From c9a3c8a972eb41d7ebff25de887adff8df99be35 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Wed, 26 Oct 2022 06:03:17 +0200 Subject: [PATCH] update week 43 --- doc/pub/week42/ipynb/week42.ipynb | 1729 +++--- doc/pub/week43/html/week43-bs.html | 2677 ++++---- doc/pub/week43/html/week43-reveal.html | 2673 ++++---- doc/pub/week43/html/week43-solarized.html | 2675 ++++---- doc/pub/week43/html/week43.html | 2675 ++++---- doc/pub/week43/ipynb/ipynb-week43-src.tar.gz | Bin 29 -> 88085 bytes doc/pub/week43/ipynb/week43.ipynb | 5872 +++++++----------- doc/src/week43/version2021.do.txt | 2216 +++++++ doc/src/week43/week43.do.txt | 1887 +++--- 9 files changed, 11893 insertions(+), 10511 deletions(-) create mode 100644 doc/src/week43/version2021.do.txt diff --git a/doc/pub/week42/ipynb/week42.ipynb b/doc/pub/week42/ipynb/week42.ipynb index ecef57bd1..7364bd9ab 100644 --- a/doc/pub/week42/ipynb/week42.ipynb +++ b/doc/pub/week42/ipynb/week42.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "2f8195a9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "dfa7bd49", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Week 42 Solving differential equations and Convolutional (CNN)\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", @@ -30,9 +26,7 @@ { "cell_type": "markdown", "id": "219b113b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Plan for week 42\n", "\n", @@ -76,9 +70,7 @@ { "cell_type": "markdown", "id": "a0a7d278", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using Automatic differentiation\n", "a\n", @@ -90,9 +82,7 @@ { "cell_type": "markdown", "id": "1f733262", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Back propagation and automatic differentiation\n", "\n", @@ -107,9 +97,7 @@ { "cell_type": "markdown", "id": "b381b780", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Solving ODEs with Deep Learning\n", "\n", @@ -134,9 +122,7 @@ { "cell_type": "markdown", "id": "5433168f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Ordinary Differential Equations\n", "\n", @@ -148,9 +134,7 @@ { "cell_type": "markdown", "id": "ba1807a1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -165,9 +149,7 @@ { "cell_type": "markdown", "id": "4be558b5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(x)$ is the function to find, and $g^{(n)}(x)$ is the $n$-th derivative of $g(x)$.\n", "\n", @@ -181,9 +163,7 @@ { "cell_type": "markdown", "id": "6ed06c5f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "\n", @@ -193,9 +173,7 @@ { "cell_type": "markdown", "id": "44cebe9d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -211,9 +189,7 @@ { "cell_type": "markdown", "id": "6e7591e4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $h_1(x)$ is a function that makes $g_t(x)$ satisfy a given set\n", "of conditions, $N(x,P)$ a neural network with weights and biases\n", @@ -232,9 +208,7 @@ { "cell_type": "markdown", "id": "c0762551", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Minimization process\n", "\n", @@ -249,9 +223,7 @@ { "cell_type": "markdown", "id": "1012f4f5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(x, P\\right) = \\big(f\\left(x, \\, g(x), \\, g'(x), \\, g''(x), \\, \\dots \\, , \\, g^{(n)}(x)\\right)\\big)^2\n", @@ -261,9 +233,7 @@ { "cell_type": "markdown", "id": "7d36e2da", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "If $N$ inputs are given as a vector $\\boldsymbol{x}$ with elements $x_i$ for $i = 1,\\dots,N$,\n", "the cost function becomes" @@ -272,9 +242,7 @@ { "cell_type": "markdown", "id": "cc238507", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -289,9 +257,7 @@ { "cell_type": "markdown", "id": "31290afe", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The neural net should then find the parameters $P$ that minimizes the cost function in\n", "([3](#cost)) for a set of $N$ training samples $x_i$." @@ -300,9 +266,7 @@ { "cell_type": "markdown", "id": "8a2114d1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Minimizing the cost function using gradient descent and automatic differentiation\n", "\n", @@ -316,9 +280,7 @@ { "cell_type": "markdown", "id": "9ce949b8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Exponential decay\n", "\n", @@ -328,9 +290,7 @@ { "cell_type": "markdown", "id": "5c77fdf5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -345,9 +305,7 @@ { "cell_type": "markdown", "id": "84f7fcd7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $g(0) = g_0$ for some chosen initial value $g_0$.\n", "\n", @@ -357,9 +315,7 @@ { "cell_type": "markdown", "id": "44c593b5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -375,9 +331,7 @@ { "cell_type": "markdown", "id": "0e41c560", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Having an analytical solution at hand, it is possible to use it to compare how well a neural network finds a solution of ([4](#solve_expdec))." ] @@ -385,9 +339,7 @@ { "cell_type": "markdown", "id": "ea0be84f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The function to solve for\n", "\n", @@ -397,9 +349,7 @@ { "cell_type": "markdown", "id": "b7ff230b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -414,9 +364,7 @@ { "cell_type": "markdown", "id": "e13b3067", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(0) = g_0$ with $\\gamma$ and $g_0$ being some chosen values.\n", "\n", @@ -426,9 +374,7 @@ { "cell_type": "markdown", "id": "e520ea40", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "To begin with, a trial solution $g_t(t)$ must be chosen. A general trial solution for ordinary differential equations could be" @@ -437,9 +383,7 @@ { "cell_type": "markdown", "id": "3b1fa662", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g_t(x, P) = h_1(x) + h_2(x, N(x, P))\n", @@ -449,9 +393,7 @@ { "cell_type": "markdown", "id": "8b1510cc", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $h_1(x)$ ensuring that $g_t(x)$ satisfies some conditions and $h_2(x,N(x, P))$ an expression involving $x$ and the output from the neural network $N(x,P)$ with $P $ being the collection of the weights and biases for each layer. For now, it is assumed that the network consists of one input layer, one hidden layer, and one output layer." ] @@ -459,9 +401,7 @@ { "cell_type": "markdown", "id": "2c141fbb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setup of Network\n", "\n", @@ -479,9 +419,7 @@ { "cell_type": "markdown", "id": "1871cb76", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -496,9 +434,7 @@ { "cell_type": "markdown", "id": "94ec0e56", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Reformulating the problem\n", "\n", @@ -515,9 +451,7 @@ { "cell_type": "markdown", "id": "813d0378", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g_t(x, P) = g_0 + x \\cdot N(x, P)\n", @@ -527,9 +461,7 @@ { "cell_type": "markdown", "id": "33747043", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "has been chosen such that it already solves the condition $g(0) = g_0$. What remains, is to find $P$ such that" ] @@ -537,9 +469,7 @@ { "cell_type": "markdown", "id": "3472f2a6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -554,9 +484,7 @@ { "cell_type": "markdown", "id": "93c47a39", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "is fulfilled as *best as possible*." ] @@ -564,9 +492,7 @@ { "cell_type": "markdown", "id": "0772bce3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More technicalities\n", "\n", @@ -580,9 +506,7 @@ { "cell_type": "markdown", "id": "84880a3a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\min_{P}\\Big\\{ \\big(g_t'(x, P) - ( -\\gamma g_t(x, P) \\big)^2 \\Big\\}\n", @@ -592,9 +516,7 @@ { "cell_type": "markdown", "id": "65375191", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "(the notation $\\min_{P}\\{ f(x, P) \\}$ means that we desire to find $P$ that yields the minimum of $f(x, P)$)\n", "\n", @@ -604,9 +526,7 @@ { "cell_type": "markdown", "id": "eed8422e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\min_{P_{\\text{hidden} }, \\ P_{\\text{output} }}\\Big\\{ \\big(g_t'(x, \\{ P_{\\text{hidden} }, P_{\\text{output} }\\}) - ( -\\gamma g_t(x, \\{ P_{\\text{hidden} }, P_{\\text{output} }\\}) \\big)^2 \\Big\\}\n", @@ -616,9 +536,7 @@ { "cell_type": "markdown", "id": "47cb2e38", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for an input value $x$." ] @@ -626,9 +544,7 @@ { "cell_type": "markdown", "id": "484a9d19", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More details\n", "\n", @@ -638,9 +554,7 @@ { "cell_type": "markdown", "id": "283b0042", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -655,9 +569,7 @@ { "cell_type": "markdown", "id": "2bdf98d6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Letting $\\boldsymbol{x}$ be a vector with elements $x_i$ and $C(\\boldsymbol{x}, P) = \\frac{1}{N} \\sum_i \\big(g_t'(x_i, P) - ( -\\gamma g_t(x_i, P) \\big)^2$ denote the cost function, the minimization problem that our network must solve, becomes" ] @@ -665,9 +577,7 @@ { "cell_type": "markdown", "id": "e87de03a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\min_{P} C(\\boldsymbol{x}, P)\n", @@ -677,9 +587,7 @@ { "cell_type": "markdown", "id": "b883c4d8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In terms of $P_{\\text{hidden} }$ and $P_{\\text{output} }$, this could also be expressed as\n", "\n", @@ -691,9 +599,7 @@ { "cell_type": "markdown", "id": "ffe08292", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A possible implementation of a neural network\n", "\n", @@ -707,9 +613,7 @@ { "cell_type": "markdown", "id": "4fcb0ec6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Technicalities\n", "\n", @@ -719,9 +623,7 @@ { "cell_type": "markdown", "id": "7a61ba45", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -741,9 +643,7 @@ { "cell_type": "markdown", "id": "09ed7190", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities I\n", "\n", @@ -753,9 +653,7 @@ { "cell_type": "markdown", "id": "47ec3fee", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -776,9 +674,7 @@ { "cell_type": "markdown", "id": "8593d76f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities II\n", "\n", @@ -792,9 +688,7 @@ { "cell_type": "markdown", "id": "31f28a52", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "f(z) = \\frac{1}{1 + \\exp{(-z)}}\n", @@ -804,9 +698,7 @@ { "cell_type": "markdown", "id": "da7aecec", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "It is possible to use other activations functions for the hidden layer also.\n", "\n", @@ -828,9 +720,7 @@ { "cell_type": "markdown", "id": "738fec98", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities III\n", "\n", @@ -840,9 +730,7 @@ { "cell_type": "markdown", "id": "60484140", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -861,9 +749,7 @@ { "cell_type": "markdown", "id": "72535cc4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final technicalities IV\n", "\n", @@ -873,9 +759,7 @@ { "cell_type": "markdown", "id": "d5f241b9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{z}_{1}^{\\text{output}} =\n", @@ -892,9 +776,7 @@ { "cell_type": "markdown", "id": "e34c683d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In this case we seek a continuous range of values since we are approximating a function. This means that after computing $\\boldsymbol{z}_{1}^{\\text{output}}$ the neural network has finished its feed forward step, and $\\boldsymbol{z}_{1}^{\\text{output}}$ is the final output of the network." ] @@ -902,9 +784,7 @@ { "cell_type": "markdown", "id": "e1381e61", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Back propagation\n", "\n", @@ -916,9 +796,7 @@ { "cell_type": "markdown", "id": "0af6f5af", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C(\\boldsymbol{x}, P) = \\frac{1}{N} \\sum_i \\big(g_t'(x_i, P) - ( -\\gamma g_t(x_i, P) \\big)^2\n", @@ -928,9 +806,7 @@ { "cell_type": "markdown", "id": "8e575647", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In order to minimize the cost function, an optimization method must be chosen.\n", "\n", @@ -940,9 +816,7 @@ { "cell_type": "markdown", "id": "03aaf438", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Gradient descent\n", "\n", @@ -957,9 +831,7 @@ { "cell_type": "markdown", "id": "130032ee", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\omega}_{\\text{new} } = \\boldsymbol{\\omega} - \\lambda \\nabla_{\\boldsymbol{\\omega}} C(\\boldsymbol{x}, \\boldsymbol{\\omega})\n", @@ -969,9 +841,7 @@ { "cell_type": "markdown", "id": "da59a12f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for a number of iterations or until $ \\big|\\big| \\boldsymbol{\\omega}_{\\text{new} } - \\boldsymbol{\\omega} \\big|\\big|$ becomes smaller than some given tolerance.\n", "\n", @@ -991,9 +861,7 @@ { "cell_type": "markdown", "id": "5898ee15", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -1006,9 +874,7 @@ { "cell_type": "markdown", "id": "206752c5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The code for solving the ODE" ] @@ -1017,11 +883,28 @@ "cell_type": "code", "execution_count": 1, "id": "8f8405c3", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 367.01\n", + "Final cost: 0.0666807\n", + "Max absolute difference: 0.0437499\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -1174,9 +1057,7 @@ { "cell_type": "markdown", "id": "c7635a55", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The network with one input layer, specified number of hidden layers, and one output layer\n", "\n", @@ -1189,11 +1070,35 @@ "cell_type": "code", "execution_count": 2, "id": "c87a3518", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 324.246\n", + "Final cost: 0.119936\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -1358,9 +1263,7 @@ { "cell_type": "markdown", "id": "208d3ec4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Population growth\n", "\n", @@ -1371,9 +1274,7 @@ { "cell_type": "markdown", "id": "80e303ab", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1388,9 +1289,7 @@ { "cell_type": "markdown", "id": "410ffa1a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(t)$ is the population density at time $t$, $\\alpha > 0$ the growth rate and $A > 0$ is the maximum population number in the environment.\n", "Also, at $t = 0$ the population has the size $g(0) = g_0$, where $g_0$ is some chosen constant.\n", @@ -1404,9 +1303,7 @@ { "cell_type": "markdown", "id": "966f9ec6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the problem\n", "\n", @@ -1417,9 +1314,7 @@ { "cell_type": "markdown", "id": "aab8b361", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1434,9 +1329,7 @@ { "cell_type": "markdown", "id": "c8dd3e3f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $g(0) = g_0$.\n", "\n", @@ -1446,9 +1339,7 @@ { "cell_type": "markdown", "id": "aaf89bbf", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "\n", @@ -1473,9 +1364,7 @@ { "cell_type": "markdown", "id": "b5c25622", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The program using Autograd\n", "\n", @@ -1486,10 +1375,7 @@ "cell_type": "code", "execution_count": 3, "id": "34787ebc", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -1660,9 +1546,7 @@ { "cell_type": "markdown", "id": "49fd09f6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using forward Euler to solve the ODE\n", "\n", @@ -1680,9 +1564,7 @@ { "cell_type": "markdown", "id": "a2b04875", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -1695,9 +1577,7 @@ { "cell_type": "markdown", "id": "04f5a4d0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "along with the condition that $g(0) = g_0$.\n", "\n", @@ -1709,9 +1589,7 @@ { "cell_type": "markdown", "id": "59eca821", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -1725,9 +1603,7 @@ { "cell_type": "markdown", "id": "4e8d49a1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Now, if $g_i = g(t_i)$ then" ] @@ -1735,9 +1611,7 @@ { "cell_type": "markdown", "id": "d7ddff32", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1757,9 +1631,7 @@ { "cell_type": "markdown", "id": "85e79382", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for $i \\geq 1$ and $g_0 = g(t_0) = g(0) = g_0$.\n", "\n", @@ -1771,10 +1643,7 @@ "cell_type": "code", "execution_count": 4, "id": "5dd0da52", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Assume that all function definitions from the example program using Autograd\n", @@ -1847,9 +1716,7 @@ { "cell_type": "markdown", "id": "dab25b3d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Solving the one dimensional Poisson equation\n", "\n", @@ -1859,9 +1726,7 @@ { "cell_type": "markdown", "id": "83838a24", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1876,9 +1741,7 @@ { "cell_type": "markdown", "id": "732ce94b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $f(x)$ is a given function for $x \\in (0,1)$.\n", "\n", @@ -1888,9 +1751,7 @@ { "cell_type": "markdown", "id": "35634df2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -1903,9 +1764,7 @@ { "cell_type": "markdown", "id": "fa26e45e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "This equation can be solved numerically using programs where e.g Autograd and TensorFlow are used.\n", "The results from the networks can then be compared to the analytical solution.\n", @@ -1915,9 +1774,7 @@ { "cell_type": "markdown", "id": "22ae35fb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The specific equation to solve for\n", "\n", @@ -1927,9 +1784,7 @@ { "cell_type": "markdown", "id": "12ec38a5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "-g''(x) = f(x),\\qquad x \\in (0,1)\n", @@ -1939,9 +1794,7 @@ { "cell_type": "markdown", "id": "c32759f0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $f(x)$ is a given function, along with the chosen conditions" ] @@ -1949,9 +1802,7 @@ { "cell_type": "markdown", "id": "69bc571c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -1966,9 +1817,7 @@ { "cell_type": "markdown", "id": "555c09cf", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In this example, we consider the case when $f(x) = (3x + x^2)\\exp(x)$.\n", "\n", @@ -1978,9 +1827,7 @@ { "cell_type": "markdown", "id": "5247323f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g_t(x) = x \\cdot (1-x) \\cdot N(P,x)\n", @@ -1990,9 +1837,7 @@ { "cell_type": "markdown", "id": "90357df3", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The analytical solution for this problem is" ] @@ -2000,9 +1845,7 @@ { "cell_type": "markdown", "id": "33be81dc", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "g(x) = x(1 - x)\\exp(x)\n", @@ -2012,9 +1855,7 @@ { "cell_type": "markdown", "id": "7fdc3446", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Solving the equation using Autograd" ] @@ -2023,10 +1864,7 @@ "cell_type": "code", "execution_count": 5, "id": "b1fb7c4b", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -2184,9 +2022,7 @@ { "cell_type": "markdown", "id": "c13e59dd", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Comparing with a numerical scheme\n", "\n", @@ -2206,9 +2042,7 @@ { "cell_type": "markdown", "id": "d268a538", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2223,9 +2057,7 @@ { "cell_type": "markdown", "id": "18236a01", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "If $x_i = i \\Delta x = x_{i-1} + \\Delta x$ and $g_i = g(x_i)$ for $i = 1,\\dots N_x - 2$ with $N_x$ being the number of values for $x$, ([15](#approx)) becomes" ] @@ -2233,9 +2065,7 @@ { "cell_type": "markdown", "id": "9454651e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -2248,9 +2078,7 @@ { "cell_type": "markdown", "id": "227cb339", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Since we know from our problem that" ] @@ -2258,9 +2086,7 @@ { "cell_type": "markdown", "id": "0fb50ca9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -2273,9 +2099,7 @@ { "cell_type": "markdown", "id": "6f2d9f30", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "along with the conditions $g(0) = g(1) = 0$,\n", "the following scheme can be used to find an approximate solution for $g(x)$ numerically:" @@ -2284,9 +2108,7 @@ { "cell_type": "markdown", "id": "6094700a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2304,9 +2126,7 @@ { "cell_type": "markdown", "id": "2e29ff0e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "for $i = 1, \\dots, N_x - 2$ where $g_0 = g_{N_x - 1} = 0$ and $f(x_i) = (3x_i + x_i^2)\\exp(x_i)$, which is given for our specific problem.\n", "\n", @@ -2316,9 +2136,7 @@ { "cell_type": "markdown", "id": "eb0fe9a1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{aligned}\n", @@ -2353,9 +2171,7 @@ { "cell_type": "markdown", "id": "c03a46bb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "which makes it possible to solve for the vector $\\boldsymbol{g}$." ] @@ -2363,9 +2179,7 @@ { "cell_type": "markdown", "id": "401c3831", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the code\n", "\n", @@ -2376,10 +2190,7 @@ "cell_type": "code", "execution_count": 6, "id": "4f67bd3c", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -2577,9 +2388,7 @@ { "cell_type": "markdown", "id": "e303dc71", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Partial Differential Equations\n", "\n", @@ -2594,9 +2403,7 @@ { "cell_type": "markdown", "id": "e30e6e45", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2611,9 +2418,7 @@ { "cell_type": "markdown", "id": "1173cba1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $f$ is an expression involving all kinds of possible mixed derivatives of $g(x_1,\\dots,x_N)$ up to an order $n$. In order for the solution to be unique, some additional conditions must also be given." ] @@ -2621,9 +2426,7 @@ { "cell_type": "markdown", "id": "87b83e1d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Type of problem\n", "\n", @@ -2636,9 +2439,7 @@ { "cell_type": "markdown", "id": "f41effef", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -2650,9 +2451,7 @@ { "cell_type": "markdown", "id": "c4a45e13", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $h_1(x_1,\\dots,x_N)$ is a function that ensures $g_t(x_1,\\dots,x_N)$ satisfies some given conditions.\n", "The neural network $N(x_1,\\dots,x_N,P)$ has weights and biases described by $P$ and $h_2(x_1,\\dots,x_N,N(x_1,\\dots,x_N,P))$ is an expression using the output from the neural network in some way.\n", @@ -2663,9 +2462,7 @@ { "cell_type": "markdown", "id": "0b04a28a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Network requirements\n", "\n", @@ -2683,9 +2480,7 @@ { "cell_type": "markdown", "id": "adcccc59", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(x_1, \\dots, x_N, P\\right) = \\left( f\\left(x_1, \\, \\dots \\, , x_N, \\frac{\\partial g(x_1,\\dots,x_N) }{\\partial x_1}, \\dots , \\frac{\\partial g(x_1,\\dots,x_N) }{\\partial x_N}, \\frac{\\partial g(x_1,\\dots,x_N) }{\\partial x_1\\partial x_2}, \\, \\dots \\, , \\frac{\\partial^n g(x_1,\\dots,x_N) }{\\partial x_N^n} \\right) \\right)^2\n", @@ -2695,9 +2490,7 @@ { "cell_type": "markdown", "id": "b4d3ebc5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More details\n", "\n", @@ -2707,9 +2500,7 @@ { "cell_type": "markdown", "id": "68189260", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(\\boldsymbol{x}, P\\right) = f\\left( \\left( \\boldsymbol{x}, \\frac{\\partial g(\\boldsymbol{x}) }{\\partial x_1}, \\dots , \\frac{\\partial g(\\boldsymbol{x}) }{\\partial x_N}, \\frac{\\partial g(\\boldsymbol{x}) }{\\partial x_1\\partial x_2}, \\, \\dots \\, , \\frac{\\partial^n g(\\boldsymbol{x}) }{\\partial x_N^n} \\right) \\right)^2\n", @@ -2719,9 +2510,7 @@ { "cell_type": "markdown", "id": "0afc4171", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "If we also have $M$ different sets of values for $x_1, \\dots, x_N$, that is $\\boldsymbol{x}_i = \\big(x_1^{(i)}, \\dots, x_N^{(i)}\\big)$ for $i = 1,\\dots,M$ being the rows in matrix $X$, the cost function can be generalized into" ] @@ -2729,9 +2518,7 @@ { "cell_type": "markdown", "id": "eb4f9d04", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "C\\left(X, P \\right) = \\sum_{i=1}^M f\\left( \\left( \\boldsymbol{x}_i, \\frac{\\partial g(\\boldsymbol{x}_i) }{\\partial x_1}, \\dots , \\frac{\\partial g(\\boldsymbol{x}_i) }{\\partial x_N}, \\frac{\\partial g(\\boldsymbol{x}_i) }{\\partial x_1\\partial x_2}, \\, \\dots \\, , \\frac{\\partial^n g(\\boldsymbol{x}_i) }{\\partial x_N^n} \\right) \\right)^2.\n", @@ -2741,9 +2528,7 @@ { "cell_type": "markdown", "id": "205b707c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: The diffusion equation\n", "\n", @@ -2753,9 +2538,7 @@ { "cell_type": "markdown", "id": "404eb094", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial g(x,t)}{\\partial t} = \\frac{\\partial^2 g(x,t)}{\\partial x^2}\n", @@ -2765,9 +2548,7 @@ { "cell_type": "markdown", "id": "0ea885b8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where a possible choice of conditions are" ] @@ -2775,9 +2556,7 @@ { "cell_type": "markdown", "id": "ff4404d8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -2791,9 +2570,7 @@ { "cell_type": "markdown", "id": "acc86662", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $u(x)$ being some given function." ] @@ -2801,9 +2578,7 @@ { "cell_type": "markdown", "id": "f2237d59", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Defining the problem\n", "\n", @@ -2813,9 +2588,7 @@ { "cell_type": "markdown", "id": "a3673fb9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -2830,9 +2603,7 @@ { "cell_type": "markdown", "id": "3966a036", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -2840,9 +2611,7 @@ { "cell_type": "markdown", "id": "d8c2715d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -2856,9 +2625,7 @@ { "cell_type": "markdown", "id": "1d27bfe8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $u(x) = \\sin(\\pi x)$.\n", "\n", @@ -2870,9 +2637,7 @@ { "cell_type": "markdown", "id": "148027d5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the network using Autograd\n", "\n", @@ -2889,10 +2654,7 @@ "cell_type": "code", "execution_count": 7, "id": "6398f3ad", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def sigmoid(z):\n", @@ -2944,9 +2706,7 @@ { "cell_type": "markdown", "id": "692b2f8e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the network using Autograd; The trial solution\n", "\n", @@ -2974,9 +2734,7 @@ { "cell_type": "markdown", "id": "aace74d8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Why the jacobian?\n", "\n", @@ -3003,10 +2761,7 @@ "cell_type": "code", "execution_count": 8, "id": "ad86c98b", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Set up the trial function:\n", @@ -3050,9 +2805,7 @@ { "cell_type": "markdown", "id": "bc714b2c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting up the network using Autograd; The full program\n", "\n", @@ -3076,10 +2829,7 @@ "cell_type": "code", "execution_count": 9, "id": "0901c4d7", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -3310,9 +3060,7 @@ { "cell_type": "markdown", "id": "7599ed2e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Example: Solving the wave equation with Neural Networks\n", "\n", @@ -3322,9 +3070,7 @@ { "cell_type": "markdown", "id": "fb75b947", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\frac{\\partial^2 g(x,t)}{\\partial t^2} = c^2\\frac{\\partial^2 g(x,t)}{\\partial x^2}\n", @@ -3334,9 +3080,7 @@ { "cell_type": "markdown", "id": "15212ace", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "with $c$ being the specified wave speed.\n", "\n", @@ -3346,9 +3090,7 @@ { "cell_type": "markdown", "id": "81bca02a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{align*}\n", @@ -3363,9 +3105,7 @@ { "cell_type": "markdown", "id": "a8ddda25", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $\\frac{\\partial g(x,t)}{\\partial t} \\Big |_{t = 0}$ means the derivative of $g(x,t)$ with respect to $t$ is evaluated at $t = 0$, and $u(x)$ and $v(x)$ being given functions." ] @@ -3373,9 +3113,7 @@ { "cell_type": "markdown", "id": "8d367f03", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The problem to solve for\n", "\n", @@ -3385,9 +3123,7 @@ { "cell_type": "markdown", "id": "4c323240", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -3402,9 +3138,7 @@ { "cell_type": "markdown", "id": "071489f1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $c$ is the given wave speed.\n", "The chosen conditions for this equation are" @@ -3413,9 +3147,7 @@ { "cell_type": "markdown", "id": "28331e09", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -3433,9 +3165,7 @@ { "cell_type": "markdown", "id": "f2f4f1f9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In this example, let $c = 1$ and $u(x) = \\sin(\\pi x)$ and $v(x) = -\\pi\\sin(\\pi x)$." ] @@ -3443,9 +3173,7 @@ { "cell_type": "markdown", "id": "777aebaf", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The trial solution\n", "Setting up the network is done in similar matter as for the example of solving the diffusion equation.\n", @@ -3469,9 +3197,7 @@ { "cell_type": "markdown", "id": "41b8d250", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The analytical solution\n", "\n", @@ -3485,9 +3211,7 @@ { "cell_type": "markdown", "id": "10183371", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Solving the wave equation - the full program using Autograd" ] @@ -3496,10 +3220,7 @@ "cell_type": "code", "execution_count": 10, "id": "3abd461f", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import autograd.numpy as np\n", @@ -3727,9 +3448,7 @@ { "cell_type": "markdown", "id": "119b3b20", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Resources on differential equations and deep learning\n", "\n", @@ -3745,9 +3464,7 @@ { "cell_type": "markdown", "id": "c0e661cb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Convolutional Neural Networks (recognizing images)\n", "\n", @@ -3772,9 +3489,7 @@ { "cell_type": "markdown", "id": "cae07a7f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## What is the Difference\n", "\n", @@ -3794,9 +3509,7 @@ { "cell_type": "markdown", "id": "15491f8e", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Neural Networks vs CNNs\n", "\n", @@ -3812,9 +3525,7 @@ { "cell_type": "markdown", "id": "d6a16e04", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Why CNNS for images, sound files, medical images from CT scans etc?\n", "\n", @@ -3842,9 +3553,7 @@ { "cell_type": "markdown", "id": "33c6656c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Regular NNs don’t scale well to full images\n", "\n", @@ -3872,9 +3581,7 @@ { "cell_type": "markdown", "id": "fddecbf2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## 3D volumes of neurons\n", "\n", @@ -3912,9 +3619,7 @@ { "cell_type": "markdown", "id": "c586e81c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Layers used to build CNNs\n", "\n", @@ -3941,9 +3646,7 @@ { "cell_type": "markdown", "id": "7ca122dd", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Transforming images\n", "\n", @@ -3963,9 +3666,7 @@ { "cell_type": "markdown", "id": "7a990115", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## CNNs in brief\n", "\n", @@ -3992,9 +3693,7 @@ { "cell_type": "markdown", "id": "fbc35b23", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Key Idea\n", "\n", @@ -4009,9 +3708,7 @@ { "cell_type": "markdown", "id": "95f90e42", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Mathematics of CNNs\n", "\n", @@ -4029,9 +3726,7 @@ { "cell_type": "markdown", "id": "1a582990", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "y(t) = \\int x(a) w(t-a) da,\n", @@ -4041,9 +3736,7 @@ { "cell_type": "markdown", "id": "8a4fae4c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $x(a)$ represents a so-called input and $w(t-a)$ is normally called the weight function or kernel.\n", "\n", @@ -4053,9 +3746,7 @@ { "cell_type": "markdown", "id": "7ddf0d4b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "y(t) = \\left(x * w\\right)(t).\n", @@ -4065,9 +3756,7 @@ { "cell_type": "markdown", "id": "af350fa5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The discretized version reads" ] @@ -4075,9 +3764,7 @@ { "cell_type": "markdown", "id": "7e68a973", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "y(t) = \\sum_{a=-\\infty}^{a=\\infty}x(a)w(t-a).\n", @@ -4087,9 +3774,7 @@ { "cell_type": "markdown", "id": "f5753124", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Computing the inverse of the above convolution operations is known as deconvolution.\n", "\n", @@ -4099,9 +3784,7 @@ { "cell_type": "markdown", "id": "e657578c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Convolution Examples: Polynomial multiplication\n", "\n", @@ -4114,9 +3797,7 @@ { "cell_type": "markdown", "id": "a7ab8c44", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "p(t) = \\alpha_0+\\alpha_1 t+\\alpha_2 t^2,\n", @@ -4126,9 +3807,7 @@ { "cell_type": "markdown", "id": "f8b3e313", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -4136,9 +3815,7 @@ { "cell_type": "markdown", "id": "31842251", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "s(t) = \\beta_0+\\beta_1 t+\\beta_2 t^2+\\beta_3 t^3.\n", @@ -4148,9 +3825,7 @@ { "cell_type": "markdown", "id": "6b8a1979", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The polynomial multiplication gives us a new polynomial of degree $5$" ] @@ -4158,9 +3833,7 @@ { "cell_type": "markdown", "id": "75fcd18c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "z(t) = \\delta_0+\\delta_1 t+\\delta_2 t^2+\\delta_3 t^3+\\delta_4 t^4+\\delta_5 t^5.\n", @@ -4170,9 +3843,7 @@ { "cell_type": "markdown", "id": "f7853eed", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Efficient Polynomial Multiplication\n", "\n", @@ -4183,9 +3854,7 @@ { "cell_type": "markdown", "id": "aa6b8f30", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{split}\n", @@ -4202,9 +3871,7 @@ { "cell_type": "markdown", "id": "3b0d50d6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "We note that $\\alpha_i=0$ except for $i\\in \\left\\{0,1,2\\right\\}$ and $\\beta_i=0$ except for $i\\in\\left\\{0,1,2,3\\right\\}$.\n", "\n", @@ -4214,9 +3881,7 @@ { "cell_type": "markdown", "id": "ca1b360f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_j = \\sum_{i=-\\infty}^{i=\\infty}\\alpha_i\\beta_{j-i}=(\\alpha * \\beta)_j,\n", @@ -4226,9 +3891,7 @@ { "cell_type": "markdown", "id": "4da60fe0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "or as a double sum with restriction $l=i+j$" ] @@ -4236,9 +3899,7 @@ { "cell_type": "markdown", "id": "17ef5106", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\delta_l = \\sum_{ij}\\alpha_i\\beta_{j}.\n", @@ -4248,9 +3909,7 @@ { "cell_type": "markdown", "id": "c75712f0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Do you see a potential drawback with these equations?" ] @@ -4258,9 +3917,7 @@ { "cell_type": "markdown", "id": "1ba6594d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## A more efficient way of coding the above Convolution\n", "\n", @@ -4272,9 +3929,7 @@ { "cell_type": "markdown", "id": "bd1b9286", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\delta}=\\begin{bmatrix}\\alpha_0 & 0 & 0 & 0 \\\\\n", @@ -4290,9 +3945,7 @@ { "cell_type": "markdown", "id": "f88ab8dc", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding $\\beta$ and a vector holding $\\alpha$.\n", "In this case we have" @@ -4301,9 +3954,7 @@ { "cell_type": "markdown", "id": "a0f533df", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\boldsymbol{\\delta}=\\begin{bmatrix}\\beta_0 & 0 & 0 \\\\\n", @@ -4319,9 +3970,7 @@ { "cell_type": "markdown", "id": "0618610f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Note that the use of these matrices is for mathematical purposes only and not implementation purposes.\n", "When implementing the above equation we do not encode (and allocate memory) the matrices explicitely.\n", @@ -4333,9 +3982,7 @@ { "cell_type": "markdown", "id": "41f3f297", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)\n", "\n", @@ -4345,9 +3992,7 @@ { "cell_type": "markdown", "id": "c7996c86", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "m\\frac{d^2x}{dt^2}+\\eta\\frac{dx}{dt}+x(t)=F(t),\n", @@ -4357,9 +4002,7 @@ { "cell_type": "markdown", "id": "55a601e4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $F(t)$ is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations.\n", "\n", @@ -4371,9 +4014,7 @@ { "cell_type": "markdown", "id": "e0006437", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4389,9 +4030,7 @@ { "cell_type": "markdown", "id": "34568d1f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Principle of Superposition\n", "\n", @@ -4410,9 +4049,7 @@ { "cell_type": "markdown", "id": "9be00807", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4424,9 +4061,7 @@ { "cell_type": "markdown", "id": "3dd03861", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "One example of a non-sinusoidal periodic force is a square wave. Many\n", "components in electric circuits are non-linear, e.g. diodes, which\n", @@ -4437,9 +4072,7 @@ { "cell_type": "markdown", "id": "8e79b05f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Simple Code Example\n", "\n", @@ -4450,10 +4083,7 @@ "cell_type": "code", "execution_count": 11, "id": "9743d437", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -4478,9 +4108,7 @@ { "cell_type": "markdown", "id": "e73902c2", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "For the sinusoidal example the\n", "period is $\\tau=2\\pi/\\omega$. However, higher harmonics can also\n", @@ -4492,9 +4120,7 @@ { "cell_type": "markdown", "id": "20805935", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4510,9 +4136,7 @@ { "cell_type": "markdown", "id": "345f9874", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Wrapping up Fourier transforms\n", "\n", @@ -4525,9 +4149,7 @@ { "cell_type": "markdown", "id": "5fa36e45", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4543,9 +4165,7 @@ { "cell_type": "markdown", "id": "c9f2fa30", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The solutions for $x(t)$ then come from replacing $\\omega$ with\n", "$n\\omega$ for each term in the particular solution," @@ -4554,9 +4174,7 @@ { "cell_type": "markdown", "id": "e6ed026a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4574,9 +4192,7 @@ { "cell_type": "markdown", "id": "b925f9a5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Finding the Coefficients\n", "\n", @@ -4593,9 +4209,7 @@ { "cell_type": "markdown", "id": "6dac2506", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4613,9 +4227,7 @@ { "cell_type": "markdown", "id": "a3582398", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "To check the consistency of these expressions and to verify\n", "Eq. ([24](#eq:fourierdef2)), one can insert the expansion of $F(t)$ in\n", @@ -4626,9 +4238,7 @@ { "cell_type": "markdown", "id": "c28b32ac", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4642,9 +4252,7 @@ { "cell_type": "markdown", "id": "e7bb2438", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Immediately, one can throw away all the terms with $g_m$ because they\n", "convolute an even and an odd function. The term with $f_0/2$\n", @@ -4659,9 +4267,7 @@ { "cell_type": "markdown", "id": "f174e71f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -4677,9 +4283,7 @@ { "cell_type": "markdown", "id": "efcb4f9c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "and" ] @@ -4687,9 +4291,7 @@ { "cell_type": "markdown", "id": "161f1560", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\begin{eqnarray}\n", @@ -4703,9 +4305,7 @@ { "cell_type": "markdown", "id": "ea40b7b4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "The same method can be used to check for the consistency of $g_n$." ] @@ -4713,9 +4313,7 @@ { "cell_type": "markdown", "id": "6ed3ab85", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final words on Fourier Transforms\n", "\n", @@ -4732,10 +4330,7 @@ "cell_type": "code", "execution_count": 12, "id": "d4395346", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -4772,9 +4367,7 @@ { "cell_type": "markdown", "id": "cbd2d810", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Two-dimensional Objects\n", "\n", @@ -4786,9 +4379,7 @@ { "cell_type": "markdown", "id": "54b08352", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(m,n)K(i-m,j-n).\n", @@ -4798,9 +4389,7 @@ { "cell_type": "markdown", "id": "e6d3c4d7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Convolution is a commutatitave process, which means we can rewrite this equation as" ] @@ -4808,9 +4397,7 @@ { "cell_type": "markdown", "id": "b348035a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(i-m,j-n)K(m,n).\n", @@ -4820,9 +4407,7 @@ { "cell_type": "markdown", "id": "a397fbae", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of $m$ and $n$." ] @@ -4830,9 +4415,7 @@ { "cell_type": "markdown", "id": "decee3f7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Cross-Correlation\n", "\n", @@ -4842,9 +4425,7 @@ { "cell_type": "markdown", "id": "336d08e4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(i+m,j-+)K(m,n).\n", @@ -4854,9 +4435,7 @@ { "cell_type": "markdown", "id": "9bf87751", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## More on Dimensionalities\n", "\n", @@ -4881,9 +4460,7 @@ { "cell_type": "markdown", "id": "1ecf964a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \\approx 10^{10},\n", @@ -4893,9 +4470,7 @@ { "cell_type": "markdown", "id": "41608899", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "that is ten billion parameters to determine." ] @@ -4903,9 +4478,7 @@ { "cell_type": "markdown", "id": "78314823", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Further Dimensionality Remarks\n", "\n", @@ -4929,9 +4502,7 @@ { "cell_type": "markdown", "id": "8c588e74", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## CNNs in more detail, Lecture from IN5400\n", "\n", @@ -4941,9 +4512,7 @@ { "cell_type": "markdown", "id": "dde50e2c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## CNNs in more detail, building convolutional neural networks in Tensorflow and Keras\n", "\n", @@ -4960,9 +4529,7 @@ { "cell_type": "markdown", "id": "29fa7070", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Setting it up\n", "\n", @@ -4973,9 +4540,7 @@ { "cell_type": "markdown", "id": "a22ddefa", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "(n_{inputs},\\, n_{pixels, width},\\, n_{pixels, height},\\, depth) .\n", @@ -4985,9 +4550,7 @@ { "cell_type": "markdown", "id": "cd11b854", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The MNIST dataset again\n", "\n", @@ -5006,9 +4569,7 @@ { "cell_type": "markdown", "id": "d58e3877", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Strong correlations\n", "\n", @@ -5027,9 +4588,7 @@ { "cell_type": "markdown", "id": "eeb50fa4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Layers of a CNN\n", "The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. \n", @@ -5052,9 +4611,7 @@ { "cell_type": "markdown", "id": "5ad42b43", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Systematic reduction\n", "\n", @@ -5071,22 +4628,36 @@ { "cell_type": "markdown", "id": "a3193d14", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Prerequisites: Collect and pre-process data" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "3cf1c5e2", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)\n", + "labels = (n_inputs) = (1797,)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# import necessary packages\n", "import numpy as np\n", @@ -5133,21 +4704,16 @@ { "cell_type": "markdown", "id": "0639c167", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Importing Keras and Tensorflow" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "c43a1a3c", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras import datasets, layers, models\n", @@ -5177,9 +4743,7 @@ { "cell_type": "markdown", "id": "beb42cb6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Running with Keras" ] @@ -5188,10 +4752,7 @@ "cell_type": "code", "execution_count": 15, "id": "9ee64b95", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def create_convolutional_neural_network_keras(input_shape, receptive_field,\n", @@ -5219,15 +4780,13 @@ "n_categories = 10\n", "\n", "eta_vals = np.logspace(-5, 1, 7)\n", - "lmbd_vals = np.logspace(-5, 1, 7)" + "lmbd_vals = np.logspace(-5, 1, 7)\n" ] }, { "cell_type": "markdown", "id": "2e4e80f6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final part" ] @@ -5236,11 +4795,301 @@ "cell_type": "code", "execution_count": 16, "id": "0bd52393", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", + " super(SGD, self).__init__(name, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/12 [==============================] - 1s 28ms/step - loss: 2.9422 - accuracy: 0.0944\n", + "Learning rate = 1e-05\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.094\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 2.9493 - accuracy: 0.0917\n", + "Learning rate = 1e-05\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.092\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: 3.0350 - accuracy: 0.0944\n", + "Learning rate = 1e-05\n", + "Lambda = 0.001\n", + "Test accuracy: 0.094\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: 3.8904 - accuracy: 0.0917\n", + "Learning rate = 1e-05\n", + "Lambda = 0.01\n", + "Test accuracy: 0.092\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 12.3914 - accuracy: 0.0917\n", + "Learning rate = 1e-05\n", + "Lambda = 0.1\n", + "Test accuracy: 0.092\n", + "\n", + "12/12 [==============================] - 0s 23ms/step - loss: 92.4942 - accuracy: 0.0917\n", + "Learning rate = 1e-05\n", + "Lambda = 1.0\n", + "Test accuracy: 0.092\n", + "\n", + "12/12 [==============================] - 1s 30ms/step - loss: 525.0207 - accuracy: 0.0806\n", + "Learning rate = 1e-05\n", + "Lambda = 10.0\n", + "Test accuracy: 0.081\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 1.3310 - accuracy: 0.5278\n", + "Learning rate = 0.0001\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.528\n", + "\n", + "12/12 [==============================] - 1s 33ms/step - loss: 1.3376 - accuracy: 0.5139\n", + "Learning rate = 0.0001\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.514\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 1.4230 - accuracy: 0.5167\n", + "Learning rate = 0.0001\n", + "Lambda = 0.001\n", + "Test accuracy: 0.517\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 2.2754 - accuracy: 0.5167\n", + "Learning rate = 0.0001\n", + "Lambda = 0.01\n", + "Test accuracy: 0.517\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: 10.3097 - accuracy: 0.5139\n", + "Learning rate = 0.0001\n", + "Lambda = 0.1\n", + "Test accuracy: 0.514\n", + "\n", + "12/12 [==============================] - 1019s 28ms/step - loss: 53.9860 - accuracy: 0.4972\n", + "Learning rate = 0.0001\n", + "Lambda = 1.0\n", + "Test accuracy: 0.497\n", + "\n", + "12/12 [==============================] - 0s 27ms/step - loss: 4.6519 - accuracy: 0.1389\n", + "Learning rate = 0.0001\n", + "Lambda = 10.0\n", + "Test accuracy: 0.139\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 0.2474 - accuracy: 0.9167\n", + "Learning rate = 0.001\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.917\n", + "\n", + "12/12 [==============================] - 0s 27ms/step - loss: 0.2513 - accuracy: 0.9194\n", + "Learning rate = 0.001\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.919\n", + "\n", + "12/12 [==============================] - 0s 27ms/step - loss: 0.3400 - accuracy: 0.9167\n", + "Learning rate = 0.001\n", + "Lambda = 0.001\n", + "Test accuracy: 0.917\n", + "\n", + "12/12 [==============================] - 1s 30ms/step - loss: 1.1627 - accuracy: 0.9194\n", + "Learning rate = 0.001\n", + "Lambda = 0.01\n", + "Test accuracy: 0.919\n", + "\n", + "12/12 [==============================] - 0s 27ms/step - loss: 5.7782 - accuracy: 0.9250\n", + "Learning rate = 0.001\n", + "Lambda = 0.1\n", + "Test accuracy: 0.925\n", + "\n", + "12/12 [==============================] - 0s 25ms/step - loss: 2.6433 - accuracy: 0.6083\n", + "Learning rate = 0.001\n", + "Lambda = 1.0\n", + "Test accuracy: 0.608\n", + "\n", + "12/12 [==============================] - 1s 30ms/step - loss: 2.3032 - accuracy: 0.0778\n", + "Learning rate = 0.001\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 0.0658 - accuracy: 0.9722\n", + "Learning rate = 0.01\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.972\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 0.0758 - accuracy: 0.9750\n", + "Learning rate = 0.01\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.975\n", + "\n", + "12/12 [==============================] - 0s 25ms/step - loss: 0.1647 - accuracy: 0.9750\n", + "Learning rate = 0.01\n", + "Lambda = 0.001\n", + "Test accuracy: 0.975\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: 0.6665 - accuracy: 0.9806\n", + "Learning rate = 0.01\n", + "Lambda = 0.01\n", + "Test accuracy: 0.981\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 0.9409 - accuracy: 0.9472\n", + "Learning rate = 0.01\n", + "Lambda = 0.1\n", + "Test accuracy: 0.947\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 2.3065 - accuracy: 0.0778\n", + "Learning rate = 0.01\n", + "Lambda = 1.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 31ms/step - loss: 2.3067 - accuracy: 0.0778\n", + "Learning rate = 0.01\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 26ms/step - loss: 0.1937 - accuracy: 0.9556\n", + "Learning rate = 0.1\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.956\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 0.3655 - accuracy: 0.9167\n", + "Learning rate = 0.1\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.917\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: 0.4640 - accuracy: 0.9083\n", + "Learning rate = 0.1\n", + "Lambda = 0.001\n", + "Test accuracy: 0.908\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: 0.3183 - accuracy: 0.9528\n", + "Learning rate = 0.1\n", + "Lambda = 0.01\n", + "Test accuracy: 0.953\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 2.3076 - accuracy: 0.0778\n", + "Learning rate = 0.1\n", + "Lambda = 0.1\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 26ms/step - loss: 2.3078 - accuracy: 0.0778\n", + "Learning rate = 0.1\n", + "Lambda = 1.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 0.1\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 26ms/step - loss: 37.0769 - accuracy: 0.0917\n", + "Learning rate = 1.0\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.092\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 165.7696 - accuracy: 0.0889\n", + "Learning rate = 1.0\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 1s 25ms/step - loss: 12.5201 - accuracy: 0.0917\n", + "Learning rate = 1.0\n", + "Lambda = 0.001\n", + "Test accuracy: 0.092\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 2.3108 - accuracy: 0.0889\n", + "Learning rate = 1.0\n", + "Lambda = 0.01\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 1s 27ms/step - loss: 2.3130 - accuracy: 0.0889\n", + "Learning rate = 1.0\n", + "Lambda = 0.1\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 1s 25ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 1.0\n", + "Lambda = 1.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 29ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 1.0\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: 28988596.0000 - accuracy: 0.0889\n", + "Learning rate = 10.0\n", + "Lambda = 1e-05\n", + "Test accuracy: 0.089\n", + "\n", + "12/12 [==============================] - 1s 26ms/step - loss: 314152.2188 - accuracy: 0.0917\n", + "Learning rate = 10.0\n", + "Lambda = 0.0001\n", + "Test accuracy: 0.092\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: 2.4676 - accuracy: 0.0778\n", + "Learning rate = 10.0\n", + "Lambda = 0.001\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 25ms/step - loss: 2.4545 - accuracy: 0.1167\n", + "Learning rate = 10.0\n", + "Lambda = 0.01\n", + "Test accuracy: 0.117\n", + "\n", + "12/12 [==============================] - 1s 28ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 10.0\n", + "Lambda = 0.1\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 0s 24ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 10.0\n", + "Lambda = 1.0\n", + "Test accuracy: 0.078\n", + "\n", + "12/12 [==============================] - 1s 25ms/step - loss: nan - accuracy: 0.0778\n", + "Learning rate = 10.0\n", + "Lambda = 10.0\n", + "Test accuracy: 0.078\n", + "\n", + "Model: \"sequential_49\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_49 (Conv2D) (None, 30, 30, 32) 896 \n", + " \n", + " max_pooling2d_49 (MaxPoolin (None, 15, 15, 32) 0 \n", + " g2D) \n", + " \n", + " conv2d_50 (Conv2D) (None, 13, 13, 64) 18496 \n", + " \n", + " max_pooling2d_50 (MaxPoolin (None, 6, 6, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_51 (Conv2D) (None, 4, 4, 64) 36928 \n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " flatten_49 (Flatten) (None, 1024) 0 \n", + " \n", + " dense_98 (Dense) (None, 64) 65600 \n", + " \n", + " dense_99 (Dense) (None, 10) 650 \n", + " \n", + "=================================================================\n", + "Total params: 122,570\n", + "Trainable params: 122,570\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], "source": [ "CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", " \n", @@ -5257,28 +5106,155 @@ " print(\"Learning rate = \", eta)\n", " print(\"Lambda = \", lmbd)\n", " print(\"Test accuracy: %.3f\" % scores[1])\n", - " print()" + " print()\n", + "model.summary()" ] }, { "cell_type": "markdown", "id": "d57a3ac4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Final visualization" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "id": "a46c8db7", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "45/45 [==============================] - 1s 16ms/step - loss: 2.8932 - accuracy: 0.1023\n", + "12/12 [==============================] - 0s 14ms/step - loss: 2.9421 - accuracy: 0.0944\n", + "45/45 [==============================] - 1s 14ms/step - loss: 2.9007 - accuracy: 0.1023\n", + "12/12 [==============================] - 0s 13ms/step - loss: 2.9495 - accuracy: 0.0944\n", + "45/45 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# visual representation of grid search\n", "# uses seaborn heatmap, could probably do this in matplotlib\n", @@ -5315,9 +5291,7 @@ { "cell_type": "markdown", "id": "0d8ef831", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## The CIFAR01 data set\n", "\n", @@ -5329,12 +5303,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "3d8a7042", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", @@ -5352,9 +5323,7 @@ { "cell_type": "markdown", "id": "2c4dab01", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Verifying the data set\n", "\n", @@ -5363,17 +5332,24 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "id": "62af6ee9", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n", " 'dog', 'frog', 'horse', 'ship', 'truck']\n", - "​\n", "plt.figure(figsize=(10,10))\n", "for i in range(25):\n", " plt.subplot(5,5,i+1)\n", @@ -5390,9 +5366,7 @@ { "cell_type": "markdown", "id": "f069982f", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Set up the model\n", "\n", @@ -5403,13 +5377,38 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "id": "c125df30", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_49\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_49 (Conv2D) (None, 30, 30, 32) 896 \n", + " \n", + " max_pooling2d_49 (MaxPoolin (None, 15, 15, 32) 0 \n", + " g2D) \n", + " \n", + " conv2d_50 (Conv2D) (None, 13, 13, 64) 18496 \n", + " \n", + " max_pooling2d_50 (MaxPoolin (None, 6, 6, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_51 (Conv2D) (None, 4, 4, 64) 36928 \n", + " \n", + "=================================================================\n", + "Total params: 56,320\n", + "Trainable params: 56,320\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], "source": [ "model = models.Sequential()\n", "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n", @@ -5426,9 +5425,7 @@ { "cell_type": "markdown", "id": "77ee1873", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer." ] @@ -5436,9 +5433,7 @@ { "cell_type": "markdown", "id": "a1c381c9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Add Dense layers on top\n", "\n", @@ -5453,18 +5448,49 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, "id": "f0dbe0cd", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_49\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_49 (Conv2D) (None, 30, 30, 32) 896 \n", + " \n", + " max_pooling2d_49 (MaxPoolin (None, 15, 15, 32) 0 \n", + " g2D) \n", + " \n", + " conv2d_50 (Conv2D) (None, 13, 13, 64) 18496 \n", + " \n", + " max_pooling2d_50 (MaxPoolin (None, 6, 6, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_51 (Conv2D) (None, 4, 4, 64) 36928 \n", + " \n", + " flatten_49 (Flatten) (None, 1024) 0 \n", + " \n", + " dense_98 (Dense) (None, 64) 65600 \n", + " \n", + " dense_99 (Dense) (None, 10) 650 \n", + " \n", + "=================================================================\n", + "Total params: 122,570\n", + "Trainable params: 122,570\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], "source": [ "model.add(layers.Flatten())\n", "model.add(layers.Dense(64, activation='relu'))\n", "model.add(layers.Dense(10))\n", - "Here's the complete architecture of our model.\n", + "# Here's the complete architecture of our model.\n", "\n", "model.summary()" ] @@ -5472,9 +5498,7 @@ { "cell_type": "markdown", "id": "07b30e11", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers." ] @@ -5482,27 +5506,49 @@ { "cell_type": "markdown", "id": "7398c3e5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Compile and train the model" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "id": "233a4813", - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "1563/1563 [==============================] - 20s 11ms/step - loss: 1.5702 - accuracy: 0.4238 - val_loss: 1.2847 - val_accuracy: 0.5375\n", + "Epoch 2/10\n", + "1563/1563 [==============================] - 17s 11ms/step - loss: 1.1986 - accuracy: 0.5762 - val_loss: 1.1051 - val_accuracy: 0.6080\n", + "Epoch 3/10\n", + "1563/1563 [==============================] - 15s 10ms/step - loss: 1.0401 - accuracy: 0.6337 - val_loss: 1.0155 - val_accuracy: 0.6432\n", + "Epoch 4/10\n", + "1563/1563 [==============================] - 16s 10ms/step - loss: 0.9467 - accuracy: 0.6684 - val_loss: 0.9573 - val_accuracy: 0.6644\n", + "Epoch 5/10\n", + "1563/1563 [==============================] - 15s 9ms/step - loss: 0.8787 - accuracy: 0.6909 - val_loss: 0.9499 - val_accuracy: 0.6741\n", + "Epoch 6/10\n", + "1563/1563 [==============================] - 15s 9ms/step - loss: 0.8250 - accuracy: 0.7108 - val_loss: 0.8924 - val_accuracy: 0.6851\n", + "Epoch 7/10\n", + "1563/1563 [==============================] - 15s 9ms/step - loss: 0.7807 - accuracy: 0.7246 - val_loss: 0.8970 - val_accuracy: 0.6846\n", + "Epoch 8/10\n", + "1563/1563 [==============================] - 17s 11ms/step - loss: 0.7421 - accuracy: 0.7367 - val_loss: 0.8826 - val_accuracy: 0.6959\n", + "Epoch 9/10\n", + "1563/1563 [==============================] - 15s 10ms/step - loss: 0.7116 - accuracy: 0.7490 - val_loss: 0.8979 - val_accuracy: 0.7012\n", + "Epoch 10/10\n", + "1563/1563 [==============================] - 15s 10ms/step - loss: 0.6759 - accuracy: 0.7609 - val_loss: 0.8912 - val_accuracy: 0.7006\n" + ] + } + ], "source": [ "model.compile(optimizer='adam',\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=['accuracy'])\n", - "​\n", + "\n", "history = model.fit(train_images, train_labels, epochs=10, \n", " validation_data=(test_images, test_labels))" ] @@ -5510,9 +5556,7 @@ { "cell_type": "markdown", "id": "5d4daa27", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Finally, evaluate the model" ] @@ -5521,10 +5565,7 @@ "cell_type": "code", "execution_count": 23, "id": "be679d5f", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "plt.plot(history.history['accuracy'], label='accuracy')\n", @@ -5540,7 +5581,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/pub/week43/html/week43-bs.html b/doc/pub/week43/html/week43-bs.html index 286995758..5e4a95be4 100644 --- a/doc/pub/week43/html/week43-bs.html +++ b/doc/pub/week43/html/week43-bs.html @@ -37,7 +37,105 @@ doconce format html week43.do.txt --html_style=bootstrap --pygments_html_style=d {'highest level': 2, 'sections': [('Plans for week 43', 2, None, 'plans-for-week-43'), ('Reading Recommendations', 2, None, 'reading-recommendations'), + ('Convolutional Neural Networks (recognizing images)', + 2, + None, + 'convolutional-neural-networks-recognizing-images'), + ('What is the Difference', 2, None, 'what-is-the-difference'), + ('Neural Networks vs CNNs', 2, None, 'neural-networks-vs-cnns'), + ('Why CNNS for images, sound files, medical images from CT scans ' + 'etc?', + 2, + None, + 'why-cnns-for-images-sound-files-medical-images-from-ct-scans-etc'), + ('Regular NNs don’t scale well to full images', + 2, + None, + 'regular-nns-don-t-scale-well-to-full-images'), + ('3D volumes of neurons', 2, None, '3d-volumes-of-neurons'), + ('Layers used to build CNNs', + 2, + None, + 'layers-used-to-build-cnns'), + ('Transforming images', 2, None, 'transforming-images'), ('CNNs in brief', 2, None, 'cnns-in-brief'), + ('Key Idea', 2, None, 'key-idea'), + ('Mathematics of CNNs', 2, None, 'mathematics-of-cnns'), + ('Convolution Examples: Polynomial multiplication', + 2, + None, + 'convolution-examples-polynomial-multiplication'), + ('Efficient Polynomial Multiplication', + 2, + None, + 'efficient-polynomial-multiplication'), + ('A more efficient way of coding the above Convolution', + 2, + None, + 'a-more-efficient-way-of-coding-the-above-convolution'), + ('Convolution Examples: Principle of Superposition and Periodic ' + 'Forces (Fourier Transforms)', + 2, + None, + 'convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms'), + ('Principle of Superposition', + 2, + None, + 'principle-of-superposition'), + ('Simple Code Example', 2, None, 'simple-code-example'), + ('Wrapping up Fourier transforms', + 2, + None, + 'wrapping-up-fourier-transforms'), + ('Finding the Coefficients', 2, None, 'finding-the-coefficients'), + ('Final words on Fourier Transforms', + 2, + None, + 'final-words-on-fourier-transforms'), + ('Two-dimensional Objects', 2, None, 'two-dimensional-objects'), + ('Cross-Correlation', 2, None, 'cross-correlation'), + ('More on Dimensionalities', 2, None, 'more-on-dimensionalities'), + ('Further Dimensionality Remarks', + 2, + None, + 'further-dimensionality-remarks'), + ('CNNs in more detail, Lecture from IN5400', + 2, + None, + 'cnns-in-more-detail-lecture-from-in5400'), + ('CNNs in more detail, building convolutional neural networks in ' + 'Tensorflow and Keras', + 2, + None, + 'cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras'), + ('Setting it up', 2, None, 'setting-it-up'), + ('The MNIST dataset again', 2, None, 'the-mnist-dataset-again'), + ('Strong correlations', 2, None, 'strong-correlations'), + ('Layers of a CNN', 2, None, 'layers-of-a-cnn'), + ('Systematic reduction', 2, None, 'systematic-reduction'), + ('Prerequisites: Collect and pre-process data', + 2, + None, + 'prerequisites-collect-and-pre-process-data'), + ('Importing Keras and Tensorflow', + 2, + None, + 'importing-keras-and-tensorflow'), + ('Running with Keras', 2, None, 'running-with-keras'), + ('Final part', 2, None, 'final-part'), + ('Final visualization', 2, None, 'final-visualization'), + ('The CIFAR01 data set', 2, None, 'the-cifar01-data-set'), + ('Verifying the data set', 2, None, 'verifying-the-data-set'), + ('Set up the model', 2, None, 'set-up-the-model'), + ('Add Dense layers on top', 2, None, 'add-dense-layers-on-top'), + ('Compile and train the model', + 2, + None, + 'compile-and-train-the-model'), + ('Finally, evaluate the model', + 2, + None, + 'finally-evaluate-the-model'), ('Recurrent neural networks: Overarching view', 2, None, @@ -83,68 +181,7 @@ doconce format html week43.do.txt --html_style=bootstrap --pygments_html_style=d ('Interpolating Between MNIST Digits', 2, None, - 'interpolating-between-mnist-digits'), - ('Basic ideas of the Principal Component Analysis (PCA)', - 2, - None, - 'basic-ideas-of-the-principal-component-analysis-pca'), - ('Introducing the Covariance and Correlation functions', - 2, - None, - 'introducing-the-covariance-and-correlation-functions'), - ('More on the covariance', 2, None, 'more-on-the-covariance'), - ('Reminding ourselves about Linear Regression', - 2, - None, - 'reminding-ourselves-about-linear-regression'), - ('Simple Example', 2, None, 'simple-example'), - ('The Correlation Matrix', 2, None, 'the-correlation-matrix'), - ('Numpy Functionality', 2, None, 'numpy-functionality'), - ('Correlation Matrix again', 2, None, 'correlation-matrix-again'), - ('Using Pandas', 2, None, 'using-pandas'), - ('And then the Franke Function', - 2, - None, - 'and-then-the-franke-function'), - ('Lnks with the Design Matrix', - 2, - None, - 'lnks-with-the-design-matrix'), - ('Computing the Expectation Values', - 2, - None, - 'computing-the-expectation-values'), - ('Towards the PCA theorem', 2, None, 'towards-the-pca-theorem'), - ('More on the PCA Theorem', 2, None, 'more-on-the-pca-theorem'), - ('The Algorithm before the Theorem', - 2, - None, - 'the-algorithm-before-the-theorem'), - ('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'), - ('Implementing it', 2, None, 'implementing-it'), - ('First Step', 2, None, 'first-step'), - ('Scaling', 2, None, 'scaling'), - ('Centered Data', 2, None, 'centered-data'), - ('Exploring', 2, None, 'exploring'), - ('Diagonalize the sample covariance matrix to obtain the ' - 'principal components', - 2, - None, - 'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'), - ('Collecting all Steps', 2, None, 'collecting-all-steps'), - ('Classical PCA Theorem', 2, None, 'classical-pca-theorem'), - ('The PCA Theorem', 2, None, 'the-pca-theorem'), - ('Geometric Interpretation and link with Singular Value ' - 'Decomposition', - 2, - None, - 'geometric-interpretation-and-link-with-singular-value-decomposition'), - ('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'), - ('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'), - ('Incremental PCA', 2, None, 'incremental-pca'), - ('Randomized PCA', 3, None, 'randomized-pca'), - ('Kernel PCA', 3, None, 'kernel-pca'), - ('Other techniques', 2, None, 'other-techniques')]} + 'interpolating-between-mnist-digits')]} end of tocinfo --> @@ -167,7 +204,7 @@ MathJax.Hub.Config({

 

 

 

-

ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis +

ATITLE: Week 43: Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks

@@ -182,7 +219,7 @@ MathJax.Hub.Config({

-

Oct 24, 2022

+

Oct 26, 2022


@@ -191,8 +228,8 @@ MathJax.Hub.Config({

Plans for week 43

    -
  • Thursday: Convolutional Neural Networks, basic elements and
    • -
  • Friday: Recurrent Neural Networks and other Deep learning methods, Generalized Adversarial Neural Networ and autoencoders
    • +
  • Thursday: Convolutional Neural Networks (CNN)
    • +
  • Friday: Recurrent Neural Networks (RNN)
@@ -220,10 +257,209 @@ MathJax.Hub.Config({

Reading Recommendations

-
    -
  • Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.
    • +
    +
    + +
      +
    1. Goodfellow, Bengio, Courville, chapter 9
      2. +
    2. Lectures from CS231 at Stanford
      3. +
    3. Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs.
      4. +
    +
    +
    + + +
    +
    + +
      +
    1. Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.
      2. +
    2. Lectures from CS231 at Stanford
    3. Aurelien Geron, chapter 14 on RNNs.
      4. +
    +
    +
    + + + +

    Convolutional Neural Networks (recognizing images)

    + +

    Convolutional neural networks (CNNs) were developed during the last +decade of the previous century, with a focus on character recognition +tasks. Nowadays, CNNs are a central element in the spectacular success +of deep learning methods. The success in for example image +classifications have made them a central tool for most machine +learning practitioners. +

    + +

    CNNs are very similar to ordinary Neural Networks. +They are made up of neurons that have learnable weights and +biases. Each neuron receives some inputs, performs a dot product and +optionally follows it with a non-linearity. The whole network still +expresses a single differentiable score function: from the raw image +pixels on one end to class scores at the other. And they still have a +loss function (for example Softmax) on the last (fully-connected) layer +and all the tips/tricks we developed for learning regular Neural +Networks still apply (back propagation, gradient descent etc etc). +

    + + +

    What is the Difference

    + +

    CNN architectures make the explicit assumption that +the inputs are images, which allows us to encode certain properties +into the architecture. These then make the forward function more +efficient to implement and vastly reduce the amount of parameters in +the network. +

    + +

    Here we provide only a superficial overview, for the more interested, we recommend highly the course +IN5400 – Machine Learning for Image Analysis +and the slides of CS231. +

    + +

    Another good read is the article here https://arxiv.org/pdf/1603.07285.pdf.

    + + +

    Neural Networks vs CNNs

    + +

    Neural networks are defined as affine transformations, that is +a vector is received as input and is multiplied with a matrix of so-called weights (our unknown paramters) to produce an +output (to which a bias vector is usually added before passing the result +through a nonlinear activation function). This is applicable to any type of input, be it an +image, a sound clip or an unordered collection of features: whatever their +dimensionality, their representation can always be flattened into a vector +before the transformation. +

    + + +

    Why CNNS for images, sound files, medical images from CT scans etc?

    + +

    However, when we consider images, sound clips and many other similar kinds of data, these data have an intrinsic +structure. More formally, they share these important properties: +

    +
      +
    • They are stored as multi-dimensional arrays (think of the pixels of a figure) .
      • +
    • They feature one or more axes for which ordering matters (e.g., width and height axes for an image, time axis for a sound clip).
      • +
    • One axis, called the channel axis, is used to access different views of the data (e.g., the red, green and blue channels of a color image, or the left and right channels of a stereo audio track).
    +

    These properties are not exploited when an affine transformation is applied; in +fact, all the axes are treated in the same way and the topological information +is not taken into account. Still, taking advantage of the implicit structure of +the data may prove very handy in solving some tasks, like computer vision and +speech recognition, and in these cases it would be best to preserve it. This is +where discrete convolutions come into play. +

    + +

    A discrete convolution is a linear transformation that preserves this notion of +ordering. It is sparse (only a few input units contribute to a given output +unit) and reuses parameters (the same weights are applied to multiple locations +in the input). +

    + + +

    Regular NNs don’t scale well to full images

    + +

    As an example, consider +an image of size \( 32\times 32\times 3 \) (32 wide, 32 high, 3 color channels), so a +single fully-connected neuron in a first hidden layer of a regular +Neural Network would have \( 32\times 32\times 3 = 3072 \) weights. This amount still +seems manageable, but clearly this fully-connected structure does not +scale to larger images. For example, an image of more respectable +size, say \( 200\times 200\times 3 \), would lead to neurons that have +\( 200\times 200\times 3 = 120,000 \) weights. +

    + +

    We could have +several such neurons, and the parameters would add up quickly! Clearly, +this full connectivity is wasteful and the huge number of parameters +would quickly lead to possible overfitting. +

    + +
    +
    +
    +

    Figure 1: A regular 3-layer Neural Network.

    +
    +

    +
    + + +

    3D volumes of neurons

    + +

    Convolutional Neural Networks take advantage of the fact that the +input consists of images and they constrain the architecture in a more +sensible way. +

    + +

    In particular, unlike a regular Neural Network, the +layers of a CNN have neurons arranged in 3 dimensions: width, +height, depth. (Note that the word depth here refers to the third +dimension of an activation volume, not to the depth of a full Neural +Network, which can refer to the total number of layers in a network.) +

    + +

    To understand it better, the above example of an image +with an input volume of +activations has dimensions \( 32\times 32\times 3 \) (width, height, +depth respectively). +

    + +

    The neurons in a layer will +only be connected to a small region of the layer before it, instead of +all of the neurons in a fully-connected manner. Moreover, the final +output layer could for this specific image have dimensions \( 1\times 1 \times 10 \), +because by the +end of the CNN architecture we will reduce the full image into a +single vector of class scores, arranged along the depth +dimension. +

    + +
    +
    +
    +

    Figure 2: A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).

    +
    +

    +
    + + +

    Layers used to build CNNs

    + +

    A simple CNN is a sequence of layers, and every layer of a CNN +transforms one volume of activations to another through a +differentiable function. We use three main types of layers to build +CNN architectures: Convolutional Layer, Pooling Layer, and +Fully-Connected Layer (exactly as seen in regular Neural Networks). We +will stack these layers to form a full CNN architecture. +

    + +

    A simple CNN for image classification could have the architecture:

    + +
      +
    • INPUT (\( 32\times 32 \times 3 \)) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
      • +
    • CONV (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as \( [32\times 32\times 12] \) if we decided to use 12 filters.
      • +
    • RELU layer will apply an elementwise activation function, such as the \( max(0,x) \) thresholding at zero. This leaves the size of the volume unchanged (\( [32\times 32\times 12] \)).
      • +
    • POOL (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as \( [16\times 16\times 12] \).
      • +
    • FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size \( [1\times 1\times 10] \), where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
      • +
    + +

    Transforming images

    + +

    CNNs transform the original image layer by layer from the original +pixel values to the final class scores. +

    + +

    Observe that some layers contain +parameters and other don’t. In particular, the CNN layers perform +transformations that are a function of not only the activations in the +input volume, but also of the parameters (the weights and biases of +the neurons). On the other hand, the RELU/POOL layers will implement a +fixed function. The parameters in the CONV/FC layers will be trained +with gradient descent so that the class scores that the CNN computes +are consistent with the labels in the training set for each image. +

    +

    CNNs in brief

    @@ -242,9 +478,1093 @@ the course and the slides of CS231 which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs.

    -

    However, both standard feed forwards networks and CNNs perform well on data with unknown length.

    +

    The textbook by Goodfellow et al, see chapter 9 contains an in depth discussion as well.

    + + +

    Key Idea

    + +

    A dense neural network is representd by an affine operation (like matrix-matrix multiplication) where all parameters are included.

    + +

    The key idea in CNNs for say imaging is that in images neighbor pixels tend to be related! So we connect +only neighboring neurons in the input instead of connecting all with the first hidden layer. +

    + +

    We say we perform a filtering (convolution is the mathematical operation).

    + + +

    Mathematics of CNNs

    + +

    The mathematics of CNNs is based on the mathematical operation of +convolution. In mathematics (in particular in functional analysis), +convolution is represented by mathematical operation (integration, +summation etc) on two function in order to produce a third function +that expresses how the shape of one gets modified by the other. +Convolution has a plethora of applications in a variety of disciplines, spanning from statistics to signal processing, computer vision, solutions of differential equations,linear algebra, engineering, and yes, machine learning. +

    + +

    Mathematically, convolution is defined as follows (one-dimensional example): +Let us define a continuous function \( y(t) \) given by +

    +$$ +y(t) = \int x(a) w(t-a) da, +$$ + +

    where \( x(a) \) represents a so-called input and \( w(t-a) \) is normally called the weight function or kernel.

    + +

    The above integral is written in a more compact form as

    +$$ +y(t) = \left(x * w\right)(t). +$$ + +

    The discretized version reads

    +$$ +y(t) = \sum_{a=-\infty}^{a=\infty}x(a)w(t-a). +$$ + +

    Computing the inverse of the above convolution operations is known as deconvolution.

    + +

    How can we use this? And what does it mean? Let us study some familiar examples first.

    + + +

    Convolution Examples: Polynomial multiplication

    + +

    We have already met such an example in project 1 when we tried to set +up the design matrix for a two-dimensional function. This was an +example of polynomial multiplication. Let us recast such a problem in terms of the convolution operation. +Let us look a the following polynomials to second and third order, respectively: +

    +$$ +p(t) = \alpha_0+\alpha_1 t+\alpha_2 t^2, +$$ + +

    and

    +$$ +s(t) = \beta_0+\beta_1 t+\beta_2 t^2+\beta_3 t^3. +$$ + +

    The polynomial multiplication gives us a new polynomial of degree \( 5 \)

    +$$ +z(t) = \delta_0+\delta_1 t+\delta_2 t^2+\delta_3 t^3+\delta_4 t^4+\delta_5 t^5. +$$ + + + +

    Efficient Polynomial Multiplication

    + +

    Computing polynomial products can be implemented efficiently if we rewrite the more brute force multiplications using convolution. +We note first that the new coefficients are given as +

    + +$$ +\begin{split} +\delta_0=&\alpha_0\beta_0\\ +\delta_1=&\alpha_1\beta_0+\alpha_1\beta_0\\ +\delta_2=&\alpha_0\beta_2+\alpha_1\beta_1+\alpha_2\beta_0\\ +\delta_3=&\alpha_1\beta_2+\alpha_2\beta_1+\alpha_0\beta_3\\ +\delta_4=&\alpha_2\beta_2+\alpha_1\beta_3\\ +\delta_5=&\alpha_2\beta_3.\\ +\end{split} +$$ + +

    We note that \( \alpha_i=0 \) except for \( i\in \left\{0,1,2\right\} \) and \( \beta_i=0 \) except for \( i\in\left\{0,1,2,3\right\} \).

    + +

    We can then rewrite the coefficients \( \delta_j \) using a discrete convolution as

    +$$ +\delta_j = \sum_{i=-\infty}^{i=\infty}\alpha_i\beta_{j-i}=(\alpha * \beta)_j, +$$ + +

    or as a double sum with restriction \( l=i+j \)

    +$$ +\delta_l = \sum_{ij}\alpha_i\beta_{j}. +$$ + +

    Do you see a potential drawback with these equations?

    + + +

    A more efficient way of coding the above Convolution

    + +

    Since we only have a finite number of \( \alpha \) and \( \beta \) values +which are non-zero, we can rewrite the above convolution expressions +as a matrix-vector multiplication +

    + +$$ +\boldsymbol{\delta}=\begin{bmatrix}\alpha_0 & 0 & 0 & 0 \\ + \alpha_1 & \alpha_0 & 0 & 0 \\ + \alpha_2 & \alpha_1 & \alpha_0 & 0 \\ + 0 & \alpha_2 & \alpha_1 & \alpha_0 \\ + 0 & 0 & \alpha_2 & \alpha_1 \\ + 0 & 0 & 0 & \alpha_2 + \end{bmatrix}\begin{bmatrix} \beta_0 \\ \beta_1 \\ \beta_2 \\ \beta_3\end{bmatrix}. +$$ + +

    The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding \( \beta \) and a vector holding \( \alpha \). +In this case we have +

    +$$ +\boldsymbol{\delta}=\begin{bmatrix}\beta_0 & 0 & 0 \\ + \beta_1 & \beta_0 & 0 \\ + \beta_2 & \beta_1 & \beta_0 \\ + \beta_3 & \beta_2 & \beta_1 \\ + 0 & \beta_3 & \beta_2 \\ + 0 & 0 & \beta_3 + \end{bmatrix}\begin{bmatrix} \alpha_0 \\ \alpha_1 \\ \alpha_2\end{bmatrix}. +$$ + +

    Note that the use of these matrices is for mathematical purposes only and not implementation purposes. +When implementing the above equation we do not encode (and allocate memory) the matrices explicitely. +We rather code the convolutions in the minimal memory footprint that they require. +

    + +

    Does the number of floating point operations change here when we use the commutative property?

    + + +

    Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)

    + +

    For problems with so-called harmonic oscillations, given by for example the following differential equation

    +$$ +m\frac{d^2x}{dt^2}+\eta\frac{dx}{dt}+x(t)=F(t), +$$ + +

    where \( F(t) \) is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations.

    + +

    If one has several driving forces, \( F(t)=\sum_n F_n(t) \), one can find +the particular solution to each \( F_n \), \( x_{pn}(t) \), and the particular +solution for the entire driving force is then given by a series like +

    + +$$ +\begin{equation} +x_p(t)=\sum_nx_{pn}(t). +\label{_auto1} +\end{equation} +$$ + + + +

    Principle of Superposition

    + +

    This is known as the principle of superposition. It only applies when +the homogenous equation is linear. If there were an anharmonic term +such as \( x^3 \) in the homogenous equation, then when one summed various +solutions, \( x=(\sum_n x_n)^2 \), one would get cross +terms. Superposition is especially useful when \( F(t) \) can be written +as a sum of sinusoidal terms, because the solutions for each +sinusoidal (sine or cosine) term is analytic. +

    + +

    Driving forces are often periodic, even when they are not +sinusoidal. Periodicity implies that for some time \( \tau \) +

    + +$$ +\begin{eqnarray} +F(t+\tau)=F(t). +\end{eqnarray} +$$ + +

    One example of a non-sinusoidal periodic force is a square wave. Many +components in electric circuits are non-linear, e.g. diodes, which +makes many wave forms non-sinusoidal even when the circuits are being +driven by purely sinusoidal sources. +

    + + +

    Simple Code Example

    + +

    The code here shows a typical example of such a square wave generated using the functionality included in the scipy Python package. We have used a period of \( \tau=0.2 \).

    + + + +
    +
    +
    +
    +
    + 
    import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t)
+plt.plot(t, SqrSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    For the sinusoidal example the +period is \( \tau=2\pi/\omega \). However, higher harmonics can also +satisfy the periodicity requirement. In general, any force that +satisfies the periodicity requirement can be expressed as a sum over +harmonics, +

    + +$$ +\begin{equation} +F(t)=\frac{f_0}{2}+\sum_{n>0} f_n\cos(2n\pi t/\tau)+g_n\sin(2n\pi t/\tau). +\label{_auto2} +\end{equation} +$$ + + + +

    Wrapping up Fourier transforms

    + +

    We can write down the answer for +\( x_{pn}(t) \), by substituting \( f_n/m \) or \( g_n/m \) for \( F_0/m \). By +writing each factor \( 2n\pi t/\tau \) as \( n\omega t \), with \( \omega\equiv +2\pi/\tau \), +

    + +$$ +\begin{equation} +\label{eq:fourierdef1} +F(t)=\frac{f_0}{2}+\sum_{n>0}f_n\cos(n\omega t)+g_n\sin(n\omega t). +\end{equation} +$$ + +

    The solutions for \( x(t) \) then come from replacing \( \omega \) with +\( n\omega \) for each term in the particular solution, +

    + +$$ +\begin{eqnarray} +x_p(t)&=&\frac{f_0}{2k}+\sum_{n>0} \alpha_n\cos(n\omega t-\delta_n)+\beta_n\sin(n\omega t-\delta_n),\\ +\nonumber +\alpha_n&=&\frac{f_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\beta_n&=&\frac{g_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\delta_n&=&\tan^{-1}\left(\frac{2\beta n\omega}{\omega_0^2-n^2\omega^2}\right). +\end{eqnarray} +$$ + + + +

    Finding the Coefficients

    + +

    Because the forces have been applied for a long time, any non-zero +damping eliminates the homogenous parts of the solution, so one need +only consider the particular solution for each \( n \). +

    + +

    The problem is considered solved if one can find expressions for the +coefficients \( f_n \) and \( g_n \), even though the solutions are expressed +as an infinite sum. The coefficients can be extracted from the +function \( F(t) \) by +

    + +$$ +\begin{eqnarray} +\label{eq:fourierdef2} +f_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\cos(2n\pi t/\tau),\\ +\nonumber +g_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\sin(2n\pi t/\tau). +\end{eqnarray} +$$ + +

    To check the consistency of these expressions and to verify +Eq. \eqref{eq:fourierdef2}, one can insert the expansion of \( F(t) \) in +Eq. \eqref{eq:fourierdef1} into the expression for the coefficients in +Eq. \eqref{eq:fourierdef2} and see whether +

    + +$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~\left\{ +\frac{f_0}{2}+\sum_{m>0}f_m\cos(m\omega t)+g_m\sin(m\omega t) +\right\}\cos(n\omega t). +\end{eqnarray} +$$ + +

    Immediately, one can throw away all the terms with \( g_m \) because they +convolute an even and an odd function. The term with \( f_0/2 \) +disappears because \( \cos(n\omega t) \) is equally positive and negative +over the interval and will integrate to zero. For all the terms +\( f_m\cos(m\omega t) \) appearing in the sum, one can use angle addition +formulas to see that \( \cos(m\omega t)\cos(n\omega +t)=(1/2)(\cos[(m+n)\omega t]+\cos[(m-n)\omega t] \). This will integrate +to zero unless \( m=n \). In that case the \( m=n \) term gives +

    + +$$ +\begin{equation} +\int_{-\tau/2}^{\tau/2}dt~\cos^2(m\omega t)=\frac{\tau}{2}, +\label{_auto3} +\end{equation} +$$ + +

    and

    + +$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~f_n/2\\ +\nonumber +&=&f_n~\checkmark. +\end{eqnarray} +$$ + +

    The same method can be used to check for the consistency of \( g_n \).

    + + +

    Final words on Fourier Transforms

    + +

    The code here uses the Fourier series applied to a +square wave signal. The code here +visualizes the various approximations given by Fourier series compared +with a square wave with period \( T=0.2 \) (dimensionless time), width \( 0.1 \) and max value of the force \( F=2 \). We +see that when we increase the number of components in the Fourier +series, the Fourier series approximation gets closer and closer to the +square wave signal. +

    + + + +
    +
    +
    +
    +
    + 
    import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+T =0.2
+# Max value of square signal                                                                             
+Fmax= 2.0
+# Width of signal   
+Width = 0.1
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+FourierSeriesSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t+np.pi*Width/T)
+a0 = Fmax*Width/T
+FourierSeriesSignal = a0
+Factor = 2.0*Fmax/np.pi
+for i in range(1,500):
+    FourierSeriesSignal += Factor/(i)*np.sin(np.pi*i*Width/T)*np.cos(i*t*2*np.pi/T)
+plt.plot(t, SqrSignal)
+plt.plot(t, FourierSeriesSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Two-dimensional Objects

    + +

    We often use convolutions over more than one dimension at a time. If +we have a two-dimensional image \( I \) as input, we can have a filter +defined by a two-dimensional kernel \( K \). This leads to an output \( S \) +

    + +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(m,n)K(i-m,j-n). +$$ + +

    Convolution is a commutatitave process, which means we can rewrite this equation as

    +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i-m,j-n)K(m,n). +$$ + +

    Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of \( m \) and \( n \).

    + + +

    Cross-Correlation

    + +

    Many deep learning libraries implement cross-correlation instead of convolution

    +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i+m,j-+)K(m,n). +$$ + + + +

    More on Dimensionalities

    + +

    In feilds like signal processing (and imaging as well), one designs +so-called filters. These filters are defined by the convolutions and +are often hand-crafted. One may specify filters for smoothing, edge +detection, frequency reshaping, and similar operations. However with +neural networks the idea is to automatically learn the filters and use +many of them in conjunction with non-linear operations (activation +functions). +

    + +

    As an example consider a neural network operating on sound sequence +data. Assume that we an input vector \( \boldsymbol{x} \) of length \( d=10^6 \). We +construct then a neural network with onle hidden layer only with +\( 10^4 \) nodes. This means that we will have a weight matrix with +\( 10^4\times 10^6=10^{10} \) weights to be determined, together with \( 10^4 \) biases. +

    + +

    Assume furthermore that we have an output layer which is meant to train whether the sound sequence represents a human voice (true) or something else (false). +It means that we have only one output node. But since this output node connects to \( 10^4 \) nodes in the hidden layer, there are in total \( 10^4 \) weights to be determined for the output layer, plus one bias. In total we have +

    + +$$ +\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \approx 10^{10}, +$$ + +

    that is ten billion parameters to determine.

    + + +

    Further Dimensionality Remarks

    + +

    In today’s architecture one can train such neural networks, however +this is a huge number of parameters for the task at hand. In general, +it is a very wasteful and inefficient use of dense matrices as +parameters. Just as importantly, such trained network parameters are +very specific for the type of input data on which they were trained +and the network is not likely to generalize easily to variations in +the input. +

    + +

    The main principles that justify convolutions is locality of +information and repetion of patterns within the signal. Sound samples +of the input in adjacent spots are much more likely to affect each +other than those that are very far away. Similarly, sounds are +repeated in multiple times in the signal. While slightly simplistic, +reasoning about such a sound example demonstrates this. The same +principles then apply to images and other similar data. +

    + + +

    CNNs in more detail, Lecture from IN5400

    + + + +

    CNNs in more detail, building convolutional neural networks in Tensorflow and Keras

    + +

    As discussed above, CNNs are neural networks built from the assumption that the inputs +to the network are 2D images. This is important because the number of features or pixels in images +grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network. +

    + +

    As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks +are the convolutional and pooling layers stacked in pairs between the input and the hidden layer. +In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D +matrices, typically 1 for each color dimension (Red, Green, Blue). +

    + + +

    Setting it up

    + +

    It means that to represent the entire +dataset of images, we require a 4D matrix or tensor. This tensor has the dimensions: +

    +$$ +(n_{inputs},\, n_{pixels, width},\, n_{pixels, height},\, depth) . +$$ + + + +

    The MNIST dataset again

    + +

    The MNIST dataset consists of grayscale images with a pixel size of +\( 28\times 28 \), meaning we require \( 28 \times 28 = 724 \) weights to each +neuron in the first hidden layer. +

    + +

    If we were to analyze images of size \( 128\times 128 \) we would require +\( 128 \times 128 = 16384 \) weights to each neuron. Even worse if we were +dealing with color images, as most images are, we have an image matrix +of size \( 128\times 128 \) for each color dimension (Red, Green, Blue), +meaning 3 times the number of weights \( = 49152 \) are required for every +single neuron in the first hidden layer. +

    + + + +

    Strong correlations

    + +

    Images typically have strong local correlations, meaning that a small +part of the image varies little from its neighboring regions. If for +example we have an image of a blue car, we can roughly assume that a +small blue part of the image is surrounded by other blue regions. +

    + +

    Therefore, instead of connecting every single pixel to a neuron in the +first hidden layer, as we have previously done with deep neural +networks, we can instead connect each neuron to a small part of the +image (in all 3 RGB depth dimensions). The size of each small area is +fixed, and known as a receptive. +

    + + + +

    Layers of a CNN

    +

    The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. +The input image is typically a square matrix of depth 3. +

    + +

    A convolution is performed on the image which outputs +a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as filters. +

    + +

    Each filter slides along the input image, taking the dot product +between each small part of the image and the filter, in all depth +dimensions. This is then passed through a non-linear function, +typically the Rectified Linear (ReLu) function, which serves as the +activation of the neurons in the first convolutional layer. This is +further passed through a pooling layer, which reduces the size of the +convolutional layer, e.g. by taking the maximum or average across some +small regions, and this serves as input to the next convolutional +layer. +

    + + +

    Systematic reduction

    + +

    By systematically reducing the size of the input volume, through +convolution and pooling, the network should create representations of +small parts of the input, and then from them assemble representations +of larger areas. The final pooling layer is flattened to serve as +input to a hidden layer, such that each neuron in the final pooling +layer is connected to every single neuron in the hidden layer. This +then serves as input to the output layer, e.g. a softmax output for +classification. +

    + + + +

    Prerequisites: Collect and pre-process data

    + + +
    +
    +
    +
    +
    + 
    # import necessary packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+
+
+# ensure the same random numbers appear every time
+np.random.seed(0)
+
+# display images in notebook
+%matplotlib inline
+plt.rcParams['figure.figsize'] = (12,12)
+
+
+# download MNIST dataset
+digits = datasets.load_digits()
+
+# define inputs and labels
+inputs = digits.images
+labels = digits.target
+
+# RGB images have a depth of 3
+# our images are grayscale so they should have a depth of 1
+inputs = inputs[:,:,:,np.newaxis]
+
+print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
+print("labels = (n_inputs) = " + str(labels.shape))
+
+
+# choose some random images to display
+n_inputs = len(inputs)
+indices = np.arange(n_inputs)
+random_indices = np.random.choice(indices, size=5)
+
+for i, image in enumerate(digits.images[random_indices]):
+    plt.subplot(1, 5, i+1)
+    plt.axis('off')
+    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
+    plt.title("Label: %d" % digits.target[random_indices[i]])
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Importing Keras and Tensorflow

    + + +
    +
    +
    +
    +
    + 
    from tensorflow.keras import datasets, layers, models
+from tensorflow.keras.layers import Input
+from tensorflow.keras.models import Sequential      #This allows appending layers to existing models
+from tensorflow.keras.layers import Dense           #This allows defining the characteristics of a particular layer
+from tensorflow.keras import optimizers             #This allows using whichever optimiser we want (sgd,adam,RMSprop)
+from tensorflow.keras import regularizers           #This allows using whichever regularizer we want (l1,l2,l1_l2)
+from tensorflow.keras.utils import to_categorical   #This allows using categorical cross entropy as the cost function
+#from tensorflow.keras import Conv2D
+#from tensorflow.keras import MaxPooling2D
+#from tensorflow.keras import Flatten
+
+from sklearn.model_selection import train_test_split
+
+# representation of labels
+labels = to_categorical(labels)
+
+# split into train and test data
+# one-liner from scikit-learn library
+train_size = 0.8
+test_size = 1 - train_size
+X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
+                                                    test_size=test_size)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Running with Keras

    + + + +
    +
    +
    +
    +
    + 
    def create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd):
+    model = Sequential()
+    model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
+              activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
+    
+    sgd = optimizers.SGD(lr=eta)
+    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+    
+    return model
+
+epochs = 100
+batch_size = 100
+input_shape = X_train.shape[1:4]
+receptive_field = 3
+n_filters = 10
+n_neurons_connected = 50
+n_categories = 10
+
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Final part

    + + + +
    +
    +
    +
    +
    + 
    CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+        
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd)
+        CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+        scores = CNN.evaluate(X_test, Y_test)
+        
+        CNN_keras[i][j] = CNN
+        
+        print("Learning rate = ", eta)
+        print("Lambda = ", lmbd)
+        print("Test accuracy: %.3f" % scores[1])
+        print()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Final visualization

    + + + +
    +
    +
    +
    +
    + 
    # visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
+
+sns.set()
+
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+
+for i in range(len(eta_vals)):
+    for j in range(len(lmbd_vals)):
+        CNN = CNN_keras[i][j]
+
+        train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
+
+        
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    The CIFAR01 data set

    + +

    The CIFAR10 dataset contains 60,000 color images in 10 classes, with +6,000 images in each class. The dataset is divided into 50,000 +training images and 10,000 testing images. The classes are mutually +exclusive and there is no overlap between them. +

    + + + +
    +
    +
    +
    +
    + 
    import tensorflow as tf
+
+from tensorflow.keras import datasets, layers, models
+import matplotlib.pyplot as plt
+
+# We import the data set
+(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+
+# Normalize pixel values to be between 0 and 1 by dividing by 255. 
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Verifying the data set

    + +

    To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image.

    + + + +
    +
    +
    +
    +
    + 
    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+​
+plt.figure(figsize=(10,10))
+for i in range(25):
+    plt.subplot(5,5,i+1)
+    plt.xticks([])
+    plt.yticks([])
+    plt.grid(False)
+    plt.imshow(train_images[i], cmap=plt.cm.binary)
+    # The CIFAR labels happen to be arrays, 
+    # which is why you need the extra index
+    plt.xlabel(class_names[train_labels[i][0]])
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Set up the model

    + +

    The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers.

    + +

    As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer.

    + + + +
    +
    +
    +
    +
    + 
    model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+
+# Let's display the architecture of our model so far.
+
+model.summary()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer.

    + + +

    Add Dense layers on top

    + +

    To complete our model, you will feed the last output tensor from the +convolutional base (of shape (4, 4, 64)) into one or more Dense layers +to perform classification. Dense layers take vectors as input (which +are 1D), while the current output is a 3D tensor. First, you will +flatten (or unroll) the 3D output to 1D, then add one or more Dense +layers on top. CIFAR has 10 output classes, so you use a final Dense +layer with 10 outputs and a softmax activation. +

    + + + +
    +
    +
    +
    +
    + 
    model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10))
+Here's the complete architecture of our model.
+
+model.summary()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.

    + + +

    Compile and train the model

    + + + +
    +
    +
    +
    +
    + 
    model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+​
+history = model.fit(train_images, train_labels, epochs=10, 
+                    validation_data=(test_images, test_labels))
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Finally, evaluate the model

    + + + +
    +
    +
    +
    +
    + 
    plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.ylim([0.5, 1])
+plt.legend(loc='lower right')
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+print(test_acc)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    -

    This is where recurrent nueral networks (RNNs) come to our rescue.

    Recurrent neural networks: Overarching view

    @@ -272,7 +1592,7 @@ systems such as automatic translation and speech-to-text.

    Set up of an RNN

    -

    More to text to be added

    +

    See handwritten notes for week 43 and Lectures from CS231 at Stanford

    A simple example

    @@ -1069,7 +2389,7 @@ samples $$ \begin{equation} x = g(z; \theta^{(g)}) -\label{_auto1} +\label{_auto4} \end{equation} $$ @@ -1086,7 +2406,7 @@ value given by $$ \begin{equation} d(x; \theta^{(d)}) -\label{_auto2} +\label{_auto5} \end{equation} $$ @@ -1099,7 +2419,7 @@ which a function $$ \begin{equation} v(\theta^{(g)}, \theta^{(d)}) -\label{_auto3} +\label{_auto6} \end{equation} $$ @@ -1110,7 +2430,7 @@ conjugate reward $$ \begin{equation} -v(\theta^{(g)}, \theta^{(d)}) -\label{_auto4} +\label{_auto7} \end{equation} $$ @@ -1145,7 +2465,7 @@ $$ \begin{equation} g^* = \underset{g}{\mathrm{argmin}}\hspace{2pt} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\label{_auto5} +\label{_auto8} \end{equation} $$ @@ -1155,7 +2475,7 @@ $$ v(\theta^{(g)}, \theta^{(d)}) = \mathbb{E}_{x\sim p_\mathrm{data}}\log d(x) + \mathbb{E}_{x\sim p_\mathrm{model}} \log (1 - d(x)) -\label{_auto6} +\label{_auto9} \end{equation} $$ @@ -1166,7 +2486,7 @@ approximation of a partition function. In the case where $$ \begin{equation} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\label{_auto7} +\label{_auto10} \end{equation} $$ @@ -2122,1207 +3442,6 @@ plot_results(results, plot_number)
- -

Basic ideas of the Principal Component Analysis (PCA)

- -

The principal component analysis deals with the problem of fitting a -low-dimensional affine subspace \( S \) of dimension \( d \) much smaller than -the total dimension \( D \) of the problem at hand (our data -set). Mathematically it can be formulated as a statistical problem or -a geometric problem. In our discussion of the theorem for the -classical PCA, we will stay with a statistical approach. -Historically, the PCA was first formulated in a statistical setting in order to estimate the principal component of a multivariate random variable. -

- -

We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see below for its definition)

-
    -
  • Each data point is determined by \( p \) extrinsic (measurement) variables
    • -
  • We may want to ask the following question: Are there fewer intrinsic variables (say \( d < < p \)) that still approximately describe the data?
    • -
  • If so, these intrinsic variables may tell us something important and finding these intrinsic variables is what dimension reduction methods do.
    • -
-

A good read is for example Vidal, Ma and Sastry.

- - -

Introducing the Covariance and Correlation functions

- -

Before we discuss the PCA theorem, we need to remind ourselves about -the definition of the covariance and the correlation function. These are quantities -

- -

Suppose we have defined two vectors -\( \hat{x} \) and \( \hat{y} \) with \( n \) elements each. The covariance matrix \( \boldsymbol{C} \) is defined as -

-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{cov}[\boldsymbol{x},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{y},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{y},\boldsymbol{y}] \\ - \end{bmatrix}, -$$ - -

where for example

-$$ -\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). -$$ - -

With this definition and recalling that the variance is defined as

-$$ -\mathrm{var}[\boldsymbol{x}]=\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})^2, -$$ - -

we can rewrite the covariance matrix as

-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{var}[\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] & \mathrm{var}[\boldsymbol{y}] \\ - \end{bmatrix}. -$$ - - - -

More on the covariance

-

The covariance takes values between zero and infinity and may thus -lead to problems with loss of numerical precision for particularly -large values. It is common to scale the covariance matrix by -introducing instead the correlation matrix defined via the so-called -correlation function -

- -$$ -\mathrm{corr}[\boldsymbol{x},\boldsymbol{y}]=\frac{\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}]}{\sqrt{\mathrm{var}[\boldsymbol{x}] \mathrm{var}[\boldsymbol{y}]}}. -$$ - -

The correlation function is then given by values \( \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] -\in [-1,1] \). This avoids eventual problems with too large values. We -can then define the correlation matrix for the two vectors \( \boldsymbol{x} \) -and \( \boldsymbol{y} \) as -

- -$$ -\boldsymbol{K}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} 1 & \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{corr}[\boldsymbol{y},\boldsymbol{x}] & 1 \\ - \end{bmatrix}, -$$ - -

In the above example this is the function we constructed using pandas.

- - -

Reminding ourselves about Linear Regression

-

In our derivation of the various regression algorithms like Ordinary Least Squares or Ridge regression -we defined the design/feature matrix \( \boldsymbol{X} \) as -

- -$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ - -

with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) refering to the column numbers and the -entries \( n \) being the row elements. -We can rewrite the design/feature matrix in terms of its column vectors as -

-$$ -\boldsymbol{X}=\begin{bmatrix} \boldsymbol{x}_0 & \boldsymbol{x}_1 & \boldsymbol{x}_2 & \dots & \dots & \boldsymbol{x}_{p-1}\end{bmatrix}, -$$ - -

with a given vector

-$$ -\boldsymbol{x}_i^T = \begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \dots & \dots x_{n-1,i}\end{bmatrix}. -$$ - - - -

Simple Example

-

With these definitions, we can now rewrite our \( 2\times 2 \) -correlation/covariance matrix in terms of a moe general design/feature -matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \). This leads to a \( p\times p \) -covariance matrix for the vectors \( \boldsymbol{x}_i \) with \( i=0,1,\dots,p-1 \) -

- -$$ -\boldsymbol{C}[\boldsymbol{x}] = \begin{bmatrix} -\mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_1] & \mathrm{var}[\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & \mathrm{var}[\boldsymbol{x}_{p-1}]\\ -\end{bmatrix}, -$$ - - - -

The Correlation Matrix

- -

and the correlation matrix

-$$ -\boldsymbol{K}[\boldsymbol{x}] = \begin{bmatrix} -1 & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_0] & 1 & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_1] & 1 & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & 1\\ -\end{bmatrix}, -$$ - - - -

Numpy Functionality

- -

The Numpy function np.cov calculates the covariance elements using -the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have -the exact mean values. The following simple function uses the -np.vstack function which takes each vector of dimension \( 1\times n \) -and produces a \( 2\times n \) matrix \( \boldsymbol{W} \) -

- -$$ -\boldsymbol{W}^T = \begin{bmatrix} x_0 & y_0 \\ - x_1 & y_1 \\ - x_2 & y_2\\ - \dots & \dots \\ - x_{n-2} & y_{n-2}\\ - x_{n-1} & y_{n-1} & - \end{bmatrix}, -$$ - -

which in turn is converted into into the \( 2\times 2 \) covariance matrix -\( \boldsymbol{C} \) via the Numpy function np.cov(). We note that we can also calculate -the mean value of each set of samples \( \boldsymbol{x} \) etc using the Numpy -function np.mean(x). We can also extract the eigenvalues of the -covariance matrix through the np.linalg.eig() function. -

- - - -
-
-
-
-
- 
# Importing various packages
-import numpy as np
-n = 100
-x = np.random.normal(size=n)
-print(np.mean(x))
-y = 4+3*x+np.random.normal(size=n)
-print(np.mean(y))
-W = np.vstack((x, y))
-C = np.cov(W)
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - - -

Correlation Matrix again

- -

The previous example can be converted into the correlation matrix by -simply scaling the matrix elements with the variances. We should also -subtract the mean values for each column. This leads to the following -code which sets up the correlations matrix for the previous example in -a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the \( 2\times 2 \) correlation matrix (since we have only two vectors). -

- - - -
-
-
-
-
- 
import numpy as np
-n = 100
-# define two vectors                                                                                           
-x = np.random.random(size=n)
-y = 4+3*x+np.random.normal(size=n)
-#scaling the x and y vectors                                                                                   
-x = x - np.mean(x)
-y = y - np.mean(y)
-variance_x = np.sum(x@x)/n
-variance_y = np.sum(y@y)/n
-print(variance_x)
-print(variance_y)
-cov_xy = np.sum(x@y)/n
-cov_xx = np.sum(x@x)/n
-cov_yy = np.sum(y@y)/n
-C = np.zeros((2,2))
-C[0,0]= cov_xx/variance_x
-C[1,1]= cov_yy/variance_y
-C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)
-C[1,0]= C[0,1]
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We see that the matrix elements along the diagonal are one as they -should be and that the matrix is symmetric. Furthermore, diagonalizing -this matrix we easily see that it is a positive definite matrix. -

- -

The above procedure with numpy can be made more compact if we use pandas.

- - -

Using Pandas

- -

We whow here how we can set up the correlation matrix using pandas, as done in this simple code

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-n = 10
-x = np.random.normal(size=n)
-x = x - np.mean(x)
-y = 4+3*x+np.random.normal(size=n)
-y = y - np.mean(y)
-X = (np.vstack((x, y))).T
-print(X)
-Xpd = pd.DataFrame(X)
-print(Xpd)
-correlation_matrix = Xpd.corr()
-print(correlation_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - - -

And then the Franke Function

- -

We expand this model to the Franke function discussed above.

- - - -
-
-
-
-
- 
# Common imports
-import numpy as np
-import pandas as pd
-
-
-def FrankeFunction(x,y):
-	term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
-	term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
-	term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
-	term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
-	return term1 + term2 + term3 + term4
-
-
-def create_X(x, y, n ):
-	if len(x.shape) > 1:
-		x = np.ravel(x)
-		y = np.ravel(y)
-
-	N = len(x)
-	l = int((n+1)*(n+2)/2)		# Number of elements in beta
-	X = np.ones((N,l))
-
-	for i in range(1,n+1):
-		q = int((i)*(i+1)/2)
-		for k in range(i+1):
-			X[:,q+k] = (x**(i-k))*(y**k)
-
-	return X
-
-
-# Making meshgrid of datapoints and compute Franke's function
-n = 4
-N = 100
-x = np.sort(np.random.uniform(0, 1, N))
-y = np.sort(np.random.uniform(0, 1, N))
-z = FrankeFunction(x, y)
-X = create_X(x, y, n=n)    
-
-Xpd = pd.DataFrame(X)
-# subtract the mean values and set up the covariance matrix
-Xpd = Xpd - Xpd.mean()
-covariance_matrix = Xpd.cov()
-print(covariance_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We note here that the covariance is zero for the first rows and -columns since all matrix elements in the design matrix were set to one -(we are fitting the function in terms of a polynomial of degree \( n \)). We would however not include the intercept -and wee can simply -drop these elements and construct a correlation -matrix without them by centering our matrix elements by subtracting the mean of each column. -

- - -

Lnks with the Design Matrix

- -

We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix \( \boldsymbol{X} \) as

-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ - -

To see this let us simply look at a design matrix \( \boldsymbol{X}\in {\mathbb{R}}^{2\times 2} \)

-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{00} & x_{01}\\ -x_{10} & x_{11}\\ -\end{bmatrix}=\begin{bmatrix} -\boldsymbol{x}_{0} & \boldsymbol{x}_{1}\\ -\end{bmatrix}. -$$ - - - -

Computing the Expectation Values

- -

If we then compute the expectation value

-$$ -\mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}=\begin{bmatrix} -x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\ -x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\ -\end{bmatrix}, -$$ - -

which is just

-$$ -\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\ - \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\ - \end{bmatrix}, -$$ - -

where we wrote $$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).

- -

It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).

- - -

Towards the PCA theorem

- -

We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as

-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ - -

Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \). -These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \). -

- -

Assume also that there is a transformation \( \boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).

- -

That is we have

-$$ -\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}^T\boldsymbol{X}^T\boldsymbol{X}T\boldsymbol{S}]=\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ - -

since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S} \) from the left we have

-$$ -\boldsymbol{S}\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ - -

and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that

- -$$ -\boldsymbol{S}_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}_i. -$$ - - - -

More on the PCA Theorem

- -

In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is -\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \). -

- -

The eigenvalues tell us then how much we need to stretch the -corresponding eigenvectors. Dimensions with large eigenvalues have -thus large variations (large variance) and define therefore useful -dimensions. The data points are more spread out in the direction of -these eigenvectors. Smaller eigenvalues mean on the other hand that -the corresponding eigenvectors are shrunk accordingly and the data -points are tightly bunched together and there is not much variation in -these specific directions. Hopefully then we could leave it out -dimensions where the eigenvalues are very small. If \( p \) is very large, -we could then aim at reducing \( p \) to \( l < < p \) and handle only \( l \) -features/predictors. -

- - -

The Algorithm before theorem

- -

Here's how we would proceed in setting up the algorithm for the PCA, see also discussion below here.

-
    -
  • Set up the datapoints for the design/feature matrix \( \boldsymbol{X} \) with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) referring to the column numbers and the entries \( n \) being the row elements.
    • -
-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ - -
    -
  • Center the data by subtracting the mean value for each column. This leads to a new matrix \( \boldsymbol{X}\rightarrow \overline{\boldsymbol{X}} \).
    • -
  • Compute then the covariance/correlation matrix \( \mathbb{E}[\overline{\boldsymbol{X}}^T\overline{\boldsymbol{X}}] \).
    • -
  • Find the eigenpairs of \( \boldsymbol{C} \) with eigenvalues \( [\lambda_0,\lambda_1,\dots,\lambda_{p-1}] \) and eigenvectors \( [\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \).
    • -
  • Order the eigenvalue (and the eigenvectors accordingly) in order of decreasing eigenvalues.
    • -
  • Keep only those \( l \) eigenvalues larger than a selected threshold value, discarding thus \( p-l \) features since we expect small variations in the data here.
    • -
- -

Writing our own PCA code

- -

We will use a simple example first with two-dimensional data -drawn from a multivariate normal distribution with the following mean and covariance matrix (we have fixed these quantities but will play around with them below): -

-$$ -\mu = (-1,2) \qquad \Sigma = \begin{bmatrix} 4 & 2 \\ -2 & 2 -\end{bmatrix} -$$ - -

Note that the mean refers to each column of data. -We will generate \( n = 10000 \) points \( X = \{ x_1, \ldots, x_N \} \) from -this distribution, and store them in the \( 1000 \times 2 \) matrix \( \boldsymbol{X} \). This is our design matrix where we have forced the covariance and mean values to take specific values. -

- - -

Implementing it

-

The following Python code aids in setting up the data and writing out the design matrix. -Note that the function multivariate returns also the covariance discussed above and that it is defined by dividing by \( n-1 \) instead of \( n \). -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from IPython.display import display
-n = 10000
-mean = (-1, 2)
-cov = [[4, 2], [2, 2]]
-X = np.random.multivariate_normal(mean, cov, n)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

Now we are going to implement the PCA algorithm. We will break it down into various substeps.

- - -

First Step

- -

The first step of PCA is to compute the sample mean of the data and use it to center the data. Recall that the sample mean is

-$$ -\mu_n = \frac{1}{n} \sum_{i=1}^n x_i -$$ - -

and the mean-centered data \( \bar{X} = \{ \bar{x}_1, \ldots, \bar{x}_n \} \) takes the form

-$$ -\bar{x}_i = x_i - \mu_n. -$$ - -

When you are done with these steps, print out \( \mu_n \) to verify it is -close to \( \mu \) and plot your mean centered data to verify it is -centered at the origin! -The following code elements perform these operations using pandas or using our own functionality for doing so. The latter, using numpy is rather simple through the mean() function. -

- - -
-
-
-
-
- 
df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - - -

Scaling

-

Alternatively, we could use the functions we discussed -earlier for scaling the data set. That is, we could have used the -StandardScaler function in Scikit-Learn, a function which ensures -that for each feature/predictor we study the mean value is zero and -the variance is one (every column in the design/feature matrix). You -would then not get the same results, since we divide by the -variance. The diagonal covariance matrix elements will then be one, -while the non-diagonal ones need to be divided by \( 2\sqrt{2} \) for our -specific case. -

- - -

Centered Data

- -

Now we are going to use the mean centered data to compute the sample covariance of the data by using the following equation

-$$ -\begin{equation*} -\Sigma_n = \frac{1}{n-1} \sum_{i=1}^n \bar{x}_i^T \bar{x}_i = \frac{1}{n-1} \sum_{i=1}^n (x_i - \mu_n)^T (x_i - \mu_n) -\end{equation*} -$$ - -

where the data points \( x_i \in \mathbb{R}^p \) (here in this example \( p = 2 \)) are column vectors and \( x^T \) is the transpose of \( x \). -We can write our own code or simply use either the functionaly of numpy or that of pandas, as follows -

- - -
-
-
-
-
- 
print(df.cov())
-print(np.cov(X_centered.T))
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

Note that the way we define the covariance matrix here has a factor \( n-1 \) instead of \( n \). This is included in the cov() function by numpy and pandas. -Our own code here is not very elegant and asks for obvious improvements. It is tailored to this specific \( 2\times 2 \) covariance matrix. -

- - -
-
-
-
-
- 
# extract the relevant columns from the centered design matrix of dim n x 2
-x = X_centered[:,0]
-y = X_centered[:,1]
-Cov = np.zeros((2,2))
-Cov[0,1] = np.sum(x.T@y)/(n-1.0)
-Cov[0,0] = np.sum(x.T@x)/(n-1.0)
-Cov[1,1] = np.sum(y.T@y)/(n-1.0)
-Cov[1,0]= Cov[0,1]
-print("Centered covariance using own code")
-print(Cov)
-plt.plot(x, y, 'x')
-plt.axis('equal')
-plt.show()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - - -

Exploring

- -

Depending on the number of points \( n \), we will get results that are close to the covariance values defined above. -The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed. -

- - -

Diagonalize the sample covariance matrix to obtain the principal components

- -

Now we are ready to solve for the principal components! To do so we -diagonalize the sample covariance matrix \( \Sigma \). We can use the -function np.linalg.eig to do so. It will return the eigenvalues and -eigenvectors of \( \Sigma \). Once we have these we can perform the -following tasks: -

- -
    -
  • We compute the percentage of the total variance captured by the first principal component
    • -
  • We plot the mean centered data and lines along the first and second principal components
    • -
  • Then we project the mean centered data onto the first and second principal components, and plot the projected data.
    • -
  • Finally, we approximate the data as
    • -
-$$ -\begin{equation*} -x_i \approx \tilde{x}_i = \mu_n + \langle x_i, v_0 \rangle v_0 -\end{equation*} -$$ - -

where \( v_0 \) is the first principal component.

- - -

Collecting all Steps

- -

Collecting all these steps we can write our own PCA function and -compare this with the functionality included in Scikit-Learn. -

- -

The code here outlines some of the elements we could include in the -analysis. Feel free to extend upon this in order to address the above -questions. -

- - - -
-
-
-
-
- 
# diagonalize and obtain eigenvalues, not necessarily sorted
-EigValues, EigVectors = np.linalg.eig(Cov)
-# sort eigenvectors and eigenvalues
-#permute = EigValues.argsort()
-#EigValues = EigValues[permute]
-#EigVectors = EigVectors[:,permute]
-print("Eigenvalues of Covariance matrix")
-for i in range(2):
-    print(EigValues[i])
-FirstEigvector = EigVectors[:,0]
-SecondEigvector = EigVectors[:,1]
-print("First eigenvector")
-print(FirstEigvector)
-print("Second eigenvector")
-print(SecondEigvector)
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2Dsl = pca.fit_transform(X)
-print("Eigenvector of largest eigenvalue")
-print(pca.components_.T[:, 0])
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

This code does not contain all the above elements, but it shows how we can use Scikit-Learn to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?

- - -

Classical PCA Theorem

- -

We assume now that we have a design matrix \( \boldsymbol{X} \) which has been -centered as discussed above. For the sake of simplicity we skip the -overline symbol. The matrix is defined in terms of the various column -vectors \( [\boldsymbol{x}_0,\boldsymbol{x}_1,\dots, \boldsymbol{x}_{p-1}] \) each with dimension -\( \boldsymbol{x}\in {\mathbb{R}}^{n} \). -

- -

The PCA theorem states that minimizing the above reconstruction error -corresponds to setting \( \boldsymbol{W}=\boldsymbol{S} \), the orthogonal matrix which -diagonalizes the empirical covariance(correlation) matrix. The optimal -low-dimensional encoding of the data is then given by a set of vectors -\( \boldsymbol{z}_i \) with at most \( l \) vectors, with \( l < < p \), defined by the -orthogonal projection of the data onto the columns spanned by the -eigenvectors of the covariance(correlations matrix). -

- - -

The PCA Theorem

- -

To show the PCA theorem let us start with the assumption that there is one vector \( \boldsymbol{s}_0 \) which corresponds to a solution which minimized the reconstruction error \( J \). This is an orthogonal vector. It means that we now approximate the reconstruction error in terms of \( \boldsymbol{w}_0 \) and \( \boldsymbol{z}_0 \) as

- -

We are almost there, we have obtained a relation between minimizing -the reconstruction error and the variance and the covariance -matrix. Minimizing the error is equivalent to maximizing the variance -of the projected data. -

- -

We could trivially maximize the variance of the projection (and -thereby minimize the error in the reconstruction function) by letting -the norm-2 of \( \boldsymbol{w}_0 \) go to infinity. However, this norm since we -want the matrix \( \boldsymbol{W} \) to be an orthogonal matrix, is constrained by -\( \vert\vert \boldsymbol{w}_0 \vert\vert_2^2=1 \). Imposing this condition via a -Lagrange multiplier we can then in turn maximize -

- -$$ -J(\boldsymbol{w}_0)= \boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0+\lambda_0(1-\boldsymbol{w}_0^T\boldsymbol{w}_0). -$$ - -

Taking the derivative with respect to \( \boldsymbol{w}_0 \) we obtain

- -$$ -\frac{\partial J(\boldsymbol{w}_0)}{\partial \boldsymbol{w}_0}= 2\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0-2\lambda_0\boldsymbol{w}_0=0, -$$ - -

meaning that

-$$ -\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0\boldsymbol{w}_0. -$$ - -

The direction that maximizes the variance (or minimizes the construction error) is an eigenvector of the covariance matrix! If we left multiply with \( \boldsymbol{w}_0^T \) we have the variance of the projected data is

-$$ -\boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0. -$$ - -

If we want to maximize the variance (minimize the construction error) -we simply pick the eigenvector of the covariance matrix with the -largest eigenvalue. This establishes the link between the minimization -of the reconstruction function \( J \) in terms of an orthogonal matrix -and the maximization of the variance and thereby the covariance of our -observations encoded in the design/feature matrix \( \boldsymbol{X} \). -

- -

The proof -for the other eigenvectors \( \boldsymbol{w}_1,\boldsymbol{w}_2,\dots \) can be -established by applying the above arguments and using the fact that -our basis of eigenvectors is orthogonal, see Murphy chapter -12.2. The -discussion in chapter 12.2 of Murphy's text has also a nice link with -the Singular Value Decomposition theorem. For categorical data, see -chapter 12.4 and discussion therein. -

- -

For more details, see for example Vidal, Ma and Sastry, chapter 2.

- - - - -

For a detailed demonstration of the geometric interpretation, see Vidal, Ma and Sastry, section 2.1.2.

- -

Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm. -First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. -

- -

The following Python code uses NumPy’s svd() function to obtain all the principal components of the -training set, then extracts the first two principal components. First we center the data using either pandas or our own code -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-from IPython.display import display
-np.random.seed(100)
-# setting up a 10 x 5 vanilla matrix 
-rows = 10
-cols = 5
-X = np.random.randn(rows,cols)
-df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-display(df)
-
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-# Then check the difference between pandas and our own set up
-print(X_centered-df)
-#Now we do an SVD
-U, s, V = np.linalg.svd(X_centered)
-c1 = V.T[:, 0]
-c2 = V.T[:, 1]
-W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
-print(X2D)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering -the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t -forget to center the data first. -

- -

Once you have identified all the principal components, you can reduce the dimensionality of the dataset -down to \( d \) dimensions by projecting it onto the hyperplane defined by the first \( d \) principal components. -Selecting this hyperplane ensures that the projection will preserve as much variance as possible. -

- - -
-
-
-
-
- 
W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - - -

PCA and scikit-learn

- -

Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The -following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note -that it automatically takes care of centering the data): -

- - -
-
-
-
-
- 
#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D = pca.fit_transform(X)
-print(X2D)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

After fitting the PCA transformer to the dataset, you can access the principal components using the -components variable (note that it contains the PCs as horizontal vectors, so, for example, the first -principal component is equal to -

- - -
-
-
-
-
- 
pca.components_.T[:, 0]
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

Another very useful piece of information is the explained variance ratio of each principal component, -available via the \( explained\_variance\_ratio \) variable. It indicates the proportion of the dataset’s -variance that lies along the axis of each principal component. -

- - -

Back to the Cancer Data

-

We can now repeat the above but applied to real data, in this case our breast cancer data. -Here we compute performance scores on the training data using logistic regression. -

- - -
-
-
-
-
- 
import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.model_selection import  train_test_split 
-from sklearn.datasets import load_breast_cancer
-from sklearn.linear_model import LogisticRegression
-cancer = load_breast_cancer()
-
-X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
-
-logreg = LogisticRegression()
-logreg.fit(X_train, y_train)
-print("Train set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_train,y_train)))
-# We scale the data
-from sklearn.preprocessing import StandardScaler
-scaler = StandardScaler()
-scaler.fit(X_train)
-X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-# Then perform again a log reg fit
-logreg.fit(X_train_scaled, y_train)
-print("Train set accuracy scaled data: {:.2f}".format(logreg.score(X_train_scaled,y_train)))
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D_train = pca.fit_transform(X_train_scaled)
-# and finally compute the log reg fit and the score on the training data	
-logreg.fit(X2D_train,y_train)
-print("Train set accuracy scaled and PCA data: {:.2f}".format(logreg.score(X2D_train,y_train)))
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We see that our training data after the PCA decomposition has a performance similar to the non-scaled data.

- -

Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to -choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). -Unless, of course, you are reducing dimensionality for data visualization — in that case you will -generally want to reduce the dimensionality down to 2 or 3. -The following code computes PCA without reducing dimensionality, then computes the minimum number -of dimensions required to preserve 95% of the training set’s variance: -

- - -
-
-
-
-
- 
pca = PCA()
-pca.fit(X)
-cumsum = np.cumsum(pca.explained_variance_ratio_)
-d = np.argmax(cumsum >= 0.95) + 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

You could then set \( n\_components=d \) and run PCA again. However, there is a much better option: instead -of specifying the number of principal components you want to preserve, you can set \( n\_components \) to be -a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: -

- - -
-
-
-
-
- 
pca = PCA(n_components=0.95)
-X_reduced = pca.fit_transform(X)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - - -

Incremental PCA

- -

One problem with the preceding implementation of PCA is that it requires the whole training set to fit in -memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have -been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch -at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new -instances arrive). -

-

Randomized PCA

- -

Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic -algorithm that quickly finds an approximation of the first d principal components. Its computational -complexity is \( O(m \times d^2)+O(d^3) \), instead of \( O(m \times n^2) + O(n^3) \), so it is dramatically faster than the -previous algorithms when \( d \) is much smaller than \( n \). -

-

Kernel PCA

- -

The kernel trick is a mathematical technique that implicitly maps instances into a -very high-dimensional space (called the feature space), enabling nonlinear classification and regression -with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature -space corresponds to a complex nonlinear decision boundary in the original space. -It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear -projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at -preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a -twisted manifold. -For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an -

- - -
-
-
-
-
- 
from sklearn.decomposition import KernelPCA
-rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04)
-X_reduced = rbf_pca.fit_transform(X)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - - -

Other techniques

- -

There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.

- -

Here are some of the most popular:

-
    -
  • Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances between the instances.
    • -
  • Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances.
    • -
  • t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).
    • -
  • Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures.
    • -
diff --git a/doc/pub/week43/html/week43-reveal.html b/doc/pub/week43/html/week43-reveal.html index bf64da0f6..47cb7d99e 100644 --- a/doc/pub/week43/html/week43-reveal.html +++ b/doc/pub/week43/html/week43-reveal.html @@ -166,7 +166,7 @@ MathJax.Hub.Config({
-

ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis +

ATITLE: Week 43: Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks

@@ -181,7 +181,7 @@ MathJax.Hub.Config({

-

Oct 24, 2022

+

Oct 26, 2022


@@ -196,8 +196,8 @@ MathJax.Hub.Config({

Plans for week 43

    -

  • Thursday: Convolutional Neural Networks, basic elements and
    • -

  • Friday: Recurrent Neural Networks and other Deep learning methods, Generalized Adversarial Neural Networ and autoencoders
    • +

  • Thursday: Convolutional Neural Networks (CNN)
    • +

  • Friday: Recurrent Neural Networks (RNN)

@@ -224,10 +224,215 @@ MathJax.Hub.Config({

Reading Recommendations

-
+ +
+

Convolutional Neural Networks (recognizing images)

+ +

Convolutional neural networks (CNNs) were developed during the last +decade of the previous century, with a focus on character recognition +tasks. Nowadays, CNNs are a central element in the spectacular success +of deep learning methods. The success in for example image +classifications have made them a central tool for most machine +learning practitioners. +

+ +

CNNs are very similar to ordinary Neural Networks. +They are made up of neurons that have learnable weights and +biases. Each neuron receives some inputs, performs a dot product and +optionally follows it with a non-linearity. The whole network still +expresses a single differentiable score function: from the raw image +pixels on one end to class scores at the other. And they still have a +loss function (for example Softmax) on the last (fully-connected) layer +and all the tips/tricks we developed for learning regular Neural +Networks still apply (back propagation, gradient descent etc etc). +

+
+ +
+

What is the Difference

+ +

CNN architectures make the explicit assumption that +the inputs are images, which allows us to encode certain properties +into the architecture. These then make the forward function more +efficient to implement and vastly reduce the amount of parameters in +the network. +

+ +

Here we provide only a superficial overview, for the more interested, we recommend highly the course +IN5400 – Machine Learning for Image Analysis +and the slides of CS231. +

+ +

Another good read is the article here https://arxiv.org/pdf/1603.07285.pdf.

+
+ +
+

Neural Networks vs CNNs

+ +

Neural networks are defined as affine transformations, that is +a vector is received as input and is multiplied with a matrix of so-called weights (our unknown paramters) to produce an +output (to which a bias vector is usually added before passing the result +through a nonlinear activation function). This is applicable to any type of input, be it an +image, a sound clip or an unordered collection of features: whatever their +dimensionality, their representation can always be flattened into a vector +before the transformation. +

+
+ +
+

Why CNNS for images, sound files, medical images from CT scans etc?

+ +

However, when we consider images, sound clips and many other similar kinds of data, these data have an intrinsic +structure. More formally, they share these important properties: +

+
    +

  • They are stored as multi-dimensional arrays (think of the pixels of a figure) .
    • +

  • They feature one or more axes for which ordering matters (e.g., width and height axes for an image, time axis for a sound clip).
    • +

  • One axis, called the channel axis, is used to access different views of the data (e.g., the red, green and blue channels of a color image, or the left and right channels of a stereo audio track).
+

+

These properties are not exploited when an affine transformation is applied; in +fact, all the axes are treated in the same way and the topological information +is not taken into account. Still, taking advantage of the implicit structure of +the data may prove very handy in solving some tasks, like computer vision and +speech recognition, and in these cases it would be best to preserve it. This is +where discrete convolutions come into play. +

+ +

A discrete convolution is a linear transformation that preserves this notion of +ordering. It is sparse (only a few input units contribute to a given output +unit) and reuses parameters (the same weights are applied to multiple locations +in the input). +

+
+ +
+

Regular NNs don’t scale well to full images

+ +

As an example, consider +an image of size \( 32\times 32\times 3 \) (32 wide, 32 high, 3 color channels), so a +single fully-connected neuron in a first hidden layer of a regular +Neural Network would have \( 32\times 32\times 3 = 3072 \) weights. This amount still +seems manageable, but clearly this fully-connected structure does not +scale to larger images. For example, an image of more respectable +size, say \( 200\times 200\times 3 \), would lead to neurons that have +\( 200\times 200\times 3 = 120,000 \) weights. +

+ +

We could have +several such neurons, and the parameters would add up quickly! Clearly, +this full connectivity is wasteful and the huge number of parameters +would quickly lead to possible overfitting. +

+ +
+
+
+

Figure 1: A regular 3-layer Neural Network.

+
+

+
+
+ +
+

3D volumes of neurons

+ +

Convolutional Neural Networks take advantage of the fact that the +input consists of images and they constrain the architecture in a more +sensible way. +

+ +

In particular, unlike a regular Neural Network, the +layers of a CNN have neurons arranged in 3 dimensions: width, +height, depth. (Note that the word depth here refers to the third +dimension of an activation volume, not to the depth of a full Neural +Network, which can refer to the total number of layers in a network.) +

+ +

To understand it better, the above example of an image +with an input volume of +activations has dimensions \( 32\times 32\times 3 \) (width, height, +depth respectively). +

+ +

The neurons in a layer will +only be connected to a small region of the layer before it, instead of +all of the neurons in a fully-connected manner. Moreover, the final +output layer could for this specific image have dimensions \( 1\times 1 \times 10 \), +because by the +end of the CNN architecture we will reduce the full image into a +single vector of class scores, arranged along the depth +dimension. +

+ +
+
+
+

Figure 2: A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).

+
+

+
+
+ +
+

Layers used to build CNNs

+ +

A simple CNN is a sequence of layers, and every layer of a CNN +transforms one volume of activations to another through a +differentiable function. We use three main types of layers to build +CNN architectures: Convolutional Layer, Pooling Layer, and +Fully-Connected Layer (exactly as seen in regular Neural Networks). We +will stack these layers to form a full CNN architecture. +

+ +

A simple CNN for image classification could have the architecture:

+ +
    +

  • INPUT (\( 32\times 32 \times 3 \)) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
    • +

  • CONV (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as \( [32\times 32\times 12] \) if we decided to use 12 filters.
    • +

  • RELU layer will apply an elementwise activation function, such as the \( max(0,x) \) thresholding at zero. This leaves the size of the volume unchanged (\( [32\times 32\times 12] \)).
    • +

  • POOL (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as \( [16\times 16\times 12] \).
    • +

  • FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size \( [1\times 1\times 10] \), where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
    • +
+
+ +
+

Transforming images

+ +

CNNs transform the original image layer by layer from the original +pixel values to the final class scores. +

+ +

Observe that some layers contain +parameters and other don’t. In particular, the CNN layers perform +transformations that are a function of not only the activations in the +input volume, but also of the parameters (the weights and biases of +the neurons). On the other hand, the RELU/POOL layers will implement a +fixed function. The parameters in the CONV/FC layers will be trained +with gradient descent so that the class scores that the CNN computes +are consistent with the labels in the training set for each image. +

@@ -249,9 +454,1160 @@ the course and the slides of CS231 which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs.

-

However, both standard feed forwards networks and CNNs perform well on data with unknown length.

+

The textbook by Goodfellow et al, see chapter 9 contains an in depth discussion as well.

+
-

This is where recurrent nueral networks (RNNs) come to our rescue.

+
+

Key Idea

+ +

A dense neural network is representd by an affine operation (like matrix-matrix multiplication) where all parameters are included.

+ +

The key idea in CNNs for say imaging is that in images neighbor pixels tend to be related! So we connect +only neighboring neurons in the input instead of connecting all with the first hidden layer. +

+ +

We say we perform a filtering (convolution is the mathematical operation).

+
+ +
+

Mathematics of CNNs

+ +

The mathematics of CNNs is based on the mathematical operation of +convolution. In mathematics (in particular in functional analysis), +convolution is represented by mathematical operation (integration, +summation etc) on two function in order to produce a third function +that expresses how the shape of one gets modified by the other. +Convolution has a plethora of applications in a variety of disciplines, spanning from statistics to signal processing, computer vision, solutions of differential equations,linear algebra, engineering, and yes, machine learning. +

+ +

Mathematically, convolution is defined as follows (one-dimensional example): +Let us define a continuous function \( y(t) \) given by +

+

 
+$$ +y(t) = \int x(a) w(t-a) da, +$$ +

 
+ +

where \( x(a) \) represents a so-called input and \( w(t-a) \) is normally called the weight function or kernel.

+ +

The above integral is written in a more compact form as

+

 
+$$ +y(t) = \left(x * w\right)(t). +$$ +

 
+ +

The discretized version reads

+

 
+$$ +y(t) = \sum_{a=-\infty}^{a=\infty}x(a)w(t-a). +$$ +

 
+ +

Computing the inverse of the above convolution operations is known as deconvolution.

+ +

How can we use this? And what does it mean? Let us study some familiar examples first.

+
+ +
+

Convolution Examples: Polynomial multiplication

+ +

We have already met such an example in project 1 when we tried to set +up the design matrix for a two-dimensional function. This was an +example of polynomial multiplication. Let us recast such a problem in terms of the convolution operation. +Let us look a the following polynomials to second and third order, respectively: +

+

 
+$$ +p(t) = \alpha_0+\alpha_1 t+\alpha_2 t^2, +$$ +

 
+ +

and

+

 
+$$ +s(t) = \beta_0+\beta_1 t+\beta_2 t^2+\beta_3 t^3. +$$ +

 
+ +

The polynomial multiplication gives us a new polynomial of degree \( 5 \)

+

 
+$$ +z(t) = \delta_0+\delta_1 t+\delta_2 t^2+\delta_3 t^3+\delta_4 t^4+\delta_5 t^5. +$$ +

 
+

+ +
+

Efficient Polynomial Multiplication

+ +

Computing polynomial products can be implemented efficiently if we rewrite the more brute force multiplications using convolution. +We note first that the new coefficients are given as +

+ +

 
+$$ +\begin{split} +\delta_0=&\alpha_0\beta_0\\ +\delta_1=&\alpha_1\beta_0+\alpha_1\beta_0\\ +\delta_2=&\alpha_0\beta_2+\alpha_1\beta_1+\alpha_2\beta_0\\ +\delta_3=&\alpha_1\beta_2+\alpha_2\beta_1+\alpha_0\beta_3\\ +\delta_4=&\alpha_2\beta_2+\alpha_1\beta_3\\ +\delta_5=&\alpha_2\beta_3.\\ +\end{split} +$$ +

 
+ +

We note that \( \alpha_i=0 \) except for \( i\in \left\{0,1,2\right\} \) and \( \beta_i=0 \) except for \( i\in\left\{0,1,2,3\right\} \).

+ +

We can then rewrite the coefficients \( \delta_j \) using a discrete convolution as

+

 
+$$ +\delta_j = \sum_{i=-\infty}^{i=\infty}\alpha_i\beta_{j-i}=(\alpha * \beta)_j, +$$ +

 
+ +

or as a double sum with restriction \( l=i+j \)

+

 
+$$ +\delta_l = \sum_{ij}\alpha_i\beta_{j}. +$$ +

 
+ +

Do you see a potential drawback with these equations?

+
+ +
+

A more efficient way of coding the above Convolution

+ +

Since we only have a finite number of \( \alpha \) and \( \beta \) values +which are non-zero, we can rewrite the above convolution expressions +as a matrix-vector multiplication +

+ +

 
+$$ +\boldsymbol{\delta}=\begin{bmatrix}\alpha_0 & 0 & 0 & 0 \\ + \alpha_1 & \alpha_0 & 0 & 0 \\ + \alpha_2 & \alpha_1 & \alpha_0 & 0 \\ + 0 & \alpha_2 & \alpha_1 & \alpha_0 \\ + 0 & 0 & \alpha_2 & \alpha_1 \\ + 0 & 0 & 0 & \alpha_2 + \end{bmatrix}\begin{bmatrix} \beta_0 \\ \beta_1 \\ \beta_2 \\ \beta_3\end{bmatrix}. +$$ +

 
+ +

The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding \( \beta \) and a vector holding \( \alpha \). +In this case we have +

+

 
+$$ +\boldsymbol{\delta}=\begin{bmatrix}\beta_0 & 0 & 0 \\ + \beta_1 & \beta_0 & 0 \\ + \beta_2 & \beta_1 & \beta_0 \\ + \beta_3 & \beta_2 & \beta_1 \\ + 0 & \beta_3 & \beta_2 \\ + 0 & 0 & \beta_3 + \end{bmatrix}\begin{bmatrix} \alpha_0 \\ \alpha_1 \\ \alpha_2\end{bmatrix}. +$$ +

 
+ +

Note that the use of these matrices is for mathematical purposes only and not implementation purposes. +When implementing the above equation we do not encode (and allocate memory) the matrices explicitely. +We rather code the convolutions in the minimal memory footprint that they require. +

+ +

Does the number of floating point operations change here when we use the commutative property?

+
+ +
+

Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)

+ +

For problems with so-called harmonic oscillations, given by for example the following differential equation

+

 
+$$ +m\frac{d^2x}{dt^2}+\eta\frac{dx}{dt}+x(t)=F(t), +$$ +

 
+ +

where \( F(t) \) is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations.

+ +

If one has several driving forces, \( F(t)=\sum_n F_n(t) \), one can find +the particular solution to each \( F_n \), \( x_{pn}(t) \), and the particular +solution for the entire driving force is then given by a series like +

+ +

 
+$$ +\begin{equation} +x_p(t)=\sum_nx_{pn}(t). +\tag{1} +\end{equation} +$$ +

 
+

+ +
+

Principle of Superposition

+ +

This is known as the principle of superposition. It only applies when +the homogenous equation is linear. If there were an anharmonic term +such as \( x^3 \) in the homogenous equation, then when one summed various +solutions, \( x=(\sum_n x_n)^2 \), one would get cross +terms. Superposition is especially useful when \( F(t) \) can be written +as a sum of sinusoidal terms, because the solutions for each +sinusoidal (sine or cosine) term is analytic. +

+ +

Driving forces are often periodic, even when they are not +sinusoidal. Periodicity implies that for some time \( \tau \) +

+ +

 
+$$ +\begin{eqnarray} +F(t+\tau)=F(t). +\end{eqnarray} +$$ +

 
+ +

One example of a non-sinusoidal periodic force is a square wave. Many +components in electric circuits are non-linear, e.g. diodes, which +makes many wave forms non-sinusoidal even when the circuits are being +driven by purely sinusoidal sources. +

+
+ +
+

Simple Code Example

+ +

The code here shows a typical example of such a square wave generated using the functionality included in the scipy Python package. We have used a period of \( \tau=0.2 \).

+ + + +
+
+
+
+
+ 
import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t)
+plt.plot(t, SqrSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +

For the sinusoidal example the +period is \( \tau=2\pi/\omega \). However, higher harmonics can also +satisfy the periodicity requirement. In general, any force that +satisfies the periodicity requirement can be expressed as a sum over +harmonics, +

+ +

 
+$$ +\begin{equation} +F(t)=\frac{f_0}{2}+\sum_{n>0} f_n\cos(2n\pi t/\tau)+g_n\sin(2n\pi t/\tau). +\tag{2} +\end{equation} +$$ +

 
+

+ +
+

Wrapping up Fourier transforms

+ +

We can write down the answer for +\( x_{pn}(t) \), by substituting \( f_n/m \) or \( g_n/m \) for \( F_0/m \). By +writing each factor \( 2n\pi t/\tau \) as \( n\omega t \), with \( \omega\equiv +2\pi/\tau \), +

+ +

 
+$$ +\begin{equation} +\tag{3} +F(t)=\frac{f_0}{2}+\sum_{n>0}f_n\cos(n\omega t)+g_n\sin(n\omega t). +\end{equation} +$$ +

 
+ +

The solutions for \( x(t) \) then come from replacing \( \omega \) with +\( n\omega \) for each term in the particular solution, +

+ +

 
+$$ +\begin{eqnarray} +x_p(t)&=&\frac{f_0}{2k}+\sum_{n>0} \alpha_n\cos(n\omega t-\delta_n)+\beta_n\sin(n\omega t-\delta_n),\\ +\nonumber +\alpha_n&=&\frac{f_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\beta_n&=&\frac{g_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\delta_n&=&\tan^{-1}\left(\frac{2\beta n\omega}{\omega_0^2-n^2\omega^2}\right). +\end{eqnarray} +$$ +

 
+

+ +
+

Finding the Coefficients

+ +

Because the forces have been applied for a long time, any non-zero +damping eliminates the homogenous parts of the solution, so one need +only consider the particular solution for each \( n \). +

+ +

The problem is considered solved if one can find expressions for the +coefficients \( f_n \) and \( g_n \), even though the solutions are expressed +as an infinite sum. The coefficients can be extracted from the +function \( F(t) \) by +

+ +

 
+$$ +\begin{eqnarray} +\tag{4} +f_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\cos(2n\pi t/\tau),\\ +\nonumber +g_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\sin(2n\pi t/\tau). +\end{eqnarray} +$$ +

 
+ +

To check the consistency of these expressions and to verify +Eq. (4), one can insert the expansion of \( F(t) \) in +Eq. (3) into the expression for the coefficients in +Eq. (4) and see whether +

+ +

 
+$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~\left\{ +\frac{f_0}{2}+\sum_{m>0}f_m\cos(m\omega t)+g_m\sin(m\omega t) +\right\}\cos(n\omega t). +\end{eqnarray} +$$ +

 
+ +

Immediately, one can throw away all the terms with \( g_m \) because they +convolute an even and an odd function. The term with \( f_0/2 \) +disappears because \( \cos(n\omega t) \) is equally positive and negative +over the interval and will integrate to zero. For all the terms +\( f_m\cos(m\omega t) \) appearing in the sum, one can use angle addition +formulas to see that \( \cos(m\omega t)\cos(n\omega +t)=(1/2)(\cos[(m+n)\omega t]+\cos[(m-n)\omega t] \). This will integrate +to zero unless \( m=n \). In that case the \( m=n \) term gives +

+ +

 
+$$ +\begin{equation} +\int_{-\tau/2}^{\tau/2}dt~\cos^2(m\omega t)=\frac{\tau}{2}, +\tag{5} +\end{equation} +$$ +

 
+ +

and

+ +

 
+$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~f_n/2\\ +\nonumber +&=&f_n~\checkmark. +\end{eqnarray} +$$ +

 
+ +

The same method can be used to check for the consistency of \( g_n \).

+
+ +
+

Final words on Fourier Transforms

+ +

The code here uses the Fourier series applied to a +square wave signal. The code here +visualizes the various approximations given by Fourier series compared +with a square wave with period \( T=0.2 \) (dimensionless time), width \( 0.1 \) and max value of the force \( F=2 \). We +see that when we increase the number of components in the Fourier +series, the Fourier series approximation gets closer and closer to the +square wave signal. +

+ + + +
+
+
+
+
+ 
import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+T =0.2
+# Max value of square signal                                                                             
+Fmax= 2.0
+# Width of signal   
+Width = 0.1
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+FourierSeriesSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t+np.pi*Width/T)
+a0 = Fmax*Width/T
+FourierSeriesSignal = a0
+Factor = 2.0*Fmax/np.pi
+for i in range(1,500):
+    FourierSeriesSignal += Factor/(i)*np.sin(np.pi*i*Width/T)*np.cos(i*t*2*np.pi/T)
+plt.plot(t, SqrSignal)
+plt.plot(t, FourierSeriesSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Two-dimensional Objects

+ +

We often use convolutions over more than one dimension at a time. If +we have a two-dimensional image \( I \) as input, we can have a filter +defined by a two-dimensional kernel \( K \). This leads to an output \( S \) +

+ +

 
+$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(m,n)K(i-m,j-n). +$$ +

 
+ +

Convolution is a commutatitave process, which means we can rewrite this equation as

+

 
+$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i-m,j-n)K(m,n). +$$ +

 
+ +

Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of \( m \) and \( n \).

+
+ +
+

Cross-Correlation

+ +

Many deep learning libraries implement cross-correlation instead of convolution

+

 
+$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i+m,j-+)K(m,n). +$$ +

 
+

+ +
+

More on Dimensionalities

+ +

In feilds like signal processing (and imaging as well), one designs +so-called filters. These filters are defined by the convolutions and +are often hand-crafted. One may specify filters for smoothing, edge +detection, frequency reshaping, and similar operations. However with +neural networks the idea is to automatically learn the filters and use +many of them in conjunction with non-linear operations (activation +functions). +

+ +

As an example consider a neural network operating on sound sequence +data. Assume that we an input vector \( \boldsymbol{x} \) of length \( d=10^6 \). We +construct then a neural network with onle hidden layer only with +\( 10^4 \) nodes. This means that we will have a weight matrix with +\( 10^4\times 10^6=10^{10} \) weights to be determined, together with \( 10^4 \) biases. +

+ +

Assume furthermore that we have an output layer which is meant to train whether the sound sequence represents a human voice (true) or something else (false). +It means that we have only one output node. But since this output node connects to \( 10^4 \) nodes in the hidden layer, there are in total \( 10^4 \) weights to be determined for the output layer, plus one bias. In total we have +

+ +

 
+$$ +\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \approx 10^{10}, +$$ +

 
+ +

that is ten billion parameters to determine.

+
+ +
+

Further Dimensionality Remarks

+ +

In today’s architecture one can train such neural networks, however +this is a huge number of parameters for the task at hand. In general, +it is a very wasteful and inefficient use of dense matrices as +parameters. Just as importantly, such trained network parameters are +very specific for the type of input data on which they were trained +and the network is not likely to generalize easily to variations in +the input. +

+ +

The main principles that justify convolutions is locality of +information and repetion of patterns within the signal. Sound samples +of the input in adjacent spots are much more likely to affect each +other than those that are very far away. Similarly, sounds are +repeated in multiple times in the signal. While slightly simplistic, +reasoning about such a sound example demonstrates this. The same +principles then apply to images and other similar data. +

+
+ +
+

CNNs in more detail, Lecture from IN5400

+ + +
+ +
+

CNNs in more detail, building convolutional neural networks in Tensorflow and Keras

+ +

As discussed above, CNNs are neural networks built from the assumption that the inputs +to the network are 2D images. This is important because the number of features or pixels in images +grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network. +

+ +

As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks +are the convolutional and pooling layers stacked in pairs between the input and the hidden layer. +In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D +matrices, typically 1 for each color dimension (Red, Green, Blue). +

+
+ +
+

Setting it up

+ +

It means that to represent the entire +dataset of images, we require a 4D matrix or tensor. This tensor has the dimensions: +

+

 
+$$ +(n_{inputs},\, n_{pixels, width},\, n_{pixels, height},\, depth) . +$$ +

 
+

+ +
+

The MNIST dataset again

+ +

The MNIST dataset consists of grayscale images with a pixel size of +\( 28\times 28 \), meaning we require \( 28 \times 28 = 724 \) weights to each +neuron in the first hidden layer. +

+ +

If we were to analyze images of size \( 128\times 128 \) we would require +\( 128 \times 128 = 16384 \) weights to each neuron. Even worse if we were +dealing with color images, as most images are, we have an image matrix +of size \( 128\times 128 \) for each color dimension (Red, Green, Blue), +meaning 3 times the number of weights \( = 49152 \) are required for every +single neuron in the first hidden layer. +

+
+ +
+

Strong correlations

+ +

Images typically have strong local correlations, meaning that a small +part of the image varies little from its neighboring regions. If for +example we have an image of a blue car, we can roughly assume that a +small blue part of the image is surrounded by other blue regions. +

+ +

Therefore, instead of connecting every single pixel to a neuron in the +first hidden layer, as we have previously done with deep neural +networks, we can instead connect each neuron to a small part of the +image (in all 3 RGB depth dimensions). The size of each small area is +fixed, and known as a receptive. +

+
+ +
+

Layers of a CNN

+

The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. +The input image is typically a square matrix of depth 3. +

+ +

A convolution is performed on the image which outputs +a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as filters. +

+ +

Each filter slides along the input image, taking the dot product +between each small part of the image and the filter, in all depth +dimensions. This is then passed through a non-linear function, +typically the Rectified Linear (ReLu) function, which serves as the +activation of the neurons in the first convolutional layer. This is +further passed through a pooling layer, which reduces the size of the +convolutional layer, e.g. by taking the maximum or average across some +small regions, and this serves as input to the next convolutional +layer. +

+
+ +
+

Systematic reduction

+ +

By systematically reducing the size of the input volume, through +convolution and pooling, the network should create representations of +small parts of the input, and then from them assemble representations +of larger areas. The final pooling layer is flattened to serve as +input to a hidden layer, such that each neuron in the final pooling +layer is connected to every single neuron in the hidden layer. This +then serves as input to the output layer, e.g. a softmax output for +classification. +

+
+ +
+

Prerequisites: Collect and pre-process data

+ + +
+
+
+
+
+ 
# import necessary packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+
+
+# ensure the same random numbers appear every time
+np.random.seed(0)
+
+# display images in notebook
+%matplotlib inline
+plt.rcParams['figure.figsize'] = (12,12)
+
+
+# download MNIST dataset
+digits = datasets.load_digits()
+
+# define inputs and labels
+inputs = digits.images
+labels = digits.target
+
+# RGB images have a depth of 3
+# our images are grayscale so they should have a depth of 1
+inputs = inputs[:,:,:,np.newaxis]
+
+print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
+print("labels = (n_inputs) = " + str(labels.shape))
+
+
+# choose some random images to display
+n_inputs = len(inputs)
+indices = np.arange(n_inputs)
+random_indices = np.random.choice(indices, size=5)
+
+for i, image in enumerate(digits.images[random_indices]):
+    plt.subplot(1, 5, i+1)
+    plt.axis('off')
+    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
+    plt.title("Label: %d" % digits.target[random_indices[i]])
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Importing Keras and Tensorflow

+ + +
+
+
+
+
+ 
from tensorflow.keras import datasets, layers, models
+from tensorflow.keras.layers import Input
+from tensorflow.keras.models import Sequential      #This allows appending layers to existing models
+from tensorflow.keras.layers import Dense           #This allows defining the characteristics of a particular layer
+from tensorflow.keras import optimizers             #This allows using whichever optimiser we want (sgd,adam,RMSprop)
+from tensorflow.keras import regularizers           #This allows using whichever regularizer we want (l1,l2,l1_l2)
+from tensorflow.keras.utils import to_categorical   #This allows using categorical cross entropy as the cost function
+#from tensorflow.keras import Conv2D
+#from tensorflow.keras import MaxPooling2D
+#from tensorflow.keras import Flatten
+
+from sklearn.model_selection import train_test_split
+
+# representation of labels
+labels = to_categorical(labels)
+
+# split into train and test data
+# one-liner from scikit-learn library
+train_size = 0.8
+test_size = 1 - train_size
+X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
+                                                    test_size=test_size)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Running with Keras

+ + + +
+
+
+
+
+ 
def create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd):
+    model = Sequential()
+    model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
+              activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
+    
+    sgd = optimizers.SGD(lr=eta)
+    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+    
+    return model
+
+epochs = 100
+batch_size = 100
+input_shape = X_train.shape[1:4]
+receptive_field = 3
+n_filters = 10
+n_neurons_connected = 50
+n_categories = 10
+
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Final part

+ + + +
+
+
+
+
+ 
CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+        
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd)
+        CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+        scores = CNN.evaluate(X_test, Y_test)
+        
+        CNN_keras[i][j] = CNN
+        
+        print("Learning rate = ", eta)
+        print("Lambda = ", lmbd)
+        print("Test accuracy: %.3f" % scores[1])
+        print()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Final visualization

+ + + +
+
+
+
+
+ 
# visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
+
+sns.set()
+
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+
+for i in range(len(eta_vals)):
+    for j in range(len(lmbd_vals)):
+        CNN = CNN_keras[i][j]
+
+        train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
+
+        
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

The CIFAR01 data set

+ +

The CIFAR10 dataset contains 60,000 color images in 10 classes, with +6,000 images in each class. The dataset is divided into 50,000 +training images and 10,000 testing images. The classes are mutually +exclusive and there is no overlap between them. +

+ + + +
+
+
+
+
+ 
import tensorflow as tf
+
+from tensorflow.keras import datasets, layers, models
+import matplotlib.pyplot as plt
+
+# We import the data set
+(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+
+# Normalize pixel values to be between 0 and 1 by dividing by 255. 
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Verifying the data set

+ +

To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image.

+ + + +
+
+
+
+
+ 
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+​
+plt.figure(figsize=(10,10))
+for i in range(25):
+    plt.subplot(5,5,i+1)
+    plt.xticks([])
+    plt.yticks([])
+    plt.grid(False)
+    plt.imshow(train_images[i], cmap=plt.cm.binary)
+    # The CIFAR labels happen to be arrays, 
+    # which is why you need the extra index
+    plt.xlabel(class_names[train_labels[i][0]])
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Set up the model

+ +

The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers.

+ +

As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer.

+ + + +
+
+
+
+
+ 
model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+
+# Let's display the architecture of our model so far.
+
+model.summary()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +

You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer.

+
+ +
+

Add Dense layers on top

+ +

To complete our model, you will feed the last output tensor from the +convolutional base (of shape (4, 4, 64)) into one or more Dense layers +to perform classification. Dense layers take vectors as input (which +are 1D), while the current output is a 3D tensor. First, you will +flatten (or unroll) the 3D output to 1D, then add one or more Dense +layers on top. CIFAR has 10 output classes, so you use a final Dense +layer with 10 outputs and a softmax activation. +

+ + + +
+
+
+
+
+ 
model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10))
+Here's the complete architecture of our model.
+
+model.summary()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +

As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.

+
+ +
+

Compile and train the model

+ + + +
+
+
+
+
+ 
model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+​
+history = model.fit(train_images, train_labels, epochs=10, 
+                    validation_data=(test_images, test_labels))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ +
+

Finally, evaluate the model

+ + + +
+
+
+
+
+ 
plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.ylim([0.5, 1])
+plt.legend(loc='lower right')
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+print(test_acc)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -281,7 +1637,7 @@ systems such as automatic translation and speech-to-text.

Set up of an RNN

-

More to text to be added

+

See handwritten notes for week 43 and Lectures from CS231 at Stanford

@@ -1081,7 +2437,7 @@ samples $$ \begin{equation} x = g(z; \theta^{(g)}) -\tag{1} +\tag{6} \end{equation} $$

 
@@ -1100,7 +2456,7 @@ value given by $$ \begin{equation} d(x; \theta^{(d)}) -\tag{2} +\tag{7} \end{equation} $$

 
@@ -1115,7 +2471,7 @@ which a function $$ \begin{equation} v(\theta^{(g)}, \theta^{(d)}) -\tag{3} +\tag{8} \end{equation} $$

 
@@ -1128,7 +2484,7 @@ conjugate reward $$ \begin{equation} -v(\theta^{(g)}, \theta^{(d)}) -\tag{4} +\tag{9} \end{equation} $$

 
@@ -1166,7 +2522,7 @@ $$ \begin{equation} g^* = \underset{g}{\mathrm{argmin}}\hspace{2pt} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\tag{5} +\tag{10} \end{equation} $$

 
@@ -1178,7 +2534,7 @@ $$ v(\theta^{(g)}, \theta^{(d)}) = \mathbb{E}_{x\sim p_\mathrm{data}}\log d(x) + \mathbb{E}_{x\sim p_\mathrm{model}} \log (1 - d(x)) -\tag{6} +\tag{11} \end{equation} $$

 
@@ -1191,7 +2547,7 @@ approximation of a partition function. In the case where $$ \begin{equation} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\tag{7} +\tag{12} \end{equation} $$

 
@@ -2151,1291 +3507,6 @@ plot_results(results, plot_number)

-
-

Basic ideas of the Principal Component Analysis (PCA)

- -

The principal component analysis deals with the problem of fitting a -low-dimensional affine subspace \( S \) of dimension \( d \) much smaller than -the total dimension \( D \) of the problem at hand (our data -set). Mathematically it can be formulated as a statistical problem or -a geometric problem. In our discussion of the theorem for the -classical PCA, we will stay with a statistical approach. -Historically, the PCA was first formulated in a statistical setting in order to estimate the principal component of a multivariate random variable. -

- -

We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see below for its definition)

-
    -

  • Each data point is determined by \( p \) extrinsic (measurement) variables
    • -

  • We may want to ask the following question: Are there fewer intrinsic variables (say \( d < < p \)) that still approximately describe the data?
    • -

  • If so, these intrinsic variables may tell us something important and finding these intrinsic variables is what dimension reduction methods do.
    • -
-

-

A good read is for example Vidal, Ma and Sastry.

-
- -
-

Introducing the Covariance and Correlation functions

- -

Before we discuss the PCA theorem, we need to remind ourselves about -the definition of the covariance and the correlation function. These are quantities -

- -

Suppose we have defined two vectors -\( \hat{x} \) and \( \hat{y} \) with \( n \) elements each. The covariance matrix \( \boldsymbol{C} \) is defined as -

-

 
-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{cov}[\boldsymbol{x},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{y},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{y},\boldsymbol{y}] \\ - \end{bmatrix}, -$$ -

 
- -

where for example

-

 
-$$ -\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). -$$ -

 
- -

With this definition and recalling that the variance is defined as

-

 
-$$ -\mathrm{var}[\boldsymbol{x}]=\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})^2, -$$ -

 
- -

we can rewrite the covariance matrix as

-

 
-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{var}[\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] & \mathrm{var}[\boldsymbol{y}] \\ - \end{bmatrix}. -$$ -

 
-

- -
-

More on the covariance

-

The covariance takes values between zero and infinity and may thus -lead to problems with loss of numerical precision for particularly -large values. It is common to scale the covariance matrix by -introducing instead the correlation matrix defined via the so-called -correlation function -

- -

 
-$$ -\mathrm{corr}[\boldsymbol{x},\boldsymbol{y}]=\frac{\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}]}{\sqrt{\mathrm{var}[\boldsymbol{x}] \mathrm{var}[\boldsymbol{y}]}}. -$$ -

 
- -

The correlation function is then given by values \( \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] -\in [-1,1] \). This avoids eventual problems with too large values. We -can then define the correlation matrix for the two vectors \( \boldsymbol{x} \) -and \( \boldsymbol{y} \) as -

- -

 
-$$ -\boldsymbol{K}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} 1 & \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{corr}[\boldsymbol{y},\boldsymbol{x}] & 1 \\ - \end{bmatrix}, -$$ -

 
- -

In the above example this is the function we constructed using pandas.

-
- -
-

Reminding ourselves about Linear Regression

-

In our derivation of the various regression algorithms like Ordinary Least Squares or Ridge regression -we defined the design/feature matrix \( \boldsymbol{X} \) as -

- -

 
-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ -

 
- -

with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) refering to the column numbers and the -entries \( n \) being the row elements. -We can rewrite the design/feature matrix in terms of its column vectors as -

-

 
-$$ -\boldsymbol{X}=\begin{bmatrix} \boldsymbol{x}_0 & \boldsymbol{x}_1 & \boldsymbol{x}_2 & \dots & \dots & \boldsymbol{x}_{p-1}\end{bmatrix}, -$$ -

 
- -

with a given vector

-

 
-$$ -\boldsymbol{x}_i^T = \begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \dots & \dots x_{n-1,i}\end{bmatrix}. -$$ -

 
-

- -
-

Simple Example

-

With these definitions, we can now rewrite our \( 2\times 2 \) -correlation/covariance matrix in terms of a moe general design/feature -matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \). This leads to a \( p\times p \) -covariance matrix for the vectors \( \boldsymbol{x}_i \) with \( i=0,1,\dots,p-1 \) -

- -

 
-$$ -\boldsymbol{C}[\boldsymbol{x}] = \begin{bmatrix} -\mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_1] & \mathrm{var}[\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & \mathrm{var}[\boldsymbol{x}_{p-1}]\\ -\end{bmatrix}, -$$ -

 
-

- -
-

The Correlation Matrix

- -

and the correlation matrix

-

 
-$$ -\boldsymbol{K}[\boldsymbol{x}] = \begin{bmatrix} -1 & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_0] & 1 & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_1] & 1 & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & 1\\ -\end{bmatrix}, -$$ -

 
-

- -
-

Numpy Functionality

- -

The Numpy function np.cov calculates the covariance elements using -the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have -the exact mean values. The following simple function uses the -np.vstack function which takes each vector of dimension \( 1\times n \) -and produces a \( 2\times n \) matrix \( \boldsymbol{W} \) -

- -

 
-$$ -\boldsymbol{W}^T = \begin{bmatrix} x_0 & y_0 \\ - x_1 & y_1 \\ - x_2 & y_2\\ - \dots & \dots \\ - x_{n-2} & y_{n-2}\\ - x_{n-1} & y_{n-1} & - \end{bmatrix}, -$$ -

 
- -

which in turn is converted into into the \( 2\times 2 \) covariance matrix -\( \boldsymbol{C} \) via the Numpy function np.cov(). We note that we can also calculate -the mean value of each set of samples \( \boldsymbol{x} \) etc using the Numpy -function np.mean(x). We can also extract the eigenvalues of the -covariance matrix through the np.linalg.eig() function. -

- - - -
-
-
-
-
- 
# Importing various packages
-import numpy as np
-n = 100
-x = np.random.normal(size=n)
-print(np.mean(x))
-y = 4+3*x+np.random.normal(size=n)
-print(np.mean(y))
-W = np.vstack((x, y))
-C = np.cov(W)
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -
-

Correlation Matrix again

- -

The previous example can be converted into the correlation matrix by -simply scaling the matrix elements with the variances. We should also -subtract the mean values for each column. This leads to the following -code which sets up the correlations matrix for the previous example in -a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the \( 2\times 2 \) correlation matrix (since we have only two vectors). -

- - - -
-
-
-
-
- 
import numpy as np
-n = 100
-# define two vectors                                                                                           
-x = np.random.random(size=n)
-y = 4+3*x+np.random.normal(size=n)
-#scaling the x and y vectors                                                                                   
-x = x - np.mean(x)
-y = y - np.mean(y)
-variance_x = np.sum(x@x)/n
-variance_y = np.sum(y@y)/n
-print(variance_x)
-print(variance_y)
-cov_xy = np.sum(x@y)/n
-cov_xx = np.sum(x@x)/n
-cov_yy = np.sum(y@y)/n
-C = np.zeros((2,2))
-C[0,0]= cov_xx/variance_x
-C[1,1]= cov_yy/variance_y
-C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)
-C[1,0]= C[0,1]
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We see that the matrix elements along the diagonal are one as they -should be and that the matrix is symmetric. Furthermore, diagonalizing -this matrix we easily see that it is a positive definite matrix. -

- -

The above procedure with numpy can be made more compact if we use pandas.

-
- -
-

Using Pandas

- -

We whow here how we can set up the correlation matrix using pandas, as done in this simple code

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-n = 10
-x = np.random.normal(size=n)
-x = x - np.mean(x)
-y = 4+3*x+np.random.normal(size=n)
-y = y - np.mean(y)
-X = (np.vstack((x, y))).T
-print(X)
-Xpd = pd.DataFrame(X)
-print(Xpd)
-correlation_matrix = Xpd.corr()
-print(correlation_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -
-

And then the Franke Function

- -

We expand this model to the Franke function discussed above.

- - - -
-
-
-
-
- 
# Common imports
-import numpy as np
-import pandas as pd
-
-
-def FrankeFunction(x,y):
-	term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
-	term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
-	term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
-	term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
-	return term1 + term2 + term3 + term4
-
-
-def create_X(x, y, n ):
-	if len(x.shape) > 1:
-		x = np.ravel(x)
-		y = np.ravel(y)
-
-	N = len(x)
-	l = int((n+1)*(n+2)/2)		# Number of elements in beta
-	X = np.ones((N,l))
-
-	for i in range(1,n+1):
-		q = int((i)*(i+1)/2)
-		for k in range(i+1):
-			X[:,q+k] = (x**(i-k))*(y**k)
-
-	return X
-
-
-# Making meshgrid of datapoints and compute Franke's function
-n = 4
-N = 100
-x = np.sort(np.random.uniform(0, 1, N))
-y = np.sort(np.random.uniform(0, 1, N))
-z = FrankeFunction(x, y)
-X = create_X(x, y, n=n)    
-
-Xpd = pd.DataFrame(X)
-# subtract the mean values and set up the covariance matrix
-Xpd = Xpd - Xpd.mean()
-covariance_matrix = Xpd.cov()
-print(covariance_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We note here that the covariance is zero for the first rows and -columns since all matrix elements in the design matrix were set to one -(we are fitting the function in terms of a polynomial of degree \( n \)). We would however not include the intercept -and wee can simply -drop these elements and construct a correlation -matrix without them by centering our matrix elements by subtracting the mean of each column. -

-
- -
-

Lnks with the Design Matrix

- -

We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix \( \boldsymbol{X} \) as

-

 
-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ -

 
- -

To see this let us simply look at a design matrix \( \boldsymbol{X}\in {\mathbb{R}}^{2\times 2} \)

-

 
-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{00} & x_{01}\\ -x_{10} & x_{11}\\ -\end{bmatrix}=\begin{bmatrix} -\boldsymbol{x}_{0} & \boldsymbol{x}_{1}\\ -\end{bmatrix}. -$$ -

 
-

- -
-

Computing the Expectation Values

- -

If we then compute the expectation value

-

 
-$$ -\mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}=\begin{bmatrix} -x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\ -x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\ -\end{bmatrix}, -$$ -

 
- -

which is just

-

 
-$$ -\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\ - \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\ - \end{bmatrix}, -$$ -

 
- -

where we wrote

 
-$$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ -

 
to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).

- -

It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).

-
- -
-

Towards the PCA theorem

- -

We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as

-

 
-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ -

 
- -

Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \). -These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \). -

- -

Assume also that there is a transformation \( \boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).

- -

That is we have

-

 
-$$ -\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}^T\boldsymbol{X}^T\boldsymbol{X}T\boldsymbol{S}]=\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ -

 
- -

since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S} \) from the left we have

-

 
-$$ -\boldsymbol{S}\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ -

 
- -

and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that

- -

 
-$$ -\boldsymbol{S}_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}_i. -$$ -

 
-

- -
-

More on the PCA Theorem

- -

In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is -\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \). -

- -

The eigenvalues tell us then how much we need to stretch the -corresponding eigenvectors. Dimensions with large eigenvalues have -thus large variations (large variance) and define therefore useful -dimensions. The data points are more spread out in the direction of -these eigenvectors. Smaller eigenvalues mean on the other hand that -the corresponding eigenvectors are shrunk accordingly and the data -points are tightly bunched together and there is not much variation in -these specific directions. Hopefully then we could leave it out -dimensions where the eigenvalues are very small. If \( p \) is very large, -we could then aim at reducing \( p \) to \( l < < p \) and handle only \( l \) -features/predictors. -

-
- -
-

The Algorithm before theorem

- -

Here's how we would proceed in setting up the algorithm for the PCA, see also discussion below here.

-
    -

  • Set up the datapoints for the design/feature matrix \( \boldsymbol{X} \) with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) referring to the column numbers and the entries \( n \) being the row elements.
    • -
-

-

 
-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ -

 
- -

    -

  • Center the data by subtracting the mean value for each column. This leads to a new matrix \( \boldsymbol{X}\rightarrow \overline{\boldsymbol{X}} \).
    • -

  • Compute then the covariance/correlation matrix \( \mathbb{E}[\overline{\boldsymbol{X}}^T\overline{\boldsymbol{X}}] \).
    • -

  • Find the eigenpairs of \( \boldsymbol{C} \) with eigenvalues \( [\lambda_0,\lambda_1,\dots,\lambda_{p-1}] \) and eigenvectors \( [\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \).
    • -

  • Order the eigenvalue (and the eigenvectors accordingly) in order of decreasing eigenvalues.
    • -

  • Keep only those \( l \) eigenvalues larger than a selected threshold value, discarding thus \( p-l \) features since we expect small variations in the data here.
    • -
-
- -
-

Writing our own PCA code

- -

We will use a simple example first with two-dimensional data -drawn from a multivariate normal distribution with the following mean and covariance matrix (we have fixed these quantities but will play around with them below): -

-

 
-$$ -\mu = (-1,2) \qquad \Sigma = \begin{bmatrix} 4 & 2 \\ -2 & 2 -\end{bmatrix} -$$ -

 
- -

Note that the mean refers to each column of data. -We will generate \( n = 10000 \) points \( X = \{ x_1, \ldots, x_N \} \) from -this distribution, and store them in the \( 1000 \times 2 \) matrix \( \boldsymbol{X} \). This is our design matrix where we have forced the covariance and mean values to take specific values. -

-
- -
-

Implementing it

-

The following Python code aids in setting up the data and writing out the design matrix. -Note that the function multivariate returns also the covariance discussed above and that it is defined by dividing by \( n-1 \) instead of \( n \). -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from IPython.display import display
-n = 10000
-mean = (-1, 2)
-cov = [[4, 2], [2, 2]]
-X = np.random.multivariate_normal(mean, cov, n)
-
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-
- -

Now we are going to implement the PCA algorithm. We will break it down into various substeps.

-
- -
-

First Step

- -

The first step of PCA is to compute the sample mean of the data and use it to center the data. Recall that the sample mean is

-

 
-$$ -\mu_n = \frac{1}{n} \sum_{i=1}^n x_i -$$ -

 
- -

and the mean-centered data \( \bar{X} = \{ \bar{x}_1, \ldots, \bar{x}_n \} \) takes the form

-

 
-$$ -\bar{x}_i = x_i - \mu_n. -$$ -

 
- -

When you are done with these steps, print out \( \mu_n \) to verify it is -close to \( \mu \) and plot your mean centered data to verify it is -centered at the origin! -The following code elements perform these operations using pandas or using our own functionality for doing so. The latter, using numpy is rather simple through the mean() function. -

- - -
-
-
-
-
- 
df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-
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- -
-

Scaling

-

Alternatively, we could use the functions we discussed -earlier for scaling the data set. That is, we could have used the -StandardScaler function in Scikit-Learn, a function which ensures -that for each feature/predictor we study the mean value is zero and -the variance is one (every column in the design/feature matrix). You -would then not get the same results, since we divide by the -variance. The diagonal covariance matrix elements will then be one, -while the non-diagonal ones need to be divided by \( 2\sqrt{2} \) for our -specific case. -

-
- -
-

Centered Data

- -

Now we are going to use the mean centered data to compute the sample covariance of the data by using the following equation

-

 
-$$ -\begin{equation*} -\Sigma_n = \frac{1}{n-1} \sum_{i=1}^n \bar{x}_i^T \bar{x}_i = \frac{1}{n-1} \sum_{i=1}^n (x_i - \mu_n)^T (x_i - \mu_n) -\end{equation*} -$$ -

 
- -

where the data points \( x_i \in \mathbb{R}^p \) (here in this example \( p = 2 \)) are column vectors and \( x^T \) is the transpose of \( x \). -We can write our own code or simply use either the functionaly of numpy or that of pandas, as follows -

- - -
-
-
-
-
- 
print(df.cov())
-print(np.cov(X_centered.T))
-
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-
-
- -

Note that the way we define the covariance matrix here has a factor \( n-1 \) instead of \( n \). This is included in the cov() function by numpy and pandas. -Our own code here is not very elegant and asks for obvious improvements. It is tailored to this specific \( 2\times 2 \) covariance matrix. -

- - -
-
-
-
-
- 
# extract the relevant columns from the centered design matrix of dim n x 2
-x = X_centered[:,0]
-y = X_centered[:,1]
-Cov = np.zeros((2,2))
-Cov[0,1] = np.sum(x.T@y)/(n-1.0)
-Cov[0,0] = np.sum(x.T@x)/(n-1.0)
-Cov[1,1] = np.sum(y.T@y)/(n-1.0)
-Cov[1,0]= Cov[0,1]
-print("Centered covariance using own code")
-print(Cov)
-plt.plot(x, y, 'x')
-plt.axis('equal')
-plt.show()
-
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-
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-
-
-
-
- -
-

Exploring

- -

Depending on the number of points \( n \), we will get results that are close to the covariance values defined above. -The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed. -

-
- -
-

Diagonalize the sample covariance matrix to obtain the principal components

- -

Now we are ready to solve for the principal components! To do so we -diagonalize the sample covariance matrix \( \Sigma \). We can use the -function np.linalg.eig to do so. It will return the eigenvalues and -eigenvectors of \( \Sigma \). Once we have these we can perform the -following tasks: -

- -
    -

  • We compute the percentage of the total variance captured by the first principal component
    • -

  • We plot the mean centered data and lines along the first and second principal components
    • -

  • Then we project the mean centered data onto the first and second principal components, and plot the projected data.
    • -

  • Finally, we approximate the data as
    • -
-

-

 
-$$ -\begin{equation*} -x_i \approx \tilde{x}_i = \mu_n + \langle x_i, v_0 \rangle v_0 -\end{equation*} -$$ -

 
- -

where \( v_0 \) is the first principal component.

-
- -
-

Collecting all Steps

- -

Collecting all these steps we can write our own PCA function and -compare this with the functionality included in Scikit-Learn. -

- -

The code here outlines some of the elements we could include in the -analysis. Feel free to extend upon this in order to address the above -questions. -

- - - -
-
-
-
-
- 
# diagonalize and obtain eigenvalues, not necessarily sorted
-EigValues, EigVectors = np.linalg.eig(Cov)
-# sort eigenvectors and eigenvalues
-#permute = EigValues.argsort()
-#EigValues = EigValues[permute]
-#EigVectors = EigVectors[:,permute]
-print("Eigenvalues of Covariance matrix")
-for i in range(2):
-    print(EigValues[i])
-FirstEigvector = EigVectors[:,0]
-SecondEigvector = EigVectors[:,1]
-print("First eigenvector")
-print(FirstEigvector)
-print("Second eigenvector")
-print(SecondEigvector)
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2Dsl = pca.fit_transform(X)
-print("Eigenvector of largest eigenvalue")
-print(pca.components_.T[:, 0])
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

This code does not contain all the above elements, but it shows how we can use Scikit-Learn to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?

-
- -
-

Classical PCA Theorem

- -

We assume now that we have a design matrix \( \boldsymbol{X} \) which has been -centered as discussed above. For the sake of simplicity we skip the -overline symbol. The matrix is defined in terms of the various column -vectors \( [\boldsymbol{x}_0,\boldsymbol{x}_1,\dots, \boldsymbol{x}_{p-1}] \) each with dimension -\( \boldsymbol{x}\in {\mathbb{R}}^{n} \). -

- -

The PCA theorem states that minimizing the above reconstruction error -corresponds to setting \( \boldsymbol{W}=\boldsymbol{S} \), the orthogonal matrix which -diagonalizes the empirical covariance(correlation) matrix. The optimal -low-dimensional encoding of the data is then given by a set of vectors -\( \boldsymbol{z}_i \) with at most \( l \) vectors, with \( l < < p \), defined by the -orthogonal projection of the data onto the columns spanned by the -eigenvectors of the covariance(correlations matrix). -

-
- -
-

The PCA Theorem

- -

To show the PCA theorem let us start with the assumption that there is one vector \( \boldsymbol{s}_0 \) which corresponds to a solution which minimized the reconstruction error \( J \). This is an orthogonal vector. It means that we now approximate the reconstruction error in terms of \( \boldsymbol{w}_0 \) and \( \boldsymbol{z}_0 \) as

- -

We are almost there, we have obtained a relation between minimizing -the reconstruction error and the variance and the covariance -matrix. Minimizing the error is equivalent to maximizing the variance -of the projected data. -

- -

We could trivially maximize the variance of the projection (and -thereby minimize the error in the reconstruction function) by letting -the norm-2 of \( \boldsymbol{w}_0 \) go to infinity. However, this norm since we -want the matrix \( \boldsymbol{W} \) to be an orthogonal matrix, is constrained by -\( \vert\vert \boldsymbol{w}_0 \vert\vert_2^2=1 \). Imposing this condition via a -Lagrange multiplier we can then in turn maximize -

- -

 
-$$ -J(\boldsymbol{w}_0)= \boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0+\lambda_0(1-\boldsymbol{w}_0^T\boldsymbol{w}_0). -$$ -

 
- -

Taking the derivative with respect to \( \boldsymbol{w}_0 \) we obtain

- -

 
-$$ -\frac{\partial J(\boldsymbol{w}_0)}{\partial \boldsymbol{w}_0}= 2\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0-2\lambda_0\boldsymbol{w}_0=0, -$$ -

 
- -

meaning that

-

 
-$$ -\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0\boldsymbol{w}_0. -$$ -

 
- -

The direction that maximizes the variance (or minimizes the construction error) is an eigenvector of the covariance matrix! If we left multiply with \( \boldsymbol{w}_0^T \) we have the variance of the projected data is

-

 
-$$ -\boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0. -$$ -

 
- -

If we want to maximize the variance (minimize the construction error) -we simply pick the eigenvector of the covariance matrix with the -largest eigenvalue. This establishes the link between the minimization -of the reconstruction function \( J \) in terms of an orthogonal matrix -and the maximization of the variance and thereby the covariance of our -observations encoded in the design/feature matrix \( \boldsymbol{X} \). -

- -

The proof -for the other eigenvectors \( \boldsymbol{w}_1,\boldsymbol{w}_2,\dots \) can be -established by applying the above arguments and using the fact that -our basis of eigenvectors is orthogonal, see Murphy chapter -12.2. The -discussion in chapter 12.2 of Murphy's text has also a nice link with -the Singular Value Decomposition theorem. For categorical data, see -chapter 12.4 and discussion therein. -

- -

For more details, see for example Vidal, Ma and Sastry, chapter 2.

-
- -
- - -

For a detailed demonstration of the geometric interpretation, see Vidal, Ma and Sastry, section 2.1.2.

- -

Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm. -First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. -

- -

The following Python code uses NumPy’s svd() function to obtain all the principal components of the -training set, then extracts the first two principal components. First we center the data using either pandas or our own code -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-from IPython.display import display
-np.random.seed(100)
-# setting up a 10 x 5 vanilla matrix 
-rows = 10
-cols = 5
-X = np.random.randn(rows,cols)
-df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-display(df)
-
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-# Then check the difference between pandas and our own set up
-print(X_centered-df)
-#Now we do an SVD
-U, s, V = np.linalg.svd(X_centered)
-c1 = V.T[:, 0]
-c2 = V.T[:, 1]
-W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
-print(X2D)
-
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- -

PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering -the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t -forget to center the data first. -

- -

Once you have identified all the principal components, you can reduce the dimensionality of the dataset -down to \( d \) dimensions by projecting it onto the hyperplane defined by the first \( d \) principal components. -Selecting this hyperplane ensures that the projection will preserve as much variance as possible. -

- - -
-
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-
- 
W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
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-

PCA and scikit-learn

- -

Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The -following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note -that it automatically takes care of centering the data): -

- - -
-
-
-
-
- 
#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D = pca.fit_transform(X)
-print(X2D)
-
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- -

After fitting the PCA transformer to the dataset, you can access the principal components using the -components variable (note that it contains the PCs as horizontal vectors, so, for example, the first -principal component is equal to -

- - -
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- 
pca.components_.T[:, 0]
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- -

Another very useful piece of information is the explained variance ratio of each principal component, -available via the \( explained\_variance\_ratio \) variable. It indicates the proportion of the dataset’s -variance that lies along the axis of each principal component. -

-
- -
-

Back to the Cancer Data

-

We can now repeat the above but applied to real data, in this case our breast cancer data. -Here we compute performance scores on the training data using logistic regression. -

- - -
-
-
-
-
- 
import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.model_selection import  train_test_split 
-from sklearn.datasets import load_breast_cancer
-from sklearn.linear_model import LogisticRegression
-cancer = load_breast_cancer()
-
-X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
-
-logreg = LogisticRegression()
-logreg.fit(X_train, y_train)
-print("Train set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_train,y_train)))
-# We scale the data
-from sklearn.preprocessing import StandardScaler
-scaler = StandardScaler()
-scaler.fit(X_train)
-X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-# Then perform again a log reg fit
-logreg.fit(X_train_scaled, y_train)
-print("Train set accuracy scaled data: {:.2f}".format(logreg.score(X_train_scaled,y_train)))
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D_train = pca.fit_transform(X_train_scaled)
-# and finally compute the log reg fit and the score on the training data	
-logreg.fit(X2D_train,y_train)
-print("Train set accuracy scaled and PCA data: {:.2f}".format(logreg.score(X2D_train,y_train)))
-
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We see that our training data after the PCA decomposition has a performance similar to the non-scaled data.

- -

Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to -choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). -Unless, of course, you are reducing dimensionality for data visualization — in that case you will -generally want to reduce the dimensionality down to 2 or 3. -The following code computes PCA without reducing dimensionality, then computes the minimum number -of dimensions required to preserve 95% of the training set’s variance: -

- - -
-
-
-
-
- 
pca = PCA()
-pca.fit(X)
-cumsum = np.cumsum(pca.explained_variance_ratio_)
-d = np.argmax(cumsum >= 0.95) + 1
-
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You could then set \( n\_components=d \) and run PCA again. However, there is a much better option: instead -of specifying the number of principal components you want to preserve, you can set \( n\_components \) to be -a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: -

- - -
-
-
-
-
- 
pca = PCA(n_components=0.95)
-X_reduced = pca.fit_transform(X)
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Incremental PCA

- -

One problem with the preceding implementation of PCA is that it requires the whole training set to fit in -memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have -been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch -at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new -instances arrive). -

-

Randomized PCA

- -

Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic -algorithm that quickly finds an approximation of the first d principal components. Its computational -complexity is \( O(m \times d^2)+O(d^3) \), instead of \( O(m \times n^2) + O(n^3) \), so it is dramatically faster than the -previous algorithms when \( d \) is much smaller than \( n \). -

-

Kernel PCA

- -

The kernel trick is a mathematical technique that implicitly maps instances into a -very high-dimensional space (called the feature space), enabling nonlinear classification and regression -with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature -space corresponds to a complex nonlinear decision boundary in the original space. -It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear -projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at -preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a -twisted manifold. -For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an -

- - -
-
-
-
-
- 
from sklearn.decomposition import KernelPCA
-rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04)
-X_reduced = rbf_pca.fit_transform(X)
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Other techniques

- -

There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.

- -

Here are some of the most popular:

-
    -

  • Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances between the instances.
    • -

  • Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances.
    • -

  • t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).
    • -

  • Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures.
    • -
-
-
diff --git a/doc/pub/week43/html/week43-solarized.html b/doc/pub/week43/html/week43-solarized.html index 36f0b0a43..d1ea1c8e7 100644 --- a/doc/pub/week43/html/week43-solarized.html +++ b/doc/pub/week43/html/week43-solarized.html @@ -64,7 +64,105 @@ div.toc p,a { {'highest level': 2, 'sections': [('Plans for week 43', 2, None, 'plans-for-week-43'), ('Reading Recommendations', 2, None, 'reading-recommendations'), + ('Convolutional Neural Networks (recognizing images)', + 2, + None, + 'convolutional-neural-networks-recognizing-images'), + ('What is the Difference', 2, None, 'what-is-the-difference'), + ('Neural Networks vs CNNs', 2, None, 'neural-networks-vs-cnns'), + ('Why CNNS for images, sound files, medical images from CT scans ' + 'etc?', + 2, + None, + 'why-cnns-for-images-sound-files-medical-images-from-ct-scans-etc'), + ('Regular NNs don’t scale well to full images', + 2, + None, + 'regular-nns-don-t-scale-well-to-full-images'), + ('3D volumes of neurons', 2, None, '3d-volumes-of-neurons'), + ('Layers used to build CNNs', + 2, + None, + 'layers-used-to-build-cnns'), + ('Transforming images', 2, None, 'transforming-images'), ('CNNs in brief', 2, None, 'cnns-in-brief'), + ('Key Idea', 2, None, 'key-idea'), + ('Mathematics of CNNs', 2, None, 'mathematics-of-cnns'), + ('Convolution Examples: Polynomial multiplication', + 2, + None, + 'convolution-examples-polynomial-multiplication'), + ('Efficient Polynomial Multiplication', + 2, + None, + 'efficient-polynomial-multiplication'), + ('A more efficient way of coding the above Convolution', + 2, + None, + 'a-more-efficient-way-of-coding-the-above-convolution'), + ('Convolution Examples: Principle of Superposition and Periodic ' + 'Forces (Fourier Transforms)', + 2, + None, + 'convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms'), + ('Principle of Superposition', + 2, + None, + 'principle-of-superposition'), + ('Simple Code Example', 2, None, 'simple-code-example'), + ('Wrapping up Fourier transforms', + 2, + None, + 'wrapping-up-fourier-transforms'), + ('Finding the Coefficients', 2, None, 'finding-the-coefficients'), + ('Final words on Fourier Transforms', + 2, + None, + 'final-words-on-fourier-transforms'), + ('Two-dimensional Objects', 2, None, 'two-dimensional-objects'), + ('Cross-Correlation', 2, None, 'cross-correlation'), + ('More on Dimensionalities', 2, None, 'more-on-dimensionalities'), + ('Further Dimensionality Remarks', + 2, + None, + 'further-dimensionality-remarks'), + ('CNNs in more detail, Lecture from IN5400', + 2, + None, + 'cnns-in-more-detail-lecture-from-in5400'), + ('CNNs in more detail, building convolutional neural networks in ' + 'Tensorflow and Keras', + 2, + None, + 'cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras'), + ('Setting it up', 2, None, 'setting-it-up'), + ('The MNIST dataset again', 2, None, 'the-mnist-dataset-again'), + ('Strong correlations', 2, None, 'strong-correlations'), + ('Layers of a CNN', 2, None, 'layers-of-a-cnn'), + ('Systematic reduction', 2, None, 'systematic-reduction'), + ('Prerequisites: Collect and pre-process data', + 2, + None, + 'prerequisites-collect-and-pre-process-data'), + ('Importing Keras and Tensorflow', + 2, + None, + 'importing-keras-and-tensorflow'), + ('Running with Keras', 2, None, 'running-with-keras'), + ('Final part', 2, None, 'final-part'), + ('Final visualization', 2, None, 'final-visualization'), + ('The CIFAR01 data set', 2, None, 'the-cifar01-data-set'), + ('Verifying the data set', 2, None, 'verifying-the-data-set'), + ('Set up the model', 2, None, 'set-up-the-model'), + ('Add Dense layers on top', 2, None, 'add-dense-layers-on-top'), + ('Compile and train the model', + 2, + None, + 'compile-and-train-the-model'), + ('Finally, evaluate the model', + 2, + None, + 'finally-evaluate-the-model'), ('Recurrent neural networks: Overarching view', 2, None, @@ -110,68 +208,7 @@ div.toc p,a { ('Interpolating Between MNIST Digits', 2, None, - 'interpolating-between-mnist-digits'), - ('Basic ideas of the Principal Component Analysis (PCA)', - 2, - None, - 'basic-ideas-of-the-principal-component-analysis-pca'), - ('Introducing the Covariance and Correlation functions', - 2, - None, - 'introducing-the-covariance-and-correlation-functions'), - ('More on the covariance', 2, None, 'more-on-the-covariance'), - ('Reminding ourselves about Linear Regression', - 2, - None, - 'reminding-ourselves-about-linear-regression'), - ('Simple Example', 2, None, 'simple-example'), - ('The Correlation Matrix', 2, None, 'the-correlation-matrix'), - ('Numpy Functionality', 2, None, 'numpy-functionality'), - ('Correlation Matrix again', 2, None, 'correlation-matrix-again'), - ('Using Pandas', 2, None, 'using-pandas'), - ('And then the Franke Function', - 2, - None, - 'and-then-the-franke-function'), - ('Lnks with the Design Matrix', - 2, - None, - 'lnks-with-the-design-matrix'), - ('Computing the Expectation Values', - 2, - None, - 'computing-the-expectation-values'), - ('Towards the PCA theorem', 2, None, 'towards-the-pca-theorem'), - ('More on the PCA Theorem', 2, None, 'more-on-the-pca-theorem'), - ('The Algorithm before the Theorem', - 2, - None, - 'the-algorithm-before-the-theorem'), - ('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'), - ('Implementing it', 2, None, 'implementing-it'), - ('First Step', 2, None, 'first-step'), - ('Scaling', 2, None, 'scaling'), - ('Centered Data', 2, None, 'centered-data'), - ('Exploring', 2, None, 'exploring'), - ('Diagonalize the sample covariance matrix to obtain the ' - 'principal components', - 2, - None, - 'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'), - ('Collecting all Steps', 2, None, 'collecting-all-steps'), - ('Classical PCA Theorem', 2, None, 'classical-pca-theorem'), - ('The PCA Theorem', 2, None, 'the-pca-theorem'), - ('Geometric Interpretation and link with Singular Value ' - 'Decomposition', - 2, - None, - 'geometric-interpretation-and-link-with-singular-value-decomposition'), - ('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'), - ('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'), - ('Incremental PCA', 2, None, 'incremental-pca'), - ('Randomized PCA', 3, None, 'randomized-pca'), - ('Kernel PCA', 3, None, 'kernel-pca'), - ('Other techniques', 2, None, 'other-techniques')]} + 'interpolating-between-mnist-digits')]} end of tocinfo --> @@ -192,7 +229,7 @@ MathJax.Hub.Config({ -

ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis +

ATITLE: Week 43: Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks

@@ -207,7 +244,7 @@ MathJax.Hub.Config({

-

Oct 24, 2022

+

Oct 26, 2022


@@ -216,8 +253,8 @@ MathJax.Hub.Config({

Plans for week 43

    -
  • Thursday: Convolutional Neural Networks, basic elements and
    • -
  • Friday: Recurrent Neural Networks and other Deep learning methods, Generalized Adversarial Neural Networ and autoencoders
    • +
  • Thursday: Convolutional Neural Networks (CNN)
    • +
  • Friday: Recurrent Neural Networks (RNN)
Excellent lectures on CNNs and RNNs @@ -243,10 +280,207 @@ MathJax.Hub.Config({









Reading Recommendations

-
    -
  • Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.
    • +
    +CNN readings +

    +

      +
    1. Goodfellow, Bengio, Courville, chapter 9
      2. +
    2. Lectures from CS231 at Stanford
      3. +
    3. Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs.
      4. +
    +
    + + +
    +RNN readings +

    +

      +
    1. Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.
      2. +
    2. Lectures from CS231 at Stanford
    3. Aurelien Geron, chapter 14 on RNNs.
      4. +
    +
    + + +









    +

    Convolutional Neural Networks (recognizing images)

    + +

    Convolutional neural networks (CNNs) were developed during the last +decade of the previous century, with a focus on character recognition +tasks. Nowadays, CNNs are a central element in the spectacular success +of deep learning methods. The success in for example image +classifications have made them a central tool for most machine +learning practitioners. +

    + +

    CNNs are very similar to ordinary Neural Networks. +They are made up of neurons that have learnable weights and +biases. Each neuron receives some inputs, performs a dot product and +optionally follows it with a non-linearity. The whole network still +expresses a single differentiable score function: from the raw image +pixels on one end to class scores at the other. And they still have a +loss function (for example Softmax) on the last (fully-connected) layer +and all the tips/tricks we developed for learning regular Neural +Networks still apply (back propagation, gradient descent etc etc). +

    + +









    +

    What is the Difference

    + +

    CNN architectures make the explicit assumption that +the inputs are images, which allows us to encode certain properties +into the architecture. These then make the forward function more +efficient to implement and vastly reduce the amount of parameters in +the network. +

    + +

    Here we provide only a superficial overview, for the more interested, we recommend highly the course +IN5400 – Machine Learning for Image Analysis +and the slides of CS231. +

    + +

    Another good read is the article here https://arxiv.org/pdf/1603.07285.pdf.

    + +









    +

    Neural Networks vs CNNs

    + +

    Neural networks are defined as affine transformations, that is +a vector is received as input and is multiplied with a matrix of so-called weights (our unknown paramters) to produce an +output (to which a bias vector is usually added before passing the result +through a nonlinear activation function). This is applicable to any type of input, be it an +image, a sound clip or an unordered collection of features: whatever their +dimensionality, their representation can always be flattened into a vector +before the transformation. +

    + +









    +

    Why CNNS for images, sound files, medical images from CT scans etc?

    + +

    However, when we consider images, sound clips and many other similar kinds of data, these data have an intrinsic +structure. More formally, they share these important properties: +

    +
      +
    • They are stored as multi-dimensional arrays (think of the pixels of a figure) .
      • +
    • They feature one or more axes for which ordering matters (e.g., width and height axes for an image, time axis for a sound clip).
      • +
    • One axis, called the channel axis, is used to access different views of the data (e.g., the red, green and blue channels of a color image, or the left and right channels of a stereo audio track).
    +

    These properties are not exploited when an affine transformation is applied; in +fact, all the axes are treated in the same way and the topological information +is not taken into account. Still, taking advantage of the implicit structure of +the data may prove very handy in solving some tasks, like computer vision and +speech recognition, and in these cases it would be best to preserve it. This is +where discrete convolutions come into play. +

    + +

    A discrete convolution is a linear transformation that preserves this notion of +ordering. It is sparse (only a few input units contribute to a given output +unit) and reuses parameters (the same weights are applied to multiple locations +in the input). +

    + +









    +

    Regular NNs don’t scale well to full images

    + +

    As an example, consider +an image of size \( 32\times 32\times 3 \) (32 wide, 32 high, 3 color channels), so a +single fully-connected neuron in a first hidden layer of a regular +Neural Network would have \( 32\times 32\times 3 = 3072 \) weights. This amount still +seems manageable, but clearly this fully-connected structure does not +scale to larger images. For example, an image of more respectable +size, say \( 200\times 200\times 3 \), would lead to neurons that have +\( 200\times 200\times 3 = 120,000 \) weights. +

    + +

    We could have +several such neurons, and the parameters would add up quickly! Clearly, +this full connectivity is wasteful and the huge number of parameters +would quickly lead to possible overfitting. +

    + +
    +
    +
    +

    Figure 1: A regular 3-layer Neural Network.

    +
    +

    +
    + +









    +

    3D volumes of neurons

    + +

    Convolutional Neural Networks take advantage of the fact that the +input consists of images and they constrain the architecture in a more +sensible way. +

    + +

    In particular, unlike a regular Neural Network, the +layers of a CNN have neurons arranged in 3 dimensions: width, +height, depth. (Note that the word depth here refers to the third +dimension of an activation volume, not to the depth of a full Neural +Network, which can refer to the total number of layers in a network.) +

    + +

    To understand it better, the above example of an image +with an input volume of +activations has dimensions \( 32\times 32\times 3 \) (width, height, +depth respectively). +

    + +

    The neurons in a layer will +only be connected to a small region of the layer before it, instead of +all of the neurons in a fully-connected manner. Moreover, the final +output layer could for this specific image have dimensions \( 1\times 1 \times 10 \), +because by the +end of the CNN architecture we will reduce the full image into a +single vector of class scores, arranged along the depth +dimension. +

    + +
    +
    +
    +

    Figure 2: A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).

    +
    +

    +
    + + +

    Layers used to build CNNs

    + +

    A simple CNN is a sequence of layers, and every layer of a CNN +transforms one volume of activations to another through a +differentiable function. We use three main types of layers to build +CNN architectures: Convolutional Layer, Pooling Layer, and +Fully-Connected Layer (exactly as seen in regular Neural Networks). We +will stack these layers to form a full CNN architecture. +

    + +

    A simple CNN for image classification could have the architecture:

    + +
      +
    • INPUT (\( 32\times 32 \times 3 \)) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
      • +
    • CONV (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as \( [32\times 32\times 12] \) if we decided to use 12 filters.
      • +
    • RELU layer will apply an elementwise activation function, such as the \( max(0,x) \) thresholding at zero. This leaves the size of the volume unchanged (\( [32\times 32\times 12] \)).
      • +
    • POOL (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as \( [16\times 16\times 12] \).
      • +
    • FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size \( [1\times 1\times 10] \), where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
      • +
    +









    +

    Transforming images

    + +

    CNNs transform the original image layer by layer from the original +pixel values to the final class scores. +

    + +

    Observe that some layers contain +parameters and other don’t. In particular, the CNN layers perform +transformations that are a function of not only the activations in the +input volume, but also of the parameters (the weights and biases of +the neurons). On the other hand, the RELU/POOL layers will implement a +fixed function. The parameters in the CONV/FC layers will be trained +with gradient descent so that the class scores that the CNN computes +are consistent with the labels in the training set for each image. +

    +









    CNNs in brief

    @@ -265,9 +499,1093 @@ the course and the slides of CS231 which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs.

    -

    However, both standard feed forwards networks and CNNs perform well on data with unknown length.

    +

    The textbook by Goodfellow et al, see chapter 9 contains an in depth discussion as well.

    + +









    +

    Key Idea

    + +

    A dense neural network is representd by an affine operation (like matrix-matrix multiplication) where all parameters are included.

    + +

    The key idea in CNNs for say imaging is that in images neighbor pixels tend to be related! So we connect +only neighboring neurons in the input instead of connecting all with the first hidden layer. +

    + +

    We say we perform a filtering (convolution is the mathematical operation).

    + +









    +

    Mathematics of CNNs

    + +

    The mathematics of CNNs is based on the mathematical operation of +convolution. In mathematics (in particular in functional analysis), +convolution is represented by mathematical operation (integration, +summation etc) on two function in order to produce a third function +that expresses how the shape of one gets modified by the other. +Convolution has a plethora of applications in a variety of disciplines, spanning from statistics to signal processing, computer vision, solutions of differential equations,linear algebra, engineering, and yes, machine learning. +

    + +

    Mathematically, convolution is defined as follows (one-dimensional example): +Let us define a continuous function \( y(t) \) given by +

    +$$ +y(t) = \int x(a) w(t-a) da, +$$ + +

    where \( x(a) \) represents a so-called input and \( w(t-a) \) is normally called the weight function or kernel.

    + +

    The above integral is written in a more compact form as

    +$$ +y(t) = \left(x * w\right)(t). +$$ + +

    The discretized version reads

    +$$ +y(t) = \sum_{a=-\infty}^{a=\infty}x(a)w(t-a). +$$ + +

    Computing the inverse of the above convolution operations is known as deconvolution.

    + +

    How can we use this? And what does it mean? Let us study some familiar examples first.

    + +









    +

    Convolution Examples: Polynomial multiplication

    + +

    We have already met such an example in project 1 when we tried to set +up the design matrix for a two-dimensional function. This was an +example of polynomial multiplication. Let us recast such a problem in terms of the convolution operation. +Let us look a the following polynomials to second and third order, respectively: +

    +$$ +p(t) = \alpha_0+\alpha_1 t+\alpha_2 t^2, +$$ + +

    and

    +$$ +s(t) = \beta_0+\beta_1 t+\beta_2 t^2+\beta_3 t^3. +$$ + +

    The polynomial multiplication gives us a new polynomial of degree \( 5 \)

    +$$ +z(t) = \delta_0+\delta_1 t+\delta_2 t^2+\delta_3 t^3+\delta_4 t^4+\delta_5 t^5. +$$ + + +









    +

    Efficient Polynomial Multiplication

    + +

    Computing polynomial products can be implemented efficiently if we rewrite the more brute force multiplications using convolution. +We note first that the new coefficients are given as +

    + +$$ +\begin{split} +\delta_0=&\alpha_0\beta_0\\ +\delta_1=&\alpha_1\beta_0+\alpha_1\beta_0\\ +\delta_2=&\alpha_0\beta_2+\alpha_1\beta_1+\alpha_2\beta_0\\ +\delta_3=&\alpha_1\beta_2+\alpha_2\beta_1+\alpha_0\beta_3\\ +\delta_4=&\alpha_2\beta_2+\alpha_1\beta_3\\ +\delta_5=&\alpha_2\beta_3.\\ +\end{split} +$$ + +

    We note that \( \alpha_i=0 \) except for \( i\in \left\{0,1,2\right\} \) and \( \beta_i=0 \) except for \( i\in\left\{0,1,2,3\right\} \).

    + +

    We can then rewrite the coefficients \( \delta_j \) using a discrete convolution as

    +$$ +\delta_j = \sum_{i=-\infty}^{i=\infty}\alpha_i\beta_{j-i}=(\alpha * \beta)_j, +$$ + +

    or as a double sum with restriction \( l=i+j \)

    +$$ +\delta_l = \sum_{ij}\alpha_i\beta_{j}. +$$ + +

    Do you see a potential drawback with these equations?

    + +









    +

    A more efficient way of coding the above Convolution

    + +

    Since we only have a finite number of \( \alpha \) and \( \beta \) values +which are non-zero, we can rewrite the above convolution expressions +as a matrix-vector multiplication +

    + +$$ +\boldsymbol{\delta}=\begin{bmatrix}\alpha_0 & 0 & 0 & 0 \\ + \alpha_1 & \alpha_0 & 0 & 0 \\ + \alpha_2 & \alpha_1 & \alpha_0 & 0 \\ + 0 & \alpha_2 & \alpha_1 & \alpha_0 \\ + 0 & 0 & \alpha_2 & \alpha_1 \\ + 0 & 0 & 0 & \alpha_2 + \end{bmatrix}\begin{bmatrix} \beta_0 \\ \beta_1 \\ \beta_2 \\ \beta_3\end{bmatrix}. +$$ + +

    The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding \( \beta \) and a vector holding \( \alpha \). +In this case we have +

    +$$ +\boldsymbol{\delta}=\begin{bmatrix}\beta_0 & 0 & 0 \\ + \beta_1 & \beta_0 & 0 \\ + \beta_2 & \beta_1 & \beta_0 \\ + \beta_3 & \beta_2 & \beta_1 \\ + 0 & \beta_3 & \beta_2 \\ + 0 & 0 & \beta_3 + \end{bmatrix}\begin{bmatrix} \alpha_0 \\ \alpha_1 \\ \alpha_2\end{bmatrix}. +$$ + +

    Note that the use of these matrices is for mathematical purposes only and not implementation purposes. +When implementing the above equation we do not encode (and allocate memory) the matrices explicitely. +We rather code the convolutions in the minimal memory footprint that they require. +

    + +

    Does the number of floating point operations change here when we use the commutative property?

    + +









    +

    Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)

    + +

    For problems with so-called harmonic oscillations, given by for example the following differential equation

    +$$ +m\frac{d^2x}{dt^2}+\eta\frac{dx}{dt}+x(t)=F(t), +$$ + +

    where \( F(t) \) is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations.

    + +

    If one has several driving forces, \( F(t)=\sum_n F_n(t) \), one can find +the particular solution to each \( F_n \), \( x_{pn}(t) \), and the particular +solution for the entire driving force is then given by a series like +

    + +$$ +\begin{equation} +x_p(t)=\sum_nx_{pn}(t). +\label{_auto1} +\end{equation} +$$ + + +









    +

    Principle of Superposition

    + +

    This is known as the principle of superposition. It only applies when +the homogenous equation is linear. If there were an anharmonic term +such as \( x^3 \) in the homogenous equation, then when one summed various +solutions, \( x=(\sum_n x_n)^2 \), one would get cross +terms. Superposition is especially useful when \( F(t) \) can be written +as a sum of sinusoidal terms, because the solutions for each +sinusoidal (sine or cosine) term is analytic. +

    + +

    Driving forces are often periodic, even when they are not +sinusoidal. Periodicity implies that for some time \( \tau \) +

    + +$$ +\begin{eqnarray} +F(t+\tau)=F(t). +\end{eqnarray} +$$ + +

    One example of a non-sinusoidal periodic force is a square wave. Many +components in electric circuits are non-linear, e.g. diodes, which +makes many wave forms non-sinusoidal even when the circuits are being +driven by purely sinusoidal sources. +

    + +









    +

    Simple Code Example

    + +

    The code here shows a typical example of such a square wave generated using the functionality included in the scipy Python package. We have used a period of \( \tau=0.2 \).

    + + + +
    +
    +
    +
    +
    + 
    import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t)
+plt.plot(t, SqrSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    For the sinusoidal example the +period is \( \tau=2\pi/\omega \). However, higher harmonics can also +satisfy the periodicity requirement. In general, any force that +satisfies the periodicity requirement can be expressed as a sum over +harmonics, +

    + +$$ +\begin{equation} +F(t)=\frac{f_0}{2}+\sum_{n>0} f_n\cos(2n\pi t/\tau)+g_n\sin(2n\pi t/\tau). +\label{_auto2} +\end{equation} +$$ + + +









    +

    Wrapping up Fourier transforms

    + +

    We can write down the answer for +\( x_{pn}(t) \), by substituting \( f_n/m \) or \( g_n/m \) for \( F_0/m \). By +writing each factor \( 2n\pi t/\tau \) as \( n\omega t \), with \( \omega\equiv +2\pi/\tau \), +

    + +$$ +\begin{equation} +\label{eq:fourierdef1} +F(t)=\frac{f_0}{2}+\sum_{n>0}f_n\cos(n\omega t)+g_n\sin(n\omega t). +\end{equation} +$$ + +

    The solutions for \( x(t) \) then come from replacing \( \omega \) with +\( n\omega \) for each term in the particular solution, +

    + +$$ +\begin{eqnarray} +x_p(t)&=&\frac{f_0}{2k}+\sum_{n>0} \alpha_n\cos(n\omega t-\delta_n)+\beta_n\sin(n\omega t-\delta_n),\\ +\nonumber +\alpha_n&=&\frac{f_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\beta_n&=&\frac{g_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\delta_n&=&\tan^{-1}\left(\frac{2\beta n\omega}{\omega_0^2-n^2\omega^2}\right). +\end{eqnarray} +$$ + + +









    +

    Finding the Coefficients

    + +

    Because the forces have been applied for a long time, any non-zero +damping eliminates the homogenous parts of the solution, so one need +only consider the particular solution for each \( n \). +

    + +

    The problem is considered solved if one can find expressions for the +coefficients \( f_n \) and \( g_n \), even though the solutions are expressed +as an infinite sum. The coefficients can be extracted from the +function \( F(t) \) by +

    + +$$ +\begin{eqnarray} +\label{eq:fourierdef2} +f_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\cos(2n\pi t/\tau),\\ +\nonumber +g_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\sin(2n\pi t/\tau). +\end{eqnarray} +$$ + +

    To check the consistency of these expressions and to verify +Eq. \eqref{eq:fourierdef2}, one can insert the expansion of \( F(t) \) in +Eq. \eqref{eq:fourierdef1} into the expression for the coefficients in +Eq. \eqref{eq:fourierdef2} and see whether +

    + +$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~\left\{ +\frac{f_0}{2}+\sum_{m>0}f_m\cos(m\omega t)+g_m\sin(m\omega t) +\right\}\cos(n\omega t). +\end{eqnarray} +$$ + +

    Immediately, one can throw away all the terms with \( g_m \) because they +convolute an even and an odd function. The term with \( f_0/2 \) +disappears because \( \cos(n\omega t) \) is equally positive and negative +over the interval and will integrate to zero. For all the terms +\( f_m\cos(m\omega t) \) appearing in the sum, one can use angle addition +formulas to see that \( \cos(m\omega t)\cos(n\omega +t)=(1/2)(\cos[(m+n)\omega t]+\cos[(m-n)\omega t] \). This will integrate +to zero unless \( m=n \). In that case the \( m=n \) term gives +

    + +$$ +\begin{equation} +\int_{-\tau/2}^{\tau/2}dt~\cos^2(m\omega t)=\frac{\tau}{2}, +\label{_auto3} +\end{equation} +$$ + +

    and

    + +$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~f_n/2\\ +\nonumber +&=&f_n~\checkmark. +\end{eqnarray} +$$ + +

    The same method can be used to check for the consistency of \( g_n \).

    + +









    +

    Final words on Fourier Transforms

    + +

    The code here uses the Fourier series applied to a +square wave signal. The code here +visualizes the various approximations given by Fourier series compared +with a square wave with period \( T=0.2 \) (dimensionless time), width \( 0.1 \) and max value of the force \( F=2 \). We +see that when we increase the number of components in the Fourier +series, the Fourier series approximation gets closer and closer to the +square wave signal. +

    + + + +
    +
    +
    +
    +
    + 
    import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+T =0.2
+# Max value of square signal                                                                             
+Fmax= 2.0
+# Width of signal   
+Width = 0.1
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+FourierSeriesSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t+np.pi*Width/T)
+a0 = Fmax*Width/T
+FourierSeriesSignal = a0
+Factor = 2.0*Fmax/np.pi
+for i in range(1,500):
+    FourierSeriesSignal += Factor/(i)*np.sin(np.pi*i*Width/T)*np.cos(i*t*2*np.pi/T)
+plt.plot(t, SqrSignal)
+plt.plot(t, FourierSeriesSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Two-dimensional Objects

    + +

    We often use convolutions over more than one dimension at a time. If +we have a two-dimensional image \( I \) as input, we can have a filter +defined by a two-dimensional kernel \( K \). This leads to an output \( S \) +

    + +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(m,n)K(i-m,j-n). +$$ + +

    Convolution is a commutatitave process, which means we can rewrite this equation as

    +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i-m,j-n)K(m,n). +$$ + +

    Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of \( m \) and \( n \).

    + +









    +

    Cross-Correlation

    + +

    Many deep learning libraries implement cross-correlation instead of convolution

    +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i+m,j-+)K(m,n). +$$ + + +









    +

    More on Dimensionalities

    + +

    In feilds like signal processing (and imaging as well), one designs +so-called filters. These filters are defined by the convolutions and +are often hand-crafted. One may specify filters for smoothing, edge +detection, frequency reshaping, and similar operations. However with +neural networks the idea is to automatically learn the filters and use +many of them in conjunction with non-linear operations (activation +functions). +

    + +

    As an example consider a neural network operating on sound sequence +data. Assume that we an input vector \( \boldsymbol{x} \) of length \( d=10^6 \). We +construct then a neural network with onle hidden layer only with +\( 10^4 \) nodes. This means that we will have a weight matrix with +\( 10^4\times 10^6=10^{10} \) weights to be determined, together with \( 10^4 \) biases. +

    + +

    Assume furthermore that we have an output layer which is meant to train whether the sound sequence represents a human voice (true) or something else (false). +It means that we have only one output node. But since this output node connects to \( 10^4 \) nodes in the hidden layer, there are in total \( 10^4 \) weights to be determined for the output layer, plus one bias. In total we have +

    + +$$ +\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \approx 10^{10}, +$$ + +

    that is ten billion parameters to determine.

    + +









    +

    Further Dimensionality Remarks

    + +

    In today’s architecture one can train such neural networks, however +this is a huge number of parameters for the task at hand. In general, +it is a very wasteful and inefficient use of dense matrices as +parameters. Just as importantly, such trained network parameters are +very specific for the type of input data on which they were trained +and the network is not likely to generalize easily to variations in +the input. +

    + +

    The main principles that justify convolutions is locality of +information and repetion of patterns within the signal. Sound samples +of the input in adjacent spots are much more likely to affect each +other than those that are very far away. Similarly, sounds are +repeated in multiple times in the signal. While slightly simplistic, +reasoning about such a sound example demonstrates this. The same +principles then apply to images and other similar data. +

    + +









    +

    CNNs in more detail, Lecture from IN5400

    + + +









    +

    CNNs in more detail, building convolutional neural networks in Tensorflow and Keras

    + +

    As discussed above, CNNs are neural networks built from the assumption that the inputs +to the network are 2D images. This is important because the number of features or pixels in images +grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network. +

    + +

    As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks +are the convolutional and pooling layers stacked in pairs between the input and the hidden layer. +In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D +matrices, typically 1 for each color dimension (Red, Green, Blue). +

    + +









    +

    Setting it up

    + +

    It means that to represent the entire +dataset of images, we require a 4D matrix or tensor. This tensor has the dimensions: +

    +$$ +(n_{inputs},\, n_{pixels, width},\, n_{pixels, height},\, depth) . +$$ + + +









    +

    The MNIST dataset again

    + +

    The MNIST dataset consists of grayscale images with a pixel size of +\( 28\times 28 \), meaning we require \( 28 \times 28 = 724 \) weights to each +neuron in the first hidden layer. +

    + +

    If we were to analyze images of size \( 128\times 128 \) we would require +\( 128 \times 128 = 16384 \) weights to each neuron. Even worse if we were +dealing with color images, as most images are, we have an image matrix +of size \( 128\times 128 \) for each color dimension (Red, Green, Blue), +meaning 3 times the number of weights \( = 49152 \) are required for every +single neuron in the first hidden layer. +

    + + +









    +

    Strong correlations

    + +

    Images typically have strong local correlations, meaning that a small +part of the image varies little from its neighboring regions. If for +example we have an image of a blue car, we can roughly assume that a +small blue part of the image is surrounded by other blue regions. +

    + +

    Therefore, instead of connecting every single pixel to a neuron in the +first hidden layer, as we have previously done with deep neural +networks, we can instead connect each neuron to a small part of the +image (in all 3 RGB depth dimensions). The size of each small area is +fixed, and known as a receptive. +

    + + + +

    Layers of a CNN

    +

    The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. +The input image is typically a square matrix of depth 3. +

    + +

    A convolution is performed on the image which outputs +a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as filters. +

    + +

    Each filter slides along the input image, taking the dot product +between each small part of the image and the filter, in all depth +dimensions. This is then passed through a non-linear function, +typically the Rectified Linear (ReLu) function, which serves as the +activation of the neurons in the first convolutional layer. This is +further passed through a pooling layer, which reduces the size of the +convolutional layer, e.g. by taking the maximum or average across some +small regions, and this serves as input to the next convolutional +layer. +

    + +









    +

    Systematic reduction

    + +

    By systematically reducing the size of the input volume, through +convolution and pooling, the network should create representations of +small parts of the input, and then from them assemble representations +of larger areas. The final pooling layer is flattened to serve as +input to a hidden layer, such that each neuron in the final pooling +layer is connected to every single neuron in the hidden layer. This +then serves as input to the output layer, e.g. a softmax output for +classification. +

    + + +









    +

    Prerequisites: Collect and pre-process data

    + + +
    +
    +
    +
    +
    + 
    # import necessary packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+
+
+# ensure the same random numbers appear every time
+np.random.seed(0)
+
+# display images in notebook
+%matplotlib inline
+plt.rcParams['figure.figsize'] = (12,12)
+
+
+# download MNIST dataset
+digits = datasets.load_digits()
+
+# define inputs and labels
+inputs = digits.images
+labels = digits.target
+
+# RGB images have a depth of 3
+# our images are grayscale so they should have a depth of 1
+inputs = inputs[:,:,:,np.newaxis]
+
+print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
+print("labels = (n_inputs) = " + str(labels.shape))
+
+
+# choose some random images to display
+n_inputs = len(inputs)
+indices = np.arange(n_inputs)
+random_indices = np.random.choice(indices, size=5)
+
+for i, image in enumerate(digits.images[random_indices]):
+    plt.subplot(1, 5, i+1)
+    plt.axis('off')
+    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
+    plt.title("Label: %d" % digits.target[random_indices[i]])
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Importing Keras and Tensorflow

    + + +
    +
    +
    +
    +
    + 
    from tensorflow.keras import datasets, layers, models
+from tensorflow.keras.layers import Input
+from tensorflow.keras.models import Sequential      #This allows appending layers to existing models
+from tensorflow.keras.layers import Dense           #This allows defining the characteristics of a particular layer
+from tensorflow.keras import optimizers             #This allows using whichever optimiser we want (sgd,adam,RMSprop)
+from tensorflow.keras import regularizers           #This allows using whichever regularizer we want (l1,l2,l1_l2)
+from tensorflow.keras.utils import to_categorical   #This allows using categorical cross entropy as the cost function
+#from tensorflow.keras import Conv2D
+#from tensorflow.keras import MaxPooling2D
+#from tensorflow.keras import Flatten
+
+from sklearn.model_selection import train_test_split
+
+# representation of labels
+labels = to_categorical(labels)
+
+# split into train and test data
+# one-liner from scikit-learn library
+train_size = 0.8
+test_size = 1 - train_size
+X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
+                                                    test_size=test_size)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Running with Keras

    + + + +
    +
    +
    +
    +
    + 
    def create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd):
+    model = Sequential()
+    model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
+              activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
+    
+    sgd = optimizers.SGD(lr=eta)
+    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+    
+    return model
+
+epochs = 100
+batch_size = 100
+input_shape = X_train.shape[1:4]
+receptive_field = 3
+n_filters = 10
+n_neurons_connected = 50
+n_categories = 10
+
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Final part

    + + + +
    +
    +
    +
    +
    + 
    CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+        
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd)
+        CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+        scores = CNN.evaluate(X_test, Y_test)
+        
+        CNN_keras[i][j] = CNN
+        
+        print("Learning rate = ", eta)
+        print("Lambda = ", lmbd)
+        print("Test accuracy: %.3f" % scores[1])
+        print()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Final visualization

    + + + +
    +
    +
    +
    +
    + 
    # visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
+
+sns.set()
+
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+
+for i in range(len(eta_vals)):
+    for j in range(len(lmbd_vals)):
+        CNN = CNN_keras[i][j]
+
+        train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
+
+        
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    The CIFAR01 data set

    + +

    The CIFAR10 dataset contains 60,000 color images in 10 classes, with +6,000 images in each class. The dataset is divided into 50,000 +training images and 10,000 testing images. The classes are mutually +exclusive and there is no overlap between them. +

    + + + +
    +
    +
    +
    +
    + 
    import tensorflow as tf
+
+from tensorflow.keras import datasets, layers, models
+import matplotlib.pyplot as plt
+
+# We import the data set
+(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+
+# Normalize pixel values to be between 0 and 1 by dividing by 255. 
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Verifying the data set

    + +

    To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image.

    + + + +
    +
    +
    +
    +
    + 
    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+​
+plt.figure(figsize=(10,10))
+for i in range(25):
+    plt.subplot(5,5,i+1)
+    plt.xticks([])
+    plt.yticks([])
+    plt.grid(False)
+    plt.imshow(train_images[i], cmap=plt.cm.binary)
+    # The CIFAR labels happen to be arrays, 
+    # which is why you need the extra index
+    plt.xlabel(class_names[train_labels[i][0]])
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Set up the model

    + +

    The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers.

    + +

    As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer.

    + + + +
    +
    +
    +
    +
    + 
    model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+
+# Let's display the architecture of our model so far.
+
+model.summary()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer.

    + +









    +

    Add Dense layers on top

    + +

    To complete our model, you will feed the last output tensor from the +convolutional base (of shape (4, 4, 64)) into one or more Dense layers +to perform classification. Dense layers take vectors as input (which +are 1D), while the current output is a 3D tensor. First, you will +flatten (or unroll) the 3D output to 1D, then add one or more Dense +layers on top. CIFAR has 10 output classes, so you use a final Dense +layer with 10 outputs and a softmax activation. +

    + + + +
    +
    +
    +
    +
    + 
    model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10))
+Here's the complete architecture of our model.
+
+model.summary()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.

    + +









    +

    Compile and train the model

    + + + +
    +
    +
    +
    +
    + 
    model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+​
+history = model.fit(train_images, train_labels, epochs=10, 
+                    validation_data=(test_images, test_labels))
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Finally, evaluate the model

    + + + +
    +
    +
    +
    +
    + 
    plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.ylim([0.5, 1])
+plt.legend(loc='lower right')
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+print(test_acc)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    -

    This is where recurrent nueral networks (RNNs) come to our rescue.











    Recurrent neural networks: Overarching view

    @@ -295,7 +1613,7 @@ systems such as automatic translation and speech-to-text.









    Set up of an RNN

    -

    More to text to be added

    +

    See handwritten notes for week 43 and Lectures from CS231 at Stanford











    A simple example

    @@ -1092,7 +2410,7 @@ samples $$ \begin{equation} x = g(z; \theta^{(g)}) -\label{_auto1} +\label{_auto4} \end{equation} $$ @@ -1109,7 +2427,7 @@ value given by $$ \begin{equation} d(x; \theta^{(d)}) -\label{_auto2} +\label{_auto5} \end{equation} $$ @@ -1122,7 +2440,7 @@ which a function $$ \begin{equation} v(\theta^{(g)}, \theta^{(d)}) -\label{_auto3} +\label{_auto6} \end{equation} $$ @@ -1133,7 +2451,7 @@ conjugate reward $$ \begin{equation} -v(\theta^{(g)}, \theta^{(d)}) -\label{_auto4} +\label{_auto7} \end{equation} $$ @@ -1168,7 +2486,7 @@ $$ \begin{equation} g^* = \underset{g}{\mathrm{argmin}}\hspace{2pt} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\label{_auto5} +\label{_auto8} \end{equation} $$ @@ -1178,7 +2496,7 @@ $$ v(\theta^{(g)}, \theta^{(d)}) = \mathbb{E}_{x\sim p_\mathrm{data}}\log d(x) + \mathbb{E}_{x\sim p_\mathrm{model}} \log (1 - d(x)) -\label{_auto6} +\label{_auto9} \end{equation} $$ @@ -1189,7 +2507,7 @@ approximation of a partition function. In the case where $$ \begin{equation} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\label{_auto7} +\label{_auto10} \end{equation} $$ @@ -2145,1207 +3463,6 @@ plot_results(results, plot_number)
-









-

Basic ideas of the Principal Component Analysis (PCA)

- -

The principal component analysis deals with the problem of fitting a -low-dimensional affine subspace \( S \) of dimension \( d \) much smaller than -the total dimension \( D \) of the problem at hand (our data -set). Mathematically it can be formulated as a statistical problem or -a geometric problem. In our discussion of the theorem for the -classical PCA, we will stay with a statistical approach. -Historically, the PCA was first formulated in a statistical setting in order to estimate the principal component of a multivariate random variable. -

- -

We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see below for its definition)

-
    -
  • Each data point is determined by \( p \) extrinsic (measurement) variables
    • -
  • We may want to ask the following question: Are there fewer intrinsic variables (say \( d < < p \)) that still approximately describe the data?
    • -
  • If so, these intrinsic variables may tell us something important and finding these intrinsic variables is what dimension reduction methods do.
    • -
-

A good read is for example Vidal, Ma and Sastry.

- -









-

Introducing the Covariance and Correlation functions

- -

Before we discuss the PCA theorem, we need to remind ourselves about -the definition of the covariance and the correlation function. These are quantities -

- -

Suppose we have defined two vectors -\( \hat{x} \) and \( \hat{y} \) with \( n \) elements each. The covariance matrix \( \boldsymbol{C} \) is defined as -

-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{cov}[\boldsymbol{x},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{y},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{y},\boldsymbol{y}] \\ - \end{bmatrix}, -$$ - -

where for example

-$$ -\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). -$$ - -

With this definition and recalling that the variance is defined as

-$$ -\mathrm{var}[\boldsymbol{x}]=\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})^2, -$$ - -

we can rewrite the covariance matrix as

-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{var}[\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] & \mathrm{var}[\boldsymbol{y}] \\ - \end{bmatrix}. -$$ - - -









-

More on the covariance

-

The covariance takes values between zero and infinity and may thus -lead to problems with loss of numerical precision for particularly -large values. It is common to scale the covariance matrix by -introducing instead the correlation matrix defined via the so-called -correlation function -

- -$$ -\mathrm{corr}[\boldsymbol{x},\boldsymbol{y}]=\frac{\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}]}{\sqrt{\mathrm{var}[\boldsymbol{x}] \mathrm{var}[\boldsymbol{y}]}}. -$$ - -

The correlation function is then given by values \( \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] -\in [-1,1] \). This avoids eventual problems with too large values. We -can then define the correlation matrix for the two vectors \( \boldsymbol{x} \) -and \( \boldsymbol{y} \) as -

- -$$ -\boldsymbol{K}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} 1 & \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{corr}[\boldsymbol{y},\boldsymbol{x}] & 1 \\ - \end{bmatrix}, -$$ - -

In the above example this is the function we constructed using pandas.

- -









-

Reminding ourselves about Linear Regression

-

In our derivation of the various regression algorithms like Ordinary Least Squares or Ridge regression -we defined the design/feature matrix \( \boldsymbol{X} \) as -

- -$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ - -

with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) refering to the column numbers and the -entries \( n \) being the row elements. -We can rewrite the design/feature matrix in terms of its column vectors as -

-$$ -\boldsymbol{X}=\begin{bmatrix} \boldsymbol{x}_0 & \boldsymbol{x}_1 & \boldsymbol{x}_2 & \dots & \dots & \boldsymbol{x}_{p-1}\end{bmatrix}, -$$ - -

with a given vector

-$$ -\boldsymbol{x}_i^T = \begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \dots & \dots x_{n-1,i}\end{bmatrix}. -$$ - - -









-

Simple Example

-

With these definitions, we can now rewrite our \( 2\times 2 \) -correlation/covariance matrix in terms of a moe general design/feature -matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \). This leads to a \( p\times p \) -covariance matrix for the vectors \( \boldsymbol{x}_i \) with \( i=0,1,\dots,p-1 \) -

- -$$ -\boldsymbol{C}[\boldsymbol{x}] = \begin{bmatrix} -\mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_1] & \mathrm{var}[\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & \mathrm{var}[\boldsymbol{x}_{p-1}]\\ -\end{bmatrix}, -$$ - - -









-

The Correlation Matrix

- -

and the correlation matrix

-$$ -\boldsymbol{K}[\boldsymbol{x}] = \begin{bmatrix} -1 & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_0] & 1 & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_1] & 1 & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & 1\\ -\end{bmatrix}, -$$ - - -









-

Numpy Functionality

- -

The Numpy function np.cov calculates the covariance elements using -the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have -the exact mean values. The following simple function uses the -np.vstack function which takes each vector of dimension \( 1\times n \) -and produces a \( 2\times n \) matrix \( \boldsymbol{W} \) -

- -$$ -\boldsymbol{W}^T = \begin{bmatrix} x_0 & y_0 \\ - x_1 & y_1 \\ - x_2 & y_2\\ - \dots & \dots \\ - x_{n-2} & y_{n-2}\\ - x_{n-1} & y_{n-1} & - \end{bmatrix}, -$$ - -

which in turn is converted into into the \( 2\times 2 \) covariance matrix -\( \boldsymbol{C} \) via the Numpy function np.cov(). We note that we can also calculate -the mean value of each set of samples \( \boldsymbol{x} \) etc using the Numpy -function np.mean(x). We can also extract the eigenvalues of the -covariance matrix through the np.linalg.eig() function. -

- - - -
-
-
-
-
- 
# Importing various packages
-import numpy as np
-n = 100
-x = np.random.normal(size=n)
-print(np.mean(x))
-y = 4+3*x+np.random.normal(size=n)
-print(np.mean(y))
-W = np.vstack((x, y))
-C = np.cov(W)
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -









-

Correlation Matrix again

- -

The previous example can be converted into the correlation matrix by -simply scaling the matrix elements with the variances. We should also -subtract the mean values for each column. This leads to the following -code which sets up the correlations matrix for the previous example in -a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the \( 2\times 2 \) correlation matrix (since we have only two vectors). -

- - - -
-
-
-
-
- 
import numpy as np
-n = 100
-# define two vectors                                                                                           
-x = np.random.random(size=n)
-y = 4+3*x+np.random.normal(size=n)
-#scaling the x and y vectors                                                                                   
-x = x - np.mean(x)
-y = y - np.mean(y)
-variance_x = np.sum(x@x)/n
-variance_y = np.sum(y@y)/n
-print(variance_x)
-print(variance_y)
-cov_xy = np.sum(x@y)/n
-cov_xx = np.sum(x@x)/n
-cov_yy = np.sum(y@y)/n
-C = np.zeros((2,2))
-C[0,0]= cov_xx/variance_x
-C[1,1]= cov_yy/variance_y
-C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)
-C[1,0]= C[0,1]
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We see that the matrix elements along the diagonal are one as they -should be and that the matrix is symmetric. Furthermore, diagonalizing -this matrix we easily see that it is a positive definite matrix. -

- -

The above procedure with numpy can be made more compact if we use pandas.

- -









-

Using Pandas

- -

We whow here how we can set up the correlation matrix using pandas, as done in this simple code

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-n = 10
-x = np.random.normal(size=n)
-x = x - np.mean(x)
-y = 4+3*x+np.random.normal(size=n)
-y = y - np.mean(y)
-X = (np.vstack((x, y))).T
-print(X)
-Xpd = pd.DataFrame(X)
-print(Xpd)
-correlation_matrix = Xpd.corr()
-print(correlation_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -









-

And then the Franke Function

- -

We expand this model to the Franke function discussed above.

- - - -
-
-
-
-
- 
# Common imports
-import numpy as np
-import pandas as pd
-
-
-def FrankeFunction(x,y):
-	term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
-	term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
-	term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
-	term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
-	return term1 + term2 + term3 + term4
-
-
-def create_X(x, y, n ):
-	if len(x.shape) > 1:
-		x = np.ravel(x)
-		y = np.ravel(y)
-
-	N = len(x)
-	l = int((n+1)*(n+2)/2)		# Number of elements in beta
-	X = np.ones((N,l))
-
-	for i in range(1,n+1):
-		q = int((i)*(i+1)/2)
-		for k in range(i+1):
-			X[:,q+k] = (x**(i-k))*(y**k)
-
-	return X
-
-
-# Making meshgrid of datapoints and compute Franke's function
-n = 4
-N = 100
-x = np.sort(np.random.uniform(0, 1, N))
-y = np.sort(np.random.uniform(0, 1, N))
-z = FrankeFunction(x, y)
-X = create_X(x, y, n=n)    
-
-Xpd = pd.DataFrame(X)
-# subtract the mean values and set up the covariance matrix
-Xpd = Xpd - Xpd.mean()
-covariance_matrix = Xpd.cov()
-print(covariance_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We note here that the covariance is zero for the first rows and -columns since all matrix elements in the design matrix were set to one -(we are fitting the function in terms of a polynomial of degree \( n \)). We would however not include the intercept -and wee can simply -drop these elements and construct a correlation -matrix without them by centering our matrix elements by subtracting the mean of each column. -

- -









-

Lnks with the Design Matrix

- -

We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix \( \boldsymbol{X} \) as

-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ - -

To see this let us simply look at a design matrix \( \boldsymbol{X}\in {\mathbb{R}}^{2\times 2} \)

-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{00} & x_{01}\\ -x_{10} & x_{11}\\ -\end{bmatrix}=\begin{bmatrix} -\boldsymbol{x}_{0} & \boldsymbol{x}_{1}\\ -\end{bmatrix}. -$$ - - -









-

Computing the Expectation Values

- -

If we then compute the expectation value

-$$ -\mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}=\begin{bmatrix} -x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\ -x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\ -\end{bmatrix}, -$$ - -

which is just

-$$ -\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\ - \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\ - \end{bmatrix}, -$$ - -

where we wrote $$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).

- -

It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).

- -









-

Towards the PCA theorem

- -

We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as

-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ - -

Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \). -These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \). -

- -

Assume also that there is a transformation \( \boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).

- -

That is we have

-$$ -\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}^T\boldsymbol{X}^T\boldsymbol{X}T\boldsymbol{S}]=\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ - -

since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S} \) from the left we have

-$$ -\boldsymbol{S}\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ - -

and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that

- -$$ -\boldsymbol{S}_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}_i. -$$ - - -









-

More on the PCA Theorem

- -

In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is -\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \). -

- -

The eigenvalues tell us then how much we need to stretch the -corresponding eigenvectors. Dimensions with large eigenvalues have -thus large variations (large variance) and define therefore useful -dimensions. The data points are more spread out in the direction of -these eigenvectors. Smaller eigenvalues mean on the other hand that -the corresponding eigenvectors are shrunk accordingly and the data -points are tightly bunched together and there is not much variation in -these specific directions. Hopefully then we could leave it out -dimensions where the eigenvalues are very small. If \( p \) is very large, -we could then aim at reducing \( p \) to \( l < < p \) and handle only \( l \) -features/predictors. -

- -









-

The Algorithm before theorem

- -

Here's how we would proceed in setting up the algorithm for the PCA, see also discussion below here.

-
    -
  • Set up the datapoints for the design/feature matrix \( \boldsymbol{X} \) with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) referring to the column numbers and the entries \( n \) being the row elements.
    • -
-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ - -
    -
  • Center the data by subtracting the mean value for each column. This leads to a new matrix \( \boldsymbol{X}\rightarrow \overline{\boldsymbol{X}} \).
    • -
  • Compute then the covariance/correlation matrix \( \mathbb{E}[\overline{\boldsymbol{X}}^T\overline{\boldsymbol{X}}] \).
    • -
  • Find the eigenpairs of \( \boldsymbol{C} \) with eigenvalues \( [\lambda_0,\lambda_1,\dots,\lambda_{p-1}] \) and eigenvectors \( [\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \).
    • -
  • Order the eigenvalue (and the eigenvectors accordingly) in order of decreasing eigenvalues.
    • -
  • Keep only those \( l \) eigenvalues larger than a selected threshold value, discarding thus \( p-l \) features since we expect small variations in the data here.
    • -
-









-

Writing our own PCA code

- -

We will use a simple example first with two-dimensional data -drawn from a multivariate normal distribution with the following mean and covariance matrix (we have fixed these quantities but will play around with them below): -

-$$ -\mu = (-1,2) \qquad \Sigma = \begin{bmatrix} 4 & 2 \\ -2 & 2 -\end{bmatrix} -$$ - -

Note that the mean refers to each column of data. -We will generate \( n = 10000 \) points \( X = \{ x_1, \ldots, x_N \} \) from -this distribution, and store them in the \( 1000 \times 2 \) matrix \( \boldsymbol{X} \). This is our design matrix where we have forced the covariance and mean values to take specific values. -

- -









-

Implementing it

-

The following Python code aids in setting up the data and writing out the design matrix. -Note that the function multivariate returns also the covariance discussed above and that it is defined by dividing by \( n-1 \) instead of \( n \). -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from IPython.display import display
-n = 10000
-mean = (-1, 2)
-cov = [[4, 2], [2, 2]]
-X = np.random.multivariate_normal(mean, cov, n)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

Now we are going to implement the PCA algorithm. We will break it down into various substeps.

- -









-

First Step

- -

The first step of PCA is to compute the sample mean of the data and use it to center the data. Recall that the sample mean is

-$$ -\mu_n = \frac{1}{n} \sum_{i=1}^n x_i -$$ - -

and the mean-centered data \( \bar{X} = \{ \bar{x}_1, \ldots, \bar{x}_n \} \) takes the form

-$$ -\bar{x}_i = x_i - \mu_n. -$$ - -

When you are done with these steps, print out \( \mu_n \) to verify it is -close to \( \mu \) and plot your mean centered data to verify it is -centered at the origin! -The following code elements perform these operations using pandas or using our own functionality for doing so. The latter, using numpy is rather simple through the mean() function. -

- - -
-
-
-
-
- 
df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-
-
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-
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-
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-
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- - -









-

Scaling

-

Alternatively, we could use the functions we discussed -earlier for scaling the data set. That is, we could have used the -StandardScaler function in Scikit-Learn, a function which ensures -that for each feature/predictor we study the mean value is zero and -the variance is one (every column in the design/feature matrix). You -would then not get the same results, since we divide by the -variance. The diagonal covariance matrix elements will then be one, -while the non-diagonal ones need to be divided by \( 2\sqrt{2} \) for our -specific case. -

- -









-

Centered Data

- -

Now we are going to use the mean centered data to compute the sample covariance of the data by using the following equation

-$$ -\begin{equation*} -\Sigma_n = \frac{1}{n-1} \sum_{i=1}^n \bar{x}_i^T \bar{x}_i = \frac{1}{n-1} \sum_{i=1}^n (x_i - \mu_n)^T (x_i - \mu_n) -\end{equation*} -$$ - -

where the data points \( x_i \in \mathbb{R}^p \) (here in this example \( p = 2 \)) are column vectors and \( x^T \) is the transpose of \( x \). -We can write our own code or simply use either the functionaly of numpy or that of pandas, as follows -

- - -
-
-
-
-
- 
print(df.cov())
-print(np.cov(X_centered.T))
-
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-
-
- -

Note that the way we define the covariance matrix here has a factor \( n-1 \) instead of \( n \). This is included in the cov() function by numpy and pandas. -Our own code here is not very elegant and asks for obvious improvements. It is tailored to this specific \( 2\times 2 \) covariance matrix. -

- - -
-
-
-
-
- 
# extract the relevant columns from the centered design matrix of dim n x 2
-x = X_centered[:,0]
-y = X_centered[:,1]
-Cov = np.zeros((2,2))
-Cov[0,1] = np.sum(x.T@y)/(n-1.0)
-Cov[0,0] = np.sum(x.T@x)/(n-1.0)
-Cov[1,1] = np.sum(y.T@y)/(n-1.0)
-Cov[1,0]= Cov[0,1]
-print("Centered covariance using own code")
-print(Cov)
-plt.plot(x, y, 'x')
-plt.axis('equal')
-plt.show()
-
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-

Exploring

- -

Depending on the number of points \( n \), we will get results that are close to the covariance values defined above. -The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed. -

- -









-

Diagonalize the sample covariance matrix to obtain the principal components

- -

Now we are ready to solve for the principal components! To do so we -diagonalize the sample covariance matrix \( \Sigma \). We can use the -function np.linalg.eig to do so. It will return the eigenvalues and -eigenvectors of \( \Sigma \). Once we have these we can perform the -following tasks: -

- -
    -
  • We compute the percentage of the total variance captured by the first principal component
    • -
  • We plot the mean centered data and lines along the first and second principal components
    • -
  • Then we project the mean centered data onto the first and second principal components, and plot the projected data.
    • -
  • Finally, we approximate the data as
    • -
-$$ -\begin{equation*} -x_i \approx \tilde{x}_i = \mu_n + \langle x_i, v_0 \rangle v_0 -\end{equation*} -$$ - -

where \( v_0 \) is the first principal component.

- -









-

Collecting all Steps

- -

Collecting all these steps we can write our own PCA function and -compare this with the functionality included in Scikit-Learn. -

- -

The code here outlines some of the elements we could include in the -analysis. Feel free to extend upon this in order to address the above -questions. -

- - - -
-
-
-
-
- 
# diagonalize and obtain eigenvalues, not necessarily sorted
-EigValues, EigVectors = np.linalg.eig(Cov)
-# sort eigenvectors and eigenvalues
-#permute = EigValues.argsort()
-#EigValues = EigValues[permute]
-#EigVectors = EigVectors[:,permute]
-print("Eigenvalues of Covariance matrix")
-for i in range(2):
-    print(EigValues[i])
-FirstEigvector = EigVectors[:,0]
-SecondEigvector = EigVectors[:,1]
-print("First eigenvector")
-print(FirstEigvector)
-print("Second eigenvector")
-print(SecondEigvector)
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2Dsl = pca.fit_transform(X)
-print("Eigenvector of largest eigenvalue")
-print(pca.components_.T[:, 0])
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

This code does not contain all the above elements, but it shows how we can use Scikit-Learn to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?

- -









-

Classical PCA Theorem

- -

We assume now that we have a design matrix \( \boldsymbol{X} \) which has been -centered as discussed above. For the sake of simplicity we skip the -overline symbol. The matrix is defined in terms of the various column -vectors \( [\boldsymbol{x}_0,\boldsymbol{x}_1,\dots, \boldsymbol{x}_{p-1}] \) each with dimension -\( \boldsymbol{x}\in {\mathbb{R}}^{n} \). -

- -

The PCA theorem states that minimizing the above reconstruction error -corresponds to setting \( \boldsymbol{W}=\boldsymbol{S} \), the orthogonal matrix which -diagonalizes the empirical covariance(correlation) matrix. The optimal -low-dimensional encoding of the data is then given by a set of vectors -\( \boldsymbol{z}_i \) with at most \( l \) vectors, with \( l < < p \), defined by the -orthogonal projection of the data onto the columns spanned by the -eigenvectors of the covariance(correlations matrix). -

- -









-

The PCA Theorem

- -

To show the PCA theorem let us start with the assumption that there is one vector \( \boldsymbol{s}_0 \) which corresponds to a solution which minimized the reconstruction error \( J \). This is an orthogonal vector. It means that we now approximate the reconstruction error in terms of \( \boldsymbol{w}_0 \) and \( \boldsymbol{z}_0 \) as

- -

We are almost there, we have obtained a relation between minimizing -the reconstruction error and the variance and the covariance -matrix. Minimizing the error is equivalent to maximizing the variance -of the projected data. -

- -

We could trivially maximize the variance of the projection (and -thereby minimize the error in the reconstruction function) by letting -the norm-2 of \( \boldsymbol{w}_0 \) go to infinity. However, this norm since we -want the matrix \( \boldsymbol{W} \) to be an orthogonal matrix, is constrained by -\( \vert\vert \boldsymbol{w}_0 \vert\vert_2^2=1 \). Imposing this condition via a -Lagrange multiplier we can then in turn maximize -

- -$$ -J(\boldsymbol{w}_0)= \boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0+\lambda_0(1-\boldsymbol{w}_0^T\boldsymbol{w}_0). -$$ - -

Taking the derivative with respect to \( \boldsymbol{w}_0 \) we obtain

- -$$ -\frac{\partial J(\boldsymbol{w}_0)}{\partial \boldsymbol{w}_0}= 2\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0-2\lambda_0\boldsymbol{w}_0=0, -$$ - -

meaning that

-$$ -\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0\boldsymbol{w}_0. -$$ - -

The direction that maximizes the variance (or minimizes the construction error) is an eigenvector of the covariance matrix! If we left multiply with \( \boldsymbol{w}_0^T \) we have the variance of the projected data is

-$$ -\boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0. -$$ - -

If we want to maximize the variance (minimize the construction error) -we simply pick the eigenvector of the covariance matrix with the -largest eigenvalue. This establishes the link between the minimization -of the reconstruction function \( J \) in terms of an orthogonal matrix -and the maximization of the variance and thereby the covariance of our -observations encoded in the design/feature matrix \( \boldsymbol{X} \). -

- -

The proof -for the other eigenvectors \( \boldsymbol{w}_1,\boldsymbol{w}_2,\dots \) can be -established by applying the above arguments and using the fact that -our basis of eigenvectors is orthogonal, see Murphy chapter -12.2. The -discussion in chapter 12.2 of Murphy's text has also a nice link with -the Singular Value Decomposition theorem. For categorical data, see -chapter 12.4 and discussion therein. -

- -

For more details, see for example Vidal, Ma and Sastry, chapter 2.

- -









- - -

For a detailed demonstration of the geometric interpretation, see Vidal, Ma and Sastry, section 2.1.2.

- -

Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm. -First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. -

- -

The following Python code uses NumPy’s svd() function to obtain all the principal components of the -training set, then extracts the first two principal components. First we center the data using either pandas or our own code -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-from IPython.display import display
-np.random.seed(100)
-# setting up a 10 x 5 vanilla matrix 
-rows = 10
-cols = 5
-X = np.random.randn(rows,cols)
-df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-display(df)
-
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-# Then check the difference between pandas and our own set up
-print(X_centered-df)
-#Now we do an SVD
-U, s, V = np.linalg.svd(X_centered)
-c1 = V.T[:, 0]
-c2 = V.T[:, 1]
-W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
-print(X2D)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering -the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t -forget to center the data first. -

- -

Once you have identified all the principal components, you can reduce the dimensionality of the dataset -down to \( d \) dimensions by projecting it onto the hyperplane defined by the first \( d \) principal components. -Selecting this hyperplane ensures that the projection will preserve as much variance as possible. -

- - -
-
-
-
-
- 
W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -









-

PCA and scikit-learn

- -

Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The -following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note -that it automatically takes care of centering the data): -

- - -
-
-
-
-
- 
#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D = pca.fit_transform(X)
-print(X2D)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

After fitting the PCA transformer to the dataset, you can access the principal components using the -components variable (note that it contains the PCs as horizontal vectors, so, for example, the first -principal component is equal to -

- - -
-
-
-
-
- 
pca.components_.T[:, 0]
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

Another very useful piece of information is the explained variance ratio of each principal component, -available via the \( explained\_variance\_ratio \) variable. It indicates the proportion of the dataset’s -variance that lies along the axis of each principal component. -

- -









-

Back to the Cancer Data

-

We can now repeat the above but applied to real data, in this case our breast cancer data. -Here we compute performance scores on the training data using logistic regression. -

- - -
-
-
-
-
- 
import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.model_selection import  train_test_split 
-from sklearn.datasets import load_breast_cancer
-from sklearn.linear_model import LogisticRegression
-cancer = load_breast_cancer()
-
-X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
-
-logreg = LogisticRegression()
-logreg.fit(X_train, y_train)
-print("Train set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_train,y_train)))
-# We scale the data
-from sklearn.preprocessing import StandardScaler
-scaler = StandardScaler()
-scaler.fit(X_train)
-X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-# Then perform again a log reg fit
-logreg.fit(X_train_scaled, y_train)
-print("Train set accuracy scaled data: {:.2f}".format(logreg.score(X_train_scaled,y_train)))
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D_train = pca.fit_transform(X_train_scaled)
-# and finally compute the log reg fit and the score on the training data	
-logreg.fit(X2D_train,y_train)
-print("Train set accuracy scaled and PCA data: {:.2f}".format(logreg.score(X2D_train,y_train)))
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We see that our training data after the PCA decomposition has a performance similar to the non-scaled data.

- -

Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to -choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). -Unless, of course, you are reducing dimensionality for data visualization — in that case you will -generally want to reduce the dimensionality down to 2 or 3. -The following code computes PCA without reducing dimensionality, then computes the minimum number -of dimensions required to preserve 95% of the training set’s variance: -

- - -
-
-
-
-
- 
pca = PCA()
-pca.fit(X)
-cumsum = np.cumsum(pca.explained_variance_ratio_)
-d = np.argmax(cumsum >= 0.95) + 1
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

You could then set \( n\_components=d \) and run PCA again. However, there is a much better option: instead -of specifying the number of principal components you want to preserve, you can set \( n\_components \) to be -a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: -

- - -
-
-
-
-
- 
pca = PCA(n_components=0.95)
-X_reduced = pca.fit_transform(X)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -









-

Incremental PCA

- -

One problem with the preceding implementation of PCA is that it requires the whole training set to fit in -memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have -been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch -at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new -instances arrive). -

-

Randomized PCA

- -

Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic -algorithm that quickly finds an approximation of the first d principal components. Its computational -complexity is \( O(m \times d^2)+O(d^3) \), instead of \( O(m \times n^2) + O(n^3) \), so it is dramatically faster than the -previous algorithms when \( d \) is much smaller than \( n \). -

-

Kernel PCA

- -

The kernel trick is a mathematical technique that implicitly maps instances into a -very high-dimensional space (called the feature space), enabling nonlinear classification and regression -with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature -space corresponds to a complex nonlinear decision boundary in the original space. -It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear -projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at -preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a -twisted manifold. -For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an -

- - -
-
-
-
-
- 
from sklearn.decomposition import KernelPCA
-rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04)
-X_reduced = rbf_pca.fit_transform(X)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -









-

Other techniques

- -

There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.

- -

Here are some of the most popular:

-
    -
  • Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances between the instances.
    • -
  • Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances.
    • -
  • t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).
    • -
  • Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures.
    • -
© 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license diff --git a/doc/pub/week43/html/week43.html b/doc/pub/week43/html/week43.html index 8c99676dd..76e1940f9 100644 --- a/doc/pub/week43/html/week43.html +++ b/doc/pub/week43/html/week43.html @@ -141,7 +141,105 @@ div.toc p,a { {'highest level': 2, 'sections': [('Plans for week 43', 2, None, 'plans-for-week-43'), ('Reading Recommendations', 2, None, 'reading-recommendations'), + ('Convolutional Neural Networks (recognizing images)', + 2, + None, + 'convolutional-neural-networks-recognizing-images'), + ('What is the Difference', 2, None, 'what-is-the-difference'), + ('Neural Networks vs CNNs', 2, None, 'neural-networks-vs-cnns'), + ('Why CNNS for images, sound files, medical images from CT scans ' + 'etc?', + 2, + None, + 'why-cnns-for-images-sound-files-medical-images-from-ct-scans-etc'), + ('Regular NNs don’t scale well to full images', + 2, + None, + 'regular-nns-don-t-scale-well-to-full-images'), + ('3D volumes of neurons', 2, None, '3d-volumes-of-neurons'), + ('Layers used to build CNNs', + 2, + None, + 'layers-used-to-build-cnns'), + ('Transforming images', 2, None, 'transforming-images'), ('CNNs in brief', 2, None, 'cnns-in-brief'), + ('Key Idea', 2, None, 'key-idea'), + ('Mathematics of CNNs', 2, None, 'mathematics-of-cnns'), + ('Convolution Examples: Polynomial multiplication', + 2, + None, + 'convolution-examples-polynomial-multiplication'), + ('Efficient Polynomial Multiplication', + 2, + None, + 'efficient-polynomial-multiplication'), + ('A more efficient way of coding the above Convolution', + 2, + None, + 'a-more-efficient-way-of-coding-the-above-convolution'), + ('Convolution Examples: Principle of Superposition and Periodic ' + 'Forces (Fourier Transforms)', + 2, + None, + 'convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms'), + ('Principle of Superposition', + 2, + None, + 'principle-of-superposition'), + ('Simple Code Example', 2, None, 'simple-code-example'), + ('Wrapping up Fourier transforms', + 2, + None, + 'wrapping-up-fourier-transforms'), + ('Finding the Coefficients', 2, None, 'finding-the-coefficients'), + ('Final words on Fourier Transforms', + 2, + None, + 'final-words-on-fourier-transforms'), + ('Two-dimensional Objects', 2, None, 'two-dimensional-objects'), + ('Cross-Correlation', 2, None, 'cross-correlation'), + ('More on Dimensionalities', 2, None, 'more-on-dimensionalities'), + ('Further Dimensionality Remarks', + 2, + None, + 'further-dimensionality-remarks'), + ('CNNs in more detail, Lecture from IN5400', + 2, + None, + 'cnns-in-more-detail-lecture-from-in5400'), + ('CNNs in more detail, building convolutional neural networks in ' + 'Tensorflow and Keras', + 2, + None, + 'cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras'), + ('Setting it up', 2, None, 'setting-it-up'), + ('The MNIST dataset again', 2, None, 'the-mnist-dataset-again'), + ('Strong correlations', 2, None, 'strong-correlations'), + ('Layers of a CNN', 2, None, 'layers-of-a-cnn'), + ('Systematic reduction', 2, None, 'systematic-reduction'), + ('Prerequisites: Collect and pre-process data', + 2, + None, + 'prerequisites-collect-and-pre-process-data'), + ('Importing Keras and Tensorflow', + 2, + None, + 'importing-keras-and-tensorflow'), + ('Running with Keras', 2, None, 'running-with-keras'), + ('Final part', 2, None, 'final-part'), + ('Final visualization', 2, None, 'final-visualization'), + ('The CIFAR01 data set', 2, None, 'the-cifar01-data-set'), + ('Verifying the data set', 2, None, 'verifying-the-data-set'), + ('Set up the model', 2, None, 'set-up-the-model'), + ('Add Dense layers on top', 2, None, 'add-dense-layers-on-top'), + ('Compile and train the model', + 2, + None, + 'compile-and-train-the-model'), + ('Finally, evaluate the model', + 2, + None, + 'finally-evaluate-the-model'), ('Recurrent neural networks: Overarching view', 2, None, @@ -187,68 +285,7 @@ div.toc p,a { ('Interpolating Between MNIST Digits', 2, None, - 'interpolating-between-mnist-digits'), - ('Basic ideas of the Principal Component Analysis (PCA)', - 2, - None, - 'basic-ideas-of-the-principal-component-analysis-pca'), - ('Introducing the Covariance and Correlation functions', - 2, - None, - 'introducing-the-covariance-and-correlation-functions'), - ('More on the covariance', 2, None, 'more-on-the-covariance'), - ('Reminding ourselves about Linear Regression', - 2, - None, - 'reminding-ourselves-about-linear-regression'), - ('Simple Example', 2, None, 'simple-example'), - ('The Correlation Matrix', 2, None, 'the-correlation-matrix'), - ('Numpy Functionality', 2, None, 'numpy-functionality'), - ('Correlation Matrix again', 2, None, 'correlation-matrix-again'), - ('Using Pandas', 2, None, 'using-pandas'), - ('And then the Franke Function', - 2, - None, - 'and-then-the-franke-function'), - ('Lnks with the Design Matrix', - 2, - None, - 'lnks-with-the-design-matrix'), - ('Computing the Expectation Values', - 2, - None, - 'computing-the-expectation-values'), - ('Towards the PCA theorem', 2, None, 'towards-the-pca-theorem'), - ('More on the PCA Theorem', 2, None, 'more-on-the-pca-theorem'), - ('The Algorithm before the Theorem', - 2, - None, - 'the-algorithm-before-the-theorem'), - ('Writing our own PCA code', 2, None, 'writing-our-own-pca-code'), - ('Implementing it', 2, None, 'implementing-it'), - ('First Step', 2, None, 'first-step'), - ('Scaling', 2, None, 'scaling'), - ('Centered Data', 2, None, 'centered-data'), - ('Exploring', 2, None, 'exploring'), - ('Diagonalize the sample covariance matrix to obtain the ' - 'principal components', - 2, - None, - 'diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components'), - ('Collecting all Steps', 2, None, 'collecting-all-steps'), - ('Classical PCA Theorem', 2, None, 'classical-pca-theorem'), - ('The PCA Theorem', 2, None, 'the-pca-theorem'), - ('Geometric Interpretation and link with Singular Value ' - 'Decomposition', - 2, - None, - 'geometric-interpretation-and-link-with-singular-value-decomposition'), - ('PCA and scikit-learn', 2, None, 'pca-and-scikit-learn'), - ('Back to the Cancer Data', 2, None, 'back-to-the-cancer-data'), - ('Incremental PCA', 2, None, 'incremental-pca'), - ('Randomized PCA', 3, None, 'randomized-pca'), - ('Kernel PCA', 3, None, 'kernel-pca'), - ('Other techniques', 2, None, 'other-techniques')]} + 'interpolating-between-mnist-digits')]} end of tocinfo --> @@ -269,7 +306,7 @@ MathJax.Hub.Config({ -

ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis +

ATITLE: Week 43: Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks

@@ -284,7 +321,7 @@ MathJax.Hub.Config({

-

Oct 24, 2022

+

Oct 26, 2022


@@ -293,8 +330,8 @@ MathJax.Hub.Config({

Plans for week 43

    -
  • Thursday: Convolutional Neural Networks, basic elements and
    • -
  • Friday: Recurrent Neural Networks and other Deep learning methods, Generalized Adversarial Neural Networ and autoencoders
    • +
  • Thursday: Convolutional Neural Networks (CNN)
    • +
  • Friday: Recurrent Neural Networks (RNN)
Excellent lectures on CNNs and RNNs @@ -320,10 +357,207 @@ MathJax.Hub.Config({









Reading Recommendations

-
    -
  • Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.
    • +
    +CNN readings +

    +

      +
    1. Goodfellow, Bengio, Courville, chapter 9
      2. +
    2. Lectures from CS231 at Stanford
      3. +
    3. Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs.
      4. +
    +
    + + +
    +RNN readings +

    +

      +
    1. Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.
      2. +
    2. Lectures from CS231 at Stanford
    3. Aurelien Geron, chapter 14 on RNNs.
      4. +
    +
    + + +









    +

    Convolutional Neural Networks (recognizing images)

    + +

    Convolutional neural networks (CNNs) were developed during the last +decade of the previous century, with a focus on character recognition +tasks. Nowadays, CNNs are a central element in the spectacular success +of deep learning methods. The success in for example image +classifications have made them a central tool for most machine +learning practitioners. +

    + +

    CNNs are very similar to ordinary Neural Networks. +They are made up of neurons that have learnable weights and +biases. Each neuron receives some inputs, performs a dot product and +optionally follows it with a non-linearity. The whole network still +expresses a single differentiable score function: from the raw image +pixels on one end to class scores at the other. And they still have a +loss function (for example Softmax) on the last (fully-connected) layer +and all the tips/tricks we developed for learning regular Neural +Networks still apply (back propagation, gradient descent etc etc). +

    + +









    +

    What is the Difference

    + +

    CNN architectures make the explicit assumption that +the inputs are images, which allows us to encode certain properties +into the architecture. These then make the forward function more +efficient to implement and vastly reduce the amount of parameters in +the network. +

    + +

    Here we provide only a superficial overview, for the more interested, we recommend highly the course +IN5400 – Machine Learning for Image Analysis +and the slides of CS231. +

    + +

    Another good read is the article here https://arxiv.org/pdf/1603.07285.pdf.

    + +









    +

    Neural Networks vs CNNs

    + +

    Neural networks are defined as affine transformations, that is +a vector is received as input and is multiplied with a matrix of so-called weights (our unknown paramters) to produce an +output (to which a bias vector is usually added before passing the result +through a nonlinear activation function). This is applicable to any type of input, be it an +image, a sound clip or an unordered collection of features: whatever their +dimensionality, their representation can always be flattened into a vector +before the transformation. +

    + +









    +

    Why CNNS for images, sound files, medical images from CT scans etc?

    + +

    However, when we consider images, sound clips and many other similar kinds of data, these data have an intrinsic +structure. More formally, they share these important properties: +

    +
      +
    • They are stored as multi-dimensional arrays (think of the pixels of a figure) .
      • +
    • They feature one or more axes for which ordering matters (e.g., width and height axes for an image, time axis for a sound clip).
      • +
    • One axis, called the channel axis, is used to access different views of the data (e.g., the red, green and blue channels of a color image, or the left and right channels of a stereo audio track).
    +

    These properties are not exploited when an affine transformation is applied; in +fact, all the axes are treated in the same way and the topological information +is not taken into account. Still, taking advantage of the implicit structure of +the data may prove very handy in solving some tasks, like computer vision and +speech recognition, and in these cases it would be best to preserve it. This is +where discrete convolutions come into play. +

    + +

    A discrete convolution is a linear transformation that preserves this notion of +ordering. It is sparse (only a few input units contribute to a given output +unit) and reuses parameters (the same weights are applied to multiple locations +in the input). +

    + +









    +

    Regular NNs don’t scale well to full images

    + +

    As an example, consider +an image of size \( 32\times 32\times 3 \) (32 wide, 32 high, 3 color channels), so a +single fully-connected neuron in a first hidden layer of a regular +Neural Network would have \( 32\times 32\times 3 = 3072 \) weights. This amount still +seems manageable, but clearly this fully-connected structure does not +scale to larger images. For example, an image of more respectable +size, say \( 200\times 200\times 3 \), would lead to neurons that have +\( 200\times 200\times 3 = 120,000 \) weights. +

    + +

    We could have +several such neurons, and the parameters would add up quickly! Clearly, +this full connectivity is wasteful and the huge number of parameters +would quickly lead to possible overfitting. +

    + +
    +
    +
    +

    Figure 1: A regular 3-layer Neural Network.

    +
    +

    +
    + +









    +

    3D volumes of neurons

    + +

    Convolutional Neural Networks take advantage of the fact that the +input consists of images and they constrain the architecture in a more +sensible way. +

    + +

    In particular, unlike a regular Neural Network, the +layers of a CNN have neurons arranged in 3 dimensions: width, +height, depth. (Note that the word depth here refers to the third +dimension of an activation volume, not to the depth of a full Neural +Network, which can refer to the total number of layers in a network.) +

    + +

    To understand it better, the above example of an image +with an input volume of +activations has dimensions \( 32\times 32\times 3 \) (width, height, +depth respectively). +

    + +

    The neurons in a layer will +only be connected to a small region of the layer before it, instead of +all of the neurons in a fully-connected manner. Moreover, the final +output layer could for this specific image have dimensions \( 1\times 1 \times 10 \), +because by the +end of the CNN architecture we will reduce the full image into a +single vector of class scores, arranged along the depth +dimension. +

    + +
    +
    +
    +

    Figure 2: A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).

    +
    +

    +
    + + +

    Layers used to build CNNs

    + +

    A simple CNN is a sequence of layers, and every layer of a CNN +transforms one volume of activations to another through a +differentiable function. We use three main types of layers to build +CNN architectures: Convolutional Layer, Pooling Layer, and +Fully-Connected Layer (exactly as seen in regular Neural Networks). We +will stack these layers to form a full CNN architecture. +

    + +

    A simple CNN for image classification could have the architecture:

    + +
      +
    • INPUT (\( 32\times 32 \times 3 \)) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
      • +
    • CONV (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as \( [32\times 32\times 12] \) if we decided to use 12 filters.
      • +
    • RELU layer will apply an elementwise activation function, such as the \( max(0,x) \) thresholding at zero. This leaves the size of the volume unchanged (\( [32\times 32\times 12] \)).
      • +
    • POOL (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as \( [16\times 16\times 12] \).
      • +
    • FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size \( [1\times 1\times 10] \), where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
      • +
    +









    +

    Transforming images

    + +

    CNNs transform the original image layer by layer from the original +pixel values to the final class scores. +

    + +

    Observe that some layers contain +parameters and other don’t. In particular, the CNN layers perform +transformations that are a function of not only the activations in the +input volume, but also of the parameters (the weights and biases of +the neurons). On the other hand, the RELU/POOL layers will implement a +fixed function. The parameters in the CONV/FC layers will be trained +with gradient descent so that the class scores that the CNN computes +are consistent with the labels in the training set for each image. +

    +









    CNNs in brief

    @@ -342,9 +576,1093 @@ the course and the slides of CS231 which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs.

    -

    However, both standard feed forwards networks and CNNs perform well on data with unknown length.

    +

    The textbook by Goodfellow et al, see chapter 9 contains an in depth discussion as well.

    + +









    +

    Key Idea

    + +

    A dense neural network is representd by an affine operation (like matrix-matrix multiplication) where all parameters are included.

    + +

    The key idea in CNNs for say imaging is that in images neighbor pixels tend to be related! So we connect +only neighboring neurons in the input instead of connecting all with the first hidden layer. +

    + +

    We say we perform a filtering (convolution is the mathematical operation).

    + +









    +

    Mathematics of CNNs

    + +

    The mathematics of CNNs is based on the mathematical operation of +convolution. In mathematics (in particular in functional analysis), +convolution is represented by mathematical operation (integration, +summation etc) on two function in order to produce a third function +that expresses how the shape of one gets modified by the other. +Convolution has a plethora of applications in a variety of disciplines, spanning from statistics to signal processing, computer vision, solutions of differential equations,linear algebra, engineering, and yes, machine learning. +

    + +

    Mathematically, convolution is defined as follows (one-dimensional example): +Let us define a continuous function \( y(t) \) given by +

    +$$ +y(t) = \int x(a) w(t-a) da, +$$ + +

    where \( x(a) \) represents a so-called input and \( w(t-a) \) is normally called the weight function or kernel.

    + +

    The above integral is written in a more compact form as

    +$$ +y(t) = \left(x * w\right)(t). +$$ + +

    The discretized version reads

    +$$ +y(t) = \sum_{a=-\infty}^{a=\infty}x(a)w(t-a). +$$ + +

    Computing the inverse of the above convolution operations is known as deconvolution.

    + +

    How can we use this? And what does it mean? Let us study some familiar examples first.

    + +









    +

    Convolution Examples: Polynomial multiplication

    + +

    We have already met such an example in project 1 when we tried to set +up the design matrix for a two-dimensional function. This was an +example of polynomial multiplication. Let us recast such a problem in terms of the convolution operation. +Let us look a the following polynomials to second and third order, respectively: +

    +$$ +p(t) = \alpha_0+\alpha_1 t+\alpha_2 t^2, +$$ + +

    and

    +$$ +s(t) = \beta_0+\beta_1 t+\beta_2 t^2+\beta_3 t^3. +$$ + +

    The polynomial multiplication gives us a new polynomial of degree \( 5 \)

    +$$ +z(t) = \delta_0+\delta_1 t+\delta_2 t^2+\delta_3 t^3+\delta_4 t^4+\delta_5 t^5. +$$ + + +









    +

    Efficient Polynomial Multiplication

    + +

    Computing polynomial products can be implemented efficiently if we rewrite the more brute force multiplications using convolution. +We note first that the new coefficients are given as +

    + +$$ +\begin{split} +\delta_0=&\alpha_0\beta_0\\ +\delta_1=&\alpha_1\beta_0+\alpha_1\beta_0\\ +\delta_2=&\alpha_0\beta_2+\alpha_1\beta_1+\alpha_2\beta_0\\ +\delta_3=&\alpha_1\beta_2+\alpha_2\beta_1+\alpha_0\beta_3\\ +\delta_4=&\alpha_2\beta_2+\alpha_1\beta_3\\ +\delta_5=&\alpha_2\beta_3.\\ +\end{split} +$$ + +

    We note that \( \alpha_i=0 \) except for \( i\in \left\{0,1,2\right\} \) and \( \beta_i=0 \) except for \( i\in\left\{0,1,2,3\right\} \).

    + +

    We can then rewrite the coefficients \( \delta_j \) using a discrete convolution as

    +$$ +\delta_j = \sum_{i=-\infty}^{i=\infty}\alpha_i\beta_{j-i}=(\alpha * \beta)_j, +$$ + +

    or as a double sum with restriction \( l=i+j \)

    +$$ +\delta_l = \sum_{ij}\alpha_i\beta_{j}. +$$ + +

    Do you see a potential drawback with these equations?

    + +









    +

    A more efficient way of coding the above Convolution

    + +

    Since we only have a finite number of \( \alpha \) and \( \beta \) values +which are non-zero, we can rewrite the above convolution expressions +as a matrix-vector multiplication +

    + +$$ +\boldsymbol{\delta}=\begin{bmatrix}\alpha_0 & 0 & 0 & 0 \\ + \alpha_1 & \alpha_0 & 0 & 0 \\ + \alpha_2 & \alpha_1 & \alpha_0 & 0 \\ + 0 & \alpha_2 & \alpha_1 & \alpha_0 \\ + 0 & 0 & \alpha_2 & \alpha_1 \\ + 0 & 0 & 0 & \alpha_2 + \end{bmatrix}\begin{bmatrix} \beta_0 \\ \beta_1 \\ \beta_2 \\ \beta_3\end{bmatrix}. +$$ + +

    The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding \( \beta \) and a vector holding \( \alpha \). +In this case we have +

    +$$ +\boldsymbol{\delta}=\begin{bmatrix}\beta_0 & 0 & 0 \\ + \beta_1 & \beta_0 & 0 \\ + \beta_2 & \beta_1 & \beta_0 \\ + \beta_3 & \beta_2 & \beta_1 \\ + 0 & \beta_3 & \beta_2 \\ + 0 & 0 & \beta_3 + \end{bmatrix}\begin{bmatrix} \alpha_0 \\ \alpha_1 \\ \alpha_2\end{bmatrix}. +$$ + +

    Note that the use of these matrices is for mathematical purposes only and not implementation purposes. +When implementing the above equation we do not encode (and allocate memory) the matrices explicitely. +We rather code the convolutions in the minimal memory footprint that they require. +

    + +

    Does the number of floating point operations change here when we use the commutative property?

    + +









    +

    Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)

    + +

    For problems with so-called harmonic oscillations, given by for example the following differential equation

    +$$ +m\frac{d^2x}{dt^2}+\eta\frac{dx}{dt}+x(t)=F(t), +$$ + +

    where \( F(t) \) is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations.

    + +

    If one has several driving forces, \( F(t)=\sum_n F_n(t) \), one can find +the particular solution to each \( F_n \), \( x_{pn}(t) \), and the particular +solution for the entire driving force is then given by a series like +

    + +$$ +\begin{equation} +x_p(t)=\sum_nx_{pn}(t). +\label{_auto1} +\end{equation} +$$ + + +









    +

    Principle of Superposition

    + +

    This is known as the principle of superposition. It only applies when +the homogenous equation is linear. If there were an anharmonic term +such as \( x^3 \) in the homogenous equation, then when one summed various +solutions, \( x=(\sum_n x_n)^2 \), one would get cross +terms. Superposition is especially useful when \( F(t) \) can be written +as a sum of sinusoidal terms, because the solutions for each +sinusoidal (sine or cosine) term is analytic. +

    + +

    Driving forces are often periodic, even when they are not +sinusoidal. Periodicity implies that for some time \( \tau \) +

    + +$$ +\begin{eqnarray} +F(t+\tau)=F(t). +\end{eqnarray} +$$ + +

    One example of a non-sinusoidal periodic force is a square wave. Many +components in electric circuits are non-linear, e.g. diodes, which +makes many wave forms non-sinusoidal even when the circuits are being +driven by purely sinusoidal sources. +

    + +









    +

    Simple Code Example

    + +

    The code here shows a typical example of such a square wave generated using the functionality included in the scipy Python package. We have used a period of \( \tau=0.2 \).

    + + + +
    +
    +
    +
    +
    + 
    import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t)
+plt.plot(t, SqrSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    For the sinusoidal example the +period is \( \tau=2\pi/\omega \). However, higher harmonics can also +satisfy the periodicity requirement. In general, any force that +satisfies the periodicity requirement can be expressed as a sum over +harmonics, +

    + +$$ +\begin{equation} +F(t)=\frac{f_0}{2}+\sum_{n>0} f_n\cos(2n\pi t/\tau)+g_n\sin(2n\pi t/\tau). +\label{_auto2} +\end{equation} +$$ + + +









    +

    Wrapping up Fourier transforms

    + +

    We can write down the answer for +\( x_{pn}(t) \), by substituting \( f_n/m \) or \( g_n/m \) for \( F_0/m \). By +writing each factor \( 2n\pi t/\tau \) as \( n\omega t \), with \( \omega\equiv +2\pi/\tau \), +

    + +$$ +\begin{equation} +\label{eq:fourierdef1} +F(t)=\frac{f_0}{2}+\sum_{n>0}f_n\cos(n\omega t)+g_n\sin(n\omega t). +\end{equation} +$$ + +

    The solutions for \( x(t) \) then come from replacing \( \omega \) with +\( n\omega \) for each term in the particular solution, +

    + +$$ +\begin{eqnarray} +x_p(t)&=&\frac{f_0}{2k}+\sum_{n>0} \alpha_n\cos(n\omega t-\delta_n)+\beta_n\sin(n\omega t-\delta_n),\\ +\nonumber +\alpha_n&=&\frac{f_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\beta_n&=&\frac{g_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\delta_n&=&\tan^{-1}\left(\frac{2\beta n\omega}{\omega_0^2-n^2\omega^2}\right). +\end{eqnarray} +$$ + + +









    +

    Finding the Coefficients

    + +

    Because the forces have been applied for a long time, any non-zero +damping eliminates the homogenous parts of the solution, so one need +only consider the particular solution for each \( n \). +

    + +

    The problem is considered solved if one can find expressions for the +coefficients \( f_n \) and \( g_n \), even though the solutions are expressed +as an infinite sum. The coefficients can be extracted from the +function \( F(t) \) by +

    + +$$ +\begin{eqnarray} +\label{eq:fourierdef2} +f_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\cos(2n\pi t/\tau),\\ +\nonumber +g_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\sin(2n\pi t/\tau). +\end{eqnarray} +$$ + +

    To check the consistency of these expressions and to verify +Eq. \eqref{eq:fourierdef2}, one can insert the expansion of \( F(t) \) in +Eq. \eqref{eq:fourierdef1} into the expression for the coefficients in +Eq. \eqref{eq:fourierdef2} and see whether +

    + +$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~\left\{ +\frac{f_0}{2}+\sum_{m>0}f_m\cos(m\omega t)+g_m\sin(m\omega t) +\right\}\cos(n\omega t). +\end{eqnarray} +$$ + +

    Immediately, one can throw away all the terms with \( g_m \) because they +convolute an even and an odd function. The term with \( f_0/2 \) +disappears because \( \cos(n\omega t) \) is equally positive and negative +over the interval and will integrate to zero. For all the terms +\( f_m\cos(m\omega t) \) appearing in the sum, one can use angle addition +formulas to see that \( \cos(m\omega t)\cos(n\omega +t)=(1/2)(\cos[(m+n)\omega t]+\cos[(m-n)\omega t] \). This will integrate +to zero unless \( m=n \). In that case the \( m=n \) term gives +

    + +$$ +\begin{equation} +\int_{-\tau/2}^{\tau/2}dt~\cos^2(m\omega t)=\frac{\tau}{2}, +\label{_auto3} +\end{equation} +$$ + +

    and

    + +$$ +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~f_n/2\\ +\nonumber +&=&f_n~\checkmark. +\end{eqnarray} +$$ + +

    The same method can be used to check for the consistency of \( g_n \).

    + +









    +

    Final words on Fourier Transforms

    + +

    The code here uses the Fourier series applied to a +square wave signal. The code here +visualizes the various approximations given by Fourier series compared +with a square wave with period \( T=0.2 \) (dimensionless time), width \( 0.1 \) and max value of the force \( F=2 \). We +see that when we increase the number of components in the Fourier +series, the Fourier series approximation gets closer and closer to the +square wave signal. +

    + + + +
    +
    +
    +
    +
    + 
    import numpy as np
+import math
+from scipy import signal
+import matplotlib.pyplot as plt
+
+# number of points                                                                                       
+n = 500
+# start and final times                                                                                  
+t0 = 0.0
+tn = 1.0
+# Period                                                                                                 
+T =0.2
+# Max value of square signal                                                                             
+Fmax= 2.0
+# Width of signal   
+Width = 0.1
+t = np.linspace(t0, tn, n, endpoint=False)
+SqrSignal = np.zeros(n)
+FourierSeriesSignal = np.zeros(n)
+SqrSignal = 1.0+signal.square(2*np.pi*5*t+np.pi*Width/T)
+a0 = Fmax*Width/T
+FourierSeriesSignal = a0
+Factor = 2.0*Fmax/np.pi
+for i in range(1,500):
+    FourierSeriesSignal += Factor/(i)*np.sin(np.pi*i*Width/T)*np.cos(i*t*2*np.pi/T)
+plt.plot(t, SqrSignal)
+plt.plot(t, FourierSeriesSignal)
+plt.ylim(-0.5, 2.5)
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Two-dimensional Objects

    + +

    We often use convolutions over more than one dimension at a time. If +we have a two-dimensional image \( I \) as input, we can have a filter +defined by a two-dimensional kernel \( K \). This leads to an output \( S \) +

    + +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(m,n)K(i-m,j-n). +$$ + +

    Convolution is a commutatitave process, which means we can rewrite this equation as

    +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i-m,j-n)K(m,n). +$$ + +

    Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of \( m \) and \( n \).

    + +









    +

    Cross-Correlation

    + +

    Many deep learning libraries implement cross-correlation instead of convolution

    +$$ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i+m,j-+)K(m,n). +$$ + + +









    +

    More on Dimensionalities

    + +

    In feilds like signal processing (and imaging as well), one designs +so-called filters. These filters are defined by the convolutions and +are often hand-crafted. One may specify filters for smoothing, edge +detection, frequency reshaping, and similar operations. However with +neural networks the idea is to automatically learn the filters and use +many of them in conjunction with non-linear operations (activation +functions). +

    + +

    As an example consider a neural network operating on sound sequence +data. Assume that we an input vector \( \boldsymbol{x} \) of length \( d=10^6 \). We +construct then a neural network with onle hidden layer only with +\( 10^4 \) nodes. This means that we will have a weight matrix with +\( 10^4\times 10^6=10^{10} \) weights to be determined, together with \( 10^4 \) biases. +

    + +

    Assume furthermore that we have an output layer which is meant to train whether the sound sequence represents a human voice (true) or something else (false). +It means that we have only one output node. But since this output node connects to \( 10^4 \) nodes in the hidden layer, there are in total \( 10^4 \) weights to be determined for the output layer, plus one bias. In total we have +

    + +$$ +\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \approx 10^{10}, +$$ + +

    that is ten billion parameters to determine.

    + +









    +

    Further Dimensionality Remarks

    + +

    In today’s architecture one can train such neural networks, however +this is a huge number of parameters for the task at hand. In general, +it is a very wasteful and inefficient use of dense matrices as +parameters. Just as importantly, such trained network parameters are +very specific for the type of input data on which they were trained +and the network is not likely to generalize easily to variations in +the input. +

    + +

    The main principles that justify convolutions is locality of +information and repetion of patterns within the signal. Sound samples +of the input in adjacent spots are much more likely to affect each +other than those that are very far away. Similarly, sounds are +repeated in multiple times in the signal. While slightly simplistic, +reasoning about such a sound example demonstrates this. The same +principles then apply to images and other similar data. +

    + +









    +

    CNNs in more detail, Lecture from IN5400

    + + +









    +

    CNNs in more detail, building convolutional neural networks in Tensorflow and Keras

    + +

    As discussed above, CNNs are neural networks built from the assumption that the inputs +to the network are 2D images. This is important because the number of features or pixels in images +grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network. +

    + +

    As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks +are the convolutional and pooling layers stacked in pairs between the input and the hidden layer. +In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D +matrices, typically 1 for each color dimension (Red, Green, Blue). +

    + +









    +

    Setting it up

    + +

    It means that to represent the entire +dataset of images, we require a 4D matrix or tensor. This tensor has the dimensions: +

    +$$ +(n_{inputs},\, n_{pixels, width},\, n_{pixels, height},\, depth) . +$$ + + +









    +

    The MNIST dataset again

    + +

    The MNIST dataset consists of grayscale images with a pixel size of +\( 28\times 28 \), meaning we require \( 28 \times 28 = 724 \) weights to each +neuron in the first hidden layer. +

    + +

    If we were to analyze images of size \( 128\times 128 \) we would require +\( 128 \times 128 = 16384 \) weights to each neuron. Even worse if we were +dealing with color images, as most images are, we have an image matrix +of size \( 128\times 128 \) for each color dimension (Red, Green, Blue), +meaning 3 times the number of weights \( = 49152 \) are required for every +single neuron in the first hidden layer. +

    + + +









    +

    Strong correlations

    + +

    Images typically have strong local correlations, meaning that a small +part of the image varies little from its neighboring regions. If for +example we have an image of a blue car, we can roughly assume that a +small blue part of the image is surrounded by other blue regions. +

    + +

    Therefore, instead of connecting every single pixel to a neuron in the +first hidden layer, as we have previously done with deep neural +networks, we can instead connect each neuron to a small part of the +image (in all 3 RGB depth dimensions). The size of each small area is +fixed, and known as a receptive. +

    + + + +

    Layers of a CNN

    +

    The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. +The input image is typically a square matrix of depth 3. +

    + +

    A convolution is performed on the image which outputs +a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as filters. +

    + +

    Each filter slides along the input image, taking the dot product +between each small part of the image and the filter, in all depth +dimensions. This is then passed through a non-linear function, +typically the Rectified Linear (ReLu) function, which serves as the +activation of the neurons in the first convolutional layer. This is +further passed through a pooling layer, which reduces the size of the +convolutional layer, e.g. by taking the maximum or average across some +small regions, and this serves as input to the next convolutional +layer. +

    + +









    +

    Systematic reduction

    + +

    By systematically reducing the size of the input volume, through +convolution and pooling, the network should create representations of +small parts of the input, and then from them assemble representations +of larger areas. The final pooling layer is flattened to serve as +input to a hidden layer, such that each neuron in the final pooling +layer is connected to every single neuron in the hidden layer. This +then serves as input to the output layer, e.g. a softmax output for +classification. +

    + + +









    +

    Prerequisites: Collect and pre-process data

    + + +
    +
    +
    +
    +
    + 
    # import necessary packages
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+
+
+# ensure the same random numbers appear every time
+np.random.seed(0)
+
+# display images in notebook
+%matplotlib inline
+plt.rcParams['figure.figsize'] = (12,12)
+
+
+# download MNIST dataset
+digits = datasets.load_digits()
+
+# define inputs and labels
+inputs = digits.images
+labels = digits.target
+
+# RGB images have a depth of 3
+# our images are grayscale so they should have a depth of 1
+inputs = inputs[:,:,:,np.newaxis]
+
+print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
+print("labels = (n_inputs) = " + str(labels.shape))
+
+
+# choose some random images to display
+n_inputs = len(inputs)
+indices = np.arange(n_inputs)
+random_indices = np.random.choice(indices, size=5)
+
+for i, image in enumerate(digits.images[random_indices]):
+    plt.subplot(1, 5, i+1)
+    plt.axis('off')
+    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
+    plt.title("Label: %d" % digits.target[random_indices[i]])
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Importing Keras and Tensorflow

    + + +
    +
    +
    +
    +
    + 
    from tensorflow.keras import datasets, layers, models
+from tensorflow.keras.layers import Input
+from tensorflow.keras.models import Sequential      #This allows appending layers to existing models
+from tensorflow.keras.layers import Dense           #This allows defining the characteristics of a particular layer
+from tensorflow.keras import optimizers             #This allows using whichever optimiser we want (sgd,adam,RMSprop)
+from tensorflow.keras import regularizers           #This allows using whichever regularizer we want (l1,l2,l1_l2)
+from tensorflow.keras.utils import to_categorical   #This allows using categorical cross entropy as the cost function
+#from tensorflow.keras import Conv2D
+#from tensorflow.keras import MaxPooling2D
+#from tensorflow.keras import Flatten
+
+from sklearn.model_selection import train_test_split
+
+# representation of labels
+labels = to_categorical(labels)
+
+# split into train and test data
+# one-liner from scikit-learn library
+train_size = 0.8
+test_size = 1 - train_size
+X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
+                                                    test_size=test_size)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + +

    Running with Keras

    + + + +
    +
    +
    +
    +
    + 
    def create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd):
+    model = Sequential()
+    model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
+              activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.MaxPooling2D(pool_size=(2, 2)))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))
+    model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))
+    
+    sgd = optimizers.SGD(lr=eta)
+    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
+    
+    return model
+
+epochs = 100
+batch_size = 100
+input_shape = X_train.shape[1:4]
+receptive_field = 3
+n_filters = 10
+n_neurons_connected = 50
+n_categories = 10
+
+eta_vals = np.logspace(-5, 1, 7)
+lmbd_vals = np.logspace(-5, 1, 7)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Final part

    + + + +
    +
    +
    +
    +
    + 
    CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
+        
+for i, eta in enumerate(eta_vals):
+    for j, lmbd in enumerate(lmbd_vals):
+        CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
+                                              n_filters, n_neurons_connected, n_categories,
+                                              eta, lmbd)
+        CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+        scores = CNN.evaluate(X_test, Y_test)
+        
+        CNN_keras[i][j] = CNN
+        
+        print("Learning rate = ", eta)
+        print("Lambda = ", lmbd)
+        print("Test accuracy: %.3f" % scores[1])
+        print()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Final visualization

    + + + +
    +
    +
    +
    +
    + 
    # visual representation of grid search
+# uses seaborn heatmap, could probably do this in matplotlib
+import seaborn as sns
+
+sns.set()
+
+train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
+
+for i in range(len(eta_vals)):
+    for j in range(len(lmbd_vals)):
+        CNN = CNN_keras[i][j]
+
+        train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
+        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
+
+        
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Training Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
+fig, ax = plt.subplots(figsize = (10, 10))
+sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
+ax.set_title("Test Accuracy")
+ax.set_ylabel("$\eta$")
+ax.set_xlabel("$\lambda$")
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    The CIFAR01 data set

    + +

    The CIFAR10 dataset contains 60,000 color images in 10 classes, with +6,000 images in each class. The dataset is divided into 50,000 +training images and 10,000 testing images. The classes are mutually +exclusive and there is no overlap between them. +

    + + + +
    +
    +
    +
    +
    + 
    import tensorflow as tf
+
+from tensorflow.keras import datasets, layers, models
+import matplotlib.pyplot as plt
+
+# We import the data set
+(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+
+# Normalize pixel values to be between 0 and 1 by dividing by 255. 
+train_images, test_images = train_images / 255.0, test_images / 255.0
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Verifying the data set

    + +

    To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image.

    + + + +
    +
    +
    +
    +
    + 
    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+​
+plt.figure(figsize=(10,10))
+for i in range(25):
+    plt.subplot(5,5,i+1)
+    plt.xticks([])
+    plt.yticks([])
+    plt.grid(False)
+    plt.imshow(train_images[i], cmap=plt.cm.binary)
+    # The CIFAR labels happen to be arrays, 
+    # which is why you need the extra index
+    plt.xlabel(class_names[train_labels[i][0]])
+plt.show()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Set up the model

    + +

    The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers.

    + +

    As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer.

    + + + +
    +
    +
    +
    +
    + 
    model = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+model.add(layers.MaxPooling2D((2, 2)))
+model.add(layers.Conv2D(64, (3, 3), activation='relu'))
+
+# Let's display the architecture of our model so far.
+
+model.summary()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer.

    + +









    +

    Add Dense layers on top

    + +

    To complete our model, you will feed the last output tensor from the +convolutional base (of shape (4, 4, 64)) into one or more Dense layers +to perform classification. Dense layers take vectors as input (which +are 1D), while the current output is a 3D tensor. First, you will +flatten (or unroll) the 3D output to 1D, then add one or more Dense +layers on top. CIFAR has 10 output classes, so you use a final Dense +layer with 10 outputs and a softmax activation. +

    + + + +
    +
    +
    +
    +
    + 
    model.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(10))
+Here's the complete architecture of our model.
+
+model.summary()
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + +

    As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.

    + +









    +

    Compile and train the model

    + + + +
    +
    +
    +
    +
    + 
    model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+​
+history = model.fit(train_images, train_labels, epochs=10, 
+                    validation_data=(test_images, test_labels))
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + +









    +

    Finally, evaluate the model

    + + + +
    +
    +
    +
    +
    + 
    plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.ylim([0.5, 1])
+plt.legend(loc='lower right')
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+print(test_acc)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    -

    This is where recurrent nueral networks (RNNs) come to our rescue.











    Recurrent neural networks: Overarching view

    @@ -372,7 +1690,7 @@ systems such as automatic translation and speech-to-text.









    Set up of an RNN

    -

    More to text to be added

    +

    See handwritten notes for week 43 and Lectures from CS231 at Stanford











    A simple example

    @@ -1169,7 +2487,7 @@ samples $$ \begin{equation} x = g(z; \theta^{(g)}) -\label{_auto1} +\label{_auto4} \end{equation} $$ @@ -1186,7 +2504,7 @@ value given by $$ \begin{equation} d(x; \theta^{(d)}) -\label{_auto2} +\label{_auto5} \end{equation} $$ @@ -1199,7 +2517,7 @@ which a function $$ \begin{equation} v(\theta^{(g)}, \theta^{(d)}) -\label{_auto3} +\label{_auto6} \end{equation} $$ @@ -1210,7 +2528,7 @@ conjugate reward $$ \begin{equation} -v(\theta^{(g)}, \theta^{(d)}) -\label{_auto4} +\label{_auto7} \end{equation} $$ @@ -1245,7 +2563,7 @@ $$ \begin{equation} g^* = \underset{g}{\mathrm{argmin}}\hspace{2pt} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\label{_auto5} +\label{_auto8} \end{equation} $$ @@ -1255,7 +2573,7 @@ $$ v(\theta^{(g)}, \theta^{(d)}) = \mathbb{E}_{x\sim p_\mathrm{data}}\log d(x) + \mathbb{E}_{x\sim p_\mathrm{model}} \log (1 - d(x)) -\label{_auto6} +\label{_auto9} \end{equation} $$ @@ -1266,7 +2584,7 @@ approximation of a partition function. In the case where $$ \begin{equation} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) -\label{_auto7} +\label{_auto10} \end{equation} $$ @@ -2222,1207 +3540,6 @@ plot_results(results, plot_number)
-









-

Basic ideas of the Principal Component Analysis (PCA)

- -

The principal component analysis deals with the problem of fitting a -low-dimensional affine subspace \( S \) of dimension \( d \) much smaller than -the total dimension \( D \) of the problem at hand (our data -set). Mathematically it can be formulated as a statistical problem or -a geometric problem. In our discussion of the theorem for the -classical PCA, we will stay with a statistical approach. -Historically, the PCA was first formulated in a statistical setting in order to estimate the principal component of a multivariate random variable. -

- -

We have a data set defined by a design/feature matrix \( \boldsymbol{X} \) (see below for its definition)

-
    -
  • Each data point is determined by \( p \) extrinsic (measurement) variables
    • -
  • We may want to ask the following question: Are there fewer intrinsic variables (say \( d < < p \)) that still approximately describe the data?
    • -
  • If so, these intrinsic variables may tell us something important and finding these intrinsic variables is what dimension reduction methods do.
    • -
-

A good read is for example Vidal, Ma and Sastry.

- -









-

Introducing the Covariance and Correlation functions

- -

Before we discuss the PCA theorem, we need to remind ourselves about -the definition of the covariance and the correlation function. These are quantities -

- -

Suppose we have defined two vectors -\( \hat{x} \) and \( \hat{y} \) with \( n \) elements each. The covariance matrix \( \boldsymbol{C} \) is defined as -

-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{cov}[\boldsymbol{x},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{y},\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{y},\boldsymbol{y}] \\ - \end{bmatrix}, -$$ - -

where for example

-$$ -\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). -$$ - -

With this definition and recalling that the variance is defined as

-$$ -\mathrm{var}[\boldsymbol{x}]=\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})^2, -$$ - -

we can rewrite the covariance matrix as

-$$ -\boldsymbol{C}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} \mathrm{var}[\boldsymbol{x}] & \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{cov}[\boldsymbol{x},\boldsymbol{y}] & \mathrm{var}[\boldsymbol{y}] \\ - \end{bmatrix}. -$$ - - -









-

More on the covariance

-

The covariance takes values between zero and infinity and may thus -lead to problems with loss of numerical precision for particularly -large values. It is common to scale the covariance matrix by -introducing instead the correlation matrix defined via the so-called -correlation function -

- -$$ -\mathrm{corr}[\boldsymbol{x},\boldsymbol{y}]=\frac{\mathrm{cov}[\boldsymbol{x},\boldsymbol{y}]}{\sqrt{\mathrm{var}[\boldsymbol{x}] \mathrm{var}[\boldsymbol{y}]}}. -$$ - -

The correlation function is then given by values \( \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] -\in [-1,1] \). This avoids eventual problems with too large values. We -can then define the correlation matrix for the two vectors \( \boldsymbol{x} \) -and \( \boldsymbol{y} \) as -

- -$$ -\boldsymbol{K}[\boldsymbol{x},\boldsymbol{y}] = \begin{bmatrix} 1 & \mathrm{corr}[\boldsymbol{x},\boldsymbol{y}] \\ - \mathrm{corr}[\boldsymbol{y},\boldsymbol{x}] & 1 \\ - \end{bmatrix}, -$$ - -

In the above example this is the function we constructed using pandas.

- -









-

Reminding ourselves about Linear Regression

-

In our derivation of the various regression algorithms like Ordinary Least Squares or Ridge regression -we defined the design/feature matrix \( \boldsymbol{X} \) as -

- -$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ - -

with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) refering to the column numbers and the -entries \( n \) being the row elements. -We can rewrite the design/feature matrix in terms of its column vectors as -

-$$ -\boldsymbol{X}=\begin{bmatrix} \boldsymbol{x}_0 & \boldsymbol{x}_1 & \boldsymbol{x}_2 & \dots & \dots & \boldsymbol{x}_{p-1}\end{bmatrix}, -$$ - -

with a given vector

-$$ -\boldsymbol{x}_i^T = \begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \dots & \dots x_{n-1,i}\end{bmatrix}. -$$ - - -









-

Simple Example

-

With these definitions, we can now rewrite our \( 2\times 2 \) -correlation/covariance matrix in terms of a moe general design/feature -matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \). This leads to a \( p\times p \) -covariance matrix for the vectors \( \boldsymbol{x}_i \) with \( i=0,1,\dots,p-1 \) -

- -$$ -\boldsymbol{C}[\boldsymbol{x}] = \begin{bmatrix} -\mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_1] & \mathrm{var}[\boldsymbol{x}_2] & \dots & \dots & \mathrm{cov}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{cov}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & \mathrm{var}[\boldsymbol{x}_{p-1}]\\ -\end{bmatrix}, -$$ - - -









-

The Correlation Matrix

- -

and the correlation matrix

-$$ -\boldsymbol{K}[\boldsymbol{x}] = \begin{bmatrix} -1 & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_0,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_0] & 1 & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_2] & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_1,\boldsymbol{x}_{p-1}]\\ -\mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_1] & 1 & \dots & \dots & \mathrm{corr}[\boldsymbol{x}_2,\boldsymbol{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & 1\\ -\end{bmatrix}, -$$ - - -









-

Numpy Functionality

- -

The Numpy function np.cov calculates the covariance elements using -the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have -the exact mean values. The following simple function uses the -np.vstack function which takes each vector of dimension \( 1\times n \) -and produces a \( 2\times n \) matrix \( \boldsymbol{W} \) -

- -$$ -\boldsymbol{W}^T = \begin{bmatrix} x_0 & y_0 \\ - x_1 & y_1 \\ - x_2 & y_2\\ - \dots & \dots \\ - x_{n-2} & y_{n-2}\\ - x_{n-1} & y_{n-1} & - \end{bmatrix}, -$$ - -

which in turn is converted into into the \( 2\times 2 \) covariance matrix -\( \boldsymbol{C} \) via the Numpy function np.cov(). We note that we can also calculate -the mean value of each set of samples \( \boldsymbol{x} \) etc using the Numpy -function np.mean(x). We can also extract the eigenvalues of the -covariance matrix through the np.linalg.eig() function. -

- - - -
-
-
-
-
- 
# Importing various packages
-import numpy as np
-n = 100
-x = np.random.normal(size=n)
-print(np.mean(x))
-y = 4+3*x+np.random.normal(size=n)
-print(np.mean(y))
-W = np.vstack((x, y))
-C = np.cov(W)
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -









-

Correlation Matrix again

- -

The previous example can be converted into the correlation matrix by -simply scaling the matrix elements with the variances. We should also -subtract the mean values for each column. This leads to the following -code which sets up the correlations matrix for the previous example in -a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the \( 2\times 2 \) correlation matrix (since we have only two vectors). -

- - - -
-
-
-
-
- 
import numpy as np
-n = 100
-# define two vectors                                                                                           
-x = np.random.random(size=n)
-y = 4+3*x+np.random.normal(size=n)
-#scaling the x and y vectors                                                                                   
-x = x - np.mean(x)
-y = y - np.mean(y)
-variance_x = np.sum(x@x)/n
-variance_y = np.sum(y@y)/n
-print(variance_x)
-print(variance_y)
-cov_xy = np.sum(x@y)/n
-cov_xx = np.sum(x@x)/n
-cov_yy = np.sum(y@y)/n
-C = np.zeros((2,2))
-C[0,0]= cov_xx/variance_x
-C[1,1]= cov_yy/variance_y
-C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)
-C[1,0]= C[0,1]
-print(C)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We see that the matrix elements along the diagonal are one as they -should be and that the matrix is symmetric. Furthermore, diagonalizing -this matrix we easily see that it is a positive definite matrix. -

- -

The above procedure with numpy can be made more compact if we use pandas.

- -









-

Using Pandas

- -

We whow here how we can set up the correlation matrix using pandas, as done in this simple code

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-n = 10
-x = np.random.normal(size=n)
-x = x - np.mean(x)
-y = 4+3*x+np.random.normal(size=n)
-y = y - np.mean(y)
-X = (np.vstack((x, y))).T
-print(X)
-Xpd = pd.DataFrame(X)
-print(Xpd)
-correlation_matrix = Xpd.corr()
-print(correlation_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- - -









-

And then the Franke Function

- -

We expand this model to the Franke function discussed above.

- - - -
-
-
-
-
- 
# Common imports
-import numpy as np
-import pandas as pd
-
-
-def FrankeFunction(x,y):
-	term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))
-	term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))
-	term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))
-	term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)
-	return term1 + term2 + term3 + term4
-
-
-def create_X(x, y, n ):
-	if len(x.shape) > 1:
-		x = np.ravel(x)
-		y = np.ravel(y)
-
-	N = len(x)
-	l = int((n+1)*(n+2)/2)		# Number of elements in beta
-	X = np.ones((N,l))
-
-	for i in range(1,n+1):
-		q = int((i)*(i+1)/2)
-		for k in range(i+1):
-			X[:,q+k] = (x**(i-k))*(y**k)
-
-	return X
-
-
-# Making meshgrid of datapoints and compute Franke's function
-n = 4
-N = 100
-x = np.sort(np.random.uniform(0, 1, N))
-y = np.sort(np.random.uniform(0, 1, N))
-z = FrankeFunction(x, y)
-X = create_X(x, y, n=n)    
-
-Xpd = pd.DataFrame(X)
-# subtract the mean values and set up the covariance matrix
-Xpd = Xpd - Xpd.mean()
-covariance_matrix = Xpd.cov()
-print(covariance_matrix)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- -

We note here that the covariance is zero for the first rows and -columns since all matrix elements in the design matrix were set to one -(we are fitting the function in terms of a polynomial of degree \( n \)). We would however not include the intercept -and wee can simply -drop these elements and construct a correlation -matrix without them by centering our matrix elements by subtracting the mean of each column. -

- -









-

Lnks with the Design Matrix

- -

We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix \( \boldsymbol{X} \) as

-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ - -

To see this let us simply look at a design matrix \( \boldsymbol{X}\in {\mathbb{R}}^{2\times 2} \)

-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{00} & x_{01}\\ -x_{10} & x_{11}\\ -\end{bmatrix}=\begin{bmatrix} -\boldsymbol{x}_{0} & \boldsymbol{x}_{1}\\ -\end{bmatrix}. -$$ - - -









-

Computing the Expectation Values

- -

If we then compute the expectation value

-$$ -\mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}=\begin{bmatrix} -x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\ -x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\ -\end{bmatrix}, -$$ - -

which is just

-$$ -\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]=\begin{bmatrix} \mathrm{var}[\boldsymbol{x}_0] & \mathrm{cov}[\boldsymbol{x}_0,\boldsymbol{x}_1] \\ - \mathrm{cov}[\boldsymbol{x}_1,\boldsymbol{x}_0] & \mathrm{var}[\boldsymbol{x}_1] \\ - \end{bmatrix}, -$$ - -

where we wrote $$\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]$$ to indicate that this the covariance of the vectors \( \boldsymbol{x} \) of the design/feature matrix \( \boldsymbol{X} \).

- -

It is easy to generalize this to a matrix \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \).

- -









-

Towards the PCA theorem

- -

We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as

-$$ -\boldsymbol{C}[\boldsymbol{x}] = \frac{1}{n}\boldsymbol{X}^T\boldsymbol{X}= \mathbb{E}[\boldsymbol{X}^T\boldsymbol{X}]. -$$ - -

Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices \( \boldsymbol{S} \). -These matrices are defined as \( \boldsymbol{S}\in {\mathbb{R}}^{p\times p} \) and obey the orthogonality requirements \( \boldsymbol{S}\boldsymbol{S}^T=\boldsymbol{S}^T\boldsymbol{S}=\boldsymbol{I} \). The matrix can be written out in terms of the column vectors \( \boldsymbol{s}_i \) as \( \boldsymbol{S}=[\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \) and \( \boldsymbol{s}_i \in {\mathbb{R}}^{p} \). -

- -

Assume also that there is a transformation \( \boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}=\boldsymbol{C}[\boldsymbol{y}] \) such that the new matrix \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal with elements \( [\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}] \).

- -

That is we have

-$$ -\boldsymbol{C}[\boldsymbol{y}] = \mathbb{E}[\boldsymbol{S}^T\boldsymbol{X}^T\boldsymbol{X}T\boldsymbol{S}]=\boldsymbol{S}^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ - -

since the matrix \( \boldsymbol{S} \) is not a data dependent matrix. Multiplying with \( \boldsymbol{S} \) from the left we have

-$$ -\boldsymbol{S}\boldsymbol{C}[\boldsymbol{y}] = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}, -$$ - -

and since \( \boldsymbol{C}[\boldsymbol{y}] \) is diagonal we have for a given eigenvalue \( i \) of the covariance matrix that

- -$$ -\boldsymbol{S}_i\lambda_i = \boldsymbol{C}[\boldsymbol{x}]\boldsymbol{S}_i. -$$ - - -









-

More on the PCA Theorem

- -

In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is -\( \lambda_0 > \lambda_1 > \dots > \lambda_{p-1} \). -

- -

The eigenvalues tell us then how much we need to stretch the -corresponding eigenvectors. Dimensions with large eigenvalues have -thus large variations (large variance) and define therefore useful -dimensions. The data points are more spread out in the direction of -these eigenvectors. Smaller eigenvalues mean on the other hand that -the corresponding eigenvectors are shrunk accordingly and the data -points are tightly bunched together and there is not much variation in -these specific directions. Hopefully then we could leave it out -dimensions where the eigenvalues are very small. If \( p \) is very large, -we could then aim at reducing \( p \) to \( l < < p \) and handle only \( l \) -features/predictors. -

- -









-

The Algorithm before theorem

- -

Here's how we would proceed in setting up the algorithm for the PCA, see also discussion below here.

-
    -
  • Set up the datapoints for the design/feature matrix \( \boldsymbol{X} \) with \( \boldsymbol{X}\in {\mathbb{R}}^{n\times p} \), with the predictors/features \( p \) referring to the column numbers and the entries \( n \) being the row elements.
    • -
-$$ -\boldsymbol{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -$$ - -
    -
  • Center the data by subtracting the mean value for each column. This leads to a new matrix \( \boldsymbol{X}\rightarrow \overline{\boldsymbol{X}} \).
    • -
  • Compute then the covariance/correlation matrix \( \mathbb{E}[\overline{\boldsymbol{X}}^T\overline{\boldsymbol{X}}] \).
    • -
  • Find the eigenpairs of \( \boldsymbol{C} \) with eigenvalues \( [\lambda_0,\lambda_1,\dots,\lambda_{p-1}] \) and eigenvectors \( [\boldsymbol{s}_0,\boldsymbol{s}_1,\dots,\boldsymbol{s}_{p-1}] \).
    • -
  • Order the eigenvalue (and the eigenvectors accordingly) in order of decreasing eigenvalues.
    • -
  • Keep only those \( l \) eigenvalues larger than a selected threshold value, discarding thus \( p-l \) features since we expect small variations in the data here.
    • -
-









-

Writing our own PCA code

- -

We will use a simple example first with two-dimensional data -drawn from a multivariate normal distribution with the following mean and covariance matrix (we have fixed these quantities but will play around with them below): -

-$$ -\mu = (-1,2) \qquad \Sigma = \begin{bmatrix} 4 & 2 \\ -2 & 2 -\end{bmatrix} -$$ - -

Note that the mean refers to each column of data. -We will generate \( n = 10000 \) points \( X = \{ x_1, \ldots, x_N \} \) from -this distribution, and store them in the \( 1000 \times 2 \) matrix \( \boldsymbol{X} \). This is our design matrix where we have forced the covariance and mean values to take specific values. -

- -









-

Implementing it

-

The following Python code aids in setting up the data and writing out the design matrix. -Note that the function multivariate returns also the covariance discussed above and that it is defined by dividing by \( n-1 \) instead of \( n \). -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from IPython.display import display
-n = 10000
-mean = (-1, 2)
-cov = [[4, 2], [2, 2]]
-X = np.random.multivariate_normal(mean, cov, n)
-
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-
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-
-
- -

Now we are going to implement the PCA algorithm. We will break it down into various substeps.

- -









-

First Step

- -

The first step of PCA is to compute the sample mean of the data and use it to center the data. Recall that the sample mean is

-$$ -\mu_n = \frac{1}{n} \sum_{i=1}^n x_i -$$ - -

and the mean-centered data \( \bar{X} = \{ \bar{x}_1, \ldots, \bar{x}_n \} \) takes the form

-$$ -\bar{x}_i = x_i - \mu_n. -$$ - -

When you are done with these steps, print out \( \mu_n \) to verify it is -close to \( \mu \) and plot your mean centered data to verify it is -centered at the origin! -The following code elements perform these operations using pandas or using our own functionality for doing so. The latter, using numpy is rather simple through the mean() function. -

- - -
-
-
-
-
- 
df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-
-
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-
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- - -









-

Scaling

-

Alternatively, we could use the functions we discussed -earlier for scaling the data set. That is, we could have used the -StandardScaler function in Scikit-Learn, a function which ensures -that for each feature/predictor we study the mean value is zero and -the variance is one (every column in the design/feature matrix). You -would then not get the same results, since we divide by the -variance. The diagonal covariance matrix elements will then be one, -while the non-diagonal ones need to be divided by \( 2\sqrt{2} \) for our -specific case. -

- -









-

Centered Data

- -

Now we are going to use the mean centered data to compute the sample covariance of the data by using the following equation

-$$ -\begin{equation*} -\Sigma_n = \frac{1}{n-1} \sum_{i=1}^n \bar{x}_i^T \bar{x}_i = \frac{1}{n-1} \sum_{i=1}^n (x_i - \mu_n)^T (x_i - \mu_n) -\end{equation*} -$$ - -

where the data points \( x_i \in \mathbb{R}^p \) (here in this example \( p = 2 \)) are column vectors and \( x^T \) is the transpose of \( x \). -We can write our own code or simply use either the functionaly of numpy or that of pandas, as follows -

- - -
-
-
-
-
- 
print(df.cov())
-print(np.cov(X_centered.T))
-
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-
-
-
-
- -

Note that the way we define the covariance matrix here has a factor \( n-1 \) instead of \( n \). This is included in the cov() function by numpy and pandas. -Our own code here is not very elegant and asks for obvious improvements. It is tailored to this specific \( 2\times 2 \) covariance matrix. -

- - -
-
-
-
-
- 
# extract the relevant columns from the centered design matrix of dim n x 2
-x = X_centered[:,0]
-y = X_centered[:,1]
-Cov = np.zeros((2,2))
-Cov[0,1] = np.sum(x.T@y)/(n-1.0)
-Cov[0,0] = np.sum(x.T@x)/(n-1.0)
-Cov[1,1] = np.sum(y.T@y)/(n-1.0)
-Cov[1,0]= Cov[0,1]
-print("Centered covariance using own code")
-print(Cov)
-plt.plot(x, y, 'x')
-plt.axis('equal')
-plt.show()
-
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-
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- - -









-

Exploring

- -

Depending on the number of points \( n \), we will get results that are close to the covariance values defined above. -The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed. -

- -









-

Diagonalize the sample covariance matrix to obtain the principal components

- -

Now we are ready to solve for the principal components! To do so we -diagonalize the sample covariance matrix \( \Sigma \). We can use the -function np.linalg.eig to do so. It will return the eigenvalues and -eigenvectors of \( \Sigma \). Once we have these we can perform the -following tasks: -

- -
    -
  • We compute the percentage of the total variance captured by the first principal component
    • -
  • We plot the mean centered data and lines along the first and second principal components
    • -
  • Then we project the mean centered data onto the first and second principal components, and plot the projected data.
    • -
  • Finally, we approximate the data as
    • -
-$$ -\begin{equation*} -x_i \approx \tilde{x}_i = \mu_n + \langle x_i, v_0 \rangle v_0 -\end{equation*} -$$ - -

where \( v_0 \) is the first principal component.

- -









-

Collecting all Steps

- -

Collecting all these steps we can write our own PCA function and -compare this with the functionality included in Scikit-Learn. -

- -

The code here outlines some of the elements we could include in the -analysis. Feel free to extend upon this in order to address the above -questions. -

- - - -
-
-
-
-
- 
# diagonalize and obtain eigenvalues, not necessarily sorted
-EigValues, EigVectors = np.linalg.eig(Cov)
-# sort eigenvectors and eigenvalues
-#permute = EigValues.argsort()
-#EigValues = EigValues[permute]
-#EigVectors = EigVectors[:,permute]
-print("Eigenvalues of Covariance matrix")
-for i in range(2):
-    print(EigValues[i])
-FirstEigvector = EigVectors[:,0]
-SecondEigvector = EigVectors[:,1]
-print("First eigenvector")
-print(FirstEigvector)
-print("Second eigenvector")
-print(SecondEigvector)
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2Dsl = pca.fit_transform(X)
-print("Eigenvector of largest eigenvalue")
-print(pca.components_.T[:, 0])
-
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-
-
-
-
-
- -

This code does not contain all the above elements, but it shows how we can use Scikit-Learn to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?

- -









-

Classical PCA Theorem

- -

We assume now that we have a design matrix \( \boldsymbol{X} \) which has been -centered as discussed above. For the sake of simplicity we skip the -overline symbol. The matrix is defined in terms of the various column -vectors \( [\boldsymbol{x}_0,\boldsymbol{x}_1,\dots, \boldsymbol{x}_{p-1}] \) each with dimension -\( \boldsymbol{x}\in {\mathbb{R}}^{n} \). -

- -

The PCA theorem states that minimizing the above reconstruction error -corresponds to setting \( \boldsymbol{W}=\boldsymbol{S} \), the orthogonal matrix which -diagonalizes the empirical covariance(correlation) matrix. The optimal -low-dimensional encoding of the data is then given by a set of vectors -\( \boldsymbol{z}_i \) with at most \( l \) vectors, with \( l < < p \), defined by the -orthogonal projection of the data onto the columns spanned by the -eigenvectors of the covariance(correlations matrix). -

- -









-

The PCA Theorem

- -

To show the PCA theorem let us start with the assumption that there is one vector \( \boldsymbol{s}_0 \) which corresponds to a solution which minimized the reconstruction error \( J \). This is an orthogonal vector. It means that we now approximate the reconstruction error in terms of \( \boldsymbol{w}_0 \) and \( \boldsymbol{z}_0 \) as

- -

We are almost there, we have obtained a relation between minimizing -the reconstruction error and the variance and the covariance -matrix. Minimizing the error is equivalent to maximizing the variance -of the projected data. -

- -

We could trivially maximize the variance of the projection (and -thereby minimize the error in the reconstruction function) by letting -the norm-2 of \( \boldsymbol{w}_0 \) go to infinity. However, this norm since we -want the matrix \( \boldsymbol{W} \) to be an orthogonal matrix, is constrained by -\( \vert\vert \boldsymbol{w}_0 \vert\vert_2^2=1 \). Imposing this condition via a -Lagrange multiplier we can then in turn maximize -

- -$$ -J(\boldsymbol{w}_0)= \boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0+\lambda_0(1-\boldsymbol{w}_0^T\boldsymbol{w}_0). -$$ - -

Taking the derivative with respect to \( \boldsymbol{w}_0 \) we obtain

- -$$ -\frac{\partial J(\boldsymbol{w}_0)}{\partial \boldsymbol{w}_0}= 2\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0-2\lambda_0\boldsymbol{w}_0=0, -$$ - -

meaning that

-$$ -\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0\boldsymbol{w}_0. -$$ - -

The direction that maximizes the variance (or minimizes the construction error) is an eigenvector of the covariance matrix! If we left multiply with \( \boldsymbol{w}_0^T \) we have the variance of the projected data is

-$$ -\boldsymbol{w}_0^T\boldsymbol{C}[\boldsymbol{x}]\boldsymbol{w}_0=\lambda_0. -$$ - -

If we want to maximize the variance (minimize the construction error) -we simply pick the eigenvector of the covariance matrix with the -largest eigenvalue. This establishes the link between the minimization -of the reconstruction function \( J \) in terms of an orthogonal matrix -and the maximization of the variance and thereby the covariance of our -observations encoded in the design/feature matrix \( \boldsymbol{X} \). -

- -

The proof -for the other eigenvectors \( \boldsymbol{w}_1,\boldsymbol{w}_2,\dots \) can be -established by applying the above arguments and using the fact that -our basis of eigenvectors is orthogonal, see Murphy chapter -12.2. The -discussion in chapter 12.2 of Murphy's text has also a nice link with -the Singular Value Decomposition theorem. For categorical data, see -chapter 12.4 and discussion therein. -

- -

For more details, see for example Vidal, Ma and Sastry, chapter 2.

- -









- - -

For a detailed demonstration of the geometric interpretation, see Vidal, Ma and Sastry, section 2.1.2.

- -

Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm. -First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. -

- -

The following Python code uses NumPy’s svd() function to obtain all the principal components of the -training set, then extracts the first two principal components. First we center the data using either pandas or our own code -

- - -
-
-
-
-
- 
import numpy as np
-import pandas as pd
-from IPython.display import display
-np.random.seed(100)
-# setting up a 10 x 5 vanilla matrix 
-rows = 10
-cols = 5
-X = np.random.randn(rows,cols)
-df = pd.DataFrame(X)
-# Pandas does the centering for us
-df = df -df.mean()
-display(df)
-
-# we center it ourselves
-X_centered = X - X.mean(axis=0)
-# Then check the difference between pandas and our own set up
-print(X_centered-df)
-#Now we do an SVD
-U, s, V = np.linalg.svd(X_centered)
-c1 = V.T[:, 0]
-c2 = V.T[:, 1]
-W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
-print(X2D)
-
-
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-
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-
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-
-
-
-
-
-
- -

PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering -the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t -forget to center the data first. -

- -

Once you have identified all the principal components, you can reduce the dimensionality of the dataset -down to \( d \) dimensions by projecting it onto the hyperplane defined by the first \( d \) principal components. -Selecting this hyperplane ensures that the projection will preserve as much variance as possible. -

- - -
-
-
-
-
- 
W2 = V.T[:, :2]
-X2D = X_centered.dot(W2)
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- - -









-

PCA and scikit-learn

- -

Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The -following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note -that it automatically takes care of centering the data): -

- - -
-
-
-
-
- 
#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D = pca.fit_transform(X)
-print(X2D)
-
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- -

After fitting the PCA transformer to the dataset, you can access the principal components using the -components variable (note that it contains the PCs as horizontal vectors, so, for example, the first -principal component is equal to -

- - -
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- 
pca.components_.T[:, 0]
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Another very useful piece of information is the explained variance ratio of each principal component, -available via the \( explained\_variance\_ratio \) variable. It indicates the proportion of the dataset’s -variance that lies along the axis of each principal component. -

- -









-

Back to the Cancer Data

-

We can now repeat the above but applied to real data, in this case our breast cancer data. -Here we compute performance scores on the training data using logistic regression. -

- - -
-
-
-
-
- 
import matplotlib.pyplot as plt
-import numpy as np
-from sklearn.model_selection import  train_test_split 
-from sklearn.datasets import load_breast_cancer
-from sklearn.linear_model import LogisticRegression
-cancer = load_breast_cancer()
-
-X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
-
-logreg = LogisticRegression()
-logreg.fit(X_train, y_train)
-print("Train set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_train,y_train)))
-# We scale the data
-from sklearn.preprocessing import StandardScaler
-scaler = StandardScaler()
-scaler.fit(X_train)
-X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-# Then perform again a log reg fit
-logreg.fit(X_train_scaled, y_train)
-print("Train set accuracy scaled data: {:.2f}".format(logreg.score(X_train_scaled,y_train)))
-#thereafter we do a PCA with Scikit-learn
-from sklearn.decomposition import PCA
-pca = PCA(n_components = 2)
-X2D_train = pca.fit_transform(X_train_scaled)
-# and finally compute the log reg fit and the score on the training data	
-logreg.fit(X2D_train,y_train)
-print("Train set accuracy scaled and PCA data: {:.2f}".format(logreg.score(X2D_train,y_train)))
-
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We see that our training data after the PCA decomposition has a performance similar to the non-scaled data.

- -

Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to -choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). -Unless, of course, you are reducing dimensionality for data visualization — in that case you will -generally want to reduce the dimensionality down to 2 or 3. -The following code computes PCA without reducing dimensionality, then computes the minimum number -of dimensions required to preserve 95% of the training set’s variance: -

- - -
-
-
-
-
- 
pca = PCA()
-pca.fit(X)
-cumsum = np.cumsum(pca.explained_variance_ratio_)
-d = np.argmax(cumsum >= 0.95) + 1
-
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You could then set \( n\_components=d \) and run PCA again. However, there is a much better option: instead -of specifying the number of principal components you want to preserve, you can set \( n\_components \) to be -a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: -

- - -
-
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- 
pca = PCA(n_components=0.95)
-X_reduced = pca.fit_transform(X)
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- - -









-

Incremental PCA

- -

One problem with the preceding implementation of PCA is that it requires the whole training set to fit in -memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have -been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch -at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new -instances arrive). -

-

Randomized PCA

- -

Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic -algorithm that quickly finds an approximation of the first d principal components. Its computational -complexity is \( O(m \times d^2)+O(d^3) \), instead of \( O(m \times n^2) + O(n^3) \), so it is dramatically faster than the -previous algorithms when \( d \) is much smaller than \( n \). -

-

Kernel PCA

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The kernel trick is a mathematical technique that implicitly maps instances into a -very high-dimensional space (called the feature space), enabling nonlinear classification and regression -with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature -space corresponds to a complex nonlinear decision boundary in the original space. -It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear -projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at -preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a -twisted manifold. -For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an -

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from sklearn.decomposition import KernelPCA
-rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04)
-X_reduced = rbf_pca.fit_transform(X)
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Other techniques

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There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.

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Here are some of the most popular:

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  • Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances between the instances.
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  • Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances.
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  • t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).
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  • Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures.
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© 1999-2022, Morten Hjorth-Jensen. 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zM3Wxt9^~{@rY9Q+=WI~BHn1Riuqh=k0hzOK*nDBzzv#CpA}_Jv97{SQ87`e{>NfBX grX>EKtC0V<|C;(&`B(W@`M*{E4GBCt!~li?0C}ATIRF3v literal 29 kcmb2|=3uaR8\n", - "ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis\n", + "ATITLE: Week 43: Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks\n", "\n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Oct 24, 2022**\n", + "Date: **Oct 26, 2022**\n", "\n", "Copyright 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" ] }, { "cell_type": "markdown", - "id": "456560f4", - "metadata": {}, + "id": "6221cc6d", + "metadata": { + "editable": true + }, "source": [ "## Plans for week 43\n", "\n", - "* Thursday: Convolutional Neural Networks, basic elements and \n", + "* Thursday: Convolutional Neural Networks (CNN)\n", "\n", - "* Friday: Recurrent Neural Networks and other Deep learning methods, Generalized Adversarial Neural Networ and autoencoders\n", + "* Friday: Recurrent Neural Networks (RNN)\n", "\n", "**Excellent lectures on CNNs and RNNs.**\n", "\n", @@ -44,20 +48,254 @@ }, { "cell_type": "markdown", - "id": "8fac133d", - "metadata": {}, + "id": "bad85017", + "metadata": { + "editable": true + }, "source": [ "## Reading Recommendations\n", "\n", - "* Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.\n", + "**CNN readings.**\n", "\n", - "* Aurelien Geron, chapter 14 on RNNs." + "1. [Goodfellow, Bengio, Courville, chapter 9](https://www.deeplearningbook.org/contents/convnets.html)\n", + "\n", + "2. [Lectures from CS231 at Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture5.pdf)\n", + "\n", + "3. [Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs](http://neuralnetworksanddeeplearning.com/chap6.html).\n", + "\n", + "**RNN readings.**\n", + "\n", + "1. [Goodfellow et al](https://www.deeplearningbook.org/contents/rnn.html), chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.\n", + "\n", + "2. [Lectures from CS231 at Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf)\n", + "\n", + "3. Aurelien Geron, chapter 14 on RNNs." ] }, { "cell_type": "markdown", - "id": "c7d1fdab", - "metadata": {}, + "id": "60686f8b", + "metadata": { + "editable": true + }, + "source": [ + "## Convolutional Neural Networks (recognizing images)\n", + "\n", + "Convolutional neural networks (CNNs) were developed during the last\n", + "decade of the previous century, with a focus on character recognition\n", + "tasks. Nowadays, CNNs are a central element in the spectacular success\n", + "of deep learning methods. The success in for example image\n", + "classifications have made them a central tool for most machine\n", + "learning practitioners.\n", + "\n", + "CNNs are very similar to ordinary Neural Networks.\n", + "They are made up of neurons that have learnable weights and\n", + "biases. Each neuron receives some inputs, performs a dot product and\n", + "optionally follows it with a non-linearity. The whole network still\n", + "expresses a single differentiable score function: from the raw image\n", + "pixels on one end to class scores at the other. And they still have a\n", + "loss function (for example Softmax) on the last (fully-connected) layer\n", + "and all the tips/tricks we developed for learning regular Neural\n", + "Networks still apply (back propagation, gradient descent etc etc)." + ] + }, + { + "cell_type": "markdown", + "id": "70b40ee2", + "metadata": { + "editable": true + }, + "source": [ + "## What is the Difference\n", + "\n", + "**CNN architectures make the explicit assumption that\n", + "the inputs are images, which allows us to encode certain properties\n", + "into the architecture. These then make the forward function more\n", + "efficient to implement and vastly reduce the amount of parameters in\n", + "the network.**\n", + "\n", + "Here we provide only a superficial overview, for the more interested, we recommend highly the course\n", + "[IN5400 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html)\n", + "and the slides of [CS231](http://cs231n.github.io/convolutional-networks/).\n", + "\n", + "Another good read is the article here ." + ] + }, + { + "cell_type": "markdown", + "id": "caad6f84", + "metadata": { + "editable": true + }, + "source": [ + "## Neural Networks vs CNNs\n", + "\n", + "Neural networks are defined as **affine transformations**, that is \n", + "a vector is received as input and is multiplied with a matrix of so-called weights (our unknown paramters) to produce an\n", + "output (to which a bias vector is usually added before passing the result\n", + "through a nonlinear activation function). This is applicable to any type of input, be it an\n", + "image, a sound clip or an unordered collection of features: whatever their\n", + "dimensionality, their representation can always be flattened into a vector\n", + "before the transformation." + ] + }, + { + "cell_type": "markdown", + "id": "c3603e8a", + "metadata": { + "editable": true + }, + "source": [ + "## Why CNNS for images, sound files, medical images from CT scans etc?\n", + "\n", + "However, when we consider images, sound clips and many other similar kinds of data, these data have an intrinsic\n", + "structure. More formally, they share these important properties:\n", + "* They are stored as multi-dimensional arrays (think of the pixels of a figure) .\n", + "\n", + "* They feature one or more axes for which ordering matters (e.g., width and height axes for an image, time axis for a sound clip).\n", + "\n", + "* One axis, called the channel axis, is used to access different views of the data (e.g., the red, green and blue channels of a color image, or the left and right channels of a stereo audio track).\n", + "\n", + "These properties are not exploited when an affine transformation is applied; in\n", + "fact, all the axes are treated in the same way and the topological information\n", + "is not taken into account. Still, taking advantage of the implicit structure of\n", + "the data may prove very handy in solving some tasks, like computer vision and\n", + "speech recognition, and in these cases it would be best to preserve it. This is\n", + "where discrete convolutions come into play.\n", + "\n", + "A discrete convolution is a linear transformation that preserves this notion of\n", + "ordering. It is sparse (only a few input units contribute to a given output\n", + "unit) and reuses parameters (the same weights are applied to multiple locations\n", + "in the input)." + ] + }, + { + "cell_type": "markdown", + "id": "fbdebd40", + "metadata": { + "editable": true + }, + "source": [ + "## Regular NNs don’t scale well to full images\n", + "\n", + "As an example, consider\n", + "an image of size $32\\times 32\\times 3$ (32 wide, 32 high, 3 color channels), so a\n", + "single fully-connected neuron in a first hidden layer of a regular\n", + "Neural Network would have $32\\times 32\\times 3 = 3072$ weights. This amount still\n", + "seems manageable, but clearly this fully-connected structure does not\n", + "scale to larger images. For example, an image of more respectable\n", + "size, say $200\\times 200\\times 3$, would lead to neurons that have \n", + "$200\\times 200\\times 3 = 120,000$ weights. \n", + "\n", + "We could have\n", + "several such neurons, and the parameters would add up quickly! Clearly,\n", + "this full connectivity is wasteful and the huge number of parameters\n", + "would quickly lead to possible overfitting.\n", + "\n", + "\n", + "\n", + "\n", + "

Figure 1: A regular 3-layer Neural Network.

\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "afadbdff", + "metadata": { + "editable": true + }, + "source": [ + "## 3D volumes of neurons\n", + "\n", + "Convolutional Neural Networks take advantage of the fact that the\n", + "input consists of images and they constrain the architecture in a more\n", + "sensible way. \n", + "\n", + "In particular, unlike a regular Neural Network, the\n", + "layers of a CNN have neurons arranged in 3 dimensions: width,\n", + "height, depth. (Note that the word depth here refers to the third\n", + "dimension of an activation volume, not to the depth of a full Neural\n", + "Network, which can refer to the total number of layers in a network.)\n", + "\n", + "To understand it better, the above example of an image \n", + "with an input volume of\n", + "activations has dimensions $32\\times 32\\times 3$ (width, height,\n", + "depth respectively). \n", + "\n", + "The neurons in a layer will\n", + "only be connected to a small region of the layer before it, instead of\n", + "all of the neurons in a fully-connected manner. Moreover, the final\n", + "output layer could for this specific image have dimensions $1\\times 1 \\times 10$, \n", + "because by the\n", + "end of the CNN architecture we will reduce the full image into a\n", + "single vector of class scores, arranged along the depth\n", + "dimension. \n", + "\n", + "\n", + "\n", + "\n", + "

Figure 1: A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).

\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "1ff26c82", + "metadata": { + "editable": true + }, + "source": [ + "## Layers used to build CNNs\n", + "\n", + "A simple CNN is a sequence of layers, and every layer of a CNN\n", + "transforms one volume of activations to another through a\n", + "differentiable function. We use three main types of layers to build\n", + "CNN architectures: Convolutional Layer, Pooling Layer, and\n", + "Fully-Connected Layer (exactly as seen in regular Neural Networks). We\n", + "will stack these layers to form a full CNN architecture.\n", + "\n", + "A simple CNN for image classification could have the architecture:\n", + "\n", + "* **INPUT** ($32\\times 32 \\times 3$) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.\n", + "\n", + "* **CONV** (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as $[32\\times 32\\times 12]$ if we decided to use 12 filters.\n", + "\n", + "* **RELU** layer will apply an elementwise activation function, such as the $max(0,x)$ thresholding at zero. This leaves the size of the volume unchanged ($[32\\times 32\\times 12]$).\n", + "\n", + "* **POOL** (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as $[16\\times 16\\times 12]$.\n", + "\n", + "* **FC** (i.e. fully-connected) layer will compute the class scores, resulting in volume of size $[1\\times 1\\times 10]$, where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume." + ] + }, + { + "cell_type": "markdown", + "id": "5093832a", + "metadata": { + "editable": true + }, + "source": [ + "## Transforming images\n", + "\n", + "CNNs transform the original image layer by layer from the original\n", + "pixel values to the final class scores. \n", + "\n", + "Observe that some layers contain\n", + "parameters and other don’t. In particular, the CNN layers perform\n", + "transformations that are a function of not only the activations in the\n", + "input volume, but also of the parameters (the weights and biases of\n", + "the neurons). On the other hand, the RELU/POOL layers will implement a\n", + "fixed function. The parameters in the CONV/FC layers will be trained\n", + "with gradient descent so that the class scores that the CNN computes\n", + "are consistent with the labels in the training set for each image." + ] + }, + { + "cell_type": "markdown", + "id": "7e0783d4", + "metadata": { + "editable": true + }, "source": [ "## CNNs in brief\n", "\n", @@ -78,15 +316,1567 @@ "[IN5400 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html)\n", "and the slides of [CS231](http://cs231n.github.io/convolutional-networks/) which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). [Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs](http://neuralnetworksanddeeplearning.com/chap6.html).\n", "\n", - "However, both standard feed forwards networks and CNNs perform well on data with unknown length.\n", - "\n", - "This is where recurrent nueral networks (RNNs) come to our rescue." + "The textbook by Goodfellow et al, see chapter 9 contains an in depth discussion as well." ] }, { "cell_type": "markdown", - "id": "abd232ca", - "metadata": {}, + "id": "3d5226b9", + "metadata": { + "editable": true + }, + "source": [ + "## Key Idea\n", + "\n", + "A dense neural network is representd by an affine operation (like matrix-matrix multiplication) where all parameters are included.\n", + "\n", + "The key idea in CNNs for say imaging is that in images neighbor pixels tend to be related! So we connect\n", + "only neighboring neurons in the input instead of connecting all with the first hidden layer.\n", + "\n", + "We say we perform a filtering (convolution is the mathematical operation)." + ] + }, + { + "cell_type": "markdown", + "id": "24a03a3e", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematics of CNNs\n", + "\n", + "The mathematics of CNNs is based on the mathematical operation of\n", + "**convolution**. In mathematics (in particular in functional analysis),\n", + "convolution is represented by mathematical operation (integration,\n", + "summation etc) on two function in order to produce a third function\n", + "that expresses how the shape of one gets modified by the other.\n", + "Convolution has a plethora of applications in a variety of disciplines, spanning from statistics to signal processing, computer vision, solutions of differential equations,linear algebra, engineering, and yes, machine learning.\n", + "\n", + "Mathematically, convolution is defined as follows (one-dimensional example):\n", + "Let us define a continuous function $y(t)$ given by" + ] + }, + { + "cell_type": "markdown", + "id": "9592a90c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "y(t) = \\int x(a) w(t-a) da,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9b85786d", + "metadata": { + "editable": true + }, + "source": [ + "where $x(a)$ represents a so-called input and $w(t-a)$ is normally called the weight function or kernel.\n", + "\n", + "The above integral is written in a more compact form as" + ] + }, + { + "cell_type": "markdown", + "id": "56b12833", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "y(t) = \\left(x * w\\right)(t).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c9163b65", + "metadata": { + "editable": true + }, + "source": [ + "The discretized version reads" + ] + }, + { + "cell_type": "markdown", + "id": "c6e217d8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "y(t) = \\sum_{a=-\\infty}^{a=\\infty}x(a)w(t-a).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0800a753", + "metadata": { + "editable": true + }, + "source": [ + "Computing the inverse of the above convolution operations is known as deconvolution.\n", + "\n", + "How can we use this? And what does it mean? Let us study some familiar examples first." + ] + }, + { + "cell_type": "markdown", + "id": "0862d627", + "metadata": { + "editable": true + }, + "source": [ + "## Convolution Examples: Polynomial multiplication\n", + "\n", + "We have already met such an example in project 1 when we tried to set\n", + "up the design matrix for a two-dimensional function. This was an\n", + "example of polynomial multiplication. Let us recast such a problem in terms of the convolution operation.\n", + "Let us look a the following polynomials to second and third order, respectively:" + ] + }, + { + "cell_type": "markdown", + "id": "53d653f1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "p(t) = \\alpha_0+\\alpha_1 t+\\alpha_2 t^2,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d03e5555", + "metadata": { + "editable": true + }, + "source": [ + "and" + ] + }, + { + "cell_type": "markdown", + "id": "c195a7b8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "s(t) = \\beta_0+\\beta_1 t+\\beta_2 t^2+\\beta_3 t^3.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "18f5c59a", + "metadata": { + "editable": true + }, + "source": [ + "The polynomial multiplication gives us a new polynomial of degree $5$" + ] + }, + { + "cell_type": "markdown", + "id": "975ab8d3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "z(t) = \\delta_0+\\delta_1 t+\\delta_2 t^2+\\delta_3 t^3+\\delta_4 t^4+\\delta_5 t^5.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "bd1518b5", + "metadata": { + "editable": true + }, + "source": [ + "## Efficient Polynomial Multiplication\n", + "\n", + "Computing polynomial products can be implemented efficiently if we rewrite the more brute force multiplications using convolution.\n", + "We note first that the new coefficients are given as" + ] + }, + { + "cell_type": "markdown", + "id": "f8860319", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{split}\n", + "\\delta_0=&\\alpha_0\\beta_0\\\\\n", + "\\delta_1=&\\alpha_1\\beta_0+\\alpha_1\\beta_0\\\\\n", + "\\delta_2=&\\alpha_0\\beta_2+\\alpha_1\\beta_1+\\alpha_2\\beta_0\\\\\n", + "\\delta_3=&\\alpha_1\\beta_2+\\alpha_2\\beta_1+\\alpha_0\\beta_3\\\\\n", + "\\delta_4=&\\alpha_2\\beta_2+\\alpha_1\\beta_3\\\\\n", + "\\delta_5=&\\alpha_2\\beta_3.\\\\\n", + "\\end{split}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7c089030", + "metadata": { + "editable": true + }, + "source": [ + "We note that $\\alpha_i=0$ except for $i\\in \\left\\{0,1,2\\right\\}$ and $\\beta_i=0$ except for $i\\in\\left\\{0,1,2,3\\right\\}$.\n", + "\n", + "We can then rewrite the coefficients $\\delta_j$ using a discrete convolution as" + ] + }, + { + "cell_type": "markdown", + "id": "c2ca08e7", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\delta_j = \\sum_{i=-\\infty}^{i=\\infty}\\alpha_i\\beta_{j-i}=(\\alpha * \\beta)_j,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8a2a68b2", + "metadata": { + "editable": true + }, + "source": [ + "or as a double sum with restriction $l=i+j$" + ] + }, + { + "cell_type": "markdown", + "id": "00e98eb3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\delta_l = \\sum_{ij}\\alpha_i\\beta_{j}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b7f39916", + "metadata": { + "editable": true + }, + "source": [ + "Do you see a potential drawback with these equations?" + ] + }, + { + "cell_type": "markdown", + "id": "5c38d50d", + "metadata": { + "editable": true + }, + "source": [ + "## A more efficient way of coding the above Convolution\n", + "\n", + "Since we only have a finite number of $\\alpha$ and $\\beta$ values\n", + "which are non-zero, we can rewrite the above convolution expressions\n", + "as a matrix-vector multiplication" + ] + }, + { + "cell_type": "markdown", + "id": "abcd5c2c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\delta}=\\begin{bmatrix}\\alpha_0 & 0 & 0 & 0 \\\\\n", + " \\alpha_1 & \\alpha_0 & 0 & 0 \\\\\n", + "\t\t\t \\alpha_2 & \\alpha_1 & \\alpha_0 & 0 \\\\\n", + "\t\t\t 0 & \\alpha_2 & \\alpha_1 & \\alpha_0 \\\\\n", + "\t\t\t 0 & 0 & \\alpha_2 & \\alpha_1 \\\\\n", + "\t\t\t 0 & 0 & 0 & \\alpha_2\n", + "\t\t\t \\end{bmatrix}\\begin{bmatrix} \\beta_0 \\\\ \\beta_1 \\\\ \\beta_2 \\\\ \\beta_3\\end{bmatrix}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a790d9b7", + "metadata": { + "editable": true + }, + "source": [ + "The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding $\\beta$ and a vector holding $\\alpha$.\n", + "In this case we have" + ] + }, + { + "cell_type": "markdown", + "id": "bf0b60a4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\delta}=\\begin{bmatrix}\\beta_0 & 0 & 0 \\\\\n", + " \\beta_1 & \\beta_0 & 0 \\\\\n", + "\t\t\t \\beta_2 & \\beta_1 & \\beta_0 \\\\\n", + "\t\t\t \\beta_3 & \\beta_2 & \\beta_1 \\\\\n", + "\t\t\t 0 & \\beta_3 & \\beta_2 \\\\\n", + "\t\t\t 0 & 0 & \\beta_3\n", + "\t\t\t \\end{bmatrix}\\begin{bmatrix} \\alpha_0 \\\\ \\alpha_1 \\\\ \\alpha_2\\end{bmatrix}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "046c406b", + "metadata": { + "editable": true + }, + "source": [ + "Note that the use of these matrices is for mathematical purposes only and not implementation purposes.\n", + "When implementing the above equation we do not encode (and allocate memory) the matrices explicitely.\n", + "We rather code the convolutions in the minimal memory footprint that they require.\n", + "\n", + "Does the number of floating point operations change here when we use the commutative property?" + ] + }, + { + "cell_type": "markdown", + "id": "e31303a5", + "metadata": { + "editable": true + }, + "source": [ + "## Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)\n", + "\n", + "For problems with so-called harmonic oscillations, given by for example the following differential equation" + ] + }, + { + "cell_type": "markdown", + "id": "e9b72ded", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m\\frac{d^2x}{dt^2}+\\eta\\frac{dx}{dt}+x(t)=F(t),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "06aec3b3", + "metadata": { + "editable": true + }, + "source": [ + "where $F(t)$ is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations.\n", + "\n", + "If one has several driving forces, $F(t)=\\sum_n F_n(t)$, one can find\n", + "the particular solution to each $F_n$, $x_{pn}(t)$, and the particular\n", + "solution for the entire driving force is then given by a series like" + ] + }, + { + "cell_type": "markdown", + "id": "f13c2179", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "x_p(t)=\\sum_nx_{pn}(t).\n", + "\\label{_auto1} \\tag{1}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "734e72d8", + "metadata": { + "editable": true + }, + "source": [ + "## Principle of Superposition\n", + "\n", + "This is known as the principle of superposition. It only applies when\n", + "the homogenous equation is linear. If there were an anharmonic term\n", + "such as $x^3$ in the homogenous equation, then when one summed various\n", + "solutions, $x=(\\sum_n x_n)^2$, one would get cross\n", + "terms. Superposition is especially useful when $F(t)$ can be written\n", + "as a sum of sinusoidal terms, because the solutions for each\n", + "sinusoidal (sine or cosine) term is analytic. \n", + "\n", + "Driving forces are often periodic, even when they are not\n", + "sinusoidal. Periodicity implies that for some time $\\tau$" + ] + }, + { + "cell_type": "markdown", + "id": "bd89d975", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{eqnarray}\n", + "F(t+\\tau)=F(t). \n", + "\\end{eqnarray}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "084204dc", + "metadata": { + "editable": true + }, + "source": [ + "One example of a non-sinusoidal periodic force is a square wave. Many\n", + "components in electric circuits are non-linear, e.g. diodes, which\n", + "makes many wave forms non-sinusoidal even when the circuits are being\n", + "driven by purely sinusoidal sources." + ] + }, + { + "cell_type": "markdown", + "id": "bb16be3e", + "metadata": { + "editable": true + }, + "source": [ + "## Simple Code Example\n", + "\n", + "The code here shows a typical example of such a square wave generated using the functionality included in the **scipy** Python package. We have used a period of $\\tau=0.2$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ca53811d", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import math\n", + "from scipy import signal\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# number of points \n", + "n = 500\n", + "# start and final times \n", + "t0 = 0.0\n", + "tn = 1.0\n", + "# Period \n", + "t = np.linspace(t0, tn, n, endpoint=False)\n", + "SqrSignal = np.zeros(n)\n", + "SqrSignal = 1.0+signal.square(2*np.pi*5*t)\n", + "plt.plot(t, SqrSignal)\n", + "plt.ylim(-0.5, 2.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "05d57e62", + "metadata": { + "editable": true + }, + "source": [ + "For the sinusoidal example the\n", + "period is $\\tau=2\\pi/\\omega$. However, higher harmonics can also\n", + "satisfy the periodicity requirement. In general, any force that\n", + "satisfies the periodicity requirement can be expressed as a sum over\n", + "harmonics," + ] + }, + { + "cell_type": "markdown", + "id": "7bf3ac2d", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "F(t)=\\frac{f_0}{2}+\\sum_{n>0} f_n\\cos(2n\\pi t/\\tau)+g_n\\sin(2n\\pi t/\\tau).\n", + "\\label{_auto2} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "34b77a31", + "metadata": { + "editable": true + }, + "source": [ + "## Wrapping up Fourier transforms\n", + "\n", + "We can write down the answer for\n", + "$x_{pn}(t)$, by substituting $f_n/m$ or $g_n/m$ for $F_0/m$. By\n", + "writing each factor $2n\\pi t/\\tau$ as $n\\omega t$, with $\\omega\\equiv\n", + "2\\pi/\\tau$," + ] + }, + { + "cell_type": "markdown", + "id": "e02fe013", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\label{eq:fourierdef1} \\tag{3}\n", + "F(t)=\\frac{f_0}{2}+\\sum_{n>0}f_n\\cos(n\\omega t)+g_n\\sin(n\\omega t).\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8cbd02ba", + "metadata": { + "editable": true + }, + "source": [ + "The solutions for $x(t)$ then come from replacing $\\omega$ with\n", + "$n\\omega$ for each term in the particular solution," + ] + }, + { + "cell_type": "markdown", + "id": "244737da", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{eqnarray}\n", + "x_p(t)&=&\\frac{f_0}{2k}+\\sum_{n>0} \\alpha_n\\cos(n\\omega t-\\delta_n)+\\beta_n\\sin(n\\omega t-\\delta_n),\\\\\n", + "\\nonumber\n", + "\\alpha_n&=&\\frac{f_n/m}{\\sqrt{((n\\omega)^2-\\omega_0^2)+4\\beta^2n^2\\omega^2}},\\\\\n", + "\\nonumber\n", + "\\beta_n&=&\\frac{g_n/m}{\\sqrt{((n\\omega)^2-\\omega_0^2)+4\\beta^2n^2\\omega^2}},\\\\\n", + "\\nonumber\n", + "\\delta_n&=&\\tan^{-1}\\left(\\frac{2\\beta n\\omega}{\\omega_0^2-n^2\\omega^2}\\right).\n", + "\\end{eqnarray}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "19a53f2f", + "metadata": { + "editable": true + }, + "source": [ + "## Finding the Coefficients\n", + "\n", + "Because the forces have been applied for a long time, any non-zero\n", + "damping eliminates the homogenous parts of the solution, so one need\n", + "only consider the particular solution for each $n$.\n", + "\n", + "The problem is considered solved if one can find expressions for the\n", + "coefficients $f_n$ and $g_n$, even though the solutions are expressed\n", + "as an infinite sum. The coefficients can be extracted from the\n", + "function $F(t)$ by" + ] + }, + { + "cell_type": "markdown", + "id": "a344869c", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{eqnarray}\n", + "\\label{eq:fourierdef2} \\tag{4}\n", + "f_n&=&\\frac{2}{\\tau}\\int_{-\\tau/2}^{\\tau/2} dt~F(t)\\cos(2n\\pi t/\\tau),\\\\\n", + "\\nonumber\n", + "g_n&=&\\frac{2}{\\tau}\\int_{-\\tau/2}^{\\tau/2} dt~F(t)\\sin(2n\\pi t/\\tau).\n", + "\\end{eqnarray}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b90251a7", + "metadata": { + "editable": true + }, + "source": [ + "To check the consistency of these expressions and to verify\n", + "Eq. ([4](#eq:fourierdef2)), one can insert the expansion of $F(t)$ in\n", + "Eq. ([3](#eq:fourierdef1)) into the expression for the coefficients in\n", + "Eq. ([4](#eq:fourierdef2)) and see whether" + ] + }, + { + "cell_type": "markdown", + "id": "77fcc155", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{eqnarray}\n", + "f_n&=?&\\frac{2}{\\tau}\\int_{-\\tau/2}^{\\tau/2} dt~\\left\\{\n", + "\\frac{f_0}{2}+\\sum_{m>0}f_m\\cos(m\\omega t)+g_m\\sin(m\\omega t)\n", + "\\right\\}\\cos(n\\omega t).\n", + "\\end{eqnarray}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6ed2ed49", + "metadata": { + "editable": true + }, + "source": [ + "Immediately, one can throw away all the terms with $g_m$ because they\n", + "convolute an even and an odd function. The term with $f_0/2$\n", + "disappears because $\\cos(n\\omega t)$ is equally positive and negative\n", + "over the interval and will integrate to zero. For all the terms\n", + "$f_m\\cos(m\\omega t)$ appearing in the sum, one can use angle addition\n", + "formulas to see that $\\cos(m\\omega t)\\cos(n\\omega\n", + "t)=(1/2)(\\cos[(m+n)\\omega t]+\\cos[(m-n)\\omega t]$. This will integrate\n", + "to zero unless $m=n$. In that case the $m=n$ term gives" + ] + }, + { + "cell_type": "markdown", + "id": "b2a0d390", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\int_{-\\tau/2}^{\\tau/2}dt~\\cos^2(m\\omega t)=\\frac{\\tau}{2},\n", + "\\label{_auto3} \\tag{5}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "62da6bad", + "metadata": { + "editable": true + }, + "source": [ + "and" + ] + }, + { + "cell_type": "markdown", + "id": "0fe4ccdb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{eqnarray}\n", + "f_n&=?&\\frac{2}{\\tau}\\int_{-\\tau/2}^{\\tau/2} dt~f_n/2\\\\\n", + "\\nonumber\n", + "&=&f_n~\\checkmark.\n", + "\\end{eqnarray}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9d8cf90b", + "metadata": { + "editable": true + }, + "source": [ + "The same method can be used to check for the consistency of $g_n$." + ] + }, + { + "cell_type": "markdown", + "id": "ea5a1ec9", + "metadata": { + "editable": true + }, + "source": [ + "## Final words on Fourier Transforms\n", + "\n", + "The code here uses the Fourier series applied to a \n", + "square wave signal. The code here\n", + "visualizes the various approximations given by Fourier series compared\n", + "with a square wave with period $T=0.2$ (dimensionless time), width $0.1$ and max value of the force $F=2$. We\n", + "see that when we increase the number of components in the Fourier\n", + "series, the Fourier series approximation gets closer and closer to the\n", + "square wave signal." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eb87a179", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "from scipy import signal\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# number of points \n", + "n = 500\n", + "# start and final times \n", + "t0 = 0.0\n", + "tn = 1.0\n", + "# Period \n", + "T =0.2\n", + "# Max value of square signal \n", + "Fmax= 2.0\n", + "# Width of signal \n", + "Width = 0.1\n", + "t = np.linspace(t0, tn, n, endpoint=False)\n", + "SqrSignal = np.zeros(n)\n", + "FourierSeriesSignal = np.zeros(n)\n", + "SqrSignal = 1.0+signal.square(2*np.pi*5*t+np.pi*Width/T)\n", + "a0 = Fmax*Width/T\n", + "FourierSeriesSignal = a0\n", + "Factor = 2.0*Fmax/np.pi\n", + "for i in range(1,500):\n", + " FourierSeriesSignal += Factor/(i)*np.sin(np.pi*i*Width/T)*np.cos(i*t*2*np.pi/T)\n", + "plt.plot(t, SqrSignal)\n", + "plt.plot(t, FourierSeriesSignal)\n", + "plt.ylim(-0.5, 2.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "51f36878", + "metadata": { + "editable": true + }, + "source": [ + "## Two-dimensional Objects\n", + "\n", + "We often use convolutions over more than one dimension at a time. If\n", + "we have a two-dimensional image $I$ as input, we can have a **filter**\n", + "defined by a two-dimensional **kernel** $K$. This leads to an output $S$" + ] + }, + { + "cell_type": "markdown", + "id": "f4a3a419", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(m,n)K(i-m,j-n).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5d7b79a7", + "metadata": { + "editable": true + }, + "source": [ + "Convolution is a commutatitave process, which means we can rewrite this equation as" + ] + }, + { + "cell_type": "markdown", + "id": "8da8d193", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(i-m,j-n)K(m,n).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a9bab996", + "metadata": { + "editable": true + }, + "source": [ + "Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of $m$ and $n$." + ] + }, + { + "cell_type": "markdown", + "id": "605828b5", + "metadata": { + "editable": true + }, + "source": [ + "## Cross-Correlation\n", + "\n", + "Many deep learning libraries implement cross-correlation instead of convolution" + ] + }, + { + "cell_type": "markdown", + "id": "0fbe6086", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "S_(i,j)=(I * K)(i,j) = \\sum_m\\sum_n I(i+m,j-+)K(m,n).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0617cc46", + "metadata": { + "editable": true + }, + "source": [ + "## More on Dimensionalities\n", + "\n", + "In feilds like signal processing (and imaging as well), one designs\n", + "so-called filters. These filters are defined by the convolutions and\n", + "are often hand-crafted. One may specify filters for smoothing, edge\n", + "detection, frequency reshaping, and similar operations. However with\n", + "neural networks the idea is to automatically learn the filters and use\n", + "many of them in conjunction with non-linear operations (activation\n", + "functions).\n", + "\n", + "As an example consider a neural network operating on sound sequence\n", + "data. Assume that we an input vector $\\boldsymbol{x}$ of length $d=10^6$. We\n", + "construct then a neural network with onle hidden layer only with\n", + "$10^4$ nodes. This means that we will have a weight matrix with\n", + "$10^4\\times 10^6=10^{10}$ weights to be determined, together with $10^4$ biases.\n", + "\n", + "Assume furthermore that we have an output layer which is meant to train whether the sound sequence represents a human voice (true) or something else (false).\n", + "It means that we have only one output node. But since this output node connects to $10^4$ nodes in the hidden layer, there are in total $10^4$ weights to be determined for the output layer, plus one bias. In total we have" + ] + }, + { + "cell_type": "markdown", + "id": "088acb24", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \\approx 10^{10},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f7112d13", + "metadata": { + "editable": true + }, + "source": [ + "that is ten billion parameters to determine." + ] + }, + { + "cell_type": "markdown", + "id": "cf524df1", + "metadata": { + "editable": true + }, + "source": [ + "## Further Dimensionality Remarks\n", + "\n", + "In today’s architecture one can train such neural networks, however\n", + "this is a huge number of parameters for the task at hand. In general,\n", + "it is a very wasteful and inefficient use of dense matrices as\n", + "parameters. Just as importantly, such trained network parameters are\n", + "very specific for the type of input data on which they were trained\n", + "and the network is not likely to generalize easily to variations in\n", + "the input.\n", + "\n", + "The main principles that justify convolutions is locality of\n", + "information and repetion of patterns within the signal. Sound samples\n", + "of the input in adjacent spots are much more likely to affect each\n", + "other than those that are very far away. Similarly, sounds are\n", + "repeated in multiple times in the signal. While slightly simplistic,\n", + "reasoning about such a sound example demonstrates this. The same\n", + "principles then apply to images and other similar data." + ] + }, + { + "cell_type": "markdown", + "id": "086cf947", + "metadata": { + "editable": true + }, + "source": [ + "## CNNs in more detail, Lecture from IN5400\n", + "\n", + "* [Lectures from IN5400 spring 2019](https://www.uio.no/studier/emner/matnat/ifi/IN5400/v19/material/week5/in5400_2019_week5_convolutional_nerual_networks.pdf)" + ] + }, + { + "cell_type": "markdown", + "id": "2a7751ec", + "metadata": { + "editable": true + }, + "source": [ + "## CNNs in more detail, building convolutional neural networks in Tensorflow and Keras\n", + "\n", + "As discussed above, CNNs are neural networks built from the assumption that the inputs\n", + "to the network are 2D images. This is important because the number of features or pixels in images\n", + "grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network. \n", + "\n", + "As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks\n", + "are the **convolutional** and **pooling** layers stacked in pairs between the input and the hidden layer.\n", + "In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D\n", + "matrices, typically 1 for each color dimension (Red, Green, Blue)." + ] + }, + { + "cell_type": "markdown", + "id": "a8c1c5a3", + "metadata": { + "editable": true + }, + "source": [ + "## Setting it up\n", + "\n", + "It means that to represent the entire\n", + "dataset of images, we require a 4D matrix or **tensor**. This tensor has the dimensions:" + ] + }, + { + "cell_type": "markdown", + "id": "e7214f44", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(n_{inputs},\\, n_{pixels, width},\\, n_{pixels, height},\\, depth) .\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8e3bd874", + "metadata": { + "editable": true + }, + "source": [ + "## The MNIST dataset again\n", + "\n", + "The MNIST dataset consists of grayscale images with a pixel size of\n", + "$28\\times 28$, meaning we require $28 \\times 28 = 724$ weights to each\n", + "neuron in the first hidden layer.\n", + "\n", + "If we were to analyze images of size $128\\times 128$ we would require\n", + "$128 \\times 128 = 16384$ weights to each neuron. Even worse if we were\n", + "dealing with color images, as most images are, we have an image matrix\n", + "of size $128\\times 128$ for each color dimension (Red, Green, Blue),\n", + "meaning 3 times the number of weights $= 49152$ are required for every\n", + "single neuron in the first hidden layer." + ] + }, + { + "cell_type": "markdown", + "id": "21175f79", + "metadata": { + "editable": true + }, + "source": [ + "## Strong correlations\n", + "\n", + "Images typically have strong local correlations, meaning that a small\n", + "part of the image varies little from its neighboring regions. If for\n", + "example we have an image of a blue car, we can roughly assume that a\n", + "small blue part of the image is surrounded by other blue regions.\n", + "\n", + "Therefore, instead of connecting every single pixel to a neuron in the\n", + "first hidden layer, as we have previously done with deep neural\n", + "networks, we can instead connect each neuron to a small part of the\n", + "image (in all 3 RGB depth dimensions). The size of each small area is\n", + "fixed, and known as a [receptive](https://en.wikipedia.org/wiki/Receptive_field)." + ] + }, + { + "cell_type": "markdown", + "id": "536b5f5b", + "metadata": { + "editable": true + }, + "source": [ + "## Layers of a CNN\n", + "The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. \n", + "The input image is typically a square matrix of depth 3. \n", + "\n", + "A **convolution** is performed on the image which outputs\n", + "a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as **filters**.\n", + "\n", + "Each filter slides along the input image, taking the dot product\n", + "between each small part of the image and the filter, in all depth\n", + "dimensions. This is then passed through a non-linear function,\n", + "typically the **Rectified Linear (ReLu)** function, which serves as the\n", + "activation of the neurons in the first convolutional layer. This is\n", + "further passed through a **pooling layer**, which reduces the size of the\n", + "convolutional layer, e.g. by taking the maximum or average across some\n", + "small regions, and this serves as input to the next convolutional\n", + "layer." + ] + }, + { + "cell_type": "markdown", + "id": "86377de7", + "metadata": { + "editable": true + }, + "source": [ + "## Systematic reduction\n", + "\n", + "By systematically reducing the size of the input volume, through\n", + "convolution and pooling, the network should create representations of\n", + "small parts of the input, and then from them assemble representations\n", + "of larger areas. The final pooling layer is flattened to serve as\n", + "input to a hidden layer, such that each neuron in the final pooling\n", + "layer is connected to every single neuron in the hidden layer. This\n", + "then serves as input to the output layer, e.g. a softmax output for\n", + "classification." + ] + }, + { + "cell_type": "markdown", + "id": "0916b1e0", + "metadata": { + "editable": true + }, + "source": [ + "## Prerequisites: Collect and pre-process data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3e8a212a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# import necessary packages\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import datasets\n", + "\n", + "\n", + "# ensure the same random numbers appear every time\n", + "np.random.seed(0)\n", + "\n", + "# display images in notebook\n", + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (12,12)\n", + "\n", + "\n", + "# download MNIST dataset\n", + "digits = datasets.load_digits()\n", + "\n", + "# define inputs and labels\n", + "inputs = digits.images\n", + "labels = digits.target\n", + "\n", + "# RGB images have a depth of 3\n", + "# our images are grayscale so they should have a depth of 1\n", + "inputs = inputs[:,:,:,np.newaxis]\n", + "\n", + "print(\"inputs = (n_inputs, pixel_width, pixel_height, depth) = \" + str(inputs.shape))\n", + "print(\"labels = (n_inputs) = \" + str(labels.shape))\n", + "\n", + "\n", + "# choose some random images to display\n", + "n_inputs = len(inputs)\n", + "indices = np.arange(n_inputs)\n", + "random_indices = np.random.choice(indices, size=5)\n", + "\n", + "for i, image in enumerate(digits.images[random_indices]):\n", + " plt.subplot(1, 5, i+1)\n", + " plt.axis('off')\n", + " plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n", + " plt.title(\"Label: %d\" % digits.target[random_indices[i]])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fa3dc89d", + "metadata": { + "editable": true + }, + "source": [ + "## Importing Keras and Tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "701912b2", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from tensorflow.keras import datasets, layers, models\n", + "from tensorflow.keras.layers import Input\n", + "from tensorflow.keras.models import Sequential #This allows appending layers to existing models\n", + "from tensorflow.keras.layers import Dense #This allows defining the characteristics of a particular layer\n", + "from tensorflow.keras import optimizers #This allows using whichever optimiser we want (sgd,adam,RMSprop)\n", + "from tensorflow.keras import regularizers #This allows using whichever regularizer we want (l1,l2,l1_l2)\n", + "from tensorflow.keras.utils import to_categorical #This allows using categorical cross entropy as the cost function\n", + "#from tensorflow.keras import Conv2D\n", + "#from tensorflow.keras import MaxPooling2D\n", + "#from tensorflow.keras import Flatten\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# representation of labels\n", + "labels = to_categorical(labels)\n", + "\n", + "# split into train and test data\n", + "# one-liner from scikit-learn library\n", + "train_size = 0.8\n", + "test_size = 1 - train_size\n", + "X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,\n", + " test_size=test_size)" + ] + }, + { + "cell_type": "markdown", + "id": "c38042b9", + "metadata": { + "editable": true + }, + "source": [ + "## Running with Keras" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "60bcf099", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "def create_convolutional_neural_network_keras(input_shape, receptive_field,\n", + " n_filters, n_neurons_connected, n_categories,\n", + " eta, lmbd):\n", + " model = Sequential()\n", + " model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',\n", + " activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n", + " model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + " model.add(layers.Flatten())\n", + " model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n", + " model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))\n", + " \n", + " sgd = optimizers.SGD(lr=eta)\n", + " model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n", + " \n", + " return model\n", + "\n", + "epochs = 100\n", + "batch_size = 100\n", + "input_shape = X_train.shape[1:4]\n", + "receptive_field = 3\n", + "n_filters = 10\n", + "n_neurons_connected = 50\n", + "n_categories = 10\n", + "\n", + "eta_vals = np.logspace(-5, 1, 7)\n", + "lmbd_vals = np.logspace(-5, 1, 7)" + ] + }, + { + "cell_type": "markdown", + "id": "c2f7b0cb", + "metadata": { + "editable": true + }, + "source": [ + "## Final part" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9ca54a70", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", + " \n", + "for i, eta in enumerate(eta_vals):\n", + " for j, lmbd in enumerate(lmbd_vals):\n", + " CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,\n", + " n_filters, n_neurons_connected, n_categories,\n", + " eta, lmbd)\n", + " CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)\n", + " scores = CNN.evaluate(X_test, Y_test)\n", + " \n", + " CNN_keras[i][j] = CNN\n", + " \n", + " print(\"Learning rate = \", eta)\n", + " print(\"Lambda = \", lmbd)\n", + " print(\"Test accuracy: %.3f\" % scores[1])\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "id": "bc03b613", + "metadata": { + "editable": true + }, + "source": [ + "## Final visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2ee8b6d4", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# visual representation of grid search\n", + "# uses seaborn heatmap, could probably do this in matplotlib\n", + "import seaborn as sns\n", + "\n", + "sns.set()\n", + "\n", + "train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n", + "test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n", + "\n", + "for i in range(len(eta_vals)):\n", + " for j in range(len(lmbd_vals)):\n", + " CNN = CNN_keras[i][j]\n", + "\n", + " train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]\n", + " test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]\n", + "\n", + " \n", + "fig, ax = plt.subplots(figsize = (10, 10))\n", + "sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n", + "ax.set_title(\"Training Accuracy\")\n", + "ax.set_ylabel(\"$\\eta$\")\n", + "ax.set_xlabel(\"$\\lambda$\")\n", + "plt.show()\n", + "\n", + "fig, ax = plt.subplots(figsize = (10, 10))\n", + "sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n", + "ax.set_title(\"Test Accuracy\")\n", + "ax.set_ylabel(\"$\\eta$\")\n", + "ax.set_xlabel(\"$\\lambda$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "76cc9332", + "metadata": { + "editable": true + }, + "source": [ + "## The CIFAR01 data set\n", + "\n", + "The CIFAR10 dataset contains 60,000 color images in 10 classes, with\n", + "6,000 images in each class. The dataset is divided into 50,000\n", + "training images and 10,000 testing images. The classes are mutually\n", + "exclusive and there is no overlap between them." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a7162ca5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "from tensorflow.keras import datasets, layers, models\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# We import the data set\n", + "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n", + "\n", + "# Normalize pixel values to be between 0 and 1 by dividing by 255. \n", + "train_images, test_images = train_images / 255.0, test_images / 255.0" + ] + }, + { + "cell_type": "markdown", + "id": "60974e30", + "metadata": { + "editable": true + }, + "source": [ + "## Verifying the data set\n", + "\n", + "To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aa17bdb6", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n", + " 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "​\n", + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(train_images[i], cmap=plt.cm.binary)\n", + " # The CIFAR labels happen to be arrays, \n", + " # which is why you need the extra index\n", + " plt.xlabel(class_names[train_labels[i][0]])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d2562533", + "metadata": { + "editable": true + }, + "source": [ + "## Set up the model\n", + "\n", + "The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers.\n", + "\n", + "As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ada7baa9", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "model = models.Sequential()\n", + "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n", + "model.add(layers.MaxPooling2D((2, 2)))\n", + "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n", + "model.add(layers.MaxPooling2D((2, 2)))\n", + "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n", + "\n", + "# Let's display the architecture of our model so far.\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "6758beb6", + "metadata": { + "editable": true + }, + "source": [ + "You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer." + ] + }, + { + "cell_type": "markdown", + "id": "36ca67d9", + "metadata": { + "editable": true + }, + "source": [ + "## Add Dense layers on top\n", + "\n", + "To complete our model, you will feed the last output tensor from the\n", + "convolutional base (of shape (4, 4, 64)) into one or more Dense layers\n", + "to perform classification. Dense layers take vectors as input (which\n", + "are 1D), while the current output is a 3D tensor. First, you will\n", + "flatten (or unroll) the 3D output to 1D, then add one or more Dense\n", + "layers on top. CIFAR has 10 output classes, so you use a final Dense\n", + "layer with 10 outputs and a softmax activation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1c1cec77", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "model.add(layers.Flatten())\n", + "model.add(layers.Dense(64, activation='relu'))\n", + "model.add(layers.Dense(10))\n", + "Here's the complete architecture of our model.\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "94226507", + "metadata": { + "editable": true + }, + "source": [ + "As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers." + ] + }, + { + "cell_type": "markdown", + "id": "6a3d9b85", + "metadata": { + "editable": true + }, + "source": [ + "## Compile and train the model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0d0d308e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "model.compile(optimizer='adam',\n", + " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " metrics=['accuracy'])\n", + "​\n", + "history = model.fit(train_images, train_labels, epochs=10, \n", + " validation_data=(test_images, test_labels))" + ] + }, + { + "cell_type": "markdown", + "id": "a1adb172", + "metadata": { + "editable": true + }, + "source": [ + "## Finally, evaluate the model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0085b6b6", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "plt.plot(history.history['accuracy'], label='accuracy')\n", + "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.ylim([0.5, 1])\n", + "plt.legend(loc='lower right')\n", + "\n", + "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n", + "\n", + "print(test_acc)" + ] + }, + { + "cell_type": "markdown", + "id": "31e5dd49", + "metadata": { + "editable": true + }, "source": [ "## Recurrent neural networks: Overarching view\n", "\n", @@ -110,307 +1900,36 @@ }, { "cell_type": "markdown", - "id": "e8a8b6ef", - "metadata": {}, + "id": "f23f49d3", + "metadata": { + "editable": true + }, "source": [ "## Set up of an RNN\n", "\n", - "More to text to be added" + "See handwritten notes for week 43 and [Lectures from CS231 at Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf)" ] }, { "cell_type": "markdown", - "id": "6569f7b9", - "metadata": {}, + "id": "900aa5d3", + "metadata": { + "editable": true + }, "source": [ "## A simple example" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "900cdf33", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Metal device set to: Apple M1\n", - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " simple_rnn (SimpleRNN) (None, 32) 1184 \n", - " \n", - " dense (Dense) (None, 8) 264 \n", - " \n", - " dense_1 (Dense) (None, 1) 9 \n", - " \n", - "=================================================================\n", - "Total params: 1,457\n", - "Trainable params: 1,457\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "Epoch 1/100\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-10-24 21:54:19.775268: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "50/50 - 1s - loss: 1.1607 - 1s/epoch - 27ms/step\n", - "Epoch 2/100\n", - "50/50 - 0s - loss: 0.4438 - 318ms/epoch - 6ms/step\n", - "Epoch 3/100\n", - "50/50 - 0s - loss: 0.3997 - 368ms/epoch - 7ms/step\n", - "Epoch 4/100\n", - "50/50 - 0s - loss: 0.3951 - 363ms/epoch - 7ms/step\n", - "Epoch 5/100\n", - "50/50 - 0s - loss: 0.3900 - 324ms/epoch - 6ms/step\n", - "Epoch 6/100\n", - "50/50 - 0s - loss: 0.3884 - 428ms/epoch - 9ms/step\n", - "Epoch 7/100\n", - "50/50 - 0s - loss: 0.3849 - 448ms/epoch - 9ms/step\n", - "Epoch 8/100\n", - "50/50 - 0s - loss: 0.3850 - 396ms/epoch - 8ms/step\n", - "Epoch 9/100\n", - "50/50 - 0s - loss: 0.3828 - 440ms/epoch - 9ms/step\n", - "Epoch 10/100\n", - "50/50 - 0s - loss: 0.3814 - 426ms/epoch - 9ms/step\n", - "Epoch 11/100\n", - "50/50 - 0s - loss: 0.3802 - 450ms/epoch - 9ms/step\n", - "Epoch 12/100\n", - "50/50 - 0s - loss: 0.3802 - 425ms/epoch - 8ms/step\n", - "Epoch 13/100\n", - "50/50 - 0s - loss: 0.3770 - 324ms/epoch - 6ms/step\n", - "Epoch 14/100\n", - "50/50 - 0s - loss: 0.3776 - 317ms/epoch - 6ms/step\n", - "Epoch 15/100\n", - "50/50 - 0s - loss: 0.3777 - 333ms/epoch - 7ms/step\n", - "Epoch 16/100\n", - "50/50 - 0s - loss: 0.3769 - 325ms/epoch - 6ms/step\n", - "Epoch 17/100\n", - "50/50 - 0s - loss: 0.3757 - 318ms/epoch - 6ms/step\n", - "Epoch 18/100\n", - "50/50 - 0s - loss: 0.3753 - 316ms/epoch - 6ms/step\n", - "Epoch 19/100\n", - "50/50 - 0s - loss: 0.3755 - 316ms/epoch - 6ms/step\n", - "Epoch 20/100\n", - "50/50 - 0s - loss: 0.3745 - 315ms/epoch - 6ms/step\n", - "Epoch 21/100\n", - "50/50 - 0s - loss: 0.3739 - 316ms/epoch - 6ms/step\n", - "Epoch 22/100\n", - "50/50 - 0s - loss: 0.3733 - 331ms/epoch - 7ms/step\n", - "Epoch 23/100\n", - "50/50 - 0s - loss: 0.3731 - 318ms/epoch - 6ms/step\n", - "Epoch 24/100\n", - "50/50 - 0s - loss: 0.3732 - 340ms/epoch - 7ms/step\n", - "Epoch 25/100\n", - "50/50 - 0s - loss: 0.3728 - 443ms/epoch - 9ms/step\n", - "Epoch 26/100\n", - "50/50 - 0s - loss: 0.3721 - 442ms/epoch - 9ms/step\n", - "Epoch 27/100\n", - "50/50 - 0s - loss: 0.3722 - 414ms/epoch - 8ms/step\n", - "Epoch 28/100\n", - "50/50 - 0s - loss: 0.3702 - 436ms/epoch - 9ms/step\n", - "Epoch 29/100\n", - "50/50 - 0s - loss: 0.3697 - 347ms/epoch - 7ms/step\n", - "Epoch 30/100\n", - "50/50 - 0s - loss: 0.3711 - 319ms/epoch - 6ms/step\n", - "Epoch 31/100\n", - "50/50 - 0s - loss: 0.3679 - 314ms/epoch - 6ms/step\n", - "Epoch 32/100\n", - "50/50 - 0s - loss: 0.3687 - 394ms/epoch - 8ms/step\n", - "Epoch 33/100\n", - "50/50 - 0s - loss: 0.3671 - 318ms/epoch - 6ms/step\n", - "Epoch 34/100\n", - "50/50 - 0s - loss: 0.3698 - 319ms/epoch - 6ms/step\n", - "Epoch 35/100\n", - "50/50 - 0s - loss: 0.3674 - 343ms/epoch - 7ms/step\n", - "Epoch 36/100\n", - "50/50 - 0s - loss: 0.3670 - 456ms/epoch - 9ms/step\n", - "Epoch 37/100\n", - "50/50 - 0s - loss: 0.3661 - 396ms/epoch - 8ms/step\n", - "Epoch 38/100\n", - "50/50 - 0s - loss: 0.3662 - 326ms/epoch - 7ms/step\n", - "Epoch 39/100\n", - "50/50 - 0s - loss: 0.3655 - 310ms/epoch - 6ms/step\n", - "Epoch 40/100\n", - "50/50 - 0s - loss: 0.3651 - 335ms/epoch - 7ms/step\n", - "Epoch 41/100\n", - "50/50 - 0s - loss: 0.3652 - 309ms/epoch - 6ms/step\n", - "Epoch 42/100\n", - "50/50 - 0s - loss: 0.3641 - 316ms/epoch - 6ms/step\n", - "Epoch 43/100\n", - "50/50 - 0s - loss: 0.3649 - 315ms/epoch - 6ms/step\n", - "Epoch 44/100\n", - "50/50 - 0s - loss: 0.3628 - 343ms/epoch - 7ms/step\n", - "Epoch 45/100\n", - "50/50 - 0s - loss: 0.3645 - 450ms/epoch - 9ms/step\n", - "Epoch 46/100\n", - "50/50 - 0s - loss: 0.3639 - 445ms/epoch - 9ms/step\n", - "Epoch 47/100\n", - "50/50 - 0s - loss: 0.3626 - 432ms/epoch - 9ms/step\n", - "Epoch 48/100\n", - "50/50 - 0s - loss: 0.3616 - 455ms/epoch - 9ms/step\n", - "Epoch 49/100\n", - "50/50 - 0s - loss: 0.3613 - 452ms/epoch - 9ms/step\n", - "Epoch 50/100\n", - "50/50 - 0s - loss: 0.3626 - 448ms/epoch - 9ms/step\n", - "Epoch 51/100\n", - "50/50 - 0s - loss: 0.3608 - 391ms/epoch - 8ms/step\n", - "Epoch 52/100\n", - "50/50 - 0s - loss: 0.3621 - 474ms/epoch - 9ms/step\n", - "Epoch 53/100\n", - "50/50 - 0s - loss: 0.3618 - 404ms/epoch - 8ms/step\n", - "Epoch 54/100\n", - "50/50 - 0s - loss: 0.3604 - 330ms/epoch - 7ms/step\n", - "Epoch 55/100\n", - "50/50 - 0s - loss: 0.3600 - 336ms/epoch - 7ms/step\n", - "Epoch 56/100\n", - "50/50 - 0s - loss: 0.3606 - 408ms/epoch - 8ms/step\n", - "Epoch 57/100\n", - "50/50 - 0s - loss: 0.3595 - 409ms/epoch - 8ms/step\n", - "Epoch 58/100\n", - "50/50 - 0s - loss: 0.3601 - 425ms/epoch - 9ms/step\n", - "Epoch 59/100\n", - "50/50 - 0s - loss: 0.3584 - 423ms/epoch - 8ms/step\n", - "Epoch 60/100\n", - "50/50 - 0s - loss: 0.3593 - 418ms/epoch - 8ms/step\n", - "Epoch 61/100\n", - "50/50 - 0s - loss: 0.3563 - 415ms/epoch - 8ms/step\n", - "Epoch 62/100\n", - "50/50 - 0s - loss: 0.3582 - 387ms/epoch - 8ms/step\n", - "Epoch 63/100\n", - "50/50 - 0s - loss: 0.3584 - 413ms/epoch - 8ms/step\n", - "Epoch 64/100\n", - "50/50 - 0s - loss: 0.3582 - 402ms/epoch - 8ms/step\n", - "Epoch 65/100\n", - "50/50 - 0s - loss: 0.3572 - 331ms/epoch - 7ms/step\n", - "Epoch 66/100\n", - "50/50 - 0s - loss: 0.3576 - 390ms/epoch - 8ms/step\n", - "Epoch 67/100\n", - "50/50 - 0s - loss: 0.3565 - 407ms/epoch - 8ms/step\n", - "Epoch 68/100\n", - "50/50 - 0s - loss: 0.3565 - 360ms/epoch - 7ms/step\n", - "Epoch 69/100\n", - "50/50 - 0s - loss: 0.3565 - 358ms/epoch - 7ms/step\n", - "Epoch 70/100\n", - "50/50 - 0s - loss: 0.3560 - 404ms/epoch - 8ms/step\n", - "Epoch 71/100\n", - "50/50 - 0s - loss: 0.3551 - 406ms/epoch - 8ms/step\n", - "Epoch 72/100\n", - "50/50 - 0s - loss: 0.3557 - 386ms/epoch - 8ms/step\n", - "Epoch 73/100\n", - "50/50 - 0s - loss: 0.3549 - 394ms/epoch - 8ms/step\n", - "Epoch 74/100\n", - "50/50 - 0s - loss: 0.3551 - 383ms/epoch - 8ms/step\n", - "Epoch 75/100\n", - "50/50 - 0s - loss: 0.3531 - 340ms/epoch - 7ms/step\n", - "Epoch 76/100\n", - "50/50 - 0s - loss: 0.3531 - 323ms/epoch - 6ms/step\n", - "Epoch 77/100\n", - "50/50 - 0s - loss: 0.3530 - 345ms/epoch - 7ms/step\n", - "Epoch 78/100\n", - "50/50 - 0s - loss: 0.3530 - 408ms/epoch - 8ms/step\n", - "Epoch 79/100\n", - "50/50 - 0s - loss: 0.3524 - 401ms/epoch - 8ms/step\n", - "Epoch 80/100\n", - "50/50 - 0s - loss: 0.3527 - 391ms/epoch - 8ms/step\n", - "Epoch 81/100\n", - "50/50 - 0s - loss: 0.3537 - 333ms/epoch - 7ms/step\n", - "Epoch 82/100\n", - "50/50 - 0s - loss: 0.3491 - 319ms/epoch - 6ms/step\n", - "Epoch 83/100\n", - "50/50 - 0s - loss: 0.3515 - 328ms/epoch - 7ms/step\n", - "Epoch 84/100\n", - "50/50 - 0s - loss: 0.3514 - 311ms/epoch - 6ms/step\n", - "Epoch 85/100\n", - "50/50 - 0s - loss: 0.3515 - 322ms/epoch - 6ms/step\n", - "Epoch 86/100\n", - "50/50 - 0s - loss: 0.3489 - 341ms/epoch - 7ms/step\n", - "Epoch 87/100\n", - "50/50 - 0s - loss: 0.3515 - 386ms/epoch - 8ms/step\n", - "Epoch 88/100\n", - "50/50 - 0s - loss: 0.3498 - 458ms/epoch - 9ms/step\n", - "Epoch 89/100\n", - "50/50 - 0s - loss: 0.3501 - 408ms/epoch - 8ms/step\n", - "Epoch 90/100\n", - "50/50 - 0s - loss: 0.3497 - 410ms/epoch - 8ms/step\n", - "Epoch 91/100\n", - "50/50 - 0s - loss: 0.3496 - 416ms/epoch - 8ms/step\n", - "Epoch 92/100\n", - "50/50 - 0s - loss: 0.3500 - 390ms/epoch - 8ms/step\n", - "Epoch 93/100\n", - "50/50 - 0s - loss: 0.3493 - 335ms/epoch - 7ms/step\n", - "Epoch 94/100\n", - "50/50 - 0s - loss: 0.3485 - 328ms/epoch - 7ms/step\n", - "Epoch 95/100\n", - "50/50 - 0s - loss: 0.3482 - 338ms/epoch - 7ms/step\n", - "Epoch 96/100\n", - "50/50 - 0s - loss: 0.3485 - 398ms/epoch - 8ms/step\n", - "Epoch 97/100\n", - "50/50 - 0s - loss: 0.3490 - 341ms/epoch - 7ms/step\n", - "Epoch 98/100\n", - "50/50 - 0s - loss: 0.3462 - 395ms/epoch - 8ms/step\n", - "Epoch 99/100\n", - "50/50 - 0s - loss: 0.3465 - 391ms/epoch - 8ms/step\n", - "Epoch 100/100\n", - "50/50 - 0s - loss: 0.3479 - 341ms/epoch - 7ms/step\n", - "0.3435967266559601\n" - ] - }, - { - "ename": "InvalidIndexError", - "evalue": "(slice(None, None, None), None)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mInvalidIndexError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28mprint\u001b[39m(trainScore)\n\u001b[1;32m 66\u001b[0m index \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m---> 67\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(index,predicted)\n\u001b[1;32m 69\u001b[0m plt\u001b[38;5;241m.\u001b[39maxvline(df\u001b[38;5;241m.\u001b[39mindex[Tp], c\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/matplotlib/pyplot.py:2757\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2755\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 2756\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;241m*\u001b[39margs, scalex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, scaley\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2758\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2759\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1391\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1392\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1629\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1630\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1631\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 311\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/matplotlib/axes/_base.py:488\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(xy) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 487\u001b[0m x \u001b[38;5;241m=\u001b[39m _check_1d(xy[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m--> 488\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43m_check_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxy\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 489\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 490\u001b[0m x, y \u001b[38;5;241m=\u001b[39m index_of(xy[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1327\u001b[0m, in \u001b[0;36m_check_1d\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings(record\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m w:\n\u001b[1;32m 1322\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\n\u001b[1;32m 1323\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malways\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1324\u001b[0m category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mWarning\u001b[39;00m,\n\u001b[1;32m 1325\u001b[0m message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSupport for multi-dimensional indexing\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1327\u001b[0m ndim \u001b[38;5;241m=\u001b[39m \u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mndim\n\u001b[1;32m 1328\u001b[0m \u001b[38;5;66;03m# we have definitely hit a pandas index or series object\u001b[39;00m\n\u001b[1;32m 1329\u001b[0m \u001b[38;5;66;03m# cast to a numpy array.\u001b[39;00m\n\u001b[1;32m 1330\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(w) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/indexes/range.py:388\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_indexing_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mget_loc(key, method\u001b[38;5;241m=\u001b[39mmethod, tolerance\u001b[38;5;241m=\u001b[39mtolerance)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/indexes/base.py:5637\u001b[0m, in \u001b[0;36mIndex._check_indexing_error\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 5633\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_indexing_error\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m 5634\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(key):\n\u001b[1;32m 5635\u001b[0m \u001b[38;5;66;03m# if key is not a scalar, directly raise an error (the code below\u001b[39;00m\n\u001b[1;32m 5636\u001b[0m \u001b[38;5;66;03m# would convert to numpy arrays and raise later any way) - GH29926\u001b[39;00m\n\u001b[0;32m-> 5637\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n", - "\u001b[0;31mInvalidIndexError\u001b[0m: (slice(None, None, None), None)" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 14, + "id": "5136a36b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ - "%matplotlib inline\n", - "\n", "# Start importing packages\n", "import pandas as pd\n", "import numpy as np\n", @@ -483,8 +2002,10 @@ }, { "cell_type": "markdown", - "id": "1193c9ba", - "metadata": {}, + "id": "3e357add", + "metadata": { + "editable": true + }, "source": [ "## An extrapolation example\n", "\n", @@ -496,9 +2017,12 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "6f3864a3", - "metadata": {}, + "execution_count": 15, + "id": "3552274a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "\n", @@ -533,8 +2057,10 @@ }, { "cell_type": "markdown", - "id": "9e07c8b3", - "metadata": {}, + "id": "7fdaffb4", + "metadata": { + "editable": true + }, "source": [ "## Formatting the Data\n", "\n", @@ -574,9 +2100,12 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "0343fde9", - "metadata": {}, + "execution_count": 16, + "id": "630d3f36", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# FORMAT_DATA\n", @@ -655,394 +2184,23 @@ }, { "cell_type": "markdown", - "id": "ca6159be", - "metadata": {}, + "id": "3ea68278", + "metadata": { + "editable": true + }, "source": [ "## Predicting New Points With A Trained Recurrent Neural Network" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "94fde416", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_1 (InputLayer) [(None, 2, 1)] 0 \n", - " \n", - " RNN (SimpleRNN) (None, 200) 40400 \n", - " \n", - " dense (Dense) (None, 1) 201 \n", - " \n", - "=================================================================\n", - "Total params: 40,601\n", - "Trainable params: 40,601\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "Epoch 1/150\n", - "1/1 [==============================] - 1s 1s/step - loss: 0.1679 - val_loss: 0.2210\n", - "Epoch 2/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0739 - val_loss: 0.0502\n", - "Epoch 3/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0189 - val_loss: 2.7614e-05\n", - "Epoch 4/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 3.1402e-04 - val_loss: 0.0393\n", - "Epoch 5/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0096 - val_loss: 0.1075\n", - "Epoch 6/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0286 - val_loss: 0.1483\n", - "Epoch 7/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0400 - val_loss: 0.1461\n", - "Epoch 8/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0388 - val_loss: 0.1138\n", - "Epoch 9/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0292 - val_loss: 0.0707\n", - "Epoch 10/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0171 - val_loss: 0.0327\n", - "Epoch 11/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0070 - val_loss: 0.0086\n", - "Epoch 12/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0015 - val_loss: 9.9361e-05\n", - "Epoch 13/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 7.4276e-04 - val_loss: 0.0037\n", - "Epoch 14/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0034 - val_loss: 0.0133\n", - "Epoch 15/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0074 - val_loss: 0.0227\n", - "Epoch 16/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0109 - val_loss: 0.0278\n", - "Epoch 17/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0127 - val_loss: 0.0272\n", - "Epoch 18/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0124 - val_loss: 0.0217\n", - "Epoch 19/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0103 - val_loss: 0.0137\n", - "Epoch 20/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 0.0072 - val_loss: 0.0061\n", - "Epoch 21/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0041 - val_loss: 0.0012\n", - "Epoch 22/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0017 - val_loss: 1.1455e-04\n", - "Epoch 23/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 5.1963e-04 - val_loss: 0.0028\n", - "Epoch 24/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 5.3367e-04 - val_loss: 0.0081\n", - "Epoch 25/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0015 - val_loss: 0.0139\n", - "Epoch 26/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0027 - val_loss: 0.0183\n", - "Epoch 27/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 0.0038 - val_loss: 0.0199\n", - "Epoch 28/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0042 - val_loss: 0.0185\n", - "Epoch 29/150\n", - "1/1 [==============================] - 0s 35ms/step - loss: 0.0039 - val_loss: 0.0148\n", - "Epoch 30/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0031 - val_loss: 0.0099\n", - "Epoch 31/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0020 - val_loss: 0.0053\n", - "Epoch 32/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 9.5439e-04 - val_loss: 0.0019\n", - "Epoch 33/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 3.2006e-04 - val_loss: 2.3483e-04\n", - "Epoch 34/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 1.4570e-04 - val_loss: 1.0170e-04\n", - "Epoch 35/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 3.5981e-04 - val_loss: 9.4127e-04\n", - "Epoch 36/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 7.8104e-04 - val_loss: 0.0020\n", - "Epoch 37/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0012 - val_loss: 0.0028\n", - "Epoch 38/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0014 - val_loss: 0.0029\n", - "Epoch 39/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0014 - val_loss: 0.0024\n", - "Epoch 40/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0012 - val_loss: 0.0015\n", - "Epoch 41/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 7.9685e-04 - val_loss: 5.9749e-04\n", - "Epoch 42/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 4.0970e-04 - val_loss: 6.9986e-05\n", - "Epoch 43/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 1.3257e-04 - val_loss: 6.4204e-05\n", - "Epoch 44/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 2.6951e-05 - val_loss: 5.2952e-04\n", - "Epoch 45/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 8.6004e-05 - val_loss: 0.0012\n", - "Epoch 46/150\n", - "1/1 [==============================] - 0s 36ms/step - loss: 2.4556e-04 - val_loss: 0.0019\n", - "Epoch 47/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 4.1523e-04 - val_loss: 0.0022\n", - "Epoch 48/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 5.1611e-04 - val_loss: 0.0021\n", - "Epoch 49/150\n", - "1/1 [==============================] - 0s 42ms/step - loss: 5.0940e-04 - val_loss: 0.0017\n", - "Epoch 50/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 4.0554e-04 - val_loss: 0.0011\n", - "Epoch 51/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 2.5222e-04 - val_loss: 4.9320e-04\n", - "Epoch 52/150\n", - "1/1 [==============================] - 0s 37ms/step - loss: 1.0946e-04 - val_loss: 1.1215e-04\n", - "Epoch 53/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 2.3740e-05 - val_loss: 5.1698e-07\n", - "Epoch 54/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 1.1804e-05 - val_loss: 1.1202e-04\n", - "Epoch 55/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 5.9073e-05 - val_loss: 3.2815e-04\n", - "Epoch 56/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 1.3068e-04 - val_loss: 5.1717e-04\n", - "Epoch 57/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 1.8867e-04 - val_loss: 5.8783e-04\n", - "Epoch 58/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 2.0755e-04 - val_loss: 5.1789e-04\n", - "Epoch 59/150\n", - "1/1 [==============================] - 0s 38ms/step - loss: 1.8234e-04 - val_loss: 3.5034e-04\n", - "Epoch 60/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 1.2723e-04 - val_loss: 1.6400e-04\n", - "Epoch 61/150\n", - "1/1 [==============================] - 0s 40ms/step - loss: 6.6671e-05 - val_loss: 3.4339e-05\n", - "Epoch 62/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 2.3730e-05 - val_loss: 1.5251e-06\n", - "Epoch 63/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 1.0639e-05 - val_loss: 5.8371e-05\n", - "Epoch 64/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 2.5247e-05 - val_loss: 1.6028e-04\n", - "Epoch 65/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 5.4094e-05 - val_loss: 2.5018e-04\n", - "Epoch 66/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 7.9956e-05 - val_loss: 2.8536e-04\n", - "Epoch 67/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 8.9958e-05 - val_loss: 2.5404e-04\n", - "Epoch 68/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 8.0570e-05 - val_loss: 1.7559e-04\n", - "Epoch 69/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 5.7644e-05 - val_loss: 8.6747e-05\n", - "Epoch 70/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 3.2237e-05 - val_loss: 2.2337e-05\n", - "Epoch 71/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 1.4742e-05 - val_loss: 1.3760e-08\n", - "Epoch 72/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 1.0293e-05 - val_loss: 1.5472e-05\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 73/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 1.7331e-05 - val_loss: 4.8492e-05\n", - "Epoch 74/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 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91/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 1.1575e-05 - val_loss: 1.1809e-06\n", - "Epoch 92/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 1.3349e-05 - val_loss: 1.2661e-06\n", - "Epoch 93/150\n", - "1/1 [==============================] - 0s 23ms/step - loss: 1.3606e-05 - val_loss: 3.0378e-07\n", - "Epoch 94/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 1.2336e-05 - val_loss: 2.5686e-07\n", - "Epoch 95/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 1.0331e-05 - val_loss: 3.4652e-06\n", - "Epoch 96/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 8.6390e-06 - val_loss: 1.0883e-05\n", - "Epoch 97/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 7.9747e-06 - val_loss: 2.1254e-05\n", - "Epoch 98/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 8.4022e-06 - val_loss: 3.1601e-05\n", - "Epoch 99/150\n", - "1/1 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[==============================] - 0s 27ms/step - loss: 7.9527e-06 - val_loss: 6.7973e-06\n", - "Epoch 140/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 7.9455e-06 - val_loss: 7.3053e-06\n", - "Epoch 141/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 7.9271e-06 - val_loss: 8.0635e-06\n", - "Epoch 142/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 7.9096e-06 - val_loss: 8.9416e-06\n", - "Epoch 143/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 7.9023e-06 - val_loss: 9.7847e-06\n", - "Epoch 144/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 7.9065e-06 - val_loss: 1.0437e-05\n", - "Epoch 145/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 7.9163e-06 - val_loss: 1.0783e-05\n", - "Epoch 146/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 7.9229e-06 - val_loss: 1.0784e-05\n", - "Epoch 147/150\n", - "1/1 [==============================] - 0s 36ms/step - loss: 7.9217e-06 - val_loss: 1.0474e-05\n", - "Epoch 148/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 7.9139e-06 - val_loss: 9.9551e-06\n", - "Epoch 149/150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 0s 28ms/step - loss: 7.9046e-06 - val_loss: 9.3551e-06\n", - "Epoch 150/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 7.8996e-06 - val_loss: 8.8013e-06\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_90071/3370771014.py:33: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n", - " next_input = np.array([[last, next[0]]], dtype=np.float64)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 0.0002401721448508743\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 6.57977975\n" - ] - } - ], + "execution_count": 17, + "id": "5ed11b13", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "def test_rnn (x1, y_test, plot_min, plot_max):\n", " \"\"\"\n", @@ -1139,8 +2297,10 @@ }, { "cell_type": "markdown", - "id": "bdc4c3dc", - "metadata": {}, + "id": "d68e5319", + "metadata": { + "editable": true + }, "source": [ "## Other Things to Try\n", "\n", @@ -1158,382 +2318,13 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "1bb14cee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_1\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_2 (InputLayer) [(None, 2, 1)] 0 \n", - " \n", - " RNN1 (SimpleRNN) (None, 2, 500) 251000 \n", - " \n", - " RNN2 (SimpleRNN) (None, 500) 500500 \n", - " \n", - " dense (Dense) (None, 1) 501 \n", - " \n", - "=================================================================\n", - "Total params: 752,001\n", - "Trainable params: 752,001\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "Epoch 1/150\n", - "1/1 [==============================] - 1s 825ms/step - loss: 0.5439 - val_loss: 6.5184\n", - "Epoch 2/150\n", - "1/1 [==============================] - 0s 41ms/step - loss: 8.9007 - val_loss: 0.3062\n", - "Epoch 3/150\n", - "1/1 [==============================] - 0s 49ms/step - loss: 0.9950 - val_loss: 2.6054\n", - "Epoch 4/150\n", - "1/1 [==============================] - 0s 40ms/step - loss: 1.4618 - val_loss: 5.9886\n", - "Epoch 5/150\n", - "1/1 [==============================] - 0s 41ms/step - loss: 4.1418 - val_loss: 4.2977\n", - "Epoch 6/150\n", - "1/1 [==============================] - 0s 45ms/step - loss: 2.7668 - val_loss: 1.3572\n", - "Epoch 7/150\n", - "1/1 [==============================] - 0s 40ms/step - loss: 0.5932 - val_loss: 0.0325\n", - "Epoch 8/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 0.1006 - val_loss: 0.3101\n", - "Epoch 9/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 1.0014 - val_loss: 0.7823\n", - "Epoch 10/150\n", - "1/1 [==============================] - 0s 52ms/step - loss: 1.7507 - val_loss: 0.6643\n", - "Epoch 11/150\n", - "1/1 [==============================] - 0s 48ms/step - loss: 1.5738 - val_loss: 0.2089\n", - "Epoch 12/150\n", - "1/1 [==============================] - 0s 50ms/step - loss: 0.8158 - val_loss: 0.0028\n", - "Epoch 13/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 0.1783 - val_loss: 0.3375\n", - "Epoch 14/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 0.0670 - val_loss: 1.0179\n", - "Epoch 15/150\n", - "1/1 [==============================] - 0s 49ms/step - loss: 0.3859 - val_loss: 1.5853\n", - "Epoch 16/150\n", - "1/1 [==============================] - 0s 46ms/step - loss: 0.7420 - val_loss: 1.7043\n", - "Epoch 17/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 0.8218 - val_loss: 1.3692\n", - "Epoch 18/150\n", - "1/1 [==============================] - 0s 45ms/step - loss: 0.6011 - val_loss: 0.8205\n", - "Epoch 19/150\n", - "1/1 [==============================] - 0s 42ms/step - loss: 0.2756 - val_loss: 0.3361\n", - "Epoch 20/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 0.0666 - val_loss: 0.0677\n", - "Epoch 21/150\n", - "1/1 [==============================] - 0s 46ms/step - loss: 0.0681 - val_loss: 3.4934e-05\n", - "Epoch 22/150\n", - "1/1 [==============================] - 0s 49ms/step - loss: 0.2152 - val_loss: 0.0207\n", - "Epoch 23/150\n", - "1/1 [==============================] - 0s 46ms/step - loss: 0.3624 - val_loss: 0.0303\n", - "Epoch 24/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 0.3974 - val_loss: 0.0086\n", - "Epoch 25/150\n", - "1/1 [==============================] - 0s 41ms/step - loss: 0.3072 - val_loss: 0.0052\n", - "Epoch 26/150\n", - "1/1 [==============================] - 0s 43ms/step - loss: 0.1642 - val_loss: 0.0805\n", - "Epoch 27/150\n", - "1/1 [==============================] - 0s 51ms/step - loss: 0.0610 - val_loss: 0.2489\n", - "Epoch 28/150\n", - "1/1 [==============================] - 0s 45ms/step - loss: 0.0477 - val_loss: 0.4609\n", - "Epoch 29/150\n", - "1/1 [==============================] - 0s 45ms/step - loss: 0.1078 - val_loss: 0.6318\n", - "Epoch 30/150\n", - "1/1 [==============================] - 0s 46ms/step - loss: 0.1808 - val_loss: 0.6922\n", - "Epoch 31/150\n", - "1/1 [==============================] - 0s 41ms/step - loss: 0.2099 - val_loss: 0.6265\n", - "Epoch 32/150\n", - "1/1 [==============================] - 0s 42ms/step - loss: 0.1783 - val_loss: 0.4736\n", - "Epoch 33/150\n", - "1/1 [==============================] - 0s 42ms/step - loss: 0.1126 - val_loss: 0.2989\n", - "Epoch 34/150\n", - "1/1 [==============================] - 0s 43ms/step - loss: 0.0573 - val_loss: 0.1570\n", - "Epoch 35/150\n", - "1/1 [==============================] - 0s 52ms/step - loss: 0.0424 - val_loss: 0.0701\n", - "Epoch 36/150\n", - "1/1 [==============================] - 0s 66ms/step - loss: 0.0664 - val_loss: 0.0306\n", - "Epoch 37/150\n", - "1/1 [==============================] - 0s 42ms/step - loss: 0.1028 - val_loss: 0.0193\n", - "Epoch 38/150\n", - "1/1 [==============================] - 0s 42ms/step - loss: 0.1218 - val_loss: 0.0253\n", - "Epoch 39/150\n", - "1/1 [==============================] - 0s 40ms/step - loss: 0.1109 - val_loss: 0.0510\n", - "Epoch 40/150\n", - "1/1 [==============================] - 0s 39ms/step - loss: 0.0801 - val_loss: 0.1045\n", - "Epoch 41/150\n", - "1/1 [==============================] - 0s 41ms/step - loss: 0.0514 - val_loss: 0.1849\n", - "Epoch 42/150\n", - "1/1 [==============================] - 0s 47ms/step - loss: 0.0418 - val_loss: 0.2759\n", - "Epoch 43/150\n", - "1/1 [==============================] - 0s 45ms/step - loss: 0.0524 - val_loss: 0.3501\n", - "Epoch 44/150\n", - "1/1 [==============================] - 0s 43ms/step - loss: 0.0706 - val_loss: 0.3834\n", - "Epoch 45/150\n", - "1/1 [==============================] - 0s 44ms/step - loss: 0.0807 - val_loss: 0.3669\n", - "Epoch 46/150\n", - "1/1 [==============================] - 0s 54ms/step - loss: 0.0756 - val_loss: 0.3108\n", - "Epoch 47/150\n", - "1/1 [==============================] - 0s 44ms/step - loss: 0.0602 - val_loss: 0.2373\n", - "Epoch 48/150\n", - "1/1 [==============================] - 0s 43ms/step - loss: 0.0460 - val_loss: 0.1683\n", - "Epoch 49/150\n", - "1/1 [==============================] - 0s 43ms/step - loss: 0.0418 - val_loss: 0.1176\n", - "Epoch 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"Epoch 146/150\n", - "1/1 [==============================] - 0s 45ms/step - loss: 0.0407 - val_loss: 0.1729\n", - "Epoch 147/150\n", - "1/1 [==============================] - 0s 41ms/step - loss: 0.0407 - val_loss: 0.1734\n", - "Epoch 148/150\n", - "1/1 [==============================] - 0s 44ms/step - loss: 0.0407 - val_loss: 0.1738\n", - "Epoch 149/150\n", - "1/1 [==============================] - 0s 43ms/step - loss: 0.0406 - val_loss: 0.1739\n", - "Epoch 150/150\n", - "1/1 [==============================] - 0s 43ms/step - loss: 0.0406 - val_loss: 0.1737\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_90071/3370771014.py:33: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n", - " next_input = np.array([[last, next[0]]], dtype=np.float64)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 0.3312563121466564\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 8.502487125000002\n" - ] - } - ], + "execution_count": 18, + "id": "1a6980f0", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "def rnn_2layers(length_of_sequences, batch_size = None, stateful = False):\n", " \"\"\"\n", @@ -1625,8 +2416,10 @@ }, { "cell_type": "markdown", - "id": "4b234399", - "metadata": {}, + "id": "4cdbd70f", + "metadata": { + "editable": true + }, "source": [ "## Other Types of Recurrent Neural Networks\n", "\n", @@ -1648,740 +2441,13 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "995afe8f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_2\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_3 (InputLayer) [(None, 2, 1)] 0 \n", - " \n", - " dnn (Dense) (None, 2, 125) 250 \n", - " \n", - " dnn1 (Dense) (None, 2, 125) 15750 \n", - " \n", - " RNN1 (GRU) (None, 2, 250) 282750 \n", - " \n", - " RNN (GRU) (None, 250) 376500 \n", - " \n", - " dense (Dense) (None, 1) 251 \n", - " \n", - "=================================================================\n", - "Total params: 675,501\n", - "Trainable params: 675,501\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "Epoch 1/150\n", - "1/1 [==============================] - 4s 4s/step - loss: 0.2307 - val_loss: 0.5958\n", - "Epoch 2/150\n", - "1/1 [==============================] - 0s 72ms/step - loss: 0.1868 - val_loss: 0.4770\n", - "Epoch 3/150\n", - "1/1 [==============================] - 0s 48ms/step - loss: 0.1452 - val_loss: 0.3583\n", - "Epoch 4/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.1045 - val_loss: 0.2400\n", - "Epoch 5/150\n", - "1/1 [==============================] - 0s 40ms/step - loss: 0.0653 - val_loss: 0.1293\n", - "Epoch 6/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0310 - val_loss: 0.0419\n", - "Epoch 7/150\n", - "1/1 [==============================] - 0s 35ms/step - loss: 0.0084 - val_loss: 9.5819e-04\n", - "Epoch 8/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0060 - val_loss: 0.0166\n", - "Epoch 9/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0249 - val_loss: 0.0468\n", - "Epoch 10/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0434 - val_loss: 0.0487\n", - "Epoch 11/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0438 - val_loss: 0.0274\n", - "Epoch 12/150\n", - "1/1 [==============================] - 0s 38ms/step - loss: 0.0306 - val_loss: 0.0061\n", - "Epoch 13/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0150 - val_loss: 3.9589e-04\n", - "Epoch 14/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0052 - val_loss: 0.0126\n", - "Epoch 15/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0034 - val_loss: 0.0355\n", - "Epoch 16/150\n", - "1/1 [==============================] - 0s 37ms/step - loss: 0.0072 - val_loss: 0.0586\n", - "Epoch 17/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0129 - val_loss: 0.0740\n", - "Epoch 18/150\n", - "1/1 [==============================] - 0s 35ms/step - loss: 0.0172 - val_loss: 0.0778\n", - "Epoch 19/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0185 - val_loss: 0.0702\n", - "Epoch 20/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0165 - val_loss: 0.0543\n", - "Epoch 21/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0122 - val_loss: 0.0347\n", - "Epoch 22/150\n", - "1/1 [==============================] - 0s 35ms/step - loss: 0.0073 - val_loss: 0.0167\n", - "Epoch 23/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0035 - val_loss: 0.0045\n", - "Epoch 24/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0021 - val_loss: 6.8130e-05\n", - "Epoch 25/150\n", - "1/1 [==============================] - 0s 35ms/step - loss: 0.0034 - val_loss: 0.0015\n", - "Epoch 26/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0062 - val_loss: 0.0044\n", - "Epoch 27/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0083 - val_loss: 0.0050\n", - "Epoch 28/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0084 - val_loss: 0.0029\n", - "Epoch 29/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 0.0065 - val_loss: 4.8800e-04\n", - "Epoch 30/150\n", - "1/1 [==============================] - 0s 36ms/step - loss: 0.0038 - val_loss: 3.2122e-04\n", - "Epoch 31/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.0018 - val_loss: 0.0034\n", - "Epoch 32/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 0.0013 - val_loss: 0.0086\n", - "Epoch 33/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0019 - val_loss: 0.0138\n", - "Epoch 34/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0030 - val_loss: 0.0168\n", - "Epoch 35/150\n", - "1/1 [==============================] - 0s 35ms/step - loss: 0.0038 - val_loss: 0.0166\n", - "Epoch 36/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0038 - val_loss: 0.0135\n", - "Epoch 37/150\n", - "1/1 [==============================] - 0s 39ms/step - loss: 0.0031 - val_loss: 0.0087\n", - "Epoch 38/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0019 - val_loss: 0.0041\n", - "Epoch 39/150\n", - "1/1 [==============================] - 0s 36ms/step - loss: 9.7991e-04 - val_loss: 9.9244e-04\n", - "Epoch 40/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 5.7611e-04 - val_loss: 1.3551e-06\n", - "Epoch 41/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 8.0269e-04 - val_loss: 5.1056e-04\n", - "Epoch 42/150\n", - "1/1 [==============================] - 0s 32ms/step - loss: 0.0014 - val_loss: 0.0013\n", - "Epoch 43/150\n", - "1/1 [==============================] - 0s 40ms/step - loss: 0.0018 - val_loss: 0.0015\n", - "Epoch 44/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0017 - val_loss: 8.9369e-04\n", - "Epoch 45/150\n", - "1/1 [==============================] - 0s 34ms/step - loss: 0.0012 - val_loss: 1.8812e-04\n", - "Epoch 46/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 6.2053e-04 - val_loss: 3.6929e-05\n", - "Epoch 47/150\n", - "1/1 [==============================] - 0s 35ms/step - loss: 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_90071/3370771014.py:33: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n", - " next_input = np.array([[last, next[0]]], dtype=np.float64)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 0.003350261226084576\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 10.474332250000003\n", - "Model: \"model_3\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input_4 (InputLayer) [(None, 1, 1)] 0 \n", - " \n", - " RNN (SimpleRNN) (None, 200) 40400 \n", - " \n", - " dense (Dense) (None, 1) 201 \n", - " \n", - "=================================================================\n", - "Total params: 40,601\n", - "Trainable params: 40,601\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n", - "Epoch 1/150\n", - "1/1 [==============================] - 1s 917ms/step - loss: 3.8466 - val_loss: 5.6772\n", - "Epoch 2/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 3.0183 - val_loss: 4.4450\n", - "Epoch 3/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 2.2939 - val_loss: 3.3730\n", - "Epoch 4/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 1.6746 - val_loss: 2.4616\n", - "Epoch 5/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 1.1603 - val_loss: 1.7088\n", - "Epoch 6/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.7492 - val_loss: 1.1099\n", - "Epoch 7/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.4374 - val_loss: 0.6566\n", - "Epoch 8/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.2188 - val_loss: 0.3368\n", - "Epoch 9/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0843 - val_loss: 0.1345\n", - "Epoch 10/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0222 - val_loss: 0.0298\n", - "Epoch 11/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0181 - val_loss: 4.3097e-07\n", - "Epoch 12/150\n", - "1/1 [==============================] - 0s 37ms/step - loss: 0.0558 - val_loss: 0.0214\n", - "Epoch 13/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.1186 - val_loss: 0.0711\n", - "Epoch 14/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.1904 - val_loss: 0.1291\n", - "Epoch 15/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.2579 - val_loss: 0.1804\n", - "Epoch 16/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.3110 - val_loss: 0.2150\n", - "Epoch 17/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.3439 - val_loss: 0.2286\n", - "Epoch 18/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.3545 - val_loss: 0.2213\n", - "Epoch 19/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.3438 - val_loss: 0.1965\n", - "Epoch 20/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.3153 - val_loss: 0.1596\n", - "Epoch 21/150\n", - "1/1 [==============================] - 0s 33ms/step - loss: 0.2738 - val_loss: 0.1171\n", - "Epoch 22/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.2248 - val_loss: 0.0752\n", - "Epoch 23/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.1735 - val_loss: 0.0394\n", - "Epoch 24/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.1247 - val_loss: 0.0139\n", - "Epoch 25/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0820 - val_loss: 0.0013\n", - "Epoch 26/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0481 - val_loss: 0.0022\n", - "Epoch 27/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0243 - val_loss: 0.0159\n", - "Epoch 28/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0106 - val_loss: 0.0401\n", - "Epoch 29/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0060 - val_loss: 0.0716\n", - "Epoch 30/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0089 - val_loss: 0.1065\n", - "Epoch 31/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0169 - val_loss: 0.1410\n", - "Epoch 32/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0277 - val_loss: 0.1714\n", - "Epoch 33/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0389 - val_loss: 0.1951\n", - "Epoch 34/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0485 - val_loss: 0.2101\n", - "Epoch 35/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0553 - val_loss: 0.2155\n", - "Epoch 36/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0583 - val_loss: 0.2117\n", - "Epoch 37/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0576 - val_loss: 0.1995\n", - "Epoch 38/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0533 - val_loss: 0.1807\n", - "Epoch 39/150\n", - "1/1 [==============================] - 0s 31ms/step - loss: 0.0464 - val_loss: 0.1574\n", - "Epoch 40/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0379 - val_loss: 0.1318\n", - "Epoch 41/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0288 - val_loss: 0.1059\n", - "Epoch 42/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0201 - val_loss: 0.0814\n", - "Epoch 43/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0128 - val_loss: 0.0597\n", - "Epoch 44/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0072 - val_loss: 0.0415\n", - "Epoch 45/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0037 - val_loss: 0.0273\n", - "Epoch 46/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0022 - val_loss: 0.0168\n", - "Epoch 47/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0024 - val_loss: 0.0096\n", - "Epoch 48/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0038 - val_loss: 0.0050\n", - "Epoch 49/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0059 - val_loss: 0.0024\n", - "Epoch 50/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0081 - val_loss: 0.0011\n", - "Epoch 51/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0101 - val_loss: 5.0639e-04\n", - "Epoch 52/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0113 - val_loss: 3.0735e-04\n", - "Epoch 53/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0117 - val_loss: 3.1448e-04\n", - "Epoch 54/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0113 - val_loss: 5.1057e-04\n", - "Epoch 55/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0101 - val_loss: 9.9008e-04\n", - "Epoch 56/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0085 - val_loss: 0.0019\n", - "Epoch 57/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0066 - val_loss: 0.0034\n", - "Epoch 58/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0048 - val_loss: 0.0055\n", - "Epoch 59/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0032 - val_loss: 0.0083\n", - "Epoch 60/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0020 - val_loss: 0.0116\n", - "Epoch 61/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0012 - val_loss: 0.0153\n", - "Epoch 62/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 9.6751e-04 - val_loss: 0.0190\n", - "Epoch 63/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 0.0011 - val_loss: 0.0226\n", - "Epoch 64/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0015 - val_loss: 0.0257\n", - "Epoch 65/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 0.0019 - val_loss: 0.0281\n", - "Epoch 66/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0024 - val_loss: 0.0295\n", - "Epoch 67/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 0.0028 - val_loss: 0.0301\n", - "Epoch 68/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0030 - val_loss: 0.0297\n", - "Epoch 69/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0030 - val_loss: 0.0285\n", - "Epoch 70/150\n", - "1/1 [==============================] - 0s 30ms/step - loss: 0.0028 - val_loss: 0.0266\n", - "Epoch 71/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0024 - val_loss: 0.0242\n", - "Epoch 72/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0020 - val_loss: 0.0216\n", - "Epoch 73/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0016 - val_loss: 0.0189\n", - "Epoch 74/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0012 - val_loss: 0.0163\n", - "Epoch 75/150\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1/1 [==============================] - 0s 24ms/step - loss: 9.6052e-04 - val_loss: 0.0139\n", - "Epoch 76/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 8.0506e-04 - val_loss: 0.0118\n", - "Epoch 77/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 7.6012e-04 - val_loss: 0.0100\n", - "Epoch 78/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 8.0467e-04 - val_loss: 0.0086\n", - "Epoch 79/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 9.0629e-04 - val_loss: 0.0076\n", - "Epoch 80/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 0.0010 - val_loss: 0.0069\n", - "Epoch 81/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 0.0011 - val_loss: 0.0064\n", - "Epoch 82/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 0.0012 - val_loss: 0.0062\n", - "Epoch 83/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0012 - val_loss: 0.0062\n", - "Epoch 84/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0012 - val_loss: 0.0065\n", - "Epoch 85/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 0.0011 - val_loss: 0.0069\n", - "Epoch 86/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 0.0010 - val_loss: 0.0075\n", - "Epoch 87/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 9.4648e-04 - val_loss: 0.0082\n", - "Epoch 88/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 8.5383e-04 - val_loss: 0.0089\n", - "Epoch 89/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 7.8291e-04 - val_loss: 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[==============================] - 0s 25ms/step - loss: 8.2569e-04 - val_loss: 0.0127\n", - "Epoch 99/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 8.0524e-04 - val_loss: 0.0123\n", - "Epoch 100/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 7.7903e-04 - val_loss: 0.0119\n", - "Epoch 101/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 7.5207e-04 - val_loss: 0.0113\n", - "Epoch 102/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 7.2874e-04 - val_loss: 0.0108\n", - "Epoch 103/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 7.1198e-04 - val_loss: 0.0103\n", - "Epoch 104/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 7.0294e-04 - val_loss: 0.0099\n", - "Epoch 105/150\n", - "1/1 [==============================] - 0s 28ms/step - loss: 7.0107e-04 - val_loss: 0.0095\n", - "Epoch 106/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 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29ms/step - loss: 6.3407e-04 - val_loss: 0.0094\n", - "Epoch 141/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 6.3197e-04 - val_loss: 0.0094\n", - "Epoch 142/150\n", - "1/1 [==============================] - 0s 25ms/step - loss: 6.3005e-04 - val_loss: 0.0095\n", - "Epoch 143/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 6.2830e-04 - val_loss: 0.0095\n", - "Epoch 144/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 6.2669e-04 - val_loss: 0.0095\n", - "Epoch 145/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 6.2513e-04 - val_loss: 0.0095\n", - "Epoch 146/150\n", - "1/1 [==============================] - 0s 29ms/step - loss: 6.2357e-04 - val_loss: 0.0095\n", - "Epoch 147/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 6.2195e-04 - val_loss: 0.0095\n", - "Epoch 148/150\n", - "1/1 [==============================] - 0s 26ms/step - loss: 6.2025e-04 - val_loss: 0.0095\n", - "Epoch 149/150\n", - "1/1 [==============================] - 0s 27ms/step - loss: 6.1847e-04 - val_loss: 0.0094\n", - "Epoch 150/150\n", - "1/1 [==============================] - 0s 24ms/step - loss: 6.1663e-04 - val_loss: 0.0094\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time: 5.474418290999978\n" - ] - } - ], + "execution_count": 19, + "id": "ebf495f9", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "def lstm_2layers(length_of_sequences, batch_size = None, stateful = False):\n", " \"\"\"\n", @@ -2577,8 +2643,10 @@ }, { "cell_type": "markdown", - "id": "7a158a39", - "metadata": {}, + "id": "4ff3302c", + "metadata": { + "editable": true + }, "source": [ "## Generative Models\n", "\n", @@ -2599,8 +2667,10 @@ }, { "cell_type": "markdown", - "id": "fbce4030", - "metadata": {}, + "id": "8c52f1c1", + "metadata": { + "editable": true + }, "source": [ "## Generative Adversarial Networks\n", "\n", @@ -2617,24 +2687,28 @@ }, { "cell_type": "markdown", - "id": "aa25d2dd", - "metadata": {}, + "id": "4cb5a4ac", + "metadata": { + "editable": true + }, "source": [ "\n", - "
\n", + "
\n", "\n", "$$\n", "\\begin{equation}\n", " x = g(z; \\theta^{(g)})\n", - "\\label{_auto1} \\tag{1}\n", + "\\label{_auto4} \\tag{6}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", - "id": "14283846", - "metadata": {}, + "id": "122d9cb8", + "metadata": { + "editable": true + }, "source": [ "## Discriminator\n", "The discriminator attempts to distinguish between samples drawn from the\n", @@ -2646,24 +2720,28 @@ }, { "cell_type": "markdown", - "id": "683b479e", - "metadata": {}, + "id": "7f4f4f8f", + "metadata": { + "editable": true + }, "source": [ "\n", - "
\n", + "
\n", "\n", "$$\n", "\\begin{equation}\n", " d(x; \\theta^{(d)})\n", - "\\label{_auto2} \\tag{2}\n", + "\\label{_auto5} \\tag{7}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", - "id": "fc5b439b", - "metadata": {}, + "id": "777cc3d8", + "metadata": { + "editable": true + }, "source": [ "indicating the probability that $x$ is a real training example rather than a\n", "fake sample the generator has generated. The simplest way to formulate the\n", @@ -2673,24 +2751,28 @@ }, { "cell_type": "markdown", - "id": "24694743", - "metadata": {}, + "id": "ca879622", + "metadata": { + "editable": true + }, "source": [ "\n", - "
\n", + "
\n", "\n", "$$\n", "\\begin{equation}\n", " v(\\theta^{(g)}, \\theta^{(d)})\n", - "\\label{_auto3} \\tag{3}\n", + "\\label{_auto6} \\tag{8}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", - "id": "6aae91d2", - "metadata": {}, + "id": "c14e4fa3", + "metadata": { + "editable": true + }, "source": [ "determines the reward for the discriminator, while the generator gets the\n", "conjugate reward" @@ -2698,24 +2780,28 @@ }, { "cell_type": "markdown", - "id": "a82d4be2", - "metadata": {}, + "id": "49d1f9a5", + "metadata": { + "editable": true + }, "source": [ "\n", - "
\n", + "
\n", "\n", "$$\n", "\\begin{equation}\n", " -v(\\theta^{(g)}, \\theta^{(d)})\n", - "\\label{_auto4} \\tag{4}\n", + "\\label{_auto7} \\tag{9}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", - "id": "29bc87f8", - "metadata": {}, + "id": "5e75a8d9", + "metadata": { + "editable": true + }, "source": [ "## Learning Process\n", "\n", @@ -2739,8 +2825,10 @@ }, { "cell_type": "markdown", - "id": "8108d824", - "metadata": {}, + "id": "a8d0e974", + "metadata": { + "editable": true + }, "source": [ "## More about the Learning Process\n", "\n", @@ -2749,51 +2837,59 @@ }, { "cell_type": "markdown", - "id": "e44e5407", - "metadata": {}, + "id": "b2e8bede", + "metadata": { + "editable": true + }, "source": [ "\n", - "
\n", + "
\n", "\n", "$$\n", "\\begin{equation}\n", " g^* = \\underset{g}{\\mathrm{argmin}}\\hspace{2pt}\n", " \\underset{d}{\\mathrm{max}}v(\\theta^{(g)}, \\theta^{(d)})\n", - "\\label{_auto5} \\tag{5}\n", + "\\label{_auto8} \\tag{10}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", - "id": "01611e48", - "metadata": {}, + "id": "4c60d647", + "metadata": { + "editable": true + }, "source": [ "The default choice for $v$ is" ] }, { "cell_type": "markdown", - "id": "d9c914c6", - "metadata": {}, + "id": "fa0e9d4c", + "metadata": { + "editable": true + }, "source": [ "\n", - "
\n", + "
\n", "\n", "$$\n", "\\begin{equation}\n", " v(\\theta^{(g)}, \\theta^{(d)}) = \\mathbb{E}_{x\\sim p_\\mathrm{data}}\\log d(x)\n", " + \\mathbb{E}_{x\\sim p_\\mathrm{model}}\n", " \\log (1 - d(x))\n", - "\\label{_auto6} \\tag{6}\n", + "\\label{_auto9} \\tag{11}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", - "id": "a613221a", - "metadata": {}, + "id": "828059e3", + "metadata": { + "editable": true + }, "source": [ "The main motivation for the design of GANs is that the learning process requires\n", "neither approximate inference (variational autoencoders for example) nor\n", @@ -2802,24 +2898,28 @@ }, { "cell_type": "markdown", - "id": "543356c5", - "metadata": {}, + "id": "badb0eb9", + "metadata": { + "editable": true + }, "source": [ "\n", - "
\n", + "
\n", "\n", "$$\n", "\\begin{equation}\n", " \\underset{d}{\\mathrm{max}}v(\\theta^{(g)}, \\theta^{(d)})\n", - "\\label{_auto7} \\tag{7}\n", + "\\label{_auto10} \\tag{12}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", - "id": "cfac2f93", - "metadata": {}, + "id": "6cdefd5c", + "metadata": { + "editable": true + }, "source": [ "is convex in $\\theta^{(g)} then the procedure is guaranteed to converge and is\n", "asymptotically consistent\n", @@ -2828,8 +2928,10 @@ }, { "cell_type": "markdown", - "id": "3d46a088", - "metadata": {}, + "id": "4ff45a4c", + "metadata": { + "editable": true + }, "source": [ "## Additional References\n", "This is in\n", @@ -2847,8 +2949,10 @@ }, { "cell_type": "markdown", - "id": "4a2a3ac9", - "metadata": {}, + "id": "a66b1c7f", + "metadata": { + "editable": true + }, "source": [ "## Writing Our First Generative Adversarial Network\n", "Let us now move on to actually implementing a GAN in tensorflow. We will study\n", @@ -2861,9 +2965,12 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "99e1c7b2", - "metadata": {}, + "execution_count": 20, + "id": "8bcd589e", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "import os\n", @@ -2877,28 +2984,23 @@ }, { "cell_type": "markdown", - "id": "75c386d7", - "metadata": {}, + "id": "502557a2", + "metadata": { + "editable": true + }, "source": [ "Next we define our hyperparameters and import our data the usual way" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "dfb387b0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", - "11493376/11490434 [==============================] - 1s 0us/step\n", - "11501568/11490434 [==============================] - 1s 0us/step\n" - ] - } - ], + "execution_count": 21, + "id": "ca9536e5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "BUFFER_SIZE = 60000\n", "BATCH_SIZE = 256\n", @@ -2919,8 +3021,10 @@ }, { "cell_type": "markdown", - "id": "536159a6", - "metadata": {}, + "id": "6ebf94c4", + "metadata": { + "editable": true + }, "source": [ "## MNIST and GANs\n", "\n", @@ -2929,21 +3033,13 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "e0a74990", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 22, + "id": "7013ed4c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "plt.imshow(train_images[0], cmap='Greys')\n", "plt.show()" @@ -2951,8 +3047,10 @@ }, { "cell_type": "markdown", - "id": "9df923de", - "metadata": {}, + "id": "dd5f06a6", + "metadata": { + "editable": true + }, "source": [ "Now we define our two models. This is where the 'magic' happens. There are a\n", "huge amount of possible formulations for both models. A lot of engineering and\n", @@ -2967,9 +3065,12 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "b8622ff4", - "metadata": {}, + "execution_count": 23, + "id": "dea2992a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def generator_model():\n", @@ -3033,8 +3134,10 @@ }, { "cell_type": "markdown", - "id": "7cff4ebb", - "metadata": {}, + "id": "77f5dc98", + "metadata": { + "editable": true + }, "source": [ "And there we have our 'simple' generator model. Now we move on to defining our\n", "discriminator model $d$, which is a convolutional neural network based image\n", @@ -3043,9 +3146,12 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "056e6590", - "metadata": {}, + "execution_count": 24, + "id": "2485c4e6", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def discriminator_model():\n", @@ -3081,8 +3187,10 @@ }, { "cell_type": "markdown", - "id": "dfaa2b71", - "metadata": {}, + "id": "da9b1726", + "metadata": { + "editable": true + }, "source": [ "## Other Models\n", "Let us take a look at our models. **Note**: double click images for bigger view." @@ -3090,18 +3198,13 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "329d3318", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model/model_to_dot to work.\n" - ] - } - ], + "execution_count": 25, + "id": "729c6ed9", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "generator = generator_model()\n", "plot_model(generator, show_shapes=True, rankdir='LR')" @@ -3109,18 +3212,13 @@ }, { "cell_type": "code", - "execution_count": 35, - "id": "64d76d91", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model/model_to_dot to work.\n" - ] - } - ], + "execution_count": 26, + "id": "1b9b268d", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "discriminator = discriminator_model()\n", "plot_model(discriminator, show_shapes=True, rankdir='LR')" @@ -3128,17 +3226,22 @@ }, { "cell_type": "markdown", - "id": "2227f7d1", - "metadata": {}, + "id": "2f7bd26a", + "metadata": { + "editable": true + }, "source": [ "Next we need a few helper objects we will use in training" ] }, { "cell_type": "code", - "execution_count": 36, - "id": "987587b9", - "metadata": {}, + "execution_count": 27, + "id": "34df3cca", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n", @@ -3148,8 +3251,10 @@ }, { "cell_type": "markdown", - "id": "897da37f", - "metadata": {}, + "id": "1ceb74f2", + "metadata": { + "editable": true + }, "source": [ "The first object, *cross_entropy* is our loss function and the two others are\n", "our optimizers. Notice we use the same learning rate for both $g$ and $d$. This\n", @@ -3160,9 +3265,12 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "0ae2012e", - "metadata": {}, + "execution_count": 28, + "id": "dadda0b7", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def generator_loss(fake_output):\n", @@ -3173,9 +3281,12 @@ }, { "cell_type": "code", - "execution_count": 38, - "id": "917cc879", - "metadata": {}, + "execution_count": 29, + "id": "ce5cb4e1", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def discriminator_loss(real_output, fake_output):\n", @@ -3188,8 +3299,10 @@ }, { "cell_type": "markdown", - "id": "7a0d467b", - "metadata": {}, + "id": "c9b559cd", + "metadata": { + "editable": true + }, "source": [ "Next we define a kind of seed to help us compare the learning process over\n", "multiple training epochs." @@ -3197,9 +3310,12 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "8d12eeb8", - "metadata": {}, + "execution_count": 30, + "id": "309b3c96", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "noise_dimension = 100\n", @@ -3209,8 +3325,10 @@ }, { "cell_type": "markdown", - "id": "ed34ee8f", - "metadata": {}, + "id": "c5cb0cb5", + "metadata": { + "editable": true + }, "source": [ "## Training Step\n", "\n", @@ -3222,9 +3340,12 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "87fe42ec", - "metadata": {}, + "execution_count": 31, + "id": "dbbe97ae", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "@tf.function\n", @@ -3254,8 +3375,10 @@ }, { "cell_type": "markdown", - "id": "7f7f7926", - "metadata": {}, + "id": "b6431118", + "metadata": { + "editable": true + }, "source": [ "Next we define a helper function to produce an output over our training epochs\n", "to see the predictive progression of our generator model. **Note**: I am including\n", @@ -3264,9 +3387,12 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "9628ae2e", - "metadata": {}, + "execution_count": 32, + "id": "cabfedf2", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def generate_and_save_images(model, epoch, test_input):\n", @@ -3287,8 +3413,10 @@ }, { "cell_type": "markdown", - "id": "1e9fbcfd", - "metadata": {}, + "id": "c5dde4ce", + "metadata": { + "editable": true + }, "source": [ "## Checkpoints\n", "Setting up checkpoints to periodically save our model during training so that\n", @@ -3298,9 +3426,12 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "7db374c9", - "metadata": {}, + "execution_count": 33, + "id": "20544715", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Setting up checkpoints to save model during training\n", @@ -3314,17 +3445,22 @@ }, { "cell_type": "markdown", - "id": "7cd90de3", - "metadata": {}, + "id": "d4e35ae9", + "metadata": { + "editable": true + }, "source": [ "Now we define our training loop" ] }, { "cell_type": "code", - "execution_count": 43, - "id": "d5e46919", - "metadata": {}, + "execution_count": 34, + "id": "24e3bc90", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def train(dataset, epochs):\n", @@ -3360,8 +3496,10 @@ }, { "cell_type": "markdown", - "id": "0e825c1a", - "metadata": {}, + "id": "1c4ba03f", + "metadata": { + "editable": true + }, "source": [ "To train simply call this function. **Warning**: this might take a long time so\n", "there is a folder of a pretrained network already included in the repository." @@ -3369,33 +3507,23 @@ }, { "cell_type": "code", - "execution_count": 44, - "id": "5467aa4b", - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "in user code:\n\n File \"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_90071/1041968406.py\", line 12, in train_step *\n disc_loss = discriminator_loss(real_output, fake_output)\n File \"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_90071/1414150303.py\", line 3, in discriminator_loss *\n fake_loss = cross_entropy(tf.zeros_liks(fake_output), fake_output)\n\n AttributeError: module 'tensorflow' has no attribute 'zeros_liks'\n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [44]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mEPOCHS\u001b[49m\u001b[43m)\u001b[49m\n", - "Input \u001b[0;32mIn [43]\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(dataset, epochs)\u001b[0m\n\u001b[1;32m 6\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m image_batch \u001b[38;5;129;01min\u001b[39;00m dataset:\n\u001b[0;32m----> 9\u001b[0m gen_loss, disc_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m generator_loss_list\u001b[38;5;241m.\u001b[39mappend(gen_loss\u001b[38;5;241m.\u001b[39mnumpy())\n\u001b[1;32m 11\u001b[0m discriminator_loss_list\u001b[38;5;241m.\u001b[39mappend(disc_loss\u001b[38;5;241m.\u001b[39mnumpy())\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:153\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/framework/func_graph.py:1147\u001b[0m, in \u001b[0;36mfunc_graph_from_py_func..autograph_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint:disable=broad-except\u001b[39;00m\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(e, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mag_error_metadata\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1147\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mag_error_metadata\u001b[38;5;241m.\u001b[39mto_exception(e)\n\u001b[1;32m 1148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1149\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", - "\u001b[0;31mAttributeError\u001b[0m: in user code:\n\n File \"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_90071/1041968406.py\", line 12, in train_step *\n disc_loss = discriminator_loss(real_output, fake_output)\n File \"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_90071/1414150303.py\", line 3, in discriminator_loss *\n fake_loss = cross_entropy(tf.zeros_liks(fake_output), fake_output)\n\n AttributeError: module 'tensorflow' has no attribute 'zeros_liks'\n" - ] - } - ], + "execution_count": 35, + "id": "0c32fe17", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "train(train_dataset, EPOCHS)" ] }, { "cell_type": "markdown", - "id": "aac7868b", - "metadata": {}, + "id": "b6556ed4", + "metadata": { + "editable": true + }, "source": [ "And here is the result of training our model for 100 epochs\n", "\n", @@ -3405,26 +3533,13 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "517986d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 36, + "id": "e70b7bd6", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from IPython.display import HTML\n", "_s = \"\"\"\n", @@ -3436,8 +3551,10 @@ }, { "cell_type": "markdown", - "id": "55e7baee", - "metadata": {}, + "id": "3559896b", + "metadata": { + "editable": true + }, "source": [ "\n", "\n", @@ -3447,19 +3564,13 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "0ea2989a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - } - ], + "execution_count": 37, + "id": "f5588307", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))\n", "restored_generator = checkpoint.generator\n", @@ -3471,8 +3582,10 @@ }, { "cell_type": "markdown", - "id": "6ab1ecc7", - "metadata": {}, + "id": "1668f66e", + "metadata": { + "editable": true + }, "source": [ "## Exploring the Latent Space\n", "\n", @@ -3484,9 +3597,12 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "9a322fc0", - "metadata": {}, + "execution_count": 38, + "id": "34603743", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def generate_latent_points(number=100, scale_means=1, scale_stds=1):\n", @@ -3509,9 +3625,12 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "0f932008", - "metadata": {}, + "execution_count": 39, + "id": "351aac6a", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def plot_result(generated_images, number=100):\n", @@ -3529,21 +3648,13 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "9e489dcf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 40, + "id": "5412d7dc", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "generated_images = generate_images(generate_latent_points())\n", "plot_result(generated_images)" @@ -3551,8 +3662,10 @@ }, { "cell_type": "markdown", - "id": "77a0c14f", - "metadata": {}, + "id": "9c51886f", + "metadata": { + "editable": true + }, "source": [ "## Getting Results\n", "We see that the generator generates images that look like MNIST\n", @@ -3564,41 +3677,13 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "fe7f1680", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 41, + "id": "c773ad5b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "plot_number = 225\n", "\n", @@ -3620,8 +3705,10 @@ }, { "cell_type": "markdown", - "id": "4c7e55a2", - "metadata": {}, + "id": "d7b4637f", + "metadata": { + "editable": true + }, "source": [ "Again, we have found something interesting. *Moving* around using our means\n", "takes us from digit to digit, while *moving* around using our standard\n", @@ -3632,21 +3719,13 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "b952e47d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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jjEYjtFot/H4/7r77bvzt3/4tFAoFdDodpqen4fP58LnPfY5stjhuHDeO20eT24edH8eN48Zx47hx3DhuHLc7c98z3PYw+E9/+lP09/djbGwMAFBRUYFAIIC3334btbW1WF5ehtfrhVwuR1NTE/L5PHbv3o25uTn4/X6IRCLkcjnMz8+jt7cX6+vrEAqFKCsrQyKRgM/nQ3l5Oex2O7VrVlZWEAqFUFlZidraWtx7770cN44bx+0jyu3Dzo/jxnHjuHHcOG4cN47bnbnvGW57RkMul+Nv/uZvUFNTA4vFgkQigerqamSzWcjlchw7dgwOhwPr6+vQarXo7e3Fj370I/B4PEQiEej1emg0GhpM6evrQzabhdlsRjweRyqVwvT0NLq7uyGVSgEA2WwWiUQC0Wi05NAJx43jxnH78HP7sPPjuHHcOG4cN44bx43jdmfue4bblk65XC7s3bsXZWVlqKqqQldXF5aWlnDkyBFYLBao1WrkcjmIRCJUVVVhamoK+/fvh1AoRHV1NaxWK+rq6vDggw/izJkzSKfT8Hg8EIlE4PP5SKfTOHnyJCKRCFKpFFwuF8rLy9HT04NsNot33nmH48Zx47h9hLl92Plx3DhuHDeOG8eN48ZxuzP3PcNtJxpisRhOpxNKpRICgQBOpxPBYBBerxcAsLKyglwuh/LyclRUVMDpdEIul0Oj0UAoFEKr1UKlUmF+fh7l5eVQKpXQ6XQoFovIZDIoFAq05nxxcRGhUIiWj9TU1ECtVnPcOG4ct48wtw87P44bx43jxnHjuHHcOG535r5nuO1EQyKRIBaLgc/nIxaLkd3VwsIC4vE4XC4XUqkUCoUCotEogsEgEokETCYTkskkCoUC+Hw+pqenUVZWBofDAb1ej1gshrW1NYjFYvL19fl84PF48Pv9WF5ehlgsLtmi4bhx3DhuH35uH3Z+HDeOG8eN48Zx47hx3O7Mfc9w24lGMplEX18fUqkUgsEgeekmk0lEIhFks1kEg0GMjo7i9ddfR6FQQLFYpIUi8XgchUIBSqUSTqcTL774ImKxGBwOB5aXlyEUCqHT6bCysgKRSAS9Xg+TyQQ+n4+xsTGEw2GOG8eN4/YR5vZh58dx47hx3DhuHDeOG8ftztz3DLc9DG6xWHDlyhWo1WoIhUIUCgWIRCKcOXMGUqkUUqkUfD4f2WwWqVQKd911FwYHB/HOO++gr68PFRUVyOfzWFtbg16vh1arxY0bN2AymSAUCmG32/Haa6+htrYWLS0tUCqVyGQypC9rbm7muHHcOG4fYW4fdn4cN44bx43jxnHjuHHc7sx9z3Db9rYcOHDgwIEDBw4cOHDgcLu4bekUBw4cOHDgwIEDBw4cONwuuESDAwcOHDhw4MCBAwcOdxxcosGBAwcOHDhw4MCBA4c7Di7R4MCBAwcOHDhw4MCBwx0Hl2hw4MCBAwcOHDhw4MDhjoNLNDhw4MCBAwcOHDhw4HDHcdt7NH74wx9CqVQin8/DYDBAq9ViZWUFc3Nz6OnpIV9doVCIYrGIcDiMWCwGg8GA06dPQ6/XY/PmzRCLxSgUCqiursba2hoymQz8fj9WVlaQyWRgs9lgtVqhUqkQiUQQCASQyWQQj8fxta997QO5/frXv0YwGIRMJoNEIkEul4Pf70exWIRIJAKfz0ehUIBMJoPRaEShUEBLSwvOnTsHsVgMi8UCo9EIlUqFN954A4VCAQqFAouLi8hkMvT/TyQS9Dem02nk83mIRCIEg0F84xvf+EBu3/ve92CxWKBQKJDP55HL5SAWizE+Po6mpibU1dUhGAwilUrBYDAgl8theHgYcrkc2WwW+XweGo0GDQ0NWFtbw9TUFPR6PZRKJQqFAhKJBGQyGRQKBXQ6HRYXF5FIJCAQCKBWq2E0GnH06NEP5Pb0008jHo9DoVBApVJBKBQiGAwim81CJBKhqakJmUwGoVAI0WgUQqEQRqMRsVgMIpEIAJBKpei/8eabb6KzsxPV1dWQy+UoFouIxWLg8XgwGAzweDxYXV0Fn89HeXk5wuEwHnzwwQ/k9qMf/Qj5fB7JZBI8Hg9SqRQ8Hg/RaBQWiwXRaBThcJg46XQ6rK2tQSqVIpFIoFgsIpVKIRAIoKqqCqFQCDKZDBqNBiKRCLFYDEtLS7BYLDAYDAgEAuDz+cjlcvTf+da3vvWB3J588kkIhUKIRCKYTCao1WosLi5ibGwM9fX1sFqtyGazyGazAICpqSlEIhHYbDYYjUbk83kAoEU4VVVVmJ+fh0wmg0gkQjwex9raGgwGAzo6OhCJRAAAxWIR6+vriMfjePTRRz+Q2+9//3sUCgUAIP/sQCAArVaLUCiExsZG8Pl8RKNRRCIR1NTUQCwW49KlS2hubgaff6v2UFFRgVQqhWKxiJmZGeh0Omi1WggEAqyuriIYDEKpVCKRSAAAvWeBQACPPfbYB3IDgN/85jdIJBIoFAqQSCSQyWRIJBLg8XgwmUyQyWRwOBzwer0Qi8VQq9VoaGjAyMgIFAoFJBIJ8vk8otEotFotLBYL5ubmUF9fj2w2i4WFBSiVSuh0OqjVahQKBYTDYfB4POh0OgwNDeHrX//6B3L76U9/ilAoBIlEgurqapjNZoTDYayuriKfzyMQCEClUqG8vBw6nQ4+nw9LS0swGAxwu92QSqUoLy+HWq1GKpXCzZs30dHRgVwuR9tVm5ubEYlEkEgkYDQa4fF4EA6HodfrIRaL8cgjj3wgt3PnztEW12KxSL7oMpkMsVgMNpsNuVwOKysrcDqdqKqqgtVqhdfrhcVioX/XYrFgaGgIQqEQyWQSZWVlkMvlEAgEAAC73Y6uri6srKwgm81CIpFALBZDIpGgv7//A7n9x3/8B2KxGLRaLWQyGfL5PEKhEILBICoqKrC+vg6ZTAaZTAY+nw+NRgOLxYL19XVIJBIAQCgUgsvlglgsRjwep+ctEonA5XKBx+PBZrOhoaGB7oNYLIZIJAKBQIDHH3/8A7n94Ac/QEVFBbLZLIRCIQQCAT0PuVwOVVVVtAU3m82itrYWa2tr9LMymQy5XA4ejwdWqxVjY2OQSqWQy+V0BvL5fHq/gsEgBAIB/V3pdHrDc+QHP/gB9Ho9UqkU5HI5lEolwuEwUqkUzGYzNBoNQqEQkskkZDIZtFototEoLl++DLVaDbFYDIFAAJVKhZmZGbS1tcHr9dJdwLb6Mn5utxvpdBparRZVVVWYnJzEX/zFX3wgt3//939HPp+HUCiERCKBQqGAQqHA1NQUampqoFAo4Pf7kc1moVQq6Yxh3x87+0QiEVQqFVwuF5RKJYxGI52/a2tr4PP5SKVSEIvFkMvl4PF4iEQiWFxcxN///d9/ILdf/epXdEfJ5XJIpVLahqxWq1EsFlFVVQWpVIrV1VVkMhlUVlZicXGRzhCRSASRSIRoNIr5+Xk0NDSAz+djbW0Nfr8fMpkMAwMDCAQCkEqlWF9fRygUgkgkQj6fx2c+85kP5Pb0008jk8mAx+Mhn88jlUrRu55IJKDX6+mOlUql8Hq9aGxsxPr6OkwmE3Fjn9nCwgISiQSqqqogk8kQCAQQi8Wg1+shk8kQDAah1WqhVqsRDocRDoc3jJN+/OMfQ6fTIR6PQ6lUQqPRwOv1IhwOQ6lUorm5GeFwGOl0GhqNBolEAslkEolEAjMzM7BYLLDZbPD5fNBoNLhx4wY2b95M8VA8HsfS0hLq6+shk8mwvr6OTCYDsVgMg8GASCSCz3/+8xt+bgqFAslkEhqNBkqlEsFgELFYDKlUCp2dnQgEAlheXkY2m0VDQwNmZmZgNpthMBiQz+cRj8fB4/FgNpvpvAyHw9Bqtairq0Mmk6EzPR6PA7i1WVsul8PhcOCrX/3qB3L7t3/7N/B4PMjlckgkEtrC7XK5UFdXh1gsBplMBh6Ph3g8DpFIhJqaGiwtLUGhUCCdTiMSiaBQKKCjowPhcBgjIyPo7OyEUqnEwsIClpeXUVlZiVwuB5VKRbFdNpuFQCDAn//5n38gt5/97GcoFouQy+UoKyuDSCTCxMQEIpEIhEIhmpqaoNVq6XkUCASIRCJQKBRYX19HOByGWCxGVVUV1tbWEIlEYDQaYTabUSgU4HK54Pf7KT52OBzw+/10l8Tj8Q0/t/fithONXC4HiUSC5eVlhMNhGAwGJJNJbNq0CcFgkF6gaDQKkUgEi8UCr9dLD14qlcL169ehVqthMplgMpkwNzeHsrIyVFdXo7KyEoFAAKurq5iamoJUKoVKpUI2m4XP54NSqdyQm8fjgUajQTKZRDAYBI/Hg16vh9frBQCUl5fD4/EgGo0im80ikUjAYrFApVIhFAphZmYG4XAYHR0dCAQC2Lx5M1ZXV2GxWOjAXFtbQy6Xg1QqhVgshlgsphdMpVJtyC2dTiMajVLwKxQKIRQKUVtbi3g8TodqPB5HMplEOp2GQCBAIpGATqdDKBSC2+0Gn8+HUqmkFzgQCEAikUAoFCIQCNDB4PV6UVZWBqlUCrvdTgHEByEej0MulyORSCAUCgG49eKVl5fD7XZTUFssFqHVarGwsIC6ujrkcjnI5XL4fD7Y7XYUi0U0NDRALBbTi+X1ehGJRNDV1YWbN2+ir68PoVAIfD4fcrkc8XgcuVyuJDe9Xo9wOIxMJoN8Pv++oJRxlUqlEIlEsNvtUKvVCIVC4PF4SCQSSKfTkMvlmJ2dhUajocsMADKZDKRSKWZmZijYjkajSKfTFPBvhFgsBrVajVgshng8DqFQiFwuh/b2dqyursJoNNIBLpfLUV1djfX1dbjdbtTX1yMcDlNiIZPJ4PV6kUwmUVFRAaFQiGw2C5vNhpWVFUxMTEAkEsFsNqNYLNLlvBGi0SjKy8uxurpKy3sKhQI9s+xAZsFLNBqFWq2GTCaDVCrF4uIi/H4/lEol3G43GhoaIBKJ6DmMRCKwWCzweDzo6OigoFAikcDn81GSsxESiQTEYjEymQzC4TDW1tYgEAiQz+fpIGeXsMVigcPhgF6vh9vthlgsRmVlJaxWK9RqNW7cuAEAVFDIZrNIp9Ooq6vDpUuXKBHh8/nI5/MIh8OUGG3EraysDMFgkAJtdnmw5HNtbQ2hUAharRb5fB6ZTAaZTAYKhQK5XA7BYBAikQiJRAKHDh2Cz+eD2+1GNpuFTCbDlStX0NPTA7lcjsXFRQgEAohEIkxPT6O+vn5DboFAAHq9HrlcDrFYDIlEgpJvVrzJZrNQKBRobGxEPp9HLBaDQCBAoVCA1+uF3+9HOp2GRCKhs1ypVILH49FW2UKhALvd/r7vNZvNlvxeE4kE5HI5+Hw+EokEMpkMhEIh5HI5UqkUuru7EQqF6JlsbGzE4OAgBZ2RSATRaBTFYhF+v58CoFwuB71ej/b2dqyvryOfz+Pq1auwWq30HrMzaiOk02nkcjkkEgnw+XzweDwEAgGkUimYTCYEAgH6/SyZrKqqoueNJTWZTAarq6tobm7G3NwcotEoeDweJBIJfYYCgQBKpRJisRgAKGnYCPl8Hvl8HsFgkILzZDIJsViMZDIJqVSKcDhMnwNwqzhRVlYGpVKJYrGIfD5PhTGz2YxQKIR0Og273U6J7fLyMhVpWAIzNzdX8k71+/0wmUx0/kqlUnr+XC4X2traKMHwer30nUciESiVSnquVSoVotEoKisrMTk5ScW5QCCAYrFIZ3gkEoFarYZEIqH7biNks1mo1Wr638lkEgBQWVmJbDZLZ5VKpaLAymQyUbKTSCQo+fV4PFSk4vP5dP6Xl5fD7/cjk8lQsFssFpFOpzfkBQA+n4/ilnQ6jXQ6jVQqhbKyMkQiEbrD0uk0MpkMstksVldXsba2hmAwCLFYDJVKBavVilAoBJVKRTGBWCyGUqmEUCiEz+eD0WiEWCymcyCbzSKTyWzILZVKgcfjUaCcSCSo8BkMBpFMJpHNZhGPxxGPx5FKpbC2tgaFQoG6ujoIhUJEo1HodDqsrq5Cp9PB6/XCbDZDIpFQID01NYXy8nIq7mYyGUxOTsJgMGzIjRUdWHLCzig+n4/Z2VmUlZUhm83SOTM2Nga1Wk0BM0sC6+rq4PV6UVlZSfFPsVikoordbofBYEA0GoVYLKYiQqkzhJ197Dljd2pFRQUAUOGMxSKpVArJZBKxWAyFQoGKenw+H36/n87LRCKBaDSKWCyG8vJyRCIRaLVaAIBAIIBUKqVN3huB3afr6+t0/7LvFLgVIzmdTojFYtTU1FDRs7m5mRLtdDqNsrIypNNpJBIJeDweyGQyRCIRLCwswGAwUJIOgIpbi4uL0Gg0G3J7L25bOsW6FDweDyKRiDKk0dFR6kwIBAKk02m43W6srKygubkZsVgM6XSaLtq6ujoUCgXcvHkT8XicqiUSiYQOVYVCAaVSiXQ6Tf+MBZcfBJZNs8BKLpdDpVIhkUggGAwCAMrKyiCTyeiyGxkZgcFgoOruysoKBT2syhGPx6HVaqliyTJXlkFHIhGIRCLIZLINucnlckog+Hw+FAoFpFIpYrEYBd7sC1xZWYHf76eXye12U9WeVduLxSKKxSLUajU0Gg2KxSJ8Ph8lJpWVlXTBZzIZBAKBkt8p69LweDyk02mqAkmlUggEAsTjcUps2M/FYjH4fD7kcjmUlZVBpVJBo9HA7/dT9YIlfqurq1CpVIjH43QAsyREp9NtyI0ljezvZc+Fz+ejCj+7GFgFkj1j8/PzWFtbg1arpcx8bm4OxWKRqrPsezQYDCgUClQhZN9HqURDoVAgHo9TZyKfz4PP51PyzSrN7DsVCARobW1FRUUF5ufn4XK5qJKSyWSwvLyMfD5P79T6+jomJiYoUEyn01hZWaGEtNQlzLpAAoGAnhu9Xk9JF6twFotF2O12RKNRCAQCNDQ0UDDKnjWWiIXDYUQiEUowV1dXAYCe6UQigUgkQhXwPwRW8WOJFgB4vV64XC4EAgEoFAqo1Wo6/IPBIHFiFzQA1NbWUsczk8nQd8ACKHaZplIpRCIRqlSW+uxYNZolm4lEgr4vlrBlMhksLCzA7/dDrVZDLpdT52N1dRXr6+uoqKhAPB7H/Pw8JR8qlQrr6+t0tgQCATqfLBZLySSIJUrsmWPPciaTgUgkgkAgoGAjFouBz+dDrVajsrKSgn/WkWadAj6fDz6fT+/E9evXIRQKwePxoNFokM1m6d2PRqMbcuPxeHQZswCTff7RaBRyuRw6nY46XslkEmazGW63G4uLi/D5fAiHwxTEsCCavWMSiQQOh4MCfBZcsmeu1PsgEAioaxQKhRAOhwGA3lMWfCWTSSwtLVFSwTrQLJlg3SK/3w8ejweFQkEdnFAohFQqBaPRCJPJRAWfQqFQMkFjCbZEIqHzN5/P03spFArpIrfb7VhcXEQ0GqVqbygUomCOfX+xWAxyuZxUByKRCHq9Hs3NzVTICAQC8Pl8FDj9IW4suZFIJHC5XJQ0sns5lUpR9b5QKND7xr4f1mHn8XgoFouQyWQoKytDLpejoltVVRUAULW/VBLEugQsmeXxeFhfX4fH44FcLodGo4FQKKQCAXCrUMruGxa4RaNRGAwGClRTqRR1OhYWFrC6ukoBNys6yGSykoXF93bHAEAoFMJgMGBtbQ2FQgGRSATxeBzhcBiBQIDOI6PRiJmZGSwsLFCQyn63SqWiM5slygCg0WjonJmbm0MgECj5uYlEIrq3WUcPuJWsMfUB+6w8Hg9CoRCUSuX7gl/WEZTJZFSwYmcm6wLm83naMp3L5RCNRumM3ghMgcAC5Uwmg1Qqhfn5eeo0FQoF5PN5rKysUIAtEong9XqxsrJCZ4vL5aKgvaKiAiaTCQDgcDhgt9shFAopiWfPMytcfBDy+Ty9m/l8nhI6p9OJbDaLtbU1umdCoRAVkyUSCSUxrCMol8sxMjJCZ4hcLodCoaAuodlsps4g+yxKIRwOIxgMIhgMUkGA3Uc6nQ6ZTAYymQzpdBpjY2P0HgSDQUxOTlJcx97TiYkJKgKz4hV7J3k8HioqKlAoFBAKhSjJvR3cdqLx3raQSqWCQqGAWCzG8PAwVldXoVarYbVaodVqkUwmsby8jPb2dhgMBsr4KioqsHXrVgC3AgsmQwqHw5ibm4PP50Mmk0FbWxtsNhtV0wQCQcnMiWWRwP9d8WEBGQvo9Xo9BZ3shYtGo9Dr9VCr1fTgMImPy+VCOBymKhVwKzusqamhNeypVIoC3I3AZAn5fJ6kIMVikeRIarUaNpsNZrOZDpHy8nLo9XpqXZtMJlitVsRiMRSLRUilUjQ2NqK+vp6SNCZHq6mpoY6IVCotyY3P59PDWVZWhrKyMgiFQjgcDpJjicViqgBJpVJIJBKEQiH4fD6SM7S0tNBlzKQnrNXmdDpRWVmJTCYDuVxOsopoNEoJ1geBVQJYhUcmk0EgEFDFigUzTELBKn4sYWUSP5PJBJ1ORy3gXC5H1QKJRIKWlhaSrahUKgrGSh2KLEEoFotQqVQwmUzEjb3YVqsVFouF/t7y8nI0NDRgbm4Ofr8fFRUVaGpqQjQaRaFQgEAgoMprMBiEw+FAJpNBbW0thEIhBUmsel/qO2WXMPsO2ffIAme5XA65XE7VeaFQCJVKRYGMVquFUCiEXq/H1NQUVldXUSwWYbVa0djYiHQ6TRVF4FbiwIKWUhfde3+eVd8tFgs9v2tra1RRZAERO4yrqqqg1+vp51KpFGpra1EoFKgCx+PxoFarIRAIYDKZUFFRQVVflsSx5+SDwKq+hUIBKpUKWq0W6XQaxWKRvmOtVgutVkuV7oqKCqqMseBAKpVSR9HtdgMA/R0GgwFLS0tUpWQJEpNubARWTczlclAqlTCZTBCJRPS7RSIRJBIJ0uk0lpaW6LzV6/WUTNpsNkqsM5kMNBrN+2SXwWDwfUEC+6xDoVDJ94FJYRKJBAWm7HcwaWyhUIBQKEQsFsPy8jK2bt1KFzM7G/l8PsxmMywWC8mC2L/v9/sxOztLQQ8LctjlV+pzYx0Dlljw+XyqtAOgijCrNMdiMUilUigUCshkMuqEZjIZOBwO8Pl8VFZWwmazQa1WU/JrNBohkUhI8svOh1LPGyuMSSQSelbX19f/vwpkbrcbLpeLghiWOOXzeUriWNFPo9GgtbUVzc3NpESorKxEbW0tgFvBPJNmbAR2JzJpqs1mo+CsUChQpZ7dp6z4w+RxsVgMwWAQuVyOpIJyuRwmk4nuKaFQSJ9beXk5AFAiWOo9ZXcAS7JYZ9Zut5PUWKPRUECk0+mo0MJUBayQwCS8TIrFJDAej4cSBtYFYAF1qVikWCxSIYUVUc1mM5LJJH137Cxi75RGo4HRaKQOCPtuWIAuEono2QBA3Tkm4YxGoxQwW63WDbmxQhNLqBQKBXU8WWcUAH23ANDR0QGDwYBUKkXnH1MXsLOOyQhZYUIoFMJsNkOtVtPfyToxG4HJ31gSwDo9S0tLFEtIJBKkUimS1SoUCkSjUZLGs87B2toaVldXUVZWhtraWlitVvB4PMzOzsLv9xPnfD5PCgn2/G30nbIkmSU7rGCaTCbp/mY8JBIJFaBYp1KhUKC8vBwqlQqrq6tIJBJQKpUwm80wGo3QarXQ6XQwmUzUEczlcqQ8KPUusGROLpejqakJarWapN2rq6v03YyNjUEikaCvrw9utxsej4e+G6VSScWBTCaDQqEArVZLhSoAqK6upuKKVCqFVqsteWe9F7edaAgEAlitVqRSKSwvL2NlZQUSiQQnT55EIBBAb28vKioqKHhg1QadToeDBw9SBSuZTMLpdGLXrl1IJpOw2+2YmJiAx+NBf38/AoEAxGIxFhYWSCqRy+XQ2NhYkl91dTU9CAAwMjKC1tZW2Gw2RKNRLC4u0n9nZWUFW7duxa9//Wuo1WpUVVVRm9BkMqG5uRmbNm1CX18fpFIplpeXceDAAfT29qKyshLDw8MQCARobGyEz+fD+vr6hrzC4TDJMVZXVxEKhTA9PY2+vj6qnjM5Q09PD5aXl9HS0oKqqio89NBD2L9/P1VtlpeXUV5eTskGO8QOHjxID9zw8DDC4TBUKhXy+fwfrL6Ul5dTZ8lms8HlclEixg59i8WCmpoa+Hw+lJeXQyAQoKOjA6lUCuPj47BYLFhZWcHDDz+M6elpXLhwAVNTU5TsMGnb+Pg4VCoVuru7kUqlsLCwsCE31upjbeSKigqMj49j27ZtJM1jz1dDQwOWlpZQXl6O8fFxHD16FK2trfB6vXRAP/bYY3A6nfB4PIhEInC73XA4HAiFQtBoNBRQv1dmshGYLj8Wi9HcCXsO4vE4qquroVKpYDAY0N3d/b65nLq6OtTW1kIqlUKpVFLnj1Vy5XI5ysvL8bnPfQ5KpZI6bs3Nzdi6dSslhxshEonAarVSFZF97m1tbQiFQlQ5VCqV2L17N+x2OzKZDG7cuAGlUolcLge73Y5sNgu73Y7GxkakUinU1dVBpVJhbm4OlZWVkEqlJOdqaGhAd3c3VQNLgcn8IpEIVWGi0Sh27NgBqVQKm82GeDyOYDCIyspK5PN5tLe3o6WlBXV1dST/MhgMeO6556iiKpFIEI/HMT4+jlgshmQyifr6egQCAQiFQlitVnpnNkI0GkUwGITT6cTy8jJWV1fhdrtx77330u9MJpPI5/PvkwouLCxgYGAALS0tMBqN2Lt3L55//nls2rQJDQ0NqK6uBp/Px+rqKtra2lBVVYW2tjaSjcpkMly+fBlGo3FDbu+VqyoUClRVVcHr9aKqqopmSFgF3Gw2I5fL0Rwdj8cjOYlYLMbQ0BCqq6sBgLqrdrsd99xzD5xOJyKRCHw+H9RqNerq6qh7shGYPJRVEhUKBfh8Prq7u2E0Gqlzkc1mSWIrlUpRXV0No9EIg8GApqYm7N27F1NTUzCZTMjn8xSc8Hg8PP7445BIJFCpVJS4sGChrq5uQ27ZbBZWq5UkOmy2oK6ujrp0wWAQKpUK+/btQzgcJukUe1fYmdzW1oaWlhaq9BcKBayvr0OtVlOC6nA4YLVa0dXVhUgkUlIOF4lEqNrNgtq1tTUcP34cHo8HTqeTZMNbtmyBWq3G1q1bIRQK0dvbi6qqKpK1FgoFbNu2jbq/7PtIpVJ4++23cf36dSwuLkKv16OpqQlKpbJkMM/kK/Pz8xgZGcH09DQmJyfR19cHvV6P3t5eALdma9RqNSYnJyEWixGLxaDRaKjAZjKZMDMzA7FYjFAoRImTy+VCsVjE9PQ0/H4/rl27hng8TolwWVnZhtzY2bm2tobl5WWEQiFIpVKSy7y3G1ZZWQmTyUTzUGKxmLp+FosFN27cQE9PDyXILBg+deoUzQyxJEQoFP7Bu4HP56Orq4uKpPl8Hl6vFw0NDXSW2Ww2WCwWWCwWLC4uQqlUYnBwEFu3bqWYYnl5GU6nk6RArAIuk8mwc+dOrK+vI5vNYnp6GjKZDPX19RAIBH/wO2USLlZgYh2m9fV1WCwWutNkMhndR8vLy6ivr4fBYIBUKsXevXsxNzeH1tZWmEwmksytra1R7BAKhbC+vg6VSgWLxYJcLve+4tQHcWNF23g8jlAohOXlZXR2dlIAzZ7tlpYWrK2twWazYW5uDl1dXaitrX2fDMxisUAqlUImk1HCqdfrqai2trYGq9WKnp4eSjxLobKyEgBIOlcsFnHy5EnE43Hs37+fZot37NhBSbXX66Vi2OzsLCkEDhw4gHQ6TXK1dDqN9fV1KoR6vV6aWxIIBPS7PwgqlQqtra2kXkmlUojFYrj//vsxPj6Oubk5vPnmm5iamkJtbS3sdjuqqqowPT2NgwcPoqKiAm63m+TyjzzyCCKRCL0rjY2NsNlsWFxchEwmw8jICHp7e3H//fdTJ+l2cNuJhlQqRbFYxP79+3Hs2DE0NDQgFothbGwM99xzDxwOBwVyHo8Hu3fvRjQaRXd3N77zne/g+vXr6OrqQigUwoEDB/Cd73wH5eXllP0eOnQI6XQaX/rSl2A0Gmkg0W63Y9++fRgaGtqQGwtWWKdFpVJh165dmJubQ3d3N7q6uiCXy2G1WnHkyBF84hOfQDabxb333otnn30Ws7Oz2LJlC7LZLCwWC7RaLbZs2YL6+nrYbDYolUq8+uqr2LlzJw0bJRIJLC0t0TD5RrBarSgWi9ixYwe6urpIlzo8PIzDhw9jbW0NS0tLmJiYwLvvvot7770XZ8+ehV6vx/e//3288sorqK2tRX19PU6cOAG3242Kigrw+XxEIhEsLy/j4sWLuOuuuzAyMoJt27aRvpNVyUohmUySDjQQCKC7u5ukWwDQ2NgImUyGs2fP4p577iGd9+DgIH0WTzzxBCorK/HCCy/gwIED2Lp1K8rKyugQKy8vx+zsLHp7e7G6uoqbN2+iv7+/ZCUyl8vB6XTScC3T+Z4+fZq+B9YSnJiYoKH65eVlDA4OIp1OQ6fT4dKlS+jq6sLVq1fR0tKCsrIyxGIxrK+vI51O48iRI5iZmaEXVCAQYO/evejp6dmQW0VFBSQSCQYGBlBRUUEB38TEBFpaWpDJZLC4uIihoSHMz8/jyJEj+P3vf4+Ojg6o1WoaYAsEAqivr8dTTz2FzZs3UyWjpqYGuVwOu3fvxo0bN1AoFDA1NYW5uTn09/fDbDaX/E5ZMsCSIaFQSPKm5eVl6PV61NTUwGw248CBA9DpdGhra4NcLsehQ4fw6U9/GjKZDB0dHZicnMSpU6egVqsxNzeH4eFh/Pa3v8XBgwdx5swZqFQqLC8v4+rVq+Dz+fD5fCW5qVQqmM1mdHd30wxSeXk5DAYD7r//fvzqV7/CzZs3EYvFMDMzA5VKhfPnz8NsNqOmpgZ33XUXjh8/jvHxcZw6dQperxfHjx+H0Wik+Z/x8XHs2bMHExMTsFqtcDqdGBsbw6ZNm7C4uLghN7PZDJlMhu3bt6OhoQHBYBCJRAJPP/00Nm3aBJvNhp6eHnR1dUGv12PPnj24dOkSDAYDfv/730MoFOLo0aNYXV3Ftm3b8JOf/ARGo5E+7+3bt0OtVuP8+fN44YUXEI1G4ff74fF4IJVKMT8/vyE3NgPALh232w2ZTIZnnnkGFRUVpFdWqVSoqqpCS0sLzV2w57OzsxOhUAiPP/44BgcHIZFIUFZWBoVCgVgsBrfbjaamJpr/isViiMViqKmpKXkRsyDMYrEgFArh6tWrqKqqwu9+9zv09vbC4XCgUCigpqYG9fX12LJlC2KxGCUvu3fvxrFjxyAUCrFv3z788z//M/bt24dcLodwOIyamhqMjIzgyJEj9PPFYhFutxs9PT3UNfogMNmVWCyGTqeDwWDAnj17YDAYcPz4cTQ2NmLTpk0oKyuDw+HAwMAAXnrpJVy9ehU//vGPMTY2ho6ODrS2tlLXo76+Hg0NDTCbzTRjceDAAVy9ehXAraRwfX0dOp0Oo6OjJd+F6elpbNq0CXv37kVDQwOamprgdrths9ng9XpJeiGVStHV1YUf/OAH9Dnz+XzIZDJcunQJPT09+Ku/+ivs2rULzc3NlATNzs7ij/7oj6izHIvFsLKygtra2pLPm9lsRjAYxD333INTp05Roj8yMoI9e/bg7/7u72C32ylhZQOhmzZtosKZzWaDQqFAb28vfv3rX6O3txcejwfDw8N0Fn32s5+lv29qagrj4+Nob2+Hx+PZkBsL+FmXTKvVUuGyvb0dGo0GNTU1qKysRCQSoee4srISFy5cAAB0d3djdHQUdXV1eOGFF9DT00Na91gshtnZWWzdupXmND0eDwXOpQoCOp0Og4ODJJnKZrOIRCK4evUqmpqaMDg4CIfDAeBW8PrNb36Thpvn5+dJrrS8vIza2loKkHt6elBbWwufz4fTp0/jq1/9KnWMrl+/juHhYTQ1NWF4eHhDbnq9HrFYjOIklqSOjIzg5MmTJNOWy+VYWVlBNBrF008/DZVKhZ/+9Kd4+umnMTY2hqtXr0Kv11PhgHXmrFYrdDodWlpaaCCfSaJaWlpKdrtzuRwmJiagUChQW1sLm82G/v5+OJ1OyOVynDt3jmRfLpcLe/fuhVAoxPbt2/HDH/4Q77zzDmw2G8rKyihJrqiogNPphNfrhdVqhd/vx5/+6Z+S0crs7CyuX7+OpqYmKqp+EAQCAYLBIPr7+7FlyxYYDAao1WqMjo5SYsUkRpOTk+jq6qLiVmtrK9ra2mAwGLC8vIxdu3bh3XffxZ49e2A2m7G6ukrJck1NDSYmJujsDYfDsNlsWFpa2pBbNBqF0+nEtm3b0NraCrvdjpGREfziF79Ab28v1tbW0NXVhbvvvhutra04fvw43nnnHeh0Orz66quUKCkUCoqvHnroIWzZsgV+vx9vvfUWRkZGcOzYMTz55JMoFot46aWX8OKLL+Kuu+7CyZMnN+T2Xtz2MDiTVIyNjZEmM5fLoa+vDy+99BIN+jAJxerqKrq6umC32/Hd736XKoXMnaeuro607MlkEu+++y5effVVfOMb30AikYDb7abuyPLycsmWIJMtmM1mCAQCCt6tVisuXryIvr4+uFwuOJ1OSCQSZDIZVFRU4PLly8jn81hdXaXhueeeew733nsvZYDsi6ysrMQvfvELCkCSySS1lkoNiC0vL8NgMODKlSukh15eXsbRo0fxyiuvYMeOHYhEIkin06ioqKD5h8HBQWzZsgV8Ph/Dw8NU/e3t7cXExAScTifp1vP5PM6fPw+/3w+dTgeBQACj0Ug63o3Ahn8EAgEMBgMEAgGmp6dRXl4OHo9HzjAejwdVVVVYXl7Gyy+/jJ07d6K9vZ1kBnv37oXD4cAjjzyCVCqFy5cvQyQSob29HcFgEC+++CLJ6hoaGhAKhXDz5k0kk0kcO3Zsw+/UaDRiYmKCWorpdBr79u3D22+/jdXVVRgMBtTU1KC9vR0OhwOrq6vUwXI6ndBoNJBKpRgdHaUA1+l00qxNIBDAyy+/DIfDgaqqKtJlj4+Pl9Qvr6+vQ6FQkJME6+SxQXkWKBuNRlgsFly4cAE+nw9XrlwhFx7myGM0GnHffffh4sWL5Dbh9XrhcDjQ1dVFVWmmkb58+XLJ2ZZsNguTyYRIJEJuX21tbZiamsKmTZtIGjc/P4/19XUcOHAAMzMzAG4Fsy6XC263m/S+X/jCF+D3+6k1297eDj6fjzfffBPRaJRa5ew7K6V1BW4FyMz5i2lRz5w5g0wmg/Lycpw6dQoLCws0YMo01i+//DL6+vqo09Dc3IzZ2Vls2rQJY2NjqKiooKB5cHAQzz33HB588EGSTqyuruJ3v/sdXC7XhtxmZmbQ2tqK2dlZqlDLZDI0NzdjfHwcy8vLZK4gFArR1dWFQ4cO4cyZM3jwwQdp9iwcDlNV98yZMzCZTKRdb21txYMPPkhnCJs9S6VSJbstTJ7i9/uhUqmg0+mg1+uxdetWrK6uwmQyYWlpCcViEY2NjbDb7dTmZkOWTMt+48YNDAwMYGhoCF6vlwLw5eVliMViep6ZVMHlcpU848RiMXVt3W43ydi2bNmC6elpbN++nTrhyWQSmUwG09PTNNT7+uuvg8fjYdu2bQiHw9i0aRP+5V/+Bb29vTCZTHQWBwIBNDU14ebNm+TkY7fbS76rkUiEClpsQH5qagrbt2/Hu+++i/vvvx92u52S/vHxcXR2dsJoNCKXyyGXy2Fqagpra2t47bXXUFtbi0wmg8bGRiQSCaytraGyshLLy8tIJBJwOp0oKyuDXq9HKBQqWegJhULo6OggGS/r5AeDQfT19UEmk8Fut5PRxRe/+EUAoDkqVq1lXchHHnkEN27cgEKhIOeeTCZD3an5+XmUlZWhsbERoVCoZKV0ZmYGWq0Wk5OTSCaTNNguEokwOjqKRx55hCS1bJDe6/XCZDJh06ZNiEajmJmZwenTp6HT6fC73/0OmzZtgsVioQ5Oc3MzyYeBW8lNLBbD/Px8Scmvx+NBU1MTzUgyw490Oo2ZmRk0NzdTVZwNUScSCfh8PnJiY8l9NpvFjh07cO7cOfT29kKj0cDtdmNgYAA3b96kYiKbAWWy243g9/vp2QBuJRPhcBitra24evUqNm3aBKfTSZXsF154AW1tbRgeHqZOHauMP/vsszh69CgVXVg3V6PR4KWXXsLCwgLq6+vR19eHcDiM4eHhklIWNsPCZD6sSHfgwAEqXExNTSGbzaK7uxuDg4OU9D3yyCPkJMrchhYWFhAOh7F3714olUqMjIwgFouhqqoKV69epTkZNqvGktIPQjQaRV9fH5xOJw3mLy8vQ6fT4eLFi/j85z+P4eFhOBwOqNVqTE9P0zn2mc98hpwQBwcHycXv8uXLqK+vB4/Hg8PhQEtLC0QiESYnJ0npEY/HMTw8TLLCD0IwGERLSwtu3ryJ8vJymEwmOJ1OACCZL5Orsw7ynj174PV6cebMGZSXl1PsMTg4iP3795O8MJlMkuR8eXkZPp8Pu3btoneBKW02ApthZWZDPB6PDFz4fD5aWloA3FJhMInz+fPn0dXVhVgshmw2i4mJCSwuLkKr1WLz5s24cOECSbi2bdsGmUyGZ599FgqFAoVCAYcOHQKPx8OZM2eQy+VoHKIU/l8Ng7/44osYHR1FMpmEwWBAdXU1hoaGUFZWhoGBAYjFYhoEd7vd9L+feOIJOBwOtLe3kwtATU0NCoUCfD4f1tbWEI/HMTMzQw5DbLh4YWGBXG82AtOkMxcnprEeGRkh+7umpiaUlZXh+vXreP755/HOO+9gaWmJKvrd3d04efIkBgYG8Oqrr5J7BLOcA25Viufm5mCz2Wgg9L2OIB+EbDaL2dlZqFQqyrgNBgPi8TisVivKy8uh1WppEIdlvU6nk17cjo4OHDt2DDweDzdv3oREIsHhw4fR29sLr9eLfD4Pt9uNlpYWXL58Ge+88w7W19exdevWDS0pgVtyM+a0wjJ6Zm3GBp38fj8mJiZw/vx5BAIBPPzwwxgcHMR3v/tdvPrqq9BqtWhra0M6ncYvf/lLyOVy7N27F52dnfB4PBgaGoJarcaOHTuQTqcxMjKCiYkJACgpK9DpdHS5+f1++Hw+VFVVYWpqCjabDXv37oXNZoPT6cSrr76Kuro6nDhxAnq9HpcuXcLq6ioaGhqwY8cOSkb0ej22bduGjo4OGpCVy+V49NFH0dnZiWKxiGg0+gcH/tRqNaampkjrzdqgCwsLpIUvLy9HMBjEuXPnwOPxUFtbC51ORzI71llJpVK4du0ayY7Yxcnj8aBUKmGxWEgaNjw8jIqKipLuHcwZaXFxEYuLi/B6vXC73TCbzRgdHQWfzyd3DFYZlMlkKBaL+PWvf43R0VHU19ejp6cHdrsdf/d3f4elpSWaA8hms5icnITT6URvby+SySSmp6fJlaKU+QBwywFuYWGBXM2Ya5vD4cCbb75JTm4s8Wbt7YqKCly5cgXhcBgVFRWYnZ2FUqnEb3/7W9y8eROZTAa5XI4G67VaLW7evIkLFy5Q8N/W1obPfe5zG3JLpVLkrGUymbBlyxaYTCa8+uqrOHPmDA0Q+/1+LC8vIxaLUXXyv/7rv/CLX/wCi4uLqKiowMrKCi5cuIDe3l5IJBIEAgGShLGLoaysjLotzMp4I1RVVWFubo6qsz6fD6lUipKs9fV1JJNJuN1unDlzBnK5HGazGYuLi3jyyScxMzMDm82Gvr4+2Gw2PP/882hra0N3dzdMJhNJWisrK6kSyYbbZTIZmpqaNuQmFovhcDhgMBhw+PBhHD9+HDwejyq0IyMjuHr1KoaGhjA6Ogqz2YwTJ04gHA5jZmYG6+vrKBaLJGscGRkhR6DFxUWEQiGcO3eOfo4luMyso1SllJkeLC0tYXFxEevr67Barfj3f/93SKVSml8Jh8OUFFqtViwuLuKZZ57B5OQkWSwfOXKEilXM4EQsFmN6ehparRZbt27FwMAASTOy2WzJrm2xWKRZQHYGV1ZWoqGhAefOnYNOp0Nvby8sFgsWFhbwy1/+krTdb775JiYmJqDValFWVkYuWOXl5bh27RouXryISCSCN998E1evXsW2bdvI6Yzds6USNLFYjKmpKaTTadhsNrS2tkIikeDmzZu4fv061tbWaK4mEAhgbW2NtNsjIyPkRqTX6+FyufAv//IvNJzL/qbnn38ew8PD8Hq9sNvtuHHjBhwOB4xGY8kETalUIh6P01ydSCSiajxLdtnft7CwQMEkGwpOp9NQKBTo7+9HS0sLBgcHKehixggTExM009HU1ESyMIPBUFIeWlFRQZ1E5tzjdrvx93//96iurkZTUxM2bdqEQqGA3//+95ienobD4UBdXR3y+TwUCgW2bduGe+65Bzt37sTy8jK5cvJ4PLjdbszMzGBtbQ2HDh2CRCKh2ajm5uaS6opisUh3gkAgoI4y67rF43HU19dDJBLhzJkzePTRR3H48GEIBAIMDg7i8uXLmJ+fp6B0aGgIAoEAdrsdHo8HfD4f586dg8FgwAMPPICmpibqxDY2NpaUw4lEIty4cYPcEVl8ND8/T9+vyWRCWVkZlpeXMTc3h7q6Opw/fx5PPfUUgsEgjh49ik996lP42Mc+hlAoREP3zDVvcHCQ5rzY7C9LNtmz8UHQ6/V0fjNLczY8nUqlsHnzZlitVgQCAczNzUEikeDKlSvUBWUFdbPZjPn5eQwPD2NkZARisZhkncyk5bOf/SzZFLPZF5YsfBCYkx6TMjO5Z7FYxJUrV6hLf/36dfzTP/0ThoeHceDAAej1ely5cgVXrlwhiefk5CReeOEFWCwWzMzM4NKlS5ifn8dzzz2HxcVF1NbWQq1WY2FhAePj45BKpSXPt/fxvK2fAsgqj1W+WSV93759NLykVCrR3t6OnTt3Ip1OQyQSwe12o66ujgZjmMZ+69atCAQCkMvl5J7y2GOP4eWXXyZ9Javc/aFhcDawxiRU7IXdvHkzDSIzRyWDwYDV1VXcc889EIlE2Lx5M/h8PmXCTNPL3IB8Ph9EIhFJM5iukWk/5+bmSmacQqGQbPfYMK9EIoHVaqX2Mav+7969+30+36xNx2x6Y7EYOY74/X6Ew2GyHWTzE8z1iWl/S7nFsOCROTyx/y5zmmA7HTQaDQYGBqjtWllZiXvuuYcCvlgshkwmg507d2JhYQEulwuJRAImkwn33nsv7TVgCSpLMkvJMTKZDLV49Xo9ebhbrVY4HA64XC6yaxwYGIDD4YBQKEQqlUJXVxf9PqVSCZfLhXg8jpWVFayvryOVSkGr1aKjo4OGs5lemulUS7knxeNxugyZXjufz6O1tZX2l/D5fJIPJJNJ7Nq1C4FAAH6/H+vr68jlcigvLwefz8euXbuwvr5OOwaUSiUlb+yCYwHF+vp6Sb08SyL4fD7KyspI3sGqEQBoyFChUMDtdsNisUAgEGD//v2kw2QmBdXV1ZiamqJh8I6ODnziE58gm1uj0YiWlhY0NDQgmUyiq6trQ27smWP2nWxwnml/mQuMWq2GSCSi+SitVkuuKD6fjy788+fPk8aWyVqWl5fpuWYSNTbzxC7DjaBQKKjFz6pUrGXOtO1SqZRkQjMzMwiFQkgkEsTbYDCgv78fqVQKx48fRzgcRnt7O7Zu3UpubPF4HHv27IFEIoHBYIDRaMTKykpJi0W2P4ZVq9nuFpvNRr+fOQ2Vl5eTNCkSieDEiROUdAC3qtWf+tSnSMPMdjhs374do6OjKBQKVIBhcotSz1wwGCQLc6/Xi2AwCI/HA71ej2KxiL1792LHjh3o7OxEfX091tfX4XQ6IZPJaCaur68P9fX1sNvtOHjwIMknWFDAuo0suWcJNzufNwLT6bPhSOZ819XVRUESGxwdGBiAQqGgOYEdO3bQfA2TZPT29iKbzWJ5eZmspA8cOEDvDNuJUVVVRdXPjcBmRpiWnw32d3V1wWKxgMfj0fwNm5FTq9Xw+Xy455570Nraimg0ivb2dni9XvT19b3PCry8vBy7du0iRyCxWAyTyYTy8nKIRKKSHQ0ANB/JCjIikQjHjh1DTU0Nurq66HNvbm6GRqOhPQg6nY52WbEktKamhgI/5iTZ19eH1dVVMj5h5w1zCCwFNp/DXHfy+Tyam5vpLGDnTFlZGZaWlmA2m3H9+nXU1NRAo9HQfox0Oo3+/n7aQ2CxWKgYyOfzsXPnTqytrYHH40Gr1cLr9ZI70weBxT3vdcJjO2jYTqJAIACbzYbt27dTzBSNRtHf3w+FQoHLly9jaWkJ6+vrOHLkCMU1bKC8q6uLBqI1Gg3Kyspo+L3UwHU8HqfAkw1pM/lTMplEVVUVLBYLmpqacPDgQdpVtbCwALPZjN7eXuzZswd79uyB0+nE4cOHyS2NWc8ePnyYpHFsXstqtZLEqhR0Oh0UCgVMJhMsFguKxSI2b95Mu6qYrK2qqgrpdBrbtm0Dn8/H9u3baRcUW7/ABu9bWlpIBsgKMps2baLObWtrKz27G0EoFNIQOOsksl0ZrOPLOl+tra1YWVmBVqvF0tISenp6YDabkUqliKfZbKaur06ng9FoJAdRFnOxHT5MqbMRmDSaFV4YNyaJ7unpocLpoUOHsL6+Dq1Wi1Qqhfb2digUCuruVVRU4OjRo1RYZM5XPT092Lx5M6qqquhdY8qhP3Tf02d4Wz/1f33Y7EAAQAOpbW1tCAaDJLWoqqqC0WgkC0zWls9kMnC5XHQAsgyPuaIAwPbt2/HMM89Qi5BpjsvKykpqv98bRLFKNbvU2T4CFsjpdDpUVFSgsrISZWVltHPgvZZ/rDLEWudarZYeqkQiAZVK9b6dEaXavOxFYxWubDYLnU6HRCIBm81GL4XBYKAhT/bSs+UykUiE7MqYSw9zhbDZbNDr9SSnYYElc2spVf1mQ57vdYhhASDz0GfWmezn7HY7KisrodFoyCWMOT+YTCb6v8ViMWnv29raEI1GyXaQPcSlXE/YzzH7N+BWQFNWVkaHLHDrsmayAOaYw75nmUyGubk52ovApEkscWRJiNfrpSEx5nNeSrPJWpTsM2GyIvYCMllbS0sLZDIZOZfU19fj2rVrVG1hDjNNTU3k/rG6uopIJELvBrvUmBtWNpstqUlnFy2r9DFXGhbU53I5SpjZkCN7fnt7e2khklAopP0FbDeOSqUix47Ozk4aLgwGg7T4p1QnCADtuGG7MhgXs9lMy7tYsPxeu0QWjDFbWJZgM00vq3YLBAIsLy/j+PHjdLEwxy029LgRmMGAVqulBZYsCD927BhVOJlPOnNusVqtSCaTtAiLnZU+n48GnuVyOS2Yq6+vh1QqpT0FbHi11GfHFiQyhyLmRsMWnzK3E7PZDKVSSZ2tiooKyOVyRKNRckBhAQerQgO3nG+YPIkNnrJzmC0D2wgsmGdJfCqVglqthlarJSMHNshaKBRo6NZkMlESE41GyZ6SzcCw7gX725jkg81/5XI5+n0bgf17zOufOVmxSzadTlMAV1FRAbPZTGccs9kViUS03G9sbIz2GbBiSG9vLxYWFigZjEQiZEFeyp6SWbgzr3xWfBGLxTTrkM/nYbFYyJqd2chWVFSQoxObiWBSOVbVZsXBsrIyjI6Oki1wOp0mh6yNIBKJYLPZaF8Ue7Y7OzvpPmB/Q3l5OTmTOZ1OOqvYLJzBYCCnQFag1Ov12LJlC7xeL2ZnZ1EsFt93j5cKrt57/zGHJNbl2LNnDxUM2VnF/h12xlssFkqS/H4/2trasLq6Sm5ySqWSEndW6AJAHehS70IymXyfi142m0VZWRkVBBYXF8mApra2FjweD9XV1SSrZXsVJiYmoNFoUF1dTffdezvcly9fhtvthslkovkAZoqxEZgr33tt1Fk302Kx0M4ZZrHKrGCZAxkrErKuAysa5HI5mrPp6OjAyMgILZ5jjn9/aB8EMxti9tiseMeSY/bPmWyNyaL1ej1aWlpI9cDOIBZHsedCKBSioaEBNpsN+XyeXD5ZTPaHihVMNseeOWZfq9FoqOjA5sCYo2AoFEJrayvFV8CteJnNnrHPUa/Xw+/3o7m5GdPT02RlzHbYlEoe2bnKLKfZ9xSNRskyl33+7G+MRqOk1GHSK1Z0MhqNcDqdSCQSUKvVdH+wPSbMfY1Zzf+h5JHhtjsaEomErLLYyysUCuH3+7Fz5873OQZEo1F0dnZSELW0tET2fM888wwaGxtx/fp1dHR0UKDR3NyMYrGIz372s2QDxh5MFhCW+rCZXS7zgI5Go5ienqbAOxaLkRRgYGAAbrcbBoMBwWAQZrMZO3bsoH9/cXERjY2NNFfAqpVtbW1wuVxk45ZIJP7gkBNbumKxWChrNZvNGB8fx9atW2n2ZH5+HhcvXqTdIyqVClNTUwgGg+SU0t7eDpVKhW3bttF2cJvNBpFIhAceeAB+v58s35j0qVSiwV5cJtlhFRKPx0PZu06nQywWw4ULF6BSqXD27Fmk02mcPn0asVgMmzdvBnDroLh27Rp6e3tRV1dHlzRrIU5OTlJlYWlpCTU1NSXlcExexqp8zEmCdW6MRiMEAgEWFxdx4cIFsiVsaWlBNptFeXk5qqurceHCBdoWzhIftrzP6/WiubmZbG2Z77/ZbKYu0kafm9frJYkT09YODQ2hUCjgzJkzAG5J05iHPXMVYYd2OBzG5OQk7HY7xsbG0Nvbi3g8jmvXrmFpaQnBYBDNzc3k2AXcChwqKysxOzu7ITfm9S2VSrGysoKxsTFy/nlvcMSqrxUVFZienqYkp7a2Frt27SLp1o0bNygAzmQySCQSuHr1Kvbt20c61bm5OQrASr2nAChBt9lsUKlUdEEyF5bR0VF6R7LZLOmd2Z4eZkrh9/vxyU9+EmfPnkVjYyNcLhdcLhdMJhN8Ph96e3shk8loC6vT6URPT0/JDp/ZbMby8jJ1UtmG8JGRERrwY+/T1NQUOUe1trYiFAqR89uNGzfQ2NiIoaEh7Nixg7bImkwmGlB/++23oVQqKbiqqqoqWWFmc2jvXYjJli4pFArq5LBEpLGxESMjI7Barbhy5QpZKmcyGVRVVeHSpUu0qJHt05BIJDh+/Dg0Gg0VhzKZzB8cSJTJZEilUmhsbERzczOsVisGBgbA4/FQU1ODYDD4vkWQzc3NaGlpoX0/CwsLeP311/Hkk0/CbDZTp4Cd/+xZYN10uVxOz013d3fJjrJOp4PL5YLFYiELX9b57OjoQENDAyV8ExMTqKmpwenTp9He3k77E1gluru7GxcvXqSAi1m3p9NpNDc3Y2lpCR0dHdBqtVR4K3WOSKVS6sgUi0VEIhGqwDMJTllZGRoaGqDT6dDY2EidoN/97nfkiz84OIi6ujo4HA60tbWhs7MTarUaKysrNCD97rvvIhAIYGpqCsPDwyT/KvWdMrcfk8lEySmzbB0ZGYHf76dChVwuh16vJ+08C6Q8Hg8GBgbgdDrJoUgul6OhoQEWiwU7duzA888/T4XIpaUlNDY2oq2tbUNu7HNly3MzmQy0Wi1GR0exefNmOuOZWqK/vx8ulwsDAwPkyNjZ2YlUKkVOkG1tbRCLxWRZXygUUF5ejqmpKTQ1NZFTIFtsthHY7hT2jLJlsWw4Wi6XI51OI/R/bXxnXW22/I51ocRiMbq7u7G8vEydBovFQlLrzZs3Y2Vlhf5+tqi4lEpApVLRfolCoYBAIEDzZwaDgQbVk8kkSYjLysqwY8cOjI+PY2RkBD6fD2+88Qa5dzY2NtLCz3g8DofDgZ6eHnKDZLNOsVisZFDKCllMVsoSHrvdDovFQnGa0WjE0tISqqqqsLS0BLVaTaYhu3btQllZGXVGjEYj1tbWEAgEUFZWRmYaly9fJmdUtrfsD+1pY/InJicXCoXkTMfj8UjpEo/H0draiqWlJZoDNRgMqKyspPfUZDLBZrMBAO2risVi6O3txbVr16jw4nQ6qbu8EWpqaqDT6cg2lw3U//jHP6bN8+l0mkwErFYrpqamUFlZSQYdu3fvhkAgwMrKCoaHh1FfX0/fZ0VFBW7evEk7zJidN3OIYzL4P4Tb7mgwFwQAtE/DYrHAbrdDJBKhsbERP/nJTxAOh3HPPfdgbm4OZrMZL730ElpbWwGAKmhra2sYHh4mfWZ1dTXa2tpoyKupqQk+n49aQtevXy8ZIIRCITQ2NtJgKrOLfOihh/D222/TocdarVarFW+88QbC4TDcbjdZLbIZgAMHDuD8+fNobGwk9yM2a8GkV2azGWKxGDMzMyUf0oWFBbS3t2NhYQHFYhE6nQ4ejweNjY34/e9/j76+PtjtdkxNTcFsNmNsbAyFQgHf+9738OlPfxoikQhLS0tUYTUajZibm8Pg4CCAWzMVL7/8Mm14XFhYoFbgzMwM2tvbN+TGghXmThQIBGAymejg8Hg8VNm22WyoqKggf2smoWKfgcfjQVdXF3w+H1UaWFLBqjTMArGmpoaq/BuBJYTM8pHp9tmh4XA4qILNWuWjo6MIhUJ45ZVX4PF4sG/fPnppBgYGcO7cOayurtJiv8bGRszNzaG9vZ1kZuySKNUSXFxcRHt7O8LhMG3VFAgEJHfYvn076Vbb29tJM3z69GlIpVLkcjmsrq6ivr4eYrEYIyMjiEQimJqaAnDL6WttbY2CwcnJSTQ3N0OpVOLSpUsl5RiZTAbV1dW0+EmhUGB2dpbkedFo9H1yDABU9WdbYFlV5t1338XHPvYxjI2NkXaaXdxDQ0PkBmKxWFBVVYX5+fmS80rArYFENjCZTqfh8Xhom7Ldbqfkhlk6GgwG3H333VhcXER9fT0lheXl5Th37hw8Hg8Nca+srMDhcODb3/42FAoF5ubmSKY1OztLA4MbYWFhAXv27MH4+Dh8Ph/4fD4mJyfx6KOPkqZ/aWmJdt2kUilKdEOhEE6cOIGOjg5cunQJra2tePLJJ3Hp0iUyn2ALLs+dOwen00nWqGznBgCyDf1/glW+isUi5HI5OQgx3/PKykrE43GaeRkeHkY2m8WlS5ewZcsWGmpmNqPl5eU4ffo0mpubaVhyZmYGIpEI9957Ly5evEhb7tms2kZgFy/rrLCkhVVFN23ahCtXrpCJBTvbX3rpJfT29sJsNpPRADtPpqamqPPILlFmYT48PEyJMytCbIRUKoX6+nraO+H1ejE/P4+9e/fiN7/5DTo7OwHcCsKYW9yePXuQyWRQX18Pj8eDsbExrK+vw+/34+6770Y8Hsfo6Cg8Hg9ZQbMB4jfeeIMW0bHzbiMkk0k0NDRQpZDZk7M9DplMBmfPnkWxWKRuU3V1NZLJJPr6+rC2tgav10tde7VajYmJCajVanrHq6urcfr0aezatQuTk5OoqamBxWKB3+8v2Yln+zIymQz0ej0aGxvB5/ORz+fxyiuvwGAwkMvW7t27ybGMJTZsCeI999yDsbExLC4uUlLGFms2NTXhypUrtKOD6dyvXbtWUirt8Xho1wq7RxwOB/bs2YNf//rXtDm7UCigtraWpChsWSrrbLAKPgBcv34der0eGo0GlZWV8Hg8eP7557F7926cPn2aLHKZBf5GKBaLqKuro26vQCCAy+WCx+PB0aNH0dDQgGvXrtEywLGxMapYLy0tIRKJoKenB5WVlfj7v/97NDQ0oLOzk4oJ165dg9/vx8jICMnPmT09q6JvBLY4jslfRCIR5ubmYDKZsLKyglwuh7m5OeRyOWzZsgVvvPEGdu3ahd/+9rfo6OhAOp3G66+/DgD0PrKhYYfDgenpaXI1Y0uXI5EI8vk8jEZjyRiOSYnYuc+sYE+dOoUzZ85gYGAA58+fp+esu7ubimXz8/O0oNjpdFIBTCwWkwMck1oz9QJL0ticSql3gVmHv1fazPZjTU9P014avV4Pg8GAt956CzabDR6PB8ViEdeuXSPL5h/+8If41re+hd/97ndQq9XQ6/WQy+U4efIkzp8/Dx6Ph4WFBTJ/WVlZAZ/PJ8e9/yei0ShsNhscDgc9C2+++SYMBgNu3LhBDn/V1dWor69HZ2cnnn32Weh0Okrq2L4qgUCAU6dOkdOeRCLB6OgohoeHoVAoEA6HMTY2Rjt6gsEg7c/5Q7jtRMNoNGJychImk4lmLt59913Mzs5icHAQX//613Hy5EkMDw/D4/Ggrq6OPORZVnr06FH09vbiH//xH1FbW4tDhw6RrGNychKvv/469u3bB6vVirq6OqyursLhcNB2043Q0NBAcxg6nQ51dXXg8/n4yle+glOnTuFjH/sYgsEgrl+/jpWVFRw5cgTr6+tQKpUIhULI5XKQyWRoaGhAOBzGt771LTz88MPU5RgeHobT6cT999+PxcVFBINBXLhwAcFgEHv27MGWLVs25NbW1oZwOEzDlMxd5stf/jJOnDhBNqQVFRW01Xnr1q348Y9/jJGRERquZS4r586dg9Vqxf333w+JRILp6Wls27aNZDt33303Ll68CLvdjp6eHmr3fRDY8H51dTVV/sbGxpBOp6FUKtHS0oIbN27gxo0bNBx63333oaWlBb/85S8xPj4Oq9WK7du3IxAIYGRkhC5oZhXodDrJppUt0GNdgFJLclwuF+bm5gDc0gqr1WoMDg5SpX/Xrl3kTgTcOvhaW1uh1Wqxb98+BAIBjI+Po7e3Fz/96U/Jxo5pWnU6HRwOB2pra9HW1oZ//ud/JsmHQCDA8ePHN+TGqk8sSGDdjdnZWWSzWezdu5dcJAKBABoaGjAwMIDTp09jz549kEqlSCaTJBcoFotk6cysRp944glcvXoVDz30EEQiEZkA1NXVobm5eUNuAoEAQ0ND5EqUyWTIClKr1WL37t1wOBw4c+YMFhcXUVZWhhMnTsDlcmF4eBg3b97E9u3bcfDgQXIM+eQnP0mDq1NTU3A4HOjt7UVNTQ3t52BOXqUCBPbZXblyBel0Gjt27EBraysuXbqEd955Bzt27CCHJrZEqqWlheaW/sf/+B/Ys2cP7rvvPiwuLmJ1dRWf//znyUyBSWu+//3v4/DhwxgYGCBJH0tq9+7duyG3TCaDX/ziFygvL6cWfXd3N1588UWq5rLq7tjYGH7zm9/gj/7oj6gqyrqDO3bswD/8wz/glVdewZe+9CW0tbVhYmKCZoyOHDmCuro6vPjii3A4HODxeKirq0N3d/eG3DQaDSUm6+vrWFtbI9nnoUOHaHhwdXWVLJ1ZIMKq8F1dXZBKpRgfH8e5c+fwjW98gxae1dTU4B/+4R9QU1MDh8MBs9lMkj4mK9kIFosF7777LnQ6Hfbt24fW1lacO3cOzzzzDNrb2zE/P4+mpiY0NzcjHA7DaDSiv78f//RP/4QtW7agpaWFNnMLhUJ885vfxOOPP46BgQHIZDIsLi7Soj3mHBQIBOi8/9SnPrUhN6FQiOXlZZrpYLrnt99+G2q1Gh0dHchms3A4HBgaGsLQ0BAN5jKXvG3btkEgEOCLX/witm/fjo997GPUDUqlUjhw4AASiQQ2bdqEoaEhXL16leYPSw3n8vl8jI2NkYyXaedv3LgBs9mMu+66CzabDaOjo3C73aivr8eePXvgcrnwzjvvQCqVkm36z372MzQ0NNC+GKlUSsYALFnv7e3F/Pw8HA4HIpFIyTtVpVJhfHwc09PT6OzsRFNTE8bHx6mLYTKZ8LGPfQwTExN45ZVXsG/fPmSzWdTV1cHtdkMoFKKqqoq6D319fVCpVFCpVJiZmcG5c+fw1ltv4e6778YXv/hFXLx4EU6nk3ZdldoVtGnTJkxOTtKySTYLNzg4SBI8luycPXsWPB4PR44cwfDwMMbGxtDY2IjW1la4XC5aTsa06Hw+H5cvX8bg4CA+9alP4Wc/+xlOnjyJwcFB+Hw+3HfffSVlNswRiXUW2VyH1+vFk08+id7eXhw4cAALCwv41a9+hbvuugsmkwkOh4Oqxjdv3sQDDzyAP/3TP8Wzzz5LKhEmKfrf//t/4/7778eOHTsQCARIU69Wq0smtkajkYxzqqurae5RIpHA7XbD6/VCq9XC5XLh8uXL0Gg0SKfT2Lt3L20xZ/OHs7OzWFlZQTAYRGdnJw0hX7p0Cfv27aPESC6X04bpUp9bQ0MDdaRYJ6y+vh7f/e53aV9YdXU1MpkMhoaGkMlk0NzcTG57ACh5HR4exujoKKqqqkixwAribNaPSYg1Gg3q6+tLJmjsu2TJv0gkgsfjwezsLPbt20c7UpaWlnD58mXo9XpIpVI0NzfjH//xH1FfX48jR45ALpeju7sbwWAQ1dXVEIvFNODPYt9jx44hEAiQE57BYKDuxwfBYDDg17/+NY4dO4a2tjbaTTYzM4Ovfe1rGB0dxdjYGDweD4xGI0QiEf7hH/4B58+fx82bN6FWq9Hf34/6+np873vfw7/8y79ArVbjrrvuovngnTt3kvKBLXVmZi6l4qT34ralUxMTE9i+fTsA0CHV0dGBBx54gPyd1Wo1BgYG0NHRgddffx319fWQSCR45JFHcPToUUgkEtreqNVqcenSJZw9exY+nw/t7e34+Mc/jsnJSbzxxhv46U9/isnJSUgkEvj9/pItQa/XC5/PR2vomcvAxz/+cdKvsuV4ZWVltLdgbm4OX/va1/DII49ArVajvLwcW7Zswbe//W0MDg7iRz/6EX71q19hfn4eu3fvxve+9z2k02k4nU7s2LEDDz74IBKJREluzBd7aWkJqVSKErbvf//7OHjwIKqrq9HR0YFNmzZBq9Xi6tWrSKfTiEQiJFHj8/nYtGkTfv/73+Ozn/0sZf5sp0V5eTmmp6cBgBbR1NXV0ZKsDb98Pp+8plmnRywW4wtf+AIkEglpQvv7+/HYY4/RDoeJiQl0dHQgl8vhwoUL1Cn68pe/jKmpKaRSKayvr+PixYtoaWnByy+/jPLyckgkEtILMrvkjVBRUYFNmzahvr4e9fX1aG1tRW1tLf74j/8Y8XgcQ0NDuHDhAlZWVmjWJR6PkysVa9vW1NSgt7eXkrGBgQHqnrHPKBKJoLu7G9u3b0dTU9Mf3P5aKBTQ1NSERCJBA1QKhQKPPvooJZQmkwl79+7F8ePHce3aNTQ1NSEWi2FtbQ03b97E+fPnaQh///79GB0dxfr6OgqFAqRSKf7kT/6EEjHmUa5SqTA6OlpytiWfz2PPnj00K1BRUYGKigr09/eTdDAcDqO+vh4PPvggDXNfvnwZBw8exPbt22khFqt+nT17lgYimXTvqaeeQiAQoGQqGAxifn6+5FIm4NbZ8cADD5BcJRQKwW63U0BVX19PFe7a2lqqYvF4PPzt3/4tampqMDQ0hO7ublRWVqKnp4fkSB0dHaipqcH3v/992rHi8XiwZcsWHD58mKxON4JYLEZTUxPpspm7XH19PcrKynDo0CF88pOfxP333087ObLZLBYWFsg6cWJigpxqfvazn0Gv19NgI9sl89RTT2FpaQmTk5OUMLElTxthfn6eNs03Nzfj2LFjtOjw0qVLJA80m834/Oc/j6mpKSgUCpw7dw67d++GyWTC+Pg4LeH727/9WwwNDdEG41gshscff5xmWV5//XUMDw9jdXUVU1NTJYNSp9OJhx56CJs3b6YkWqFQ4KGHHkJDQwMefvhhGI1GOBwO+Hw+/PznP8fU1BQOHTqE2tpa+P1+pFIp3HfffRgdHcVPfvITmg2QSCSkBz937hz0ej0ikQhJ206fPl1y8adAIEB7ezvtKwFudWAOHTpEszuTk5NYX1/Hrl27wOfz8eCDD9KunHPnzuGHP/whzp8/D5PJhIMHD1LAura2hmAwiH/6p3/Cj370IywvL+O//uu/4PF40NraioaGhpKfWywWw4EDB5DP58lCenp6GidOnEBnZyeqq6tRXl6Onp4ePPDAAxgdHUU8Hsfly5fR29sLtVpNMiVWiT979ixmZ2chkUiwZcsW7N27F7FYDK+++ipefPFFSKVS7N69m2YpS3H79Kc/TZ0MPp8PrVZLe6asVivJoNiz39zcjHw+j/3796O7uxsGgwGtra3o6OhAOBympLq6uhpbt27FX/7lX8LlctHCPiZXYvMgG8Hr9UIikWBqagoulwuFQgEXL17Enj17UF1djb6+PpoB+vjHP44XXngBuVwOTz31FPh8PoLBIBwOBxobG7G0tITm5mZKNN955x2Mj4/jM5/5DJ5++mls27YNkUgEAwMD+MQnPkEdxY3A5ivZzBhbQHry5EmUlZWBz+fj+vXriMViePjhh7G8vIzNmzcjHo9TRZxZxD/xxBPkCsmSKqFQiEcffRQ8Ho+6qcViESaTiVzeNsLi4iJZ07KYiakeCoUC7baqqKjAtm3baC7h5z//OYaHh3H+/Hm88cYb1CG+++67odFokMlkMDU1hUuXLmHz5s24dOkS5ubm8PLLL8PtdkMgEMDj8ZS8G1gM6Pf7yRUrmUziS1/6Enbs2IF9+/aRZPrw4cPI5/Po6OjAm2++icbGRggEArz55psYHh7G3NwcDh48SNbPLpcL09PT2L9/P9RqNb7yla+QnCgajZLsdCMEAgEYjUYyO1hcXITdbsdDDz0Eu91OsuNisUjWzgqFAnK5HP/xH/+BL37xizCbzWStbLfbsbKygpqaGpp5OXjwIKampuD3+7G6ugqLxYK2trY/uAvN4XDgwQcfhNvtJiOPTCaD7du347nnnoNOp6M9PQ8++CDMZjOEQiFGRkbI9IAtc/V4PPjSl76ElZUV3LhxA4ODg3jrrbcgkUjI8EMqldICZ7af63Zw24kGSzLe224Ui8U4d+4cTp48iXfffRdvvfUWLUfas2cPFhcXceTIEYyPj9PimdnZWTz22GOYnJzEgQMHoFQqMTs7Sxsu9+7di97eXmzduhWhUAhra2s4deoUVbc/CKzKyFakp1IpbNu2DW1tbcjn83jnnXcwNzeHSCRCtqdSqRQPPfQQ6dlbWlooKWGVi9raWlpqxefz8Wd/9mckJZqensZvf/tbhMNhkjF9ENRqNVwu1/sqzDwej3YABAIBJBIJkhnt3bsXP/3pTyGTyUiKsn//fiiVSnR0dEAkEqGrqwsmk4lW1FdUVOALX/gCbDYbUqkUDc43NzfjpZde2pAbS+LYIDnz0b969SoOHjwIoVCIuro69PX1wWQywWQy4ezZs9DpdCSl2759O27evAmr1UobRY1GI+rr67F9+3ay5r169Sr8fj/Ze3Z2dpKxwAeBbc1Op9NYWVnBtWvXSGJw7NgxqNVq7N69G3v37oVUKsWnP/1pXLlyheYsmOvE/Pw8tm7dik996lO49957adDKZDLhxz/+Merq6jA2NkamBVqtFlVVVSVnDZgsjAWHFosFW7Zsoe28TIbBpDpbtmzB2NgYTp06herqarS3t6Ovrw+JRAJf+MIXkMlkcOLECUgkEgpennvuOdx99914++23aZdDV1cX2traSi4BYz7wZrMZCoUCKysrZPHJJGJsM7NGo4HRaMT8/Dw++clPQqfT0eEzMzODo0ePIhwOo66uDi6XCysrKzAYDHC5XPjLv/xLXLx4keyPHQ4HOjo6Sg5bA7dsfd1uN7RaLWljDx06BJFIhIMHD+LSpUvwer3QaDRIJpM4dOgQ1Go1uZhYLBbU1taS7Sqbh2CbUQuFAmZmZnDixAmEQiH09/dDq9WCz+ejvb295MI+pj9vbGyk3RPNzc24cuUKdDodxsbGcOXKFZI+sO/12LFjiEQiUCqVaGpqwvz8PBQKBS36YwvrPve5z0Gn02HXrl14/vnnsX37dpKv1dXV4ebNmxtyMxgMuHnzJlnkulwusktsa2tDJpNBsVikLbN79+7Fq6++it27d2Nubg48Hg+dnZ3kMra0tITu7m5qnQPA9PQ09uzZg2KxCIvFQjIjs9mMK1eubMjNYrFgfn7+fXs3qqur4XQ6yUWHyfcaGhrw9a9/HW63m2RbVqsVVqsVw8PDqKqqwtDQEB544AGSuw4MDFDgyjoAbG9KbW1tyfZ9sVhEKBSCTCaDTqeDRqOByWTC6OgoamtrcfbsWUgkEhiNRpLg/e53v0N9fT2EQiH6+vpw+PBhOBwObN++ne4Z5ohXXV2N/v5+PP7447Db7diyZQudq42NjSU/N4PBAL/fT7+/urqaXI527tyJK1eu4ObNm3A4HBgZGcGJEydgsVhgNpsxNDSEcDhMm4ebm5vJhY1Zwy8uLmJkZASf+MQnaCh7YWEBw8PDMBqNJYfojUYjFhYWSAqXTqeh1Wqh0+lIWjw7Owu/34+pqSnqEOzYsQNnz54lzXkkEoHD4YDNZsN9991H77VMJsP09DRtGN6zZw/S6TSuXbsGqVRaUvv9Xuknu+8bGxsxOjpKZ4LH48HCwgJeffVV9PX14Te/+Q0efvhhWK1W1NfXo6amBnNzc9i5cyf+7d/+DR0dHaitrUV3dzcOHDiAl19+GZ/5zGdoadrKygpGRkZgMBhKdg0AUOKXTCbhcDhQX19P5i6sCBePx6FWq3HgwAFcvHiR5hvYQLVarcZnPvMZlJWVkdvP+vo6NBoNFZM8Hg/6+/uhVCoRDofR3NxcclcF281gtVqRSCQwNzeHvXv30nnJZIAajYZ2/iSTSXR2dqKnpwdHjx5FT08P3nzzTXR3d1OBqK6uDj09PdiyZQtEIhEqKipINsxmUNmcaSl4vV4yjlhZWYFcLodQKERTUxMF55lMBvF4HB0dHbh27Rr27duH5eVligeCwSDa29tRLBbR3d2NK1euYGxsjCziu7q68Itf/AJlZWVUVCoUCiUd/wwGA+x2O1QqFTlosRiISQNNJhP6+/uh0Wiwc+dOXLt2DVarFe+++y4mJydpR9Xx48chFotpdoW5mZ07dw79/f2IxWIkr5+amoJUKi25NyuXy8Hn88FgMKC5uRm1tbW0tLa3t5ckiTabjeKLZDKJkydPktFEZ2cnqYYWFxfpexSLxaiqqsIrr7yCPXv2YGVlhdQBrNlwu8Pgty2dYjouZtPq8XjgcrnQ2NhImmFWGQ+FQuju7iarN2YJybKtQCAAoVCIUChEL040GiVHF2a5Njk5Cb/fj5qampKBH3M8YK4RTH/a0tKC8fFx2hjMXErYYLtQKITdbsfa2hqKxSLNJWg0Ghq29vv9VLFl2k82zc+cgkohnU7TzAQ7LFgLkdn9CQQCao8KhUJEo1EsLCyQjpJpwcPhMN555x1oNBpUVVWRM1MikYBWq6WKOXPFYEnTRkilUjCZTOTFLBAIaA4ilUrREhq2aZO1DZk1Ktt5kMvlEI1Gydlkbm7ufYPSTU1NqKysRDKZRCKRIEtd5kz2QYjFYvRssOQhmUzi0qVL2L17Nw3lsqSXdaScTidZIrPfYTQaaV9LOBymi7O2tpY6Xcw9A7gVnJQa4gyHw1RxW1tbI3kaq9gwrXwymUShUCDfezYkz35/IpFANBrF5OQkDh48iNXVVXIzcrlcqKioIO27QqGAVColB7KNUCwWaRASwPu+h2QyCbVaTYNeNTU1JAFhMreamhqUl5fD5/PB6XRCKBSSrW4wGCTLXZfLRY4VbIgvGo2WdD0BbkkAmVySbWJng4DhcBjT09PkisKcmNbW1ijhZ++K3W7H3NwcUqkUrFYrxsfHSdrEdt2wTbLMBYTx3wjsnODxeHSAhkIhsqBljmDMUYxZ3FZWVpIuuaysjGYxWILHBvomJydJAsTsQvV6PQ04l9rUzDqXzI2HuXOxy4otUX2vcwzT4rPnBwANjbJu3MrKCgUXjAeTNTLtMXNE2ghs5kmn05EJBOsqs3OPbbpdWVmhWaHFxUW6DIPBIN566y0MDAxgfHwca2tr73MSczqdqK+vRzKZxPr6OrLZLCQSCaRSaUm3LtY5ZW5OAGiok50R7L9jNpuhVqtJTsuCSibL6+zshFgspuQglUohFAqhrq4OxWIRN2/ehMlkoi4Gm13ZCEz7zSqlXq+XihThcJjcblhXmgWcLElg3N588000NzdDKpXSksC1tTW6j6PRKHXek8kkIpEIkslkyW43c1xkrm8sGWUGAYlE4n0OUVNTUwiHw2hpaYFWq6XA5vr161Cr1RgaGqJnKhqNIhQKweVykW0vu3uYY2KpCjPTu+t0Ojor+Hw+uQyx3VnMCZDH42Fubg79/f3IZrNkj80WWKrVaipiMWtw1pljzwhbVvfez+KDwGyh5XI5IpEIGV0sLy/TklehUAiNRkPdABZTJBIJcntjXX+VSkWL3ZjUh51F7P5nsnEAJbstIpGIZhKYgxar6mcyGUQiEZJSAreKQna7nWZcmNkPG7JnMiTm/MYkSUxhoNFoyPpeq9WWNG0Abslqs9ksUqkUnWnhcBhms5mSARaLsC4EiwMEAgHtaMvn81hbW6O5CovFQvbpbHbH5/NBqVSSs2ap+RG2p6RQKNDfy+ZbGhsbaRaEdfqY+Q/7bCsqKmCz2eD1enHlyhXar8OKgB6PB2tra1hYWKBFmqyIvL6+XvJ5Y88AizXYGgngVuGosrKSzivm3DgxMYFYLIalpSWUl5fTM61UKrGwsAAej0ezIez8YlbHWq32ffFlqfm49/G8rZ8CqErFHHtYy2nbtm204VQsFmNpaQl2ux1tbW20GZfZrzHrvcHBQSiVSkxMTKCurg4GgwFutxupVApzc3NktRUKheDxeHDu3LmSmk3mQMIGoldXV+liYnaafD6fqjMsoWALfVKpFDQaDWQyGX35fr+fgmPm3sOm7VlljPm8l2pBMy0ge0ljsRhEIhFZxaZSKVr4ls/nMT8/j9raWhrmZZ0O5k//0ksvYWBggJb/MacBNmTJugSspVVKnsQSwWQySd7rbOP75OQk2dcx+0f2Ys/PzyMej8Pj8ZCl6XvnacbGxsDj8ciijW06HRkZoQuOvfAbgclAmPMUc+5YWVnBzMwMLBYLdbwKhQLa29vR1NQEl8uF9fV10sszv+fh4WGoVCqyJi0Wi6QNb25uhtPpJGtPZue2EZgzFbvcXC4X1Go1FAoFRkdHyWKWubywQHh2dhY2m43aquXl5Thz5gzm5+fJdq9QKJBsLhQKobq6Gj6fD1NTU/QdlOKWy+XeZ2nHHDESiQQNwfL5fLpsMpkMjEYjRkdHydGLOcm8++67qKuro/eD7VlhFe7m5ma43W7o9XoUCgUKHEshmUyirq4OHo8HhUKBDmk27M8CW+aeNTMzQ9uYWcLArEPZICh7n00mE2mD33rrLchkMkSjUZJXMDemUmAJp0KhgFarxczMDPh8Pm3fZksGmR89+/35fB4ejwcSiQQqlYokIgsLC+jp6UEsFsP4+Di5Ce3Zs4cKImxuoFSlNJFIoLm5mX6WXXZCoZDOZgAkqRwYGEBnZye5W7HPwWw205LBVCqFSCRCVsvZbBaBQICGmdmwZTgcJg32ByGVSpGBQFlZGeRyOUZGRsj9hX2OLpcLkUgE8/Pz0Gq1WF1dhUAgQCqVoueBSRpeeeUV6kTNzc0hFArh6NGjNADNgh+dTkddoQ8CuzfYZ8vOW7ZjpKKiAmtrazT0HI/HsWXLFiwvL5PddywWA5/PJ52z1+tFV1cXLa+cm5ujAhcLepkjXqnBYbaPRy6Xw+l00rJHtglcqVRCJBIhGAwil8vB7XZDrVbD7/fTnqPV1VUMDw+/z9ueVZFTqRSCwSCGhoZgMplIY84G6Et9p5FIBGKxmN43NhfEglX2HGo0GtTV1WF6ehpKpZIUAwaDgc6/jo4O/PVf/zW53LCCYiqVwpUrV+h5Y7IM9hluBLYokAXm702wVldX0dTURP8NtVoNt9sNuVyO4eFhrK2tIZ/PU6J/6dIlcverqqqi5bcqlQpjY2Ow2WxUjGSL40oFfuzvem8SwBZ7so3xbPcSG1RmjlZsV0M+n0cikaAAlDkFvtfe+ebNm9ShZokIeyf+EJjVuUAgoAF0FnfFYjE6D5jBxPr6Ou024fP52LZtG2ZnZympUSgUpMpgRWSWPLO1ApWVlSU7aExZ4vF4KIkOBAKYmZmhreHMSCifz2N8fJyMdNggOLOdZRbn8Xgcu3fvJnUEcEu10dXVhStXrtA+JLb3ZSOw/U7sM2ZFivX1dUxMTJCt/+LiIsWGXV1duHr1Kng8Hu09yuVyGBsbo11qzM6bxThzc3O0wI/NCDKzj43ATA3ea73M7J0vX76M7du3k8qArQI4f/48crkcdd7ZXA4rSKjVaiQSCbK212g0mJ2dhdVqJTdC5oTFeP4h3LZ0ijnOtLe3Y9OmTaisrIREIsEvfvEL2pDIvPvn5+dx8+ZNCAQCPPXUU3j++edx9uxZuN1u0s+zTaZsCRdrqwWDQdoS6XQ6yXmm1GUC3JohWVhYoGwrlUrh9ddfp67B5OQkhoaGcOPGDfj9fvIFZt7n+/btw7Zt25DNZvHaa68hmUxCo9GgoqICUqkUQ0NDFFgEAgF4vV7yKS9lP6bX63H9+vX36e7YAxePx0lDyCoUbIOvXC6H3W7H4uIiLRZsa2sjT22z2Yza2lo0NzdjYmICb775JgCgtraWhpGYNeJGkEqlcDgc9JLGYjHYbDYsLCzA5/NhZGSELHTj8TgKhQKcTicikQieffZZsu5jbXKxWIy2tjbU1dWRjSurQrB2I3uAWWtuIygUCvpdOp0ObW1taGxsxO7du0nWIZFIyE5ULpcjkUigtbUVZ8+exfj4OC2pEgqFNJcA3Gq9u91uTExMkI0jC1JZ9baurm5Dbsz+02g0oqqqijoDp0+fxq9+9Sv6W51OJ55//nmMjo5Shcbv95OUo6amhlrEV69epdkIFtC8/vrr0Gq1MJvNiEQi8Hq90Ol0JR2xZDIZXC4XuWLMz8+TXIrpdxsaGlBWVoa5uTnMzs7SHEJ5eTk8Hg+uX78Ou91OwfTVq1ehVCrR2tpKVnqHDx8mfafP50MwGEQ2my2Z2AK3ZDasiswSolgshjNnzqC7u5v2ptTX12Pz5s0QCASora3F9PQ0XnrpJYyPj6Oqqgq7d+/GkSNHsGXLFvT09GD//v3YvHkzdSTC4TAUCgW2bNlCl9Z7OwIfhGKxiMuXLyMQCJDU0e/346mnnsKTTz5JSbzL5cLIyAikUim2bNmC1tZWkjB4PB66wJlNr0ajQV9fH/7oj/4IJ0+eRHt7O0wmE1Xy2Rb3UpV5tlWeVaQ9Hg+y2Sw5NFksFuj1eqyurtK2Vz6fj927d6OzsxOhUAhOp5P2HrGCEPPn9/l8uHHjBiYnJ9Ha2kruYCz4LzVrYDAY4HQ6MTQ0BKfTiXw+T3a+UqkUNpsNMpkMSqUStbW1uHjxIlpbW9HT0wORSESzLg8//DBef/11jIyMYHx8HEtLSzRozwoE6+vrqKqqonOTOfmVeh/sdjucTidCoRB1bKenp2k2SyKRkPOf1+tFR0cH2UEyF8D19XX827/9G1577TVEIhE0NDRg+/btsNlsOH/+PORyOe1tmZ2dpSVYpfTVLDAwGo3Q6/XI5XLIZrP49a9/jeXlZUxNTSESicDlcuHJJ5/ExYsXSY7x2muvYXp6Gmq1Gr29vbh06RL+z//5P1QEYQU8tldg9+7dEIlE5EzD7NpLvQvLy8skcwKAy5cvY2FhgZaEyWQy6PV62hVjsVigUqmQTCZht9spYVIoFFAoFJiYmCCJocViQUtLCwX0bJC3qqoKMpmspJRFIBBgZmYGw8PDcDgcCAaDGB0dxYULF/Daa6/Rdmu73Y6ZmRmaHT179ixVrkOhEJaWlrCyskJFR7bXIBAIUDG0vb0dKysrcLvdCIfDWFtbK3nGse4JUwVEIhGsr6/TLBqLnRYXFyk+qaysxNTUFIaGhmhxLI/HQ1VVFS5cuECLBwuFAubm5nDhwgVaYqpSqRCNRmkRWyk7e+YiVFFRge7ubtTU1MBms2FychKxWAxGo5HmHEdGRkgKXF5ejsnJSXqnGxoasLKygt///vdUUNRqtWhtbYXb7SbOrAioVqtpt9BGyGQyZCTEgnkWdz3zzDPUcQRuqQiYvSqznmZdApFIhKamJnJNs9lsZErAdo4xB0+mzGFWvhtBJBJRkctgMFAxjhUM2eB3Pp/Hs88+S/vJYrEYOYWNj48jm83i8OHDKBaL8Pl8lBAUi0UolUpUVla+b040k8lAo9HQ3OcHgW1KZ+58TLExMzMDiURCyhePx4OJiQkqprCu08jICM1HXb16FQ6Hg7q35eXltAmez+fj7rvvxoEDB0hqxRoMt4PbTjTcbjfEYjHOnz+PV199FbOzs1AqlfjXf/1XsiE0Go3o7e3F/fffD4fDgfX1dezYsQP/7b/9N9qKyOfzcfr0aTzwwAMUxLOFKl/+8pdp0/TS0hKOHTuGP//zP4dGo9nQ3otx6+vrg0ajQU9PD06dOgWpVIqTJ09Cp9PhwQcfxPHjx7Fnzx488MADWFpaAp/Px/z8PL785S+jpqYGTzzxBC5dugS9Xo8jR44gGAzSwBQA3HXXXXC73XjkkUeofSmXy/HWW2+VTDTGx8dx//33U3vLZrNBo9Hgy1/+MrZs2YKdO3diYGAAx44dwxe/+EXYbDb09/dDpVLh0UcfxdatW8mDOpFI4Gtf+xpVGiYmJnD58mV8//vfh9frRWVlJS5evAiNRoNdu3aRZdxGkMvlpEUWiUTQarVIJpM4cuQIJBIJJTsulwtarRZnzpzB4cOHUSgUcPToUaTTaZw9exYtLS0YHBzE448/Tnp0k8mEqqoqPProo1hbW8OZM2ewvLyM/v5+7N+/n/SmGyGRSGDz5s3U3vZ6vbhx4wZJBViFnr0AzzzzDE6cOAG3242HHnqILN9aWlrw3HPP4cSJE1hbW8P+/ftx7733orm5GZ/85Cdpy/jVq1chEAhQXV1NlbqNwCo2r7/+Ot544w0sLy9jYmIC9913H1nW6fV63H///Th9+jT6+/vxd3/3dzAajTTgzraly+VyfPnLX0Zvby8lO2VlZTh27BjpPcPhMBobG7F9+3b4fL6SHQ2Hw4GKigqYTCb09vbi+PHjKC8vR1tbGwQCAcbHx2kbb01NDQQCAW7cuEF7MIBb1aXNmzejvr4ed911FyoqKqg1X1FRAY1Gg5deegmRSAQmkwkDAwPo6+sDgJJyDAAkYRsZGaG9POfOnUNTUxNee+01pNNpTE9Pk8tWf38/WlpaUFtbi49//OPo7u6mAeXvf//7yOfzuHLlCtxuNy5evIinnnoK27dvx5UrV+BwOPDSSy/BaDRi+/btWFhYKNl9TKVSuPvuu8kxTSwWw2Kx4Ac/+AH+7M/+DHq9HlqtFo2Njejs7MT58+eRSqXgcDhw7NgxnDp1Cnv27MH+/fsRjUbx+OOPk/QiFAphdnYW7777Ll5//XWS3u3cuRNbtmzBhQsXMD4+viG3QqFAdqtKpRK7du3C0tIS7r77bqTTaTz77LM4c+YMrFYrvvKVr0Aul1MCwBYissovj8fDtm3baCEqSzY+97nP0Rb6trY2bN++HW1tbdT12AhsHkGtVkMul4PP58Nut+Po0aOYn5+nBWjl5eUYGBggR6C5uTkq9rhcLpJI/vf//t9hNBqxa9cubN++HTqdDocPH0YgEKBZlsrKShw6dAh+vx9nz54t+bw1NjbSbhS2H+nhhx8mqUFbWxvuvvtuPPjgg1hdXaV5wfvuu4+q/n/6p3+KRx55BA0NDRgfH8fY2BhtAP6zP/sz3LhxAxqNBvv27cNjjz2Gu+66izrsGyGZTMLv92NwcBDZbBYDAwOQSqX45S9/CYPBgMOHD2PHjh2477778J//+Z84cOAArl69iqamJnzlK1+hzeh/8id/Qkvcqqqq4PF4SIJx1113IRAIIBAIoKamBsePH8exY8eg1WpLauZZEsGkEZs3b8bHPvYx7Nq1CwCwZ88e0vyzanY8HsfCwgIaGxtJVnvffffht7/9LX7yk5/g4x//OA17ZzIZksm1tbXB7/fD5XKROuHEiRMbcsvn8/Q9VlZW0pDwpz71KezevRu9vb04ePAgTpw4gf7+fiSTSezevZuSIbVaDZVKhe7ublRVVeGrX/0qdQKYC9Uf//Ef49KlS9BqtaisrMSpU6dw4MABDA0NldwMDtwq9hkMBmzatAknT55EdXU1HnvsMSwvL2NhYQETExPIZrPo7u5GKBQie1m5XA6Px4Ph4WGYzWa88MIL+J//83/Sfg/Wsfj0pz+N+fl5moNiJh1sP9VGWFhYwMDAACYnJzE+Pg6RSIREIoFvf/vb5JzY3NyM48eP49FHH6VN07FYDLt378aWLVtIhrh582Z8//vfp++DSac+/elPo6+vD7t27UJHRwfa2tpoHwbrKnwQmDug0WjEli1bcOTIETQ1NeGb3/wmampqKClkM3TMDEQsFuOv/uqvqAtZUVGB//W//hdOnTpFjnVTU1OYm5ujmajKykqSebKO2KZNmzbkxhIBJvPl8/lYWlrCyZMncenSJQQCAbhcLtTX1+Ob3/wmJc7hcBjd3d0IBAJ47bXXoFarce3aNfzFX/wFOjo6cOjQIfT09EChUODkyZPwer2IRqN45513YLFYcPjwYTojNsLc3Bza2trgcDjI9rmqqgrf+MY3oFar4fV6Serb29uLV155hQb+/+M//gN//ud/jra2NuzatQu7d+/Gj370I1oC+PGPfxw/+tGPsGvXLpw7dw7BYBBPP/00pqenIZFIMDs7W3IG7b3gFUuVXN6D//zP/8TY2BhVDZeWluDz+dDV1YXW1laaZXA6nfB6vTh69Cj6+/tx/fp1Cqjm5+fx85//HN/+9rfx13/91/jOd74DnU5HWzSffvpp/M3f/A3efvttVFdXkw6U6chaWlo+kNuTTz5Jy40ikQh8Ph85htxzzz04cuQIVCoV5ubm8MQTT2BlZQXf/va3EQ6H8dxzz6G9vR379+8nucP/h73/jI7zPM/E8Wt6n8EUTAMGvVcCJAj2TlFUr7ZsSZZsK45s7SbZbIqTnOz5ObaTEx8nTpw49tqObCuOLUa21akuNrATvXdgBjOYgukYTMf8P3DvO9L+ibH2nHzk/cXnyCD54H2f93nucpWXX34ZDocDr776KkpLS3HkyBH88Ic/xB/+4R9iYWGBTU+CwSC6u7vh9/vx0EMP3XJtP/vZzzAwMMAFlUgkwv33349f/vKXePbZZwHc7FYuLS3hJz/5CQwGA1KpFA4fPoz5+XlYrVZWqurp6cHw8DC2bdvGHwhw0zxMoVDggw8+wNGjRxGLxRCJRGC1WpHNZreUf/zud7+LWCyG8vJy7qhRF/Ezn/kMdw68Xi+Tu77xjW/gj//4j3H9+nWoVCrU1NQwyZO8H6g7UlFRgUgkwjKIZGhDl1MgEMDXv/71W67t7//+7+FyuVBfX89GR/F4HKdOncJ3vvMd/OxnP0M2m4VWq4VEIkF7ezvm5ubg9/vx5ptvYseOHeju7sa5c+fQ09MDkUiE5uZmhiOk02mMjIygvLwcL7zwAh544AF2SaUJ0x/90R/dcm0/+MEPoNfrmUMD3Ey2Dh06hHw+j3A4zAprHo8HDz30EHeJJicnYbPZUFZWhuXlZVitVly8eBFf+MIXmMy/urrKRn+nT59m87eFhQXs2bMHsVgMDz/88C3X9utf/5phaSSb29vbi1deeQV79uzhgpR0sTc3N/Haa6/h5MmTjPdPJpOYnp7Grl27GFI2NTUFvV6PhoYG9PX14Rvf+AauXbvGE63NzU1YLBZMTU3hscceu+XaAOCnP/0plpaWsHfvXobr7N27F1/96lexZ88e1NTUQK/Xs1EjcZhyuRzDKki/W6PRoLq6mj0EiPdF6lV9fX0MU6Rusc/nwz/90z/dcm1/8id/AolEAofDwep4nZ2deP7553HgwAG4XC6W5CVsOE1HaYKQz+cxMzPD7r9lZWV47733cOHCBayvr6Orqwuzs7P4vd/7PczNzaGtrQ0KhQJvvPEGEokE/vIv//KWazt9+jRzJihBrq6uxtLSEnbv3s3O32NjY5iYmMBf/MVf4F//9V+xY8cOKJVKVuGam5vjzjZBPGiqJxAIcPLkSSwtLbHMMnXVXC4XDhw4cMu1/fM//zNCoRA0Gg3j+nfv3o3R0VHk83mcOHEC6XQaKysriEaj2LdvH7LZLF5++WXs2LGDE9rr16/j0UcfxUsvvYR77rkH/f39GBsbw8bGBpxOJ37/938fbreblfxKSkogEAgwPDyMP/3TP73l2v71X/8VY2NjXACJRCKYzWZcvXoVf/VXf4VoNMrT7x/84Af4y7/8S1itVgSDQbzwwgvo6enBXXfdhdXVVWzfvh2f+tSn8Oyzz/KkJ5lM4uLFi3jooYdgMBhY2tfr9eLEiRMYGBjAc889d8u1/cM//APjp2maOj8/j7fffhvPP/88w/ZogtXe3o6/+7u/wx/8wR8wcXR5eZkhokNDQzh8+DB/18RhIIPH0tJSnh41NDTgF7/4BX72s5/dcm0//OEP8dprr+G+++7D+vo6JicnsW/fPpw/fx4nT57E6OgoTCYTY7aXl5eRz+dx8uRJqNVqCAQCbl75fD589atfxfe+9z1WFSKYzn333YeTJ0/i6NGjjLu/++67sby8jD/7sz+75dqef/55JBIJ2Gw2Fj8g3gCZter1ely+fBm/+c1v8Pzzz+PFF19Ea2srXn75ZXR2drKviMPhwFtvvYVnnnmGkyfy3nr88cehUCiwsbHBwiRkJLmVrOfPfvYz+P1+NDY2sgcX7f2TJ0/C4/FgY2MDg4ODeO+99/C1r30N8Xgcer0er732GioqKlgAQyAQ4OzZs5wjER8plUrh0KFDGBkZgd1ux7Vr1xAIBFiA56mnntryuSWTSZSUlDB0tr6+HmfPnsW9997LjY719XWsr6/j937v9yCVSvHjH/8Y5eXlaG1tRUlJCcLhMDweD/72b/8W//Iv/8LTK+qcl5eX49ixY3C5XExwb2xsxNzc3Ja5yKlTpxhe73Q64XK5WPb3iSeeQDKZRHl5OaampvDzn/8cX/nKV5h/RZwOs9nM8NLvfe97MBqNMBgMcLlcmJmZQW1tLZ588kkMDAygq6sLi4uLuHHjBoCbkNNvfvObW77TTCaD5uZmLqC1Wi3+4z/+A0899RS+8Y1voLa2Fjt27IBWq2U+hNFoxN/93d+xQit9Q4lEAtXV1VhcXEQqlYLFYuHJbDqdhtlsZjllykt37tx5y7V95zvf4QkQFdzDw8P46U9/il/96lfo7+9He3s7FAoFT1Xef/995oE89thj6O7uxvr6Oi5duoTNzU1sbGzgN7/5DTKZDI4cOcLCNU6nE0eOHMHy8jIGBgYgFouxtLSEv/u7v7vl2j4a/0+GfeQo6fF44HK5uJPh9/sZk+n1euHxeJBKpTA5OYmXXnoJDoeDN0RNTQ1+8pOf4Hd/93dx4cIF+P1+JoG5XC5885vfxEMPPcSXg0qlwrlz5xAIBLY8eHw+H8xmMxPNgJudia6uLtZdJvnT9vZ2nDhxgs2pCHN7+vRpHnWnUikuqsxmM5xOJ+rr63Hu3Dlcv34du3btglwuh1AoxMWLF4uO3RYWFtgwKp/PM2nwnnvuwYcffsjSpk6nk90+4/E4ZmZmeORsMBhw6NAh/PznP2fiHRkfFQoFjIyMYO/evSgtLeXuXmlpKT/TreKj3BQi+icSCSZd6/V6hEIh5PN5xsnTpen3+1FeXs78jFdffRWPP/44O0oTfCKZTHJRFggEsLy8jM3NTbS1tXEX/FZBJHKaaFCBotVq+YMgYQIikYlEIkxOTqKnpwdGo5EPPSI0Utctk8mw1PD58+fR29uLQCDARD0ARTGbxLMhPC0lHeFwGBMTE2htbWUo2ubmJs6cOcMFXDweZ4drs9mMt99+G3feeScGBwcxMzODtbU11lTft28fd1CTySRKS0sxOjrKRka3Cur6ktt3oVDA5OQk7+NMJsNFIv19J0+exPnz52GxWBiDmUgkMDw8jPr6eoRCIXzhC18AAJZSHRsbYzlOwn5GIpHfqjq1vLyMpqYmJsJubm7i3XffRVlZGU8UCe/qdrtx6NAh+P1+3Lhxg4nGBKekf390dBSdnZ3MLTKZTPw7xONxJlESf6BYNDY28h6gC+Opp55COp1GoVBAX18fzp49i7KyMpSXl2N8fBxSqRTl5eWs9GUymfDSSy/Bbrdj3759sNvt6OjoYBO69vZ2LCwssGIXiS4UW1s4HGZp3nQ6jbW1Nbz++us4cuQIFhcXuThzOByIRqP43ve+B6lUCqfTiUAgwCarBoOB1YGUSiWmpqa4MUPCC7lcDmfOnMHS0hLUajV6enqKyhiGw2GYzWaGMMViMVy4cIGTR6fTieXlZVy/fp0NRWnSc/78eRYMUSqVeOGFFyAQCPDuu+9iamqKBTL8fj+CwSDr/QsEAlgslt8KqSXuDpGCCX6Sy+Xg8XgwPj7O8ICmpibEYjFIJBJ4PB6cPHmSFZBogvrZz34WMpkMw8PD7BVBJoMHDx7Eyy+/jOnpaT4XiinrkeBGNptljkFJSQkefPBBqFQq2O12LC0tQSwWY+/evTAYDHjiiSdYNCCdTiMajbLLN3GITCYTAoEAPvzwQ+Z57dq1C++//z6fz5cvXy7KkaOpglqtZl7i2toaOjs72fRsYmICQqEQ1dXVzHe7ePEidu7cyXvJYDDg9OnTuOOOO/DBBx+gra0NpaWlmJmZQTwex9jYGL785S+jUCigrKwMIpGIC4atgsjF6+vrLEU+PDzMho+JRAI3btxAJpPBM888g5GREZ4mr66uIhwO4+zZswiFQqitrUVraytOnz7N6pVKpRJdXV3w+Xzo7u5GIBBgAzqfzwe3271loUF8GqfTiUQiAb/f/zGPi7W1NZZC7enpwdzcHJunnTx5EoFAAFeuXEFdXR1OnjwJhUKBmZkZuN1uFqMgoQTichAsye12F+1+E+yLZKMTiQR8Ph8nyE1NTZiYmGB5ViogYrEY3G431tbWYLFY0NLSgmAwiMcffxxvv/02QyR9Ph9z0CKRCJaWljgfI47pVkGeViKRiGG80WiUhSTi8Tj6+/uZj0aoAfIVowRZLpfD5XKhvb0dY2NjPIkgn57R0VGo1Wq888478Hg87JdTDMZNog4TExMs4KJUKnH8+HH8+te/Rl1dHUKhEEZGRnDgwAEWN5DL5XjwwQfh9XoRCoVQXl6OaDSKXC4Hl8vFQj+Dg4OYnZ1l/5H5+XmGwV27dg1isXjLQiOVSrFBslgsRjgc5uZrLpeDVCrFD3/4Q1YqI5TI8PAwcrkcFhcXoVQqMT09jRs3bsBoNEIkEuHYsWNsW3D69Gk8+OCDSKVS+OUvf8m5Zy6XK8pB+2h8YuiUWq1mbLVCoYBMJoPdbsfp06dx48YNTuAomY5Go4jFYmyulMvlUF1djdraWnR3d+NHP/oRTCYTbDYbRCIRYrEYy/x9+OGHTFIeHx/nbupWIZfLEQgEeKNR4pnL5ZigR5hk0gPPZDLIZrPs8Lq5uYn5+Xkmg1JSEw6Hsba2BqvVivr6euzcuRORSAQej4fxq+Xl5VuuzWg0YmNjA5WVlYx7i8fjePXVV5HJZLhzG4/H+RkdO3aMcd7z8/PsjG0ymSAQCBCPx2G327nQOXPmDGv3k+QfFSHFCH9arZbVYAhmZTKZYLVaMTk5iXw+D7VaDblczq7j27dvZxlc+ogJ/0hwKJpc0LsZHh6Gw+Fgb4LW1lY0NjYWxWySCytB1+hjsFgs6O/vZxMkn8+HUCjEMpY+n4+JkgCwY8cOVnaYmZlhbCSpj0mlUu4ujI2NYXZ2lhP9raJQKDCBnkbuoVAI//AP/4Dp6WmYTCasrq7i4sWLTIKlS4b4Rs3Nzejp6eFDisjGxCP58MMPMT09zaRMSpqtViu2b9++5doEAgGuXr3KfgnkNJpOp9lNWSgUMiGwoaEBNpsNNpuNSYUqlQpWq5XxnzQBIRUKKuLsdjssFgvLjRI8pViIRCJcuXIF4+PjHxM52NjYgEajQWVlJTv9DgwM4Ny5c9jY2EBLSwtCoRAkEgm6urrQ1NSEuro6XLhwAa2trVwkkxcMkac7OjqYOE6Qia1CJpNhYmKCRQ5IoeXq1auIx+PYtm0brFYrE2R1Oh2eeeYZ1NTUYHJyElNTU6zS5nA4MDU1hcnJSRiNRtTW1kKpVPK3mclkYLVaMT8/jw8//BBra2tFYV35fJ7NEj+qbDM8PMxwNEpABAIBbDYbK28BN3k5PT09/F6JhE3GfCaTCUtLS9zp7OjoQFdXFyuEFdPAV6lUrJdPYhuhUAg///nPuaNJAhszMzOsRU9eDCSx6/V6kclkMDw8zOaZ1JWsqalBOBxGXV0dew6RsVUx9TryF6EiiwyniKtw6dIlTE9PAwCqqqqQSqWwe/duTE9PMyQ2Go1idnYWc3NzGB8fh1wuR1lZGaqrq1FeXs6qi7FYjInjNCUqZq5JdwPBKEUiEQKBAGZnZ7G8vMzqcaRAdPnyZeaUKJVK2O121NfXo6GhgY0u19bW4HK5sL6+zhMmiUTC6ngkTtLQ0FBUCpWUIKmBQ8T3/v5+AEBHRwcaGho4sVtbW8OBAweQz+dx7do1ljs2m80sg08eFKSs6PP5MD8/D4fDwV4uq6urcLlcRX00iC9I/Evihr3//vtMlCfX6kwmg5GRETQ0NHABQkZyhCTQarUsQFNSUgKdToelpSWMjo7yO3U6nfB4PNBoNEWLIGqKZbNZxt1TjkOqVSQB39DQgI6ODmxubmJqagpjY2MQCASora1FJpPBlStX8OGHH0IqlbIXlNVqxfXr11FTU4OmpiZu3q2srPzWQkMgELASklQqZQPL/v5+vhvpfiQyu8vl4vVEIhFEIhHm2fzqV79CIBCAz+eDVCpl80uFQsGGbvPz85iZmcHm5ibKysq2XFsmk+GmqFQqRTqdRiQSwXvvvYdsNguHw8G8OYfDgWQyydLeV65cwdTUFPuPnD17lgV4qqurWRiDZLAJhkYqTMXgjRSkOqbX61FRUYFQKIRLly5BqVSio6ODoc8ulwsLCwsoKSnB5OQkfD4fCoUCjEYj6urqkEgkMDAwAK/Xi2AwyER/Uil0OBzIZDKYmZnB7OwsT1CKPbfl5WXmZpCBZW9vL2ZmZlhKv7S0lGF5FRUVTFtYWVmB0+mE1WpFZWUlmpqa4PF4EIlEYLPZ0Nvbi4qKCrz66qssBJHL5bC2toaNjY2iMO6PxicuNEhSLhQKIRqNQi6XQyqVoq6uDm63m9nxGo0G5eXl8Pl8sFgsLDlH0radnZ18gEqlUj78y8rKcOLECU6oCB4A/OfEotjDJlk0qVTKXIOqqioIhULYbDbY7Xa+ZEmCjWAkGo2GnWY3NjbYaJAItQRDisVi2LFjB29YIm0Ve9jEfwDA5HJStaHROxGLFAoF7HY7Dh48yFh8jUbD0qKbm5uoq6tDJBLhPyOTyXDw4EGkUimGBygUCgiFQni93qIJgkQiYXL72toad9iIlEvqP4SuI6PF9fV1xvCLxWImrxOMJhwO8wdHY1+SQrVYLLBYLHygbRWJRIK7LwKBgDsOJCRQVlbGVX9zczOb/FDhI5VKGc9J/g+hUIil/JLJJCchOp0OJSUlMJvNUCgUCAQCRYsg0p8mGItGo4FOp/uYEhHJNEskEvh8PvZAKS8vZ0lhiUQCi8XCaiT0v5SgUKeTDm/6HooR1UlZjXxEKJEj3wq6XIj0Tu7kJFeYTCb5slOr1TydCgaDzGexWq2svETKR0KhkPdgsaBklN6TUqlENBqF2WyGSCRizxqFQoG2tja43W44HA6IRCI0NTWhoaGBhRBIdpQmFQqFAhaLBfX19QgEAmy6Vl9fj6amJkSj0aKcpY8qIFHX1OPx8J+x2Wyoqqpignc0GoXJZOLJrsfj+ZjMbW9vLyd2NAFVKBSsQT49PY3l5WWeChfjBVGRRNM4mUzG7r4EowyHw0gmk6iuroZGo2GFLa1WyxMHkookiWWHwwGz2YxQKMQwg1gsxtAbklIthrClpKRQKEChULDqFjWfjEYjysrK0NLSgoceegjJZBJGoxE6nY7PWbFYjMbGRkQiEej1eojFYjQ3N6O+vh46nQ4HDx5kw8GSkhI0NjbC4XAgEAgULR6pMKbzh1x+d+7ciXA4zNMukgPe3NxklUKFQsFnhc1mY9lgMqZUKpVYW1vD4cOHWTodACcO9E1vFfT30T1AEz5SZaNzvFAoQCKRwO12o6qqiqcksVgM4XCYZVOpGUOiAclkEna7ndUSCZNOSoHFhBHoPiXiN52HlZWVCIVCcDgcsNvtsNlsaG5uRjqdRllZGUvNFwoFZDIZFgBRKBQIhUIQiUSQyWTMGxoeHkahUGAJUboXixVBtH/z+TwTpYkbR82OsrKyj8kVV1RUYGBgAHq9Hna7neXQyYGaIH/0PZWWlrICJUmM0jsqRrjOZrMsQkLFC6ESZmdnmadKsuEajQaHDh3CxsYGmyGSQeu7777LPglyuZzhdXv27OHGVDqdhkajYdfv34aEJ74HnfsSiQRarRZ+v5+VvmQyGQtL0KSKnOtJsc3pdMJsNrPyn1qthslkQnNzMwCw0A29a/I42iroPCIJfYLWbdu2jc87s9mMiooK5qjW1dUxMoGUFNVqNVZWVrC2toZAIMANtdraWvT29mJ6eppNUkkBk+SOtwraZ3Q3Go1Glsjd3NxESUkJf1sqlQp+v5+/YRKN0el0sNvt7KO2urrK8se5XA6tra0sCaxQKBjqSw2SYt8CFSMkTx4Oh7Fv3z4EAgGsr69zwavVajE4OMhw2ebmZsTjcQwODsJms/HPkZQyNcAbGhrg9XoZuVNSUgKDwYB0Ov1frzpF6gg+nw9er5c1hR999FE27qDRb01NDRKJBHQ6HROJs9ks40nn5ubw5JNPYmNjg/FuVVVV6OjoQCwW481NUxPSPd8qSGGGPka6BFpbW1FWVgatVouOjg7s2rULZWVlWF9fZ2fixx57DD09PchkMjCZTHwokSQZyeiRjCQlvFQBFoPYADcJf4T5jUQiPII9fvw4q+OQaQupGJDpWldXF7Zt28aKSqRTLpPJEIlEIBKJ0NXVhSeeeAISiYRJgJT8UfK9VeTzee7QkCxaOp1GeXk5enp6GKJBqlGk+W8wGLBz506Ul5ezfHAgEEBFRQW/J3KKJuUrMkykycPw8DAWFha2XJvX6+WkjvS6y8rKUFFRAYvFgq6uLrS0tKCzsxNtbW1wOp2cZFZVVcFoNLJC0urqKg4ePMjFKnFYiI+QSqXQ3t6Offv2cfVfzA9CIBDAarUCuHmxKJVKVFRU4Mtf/jI6Ojrg8/lYNaS0tBQikYiLXL1ez11fj8fDimLNzc3sUaLX6/HAAw9woe73+1Hyf4wEE4lEUYOyXC6H2tpafqfUQTOZTKisrOQijGSbiSxGXROCe5BMKznV+v1+zMzMYGVlBXa7nSccJP8ZjUbh9/uLkoYBsHJdXV0dqqqqeM9UV1fzJIzGzI8++ijq6+tRXV3NbsvE1xKJRJibm0NDQwN3Xskzo7y8HBsbG8yXMJvNaG5uZl7CViGRSGC1Wjm5E4vFbFhFz424AYSnnZycxNLSEl8OoVAIOp0OAoGAiaD0XKmwTafTEAgEDIkjJ/RiMJuSkhK0trZ+bFprt9vR2NgIq9UKm83G01wy8FOr1WxQJxAIOOkXiURcvBPPwe/3o62tjX03ZmZmWAFwc3OzKOyBvGJINpS+v66uLkilUshkMmi1Wuzduxef/exnGQZIcpQkkUp8t97eXuj1etTW1rL3THt7O/x+P/s3WSwWdHR0QKFQFJ3aZjIZ1NXVsbpZJpOBUCjEiRMnIBAIUFdXx2dGeXk55HI5PB4PGhoaWL1ILBZjz549KC0txfHjx5nEnslksLKygq6uLr7HaDphNBp50r5VBAIBVFdXI5lMMkRVpVLh5MmTfJ9Sw4069HSxLywsYGxsDJOTk6y/73A4EIlEuOEUCARQV1f3MU8dhULBKlrUACv2TkkWuqqqCl1dXTh58iRDf0gC9eTJkyw2AdwkQ5MHFU2F7r33Xvh8PmxsbLBh2fbt21kmP5lMoqKigmXxixVoJF1OcvnU8Pj0pz+N9vZ29jygxh0l/KOjozhy5Aj27dvH0JJMJsMJIBUJRqMRe/bsYbgTKSfRxLFY05OkSQmuE4vFIBAIGHpGk0iCZ/r9fnaBv/vuu1FXV4dkMon6+npEo1EcOHCAC0pq6H7uc59jN3u5XI7t27ejt7eXG7dbhVAoREVFBeRyOfPj4vE4Ojs7WYpao9GgrKwMzc3NiMViaGhowNLSEkpLS7kRp9Vqsbi4iGeeeQZqtZonQqlUCmVlZVAoFPwMiHhM6nFbhUqlgk6ng1AohEwm4/v76aef5gk6+f1QXtbU1IShoSEcOXIEbW1t2NzcRENDA0pLSxGLxZDNZrGysoJQKAStVosjR45gaWkJLpeL9yKducVkz7PZLKt+0eSY9snGxgb7l5HMt0gkYmRHeXk5F0KkgEnQQirGU6kUqqqq4HK5uNHS3t6OxsZGlmgu9k4bGxsZtkvNcVKuunLlCqanpyEWi7Ft2zaWCBYKhdywJJVKgqY3NDSgtbUViUQC586dg1QqRWdnJwqFAkts19fXczPmk8QnLjTMZjP6+/uZ4Eh6uz/60Y/wxBNPcEJFeL7u7m788pe/xIEDBxAIBNid8Atf+AJ0Oh3efPNNVqoiBYz77rsPX//616FSqaDX6xk//IUvfKHogS2XyyGTybBr1y40NDRAr9fjnnvuwQcffICqqiqMj4+zVCO5gw8PD+PQoUOQSCTo6enBc889hz179kChUODixYucJJT8HxOW8+fP41Of+hSGhoZw4sQJqNVqLC4uoqWlpSh+maAkdrsd+XwePp8Pe/bsweuvv4477riDzc5cLhcuXrwIqVSKF198Efv27cPU1BTS6TSam5uRSqWYAPjEE0+wPK9SqcTzzz+PO+64A+Pj4zh+/DiPWGtra4seilScVVVVsW9JRUUFpqamUFNTwxW/XC5HaWkpdu3ahVwuh127duH69esYHR3FxsYGjwp9Ph96enpQXV0Nu93Ocqg1NTVYXl5GZWUldwj37dtXdL81NTXhypUrrNZFuOS5uTns27cPly5dQjweh9/vxy9+8QtWBPrsZz+LnTt3MnEtGAyiqakJ9fX1eOihh1hXPRAIsPv1a6+9BrfbjXw+D4fDgc997nNFDSJLS0tx8eJFnkItLi7C7Xajv7+f3YD9fj/W1tYwMjKCZ599Fq+++ioT3UpKStDe3g6RSITt27fzx5rP52EymTjhOn78OPx+PzY3N+H3+7GxsYHu7u6i6h0ajQZ9fX28NuJPLS4usoJIoVBALBbD1NQUjh49imAwiPb2dhQKBS6G3nnnHXR3d+PFF1/Erl27UFlZicbGRtxxxx28/nfffZclKQk+V8x5GwAaGhowMzPDfg/JZBJHjhzB8PAwtm/fjp/85Ce4evUqw+Z+93d/lxVqXnvtNTY1e+GFF7Bv3z4mNJJMH3BTHOG+++7D3NwcwuEwTp8+jTNnzqC5ubnot6rVapFOp3HvvfeiuroaCwsLuP/++9Hf349sNotAIMAYb6lUiqeeegpzc3M4efIkd+zb2towPj6O5uZm/Nu//Rt27NiB8vJyVFRUoLGxEUNDQ2hsbMSvf/1r/NEf/RGOHz+OyspK/MEf/EFRc81YLIalpSXmoZES3ZkzZyCXyxku5XA4WEXl9ddfZx4BwXw0Gg0ef/xxnjYQB+C+++7DysoK9u/fD5fLhcbGRk72yMV2qzAYDIxJX11dxfLyMrZv347z58/D5XJxQvzBBx/gZz/7GfR6PU6fPs0FwyOPPILjx4/j3Llz3DTYuXMny7+Wl5fj7bffxle/+lWMjY3B7Xbj+vXrcLvdOHz4cFGjw1QqBafTCZvNxsRltVqNl19+Gfv372cRE71ej87OTtTX1+O73/0uQ+Ta2tqwa9cuuFwu7N69Gx988AF6e3shEolQWVmJY8eO4Stf+QqefvpphmmQyhUA9iy5VRQKBYyOjqKjowMtLS3sWfCrX/0KVVVV6O/v54aZRqPBkSNH8Morr+DAgQP48MMPkcvl0Nvby2ISJpMJv//7v4/m5mZOBuRyOZ5++mmezC0tLWFiYoLlN4u9U6FQiLq6OpabveOOO/Diiy+irKwMFy9eZEjI+vo6HnjgAaRSKTzxxBN4//33WQjgjTfewJ/8yZ+gtrYW+/fvZw4nNWq+9rWv4b333oPJZEI+n4fT6YRQKMTQ0NCWayNJV4fDweamNNGorKxkvxZSJCstLcXly5exfft2XLlyBSsrK5DL5XjrrbdgNptx6dIl9Pb2wmw287Qsm83iU5/6FDweD3bu3IlDhw6hvb0ddru9qMokTZ7oOxUIBOjo6MDo6CgeeeQRhkbv27cPoVAIcrkcb7zxBvsdaDQaHDt2DFarFU8//TRUKhVj941GI3p7ezE/P48vfOELeP3112Gz2VgoRS6XF+3M22w2CIVC1NbWorS0FNFoFF6vF//+7/+OvXv34uLFiwgGg6iqqkJtbS0OHDiAl156CfX19QwJ3r9/PzfuJicnsWfPHm6ayWQyfPe738XJkyd5kjU3N4f5+Xmo1eotxXwAoKKiguVos9ksFhcXceLECfT39+PEiRMYGhrC0tIS1tbWWFDlP/7jP6DVajE1NYW6ujocPXoUb731Fr785S+jqqoKzzzzDNrb22EwGKBSqfDGG2/gy1/+MsvwE6+ut7e3aDJPjfS6ujoUCgUMDQ3xFGf//v1Ip9PclHj11Vexa9cuvPrqq1Cr1XjllVdw/fp1bGxsoK+vj4u4hx9+mNEL27ZtYxjXyy+/jMrKSoYmOhyOot+CXq9HXV0dDh8+zF45LS0teO2112C326HX67mBqVAo8L/+1//ClStX0N7ejlOnTqGkpARPPvkk3nzzTZSXl8NoNOK5555DS0sLT1iJe3Lp0iUAYJ4SNQE/SXxiMrhMJsPnPvc5jI6OQqFQYNu2bawy9eabb8LlcrFEWkNDAy9udnYWOp2OSVEEJ7BYLBgaGmLoQSQSwZ/8yZ/gL/7iLyCTyXgqEolEsLm5+Vt/IZIOow1AykdkikWjZHKYPnXqFDo6OrhCpm55WVkZenp6GC9JONCOjg6W9CQHbIlEguXlZajVatx55523XJfH42EpSurUzMzMoKqqCrFYDB6PB4FAAEKhENu3b2dXarlczkoOvb29qKmpwY9+9CP8zd/8DU6dOsVwHa/Xi2QyiRdeeAGtra24ePEiTCYTJBIJe5RsFS6XCw6HgyFAiUQC165dg0ajYbOb9957jy8Ni8WCF154AYlEgvWZg8EgxGIxrl27hvb2dkxOTrJLsVarxfDwMHp7e3HixAkmQAmFQjidTrS2tm65tkQigZ07d2J1dZUnCG63m0nWDocDo6OjcLvdkMvl7DhPhlukQOR0OvH666/j+eefx7e//W2UlZXB5/MxUUyn06G1tRXLy8uMDf5tl3AkEkF9fT0SiQRMJhPKy8vx/vvvM6yttbUVfX19AICHHnqITSs3NjZgMBhw/fp19Pf3o6urC8lkEi6Xiw3BxsbG2FyQMOxisZgLRtJ33yoIY55KpZjTQI7psVgMpaWl3DGRyWR4+eWXUVtby/4cs7OzbLb2N3/zN8hkMhgYGGD97ZWVFVbuKSsrg9Pp5ASWsNrFggjmBEsifxOa6LS0tKBQKCCXy2FmZgbXrl1DVVUVMpkMF2Verxft7e147bXXkEwm8Zvf/AZ33HEHlEolVlZW2Ay0urqaDdrIdK+YBr7X64XVakU0GmXeg8vl+thUsKSkhIUFvvvd7+Lxxx/H888/zxMQp9OJK1euoL6+HgcPHmSzNJoKkSLPAw88gFOnTrGs49mzZ4tCCwqFAqqrqxlqJJFIMD09je3bt2NjY4NN2wBgYmICNTU16OrqYhdmj8fD0zK3243q6mp88MEHkMlkfM5JpVJOzEZHR5lXVaw4A27Kizc2NiIej2N5eZldbkmr/6N46fr6eqhUKjz++OP48MMP8dZbbzERm6Aaa2trOHPmDGvlkxrcqVOnGJoxMzOD5eVl1NXVFeUsEVSPpozU0d+1axd+8IMfsGLi4uIiYrHYx9R+aEJuMplQW1sLj8eDPXv2YHBwEF6vF9PT07Db7fj85z+Pv/qrv2JRCbFYjEKhgMXFxaIkU6VSydMYs9kMi8XCyj/j4+NQKpXwer1YWFjA+vo6YrEYamtr8dJLL0GpVCKZTGJoaAiJRIJlK61WK+bm5jAyMsIY6tdffx179uzB8vIyqqurkcvl+AzfKkgCmyaU+Xwe586dw+7duzEzMwOn0wkAzHPRaDTo6OjA4OAgHA4HNBoNbDYbTCYT/uEf/oG9LACwPxR1eru6uqDT6XD58mUWiyg2GV1bW0NDQwMWFxdZatzn8+Hy5cs4ePAg6/8rlUocPnwYyWQSdXV1+NrXvga1Wo3Z2VluTDY3N2N5eZnVpUjVrLOzEysrK9wcUqlUDJksZqBG/lElJSWQyWSIxWJ4//33odFocP36dQA3/UjIq0AikSAQCGBycpI5cgqFAk1NTczhtFqtkEgkGBkZwcTEBI4fP46lpSU0NDRAIpHg8uXLcLvdLKla7LlVVVWxx0tNTQ2kUik+/elPw+fz4f7778fAwABmZ2e5497S0oKLFy/yHvB4PJBKpYhEIujq6kJfXx/m5+cZIlxSUoL33nuPVUOJ9xKNRvHee+9hz549t1zbyMgI31MEUZucnITBYMClS5ewZ88ebnoSj8br9aJQKGBhYQGZTAbd3d3Ytm0bzpw5AwA8XV5dXcXs7CyEQiG+//3vo6KiAg0NDXy+X79+HQcPHtzyufl8PlRWVmJjYwPl5eWoqqrCzMwMC1SQLK1KpcL27duZDF5RUYGOjg6cP38eP/nJT6DT6dDc3IyxsTEcPHiQhQZmZ2dhsVhw+PBhPtdoGpbNZosKrKRSKW6ckmAOEevp+yf/rjfeeINznDfffJNhWXTvCIVCfOMb3+Azm6Y0U1NT2LNnDxobG3H+/Hn+LhobG/HGG2+gq6try/VRfOJCQ6lU4q/+6q9QVVUFq9WKjY0Nhj0olUqcPHmSvTNKSkrQ3d2N73//+xAIBIxJpo80n89jx44dyGaz7P5Mv1BnZyePhyk5I/m3rYIwxmazmf+NxcVFTE1Nobm5GXfddRebk0kkEuzevZsNgk6dOoVoNMqun0KhEN/73vfQ2trKxHelUolXXnkFX/nKV3Dq1Cn+UO12OxwOR9GkVK1W48qVK9jc3GSCt8/nw+LiIgKBAD7zmc/g9ddfh9PpRE9PD86dOwefz8fQA7VajWAwCK1Wi1gshn/6p3+C2WyGVqtFMBjE2NgYq2cMDg6yahBhw4tBgIxGI0ZHR5lATkktJUThcBitra2wWq1IJBLcqSSTP5PJhOrqak7+XnzxRYb4UMeEHDi1Wi02NzexuLiIdDqN9vb2ohhhOrjosCUyWiAQ4I5EW1sbqqureZS/e/duLC0t4Y033mB1DJlMhmPHjgG4WSBYLBbs3LkTOp0O3/72t3HPPfcwOZZ4Q5lMhhV+bhVarRZ9fX2QyWQ8tvf7/fjrv/5rHD9+HL29vZDJZOjv78fk5CT+5m/+BlKpFIODg1hYWIBOp+PCaXBwEOl0GseOHWOnYOpYzs7OwuVy4ciRI9DpdDx6pmTrVlFSUoJf/epXaG5uZhJ3NpvF6uoqampqkM1meXRMnAaHw4EPP/yQ1Tr27duHvr4+TmJJT56wmZFIBBMTEyx9PDk5CalUiqqqKjz44INbrg24Cclwu92MXY7H47h69SpOnTqFp59+Gjt37mQynNvtxv33389kzFOnTqGyshJ33nkn6urqcOTIEfz4xz9Gc3MzLl++zHuDYDG7d+9micVsNsuGXFtFZWUlhoaGmMuysbGBCxcucAFGU93l5WWGIPT19WF9fR29vb3IZDLweDzo7e1lqWdyAa+srITJZILRaMSFCxfY8ZlUVA4ePFi00CBYFjnfkjfH2bNn8Tu/8zvwer1Ip9OIx+N8XlLhT1ASgqyMjIxgbW0N+/fv5+QqkUgw34AczEUiEXw+HydeW4VGo0F/fz+OHz+OpqYm9kMgQ8+WlhYsLCzg0qVLOHfuHB544AHU1dVBKBRyd7m5uRl33HEHw2iOHj3KCiyvvfYalpeX8dxzzzEhUyaTMZSkGKzWYrEwnvyj8LGhoSH4/X6+hEkuefv27fjOd76Dt99+mxPSyspK2Gw2nD17Ft/61rfw7LPP4v7774fH48HFixchEAjw+OOP49SpU1Cr1SwJvHv37qKTILPZjHfeeYcLSOKuRCIRlJaWIpPJMI6bYEJarfZjECSbzca8nzfffBMNDQ04cuQI7rrrLgwMDOD111/H/fffj1QqhY2NDUxPT2NzcxPHjh0reqdSAUrGjZFIBEajEfl8HgaDAUePHmVJzOHhYTz77LNsVkl8oYWFBYTDYVitVly4cIFJyHa7HUePHkU8HkdfXx++/OUvY2BgALlcjvljxXwNLBYL3n33XTgcDqhUKuRyOXR2dmJwcBA3btyAUqlkJ/Vr166hq6sLuVyOYYV0zldUVOD06dNYXFyE0WjEgw8+iNraWszPz7OzdF1dHc6ePcuu48Sv2yqMRiPvYQCsfjc5OYnt27ejtLQU9fX1WFlZwauvvoq9e/eitbUVU1NTeO2119iLjPyoSDJ2586d2LFjB4RCIX7961+jt7cX3d3dAMBNVKD43aDX6zE4OIjS0lJYLBZsbGyw3LfNZuMOdjqdxujoKHbv3s1mrJRQb25u4q677sLp06dx6dIl7N69G11dXZiZmcHk5CQ332pra7Fr1y52Hido1FZRV1fHUqw6nQ75fB5ra2t4//33WUmJvFbS6TSeeuopPPzww7h69SpzDenfuXLlClwuFz7/+c+j5P8YG1KD+M///M+h0Wjg8/kwODiIRCKB7du3FyVcf1TFkVA4JF4wMTGBTCYDpVIJtVrNfBQSpnG5XNi2bRtTBUiB6oMPPsD27dthNBrR1NTE6JXa2lr2B7NYLNDr9VheXt5ybdXV1XjhhRdw/PhxPlPJYiIej+P48ePY3NxEKBRi80gq1C5cuICBgQFoNBr09vZiZGQEV65cwcjICMOUW1paYLPZsLKygvLycjQ1NTEioaenp2hh+9H4xNCplZUVHDp0CCaTCQ6HAx0dHVheXsaJEyf4ACSFC4fDgcnJSRw5cgRisRgVFRWw2+2oqanBZz7zGZw9exbpdBqrq6vc4SbIAh2sKysrsNls6OrqQjabxYcfflh0bTU1NfD7/RgbG4PP54PD4cAjjzzCRneJRIKTzLfffpsdMQ8ePMjwJ6FQyNWbz+eDXq/n8WxrayvOnTuHQ4cO4eTJkzhy5AiTkYolzIlEAs3NzYyLF4lEaGhowLFjx2Cz2RAKhRhmREnEnXfeCaVSCY1Gw1Avs9kMk8mEAwcOYGNjAw6HA+3t7XA4HDh58iRj+z5KIJLJZEXJYYVCAa2trQiHw8yhEIvFLDtLxluk/x+Px5lEVlZWBq/Xi7Nnz7JGe11dHcRiMTo6OlBaWoqVlRXs27cPCwsLsNlsPF3RarVYW1tj6btbBY2CiZhEvhVNTU3MjfF6vQDAJNJUKoXZ2Vns2rWLcZ01NTWYnp5GJBJhWcxQKASXy4W//uu/Rj6fZy8D4D85CsVGqdlsFjt37mTX0aamJjQ3N+Ob3/wmc4pI3OCLX/wirl69CrvdzsRNIgN2dnbi/PnzrE1NhNVkMslQjKeffprVP6hr4Xa7t1xbLpfD4cOHodFoWPqR5G2npqZYzY3G1JOTk6wQc+jQIYhEIkxNTWHfvn2YmJjAww8/jIqKCuzevZu7bKSURbKWJI/4zjvvMPZ4qxAKhYzjJYJeMpnEH//xH0MkEsHv9zNcZv/+/ejr64PZbMaFCxfw2GOP4fDhw4y1P3/+PE6cOMFkPI1Gw6T+lZUVLoZI6YyU47YKl8uFrq4uRKNRLk6IaEdwKXqmNpuNBSdoxE18LqvVCqPRiC9+8Yuw2WyssEYdosnJSQgEAqhUKobrLC4uFp22yGQyWK1WRCIRVugKhUL4b//tv2F5eRl79uzBtm3b0NrayvCGfD6P+fl5xoVTx9ThcHDHlhRmzGYzTpw4AafTifb2dj6LqNgo1sVNJBJoa2tj5RuxWMx7iEi0ANDa2oqHHnoIly5dgkwmQ19fHw4fPoz6+nosLCwgHo9jdXWVyfyEpzYajXjyySexuLiIbdu2YXJyEjMzM4zZLpaULi4uor29nXHUhUIBoVAIra2tqK6uZl5Ae3s7SktLceHCBSwtLSGRSGDXrl0wm81wu91IJpMwm8347ne/i7m5OfZu0Gg06O7uxpUrV9h5uK6uDhaLBaOjo0UhvysrKzh+/DjDXHfv3o2NjQ00NzezxGp5eTlqa2tx6NAhOJ1OmEwmvsfoTNy5cycSiQSOHDkCqVQKnU7HJomf+tSn2DtldXWVu7n9/f1F1bo2NjYgEAiYC9Td3Y3NzU1UVVXB7XZjaGgIV65cgUAgwF133YX+/n6eGh08eBA7duxAZWUlDhw4gOvXr+O+++6DWq1GV1cXbDYbZmdnGUu/uLiIoaEhWK1WdHV1sRjBVhEOh/msIs8Q4nVNTEywil5nZycee+wxRi1Q4iQQCDA3N8ed8wceeAA+nw8jIyOYn59HIpHA0aNHmTt64MABHDp0CLW1tXzXbRUk/CGVSpnzpVKp8MQTT0ClUmHbtm0QCoUoLy/Hc889h2AwyNj3xx57DI8++ijq6uqg1+vh8XiwY8cOZDIZBINBRoXceeedHzNJ7OnpYehVMVitz+eDwWBgV+dIJILKykp89rOfhUqlwrFjx7Bz50709PTgnnvu4cacTqfDo48+imPHjkGpVGJmZoZzI5LDNZlM6OnpwV/8xV+wbDepdTY0NPDZuVUEAgE2p11ZWWEOzqFDh9iIubm5GXv27MGnP/1pljune9RisbCAhEQiwZ/+6Z9ibm4OYrEY1dXVcDgc7BVSWlqKeDyO3t5eHDt2DMvLy0WnBqQaqlarWbBAJpOxCeTu3bsZRULiDocPH0ZHRwf+/M//HIcOHUIsFkM+n0coFMK2bdtgsVi4GF5cXERjY+P/Hy8rm81ifHy86J01NzeHgwcPckOf+FkkSW6xWPg8e+CBB/D+++/DZDKho6ODifazs7Ow2Wy4fv06fvCDH+Do0aM4cOAAampq+H385Cc/YZXSo0eP4jOf+QxCoVDRBtRH4xNPNEhpoKKiAiKRCE6nE+FwmI1CPB4Pcrkcm5ENDAygpaWFiZElJSXQaDSYm5tjhnsmk2FCDBEOJRIJlpaWkMvlODGrqqoqSiSi9ZByTiwWYwmulpYWVjiiA8doNMJkMnF3nchihDOksfXExATjPsm2fmZmBjU1Ncw1UCgURQkxMpmMNfJJvYYKMpVKxcoWer2eK3nq+hBetaKiAul0Gq2trTCZTKipqUEkEkE8HucPkjgbVquVJXltNlvRi46Is/l8/mMu4sTdIDK5yWSCSqXC/v37MTU1hfLycvb7IEIkTTHIpI4SGgBsd0+QJDJ3KwazIdISeQwAN7s1N27cwLFjx5jsSoTFxsZGjIyMIBqNsoKOXC6H3+/HyZMn4Xa7odFouCChxK+1tRVjY2OsokakrWL+IwTfouKPnhkRQSmZzmazDEnzer3Ys2cP62iTYs2jjz6KVCrFhHlSRKMDgqSdaW8bjcaiyXw6nUYul4PJZGLyMUnbNTU1wefzsfSlw+Fgw6uamhrodDpWuyDvFNpvEomEDQDpvdF0RqlUwmAwsCdNsaDEfW1tjTt25NhrMBgQjUah0Wig0WhYXSYUCqG+vp4v7UgkgtHRURQKBYhEIu4Iq1QqiEQiTE9PM2yKBBZIJKJY54o6uKQQRQpsVFSNjY1BJBKhvr4ejY2N3H1aX19nHodcLofT6YTRaOSJ6MzMDIxGI3K5HHveeDwemEwmLmLo2WwVJP8oEAh4olFRUYFkMslGjxqNhs34WltbMT8/D+Dm+UjmbaRkZjKZ2LSKutSkgAf8pzpNKpVi/PxWQUaPqVQKwWCQx+qkbjI3N8edzoqKClRUVCAWi6GqqoqhSWKxGB6Ph6Uxq6qqOBGkBD4cDiMcDqOsrIwlnzc2Nop2/ICbXhqkoETiHEtLS6ioqGB1O1IRy2azmJ2dZR8nUkMbHh5mCLDdbudnA9xMfNVqNaLRKDY2NhCLxfhOKZbMU7ON1K1I4l0gELDMM4lhUFPI6/XyNweAjbSItEzQv/X1dbjdblaPoWdL+/qjsKitgsRLSJqVfJfIt8JiscBms/GkqLq6mj16qFANh8PYvn07uru7sby8jHQ6zc8tk8mgo6MDmUwGMpmMjdcqKiqK7jeBQMC/D02gY7EYysrKoNPpEI/HGV4ok8ngcDgY3hMOh1na9tq1awDAgiUkwUxePTU1NTh37hzsdjsLkpBU9lZBpreUKGazWVau2r17N/x+P0KhEEpLS6FUKiGRSHD16lVYrVZW8aKpL0GvCLaTTqeZKF0oFHDx4kXm7xAxv9gEjQQRSOyC8gUqOMRiMcuWGgwGlt7v7u7maajZbGb1rPLycr5/ic+5srKCHTt2sFInKdLRpGurINhpJpNhlTRSgVMoFLh27RpUKhVqa2thMpkwPz/PkrbpdJo5RfPz89i3bx86OjrgdDoxNjaGQqHA0q8E9zYajQw1UiqVRQs0sVjMSpNkoKnVapFKpVBbW8vmlMQn1ev1zM0iNEppaSmcTidzngipI5VKoVAosLy8jI6ODgwPD3OeRIJKxYQRKF/UaDRsyUA8onQ6jVdeeQUul4vNQI8dOwaVSsVCOKQCl8lkmM9CE+hMJgO5XI5oNIrS0lL4/X44HA72mqO99EniE080aFQkFAqxvr6OpaUl9lYg7HYqlWLNb9LGJ3wtJQ1TU1Oc5BsMBu40SaVS9tugzkggEGDPhmJjXiIyf3Q8FwgEMDc3x1CPTCaDjY0Nlh4jvXufz8eSbQTxGhsb4wOLSORkSrayssIqO2TcVuzgoU1DKj4SiYSNcqRSKaLRKJLJJE8eiFtAxHFShqLuAql4+Xy+j6kFkLykVqvljj35gGwVJCdHxRepUdBHRV1Xep56vZ6xwiRVaDAYOOGji3FpaYl14MPhMBcVJLEqkUjY6G+rIJw+OduGw2HWgacCgz4Ywk6ur68zPI0SL3IXpySVEn2Ce1AxRskoyeIW6+Bms1k29gLAqmTJZJJH5VSMkI9LOBxmkrzFYoFUKsXy8jJaWlq4o0DJApEv5XI5BgcHEYvF+KKh7vRWkUql2Gchn89zsUiKGHT5knJLc3MzY4DpuzObzSz5t7q6yrKqdDCRUhVpxodCIQA3Cfy/rdAg88TNzU0EAgG43W4uUElilDDApHBB8KKFhQWsrq7yZS4UCjmpoClRNpvF9PQ01Go1f9d0cdHEYav4qJob8TLsdjvW19e5k0sS0MlkEg0NDQgEAvD7/RgZGUEwGOQJAJHoyJG1v7+f+ThWqxVOp5ObEHT+FftWk8kkJx+056nbTucBwSVJypSmC+QATpAjkp+m/Q6AicJ0geZyOX6/5eXlRRs9lOxRck2QPJ/PB51OxxNFKkRo4tTW1oZwOMx7Mx6PIxgMYmZmhvc7qeNMTk5CrVYzXpy+AXI7/23vlDqFJO1JEsY0IaIzvra2FgsLC3A6nZiYmGDFmdXVVQDA0NAQysrKkMlk2JxteXmZp8kbGxsIBoOIxWIsvrBVkPoc7QGn04l8Ps9neTAYZGUg6tSThDud+SKRCMFgkN8Znf/UCFpeXubEnAwoU6kUQwO3CuKZECcoGo3yHUoy5pRETk9Ps8rQ5uYmJiYm4Ha7kc1m2cRtfX0dcrmc4RsE1auqqmJYI6mKkYphsbX5/X7G61Pzggotj8fDRSjBuXw+H0OYNjc3WUFToVBgdXWVm3iFQoE9h2QyGWZnZxEMBhkOaLFYisJF6NsUiUT87RFXku5GgjcuLS2xIa1er0csFkMymeS9LZPJ4PF4UFNTw/9NKpViZWUFRqORXdYXFxd5elCsWSGRSLhhRfBq6porlUr4fD6srq6yshLZBBgMBni9XiQSCd7TtBeogUp33OnTp7nZBtxUBKWGYbEmj0QigdfrZY8YgvpFo1EoFAqMj4/zvvF6vTAajfB6vSxHTtNncqynhgR9C4VCgf3H3G43NjY2MDMzg4WFBZSVlRVFV9D9QtLD6+vrDFMlOXsy8A0Gg+xgns/nGWpL33pFRQU//2w2C6FQyL5RJNFLk0QicBfLL4lfR2pXwWAQKpUKi4uLMJvNWFpaYun5vr4+dHV1wev1Ynl5mXl51OQxmUzMOabcO5lMIhgM8v0rkUgQj8f5ni/23D4an3iikUwmuVJNp9NMRvsoaSUcDjO5hXBzZPRHJjtqtRpOpxPXr1/H7/zO7zCWjS7C/v5+hv5Qojw6OloUM08HRyKRYK10SphmZmZYxz4ajWJwcBANDQ24dOkSlpeXOcnK5/OsrlJRUQGNRsOyhWtra8jlchgcHER5eTnLS9KD/m3dPqpg6WKQyWRYXV1FWVkZu0PS5iX9eoK5qFQqTh7X1tYwPj6OpqYmKJVKLgzS6TRGRkagVqvZTRe42ckrZjxHLu9U8ZM0Gpme1dTUYGxsDNPT09y91mg0GBgYYDdK4otMTU1BIBBg27ZtkEqliMfjDMmhA526IplMBk6ns+hFR2pRfr+fE9Nz586x82Y2m+UDg9ZBRKuVlRWeSGQyGZw5c4ZJuACYYBWNRjE9PY36+nq+FAh6RsZ5twqhUMiwNrrwaSxM423SJXc4HFheXkZXVxdmZ2fZY4D0x0+fPs3mRITdnZychE6nY0zv+Pg4T97Ky8uLYl3z+Twrj8RiMfh8PpYeXllZQXd3NxKJBJxOJ1wuF0pKStDQ0IC+vj6srq6yR4VQKMT09DR8Ph+6urqgUqn40CZCbUtLC/x+P0PiSNihWAgEAmi1Wk7MKIGhxKCyshJTU1OYn59njXSBQIC1tTUusumbJQJkX18fd2iIQDw4OIiKigrkcjkeW2u12qLje+Cm+gklUJREhcNhfv6RSARjY2OYm5tjyIvT6eRCk6CF4XAY165dYxjm2toaBAIBhEIh3n//fT47iYNERPatIpvNsilbJpPhRNfhcCAUCkEgEHAxTsm91WrFzMwMgsEgd0OpuBgYGOCCirr41BDJ5/PswK1QKFiSfKsgPxSJRMLclbfeeos9KqgAo+YJNV1oSpHL5bjBcePGDchkMlaqIRVDv9/PvIOlpSX+PX7b+ySfI0piFAoFc1jGx8f5oqdGkkwm45/1eDyYmpqCzWZj123aGxTUeKO/k+444gkU6/iRrw6pr9H0zmazob+/nyVD19bWEAqFWB1udXWVHe+tViurxO3bt48d7NVqNctj036hYoQSmmKJH4kUEJxDo9EgHA5jeXmZZUszmQzGxsYwPz+PkydPYmJigotvmsIoFApMTk5yQ6G0tJQblYQEiMfjCIVC7Gvi8/mKSp/n83kWyiDvi0gkwsUHTamSySQWFxexvr4OhUKBpaUlhtuRH4Ner8fCwgJ27NjBoiiBQACjo6PQarXst0J+BGROWWy/tbe3A7jZADUYDHj//fdRXV0Nl8vFSSMRkVOpFFpaWjA6Osp50Efh5KQuR1MMcrNXqVRoamriZ0YczmJTg83NTfYmymaz7Ci/vr4Oj8fDctNKpZKnBG63mw33VlZWWLgml8vhxo0bEIlEsNls3HRzu924cOEC5zgf9R4plieRLxP5PMViMczNzfE7IX+lsbExrK6uorOzE3V1dbh8+TJ72FAD8fz58xgYGGAJdbrXo9EoFzwjIyOshmqz2YoiGEh8hr6jXC6Hubk5bqyVlZVBrVazNwZxJQwGA5aWlri4XFtb+9g7EolE7B4vEAjw4YcfQqvVsqeMSCTCyspK0TMulUqxqfLKygqGh4eh0WiY/ysUCnHjxg1cv34dLpcLfX19mJubY5gxSfaSMqDX6+WGaC6X43uYuMo06aD7sZigxEfjExcaVquVHYeJNCSRSFidg+AG1DG55557cPnyZXz44YfYsWMHysrKmOBDUqQDAwMwm80Qi8VYWFjA22+/zbhvglYRzKiYw6pMJuOHR/8Gjd5I3Sjyfxwt6SKlC8Dj8aCurg4NDQ0so0odeDJKAm6qgDQ2NvLYTavVcvFULJnX6XSYmZnhLnUqlUJ1dTUbyLW0tDAPg5LE0tJS7lwTsY4q3fHxcdYHLykpYaw4AL7Eq6qqsL6+zgpIW4XD4cDMzAzjG8nwhxxwyUWauiAikQiZTAZ33HEHS41mMhk0NzezJCXBpgg2ZLfb+YOenJzkaY1MJkNLS8uWazMajbhx4wZ3w8j85uLFi4xJtFqtTLjq6urC/Pw8MpkM+6rQx03a8iaTiYmNtKdJ6pFc0LVaLeOZtwoa3dKIHbh5iF+9ehVtbW0MJVpfX2clNq1Wi8nJSe6qGAwGrKysoKmpCePj47h06RIX5aurq+jr62MCo9lsRjweZ83uYqZzEokEHo+HO4h0YJCEst/v5wkJdcHlcjnuv/9+nD17FvPz81hfX8fBgwdRUVGBtrY2DA0NIRKJcEFF/B8addPzCwQCRbktFKRiplarOfGTy+Xo7OzE66+/jpKSElgsFqyuriKRSDDhlZRfVCoVhoaGOKGur69n80xK+qhBQAUmGXsVk3umMbREIoFSqUQikcDU1BTUajUuX77MBFzCw54/f56VVZRKJZxOJ39zarUaO3fuZEx1MBjE5cuXmYsWDAZx5swZyGQyLqaKcTSIwEgjeur2LS4uor6+HlqtFgsLC5DL5di5cydKSkqwvLwMo9GI2dlZfsbNzc3cXauvr+dvhCBrSqUSsVgMKysrrH6nUChQXl6+5dooGaDin6YQpAJHxRjx1QwGA/L5PAYGBnjKuby8zE62w8PDmJycxKFDh1BTU8NJ4szMDKsnkQY+mcZuFVKpFFNTU+zUDIB5gGKxGFVVVewC3tHRAbfbDYFAwFh+6qJu374dL7/8MkpLS/nsXVtbYw8M4l4RZIbOzWLfg16vx/T0NE9Taar9zjvvoKmp6WOTFupOtrS0wGAw8MSORFAOHz6MM2fOoLu7m6e2JFKxY8cOTo5JFauurq7oBC2fz2N6epqVoTKZDJsVVlVV8bdLMOWysjKEw2FcuHABRqORMeiBQIDhVSR5StwkKqDIEJBkaTc3N7F///4t10YTRWqQ0cR7fn6euRnr6+vsGk+5icVigd1uRzabZWEYv9+PvXv3slodnSMjIyO8Lz/qjRGJRFBVVbXl2oCbimh0disUCuh0OiwuLmJsbIxVHqlJVV5ezpBYuhdpAqVWqzE6OspnbDqdRjQaRSKRwNzcHBwOB5RKJRYXF7nBUgxmQ7Kx5D2TTqcxPDwMgUDAnBiSvS4pKcHi4iJPMgiWI5VK0dXVxc1iUigiSCd5X9F7osKXOINbRSwWQzweRyKRYL8O8i0aGhpCfX09wuEwO5uPj48zPC6bzeLatWsYGxtDW1sbIwZIEZFQLaTkmU6nIZFIUF9fz5CgYjxbmUyGkZER/pbp/hwYGOApFcHuPB4P+2IQ19BqtUIul/M5SwIbLpeLJYk7OjqwsLDAUy3gprALwUy3CoJkTU5OshkgwQP9fj9WVlYYNtjS0oLx8XHs3r2bczeiBHg8HtTW1nLT2Gq1orq6mg1OP2rASg0qanx+khAUfpuV5O24HbfjdtyO23E7bsftuB2343b8P8Yn5mjcjttxO27H7bgdt+N23I7bcTtuxyeN24XG7bgdt+N23I7bcTtux+24HbfjvzxuFxq343bcjttxO27H7bgdt+N23I7/8rhdaNyO23E7bsftuB2343bcjttxO/7L43ahcTtux+24HbfjdtyO23E7bsft+C+P24XG7bgdt+N23I7bcTtux+24Hbfjvzw+sY/GP//zP0OtVrPXQ0lJCTweD2ZnZ9HV1cW63eQoSs7ZRqMRr732GgwGA7Zv387uxxUVFVhbW0Mmk2H94Uwmg8rKStjtdjZEC4VCbOj1P//n/7zl2v72b/+WnZ/JWVUqlWJsbAwNDQ2oqalhB1qj0YhcLoehoSHWPM/n89DpdKirq8Pa2homJydZq3xzc5O18lUqFfR6PZaWltj9k+zl77zzzluu7fvf/z7y+TySySQbQwkEAsTjcVitVsTjcUSjUYjFYpSWlkKv12NtbQ1yuZx9LVKpFEKhEBwOByKRCGtzk0slGQ+SNwcZ6tDf82d/9me3XNvrr7/O+tnkgRIKhVBSUsIGcuS+HIvFUFVVBalUikuXLqGxsZG9SMrKythxd3p6Gnq9HiUlJRCJRPD7/QiHw1Cr1WzmRoZEoVAIzzzzzC3X9uMf/xiRSAQymQwVFRXsyEp+I6FQCBqNBjabDXq9Hj6fj30D3G435HI5bDYbOx0PDg6ira0NuVyOHegbGxtZE7u0tBSrq6uIRqMwGAyQSqV48sknb7m2H/zgB1hfX0dJSQkUCgUbHZIbaTAYZIdkMi2zWq0IBoOsJR6JRLCysgKpVMrOniUlJexhIBAIUFlZibq6Ot7/5MouEonwpS996ZZr+/a3vw2DwYBUKgWlUgm1Wo1oNIpUKgWLxQKdTodIJIJkMgmFQoGSkhLE43FcuXIFWq0WUqkUIpEIGo0G09PTaGlpgdfr5b0vlUp5j5WUlMDtdiOdTqOkpAQOhwMTExP46le/esu1AcAvf/lLNkUkn5h0Oo35+XlotVoUCgU4HA7I5XL4/X5kMhmUl5ezSZtMJoNEImGH0rm5OTZIWltbQyAQgEKhwO7duxEKhSCXyxEMBhGJRNiY8+mnn77l2n74wx9Cr9ezbrxOp4PX60U0GoVarUZjYyOi0SjS6TR0Oh02NjaQTCaxsbHBBnmVlZXsiD0wMIDt27ezc3EikcDy8jJqa2uhUCgQDAZZI99oNCIWi+GLX/ziLdf2ve99DwKBgHXNyShuZWUFNTU1bEpGeucSiQRVVVVYXl6GSqVCOp1mj4K2tjZEo1EMDw+jvb0darUa8/PzcLlcKC8vZ+MrOvuy2SxEIhH+8A//8JZr+4M/+AP2kADA+vbkxF1bW4tQKMTeRKWlpQgEAjAYDGwSplKp2J34jTfewIMPPoh8Ps9O2AaDgc9eMv4ioz+pVIpvfOMbW75T8nySSqXs+RSPx9mwLBqNIhQKYX19HY2Njaiursb4+DjcbjeUSiXKysqg1WqxtraG5eVlGAwGVFdXI5PJwO12Q6/XQ6VSQSKRYHp6Gpubm9DpdFCr1UilUnj22WdvubaXXnoJuVwOyWQSKpWKv9vNzU34fD5UV1ez2WJJSQlkMhmbsZLvQ3V1NRsfSiQSrKys8F4oKSlhr4bNzU0sLS2xCWNnZydisRjuvffeW67tu9/9LrLZLPuoZDIZLC4uIp1Oo7KyEr29vVhdXcXY2BhCoRAOHz4Mg8HA3hXV1dWora1FLpfD1atXkc/n0dTUhFwux+fs9u3bMTs7i/b2dgwNDSGXy0EgECCVSsHhcODTn/70Ldf2ne98B9FoFBUVFTCZTHzXkTEpAN43SqUSY2NjqKysRDqdhlarhUgkYiNI8igqKyuD3W5nv6v3338fdXV1CAaDbGyWTCaRy+Wg0+m2fKfPP/88RCIRhEIhjEYjJBIJRkZGIBKJ0NbWBqlUyo7aJSUleO+99/gesVqtCIVCWFlZgUgkQmtrK/r7+1FSUoK6ujokk0mMjo7C4/GgubkZbrebDXITiQT7dD333HO3XNuPfvQjpNNpCIXCjxl/ejweVFdXQyaTIRqNIplMQiqVwmKxIJvNYmJiAlVVVSgUChCLxejs7MTQ0BB8Ph+SyST0ej3nX7Ozs7BarVAoFHxXi0Qidpnf6gz51re+xft5Y2OD7xwyWq2oqEAgEMDq6ipyuRzEYjF7hdF5ZTabYTAYMD4+jpWVFTQ3N/M5BIB9pfL5PPx+P+d05Ln05S9/+ZZre+uttxAOh5HJZNjYk4yWC4UC9Ho9u78nEgm+ZwUCAQKBAKRSKcxmM0pKSuDz+T72PMmPIhgMQqlUoqamBn6/H4lEAmKxmI0PP/vZz95ybS+99BIbX5J3Dxk5036lvUFG2w0NDeyzQfmETqeDQqHA6Ogoamtr4Xa7EYvFoFarea35fB5isRiBQIBNMQuFAh544IFbru2j8YkLDTLrcrlciEajMBqNSCaT2LZtG8LhMB928XgcEokEVquVrc5ra2uRSqXQ398PrVYLs9kMs9mM2dlZmEwmdukMhULw+/2YnJyEXC6HRqNBNpuFz+djs6VbBdnBU2IuFoshFotRXV2NRCKBWCyGpaUlJBIJJJNJpNNpiEQibGxsQK/XIxKJwO12QygUshFRMBhEKBSCTCaDWCxGKBTi5MLr9cJkMrEBjEgk2nJtiUQCBoOBHZDz+Tw7hZPxGhkCSSQSLCwsQKvVskHfxsYGb5CZmRnodDo+QAHw5p+enuZCgAzLyNV3q4jH47DZbPD7/XwZb25uQiqVsgkbPTMyVtNqtVAoFJDL5VhaWkIgEIBarYbb7UZdXR0kEgk/z1gsBqvVitXVVbS1tXFCTW7hdADcKjY2NmAymRAOh+HxeNhJO5FIcAFFZnwlJSXI5/N8cKpUKuRyOb58NzY2cPz4cfh8PnZFVSgUuHr1Krq6uqBUKrG0tASRSASJRIKpqamizuB0UQiFQmxsbCCTybCjaiqVQmdnJzvVbm5uor6+HpcvX0ZpaSkkEgmbE5HJDyW0uVwOBoMBra2tCAaDyOfzuH79Oux2O79vMhDbKshhORwOI5FIIJvN8sWRTCYhl8vZ0MtgMAC4aRxmMpnYlImc2Mkgj1yEFxYWuEBzuVxcMNPBOjs7W/Q7BW6aRpHxEa0NAMrLy5HNZnnvaDQaTgjMZjMnVhsbG1zIra6ucmFOxl0KhQI2mw2BQACZTAbr6+t8IRRzQgZuJifk3r2+vo6NjQ1uWoTDYTai/Oilvra2BpVKhZqaGojFYnaI9vv90Ov18Hq9sFgskMlknORPTk7CZrNhc3OTk7iJiQl2dL9V0LdCz4u+1bKyMt6TZO5Gl2AymcT6+jo2Nze5WSAUChEIBHgPb2xsIB6PY319HTabDbFYjI0D6RLc3NxEMbulTCYDq9XKZxo5WtPeJuNFMtmTSCR8nqjVaiQSCTa9CwaDbPCXTqfZ6I0aTyqVCqurq5BIJBAIBIhEIshkMluujZJIel9CoRBCoRAymYxdyVOpFPL5PAqFAhKJBGZnZyEWi2EymdggcW1tjc9Lt9vNxaTNZoPRaITT6YRGo2GnXnK4LmZStra2xo7y1Kgj479MJgOXy4VCocDNKZfLhUwmwwWlVCpFLpeD3+9nw1Sv1wudTscFvNlsZhMxjUaDQqGAeDyO6enpokaz2WwWVVVVbLpIxp5Go5Ed0yORCIRCISoqKjA/P49sNovV1VV2qR4aGkIymUQwGIRGo8Hc3Bx/S2TOtr6+jsHBQayurkIoFLLJm1i8dWqyvr7OBofU0AwEAvw76/V6buBRoURJK+11SpBLSkqgVqvh8/n4zM1ms6itrUUwGOTChBqUS0tLRc1cU6kUJBIJCoUCVlZWeF9pNBpO8BcXF7G5uclFrUajwfj4OMrKyhCNRjE/Pw+xWAybzQa1Wg2n04l8Pg+VSgW1Wo2Ghgasra1xoUx3g9vtLnqnUnItEAj498xms2hoaODzbn19HVKpFCUlJchkMtw8ov+vUCjAbrdjenoaDocDCoWCk3CHw4HKykqsrq7CarUin88jFotBIBBAp9PB4/FsubZYLIby8nI2taOzRCAQwOfzQSqVcoMMuJkzfdT8VywWI5PJcHPSZrPB6XTCaDRCJBJhfX0dEokEwWAQlZWVWFlZgclkQjabhcvl4nN0q+dGTelMJsPFPv19GxsbkMvlKCkpgVwux+rqKioqKrhYisfj7DweDAbR29uLxcVFborZ7XbOm6LRKOfQEokEiUSiaH5J5sRk6qdWqyGVSiEUCrG0tITKyko+T1KpFP+82Wzmpl0ikYBKpUIgEIBGo4HX68Xm5iabLK6urkKhULDhpk6ng0qlwtraWlG394/GJy406KMWCAR8yOfzeXbQVKvVfEhQgtXY2Ih3330XADi5q6mpQSwWw+DgIBKJBMrKyqBSqdjROZfLQaVSQaFQcAFBnY6tQqlUIplMQiKRQC6XQ6VSQS6X84VaXV0NlUqFRCIBj8eDzc1N7ly63W4+RGkSUCgUUCgU+MCmalUikaCiooIdSSnJDIVCW64tmUwiHA7z30m/3/j4OEpLSwGAkyS6eOhZjY2NQSqVwuFwQK1WIxAI8ASJHIqpa57L5bC5uQmtVssHyEfdZm8VVPVSB0YoFHKXUalUIhQKcfK5sLCAyspK6PV61NXVwev1IhAI8L9NxU48HufDlapmANxJocIpm80WvYSpEyKTybhgom6/TqfjgiiVSmF+fh4qlQomkwlKpZITG4VCAZFIhLq6OnZTjcVinLgsLCxgbm4OZWVl7FCrUqk4adoqPuqkSsVJLpfD+vo6crkclEolBAIB1tbW2D3YYrHwYUPvRyAQwGAwQKlU8sWr0+nY7ZU62JSQx2Ixdh3eKkQiER++9Nzz+TwWFhZQVVWF0tJS6HQ6BINBLCwsQKFQwGKxQK1Ws1MwfX8ymQz9/f0AAIPBwN+kRCKBwWD42FQrFArB5/Pxnt4qyJGZpifkTJ3P5/nbogKXEkhy2QXA+zkej8NoNHKColQqIZVKAQDz8/OwWq1cZNOzlsvlRRMYiUQCn8/H+yaXy/GfLxQK7MpOjscCgYAnH9S8EIlEiEajUCgU8Hq97Jgrk8l4MpDP5/k7o4uFiuStgia1dO4CNxN8n8+HqqoqrK2tceG4sbEBtVr9sY6YQCD4mHv15cuX+eylM8nhcGBpaYldcqkpk8vlil52NLGhfUdTsYmJCVgsFnb/lkqlWF9fRzabhcFgQDab5SSREgixWIzKykp4PB5IJBL+HjweD08aVSoVIpEINjc3YTAY+M/eKsgFPZvNQiwW895aXFyE2WyGWq2GXC6HTCbjgkOj0SCdTvP7FgqFSCaTPE0iN2Jy/ia3baFQCJvNxsVLLpfjPXSrEIlEHytugZtO5svLywBufrsymQz5fJ7PEoFAwFNbiUSC0tJS3g80FRCLxR9ryszPz3MTihoy6+vrvF+2WhvdjbSHyTleKpXCbrdDLBZDoVBAq9XC4/FwYUkJO90p9fX1mJyc5M49Je+EeKB7IRgMsoN3MadmSuQLhQJ/2zabDeFwmKfBKpUKGxsbmJ+fh9FoxObmJqLRKGQyGTd6dDodFx90pmcyGb4TqZg3GAxIJBI82aRC/FaRy+W4s0woAZlMBq1WyxOA9fV17sZ3d3fDarXC5XJhcnIS6+vrMJvNPCGbn59HRUUFmpubuXiXyWR8x9AUZHl5GRsbG2hvb99ybdTcot8rm81ic3MTq6ur/F199PwVCoXsOB4KhXjyTw7kuVyOzzoAcDqdKC0tRTweR1NTE7/XWCwGmUxW9G7QaDRcmAuFQp5wzs3NQa/XI5vN8vQvm80y+oPy0nQ6DZVKxbnkxsYGUqkUwuEwTCYTLBYLfD4fn2f0jVPuUsx9m759ynGFQiEKhQJcLhdPUuj5kts2/R4LCwsQCAQoKyuDRqMBcPO7puYXTfBoAkMon2Qyyec3FZK3Ciqu6Fyn5sH4+Dg2Njag1Wohk8mQSCR4qk/nDaE5ZDIZ1Go1XC4XN+PofSkUCly/fh06nQ4ajYZRNLS+T1pofGKOxkdH8hqNBiqVClKpFENDQ/D7/dBqtbDb7SgpKUEymYTL5UJrayuMRiN328rKytDb2wsA8Hq9DLWKRqOYnZ2Fz+dDJpNBS0sLKisrP3Y563S6LdemVCr5AqfDv1AoIBKJcBe+srISFosFAoEAoVAINpsNBoOBx0Fmsxl2u52rdrlcjvr6etTW1jJkgyBfVVVVPBGRy+XIZrNbro26iVKpFBqNhpMY6t5SMkTdHTr8aYxJUDWz2Qy9Xs9QDbr86ZJsamrii1ij0TDMrVjyQgcBXRj0oUqlUp4EKJVKKJVKnh6IxWJoNBosLS0hHo+jpKQEYrEYBoMBk5OT8Pv93PWor6/ncTUFfUQAina/qVtOlXVJSQnS6TQKhQLMZjNEIhFKSkq480JFK3X51tfXkUgkGCqxsLAAt9sNAHyIGY1GLC8vc5KYyWQgFAoZirNV0MdGH6ler//YKDoQCGBzc5PHuy6XC729vUilUtyFoYvIYrHAarVyckZ/PhAIYGZmhi81mizRoVXsuREsSSaT8cURDAa5kDCZTFAoFHC73VhZWeEJYDQa5fXp9XoIhUKMjIxgbW0NOp0Ozc3NaGxs5OlmeXk5qqurAQDhcJihNsWC9j1186mzvbCwwBBGKiQjkQj0ej0XvjStpMLTZrMhGo0yFIu6daurq1hbW+MkhDqlMpnst54joVCIu5IqlQoikQibm5vcQQb+86IAgLa2NhiNRqRSKd6TNLWk/adUKjk5DQaDEIvFsFgs0Gq1/I1SYbBV0KVUKBSwubnJHUmv14tkMonNzU2+BDc3Nzm5USqVPNlSqVSw2WzQaDTw+/1ckFgsFpSWlqKkpAR6vZ4TcCq2aKK5VXz0rKJnTb+LSCSC3W6H2WyGyWSC0WiEWCyGXq/naQYlNdTYMZlMEAgEMJvNqKysZCgnQR7pm8hkMrBYLCgvLy+6tkgkgkQiwZM6uthzuRwcDgcsFgvMZjPKysoYcqZUKrkLKBaLYbfbORFTqVSorq7mhoTP5+N3TAk4TQ/lcnnRtQHgvUtd2WAwyOecSqXiyRklrf/3tJm6mFevXuXCVyKRMNzJ6XSirKyMp3LUHKLi/VYhFArhcrl4D5eUlPAkvbq6GhUVFSgrK4PJZILBYOC7w2q1IhqNclJP9yUVemazGTabDQqFAtlsFkqlEvX19aisrOTvuqKiAjabbcu1UZONzlij0chNCpqW0DlCk73NzU2GQVFRSc1LaiSUlZWhpqYGWq0Wfr+fE0MqWqghUldXt+XaqEEmFAqh0WgYUqRSqbC0tASfz8eNHI/HwxN5o9GIiYkJrK2toaqqCl1dXZDJZPD7/aiqqkJNTQ1KS0sZPkz3NU15A4EAJBIJampqiq6N9hgV1fl8HsvLy8hkMlAqldDr9Yy4SCQSfE7TPlWr1dBoNDAYDCgUCvD7/fwNUiFEsJqSkhKe5CQSCTQ1NW25NoPBAI/HwzmH0WiEWq3G+vo6AMBoNHKeR++/vb0ddrsdMpkMIpEIarUaZWVljASh4l+j0fDkKJ/PQyAQ8BQhn8/DbDYXLTTEYjEkEglkMhlUKhUn3IRyIWglwZSsViufnfQ7mUwm/u+EqtBqtdwADwQC0Gq1nIPEYjEEAgHe71sFNWDFYjHUajXnzAsLC9yIUKvVEAgE3Byhwoj2KuVX1Fii/65UKnniTvc75T7BYBACgaBo0f3R+MSFBl0YqVQKLpcLHo8HMpkM9957L0KhELq7u7lDZrVaGUqi1+tx7NgxWCwWuN1uJJNJOJ1O7N+/H8lkEgsLCxgfH8fq6ip27tyJUCgEqVSK+fl5SKVSxgzX19dvubZoNMowG7/fj0gkgqmpKezYsYOrY+oOdXV1weVyoampCQ6HA48//jiOHDnCHQKXywWbzcbFBsFHjh07xlX10NAQotEob1w6kG4VUqkUVVVV0Gg0sNvtKCsrw9jYGHbt2sUQM3pOdXV1WF5ehs1mw9jYGO688040NzfD6/VywvjMM8/A6XRidXWVcaaLi4uIRCLQ6XSc7G9ubvLUYauIxWKw2+3MN9HpdBgbG0NLSwsikQgcDgeAmwXBgQMHsLCwgEwmg4GBAajVauRyOSwsLPDGrq+vRyqVQk1NDTQaDWZnZ1FeXg65XA6LxYJCoYC6ujp0dnbyaHCriMfjCIfDcDqdcLlc8Pv9cLvdeOihh7j4SiaTfPBTR2Z+fh67d+9GU1MTSktLcejQIfz617/Gtm3bUFdXh4qKCgiFQvj9frS0tMDhcKClpYUhfAqFAleuXCnafaFJ28rKCsM5hEIhOjs7UVpaCrfbjaWlJe7chsNhyOVyVFRUcEeooaEBhw4dwuTkJMxmM/L5PCebAoEAX/rSlyCTyaDRaLhwUSqVyOfzRS+TWCwGo9HIHdBsNou1tTXcddddWF1dhdPpZChiT08PtFotent7IRaL0d3dDYfDgUKhwLjkXbt28SSOfudUKoUPPvgA/f39WFpagsFgQENDA9RqddEuKXATJ6tWq7G2tgaXy4VIJAK5XI7y8nIsLCwwfCWfz6O8vBxms5n5M1KplLvgVqsVAwMDfCHr9XpObh9++GHmv1ARIhaLf+v3kMlkYDKZEIvFuKguFAoMpbRarVhfX+dRslqthlKphMvlQm1tLYxGI+RyOQ4dOoTZ2Vk0NzfDbDZDLBYjFothbW2NpyaRSIQhJVarFblc7mMF+a2CEmrqOhUKBdx7771IJBI4cuQI89v27t3LF7bX60V1dTU2NzcxMzPDk8ejR48inU5z8plOpxkGkMvl4PV6GecuEomKJvNisZjhUjTtnZycRFVVFbxeLxYXFxk3rVAooFQqYbVasbCwAKlUirKyMtTX16OzsxNut5sn49TJC4VCKC8vh8vl4u40naVOp5OnpreKjY0NWCwWyOVynjRks1ncc889cLvdKC0t5d8XAH/3EokERqORi5MdO3ZgdXUVcrkciUSCYVKTk5OwWq1YWVnB2toa3G438vk85HI5CoUCdu3ateXaYrEYNBoNd2A3NjZ4Yp1KpVBfXw+lUolYLIZCoQCtVouOjg6Mj4+jvr6eYTWbm5sYHh5GbW0tSktL4XA4uNvb09OD7u5udHR0MD+GuBMEAblVBINBdHd3w2g0wmAwwGKxIJFIoLKyEkNDQxgaGsLw8DDDqDc2NtDd3Y2VlRU0NDSgubkZdXV12LNnD65cuYLOzk6EQiGG6w0MDKBQKODixYtIpVLIZDLQ6/XQ6XRYWlrC2bNnt1wb8TPoO1CpVBgYGIBAIEBDQwMcDgcX0ceOHYNcLucmHf3/arUara2tKC0txcmTJzE5OclJY6FQwPbt25lXsri4iIqKCrS3t2NwcBDj4+Nbro0m2svLy3A6nQwbokLHbrejoqICdXV1aGlpwerqKrLZLGZmZrgJOjo6Cq/Xi/n5edx9991YWlrC8PAw5ufnubk4NzcHrVaL4eFhiMVitLe3QywWF31uuVwOlZWV2NzcRCwWY9z+vn37uBlFUO/Ozk7kcjns3r0b4+Pj2LVrF6qrq7G4uIgLFy7gpZdeQkdHBxeZtbW1OHDgAGprazlJvnHjBgCgsrISAoGAG123Cr/fD6PRCL/f/7H8Zdu2bUgmk9ykLSsrw759+xAIBFBVVcXImY6ODlRXV6OkpASzs7OwWCzY3NxEe3s7LBYLJiYmuGGayWSwsrLCjafl5eWiEzSZTAaTycR3aiaTwdTUFGw2G/P/qBlDObJMJsPMzAy6urpgtVoRDochlUqxurqKzs5OPlupOK6rq8Pw8DBDvYjXsra2VrQIIphbNBpFIBBANBrF0tISenp6IJPJmKdFk/qVlRXo9XrcuHED1dXVqK6uZmRLPp/Hvn37uDijadGuXbu4MbS6ugqdTofy8nL+uz5JfOJCgw7OI0eO4OTJk6irq8P6+jpGR0fx4IMPcudkdXUVq6urOHDgAOLxODo7O/G1r30N/f396OjoQCQSwdGjR/G1r30NNpuNu3vHjx9HOp3GV77yFZSWlqKjowMejwcLCws4fPgwrl27tuXa7HY7CoUC9u7dyweqTCbD0NAQ7rjjDibxjY+P48yZM3jooYdw7tw5GAwGfOtb38Lp06eZvHb33XfD7XajrKyMK0uXy4WLFy/innvuwfDwMHbt2gW73c54wGLwpFwuB6fTycTkeDyO8vJyvPbaa9i3bx/Dn9LpNMbHx5m47nK5cPnyZcacXrp0CR0dHbh+/TqamppgMpm4skyn0zhx4gSmp6eRSqWwvr4OkUiEQ4cOoaurq+h7pWLAaDRifX0dYrGYx50ulwsGgwFVVVWwWCw4evQo9Ho9WlpaoFQqcfz4cXzuc5+DQqFAW1sbJiYm8PDDD0Or1WJ2dhZDQ0P4zW9+g2PHjuHs2bPQaDRwuVy4fv06Y5i3CovFAoVCgT179qCurg7hcBgbGxv4xS9+gW3btqGyshJdXV182B08eBCXLl2C0WjE66+/DrFYjDvvvBN+vx+7du3Cj370I5SWlvLvs2fPHmi1Wly4cAEvv/wy4vE4k83oIN8qKHG1Wq2IRCK4fv06HA4HXn31VXR3dzMGt6qqCrW1tejp6WHsKwAcOHAAJ0+ehFgsxuHDh/GP//iPOHz4MI+iq6qqMDw8jBMnTvDPFwoFuN1udHV18WTmVqHRaDA1NYVt27bh0KFDqKurQ0NDA9xuNyorK+H1ehlaJpfL0dHRgW9/+9v8uwiFQigUCly6dAldXV34i7/4C+zfvx+NjY18Wc7MzOB3fud34HQ6UV5ejvX1dSYUFntuADjhp25kSUkJNx1aW1uh0+lQVVWF8vJyxGIxhhmUl5ejr68PANDZ2YmRkRHU1NTg5ZdfZihhMBjE+vo6ZmZm0NvbC6vVyrhxSuqLFZAGgwHr6+s8saWCa3h4GPfeey+LNhCUJx6P4xe/+AU0Gg1+/OMf4xe/+AVGR0dx/fp1hgYZDAaeMtntduj1ejQ1NTHpncQempqaik74RCIRwuEwdu7ciZ6eHhiNRmi1WoyMjHDxks1mEQ6HMTExgY6ODu7ENzc3o6WlBUajES6XC/v378eZM2dw8OBBWCwW+P1+LvqqqqowPj4OmUyG9fV1RKNRVFZWMpznVkF8Gep4WiwWPPjgg/j5z3+Obdu2cRFNSeadd96JmZkZNDc34+zZs/B6vXA4HIjH42hoaMD09DQeeeQRZDIZrK6uwmQyMb5fLBbD4XAwpEin0xWFJ5WUlGB+fh6FQgH19fXo6elBe3s7Xn75ZXzqU5/CysoKvF4vvF4v+vr6YDab4ff7OWEyGo1obm5GMBjE8ePHAQDd3d3IZDKM5U+n02hoaIDZbMaRI0dgNBpZAGNqamrLtdE0vKKiAvX19YybJrjR4OAg0uk0wxq7uroQj8exfft2fPvb38b169c5AZRIJBgYGIBUKoXL5cL09DTC4TBGR0fx6KOP4t///d+ZLD4/P4/9+/cXnRrs2bMH169fx+rqKqanpzE3N4d77rkH4+Pj+NKXvsScxrW1NZw6dQpf+tKX8B//8R9ob2/HBx98gFgshvb2dgQCATzyyCNwuVw4ePAg6urqsHfvXjz99NNMRI3H47jjjjvQ0dEBiUSC1tZWmEymLdcmlUoZziESibC4uIiGhgZ4PB7mPlF3eXNzE88++yyEQiEcDgcmJyeZZ+b3+/Gzn/0M7777Lv77f//v2NzchNPpRGNjI8RiMe666y74fD6YTCZcvHgRly5dwn333Vf0jCspKUEikcD27dvR0tKCYDCI8+fP43vf+x46OjoY+kRY+GQyiX/8x39Ed3c3jh8/jieeeAKf+cxnUFNTg6amJrz44ovcODKbzfjsZz8LhUKBv/zLv+Rzkhoo+/fvLzqxFQqFuHLlCj+L6upq3ss0xchkMtypvuOOOxCLxdDa2oqpqSlYLBbcc889CAQCXNjcddddqK+v5+nG7Ows9u/fj9LSUjQ2NvIU9VOf+hRDh28VJDDU1NTEyJPKykr87//9v6HVarG8vAyv14srV67gH//xH9Ha2or33nsPJpMJZ86cwY0bNxgeW1lZCZFIhN7eXsRiMcRiMTQ1NUGlUuFrX/saC8lMTEzgwoULzFvcKkpLSzE1NQW1Ws28mZqaGvh8PrS3t0On08FoNDJs68iRI9xQnJmZgUqlQmVlJWZnZ7Fv3z7eP6WlpVyUX7hwAZ2dnczRJYhmTU0NJicnt1xbOBzm5qRMJoPZbMa+ffuwtraG3bt38/ljNBoRj8fx3HPPIZvN4sSJE3juuefwi1/8ghsUFRUVePnll1FeXs7wRKFQiPfeew+PPfYYi1wMDAygr68Pdrv9t973FJ+Yo0EwmdHRUR675XI57NixA2+88QYTGQkW4/f70dHRgYWFBfz1X/81d6eXlpYwOjqKmpoapFIpKBQKJJNJnDlzBm+99Rb++I//GBsbG3C73TwdcblcsNvtW66NOl1Xr15lfKTL5cKdd96J06dPY+/evYjFYkin0ygrK+MK7/Lly+jp6YFQKMTQ0BCTmrq7uzE+Pg6n08lj5nw+jwsXLiAQCECv10MkEqG0tBThcBiRSGTLteVyOZSWlmJ8fJxhCel0GocPH8YHH3zAlXxVVRVaW1uxuLgIv9+PyspKxONxOJ1OhhSMjIxAo9HAYrHA6XSyukgoFMKbb76JxcVFOBwOJr6NjY0VxS9ns1keGxL2t6WlBZOTk9i2bRvEYjEWFxcxNzeHYDCIo0ePYnp6GsBNiM7KygrcbjdzOZ599lkEAgHE43EolUq0trbyRo3H4wwboedSDHs4PT2N5uZmzMzMMIRKoVCgsbERY2NjcLlcjBsXi8Xo6OjA8ePHcfbsWXzmM5/B5uYmBgcHEY1GGed99uxZmM1mhk80NzfjM5/5DHd1aZSeSqWKEiWlUilP0NxuNxKJBAQCAXp6ejA1NYU9e/bw5C+ZTHIHhMal77zzDgQCAXbt2oVoNIpt27bhu9/9Lrq7u2E2mzE5OYnq6mqEQiE0NDRgcHCQ8bELCwtF32kkEkFbWxt8Ph9D9ubn5xEOh7Fjxw4oFAosLCzwgUZKG8S3USgUfFEvLCzgySefxMDAAFQqFSvpEDdAq9Vibm4OJpMJ9fX1iEQiRTvfALC6uoqGhgbEYjGGeM3MzCCdTmN6ehqNjY2MJ6UxOcFT7HY7F0qUpO3duxfnz59Hd3c3dDod3G43du/ejcHBQSwvL7MiC0GLikFZVldXeTxPvKnx8XEcPXoUbrcbCoUCk5OTyGaz6OzsxOXLl9Ha2orV1VU8+eSTTEy1Wq1IJBKYn59HNBrFoUOHoFarMTw8jPX1dTgcDly/fh1isZihQ3Nzc6yYc6sIh8NoamrC4OAgbDYbzGYznE4nADDshuCcHo8HAoEABw8ehNfrxdmzZ2Gz2fjcuHz5Mo4cOcKkdyIjRyIRuFwu+Hw+7N+/HxaLhcn0a2trW66NiJVqtZqFN6anp3H//fdjaWkJFRUVGBwchFAoRG9vL77//e8jFovhzjvvRFNTE8ObaGrhcrnw+uuvw2azQSAQwO1249ixY3C73SxGQgRlgixuFaFQCGVlZUgmk1hbW4Pf78evfvUrHDp0CMlkEn6/HysrK1CpVNi2bRvef/999PT0IBgMMuyVYDQ/+MEPANwsdIlkSd+4TCZDT08Pbty4AZfLhXw+j+np6aJFEMGJ6LImDhFx81pbW3H16lXMz89DrVbj6tWrrIb47W9/G9FoFJOTk5idnUU0GkVrayteffVVHDt2DHV1dZiamoLP58PU1BR2796NRCIBk8mEVCqF2dlZSKVShjL/3zE+Po66ujrGx6+uruKHP/whdu7cCafTifvvvx+jo6OIx+PYtWsXvvWtbyGZTGJ4eBharRYbGxtwuVwwm80YGRnBtm3b8Oqrr6KmpoYLxc7OTvzmN7/B3r178Ud/9EdoamqCzWbDyy+/zJDMW4Xf72fUBEFaCIng9Xqxa9cuuN1uDA4O8r1+5MgRDA8PM9yFEusvfvGL+OCDD7C8vIxsNgu32414PI4TJ07gBz/4AbRaLU9iqBh+5JFHtlyb2+1Gb28vcyZMJhMeffRRLC8vszLh66+/zly38vJy1NXVQSaTYWJiAuFwGHa7Hd3d3bhx4waamprw/vvvo7u7G2tra5iYmIDT6cSlS5fw4osvoru7m9XI+vr6it5bJHBAUG+RSISpqSnmdmq1Wm44ADeT02w2i4sXL0Kj0SCZTKKsrAw2mw1LS0u4du0a3zfELYrH47hx4wauX7+OxsZG7N69GwDwyiuvFC2CAoEAq3wRV1coFOLhhx9mDtjMzAw2Nzexf/9+/PCHP0RjYyMXRAKBgCF71DAjYQ6v14tr164xh4GQBARH3djYKDptIQgzIVgCgQAWFxdhtVpx48YNNDY2YmZmBhKJBO3t7fj3f/93VFZWoqmpCWKxmNW3qqurOX9aX1+H2+2G2+1GJBLBI488wuslZSsSoWhsbNxybdT0TCQSLHgyODiIJ598Eu+88w46Ojo4R6mrq0NfXx9qa2tx5swZPPbYYygpKcHS0hIUCgXeeecdFv8gdUmfz8fTLqPRiEAgwNDt3zYV/Wh84olGoVDAK6+8gpGRESSTScZFXrt2DSaTCbt374ZUKoXb7YbH4+GH6PF48LOf/QyLi4tobW3lkRCNvXw+H9bW1pBIJDA9Pc3KTDqdDoFAAPPz88zI3ypo9KjRaFBZWclY4EQiAbvdDpvNhpKSEmxsbGB8fJy7ek6nky//trY2nDx5EgKBAIODg5DJZLjjjjvQ3d0Nr9eLfD4Pt9uNpqYmXLlyBR9++CErCOzcuXPLten1epYXDQQC8Pl83FmprKzEoUOHUFlZCafTibfeegs1NTW4++67YTAYcOnSJfj9fu4CUTFiMBiwa9cutLW1cRdCqVTiqaeeQnt7OyuLKBSKorAuUm5aWlpi6UO32w2LxYKRkREIhUJWsaFOtEKhQKFQwIsvvoiRkRHU1taiq6sLCwsL+OY3v4nl5WVWUCFpPKfTie7ubiSTSUxNTbFUaTESfSqVYuUqs9mMnp4emM1mvPXWWzh79iySySQ/U5fLhfX1dSwuLgIAfvKTn+CFF17A0tISysrK4PF40NfXx4cyKakQSZ8uBKfTidHRURiNRrS1tW25NqlUisXFRRiNRtxxxx246667IBAIMDc3h0gkguHhYVy/fh3Xrl3DyMgILBYL7r77bkSjUUxPTzMGm+Bvw8PD3BVeWlpCJBLB+fPn+eeoSCPsa7HON6mdkGRnNBrly+z8+fPQ6/VMQJyfn8fPf/5zhhm89957GB8fR0lJCUwmE5MkbTYbbty4gYsXLyIWi+G9997jbiopdtG3W6wIAsC4fOISSSQSnhRQ4UZ/z/z8PBKJBIaGhhhXSqS/nTt3oqmpCZcvX2aCsVQqhUwmw/j4OHM6GhoamIRMcpPFnh19ByKRiBXpRkZGANzkydTW1kIikeDs2bN46qmncMcdd0AkEuHy5cu4cuUK5ubmsLS0hJKSEly7dg0ikQgLCwtMWj9//jyMRiM+/elPo6GhgYm29fX1Rbu4BoOB9y1JOWezWSY9bt++HXa7HaFQCLOzs5DJZLh69Sqam5uZcEjE/7m5OYa+ELGXvguhUIjPf/7zfGkT96AYvhoAc+kIv63T6bC+vo5MJsPExFwuxyIHdXV1CIVCiEQi8Pl8WFhYwMzMDPR6PRwOB2KxGDKZDGOC//7v/54x/bW1tVycyGQybNu2bct1EfRIIBAgFoshGo1iz549OH/+PAYGBlBaWoqysjLEYjGcOXMGkUgESqUSfX19mJychNPpxMrKCkKhEO677z709fXh7bffhsfjYZLp9PQ0q7rRPWWxWNDW1lY0YQZuJqYkrU6wi4GBAZ7Y7NixA7t27YJGo8H+/fvR2dmJ+fl5TExMMIQ0FovBYrFgaGiIpS1TqRT0ej1OnjzJwhUf5cOUlpYWTUqJb0Zd3MrKSpSXl2N6ehoLCwu4cuUKlpaWsLCwgPfeew92ux3/43/8DywuLqK/vx/nzp3DuXPn+Fz+wz/8Q8hkMoaSnj17Fj/96U/x//1//x/MZjM6OjqwurqKkZERtLa2FlWIM5lMvJ9KSkoY0XDt2jUWaqBkk/iVb7zxBotpuFwurKysoLa2FleuXMH169dx/fp1bvpZrVa89dZbEAqFqK6uRjQaRV9fH65cucLKQFvFxsYG3n//fX4nZrMZPp8PP//5z6FQKPDoo4/iyJEjsNls3Kw8dOgQYrEYzp8/z0pFfr8fqVSKJxokdx+LxbC4uIhUKoWHH34Y0WgUExMTcLlcfO9uFZT0UoNhcnKShVQMBgNOnDjBE7mZmRlcvnwZTU1NbEVAENd4PI5Pf/rTWFxc5EIsEonAYDBwY/Xee+9FMBjEqVOncPr0aZ7KbBUCgQDvvPMOc1QaGxsRDodZzpasBPx+P9544w00NjaitrYWAoEAv/jFL/Daa68hGo2irKyMRYBI7tZqtUKr1eKVV14BAFy+fBlarRbt7e3o6upCS0tLUa4BNc/UajUrlW1sbKCvr4/FPPL5PFwuFzdVFxcXoVarkUwmUVtbi507dzIf6aN8krq6OpSUlOCnP/0pBgYGWGVKqVTypKRYs0KtViMYDLJ8t0ajwd69e/Gd73yHeV5EtB8cHIRSqcTg4CBDeQnaR40Sh8MBkUiEzs5OdHd3AwCrwf7qV7+CUCjks1QikTC8/rfFJy40aNxIGDLq8h8+fJhJoIR73LdvH1fsbrcbNTU1TEokbkJvby93CqRSKTKZDJ555hm8+eabjNHWaDQ8PShWDYvFYpbHjEajDJ2y2+0MAaKR7IEDBz6mx08wBMLorq+v86iaMG9yuRwGgwE+nw82m4117+njKVbVkQoBfcwqlQqTk5Ow2+1YXFzEWBgjkgABAABJREFUysoKS4Tu3r0bi4uLEIvFSKVS6OjoYGypWq3GysoKK2cFg0GkUimUlJSgra0Ns7OzCAQCPO4nPHkxZScqIoRCIV+aJKNHlxMpKahUKrjdbiY0HTlyBAqFgjtYlZWVqKio4E6B3W5HW1sbPvWpT7HMbWlpKZqamlgTvKOjY8u1qVQqhpNQt5agI8QJoA6g1WrF9PQ0IpEINjY2GFZiNBqxc+dOpFIp3HXXXdz16+3thdFohE6nQyKRwMGDByGTyWA0GlFaWgqPx1NUQjYcDrNks9fr5QORCHKHDh3C3r170d7ezjKJTqcTCoUCZrMZjY2N2LFjB2pra7GwsIBjx46hurqaia9E3CVyrUajQVlZGYxGI6ufFXunKpWK/WGIHN/R0QGr1QqBQMAdlqqqKp5M+Hw+PPjgg2hubkY8Hkdrayu8Xi927NjxMVlmm82G/fv3s6QgkctsNht36H5bENckGo0iHA4jn88zXIFUnWhqRPjZ/v5+VFVVQafTsT9GOp3mA5zIdtRkEAqFPEImKIDX62Vy4a2CNNDJG4XgMaRs5HA4YLVa0dDQgGPHjrGE5vz8PCwWC7q7u3Hw4EEcPHgQTqeTO22ksqZUKnHHHXdgaGiIR/oqlQp2u50hVluFWCxmEjhNnoi4TORmmuA0NzfD4/GgpKQEy8vL6OrqgsViQSqVwp49e1gjnyZxer2e1chIlYYuPIVCgfX19aIY5lwux8UvdSKJ46BSqdDc3IzKykpOmKgJsra2hp6eHtTX1/N0gqZ4Go0GFRUVLPxw6NAhFprweDz8O5ByzFaRTCY52SeZZqPRiK6uLszMzCCVSkGn06G2thZNTU1Ip9OwWCxQKpW4++67uSDK5/OYnJzE3r17+Vml02n4fD40NjZyokrTDZK6LiZZnMvl0NDQwOpUOp0OmUyGPQ3y+Tx8Ph8XyW63m8UGiNcVjUZZ4eeRRx5BaWkpNjc3uWh3uVwIhUKwWCzI5XLsr0HT7K2C+BzhcJh5k0RMn52dhUKhYN7C7t27UV5ezgiEz372s9i/fz9z15LJJO666y5UVVUx8dtsNuPkyZMYHh5mYQVSGKQif6sgIjepOGk0GsTjcbS3t0OlUrEIh0AggMViweDgIDQaDdbW1pi7BwAOhwPBYBCf+9znIBAIPjYZo84ucJNLQx1qymu2ChLrIOUrkv3et28fP0eLxYLGxkY0NTWx5PPU1BRzRhKJBEu9/v7v/z43Oefn57G2toY9e/YwNI3Oj/r6emg0mqJc1kQiAZlMhkAggGw2y9LgJPlL/LNMJsMJ+/LyMioqKlBdXQ2v14vLly8zxOwLX/gCKxMFAgFMTEygs7MTLpeLeXg0TU4mk0VJ9LlcDnv37sXm5iYCgQAriu3YsQPLy8v8jba2tjLEjriSe/bsQUVFBftYqFQq7N27l7mS4XAYOp0OJ0+exNjYGCQSCbxeL/OV/H5/0UaKUCiEVqtFMBjE4uIi++tQs5zQPWazGbt370Y+n4fD4cDa2hr2798PpVKJM2fOoK+vj9E5dFfRFPro0aO89kQiwXeL2+0uei9QcUlKn6RKuGfPHkbjECStra0N8/PzqKurg1gsxs6dOyGVStHf3w+Px8MeKRMTE3C73QzLuueee7CyssKcFLp/yA/nk8Qnhk6RstBHpRUBoKWlhQ8jtVoNh8OB0tJSJujp9XoYjUYm4FCyQt01g8HAifqePXtw6tQpVtUghr/JZCqK56ePjciv9BFtbGygsrKSO6Z08TkcDk4cyAiOul1kPkPKFMBNMhMZRgHghJwUgopdJmKxmGFC1EEiyTWv18u4fRqXzc3NsfylUChkFYjZ2Vn2lCAzrY+qChDWmFShaDRcbCNQYkedZVIbIswmJQ0kuUtTCuAmTtnlcvH4n7wfSANao9Gw4k57ezsTbcPhMKLRKFffWwWR5GlkSdh4tVqNkydPcseaVEVIJchutyOZTLLpGu1bn8/HZGylUsnmd7W1tZDL5TCbzVykEtlzqyCiMhV7qVQKWq2Wu2skikAwJCIqm81m5mrE43GEQiEuuKhIIZgYKWlQEUiEbPr3tgq1Wo10Os3eBwQ5k0ql2LZtG6tK/N+mSqlUCmVlZcjlcgwzJDIZmUkB/9lwMJlMGBkZYenddDqN0tLSooUtgI99V6TeRFOOgwcPsooU7R36M7SvPypbGwgE0NLSAr/fz+pjarWamwBU3ANgCFCx70EgEECr1fJhHQwGYTabkUwmYbVaOaklydaFhQWW6s1kMgyBJB8aKjIJLy6Xy9HW1obh4WGWWCQVlN/mVUGkZIILktcAERpdLhdD5UipSyQSIRKJoLm5+WMQo5aWFuYD0VoNBgMCgQAaGxsxNTXFEo7k/1NMEYsUcAjOls1mmYxJ0qBUZAiFQnR3d7PiDSlQERyXCruamhrU1tYimUzC5/Ohs7MTV65cYeW5QqHA5lTFut90hpGsaTKZhMlk4iR4fn6eScDUCCKVMdrPRNjMZrOsXmiz2XjyrlarMTExwUULfaMikei3QjCtViuvjxS7CKZDd61Op4NWq+U1fNQw7KNS2bQmgsNms1lWLaKkgeR8KZErtt8oSaQCRa/Xs3wsAJYfpqJ1ZmaGlSeJ0Eoww8XFRSYi5/N5lJWVobKyEq+99hq0Wi1zbejOK+a5QB1u2geFQoGbYSRHT+RW+qarq6sxNDTEP0cFTWtrK8uA+nw+JrfL5XL09PRgcXGReR8EKSkG1TMajSydSg0yjUbD/iP0HlQqFTweDzdm6G7L5/OcF9H7qq+v/xgkqLq6muFfdrudZUlJTXGrIM8tai6KRCLOrVQqFauMmUwmNDY2Ym1tjScsYrGYpdpJhGDnzp1ob2/H9PQ0qzw5HA4+F+lbAP7TTG6rIK8Sem80iaemhUgk4juI3hHxwghKShLG9LOUU5FiqMViYcNosViMyP8x+yPPqGL7bX19HXq9HqFQCB6Phyeh+XweJpOJ91V5eTnS6TRWV1eRTCa5UULGiDqdjpXPaM+rVCo0NjbCYDCwIhipfpEC11YhlUq5uUAID7Vajb1793I+TDnARxUbqRkplUq5KAwGg2hqauIckM7A5uZmTE5OctOcFByLPbP/Oz7xRIOk1ghXDYBdAvft28fJKFVj7e3tCAaDaGhowPLyMktpnjp1CvX19ejv70dbWxsnso2NjSgUCvj85z/PEox08VIivVVQ98FqtXJXzmKxYGxsDL29vczvmJubw8WLF9HY2Mikz8nJSYTDYTYsaW1thUajwa5duxgPW1lZCYlEgk9/+tMIBAJswuJ0OpHNZotuBIJwUWc5HA7DarXydKS0tBQikQhLS0vo6+tjic6mpiZks1nYbDZUVFSgr6+Px6dkEkXmfV6vF42NjSxru76+zlMSmtTcKuiyl8vl8Hg8GB0dZdUkwuJrNBp21y4rK2NMJ7m/7t+/n/0EBgYGOEEnJZXr16/j8OHDjCefnZ3F6OgopFJp0XdKvhM01SKpxOHhYSa60nOfnJxk5ajm5mZEIhFWCBsYGEB9fT2uXbuGvXv3spuy2WzG+Pg47HY7PvjgA6jVak4yHA5H0c68QqFgVZjGxkbY7Xbs3r0bAoGAL/WPmhlS94q6nPPz83jnnXfwb//2b7BYLGwERvudHFlpeqhUKtnZvrOzs+gIWi6X89SjUCggFovxZIAkLk0mE+Ov6+vredry6quvYnR0FLlcDpcvX0ZNTQ0WFxfR0tKC9vZ21ssn4vaZM2cQCoUwOTmJoaEhNpAqFmq1Gl6vF3K5nC/tkpISjIyMYPv27byvadK5c+dOrKysYPfu3az01t7ejlQqxQpzLS0t7KlCnX+bzYbJyUk0NDSgurqaZVOLrY9M4UhxKxQKMSfIaDTi8uXLWFxcRDKZ5K68yWTC3r17MTY2huHhYfh8Prz77ruor6+Hx+NBfX09K66Qf0NXVxerzJHqEXVgtwoqykgWlLwb3G43HA4HBAIBq2QlEgk0NzdjeXmZOVxGoxHl5eX8Xkk6FgBrya+vrzM2nIphUjUqBokj2AHhiwuFAvslyeVyvPfee4jFYiy//eijj7Lu+9jYGO9/SmJCoRCTN2niQnLK6XSalVIikchvlaIWCASYmJjgRgCZAM7MzODo0aNYXV3F8vIyYrEYtFot7rnnHp4Ynzt3DplMBo2NjfB4POjq6kImk8HevXt5CrRjxw4masvlclitVuaD0GSt2HMLh8MoKytjd2GRSASPx8OEWIvFwpNO8kshM1xq/hA38dVXX2WIE0GjCBr3zjvv8BSfJG7n5+e3XFs+n8fKygpzmsLhMJ9H1AUmrhcZfb3//vvI5XK4ceMGcrkcysvL4Xa70djYiImJCahUKkxPT2Nqaop/jgx5a2pq+D07HI6ixGFKPKk5EwqF+AzJ5XKYmJhAPB5nDs6dd97Jjc3r168jFouhrKwMXq8XBw8exNe//nWo1WrMz89jZmaGfbn279+PeDyO7u5uFv2gTu5WQSgHOo/C4TA6OjqwvLzM/jCJRAIulwv9/f1oaWlhtEU4HOZJEQkNfP3rX8euXbtQWVkJrVYLm83GqpkbGxswGAxYWlrCjRs3IBKJiq4NAH8/EomEzydqwvh8PlZE1Ov12LFjBzdsvV4vhEIhc2NyuRz+5V/+BZWVlR9zDE8kEnjwwQehVqvR1tYGmUzGhdzs7OyW65LL5RgaGuJCnqbqQ0NDaGhoYP+tRCIBv9+Pzs5OlucfHByESCTiu5+kiJubm9HZ2cmTh/7+fuzcuRM+n4+hwzdu3IDFYkEwGNxybQRHtlqtsNlsH/PQ6ejogNlsRkVFBaRSKfx+P3p6enD58mXk83n85je/QTabxd13342amhr2TyIlPHI9d7lc6OrqYq4k5QImk6komkev1zM8jEw+yQOrq6sLQqEQbrcbo6OjmJiYYH4KncukLEZQ7lQqhX379nEObjKZWEhleHgYZWVlXOR2d3d/IgQD8P8w0XC5XDyCJj8NkiiUSCSor6/Hj370I0SjUTz44IMsMfbGG2+gubkZwE0nVBqZk0Se1+tFRUUFS701NDSgoaEBPp8PYrEYwWAQ/f39ReFJ8/PzaG1tZXURvV6P1dVV1NfX4/XXX8eOHTuwsLCAyclJWCwWjI6OYnNzE3/7t3+Lz33uc5BIJFheXubOeWlpKWZnZ3H58mUAN9UB3nzzTXbcpi4YHZytra1brs3v92P37t0sM5pOp5FOpxEKhWAymbC4uMhW9AQbGRkZQSQSwenTp7G6uorDhw+jtrYW4XAYu3fvxvnz5+H3+9nYr76+HrOzs2htbeVRKMmhFoMnZTIZdnVNJpNQqVRMamptbWUiJMFsgJuyh1KplB2RqXt65swZJgguLi6itLSUE0UiYp0/fx5WqxUOhwNzc3NFpVDn5+dx8OBBjI2NwefzQSgUYmJiAk899RT+5V/+BXK5HMvLy+yJkkqluFiLRCK4++670dbWhkuXLqG5uRn/9m//hkuXLvFYOJfLwWQy4fz583A6newCS54bABij+H8HJXlra2vY3NzkjiR1ubdt24arV6+yoADt5TfeeAPd3d0MzWhtbYVUKsX09DQmJyd5QkU+B6QNPzQ0xMUfFatbBY2oydiP5JjJXyKTyeDcuXMoFApMOK+oqEAymcSOHTu4i0WXolarxfj4OONjyVjvtddew/79+zExMYGqqipYrVYeyRcLco0l6VmBQIDFxUUcPHgQL774IsRiMbvGV1dXM4Rnbm6OPT8+arYEAP39/cwLKC8vx+rqKn7961/jwIEDeO2111gil5RbtgoaXROcSyKRYHZ2lt2Vc7kcZmdnkcvl0NPTg3fffRf79+/Hb37zG7S1tSGdTuOdd94BAH5/5EmxuLiIqakpVugiDfxYLIZ8Ps9GV1vF/4+9/wyP8yzTBuBTM5oZjTRNmqKRNKqj3oslucXdjg1xEockTuIkQJawLCULuyywsCwvsPSyvGQJJQUCJKSQaseJU9wtN/XeRxrNaDSaqulFmvl+ZK/rdfazhnzfsT99HQc/CMG+53nu576vchbq/pPfBHU/ScqbdM5zcnKgVqvx7rvvsktvMplEd3c3EokENBoN/uu//gv/+q//itdeew0KhYJNIw8ePIhz584hLS0NMzMz0Gg0kEgkWFxchEAgYBW0/xlmsxnl5eXszqzRaDA2Nobq6mqcPXsW7e3tmJ+fx+zsLIqKijA9Pc2Gmq2trbxPS0tLcerUKczPz2P79u0YHx9nhbLMzEwMDAzwJJgmvSaTKWWn9H+awMbjcfj9fuzdu5fJpsePH8eZM2dYA7+wsJAT7MXFRbjdbigUCly4cAE6nY5FREKhEK5cuYLc3Fw+n4mDlZ6ezhCS9YImHzRx9/v9mJmZYQIwyd3GYjFUV1ejvb0dc3NzkMvlMBqNzIEUCARYWlrC5OQkqqqqUFZWhqmpKfT19aGoqAgOh4PPyPn5eXZpT/WtpqWlYffu3SzTSpNrclMvLS3F5OQkJicnMTc3h/Lycuzfvx+PPvooi1YsLCxgfn4ev/rVr3D48GFMT0+zJwNN+kjCemRkhI3yBgcHU2LmLRYL9u/fj/HxcTY627VrF0+3hoaGuOOaTCZx5swZFBQUwOv18sSXXNdPnTqFI0eOsOcMwYCVSiWeeOIJNp6sra1FY2Mji1+sF729vbjjjjvQ29uLyclJnjoajUbmSPT29nKzZmRkBGtra+jt7cWWLVuwuLiI1157DQcOHEB+fj5KSkrw3HPPYW5uDjKZDK2trSz4oFKp0N3dDalUyhxGuruuF7FYDBUVFQiFQvD7/YjH4zCZTKiqqsLc3BxPXpVKJSorK3H16lV0dnYiFouxCIDX60VbWxt6e3tx+fJltLW18dl/6tQpbNq0ieXxbTYbTxEIArhe2O12Viq9lru4Y8cOvP3229izZw+Gh4cZRnX69GmUlpbixRdfRFtbGxwOB4aGhuB0OlFcXMzcPeJxicVirKys4JlnnkFRURF6e3vZhK63txe7du1ad23hcBibNm1iqGp2djbOnTvHylsOhwM9PT2w2+183u/ZswcmkwmTk5P89+Tl5eG5555DdXU16urq0NDQwJNJl8uFxx57DK2trYhEIuyrtrCwwA2h68Xa2hpsNhvDFil/LCoqwtDQEID3G+xOpxPbtm3jJqTZbIZIJMLAwACmpqbYyFgkEqG/v58bEktLS/D7/YhEIrjpppvY10wsFrPK3YeJD11oaLVajI6OQqfTMefi1KlTTBr68pe/jIMHD6K/vx82mw1lZWXsWzA7Owu9Xo/9+/ejtbUVP//5z1FaWoq9e/fymGZ0dBQnTpzAzp07kZ+fj7KyMiwvL8NkMrET8XpRW1uLlZUVVFZWAvh/qkGf+9zn8NGPfpTlW2nUFQ6H0dnZid/97nesRAGAlX3Onj2L/Px83HXXXZBIJBgfH8fGjRvZyOTWW2/FhQsXMDs7i5aWlpSuuRaLhSt5g8EAhUKBixcvwuPxoKqqCjfddBNmZmZ48rC8vIyamhqoVCrs3LkTbrcbw8PDaG1txRNPPIGenh5UVlZCqVRCo9EgOzsbJpMJpaWlqK2txf/9v/+XoTxCoRAf+chH1l2bUCjElStXuCqOxWJ8satUKmzbtg0mkwmnT5/G3NwcNBoNPvrRj8JisaC/vx99fX3YvHkz9uzZw0TDe+65hzsAY2NjMJlMaG1tRUlJCR9ApJaVqlKPxWL44x//iLy8PL70m5qa8OqrryI3N5el3ILBIIaGhvDXv/4VDz/8MHe5aUS8ZcsW/OxnP8Px48fx2c9+FrW1tRgZGWGezM0334yysjK8+uqrTFQtKytDU1PTumvT6/U4deoUsrOzsXPnTtTU1ODs2bN4/vnnUVdXh+npaVRWVqKqqgorKyvQarXo6OjAL37xC7S3t6O6uhrhcBjz8/NIT0/HV7/6VXz605/Gpk2bIJVKMTc3h0gkwhLARPIlqeP77rtv3bUJBAIMDQ3BYDCwMR9dZCRPWFxcjMHBQVitVhiNRmzfvh0WiwUnT55ERkYGy0Q/9dRTTFIl8zGr1YrTp09DrVbj1ltvRWtrK6anp2EymbhrnSqam5sxOjrKcBuJRMIXA8HJ6urqkJaWhjNnziAtLQ0333wz+vv7MTQ0hIqKCtTU1MBisSAWizGkgbo3ly5dwsWLF3HffffhqaeewsGDB3Hx4kXY7XbceeedKfHVWq2WIQBFRUXMp5JIJLBarVhaWoJKpYLFYsGlS5cYp79jxw643W52PRYIBJicnGSoRENDA5Pmurq6sHPnToYqZGZmsktvqrWRASMViyKRCDabDZOTk9i5cyfKy8vZfOvSpUvIyclBRkYGqqqq8POf/xxGoxE333wzMjMz0dTUBI/Hw104Unai8/fAgQNwu92YnZ3laUiqyy4/P5+x1dFolDG8f/3rX7Fz507Mz8/DYDAgFouhv78fn/jEJ9Da2or/83/+D8xmM/vsNDQ0oK2tDSaTCVNTU2hoaIBMJsPIyAj6+vpQX18PhULBHgPRaBQVFRUpCYkKhQKTk5Po6+tjeeGZmRk8+eST+OhHP4qmpibs3bsXDocDAwMDAMDP5y9/+QuKiopw8803o7m5GTk5Obhw4QLKysrw7rvvcoNofHwcY2Nj+NKXvoSCggKcO3cOLpcLGRkZKadUEomE5VQ1Gg0EAgFqa2vx5JNPQiQS4aabbkJnZydcLhd8Ph+TfsViMRu0Go1GlJWV4be//S0OHToEpVLJXV2CKZL/hcPhYBgvNcrWi/r6ehw9ehTFxcXYsmULhEIh3njjDQDAW2+9hfb2dpw8eRIzMzPYsGEDlpaWcMcdd3BC7/P52Btlx44dOHToEL7+9a9jw4YNyM/PZ26Tw+HAli1b8NZbb2F6ehqxWAzFxcXYvn37umtramrCm2++iWg0ivLyckQiEbz55pt8vz/44IMYHh6GQCBAe3s7ZmZmmAfzwgsvoKKiArt372YZ83A4jPvvvx9tbW2Yn5/HlStX4PF4cOeddyIQCMDtdmNgYADLy8swGAzcdLteqNVq/P73v8e2bdtQUVHBjtqXL1/Gzp07cdNNN7HARCwWw4kTJ/Doo4/i9ddfRyKRQF1dHatifvGLX4RYLGZZ/aWlJXas9/l8rOAVi8XYEyeVsWZ1dTUuXLiAxsZG5uzMzc0hMzMT5eXlqK6uRiAQQFdXF55++ml8/OMfZ/8Ek8mEaDSKwsJCFBYWQqfT4dvf/jYnszqdDktLS/je976Ho0eP4ktf+hJWVlY4Aadp+nqRl5cHs9nMJsjFxcWwWq3Yv38/vv3tb8Nms6GgoAAZGRmwWCyIRqPo7OxEb28vLl26BJlMhpaWFhw6dAg9PT1cvALvQ+u1Wi2Ki4uZ/0UwOr1ej5aWFoyMjLB89f8MqVSKmZkZFhcgWNiVK1egVCqxfft2FBcXY2BgAF1dXQzjrampwfPPP8+qqIcPH0ZDQwO+//3vMw+FTGd/9atf4cc//jFmZmZQVlbGjcL6+vqUDSjvf5s+Z2RkQK1WQyKRYH5+Ht/+9rdx6NAhVv4aHh7GyMgIFzmbN2/G448/jnA4jLa2NoaCP/nkk+jo6GAfGUIw1dbWMtySmg3UYPww8aGhUyMjI9i8eTMAcEJRX1+Pw4cPo7S0lEmlmzZtQn19PU6cOMHqIA888AD279/PMmwET+jq6sKZM2dgt9tRV1eH22+/HaOjo3j77bfxxBNPYHR0lMlLqT4g6lTMz88jEolwUfTjH/8Ye/bsQVFREerr69Hc3AyVSoWrV68iGo3C5/MxDEwgEKC5uRlHjx7FJz/5SaSlpbE+dTQaRV5eHmsSO51OLobImG29KCgoQHNzM4xGI4xGI2pqalBaWorPf/7zCAaDuHLlCs6fP4/FxUXmkwSDQValInhFSUkJWltbueChzgEVdXNzc/D5fGhqasLmzZtRWVn5N52Q19bWsH37duYyFBQUoKCgAB0dHWycs7KyAqPRiHvvvZfJ3JcuXcKePXuwefNmNmCjTvCZM2eYgEsQtGeeeQZut5uJiR6Phw2H1guxWIzKykrG2RcXFyMWi8FoNEKj0WDv3r245557cNddd6G5uZnfA3UERSIRRkZGWHnoqaee4hFjMpnE5OQkWltb8cwzz2B+fh6jo6OYm5tjx+RUWFez2YwjR46gra0NGRkZPA06cuQIysvLcf/990Or1cJkMsFut+MPf/gDxsbGsHfvXpSWlsLhcCASieDOO+/E4OAgHn/8cfYIoO6L0WjE2bNnkZOTw90GiUSC119/PSXkIRAIYPfu3SzD5/f7MT4+jo9+9KNoaGhg192WlhYcPnwYg4ODCAaDuHTpElpbW6FQKNDb28uywAaDAWfOnMHk5CQrU+zYsQOBQABvvvkmXn31VWRkZGDbtm3Mz0oVS0tLkEgkGBsbg8ViQSKRwIULF7B9+3YUFRVhw4YNzGe5/fbb8corr2B1dRXPPPMMBAIBPB4PTCYTKioqMD8/j6qqKphMJly5cgUnT57E8PAwPvGJT+DZZ5/Fxo0b4fP5sGnTJtx9991IJpMp3+vc3BxL0y4tLcFut/PEMpFIoKOjg7+RjRs3MmfiD3/4A/r7+3Hu3Dm8/fbbCIfDmJycxK233soE37GxMXR1daGtrQ1dXV2YmprCG2+8wQZ1Npst5ffgdrsZDhMOh1nx58iRI5idnWU4YzKZRHNzM/x+P2O2f/Ob3+Af/uEfkJubyxLBs7OzWFxcRElJCfNK9uzZg7GxMTgcDpYQra2t/ZteQWSwRSZ7ubm58Hg8uOeee7C2toZdu3YxHG7jxo14+eWXYbfbEY1Gefwej8dZzeWOO+5gKJHX64VYLEZ7ezsbvg4MDECpVKKjo4OfR6rvYdeuXUw0rqyshEKhwLe+9S3odDqUlpYyfG/37t347ne/i/T0dBQXF3NiTN3erq4u3H///VAqlbjvvvtw//33o66uDkeOHOFk4NixYygtLcUdd9zB8Kb1wm63o76+HpFIhLknx44dw6233oqOjg7s2bMHZWVlUKlU0Ov16OnpwUMPPcQQ0dXVVbz55pt45ZVXMD4+jvr6eszOzjK/USaT4dChQ4xGIM5idXU1lpaWUkJ+JycnkZubi6mpKfT09LBx6pe+9CXmCGi1Wtx888145JFHYDQaebpSUVEBm82GV199FYuLi3A6nXjttdcAgLknp06dYvGJWCyGYDCIwsJCngylurcsFgvuuOMOVFZWor6+HrW1tezNRPwzkqI9ceIEpqenGRZ58803s9pbfn4+JicnoVAocOjQIfzud79jE7L8/Hw88cQTSE9PR19fH2pqanD33Xezutp6QdCTqakpTE9PQ6VSwW634/Dhw3j77bcxNDTE0OidO3figQce4Mlyd3c3zGYzQ/JKSkrw6U9/GtPT07jpppsYHTA4OIizZ8+yYlZWVhbKysrgcrlSijZYLBa0tbUxpzYnJwcikYiRJ6FQCDabDQaDAd/73vdYCtXr9eLw4cMs4GMymWAymZCRkYH8/HzIZDL4/X4IhUL853/+J6qrq7Fx40ao1Wp88pOfxPe+9z1UVFSkbJA5HA72ZCI+RSAQwJ///GfI5XIEAgEkk0kUFxdjw4YN7JeUkZGBmpoaJtG73W5cvHgRRUVFnPdJpVIMDQ1heXkZ58+fx6uvvopgMAiFQoFgMMiQ9fWC7l+C5pIHycc+9jGe3FCj7I477sDly5dxzz33YGlpCZ///Odx+PBhJBIJzM7Owm634ytf+QoaGxtZ2CEvLw9vvfUWrl69iq1bt2J2dhYSiYShy6nglwD4zp6dnYVSqUQ8HsdnP/tZ/t/VajW2bt2Kffv24S9/+QtKSkrQ39+Pj3/84+w1Nj8/j9OnT+Pzn/88RkZGcOzYMQwNDUEmk0Gv1+P1119HMpnE6OgoYrEYJBIJLl68iN7e3pRro0hLprpBronHH38ccrkcIyMjyM3NRWNjI5xOJ86cOYOHHnoIL7/8MkKhEIqKilBZWYmlpSU0NTWxrnJxcTHDMXJzc/GLX/wCn/nMZzAyMgKv1wudTodIJIL6+nqumkg/fPv27XjrrbfwL//yL+uuzWKxwGAwQK/XY21tDUNDQ1hbW8ORI0c4MZuenkZfXx+OHDmC06dPY//+/Xj33XdZtzoej+Ott97C4cOHsbCwwBKK8/PzyMnJQUdHB2ZnZ+FyuWCxWJh4Nz4+jp/85CfXXdtjjz3GKilk5768vIydO3dCr9ejq6sLEokEMpmMjXRefvllVqwg111Sd6isrGQSNuHvnnrqKfzd3/0dY5xp7LWysoK8vDzce++9113bX//6V6ytrbH9PBUIkUgEzc3NSCaTTBQrLCzE6Ogoq1rY7XZ2wFQoFJDL5XjhhRewadMmJiERCbq9vR3Hjh3jjngymWTIxHpdhB/96Ec8DiQJucrKSrz44ou4/fbbmQNC3c/m5mYUFRUxtri8vBwFBQWYnp7GwsICLl++jM985jPsok5Jht/vR29vL1pbW9HX18cdNq/Xyx4T/zN+8YtfMAmMiMoqlQrvvfceu/KurKxAIBAgPz+fXa/D4TATOwkqMTg4yMoni4uLEAqFKCgogM1mw5kzZ5jHQ26rgUAASqUSX/ziF6+7tl//+tdMfL1WpnZlZQUHDhzAlStXMDc3x5LIer0ebW1tOH78OKamppCVlcXKHlarlTvLyWQSLpeLJ0a33HILurq6IBQKkUgkmFDvcrnwiU984rpro2eXkZHB41cim9JE4PLlyzyJIUdowt+aTCYUFBRAp9OxDPOTTz6JgwcPoqioiB24z507h3379uHMmTNob2/HyMgIVlZWsH37djZyWu/ZZWVlISsri4mh9fX1mJ6exp49e/Dyyy8zRGt5eRlisRh33nknnn/+eZSVlUGtVsPtduPy5ctoaWnhP6ugoADBYBCjo6P8vOhby87OZqd6APjCF75w3bX96le/YvM2ggBZLBZuQLz00kuor69nfw3yBjpw4ADOnDmDrKwslJSUcNPE4XAgHA6jqqqK5ZrPnz+P/fv3w2KxIDMzE2azmSUja2pqsHv37uuu7bOf/SzjtEOhEJvjvfTSS9i5cyccDgeKioogEAhYRYzgpiRvS/AhmkZu376dZSozMzMRi8Xg8XiQm5uLZDKJ8fFx+Hw+5qat9z3813/9F3/vq6uriEajMBgMWF5eZjUpcg8OBoMM6Zmfn2dirkKhgNlsZjGGgwcPwmKxYHBwECsrK9i6dSvkcjmqq6vR3d3NCQ8ZJ673Tn/yk5+gpKSEndDD4TAbfZL3EimDkQcCTTRIWCIej3+AD6PT6eDxeNDf34+JiQno9XqGQJFkfDAYhFarxdraGg4fPnzdtT3++ON8HpEEc1ZWFsbHx1FRUcHKeKWlpdDr9QwT6u/vZ1lNhUKBQCCAj3zkI/jXf/1X3HfffVAqlVhaWkIoFMKePXsYjiaXyzExMcGQw7GxMfz7v//7ddf2z//8z8y7JAgdNYp2796N1dVV6PV65o/t27eP5dKpSVFWVsamhpOTkwCAO++8EzqdDlNTUxgZGcHdd9+N//zP/+Q9UVhYiJtuugmrq6t48MEHr7u2n/3sZ1CpVDz9qKurQyQSwZkzZ9gvgxQNdTod1Go1BgcHUVVVxb4JxBcQCAQM26QGWW9vL37729/imWeewenTp9He3s4Fg0AgwJUrV9a9t7761a9CrVaz+ARNMpaXl2E2m1lmf3V1FUVFRXC73di6dSsmJibw3nvvcW43NjaGrKwsmM1mGI1GjI+PIysrC7W1tZiamoLRaERJSQmWlpZYeIH8xw4cOLDut0BNBeqUT05OYnh4GHfccQdL7RL65Utf+hJ+9rOfYd++fRgfH+d7dWBgAA6HAw8++CB7gJB3kVKpRF5eHt58800YjUbmPtpsNhQVFeFTn/rUddf27LPPAng/oR8bG8Pc3Bz27duHHTt24MyZM9DpdOwbEwwGceutt8LlcuHKlSu4ePEijEYjN6YIikqSzIS+qa2txauvvsoeXPQt6PV6lJSUoL29/bprO3bsGEsAy2Qy9t8gFcPjx4+jrq6OzzxCONTW1sJut7O0bX9/P1ZWVhidcuHCBXi9XlbDJKEFh8PB3nF+vx/JZBKf//znr7u2a+NDQ6fcbjcz1VdWVmCz2WCxWFBRUYHMzEwYDAa+mLxeL5qamlgKjmRISfnE7XbzpaJUKrG2tsaLJiWNnJwcjI6OstV8KuJwNBrlUbDH42FdYeB93DUpERBR/Vo8LI2HCOO/srKCkydPQqlUorCwkIlPoVCI7dydTier10gkkpSKWJQYrq2tMUk7HA6jq6sL27Zt464S4c2J8GU2m1nal0htWq2WfUcokSVyF4316FAE3icxpSKDJ5NJ7jQC78Mz0tLSOAkhpZNgMIiSkhIoFApIpVKWay0pKUFeXh7sdjvMZjNfhi6XCx6PB9FolEnfpA5CJH+/359SoYjIomlpaQw/8Hq9LEG7vLwMn88HgUCA6upqlrg1GAyMgddoNMzFSCaT3PkNh8OsEkN/D8HziHydyvGSuDE0VqaRIqlXeDwedlVeXFxkvsvc3BxaW1uRkZEBj8eDd999l8eadMEQHIMO8XA4DJfLhXg8DolEwkn6ekFYfur0Li0tQS6XIxaLYWVlhdWLqNNaWFgIj8cDrVaLSCTCBnnvvPMOqqqqkJGRAa/Xi2QyCafTyd+43+9HKBRiw01S9PlbHA3iCWRnZ7O6E0lgkns0KU6RaszU1BQ6OjoQj8dZ6jMtLY15NVR002VLUyb6dshIb21tLaV3i0gkYu+MawmSJPHs8/kglUq5+6XRaDA7O8s8EhLDICJ7MpnkPycSiSAQCGB1dZUnl9d6TahUqpQkf4LCJBIJ/jOJQ1JRUcEYZFI+I3EMWj+p/CwtLeHy5cuIx+PsMUNEQKfTiZmZGTbPW11d5f2X6rnRdx+LxSAWi1mVhL47AKzoRmfOwMAA8vPzOXknXplKpYLb7WaJ8pWVFTidTi7OAbBgBsFhUhFgo9EoFAoFe6yIRCJ4PB4IhULk5eXBZrNhenoabrcbOp2OSaJUiBMElZI/p9MJu92OpaUl9vogpT3C75ORFSmXpdpvJGRCdww1I65VfiH/hLKyMszOzjKRVCKRcOKakZGB4eFh5jXR/gyHw6yaJRKJWOlOo9GklPAOBoMoKyvjophkZEkKlCRRSfWPXJGJO0fqNr29vTAajazKRkVYIpFg0Q5SVyKDOLlcnlKyWCQSIRQKQSwWw+PxwG63w+12QygUMg/S7/fD4/HA6XRCqVSyJxBBfKnoBN4X3zAYDHzW0Z9BxGOlUslmj4uLiynve/oGgfc74TTtIyljm83GTbpEIgG1Wo35+Xk4HA6GJxLaoqOjgydC10IYt2zZArfbDYfDgf7+fhQWFrIIDImuXC8CgQDDjencpeYVFStut5uLV5I3lslkLFrj8/kAvA9t7uvrY5lZpVLJzzcajcLpdLL8qUQi4YnEenGtZ5fb7Ybf78fq6iorrJHiKf3z1dVVFnqgs9/n82FpaQlGoxFjY2MwGAwwGAywWCyYnp5GIpHA7bffDuB9jpDT6WQYWqrnRoUZ7T3KXQkWR02LSCTCtgJCoRCVlZXMjUsmk8wXMZlMUKlUH5Asp7zQ5XKxaAzByFJ5yFEOQwqXHo+HSftkwgy8zzWbmZmBVquFSCSCw+HA4uIi2wGQiAXB8GpqauByuRAOh1lZkSaWDoeD5YH/lvgLxYcuNCwWC4qKiljpyO/3Y2FhARs3bvz/coecnZ1FbW0tuzHThIJkMi9evMjYW+oEWq1W1ujOyMiAVCrlsdTZs2dTSsqRLB5d9IFAACKRiMdVNJomkuf09DRKS0uZBO3xeNji3uPx4NixY9i0aROb/y0vL3+A2Euyg6FQCMD/U125XoRCIe6WicViVsVaXFzkjhMdboTRrKyshMViYZwvqWwQiV4ul/MBn0wmGfNfVVUFs9kMt9uNtLQ09hdI9dxI2YUkHakrOT09jR07dkAgEHDiFYvFoNVqMTg4yKpZJHl76tQpdntXKpXsQ5Kbm4vLly+jqqoKVqsVOTk5SCQSnHSnCiqasrKyoFKpMDExwd1f6p6TggMlH/SObTYbJBIJ5HI50tLSmPTe0tKCQCCA4eFhxo1S95QKCJKgXS/IDCsajUKj0TBRNTs7+wNrtVgs8Pl8PEJfXl6GUChEJBKBzWZjuIndbsfx48ehUqkgEAgwNTUFr9eL/fv3Mzmbktns7OyUGGHqVlJHmkwLyQmcJl6Es7RarVAoFHA4HFCpVBCJRFheXkZ/fz9zkggqSL/d4/HgypUr0Ol0zBkgkvrfMvARi8WsFkJdEipklpeXUVlZyZegQqFgzfL+/n44nU6sra1xYdjV1YWqqirYbDaGZFitVsjlcgwNDaG4uBizs7NwOp1sDJgqYaYgeWehUMiKRHQuBQIBvqgNBgN3waemppBMJiEQCLBx40ZWrwHexwiT+yw1WagQJF1yg8GQUrbY7/ez/GMwGGQIh8vlwsjICHQ6HWKxGObm5pCdnQ2lUonGxkZcvXqVTZmo2zU0NMSJPCWedD5NTU2xgR9xoyihXi/S0tJYklyn0yErKws2m42T3pKSEiZVU0FECTu5zBL80uv1IhaLMRkdeB9uR47tpLglEAigVqsRjUZTXsThcBjZ2dlMYszMzGRIALnJd3d3Y2lpiYvpDRs2YHR0lM9Zko4dGxtDJBKB2WyGQqFAZWUlJwgjIyPQarVsukey1KmSeWqkUHOBfit1dmlPmEwmOBwOzM3NobGxEWazmaWMScI4PT0di4uLsFgsyM3NhV6vZ3IwYdUp8YvH49yAWC8CgcAH+HErKyusckSS4WR+S4VyW1sbfytutxsSiYQN1a6V/SVpZWqwdHR0cGJK93cqCGYymYRUKmXyNzUS9Ho9lpeXkZaWxgUBdZpp+rO2tsbGs3K5HN3d3QyvIU7V4uIi1tbW0NXVhfz8fBiNRthsNrhcLm5GrhcejwclJSVMOieFLfrt1EWfn59niFZZWRn+8pe/MFwqMzMToVAIExMTLLufTCYRDoeRmZmJjo4OLCwsoKSkBFNTU3A6nSwIkJeXt+7aSACAjGpJRpaKdoK2k2AF5QN013u9XoZ2TU1NsTSuTCZjJ2kqfEtKSphjSPdDKr4SNRnITJAKx8zMTObGzc3NweVyQSqVYmJiAo2NjR/Ih6iJJRaLcfbsWezevRtFRUVIJBIYHBzEhQsX+PlarVa+3yhvWC/i8TgX/TR9o+dIBYff7+cGU19fH9rb2yGVSrmAo6Kc8i2Xy8US8/Q9+v1++P1+LC4uwu/389Sfpt3XC9rX1NRNJBJ8BsTjcYbKzc3NcWHU2dnJ97vNZkN2djbq6uq4WRIMBtHZ2QmDwYChoSEWLyJfFBIiIgnrDxMfmqNBKkJ1dXVobm6GwWCARCLBH//4R3anJT8GgigJhUI888wzeOmll3DmzBlYrVbmHVCHgYhPDocDALjz0tvbC7PZzFVZquQqJycHPT09H8AV04VK3QuLxYLl5WWkp6djYmICxcXFyMzMxOzsLObm5iASidDW1oba2lr2t8jNzUVpaSmqqqowMjKCd955BwBQWlrKZEuSCl0vaMRIOL/a2lpUVFRg27ZtLFMokUhYipUOmZqaGpw5cwbDw8NsjJaens6cCeB9bLTVasXIyAikUikyMzP5oicPhbKysnXXJpVKGQLm9XpZnYA+IAAoLy+HRqPB1NQUJicnmSdB3cCenh7uskmlUly9ehUymQw1NTXIzs6GXC7Hvn37oFarGSZBevCpCrRkMolLly7B7XYzWd3hcOCZZ57Bn/70Jy72LBYLBgYGWPe8pqYGHR0dkMlksNlsnCyS5LFSqcSGDRvw8MMP4+DBg6irq4NOp+MpA7mkp7qE1Wo1zGYzrly5ArPZzHAt+nOKi4shlUohk8lQWlqKCxcuoKamBi0tLRCJRMwnuf/++3HixAkMDAywogZBk6iQdLlcKCws5D1MqmrrBanYaLVadmqNx+N47rnnsLCwgLGxMfh8PlgsFvzpT3/ChQsXkJ+fj1OnTuGtt97C+Pg4FAoFWltb0dXVhd/+9rcslUxNAfIP2bZtG3egCPNaW1u77tqA9wUIJiYm0N/fD5PJBI/Hg8HBQXZcJuft2dlZTExMMO/rzJkzjIMnOOPi4iLrlZMfBGF0yTx0cXERVquVk9tUe04ikWBwcBAFBQUsi1hcXIzR0VFOfog/NTAwgEQigY0bNyIvLw+jo6O8B8rLy7G4uIijR49yIqlSqVBTUwOr1Yrz58+zclsikeBufSoiPXUOSW+einziYRDxe21tDS+88AL7TJBySXd3N4aHhxGPx7Fv3z6GRVLimUwmIZPJYDAYPsDxisVirEKT6p2+/fbbsFqtXDwvLCywTKzRaER2djZcLhcGBgZgMpnY8JIMRjdt2oTNmzfjpZdewqlTp1htKTc3l6GElLjSd61QKFgad71QqVQwmUw83TKZTFhbW8OJEycYH013S39/P0uzLywsYGFhgXkNBw8eRH5+PidRdDfU1NSwSlp9fT1sNhuTj+vr67lAv16kp6ezCz01xnJzc3H69GkuNlQqFcMFR0dHceTIEdTU1PAZnZ2djYaGBhQUFKCsrIzXVVlZCaPRiLW1NQwMDLAvhsvl4q56qjNOqVTirbfeQigUYr+payFcHR0d6OzsZI7L2bNnUVxczKaaOTk52Lx5M/7pn/4JIyMjMJlMGBoaYi+BkpISnD59mjv1MzMzOHXqFK5cuYJAIJCyU0pGly0tLSgpKYFUKkVnZyc3yygxo4Tw+PHjLIVMctJ0ZhEBub+/n+88KoZisRhKSkrQ2dmJ8vJyRk2kUplUKpUQiURobm5GZWUlRCIRpFIpjh8/jtdffx0qlQpisRgOhwMLCwsIh8PYtm0bHnjgAfbnIi+jY8eO8R7Q6XRwOBx48803EYlEYDQaceedd+Khhx7iuzgcDqcUMVEqlTyNoelwcXEx5ubmMDw8jIWFBSgUCoRCITz77LPMWXznnXfw9NNPY2BggH0bxGIxHnroIUgkEoTDYZafTSQSKCgogFgsxs0338wNkJqampSFhlQqZXndmpoavvPeeOMNmEwm+P1+nDt3Dm+++SZ8Ph/cbjdaWlpw8OBBeDwezM7Osslmf38/Tp06xZMVhUKBbdu2IRaL4cqVK2hsbGQz2+LiYtTU1KQkNa+urnLRSlBYjUYDhUKB7OxsZGZmsuHfM888A7PZjOnpaVy+fJlRCvTMyXiX5N1FIhFz+PR6PQoKClhllfZDqu9ULBYz344K8OnpaZw/f57Vo3w+HwKBADQaDSoqKjhPCQaDPMGgCcUbb7yB3NxcqNVqzrtGR0fR19cHmUyGrq4uBINB5mn9Ld8sig890bBarWhpacHZs2eRTCZRVFQEmUyGRx99lIng1KlraWnB5cuXUVpaii1btqCpqYnH6gKBAK+//joOHz7MYx4ymvvc5z6HRx55BDk5OZifn8eBAwfQ1NSEnp4ebNy4cd21DQ8P46677mKJPNI9/9znPoe8vDwmflGyMDo6ymPJj3/84+yKSVi7f/7nfwbwfnfYarWir68PP/7xj/HEE0+gtrYW586dQ1VVFWpqalgmbb0IhUJoa2vD0NAQXC4XH/wkvWo0GuFwOCCRSHDrrbfi+eefxz//8z/jJz/5CY4cOcLEq7vuugs/+MEP8KUvfQlzc3PYtWsXVlZW0NfXh9bWVvT29kImk+Hq1asoKChAUVERTCZTyrWZTCa0tbVBq9Wy4zHpl1PXHwDj0u12O3p7e+FwOFgnfG1tDW1tbRgfH8dNN92EK1eusFlRQUEBJBIJy7rqdDoYjUaWFUwFs4lEIox1dLlcvMaf/vSnzINQqVSoqKiATCbDuXPnsGnTJlitVt5bNpsN9fX1uHLlCj796U/jnXfeYXM5cs29ePEiOjo6AABbt26F3W7HX/7yF5Zyvl44HA5UVFSwYodAIMDs7Cw++clP4oUXXkB1dTUng1VVVQDAEKD8/HyWDSYIwr/927+hu7sbN910EzIyMjA6OoqGhga43W6UlZXhwoULKC0tRXNzMwYHB3HmzBk0NDRcd23hcBgOh4Mv8E2bNsFut+PPf/4zJicnuevY3NyMT3ziEwgGg7h69SoqKyuxf/9+XL16FXa7HV//+textLSEp556Cs899xxL6BkMBnYKdbvdKCkpQUNDAwQCASYmJrhhsF6sra2xAZXBYEBOTg47kNNeNhqNqKyshNPpxNTUFLZt24aTJ09Cr9czH8hoNGJpaQmPPPIIfv/73wMAq1B9/vOfx+9+9zvs378fBoMBTU1NSEtLw8mTJ7F169Z11zYzM4Nt27ahv78fGRkZMBgMCIVC+OY3v4mzZ8+yqzupIdGUNhAIYNu2baxGIpVK0dbWhk9+8pM4f/48GyIGAgE8+OCD/K0QpO1aWN96QYUAQaYSiQTm5+dx55134oUXXkBVVRUSiQSMRiPa29tx+vRp3HzzzVhZWUFTUxNmZ2fx1ltv4XOf+xy6u7vxta99jWEtLpcLQ0ND2Lt3L06cOAG/349Lly6huLgYhYWFuHz5csr36na7sWXLFthsNggEAhQVFeHixYv45Cc/icuXL+PWW2+FXC5nXyVSPhKJRIwFn5qaQllZGfbu3YuKigr09vZiYGCAnxG9n0OHDjHsLBKJ4NKlSymbKSTHea2Ax1tvvYUvfvGLWFhYwEc/+lE4nU5MT08jKysLTqcTVqsV5eXluOWWW9h76fbbb4fH48HXvvY19PT08DQ8Pz8fBw4cwEsvvQQAqKiowNatWxGNRvHee++lLLyj0SikUikGBwehUCiQn58Pj8eD73//+yxdS5OYjRs34ujRo/wMt27disXFRXi9XuTl5eGNN95AUVERMjIyUFtby9zIO++8Ez//+c9RV1eH9957j12JE4lESg38YDCInTt3YmFhAS6Xi03aHnnkERw9ehQdHR3cISWzNK1Wi+9+97soLy/H1NQUzGYz7rvvPhw6dAi33norvvWtb3HTyWKx4J577sGf/vQnvP3225idncUXvvAFSKVS/PSnP02Z+NG3NDIygsLCQrS3t+PVV1/Ffffdh8HBQWzfvh0zMzMQiUS455578J3vfAcdHR04ceIEmwNmZ2ejo6MDRqMRhYWF+N3vfoe+vj5kZmZCq9Wivb0df/rTn5CZmYn09HRs2LABeXl5zENM9dwaGhrQ29vL6opTU1P41Kc+hccee4yFSvbt24cNGzZgZWUF+fn5GB8fZ58clUrFctGbNm1Cd3c3Ghoa2GPiH/7hH/CjH/0IPT09LGLQ3t6OH//4xyn3GxmaLiwssOfLxMQEbrnlFjzxxBPYu3cvLl68yAIa1xowb9q0iXlAe/fuxVNPPYWPfexjiEajqKurY/PX/fv349KlS6wgptPpUFJSgsnJyZQKmE6nExs3bsTAwAB7S9hsNtxyyy3c3Cb/m8rKSoZERqNRfO1rX2NZ/5qaGmzYsAG7du1CV1cXc7oA4OLFi3j77bf5N1FO8sYbb6SUBZZKpSgpKeHps0ajwdWrV9HQ0ACtVovy8nL2UCoqKoJGo0FbWxump6exe/duCIVCZGRkYPPmzXj22Wdx6NAhDA8P4+rVq/D7/RAIBOjo6IBcLkd+fj4qKytRVFQEkUiE48ePo7m5OeW3cODAAZw9exZ+vx+FhYXYtm0b2traYDab0d7ezjBWo9GIH/3oR6iurkZtbS0OHjzI0roCgQB/+MMfcMstt6C/vx9KpRI+nw9nz57FwYMH8c477+CWW27BxMQEw+oIqvlh4kOTwZ988kkMDQ2hra0NmZmZmJ+fh91uR2NjI2pqaji5MZvNWFpawv79+9HR0YGenh6u+qanp/GHP/wB3/zmN/Gtb30L3/72t3ms7XQ68eyzz+I73/kOE49CoRCbb62tra1rE//000+jt7eXqzShUIjbbrsNf/nLX/CZz3wGwPuksbm5Ofz+979HTk4OIpEIdu7ciZmZGej1ei5A2tvbMTAwgObmZoZ2AO+bwkmlUrz33nvYvXs3fD4fvF4v9Ho94vH4uooKP//5z7GwsMAyjJmZmfD7/Xj++efxn//5n3j66ac/4Pba0NCA6elpLC8v44033sCGDRvQ2trKxFYixhIEJhqNYnBwEAaDAX/84x9x++23s5sxTXG+/OUvX3dtL730EnfjkskktFotOjs7mZS0tLQEg8GArKwsrKysIJFI4PXXX8eBAwd4FBoOhzExMYGNGzcybGt8fBzZ2dmorKzE+fPn8R//8R8sBXetqsT4+Djuueee667tK1/5CkQiEauHkKLWU089hW3btmFhYQFbtmzhTgmpLGk0GuZurK2tsapIYWEhCgoK8M477+DcuXMIBAJoaWnB1NQUHnnkEUxPT/MI/dixYwgGg/jmN7953bX913/9F9xuN2OJhUIhNm3axAIEN998M6LRKCwWC5NF4/E4XnnlFWzYsAE5OTnweDy4evUq7rrrLrz44ou45ZZb0NPTg+HhYYRCIZjNZvzjP/4jrFYrq6qpVCqkpaVhYGAAX/3qV6+7tl/84hdcuNJka2ZmBm+99Raeeuophp/5fD7YbDY0NDTgZz/7Gb74xS+io6MDgUAA8/PzDNfr7+/Hzp07+f0Tt+LaLghNaCorK/Hss8/i6aefvu7aAOCpp55CMBhEXl4eotEoY1LJVyMvLw/Z2dm4ePEiXn75ZS506urq8Morr6CpqYn9OwoLC/Hmm2/iU5/6FCQSCSYnJ/Hee+9hZWUFR44cgVQqZc15IkEnk8l1L7ynnnoK4XAYKpWK4YIVFRU4ffo0Dh48yIU3NSYeeeQRiMViPPHEE0z8JBO2xcVF/OhHP8Jjjz3Gxmbp6ems6rJnzx4sLCywektVVRWmp6fXPUeefvpp7gymp6fzRfvCCy/g4x//OP7jP/4DRqMRGzZsYDdcMn382c9+xiqBY2Nj2Lp1K3elTCYTIpEIX8gECSS/CLvdjl27diEtLY0L8v8Z//RP/8TwHerOKxQKTE9Po7a2lv1fyAvlu9/9Ln75y1+itLQUfX190Gg0aGhoYHPSv/71r7jllltYTSk9PR1dXV149NFH2Z/J5/MhFApBLpfD7Xbjhz/84XXX9vLLL8NqtSItLY09SIqKinDq1Cns2LGDIQTkSt7Z2Qm/38/mg5FIBJOTkxgcHMS9996L559/Hp/97Gdx7tw57hjSBKG8vBwvvfQSF1AE9brrrrvWXdtLL72ElpYWvhdyc3MxOjqK2267DdPT08yjee+997BlyxbU19czz4Vkvs1mM7q7uxmuIhQK+fcIhUJIpVK8++672LdvH0+4FAoF9Hr9ug28H//4x+z5FAqFoFQqsX//frzzzjvYvn07Xn75ZUxPT6O5uRkPP/wwlEolPvGJT2DTpk3suySVSnH27Fl85CMfwcDAADZu3AiLxcKE9JGREdx88804cOAAXn75ZYyPjyMWi/G06tvf/vZ11/brX/8aiUSCicx+vx8f+chH8NOf/hQPP/ww+vv7sW3bNmRnZ2N4eBhnzpzB/v37GUJGpFmBQIDLly/j3XffxV133YXJyUl2CyeIZGNjI4qKinD27FnYbDZs374dFy5cWJeo/o1vfANpaWno7OzkiVs0GsUPf/hDbNmyBRUVFby3aN87nU78/Oc/Z1d0Mi2dmJhAMBjEZz7zGZw4cYKNeWUyGRobG/Hyyy/jnnvu4altdXU1rFYrbrnllnXfaV5eHk9B4/E4hoeH8c477+BjH/sYAoEAFAoFXC4X7HY7vvOd7+Ds2bOwWCzs55CXl4eRkRHmiRD/iSBpXV1dKCsrQ0tLC/R6PZOzSQ75X//1X6+7tieffBLDw8OsmhSNRlFWVgaHw4FHHnkEw8PDmJubY0jy3//936O7uxuLi4vIzMxk6Wq/34+ioiL88pe/xL//+78jmUzi/PnzmJycxOc//3mIxWIMDw+jp6eHG0MikQjFxcXr3gsvvfQSysrKEIvF4HK5GAZqMpnwy1/+EleuXOFz4urVq7j33nvxu9/9Dt///vfxxhtv8DQ7Go2ioKAAFy5c4IKSvFUuXryI7373u+jr68Pp06dRXFyM/Px8mM1mZGVlrZsnnT17FqdPn2YHco/Hw5zCr3/968xJHh4extNPP42vf/3rLLTy5z//GbW1tdi+fTv7Vn3rW9/C9u3bodfrkZWVhWg0irfeegt33XUX/vCHP6C9vZ0hgDU1NZidncXOnTuvu7Zr4/8nwz5yzF1cXOSxssfjwfLyMiuuLC0tYXFxEZFIBGNjY3jxxRdRWFgIlUqFSCSCsrIy/P73v8ff//3f49y5c1heXkY0GsXa2hoWFhbwve99D3fccQeGh4eRmZmJrKwsnDlzBg6HY91NOjs7C6PRyGTXZDKJvr4+3HLLLTh58iR27dqFwcFBmM1mtLW1MQZucnKSlWFycnKwY8cO/PnPf0Zubi7GxsZYEz6ZTGJwcBBbtmyBVqvFmTNnAICVO1K5mDocDpbTc7lcjEFVKBTo6upiYu7Kygqys7P5chgbG0N7ezvUajUnJ3SBEEGb7OrJ+6OzsxMOhwM+n4+Jk6lIddQxJ7fvZDKJsbExVq6JxWIsn6fVajE0NMTVM43XSJpwYGCAdcMfeughAO97BhBJkWRWyQ3T6/WmJEoCQFVVFf8OSpw+/vGPsxTk+fPncfr0aRQUFMBgMGBkZISxzbm5uUwIf/HFF5Gfn4+tW7ciPz8fjY2NbJDX0NDA7rYjIyMQCoV/U97W4/FAp9MxhImUjnbv3o2FhQWYzWbMz8/j6tWrbO5I0I2zZ88yCTQzMxN//OMfkZaWhrfffhvj4+N8MCwvL8PlcrF/A7kEp4IQAu93OCjBIe6DSqXCoUOHkJWVhfz8fMzNzSE9PR1btmxBTk4O7r//fia/E+aZXL6JC6PRaOBwOHDy5El4vV4UFRVh48aNePfdd9kZ/uLFiym78gAYzx0IBFh+eWBggM0Lg8Eguru7EYvF8KlPfQqDg4PQarVobm6GzWaDx+NhyIXRaERdXR2OHz/OJMfMzEy0tLTAbrejtbUVDoeDpT7tdjusVuu6FwpBq0hmOBgMwm63c/JeXV3NrsNZWVlcQPh8PlitVjidTuTm5qK2thYulwtHjhzBW2+9xaPqa/XkvV4v5ubmGA5G/LD1gkwiR0dH2XArMzMTe/fuxUsvvYTy8nK43W4MDg6yMRN10g4dOoSlpSVW8SITu4WFBRbC6Ovrw9TUFHt8zMzMQKVSQa1W48qVK0hPT1+30CDzMXKvJ65Mfn4++/PMzMxgcXERUqkUFy5c4G+HJs1DQ0NYWFjACy+8gA0bNuC1116DwWBAZWUlq594vV5kZWUxP5AaNKl05icnJ1FbW8sCEWlpaXj88cfR2dmJaDSKm266CQMDA7BYLLwXQ6EQ9Ho9F/ZZWVlobGzEiRMn4HK58Mwzz3AiSr8/FothYGCAp9bA+xDbkZGRdQsNk8mEzs5O6HQ6xONxhEIhnspfunQJjY2NDI+juyASiaCrq4uhv8D7kJji4mKsra1hbm6OpZlJVaupqQkFBQVYWFhAIpHgBlUqgRW73c74cr1eD5FIhFdffZVFBu699168+eabWFlZwYULF7hJtbi4CAAMS2lra8O7776LsbExCIVCVh9bXFxkWfbnnnsOi4uL3ODYtWtXSklPp9OJkpIS9PX18fP+9a9/Db1ej3PnzmF+fh7nz5/nqWROTg5MJhNGR0chFouxceNGaDQa9PT0YGJiAkVFRXC5XKymNT09zYpbAJgsbLfbceXKlXXXBbw/sc3JycHExATnFn6/HwcOHIDBYGBFsLS0NDQ3N0MsFuPChQtob29HMBhkHH9paSl6enpYgpc65IODg6iursb4+DhuvfVWmM1mllYlP5z1Cg2Xy8VNQyJDr62tcfJId5VOp2NJ+C1btrBsrt1uZwXPZ599lrH7O3bsQCKRYKTI6OgojEYj+vr6eMp87NixlNK78/PzUKvV3AQm3k1bWxu6u7uRkZEBn8/HXh6Li4sMK6cCIy0tDZWVlXjmmWdw4MABnDp1iu+UnJwcWK1WNtojB/fl5WU2AV0vyNT2Wj6Sz+eDSCTCxMQE//Pi4mJWY62vr8eZM2eY43r16lVuxMRiMTidTp4SR6NRVFdXY3JyEjabDc3NzUgkEjCbzXA6nSlNP6emplBcXMweUJFIhEU3+vr6EA6HMTw8DLfbjcbGRkxOTsJgMODdd9/Frl27UFZWhnA4jPPnz/N5PTAwgP7+fp58bN68mRUOCdZNBqF+v/9/t9CQyWTcKSWLc5VKhePHj2Pz5s3YunUrYrEYJ/qUrJDZGOHVSZ7x8ccfx9133w2hUIiFhQV4PB5IJBJotVqcPHkSxcXFrBe/tLSUkgehVqsZS5tMJtk/4LXXXoPRaIRYLGYlEIlEwjAUk8nE3hsZGRnIzs6GRqNBWloa/H4/SkpKsLy8jLNnz6K7u5uxy3a7HV6vl4uQVARYciROJBJ8OfT09CA3Nxc9PT0fUJChDUJcBsKrA8CGDRtgs9mYMKfVavnvz87OhlgsRmlpKc6ePcsE6mQymRL3nZaWhsuXL6OwsJA/8JmZGZajIzInkWMrKyuRl5eHvLw89iChzphKpcLCwgJPQEit4NpCaXV1FXK5HHK5nAm164VEIsHo6Ci0Wi2TrL1eL8vnNTc3Ix6Ps/OxUqnEpz71KZw/fx4DAwPweDys0V5YWIiRkRGo1Wrs2rULRqMR8/PzUKlU/Pv0ej2mpqZgsVjYsXi9yMrKgtVqZVUdIvn++c9/RlFRETZt2sT7cHJykqWD8/PzGesZCoUYvz8+Po729nbuDgmFQpSVlcHj8aC8vBx6vZ5d0MkvIdW34HQ6WV1iZWWFE4/5+XnodDpkZmbC5/PB7/djYmICLS0tuHTpEjIzM6FWqz+QGNPaw+Ewvz8iVzudTj44BQIBKisr/6bqFDlC06VK0sDvvvsu2tvb2euDCMgEhaACRCwWc1d3bm4OCoUC4XCYVW5CoRDm5ubYIT0QCGB5eRkikYgTulTfA7nQi8ViqNVqCIVC9PT08NSMeA1KpZKTdfIQCIfDLC0sk8nwpz/9Ca2trUgmk1Cr1aivr+f9ZbVaWb2N4BpEjF4vaKpE+NnJyUl0dXUhKysLxcXF8Pl8iMfjWFhYgN1uR3t7O8bGxpiDQe/WbDZjZGQElZWVLHogl8t5PxcWFmJ5eZklS6mztV5EIhEuIgjWRc7YwPtmXFS8SSQSLC8vM6Gdzl6VSgWlUonJyUkm9hLfg2CHVVVVUCqV7KdBU8tUGvjklAuACfBZWVmYmJhATk4OKz2RXLBGo4FWq+VGCElEK5VKrKysQK/X8zSBlJPGxsawfft2LCwsYNOmTejv74fdbkdFRUXKtWVlZfE5SB1uIuST+hsZa4rFYk52cnJyWHb82g71s88+i61bt0KtVvNZQ7K2RPwncrtCoUgJexCJRPysaKqfnp4Op9MJi8WC3t5eLC0tIRKJ4OrVq9xVJrw4+SXReUFS2tRoIyK3VqtlPt+1ipOpSKYZGRmsJEkyoQRls9vtqKqqgkQiYRl6Mu577733EI/H2WWb5E5JqprUAwUCAYqLi/lbo6ZqLBbD7OxsSj8Ikr6mqVhxcTEyMjJYPKOhoQHBYJAbUiSb/8c//hEAUFZWhtLSUuTl5SEYDKKqqorVGuVyOd+BNNkzm80YHx9n5cBUEPNEIoFQKMTCC8SPvXLlCu655x52qo7FYlhcXMTy8jJLmtJ9JZfLeYp29913w263sxS+QCDAyZMnsbi4iPvvvx+jo6OYnJzE6uoqK2auF7Sn6e5xuVyYn59nyL7b7WYhHmrS0UTS4/FArVZDpVJBJpOhqqoKaWlp8Pl8bPI3Pz+Pp59+Gps2bWLuF6lX6fX6lMp1pFBGsvUkNrSysoKuri6kpaXxnqQGIeWEJDVLXN2JiQlYrVbs27cPMpkMs7Oz6O3tZTla4m8Qxw7A3/QbA97/XuVyOU8gScmPiP9U9NKEMy8vD0NDQx8Q9RkcHOTpMwltkFDGoUOH4PF4+A4ilbxUXjzXxocmgxOj3+12Y2Vlhcks5eXlsFqtnBjI5XIYDAbY7Xbk5uaybCVJ2zY1NbGkqlgsRk5ODjQaDQoKCnDzzTezBC3pPAPgDbhepKenc0KuVCpRUFDAyTbpk1NnSCqVIj8/H9u3b4fdbodGo2H8sMfj4ZGs978dF8m9ePv27TwyIjkx4pek2gjBYJC7pKTOAoAJ8QUFBdw5rKmpYXKgUChkNYesrCxUVFRgcXGRJ0c0HguHw8jIyODxNhHHpFIpHA5HykIjkUhAIBAgPT2d/0MyvpR0SqVSJpbThqVDORwOIy0tDUajETKZDDKZjCUdTSYTLBYL9Ho9K0ORNJxAIOBnuV5cq85EXQRSwgDAJE4ieK+srECj0fCUbXFx8QMyt52dnaxOlpGRwe+QzCcnJiYwPz/PE7pU3BaSPCbyFSlbUdFCLvS1tbW44447EA6HeRwukUhYgraqqooJnenp6aipqUFFRQW7jQ4PD0OhUEClUqGqqgqFhYVwOBwpyWGrq6vIzc3lfU8SemVlZbDZbLxvk8kkRCIRrFYrSkpKuOjz+XzweDzIysriYnJiYoKl7giXTvKPdGmJxWLI5fKUXatr9xyR8YhUl5OTA7lcjuzsbBQUFLB/AU1Pent7WaWFJKBtNhuMRiPD1+gw1Wq1TI4jadZrC99UQdwj2usEA1peXuYuq0QiQU5ODvNsSC5UKpWy+ofZbObChiY+ZLQGgEUqyIeFisj1gp4XfXNqtRqJRIKhj3TJ0jNYXl7md06S0EqlEvn5+aygYrPZ4PP5mPxaV1fHRZRUKmUhAZKXTBWkFJaZmQmZTMbTqtXVVT7f8vLyUFBQwARzn88HnU7Hk3KCzJAEOjWt6JsiBbGFhQW+LP+WqAQ1mQiOEY1GOYFeXFzkKTj5FWVkZHChRWc2Sa7SbwPAe5FMwcjXg0QIKioqeGq3XpAcdlpaGisyKhQK1NTUQCKR8P6JRCJchBQVFfHeCQQCCIVCUCgUjPFPJpPIz89nL4L9+/cjFoshOzubO8JCoRACgSDlcyOXe2p+kXR0TU0N+vv7WbZdo9FAKBQyzDAej6Ourg56vZ6LGhIQIWltgrFRkaRSqZCbm4vW1lbU1dXB7XannMQDYAiuRqNBSUkJF7Ik+pGXl4eysjLurKenp7OHwOTkJLq7u/kOa29v5zOR9hyp39XU1CAQCPDzIine9YLI8tSQpSQzLy+PnwchKGQyGS5fvoyKigqeUJKXhlgs5gloMBhkFT46+4iTQHDhgoIChi6nCpLEp8KWSO8EAyeiPJ0tOTk5iEajqK+vZ0dxtVrNjYtrpYhVKhWEQiFmZmaQSCQQDAYhkUj430ul5EhnDOU1dFdS05dsCORyOZRKJXubkXkcTVdIQSuZTLKBqEAggN/v5zNNJBJhbW0NEomEc5BUhS39+aTaRRL51PwuKSlBVlYW722v14vm5makp6dzY5ag7olEAlVVVexDQ80suovJqDSRSPCdkZWVte7aCPZJSmikatrc3AyPx4PMzEzk5eWhuLgYer2eOYHA+82llZUVmM1m5OXlIRQKoaKigqdEJF2vVqsxNzeHaDT6gaYd8cE+THzoQiMYDHIBsbS0xHrud911F1djBNMoKytDMBiEUqmExWKBSqVCPB7n0ej09DQeeOABVipQKBQoKSlBY2MjfD4fX940ESEJsPUiHA5Dr9fD5XLxeF0mk2Hv3r1IS0tjCTeVSsXdH+qMtbS0oLm5mdWeVlZWUF1dzWZ9QqEQLS0tuP/++yESiWCz2bgIuVY1ZL1YWlrihJh09YmsnZubi5aWFtTW1qKpqQn19fUwm83Izc1FRUUFSkpKuHspl8sZI0pFF/FEiCsRiUTQ0NCArVu3YsOGDdBqtSkvutXVVRiNRoZuUXdQo9GguLiYC51wOIzFxUUsLS3xQQWANaBJRpZG+9QNtVgsyM/PR1ZWFnslkL8EdTXXCxrZ0wWQnp6OcDjMRFyxWMy8BcKVk5kOHShut5v1vcnUjdZNxRl1TIeHhxEOh5Gbm8seDusFHQIktUrvqaWlhfX6FQoFtmzZgvvuuw/hcJg1pwkXWlZWhtbWViwvL6OzsxPZ2dkwGo0oKyuDTCZDQ0MDlpeX2a+GTDKlUmnKCRpNosLhMCdrWVlZOHDgAH+jVMQXFRXxgQG8D0EcHh7G2NgY+ykUFhbC6/Xyge1wOFBeXv4B7xXS8L7Wv2a9ILlmSqyoAD18+DAaGhrYK4IaAiR1PTQ0hF27dmHr1q0QiUR8QVLiTHr8arUamzdv5ukQqTqRXGaqhgURmTMyMhjj7vf70dTUxFM1uVzOBnY+nw+VlZWYm5uDVqvlAl+hUMBkMuFTn/oU48EJ719QUMDSnGTOWFBQwGpj60U8HodGo2E+m9Pp5N8bCoXY34ekl4VCIV86BoOBJ6CkXtfR0fGBRD4SiaCkpAQLCwsfUDOqqqriJCfVO9VqtTytpjOWeAsqlQoKhYKVy+i8B96HF2k0Gp48UaE3MzODjIwMllMlA8mZmRk4nU6egvythkV6ejrvo0QiwYVuZ2cnk2Ppm6yuruYmhM/nQ0VFBROmhUIh0tPTuVO4tLSEcDgMg8GAXbt24erVq+xFUlpaisbGRlZQXC9isRirN2ZkZHACS/s3JycHRqMRjY2N2LJlC0OkSCaYYJ6xWAz9/f0oKiriJIXO5urqapal9fl8EAqF0Gq13CxZL2jf0nlNuv6dnZ2IRCLIy8tDRUUFampqUF1djZKSEt53RP6ura1lsy8yFaRzm9ALVBT7/X7+hqjpt16QrLtQKGS1uYyMDBQVFbEMKxkfkgIhPYPMzEy4XC6Mjo6yKdq+ffsgl8uhUCi48KOpPIkW0J6trKxM6blAMJja2lpUV1ez6tjhw4eh1WoxMTEBr9eLnJwclJaWctO2s7MTu3fvZtgieVPk5uZyoi2TyRiympeXh0QigUgkgs7OTm7SHjt2bN21UQJ+LewvMzMT27dvRywWg8ViwezsLCu5CYVCbNiwAfn5+ejs7ERdXd0H+CVjY2PMS+jt7YXX68XevXuh0+kwPz+PQCCAoqIiNDc3Q6VSpcxFQqEQF1XEGauoqEBFRQXkcjmKioqQn5+P0tJSVFdXY3p6mmGr+fn5yMzM5ImdzWbj+5k8SaLRKLZu3crS9fF4nMWJrvUwuV4Q3MxkMvEdJxaLsX37dtTX17MMtFwuR21tLZRKJQwGA6uWqVQqLnBI0fDy5cswm80QCoUwGo2Qy+Wsfkr0g3g8zkbO64XP5+NCg5ppcrkc9fX1AMDTzaKiIobtRiIROBwONDc3szgP3VE0sSKD6dzcXGzYsAGnT59miWuZTIb8/HxotdqUin/XxocuNHQ6HXp6ephUS52Uxx9/HPfff/8HDF2sVitaW1vxl7/8Bdu2bYPD4UB+fj6Ki4vx0EMPQalU4o033oBWq4VWq4VKpYLD4cCtt96K7373u8jKykJ2djbGx8dht9vx0EMPpVQ9uRbvS/CGzZs34+jRo3yIJJNJLCws4MKFCxCLxXjuueewdetWjI+Ps0FJJBJhQuz9998PjUaDeDyOzMxMPPXUU9i3bx9GRkawd+9ebNy4EXq9HkajMWXyUl1djUuXLrH5FWHhp6ensXXrVnR1dbEK0bPPPoucnByoVCrcd9996OjoYIKpy+VCdXU1KioqcMcdd7DHgMPhwMTEBPbv34/XX3+d9aELCwvx4IMPpsThyuVynD9/nic9xLUxmUysppNMJuHz+TA+Po7du3fD5XKxUzQZr504cQKtra147rnnsHHjRhQXF6Oqqgr79u2DSqVCQ0MD3n77bVRWVrK5EMnqrRekKHHw4EGUlpZidnYWt912G3p6elhLnzD7YrEYH//4xzE9PY0DBw7wNKG+vh4jIyOoqanBn/70J2zYsAEGgwFFRUWoqqpCf38/qqqq8NJLL+HLX/4y9u7di+LiYnzxi1/8m2Nes9mMYDAIm82G+fl5tLW14ezZs1hYWOBk/b333sPTTz+N7OxsHD9+nAuGO++8E3v37sWZM2e4uOzo6GBsqsFgwFtvvYWvfe1rGB4ehtVqxdWrV2G1WrFz586UZoLJZBJDQ0NobGxEbW0tS1r+9a9/RUlJCXp6epCZmYm6ujrI5XLs2rULr776Kis7ra6uorOzExcuXMBDDz0EjUaDf/zHf0RNTQ0UCgUqKiqQkZGBT3ziEzxhmpubY/xzIBBYd22050gdQyQSwWKxcFJVXFzM3iMOhwNnzpyBVqvFxYsX0dbWhkuXLsFisSAjIwNvvvkmdDodurq6GOdOk594PI67774bi4uL6OjowI4dO9DQ0ID8/HyEw+F115aXlweBQACj0QitVsuws2eeeQZbtmzBhQsX4HK5UFJSAqPRiG3btuHFF19ERUUFJiYmoNFocNNNN3FDYGxsDJs3b+YJm0QiwS9/+UscOHAA2dnZ7FczMzMDmUy2rtgF7bm0tDSUl5cjmUwyzlsmk+Gmm25CNBplqOVrr72GjRs34rXXXoNMJsOrr76Kq1evIhQK4fz581wofexjH+Pki6CIa2treOWVVxhvTN9sf3//umsjjwvi3lgsFtTV1aG7uxstLS0YHx/HxMQEbDYbAoEA8yMUCgWGhoYgFov53BUKhbh48SL27NkDpVLJ6nXnzp3DoUOHIBKJsH37drjdbrz55ptsuLVekE8NdSPlcjm2b9+OtLQ0bNmyhZtmarWaDf1OnTrFcB6j0Yja2lpYrVZUVlYiFouxoks4HEZFRQV+8IMf4KGHHkIymURdXR1GRkbw/PPPQyKRpFR2amhoYHlJMvmkTnpbWxuOHz8OqVSKlpYWFjN4+eWXcdttt0GhUGDHjh3YvXs3jh49in/8x3/E4uIiSkpKeJpfU1ODX/3qV9i/fz8SiQQTk6kjOzAwsO7aDAYDd5GJv7a6uorTp0/j0KFDeOONN9Dd3c2Qkk2bNuHxxx9HYWEh+5QQdpvggySSIRKJsGnTJvzqV79CRUUF+vv74XA48Je//AWvvPIKNm7cmNK3BXi/y7x161ZoNBpMT0/DaDQyd+7y5csIhUJwOp04f/48nnjiCRQWFrKpKEn/vvzyy9i9ezeWlpbQ0dHBsCVSEbr//vvx9NNPw2AwoLS0FPn5+di4cSNGR0dT7rfBwUH+bsgQz+PxYNeuXQzPcrvdcLvdeOyxx1hVMi8vD01NTdiyZQtCoRC+973vobi4GGVlZSyMo9FocPHiRezatYvhu6FQCMlkErfeemvKqUEoFGJ/H+JzNTc3Q6PR4Pbbb8eJEyegUqmwc+dOqFQq7N27FxMTE2hoaMBjjz2Gl19+mRP3e+65BzU1Ndi3bx8TsWtqaqBUKvGjH/2IDSZJ0hoAXnjhhXXXJpVK0d3dzdMP8sr6zW9+g3379mFubg6hUAhVVVVoampCZ2cnJBIJKisrIRAIsGXLFtx2222wWq14+OGHuaD2eDzIyMiA0WjEb37zG9x///1wOBxoampirzcA6OzsXHdt1GAm/6dwOIxDhw5hamoKCoUCFy9eZFGTtbU1bN26FUNDQ5BIJHA4HEgkElAqlexV9PTTT3NBLRAIsHXrVuh0Ohw6dAhWq5XFSAj2mIrLSs2jgoIChrBu2bIFx48f5++eTI+vFV4xGo04d+4cQqEQmpubsby8DIVCgXvuuQdFRUUoKSlBJBLBhQsX8NJLL+GLX/wi8vLycOjQIWg0Gng8HtTW1qZsyF4bH5qjIZFI8OCDD2JoaAhSqRTNzc2sMvXGG29gYWGBjZUqKyuZlDg1NcW4RUpiCN7R39/PH4rX68VXvvIVfOMb34BEIuGpCHUnUuFJFxcXcdNNN2F+fp5N6iYnJ1FSUgKfz4fFxUU2eWpra2PH7IyMDFaD6ezsRFlZGR5//HH84Ac/wPPPP89jOupe/fGPf0RdXR0uXLgAjUbDRiypktJgMIiOjg7YbDakpaVBr9fDarUiGo0iEAjwwWy1WpGRkcGwgWAwiPb2dnahNJvNOHr0KJ566in89Kc/RUFBAex2O4aHhxlvWldXh/n5eSwsLHDXP1XiF4lEsGnTJkQiEbjdbohEInYl9/l8zI+gTvMrr7zCmviNjY2Ymppio7of/OAHiMVijDcMBoOwWCwQCoXQ6XQ82jUYDJDL5VhYWEjphLy0tAS9Xo+VlRV2Q19YWPjA9EilUjE5/pe//CWOHDmCp556iicgZrMZly5dQkVFBbZv3844WZq6yGQypKWl4fbbb8fzzz8PgUAAnU6H06dPp4SxWK1WVFVVwe/3Y35+nh2VyXuBzCNLS0tRUVGBrKwsHDlyBCdPnsSbb76Jubk5VFdXM/TG6XTi1KlTCAaDjKMPh8N4/vnnGWozOTmJ+fl5lJeXo62tbd21ZWZm8sRDp9MhNzeX1ZJGRkbYAGl2dpaTNKPRiBdffBGZmZkIh8Po7+9HMBjE8ePHUVBQAL1ez0TEUCgEjUaDo0ePYvPmzZifn0dpaSlWV1cRDAb/pmGf0+lEZWUlTCYT4//tdjsuXryI7du3Iy8vD9PT08jMzMTOnTt5RP3tb38bMpkMU1NTkEgk2LhxI2pqatiFOBQKsUJXU1MTcyFItYMgdqkSGCKZ2mw2ZGRkoKysDGKxGIcPH4bdbsdtt92G3t5eTE1N8TSgtrYWFy5cAPD+GHtxcRFisRherxctLS04f/48S23a7XaoVCq88847rKpH6iMrKyt45513sHnz5uuuzW63o7i4GKFQCAaDgSUjCRNMhk9ZWVloa2tjMnhRUREaGxtx9uxZ/P73v4dSqURNTQ2Gh4exfft2JswT5nvnzp2818jxPR6Pp7zsCMtNbvFra2sYGhrCbbfdhrfffpu5DuQfMDAwAJ1Oh+eee44np9nZ2XjggQfw/PPPY//+/Zifn4fFYmGn7I6ODvzyl79ES0sLFhYW2M2ZIFHrxerqKsrLy/kcXVlZYQ8kalbQtCMajaKiogI7duzAyy+/jP7+fp5kxWIx/PCHP8S//Mu/wGKxYN++fcjIyEBPTw82b96Mb33rW7jrrrtw7NgxNvKkpGy9GBkZQXNzM+bn57mTu7i4iFgshn379qGpqQlzc3Po6emBy+Xi5sGZM2fg9Xp5CrNz506YTCYUFxfj1KlTKC0t5eZQeno6zp07h8LCQiiVSu7uLy0toaKiYt212e12pKWloaKiAkajEXNzcxgbG8POnTtx5coV3Hnnnejp6UFfXx9aWlqQnZ2NrVu3YnBwEFevXmVDOalUirm5Odx222148sknUVFRAZFIhHfffRfV1dV48sknsX37dly9ehXV1dWQy+V48803U8KTCKLX19cH4P17ICcnBwqFAsFgEEajkad80WgUn/vc57C6uoqOjg40NDRgZmYG4+Pj2LJlC5u3TU5OoqysjPkepCq4detWNvokEm8qY00qjglJMTk5ienpaRw5cgROp5MnfkRMJt+RP/zhD8xTzc3NhU6nw4ULF/DII4/weZFIJDAyMsIGpVarlSe5lKukiuzsbHg8HqSnp6O8vJy5U0KhECdPnmRj2YyMDGRlZWF8fJzlZG+//XacPn0azz77LGprazEzM4P+/n7827/9G9xuN2ZnZ1k04YknnsDnP/95DAwMwOv1wuv1IhgMrqucBLyfJ23atIm900ix7JFHHsHZs2chl8uxuLiIV155hRvUdXV1ePnll7kIqampgdfrxWOPPYbPfvazWF1d5QkNTTqOHj2KwsJCvP7661hbW+Np63PPPYcvfvGL110bCQZEo1GGwpHYgMPhwN69ezE4OIjx8XEUFxdDp9Mx2mJ5eZk9a+rr69Hd3Y0NGzawcE53dzdsNhv0ej3nrJmZmdwMWFxc/Js8W5KTJkTRu+++C7FYjNdffx1yuRyBQIDz6IaGBrzxxhtYWlpCXV0dSktLodPpoNFo+FuOx+MYHBxkM79NmzbhxRdfZONZavCaTCYYDIaUDTKKD11oZGZm4jvf+Q5jP0OhEIqKirjjf+DAAR4tqVQqtLa24te//jWTcsiLgQjjGzZsQDweZ9fsSCSC8fFxNDU1MZSDlDiIWLVeyGQyXLp0iXG+JL1Ljqr33nsvjh49yrrCZ86cYXOYlpYWyGQyuFwuKBQK+Hw+PProo9DpdCz1Rqx9wnuS2hJh/lONBCnBIB8RwkOSFwV13ktLS7G8vIxkMolNmzZhbm6OlRpqa2shkUiwZ88eAO9rw+fm5qKjowNKpRI//elPccsttzBelqrWWCz2N82s/vrXv6KmpoZJ3ESwLisr+wCxlMZohYWFOHnyJBYWFhimdf78ebS1tSEUCqGrqwsqlYoPf6/Xi9HRUQSDQSwtLWFsbAxisRglJSU4dOjQumsrLi5Gf38/c0VCoRDOnTvHBQ5N2Obn5xnucv78ee6YEqGts7MTTqcTXV1d7AJOju9qtRrnzp1jp2yLxYKioiKWe1sv5HI5enp6sHfvXlRXV8NiscDj8WBychJKpRK1tbWYnZ1FV1cXzpw5g9tvvx3l5eUQCATckaduEJk2XTs2f/311zE/P4/Pfe5zCAQCyM7OhkQiYS5UKhihTqfDiRMnUFpaymZKZJao1WoRi8UYl38tLpy6vmKxGHl5ecxfeeONN1BZWYldu3bhIx/5CHp7e3H06FHcdtttDKeYmJhAIpHAnj17Un6nAJCbm8ta5llZWVhdXUVTUxP6+vrQ3d3NOGWr1YorV66gpaUFq6urbHZJe7uoqAjHjx+HyWSCWq3GoUOHYDQaMTMzg4WFBQSDQZSXl+P06dPsOq7T6VKOobOzs9HX1wetVovc3FyEQiHY7XY8+uijfClTQjo0NMQeJR6Ph5P9RCKBj3zkIzh+/Di6urqwadMmtLS0YHJyEmNjYwgGg6xpvnHjRnYez8rKSpkoXKsORxetRCJBIBDA6OgoYrEYcwiI80GiEgsLC2hubma4KilQvffee2hra4NarWYlG5qMTE9PQyAQIDc3F9nZ2Zifn193bQaDAQKBgBseNEUjH4msrCwsLCygq6sL09PTOHz4MA4cOMAmeAQHc7lcEAqFeOONN7B582bGGU9OTsLhcKCsrAzFxcWwWq2wWCy8D1IFyVcSxyUWi6Gvrw9ra2tsxDYzM8MGjHQp07cyPj4OALjllluwceNGPP744zh48CAbil65cgUlJSWorq5m1+7u7m6IRCLs3r07ZVKq0+kwPj7OPKWVlRV4vV6cOnWK+R0mkwlTU1OIx+O45557WLWOmmVk/OhwOPDSSy/hgQceAPA+ZIyIyHV1dYjH4yx4QcVWKjz/4uIiw1kIpmM2m/Hss8+y51IymURmZiZUKhXMZjM+8pGPoKqqCpcvX2a+244dO+BwOPDee+9BJBKhvr4ePp+P1biI2E/QHYJqUA5wvaBkqqCgACKRCMFgEOnp6dixYwd7y0xMTODKlSsIhULYtm0b+vr6mHum+m8X85ycHEilUhw9ehQ1NTUoLy9novHAwABefvlldHR08F2/uroKqVSaEmZDyedrr72GiooKtLW1IR6P44knnkBFRQW8Xi9LOZNJKU1+qqqqkJ2djdXVVX5+x44dQ2dnJ6qqqli0xOv1YnFxEYcPH8Y777wDkUgEpVIJt9ud0kywuLgY0WgUZrMZDQ0NKC0txdGjR3HlyhV85jOfwcaNG5mTR8IGH/3oR/HlL3+Z5a337NmD3t5eVFRUYGZmBseOHUNzczPq6uowNzeHwcFBFBUVIS0tjZWkSktL4XQ6UxbdQqGQVSlJvjsvLw/z8/OoqqpCfX09nyOULxoMBoTDYezfvx8zMzN49913cfDgQVy8eBGf+tSnsGvXLuTn52NpaQk9PT3wer04duwY7rzzTqjVagSDQeY77dq1a921ZWdnQ6FQQCgUwuv1YnZ2Fi6XC+Pj44zaKCoqgt/vx9tvv43du3ejpqYGNpsNKpWKp3jZ2dmQyWQ4ffo0NyGCwSDvGYKJjoyMMCeSVAPXC8qxiOunUCjQ0NCAkydPoqmpCVqtFj6fj7m0ZWVlkEgk8Hg8fMcSAoWmIxaLBTU1NcjKysLQ0BCefPJJ3HnnnTh//jw2btyIYDAIh8OB7OzslGiea+NDQ6csFgt27NgBjUaDwsJCNDY2Yn5+HjfffDMnK4RDKywsxNjYGHbt2oX09HTG15WVleHee+/lB22z2ZhkRjAZSoIsFgvy8vK4wjp58uS6awsGg6ipqWE+gVAoRGVlJfbs2YO8vDy43W7k5+ezBr1QKMT+/fuRmZkJuVwOn8/HiYhGo8G2bdvYEbWhoQGFhYU4cOAAY5evJUgSAW298Pv9yMjIYJWU7Oxsxs4WFxdjdXWVpdWIHByJRDA1NYWNGzeyalNZWRnjO0nu1O12Y2FhAd///vextrbGPhDA/+NPpMJWr66uYufOnYzLUyqVLG87Pj4OkUiEcDjMilljY2OYnp6GRCLBjh07+HDYunUrRkdH8bGPfYxVl6qrqxGLxVh2k7CVJBV64sQJlq69XiwsLKClpYWxkdS5IZgBcSFIOSQcDjOGtrCwkMecer0earUaf/d3f8cGejk5OdyZGhsbY0xuU1MTKioqYDKZUhIlg8EgKwhZrVakp6fzcyDiMQDU1dXhjjvuQFdXFyQSCc6fP4+dO3eioqKCOy02mw0VFRVwOByYm5vDwsIC1Go1HnjgAZhMJjQ3NzMWNh6PQ6lUpjTwsVgs2Lt3L8P6Nm3axN0ekn41GAwwGo3YsWMHzGYzNBoNF660Pzs6OhAMBrFr1y6IxWLuhqrVatx9992Ynp7mkXJRUREqKyvR09OTUhELACcfQqGQFSxIdnV0dJTJc01NTbjnnnugVCqZAFlbW4u0tDRMT08zHvX222+H3W7H4OAgZmZmEAwGsXv3bsZUb9u2DTt27GCDP+JqXC+ulfSkTlxxcTHuu+8+ZGVlYc+ePejo6EB7eztuueUWLviVSiXuuusu7NmzB5mZmZicnER2djYKCwtZDlej0aC9vR3f+MY3GIridDo5aaP9vF6Qqh7h20mkYtu2bWyoRVNWEgPYuXMnGhsb8fWvfx07duxgPK/b7UZzczMrr1itVphMJlZruZbHE4/HMTIykrL7HQgEEIvFMDk5yZw1r9eL9vZ2rK6uIi8vD0ajEbt378bDDz+MrKws7ooR14ZI2kqlEg8++CAWFxdhsVggk8mwYcMG1NfX8/e8urqKkpISlJWV8RR7vSCcs8ViYXfcRCKBvXv3cgfSYDAgPz8fVVVVUKvV0Gg0nExt3boVpaWlDPV6+OGHsby8jNnZWahUKhw8eBBHjhxBRkYGbDYblpeXsWXLFnz0ox/F8vJyysJ7fn6ejTeJLxIIBLBr1y4+jzo7O3HXXXfhq1/9KpxOJ2699Vb4fD5WVCPz17W1NTz11FPMl9Hr9azKdPbsWUgkEjYGu/nmm3mfrxdCoRANDQ0YGhpCV1cXnE4nkskkDh48yIaaZCAnEonYBf6VV16BQqFglUGr1QqPx4OKigqsrq5ibm4OVqsVcrkchYWFzDehM538tlJBHN1uN9rb27mhZTAYWBCBJlfj4+MIBoPQ6/U4fvw4T6rIn6K/vx9yuRwnTpzAvn37mG/n8Xhw6tQpNo2USqWc9BMXkiRerxfBYBCtra2cbLtcLiQSCTzwwAPw+XxobW1lX6jW1lb09/ejqakJTU1N0Ol0SEtLg1arxd133w2VSoV7770XExMT8Pl8PKUqKirCzMwMNwClUilisRirAq0XZEpJEKrR0VHIZDJ84QtfwPLyMoqLi1FRUYEDBw7gX/7lXzA3N4cTJ05wI6ytrQ0lJSXYuXMn1tbW8KUvfQnDw8MYGhrC9PQ07HY73xvj4+PcSC4sLIRcLk9ZBJEy1erqKnNF4/E4dDodTCYTo2BIypWEPkhsgBq4ExMTkEgk+O1vf8sKVTqdDtXV1fjRj36ETZs2oampif3WZDIZnE5nSlQKnYUWiwWhUAh5eXmQSCQM38rOzmZ+59///d9DJpPB6/VieHiYm0KhUIgnP/v378f09DQSiQRycnKQn5+P+++/n+9Buos3btzIUMf1gpoGVHyRRUFxcTEXMeRAnp6ezggEjUaDTZs2IT8/Hz6fD1KpFOnp6Xz22u12RKNRyOVy3HPPPbDb7Th06BDMZjP0ej3DT1NB9a6NDz3RICUVMiIym83weDxssrW4uMiXSkFBAXp7e5kYQ0RsuVyO6elpZuETA560lok8Nzc3h9XVVe5AkH7veiGRSNj7gFSJqOghdRGSgVUqldzdIkMUIoGSy6VGo0FZWRm8Xi/8fj+TV4mzodfr4fF44HQ6kZeXl5I/IpVKWQY0FAoBAGpqatDd3Y09e/bwBUPKXFVVVRgcHORxLY35l5eXceDAAT6kqSChpLmurg7Dw8O8sag6TeXxEY1Gsbq6Co1Gw+RorVYLm82G6upq2O12BAIBvhTIYK2srIwVZQjXTZu3rKyMO24k7Qu8TzQmtbGcnBxotdqU2tWkikPkTKFQiKamJi5ahoeHIRQKUVFRgaqqKu7CBgIB5nFkZGTAbDYz/loqlWJychJqtZplL7ds2YLFxUWGOhAJMVURRGaF5KoplUrZ80MqlWJ6epo710VFRSzXSuoUGo0G6enpLAlKyk+UPBcXF7Opn8fjQUFBAeuBh0KhlN1lKuDp8vH5fKzeUVxczH4cpCZTV1eHpaUlfjcA2DCQyNQEYQsEArBarRAIBFhdXeX10/uhAy9VpKWl8f+POswkQ6hUKj+gMCKRSFBYWMjQI1LRyM7OZj17jUbDvAWHw4FkMom0tDSUlZXhzJkzLH9J3jV/S96WFGYIS00dxOLiYpb3JGIpKRq1trZCLBYzmZXkSQ0GA3/X10qobtiwgVVeiJRLnhfrRXp6OtbW1lggwefzQaFQIBKJwGg0Ii0tjUny4XAY2dnZMJlMAMDTWq1WC7PZjPLycoab+f1+Jv7Nz8+jsbERAwMDDLskUQ+NRrPu2ggHTKpbPp8PVVVVAN4n7k5NTbF6E5Fce3t72RRSIBBAIBBwd5sM74h8mJuby+TZ2dlZFiYgH41UsC5KrNbW1hAKhVhJzel0stkfSYzTPUR6/vTN5uXl4cqVK4jFYiyNSr8nEAjg4sWL2LFjB7xeLyvQpaWlset5qiDp2mQyydBJt9uN6upqzM/P8z0llUpZRIN8MEgVMBwOs5Qr+Q0EAgHeV6TORfeCTCZjSeb1gtS/qBEQiUSYx0hcFZVKxcpKHo+H/TQMBgMKCwu5ARAOh2EymXhKTr4np06dAgAuzPV6PTQaDYLBIKxW67pro/1KSQ4pj10LWyTZYKlUioaGBlZ4JK4UyZFSA4Pe98rKCgoLC5GRkYHc3FxYrdYPyPzSxGG9UCqVMJvNfH8EAgHm6ajVajgcDlY2oybYm2++CbPZzGo+iUQC09PT2LFjBwwGAyoqKpBMJrG6ugqFQsE+DRcuXEBNTQ1zPui7WC+i0SgL5ZCwAXXaRSIRzGYze7msra1xE7axsRE5OTlQq9Xw+Xy4ePEiDAYDT6hIrUupVLJ0P527ZO6q1WpT+t0QXIgmRyR0QfKw3d3dnHALBAI+y+huIq5ZNBqFSqVi/wlqCFMTgSZgpECWmZmZsokCgAvMrKwsVk4j5S0SSJqdnWUuSHFxMVwuF4uKkHDFysoK31+VlZUAwEgXoVDI3JS1tTX26aivr08JMSe+TVFREZxOJ4aHh7nBLBKJYDKZIBKJOL+h78/hcECr1UIsFrNiJhXBJMFMMvbEl6VvivxXCgsLU+Zw18aHnmjQmF4gECAQCLDcFXURLRYLIpEIk848Hg9CoRDjzUntg0bFJpMJOTk5rJYiFotZ1o7woQ6HAwsLCxCLxSk7Q3QpkvoRbSa73Q6xWMyMfJo8UNeDiOP0UVGHkpSy7HY7LBYL1tbW+OAmhj5NE8hHYb2gA4I+OI/HA6FQiNXVVS4wSOmC8NdEiiSPD+I0VFRUsGMjmW4RjIcKHjI5JFncVJj0SCSCcDgMuVzOzp4kK0uHDx3KIpEINTU1bN5E70in0yEQCPDkRS6Xs+yuUChkpSoyK6SLt7q6OuUmvVb1i3gZZO5VUlICq9XK5nRE8HM4HFheXsbg4CDDMEj+c3BwEFKplF1Bw+EwFhYWoNfrYTabuVilvZjqnVKCTIpIBCuz2+3cMaNC0OVyQafTwWKxoL6+nlUfyGTI5XJhcnKSFbBI7WhsbAwymYzx/1QERKPRlGRwgUDA357T6YTZbMba2hrvXZfLxYcNTRAikQh/Q5ScuFwulj2l/U5Gl/Pz81wwkJFiJBJhiFuqIDNCOnxpSkUFzeLiIhdUNpuN32EwGMTY2BgSiQQf7lKpFDabjZsDJGlIycHU1BQnYB6PB7m5uSk7fqQmQs0PKqSJ22K321lZiMz9wuEwcnJysLS0xBcYdfivbX4Ql+j48ePIzs7mhGBlZYUbEam4BiQfq1KpmBsWjUZZtpEKDZKXNhgMGB8fx9raGkMMaW8UFRXxbyTfgLS0NJhMJjaYIplI8tBJVaBRkURdclLIIu4B8Zh8Ph/cbjcX2eRbEgqF+Izwer3cDKAkmjhQGo0GCwsLfDFSMZ8qmReJRHxpSyQSNhidnp6GUqlEIBBAPB7nZC0YDMJsNkOlUnFnXSaTwe/3Y3V1FePj48wRIPNagtOurKywis3y8jK0Wm3KpoBEIuFzlGBblCRlZWVhenqa5eRnZ2chFAr5mwuFQiwdarfbEY/HceHCBcTjcW5Q0L6pra2F3+9nCCE1IlLtN5FIxE02KlByc3O5+KHvKCsri6VliTdC5mk0PaICT6vVcnEWCAQwPDzM9ydBIknhLNXaaG/+T5n1ubk5CAQChlLRn0dmeA6HgyEqRUVFfO4T6sBiscBut7MqV2lpKdxuNzIyMlgNj6Di6wU9GzLXJVWp6elpZGdnY3l5mV3tE4kEtm3bhjNnzvA5QN/5zMwMCgsLMTU1hZycHD6LiDPa2NjICmOk5kgeCameG03vry3WiR9KHkBzc3OYm5vjM9VgMMDr9cLpdGJlZQWjo6NsVFxYWPiBZhN5X1Fe4/F4sLS0BJFI9DdJzXSGXWvS6/P5+I4m08+VlRWWHhcIBFhZWWHRCKVSyTwfanqQBO7MzAzEYjE3YOj8KygoSFkEAeCGDCFYiF8olUrhcrngcDjYyJryO+JDksQ3ABQWFjIkUSwWs0EuceSmpqaYm0HeW1NTUynfqd1u5xzN6/WyFO+1xsvUuCVIuc1m40YDWSFQDkcNSfI6Wl5eRlZWFjesqDlI996HiQ890QiHw9yJI31i0tUn3LjH42FiIfkuyGQyhMNhBINBlgU1m824evUqHn74YcYRk3ZvT08PV6F0CA0NDaXkGhB2MhQKcaIkkUhgs9kYxxmJRPhyTiQSrGpFUw9Kup1OJ0ZGRlBdXY3MzEyuSklNQiaTsYMzANhstpQ6x6QWRZVqIpHAmTNn2L2XPnTqkup0OiZrWiwWnkjEYjGcOnWKu2kAmKS5srKCiYkJ1kAm4yORSASj0bju2tbW1thXwOfzwW63w+FwoKioCBaLBa2trXzxkjN8ZWUlzp8/D5vNhtzcXJSWlkIgEGBiYgJ2ux0tLS3IysrixJSkT2tra5kYBYBFAFJFUVERd3xXV1cxMTEBj8fDOuA0npyenmaoEHWTqLCkw+7KlSvYvHkzE8tJy/3dd9/lfUzdBRqTrhcymYxlD4kf8uabb0IgELBKBF00drudC2Dq/NF4eG1tDd3d3XwIymQyVm1bXl5mTsTc3BwXj6ngNcD7CYLBYIDD4WC4ntvtRl5eHnp6elhm1el0wu12s4qYzWaD3++HQCCAXq9nNbGtW7eyE7tMJmNvHBpXUzFCCWqqBIH2XH5+PkMYyYiRig/ynaEuaCAQYEIpXbRFRUUwGAzIzs7G7OwsNmzYwJKeDocDQ0NDUCgU6OjoYDUfuvhSdUoTiQTq6+uZZEn66YFAAIuLiyxPTJ0zcpudnJxEIpGAxWJh0YnV1VV0d3dzR5lgKkQCpfPpWn+PVAoeJBxBz311dRXT09NcsBcUFEAmk7E3BnGCcnJyOHHw+/2c8NP0RCgUIhgMYnl5GWlpaTh58iQUCgUnskKhEBaL5W/uO5q4KJVKJBIJTExMYGlpiRs/NGanfUyJx9zcHPLy8ljq1Ov1MgyE+Bd2ux0DAwOora3lTjQRYOnPXi8o6SS57PT0dFy5coXlZzUaDZuV0oSUzojV1VVcuXIFRqMRBoMBc3NzLI+qVqshEAiQlZXFsEGLxQKXy8VJsNVqTTk1EIlEUKvVPDmiTmZGRgbLF1Oh5vf7UVdXh7KyMhY6IG7GtdLKLpeL5dAjkQh7MHi9Xi70gPfv81SwWsKVRyIRKBQK/tai0SgLMqysrCAajUIgEKCwsBDl5eV49dVXWfSkrKwMjY2NuHjxIhoaGliwgKZI5AlSWFiIubk5rK2tsYlnKr4SQRHpziZYilwuh1arRTAYhEgkYlPAQCCArKwsHDt2DMFgEGVlZZBKpRgfH8f8/DwcDgekUikXST6fDzt27OCE3OPxsGKX0+lMOTVwOBwwGo1srEoCBPF4HMXFxVCpVBgeHkZ3dzcT+QmiPTExAQAfENQ5d+4ctm/fjsrKSgQCASwsLLABZ2trKyYmJth3jNSH1ou1tTWexAFglAXxMEnitKysjCHEi4uLEIlEWF5eZmGGjRs3suwxNUypsatQKCAQCDA5Ocn3NTVbUn2nKysrLLVKhavJZOIGnEql4oYEeVW0t7fj6aefhl6vRzgcZpNem83Gk0JyWl9dXeUi0+/3s2QzffepiiAA3JyjQsZkMmHz5s2s1CmXy2GxWNDV1YXGxkbmF1PBT7LmUqmUcymPx8PTt8XFRezatYv3LhniUeG3XhA/gxoARUVFzOWjotPj8SAWi8FqtX6gYUk5OhUOhM4wm808vfP5fDwUKCwsRFpaGhfxtNYPEx+60NDr9bh8+TIUCgWTIkUiEU6fPs1GR1QhRyIR3HLLLbh48SJOnjyJDRs2oKCggMcuJOHa29sLnU6H9PR0zM7O4q233mIsP0GrCAJFo/jrBTnKXus4XFpaymPo2tpa5mFQcq3VatmEKSMjg196WloaRkZGWMdfpVIxBwAAJ44lJSVMHEyFPVSr1eju7kZBQQECgQAbaF24cIFxbnq9nkmbLS0tmJmZQSwW48qdEgTvf/sZaDQaOBwOhpPp9XqWFyW9bRqdpXK8JGwtObXm5+dz53thYeED3Re73Y7V1VVkZGTgtttuw+nTpzEzM4NAIIDt27ezcVV/fz+8Xi+bkRGPhSprWqPD4Uh50RG8TSQSITMzk3GfMpkMFy9eZOIyiQ2cPXuWVXwyMzNhNpv53chkMnR0dGB1dRX79++Hy+XCxYsXmRfkcrlw6tQp1nYnqN96QUktFYk0haCOKBU8xB3KycnB2toaent7ofpvM8T5+XlMTk6irq4OAwMDGBsbw44dO/gSnJubw+TkJCs7EfyDTDLXi+zsbExMTPBki6Z4J06cYE198hHR6XSYm5tj6IBMJmPjI51Oh507d+LUqVNobW3lS4UunQ0bNnDS7nA4WMkk1SQIAE+gyEmWpnwzMzPMzQgEApidneXig7qp+fn5iMfjmJyc5HVs2bKFsfxqtRppaWkYHBzkZ0wTOeINkH/D9SIej7O0M0EFBwYGuOMMgE0ZVSoVTCYTTzJkMhm7uba0tHAzhVTI0tLSIBQK2VOHngUVccSTWi8kEgkGBwf5m6Hvsre3F7m5uZibm2P42OLiIvtikFyyXq/nBJaU3LxeLxYWFpCRkQG1Wo3GxkbWpqfzjoiOqYicq6urMJvN0Gq1DPEZGRlBQ0MD+vr6eKy/trbGEBlqCpWWlsLv98NkMqGiogLDw8PQ6XRQq9Xc5ZbL5byX6aIjURFKiNcL6iiT9CQVukKhEP39/aisrOQpo16vh1QqRXZ2NkZGRvhcdrlczHno6enBI488wkkAdQPdbjdr/9O5eG0j53qhUCjQ39/Pqlck0LC0tASLxYKysjKMjo6y99LQ0BB0Oh3vP5VKBb/fj6mpKVb1oslgMpnE3NwcZmZmMDo6yrAhgspkZWWxx8P1giYltE9jsRhcLheqqqrQ29uLkpISOBwOSCQShoIeO3aMO8Srq6sYGxtjFa0LFy6gtbWV3aUzMzO5kCKcO/HqCKq4XohEIoyNjbHrM+UdBKMhOJ7b7cbZs2dRVlaG3NxcVkNLJBLIy8vDjh078Morr8DhcGBychLbtm1Deno6TCYT7HY7du3ahcHBQYYNkrdXKhhhLBbDlStXuNjLyMjA4OAgBAIBbrvtNiwsLHzg7n7vvfewdetWTE1NwWazcTHW1NQEo9HIE9T6+nrMz8/j0UcfZTVD4mSYzWYEAgHk5uamNEylxoher2fvrJycHIb0yOVyaDQamM1mvPXWWwyLouaZx+Nhc8tYLIZ3330X999/Pxe1BMujRnR7eztGRkZgt9vh9/tTKmASDJQMdQmmSNLSBoMBVqsVQqEQZWVlMJvNSCQSzGshFA01brq7u3HkyBGIRCLOIRKJBGw2G/tKzM3NwWKxsHfaeqHT6T6AcqCzp7u7Gw6HA52dnZDJZAzFjEQimJ2dRW5uLnp7exm5kJ6ezvBemvYJBALm1l2+fBk1NTW8JmpopWoWa7VanvLS1HXjxo0YGRnBc889h7a2Nm446HQ6PkfJUJF4knK5HBcvXsRdd93F4iT0W2Uy2Qe4p3TuWiwWhoD9rUhLpmIy34gbcSNuxI24ETfiRtyIG3EjbsT/H/GhORo34kbciBtxI27EjbgRN+JG3Igb8WHjRqFxI27EjbgRN+JG3IgbcSNuxI34X48bhcaNuBE34kbciBtxI27EjbgRN+J/PW4UGjfiRtyIG3EjbsSNuBE34kbciP/1uFFo3IgbcSNuxI24ETfiRtyIG3Ej/tfjQ8vb/uQnP4FMJmOnaK1Wy3KehYWFLN1F+vNTU1OoqqqCw+Fg3wpyerx06RJkMhl0Oh1LmZHcYTgchlarxdraGstFZmRkYGlpCd/4xjeuu7Ynn3ySpVOVSiUyMjJgt9tZHkwkEiEcDrO+eXp6OkpKSjA/P8868uS6G41GWcZLr9djdXUVy8vLkMvlbIhDEpdkohOJRPC5z33uumt75pln2EU6MzOTtc5Jqz4/Px+RSARutxterxcikQh1dXVsDER+Bzk5OZifn2dTNPIHcTqdrBVPUnTxeJwlRGOxGB5++OHrru348ePsgZCens4mLVqtFh6PByqVCqurq/D5fCzhWVZWxhrV9N7IoCYUCkGtVrNhH7lQz8zMoLKyEvF4HLFYDNFoFJFIBGlpaTh8+PB11/azn/2MNb/JmZW8HILBIORyOTweDxKJBBQKBex2O8rLy2E2m5GVlcVGRmTKQ+vNzs6GUCiEz+djB1Jy0Sad8fT0dHg8HnzrW9+67tp++tOfIiMjgzXEpVIp7HY7YrEYa+uTMVgsFoNIJEJhYSHcbjebEkkkEtbkX1lZgUqlglarhc/nYx1rMkAjbX3SwxcIBPjqV7963bX95je/YeM2klUNBoPw+/0oLCxESUkJLBYLG1EqlUqWG5ZKpeygTmZD2dnZiMfjLN9KuvB5eXlYXV1liT563unp6fjSl7503bUBwH/8x38gMzOTjQDJjDI/P59daycnJ+H1epGdnY309HQoFArMzMywWzfwvlSjQqFgyVIybyJjPJLOTCQSUCqVrPvv8Xjwox/96Lpre/LJJwGAvz2pVAqPxwOlUslO6uQTIhQKsby8DJ1Ox/LGpBWfSCTYS0YoFPIzpu8oKyuLTRzJV8LlcmFtbQ1f+cpXrru2Rx999AMGnEKhkJ/F6uoqSkpKYLfb+XyOxWLIycnB2NgYdDodgPfNQ9VqNTvNarValmlNJpPQ6/WwWq3QarXsCkueMaFQCA899NB11/bzn/+cPQhkMhk0Gg2bvdHvJX8MsVgMn8+H0tJSDA0NsdmXWCxmQzh6Z3SWuFwuljsn/x0yh00mk1hZWcG//du/XXdtv/jFL/jdSKVSfm8ajYYlSF0uFxtZknz01atXkZaWxjLhKysrUCqVLMksFAr5zCT/h8zMTCSTSTZ3I/nRH/7wh9dd27PPPsu+EbQPHA4HPyeSkQfAZ0Zubi6CwSDfW8D78ptjY2PsMFxQUIB4PM4+RiRJT/4k8Xgccrkcfr8fH//4x6+7tsceewwqlYr/LvItysrKQjweZ6NWm80Gt9sNjUbDbuAkn02SwrOzsywXnpuby88zFoshMzMTEomEfwu5xYdCoXXvrW9+85t8hpA/Dp2/arWa/YPIWyEzMxObN29Gf38/n2N03ofDYYjFYvbfIv8D8pQgs1PyuCFvma997WvXXRudvySxm5aWhmQyyedTVlYWlpeXEYlEoFQqkZWVhaWlJfaPoL8jNzcXTqcTkUiEZZIpj6E7NDs7G6FQCCsrK/xtx+Pxdd/pb37zG95T6enpSE9Ph9/vRygUYhl/8qmis498u8RiMft1GAwGXLx4ke9SnU7HOQz5AUUiEZYVX11dRSQSgUqlwoMPPnjdtf3qV79iPzOpVIqsrCzOyyjHSE9PRzwe5/NOpVLh4sWL7KOWm5uL4uJiDA0NsdGmwWDAysoK5ufnUVRUxDmXz+djjxty8v70pz993bX95Cc/gUQiYfNkhULBZrLZ2dlsHOv3+/nb12q1bKyrUCjYr2d5eZnPSZVKxbL4sViMzZ1pD1BOJ5VK8Q//8A/XXduf/vQnJBIJzltIzpfuT5VKheXlZTaKJin1QCDARr20z0n6Ph6PIzc3l3PzRCLBOWg4HEZGRgbbCsTjcXzhC1+47tqujQ9daJDZiFgshlqthkqlQjQaxeLiIsrKyrC6uorc3NwPPJxAIACj0YjXX38dOTk5aGtrg1gsxsaNG1lbPRaLweFwwO/3IxaLobi4GPn5+ZDL5WwwRC7MqdZGBiV0EFDyKBaLUVlZiVgsBq/XC7/fzx80fbxarZa1hrOysnDhwgU0NDQgLS0NSqUSCoUCgUAAaWlpfPl5PB4IBALk5eWldBtWKBSceJDeN10elIRKJBK+bEtKSiAWi9HV1YWqqipkZmYiMzMTBQUFKCwsRDKZxMTEBNLS0iCTyaBUKnkjyWQyNsEj589UrrkymYwTRUriKCEnB/TV1VUEAgH+88kMUK1WI5FIQK1WQ6/X48qVK1AoFPD5fNBoNNDpdOzonZWVBbVajcXFRd7oarU6pW9AMplEdnY2X+AymYwTxdzcXCiVSvZkyMjIQFNTE/x+PywWCxQKBV+6CoUCExMTqK2txdLSErty5+fnw+12s9uy1WplYyBy71wv8vPz2W2T3F9Jm52MFynZpSKZnFd1Oh0kEglEIhEXr36/n91TpVIpm3glk0l+hhkZGR9IetYLMoWkC5zMpTwezwc8YihhEYlEqKio4MOd3FQ9Hg/a2tqwsrKCgYEB6HQ6bjSQiRI505P7L723VJGTk4OcnBw24qMz5fLly6ivr0d6ejq0Wi1KS0uRl5cHk8mEwsJCzM7OQq/Xf+DiGRsbg8Fg4CSAzOxo71VXV8NisbDhI11Q6wXtd9LLV6lU7HFgt9tRWloKl8uFaDQKhUIBrVbLxp3k5VFSUsLv32AwwGKx8G8tKipCIBCAwWDgZ7a0tASpVIoNGzak9Kogj4+amhoYDAasrq5iZGQE8XgctbW17KNAF3woFILVakVDQwMn+clkkk1MnU4ncnNzIRQK4Xa7YbVauYCkv4/8Kbxeb0rvAJVKBaFQCLVazWfqzMwMhEIh6uvrIRaL2T9GpVLhnXfegc/nQ3V1NfR6PdxuN5vddXZ2oqenBxKJhI24lpaWMDIygpqaGoyMjKC5uZmN2ciQbr2ghFSlUiEtLY09g+h50PlBxZHX68XMzAybARoMBuTm5iKZTLJuPhUoGo0GWVlZWFhYYM8S8u2gRIK+3+tFdnY2m2nF43H2b4lEInz+BQIB9jzJzc3lwsThcEAsFkOn00Eul6O4uBijo6MoKSmB1+uFUChEfn4+XC4XYrEYDAYDlpeXueigxGm9SE9PZ7dpcmEG3jeoJb8c8oZJS0tDRUUFkskkKisrMTAwgFAohHA4jEgkgtbWVqjVaoyPj8Nms3GTEQCmp6eRn58PhULBruHkhbFe5OXlwefzcRKfnp6OyspKvPPOO6iurkZhYSE8Hg+cTic7V4tEIj4PKOlfXFzE/v37MT4+DrFYjEAggJycHGg0Gpw8eRJFRUX873o8Hm7opfJ/UiqV3EyiJJR+DxkNKhQKvjPy8/MxNDSEbdu2cUK3tLQEk8mE9vZ2DA4OoqCggN+Xz+eDWq1GZWUlent7uTCiPedwOFKuzW63w2Aw8O8ld2ryiSE/FyrampqaMD09jfn5eYhEIqhUKkQiEVRWVvJdp1AouFA0Go3wer3Iz89HIBCAxWJBMBhEWlpaylyE/D8KCwshEom4Werz+VBUVIS1tTVu3szNzbF3mlgs5uccDodx6tQpNDQ0IC8vD3a7HQKBgL1PKCcqKSnB8vIyzGYzXC4Xe2ysFzKZjI0wc3NzoVKpoFQq2byRjIF1Oh0be2o0GojFYpjN5g/crRqNBlNTU9ixYwe8Xi/W1tbYM2Z2dpabBNREWVtbS+n2HovF+BtVqVRIT09nzzP6RmOxGPLz81FaWso+HiUlJTh37hxkMhlqa2uRnp4Ot9sNk8kEhULBJofRaJSNcnNyctiImBpeMzMz667t2vjQhQYZsC0sLGBlZYU7B83NzfB4PHy5UidLr9djaWkJ8/PzMBqNiEQi6OnpgUKhgE6ng06nw9TUFDQaDTv9ut1uLC8vY2xsDBkZGZDL5YjH47Db7ZDJZOuuLRgMcteNjHAkEgny8vJgtVrhcrkQDAaRTCahUqkwMzPDxVFmZibsdjtmZ2eRTCZRXl7ODrbRaBRLS0vw+XxobGxEX18fu6MLBAI2kqMq9Hrh9/uRl5eH5eVlNjkkF08yrwsGgwiHw5BKpfD7/VAoFJBKpcjIyMDc3BwcDgdkMhmsVivKy8u5YrVarfD5fNDr9bDZbKivr0cwGER2djYkEgl3l9YLqlKpmKADizoYVN1mZWWhoqICa2trfPkkEgksLS3B4XAgGo1CIpHw/qAuCHWTEokEZmdnP7C2awuu6wV1qumAp4NHLBZzckAmcjk5OQDev7g1Gg13rSjpzsrKQm5uLjtXz87OYnV1FVVVVR8wu6ECZmpqKuV+s9vtvDej0ShPaDQaDZt4UTJOScTy8jKcTic8Hg+bkOXn58Pr9UIul3OXVCwWQyaT8YGh1Wq560ATHvrIrxf0TGkqR/uNHGOvNewj599wOIxAIIBEIsHOuAKBAA6HA6FQiL8tMl2ii566lNTxpMMxVZBBFx3SZJZJCVYwGITNZoNYLOZkoKqqip8h8P5ZREkGGT/SvvD7/aivr8fY2BhPrsrKypBIJDA+Ps575XpBbsw0/RSJRNwgiMViWFhYQDKZ5HexsLCAWCwGqVTK3wxNQM1mMzo6OrC0tMRT1mg0Cp1Ox4Z6crkcyWQSfr8fExMTbJJ0vbj2ohUKhWx4ZjQaEQgEkJ6ezl1imlDOz8+zAyx1b1dWVqDRaNDQ0IDZ2VkutKuqqmA2m7G2tgaXy4VwOIycnBwIBALMz8+nLG4jkQhEIhGSySRPyqihQNMuk8mERCKBqqoqlJaWQi6XY2RkBAUFBVhZWcHMzAzS09ORl5cHmUzGa6EpV2VlJZxOJ7u1k8Ot1WpNeY6Q8VU4HOazNh6PQ6FQwOPxoLKykk3UAPCkkRIxs9kMp9OJkpIS7hjKZDLE43FYLBZ+tqurq7BarZyIhUIhBAKBlEnp8vIyMjMz+ZumopVc0+k8poLXZrOhqKgIS0tLbOS6sLAAAFykmUwmnk7m5+fzXbGyssJ3M02aUiXzoVCInzmdJUqlEktLS1z0UKKZk5ODgYEBFBYWwmAwoKCgADMzM1haWuLmV1FREU9SqKA3m81sREp7mBoJVPBeL2w22wcS+mQyCavVyoazdrudO8f0DS8vL0MqlbITdGZmJtRqNRvo9vX1QSgUssksTcfr6+v5uyKDs1RGh4lEgrva5Ma+srLCk2+32w2fz4e1tTVGNdBdQs7ZZKZnMpm4GUd/t8vl4sm2RCKBWCyGQqGAUChkp/j1Ym5uDqWlpQDAd2FmZib/uQqFgvchvSun04mJiQl+jmT0RigDj8fDRr4qlQpLS0uYm5uDXq/nPEcikfD9sV54vV6oVCqEw2E+x5eXl1FVVcWoFLPZjEgkgvz8fMzPzyMtLQ1OpxP5+fnQaDTweDyw2+1sJDk/Pw+VSoVEIoFoNAqZTIajR4/yPdvY2IhQKMQIgvXC5/PxPl5cXOT8g/YtmQcLBAJYLBbe91SYBYNBLC8vo6ioCFarlZvT8Xiccxy6N6lRRibJdrs95XdKSAVCLiSTSW4ghsNhRKNRrK2twW63IxgMorS0FPF4HGazGUqlEuFwGH19fXx3CQQCBAIBPndpukjvmZAtALCwsACxWLzu2q6ND83RoBE1fdzUzR0cHOTJBDndWq1WThACgQBXRVKplC/9vr6+D2xYgjasrq7yj4xGo/zPUtnXJ5NJhEIhXlc0GkVGRgY7C9OY2eVywe12879H3aLV1VVoNBrI5XIolUruFqnVan4ZBJ8i2E48HuciJDs7e9210QVPoz+BQICcnBz+yKmrnkwmMTs7C7/fD6FQiPLyck7kBQIBf8wTExNYWVlh6E9mZiYnYNFolN1VaayVampA3XjqCBNUjSYt1K2iTpNAIIBCoYDBYEAoFGLXyLW1NahUKk5QaTojkUjQ09PDEAOCddF7SHXwUOUskUj4ndIYnqZSSqUSwPudgLm5OR5Jksv7tVOunp4eBAIBvmBUKhVEIhFycnJQVVWFgoICrurtdjtfrteLQCAAp9PJ0JX09HSGpCQSCYbIrKyswO12c0dCq9ViYmICMzMznNyTkz0lZeR+TN0fpVKJgoICBINBTE1Nwe12pyyCyLWTCjVKYsxmM+LxOJxOJ0NPvF4vj9AlEglsNhtP9bKyspCZmcnO2PTfs7KyUFJSwsXbtY7EqQpuCoLCELQvLS2NIWwWi4WnkU6nExcuXOC9LJfLOSmh9dXV1cHr9SIcDkOpVHL3GQAnWsXFxQgGgzCZTDzhTLXnCNJHXXKxWMwO6NFolM89p9MJp9MJv9+PzMxMLsKoOAeA/v5+/m10EUxNTaG7u5vPReoOuVyulN8qwaYIouBwOCASiRCLxfhMo64pTeeqq6vh8/kwPT0Ns9nMo33qgBGsi6BWLpcLgUAAhYWFKC8vRyKRgNPp5MnTekHQkvT0dIb8SCQShjTOzs4iEAhgeXkZly9fhlQqhV6vh0KhwNjYGDweD3Q6HXczZ2ZmoNVqUVNTA51Ox3A12n96vR7BYBDz8/MIhUKoqKhYd22UVF/b+SOow9LSEsRiMXJyctg1WCKRID8/HzqdDuFwmLuOVHSJxWKGItK9Rt+zWq1mKF0oFOLG0XpBZ4xMJvtAc2lxcZGnFQqFgruS5B4vEAgwNzcHt9vNDTnanzQNIbiuxWKBXq/nO9rn83Ezjoq19d6pw+HgLjo1rSiZicfjfC+53W6+A2gP0tlNhdmlS5fgdDohk8mQm5vLRTPtR3KGDofDXMSkCp/PxwUmNZRMJhMWFhZgs9kQjUbh9XoxMjICs9mMqakprKyswGq1cldXrVZDKpVibm6O1xqPx/n8pjv+WpgVFWypnlsgEOD9QUXowsIC4vE4T0Wo0IrFYmhra0M8HudmXF5eHkpKShhJEI1GOTehhoLL5WJ3d0KFELw2VQSDQYbNAOCJxsLCAqRSKXQ6HVQqFaRSKbRaLcNIKX+i74Gm21TU0QR0bGyMXeHpnKLmml6vX3dd4XAYdrsdbrcbTqeTYXoajQbLy8sMUw8Ggzz5raysREZGBkZGRmCxWBj+RZAqlUqFsrIydkI3m83IzMyEyWRidATdRamme2lpabDb7fzcl5aWEI/H2alcpVIhOzsbiUQCi4uL/A0kEgkIhUIuhu12O+9del9UwMzPzyMvL4/hTYuLi7Db7TxhWy/EYjFDZoPBIDcGCfpKjctEIoGFhQXY7Xa4XC52Uqe7yul0Qq1WY2ZmhqG0dJ7TmUEFD53lPp8v5Z11bXzoQoMuSoJ8EKb0/8Pan0a1eZ5r3/iBAAFCIAkhNACSEPM8Y7ABG+MJJ05iZ2wz1k3TdEz30N326drvftp02M9OOux2d0zbzEOTOPEQx3M8G2PmeQbNEgIJMQkhBr0f3PP8J88yat+1/tda/ZCU2DfSfV/3eZ3ncfyOrq4uHslqNBo+lVosFuTn57OngLqqW7ZsAQCWitCDMzo6yjrLvLw86HQ67nzTC3HTX+JvHUcagScmJiIiIgKTk5M8thIKhfD5fHC73Sxj8Xq9mJqaQlhYGHQ6HXJychAVFYW1tTXuGCgUCsTFxcFsNiMlJYV1pevr6yzFCtWJpMKfvABUJAmFQkRGRvKNQTcHFX9xcXFcPNNIjDTXLpcLwWAQGo0GmZmZLOWgRR4JACGLUtJoktY1KSmJb07q6FIHkbTTycnJSEhI4AORTqdjGVUgEIBEIuHfk6QB9KKm7gL9nqE683SgJQkQTSjcbjdmZ2cBAImJiYiJiYHNZoPVauWieW5ujjdEmUwGgUDAB2KJRILc3FxkZ2fzlC4lJYU7PeT7CPWiI41vWFgYH5SVSiV7FD6t3abfUSKRQKFQ8ASE/g6SDNJmRIc+n8/H0jyZTMayMBpLh7o26vZTt5VemMvLy7z5UaeZCkJ6mdAES61WIy4uDi6XCz6fjwsEhULBGyvJqUiHS1O5UItG8+vr6zwJoe6xw+FAZGQklEolb6DU8UlMTOSDTXx8PNRqNbKzswHcfrkrFApkZWUhLS2Ni4iMjAzI5XJMTU3BarXyy3qzRZsmSdhI1+52uxEMBpGUlMQ6YjpoUlfx05NJsVjMcjCSN9KhYHJyEmazGcnJyZidnWU9+draWsiGRXR0NL8U6OAfExMDr9cLp9PJ4+6lpSXYbDbMzMxg165d7KuhfSUrKwtLS0tYWVmBUCiEWq2GUCjkly/JTXQ6HVZXV+H1eiGXy6HVaje9NvouBQIB4uLioFKpeJ8zGo2YmpriAt9ut3P3Ui6XY2BggCcGpaWliIqKgsvlgl6vh8FggEKhwNzcHHekqQnl8/n4sGUwGDa9NmpYrays8L5KPjvSLSsUCiQmJnIxmZmZyVNH+j1IAhsTEwOXy4WVlRVIJBI+2AqFQhQXF0OhUPB099OemjstmpDQz5Jcy+PxcIOBuv9LS0ssb1lbW4PdbofP50NiYiL/e5vNxtMauhdo4keTLipy6Jo3W8FgkA9ywO1DDBXon36/+P1+TE1NsWyEvpfw8HDo9Xqkp6cjPDwcN2/exNzcHB+IqaMfFxeHhIQEntAsLCxApVKFLObj4uLg9Xr5Pl5fX0d0dDSGhoZgNpshl8sRFxcHv98Po9GI0dFRLmSnpqZ4SkOeGvIx0ftvbm6OD6Eej4cbeCTHDdX0pEPmpxt9ERER8Pv9cLlcWF5eZika7a0kO6NCMS4uDpWVlZBIJPB6veyh0Gg07BOixiMpL2h68/dkXdTko4Mg+dpmZmb4na1QKCCXyxEdHY2IiAgkJydzcenz+T4jsSVv0+rqKjf4IiIiMDQ0xP5Durb09PR/6HOjCWliYiICgQDsdjvXXQC4kaJQKKBWq2GxWGA0GiEQCJCXlwelUonFxUVoNBr+zMj7mJuby14cu93O93ioqSh5zKjRRAf/+fl5luYnJSVxUzYuLo49q2QjoEY6TbVoyiWXy9nvkJCQgKioKNjtdlitVpbaUW2y2bVNT0+zV5cm7uRPpHqYpl79/f1YWlpivwXJvsivSdN2AFzLrKys8DSZ/plULKHeWZ9e//BBgzSffr8fFosFdrsdUVFROHDgADweD8rKypCcnMwdp4iICIhEIshkMuzatQtKpRI2mw3Ly8swm82oq6vD8vIyJiYm0N/fD4fDgaqqKng8HgiFQoyPj0MoFLIeOVTXiuQcNCHR6XSsxaSTKHDb3E2mSTo9FhQUwO/3o6+vDyqVCna7HY899hiGhoZw7do1DA4O8gibRrZ9fX2Ii4tDcXEx/H5/SJ3a/Pw8NBoN68rpv8/Ly4PX60VqaiqA2y/E+vp6TExMIBAIoKOjA2KxGGtra5iYmOCOR2ZmJvx+PwwGA+Li4jA6OoqUlBRER0ezljgjIwPFxcU8Jt9sfVoCFxsbi9TUVDidTqSmpsLlcnGXkl6opHW22+0ICwtj+ZZQKMStW7e4GImOjsbi4iImJiZw8OBBmM1m7orFx8fDYDDw9CTU50ZTAirqZmZmsH//fjgcDpjNZpbUVVZWIj4+Hlu2bEFERATKysrYz0KHm+rqajZbUdHu9/tx4cIFtLe3w2g0IiEhAVlZWRCLxSElNgKBAEVFRXwQXl9fh9PpREZGBtbX15Gfnw+dTgeVSgWVSgWj0QixWIzm5mZs2bIFmZmZmJqagsVigdlshsFggNPp5MlBTEwMamtreRMfGhpCTEwMv7RDXRsApKSkAAB3goPBIA4cOIClpSXs3LmTfVDbtm3jg67T6URaWho2NjYwMjLC07PGxkasrKxwUbyyssKyjrW1Nfa90EuY/u7Nls/ng0qlgt/vh91uh9Pp5E0rKSkJ8/PzWFxcRFZWFh544AHebGNjY7Fnzx5+tmm6JZPJIJPJEB8fz5r71NRUPjQdP34cYWFh2Lp1KyQSSciNkSYnBDnw+XwYHR1FaWkp/H4/MjMzIRKJuOMTHx+PoqIi9Pf3IzMzkyU/Gxsb6O7uRnp6OhQKBVJTUyGTyRAMBlFZWYmysjIUFRUhGAxCoVAgPT2d78vNlsfjgVKp5IMjaWYzMjLgcDjYpEwyGYfDAZVKBZ/Ph5ycHOTm5iItLQ3FxcW4efMmAoEAenp64HK54Ha7MTMzg4yMDFitVvT19aGtrQ0REREsgQ21aLJqMplgNpt5ermxsQG32w2NRgOtVouMjAzk5eXB4XBgdXUVIyMjPL3r7e2F0+nE+Pg47rrrLhiNRnR3d7PXIzo6GmNjY4iPj0d3dzciIiJQWFiIiIgIXLp0KeT9lpCQAI/HA4vFwvfxgQMH+NC6vr6OpKQk7N69G5OTk0hPT4fdbseuXbtQUlICk8mEzs5O7iyTfl2pVEKtVuPLX/4yGyxHR0chlUqhVCrR1dWFxcXFTa+NtOO0xwUCAQwNDUGtVnP3mA7v9O6NiorCyMgISktLoVKpuFvscDj4fSQUChEMBjE7O4uMjAx0d3dDLpcjPDycO6YzMzMhPUHU5IiOjkZmZia2bNkCs9kMrVaLsbExqNVqLkzJeymVStHS0oLk5GSoVCrIZDLU1tbixo0byM/Ph1qtRmRkJL9bt2zZgrCwMKSlpaGzsxNOp/Mzz/Vmixo8JPEhqdmBAwcAALt27WLVQHZ2NrxeLw4dOgSHw4GYmBgEg0G43W6sra3hrbfeYrAINQDn5ua4caTT6bg7rNfroVAo/m5xJZPJWC1AQIDy8nLYbDaWFq2trSEjIwOTk5MIDw9HIBDAjh07IBAIcOPGDSQlJaGkpATJycnw+Xxsgl5bW0NOTg6mpqbg9/sxMTGB4uJi7NixAwMDAyEnGjR1okmcSCRiSALBFmgqFAwG0dLSAofDwRMfqVTKB8pLly5Bq9UiLCwM6enpiIiIQH9/P8rLyz8DoEhPT0deXh5LckOtwsJC/nv0ej0sFgv6+/tZSUP1SGxsLE6fPs3emocffhgZGRnwer0Mq/na176G0dFRDA4Ooq2tDSMjI+xZysrKgkgkQmVlJRobGxEREfF35XA1NTWQy+XQ6XQoKyuDy+VCbm4uT+XofUFSYIVCAbvdzlOytbU1rm/Lysp40hEZGYno6GhkZGRgYmICADA4OMgT+qtXr6Kvry/kd0rKIZpY22w2FBQUwOFwICkpiSfS5CEtKSmBSqVCQ0MDwsPDcePGDZhMJnz88cfIz89nLwv5kQoLC/lQNTc3B4VCgcLCQmi12pBNnk+vf/igER0djWAwiJ07d6KpqQkZGRlYXFxEb28vDh48yJ06h8MBh8OB+vp6LCwsoLi4GD/4wQ/Q3t6OoqIieL1eNDY24gc/+AHUajUiIiKgVCqxe/durKys4Ktf/SoUCgWKiopgt9sxMTGBhoYG3Lp1K+T1LS8vs9bd4/GguLiYPzAAbHi9fPkyDh48yN6C5uZmpiO9+uqrSElJwYcffojGxkZs2bIFiYmJfCpVq9UYGRnhG62zsxNVVVWsf99s0WGATL4RERF8erdYLEhISIBer4dSqURjYyNkMhny8vIgEomwe/duPPHEE4iJiUFBQQEGBgZw//33Iz4+HqOjo+jq6sIHH3yAXbt24dKlS0zxaW1thUAg4HHdnRaZ+ag4tNlsiImJwV//+lfuplGnLTU1FTk5Oey7oAMJmU2feeYZNDc388szNjYWi4uLsNlsyMrK4gdycXERi4uL0Ov1IbvfcXFxGBoaQklJCXbs2IGMjAxkZWXBZrNBp9PB6XSy1Cc6OhpFRUV48cUXoVKp2EMTExODGzduoLS0FN///vdRV1eH7OxsLoBGRkbwpS99iadVi4uLsNvtSEtLY2LEnZZMJkNzczNLpmjc3traiqysLDQ3N2NychLA7aL/O9/5DlZXV5GRkYGxsTF4vV6IxWJYLBakpaXB6/UiISEBpaWlSEtLw9TUFI4fP45vfvObrI1sb29HV1cXsrKy0NXVtem1hYeHY3Z2FlVVVaisrIRcLkd8fDx6enr44E3dp4GBARQVFfFGm5ubi7y8PMjlclgsFtTV1eHixYvYvn07lEolXC4XLBYLvF4v9Ho9+vv7+Tudm5uDTqeDyWQK+SwoFAqIxWI8+eSTuPvuu1kHLxaLUVdXx519otMkJyfjxz/+MYqKimCz2RAbG4u8vDycPXsWWVlZ+Pjjj1FWVobIyEisra0hOTmZpYenT59GTU0N+vv78frrr8NgMIR8EdPUQKvVIjMzk437AoEAmZmZXGySPK+0tBQLCwsoLy/Hiy++iNbWVlRXVyMtLQ2RkZHo6OiAUCiExWLB8PAwZmdn0dvbiwcffBBvvvkmIiMjYbVaMT4+jrq6upC6dCp4SOO+tLTE5r3nnnsOr776KsbGxrjIeeqpp2C1WtHU1ITr169DIBDg3nvvRUpKCp566im0tLTg4MGDyMjI4C6m1WpFQ0MDJiYmkJeXx1KZpqamkJ1S0iGXl5cjLy8PbrcbV65cwW9+8xsUFRWxZjouLo4na//93/+NsrIy7N69G4899hg+97nPwWAwICcnB++88w43GZKSkvD5z38eMTEx+Pd//3fo9XqkpKSwrLSuru7vTrtJRkkTair6iFxjMplw69YtLC8v4xvf+AY6OzuRlZWFa9euwWQyITMzExEREWhoaEAgEMC+ffu48ULNikceeQRjY2PIzc3F0tISzGYzKisrQ3YjFQoFhoaGIBaL2ZtiMBgwNTWFwsJCJm/RZGTnzp3cbR4ZGUFsbCx0Oh1GR0dRW1vLexZN93Jzc3H16lUUFxdjYmIC8fHxLBEyGAwYHBzc9NoyMzPhcrkglUpZIvPMM89gcXGRO9zJycnQ6XSw2+1MAyPpEk0u29ra0NTUhISEBNx///1saF9aWsK7774LqVSKqakpFBcXY9u2baioqODJ0GYrMjISKysrqKioQGlpKcNFpqamsG/fPvzhD3/A4uIiCgsLsbCwAIVCgUAggPz8fERHR0Ov16OpqQnV1dXw+XwoLy/Hrl27MDk5iatXr6K/vx+/+MUvIJfLGfri8XiwuLiIvLy8kBONjY0NLC4uoqCgALm5uYiLi0NaWhqMRiP27NmD6upqlJSUQK1WQyAQ4MEHH8TNmzdht9vx3//933C5XGhoaMDY2Bg6Ozvx3nvvsdeGJiurq6uoq6uD0WiEwWDA8vIypqensWXLFrjd7k2vTavVYmVlBSUlJdBoNDxpBID77rsPEokExcXF2LJlC8RiMcrLyyGTyZCSkoK4uDjk5+ejqKgIfr8fWq0WMTEx+PznP8+qgZSUFHzyySfIzMxEcXExsrKyYDAYkJubi8LCQoyOjm56bQQVqa6uBnCbyOZwOHD//ffja1/7GhITE5GRkQG1Wg2v14snnngCH330EU8sy8vLUVFRgQ8++AAbGxt45ZVXoNVqIRKJUFFRgW9+85uIiYnB9u3b0d/fD6PRiK6uLjgcDtTV1YX0FpI6hqYPo6OjkEgk6OzsRGpqKgQCAfLz85GZmQmz2YzCwkJcvXoVw8PDOHHiBKxWKwoKCpCens5NssrKSmxsbGBgYABDQ0NYWlpiWWZBQQGrcqj5t9nKycmBWCxGaWkpiouLoVQqkZ6eDqPRiCeffBI9PT0YGxvjydKBAwfYJN/V1YWYmBgcOnQIDzzwAP75n/8ZLpcLFRUVkEgkWFxchNFoxIkTJ/CVr3wFy8vLUKvVcDgc6O7uRiAQwJUrVza9ts98hv/QTwEs4ent7WXpw9raGioqKvDRRx9BLpcD+P/pTl0uF4qKijAxMYGf/OQnmJ2dhdlshtFoRG9vLwwGA6PMiBZw6tQpfPvb32ZiCk1HCH+52VpZWeGJAXVuqDMUFhYGlUoFt9sNh8OB1NRUWCwWnDx5ErW1tcjPz8fKygoWFxexY8cOTE5O4vHHH4ff78fNmzcZNzs7O4ujR4+yPIxO0J2dnVheXkZTU9Mdr41QgETJkMvlyMvLw+DgIEpKSvgmHhsbg9vtRmNjI4aHhwHcPghYrVbYbDbW5j/77LNM6RKJRMjPz4dAIMC5c+ewsLAAt9vNVC3qNG62CJ06PT2NuLg4yGQyJCQkYMuWLYzuNJlMCAaDyMzMZEwhjQLdbjfCwsLgdrvR0dGBmpoa3Lp1i7GoVLAKhUKedhE9w2q1csf9Tsvr9aKgoABTU1NYWFjgKdfs7CwqKioQExODiYkJfoES/o08LUQio4nQ448/jo6ODsTGxkIikXAHh6YsY2NjSExMRGZmJrxeb8jO/PT0NDIzM1lWQAi93NxctLa2oqSkBGazmScAH374IfLy8tDV1cW4Z5oovPvuu9i3bx8WFxcxPDzMMhyJRIKPPvoI4+PjSE9PR0VFBebm5tDV1RWyWJ6dnUVOTg46OzuhVquRlJQEs9kMACwHIoMXTaa2b98Op9OJS5cuQa1WQ6lUwmw2o7m5GTt37mSDGJmkvV4v6z3r6up4VE2SolBrdHQUAoEAJ0+ehEQiQWlpKZKSkrBnzx4cPXqUx+8EHHjggQfg8/nw1a9+FU888QR7qLKysvDee+/hqaeeYmQpSYaITEbepccffxxra2s4ceIEzpw5g507d97x2lZXVxEREcEFG0kH+vr6IBQKkZ+fj5aWFoyPj0MsFqOlpQUJCQkQi8V48cUXMTc3h8HBQdaD5+fn49ixY9i1axcyMjIwNDSEqakpDA0NoaamBktLS0hMTITf78fo6CiEQiFLS//vFQgE2JhOXWmXy8X+M7VaDavVyj6UU6dOsUyupKQEy8vLOHbsGBQKBWt/P/zwQwDgrpparcbKygrq6+tZ3rW+vo4LFy5g165dm36nNpsNW7ZsYRlCYmIiHnzwQZhMJni9XszOzuLEiRNs6kxJSUFGRgaioqIwMDCA2dlZaDQalJWVoa2tDTk5OTh//jzKysowMzODgYEBmM1m3LhxA++88w7Kysq4o33t2rWQ0lXy6ymVSoSFhcFms2FgYAB5eXmYn5/HzMwM70URERH857e0tMBgMECtViMxMRESiQSvvPIKdu/ejaioKCQnJ8PhcODKlStYW1tjM/GlS5eYEEiehM3WyMgIpFIp4uLiGDE9OTkJlUqFtrY2ZGdnY2RkBJGRkSgsLMSbb77JEt+IiAieOqelpfE7g5o7NpsNXq+Xp4IymYzJdWtra5iZmWHp4WbfKRmu6bD24YcfIjMzE8PDwwgGg+ju7mb9/OzsLC5duoTMzEx0dHQgMTEReXl5WF9fx8mTJ/H444/zFDo+Ph4NDQ1ITEzExMQETp06xXse0QT/3kSZJtZms5nx4Gq1Gjdv3kRxcTHcbjfLaQKBAP7yl7/grrvuwtatWzE8PIz3338fubm52LVrF4xGIzo6OqBWq7Ft2zYsLCxgenoau3btwtGjR6HT6Viie+XKFRw6dGjTa1tdXeXvnqatbW1tKCgowNDQEFMIFxcXERUVhZdeegn33XcfHA4HDh06xCRPmUwGt9uNe++9F6mpqSxdm56ehslkQnJyMvsNyCfndDr53X+nRXuzw+FgBG1HRwfy8/Nx8eJFZGdnw2azsRx8eHiYD0Tka6I9i6YTr776KrKysliKvnv3bpYk0aR/bW0NKpUq5LMwNzeHzMxMjIyMID4+HocPH4bJZILRaITf78fJkydZxp6YmIi1tTU88MAD6Orqgl6vx+nTp7np09zcjKeeegpdXV0wm818D9y8eRPZ2dlMxTQYDAgEArh48SKDTe60KLaBJEPksykpKcHAwAD/u9jYWBw6dAgTExOoqqpiaur09DSOHDnChKyzZ89i3759rNKIjo5GQUEBTCYTWlpa2J+zvLwMlUoV0qMxNDQErVYLu92O6elpJtKlpaXhww8/xMGDB3Ht2jX09fXxNNLn8zEJzGq1wuVyYcuWLfjDH/6AuLg4tLW1Yfv27VhdXUV7ezsKCgpgNBpx9uxZ3HfffZBKpSzH/UfX/ycz+NGjR9HT08PGN61Wi1u3biExMRE1NTVsnLTb7bzZ2e12vPrqq5icnER+fj6/JPV6PWMjSV82PDzMxisqKMbHx5mcsdki/Blp34DbXR0qmFZXVzE9PY3+/n5cvXoVHo8Hjz32GJqbm/GTn/wEp06dglQqRV5eHlZWVvDGG29AJBJhx44dKCwshMPhYHzrtm3bsLKygu7ubvT39wNASO1hdHQ0ZmZmYDQaYTQa4XQ6YbPZoFQq0dPTw+Qd8qsQ7jIYDOKdd95BT08P0tPTUVpaiomJCfz4xz+GyWRiD8Xq6iq/iMvKyrC8vIyhoSEYjUY23G22UlNTMTo6ylMGGsf29vYiOTmZjVI2mw2XLl2CSCSCUqmE0WjE66+/juHhYeh0OlRUVECn0+HIkSPIy8tDcXExmylpWkAvBpfLxXrrrKyskPeb1WplQ/Xc3BwXKFeuXIFMJkNZWRlUKhXGx8fxxhtvsJfg3Llz6O/vh1QqZWxlWFgY1Go12tracP36dczPz+PcuXPchRaJRDAajXwPhjKDk76eshbm5+dhs9nwwgsvQKvVIisrCyUlJdjY2MCJEycwNDSEyclJGAwGNjNXV1fj4MGDqK2thcViYfIaFUPDw8OYmZnhwsbn8yEqKgrZ2dkhTXUkE1laWsLU1BRMJhMjEP1+P8rLyxntOzo6iqioKLS0tCA3N5cNpIS/HRsbQ1dXF7q7uyEUCqHRaBAVFcUAgy984QuM9BSJRIyQDLWSkpIwMzPDaEWSIszPz2NhYQFf/vKXkZycjFu3buH06dO4ceMG6uvrkZSUhF//+te4ePEi9Ho9H3A2NjYwPT0NnU4Hg8HAB/rl5WVkZ2fDaDTiZz/7GV5++WXk5uaivr4+5PVRgZWRkcFylY6ODkRFRWF9fR0VFRWorq5GXFwc6urqUFxcjPHxcQwMDDDvfn5+nqUzRCchrHJTUxN3qD6NZ1UoFCELZrPZzLI18hnNzMxwsbl7925UV1cjKioKw8PD6OjoQG1tLdbX1+FyueBwOGC327mhMTQ0hPr6euzbtw/V1dVITEyE1WrlUfvq6iouX76Mjz/+mHX3my2fz4fz58/z752UlISpqSm88cYbiImJwYMPPoidO3eyXFWhUGDHjh2Yn5/HlStXmKJE+QI00SBM7/z8PCYnJ+H3+3H//fdjbm4OAwMDsFgsvA9utmJjYzE2Ngaj0YikpCTU1NRwYyszMxNSqRSFhYUQiUS4ceMGNBoNo3/JuE/FSk5ODt5//32WaimVSggEAhw9ehQdHR3Izc3lz9xisSAjIyPkJIiaGWKxmJn3Pp8P165d4w4k/VnUSJqcnIRYLMby8jLS09NRVVUFkUiEzMxMhIeHQywWIzk5GRkZGZBKpXjllVfQ0dHBlCmRSMSTklDwhuXlZZhMJvT29mJoaIgnA0NDQ8jKyuJ7dWpqiqEHBQUF/G6kCTblHF26dIkpZ4FAAL29vejv70dsbCy+8pWv8KGHgAqhDhorKytoaWlBZ2cn6/cDgQBeeeUVGI1GTExMMKI6Li4OeXl5+Jd/+RdoNBocPXoUc3NzqKqqYjhISkoKKzLIr9jb24uWlha+v2mCNj8/j5MnT256bRsbG7h58yamp6f5kK3VanH27FkEAgFkZmaioqKCm3CJiYnsi3znnXdw+fJlnixQPlB4eDji4uJQWFiI2tpaGI1GREREoKCggIs+s9kMj8cTUmIOAJOTk0z1UygUcDgcTLybn5/nRtT09DSTzNxuN78rDAYDT+SJTEeZIHl5efjwww+ZRCYSiVj+tLCwAIfDsel1JSYm4urVqzAajXxNPT09uHDhArq7u3H33XfzQej69euYnp5GTk4OJBIJOjo6AAA1NTWQSqW466678NJLL8HhcEAkEkGr1UKhUODUqVM8id7Y2MD58+dx/PhxrKysICMjY9NrW1tbY6mkRqOBTCaDzWbDW2+9hezsbK49JyYm0NbWhoWFBc5C6+zsRDAYxP33349vfetbAG7Xi1lZWVheXmb/DT0njz76KIRCIcvFdTpdyAka7aFra2tMfZNKpfjwww8hEAhYTgUAJpMJ5eXlWF9fZ+llUVERKisr4ff7kZyczFS2T2eQXbt2DVFRUUhLS0NbWxu6urpYshVq7/30+ocPGoQDpYkBkUYaGhrY2CoWi5Gfn4/a2lruXtlsNhgMBjabVldXIzIyElu2bIHH42GUYCAQwNNPP42TJ09yUAt12f+eGZxeNhTQMzs7y0hYogstLS1BIpGgpqaGQ61SUlJw8OBBJCcnY2RkBIuLiwgEAqitrcX4+DisVit8Ph+SkpJw6NAhfiHQQYsOS6EkQHSIEAgEbGilYpMKDjIEx8bGwmazsblv586diImJQXd3N+Lj46HT6aDVajE4OMhm8IKCAjz00EOMuVUoFMjJyUFGRgaWl5dRVFS06bWR8Y66/PHx8TAajdDpdBy+R4QmtVrNxeH8/DzuuusuPnQAwPDwMD7/+c8jOjqaA9/EYjG2bt2Knp4eDnQj8x/9WaE+NwqHWl9fZ4N6UVERVCoVwsLCuMgi3w1Rbg4ePIjc3Fx+4J1OJyoqKvgQSzK4uro6NjgR5YV0xKEmGnRv0wuOWN1VVVWcc+LxeKDT6bB161Z+LhYWFlBVVYXY2FjcvHkTJpMJbrcbe/fuZawgGcqLiorYkCiRSLirSmFLmy0KZKLAPmLI6/V6niSZTCYIhULk5ubCbrezfKS0tJSlDVu3buUQNep+kMSGAuw+jcmk3Bwyx262gsEglEolAoEAI/7oWZiamuKNi/DUV69eZfRiQUEBEhISEBYWxveQ3+9HZ2cnWltbsby8jF27duGhhx7CwsIC6uvrIZfLUVlZierqaiwvL4f8XtfW1pCVlcVaaIlEgkAggLS0ND5oEOFDLpczzpQ0/qTLzsnJgUAgwAMPPACFQsHhbTabDRaLhf0WFDQl/VtwE+ma77Smp6f52kmXLhAIUFJSwsQQg8GA0tJS7Nq1C2lpaQgLC4PP50N1dTX0ev1nfGIJCQno6OiAxWJhiltpaSnsdjtrobVaLQoLC5GamhqSLhIWFobk5GT2GBEiura2lj13SqUS2dnZyMnJYXrY0NAQ8vPzORMjJSUFMzMzeO6557C+vg6bzYbx8XHMzMxg69atmJ+fR2FhIdN2MjMzERcXF7K4Wl1dhV6vZzymxWKBwWBAQ0MD2tvbeUpKfkLCN0qlUr422jPW1tbQ1NTE+/Ts7CySk5Oxb98+WK1W/rwpSyqUNAkAU/zcbjcmJydhs9kgEon4vUKqATogra+vc7e8rq4OIpEIFy9exLVr13jqTx1Qmlo2NjZyt5VoWURfClXAREZGIi0tjSXTtDIzM+FwOLhxoVQqUVdXxx4vr9eLiooKxMbGYmZmBunp6dBqtfzZpqWlccMlLy+PpcsymQw5OTnIyspi5PJma2lpCRkZGWx8TUpKwsDAAPbt24f8/HzU1dXx51BaWspQh5aWFkagr66uYmFhAePj4wzEyMvLQ2xsLMbHx1FbW4uRkRGo1WqmK6ampkKtVodsppBHhoo0wr4+8MADmJubY5KUTCZDQ0MDU+k8Hg8aGhoY50rIbLpuQiaPjo6iuLj4M2hwQkn/PXOux+NBbm4u+2doLyUkNzUSs7KyUFZWxhkZ5GeIiYlBR0cHOjs70dPTwzAJOiTS80xByGFhYSgpKUF5eTnXJ5stKvYJU76+vg6RSMSTYJraJCUlobGxkacMPp8PBQUFjP+PiYmByWRCfn4++10pRPN//ud/PpMfkpycDI1Gw9/RZisYDHLu26cP0Hv37uWDUVhYGFJTU1FcXMxZUCQnTUxMxODgIMxmM4LBIEpKShg2RHJ/ytMRi8XweDyQyWRIS0vjbJfNFlkPoqKisLy8zFLy5ORkFBYWYn39dgixQqHgwELyMJLkbXl5mQM477//foZfEA6aauvGxkaIRCLEx8dDJpP93YntZ67zH/qpv/1CCQkJnwnHAoC8vDzMzs6ytCc1NRUKhYJlCyShCQQCsFqtXORR15QIRgCwdetW/PWvf2X5CPkDEhMTQ3oNyFhM5CPqSFMSNOlSiZ9MuQ4pKSkcYkOFnkAgQFJSEv8zPUwkeVpYWGDMHCWNhyIUEYGIiCJEQiL+/qeTuamAILpMWVkZY/EiIiKQn5/PaDKif1ECa2FhISfnkgGNyCmbLQrhI7IT0Y/ooSJds1KphFgs5ukMjW0XFhbYdLu0tMRppnTwkkgkSEtL40RsMtsRcSQUm5+0l5TNQAc6oVCIkpISpo2oVComwVDnODk5mRPNyWhMki/a7OjgnJiYiJ6eHs7pIJpFqMKKumVE0FpdXUViYiIf0IxGI+Lj45GamsoFn1arZaMkoVD7+/shkUiYjU8oOpVKhYyMDNy8eRM2mw1JSUnsqyBD6GaLUI+EoiNJSFRUFCQSCReWNMal/A6v14vc3Fx+hoDbz/ba2hrfS4QCnZ6eRnZ2NoaGhhjJS6z5UIcguifIYDw3N4epqSk2uBYWFsLlciEiIgIpKSlcAE5OTjLhKjY2lnGEpaWl6O3tZcLMp8lQ6enpjKImshV18jZbhGCkgyPJlEQiEVJTU3nvo0M54QEpSJHCLImJr1ar+RBCFBoiKlGgIz1Xn049v9OKi4vjz58wvGlpaWwmnpiY4AMy3Xs0aUpJSeFsGY/Hgx07dsBisfB0MS4uDlKpFFKplP+bjY0NbGxsQCqVIjk5OeREmSg/dK9RB5ZCOul3jY2Nhd1u5wMTNSPW19f5XUCfSWZmJtPjBAIB0tLSmGin0WhgsViwsrLC2OXN1srKCofpEZ0lMzMTWq0Wly5dQnt7O99XdAimqRsd5ohESPpnwpwStaqiooInv5QfRPdxqEUoVJlMBo/HA7vdDoVCwZI1ou7ExsYiJSUFKysrcDgcXBiQidhut0MikUAqlfJzTpj47OxsJCQkMBqZZBOE+d5sUU4L/Q5kxqXGQGxsLB9UCARBIIbMzEymEkZGRiI3Nxd9fX3cPKKiTC6XIyIigmmARO0hQs5miyTSwO2pPMFHKioqoNFouDCj5gAA3jMIv0zYb6KQKRQK5OfnIywsjANeaU/v7e3lzjBBPjZbREgkmlMgEGDfolqtxszMDGw2GyIjI3nqRHkiubm5XFNQACwFjhIxcGNjA5WVlQBuTyfo8yR6Xah3KgCup6hhSE1Ei8XCqGEiUpWVlTHhke5roiHR56/X6/n9Adz2a1HdtbGxwWAVku9utgQCAd+XlHtB722C6VATjqYUdrudg3/pMEHZShkZGUzzmp6ehkgkgkql4j+Xah862ISq4YjiRAf2mJgYxMfHIyEhAXNzcwycoAM+FeGEr11eXuY8uJycHJ4YE9Le5/PBarWiqKiI906Se9IztNkiawA1Eubm5hjRrdPpOFMnIyOD87kog4yeN6LRkdReJBIxOILqE5Jd6/V6eDwersf+nj+Zv99/6KcAxg4SchQA68Fqa2v54aWuSWFhITv8TSYTI0j/+te/IjMzk7VfVGRnZ2cjGAziC1/4AqM1qZMiFothtVo3vTbSGJOLn7rApEUUCAScsnrt2jXExcXh8uXLWFlZwfHjx7G4uIjy8nIAtzextrY2lJWVwWAw8Aua5CUDAwNQKpWYnZ2FyWTiJNbNFuUZEB+9t7cXCoUCNpuNfQJk8KQieWhoCBsbG3C5XEhLS0NdXR0ngXd0dPDhgeg4ra2taGho4Id5dHQUvb29EAqFIT83etF9OpyQxmGUeEs3X1RUFDIzM9Hd3Q2NRoOWlhZMT08jOTkZgUAAqampuHHjBp+EKU8jKioK+/fv53E9HQT/nnE4OjqaO9zBYJATx0mfSshTMrOSESsmJgbHjh1Db28v1tbW0NzcDIPBgMnJSeTl5aGwsBDx8fEcvEP6VI/Hg8HBQXR1dTHSd7NFG75IJMLs7CxGRka4y5+amgqRSMQEIDJQ0T3odDp5mkJITIvFgtTUVKZUEQu8vLycmfqURk+UkM0WsfRpMyPdps1m42Af6rguLS0hNzcXJpOJsXtyuRwpKSn8uSUlJXFyKSEVFxcXWU8vFAqxsLDAtKVQkjMAnIpNAXaE7G1tbUVTUxOGh4e5kPB6vdizZw88Hg8XH7Rhj46OYvv27ZDJZGhsbERNTQ1kMhlGR0dx9uxZ7Nq1Cx9//DFWVlbQ19eHa9eu/d1wIalUyl1q2i/Cw8Nht9tZ469UKqHT6ZiMMjMzwwcnahS0tLRAIBDg2LFj0Ol0SElJYWkUSdDOnDnDDRAqEkLR6wwGA5aWlpCcnIzIyEjMzMwgLy+PAyePHDmC/v5+uFwu9vWYzWakpqbyc5SSkgK32w2dTofo6Ghs376dDzAUoJqXl8e0u97eXnR3dyM6Ojok+YSM25RMPjs7i6KiIphMJjYLLy0twWKxoL29HXl5eZ/xvlFq+urqKtLT0/H888+juroaOp2OUcYWi4WnIQkJCTAajWhra0N4eHhISQbtvQTjIJTn2NgYdDodB6CRTIs6t2FhYTAajazHttvtMBgMPCmXSCRMTlteXkZtbS1MJhM/a8PDw4z53GyRPJSe+dXVVQ5gKyoqQlJSErRaLYRCIVwuFyorK9Hc3Iz19XV88MEHWF1dxV133QWDwcCUJaVSyV4MrVYLi8WC0tJS9q7RO5UmpJutmJgYDialRlMwGERraytLdoRCIex2O1paWqDRaHDr1i1s27YNBQUFKC4uhl6vZ88JBSsSZc5gMPCh12azISIiggPX4uPjQ75TCUZCB0bgtiST9sexsTEmlQ0NDUGpVEImk6GgoACTk5OM1id/Gk1n6eBKBvM9e/awRGxwcJAnHCMjIyGfBSqwg8EgBxx2dHSgpKSEJymTk5O8x546dQqRkZFob29nyt7s7CzS09OhVqtRUVHBDb2ioiLExMQgLS0NXV1dSE1NZVSp3+8POUWTSCQYHx9njyUpGrKysniStLS0BKvVitHRUWRmZqKnpwdOpxPnzp1jGphGo8HBgwfhcDgQGxuLoaEhLC4uori4GEajEfX19UwCdLlc8Hg8vB+FurZLly7xdIHQuOPj49i/fz97FiwWCy5evIiGhgaMjIxAq9WiubkZKysryM/Px9zcHOLi4nDp0iXs37+f61av14t/+Zd/YXgPeV28Xi/CwsJCfqfx8fFMvxMKhVhdXUVCQgKuXr3KHkNqmHd2dkIul+PIkSNITk7mIOji4mKoVCqUlZXBbrfzNIkOuRMTE9yUpLqTzOChDkFEEJNKpdycJQQvIZQpu40ObiQlHx0dZfrYxYsXIRQKcfToUayursLhcDB2vKKiAlKpFB0dHQxWIB9IKO/0p9c/PNEgQg4AztNQqVTcScvMzMRLL72Eubk5HDx4EKOjo1Aqlfjoo4+Qm5sLANztnpmZQVdXF7q6uuB0OqHVahl7SDf91NQUIiIi4Ha7+QHcbFGBTFQnj8fDXOPZ2Vk4HA6eCOh0Oo55p25eSkoKhEIhhoeH4XA4UFRUhKmpKURHR3Piuclk4k70wMAAhEIh9Hr9ZxI977QCgQC0Wi2HOsXGxrLBj+Q0n5YAAbdTXkmnRwjBlZUVXLx4Effeey96e3sxOTnJqdFVVVW4desWRCIRrly5ApVKhdTUVIyNjYUcu1GHNBgMcudzcXER8/PzCAQCSElJYVZ5XFwcurq6sLq6ihs3bqCyspKDeYRCIbxeL9RqNY4fP47s7GyEhYVhcnKSU0UPHTqE69evw+VysakrlFRkeXkZGRkZnwmG6urqQlhYGBwOBwKBAC5fvoxgMMhTE61Wi+XlZVRUVGBmZgZOp5MLnfj4ePT39/Pmt76+Dq1Wi+PHj6Ourg4DAwPQ6/VQqVQ8lt1sBYNBNpJRF8NqtcLhcGDfvn3IyMhAW1sbXC4XlpaW0Nvby51Bk8mE+fl5lJaWIiUlBS+88AIyMjJYEmKxWNDW1obp6Wl0d3ezxJDQzZ/uHt1pkexFIBCw6Z0yX4aGhjgnJCEhAXK5HOfPn4dOp4PD4UAwGERbWxvzt//nf/4H3/ve93Ds2DHu4IhEIhw4cABXr15FWFgYxsfHkZiYyPxvgUCAvXv3bnp9HR0dPA0jWZfNZsNDDz2EH/3oR0xGIcni6OgowsPDcfToUTz11FPweDzo6upi4/KBAwfQ09PDaOeuri50dHSgvr4e27Ztw40bN1BWVsbysFATPr/fD7/fj7m5Oc6YGR8fZ3MySRcCgQBycnJQWVkJo9GIuLg4pKensydNIBDA6XRiZGQE2dnZMBgMGB0dRWdnJ7RaLaanp6FWq+H3+2EymZifH+qeGxkZweHDh3Ht2jVMTEwgOjoaHo+HTf2E2SX09a9+9SuWTlJX1u/347vf/S7jJzs7OzE1NcWdPrfbjZWVFTamFxcXM1kkFOK2o6MDhw4dQkdHB0ZGRniCRthLl8uFjo4OPtj39/djfX0dHR0d2LZtG+x2O44dO4ampiZoNBro9Xq88847jIUuKyvj31MqlaKtrQ0xMTGorKxEUlISYyHvtBYWFhg9W1RUhNraWty8efMzUg6fz8fIVJvNhpGREQY+0MGWdPC7du2C1WplhCtNNrq7u6HVauF2u1FRUYGSkhIYjcaQJLHl5WXU1NSwtFEmk+Hq1auM7Z2enkZ7ezvnVIjFYqYjUXMjLi4OarUa77zzDnJycpCfn4/CwkKesLndbvz2t79FWVkZ054WFxdhsVi4gXCnRVIogiuQvCcpKYk9W+S/q6ysxPXr11FQUICxsTGGxfh8Pg6TfOSRR/D73/+e7z0qdqkQWllZQV5eHpKSkhi2sNki6SFN5Chte/v27bh48SKSk5PxwQcfYGFhAUVFRRgZGUFiYiIuX77MAICJiQlERERgdHQUt27dgs1mw9jYGPR6PdOiTp48yQd7mpbTf7PZIqkLyS5J7bF9+3a0t7dzTUFhdHNzcwgPD+esA4FAgO7ubphMJshkMnz961/HkSNH4HA4+Jmfnp5GbGwstm7dihs3bqC4uBjFxcXcMNts0cHA6XRCIpEgNjaWJ5iE646NjYVCoYBAIMC1a9cA3JZF07SA0r5PnToFo9GIkZERloBPT0+jqqoKgUAA3d3dnNUUCARgs9lCTtDGx8eRmZkJlUrFABxSd7S0tOCuu+7iv58adiKRCL/73e+wuroKtVoNk8mEvr4+TE9P46233mIptEgkwsDAADcDBwYGEAgEuCuv1WpD5t3Y7XY2mZOkPCIiAjt27MC5c+eQkpICl8uFhIQEVFdXc0hpZWUlMjIy0N/fj46ODm5QGQwGjI2NsfxMo9HA5XLBaDRicnISra2t0Ol0EAgEuHDhAmpqaja9Njq4kow3Pz8fPp8PWVlZOHr0KKRSKSYmJjA7O8ue1crKSrzzzjucF7W2toZgMIiRkRHOZbpw4QJPjsjjRSQ6kgW2trYiOTk5pDyf1j980FAoFBgYGGC3Pp2CRkZG0NzcjH/913/FgQMHGBlmMBiQkZEBrVaLiYkJqFQq7Nu3D2VlZfj5z3+OtLQ07N69m2VEAwMDOHPmDBoaGqDRaGAwGOByuTA5OckJzpstMqGTFtRqtaK3t5dRlDk5Oejo6EBHRwcbkh944AHk5OTgjTfeQF9fHzQaDbZu3QqPx8PorvT0dAiFQrjdbpjNZtTX16Ojo4MTYMPCwrho2GyFh4fj1q1brIkMBAJwOp0sS6ivr8fk5CQuXboEo9GIxMRE3HXXXbBarejq6kJnZye2bt2KXbt28fjukUce4W7Y4OAgJicnUVZWBr1ez/kcFOgVqmtFAT6kE56ZmYFarYbL5cLu3bt5E3K5XLhx4waKioq48L1+/TrkcjmKioq443nlyhV8+9vfZm6zXq/Hz372M+5skf6TKEGhdJECgQC9vb1ISUnhYD4qTpRKJe6++27odDr09PTAZrMhPT0d27dvh9VqxSeffILo6Ghs27YNRUVF+Mtf/sLm3ujoaERHR7PBXS6X45577kFZWRnGxsYwOTnJ4Tmbrfj4eAwNDfGkRyqVwul0wul04vXXX0dZWRkaGxsxPj6Ot99+G3fffTeSkpJ41D01NYXOzk48/PDD+Na3voV3332XJ4EU4PWLX/wCDz74ILZt2waPx8MAgPj4+JCdebomOoxFRkbC4XBgZGQEDQ0NnPVhMplw8+ZNJCQkIDo6GtnZ2fj5z3+O9PR07N27FyKRCMXFxWxmFAqFbFSn57SpqYlNcDQNCVW8ALdH9263G+np6dDr9YiOjobRaMT777+PwsJCPP300/jzn/+MN954g9Gazz//PL74xS+yufjw4cM8Em9ubkZkZCQWFxexuroKjUaDyclJWK1WVFdXIywsDB9//DHsdju0Wi2uXLmyKdkpKioKZrOZJXUUAPXnP/8ZkZGRqKurY3Pk/Pw8G5KFQiFycnKQkJCA9PR0GAwG/OEPf8DBgwc/E7pFxLWlpSUea9PEkF78oZ6H3//+9zzFoxDRrq4ujI2N4eGHH8b6+jpGRkYwMjICnU6H3/72tzh8+DC+/vWvIy0tDf39/XA6nXC5XDh16hQeeughJCcnY2xsDO+++y6EQiH+9Kc/4cUXX4TT6eR8GaFQGNIsKZfL8fLLL6O+vh6ZmZl8AGppaUFDQwPq6urg9XrR09ODQCCAM2fO4Ne//jWOHz+OjY0N5OfnM4ziW9/6FoRCIe6++250d3dzB5waIAaDAampqQgEAqxtDjXhq62t5UwYs9mMqKgodHd3M1K1rq4O7e3tTO9xuVzYs2cPXn75Zfz85z9HXl4esrOzMTAwwO+LgwcPQqvVIjIyEgqFAqdPn+ZGR15eHocf7ty5M2QXNyYmBuPj4xAIBIiNjeXMolu3bkEikWD79u3Q6XTo7u7GjRs3WJaSm5uLv/71r7BYLJDL5Xj44YdRWFiIn/zkJxyqp1arMTc3h9/85jf4r//6L4yPj8NgMHDjpqCgIGTzjibqUqkUqampiImJwbVr1/DLX/4SX/rSlxj/bLVa8frrr7OmfmlpCYODg4xJtdlsuHDhAnp7e7F//37s3bsXJpMJH3zwAXJycrB3715ERkYybrSlpQWZmZkh95Hp6Wl88MEHqK2tZa/GwsICfvCDH6CxsRH79u2DVCpFe3s7vH8L1bXb7VwLpKeno7i4GAsLCzh+/DisViuef/55dHd3Y3R0FFeuXMHY2BgeeeQR9sRRLeDz+ZCXl7fptcXHx/MUm4IlR0dH8YUvfAH3338/du3ahZiYGLS2trJs79ChQ3C5XJw1Fhsbi6997Wt45JFH4Pf78eijj7K3w+Fw4MyZM3jqqacwNzeHXbt2ISoqilG1mxH1ALDs1GQyQSwW8yH16NGjqKiowOLiItxuN+9L1dXViI6O5oaLQqFAZWUlpFIp/vSnPyEnJ4cl0UlJSfD7/Xj++efx5S9/mb2ZRNei+2izRbk/dFDS6XRMFXv44YcZE5yVlYW2tjZ0d3ejrKwMAwMD7JFISUlBXV0dvvrVr/J3R14F2vs/+eQTzsAYGxuD3W5HZWVlSKAPSfdzc3Ph9XphtVoxOzsLo9GIoqIi3Lp1i712IyMjWF1dxQ9+8AOmWaWnpyM5ORkFBQX42c9+BoPBwPXM1NQU7HY7I/sJgW+xWJjcFepZyMjIwPj4OEtIIyIi0NXVhbNnz3LwLh0+wsLC2OvY2NiIvr4+JuQBt1UkHR0dqKioYNAKNd1osulwOFjeRUS1f2T9w9Kp/v5+bN26FQC4ECsoKMDDDz/M7P/4+HjU1NSgoKAAZ86cQXp6OqKiovD4449j3759PK4jbeCNGzdw+fJlTE1NIT8/H/fddx8GBgZw9uxZ/OlPf8LAwACioqJYZ7bpLyEQIDk5mVF3Go0GQqEQzz77LKKiolj3XlVVhaeffhpVVVVQKpXo7+9HQUEB1tbWcO3aNTZzfu1rX8Pg4CB3hK5fv46cnBycPHkSarUaUVFRCAsLw8bGBmN/N1vr6+vYvn07j8KSk5ORnJyMqqoq7sjMzc0hPT0dn/vc59jMffPmTezatQtbt25lrV9sbCxmZ2dx+fJlzMzMsPejuroab775JodSeTwezM7OcsDVZmtsbAwqlYoJAk1NTVhYWOBOMMkJlEolvvjFL2JwcBCxsbG4cuUKk4D6+vo4hO/555/HrVu3uDu4uLiIZ555hk/OZ86c4ST5wcHBkMX84uIiGhsbGfu4sLCAoaEh3HXXXbxxqNVqlJaW4uGHH0ZPTw+WlpZw8+ZNlJWVIT4+Hh0dHZibm+Pwm8uXL2NkZARRUVGorKzEjh07sLi4iFOnTuHo0aOIjo5GfX09+4w2W6RFpsRonU6HpaUlHDhwgAvU9vZ2LC4u4rHHHoPFYkF5eTkncsrlcsYnv/rqq0ytoOTjiIgIPPnkkyzfuHbtGvsPKIdjs+XxeFims7y8zBSWRx99FBMTE1xAkCmNUIYikQi///3v8ZWvfIXpGk6nExMTE7Db7dDr9dDr9RAKhdi1axcGBwcxPT0Nl8sFlUqFvLw87nqFWvTS7urqwvvvv4/jx4/j5s2b+NrXvsZa+LKyMi6Qt2zZgunpaWamLywsoL+/H3q9Hj09PdBoNHzYJnnGd7/7Xbz88svo7e3F3Nwc7r33XjzzzDNITU3F7t27N722qakpDvBcWVlBMBjERx99hHvuuQdVVVXYtWsXDAYDpFIpVCoV2tvbcfjwYSQmJrK++tSpU/jwww8xNDSEgoICTExMsN9MLBbj4MGDPB32+XyMK3U6nSE7fkKhEHV1dZwKTvkcDQ0NKCwsRHJyMuM677vvPu6o19TUICEhASqVig9Q/f39bJQvKyvDzp07kZubi+3bt2Nubg7Hjx/H9evXodFo2EQfygfh9/tRW1uL0dFRjI2NQfq3bISHH34YZ8+eRW9vL8LCwpCTk4OGhgY8/vjjPIUkqpNCoUBFRQX0ej2eeeYZjI2Noa6ujqe1PT09uHLlCm7evInW1lbExsbCYDDA7XaHBBCcOnUKMzMziIiIYDPvyMgI7r//fqSlpSElJYVTyDMyMvDJJ5/A5/NBrVZj//79KCwsRFZWFr70pS9hdXUVubm5OHv2LM6cOYPe3l643W7ExcWhs7MTRUVFsFqtXBS+9tprITvM8/PzkMlk3CknT9f9998Pr9cLh8OB5uZmLCws4NChQ2hpacEjjzwCp9OJr3/963j44YfZbzg1NYV/+7d/Q1FREQMK1Go1Tp8+jdbWVtTW1mJiYoLJQZOTkyEx2eRzIspPZ2cnAOD/+X/+H5Y3Dg8Pw263o7S0FMnJySydoXcWmarLysrwrW99i9HLiYmJSE5OxuHDh3Hs2DGWvFKThJpymy2pVIrvfe97rFCoqqpCeno6vvSlL3HWz8TEBBOWVldXUVFRgYGBARw6dIjD0XJycrCxsYEHH3wQCwsLKC0tRXp6Ovx+Px588EGsrKxgx44d3OgkUlSoDq7D4UB5eTm8Xi9MJhM8Hg8cDgfefPNNyOVyxuyrVCo89NBDUCgUqK2thU6n4/qos7MT586dw969e/Hoo4/iF7/4BXp6erC2tga3243ExESWTb311luwWCxQq9Wc7LzZIuQuGcAJVZ6ZmQmn0/kZv2xFRQXOnj3LdcfTTz+N8vJyVmasrKxAKBQiOjoaZWVlTI1744030NraCofDgbW1NZ4gj4yMhJQA6fV6pKWlcaI4NevuuecevP3223jnnXdw4cIFxMTE4KGHHuIMp/DwcOTn5yMQCODkyZO4ePEiXC4XqqqqYDKZYDKZsLKyguTkZPz7v/87y7hIwkwo5FDBmktLS3y4JL9DdHQ0cnNzIRAIsHv3bmRnZzN0QCQSITExEZ988gk6Ozt5TxCJRKitrcVDDz3E5ndqmuzbtw9ut5ulwGVlZdBoNOzF2mxZLBZs27aNIQDkLVSpVAgGg0hPT4dYLOZGzeTkJDY2NtDb24vt27ezbIsCVxsbG+H3+7GxscHqgZmZGQ53vX79Opqbm3Hu3Dm88cYbf9eHRiss+Pcqg7+tl156CXFxcejv74dSqURRURFmZmZw+fJlHD58GB988AF8Ph/jPZ1OJ3cN2traoNPpWMaiVCrxy1/+Es8++yz6+/vh9Xr5RFxQUMDGVxqhbt++HadPn8a3v/3tO17bX/7yF4SHh7PXwOv1Mm0kIyMDkZGRLBmRSCRYWFhgw257ezuysrKQnZ3NRqfi4mKcOnUK9913H8LDw7l7LxQK8dFHHyE/P59HnaQnbWxsvOO1vf/++5zIuby8zAcEv9+PkpISBINB7kakpqZiYGCAKSqEmyUqRlxcHN59913U1NSwCVkmkyEiIgKVlZX46KOPuFsfDAZRVlbGD8Kd1ttvv80PHNG9yHSl1WoRHh6OxcVF7twtLS2hpaWFCyiZTAaVSvUZIz29LCg1e3BwkNO2b9y4AZ/Pxy8Sl8uFL37xi3e8tt/97neIioriQylhaufm5tDU1IRbt27BaDTyg61SqVBeXo6PP/4Yo6OjiI2N5YAhm82G0dFRFBYWcios6Tbvvvtu3Lhxg3MbiGLidrvx1FNP3fHafvaznyE2NpYlAsDtw25NTQ2uXbuGsLAwzM/PcwctOjoaAHDu3DnMzc1BrVZzYKFKpeLwuYSEBNaye71e1NTUcIDg/Pw8T5jCwsLw8MMP3/HafvOb3/Akj3waVqsVer0eZWVlOHLkCAoKCjhfg4J7mpqacPnyZcTGxkKv12NlZQVqtRrT09OMihUKhZicnMS1a9eYtCMSifiAT8XuZs8C3XPXr19HVVUVb6IKhQIffPAB7r33Xma9T05Oore3Fw0NDUhNTYXZbMbY2Bjy8/ORlZWF2dlZ9PX1YXR0FI2NjUhJSYHdbsf58+cRDAZx+PBhdHZ2sqyRDHyDg4P46le/esdre+GFF9jcuLKywgZMSgTu7+9nAldERAQSExN5okHUjtXVVcbs0r00OzuLrq4uDA8PQ6VSobGxESaTiRHeS0tLTDfa7Hv93e9+x4dfIp3Fx8fjpZdewr/+678yYpkyBkiaubKygpmZGSwsLLCURSqV4vTp06iqqsLS0hJUKhWUSiXeeustiMViVFdXM56aqCPt7e34t3/7t02fB5oGpKSkID8/H36/H5cvX+a8jKWlJQC3tfRyuRw9PT3Izs7mTAcKGiMYh91u55dlR0cH/vCHP+DNN9/EpUuXUFlZiYSEBDZr37p1i3N0/u/1+9//nnMkDAYDIiIicObMGZY+OJ1OuN1ulgOXl5fD5XJBLBajq6sLBoMBxcXF2NjYwLFjx5icRybryMhIyGQyzM3NITo6Gs3NzYxk1+v1MJvN+OEPf3jHa3vrrbcAgO9LCnXbsWMHLl++jKSkJA7BW1pawj333AO3241bt26hubkZ6enpqK6uZmiH3W6HRqNBMBiE3+/ncMujR49i69at8Pv9GB4e5uaAXq9nY/H/vf74xz/y1MZqtcJqtSIqKgrj4+PIz8//TDYC7TcXL17EoUOHGDZBaoDY2FjGPK+vryMYDGJ9fZ2fYQoEvXLlCqeep6SkYPv27Xe8thdffBFTU1MMtCAJsdVqxf79+zExMcG0MpLtmkwmdHd3Q61WM/Xq6tWryM7OxtTUFGpqajA2NsbgkpGREQ4mJOM8+Xz6+/tx+PDhO17bn//8Z6Snp3N4ZmpqKiszSkpKcPbsWWg0GpSXlzNCXKvVsrSRAkdbW1shk8lw4MABnD17lr2nlLXy8MMPo6+vj9ULdDA0mUx45pln7nhtP//5zxEXF4fV1VXMzc3x37eyssLFNzUFY2NjeQpAVDmlUgmn04nf/va3+MpXvoKhoSFUV1djdHQUfr8fKSkp3BgeHBzk4vnT9LHNCHGvvfYa1tbWoNPp4HK5YDabodfrObeMni8icWk0Gly8eJHphGSgp/uL9kLy3URGRrIMaWlpCcvLyxAKhYzeHxkZwYMPPnjHa/ve976HhIQEGAwGrKys8Pt5ZWUF9957L8bGxhAbG8vY8P3790MgEGBkZARyuRzJycn8fBICnqa7lKFz6dIl7N69G319fez5W15ehsFg4PTzO6333nsPc3NzPEVLTU3F6uoqzpw5w6oU6d/S4MfGxpCXl4fp6WnGsBP1lSR85O2IiYmB2+1mWW5CQgKuXbuG/Px8nswTRW2zz+3T6x+WTnk8HsaNzs3NweFwwGq1IjMzk/X9VHB4vV4UFxczBpSCr0iv5/F4EBERAa/XC4lEgvX1dSwsLPAGSWabgYEBTE9PQ6/XhzQ1+/1+JCUlQSAQcNo1JYQTF9jn8zEBgiQlRPSIiIjAysoK1tbWsLCwwBSi0dFRLuwoJCwlJYUpBWTkJMLWnVYwGIREIuGO4Kf/vOXlZabXLC0tQa/Xs5ma5Fp6vR5qtRpTU1Mwm81c4LjdbszOznJnwWq1IhgMMuqOxsmhCEVUTFJH0O/3QyqVYm1tjQ9D6+vr/NkolUr2MJB/BACb8Hw+H5KTk2G327G6usrhSyKRiCVmdOAjveVmi7wG1Jl3Op2Ii4tjLCrRlUjXnpqaitnZWSgUCqaL+Hw+nDt3DtnZ2YiOjobX60UwGMTMzAzfq1SAUXDk/Pw8UyI2W1RsiEQizM/Pw+/3c7Dkp3nkFEBFwYCEGCVTME3ESENOpmCJRMLkNqKbkVSE7qHNFkl0KKGWCkUy99GmQjSPjY0NqFQqDkaktF+n04mWlhasrq7ydxoMBtkkNj4+zsGGREVxu90hO7jA7a4adXHsdjvCw8MRFRXFNCXyZ9Dztba2hqmpKczPz2NxcREmk+kzqFnCCRMBjCQ6Dz30EOx2O7984uLioNVqQ14f7VUU9EiHbolEwhJPus9JxjMxMcEGXDpgE2Kwr68PaWlpXGQnJiZieXkZwWAQs7OziIyMhMfjwfLyMhITE/nn7rRoryQwAmELib9OzxQV7CUlJQzhCAaDEIvFWF1dZS007eMSiYSlZyRvCAsLQ0tLC6Kjo5lQE6r7TfcTcLtL73K5+DPMysqCw+HgpsnGxgbkcjlMJhPnnwC396L5+XlUVVXBbrczSpLkeCQhnJ6e5m4udepCyZMoaV4sFnNOB02liYRIacvT09OoqKhAZGQkh3otLy/D4XAwYnp+fh7BYJDTxslDRx3djY0NhIWF8X0cao+j74/uPXonkvSM/Ae0vxCcgOQj9J0TKpPkX59GXBMy2+12QyQSMXmK9sPNFgXChoWFMbI8LCyMu+Kjo6OsoQ8EApDL5VhZWcHly5chl8uRkZHBYa5E5yL8LUETxsbGWA5ElCyaDre0tGx60CD6UCAQYGkOXSt5Q+gdQP4jl8vFeVGrq6u8P6ampmJ6ehqdnZ28dwoEAszMzCAxMZEzpcjrRu/czdbGxgbXIXNzc0x1pP2EphaDg4Osmx8YGIDVakVubi7i4+O5APV6vXwdXq8XdrudfX/BYJA9n16vlw9ef08ORwcWojQRwIGKbzIXA7cbGn19fYiNjf0MYZPIcm63m38vAqS0tbWxPNrn82FxcZGD7ux2+6YHDWq2RERE8EGHwBILCwvY2NjgzCEqkp1OJx+ugNtTX8p5ys/Px+DgIAYHByGRSDjM8dChQ/jkk09YzkVhe6HITuR5dLvd/Dv5fD4oFAr+HqjoJmn21atXkZCQgNHRUZ6GUQ1Dwb1k4nc6nSypn5qa4rBhOiiRzPpOi6TvRDuk5hARt8jLSwhjeo9RyDAduIi02tLSAp1Oh/n5eVitVoaOEHGOqHVUr/w9+At/hv/QTwGwWq3QarWQSCR8Q1ssFlRXV3OKs1AohMlkwsTEBPLy8iASiXDp0iWeUFAHt7m5GWKxGP39/TAYDMykp4Rc2thofHzlypWQZh060BB/mfRva2trGBgYQHp6OofPUeEml8sxNjaGpaUlxl7GxcV9xhdCI3/Cd1Kac3d392eQZaFeJrSB0kuFuqE+nw9jY2PYsWMHJ69SjodCoUBPTw+mpqY412F1dRUXL17kRHWJRMJ5IUqlEi0tLRxok5CQgI2NDRiNRpSVlW16bT6fD9nZ2ZyBQEVpREQEf98AOKyopqYGhYWFuHDhAstYrFYrlEolJicnAdw+rMzPz3MWyurqKjweD/PCia41NzcXUrNJGw91zI1GI1Mc4uPjIRaLeVK1trYGm82G+Ph4TE9Pc3aKy+XicJnw8HDMzMwwJYE2sVu3biEpKYk9DZRyG+rayDT86UPA4uIiJicnkZiYyBjoqKgozM3Nwe12M5OeCgB6IVLhTi+X8PBwzlzp7OxEfn4+B9wRJzvUKJXQg/Szq6ur/FLo7+/nDcVoNEImk0EikaCoqAitra0ICwtjdCtRh+iFRAXx0tIShEIhRkdHOcCPJjYEEQi1pqamkJmZySFgEomEEZ90UCMSVUJCAhwOBx/+AoEAJiYmMDc3h6KiIlgsFigUCoyMjLDplHCFp0+fZqmC1+tl7GOoZ5WKlWAwyJ8xUWOo++X3+zE5OYnp6WnW6JrNZu7iklmfSEX0fKhUKjYuU9Cnw+HgLBF6CW225ufnecJIRTyBGIjERghfermQFpzCVWky2tbWBoFAAL/fD7lczgW8TqeDXq9HREQEJiYm+HpoUrnZmp2dhV6v5+720NAQ1tbW+DkhOhsZXMmD9/bbb7NcSiQSwefzYXh4mKd39BIXiUSoqqqCxWKBXq/H6OgoZmZm2HQfyiNHZB2xWAyn0wmr1QqpVMrYZMpgWlpaYiogkenkcjlP0nJycjgTxOfzcRZNXFwcY5SpOKcinuSQmy0q0sPCwpCUlMTd5tnZWf7v6c8UCoXo7OxkKhZNU2jfpneM2+1m5DfdVwsLC+xToMYGkaw2W9TIob2HvluazCclJcFisfDEOS0tDbm5uTh37hyysrJ40rOysgKz2Qyr1Yr77ruP92WLxcISVJ/Ph87OTkZyms1m+P3+Ta+NgAcWi4UL0ImJCUgkErS2tn7mAOTz+XD69GnU19czNIASrqnoFwqFXFCTBEUkEiEpKQlJSUlckHk8HoyNjYWU6hF2nZQO5Auam5uDyWSCRqOBzWbDjRs3sLGxgUOHDrF0Frj9nH+6CdHT08PktEAgAIfDgZiYGJ6OEnrc5/PBbDaH9GRSUUiTIPqMgNtBpTKZDC6XCzMzM5xJRg245eVlWK1WCIVCHDx4EJOTk/B4POxXoHwKQhUnJycztY0aq2NjY5seHgnhT7VYeno6ZmZmYDKZYLVakZaWhrGxMe7yU83idrthMpmYNlVUVITOzk5kZ2cjEAjA5XKx8mNubo6bYkSTm5qawtTUVMj3PR1kp6amuJagzIuxsTE2u8/NzSE2NhYTExM8qb1+/Tp7sBYXFzE3N4eJiQnExsaioKAAXq+XQRKzs7Pc+P40bTMUSWx2dpYzzug77unpQUJCAiYnJzk0mb7Pnp4e1NfXMxSBmtMESKKmilwu5+bLyMgI0zQ7OzsxOzvLMQ2hapFPr3/Yo0Hjyfz8fJSUlPCY7LXXXuMOAmVFjI2NobOzE+Hh4XjzzTdx5MgRXL58GTabDTqdDhkZGYzYIhMgJc/SQ9bR0QGz2YywsDAOx9tsRUdHY3Jykru+i4uL0Ol0GB8fx9TUFLq7uyEWi7lbsrGxAbPZjPn5ebz77rtM8CBsp1AoRF5eHgwGA3cYlpaWEAgEkJCQAJlMBp/PB7fbzcXNZismJgZWq5XJP2NjY0zyoI5SRkYGEhMTMTo6ipGRETaiq9VqOBwOtLe3c+eUigexWIzc3FzIZDJG8RESdGpqihn/ofwjMpmMD2Dz8/OsZx0eHsbg4CBUKhVLnFpaWjA/Pw+BQID6+noUFhbyRkpZKnTIpDyIqakpdHR0YGBgALm5uXA4HJienmZOdigdLh1GFAoFEhISsLa2htXVVbzzzjuwWCwYHBzkU/frr7/OmvKLFy/i9OnTGBoaQnx8PMrKynDjxg384Q9/wMLCApRKJR9u3W43/H4/6uvreWNfWlri8KbNFqHhqNs4Pz/PBe3ExAQ/H0ajEWfOnOEwssHBQdy6dQvz8/OQy+XMkyf0amJiIjY2NjA6Oopr165BKpXyhGphYQFutxtSqTSklp8wjYSIpYMq+TDI+L2+vo53332XM2iIRNPW1oa+vj6srq5iz549LO2jgpi64ykpKdi6dSuysrK4OyyRSEKmvQO3AQSdnZ3wer2QSqUsW3zppZfQ19fHGTDEmx8bG0Nvby/6+voQDAY5oZVY4xMTE8jIyEBSUhIkEglSU1M5EGnbtm0cbkVSOeLC32lFRETA6XQyu3xjYwNKpRKXLl3iwwblSlBI2KOPPorc3Fx+nmUyGXsmDAYDlEolmxjT09Oxvr6O7u5urK+vY2lpiTXbhBLdbKWmprKxMCcnByqVCj6fD7du3cLFixe50ycQCGA0GvHee+9h69atePzxxxEVFcX3DplxaZJ38uRJHDt2DFarlUOc9Ho9Pv/5zzOGm/IuQn2nkZGRKCkp4SIzJiYGH3/8MY4fP84o1OnpaS4w6+vr8fjjj3OAKeXefPTRR/w5JyUlYXp6GqdOnYLf70d6ejoeeOABHD58mPfG5eVlFBcXb3ptRDESiUTIysqCwWDA3NwcPvjgA/YAkhdvfn4eRqORDdNutxsRERGch/LXv/6V8df0EjabzWhtbYXT6YRAIEBkZCQHEqalpYUsSmlaR1PO5ORkluTKZDKIRCIOh3vzzTf50NPS0sL+MAA8daQ0daFQyBN72seTk5OZ3kifeaj7jWSBVFBSinhpaSkCgQB2797N4YEOhwNSqRS1tbX4yle+gsjISJhMJhiNRu42nzhxgtPFgdukMo/Hwz4sahDQJHyzDi4A9gSS/9Dj8aC9vR3d3d2Me6YpxPj4ODo7O1FRUYGDBw9CoVAw2tTlcrHsJTU1FVarlYmQxcXF2LFjB7Zs2cIZMZ9GooZa3d3dfHgMCwtDb28v/ud//gfz8/MAbntMoqOj4XA40NXVhZqaGoSHh+PEiRNobm5mXwcA9PT0wGQysXQrKioKxcXFGBoaYrXD6uoq5ufnOa9js0UNIZpCuN1uWK1W9n2RxIjM31arFQ8++CBqamo460ev1+Ouu+7iKVogEMDQ0BA/Y9nZ2fD5fEhJSYHH4+Euf1JSUsipAcFSCDdNB2WTyQStVguNRgOxWAyz2YwPPviAG8Q0laX8h/z8fNTX1+OVV15BWVkZSkpKOGOioqIC/f39qKmpYZWBy+XiA/hmKzw8nJ9v8hdptVqYTCYMDAywyZrITW1tbcjLy+Pm5vDwMHp7e/mdQhMUmq4Rbay8vBx6vR46nY6/C4VCgcLCwk2vTSqVYnx8nAEUZKB/8cUXsbCwAK1WC5FIxFJboqc5nU40NzdjeHgYs7OzHGWQkZEBtVqNkpISNDY2Qq/XM75YLBbD4XBww0WlUoVUfnx6/cMHDZvNBqFQiKtXr+LUqVMYGRmBWCzGr3/9a/5AFAoFysrK8OCDD/JNs23bNqYQ0Aj++PHjePjhh5GcnMxhKBEREfja177GCd0mkwlNTU3453/+Z0gkkpDITJFIxCjFyMhI7rrs3buXT6MTExPczbp06RL27NmDjY0N7Nu3j0e+OTk5aG5uxjPPPMOmmaSkJKSmpuLJJ5/EzMwMLl26BIvFgqqqKuzcuRNutztkF2FycpILk7KyMuzfvx9qtZpvxL6+PkajkT68o6ODczCA292l8vJypKen4+6772ZdJQAkJydDIpHgo48+wvz8PKfIVlRUAEDIG2FjYwPp6elwOBwQi8Woq6uDyWTCPffcg5WVFbz77ru4dOkSNBoNvvGNb7BWlSRcJBkhFnV1dTWHLNJh4/DhwyzLyMvLw9atW5GXl8dTj83W8vIyU4VWV1d5s3vjjTcgl8uxZ88ebNu2DQ888AD+/Oc/o7GxEa2trcjKysI3vvENyOVyTE1N4bnnnuPwu9TUVDgcDpb83H333fB4PPB4PNDr9di/fz+ampoglUr54LvZSktLg1wuR0lJCQ4cOACtVounn34aFosF4+Pj6O/vx+rqKoqLi9mDZLfbIRKJ+AWjVCrx4Ycf4kc/+tFnRtjh4eF44oknWL9LB+64uDjObtls0UGAimWBQACTyYQDBw7gxo0b3IVKT0/Hd77zHfT29kIul2Nubg7FxcXweDw4ffo0d76/+93voqCgALt370ZpaSliY2Nx4MAB7pR+8sknUKlU2LNnD39nodbMzAwKCgoQCAQ4L2BpaQlf+9rXeBNzuVyIj4/HQw89hPT0dLzwwgtsrnY6nfjoo4+g1WoxPDyMH/7wh3C73SgtLWUCx4EDB2C323HkyBF8+OGHyM7Oxvbt23HhwoWQnSsKnCN2vEajgUwmw09+8hNERkbCbrfDbDYjMjIS1dXVXKBmZWXhi1/8IiorKzlo6+rVqyyBo0NrW1sbHnjgAQQCAUZbazQa1NXVwWAwhEwtJ2/PzMwMhoaGsLKygsbGRvznf/4n1tbWMD09jfj4eDz99NN46623UFJSgtnZWVy4cIH57GNjY9BoNNi5cyfy8/ORkJCA73znOzhw4ACMRiMefPBB/OQnP8GPfvQj9PX1YcuWLdizZw+cTmfIAmZpaQmFhYUYHR3lRoPJZMLTTz+No0ePoru7G5GRkdizZw/+6Z/+iZO9h4aGYDabYbPZOO1Zq9WipqYGYrEYFRUVaGhoQENDA77yla/gypUreO2119DS0oLGxkZ86UtfwvLycsimAE20Ozo64PV6kZeXh2AwiKeeegrZ2dm4++67UV9fj/vuuw/f+973uOg8c+YMiouLER0djfPnz8Nms+Gf/umf8O1vf5uN6PHx8XA6nSgtLcWNGzegUqkwPDyMkpISNDU1oaOjgwv0Oy0i8/l8Pi5iiKCjUCiwc+dOCAQCTE1NQavVIjExEeXl5QgLC0NjYyMyMzMRHR2NrVu34siRI6ioqOBcpfPnz6O9vR1qtRpZWVnQaDTIyspCZWUlo15D7b9Wq5X166WlpTxJ+fznP89adAB4+OGH8cMf/hC3bt1CYmIi2traOOBrbm4ONTU1WFlZwfPPP4+Ojg7Gg2ZnZ+OnP/0p+vv7GeYwMzMDl8vFzbbN1uzsLORyOSQSCaKioqDVavHss89i586dGBwcRH9/P4xGI+bm5iCXy/Gtb30LN2/exMjICGpqalBUVASRSMQFc1ZWFifX+/1+nDt3DiqVChcuXOAMGEohl8lkIQlFPp8PcXFxaGtrg8PhQF5eHtLT03HhwgUkJSVh586dyMnJwf79+/GrX/0KIyMjuHHjBhISElBXVwexWIy5uTk0NjZCpVLh7rvvZqmZWq1GRkYGHz4XFhYwNTWFyspK7N+/nxOsN1skf56fn4dCoWC/wkMPPcQBkZQpQ4GF8fHxGB4eZpl0W1sbPvzwQ/h8PpSVlWF0dBQlJSVQqVS4ceMG9u7dyx1yr9fLGOjjx4+HnFJ5vV7s2rULYrEYQ0NDjJvfv38/1woEabjnnnsgFovxne98hyMRkpOT+TtyOBz44he/iFdeeQWFhYWorq7G3NwcXnzxRZhMJn53V1RUoLS0FLOzsyHzIAhjv7CwgEAgwKGwX/jCF7C+vo6GhgZUVlaivr4ezz77LJaXlyGTyVBaWopf/vKXqK6uZs+yQqFAU1MTy8QoJqK2thZOpxN+vx8DAwPcmH7ppZdCJtH7fD72HFHgYWVlJd544w0IBALk5ORAoVBAKpWitLQUubm5SE9Ph0wmw3PPPYe7774bUqkU2dnZePfdd/H444/DZDKht7cXJpMJIpEIW7duxeTkJLZs2YJAIICKigrs378fs7OzKCgo2PTaPr3+YTP4n//8Z/T29qK8vJwd6lNTUygqKkJubi6mp6fh8XhgNpvhdDqxb98+VFVVob29nbszY2NjeOWVV/Dv//7v+I//+A/84Ac/4ATJmZkZvPXWW/jhD3+ICxcucFw6vbjX19c3/cB/9atfYX5+HikpKdz9po7F5z73OczMzGBpaQlOpxMOhwP5+fn40Y9+hG9/+9ufIZhQWufMzAxu3LiBxcVFjm73er2499570d/fz2FnpNWbnp7G888/f8drO3LkCMurgsEgFAoFtmzZwgY9p9OJlJQUZlpvbGzg+PHjaGpqYq/E8vIyhoeHUV1dDbPZDI/Hg6GhIchkMmRlZeHatWv40Y9+xFhEklcolUoMDQ3hkUceueO1ffzxx+yZoAC6tLQ0ptVQ8ndfXx8GBgbw/e9/H3/+859RUVEBkUiEmJgYNhnRRIAkRUQRCQsLQ1NTE4xGI48PqfttsVhQX19/x2v75S9/ienpaURFRXFq7/j4OE6fPo2//OUvGB4e5kRLh8OBwsJC/OxnP8O3vvUtVFVV8WGVEuG7urrQ0NDAnzHRNyioUKFQcDczKysLb731Fl599dU7Xturr74Kl8vFZlaz2czhc01NTbDb7SwHOHfuHH7wgx+wIfX48ePQarXYvXs34uPjERYWhkuXLvFzEB0dzUnXO3bsYLLSrVu3MD09je3bt8NoNOLJJ5/c9NoIgUneo/j4eLz77rt48skn8eJoq3AAAQAASURBVKMf/Qjp6emoqKhAfHw8d2flcjl+9rOfMU1ucHAQtbW1WFpaQlpaGiYnJ+H3+zmvgmQRSUlJaG5uxtTUFHbu3ImwsDBUVVXd8doA4P/8n/+DkZER5Ofn84RPIpHg7bff5ukPTdbi4+O5wKXxN2XlvP3225DJZIiOjsaePXvg/Vs4YiAQwIULF3D48GFcuHCBde7r6+tISEiAWq3Gs88+e8dr++CDD3DkyBGUlpZy1odSqcTAwACb/sivcuHCBQ4nIx+WUqlkRG5bWxv0ej2ni5OMMDw8HDExMTh//jz27NnDk6T4+HioVCpUV1ff8dpeeOEFXL58GfX19dBqtWzaf+655xgOsb6+jrKyMhw4cAACgQCzs7P48MMPWapEBwCHw4Hvfe976OrqwrVr1xjz/Pzzz6Onpwd//OMf8fDDD3M2THp6OlZWVjY1mX7/+99HWFgYtmzZgvj4eCwsLGBlZQX/+Z//iW3btiEzM5O/q8TERBQWFmJmZgY///nPOciSCsfh4WEsLS3h2WefxZkzZzgjSSwWo6ioCB988AEeeeQR2O122Gw25OTkwGaz4e67777jtf3+97/HwMAAT8NUKhXCw8Nx7tw5/PCHP8T09DRSUlLg9Xrx4YcfMna4uLgYp0+fZnlpfX09lpeXMTAwgIKCArS0tAC4PY1ub2/Hl7/8ZRw7dgyFhYXs8WtqasLZs2fxzW9+847XduTIEc7jIeOlx+PB5OQkfvWrX+HWrVvw+/0YGRlBa2srPve5z+GPf/wjfvKTn+DkyZOQSqXIzc1lqs7169cZs0n5Jc3NzXj++efR2dmJS5cuQafTQaPRwGw2IzY2dtN3w+9+9zvWtMfGxsJmsyElJQV//OMf8S//8i+fyXUwGo2455570N7eDoPBwDkNERER6O7uxj333INf//rX+NKXvoSzZ89ywKFEIkFhYSHeffddlJaWYn5+HgsLC0hISIDP59s0P+DFF1+E3+9HWVkZbt68iVu3buGZZ57BSy+9hK9+9atoaWlhr5zNZsO3vvUtyGQyvPfee7DZbKiurkZWVha6u7uhUqnwwQcfYNeuXUhLS4PD4UBvby+qq6uRk5ODl156CXv37mWv4NzcHOx2+6Zm8Ndffx1dXV3Q6XTIyspCREQELl68iObmZrz33nt47733uB6anJxEREQEJicn8Y1vfAPnz59HREQEcnNzIZfLIRKJ8Kc//Ql79uzBJ598guXlZVRWVkIsFmNjYwNvvfUWnnvuOZ5CREdHM1Fts+9Uo9FwYO38/DwaGhq4STE2Ngan08lkssTERN4733nnHdTU1KCqqgpnzpxBZWUlLl++jKeeegomk4mTro8dO4YHHniAQTDz8/MICwtj6em+ffvueG1vv/02ZmZmmDxIwYmvv/46nn32WSwuLrLHMiYmBg6HA3q9HhKJBO+88w53/kk6KhaLUVpayiCZiIgIXLhwAaWlpQwKIK/rhQsXcO+99256bS+88AJPi8ifJ5fLcfHiRXz961/H6dOnoVKp2LReUFCAixcvwul0oqOjA/fddx/27dvHWO3f/va3+O1vf4uTJ09icnIS+fn52Lt3L27dugWhUAidTsf1CR1u7rvvvjte27lz52AymZCQkIDV1VUGWzgcDhw6dIg9n9S0vffee6HX6/Hb3/4Wdrsdqamp7KfKycnBL3/5S3zxi19kUz3R5fbu3YszZ86gqqqKQS0ElAiV80Hr/1Ngn1QqxcbGBux2OywWC3w+H2ZnZ+FyuVjr63Q6YbfbOaXyvffe47E/aStffvllfPnLX8bVq1fhcrlY12ixWPDjH/8Yhw4dQl9fH0QiEWJjY3H58mVMT0/je9/73h2vjTwLZCKihPLl5WXcuHEDMpmMH5i0tDQEAgF87WtfAwAmpZA/49ixY3j00Uc5iXt9fR1WqxXLy8sYHBxkzrTJZMLGxgYKCgp4enCn5fF4YDAYOO07GAxyaJHZbEYgEODDjkKhQG9vL5qamnDlyhUolUpO015aWkJ3dzdz6mmjIwxtX18fI2ApGdbr9bI59E6L9H0AmE5z4sQJ7Ny5E5OTk5DJZBAKhUhNTWUmOwU1ES2H0H9WqxXbtm2DSCTC0NAQpqam2LS7uLiItbU1XLx4kUO4KisrQ+LuyMS/urrKG4xUKsXBgwe5I2o0GhEREYFt27YhISEBjz32GMbGxvj3mZub45RvIkklJiZienoan3zyCbxeL7RaLaqrq3H+/Hn+zpubm0Nq+ckXYjabsbS0BJfL9ZmMC+rMBYNBVFZWMoEkGAyiqakJ09PTuHnzJjIyMtDU1ISYmBiMjIzAZrPB5XKxftLtdrOXIyIiAgkJCbDZbCGnBpQEPjAwAJ/Pxxrv3bt348iRI8jIyGDtb319PaampjhF9+DBg3A6nfB4PGwWW1tbg8Vi4QCwzs5OjI6OoqGhAbGxsRgfH2c5161btxARERHyoDE7O8vkIaKdWa1WfOc734HZbEZ6ejrjAHU6HdbW1uDxeHDy5ElER0cjIyMD+fn5aGpqwmuvvQalUgmPx8MHpuHhYXz00UfYvXs3kpKSMDMzw5MCq9Ua0shJXZukpCQ2vvX392N5eRk3b95EUVERy9AqKysZU3zjxg3odDr2MUgkEuh0Oqyvr8NoNLLJj/ag4uJiJCcns8ac5G6hgBc+nw8NDQ1YX19HX18fAoEAzGYznnvuOYjFYggEAly6dInRknv27MG7777L905kZCSHK5WUlLDZUC6Xs5zopz/9KecyTE9Pw2AwICsrC52dnSG733SIo85nbGwsFhYW0NTUhJSUFKZuhYWFoaSkBEKhENevX0dlZSWWlpbYY5CWlob29nZoNBqMjIygvLycg61ycnIwNDSEe+65B2azmbGvlO2y2UHD4XAwOnRtbQ2Li4uIiIjgEEUKdRWJRNi1axfOnDkD4HYStEKhQHh4OMLDw9He3s6+he7ubgZPJCQkYMuWLXjjjTfQ3NzMkk9CSIbafyl7w2KxIBAIQCqVYn5+HpGRkRgeHuZ/r9PpmPJYUFCAy5cvs6+vtbWVzbmBQAAzMzO8P6ysrCAnJwcjIyNwOBz8vZvNZszMzISUwxHS02q1Ijo6Gqurq2hra8M999wDj8eDmZkZ9PX18f9fUlICv9+P69evo7i4GGFhYTyZfPPNN6HRaDAxMcHhtceOHUNNTQ1GRkYwOzvLJuuNjQ34/X709fVtWsDY7XZs2bIFAoEAYrEYarUao6OjePLJJyGRSJCbmwuj0Qiz2cwTiYmJCfT09CAuLg7Dw8NMt7tx4wbuvfdeGI1GzM7Owu12Y2xsjBtTW7duhclkYtJbeHj43+3M02F7ZGSEk95JtjM/P4+JiQksLy8jPDwc1dXVWF9fx7lz59igfuXKFSZ1ZWdnc+OF8qdGRkZYcvrOO+8gLi4OsbGxWF9fDxmCS9JIen9FRkaipaUFCoUCfX19TA+j+z8vLw9Xr15FcnIydu7cCYfDgTfeeAPR0dG4fv06EhIScPbsWQ7snZ2dRX5+Pi5cuIC8vDykpaVxMDKZ2Ddb5BkkSZ9UKmW4AUFJ6F0wMzOD0tJSHD9+nKls5eXl8Hg8uHr1KlJSUrC+vo7Lly/z/kqSIvL12Ww2BAIB+P1+rK2tob+/f9ODBgBWHCwuLnL9R02F8fFxtLS0IC8vD/fddx/W19dRU1ODmzdvorKyEiKRiA82TqcTjY2NGBoaQmpqKuLi4uDz+XD58mW43W6UlJRgbm6OpVB+vx8ej2fTg8bIyAiio6Mhl8uxsLCAmZkZzrobGRlBbGwsPB4P3G43y7aoEaDX6zE2NoYTJ05ALpfj7NmzvAcKhUKG+TgcDty6dQtZWVmYmJhg2hlwO2jx/68HDbFYzB1mSvWUSqX4+OOPsXXrVtTW1iIQCPBJiIo8jUYDj8cDsViMtLQ0xm6+9NJLeOihhxAeHg6LxYLZ2VlERUVBoVDgk08+gU6n4xwACjLZbBG56dPXRqfYwcFBbNmyhc1sKysrMJlMuOuuu/ghIakJ+ThIDrW2tsYPR3R0NCc1E9KUNImhtIdEcElNTUVCQgL8fj8HrFCMO/k/YmJikJWVBbVaDbVazfgx6nZKpVJYLBaegJC5XS6Xw+FwMAEoLi6OTYp0rXda6+vrGBwc5JEwkZS6u7uRnJzML1a3242wsDAOISIsKmnjqVigTVipVH4Gh0tG96KiIshkMjY/h8r4kMvlmJmZgUKhQGRkJObm5rhgM5lMSEpKYurTwsIChoeHUVpaips3b7LEKykpCdHR0ZiammK08PLyMn9GhD+cmZnhF7VAIEBWVlZIyRnpoynfRCgUMmVmcXGRgQQKhYJR0CaTCUNDQ0yQSU9PRyAQwM2bNzE8PAyDwYCysjI2c506dQoPPPAAwsPDYTKZ4HQ62chGL8nNFhU6crkcMpmMx/NEdZmfn8fq6iosFguP3gcHB9mDQZ+d2WxGf38/srKyWMYXFxeHxMREBINBRjdSGjThpUMtMssBt0OZoqOj+SBDpj+inCwtLXFWRWtrK7+I29ra4PV6WWdL/xwbGwuBQACLxYK3334be/fu5VA3qVSKgoKCkIQi0vKS+ZKIS5SU7fP5OCBSKBRykZiQkMCIarVazX6Yt956C7W1tZDL5dBoNHzvkuSDJoCk8U1LS9v02mJiYuByuRATE4O4uDhERERgaWkJt27dQlVVFaOPqdCYm5tDYWEhfv3rX/Pkj0AKDoeDqWZkoqdmTCAQQG1tLaxWKzo7O9kwGspPJZFI4HK5eGpH3yuBFgoLCxnbbDabkZCQgKqqKrz22msAAIPBgLS0NKjVaiwtLSE7O5u9SUTfGR0dxejoKJRKJcxmM6fcKxSKTadAwO192+l0cgaM2+2GzWbD7OwsH7qIzkVGTJVKhZ6eHgwNDaGsrAx79uyBRCLB0aNHER4ezunl4eHh7NVKSkrCrl27IJfLeS8h78Rma21tDSsrK2xUJZT43Nwcbty4wcSnubk5uFwuuN1ulv6tra1xwrTZbMbw8DBsNhv27NkDsViMiYkJdHR0ICEhgX2OpLOnZznU/puYmIjBwUFOeaeGEZmVCwoKEBMTA7FYzB3epKQkXLp0CUtLSzCbzZiamuJUeoKShIeH87O9sbGBGzduoLCwEFeuXOHGFSUvh1q9vb3IzMxkMILRaOQiNCEhgcl49PwDwLZt25hQKZFI4HA4EBERgddffx2ZmZmMIaVCt7+/H93d3dw4sVgssNvtIRuL4eHhmJ6e5r2D4DnDw8MYGxtDWloav3uoXtq6dStOnDiB8fFxaLVaxjcfO3aMJUxUsC4tLfEhnWQ3c3NzmJubg06nC3nQAG4X9NTABW4bndva2nhyTWAIAEhISMDKygokEgkbh5OTk/mzcTgc2LdvH3w+H6dau91uZGdn8ySd/EL03t1sxcbGwufzIRgMspcBuP2+cTgcSE5OZvUCwUPq6urQ2tqKlJQUPlyQod1isSAjI4M/bwJ0TE1NISEhATqdju+x7OzskJ8b0S3psKdQKDA1NYULFy7g0KFDiIyMhFgsht/vZ5moRqOBw+HgPTQ9PR2pqano6OiARqNBZmYmFAoFzGYzrl27xgft+Ph4dHZ2Yn5+/jO+qM2W1+tl7G94eDhDC7q6urB161bk5+dzfsbQ0BCqqqogEAiQkJCAixcvIjExEXv37uXMrk8++YSzgxYWFtDe3o6RkREoFArEx8djaWkJHo+H/Yih/KKfXv+wR2NjY4NHacQ0psRYm83GxJm4uDikpKRgamoKSqUSy8vLPOoLBAIoLi5m845QKERCQgIbbPbu3csJySTlAW6TakLdCJGRkQgLC+ONhore4uJiiEQi5naTSoxIGYuLi+x9iIiI4K4kyY/IJEOZETSRCAaDUCqVUCqVbJQN9bkJBAJERETw/3w+H5NP5HI5YmJiGPcaDAYZf0rUlbCwMA5eoSmL2+3mBGRC+CmVSqZGEVUmFC2GsjnoZE96V+pKRkdHY3Z2FsvLy/zAEp4uPj4eSUlJkMlknCJJOLbU1FQkJSXB4/EgISGB6SQkWSIEbSjVHulJKc+AkI0Gg4HJGwKBgDcmm80GvV7PByt6IdIIXSQSYXh4mM3vy8vL0Gg0jLejYCIiyYQycRIhgwKLaJJGODvyIhGKOC4uDjt27IDP5+MDI21GZ8+eZVNodHQ0y8S2bt0Kl8uFpKQkrKysIC4uDgqFgjfjzdb6+jobK6nA2NjYYPmeVCrl35WQnPSZyuVyKBQKSCQSaDQaJuIQ+Ym6P/n5+WzCi4mJYaM+TbdCrY2NDaSkpDAJhw4HnzbHEf1rZWUFNpsNUqkUHo+H8dIAWCKybds2xooGg0GsrKygoaGBKSB+vx/R0dGM9gwFRyBcaVhYGBPy4uPjkZubi6ioKJ50+P1+LiS0Wi2Wlpa4W+7z+Tg5nu5xKkxVKhX27duHQCAAmUyG+fl5Nu4LBIK/G/wJgKWJSUlJkEql/BKlbBqSIJnNZlRVVXGhEwwGOchpbGwMlZWVGB4eRjAYRHx8PONHaRJIDZZgMIjFxcWQPjS6z6nRQwWwWq3mgxQ9H2KxmNOfadpGhBahUIipqSkkJyczypKyisifsrCwwIUchbSGQhaHh4fz3kr4SypU6OBG+HWazG3fvh1xcXHIz89nNDR1u6mIpgPlxMQE9Ho9TCYT+xLCw8MhFArh8XhCfm40bfX5fIxcXV5e5qaaXq9HbGws0wq9Xi9KSkrYoE6NHyrMsrOzsbq6itjYWD7w0N4ok8lYUisWi5GYmPh3k+jlcjlT9aipkpSUxJlJCoUCaWlpDHeRyWSw2+2Ynp7mKXRRURHLUYleuLa2BpFIBJ1OB5/PB5FIxD8fDAZhMplCBqZ+mtIYFxfHhzmSwtL+qtfrUV9fj87OTpZ8kpadMlMAcH1C/kKZTIatW7cy4MHlckEgEPD9+feehYyMDDbz0ndMqdJ0+NDr9aioqMD8/DzUajWnWEdGRsLr9SIxMRHr6+t8j0ulUk4dp4aJWCxmOM2nEfqbLcqgoIYSvaOotqDMisTERGi1WrjdbuTl5fE0QiKRIC8vD4WFhYxVJepQTEwMH2bpZyk3QqPR/N331traGuPWqfHi9/uRkZHBiHyKI6BrIiM84b0FAgF27NiB5ORkZGdnQyqVQqvVspqGYBlE3KLnAABUKlXIz44aAOvr65DL5UzwW1lZQUFBATIyMhATE4Pp6WlMTEwwvUuj0SAmJoZBAORbnpmZ4X02GAzCYDDwIYamrpTdFgqwIhAIsLS0xM1HqVSK2dlZSCQSnvSvr69DIBBwDgpBNmivVqvVKCgo4Kkv7eNUS5aVlTF5jaZmlHEUKrX8M9f5D/0UwOScqakpOJ1O7i4++OCD3DUheYvBYOCbjQzYRBAgKtXjjz8On8/Hpzi9Xo+ioiLMz88jJycHy8vLPBEhAsFmi8ZtxIafm5vDysoKUlJSUFlZybIg6swRs586aykpKQgLC0NCQgKmp6eh1Wr576OE7cTERGRkZGB0dPQzLPXu7m7u0N5pra2tIT09na+Nut2JiYnQ6XTcEV9eXobdbme6AXWGCYdKnRaSa1An2Wq1QqPRcMG2vLzMGLW/N66USqXIz8/nB48e9uzsbKhUKqjVap525OXlMdJNIpEwUYMY6hT2QsQYGp0XFBRw7gaNyenFGIoARNMeOqFTunJTUxPfa3QY1Wq1PL0AwOO9wcFBzntITU2F1+tlAsP09DQyMjI+k29C/GqiiW22COlKHQTSodK0hiZD1DFxuVycZn7XXXchIyMDy8vLyMzMxNzcHOPmZmdn+dD+xBNPYGBgAG63G9HR0SgvL2c6S6hifnV1lelVMzMznIosl8t54kKHccLTUT5DSkoKFAoFv1RJBkWHdDqQ6vV6Di0iylJ2djYXhqHWwsICdu7cCbVazV0YlUrFoY4k9yMOPj2THo+HpwwkOZPJZNi+fTt0Oh3q6+uRkZEBv9+PRx55BEVFRZwxYzAYoFKpmGm+2SK+OH2/JCPYunUr4uPjkZCQgPT0dBQVFWHbtm0skSIKx6fzerq6uqDVarm4o+c4JycHPp8PCQkJjKwlY2Cojt/Kygo3AehQJZfLOXQvJSUFarUacXFx3F0TCoU4dOgQUlJSoFQqGWBRWFiIsrIyRgjTS1aj0TCWcn19HampqdDpdFwEh7q2nJwc5OXlIScnh42VDz/8MBQKBYaHh/nvSktL40bVli1b0NjYyBI86p4qlUosLCxgfX0dYrGY5Y1UkPn9fmzZsoUbUx999NGm10YNMuB2oZWYmMhkMgp3JfNmSkoKJBIJDAYD8vPz8eijjyIjIwO9vb24cOEC1tfX2U9BTZq1tTVs2bIFo6Oj6O7u5mRm8kiEMjWTL486wUQw3L59OwoKCnj/jYuLQ15eHiQSCVJSUhAIBKDX6yH9W+YRyeKKi4vR0tICs9mM8PBwpKenIy4uDkajEZGRkSxrpiIrVDbK/Pw8srOzERYWxrAWhUIBg8EAmUzGpEiJRIKCggKMjo5ienqaMzFkMhkyMzORlpaG+Ph4FBYWYmpqikleSqWSsbgbGxvIyspCeXk5Z/2EknVRQyM6OhpKpRJ5eXnQarWMJabvuba2Fp///Od5Tx4YGGCjsc1mg0ajQUJCAr7whS8gNTWVu+/x8fHQarVQKpWorKxk78z/3Qm/0woLC8P27du5pqBGFhmAXS4XfD4fUlNTUVhYyAftpKQkPPLII0hLS8Pw8DAWFxe5QUGTT6/XC6vVioaGBjgcDiQmJmJubg4CgYAVDET5utOKioqCWCzG0tISvF4vFhYWMD8/j9LSUhQXF3PtVlFRgaKiIgiFQhQUFMDtdiMmJoYnQfR+q6ysZNkbgT2IShYfH8/ycvIMhSI7kdGbpKnUPddoNOyXW11dhUajQU1NDWdaEJCDJv65ubmorKzEk08+yUGbFOBIBwuSetO7b2pqKuQknrDv1MAVCoWIiYlBbW0tgsEgEwXpfiKvoEKhwIEDB1BUVMTGfWrUDQ8P4/r16xgbG0N4eDhycnKQk5ODqKgoJCcnQ6/XIyUlhbM+NltisZhrrYiICKSlpUEoFOKJJ57gQ9/09DRPgAi9bLfb+Z1JiOfZ2VmUlJRw/RgIBJCcnIwHHnjgMzV4Tk4OiouL+bv6R9Y/fNBISkpCe3s7UlNTkZyczJ27l156CY899hjrWcmAVVZWhrfffhv19fWYnp5mRvbhw4chkUhw8uRJJlUR5eeee+7B888/zzxr0vofPnw4pC6duu+Utkza+6GhIRgMBsbmRUdH86h9bW0N1dXVaG1tRW9vL3w+HyYmJiCVSllOkpaWBo1GwxhZg8EAk8kEnU6HQCDAprZQKy4uDteuXUNsbCzLH+RyOSYnJ5kFTbzkoaEhNDY2wu12c4p1YmIiZDIZzpw5g7KyMrzzzjuorq6GTqdDdnY29uzZA6lUisLCQk6YpjArnU4Ho9G46bURzpFCcRYXFxEWFoaLFy/yOI7SJkmHe+LECURERCA1NRVpaWnsb3n00UdZAkbeiXvuuQdWqxV1dXWwWCzIzs7mlz6FyGy2gsEgent7UVRUhLy8PEaBvv/++9Dr9Whvb2ecXVxcHHbu3ImjR4+ivr4en3zyCRcA169fx+HDh5GYmIjnnnuOQ5GI2PLUU0/Bbrfz6HBgYIBxr5st6hLS5xYWFoaioiL09vbigQceQFpaGnbt2oXa2loukD/66CNs3boVbrcbcXFx2LVrF1QqFZ566inExsay50Eul2PLli0YHx/H4cOHceLECajVat5sSY+52aJDf0ZGBoLBIGvjiSpGKbCRkZE4duwYqqurcezYMYjFYhw9ehStra3w+Xy4du0aJ33ff//9SEhIgEgkQklJCVZXV7G+vs7m2aKiIr7vurq6Qj4PIpEIi4uLqKurQ0JCAlpbW7GyssKQhGvXrnHwo1gsxo4dO3Dy5En88pe/xJtvvomPPvoI0dHRuHz5MkwmE37zm9/gwQcfZCMkIbM/97nPIScnh9OxvV4v67o3W4WFhYzInp2dxeLiIrKyspj69vHHHyMmJgalpaUcdkmJ5vHx8dixYwcaGxtx4sQJPPfcc7Db7dDr9WxuzM3NxW9+8xvs27ePwxpJl768vIzu7u5Nr40IfbW1tTAYDIzIfvnllzE/P4/XX38d6+vrKCwshEKhwOOPP4733nsPhw8fhtfrZe3/9evXUVFRgatXr2Lbtm28LxkMBly5cgWPP/44p5t3dnbixIkTiI6ODlnArKysoKenh++B6elpngjv3LkTwWCQ83Q8Hg9++9vfYnJyEg8++CDUajWKi4uxbds2+Hw+/PjHP4ZOp4PBYGAYSGJiIpqbm7Fz507YbDY2ZQaDQdxzzz0h8eI00SZ998LCAioqKnDr1i3s2bMHk5OTGBsbg8lkwsjICJaWlnhP/P3vfw+NRoMf/OAHLO+y2Wz413/9V6hUKigUCuzduxeXLl3Cf/3Xf0GhUODuu+9GTU0NDAYD9Hp9yEZPZGQkHA4HBAIBh0MePHgQo6OjiI+PR3NzMwftra+vo7a2Fr29vZy+vLGxAYlEwtk2r776KnvRBAIBamtrkZSUhIMHD/Jk0Ov1snwvlH8kGAyiu7ubg3D7+vqwtraGq1evoqCggJGlVBCVl5fjww8/xGOPPYaSkhLukL722msIBAIYHh7Gvn37WFabm5uLl19+Gbt374bZbEZ1dTVEIhE8Hg+2bt2K3t7eTa9Nq9UiLi4OSUlJWFxchNFohF6vR1tbG7q6uvDGG2/wNCksLAzf/e530dPTw1QuhUIBuVyOF154AY8++igGBwcZeUrhk5cvX8azzz6LrKwsBAIBnDt3Dp2dndi9e3fI5zQqKor19xqNBsvLyygsLMTExASampowPz8PpVKJxMRE+P1+7N69G83NzaisrMTi4iIyMzPR1NSE0dFR3k+feOIJzveQSqX4xS9+gWeffZb9Re3t7Whvb2cp32ZLLpfDaDRifn6e1R8KhQI//elPodPpYLVa4XK50NPTg2PHjqGuro5TyEmqvLy8jCNHjuA73/kOrFYr9Ho9T30A4PLly3jsscfgcrmY9nbp0iVUV1eH/E51Oh3ee+89Duqdnp5maRHlPxBQY3BwEAcPHsTKygqefvppNDQ0QKPRYHFxERMTE/D+LX+toKAA7e3t7ENxu90oKirC2toap74vLi4iNzcXra2tm14bhaPW19ez1L2urg7Xrl3Dli1b8MEHH8Dn86GkpARarZbx9l/96lfh8/k4THFtbQ1NTU1wOp04dOgQ9u7di6qqKiiVSpw4cQLPPvssent7ufkyOjrKpMnNFk2xCWNLDYbm5mY8++yznMtjNpvR0tKCkpISvPDCC4z9j42NRUlJCVpbW7Fr1y7Y7XYolUqkpKRAq9WyZ3Xfvn1YXFxkI/7U1BRyc3NDHh4/vf5hj0ZUVBSeeOIJ9Pb2IiYmBiUlJUyZOnnyJCwWC5xOJ/PKyWw6OjrKGl4q/kgW09XVxS8Nr9eLf/u3f8P3v/99ph7QyXtjYyOkftlisSA1NZU7h6RdplN+fX09zp07h97eXqyvr0OpVOK1117D0tISUlNTERERwZ2WW7duobCwEIODg5yMHR8fz/6MvXv3sqZcIBDAbDYjPz9/02vz+/2oqalhUw9h6ijHQ6FQcFc4KioKH374IePHioqKuFMmk8nw05/+FIFAgLW3S0tLfDJPSkriMSsV/xaLJaSePxgMIi0tjcdrZEIsLy+Hz+fjsDvgNibSYDCgtLQUCwsL8P4tqZRkIDabDWlpabhw4QKioqK4EyYUCnH58mU2upNXJZQRHLhdIJAeOSkpCUqlEn19fQCA/v5+iEQiOJ1OTExMsHY4PT0d7733Hm+IXV1dWFpawscff8yYPTKX+nw+JCYm4sSJE2z4S0tLY/1wKAwqZatIpVJERUVhfn4e58+fR1xcHG9YN2/eRHh4OAoLCxEZGYnp6WkMDg5y8F5MTAxycnIQCASYSR0ZGYmenh4MDAxg9+7dMBqNnEvQ3NwMm83GKNrNFumiiWWu1+sxMjLCGm/C0sbGxqK8vJzN4FqtFkVFRbhy5QpefvllNlX29fVh+/btKCsrw/T0NOvkGxoaMDIyApPJxFMdol6EWlTEk968oKAAbW1tHIbV0dGBrKwsDlTctm0bjh49ynCH8fFxtLa2YsuWLejs7MSTTz6JoaEheP+WnE0vo8XFRQwPD/PBgzrH6+vreOKJJ+54bf39/azZJ98R3eN79uxBcXExjEYj2tvb+aWVl5eHy5cv89+fk5ODhoYGTE5OQqfT4eLFi0hLS+NGQkREBK5evYrU1FRIJBKIxWL4fD44nc5NU3MB8GSRvBDAbVNsUVERMjMz0djYCKPRiPPnz3MxlZ2djT//+c+sITYajZzMLpPJYDQaOY19eHgYb775JkQiEXtOALCMKZQBlrp9lIY8MjKCsbExPProo5iZmWFpoc1mw9WrV5kW88orr7C3TqlUIikpCdevX8c3v/lNnDt3DlqtFhsbG6wHT01Nhc1mY+liXFxcSBkLcLuhUV9fjxs3bmB+fh6xsbH8Z128eBErKyss+V1bW4NWq8W7777Lh/5Tp06hp6cHW7ZswcmTJ/GHP/wBRqMRR44c4WwP8kjU1NRwBhHJPmjKeqc1PDwMrVbLmUKxsbG4du0aoqOjMT09jd27d7NXhPT3YrEYUqkULpeLs1foGaqoqGBYCOFVVSoVRkZGOBSRDrV2uz3kdM9ut2PPnj1obW3F/Pw8NBoNwsPDuVBNT0/H7Ows5ufnkZqayl35trY25OfnIzk5mT0UKysr+N73vodf/OIXKCkp4RDe8+fP8/S8p6cHbrcbKysrnJO02SJJD90XlOadnZ0NrVbLzc6LFy/iN7/5DZqamnDvvffio48+QnV1NeLj42GxWLC4uIgXXngBGxsbuHLlCucpXb9+HUVFRXj77be520wSyVu3boW831ZXV1k2R0GuRJH8y1/+AqFQiJGREUxOTrJ36/z58xzASGqGhYUFxor39fVxozUqKgqNjY1oa2tj+RvBDrxeb0gvq/dvqeyJiYkcdtff38+S+IKCAhiNRgSDQVRUVLBv9I9//CMefPBBlrLW1tbijTfeQFdXF2JiYlBYWAiPxwO73Y4dO3bgj3/8IxYWFvDQQw8hPj4ek5OTGBwcDIlC3djYwP79+znFfnx8HAKBgIv2hoYGnD59GjabDYWFhTh//jyGhoawurqKe+65B9HR0XC73ewrbG5uxvbt26HRaJCSkgKn04nW1lY8/vjjuHr1KoOLYmNjIZFI+CB3pxUIBHj/9vv9DBkisMqePXtgsVjQ0dHBwXfvv/8+NBrNZ5QxJpMJ8fHxfIAkzzDBNP73//7f2L59O86cOcOZGKOjoyHR4uQPI2kW+WGUSiV6e3sxNTUFgUCAoqIiZGRkwOv1Yvfu3XC5XEz5ownUBx98gMrKSgC3672FhQWuWc+fPw+r1cp7bnh4OMbHx0OixT+9/uGDhkgkwg9/+EPWGvt8PpYYiUQiNDU18QhYKpWirKwMv/vd7xAWFob5+XkkJCRAIpGwAaqiogKrq6tQKpX8IhsaGmJ2OQAmv5D+c7Mll8vR29vL42TyQrhcLojFYqYhqFQqHi+Wl5dzyF9iYiLS0tIQExMDp9OJd955B1KpFKmpqVAoFJibm0NLSwsnW29sbGBychIrKysoLCwMqeeXSqV4//33kZubyyZuSmA0GAyfMeXGxMRAqVQiNTUVn3zyCSwWCwoLC1FbW4tr167xAeDGjRuQSqWQSqVISEiA1+vFwMAAEwUoMViv1+PgwYObXht1+Yhasry8DK/Xi0uXLuFLX/oSnE4nb3r0HdAhLCoqinXPlGQ+MzODuro6+Hw+ZuPPzMygoaEBfX19SE5ORnh4OKampuD1ev9u0NaZM2f4ILS8vMyBhAqFgh9+v9//GT09BWAJhUKo1WrWR548eRJZWVnYuXMn9u/fj46ODpw4cQL33nsv/H4/pxJvbGxg165df/d+o+AxAEwFGxwcRHl5ORQKBTIzM2G1WnHs2DFs27YN+fn5GBoawvHjxzlvhrJaKBG1qqoKFRUVEAgEOHLkCLZs2cImSvKVAEBubu6m1/ZpwhlNDEmXPjAwgEAgwAFsERERbOyampqCxWJBSUkJyxqJQHXhwgWUl5dDLpcz/YcmI2NjY6z/lMlkIbsvwO3Cz+l0cnrv6uoq2tvbYbfbIZVKsWfPHn6BUhDcvffeC4/HA6VSCY1Gg7S0NNjtdggEAvzHf/wHwsLC8L/+1/9CY2Mj050ITW21WjE/P4/19XXGvIa654aGhpgfPzc3B6/Xi4sXL2JxcRFRUVGYnJzE6Oj/y95/R7d93vfi+AsgMYhFgBjEIjjAvUXtvS3J8o4dO3EcN6tp2uTcpk3TnI57k7ZO0yR17k3atLlx0jqOnbhOvB3Zli1bsvagxL0JkgCIvTcIgL8/1Pe7Uo8I+/s790+9z8k5sSxLDz+f5/M87/EaM1hZWcEjjzyCU6dOsaqJ1+tl1bFAIIDf/va3eOyxxwCAOWBSqRRdXV1YWVlhOFcsFkNNTU1ZrsHq6iref/99rFu3DgaDgZ1629vbcerUKRw9epTd1jdu3Ij5+Xl873vfw65duyCRSBiWo1QqEQqF8P7772Pfvn3Yu3cvgsEgBgcHsX//fnR1dUGr1fJ7JC5eOYdrSoxfeeUVtLS0YP369VhZWcFTTz2FlpYWRKNR9PT0wGQy4fTp03jzzTexd+9eANeJmBqNhiXJI5EIXn/9dWzevJndfY1GIzc2Hn74YRw/fhwikYhxyOUaPaurq/jZz34Gs9nMXc/Lly9DIBAgGAziL/7iL/DCCy/g7bffRktLCw4cOICdO3dicnISWq0Wfr+f373RaMSjjz6KRx99lOFC586dg1KpxKZNm1gePZfLYXR0FFNTU2vKxwLXDVMJ5x79T7NP8qigSbXNZkMikcDbb7+N/fv3s/EpQSqIyK9QKPD+++8jl8ux7wC9l5qaGlRWVmJsbIw5aqQyt1aQgWxrayuT24Hr9xlxt3w+H4aHhxkC+cgjj+DXv/41xsbGuLg5ePAgjh07hgMHDuD06dPMebrzzjtx5coVVvDp7u5m+CtBY9cKuVyOoaEhVh+MRqN48803+We22+2YnJyESCRiKesXXngBzz33HAQCAe6++2709PSgoqICDocDW7Zsgc1mYzd7k8mEWCwGgUCAL33pS3jnnXdw7tw5JJNJnrSuFQqFApcvX2bvrKqqKggEAkxPTzOccWFhgbH9drsd+/btYwVP4PoZ2dDQgOPHj+O73/0u9u7dyzy6ixcvYmZmBl/84hcxOjrKOVgsFoNOp8O2bdvWXFs4HObGJkG2b5R7Hx8fx/j4OOLxOGZnZ9Hf34+Ojg7I5XIMDw8z74QaiD09PSgWi5ienkahUIBQKMTIyAiy2SwOHDiAp556CkajkYV9yhVBPp8PJ0+e5PMtHo/jpZdewrFjx3DHHXegra0NJpMJbrcbS0tLsFgs2LRpE37yk5/gl7/8JWw2GwYGBmCz2fDGG28glUqxKAAZcBKkWK/XM7yJIKHlRBskEgmWl5fZtLpYLCIWi8Hj8eDZZ5/Fpk2beGpMZzmJ5PT29rLhYyaTwenTpzmnam5uRn19PRKJBF5++WX+cycmJlAqldDQ0MCGs2uFxWLB6Ogoe9kR7/Ib3/gGvvjFLzI3aWRkBM899xy+8IUvQCQSoVAo4O2330Z3dzdzS6uqqjA6OgqdTof+/n5oNBp4vV7Mzs7ikUceYcUyt9uNdDoNg8GA2dnZD0X1AP8foFMulwt79uxhjCsp6Rw6dIiTPMKL1tXVYWJiAvv27UNlZSU7OzY1NeETn/gEH4gej+emKv7uu+/m5NHlcsFkMmHdunVYWVnBiRMn1lzb6uoqurq6EIlEmENRWVnJ6hBCoZA739SRJ9K1xWKB1+vF+++/z34Kzc3NqKysRG9vLycrO3bswPz8PEwmE3f7VCoVExTXikKhgL1790KpVEIikaC6uprlbekwzGQyKJVKqK+vx8TEBGZnZyGRSLBnzx5UVFRgcnISO3bswPj4OD72sY+xqRV1xKmybmxsRHt7O3skvPXWW2XxpHR5RqNRNhoit9bFxUVs27YN/f396Orqws6dO+HxeFAsFjE3N8c4TerQ19XVoa+vDy6XC7FYjAmrhw4dwtLSEnp6erigoWKjHH7Z5XLh4MGDrNC0detWpNNpdHR0QCaTYd26dbBarbDb7dizZw+Wlpag0+kYi05SeYRh37dvHxPxFAoFtFotPv7xj7MHiMfjgc1mQ2trK65cucKj4FuFz+dDd3c3Y3qNRiPkcjk+9alP8ShSKBTCarXij/7oj1ilyu1245FHHsFDDz2E5uZmJk9S9ygUCvHkj9Q8IpEIUqkUNm7cyNArwoHfKtLpNHN/yINEIpFg165dbNKk0+kgEomYbL9371709vbiL/7iL7Bnzx5OzMPhMPr7+1FbW4tCoQC32w2Hw8HY7Rt5MisrKxgbGyvblQfA+vfpdBpLS0tIJpPQarXYt28fVCoVF5Ojo6O4ePEiLl++jK1bt0Kr1eK+++7jQuHo0aNsukhk71gshpMnT0Imk7GbuMvlYtw/KY6sFSRZCID5SMlkEvv27YNcLkdfXx82b96Mhx56CH/+53+OYDCIe+65B/F4HM3NzUgmkxgaGkJ3dzeKxSJ+/vOfM1yT/BvMZjNOnTrFXdhNmzbh0KFD3NFcK4LBIJqbm/kyIpKl2Wxm93FyeibIyB//8R9j8+bNN2HB161bxyZmwWAQy8vLCAaDUKlU+MY3voFr164hGo3C6XQysVkkEpUlmqZSKb7kBQIBSyk+9thjiMfjGBgYYDz4wMAArl27hr6+PvT19THXS6/X4+Mf/zjUajU+8YlPYGpqCvF4nDuJJItKDZmqqirk83l2kl8rQqEQtmzZwlhmo9GItrY2HDp0iOVeCS9fVVWF1157jb+ZT3ziE3jggQdYjMBsNuOBBx7A3NwcmpqacPDgQfT29mLv3r24evUqPB4PJicnoVAo0N3dDaVSCbfbveba6NtxuVyMUZdIJNi8eTMkEgk0Gg13J7/4xS8yh2l0dJSbCOl0GuFwGPPz8zh8+DBmZ2dRKpVQU1MDs9mMT33qUyxXTWfjli1bGLK3VsTjcdhsNni9Xp4iETezpaXlJnK1WCzmSZtSqWTlxFQqhbm5OYaVEMHc7/fj9OnTDIUjzD2p/o2Pj5c1ryRxGFKuoj2kUqn4WyS+mUajweDgINRqNX7/938f3/jGN9DR0YFSqYTt27fD7Xajs7OThVMIitLe3o5IJIJgMMgF144dO5BMJssmzKFQCJs2bWLhgY6ODnR3d7MC0Lp169DU1AS73Q6bzYZjx45h9+7d7LElFou5ARuLxfDoo49CJBJBp9OhqakJdXV12Lt3L2ZnZ9HX14dEIoHGxkb09fXB7XaXlcgm82K32828skwmA71ejytXruDq1atQq9XYuXMnBgYGMDw8zBLuBO8j9aahoSE0NzcjHo9DpVJB/Z9u53fccQcEAgFkMhlaWlrQ2dnJogYSiWTNtQHXVcFyuRyy2Sza2tpw4MABfPWrX8Xs7CzeeustzM/PcxP52LFj7MYtl8u5WRKJRODxePCjH/0IPp8PPp+PFZ0++clP4tlnn2XJfIFAwKiEcsVjIpGAVquF1+vF1NQUvF4vBAIB2tra0NnZCaPRyOdKV1cX31kklkNO7+RlREqYJHA0MzODT33qU6xEuHHjRhw4cICb7uWantFoFOvWrYNer2fBGJPJhH/4h39gd/vt27fjrrvuwqc//WkWNkqlUvjjP/5jHDhwAAKBAF1dXVhcXMTWrVsZmkkcrI997GNwOBws1GI0GtkkkGRuPyw+8kSD/BNsNhsqKioYO7eysgKTyYTl5WUUCgWYTCZYLBYMDg4ygY1eqFKpxOzsLKtl5PN5JpkSOVgkEmFhYQGFQoFVgwgHuFYQ8ZsUJQQCAYD/4m54vV5UVFRwp5Q6Vlarle3n8/k8XC4XTzESiQSWl5c5iQau4whjsRhj+MkUrxw8KZfLoVAoQKfTQSqVMqzB4/Ggvb0dPp+P1TPq6uqQy+UQCoXQ1NTEEruEiW9sbOQDh7qoer2e//75+XlWn6ipqeHu8VqRz+fhdDpvkkaz2WzIZDKor69HNpuFUqlkM76uri7Mzc0BuK7mQqYy9OHqdDqsW7eOISpEjKIEirSoSbWnnPQuFaKUUMTjcYZk1dfXQyAQ8GVHhSbpQ1NSRMmsWCxGqVRiKcBkMgm32w2hUIhCoYDl5WVWsSKRgHLYauq+UoJNKh6hUAhbt25l2VC9Xg+ZTMZ65UajEWazmUmtbreboVcEd8rlcpBIJNBqtVhdXcWZM2eYh1IsFlnCeK2orKxEsViE0WhkWVNS8LDb7RAIBNzRIBKsw+EAACa5EjaWlDRoqkid7cXFRfT29mJoaIi/BRJ/KDfmBa6PdsmoCgATg5VKJY4cOcJESZFIhImJCcZIb968mQ2gYrEY3n33XdjtdsjlcmzYsAFnzpxh5aelpSXs3LmT/Tg8Hg9SqRQaGhrKFrcAWLqWTDnJH6i9vR2Li4sQi8UsUEGCC+SDQQpyNP4nj4F4PM5eMhUVFayWQupkCoUCdru9bAG5urp6U4NEpVKx+o7VakUgEODudiqV4qbBysoK5ufnIZPJ+BwlKV4ithKJP5FIsAqTzWZjUjyplq0VpHJF7zSZTHKiqNVqEQgEIJVKUVdXB6PRyJAkclk3GAwolUqYnZ3Fnj17YLVa0dLSwtwKauh0d3fjzJkz6OjoYM4HiTusFVRsE3GUGjqBQAAdHR08XSUFn+bmZiwvL8NgMCAajbIKy9DQEHbv3s2JQCKRgN/vR01NDbZt28bfE91B9A2WK4LIjJU8EEiSmoQZfD4f5ufnuUtP0utSqZQlmMkkTCaTsYErAIZkVlRUsEdUsVhkrk53d3fZe8tkMsHpdHIyLxQKYbfbcf78eaxfv55VrgglQD5BcrmcvxOVSgW/34/Ozk74fD5uKorFYibCE9lZpVKxKRt5L6wV1FUm2ByJVkxMTPCUS6fTQafTwWg0orGxkc+dqqoqaDQavj8pmYv+p4u1XC5HJBLB2bNnGVIkEAiYO2AymcrCfvP5PBfmdHe3tLSwrOzc3BzvqWKxiHXr1mFhYQGZTAaJRILJ1teuXcPRo0c5v3K73dzoI07m2NgYCoUC0uk038flzhBSNcxms6zsZ7PZ4HK5WJGJiN10Bo+MjKC5uZnP9VQqhZmZGVaqIi4gGfYJhUKYTCbOr8hWgBSa1gqa/BB/Ri6Xo66ujpsz4XAYFouFJXcPHTqEsbExVp60WCwwm81YXFzEI488gmw2i9bWVp62VFVV4dy5czCZTDd5RBDXqNydSo0slUrF0uVSqRRut5u5LU6nk4UJ6P2TuSwA/vv6+/uxYcMGhMNhTE9Ps7JTPB5HS0sLywwLBAI+r8oVQYRW0Ov1WF1dRSAQgEKhQFtbG/NIic9UXV3NU+NMJsMqh/TPdXV1EAgE7D9FIjeJRAISiQTj4+PYtGkTw5otFsuHylBTfOSJBv1AQqGQCVi5XA5zc3PMFaAHk0gkEIlEeLxCHXuhUMjQBIfDgZqaGlbBIVwjSX0KBAIEAgE4nU6IxeKyVR3hbKkjSgcxHfYkrwiAGfiE6w+Hw5xckhMiJbELCwtMnItEIlxUkDStSCTiQ3ytIEwfJYpU9JDqD0ngSaVSiEQidHR0MF6fngOR3sgvgyRpyfSHlKpIfpEgGO3t7WULjUwmw8muQCDg90XEQVJJIAk1hUIBt9uNyspKltnUaDSQSCQsBUwSsQCYYE2JbqFQ4DWSG+9aQYdeOp1GMBjE0tISisUiCoUCJ/UrKytIJBIYHx9nKU1KfCmpIwUjSlgIS5pIJLC4uAiBQIDl5WU2fCMZvHKXCT2riooKfhbJZJIlnRcWFhhutrCwwF1ekjTNZDJcDNFYtqmpiX9NLBbz4R8IBOD1euFwOBAMBmE0Gst+3CRlRxKPyWSSR7V0mItEIpZItlqtmJycRLFYxJUrV7C4uMjP3mazcSOAxs8CgQAOh4OTXIFAwBKYH6YORxGJRFjSlZIz+i4B3ATlK5VKWFxcZO4MJXJXrlwBAIZwTU9PM9mQEhfqNKXTafj9fta4XyskEgl/c6Sqp1arEQwGIZfLMTs7y/Le8/PzqKio4L2TTqe5QPT5fFhZWcGZM2eYjEewiGKxiM7OTi4a/H4/F9TlLmJ6XqR8REkdueAScXh1dRXLy8uQSqVYWlrCysoKfD4fq93Nz89jfn4e4+PjrGAFXL9ML1++jPr6egiFQi68s9ksUqkUY4BvFQTBIAd0Kh7JqJJMKIncvWvXLpw8eRKxWIwdzYVCIebm5lBXV8fqYJSoVVdXw+v1ore3lxs8pK5HTutrxY2qffTz0PQQACvzVVRUQK1Wo6+vDw6HA6lUCqdPn8bCwgLkcjmi/2lI5vP5YDQab+KHxWIxNDY2MgeC1JVUKtWHfg9UwEskEqyurrIfTVVVFZvDkkEueVAQP61QKHCyUFdXxyIK5OsTi8WYUzUzM8PcDJLdLEccJtlkUpyjwob8g0iamBTOWltb2QhufHwcfr+f4VUCgQAul4v3FP2MsVgMdrsd6XQa1dXVPMGl971WVFZWIpPJsB8NdVklEgknWPSN+Hw+hrPRWRKJRFAsFjE/P4/GxkYMDw9zDkFEYiJ0kxpYOBzmieiHrS2dTqOqqgrFYhEej4dhucQ5pYZWMpnkvz+bzbIMe1VVFS5cuIDq6mrMzs4yh5TMjSORCIxGI/x+P3/fPp+Pi7W1gu4ruqPJNZqKBI1Gg3Q6zV4VJpPpJv4SNebouc3OznIxWywWkclk2CukVCpxnhKPx1n6da24UUabvGMIgl1fXw+r1QqtVstKl+3t7bh48SKfu4Sa8Xq96OjowAcffHCTBPTKygrOnz8Po9GIZDLJz4DUNgmWfKuoqqri5m1NTQ1UKhVzYpRKJTcdSNq5paWFvcdcLhc/XzK9zmazsFqtCAaDzJGcnJxEa2sryw6TX1Y4HC579lJuRhDXXC7Hzd90Os15Azmaq9VqLladTifnv/RrbrcbCoWCm3bUuFP/p5moVCrl+54Kwo8SH3mikclksGHDBkSjUeRyOSbc3kgEjUQiTBglCUaFQoFMJsOdETJ8u3TpEr7whS9wh42S1itXrrB83erqKiKRCEZGRtjB+lZBbuX0kmikYzabWd6SMLPU9VcqlRgcHIRUKmVMHenf3+hgm0gkGMpEY3viJZA7b7mklHSXgevjaJIFpE7CwMAAa7PTRmxtbcXp06fh8XgYFiEUCjE1NQWfz4d169ax4yN140ulEutLU/eRiPZrxcrKCncT8/k8QxHq6uq4k0NJIRGHiFxIxD3qGIrFYgwODjI0amVlBTMzM4xvLhaLcLvd8Hq9rNFMLt63CpFIxJ3aTCYDoVCIcDgMk8mEK1euQCqVIpvN8sdISl0ej4d13o1GIyt27dixA7lcjvckebzQz03FCCXQHyZ3RxAb0qJ+55130NjYCKfTyclFMBiE0+lENptFZ2cnk7PIqZkgg6T2Rd3M5eVljI2NQS6X8wifus70sa8VyWQSdXV1/HMVCgXMzs5y0UkYY/LGaG5uZgOfhYUFLpKoo0F/V0VFBbugCwQCnDhxAiqVig/6iooKuFyuDx2Pp9Np9Pb2YnZ2lgvlbDbLF3JPTw+GhoawtLSEfD6P8fFxNDQ04IMPPsDOnTv50l2/fj1OnTqFzs5O7gCHQiEkEgmYzWa8+uqrPF1IpVLMbdq8eXPZPUddKCpq6BmQTDBxvBKJBLq6utDU1MRJFHEz9Ho947OpwFQqlZy00x6jRgdw/Xz9MGngjo4OniTH43GIxWKGOE1NTXGhTc+AknSSjQyHwww1euONN1gWlC4OMrYLhUKYmJjgRgrBINeKQCAAu92OUqkEv9+PaDSKWCyGlZUV1NfXQ61WY3R0FJcvX2ayPMGVpqamAOAmEZEPPvgAu3fvRmtrK5LJJH9Tq6urGBgYwNTUFHsticXisvhqSqrp3ebzeQwPD8NiscDhcECj0XABTopTN5pCdnR0sDjBsWPHUFlZifPnz0MsFjMM5Uc/+hF27drF3xX5NHwYPAkAN0voTnI4HOyho9VqoVQq4XK5cPbsWfakuBHPb7FYGEpG9wf5P6RSKSwvL2Pfvn28JuqAk+LgWuF2u2GxWNgLoaqqiqcZNLGbmZlhwZHa2lqWKKbpdqlUYh+qWCyGTZs2cQORJjO5XI6hv7lcju/jcnwlug/IBC6TyfDdQ2affr8f8/PzSKfTOHr0KOLxOA4fPoxXX32VCe3UmX/11VexZ88ebpZNTEygqamJOS6xWIy/G2oarRXUsKI7z+/3M6cxnU7zeU5wZXK/jsViUCqVfB6WSiX86le/YuVIq9UKlUqF0dFReL1elmulMwUA3ynl9lo6nb6paKTpk8PhgFwuR21tLTfPyFPp8uXLbIRM56nf78epU6eYq0scIyJ1W61WnohR4ltu2kLcBprs+P1+LC0toaOjg6GIFy9ehNvtRl1dHXsAJZNJtLe3M6Sb+EwLCwuwWCzQ6XTI5XLskk3GtOQlFY1G4Xa7UVFRgUOHDt1ybSSUUyqVONelYpHEfUgV8/Tp0+z8XSgUMDc3x/4o8/PzKBaLOHbsGHbu3Amz2Yx4PM4NIplMxvsklUohmUyy8M5dd911y7VR0Uk+aCaTCS6XC6Ojoyya4fF42JySoLYmkwnHjx+HWCxGe3s7rFYrrly5wg1tKlppqrGyssLCMrlcjqcwHybqQ/GRCw2j0YgLFy5ApVKxeZpIJML7778PqVQKqVQKoVDIXeG77roL586dw4kTJ7BhwwZ2mibnTrVajcHBQRgMBlRWVmJ+fh5vvvkm8wwIWkW8j7a2tjXXVldXh+npaf5ISPpwdnYWY2NjrBlOkAgit91xxx149dVX4fV6kc/n+SLftGkTw6ZIQ95sNrNM78TEBHd7JBJJWea9SCTC8vIykskkpFIpzGYzd+WdTudN3T6aIEilUtx77714//33MTc3h2Qyid27d8Nms6G7u5tx1DSCrampgUQi4eSMRsuBQKBs8qJUKnmkTRcQqfO0tLRApVLx6H7Tpk1Qq9VYXFyEVqvFzMwMy612dHRwF5yMuMiQhrgc8XgcLpcLXq+XTe7K4XA1Gg2mpqbYxZTIgW+99RZ7EZRKJR59LiwsoLOzk03BcrkcKzLs3bsX7733HhvP0J7y+/3YsGED46gDgQAbLpXz+ADAI1lKVKqrq+FwODA6OsoKaASZsVqt7HtAe4YmKQqFAiMjI/zebjRMm52dRV1dHWQyGXdZtVptWXiSRCLB8PAwv3faW4ODgzxtoRHr8vIy+2LEYjFYrVYYjUZOrGOxGENLnE4nS+v29vZifn6epzMAmLharvsCALW1tbh8+TKqq6t5CmcymRgfSu6rBJkLh8NoaGiAQqHAsWPHUFVVxTCzyspK1NTUIBQKob29HcvLy7h48SImJibwqU99CidPnmRDQSrUiFx/q1CpVLh27RrDhUhowOv1wuVyoampCePj40wKHRkZgcFgYAUytVqNRCKBmZkZqNXqmyB7q6urWFhYwNzcHMbHx29K4jQaDRNb14qamhq8++670Ol0qKqqYvEBs9mMYDDIkwjgekLhcrnQ3d2N48ePY3JyEr29vWx69dhjj+HChQvsqhyNRrGwsICxsTHU19fDbrfD6/VywUWwnLUin8/j4sWLXFBJpVIMDw9DKBTi3nvvhdPpZBM3AHj33XexY8cOzMzMwOPxsBJaX18f7HY7PB4PQ6UWFxfxox/9CI2NjWhpaeGEj/g9ZJy6VhAnTqFQ8KSYGlMEkaW9TTKkJExA7291dRU1NTXo7+/H3/3d3+Ho0aPQ6XTMPxwYGMDi4iIGBwd5UkvnU7nz12AwsH49PcdisYjLly8jEAhg8+bNUCgUDIPLZrOYn59HbW0tBgcHkUgkWC0xEAiwHDqhB4iLdeHCBXR0dDB/TiaT8SRwraD9SR1Tr9fLCdDo6Cja2tqYaFtXV8fnukKhuIkv1NjYyHh04gaQT0UkEuFpNHl9SKVSfl9rhUKhYG8LKoSpcCL+HXXFqQGp0+lw+fJlVgfs6OhgPgAJZpCXRiQSQTabxRtvvIHf+73fw9mzZ5kM/WFeQRKJBIuLi4z9p++bJtsk/05d7UAgwOiGG/3IKOldWFhgJU3iy2SzWUxMTGB1dRUNDQ0AwMV9Of5IZWUlIpEIwyu9Xi9POqmzXldXd1MCXltbi6qqKkxPT6O6upohVtlsFuvWreMpIwAWznn55Zdx33338V1D51W5ZjFNBgjCGY1GYTAY4PV6mf+m0+m4GaDRaLBlyxY0Nzfj6tWr7JVCjU2lUsmSshUVFVCpVHzPEzIkmUwyJ6ccjFupVOLcuXMMZ6cptFKpxOXLl9Hd3Q2ZTAafz4dEIgGXy8X3osfjAXBdpOXOO+/EiRMnIBaLuQDJ5/MIBAKwWCyYmJhgODYZYJPCZbl36na7USgUGAKfz+dZ4XPDhg3weDyIRCIMXXe5XDwsEAqFiMViMBgMaG5uxuDgIJRKJfM8PR4PZDIZLl26hI0bN0IqlWJ0dBRutxsCgYBVqj4sBKvl5B1ux+24HbfjdtyO23E7bsftuB234/+P+MgcjdtxO27H7bgdt+N23I7bcTtux+34qHG70Lgdt+N23I7bcTtux+24Hbfjdvw/j9uFxu24HbfjdtyO23E7bsftuB234/953C40bsftuB2343bcjttxO27H7bgd/8/jdqFxO27H7bgdt+N23I7bcTtux+34fx4fWd72e9/7HhQKBbtY6/V6hMNhpFIp1NXVscQeaUjPzMygra0NgUAApVIJGo0GWq0WhUIB58+fh0KhgMFggFQqRT6fRzqdZk1svV7PRkFk2e71evGXf/mXt1zbk08+yRr3SqUSKpUKS0tLUKlULBdGXhErKyuQSqUwmUws7yYWi1niNRAIoKamhn8fyT+S6zlpaJPJEjk7fuELX7jl2v7u7/4OBoMBsVgMWq0WWq2WpcdIj7xYLLL5kcfjgc1mYydRMvOTy+VYWVnB0tISNBoNrFYrS72RYY1AIEA8HkcikWCt7EAggCeeeOKWa/vHf/xH1kgmPwzymaBnGYlEWKaRzI+WlpbY7Iu8KMjpOJ/PQ6PRoKKiAvF4HIVCAZWVlSxXWiwWIRAIWGrvf/2v/3XLtf3whz9kEyfaA36/n70C6uvrkUql2J8lk8mgpqYG0WgUYrEYSqUSCoWCHTYdDge0Wi07whYKBZY3JOOe1dVVllhMpVL4+te/fsu1ff3rX0c6nYZQKIRcLkdlZSWbpAkEAtTU1LBhFBmxkeQyhVAohFQqRUVFBYDr8oT0d5MGez6fh0Qi4f+e3lNlZSWefPLJW67tZz/7GYDrEqdk/kQmWOSiTh4hFRUV8Pv9MBgMKBaLWFlZgUAgYMM68mohHxXylUilUpDL5TAYDHC5XOyjQnt5recGAH//938PkUgEgUDAxkdkyklmUqlUCrlcjv1w6uvrMTg4CJ1OB41GA5VKBbVajUuXLqG5uZndlYHrPiL5fB5ms5mNjLLZLLuuCwQC/PVf//Ut1/bEE08gn89DIBDwr5FkoEwmg16vx8LCAlKpFPR6PUqlEpaWlqDX6/k8jMfj7JhK3iAikQgrKyvIZDLQarUst3ijazQ5Cv/N3/zNLdf23HPPsUkmPe9AIMCyyCQ5DoBNQGtra9mMjrT2DQYDJiYm2GjLYrGwxn1VVRXLl5OnDplNJRIJPP7447dc27PPPsv+HSSBSWaOmUwG6XQaKpWKz/poNAqTyYSpqSnWmCdt+nw+z1r5arUayWQSoVCIvYhqa2sRCoWQz+dZenZ1dRWf+MQnbrm2p59+mmUuab+RBwFwXZKTJNnpjqipqUEgEOD7anV1FXq9HuPj4+yfVCwWUVFRwTLlZrMZNTU17HlDHjnFYhGf+9znbrm2f/3Xf+X3VllZicrKSiQSCaTTaajVaphMJkQiEeRyOf6O6Uyj510qlWC1WnHu3Dk+dw0GA7LZLMLhMEt3ZrNZPm8KhQKy2SzUajU+/elPr/ktkBEiSa4mEgkkk0koFAp2ek6lUlhdXWXHbHovJCdP/gupVAqxWIw9lGifVlVVQS6Xs1wmSadWVlbia1/72i3X9swzzyCfz6OyshJKpZI9bEja02azscR6LpeDRqOBTqfDtWvX2NdKo9Egl8vx/8gJmwwY6VnR/hUKhXxuSSQSPProo7dc2w9+8ANUVVXxt11VVYWKigq+w8ivKZ1OI51OQ6/XQ6fTYWlpCaVSCRUVFZBIJBCLxYhGo2xMWCgUWIJVJBIhk8mw5D55HQWDQVRXV+OrX/3qLdf25JNPsukeuVy7XC4+K6urq9koTyAQIJlMor+/H4uLi8hkMvy8RSIRJiYm+OylPInk3MkDjYxhaX+Xu+9/8IMf8PPSarUQi8WYn5+HQCCAUqlEPB5ns0hyIBeLxfB6vbw/yXQzHA6z0WxDQwObGgNgM2F6jqurq2xG/Nhjj91ybd/5znf4WyfT2lKpxHLmWq0WoVCI7yFyRx8fH0epVIJAIIBMJkNtbS07ry8tLbH5bigUYqNo2sN0D4nFYqRSKXzzm9+85dqeeuoplvUViUQQiUTs00Z7kCR+yRSbfD/cbjfkcjkaGhpgMBhw8eJFVFdX811BptcajQYymYzNK3O5HBvtAih731N85EKDTIHEYjG0Wi3UajVyuRy7GhcKBfaxoAQrmUyyIU5NTQ3Wr18PsViMLVu2wGazIRgMso5wIpFAPp9HfX09zGYzb65wOIx8Pl/WpIxcOMloBrjuw0C6zJlMBrFYDJWVldDr9dBoNPxRUgKYTCYRDodRV1eHaDTKByAlf+RO3NTUhHA4zB8s6YevFUajEdFolD0IamtrOWkmPwmlUgmTyQSNRgODwYDFxUW0tLTw4UtulNlsFouLizCbzcjn83yxt7W1sV+CxWKBx+NhDwu6oG8Vq6ur0Gg0yGazXOiQOVFtbS2qq6v5sJVKpejr62OdaJVKxcmKSqXC1NQUOjs74fV6WRfcbDbzsyLXyVwuB7VazU62a4VUKoVMJuPCp7q6GqVSCbFYDAqFAhaLBbFYDLlcjt9jJpOBWCzG1NQUG+D5fD7+eNrb21FZWckXpNfrhd1uZ2M1Stq0Wm1ZPwj6feTkXSgUYLFY2HvBZDIhHA7zRafX6znJJLMbuVzO2tuvv/467r//fk4ISqUSX4ZUSEokEr5sbjws/3uoVCou2CUSCdRqNftC+Hw+NDY2stmiSqViLwEAcLlcUCgUaGho4KTJarXC5XIhmUyiqqoKNpuNvUFKpRLS6TSbMG7YsOFDfTRI395ms0Gn06FQKECv1yMQCMBoNAIA73mZTIbR0VHIZDJs3LgRKpWKC9jp6Wn2bLFYLDCbzazX/s4777CDuEgkYkdc4LqvwlphMBiQy+UgFApvMrFcXl5GY2MjEokEKisrIRaLkclkUFtbC7lcjvHxcchkMojFYhiNRva6oQJcoVCgvr4ecrkcMzMz0Ov1NxXeFRUVXGCuFRqNBpFIhP/Mqqoq1NXVIZvN8ndM7vTRaBS1tbWcsAUCAYjFYhgMBtZIJyPEaDSKiooKmM1m/gasViv8fj8XHRKJhM/VtfYcAPYMoH9eXl6GVquFQqHA6uoq4vE4IpEIOjo6EI1GsX79evZnoGLJYDAgmUzepB9vMBggl8uxtLSE6upqqNVq5PN5pFKpso6+tKbq6uqbGiIajQZGo5G18Mk9V6VScRJMDRYy86NvIBQKYdeuXey1IJPJYLFYkEqlWHueXOUTiQQ3Em4V1dXV8Pl8sFqtEIvFSCaT7KVjNBpRUVHBZwo58fb19WF2dhaLi4sQiUTsNNza2op0Os13BSULdrudzWuTySRcLhebqJUzxauuroZOp4PH48Hq6irUajUXecViETabDSsrK/B6vez4LJfLuclC5plUWMzMzLCZHZ1t5OtRUVGB5eVlmEwm6HQ6dpkuF7W1tcjn86iuroZCoUCxWMSVK1dQV1fHxbXBYIBIJOIc4M4778SlS5cgEAjYQHbdunVoamqC1+tFJBJh02FKwOgbIGPVcs7b9NzoXVECLBAI+JuKxWL8XVJDjzxbqNkllUrR3NyM+fl5KJVKXLt2DXK5nL+jSCQCqVTKv5/Oubq6OjQ1Na25NmpaKRQK/v9kBFxdXY1sNsv/jjyx1Go1J8VUsOdyOfT390Mul7NDPd23NpsNMzMzOHToEFwuF6anp9mR+sYGzn+PmpoaJBIJbgKSSR950uzYsQNOpxPxeBw6nQ6RSIS9jchHjIpUaurQ90n5IeU68/Pz7PqeyWRgsVjKOlzn83mYTCYIBAIuTpxOJ5aXl2E2m6FWq1FZWYlisQi5XA6fzwe/34+uri7Mzs4imUyysbVMJsPExATq6urYrFMoFMJqtUKtVsPv93OjrbKykk1x1wqlUsneOdTc1Gg0vPfUajX72+VyOfZdkcvl3LSsrq7mZkBzczNWV1fZHyocDqOlpQUnTpzA3r17kc1mkUgkkEqlyt4J/z0+cqFB5nDkRKvVapHJZNDf349IJMKJSSKRgEgkgtFohNfrxeLiIux2O7LZLK5cuQKVSgWDwQCDwYCZmRnodDrYbDZYrVaEw2F20qTKdWVlBT6fr6yBTyqVQk1NDWKxGF/eVVVVXJkD4MkAOTSqVCruulG3UyaTsTHNjYkkFTpTU1NoaGjgbhJ1muhyulWk02n+MJaXl7lrnEqlEAwG2RwtGo2yPTwlOORyGolEIBKJkE6ncfDgQfh8Prjdbk44Lly4gHXr1kEmk2FhYYEdKycnJ8uaMlHFGolEkEql+AOlRIqSwkKhgJqaGgDg7hAdetSxIFdRctWen59HoVBAW1sbnE4nBAIBEokEFzAzMzNl32k2m+UOVzKZ5IkXGSqRmSJNNcglXC6Xo6mpibuDGo0Gfr8fGo0GXq8XtbW1kEgkKJVK6O7uxsTEBEwmEycV5EZdrkDL5/MwGo28v6hTFQgEUF1dze7A5MJKnbtEIsGXSzgchlwuRygUgt1ux9LSEnfcyfgsHo9DLpfD4/FwNy0ajZY18CEHa3LNFYlE3GXJ5/NwOp18mQkEAjidTuTzeU6o6DIhZ9ZNmzbB6/WiurqanXwNBgOb/SmVSu52Tk1NlT2wgesTB5qcxWIxJJNJ7spR9zGRSHBXkQr+YrHIHdVMJgONRgO1Wg2FQgGfz8cJ0MrKCux2O0KhEBcm1JleWFhgA6Vbhd/v5+dAf9bKygo7VKfTaU4GKdmtq6vD+Pg4/7vV1VWYzWZMTU2hrq6OO9H0e+vr6+HxeGA0GnlCJBAIUF1dXdY51+/383SWmgzA9UQ6FAohnU5DKpVyYUmTUa/XC7lczsZ0ABAKhbB582Y4HA4EAgFUVVWxezYZf9E5LhKJOHFbK+LxODehisUiwuEwFhcXUVFRgcXFRTQ1NfGEVavVIhwOc5fWYDCwSzrtUXrfNH2LxWIolUqcqBaLRQiFQlRWVnIXcK1Ip9PQarV8QdIUZG5ujl2DqcCorKyE3+/nBhMlf8ViEWq1mhPp2dlZLgQSiQTq6+sxNDTEBmcAeApJxnW3ioWFBTQ2NgIAn5symQyxWIz3L71rmiwFg0FMTU3xd01mbjSRjkQibPqqVqvh9XqxsLAAo9HITurkPkx76FYRCoUgFouRTqchl8tvKvx8Ph9SqRQ3m3Q6HRvY0v1O32R9fT0qKirY9dnv93MSJZVK+c+XyWTcqQ4Gg3z33ipuvONXVlbY9I6eJRVt5Iw8OjrKjSqTycTJOhWq4XCYz1i6n8hAl74Bcu2ORqNlE6xYLAaj0YhwOIxkMgmhUAij0Qi5XM5NtmQyya7xWq0Wfr+fzW8zmQySySQKhQKb1GWzWRiNRhiNRp4MUWPRbrdzIe3z+TA3N4e9e/fecm3UeC0Wi3x+m0wmfuZ2u53RBwaDAfPz8zydtFgsSKfTcLlcyGaz7GQfi8VgMplQLBYRCoX4fltYWEAkEuGJExU4a0UkEkFFRQVPDujnbGtrw/j4OEZGRhAOh3nfuFwung7f2NQzm83cSBgZGWHDYeroT0xM8JlDTRSn01nWBJdcu2mqQu+5sbERarWaDU/p7I9EIlCr1chkMujo6MD8/Dzm5+cRiUTQ2dmJ3t5euN1ueL1eVFRUoLa2FvF4nCcRwWCQJ7XU4F4rAoEAO8JTQUHoAiro5+fnuflEBavb7ebnUiwWOVdxuVxc3JFZaCqVQqFQYAPPQqHA5+hHLTY+MkeDEgOq6ugyHh4e5slERUUFcrkc3G43lpeX0dbWxh8VJcVNTU0olUq4evXqTQekRCLhpJYeBl0yVDGvFZlMBpFIhF8O/Rk+n487rDTKpkSM1jM7O4tgMMgdm1KphJmZGYZtSSQSHudptVqGEYnFYobOlCs0qGNJfw4VDC6XixNeghzNzc2xmzddOH6/H36/H6FQiDtns7OzXHwolUqEQiHMzs5yAktOs5QMrxW0ycipmkb98/Pz3L2lDvD8/DwWFhY4WY5EIohGo5ygSyQStrCXyWQ89RKJRKipqUFbWxtDSsLhMHw+X9mulUgkYqd0gUDAHxt1caizVCgU4PF4EI1GoVAooFar+WerqKjgcT1dKvQuKisr+ecnp/pCocCTtQ9z46RnRQeYXC7nhFmtVkOv16OmpoYvQ5VKxR9tIBBAIBBAKpVCZWUlw8Ao4ZTJZFheXubLWy6Xc2JQU1NTtitfUVFxU5EGXL94yRWViplisYhgMIhgMIhEIgGZTMaJfKlUwsrKCgDg2rVrDH+jAnlmZgaXL1/mb5uK41AoVLYrT++VvlMquskxlg45Klbm5ub4UIvFYuyYSlMyKj5olE0QzFgsxg7QlOAEg0F2h14rCLZCnW+CXHo8HuRyOT6ryC2ZnJIpeSbYHrm3FwoF3n8AsLS0xEm8Wq3m0TkVynq9fs210bdCsJWqqipIpVIsLy/ztEKlUrH7sFKp5C7fwsICwuEwN29oH9A0hOCFLpcLRqORz/N4PM5u3uUSv5WVFYRCIX5XpVIJUqkU8XgcxWKR9w9BKehnoek3fUM0IQuHw9zV1Gg0PB1XKBSQSqWorKzk95zNZssWt5WVlfB6vdyYKBaLMJlMnAgA1yfi1Jigu81isWBhYQFutxsymQwmk4khZLQfi8UiqqurEQgEGGZKRTc58dJ5tFakUikkk0m+sGmi4XQ6OeFTq9WoqqpimJdMJkMqlUIoFOIzgmDN1E1dWVlhiEuhUMDExATC4TACgQCvrdy3QAk8QaHo/CWYI91l5O4di8VQU1OD2tpa+P1+hlhbLBYIhUKEQiGo1Wp+n6urqyiVSuzkXVNTw/dzJpO5CWa61n6jvZVKpVAsFpHL5aDT6fh7CwaDmJ2d5a6xw+HgZJGaP5RQV1ZWwmQyobq6GtFoFIFAAB6PB2q1mqcaBG0qF+l0ms8L6v5XVFTA6XQiGAxywUzNHIKq2mw2xONxTsjpTh0fH+cJNp1JTqeTE225XI5MJgOPxwO32132TqWJOTUYAXCyWSqVuOmRyWQwNzeHiooKLjiDwSBPn2QyGUqlEiYnJ7krT43A6elphlnRfUoFLb2XWwU1F6jopG5+NpvlM5xgmbTfNRoN5yKBQADpdJpzKnI6l0qlKBaLCAQC/C1TzpZOpxlS+GH3PTWWb8xl6V3SRCOdTiMUCmF1dRXt7e0IhUKYmZlhFAYVQcvLy1AoFDCZTDAYDBAKhZiZmeGzgCYwNKmiafytgr5PmUwGqVSK1dVVzi+Xlpb4bqisrGR4HhUkBAWj+76jo4OhcQTvSyQS3Iwix/bl5WX4fD4A4Bzjw+IjFxqUZFBySzjha9eu8ZidxkiZTAZOpxNdXV3QarVIp9MolUqwWCzYvHkzAMDr9fJDjMVimJmZgc/nQz6fR2dnJ+rr67mDRRjxtaJUKiGRSDA2n3CRKysrjH0H/utQp+48FTeE2zQYDNBoNIzLJGwbwRra29tRU1NzEweAkqxyz40uJ6VSyZAzGk8RrvBGOIDFYuHOcjKZZKy5VCrF/Pw8J430UWq1WiwuLvKhls/nIRQK0dzcXHYjUNFIXBCaUIRCIS5W6OB2u91wuVyMJY7FYnzJaTQaCIVCLjqrq6vR0dGBtrY2noRZrVbuOBEsoVwRJJPJEA6Hkc1muXtHByJ13oH/SpgAoLu7m7uX9Fyp80XPUCaTMSY8FAqhsrIStbW13OnJ5/NQKBRlP+4b941QKLwJmkMwFIPBAJ1OB61Wi8rKSj4UaQIjFAp5SqDT6SAQCGAwGFBfX89dEkq4KFnJ5/Oora2F1Wotuzbgv/DPhLenA5BgKDQBIt4HdaOpIFMoFBCLxbhw4QJD10QiEfL5PBwOB5aWlmCxWHi6RJ0OjUaz5tqA64kUFQaVlZU8bqcuI8EBaZSfyWRQKpU4USEYDzUe6MC0WCxoamqCSqWC3+/nhJqKtGAwiGKxiObm5jXXRvDLfD5/04W8uLiIfD4PmUzGiRIVGVRo0zOnLhDhlP1+P78zmnoRXEKtVnMXl6B9awUl6lTs0IVA0wGLxcKwuVQqxR136kTRZJV+nSaihNWmApg610KhkLts9N7K7TkqSm7ke2UyGcYx0xlJZyoV+4lEgi976vbSuquqqlBTUwOdTsffLU3iiHeTzWbLFrcEA6GuJEGCurq6buoUZrNZxmoTtJE6zQQrJdgeXbpU5Ljdbp7o0JpoP5Qr0KqrqxEKhRAOh7nhQwU0TWc1Gg30ej20Wi0XWRaLBclkEn6/nycCuVwO8XgcoVCIE1JqBlVWVmJychKhUOimaVW5aTedtZRY0wSe0AVdXV3Q6/WIRqMYGRlBsVhEW1sbFAoFFyh6vR61tbVcPGo0GrS2tnJjgfgOdL7RJKdQKHxoAkPfH02MhUIhd4ipyRCNRnm9ZrOZseUEl7ZYLAx1EgqFPAH1er3cVaeuNRXwKysrnD/cKkqlEpxOJ3M8KdFdXl5GIBBALpdjTuiNzSOLxcLJbE1NDfR6PcRiMVwuF//9VMBGo1GkUinY7XaeHiwsLHDDtNw7pWRZoVCgtraWmylCoZChS+l0mnlM0WiUi+h0Os3vkJp/NGWnJN/v96OiogL19fU38eM+rFlM8DGBQMCTQ4lEAp/Px1Dr1tZW1NfX8/lKvEPKx4jvQoWSRCKB3W7nqUwsFoPdbmd4GD1/qVRadr/Rd53P53lvEvyeCj6j0ciICIFAgNbWVmSzWYyOjiIYDKK2thbr16+HRCLBxMQEhEIhenp60NzczFxEalKo1WrOX6VSadk7le4EykXovMvlclxY6PV6VFdXw+/38xldKpVu4n4SVLCtrQ21tbWMaiForVAoZIRRLBbjxl+5ie2N8ZELDUqgstks49MkEgnuvvtuhMNhDAwMMNaNOnV0MR84cAC1tbVccS8tLWHnzp3IZDKYn5/H2NgYPB4PNm3ahHA4DLFYjLm5OYjFYlitVhQKBbS0tJTdCA0NDVAqlTCbzbBYLBgdHcWWLVsYxkVraW5uxuLiIkwmE0ZHR3H48GF0dHTw4VIsFvH5z38eS0tL8Hg8iMfjcLvdcDgciEajqK6uxsTEBPx+P0ql0k0Y1VsFHQ5LS0twOp3w+/1wu9144IEHuMAhYk1zczN3x+fm5rB161a0t7dDr9djz549+O1vf4v+/n40NzfDZrNBKBTC7/ejs7MTdXV16OzsZChaVVUVzp8/X7ZLGo/HodVqubO9srKCYDCIO++8Ex6PB0tLS3xQE0Z+8+bNqKysxMDAAOrq6hgHWCqVsGXLFp4oEZcjm83i3XffxZUrV7CwsICamhq0trZCoVCUPbDz+Tx0Oh0nMDdyf0KhEIxGI5LJJDweD6qqqqBQKCCTyeB0OvlwkUql2LNnD2ZmZtDR0cGVfTweRzAY5KlJNBpFKBSCUqmE0WhkstxaQeNb6ggLhUJMTEygoaEBXq8XDocDY2NjcLlcqKqqgkwmg9FoxPz8PMRiMSwWC1paWtDX1we3280XACUl4XAYVqsVTqeTp2i0r5eWluD3+8u+U6VSyd39dDqNmZkZrFu3DtlsFi0tLTeNgVUqFXp7ezE2NoaWlhYoFAo+oIaGhmC326HX61FXVweNRoPV1VVs3LgRAwMD6O3tZaKs3W7n914uiJ9BhHS5XI7BwUE+nIm7o1AocODAAT5oM5kM//sbE50jR45gYmKCk+3V1VWsX78exWIR7e3tcDgcsNls6OnpwdWrVzE2Nrbm2gqFAurr67m7R1jZHTt2cAFOMM2+vj4UCgVs3boVY2Nj2LJlCxobG+FwOPDBBx/ghRdeQG9vL2pqaiCXy2G327Fr1y7Y7XZO4C9fvgwAqK+vh0Ag4OL+ViGRSKDT6fhbzefzmJycZIEKIo4SDIoS8Onpaaxbtw5Go5E72R6PB319fSyEQEVec3MzhoaGGJJAMIZgMFiWe1NdXQ29Xo9QKAS/3494PI5r167xRI8SPgBoaGiAUChEQ0MDlpeXIRAI+OIirPzhw4dRKBQ4sSsUCtDpdPB6vVhZWWEBgqamJshksrK4eYIvUGJVV1eH8+fPM4+Lkja6aKnbd/r0aezYsQP9/f2IRqPw+/2IRCIYGBiAw+GAwWBgvHVXVxf8fj9MJhOmp6ehUCjQ3t6ORCJR9lslSBZ1SWUyGQQCAZ9nVNQT3PHChQvweDzM21Kr1TAYDLBarXj//fdZEIQgMGNjY1i/fj3DRYDrnI3Ozk4kEomy73R1dRWtra08USmVSjh//jxqa2sRDAah1+v5/Lj77rthMpkgkUhw/PhxWK1WaLVahkm63W709/djdXWVib5+vx81NTXw+/08zSf+Bu2pcvvNaDTy1IDOu6qqKgwPD3NjYXV1lb9njUaD6upqrF+/HlVVVfB4PGhpacHw8DA2bNiAy5cvY2hoCJlMBuvXr8fHP/5xnpBPTU0hHA4jkUjg8uXLqK+vX3NtlZWV0Ol0jPCoqalBKpVCW1sb8xnpvOzv72f46YULF3jSMT4+jqGhIYyPj2Pbtm3Q6/VobGyEUqlELpfDzp07sWHDBgQCATgcDs7NKNleK4h8TtA1gjprtVqsrKwwv0Oj0eCee+5hfg5x8qqqqhjGtbCwgEcffRSrq6tQKpWora2FRqPBtm3bmI8mEAiwbt06bN++nb/ntYKSceKl6vV6vP7665DL5QzhIrELk8mExcVFVFVV4erVq7DZbKirq+Mihe5bgjsSgZkEdWpqarC8vMzT3KtXr5adBNH5QxwIKvySySReeukl5rYJhUK+Wwm+1dfXx3yHdDqN4eFhdHd349KlS/B4PMwN3rJlC9xuN2w2GwqFAqqrq5nTV+5OpYnE0NAQ50XRaBSbNm1CT08P8vk8ZmdnEY/H0dLSAq/Xy7D3VCqF5eVlpFIptLS0YGxsDHq9nie3sVgMEokEnZ2dzKGsqqqC1WplTl+5BtRN6/xIvwvgscy+fftw5MgRNDc3I5lMYmRkBPfffz93OT0eDzweD3bt2oVEIoG+vj5861vfwpUrV9Db24toNIr9+/fjW9/6FkwmE2/4gwcPIpfL4Q//8A+h1+vR29uL5eVlzM/PY+/evbh48WLZjbC0tASTyQSVSoVEIgGr1YpXX30VO3bs4MM7l8thbGwMTU1NiEQicDqdOHfuHGPDz549i97eXly6dAnt7e3Q6XRMAMzlcjh06BCmpqaQzWa587Znzx6sW7duzbXV1taiqqoK27ZtQ3NzM3cGnnvuOfT396O+vh7r1q3jpGT37t04e/YstFotXnvtNVRWVuLw4cPw+/3YsmULfvrTnzIsp6GhAdu2bYNKpcIHH3yAl156iUddHo8HUqkUs7Oza65NqVRicnIS/f392LNnD5qbm9Ha2gq32436+np4vV7mG0ilUvT29uL73/8+b2ahUIiqqiqcPXsW69atw1/+5V9i586daGtr4y709PQ0vvCFL2BpaQlWqxXJZJLJteXWVlNTw+N3wvSr1WoMDQ3h7rvvZnI/wYwSiQSee+45KJVKPPXUU3juuecwMjKCS5cusYpYTU0NT3FojNne3o7V1VUumrLZLNrb28viSemiog52bW0t7r//fvzyl79Ef38/j3ZlMhnS6TQOHz6M6elpdHR04P3334fX60VdXR0SiQRaW1sxNTWFBx98EPl8Hh6PBzqdDrOzs0yQraur464FYdvXCiIJ2mw2tLS0wGAwcKe0paUFV69e5Y5WJBLBunXrkEgksH79enz/+9/HpUuXOGkWiUQYHByEWCyG0+nE1NQUIpEIRkZG8NBDD+HZZ5+FSCSCy+XC3Nwcdu7cyd3KtUIsFuPSpUssNOBwONDa2spkUIJp0LTgD/7gDyAUClFXV8cwkLa2Nvj9fjz99NN4++238ZWvfIUVoNra2lBZWYk777wTPp8POp0OZ86cwdmzZ3HPPfeU3XNCoRDnz5/nv6+xsREDAwOcxFBXSyAQQK1W44477kA8HkdXVxcmJydRW1uLu+66C4FAAD09PbyOlpYWnm7MzMxg586d0Ov1aGtrg1wuh8lkwsc//nHmUNwq9Ho9JicneeSuUCjQ1NQEn8+Hnp4ehhXI5XLk83ns27ePO+HT09OQy+Wor6/HzMwMduzYwc9Br9ejtbUVHR0d+OCDD9DX18ccNlLVa2pqwsTERNnvIRaLoaurCxKJBJOTk+js7OQ9T2dpIBDA6Ogodu3ahfn5eSaKNjc3o7u7m8nT58+fh8FgYL6B3W7H6uoqrFYrY5uJK0gJ9FpB05v6+npWY6qpqcHk5CTuu+8+KBQKZLNZ5iS1tLRAp9Ox6IdMJuPOsVarxdTUFLZt28ZTyMrKSiwsLOChhx5CMpmEXq/HyMgIhoeHcccdd5Ql51LB3d/fD7PZDJ/PxzCw++67D9XV1ejr68PmzZuhUCiwfv16VhxUKpXo6upCb28vstksN5c++clP8oTZarXixIkT3NRobW1FU1MTOjo60NPTg5mZmTXXRt+NTqdDZ2cnOjo68Mgjj/B7mJ+fR01NDcxmM5aXl1l1bWBgAI2Njejr68OWLVsglUqhUqnwH//xH9Dr9WhoaGD+4Pnz57Flyxa88sorsNlsqK6uRk1NDT73uc+VhcPpdDrU1taivb2d1XC6urqwtLSE++67D263mydYuVwODzzwAOrr62EymfDP//zPOHv2LKxWKxOK/+Zv/gb33nsvtFothEIh6uvrMTk5iUgkwnu5rq4O7e3t+PKXv1z2/KWEdvv27WhqakIgEMD8/DyeeeYZ1NfXc7JN75E4CJs2bUI6nUZ1dTX27NnDDcUzZ86gVCoxD0Umk+HUqVM4cuQIAPAETC6Xo7u7u+zaampqYDAYsHHjRgDAxYsXodFocPz4cXR0dLDK3MrKCjweD3p7e1lIh/783t5eiMViPPzww/inf/onFqIZHh6Gy+XC2NgYWltb8eSTT2JpaQlXr17FyMgI2traGK611jt1Op1Qq9XciPnCF74AgUCAvr4+TuRpOv/4449jcHAQZrMZr7/+OsbGxpgUbTAY0NLSgpWVFUZiRCIRnDx5EnfccQfva7/fD6fTiR07dsBsNq+5NqVSCY/Hw/Byv98Po9GIuro6/PVf/zVUKhVUKhVCoRBee+017N27F++88w4MBgN+8Ytf8B3n9XpRKpVw8eJF3Hvvveju7uaz780330R3dzcikQiMRiPcbjc8Hg/2799f9p1WVVVhfn6ekQp+vx9msxmLi4tobm5mjiNNwTs7OyGTyRjePTAwgPr6epw9exalUgk/+clP0NLSgs2bN2Pfvn3Yvn073njjDb7DqAFQVVWF9evXl72zboyPTAankc7IyAjDCgqFAjZs2IDXX3+dOxFUGfr9fvT29mJ+fh7f/va3uau/sLCAkZERNDU1IZvNoqqqCplMBu+99x6OHTuGP/uzP0M6nWbpLaPRyPKBawUp14yNjbFyUy6Xw969e/Huu+/C7/dDq9WioaEBXV1dcDgc8Pv9qK+vRyKRYDUTqVSK4eFhrtCXlpYYMxcOh/HGG2/A4XCgrq6OMYWjo6Nlq+GpqSl0dHRgenqaIVRVVVVoa2vD6OgonE4nj6crKyvR29uLgwcP4v3338cnPvEJ5rPEYjHG77///vswGAysSNLR0YFPfOITDGOhEeWH4Zej0Si6u7vh8/kYejY3N4dIJIINGzbwJqbE40tf+hKA6wQkKjIoMZyfn8djjz2GwcFByOVyVgPJ5/Pw+XxQqVSYnZ2FTqdDS0sLotFoWQiQx+OBTCbjsR0Vifv372eowsTEBFZWVtDX14dz586hq6sLHo8Hjz32GBONiUA3NzeHWCyGPXv2QKFQYGhoCMlkEnV1dbh06RJj5YkDY7PZ1lyby+ViQnw8HsfCwgKmpqZw7733YmFhATabDVevXoVQKMTmzZvxL//yL4jH4zh8+DDa29shkUj453c6nXA6nXjttddY2cLtduPAgQNwu92cUBEhi7gIawURICmRJLjF6OgoxGIxurq6cOHCBR6NX7hwATU1NVAoFPj+97+PWCyGiYkJxpZ2dXXhlVdewYEDB9Dc3IzJyUn4fD5MTk5i69atSKVSTFicmZmBWCxmeOStgg7pG6FATqeT5aaps3P16lVcu3YNkUgE+/btw9DQEMOEKOn/3Oc+h3fffReLi4t8qSQSCRw6dAj/+q//CpVKxckiFXYPPvjgmmujbiTxUyoqKjA5Ocm8LJVKBafTybwF6gSeOXMGSqWSFUxMJhMWFhZw8eJF/sbEYjEWFxe5K3rp0iW0tbVh69atAICXX365bBd3enqaoVaEOXY4HDAajbh8+TLa2towPT0NkUiEnp4ePPvss6ivr2elNb/fj2KxiMbGRkxNTQG4Tsx3u91wu92IRqN48MEHWUCBEsRCoYBgMIi2trY11zYzM4P6+nom9qfTaUSjUe583sjhkMvleOedd9DQ0MCFcDgcxvj4OHQ6HdavXw+j0YjTp0/z3z08PIxisYiWlhY4nU5YrVYu3EgBbq2ghgSd8cQPuuuuu3Dp0iWeShKn5dy5cwzbCAQCGBoaQnV1NcxmM9xuN6qrqzE7O3sTlwe4nrQFAgGG22UyGUxMTCAajWLnzp23XFswGARw/awj/Pzg4CC6urrw3nvvoa2tjfe0XC7H1NQUk3ep2UHfH00nnn76abS2tvIZc/DgQSwvL2NxcRHFYpFVgoxGY9lmis/nQ21tLebm5ripdP78edx1110Mp3j11Vd5b2QyGbz++us8PXI6nXA4HHjvvfcwPDzMzUhS5Kqrq+MudH9/P3MWPB4PC2WsFaOjo6x6RzyIU6dOYdu2bbhy5QpDqWia/Oabb+LIkSMolUrYunUrFhYWMDw8jO3bt0MgEGD37t2Ynp7m+2ZychJ+vx9qtZq5KiRysLCwgNbW1jXXRrzNs2fPQiQSwWq1or+/Hz6fj5UZh4aGEI/HUVtbC4FAgD179uCll15ixTL6GejnTCaT0Gq1CAQCuHLlCkZGRhjW19zcjNraWhSLRUxOTuLixYu46667brm2lZUVTE1NQa/XszzsyMgIzGYzPB4PE4mrq6tZfttut+PFF19kyWW9Xo/m5mYcP34cR44cYTWuG60IXC4X7HY7VCoV/0zUOV8rAoEAWltbeTpA3L+6ujq89dZb2LdvHxddBw4cwK9+9StUVlbCbDZDoVDA5XJhcnKSp6+lUgk6nY7fYVdXF+688048//zz3ICiqSQR0ct9CyTiQXzgq1ev4v7778elS5eg1+vhdDpZcvjcuXMArk/rDx8+DJFIhNnZWeZR9fT04PLly+jq6kIikcDw8DCam5sRCoUQDAaZ9K5QKDA1NVX2vp+bm4PFYmFRA+KuUXOeLCYqKyvR0NAAn8/Hap2hUAhOp5Mh2xKJBBs2bMCJEye46UMQrrGxMahUKgQCATQ0NKCqqgrXrl370MYifxcf6Xfh+ij15ZdfxvDwMEt12mw2XLx4ETqdDlu3bmXS6fLyMl9gy8vLePrpp+FwONDV1cXj+IaGBibtBINBpFIpfqg3kuzm5uZYDWWt0Gg0TAwMBALw+XzcAa2vr8eePXtQX1+PpaUlHDt2DE1NTTh69Chqampw9uxZ+P1+NDc3Y/v27VyM1NTUYMuWLeju7uZLRSaT4fHHH0dPTw/j6Uiedq3IZrNwu92Ix+PcTTAYDDh27Bjef/99lmELBAJwOp1IJpNwOBwAgH/7t3/DL37xCywsLMBisWB5eRmnT5/GwMAAJBIJ+5gQBplw2EtLSxgZGYFWq0V3d3fZd0oSg+FwGLFYDFarFc3NzTh16hQ0Gg0GBgZgNBoxNzeHX/7ylwxrOX78OMbGxqBWq6HT6ZiUazKZcPnyZZw5cwbxeBzHjx/nLjmpYtF7Llegra6uYmFhgXG3Go0G0WgUw8PDAMAYVZFIhPfffx+PP/447rjjDlRUVODcuXM4f/48ZmdnsbCwALVajYsXL6KiogLz8/PweDwQCoU4deoUtFotHn74YcadVlRUcFezXBB36Ea8KHk4jI2NMS7V4XBAIBCgubmZCcM+n487uhqNBnV1dYjH4+ylkEwm8eSTTzKcwm63c+IgkUjQ399fdm2UEDU3NzOMZnBwkKciGzZswJYtW6BUKrFz50709fVhbm4O4+PjDOeji/DatWuMMybZYrq06T0Q50Sv13+o6pROp+Nno1arYTabsbq6iosXL7KPDSU2JBLx+uuvIx6P30SEtNvtOH/+PC5duoRLly5xM8FoNOLYsWMQCoVobGxELBbD6dOncf78ecbprhWUkJPS2cTEBObm5hgzfOjQIezbt4+nBOfOnUN7ezsrhUilUlitViQSCTz88MNwOBxc7ESjUdTU1HBT5O6770YoFMLzzz+P3/3udwxhXCuoKCcSpFqtRjqdxunTpxl3XywW4XQ6cfz4cSQSCTgcDigUCmQyGdjtdmzatAkymQwtLS2oqKhgmejm5mao1Wr8+7//OwYHB1llijgNpNq1VggEAni9XuaFbd68GU1NTTh37hwrw5GSn9/vR1tbG6LRKBPmCRrY3t4OuVyO48ePo6GhAWazmVUBfT4fQ76IkE8J/VoqO8B1mI1QKGQICCXJx48fZ0iQwWBAdXU1gsEgtFotmpubWZpzdXUVvb296OvrY65INpvlvz8ajXJnnOCERqPxJlJ5uXA4HPB4PBCLxdDr9fB4PKzeFo/HWbQiEAiwWhiRTCUSCZqamlgIg7yESBWos7MTL730Empqalgu2ul0YmxsDIlEoqwC242iDfTsW1pa8Fd/9VeYmppCU1MT7r33XmzYsIGJ9ps2bcLCwgKeeuophMNh3HfffTh8+DAnyyTDS3fqiy++iNdffx27d+9m7hAAvsvWCq1WC7fbDb1eD6PRyJBnKhxJXpYI4Bs3bmS/gmQyCZPJhK6uLlRWVsJoNCIWi+Hq1asAwHeEz+eD2WxmqWCCBCqVSmzYsKHs2i5evMg8HZqU/frXv0Z3dzcMBgN27dqFnp4eVp+rqqqCyWTC/Pw8AKC3t5d5oUqlkonoxOPZsmULqwgR/NflcqGioqIswb+6upp5fzKZjEUlSD2zu7sbIpEIV65cwWuvvcbwYeJbERl4dnYWAoEAr732GktRkwJaqVTCyMgIlEol+595PB7o9fqy0z2ZTMYiFjRlHBkZwTe+8Q2srKxgYmICoVAI8/PzeP7557G0tIR9+/ZhYWEBly5dwvLyMurq6hh2+cwzz2B1dRV2ux3Nzc0IBAL44Q9/COD6WUocEzqfyp29pVIJExMTyGazqK2thd1uR6lUwsmTJ1lQiIjhBDGqr69nnhVN1ohsPzg4yOc3qexNTExwYVdRUcFSypQ7rBUk56zT6VBXV8fTvuHhYdTW1uLChQtc9B87dgz5fB52u519ZoLBIJaXlzE7O4toNIq/+Zu/wcLCAktHGwwGDA8Pc7PdarVicHAQv/nNb27iSn5YfORCgyALhN8lTf+9e/cysZew0zt27EAul4NIJILb7WYsbSqVYt7E5s2budtIqkuf//zn8cYbbzDJS6lUsglRuW4fbVBKCOjFmc1mOBwOuFwullbdunUrHA4He2709vYyBpwqY8KuhUIhNjbq7u7GzMwMAoEAcxuIJ1COjCiXyxmKQ5dGJBLBpk2bmK9AXWej0YipqSkmV2m1WqhUKmi1WmzatAnZbBZ33nknd5o3b94MrVbLPhG7d++GRCKBVquFXq//0C7CjUaA5M9QKBT4shQIBNwJpWqYjPvuv/9+dHR0IJFIoKurC16vFxs2bLhJetBkMmHnzp1M/CQFBJPJxN2etSKVSjHsjEjaBH/KZDKoq6uD0WhEa2srDhw4wJjCubk51NbWYmBgALt378bu3buxtLSEO+64g0eflMDfcccduHbtGuLxOEvJkvFWuQ+IFHHo/5OZE0FXOjo6UF9fzzwRKkiDwSA2btyIlpYWTrDC4TBaW1uhVCphs9lYIGDPnj3w+/1YXV1lZaHa2lrGJZdbW2trKwqFAmM98/k8GhsbudDw+XzcJXO73UyaJ+O+WCyG9vZ2CIVCPPjgg2xOF4lEeMoSDodRW1vLpF3qHH0YlIUOJ1IdIzxoT08Pmx/SAVtbW4urV6+yrCfxlQCwDvqnP/1pJmreuK+pEIrH49zZpzOp3J6jy4L8FkiViw5+j8eDfD4Pi8WCQCCAxcVF2Gw2NDY2wuv14ty5cwzj+uxnP8ueGYFAAOPj4+jr64PT6YRCoeAGCxlOlrtQiKwaCoXgcDhYQYUaNjRhNhgM2Lp1K3cDg8Egdu7cCZlMhvfeew+nT5/mCbHRaIROp0NFRQXS6TT279/PfIVUKsXfFOGm1wqpVMoqgSQm4HQ6sWnTJiQSCZhMJv53JDMNgBWBSHVLKpVyU4WwxDabDXK5HDabjeEyxIsiVbtyXINcLsfcQZJppX1AiTbh6XU6HVwuFzQaDQqFAgYGBlh3nqa7AwMDvFc0Gg22b9+O3t5elhEmg0aVSsWcsrUiHA6jo6MDRqMRGo2Gzx3q7pPJV2trKwYGBtgjg3xlqqqqMDg4iKtXr2J4eBhisRgmk4mnT5OTk+jq6mKpYZoerF+/HvF4vGxSSlNp6i6TSlRrayurQRL2m6ZSd999N8xmM7q6upDNZnH27FnmPvb19cHlcuHq1auIRCJobW3Fhg0bkEgkUCgUGIKhVCqRTqfLdpiJ60CNTEpwCbJLXh4ikQiNjY145513WOmwoaGBeWcajYYLqFgsxsR7khw1m808LaLCIZlMYnFxsex+Iz6GRCKBRqPBzMwM+5hQYre6uorm5mbOS/L5PItZpNNpmEwmRKNR1NXVsfIkSc43NDRgbm4OdXV1AMDTR7FYzIIrt4pkMon6+nqGJtH0rbW1lT3EWltb0dvbi/Xr1+P8+fNsbEhoCoFAAKPRiKamJhw6dIihPuTb093dzcVHMBhkJbL5+fmyayM5diq8SRjnM5/5DIvu6HQ69PT0YNu2bSwVXigUsH//fpjNZiwtLaFQKMBms2HXrl1IpVL87tRqNbZv3w4ADDekfV4qlfhZ3iqKxSL0ej3nb6SUtm3bNng8HoY+1tTUoL29nfm7ZrOZYa20fuKtRCIRJvFns1kcPHgQwPV7jQwHFQoF+1StFcQZ9Pv98Pl8rB5YXV3NxqcdHR3o7+/HunXrsLy8fJMiI+XX+XweXq+Xc8j6+nq2jNi0aRNPj1KpFAwGAxoaGpDJZMrepzfGR4ZOVVZWoqamhnF2dLF3dnYiEokgHA5DoVCgrq4Oer2eSXDkCJ7P55kcazKZUFVVhdraWjZqAYBt27bh+eefZwUcwpXpdDomE661NqrSqaMaiUSYQEijdfogZ2dnWdaUFASqqqowMzPDfhdKpRKlUgkikQh6vZ6LEK/Xe5MjKY2q1goiohPsgXgFCoUCR44cYVM6UtghhSWz2YxMJnOTEkpNTQ18Ph9sNhv0ej0TIePxOOx2O6RSKbuQE9mn3LSFkgPSRCZYl1gsRn9/P48vqetCRUc2m4XFYmE3UJL+JBlGusCoONXpdBgeHmafDjKxK1egkXM0SbGGQiEYDAZkMpmbiIC1tbVQq9UsUUiqOQSVE4vFEIvF7K1A+H+pVIru7m4MDQ0xobBYLLIiUjl5RcI9Eh9iZWWFpZGJ/EdFhlAoxMDAAB/qpEBF8EMqnpqammC321kPva+vD+fPn+dL/sbRNBGpbxUkW0kKFKSKRfAh+n5pPE64XPISIMOjG6VnqQihqYLf70dDQwMikQiPeInDRVCStYJUh+hnIgdXep7kk0HvQqVSobGxkQ3w5HI5q8p1dXWx6zSNhOm5b9y4EQ6Hg3kfN8pIrxXkl0PQC0o+CTvvdDq5e9TW1oZgMMgJZmVlJUtzEqGeCHlTU1NIp9PMt6G9SspaABjKUO65JZNJaDQahMNhxsVbLBa+xOj5WK1W5HI5HvVT8Urmg2R6J5FImPMjl8vR1tbGSRqpxaRSKVa2WytIipvOS5pCUJJB2F5q7pBXBclq07lN5Gyr1crNFwBshtXd3Q2FQgG9Xs+GeR92N5CiFym5kLIOAJ5M0PdULBYZkkOS3DQNIVn0QCDAHB1KLqgoJQEBUkKqqKiAy+Va+2PAfxluicViNjYzmUxwOp2QSCQ3SZAPDAywYACJB1ATjd4POSETAoDUDDUaDcvJ1tTUYGlpqey6yO2ZEn46Q5qamjgxpn1DjaOKigrU1dXxRDCZTGJqagpVVVXc3KApW19fHzeR5ufnWUSEGlDUKFhrbUqlEsvLy5z4ZbNZllSn84WkW4PBIAsh0FlH9y4piLW0tLDKJSVP1HjRarV8V5E/VLlQKBTcSCI/D5vNxqqDxAnt6urifUWcGmpgkV+Rw+FgGV6aztXW1sLhcHCj8Mak+cNIzer/lHEnrxWSOzeZTPzrSqUSBoOBi8CGhgYmTpPfSTAYRH19PUsRU6FQVVXF4hM+n4+5A263u6yyE6l1kdRqJBKBQqFAW1sbVldXuVlJEud2ux1+vx8SiYSlb0mpkjy9yPyzUCigo6MDVquVc7wbnb6tVmvZRjYpA9KZJJVK0dXVxUIJ1GRTKBQQCoWc0FOThNTM6urqcOXKFWzfvh1jY2N8ZkqlUphMJrZMIMVIyh/K3VnkN3SjwTRBrUkIgaDRBPtSq9Worq7GwsICCx7E43GIxWK0tLQwHJXyIBLRoAkX5TgzMzMfet9TfOSJBkmX3SjZRwfvjh07eBNQJ6ynpwehUAitra1YXFxkUs7zzz+PlpYWXLlyBd3d3Wy8RBvqM5/5DGuW04VASf5aodVqkUqluCNPVbbP54PJZOIuxcLCAk6fPs2mXO3t7VhZWYHJZILNZsPp06fZRMhkMvG0gKzu29raeLRHzq+1tbVlK87a2lo4nU6ezkgkEthsNgwNDaG3t5clPgFgYmKClaPIPZdUVwYHB9HS0oKLFy9i+/bt7PJqMBgwNjYGs9mMd999FwqF4qYqvdzUQCqVsinR6uoqu24uLi6ydKlOp0NzczM0Gg1aWlqwtLSEqqoqvPLKKxgZGUGhUMC5c+fQ1NQEh8OBzs5O9PT0QKVSYXl5GZlMhjHH4XAYExMTuHbtGjuTrhVkWEfwhnA4zLwWrVaLc+fOweFw8Ei3qqoKOp0O27dvx+joKIaGhuDz+fD222+jpaUFy8vLaGlp4Qs6lUrB4XBg3bp1rEam1+s5ofswKT4AjGVfXV1FU1MT4vE4pFIpjh8/jng8zlLIDz30ECtSjI6OcpeXktJwOMywETLsI0ngXC6HxsZGhml8mGSxWq1GJBKBxWJht2hy3iVMcG1tLcvoNjQ0IBgMssEmXbQXLlyAUCjEK6+8gvr6elitVoZGicVimM1mvPXWW0wYJonbubm5NdcGgBMSkn8Nh8NQq9UYHh5mxZVEIsHmXocPH+amxKVLlxCPx7njvXv3bvzt3/4tFAoF5ubmGGedyWSwc+dOJBIJDAwMIBqN4tKlS4xrLxf0vEUiEe8Zgn34fD5otVq0trZCo9Fgw4YN0Gg03HUicy5qfPz4xz9GfX09VldXUVlZib6+PqRSKdx///3sfEsyiyqVqiw5l2CORqMRJpOJ8dSkGmYwGGCz2RietHHjRpw7dw7FYhEvvvgiVlZWcPToUXbSzeVyqK2t5eaLzWaD0+nEunXrmINF569Opyt7EVORRRKWJPBx9epVJiYStpvkWKPRKDo6OlBZWckkYRJ8mJqaQmtrK/NuSqUSFhcXuXOu0+nY3IwKrLVCqVRidnaWzf/ovAsEAlyEkkeU2+3Gjh07GDZIsrNCoRBjY2Po7OzEU089xV1glUqFxcVFOBwO9Pf3w+PxQCKRsMmeSCTC+Pj4mmurrq7G3NwceyYkEgme0ra2tnKR5HK5MDMzwypJXq8Xx48fx/z8PDQaDcxmM+6//354PB7I5XJMTk4imUyir68PCwsL2LVrFzu2+/1+hMNhNgktFySXSucYKRaRcl1raytPGXt7ezE4OAipVIpwOIzGxkZGO+h0Orz66qt8n5Jbc6FQwJEjRzA1NQWbzcYGllSErBXUvVf/py8HycZ7PB7urKv/08wzFovhoYceQiaTQW1tLcbGxtiTxePxoLu7G2fPnsXevXsZLknKZnSuUFeYYF/l4HAymQwul4s5W1NTU2yW2NTUxNLsJBZit9uxvLzMhSBJ387NzWH37t148803YbPZ+O+32WyQSCQ8mSNTXb/fD7FYjOnp6TXXVllZiUgkApPJhMbGRpb0Hx0dxebNmzE1NYXR0VHMzMxgfn4ehw4dgtPpxNatW7lBSNPZqakpho56PB7Mzs4il8thZGQEBw8eZLlqMsXr7OwsmydVVVXB6XSioaEBNTU1CAaDUKlUrJxJDeTh4WE89dRT0Ol0+OUvfwmhUIif/vSn8Pl82LZtG8LhMHMdiJ8mlUrR2NiI6elpVFRU4MSJE3yekZBCOWUnapzf6GlBAiAbNmzA/Pw8K9+VSiUcOnSIvUaWl5dRWVmJ5uZmbuzl83lWdDOZTKirq2OOaSAQ4POtoqICTU1NZXM4lUoFj8eDtrY2bN68Gf39/dixYwfm5uawZcsWjI+PMzpnaGgIGzZsYD7yyMgIS5onEgkYDAZcvnyZifJk+ud2u7F582ZWN6PmTENDQ1lKw0177yP9Llw36qHRF/lpkGSnSCRCS0sLfvrTnyIWi+H+++/HzMwMamtrmSAGXD+4CEJy7do1XLt2DV6vFzabDZ2dnfB4PHzI+nw+xlVeuXKl7Ebw+/3YunUry7OSVjRdSg6Hg42haDQ9PDyMaDSK3/3ud/B4PNi7dy/sdjsikQi2bt2KU6dOsfFRVVUVWlpaMDMzw10IInbLZDL09vauuTY6MEZHR+Hz+SAUCjE+Po7HH38cP/7xjyGVSrG4uIhwOAyTyYRsNssFUTQaxdGjR/kw7OjowDPPPIOzZ88yfIOk106dOoWlpSV2lCXPDQA87v/vQXCNlZUV9kG4du0aBAIBjy9PnjyJ1dVVNDY2IhwOw2azIZPJYMOGDdzRra+vx/LyMlQqFZOGaPphs9nw6quvYufOnRgfH0dDQwOMRiPDU9YKgnBEIhHuEM7MzLArdaFQwMzMDAqFAjZu3Ii3334bO3fuxIsvvoju7m7kcjm89dZbvD8IlqRQKOBwOPgyXl5eZoNIMhjT6/Vl99vS0hLjPskHY2JiAu3t7Th16hQ2btyIxcVFzM/Pw2azsZliVVUVBgYG+Fk3Njbivffew+LiInbv3o3JyUlWppDJZBgaGuKpHE3dHA5H2c53NptFNptlEmwikcDc3Bza29vxzjvvsNxtPp9He3s7Nm7ciIWFBSiVStjtdoYjkC799PQ02tra0NTUhJmZGZYTpEQtm81icXGRDew+rMPhcrlw+PBhTE5OwuVyoaOjA/v27ePu2sjICPswrK6u4uTJk7BYLIxDJzOz1dVVvPfee3j00Ue5Q6lSqVBfX4/q6mo89dRTqKiowPj4OLuxkhjBWpHP59HS0sL66isrK3A4HGhra8PCwgJPw6qrq9Ha2opLly5h8+bNyOfz2LRpE5aWlhCNRrF+/XoMDg7iwoULTG4OBAJ47733sHXrVnR2diIajXJiJBaLueO7VmQyGWzduhUikQgNDQ3QaDT44IMPWN2KiKLURSR5YIfDgenpaXbyNZlM+PWvf4329nZ0dXWhp6eHJ1mhUAg//vGPMTAwwL4SyWQSTqezrKTn4uIitm7dikgkwqp61BV74YUXsHfvXjgcDpbKJrlO4pd0d3dzV83lciGdTmNkZAT5fB4SiYQn5U899RQAMDRDJpNxEbNWhMNh7Nq1i/clyZtTg4dcdGkKeu3aNSiVSoyPj8Pn80EsFqOurg49PT1466232AV7YmKCk46+vj5cu3YNW7ZswcLCApNRyWR1raDCwOv1spxlLBbjbjth34m8e/r0aQDXBUbI94iS02PHjmFhYQHT09M8DQgEAti0aRPy+TyGhoZYCjOfz8PtdpedUtHETKVSsTfBO++8w74yiUQCJ06cgEajwWOPPYYzZ87goYcews9+9jOGB9lsNjz44IP44Q9/iL1798Jms0GlUrHYxMc+9jEMDw9jdXWVnYoB4PLly2Uhv8FgEAKBgGGlmUwG/f39qK6uxq9//WsA16Vca2trsXHjRoyMjCCVSmF0dJR5fU6nk5tmdrsdHo+H1QupyeNyuXD69Gm0tbXBZrMhGo3izJkzANa+U+PxODZt2sQkaZrw+f1+XLhwgQ0Tq6urYbfbMTc3B6VSyU3S0dFRLsLz+TzuvfdenD59GhcuXGCRBZVKhQMHDkAmk+H06dNcaDkcjrLnG+UY0f90oaeknDhyy8vLDDMMhULMZXzhhRcYLeD1epHP5zE/Pw+9Xo/FxUWevDkcDgQCAfzbv/0b8vk8f9s6nQ6Tk5PYsmXLmmsrlUq48847mc9KsrNf/epX8bvf/Y7ft9VqhcViYc82t9vNTcNr164xNPib3/wmJiYmeALodDrxxhtvwGAwcMNDp9PxnVMOqre0tMSN84WFBf4G6urqcOzYMWzdupWlgO12O58Nb7/9No4ePYpkMomxsTGsW7cOdrsd77zzDjer5XI58/ModxkZGQFwvYFerjgD/stfiSwX8vk8PvjgA2zZsgVXrlzhKQShW5RKJc6cOYOf//zn3BSqrKxEY2Mjzpw5A6/Xi7vvvpvv+FAohAMHDmBhYQFNTU04c+YMlpaWuGH+UeMjFxp6vR7j4+MwGAzMuXjvvfeYFPm1r30Nd999N65duwaPx4Ompib2e5ifn4fRaMThw4cxMDCAJ598Eo2NjTh48CA0Gg3y+TzGx8fx1ltvYe/evTCbzWhqaoLf74fD4WB36bWCOj7AdaKPSqXCuXPnEIlE0NbWhp07d2Jubo5fmt/vR0dHB9RqNfbu3YtwOIzR0VEMDAzgqaeewpUrV9Da2srmThqNBg6HA42Njejs7MT/+T//B9lslkdid95555pry+fz+MUvfgGTycQHaV9fH15++WXU1tZCIpHAYDAglUphZGQEv/nNb/CFL3yBzVUIyrF9+3b84z/+I373u9/hD//wD9HZ2YmxsTHmohw6dAhNTU14+eWXmYDc1NSEvr6+NdcmFAoxMjICq9XKxnzFYhGDg4Ms1VlfX4/h4WG43W7Y7Xbs3r0bLpcLJ06cgFQqZYzyz3/+cyYfk1GR2+3G+++/D61Wi3vuuQcDAwOYnZ1lh9Zy71Sv1zPchEaQExMTkEgkLHGpVqvhcrlw/vx5VFdXI5fLYc+ePQiHw6iqqoJKpYJQKMT09DSWl5cRiUTQ09PDeEzqZBEshroQN47PbxVms5klB3O5HCuc/OY3v8HevXuxuLgIq9WKfD6Pa9eu4fd+7/cwMDCAb37zm1haWoJUKkVtbS16enqwfv16OBwOzMzMoKenBwqFAmNjY7h69Sq6u7v5cJ+bm+NuYjk8qUQiYZlXnU4HoVCIzs5O/OxnP4NIJMLOnTuZUBqPxzE+Ps5uv2RIabfb0dTUhJ/85Ce4//772S2XTJkoCa2vr0cgEGDYIiVL5aKvrw/Hjh1DLpdDc3Mzstksjh07xhCgT3/60xgdHYVQKMTGjRvZHbxQKOA//uM/0NLSgv3797N0cyaTwac+9SmsX78ei4uLuHjxIiKRCB588EHWKB8aGoLf74fVamXM/a2ivb0dZ86cQW9vL/NPFhYWIJPJ0NzcjPb2diSTSZw9exZPP/00Hn/8cTZidDgcyOVyrKZjMBjwrW99iy8Ng8EAr9eLJ554Aq+99hq++tWvIhaLcXFAE8S1oqqqCnNzc0ySJ+jVxYsXUV1djd27d6O+vh5DQ0M4e/YsQ2Y6Ojrw/PPPsyfLww8/jJ6eHnz7299GIBDA6uoqTCYTYrEY/vmf/xnf/e53MTc3h6amJm5AkPTsWkHKPps2bWJpRvpZmpub2USNoFvUXR8YGGCzsGg0img0isHBQXg8Hvh8PuzevRtyuZyhrgBYPpr2xcaNG1FbW7vm2kidihobpLw0NTWFN998E4888gikUilmZmaYq7Vjxw6GlpH5p1wux969e/Hiiy/CYDCwL825c+cQDAaxbds2GAwGVFVVobq6GpFIhDmDawVBXRYXF9no0WQy4eWXX8aGDRtYWp2+MZKLpeaBXq/Hxo0boVar8dRTT6G9vZ29BEjK92//9m/xxS9+Edu2bcPw8DBzCskEba3QarUYHh7md0XQpmeffRYPP/wwPv/5z2P37t2YmJjAM888g3Q6DbPZzDhumgD+4Ac/wNatW/Hb3/4WLpcLBw8ehFKpZFnZxx9/HPv370c8HmdCLEnMrhWlUgnXrl1Db28vNm/ezHK53/nOd/DEE0/A5XLxeT82NoadO3cyJOmll15iWXidToeBgQHs2LEDO3bswJ/92Z+hUCjgqaeeQiKRwN13380kY1Kf+8pXvlJWpjWdTuPkyZMsQUwGoK+++ip0Oh178tD69u7dC6VSiRMnTkAsFrNcrNPpRLFYxNjYGD71qU/B5XKxAWYwGORmAgBcvXoVFRUVPCVfKwglQFBGq9UKkUiE8+fPo7GxEfv378fU1BRPTvP5PBYXF9HQ0ICJiQm4XC7YbDbcc889mJqawrVr1+BwOPDAAw+gr68P77zzDkKhEA4dOoTLly+jsrISc3NzWF5eRmdnJzo7O9dcG3Fp9u3bh5aWFuYuHDlyBA8++CAef/xxvPfeeyxO4nA4cPfdd7O/Dbne33PPPfjhD3+Id955B7W1tUgmkyyMEA6HMTs7C61Wy6qiBKMqR1QnxTEypQauFy7f+MY3UFdXx0Z9TqcTH3zwAWZnZ3Ho0CHYbDa88sorqKurw549e/gZP/HEEzh06BArIWYyGfT29mLbtm2YnJxkawiv18vPfK0g7xi73c4IlnA4jMuXL+MrX/kKOjo64HQ6EQgEWAxg9+7dmJqawpUrVzA7O4tkMsmWDxs3boRQKITJZILZbIbL5cJvfvMbGAwGnD9/HteuXYPVaoXBYMDIyAj+7M/+bM213RgfGTo1NjaGbdu2AQAnid3d3Xj44YfR2NjIROGtW7eiu7sbb731FqvlPPbYYzh8+DCPhwkSc/bsWZw8eRI+nw9dXV247777MD4+jrfffhtPPfUUxsfHmZxZbpRqsVjQ398Pu90Ou92Ojo4ONDY24stf/jJSqRQuXryI06dPY3l5GfX19YxhI1Uqgs00NDRgYGAAW7ZsQbFY5O4jFU7Urerr68O2bdtYRrAcrEAsFqO1tZUvHdLSttvt0Ol0OHjwIB555BE89NBD6O/vZzlI6kKLRCKMjY2xgdLPf/5z1NTUsNrG9PQ0BgYG8Oyzz2JxcRHj4+NYWFhg19hyzrTJZBL79+9nucxEIoHJyUkcPXoUPT09sNlsMJlMWLduHR5++GEMDw8jlUrh/PnzGBgYgEqlwuDgIEvvWq1WnDx5EtPT05BIJNi4cSP27NmDZDKJY8eO4eWXX4ZUKsWuXbuYy7NWLCwssDSt1+tlecCuri6USiVs2rQJFosFFosFW7ZsYRfbf//3f8e1a9fwwQcf4O2330Ymk8H09DTuueceJjdOTEzg7NmzWL9+Pc6ePYuZmRm88cYbbJ7n8XjKGvYR2YtM9mpraxGJRPDII4+gWCxi3759DBHYsmULXnzxRfh8PuRyOQwMDMBqtWJlZYWVJx544AGGOUWjUYjFYmzcuJENLklic9OmTR+Kw/X5fOju7kY2m2V+x+uvv4577rkHmzZtwoEDB9DU1AS1Wg2j0YgrV67gs5/9LMP1CoUCjh07hpdeegmTk5Po7u7G/Pw8c6YUCgXuv/9+nnCm02kmW3u93rJdUuB6U+CBBx5gGEJnZycuXbrEKj6kT55IJPDWW29hdnaWL8hDhw6xOpjZbMb09DRUKhXuv/9+/N//+3+ZyGs2m/HUU0+hsrISV69eRUdHBz7+8Y+zUli5ta1fv54vrpqaGohEIp7MptNpeDweWK1WPPHEEzh9+jTsdjui0SgefvhhFrhwOByMoSb5RZIL/MEPfoD29nZs2bIFWq0Wn/nMZ/DEE0+gpaUFn/zkJ9dcG5lAZbNZ5no0NjbiYx/7GE9Hzp07h0QigQceeAAXLlzAI488Aq/Xiy9/+ct4+OGHUSqVWFHn61//Onp7e1kIwGQy4c0338SlS5ewY8cOzM/Ps6qRw+EoC9cj6CsZX9KF3NfXhytXrmBlZQX5fB4qlQrd3d2IRqMoFAp45513mPdDOPYHHngADz74IOx2O3srZLNZ6PV6ZLNZNDQ0sDpcPp/HxYsXceHChTXXVigUIBaLmTg/MDAAk8mEr33ta6iurobT6WR409atW2GxWJgUbjabodVqIRKJYLFYeNLg8XiYsKzRaNDR0YHjx4/D6/XixIkT3J0nqeC1IhgMQqPR8JS9UCggGo2ysdaN/MUNGzbg7bffZmjs5z//eaxfvx5zc3MMayEO18DAAGvq//KXv2RzMMLfA9flksspO7ndbvT09LDcaX19Pfx+P/7xH/8RFRUVOHv2LOLxONra2rBt2zZuFBCB2Ww2o7W1FX/0R3+EpaUl/M//+T/R0tKCnp4e9PX1wWKx4M4770RFRQU6OjowNDQEgUDAE2BSXrxVZLNZHDp0iKVnCbf/jW98A6FQCH19fVi/fj3zVd566y2IRCKEQiH81V/9Fdrb2/Hyyy9jaWkJr776Kk6fPg25XM5CLwaDAV/5ylfwP//n/4RMJoPH44HBYEBtbS3ef//9svtNJBLxHWU2m7FlyxYEg0H83d/9HYrFIhKJBK5cuYKpqSmUSiU8/fTTfOYbjUZIJBKIxWJ0dHTg7bffhlgsxpkzZ9DQ0IB169Yxj4iaiZTEb9++HUtLS2X3240mw2T6SdCos2fPsupeIBBATU0N+8O4XC489thjuPfeezlf2r59O7785S9DJpNxYyyVSmHdunV48cUXmf9KTUci468V0WgUR44cwalTp3DhwgVWv3z11VchFou5YFi3bh127drFyoVjY2MMGyIvjmAwiIGBAbz55psoFosIBoN45ZVXsGHDBohEIqxfvx5SqRTt7e3o7e1FIBAoy7MljuzY2BhPNBYWFvCnf/qnOHr0KJRKJVQqFTo7O3Ho0CFs3LgRJpMJPT092LhxI7RaLSYnJ/Huu+9idnaWp34WiwWNjY3sQE93DDl70xSn3DudmprCPffcw14/wWAQOp0ODz30EIaGhmCxWNDQ0ACDwYB0Oo2vfOUreOaZZ5BIJPAnf/In2Lx5M3w+HxobG2E2m2G32zk3SSaTOH36NO666y7EYjFs3ryZCe5bt25l75+PEoLVcqzXG+KnP/0plEolxsbGUFtbi97eXgSDQZw8eRKf/exnWWvZZrOxOUlfX99NbpoEsamtrcX//t//G3/wB3+AsbExRKNR7sB0d3czGYi0/gmruFb19OMf/5iVeai69Pv92Lt3L4xGI86ePcsjeDLjevHFF1lZg3B8pBDT2toKp9PJHT23242f//zn+NznPse4esKlx2IxmEwmfOITn7jl2v7hH/6B/UBIhrK1tRUvvPAC7rvvPnaMpK52f38/41VdLheam5thsVgwOzsLp9OJCxcu4A/+4A/YqdxgMKCxsRGJRAKDg4MYGBjA1atXEQgEsH37dkSjUfa/+O/xL//yL5BIJFz4kUxtLBbDkSNHcPHiRZY6I7fN9evX43e/+x1mZmYgl8vZNMrtdnNXfnV1lV2Co9Eo7rrrLpw9exYVFRVMfjIYDAiFQvi93/u9NddGSjFE9O3u7sbs7CwOHDjAh1l1dTXjUx988EE8//zzaGpqglarRTgcxoULF7Bu3Tr+sywWC1KpFMbHx3k9NIonoysSMvjKV75yy7X94R/+IePu0+k0VldX0dLSgt/+9rfYu3cvAoEAO7cvLi6itrYWXV1dAMDytgRtoqnV7t272YOApP4ikQiTaScnJ/liVyqV+OM//uNbru173/seX7C5XA6ZTAalUgkNDQ3sNZNKpXhsSoczKYIRefFGzgmZp127do0xx/v378fi4iLLUKdSKej1ehSLRTz88MO3XBsA/Omf/il3hQgORsUxmRMZjUbm89xxxx0sEe1wONDS0oKmpiY2DyRM8oMPPgiDwYCZmRmMjY3h4x//OH7wgx+wAEBdXR127tyJQqGAT3/607dc25//+Z9Dq9WyIABNMkiikiSySdkkHA5jx44dmJqawrvvvstn38TEBORyOZaWlmC32zE5OQm5XI7Ozk7MzMzAbrezi7xOp+Mkh6SDbxXPPfccADD0ZGFhAXfccQf27NmDkydP8kVChPV77rkHoVAIFy9exLlz52C327kgl0gkWF5eZmlh8tvp7OzEyy+/jG3btiGbzWJqaop9TxoaGtjo67/HP/3TP0EsFqOmpgZqtZqbG//2b/+Gj33sY1hZWUE4HMbS0hJisRg+9alPYWRkhEmJKpUKKysrGBkZQXNzM8bGxrBhwwZUVFSwd0E+n0dvby8uXrzIUqjUmR8ZGVnzW/31r3/NEBngOom6vr4edXV1cDqd0Ol0SKVS8Pl8KBQK2LdvHz744AMmF9MUhMQeXC4XamtrWR1oZGQE165dw3333YdwOMzCIQKBgEm6hw8fvuXannzySSiVSqysrLA8u8ViQS6Xg91uh9frZWIoKW+dOnWKZXOJH/jjH/8YX/rSlxieMjMzg2w2C6vVyo26iYkJHD58mPHrVMi1tLTccm1/9Vd/xapJAoGAZdhLpRIOHjyIs2fPsgu9WCzG1atXsbS0xDBp4lg6HA5s3boVXq8XVquVG2BSqZRd6u12O0ZHR5HP55n4n0gk8OSTT6753MgcUigUsvjA+fPncd9997GaGRWY4XAYRqORDSDVajU3kgjelslk0N7eDqlUyvj03/zmNzh48CDfJdFolKdVjz766JrfArl1kzIiGcYdOXIE09PT3HSSyWQwm804ceIEPvWpT/GEnLyZHnroIZw6dYq5NvF4HIVCAZcuXcKXv/xl9nQheXvgeoPuiSeeuOXavve970EgEPD0jCBdxNsig9xSqcR3Kt1zRLg2Go2oqanBv/zLv0Cv16Ovrw8ikYj9py5duoSjR4/C4XCgoaEBS0tLDLluamrCgQMHbrm2F154gfkwuVwObrebGxD33HMP53Xk0L5jxw788pe/xMDAANxuNxQKBbujE/l7ZmaGDTvPnDkDrVaLBx54AA6Hg9XPGhsbsWHDBjz33HP41re+dcu1/f3f/z0re5JPm8vlwtDQ0E2eYKTQVV9fzxzPEydOsDN9LpdjA9+LFy8yJHJ5eRl+vx+dnZ24evUq1q9fz3woamr8yZ/8yS3X9vTTT7NgUSaTQTAYRDabxcWLF3H//fdDLpcjGAyioqICBoMBU1NTaG9vx/LyMlZWVpgnS2qt77zzDj75yU/CZrMxb629vR2XLl3C/Pw8C37QRFSn06G9vf2Wa7sxPjJ0itj8dCh6PB64XC4mhlmtVr5IotEo+vr6WEaQ2PqEDyVDEvpw6WChS4+UeMbHx9kgpBwZnKrdYrHIJO1MJoOzZ89i165d3GUltQiVSgWTycQdgIqKCiaf6vV69vagMS4RRAm2QMkLcH00Xw5HRyRggUDAmy8ajbIELelzC4VCtLe3s8St1Wpl/gDpzet0OqyurvILz2QyGB8fZ/UUmUzGMDNSCSnnhEwqSNQl93q9UCqVjK8sFosMJQKuS69FIhHuLorFYqTTaRw/fhxtbW2QSqWIRqOs0kL7IZFIIJ1OszljPB5n7fW1QiQSsXfGjWTcRCKBfD6PeDyOqqoqTiB0Oh3m5+fZA4CECUg+mXT0icNAahQ04brRB4PG02sFJeiEQ6ekh94PAFb+ojH10NAQzGYzCoUCstksc3zUajXL2UkkEsRiMSbCEQacxAuo6/lh+vck71ooFJBOp/mSIJgiXczxeBxNTU2Yn59nYrBEIoFEImFlrtHRUe660HMmpZFIJAKRSIRwOMzE3HLYalpfOp2GWCxGJBKBz+dDOBxmdZhcLodEIoFIJIJgMIjq6mr2aCHPDjLpAq6LLZBKUTQa5T+DtO/JH8Hv92N5eblsByaZTDKMkUjAgUAAyWSSpxvhcJgLMVI/It5BRUUFS63m83lcvXoVIpGI5SHpZ8jlcggGg/B4PIjFYpBIJGx6tVZQ8UPPkM5PMmkikm82m2XZ7YqKCrS2tuLy5ctMeiYvFofDwWRNkselczMUCrFfBCWF5Uh/pVLppvOXgiYbi4uLSKfTEAgE7LI7MzODzs5OvhNEIhFDeWjtpFwkFov5/iE+hcFg4GS2HJQll8txI4nOHoLRAdf9cEhRyOVy8XdDDRiS1iZNfJKupfOfPHDItZym2zTxKCe9S5c2nU/UzSZoIk0q6JzM5/MYHR2FXC7nSQ3BSysqKvheSqfTrLR2+fJltLa2soRxMplkDiOJHdwqaPpBKAQ6D0dHR3H33Xejvr4e4+PjiMfj6OrqgtFo5HMzk8mwXLvJZOLEXyKRIBgMsrohPR+SZSaRjht/5rXeKSWUpOZH/ijUpVcqlSz12dnZydNhIpGTouWpU6f4OVByHQ6Hcfr0ad7TuVzupmZKuUlQIBDgJJ4mVDRZJl5rdXU1PB4PPB4PuzcvLS3xzyQSidDa2srTtJWVFUSjUeTzeVRVVfHdRtyCVCqF1dVVLvTXihunkqQAGQqFeCJPJo00gZBIJJz8JxIJNDQ0MBeEvKBMJhMSiQTvqUQiwea31dXVEIlE/H1OTEysWWiQwhVJDhOfy2g08kSUFOui0SjLrG/bto25SsB1LgL9TPTPhUIBNTU1nEsQLIvMgIkPtVZkMhmYTCaGD9JZSGcdTayDwSBWVlbQ0NCAcDgMl8vFe6qyspJloS9evMgTbrVaDa1Wy89eo9HA7XYz8b5YLJaF1MZiMXZEpwa43++HxWJBMpnE3Nwc+zsRKX58fBwqlYobTFKplNUnadqVyWRY9XRsbAyBQAAAuKgErtMUyp1vN8ZHLjQIK0YqTIlEAk6nk81jbnS/nZ+fZ6vz999/nycUJH167tw5xqJT99ntdrO7sFQqRVVVFUMCTp06VVZZhIhfpDxFDrDLy8vcgaUkpFQqoaurC62trXC5XAiFQsyDoGSHCIEkObu6uor29nZkMhm0tbVxlU5ybx9m7najKy6pqlDnnDr7pAJDSSWRoEnJhA4vm82Gubk5rFu3DslkEqOjo4zvpq44Edjpkl4r6OCUyWTs2m6xWNgJnDYudYLcbje7Q5KGvd/vZxIWPT/qWGezWUQiEVy8eBEGg4EJ5qQMVg4jTEGHa0VFBRYXFxGPx3mPJJNJTgqtVisfMjMzMyxdt2XLFlYjAsBa9wSPubHQCoVCLHdH3chbBSmHkOyrXC6Hx+PhhLyhoQHLy8sIh8MQi8VcPBAWHwBDIOgCmZub433k9XrZpZbgAUKhEFqtFrlcrmzSR0UtiR/Qn0fjefrZiLi3sLCA3t5eLC0tcReQ5DErKyuxvLzMXVyj0QiDwYD5+XmMjY0BACfLdEiVk1MGwFJ8RP6+0cHd7/czHMDn83GHnqYshK8nk8zLly+ju7sbVVVVjHleXl5GsVjE2bNneRTs8XiYyFduRE5kduoQkqcIJQ2UdJGIAE2FSN0uGo1iaWkJer0eMzMzLL2qUCg4gaEirqGhAUKhED6fj7+JckpndNkKBAJOsmmtVHAkEgkurK9evcpeC1QkUaFN6mahUIilq+n9JRIJJBIJJsYSUZWmfGtFRUUFP1uFQoFQKASFQoFoNMrJBnnOzM3NcTJI/63JZEJHRwfGxsbg8/mYg0VOw0Qw7+zsxPz8PGKxGCoqKlBZWVlWlYUSXJIClclkbDxGcsHUZPJ6vVhcXIROp+O9QmTJeDzOTuyktEbqP2azmeWYfT4fw8EI575WEARSLBZDo9HwOwauQ5c0Gg38fj9f/HSHUhLgcrkgFovZdZuSG5JyJd+G6upqWCwWLCws8DQzEAhgdnYWu3fvvuXastksy5ffKNs7Pj6OyclJVgMkLoHVasWBAwfwk5/8hGF+BoMBfX19+P73v8+keyo+JBIJJicnmSOUTqe5GA8Gg2Uhv6TqRl4yKpWKm4ZLS0ucGNJdvnPnTiwvL/NUMhaLQaFQcHGzuLiIXC6HSCTC58/ExAR27doFkUjEoicrKysM2VkrYrEY2trauHFA/k+VlZXM8yRVK+r60xSvt7eXC8z+/n6Mjo5yXkR7kfx0xsfHWUQAADff7Hb7mmsDrhcbVIjS+ZbP53HlyhU0NDRgZmYGuVwOOp2OjZJJSZSmOuTHlE6nsbi4CKFQyPC1aDTKaAv6DgqFAk/s1gqaNKVSKYbuSKVSNDc34+LFi7BYLAiHwwyNcrlcbLfg8/lY1TOdTuPEiRPc6NNoNCz84HK58P7777NRoVAoRDgcxuDgYFmH60wmg3Q6zQUTwbnj8TiCwSBPT2ZnZ1FRUYFdu3axpG8kEuG1aTQaJBIJ5mRRQU0NRalUip07d+LYsWMIBoNceH7YGUKFNok3+P1+DAwMwOl0csM4EokwlM3r9SKbzbLkLTWxCT53/vx5lkGmSbTVakVXVxei0Sg3ub1eL1pbW8vuN953H+l34To5VywWo6urC/39/TyW/cUvfoF0Oo1sNgulUsl260RQevbZZ/Hb3/4WJ0+ehNvtZowfdSmTySR8Ph9XTNQlHRwcxNLSEgQCAXsirBUEU6ADrrOzEy0tLdi1axfLdkokEpaJlclkSKfT6OjowMmTJzE6OsqGcpWVlezQCFzHNbrdboyNjaGqqgoymYyTS6qkyxGJVldXcf78eYTDYTZlCgQCePbZZ/HMM8/w5qVRHHkAdHR0YNOmTSzDR4k26RpXV1djw4YN+MIXvoC7776bdZ2pOiUzmA9zLY/FYtDr9exavLKygl//+tdwOp2YmJhgJZhnnnkGZ86cgdlsxnvvvYc333wTk5OTTOo8e/YsfvKTn7DkLxWQZHpIhza5mROmca2QSCQYHh6GxWJBX18fGhoauIuWTCah1+uZZzM0NIRSqYQtW7bAZDJhfHycpX+bm5uxvLyM1157jRNwtVqNjo4OuN1unD59GmKxmItQmiSUI3FWVFTg7bffZulNUkIiGTm73Q6NRsOScg6HA3a7Hdlsls0et27dim3btuG3v/0t3nvvPVaCqq2thcVi4QKTFMDIpIqkcdeKyspKdlMnYx7CFlOxoVarYbFYYDAYMD4+jkcffRQdHR2ctGo0GvT09MBisbBufmNjI1pbW2G321EsFjE0NIRiscjFHXWhPqzQIGPEdevWoaGhAVVVVdi8eTNKpRKP8gFwIv273/0O0f+U9SX5YdpDS0tLSKfTuHbtGhdflCDl83k0NDRg8+bNaG5uZqI6QdhuFdXV1TzxWFlZYYOrhYUFjI6Owul0MmTmueeeYy7U8ePH8fTTT2NoaIhlQMViMT772c+yzns6nWbPGovFArFYjEOHDsFgMDBp+8NMIqn4IgigTqeDSqWCRqOBTCbjEfazzz6LpaUlzM7O4sKFC6wsQz+XxWJhvxGS0yTuktFohMViYaW/2tpaNk9dK8g5Nx6Po66uDm1tbaya9u677wIAq+C9+OKLLGUulUrh8Xh4KrOwsIATJ05gaWkJExMTsFqtPCmgjumePXvQ398PgUAAl8vFnjNrBXGuaBK7YcMGdmVeXV1FXV0dstksFz8XLlzgiSOp29XW1jJsuFAoYGRkhIvXoaEh/Pa3v8XExARWV1dx7tw5nDt3DnNzcyxxu1aQOAhNIUKhEFwuF+89mqAQ+dvlcuGhhx7C1q1bWVa7oaEBR48e5QQwn89jcnISc3NzkMlkaGtrQzqdZpJoMplkOGS5tRUKBSwtLWF5eZmTXJKzHRwcxMjICPr7+2E0GvHjH/8Y58+fZ+IzTe/JM4sKt+HhYQSDQTQ3N2PDhg343e9+h5qaGlRWVmJxcRGFQgEqlYqV/9YKvV6P8+fPY3BwEPl8niepNFEzm80MS6G9Sz5ApHQpFoshEAhwzz33wOl0cqPPYDCwn8DmzZvZqI1gL4SnXyuMRiNPzWj6ZjKZEAgEGB4WiUQwPz+P6elpNhqmQszj8SAYDCIQCODYsWOcf1BTiNQyp6amsHXrVmzcuJFNBaenp8tOlKurq5lvoFAo2KwTAEwmE+644w60tLRAKBQywoKg3PPz8zh16hR/l1qtFjt37kRPTw8++clP4v7774dQKMTVq1d5n9FZSpNh4vjeKijx9ng8UCqVWLduHRQKBV5++WVcunSJkQzhcBjXrl3D66+/jp6eHp4cT09Ps+qdSqXCO++8g6mpKWg0GpaxJqXDjRs3QiAQYHp6GpcvX8b09HTZs7dUKmFubg7ZbBY1NTW8l65cuYKxsbGb8kEynK2rq8PGjRsZEWK1WrFnzx6WICcZX+A6WojELTo6OqBSqZDJZBAIBNgrZK3o7Oy8STTC4/FgbGwMXq+Xizefz4eTJ0/ivffeg1qtxqFDhxCLxeB0OjE2NoZQKISNGzdienoaZrMZCwsLaGxsZC+1q1evAgBbW8jlcuRyOczOzpb9Fm6Mj1xouN1uiMVifPDBBzh27Bimp6ehUCjwox/9iDFier0eAwMDeOihh+BwOBAKhbB9+3ZWvSBDkldffRUPP/wwLBYLpFIpUqkUKisr8Ud/9EeYnp5m980jR47gT/7kT1BdXY1Dhw6tubZ0Oo3169dzl87r9WJwcJBhPel0GrOzsxAKhbjnnnvw/PPP4+jRo3C73Xj00UdRX18Ph8OB9vZ2vPDCCzh69CiCwSD27duHBx54AG1tbXjkkUfYZfzSpUuoqKiAzWZj2NZakc1mcc899/BBQqSv73//+/jqV7/K404iyhE+2OFw4MiRI/jYxz6G3bt3Y9++fUgkEvj93/99hnpFo1FMT0/jvffew1tvvcUqFDt27MDGjRtx+vRpjI6Orrk22sznzp3DysoKX2C//OUvodVqcccdd2D79u148MEH8bOf/Qz79+/HpUuX0Nraiq985Ss88vsf/+N/oLm5GefPn0ddXR08Hg/jFu+66y6Ew2GEw2E0NDTgzjvvxJEjR6BWq7m4vFXMzc1h69atGB8fx+joKENu/vqv/5o7/m1tbbjzzjvx+OOPs3xsMpnErl27WI2lqqoK69evx3e/+10AYMPBZDKJT3/609iwYQN27tzJxGRyMV1eXl5zbeFwmF1GhUIhbDYb0uk0PvOZz8But+Oee+7B1q1bYbfbsWPHDtZMF4lEsNlsiEQiePfdd1lz/Etf+hInLXRI0JSjo6MDFosF9fX13MkiF91bBUFFSG/fbDZDo9Hg29/+NntDLC0tQSQSYcuWLSxr2trais997nN8OJpMJnzwwQeorq6GXq/novDy5ct48MEHkc/n0dXVhUQiAbPZjJ07d36o5jcAfvZjY2PscHzt2jUcPXoUFosFDz30ENrb27F582Z87Wtfw+joKKRSKd566y0IBAKGgO3cuRN//ud/jgcffBAA+LIhnsPIyAgcDgcuXryIzs5OVgMrhyclCI/X62Wo39TUFO666y52b6Wpzic/+Ul0dXUxhOqhhx5i+ODBgwcxPDzM8ont7e2wWq1IJpM4fPgw5ufncfbsWfzqV79CsVhEQ0MDpqenyybMVVVVaGhoQDqdhlAohE6ng8fjgUAggF6vx759+3hCYrPZoNPp+Ezcv38/WlpaIJVKubjdsGED0uk0Ll26hHfeeQdXrlyByWRCa2srE3kpiTl58mTZBIZUW2KxGBwOB6sFfvOb3+QmUXV1NbZt24Yf/vCHmJmZwbe+9S2kUinccccd2L9/P7q7u7Fz507k83kmEJNSytGjR3Ho0CEMDg5yd7Cvrw9btmxhyORaQVy1XC6HhYUFBAIBDA8Po7m5GefOncPCwgIWFxcxMDCAf/iHf8Ds7Czuv/9+LCws4ODBg9Dr9XC73bj77ruxfv16VoKzWq1savVXf/VXmJ+fh91uh8lkwpEjR3DgwAFWLForqIiJx+PQ6/XYunUrUqkUPv7xj7MJI3UhyV9JpVJhamqK1d0uX76Ml156Cel0GgMDA8zzI27ioUOHsLq6yhLRFosFRqMRr776atkOs0gkYsJnc3Mz2traMD8/j69//evcAHQ6nVCr1fjGN77BE14STgmFQnjllVfg8XgwOTmJ7373u+jp6WGZ0Lfffhvf/OY34fP5YLVaYTKZ2NAvFoutCekCrpPo6blSRzcUCmHfvn2wWq3Yv38/89K+853v4OWXX8bOnTtx11134bHHHmMBB4lEgu985zv49re/DZ/Ph1KphHQ6jfn5efT09ODixYtIpVI4ceIE5HI52tvbMTY2VrYISiQSDGUhz61f/epXLF9OUr42mw1PPvkkAv8/9v40utGzzBPGf7J2S5ZkSZYsyZYsW953l5fa96qkspCFJCSQpNlCT2/AzNAwbwPd000PPXQD3QTodEM6TUIICZAEqGyVVJLaV+92ed837btkWavfD+G63qo5ZSX//5mPdZ3DORxwVd1+nvu572v5LV4v7rvvPnblLisrQ3l5Odra2hCJRPDYY49BJBJh//792L9/P0wmE5544gksLCxAr9fD7/dDoVCgvLwcSqUyr9Q+qZdREUQFB0ExT5w4wfe0QqHAf/7nf+LIkSOwWCz40pe+hHvuuYcnsyR9e+nSJZw6dQrz8/NobW3FP/zDP0AqleKrX/0qLl++jFAohIaGBojF4rxCIeTZkkgkIBQKUVVVhW3btmHv3r0sU9zU1IT7778fP/3pT9HZ2cnk7LvvvhvxeBzPP/88TCYT7rnnHjz++OOMyiDY9de//nVEo1GMjo4iHo/jtttuw1133cX2AvmCeCLUUJRKpfje976Hz3/+89i1axeamppQX1+PAwcOYHV1FW63G4uLi3jsscdw4MCBG6YhJpMJv/jFLxjhEwgE8IUvfAEvv/wy/uqv/gpDQ0Oor6/H0aNHodVqt5RSBj7wFSE/nng8ziIpDz74IGKxGO6//348+OCD2L59O0pLS3HixAmMjo5Cq9XijjvuYIQMFWW01oWFBQDAxz/+cbz22msoLy9HUVER+vr62Dn8zJkzW/Ko/s/4yGTw//iP/8DIyAi2bdvG+uVutxstLS2or6+H1+vlMYvL5cLtt9+O7u5u9PX1ccdtZmYGP/vZz/DNb34Tf/M3f4O//du/ZaMan8+HF154AX/3d3/HxEoa8ZE78VZJwve//30sLy+z9CeNx1966SX88z//M5599tkb3I+bm5sxMzMDj8eD119/HZ2dnejo6MDp06fR1dXFShj0ESaTSQwPD6OsrAzPPfcc7r33XnapJhzeV77ylZuu7atf/Srjj0lxq7W1Fc888wz27t2L5eVl7Nq1izuapABFXQrqypDCTnl5OSwWC9555x2cPXsWsVgM7e3tmJ6exhe/+EXMzMwwnOS1115DPB7HN7/5zZuu7V/+5V/g9Xpv8C2YnZ3FW2+9hWeeeYYhXpFIBE6nE83Nzfje976HL3/5y+ju7uaCkGBng4ODOHDgADteEpEwmUzC5XKhpKSEVbVqamrwwgsv4Nlnn73p2p555hmW8yOYQ3V1NU6dOoW7776bCyjCcX/xi1+ERCLB008/zWM+Mq9bW1vDd77zHfzrv/4r5ubm2JmZFIQOHz6M5eVlVgqqra3FzMzMlipA/+2//TeGFtHkgBznGxoa2Bl2ZmYGKysr+Na3voUnn3wSdrsdAwMD0Ov1aG5uZqPI3/zmN7jrrrtY6UkkEuHChQv44Q9/yH40kUgE6+vrKCoqQiAQwP/+3//7pmt75ZVX8PLLL6O9vZ2/A6PRiLGxMdxzzz2YmZmBxWLB2toa3n33XT4kiUtCkstLS0vo7e1liA85FJNyk1wux8mTJ3H06FH2WlCpVCgtLc2rl/7UU08hl8sxyToajeKOO+7Ad7/7XTzxxBMYHBxkg7HR0VGcPn0at99+O0O1lEolyxZfvnwZJ0+exIMPPshmTI2NjQypa2lpYQKt0+nEvn37cP78efz1X//1Tdf2j//4jzCZTAyxSafTGB0dxTvvvIOPf/zj7N/g9/vhdrvxd3/3dzhz5gxWVlbYa8JkMuHatWvYtWsXSyJSEuZyuXDhwgVUVlaye+7Q0BC8Xi+6urrw3nvv4f/5f/6fm67t5ZdfRmVlJVKpFPx+P0Pz5ufn8eSTT+LKlSvY2NjA1NQUrl69ikceeQQ/+clP8O1vfxuvv/46T/GSySQsFgvOnz8PuVzOPA2Px4OLFy/iW9/6FgYGBnDq1CnYbDaYzWYsLS1BoVDg4Ycfvunann32WVabIm5AVVUVZmZmsGPHDpw9exZms5ldyA8fPoynnnoKXV1deP7552EwGLB7926sr69jYmICtbW1yGazkMlkDJmz2+0oKirixIqgcqRMtxXh+qWXXmI3b5lMBqVSCbVazURyrVaL2dlZ9jMgAZORkRH09/ejtbUV7e3tfM499dRT+NM//VP25jh9+jTefPNN/PM//zP+8R//kRXfaDJXWVnJxfDNvgWz2czmppFIhMUk6DyhaXxlZSX0ej2bCL744ovYsWMHuru7ceLECXR1deH06dP49Kc/jcXFRaTTaRQVFeF3v/sdHnjgAaTTaZ46EfSzrKxsy+f2ne98hx2sybAxm83i29/+Nv78z/8cJ06cgM1mY5fw7u5uvPPOO3j00Ufx3HPPQSQSoaGhAalUCuXl5fjpT3+Kxx9/nKcCRKT/zne+g//xP/4HGhsbEQwGkcvlUF9fj7Nnz+K73/3uTdf24osvIpvNorq6GsXFxchmswzhveuuuzA4OMjCBWfOnMFtt92G2dlZlJWVYWFhgQn7w8PD2LlzJ1paWvDcc88hFArBaDSiuroa58+fZ2lQ8v8geWadTrcliuFnP/sZ+wyUlpbCZrOhsLAQx48fx86dO2E2mxEMBnH16lWsra3hm9/8JjY2NpgX0tjYiLKyMpw7d47VD6uqqpiXQ5yWu+++G9/73vdw5513IhqNwuv1MjG/ubl5y3cql8uhVCoZKvupT30K6+vrqKysxKuvvoqjR48ik8nge9/7HoxGIx577DGcO3cO/f39uOOOO1BbW4uf/OQnOHjwICYmJtDR0cF8vvX1dXz5y1/G6Ogofv3rX7M6VigU4uLzc5/73E3X9uyzzyISibB3WiQSgc1mw1tvvYVnn30WZ86cgU6nQyaTYZjXv//7v+N73/seXnjhBUgkEjgcDobnOZ1OVFdXc/4WCoXg8Xhw9OhR/OY3v0FnZyer5MnlcsTj8S1zuL/+67+G3+9HUVERqqurYbVasbS0xM2kQ4cOYXV1lZurBQUFuHjxItRqNUMoSZ1LrVbj+PHj+NM//VOGzc/PzzPRfmFhgYWLcrkcS/Lv2bPnpmt77rnn4Pf7UV1dzVDSpqYmnpadOHECd9xxBzY2NvCzn/0Mf/3Xfw2BQIDJyUksLi6ivb0der0e77zzDpRKJcNtE4kElpaWcPXqVSQSCTbRpAk4TS9PnjyJb3zjGzdd2/Xx/5NhH+Ey19bWsLy8zFh3j8fDygwulwtra2vY2NjA+Pg4fv3rX6O8vBwajQYbGxuorKzEf/7nf+KP//iPcfbsWXg8HiSTSWSzWSwvL+N//a//hfvvvx+jo6M8pjl9+jS8Xu+Wl7DX62XZS7/fz0ZTKpUKFy5c4PFVOBxmLKxQKMT4+DjLj1HSSQkVEdOIhFRSUoIzZ86gp6eHZfAIW5cPswkAtbW1/LNSqRTpdBp/9Ed/xCSxc+fO4dSpU7BYLCgrK8O1a9f4IjAajUwI//Wvfw2z2Yzdu3fDbDajpaUF4+PjXDzNzc3B6/Xi2rVrTP7Lh3UlQhSRzQhWRGoFNEYTiUTYtWsXtFotHn30USaYE8aeXL7pw9Hr9fB6vXjvvfcQCoVgtVqxfft2nDx5klUvLl68yASumwV1OMhZlJRhOjs7oVKpUFdXxy7SCoWCC4hIJILV1VX4fD4YjUY0NDTA7/fjU5/6FN566y2Gl13vXRAKhbCwsMBSki6XKy8Bi9zOg8EgNjY2kM1msbq6CrPZDK1Wy9OItbU1yOVynD9/niXqzGYzYrEYRkZGsLy8jF/96lfo7OzE7373O5SVlaGmpgahUAh2ux2hUAgKhYL5UFQs5/M0mJ+fR09PDwwGA2OKr127hkQigUuXLqGlpQVisRgKhYL3/sbGBi5cuMCXIgCWtKTuBkkMU6JDEpXLy8tMak0mk3lFG+i9VlRUYGBgAH6/H9lsFk899RRKS0tx9uxZLC4u4ty5c0xg02q1mJ+fx9jYGCQSCXexSSLSarXyQZtOpzEzM4O1tTWGBEQiESwtLcHtduPKlSt510ZJbDgcZnWebDaLrq4uhteo1WoYDAaWmt61axeTD91uNzujv/DCC2yKtX//fuRyOYyNjaG7uxtjY2OoqqrCwMAAUqkUcrkcXnvttbx+EKTcsry8zIIFkUgEYrEYk5OT/L/bbDZWBGxqasLp06fZp+Lq1atcHBPBmaaKNHmZmpqC0+lEW1sbcrkclpaW4PP5eFp6s4jFYujo6IDP5+MzRyAQQKVSMU6YxBfS6TQ7/Z4+fRo7d+5ELBbDlStXIJVK+eJtbm5GLBbDysoKC0rI5XLU1tbC5XLB6XQiEonA7XYzBOlmEQwGmc9FvBuSbg2Hw/wNk3vze++9B6PRCJ1Oh7a2NiiVSqyvr8Nut2N5eRkf//jHoVQqMTQ0xNwrahbs2bMHhYWFzJMTCAR53d5JxYY4cGKxGJcvX0ZJSQlGR0eRTCb5uY+Pj6OhoQFnz56FxWLBwYMH4XQ68fzzz0Mmk+H8+fPQarUsiRoKhRAMBtHY2Ih3330XDQ0NzA/weDzweDx5p1SkuEhyvpOTk0in09i3bx9GRkbQ0NAAoVAImUyG8vJyvPrqq0ilUvjlL3+Jw4cPI51Ow+12Qy6Xs6Gjz+djz6qFhQV87GMfw/PPP4/t27cz/04ikbBZYL53SqRhgt3NzMzg8ccfx1NPPQWDwYC33nqLicM9PT3Q6XQ4d+4ck82Li4uRyWTw+uuv48tf/jIuXrzIe/fKlSsseUxwNoPBwLBUggbfLKhJSAkzQWJ7enrYnJEgdVKpFK+//jqsVivOnj2Lnp4ejIyM4OWXX0ZtbS0OHDiAa9eusSDB4OAgTp8+DZFIhMrKSnR0dPC/QZj7119/fcsCjSYYdM9vbm7i2WefRSqVYmM9gqN2dHRAq9Vibm4OS0tLEAqFbFoqlUrxwx/+EF/84hfx2muvMRSXpqdPPfUU2tvbMTMzw02b0dHRvHnS9PQ0LBYLRCIRbDYbhEIhgsEg7rvvPqysrECn0+Hy5cvsIRIKhfDxj38cP/3pTxEKhVBTU8MIhldeeQWf+tSnGLbqdDpZxYog8yQMEQqFWM53qyDpaMpRSTmNpkhOp5Mlj8nokRovRJBXqVRoa2vDyZMncf/992N1dRVOp5N5GrlcDtFoFHa7HSMjI5icnGQ58IWFhS0LjaWlJezZs4cFiYRCIf7t3/4NpaWlUKlUcDgc7Guzbds2NoReW1tjE1mj0Qij0Yif/exnzBWiqReJM5nNZpw7dw65XI75NoWFhXn9bm7Yex/pp/BBJ5G633K5nDtCb7zxBnbu3Indu3cjlUohm81ic3OTE1Cz2YxAIAClUgm73Q6lUgm5XI6f/vSneOihhyAUCrG8vIxgMAipVIqSkhK89957sNlsSCQSnOQ4HI4t11ZSUsIYe0qW+vr6YDQa0dfXxyRIsVjMSkChP8iMklMo8IFWOalBEFGaDtvi4mJIJBLY7XacOXOGyd2k9rBVSKVSjI2NoaSkBMXFxUwAJpnLtrY2pNNpxgyr1Wp8/vOfx7lz5zA0NIRgMMguoeXl5bh27Rp0Oh0OHjyIqqoqLC4uMokqlUqhtLQU09PTWFlZYSfqrYIcKUtKSiAWixEOhzmhXFxchMFgQGFhISKRCKLRKCYnJ9He3o5Lly6hsLAQOp3uhsSd5NWIYFRUVASfz8eEOkpwCgoKUFNTk1dZhIiVBQUFkEgk0Ol0EAqF6Ovr48kPycRSp3R5eRlVVVXsFkvKMUqlEj//+c/R0dGBzc1N6HQ6NDU18TNaXV2F3+/H0tISCgsLUVJSktfRd2Njg4sI4kGIRCKGNJlMJi6QpFIpX+rpdBobGxuQyWQs7UlQQUp0SD97enoatbW1UKvVrJhC0618njIKhYLxyuRaLpFIMD09zSo1ZMAokUg4edVqtXC73VAoFDd09V944QXs3r0bOp2OdbNJ1lYqlbLSESWWH4bZlMlkTOQleVXyAnC73aitreWEc2VlhY373n33XaTTaSwsLHBX2mQy3SDDSMmRzWZDLpeDTqfjhgi52ebzqiDoBBH1CWd85coVPPzww+yiTZ1Nj8cDmUzGssSkqkQToYceeghutxttbW0sTvDee+9hbW0Njz76KMbGxjA1NYVMJsNqd1sFKW0VFxdz4k7f7IULF7hjHw6H+ZIgeAmprhCXbXJyEqurqzh69CiUSiXm5ubQ398PrVbLnDjiAJCYRD5VFoFAwKN2iUSCdDqN4eFhXLp0CZ2dndi7dy+uXbuGwcFBLnA2NzeRTCb5e6irq0N9fT0uXrzI53RNTQ1UKhXee+89+P1+mEwmqFQqTE1NYWJiAtFoFGVlZSw+sdV+I2UpUsFZXV3FG2+8AbVazRKciUSCTdZcLhdcLhcbslJRo1KpmJdGfCuaHBQVFUEoFDJvL5vNQiwW5+XvAWBDSIJCSaVSdsa22+1sRAZ8IEFJCmb0DC0WCzQaDa5duwan04nbb7+d+S4EX66trUUymeTpGkmz50v86Dwj4QUSHvH5fGhra2OzTSrISRyE1ASz2SxUKhUqKirw4osvYnZ2Fvfddx/7qPT29qKvrw9Wq5XPSI/HA7FYjKKiorwNMoLB2e12dqUuLS3Fb3/7W4TDYTzyyCNYXl7m4qW3t5ebYAsLC/D5fGzIm81m8fvf/x4PPPAANxbKy8ths9kwMzPDaI3l5WVEIhEEAoG8EKD19XUEAgGGThFR/erVq6iqqoJSqWTkh9vtRnNzM95//33Mz8+zBGp5eTl27NiBa9euYWRkBKurq2hvb4fD4YDL5cJzzz2HkZERKBQKNiSlojFfnqRWq/le1Ol0N7hG0+SMBHnIKFCpVGJ6eprfJ3X10+k0BgcHWchAIBAgGo2y0TIl8iSJHAqF8sJW6dsUCATMEUkmkwydNRgM/LOUw7W1tWF+fh6XLl3i5iPZDRCPj6b1CwsLsFqtSKfTGBoaYgEYEnTZtm3blmsrLCxENpuFRCLhZ+B2u3H58mUcOXIEBoMBNpuNm2TJZBJdXV1YXl7GysoKT1S7urpYOIWknglQNDg4CLlczkRyv9+PYDCIpaWlvA0ooVDIyBOSISb+hVAoxKOPPorZ2VkMDAwgkUiwAp1IJEJJSQkCgQCSySQaGhqwZ88e/OhHP0Iul8OePXtQW1uLxcVF5ohtbGywKhaZYV//XvLFR+Zo5HI5Ht2Gw2EmEjocDraNT6VSKCoqQllZGct1ke8EyRi2traymRlpr+v1elgsFtx2222scEA69sAHJmT5fqF4PM7dbxoLA2DSOR3GOp0O9fX1TJYVCoWsCKNQKFBdXc2jUVJxSaVSSCQSPBpUq9XQaDTsAvth1bBQKMTGxgYrLmk0GlasAT5ISisqKpjgTXJlNC1aW1u7Qea2p6eH1VtkMhm7UpOJIo3EaNKUjz9CGGHyWyCpy8rKSjidTlZmIGm+1dVVVFRUMN4wEomwA24kEkFhYSEmJyfh9/tZGcVsNrPcKCVwEokERUVFeT8gACz1uLm5eYO0pMfjYak+qVQKrVYLj8fDymDkck6diKWlJe7w00SFDOpo/1CnRiQS8UWRL0j+tLCwEEqlkhMZImDpdDqYTCZYLBakUimo1Wo2g6LJIBG8ieRGRXoqlYJOp8Pc3Bx8Ph9f+CaTiRU5topcLseqQoQBValUqK+vZxd6KnioCLFarfwMYrEY1tfXOami5282m1FRUYHS0lLcfvvtSKVSKC4uRiQSQTKZZKnAfGujiMVifJ5UVFRwUUZCByaTCZWVldz1F4lELPVHBL7NzU0kEgl2MqVuNSWo2WwW9fX1iMVivC7q6OULuuCoSCNieSgUQmlpKZPRc7kc3G43J39NTU3sKK7T6bigJeUQANBoNBAKhZidnUUul0M8HudLn9SdtgqaGpIyFil/UGJBuGoi1YdCIbS1tUEkEsFkMsFkMjEUNJfLoba2Ful0GgqFgot4+sbJPJIkE8nrY6sQCAQstlFYWMiCFySFbLfbYbFYUFJSgqqqKkxPTyMcDvOzIWWUkpISFBUV8bSQiu7CwkLmAxFxOpVK8feT727IZrOoqqri+0an00Gn07FqDhmnktkdAPb1IElcUhzyer3c9KE9W1BQwMWEUCjkZ0B/Rz4VIIFAwKpEVIiKRCJOSM1mM/sb0eSuoaGBGxfkeEy8ByqMgA84PVQw0s+ura1BLBbDbDaz/89WkU6nUVhYiFgsxnwRhUIBm80GpVIJrVbLDTKn04ni4mIcPnyYk0q/349QKMQE7Vgsxvh72gdFRUUsR06kbZ1Ox3LJWwV9T7FYjFXYzGYzq3jRPWM2m9nBnRIkUmwDwKRmIo/b7XZunO3du5fhsfR+SIr4w1QmLRYLF5zFxcW892dmZlhWXCgUMtKBoDuJRAICgQB6vR5GoxFTU1MsVCIUCqHValFWVoYjR45gamqKm3oGgwElJSVYW1vLy5FbX19nRAdJutL5QN8t5SUkerG5uclSrETyNxqN/LPkq0HfJd0zEokEyWSS93c6nUZtbW3e55ZOp1lim1AWlAOSvxEV9olEgr/dqqoqbrJkMhkolUo4HA5unGm1WmxubqKyspKN8ujMIc+bfEauMpmMxUkoQSclO5JY1mq1rLJHhYbH42HFP5Jdp1xzbW2NpbSDwSBMJhMWFxexuLjIwkrUBM2nOiWRSPgcouY/GeteT9wmf7iLFy/CbDZz3kkNNRIp2bFjBwQCAUP0SbHr0qVLqK6uhtfrhUwmg8lk4m/3o8RHLjTi8TgXEC6XC7lcDrFYDA8++CBvOhrJVVZW8uG2srICjUaDdDrNONeZmRk89thjrMRCnY+WlhZEIhFOGGkiQvKLW4XL5eJknfwSLBYLrFYrjEYj2tvb0dDQgNbWVjQ1NXGVWF1djYqKCuh0OhQXF7PG+759+/jyovHa9aZFzc3N2L17Nzo7O3nTbRWknU3JEFWdzc3NXOQolUpoNBruaJAhl1wu58SGpP/uvvtutqQn8zt6jgKBAKOjo0gkEjAajVyJbxVerxd2ux2JRIITXYVCgWPHjvH7pILParXyIQwAc3NzGB0dxfj4OGZnZxH6gyMsHQ6kNOFwOPiwKCwshFwuRy6Xg9/vz6t6QiRrmUzG3IBoNIrW1laeDBUVFcFisaC+vh6RSAQ1NTWMwaVCUKVSYX5+Hp///OcZ3y+Tydgci6RWhUIhO43TyH2rIHNHms5R97+mpgYOh4MNoWjETvsbAOx2O/R6PWKxGHfjCZYjk8lYhYVMEGdnZ+Hz+XgKQiPLrSKVSrEiHGHSAWDnzp1QqVTQarWoqqpCS0sLdu3axRApkuIlyB2N6knwgLD3xJVaX1+HVqvlS5PUWT4MRkhy0UKhEBKJhBM8cqEn+byKigpWXaN/q7CwEH6/H2NjY2wmd/ToUe4cUoFF3Soi4NPzr6mpwdLS0pZry2QyfH5Qh7CwsBD79u1DKpXCysoK5ubmWPlLKBSis7MTZrMZPT09aGxsRHFxMRcp4+PjzJno7+9HKBTiDtji4iJisRisViva2tqg0WjyniME6aIuNSU9+/btQ1NTE0wmE1+IDQ0NUKvVKCsrY/Utkiskl+vW1lZcvnyZYRGU+C0sLDAhnkydyOg0X1CyQom/Xq/Hww8/zApTUqkU9fX1OHjwIEMu6Tml02lMT09jaWmJO7qpVApjY2OYmZlhJcFEIsEiDWazmc/vfOcITSpIkUkikaChoQFtbW2ora1FZWUlK8BVVVVhfX0dDocD1dXVqK+v5+KNzpg77rgDGo0GpaWlXMSbzWYuPoAPFNPIVyVfYUtY7Hg8jlAoxBy+9vZ2tLa28l3a2dmJlpYWSCQSNDU1Ma7cYDBArVbzBKCrq+sG5Z2Wlha0t7ezK/vi4iJ794hEorwQTDIeI8+G4uJitLe346GHHoLVamWvJSpIRCIRTzOz2SzLhRPZ22azsfQpdXdJAU6r1UKr1aKuro75hfmKbupwp1IppFIpln8/evQoNBoN30d2ux3bt29Hc3MzG/Z1dHSgra0NBoOBicKf+MQnIJfL0dTUBKPRiNXVVZSXl7NqIjVLbTYbamtr8zZTRCIRWltbYTabWQbZ4XDg0KFDXLwWFRWhvr6eBW7q6+uh0+lgs9l4ykINU4fDwZy0aDSKdDrN5sAErdu7dy8OHToEoVCYV4Y6GAxCp9MhFotxJ5s8a0pKSnhiWFFRgcOHD6OgoABOp5MFHegbp+lqXV0dampqGNNPxpbEhaVGQHFxMTcEtwqasNEZTEaM+/btQ3V1NX9HJPNeUFDAxoO7d+9mWCDZEjgcDr4PqIDPZDJwuVzYu3cvTyfMZjOEQmFevzES3BCLxVxYJJNJbNu2DcXFxVhcXOQE3Gq1oqKigmFbra2tcDgczPNVqVSwWq0YGBhg2LXf78fu3buZU0r7Ta1W83e2VUilUtjtdjaftlgsMJvNqKurw+7du9lPraenB/feey8T2emcJiTF9PQ05ufn8dnPfhatra0Mqaeps8/nQ21tLRe/TU1NSCaTHyp7TvGRCw2DwYC+vj6udKjr+dOf/hSPPvroDYZVq6ur6OjowC9/+Uvs3buXiUo2mw2f/exnoVar8frrr7NSFSkQfexjH8O3vvUtKBQKFBcXY2JiAm63G5/97GfzKhTV1dXh0qVLyGQySCQSvGlmZmawe/duXLhwAdFoFB6PBy+88AIrPX3yk59Ed3c3E4f9fj/q6upQXV2N+++/HyqViqXlJicncfvtt+P3v/89VldXecT6+OOP58WlE07v7rvvht1ux9zcHO655x709fVxl4zkFyUSCf7oj/4IMzMzOHbsGCwWCxoaGtDU1IRr166hvr4eP//5z9HZ2YmysjJYrVbU1tZicHAQtbW1ePnll/GVr3wFR44cgc1mw5e//OW8cIzNzU2MjIygpaUFDQ0NLFX6m9/8BhUVFejr6+NuYlFREQ4ePIjf/va32Lt3L9577z1kMhn09PTg/Pnz+OxnPwu9Xo8vfelLLNFGSjef/vSneYqzsLDAePvrzb3+zzCZTCgoKEBVVRVKSkoY1vWLX/wCu3btwvnz5+H3+1FRUYGqqirs3bsXv/71r1FdXY3JyUno9Xrs2bOHC8fx8XHs3LmTp0RSqRRPPvkkjh07xvrbMzMz7Eafb8xLHhcajYZx5I2Njejt7UV7ezsmJiYwOTkJp9OJWCyGnp4eLqhHRkYgkUiYTC8UCnHx4kUcPnyYXeotFgvOnj2L++67D2KxGPv27UMgEMCbb76JWCyW1ySHJP9IFz4Wi3GHn1zd5XI52tvbkUwmYTKZ8Morr+Cee+6BSqXC/v37cejQIRw/fhxf+tKXsLa2hoqKCoYK1tfX48c//jFuv/12xpWSbGsikcDQ0NCWa6OQSqXYvXs39Ho9ZmZmUFVVhWAwCIPBgMuXL2N9fR0+nw/nzp3D008/zaZoNPqtqqrCK6+8gkOHDsHlcqG7uxuVlZWw2+1wOBwYGhrCo48+imeffRZlZWWw2+0wm83Yvn07Y69vFuvr6+y5EggEMDw8jLa2Nuj1etx77704ceIENBoNDhw4AI1GgyNHjmBychLNzc3413/9V7zyyivs7Pvwww+zcggpf9XX10OtVuM73/kOmyWS2hgA/OpXv9pybWKxmJVeyITxvvvuw/T0NFQqFS5evMhGe9lsFrt378bIyAg7Q1OXmLyGnn32WYaTFBQUYPfu3TAYDLjvvvuwurrKIgwEk/P7/VuuTaVS4a233uIE1ul0Yvfu3RgeHsadd96JyclJLgyj0Si+9KUvsfcMPSOLxYJXX30VVqsVkUiEpUVpcriysoLDhw/j2rVr0Ov1MJvNjC2emJjYcm0ikYilmIVCIXvFpFIpOBwOnDlzhmEGuVwObW1t3CihCTKpVjU2NuL06dNspEZF7OTkJPbu3YtAIMBGh+RpkO/e0ul07PgcCAQYxvoP//APsNlsLDQyPDyM3/3ud9izZw/m5+fxiU98gmGtiUQCL7/8Mr72ta9hZWUFFRUVTHwFgNOnT+PRRx+Fx+NBVVUV+vv7cerUKWzfvh0jIyN5nxs18OhurKmpYcjvm2++iXg8jm3btmHXrl0oKSnB008/zfywqqoqdHd3w2az4ciRIxgcHERxcTHzYbq7u/Hcc8+hvb0dv/vd79Da2srFZWVlJXOdbhYtLS2IxWKs0nbp0iUYDAasr6/j6NGjeOeddxCPxxGJRPDyyy+jpqYGP/jBD3DXXXfh/PnzWF1dhUgkwnPPPYff/e53eOqpp7C5uYnp6WmMjIxgbW2Nn3MikUB9fT37esnl8rxrI94HSbgTJ3NiYgIHDhzgAqOmpgZnzpxBeXk5Tp48iUceeQQOhwPNzc1ob29HJpPB/v37MTo6iqqqKgwPD2N8fBwymQz//u//js985jPcvCQoJDUPtgq73Y7+/n6GNmWzWT4vpVIpZmZmEPqDd9mFCxcYNlZdXc3QRlKavO2227C0tISWlhZ2E1cqlRgYGIDFYsH7778Pm82G8vJyaLVa2O32vEazDocDer0eXV1dqKiogEwmw6FDhzA3Nwez2Yznn38eNpsNd999N8LhMPbt24cLFy6goaGBJ51ms5k5Q++++y4OHjzIyBCLxYJf/vKXKCwsxNmzZ7m5qFareRK3VdDE4sCBA6xQ6vP5MDw8zHCohYUFblLs2rULTz31FA4dOsSTWZlMxtLZBGfU6/U8bf7Zz36GL3zhCzh69Ci6urqQzWaZZ5oPKl1cXMx812AwyBxNku1fXV3F2toaLly4gJdffhmf//zn8ZOf/IT9b7Zt24Y77riD/Xqef/55dHd3Y25uDm63GwcOHMDU1BQeffRRtn2gidNjjz2G3t7eLdd2fXxkjoZUKsXjjz+OkZERyOVytLW1Mdbw9ddfx/LyMsuO1tTUYHh4GHv37sX09DTUajU8Hg8npgTZGRwcZJhQKBTCV7/6VXz9619nlQ3q9ORyubzY73g8ju7ubpZ7LC0tZe110jQmrKNMJmOoSjweR1dXF7vsLi0t4fjx43jmmWfw3e9+FxaLBW63G6Ojo1xhNjY2YnFxEcvLyzyRyJcwu1wulJaWIhwOs+M4XWwUGo2GCehPPvkkPvWpT+GZZ57hCcjS0hKPrvbt28cGeKRuQuO/e++9Fy+99BIKCgpgMBhw6tSpvB9QYWEh47gNBgOMRiOrOZH8qMvlwtzcHCe4VVVV+PWvf82X3ODgIOLxON544w2WTpyZmcHw8DDW19eh1+tZcWNxcRF2ux2ZTAbxeDyv9CORhp1OJ2QyGSorKyGRSPCJT3wCbrcb99xzD/r7+xk/urm5iYaGBpw/fx7AB4fD2toaEyPb29tx7tw5zM7OQiwWw+12Q6PR4J133mGFM1LhCYfDeOedd7bU/SZsPjmeZ7NZjIyM4J577sHbb78Nv98PjUYDiUQCr9eLoaEhGAwGvPjiiww9KC4uxmOPPYaXXnoJt99+OxYXF7GyssIu3t3d3XjyySfR3t6O5eVldsCmLsNWce3aNbS1tWFxcZFJYGtra0ilUjh69ChaW1uxsLCAvr4++P1+LjJPnz6NUCgEtVqNuro6HDhwAPPz87DZbHj//fdht9sRiURYFevs2bMoLy9nbfb19XW4XK68spQAGG5G2tyk/KNSqRCPx7mrTA7Gf/Znf4ZMJoPu7m40NzdjdnYWExMT2LVrFwoLC7G6uoqpqSlUVlYy3yMQCHCnKxwOIxAIMDcgn6QnEXNFIhEcDgdzbYRCId577z3MzMxA8wcjKYVCgYmJCUxNTaGmpgb33nsvTp06hRdeeAENDQ2YnZ3F4OAgvvGNbyAQCGBubg4KhQItLS14+umn8ed//ucYGhpCKBRC6A+mWFupOgFg4nsymWS+BZHmvV4vS+pOTEzAZrNxx1aj0cDj8bDHSVNTE3p7e9HZ2Ynq6moEAgH09vbC6XSitLQUU1NTqKioQGFhIRePa2trebuRa2traG9v52JQIpFgbm4OHo8HZ8+e5dE/dfx7e3tRVlaG6elpDA0NselpfX09hoeHMTc3B4fDcYO/RyQSQVtbG08tCZ5ICjVbhVAohNFoxOLiIkpKSqDX6/Huu+9i3759XEy88cYbiMVi2LlzJ3MOV1dX2Ugul8th27Zt+P3vfw/gg4lFKpWCy+Vi3uK7777L8tEKhQJSqRTLy8t5IUChUAjpdJonnH6/nwupQCDARNDNzU10dnayeelPfvITPPjgg0gmk1hdXcXu3bvx/PPPM867ubkZgUAAa2tr2L9/P37yk58gGo3ioYce4gnv+Pg4mpqatlwbQXgJpptKpTA8PMx+UF1dXZiZmcF7770HuVyO+vp6tLS08D1KkKuCggI8++yz+PrXv87QGo/Hw7wIv9+Pw4cPw+l0MuH6+PHjWyonAcCJEyewY8cORibs2LEDFy5cYHjl0aNHMTU1hfX1dRw4cAAbGxu47bbbsLy8jOLiYi5QDxw4AJ/PxxyJ6upq6HQ6TExM4LXXXsPY2Bjm5uYYgkak7NXV1S3XZjAYUFlZibGxMXbfHh0dRSwWw5EjR/DKK68gm80yzHtmZgaVlZUQCAQ4deoUPB4PbDYbDh06hP/4j//Ajh074Ha72U18fn4ehw8fxve//32EQiGoVCpGI1it1rwIhtnZWRw+fJhFKOhsIxNFiUSCa9euoaCgAI2NjXjttddw9uxZfOYzn4Hf78fw8DAMBgOqq6sxMjKCr3zlK+w5RUnt6OgoHnroIfbfov2rUqnw7rvvbukMTgVmb28vNBoNCgsL8dJLL3EBf8cdd8Dr9eLKlSuYn5/H+vo6c/CsVis8Hg+uXr0KrVaLixcv4oc//CHeeustVFZWIhKJ4OLFi9DpdOjs7ITb7cbx48dZvvz8+fN5lRILCgrYDJIKKjKJnp2dZSGidDrNvjektkdIoKqqKlRXV2NhYYEnVL29vQx9euuttxgyR6avDoeDp9BbxdLSEqqrqyEQCLCxsYFkMomzZ8/C4XCgtLSUJ4rUtAkEAkzUt9vtGB8fx9jYGOx2O7xeLx544AE8//zzmJycZBXHY8eO4f3332d/t4WFBc4J8/nbXR8fudAoLCzE3/3d3zFOe319nck1hYWFOHbsGI/1NRoNOjo68NRTT0EgEDB/gEZB2WwWnZ2dSKfT7Oi9sbGBiYkJtLa2MjyHVAKi0Sir9twsKHGk0Thhqr1eL4/Km5qaYLfbmYCzY8cOLCwssNpLQ0MDpFIpfwgkddfd3Q21Wo3vfve7uOuuu9jkhjC/ZMizVdhsNgwODjJ5en19HWfPnkVLSwump6d5UrS4uMhQoXPnznEnnIinPT098Pl8uHDhAruA22w26PV66HQ6nD17ll28ycV93759eQsNg8GAEydOMKkukUhAp9Mh9AeXSXIb3djYuAHvT8ZhEokEJpOJOSKvv/46ampqcPDgQdxxxx3o7+/H8ePHcc8992BjYwPr6+uYnJxELpfD4cOH877T4uJiDAwMoKSkBEajEevr63C73fjhD3/ICSCRp0dGRvhADgaD7KWSy+Vwxx134I033sCFCxewY8cOtLe3Y2pqCuPj44jH44zxvF75hMaQWwVJ2FHxSZMgUndRKBRYXl7GhQsXMDMzg0984hM4duwY7r77bpjNZpZz9vv9EAqFeP3117Fz5060tbUhGAxiamoKXq8XlZWVsNlsWF1dZRdggs3le6cTExOsuR8OhxEKhfD+++8jFotBKpVifn4e09PTSKfTePjhh3HmzBkm4JKZFSkSvfzyy3jssccAgBVBZDIZGhsbWbqPMPdarfZD9cg1Gg1OnjwJi8UCsVjMHjr79+/H4uIiHnjgAUxOTuLKlStYX1/H3r17MTAwwFwgjUaDbdu2QavVQi6X4/jx46ivr4fD4YBMJmOjuFdeeQXd3d3sup3JZCCXy/OqZNhsNiSTSSwtLaG5uRl2ux3Hjx/HlStX8F/+y3/B9u3bmYe0vLyMHTt24M4778RXvvIVCAQCdHd34/Dhw+jv70d1dTVmZ2fx2muvoa2tDY2NjWy4ZbVaIRAIsLi4CJ1OB7vdDp/PlzdhLi4uZgx+KBTC3Nwc/H4/JiYmGDJitVoRjUbx9ttv49ChQ6ivr4fT6YRGo0EymYRYLEZxcTGUSiVOnTrF0xeSpyS+mUgkYiMn4qfdd999W66tvLycZRnFYjHjhckI7a677kIul8OJEyfwL//yL3j44YexZ88e/nPEfautrUVvby+MRiMbiRLs6fLly8yvIGgN8SfyYeaJr0LQN6/Xi2g0ih/96Efo6OjA9u3bcfToUQwPD+OXv/wl2traIJPJUFFRgcnJSeaSEJyLEnk6B3/3u9/hO9/5Dk6ePIlXXnkFWq2WmwmxWCyvr0EgEGCREGpaXS8NPjY2hrGxMUQiEczMzKCtrY3hXMPDw8xboylVc3Mz85ioQBgZGcHGxgYOHz6Mp59+miVXr+f3bPUtEK+CjHWdTiefu/39/eweT9yU8fFxxpUrlUpUV1ejrKyMp0oEh5FIJKzYQ3fppUuXGC4VjUbzKmIZjUYcP34ce/fuBQBWNnv22Wf5XkylUhgdHWU38BdeeAGf/OQncfr0abz77rtoaWlBc3Mz3G43BAIBTyAIYfHjH/8Yn/vc51BbW8uEa6/Xi6KiorywWoVCgWAwyFNHcnP//e9/j4aGBphMJkxPT2NxcRHAB5N78nTp6+tDTU0NrFYrv9/Lly+jtraWcyDyz3niiSdw/vx5TiRpkpnvWyCeDsHeqWk0MjICj8eDxx57jIuIubk57Ny5Ezt37mTTxtLSUigUCvafqa+vx8zMDGQyGatoVVRUwGKxIJPJMCyY4LL5Ema5XA63280NratXr7Lf0sWLF9HY2Mgu9Q6HA3fffTebCmezWYZ9kVQwqf4RjHnnzp1IJpPo7e1lKB3tscrKyrzfqUQiwa9+9SvY7XZUVVUxD+LcuXPo7e1FVVUVlpaWsLS0xP5n5BL+4IMPcsO8srISb731Fk9I/viP/xiBQADPP/88SktLIZfL2bCT4OUul4s9u24WZJpL8GEqtv1+P9LpNHbu3ImxsTH09vYyT/rzn/88nnrqKfz7v/87urq6cPvtt2NjYwOpVAp///d/j4cffhh/8id/wgglvV7PzXsyUa6srEQsFuPm0YfFR4ZOraysYP/+/dDr9SgvL0dLSwsWFxdx2223cQJK1Vd5eTnGx8dx8OBBiEQiWK1WmM1mVFZW4pFHHuFLzul08sVE8CJKbFdWVmAymdDe3o50Oo333ntvy7VFo1Em7lH1SVhym83G2DzgA6nZUCjE6grbt29nedvKykpMTk4iFArB5XJBIBCw7f23v/1tZLNZxpACYMx6PkWW5eVltLe3M76aCEUEbaGKllR0EokE48nLy8sZKlFaWgqdTofPfe5zMJlMqK2thVar5S7t+Pg449NbW1tRXV2N+fn5vHjSlZUVNiGrqqrCjh07uOtUWFiI9vZ2rsb379+PpaUlrrq7u7v5OXd3dyMej+PgwYOQSCTc5dbpdHjooYcwMzPD0A+r1Yqamhr09fXlxfcR0VYsFnPX12az4ZOf/CQUCgUOHz6M7u5udHV14a677uLCUK1W48EHH8Thw4dRWFjIJm7l5eUsh0sj2q9//essB+zz+VBaWoqamhp+J1tFLBZDKpXC1NQUCwuEQiF0dXUhk8nAZDKhqqoKhw4dwhNPPMEwEZKxJGgTvffHH38ca2trWFlZgVKpRGdnJ5qamvi9ZzIZVFRUoLKyEouLi3k5GouLi9wNTKVSnPAcPHiQ90ZPTw8efPBBfO1rX4PP58PHPvYxPsBjsRiGhobQ1NSEbDaLZ555hjkppaWlrBZ15swZJqF1d3fjtttu4/eVLwKBAJspajQalJWV3YAbD4VCmJiYQDweR2lpKd544w1UV1dj//79UKvVmJycxODgIGuEHz16FNlsFoWFhQgGg3j//fdRXV2N/v5+ltYMBoPMscoHeyA5RoJQjY2NQalU4i/+4i+401hdXY1jx47hL//yL7GwsIATJ04gkUjg0KFD2LZtGyoqKtj06L/+1/+K0dFRjIyMYGZmBm63m7+ViYkJbrSQGVI+13LiSq2srGB9fR0mkwlSqRQ9PT2QSqUoLi5mbtYf//EfMzFxdHSUIaqkiDM3N4fbb78dMzMzyOVy0Gq1MJvNePTRRxEIBFhWu7u7G9u3b2do3FZB6k8kyV1fX4/p6Wk+D6anpxGJRLBt2zb8xV/8BSKRCIqLiyEWi9HV1cXuw9RoOXDgAAAwkTiZTOLYsWMIh8OwWq0sS0sCDh9WfJM4BGnVSyQSHD16lPdeMBhEUVERbrvtNigUChgMBkxPT3Mxu7m5CavVCoVCgebmZrz55pvo7+/H4uIiVCoVnnnmGXi9Xjz88MPcGd7c3MTc3BxPWG8WUqmUp3LEkUokEigpKUFfXx8GBgag0WiwZ88edHR0YHh4GEtLS4jH43wvEGF5aGgIDoeD1bFo8nb06FEm1FdXV6OhoYFJoB92xpFk8ezsLBtLbt++HdPT02hpacG+fftw++23495770VlZSUOHToEqVSKb3zjG+jq6sL09DRMJhPuvPNO5v3RvxmJRGAymTA7O8tQpqamJvT09ECj0eTlj0QiEezYsQNCoZBVuAgCQ5xJs9mMnTt34p577sHa2hq+8Y1voLe3F5WVlawapFKpMD4+zrClbDaLubk5TExM4C/+4i/wm9/8Bj6fD0tLS+y31N7ejp6eni3Xtrq6yqIgpMjkcrnwV3/1V6wQWVZWhm3btuHYsWNwuVzceLjzzjuh1WrR19cH4AN1wwceeAAlJSUMJ/J6vWhvb8fp06dhsVj42yVkSD5uC0GtQn8wKzSbzQiFQvjUpz4Fh8OB0B8MgIkndfnyZf55ulOmp6dhs9mwvr6OJ554giGglZWVyGQy2L17N9577z3o9XrMz8+zF1g4HM6bMANgrmAwGERJSQlaW1uxvr7Oe5VEYbRaLX75y18yr6ezsxPl5eUwm8248847UVFRwUI4JKhAeRopTRLHU6fTQalU5uVoBAIB7NixA9lsFjMzM1hYWOD8MhKJwGg0wmq1or29nSFdHR0dsFgsGBoaYv5lcXEx5HI5HnroIaysrODtt9/G+++/j3Q6jccffxzXrl3DXXfdBZPJhKKiIhZJysdPJsL9tWvXMD09jWg0inA4jDvvvBOXL19GX18fhoeHoVQqsXv3bjYTHBgYwH333YeWlhZG9xgMBrS1tbF9w+LiIjcA3G43mpqauKgUCAQIBoN5p6LXx0eeaJB6j9VqhVAoxNLSEoLBINLpNEwmE9bW1jjJslgs6O/vZ1KiQCCARqNBUVERZmZmWAEllUqxyg4d+GKxmPWaqYtJuNOtQi6XszLD+vo6gA8IVr29vTh8+DAnXKR+VVtbi+HhYYZVaLVayGQyeDweHDt2DKurq9y5INt5n8+HxsZGllsjl3OJRJLXqVkgEDD8h4zPWltbEQgEYLfbMTo6CqFQiOrqatTW1rLRVSwWYx6HTCbD0tISdDodywtPTU2xgc3w8DB27dqFtbU1diMmH4h8FScVe3K5nKcXUqkUAoEANpuNFSZITq6xsREul4tVw4APcO1LS0vskkswsVgshtXVVRQUFCCTybAcLD0DrVabt2tFXTQ6HKl7TgWHSCRi3X4a3WYyGXR0dEAikTAhnGRdy8rK+P0TxnBlZYWNe0pLS5kwRvrwWwUp+5CyVSQSYUWNRCKB6elpHqXSRdjf388EOZLvI9lacgwPh8M8kiVc69zcHGPGyUcjH14eAEvXbm5uIh6Ps4paXV0dFhcXIZFIWGSB5FDJB4M6xkTmXFlZgUwmQyQSYS8EoVDIClj0HSiVSpYWzhcSieQGsiepaF0PcyN5Xuoek3IccWtIYcRkMqGhoYFJ6eFwGOXl5ZDJZEzqJDNKOujzkdWTySQrdohEIpbEpqSYNOZJ276oqAiRSAQtLS3QarXQ6XQ8pi8rK4NYLGZ5xWw2C7VazbLbJO1LhpYlJSV5kysqlBQKBbLZLMv3bm5uskjH3NwcZDIZqqqqYLPZ4Pf7GfJBYgLhcBiFhYUoLi5GTU0NAPAkWCgUoqioCB6PB9lsln06mpqa4PP5tlybWCxGIBCASCTib7q5uRlFRUU8/aaO4/r6Oht5WSwW/h6IdHz06FH+82tra4jFYpDJZJx8nDt3DlqtljHMxLvYKmiPb25uIpPJYGNjg58bSRETzEehULD/QWNjIyc2JJOt1WqxtrYGg8HAkwfyTCBYAp2XJO+e71slCB51EzOZDKxWK/sGFBQUMLG7vLwcYrEYIyMjjGcHPoANE2+C1HbovCwqKkJBQQFMJhMmJib4DCS+WD4IJk2CSKUoFotBp9Px3qUpHKkJGo1GrKysYHNzExMTE1Cr1Whvb8fCwgIMBgN3W91uN0sbT09Po76+Huvr6ywOAYCnR1sFqd3Rsydls2g0yvcG3V0ajQaTk5N85pHsul6vx8bGBmpra5FIJFj1h8xkyTfF5XLBbDbzHtVqtXkn8Uqlkv16aNpkNpsxNzeH7du383diMplYPn9hYQGBQABisZg9Zkg9MJfLoaysjJ81FWqVlZXo6+tjSHEoFOLvd6sQiUSYn5+HXC5nURaz2Qy/34/i4mJWHiTydE1NDaLRKBobGxEOhyGXy1lgo6amBgaDAc3NzUgkEkilUrDb7awc5/F4mGgdjUaxsbGRV4iDIFharZZVykh9tKGhAU6nExsbGyy80traCq/XyygOUvOanp7GsWPH2F+HjDDD4TCrnl69ehWlpaVsWZBIJHjCdLOgO4uEG2KxGEN+o9EoJiYmmMCt0Wj4mRC6pKKigv02dDodFhcXYTKZGNpPMuLNzc1syhsOh5FMJnmCtVXQ/XR9YavVallQZXJykhsyq6uraGxsxPDwMHbs2MFGhhsbG/B6vSgtLcXa2hrq6+tZdryhoQFvvfUWamtrEY/H2SRTJBJBLBbn5aBdHx95okEHckFBAWKxGBYWFpBMJtn6fGVlhTWTo9Eov3xy0M7lcigoKGBYx/z8PG8qn8/H1vaZTIbHmaRfTQ9wq6BLhC7tYDDI41wqMEiBgC46IuJeLydG7uI0IiS5NLJeN5lM8Hq9LBtGUmX5DkWpVMqHAPEyyLStoqICq6urbJxHZFev18skQILXuN1u5HI5DA8PQy6XY3R0FH19fUgkElheXkZpaSk7khKZViKR5DWeo4uMfn5paQnZbJaVH2j8Fo1GMTY2BpPJhI2NDealUNJJxi6ZTIYxozRWX1xchEAg4CSRpFUJRrZVkKoOFaJk9kXcEbfbDafTCZ/PxzruiUQCWq0WLpcL8Xgccrmcpw/XG4kRH+aNN95AcXExK+qQARd1yrYKKkSog084a+JFLC4uMhSGLhYiwxPsrKCggCUgqWikBJ+6iHq9HsvLyzzaJ6hXPniSVCrl74dG5BqNBj6fDwqFAjMzMyxRPTc3B6FQyO9mfX2d5VbdbjfS6TTOnz+PdDrNajbAB51mIuFtbm7C4/FwwZrvuQEfJDB0KJJELYkEFBQUMJSKCpiuri7E43F4vV6G9pAzq1Qq5WnmysoK3G43q1+RB4FMJoNUKmXTznzTIJJvpMuMkgvidlHCubCwgIWFBej1ek70Q6EQfD4fwuEwxsbG2NypvLz8hgKbPHXoTAoGg3C5XBCLxR9aQBKRmbp7IpEICoUCcrkcfr8fXq+XzVSvl1p1uVwsuwx8kMitr6/DYrGwDGU4HGYX+unpaeZm+Hw+mM3mvMZz5I9DPgo0XQTA0rF09obDYTgcDiwuLiKVSrHKCnVOS0tLMTY2Bq1WC6lUyphzl8sFjUbDhTLJX9IUbKuggonUU6goId8TSvIJpltQUIBAIACNRsPfAzXISF5Yr9fzfiCfAYFAgJGRESaVk/xzvsSP9OzpLikoKGA34aKiIhapIFEJk8nEEvKUEGYyGczNzcFut2NmZoYLRlJ+Iiw2+TpQQ4mk2bcKKrhUKhVUKhXLAQeDQej1eia9h0IhxGIxFBcX813V29vLyj+EJKAmFvGvMpkMvF4vK0MWFhbC4/Ew7CpfQ4Cmd7lcDhsbG4jH4ygsLOT7hKCS8Xic77fz58/DYrHA6/WyZOfU1BTUajULKpBEq0AgwOrqKvPFSOGKpEbzyQJLpVL4fL4boNU0iXU4HCybT4koOZTncjlGURCcXCaTYXx8nBWAKCF1uVyw2Ww81SOZeQAfqki4vr7O3Cfi4TidTn6WxB2Jx+MwGo0IBoPMb6OpKn3fxEHyeDysuuTz+aDX6+FyuVBSUsLKmQUFBXmbUHQfkCEq+UFp/mAGSM+Wvn0y26V7jCCRw8PDKC0tZbEEl8vF9gt0vgQCgRtUD8mMb6sg+WESgCA7gGg0CoPBgJmZGXi9XoTDYSwtLbE3Dz1jkgzu7e2FSqXC+++/j+LiYm6+k5FjR0cH5ubmuEkZiUS4cN4qqJlQUlLCyBrNH+TzS0pKeBpCXE21Wo3FxUXU19ejv78fIyMjnKeR75lKpeJJslQqxfvvvw+ZTMbS5wSJKygo+MiqUx95opFIJLj7m0wmmQxMxiyUjHi9XkxPTyOXy2FzcxNKpRKJRALxeJyJNGRg88QTTyD0B+M8kUiE4uJi9PX1QSwWs/ZxMBjEyMhIXh4EqUVRJy6Xy+H06dPYu3cvJ0yrq6vc/TYYDKivr8fo6ChWVlZ4IpFKpfD+++8zuRr4oOPq9/sRDocxOTmJ6upq7nYS+SefVjoAWK1W7pZnMhlMTk7eoEZAEIeZmRmGMS0tLXG3hjprwWAQV65cwc6dO5lYTvJtJ0+e5PdBHUqCWmwVYrEYZWVl8Hq9DDsLBAIwmUzo6+tjGVhydPX7/WhubobT6UQ0GkVBQQF3Bk6cOIHdu3ez2zl1uWg95LBOhw7to60il8uhqamJHT3JRyAWi2FtbQ3JZBIikQiFhYVMkCVicC6XYzw2Xca9vb3ciSd4z+rqKs6ePQupVAqn08leIqSzny+oi6lWq5HL5TA5OQmXy4WioiIeLVIhSiZZZBhF3Swyb6SEiyAgbrcbQ0NDaGho4O69SCS64e/O905JwpAmA5FIhC8B6jqTEU9jYyMqKyt5pEzcjOslgqnjRdKBBN0IhUJcTAEfnBH5YIQAGLpG2F2C85AkKB3oMpmM37dCocBrr72GeDyOyspKyOVyTExMsKwgwWJIHnT//v1cLASDQVbG8vl8eWVaSVaSDmeakC4vLzPUgoQJCJpI3gSk5KJWq7F9+3bEYrEbdN3J2Z6IgFNTU3wOUAGc770C/5/OPEmazs/PY+fOnfB4POzFsbKyggsXLqClpYWTZ0pCSM5ZLpdjeXmZ5RRpirS2toaDBw/yO6DEi4qrrSKZTLIaCU1QSQmP3uXq6ipPFqiIILwvTRLEYjEGBwcxMDCA2tpahn/R/ojH49i1axfLT8tkMub9bRWEjSaJSIFAgKWlJWzbto0LWPJfoMZMRUUF85aMRiNyuRwLgBw+fBhzc3MoLi5GYWEhwuEwm3Fubm4yL5C+03xcL3o/NHHa3NzkQmp+fh4KhQJGo5Hvm5KSEqjVavT29rJpLt2tHo8HZ86cYe4k8XiIp1ZWVobl5WXE43He4/kSP2oiRaNRZLNZbmCQnCxJTdOEIhwOo7a2FisrKxAKhQgEAtylJ3O3dDrNxdPGxgZMJhO7brtcLp4CkQHsVpHNZpm7BnyQWC0vL7MDs0qlYtEVajbQuwkGg/xO33jjDeh0OvaIsdvtGBsbw5tvvoloNMoSr8FgEJFIhBty+c44t9uNxsZG9t8gJ3qj0cjNWeLN0UQtlUoxD5GeA00XFhYWoFAo+N0TkiQajaK9vZ1hbQB4crpVRKNRlJeXM4z3eu+FWCzGqICNjQ1sbGxgYGAAcrmc1Z1ozxDPlhpUlLRfu3YNm5ubKCkpQSKRwMrKCrLZLDY2Nj5U3pbeORmRUnPRbrdjfX0dtbW1XCy4XC5sbm7CZDJhZmaGGzlSqRS1tbW4ePEiioqKGIpJtgRSqRQLCwvYuXMnzp07h5mZGZ605VN2ou+BchmSg00kEix4RP8Gqfh5PB4YjUa4XC7mTKysrKC0tBSRSATLy8soKiqCWq3G5uYmO9wTTJUMQKmI3ypIGZUmVOvr6yyPPzMzw3cC5alDQ0MoLy/H0NAQpqamUF5ezl4b0WgUPp8PIyMjfHeurq5ic3MTvb29LD9PzV86kz5KfORCo7S0FJcvX2aTl1wuB7FYjFOnTkEmkzE0iDrWd911Fy5evIj33nsPnZ2dsFgsPO4medn+/n5Wmpibm8Nbb70Fu92Ouro6hlYR7yOf2YtOp0Nvby8sFguT1nQ6Hc6fP499+/axEpXb7WbOxOzsLFecCoWCH3LoDz4Qer0eXq+Xu2XUaaPqlzwZiHuwVZCjpFgsRmHd2mK1AAEAAElEQVRhIeOzlUolLl68yLhKIs3TZUFjyqWlJYRCITQ2NkKpVKK7uxuZTAa33347/H4/Ll68yPwWv9+P999/nx3WCbK2VRQXF2NycpInNNSpOXHiBHslkFeHwWDAwsICQ1VoBB8IBGAwGHDgwAG8//776Ojo4ASLErDOzk6+FKij5HA48k5b0uk0j3nLy8uRTCYxNDTEnXoA7Iau0WgwPz/PkwylUsndn/b2di5sKyoqmJAoFArZewUATzGuh1RsFZlMhpUhCJpx7do1NDc3Y2BgAFarlV1mi4uLGT5CB2c0GmWVk9HRURgMBuh0Ou6CUZeDoDUikYgPdyL1bhUqlQqDg4OQyWTcpZLL5XC5XFhZWWFFFPKaGRkZ4Y4d8Sai0Simp6dZOYsgceSwOjs7i7GxMYYz0fiWTLLyhVgsxvj4OCwWyw1nBkHuCEoTCARw5swZVFZWwmg0srJXLpeDyWTC/v378eqrr8Lr9WJqagp79+5laIDb7cbBgwcxPDzMEBPy5clHlqRitbS0FOl0mgnuBDciyMXS0hLeeusthkWR/GswGGRFmVQqhZMnT+LRRx/lAo0gZtSo6erqwrVr1+B2uxGNRvN2rgwGww0TStoPvb298Hq96OnpYV15cmufm5uD0WhEf38/otEo/H4/RCIRvF4vNjc3odfredKs1+shFotx+fJl1NfXY2VlhX9vKiS2CppWkOEjKXvRt0DJLQDuQra0tDAROPQHOVYyNiXZ3evvFYIQEaxNr9cjm83ynt4q9Ho9w76oWRIOh5l0uW3bNszMzMDpdLIvTyQSgUajweDgIMu6FhQUIJFI4NVXX4XD4UAqlYLf78fy8jJPaktKStiHhd5PPhdpkUiEYDDIsBVKSghiJBQKOREIhUKcsBJ0Vq1WM8RqY2MD7e3tLP0KgGXBf/vb3+Lee+9lBSDq0OZr3iWTSQwODjK87XpVudHRUe58E6RwdnaWzfHIqZjOtKKiIpw/fx4Oh4MnJX6/H21tbRgYGGBiKRVdhELYKlQqFdbW1hAIBJBIJDA/P8/FDmHmqclD0129Xo/XXnsNu3btQl1dHd9rQ0ND6OrqwnvvvQexWAyNRoOuri709fXh/Pnz0Gq1mJ2dhUKhYK7djh078r5TaoySKhhNBGw2GzQaDZsSC4VCbi6RCSkpkE1OTrISEBGLnU4n+vv7YbVacfHiRUSjUU5Q6c7Pl4uo1WqMjo7CYrFAKpViaWmJITIOh4Ol8skQt6ysjP1aCD6UzWbh8XhY9Yk8rmjSc73fRX9/P0/qqYDfKgoLCzE/P89qecAHDVq61+vr6xGPxzE3N4f19XV0dnbi0qVL0Gg0GBsbY6Phuro6SKVSluUmvkMqlWI+FE3VioqKkM1mGcqUb23ExSREz9TUFHN9iouLOQEnsRClUon+/n5EIhFGY3R0dODKlSusluVwONhRvbi4GCMjI7Db7QzhI5PEtra2LddGjT9quhJU/6mnnsKxY8dYUjyZTCKdTuPOO+/ElStX0NfXh23btjGSqLCwEHNzczh27BhL+9NEs7y8HE1NTRgYGGBjRzJazafieH0INvPNAW/FrbgVt+JW3IpbcStuxa24Fbfi/4/4yByNW3ErbsWtuBW34lbciltxK27FrfiocavQuBW34lbciltxK27FrbgVt+JW/F+PW4XGrbgVt+JW3IpbcStuxa24Fbfi/3rcKjRuxa24FbfiVtyKW3ErbsWtuBX/1+NWoXErbsWtuBW34lbciltxK27Frfi/Hh9Z3vZHP/oRlEolS/aRm+/09DTa29tZzlQkErHUHLmJ/v73v4dWq2U5rVwuxxKgqVSKjaZSqRRsNhvMZjO77gYCAaRSKcTjcfz3//7fb7q2n//856zDTi6kCwsLGBkZQVVVFcxmM9LpNOvjj4+PIxKJwGazoaSkhLWni4uLsba2hvLyctZnJndPn88HnU6HpqYmRCIRAGBTu3g8jj/6oz+66dr+7d/+jU3TSB4uFAohGAzCYrHA7/ezjjMZNpWWlsLv97PEaigUwsrKCiQSCeLxOJu9RCIRrKyssJM3yS6Se2UkEoFQKMQXvvCFm67thRdeQCqVYhO7jY0NrK+vo6SkhD1HyP1dJpOxvKHf74fBYIBUKoVYLGat5tnZWayvr7NJWSAQQCwWg1arZYk7jUYDlUqFcDiMcDi85Tv98pe/jIKCAjYgymazEIvFLNlaVVWFQCAAn8/H2t1k6EYeG+Sl4Ha78dprr+G+++7j31MikbDL68LCAu+BTCbDksR///d/f9O1/eM//iPcbje73pN0nlgsZn178p8gE0jS3ieZ5XA4jIqKCsTjcdZoJ033bDaLeDwOiURyg3QhOQ2HQiF84xvfuOnannnmGZbh0+l0EIvFGB4ehlAoRFNTE/+dhYWF0Gg0eOedd3hvkpkRaeE3Njair68PGo0GDocDiUQCIyMj7B66urqKtrY23pfxeBwbGxv4sz/7s5uuDQB++MMfshQfyfF5vV4kk0loNBo4nU6Wzkyn06isrOT3kclkWFZbIpFgfHwcNpuNjeAKCwtRUVEBmUyG3t5ebNu2DUNDQ+wfQhK9f/Inf3LTtR0/fpz3WnFxMe/TtbU16HQ69kogQ9L6+nqEQiGUlJTA5XKxUzr5AJEbcyaTQSqVglAohEKhwNLSEmw2G+vFx+Nx9nJ45JFHbrq2X/7ylywnWlxczLLK5HVgNptZrpscf9966y3s3bsXuVwOgUAALpcLPp8PXV1d7GFRXFyMjY0NBINB6HQ6WCwW9Pf3s8cD+YZ4vV587nOfu+nannzySXbuVqvVSCQSWFhYgN1uZ4140moPBAJoampis7JIJAKlUgm73Q6LxYJ3330X4XAYdrsd8XicHXI1Gg1CoRDq6urgdDrZd4Q8nL72ta/ddG3/8i//AolEwr+bWCzG2bNn4XQ6UVJSgq6uLjZ+XFlZ4T1cVVWFS5cuQSKRoLa2FqWlpWwWKpfLodfrkclk2JhUpVKhvb0dIyMjvE9Iunar7+GZZ55BKpXi/U5y1mVlZfB4PHC73WyiRqaTFosFPp+P/w36Nufm5lBUVITBwUF2t6c1kLloMBjk70etVqOyshIHDhy46dpeeOEF9j4gE950Og2n0wm9Xo/y8nL2FiD/mcrKSszMzMDj8UAqlbLXSCQSgdlsxubmJpvSKZVKDA0NoaysDDabDZcuXeJvtKioCOl0GnfcccdN1/byyy+zjxV5qZBsNvlxyWQyljEnr4JgMMjy6JFIBH6/H3v27MH09DQqKipYGt/tdsPtdrPnkkAgQElJCd+BGxsb+PKXv3zTtf3N3/wNmzWWlpZCLpdjcXERuVwOQqGQTVJJLpfO9IqKCgSDQUilUqhUKqRSKTaIlEgkbAq6uLiImpoa6HQ6zM7Owul0shy+xWJBOp3GV7/61S2fm8fjgclk4mdAZrpWqxU6nY6doqVSKUQiEQwGA8bGxqDRaKBWq6FUKlFYWIje3l643W7U1dVBJpOxFQHdv+TNQfctyVM//PDDN13bd77zHSiVSpYAFwqFbAa9vr6Orq4uNpTzer2oqanB5uYm1tbWIBKJ2NeK/FmuXr0KtVqNkpISdsAmJ/ZcLsdmtuTRtbq6iieffPKma/unf/onNkgtKipCYWEhAoEAgsEgYrEY1Gr1DUaZi4uLbDTr8XjYUyudTsNqtWJxcZFln0kSXKFQwOPx8O++vr7OPm2JRAL/8A//cNO1Pf3001hdXUVRURGqqqpQUlKC1dVVTE5OorKykg2INzY22OuGPKsGBgaQTCbZn4ueYzKZ5PuXDE1VKhVaWlrYt+X6v/OLX/ziTdd2fXzkQoNszJeXlxEOh6HT6ZBIJNDW1oZgMMgHWTQahVgsRmlpKVwuFxYXF1FVVYWNjQ309fVBpVLBYDDAYDBgenoaer0eVqsVZWVlCAQC8Hg8GB8fh0wm4wPH7Xbn1dWOxWLsZkiawplMBo2NjfB4PJwIUDJitVrh9/vZ/TMcDnNhQZ4DiUQCFouFjXdsNhvW1tZw7do1NnLa3Nz8UBMwSuoKCgqwvr7OFws5q7a2trKOfC6XQ3V1NS5evIiSkhKIxWJEIhF2YPZ6vVCr1ez0q9Vq0djYCL/fj2w2i6tXr8JsNnNyTkXQVuF2u/kZJ5NJds/V6/XsHksmMuSe6/F44PP5EAwG2fPBbDazAVEymWQDH9K8J8dmiUTCFwBpb28VqVSK/ScEAgGbB9IzcDqdfAhRESKXyxGNRtkgjRIcv9/PJoj0EZEBXSQSYX10ulTIRC/ffiM/BDKvItfRWCyGlZUV6PV6Th5GR0ehUqkwOTmJQCDAv/vy8jJyuRwaGhowPDzMBz8Zw01MTECtVrM5Y2FhIUQiEbsb3yw2NjYgFouxubnJhknkDUKH7Pz8PHK5HGpra2G321FUVIRr167BYrEgHA5jdnYWIpEIJpOJDTaz2SwnMDU1NfD5fGyiSPttdXWVPU7yPTudTneDb0QkEoHJZGIH51wuB7Vazbrv+/btw9DQEDsjk0lYOp2GXC7H3NwcJ+zpdBp2u50NxXK5HLRaLXuU5HORpj1PXjmBQACLi4sQCoVYXFzkokcmk0Gn0yEQCECtViOTycBgMLCBYUFBAVKpFBeNm5ubbNSVy+XYdyGbzaKgoIATTaFQuOXa6M/lcjn+d8LhMJRKJRf1kUgE2WwWKpUK6+vr/G1fbz4aDocxPz/PTQxK5sloKhQKsQOvSqWCUCjky32r8Pl8XGBQQiWXyyGTyTA7O8v7uaCgAAqFAl6vlwuzkpIS5HI5zMzMsDmi3W7H0tISVCoVNBoNUqkUEokEdDodpqensb6+DqVSyZd3Pl+ZTCYDkUjEhm7kmms2m+F2uzE1NYVMJsP+EolEAmVlZchms9zMWFhYgN/v5/NkY2ODneypYbG2toa+vj643W72JEkkEnl9W8LhMBf3sViMDVCpKKNkjb4JnU7HuvhkREYu27FYjI0wS0tLUVpayu+WGhlVVVX8ntxuN2ZnZ7csNDY2NliT//o9ZjabbzAoJG+T3t5e/tnV1VUIhUIIhUJ2kK6trcXU1BSCwSAXT7W1tVhbW4PL5eJ3SOZ4+fT56e6h85WMahUKBZ8/9A16vV42iaUiidzRNzc3kcvl0NjYyM7vEokEFosFSqUSMzMzOHz4MNbX12/4u/O5vYdCIRQXF3OjlZ4dNRiEQiG/L0pyKXdSKBScJ2QyGc6rVldXkUgk2FMpEomgtLQUoVAIIpEIWq0WAoGA/RDy7TetVsuGiRsbG9jc3ERxcTGSySQ7cC8vL99wZigUCojFYr5PGhsb+S4gQ2H6+6ixR3c8Jd/JZDJvLkL/n1gsZtPl68+wjY0NeDwexGIxyGQyfl50L5E/jlarZX8N+o6oeUAmwVarFWtraygpKblhv24V5DVC5yd5WWm1Wrjdbs4pyCtNKpXC6/XyXU4FORnUUn5CHhtU+FMTOZ1OI5fLscFzPoNI8qXb3Nzk5mQwGER1dTXcbjcUCgWfx9Q4o2elVCqRy+UQDAY51+zu7sbExASy2SwsFguqq6sxPT2NSCSCxcVFboCLRCIEAoG87/T6+MiFBk0p6IFSF3x4eBiFhYVciSWTSU6aa2tr8fbbbwMAJwWVlZWIRCIYGBhAPB5nZ1jqxmUyGSgUCsjlcnYrzGQyeV1zya5eKBRyZUsd3cXFRTYto+5kQ0MD6uvrIZFI+IChhDGZTGJ5efmG35EMhmhzJZNJdqeOx+N53V+vN5gTCoXsuEsXBHV1fT4ffD4fEokEH8703ChZoIQpGo3yv0tGNwKBAIWFhUin0ze4tecrgugSo8OJXLF9Ph93PKhTLRQKudgrKSnBlStXUFhYiNra2htcqCmhpe4cuUZTN8Tn82FpaQm5XC7vB0SVNbnSUsdgbGwMRqMRGo2Gk0c6tLVaLdLpNGKx2A2OvyKRiAtF+qgLCwuxtrYGrVbL5k6hUIgTU/qzNwuxWAyfzweZTAapVAqBQMDO7XQok8MqGTPp9Xqk02k25aKpFCUY5LBKFxF1ue12OxsS+nw+dl3dKuhSlUgkbIZHXTK3283JSSwWQygUQkdHB0pLS7G8vIzx8XHEYjEYDAb+PWZnZ2G1WlFfX8+FHn0rZIRJCfn6+jqam5u3XBvwgXEeJdzkak/vViaTwWKx8PexurqKWCzGxlIzMzN8yGUyGU5SqbClzn02m4VGo7nBbZccm/MVkNTFp793c3MTMpmMje7oUqAkMp1OQ61W85lz/TrI8bywsJCNPalI0ul0bKhI7yKdTsNgMOR9r3QBUTGUyWQ4MacGRjKZxNLSEmpqarBt2zYufKVSKUwmExtOxeNxFBUVsaEcTRiz2SysVitUKhWWl5cRi8VQWlr6oY7qlDwWFhaipKQEZWVlbKQlFou5S0dJCPBBMuh2u/kyJaND4IMGjUajYTPJixcvwmq1QqvVsgmf3++HQqHIa0oKfGDUR3uN7he/3w8A7IBLrr+RSITdkz0eDzu7S6VSVFdXw+VycZEjkUgAgA0Bq6qq+D3Tubq+vr7lutbX19lgjYodoVCI5eVlTiCkUik3Tah5YbVaMTMzg1wux9OtdDrNhrK0l9PpNJaXl5FMJrFt2zYoFAokEgk4nU44nU6UlZVtubaCggJ2iE+n04wY8Hg87O6dTCahUql4Skr7nPIAcoInozSBQACDwYBEIsEGuWQEV1JSwgUR/b1bBZnD0URAKBQikUhgaWkJ1dXVAMB3MyVzJpOJnxGtiZpf7777LhQKBTKZDL9XakzKZDI+z6npRwn5zYK68lRoy2QyAIDH4+H9UFRUhFwuB7/fD6VSyTmVUqlEMBhEMBiEXC7nc9DpdCKVSkGr1UIkEmF2dhZra2swGo2IxWLsCJ5IJPIaz+VyOTbYo6I/lUohFArxXUG/aygUgt1uh1QqhVKpZNdrmvwTcoWmxSqVivMtuVwOr9fL31Y8Hmf0w1ZBjU6aJBcWFiKXy8Hr9cJms/EUL51OIx6P8zRPIBBgeXmZz2qaMFCRdv33sLi4CJVKhYKCAqhUKkZwECIh33OjZ0SFBACsrKxApVJxMh+Pxxm1YTAYIBAIMDo6inQ6zYgHqVSKa9euobS0lAsJ4IOmr9/vR0NDA0+pqKmV751eP/GkBqZer+emrs1mg9/vZ5NFmrZNT08z8oEmGblcDhMTE4hEItDr9Xz3DQ4Oora2lgsuapzly8n/z/jIHA2BQMAHDzkXSyQSdpJUqVQwm83QaDRIJBJYXl5GY2MjdDodO0xbLBb09PQAAHcxRCIRwuEwpqen4Xa7kUql0NDQAJvNxg+Pxp9bRWFhIR8+RUVFMBgMEAqF/ODlcjnMZjNKS0sZumQymeBwODA9PQ2v1wuLxYKamhpEo1FOXjKZDNbX1xEMBjE/P49UKgW73Q6RSIRQKMQHab4PSCwWs+spjZNzuRxSqRR3XHK5HEQiEWKxGJaXl9HT08MVOMFGCgoKYDQaUVpayokZ/XlyR6YLl5JUgUCQ91AkK3mBQACpVAqFQgGj0YhEIsGbj4otSgCuH0dSp219fR3RaBSpVIq76XQgra+vo6CgACaTCcXFxQxRCIVCeV2kpVIpJz4FBQVcRAIfFGxmsxkGgwF6vR46nY5H5tQtiMfjDEWTyWR8SRsMBnZoDYVCCAQCyGQy/OxSqRSMRmPeS1gul/PkQCgUYnNzEzqdjl2RbTYbd1vEYjH0ej0cDgcaGhr44rke5kNddpPJBLVajUAgwF2W8vJy2O12vhSSyeSHJn10gBQVFXGHVKFQYGFhgbsccrkca2trCIVC0Gg00Ol0GBsbg8/nQ0VFBdrb2yGVSuHxeFBRUYHKykp2GvZ4PNylUyqVWF9fh9frhVgszuvSTOujJF0sFvNBnUqleI8rlUpEo1EsLS1BIpFgbW0NKpUKoVAIoVCIJ4oqlYqTbq1Wy6NogpdRx2pjYwMulwvLy8u8h7bac5FIBKFQiAs0Ss7IMZ2+g0wmA6FQyIVPNBrlg5gKSDpD5HI5J8h04dBkjy47givke27r6+vcQKB/lzp91NGlZHB9fR3V1dXc6aIEp6urC2q1GqFQiJN5s9kMtVrNibhMJuNuIE2o8yV+lOhubm5CIpFAo9GgtLQUiUSCoUYEeaRvpKysDEajkYvXUCgEuVwOkUjE37tUKuXfk5o7DQ0NqKio4IReIpGgpKRky7URxIq+O/ozlDiUlZWhvLycJ5TJZBIGg4HhFplMBnq9Hg0NDWhqaoJAIEAikWCX+OubDo2NjaiqqoJUKuWzJ1/kcjksLy8zBEgmkyGbzWJtbY3hhATFozNWq9XCYrGw269Wq+WEeWVlhZMvgveFQiHE43FUVVUhHo9jYWEBCwsL8Pl8eQs0mhYUFhZic3OTvztyQqfJjtPphM/nQzweR01NDd8hMpmMO8kKhQIDAwPsQF9eXg6j0QiPxwOxWAyFQgG1Ws3fx8bGBoxGY961EVzr/3xuIpEIGo2G7yyJRIJEIgGVSsWdZiog9Xo9crkcXn31VcTjceRyOSSTSYZVUfFBHflIJIJIJMJ76WZBkDUqZEpKShhiQi7blZWV7Lotk8m40SkSifgbl0gk/P6pONZoNOwm//7776O6uprRElRI52tWEAyc/j2CmFGnnc69zc1NPgepKKIimHKdWCzG3fBwOAypVMrvjCDO2WwW0WiUIdz5zjfab/Q7l5aW3gDDSiQS3MiJx+Pwer0oKSlhpAcAngZYrVZutmo0GlitVp4YULOIzp7V1VWk0+m8k0f65pPJJADwe3K5XHy3V1dXQ6fTcbFaUVEBnU4Hv9/PUzWDwQCxWMwQuevvBNqTGo2Gm4V0HioUirxro4ZsQUHBDc1wgniKRCJ4vV52HG9ra0M0GsXi4iJDK8vKyiAUCjE/P89rzWazGBgYwMzMDOeJOp2OJ8nXN58/LD5yoUHJ3cbGBpaXl7G2tgapVIq7774bgUAAHR0dPJ0oLS3l7lZxcTEOHz4Mo9HII8ClpSXs2bMHiUQCc3NzuHbtGpxOJ7q7uxEIBCCRSDA7O8vYxEwmw52Km8Xi4iInetSJogoxHo/DarVy9661tZU3w+joKCorK2G32yGTyaBUKrG2toba2lpOWgsLC2EymfDZz36WuxXRaBS1tbXo6enhEe1WQZf1ysoKw3QKCgrQ2trKeLqFhQXuyNOLt1qtKCkpgU6nQ01NDfbv34/x8XHeAARLEggE+MIXvgCpVIqioiIuXAoLC5HNZvMmfgUFBWhpabkByuFyueBwOHhEarPZeBS/sLAApVKJixcvoqenh8dzy8vLWFpaQmVlJWNwKZndvXs3/H4/0uk0JiYmIJfLUVVVBaFQCK1Wu+XaRCIRw6U2NzdRUFCA8fFxVFRUwOVyYX5+HteuXcPKygrkcjkKCwtRWlqKubk5Hn9XV1ejtbWVk/brpzeBQABlZWVYXl6GTqdjOIXFYsHS0hI8Hs+Wa/P5fNi5cyfUajUUCgUMBgNisRjMZjOmpqa48NFqtThw4AAKCwtRVlaGlZUVNDc3o7m5GYWFhXA6nZidnYVMJkMgEEBlZSX0ej1PqKh4OnfuHEKhEIxGIyQSSd5DkaZki4uLWFpagkAggFqt5g6a2WyG1WrlwsfpdCKdTmNqaoonUCMjI3C5XJidncWdd96JhYUFDA0NYXZ2licPMzMzUKlUGBoagkgkQnNzM0QiEU6dOrXl2oAPEmbiJxgMBtTV1TGcYnR0FNeuXcPMzAwkEgn27NmD4uJi7Nu3DwqFAgcOHIBer4ff70dlZSWGh4dhNpsZ519YWIh4PA6bzYarV69iZGQEU1NTsNls6O7uZhz5VkFFNHWAIpEIBgcHmWMUCoXgdrsBABUVFSgoKEBFRQXW1tYgEAh4KkDfwe233878jHQ6zckNwYZWVlagUChQWVmJwsJChpJtFcXFxchmswiHw3zBb9u2Daurq3C5XFhYWEAmk4HD4cD8/Dw3I/bv34+CggJcuHABBoMBbW1tsFgsjAOmzlpdXR3zAubm5tDa2or9+/djbGwsb3FLv0M8Hsfy8jKmpqbQ29uLY8eOQSAQoLy8nGE0d911F/x+P9ra2hgzXFlZCZlMBolEgkAggPLyciQSCWQyGd6HO3fu5BE/7bmKigpu9mwVuVwOBw4c4IYWXbB1dXXMZRMKhdBoNDCZTDAYDDAajXC5XHjkkUfQ1dXFkCmPx4M9e/bA6XRiYWEB4XCYC51oNMrwKZlMBofDgWQyifr6+i3XJhKJGMpD52E8HkdtbS2EQiFUKhVKSkpQVVWFtrY2bGxsoKioCJcvX4ZUKkUmk8HY2BiGhoYwNjaGnTt3oqSkhOGQyWQSe/bsQWdnJ7xeL+8J4kDmK9Co4UJFTiAQgNPphMViQTabZZ4ANZsWFhZ46kc8SQCMoy8oKEB/fz/DkgFg3759mJqawvT0NMLhMMrLy9Hc3AyZTJa3w5zJZHiqks1mGSbZ2tqKwsJCOBwO5lSYTCbupguFQuzfvx/V1dXI5XJQKBQYHBzEww8/zJO7qqoqmEwmPPbYY9izZw+0Wi03/AgCmK+AXFhYYH7BwMAAJiYm4PF4sHfvXszMzKChoQHAB5Ndq9UKr9cLjUaD2dlZuFwuAIDRaERVVRWuXLmC/fv3o62tDS0tLbBYLJBIJGhqakJjYyPDf202G6xWKwDk7TITh4wg7sAHMLSenh44HA4olUrEYjFuUL7zzjuQy+VYXl5GWVkZmpqaUFxcjPn5eeaZ5XI5LiwTiQTuuusuuN1u2O125jp1d3ejoKAg7ySeoHTRaBTT09MYGxtDX18fmpub0dvbC4PBgI2NDT7fo9EoT6w7OjpQVFQEl8sFo9GIU6dOYd++fZiYmMDm5iZPNtra2rC6usoTTYvFws2D1tbWLdeWTCZRWVnJaI1kMgm3243du3czRG5tbQ0bGxuwWCwMOTx//jzq6upgtVohFApRV1eH5eVl3H///dycrKioQGlpKdRqNaqrq3my2djYiK6uLiSTSUxMTGy5NuI7U0EsEAhw6dIlnk4QCkmlUsFoNMLhcGD79u3Q6/WorKxEcXExiouLsW3bNszOzqK5ufkGThMAfPazn0V/fz+Wl5cZfkZIg//rhQaNTA4ePIhjx47B4XAgFothZGQE9913H28+Gsvu3bsX0WgUra2t+Nu//Vv09fWhpaUFoVAIhw4dwt/+7d/CZDJBJBLBaDTiyJEjSCaT+NM//VOUlJSgpaUFa2trmJubw4EDB3DlypUt12axWCCVSrFjxw5YLBa4XC6Ul5fj2rVrqKurQyqVwsLCAq5cuYKZmRncdtttOH78OJqamvgFEOa6qqoKv/jFL7Bt2zaGg1VUVCCTyWDv3r3o7+9HLpfD+Pg4pqen0d3dnbf7QlMbwlRevXoV5eXl+N3vfoeOjg7Gy1dUVKCqqgpdXV2IxWI88tu7dy+OHTsGkUiEAwcO4Ac/+AEOHDiATCbDhOKhoSHcdttt/PObm5tYXV1Fe3s7VldXt1xbcXExLl68yJCpdDqNSCSCq1evoqamBhcvXsT8/DwAoKysDF/72teQTqfhcDgwMzODUCgEpVLJ8I1QKAStVov29nbY7Xa43W78/ve/xxe/+EUEg0FkMhn09fVhcHAQNTU1GBwc3HJtcrmceRNKpRJGoxH33Xcfnn/+ebS1tXGxRePo22+/HVNTU6ivr8epU6d4D0SjUdTU1GBychIPPPAAUqkUY3UpoRWJRCgvL4dUKkU2m2Xc/VZhs9nw5ptvQiqVwm63o6SkBJ2dnUgmk/jKV77CBdDw8DCefvppPPjgg7hy5QqqqqqwuLiIzc1NmM1mTExMcOLc3d0NANDpdLjjjjuwsLCARx99FHNzczh8+DB3NT7zmc/kTaw0Gg3i8Ti2bduGhoYG+P1+nDlzBj/+8Y/R0tLC0Cc6mBOJBH7wgx+go6MDR44cwaOPPopHHnkElZWVqKurw4svvshwOoPBgE9+8pOQy+X45je/iYqKCpSVlTE0ac+ePR/axaWOW2FhIVZWVnDhwgWoVCpUVVXhM5/5DBfWlZWVEAqFeOSRRyAUCjE8PIyBgQE0NDTgjjvuwMzMDD73uc/hRz/6Ee69917s2bMHdXV1UCqVOHv2LKqqqnDy5Ekm2BuNRjzyyCOYnJzMu+fC4TAaGxshlUoxMTGBhoYG3ld1dXXQ6/Xwer0YHR3F3r17MTc3h6mpKUQiETgcDjQ1NTFc7tKlSzAYDLz3q6qqsLm5ibKyMi4OiPOWrxMJgGFWTU1NqK+vR1FREex2OxYWFnD06FFs374dbW1tDI968MEHcenSJaytreEHP/gBPB4PDhw4gJmZGQwMDODXv/41zGYzxGIxT8nS6TT27NmDhYUFVFZWIpFIwOv1oqenh6FGNwuxWIz+/n7odDpUVlbCaDSio6MDZ86cweHDh3lSEAqF4HK58PWvf507dufPn0coFIJer8elS5dwzz334MqVKygvL4dMJkN5eTna29vx7LPP4oknnkBBQQH27dsHqVSK+fl5yOVynDlzZsu1FRUVYWBgAK2trdi3bx8qKytRW1uLubk5fOxjH4NarUZxcTEKCgrg9/vxhS98AYuLi6ivr8cvfvELTExMoLa2lov/F198EVarFfv27YPD4cDGxgYymQxqa2uxtLSE7du3Y3NzE729vQiHwxgdHd1ybclkEgqFArt27WLC/dzcHH7+85/DZrNxA6u4uBhlZWWora3F2NgYuru7sb6+DrVazYmow+HA+fPnGW5CePkzZ87g2LFjAD5oLBGmvampKe8ZV15ejsuXL8NisaC8vJz5JATjfemllzA4OIjV1VVcvHgRR44cQX19PRobGyEQCGC329HW1oaxsTFYLBbs2rUL9fX1zKdsa2vDyZMn0draiqKiIjgcDoTDYfh8Phw9ejRvwtzQ0IBoNMowG4lEgp6eHggEAmzfvh3Dw8MYHBzE1NQUBgcHsXPnTiwvL0Oj0WBgYACxWAwtLS2cvJ47dw5FRUUQCAQMeX3jjTdQV1eHyclJhtSJxWIcOHAgbxOqpaUFAoEAR48eRVtbGwQCAdra2vD000+jp6cHJ0+e5MZXNpvF5z73OZ5SBYNBNDY24uMf/zj0ej0+/elP43/+z/+JsrIyvPnmm7h48SK6u7t5CvbCCy8gGAwyHOrDOsxSqRTBYBBGoxFqtZq5J0tLS6ioqMD8/DxisRjKysrQ3d2NJ554gpuqBNOrq6uDRCLBPffcA6fTiXvvvZdRE5ubm3jzzTdxzz33QKlUoqysDE6nE2NjYyguLs7bWKSpeEVFBQwGA3Ns33vvPXR2dmJychJKpRIGgwEulwuPPvoohEIhSktLGZZnt9sBfABhLCgo4Dt5dHQUs7OzmJmZQVdXFz8Dv9+P0dFROJ1OvP7661uuTSaTwePxoKGhAdu2bUNlZSXa29tx6dIlNDQ0QKlUMpH+/PnzePDBBxlqeOnSJYjFYuzduxcejwdqtRoPP/wwiywsLS3B7/dDp9NhbW0N586dw+rqKq5evYqrV68yB3araGpqwsLCAkMInU4nQqEQ3n77bRw7dgw+nw9GoxHbt29HQ0MDdu7ciTNnziCdTnNzuKenB/Pz8/jMZz6D73//+7Db7TCbzWhubsb27dvx1FNPYc+ePcwVJr7boUOH8nL3ro+PzNEgIt3IyAg2NjaQzWaRyWTQ2dmJ1157jbG+VLV6PB60tLRgbm4O3/72txEMBrG0tMRqUJWVlcydSCQSeP/99/Hmm2/iL//yL7G+vs6VJ+HH8z1sGnNGIhGGaFGXLZPJ8OFSUlKC0tJSnDt3Dm63mw9Sv9/PKlAlJSV44IEHcP78eR4bUge9paWF1bb0ej2kUikuXbqE4uLiLddGHehgMIjV1VXE43EIBAJ0dXVhYmICO3fu5CkR4YQnJiaQTqehUqlw4sQJPkDD4TDa2trw5JNPoqOjAwaDAePj47Db7QgEAqipqcHAwADjzOfm5vJ2EbxeL6qrqxlHXFZWhnA4jPr6ely9ehVtbW1YWlriseGrr76KhoYGDA4O8uTEbDZj165d+NWvfoXbb78dsVgMk5OTTKhVq9V47bXXMDs7i6qqKnR2diIcDmNwcDBvl5QI1aRMs7CwgMnJSdxzzz1YWFiA1WrlcXxPTw+eeuopRCIR3H777airq4NUKkUoFOKpxfLyMo4fPw6TyQSBQIDV1VUcPnwYq6urnOzRaJWgbVvF0tIS6urqkMvl4PP5kEwmMTc3h4ceegivv/467HY7Ll++jEQigdbWVjz//POYn5+HSqXCxMQEqqqqUFpain379uHHP/4xmpubMT4+zuPOkpIS7Ny5E5cuXcKRI0dw9epV6PV6RKNR/OhHP+IR7s1idXUVPT09zJnQ6/V48MEHsbi4yGpnx48fZ0JbWVkZHA4HpFIpxsbGEAwGYTab0dHRgd7eXtTV1eHkyZPo6OiAz+fD2NgYlpaWcOHCBbz44ovo6Ohg1Z5z587lHfMCH0w03G4343CJrBqPxzE8PIxYLMZwNoItLS4uMn7b6XRCJpOxqtLHP/5x/OAHP8COHTsYiiIWixEIBHDnnXfC4/EwROSdd97hc+pmMT09DZvNxhwfghPQNC+dTsPv9/PaTp48iYqKClRXV8NgMCAQCGBsbAx6vR7btm3jsyaTycDn82F4eBjZbBbV1dXcJdzc3ITH42GYx1aRTqdRXl4On8/HXaje3l40NTVhYmKCFXFisRikUil++tOf4t5774XT6cT999/P0KPi4mL4/X7cc889KC8vRzKZZPjl4uIiLBYL8740Gg3j1fNBzoLBICfGY2Nj8Pv92NzcRH19Pc6dO4fW1lZMTU2xkMP8/DySySRMJhN3ku12OzweD06fPg2v18vCDkTW3rNnD1544QVoNBouhIxGI/r7+/MWaWNjY9izZw8mJydRUlICi8WCwcFB7Nu3j59fMBhEMplEU1MTYrEYLl68iGw2C4fDwXhzmroRlOmtt95irlVBQQEnU0SIpoQoH3SVcPIXLlzgb7GtrQ1utxuTk5NoaGjA0NAQIpEIjEYjBAIB9u/fj1dffZWJnH19fRCLxRgdHUVZWRmLLXi9XvT19WFkZIQJ5A6HA0ajEdlsFhMTE7hy5Qruuuuum65tamoKLS0trPwok8mY40YcIxKDIIGLq1evwuv1Ml5eKBSip6eHp/RDQ0Nobm5GKBTC1NQUN5sMBgPm5+dRX1/PE/O6urotn9vi4iK2bduGubk5zM7O8nRHrVbzRJXg1jqdDuPj49i7dy8GBgYYnh0IBHDq1Clsbm5i+/btDNGJRqOYnJxkBAY1HgiOTFyprWJtbQ1lZWWYmZlBNBpFNpuFx+Nh9INarcbMzAzC4TBEIhFDbzOZDO644w6kUimeWOn1ehw5cgSvvPIKioqKEA6H8atf/QoHDx7EqVOn8OlPfxovvvgiP1+3241Tp05x0/H/DCI8WywW5gL5/X6Ul5fj0qVL/BzcbjdGR0fhcDgwODjIEyk6dxsaGrCysoI777wT/f39KCoq4om0yWTCwsIC1Go1otEoKisrIRAIsLKykndKRbw6Ej2Ry+W4evUq88/a29sxNTXFedzQ0BAmJycZQkjcL4vFwvxVsVgMr9d7g1Knx+PB/Pw8o0FI2Svffe92u+FwODAyMsKqU4ODg2hoaEAoFMKZM2eg0Wig1+vR09OD3//+92hqakIgEMDBgwchFos5B9XpdHjqqad4MkDJe3d3NyuFknAGcXSGhoa2XNvU1BTMZjPzlDY2Njh/IFWpS5cuoaCgADU1NXw2p9NpdHR0IJFI4Ny5cygrK4NYLMbDDz+M5557Dq2trbDb7SgoKMAXv/hFzM/PY//+/ZiamkJVVRVUKhWrXX2U+MgTjc3NTfz2t7/F8PAwK4BYrVZcuXIFer0eO3bsgEQiwerqKtbW1rC6usr//dlnn8X8/DwaGxv5w6qoqEAul2NFnXg8zt0DSlC9Xi9mZ2chFovzqsWoVCqMj48zLj2ZTCIYDDLUw2azwWQyIRgM4syZM1z90qULAHq9nvHDvb29TPokJQGS5CstLYXD4cDi4iIGBwdhsVjyJi8SiQTz8/PQ6XQ4evQo7rjjDggEAp4IDA0N4erVq7hy5QqGh4dhNBpx5513IhwOY3JykjeG0+lEJBLB0NAQd/sXFhZ4o9PPRaNRhoxpNJq8HQ7qotCHHYlEsLq6in/6p3+C1WpFTU0N2trakMvlcPz4cUxMTGB+fp7VdxQKBbZv34777rsPu3fvxvLyMiuIUTI/OTkJn8+HI0eOQCqVMleFJCPzBXVpioqKoNVqoVarEYvFkEqlcO3aNVbnoG6jw+FAIBBgiAt1m4uLi1FeXo5IJMI8klgshu9///tM1CJsNanutLW1bbmuVCqF9fV1rK2tYX5+nqGEX/jCF2C1WtHY2IidO3eivLwcfr8fQqEQ9957L2MzCWdKcnZvvfUWHA4HvvCFL+Dhhx+GWq3GwsICUqkUy4BubGww/O7gwYNbrm19fR0nT57k5MRgMMDtduP555+HXC7Hgw8+iIMHD8JkMkEoFKKkpAT79+9HJBLBmTNn4HK5GCaysbHBEw2ZTMb44fn5eWxsbODjH/84wuEwxsbGsLy8zPjXfEGdsUwmg2QyiUwmg4WFBfznf/4nwuEw7rrrLu7SPPPMMzhz5gzsdjtsNhsCgQCUSiXa29uhVqtRX1+Pn/3sZ+ju7kZnZydaW1vR1NSEXC7H0JDi4mIMDQ3h1KlTEAqFOHr06JZrEwgEcLlcKCgogMPhQE9PDyorK3Hx4kUmIZKUocfjQW1tLXfjqUA1m82oq6uDQqHAO++8g4qKCpjNZggEAiZDO51OtLa2MoySeCtbKQDRc7t06RLzGYLBIKxWK95++22kUilUV1ejs7MTOp0Oy8vLzB+QSCR48cUXcfr0aajVarS2tjJ3iL6t5uZm7N69m+EvTU1NzGFaWlpCIBDIC12l/Ukdz7a2NlitVrz44otwu918LlOC+9vf/pYTgEAggNnZWaysrHDx0NTUxGcvyUW+/vrr2NzchNVqRVtbG/Nz9u3bh507d265NofDwapRxFsrKSnBq6++yhBTSpJeeukl/PKXv0RHRwei0SheffVVXLt2DVVVVbjtttvQ3NwMpVKJrq4u3H333airq8Pi4iKuXr0Km82GHTt2IBqNYnx8HE6nE1qtNu8kSKfT4cqVK6yw5PF4sLS0hBdffBFNTU0wGAzYu3cvmpub4XQ6WS7VZDJhbm4OwAcddEr6i4qKmNRJIhrbt29n2KVYLIbb7Wb56nznr0qlgsfjwfLyMlwuFxN6A4EAFzUikQgDAwN44403GIvvdDpx+fJljI6OsoLU8ePHcenSJVZ3GhgY4ORUr9ejs7MTFosF8/PzmJiYgNFozKteF4/Hcf78eea5abVamM1mbja1tbVh586dLF2uVqtx8eJF3sfbt2/Hvn37sG/fPtjtdszMzKCsrAx2ux12ux1GoxHFxcUIhULo7u7mRhs1BT6sIXDt2jVWECNu2auvvsrnFwDmIQwPD+PQoUPY2NjAO++8g7Nnz2JiYgKXL1/G/Pw83n//fRgMBlRUVDDPYGFhAT09Pbh06RJSqRRWVlaYv1RbW7vl2khMhPgoRqOR4e2UNGazWfh8Prjdbrz00kv8zZFATElJCXw+HwKBACYmJljxr7S0FEajEZcvX8bp06cxOTmJoqIi9Pb24sSJE1AoFBgeHt5ybWq1moV70uk0F2lnz57F4OAgT4WDwSDfN6SiRtPdI0eO4IEHHoBWq8VTTz3FMCutVgun04kXX3wR4XCYzxeC3mm12rz3fWFhIQtjhMNhrK+vo66uDm+//Tay2SwaGhqgUCgQDodZrZT2wpkzZ3D16lXmhGWzWfzHf/wHCgoKsHv3bnR1dUEikcDpdEIulzOsNZ1Os7paPlgXNdXkcjna29uxZ88eZLNZvPnmm7h06RKMRiN27doFg8GACxcu4PTp0/D5fGhsbMTq6io3IGdmZiAUCrnBTM36dDqNH/3oRygqKkIikWC5/HfffRfpdDrv2q6Pj1xo0HhYp9MxyTGbzeLAgQNMCFYqlWhsbMTu3bu5Y7q6usoY5Hg8ju3bt0MsFqOnp4dVWUgN5vOf/zxef/31GxSMiouLP5QMHo/HmRhEB2w2m0V9fT1jXUnZx2azIZFIYM+ePQgEAvB6vdylJNjBnj17WO6R1JIaGhqYqES+GAR7yXfwBINBlvd1uVwIBoN8CW1ubmL//v3YtWsXmpubUVVVBb/fj6WlJSaO1dbWorOzE1VVVQyjocRLo9Gw/ByRgYqKirj4cbvdH6r0QKRKIuNKpVJ0d3djc3OT5VhtNht27tzJ7zcajaK7uxsKhQKXLl1i2bPbbrsNqVQK0WiUCeUtLS1wuVyQSCRM+CPOQL4uKam90H8nohNBYerr62Gz2TgJjkajkMvl7BNAeEdSIqqpqUFRURGsViuLBezfvx8ej4dxxBKJBEajkRPgrYKUG4g/UlZWhqqqKmi1WiwvL2NiYgJra2sspTw9PQ2TyQSVSoWGhgaoVCpWGJPL5dxtIzlWgUCA2267DefPn8epU6cwPj6O8vJydHd3o6ioKG8Xgbo6xJNRq9WYnp7G7t27mf9kNBpRW1uLuro6PmwnJibQ2NjIF1FZWRl8Ph++9KUvIZvNYnV1FbOzs8xPiUQiaG5uhkKhgNlsRnV1NYqKivImpAAYM0sXCZG/aXoRj8d5etrT0wOPxwOhUIja2lrs3LmTJwfZbBZTU1PYs2cPpqamMDU1xSIPX/ziF5msSOo8arWa8apbBfG0SLGGRC26u7sRjUZhMpn4/ysuLmZMNXUvifwqk8mwsLDAME6lUgmr1QqFQgGr1Qqj0Yi6ujrmHmk0GpYX3Cqom058DyKdP/DAA4wTJt8GglaSBOGBAwdQW1vLEzPyfrl8+TKT0aenp9Ha2sqdy2AwyOIaRBzdKkgWmCZtCoUC6+vrqK+v54SVuDn19fUIBAIwGo2w2Wzo6uqCRqPhEXw2m8Xu3bsBgJ8VAOzYsQPhcBgqlQparZb3TSgUyju+p6aVXC6H2+3G8PAwotEoysrKmLxJZFCSCrfZbAiHw3zm9ff3817Yvn07+vr6uInV2NiIxx9/HNPT06y2ZzQaYbVamUeyVSSTSeZj0DOenp6G2WxGLBaDz+fD2toaNjc34XA4GJeeSqVQWVnJMsYmkwmhUAjl5eXweDwsDiCTyVBRUYHZ2VmUl5fzHie4EUFNbhYCgYBFL6iTTUIB09PTTLZVKBRobm6Gy+VCbW0tVCoVenp6YDab4XQ6MTMzw2T0RCKBlpYWmM1mjI2NIZvNYmxsDMAH8DCS26YG0lZBngE+nw+Tk5Nwu90Ms9zY2MDa2hp8Ph8UCgXD3srLy7GwsMBr9Hq9KC0t5cmL0+nkb4aIycFgkL13iERNakdbBanHBQIBRKNRKBQKRKNRVFVVsZgEFUYkpEF7ieSJI5EIrFYrd81TqRRaWlrQ09PDfDGCWwcCAUilUmg0GqysrOQt0IgsLhAIWK2quroaUqkUMpkMcrkcGo0GFosFRqMRUqkUGxsb0Gq1TBZeXV2F2+3maS9JUJPARV1dHdbW1pg4brPZWDY933SPim3izxJRvbGxESaTCRUVFfzvUN5gsVgQCARYLGVxcZElxzs6OjA1NcXcJ5FIhIMHDyIWizFkn1SgSHwiXxgMBuRyOcTjcZ5+kFcG+XWR0uHExAR7shGvMpFIoLGxEbFYDDt27GA1NLVaDYPBALPZDIlEgqqqKubJkOqTXC7fcl0CgYCb64TMAcDCTHQvazQaNDY2wuv1wu12IxaLwWKxoKCggD3shoeHce+99/IzJllhas4Rakiv16OiouIGgvyHxUeGThG5ldSE6GE3NDQgGAxytV5eXo6SkhLGMRYXF0On03HlTR0ZuVwOo9HISQcA7Ny5Ey+99BLDbhQKBat7EAnzZkFSqiQtGY1GmQwtFAqxtLSEgoIC1NXVsUKKQCBAVVUVent7WZmDpGxramogFAqRy+WYFEprJcImmRem0+m8PIhsNouysjL4/X7WmqaPk8jHRJymbi8Z5RBXIxqNIhAI8EFNRQopZBE+j1Q9aBRL/95WkUgkWJuf5OP0ej2boC0sLEClUrHyEUkrEuSMvBCuXbsGtVoNq9XKRcX1059Lly5hdXUVBoOBK/UPU9khVSPSr06n09zpItIkFRkFBQXo6OhANptFNptlEjVB/IhoX1lZyRee2+1Ga2srLl26xBcIqeaQEshWIZPJ+FKizqvJZEJjYyM2Njbw7rvvQi6Xw263o7Kykr1gNBoNq6TQ90CwkeXlZQQCAU6GST1mdnYWWq2W1ZmogNoqdDodHzw0michhLW1NQSDQVayoDE/AJaopuSfDmelUonq6mqEw2Huytjtdk7ezGYzS2jSgZsvSGWLVJcoMYtGo6irq2NFMr1ez8XZ4OAgT6RIipQUYmpra1lel4psjUaD5uZm5h9QckqqUlsFQblIkpWmEJTYbm5uspwtqT2RxCbJSysUCvZiKSsrg0wm44uRZHCbmpqgVCpRUlLC38KHnXFSqZQLE1LUIXiFyWSCz+fD6uoqxGIxHA4HNBoNlpaWOOGnCzyXy8FkMmFlZYX13cnAqqurCwAwPz/Pv0cikUBBQQHC4fCWa6OmAPE8kskk+62UlZXx72WxWFjsgJQCm5ubodVqMT09jZGRERYLUalUsNlsLJqwd+9eXL16Faurq3wpk9xvviAuGwCenDscDiZsU6PBaDTyf5aXl6FWqzmBv14H3+l0svCDQqGAVqtFRUUF+vv74XQ6IZFIeGqpVCo/lLOkVCq5IUIyx1arldXzSPKzsbGRpURJLYsaMaSURZwVg8HAKmJGoxHz8/OsJEb3ARX2WwVN8Om7lkql/PNkKEcJHUGISdmRPEpmZmYgEonQ0dHBRVBRURELK+RyOTgcDm6WUSebCLFbhUKh4PuPcgexWIy6ujpWGSsoKGA1tbGxMX6elFcQNKW6upoFY6joJAhPRUUFpqenmfQeDodhNBpZsv1mQYktmQOSYtv6+jqsVitWVlYYqkrSrQMDA6y+RXDFsrIyjI+PM8+VeHJCoRAul4v5aCRao9PpOMncKuheKygowMbGBpLJJJvYUdFC6k5qtRp1dXVIJBLcJATAibndbodSqcTCwgJPHqmor6yshFgsZsW6631htgpquIRCIRZo0Ol0EAgEqKmpYTgtqXrV1dUxnJbeKUn7B4NBNDU1YWZmhqeY5eXlUCgUmJmZ4Z8n2wSS0t0qrs9TCO5NeUZpaSlPuUgMIRwOs3AC8XuJ6zs1NQW9Xg+VSnWDulcmk4HRaGQZdhL+oIbDVkFKYiQlTEqCpaWlKCkpgUgkYki+UqlEW1sbpqen0dnZyQMDam4uLCygo6MDdrudUUSxWIyFLbRaLUudi0Qi+Hy+DxUw4ef2kX7qDw+b5Oiu913wer3YvXs3V0DUkWxubuakfXFxEaurq1hZWcFLL72E6upq9PX1oampiRddW1uLzc1NfOYzn4HJZEJRUREfNkqlEisrK3kftsvlusFpOJfL4cqVK8jlcozbJB322tpaVk6iQ5MgIHNzcxgZGUFHRwfi8Th6e3tZBqy2thZOp5M/ukwmg7KyMkxNTW25NuoKVVdXo7a2FmazGTt27IBAIGA30IWFBXi9XiiVSu4000h/dnYWJ06cwM9//nNOyEwmEz8bo9GIdDrNkybacGQGmG+TUoJBf2ZqaooVB8rLy9lXJBQKsUZ3QUEBd3NNJhP27NnD473l5WWUl5ezSpXJZIJGo8G2bdt4YkBwIOqYbRV0KFFnY3Nzkz1YZDIZ3nnnHUQiEVaUePDBB1nnenR0lJ9TIpGAUqlEIBBgSAsZ9kmlUpSVlSGZTMJut7P0scPhyMsfoWkRqUuRESAJC/T29iISibA77MMPP4yxsTFWg0qlUtBoNOwWvrGxgc985jPc2c1ms+jr68ORI0dYGnVkZARXr15FY2Nj3mSZLiSJRMKOrC0tLVhcXGQvEVIH6uvrQ0NDA8RiMRobG1m/3Wq1Ip1Oo6qqCt/61rewfft22Gw2qFQqmEwmLC8v8zREq9ViYWEBvb293BnMFyqVCtPT0+wPQhK2ABjXSs656XQa3d3deO+99xAIBNDb24tAIIDi4mKemF24cAH3338/iouLmevx0ksv4e677+ZCiA7Snp6evM+ODk+6gEioYmBgAGazGYuLiygpKYHD4WAiZSgUQn19PUQiEUpKSlBRUcFdz8nJSdTU1HAzIpfLYXFxkTuepFVOxm75cMKkjEYXrcvlQigUQn9/P9ra2jhJm5+fx8WLF1FZWYk333wTYrEYfX19iEajzBUjZZ3Ozk42X2tpaeHimAo78pPZ2NjA+Pj4lmsjUzKC4oRCIZ5saTQafi9U0Dc3NyMSiWB6eppVU8rKynDx4kWEw2H84he/QFFRESfr1dXV0Gq16OzsxNjYGEMf/X4/jEYjd+tvFgUFBWw4Rf4NBQUFWF5ehlar5QSGRCI6Ozvx7LPPsnADEY1lMhmMRiN+9rOfobq6Gk1NTTCbzUgmk7h8+TKOHDmC+fl5htc4nU4YDIa8BRoJIhAsYXJyEqWlpcjlcoxtp2dA8E7i2eRyOeaLzM7OYt++fXjrrbfY86ampgZWq5U9dJLJJGQyGctTSySSvPfW9U0KMgolPmVnZye/W+I11tbWclebJmdVVVVwOBzYt28fJ1ITExOIxWLYvn07AOBTn/oU/3tDQ0O4cOECF+RbRTabRW9vL6RSKXduJRIJ+vv70dzczFBpgtW0t7fz90ZmwPX19VhbW0NpaSmampoYfpdMJlFWVsbiDj/60Y+g0Wig+YNxJHEstgpKYKnISaVSsFgsWFtbQ319PQoLC2G322G1WiGRSLBr1y42PnO5XNBoNKivr8f6+jra2tr4LHO5XDh37hwikQjGxsagUqkwNjaG1tZW7pq3tLRsuS7g/8uTSCaW5PQBoK6uDuPj4xgaGsLKygrDMYuKihAIBNg02eFwoLS0FDabjSdi1xt1rqyssKO7x+PBzMwMXC4Xu29vFUqlkqF/KysrmJiYgFarhdfrhUwmQ39/P/x+P2QyGasknTlzhhUQ5XI5uru7odFoMDU1hXg8jra2NpY6p4nStm3b4HQ6GRlwvUrqViGTybCyssKcKjK+ffvtt9HY2MhNx0QigampKVgsFrz77rs4cOAAS93u2rULwAc51+rqKjo7O6FUKuFyuTA+Po5XXnkFJpMJJ06cgE6ng9lshlar5eJ0q5BKpVhbW4NSqeSpODUKm5qa2Bx1cXERo6OjOHTo0A0Fvk6nYyhqJpPBK6+8wopmqVQKuVwOg4ODrDZJQimhP9gTfFijh+IjTzRIWQgA+2mQnKhYLEZ1dTV++tOfIhwO47777sP09DSMRiNee+01lvjz+XwMbxkcHMTg4CBcLhesVitLbdbU1KCm5v9l7z3D4yzPfPHfjGZGo+mjKWqj3nuxiiUbF7mDDYRuWggJCQnJkrKbbcnJSTbZzW4ah5AQAqQQCCGmBBswuHdbsmXJ6m0kzahMU5mqqRr9P7D3feB/WQPnuvaj709Jrth+5n2f93nu8islcDgcEIlEWFhY4ItyrZiamkJlZSW7Nur1esagCgQCtLW1YWJiAjabjbFpkUgEBw8e5O600+lEYWEhJBIJE/Doci0uLsb8/Dwr5AwODqK0tBQKhYIlI9cKl8vFCUw8Hmd4EiWadXV16OjowMDAAKxWK//ud955Bw0NDYyjrKyshEQiwcjICIaGhiAWi2EwGKBUKuF0OpGTk4PV1VVWAqFOWaIuByXvZPJCB4XNZsPu3btRVFSEK1euwOl0IhAIoK+vjz9IIujW19fDZDLhJz/5CYqKihhOMz09jStXrsDlcuHatWsMlSOsb1JSUkLejdVqRVFRER80dEmUlZXhzJkzaGpqgsViwcTEBBtYBQIBpKSkoKGhgTtW+fn5OHnyJCwWC0ve2e12LrCuXbvGkqNkfjU5OZkQDkcEVlLmIYMt6nySMklXVxcTy2tqajA0NITR0VGcOHEC1dXV2LZtG771rW/hK1/5Cv7617+y7rbT6cQjjzyC3t5eLu5IG/vSpUsJk5erV6/ijjvuwNWrVzE6OspmPoWFhRCJRHA6nQwFqaysxMDAAFZWVnD16lVs2LABc3NzePvtt7Fnzx5kZmYiLy8Pf/nLX1jauKGhgfkpGo0GV65cQUpKCpqammA0Ghk7nui9UhFBBoWdnZ246aab8Nprr0Gn08FoNHJiSAVCb28vTwFo8vCb3/wG99xzDwYHB1FXV4fx8XEcOXIEOp0O4+PjmJ2dxfj4OOvsWyyWT5TJbm1tZdMsIglKJBIcOHAAW7duxeTkJF+6JCV67tw5xv5SYj0zM4Pl5WX09fXx9IEmvi+88ALvIyLFUxGTaM8RT4mK9cXFRWzevJklVVNTUyGXy6HX6+HxeBiOQdDRa9euwWKxQKvV4qtf/SreeOMNJtcTjFQul6OtrQ0XLlxAbW0tamtrcfLkyYRdXOp+Dw0N8ZR0fHwc4+PjTLi8dOkSkpOTsWfPHpSUlGB4eBgWiwUWiwV6vR6pqanYsGEDnn32Wfz85z/HG2+8gQsXLrCxoVAoxK9//Wu0t7fzGUUwiUSmeEtLS9i6dSsXDTk5OZxwdHR0oLS0FKdPn8bU1BR7f9TX16Ovrw9zc3MA/q/b84kTJ3D33Xfj+PHjOHfuHPPMiMAuEAgwODjIXJO+vr6EXVxy4iW5SJqiOZ1OdHR0MAldrVajsLAQZrOZC7BYLIb+/n4udCORCG677TacO3cOHR0dH4O2bN++HTKZjNWVNP9tAJpIJtvpdLKqFhXAJEE7NzeHubk5jIyMcIJ3+fJl1NbWore3F0ajkSFxV65cwalTpxAIBBhCSHywjIwMHDlyBEajEQsLCyzzOzY2lvBbIH4STRoINrhlyxbmEU5MTCAlJQXl5eVwOp3YtWsXotEo3njjDbjdbpSUlCAcDuN3v/sdHnjgAbjdbp7yBINBbNmyhfkR77//Pk++aGK6ViwvLyMrK4v5CDqdDh6PB4WFhXjttdfYRNHlcsFms+ELX/gCwuEw3n//fdx8881MSHe73Thx4gRuv/12HDlyBD09PfD5fCguLsbevXtx6dIlVrLzeDyYnJxkEZhHH330umubn5/nvOWjZow+nw9qtRrr1q3D2NgYFhYWuAEbDodx9OhRNh6mid2BAwdQWlrKxatWq0VBQQFSUlL4Th4bG0NWVhYbcSaCX/p8Pp54Ud43PT2NpqYmvP/++7jvvvvQ1dXFRpPXrl1jeWmJRIKhoSGMjIxAp9NBrVYjGAzirbfegk6nY64UJc0EDyVfkk/qzDscDmRnZzNUOhAIYG5uDkajEX19fYwOmJ+fZ/5beno6Tp06xSbKNJ149tlncfz4cVZqFIvFqKqqgkgkwuDgIBYXF3H8+HE0NjZCo9Ggs7MzoVoX5TXUKPH7/bwXaHrd1dWFyclJbNiwAS6XC7W1tXjmmWeYW1NXV4fNmzdjcnISmZmZOHz4MNRqNbKzs6HRaDAwMIDz588zbHhiYgLJyckoKCjgM/KT4lNPNAwGAwYHB7mjr9frcfLkSRw5cgR/+MMfoNfrsW/fPlRVVcFmsyEzMxOlpaXIycnBxMQES5B+97vfhdPpRH5+Pr74xS/iF7/4BZ544gmkpaWht7cXWVlZyMzMxM0334y6ujpotVoUFxdj+/bta66NOuxkXmO32zE3N4fR0VGWEKXu0dmzZ+HxeBjz29LSgltuuQU33XQTMjIy2IjQYDDg3nvvxaOPPor6+npcu3YNr776KkNluru70dnZiYKCgoQkzvT0dJw8eRK9vb0wGAzYuHEjgsEgfv/73+PUqVN4/fXXkZmZiTvuuANlZWVIS0vDnj17mJxTVlaGqqoqNqz77ne/C4VCgS1btqCxsZFxlOSym5mZyXhprVaL+++/f821kQoSdRw0Gg3sdjvsdjv+9Kc/wWw2Y9u2bSgvL8fbb7+NiooK7tLJ5XI4HA50d3dDpVLh61//OpaXl3miRfjHX/ziF9DpdNiwYQPLEIvFYu4SrRVULbtcLoyPj2NqagopKSl4/fXXkZWVxZ1hnU6Hnp4eNDQ04Mknn8Tc3By6u7sxMDDARpLr1q3jMXhqaipX893d3aiqqkJJSQni8TjMZjN/RKRLfr3weDwYHh7GwMAAZmdnMT8/j8uXL8Pn8+HatWssd7u6uopnnnkGU1NT+Jd/+Rf2PSAowd69e/H8889zd66srIyT5JdeeglPP/00HnzwQeTm5jIHJyMjI6ECm06nw+9//3uo1Wps3LgRJSUlMBgM6OjoYPlSOnhnZ2fx61//GlVVVYzvraysRH19PRwOB77+9a9jdnYWe/fu5e+DLnaaJn3U7IwmIomCMMhTU1MIhUIoLS3F9u3bWQEmJyeH4YANDQ1sIlVRUYGxsTEuonU6Hb7yla/glVdeQUdHB3w+H0OJioqK8OKLL8Lv97PazTvvvMP4+7VCJpPh/fffx+rqKo+IqVAvKipi3HEkEsHc3BzDihoaGrB582YYDAa4/9sE8urVqzh58iROnjzJsFOHw4GpqSkAQF1dHVpbW7nR0dTUxGam1wtyziaIXkNDA2QyGT73uc/BZrOhsLCQuQUffPAB3nnnHWzfvp05OZFIBHK5HE888QQOHjyIn/70p2hra8OuXbtQU1MDuVyOq1evorCwELFYDNu3b+dpn1wux1133bXm2jQaDd566y14vV6Ul5ejsbER8XgcOp2OJ6F33XUXiouL8dprr2F8fJw9YyKRCCwWC0QiET7/+c/jf/2v/8UiDSTb6/F4cOnSJdx555144IEHWHQgLy8PJSUlCcngIpGI+VKrq6uwWCyYmZnByMgIuru74fP5UFdXh+rqap7ebNq0iYmVGo0GCwsLSE9Px/bt2zE4OIjCwkLcc889qKiowODgIHw+H3p6etDc3IyHHnoIubm5CAQCDI1dK5aXl3H69GmeapBHxcGDBzE/P4+NGzfi5ptvRn5+PhdMW7duRTgcZt5SeXk5pqenYTabceXKFezYsYMNxHbs2IHCwkIYjUaGWXV3d+PcuXPMGVwr0tPTMTY2hqmpKczNzSEWi6GhoQEnT57E1atXkZycjPvvvx9bt25Ff38/9uzZg7/+9a/o7+/Ht771LXzve99DX18fampqoFarGe67fv16VFVV4dq1a/jOd77DzaasrCwoFAro9Xps3rw5YcNCr9djYGCAIX4lJSUIBALYv38/Ll26hNTUVG42HTp0CEajEf/5n/+Jnp4ednUnGN29996LU6dOsXR2cXExTp8+jT//+c/QarXYv38/duzYwbw+k8mUkHeTm5vL0OLGxkaYTCaoVCo8++yzbIxG3Erycvryl7+Mv/u7v4NUKoXZbMbZs2fxwgsvoLm5GePj4+jv78fNN9+MsrIyvPjii/jb3/6GWCyG/fv348qVK/jjH//Isv233357wucWiUTQ2dmJiYkJyOVyBAIBXLp0Caurq0hJSWEoNUGrtm/fjpycHJw4cQIffPABhoaGeELscrkQiUTQ0NCAwsJCjI+P46233sLw8DBzyoAPGxHES0u0tpSUFFRWVmLTpk2ora2Fz+fDwYMHUV9fj5dffhlerxcejwff/va3cfz4cXz+858HAFy6dAnj4+PIyMjA3r17kZSUhDfffJNzUFLsIpGMffv2QaVS8T1CxPy1gjiVSqUSKpWKPYFoH0YiEeTm5kKtVuPKlSvo6urCli1bkJeXx2iOV199FVarFbfeeiv+z//5P/j1r3/N3Kxr165BrVbjJz/5CTZs2MBiDwMDA8jPz8e+ffvWXFtKSgrnaoWFhdi0aRNzV2jvkB/P0NAQDh8+jJaWFmRnZ6OoqAhtbW0wmUzo6urCV7/6VUxPTzOJnjxTTp8+jerqakSjUVRWVkKlUrFx7CdBQyk+daExMDDABzolGVVVVbj33nvZM0GlUqG1tRVVVVX44IMPWMnnoYcewu7du1kGkxwbiQXvcDhQWVmJ22+/HYODgzhy5AheeOEFDA4OMpYxUQITj8dRUlLC3YTdu3dDLpfjs5/9LEpKSgB8SObZsmULbr75Zly5cgUlJSWMf+vu7sbZs2cZ39fe3o7e3l6ugqVSKZ588km2gheLxWx61Nvbm7CjZrVa8cADD2DdunUMk5DL5XjggQdQVFSEBx98EAaDgTulf/jDHzA0NIQdO3YgPz+fNdHvuusu9Pb24vnnn2c8IGGBCwsLcebMGaSmpjJkJzk5GQcPHoTZbF5zbcFgkJWRVldX+YLct28f9Ho9hEIhurq64Pf78eCDD2J6ehrr1q1DIBBAamoqdDodQqEQLl26hD/+8Y9YWVlhBQS5XA6RSITPfvazjP87d+4cVldXYTQaWXVrrXA6nYzDl8lkSEtLw9LSEu677z6srKygvb0darUaMpkM69evx5tvvgmHw4FwOIyGhgaYTCbG2y4vLzO8xu/3w+12QyKRoKmpiU0k6WMnknsi/HIoFOKkkAifpIBz7tw57sr5/X7cfffdCAQC+NnPfobNmzfjn//5n/HII49AKBTi7NmzuHjxIvLz82G1WqHVatHc3IwtW7bg61//Oqqrq5GcnIyhoSFkZmayOVsiJbFQKISNGzdibGwM4+Pj0Gg0cDgcuPfee3HkyBH09fVBIBCgrKwMW7duxUMPPcTyyFeuXIHVauVCKS8vD1/84hcxPj6Om266Cc3NzayOdubMGVy6dAmXL19mKWmCsiQKIjPOzc3BarWyydn69esxNjYGuVyO2dlZjI2NIRQK4ciRI7jtttsQDofx5JNPQqFQ4IUXXmC1jG9/+9vc1SZsb2VlJcLhMG6//XaW4P7c5z7HkKW1giCcbrebu3qZmZmora1FV1cXotEoIpEIVCoVqqqqGFtLKl/EJcrJycEdd9yBu+66C4WFhexMS9KDoVAIeXl5EAgEbKrX2dmJjo6ONddms9mwbt06uN1uJj3abDa88sorPAkjZaJ77rmHmxq5ubl87nZ3d+Po0aPYtWsXHnjgAfziF79Ab28vu6nr9XqGTf35z3/G9PQ0Q1ASda7cbjfKysoQi8XgdrsZFmkwGGAymdDa2oqysjK0tbXh4YcfxsWLF9mXZOPGjTAYDOju7sahQ4cwPz+P5uZmFBYWYmlpiZUISeHs6tWr6OjowPj4OHfVEyXMBPUl74Kmpibk5ubijjvuYAOuiYkJCAQC7N69G8PDwygqKsLZs2cxPT0Np9OJubk57jDv3bsXFosF7733Hjo6OqBWq/G1r30NoVAIAoGAYbYET0yUwBBkkdTK1q9fj/n5efzwhz9kV+Wuri6MjIwgHo/jj3/8I59d6enpfP6Xl5fjyJEjkEgkOH/+PPLy8lBfX8/8QFLlI2WiDRs28HmzVoRCIRZfMBgM3GT4xje+AZPJhK1bt2J0dBSDg4PYt28fwuEwtm3bhv379+PMmTO455570N/fj0gkwv4MqampuHbtGjo6OmAymfDcc89BKBRCoVBgcHAQq6ur0Ov1mJ+fR319/Zprc7lcePjhhyEUCjEyMsITlldeeYXVgVwuF79vtVqNJ598EmKxGLfffjt27tyJ+vp63HrrrThz5gxDoj/44AP4fD489thjuO2225hLNDw8zKiLv/3tb7h69eqaa5uammKT2ImJCSQlJSEQCOCFF17gRK2trQ379+/Ho48+iqeffhpXr17F/Pw81q9fj6ysLGi1Wvz93/89IpEIHn74YUQiEVy6dAlOpxOtra3YtWsXXnvtNQwNDSESieDxxx/H/fffj1deeQUHDhxYc20LCwsQi8UIhUI8LdbpdGhra4PNZmO1SuIrkliJTqfDCy+8gAceeABGo5EFaUKhECsWkWTsZz7zGRarIGES6oonEqYhYaCOjg709PQwd2LTpk04efIkmpubkZeXh82bN+O3v/0totEoQ4Vqa2sZOudwOOBwOHDbbbdhfHycOVQSiQR33XUX3nrrLfT29mJwcBBTU1NwOBwIBAIJYWcEO1xeXkYoFGK/kltvvfVjEOuWlhb88Ic/5MLK4XCwg/nGjRuxZcsWKJVK3H333bDb7ZiZmWFX9VtuuYWVWElcRqFQYHh4GCdPnlxzbfn5+WhqasLExAS6urrg8XjgcrmQmpqK4uJi/sZJ0IEkgi0WC0+jCHp15swZfOc732EawejoKJaXl/HEE0/A7XZj/fr1uHjxIpKTk1FeXo7BwcFPXWgIVhOxrj4Szz//PJRKJQYGBpCWloaamhrMz8/j9OnTePTRR/Hmm28y4amkpAR2u52r0itXriA3N5cx4mlpaXjqqafw+OOPY2BgAG63m81zqqqqmDBsNpuhUCgYf/oP//AP113biy++CADc+STiGek4Ly0tYXJyEisrK0ycpK4EEaP8fj+7so6MjLC8HZHLzp07h127duHtt99GdXU1ioqKIBAIMDk5CZ/Ph69+9avXXdtTTz2FlZUV9t0QiUTQaDQ4fvw4srOzodVq4fF4mLxmMpnY/0KtVnMn0+fzcfHz8MMPs7JDVlYWbDYbTp8+jYyMDBgMBni9XrhcLvj9fqjVanz961+/7tp+9rOfMUGQMLFCoRCtra04d+4cBAIBuy6XlZVBKpUC+NCPwOPxsBoE4bOPHDmCkpISJmuTk2Rrayvm5uaQn5/PXQni09x7773XXdtXvvIVrK6uQiQSYXl5mRUy3njjDWzduhUulws5OTkQCoWwWCxIS0tDZWUlALC8LWmVE/l48+bN7I9AZFQy71ldXcXw8DC8Xi93QdZ6bv/1X/+F0dFRhqTQyJ+8AcjIsaCgAO7/9mFYWVlhhRPCZVosFtx6660QiUTIz8/Hm2++if7+fqjVaqSlpWHdunXYsWMH3n33XZ440PP72c9+tuY7Jb1wk8nEBPXTp08zCTEQCAAAT4R6e3tRWlrKfg8kyUkqJYRlXl1dxdWrV/Hcc8/hlVdewalTp9DU1ITU1FSIxWIIhUJ0dnbiy1/+8nXXBgC/+c1v+LsifKdGo0FSUhJqa2sxNTWF1dVViMVirK6usjFZbm4uBgYG+O8ZHBxERkYGOjs78fDDDzM8yuv1oqOjAxqNBu3t7SxBSLjo/v5+PP7449dd2zPPPAOJRILU1FRoNBrmov3+97/HnXfeyV1Iq9UKj8eDBx98kOEx9K1Go1H09fWhqKgIAwMDTLpbXFxkyeKamhp0dnYiPT2du1qa//Yo+trXvnbdtb344ovcNSS5ZtKEr6urw5EjR7gYTUtLw/j4OHJyctj3iLTzL1++DK1Wi3379uHIkSMsgUvO0ffeey9Dcvx+P+RyOfMVvvjFL153bb/61a8YGkbylFlZWXjjjTfQ0NDAF35NTQ0KCwt5/J6eno5XX30V4XAYFRUVaGxsxPLyMk6cOIH9+/fj6tWrmJ2dhcFgQHl5OXdgA4EATp06BZfLhQ0bNqC2tpYx0Nfbb9PT08jNzWUYllarxcmTJ9Ha2sqTUoIj0QVN3k65ubmoqKhgaezBwUGUlJSwsh41fQoLC7G4uMgqUaSkNjw8vOa99cwzz7BbN3HbZmZmsLS0hD179mB0dJSbJzKZDJmZmThx4gQefPBBuFwuhMNhng7efffdOHPmDH9DpGpz+fJlfPWrX2VuAamJAR8mxT/60Y+uu7Y//elPKC0thdPpxNLSEpRKJUpKShgemp+fD5vNhqGhIfj9fmzcuBE+nw8HDhxAKBRCY2Mj1q9fD5VKBbvdjueeew6bNm1ixbacnBw4HA7E43GWB3Y4HEhOTsb27dtZweh68bvf/Y4TnKWlJU7WxsfHGd5IBf309DS2bdsGq9XKYic0gbXb7aiursbAwAAyMzOxtLTEDa6KigqGcAeDQVy8eBHz8/Ooq6uD3W7HN77xjeuu7Rvf+AYkEglKSkq4sSgWi/H6669j165drCJkt9vR2dmJ0tJSXL58Gb/97W/xwQcfQCKRQC6Xo7OzExs3bsRvfvMb/PKXv4TZbOYJTn19PXNhent70d/fD4FAgMbGRgwNDeHpp5++7tpee+01RnyQuzoJHphMJpaK93q9MBgMaGlpQTQahd1u5+9QpVLB4XDw+hsbG6HX69mjzGAwoKenB0qlEvPz8zCbzRCJRGhsbEQsFmNuzv8/vvOd78Dr9cJoNCI1NZWnyUNDQ9zQrKysRF5eHvx+P65cuYL29na+E9RqNRsSBgIBeL1erKysQKfT8RQiEolg48aNMJvNLDNOhfvWrVtx6623Xndt//AP/wChUMieY2T4a7PZUFBQgP7+fvYfIp5NKBSC0WhEdnY257Jzc3Ooq6tjCwfgw1zF4/EgJycH9fX13GwLBAIsfKJSqfDtb3/7umt79tln2euHVM6amprw+uuvY8+ePez/FAwGEY1Gccstt7CIAuVkOp0OExMTUCqVCAQCjJ4hvtH58+cZNm00GhEMBrG4uAi/34+VlRXcd999113bR+NTTzQWFxcRj8cRjUaZVT85OYni4mLIZDJWWCF1mtraWpZPJeUVwqsvLi5yAkiELZJ+I6UkwqRPTk7yuHut8Hg8/OJIEjAWi0GlUgEAExF9Ph8rsxw9ehRmsxk2m421kZeXl+Hz+TA4OIiUlBREIhG+cGdmZhhvOD8/zxhhUg1YK6LRKEsXGo1G9hCIx+OQy+WsNW6xWHDlyhU2uhkfH2fjI5/Ph2PHjqGwsBA2m42TQfrgrVYrCgsLIRAIsLCwwF4VhH1eKwj6QbCCYDAIpVKJ6elp3mQikQhqtRpqtZrxw2RSRiQ0UvpSKpXw+/2YnZ2F1+uFWq1GcXExnE4nKzIQ7pOKh7WCYCJqtRqa/5YSpt9Ef87j8WBxcZHVgK5duwaXy8UQCPIUof21urqK5ORkRKNRJskT54RkP2OxGGKxWEJSM0npUqFBEpDLy8ts0EiyhaS0Njc3h8uXL/OzIPWlzs5O9tRYXl7mhN3hcGB1dRWXL1/G0tIS5HI5d3ASTVs+yt/wer1wOp3ceS8pKWES4Pz8PHeCLRYLzp07B5vNxqpBpIiytLTERn1OpxO5ublMYHS5XOjp6cHY2BisViv6+/thtVrXXBsATl6JXEZjbZpI2Gw27thLJBK4/9vllAw/ycNHpVJhenoaWVlZiEQiGBkZgcVigc/nw4ULF1BUVMTET1LwInWOtYJkcAlPS2cCTTaAD+EuJClos9kwNjbGl8L8/DyWl5eZnEwTFr/fzwpy9PtJxjoQCEAikbA0d6K1CYVCCAQCeDwe9r+JxWL8vZOHw/DwMHeJZ2ZmOEEQi8VQKpVwu92Yn5/nb4l8j6ibSVPXWCwGh8PBTrBrBa2F1PNWV1fhcrlQUlKC5ORk9koKhUJ8DsTjcW440Lc9NTXFTr60F0k6XSgUIhgMsioaqfXRZCfRftNoNEhNTWUuHgDU19cjGAwyrIwgXiQNHY1GEYvF2MSTuHB9fX0Qi8XIzs6GSCTC0NAQiy9EIhE+R9xuN99da4XL5WJCKk2DiDBPOO+SkhLm3JDfkdVqZbK2WCxGSUkJdDodY+7dbjdCoRD/90AggHPnzsHlcsHj8TB/JxF0NRAI8PlKZrNTU1MYHR2FRCKB1+vlKZ5QKGROCPkykOoQwWtoqkQiC4uLixgdHYVCoYBCoUA0GuWzmPDrawVJz1LnnaZJ0WiUJWvJw4kKtWg0ivz8fC4qU1JS4PF4GD9PiRVJeTudTk7ISf49Ho+zUMRaQbAiku0nJau6ujouPlNSUljVzOl0MhSXFAFpHw4PD3NSSqpkU1NTuHTpEkQiEaampvh3fxq1LpL+J7QB8VFIpp9yGnrvdrsdY2NjkMlkrIi2srLCPIeMjAykp6ezmA7lb+R6TiIypApKDa7rRSQSQX5+PkPLVCoVsrKyoFarEY1GGe5GkNuMjAzOIRYXFxGJRFjNkYwVSZKZzn3iJKWnp7O6pEKhQFJS0idKZJOSI+WStNdp0kVeZ6S4R81bp9PJZ79SqcTY2BiuXLnCPC5SOnU6ndDr9azeRT5TpKq1VlCRnZaWxmt0Op2cP6+srLBMeU5ODt/tV69excTEBCMfZmZmWFnuoxNiUiIj9b9AIMDwYCK/f5r41GTwmZkZhgCQ5CrpirvdblRVVUEikTBBt6KiAjKZDKdOneIJBXW+L168CIVCgYGBARQUFECn02F2dhahUAhjY2Os6ex2u2Gz2XDmzJmEiiykL06X0czMDMuB9vb2sjb24uIiw1D6+/sxOjqK3NxcluXMyMjAqVOnMD4+zrh46iLLZDK43W7uxAwNDfGlmYhUR0ZWRFInArJWq2U5u5GRETZjI7gL+QeEQiHYbDaGETkcDrz33nvQaDSsqOJ2u7F7925OHqkq1Wq1CfGkVNR9tAggMhEVKXQ4eTweLCwsID8/nzuD9FFQlT8/P49IJAKlUvkxud3u7m6WZ/T7/eyXkEi7mhRXVlZWYDQaIZfLYbPZkJ+fj0AggLy8PMzNzXFC6vF42GiIcNF0sLvdbkQiEZjNZn5XxOlJSkpCNBqFy+WCUChkgmGi4tHhcPClJZVKIZFIWJFiaWkJmzdvRk9PD5sr5uTkYHFxEd3d3ZBIJMjOzobRaERGRgbeeecd+Hw+HmMTVtztdsNsNrOBHklE035aK5aWlpCXl8ddu+Hh4Y9JHZPCGJGCMzMzUVBQgFdffZXhUjKZDMvLyxgZGeEJCiWgMpkMzc3NmJ6eZt7L/Pw8d8oIXrhWUJEWjUah1WqhVqtx5MgRTppDoRBcLhcf0IuLi1hYWMClS5f4oIvFYtDpdLh8+TJuvfVWTvZIZ31qagorKyuw2WwMT3K73QnfKUVSUhIXawqFAgsLC+zXQDKO5OtiNpsxPT3NviZJSUnIyMhAeXk5BgYGuBFAssZEEI9EIqioqMDExAQnfSKRiKWGrxd0aZABmNVq5e/SYrEgMzMTs7OzuHDhAuLxOO644w6GMgJgaWAyeCKXcrqcyTCKlI+IaLm8vAyr1ZpwRE4FLclPkrIYKXARfHVhYYFdkU0mEzo6OngPkLGp0WjE7Ows80UCgQA3tBwOB3deDQYDK6oUFhYmXJvBYEAwGGQYVGpqKlpaWvDXv/4VO3bsYIhDZmYmvF4vBAIBRkdHEQ6H2em7qqoKAwMDmJmZgdlsxvr166FQKLhgGx0dRXp6Oiu9UcMjkYS3x+NBaWkpexqQR4pIJMLk5CTy8/MRjUYxMzODyclJ5OXl8aSspqaGvV7q6urQ39/PdyhNcoVCIYuX2O12foeRSARerzfhcwuFQrBardwABD6ET/v9fshkMn6XkUgEJpOJz1OStw+Hw+jr6+MzMi0tjf2uQqEQQ1ZlMhkaGhq4AAyHwzwtvummm667NuKrUWFM6ohlZWXw+XzQ6XTMNaQmREVFBVJSUjA9Pc2SqwqFgk3LgA8TUJKJnZ+fx/T0NGpqalgtSS6X8x23VlDBabfbkZKSgkAgAL/fz9P8zMxMvnObm5vx3nvvMSSF1BWBD6X+f/KTn6ClpQXnzp2DQqFgo1+r1cqNCZq8UmOLUAfXC/J8oOYvFVXr1q1DT08PDAYDy8G73W5cuHCBRXUoeaZcaHFxEQaDARqNhvkPRConIRZqlpFDN73jtd4pkbhJGQ74kHM4OjqKPXv2YHBwEIODgyguLmZ1TIfDwWecRCKB0Wjk4pagW16vF1arFQ0NDRgaGkJVVRUCgQDL4AcCgYTwdwDcGKdGH/lxpKSkoLq6GhcuXIDH4+HCKDc3l+FoNDlSqVQ4dOgQ3P9tuFpSUoL5+Xl0dnYiIyMDdrudGwTUMAeQUBiBmuP028PhME9FSOCAhBYkEglPtIlHtLKyAofDAb1ezzLxhGhQKpWw2+0QiUSs0jU4OMiN/E9S1fto/D+RwSUSCSorK1FXVweTyYTk5GS89NJLjF1TKpUQi8Xs/pmUlIRXXnkFb7zxBk6fPo3Z2Vnk5uay+RAZx9BBD4Avw6tXr8JqtXIClihhVqvV0Ol0MBgMyM7ORl5eHlQqFQ4ePIhXX30Vqamp0Gq1sFqteOONN9Db2wuBQMBJjdvtRkpKCievMpkMly9f5orXZrPBYDDggw8+gOa/TVm8Xi8rFSXC9+l0OlitVnR2dsJqtWJlZQUzMzMwGo2QSqXIzc1FSkoKFAoF8vPzcf78eZSXl6O+vp6Tpmg0igcffBAffPABrl27hv7+flgsFu5WE+ZyYWEB2dnZiMVimJmZwerqasKkVCAQYGxsDIuLi3yIkBwcKWKZTCZMTU3hgw8+YCO3oaEhdHZ2wuv1stZ1dnY2zp07xx39eDyOsbExnDt3DhqNhqtqmjJoNJqEjupJSUk4cuQIy4KGw2F24F5YWEBhYSE7u1+7dg2Tk5NsjEjram1tRVtbG9544w2cPHkSY2NjiEQi3BG12WzsgWCz2djckaRx1woqUMh8idSGyOOjrq4OFRUVUCgUiEQiGBsbYxLx6dOnYbPZUFNTg7179yInJweXL19mEySSZCVflZKSEsjlcoyOjqK/v5/3SaJvQSwWo66uDiUlJRCLxUhJScF7772HgwcPshSky+XC9PQ0gsEgNm3ahIceeog7LLFYDF6vF++88w4kEgk0Gg0f4IcPH0YoFEJhYSHuuusuPProo8jIyIDNZkMwGPxEp1Ayc6MilA7HK1euQPPfTvZ0ObzyyitITU3FzTffjKmpKZ54zMzM8ITBarWy4ENZWRn0ej1MJhN6e3shlUrhcrnQ39/P+unnz59fc23xeBxDQ0Pwer3Izs5GaWkpNwaOHz8O4EOs68TEBN58802W5JZKpdwNJP+ZEydOwGq1YmhoCCaTCSaTCbOzswiHwwiHw9iyZQvq6uogEAgwMzPDvi6J4tq1axAIBGy61dfXh2eeeYYle6mgsdls6OnpQWtrK5KSknDo0CF2Rqbuf29vLywWC3Q6HZ/ltbW1GB4eRl5eHjIyMhCNRuH1enmSuFaoVCpcvXoV09PT3F1OTk7GlStXeFxfXl4Oi8WCw4cPsz8GTcMikQjD+CwWC5qbm1mrPy8vD4FAAN3d3dzEIPgOddPXrVu35tpkMhlPdsjQamlpCadPn2ahAa1WC5fLhRdffBFjY2N47rnnWPue4HJUJJKaTiQSYS4EJV6k4Ed+NeRBtFakp6dzkUbJQEZGBu9ZgnZOTExgdHSUpx8SiYQ70PPz8/xdOp1OzM7OwuVyYWpqCr29vXC73RgZGUFrayuampqg1+vh9XpZAjTRcyOzSZ1OB4VCAZPJxDyUvr4+eL3ejxUN+fn5LMJCKnw+nw9ZWVlITU1FbW0td2qLi4vR0tLCSRhJasZisY91n68XlAzTuzOZTPD7/cjLy0NfXx+kUinq6+tRWVmJlZUVnD59GkajEWazGYcOHWLup06nY1nXSCQCkUgEnU6HoqIi9hCamJhgbx+aNiUKo9GInp4eTE5Owm63M/rg3/7t32CxWLCysoL+/n4uwhobG7F582YuAl0uF2ZmZtjXqLCwEN3d3RCJRKitrWV3+sHBQaSnp6O8vBxisRhzc3NYWVlJmCeRxCzdU2q1mmFRJMFPZ8Hq6ioXwCKRCAUFBUhNTYVUKkVdXR1GR0fZ1DgWi8FqteLMmTM4duwYNP/tQ0LoBZookRDG9UIikeDUqVO4dOkS+vr60N3djffffx+zs7MMuUpNTcXs7CysVisikQisVit7ZdD0Mzs7G9XV1bBarRgdHWWuw+HDh9HZ2cmTP0KsLC8v872/VtDzjUajDOf1er146623PrYeoVDIxQgl4b29vSznb7Va2eW9u7sbGo2G4UhyuRwDAwOM/pmcnMTQ0BDnrmuFSCTiXFCn0zE/8ejRo1heXmZfLbvdjjNnzrAqVk1NDaqqqqDVaqHRaLBt2zZcvXoVf/jDH7iBMjc3x4Z+5FNDDbzFxUVoNJqE4iofjU9daMzOzkIikeDs2bM4fPgwjz1/+ctfstSqwWBAQ0MD7r77bkxOTmJhYQEbNmzAl770JbS1tXHn9+DBg7j33nuRlZUFqVTK1eUTTzzxMbLKnj178M1vfhNqtRq7du1K+LDlcjk++OADHDlyBNPT0xgYGMBdd93FZl2pqam4++67cfDgQTQ3N+NHP/oRDAYD6urqeDNqtVrIZDI88cQTH9sAer0ee/bsYSUfj8eD4uJitLW1cTW4VrhcLhQXF7OMJZEPd+/ejfHxcT7IMzIy0NraisrKSi4AaCQ6MzMDv98Pn8+H73znOzAYDLjpppvQ1tYGrVaLnTt38kXX3d0Nk8mEHTt2wOVy4fTp0wnfK2H06urqsG/fPuTk5OALX/gCK5kMDAyw1TxxaagYo4QmLS0Nb731Fn74wx+yfwNNLB5++GHGi1PhqFQqYbFYEh6Ki4uLjLsWCoXIycnB8vIyPve5z6GwsBC33norWltbUVhYiI0bN7LWvFgsRk5ODpaWlnD8+HH09fVhx44d+PKXv8xFycDAAHe7zGYzysvLkZWVxV4Rg4ODrC9+vSDZuOXlZVawOXv2LNrb2/GLX/yCZfgKCwuxf/9+uFwuVFRUsAyxw+HAyZMnkZKSguzsbCQlJTFGc3l5GadOncK2bdvw9ttvs374unXrsGXLFvh8voTGboFAANXV1RgbG8PExARPGb/whS/gb3/7G65duwaxWIydO3fiG9/4Bjt7Dw8Pw2q1MoSmqKgIOTk5aG1thUKhQGNjI6vefPnLX8aZM2fw0ksvoaOjA9u2bcNjjz2GYDCIioqKhPvN6/UyFv/06dM4fvw4kpOT8fDDD8NsNmPLli0wmUzYuXMn/vrXv+LatWuora2FXC7H/v372XunoaGBtb5JKWxmZganTp3CZz/7WfT396O2thbxeBwbNmzAjh07MDExgU2bNq25tqSkJBQUFLBcJHEg/vf//t/c7FCr1Whra8PTTz+NsbExfP/730cgEMDOnTuxbds2VFVV4aabbkIkEsETTzwBq9UKl8sFtVqNW265Bbt27cLVq1ehVCqh1WpRW1uL9evXQ6/XJ1TaWV5ehlKpxJUrV2Cz2VBRUYHCwkIcP34cRqMR7e3tKCsrw80334ynn34ao6OjuHDhAlJTU3HTTTdBoVDA4/Fg27ZtSE9Px969e5lQmZGRgaKiIsbE0x5ramrCzTffzGpga0UoFMLmzZsRCoVY1GJoaAhPPvkkTCYTsrOzoVKpYDQasX79evbCCAQCLIqxtLSEHTt24KmnnkJ7eztcLhdGR0dZ8erRRx9l+e8///nPWF5eRm1tLUKhEM6cObPm2mQyGXOTysrKcNtttyEzMxNbtmyBwWCATqdjqep169ZBKBTipz/9Kbq7u5Gfnw+j0cimc+fPn8cjjzyClZUVjI2Nobe3FzabDa2trXC73TAYDDAajXj44Yfxla98haGpawV5m8TjcSiVSigUCrz66qssw726uorZ2Vnk5OTg5z//OVwuFz7zmc8gOzubzRCzs7NRV1cHr9eLhx56CCKRCFu2bMGWLVuQkZGBxx57DFNTUwyPkcvlrBSXqEG2srLChl8EHRQKhbjvvvuQnZ2N3NxcnvAlJSXBYrGgs7MTBw4cYO+A2tpa3HPPPTh69Cjq6+tx8eJFjI2NYWBgAGfPnoVOp8OZM2eQk5ODU6dOIR6Po66ujuEga0UgEGDlS3Jhr6urY8hOWVkZUlNTUVFRgS996UvMmfH7/di6dStkMhmcTic2btyIzs5O/OM//iNKSkoYXREIBLB161b4fD4+/zIzM1FVVYWcnJw1+UD0LVRVVWF6ehoej4cnjV/+8pfR3NyMO++8E+3t7cjNzWWp2fLyckSjUTQ2NiItLY1la0tKStDS0sJiKv39/YhGo/iHf/gHuFwutLe3Y3BwEFVVVbjnnnswMjLCTutrPbeSkhKsrKyw1wlNcEtLS5mDs7y8jNLSUuj1etTX1+PgwYNISUnBwsIC3nvvPczNzWHv3r3Ytm0bLBYLUlJSWN2tpqYGly5dYqno5uZm1NfX4/jx4wmnLeFwGLt370ZLSws2bNiAPXv2MGleo9HAbDZjeXkZhYWFqKurg81mw1133cXJrtFohF6vR35+Po4ePYq9e/dicHCQzRB37tyJffv24fXXX4ff70ckEsHmzZs5T0o0pVpZWUFBQQGjUjZu3AidToeHH34Yy8vLWLduHbKyslBUVIR9+/bh/fffx7Zt27C6uopHHnkEBQUFmJ6eRnl5OTweD772ta/xRJYMVVtbW3HgwAF88YtfhNfrRXV1NR577DGkp6cnJNGnpKRg06ZNSEpKYtNlrVaLL3zhCxAIBNDr9aipqcF9992Hf/7nf0Z7eztuuukmCAQC9jszGAzQarWYnZ3FU089xQqxjY2NMJvNePDBB5k0Pzc3h9bWVtx2222Ympr6RINeik9NBn/xxRfR19eHdevWse67w+FATU0NysvL4XK5mChpt9uxe/duNDc3o6urixP48fFx/OEPf8B3v/tdfO9738P3v/99Nh+an5/Hn//8Z/zgBz/A8ePHObGkpI4OkOvFb37zG3ZSpJficrmwZcsWrKysYGlpiTHMc3NzuOOOO9icZmhoiDGRFosF6enpOH/+PB599FHk5uYydImM/t577z0UFxezRFxbWxu8Xi/uvPPO667tmWeeweLiInfikpKS0Nrair6+PqysrGDXrl0Ih8OsQLBx40ZEo1G89dZbaGxsZJfRy5cv4+6778aBAwewd+9edHV1ob+/n6ENTz75JGZnZ+F2u6FQKBiXfu3aNfzjP/7jddf2xz/+EU6nk4nAZDw3MzODPXv2YG5uDsvLy6xW8/3vfx8+nw9arRYHDx5ETk4OduzYAZVKBYFAgFOnTvH7JPx9KBTCli1b0Nvbi8zMTHR2dsLlcmHz5s2YmprCZz/72euu7Zvf/CbcbvfHsMgqlQrj4+M8LYjFYowv/Ld/+zc8/fTTyM/PR3d3N/R6Paqrq5GRkYGcnBy8/vrr2Lt3L8vvkdnbL3/5S/Z8IadRMin68Y9/fN21/ed//ieT02lUnpqaildffRWf+cxncPLkSdjtdmRmZmLnzp0wmUywWq0Qi8X4zW9+g+LiYmzYsAH9/f3YsWMHHnzwQbz44ovMwRgbG8N//ud/4s0338Szzz6LdevWsXLLhg0bEI1GsX///uuu7V//9V/Ze4KmM+FwGD/+8Y+xYcMGVqIgb5Lq6mrMz8/j5z//OZsxkhb5yMgIAoEAHn/8cXzwwQew2+18OdbU1ODNN9/Efffdxxj/srIylsNdK771rW9x4a9Wq6HVapGamooXX3wRt99+OxeLkUgECwsLkEgkOHPmDE+g6uvrUVBQgMuXLyMzMxPd3d3YsmUL0tLSEAwGMTg4iJ6eHtx9992wWq0wmUzMGaCpyP/6X/9rze+B1KbIzJII2K2trTh79iwbcZEE7LPPPoumpia8/PLLMBqN2LhxI5aXlzE8PIzS0lKGJxG+OT8/H0qlkpM+6n6np6cjEAhg9+7d113bn/70J/T09CA3NxclJSUQiUQ4efIkLl68iAMHDuDAgQN8zpICzOTkJL72ta/h2LFjEIlEKC8vh06ng0wmwwsvvICdO3fixIkTCAaDaGpqgkKhQDwex5///GdW6fH5fJBKpRgdHV3zjHv22Wdx7NgxVFRUIDU1FQqFAk1NTRgeHoZarYbH40FNTQ1GR0fxy1/+Ek899RRefPFFrF+/HgMDA0hPT2eFtaysLLz44os8HXe73ZiZmUEwGMSTTz7JYgYzMzNISkpCfn4+3n777TXf6V/+8hdWbSIpahKN+MpXvoJz585BKBRiamoKFy5cwMaNG+F0OnHTTTdhYGCA3djn5ubQ3t6OQ4cOobCwEHl5eQzVI08V8m+YmZlhQ1ar1brm2v7whz8wBI5M0GQyGQ4dOoS2tjYmKF++fBlzc3P47ne/i1AohMXFRZw7dw6VlZUwmUzMr1KpVCgsLGTOBxHC9+3bh5/97GdMBCWYmEQiQXV19XXX9tRTT0EkEmH79u2YnJzEBx98gMrKShw4cAD/8i//gtXVVZjNZoZNSqVSNDc3o6ysDIuLiwiHw+zj1NHRgXA4jHXr1nHCSipgW7ZsgdfrRVJSEqanp7mhWVhYiB07dqz5LYRCIdx0001wuVw4f/489Ho9Ll26hO9///t4+eWXuXDzer2oqqpCYWEhkpKS8Kc//QnZ2dkoLCzkwvGZZ57BE088weIngUAAQqGQp4GVlZUYGxtDOBzG5s2buWC5XlD+UlJSwr5MZWVlPKUAgMbGRkQiEZw5cwbNzc24dOkSduzYwXd5KBRCbm4uzp8/j6ysLDQ0NKCrqwsCgQD5+fk4ffo0mpqamBNEfNOGhgZIpVJ897vfve7afvKTnzAnljgX5eXl+P3vf4977rmHpzzEN6CCrKioCNeuXUM4HEZmZiby8/PZI23z5s18TyiVSvztb3/Dr371Kxw9ehRTU1NsbGoymeB0OlmS9v8fv/71r+FwOKBUKpGcnAypVAqj0YgDBw5g3bp1qKmpgU6nw8jICI4cOYIvfOELDMfq7e1FcXExbrrpJj5vnnvuOeTn5+Ott96C3+9Hc3Mzk97XrVvHDucpKSkQi8U4ePAgfvWrX113bT/+8Y8xMjKCpqYmSCQSzMzMMF/j0UcfxY9//GM0NzdDp9Oho6MD9fX10Ol0SElJwU9/+lNUV1ejubkZR48eRWVlJSYmJvDYY49hZmbmY5zGgYEB7Nq1i1VbR0ZGsHnzZvT39695hjzzzDOQSqXMjSEYYU9PDyorK1FYWAi73c5c1YaGBnzrW9/Czp072XhTKpWipKQER44cweOPP86S0G63Gz09PTh58iS+973vAQDMZjOsVivnSQUFBQnve4r/J8M+gofMzc1henoay8vLrNSg0+mYQDQ3N8dusgcOHGDjj1AohIKCAvz+97/Hl770JZw9exZOp5Pxx9PT0/jRj36EO+64A/39/UwwJEOkf/7nf77u2sjsiDgDBLVZWlrC4OAgj5P8fj/i8TgbuJEyFRFTyZxn9+7d6O7u5hEwqT1s3LgRRUVF6O/vRzAYhMFgQF9fH0ZGRta8hJeWlpgEDnzY0T179iy2bduG6elpWK1WWCwWXL58GWazGWq1GklJSQgGgzhz5gzjKWUyGV566SUIBAIcOXIEw8PD8Pl8bMJGh7fdbmd5tUQTA1obFRhESCYSPZGvyKioqamJFW9WV1exZ88euFwuXLp0CUVFRdizZw9SUlIwOjrKZDqv18ujY+JykKcAjfjXCjKUI5dzwpOSYyZNI+bm5pCSkoLz58/zM87MzITf70dfXx+mp6fx17/+FY2NjXj77bdhMplQUlICt9uN/Px8xjES5ygajXKCvlZYLBbU1NTAbDazsRuR4QcGBmA0GtmE6qWXXkJOTg4yMzOZa0QKVXl5eZicnER7ezsmJiYwPT0Nu92OaDSKXbt24Yc//CGysrIwOzvLZmxnzpxJaBC5srKC1NRUjIyMQC6X82/bs2cPTCYTlpaWWD6wrq6OJTGbmpoQCAQQCoUgl8uRn5+Prq4ulsRbt24dxsfH0dvbi7KyMgwPD+PWW2+F1WrFxYsXGQr5SYWGVqtFQ0MDLl68iKmpKWRkZCA5ORl33HEHRkdHGWak0+nYI6WiooJVSIggF4/HMTk5iaqqKjZrIjUMuVyOQ4cOobGxER988AEXwkNDQwllM/1+PxoaGpjUTcpoRDz3eDzw+/1M8DQYDHw+tbW1we/3o7OzE8nJyVhYWMDk5CSqq6tZTMLj8TBHpbS0FHa7HTabDV6vFw6HI2HHz/3f5ms+n48JuSqVCo2NjRgYGIDX62W1uqSkJKxfvx4rKys4evQoE0nPnDkDuVwOqVTKkp5knOh0OjE6OoqSkhKUlpbiL3/5C0N1iCe1VkxMTODWW2/lBNPv9+P555/nIm1lZQXz8/Pw+XwMBcnOzmYunEAgQFdXF5aXl5mcTQRoMhbLz8/HqVOn+O8gEQ86E9aKq1evoqioiHl/dK6Fw2F0d3cjJSUFDocDsVgMFRUVLMgwODjI75pkr3t6erB+/XreZ1arFZOTk7h06RK+853vICsri8/PWCzGEJ21wuVyQa/Xs0EZQbJaWlogl8tht9s/5kn07rvvIicnB2fPnkVLSwv6+vrwxhtvoLS0FFu3bmXzzUgkgp6eHpw+fZphLw0NDfxvhEIhKBQKvPvuu/jpT3963bWJxWLodDpMTU2xUZ/b7cb3v/99iEQieDwepKamsht9RUUFLl++jLGxMeZrCgQCmEwmKJVKXL58GdFolCfhly9fRmVlJY4cOYJdu3axXDXxJxLJ2RNMu6+vj03KVCoVHn74YbhcLhQUFGBgYICntxUVFQwvMxqNWFpawsmTJ9kZvrm5Gb29vQgEApifn+f9oVQqkZeXB61Wi5aWFjidTpw4cQJCoXDNQsNut2Pnzp0syR6LxfDWW29xASiVSjl/EIvF6OvrQ19fH6xWK4qLi7nDPDMzg5dffhn79+9HUlISQ457enpgtVoxPz+PDRs2wG63w2AwMA8i0eRRKpWyuzzxW2ZmZtjrhCSQ6SxPTk5mta7p6WnG8586dYqnoO+88w7S0tKQnZ2N5ORktLW14dixY5icnIRKpWJZb2ryrBVTU1PIzc3F/Pw8lpaWkJSUxII4oVAIx44dQ39/P3MQ33rrLezevRvnzp1DIBCAxWJBLBZj1M3jjz+O8+fP49Zbb4VUKoXf70dPTw+2bt2K5ORknDhxgpsiMzMzCflK8/PzKCkpYb6JyWRi8v7rr7+OtLQ0vrt1Oh3cbjefpTt27GAuSU1NDfr6+pCbm8uw2vn5eSQlJWFhYYGNT8+fPw+lUonCwkIcOnQo4bdARpgk+kDCICqVCuFwmDkUKpWKG1zFxcXM8zGZTBCLxejs7ER1dTW++tWv4ty5c/znXS4XsrOz8fTTT+Ouu+5CIBDgpuzY2FhCnu1H41MXGgqFAi6XC8nJyWzsotFo8N5776GtrQ0bN25khYTV1VUef2ZmZmJxcZGx5QqFAikpKXj++edxzz33cCeDpg4GgwEnTpxAbm4ugsEgpqamYLfbUVRUtObaiKhKTsPxeByLi4t46qmnkJubi82bN6O7uxtXrlyBSCRCTk4OXzg6nY7x2ORsTGpSLS0tUKvVmJycxIkTJ3g0LpFIEAgEEIlEeAy9VpAvACm8kKPvyy+/zNAU6riOjo4iGAxifn4emZmZjNsnpRuSYG1qauJigOAeS0tLKCoqQnp6Oqv2xOPxhAQs8s8gLopEIuFEjqTLSA+fJI0tFgsb8qSnp6OwsJB1vkdGRvhSCwaD6O3txeHDh3HXXXfxeJ2UPOh3rhWhUIiLCLFYjHg8DpFIxJCmjIwM9hJJTk5m9/JoNMoqUCQ7SnC89PR0LkIJnlZaWgq1Ws04Y6FQyL97rSCDSJVKhbS0NCZMkvpOKBRCZmYmVldXMTk5+TF5W1LOoPdJmNmFhQVWBJmdncXMzAxuv/12Hut+lBydiBymVqu5gNZqtSzr2dnZCaPRiOrqapYetlqtSE1NRXNzM1566SUAQEFBAfLz85GRkcFyz8SvIcWisbExngIRR2JpaQkGg2FN+UKK5ORk7rgLhUJ4PB44HA7Mzs7C4XDweyE8fjQaxaZNm/CXv/wFtbW1SEpKwvDwMFQqFV8y1PmlDh85u1+4cIExpD6fDyaTKeF7JYgC8CFmOBqNore3F5cuXUJjYyM2bdqEgYEB9PT0IBKJYH5+HqurqwiHw7znysrKUF5ejosXL7JEb0lJCVQqFU6cOIGFhQUmBpKhKK0tUcJMyih6vR4SiYQJ1CMjIxgfH+dJCWGjV1ZW0NbWhkOHDsFsNiMnJ4eliMl8U6/Xo6mpCTKZDIFAgAtOUjwihSKCJqwVCoWCm02hUAhOpxMymQw+nw9LS0u47bbbuDNPEoyUpHs8Huh0Ouj1er4AS0pKYDQaUVlZyRPVnp4e7n4DYDy00+nEQw89tObagsEgE7zJmToWi2FiYgIjIyPIycnBwsICexxQg+P9999nbtnCwgJOnDiB4uJijIyMoL6+niVK5XI5ampqMDk5icrKSuj1elitViasJ7qIl5eX+ZwmMzCr1YrLly8znJcKGofDgerqapw8eRKTk5MsMJKdnY3W1lYMDAygr68Ps7OzqK+vR1FREex2O1566SV2L15cXMS1a9eYm5HoTqUzVa1WIxwOM/9qbGyMeVsEQUlNTYVSqWRc+sjICCsvpaamYm5uDn19fVCr1Xz/Eg+S1hGJRKDT6Vh9K5FwA0m+x2IxpKWlwWAwwOPx4I9//CN2794Nh8MBoVDIzs8EmdRoNLh27Ror8EgkEnR2duLIkSPYtm0b85ImJyeRkpKC+vp6DAwMMLae1COj0eiaawM+bPZQkUx8kcOHD6OgoAB33nknrl69iuHhYUilUuTk5KC0tJTJ/ARhJJ8np9MJqVSK/Px8zpWImExSzVSQpqWlJVSZTElJYTlnMtO02+3o7e3lYoGg2lT0f5TjRPmP1+tFT08P87HS0tJYbrmqqgpXrlxhrqZSqUQkEsH4+DiTjK8X4XAYV69ehVqtRnp6OrRaLRf0tPdkMhnzHygxF4vFWFhYQDgchkgk4gLv8uXL8Pv9+MxnPgOVSoWOjg5eF/HBSHxEJpOx4tlaz83hcGBiYgJ5eXkoLy9niK3H42EUjUAggFQqhVarRVVVFY4fPw6r1Yrs7GzU1NRAKpViYGAAw8PDMJlMjIgYGhrC6uoqNBoNwuEwN80AfCJfiegINBGnJtzAwADL7VORKhQKsbq6irvuuovd20lRDgCOHz+O1NRUCAQCVFdXs6KqwWBgHi/x0Ww2G5PQP018ao5GPB5nZrrH42G1naKiIpYzpeTAZDLB4XAwnIFG8pFIBLW1tWy0Rpr1er0eWVlZ2LVrFztLx+NxHjU6HI5P7OJSEkmHNnkRkNwg6buLxWI4HA5UVVVxdWo0GiEUCiEWi9nxkdaVmpoKmUyG0tJSVmOIRqNISUnh9RUUFKy5NjoQyXlTqVSy1BsdrllZWaioqMAdd9zB8p2kC03Se2QkptVqGQZB6gs0XlOpVNBoNCgtLUV2djbLJ64VpHBCUm9ktJeamsoXilAohEgk4kNjy5YtWF5eRnp6OkMHMjMzceTIEVaakEqlkMvlWF1dRVtbG5xOJ5N+lUolDAbDJ8rb0nsl+V2FQoHk5GSGrRCZnGBvkUgEarWaDyKavhHRi2SVqRCmZz8xMcEKIykpKXzZJJJ+JO8DColEwgpZ5CotlUrZYVwkEnHCX1payoaAhE1XKpWwWq3Q6/XIycmBUqlEVVUVE8Np39IF+0nkMJILTUlJ4UlIRkYGXwz0jhUKBTo6OlBcXAyfz8fKTyTXSQpeBCVQqVRQqVTsRUMwDPp+rVZrQkM8AIjFYkw4p0OUviviMREhkgo/SnYKCgqQm5sLg8HA8srJycmYn59nAiN1YCiZ+qikZEpKSsL3Sl08Wltqair/PiK7ZmVlwWAwoLCwEGNjY/B4PAyLJJltg8EApVLJEzkqbGUyGSorK1mKkhSoaI8mOuNEIhGKiopYNSY5ORnhcBjl5eWsBa9Wq5GXl4fGxkZ4vV5kZGQgHo8jKyuLYUgkoUjvi6Qkg8EgqqqqYLVaoVAomIRIZ3WiIPUrUkwh+Uzy/9FqtawCmJaWhlAoBJ1Ox67oJH1L0+K0tDSMjY1xh5J4XXa7HS0tLVhZWWG1NPKzSPTcqKlE8tPBYBDV1dWw2+2MV/f5fPwbysrK+NlkZWUhIyMDxcXFkMvlKCsrYzEIUpqjaWBSUhLLvobDYZYATRRZWVlYXV3lpJH21/j4OJNOyQVcJBKxkhEZrhFPgaZc1CFNTU1lrh5N5oPBIBvwzc3NJVQ5A8CogGg0yt34cDiMgYEBhsjGYjEWIMnNzeVCmCTG5XI5eyHQeSyXy7nDSg0aIp7St55I5Yzw9JQ8kXoOiY3k5uZCp9MhLS3tY/cm7Wc6K+LxOLq7u1kSlBJFahhEIhHI5XL4/X7+juVyeUK3949K/ZJ0bWZmJhoaGuB2u5lQTRMjOlcpHyJxnFgsBpPJxEmwyWSCwWDgKRFNRalZmJqa+qmSPlJX0uv1yMzMhEAgQEZGBovwkPAHqWNVVVUhFotx8k8+YHRuxWIxliungp1MZRcXFyEWi3mq8UlnLxGqiW+5sLDAxG7iFBCnie578tcgyFdTUxMb331UAMLr9aKtrY2bfJR0U7GRqLANhUJITk7mfeDz+eD3+9l/S6vVQqFQ8N4hpcLJyUk21aTnTufWR9UGV1ZWeJpK0vgEexSLxQnvBeLxLi4uQiAQQKPRIBaLoaioiHNYIrAHAgGYzWbk5+dz/kVoIpPJhLGxMRYDUCqV0Gg03Pwkrhk1vSmvTdT0/Gh86kKDFIdIspHG8XfffTdXQVSVFRQUMGFrZmYGGo0G0WgUFouFVakeeughLC8vIxwOQ6VSIS8vDzU1NfB6vSgrK2NpQFLgSdRFoE1ID570jb/85S+jpqaGO6UkvUeSbXq9HlqtlmEZxK5ft24dqzlEIhFotVrcfvvtXDg5nU5oNBpOaKjLdr2Ix+NQKBRYWVmBTCZDXl4edDod6uvrIZFIeMy1YcMG3H///QzJogpdrVbzlMDpdLJSR2FhIQoKCqBQKFBdXQ2n08mKPDR9ILLxWkFYSIJqUedPq9Vifn4eSqXyY9W+0+lEdXU1cnJycMstt6CoqAjBYBDFxcXweDzYtGkTy2hS8fnwww9jcHAQCwsLkEqlWLduHVpaWrjIXCukUikMBgNPwEiCuKSkBEVFRZwgpaWloaCggPcQ8CHBXa/Xw+/3w+l0MkGd5P9SU1MhEolYupCwxjQFocp9raCJBZkZuVwu1mY3Go2oq6tj+FRtbe3HCsympiaUlJQgMzMTGzZsQH5+PkpKSrhDQwf9bbfdhvPnzyMtLQ1yuRy5ubmorKyE0WhMuDaSeayoqEBZWRl3ye+9914YDAaMjIzwhZefn89Ng5aWFmzbtg1yuRxms5kve5rYrKyscEd4/fr1nMSSSzo1Cd55550110bPzmg0crKQlZXFRPzy8nJO5k0mE/Ly8pCZmYmFhQWeCqSnp6OiogJZWVmor6+HXq+Hw+H4GNxv06ZNvNeowFepVJ8oqQx8OCmTyWSc+Ov1etx333082SFX1Pb2dh5NE98lGo2ypwh1m8nkjWCHFRUVCAaDLF2YmZmJ4uJi5OXlJST9CQQCbN68mZV/CCO8Z88eGAwG9mEh1RUqGo1GI+677z7k5+djZGQEfr8fmZmZyMvL4y4V8SC2bt3KZlRkIkomTon8IIgbR6ZR9G9T8kfa9mq1GiUlJSzzWVxcjOrq6o+9m3PnziEej8NsNrNww0fH/QUFBXwxtrS0ICMjA/39/WuujeBf5D3i9XoxOTmJtrY2rKys8BlMRHabzQahUMiqKpmZmaipqcH9998PmUyGffv2cVJFHWuSq6YmHABWrEo07SYloczMTCQnJ0MsFqOoqAjbtm1jd2WlUony8nIWQyGeTW5uLnu+kBR4UVERe/v4fL6PcblI33/Tpk3Ytm0bw+XWCplMhng8zmpyBKWg96fX61nljBJQIheTmlxaWhrS09PhdrvR3t7OCjexWAxzc3PYsGEDN1SoK0+ohEQJDMF+lpaWuLNPYhF6vR6FhYVs0FlUVMTTSYlEgr1796KhoYGlwmOxGJqamtgfjDxSqqurMTc3h/r6ejahpaQr0drIwJXylaSkJAgEAhYxIbW98vJyNDY2IisrCwsLC9DpdKitrUV+fj5EIhH8fj8UCgU/w4/+26R8SSTp5ORklJaWsgLUWkG5ETUvSd68vb2d7xSlUonS0lI0NTUxbyg3Nxfr169HSUkJN26ysrKQnp4OpVLJ7zs5ORlDQ0P89xC8np5JoqI7KSkJhYWFMBqN7O0zMjKC4uJiliienp7G1NQUuru74ff7uSl08803c+OYeLx1dXVYXl7G4cOH8cYbb2B8fBybNm1iTzbyL6JJ8dLS0pprW1pagslkgl6vRzgcxsTEBGw2G3t+0N6gBpDD4cDU1BTi8ThuueUWVFVVcSPeZDLhlltugVAoxPj4OKNqSOo2NTWVlQAzMzP5u1orQqEQ/3ter5fVtG6//Xa0trZyQzQ5ORmpqansHzM0NIT+/n7m3VDjMS8vj5tmdGbQ9IsKYalUivT0dKjV6oTn20fjUxcaRqMRXV1dyM7ORlZWFhtZPf/883jwwQc5gfd6vZidnUVDQwNeffVVbNq0iclnubm5ePTRR6FWq/Huu++yUpVGo4HL5cKtt96Kf/u3f4NcLodWq8Xw8DAcDgceffTRhHh+g8GA8+fPc+dycnISs7Oz6OrqQnFxMTo6OuB0OjE/P4/e3l48/vjjePvtt9n8SKPRoLq6GklJSUxYA8CO3pTk79ixg51D6WJvaGjgpOF6kZqayhwIMiNbt24dzpw5g+npafT392NoaAjHjx/HH//4R2i1Wrz33ntcMNx1113YsWMHTp8+zR3H5uZm1ks3mUx4//338U//9E88fr18+TJmZ2exdetWjI+Pr7k2sViM2dlZrtIFAgHjCO+66y7k5+dj+/bt2LhxIxYXFyGVSvHOO++gra2NyUXbt29Heno6HnnkEcjlcmzdupV9H1paWmA2m/Hoo4/i0KFDnMQRwTSRvC11DWlEPzMzg8rKSly5cgX19fUYHh7GyMgIY3RbWlq4aO3r64NEIkFzczObpl28eBHbt2/n8X1WVhbOnj2Lz3zmMxCLxdi8eTMWFxdx+PBh+P3+hMWj0WjE9PQ0DAYDk682b96MaDSKDRs24NixY1Cr1aivr0c8Hkdrayuee+451NXVcaKfnJyM48ePc6d0w4YNGB4eRl9fH0QiEZ577jlIpVJ4vV5s2bKFx7dlZWWYnJxcc23kFltSUsJTLerEtLe3Y3V1lSU7FxcX8etf/xqTk5O4++67kZGRwS7Ly8vL+NGPfoTc3FwUFBTwSFav1+PixYtob29nSCB1R2699dZP7KqRHwqRtHt7e3kf9Pb2IhwOs7P8xYsXkZOTgxdffJGnA4RPXlpawvnz5/Hzn/8cO3fuhE6nQ0FBAfbv3w+n04n77rsPGo0GZWVl3ElTq9U4derUmmtTqVQMmRGJRLDZbNi4cSN6e3txyy23YGRkBCMjIwxNe/LJJ/n3lJeXY+fOncjKysJbb72FnJwchl+SPr1QKMTMzAy2b9+OgYEB7iqmpaUhLS0Nw8PDa64tOTkZw8PDyM7ORmZmJnflJyYmsGfPHni9Xp7GhkIh7NixAxcvXkRTUxP8fj+Ki4uxZ88ejI2NITs7Gz09PXj44Yd5OqbRaPCLX/wCjz/+OAQCAZaWltDV1YWuri4kJSVhbGws4TulySd5SDQ3N+P48eO47777kJOTw2dtd3c3WltbcfnyZZagfuihh7Br1y5cvnwZ99xzD9xuNx5//HHk5+dDpVKhuLgYR48exRNPPIGXX34ZRUVFiMViWFhYwC233JIwuTIajRgdHeUpsd/vR2pqKvx+PyorKyEWiyESiZCdnY1NmzahuLgYx44dwxe+8AXmjywsLKCvr4+/J5qW6vV65sLs27cPx44dQ3t7O7Zt24bKyko0NjYmPH9jsRjeffddBAIBqFQqNhMdHh7G1q1bucAoKSnBmTNnkJ2djWPHjmH//v0oKipCdXU16uvrEYvFsGXLFvT396OwsBC9vb1sVvncc8/hc5/7HHdwR0ZGMDs7i9raWnR0dKy5tsrKSgwODkKr1WJxcREXL17EunXr4PP5sHHjRiwsLLDSzszMDBobG1kC9ODBg7DZbFCr1bh69SpSUlKwuLiIiooKNqrbtWsXCgsL8cUvfhHz8/PIyMhAUlISlpeXeaKzVuTl5UEgEGDr1q3Iz8/H4uIi2tracPr0aaSnp+Ptt99GRkYGWlpa4PV6odVq8cILL6CsrAwFBQVobm7Gxo0bsbS0hLvvvhsTExPYvHkz85Hkcjnef/993HbbbQzBAsBcnUShUqngdDq5MCCVOrp/enp6mPR+6dIlbN26lf0wwuEwqqursWPHDvj9fuzYsYMl9GnSeNttt+HSpUvIysrC97//fZ5QzszMoLq6OqHcaEpKChdzdrud4TOHDh1CdnY2Ojs70d/fD7PZjJGREWRnZ8Nut6O+vh5WqxUZGRnYuXMnDAYDWltbcfr0aXzmM59haHBycjKWl5fxzW9+E3K5HOXl5XyXbtq0KeG0m8xITSYTlpeXMTk5iQ0bNrB4UF5eHhfQ09PTqKiowMGDB7Ft2zbmcUajUfz2t7/lZs8jjzzC01qxWIyf/vSnSE1NxdGjR5Gfn4/q6mpkZ2cjJycnYcKclpYGs9mMlJQU5i3ceeedUCqV2LdvHwYHB3kiFI/HcfvttyMrKwsPPfQQSktLUVlZiY0bNzJa4ty5c+y6npycjNbWVrz99tvIzs7GyZMnsWHDBpSVlUGn02HLli0JZagJ4lpVVQWj0Yjx8XEEAgF0dHQgOTkZJ0+ehNlsZtPM+++/H++++y6ampogl8tRXV2NpqYmTE1NMTImKSmJC8iqqiqsX78et956K9LT01FTU8OIAxIC+DTxqTkaJEFJ+s91dXWMH3333XeZDENd597eXmzatAljY2OMG4/FYmhpaWFsZU9PDycqbrcb3/72t/Gv//qvSE5O5qkIdSwSeQe43W4UFxcjEAiwjv6xY8dQVFQEt9uNyspKnDt3DgBwxx13sIng8vIyUlNTcfnyZXR1dbFb7PT0NIRCIWw2G/r7+9HT0wOlUskVpkgk4nEWmQ+uFbOzsygtLYXP54PFYsH8/DzMZjNqa2v54yHXUko6H3jgAZw4cQKHDx/G1NQUysrK4HQ6UVZWhvn5eZw8eRKBQAA6nY4t4V977TWUl5cDAEZHR2GxWFBUVJRQY54gRTQi83q9OHbsGJP3AODSpUtISkpCdXU1dy2HhobYeI9UNcgQhuTYent7MTg4iB07dmBqaoo9HS5evIjZ2VnodLqEZi+ETfX5fOyO2dfXh9tuuw1HjhxhLw7yhLh27RqMRiP+8pe/IDc3l+WKH3roIbz22mvYvXs3dzOou9rc3Iynn34a9fX1mJ6eRlpaGkwmE7tjrxUulwsqlQp+v5+7ZkSM+uMf/8hkZ7/fD6PRiOPHj+OOO+7AxMQE42DJm+XatWvQaDRob29nNaWTJ08yjjgQCOD06dOwWCzsDpxIMpMKKXKQHh0dxfj4OB544AHMz8/zdGh2dhZnz55lydY//OEPzJNKS0uD0WjE+fPn8Xd/93c4evQocnJyGPtps9mQnZ2N2dlZht8RXPGTgoyUkpOTObkg13a1Ws3Plbxw/vrXv2L79u04fPgw9u/fz+pB0WgUJpOJ1UfMZjO/VxIdWL9+PYaGhhhzrFKpUFdXt+baqINJpkwSiQQTExNwOp04e/YsT2FownflyhUeOV+7do2hYOXl5ejt7cXExASKiop4MkoeHeRxMjU1xRDAhYWFhMUtdWTJBdfv9+PgwYPYs2cPfve730EikWB0dBSTk5PQ6XTIzMzEsWPHkJqaym7zBBGqqqrCjh070N/fzw2c5ORkbNu2DVeuXIFer+cpWnJyMtxud0I8/+LiIoqKilimPDk5mVWQ3nnnHSaolpaWsjvuAw88gP/6r/+CVCpFQUEBm5W2tLTgl7/8JUMayDOorq6OCcQSiQQdHR18xiVyaib4B50nOTk5CIVCOHnyJFZWVpisThK8c3NzcLlczPci7tS2bdswODjIng3FxcXo6urCBx98gK9//ev4y1/+wk0a0uS/cOHCJxZBBQUFGBwcRDQaRTAYRH9/PyeZb775JnefyaytoKCAFf6cTidyc3Oxbds2vPjii2htbYXD4YBarYbNZsPk5CS2b9+On//85wxNJPhoTk5OQsfhzs5OtLe383RYr9ejv78fOp0O7733HnuGqFQqtLS0YGFhgcVhiO/n8Xhgs9nwxBNP4NixY3j99dcZ1kl/trOzE01NTQiFQujo6MD8/Dyqq6sTPrfBwUE2AKQCt6Ojg7/L22+/HT09PRgeHoZYLEZ7ezuT4Q8cOACv18vQEJfLxXC04uJipKSk4O2332asvFwuB/Bhsun3+/Hyyy8DwJoSt2SUS+aBw8PDGBoagkAggNPpRDAYZAhtamoqOjo6cM899+C5556DWq3G2NgYK3K9/PLL8Pl8nHza7XbOU2hCQNN3hUKBqakpdHV14bbbbrvu2iQSCRsS0n1ttVqxc+dOOByOj8FrSdzHaDRywdnV1YWrV6+ipqaGzZatViuWlpaYlFxQUIA333wTkUgEZWVlsNlsmJqaYgnptcLhcDA/hvZ8V1cXvvrVr+Kvf/0rKisrsbq6ivz8fDz22GMwmUw4dOgQDh8+DJ1OB5VKBZ1OB5/Ph+rqavzoRz/CY489BqPRyCalBGM2mUxsHkzIit7e3jXXRgIDWVlZmJ+fh9Vqxfvvv49Tp07hscceg8FgYB5LeXk5jhw5whMXMoRcWFhAc3MzVldXMTg4iJycHNx0003o6OjA888/j71792J8fBw+nw+nT59GSUkJDAYDLl68mPA7JUQBQbuUSiUT0k+fPs2FitfrZU4fyaQTZFcsFvP3SsiFrq4uBAIB5u61trYiJSWFUUk09U7Es/1ofOpCQyaT4Qc/+AHy8vKQnp7O5kU0jtuzZw97Z5ARybPPPsuScSRnSUTFxsZGRKNRPjBCoRCGh4dRW1vL6itUDBDOd60gYiglSWKxGE6nE//+7/+OHTt28Ivo6urC0NAQ/uM//gMSiQTd3d2YmJhgAtLs7CxjNrdv385Opps2bcKJEycwNjaG6elptLe3Q61WM66PEvzrhVKpRFdXF3bs2MEyd0tLSxgdHYVarWaH4AsXLuD06dO4/fbbUVRUxN1PqVTK3VIyWPsoxOXgwYOwWCx44okn4Pf7odVqeYN7vd6EkDOdTsd/J/BhEhgIBDA0NIR169YxFn5mZgZvv/02NmzYgMrKSgwPD+PgwYPsmzI5OclkbZvNhubmZjQ2NkIoFOKNN95AS0sLGhoaAHxIzqR/L9FzM5lMjNdUq9UIBAJYWFjA8ePHUVFRAblcjunpaVy4cAHj4+O49957sWfPHuzbtw+ZmZksmbywsICkpCS8++67aGtrQ11dHT9/OkByc3OZgC2RSBIqdwDgJJ6KM4KGjIyMsDxsXV0dRCIRxsbGWH3n0KFD0Ov1DH1RKBRoa2vDwYMHUVxcjN27dyM5OZllNQnfnJOTg8rKSiwsLGB+fh6lpaVrro1M6t5++20UFxdj3bp1iEajeOGFF1BcXAy3282yv+fOncP777/PCiqlpaVsmmWxWLC0tIR33nkHLS0tKC0tRSQSYSjE3Nwc7r33Xhw9epRxuIuLiywdmmjPkVoaGTkRxJJ08CcmJtDT08PYYEp2z549C71ej5SUFMRiMSiVSrz88su4+eabWQnDZrMhEomgvb0dXV1dLKVLyU+igzE7OxtTU1M8BhcKhUhOTsb09DTC4TD27t2LeDyODz74AE899RTuu+8+3HTTTfznlpeXoVarUVpaiitXriAtLQ0ejwcGg4Gxrx0dHayoRSpIRMpMRPpTKBS4cuUKsrKyYDQamXs2OjqKUCgEiUSCqakpJmQWFhaivb2dkz/gw8lvXl4ejh49iv/6r//C1q1bUVBQAJvNhs7OToyNjeFLX/oS+vv7+Wz3eDzQ6/Voa2tbc21arRaHDx9GfX09srOzGVK2srLCpMvLly/DbrfDaDRiZWUFo6OjuOmmm3Do0CFotVrmn6SlpUGlUuHixYswGAxQqVTcpZ2cnEQkEmGMtEKhgFKpTNjFlclkOHbsGEwmE1JTU2GxWFgRZ2ZmBps2bYLdbmdJWpvNhuXlZcRiMX6nmZmZTLb+4IMPEA6H8fnPfx7l5eWIxWLo7Ozk/X/q1CkunsgQa62Qy+WsWhcMBrG0tISUlBQcPHgQFRUVyMjIwNjYGMPWSOnn4sWL6OrqQklJCXJyctDb2wu5XI6Ojg5uamm1Wuj1ethsNjz22GM4f/48O6sTfCbRflMqlTh69CiWlpaQnZ0NvV6PkZERdHR0YP/+/ZDL5bh8+TI3T4iX19fXxy7klZWVrIZVVFSEkpISFk4AwGapNBUm6KpKpUoIcdRoNJicnOR95nA4UFBQALFYzGpzq6urrDan0Wjw8MMPY//+/XjzzTchlUoZPkWwR+JelpaW4vOf/zyee+45KBQKlJaW4ujRo5icnIRCoeD7OlGEQiEsLCwwX4nEK7Zv3w6BQICDBw/ivffeQ0VFBVpbW3H48GEYDAbk5uYyGVur1SI9PR2XL19mnqVarf4Y/LCmpgYXLlxggrNcLk+oNLm8vMzTAFI6CwQCLOtcUVGBgYEBDA4O8vdJHCBCbSgUCoYWjoyMYOvWrfz8+vv72YCY/n5CLpCoxFqRm5uLt99+Gzt27EBFRQVyc3MxNzeHp556CtXV1ejs7IRQKITX68Uvf/lLNDc3M3z1tddeQ0NDA/bu3QupVIrBwUG0tLTA4XCwSubg4CDz21wuF/MWkpOTUVJSwlKv14ucnBzMzs6yKAp5o+zevRuTk5OQyWSorq5mh3QiU4+OjrLRa3Z2NuRyOXp7e2G32/HKK6/gzjvvRH19PeRyOQYHB+F0OpGfn4+UlBTeY2q1+hMNei9duoSamhrIZDI2Wjx//jw0Gg2qqqrQ19eHmZkZzj1Ihe3gwYOYnp5Gc3MzUlJSEAwGGTJNZsgTExM4fvw4NmzYwL+P1PjIN+XTxKeGTs3MzGDLli3Q6/XMordYLNi1axfS09NZ0o8wjkNDQ2hvb2eVCSLB7N+/H6dOnUI4HIbNZvtYt23fvn3sNjozM4OMjAzU19cjGo3ixIkTa64tGo2iubkZubm5yM7OZuWXH/3oR8zxILL55z//eXR0dLBSzUfH/rW1tThz5gza29thsViwurrKilaPPPII8vPz8cgjjyAWi2F6epo7X8Tav14EAgFUVVVhZmYGs7OzEIlEGB4eZjgSJd2VlZW44447cOHCBSQnJ+PcuXPYunUriouLMTExwV3w4uJi9gSYnp6GTqfDQw89hMnJSdTV1WFoaAijo6OcwCXq4BIpXiKRsNKMXC7Hgw8+CLlcjrq6OgiFQphMJjzxxBNMPJudncV9992Hu+++G0VFRdBqtZibm2N98IWFBZ5g7d69m5OdQCCApqYmhl4lgpyRosno6CjsdjuSkpLgdrvR1NSEWCyGjIwMFBYWslkcQVhIspdcSYlA/vDDD2Nubg4zMzNsQFdVVcXE9lgshry8PBQUFMBisSTkQVBhTHhFIij/+7//O6tYjY6OYmhoCCqVijkdVVVVuPXWW7Ft2zZs2rQJDz/8MCYmJvCtb30LPT09eP/99zEyMgKTyYTKykqG7tChk5+fj2AwmHASFAgE0NDQgJycHIZ9xONxPPTQQ/B6vWhoaIDP5+P/3NPTg9raWtTW1rLjtMFgwD333AONRoP9+/djZGSESa7Dw8PIycmB2WyGw+FgkjWROhOtDfgQ2qXRaLgTRkpWLS0tbIpEuPSmpiZkZmZieXmZYV0EW6yurkZKSgp+/OMfw2w2syu1Wq3Gfffdh8HBQTZKSkpKQklJCSvMrRWk/iSRSGAymVBeXo6xsTG0t7dDIpFgbGwMXq8X69atw9e+9jWGZYjFYjQ1NaGurg45OTnIycmBVCrlAm5lZQV+vx/hcBh79uyBx+NBTk4O8wZisRgLB6wV1BEjTkF5eTmqqqrQ1taGyspK9hcpLCxETk4ODh8+jM2bN2N1dRUGg4Fx6lT8PPDAA9yZKigoQHZ2NkMta2tr4fP5kJ+fj9raWi7C14pYLPYxWB45fK9fvx4KhQL19fWsbLd+/XqIRCLU19djdXUVt99+O190ubm5+MMf/oDPfvazrMjX1tYGk8nEqofr16/HxMQEZDIZMjIy0NnZmZBr4PP5sGvXLkilUvblaWxsxK5du7B+/Xrk5OQwDj4tLQ1qtRo7d+6EUChEY2Mjdu/ejZ07d2Lnzp3sc1JZWckE++XlZebgUXeUMP2XL19OSFSfnZ1lcQuJRMIKQv/yL/8Ci8UCjUbDuv979uyB3W7n7/qWW25Bamoqurq6AHyY3N51111cTFLDob6+HqdPn2Z37szMTEYRJII5ut1u9hsAwJPChoYGLC0tQSqV8nl2++2387TPZrOhtLSUJ8NCoRCvvfYa3G43hoeHoVQqkZqayrL2BEMcGhpCS0sLbr31VkxNTSWUenY6nUhLS2MCPe19chenAo6Mdi9cuIAf/OAHkMlk+N73vofPfe5zbHpot9uxZcsWzMzMwGw2Y2xsjJso/f39mJub49+am5vLsM+1ggzYyDeHpGeNRiN6e3vhcrlYSl8oFGJoaAi5ubkYGxtj6A1Bi6emprBv3z40NjaiubmZvcUikQhmZmbYtfujE6REhYZAIEBJSQmcTidPcvx+P9ra2lgsJTk5GcXFxSgtLWWp+qWlJRQXF6OgoICljU0mEx588EHYbDYkJyezUmFNTQ0EAgFqa2sBgN/34uJiwqZnNBrFli1bsLq6CovFgrm5Ofj9flYRq6urQ1FREerq6nDnnXdyQ6Cnpwft7e3Izs7GwMAAuru78dJLL+Gee+6BTCbj+6i0tBRtbW2wWCyorq5mAZ6srCyMj48nVPxbXFxkAjVxaPV6PXMAKyoqWGCAhBEqKiqwuLiI1tZW1NXVMfeyt7cXGzZsYCPMSCSCubk5tLS0MA+ExF/IuoAK8+sFGasSP4xUpurq6tDS0gKj0Yj09HSUlpZi3bp1UCqVjIhob29HeXk5DAYDtmzZgmg0ipqaGkxPTzOfmPLoN998k8VWMjIy2Kk9kXz3R+NTTzRozJaTk4OkpCROFKLRKDIyMjA3N8cJYFZWFq5evcqYTOoqKJVKjI+PIyMjAwqFApFIhBWA4vE449ympqbY7Ieq9ESqAKTdTuRN4MNuEcFnSJmCbOoDgQDsdjva2trg8XhYZWZ+fh533303K6CQ8klubi7S0tIwNTWF4eFhaLVaSCQSrK6uQqfTJZRGi8VirHq1sLDAahFE8iFMHSkOEbY7Ly+PXcnJdTMlJQWzs7PIy8tDMBhkNSMy9VtaWkJWVhbL7y4vLyckcVLnWiaTIRqNIhqNspxja2srnE4ny6mSkVpHRwfS09ORmZnJhODZ2Vnu7ufm5jLJ/6MGQOfPn0dlZSWUSiWTORONx+PxOAQCAeMivV4vd/KDwSDGxsYgEAgY5kR4YLFYzIowQqGQZWvJMdzj8bB0HxGiKXEhGJtKpUooxUcyekKhEKFQCJFIBAaDAQMDA2hqauKuVDAY5D1O8AwyXRKJRJiZmUEoFMLAwAAEAgGmp6dZ6YuSSZVKxSpGmZmZ2LRpU8KkT61Ww2q18p70+/18Mep0Or7osrOzkZ6eDp1Oh8OHD7OhoNFoRDwex/j4OLt0FxcXs0KHSqXC/Pw8qqqqcP78eb5UFxcXPxHWBfxfpTMi9xPplCaklHSFw2EsLCxg27ZtcDgcyMvL42+VkiiXywWhUIj6+nqeVNA+KCoqQl5eHndgXC4X5HJ5Qq4XjdhFIhHjYqurq6FUKnmKS9K0y8vLMJlMmJycRFZWFu850jPfuXMn/3m6NKVSKcsAnzt3DqmpqewxQRCytSISiUAsFjPROhqNori4mN1ZzWYzFywrKyuor6/H1NQUgsEgfD4fG3X19PTglltu4XN7dnYWNpuNceLV1dUYGBhgVSyCgiRqCpDLt0QiYbUdhUKB+fl5mEwmnixlZWXxOujfJSWdcDjMqn8TExMoLi7mMyk7O5sJpTabDQCYM0QwobUiFAqxDK1IJEIoFGJCZEZGBtRqNYsTUCc9Ly+P1aW0Wi0Xw2KxGKFQCLW1tXC73TxV6+vr43NEoVBgfHwcSUlJKC8vT1hoKBQKdHZ2fuwZZmZmYmJiAuvXr+e9mJGRAYPBwMaCpOZDPi5Eho7H4wwLCQQCnAQUFBSgq6sLBQUF/I4Ih71W0DlIU276NxYXF1FWVoalpSUEg0FWciotLeXz32g0IikpiflxJKdKvBoyFyODxlAohKWlJZjNZmRkZCAzMzNhw0IkEiEYDCIjIwMSiYQbS5FIBMXFxQzNouKe1NBSU1NZvEQqlWJychKZmZkMa87JyYFYLMbo6Ciys7ORkZGBjo4OGI1GhoWSH9RaQQpDlB+QiiSdHaOjoyxQQDClyclJ5OTkIBgMQqlU8nSPCsL29nYsLCzA5/NBp9PBbDZDIpGgt7eXFZoIUZDI4ZrMb0m2mXgVBIEmt3CaSJBAQ0lJCYLBIJtXulwumEwmhgiSSmhxcTFyc3PR1dWF+fn5j00IP0nQJx6Pc3N0cXERbrcbDQ0NrHBEHlfUaCkqKsLJkycRjUaRlZWFnJwc+P1+DA4OYvv27SyXPTQ0hGAwCJlMBqvVitLSUv5eSZ6WBEXWCpLSlslkzN/V6/XMcSG4EfHTiCdVWVnJapMrKysIhUKoqKjA+Pg4ysrKYDabWbnO6XSioKAAR44cgV6v54meSqVKqM4pEAjgcDhY6Y7WQn4XTqeTlWCp6AU+LAC9Xi8rwJGZqc/nY8UxmnqWlZWhs7OTpdkjkQjcbjdKS0sT8kc+9gw/1f8LYAyYUCiE3+/nMb3ZbGan1lAoxBU5jYSJQxCPxyEUCjE8PAy9Xo/JyUkm5ZGc5uLiIkukCgQCuFwuTr4SjaCj0Si8Xi8/RMJoU6IXCAS4GHG5XOwWnp+fz2RMiUQCi8WCiooKjI2N8SEWj8cZwymVStHd3c0O0tR9TdT9Judoko9NSUlBWloaY2mJHEyFiNFoxMzMDKqqqpjkpNVqWQt5dHSU/+2VlRVEo1EMDQ2x0QsAXk84HE5IqhMIBJxM0CVM8npUWPl8Pvh8PpaFW1hYgFarZRw7/VvJycmYm5tDQUEB/2/kokkJrt1ux+TkJObn55Genp6wQCMTRcKYR6NR6PV6WCwWVsGZn5+H1+vlC3lubo4/PFK7WFhYYAMdgquQh4fZbIZer+dxJ2ESScZ5rfhooUGQrkAgwPwluoQpWQ4EAvB4POwfQNJ13d3dAID+/n6WPCRoCHVOaAS/tLSEhYUFLvQT7TeSziM9d6lUyqpHZKRIidqmTZtw+vRpxlsSadlsNiM7OxtjY2NITU3l5FatVsNut6OmpoYNMIPB4MeSikRBeFPCPpMWN3XoyHCToCSkwEKwKJKlPH36NMRiMfr7+xkWk5KSgtXVVfT09EChUHDxIpPJmOeTqIBMSkrihD4YDMLtdiMtLQ0AWLaTvhmPx4OioiJYLBae5tBl7Ha7kZ6ejsHBQe760O+22+3QaDSwWCw8ZaBOe6Jmikgk4gt7ZWWFL3By5XW73exm7Pf7kZ+fj97eXoRCIXi9Xr4kOzo6oFarMT4+zvAG6i6TQ7nT6UQ0GoXD4WBFr0RmW+RbRNPpjyqUCIVCDA4Osuyy1+uFTqfjd9rT04OJiQl+7lQo6vV6zM/PY2Jigg1ZjUYjJicnEYvFYLfbYbfbGVeeaL+Rmh51yWdmZmC321mlJx6P8/+PuBL0m+mctdvtMJlMsNvtyMrKYkLk6uoqFhcXudtH5xB5LyRSsyFpZpI2JcnOkZERFBUVYXFxkc8C4j709vYiHo9/zJiV7qahoSFWu1pdXYVEIoHdbkdubi5PzkgVEEDCe4vIudSpJeNEm83Gjts2mw1Op5NNT6VSKVQqFdxuN5+71P0m4uvw8DD73CwuLkKlUsFut3PDpb+/nycea4VMJuP9IhAI4PP5WOyFind6H11dXVAoFDCbzQxlDYVCLJpAkDBSzxIIBHw3ZGdnM+rA5/Ox2Egi+CUVo9ToSkpKQlZWFqtPejyejzVeNRoNZmdnmQxO/E+STLdYLFAoFLDZbLDb7RCLxWz6SQ2KUCiEUCgElUqVsECjM1ylUvE5QoqbtDdoYvLRYom8KgiiSUI0lFssLi7y30ET9ImJCfZTW1pa+sTmnUAg4EJLLBazc/nc3NzH7i2fz8cQwJGRERZ4iMfjDJGtqqrC4OAgq6KRjOv09DS0Wi36+/v5ng2FQsjIyGD/pOsF5ZJCoRDxeJzh0j6fj9UtScGTJMfNZjM3cElWfX5+HoWFhTCbzcjMzITNZuOJO0GwqPFMimrUdFsryAyQGvYf3ZvxeBzDw8PcMJ6dneXCjXIvkh2fmJiAUCiE0+mEyWTiSQuZyhYUFMDhcLA1wdzcHHO+Pk186olGMBhEY2Mj3G43wuEwZDIZ4+3pgKaKbmxsDPF4HKurq1AoFAgGg9wVUSgUbEr02GOPwe12w+FwsNxaV1cXdx9Iwq6vry+hNBrJMHq9XsaPUTeHIDxOpxNCoRDZ2dmwWCyor6/H2NgYy45Go1EIBAK89957CIfDTDCfmZnB0NAQ1Go18xbIDEUqlcJkMiUkwVLHgTrqUqkUhw8fZs1w4hCQ6yZpGNOUgmQiV1ZWcOXKFSQnJ8NsNvNhTy7cN998M65evYqpqSnI5XKu/BMFYQkBsOfCsWPHkJ+fzx0EAOwzQRV5X18fv7OPQt/8fj8TyUiibmBggPXnl5aWWDfc6/UmHI8DYI1ptVqNeDyOkZER2O12PoxI1pF+Lx2I5DhNHT+3283JIMFTHA4Hrl27hoqKCkilUng8HiY109+9VpCON/Dh4R0Ohxnqd/nyZVgsFvaroCSCitWZmRmWAHU6nUhJSWGta0rErFYrpFIpenp6sLq6iqGhIb7kMjIyEk4NqKNCECO67KPRKHJzc6HRaNDf348rV66grKwMW7duZTlHUpD4qKDD2bNnsXnzZpSUlMDv9/O+WF1dRUNDA0ZGRrijJZFIEmrMA+BCgfDVJKdKxE5SnJLJZMjPz0d3dzdWVlZw9epVbN68GcnJybBYLDCbzbj//vsRjUYxMDAAg8GA1dVV9iO5evUqAGDdunXc0CBDxbUiHA6jsLCQp4UA0NPTg9LSUojFYkilUszOzsLj8bB2fXJyMnp7e5GVlcVdc7FYjJ6eHnR3d6O0tBQKhQJutxuLi4ssF7thwwZ+P1KplPlrawUpElHR4nQ6MTQ0xEov9H253W5WbdFoNOzzQQlJPB7Hq6++itzcXExNTXFnsr+/H3a7nc+WUCjE3Sr6xtcKr9fL8qcEPzxw4ACys7M/lgARh4XMwchATqPRoK6ujsUiKNEJh8OYm5tjnhM1kqi7q1ar2XdmrRAKhSgtLYXFYmHO1/T0NHJzc9lUzefzMfkbALq6uvhcdblcKCkpYbzz4OAgK9xQgVZQUAChUMhFNl3UY2NjCbuRDocDlZWVEIlEDLWZnZ3lJg99I263G0qlkjv1aWlpWF5eht1u599AnBKSQKXJJikK1tfXw2w2c8FLRf1aQdw4kq0PBoPcpFteXoZcLkckEuFJmtvthtFoZF4LNeoI03/69Gl2OyeJ2+7ubmzcuBGTk5N8ltM7TzQ1oPdISQ4V0waDgZNl4oZ2dnaymTCtnyCfNE10OBy46aab2KuFnk8sFkNdXR0mJiZgsVhYIjTR5JE8mOgZmUwmbiy5XC4uPiORCDeNFAoFxsbG0NDQAI/HwxA5asqdPXsWfr8ffr8fgUCAhWhImIC+BZPJlHDyGAwGIZfL4fV62TeMYHAkEkBwOuLejoyMwOPxIB6Ps0KoVCpFV1cX3G43qqqqOPkcGxvD+++/j3Xr1mF5eZn/DuLikNnu9YJ8ogh9IhQKce3aNaSlpSErKwtKpZLP5FgsxrBDenZOp5PtCy5evIilpSW0traiqKgITqcTZrOZ/XlIfIJMJgUCQcKEmWRpqXgmdb3NmzdzzkaqnYRE0Wg06Ozs/Ji4CN17RUVF8Hq9LKtNcruzs7NYv349i6MkJSXB4XAkbCySKBAVttFoFDMzM2xE6nA4uFlBHCSCdVGTXSwWs4fYysoKIpEI/6bBwUHMzc0xv4rueoLQ/48XGunp6ejo6GDTsHg8DrFYzOQ3qVTKPzQUCmHv3r24ePEiTpw4wXrR9GGnpqZCo9Hg6tWrMBqN7N75/vvvIz8/H2VlZQytIt5HIgKsXq+H2WzG6uoqJ4jxeBwdHR2oqqpi8y2/38/KWCqVCkNDQzwqSk1NxczMDMrKyjAwMIALFy5wkWSz2XDu3DnEYjFotVoYjUZWQ6Lkeq0gE0EysqEpxOLiIgKBAAoLC1n+try8nDsyV69ehUajgUwmg8ViwejoKCorK3Ht2jUMDQ1hy5YtKCgoYLfP0dFRxiBS95a01RPF6Ogo/66UlBR2Qu/v70d2djZX8ATBIbgYjV0FAgHm5uagUCjQ19fHahZ0SQYCAYyPjyM7OxsymQyTk5P8cSQiI8ZiMVitVhgMBoY6DAwMoLq6Gt3d3cjJyeEOPBHniMSZn58Pn8+HyclJFBcXo7+/n91AKfGn8TXtGRo7k4FXIk8DrVaL6elp9gGhInp6eholJSXsPUE67ZTcTk5OorOzE+Xl5cjNzeWCpaurC7fffjtP2paXl3Ht2jXk5eUhIyODFU/I1ChRdzkSiaCzs5MhH1KpFL29vRAKhbjtttuYW0Td8+PHj2Pjxo0YGxvjrpnP50NtbS0KCwths9kYKmWxWPDLX/6SFdKIk2G1WtnAiCYAawWRq+kcoWTO4XDg/vvvZ6Kh2WxGUlIS9uzZg6WlJdZHp+QpNzcXP/7xjxGNRvHDH/4Q8Xgc/f39iEajqKqqgkajYeJlRkYG8vPzGaqwVtC0IhaLwe/3c6ec9lsgEOA/T7+9pqYGSUlJkMlkDBsKhUKw2WzsYPvR85G6ewqFgrv8JE+ZSJWFCixSiBOLxdBqtTxpjEQimJqaQigUgslkgsvl4mnoR32OyI+BdN4JRjo7O4tQKMQutYT1pkL1k1yko9EoTykkEgkMBgNOnz6NmpoangrTJMZoNGJgYAAOh4MJsjabDa+//jqr/pFkJHH8AoEA+6EQr4GS0kSGVjKZDMPDw9z5n5ubQ2ZmJrq7uyEWi1FfX89daI/Hg4aGBu5uDwwMYGVlBVarFWazGT6fj/chnbECgQAWiwUFBQWYmprC6uoqexORkexaIRKJuIkWDocxOzvLXUlqClAhlJSUxE2SK1eucGPC7/djZGQE+fn5cLlcDNuy2Wy4evUqcnJycPHiRTb1IgKswWBICDkj53HyAiGT0cbGRp5+kX/LiRMn2BxPq9WioKAAq6urfHZQ44V+A6msLS0tIR6PY926dbzm5eVlyGSyhO/UYDDAbrdzIUzrEYlEmJqaws0338zJEBmPFhcXo7Ozkxs7ZLBKUyKFQoGsrCyEw2HEYjEcOHAAJ06cwA9+8APu7pJPUiKe18rKCqamptjkk9S4FAoFhoeHWUBgeXmZie9UkKnVavYpIw8sgsSQMILb7YbX6+VkkZpYfr8f3d3dCZEfZLKYlJSE1dVVnoQYDIaPCcksLCxgcXEReXl5uHz5MvR6Pc6fP4/09HRUVVXx+bCwsMB3PfkVOZ1OpKenY2xsjBEZZOyYiING0FGavspkMhYecTqdaGho4EmezWZDeno60tPTOR8hHxzyniEOLu0L8jSqrq6G2WzGxMQEox7IY22toCmlQqGAQCDgc+7QoUOIRCKor6+Hz+eD3W5nD5WSkhKG6BHCZ2FhAXq9HrW1tSyBTEULmTqTcTDdjzRxXStkMhkEAgF/o16vF/39/SzARPt1cXERDocDJSUlPG0ym81MkjeZTLBYLMjKysLi4iILbVD+YTab0djYyI1FmlwnIqp/NASrn2TPfCNuxI24ETfiRtyIG3EjbsSNuBH/j/GpORo34kbciBtxI27EjbgRN+JG3Igb8WnjRqFxI27EjbgRN+JG3IgbcSNuxI34H48bhcaNuBE34kbciBtxI27EjbgRN+J/PG4UGjfiRtyIG3EjbsSNuBE34kbciP/xuFFo3IgbcSNuxI24ETfiRtyIG3Ej/sfjRqFxI27EjbgRN+JG3IgbcSNuxI34H49P7aPxzDPPQKFQYGVlBTqdDhqNhs2U6uvr2e9CJBKxI6Tf74dOp8PBgweRmpqKdevWsds2+SCQkc7c3BwikQhyc3ORmZnJBnyLi4vs7v2tb33rums7dOgQaw2Tl8fi4iI0Gg0b7wmFQtZpzsvLg0QiwYULF1BaWspmVFlZWQiFQlhdXcXIyAi0Wi00Gg2SkpLgdDrZnZXMhMjIanFxEV/4wheuu7af/vSnSE1NRSgUgkwmY3fQUCiEtLQ0qNVquN1uBINBdpD0+Xy4dOkSa2wnJSVBqVRiZGQEFRUVsNvtkMvl0Gq17LAqFArZZTQcDkOj0SA7OxuDg4P4p3/6p+uu7Ve/+hUEAgGb6pDrO2n6+/1+pKSksGu2WCxGXl4eLBYL5HI5wuEwvF4v4vE4qqqq4PF4cO3aNVRXV7Mb6/T0NEwmE2KxGJRKJeLxOJv5JCUl4Zvf/OZ113bgwAHEYjE2GaJnGI/H4XA4kJ+fj4WFBf6tZDADfOj8q1AokJ+fz8aHYrEYMzMz/Js0Gg17g8TjcUxNTbF+eW1tLbxeL/bt23fdtf3ud79DUlIShEIhdDodxGIxent7kZSUhKqqKkgkEtaD12g0OHr0KDQaDVJSUpCeno7FxUXMzMwgKSkJlZWV6OrqgkajQVFREYLBIPr6+jA3N4fy8nLMzs6irq4OEomEnctDoRCeeOKJ667t8OHDbAhFTr3kE7C6ugqtVsvu74FAgPegQCCAy+WCRCKB0WiERqOBw+HA4OAg8vLy2EBIKpViYWEBMpkMBQUFcDqdCAQCEIlESE5Ohs1mw/3333/dtQHAr3/9azawoucjl8tx9OhRNg9cWlpih1MykvT5fJiZmWETx7m5OezevRvDw8PsQp2amgq9Xo8TJ06goqKCv8+lpSVeo0qlwmOPPXbdtb366qvsOkt/J/kYFBcXs+8H6YhrtVrU1tZifHwcFosFYrEYGo0GIpEIgUAAy8vLSE1NhUqlYnf4oqIiuN1uZGZm8rdGniYrKyv4+te/ft21Pfvss1hYWIBWq0VGRgabgs3Pz0MoFEIikcDn87EmOmn5LywswGq1Qi6XszcBGeNt2bIFbrcbsVgMGo0GADAxMYG0tDREo1H2IlhZWUFKSgo++9nPrvk9RCIRiEQi9usQCAQwmUxwOp1wOBwIhUK8R+LxOLKysjA/P8/mj7T/JyYmoFQq0dPTA7lcDoVCwcatUqmUv2fycVKr1SgoKMDWrVuvu7annnoKwId+RkqlEgqFAi6XC06nk/d5SkoKXC4XrFYrcnNzsW7dOlgsFkxMTCApKYk9WaLRKKamprC4uAij0cgGfXRvpKSkIBaL8TmVlJQEj8eDf/7nf77u2n7wgx8gIyMDAoEAYrGYdfTj8TgyMzNhNBrh9/uxsrICuVzODsBpaWkYHx9nx3QyjhwbG0N2djYEAgF7wGRnZ0Oj0fB3Su7TKpUKy8vLePzxx6+7tgMHDkCj0bDLtkgkQlJSEt8F5JxO5xyZx3k8HlgsFuj1euh0OjgcDuh0OoyPj6OpqYm9guh+pue2urrKTtqxWAwAsGfPnuuu7fnnn+d9RI7kTqcTAoEATqcTer0eS0tLCIfD0Ol0yMzMhEQiQTAYZO+MlZUVTE5Owmg0ore3F8XFxTAajeyhQ2fT7Ows9Ho9ent74XK5UFZWBq/Xu+Z3+pOf/ISfl1KphEwmY+dsv98PtVrNd/rKygosFguampoQCAT4N0gkEkSjUeTk5MBisUAmk0GpVPLa5HI5nE4nFAoFkpKS+D4Vi8UIBoP4j//4jzX3m1wuh16vh0KhQDgchtlsxtLSEioqKhAOh6FQKKDVaiEWizEyMsJmkn6/n93MFxYWUFpaioWFBRQXF6O3txdOpxNisRgejwc6nY6N65aXl9lPa25uDj/+8Y+vu7Zf/epXCAaDSE5ORlJSEpsmkx/U0tISotEogsEgQqEQSkpKkJWVhZmZGQAfnmmUJ8zOzsLr9SIUCrGzPQDk5ORgfHwcGo2G3ceDwSB7inz3u9+97tp+85vfID09nd3BRSIRXC4XgsEgUlNT2cWd8lQym45Go1Cr1YhGo1heXoZUKoVcLsfi4iKbnEYiEb4PyLvL6XRieXkZ8XicPbi++MUvXndtv/3tb9l3QywWIxAIwGw2Y35+HgUFBZxXAmCX9oceegh9fX3so5OSkgKxWIxIJMLfVHFxMZvyCYVC2Gw2lJeXY2hoiE0dZ2dnodVq8fd///fXXdtH41MXGuS2PT09zZspGAyirq4OS0tLfAH4fD6IxWKkp6fDbrfDYrGgsLAQoVAIXV1dUKlUMBqNMBqNGBsbg16vR05ODkwmExYXF9nxloxXotEoHA4HFArFmmvz+XzIyMiA0+nkC4icismYJxAI8EP3+XxQqVRISUmBVCrF1NQUXC4XFAoFZmdnUVRUBLFYDLfbzZs2PT0dNpsNVVVVCAQCbG7jcDgSGqqQqzclO/Sx0MFHrtSxWIwN4LRaLR8GdNHTBkhLS2N39omJCcRiMZSWlmJ6ehoCgQA+n4+Tx7GxsYTPjdYdDAYBgJ8buTvTZpdKpZysBoNB+P1+xONxLtyEQiFcLhdfOsvLy/D5fPD7/cjIyIDX6+VEhpJVSmLWivn5eYhEIgSDQS5i5XI5hEIhIpEIpqen2RyLXEcjkQgXRhKJhB3hrVYrmpubYbfboVarIZVKEQ6HYTQaMTc3h1AoBKVSidXVVfh8PoyMjEAul6+5tlAoxInBzMwMX8ZKpRJCoRDxeByTk5OIx+MoLS1Ffn4+lEolBgYGkJWVBY/HA7PZDJFIhIyMDCgUClitVk4oFAoFSkpKMD8/j+TkZITDYT5wZmdnE+43p9MJmUyGaDSKSCTCxRe5ptOBRw6vNpsNOTk5XLz6fD52cF1YWEBLSwsmJyfhcrmQkpLCByIZudH3TodcIhdTALDZbFzY0DOcnZ1FZmYmYrEYHA4HJ/P0zslBPRQKsTmeTqeDzWaDTqdDd3c3kpKS2Lm3qKgIVquVi1+6ADweDzIzM9dc29TUFBsQ0Tcmk8ng8XiwsLDAF4PP50M0GoVSqcT8/DxGRkZ4rWSCl5ycDLlczmZM9MztdjumpqaQnp7OZxS5eydyHPZ6vdzkmJub47NjZWWFEz6JRAKhUIiZmRnY7XYoFAoufiiRycnJwezsLDIyMtgxns4n+h4jkQiMRiOWl5fhcDg+0Z3W4/FwAe33+yEUCpGens7NCCrqw+Ew4vE4dDodv2OpVMpnCiVSer0eoVCITbno3ZHrc2FhIdRqNYLBIBwOB8xm85qFRiAQQE5ODpve0f9GyTgZ3gkEAuTm5sJisaC0tJRNvCwWC4aHh5Geno7k5GS0trbixIkTcLlcbBQqEAjQ19eH2tpaRCIRCAQCfqbkNn69WF1dhUwmg9fr5Wfv8XiQn5/PTTIyASQDMo1Gg2AwiPLyckxMTGBiYoITxZqaGszOzsJutyMpKQlpaWnwer1wuVzc2FtdXcXq6iosFgsn9NeLUCgEoVCIWCyGWCyGSCTCSR41jKhIJGflubk5iMViSKVS3otSqRSBQIAbkwqFgs+maDQKr9cL4EPTXa/XC7fbDYlEwv/79SIcDnNDMR6Ps/EcNd7ofFQoFEhNTUVPTw9KSko4b1laWkIwGIRGo8HS0hJSUlL4t/r9fiwsLEAsFmNiYgJer5cNHAHwHk/03KRSKaLRKDweD5u/pqamwuFwIBwO89lHDT6XywWBQACVSsXNtWAwyEZ/gUCAmzNUXFMOQs+A3pNKpVpzbQKBgO9h+h3RaJQbRNTkDAaDSEpKwsLCApKSkhAIBKBWqwEAdrsdQ0NDyMvLQ1paGkZHRyGVSrG6uorR0VFUVFRgenqaHeHVajWb0kml0jXXRoUnFTXBYBCRSAQLCwsIBAIoKyvjuzolJQUjIyNsZkhnKADOUxwOB9LS0thgz+fzwePxsNGmWCyGz+fDysoK1Go1N5uvF9TcW15e5mJgZWUFOTk5EAgESElJweTkJJaWliCXyxGLxWAwGLC4uIiioiLYbDbMzMwgNzcXZrOZc2kqwMhw0u/3IysrCyKR6GOO5YlMZqkYW1hY4IYT5Xwej+djOaRAIEAwGITP52PT5ZmZGXg8HuTm5iI5ORlqtZqd1Cn3IBNhuvNVKhUX0Ym+hY/Gp4ZO0ZSCPhI6THt7e3kyQZXo7Ows5ubmUFpayhdMNBpFSkoKCgoKEI/H0d3d/bELmR58LBbjZIvcpWOxWMKHvbKywh+HSCSCUChEamoqJwrU8V9dXcXExARfOkVFRbDb7XC5XHzQSKVSjIyM8MVGnVen0wng/x5yy8vL8Hq9/KGuFdS5IBftcDiMlZUVXodIJOKPeGJiAlNTU/D5fFAoFFhaWoLb7WZn3eTkZHR1dXHHhSZLdJCVlpbyRiUnSCoi1nputB5yLI9EIrBarYhGo5ifn8fy8jJCoRDcbjdEItHHOteBQAByuRxyuRwymQzXrl2DQCDg/y6Xy7mzkZaWBo1GwxdBokuOnhs5OdMhIJFIMDs7y++B9uD8/Dzm5+fh8/kgk8m4yKHLEAB6enoQi8W46xoIBDA2NoYrV67wHl1ZWeHDLdE7jcViWFlZgUgkgkQi4WeiUqngcDgwMTEBv98Pp9OJjo4OnmSQG/3S0hI7dorFYpjNZhgMBpSXl8NoNCIcDgMAP0NKtsj1vbi4eM210ftWKBQfK6bn5uZ4WqFSqdhNWqlU8jSLurVU5NNzpmkIJWczMzNIT0/n754SBFrzJwVNwQBwET05OYnp6WnYbDbuZg0MDMBqtWJsbAwejwezs7NwuVzsBpuSkoKpqSl2yqbEhQp6cmql84EKo0QRCAQ+5gpOzrfk2PrRLrjBYEA0GoVMJkMgEMDCwgLcbjd3TWly5HQ6ueM4NDSEWCyGoaEhLC4uwuVyYWlpCRKJJKGLtEAggMPh4KLHbrcjGo1idnaWu+7k/Ds3N8dOu9RZp6LO4XDwO3C5XJBKpVzA/H+s/Xl0m3eZ94+/vUmWrH21LFuSJe/7viXO5qxtmjZp0xRogXYoU7aBGWAYhgdmGCgwXygDM1MYKAUKXaFNmyZdkzS7EzuJ9323ZcmWtVjyIsuyLf3+6HNdT/KcWO1zzu9zzpzDtGl7S7rv+3N9ruv9fr0nJydhMBg4OdjpdMLlcvGkaLMVDAb5d6PE7oSEBNjtdng8HkQiEQiFQj74x8XFIS4uDiaTiYsFiUTC77i+vj7umq2trWFtbQ12ux3T09NQq9WcUj0zMwOHwxHzHReJRLixEw6HMTs7y7+vVCrl98HGxgZPI+bn5yEUCvl+W11dRTAY5ATl1NRUZGdnQ6/XY2VlBX19fZDJZJyiHQqF4PP5uBGy2UpMTMTi4iJWV1dv2yPp+6LpWDAYhNfrRTQaRV5eHrxeLz8TEokEaWlpUCqVXMgbDAbodDrEx8djeHiY72lKZ6fpdGLi5n3GaDQKj8fDzQOxWIxIJMLNQOoWx8fHc10glUq5QKRDCU2C6HN6vd7bkoXp3QuA//+EhASEw+FNrw34sLkYCoX4u6OutFAohEAggNFohEwmw9TUFB/4IpEIPB4PZmdn4ff7ER8fD5lMBrfbzc0DqjOmp6fhdrv5OUpMTOTfI1ZRGolE4Pf7uYinA+T09DRkMhm0Wi30ej2EQiEXptR4pVRwelcIhUKMjY0hLi6ODxLUeL218REMBhEOhxEfHw+DwbDptcXHx2N5eRnhcJjvN1JtBINBnljRATccDqOkpIQTqtfX1yEQCKBWqxEMBnH+/HmuMVQqFTIzM3kfiIuLQyAQwMjICEZHR7GxsRHzEJSUlMQNu0gkgsTERJ7IDg8PIxwOs4pDqVRCIpFwmjfVJz6fj+8HavR5PB6srq7CYDDcNtmn9wo1pukAcadFE3RKoqf9kd6TKpUKcrmca9+EhASo1WpEo1FMTEzA6/XypCghIYF/Y0qDFwqFfL/Q5HVtbY3v21jNirW1Nd4TqckViUTg9Xr54KlUKnnKnpiYyBNEasLRvp6eng6Px8O/Bf0uHo8HMpmMm5j0XalUqpjvt9vuvY/1p4Db5DNSqRQpKSkQCATo6OjA3NwcZDIZ0tLSuONit9tRWFjINyV1ymtrawF8eDKml10gEMDw8DBcLhfC4TAKCgr4hEUSGyrG7/gh/nfhT18iFb8CgQBJSUncaReLxfB4PFw0S6VSLuzpJaJSqdDf34+5uTnuJGVnZ2N1dfW2B4VepgBiTg3+7+4FnS69Xi/m5+cBfNjNEYlEcDgcLA+h74UKF6VSifj4eD7YyeVy5OfnIzc3l0/I6enp3JEleQHJvO60qHikm5QOG7Ozs1hZWeEihbr0VExTYbW2toaUlBQYDAZIpVIe+UkkEuj1emi1Wi6AdDodj3qpa0MjvTutWzsUIpEIiYmJfAiIRqPQ6XRISUlBKBRiCQZtMrdOsCQSCQQCAVpaWvjBpjHh+Pg4pqamYDQauctFB07qYN1pbWxs8PcilUq5e5uSkoKJiQm4XC6kpKRAJBLB6XTC7/dDoVBArVajr68PHo8HFosF5eXlEAqFmJubg8VigdVqhVarRSAQ4C4+HbqDwSDcbjeSkpJgtVo3vbbExETepFJSUiCVSpGUlASfzwe5XM4b8Pr6OpaXl5Gamsq/idPpRDAYhEaj4b/ucDiwtrYGmUyGlJQULC8vw+128yYXHx/PRSsALhA3W1KpFH6/nzs5GxsbSE5OxsDAAKampqBWqyGVShEKhTAxMYHh4WHuXLtcLng8HqysrEAsFiMajaK/v583ahrdz87OsqSQGgMkV4jVsJDL5fB6vfD5fNwciI+P58MsbXRarRZqtRrJyclITEyE0Wjkg2UwGLxNVuj1enmzoMZBYmIiBgYG4PV6b5sK2Wy2Ta8tJSWFpyxUqNMhTyAQICcnh7tgNG1ZX19HMBhENBrlIj4QCEAkEsHn83GhqlarERcXh4WFBZbhOJ1OTE9PY3FxERqNht8rd1qRSAR2ux1ra2tITk7mSYvT6eRC/dZ38eLiIlQqFYxGIxegKpUKWq0WAoEA09PTLFdaXFzk7215eRk2mw3Ly8uYmJjAxMQEPB4Pd2nvtOhz0fdFvwdNUOhdRfIpkldRI2JhYYGLMZp+KxQKlJeX85R7cnIS6enpAD7cCxYXF3kaSVPqOy2SWIbDYayvr7Mcz+Px8KEqNTUVIpGIC4KcnByEQiH09PTA4/FAr9ejsrISQqEQ/f39iI+PR3FxMbKysrCxsQG/38+TWpIB0+8U6x1HUjz67lJSUvj5ob8mEAgQjUZ5QmQ0Gvk+I0kITS5IlhEKhRAOh3lPpD2UCi0AXPRutqLRKB9o6FoTEhKwsrLC9z59706nE0KhkK/r1kYCNSL9fj8WFhZ4/5fJZLfJT+fm5niCmJycDK1WG/N+W1lZ4cMTHRRnZ2eh0WiQlZWF7OxsqNVqLtwtFgvUajW8Xi/cbjfW19f5fUaHXjrsU9OT5I5039AeGWsSTzLvW39/nU6HxMREVi7cKsHZ2NhAUVERvxMAQCaToaamBtFoFJcvX8b8/DwCgQAUCgWqqqoQDAb5QBUIBDA1NQWHw4GlpSVuXt1pSSQSfg6Sk5O5iZqYmIilpSWWiEokEv4+CgsLb/vtFxcXeTpF9yU1lgwGA8xmM6LRKE8dEhMTWV0Ra1+g3/BW2aFcLsfi4iLcbjfEYjEMBgM3QABArVZDIpFgcnISKysrMBgMEIlE3EAPh8MsMUtNTYVAIGAZ+62NK7qPNlsJCQksDSdFBTVLJBIJ0tPTIZVKudl2qySQpkzUBKXnkdQzYrEYcXFxPIVeWVmBUqlEcnIyf3+x3r233Xsf60/97w+UlpaGUCgEu93OD/A999wDn8+HiooKGI1G7sAmJiZCLBZDqVRi9+7d0Ov13H2amppCY2MjVlZWMDY2ht7eXszMzKCmpgY+nw8CgQCjo6MQCASs74/VxV1YWEBaWhq/oORyOXp6elBQUAC/388nX4lEgm3btmFsbAzhcBhtbW2QSCRYX1/H2NgY1tbWMDY2huzsbIRCIVitVkilUgwPDyM9PR3JycnQ6/WIRqPIyspCaWnpR56GFxYWoFaruetOk4K77roLMzMzmJqaYmlYdXU1ZDIZamtrkZiYiIqKCmRkZLCGORKJoK6uDtFoFOvr66xbD4VCOHv2LG7evImJiQmoVCrk5OTw6DjWog2SbuhoNIp77rkHy8vL2LVrF3tmtmzZwge22dlZZGZmIhKJ8Oh0cHAQTU1NPGqjly3JddbX1zE7O4tQKMQvB/pvb/a9SaVS7tgEg0H2A4VCIWRnZ98mO5DJZCgpKUFvby+ys7NZjhSJRNDZ2QmbzQatVouMjAwolUpEo1FUV1ejoqICJSUliEaj0Gq1sNls/N1utmjKNTk5iampKcTFxUEul3MnIS0tDSaTCVlZWSgoKMDMzAzW1tYwNDTE49Du7m7Mzs5idHQUd999NyYmJtDZ2YnR0VGWl42MjEAmk6GzsxOJiYkoLi5GYmIizp8/v+m1CYVCaDQavt/C4TAGBgZgMBi4406HPHqehUIhhoaGuHiiDvvMzAxKS0v5NyOtfFZWFjo7O6FWq5GQkMCdHY/HE1PyAHy4EZOfhjp3Xq+X/TC7d+/maWRubi78fj+OHDmCmZkZ7sRQt+bFF19k/S41FgKBAHeazWYzd2gsFgu0Wu1HHiAVCgV3++hFSxsEHU5JttfS0oKZmRmeqigUCuh0OqSnp+P8+fM8WrfZbEhMTERvby8qKyv5cAQANpsNBQUFXNRttiKRCOrr66FWq2E2m1FRUYG5uTnk5+fD5/Ox/IqkcfPz89BqtSxnWVtbw/r6Or9jKyoqeNJBchfySABAf38/5HI50tPTcenSJfT09Gx6bdR9pKm2SqXC8vIycnNzkZCQwIWHzWZDWVkZTxNaWlp40tHX14fOzk709fWhoaEBWq2WJYerq6tobGxEVVUV3G43xsfHeT+SSqUxCz/6TUmfr1KpoFKpsL6+jvfee4+bHuQFVKvVKCoqQnNzMxQKBTQaDaRSKaqqqnDhwgXk5ubC7XZjdnaWJ3CFhYVoa2sDAJYy0XQk1lpfX+fvjd7xJPV4/fXX4fV6+TBP7zqSa5WWlkKn0/H7saurC0VFRbh+/TpmZmawtLQEn8+Huro6OBwOmEwmrK+vQy6XIyUlBcPDwzHfcQqFAhaLBevr6/D7/Vy86HQ6pKWlsSzL7/dDo9Ggo6MDq6urSE9PR2VlJXuQSAu/bds2biQYDAZEIhEUFhYiGo3yhFwikUChUKCrqytmg2xubo7fOdTII8kS7aU9PT2YmJhAaWkpd6AjkQi0Wi1LcMkLRO9VpVLJ3WnqgM/NzWFgYIDfgW63G2azedNrW11d5bohPj4eq6urcLlc2Lp1K/tWSQJkNBoRFxeHmZkZXLlyBXl5eTCZTEhISEBeXh7sdjuOHDnCE1wqSuVyObKzs7G+vo5oNIrCwkJUV1djdXUVAwMDm14bddHj4uKg0+mQlZWFQCAAl8vF7zuFQoH09HTYbDYEg0EMDg4iEonAYrHwu5qK6IMHD7JXyOv1YnBwEPv27cPa2hq0Wi08Hg8KCgqwbds2bizH+t7W1tbg9XpZ4jU4OIiGhgZs27aNpWck3W9tbWUPXUpKCsu+pVIpkpOTcd9992FpaQk5OTlISUnB+fPneYpOTYW4uDhIJBL4fD6eVtxpCQQCnuyQkoE8FtPT0zxxTElJQXV1NUZHR7nB19jYCIvFgunpaYyNjWFychJms5kPunS4TU9Ph9PphFKp5Hd3bm4uP4uxVmpqKhYXFxEXFwer1YqkpCTU1tZy49nn82FiYoI9v5FIBL29vSgsLERBQQGysrLQ1NSE1tZW7N27F6Ojo3A4HJiZmYHdbofRaITf74dKpeJ6KyEhATMzMx9LwQD8Pxw0SIe3a9cuHDhwAFlZWVhaWkJ3dzcOHz7M3eGZmRnMzMxg27ZtWFxcRGlpKb7//e/j5s2bKCkpgd/vR1NTE77//e/DYDAgMTERer0ee/bswerqKr74xS9Cq9WipKQETqcTY2Nj2LlzJ1pbW2NeHx0G1Go1lpaW+BS6sLAAu90OlUrFusKmpiYolUoUFBRALBZjz549+PSnPw2RSISioiL09fXh/vvvh0wmw/DwMDo6OnD8+HHs3r0b58+fh1Qqhd1ux/Xr1xEfH89yhDstqVSKgYEBlJWVYceOHcjKykJOTg4cDgfMZjNmZ2dZhpScnIySkhL87Gc/Q2pqKo93RSIRmpubUV5eju985ztobGxEbm4uF7ZDQ0N4/PHHMTU1xaY8p9OJzMxMjIyMbHptdBquqalBdXU11Go1ZDIZurq6kJSUxC+g+fl59PX18cOWlpaG/Px8FBQUQK1Ww263o7GxEefOncP27duh1+sxNzcHu90Ov98Pi8WC3t5eHhGSJnBycjLm/UaGODLqUYc5Ozsb7e3tLLmYn59HeXk5FhcXUVlZiZ/97Ge4fv066urqkJmZiaSkJLS1tUEgEMBut2NwcBDz8/Po7u7G0aNH8cILL7BZfHR0FI2NjTFH0KR5r6ysREFBAbxeLy5evIinn34aJSUlcLvdEAqFkEqlPB365S9/iYqKCuzZswcPP/wwPvGJT8BqtSIvLw8vv/wyH0Z1Oh0++clPQiQS4bvf/S4sFgvS09NZ4tfY2BhzuqfVajEwMMASColEAqvVCpfLheLiYsjlcpafhMNh7Nq1izv0Q0NDSElJgdlsxvDwMLZu3cr3j1arRU5ODvLz83Hp0iWUlpZibGwMMpmMpUtWqxX9/f2bXhvw4YRqdXUVVVVVKC8vh1QqZdPo/v378Zvf/AZLS0soLi7G4uIitFotwuEwCgsLkZycDIvFggMHDqCurg7BYBCVlZXYvXs3xsfHcenSJfT29uI//uM/WItNXbylpSUUFBTE7FyZTCasrq6irKwMaWlpPJkCgPvuuw9yuRylpaWora2FRCJBZWUllEold40KCwtRUlKCUCgEk8kEkUiET37ykzyNTE9PxwcffIDs7GyUlpYiJycHVqsV+fn5KC4uxvDw8KbXlpiYiPHxcZ4+DA8PQy6Xo729HRkZGYiPj0dhYSGys7MxNTWF4uJiXLp0CYODgzh58iSmp6dRVFQEm83GvqLq6mpEIhH09fVhYGAAy8vLDB0oKiqCRqNBYmIiNxU2W6urq0hJScGWLVtgtVrhdrsxNjaGP//5zzCbzdx5pu8qNzcXfX19qKmpQTAYhFwux44dO1BWVoasrCxcuXIFkUgEbrebIRoXL15kczBNmVJSUlBUVBRThmk0GuF0Ovk7FAqFqKurg81mw9e//nX2SWm1WkxOTmJ2dhZDQ0O4//77IRQKuaj493//d+Tn52NychJGoxFZWVmor69HSUkJ/vKXv2DXrl0wmUywWq0sx7JarWy4vNOSSqWYmZlhSe3c3BxSU1ORkZGB7373u5DJZJDJZPB6vTh58iR27tyJM2fOQKfT4U9/+hPGx8eRk5OD2dlZRCIRtLa24t5770VRURGysrJQVFSEd999F0VFRZifn0dqaioXEE1NTTG/NzrAlJaW8j+n1WqRnJyMuro63hs0Gg2USiX279/PB+9Tp04hISEBe/fuhVQqRXFxMdrb25GZmQmRSISEhASkpKTA4XDgkUceYZm1RCKBWCzG3XffHfOgkZaWBpFIBL1ej1AohOHhYeh0OhiNRhw4cACvvfYaXC4XhEIhpqamsHXrVohEIjQ2NuLKlStwu93YsmULiouLsWvXLly9ehXZ2dlwOp04e/Ysrly5glAohJqaGoTDYTzwwANISkrCyMgIKisrWR50p0UTkIKCAlRWVsJqtaK8vBzXrl1DQUEBTxD9fj+uXLmCo0eP8hT62rVrSEpKwrZt2zA3Nwe5XI6HHnoIKpUKkUgEU1NTXOg7nU5cvnwZDocD169fx/Xr1yGVSmN60FJSUlBQUIDCwkLMz8/j4sWLEIvFcDqd+MQnPoGNjQ3Mzc2hq6sLFy5cwGc+8xmMjo5CIpHgv//7v7G8vIwjR47wpOLFF1/E//pf/wtVVVXIyMjAvn37kJ6ejoqKCoZRjI2NYWBgALt27YopOSOJmlarhdvtRnNzM4qLizE0NASFQoGpqSmsrKxAKpXCZDLhM5/5DNra2lBQUAChUIj8/Hwu8o1GI5qbm2Gz2ZCSkgKtVovi4mJMTEzg2LFjWFpaYt/w7Ows8vLyYsq60tPT0dfXx9OS6elpiMViVFRUYPv27WhububpwMrKCj7zmc9gbGwM6enp7CWhgr6hoQGnTp2C0WhkG0Frayump6dx6NAhnD9/HpOTk+jo6EB/fz+MRmNML2tGRgbsdjvXuqurq9i+fTt7Bj/44ANs374dn/rUpzA2NoYjR44gLS0N6enpXHtrNBqMj4+juroavb29ePDBB3HPPfegtLQUYrEYXq+XnzVqIq2urqKhoSGmpPbW9bHN4DR67e7uRigUYk1rVVUVTp06BbVaDeD/aMTn5uZQUlKCsbEx/OhHP8L8/DympqYwMTGB7u5uWK1WNvasrKzg3LlzeOedd/DNb34TwWAQDoeDpyN2uz3mA7S2tgadTscjXbVajYKCAvT396OsrIw36ZGREXi9XjQ1NWFwcBDAhx3W6elpOBwO+Hw+SCQSPPHEE6zbFIvFKCwsRHx8PE6fPo3FxUV4vV7Wt1J3e7Pl9/tRVFQEl8uFxcVFntbMz8+jqqoKIpGITX0ymQxf+MIXAIB9I6TZpanLI488gra2NqSkpEAul3On1eVyQSaTYWRkBBqNBtnZ2fD7/TGnBvPz88jLy0N7ezvreqempgCAR7FEtXA6nYiLi8P27dsxOzuL8+fPw2AwQK/XY2pqClevXuWXCZm5qBtmt9vhcrnQ2NgIvV7Po2siC232myYmJnKhSzKVnp4eCAQCFBYWoqWlhV+ELS0tUKlUkEgk+NnPfoZAIID+/n7WMhcWFuLEiRPYvXs3srKyMDAwAJfLhYGBAdTX12N5eZlNqMPDwxAIBCzz+7+Xw+FAbW0teyY0Gg2OHj2KyclJ+P1+zM/P4+TJk2wETE9PR1ZWFuvP5+fnkZaWhoqKCty4cQN5eXk4c+YMKioq4PF40NfXh6mpKTQ3N+Pll19GRUUFTwEuX74cczxOL2Yal1IHODU1FTdu3EBubi6GhoaQlJSE4uJivPDCCzCbzcjLy2Oq0sbGBjIzM/kZWVpagsPhgMPhgN/vxwMPPIDFxUUeo5IUy+PxcBdmsxUfH89TuqmpKR67GwwGXLt2DaWlpfB6vZiYmGDj/+9//3vcfffdaGhowODgIF599VXk5+dj9+7dmJiYQFtbGwwGA7Zs2cLj7N27d+ONN96A2WxmWeLFixdx5MiRTa+N7seZmRlsbGwgFAqhra0NhYWFOHfuHHJzc+FwOJjuNDg4CIPBwM8pNQzI2AoAzz33HHJyciAUCuH3+7Fnzx44nU5MTk5iY2MDs7OzWF9fR2pqakwJptvtxtraGmuQSW9bVlaGvr4+/mspKSk4cuQIxsbGUFNTw+Q+t9uN1157DQKBAEqlEu+//z7279/PE9bk5GQUFRVhcnISLS0tsFgs/BynpqbG3FDi4+ORkpKC5uZmvt/LysrgcrmYlNfZ2YmFhQU2aO7YsQOvv/46JBIJIpEIbt68iaSkJPT09HCzRK1Ww+124+bNm+ju7mYDeVZWFvR6PTY2NjAwMIDW1lYcPHjwjtc2MjKCtLQ0JiWJRCKcOHECycnJ6OzshF6vx9jYGBN0BAIB2tra+JqIkFhaWorr16/DarWipaWFdd5LS0v4/e9/jzfeeAMrKyuQyWQsQaUD02bL5XLBbDZjZmYGKysrmJ+fR3t7Ow4fPozr169Dq9XCbrdjZWUFaWlpuHr1KoAPC8b9+/dz8Ut+oOLiYty4cQOFhYVYXFxEV1cXsrKy4PV64fF44PP5kJiYCIlEgsHBwZg+CJ/PB6PRyAUe0WwefvhhOBwO9sSRZLS/vx+VlZWw2+3Iy8vjBppOp8PNmzeRn5+P2dlZnqKQwbavr4+fjdXVVZ7Sx5Jg0rR7YWGB/YkDAwPQ6/UwmUwoLy/H0tIS+/TIGH/q1Ck89NBDcLvdaG1thVarxfj4ODQaDXfPU1NTMTo6ioKCApw4cQJisRitra0sBT5z5gwAbErWc7lcyMrKQnd3NxtmOzo6WF1x8eJFnpTV1tbizTffRFFREXw+H3bt2oWkpCS0t7fz/f/rX//6tu8mGo2ipqYGS0tLbFS/1cje2dm56fdGhu9AIMBTEq/Xi8cffxytra3w+XzQaDSwWCxQKpUYGxvjw9o999yDSCSC1157jafsNTU1uHjxIhYWFuB0OvHee+9hz549uHLlCoqKilgKFwqFMDIyEnNqsLKyApPJdBtdsbW1lelWZrOZG9kulwtxcXGoq6vD2bNnUVZWhkAgAKfTiYyMDHR3d2PXrl24cuUKy72JQPj222/z+4CkpW1tbTE9mf39/aiurkZXVxdkMhnS09Nx+vRpbN++HePj41wL0nNWXl4OhUKBV155hZspN2/e5MkfPbvT09MIh8NIS0vD4uIi2tvbodfrmeK2srKCy5cvc2292bOgVqshl8t5UtPV1QWTycTS1bNnz0Kv1+PYsWMYGxtDS0sLVlZW0NHRAbVaDbVajeXlZfT39+PQoUNYXl7m7zkhIQH33HMPTpw4AbPZjLa2Nuh0Ouj1enR3d8dUGt26/p/M4G+88Qa6urqwsrICtVoNk8mE1tZWaDQa1NfXs1nX6XRyYeJ0OvHcc89hfHwchYWFPPK0WCyMKiXTGb38iATgdrsxOjrKlJvNVnJyMjweD+t2Z2dn4XA4oNfr0dXVxfQL8oQQxjQajeLll19GV1cXbDYbysvLMTY2hieffJKpHCQ7oOKvoqICKysrGBgYwMTEBOPKYn1v5OwnXTQVnhcvXoRSqURFRQW/4J5//nn2OZw+fRq9vb38YqLxmMFgwI0bN3DlyhUsLCzg9OnT3MEXi8WYmJjg7zKWUVKlUvHY0OVyYXJyklFsoVCIR+A+nw/Dw8MQCoVoaWlBfn4+G4ioszQyMoKOjg50dnZCIBAgLS0NQqGQjfiPPvooAoEAAoEAG4ry8vJi3nMOhwNyuRxZWVks86GXwsbGBqqqqlBXVwepVIrGxkaUlpZidHQUfX19jGGm4qajo4O140QoOXDgABcC9HKLj4/n8fpmKxgM4syZM/zvJiPf888/D5FIhKNHj2LXrl1srNVqtdixYwcWFhZw8eJFJjyRqZImGsnJydyZGB8fRygUwv33349AIIC+vj7Y7Xa+Jzdbt2rFbzX6Xb58mf0AGxsbsNvtfHAeHx+HRCLBysoKbDYbampqIBaLkZ2djYSEBEgkEu7iKhQK/PGPf0RbW9ttRlGalHyUyX91dRUtLS1ob29npHU4HMYf//hHTExMYGxsjDGHUqkUBQUF+PrXv460tDS88cYbCAQCvNHSC5mmqaS57+7uRktLC/9WNA1aWFjAW2+9FfP6xsfHMTMzA4FAAK1Wi5mZGaaQkY47ISEBbrebqVxkWBQKhbBarfD7/fx9EpZZLpejoKAAr7/+Ost3xGIx7HY7ent7sbi4GLP7vb6+DrVaDb1ez+Zfh8OBF198Ebm5udjY2IDP58PY2Bhu3LiBxcVFFBYWYm1tDe3t7YhGo7j//vsZy2mz2ZCTk8N0G5JmymQyfOpTn4JAIGCpp9lsjjkJUqvVaG1tZakQkd5efvllFBUVQafTYdu2bSguLsbMzAz7DgwGA0u1SkpKkJyczL876cbJK1NXVwe/3w+9Xs/TVkJExzLRkz+A0KlkrD1+/DhGR0eZzgUA165dw+TkJDdC9Ho9cnNzkZOTwxs53Z8kM/H7/XjyyScZZWkymbC2toa5ubmP9GhEIhH09/fzf8tmsyESieDChQsQiUQYGRlhb5zb7UZqairLAUlDTV6guLg4tLW18fNO5tn+/n4EAgHYbDYkJCRwAR4XF4esrKxNry0lJQU9PT0IBoM8iUpNTcW5c+duIzWRRJbkUHFxcRgbG+OiNTMzk5t+crkcqampUCqVWFxcxLlz57CwsICsrCzEx8djamoK09PT0Gg02Lt376bXlpiYiOHhYTbPkhl9cnKSZZ+RSATz8/NwOBxMCZydncXVq1extLTEskKhUIhz585hdnaWn0Oa6CYlJUGn02Fubg7Nzc18CI61N9AzTZP7YDCIvLw8vP/++9jY2EBBQQFSUlIQCASwsrLChMe1tTVcvHgR169fZyXDxsYGnn32WcTHx2Pr1q2orq5mSatIJEJZWRmMRiOrDgQCAUpLSze9NplMhu7ubgQCAWi1Wm4mNDc3M4zB7/fj2rVreOONN5Ceno76+nokJiais7OT1QlUqOfm5mJ+fh4GgwE2mw0rKyt45plnIJPJUFxczJ5Yep9WV1dvem0JCQkYHBxkNH1RURGMRiPC4TAmJiawsLDAvke/389yJZLaDQwM3AYSevfddzE+Ps7eBZ/Ph9OnTyM+Ph46nQ5arRZGoxFpaWmQyWQx3yFyuZy9WRKJBF6vF16vF6+88gpyc3Nx5MgRaDQauN1uLC8v46WXXoLNZkNGRgYmJiYgFovR1NSE+vp6ZGVlMTY4Ly8PFosFPp8PIyMjcDgcyMjIwOjoKK5evYqJiYnbfLd3Wkqlkumu1EyJj4/HjRs3YDabUV5eDolEggsXLuDHP/4xEhISUFVVxbWAx+Nhz6XZbGY5aUpKCh9w/vznP6OqqgoNDQ0oKyu7jUj2UVJpWh/7oEEYVdJlE3ln586dbDqWSCQoLCzE1q1buZvrcDhgtVrZ4FJXV8caMp/PB7FYzESQz33uc3jrrbfYiCSVStmpH0suQoeI+Ph4aDQa7nYRbhL4cCJDL1+Hw8Fm1127dkEkEqGzs5O/bJPJhP7+fjaDFxUV4cEHH2TMrVarRV5eHucelJSUxLy2lJQUxkgSdrWkpITRiNRFtlgsPJlwuVw4fPgw8vPzuWiYnZ1FVVUVH8Zos25sbOQxP5GFSGMaa6Jx62ZOXVnSsxNdaHJyEgKBAPn5+XA6nVAoFJicnER5eTmP0xoaGiAQCKDX69kYSKZZuVyOaDR6G/5UJBJhaWkJer0+5v2Wk5PDtAu5XI5wOIzMzEw+aLhcLu78OBwOKP43912n02FjYwOBQAB5eXmIj4/HAw88AK1We9sGZLfb4fP5oNfrsb6+zvkaNCHbbJHxkUyShBLeunUre4yoSMnLy2Ok3sDAAAoLC6HVarG8vMyUh69+9avY2NiAw+FgBnZDQwMWFhZQXFyMlJQUhhJIpdKYXQQiqXi9XoyPj8PhcEAsFvPBniaROp0O9fX12NjYQEZGBjweDxobGyEWi3Hu3DlcvnyZJ4nU0abpVlNTE3fPyXBHVKhYBSnw4YEuKyuLiTw6nQ59fX3Yv38/CgsL0djYyP+98vJyhgC0tLQw9pkoNoTgk8lkvIGPjo5i69atGBoagsFgQDgcZkKJwWCIebj1+XzIz8/nYkilUnFhSsQNvV6PnJwcVFRUsHFULBajuroaIpEIbW1taG9vR1dXFwQCAQwGA0956PefnJzkoresrAyVlZX8btlsRaNRRpSSJyMSiWDfvn2YmJhgCU5GRgZKS0uRmJjI91llZSU0Gg36+/sxNTWFaDSKsrIy+P1+LuzJW0Xfl8/ng1KpRGZmJmeUbLbIT0OIYaVSieHhYdbpezweOJ1O9rYRgjscDsNqtXKmg8FgYE/d3NzcbSZci8WC0dFR9tvRJE0gEMTciAUCAWvySUuekJCA6upqJrGsra3BaDRi69atWFhYwJ49ezAzM4OdO3fCbDZjfHwcc3Nz8Hq9MJlMbNQlwkt1dTXnGhCmMzMzEwkJCR/pCdJqtXx4JVlNQ0MDZmZmWMKnUqmQl5fH+Pa0tDSWQW5sbECj0SAuLg7l5eWYn5/nhlsoFMKePXsAfCixIBIWwSVmZ2c3vbZQKMTNIpp8aDQaxm7TdZH/xm63M6GNwCdElKMCnchk5EEjbw01K6xWK2PuYzXIgsEgNzWocKR/h8fjQXZ2Nn/vQqEQCwsLWFhYgFarRVZWFt83NTU10Gg0ePDBB5GRkYGcnBwkJydjbGwMSqUScXFxSE1NZfiI0WjE6uoq6uvrN702ANDpdEwAou+OsjLogKhQKKDX6zEwMMDEML1ez9j7wsJCLC0tob6+numXcrmcPTICgQA2m40PjZRvFAuwAgAWi4Une2SOJ7oT3UfU4Dl16hRGRkYQCoVQXl6OrKwsvk+zs7PR0NCA3t5e9Pf3Q6FQ4L777sMnP/lJfnfQvSGRSG7LC7rTosMfGblvNd4nJiZyI04mk8Fms0EkEnEuGQE/yEA+MTGBrKwsVFRUoKqqCunp6RAKhSgpKWHTONWrBNmI5aeiSczS0hLLOekAPTQ0BJ/PB7/fz03FxMREPjAZDAamUZH6xWg0Ynh4GOPj4wxAIrO/RqNh+hYZ2mO9ez0eD9d65NEjWA3VRGQwT09P5+YoAOTl5UGpVCIYDPL9TdNkIl7pdDrU1NSgo6ODDzOkpJmbm/tI+Autjy2dItMKYa/oASooKOAAHtrQtVot42CVSiXUajXC4TCmp6e5OKZOOIVxAUBDQwNeeeUVJgcRNUej0cT0QZAmlgw4RGmiXAV6IVH4DE0pAKCiooKJKYmJiSgsLGQEH52Gk5OTIRaLUVxcjHA4zIFAgUAAAoHgI2kKxI8nupNIJIJAIEBZWRmTd6jLR4cOMoutr6+z9o4Mp4RLBf7PAZBChSinY3V1FVqtNuZIkDYCInyQOYl4yna7neVeSqWSNwm/34/8/HxEIpHb7oP19XX+TgQCAVQqFdxuN3JzczEwMMAvEcKqxcIrEu4zPj6evRm0mWRkZPB9KJfLmRJCdCQyBxLth6ZAdAghLDHRnubn51lSQF6kWEg5IiPR90WBiqSdpX9fSkoKnE4nH/boxbixscH3Pv13s7OzmTIWHx+PzMxM7qKmpaXBbrezFj6WVI9G6Eqlktn21L2hooQO3Onp6VhdXWXpBm3Y4XAYTqcTcrkciv8dhiiXy7nTkZubC5VKhXA4zHzx5eVlRi7HWoT+Az4sfpKTk7G0tISqqiouXgBwoQKAUZmEEqYutVQqZcxsYWEh4uLiWKqj0WhgNpvR3d2NxcVFCIVCmM3mmMU8AH7fEMufZF12u53DpKjLXlFRweS4uLg4zM/PM6mJPqPFYuFnBvjQ3xMOhxlFS8AGkixutiiPgIoVCmlSqVTcOaXNigpckkTRdIXQmHl5eRCJREzoIWrf9PQ0SkpK+H4m+AWZxmMtgmpEIhEmCJlMJiY4ra6uQqlUorCwkDNyyLdCEwXKthkfH2eUMDHu9Xo9xsfHuXFDhcGtpJc7LcqGIR/E2toaSw3Ik0Mbs0ajuSOa2+v1YmNjAwsLCxCJRFCr1fwcKRQKlJSUwOPx8F5D+miJRBJzb6DvnmShycnJKCwsZIQyNT0ok6C8vBxOpxMmkwkpKSmQyWRISEhARkYGbt68iS1btqC3txeBQIApQgaDgTMtiOBHcpuPQsgSzpfuHcLaU4YUcfbJGEsIU4J9kO6eCkbyGNB7bX19HeXl5dyIi4uLY98g+WrutCgbht7RRLQiT4tUKoVer8f09DQ3owYHB7k5Svsx3efl5eUMu5idneXQP8Kx04RbLBbz5GCzdet+S54EMhCnpqZycUbI2EAggJmZGRiNRjbfJyQkwGKxYGhoCBqNBjKZDNFolM336+vr0Ov1HLCqVCo5TyeWHJkyFigugPYD+vcvLS1BIBCwtDUQCGBwcBAmkwlpaWmMmqduvFwuR1paGsca0G+TkZGBhYUFzisjImWsYj4SiXA2DoEbDAYDB/1qtVr+7QipTHULQTgMBgOEQiHEYjHy8/OZxjkzM8MSZ5qAk7eQ/kysQ1BcXBy/SymMMycnByqViieY9BzLZDJUV1fj+vXrCIfDPDWirBcCUiwvL2NsbIypT9SsWV9f5ynXxsYGA3g2W9QYomyw+fl5RtGSuoAavXSocLlcDEYRi8Vci9G/g6ZFSUlJMJlMEAqF6Onp4fBIqvtuRfF/1PrYEw3CcBKWDgAnJG7dupULAOpwFhcXw+v1IicnB5OTk4xufeWVV5CdnY2bN2+iqKiIb9zc3FxEo1E8+uijjEslEwydiDdblANBeQHd3d3QarVwOBzsYZBKpZDJZFzADwwMIBKJYG5uDpmZmWhsbGQKQVtbG28QZIq7fv06du7cyQXB8PAwuru7Gce42SISBnHsFxYWoNFo2FQ4Pz/P2DulUslmTtISd3d3Y319HVevXoXVasX4+DgKCgpQXFwMmUzG4V2kI/f5fOjv70dHRwdvBJstykigosPtdiMxMZFHeHFxcZz0vry8zGZIwmOq1Wqkp6fztel0OiZyEKZuaWmJvQiETCQaVKwCQfG/w5SMRiOnWSckJHDnJy4uDnq9HmazmQkpHo+HgyJpY2xpaUF8fDxrDNPT03njIInXe++9x4ZmQtyOjo5uem1k3BYIBExjKSkpYaIEMbftdjtu3rzJKdVkwhOJRPyCstls+MEPfoC6ujqYzWbIZDIubGkaolKpMDExgRs3bjDtYbNFUr3U1FQYDAasra1BIBAwlUun07EWdm5uDtXV1bh69So2NjZw/PhxrK2t4e6774bVamX6k16v55eoyWSC3W5HeXk5+4joOdVoNDEnjwCYWpadnQ2TyQTgww7g/Pw84uLiMDIywh1P0l0rlUoUFRVhfHyc6SXkF6KXKB3CyGC+d+9eljj09/fzhGNoaGjTa5PL5RgdHeVu3+LiIlJTU5GTk4OcnBzm0E9PT2N4eJhTcWdnZ3H69GnuhKalpeHw4cOYmZlBSkoKBgYGsLS0hNLSUkxMTGDbtm3c8Zubm4PP54PNZot52JDJZEwko+RglUqFS5cuoby8HDqdjps27e3tUKvVeO2112A0GjE2NoZoNMrG3oqKCjidTp7Y0GFtbGwMmZmZbGSWSCRsBo9lzhWLxZienuYchcHBQQ4ktFqtjEIls6/NZmPCCoUWUp7M9u3b8e6778JkMiE/Px85OTm82clkMqyurnLIKRVtsX7TpKQkTE1NcTEaDofZYFlWVsZT+uXlZTgcDhw5cgStra0oLi7mUL49e/bAYrFAKBRiYGAAJpMJKpWKp33j4+OoqqpCUlISjEYjoynps2+2qCF3a6ZFYWEhBgYGUFVVxXk8AHh6RRu90+lEYmIid+hpGkWYY4PBgIyMDFy9ehWFhYVwu93QaDRITk5GQkIC8KfwSwABAABJREFUTw9i/aZUeNLkZ35+Hh0dHTCbzYzBnJubw9WrV5GXl8dJ3ITpFAqF8Hq9qKmp4eafz+eDx+PB+vo6RkZGUFZWdltuwPr6OkwmU0w5skqlYokrJXyvrq5CJBIhMzOTMyaomxyJRNjjc/nyZfbWXblyBUtLSxzk2tHRgd7eXiQmJmJ0dBQWiwULCwts2nW5XEhOTsa1a9c2vbbk5GRMT0+zb4nQ8O+//z4KCwsZC7qysoKhoSEYjUacPXsWO3fuZNTtli1b+Dd3OByoqqqCRCLhsLzjx4/DYDDgvffe4+RzKnpj1SICgYADIKmZWVBQgIWFBeTn53OziAJVjx49irW1NeTk5HBwHh2OZTIZ7HY7jh07hrKyMszMzOAvf/kLfvrTn2LXrl3o6urC+Pg41tfX2Y8Y69AtFAoRCoV4gj0/P8/NTrFYDJFIxM0uh8OB5ORkLC4uskxVp9NxvZGdnc1NsN7eXnR2dsLhcMDr9aK+vh4OhwPRaBRzc3PweDwwmUwx/SMKhQJjY2OMuKacrcnJSdTW1jJenSR7W7du5RwWIp3RJJY+W1FREQDwwYLohKOjo0hPT4dMJsPKyspHKgQkEgmj5YlGajab4Xa7kZmZyf6qUCjE0t/Tp08jIyMDDocDQqGQwyxXVlY4325hYYFxw9evX0deXh4ikQiys7P579fV1cHr9ca8Plofe6Jht9t5RE15GqmpqRgbG0NSUhKys7PxzDPPIBAI4PDhwxgeHmYDVn5+PgBw18fj8aCjowMdHR2YnZ2FyWRiDCht6i6XC4mJifB6vbh582ZMFF84HIbJZMLo6ChWVlaQkpLChlcyxt0qTwI+NO6QDpn4+Kurqzh37hzuvfdedHd3Y3x8nNnuNTU1aG1tZQIK0UFGRkZijrZWVlaQlZXFp9719XV0dHQw1i4cDuPChQuIRqOMIjOZTFhZWUFVVRUHDFEBK5PJ0Nvby0UKJVS++eabaGxsRF9fH2PwyEC62aJOCyV4Urfd7/djYGCAszhUKhXUajXOnDnD5sVoNIobN24gEolAo9Hgv//7v/Htb38bJ06c4E6rWCzGPffcg0uXLiEuLg6jo6PQaDTM6Y+Pj8e+ffvueG3UAQgEAhxsNTo6ysZpwt2Gw2Hk5eWhuroaExMTkEqlsNls7A+Kj49nkkxubi6sViuGh4fR3t4Ok8kEt9sNg8GAUCjEo8FgMBjze2tra8ORI0fQ1taGoaEhngIRxnRubg5tbW18AOzt7cXGxgba2tqwZcsWOJ1OnDhxAgcOHEBaWhosFgtefvllTExMQCKRoKKigg34CoUCN27cgEgkQnV1NXQ6HevaN7vf6uvrWQKnVCpx6dIlRjiSudblcnG2CVGbhoaGIJfLIZVKYTAY8PLLLyMvLw+FhYUoLi7mSZHX68WvfvUrVFRUIBQKMZvebrfHRD8CYKkaSWN8Ph8WFhawfft2nDt3DkajEcePH8fi4iJKSkq4q3fhwgUsLCzA4/FgbGyMddqtra1wOBwYGRmBxWJBfn4+pFIp3nrrLRiNRp5w0iYWi+xEBwMqPEhLTZkdlCVCI/7Lly8DAAYHBzkjhwrnd955BxMTExgaGmL5ptvtZpJNZ2cnFyHhcBgOhyPmNMjpdKKyshJXr15l6UhiYiJ27NiB06dPIz09HXNzc1CpVEwFGhsbQ3V1NbKystDb24u2tjY+mFutVoyMjLDEKy0tDXNzc5iYmMD4+DiuX78Os9mM+Ph4nD17NqZcZGFhATU1NVhdXWWkKUlCW1paOJRQLpfzZiqVSllG2tPTw1PScDiMe++9F5cvX0ZLSwsn0spkMuzevRtisRiXL1+GVCqFQqFgM2+s3zQzM5ObAoSX1mq1OH/+PAoKCtDV1YWNjQ00NTXh9ddfh9lsRk9PD6PNgQ85/AqFAmfOnEF5eTlPOFZWVjA6OgqTyYT4+Hj09vay3CEUCqG7u3vTayM6GMEPNjY2MDk5iYyMDLzzzjuor6/nd63NZkN/fz8SExPx/vvv4+6778bS0hJ6e3tRXl4Om82GM2fOsDGcNNYejwfHjx9HUVERX4tarY4pmwLAGGx6p9HUsqSkBCMjI8jMzGRMcCAQYIT2888/jy1btrBXgfKAiHJDycPDw8PIz8/H8PAwZ5FQITc2NsaF2Z0WyUMAcHOQDjkXLlyAWCxmyU5jYyMnaEulUnzwwQc82fZ6vejq6kJdXR3m5uaQm5vLhCN6xsmYTh1wwiBvtlwuFzIyMjgYbnl5GU6nEzqdDt3d3Tzl9ng8nFCdmpqK8+fPQywWs8R6aWkJv/71r3H27Fn2mCUlJaGoqAiJiYno6+uDz+fD2bNnUVVVBYVCgdbW1pi1yMDAADdbaUpHKoHFxUV86Utfwvnz53H58mVoNBqcPn0aMzMz+MY3voHHH3+cZedmsxnnzp3D+vo6w2QoKyIuLg7Nzc2YmZlBcnIy4uLiOPgy1lR0aWkJFosFbreb80GOHz/O8lnKKKL3/ze/+U2cP38eY2NjXETTu+KFF17Aww8/zHhbyoLYtm0bfv/736O0tJQDRUlO+FHvkNLSUkQiESwsLGBpaQk3btyAWCxGd3c3w2RuDbKuqKjAK6+8wpOwjY0NJvAFAgF0dnZyJhRhczMyMjA9Pc2gHblczgb/qqqqO16b1+tlJL1SqWR5LaGy6TkvKCjgJlJVVRXa29s59JLUR3K5HLW1tUyBS01N5ciI7u5u3udpetPX1/ex8bYf+6Ch1WrR19cHnU7Hnotz585haGgIV69exTe+8Q3cc8896OjowMzMDKxWK7KysmAymTA2NobU1FTs378fFRUV+PnPf47MzEzs2bOHJU59fX147733sHPnTqSlpcFqtWJubo4NPbEeoISEBLS2trKumhJgI5EIFAoFtm3bhvHxcZw/f56xa3fffTemp6fR0dGB9vZ2NDQ0YPfu3SxPeOihh7g73N/fj/HxcVRUVMBisTBBgtJWPypMsLu7G+np6RzMR0WnXq/HwYMHYTab0dXVBYfDAZvNhu3bt2N6ehoffPABkpOTsWXLFpSUlOD3v/89G6NJw+hwOHD+/Hmo1WocOnQIFRUVGBkZwfj4OAdwbbao60QHnqSkJMzMzGBoaAg7d+7k0KfJyUlcu3aNkyBzc3Px85//HDabjbtspaWlmJ+f5245vWDoNz1w4ACbVWkaEqsoJdoFycLi4+NRUFCAZ599lvnUZMRdWFhgs7RAIEBeXh5UKhVsNhusVit+85vf4PDhw5DL5Wwkow7K8vIydwBIQ0lF5mZLrVbjD3/4A7Zt24bs7Gz4fD4Eg0G0tLRg586daGxshN/vR1dXF8LhMN577z3813/9F958803mx1Nq9Ne+9jUIBAIcPHgQnZ2dmJ2d5YKNumkZGRmc5koTkc2WSCTC6Ogod9Roc29tbYVcLsf27dthNpvR2dnJWQHhcBj5+fl45ZVXYLfboVarcezYMRQXF+NHP/oRJ48aDAYEAgE8/fTT+P/+v/8Po6OjsFqtfIguKir6yORtt9uN48ePY+vWrezVWFxcxPe//300NTVh//79UCgUuHnzJvx+PxYXFznxOC8vDzabDaWlpVhcXMSbb76J6elp/OAHP0BnZyeGh4dx8eJFjIyM4KGHHuKEWZvNxuFoBQUFm14byXAmJydZ9mIwGPDGG2+gqqqKNf50r9TV1XFnjdLdq6uroVAo8Lvf/Q55eXns49HpdAiFQvjBD36Av/3bv0VDQwO6urqwtLTEkshYxRXJR/Pz8+H3+zE9Pc16/JKSErS2trLHaGhoCGtra/j+97+Pc+fOQaFQwGazwWg0oqioCE899RSsViu/i1wuF5xOJ+O2s7KyMDIyArvdznSsWM9qMBjEhQsXGPNLXck333wTGo0GW7du5QC/+fl57Ny5k4s+gUCA3NxcxpBvbGygt7cXDz/8MKanp7GysgKFQsHoZ5JZtbe3IyEhAQaDIeZk1Gq1MiGNvFt+vx/nzp3D4uIifD4fUlJSMDMzg6effhpNTU0sHZycnERmZiZqa2ths9lYVkDSGspIILkoeSc++OADjI2NQSAQxPRTkYGfwm6BDxt6//RP/4SMjAwO6rPb7bh06RJGRkawb98+mEwmnDhxAhkZGdixYwcyMzPR1NSEJ598Evv27UNGRgYWFxfZP9jQ0ICBgQFGzs/OzmJ6eponindaJNslH4nZbMbExAQuXryIo0ePoq+vj9HMFIwaiURQWVmJl156CVKpFGVlZRywODAwAIFAAKvVitLSUjQ0NODEiRPIz89HQkICCgsLMTY2xs2oWP49Mg7X1dVxAXj16lW0tLRwXkhvby8ikQg3Zv75n/+Zu7WkRZdKpTh+/DjTf7KzszlTpby8HMPDw2hoaEBHRwdPYRISElBcXLzptclkMtbNA7iNtNbb28uG/rW1Nbz//vtYW1vDE088gebmZmRkZKCtrQ3nzp1DaWkpDh06hF/+8pfwer146KGHIBQK0dnZCZ1Oh5/+9Kf4+te/zgANoVCIzMxMbNu2Lea15eXlYWFhAYFAgA+jH3zwAU+wJRIJ7+VutxsPPPAA58TExcWhoqICaWlp0Gq1TCx88skn4ff78Yc//AH5+fnIzs7GZz7zGTidTq5D8vLyYsq4qVGcnJzMHoypqSm0t7fj3nvvZVw5yX6bm5sxMTHBGO+enh4kJCSgtrYWOTk53CihxpnH48HQ0BBCoRBPMKkpZLPZYnr3yPtJ35dKpcLIyAjefvttPPLII5wb4nA4cObMGUxPT+OJJ57Avn370NXVhZSUFKhUKty4cQNKpRKBQAA5OTk4duwYJ6yT9JDeIVQL1NXVoby8fNNrW1paQnJyMmeUUdaJVqvFBx98gMLCQmg0GvT29uLcuXMoKyvDfffdh5MnT2JoaAh5eXnsQxKJRLh69SrLLV0uF/r6+jA2NoaDBw9CKpWiv78f09PTjLaOBR+4dX1s6VRvby8aGhoAgAvYoqIiHDt2DJmZmWxirq+vR1FREd577z3YbDYIhUI88sgj2L9/P8sRSPvc3NyMCxcuwOVyobCwEPfddx/6+vrw/vvv43e/+x2j79xud8ziamNjA9u3b+dRv9FohNFoRE1NDXdOibzxiU98gs3c165dw+7du9HQ0MBa5pSUFMzPz+PChQvweDzs/airq8MLL7wAn8/HJ+H5+XkOVYt1IzQ1NTFqdHFxEQMDA7j77rtRXFwMk8kEg8GA8vJyHDt2DF1dXVheXsa1a9dQUVEBmUyGtrY2BAIBDtC6cOEChoaGIBQKUV1djR07dmBpaQnvvPMO3njjDSQnJ2Pbtm3sl9lsUWePCix6aRFzmUbjJDMgnKFYLMb//M//4Atf+AJTcGZnZzE2Ngan0wmLxQKLxQKBQIDdu3ejv78fbrebOfHEvo7Fh3a5XCgqKuJgm2g0ilOnTuHQoUOoqanB7t27YbVaoVAokJqaips3b+Kxxx6DRqNhfeY777yD119/HQMDAygqKsLY2Bi/MCQSCQ4fPsyTumAwyJjX2dnZmN3lUCiErVu3Ynh4GCMjI1AoFHC5XDh27Bjef/99dHd3Iy4uDnl5edi5cyceeeQRnlbduHGDO2ZVVVWwWCz4/Oc/j5GRETQ2NvLkrKurCxcvXsS1a9dw/fp1pKSkwGq1MtN6s0WhXtTBz8jIQGZmJu6//374/X7MzMzg6tWrWFxcxJEjR9DS0oKHHnoIs7Oz+PKXv4xjx44hEolgbGwMLpcL//iP/4iSkhJ+2RoMBrz77ru4fv06tm7dypscyfpisdKBD1/a3/72t1FYWAiz2YyamhrYbDY8/vjjnL0yNjbG9Ke1tTVUVVWhr68PR44c4VA5GuUePXoUi4uL3NUNhUI4evQoVldXsWPHDm5SUN5LLHCDx+NhEyhBCPx+P7KzszE7O3ubn6yqqgrvv/8+vzM+97nPobKykqeqlIadnJyMiooKpow9//zzHKpG+mPgQyxxLHnS8vIyH5JEIhGysrKQnJyM/Px8xMfHY8+ePcjNzeUiXywWQ6PR4IMPPkB7ezump6eZ1LV161Y8+OCDWF1dRVZWFh9k9+/fD6/XC6VSiaamJi4qyLuz2aLJMRmV6+rq4PF48MMf/hAbGxtYXFzEzZs3Ofzrueeeg9/vZx+WUChk4MT7778PgUCAK1euwGKx8PSAtNVxcXFwOp0oKCjAli1bMDU19ZGGa6VSiQsXLuDMmTMYGBhAS0sL/v7v/56bGBQW94UvfAF9fX0oLy+HUCjEvn37kJycjPPnz2Nqagr9/f348pe/jOnpaS5OMzMz8Ytf/AJnzpxhpG9aWhrq6+t539hs0X7V29vLE42JiQl8/etfx913381y34KCAuzbtw/V1dUwGAwoLi7m3KOBgQGcPXsWIyMjePbZZ+HxeGA0GpGZmYnl5WVkZmbi1KlTCAaDmJ+f52T5aDQa83sbGxvjw8Otadp5eXlYXl5GdXU1o8CXl5dx+vRp9kg89NBDKC0tZYDB/Pw8GhoaOMNjfn6e5TqvvPIKVlZW8M4772BqagoajYbNwJstr9eL3bt3w+PxoLu7G06nE1NTUygvL0c0GsWePXtwzz33YM+ePRwA6vP58Oqrr6K3txdvvvkmfv/73zPVq7GxEQUFBSzNWVpa4gZjSUkJlEol05ZIvrnZImkfkRvJPH3o0KHb3o21tbX44Q9/yBJgl8sFt9sNrVaLrVu3YseOHZBKpTh69CgfDAOBAIRCIe6++26Ge1C4nkQiwcDAAM6dOxfzWVhcXMTw8DCb/efm5nDs2DGsra3h1KlT+OCDD5iKSB6yYDCI++67jymeBE945plnMDExgT/84Q94++232dP1y1/+Er/5zW/w/vvvo6uri2Xm1Dm/0yL0bkJCAge/UmOppaWFYUJisRi7d+/G8PAw57Lcf//9qK2tRSQSwerqKjc2dTodMjIyYDQaoVarkZWVhYWFBeTm5iI+Pp4naK2trbh+/fqm10YTsvfffx/nzp1DYmIiLBYLPvWpT6Gvrw9DQ0MIBALIz8/HJz7xCQBgjyplEQHg7LnvfOc76OnpYWm8QCCA3+9HW1sb12tmsxkWi4XBDpstaqjSBIMw5zk5OSwtnZychEqlwte//nUEg0GMjY1hdnYWWq2WZa0HDx7E4OAgg0lIGmowGPA3f/M3ePXVV2Eymfi7UCgUnDv2cVZcNFa1d8t65plnIJVK0dvbC71ezwa4Cxcu4LHHHsPx48cRDAZhMpk4RIg6j4TaIvmPXq/HL37xCzzxxBPo7e2F3+/njh/xl+fn5zkjgXS73/zmN+94ba+++uptCbB0QAiFQigrK0M0GuXwrYyMDPT19XGnyeVycToyGZf+8pe/oL6+nvGOSqUSiYmJqK6uxqlTp3iSEI1GUVFRwRv9ndavf/1rJl/ciqkNBAI4cOAAWltbMTExwcVBamoqKisr8fbbb2N4eBgpKSkcBOZwODA8PIzi4mJOSJ6bm4Pf78fBgwfR3NyMhIQENgLSePizn/3sHa/t6aef5tA78mlMT0/DYrGgoqICr732GuMpp6amEB8fj46ODhw4cAAXLlxgwyThDd1uN1ZWVpCbmwuBQIDx8XFcvnwZ+/fv55Abyk4wGo3Iz89HU1PTHa/tpz/9KRtpV1dXWRNMCc8kUSAfiUaj4YmGTqdjQ6rVamXeOHkBOjo6WEfe1NSEyclJxikvLy+z9vfYsWN3vLannnoKCoUCc3NzSE9PR2FhIUKhEC5cuMB5GaT51Ol0UKvV6OrqYrOXx+NhbSNRU5xOJ3cW2tra8Jvf/AYvvPACzp8/j+rqaqhUKiartba2ct7K/71efPFFAB92r/r7+zExMYG9e/dix44duHDhAnQ6HYLBIOx2O5aXl3Ho0CF4vV60trbi6tWrsNlsqKurY6ym0+lEWloaotEoQqEQhz698cYbaGhoQCgUwuDgIB8iLRZLTIzhz372M7hcLjYxkjRxenoad911F3c1KdH7yJEjmJycRGdnJwwGA6xWKzY2NnDp0iXk5ubC5XKhvr4eIyMj8Pl8iIuLY+0zFdbkJVKr1ejt7cVjjz12x2v7+c9/znrxQCDAfq7V1VXYbDb2h4hEIqSkpMBkMuHixYtMIdPr9ZidncWvfvUrfOELX8DAwADq6up4Yyf6idvtRn9/Pxf2Ho8HNpuNwynvtL797W9DpVLBarVidXWVwzBXV1dx7733YmRkhI3LAwMDuOuuuxAfH4+hoSGo1WoYjUb+PQktTRM3ylw5f/489uzZg56eHvY6rayswGq1QiAQbPo8/Pd//zendWdkZCA1NZUnLgcOHMDQ0BBcLhcTidLS0vDBBx/g4YcfhtvtZkPxxMQEjh49iosXL7KfhYg7169fx5e//GXI5XKMjIxgfn6eGxUTExN48skn73ht//qv/4rk5GSoVCo+aEulUpYYeb1eGAwGPkROT0+zPyMzM5M19zdv3sSxY8cwPj7O8koCPhCimaR7zc3NmJ6exo4dO5CQkLCpPPTHP/4x5HI5lpeXeeIyPT2Nzs7O23KQSNZ7K2b4gw8+QCQS4b8XDAaxf/9+zoeIj4+H0+nk8Lj29nZUVlZCq9UyACIYDOIf/uEf7nhtr7zyCn+/hAAlyWZ6ejr7p5aXl9Hd3Y2amhpOVCcTfzgcRk9PD4cR5ufnM8EpNTWVs0veeecd9gtR6nBSUhL2799/x2v7l3/5F9TX17NGHwD0ej3LfCn/h8AWwWCQzfR0oKMU7bS0NKytrXHeCGW1UNYL0bIcDgd/drfbjU9/+tN3vLZvfvObHCobDAYxMzPDh1mr1Yqenh4YjUb2qikUCoRCIS6Kqe5xOp0oKytjwiTwoZwtEAhwVgjVB+SfoRyXf/zHf7zjtT355JNMM0pMTIRMJoPRaITP50NZWRn+/d//Hfv27UNeXh56e3uRnZ0NrVbLAIzR0VGMjY0hJSUFdrudk8HLyso4eG58fBxSqRRutxv79u1jSS2ZoDf73p5++mlMTU0hJSUFBoOBJ0IkUaqrq8P169fh8/lQXFwMo9GI0dFRKBQKzgoiYEg4HIbf78fhw4eZMkmkU8Ker62tcXYaUQV/+ctf3vHafvvb37KBXqfTMVKWwAivvfYacnNzYTQaWSK2srKCuro6XLt2jf3D1IynrDe/3880KNqrLBYLJ7K73W6mYn3729++47X9x3/8B9LT07GxscEQI6lUepu8ltDlJLUPBoMcSFtZWcmqo/7+fuj1en5GCQigUqlw9uxZZGdnIz4+Hm63m6ld4XAYf//3f3/Ha7t1fWzpFI2YaROemZnB9PQ0srOz+cuiItLv96O0tJQRt0TVID0yBQf5/X5G9FEYDdGCVCoV+vr64Ha7OcJ9sxWNRiGXy1kvJhaLERcXx4UvmeWWl5dvw7t5vV5MTU3BYrHAYDDA5XJhamqKC1ev18tdICqG6EElszoRbTZb5IOgqcHs7CykUilr9Yj8RJ4AetlotVp+cILBIE6fPo3c3FwkJyfD7/cjGo3C4/Hwd07INgpAJP1wLK8BSYiIFEOjNzK6CgQCDlRLS0tDJBJBamoqhw8ajUaWW7S0tHCng1CWMzMz8Hg8GB0d5U1zfX0dKysr8Hq9MQ1/dN9QWGEwGMTKygrkcjnL7eLi4jh3wmq1YmxsjI3LREWhxPWenh7u8gEfSlFWVlYQjUb5RURYRY1GE9McRt8J8OEEgbCThOSlsBsiVKjVakxOTsLtdrMEhYqrmpoa1uzeKinbsmULB611dHQgIyODi6NYpmHSo9J3SM8ZybvIFxEKhZCamsom+5ycHPbcRKNRRp+Oj49DoVDchkImWYbX64VYLGbyFN2bsRZpUsPhMMuG6HmllyDd90TjmJubY/4/sfOXlpaQkZEBt9uN9vZ2vpfj4+Ph8Xig0WjQ3d0No9HI3iN6ljdbkUgESUlJTKChTjtJ7GhScSvtpqenBykpKVhfX+dnlUhkXq+X791IJIJIJIIbN24gJyeHUcFLS0scwud0Ojc9aJCXyuv18j8XDAah1WoRjUaxsrKCQCDAo/Tk5GRcunQJKpUKw8PDPNWh909HRwfcbjdUKhW/l0hy6nK5OCg0Pj4eq6uraG9v3/Sg4Xa7IZVKeRJEByAy6qempkIul2NmZgYzMzPIyspiaYRUKmVSU05ODtRqNVMB/X4/kwMJNNLd3Y2EhAQsLy8jGo1CpVLxs3intbi4iPT0dJbUUggsSW2JTBeNRrmLSwZh8oFQoUAGT/LqUKIzkYBuldWJRCKEQiE4HI5NDxqkaycTJz07ZACXSCTMul9bW2Pm/vT09G30otXVVQQCAbS2tmJxcREJCQlQKBRQq9VwuVwMVHA4HGyapudhs0WUKjrE0HUlJiZiamoKQ0NDPJEhFP2t+/2tvx/JLChni/YpovARJcrhcHCuRiAQ2PTaqJgMBAK8N8/Pz7O5nQ4ghFInGAsBQtRqNe8NRDUkgzQdQiis1263w+12878jNTU1pkeDMsCos7y6usoFpclkQiQS4QMvEYP8fj+nctPnIX+Q2+3G/fffz4ABr9eL5ORkaDQaDAwMMK6cfqdYkl9C4t9KsKJ3HZEZk5OT4Xa7MTw8jNzcXA7qXV5exuTkJHw+HxOi6J+jKS89l3fddRdOnz4Nj8eDubk5zMzMIDExMeb9tra2xkAWynoiTDBJyyn2YHBwEBKJhO8xoitRFhvVGVTfkWdBLpcjFAphaWmJD9H0bon1DllaWrqNIEcks5SUFN4nCJOt1+sxPz/P3lDyAsvlchgMBm7UUz1FtMxgMIj8/Hysrq5iaGgITqeT3zux9tRbSYRJSUks+1xdXUVRURHLbEn2F4lEeAI2MzODQCDAYdqkhqBJB+0zQqGQPcUECiAU9+Tk5KbXduv62AcN0nTK5XKmB9ntdg5SKioq4jHN2NgYCgoKIBaLcf78eZ5QEJb16tWrkEgk6O3thdVq5RwEGsUmJydzMMvMzAwuXrwY8+Em7CYVWdTlDgaDGBkZwY4dOxAfH8++inA4DK1Wi66uLrhcLuYQr62t4dy5c5xaLpfLWauo1+vR0tLC6cAqlQqRSAQTExOoqKiIeSNotVru5k9MTDAJRiaTQSKRICkpiYkbDocDMpmMTVFJSUmYm5tDR0cHm7eIYw6AqUetra3Q6XT8AltfX8f8/HxM3TdhOIlMsLa2xgVSb28vdDodB+YolUrI5XKUlJTg+vXriIuLY7Ts+vo6uru7+aGjgn15eRkCgYCZ+kKhkEfiRDXYbFHxSR0uAJidneWXY3p6OkKhEMbHx+F2u1mrPjU1xUhewoomJibC6XRienoaer0eqamprN3t7e0FAH7o1tbWuMjYbM3Pz8NisbAJkcKCyMBOGMTJyUmmEFmtVrz00ksslxKLxdy5IM0sFYxisRg1NTWw2+2wWCwYHh6Gx+Phlxd1e+60CP1K3ZdbiS504KBNVCAQoL29nTMgaGpCh056pshwRi9lSvgl/wR1UYhkFWuFQiFYrVbY7XaWZYyNjUEul+P69ev8HFIn8t1338W2bdu4m0rp2yTDFAgEXOyTFEQsFjMJhGgaFIoUS3ZGWn+attB1AB+GRyqVSiaVUJIzHexXVlbYOHr48GGMj49zUUhITMJPyuVyGI1GTExM8FTO7XZjZGQE27dvv+O10YHM5XLxe4AKlZGRETaUk/Z6bGyMp2dXrlxhzw4FiVFnkjajzs5OlrncWkRTUdLf37/p9xYIBJCbm8sbOBWXiYmJGB8fR2ZmJk9Lx8fHYbFY2KBeUlLCh7iysjL09PTwXkAgCKLc9PX1sVEf+PCgt7CwAJvNtum1ra+vs9fO7XZzlhGx8/Py8rC0tISZmRluAojFYvT19XGidHJyMiorK9HT08N+BJ1Ox4F1IpEIAwMDqKmp4ec5ISEBk5OTMYsECkukjvXy8jJmZ2cZekBmaSKxbdu2jQ258/PznDNFAXjkLaLDMEk2kpOT0djYiHfeeQcej4cPdx9VMFMXl/xhbrcbCQkJSExMRGJiIhedlAkzNzeH5eVleDwezM/PIxgMsqSSGjt0TzkcDsYAE66YjNYCgSCmVJrkzYRJjouLw/DwMHw+H7/z6F6kQq26upoNuGazGTk5OcjLy+OCenl5GXq9HvHx8aw4IG/arSnJBCaJtai5Q82+paUlRvoXFxejubmZpdAUiDkzM8OyRYPBAJlMhpMnT8Lv92N+fp7zS1pbW2EwGG7T5FNODYCYUmm6V2lPXVlZQU9PD9LS0tDW1ob09HRMT09jamoKo6Oj2LlzJ27cuMGyUdqjSCUwOjqKQCDAmn3yxVLDhmhP4XCY8fKbLTpck79zaWmJu/SJiYk8gSNflEAggFqt5v2GDsZ0fyUlJeHGjRvcGB8dHcXw8DBjXam5S/d4LA8aEawIMU5TVpfLhZmZGeh0Oi7MaY8gpL/T6YRUKoXRaERJSQkuXboEq9WKmZkZxMfHQyAQwOfzcTgfwQgoLoKM15stypSi/x0Oh+F2uxkskJSUxPSv4uJieDweFBUVcdPH7XYz8CEQCHAwITWZ7XY7NwaEQiFDmpRKJZvfP8762B4NkjgUFhairKyMZQB/+tOfuAtJORYjIyNs1nvhhRfw2muv4cKFC0wsoNAmuqFInwiAu8ttbW2YmpriMRPRou60RCIRpqen2eg3MjLCLzt60WdlZUGj0WB4eBhDQ0MIh8Ow2WwwGAyYmZnBzZs3uSMuEolw/fp1SCQS5OfnQ6lUMjKTRl4ul4tPo7FOw7RharVapnSsra3h5Zdfht1uR39/PxYWFjA9PY0///nPuHLlCtLS0nDu3Dm8++67TIqoqKhAc3MzfvOb33DgFR3SvF4vQqEQtm3bxp15SomMZX6ljhVlG9CBi3wYZPze2NjAX/7yF0SjUS6Y7HY7bty4gZ6eHqytrWHv3r0sUaOCncKh0tPT0dDQgJycHA6AksvlyMnJ2fTaKMGdxuiRSAR6vR7nz5/nw4ZCoYDRaOTQt0996lPIz8/n312pVPKY1Wq1Qq/XIzMzEzk5ObDZbNjY2EBnZyc2Nja4Y0SbSqyDhlwuR1JSEsrKypCTk8NymrfffhtvvvkmP6hutxt2ux0rKyvYtm0bHnnkEQ6TpHyUU6dO8WfR6XRwu9145513EAqFYLPZ8MADD+Cxxx7j+3RlZSWmAYtegNTdMhqNzEonbjYZ31544QVMTU1hZGQELS0t8Hq93Omh6RQFJRGCNBwOo7+/H6mpqTAajUyEo88V63sDPpR0UUgb8OGk9ObNm+js7ITdbmcZkkQiwejoKNrb21FVVYXDhw9Dq9XC7XZjcnISc3NzLBciWgeR5kpLS7Fjxw7U1tZyp5X+uViFH73QaQrh9Xp5E6UOFnXcRkdHMT09jaNHj6K+vp7zVywWC+6++26eCIXDYQwMDGB0dBRisRi5ubkIBoMcTEYTCAqB22wlJCRgdnaWQ0mNRiNMJhMmJyfR19eHuLg45uEPDQ3hxo0bKCgo4MbE4OAguru7+VkiSgpNiShRtrKyEhaLBWazmT+vVquNaYBNTU3lyZ7X6+XCwu12o6enh5GOY2NjGBoaglQqZbw0PXOUUPvOO+9gbm6OSTUTExPo6uqC3+/H4OAg6uvrUV1dDY1Gg4WFBQwNDcWcPgoEAly6dAk3b97E0NAQd+5UKhXsdjvKysoYHUzAC8rYcTqdTHuj5PJ33nmHNcoUUHft2jUkJCSgoqKCMfATExMf2RSIRCIYHR1FKBSCSqXijJ+bN29yo4e6pomJiTzFq66u5kIpPT0dO3bsYAiHxWJhAIjP58Po6CjW19eRn5/PyEzyy8WaxKekpODq1as8ZdXpdLynWq1W9uFcu3aN996enh60t7fj/fffx9jYGLRaLXJycpCZmYnLly8jEomgrq4OVVVVWFlZwauvvop33nkH4+PjaG5u5vuGCuLNFgXaUh6I1+vF6uoqvve972F8fBxCoRBlZWUoLy9nzG1DQwMWFxdx7do19iyFw2E2asfHx8NisaCmpgbl5eU82aUDG8mTSIKy2UpKSoLT6eT9jybur7/+OhwOB6OWqfG5sLDADZGuri7GfE9NTUGn08FisaC9vR0KhQIVFRV8T/T29rJSZHx8HP39/VCpVDF9NwkJCQwuKCoqQklJCfr6+nDjxg1cuHABy8vL6OjowPvvv4/Z2VmIxWI4nU4mr2VmZnLtd+nSJbz00ksQCoWcWzQ5OYmkpCS88cYbmJ+fx9zcHH+e9PT0mIZrQu+SjK20tBRzc3O4ceMGuru7YbVa4fF4+HNTXUghjXFxcdiyZQu++MUvci4QyR+tVitP0Kk2oCbB8vIyRCJRzH0rISEBo6OjPNEiRcqlS5fgdDqhVqshEongcDjQ2trKTTcK0bsVQGOz2WC32zE9PQ2tVova2lrU1NRwDUH7o1AoZJN5rN9UoVAwwpkmeeTNJaIjHf5CoRAkEgni4uJgMpk41JcaCAqFAp2dnTwdCwQC/Ge2bt0Kq9UKv9/PE9aPSlS/dX3sg4bD4eCX9jvvvMOj0//6r//izUir1aKiogJHjx7F+Pg4vF4vtmzZwpQVCg568803cezYMRiNRj4lJiYm4ktf+hKGhoawtLSEyclJHDhwAP/wD/8AuVy+6fgZAMbHx7ngrKiowF133QWDwcAbbU9PD6MfSfff1tbGRhzgww5OZWUlbDYbDh48yNps4EMCh1wux6lTp/jFUF9fz8ixWPIkerFfvXoVa2trXJQ8//zzUKvV2Lt3L7Zs2YIHHngAzz77LJqamnD9+nXk5OTgK1/5Co+/v/rVryIrKwvXrl1DRkYGZmZmWI508OBB+Hw++Hw+WCwW3HXXXThw4AAUCgUf4O606CBAxXx8fDwmJydxzz33oLm5mTuyNpsN3/rWt9Dd3Q21Wo1AIIDS0lL4fD68++67kMlkuHHjBv7pn/4JRUVF2LNnDwcx3XPPPdwB/+CDD5Camoq9e/fy97LZojA8yilIS0uDUqnEj370I36ZT01NISkpCXV1dYzsy8nJwd/8zd/wZmwwGHDp0iXI5XJotVo+eN24cQMPPPAAwuEwI5DT0tLQ2Nj4kYz55eVlFBcXY3h4mOkyk5OT+NznPoc33ngDnZ2dSEpKwt69e/H3f//3nOw9MDCAqakpOBwOTsg2mUyor6+HRCJBVVUVdu7ciZ07d+ILX/gCLl68iD/96U9oaWlBU1MTHn/8caysrMQ8PIpEItZ5UlFKqdFarRa7du1CfHw8XC4XTCYTNBoNKisrERcXh6amJmRnZyM5ORkNDQ147bXXUFVVxTkyZ86cwc2bN2EwGJCTk4O0tDTk5ORw4UebVaw1Pz/PgWlCoRAmkwlPPPEEdu3ahf7+fjbGBgIBqNVqfO1rX8O1a9cwNDSE+vp6lJSUQCwWczGfk5PDKeyhUAinT59Gamoqzp49y3km1NlUKpUf2f3W6/WcIkzYwgcffJDDrajDRJurTCbD4OAgSxZu3LiB119/HcFgEBUVFRgeHkZZWRlSU1PR3NyMffv2IRqNwmg0wu/3w2g0IjU1FW+++WbMQCsKCl1cXOSwpJSUFDz66KPY2NjAzp07UV1djW3btuGJJ57AysoKlEolysvL8Ytf/AJ1dXXsm9NqtThw4ACn2NIGunXrVu6U9vX1cePmmWeeiVkkLC4uQqPRIBKJsP/hpZdewvbt2zEwMMAdbJPJhJ///Odwu904fPgwpzGnp6cjIyMDZWVlWFhYwCOPPMLo3h07dsBgMODxxx9nYqDX6+UDpkQiiWnwJ9Tk7t27ceTIEezbtw8SiQQ/+tGPEBcXB5VKBZFIxGGza2tr2LVrF9LT09lzc/bsWfT19aG/vx//8i//wsFVXq8Xzc3N+Id/+Ac22Xo8Hm7GOZ3OmPcbABw6dAgikQh2ux1OpxNCoRBPPfUUPve5z2HLli0oKipCfn4+du7cCYfDAZfLhcnJSTzyyCPYuXPnbdMQg8GAF154gZUDPp8Pn//85/Haa6/hn//5n9HZ2Yn8/Hzs3bsXKpUq5iTebrejsrKSlQtEwsvNzYVQKERRURGysrJQU1ODz33uc7hx4wZ27doFpVKJhx9+GHv37oVGo+GAOJPJhAsXLnDn3WKx4Bvf+Aa/h1QqFWpra1FQUID+/v6YzwJJUNfX17mhVl9fj//1v/4XJ9NPT0/D4/Fw2jdNzH71q1/h4MGDCAQCMJvN/AxeuXKF5U7j4+P453/+Z1y8eBF9fX0YGRlBRkYGvye/8Y1vxLzfyEdFxDW1Wo1Pf/rTCAaDqKyshNFoRFZWFu655x68++67aGpqQjQaxWc/+1me9ubn5yMQCOArX/kKN1hIXlNfX4+//vWv+PznP4+FhQUUFxfj8ccfR2pqasxmRTgcRnl5OWv5MzMzodPp8J3vfAfFxcU4fPgw8vPzcejQITz11FN444038P3vf59rkGg0ivz8fJ6C/uM//iPefvttDA4OMpiiqakJd911F775zW/CYDDg4Ycfxle/+lWWMG+2fD4fqqqq0Nvbi4sXLzI4YOfOnZw7ZTAYmN45MzPDuSLHjh3j+8vr9TKZi8L9tFotcnNzcc8990AgEKCkpATNzc1MSnQ4HOjr69v02hYWFnD//fdzjpLRaERGRga+8pWv4ODBg3jwwQdRVFSEjIwMNDQ04K233uJ3xCOPPIKmpiaWZfb09ODv/u7v2DNMmTg/+clPcOrUKXR0dOD69eswGo2w2Wx47733Yr7fQqEQsrKy+N1bWFgIqVTKzXyn08nNRavViitXrmB9fR03btxASUkJCgsLIRQKGfjx5S9/GUNDQ7wHJycn45FHHsEvf/lLblpWVFSwjyfWIejW9bHN4M8++yy6u7tRWVkJsViMyclJuFwulJSUID8/H263Gz6fD1NTU5idncX+/ftRU1ODmzdvcid1ZGQEf/zjH/Hd734X//Iv/4Lvf//7PMbyeDx48cUX8W//9m84e/YsTCYTa5tp7LbZZvfaa6/B5XJxF4JOimRYnZ2dRXp6OnPxI5EI3nzzTRw4cIBHZxQ0VVdXx6OrgYEBKJVK5OTk4PLly/jhD3/ImNClpSXusg8MDOChhx6647X94he/gNvthlAo5O7U6Ogo3n33Xfz+97/H4OAg4uPjsbCwgJmZGRQXF+Opp57C1772NdTU1PChKykpCQkJCejo6MDOnTv5cxAlZ3V1lUkC1BnIycnBiy++iOeee+6O1/bcc88x2pS0vjKZDH/5y1/wmc98Bj/84Q9hs9lQVVXFHHFK1n3qqaeYPNbf34+tW7cy5WR8fByhUIiNRWRY1Ol0uHr1KlwuF3bt2oW4uDjU1NTc8dqOHz+O1157DeXl5cz51uv16OvrY/Mr+UHOnj3LmzL5dfR6PSNyb9y4AYvFwuniGxsbLBMTiUQ4c+YM9u7dy5MaOqlvRoz5zne+g7i4ONTW1kImk2FxcRGrq6v4yU9+gi1btiA7OxtKpZK1tDSy/PnPf474+HgolUoutgcHB7G8vIwnnngC7733HmZnZ5Gbm8sF1PHjx/HQQw/B6XTC4XAgLy8PDocDBw8e3PRZoA6O1+uF0+mEz+fD+Pg4/vM//xOtra0IhUIYGhrC9evX8YlPfAK//e1v8aMf/QhvvfUWFAoFa0VpAyZsKvHMr169ih/84Adob2/H+fPnYTabkZaWxma+zZ4F4EMzeCgUQkVFBa5du4bW1lZ8/vOfxzPPPIMvfvGLaGlpQUpKChYWFuBwOPC1r30NSqUSf/3rX+FwOFBXV4ecnBx0dnYiNTUVx48fx+7du5GZmYmZmRl0d3ejrq4OeXl5eOaZZ7Bv3z72RwUCATidzk3N4L/+9a/Z+Op0OrGwsICdO3eyLn9kZASzs7NM2dJoNPD5fNjY2MDLL7+M+vp61NTU4L333kN1dTUuXLiAz372s5icnGSO/4kTJ/DAAw9gbW2Np5lE/khPT9/UAPvTn/4UQqEQGo2Gu6RqtRrnzp3Dl7/8Zbz77rtITU2FQqFAIBBAUVERzp07h9nZWbS1teG+++7D/v370dnZCZfLhV/96lf41a9+hbfeegvj4+MoLCzEvn370NraCoFAALPZzO8WOtzcd999d7y2P/7xj5iamoJQKGR8p1gsxsmTJ9HQ0IC0tDTMz8/j+vXrcDqd+O53v8tSmcuXL6OwsBDp6em4fPkyZmZmIJPJYLPZ2PtCUoV77rkHTz31FO6++24sLi7C7XYjLS0NAoFg04nLf/zHf2B5eRm5ubmca1JaWooLFy7AarUy+IFwvj/5yU/wn//5n7j33nvx1ltvwWQyITc3F93d3XjsscfwP//zP7jrrrs4zE+r1eL1119HY2MjJicnMTExwYn3nZ2dyM3Nxd/8zd/c8dq+973vwev1QiqVcoDl1NQUurq6oNFo0NTUBIfDwQ2l+Ph4XL16lSU3qampEIvF6O3thVwux8mTJ/HFL36RISDj4+OQy+VITU3FxMQEhEIhk6Tm5+dRXFyMxsbGO17b2bNn+TrUajXT1S5duoQ9e/awlI8CInt6evDoo49iaWkJzz77LAwGAw4dOsRBqidPnkRjYyObbxMSEvD666/j/vvvx9jYGPLz81lPb7PZEA6HcejQoTte209+8hM4HA7+ThITE1FTU4Onn34aX/rSl9DZ2clhiZOTkzh06BBOnjyJUCiE0tJSSCQSDA0N4bnnnoPVakVSUhKampqYaEbUwb179zKQpq+vj8N9Q6HQpr/pT37yEwwODqK6upp9nXSAeeyxx/CTn/wENTU1UKvVaGlpQXl5OXfEf/azn6G4uBg1NTU4ffo0I38ff/xxljTR6u3txb59+5jwOTg4iO3bt6Onpwff+9737nhtTz75JPr7+/HII48A+JB0V1xcjCeffBKf+9zn2OQ/ODgIgUCAxx9/HJOTk1hZWcHbb7+NxMREpgWeOnUKFy5cwI9+9COEw2HOZOjo6MD3vvc9PPXUU1hcXERGRsZtCdKbQUyeeuop9PT0cJ5FfHw8Ghoa4PV6uemr0+mwvLzM4b96vZ6zpoxGI/tms7Oz+TB14cIFpmW2tbXhG9/4Brq6utinFg6HsXv3bly5cmVToMQzzzyDhYUFiEQilg0qFAq8/PLL+Na3vgWHw8F0PGoyeTwefOUrX8EvfvELyOVybN26FfPz8xgfH8fExAS+8IUvwGazMYWUcse6u7tvo5ARbGKz++1Xv/oVhoaGUFtbyzJnk8mE559/Hl/+8pfx0ksvwWazITk5GUNDQzh06BCWlpaQlpaGEydOMCH05MmTaGpqwsWLF5GXl8fSK/Kbkbzw+vXrGBgYgNlsxqOPPop33nkHn/vc5+54bbeu/6fAPnKvO51O1m7RiEytVsPn82F2dhZOpxOhUAj9/f3461//ioyMDKYrWK1W/OEPf8Df/u3f4tKlS2yA2tjYgN1ux5NPPokjR46gp6cHYrEYKSkpuHDhAtxu96bOe5/PB6vVymnf0WgU/f39TEsKh8OMCNVqteju7saBAwdw8eJF6PV6qNVqhMNhLC8vM/HD5/NxQdLc3MyGYroxKCnZ7/fHTEckxB0Zl6LRKBQKBQ4fPsyd7omJCSQmJmLLli1QqVR4+OGHMTIyAgBs9KOUb9pENBoN3G43PvjgA/j9fphMJtTV1eHMmTNs1rl69Sobg++0KAm8r68PwWCQtft79uzBa6+9hqysLPh8PnR1dWHbtm2sU01OTsbhw4cxOzsLn8+H9PR0BAIBrK+vw263c7Bbe3s7hoeHsXPnTqSkpDAlQq1Wo7W1lTeIO63x8XFO3SSzVG9vL1ZWVnDt2jWUlJSwzItQj6FQCM3NzVzoAB/KnMxmM6MjyexKBWJpaSmMRiN7BkhO9lFJ9EQ3IY3x4uIiDhw4gPT0dCZbxcXFoaysjHGd1dXVnNKZkpKCzMxM3Lx5E2lpaRgaGkJlZSWHqOXl5WFgYACHDh3C1NQUI2kpn2SzgwYFPtrtdoTDYaZyJCUlYXBwkP+62WxmclxRUREuXLjA4/zr16+zoTkcDrOshe7HvLw8DA0NYWZmhokjVCRQzsFmy+l0ora2FvHx8ZBIJDAYDBgeHsZnPvMZyOVy5OfnY2JiAlNTUzyRGBsb481hcHAQHo8HwIfP5b333ouJiQnMz8/D6/ViZGSED+MNDQ2YnJxkqUJCQkLMTinRf8gvRcnyWq2W6Tn0+fr7+1FQUIBLly7BaDRi165dmJmZwfPPP4/k5GRcuXIFKpWKca2ktS4sLMTZs2c5QIlkOqRvj7XImL60tMRSsOLiYnR1dWF0dBQtLS0oKCjAfffdh42NDdTX1+PatWuorq5miYFGo8Hs7CyampowMDCAjIwMppRQR7CsrAyBQADj4+NM3/L5fJseNCh1mgJWKUiztraWAxAXFxeRlJQEoVDIBfylS5dQW1uL7u5uJrfs3LmTAy7D4TA6Ojpw4cIFJCYmwmq1oqKigv8bJAV466238LOf/eyO10am4snJSTa8tre3IycnBxqNBn6/n3NHiouL8f7773P6MYEuyBP3yiuvoLS0FDdu3EBhYSGmpqbwu9/9Dqurq2zwV6lUWF5e5iDRWLp0Mv7S3kehZDSpmZmZYUoO6ecp9ZoMyzKZDGVlZThz5gyOHDnCnWDyaZCkIjMzE93d3RgcHIRMJkNRUREmJiY2PWhQRk58fDx75eLj41FbW4sLFy5ALpezNIOkSqdPn0ZSUhJMJhPnRNw6Ibx58yaWlpagVCphNptx4MAB2O12aLVaDA4OsuRzaWkppteAfisyTUejUZw+fRr33nsv1yOjo6McEEnepxdeeAHj4+N8QNu+fTv6+vqwZ88eXLp0ial/BFV477330NTUhOPHj0MgEHCQbqzcFpqikE+Aivf5+Xm8+uqr0Ov1sNvtfJih+y8nJwd79uxh+XFJSQm6u7thNpsZrezxeNhDSXv8lStXOKT25MmTMSVnwWCQ1QcymQwJCQmYmZnB4cOHuaFJE83V1VX89a9/hdlsxqVLl1BZWcmNQ7/fD41Ggx//+McMUyC1islkwrPPPov8/HxcvnwZg4ODrIaJZVR3uVzYvXs331NisRhJSUmYnp5mouTY2BiWlpbg9/uRmZmJF198Ebm5uVy/JCUlQa/XsyTKZrOxVH15eRlNTU04c+YMEhMT4XK5IBaLIRaL0dLSEvM3pXcgyfrJMP+9730PFy9ehFAoxPnz5xEKhdhbmJSUhJdeegkCgQCZmZlQKBRYW1uDz+fD9u3b8de//hWrq6tITU1FdnY25ufneT+9fPkyJBIJ+2JihQkuLS0hKyuL/SuLi4tobm5GfX09gsEg6urqYLfbmZLa3NzMeRk6nQ5msxkikQjZ2dmYm5tDOBxGNBqFz+fj3zUSieDtt9+GWCzG+vo6SktLIRAIcPz48ZjXduv62AcNijoXCoVsDFEoFHj77bfR0NCArVu3MoqNHlahUIi0tDQ2tmRmZjJK9ZlnnsGDDz7ICY3z8/MQCoUcNGI2mznbgUKkNltxcXFoaWlBRkYGJ7KOjo4iJSWFdXr08heJRMjJyWGEGt3U1MVWKBSw2+08ASFKjlqtxszMDBMNpFIppFIpkxA2W5TOqtVqkZSUhEAgwMXu5OQkdDodxGIxFhYWsLi4iMHBQZSXl+PatWuM5tTpdIxHI3zvysoKXwehQOlFRIVVTk5OTFkXAJ6MqNVqKJVKDA0Nobm5GSkpKTCbzVhYWMDa2hrsdjtcLheqq6vR39/PGlS6vqmpKfT29iInJ4flaFKplLnoGRkZmJub4yTtlJSUmAQgwvYReYGoJcPDw0z3oZBD2gTsdjtUKhWPJQ0GA/tNXnzxRR5jE9qNsLZkYKfkYJlMhszMzE2vjdJtKQjMbDYzj1un06G4uBjLy8uw2+2YmpqCSqVCTU0N/vSnPwH4MEQsMzMTBoOBu61EMpNKpfD7/RgeHsbw8DD0ej2mpqZ4s9JqtTHZ/EQgoWAgSmoOBAJobm5GXFwckpOTEQgEWFNPwID19XWoVCqkpKRgamqKN4m9e/dCIpFgbGwMbW1tjKIk/wZ5dwDEzJSh1d3djezsbCZ1TUxMwOfzwePxQKVSMamMAgoBYMuWLYyBJIJRYmIi/vznPyM7O5vxrUQK6u3tRWdnJx9kSZqyWcIqrYmJCW5wAB+asG/cuMHTOiKBAR8yzFdXVyGXy+HxeFgSRf/9mZkZ7N+/H8FgkBO3vV4vcnNzeXpIWl56njdbpMmnSZxWq4XL5cLZs2dx5MgRJCUlQSKRIBQKsXQvLS0NMzMzaG9vx9raGmw2G4eCpaWlMb5yamoKly9fZiqXTCZDe3s7TxIAxAyNCgaDbAym7IepqSlcv36dCSc07Xa5XCguLsa5c+cwPj7O086MjAzU19ejt7cX3d3d3CXMysrC7Ows/vSnP3Gyss/nQ2dnJx/MYu0N6+vrDKwgHbJYLOZCnKRNc3NzWF9fx+LiImw2G4LBIE/niZhDKcqEKyeEJAAGE7jdbsalk4xss0WJwZQ8LZVK4XK50NLSgj179nAhMDk5icuXL2N1dRXV1dWs8Xa73YiLi0N1dTX74ighnoQKHR0dEIlEbCQnkuLU1FRMMIJUKsXk5CSSk5N5H5yfn0d3dzcikQiKiorYyySRSLgApzRsylQwm80YGRnBmTNn2GNAPsKNjQ1Oiqf3NQWpxYKYkPdpY2ODzdakqKivr+fmyvT0NBYWFpgEJZfL0dzcDKVSCaPRiISEBJjNZoyNjbFygQzFo6OjTEukop4Q+LE8GkS5GhsbY9yx1WpFIBBAIBBgaRO9h5VKJYqKinD27FlMTU0hIyOD/UC9vb0YGBhAeno6UyD7+/u5Wbm6uorBwUEukik7J9ZvSpIy8tMRgpeeW5Igk1mdKIPj4+MAwHXM9PQ0enp6kJeXx8AIQuzm5OQw7Y1ooFSsb7aSk5PR3d3N3/Pa2hpu3ryJmZkZpKamIj8/Hzdv3kR7ezsnVxNIx263IzU1lbOVyIdByGmSgMrlcigUitv+Pk0nYhn8RSIR17xEPJucnMRPfvITllmPj4+zL4+yLXp7e+HxeDA8PMwZaBUVFRgYGEBmZibntoyNjaGnpwcKhQIWiwVSqZQ/x8bGRkzDdUpKCv9GBG0gklhXVxfXmCqVioNGqSmwurqKrq4uLCwsYGBgAHK5HIFAAPv37+d7nwIPk5KSkJ2dzbVfXFwcH0Y+zvrYHo1IJMJSAdJuCQQCZGVlMZaOmOzp6emM1bs1NTQcDqO0tJRvvltDTYxGI/bt24fl5WUsLi4iEolwV9rlckGn08W8Nhqh0v8Fg0HeVGg0GY1GeeJBZkQi/cTFxfGmSDhMGj9PT08zalOv1zM2ND4+nilDmy3SfVMWBGFCiTwgEokQHx/PHRCHw8EPBwB+gElOIhaLMTg4CK/Xyy/3tLQ0Hs/SiJu6PbE2E9rk6POr1WpEIhGWoSkUCv73UeAYXbdarYZWq4VcLkdaWhqTjmZmZrCwsIBQKIT19XUUFhbyS0skEnGH4aPS3iORCFOPSHMsk8mQn5/PmsK1tTWEQiE+hJhMJiwvL3PoEnVwBgYGEAqFEI1GkZaWBovFwkn14XAYSqUSCwsLbIynMJ/NFuGP6dBNxbnBYODDCqWBSiQStLS0IDs7mydGRAOi9E2j0cioRQq9MhgM8Hg8LBGhZ4RkfZstmnwFg0HuaK6srHAxZrFYWBsaFxcHv9+PsrIyJCYm8uGbXuCRSAS5ublYW1vjVFTy8xCFiSSEEokEGo0mZtcKwG30N6JxAGD5H5HWLBYLtm3bhvb2du6kkWk/KSkJlZWVAMDvlpSUFKbwNDQ08GYyNzfHdA+Xy8XEojstIuzQIZreKfReSEtL4ywcMtMVFBTwNEIul6OgoADFxcXceSZimkgk4oMZ/VnKtEhLS0MwGIwZYAmAD4wbGxuM6KTuNmnmRSIR3G43xsbGmJCVlpYGkUiEhYUFAGDvnMfjQSAQYB+L1WrlQwyhTNVqNQQCQUxwA/Chj43er1TIabVajIyMsNQrISGBQwVFIhHTpuLi4jgNemhoCAKBgJsmKpUK6enp2LNnD4aGhrjJotPpoNVq4XQ6Y/qpgP9T0NP/Junb4uIie7foPopEIti9ezcXpGazGXq9Hvn5+Xyf0YQvGo1Cr9ejqqqKQ/9mZ2cRjUZZUhnrN01OTmYqXGJiIrRaLZP5KJWb3iGEDK6urma/ITWXqLhISUmB0+nkA/z8/DwMBgMmJycxOTnJwBYyrsaiTgH/p4hJTk5mGg5psrVaLYqKitDQ0MATMKvVColEguzsbC6i6JCiUqmwtLTEfpiEhATYbDZuki0vL0Oj0XAIW6yGBRU3q6urLH0GPnzGJicnYTAYIBaLkZCQwEndLpcLc3NzTI6Sy+Wora3lWoQOLrSPlZSUML2RvCC3pklvtuiQScbjxcVFLC0t3SYjlkgkjKD2er2QSCRsYqcmGgCmFQYCAb7ujY0NFBQUIBgM3iajpAlsrDqJYAFUvNKfNRgM3DQjbwDJ1yhBnSSZ5EmkEE0iiW1sbHDIHtVdCQkJLBOORqMxD0EJCQm8B5M52+PxcCK4SqViWWZ1dTXLCaVSKeRyORITE7nhRdNPei6USiUj+v1+Px8mZTIZBwHGMoPT/hYXFwexWAy9Xs+SJopjoD2G3vPFxcVYWFhAdnY2T04JL0yZL9Rkps9JsnQCc9D+HWvRn/P7/SzPpfqDlEX0O2RkZPBnoPp7fX2dJXHBYBDV1dX8XiDZalpaGodJ0n5GgJRYQInbrvNj/Sl8KCugAwRhzJaWlnD06NHbwnYUCgWsVitvptPT0zw2Iq/ByMgIHnnkER73yGQyWCwWlJSU8M26srLCExHCam621tfXYbPZGGVGP6hGo4HZbIZEIuGCmjSYxIcHwPkA1IUiGQ514Kenp5GWloaUlBSmFRAm8qMkDzRRIcQmJVMfOHCAvzM6VJlMJp5eAOCTbn9/P58wMzIymMtN4SlZWVm3ZYgQA9/r9cY0hxHfnDwyhAtUq9U8wvZ4PAiFQlAqlUhISGB+dnp6OgdD0cNdU1PDEy16CC0WC4eLEQWKkjljbSb0kqONlzqDDQ0NkMlk3DUoKSnBli1bWCLl8/kYbRsMBll+YTKZuCim3zsvLw/BYBAqlYoLITLIxuouk3yooKAAeXl5jBo+duwYywD8fj9UKhUyMzP5YF5bW8vGMOrYk5+E2OESiYRlcAaDgWkRtbW1fBA/derUptdGPiTqnlMuxPbt21FUVMSsdKlUymm46enp3P1RKBS8aRmNRpSWlqKlpQVTU1NcHEilUkxMTCApKYmlkmtra4zji7XogEkdz4KCAphMJhQXF3OYEZkoP/nJTzKOs6+vDwMDA+jr64PD4UBaWhpUKhUeffRRZGRk8GRAJpPBZDJBr9ejurqaPSoqlQpms5mRn3dapGFfXl6G3+/H4uIiFhYWUF5ejtLSUn63VVVVoaSkhOktXq8XIpGIpy1SqRRisRjV1dWYn5/H/Pw8ZDIZSkpKUF5ejnA4DJlMhsnJST7QfxQ2k3DS1OCgTW3r1q2IRqNMUqPvZWVlhTHe99xzD0pKSrC4uAiXy8UNgMHBQVy5coXxqXl5ecjLy4NQKITRaITFYmHkYqzpY2JiIkpLSxlhnZSUhKysLDQ1NWFtbY3vt/z8fIZ65Ofnc2YMdU7Jq5KVlcWb7eLiItbW1jhxl+Rr27ZtQ1NTExISEj6SAkT3RkJCAtbX1zE7O4u8vDwOy6NOK3Hm09LSOIRuy5YtKCgowP79+yGTyVg3T+GQKSkpsFqt8Pl8vOkSLEEkEsXsRhKgISkpiQ8Wq6urqKyshFKp5Oyd5ORkmEwmWCwWCIVCJs9lZWVxV5Lu+/b2doRCIW6Ubd26lbu41PSjML1YWRWULqzT6WA0GmEwGBAOh9HY2MjZH5SYbTQakZiYCLPZjISEBBQXFyMzMxOLi4vw+XyYnJzE/v37GalKhVtqaipGRkaQlpbG0jqVShUzhR74cG+grCR6f6akpGD37t1wu93cRJHJZDyloHA2i8WCzMxMmEwmFBQUIDk5meuN0dFR9PT0wG63o7i4GGazGbW1tYwMJVoXoVjvtCjPQ6PRYHV1FWNjY6yEUCgUUKlU/IwQLnRiYgKRSAR33303ioqKuGmbnp6Ou+++G/Hx8UzSVKvVjLqlbJz09HSkpaXxAX6zRc0E8otScGBVVRW/j8mTuXv3biQnJ3MzcevWrQxHWF1dhVQqxZe//GVkZGQgLy8PRUVFKCgowNGjR7keIY0/Tb5iHTRIVUGTx7S0NG6uCQQC9qTt3bsXn/rUpxAKhZjIVFRUxFNuyjtJT0/nfBXKtYiPj8fg4CBPbgkN7ff7PxJnT3lbNJXS6XT40pe+xGGn1LCggwbtw7t27eIAW8LY7tq1C5OTkxgZGeGAvz179iAtLQ1GoxEpKSl82KJD3GaLpll0CL5VMp6UlITCwkKuh4qKiniaSSSwrKwsbNmyBQ8//DAsFgsOHjwIt9uN+Ph4ztshWh3VSlQLEpny46yPfdDQ6XS4efMmR7pTt/iZZ57Bww8/zKcdMnFWVFTgpZdewrZt29i0Zzab8dhjj0Eul+Ott95iUhXRkQ4dOoQf/OAHzMQfGBiAy+XCY489FpNQJJVKcfnyZf6ByDMyPj4OkUjETG0aETU1NcHr9XLCtkajgVKpxHvvvYeKigq8/PLLqKurg9lsRm5uLvbu3QuFQsEa3pycHNbRms3mmBHx0WgU3d3dKCkpQUFBAWNUX331VVgsFty8eRNisZhpAbt27cIbb7yBbdu24YMPPsD6+jpqa2tx5coVPPbYY9BoNPjqV7/KuEKiBH32s5/lcJ2JiQn09fUxinazRQfErKwsRKNR9hVIJBI0NjZyInJSUhJOnDiBuro6nDhxAhKJBG+88QauX7+OYDCIy5cvc9L3/fffz8amsrIyrK2tsUmKfAH0/XV0dGx6bcXFxTwKnJ+fx9LSEnJycpgO9vbbb0MkEqG8vJyTyY8fP457770XMpkMO3bsQFNTE06ePImvfvWrcDqdsFgs3GXLz8/H008/jf3797OOmTBxKysr6Ozs3PTaaHOnz+F2u7mDtWvXLsYiEgnsV7/6FcbHx3H06FEYDAaUlpZiy5YtCAaDePLJJ2E2m2G1Whl+oNFocPXqVezatQsOh4M7V9FoFIcOHYrZUUtKSmJGN4UcHj58GMPDw5DJZLh69SoH7W1sbGDr1q3o7u7mxGoKY6IMlOeee459QfHx8di6dSt0Oh0OHz4Mh8MBhUIBv9/PMrRYfiUAMJlMkEqlnN4+MTEBi8WCGzduoKOjA88//zx8Ph8HA/3TP/0Turq6mH6l1WqhVqvx05/+FJ/61KfQ39+P+vp6LpwUCgUuXLiAJ554Ajk5OQiHwzh9+jTa29uxZ8+emL+rWq3mNGqScmm1Wvz4xz+G2WzmoLSuri6cOHECjY2NGB8fx7Fjx7g7tbKygtdeew3f+ta3MD09DYvFwpMVALhw4QIefvhhzM3NwWazoa2tDefPn0ddXR26u7s3vTYqxLdt28ZS0MbGRly+fBm1tbU4fvw4gsEgysrKYDKZGEP5xS9+EcFgkNnz6+vrOHDgAGZnZ5nCVFNTA71ej5MnT+KJJ55Ad3c3H4iHh4dZRrPZWl9fx1tvvcUEIJI+DAwMYOfOnXzAyMnJwcWLF5GRkYEzZ87gE5/4BLKyslBcXIzy8nKsr69jx44d6Onpgc1mQ1dXF/r7+5GcnIzf/OY3ePTRR3n6S7I+OghvtkQiERYXF3kS2tfXx5/noYce4oMIdSglEgl+8IMf4Ac/+AHm5+fR39+PyclJPPfccyz5pMYA3c+tra3Izc1FW1sb05amp6eh0+liar9pYrFz506YzWaMj4/D4/Ggq6uL5VATExMIBoNcEPz6179GU1MTF0vJycloaWlhyUtubi40Gg1PJ//4xz/i85//PPbu3Yvq6mpsbGzA4/Ewxn2zpVAoOHmammvZ2dl44403sH37dszNzeHs2bN49dVXcf78edTX12NpaQkDAwO4evUqtFot7r77blYQXLx4EceOHYNarYbdbsfAwAC+8IUv4NFHH4VKpUJjYyMkEgmCwSDMZnPMwyNNoEleNTIyAo1Gw4bo9957j/Xyf/3rX1FRUYHExER85StfQXd3N06fPo3z58/jT3/6EyoqKnDz5k088MAD7C0JBoP43e9+h0984hO4fPkycnNzodfrubnY3t6+6bXp9XqMjo5CJBIxVvv++++HVCrFPffcg76+Ptb6RyIR3HfffTAajXjkkUeQm5uLwsJCbN26FQaDAWtra/x8U6BdfX09Tpw4gYyMDJw7dw5btmxBXl4e1Go1duzYEbPpqVQq8cwzz7AUaGhoCEqlEu+++y7q6+vhdDpx6dIlnD17Fr29vThw4AC6urowNzfHmGfytUxMTODUqVM8cUhOTkZZWRlef/111NbW4sSJE6itrUVjYyPKy8uxa9eumNdGkwWPx8Oeuy1btrDk7uTJk2hubkZLSwtOnTqFvXv3Yn19HWlpadi1axeeeOIJPPzww3A6nSgqKsLFixdx+PBhzrOZnp7Gv/7rv2Lnzp0YGRnB3NwcxsfHEQ6HsX//fp723mnRPkoHmqGhId7j6+vreSI2Pj6OK1euQKlU4tq1a9i/fz97zCj3Jj4+Hj6fj4lvCoUCtbW1OHPmDA4ePIipqSnYbDYOL6VMtc0WTfrpndTX14f8/Hy0tbUhKyuLJzHkU0lOTsb8/Dyys7ORkZHBaO+XXnoJWVlZeOuttxhmodfrUVZWhs7OTnzrW99Ce3s7KioqYDAYkJCQgNraWqbIfdT62B4NoVCIT3/60+ju7oZIJEJZWRnrbt966y02OInFYuTk5LCBmPRppIGtra1lOVFHRwcXUYRL+853vsOjbeosRiKRmJr5UCiE+vp6Ni0mJSXBYDAwM51OvsXFxRAKhXj99ddhs9mgVCpRUlKC4eFhdHZ2QqlU4sc//jHC4TBr0ZeXlzE9PY2EhATu7kxNTfHYzm63szn1TkssFvMLUafTQa/Xo6enB8CH5AixWIzZ2Vk2OlEA1V//+lcuXDo6OrC8vIy3336bcZhkGqZOARFeJicnkZmZyePeWFpXl8sFs9nMXH+LxYKhoSHW7pOJMyUlBZWVlWwGN5lMKCkpwcWLF/GHP/yBTbw9PT3Yvn07KioqOF1Ur9dj586dGBoaYoIF+T5iFaW9vb0oKyvD5OQkdx2dTifC4TD27t2L0tJSTExM4ObNm/B6vXyQu3DhAmsL8/LysHPnToyPj8NsNuPcuXPIzMzkA2diYiIuXbqEjIwMyOVy3uhmZ2c3TWgGwJ3luLg4BAIBDA0NYWRkBJ/61Kfg8XggEok4WOvSpUuwWCyQy+X44x//yF4kvV4PnU6HK1eu4O/+7u9w+vRpTo4lfX9GRgYcDgdLK2hMHGsNDg7CZDJhdXWVJV2XL19mA9uePXvQ1dXF5AidTgeJRAKFQoG5uTnOECkqKsKNGzdQVVXFcIQbN26wZnZoaIiDB+lw5nQ6Y8rhALDciD4jpXnn5ubCZDJxo+LcuXN4+umnceDAAdx77704deoU6urqIJPJYLfbsbS0hJ/+9KeIRCK4ePEiZ8hcuXIFJSUleOmll7hLT5K61tbWmNdG42eNRoOlpSUOriTJKBloo9Eoqqqq2PP129/+FkePHsXq6iocDge2bt2K559/nvXxxcXF8Pl8cDqd2LFjB377299icXERDz74IGQyGXPwi4qKNr02CqcjGeDKygqam5sZ2LB3717Y7Xa0tbXBaDRibW0Nr776KtLS0ngaTCGSMpmMD/qUSk5F77/+679ysUYBXcPDwzG7kTqdDlarFX19fVhbW+MgsKWlJezZswfHjx/HxsYGPzMjIyNMBTp//jzm5uZgNpvR1NSEZ599FvX19Sxzm5mZwfj4OHbv3o2f//zn8Pv9kMlkPB02mUwxO35dXV3QarXcXZXJZBxCGhcXh4WFBchkMqSlpbFfS6fT4ZFHHoHFYoFWq2WJH+UJEWShv7+fCWPr6+v8uWkiTZLizRYR6KamprjLHwwGuVjNycnhIoHyW+rr63Hp0iVWGNhsNmRnZ2NiYoKnQETlEQqFePfdd1mWNjU1Ba1Wi6ysLJ5abrYoz2B+fh52u50VAcXFxWhra2ONvFgsRnFxMUuTFAoFDAYDN/eysrKwsLCAiYkJTExMME2PvGSvvfYaAPBUnKANsSS/lINAhxHy4NB+XVpaypKdjY0NvPPOOzh48CAX7TRVcDqduHLlCuLi4nDt2jWsr6/zuxAAF83nzp3jz7WwsBATL+52u2G1WmE0GuHxeDA1NYV3330X58+fx+OPPw6tVou+vj5MT08jPz8f77//PktQHA4HJicn4fV6UVNTg2g0ir6+PphMJjQ2NqKlpQXPPPMMDh48iJGREaYq5eTkQKvV4urVqzGfBY/Hg6997WvcAKOgzvvvvx/Ah4fmubk5GAwG7Ny5kyXrTqcTzz77LDd5tm7divT0dNy8eZN/e/J6RqNRDiU+d+4cenp6mF53KzXr/14ikQilpaVwuVzweDxob2/HyMgIqqurcfny5dvkjyqVCmNjY7BarTh+/Di2b98Op9OJpaUl7N27F+fPn0dGRgZ6e3tZjrW0tAS1Wo0rV65g165dTMZcWlqC0+mMmRNEqOtQKIT4+Hjo9Xp4vV689957qK+vB/BhTVBVVYWamhqeGtA7gWTUs7OziIuLw5UrV5CamsqheISfP3PmDEwmEytoVCoVJiYmYk66dTodlEolRkZGIJPJWE5Kh36aTCUmJrJ0igKnadpPBvze3l6srq6isLAQVqsVs7OzGB4eZsBENBrl75Q8z/9/TwYXi8X4t3/7N9a3B4NB7gCIxWIcOHCA5RoULvPrX/+aX+YqlYpHthsbG6iqquLIdiokBwYGUFpayi9q6i6QfnCzpVAo8OqrryI/P59N3Gtra5iZmYHVar3NzEwauYyMDHzwwQc8Jt26dSsuX76MyspKBINBNDc3Q6FQ8LjT7/ejr6+P01v7+/tZ83j48OGYN8J7772HzMxMHnsTbUKr1XIBEQqFbvMiEIFDIBBwqujCwgLeeust5OTkYNeuXbjrrrvQ1taGkydP4t5770UoFOK0adIax/rebiV10XSJ9KV9fX0Ih8MQi8WQSCScBEthhRR2RRI4IlCdPXsWlZWVUKvVTE6iycjIyAg/qCQNiPW9DQwMcFZBIBCA3+/HuXPn+MU8Pj6O4eFhrK2t4aGHHsLFixc5GXR2dpa13m63G6+99hpj/WjMn5ycjMLCQkZFDg8PIxAIQKVSxfRB/P/Y++8wOc/yXhz/TJ/Z6Tt9e+9dbdWLZdmWLVtucsE4lBiMYyAJEEiuNMLJOQRykhByEk4wBgKGgHGTmyQXFatrV9v77myb2el9Z6fPfv9Q7vtr/77asX/XlT91XxcXxqg8+77P+zx3+RRK2l977TXU19dj06ZNyGQyeO6551BfX49wOIz29nbYbDacP38eJ06cwP79+wEAjY2N0Ov1yGazWFxcRCgUwhtvvIFt27ahsbER6XQaVqsV4XAYKysreOSRR1jNhZReWltbN1ybXq9nbkA4HIbdbkcgEMDk5CRP5ioqKhCLxXDq1CncdtttaG5uhsvlYoiMRCJhLPGZM2eQSqX4cKGfvbi4GGKxGGNjY8wXIjWyQqFUKjE0NMSKa+FwGCdOnOA/u7a2FpOTk5BIJHj66acBAC+++CJ+9atfQSAQ4MiRI2hvb4dIJML8/Dx6e3tRUVHBzuw2mw2RSAQCgQBf+tKX8O677+LSpUtYXV1lI8CNIhgMclOC4JYflpEeHx/H+Pg4otEoZmdn0dXVhebmZiiVSgwPD7PBHU2D2tvbkcvlMD09jWw2C6FQiJGRESSTSRw8eBDPPfcc444/zKO5WVBiRMapuVyOL6kXXngBW7duZQMw2sMkB9nR0cHGhYlEgonFsVgMdXV1qKysRCwWw6uvvsp/7sTEBPL5PKqqqtDZ2VkQEkdOzdT5I8f048ePo6WlhZXF6Hu32Wyoq6vDpUuX0N/fj4aGBlRUVPAzvHLlChobG/ncJ938p556ChcuXIDH40FZWRknH4WKoPr6en72Go2GRTWGhoZ44l1SUgKHw4GZmRmUlZXhjjvuwObNm1FVVYVEIoFMJoPy8nJMTk7yVKikpAQlJSWQSCRsAllbW4tz584hk8mgoqICDQ0NBZspUqkUv/3tb1FdXY3a2lrodDpuDPT19aG2thZLS0tYWlrC/Pw87rrrLoa5Pfzww9yIq6mpwYkTJ7hY/OIXv4hgMIhf/vKXsFqtbGhLmG9qIu3cuXPDta2trWF6ehpFRUUMpxwbG4Pb7UZVVRX7aQwODmJgYAC9vb3YunUrc0GcTifDSRKJBPr6+nDo0CGIRCLMzs7i/fffx89//nO89tprePfdd9kRm7g79ExvFsS7W19fR2VlJSoqKmC32/Hqq6+itbUV+XyeG1QqlQr33nsvxsfHceLECWzevBlqtRoymQzV1dXcnSY4EDlPO51OWK1WhgqPjo5Co9Fg06ZNBe+GiooKVvhRq9UoKSmB0+nEnXfeifn5eS7MyHVZIBCgvb0d09PTeO+99wAA5eXlfKa43W688MILePDBB9mb6sNSuwqFAna7HcCNZLdQQ9btdmNwcJD5ClarFfF4HP/rf/0vHDp0CK2trRCJRJiamsL169dRWVmJxx57DLt374ZOp2P+ZWNjI5tK7tmzB0ajkc1v33zzTVRVVWF2dpbl3wnGeffdd2+4Nq/Xy+cW8enIy2H//v3Ytm0blpaWWBlLr9fj9ttvRygUwuTkJBeCiUQCpaWlcDgc3ECcn5/n39fc3MzGpGRWRxy1jUKv1+M//uM/sHnzZobor66uora2llUN6R00NTVxPku5GxnpkdEhSdY/8MADkMlkuHr1KhwOB4v3EAmcIISFYF00LafJIynx7d+/H/F4nNEIJFtsNps5h1tYWGBvnCeffBI//elPAQCXLl1Cc3MzFAoFo5Ty+TzuuecerKyscN6i0+lw6NChDdf24fjE0CmHw4F9+/bBaDSyMsLi4iLuuOMOTo6pS1JeXo6JiQkcOHAAYrEYFRUVTGx67LHHOHlxuVxsFJdKpXDkyBFOuh0OB2w2G7q7u5HJZPD+++9vuLZsNov9+/fzAaLValnelpIWwoZWVlZiYmICs7OzkMlk2LdvH0QiESYnJ7Fr1y6Mj4/jwQcfZCO1pqYmpNNp7hxWV1ejqamJvSVOnjxZEPftcDhw++23w2q1ora2lmXHmpubUVRUhO7ubu5M7du3D0tLS9yB2rp1K/R6PXK5HLZu3Yp4PI4DBw5AKpVyB95gMODYsWOYnZ1l2Axdcv39/QVH0DSmJqUHpVIJmUyGPXv2sGGZ0Whk2bhUKoX9+/ejo6MDf/Znf4Z9+/axqkcwGERXVxcsFguy2SycTifm5+fR2Nj4/+GiZDIZjI2NFZwakHQnAIY0rK6u4sCBA1Aqlejs7MS2bdvw8MMP45vf/Cb8fj/uvfdeRKNR1NXVYXV1FUNDQ2hra0Mul8Pzzz/PmFOr1co4UJKnEwqF2Lp1K+644w4mWm4U8XgcPT09qKioYO3qfD6PT3/604hGo+jp6WF8f09PDwYHB9HZ2YnOzk6YzWbGZh87dgw6nQ6PPfYYpqamEI1GuWtdUVGBubk5Lo4VCgU7LBfCVtMzdjgcWFtbg81mg0wmw7Zt29jMKpFIwGKx4Itf/CITTUdHR7nYJBUhu92OO++8E7Ozs8jn8yguLkZJSQmeeOIJljWmfdrb28vQs0JBXTLClNLz0Gg0/O6I/6PX63H9+nXodDp84QtfwLe+9S0m5e7cuRNOpxMtLS0syECY5aamJoRCIVb8aGhowK5du1gKcKMgbwDCc1MXl6Q5yaF39+7d6OnpwfDwMJaWlpjIT5NZsViMoaEh7uZqNBrodDrI5XIcOnSICXn19fVoaWlhgn4hYl0sFoPBYIDb7cbU1BR3xxobG9HS0gKr1Qqr1crwi76+Pmzfvp07XsFgkBsvUqmUE24ihM7MzOCJJ55gBbYtW7bg4MGD3Pgp1LCgpDKbzTL50u1248/+7M+wuLgInU6HsrIybNq0iWFb9O3cfffdKC4uRn9/P4Ab0+mHHnoIJpMJVVVVXNh2d3fj7NmzKC0t5X1I0/BCUEJSMIrH43xBGo1GWK1WlqA1mUw8UaFJMMFbysrK0NXVhY6ODvbImJ2dxbVr1zAzMwORSISOjg7uSpaWluLOO+9keFEhojqpJJGPx8LCAnPHotEoLBYLKioq0N3djb179+LixYvo6elBaWkphoaGMDc3x+9GoVDg2LFjcDgcOHXqFE6fPo1MJoMnn3wSY2NjuOeee2Cz2VgJkKZeGwWR05PJJBfOer0eu3btQl1dHcMv9+7di/vuuw/BYBDxePwjRqCUxEWjUXzta1+Dy+ViEn1DQwNeeOEFTE1NYdOmTVCr1RCJRDzBKbQ2krclr6B8Pg+RSITNmzczz0oul8NsNqOuro7PAHJRJ9z/jh074PV60dvbi2w2i7KyMuawPf7444jH48xNIxNGMj8r9E6rq6u56DaZTGyKur6+jpaWFshkMlZizGazaGlp4b3Q1dUFnU4Hs9mM4eFh7Ny5E2KxmCdxJA9OPBCadpHHUSFlJ4vFgl27dqGoqAirq6s8Sf7617/Oyl1GoxHbtm3Drl27GMY7PT3Nd5HT6cTk5CRGR0fxox/9CFNTU5ibm0M0GoVcLsc3v/lN2O12fP7zn2eCtlwuRzQaLZiL0D6Ox+N8N8tkMua59ff3M4xLIBDg+vXrXDhR85s4mHq9Hvfffz/sdjui0SiEQiGKiooYKkmu94TQcDqdBXO4YDDIU5/x8XH4/X40NTWho6ODJ5BkZEhogh07dnC+mc1mEQwGWaHrwoULCIfDGBkZwdzcHOLxOB5//HGWdCcFQuK5Fpru0aSGpLFVKhVWVlZQUVGBUCiEdDoNt9vNvF4yBU0mk9i8eTMqKipYJa25uRnFxcUIBAKsHOv1etlAdXh4GD6fD1qtFjabDQKBoCCa58PxiScaUqkUS0tLqKiogEgkwtLSEkKhEDKZDGw2G1ZWVpDNZmGz2VBaWorr168z2VQgEECn00GtVmN2dpaVbUizN51Os5yXRCLBwsICq2YolUrGOW8UpD5hNBohl8sZy+1yudDU1ASPx8MqN+Xl5UilUggEAqipqeELkrgE1dXVMBqNbORDBoD0QO12OytVFBcXw2Qywe12F1wbqUvR9EImk0EgEKCyshICgYAVKdbX19Ha2gq3281/BwCWWiRNY6vVytKlTqcTQqEQ2WwWKysrrGL1YU33DV/+f6lYWK1WNg0kibva2loIBAImKpLTMMnckaoOyWOS4g1NoIisuri4iI6ODgwNDTFnhIQCPk6DmaRrSTKRvFqampqwuLgIqVTKYgH0UZAPBimNJRIJBAIBOBwOPvBWV1eZdEZYWNIBV6lUqK2tLYg91Gq1WFpa4sNpdXWVEwqDwcAEzvLyclitVhgMBrz99tvsZG42m5HP5zE7O4t9+/axfOH6+jrDO8jf4sKFC2hubmbOB4kAbBRkPkmKMUSyIwI/HSo0PaisrEQgEIBcLv+IwkwkEkFRURHLPgI3Cj4ilqnVani9XlYbkUgkaGtr+9iDh7rxBAEjEYGJiQme2BiNRk4Gq6urIZFIuPus1+v5u6QkmNx9qbN+8eJFhjsJBAKG7dhsNlZ0uVmQlCfhu8nR2OFwwGAwQCgUMrG7vLwcEokEIyMjqKur470cj8cxMzPDSlXEfyJFEKFQCJvNxrKVKpWKBTEKEa7lcjnLcJNks1wuh9PpZP7I8vIyE+wlEgkr7hFPS6vVQiQSoaurC5s3b0YwGMT09DQnKqSSQlK+AoEAyWSSYT0bhUqlYlgaTXRKSkpgt9vR29uLYDDIz58EJEjSWCKRwGQysRIa/XdZWRn8fj/i8TgXYDU1Nejv7+diKRwO817cKPL5PHw+H6uJ0flZUlKCZDKJSCQCjUYDs9kMnU7HRYZAIIBYLGahh/fee489T5qamtjxmtT+mpqa4HA4oFaruSFAyeFGQWckCRCsrq7yBJ38mojATRxBmhgR/4f8NgwGAysuEVSEVHja29uxtrYGtVqNSCTCrtUul2vDtRHPRiwWI51OI51OY9OmTQydnJ+fRyKR4KTYYDDAbrfDYrEgnU6z39LCwgJaW1v5TCYZWrFYDKlUCqvVyk1Fgj6TouVGQfcdkWWpEKKzJRwOc/NsfX2d5e5rampYqIHU36iZV1NTwyZvYrEY/f39XAyTEhU9h0LwJFLVI1EWADAajbDb7QzzU6lUCIVC8Pl8zEUiIzw6h5PJJFpaWjA7O4umpibMzc1BKBR+RP3x1KlTMBqN3PnWaDQFO/NSqfQj0ucfNpqjyRUhVGgPOhwO9nkg3g918/1+P/tYUB5oNBqhUCgwNjYGsVjMDRFSX9ooaN1k6EgKfj6fDxUVFZicnGTzOBKbWF5eZlds4iosLCzg8OHDWFpa4nOBFBNjsRgqKiowPT0NmUzG4hDZbLbgt5DJZNidPBAIIJlMMuncbDazYA35tFGTlfwoSEiIfLvIlDISibDYjtvtZj4zQboEAgGqqqoKCkqIRCIWoiD/slgshsHBQf42iouLkc1msb6+zl4xpBJYUlKCsrIyVnNcXV1lsQdSJguHw9i0aRMCgcBH0AukRvVJ4hNPNAhSIxQKmcSZSqW4InM4HHwpxWIxHqUTIY4kaAkOMz8/z5J3JPVJm8zj8UAgEMDn8zE+tFBHjTDLpADgdrvZnIsSE9oMEokEzc3NsFqtkEgk/HcROZX8MujCSCQSEIlErFQVCAQQj8d5fNrU1FSw0KDkhNwil5aWkMvl+MUHAgFkMhkm8tCBS0k5JcS0wSkRJcx3LBZjMyqqVkn2ldSjNopcLsdSrrTJCFJBiimkehAIBNh8KJfLob+/H4uLi/zzVVRUcNFIcm8CgQDz8/N8+AoEAq7UP05JjBQ5BAIBK5zpdDr4/X4olUrMzs6y1LLdbodIJOKff21tDTKZDAaDAR6PB5lMBhcuXOCxJHkzkFQgyVB6vV4uCgslfQSpIZdxUpWanZ2FXq+H1+tlnf18Po89e/bg7NmzfLDQc5+bm0N5eTlmZmb4MMhkMtBqtXC73ejo6OAuDSmdkZt5oaBCjy5aIrEqFAoEAgH4fD423aTDlrhC2WyWMcrl5eVYW1vjDiCZRxL3ZmZmhrkZJKM6MzNTcG1isRiJRILx8AQVk8lkaGxsZNlNMq4iZR2CQ9BY2W63o7q6GsPDw0ilUkin0wiHw3C5XGxER6pbwWAQHo+HcbIbBUlIkjy0UChkF2aSziXdeTqQSdKb4Dmki15dXY3Z2VkuzEjzf3Z2FtXV1ew5Qc0HkqXdKBQKBScAxcXFTLomxZRYLAav14uVlRX4fD52xxUIBAybIU6Z7r/MUymZp87e5OQkm40RAdjv9zOGd6OQyWTw+/0smZhOp9n1nibBsViMk2SVSoXh4WHk83lORAhCK5fLMTExwaomlCy73W5UVlaymSLJfgMoKC+ez+f5PKDEdG1tjZtfy8vLCAQCDEEgmKZYLIbD4UA4HGZxCPLoIXIwNWE8Hg9KSkpYKtLlcmFpaQlms7ngcyOYEMltErQtFovBbDazKk0kEsHS0hKqqqrYgJbggQDYgO306dMs5alWq9kssaenB3a7nacF5EZfSCgEAE/eALDsKHl35PN5TlZ9Ph9PUdPpNE9TJRIJJiYmIBaLMTc3x9LhROjv6+uDWq2Gz+eDRqNhWXp6b4WCmlXUGMjlclhbW2MZe5IupbvswzkF4ctnZ2dhMpm4GeN2u7nrPjc39xEVOVK0pP/eKOh8EwqFyOfzrCAVi8WY+0W5SCqVQnNzM+bm5njSr1Ao+J4jjxHyw6HzmiBYs7OzXBiHw2E+ozeKDzc6RSIRnxlzc3PsP0Ywo/X1dYhEIszNzUGv13PRXVtbC7fbDZPJxIIspOJIjQ+TyYShoSGWMabiplDC/OGGMxXv1LikyRp5t5EyIxms+nw+VoZaW1vj7jtNmMnfhYRAlpaWIBQKkcvl+L4t9C2IxWLmhpnNZgiFQiwvL/NzE4vFnHOR2MzS0hJSqRTm5+eZWD02NgaLxYLTp08zooWa5AsLCzCbzdz8XV1d5SZDoWYFycITMoiaKbOzs1AoFAgGg1AqlYwIoSlYJBLBysoKhEIhjEYjFygkxUwNYYVCwTy3kpISzhcIhVAIRviRZ/iJfhVuyGht3ryZ9byJqPxhci9V6TMzM1xlkpkPQSVUKhWbOT311FOMFxOLxdDr9ejv72eZO5IUGxkZgc1m23BtpCsP3PCdIPt26kb29PTwGJQu2oaGBpw/fx4ulwsWiwXV1dUsf+bxeBgPSQk9fVCkm+/xeACAx/cbBY3ffT4fH0DBYBA2mw39/f2Qy+VIJpN8oZMalsvlQiwWg1AohNVqZVWsXbt2IZVK8bMlrxLqttBmp+S+0MGzurqK8vJy/r3ZbJY7KG63mzHthM2sq6uDUChkkhIRlfx+P49OgRsJWzwe5zHn+++/D41Gwxe+SCSCw+EoCBWRSCTcjSXjPvo7SIaX4BCxWIwJTA6HAwqFgrkZJpMJZrOZk2y9Xg+1Ws2QAHpOpDREe72Q9C51ovL5PLxeL8LhMF9ClZWV0Ol0GB0dRV9fHxPSy8vL0d7ejqmpKQD4iGjCBx98gL1796KhoQGrq6tYXl7mJKunpwdTU1PsLSOVSj9WUo4KV7ok5+fnGSZgMBjYuOnixYvo6OhgLhQlbqWlpQzXou+FLrN4PI6VlRVWEaFki/TcCymw0bOlJJYmZdevX4dIJIJSqUQ2m4XX64Xdbsfa2hruvvtuRKNR3HnnnTh+/DirQtHU4Pjx49i3bx+TVicmJlBTU8OQm0gkwnAzgrkVem5ra2t8aa6vr/PkbH5+nhWHxGIxyytqtVr09fWxoROddV6vF+fOnWMuG/Fl8vk8Dh8+jLKyMiwvLyMej3MCWGiKlsvlWKmGzluaXJL4BSnanT9/np2/s9ks5ubmUFpaColEArvdzgTZ3bt3o6SkBNFolMfl5DOxtrbGXXbCGG/kRu/xeNDa2soXbi6Xg9PphMVi4YYUcawI3ppOp2GxWJjfRX4edOlSd5Cmh+RJ0N3djbm5Oe4qUydzoyD4B3XoMpkMk1KpECDFqUAgAKvVyoZZp0+fxtraGrq7u1FbW8sQUZqwkgdIPp+Hw+FAS0sLXC4Xe+kQb6hQ0DdASRYV7ySkkkgkWFGKCNAkM0oNIPJ5ikajWF5eZtEI6qiSjDwVtyKRiB26N4pUKsXNN/o5hoeH2fCvqqrqI74p5L107tw5GI1GngQRb6+kpASDg4MsOuJyuTA2NsbJGiVoBJ8plFylUimeiNDEhXDzVIyTCW74v1ykZTIZrly5wr+X1PVCoRCam5tZ1U8gEEAoFKKrqwvxeBxisZhzHZJuL1Q8kiwtFa9k9rh37154PB7IZDLEYjHuViuVSuh0Oly9ehXd3d2IRqPcmE2lUgy/JLiLw+FAPB6H0+lEb28vN8lEIhE3DDYKeudra2uQy+XI5/O4dOkStmzZAqvVipGREUxPT0Or1TJPory8nPOUdDoNl8uF69evs0phOBxmZcrl5WVuzup0OhgMBob1TE9Pw+VybWg2S40Fg8HA58jQ0BDnToTmIBg3nS8XL17k565Wq1nZjpJo4jqQqAVNqZaWlrC+vg6tVovy8vKChQbdcUSIlkgkyGQy8Pv9MBqNUKlU3KgEbnA68vk8cyCKioqwtraGvr4+bsiSHDoARgT09/ez1DEVDqQWWWi/0XkgFot5smEymbC+vs4TEspRiPs5MDDA3EDaN+SJQpNesgcgc+T29nbOH8RiMQKBQMHc98PxiQsNq9WKK1eucOeBHsaZM2fYfIYqqmQyiXvuuQeXLl3C+++/j82bN7NqB+k/63Q6XL9+nas4u92OEydOMAeCKjnifTQ2NhbcCMS0p5EOTQyWl5c/0mEmHXm5XI777rsPZ86cwdzcHFZXV7F3715UVFSgra0Ng4ODCIfDyGaz7PYok8m4o0RjWurGbBR6vR5TU1PsNkwjwpMnT7KPQz6fZ0zpwsICWlpa2KgplUohGAzCbDZj//79OH36NHp6ehCJRPjZeL1ebN68GU6nE8CNRFgsFrNqyEYhk8lYgx0AP6Pr169zkkCblcxfqqqqEIlEUFZWBqvVyol/JBKBx+NBOBzG8vIy5HI5DAYDOjo6GCtJUo9ESC50YGs0GgwODvKBSGR5t9sNh8PBKjdE8h0ZGYHZbGZFGN1/uX3OzMxAp9N9BHZGyhhzc3MYHx9HaWkpm6fp9XomKm8U6XQaV69e5aJFLpdjeHgYQqEQ9913H5aXl/kQBm7ALnbt2oWZmRm4XC5W8+rs7ERtbS1cLhdDpRYXF/HDH/4Q1dXVPLKmjiZ1GwphNs1mM+uH01pzuRz6+vrg8/mwbds2qFQqqNVqdkMnyMP169cRi8UQCAQgFosZT01eK9T9kEgkuHLlCpqbm+FwOBhmRaT/QkEY0mAwyAcWFSjEOSLn8pWVFf77+/r68O6777IfQyqV4mRfJpOxl0YoFEIymcSbb76Jz3zmM7h48SKTjD/Ou4WSAkp2KJmjjqhIJGKiZjgc5suOpCK1Wi1DrJLJJLq7u3kqBdwwOYtEInj11Vdx9OhR/r6os12omaJWq3Hp0iWGe9L0Ta1Wo6+vD21tbSgqKoLH42HIA31vBAuoqanB4cOH8f7770MqlXIBQj4BpaWlmJiY4O4qdWM/DgIkFou5GUTKW+l0Gn6/nwtvMmIl1TrgRiee3u/q6iqmpqZQXV0Nn8/HpGdKbCoqKnDp0iX2ZqCmBUFkNwqaQlARRV11i8WCaDSK6upqFl+IRCKQSqUwmUyMs6fkLhqNMn8uFosxBCeVSjFcIhgMQiQS8TSa5EM3Cpoi0pro95BDOGn809qnpqagUqlw/fp1NmnLZDLo6enB1atXWTq2rq4OAoGAyfQjIyOorq7G4uIiO8sLBAJ0dXVtuDaTyYTp6WmYzWYkk0neqxqNhqGmdJ+p1WpEo1HodDqGABPmnr7t/v5+2Gw2hr+sr69j7969LGAyMzOD6upqlJSUsBfFRpHNZhEIBLjhSR5UMzMzsNvtOHToEKampjA0NIRIJIL6+nr09/fDbDajvr4edrsdbrcbNpsNExMTcLlcLDuezWYxMzPDHVua6tF5KhAIPhaOvLKywrKipCT5+uuvI51Oo7u7G7FYDG63mwukhoYGlqknNEggEIDRaERnZye8Xi9kMhmfTx/ObcjckLyfCk2CaDJA0xT6c3w+H7LZLEPxvF4vBgYG+L1Sg8Pj8SAajUIsFsPj8XASSs0+h8OB9fV13HHHHSgqKsL58+d5EltUVFTwbqDmKK2fpoPJZJI9YBKJBKRSKcPaSXqb/DeoeUXFp81mY+4QcEMyv6amhlENNIElmfaNQiKRYGlpCVqtFuFwmAttn8+HVCqFqqoqzksIUt7U1MRKTwqFAmKxGHv27MHY2Bg8Hg8XwtR4I1+adDqN6upqLCwscB5VKBchkSAi5JNRNU39PwzvpOluKBTivyMYDKKiogJFRUUYHR3Frl27GLlDxb9MJkNlZSXm5ub4fqUCrpD4wIdDsP5xdrS34lbciltxK27FrbgVt+JW3Ipb8f9nfGKOxq24FbfiVtyKW3ErbsWtuBW34lZ80rhVaNyKW3ErbsWtuBW34lbciltxK/7b41ahcStuxa24FbfiVtyKW3ErbsWt+G+PW4XGrbgVt+JW3IpbcStuxa24Fbfivz1uFRq34lbciltxK27FrbgVt+JW3Ir/9rhVaNyKW3ErbsWtuBW34lbciltxK/7b4xP7aPzLv/wLVCoVm+PpdDqsrKxgZmYG3d3d7OkgFouxvr6OSCTCTtvHjx9HcXExNm3axH4GFRUV8Pv9rOG+srKCdDqNyspKlJSUsIZzMBhEOp1GPB7H1772tZuu7Xe/+x37UchkMnaUJMdvhUKB+fl5uN1uSKVSaDQa1NXVYWhoiHWCc7kcYrEYdDodrFYrG4JlMhnMzc1BpVJBr9dDo9GwK6pAIIBer8fVq1fx9a9//aZr+/Wvf41MJgOJRIKioiLWsJ6bm4NGo8H6+jrKy8shl8vh9XrZ8IdMq8iARSKRIBaLYXZ2lo3zyJROoVBg+/btCAaDkMvlbBZErt6f+cxnbrq2f/3Xf2V3djJoI3+JtrY2lJSUsDulzWbD/Pw8ysvL8c4778BisbBus1gsxtLSEsrKytg8i0wAzWYzRCIRysrK4HA4kM1m2U3bYDDgS1/60k3X9o//+I+IRCKoqKiA0WhENpvln5d0+MlBnTSgKysrkUqloNFoIBKJEI1G4XQ6YTabEYvFUFpaipKSEvYtePfdd1FXV4dAIACdTscO3NlsFlqtFk8//fRN1/bDH/6QjZyKiorYcTaVSkGn08HlckEsFrNBWE1NDXK5HKRSKWudSyQSSKVSTExMoLKyEmtra+xHUVVVBblcjr6+PmzatAlDQ0NsUkg+IBs9t3/9139ljf2ioiLodDoolUq88847bBwYCoXg9/sB3NDhLi4uZh10ciZdWVnBnXfeicnJSfYFKC4uhtFoxPvvv4+WlhY2FQqFQmxupdFo8NRTT910bQDwm9/8hl3oP2xMpFKpWO+cTM90Oh30ej0qKiqwtrYGv9/P71yn02FxcZG17tPpNEpLS2G1WnHp0iWYTCbo9Xo2wSNn9Gw2i4ceeuima/vFL37BWuZarRYqlQo+nw/9/f0oLy+HyWSCQCBg0yb6u61WK65duwaBQAC5XI7FxUV0d3ejpqYGbrcboVAIbrcbPp8PSqUSVVVVfFamUimsrq7yN/ipT33qpmv7m7/5Gza1or0TjUaxsrICo9GIXbt2sY+NSqVivwKXy4Xa2lokEgkEAgHI5XLWVl9fX4dOp8Pa2hp8Ph/Ky8tht9tRU1PDv5bM7Obn5/HP//zPN13bSy+9hGQyiUwmA7lcDq1W+xFXaL1eD7lcDoFAAJFIxGZyoVCIDe/INHD37t2YmZlBVVUVVldX4XK54PF44PF4YLPZIJFIIBAIYDKZIJfL4Xa7kUwm8Yd/+Ic3Xdtf//Vfsxs1mRuSc29paSnfL2R6Rh4ziUQCcrmcfXKi0Si7mZeXl7NW/+rqKiKRCCorK7G4uIiioiL2A8jn8zCZTPiTP/mTm67txz/+MWQyGfL5PJRKJd8BAoEAXq8XRqMRoVAIqVQKBoMBJSUlkEqlbB5H9/H8/DzMZjOGh4dRX18Ps9mMbDbLZmE6nQ5OpxNGoxHDw8Pw+XxoampCNBrd8Lm99tpr8Hg8UCgU/AzoLrRYLFAqlWzUSq7R1dXVmJ+fRygUgkajgVKpZBdyv98PtVoNo9HIHitarRaBQAAlJSVYW1uDSCRCPp/H6uoqBAIBPve5z910bf/xH/8BrVaLZDLJRp9CoRDz8/OoqqqCVqtlt2qx+EaKE4/HMT09zcaa9B2vrKxg+/btAG6cO3TfiUQiOJ1ObNq0ie8ycpqORCJ49tlnb7q2f//3f4dQKGRDQQDsWE5u7WSclsvlUF1djVAoBIlEwsaSwWAQi4uLfA5v3ryZPTfIW4JcmsPhMNLpNMRiMcRiMRYXFzfcb//6r/8KAGxWqNFo2LOjsrIS8XgcwWAQEokEFRUVUKlUuHr1KrZu3YqFhQUoFAo2Ly0pKcHw8DA6OzvZaZqMddvb29l7hL4hyvsef/zxm67tueee4/NWLpdzXrK6uoqKigrOa9LpNBsoyuVyNo2lZx2LxWAwGBCNRtmPhXyRyM+suLiY7y3KF7LZLD7/+c/fdG3f+ta3YDKZ2E+E/GskEgnW19dhNpvZ0BYAamtr2fzRbrezQaNYLIZcLkdXVxcuX76MmpoaSKVSzheWlpawe/duTE9PIxQKQSQSQa1Ww+v1bnj2Pvfcc/zn0t1NhraVlZWoqanhfECtViMcDkMmk8FoNCISibAbODmw03dNPjlSqZSfu1qtRiQSgUKh4HxUKBTimWeeuenaPhyfuNAg47rl5WVOEhOJBLq6uhAKhVBcXMzurRKJBFarFW63G4uLi6itrUUymUR/fz/buJvNZszMzMBoNKKiogJlZWUIBoPwer2YmJjgDZrJZODxeKBSqTZc29raGqRSKdLpNCKRCPx+P0QiEXK5HDvd0iVotVoxPz+P4uJiOJ1OSKVSlJWVoaSkBBqNBtevXweAjzhAplIp1NTU4OLFi1yIkOskOShuFJlMhk3CMpkMm2iVlZUhk8mw8Rc5tkajUZjNZjgcDqhUKqytrSEQCHDSoNfr4ff7IRQKkUqloFAoYLPZ4PP5+OJLJpNYX18v6AoO3Dh8ZTIZ/3lyuRyhUAjl5eXsnO1yuSCVSiEUCrGysoLGxkZkMhl2wSQ7ejL+0Wg0fBnGYjG0tbVhYmKCf7aamhrk83lMTk6iuLh4w7Wtrq6yIRQVrXQIpVIp6PV6xGIxRCIRyOVyKBQKKJVKLhhXV1eRSCSg1+uh0+mgUqng8XgQCASQy+WQyWRQW1vLBjsikYiNfxYWFgqagFEBTX8PcMPs0GazIRqNsguvVquFVqvF6Ogo9u7di6GhIdhsNqTTaTYRy2QyUCgUsNvtXFBnMhlUV1ejqKgIgUAA+Xye3W6lUikfaDcLl8sFrVYLgUDAB6HT6WQTS4/HA6/Xy9+pUqmE1+uFQqHgi7uoqAgGgwEulwsGgwEDAwMQiURsXFlXV4elpSW0tbUhEonwuiORSEFzIQBsiAeAL0lyNaa9JBAIYLPZUFZWhldeeQV1dXXYtGkTDAYD5ufnEY/HUVFRwUZCFy9ehEAggNPpRDgcRmlpKRwOB5RKJTKZDAQCAUKhEBYWFlBXV7fh2sjcUKFQIJPJsKsqmRJRwUUX/ujoKCc1NpuNk2dyjQ4Gg3C5XOxmq9Vq2TSUmgf03YfD4YIu0rFYDEVFRWxMRb+XXLapYUMuwk6nE1u2bEEwGMTS0hIymQzS6TQnEgKBgE0PP+wC7vV6+buJx+Pw+/1cmBd6p1KplM8BMtdSKpVswCUUCnmNZM7lcrk48YxGo+w03draitnZWaRSKUilUpSWlkKlUmF2dhYHDx7E2traR/5stVq94drW1tag1+uRzWa5yREKheD1elFdXc2FFDm0azQaboiQQSsZ1VGhmEql4Pf7OSG2WCzwer3sniyXy/kMpHPyZpFKpdjklkxJk8kk9Ho9J2bADZPL4uJiDA4OoqGhge9fMjLT6XQIhUJQKBQQCoVcZAQCAXaDj0ajbKIH3HDsLuSGTIVONBqFw+GASCSCzWaDTCbje0Mul2NtbY3vHIfDgVAoBIPBwM++qKgIoVAIOp2OzxlKunO5HFZXVzl5JDM8OkMLPTf62QUCARvrUvOTkkGNRgO1Ws0FILl/p9NpLlDEYjGMRiPOnTuHeDwOkUiEbDbLe5CaipTgLy4uoqampuBzKy4uxvr6OhvixeNxeDwemEwmFBcXcxOQTJC7u7sRj8cRDocRi8Xg8/mwtrYGtVrNCTHlNqlUClqtFqurq6ipqWEX8Uwmw7lKoW9BJpNhfX0dq6uryOVyvMfcbjefJ3Tn0p83Pz+PRCKBcDjMBXQkEoFIJEIgEIBUKkUmk0Eul0NZWRmmpqYA3DAEFolEiMfj8Pl8kMlkG64tkUjwM0smkwgGg8hkMlAqlQiFQlyQ068FwMWfXq/nn0WtVmN2dhatra1smktms/X19ZiZmWEjSa1Wy+cQFSo3C2ra0DMjw0u6L6lwFIlEsFgsGB8f58ItkUhwfrG4uIji4mK4XC5ks1k4nU5+JiKRCLFYDAsLC0gmkx8p7gt9p2TWR/c5mWcXFxcjkUhgZmaGzaLJTLW6uhp2ux0ajYabTAKBAEKhEEtLS3y2hkIhfqb089IdKZVKObf4JPGJCw3afJTACAQC5HI5DA8Po6ioiLsAqVQKXq8X+XwejY2NOHXqFABwQkUV1sDAAOLxOEpLS/mwJ7dr6j6mUinEYjFOQApFOp2GQCBgF8Z0Og232w2hUAiBQAClUsmbHrhxOdKhnEqlkE6nIRQKUV1djWAwiHw+z//OYDBwoUPVn1gsRiKR4G7FRpFMJrnTK5VKEYvF4Pf7kcvl2I4+l8shHo+z+242m+ULgfwUqVJ3uVwQCoWcdALA3NwcrFYrJBIJP2vqrlJHZ6NnFggEkEgk+KAnZ02Hw8Hdf4/HwwdRNBqFWq3mj1+pVEIgEKCsrAz9/f2QSCRcGQeDQQA3uuZUyJHjMrmrbxSUJFMyTslnKBSCw+Hgd7q2toa5uTkYDAY+AGUyGaLRKGKxGLRaLRcf1D1Np9NIJBLc4aREnpJllUpV0Ck0kUggEokgn8/zByqVSiESiSCXy1FaWsqXvdPpxOrqKpaWlpBMJjE7O8sur9lsFiaTCfl8nh2F8/k8gsEgcrkcdDodLBYL1tbWuCNOBXWhiEajvCfpAJ6fn+dpXjabRTgchtfr5WdTWVkJp9MJhUKBiooKGAwGKBQKzMzMcAeQ3KKpGCJX41wux8+kUNIH3Ph+aLonkUiQSqWgVCoxOTkJi8XCyd6HXYbpcqOJHwDMzs5CLBZjamoK+XweGo0GRUVFAMDPUi6Xo7q6GvF4HIFAgN/VRpHJZBAIBGA2mwGAz4pUKoXS0lK+bCjJpIR8fn4eer0eKpUKEokECoUCZrMZdrsdMpkMNpsNyWQSbrebGxMdHR0QiUQQiUS8FwvF+vo6xGLxR/YaNWGoIKWCYH5+HslkkicI09PTEAgEKC4u5n0Rj8c/cj6sra1haWkJfr8fxcXF0Gg08Hg8CIVC7IK9UdBEgCaw9DMtLS2hvr6enyElkdlsFjabDZlMBuPj4+x8bTKZIJVK8d5770GpVCKbzUIqlUIqlfKzk8vlkEgkyGaz/I2XlpZuuDaatNDlqFAokMvlMDY2htbWVl5zIBBAIBDgi5/OHXo3BoMB5eXlcLlcWFlZgUwm4+8im81ibW0N3d3dSKVSCAaDXDgVapABN851+vvy+TzW19eRTCYhk8m4yEomk1haWkIoFOKixO/3c3JmtVqh0WgwMTEBvV7PZzIAOBwOTuRjsRjEYjE7tX/ct0B7iO5Uuu+AG0W30WiEXC7nxInuUZfLxWcaNdkoeab7RSwWw+l0Ip/Ps0s1FXzUJd4oqINNLucSiQTJZJKbbdQEDYVCWF5eRiwWQ2trK0pLS9kt2WQyobGxETKZDNPT0+yaTE7b5MhO0ym73c5FeKEzjhp81BBLJpMwGAzcRKWpHLlTp9NpTmS9Xi/W1ta4aKDp2odzkHA4jNXVVXZsNxgMCAQC3GAwGo0f+06pIKXCi6ZOFosFcrkcPp8PdrsdVVVVPJ0hB2u6W+n7U6lUqKur46KcCoWmpiZIJBIsLCzA5/NBLBbDZrNtuLZsNotgMMgTJ5pCrq2tcTedEuBgMAi1Ws35GeUCNAHS6/UfaWgA/+85IBQKueFDk4lYLFaw6Uku3vTrU6kUcrncR9AmAPgupT0ZCoWwsrKCiooKVFRU8HuenJyEWq2GQqHgBgMAXoNMJkMsFkM8HodSqYRSqdxwbVSsUw6nVquh0WiwsLAAoVAIpVKJoqIibm5ms1kAN+5hnU4HrVbLd7dUKoXD4UBDQwOfb9lsFrFYDBqNhpvR4XCYmz0fd99TfGKOBnUbqJNGY5XBwUF4vV5oNBqUlJRAp9MhkUhgeXkZra2tMBgMDGsqLS3Ftm3bANyooAk+EIlEMDMzw7b2LS0tqKys5EkEjdsLBY0WlUolrFYrV8B+v587/2KxGMFgkB9+eXk5iouL+ddR9zKfzyMej3+kyyUSiWA2m1FaWsrFARVbhTYpTS3y+Tx3c2KxGOx2O3K5HHe98/k8wuEw9Ho9UqkUioqKOLmgUZXNZuNOAyU0AoEALpcLfr+fE3KCCchksoLPjTY5VenUiSPIAnVipVIpPB4PdwONRiMsFgt0Oh00Gg1sNhsaGxsBgA/xhoYGVFdXY319HQaDAXV1dTAYDPB4PHA4HFCr1QXXRh3SfD4PsVgMg8HAhRlNzujZJZNJ7uwQDEomk/FFSe9hfX0dpaWlqKmpgUajgdfr5Q+FihYqAgt1vimxpvdAMAE6+CwWC1QqFWKxGJaWliCVSrGyssIXGHW0LBYLNBoNj6SLi4uhVquRzWYZ/mYymaBSqThRXV5eLlg80ng0Ho/zgSiXyzE5OYmlpSUYDAao1Wokk0ksLCxgZmYGiUSC4SmUwND+m5iY4AmkRCJBJBJhCGIwGOSuLCW/H9cQoIQCuNGppT0Qi8Wwvr7OHRkqrhoaGtDc3IxgMMiJoFqthtPphEgkgt1uh0qlgsVigdlsZgiWSqVi6A91NqnZUCiou5/L5RjWRQUVFaXhcJinrCUlJUilUpyE0R7TarW8H+j8cLvd3OGsrq6GTqeDQCDgs6vQOSIUCjlBUCqV0Gq1nLzRZI1+ZoKkUneTulGUmOj1er7QbTYbLBYLxGIxwuEwpFIpGhsb+XmJxWJEo9GChQadg9TckMvlfOlTYpvL5bi5QF0+mUwGn88Hr9eLbDYLo9GIfD6PV155BfF4nJNQglVR8ZHL5fjfU6e+UBA8LZVKcSOE4EkEBbZYLJzUlZaW8rRPLpdDpVKhqqoKDQ0NAG5MDYuLi9HY2AiLxYJkMoloNMoJBcFrqUDdKGj6Q98DfUN0tqjVat4TVNzQe6MJVDKZ5PsiHA4zlImKRUpmCZZFkBO5XA6TybTh2ujXZzIZmM1mlJeX8ySAEialUgmdTscdfCoAFxcX4XA4EIvFeLpOXWg616mDTxGLxTiJzmazqKqq2nBtQqEQer0eEokEMpkMOp0OUqkUPp+Pzzv639euXePvt7S0lO83gqMVFRVhcnKSG3cKhQI6nY4n3yqVCuvr6/D7/QiHw9wg2SiKiooYPkxB8NVwOAy5XM7QH4fDAbPZzO89Ho9zo9VgMECpVMJms0EoFMJoNKKurg4VFRXI5XJc5NHUP51Oc45Q6LnROyEILzUJKLfTaDQQi8V8BpaXl0Ov1zNUWKvVcuEB3GjqGI1G/jkITtPY2AidTveRSQxNujaKD3fL6SxbW1tDMplEUVERxGIxQ+LofCgqKmKIEZ39paWlDEHUaDQMJaXJnE6ng1Ao5ElEPp8v2PSknC2Xy3FTgb49ggnShI7yS7PZzJNaQmf09vZCo9Fw7mu1Wrmw8/v9KCkp4bOZJg2JRKLgGUL7hmCCYrGYC1WRSMT3TDab5XdMMFahUIji4mJUVFTAZDIhk8nw5JeQMjabDWtra7wXaN8BN/LHj3unvPc+0a/6rx+opKQEyWQSy8vLfPAdOXIEwWAQPT09PJ2wWq28MfV6PQ4ePAiLxQKn08mdrt27dyORSMBut2NsbAwulwtbt25FMBiEVCrF3Nwcw5qy2Sx3xm4WwWAQWq2WcXnpdBqxWAw7d+6EXC7nMRZh9nK5HFpbW9HU1MQ4ObpwXnzxRb6Y6UAfHR3lqrG2thbBYJBxqfSxbxR0Ufn9fiwvL/NhU1ZWBrvdzh85jR3NZjNMJhOP82h0ZrVacf36dXR3d0Mmk/HHDwAPPvggHA4H/3oq4KgLvlGsra3BarUimUxiZWUFbrcbqVQKMpkMZrOZP5KGhgY89NBDiMViMBqNUCqVOHToECorK+FwOJDL5WC326HX65nHQpdLeXk5d8CPHz8OgUCAHTt2QKvV8tTmZkH8DEqWlEolrl+/DoFAgIaGBpSXl2NtbQ0qlQoHDx5kWEAikeD/X6VSobW1FSaTCXfddRcmJiag1Wq5WNy0aRNyuRyampowPz+PiooKtLe3Y2BgAGNjYxuuLZfLobKyEhKJBGazGU1NTQiFQkgmkxgdHcXY2BhmZ2chlUqxe/du6PV67N27F0qlEvv374fRaEQgEEBNTQ2Gh4dRUlLC3aGioiLE43FUVlbi2rVrGBkZwfT0NCorK7F161YIBIKCo3uBQMAdEdoLgUAAR44cAQAcPHgQxcXFSKVSaGxsRDgcxgMPPACXywWFQoH19XUEAgFks1n86le/4ukVNRYikQgsFgsSiQQqKyvh9/shkUhQVVXFvIhCQZOaD+Nt+/r60NXVhVwuh5KSErjdbkxOTvLUr6qqCrOzs+jq6kJNTQ1UKhXuuOMOaLVaPPjgg8wZi8ViGBsb48ZBIBDAK6+8gqmpKWQyGczOzhaEJ2m1WlitVu6wUtdMoVBgeHiYC9H19XVUVlYin89Dr9dDq9Vi06ZNDG+sr6/H8PAwNm/ejL6+PgwNDSGRSGDTpk04duwYT8ympqYQDAYRi8XQ19eHysrKDdcWi8WQz+f5m45EIpiamsKjjz6K2tpaSCQS/vM6Ozu5O5zNZnH48GFUV1fD5XLxJG7Lli3w+XxQq9Worq6G1WpFTU0NtmzZgvr6eiwtLUGv16Ojo4O71BsFTWDpQqIJS2dnJ4qKilBXV8ecCoLfSCQSiEQi7Nu3D/X19cxTGBwcxKOPPspnXm1tLWw2Gz796U9j9+7dDD3J5XLQaDTMCSn0TgUCARwOB6anp2G32xEMBvHVr34Vfr8fLpcL09PTiMViKC8vRyAQQFtbG/+8a2trWF5ehsfjweLiIvR6PYqKitDc3IympiZ+fiMjI7hy5QrOnj2LfD6PkpIShEKh/0/S+eHwer0QiUSM56bOvFwu50J2dHQUCwsL6OzsZDgxcT+Im6BWqzE4OIj29nbmxBQXF0Or1XIH3ev1YnJykjlOPp+v4H4juEkgEGAIBQD09vairKwM3d3dSCQSGB0dZbjg0tIST9aUSiX0ej1aW1vhdruxa9cu6PV6vlO9Xi/nEwaDgTvNSqUSHo8Hs7OzBZ8bwTYI5up2u1FbW8tJJO27zZs3M//D5XJh7969DD9LJpM4efIkbrvtNszOzjJvweFwoKenh2EpKysraGlpwYMPPojq6uqCUBZqjBGEq6SkBHa7nXk/ExMTcLvdqKysxNGjR5HL5VBbW4uxsTFOFBUKBXbu3AmhUIiamhpMTU1hcHAQV69eRV9fH9LpNGZmZmCz2TA0NMQTg0AgUPC+JwRCJBJBKBTiM66jo4NRDC6XCzqdDrt374ZCocCePXv4bqdvoampCU6nE9u3b4dGo8H8/DzC4TB/q5TnXb58mb+VeDxesPstEAhQWlqKQCDAsOJ0Og2j0QipVIqamhpuMrS1tSGVSqG9vZ0nClSgbt68GdPT0wyHpynI+vo6amtree8sLi4im83CbDZDrVYXvLeIR0SN3PLycp40zs7Oor6+nrlZFosFU1NTzOloa2tjHpzVaoXdbkd9fT2i0SgSiQSi0SjsdjucTidD38PhMCwWC6qqqhCPxwtO94ifQRDxtbU1nD9/HlVVVZBKpdw4UyqVaGhoYN4MTSWo6bl9+3ZMT0+ju7ubJ3nEwbVYLHC73SgvL4fT6eTcNxAIYGFhYcO1fTg+caEhl8uxvr6OAwcO4K677kJdXR1WV1cxMjKC+++/H/Pz81haWoLL5YLL5cKePXsQi8XQ2dmJb3/72+jv70dHRwfC4TBuu+02fPvb34bNZoNYLIbFYsHtt9+OVCqFZ555BiaTCR0dHVhZWYHdbsf+/ftx9erVDdemVqthsVjQ2dkJvV6PcDgMm80Gg8GAhx9+GL/+9a8xMDCA1dVVTE1NQa1W44MPPuCXec899+Dw4cMYHR3Fgw8+CLfbjcOHD8NkMvGhRTj7sbExlJSUYGlpCSMjI+jq6ir4sCnhpwOIPuJEIoHW1lZotVpUVVWhrKyME/vV1VWUlZXh/PnzAIDOzk4MDw+jpqYGr7zyCrq7u5mks7q6iunpaWzbtg1WqxWZTAYulwuBQABqtbpg14q65b/3e7+He+65hyE+KpUKu3fv5gTCYrFAIpGgtLQUf/u3f4uOjg44nU4olUq0tLTg1KlTaGhowFtvvYWenh7ubJWWlkIkEqGurg4nTpzA9u3bMTY2hl/84heoqakpOLqXSqW4du0atFotRCIR5ufn0dDQgJWVFdhsNsbhUmfj6aefhlAoRHl5OSYmJpDNZtHY2Aiv14uf//znOHXqFL785S8jn89jaWkJjY2NEIvFOHz4MDweD4xGIy5cuICLFy/i3nvvLXjR0YdfVFQEh8OBixcvQqPRoLa2Fp/97GdhMBjQ0NCAmpoaiEQiPPbYYxCJRBgeHsbAwABaWlpw+PBhzM7O4vOf/zz+5V/+BUePHsXu3bvR1NQElUqFDz74ALW1tXj33Xe5u2WxWPDYY48xBvZmQXCkzZs3o7u7G2q1midJd955J/7v//2/WF1dRXt7O2KxGEwmE9LpNFpbWyGXy1FVVYW77roLvb29WFtbw6ZNm3Dw4EHMz8/jgw8+wNjYGP7xH/+RMeJCoZDx3i0tLR870VhfX8fy8jLDo2QyGTZt2oS+vj4YjUbY7XYu4EQiEXfwDh069BGo4wcffIDu7m688MILcDgcmJqaQigU4s7Mzp07IZFI0NXVhfn5eQwNDeHhhx9m4ufNgiZ1TU1NnCy3trZiaWkJR48ehdPpZJhPKpXCAw88gMrKSthsNvyf//N/cPHiRZSVlTG872/+5m9w3333cSJVWVmJyclJhEIhTE5OoqWlBeXl5WhqasKzzz7LY+2bBRWHVquVYRT79u3DzMwMjh49CpFIhJqaGmi1WvT19aG1tRWTk5NQKBR4//33UVNTgz/90z9FeXk5zp49i7fffhu7d+/mCWFxcTGCwSAee+wxvPPOO8jlcpiZmcHS0hLuuOOOghdxS0sLYrEYNxmkUim2bdsGgUCA3t5eDA8PY3BwENPT0xgcHMSOHTuwvLwMnU7H53JHRwfDQc6fP8+4YIJivPXWW2hqasLU1BR3CiUSCfbv31+QBxGJRODxeLB7924cOHCAp+7f/e53uVvt8XiwsrKCXC6HhoYGSCQSbNq0CRMTE1CpVPx33HHHHXj77bdRV1eHM2fO4O2330Yul8Nbb72Fo0ePoqamBu3t7XC5XFhYWMD+/fuRyWQ2XBtBU2kqMjMzw1Pzu+66Cy+99BI8Hg/DUnbt2gWFQoHdu3fjwoUL8Pl82LlzJ9rb23HgwAFcunQJ9fX1WFlZwXvvvYcLFy4gmUxi69atSKfTeOihhyCRSDA7O4tNmzYV5HoRAqC9vR2VlZUMvfvtb38LpVKJN954g+8KsViMlpYWKJVKtLW1Qa1WY9u2bTh48CDUajXa2trw85//HCaT6SP3m9lsxtatW6FSqWA2mxlb3tvbu+G6AKC9vR1OpxM7duxAb28vRCIRFAoFVlZWsHPnToyMjCAQCHDifvvtt+PMmTMwm80YGxuDxWLB0aNHIRaLsXPnTvzpn/4pHnzwQZ70VlVV4e2338Ztt92G6elpbN68GZOTk3jttdfQ2NjIkOCbBTXYamtrYbFYIJPJsGvXLvzbv/0btm7dCrPZjC1btkCj0eD48ePcrCP+T29vL3bv3s3w8QsXLqC2thZutxsejwfFxcW4cuUKHnnkEXg8HmzZsgU2mw1FRUXYvn17QY4G4fS7urrQ1tbG99Tbb7+NQ4cOoaSkBJFIBJcuXcLrr7+O+vp6/PKXv8TMzAxeeeUVBINB7N69G11dXXj22Wfx/e9/H5cuXcKbb76JsbExlJWVwWAwwGAwcGOImgA7duwoOBUlrlpnZyeam5uh0+lY/GDv3r24ePEiN6f6+vpQVlaGiYkJWCwWFBcXM1pldnYWlZWVLGpBDaiFhQU4nU7o9XpcuXIFQqEQk5OT6OvrQ1VVVcHOvEgkgsPhQHV1Nerr6yGXy2EwGLC+vo5nn30Wb775JjfnLly4wPvGYrEgEomguLgYVVVVuHLlCuRyOWKxGO666y40NTWhpaUFt99+O6MbpqenUVJSwoiVu+++u+D5ZjKZoNFoGJ1TVlaGL33pS5icnOSzjabmbrcbzzzzDEN2lUolqqurYbPZ4HA4oNPpIJfL8dnPfhY1NTUYGxvDqVOncPbsWQDA+fPn+Y4qLi7G4cOHC05bPhyfmKNBikwjIyMMt6EK8o033uAXRSM1r9eLjo4O2O12/M//+T8RCoWwtLSEhYUFjIyMoKamhvGCiUQCp0+fxttvv41vfOMbWFtb40TWarVieXm5IMnU6XSy0gmNUc+cOYN0Og2bzYYHH3wQc3NzzA0gxZ0333wTmzdvxvLyMrxeLxobGzE9PY2uri6MjIygtLSUO/iXLl3Ciy++iMcee4whTl6vF6+99hocDseGa3O5XGhoaOCLsaioCNPT00ilUpiamkJjYyOPkQlbToRR6u66XC4mBu3cuRPnzp1DT08PtFotdxYGBgawuLgIqVTKyTfBoTaKmZkZCIVCvPnmm9Bqteju7obZbMahQ4fw6quvspICqYE89NBDWFtbwzPPPIMnn3yS4RsNDQ148cUX8ZnPfAYikQherxdOp5OVJ4gsCQCf/vSnkc1m8frrr+PkyZM4cODATdfm9Xp5MkYj8uXlZdhsNrjdbvT29sLpdGJgYACDg4MIhUI4cOAAhoaGeMJFI8jPf/7zeO+997C4uIhMJgOn04lYLIY77rgDP/rRj6DRaBAMBhlOF4vFNlQmAm505T0eD/MCIpEIcxeGh4exurqKYDDIY/BEIoHFxUVEo1Gsra3x+ywpKcGJEyfw4IMP4gc/+AG2b9+OfD6P5eVlSCQSBINBPmiIM/LOO+8UPBSp2FpfX8fS0hIikQh0Oh1sNhsuX76Mzs5O7kQQQff555/H3XffjR07dmBqagq/+93v0NzcjIMHD2JhYQHXr1+HzWbDzp07max48OBBvPrqq6isrIRCoYDT6cS5c+fwwAMPbLg2ANy9JU4SwftSqRTMZjNsNhvm5uaY2Dw9Pc14166uLua+ZLNZ/OxnP0MqlUI4HEZbWxsEAgHcbjduu+02fPnLX8YTTzyBZDLJU4rTp08jm83i29/+9k3XNjo6irKyMi5iU6kUzp07hx07djD/iCBMRUVFOHHiBO666y7k83ls374dCwsLGB4exs6dOyEQCLB3715MT0/zeH9ycpLfJZHNady+sLDA0Jybhd/vR2lp6UdImisrK6isrMTo6CgXBiRKQDyflZUVFBUVYXFxkYngra2tOHDgAE6cOAG73Y7S0lL09PTA5/Ph+PHjCAQC3H1OJBIYHh4uCBdZXFzEpk2bYLfbMTc3x9herVaLM2fOAADDXw0GAyYmJrBnzx4MDAwwXDYYDOLMmTNYX19Hb28vK7jFYjFMTU3xRDwSiaC1tZWJloSb3igymQwMBgOSySTzK6RSKe6//34meQPgKfH09DRsNhuuXbvG4g6vv/464vE4rl69ij179mB1dZXP7YGBAT6nl5eXGbIkl8tx+fLlggRY4rvR3VBcXMxcpYqKCnR3d3NiTue5xWLBG2+8gUcffRQ+nw9Xr16FyWTC/Pw8jEYjpqamsGPHDlitVszNzaGlpQWvvfYaioqKcPXqVVgsFphMJrz77rsAsKEKECnkEXxqdXWV4cN+vx+tra0YHBzkyfPi4iLEYjF8Ph+6u7sRiURw4sQJaDQaJr0ODg4yn4SIvKSCRmpO9E4KTR6np6dRWlrKYjNerxcWiwUzMzP4zW9+gzvuuIM76nNzc/jGN74Bh8OBEydO8GSBOEr0Tl955RW0t7cjkUjgpZdegsFgYEgMNU5zuRxGRkYK3qnxeBw6nY7VyEgZ7s///M+xuroKh8OBS5cuwWAwYN++fZienoZUKsWRI0fgdrsxMTEBhUIBrVbLk4TJyUm+SwQCATo7O3HhwgVWzSIeDnF5NorFxUWeQtNkM5PJoK2tDfPz83C5XHA6nZxERiIRHDp0iMnsDocDH3zwATweD+bm5pjDsXPnTigUCk64W1paMDMzg9HRUT7rrl27hnw+jz179tx0bR/mi9Bkixq0Fy9eRGNjI+x2OzKZDO+vtbU1DAwMYPv27QiFQjzJoOYfTY79fj8EAgG2bduGEydOwGg0YufOnVhfX8fc3BzOnj2LkpIS7N2796ZrW19fR3FxMdxuN0/Mzp49i9/7vd/D4OAgTyOJC/bmm2/CYrFwUr6wsACv18uTXJPJhIWFBW5C0z87HA5UVlbC7XYzZP2DDz4oOLElTgqhaubn5/HKK68wRJEmhDSJeffddyGVSjE6Ogq/3w+VSsUcWp/Ph3vvvRdzc3MQCoUswKLRaHDhwgVMTk6ivr4eFouFc0Or1brh2j4cn3iisb6+jldffRXDw8NIJBKMmb969SqMRiO2b98OqVQKp9OJlZUVOJ1O/uef//znmJ+fR2trK4+Kq6qqkM/nGRdO8mOE06Mkdm5uDhKJpGD3hZITp9OJQCCAVCoFtVqN+fl5vPPOOywjSZjZkpISdHd3o7S0FFeuXEEkEkFpaSmmp6ehUqnw8ssvY2BggIlay8vLDPkYGBjA+fPnMTAwgEgkgpaWlg1l+AAwblAqlUKtVjPOXSwWIxQK8cgskUhgbm4O8Xgcg4OD3DUnsuzWrVvR1NSES5cusfKNVCqFTCbD2NgYY+obGhoYh2owGAomCHRprK6uQiqVMvSFSJZf/OIXUVpaiqtXr+LEiRO4ePEi9uzZA7PZjB/+8Ic4ffo0qqqqsHv3blgsFuTzeR7JE+lfIBAgkUigsbERCwsL+N//+3/jpz/9KZqbmzc8dIAb3WUibFF3Y319HVevXkU2m2VFCcKsy2QyvPHGG4hGo8hkMlheXobD4UBtbS0uX76Ma9eu4dq1a/B6vTAYDLBarXj77bdZACASieD8+fO4fPkyX3obBUFmKBklpaqf/vSniEQiuOeee9DW1oaFhQU8//zzOHfuHKqrq1FZWYlgMAiVSoXu7m5otVo0NzfjZz/7GbZu3YrNmzejs7MTbW1trPZA72RoaAhnzpyBSCTCoUOHNlxbKpXClStXMDAwwJLR6XQaP/vZz7CwsAC73Y5EIsFch5aWFnzta19DSUkJXn31VUQiEWzdupWlMcvKynhamc1moVKpGCYC4CNE9Wg0ijfffHPDtQFAfX09H8C1tbVob29nLhIJEhAs6cyZM+jp6YFKpYLT6cRf//Vf45e//CWKi4vx+OOPo66uDh6PB1qtli+0iYkJXLhwAU888QS2b9/OXC2VSoWmpiY8+eSTG67NYDDA6XTCZDKxjHAul8O1a9dYcYOmE/Pz89iyZQtDFlZXV2Gz2dDa2soyxJFIBAMDAwDAfBJqIJAwgtfr5YRz8+bNG65NIpGgv7+f97xKpUJ1dTWcTieGh4eZWBuLxbhgKi0txUsvvYTh4WEu5MrKyqBWq/HNb34TwWAQ99xzDzZt2oSBgQGcOXMGCwsLaG1tRTQaxezsLI/Ka2trN1xbPB7HhQsXWJq5uLgYJSUl+Ld/+zdEo1F0dXVhx44d0Ov1WFhYgFarxaVLlxAMBlFfX4/e3l7s3bsXe/fuRXV1NWZnZ1FWVobq6mpUV1fDYrHwpHrr1q1IJBIsz+v1egviq1OpFKt6kTgAkXVlMhna29tRVVUFo9HI6nQkLvD222/D6XSira0Ne/bsgUAggMFg4CkJEWTHxsYgEolQVVXFjSEiNRdam1gsxszMDBO8SWxlcXGRlbxI/cXpdCKZTEIikcDtdvM9YDKZGMZ8+vRpuN1ujI2NscDDzMwMTwi9Xi8uXrzI++PjIL9erxdKpRKNjY1ob2/nKSbBpwmmSeclTb7feOMNjI6OcpOFcOf0vAgzf/bsWSaM038MBgNPWTeKtbU1/nPLy8thNpt5qkKE9w/zHk6fPg2xWMzTAzrbZmdnUV5ezoVxNpuF1WpFe3s78vk8fvCDH2BkZASrq6uYnZ3F0NAQ8+c2Cq1Wi3A4jFQqBZvNhoqKCkxOTuLVV1+FQCBAU1MT6urqkM1mcenSJZjNZtx///1455138Prrr+PatWuIx+OoqqqC3++H1+tFbW0tKioqGNN/+vRpjI2NIZvNshoYyc0/8sgjBdd2/vx53qtarRYulwsXL15ENpvFkSNHeAJ76dIlTExMoLa2FjMzMzh9+jR8Ph9KS0tZDZRQDvS922w2XL16Fe3t7XjggQdQUVHBuWBdXR26uro2XJtSqWQeI/FCRCIRzp49y3uCCNXXr19HZWUl9u/fj+LiYlZW2r59O4qLi9HR0YFsNsv8g5aWFoY3End0YWEB09PTDMsv1JkXCoUYHx9n1cpQKASxWIwLFy7A7/ejsrISQqEQc3NzuHbtGqqrq3H77bezbQHJqhNFgNQMieeRy+Xg8/kYqkrf+NLSEhKJREE4HMm8E7xzbW0NtbW1MBqNzDcjpc/r168zkoAmiGKxmLlAFosFL7zwAsbHx/nMIGGFuro6hj/GYjE4nU5u3H+S+MSFBkFRDAYDE8JyuRz279/Pig2Eid+1axcT75xOJ2pqahh73tvbC4lEgm3btnEXmZR0fv/3fx9vvvkmcxYIO/dxZHBSJyFinUKhgMFg4DEVVWVEhKGxPcm8eTweTswJkkE+CIRLJJ8Qi8UCg8GApqYmViEpBAEC/l+CGGEjc7kcQ3cIHkAqHouLi7BYLOjv7+fDgPwxUqkUtm7diqKiIhiNRlitVhiNRk5+du3axdW7TqeD2+0uiCddX1+HxWJhwhaR6akApCKFSKMffPABy8G2tbWhuLiYR+r00QwMDODatWtIJBI4ePAgjh07hlgshj179sBgMGDLli3o7e1FIpFAWVnZhmsjIjdd6Gq1GrFYDO3t7UxS/bC85MDAANRqNfx+P5qamhgyRrjrJ598kkeIFHK5nLHH0WgUlZWVaGpq4r1b6LmR90Qul2PyN12s8XicJ4Dbtm1jLHZjYyN27NgBs9nMHefp6WnWzp6enmbY31e+8hXuZJLMolar5WRko4jH46irq2Nitdlsxvj4OO688060trZi9+7dvG9on6fTaVy5cgV6vR5Go5GVOKg7rdFoGBoxNzeHXbt2cec3nU5DpVKhvLwcNpsNTU1NG64NAOvnA2AMKCUclLCFQiGWLTx16hR3Z3bs2MHKY+SN8+lPfxoGgwEOhwPBYBBWq5UlhWlkTpK8DoeDp5k3C0p6qEFCaleNjY2sTEaky+rqarz77rsIBoPweDyoqqpCbW0tF4bE1YhEItDr9TCZTNyRLikpYelQ6tyurq5icXFxw7VJpVKW2V1fX2f5w61bt/IUi2BYPT09GB8fR1VVFas5CYVC+Hw+nkj19PR8RN99fX0d27ZtY8glESyLi4shk8kKqiep1WrU1tbC7/djamoKHo+HIYPE//L7/Zy0kh/PwsICGhsbWTqWEinC05NCEcnmhkIhLgBJ7IC8QDYK6iISJv/DHi7pdJr/m55LOBzm5L+hoYEJw4TDNhqNTFglKF9LSwt36qlIoXO7kPjA2toaS2oLhUKGCxYVFcHv96O+vp4ha6T2E41GYTKZUFdXxzC1rVu3wmg04tixYygvL0dDQwPkcjl3xAUCAT83gmalUqmCMMJgMMg+SsvLy3C5XCwnTY28xsZG7N69G7t370Y8HodCoUAoFEJlZSXLI0ulUkxOTqKmpgZVVVWorq7mCaXNZoPH40E8Hsfa2hpLuZMcbaGgnIGI+hqNBvX19Uz+pSnRpk2bsLCwwBOjjo4OTp6oMGpsbGQ/JpICJX+l4uJiSCQSGAwG2Gw2aDSaggkzKaolk0nk83lumGk0GlbIk0gk0Gq1KC8v50ZDNpvFbbfdhqamJvj9foZjUzOOpGQXFxfZa0cmk8HtdsNqtfKUr1CetL6+jrq6Oi5slUolFhYWUFRUhJWVFXi9XoTDYX6WbrebE2sSKiEujd1ux6FDh/g8TCQScDqdaGhoYKEOEpagaXNPT8+Ga6NmL50hpNZIhY1Wq+V3QxPKtbW1j6iHSqVStLW1IR6P43Of+xzLEQcCAZ6k0ztZXl7mxkihHIniw5LxZrMZ1dXVaG5uhtPphEql4jP4iSeeQCwWY/J/U1MTqymWl5cDAPR6PWZnZ1nSvrq6GgcOHMD4+DhPuEk2XiQSFZwa0B1FBTQJb5AYEPGscrkc6uvruTCiHICefTweR3l5Od9ZhGKhaeHa2tpHRBLojP44CwWKTwydIiWLDxuyATfwuaFQiDu1ZGxFUBm9Xg+DwYB0Og2Hw8FsdsKmUsIG3MDx/eY3v2FyNslnGY1GeDyeDddGyS55ZZAkm8VigVAoZPMv4oSQJCeNusiohjCihF2jDSoSibC8vMyYNNKOJhxdoQ4HqR8BYLk7mnLs3buXE3xSo6DfIxaLoVarPyJb6/P50NLSwvALOmgJF6lUKlmlhBL/Qp15Ir+SdCThgaenp9He3g6v1wuxWMy486KiIjaH+nBinUwm0d3djZGREca50qWmVCpRW1vLZDBSBPkk2tU0/aL/pNNpVoIhcj6p3Wg0GlRXV2NwcJB/nUKhgEgkQmtrKx/2Ho+Hdejlcjm2bNmC+fl55n2QAkWhSp3IVKTeQAVbLBZDU1MTHA4HwuEwjEYjX06Dg4MoLy9nwQJSySJSNpk0qdVqlJaWQqfTMdmNTIUo6YtGoxuujaQ4AXBHZXV1FZs3b0ZJSclHJPToW6Z3KJVKWbWCJAUVCgVMJhNaW1shEAgwNTWFlpYWGI1GVFZWYmRkBLFYjJOQjxulkgdKLpeDy+Xi74suDYKaFBUVMYGZCu7m5mZEIhHkcjl4vV4Eg0H4fD6GrwFAZWUlGzItLy8zwZ4uiImJCVa+u9l7VavVWFlZYfM2IqvqdDrej5QMEzSQRBgISre+vs5KJfX19ayeR9+x2Wxm8Qn6eUl2c6MQCoUoKytj1TFSnEulUqirq2PJcfp3KysrWF9fR1NTE6s10ZSMRuVutxvAjYSXYJ21tbVskkU4ZPrONwpSvonFYnyWSyQSNDU18ZqEQiFKSkqg1WoxPj7OZyCd85Qs1NfXs08KwaII/lBVVYWZmRkYDAakUinuohfCzJOqm06nY+6YTCaDy+VCSUkJwymBGzAEUrNKp9OoqKhg3HI+n2dyplKpZH5HcXExLBYL6urq2GdJKBSy8lmhxI+gvvT3U+FD3X3iHjocDm6GTU1NfUR5iyBVarWauXvr6+twu93shUFwYiKQk7dFofOX/DkEAgF7XRDfi5R2iHNISnZCoZCNJ6kIyuVyEAqFiEaj6OjoYGEU4hSRYiAVGvTzFCLR07lOa9J9yGyVuGUSiQQ1NTUsz67RaFh6FAAXdtFoFM3NzawyRo2idDqNO++8kye1dN8DKFh003dEDU+9Xo+qqirmV5FccyQSYQEX2scWiwUKhYIRHmQpQHkQJcv79u1jSXsSppFKpTAYDAX9bqjYogkynWUajQZVVVVYWlpCPp/nd+rxeFjFjHI48j6iqQjdvdRAbW1tZQ8ManRQA7KQ9C6JOwBgxU/gxn1GXXQ6U0i1i5p1ZIsgFoshk8m4iM3n8wxXJl4F5WqkvEb5SCEvNDpbad8IBALmjxAMELiBwiAhH7/f/xE1K1Ie1Ol0iEajLEtLql1UUJECH01DiXdR6LkRuoIm96lUCouLi8xvpOdUWlqKubk5pFIpVlsljhXJ1VqtVl4H7eGioiKUlJRw/k4KYEajseB++3B84omGTCaD1+vlDwkAYzJ37drFCQp1c9vb2xEIBNDQ0IDFxUU4nU44HA785je/QX19Pfr7+9HW1sZFQWNjI9bX1/HZz34WNpuNPyzgxoddiAdBLoaVlZWskkPYbKvViuHhYU4GM5kM6uvrWZ+ZeAxkOPfoo4/i7NmzqK+vh8PhYAk6j8eDnp4eKBQKRCIRDA0NYWlpCd3d3Xy53ixUKhXcbjdv6HQ6DZ1Oh+HhYcbAUbVO3SmHw4Ht27ezL0F7ezuSySR3HFpaWiCVStmjgwzDJiYmeDQol8thNBoLVpyER6dpCSXI165dw1133YWpqSlOXMPhMA4dOoRgMMgfDen4z8zMYO/evdDr9bjtttuwfft26PV6zMzM4NSpUzh48CDeeustpFIpjI6O4vz58wUvOQCsSkMHYzAY5OeWzWYxPj6OWCzGplh33nknF6/Xrl1DNBplHO/evXvxne98ByqVCnNzc4ybTyQS2L17N2KxGHp6evhnJ/WgjUKj0WBmZoal40jCFgDa2tq4k0mGcVu3bsX777+PYDCIvr4+BINBVsgSi8W4ePEiHnjgAYatxeNx/OY3v8GRI0c4KSTPjm3bthUsNIqKiuB2u1FfX88O6uRcKhAIMDs7y3ue8OB6vZ5xuiS9RxwDkq0kCB8RzA8dOsTQi4mJCZ5wTE9PF3yvNKGkru3KygoOHTqEWCyGsrIyNiuipLS3txexWAxmsxnhcJi7SaRF/q1vfQvr6+ssDQjcKGYOHz4Ml8uFbdu2IZlMMpepUFOAkiPdfzmSUxLjcrmYsEvwmkgkgocffpg7o2NjYywx63K50NbWhosXL2L//v1YXV3FxMQEMpkMG3HOzc2hvLwczc3NaGhoQFtbW8HxPTVFSEqZNO3fffdd9PT0YGlpCYuLixgZGcHLL78Mk8nE01GS+C4tLUVpaSmqq6vhcDhw4MABKJVKrK6uYn19HdeuXcP9998Pr9fL+4fOpVAotOHacrkc+vr6IJPJmGwtlUpx/fp1JhPbbDZotVpOiIPBIIxGI5uzNjc3Y2VlBVarFW1tbdixYwe/SxLKqKurw7/8y79Ap9Nxo0Wr1RYUbiAPkIaGBphMJuRyOdhsNsaPz8zM8LSaprh0pn54kkPTF8JEk5xkeXk5N8/Gx8dhMBjg9XoxPz+Purq6gp15KohpXfPz8yzFTo0ukg+nomJkZARerxfnz5/H4uIi1tbWcOHCBayursLtdkOr1WJwcJBVjAhL/2GzVI/HwxySjUIulyORSMBkMn1EBn51dRV1dXXsQj03N4dYLIaKigpWqhobG+OJdTqdZrEHlUqF1dVVxONxFjWoq6vjAj6bzUIul7M6zkZBXigAuHFI8NXOzk7MzMxAoVCgqqoKCoUCt99+O0wmEwKBAAYGBuB2u9n8kgrvrq4uhoFFo1H09fXh2LFjDEO9fv06xsfHEQgECt4NCoUC4XCYVRXlcjkrhh0+fJiLSJ/Ph9/85jc8cdNoNDh//jwWFhZY1UggEOA///M/YbPZeFq5efNmJBIJtLe384QmFArBbrejtbW14MSW5GKpKZnP51FfXw+1Wo3t27dDoVBwd55U4wjBQk3LlZUVzMzMoLOzE5OTkzhw4AD7UlgsFp7sATfyC3pXJpOpYC5C52ZlZSXfW0qlEsvLy2yjQGqPFRUVbC5oNpuxtLQEn88Hp9OJX/7yl0ilUnj11VdRWlrK3ydJFZOIDpkSezweNj7cKEjOlwpIUmi8evUqNm/ezIVONBrFxMQE9u/fj4WFBW4SUJM1Ho+jubkZU1NTKCsr4ylJMpnE5OQkDh8+jJWVFRQXF3Mzq6ampiAniO4+kp9fX19HRUUF/H4/w03FYjFUKhUrm46MjHAO19rait7eXs4NgsEgqqqq0NfXh+HhYVRVVWFtbQ3Nzc2Ynp5GQ0MDFyNbt279759oLC8v8+ie9HhJrksikaC+vh4//vGPEYlEcP/992NmZoaJa83NzQDALrN+vx+Dg4MYHByE2+1mHB0RpxsaGuDxeBj/3N/fXzCZJ9xbfX09UqkUXC4XbyrS2b9+/TrcbjfjbO+9914sLCygtraWNZhtNhvOnTsHl8uFqakpvvzm5+fxF3/xF1AqlZiZmWGY1vT0NLxeb8FD0eVycSeD5NHm5+exd+9e/Od//ifEYjE8Hg/y+Tybi4lEIszOzsLhcPBkg7gAANDf38/yhWVlZXC5XHjppZewZ88eHD9+nCVy/X5/QWm069evs5ETeXU4nU4cO3YM/+N//A+Wl11bW2Mnd5FIhFdffRWf+cxnEAwGMTg4yBfhkSNHMDw8DIvFgvX1dQwODuL69evYs2cPdu7ciYsXL6Knp4dxhYXk7hwOB+68805MTk7C4XCgubkZBw4c4KJoZGSE5frW19dx9uxZlJaWIhwOc1W/vLyM9fV1nD59Gp/61Ke446zRaFBZWQmtVovnnnsOIpEI4+PjaGlpQUdHB2ZnZwt2X8g9k7SqRSIRrl69it27d+M3v/kNDAYDd8vIvXbbtm0YHh6GSqWCyWTiLvyPfvQjHDt2DOPj4+jq6sLs7CxOnTrFyh1OpxOzs7PsTbC4uPixUs9EvqeufzQaxd69e3H69GmUlpbi5ZdfRiwWQ0dHB6anp2E0GnH27FlEo1H4/X7Y7XbGj1+9epXXUFVVhebmZqjVarz55pvsN5DL5bjonZmZ2XBttL4dO3YwhIqmZES8/cUvfsFTlKKiIszMzPCvJ3w0qY0Fg0F85jOf4bGwy+XCK6+8AqvVisbGRiZgE4H7+vXrBbleBDskkmUikWDllP/8z/8EAL5Qt2zZgpGREZa/JonZ5eVlLC0tQaFQoLa2lgnINPUhsuL58+fR2NiIiooKhMNhXLhwAQA2hBfQtIcMJbPZLPr7+/HQQw/B7XZz8gIAW7duxYEDB+BwONhorKysDCKRCBMTE7h8+TLGx8f57yZYXjgcxg9+8AMIhUKcP3/+I9PEQpLKoVCIJyDkWREKhbBv3z5MTk5ifn4edrsdCoUCzc3NrOCUyWTw0ksvIRwOo6GhAalUCs8//zw+9alPIRwOc4c5kUhg3759OH36NMxmM06cOMEylTT92yiIqO52u1l+l0yoyOTs/PnznCyTZCop7k1PT2Nubg5dXV3wer1obm7G7Ows5ubmANzg9XR1deHkyZN46qmncPz4cQA3FKU8Hg8uXLiAr33tazddG60HuDGVp3PLYDDg7NmzDHsjFUC9Xs/+Gu+//z5yuRzMZjMCgQCGh4fR29vLoibUACHYHHXPyb8kGAwWnNpSAU3QmOLiYp4GUyeduvIkr0rdztraWvblKSkpgcvlQm1tLavNEZePoJtksrq6usoNh0J3A5lKEkcnEonA6XRi//79LPjw9ttvI5/P8xS0vr4ei4uLLPc7Pz8PpVKJF154ASaTCVu2bEFTUxMbJba3t+Py5cvYsWMHFhcXIRKJeFJFDdCbxdjYGHbt2sUJZlFREa5cuYLHHnuMCwmtVovq6mp85jOfgVwux/T0NC5cuACr1Qq/34/z58+jvb0dTU1NCIfDmJqaglgsht/v55+dxCPIu0gmk2FhYYGT/JvF7Owsmpqa2IS1qKgIdrsdTU1N+NnPfob29naG4BAcdefOnXj77bexa9cu9p6amppCZWUlcwMuX76M0dFRZLNZ1NXVoampCePj40xUB4Cqqiq888472LRp003XlkwmEY/HsbCwAJPJBJPJhNnZWdx55504ffo0NxQdDgeqqqpgt9uxbds2CIVCSCQSzM3NYWVlhQuPaDSK+fl5GAwGRKNRTE1NIZlMcvLvdrthNpuh1WoxNzdX8J2SrD8ZR9P3IxKJ2ETU6XTy+z5+/DgSiQRsNhuqqqr4/pRIJHA4HFAqlYjFYjx1oQkyJfrEh/P7/bh06RI0Gg3uueeem66NGmdk4ggA7733Hpvvzc3NoampCc3NzZDL5Th16hRDDFdXV+H1erG4uIhYLIbjx4/jJz/5Ca5cucLWCQMDA5DJZHjrrbdgtVoxMzOD5uZmzk8+KRn8ExcaJpMJ4+PjMJvNzLk4ffo0pqencenSJXz961/HkSNHMDg4CJfLhZqaGjaYsdvtsFqtuPPOO9HT04N/+Id/YMKMXq9HOp3G+Pg4Tp48if3796OkpAQ1NTXcGaKDbqMgybJUKoWdO3eiubkZFy9exPvvv4+dO3fCbDYzLIYIWUQg/uu//mvs3bsXDz30EKsDfP7zn2dNYjpgvve97+HQoUPYvn07j11pnLZv374N19bV1YXx8XHWFieIyaVLlxCLxRAMBhmWcvbsWQgEAtxxxx0YHBzEyMgI6uvr0dzczMoS9HN0d3dDKBTi8uXLuHTpEh5//HE8//zzOHLkCC5dugSPx8NyhhuFWq1GIBBAbW0tqqqqIJfLsbCwgN/97ndob2/H7//+7+MnP/kJfvnLX7KSxne+8x18/vOfxy9/+UsoFAp87nOf42nQpUuXGAOdyWRQUlKC+fl5OBwOrprfeustdss8d+7chjCWzs5OvP322wwNSSaTePvtt1FaWopcLocnn3wSo6OjEAqF2LJlC+bm5pjQ99vf/hb19fW47bbbIJfLMTs7i0QigSeeeAKbNm3C4uIirl69ilAohIceeohVooaGhjgpK2QYRQSuhYUF6HQ6NDY2ora2FidPnkRXVxcqKioQj8chk8l4UpLJZNDS0oIXX3yREzeDwYBnnnkGP/jBD9iwjdxda2tr8ZOf/AS7du2CXC5nNa2enh4cPHhww7X5fD68/PLL2LVrF3M1YrEYvv3tb+O2227DnXfeCZ1Ox8TiWCyGlZUVJkvX1tais7OTDx6Hw4HvfOc7GBoawszMDM6dO4fZ2Vk8+uijLNlcW1vL4+eWlpYN1waA4S+070gRKZfLYWJiAmq1GlKplD1nnn32WXR1daG/vx+vvvoqurq6UF9fjxdffBGlpaU4fvw4XC4XioqKoFKpoFKpcPLkSezatQsCgYBlqDOZDDo6Ogo2LPL5PAYHB9HR0YFt27ZBIpFgcnIS3/3ud/G3f/u3cDgcWFlZQSgUwtjYGHbv3s0d71deeQVVVVXYsWMHjEYjenp6sGvXLuzatQvf+MY3kM1m8dxzzyEWi+HIkSNYWVlBeXk5Jicnkc/n8eUvf7kgXKS2thaBQIDhn9lsFlu3bsUf/dEf4amnnsIXvvAFxshTd5gkOjs7O3H69Gn87Gc/w7Fjx+D1etk8cPPmzWxeZTabcf36dZjNZhiNRkxOTiIej/Mkd6MwGo24evUqY3wpuXz22WfxxS9+keUqp6am8Prrr+PIkSP4u7/7O/ZEoSRKKBTikUceYSnSL3zhC4hGo/jhD38IsViMXbt24bHHHoPJZMI777wDv99fEFYA3DjjxsfHOWmnqeHU1BQkEgk3zbLZLDweD44ePQqBQIDz58/j4sWLsFgsrLCk1Wpx9epV7Nu3D/fffz9mZ2fxi1/8Am+88Qb+4i/+Av39/ejq6kI4HOZEcyNVPeAGLGRqagq9vb3o7OxEPp/HpUuXcOXKFYaOkS8MOc3/2Z/9GQYGBtiYjJLyl19+me+I+vp6hpZ0d3dzsT44OMgFu0gkQnt7+4ZrKykpweTkJOPsSXr63Xffhc/n48R8cXERdrsdO3fuhEaj4eKJ4HJ+v58nRA6Hg9+DyWTC3/3d3+H+++9nrxylUsl7k5qaGz23kZERmEwmlJaWsmfBv/3bv+Gxxx7Djh07eLIRi8Xg9Xpx9uxZHD58GL/61a9QXFyMhx56CB0dHVyAOhwO3H333az+I5FIMDw8jMbGRjz44IMYGRnBxMQE2traCnaYq6qqcP78eTY4TCaT6O3txXe/+10cPHgQO3bswNmzZ3H9+nXI5XI8+uijyOVy+NKXvoS5uTmWMad7bGpqihtlDQ0NWF9fRzgcxte//nWMjo5+xKmdGjcbhdFoxMLCAvNlSKDm/PnzqKmpgcPh4KKPDCDn5ubw/e9/n9/N0NAQFhYWsLCwgJdffhmZTAZ79uyB1WpFf38/gBsQwK6uLtjtdpSUlLC/WqGmJ/mi0JSCpvgzMzNobW3F/Pw8c2bn5+dx9epVdh+nzjyt2efz4bbbbuMpn8/nY381u92OAwcOYGJiAj6fD4uLi8jn8zxBvVlotVrmdDQ3N8NoNOK1117Diy++CKPRyEIrp06dQnFxMQ4ePIgtW7bg3XffxdDQEKxWKzZv3oyGhgYMDQ0xgT+RSDD8/vjx43xOUnPyw/4gG0VzczMWFhbYHNnj8aCzsxPnzp1jmPPMzAxOnjyJfD6Pffv2IRKJYGJiAvF4HMXFxVCpVEgkEvjjP/5jvPvuu3A6nThy5AjkcjlOnjyJ5557Dvfeey+LI73zzjsQCATMS/wk8YmhU2NjY/wyotEoiouL0dbWhkceeQTV1dWMg9y+fTva2tpw8uRJ1NbWQiaT4dOf/jTuvPNONhKLRqPQ6XS4ePEizp49C4/Hg9bWVhw9ehTj4+M4deoUnnvuOYyPj7N7bKGLLhqN4pFHHoHZbGa3R7vdjsrKSgwPD7OmNeG+P/jgAzidTggEAnznO99BVVUVrl69is7OTjYjIr+MtrY2VFVV4Xvf+x4UCgVPTLZs2YJDhw6xnORG4Xa7IZPJMDExAYfDgXw+jwsXLmDv3r2oqKjA5s2bIRaLYTabcfToUbzyyivIZrN44YUXIBQKEQqFMD8/zx0ZIlNevXoV77//PkZHR/GZz3wGv/rVr9Db24toNIrt27fj2LFjrPm/UVCSODg4iN/97nc4fvw4Ll++jD/4gz9gJY6enh587nOfw7PPPott27bB5/PhjTfeYOfrsbExVFVVsfHcxMQE5ufnWQXnW9/6Fn76059iZGQEkUgE9913H77whS+gvLwct99++4ZrczgceOCBBxhW0tLSgmvXrrGRjNVqZSnakydPYnZ2lqUi77jjDjQ0NDCWcnp6GhqNBvfffz/+/d//HQ6HA3q9HiUlJXjuuecgFosxMDCA5uZmHDt2jCcVG0VxcTHrti8tLWFlZQX9/f3o7e3FzMwMlEolnE4nZmZmkEwmcerUKdx3331IpVL46le/CpVKheeeew6Li4sIh8P4kz/5Ex4/E26ztbUVqVQKR48eZRnpz372s0wk3yh0Oh3+9E//FK2trWzyV1tbi6eeegperxfLy8uw2+0sZ5jJZLB582aMj4/jgQceQF1dHXe58vk8Hn74YcRiMXR3d6O2thbJZBIPP/wwUqkU9u3bx00Ag8GA5eVldHR0bLg24EaXhrqGJNIwNTWFT33qU5DJZHj88ceh1WphNpvx1a9+FWfOnMG1a9fw3nvvobS0FLOzszh//jy+8IUvYGFhAa+99hqUSiUCgQDm5+cRiUTw7W9/G2+99Rbq6uqgVqvx+OOP45lnnoFQKMSxY8c2XFsymeROu8/nQyaTQTQaxbe+9S2GEmzatAlVVVUQiUQ4efIkGyL++Z//OZqamvDqq69iaWkJx48fx/nz56FUKhnnbTab8eUvfxl/+Zd/iaKiIrhcLpjNZlgsFpw5c4aVvG4Ws7OzzPugyYjX68Vf/dVfMb6cOlc6nQ6Dg4P4/ve/jzfeeAMul4thiJ2dnbBYLPjzP/9zHD9+HP39/RgeHsbJkycRiUQwPDyMRx55hOFfR48eZczzRuHz+fDkk09CKBRiamoKKysrWFlZwQsvvIDl5WWsrq7C5/NBJBJhy5Yt0Gq1+OpXvwqJRIKjR4/i0KFD6O7uxr333otz586hsbERLpcLJ0+eRCwWw1NPPYX77rsP8/Pz7ORMU/BXX30V169f33BtS0tLDBmKxWKIRCIYHx/HI488Ar/fj66uLhw4cIDXsLy8DIvFApvNhj/8wz/EbbfdhtLSUjz11FMQCoW4++674XQ60d/fz+u56667MDAwgK1bt3L3ta6uriCkC7jRjTx48CD8fj9GRkb4PCHTrNtvvx1HjhzB7bffzkabwWAQv/vd7zA2Nobjx4/j+eefZ7GI3bt3o6WlBVqtlmWgHQ4HJiYm0NHRwXwBagARR+dmsbi4iF27drEcLHGpvvnNbzJOfnx8HMlkEvfccw9mZmag0WiwvLzMCkk2mw333Xcf4vE4HnroIej1ejQ3N6OkpAQqlQrf/va3EQqFoFKp2PODeDCFknmpVIo9e/YgnU5zQ06v1+PLX/4y+vv7MTc3x+gJggb9yZ/8CcRiMf7u7/4O99xzD95991309fUhl8shl8uhu7ub/W9MJhPuuecexONxNDQ0YGpqiiFQsVis4NpWVlZ4ykuF0MDAAO6++25cvnwZDQ0N2LNnD+688058/vOf5873yZMneWJFE4yamhr88R//MQ4dOgSr1Qqn08kcs5/+9KfYsmULlpeXWYHy3LlzBaFTS0tLqK2t5YmS2+1Gf38/nnjiCSiVStx999249957metDvLPXXnsNU1NTWFxc5ClvUVERHnroIeRyOQQCAQQCAf5ZiL9JZqUTExM4d+5cQR5ELpfDgw8+CKFQiNnZWYyMjODs2bPYsmULnE4n9u7dy5xDUrYTCAR49dVX4XK5WJVRpVIhFAqhsbERarUa5eXlqKioQGlpKbZu3YpoNIry8nKeqDc3N0MgEBS879PpNDo7O5HJZDAwMIDLly9DIpHgu9/9Lo4cOcKiGm1tbdi0aRM3hLdu3Yq9e/cilUrhnXfewfDwMC5cuICnn34a6XQaW7ZsYYWsJ554AhMTE+js7EQ8HkdbWxtuv/12dlnfKCYmJlBRUYH5+XlMT0/D5/Ph/fffx7Fjx7CwsMDeJrt27cKf//mfY35+niWLH330Uezdu5d9e65du8YS+0ajkVVSv/e970Gv1+PYsWMwGo0oKSlBeXk5wuFwQWPjD4dgvdDM6EPx4x//GGq1mg1vOjo64Pf7cfbsWXzuc5/Dyy+/jLW1NVRUVKChoQFut5s/THK9raysxMrKCiwWC/7pn/4JTz/9NMbGxhh7nUwm0dbWhkwmg1AohLm5OahUKuzduxcnTpzAN77xjZuu7bnnnmMlFp1OB4VCgWQyCbfbjfb2dng8HlayAcCH4G9/+1tUVFQw3rm9vZ0lz5RKJXQ6HeLxOCuibNu2DdevX0c+n0d5eTnEYjHGxsag0Wg2TJr/6Z/+iUmmJFeYzWYRDAahUChw5coVGI1GKBQKBINBhjg0Nzdjfn4epaWlMJvNcDqdsFgs+MlPfoIjR46goqICYrEY0WgUH3zwAQ4dOsQf5tjYGCKRCPbu3QuRSLTh1ODXv/41Lly4wEZCIpEIJpMJL7/8Mu677z4+tObn5zEyMoL9+/ejvLwcS0tLmJ2dRWtrK+NpR0dHMTMzg9tuuw1lZWVYWVnBu+++i/X1dXzuc5/DwMAAT4CILDgxMYFnnnnmpmsjyVUALI84Pj6OhYUF3HbbbSxFGAwGMTExgUOHDjEWnwozclUljW0AeOihhxgGNjY2hmPHjuEf//EfGZJTXl7OZoUbSaH+6Ec/QjgcZhULAEwW7uzsxMLCAnMb1tfXsba2Bq1Wi8rKyo98mOPj4ywJ+OSTTzI8KhqN4sqVK9DpdDhw4ABOnTqFXC6H6upqVFRUYHR0FE8//fRN1/b3f//38Hg8kEqlrHZEY9TDhw+z1wE5ej/wwANYXFzE0NAQbDYbampqkMvl8MEHH6CxsREejwfbt2/nsa5AIGAde5KJNBgMfKCNjY0VlHv+4Q9/CLPZDI1Gw2Zs5IhMUIFIJMJcsOvXr8PhcHCHrL6+nqcixFegzjbxDQiiFo1G0dbWxjAyImV/5Stfuena/uEf/gGtra2s1kEdtMuXL+Po0aOYnZ1FLBZjhRpSuSKXXZ1Ox9CFQCCA0tJSJBIJNDU1sVxoIBDA7373O9x+++0wGAwIBoOM6Q6FQqwo8/8b3/jGN1heUqPRsDFYNBpFSUkJWltbMTo6CpfLxT4EDz/8MBQKBRQKBZaWltgQbmBggFVOyNzqypUr2L59O8rLy6HVanHq1CkmAxI/5Lvf/e5N1/b8888z6TkUCnFXdHZ2Fvv378f8/DxMJhPLot52221YWlpCJpOB0WhkyBCd19TkCYVC8Hq9yOfzaGlpYUhtIpHApUuXuFBwu934oz/6o5uu7etf/zonyiRrDdwwn9qyZQtmZmaQSCSg1WpRUlLCymBLS0vcPFOpVPzPBNUkR17C3IdCIQQCAfbBIeUtk8mEH/zgBzdd21/91V9h+/btiMViDOkjmfDi4mI25iTi89raGoRCIbq7u7GwsMCE8MnJSXYuJrUkr9fLCdC+ffsYYut0OgHcwM9TgXiz+PnPf85FRiwW4yl+MBhEW1sbQqEQNw1oqnTy5EkAN+TmrVYrTxSLi4sZvkTT3aKiIshkMqRSKe7EB4NBVuhTq9U4cuTITdd24sQJSKVSTExMMOSYChStVssqTqRMVFVVhaqqKoyMjODcuXMwGo2or69HJpNBeXk5/vmf/xnPPPMMFhcX0dfXh2w2i6NHj3JRH4/H+X7Q6XRwOp34/d///Zuu7e///u8RCARgsVgY/pXJZBCJRNDR0cEEe6/XC5/Ph8OHD+PatWuwWq0sDLG2tobFxUX4fD5UV1ezNDBNSAjlQaRcgj2T8tKjjz5607V9+9vfZgdugrtWVFSwvxKdBxKJBEtLS2hubkZ5eTmGhoZYtpvOikOHDnFRPD09zbD0XC6H3t5e5HI5DAwMwOVysbSw3+/HX/zFX9x0bf/wD//ApH6BQMCcx1gsxk1GrVbL8FFCnJDxntVqZZl6cognwYmlpSXMzc1BrVajoaEBiUQC165dA3AD+mg0GpFIJDb8Fv75n/8Z8Xgcq6ur0Gq1KC0tRT6fRzgcxqZNm/idzMzM8BnW09ODDz74AC6XixEPxC+lO8Jms2F9fZ0d2evr6xlBU1lZyd5hy8vL+Na3vnXTtT3//POoq6vjs5KQJC6XC8FgEAaDgQVSAKCvrw8HDx5kqB7xeK9evcpIibGxMdhsNsjlcp7et7e3I5VKMX+HHMX1ev2GXjwfjk8MnQoGg4xLj0QicLlccDgcLPtXVlbGBmHhcBidnZ0sE0p6vSQlSEoJdMHmcjnEYjGsr68jmUwyjGR8fBw+nw9VVVUFyeAkhUkW7iKRCBKJBGq1GpFIBJOTk/wREDGHDkmj0ch623a7nbvQJSUlGB0d5VHezMwMKisr4fF42D+BpDgLEWKoOiSZQVLhSCQSrIhDilM0cp6ZmcHWrVvZKMlgMEAgEDA5MJPJMDZRKBQycU8kEiGRSCASiWB1dZUVBzYKv9/PI15S2pHJZCguLoZIJGJ+Bql4ELyAHMwXFxdZvcjj8WB9fZ0l/Px+P8LhMIaHh3Hs2DGWCyXZ4oqKioJrk0gkWFtbYyKqx+NhXCSRW2OxGF94Wq2WL1ZSraD3BNy4wMvKylgxiv4M8jLQarWsW76yslJQ5Wx1dZVdVkmNhLCaq6urLPtGijQrKyu4cuUKHnroIcZNl5WVcQewtLQU6XQai4uLTEa9ePEivvKVrzBZlgjahAPdKIhgTt8CjcFp762trbHCFBnZeb1eNgYj6cPV1VWUl5fD5/NhYGAAa2trnID7/X4YjUY2tSSi2cd9CwBYbQMAQwfJkFEkErGHDI2Wc7kcqqqquKheXl5mXwSdTscKRmRWScooRHbz+XxwOByMwS/kHUCynaTqk8vl2LAynU5jeXmZVdUcDgdaWlrgdDqRSCSYRE4KMefOncPKygrq6+vZ6yEYDOL8+fN85qVSKfh8PsTjcZhMpoIdPwA85qZ3Sw0Ver8ktZvJZCAWi/Hb3/4W+/fvh1Qqhc/nQyqVwuzsLBYXF9nnwO/3I5PJsBlgb28vLl26BJFIxHv544w/SXqWJMOTySQbjFqtVk5w/X4/YrEYUqkUMpkMqqurOTki+BYVNaTvTqaDRqMRGo0GuVyOvWGoaVNI7pnkw0nhikQaiGM4OTmJTCbDCTBNeMkZ/sMqY0VFRfyONBoNEokEQqEQi0LEYjEkk0mEw2HmrRSC6tF+jUQifJaEQiFO3KkAIedrIquTghmplsXjcW6WkfcL7a9YLIaioiIsLy/D5/Pxn2G1WgtyNOhOpvOIfi3BOOjnJPVGSkxIdphIzoFAABKJBAMDA6y+RF5H1MgzmUy8b0h1q5B3APEVSUKV7ptkMsny3IuLi3znVFdXY25ujnMTrVaLTCbDRngk8JJOp1lZc3x8HHV1dXw+kvKUWCxm6fSbBfmEUCKaTqeRyWSwtrYGq9UKj8fDvJL19XU2MJ6YmGAfnZWVFQQCAVRVVTHvYGVlhff6+Pg4tmzZAofDwdwpMlAr9E6pOCYjQeIhkdomSWALBAJEo1FWTyK1S7obKisrmfvg9XrZX4ImwWRESM0kOkfm5+cL7jd6fmTSK5FIYLPZ+PtUKpUsokLnFrnEkynqysoKBALBR749mliMj4+jvb0do6OjfI+SmEAh00/KL+j7+rA/2Pr6OlKpFILBIEs80/9HuSh54VBDamJiAvX19cy/VSgUbPZJfzbxz0hFcKNIJpPIZrPcdCLS+uXLl9Hb28uiIMPDw1zwnT17ln3cCKal1WrZ3Z0ankT+J/5lKBSCUqlk5AXBvD5JfOJCw+FwoKKiAlqtFlKpFLFYDMvLy+jt7UX4v5x5pVIpYzZbWlpQVFSEM2fO8IRCoVDAarXi0qVLUKlUGBsbY6w6GRLNzMywyUk4HIbL5cK5c+c+lrhWU1MDl8vFkmJ0eJNax4cP4KmpKVRXVyOdTnPBQI6kRNgOhUIMbSD1g3fffZfhCwSDkslkBcm5dImSfBglukRka2ho4OKDNPyLiooYT5vL5ViekxwyXS4XysvLkcvl4HQ6oVarMTIywt1J6lZHIpGCybzH40F9fT0mJycRi8Wg1WqxuroKvV6P5eVlKJVKVqIqLi6Gy+XizUx4R+rULC8vw2QyYXp6mpNHIs2fOHGCpeWoYw2ApwE3C5IIJfI3FU9WqxVerxcCgYALAvI6oUQ5l8vB7XZDoVBArVajr68PbW1tDHcKhUJYWVlBLpfDxYsXUVJSwsRdcs0uJAtM5PlMJgO9Xs8dYFIySSaT8Pl8nMAHg0EEAgFcvnyZzbyy2SwMBgOuXbuGe++9FxMTE/D7/SzDSR1Ll8vFB2U4HC5IZgZuHDw1NTVYXl5GPp/nAlqr1eLatWucqFCH9MSJE9izZw/sdjsA8PoI5kguonTIAzeUrcxmM0MVCQ8+OzsLi8VScH0APkJEJWwuFV0LCwsMq1pbW4NCocD+/ftx+fJlxGIx9k4hhRWa1lCisra2hqKiIvh8PlbKoKKRPA82CuI+0Deo0WiwtLQEvV7P5kk2m43FLHbv3s1wl8rKSiZqk8rX4uIiUqkUK36trKywK7ZEIoHf74fb7eZEpBAWlySws9ksf9dUNNEFR7BOkvt+//33MTc3x8ZLZLRF4/XW1laUlZVBr9cjn89jaWkJy8vLmJiY4LOQ5IQLYebp/CKibFFREVKpFJqamtjHyO12s8ETYYfpgiXoFxkzkp8IKRNRM4GgedSVp0Kp0N1AiS9x22KxGPx+P0tT0/cbCAQwOTmJuro6No0l9SwAqK6uxtLSEn+D9fX1iEaj3MQqLS2F1WrliaVarWY54Y1CqVTyz0Z+ITMzMwgGgzCbzVCr1awIRvfGli1bMDQ0xHr4DQ0NTPAlt3KSdSeomM/nw/r6OlwuF09axGJxwSKIPErIpVksFnN3moq2VCqFdDrNkKOenh5WH8vlcpBIJKirq4PL5WKPjEQiwc726XSaO82hUIgbI1R0bhQ+n4/VkUh6eHx8nKfIxCcg4Ra6d3w+H5PBSX1wZmYGNTU1GBsbg8lkQkNDAyKRCPr7+1nkxOfzcRMwHA6zKtLNggpRKgqoOScQCOB2uxGPxzE0NMTKRyMjI0ilUnj99de5SZRIJFBdXY1kMompqSlEIhH+vlwuFwYHB9mPiQpR8gyqrKzccG0kHUx5D50FxB8lMjoVF+l0GjabDYuLi5ifn+dG1ebNm3kqTkIAANgxnL5zmvqTAE6hZJ7ue/pW6Uw0m83Mp0qn0yynSlwTkpEmSW+C062vr8Pv96OkpARFRUXIZDIYHx+H3W5HLBZDOp1muWdqKGwUgUAAJpOJ3cjJOiCZTGJ8fJwnd/TdUpOXfpbFxUUsLS3xWUTNNq1Wyw1V4qOUlZXxvUFE70IKndR8CwQC8Pv9bGYajUZZFr+vrw8TExMoLi6GzWbDysoKTz3EYjFKSkpQV1eHX//619i0aRP77pDkPjUlSH2ScsDi4mK2bvi4+MQcDYJgtLa2oqurC2VlZZDJZPiP//gP7pISpmt2dhYDAwMQiUR44YUX8NJLL+Hs2bNwOp2orKxEXV0dd5/JMI86GKFQCBKJBNevX8fS0hKrRBUi51qtVnY7ViqV/OeeOXMGnZ2dbPRSW1uLTZs2QSQSsUQmuZiWl5djz549uOOOO7BlyxZ0d3fjwIED2LRpE3cMSdN6y5Yt3KEkR8WNggh/g4ODmJ+fRygUwvDwMM6fP48TJ06wWo3dbsfU1BTzXM6ePcsXQTgc5g6Ny+XiDiqtgQq31tZWduKMRCKcuG4UWq0WAwMD3B1uaGhAY2MjfvzjH2N0dJRhEH6/n5UTRkZGMDo6ylAEGhGTQVRdXR2rOZSXlzM5cufOnejs7IRQKGSZ3kJqXdFolOVUSaZw27ZtPFolnxZKnt566y2Ew2HU1dXBZDIxPI1Gp2traxgcHOREnToB6XQaVVVV2LZtG+rq6vhibW1t3XBtVNULhUIYjUaYzWakUin09fXxyLa0tBTRaBQvvPACiouLcfjwYSwsLHB3x+FwMDRsaWkJk5OTMBqNaGpqgtFoRFlZGYaHhyGXy+Hz+TA6OorZ2VnkcjlWKLpZaDQa1NTU8Hg2GAyiv78fQ0ND7OZLU4i5uTkMDAxg8+bNuP/++2EymZgg5/V6MTs7C6VSifLycjgcDkxPT0MikaCzsxP79u3Dtm3bWBGNft/HFUKkqU+HXmVlJe6//36MjY3BbrczBJIKcSIXAzeSba1Wi66uLnR3d8NsNrOwA8EhxWIxFhYW2O+AOs80DSvU/TaZTLh8+TKuX7+OdDrNOuF0MZSUlLCKD03mSF2MFPTIXPDee+/F8vIyUqkULBYLzGYzpFIp6uvrsW3bNphMJojFYp4axuPxgsm8UqnE4OAg7HY7F7GkJEQkTurK22w26PV6HDhwgGXFJRIJqqqqYDAY0NLSgosXL8Lv9+PAgQPYsWMHF799fX0IhUKIx+OshKTT6TiZuFlQV5sK77KyMqyurjJcRS6Xo7u7G62trcjlcjh79izMZjPm5ubw+uuvMxePjFAnJiaQTqdZv5/kVFUqFex2Ozc/KBkuFOl0GteuXWMvGLPZjFgshqtXr2JychJyuZwNFIkc+9BDD7GPBHEDCb5qt9tx+fJlzM7OIhwOMzRtYWEBKpUKZWVlKC8v5++iUBOKfH3I+ykQCCCVSuEv//IvMT8/D5lMxnudZG537NiBWCyGy5cvY2BggAngZAomFApRVVWFrVu3oru7m5tsNOWKx+MIhUJsxrVR0DQilUqxdD01kdLpNDQaDdrb27Fjxw40NzdjcnISJpMJO3fuRE1NDe/pzZs3o7q6mh3dg8EgywnPzs7C6/XivffeYzNNEjMo5FVB9/uHeRBUpJHwTHt7O8urer1etLS08H2wtrbG8C2hUIiWlhasrq5CIpEw9I0SaPKSoqYZCcZsFKlUigvHsrIylpsXiUS4dOkSTzMmJycxMjKC3t5e3H333WhoaEBfXx/7Y3R3d+OFF17A4OAgkskktm/fjr1790Kj0bC5Wm9vL4LBIJaXlxGLxaDX6/HYY48VfKcTExMsIU1Y+6tXryKdTsPn83ExfunSJQgEAoZBffDBBxgbG2MVup/85Ce4du0aZDIZWltbsXnzZrS2tqK+vh5vvPEGbDYb4vE4T5p0Oh3q6uo2XJtUKmVoGkFctVotfvvb33JOGIlEMDY2hr6+PiSTSZhMJgwNDcHj8XCTsKamBjKZjJEd1PwQiURwOp0spEBIm0wmA5fLVTAX0el08Hq9LKQgl8uRTqc5p52bm2MYETl+19bWIhaL4ezZs5iamkI6nYZQKER1dTUeeeQR3nsrKyu4du0ao1vuuOMONDc3c4OdhFo2CpFIhJmZGayurkIqlbJ3z+HDhxEKhXD16lXodDp0dHRAoVBgYGAAhw8fZgK6y+VCRUUF3G432traMDU1xfubvLvKysqgUqkY0kYmr1artSDB/8PxiQsNp9MJqVSKDz74AG+//Tamp6ehUqnwwx/+EBKJhJUkenp68PDDD2N+fh6BQAA7d+7EF7/4RezYsYO7+8ePH8cjjzyC0tJSHreKxWL8wR/8AaanpxmWc9ddd+GP//iPodVqcccdd2y4No/HA7/fz6pBEokE586dQ0NDA06cOME4VpLq2rp1K5qamlBdXY2jR4+is7MTXq8XExMT+N73vodcLocrV67A6XTiwoULeOGFF7Bjxw5cuXIF8/PzeOONN2AymbBjxw7Mzc0VHKWS9GZJSQnKyspgs9lgMpnw+OOPY8+ePawidPfdd/NhvGfPHphMJtakVqvV6OzsRHl5Ob7yla8wcYngA88++yybRZWVleHBBx/EbbfdhqtXrxZ0vfT7/Whra+OLo6OjA/F4HH/wB3/AGD+v1wuNRoNjx46htrYW3//+9zE5OYm2tja43W688cYbqKiowNTUFP7mb/4GgUAA3d3dqKurw9zcHCvsvPTSS3jllVfQ2NiIvXv34r333mOnzJsFfbBjY2MoKirCli1bMDg4iLvvvhulpaV4+OGH0dTUhG3btrECB6kk0ChQr9dj9+7d+OY3v4mHHnoIwA25tunpaej1etx1110YGRlhcn1LSwv279+PxcXFgg7X0WgUmzdvZlfr9957DzKZDE8++STm5uYYE33o0CH89re/xdDQEDo7O6FUKvHYY4+xf0xPTw9D/ux2O2QyGRwOB86cOYPf+73fw+joKKvR7Ny5E7fffjvsdju7xd4sQqEQe7PIZDJUVFTg6aefZqWNsbExntgYDAb84R/+IS5fvozp6Wls374dHR0dKCoqwt13382yoxaLBY2NjUgmk3jnnXdgtVrx3nvvYWVlhQ9yuVwOvV7/sSoUer0e5eXlsNvtWF5eRjwex+uvv46nn34aJ0+ehMFgQF9fH86ePQudTocXX3wRarUa09PT+MpXvoLq6mq899570Gg0WFhYwM6dO6FWq7F//3709vZCr9fjn/7pnxhvPzY2hvLyctzx/7D3n8GRnmW6OH51t9Q556ycw2g0UZNnPNEee2yDc8AYrwl72OXskpaFpWBhj/dAsVBg2F0bjG2w8XpxjjhNziONNKOcWlIHdavVanVWd6v7/2G47zP+/UeNT9X5OHcVZVO2R4/e932e5w5X2LcPCoWiZHeIoIQAGLY1Pz+PXbt2wel04oYbbmDjtsceewyvvPIKtm7dioMHD+KBBx5ATU0NotEoJBIJHnvsMfzLv/wLS1enUilMTEygra0NZ8+eRTKZxIcffsgkbuqorhSk5EfExK997WuoqanBzp07GUKydu1a7Ny5E2q1GuFwGF/84hehVquxZs0aRCIR/Nd//RcGBwcxPDyMQ4cOQSwWs1SrRCLBnj170NPTg0cffZQhazt27GAOzkpB2Hij0QiLxQK3242Ojg42XmtsbIRer0dzczM+//nPcyKZSCSwc+dOyOVyhEIhbNmyBWfPnsU3vvEN1NfX87Q7mUxi586diMfj6OrqglKpZKEOt9uNzZs3r7i26elp3HXXXQwtMRqNOH/+PH7wgx9gfHwc99xzD0MPPvvZz6KnpwfvvPMO/vu//5t5I4ODgzz1+Y//+A/s27ePyZoHDhzAzMwMRkZGoFAocOrUKRQKBVitVpw4caIkUX1ubg4VFRUM7aIm07e//W1IJBLodDpOwMntm8i4v/zlL3Hw4EEsLi6ioqIC0WgUDocDJ06cYEjo5OQkvvWtb+Ho0aMYGBjA2NgYXC4Xdy2/+tWvrri2fD4Ph8OBmZkZZLNZtLe3o6qqCtu2bYPX64VEIoHP58OZM2dw6dIl6PV6OBwOeL1eho709fXhT3/6E2QyGe644w5UVVVBoVAgGAyiu7sbTqcTN910E774xS+isrISFRUVkMvlOHHiREkCbLFYZLnRoaEhjIyMoKqqCu3t7RAIBCxRrFKpmG93+PBhJBIJrFq1CmazGRUVFfjWt77FHWatVguHw8F8yaamJrz//vsQCASYmppilcyGhoaSEEfyDSEFRL1eD6/Xiy9+8YvI5XJYvXo1tm3bhi996Uv4p3/6Jzz11FNwu92QyWTYsmULmpqaGDqkUqnwwAMPcOO2WCzitttuw3vvvQehUMhmvuvXr8dNN93Evhsrhc/nw44dO1ioZe3atejt7cVPfvITfPjhh7j77ruxbt06bN++HU8//TRMJhM2bNiAixcvIp1Oc0PH7XZDp9PhO9/5DqxWK+x2OyoqKrBhwwb867/+K/bu3YvNmzezNDNNx0vlIjRpOXPmDC5fvox0Oo18Po9t27ZhZmYGa9euxbp167Bx40bs2bMHAoGAlU1vvvlmNDQ0QKFQYOPGjSgWi+js7ITD4UBbWxscDgcqKyvxyiuvQCaTweVywWAwoFAowO/3w+PxoKura8W1URJ/tamkWCzG/fffj7m5Oeh0OkgkEphMJlbgSqVSsNvt+PznP8/S3bfddhs8Hg+be/b397NIxq5du5BMJvH73/8egUAAXV1deOCBB9DQ0MAGvCt9b52dnYzMqa6uRiKRwA033AAA2LhxI6qrq7Fu3To8+OCD3NAcGhrCnXfeCYVCgffee4+pAocOHWJYnEAgQHNzM/MyiVtHLujhcLikSMjV8YnJ4L/+9a9x6dIl7vBPTU0hGAyivb0dTU1NmJubQyQSwfT0NGZnZ7F//36sX78eFy5cgE6ng1wux9jYGH7729/iO9/5Dr773e/ie9/7Hh/g4XAYzz33HL7//e/jgw8+gNvt5pdqs9n4wrpWkLzY5s2bubu2efNmfPOb38SmTZtQXV0NnU7HVTNZrBOhmPDlsVgMKpUKVVVV8Pv97H5KXIOamhocP36cOx/kXBsMBvHzn//8mmv7zW9+g2QyyWPY+fl5NsG6uvt46tQpvPTSS/jNb36DP/zhD2hpacHLL7+MVatWYevWrRgYGIDL5cLbb7+NRx55hB28P/jgAywuLuK+++6DTCZDKpVih1siG914443XXNu//uu/YmRkBC0tLYhGoxgbG4NGo8Hzzz+Pd999l0e1pD5B3hLUuTQYDEgkEnj++ecZ67l3715Eo1GGV33wwQd4+OGH8cEHH7Cvw/LyMne0VyI1/+pXv0KhUEBtbS0TG2+88Ub8+Mc/xl/91V/h4sWL2LZtG3Q6HS5fvowjR45g//79DF1QKpVQq9UQCoU4c+YM3n//fdxxxx0YGRlhVScyj2pvb2e53UAggO3bt+PEiRP4p3/6p2uu7e///u+h1+vZy0Sn00Gv1+PXv/41br31Voa6ZbNZdpE/evQoHA4HAoEAVq9ejerqapw7dw52ux09PT3YsWMHLBYL0uk0BgYGcPHiRdxxxx2Ynp6G0+lkCWGaiqy0th//+MfIZDLo7OzE6dOncfbsWTz66KN44okn8KUvfQlnzpyBQqFALBaDz+fDV77yFeh0Orz44ovw+XzYuHEjy/BZrVa89NJL2L17N7t0UxeusbERTzzxBCfw1EHy+/0lyeDPPfccenp6sH37doTDYQwNDWHjxo34t3/7Nzz88MOQSqWYn5/H7OwslpeXcd999/FedDgczB2iETmpblAyn0gksLS0hDvvvBMSiQT9/f3cIbfb7RgYGMCPf/zja67tD3/4AzcGyJQtFArh4sWLOHjwIC5evIh169YBAI4ePYp9+/ZhfHwcTqeT9d+VSiX6+vqwadMmtLe345lnnkE0GoXFYkFdXR1OnDiBpqYmVuchZ1m/3w+DwbCiX8XXvvY1+Hw+dtumhObnP/851q9fj1tuuYV5ck899RQymQy2bNkChUKB06dPMy/I5/Nhz5497D3U398PmUyG6upqXL58GZWVlThz5gzuuusuzM3NYX5+Hk6nE+fOncPjjz9+zbU9++yzyGQy2Lp1K+bm5nDixAkYjUacPn0a3/ve9/C73/2OvYqIoF9TUwORSIRnn30WLpcLNTU1OHHiBB566CH84he/wF//9V8zVpw4CCKRCK+//jpaWlowOjqKpaUlbN++nQuWlfYq4e0VCgVkMhk6Ozvx3e9+l+XViR9XLBaxadMm+P1+XLp0CUKhkHlWOp0OS0tLqK6uRlVVFR577DH27rl48SIeffRRTExMcDff5XJh165dePbZZ1cUMXnsscfg8/ngdDoZm75+/Xo8/vjj+Ou//mv09vaiurqak91bbrkFr7/+OjKZDFatWgWlUomRkRE8/fTTqK6uRnl5OW644QaG6c3OzsJgMGDv3r0srDIwMIBQKMTQnM997nPXXNsbb7yB06dPw+l0MvS5oqKCzSqHh4dRU1MDmUyGyclJlvAkN2viE4XDYVRVVeEnP/kJT/kIqjI5OYkvfOELeOWVV9hEz+FwoKKiAmNjY/jyl798zbX99re/RSQSQWNjIzKZDPx+P8O1tm/fDuBKw2VmZgbhcJj9YhQKBZqbm1l45uTJk2htbUVvby+am5sZRnP27FmcPn0a//Iv/4LnnnsOLpeLyerBYBDNzc04dOjQNdf285//HDKZDMVikaEv27Ztw+DgIHbs2MH+Tj09PZifn8c//dM/4fz585iensbZs2dRXV3NyTI1Rnft2oWpqSn09/cjHA4jkUjgoYce4kJ4fn4eBoMBDQ0NOHbs2Irv9PHHH0c6nYZUKsXw8DDC4TDuvfde/OhHP8Lf/u3fMpSPoNQ//vGPUSgU8Mwzz+DixYsQCoXo6Ohgv5KNGzdyw+fo0aPo6elBPp/H17/+dYRCIU5GiXT+wQcfrCja8Mtf/hKRSAQNDQ2Ynp5mQ9Fnn30Wn//853nf5/N59PX14fvf/z5LrdI54HK54PF48PTTTyOfz+PQoUNQKpUYGxvDhQsX4Ha7sWHDBnz00UdwuVzMmaiursb8/DweeOCBa67tBz/4AWZmZpjTSzyR+fl57N27F6Ojozhx4gTLMnd0dPAkuKGhgcnWb775Jh566CE888wzbJZKkxCDwQCTyYTvfOc7iMViEAqF3Lirr6/HXXfddc21Pf3005iZmcGWLVvYrHdoaAjZbBZf+cpX8MMf/pCLUoVCwXLm6XQaP/7xj7Fv3z4cOHAA58+fR3t7Oy5duoSuri709PQgFovBarXi7bffxgMPPIBz585h9+7d+OCDDzA+Po4HHngAly5dWlG04er4vzLsI1UAv9+PmZkZpFIpZruTgsrs7CwTSAYHB/Hiiy/C5XJBq9Uyfvypp57C5z//eRw7dgyhUIi7CjMzM/jhD3+I22+/HZcvX2bI05EjRzA3N4d/+Id/uObaqANNJMJCoYA//elPcDgcOHnyJPR6PbLZLDweD1f1oVCI3UqLxSJDtOLxOI81V61axS7URqORMYKkwkFdvFLkXHICJ/ypRqNhFQexWIxkMonz588jm83ikUceQV9fH0wmEzo6Onikd/jwYTZyaWlpwVtvvQWz2cyEptWrV7Nz+dzcHOOdg8EgfD7fioXGwsIC47ep4PF6vfjGN77BUnhk4EOdt0gkgjfffBNSqRS1tbVoaWnBgQMH8Mwzz8BisSASifDoc3h4GG+88Qb27NkDs9n8Md17r9dbkuQUDodRWVnJB/Ly8jJ+9atfwWq14tixY5iamsLx48chlUrhdDqh1+sxOTmJgYEBiMVibNy4EUajERcuXMDw8DDcbjfm5+dZbWRsbIwJ8MCVKQUpzZw9e7bkXtDpdOjs7MSpU6fg8Xhgs9kgkUhw++23Y2RkBE6nk1WYDAYDLl68iObmZpYrJrhMoVDA5OQkWltbudNM8AKFQoHXX38da9euxbvvvgu1Wg2BQIDBwUGsXr16xbX5/X42MiITytHRUXzmM59hOT+Px8OyvMTh6Ovrg0ql4gsIAE6ePIlDhw7B4/Gwqs7Y2BguXryInTt3spkVjYXJQbZUBINBOBwOyGQyJsEPDw/jpptuYu4XcWBI472iooJFGsjLorm5mfksmUyGybLj4+PYvXs3Xn31VaxevRpjY2O8T6RSKTZu3Lji2hYWFqDT6Xial0qlMDY2hgcffBC/+tWvPmYWF41GsWHDBhgMBhw/fpzxtzqdDvl8Hm+++Sa+8pWv4NSpU8wbOXv2LAYGBgBcwf16vV6YzWaUlZVhdnYWFotlxUIjFArBYrGwIIROp8Pg4CAXD/l8Hk8//TR6enogk8n4+yPCK5kWmkwmPPXUU8wnorM5HA6jp6eHeSBPP/00NBoN8xdKvVeCzV66dAmBQACJRAJqtRoPPvgg5ubmGAPf29uL8vJyNDc3Y25uDm+//TbzWT766COWb12/fj36+voYekHCDSqVCpWVldDpdNiwYQNCoRA+/PBDCIXCFQuNfD6P9vZ2KBQKzM7Ost/O1q1bsWrVKhw7doxdyltbW+Hz+dDX1we/34/KykoEAgGMjo7CYDDg8OHDWLNmDXMvGhsbMTMzgwceeADpdBqbN2/G2bNnWTo0kUiUJEsSmZUgW8ViEe+99x4OHTrE9+r4+Dg0Gg1qamowNjaG7du34/e//z0n93K5HNu3b8fAwAD27NmDY8eOwWq1csKfSCTw7rvv4oYbbsBLL70EsVgMt9vNRogrRTgcRnV1NRQKBTKZDIRCIUNQqIFF3Jj29nZMTk7y1MDtdsPr9SIWi/Ekz2Qy4dy5c+xqH4vFYDQaublltVoZQkYk85ViYWGBOZkkglJeXo50Oo25uTmWl9VoNBCJRNDpdCyU0tvbC41Gw+TW5557jgVQyBm8UCjAZDLh5MmT2LBhA06dOoXKykrYbDYEAgHMzMysuLZkMgmz2YxAIACxWAyZTIbTp09DLBZjcnIS0WgUMzMz3Dx79913ce7cOdTV1WHnzp1sSup2uxEMBlmV0OVyQSgUYmRkBA8++CAmJyeh0WiQTCYxPj7OZrqlOBqJRAImkwnZbBZVVVXQ6/X44IMP8Dd/8zfQ6XS4ePEiq6x5vV688cYbnAt1dHTA6/Xi5MmTqK6uxtDQEJqbm9Hf34+ZmRnmPLpcLly4cAFr167FuXPn0NfXB4FAgLVr15YUlCABHhKHkEqlOHfuHA4ePIhUKoUNGzYwvLO+vh6nT59mc75cLoeTJ0/yHZ9KpVBfXw+v1wufz4fZ2VksLi6y+t3GjRsxPj7OZPULFy6UFAkJh8PchA6Hw5ifn4dQKIRWq8W5c+fQ0dHBcGDyktFoNLh06RIikQirFD700EP46KOPUFVVxXyVVCqFkZERXLhwAXfccQe2b9/OvB3KTUtB9ZaXl3lCQf5JZWVlqK2txTvvvMNFPUFBbTYbVCoVgsEgn5knTpxAsVjEBx98ALlcjmeffZYFnCKRCLq6ujA5OYmlpSW8/PLLEIlEcDgcPPH7JPGJCw2lUom5uTlIJBLIZDJIJBJotVq89dZb2LRpE7Zs2cIkOjrkJBIJ7HY7IpEIlEolqqqqoFQqIZPJ8MQTT+DOO++ESCTCzMwMO9+aTCZ8+OGHqKioYEzk7OxsSXyfSCTC6dOn+dIkpn4qlYJKpWJptcnJSVy+fBkCgQC1tbVobm5mlZu2tjbI5XIsLCzg1VdfxebNm5mQRBjxVCqFhoYGZLNZ/sBp6rFSEFEzmUwye1+r1eL999/HunXr0NbWBrfbjVgshmw2i76+Pmzfvp0LELFYjNbWVlaoIMw5SdqlUil4PB7Wbie3x/LycpjN5pIELIVCwSTgiooKSKVSJmd6vV4eddNo/8KFC3j44Ydx7tw5ViY6f/48q10Eg0H+/3ShzMzM4Pnnn8e+ffuYGKvVatHa2orp6ekV10bqGCKRCHa7HcViEePj40in0wgGg2hoaIBEIuGEjYz7PvjgA+RyOcZMazQa2Gw2Vv4hvDJJyBUKBfaASKVSDGMqJdkmkUgwOTmJsrIyJi5SUUfEv9HRUWSzWeaQbNu2DX/4wx+watUqdqxWq9WcpJKcJ0mKTk5OQiwW4+TJk3x5kGtuKU8ZAGz0SFM3j8eDSCTCrrLhcBjj4+P8PgBg8+bNyGQyzK8hotizzz6Luro6OBwOSCQSlpCmxHH9+vUAwJcNKaesFKTAMTExwYROsVjMLuUCgYANEcmArqamhpWl5ufnudNNv5PFYkE4HIZSqcTevXvR2NiIjz76CKtXr4bT6URFRQVUKhXDPVaKpaUlNj8iFSyr1YpXXnkFi4uLuOeeezAzM4OpqSnEYjGcP3+enZk9Hg9DXEhV6rXXXsOnP/1pFnVwuVzcraUp8MzMDP8upUbkJpOJ9w4AVkcjLgoNpsmniNyJo9EoT3QMBgOToOlsKy8v5+YKkd0tFgv6+voYgkOk3pWCzsB8Pg+LxcLEyaeffhr79+9HMBjki5kkMrdt2watVove3l5WoROLxTh79iz+9Kc/4YYbbmDFmcnJSchkMvY3mp6ehtlsZnhRqTNucXER8/Pz7IybTCbZe8Dv96OiooInSktLSwyHpa63UqlERUUFrFYrwwb8fj9z/oxGI5N8169fz1KkiUTiL2rgSyQSvjPpvyFkQFdXF7RaLWKxGCftlDxrNBqcPHkSOp2Op3wkBKLT6VBfX8+qPePj4xgfH2diLkFZyOV5paB1kZM67dPBwUGsW7cOSqUSk5OTGBsbQ7FYhNVqRS6Xg9lsxnvvvQetVoumpqaPiYjU1NTAYDDwnU6JDsnbFotFJBIJNo9cKaipaTabIZfLsbi4iO7ubhaNufnmmzE7O4uRkRFW1jEYDJzES6VSaDQa6PV6nDlzhhsBNTU1SCaTCAaD7L9iNpuxbt06Nisjk8KVwmazIR6Ps89AIBDg6fYHH3zAE6p0Oo2ZmRmk02m0t7cjl8vB7XYzJzKVSiESiWDnzp1sgmg0GuFwOPDUU0/hlltu4cSX7q1Lly6tKGUPXClsx8fHUVFRwTyX8vJyHDt2DGq1Go2NjRgfH0dvby+8Xi9aWlowPj4OqVQKrVbL6mtWqxVWq5W5XxaLBblcDvl8Hl6vFy6XC4ODg6yMRpPmUsIIRFDPZrMMfZ6bm0NfXx+cTicaGxuZD0G5x0033cTqTMAVmKRYLIbZbMYf/vAHfOYzn4HT6YTRaEQwGERvby/nmWQSSsp4pRJm4iPo9XrOuebm5vDOO+/gzjvvxMTEBBQKBex2O2ZnZxkGDVyxBiABBPoZyWQSIpGIzQJJmercuXO4++67MT4+jkwmw/+dULgywyGTyUAgEEAul/MzLC8vx/Hjx6HVamEwGCAUCpHP5xGLxRjudO7cOf65er0e9fX1+OlPf4ry8nJs2bKFeYixWAyvvvoqHA4H7r//fjzzzDPsQk58mk8Sn5ijQTKtkUiEpTzFYjFqa2vh8/k4UVapVHA6nQgGgwwFIbk3whfTwSIWi6HX63kD7du3jycGhUKBJwXBYLDkwUPawyKRiEk1dFiJRCIYjUbu6FHXyuVyQSQSobGxkXHoVVVV7AZOkwqZTMawB9KxJ6WpxsZGLC4ulvxIC4UCvzSxWMwyjXq9HiqVii8Li8UCtVrNqj8Z18gAAQAASURBVBbd3d1sKkdEwEAggJqaGpb2UygUUCgUMJlM8Pv9PK6NRCKsYFEqeSkUCnA6ndxFpuLgandvOmCXlpbg8/mg1WoRiURQX1/P0wmZTAatVovNmzez7j3Jvu3cuZPJ6SR7KZfLmSBWKhKJBH93JHFKEnNkqlVdXY2amhr09PSwpvjy8jJGRkZw/vx5rubXrVvHXTkqhknxq6mpiRMDSoZKEbBI/jSfz0Mul7OkIsmqkuqR1+tFOp3m6RdBLyoqKmAymVBXVweLxQKJRMJyywSRcrlcrLBBBkhEPC313KgjSAc7HcJUfNMUoLKyEtu2bUNPTw8sFgs/F0os16xZwz+7UCiwbKtOp8OmTZsY8x4KhTgJCQaD7Kfwl94rEZfpzyWpwdraWojFYhZsINgGFWA6nQ5utxuNjY0sFUwNDYvFwpciaZ2TWkapbtrV75XWJxAI2KGWOr/0Hux2O1avXo1MJgOdTgefz8cqQQB4mkDk8aqqKpjNZkilUmzbtg3RaJQJtqSUQ2fDSkGyn9Q1o2aO0+lkiU6Xy4WmpiY0NzejUCigs7OTjQLT6TSGhoYQjUaZzyEQCKBSqaBQKCCRSHgETnBWlUrFHhF/CScMgDt9pNOvUqkQj8dZDpO4PtFoFDqdjhWf6LsvFAro6elhSVGBQACpVAqpVIrGxkZks1n2SqHJoEKh4G/8WkHf/f+XkKpUKjEzMwO1Ws3+JyqViiWMNRoNd3/n5+eh0+lgNBqh1Wq5sDEajaipqYFer2cSM8mYUvOo1N1Afw7JtV7tMzQ1NQWbzcayncS7IXlUUo7SaDTYsGED36lUIFDC1t7ezmc4NYySySTjzFeKq4sMOldFIhHMZjNP/yUSCaRSKQwGA2QyGZvBicVibkiaTCZO6AQCAXso0fcQDAZhMpmgUqkYPnJ1UrZSaDQanrAoFArE43Ge8lOzkTgS5K1A5HEiCwP/R442Go1CqVSyuInVasXc3BzC4TCKxSIne3RGrRSkukbPn0Q5qBlA94NcLodWq0UgEOA9QBLeBD1bWFhg41RKKDUaDdxuNwuceL1e5iyRut1KIRKJeMpD0qoGg4GbhHq9nouw1tZWthbQaDSorKyExWJh2HgwGGToscPh4H1A6nuktEhkYsonVop8Ps/y96RqSLLOyWQSYrGYFd8kEglCoRDLj5vNZl672+1GXV0dnE4nk+atViub3pLADjXhisUi360rRVlZGcNzs9ksxGIxrzMSibDcPwmZAGDeVH19PWw2GxfrhDIgqViTyQSdTsfE/mAwiLKyMlgsFhiNRpbkXinIhwoAi5OQUA0JmtAZRxQEmUyGQqGA+vp62O12bn6TIiWJNdB70Wg0mJiY4NyN/veXzt6r4xMXGslkkguI2dlZrirvuOMOdmksFovQarUsO6bRaOD1evlwJp+AsbExPPDAA0ilUizBVVlZifb2dsRiMTQ2NrKKlN1uZynRlYLUsGpra1FZWQm328067cViEclkks3w7rjjDtTV1THmvKmpiV+ASCTC2NgY6uvrEY/H+aEvLS3B6XRyZ2FkZARmsxlNTU2IRCIlYQXkgUDSvrRZ77rrLrS1tUEikaBQKEAmk8Fut7O076VLl7Br1y5s2bKFPSwIZ07a3FRIbNq0CWq1mjXF1Wo1E0tLFWjxeBy7du2CzWZjCVWr1Yr6+no+fAk2QUUhddykUimMRiNqa2tx4MAB6HQ6bN++HRUVFdi2bRtqa2uRyWRw9913o729nXXiq6urYbVaMTo6yiYy14qr3WhJYYsOEpvNxiNuUlhRKpVIpVLQ6/WsgT8wMMAqCnv37uXEiZTJiFtAnAr6Duvr60tOW6jzTwmWw+FgbHFTUxOqqqrgcDjgdDrZiXd+fh719fVQq9WwWq1obm6Gw+HA6tWrueNC7zAcDmPbtm2QSqVYs2YNS/+R4kipg4ecuqVSKSwWC5qbm+F2u9HW1sbwK6PRiC1btuDee+9FOByGSqXCwMAAhoaGMDAwAJ/PB7vdDr1ej89+9rNwuVzsF6JWq+F2u2GxWLBu3Tr4/X5EIhG+ZEpN9wBwwU9JO7mEF4tF2Gw2FkuoqqpiHHogEEA+n2c1rurqatTX10Mmk/FIfvXq1XC5XKyIduONNyIUCmFychJerxfz8/NYXFwsCXOUSqX8XrPZLE9w9u7dC61Wy53hqqoqbNy4EW1tbWzY19nZiY6ODpjNZiiVSmQyGdx1113c3LBYLNzgIKlnasJUVFSgoaGhZAFJ35xWq4Ver4fL5UJ1dTVaW1v58ifFod27d6NYLGLVqlXM5XA6nSwRK5FIsHbtWjZyMpvNaG1txT333INgMAiPx4NAIMBJAunurxQkCEDFJxWm99xzDyfjZDZJhSS5GR88eBCdnZ1sRpbP57Fu3TqW1i0vL4fL5UJbWxv8fj9Wr16N+vp6hrrQZb1SkDtwPp+HRCLhos/pdCKdTjPsrqamBuvWreOOH/27S0tL6OnpYUU0MkojmI9EIkFjYyNUKtXH/A9oulGKAJvNZiGVShlSa7FYoFAosHv3blYAomSVphQ+nw+RSASVlZX8TJubm7kYS6fTGB8fx+XLlzEzM4O2tjYm6pKy0NXS2ysFnTnEdaMEubm5mc9kl8uFrq4uHDx4EA6HAw0NDVhcXMSWLVuwZcsW2Gw2FoDJ5/OIx+NIpVJwOBzYuHEjczNJNICSXJKQL/W9OZ1OhEIhTExMcHGxb98+lq0HwO/BarXC4XBwI4kUiAYHB+FyuVBXV8cCGhaLBZ2dndi7dy8rZ/X19X0ssS0lb0tKj+SlVFVVxUqGa9euZb8Rq9WKNWvWYG5uDgMDAywdOjs7i1QqBZPJxNN2QgxQsv7II49gYWGBFcBsNhvq6+s5AV4pJBIJOjs7Oceoqqpibg01dUjE5J577oFGo0F9fT0EAgFMJhMn1gsLCxgbG+OmI3EXjEYjtm3bxo06j8fDMu7pdLokiZ6KabVaDY1Gw/c0Ka6RR43JZMLatWuhVqsZJkUqSFc3hR999FGGZmcyGUQiEdxwww0QiUTQ6/WcP1FRWCqHI2L+7OwsvF4vMpkMJBIJQ/BpEre0tASXy4V169bB7XYjkUigubmZhXza29u5yUd+OcQdW7duHSwWC/r7+1nExeVysSXCSkENbGoeqFQqiEQidHV1we128141Go2oqKiATCZDLBZjNb+rYZANDQ1obW2F3+9nH7RCoYBPfepTzLF2Op2MwqGc8JPEJy40zGYzLly4AJfLBYfDgVQqBbVajSeeeAL3338/TCYTRCIRk0w7Ozvx/PPPY9u2bZibm2NlgocffhgajQZvvvkmK1VptVrMzc3hlltuwT//8z+zZv/Q0BCCwSAefvjhkgY+9fX17E45NzeHdDqNXbt2obe3F2vWrMFTTz2FM2fOcEfo85//PLLZLLq6uvDaa69hdnYWKpUKzzzzDLZs2YJEIoG6ujr09vbi/PnzAK5Yvd9yyy0YGxvDwsIC3nrrLXz00UdoamoquYGoq+dyuRiSRJ3JiooKHovPzc3hyJEjMJlMOHXqFNasWYPTp0/D6/VCKpUynplwo5R0NDQ0IJfLsSne+vXrsWPHDrS1tX2sG3utkMvlSCQS2Lp1K/R6Pc6dO4elpSVWmzh+/DhfDEqlEjt27MCbb76Jn/70p/j973+PN954A1KpFEeOHMHU1BQef/xx3HHHHZBKpQyDSSQSuOeee9DY2IiWlhYsLS0xvr3USBC4cjBu2bKFsbw1NTXMK6GRdzgcxvHjx/Hkk0/C5XKxxnN9fT1qamrw0ksv4YYbbsDs7CzWr1/PZM7a2lr09vbi/vvvx9NPPw2n04mqqirY7XZs3LiRsfTXCoVCgUAgwCTtvr4+7vgS5ESv1yOZTOLUqVNwu9349a9/jZaWFiwsLGB6epqlHE+cOIGf/OQn2Lt3L5OB77nnHoRCIdx9993QarVobGzkJFSj0eDw4cMrrs3tdnNnI5FIwOPxoLKyEufPn8fFixfxu9/9DpFIBBqNBgKBAN/85jfR19fHKlomkwkGgwE/+tGPcN9992FwcBBdXV1sLKbVanHkyBF84QtfQH19PbLZLN577z309PRgz5496O3tLflOKXGsqqpCIpFAb28vBAIBXC4XTCYTjhw5AqvVii1btkCn06G9vZ1VOUZHR9lcLhaL4f777wcANufT6XQ8eVSr1axEplarOYkpJb/b3t7OyjQAGAecSqWwd+9evPfee0gmk4jFYvjjH/+I+vp6/OxnP8PBgwdx4sQJ+Hw+lJWV4ZlnnsGrr76KX/3qVygWixgdHcWlS5fg9/sxOTmJu+66C+l0Gk1NTewXJJPJMD4+vuLa1q9fj4sXL8Lr9SKXy6Gurg5f+cpXcO7cORw4cAC//e1v8fvf/x6nTp3C2NgYDh48iN///vcMv9m1axe+8IUvIBKJYM2aNfjggw/w2c9+lhX51Go1Ll26BIlEgunpabhcLr68d+/ejdHR0RXXVllZCYFAgJ07d6KqqgqRSASbNm3id/nqq6/CZrNhw4YNiMVi0Ol0ePLJJ9HY2Ijq6mqsX78eW7ZswcLCAu644w5MTExg+/btbNinUCjwzjvv4NChQ5wsAlf2AkH3VgoqQtvb2xEKhXD8+HFUV1fj/fffR1NTE3eJC4UCLl26hJ07d+LUqVNsPtre3o7bb78dp06dQltbGwQCAT772c+yfGc8Hscf/vAH7N+/H++//z536QcGBrBq1aqSqiw6nQ6xWIyVzsbGxmA0GjE8PIzt27fj3XffRSaTQTabxYsvvojOzk6UlZXhy1/+Mi5duoT33nsPhw8fxjPPPIPOzk5cuHABn/70p7nRlkql8OSTT+Kee+7B8ePH0dDQAIvFwvCknp6eFde2vLwMpVLJ5l3AFRn5l19+GTKZDCdPnmR4bjQaRWVlJaLRKHbt2sVczK6uLiwtLeGhhx7CpUuXuENbXl4Oo9GIixcvYt++fThy5AiOHTuGl19+GUePHmXTsJXC4XDA4/FAIpEgk8lgamoK1dXVOHPmDJqbm/Hiiy9y48br9aKjowPd3d0wGAx48cUXmRvzq1/9CgcPHsS5c+ewc+dOTkiJ+7Jr1y6Wn+/r68Po6Cja29tLnr86nQ4DAwOwWq085b3hhhvQ19eHqqoqTgxpKnrffffh5MmT7LrsdrthMBgwODiIlpYWTE1NobKyktEVAoEA//t//29WHLv11lvhcDiYw1WKP6LVauH1epmsvri4yHtg165d8Hq9iEQiPKn4m7/5G3zwwQdIJBKsWHb33XejtrYWN9xwA+rr69HZ2ckmoR0dHXj99dfxyCOPcFFApHz6HVYKhULBDu+pVIrPpLNnz8JoNOLYsWMwGAxYs2YNtFot7rzzTszMzOCGG27AwsICLly4gHPnzqG7u5sLPaFQiN7eXuRyOZ7WdnV18R0wNTUFj8cDq9XKxP1rBb0PmkItLy+zEA4JLRBCYWFhAVu3bsUf/vAHbN++HT//+c/x/PPPY25uDm+++Sbi8Th++9vfwul04siRI3j11VcRiURw4sQJ3HLLLaxYOTExgQsXLrBx3kohkUiQSCQ45zpx4gT27duHRCKBG2+8kT3XTCYTPvroI2i1WrzyyitQqVR44YUXOHeJRCIMrVyzZg18Ph9CoRAqKysxMzODhx9+GJcvX4bdbkehUGC4ut/vX3FtV8cn5miQfOelS5cgk8nQ0dHBKlNvvvkmZmZmWDmivr4efX192LZtG0ZHR6HRaBAKhZDP57FhwwbG8l68eBFGoxGpVArRaBRf//rX8Y//+I+QSCQ8FaGqqpTGfG9vL+rq6qDVauH3+3lU2tTUxORDIsqMjIzg7NmzqKys5G6fTCZj6/jXXnsN6XQaL730Evbu3Qu5XM7jyf7+flbroIMhFAqV7EQSbpsw/STvd+rUKWzfvh02mw1jY2OQy+XYuXMncy2+973vQalUYnR0FBKJBBs3bkRTUxOmpqZYXcrn8yH6Zxd2OkCmp6ehUChgsVig0WhKmlkRRp/Mg1pbW3H+/Hk2X+vu7kZ9fT0ymQwWFhawefNmvPLKK0gmk9w5O3fuHDZs2ICenh585jOfYXgG4dTJ3Xx4eJgLDyJmLS8v48EHH7zm2oRCIeRyOV+G1Mmlrn5NTc3HVIb++q//Gvl8HuvXr0dbWxvGx8cxNDSEzZs3Qy6Xw+fzYWRkBNXV1cz3iEQiUKlU2LJlC497s9ksK2WsFDQWl0gksNlsbJhEBldqtZp9BNRqNf7rv/4Lu3fvxttvv4177rkHhUIBPp+PO0iPPPII5ubmmIOi0WiQSqVQVlaGjRs3YnBwEF6vl4v7jo6OFdc2Pz/PJL1AIMAGWA0NDQzn8Pl8+Oijj/D444/jwIEDOHToEN544w1WESHzwx/96EcoFAo4evQom8ydOHEC7e3teP755xl66Ha7kUwm/yKJHvg/mFIyoCNDwPLycqxevRqtra04efIkjh49yt2WdevW4d///d95pDs8PIxYLMYJwr//+7+jpaWFuS0jIyPo6OjAvn37WO3MaDTydGSlePfdd9HV1YXp6WlYLBZ0dXXh5MmTDMfbu3cvRkZGkEqlODHZt28fZmZmoNPpYLFYYLFYsHPnToTDYXz00UeYnJzkjin59pBxFHEmCOpSij/y4Ycfcid1dHQU/f39+PDDD1mjf9WqVQxncblceOmll/Dhhx8ygfyVV16Bw+HAli1b8MMf/hBf+9rX8Itf/IK9JXp6eiAUCmG327F9+3YcPXoUMpnsY2aqK8XAwAAsFgurQ5WVleHMmTM83br11ltx8eJFDA0Noby8HLt27UJnZyfm5+fx4osvMtGSGk4El6irq4NMJsOrr76KpaUlDA8PM2mTEubf/e53ALCixK1QKMTw8DCrxJH86dVQBaVSyVOhUCiEHTt2oLu7GxaLBdFoFKlUitXHtm3bhkuXLgEAY+Pvuece9Pf3w+FwMA8gl8shFovxVPdaEY1G+Q4BwORUalKsWrWKFWmWl5fx9ttv4+DBgzh+/Dg2bNjAUx2/348TJ05AIBDg9OnTyOfzrHoDAC+//DKr7RCEIhaLlTSvpCLPYDBgbGwMZ8+ehc1mw549eyAUCtHS0oK+vj4cPXqUyfl0/wJXivShoSGIRCL85je/wU033YTJyUm4XC6k02mcPXuW4dW5XA5dXV0oFAqYn59nv5+Vore3F11dXWw4azKZMD09jc985jMoFAqoqanB5cuX+e+pyIrFYrj99tuRzWYxODiI733vezh//jybZRJE5OjRo5yjLCwswGKxwGq1skHbjh07Vlxbf38/KyUS/OQ3v/kNdDodnn/+edx///0oFAoYHBzke7K6uhovvvgidu3ahW3btqGtrQ3Dw8OYmJjAq6++ypMugiMtLS1BIBCgra0NFy9eRCqVglQqZd7MSrG4uAiVSsWQ90QigWPHjnFjhYx+o9EoxsfH4XK5sHbtWng8Hjz55JOoq6tDZ2cnFhYWWKRjcnISCoWCJ+JGoxHnzp1jFAsJ/ZBIxEpByJGJiQmIxWJ28O7q6oJOp2M+mUKhYPL31SpL5Hbv9/vxxBNPwOl0Mox3ZGQER44cgUwmw9atWz8mqkEIlbGxsRXXNj8/z2INpFbq8/nQ3t7OPNqRkRHE43E0NTUxAb23t5dh+yTocPr0aRbzqKysZB5GPB7HhQsXeMoNXLkrybF+pfB4PHC5XAwZDgaDeOaZZ6BSqXD69GlG7igUCtx6661QKpWc/7a3twMAn+8ulwtnzpyB9s82CeTf5nK58Nprr6GiooIFGMiAspSgz9XxiQsNuVyO73//+6isrGSdYOqcyOVyHDhwgL0ztFotOjs78atf/YqJmyQFShjStWvXIpfL8QWVyWQwNDSEVatWcTeDujJkRrNSUOJGDpfxeBxnzpzBCy+8gIceegjr169HNpvFyMgIfD4fDh06hEQigenpabzwwguoqKjA/v37UVtbi127duHJJ59EU1MTTp06hbm5OYjFYuzbtw8CgQBdXV2YmZlhp0uC9awUFosFf/rTn3gMls/nsWrVKvT09OD8+fOs/uLz+XD27FmsXr0a+XweOp2O3dVJKeStt97C5OQkDAYDbrvtNtTU1GB8fJz9CGpra3H48GFWKjCbzSWnBiaTCbOzs4zNy+VyuHDhAvx+P7RaLfbu3Quj0QiFQsEulocOHUIkEoHFYoHdbmcpYKFQiO9+97sQCAT41re+hRtuuAHBYJA7JCaT6WOkRrvdvqLCDgAmzDscDpSXl7PXyo4dOzA1NYVPf/rTGB4extmzZ5FKpZhvQNhHrVaLNWvWQK/XQyaT4fXXX0dTUxNqa2sZa9jb24uXXnqJvw/CMctkspLqHaRORoZrZC7k9XoZBjAxMYGLFy+yhDIpmhw7doy12vP5PFQqFX73u9/hxhtvhMViQSwWQyAQQDabxa5du3DhwgVOkpRKJUOrVgqFQoHe3l7mEkSjUbzzzjtIJpOQSqWoqanhg5mkhV988UU899xzEAgEuPnmm9HW1gaRSITJyUls3LgRbrebHc9tNhvzkr74xS/i/fffx6lTp5BIJHjyVSo6Ojpw5MgR/qbS6TTUajV++tOf4tFHH2USOhFPqTvkdrvh9/vR39+PRCKBpqYm1NXV4d1330VzczNUKhV3x9944w189NFH3H3r6elhidNSCYzFYsHrr7/OPiUzMzNMaiZYYDabxeXLl9kN/LnnnsO9997Lfirt7e1oa2tjUQDyR6DJ7eOPP47Pfe5zaGhoQCQSQW9vL+bm5qBSqUpOW+i5UkdUJBLB4/Hg3LlzWL16NR555BHMzs7i6NGjeP/991mrv7+/H6+88gq2b9+OXbt24dKlS3C73azW9Kc//Qlzc3OIRqNobm6GwWBgQnE6ncaFCxewuLjIuuzXCq1Wy6okJIxAcquknEb4dIFAAK1WiwcffBD33HMPXnrpJUilUoZPESSTODwNDQ343Oc+h//4j/+AUqlEQ0MD3nvvPUxOTkKpVKK5uZkFLa4VRqMRIyMj7L1Abu+HDx9mT4/BwUF2kSYok1qtxsWLF1FRUcFwTbvdzg2YVatWIRqNoru7G263GwKBgHHZdKYvLS3h8uXLK65NrVazWgz9nImJCbzyyitoaWlhV2FyP77lllswMDCAd955B2vXrmWBg6qqKv4dSZ1raWmJBSqsVitzGi5fvvwxb5WVolgswmw2s/xmZWUlVCoV3n//fbS0tPAUks66TZs2wefzYW5uDoFAgJXj3G43uyQT+ZuKN5FIBLlcji9/+cs4ffo0BgcHkclk2HNlpaioqOBCgsjTMpkMP/vZz/DQQw9xkUUkdXKAf+GFF3DmzBls2LAB27ZtY2l6q9XKvBdSqItGo7jpppswOzuLgYEB5n1QUr9S1NXVQSKRfMzQldSdamtrcerUKea5HDt2DM899xz++Mc/4mc/+xlP+6VSKUM2FxYW+DuMRCIMOT58+DCqqqr4GQsEAjZYLbW2/v5+pNNpuFwu9kegBgvxDGdmZtg8eOfOnTh8+DBqa2tRW1uLYrGIYDCIpqYmVsh0OBxMriZhha6uLpw/f5677QBKTkWVSuXHlJOWlpYglUrx0UcfAQDsdjsrdyWTSdx1113cRIpGoywtrlQqEY/H8ac//QkdHR246aabIJfLMTQ0hOHhYcTjcVauGxsb4zy2FFF9YWGBzyXKg0QiEX70ox/hlltuYVnkqakpvmO3bNmCS5cuoVAowGw2c978xhtvsF1CV1cXn7Hl5eVsPNnb2wuPx4Pl5WWWt10pmpqauDFGsMGRkRH4/X7OR8jFnfLyf/iHf0BfXx/6+vq4MZtOp/H6669jeXkZ4+PjqKmpQV1dHWKxGLq7u2EymdDU1IRCoYBoNMqc27+ESqH4xNApr9eLHTt2wGg0Mt5samoK+/btY2M56jiS6sCuXbuYSEOJ5T333IPDhw9jaWmJFRmEQiGWlpZw8803cxfI6/XCZrNh9erVyOVy+PDDD1f+Jf5MQCSs7YYNG5BOp/G1r30NIpGI3TidTie2bt2K48ePw2w249ixY7j77ruxc+dOJBIJZLNZ1sfX/tnhmTptZrMZXq8XAwMDSCaTqKioQEtLC6xWa0n314WFBezYsQMikQiLi4soFArMFxkYGGBc+qpVq3D33XdzR9xms7FZytjYGJO7b731VgSDQfT19WF8fBzJZBI33HAD8wu2bduGHTt2oKamBrOzsyXxy+S3kEqlMD09jUQiAYPBgF27djFhLxqN4vLlyzh79izOnz+Prq4uGAwG3HrrrVwo3HTTTXA6nTh48CAn8YuLizhy5Ajkcjm7iXu9XsbBEvlspYhEIli3bh20Wi1X2IVCgcna0WgUQ0NDSCaTsFqteOutt1BXV4cdO3ZAo9GwG7tKpcK7776LvXv3MoGL5DTr6urQ3d3N5Esi09G0ZqUgUvX09DQWFhY4SdywYQOqq6v5sm9qasK6detgt9uRSqX4PVPntK2tDTKZDI899hgXjJlMBhqNBnfffTcGBgZQXV2NmZkZiEQi1NfXw2AwlDQ6JOEEwmzSpaZWqxGLxVBbWwun08kkNBo1P/roo/jmN7/Jh8nmzZvh8/nQ3NyMTCYDuVwOvV7P7s0LCwsIh8MYHR1FfX09Qw5LESUBMBSLJGRVKhV6enpQX1+P4eFhbNy4EXa7HS0tLfirv/or+P1+3HXXXYhEIvjUpz6F6upq9Pb2oq+vj71VyONlbm6OJ4U1NTV8OTudTp7WlCogY7EYurq62KuDFH527979MdL5pk2bcOjQIfj9fnz729/G+fPnUV1dzbKBarUag4ODuOeee1BbW8vJ2NDQEL785S/jv//7vxEOh9kjZffu3Vi9enVJxRhSmqMiQywWw+Fw4Otf/zrcbje7DldVVWHNmjU4ceIEm8bdddddrCjjcDig1Wpht9shkUjQ1dWFXbt2scKcx+NhGCqJMZAh60pBsI1CoQCdTsc+MS0tLdDpdKzPbzQaceDAAZw8eRLf//73IZfL8d3vfpd5QPX19ZidncWOHTvg9XpZtpO+gcuXL8Pv90MqlaK1tZU7bKXMBBcWFqDX65lvlk6nEQqFcPvttyMQCMBgMKCurg4333wz7rvvPpYJJUMuavw4nU5MTU3B6XSivb0djY2NzAcsKytj8ZGRkRHuhheLRWzdunXFtRGEhwjnpECzdu1aSCQShi6ZzWbU1tbyXnM6ndixYwccDgfEYjE2bdqEUCiEjRs38lqJC3DvvfcimUxienqa/YM6Ojogl8tLmpKS6g2dg263G4ODg7DZbPB4PCgWi+xcfu+992JsbAxCoZBVE/ft28dqgST3PTIyws7aMpkMBw4cQDAYxNTUFPx+P5xOJ583s7OzK65tfn4eVqsVMpmMmzDj4+O49957MTU1BbPZDLPZDJPJBJfLhUAgAJlMBpvNhocffhjNzc3I5XLYsmULysrK8K1vfQsnTpzA4OAgc0b37NmD9957DyaTCR6PB0ajkRN1j8dTcm3UGKNnODAwgA0bNsBqtbJRZXl5ObuXR6NRtLe3Y+3atZidnUVPTw/sdjuGhobQ1tbGUqcWiwUOhwP19fUYGxtDc3MzC3LEYjE+v1aK/v5+NDQ0IJ1OY3JyEn6/H7lcDp/97GfR3t6Ouro61NTUYNOmTbjzzjsxPT0NiUQCgUCAbdu2oaqqimFL4XAYa9asQSAQ4CKeXK8HBgaQSqUQCAR4vcFgEDfddNOKayO5YxLIUCgUKBQK2LZtG2QyGWpra2EymWCz2dDW1oahoSFUVVXBaDRi3759WLVqFQvZLC4u4rbbbsP8/DwGBwdx+fJlTE1NYdOmTZiensbS0hIuXrwIpVKJtra2j4mfXCuIe+z1ejE8PIzZ2VkIhUL8zd/8DUMEGxsb4XK5YLfbMTc3h3g8zrwjUjKjBgRx0Lq7u1ml0+124+zZs9wwIL4E7cWVIhQKccO5t7cXiUQCCoUCW7duRXV1NXbv3o0dO3Zgw4YNWLVqFUZGRlhl02QyYWxsDG+//TbL9nZ0dCAajcLv9+PChQs4duwYXC4XxsfHEQqFMDAwwMiN2dnZkrnI1fGJJxpisRjT09NMMKEki0zn/H4/8vk8bDYbHA4Huru70dzczFhwrVYLlUqFsbEx2Gw2KJVKZLNZJqKQ7GV5eTmTiKg7TfjPFX+JP3eWw+EwCoUCysrK2FmbVCdUKhVUKhWTJiORCOrq6tjIJBqN4tKlSzweI4lLhUIBkUiE4eFhhk0pFApm3avV6pLjIzJPoiQhm80iFovB4XBAo9HwB1leXs6wB7K0X1hYgFwuh06nY1gKEbBJnYg8QKqrq3HkyBE+3EnLvhSJvry8nCFdAFhNTKVS4cCBA2w8VV5ejsHBQcbkb9iwAYODg6yQ8cEHH6CmpoYJbydOnEA+n+ep0datW9mPIxAIIJlMMnSt1PdGzwy4cinTaJ4UKJLJJKsotLW1IR6Pw+FwsIETSeRS0abX63nNNG4kki69a3K4JgWiawURyeg7kMvlbBhH3X+xWMwGjTTdqaysxOLiIvL5PKRSKRMzhUIhVq9ezZMKjUaD7u5uFjcgKBN5pJTiKxEhkMbGZAI2ODgIjUbDngFGoxFWqxVVVVX8HZCqU3l5OZOJpVIpon92HaaE8eTJk6xAJxAI2L/GZrOV5CsBYBUwUuOi57i8vIzOzk4AV3hNsViM8aOkOtXX18fnzdLSEnvmbNq0CZOTk8hmswwJIEEHwtLS5VVqfYSZNxqNKCsrQzqdRlVVFeLxOMP+BAIBLBYLtFothoeHGW9N0ymj0YhMJsMXus1mw9TUFHK5HOrr61FVVcUwU7vdDqFQiFwuB71eX3JqSzLKJGtNPjY0ZaIzgxSvFAoFZmZmsHHjRk6ECbK4a9cu+Hw+1NXVYX5+npOjUCjEqnxkTLW0tAS/34/BwUEcOHDgmmujZ0XkXxKuyGaz3OElMispQJFoQTqdRl1dHaRSKSYnJ2G32xGPxzE1NQW3243y8nKMjIzA5XLBZrPhzJkzLMEMXJnwlHqnS0tLnCwtLCwgmUwyFEMul3PBS/AYkUiEiYkJuFwuFv+QSCRYXFzkxFypVPL9J5PJ0N3dDaFQiPHxcdhsto8l6KXOESJJq1QqTk5pQk7EY5KjLRaLyGQyGB8f56KWSOkkravT6dh8jCauZFRGJo/0V0IWrBRisZj5bmVlZZBKpXA4HJidnWUDPFKvofPM6/VyokUFWV9fH+rq6lBdXY3Z2Vkm4FMnlN6jyWRiZStybV4pSN2LFLlIOdBsNkMmkyESibAASzKZZDO/PXv2MMdJLpdjeHgY5eXlfJ7SMyfYK0HW6L0TJLuUQpFWq4VcLmevkPLyclb+Aq4khgqFgnOU++67D2NjY3A4HFwEk4IZSeG2trZCJpOhrKwMzc3NLKBBJoxCoZCNjUtxNACwmABN7pqbm5FIJFgQR6FQMBx4YWGBuSIqlQplZWXIZrM8FSDLAjKIo4lldXU1BgYGGAJMgiulut+k6ERQo3Q6jWQyCaPRyF4zpEwYDAZRXl7OjWCZTMakblJsJOEN8vcoLy9HIBBAZ2cn8vk8C68YDAZWdFspqHFKIiyk0Gm321l4hv7Z8PAwE64VCgUbsZaVlbGIkFAoZD4HqZqp1WqsXr0aIpGIpbYB/EXuHqFRqNFD0rOFQoEFfRQKBU+JCY5PhTqp0E1PT6Ozs5PNlEmSnYxey8rKMDQ0xHczcOWeHhoawsGDB0t+c8D/xUSDSCdCoZBJpktLS9xVJzZ+oVBAPB7HwsICUqkUSytSgk84vsnJSej1eiQSCYTDYYjFYkQiEeTzeYYdkM68WCwueQnTYVMoFDA3NwefzweTyYR4PM4XCCWnPp+PSWI0qiZVm6mpKQiFQk7+KREi8zmlUsnqSaQxLxQKS150ZWVlCIVCjKGjoqhQKDCmMJlMcgdAJBIhGAwimUxicHCQJWiDwSBkMhkCgQBaWlpgNBp5VE6J8ujoKPtKELa0FJEIuNLxI3ngdDqN5eVlzM3N8fMuKytjsyMa5ZNMIsmhXrhwAcCVS72mpgYjIyOMY45EIqy0RJA7GlOXSphJnpAUiQgX6PF4IBQKGUpFqibr1q1DMpnk5F2v17MMoEQiQTwe58uQJBXLysqYvEoHb6FQ4ItopaALmsbU8XgcYrEYXq+XZWILhQLS6TQWFhZQVlaGRCLBsCiS8jxy5AjKy8tx+fJlNh0koh51XKh4kcvlnPiUIq5R0keSidlslqFbDQ0N8Pl8XOAHg0HU1tby90hTHerAV1VVMbk9m80i+mdd+KGhIbhcLk5QCeuu1+tLdiIpSKWIZD0bGhpYwpgkfUOhEI4cOQK9Xs8QJipuTSYTY3UpSY9EIgyxpAnQ4uIiy2XSZVBKPYkcX0lOkmAUtL+vlgil8+3EiRNwOByYm5tjFbiRkRFoNBqMjo7ypJfUUXw+H/OLSOHq6neyUigUCv53CFpAZDyScCVfl1wuh5aWFgwNDaGmpoYlr/P5PGZnZ+FwOBAOh/m/DwaD/My1Wi27BwNgycZSXVy65MvLyyEQCBCPxxEKhdi3heSk5+bmcOHCBSiVSoyPj6OsrIyNGKlAIzI0FfwCgYB9X1wuF0t3x+Nx7j6XShIAsHoYebRIJBJcvHiRmznz8/Pc0SPfB6vVyp32bDbLAgr0fH0+H3MxLly4AIlEgrGxMXZ6J2f7Urh0AKzOSMZ4VDjROUBqTwRFuvpuTCaTyGazGBsb+5hK0ezsLPM+yBNEo9Gwp8/i4iL/daWgO5WKIUoCl5aW+E4nmJzH42HRgYqKCoRCIYZq0h6n90C6+/F4nIVRSLY4nU4jFosxpHKlKCsr4/1EpsHku6BSqdDX14fZ2VksLCyw7wIJWYRCIWQyGeRyORw/fhxisRinTp1iWF2hUEAsFsP4+Dg3oUg1KRKJcGNmpaCEnJo1wJUiirgKVNgvLCwgkUgw8ZfMhPP5PHK5HM6fPw+Hw4Hp6Wnm7JBnDjVAqDlE+95ut2NwcHDFtclkMi7yr25sEhw1Ho8zETydTsNisTD3k4jiyWQSly9fhtFohMfj4UZuIBBAOBxGf38/mpubMTY2BqVSiVAohNHRUZSVlZUsgugMo8YuGTGS4fHVClIzMzPI5/MspU+qXMSfIHKz0+nE8vIyP+vZ2VlWm6N3TSgG8pO6VlCDmJ4bcU/pu/b5fPz8JicnGT5IHj8kyz82Ngar1crwV5LuFggECAQC7MFEqAqCj5WCOJIiKfH9aE+TOhj5eABX9pzVakV/fz/nd3K5nD111Go1/H4/jEYjysvLWQKZxJEWFxe5GKem1186eyk+8UQjnU5j7dq1iEajWFpaglwu585kLBZjvfu5uTmMjo6yl4JSqeTqlKqs6elpnDt3Dn/1V3+FaDTK2sE6nY7xanq9niUTL126VFK9g6Ah9GKJQEMJfEVFBYaGhtgIxWQy8QVG8rtWq5XlA8ntl154MpmEwWBAT08Pd6yJa6BWq0ualhAfgRJ2mp5Q8UHSfDTOTCQSkMlk8Hg8jP13u91wOp3Q6XSYmJjA2rVrUSgUuLC6dOkS1Go11q9fz5uK5ENLEUxTqRTa29sZmkXOzoFAAKlUCm1tbejt7cX09DSy2SwGBgZQWVmJY8eOYevWrSgrK8P8/DzWrFmDo0ePMune6XRyV81ut+O1115jIyt6J6SetVLE43FOPkgaj6A2JpOJ/R7oMqOR4RtvvIFkMonq6mrIZDIMDQ1hamqKSaY0RYrFYtixYwdPJRYWFjh5oSRspaBCgTDpgUAARqORC0+6GORyOaqqqtDT04Pl5WV0d3dj+/btkEgkmJqa4nF/LpdDf38/TCYTisUipqenYTQa0d3dDQDMNSH/gFJFEE0K0uk08vk80uk0uru7+dujA3piYgKpVAo33XQTYrEY9u/fj9deew0ejwexWAyjo6OoqanBa6+9hh07drB6yODgIKqrq3kis7i4iIWFBeZtlCqCgCtdwYqKCvh8Pi7a3W43O7sTPjr6Z4MxKpgDgQDWr18PoVCI+fl5VFdXY2JigtWSfD4f87my2Sz6+/vZ7IpgkVcn0NeK5eVlXhtwpTs5MzODeDzO0oskEUvFKSXQtM5CoYC33nqLpwoAUFVVhYGBAbz99tuIx+PYvXs3u8STe3I4HGbN/WuFVqtlyEU6nebE2Gg0cqMmm80iEAhwkUv7jGSEyXjvyJEj7FprNBpZLloul3NhPjY2xmdRXV1dyWYK6cPThZjJZLggpN+PuHpnz55lc9e5uTksLy9zE2V5eZmVAbdu3cqSkgC4wOro6MDExASmpqbY0bmUFKpQKITBYODOdiqVwuzsLOOT5XI5yxFPTExwgQ6AMet0zhw9ehT79+9HLBaDWCzm3yn6Z1+UWCzGstjEESjV/abiUCwWc7eYxDMIqiiXy/ln0JT9zJkzH5voaTQaLCwssAIiTbmFQiE6Ojq4KUN3NhV+pYpuSv7JsymTyWB6ehr19fVIpVLcxFhaWuKiiO56mroScfTy5csYHBz8mC8L8fdqa2sxPT0NgUDAiaPdbofX6y35TolPQU0devYNDQ0MCaIETyqVQqFQYHh4GB6Ph7vjJLhBSTV1nqkZFg6HMTQ0xAR9lUpVMiEFrnjwUPOLeFmHDx+G0+nk5HRycpIL+LfffhtCoRAfffQRK+2VlZVxo5HEX6gJlkqlmBhttVq5kKV3Wwpdkc/nWZVveXmZE1zK25aXlxEKhdj4jryrjhw5wl4VtDaBQID+/n5s374d1dXVCAaDmJycZLhULpdjWwESPSjVSAHA5z1Nf0wmE0KhEPNoEokET/dJiCGRSECn08Hr9bJPi9frZY6PwWBALBbDzMwM3G43ZmdnEQ6HGV0xNTWFYDBY8rnlcjnodDo+3yQSCWKxGPL5PIaGhgCA34tIJEIkEkEoFGIJe7VazU0E8tM6c+YMe7XF43GcOHECLpeLec4ikYgROKU8jGgKRcakNHUvLy/HzMwMqqqquFFAZ83y8jKf/cQ3EQgEePfddyGVSpl3EwqFMDY2Bo/HwxBwMt8mCepSUs9XxycuNKxWK86cOcMusITdPXz4MFdm1O3JZDI4ePAgTp06hQ8//BBr165lEyfqcmm1WnR3d8NsNrNb7DvvvIOqqio0NjYytIp4H6UcVoErbsgkyUcPQSqVYtWqVXj99deh1Wo5YUkmk7DZbEweJ1WHixcvMr6flGIIMnF1EqpUKiEQCFif+C+ZCZInBHFRqNNCmP1EIoGJiQkuPoRCIZOtc7kcRkZGGNqwefNmVsGi9fX19UEul/PlSw6eJDu4UlgsFpw/f547dfPz8wz1IHIYmeZQV49Gpm+//TaTpunA1uv1mJ+fR2NjI/x+P86ePYvBwUHcf//9rPpAxjVqtZqhMtcKgms5HI6PfVtWq5XHmASBO3r0KKqrq2GxWLC8vMwkLJvNhh07duDll1/G3NwcRkZGWOd7cnISwWAQu3btQl9fH5ulkX9LKfM0iUTCZl80sZqbm0MwGMS9997L6xofH4dIJMKBAwfYfAkAwy4qKirw2GOPIZfL4Qc/+AEKhQIuX76MXC6H1tZWJiC+9tprsNlsDN0rRWhWKpXsbUHdtVgsxk7pV3c+yOTRaDTi/PnzeP/995lbQhAX4iaQl8bCwgIymQzefPNNPPTQQzh58iT6+vrY7LFUsgyAVWxoWkW8p7q6OlZgI2PHjo4ODA0NYdu2bejs7EQkEoFOp2NMvsfjwSuvvAKJRAKLxYKysjIsLCyw2RkdkoSpJV+TlYI6OtQ1p4SAfCQsFgvEYjErx5EnyRtvvIHNmzezuZZUKkVvby/WrVuHDz/8EOXl5dBqtVi3bh0uXLiAEydOMK6cTDcDgUBJfHUymeSRt9lshlqtxuLiInM3WltbMTMzw+7hFouFu95ESk+lUtyIoW61VqtFKpXijq9Op8PIyAgymQw7qns8npIysiQqQdMoOuPKysrg8Xhw4403cuOJJD/r6upw9uxZyGQyGI1GvgCVSiX6+vqgVCrhcDi4I/7iiy/iww8/xPe//30mnMpkMpjN5pI4YZq6U3GeyWQQjUYZzhmJRLhoIENWmUyGY8eOMc4/l8tBIpFgbm4OP/nJT9ibhhKbPXv2sFEsdbsFAgF3aVcKUqiixp1cLofb7cbo6CgmJiawd+9eDA8Po7e3F4uLi6irq8OFCxdgNptRV1eHiYkJzM7OwmazYXBwEIFAgO+UfD6P0dFRbsJRcUqJMn0TK0U6ncb8/DwTfcmHRqFQYHJyEhUVFfwNmc1mTiL9fj+mpqZgMplgNpuxvLyM22+/HU899RTcbjekUik7rsdiMQwMDHCzi+AaBN9ZKchskaSJ6RyhZsCBAwfg9XqZ3yORSFBfX49cLoeLFy9CKpXCZrMxt4gK7ba2NgiFQhw+fBgOhwNDQ0N8V5BK3l+6G0giV6fTMfxobGyMCzJqdBDUtKysjDl1BJtJJpPQ6/X405/+BJFIhB07dvDzoMl4PB7H5s2bce7cOczOznLHvRSXKhaL4dSpU9xopWleZWUlLl26xPLw1CGnyVJjYyMaGxsRCoX4bNDpdKzMSVBSo9GIUCiE4eFh7Ny5k5sI5I/zl5p3Xq8XKpWKm0LEA1CpVDzZJvijSqVCTU0Njh07hrm5Od57RJ5fvXo1y9lXV1djZGQE4+PjWFpa4uaK1+tlFEmp5h0VL0QEJxXOkydPwul0smodNdutVisA4PDhwygrK2N+KfmR0Tms1+sZaQNcUe+jJkahUGAoJXE1rhVisZil/61WKwKBACuPkUE2IYwImVNfX8/3OK2JvoH9+/dzDpLL5Tif9vv9sNvt0Gg0SCaTnJf/pcYihaD4l8rM63E9rsf1uB7X43pcj+txPa7H9fi/jE/M0bge1+N6XI/rcT2ux/W4HtfjelyPTxrXC43rcT2ux/W4HtfjelyP63E9rsf/87heaFyP63E9rsf1uB7X43pcj+txPf6fx/VC43pcj+txPa7H9bge1+N6XI/r8f88rhca1+N6XI/rcT2ux/W4HtfjelyP/+dxvdC4HtfjelyP63E9rsf1uB7X43r8P49P7KPxi1/8AkqlEsvLyzAYDNBqtfD7/RgdHcXq1atZV7esrIy15xOJBAwGA1577TXo9XqsWbOGNYLdbjfC4TAbN5EDa0VFBex2O1udRyIRNt/7+7//+2uu7cknn0Q0GoVEIoHb7WZH7FAoxOYkKpUKNpsNOp0OwWAQU1NTMBgM8Pl8rKtNxio9PT1obW1FPp9nR92GhgY2nzKZTAgEAlhcXGQt5AceeOCaa/vP//xP6HQ6JJNJKJVK1oZeXFyEUqlEQ0MDFhcXsbS0BI1Gg1QqxWZbw8PDsFqtqKioQDAYhEajQXd3N9asWcMO48lkElNTU6ipqYFMJsP8/Dyy2SzEYjGb1Xzuc5+75tpefPFFNnVTKBTQ6/Xs7h4MBlFVVcU671qtlh22AbCXR1VVFfR6PRYWFlBeXg6v18tGX2QyRjrS5Gkgk8mwatUqxGIx3Hzzzddc2xNPPMHux4VCAdlslvXzybiKfATEYjEsFgtyuRybChaLRZSVlWHVqlW4ePEigsEg0uk0dDodrFYrFAoF+xLIZDL+VkQiEYrFIiQSCf7u7/7ummt7/fXXAVwxy9HpdOxp4Pf7YTAYkMvlUCwWWb+6qakJ0WiU/QZyuRzEYjEbUw4NDcHpdLKxDpnrTU9Po6KiAuXl5bwHyOXznnvuuebann/+eQSDQTidTojFYnZ6jcfjqKurY9+PeDwOoVAInU6HVatWYWxsDFNTU+z5QMZMqVQKer0earUayWQSi4uLqK2tRTQahd1uRyKRgNfrZS3w5eVlfOUrX7nm2gDg2WefhU6nQzabhUajgVKpZMdol8vFGu/FYhHl5eXsAWC1WnHu3DkIBAJIpVJMTU1h9erVqK6uZhfg2dlZzM3NQaFQoLKyks8jMnSk7/K+++675toef/xx9jQgAznyDyANdaVSyT4RtbW1cDgc6Ovrg1qthlgsZtduuVwOhUKB2dlZ5PN5CAQClJWV8Xe3b98+eL1ejIyMIBaLsfvtP/3TP11zbd///vdhs9nYjKlYLGJmZoYNzsxmMxKJBPsQBINBFItFWCwWNt+j700ikWB0dBQul4vdsgHA5XKxe3IymWRTOLVajVQqhS984QvXXNsf//hHhEIh2Gw2Nk8TCATw+/1wu90wGAyIRqPsyk3eAQMDA9BqtfwdyOVynD9/HsFgEI2NjZBKpeylpNfr+Ryi35P07NVqNe6+++5rru1Xv/oVtFotG0AqFAq+nxwOB5vCkn9ORUUFcrkcAoEAmw5WVFRAIpGgp6eHfYVcLhcWFxcxMjLC5nzkMF5bWwuLxYKpqSlks1l84xvfWPGdKhQKGI1G/q7Gx8exsLCA5uZmLC0tQalUQqfToby8HMPDwygrK2NTP61WC5VKhfn5eTQ0NGB+fh51dXXo6+tDKBRCeXk5FhcXYTAYUFlZCYVCwZ4pyWQSfr8fjz322DXX9sMf/hCJRAK1tbVQq9WIRqOYmZlhMzSdTsd/XjgchkKhwPj4OBwOB3vvhMNh9PX1obKyEmKx+GP3G3DF7V6pVAK44i1EZ7lCoYBAIMDXvva1a67tl7/8JQCwWSGZAM7Pz6OiooKN4srLy+F2u6FUKnH27FmsX78eHo8HMpkMYrEYk5OTsNvt6Ovrw6pVq9hjhowA29ra2HtEKpVCpVJx/nLvvfdec23//u//zsaEKpUKy8vLGB0dhUKhgMvlglwux/j4ONLpNKqrqyEUCjExMcHnITmx0xm4sLAAnU7HJpJisRhmsxknT55EV1cXpqenIZPJIBQKMT09DZPJhE996lPXXNvzzz8PuVzO5zpwxZPkwoUL6OrqgtPpxPT0NKLRKMxmM3tyAUB/fz/y+TwbFJNXCPl2kEeGz+eDRqNBNpvFwsIChEIhO2o3NTVh9erV11zbj3/8YwgEAjY6zWaz8Hg8EIlEKCsrg0wmY4+aQqEA4IrXlk6ng9/vZ18zcj4nc975+Xn4/X5Eo1FYLBbodDoolUqYTCZ4vV7MzMwgl8vxfrxWPP3001heXmaXcrVaDa/Xi3w+j3w+j/r6eiQSCYRCIb6rzGYzCoUC3zvknaFQKFBVVYXh4WHkcjn2xBIKhbBarcjn82wITGazVqt1xfPtt7/9LWZnZ1FZWYmKigqUlZVhaGgIQqEQEomEzZaj0SgWFhawefNmKJVK/h1SqRR0Oh1qa2tRKBRw6dIlyOVy9hvKZrOw2WxIpVJs/Eqmn3K5HD6fD1/96levubar4xMXGvl8no3K6PBKp9Po6OjAwsICJ5vxeJxdK2dnZzkJzmQyuHDhAtRqNcxmM8xmM0ZHR2E0Gtn5mhwVBwcHeWPncjkEg0E+kK4VqVQKRqMRCwsL7ApKhkThcBhSqRThcBjRaBRarZY/mqvdTylRTqVS2LNnD4LBIHw+H3K5HGQyGc6cOYPVq1dDLpfzBigvL8fQ0BBqampWXBsZrAmFQv7gyKyMLjhy0Uwmk8hkMnxwV1dXo6ysDPF4HDqdDqFQCDqdDrOzs7BYLOwW2traisHBQb7oyWBvYGCgpKtkOBxmZ1QqFMl4LZvNYmZmBsViEVKpFAKBADMzM8hms5DJZLyxyWl6enoa69evx+zsLDQaDTt2m81m+P1+ZDIZqFQqTsCHh4fZOflaEQqF+OcsLy+zgQxtatqEYrEYWq0W2WwWLpcLAwMD/M+KxSLsdjuGh4fhcrkgk8nYedTlcqGiogKBQABWqxXLy8ucIGk0Gvj9/hXXFovF+LKiQnZqagoikQhTU1Oorq7G8vIypFIpDAYDIpEINBoN8vk8zGYzotEoYrEYP2eFQsFuveQ2XigUIJFIkMvl2BirrKwMAoEAIpFoxbV5PB5UVVUBAKLRKCe95ByqVquRzWYRj8eRy+WgUqkQDocxPDzM75/MgiQSCRQKBRYWFpDP5yGVSqHVajE7OwuPxwOr1YpCoQCxWMyuy6VcmoErJkLLy8uQyWTI5XKYm5tDJpPhNVNxRM6+ly9fhkajYQNGunyp6IpEIggEAigvL4fFYuFkJhgM8gWnUqnYwJKS6msFOYAvLy/zN2uz2SAUChEOh1FTU8OXntlsxsTEBCfRDocDqVQKXq8XmUwGGo0GKpUKi4uLsNlsXLQolUokk0l4PB4sLCxApVJBJpNxUrlSFItFdokm66PFxUVUVVVxIu3xePibJ+O+dDqNpqYmTExMYGJigpPY9vZ2+Hw+zM7OQiQSwWKxIBaLYW5ujptAxWIRxWIRU1NTyOfzK66NGi6pVIpNoIrFInQ6HZaWluBwOACATdYA8KVLzYnl5WW0tLRw8kkmp/TnyeVyNkkkI9Hl5WVutqwU6XSaC2JqJIjFYnR3d/O3TuaUtK/Ly8vZ7TuVSmFycpLP+3g8jlgsxkasy8vLMJlMmJmZYTdoMmpLJpPscnytEAgE0Gq1AIBIJMLfvUQiQSAQgFarRTweRzqdhkgkYsfgZDIJjUYDAJidncXg4CAqKythsVgwMjLCjvUjIyNobm7GzMwMlEolhEIhNBoNG1tSEnmtoPMzGo1yoyKZTKKpqQkA+FtZXl7mc6pQKLDDfDweRyAQYOdqq9XKZwydc5QMBYNBJJNJvmcCgUDJu4GcyYvFIr9XSj7J4I3++eLiInK5HJaWljA5OYl0Oo1oNIpCoYBCocDmr/Pz8xCLxXzeOp1ODA8PA7hi0ErPfW5ujpP+awU1RyixE4vFfFeMjIxwQ0+pVGJsbAzAlSRzamqKi0EqNMiAj4whI5EIpqenoVQq+d+Zm5vjd0p7dqWgb5q+vaWlJQQCAUilUqTTaWSzWc6T6FwaGBhAVVUV3G43u3I3NTUhGAxCq9ViaGiI9wA1UcViMSKRCIRCITcIRkdHodfrV1xbLpdj0ztyuKbneXW+RAWZQCCAXC6H3+/nc5HW197ejnQ6zcW4xWLhphoZ1i0tLSGXy/3/Fb/Xinw+D51Oh/n5eeRyOW5+kBluMplEKBSCQqFAZ2cn3nzzTYhEIqjVauRyOYTDYSSTSZjNZuRyOcjlciwtLWFpaYnfl0KhQCgU4vORmhcej6ekmeDMzAx0Oh3vU7FYDAB8BpWVlUEoFMJoNEKhUGBmZgZisRgulwsWiwXRaBTRaBT9/f3IZDIwGAzc/BcIBLDb7VAqlWxaS+9JJpMhnU6zAehfik8MnaJNSxuAEsC+vj6eTIhEIiwtLcHn88Hv96OhoQGJRIJfqkwmQ3V1NQqFAnp6ej6WzFCHOp/P82VDSRdtqpWCLhCJRMIXBV36lIyLxWJks1mMj49jbm4OarWaE7BQKIRQKMROqMlkEmNjY1x8UNdobGyMuyX0gK1WK3cHrhXl5eUIBoP8O9CFTV1v2lD5fB6BQADRaBRKpZIvIJFIBJFIhMXFRchkMk6w6PctKyvjS5cc1/P5POLxOBdTK4VIJPpYIQRcSfR8Ph+AK6669J7D4TDC4TDi8ThfJIlEAoVCgTsCFy9e5A1Ihd7o6CjOnz/P3wEVefPz8yUP7Hg8jlQqhWw2y0VGoVBAIBDA0tISfzdCoZA7NMlkkhP7aDTKUx2j0Yh8Ps/PELjSQVtcXEQ8HodWq4XVauUuPjlirxR0GObzeb6wpFIpYrEYlpeX+RmUl5djeXmZDzGa9lGyQ10LmppR91ImkyGRSECpVEIqlXLCs7i4iEwmU/ISBq64SCcSCU6qxWIxlpaWMDMzw0myVquFTCZj52PqdM3PzyMajbJLcyKRQDAYRCgU4kNmcHAQ+Xweg4ODiEQimJub40uVXFH/0rOj50TdQ3JspfcTDocxNjbGk5fJyUlOsumgo2SfXHY1Gg2i0Sjm5uY4USP39kKhULJAA8Bd8uXlZSwvLwMApFIpRCLRxzr16XSaXd8pQQmHw5y0yOVyFAoFDA0NIZfL8becz+cxMjKCdDqNsrIy3qdUnNHvfq2ghgNdUHSeUkFKU6hUKoX5+XkUi0U0NjZifn4eo6OjPEG12+3cBVQqlbDZbDCbzRAKhRgdHeVvhyY6yWSSJ0MrRaFQ4GaAUCiEQqGAWCz+mCMtJQzUaZdIJFAqlXwWkMMs7ZFwOIzZ2VkUCgUYDAZMTU1xYkWTTa/XCwDc6VzpudEkhZyZzWYzZmdnEYvF2Jk9m81idnYW8XgckUgEbrcbZrMZcrkcy8vLiEajEAqF3ByiZ+RyuZDJZKDValFZWYnm5mZIpVLE43G+0FcKOruy2Sy/U51Ox27tIpEIAoGAi8hsNov29nb+digBMxgMSKVSOHz4ME9u9Xo9qqqqeNIoEAiwuLiIsbExjI+PY3l5GWq1uuRzKxaLiMVinGAIBAJYrVZ+b9lsFqlUCrOzs8hkMnwXejweLrjpLqNGCXBlTymVSp5wq9VqbhYRgqHUO6XJAxUFlCjGYjGEw2HI5XIYDAYUi0VMTEwgkUjwz6cCloqjfD7PTts6nQ4GgwGFQgH5fB6ZTAaVlZVwu93IZDLwer2IxWIl94LRaOT7J5VK8c+iM4VyEXLmXlxc5IkWcKUxmc1muaFBZ7FcLofL5UJ9fT0ymQxkMhkUCgXKysoQDAYRCAQgFotLJqU0iU2n0xAIBJw/0CTA6/XyJNbj8SAQCHBxOzs7y3uUHOynp6cxOzuLsrIySCQSJBIJnuw7HA5YrVYkEgn4/f6P5QnXCroL0+k0CoUChEIhhEIhfzs0aaepj1KphMvlgkgkYtdxjUYDt9vN07VkMsnPUSgUIhAIIJvNfswVPZVKcUNwpaDi9OqGBE0oqIGpUqm4GZtIJLgI8Xq98Pv9WFxcRFlZGUwmE0ZHR1FWVsbu60Kh8GNoCgB8LqjVas4RVwpq3iWTScRiMWg0GoyOjiKRSMBut6O+vp4bZmKxmJErdO5QTuv3+xltQ/cLIYCWlpYgEonYGfzqhtoniU9caFDiSIk3XSYXL15EKBSCWq2G3W7nLtrMzAxaWlr4ECwUCnA4HNiwYQOAK50HusAWFxcxOjqKYDCIbDaL5uZmHldT96rULyQQCBCPx1EoFKBSqRiGUCwWYTabIRKJoNVq+TBLJpNwOBzckSd4iVQqhVQqxcTEBCfbCoWCD66pqSl+4NlsFkKhELW1tZykXyvkcjkikQiP9BQKBScuNBUAwB8xALS2tsJgMCCTyfDaqQtM65TL5R/rAJSVlcFisXBHNpvNQqlUljwUKdGnxK2srIwvfnp2CoWCpyzUbaNOPhU9SqUSYrEYZ86cYXgYwX0mJycxPT0Nh8PBhxyNh3U63YprW15e5vVcnfwRHEEul3NSTkUGTXPod1IqlVCpVNDr9SgWiwiFQrBYLHA6nchkMpibm0MymeRvQ6VS8eXf2NhY8rnFYjFEo1F+JgqFAul0mruQVODl8/mPFYXxeByZTIanA4lEgg9LmUwGvV4Po9HI75e6fJlMhjvGpQo0GhdHIhHuNtJBRpMyGnkbDAYuZBwOB49/aUy6tLSEWCzGnZxcLoeFhQXujA0NDTG0iCaZpaZ7FPS+lpeXUV5eDqFQyJ318vJy7ozSJNNut2NpaYkhhcViEQ6Hg0f0QqEQarWaL8RisYhkMsndfoFAwAVrqa4aNUpoukBwPLr44vE4isUiUqkUxsfHoVQqufu3sLDAY+j6+npuKlCiSF3YUCgEkUiEiooKSKVSfq9/qZkiFou58KaLVafTIRwOw+v1wmAwMAyQLlNKSi5fvoxwOAyLxYI1a9ZAIpFgcHAQQqEQbW1tqK2t5WSaJo5arRYikQi5XA5SqbTkXi0vL2eolUQigU6n4y5pKpXifUKJKz2zQqHAhTclltS8oCRMIpHAYrHwz6Gi5urJQqn9QPAreobUxdfpdCgUCrBardDpdBCLxfz+5+fnYbfbYbFYIJfLuRFDjRKhUAiRSASDwQC3282FOn1vhUKBm0E0AbhWCIVCRCKRjz1js9nMhbFUKoVarebnu7y8jNbWVoa/AYBarcb69etRLBZx/Phxfm5arRZr165FKpWCWq2GyWTC4uIipqen4fP5kEgkoFKpSn5vNHElNAOdt1cXDMvLy9yEqK+vh0qlQiKRQDweh0AggNPp5KaZUqmEUqmEXq+HwWBAOBzG5OQk3G43F8rUSCi1T2kvZjIZbvZks1ne9zRVoMbR8vIyXC4XdDodysrKIJfLeQpATSCRSASj0Qiz2cxFslAoRENDA+cNBHEulZRKJBKGG9P0QiQSQSgUwmw2w2azMURWr9cjm81CpVKhtraWn4FMJoPNZkM2m4XP5+Mz32Qywel0crNXJpNx0Uf3MD3rawVN7+iupH9fpVLxd0/39/z8PDweD3+PU1NTPPEjyNrVcHWj0ch7Y35+nt87Fao06Vsp6Kyh4sBkMqG8vJwbxASfNBqN/JwtFgvfoUtLS7DZbNi2bRuEQiEymQyWlpb43E+lUgyTp8YbwfuLxSL/udeKTCbDBTDlegQxJzi+yWRCJBLB4cOHodfr4XK5kMvlGNZL94hYLOYpBTX8qCkJgKGddM8RamClUKlU/A1fPT2jhoxOp4PRaIRUKmX49po1a/gup4ap2+3G4uIiQwe1Wi1MJhNDQoPBIH/PdD4nEomSjZSr4xMXGiKRCHa7HZlMBjMzM/D7/ZBIJLj55psRiUTQ2dkJh8MBhULB3WFKBnfv3g2LxQKfz4d0Oo3p6Wls3boV6XQaExMT6O/vRyAQwPr16xGJRCAWizE+Pg6xWMzY9bq6uhXXRp2z6elpzMzMIBQKwefz4fbbb2dOCY25a2trGQc6Pj6Orq4uNDY2wmQyYceOHfjjH/+Ijo4O1NbWwu12QygUIhQKobm5GS6XC83NzQz3kslkOH36dMnudzabhdFo5AuWDlPiU1DVHwgE+OCQy+WYmZlBTU0NJ4M7duzA6OgompqaePNTB4emJtFoFPPz81CpVIz3K9W1isViUKlU3JlKpVLMuclkMqirq/sYXEOtVqO9vR39/f2oq6uDUqnE9PQ0CoUCent7UVNTA5PJxId6sVjEunXr0NnZifb2dhSLRZhMJtTU1HA3d6XI5/OoqKjgTjIlMlu2bGEYSjKZhF6vx6pVq5DP59HV1YX+/n5s3LgRVVVVmJycxLFjx/Diiy+ivb0der0eCoUCNTU12LZtG2pqahgCcP78eQBARUUFdzxWCo1GA5PJhPn5eYRCIcRiMVy8eBF6vZ67UMFgEABQWVkJoVCIyspKxpLShiZ+wf79+/nyoS6b0WhkPofX62UonVwuL9nhWF5e5kKbEiAavdOlNDk5ybC4M2fOIBAIwOPxcGfcbDbD6XTi8OHDcLvdEAgEDBvq7+/HmjVrMDg4CLPZDACoqalBc3MzJ3+lQqPRwGq1YmlpCfl8nr8/mUyGvr4+xnwXi0V+/zqdDhqNBmvWrIFMJkMgEGAs+tq1a3H+/Hn09vYinU5jzZo1uPPOO/kSHR4eRiQSQTwex/nz51FRUbHi2oh/RdwEGi0T76a6uhrAlST1lltuQSAQ4Kmk0+mETCbjzq7H48F9992HYrEIlUrF+OBNmzahoqKCceirV6/G5s2b+f2vFPRNUEJFBVQikcDLL7/M3CyhUMh7lvhIq1atgtls5n3e19eH1tZWnDt3juEtkUgEGzduhM/ng9vtRj6fh0ajYU5Jqb1KnCOCzgLAwsICNmzYgNraWp6MCQQCOBwOvPfee5DJZJiZmYHT6URrayt0Oh03Jei9WywWLlgOHjzIvLFMJoOamhqsX78eQqHwL8IeTCYTEokET5M//PBD3H333bBarZzg2O127Nq1C0tLSwiFQjh79iwWFxcxMTGBy5cvQywWIxQKoaqqiieVlOxWV1djYGAAAoEAx44dw9TUFHK5HHdySz03pVIJgUAAs9mM2tpaLC4uIhgM8t7VarVwOp2oqalh3l6hUGAO0vz8PAwGA5RKJQ4ePMh8nPn5eQwPD2Pfvn3I5XIwmUwIh8Nobm7Gtm3buNG3UsTjcej1ei6wRCIRampqoFKpMDIywgUpTSKJ39LR0YHOzk6oVCpkMhlYLBbMz8/DZDLB4/FwIzAWi6GpqQnDw8MIhUIIh8NobGzEli1bGBazUlBjZ3FxkWGdKpWKpz1er5cnmlu3boVMJsO2bdswMTEBnU6HVCqFmZkZNDY2wufzoaurC2q1GpOTk4hGo6ipqYHNZuN85fTp05DL5WhqakIymSxZoI2MjKCiooLPH4PBgHw+z5NE4jmqVCps3LgRoVAIgUAAarUaFosFbrcbjY2NaGlpwdDQEDcTBQIBPB4PXnvtNX7vVPjW19ejpaUFyWQS09PTK66NIE7U1CS+D/FRzWYzxsfHMTU1xfzMbdu2YWFhATU1NaiuroZarYZAIMA777yDPXv2YPXq1ZBIJIxEIc6i1+vF6OgoTCYT1q5dC7VajYmJiRXXJhKJGH5JTSu5XI6JiQn4/X7O76amphAOh+Hz+bC0tIRIJIJYLIbJyUnmME1PT2PPnj2Ym5vD7OwsUqkUysrKUFdXx5A2QrWYzWaGOZYKgoObTCaGuOn1epw6dQplZWXweDwYHh6GUChEc3MzGhoa4HA4IJfLuYENAMePH0d7ezs3oP1+P5LJJHQ6Hfr7+3kKJxAIePJcqpFNRQHdcYVCAZOTk9ixYwfC4TD6+/sxPDyMYDDIjS23283TYEIidXV1wefzYcOGDZwjESSrqakJx48fh0AgQHd3N9RqNdra2hCJREo2x66OT8zRINznrl27IBQKMTc3h8nJSYyPj+O2227jTRoIBBAIBLB//37E43GsWrUKX/3qV1FVVYXbbrsN0WgUN9xwA/7xH/8RjzzyCNLpNAwGA2praxEMBvGlL30JhUIB7e3tOHbsGJLJJHbu3ImzZ89i27Zt11ybxWJBIpFAS0sLysrK4Pf7kUql8Nxzz+G+++7D4uIi4y9nZ2fR0NCAkydPwmaz4fXXX0dbWxv279+PUCiEjRs34oknnsCePXuYCKnX6xGJRHDs2DHmo9DUQCqVMtbyWqHX6zExMcFTIEoSPvjgA9x///08UiO8oc1mw3PPPYf169fjySefhFKpxJo1a+Dz+aDX67krS10Nu92O8vJyNDY2olgswuVyYXp6GplMBo2NjSU/BKlUilwuxx2lQqHAnZy6ujr09PQwjM3j8eDAgQNYWFjAmjVr8I//+I/o6OjAo48+CovFgvLycnR3d6Ourg4zMzMMp/H5fPjMZz6D3//+97BYLPB6vRAKhdi6dWvJA1soFOL06dMwGo1wuVyQSCQwGAzM3UilUhAIBIxz3rt3L2KxGB/QNTU1qK+vx4kTJ9DW1oaysjLceOONvNlCoRAmJiawdetWmEwmNDQ0IB6Pw2g0YuvWrXjnnXdWXJtMJsPi4iJaWloQjUYxNDSE5uZmnDt3Dlu3boXBYGBI3vT0NG677TaMjIzwJVRbWwuVSoV4PA6RSITTp0/DYrEwrr6mpgaXLl2C0+lkHH0ymYTFYoHZbMbS0tKKa6ORdkdHB5LJJILBIGprawEAt956K4Ar4326bCsqKnh6tLy8jJqaGlRWVmJ+fp6L6XvvvZcLMqfTiQ8//BB1dXVYtWoVBAIBlEol44kvXry44troZ0ulUu78zc/Po6WlBWfOnMGXv/xlnDp1ivkIS0tLuP3226FUKhGPx/H4449Dr9ejs7MTCwsL8Hq9eOWVV/DVr36ViYcVFRUYGhrCwsICv5d8Pg+bzcYX6kqh1+thNpvhcrkwOjqKs2fPoq6uDu+99x527tyJhYUF7ugFAgG0t7djamoKi4uLqK+vh8Ph4CnlXXfdhV/84he45ZZbMDw8jOnpae6+bd68GT/5yU9gt9sxPT0NqVSKhoYGBAKBFdemUqkQCAQYWx0KhWC1WiEQCHDo0CEsLy8jkUhgcHAQH3zwAb71rW/h/fffh9lsxs9+9jN0dHTg0KFDDEc6e/Ys7r33Xu6wCoVCvPbaa7jhhhuwsLAAq9WKkydP8nnd39+/4troXVosFt6bJGbQ2trK2Hin0wmHw4Ha2lrE43E0NDRwIalUKuHz+XDo0CEcP34ct956K0KhEBdWb7/9Ng4dOoTFxUU4nU6Mjo4ilUqhoqKiJOSM+CptbW08Id+8eTOOHj2K7du349y5c4jFYjxt/eIXv4gLFy7A6/Xyvrz11lvR3t6OaDSKP/zhDzyBWFpawunTpzEyMoKbbroJWq0Wt9xyC7LZLBKJBCYnJ/Hee++teG8pFAo0NTVBqVRicnISPT09qKiogN/vx9/+7d9iZGSEz6nh4WF87Wtfw6lTp6BUKvnbuv3229Hb2wuv14vnnnsOzzzzDDweD5aXl9HU1IRCoYDOzk54PB4YjUZMTExAIpFg165dJaEsJKhht9vh8XgwPj6OYrGIgYEBHDhwAH/84x+h1+shlUrh9/tx//334/Lly5BIJDhy5AhsNhs2btyIubk5zM3N4cUXX8Q///M/Y2xsDH6/H0ajEZOTk9i8eTNeeOEF7N27F2NjY8hms+jo6CjZKVUoFJibm0NHRwfKy8uRyWRQUVGBp59+Gvfffz8WFhYwNTXFzYdHH30Uv/vd7xAMBuH1etHa2op9+/ahoqICzc3NuPPOO9He3s78g4MHD7KQzdjYGDo6OjA3N4dYLIZNmzaVLILq6+vR19cHh8PBnV9qzBiNRkxPT3NX+9SpU/jKV76CX//612hubmbCskAgwNjYGD7/+c/jhRdewNatW5HNZhGLxVBRUcHQ3lOnTmHt2rVM9m1oaMDg4OCKaysvL0coFEJnZydyuRw8Hg+SySQmJydx77334vXXX4dSqYTT6UQ8HofNZsPo6ChyuRzOnz8Ph8OBjo4OqNVqrF69GqlUihEjdP4MDAzg7/7u7+DxeKDX63Hs2DEuWHbv3r3i2gwGA4RCIQwGA/x+Py5cuID6+nqMjo6iq6uLG0QTExOYm5vD/v37MTk5yQ1ju92O2tpajIyMQCaT4Zvf/Cb+5//8n/xnb9iwAePj4wiFQsypI1hWoVAoOUEjmDF19onbIRAIcMcddzBMi5oedXV1OH78OFwuF0PtrFYr4vE4Wlpa8OGHH+JTn/oUIpEIgCvw+/n5echkMly8eBF1dXWYmprC/Pw87rrrLgwMDKy4NuAKzFiv18NkMvGk9qtf/Sr279+P5eVlOBwOSCQSVFdXo6Wlhe/d3t5e7N+/H3v37sX8/DxuvvlmfP7zn8fvfvc7DA0NoVgsorq6Gq+//jruu+8+WK1WmEwmBINBSCQS3HnnnThz5kzJtVEIiqXYQ1fFT37yE35wBGWhC/zYsWM8TqTu0po1a7B792689dZbcDqdXJHmcjlcunQJ2WwWmzZt4vFrOBzG22+/ja997Ws85lIoFFCr1SgWixCLxSsqPfz93/89mpqaPlbVlZeXo6GhAblcDjMzMzy2o4QzFovh8OHD2LJlCysikbrM6dOnkcvlWDkgk8nwhUAXEuHKSbHp29/+9jXX9otf/IIJvMQpGBsbw6pVqxjqc+7cOR49nzp1Ci0tLchms6ipqeHxXkNDA8bHxzE+Pg6dTocdO3YAAHp7e3mEdenSJR4X04fvdrtXVAX45S9/yZMRAAyjIewvJYAEEzEYDExko1E8TWlOnDgBt9uN8fFx7N69GzabDUNDQxgdHcXu3bshFot5jJ3JZLj4W0kR6xvf+Aa0Wi13/eRyOSdJBJMhAmZTUxMMBgNUKhV++tOfQqVSweVy8WF/6tQpVkpqbW2FWCzG2NgY41+LxSIaGhrQ1dUFAHjrrbeg0Wjw9a9//Zpr+8///E9YrVZks1kEAgEsLCzAZrPxQUJwtqWlJYYgVFZWIhQKwWw2M4HZaDTCbrfDarXi+PHjrDiRSqWwvLyMuro6DAwMwOl0cgeRFMVWUgD69a9/zYkeEdyz2SwXYA0NDfD5fIjH41AoFIhEIozfJE6OWq2G0WjE8ePHsWXLFrzxxhuor6+HRCLB7Owsamtr4ff7ueAmZSXiZ3zxi1+85toA4N/+7d9YhYwUmhKJBE9HiAtWKBSYC3TgwAG+JCiJ2rx5MytkaDQayOVyhr6EQiHMzc2hs7OTIXeRSAQejwf19fV48MEHr7m2H/3oRwAAk8mEVCrFykMSiYSJfaSeo1arkc/nYTAY8NJLL8FoNPK4uba2Fu+99x46OztRKBRw+fJl5HI5WCwWpFIpWCwWRCIRqNVqKJVKFAoFRCIRJJNJfOc737nm2r7zne+weAFNq0KhEG677TbuHM7MzMDj8XASD4DJ/AQnUSqV3PhYWlpCS0sL4vE4+vr6oFKpoFQqUSwWodFo+KyXy+VIpVL453/+52uu7bnnnkMoFEJLSwvvy2AwCJfLhdnZWWzcuBFLS0uM9a2trcXAwAB366RSKex2O5qbm+H1etHQ0IDu7m6GpI2NjcFms8HtdkOj0cDn8/Gkzuv1wmq1rqhe9/3vfx/r1q1jaBsRgBsbG3l64vF4IJVKsXr1avj9frS2tuLMmTM4fvw4stks3G43mpqacPjwYZ6O33TTTchms/joo49gt9tx/vx5WCwWVpGSyWQs+PA//sf/uObavvrVr6KpqQmLi4uM5V9aWsLatWtx+fJlRCIRGI1Ghhc7HA5Eo1FMT09Do9GgWCzyfj137hxEIhHa29sRi8Xg9/uRz+exZ88eHDt2DK2trYhGo8xRIHLzSqpT3/72tz8GJyMOoEAg4AbR6OgoIpEIc8kikQjq6uo+RtIm6LRer8eFCxeQyWRgt9tRV1eHTCaDoaEhVFZW8j4Ih8OYnp6G3W7HD37wgxXXRrwJOitIBUyj0SAQCHBjbuvWraz2Ro0Xr9fL9+34+DjOnDkDrVaLtrY2yGQy+P1+zM/Po7m5GeFwmNXOcrkcxsbGUCgU8Mwzz1xzbc8//zx39wleTBweUsNUKBTMM2ltbUV3dzc8Hg/q6uqgUCi4c378+HGkUim0tLRAr9djZmYGY2Nj2LZtG44cOYJVq1YxbFKtViMYDCIej+PLX/7yNdf2v/7X/0I4HObJz+LiItxuN8bGxvjdEjycoNukVlhdXQ2/34+hoSHmvJjNZszPz6OqqopVDlOpFFQqFd5++200Nzejrq4Ofr8ff/zjH/HZz34W+/btu+baHnvsMZjN5o/lIsRbnJqawuTkJCwWC5OVC4UC/9kkSDQ1NcV8K4VCgeHhYVYplMvlMJlMePPNN3nCTII68XgcZWVlK+ZJ3/nOd1BVVYVEIgG5XA6xWIyenh6MjY3hwQcfZOVPug+y2Sza2trQ3d2NhYUF3qM01bNarbh8+TL27NkDo9GImZkZOBwOhusTggG4ouJVXV2NL33pS9dc249//GOGwRHsOxwOo6qqinObqakpqNVqbNu2jcn0PT09DKkm6kB5eTlOnz4N4IoCocvlYqgUNXiI7xoIBJjzuVKedHX8X5HBX3nlFfT19fEUwu124+zZszAajejq6mIisd/vh8/n479/+umnMTk5yRdRsVhEZWUly6gSK394eJiJLRqNBnNzcxgfH2cVkJUik8nA5/MhFovBbDZj3bp1MJvNePvtt3H48GGk02nG5BNZZ3JyEgDw1FNPcRfI4XDA7/fj+PHj6OzsZBm3ZDKJhYUFTgKpM3Hp0iUYDAa0traWfG5EehOJRDyO6uvrA3Bl9FVTU4Py8nIcPnwYn/nMZ7B3716IRCKcOnUKp0+fxtjYGDweD7RaLc6ePQuRSISJiQkEAgEIhUIcPXoUBoMBd911F+PDRSIR6urqSmIPgf8jR1dbW8vQuO7ubiawrV27Fhs3boRKpcLWrVuxatUqjI+PY2BggC+SWCwGi8WCixcvcuGYyWSg0+lw4MABhg9cXQCZTKaSpGaSYSW1rsHBQYyPj/PIct++fdi1axcMBgNGRkZw6tQpNDY2smKOVCrlzsxdd92FyclJqNVqJurp9XqGVdx8882Yn5/HCy+8gLfeeovxwyuFQCDA7Owsc3Q2bNiA6upqnDp1inGmGo2G4RYNDQ2IRqO8SUmStLGxEQqFAu+99x4qKytht9shEAhYNSkQCGDVqlVcoBKxfOfOnSXf6eTkJJMDSYqZVL6owyISiTA3N4f5+Xls2LCBScPU+SBRAiKWEvGrubkZL7/8MvR6PfR6PcP8+vv7WWmmVJCktMlkgtVqRbFYxPLyMs6dO8cENepuTU5OYt26dSgrK2MlEpvNxpNLq9WKxcVF9PT0AADvi2AwCLvdzpK8BG9TqVRYu3btimvTaDRMzJfL5VCr1awIJZPJ0NraivLycly4cAGvv/46wxKJnxOPxxGNRjE2NgaBQIDXX38diUQCarWaFY5IQlClUkEqlWJ2dhaBQAAmk4mhWdeKQqGAwcFBhqPU1NSgUCjgyJEjkMlknABJJBLMzc2xLDbxcmhCRIT27u5uJh3TxTw4OIjFxUXU1NRAJBKxNKNAIOCp2LUiHA7DZDIx9pu4DTqdDj6fj2U+w+EwgsEgXnjhBRSLRd6rhUKBoT2RSARDQ0Ms+Wm1WmGxWHDmzBkcOXIEw8PDUKlUOH/+PN59910oFAo+S68VarUaPp+P+UCkuPbkk0+is7PzYxLL77zzDmZmZnhadvz4cSwtLaGtrQ1Op5NlypPJJGZmZvibCgaDeOCBB3DLLbdALBbD6/UimUxizZo1JaF6arUaly5dwuLiIkMyEokETp48CaVSyTDM06dP45VXXoHT6URXVxfKysrQ29uLaDTK50YymURDQwM3PWpqapBOp/HEE08w1EEulyMcDvPZsG7duhXXJpVKuYllMplYOaynpwcCgQAmk4mLqYGBASQSCdx6661IJBIYHx9HKpWC3W5HS0sLqqurce7cOWzatAl79uxBdXU1QqEQQ+hyuRyGh4f5PanValRWVq64No1Gg+PHj0MkEqGyspKLi5MnTyKfz+Pmm2/GoUOHYDAYcOrUKQwODqKmpgajo6P46KOPMDc3B4fDwaqW5eXlcDgc0Ov1sNvtsNlsOHv2LNra2nD77bfD7XZzTlNbW4uOjo4V10YTHOIW0FmcTCaZZzA7O4uTJ0/i3Llz0Gg02LJlC5RKJf7jP/4Db775JsM2I5EIJicnYTabYTQaYTAYIBKJ4PF4sGPHDoa5jo6O4vjx48x/LPW9qVQq5j5Qkt3f3w+JRAK73Q63280CER0dHfjsZz+LCxcu4Otf/zq6u7uxY8cO3HjjjVCpVOju7kZVVRXUajXf8dR4XL9+PSQSCU6ePIne3l6sWbPmL8KTqHCle0upVCKVSqG+vh7r1q1DoVDA2NgYgsEgUqkU1q5dC4VCgeeffx7nzp2D1Wplvtno6Cjq6+shl8uxsLCA8fFxvPfee2hqasLGjRthNBoRjUZZXbLUBE2r1UIul8NoNEKj0UAmk7FoEPFUGhsbkc1m8fTTT0OhUODMmTOwWCzMj6MJyszMDF599VVUVVUhHo+zuuHrr78Oh8OBdDoNs9mMYDCIy5cvo6ysrCTvkVSzADCkX6FQYHJyErOzs3C73airq0M6ncazzz6Lf/u3f2Pp2qeffhrnz59n+Cbx8jQaDSNIFAoFHnvsMYb1nj59GkePHkV/fz98Ph/m5uZKvlOKT1xokJcEfez5fB7Ly8vYuXMnKwYolUq0tLRgy5YtWFpaQnl5OVdkpGqzceNGlJeXY8OGDYhEIlwhZrNZPPLII3jzzTe5861SqaDT6f4iGVyhUKCxsZHJzyTttX79euj1etTX13O3nmT2iKBJ3QCDwYD169cjk8ngxhtvZGjMhg0bWB0gmUxi+/bt/BJMJhNj7FYKgryQisvVpNR0Og2XywWr1Yr6+nrs3r0b8Xgci4uLGB8fh8ViQWdnJ7Zv347t27djenoae/fuZfgE4Xj37t2LixcvMvFKoVDAbrdzl6LUO62vr2cFEyLXkk/F8vIygsEgj5F9Ph8T081mM5aXl7k7KBQK8elPfxomk4lHxj6fDzMzM4hEIrBYLEx4pu4QdbFXem6UNJEaC6leUQJEKhIOhwNzc3OYmpqC2+1GVVUVZmdncerUKRQKBUxPT+Phhx/my3Fubg4DAwNYtWoVSz9SsUtEzlKJFammELGOxA/Wr1/PI2f6ZyRHDIBVlKjLJZVKucCdnZ2FUqnkg4L8YAgSRwoc5eXlJXkQkUgETU1NTHKlb4AUf+RyOSwWC+rr69HZ2ckeGXK5HOvWrYNMJkN3dzd6enrQ19cHsVgMm83GHfKhoSG0tLSwnK9AIEBHRwfWrFmDWCz2F1WnSPaPmhCUfBOMhgiS5eXlqKqqwvvvv49IJIJgMIjKykrmAel0OuZqLC4uMiGPpFrtdjtzo0hkIZFIYGpqasW1JRIJVFRUfEy6kzDQkUiEVV/a29uxZs0anD59miVcaapCyjzV1dXYt28fw5AsFgsymQxaW1u5+KCRt8ViwcTEBEv8XisIX03nG00YN23ahEAggI6ODt7zjY2NCIVCXNC2tbVBo9FgeXkZRqORuSELCwvweDzweDzIZDLYs2cPgCuXbiQSQbFY5MuevuFrBXU3iTNSLBZRV1fHOvy05x0OB3cRM5kM9Ho9nE4nRCIRfD4fJxDRaJSViEgQobGxEX6/n4njFRUVcDgc3Okv9dxIxQq4chkHAgHY7Xb4/X6cOnUKkUgE1dXV2LJlC4tgFAoF7N+/H7W1tUx8vHTpEsMPqqqq+JmSGhZBC4jISsovpaKyspILWolEgmQyCaPRiPn5eX5XKpUKzc3NeOONNzA2NoZMJoPVq1ejtraWv4W6ujps2rQJ/f39GBwchFarxa233op7772XPQ+oq6pUKj/mfXOtIN7K1VODVCqFxsZG5HI52Gw2VFdXw+VyMUGZUAMOh4MbGSTgsXbtWhw/fhxer5cFFxoaGliEwOv1siy+QCAoWaAVi0XU1tYywZZUkgh+TPAYtVqNuro6Vkwi2DOJfmg0GkxMTGDv3r185qTTafh8PtTX18Pn87FCYENDA1pbWyEQCNDZ2VnynVZXV7M6YFtbG1QqFerq6hheaDAYoNPpUFNTw+pvNTU1aG9vh81mY1lu4nXNz89DKBTCZrOhs7MTnZ2dGBwchNPp5PODRHhKEdXpfKYEkSbaW7ZswdTUFORyOU8OKysrWXRlYWEBa9eu5emCSqVCPp9nvis1bgcGBiCXy9HT0wOr1QqtVou6ujrU1dVBJBKVvO9J7poa0SSxSjLDUqkURqORZXTj8TjzN9rb22G1Wnn6HAgEePpH0wuz2YydO3eyn5HP52N0QDgcLikoQd82cWUIgrlhwwZEo1E0Nzfzt2OxWDA3Nwe9Xo+ysjLs27ePkSmE0Dl06BBDnbRaLaRSKRwOB3p7e+F0OllUxOl0Mn9xpaApHuVwlLfZbDZYLBbYbDY+Z9etW8ffld1ux4033gi9Xo+xsTGGRd50003sK0fn8l133cXNNvrdqClit9tL7gWKT8zRKCsrY6gEAK5wmpubsbCwwCNUMt0KhUIAwJJx2WwWXq+XFRVkMhksFgv0ej0TCDdt2oQXXniBVY0UCgVUKhWMRiOTa68VVquViXMqlYo5D0qlEgcOHIBcLmdN9nQ6zepPdrsd6XSazfTodwwGg3C73TCZTEy+jcViqKmpYXlEGnf/JXIYjc1IJnZ+fh5ms5mNXYgUS4cFyXWS3wGZLYnFYojFYtTU1LBSAClQtba2ore3l0lxdPmRqstKQXKkpGdOaiokpUfv+mpTpFwux0oomUyG1YgEAgH7BdAIlOAdlZWVrOlMY/ZisVgSI0z8F1IlIlUQUj+YmZnhS7mhoYHlMEnyLxwOsxIHeXy0tbVheHiYyWEul4uft0aj4YSUZOBWCpJFJogeTSHo0CfoCUlrksa2RCJhoiORgaPRKJxOJxfCAJgv0drayjC1hYUFZDKZv7gXAPCeEYvFbAZns9kwMzPDmvCkSEV8B/pWqZsbj8f50qqsrGQfCgCswkJ4UFKSKUVEpCB/HEoalUol63dTR4W8W0QiERtmEceKiiISVKDRPh3+RHClQthgMPAlMjExUZKjkc/nof2zPDT5hhCcjLgMtMfMZjPi8Tjy+Tzj2endkjdJRUUFQ1XIq0Ymk2H16tWwWq0IBoOs7Obz+Up2Iyk5ou9EKpWipaWFlUuuhhkKhUKGAVHhSnBDl8uFCxcuYPPmzejv7+fvmC4XgvqRqo1UKmX1oVLvlHwRSO3FZDIxZDASibAKk0ajQWNjI5uzUQOJzp+qqirmhBHpmibc1dXV7GVBZzjBWlYKMj4jtRnq3JH8Kcmw19fXQ6/XsxGpTqfjn0ckbIKKrFmzBlarFeFwmLuvhN9WqVSsqkY+AiuFWCz+mCcCFYIEFyafIJfLxZDC4eFhuN1unn6SbGtjYyM0Gg3sdjvDnYAruHyCPJNPjEqlQigUKukpQxBVar4QpJYKyrGxMcZ/K5VKNj8kuFsmk2EJ70wmg5aWFnR3d7PKXllZGRvzhkIhhpuRPPLi4uKKayNVKjJlpfdLkxASKKH3HAwGeZJLuUj0z2ZkNBWhe3phYYE9XUgRrLKykk3Menp6SqIE6C6l5gMpMNJ9TPuETDaJ0Kz9s8S61WqFRCKB3+9HV1cXw7BIRZOmvVd7tJBBHEG6Sz03kkOmb4RyGLlczupOdGeRNHdHRweLpFARRCbCMpmMeQs6nY59z6hoiP7ZKJNyulLPjfKIqxW+6ExcWlpihSWv1wu9Xs9TcIJ7Xm0KebXMOHGqSH6ZDFmpyU2ysCsFSZqTwiQRw0nwJhqNory8nOHTFosF/z/2/jO6zfPK+sY3CRKNIArRSBDsvXdRjVRvliyXWJa7Eztl0uNMysxkkkl5nMRxnJkkkzjFJY7LxOO4K7Ks3gsl9k6QIAmCYEEnKgGQfD8o5/zl9y8iftd6PupaK2tlJRJ1Ebjvq5yz92+vWbMGw8PDKCwsZKuBQqGAXq+HVqvln0cUVMJk63Q6zg4iXH68d4GeCfo7kUiE9zuCXdDvrtFoMDY2BovFAp/Px108kk1R97upqYkvTB6PB7W1tVzMU6vV3OmgdejjjI/d0SA0I9EAgOsHIrvdjo0bNzLBgMJ9qqqq4HQ6UVxcjMnJSUxPT8NqteL1119HUVER2tvbUVlZyQtlSUkJVlZW8KlPfQoZGRkc7gaAKzCrDb1ej6mpKe6AEHKtu7ub9Yh0aBocHGRyFCU2E+mHzMxtbW3YsGEDpx7rdDr09/fDYDDgxIkTkMlkLIWgis5qgxLOyWztcrlQUlKCvr4+bu2SWdLn80EikUCj0WDDhg3o6+tDd3c35ubmcPToUdYkFhUV8aGQzFx1dXWc8KjVahnbG6+joVQq4Xa7kZmZCZVKxZuAzWZjU5per0dOTg6Uf+fEOxwODmOkg92VK1eQmJiId999Fzk5OTAajSyNEgqFMBgM+PDDDyESifjmn5iYiLGxsbjP3OLiIlNe6PemfIi5uTmo1WoUFxdDpVKhsbGRiSIkayL6WSwWw29/+1vk5OR8JDE8EAjgrrvugkwmQ2VlJYfOyeVymEymVedFF5kbE5/1ej06OzthMBgwOTnJWn3ySxC9gdB9dDglMlJxcTGi0Simp6exvLyMyclJ9nOQgToUCjF5aLWhUCgwNjbGFVKfz8cds+LiYt6kiApC9KbZ2VkcO3aMySwGgwF33XUXh2eRNrempgYTExNobW3lKun8/DxcLhcKCgr+4WWDErSVSiVUKhVTaGZmZrjqr1QqOSTowIEDXCnq7+/nfJGZmRlUVlbi4sWL2LJlC8vriNIlk8kwNjaGrKwslJWVobi4GJWVlXGr3xRklpGRgby8PKjVauh0OvT19aG5uRnDw8Po6+uDyWSC2WzGrl27MDU1hXXr1iEUCn2k6jM8PMxyvpmZGYyOjmJxcRG9vb3YsWMH442JQV9eXh63a0DFmxszLch309jYyHkBwPVD4q5du3ijtdlsSEpK4pRnCr4qLi5m5GZWVhb7w+x2Oz9zAoEA+fn5cde45ORkloZShXNqagoAUFpaisHBQTYsRyIRFBUVITU1lTtVMpkMhYWFLPeizg4x9QUCAaxWK2677TYA18M8R0dHMTs7C5FIxNrmmw067Obl5bHnp6GhAZFIBDk5OVyZp0A0Kg4o/x6YReZ1m82GBx98kOU55FEUi8XIz8+HUqnEmTNnGPdKnpV4HT4yBZN8MBwOo7y8nIlMdAC80XhK3gKi7tEFVC6XY2pqCgcPHkRtbS1mZmbwv//7v3j66aexdetW9PT0YHx8nAlIRIZabSQlJbGMhcywdPFfWlrCX/7yl49o4MkLoVAo0NfXxwU1olBarVbce++9MBgM/Lk5nU60tLSgtLT0I9h7lUqFq1evrjo3sVjMagTah+mZWrduHRP2CLVaU1PDh2vq0NhsNphMJtTU1GBoaAhbt27lQ6per0d6ejpLI4kodyO1abVBcnBCuIfDYWRmZmJiYgJCoRAmkwmev2c8UbeKDszU6RSJRDh37hzy8/ORkZEBg8HAHrP5+XmcPXsWtbW1uHr1KlwuFyQSCdLT01FSUhL3UGo0GrG4uMiEUOrmE5VxYmICU1NTMJlMOH36NAQCAVwuF/bu3YvU1FTk5uaitrYWAoEATU1NHKwMXF+fKioqMDIygqysLL6Q9Pb2oqenBxkZGXGBEsnJyaxeCAaDfGaamJhARUUFgOuXEa/Xi46ODlRVVWFmZgZSqRRXr16F3W5HTk4OpFIpd7aooDE3N8eScFLlUDefZIDx9i0qchEyXyaTIT09HW63G0ajEa+99hoAoL6+HjKZDOvXr0d+fj7EYjHGxsagVCpRX1/P3psjR46wCmJkZISldVu2bEFycjIHl1LIXzwzOIWeEt2MLnk2m41x4vR5OhwOVFVV4aWXXkJnZyfOnj0LuVyO3bt3Izc3F6WlpThy5Ag2bdrESHGRSMTBnykpKSgvL+fMmk2bNsUtZN84PnZHY2pqijcAugGlp6fDbDYjOTkZRUVF+OMf/wiv18sGRb1ej0OHDjFL3OFwQCKRwOFwoKurC11dXawjKy8vx8zMDB+I5ubmWJvd3t4eF684NjaGTZs2oa+vj1/ygYEBPProo/jtb38LsViMyclJNr6Gw2FMTEzg/Pnz8Hg82Lt3Lx9aysrK8PLLL+PixYssyyG05NmzZ2GxWDhhmTI3AKzaTg0EAmzIJJ2wyWTixOxYLAaTyYRYLIampiYcPXoULS0teOutt1BZWYnFxUV8+OGHAK5vsBQkRaQSOgDabDYmRNFNWKvVxv3c6BZM5Cufz4exsTGUlpbi+PHjjLuNRCIoLS1FU1MTIwoLCgpY/kI5CCMjIygpKUF+fj5MJhM6OzuRnZ0Nu93On/vk5CQH7P2jjkZRURHj7qLRKMbHx1FSUoKJiQnu6CgUChQXF+Pq1atobm5GJBLBmjVrYLFY4PF40NDQgI6ODly5coUrkXa7HadOncK6deuY9kEHXaFQyHKx1cbk5CTWrVsHt9vNJlNqw77xxhvYsmULxsfH+RCl1WqRl5eH8+fPswGQEI9WqxXBYJABCRRklpWVheeeew4AeAOSSqV8iVlt0MWAEtqpOkhdAPILUMX5/PnzAIDh4WFO0yXe9wcffICJiQmMjIywBtdut2PNmjWIRCLo7u5magcx3+O17oHrawBVmW02G0KhEGpra6FQKPCXv/wFAHizb2pqQm9vLwKBAPr6+tjLNDU1BYvFAolEgoKCAszMzMBms8Hn80EkEsFoNLLGvqSkBNnZ2fB4PLhw4QKA1d9VWh8onMnlcnGmTFtbGwdRpaSkwOl0skfqjTfe4C7k7OwsIpEIzGYztFotJicnuYs0Pj4Ou92OF198kat2lZWV0Gg0GBoawtq1a1f93CwWCxdvyBA/OTmJrKwsfPDBB1i3bh3jdgsKCjA4OIikpCQcPXoUe/fuhd/vR39/P+rq6lBQUIDjx48zdjYlJYV9SbTu9Pb2ArjuqYl3AaLvtKSkhFPPCcnp8/kYS2wymT6STL+4uIhjx45xpZc042+88QZKSkogFou5ikYXAbPZjOzsbJhMJmRmZnLOTryMj+npaaxfvx4mkwkKhQJ5eXno6upCMBjEyMgICgoKMDo6iv7+fiQkJODatWtoaGhAX18fWltbGWNbW1uLK1eu4Pbbb4dUKsWlS5fQ29vL+5RUKkVOTs5HUrtHRkbgcDiwZs2am85taGgIcrkc4XCYQyypEuvz+fDFL34Rp0+fxvnz56HRaHDs2DHMzMzgG9/4Bj7zmc9wJywnJwenTp1CLBZDaWkpOjs7EQqFkJGRgYSEBFy8eJEToBMSElg684/wttQ5m5mZwdjYGKRSKfbs2YM//OEPKCgoQCQSYSN9W1sbOjo6oFAoUFJSglgsxhdJq9WKuro6jIyMwOl0MgjC4/HgtddeQ3JyMhfFqAtLRJ6bjdHRUZSWlrKnjDCopaWl+NOf/oSqqipYrVaW/FqtVmzYsAEffPABNm7cyMWR4eFh5OTkYHJyElNTU7h8+TL6+vpYFlRaWsrQAip05ubm4tixY2hoaLjp3JaWlpCfn89nFsoxyMjIwPnz57Ft2zYsLi7CarWygZhw+wcPHuQzj0AgwJ/+9CesWbMGb7/9NheYrFYrUlNTIRQK4XQ6WWGxtLSEK1eurPqsAdfPD5WVlbh06RJGRka4sLh582YMDQ1hbGyM6ZhZWVnczX3zzTeRkZHBIW30ZxYXF3HhwgVkZWVhfn4eV65cgcViwY9+9CPMzc1hZGQEUqkUNTU1XJRebdAacmMGFXUSLl26hKamJvj9fhQWFmLTpk3Izc3FxYsXcfnyZZSWlmJxcRHnzp2DUChkaV1nZydDAWpraxEMBtHd3c1dOrlczkF+8da4SCSC1tZWWK1WOBwOhpKMjo7CbrdjeXkZp06dQl5eHiOmr169Cp/PB51Ox/sXrY1arRZDQ0PYvXs3gOvI2/7+fkSjUVRUVMBkMjE5MSUlJS4WeHx8nLv7EokEsVgMAwMD2LRpE8xmMwoLC/Hee+9hamoKe/fuRWZmJgwGA8vFOjs7MTU1hbS0NLz77rvQarV4++23OcSVOmjHjx9HVVUVJiYm4HQ6WaERj7h64/jYFw2tVouBgQHodDr2XJw6dYqNuN/4xjdw++23o6urCzMzM8jPz+csCrPZjPT0dOzevRv19fX4xS9+gby8POzYsQMqlQqRSAQDAwP48MMPsWXLFhgMBjaMjY+P/8MAn0gkgj//+c/IyMjgG3ZNTQ3eeecd1gTTF97b24u//vWv+MxnPsNaTpLobNiwAc888wwOHz6ML3zhCygvL2fNq8FgwK5du5Cfn4933nkH4+PjSEhIQH5+PmpqauJ+biTVIRnD4OAgV+JmZ2ehVCoZp6hQKLC4uIjNmzdztYJeiJGREWZKV1VVsYGJqrokKbqxZRhvMxGJRLBYLCgpKYFGo2EG9PPPP4/k5GS0tLSwUZhMfxRhX1pairS0NOZr//73v8ddd93FQT4UUBYOhxEIBJCTkwO73c5GWzoErzZKS0tx4cIFVFdXs7+DdLi0CZBx8qWXXsKjjz7KmMzx8XEsLi4yOUGn0+EHP/gBH54oGfjJJ5/E+++/jyeeeAJerxft7e2Ym5vjcMHVhlQqxZEjR7BmzRrWvNKfJ900GU9tNhtX/olClJiYCI/HA4/Hg46ODszMzGBubg6bNm1CSkoKyxIAoLa2FktLSxgbG+PLKFUbbjbImDw5OcmBhRkZGXjnnXfQ2NjIjH36LtauXctJxmNjY9BqtWhqaoJSqcRzzz3HiOSUlBRu6/7oRz/C5z73Oaxfvx49PT3w+/2cwfGPAnyWl5fR1dWF6upqNDc3Izk5GUNDQ/jpT3+KJ598kpNU3W43+vv70dLSwqm5b7/9NnJzc7F+/XpoNBrU19dj48aN2LhxI775zW8iFovhueeeg8/nw+233w6bzYasrCwMDQ1heXkZX/7yl+O276n7SNI3o9HIJI68vDxs27YNw8PDTDqKRCKYnJxEbm4uBgcHYbVakZ2dzUjbrq4ujI+P4+6770ZNTQ2OHz8Op9OJXbt24dq1a0hKSsLY2BhsNhvKy8tRXl6+6tyInEXBqMD14s+//Mu/cDptJBLB1NQUzp07h9HRUezatQvZ2dl49913kZWVhc2bN/Pv8eSTT2LXrl3IysqCz+dDKBRCdXU11q9fj6GhIbS2tsLn82F2dpZ/r9UGHYLa2tq4ujo9PY3Lly9j586d3F0jGU1ycjK2b9+OiYkJvPTSSxgbG0NiYiKqq6vZ70D+tMTERJw4cQJHjx5FWVkZtFotB61SxzXe5ba8vBx9fX0skaUD7fT0NBISElBaWorS0lI4HA4MDQ3h85//PGKxGPLz8/G73/2O5U+BQACf/OQn8ctf/hK9vb345Cc/CZFIhOeffx5CoRCXL1/Gvffei+HhYcaqbtu2DZs2bVp1bnK5HKWlpVhYWGANt1qtxsmTJ5Gfn8+Ev5qaGmb/33PPPSxDIr+AwWCAVqvFq6++ipycHDz55JPweDx48cUXUVZWhqKiIjz66KOw2WwYHx/HwsICSktL4+rSBQIBfD4fysvLPxIMuW3bNvzsZz+DzWZjqQsRs774xS/ihz/8IfsnKPtofHycq9xEXrPZbGhra8P+/ftZ7nojRjSejFCj0WBiYgL5+fksK05NTcX58+eRn58Pq9XKYY9SqRQejwdjY2N4+umnkZKSgvn5eXR3d7M/6a233kI0GkVrayvS09PR3t4OAIzaNZvNMBgMXGSI5zXQaDS4evUq/H4/eyzoUpqWlobq6mo4nU54PB72P9xzzz145ZVX0NPTA7VajZycHGzatAnXrl3jd7W2thZzc3MYHR2FSCTC6dOncd9996GrqwtXrlzhkNGxsbFVgRc6nQ7f//73sXfvXmzZsgXT09M4fvw4jEYjdu/ezdjia9eusRRr/fr10Ol06O3tRXp6OsrKyhAKhfB//s//gVwuR15eHmw2G5v4a2trMTExwZ2SpKQkVFdXo7CwkD1gNxsU9kf7Dfl2zGYzf8f0fclkMmRkZODb3/42/vu//5thPXq9nqWalDdDRvuBgQFcu3YNRqMRa9euhclkYnVDYWEh7r333lXnFggE8Morr2BpaQl5eXkoLCyEWq1Gb28vPB4PcnJy0NvbyxeC6elpfPazn8U3v/lNJCYmYteuXWhpacHKygpMJhN++ctf4rHHHuN4Aa1Wi2vXrjFkhnxUJAeMV4Ainxh5MUjOfe3aNRQUFGB8fBwtLS0YHh7GhQsX8G//9m84evQoHnnkEbzyyisAgKKiIiQmJnJW0ezsLDZv3gyhUIhDhw7h2LFj2LFjB++HJEGWyWRxkcU3jo8tnerv78f69esBXDcVpaWlobKyEgcPHkReXh7m5uYgl8uxbt06VFZW4sMPP0RBQQFEIhEefvhh7N69m6Uc1Ba7ePEizpw5g7m5OVRUVODOO+/EwMAAjh49iueeew4DAwNsCI5niBEKhSguLmYiUU5ODuNhNRoNduzYgfvuuw8HDhzgFyEajXL1Pjk5mcNSiouL8cILLyAtLY2pOCMjI6ivr8err76KyclJDAwMcBuTNH+rDcKZBQIBzM7OYm5uDsPDw6ioqMDy8jLWrFmDzMxMZGZmYu3atSwV+NOf/oSuri6cO3cOR48eRSgUwsjICPbv38+m7cHBQVy8eBENDQ24ePEiTCYT/va3v7GJjYKAVhtzc3OorKxkXfXKygoOHTqE/fv3Y82aNdi+fTvLAmgBfuyxx1h2FovF8MEHH+Dtt9/G0NAQKisrYTab2V8jk8lw1113cTeMuPelpaWYnZ2Ne0CwWq1oaGhg/09aWhpvVIcOHWL8qNFoxJNPPonz58+joKAAHo8HBw8eZNjA+Pg4xsfHGaFJmQwCgQD/+Z//idLSUqxduxZqtRqf+tSn8OSTT6KoqGhVlDIAlvpRQCId/mpqatDe3s7aS7lczljJWCyG48ePs3eF8kvuvvtu3HPPPSgoKGB8J3HSw+Ew64cpjb6trS0uu9rhcEClUrEBnCqHZIq80RPV2NiIo0ePshTw05/+NBoaGjA2NsZSIEpTrq+v5036lVde4bA38iwA1yu4RMBYbYTDYQ4Ro1b5wsIC/uVf/oVlDg0NDewL+fDDDzmg6t///d9RWlqKd955BxaLBe+99x7Onz/PXH2izn35y1/G9773PUilUszMzECn00Gv1+P06dNxPzsyUM/OzsLhcPDBfd26dbh48SKT0MgQNzExgQMHDsBqteLhhx/GHXfcwTkpGzZswJe+9CVIpVL09/ejs7MTgUAAdXV1eOuttzgMjcKcqPq72iAPWX9/P3c0JiYm8M///M8sbZDL5SgvL8euXbvQ1NSEjIwMVFVVoampCWq1GkNDQzhx4gRGR0fx/PPPMw0lLy+Pk9Tp3XK73VzRXFlZids1cDqd7NkKhUJISEj4iFGdZD4rKyvsV5FIJFCr1Xjuuefw4IMPQqfTYfv27RzIR/Su8fFxJCUl4a677mKtNQX/KZVK9Pf3xy2mjIyMYOPGjTAajTAYDBwWWlxcDKVSiZqaGgY01NfX4y9/+QsSExPR2dmJL3zhC9i1axf0ej2eeOIJaLVafOELX0BjYyMEAgEkEglKS0vZC6DT6WC329HU1MRI5nPnzq06N+qWmEwmxr7Oz8/j4MGDiEajOHToEE6ePMneM/JDBYNB3HnnndDr9ejp6UE0GkVmZib++Mc/YmJiAi+++CIOHz4MsVgMvV6PX/7yl/j973+Po0ePoqenByaTCb29veyjvNkgqg/JwEgV8MorryAYDGLTpk0wGAwwGAzYu3cvgOtFhJqaGmzduhVFRUWQSCQcyEnhvH6/nzOQ/vmf/xkzMzPsPaG1xOPxxNV+WywWpq5RKFt7ezseeughpKSkYO/evdi/fz/q6uo+4u169913MTw8jMnJSe6kSqVS3HPPPeyfdDqd3A2idZECQQcHB3H27Nm4a1wkEkFjYyPTGEmF8L3vfQ95eXksJ8vOzsamTZtQUFCAoqIipKWlQaFQwO12s68xFovh8uXLaGxsRG5uLvLy8pCZmYnbb7+dfShCoZDlzQQjWG2MjY3hX/7lX5i4JJPJYDAYcPfdd6OjowNerxeFhYXYvn079uzZw3J2Wu+o67hp0ybs2rULjzzyCFJSUliuHY1Gcd9998Fut0OhUGDfvn340Y9+hEceeYRBKKsNSrW+evUqLly4wCj4hx9+GGKxGCkpKaisrMTOnTtx++23IzMzEw6Hg4MaKysrkZeXh+zsbCQnJ3NRLRKJcCjiAw88gOHhYZw5c4Yl8zMzMzh79iwH9t5sSCQSbNq0Cenp6VzI+/DDD3HXXXehpqYGe/bswe7du7Fp0yY0NzdzuGZaWhr27NmDYDCIV199lZG4zz33HMvnyCvz1FNPwWg04utf/zpLSGtqaiCVSuMW2Wl+g4OD6OjogMlkwvz8PO6//36WxZP37f7778ezzz6LJ554AlevXsXnPvc51NXV4cqVKwiFQigpKWEAxszMDNra2rCwsIAf/ehHMJvNUCqVHGWxceNGztP4OONj52j88Y9/RGpqKvr7+6HX61FdXQ2Hw4EzZ87gsccew1tvvYVgMIjs7GwUFxdjdnaWF3FK5KUwIr1ej//6r//CP/3TP6G/vx8ej4erpZWVlRyPTvkNmzZtwpEjR/DNb37zpnN76qmnWAvq9Xrh8/lQXFyMN954A3feeScn4o6Pj8NkMqG2thbZ2dlYWlqC1WpFYWEhMjMzMTo6iqmpKVy5cgX/9E//xCGEOp2OcWQdHR2or69HZ2cn7HY7NmzYAI/Hs2p2wLPPPouUlBSkpKRgbm6O5RKjo6PYvn07HzoUCgXm5+chFApxzz334PXXX0d+fj7UajVcLheuXLmCuro6/lmEVxsYGIBAIGAGOSVKUqI5gFW52k8//TQf6CgThFJntVot+vv7EQgE2IdAzGehUAidTge/38+JyQsLC9zGdbvd6OrqwvDwMNLT07Ft2zZMTk4ysjgQCECr1WJpaQkHDx686dy+/e1vQ61Ws7SCOhkUgpeTkwOLxYJYLIbs7GymZwwPD+PEiRP8HA4ODnJ4WEFBAYaGhlhraDKZOKBudnYWGo2GD62E5r3Z+O///m8IhUKkpaUx+g64jkr+xCc+gWg0CpfLBYvFAq/Xi4ceegi9vb1QKpVsrKc8mcLCQvT39/PhhbS4kUgE1dXVaGtrY1MXdQ16e3tX/U5/8YtfMLbR6/WyRnhxcREFBQWcCCyRSHjDO3v2LB/e9Ho9Zmdn8dvf/haf//znWdJDhyGj0ciX/8HBQezevRtOpxMOh4MvS0VFRTedG82voqKCzb9kpL98+TLuvPNO3pCEQiGnp5IelszadHl2Op2MBCwtLYVYLMb8/DycTif++te/YseOHfz+eDwe3sgffPDBVd8HCiBMTU1FQkIC5ufnubrs9/uRmpqK5eVlflepKhYKhTj1PC0tDc8++yy0Wi1qamqQnJyMQCDALfW9e/difHycjasulwvZ2dnIz89ftUL0k5/8hMl3KpUKOp0OVqsV3d3dnHhLCNbFxUU2bwLXk7ApGG9xcRHBYBC7d+9GW1sbS+iI1lNeXo7Ozk40NDSwHn1mZgbBYBBf//rXbzq3119/neWTsVgMKpWKJWhGoxGxWIwr6VqtFs3NzRyeSqhjygGYnZ1FW1sbGhsbmTJDB6iuri422o+NjSEpKQmNjY3cmbvZ+NWvfsUejYWFBTidTtTV1eHixYtMlxKLxWwQpsMKFYXoIKxUKnHgwAH89Kc/xaOPPspmcJvNhpaWFly8eJE7zyQJ1mq1mJ6exn/913/ddG5PPvkkX75IepeZmQmXy4Xa2lo89dRT2LVrF0pLS9Hf34+ioiImq1GX02w2cyebZBu1tbUcyjg+Po7U1FTY7Xbs2rWL/TOUi7Fapsz3vvc9LC4uIi0tjb0NRAK655570NPTwxIbwnabzWYOzBSJRExMIggMADbPLy0t4de//jW+9rWvYXp6GiaTCRKJBFqtlguS//Ef/3HTuf3gBz/g9Yq8LdnZ2bh8+TJqamowMDCAnJwcJCcnw2KxoKysDFlZWeju7mY0ttlsxpUrV7Bz505YLBaWds3OzrIxf+3atVhaWkJnZydmZmaQlJTEyOjV8m7ee+89+Hw+LC0tcdFQpVJhaGgIBw4cgM/nYzgCcB0Hu2fPHhw9epQLNnQpb25uxoULFxh3qlQqWX3Q09PDJujBwUEEg0FUVlbi9ddfxy9+8Yubzu2nP/0ptFotMjIyOGU7Pz8fHR0duP/++2E0Glny5HK5kJOTA6fTCQDIy8tjz6PVauXCskajQXt7OxcEqqurUVdXx4GOJAfU6/XweDz49Kc/fdO5/exnP+MsJOqgG41GfPDBB9ixYwdHBBCha2BgADMzM7jjjjs4DFUsFjPmdmJiArW1tdDr9VheXsb09DTnLvn9fuzbtw+BQIA9kTabDd///vdvOrenn34azc3NmJycRDgchkAg4AskXXgCgQD7aQ4fPoyWlhZMTU0xpIPOHAKBAM8//zx3cDMyMrioQNlWDQ0NnMNVWloKt9uNO+64Y9XvNBgMsv8mHA7zXIuLi3Hy5EmkpqaitLQUBoOB81MUCgVef/116PV6lJWVcQdoeXkZpaWl/JwKhUI0NDTg9OnTaGho4Gwu8mWurKyseha5cXxs6ZTL5fpI0MzMzAysViuKiooglUphNBr5YOXxeFBTU8OIWyKlkJabTHe0+VNlh8hQhH8dGBiA3W5Hbm5uXDN4YmIioyVpc/V4PIygJY4+teYIcWs0GtnbQIulRqPhGHhCnQ4MDCAcDvO/Q1Iu0tPG06klJydzdsaNRmbSIi4sLLC+DgCntwYCAUaqkSmQDjb0c8hAFovFkJOTww8Q3eSVSmXcPAj6bkgzGwwG+cBEkjY6CC4sLCA/Px9msxl6vZ7TnQnJKBaL0dfXx9VR+l2IukOhYbTIajSauFhgCnEj1DGl0fv9fu5uEBUkPz+fyUzkiRAIBEySiEQi6OzsRHJyMtNAEhISuLVPbHlqtxOic7WxvLzMz+2NXg7qbExOTnLVjhKdTSYTysvL+R0gGgz9rjfmFgiFQn7fnE4nIpEIdDodAxLiyX+odUrPikgkglAoZAkbdSrIHxOJRNDX14eUlBTWC5PUTyAQMOGMws6Wl5dx7do1FBcXQyAQIBgMwu/3M7KSTPurDaKHkKGW8JbUsSGoQzgchtVqRXl5Oaanp5lSpFQqmUZ39uxZ/vfo4O9yuXD+/Hn+fhYXFz9yuY1XjbxRrhGNRhEKhRhJ6Xa7MTc3x8++y+WCSCRiw7nP50Nubi6USiX7c8xmM5NI6PMh7Ttp+YmsBFwHVax20aAq68rKCnep6NkBwJ06h8PBpByXywWr1cqfTVJSEhYXF+H1etHW1sadvRvpPCT3mJ6eZnP70tJSXCnh0tISX1zpGaN33Ol0IhgMch6RQCDA7OwsI6/9fj8js7u6ujjHgAgxXq+Xc1zogO35e/Ac8d/jrSP0uRMO22azMUBCq9ViZGSE3xWv14vExERMT09DLBbzv0nv3dDQEO666y5IJBKEQiF4vV44HA44HA44nU6Ew2FGg5OnKZ4PjT43Mm1SlZMIZiQFttvtMJlMKCkpgc1m49+ZfIe0HtHfo8oxhfLddtttOHbsGOvL6dAc7zslghTlXBCti/IkKO9qcnISbrcbYrEYnZ2dmJubw/T0NPLy8qDRaODz+XhvT05OxvLyMj/vlIYcjUaxtLSEmZkZ+P1+RtWvNsg74vV6sbCwwJdbsVjMnUK73Y6EhAQsLCxgZmYGDocDdrsd5eXljMXNycn5SOdhbm4OTqeTu62bN2/GyMgIh3PSM0QZXDcbgUAAaWlpDJgBwDQ22kunpqZY2qtUKvmgTEhcn8+HU6dOMYCBMhwWFhYwOTnJWVU0T5KxLC4uxu08Jicn88GXMncCgQCUSiVDfCKRCHsw6LMNhUK8ziQkJHDBlBKy9Xo9FhcX4Xa7MTw8jIyMDABgKEd6ejrLhFYbfr+fzxwUEOn1ehnYQOdOIncRMYw6MQaDgQMzZ2ZmMDExgR07dvBZaWlpCR0dHdi2bRskEgkXzohuGW8Nof1QrVZjfn6e182TJ09i27ZtHCRM5ymRSMSUwdnZWQQCAajVauTn538ECz84OIiEhAQuECUmJnLwIAXrUfbXamNxcRFSqZSLdsB1pQq9b7R+JCUlcfGsq6uLIyWA62fl6elphi5RV5HOLxRwSbAikoVTQfTjjI990SCdLgWR+Xw+TE1NYe3atfDckLg8OTkJs9mM8vJySKVSnD59mjsUpN+9dOkSZDIZ+vv7uWo/PT2NcDgMk8nE7HUy6Z49e/Yf/kJ0IEpJSWGSD1X1CUu4uLjImRZEbaIFTiQS8RdBCdd1dXXw+/3o6+tj3f6mTZtY8iGRSPhg+I8G8ZAFAgEmJyexsLDAvyslMy4tLcFoNCIQCHCAGmUorF27ls1VwHUtPkkc6OJGbT3SuBqNxrgIQ7qcUVIscJ36QRse/X0ysU5MTKC6uprxaNT68/w9pIdMc0Tt0Ol0MJvNTJugwzxVM+NRT8gsTkGJtPCQJIiqXmSEp67L3NwcV1MtFgu0Wi1MJhNjYWUyGR9IaaHNzc1FYmIi5ubmGPEXj9YFXN/EifAhk8ngdDohk8kYdUdUGqJr0QGa/m5GRgbKysrQ39/PlVyqypBBPBKJoLy8HGazGV6vl7GQ8QhAVCkTCoWMG6R3Z3p6GiqVCvPz84z/pfeSDodWqxVCoRB33XUXxsfH+bBKiFm/3w+HwwGFQsE0Fep62e12jI6OxtWlEwWM0p3lcjksFgtUKhUsFgsfqAkY0dLSApvNxl0sr9cLmUzGnTNKsqUEVpvNhsHBQbS2tjKyjwhpwWAwLt4WuH4ooEsVPXORSATt7e3Izc2FyWTC4uIiNBoNB4nSJk2dE2KZB4NBTE5OMrqRDpPUKSUZXSwW4+5TvO+VEuMBsBSTaCJlZWUwmUwYHR2FQCBAa2srm4XdbjcT+VQqFXw+H3t46NJJJCsiCH3wwQcsTYjFYnHXXwJe0Ma2tLSE6elpNDQ0oKuriyvY0WgUHo8HFy9ehEAgQHp6OuONFxYWuINFfHmv14upqSk2lRPJ58aN1G63x5UA0WdLgIfFxUWMjY0hLS2NCyV0uXY6nbDZbJyybrfboVarkZeXh5SUFHR2dmLfvn0MIlhYWGAvyszMDHJzc3ndSkxM5GDSeN8pXTQEAgFCoRD6+vpgMBjQ0dHBUAOLxYKxsTFs2bIF165dYwkkIdapozw2Ngav18sHAzoA0SGou7ubLwmEIo83qKOclJTEEiSFQgGTyYTs7GxMTU1xoC4hX2lPo4wWsViM4eFh5OXl8TNG+NGqqio+uJEkjNaheJky1CmgQykduOmyS90mulzQ70sJ0/RuNjY2YmxsjP8czW9ubg5jY2MMdqBDV0JCAsbHx+MemAmVT8b2lZUVPntYrVbec2jfIe8VpXJnZGQwHvf48eOc8JyXl4fR0VF88MEHDNLIyspCV1cXEyH9fj+qqqpWnduNIX2U3RCNRpGTk8MEqUAgwPQwAgiEQiGMjY1heXkZmZmZyM/Px/nz5zmzSywWQ61Ww+12Y3x8HFKpFNnZ2RxoTF2geOckKuSGw2F+DgiV73a7odPpMDMzw6nvBEe5cuUKJBIJKy5qa2vx6quvwm63c8K7y+ViyV4sFoPRaERPTw+v1RqNJi4im4ob1O2OxWLsIdRoNOwhpTBoItY5HA54vV54vV643W7k5eXhpZdeQklJCV+mBgYG4Pl7mK9Go4FMJkNfXx+jyhcWFvhsdrNBl0wq6AJgQqrJZEJOTg7cbjdGR0d5LUxPT8e7776LNWvWIBaLYXR0lH/G/Pw8dyRJIXDj2knB0eQzjSdbvXF8bI8GmfAqKipQW1vLEoo///nPCAaDTCNITk7G6OgoOjs7IRAI8Oqrr+LNN9/EmTNnmJBRWFgIn8/HAU9zc3NMJKDKd0dHBywWC+t94yWFrqys4PLlyxxMlJaWBrvdjldffRUvv/wyL6gkNRCLxWhqakJZWRnWrFnDuEy6BKysrECv10OhUKCxsRGf+cxncPvttzO3/saFlChMqw2RSISenh5kZmaipqYGubm5yMnJ4TRVStQlBOTy8jLWrl2LjIwMDAwMMF6XEIvvv/8+Xw6USiXKysowPT2N8+fPc/gMERUoF2S1kZSUxFhKqjaRlp0uG8q/B20RZu3BBx9EWVkZ3G43PB4PVCoVqqqqeAHS6/XIy8tDcXExCgoKsLS0hO7ubiwtLfEFiszP8T43hULBmvVoNMphahMTE+jr68PU1BTkcjmCwSBee+019swcO3YML730Erq7u7lNLxQK8dhjj3GlIRgMYnFxkRdOoVCIXbt2QafTIRKJoKysLO5Fg1KaFxYWkJWVhZKSEkilUnR3d+PEiRMAwESyt956i7WuRHBxuVwIBoOYmJjAyZMnYbFYOIDJaDRyNZmgALW1tUhISOCWdTz4AMEQqAtB0gX6DOlASOZvq9WKAwcOYN26dZxvkpubi7179/JCHIlEmEoilUpRUlLC7VpaxEk2948WHq1Wi8uXL6OjowORSIQ7W9QdImMrJaFTZsXCwgJLUggNuX//fq4OUiiTUChEUVERmpubOeCO5ELkQ4j3zJEXQiaTQaPR8CKfkZGBnTt3snGOuqMkwzSbzTh79ix/j2q1Gi0tLaiqqsIDDzyAu+66i7X/9JnR800VWPLArfbMjY2NcdAdfSbt7e3o7+9n7TfJcPx+P7KysjhRNxqNwmg0YvPmzcjOzoZQKERubi6vDy6Xi4EDZWVlkMvlCIVCfJCPp8UlsAUFLioUCpZFhcNhFBUV8X5BHVmaJ8nSxGIxamtrmUxElwqLxYKzZ8/i+PHj3KGlAyYdMicmJladW2ZmJi5evIhgMIisrCwmQwWDQSYUSqVSBINBDAwMYGVlBdXV1RgeHsb777+PUCiEhoYGVFVVITs7G48++iiuXLnCMhryAxqNRgZhUGEmMzMTdXV1q86NihVCoRCVlZWorq5m0+qZM2cQCATQ1dWFo0ePYnZ2lrGdhN7Oy8vjvfjcuXP4n//5H0YlazQaTE5OIjk5Ge+88w5r/+lCbjQaUVpauurcyMuVnJzMacixWIwLTFlZWUhJSeEqv9vtxu7du7F//340NzfD7/djdHSUMaTU8Zmfn4fJZMLk5CTLxMiUT1KSj5MHMTg4iPT0dFRWVsJgMCArKwttbW0s71peXobP58OlS5eQkJDAMqhz586xH3NlZQXPP/88rl69CpFIhIqKCjQ2NqKiogJFRUU4dOgQMjIyEAgE4HA4GHscL8xVoVDg+PHjfJ4ArpP6ent7mWbV2tqKoqIiTE9PY2hoiPN25ubmGOXd0tLCVKdz586xqiEhIQEDAwMMXKCuDnUz4qF3icZJeT8qlQrbt2/H8PAwG7qXlpZgsVhw6dIlJCUlYXh4GPn5+Zyd4nK5UFNTw6bt1NRUDA8Ps3zLZDKxR3Pt2rXQarXweDxYXl6O67NNS0uD0+mEUqlEdnY21Go1otEoXnnlFfzlL3+B3+9nyElfXx+MRiO8Xi+0Wi13EeRy+Uc6OpcuXeIg30uXLmHDhg3Q6XRQKBScIE5Y23Xr1q06N5lMhmvXrmF6eprDF3U6HTZv3oxYLIYNGzZg3bp1HGgoFAohl8s59E4ikXCYqlAoxJkzZ+D1etHU1ASpVIqXX34Zf/rTn9hMX1FRgVgshu7ubpa9rjYIKOL1ehl4kZSUhPPnz+P06dMIBAIwGAxYWlrC8ePH4XA4sG/fPmRlZXGnLycnB3fffTdmZmaQkJDAYZ0kZ6XgS1p/SVqXl5cXN7X8I8/ex/pTuF4NFQqFOHfuHD744AOMjIxAJpPh17/+NSO7tFot6uvrceDAAYyPj8PpdGLDhg1MqKEwqPfeew8HDx5EZmbmR27RX/ziFxm7Njk5iT179uDrX/86FAoFdu3atercwuEw9u/fz8g3CqL7+c9/jieeeIK19EVFRaiqqsK5c+e4Ur9nzx584hOfwKZNm7B161b4fD589rOf5QqTx+PByMgITp06hQ8//JBbuhs3bkRTUxPOnz+Pvr6+Vec2NjaGdevWYWBgAH19fRwA9d3vfpc3j5KSEtx222149NFHEQgEuFPS2trKBCCJRIKGhgb87Gc/AwDWpvr9fjzyyCNobGxES0sLKisrUV5ezmnDNptt1bktLi4iKSmJcxQMBgNUKhV+/OMfc3aFxWJBcnIy1q5dyxjG4uJiPP7443yIycjIwLlz56BQKKDVapmec+3aNdxzzz2IRCKoqKiAz+eDwWBAS0vLP2Tzk7xodnYWwWAQEokEw8PD2LdvH6cYU9fkgQceQEVFBUuoDhw4wIjMHTt2oKenBxqNhjWPRqMRfr8fu3fvhtlsxsWLF/E///M/WFpaQm5uLkZGRuIe5ilbwOv1Ynx8nOlo3//+9/lSrFAosH79evzqV7+CyWTCD37wAwQCAezcuRPbtm1DZWUlWlpaEIlE8MUvfhEWi4VNdHv37sWuXbvQ0dHBleiamhqsXbuWpXWrDQp/JD38unXrEAgEcO+993IgGJmQ6UAil8sxPDzMFLBr167h7bffRjAYRH19Pfua0tPTcfHiRezatYszBjweDzIzM5Geno733nsvblUeuG5WJ8kYHSicTie2bt0Ko9GIbdu2sU/opz/9Kd555x20tLRg3759ePjhh9nwLxKJ8NOf/hQ//vGPMTc3x8nFZrMZVVVVaGtrQyAQwMmTJ5GSksI693gVZpIhkM+DLhyUXfHhhx/C5XIhNzcXKSkpePHFF7Fjxw5kZmbiq1/9Ku644w7ulhH69vLlyzh9+jTGx8dRU1ODn/zkJxCJRPjWt76FK1euwHNDsuw/QgPv378fEokEU1NTsNlsEIlEeOaZZ/DpT38aGzZsQGVlJcrKypgoMzc3h8nJSTz88MPYsmXLR7ohGRkZePXVV7nL7HK58NnPfhZvvvkm/u3f/g3d3d0oKyvDzp07kZaWFjcNORAIoLi4mKVq1Ln1er0chkcXX9KY19XV4b333mPK0OHDh2Gz2bBv3z72dFGlktJ/L1++jPT0dIRCIaxZswZ1dXU4ceIEX65vNsbHx/HYY4/B5/NBKpViy5YtjJa8du0a460DgQA2btyIvr4+pKWlYX5+Hjt27IDVasUvfvELjI6O4siRI/jtb3/LQauksybPRmNjIzweDyoqKrBhwwYMDg7GzeOJRCKoq6uD0+mEz+djs/B3vvMdVFVV4a677kJZWRn279+PZ555Bu+88w5+8IMfsMxtZWUFZWVlDJz41re+hcOHD3OScllZGbZt24bbbrsN3/zmN5GRkYGHHnoIX/3qV1nuGu87zczMhN1ux9TUFEskN2/ezFLioqIi7N+/H4888ghX8BcXFzE0NMT67oSEBGzZsgVFRUVQqVS45557sG7dOs7aunLlCgwGA18StFotOjo6MDIysurcpqensXnzZr7cNjY2oru7G7/4xS9w8uRJ3HfffWhqasKmTZvw0ksvsS+oq6sLoVCIux7Z2dlQqVT47ne/i/T0dBgMBuTk5KC5uRlPPfUUdu7ciQ0bNjAF6MYu+mojGAxi37593Amgv0eIZYvFgv7+foyPj/NFRCgUor29Hdu2bcPMzAxefPFFFBYWQiQSseGazNJGoxEPP/wwV8yrq6tRU1MDmUyG+fn5uGAaMuuTIZ4Kuo888ghLuq9duwan04m1a9fC5/Phm9/8JgoLC/Hggw9i48aN6O7uxvPPPw+fz8fY2gMHDuDuu++GWq3GM888w9LDqakp7g59+OGH/3DtLSsrQ3t7O9ra2vjQ/G//9m8oKSlBZmYmioqKsG7dOtx22204e/Ystm3bxkZxk8mEEydOIBKJwOl04kc/+hHcbjfKy8vx+OOP42tf+xruvPNOBiucO3cOWq0WW7duxdzcHE6ePLnq3BYXF7FmzRqIRCKmRXq9Xs6SoiykyspK7N69m6WECwsL2LhxIzIyMnD58mW8/PLL2LBhA/77v/+bg6WzsrJQU1ODuro6/P73v8fLL7/MGSbku4iX/URkNK/XC7lcjq1bt2JpaQlf//rX4fP5sHbtWpb77969Gxs2bIDL5YJOp8Pdd9+N7OxszMzMQCKRoL29HVu2bMH8/Dz7EdVqNWdxNTQ0QCqVQqfTobi4mFH8H2d8bDP4888/j97eXv7HJicnMTc3h+rqapSVlcFut7MBdnZ2Frt378aaNWvQ3t4OlUrFXoY//elP+O53v4v/+I//wA9+8ANOF3Y4HHjttdfwwx/+kM28JGGgNu9qFZhvfetbrHknqlVNTQ1eeOEFtLa2YmpqChs2bOBK9dLSEodSkXdjaWmJ9ZhZWVnIzMzEsWPHcO7cOfj9ftTV1cFkMuErX/kKRkdHUVlZCYlEgkOHDiEQCKxqDnvhhRcQCoWgVCrZp1FUVITTp0/j9ttv50sK6fO/8pWvQCgU4rnnnoPRaERFRQUH69lsNjz11FP47W9/C7PZzKnWRF/avn07pqammLJUUlKC0dHRVQlKb731Ft58803U1dUxu12v12NgYAB33HEHRkdHkZmZCZvNhhMnTvBhhvCfhA62WCy4du0aS5BISx2NRvlGf/z4cezcuZPNiHK5HOnp6auaOH/2s58hIyMDKSkpkEqliEaj6Ovrw7Fjx/CJT3yCsyUokfeHP/whzp49C6vViqmpKdZ29vf388tFYXFLS0uYnZ3FxYsXkZ+fz0nN3d3dTI05efIk/vVf//Wmc3vppZeYNkXSBOLxr1u3DufOnYPBYIBCoUAsFsP27dvx7LPPoqmpCa+88gp0Oh02btyIYDCIoaEhlJSUYGlpiXWs1CpPTU1lZjUhX4lgRgzu//d49tlnuRphs9mwsLCALVu2wG63Qy6XfyToLD8/HxqNBi6Xi0O41q1bhzVr1uDDDz9EU1MTzpw5g09+8pOYnJxENBpFamoq3n33Xdxzzz2IRqPc2aGDBqESVxt/+ctfsLS0xAcPCjjr6urCvn370NXVhaamJgDA2bNnsWvXLoyNjcFoNGJiYoI3/Z6eHqxfvx7V1dX485//DI/HA71ej6KiIly4cAFlZWWYmppCdnY2ZzfYbDbWyt5sPPXUUxz2RfLABx98EMFgEPn5+Xj77bexc+dOxGIxPPPMM9Dr9Xj44Ydx/vx5dHR04LbbbkNJSQn+8Ic/YOvWrRgaGkJ9fT0f6oLBIL72ta+hr68Pb7zxBoxGI8uo8vPz4XQ68fjjj990bt/73vfgdDq5opadnQ2LxcKX6G3btmF6epoN2ImJibh06RIUCgWi0SjS09OZgKVQKPD+++/jC1/4AstKx8fH2cw+MTHBm8jy8jLjtFtaWm46t6effhoKhQKpqakMiCgrK8OLL76Ie++9FyKRiJO4KWRrfn4ehYWF6O7uxuLiIgwGA/Ly8pgqt2nTJgwPD3Mo3TvvvIPf/OY3OHbsGCYmJjgI02g0Yn5+ftXP7fXXX0dHRwfWrFnD/PudO3fi0qVLnLGg1WoxMzODa9eu4amnnuLu3O9//3uEw2HU1taioKAABoMB4+PjSE9PR0dHB+bn56FQKDA+Po7m5ma43W6WaanVarS2tqKrq2vV9ffJJ5/E4OAgHn74YQDXCVlVVVV48skn8elPfxpGoxFDQ0MYHh6GUCjEZz7zGSYmHT58GElJSaioqEBOTg4OHTqEM2fO4Mc//jEikQh8Ph/C4TC6urrwve99D8888wx8Ph/jvqnyvRrE5Ic//CEmJydRV1fHHr3bb78d3/3ud/Gd73yH50TFuNtvv50lQa+88gpfUpeWlnD69GksLi6iqqoKfX19mJyc5NBUlUqFixcvsgSbOpQZGRn42te+dtO5/eY3v0EoFGJZlsPhwAMPPICnn34aX/3qVzm3amhoCD6fDz//+c+xvLyMP//5z+jq6kJiYiJqa2uxZcsWnDp1CmvXroVcLsfExATOnj2Lzs5OxGIxfOtb38L8/DyysrK4o5GdnY0TJ07giSeeuOncnnvuObS3t3OALO0NZrMZjY2NeO6551j9QTlA7e3tOHDgAPsUlpeX8eGHH2LDhg04fPgwhxDOzs7CbDYjMTERt912G0ZHRxEIBBAOhxGLxTi/5gtf+MJN5/byyy/DbrdzdsTKygoeeughfOc738FnP/tZHD16FMXFxVz4IOCE3W6HUqlEXl4e5HI5pqen8cYbb6CyshJFRUUfoaaJxWLk5ubimWeeQUtLCyNbybuy2ty+//3vY35+ns+KIpGIPcH5+fkcCkyFzNbWVnzwwQeIRCLcpbPb7Th58iRaWlqQk5ODrKwsdHR0sPIiKSkJ+/fvx5kzZzgwMhaLcXjuaobr//zP/8To6OhHQh3T09Pxve99D9u3b+cOLcm/PvWpT+F3v/sdysvLsbKywgHUo6OjEAqF7P9NTU1ljK1AIIBcLofH44FWq4VarYbH48H58+exY8cOtLa23nRuv/3tbzE7O4ucnBz2FVZWVuLcuXPQ6XTQaDTw+/1Mo/rSl76EX/3qV9i4cSM2bNjAPtyqqir4/X689tpr2LZtG06dOsU5L6+++ipfjhcXF/nMum7dOvz1r3/F9773vZvO7cbx/ymwj9KtbTYbVzmoJUt0l9nZWdhsNoTDYQwODuKNN95g01M4HEZ+fj5efPFFfO5zn8O5c+cwPz/PerWpqSk8+eSTuPvuu9HX1wepVIqUlBScOXMGdrt91YMfAJSUlLDGUiQSIRqN4tFHH2VsK7WSMjMzYTQa0d/fzwcjMhFpNBq88cYbMBgM2LhxIwwGA6qrqzE4OIjk5GRUVVXBbDbDbrejv7+fzbDxqggOh+MjCcCBQABzc3NobGxkjvrAwAATB+gCQfpfh8MBvV6P8vJyOJ1OPPjggzhy5AhLuG7MffB4PJiYmGAz4OzsbFzDH22QOp2ONez9/f0IhUK4fPkyqqurkZyczLH0FHV/8eJFTuEErreLc3JyGLlJxBY6wNbU1HASKemFSbO62qADttfrZarR0tISmpqaEA6HMTY2BoVCwbfrF154ARs2bGDzLlVSAOC1117jALbNmzdjeXkZAwMDWLNmDQYGBlBQUIDOzk5EIhEsLy/j0KFDcbMq/H4/6uvr4XA4+PtPSEjgdF6v18smfeJk03O8fv16+P1+tLW1QSQS8SGPXnRiwJPxqqSkBLOzs5iZmcHCwgLm5ub+YSVSpVIxpYmS27VaLfr6+pi3Dlw3H5eXl+PcuXPIzMzE1q1bMTMzg1deeQVisRgXLlxAWloajh49CqFQCI/HA7fbjYqKCpw4cQLl5eXIy8tDNBplDGY8Ux1wXRqpUqk+IiEbHR3FI488gmeffRY6nQ5HjhzhjJvm5mao1WqcP3+ezfsqlQqxWAx/+9vf8LWvfQ2XLl3i76GtrY2TVEk2ptPpWCao1+tXvWhQB4NkiSsrK3jppZcQiUQ4WM9isXCnJy0tDWazGRaLBQKBgIMkRSIRfv3rX+MrX/kKDh06xPLDhIQEbNu2Dc8++yzq6uowOjrKF2kKClttEGqY1klCa2dlZbH0k9C9pO2nyzCZ0Kk1f/z4cdx9992Ynp7GzMwMS5BIbpKXl4fe3l4MDw8zonliYmLVi4ZYLEZBQQFsNhsTc6xWK6qrqxm8QIGKCQkJXJyIRqNMQJqbm8Pp06eRkZGBwsJCfgezsrK4Inz8+HGMj49DLpczBnpiYiJuhXliYgIlJSUAwIe4S5cu4Z577sHZs2exuLjIwAWSo9hsNigUCmblv/3229iyZQv6+vqwdu1aDA8PQyqVwmAwcBfsRs9GR0cHy+vifafBYBByuRzXrl2DXC5nJPldd93FXf3Z2VnGj7/xxhvIycnBuXPn0NDQAL1ej5WVFdZ4/+QnP2F0NqkHsrOz8fzzz6OsrAznz5/H8PAwqxPi5RgFg0GUlpYyUpgO6vv27cPg4CCWl5fZw5WcnIwjR45gZmYGVVVVyMnJQVJSEvr6+uByueByubBmzRrMzs5ifHwckUiE15WWlhZs2LABJ0+ehEqlglKpZAn0aoMkx5FIBHl5eUhLS8OJEyfwla98BSqVCl1dXZDL5WxuPXToEIaGhtDb24va2lpYrVYuMg0NDXFeFnUKl5aWkJWVxReGq1evoqenBwkJCWhsbIy7/rpcLhw8eBBmsxkulwsajQYzMzMsj6JsFOp4GAwGeDwevP/++9xFys7ORkZGBt544w20trZyWnh6ejrm5uZw+fJlbNu2DVKpFL29vSxVIo/DaoOymsLhMKampjA9PY13330XqampkEqlWLduHaLRKD8fBBV59tlnkZ6ejqqqKr6EhMNhnD59GikpKcjIyGCv0uLiIvLz87Fjxw7OZSotLeUMm9UGeQ3kcjnLLLu7u1leR3J1Og8kJSWhs7MTS0tLHNxJnYdAIMB7C6HeKXTzueeewx133IGenh6kpaUhEongxIkTyMjIWPWiQZ0gKvYlJSWhra0NmzdvhkKhQF1dHdrb2xnfPzs7i6amJvh8PvT09GBubg75+fn8LlRXV2NmZoZpiteuXYNQKMTWrVvhcDjQ19eHjIwMaDQapKenxz0nraysICcnh9/RSCSCc+fOQSAQoLCwkDNhaC3t6OiA0+nE/Pw8Tp06xf5aIpsmJibiT3/6E9LS0iCRSDA/P48vf/nLeOGFF1BbW4vOzk7eK1599dW4UuQbx8e+aMhkMtjtdohEIg5gUiqVOHz4MNavX4+NGzcyiYIIHiKRCAaDAS6XCzKZDHl5eZDJZJBIJPjjH/+Ie++9FwKBAFNTU3C73RCJRNBqtTh58iS/mHRwjaeLFIlEGBgYgFarhUqlQkpKCjweD+NLa2trmZlM1fhPf/rTOH/+PLq7u+F2u1FWVoZIJIKsrCz09/dDrVZj69atKCgowOTkJJMZaEMymUyM7YsnKyBKQ2JiIoRCIdRqNQQCAdrb27m7QhQHqoBPTU3xxh0KhSASibCwsACZTIaXX34Z9fX1WFlZgVqtRmVlJc9jenoaTqcTFosFUqkUWq02btuNKDFUTddoNBAKhTCZTGyQ7Onp4YWHDIBpaWkcdX9j1+G1117Dxo0boVarYTAYWLqhUCggEongcrmYvkFhP6sNksIQy5w07W1tbbjvvvtQVVX1kVA8Yr3b7faPEJ/oUHPvvfdibm4OtbW1bLA/efIkbDYbHnroIQwMDGBkZASxWAxarTZupkFCQgLrwskk2dPTw8zz1tZW9Pf3o6urC5FIBA6HAysrK4yfE4vFKC0tRVlZGS5duoTk5GTOcJHL5Th58iScTicyMjIgl8sxMjLC1TnKA4g3KNiQDhIikQjXrl1jj4LL5WJPVFpaGpO4aJ43ZhTMzMxg9+7d7CkhSSQZ2qhLlJyczKSNeIMMZUTnIVPwO++8A6/Xi/vvvx9TU1MMTKDwKErEdjgcKC4uZqrUe++9h3vuuYcvoqTDHx0d5U7r1NQUpw3HkycpFAp+39RqNWN0yXhNAZQmkwn9/f146KGHIJPJWJcsl8u54xCNRpnuceNmR0GkoVAICwsLCIfDWF5ehsfjiauZl0qlWFpaYv1vamoq5ubmcOXKFezYsQM6nY5Tjs+fP4/FxUU0NTVhamqKq5IJCQloampiIAN1A6ip3dXVBYlEwkZyp9MJt9sNi8US9+ItkUgwMTHB2RihUAizs7Po6enhDc7n8zHkQK/XQyqVsgdNq9V+xIdDa4Rer4fdbsfi4iIqKyt5Q9ZoNJzPMDo6ygjOmw1CW6enp0Mul/PzShhSWpPooJOamsrUF6lUioKCAq6sBgIBPPfcc9i3bx9Xha1WK65du4a8vDyUlJSwUTUajSIcDseVFhDRhZ7d1NRUzgeiXBSSq9L+QIRGIh9ptVokJyfDarWir68PpaWlTLFyu90IhUIoLi7mtUOhUEAqlf5D+ACtWykpKUhISGCCJCWsG41G9j+RmfvOO+/EX//6V8RiMS4oAdchIJcuXYJQKGTfnEKhwNGjR5GcnIyamhpWG1Bqczw6HGVgEcLZ7/cjOTkZ586d4+Ld2NgYuru7YbVaUVFRgbGxMYjFYgZu0LpDBznKaCI4g9VqRVZWFhcZ1Wo1y8fieQtlMhnv7TKZjP0dlDQvEongcDi481dQUICWlhY88cQTLBFcWlri75UuskRuJFM1qUtqa2thMpkgEAhQVlaGwcHBVee2uLiIM2fOQKlUMvXP4/GgoKCAi5MWi4UPtvfccw+CwSD7jGjtptBmApkkJCQgMTGR8e0jIyOs6bdYLHwxj0cjJOBEIBDA1NQUhyyfOnWKw5+B6z7b0dFRPof19PSwfyUzMxOFhYXo6+tjYpZUKsXKygqWl5cxOjrKHXMKNk5MTGRa22qDyGU6nY7fBfpOlpaWkJCQwPLTK1euwO12o7W1lYmOnr8HGOv1ehw9ehSxWIyhGUT5ys7OxsLCApKTkyGXy9l3Q3vSamNubo6fz8TERIhEIiQnJ3M3eOvWrRgdHUVvby9DQ8i7qFaruQvd19eHDz74AE8//TRjoGOxGNrb2+H3+5GdnQ2NRoOcnBz2ShLA4eOMj+3RWF5eZpmF1+uFWCyGUChEYWEhpqensbCwwB8a8Zj1ej3rFOm2VVNTw4sdZRFoNBpkZmZi165dTHCh7AT6MOOlcRLne2ZmhrGuRCICrhs5c3Nz2eDt9Xqh0Wi4I2Oz2T6CuW1ubmZikFgsRmJiIles0tLSWONI3RyFQhH3syO86MrKCptx5XI55ufnGakpEolYG1xaWsp4SIlEwtVMi8XC3QdayCg8D7h+IKKKKt1S4xmJlpeXeSMhrbZcLkdZWRm/6LRh0iUkOzub/x0iOcjlcgwNDfHvaDAYkJuby2nwkUgEKpWKXx6BQMALU7yRmJiIxcVFvgTRYuTxeJCeno5YLMa/49zcHB+ayVhJqcF0KSOCDgAolUoIBAKmaQQCAT5gpqamxiVkUFWWFjKCDxDOl4KVtFotCgoKYDKZ4PV6+d/3er28maSmpqKiouIjAWlSqZQ9LaRtJ5yfXC6P+y4kJCQgEokgGo3yhSopKQkFBQUc0KRUKqHRaJCdnQ2n04ny8nLuRigUCpSXl6Oqqoor4qQlpoMk+aoUCgVsNhuSk5NhMBgQDAbjYoEB8OdPCc06nQ4Gg4EXU7pYGgwG1NXVIRwOM26VEL8A2HBN5nHSt4vFYrS2tnKlhj4Hqs4RHedmgypg9HcIqJCRkcHfM60pBCJYWVlh3CGRafR6Pf9ZytWg75HeLdKz03cVjUa58n6zIRaLOR8lKSmJ8wbEYjEfGqha6vF4+KIxPz/POGiHw8FrCcnxCG3tdruZzEO8eJJ3EKko3qANR6PRwGAwMJ6V4B5KpZK/a8pLisViSE9PZ1mDUqnk55wIgoRYpkqrTCbjKjp1NeKtIyTjpIMvoTvJF1FVVYW0tDSkpKSgoKAAU1NTyM3NhUwmQ1FREf93ogeRkZ+02gkJCcjPz8fo6CgfRIqKitDU1MQFudUGGfLpsELvNSGRVSoVMjMzUVBQgIKCAjadp6amMpXLbDZzp8toNCIUCjH1jhLAKeuHMh1oTYz3LgDXO3yEPCWkNKV4azQafiYpv4rWaEJQq1QqrFmzhgN6l5aWUF1dzfkMFRUVmJ2dxfz8PF+0yJgf7wAjEAigUCiYwkda8kgkwvuAWCyGQqFAZWUlI/IVCgWfAwh+QkQd6iYUFBRw6K/X64XL5YLT6URaWhp7++JRp+jMQmQsAjMQ5ZJQr0lJSbyuUUesuLiYpW0ajYa7lVRwJGlac3Mzo9tVKhX0ej0T5OKpKyjAlQJhy8rKIBQKUVBQAJfLxYhU0uEPDQ1xTEB1dTWMRiPkcjl/Nlu3bkVCQgJX0z0eDyO4aT5EoqNO+2qDDMiEGqYuDRGdyLxuNBpRWFgIi8WCxcVFDmlVqVSQSCSsrKivr+eDvEAg4GLY0tISX+wFAgGvNfEO8zcS+VQqFTQaDcu7iIa3srLC+yMVPYPBIKs5aJ0QCATQarVM2ZNKpVheXkZFRQVTCMk8T3tEvM4jrd9EoFOr1RAKhSgpKYHFYoFCoeB1lDJEKisrYbPZoNPpYDQa2Yc4NTXF75dMJoNarWZ5vN/vR1JSEmOuKUPk43o0PvZFIxAI8AWCsHl+vx8HDhzgTZUkB/n5+XwQsVqtzG4mA9Lo6Cgefvhhpv/I5XLk5uaiurqaF0eqLBoMBmZtrzaSk5ORnp7OXzgFaFVVVTHOVCaTQalUMplhcHCQq3B0YKU2/+23347s7Gz+IumhoYNgX18f0yGo0rjqB/z39vWN7GsyUlH3JTU1FZmZmSgrK8PCwgKKi4tZj67T6SCRSCCXyzE+Po5Pf/rT3GIk1ntmZiYkEglXaihpnOQnqw3auOjAQg/N+vXrIZfLkZaWhoKCAlRXV2PDhg0skSKjH0nHIpEIurq6kJ2dDb/fz74F8tVQSiah2uhGHa/6TRUD2gSA64v4pk2bEIlEYLVaYTab4fP5mO7Q2NgIg8GA5uZmVFRUMFtdo9FgcHAQIyMjuHr1Kus2qRI8OTnJt/ba2loolcp/mHhJh086+Gs0Gtx3331MmBKJRCgrK8PWrVs5u4DmEo1G2RhIPPVIJIKBgQGMjo5CpVKhvLwcoVCIzfwGg4EPPvHITqStDwQC8Hg87Fmqq6tDTU0Nv5+NjY2orq5m4g0x7YnKQZt+U1MT3G433G435HI5BzJR8vnk5CSWl5dZHhgv/RUA698jkQgikQhjpXfu3AmlUomxsTF4/h6ctnbtWlRVVXGLub6+HrW1tdDpdIxFPXjwICQSCSorK6HX6zE9PY2srCyo1WpO+ZXJZMjJyUFJSUncQ6nb7YZarYbf74fL5eL8BXpmqcOUm5uL7du3c1WbAAD0TMRiMQYPFBcXMz54aWkJer0ecrkcycnJfHFUqVRcaFhtJCQkcJWTLhaLi4toaGiASqXC5OQk7HY7xGIxsrOzkZuby5S1mpoaFBYWYnFxET09PZDL5cjOzkZnZycz5p1OJzZu3Mh+MPrcFAoFIpEIo5xvNqLRKK+DdIAyGAzYunUrH3xSU1NRUlKCpqYmTr7NycnB2rVrUVxczBdMAgukpqZCo9FAp9NBJBJxpTY1NZVlu4mJiYwujTc3tVrNBR7Kh6Hwu9tuuw0bN25EVVUVKisrMTQ0hKamJoYvKJVKzM7OQiKRwOFwoKGhgXNxCFG6Y8cOAOALTU1NDTZv3sxBm6sNQtOSHj0zMxNSqZTlOXq9HgUFBWhsbMT27dshFou5G7Bx40YO16Rsmi996UvIyspCaWkpQ0EOHDiAwsJC9rnRYTMYDMa9aMhkMu5iEb5WIBCgvLwcCoUCYrGYq8h1dXUQi8UYGRlBTk4OioqKkJmZyXNJSkriTndjYyNjz9evX4/i4mJIpVIMDw+zqiHeoQ+4vsbV19czBp/C5BobG1nKaTQasXPnTtx///1QKBQoLi7md4gwyoT9JLkWoU41Gg1aW1tZSkmSSOrWxbsEJSQksOeR3gehUIiqqir2KZSUlKChoQH19fUQCoXo7u5Gc3Mz9uzZg23btqG8vBzp6en8OdJ3kZaWBrlcjo0bNzIanSrl5L+LNxITE7FmzRoUFBTAaDQiLy+PE72psJKfn4/du3fjgQcegMfj4XVGq9WiqKgIJSUlyMjIgMvlwrZt25CZmcnnP9qrysvLIRQKMT09zYVbOsfF+9yysrKwvLzMhMZAIID9+/ezvIzoZFu2bIFSqWTz+86dO1FbW8vFwUAggMbGRn7X6f3S6/UoLCzkQu//u2u92tBqtZx+npGRwdQ+AgokJibC5XIhNTUVe/fuRW5uLqLRKCwWC0pLS1FaWsqo45qaGuzcuRMymQw1NTXIz8/n71AikSAQCMBqtTJtlQiiqw2VSgWFQsEWAaVSCaFQiMbGRgQCAd5PCwsLcfvtt0Oj0aCyspJN8TKZDFKpFHl5eSgvL0dXVxeDaajIp9PpMDU1hYmJCZjNZuh0Oqxbt44/k48zPvZFQ6fTob29nY3SVMn+4x//iIceeugjIWnT09Oor6/H//zP/6C1tRV2u52JDo899hgUCgX+9re/MalKqVTCbrdj//79+NGPfsTVkKGhIczNzeGxxx5jqcfNBumQb7/9duTl5cFsNuOOO+5Ae3s7M7YJwScUCvHoo49idHQUe/bsQWZmJsrLy1FZWYn+/n6UlZXh5ZdfRmNjI4xGI7Kzs1FSUoKuri6UlJTgzTffxDe+8Q3s2LEDOTk5+NrXvhZXZpORkYHExEQUFBRAq9XC6/VidnYWr776KjZs2IALFy7A6XQiNzcXBQUFaG1txRtvvIGioiIMDw9Do9GgpaUFMzMz2LRpEwYHB7F+/XruxIhEIvzqV7/Cnj17oFKpWPNOqerx5BhVVVWsbXS73fD7/UyPaWhowOHDhyGRSFBXV8cGvbfeegt33HEH5HI5Nm/ejG3btuH999/HV7/6VdhsNuTm5nI1sqysDL/5zW+we/du1n8TVjYUCqG7u3vVuQWDQc4Ncblc6OnpQW1tLTQaDe688058+OGHUCqVvOjs2LEDw8PDqKqqwm9/+1u89dZbXPG477772JxIZK2ysjIoFAo89dRTHEbodDp5Tv/7v/8b93k7cuQIH65nZmawceNG9PT0YO/evRgeHsbw8DAz4smgODMzw/PIzMzE22+/zS1TwrFSOKHVasX27duZ1U2BRHq9HkNDQ6vOTa1WY2JigqVCDocDWq0WP/nJT5CTk8MBbj09PXj33XfR0tKC8fFxHDx4EDqdDlKpFKFQCG+++Sa+/e1vw2q1Ijc3Fx6Ph/MKzpw5g4ceegjz8/MoKChAR0cHTp8+jbVr16K3t3fVuQFAdXU1/H4/U70uX74MnU6HYDCInTt34tixYwgEAlhYWMCbb76J4uJi/PKXv8S+fftw4cIFTE9PIykpCX/+85/x7rvv4tlnn8XKygpMJhN6e3ths9n49wmFQigrK+NMHolEwr6dm428vDw2+dLFgAzLIpEIo6Oj8Pw92+fixYsszSoqKmIpHBGBdu3aBYvFgurqak4Tl8lk6OzsRGZmJk6dOsWGxbS0NOTl5WFmZmbVuVHHYsuWLcjJycH4+DgcDgd6enpYDkUm6cLCQmzYsAHPPvsstm3bxt0ysViMK1eusByHCFAZGRnIyMjAn/70J3z2s5/Fzp070dTUxNU/2gRXGxKJhLs6ZFjNzs7G+++/z9jRvr4+jI2NYXh4GFlZWZidnUVdXR0sFgujg4mSdubMGdx1111IT0/H0tISRCIRJ5PTuuL3+7GwsIDW1ta4gVaUdRKLxWC321k+2dbWhr179yIQCPCeNjIygm3btmF0dBTBYBBXrlxBSkoKPvGJTzAwYnJyEhUVFVyYyczMxKuvvoq7774bL774IhobG5GVlYXExESUlZXFreKqVCr88Y9/5MDWkZERqFQqHDlyBOvWrYPNZsO5c+dw4sQJ9Pf3Y8+ePejp6cH8/DwkEgmTlQYGBjAxMYFDhw5xngHhgt9++200Nzfj3XffRXNzM1paWlBXV4etW7fGPVzl5ubC6/UylMJqtSIpKQlHjhyBzWbD6dOnIZPJUFxczEb1hYUFyOVy9Pb2Ym5uDrFYDP39/SgtLUVHRwfS09Ph8XgQiURQVFSEkZERPProoyyvM5vNaG9v5wv9akOpVLJcmGTaJL3aunUrrFYrd54CgQC+8pWv4MSJE4zczcrKwn333YfCwkJs27YNxcXFqK+vx9zcHKLRKGpra/H+++/j05/+NFf/3W43d7vidQ2Sk5PhdDo5g6C7uxtZWVk4ffo0NBoNB8fKZDJOczcajdiwYQPa29vZd7C4uIjs7GxcunQJBw4cYNUEvTOtra147733oNPp+AKyZs2auB1luliqVCq0t7fjhRdeQEFBAX7zm9+grKwM4XAYarWaq9Vf/OIXGWpw6dIleP4esHzo0CF861vfQigUQk5ODpMO9+/fD5FIhAMHDqCtrQ2xWAwTExMYHByERqPhivlq3+np06eRlZUFuVyOrq4uzM/P4/Lly7znTU9PY2RkBBcuXMAdd9yBrKws7Nq1C1KplLHkhw8fhtPpxMjICIxGI0uWBQIBzpw5gx07drDkurOzE8PDw2hoaIgrMacitl6vRyAQQGdnJ8v86JwKXL8A2+12HDx4kBPp33vvPf7/f/e738FoNCIYDGLLli1QKBRQKpWoqalBWloaGhoaIJFIOLSVPrePc/bVarUcELhhwwb8+Mc/xqZNm9DV1cUFkcLCQjz++OOYnp7GbbfdBr1ej5ycHNTW1kIul+PrX/86y7jorEzFl0984hN48803sbKygt7eXhw5coQLph9nfGyPBqHWent7IZFIUFtby5Spv/3tb5iammJca3FxMXp6etDa2sqazvn5ecRiMZYl6fV6dHV1sYSJ8Hzf+c53mIhDVdnl5eW4ev7Z2Vlu387NzcHhcGBqauoj1SRq3brdbvzqV7/Cgw8+iBdeeIE7IBaLBZcvX0ZRURE2bdqEtrY26HQ6Zn9TCM+dd96J119/HYmJidDpdDh9+nRceZLD4UBubi6H3+Tn50MoFOLgwYOYm5vDHXfcgY6ODtZ5r6ysoLy8HBcuXABw/YBhs9nYjFtXV4fz589zUNLc3ByUSiWOHTvGpC6JRMKt9WPHjq3K5+/v70dtbS0mJyeZXmGz2RCJRLBz507U1NRgYmIC7e3tcDqdqK6uRnl5Oc6cOcOLDlUYxsfHkZOTg1OnTiEvLw8LCwsYGhpCUlISzp07h6ysLM4pCAaDmJ2djavZJENzUlISCgsLkZGRwRWKkydPciWK8HZDQ0MYGRlBcXEx7rzzTpw+fRqvvfYaysvLMTY2hq6uLvz7v/87XC4XzGYzUlJSUF1djeeeew5f+tKX0N3dzXKIQCCA++67b9W52Ww21NXVYWFhgROrzWYz5ufnce7cOZaZUMv12rVrMBqNMJlM6O7u5qTXsrIy9PT0wGw2c6UlEokwyam2tpa7W8q/pzc7nU5um99seDweRKNRpk04nU6+rLhcLjb2rqysoLGxkYMk//CHP+DAgQNYXFzE9PQ0Nm7ciFdeeYV1+1VVVXC5XLDZbNi8eTP+8Ic/wOfz4d577+Vu2+DgICorK1edGwB8+OGHWLduHev+161bh4sXL3IC9c6dOzEyMsKLcTgcxq5duzA1NcUyAb1ejy1btsDhcODUqVMYHx9HUVER1Go1hoaGcOjQIQwMDMBsNrPUi9ro09PTq85tbGwM27dvZzAAPW/p6enIyMiAUChEf38/EhMTUVFRgUOHDuHcuXP41Kc+BafTiZ6eHuh0OhQVFaG3txff+MY3OMuGKnx9fX249957sbCwAKvVyt+FXC7HiRMnVk0GT0xM5FBDurRQJWxsbIwrttFolHNSiIBG3eiCggIUFRVhYmICcrkciYmJ7HsQiUQ4cuQIB1BR2GVhYSFDBVYb5D1zu92IRCIs89y5cycHaJLvgqAhlMtDh56Ojg5UV1dziKvFYoHb7eYAtPz8fLz11luIRCIoLS3l9N/09PRVzf3AdXNuUVERUlJSuAt/+PBhbNiwASqVCp2dnZidnWVpQG5uLnd5HA4H2tvbP+Irqqurw9GjR/niJpFI8Itf/AI///nPkZeXx3JYj8fDuOrVhsPhwNe+9jU2iXo8HnR3d+MTn/gEACA7Oxvz8/PIyMjAli1bWEJss9nw/PPPM51m48aNMBqNaG9vR1VVFXv+qDo8MTHBl9u+vj72DFGGwM3GjcF7wPXOvsViYdMxFbsoN+D06dPIzs6Gx+PB5s2b4fF4cPr0abhcLrS0tODEiROIxWKora1lOXB/fz9jqFNTU5GVlcVy3K6urlXnRjJUkm77/X6cO3eOixcOh4MvmGNjY8jKykJjYyMmJibw3HPPoaioCPX19XC73Th//jxf3FNSUjAwMIChoSFoNBpcvXqV1RgErKH08njfaU5ODnJzcznL5Pz587j//vthsViwZs0adHZ28vd69OhRFBQUwOl0orCwEAsLCzh9+jRUKhVGRkawZcsW1tsT8WhwcBDnz5/HY489xkCJUCiE9vb2uHsqJVzbbDYkJSWhtrYWmZmZeP7551kyOTY2xl7cpqYmltfQGp+eno6DBw/i5z//OW6//Xb84Q9/QH19PYLBIA4dOgS/349PfvKT3BVNSUnhwNN4FzSS/lE3Q6lUcnEXAIqLizEyMsJAjXA4DIFAgGPHjv3/eW9UKhV+/etfY9u2bUxycjqdkEqlMJvNmJqaQjgcRn19PQwGA3p7e+MWK6jgS0GfBA+4dOkSgOtqg/7+fj47Xb16FU1NTfjLX/7CsqWlpSXs2rULQqEQV65cQV9fH9NOyadbXFyM5uZm9Pf3w+FwMMkxXiGb5OjZ2dmYnp5GZ2cnHA4HvvKVr+D48eMcdNvW1objx4/jgQcewOLiIgYGBuD1elFUVASxWAybzYbFxUW88soruP/++zE3N8d73a5du3DkyBFs376d5b5kVfi4gX0f+6IhlUrxwx/+kLX3wWAQ2dnZ3Dbfs2cPG0WVSiXq6+vx7LPPIiEhgb0N1IZfWlpCY2MjotEo3xLD4TCGhoZQU1PDVAciIZFedbVBBhWpVMqmrXPnzqG6uhomk4m7MZOTkyxjOn/+PPx+P5qbm9lQ3NzcDIfDgYsXL3IKeE5ODjQaDdRqNc6dO4fk5GS43W5OSt+0aVPciwZtZlqtFnq9noOifv3rX/PheXl5GYuLi+jt7cW6deswNzcHt9sNp9PJMrXbbrsNhw8fxsWLF7Fu3TrU1dVhZGQEg4ODCAQC0Gg0KCgowNq1a9lIm5KSEnejIx0mZSl4vV54PB6cOnUKfr8fIpEI4+PjMJlMiEajuO+++3D27Fk2L1N4GiWrvvnmm4xqpEArsViMiooKRmySXyEtLS3uy52Tk4PFxUVYLBZUVVUhLy8P77//Ptra2vBP//RPzIceHh7G1NQU1q1bh7179+Ib3/gGEhISsGbNGmzfvh0dHR0oKirC2NgYDh06xIE4ExMT6OnpQXZ2NqeyUgqww+GIe5jPyspiBGhycjKbsAjNuG/fPkYU/td//Rfuu+8+tLS08N8LBoNQKBQoKSnBtWvXoNfrOXyIpGxXrlyBWq2GWq2GRCJhlOTs7GxcyQMlK6tUKpYN0qFDpVJhYGAAAwMDWFhYwOjoKGpra1FWVoaUlBT09PSwxCQQCHDLn9DP5Fvq7e1FOBzG9u3b8dxzz7EUJhQKxYU2ANfpF++//z7j+ojS9dJLL/H7RiY6WiRfe+01PPDAAzhz5gxOnDiB6upqPlBRZUkul3N39De/+Q0ef/xxlJSUwOVyMbY4NTU1rpSQPCd0IKWLcm9vL+bn5/Hwww/zJYKkH+vXr4fNZuPE1ZSUFFy6dAkTExMoKyvD6OgoxGIxkpOTIRKJkJubi8zMTMRiMfZuUarrPzrM/+///i+HJJEO/vz587h27RoKCgpgsVhgsVg4H4hSwg8cOMBFm/z8fBw5coQ7JJ/73OfgcrnwyiuvID09HRKJhAMel5eXeQ3asGHDqnMLBoOYmppCeXk5kpKS4HQ6EQgEcPHiRWzcuJE3ZgIutLS08IWG5BYymYwlCMPDw9iyZQs0Gg08Hg/6+vqQk5PDuRpUMVar1fB6vcjJyVl1bikpKbwn2Gw2rnbTZWJkZIS9aFRs2LRpE5RKJbq7uyGTyVjSUV5eDr/fj9raWu7K+v1+vPvuu5xh8sEHH7DE1Gg0wuFwrDq32dlZTnY2GAyMrv7JT36CnTt3oqKiAgKBAMPDw+jo6EBOTg7uv/9+tLS0QKlUMomupKSEgxtbW1uh0Wg4KPVvf/sbcnNzMTo6iubmZg5ijEQi2Lt376pzIyIY5RPdmF4/OjqKvLw8DAwMMBxEo9Hg/fff5wMcGV7vvfdevP7661hZWUE0GuWMlXA4DKPRCK1Wi87OTsjlcvY/pKWlxZURFhUVMR0xKysLCQkJGBkZ4SIG6dynpqaYnLZlyxacPn0ahYWFKCwsxMrKCubm5rjYQ+CUpKQkDoW02WxYt24drl27xqGkAOJWcb1eL4aGhrBr1y4mY0WjUfz1r39Feno6+4oI7DA9PQ29Xo/MzEzGEtNcHA4Hrly5go0bN3J69fT0NFJSUlBRUQGn08ndQQB4+OGH40pZhEIh46UbGhoAXMehm81mbNmyBbt27eKu9MDAALKysrCwsICJiQkUFxdjZmYGPT09/K5S96moqIjJdYR5bmlpwaVLlzA3N8ce3i1btqw6N0JfFxUVIT8/Hzk5OayCmJqaQlZWFlQqFaxWK9544w0UFhZy95YKLCqVCnV1dTh8+DDy8vLg8XjQ2toKsViMnp4eXLt2DePj48jOzsbExAR7JS0WS9xCtlarRVdXF6sOYrEYNBoNLl26BIFAgDVr1iAhIQHhcBi9vb1YWVlBeno6lEolQqEQS5DWrl2Ls2fPciG9rKyM32+LxcK0vtnZWaYbEi54tUH7XnJyMgoKCrhr/fOf/xxHjhzBSy+9xOGRoVAIZ86cQUNDA585bTYbZDIZotEovF4vKioqIJfLeR8h//LatWuRlZWFq1evYnFxkc8+8c6+N46PLZ2yWq3YvHkzm5Sqq6sxOTmJXbt2sYmYKl9Ea9i6dSuSkpKQnZ0Ng8GA/Px83H///czVphsRmX5vv/12JrFYrVZkZGSgrq4O0Wg0bqDK1NQU6urqOECNDJMkCaKKnUKh4OTR3NxcNl8lJiYyG1mtVuPxxx9HRkYGSkpKkJaWxgztwcFBJCQkICUlBTU1NSgqKsL4+Hhc3TeZ06jC4vF4kJOTgwceeAApKSnYvn071qxZg6amJuzbt4/zABQKBQ4cOIDt27dDKpVyWz0rK4txuBqNBk1NTfjOd77DyF2Hw4H09HRuacfzGkxOTqKqqgrAdb+GTCaD3+/H1q1b+Xdsbm7GgQMH8O1vfxsOhwP79+/HwsICV5K6u7tRWVmJpaUlvPDCC6wjJn2ywWDgl4s0ort27eLPZLVBlRqSUA0MDEAmk+HLX/4y5ufnWQu8Z88efPOb38TExAQ+/PBDhEIhbNu2DQ0NDcjNzeWgsieeeAJ9fX3o7e3F6Ogo5ubmGIU3NDTEl96srCw2aK82iOBCeOSysjKYTCZs3bqVqV0LCwtoaGjAl7/8ZSwsLEClUiE5ORlNTU2cJk2XXlqAKR17cXERe/bsgdfrRXZ2NrxeL+NqRSIR+4duNkQiEaRSKaanp9lLEwqFoNVquTWvVCrR0tKC+vp69PT0wGKxIBAI8HtARuru7m6ussnlcq4a7dy5k02DRUVFKC8v5xC7f+RtWVhYwLp16yAQCJiqoVAosH37djY4GwwGrF+/HnfccQdsNhv+/d//HdeuXUN+fj7TluRyOQYHB3H//fdzyqvZbMbQ0BC+/OUv469//SscDgcsFgtnzNTV1aG5uXnVuZHHy/P3QEDSBj/44IMoLCyE5+/hneSruXLlCv95eo9MJhNycnIQDAZZTpKfn8+HtY0bN+LkyZPQaDQYHx/nHB+v1xv3MO9yubBu3TosLS1hdHQUExMT7IFaWFiAXq9HdnY26urqsGnTJly8eBH19fXIzMxEd3c3U3fINHnvvffCarXi6NGjOHXqFKLRKB555BH09/dj3759zH4nUEc8j1xCQgKKi4sxPz8Pu93OgaPr169HJBJh/TtpvEdGRjA9PQ23280Hi6SkJHi9XhiNRjz00EOYmZmBSCRCXl4eMjIyUF1djYSEBK5ap6amcrEi3tyoYz0zM4NYLIacnByoVCrs3r0bYrEYe/bsYcNjdXU1AoEASktL8dprr6GlpQXFxcUwm818WTcYDDCZTOwjWFlZwfr16/lyRdkeRCOLNze9Xo+NGzeySTMxMRElJSX4xje+Ab/fz4CJ5uZmbNy4kYEsIyMjGBsb40Pc0NAQ+vr68Lvf/Q7Dw8MYGxvDwsICxGIxvv3tb8NsNuPxxx9HUlISV4zJdLvakEqlKC0tZb+SRCJBQkICWlpakJaWxjkmxcXFqKmpgUgkwv3334+1a9eitLSUATApKSksk6M9ifw15eXlsNvtaG5u5o5ddnY2UlJSVs0NAK534ktKShAKhfgwFI1G8alPfQrV1dUoKipCQUEB1q9fj3vvvRcWiwUikQgJCQlobW1FXl4elEol7r333o/4bsggn5eXh4MHD2JgYIApYJmZmSguLsbc3FzcCxq9Y93d3ejq6oLX6+VOjl6vZ99Gbm4u6urqYLfbYbFY8MYbb0Cn0yE9Pf0jErL77rsPY2NjXEWenJxEY2MjU7LGxsbg8/ngdDrx+uuvxw0Ppiyl6elpTE1NMa3p85//PHp6ejhDRCgUoqWlhUOZCScsEok+4jNbs2YNkpOTYTQakZmZiVAoxBQ2MuDv27cPBw8e5ALfaiMxMZFVKdSRSklJQXNzM58ByENXVlaGyclJ7N27FxcvXoTVamXKlF6vh0AgQHZ2Nkuu29raYLVaceedd3JRUCQSYWZmBqOjo+ynXG04HA60trbCarXyOz06Oordu3cjLS2Nk8JLSkpwxx13sBwdAPsCA4EA46u3bt3KYI+5uTn4fD48/PDDMJlMKC8vh9FoxCc+8Qk8+uij7P9bbfj9fkgkEly6dAkdHR0svX788cdhNpvR1NTE/sHGxkbY7Xa43W6YzWYUFBRw+jd5h+m5T0hI4FDhzZs34+zZs5idncXExARycnJQX1+PmZmZuACTG8fH7mhQOzw7OxsCgYBb29FoFBkZGbDZbIjFYsjIyEBmZiY6OjrYOEZOdQotycjIgEwmQyQSwcrKCucXEKGEzFdutxspKSmsEV9tJCQksDSJAuNqamo4Jr2vr48NgCUlJRyu5vf72cchFothsVigVqsZ4TsyMgK1Wo1YLIaenh5s2LABNpuNU6aprUwSmdXmRpsIVXVIq0q8ccpjIIlFLBZjoxgZwgk5S5sYVdSi0SisViun0qanp7PRWiAQxGV+A/+/xPeVlRUEAgGWNpSWlnJbnIxcXq8X8/PznINB1fZQKMSZBbSJUY6EQCBARkbGR9juMpmM8b2rDaItkPYyHA4jOTmZD+x04+/v72diycLCAqqrq5GWlga1Wo2FhQVcunQJRqMRycnJHDa4tLQEhUKB9vZ26PV6GAwGRtyR3jGeqTk5ORkulwtJSUmsc66qqmICSzAYZJ1xMBjkYLbMzEwmbhEuc+fOnfz3bTYbV87cbje0Wi3Onz+PtLQ0lnIIBIK4cyMpWTgcRiQSQSwWQ3Z2NqxWKyPwyNidlZWF5ORk9Pb2orCwkDslgUCA0dAymYwrgBTYl5iYiIyMDAwNDfHzSFAHWmBXG0Qf02g0DG3Iy8vjjAUitOj1eiiVSg4GMxgMiMVifPgNh8N82CBaUjQaRXFxMfLy8ljKaTAY+PNOS0uL2xlNSkrC+Pg4a+bpYOl0OqFSqZi2RiQvwoZWVFTA6/VCIpFwCmtxcTF0Oh2qqqq4gkzmy9TUVMzPz8NgMPD3GQ6H40pZKBmYjP5+vx9KpRJpaWnw+XwYGhpiA7dSqeR/l7qvRE8i8/rk5CQyMjLY8L6yssIdrGAwiNTUVHi9XiwuLnIWQLzPTSAQQPn37BHyVYRCIZbJ0GdK74xAIEBxcTF3cxITE2G321mmAYDpg0VFRcjJyUF7eztLCmZmZlgmGu8wT1k1KysrvF7W1tbCbrejrq6O2fxSqZRxnKTjJwkqUaoIc15dXc0XC6FQiKmpKSgUCs5aWFxc5MsqUQVX+04pF8br9WJ6ehp1dXVYWlri+ZBigL5nq9WKoqIipoRlZGTA7/ejoKAADocDJSUlGBgY4H2ZyIX9/f2cJUNkrHidUTJU03dH6OhwOIysrCxMTU0hMTGR0b85OTkcTCmXy5GZmcnVaOoUUEdoeXmZ12StVgubzcbrIpHB/lEeDxVSxGIxRCIR/2wK9yX0ukAggNvtxuDgICoqKjiRmboH9F7k5eVhaGiIIRdECRoYGGDJUigUYiTqakMsFnOnnvYh6mgSoai0tJR/jvLvqFmhUAi73c4HbK/XC5VKBZ/Px3uzQCDg7J6ysjL2Y1CmjlAo5M7VzYZUKuXCKxGEiouLIRAIUFVVBavVys8EFXzpPR0dHYXP54NYLEZ/fz9yc3Mhl8tRXFzMF4Pa2lqEw2GsXbsWDocDGo2G3wGSyK82yANGFCUqzjkcDshkMjgcDkgkElbGNDQ0YHBwENu2bYPZbGbQztWrV/kQXFdXx1SvjIwMRCIRrF27FkeOHEFhYSF3rlNTU+Oek+jP3Uh0rKysxMzMDPLy8jAxMcFhscvLy1i/fj3sdjsEAgGfLYHrRWcqfKxZswYTExPweDy8DpBsk7rfFPcQDza0vLyM2dlZ7pwIBAIG8JB3kd4FUkYEAgGmqCqVSn4HCSpUUlLCfhryyWm1WiaT0rpMNMWPMz52R4MQV4mJifD7/ZiYmMDi4iLGxsbYKU9MeJ/PB7fbjWAwCJ1Oh1AohOXlZSQmJrJUZ3x8HGlpafD7/cyGdrlciMViLIkgBj6x6OPNzePxMK4MuK6b8/v9yM3NxfT0NH8JxBW32+1sinU6nRAIBJibm8Py8jJ6enogkUjQ19eH9vZ2hEIhTE1NIT09nasjwWCQ5x0vFI+wmnShohC3/v5+SKVSzM3NYWZmBg6Hg8P9KNiHEjWpQhiJRD4SEEeek8OHD0OlUvECSFUBqhrH+9zos6aXSalUMj99dHSUccZmsxkCgYAN6MFgkHGwdCO+cOECotEoU4qA61X68vJyXjDn5+c58j7e3AgVShkddFicnp7mzYXauhMTE9BoNFhZWYHRaITH44HD4YDX68XAwACHnNHhgS6jlL1CxBu3243Z2Vk29K02BAIBIpEIZ0dQFwoAY4ATEhIQDAbh9XpRWFjIbH7SrlIlPD09HQMDA0hLS+OKEXBdVqFUKvmyR3KApKSkuJduQiqmpKTwxY7SoVNTUxkYQFpjWoAJvUshY2azGXl5eZxmSjrTUCjE0glKN41EIlhYWIBEIolrXAOuXzRoPQiHw1xVo02IqDiBQIDXkAsXLiAzMxN2u5353yMjI1AoFDCZTNxNpfyU6elpFBQU8PpD3gwqbKw26P0irwwhO2dmZni+tEEHAgHo9Xr2JTgcDkZy0/NAB1Cq9IvFYt6AZ2dnefGmzz3exZuIJMnJyXzRD4VC8Pl80Ol0GB0dhd1uh9frhcViQW5uLiwWC/8ehOWlcLhTp05xQBphS2dnZ1FfXw+z2cxyBEp9/0dhW9FoFHK5nA+ldKGlQwPR6ehARFIxp9OJcDjMMjKv18v0HCIDxWIxOJ1OLC8vw2w2swGYLszx3lUiTdF7v7KywjlAiYmJvMYKBAKkpKRAIpGgra0NhYWFMJlMcLvd0Ol0LGUhaSJRFoVCIdrb2zkhPBqNciAWSRLiPW8LCwucJ+Pz+TA/P89a8HA4jPHxcUxOTmJlZYVx3IQDNhgMKCgo4GeJ4B1E/BOLxZienoZWq2UZGO0J5NNYbUSjUS7IUEAbZe2QX5GUCPRsT05OcvihWq1mT5FIJMLs7CxfMkOhEJ8jCDcPgPXfMpmMc0JWexfoQEaHMsq/8vv98Pl8bAQnOiSZx0k6FwgE0NfXB41Gg4mJCaSlpcHr9fJe3N/fj/LycoyOjkImk2F+fh4mkwlJSUlxTc3UOaHLc0pKCmPrl5eXOSuBpMeESpXJZBzyR0hSj8eDzs5O7txRMZK6c16vF6FQiN/7tLS0uHsD7ZmESw2HwygsLMTs7CxKSkr4AhIMBmG1WvmCTXRL4Pp+TmGzNpsNarUaFosFExMTvFfn5+cjIyMDwWAQZrOZ34t4HTRC61JHnDqGDoeDL2w3ooOzsrJw4cIFlJWVQalUMsnPZDIhMTERIyMjjNolPO7AwAAXeQnTvLKygtTU1LiZMrQfEVrd5XIhKyuLvZYTExMsNR0bG0NqairvESaTiYumo6OjWFpa4jWEOh8SiQRXrlxBQUEBFhYWsLCwAIvFgtnZWSgUirgybirQ0xmG8pyoyDMyMsKKHK/Xi9zcXL4wut1uhMNhljMSZIHO2tQpGx4e5m6vVquFy+XC1NQU+0I+zvjYHY1QKMRVc+I1h0IhDp6iA6bdbofJZGIjmkwmYwY0mRgtFguuXr2Kz3zmM/B4PJibm2MsYnt7O5KTk5GWlsYhNb29vXERhsB14xxV8mOxGOPyyLRHWt/R0VGkp6ezppmqqpRx4Xa70dbWhvXr17OxnBbV48eP8+9MpkepVBo3uXF5eRmVlZWYnp5GNBqFSCTiKiIZcJKSkiCVSrG4uIi0tDSmKywvL3PYFh0Ar127xl0Ckh5NT0/j3Llz3A4MhUJ8IIt3G6agF1qUib1OByLaGCYnJ7lym5+fz4s2eTNuxPBS9ZdeXpLkeDweJiHR8xSPLEJsedqoCaU7NTXF0hky15OEjTIdiBqkUCiwdu1a+P1+bmFTRZh+Bi1K9LzQJS7eJry4uIiCggL+nAGwcY244dPT08y2pktET08PMjMzeZFLTk5GV1cXOjs7UVJSwhkIhMoLBALYsGEDY4qJDR8v04B8TXTQWFlZ4csKGR71ej13iQiXee3aNeb+0/s6Pz+Ps2fPsh/L4/HAbDazZ8hoNHILnr6reIdl+l5zcnJ4gVIqlZiamuLkarlczjAHqliurKxwy1ev12N5eRmHDx9mbCkA1ox/8MEH8Pl8jJ91u9180HU4HHGfOZ/Ph6ysLCbUUZtdqVTC7/dztzEcDiMcDqOzsxMSiQQXL15EeXk5//7kQ6NLOXHc+/v7sbKyAq1WyzKDpaUlhMPhf4i3BcC8d+oAk4SHoBtU1XI6nUhMTMT8/DwjUAlha7VakZ6ejoWFBUxNTSE1NRUKhQIrKyt8mCEyXGpqKgQCAfx+f9xCD5k+SQZGPjaq7pIenZ5P8laRHIm8NWKxGO3t7fB4PKisrIRWq0UwGITJZMKRI0fQ0NCAYDDIP4MOdfEOfrFYjCmJRM/q6+uDXq9HZ2cn+zeo25GXl8eFtJGREZjNZhQVFTHvnoy5lH1AnUkK/aNAPeC6vCte95E+V9JiU2o5IYB7e3v5Qk2mUEqsFovFiEQimJmZQUdHBxNvPH8PYCMufiQSQSgUYpgEcf5HRkYwMzODtWvX3nRuN3ba6YBHfpNYLIampiZYrVbMzMyw1yU5OZkT4Km4sbi4yBkjVIAhc25CQgIyMzOhUCg4r4MkQvEOV7FYDEVFRbDb7bxP0DtL/9v8/DwsFgtnlSQlJeHMmTNMaaK9LSEhAf39/di0aRPy8/MxNzeH8fFxlkvR90tmfbqsrjZEIhFXhMPhMK8j1Emi35P+LOF2qcAFXC+yqVQqPpjm5eXBZrNxAVCtVqO7u5sLf6SZJ1z4aiMSifD+QflTpaWlTGTLz89HOByGz+dj7LjJZEIkEkFubi5cLhdmZmYgk8kwNDTEhVx61jo6OlBfX4/Ozk7cdttt+OCDDzA2NsYX1nhmcJLi0VpIpnVC+ZNnam5uDlKplGlKx44dg1ar5SR4vV6PDz/8EFarFQMDA5zfNjg4yJ2zNWvW8PpMHbp4F7RwOPwRMBCFiAaDQQwMDCApKQlDQ0Nc5Jmfn8f69etx4cIFNnlTJ99ut8NsNiMYDGJhYYHPD0NDQ9xxp5+v0+mg1Wrj+iBCoRBj0ikotK+vj8NlqfhHZ8uSkhJ4vV7eb2kPooBlKhpQYYtkVJs3b8aZM2eQmprKWUw3Zm/8o/GxLxrp6em4cuUKh1hRANLp06chFotZtkQBb/v27cOlS5dw8uRJNDY2IjMzk+UfaWlpUCqV6OjogE6nQ1JSEsxmM44cOYK8vDzWeFIHIDk5Oa4WjDay5ORkSKVS1t3LZDJcunSJq2hkTKfDE0kdLBYLPB4PKioqIJPJsGbNGsRiMezevRtOpxOXLl1iD4nT6cSpU6d4kSBZ2GojGo2yHCMrKwuLi4vo7u7mLgIAThxXKpUYHx/nKhsFRQmFQsbaXb16Fbm5uVwBEAgEHEIEgCtWdFOPt/AQRo42OXowZ2dnYbVauXXs9/uRmZmJ3t5erhArlUoolUr4fD6YTCbmNy8vLyM9PZ1pJ2NjYxgYGEBmZiZfBCg/JF7CNV240tPT2ahEVSepVMoSGovFgiNHjrAsSqFQcKIx6WEjkQiOHz+Ohx56iC9AJOGiS3NTUxMnnVJFbLVB3QoKLaRqSGdnJwca0kWFpBnV1dUcmkOoWCI3UPL1je8RVWtJOkYGT/peVhtJSUlwu90s9aFDpkQiwdzcHAQCAbKysrhiRmZEqn4oFAqWWIXDYdTV1TGSFgAjmt955x3ceeedTE2ihekfFQTkcjlsNhtcLhdrrKlCTV4DCpSjbptGo8GhQ4ewYcMGlJaWcrZNd3c3mpqacPLkSQ6Fa2pqQnt7Oy5cuIC0tDSMjY0hJSWF8cLr1q1bdW4KhQJ9fX3IzMzkRHlKci8sLGTMNZGFjEYjZ4+QtIkOOcFgkC/60WiUuymUVZOYmIiOjg6WH7rd7rhaXDqMkMTE7/djZGSEPSsqlQoejwcrKyt8kJfJZOjo6GD9cjQaRX19Pdra2qBWqzE1NcVmSgJu9Pb2Ii8vj6VoFERYW1u76txUKhUHlK2srPBFTKvV8mGaJEwulwu5ubm4evUqNBoNLly4gPT0dH5/c3Nz4XQ6WU5Az9b8/DzDJKgSRzKLeJ6lUCiEt956CxqNBikpKXzIXr9+PU6ePImqqire8ImT73K5YDKZUFJSgry8PKSkpKC/v58rt42NjdwJpBC2aDTKFyEymS4vL8eFcWi1Wn6+bkwbpjRokvXMz8+js7OT5aE3hs3R3Ofm5rhjRIUhq9WKlZUVRoCeP3+e9zxKPV9tKJVKLg4RIIKe+/r6ek4UJlmKxWLB1q1bMTMzw6bjkZERlgorFAoYDAYuIgoEArz99ttIT0+HTCbjixMVB+NBJUgSS/svgVNyc3PR29vLJmbKvqB0Zco0oGeATLpvvfUWdu7cyXJNjUaD+fl5hhIQFYguk/GkUxQ4TKnwKysrcDgcvC/V19fDZDLB5XJxUOT09DQDScgQ3draioqKCvT09GB4eBhpaWlwu91MxLvrrrsAgFHypJiI9y6oVCreA4jCR8nPJJl0OBxM+hweHkYkEkFbWxuOHTuG8vJyVFdXw+FwYGhoCBs2bIBOp+P9gxQidBGiYjPhpePt9/Q7kMyI9te2tjaUlZWxD8Pv92NsbAwajYYVBfTM0LuRnp6O2tpaeL1eLkYQ9px8IuFwmAOlTSZTXBlhWloaent7WVKfkJCAv/3tb5BIJFi3bh1UKhXMZjPvVyaTCT6fD2lpaUhMTMTU1BRCoRD27t2LY8eO4Z133kFubi6KioqQmpoKt9uNrVu3wmazIS8vj72Bi4uLGBoa4mT2mw06i1AhiMirEokEExMTHDdAnkraox0OBxMsiYBI6GYqztKZWiQSYWxsDGvXrsWZM2dYxtbf3/+xzeAJK/8oyvfWuDVujVvj1rg1bo1b49a4NW6NW+P/4/jYHo1b49a4NW6NW+PWuDVujVvj1rg1bo2PO25dNG6NW+PWuDVujVvj1rg1bo1b49b4vz5uXTRujVvj1rg1bo1b49a4NW6NW+PW+L8+bl00bo1b49a4NW6NW+PWuDVujVvj1vi/Pm5dNG6NW+PWuDVujVvj1rg1bo1b49b4vz4+Nt726aef5jj61NRUDu4IBALIyspCUlIS7HY7Z0UQHtBut2N5eRkqlYpTti9fvgyZTAadTsdYO8KZhUIhTkMktKtYLMbs7Cy+853v3HRuhw8f/khiL4WlaLVauN1uTt9cWFhgbGx+fj6nYtO/LxAIOPhLrVYzg5ySisfGxlBcXMy4ysXFRYTDYSQkJODgwYM3ndvvfvc7JCcnc4gVzY2Y/bm5ubBarcwzVigUWFpagt1u51wDQhLOzs5CpVIhGo0yOs/j8cDr9SIjIwOxWIyDESk5PCkpCU888cRN5/brX/8aKSkpzPWmJEsAjFecm5vj7zwSiSAtLQ2Dg4PQ6XQAgJWVFajVamasE/t+YWEBKysrSE9P59AoShCVyWQc0vbYY4/ddG6vvfYaB6RR+Jzdbuf8EkIsA2AEm16v5/AtSpbV6XQYHBzk0Dlio9tsNkgkEsY1E1qRgu18Ph8effTRuN8poUYpoCk5OZlDmghfS+FLs7OznAoKgOdLYTpLS0tQq9WM7KPnizj7lMmhVqsRjUZXndvTTz/NDO3U1FTI5XLMzMxAIBBApVJBIpFgZmaGWemUY0AhaXK5HBqNhkOmfD4fp01Ho1G4XC5GtVIqKHHPCUX6+c9//qZzA4B//dd/5c+bwqq6u7tRVFTEoXeE4iMkLz3TPp8PEomE8x0WFxehVCoxPz/PeERKJ3a73SgpKWE+PeWDeDwePPvsszed209/+lNGtNL3ury8zBhntVoNp9PJuRGUkE68dpo3BfnJ5XJYLBakp6cjKSmJg0EJS025AQkJCZzY+v3vf3/V71UsFiMWi0EsFkMoFMLpdCI7O5sDMIlTHwgE4PF4sGbNGly4cAGpqamMvxSJRIjFYhzuFY1GGc1LGRUFBQX8LhCH3ufzrTq37373u0hJSUFCQgJkMhmEQiEmJiZgMBgwNjYGvV7P2R9CoZCDFV0uF3+HaWlpvDZOTU0hLS2N9wGv18uoRblczu82rXPhcBhPPvnkTef2gx/8gINcaR3xer08h/T0dNjtdgSDQSgUCg6u8ng8CAQCjPilUMilpSXO5ADAjHrC51K6s1gsxtLSEpRKJT772c/edG7PPPMMBwlSajqFK1L4aDAYZLS0VCqF0WhEW1sbBylKpVIoFAoOJZyfn+dwM0ooT0hIgFgsRigUglAoZOR5OBxedW4///nPOUyPcpL6+voYNy+TyXiPTkxMRDAY5PA3wr8KhULI5XLOjwGAsrIyxGIxDlaj/Bir1crPIYWTrvad/vu//zt/p5RrEY1GkZSUxOuq1WqF0+nkRG6RSMTPM+2TtPdnZ2djZGQEYrGYc3ko50MqlcLv93Nui9/vh91uxx/+8Iebzu0nP/kJotEo53KJRCJMT09zQKJGo+GMEJ1OxyGslIchFAqhVCqRnp6OoaEh3ldozaN3lL5DsVgMr9fLwb5isRjf+ta3bjq3Z599Fn6/n38mBRjn5ubCbDYjIyODM0wouHT79u1oa2tjJLxYLEZaWhpCoRAjeim/we/3c5jj1NQUh2VSorbFYsF//ud/3nRu3/72tzmIkdYDQqDTz6T/xGIxOBwOVFZWwmw2c35acnIyMjMz0dPTw7hzwrRTIr1QKPxI0GpiYiJnlP3kJz+56dz+8pe/QKPRICEhgX8fygAihG0oFILf78fi4iLkcjknc8/NzUEmkyErKwsqlQpDQ0MfCctcWlri7DelUomEhAQ4HA5eqxUKBQKBAO68886bzu0Xv/gFUlNTOahPKpVienoaOp0OFosF2dnZcDgc8Pv9nB0jk8k+cnbPysrC/Pw8Zy1lZmbyn5mbm+PQSQoeDAQCHLC7vLyMb3/72zed243jY180JBIJR5ir1WoolUosLi7CZrMhPz8fsViMg8DowOH3+1FQUID33nsPaWlpaGhogFAoxNq1a/kDiEQisNvtHGSTk5MDg8HAzHA62MSLiJfJZHwYoUWHFpGVlRXeJPx+P6cohsNhRKNRqNVqLC8vQ61WIz09HW1tbZDL5VhYWIBGo4FOp+O0cQrLsdlsnFOhVqvjZlUQT50WKlqE3W43hwVJJBI+JCcnJ6OoqAgSiQQpKSnMh3a73WhoaIDX60V3dzd0Oh1f/ChkKxaLITU1FcvLy/wgxKMXU8ZHWVkZjEYjYrEY+vv7EY1GUV5eztkYxHoPBoOYnp5GVVUVPB4Pf74UbuRwOKDX6yEQCOByuTi0jrIVotEohxJ5PJ647GqVSsWLQTQa5RyScDiMlZUVqFQq+P1+zM3NcRIzXUzsdjuEQiF0Oh1SU1ORk5ODgYEB5ObmcoK8wWDgFFuj0Yj5+Xm+dNBBfbWhUCiQkJDAmSASiQRer5fTfTUaDeRyOS9GBoMBvb29aG1txfLyMlwuF2ZnZzE+Po6mpiYO8qN/d2FhAWq1GsXFxejo6OAQPrFYDJlMFjd9WyaTcWgiceEp0Zfej5SUFOh0OhiNRk5VFwqFsFgsH3nuNBoNTCYTNm/eDI/Hw2F7AGA2m/nSRrx0yuuINyiFlA7aAKDX6zE0NASNRoPW1lb+TintlC5HTU1NCIVCnDkSi8U42EipVHJQYVZWFm/GxOinCzUdUm82IpEIMjIy+LtdWVnB1NQUbDYbDAYDlEolkpKSsLS0xDkf8/PzqKiowOjoKPx+PweYSqVSDA4O8lx8Ph9nb9DlKBAIcGAnbU6rDalUCqVSiaWlJWg0GqSmpmJqagp+vx8pKSkoKSnB0tISZ8fMzs7C4XBArVZzEjetEU1NTejo6OAsGrpkl5WVIRAIYGRkBImJiVAqlUhJSYHf7+c18GZDLpfzJZpCTNPT0+F0OvkQIBaLmd/vdrs5lG5+fp433UgkgoKCAg4wpbR6KjbMz8/zWhAMBjkdOh7LnYoSADhniZ4Tyquh7yExMRE5OTnIyMiAzWaDXC5HWloaB6fqdDrk5OQgEonAarVyIWF6eprzc+gSTSnX8fJ4UlJSkJ6ezknZSUlJ8Pl8CIVCSEtLQyAQ4EwZukDb7XYYjUYOxKN1ISUlBS6XCzKZ7COXasquAMD5LoFAIG54GnA9R4PCSxUKBR+Wurq6UFRUxFk+8/PzcLlcaGlpwcTEBGKxGNLT05GWloalpSVMTk7ymkHFJpvNhkAggLS0NGRlZWFlZQUlJSUIBoNwuVz8e6w2YrEYVCoVf/f0jNA7SxdeKoRkZ2fz/242m+Hz+fjwKxaLodFoMDo6CqPRyBkgWq0WFosFDQ0NGBkZgdvtRigUQmpqatyzCACUl5fD5XJBIBBwGvvk5CSMRiMf8peWluD3+zmQlH4Xyk2iYEgKG1QqlXA4HJifn0dmZiZqamowNjaGSCQClUrFFyIqAN5s0BmEcrAosM3pdPKzSAdygUDAwZ7FxcXw+/181rDb7UhJSUF9fT2Gh4dhs9k+8g6np6fzZ05nkWg0iubm5lXnRmc0nU6H5ORkPrA7nU4+b1A4XUpKCjQaDXp7eyGRSPgZ93g8MJlMqKio4HeDCpNyuZz3eAAwGo0ccEpFpdUG7Y8AuJhIQc0KhQI6ne4j6+uNQZzBYBDLy8uYmZnBzMwM8vPzOTjQYDAgGo3CYrHwhYL2NSrCeTweLuLebOTk5MBisWBpaQmpqakccNje3o6ioiKMjY3xhYXmX1VVhVAohLGxMV47m5ubMTg4iIqKCpw6dQparRYpKSlQKpVYWFiA0+lEa2sr/H4/Ojs7+bmI957eOD72RSMWi0EkEmFqagperxdqtRqhUAi1tbVwu90cKOPz+Tg0ZXZ2FpOTkygoKEA4HEZ7ezvkcjl0Oh10Oh0HpWRnZ8NoNMLlcmF+fh6Dg4MQi8VITU1FNBrlW+Fqg27W9CXT4kuVwuXlZUSjUaSkpKCoqIhfcoFAgOXlZczOznJlSiQS8e9JN2lKjF5eXobZbEYgEOAQKjporTbo/wuFQvxFLy8vIzMzEwD4QRSLxUhOTkY4HObb8fLyMnw+HyfuUuWNNksKlsvIyMDCwgIHBwoEAg7hi3fRoAMTLYpSqRRJSUkoKCjgG7DX6+Wbf1JSEiYnJzk5eGlpCaFQCF6vFxqNBlVVVTCbzXxIKSkp4ZeAKsFUAZicnITX6111bvPz85BKpdw9ooTd5ORkOJ1O/o4pUXhmZgbZ2dmYnZ1FSkoKfD4fp1Y6nU40NzdjfHycO0U3Lk6UlJmcnIzk5GTuisT7TqkbQKFWXq8XMpkMEokELpcLCwsLXGUIBoP8LNOCRiGE4+PjfFENBALwer2c7OzxeCASiXihFAgEnMa+2lhYWIBMJkMwGITNZkMoFOLqKr0bQqEQiYmJsFqtmJ2dhUwm4woQJYJnZ2djenoaGRkZ8Hq9iEajfEikZyoSiUCn0yEYDGJubo7DoOINn88HqVTK3Uuq1tEGSIWHhYUFrgY2NTXB5XLBYrHw8+ByuZCcnIyEhATuLNIhyu/3czo8BRdSyFC8iwaF3VE3DrgegJSXl8dVbgooNBgM3C0NhUIoKyuD2WyG2WyG2+3mcKvp6WkO2tPr9ZwOS4WWlZUVTm+ng9HNhkAgwMLCAq9l8/PzmJ6e5k6YxWKB0+kEAK5Iqf8f1t48uu3yTBu+LNuSd+2ybEm25H3f7diJEyfOTgjfRY/pAAEAAElEQVQkYQkt0ALtUNrSaad96fLOtDNd3850G0q3GdoCZVIKNEASQgLZVyfe992WJVmWtUuWLVvy9v3B3PeE94tVvnO+5xzOKZmJeSz9fs9z39d9LXI5AytUeAaDQUxOTvLzRoUdNdYE2lCwGk2HIp1xBETQMx0Khfjv2+12hEIhbt6o2HE6nYiKiuICY3FxEYuLiwgGg0hKSsLCwgKHrhEqTefuncj0yspKxLR3upvo3F1YWOBJRXJyMocsElJqNBr5OVxdXeXnJS4uDi6Xi/dPied0l5nNZg6GjIqK4oRlmhDfbVGiOKHHwIdnckZGBqKiohAfHw+j0Qiv14vExESsrKwwkyAnJwc2mw3T09PIzMzExMQE39GEcIdCIXi9Xg5cjYmJgUql4mlSpOKKws/o7omOjsb09DSfe9PT01z4qVQqtLa2IicnByMjIxwiRtPBqakpTkZOTk5GcnIyACA3NxdXr15FQUHBR6b+8fHxEYM/KZV9fn4eq6urPCVcWlpCdHQ0/H4/A0qpqakYHBxEZmYmFhYWsLi4yGepyWSCTCaDzWbDysoKrFYrn8UEGk1NTfF0mJ7xSM0jvQM0gSPkPy0tjcNi6TuVSqVoaGjg+yYnJwc+nw/T09NYXl6GUqmEWCyGw+HA3NwcJ3sLhUK0tbVBrVbDYrEwck9T80ifm1Qqhdvt5nOUmjRie9B0uKqqCqdPn+Zp3vLyMlwuFxYWFqBSqbC8vMxTMwrWBMCAAD1bEomEn4FIzS1N2+g7XVpaQjgc5ukDhUHT1CA9PZ2nPhkZGfB6vbBYLHxnUE2kVCoRCoUwPz+PmpoavPfee9iyZQvfgSsrK3C73dwM323RM0aNS1RUFJxOJxfhCQkJPJ1NSUmB0WhkYCsrKwuLi4s8WSeQhKbk4XCY/7y7uxslJSV8/hKIS1O2uy273Q6xWMwgOtUcBQUF3EBSLadWq3HmzBme1CuVSn7WV1dX+ZyOjo5GTEwMv6tra2sYHR2Fw+FgMI4CASN9bneuj63RoPReujCioqKwurqK3t5enkwQncFqtWJmZgb5+fn8YBASlZWVhbW1NXR1dWFhYQEikYhHjH6/n5MuKbWZ/izSoUjj9dXVVd4rTTRoRBgVFcUPqkAgQEpKCo+mw+Ewj4Lp4aGRGo2aOzo6eGxGaBIl3VIRvNHeQqEQo430glPR5HK5GJ2jMSqh6jabDQsLC3yBEc0kKiqK/z0xMRF6vR6JiYmMYFNzFalwAcAvw9LSEjweD4/97yzk6MC1Wq0IhUIoKCjA3NwcxsfHYTabmYJDSEN0dDTEYjFTrdxuN+bn56HT6ZCTk4O1tTUu+iKh39SYUYdOY+GZmRmeVqSkpDAyRcWqQCDA1NQUPB4PN6sAuBglZGtpaQnT09NQq9X8/M7NzcHn8wFAxEORGtpAIMDf7crKCiwWC5aXlxnZXFlZgdlsRjgcRnV1NZaXl7lRTUtLg16v5wMiFAohOTmZaRDUcFCCOhXglBy+0YqKioLdbue/Pzs7i+XlZVitVqaFUJIzISuBQIBRLDrg7HY7AHBhTNMUSh5OS0vjQmJmZgZ2ux0ymSzilArA/wv5psvbbrdzWilNPYxGI1NEoqOj+ZkjJJJG/3ei1sFgEGazmWl6dEk6nU4GDDZahCjT5UlnFjWWNNEIBoNwu91YX1/nA31sbIybzfT0dEilUszMzCApKQlpaWlQqVQQCAQYGxvjZoiKXJpk0u9wt0WXT3R0NNPHqLgIhUI8PQiHw+js7ITX64VYLOYmnJp8muCGQiGmW8nlciiVSjidTqyuriItLY2Re6vVymdypL35fD7Mz88zbQQApqenkZKSAqVSidTUVIhEIi6aqUC32+38jBLFbXJykgt/SjS32+1wu91ISUlhqisBIJGK0ri4OH6+acJAZ9D8/DzC4fBHaDEEzsTExMDlcvGzREDQ7OwsJ3gTfdDv98NqtXISOKHUAoEgIkC2urrK6D7tlSgy6+vrkMlkEIvFfKdGR0dDLpdz4eZ2u5GUlASpVIro6Gj+HJOSkpguRN/J2toagx1Ei4vUBK2trcHtdjOVmKgVdCfRHRUMBjE2NoaVlRWYTCbk5ubC6XRibGwMTqeTJ3HUWEokEqSnp0OpVGJ5eRlzc3OM2BLIJhKJEAqFNtwbgQF33l1ut5uLU5qUrq6uIhAI8Lnm9XoxMzODcDgMpVIJjUYDmUyG4eFhxMXF8X1EyfYymQwA+Pd0u90MzkVaxGKIiYnh843eXeDDe5eeE2qe6eyhAl4ikUCv18NoNDJgolKpuK4JhUJ8bggEAsTGxkKhUER83ghsoLuQKFLLy8vQ6XR8rhD1iQpUSo6emZmB3+9HTEwMlEolxsbGEBMTw+nrAoGAi3I6AxYWFhAOh5GSkhKxQaMajYr/5eVlnig6HA4Ggv1+PyYmJvieSEhI4Ho0Pj4eWq2Wf5ZIJOL3MC4uDna7nSmkBNT6/X7Mz89HZKXQ3UssGKrX7jxD6L53OBz8niYkJMBkMsHtdkMikXASuclkYgqaSqWCQqFgVk1sbCy0Wi18Ph9GR0extrYWEeS5k75PNNKoqCjYbDbMzMzwfb++vo7Z2VmmaS8tLXH9arPZMDQ0BI1Gg7GxMWY10JlHQBR9P3ROEaj6cdbHbjSIP0sIJPFdu7u7eSRF9ILFxUVYLBYUFxdDLpczaq/RaHh8Rh0eFVVjY2Ow2+0Ih8MoKipCZmYmTwyoeN1o0Uu6srLC2o/Y2FgIhUJGxOjwMplMvBeZTMYHUWZmJl/ChBzQB040ntXVVchkMkgkEr6siEK00aLClsaI1GzMzs5icXGRCzziG4tEIubaEfKWmJjIiAiNv5OSkpCamgqlUskPE9GpSGuyuLgYsZinQxAAN2Hx8fHw+XyYnZ1FSkoKj+2tVitcLhd27drF2hBqjPLy8hhBEwqFSEtLg1AohNFohN/vh8vlgk6nQ2ZmJvNv5XI5MjIyIn6n9L0lJiYiOTmZH3qxWAyNRsNo6MLCAtRqNf/eMzMzCAaDUCgU/OdWq5UPL/qdnE4nFy4CgYCLagA8Kr3bIp723NwcU3RiYmKwtLTESHpiYiJiY2P5+8rNzWUNAU04amtrIRaLGWmmS5gObaKcEJWJpi6RENzExES+TIjTSk2UUChEXl4eo5qE6lIxQZRHavrpIKECWi6XIyoqCnNzc5DJZBCJRJiZmcH09DRTxgwGw4Z7A8CIJWlZxGIxT+poQkd0J2oAqeigBnFpaQlyuZxpayKRCGlpaUzd9Pl8EAqFyM/P57OKLsxIjQbpBwjpEwgEkEqlcLlcmJ6eZnplfHw8I1t5eXlYWlpCf38/Uwerq6shEokwNDQEgUCA0tJS5OTkYHV1FT6fD+vr6wgEApBIJKyLiouLg1Qq3XBvhKoSjUKr1UIul/M7TAhocnIyZmdnMTMzw9PZubk5LC0tQSqVoqCggIv+2NhYZGZmoqqqCkVFRfwc6PV6qFQqfp5XVlYinr9RUVFYXFzk74masdnZWSgUCuTk5CA3N5c1ekKhEHq9njUvTqfzI7QJr9fL3H2iENA+SG93p64hUuFHgAedu0tLSxAKhfD5fAgEAlxg0tkRHx8PmUzGTQRpJAh0ogaUgB4Cc4gjT+c4FZo6nW7DvdHndCd9TiwWIxAIcJGelpaGxMREBl7kcjmSkpJgMpmwuLjI6CIBc+FwGHK5HHq9Hmq1GkKhkLUGdKe43e6IhTztjUCUxcVFBpT8fj/m5uaQlpYGrVaLUCiE4eFhpsGlpqZiZWWFOeBqtZqLP6lUypNxsVjMExHSYdI9Se/wRis+Ph6BQIABHvqOlpaWWBtFvHWPx8ONLZ2DRDesr69HSkoK1yJqtZrpaS6XiwEDQqGdTicWFxeZjXC3Rb8f0eHkcjnfwdTcZ2ZmoqioCOnp6axHoiZ8eXkZCoUC+fn5UCqVDCTQ+UZAp1wux9LSEtRqNevF1Go1srOzN9wbNWV039Gfra2tIS0tDVKplCdmly9fZmob1St36rZIh5WQkMDUVbqzAbBmju4VtVodsWCmv0sNWUJCAjIyMiAUCnkqRLRQOluSkpIY2BIIBJDJZMjOzkZCQgI/R/SMJScnw2g08hlKTIalpSWm4G60CDwkkJ3+oXdhfX0dYrGYdQxJSUnIzMyEUqnEzMwMgz6pqalYXl7muo+AH7oD8vLyGPgJBAKYmZnh73ajRfcxyQXovDebzZifn2dQNioqCiaTCenp6cjMzGRmyOzsLBwOB1PZCdyUSCTM7iCdF02Tia1BbIGPsz52o0GcsqWlJeYti0QiHDx4EB6PB1VVVdBoNMz1I56tVCrFrl27kJqaCqvVisXFRZjNZmzduhWLi4uYnJzEwMAAbDYb6urq4PF4IBQKMTExAaFQyNqB3NzcDfd2J5UrMTEROp0Os7OzHxG50OFGB6FEImFuoc/nY2F4a2srF8BxcXGYn5/H5OQkDh8+DLPZjLm5OdjtdqSkpCArK4unJ5GWVqsFAH6Y19fXcfDgQSwsLKC5uZl1KVu2bGFq1OzsLAwGA4+t4uLiMDIygp07d/JInC53ohKtrKxw10riZ/pv3215PB6kpqbyJUwvC43lBQIBCwr9fj9sNhvUajWCwSAKCgpQWFgIg8GA8vJy3Lp1C+FwGL29vXA4HHC73XC5XMjJycH09DT6+/vR3t7OI7zZ2dmIn5lIJIJCoYDL5eKCc3h4GGlpaXC73czZJBrU0tISRCIRRkdHUVlZCbVaDa/Xy5zb8vJy/lzW19fh9XqRk5ODnp4eftkJiXG5XCzY22hJpVKepHm9Xvj9flRXVzNVhvjKOTk5MBqNrEnYvn07BAIBbt68CZVKhYqKCmg0GkaWCMUtKCiA3W7H0tISJicnUV5eju3bt2NwcDDiwbO2toaGhgbI5XIuIh0OBwoLC+HxeBidI+qZ1+vlAzE2Npb3QO9iVVUVI4Ikcs3JyWH6zdDQEMRiMbRaLa5du4b+/v6Inxuhy0RX9Pv9GBkZwSOPPILs7GzExsZiZGQEHo8H5eXlrKtYWVnBPffcA4PBAJvNxvqX2tpaOJ1OJCcnw2AwQK1WIysrC7W1tcjNzYXZbIZUKkVZWRnC4TBsNtuGeyNuLGmPSJw3Pz+Pt99+mzU9xNGdm5uDVCqFWq1GeXk5FzPBYBC9vb0oKSlBW1sbbDYb5ufn4fF4UF9fz5x+KuATExMxNjYWcTJKDZDT6cTU1BSPsQ8fPsyAyvT0NNbW1lBTU8Mo6uLiIrZt2wapVAqj0chFWH19PSYnJ+FyueD3+zE7O4v4+HhYrVbExsZiaGgISqUS1dXVXGhutEKhELKysnjKEgqFYLfb0djYyIjhzMwMlpaWoNFoGHG7ceMGCgoKkJGRgejoaBQUFMBiseDIkSNsfEAFs1gsRm5uLk8TiouLUVtby4XuRmthYYHfb0I9k5OToVarIZVK0dzczN9zSUkJAoEAYmJiYLFYUF5ejurqam6QJicnkZWVxaglaVwefvhhpKenQ6vVwmw2Mz2HGtGNllAoRFlZGet+qBkm7jyh6ImJiaitrcXExAQDLlu3bmUjkcnJSdZCLC0tceMik8mg1WoxMzPDYJlSqUR+fj4AMNX2bov0WETDoue9vr4ey8vLTO1cX1/nfWzbtg1//OMfodFoWHuRlpaGkZERbNmyBQ6HA62trejv78fa2hry8vIwNTWF5uZmpp3k5uaywH2j5Xa7md8eFRUFnU4HjUaDpaUljI+P888QCARITU3FyMgIlEolYmNjUVJSwnoutVqNyclJ5ObmYm5ujvVVk5OTsFqtPAEjDaBer2fq0EbrTh0SgZh0ZhKVhwpgEjWTCL6iogKxsbFwOp3Iy8uD1WrFli1bEAgEMDAwwO/vnj17mLaZkpLCgvC+vr6IxTwAnqQolUpmQshkMrS0tCAmJgZTU1MYGRmBQCBAUVER8vPzodFoePpM+pTr16+jrKyMaUM0mZNKpSz+p8kkTXcj3Vuky/H5fKy9slgssNvtkEql0Gq1SEpKgl6vx/79+xEVFYWysjKetkxNTcFms2F1dRUWiwWBQABms5lpQMvLy9i+fTvTDqkBJU3Y9PT0hntbWlpCZWUlf9YE8NTU1GB+fp7ZAzR5stlsbMbz4IMPoq6ujlkXd+oQSVowMDDAeigyb5HL5SgvL/+bE1u/38/nF+m5ZmZmsH//fmZMEBgeFxeHrKwsppUZDAYoFAoolUrcf//9GBsbw9atWyGRSJjyNjc3xz9rfX0d3d3dPN0ZGRn5m7UvrY/daNA4uLm5Gfv370dOTg7m5+fR19eHw4cPw2g0wmw2s+hl27ZtCAQCKC8vx3e/+110dHSgrKwMPp8PO3fuxHe/+12kpaUhJiYGqamp2L17N0KhEL7whS9AqVSirKwMMzMzmJycxI4dO9Da2rrh3ohaQUW11WpFfHw8Xn/9dUa+CRXX6XQoKChgGgU1JCQ4evrpp9HS0sKFLnHcrFYr8vLyuFgjcZRer484NYiOjobX60VdXR1qa2shl8uRkpLCzgh2u53H2YODg3zxpKeno7CwEEVFRZDL5bBYLNi6dSsuXbqEpqYmpKamwuFwwGKxwOfzQa/XY2BggPfm9/uRmZkJk8m04d4yMzPhcrnYnYg420ajEV/+8pfxyiuvYHx8nIvqJ554AtPT09i/fz9u3LgBgUCA+++/H1qtFk888QRu376Nw4cPIycnh9GR6elp7NixA5OTkygqKmKe9P79+yMi80qlEsPDw0w9Iacwu92O0tJSRnWI/tXc3AylUgm5XI7R0VEkJiYiMzMTY2NjaGxsZFclpVKJvLw8FBYW4tq1aygvL8fk5CRSUlIY5c3KysLQ0NCGe1tbW8P8/DxKSkpQWFjIRe7U1BT27NmD+vp6VFRUIC0tDQKBAA899BBu3bqFmZkZPP/883A4HNixYwfGx8fR1dWFN998kzUjJEpdXl5mgSXxPJ1OJzZt2sRc/LutmJgYGI1Gnj6MjY1BLBajq6sLOp0OAoEAxcXFXISXlpbi2rVrGBkZwalTpzA9PY2SkhJkZ2ezE1ptbS3W1tYwODiI4eFhFqkuLCygpKQECoUCMTEx3BhHWoT6qNVqRme3b9+OsbExHDp0CNHR0cjKyoJYLEZ7ezuKi4sxPDyM+Ph4XLx4EVlZWfjWt74FnU6HK1eu4MyZM9i6dSs3j+RW8olPfALnzp3D6uoqxsbGYDabsXfv3ohTg+TkZNhsNtZ9OBwOqNVq6HQ6fPvb30ZKSgoLFE+dOoUdO3bg/PnzUKlU+NOf/gSj0Yi8vDzMzs5ibW0Nra2tuP/++1FSUoKcnByUlJTg7NmzKCkpgdfrZUc2m82GnTt3RqQ6EgqXl5cHrVbLSNrAwAAaGhqQlZXFZ/K5c+fQ1NSElpYWrKys4J133kFiYiLuvfdeTE5Owmaz4V/+5V/w8MMPo7i4GDKZDKWlpdDr9Thw4AAuX77MNM3r16+jsLAwIs0mLi4ODocDRUVFqK6uRlZWFiorK3Hr1i0UFRXxNMzn8+HGjRt46KGHGMG8desWYmNjsW3bNjgcDojFYjzyyCMswibtCZlwXL9+HVarFW1tbWhra0NycjLS09M33FtSUhImJiag0+lQUlICjUaDoqIiqNVqpKWl4cqVK/B6vUhLS0N6ejoOHDiA9PR0xMTE4MKFC/wukfsYTc2MRiM6OjrQ2dmJ3t5e7Nq1C1NTU6isrGRE8Z577olYwGi1WgwODvIkZHp6GgkJCaiqqkJTUxNu3rwJr9fLjlGf/vSnMTk5Ca1Wi5GREczNzaGoqAg5OTnYvHkz3n33XWg0GqYnt7a2Ynp6Gvfddx8uX74Mk8mE7u5upklE4vNrtVpYLBZs3rwZW7duZU3E8ePHcf/997OOkArzpKQkWK1WAB/SLUlL8NJLL2FhYQG/+tWveDpD9Nzvf//7+N73voeoqCgkJyfjxo0bOH36NLsabbRIL2IwGJCbm8vF3/r6Op599lmcPn2aaV83btzAzp07MTo6itTUVPj9fshkMuj1ety+fZupdfv370dBQQGKioqwe/dupKamIiUlBaOjo3w2x8fH48CBA3A4HBvuLT8/Hx6PBykpKfB6vbh27RoWFhZgt9uRnp7Ok7r19XVMT0/j0UcfhcViwezsLM6dO8cau9dffx1qtRrvv/8+Tzjq6urQ0NCAtbU19PX1YXh4mNFw0ruZzeYN90YC/5WVFdjtdjidTjY2eeihh1iDQS5Kubm5uH79OuRyOebm5hgkDAQCKC4uxsWLF5nFIpfLkZWVxZSm7u5uGAwGWCwWjI+P4+jRoxEnQZmZmZifn0dhYSFSUlIwNjYGr9fL1BxyCgsEArDZbEhNTcXPfvYzOJ1OyOVyFBQUICUlBVeuXMGBAwdgMpmwZ88eKBQKSKVSVFRUwGQy4Wtf+xo6OzsRFxeH7u5unDt3jumKGy1yUb1Tw0ni+0996lP8TLpcLly5cgW7du3Cn/70J+Tm5uLEiRPo6elBcnIyJicn0dfXh7/+9a/QarXQ6/XQ6/XIzc2FWCxGVlYWrl+/zs2T3+9HWVkZ2tvbN9wb1ZM0ySGhvtlsRnp6OkpLS1FcXMyfvd/vh9/vxyc/+Ulmn2RnZ+PChQt8n8hkMtY5RUVF4cKFC+jr60NfXx8qKipYL7xp0yY0NjZuuLc718cWg9Oov6+vj3leKysrqKmpwbvvvgu5XA7gf7j1DocDZWVlmJycxI9+9CN4vV6YzWZMTU2hr68PWVlZWFpaQnx8PBYXF3Hp0iWcOXMGzz33HLsb0XTEYrFEvExItEbIplQqhUwmw6ZNm+BwOKBSqWAymbC+vo7c3FxMTk5CIpEw7YKs7NxuNzo7O9HQ0IDW1la2k6VCXygU8tSGHIKmp6cjjt28Xi8KCgrQ1dXFXG06DGj0T0IjmrA0NTVhdnYWly9f5pGp2WxGS0sLmpubIRAI2OFmZWUFPp+Pu/+tW7ciNTWVR8mRHAvC4TCP5gjNJ7SOdATT09Ps3HDmzBmmelVUVGBxcREnTpyAUqlkjv7bb7/N30lcXBzS0tIQCoWwbds2mM1maDQarK6u4sKFC9i1a9eGexsdHYVEIuERuNPphNFohFqtRnt7O/Lz8zE6OsouCseOHUNmZiYKCgoQExPDkyyDwYCRkREA4IbRarXC5/PhwQcfRCAQgFQqRVxcHFOxXC4Xo353W8RpdblckEqlkEqlaG9vR0lJCfN9Z2dnmfv54osv4tChQ7DZbDhy5Ai7tZEw7/7774dOp0MoFILT6YTT6YTJZGIkyWg0svZmdnY2IpefLKZJZ0B86oqKCgwODvKfJSYm4siRI5icnERdXR07vDmdThw/fhxCoRBSqRQffPAB9u3bx3qiuLg4lJSUwGQy4fbt24yaLC4uQq1W/02NhsvlgkajYYE5fRaZmZno7+/nxoDoGTQyn5mZYc4r6YeKi4vR3NyMs2fPsgtWVVUVnE4nTp48ye5b5MJDzf1Gy263IzMzkzUNXq8XXV1dOHz4MNra2qBUKmGxWLC4uIj09HS0tLQA+JAmsG/fPsTGxmJ8fJzd4EpLS7lZCgQC6O3tRU5ODk/7PB4PYmJikJSUhJGRkYgUTKfTye5pxMeen59HcXExbt68iczMTLjdbohEIjQ3N6Ovrw8CgQB+vx9ZWVkQCASwWq0QCAQYHBzEV77yFRiNRkRFRcHhcGBmZoZ/P7/fz+cA6aIicZjtdjtycnLQ19eH5ORkJCQkoLu7G0VFRfD5fLh69SokEgkUCgU2bdqEkydPoqSkBB6PB83NzYiNjUVXVxfm5+chl8vx29/+lul/gUAA6+vrqKur48KWdEqklerp6dlwb6urq9BoNOju7oZKpYJYLMbJkydx5MgRDA8PIzExEV6vl+lmUqmUaWTEK5+enkZqaipu377NTjDk5iYUCpGdnY1Lly5xMUOc5paWFtaI3W0NDQ2x6xxpBqlJNBqN/J7T81JZWQmJRILXX38ddXV1CAaD6OjogEKhQHd3Nz+DdGanp6cjEAigq6sLqampbI+7uLjIBeRGa2BggC2nicaUlpaG733ve3jppZdQVVXFRhjk7jY6OorCwkKerMnlcuTn56Ovrw+7d+/GxYsXIZFIkJGRgUAgwNTrq1evorq6GvX19QxeRhKZkn5ldnaWhdBXrlzBpz/9aXR3dzPFhQwsTp8+zUwGADwRJGBJqVRiamqK3drof5PQfnZ2FqmpqRAKhbh27VrEaUtfXx9KS0sxMDCAubm5j7jsDQ8Pw+PxsN6qqKgIp06dwic+8QncuHEDWq2WtYslJSXo6enBoUOHMDAwgIGBAfT19WF5eZmnqA6HAx988AH0ej0LeyM5YplMJqa2EhW+r68P4+PjXEDPz88zpefs2bMoLS3Fu++++/+KDRgcHITBYMCf/vQn7N69GzqdDhaLBQUFBfzMkwYSAF555RV2Krvb8vl8XEeEQiEUFxdj8+bNaGtr4zuAKJpra2s4dOgQ0tPTcfv2bSgUCthsNgYqWlpaUF5ejtu3b0MoFGJ1dZWNJV588UWeENTU1LD2LxLzg5wFqcaRSqWwWq1obGzElStX2EEtKysLeXl5GBwcxP3334+Ojg6kpqayA1s4HMbg4CAKCgp4mjc1NYW5uTlcv34djY2NKCoqwvr6OgwGA1ZWVjA8PBxxEmS32zE6OoqsrCwolUrIZDLWLtrtdqafr62tQa/XQywWQ61W44UXXuBpBmlXadI4OzvLE0CyBo6Pj0dDQwOmpqZYazIxMYHx8XFs3rx5w/3R+v8kBn/nnXfQ29uLxcVF5ti3trZCoVCgoaEBQqGQheBU0M3MzOCVV16B0WhEcXExF7R6vR5ra2ssAl1YWODLlvIknE4nJiYm2B1oo6XT6TA2NsZTBqKc9PX1QaPR8FjLarUyWpeamoqpqSm8+uqrGBkZQWZmJmpqapCZmYnjx4+jqKiI6RBE99JqtTwSdjgccDgciI+PjzgeJ4SVUA2TyfQRQVF1dTXS09Ph8XgwNjYGkUiE27dvo7CwkDnpZH87Pj6O7u5u9PT0QCgUMkLi8XggEAjw5JNPcsdKIqmCgoIN92Y2m5l6JRaLUVhYyBZ6q6ur2L17N+rr6yESiTAyMoLOzk40NjZidXUVDoeDBUfj4+Nwu90YHh7Gtm3bsG/fPtTX10OhULAtJPmjX7lyBe+99x5zXjda9OKT2JHG3devX2eXKBqTnjt3DoFAAEajkR12srOzUVdXh4SEBOTm5rLVoEajQU5ODiQSCV5++WV0dnay6weN7IkqsdFaW1vDrVu34HQ64fP54PV6kZGRgQ8++ADhcBi5ubmoqanhBlWhULBm6C9/+QuuXLkCsViM8vJyrK6uMi81OTkZpaWlaGxs5MuORv0+nw9msxkejycijXBlZQVyuRypqanMMbZarfjzn//MFqgejweTk5Nob29ndGp5eRldXV1YX1/HAw88gK985SsAgOzsbOTl5bEjEOmTUlJS8Oijj0IoFMLlcmF5eRmZmZkRTRuADxvQjo4O+Hw+FssaDAZYrVb09vayqDYQCKC/vx9arRYajQbHjx9Hb28v29lqtVokJyfjG9/4BjweD+69915UV1ejq6sLly9fxtTUFIqLi9m4wGq1sqNapO91aGgIS0tLSE1NRXZ2NtbW1nDlyhXEx8djfHycdVROpxNqtZqngsTZJzvqqKgodHZ28nNLTf3Q0BD8fj+ys7MRHR3NZ1ZUVBRycnIivg9EVyPhX0lJCd566y14PB5GHQl5ys7ORiAQQCAQwLVr13Du3Dk4HA5s3boVjz32GH76059ibGyMfeUFAgGOHTsGp9OJ2tpaxMXFsamD0+mM+LklJCSweJQyKgoKCvDBBx9gdXUVRUVFSExMhN/v/wjHfXl5GVevXkVbWxs/D6urq/jDH/4AgUCAxsZG1NbWMv2R6CVkq0zUyPLy8g33JhKJ+DKfnp5Ge3s7xsfH0dLSwpbDKSkpWF1dxfj4OHw+H+dCEYWAjCiqq6tZD0Hc9piYGJw/f56fH9JEEcUukrOeWCzG1NQUJBIJ21K73W68/vrryM/Px5EjR6BQKOB0OrGwsIDXXnsN2dnZ0Ol0zI/fuXMnGhoa2PEpJiYGBQUF0Ov18Hg8/OzrdDpMTEygpaUFU1NT0Gq1EfVUxHMH/kev1t/fj5/+9Kfwer3QaDQwGAyIiYnB4OAgpqam8LnPfQ7JycloaWnB/Pw86uvr0dTUhNzcXLS2tkKn0zElIxgM4ubNm5idnUV2djYEAgE6Oztx48YNdonbaFGzTAYBXq8XMTExuHHjBlwuFzIzMyEQCDAxMYG2tjYYDAYuhkmTQ1buNpuNqSAEzhGwRZRLMgEgWlykeysxMRGdnZ0Qi8U8GSaAlswClpaW0NnZiXfeeYcnDGR+MTg4CJfLxTbbp06dYpt9ym9wuVwoLS3Fzp07odFoYDKZePISqXmkiYZCoWANqkajYR6+SCRCQUEBwuEwXnnlFSQmJuL27dtITU1lDZpGo4FEIoHFYsGJEydgMBhYT7C0tIRTp05Bo9FgcXGRzR76+/v/5tk7Pz+P9vZ2eDweqNVqpKen89SSdG50xg0PD6O7uxu7du2CRCLBuXPn4Pf7sWnTJnai+u53vwuhUMh0IplMhvb2dhQWFrIO99q1a/jggw+QkpISEYASi8WYnJxksHN8fBwKhQJXrlxhyrtSqUR/fz9OnTrFU5/u7m588MEHuH37NgOaOTk5nIulUChQUFCAtLQ01jXr9XrODCOznUiNBv1uxIYgYbrZbGY6KIH1Z86cQU9PD44dO8Z1NumbSKN08+ZNdHZ2Yn19nWspgUCAzZs3s+MkMRuIPv9x1sduNFZWVpCfn898Vwov2rFjB3slJyUlobi4GI2NjWxxaLVakZWVxUK0+vp6xMbGYtOmTfB4PEhISGAnhc9+9rM4ffo0FhYW2BeYHDUioQiBQIBFUWKxGCkpKWy9ReF7lP2RlpbGIpm5uTkcOHCAmw4AGBkZwSc/+Uk+kBISEpCUlITNmzejt7eXuXfJycmQyWT8szZaFCx3p3VfbGws9Ho9T0RMJhOEQiEKCwvZKcBkMqGyspLzITZv3sxFAQmTiJ4kFovZi5uKZhp1U7DU3RZ5sgP/k3EhEAhQUVHBHvxEg9i1axcMBgNbF9bX10Ov17ONIek7Ojs7mSMZExODyspKzMzMMAc5IyMDpaWl0Ol0EVFSKgDcbjeMRiOsVisSEhK4QaWJmkqlQkNDA+cnuFwubN26FQkJCbh06RKuX7/OEzFC3GmCtHPnTkb3KUSRHGQiFcyk9aCMD7LZffDBB5mTTvaFO3bsYAtBj8eDHTt2ID8/n4WBVJjcvn2brS7HxsaYn0k6CsptEYlEEek/6+vrbJ1KNL+1tTXs3buXeazEay4vL2cHkYWFBVRXV0OhUGBoaAhmsxnr6+uoqKjgSyg5OZn1QeTU5vF4IJVKYTAYGA2LtIRCIRc49DvFxcWhrq4OAoGAA4QKCgpQVVXF+ScJCQlQKpU8tiXBbFVVFfviU4YP0ctoJE7vKiGvG63V1VXmUFNjKpfLsXnzZthsNlRUVCA9PR0ymQwFBQVwOByck0J0Psq5iIqKQmVlJbxeLyNXS0tL2L17N4APwRGPx8MHOgkcN1rUCJMYk5qUnTt3sg0hcaiJxlJTUwO/34/m5mZoNBpYrVamK1LQYFRUFNLT05n7HBcXx9a95BZzZ9G50VKpVEyLpMlMbW0tv1/kpEOZKcTbJqR4cXERxcXFmJ+fR0NDAyOQ5FOfnp7O0wNqzOLi4piqsdFyu91MKxCJRFCpVJDJZGxAcKeAuqKiAh6Ph8+VnTt3oqCggIWmJAhfXl6GWq3mJlIul2NsbAzAh410ZWUlqqur2VZ2o0VCSgqBW1pagsFgQFFREUZHR+HxeNimlZ7L6elpppOSG9XKygoSEhLYNcZoNLKxCgnqFQoFB+yRKUWkd5UKFuB/XJTi4uKwbds2KJVKSKVSpKSkoKqqCk8//TSLhe12O/bv388aA6IGqtVqJCQkoKGhAenp6TCbzWhubsbq6ir0ej0LaEkkHWnaDXw0y0ilUsFgMKCwsBBWqxVJSUl8ljz22GMIBAJskkFmCHQGAh/q7cbHxzE5OYlwOAyDwYDm5mYMDg4yMEM275QvsdFaXV1lHRw5foXDYRQXF8PtdrNpQGZmJkpLS9mAYnV1lal9IpEIarWa73kyclCpVHwOLi8vIz8/n5tbsjCPROsiijlpW8hsZ9OmTfD5fCgqKmJdV2pqKpxOJ2QyGWJiYrB3715kZ2fD7/cjMTERKSkpuP/++zlYU/LfNvMajQY9PT3s6En6inA4HPE9XV1d5UyZYDDIrm9btmzB2toa1Go1UlNTodFomNZLDmjAhzXg0tISysrKIJVKce+997KxCAnAH3jgAQSDQZSUlCA9PR3l5eWoqKjA+vp6xPeU9A9Uc1BWW3V1NQYHB/nMo+eWTDCio6NRU1ODnJwc1oOQQUNnZyc72Mnlctx3333c6K6trSE+Pp7NRyIB2Xda6FosFlitVng8Huj1enZGdblcSElJwebNmyGTybB582YGxSgEt7a2lnWmxNqhZyQrKwsTExNc+2VnZyMzMxM9PT3wer0b7u3O9bGpUyQuIwtZukyKioqYS0ehPkqlkh94oh5RyBF5ZBNKT85PALB582a8/vrr/KWSrkKhULDd5t0WecXfGUgTGxvLBRehkKmpqR9xtCFqSiAQYIenhYUFTmymF0MsFsNgMLD3ODlukWdxJNSKDmniAJJQj0JUqCgnqkooFEJ0dDR8Pt9Hum/6rMlOkbp1ujTz8/N5zEZWxHdeFndbycnJ/DOio6P5oiMR9uTkJNutpaSkQKfT8bSExFnr6+vweDzYvn07LBYLT3qSk5MZEaS/Q24sEokEGo0m4pTqzuAbEpuRJSEVc4QSk/sJUV4oZDEcDmNmZgZisRgSiYQ/c7LrzM/Ph0wmY5vEO8OsIiFDd9rm0UiUND1paWlwuVwsqqXpidlsRjAY5GRcsiAmehq5hNFnVFtbCwDsyU1j47+FkpJlIhV4lH5OvMtgMMjFBznBECWKEAyyEy0oKOBJFAl9g8EgpqenUVZWxkV8XFwcBzVGQoYAcGidxWLhNGPSROTk5LBVIf0ZhWMWFBRwlgBZdZIRABXolEeQkJDABWlGRgbzt+9079nofaDmjqwpi4uL2VVF8t/p6FTIUBOdkZHBl290dDR0Oh06OjqwZcsWDAwMsHCdqITk4U68b6ICRaJO0ZSX0Cey3SV6ocPhQExMDOcV3JlETL731IhR9gYlYdP7RPvo6emBVCrFysoKJ6JH0mjcCRgQ+kbAAlE8yIKYzi8K+dTr9Yzo6vV6jI6OcuAliRF9Ph8HwlIYJ+nG5ufnI6Lf9L2S2JIoUYuLi0zzo+eJ6GjEb74TJCLRZTAYZIdFoiESJY1MKsgxkax8N1qUAhwXF8cFRl5eHmQyGTIyMlh0TbTO2tpatLW1IRwOM2WRXNjIEXFhYQGTk5OIi4uDWq1GKBRCfn4+VlZWuJgiJDOSAxvZVhPau7q6itTUVOTl5XEmBzkOkXuXyWT6iHsTgQICgYAdxehzXVhYQEZGBgwGAwu5ydUKQMTzl84Iet6oWaa8IXqPiBpis9m4DiBQkN5VyX/b2d+ZraVQKKDRaPi9ouKS7spINBu6f+nep6nt3NwcVCoVYmJiOGeKpk9EFaOmmwpjOvdJH0vhgtQkkpsYGUrcWSts9LzR+U53IbndkZNkbGws0tLSEAgEkJqairq6OoyMjCAnJ4efMzLVUSqV/PPItINCdcm1jmx06R3aaMXFxfF3Q+99bGws3/d3OrORo6LT6YRer2dnNK/Xi9jYWBaJ03cQDAbZOEalUiErK4vPvaWlJczMzEQ0u6D9kbsfib7pGSWbWAIAyHhCqVRCp9Nxevri4iLbFhNFm2j+9D1OTk7y/UB1YiTQk6hRZG1OVOm4uDhkZ2dzNhdNr6ghMhgMTMMmS+OoqCio1WqUlJTw/tbW1tg+2uv1wu12QyaTcf5aJL3onetjTzREIhEcDgdf5MCHzYfT6URjYyMr+wkZLi0thdvtRl5eHkwmE6xWK6anp/H6668jNzcXHR0dKCkp4Q80Pz8f6+vrePLJJ9nKlVC0pKSkiKI6KkrJBnR2dhb5+fmIjY1FYmIiLBYLoz4ikQi5ubno6elhjp/T6eQvQafT4ebNm5zdQHkaIpEI99xzD1NraDz+twTX5HNMBZvT6URMTAyPs+9EYhcWFlBYWAiTycTWonK5HFqtFi0tLcjKyuJ0Wvq9yRmrqqoK7e3tEAqF7LhA05eNVlZWFhYWFqDRaBAbGwuXy4WioiJYLBZERUXh+PHjGBgYgMPhwMTEBLKzs2E2m6HT6Zj/p9VqOfAmLi4OTU1N3MBQKCMJQldWVtDX14eenh7ExcVFdCgisRyJNsm+LSUlBWVlZVCpVGx/53A4UFtbi5aWFqyuruKtt97C8vIyDhw4gKysLBYgp6am8qWdkZEBi8WCyspKvgzpeaPR8kaLxKHUaM3OzsLn86GzsxMVFRWcBG00Gvl7O3PmDNOGyArW6/UiOzsbaWlpqKmp4Wa3rKwM8fHxMBgM6O7u5sOKkkwjCdVTUlIwMTHBtpHLy8uQyWS4du0aKisrGdEl7rZcLsfx48eh0WgwOTmJ9fV1lJeXQ61Wo6qqCjMzM3ypxcfHQ6lUYnJykqdbWVlZnGZrMBgiplsDYN40GTTQu3X+/HlUVVXBbDbDZDKhr68Pb731FpRKJUwmEzf/oVAIGo2GqRvT09Nobm5m04b19XW0tbXh8OHDcDgcyM3NRUZGBk9OIyEwBJDcmWlBYvSamhpMTk6yFzxNiaiBnpmZQUxMDHJyclgIGg6H2U44LS0NOp0OLS0tKC4uhtPp5OKeBPCRChhqVIj+QLk1ly5dgsFgwK1btxgJtFqtqKysxPXr11FRUYEbN26w/oT0LfHx8Thy5Aiio6MZFJqcnER+fj7OnDmDwsJCzvKhpmOjFRcXh+npabYsJovXDz74AMXFxTzRW1xcxOjoKDQaDS5cuIAdO3aw1e2WLVv496RpTFJSEmZnZzE0NIS33noLaWlpeP/99yGXy3mylJGREfFuUCqVcLvdTPmg/BPKFbkTAKKpCr0XN27cwODg4Ef2TcYbZDstlUrx3nvvseOc2WzG2NgY229GmlJJJBLWC9J0RiQSwWQyYdOmTZw1QqBEY2Mjpqen4XQ6OXCLUHlKdy8pKQEAbiyioqKQnZ2NiYkJBowWFxf/JsUxJuZ/0qmpgCX6YEVFBYLBIFtmvvbaa5yRpFQqcevWLczNzaG0tBQKhQKZmZmM9BNVVKFQMA2UKHp2u51zhnp7eyPubXFxEQkJCewkFx8fj9bWVp7iraysYG5uDkNDQ9ixYwdzy6enp7GwsIDk5GS+b0dGRjhtnSYIw8PDuOeee/j8u/M9jaSDIDMWAkkp2+HSpUvYvHkzFAoF5xpNTU2hrKwMLS0tyM/PZ0e81NRUzMzMMAVHoVBgdnYWgUAASqUS3d3dSEtLw+TkJBugUKEYaWpA90FsbCySkpKQlJTE7oxarRZ//vOfAQBVVVXM4qDfd2JiAhKJBFVVVexadfbsWUbSR0dHecKwY8cO1tARW0ClUmFwcHDDvSUkJMBms7Hl9+zsLIRCIQYHBzmIlyioHo8HGo0GYrGYqW1UA46Pj8NgMKCvrw9FRUVwuVwwmUxYWFjA66+/joqKCgAfglJGo5EnhF1dXRvujWoDsVjMzn/kWlpVVYXFxUU4HA4YjUbMzMyw0ya5mpEV+oULFxAfH4+qqir+jOl5npqaQk5ODjo6OphqT8yeSEssFvMZTcCJ3+/H7du3UV5ezlRnt9uN6elpKBQK1nQIhULO+7h48SLLDGpqajjocGlpCd3d3UhPT+fAY6PRCJ/Ph7Kysr856ab1sScaFouFKQ+Up0EfZGxsLHJzc/Hiiy/C7/fj8OHDGBsbQ2pqKt59913mcdGUwOVyobu7G93d3ZidnUVGRgaKiopgs9mQl5eHvLw82O12xMTEwO12c3G20SKkgZJ9tVotCwfD4TC0Wi3nPCQnJ6O7uxvLy8u4efMmamtrsbKygqGhIfZXT0tLw8mTJ5Gfn4+oqCgYjUaMjIwgNjYWR44cwY0bN+BwONi3PpLdHSF7AoEAYrGYgwF9Ph+Gh4c5dZcEWOfPn2dB6vr6Otrb27G2tgaFQoFf/epX+Na3voUTJ04wSp2QkICDBw/i2rVriIqKwsTEBBQKBWccCAQC7N279657Gx0dxVNPPYXr168zCubxeFiYXlZWhoGBASwtLSErKwu//OUvsbi4iLKyMmRmZrIxwDe/+U0MDQ1BpVKhq6sLdrud0TTybCc+OFmWXr16NeIlvLi4iIaGBqaZSaVSXLt2DaWlpdzgdnR0wG63c37Irl27YDQaMTo6ypdXWloa/vKXv6CgoADFxcUoLS1lkZPb7cZvfvMbVFVVYWlpicVwFoslohiRKEJ08NBEr6mpCR0dHRzGk5iYCIVCAb/fz0hZSkoKo8YmkwlSqRTPPvssjh8/DpvNxt8BuUds3rwZN2/eRHl5OcrLy3Hp0qWIITkzMzOorq5GS0sL0zpiYmKwfft2nDt3DlqtFg6HAzKZDPX19Xy519bWIicnBwMDA+js7ERmZiYkEgmysrIwPj6O3t5e1gU5HA5MTU3BaDSira2NOdEXLlxAQ0PDhnsDwBkJlM2xsrKCjo4OPPjgg5zdQhzouro6NDc3s4Wnw+FgT/6hoSHcunWLLyKfz8ccaJ/Ph+effx4CgQDXr19nusj6+npEQSK5cLndbkxNTWF1dRUmkwk6nQ5nzpxBQ0MDv5fZ2dkYGhpCTEwMPvjgAxw4cADz8/MYGBhAZWUlsrOzcf78eRaGJyYmQi6Xw+Vy4a233kJJSQn6+voAfNi4/i27Z+IuU8qtzWbD0tISduzYgYmJCaaquN1ungCvra2xOH19fR2dnZ1Ma/jOd76Dd999FwqF4iPF6s9+9jM88cQTuHjxIje7RPHaaNntdkYyCTCZmZmBSqVCX18fWy66XC4uNNVqNevlqPmcn5/Hb3/7W1y4cIHtj8mSlLQAHo8HFy5cQE1NDSQSCVpbWyNSgOiZuTM/gRooCvGiPKDFxUW+J8j8gy59nU6Hd955Bw0NDTCbzbxvn8+Ho0ePsoOi1+tli+s7cwXutsh2e21tjffX3t6OhIQE9PX1cWr0nQG5VVVVeP3115mORja04XAYfr8fPT09cLlcWFxcZNtcnU6H6elpNhehyVhPTw9qamruurdAIIDa2lp4PB6mCBIv/tatW1CpVOju7obX6+U8ivj4eLzzzjtob2/HysoKi25v3ryJ6upqiMVizM7OMkC3srKCP/3pT3jwwQdx8+ZNzq5oaGjA2bNncejQoQ33plAoOMiXdIqUCh4XFwer1crT6ZMnT3LmiF6vh9VqZQH09PQ0EhMTEQgEkJaWxo6CGRkZGB8fh8fjYW2By+VCS0sLUlJScO+99951by6XC42Njejq6mL3w+LiYmzfvh0DAwOst/P7/ezy09jYiB/96Ed45JFHmOJ4/fp1eL1e5ObmMo2OGp719XWcPHmSXZBoEkt3/0YrHA5j27ZtmJ6eZi3m1NQUxsfHuXkl4OLee+/F+Pg42traEAgEoFKpsLCwgP7+fn4myRly3759AD60vB0YGMDy8jKKi4sxNjbGNU5iYiJrzO62pqenUVpait7eXjbJSEhIgMFgwNWrV9Hc3MxidIlEwkDvd77zHZ4Ok3b4P/7jP5Cbm4vu7m6e7EulUtTV1eHEiRMoKCjAhQsX4Pf7mSYUCZmnadba2hqD7AS0EmhGEyKtVot33nmHp4eUJ0bOYuvr6zhy5AiGhobYAIasi3fs2AGpVMqmCsnJyRgeHsbWrVs33BuxMGifcXFxCAaD2LJlCzo7OzE3N4eenh5ER0dj+/btkEgksNlseP/995mSSfrP4uJiFBcX49y5c9i0aRO2b9+O9vZ2FBUVsQ6abKhjY2MxMTERkYp85/rYjQY5DdDoiTjwo6OjaGlpwf/6X/8LBw8eRHd3N2w2G9stZmRkYHJyEmq1Gvv27UNVVRV+/vOfs0BLKpWyGv/999/Hjh07kJ6ezn7lRqMRsbGxES8TsVjMqbHESUtLS4PD4cDu3buZuuNwOHDz5k2UlZWxRuTGjRuQy+UoKytjlP3q1at47rnn2AFCr9fjZz/7GSd10giZ0MBIfuQ0RbHZbEzpstlsGB0dxY4dOzjIy2Qy4datW3yY5Ofn4+c//zmys7MZOS0vL2fhMQUxWa1W/tz279/PQl+ahkQqmAUCAX73u99xqFZycjLMZjO6u7vZkm51dRWjo6MYHR1FZmYmfvOb3+Cpp57Cs88+C4PBgIGBAQ59OXPmDB5++GFoNBqMj4/jjTfegFAoxO9//3v89Kc/xezsLDo6OjA1NQWhUBhR/BofH4+JiQlGSSkHpbW1FWKxGE1NTcwTvHnzJh8+hYWFeP3112GxWCCXy3H06FGUlpbiRz/6EZxOJ3u7+/1+/PrXv8a//du/YWJiAllZWUxVID/9jRahgmS7S7qKJ598Eg888AB27dqF+Ph4tLW1oaOjA0lJSThy5AgHWpGj2he/+EU88sgjWFpawqOPPsraDjoInnjiCfj9fuzatQsikQh2ux2JiYlobm7ecG9UZBYWFsLn82F6epp1AmVlZWhtbUV8fDwkEglGR0exvLyM7373u7h06RIkEgmys7Oh0WhQUlKCn/3sZ8jKyuIEa7vdzg5Rs7OzyMnJwfj4OCwWC0wmExc9kVZ2djbcbjcXZpTZ8Q//8A/4u7/7Ozz99NOIj49nXcrKygrOnDmDnTt3cqP18ssv4+GHH4bD4eDDu6amhmllKpUKnZ2dnLpKlrxVVVV/kydM1pTkcmexWPDNb36Tk3PD4TAsFguuXbuG8fFx7N27FxkZGThx4gR0Oh22b98Og8GAnTt34oc//CH27t0LnU6HQCDATfrmzZvZOCEQCGB2dhbT09MRAywTEhLQ3t7OCDCFN/7Xf/0XlEolHnvsMbZYDAQCePXVV/GJT3wCy8vLuHjxIl8qi4uLuHr1Kv7t3/4N3/rWt7C4uIibN2/yu/bAAw9Ao9HgwIEDsFgsbBgQybY4JSWFz1zgQ3tT4sEPDAywaH55eRkffPABlpeX8cwzz+DmzZvQ6XTo7OzEpUuXUF5ejvvuuw/PP/883G43HnnkEYhEIvT09EClUuEnP/kJvva1r2FqagqTk5MQiUQwGAzYtm1bxO/UarUyxS8+Pp5tZM1mM9Mnh4aGMDAwgPvuuw+5ubn4+c9/Dq1Wy2Jwl8uFqqoqnDp1CjKZDA8++CCGh4fx85//HHv37kVubi6OHj2KK1eu4NSpU/B6vaipqYn4vEkkEqYIy+VyyGQyjI+P47333sPjjz/OIYxWqxXnz5/H9PQ0nnnmGezduxe9vb0fEblKpVL4/X7k5eXh6NGjCAaDuHz5MgvdSUfU09OD2dlZ1NfXo7KycsO9qVQqjI6OIiUlBRqNhmlIt2/f5ruSQIdz586hpqYGgUAA3/72tzEwMIDx8XE4HA585jOf4f05nU6e+HZ1dbEjYygUQnZ2NioqKpCamgqpVIrPfvazG+6NKMcJCQkoLCyEQqHAiRMn8Oabb0KhUODll1/G1NQUPvjgA8hkMuzatQu1tbU4f/48enp6oFarUVNTg7y8PC7CSJBL076TJ0+isLAQkv8OICY9yPz8fMTpHp37pGFITEzE73//e0xOTuJTn/oU7HY7T+ZHRkZw4sQJ/OQnP8GlS5fwi1/8Atu2bcP+/fuxZcsWdHV14dSpU/jmN7+JoqIieDwevP/++2z1Pjc3h7y8PNy6dQsulwvNzc2YmZnZcG8LCwv4r//6L3ZjzMnJgVwuR19fH1P++vr6uCGwWq14+umn8dxzzzFgSVbiY2NjeP755/HUU08xrUipVKK9vR1RUVHsXERU9ZSUFNTX12+4N4VCgfPnz3O0AZ0pVqsVO3bsQH9/P6dwEw3/7NmzUCqVUKlUPO3V6XTo7u5GQkICMjMzceTIEXi9Xpw5cwZ+vx81NTUMXFDeh16vj3j2EkBB9uYLCwswm81sjnPfffdBqVTi4sWL+MUvfoGsrCzExsZyjgjR+Pfv34/f/va3+O53v4ujR49yIKZarcb4+DhmZmawdetWNghxOp3suLiRqU9SUhKMRiMSEhKQmJgIvV6P4uJinDp1CsvLy+wCRrkuFKZpMBjwxz/+EaFQCAcOHIBarYbL5eLBgEKhwNLSEq5cuYKBgQHs27ePXa1OnjyJoaEhPPDAAxsCFf/3+tjUqYGBAbaxomTgkpISHD16FAaDgUPsGhoaUFJSgvfffx/Z2dkQiUR4/PHHsW/fPohEIgQCAczNzUEikeDmzZu4cuUK7HY7iouLcejQIQwODuKDDz7A73//ewwODrLDS6QDe3x8HGq1Gqurq8jPz8f+/fsRCASwZcsW3Lx5k7u81NRUfOYzn8HQ0BASExNx9epVbNu2DSqVCv39/RzC9/3vfx+tra2MlszPz+Ppp5/mQ+j999/nRPShoaGITZDH44FSqWR+OF2Sjz76KCYnJ7G2tgaHw8HCWxKuJSQk4He/+x0+//nPs4PQ7OwsJicnMTMzwx7MQqEQu3btwtDQEJxOJ3v/FxUVsZZgoyUUCrF161ZOBRcKhbBYLNixYwdKS0uh0WhQWFiIXbt24dChQ6ioqMDU1BQaGhogk8mgVquRn58PhUKBgYEBbNu2DXK5HFVVVWhubkZhYSGamprg9/tx8uRJ3LhxA+np6aivr+fR90aLeOZLS0ucsGswGPDAAw/A5/PBZrOxc8yRI0dw+/ZtPPLII5idncWzzz6Lo0ePYm1tDZOTk7Db7fj617+OsrIyvtzT0tJw9uxZtLW1obGxkQuXrKwsGI3GiE4PNpsN1dXV8Pl8bGdps9lw7NgxTjsmAeTDDz8MpVKJxsZG5m4TbencuXPYu3cvHn30UfziF79Ab28vVlZWGJUm2tSf//xnWCwWRiP/1mVSVFQE4MNLLycnB3FxcSgsLIRAIMDu3buRn5/PnE5yIbl48SK6urowPT2N+fl5JCQkoLGxEQ8//DDrJ3Q6HcLhMPbt28fWsTt37kRVVRXS09NZGxNpjY+Pc7KvSCTiCcs///M/M9WR3CwkEgm6u7vxk5/8BO+++y5sNhuio6Mhk8lQXl6O1NRU/NM//RNOnjyJjo4O9Pb24v3334ff70dvby+OHj0Km82GkpISHDp0iPMSNlpEARsYGOCJxtTUFL72ta/hwIEDnDdTVFSEvXv3ora2FmlpaSgtLeWMnOHhYVy4cAHj4+P4wx/+wFowg8GAhYUFGAwGvPvuuwgGg/B6vcw3Xl9fjyjy9/v9OHr0KMLhMCYnJzE3N8dACnn+5+Xl4d5778Xjjz8OkUiE7Oxs9PT0oLGxETk5OdyIT0xM4Ktf/SpaW1shkUhQWloK4MML9bXXXoNKpcKvfvUrXL9+Henp6TwF2GiFQiE+Y5eWllhIf999933kPdq0aRN+8IMfICoqCqmpqezlT+/H9u3bkZycjIceeoibL7/fD5FIhAMHDrARhFarRXZ2NpKSkjA8PIxLly5tuDeBQACNRsPNdW5uLnQ6HdPt6N4Qi8Wor6/H+fPnsb6+jqqqKhw5coR9/fft24dz585hz549HMK5sLCAw4cPY+/evWhvb8fZs2cxODiIzMxMlJSUsBvZRouEvR988AEuXbqEmJgY6PV6PProoxgcHMTo6Cj8fj8KCwvxiU98AsD/TO8puwYA56f84z/+I/r7+3HixAn09fXxhL6zs5PtTzMzM6HX69mgINLzduDAAXbtMplMiImJwWc/+1m2MQU+bEi+9a1vYX19HYcOHcKrr77KNu5jY2OYmJjApk2bmIIzPDyMd999F4ODg/j617/Oon+aRA8NDWF4eDiitjAcDqO8vJwbFspi+fGPf4yDBw+yOURJSQmqq6sRDoextraGuro6NDU1IRQK4dy5c+jt7cWNGzfwzDPPIBwOo7a2FmVlZVhZWcFjjz2GoaEhlJeXc17Q7t27IRAIIlqfk4kJ2X+bTCZkZWXhX//1X6HRaBAKhRAMBqFUKtmi2Gg0ora2Fvfccw/i4uLYUezixYt4/PHHcebMGZw+fRo9PT1cJ3R3d3OIr06nQ35+Pk8IN1rx8fFoampi45zFxUW8//77OHz4MMrLy7F//37s27cPTU1N2LRpE7Kzs1nTt3//fgSDQRw7dgxdXV0YHx/H73//e/h8Pg4HDoVC+Nd//VdotVp89atfZYChvLwcCQkJEeukhYUFNDU1QSgUYmlpCWKxGGKxGI2NjTh16hSbH4TDYVRXV2N9fR379u3D8PAwlpeXWR+oUqlQU1PD5xGBJa2trUhLS8Ply5fx2muvoaOjg2ujK1euRNSPREVFoaCgAB6Ph01SVCoVvvrVrzIdmihbDz/8MACgubkZW7ZsQWlpKevmSC/66KOP4tKlS6xtWl9fR21tLdPlx8bGkJCQgOLiYqb0bbTW1ta4OSFw9N1330VdXR3b/UokEqjVamRkZOA3v/kNv9df+MIXkJSUhHfffRdSqZTPkEAgwDVAVVUVjh07hpGREWzevBl2ux1HjhzB888/D6lUGtF84M71sRsNajJCoRB39EKhEFevXsXBgwdx6dIlnD9/HkajEUKhEE1NTZiamsLevXvR398Pn88HmUyG0dFRfPazn8Xg4CB27tzJ/tsUyrV9+3ZUVVWxE4LL5cIDDzzArh53W3K5HF1dXVhYWIDX62Xf8/HxcRQVFXHSL1F5tm/fjjNnzmDbtm0YGxtDVFQUSktLYbVaoVarYTKZUF5ejmAwyDSV4eFhNDU1YX19nZOtx8fH2V890t4mJyc5WIp8mC0WCw4fPoy+vj6oVCrU1dXxi9Xe3o709HRcunSJnWGmp6dxzz33QCgUQqlU8thYIpHg6tWr7DNPyZRDQ0Ms9NpokaC6oKAAYrEYc3Nz2LFjB9555x089thj7JCVmpqKgoIClJWVQS6X4/DhwyxelEgk6OzsxK5duzA+Pg6bzYYPPvgAMzMz2Lx5M65fv46CggKeGpEIa9u2bRH1I6RpoUvx1VdfRSgU4q4d+PCSW1lZQXt7OxobGzE0NITo6GjcvHkTPp+PRceBQAA3b97kQoWcXIg/GxcXh6KiIgSDQQwMDLDobKOVnJwMt9vNrhEqlQolJSU4c+YM8vLycPXqVaysrKC6uhoVFRXYsWMHU0cmJiaQn5+Pe++9lyccCQkJqK2tZRMAElnv27cPk5OTyMzM5MaEgho3WouLi4wgSqVSFg7a7XZUVlayGCw9PR2Li4uoq6tjH/vc3Fw0Nzdjx44dsFqtKC4uRmpqKrZu3Yq0tDRs2bIFn/rUp3DhwgWUlpbi6tWrnORK3NNI/GUAUKvVsNvtiIuLw9raGlwuF1QqFfx+P08Mu7q6MDc3x0FZAPCHP/wBu3btwoEDBzjIMC0tDVlZWTh69Cjq6uqQnJzM1rv/9E//BJ/Ph8TERMzOzrJRAmVf3G1Rnkx6ejoUCgWSk5NRXFyM7u5uAB+CBmQMQReh1WqFVCplm24SF5rNZubpLi0tsWC1paWFrUiFQiEyMjKQn58PvV4fsYGUyWS4fPky5ubmkJ+fj+bmZpSXl+PmzZvIzc3FSy+9hOvXr8Nut8NisSA7OxsWiwXbt29HOBxma8WhoSFUVlbi3LlziI2NhcfjYUvm7Oxs/P3f/z1GRkaQkpICu93OuQQXL16M+L2SBoo42UKhEFeuXEFmZiYmJiaYI3zjxg0UFhbi1VdfhcFgQGZmJguK//znPzO/+tChQxwsJhaL0dfXh3/6p39iXnFcXBw3qpHoSeSUkpaWxontOp0OwWAQKpWKM2ri4+MxOjqKp59+GseOHcPS0hKuXbvGwI3L5UJtbS3m5+exe/dudqChqV5FRQWn0pPLGTVOGy1KkK6pqeF3LD4+HlKpFPfccw96e3sxNjbGosv8/Hy88847TL8ksfrg4CCio6Px+uuvo6ysjM+4tLQ0Tnenu4fErAkJCRHvrZiYGHR1dTGFo7Ozk/n9FRUV8Pv9yMjIQHV1NVQqFY4ePQqfz4fGxkbWMlRWVsJoNPI0jBp1YixERUWhurqaQars7GxIpVJUVVVFPOOUSiX8fj+/i+Xl5WhqakJUVBQeffRRJCcno7CwELm5uZifn2eNFE3Us7Ky8Mgjj8DtdiM3Nxe3b99m8TsF6F66dAnbt2+H3W7/SOhvXV1dRPMBuldI80m5NydOnMD8/Dzy8vLYICQuLg6bNm3CH/7wB6ytrUEqlaK4uBh6vR4dHR3YuXMnBgcHOQVeoVBAr9djcXGR71udTseOj3v27MHExMSGeyNdYWpqKpaXl+HxeFBbW4vl5WU0NTVxcnlJSQm7dM3Pz2PPnj3Q6XTIycnBzp07sWvXLnz605/G9evXmUmg0Whwzz334E9/+hPsdjuef/55xMfHM/pvMBgifm5KpZLPQ61Wy2DrX//6V2zduhUGg4F1BTRJvnbtGg4ePIi8vDx2krp48SL27t2L1dVVdgmsr6/HZz7zGQDA3r170dPTw1rj3NxcPPnkkxE1QTk5Obhx4wZCoRAk/51nJZFIOEy3o6OD7erz8vKwfft2zM3NccJ5RkYGdDodh/ympqaiuLgYUVFRsNvtbBdbVlaGxMRESCQSOBwOTE5OYnFxEcPDwxvujdbWrVuRm5uLmJgY1hZlZmbi7NmzGB8fZ0qrwWDA22+/za5Uer0eTU1NSEpKQmNjI2ZnZ3HfffdBLpezeUNbWxsqKyvR29uLyspKTn2n+urjrI/daFDQzPLyMruGGI1G5Obmsi4iLi6OEwXLy8vZ4pZcXEhURhw8n8/HXPpAIMDuP0tLSyzgNRqNMJlMEQV/oVCIg+1I8CSRSJjbSm4JxG2Oj4+HWq2G0+mEy+Vipwav18v/f1KplKknhNJR8mJaWhprQtbX1yPSCkigSdxPv9+P2NhYjI2NsdsUOUQRKkzFGIUkZmZmIiUlBbdv38bIyAiP04xGIztqTExM8CFJDkbkhrDRomj6UCjE/txEk0hJSWFut9vthsViYaSG6E+hUAjLy8uYmJjA3NwcPxuU/jo/Pw+JRMLC4du3b2N0dJRdICJNDQjlBcDiXJ/PxxoSh8PBvuZqtZodQ/Ly8hAbG4u1tTV20vD5fDAajQgGg2wPurq6ynQWEoiS81RcXFxERyxyEyFfc0qZX1lZQTAY5KkFoXNJSUkYHBzE9PQ0lEolcxyTk5O5mVYoFHzZEb+YRKvktkRFZCRaF+mB3G43HA4H5ufnGZWinzczMwO32812qLdv3+YU8cXFRRax+nw+dHd3w263s2OJ2WxGOBzG7Ows7HY7OyoJBAKEQqGIxQstmUyGpKQk1rfQO0rOQB6Phz3yQ6EQ3njjDZjNZhiNRszOziIUCmF8fBwmkwn9/f1MbVleXuYwQI1Gg76+PraBpUlgJOSKCjD6LukZIAF4UlISAoEAI7W0197eXjgcDszNzXF2hcfjQWtrK/9vsjFcX19neggZZMzPz3PRGWkRakdhdZSZQ0j9ysoKN/t5eXmYnJxkMTXZwCYmJjL1ggqKYDDI7yQAvP/++4iKimIkjaYKGy2yIicXplAoBJ/Ph4SEBNbfra+vw2azIRQKcQ4F2XHSNDU5ORljY2Nob29HVlYWUlNTsbq6ys8yWbRSkU/ocCTBJE2kPR4Pv/tTU1M8OaSQTJfLxaJ1+g6npqbgdrsRDAYxPj7O0ydy/oqLi2MRLtktE5BCZ02k521+fp7zOoAPed/z8/N8dolEIjaIICqaQCDA7Ows70skEiEtLQ1TU1P872tra/B6vXx3Ah/ekaOjo5x3QDrBjVZMTAyEQiFCoRBbp1NYIlH0Jicn+RlbW1tDR0cHRkdH0d/fz5x3m80Gn8/H1toOh4OdG7u7u+F2u1nv4vf72dI30jnicrkgFAp5Yj83N4eFhQUsLi4yoEjaGnK1oyTz6Oho/nzon9u3b7MujmzHLRYL3G43xsfHMTY2hqmpKbZVjhTQOz8/z3cnuZyRNTFlI3g8HtbkGAwGttonkHRiYoJD6qiRpqyVxcVFdkwjmqrNZmPqeCR3OKpFqIgkK/yLFy8yOELvAlmjUygw5Yg4HA4sLS1xcysQCDA0NITZ2VlukonaSdN+Au4isSvoXQ6Hw1x3hcNhFBUVsdkECbLJUdJoNDL6Tk5LPp8PCoUCWq2WM1Lo+TWbzRCJRJDL5R85Q2dnZyOeIeQSSdMH+p6GhoYgEonYcW1iYgIulws6nY5z26hGItc/k8nETBl6FokqRa6ZlJNEmrFI+hFyqqRnOykpCVNTUxzsODU1xTkzVPcODw8jGAxicnISoVCIG/eOjg6MjIzw+2OxWDhvSy6Xw263Izo6mrVfy8vLEZlGd66PrdEgDjGloQYCAVgsFtTX18Pn86GkpARCoRAmkwmTk5MoKipCQkICLl++zOFK9Iu2tLQgKSkJAwMDyMrKglwuh9VqxdLSEsbGxphLSxSZq1evRrRtCwaDyM/P5+wI6iTJd5z4dyQwbGhoQGlpKS5cuMDFA01BjEYjALAtGyn/CQGgyyglJQVLS0vw+/0sprzbIntIemhozOd2uzEwMACVSoVwOIypqSl+ScvKytDW1saXPSVW9/X18QXk8Xj4ZwqFQoyNjXGAH6HKJIbfaM3NzXEhtra29hFRPCVTU1iW0WiEUqlkDioFNopEIqSkpKC9vR0CgQBLS0uQy+V8cNOoPiYmhic7wIfocaQxL1nmRUVFQaVSITk5mZ8jajgCgQC7CXV1dfH4kQJs6Fkgm0C32w21Ws3TEnLyoMChQCDAjlmREDXiVqakpLCQigTOJpMJ6enpsFqtuHnzJtbW1nDkyBHm/9PnTlZ8VKiurq4iLi7uIy4qIyMjUKvVbGccDAZhNpsjjlJFIhESEhJgt9uxurrKh3JSUhLGx8cRDodhtVq5IZycnGQruxs3brAmhpriyclJJCYmoqSkBD6fDz09PcjIyGCBHwle6cKP5IhF32tcXBwf8h6Ph+klVBiQVXFUVBRkMhkuXryIiYkJqFQqyOVyfhacTicuXrzII2apVIq1tTWYzWZYLBYMDQ3x5ev3+5mbvNEiD3e6dBYWFjA7O4u5uTm4XC4UFhZibGwM4+PjiI6OxrZt25CYmAir1Qqv18u5P1KplC/apKQkvmyIDhIXF4etW7fizJkzcLlcSE5OxsrKyseytyXLRqIWkcvftm3b0NHRgRs3bmBxcRHl5eVcEFDKd1JSEqKjo2GxWNjCkBo+m832kXA7mgYRtfNvcXEJCJibm+OijhD60tJS3Lx5k1FoCnekAkkqlSItLQ0pKSk4deoUfP8dgpmXlweXy8W0h9nZWT7bXS4XT4CIFnS3FRcXxwGkZHNrNpvZ9rS+vh59fX1wuVwQi8UYGRlBeXk5TCYTVlZWsLCwgGAwiOTkZJhMJiQkJMBoNCIlJYUtNqn4MBqNyMjI4MKBfsZGi4A1sqQmGpHdbofNZuOQNsojcDgcbGM+MzOD5ORkaDQalJWV4dq1a8jKyoLNZuO8D4/Hw+F8ZLFNNpxkmxvp+xSJRLBarQiHw0hNTWUjguHhYWzZsgXT09OwWq38fba2tnLTodFokJmZyQ1oX18f4uLi2Lo1HA6jra0NcXFxqKmpQV9fH+bm5pCQkIDOzs6IdvZut5uLI8qrIICSGAD0u6pUKm7kQ6EQ1tbWYDKZYDabuSkmcFEsFiM2NhbBYBBra2sYGxuDVquFyWRiNgTwPwGGd1vBYJCpW2KxmE1gyIae0sLpuyf74enpaQYQ5+fnkZubi6GhIRQXF/MZTY5sJpMJBQUFKC0thcPhYHt0g8EQkapHzWVUVBQbcXi9XgwMDEChUHB6OoVvSv47XNHlcjGo4fV6YTAY8Morr3Ae1OrqKgYHB7nIVygUSEpKQn9/P9uBU62x0aKGnBoAcjmju5RcTSm7amBgALGxsXA4HAzQKBQKxMfHc/M6PDzM5gAEuBQWFnLdQiCQy+WKaBJCzxHw4Z2wtLTEDogrKyvIy8tDb28vOjs7IZfL8cQTT0AgEHCWTVJSEut9Z2dnYbPZGMymBoYCJGliEhUVxb9vpM+N7io6D2JjY3Ht2jXO3iAXL7fb/RFAh0CHxMREeL1ezM/PY3BwkB3O6NzxeDwwmUwcxkv7iYmJYdDo46yPPdGg0VZxcTEqKiqg1WohEonwpz/9ibm59IuOj4+jq6sL0dHROHbsGI4fP44rV67AarUiMzMTOTk5/DJSkjc5zVAB1tnZyemGcrmc6TJ3W1KplNO/5+bmYLPZsLy8jJGREQwNDUGtVkMmk8HhcOD27duYm5uDQCDAtm3bUFpaysUiZYJQsyQUCpGUlAS73Y7Ozk4MDg6isLCQO+W1tTWEQqGI3ENKbIyOjoZcLkdaWhpz0RMSElj4vbq6ijfeeIMdDsj9qL29nV/YPXv2YH19nZHk5eVlDvzSarXYvHkz8vLyOEtELBZHDHuhpNbs7GwUFBRArVYjGAyitbUVly5dgkAg4O5/amoKb775JjZv3swccLfbDYlEgr1798JkMiEQCCAYDOL06dM4ceIEF0JSqRR6vR6f/OQnWdxIQYgbLbpwg8EgEhMTWZCYkpICqVSKhIQE5gkfO3YMZrMZ4+PjuH37Nk8oADDKTXQLoVCI2NhYhMNhfjY0Gg07mxGKGonyAAA9PT3cBEVFRaGvrw+/+tWvMDc3BwA8GrfZbOju7kZDQwOio6Nx6tQptLS0sK4DAHp7e2EymdjKWCQSoby8HMPDw9Dr9WzvOzc3x3kdGy2yBhQIBOwHn5GRAZPJxJcwoUujo6PsKkEo5cjICPr6+ngqRA4jFD5FtI3q6mro9XpkZmbyBEupVDLff6NFSCYlrdJFKRQKmZPv8/mwvLyMtLQ0SKVSNDc3M/pPLmRyuRxFRUXsVtPc3IzNmzdjYWGB6XRer5dDr5KSkti7fqO1traGiYkJnqiqVCrMzc2ho6ODQYHExESsr68jJiYG8/Pz0Ol0qK2t5WmvVqvF9u3b2bBBr9fz+eDxeDAxMYGVlRXm/lPSsMPhiDg1iIuLY+4u0Y1EIhHGx8cxOjrK5+nCwgKuXbuGs2fPIiMjA5mZmezUZTab0dPTg/Pnz+OFF17A888/z6Fuq6urKC0tRXx8PB555BEsLCygr68PQ0NDmJub41yXu63Y2FjMzMzweRQMBjE3N4e3336bLV8pKZuaEaLL9fb28lTLbDZDpVJBr9ejq6uL7TTpcx8YGODpuNFoZH3c3wqwpOaRXOVUKhVcLhdTfKjoInOP/fv3Iz4+noNQU1JScPToURQUFHBRo9FoIJPJ4PP5MDAwAL1ej+HhYZjNZk5ApyZioxUdHY2JiQl24UpOTubE4pmZGcjlcsTHx8NqtaK1tZVBECqGfT4fTzGIKkdT002bNqGuro51haQ/pJA2Ct3baBFSPT8/z64+iYmJuHbtGlpbW5Gens5hsvTeOp1O1NTUwGKxwGg0sjsbWc/GxcVxajnRFIlaMjIywu58MzMz2LRp04Z7I2oJTaEIoKEagz5T0vjNz88jOzsbgUAAV65cwcjICD+PBoMBR48eRUJCAgQCAWZmZtDW1sa5XHv37uU7/+rVq5iZmYl4b8XFxaG9vR3Ly8vIzc1FTk7ORybRNTU1jPj7/X7cuHEDa2trqKysREFBAXJycpCTk8PheC+99BKmpqZw5swZXLt2DYFAgI0ByDaWnoe/hTAnJSWhvb2dzRFyc3OhUqmwfft2rKysYMuWLWhoaEBSUhKHCKekpHA2BBX+MpmMqZF+vx+1tbVISEjAq6++ipdffhkpKSmoqKhga+uenh5cvHgxIvODpnh6vR6lpaXIzs5GdHQ0vvvd78LhcHCkwMjICG7cuIHp6Wnk5eWxadCNGzf4zBgcHITJZOLpZU9PDywWCzfrlFlBAJRQKER1dXXEvVFWDrn00b26uLgIrVbLmrPjx4+jo6ODYxFMJhMHNXZ3d6OwsBCXLl3C+vo6W0yTTlKpVCIcDnNzQM1XJLMhAjwoMJUmJqTZ2L17N+RyOXp6enDy5EkEAgE0Njaiv7+fJ4lknEOaEtLzra2tMVV7dnaWnVG9Xi+cTiekUunftLOn9bEbDavVCqFQiGvXruHMmTMYHR1FUlISXnjhBS4ylEolqqqq8NBDD8FoNMLtdmPLli343Oc+x8nWAoEAJ0+exNGjR6HRaBjNiYmJwRe/+EWMjo4yGrR//3589atfhVgs3tCilR6E7Oxs2Gw2JCUlYevWrTCZTLjvvvuYenH58mWkp6fjS1/6Egeymc1mxMTEcL4Cdfv19fXsg03NxlNPPcVdflFRETZv3oyioiKeemy0qBG4k/ZgMplw8OBB3Lx5Ex6PB9PT08jOzsY3vvEN9PX1QS6Xw+/3o7y8HB6PB2fPnuWpwTe/+U0WplVWViIxMREHDx5kdP7ixYtQq9XYs2cPFzEbLb/fzwFzw8PDCIVC2LlzJ3784x9jZWUFTqcTKSkp+OxnP4s///nPqKiogNfrxYULF5gONT4+jvT0dDQ3N6O4uBgymQzf+MY3cPDgQUxNTeGhhx7Cj370I/zgBz9Af38/Nm3ahD179nB65kaL3L6CwSC/4IRyK5VKNDc3M2qQkZEBhUKB6upqREVFYefOncjNzUVcXBw2b96M48ePo6amBsFgEG1tbTh//jw6OjqQlpaGvLw8pKenIy8vD7W1tVAoFLhy5UrE75QQzvb2dthsNhQVFSE7OxsXLlyASqVCc3MzCgoKcM899+CXv/wlRkdHcfPmTchkMmzduhVJSUnw+/3YuXMn1Go17r33XuY/p6WlIScnh5uoQCAAu93OYkFK/t5ohcNhKBQKpiJSkfbkk09idXUVO3bsQG1tLbZt24ZnnnmGw4cqKyvx7//+76ivr8fs7CzKy8uhVCqxf/9+Hv1TQURcTkIQCSl78cUXN3THoEWOdCTofO6555CdnY0dO3Zw9kdNTQ127NjBziOf//znkZKSgurqang8HrzxxhsYGhrCyMgI7r//fubtTkxMQCQSYffu3ejq6sLTTz/NycPbt2/nRi7Suu+++5g6MTMzA5FIhJ/97Gf47Gc/iy1btqCkpASFhYWsY7Hb7TCZTHj88cexY8eOj0xD0tLScOzYMZ7kejwePP300zh+/Dj+9//+3+jp6UFhYSH27NkDmUyGqqqqDfc1MzODkpISRgbz8vKQlpaGr33ta1hbW2Nq47PPPov3338fgUAA7e3t6OzsZFG72WzGpz71KXziE5/AH//4Rzz44IN8Lm3atAl6vR5vvvkmUlNT8clPfhJf/OIX8eijjyIzMxMdHR0b7m11dRVZWVkIhUJQKBRobGyEXC7Hpz71KQSDQU6bzcnJwcGDB3H27Fns3LkT6+vreOKJJ5CVlQWLxYLCwkL4/X586UtfglKp5PTuwsJCNDQ04M0338TTTz/NGQ1/93d/B7VaHVGjQVaqVBzeGVan0Wjgcrl4AtHf34/R0VGMjIzA6XRi9+7dUKvVsNlsEIvF8Pv9eOCBBzj/h6gxL774Ik+VpqamIBAImEsf6X2Ym5vDAw88AIFAwHlGOp0OX/rSl3Dvvffi4YcfRklJCXQ6HTZv3ozTp0/zVOXxxx/Hzp07eerU39+Pv//7v2dnOsp2+fGPf4x3330X3d3daGtr42Tl999/H2VlZRvujRK+KViXnJAOHDgAuVyOd999l23MyWb961//Ol577TXWIRAKb7PZcPz4cW4uCJA4dOgQsrKyuPEgOltPT0/E85eQWaL8URDfY489xgWQSCSCUqnEtm3bGEBLT0/H5z73OeTl5SEUCuHw4cOcX3Dz5k0MDAww/a25uRkLCws4duwYbDYbGhoa8PjjjyM/Pz9imODS0hIeeughjI+Po7OzEzExMQgGg3jmmWewdetW1NfXo6ysDA0NDXjggQcwMTGB2tpa3Lp1C+Pj4+jp6UFLSwump6eRnJyMX/7yl1hZWcGBAwewbds2xMXF4fLly2hpaYFAIEBnZyc2bdqE/fv3s335RisUCqGurg4ikYgpxX6/H9u2bYNUKuUpVklJCfbt2wev14u4uDjMzc2hsbERaWlpuHXrFl599VVs2bIFv/rVr3hCpdPpUF5ejsrKSvzHf/wHXn31VVy+fBnR0dEcqBspfXttbY0nxj09PWwW8vzzz6OjowMdHR0IhULYs2cPfvSjHyEmJgYajQY3b97k6U5BQQE++clPYnZ2Fl/+8pchFApRV1eHffv2Yffu3Th+/DiDbCaTCYWFhaioqOAmbqPl8XiQn5+PQCCA1dVVaDQatkK+ePEi05wPHDjAjpehUAilpaV4+OGH2Txg9+7dGBwcRE1NDebm5rg5JFMeyvKgcMySkhLI5XIUFxdvuDe/34+YmBgORa2qqkJ2dja2bNkCr9fLE32DwYBnn30WQ0NDMBgMSElJwWOPPYYtW7ZAJBJBp9NhaGiIM0lqa2sZ3PnhD3/I9rhdXV1sB97f3x/RevfOFbX+MRM3/vCHP6Cvrw/V1dVISEiAyWSC3W5HWVkZCgsL4XQ6WZU/OzuLffv2oa6uDh0dHYxAj4+P4+WXX8a3v/1t/PM//zO++93vMt3B5XLhz3/+M773ve/hwoULyMjI4EMkLS0Nq6urGx7a7733HudRkNexwWBghyRK/u7v78fg4CD+8R//EX/4wx9QU1ODhIQEpmuQt7bH42G3o/j4eOaO79+/H1NTU0wtoamBxWLZ0GLxlVdeYbcXGjelpKTgjTfewKc//Wn84Ac/QHZ2NmpqatiPeXl5GXK5HD/72c/Y3WtoaAiNjY0s6DEajVhaWkJqaio71oRCIahUKrS0tMBut7P3dF1d3V339pOf/ARXrlzBtm3bON1Yr9fjy1/+MhoaGtj3vqqqCgcPHoRAIIDX68Xbb7/Ngj6iWdlsNnzrW99Cd3c3rl+/jsuXL0Mul+P73/8+ent78Z//+Z84evQopqenYbPZkJ2djVAohKeffvquezt+/DgXpG63GzMzM/B4PDAajfjlL3+J1tZWLC0tYXR0FG1tbfjEJz6B//zP/8SPfvQjnD59GhKJBIWFhXzA3bhxg51n6MVsaWnB97//fXR1deHy5cvIzMxEeno6zGYzEhMT8cgjj9x1b6+++iq6u7uRmZmJvLw8xMTE4NKlS2hpacGbb76JN998k595o9HICOyXvvQlnD9/HjExMSgsLOSwnN///vfYs2cPLl68iMXFRdTW1iIpKQlra2v485//jC9/+cuIjY1ll4rR0VE88MADG36nNPUgZFkul+PSpUt49tlncfbsWajVakj+O7G9pKQEly5dwuzsLDo7O3Ho0CHs27cPPT09sNvt+M1vfoPf/OY3OH36NIxGI4qLi7F37160trZCKBQiMzOTU6ipudnI/x4AnnvuOVitVhQUFPAotrq6Gi+88ALq6upw3333QavVwmw246WXXsLS0hIaGxuRmJiIW7duQalUQqvVwmq1Yvfu3TxdHBgYQHx8PLKystDf3w+9Xo/bt2/j6NGjcDqdcLvd0Gq1aGtrw69//eu77u073/kO3G43o30ZGRkwm83o7e2FQqHAzp07YbVa2WiAxOWEjqnVaiQkJGBgYABisRinTp3CF77wBahUKrjdbhiNRojFYqjVakxNTUEkEvH37PV6UVpauuHB/cc//hGjo6NQq9XM1bbb7bh69Sree+89vP3225yfMzw8jLi4OFy7dg2f+cxn8P7778NmsyE1NRWNjY0oKCjAiRMnoNVqMTExAZvNBqFQiMrKSpw/fx5///d/D5fLhVu3bkEqleLIkSN4+eWX8c1vfvOue/vxj3+MkZER1NbW8mSK9BpPPfUUfvzjH6Ourg5yuRy3b99GZWUlo/U//elPUVpairq6Opw7d46pIn/3d3+H6elpmM1m/u8MDAxg79697Go4MjKCpqYm9Pf34zvf+c6Ge/N4PNi5cye8Xi/GxsbQ0NCAt99+G0ePHsXo6Cgj9ktLSwA+vLz37duHqakpxMTEIDo6Gl1dXZifn8df//pXfOUrX4FcLud08j/+8Y/4yle+AqvVykUSiW7Hxsbw7//+73fd24svvshUmpiYGNZr/OUvf8E3vvENDl5cXV3F22+/jWAwCJfLhS996Uv493//dzYPIdrW1NQUPv/5zzN6Pzw8zE1AX1/fR5y+EhMTEQ6HWSR7t+cN+JCO0t7ejsHBQezZswdGoxFPPfUU3nrrLQAfZs+0tLTg17/+NU8TP/jgAxYOE2hGRZpIJML09DTTSp999lm0trby/eN0OnHt2jWkpqbiiSeeuOvefvCDH8BisfA5J5VKWWu3Z88ejI2N4caNG5idnYXBYEBFRQVPNInu09raitOnT+OJJ57An/70J3Yho0mIXC6HUqnEt7/9bWZASKVSZGdns4Xw3dZvfvMbjI2NsYMdGbY4nU48+eSTeOmll5gqGBUVhfz8fLzxxht48MEHMTIywtTL3t5etLe3QyQS4XOf+xzcbjc6OzsxOjqK6elp7Ny5E9nZ2Wwh7fV6kZOTA4FAgKeeeuque/vFL36B8fFxNDc3QywWIxgMQq1W4zvf+Q527drFgadLS0sQCAR48skn8bvf/Y41VhSkPD4+DqFQyK6ZycnJbGMbHR3NlGIKE/T5fLh+/Tp27969YZ303HPPsc7WarWypavRaIRCocDi4iI3NQKBAPv374fBYMBzzz33ERtdqVSK119/HTqdDqWlpazvGB8fR0xMDB588EHExcVhdXWVqav79u1Dd3c3/vmf//mue7t69SrGx8d5IhgOh7GysoL//M//xAsvvICWlhYOS6QEcgqENBqN0Ol0SEhIwPnz56HT6eByuXDw4EEsLy9jaWkJIpGItRNEC6T8D7rP9u/fv+Hz5nA4sG3bNkRHR/Pk5cqVK2hubobL5cKWLVsQFRWFq1evoqSkhCmdcXFxqK+vh0KhwPXr13kKRI2yxWJBdHQ0uru7sXv3biQlJTG1TyAQsA39pz/96bvu7c71/ymwj1IGZ2ZmYLFY2KKRRlsejwezs7MsOBsaGsKbb77JFB0KfnvppZfwuc99jl09iOdnsVjwwx/+EEeOHOHRTmJiIq5cuQKn04lvfetbd93bnbaVoVAILpcLp06dQnNzM4xGI3PhdDod5ycIhUIWPxE9RCaTYXp6Glu2bGGOn91u50RT8tC+dOkSpqamkJSUhNra2ojjI0oCHxwcZP5mQkICd9g5OTnM09+2bRsLbuLi4nD48GHMzs7C4/FAq9Vy4qnFYuGLsaurC2NjY9ixYwcSExM5wVMul6O1tRUxMTEbNhrBYJBR2P7+foTDYZjNZnz5y19mHu/ly5fR1tYGm82GPXv24I033mBRWWxsLHOEKyoqWBQsl8uRlZUFv9+P//N//g/nWTidTmRlZSEvL49dwjZaHo+H07tpnDg3N4fY2FiMjIzwn2dmZqKsrAwulwslJSW4cuUKUzTa2tq4UAiHw3C5XDzhCYVCKCgo4ERT2r/ZbIbL5Yo4Hvf5fEhJSUEgEMDo6CiPmGtqajAwMIC5uTl2jIiOjkZ9fT1WV1dx7tw5Fm5dvXoViYmJTJ0juh/RN0ZHR5GXl4f8/Hz85S9/QXJyMhITE7G6uhoxIBIAWwSSF7fJZOIwpImJCdy+fRtFRUU4dOgQVldX0dDQgFu3bvEInMTps7Oz2LlzJ4aHh6HT6ZCcnIxgMIgrV67A7Xaz+4zRaITf78fS0hI8Hk/ERoOctki8KJVKMTQ0xM3DysoKXnnlFXR1dbErGNnGrqys8AGnVCrx0ksvsXCbzhiXy4Wuri7mvb7yyivsEOV2u7mYvNsKhUIQCoV8FjkcDgSDQeh0OiQlJcFms7FIVavVYnp6mn+PUCiEQCDAtIHz58/jyJEjsFqtLIImMXIgEGB3JXJ4KikpwdTU1IaNxvT0NKKiojg9Vy6XQ6VSYceOHeju7kY4HMZ7773HzjEJCQloampi15v6+npoNBrYbDZcv34deXl5SEpKQnJyMhdVFy9eRHV1NaampnD27FksLy8jFArhrbfeYkH83ZbL5UJeXh6LLrVaLQd1/fWvf0VqaioHVlHhQc/37t27mQ5aVlaGvr4+ZGZmwmw2Y2hoiClObrebKXA3btxAcnIysrOzcerUqYh0kYWFBeTl5cFms8Fms8Hr9aKzsxOFhYUYGBhgMen09DRMJhM2b96MiooK/PjHP0ZlZSXy8/ORnJyM+vp6nD59Gk1NTRgfH2dxKp2FL7zwAqqrq5GRkYGUlBTO1om0N3pniC4cFxcHp9OJ73znO7h69SpEIhEuX76MpaUlhMNhBINBxMbG4rXXXoNQKITBYIBEImH9YFNTE958802EQiGo1Wrk5uZyLk9FRQWuX7+OpKQkyGQy+P3+iBPlsbExTkJXqVSIiYlhoTZZbY+Pj2NxcRFFRUXo6+uDSCTiSefU1BQuXLjAxia7du1CW1sbU0MzMzMhFArx5ptvQqlU4uTJk6yls1qtER0JXS4X5ubmkJycDJfLBbfbDYFAAIlEgra2NlRUVLAFP30X5Fzm8XggFAqRkJCAJ554ggPqSEQdDAYxOjqKjo4OPPTQQ2hqakJfXx/z0dfX1yPq96jQk0gkSEhIgMfjYcDDbDazoQqlei8vL2NychL/9m//BgDsQOfz+ZCcnIyZmRm0trZyFpBcLmdKq8/nw8TEBLMmZmdnkZqauuHe1tbWUF5ejpmZGRYIt7a2Yvv27RCLxaisrERHRweCwSBnJdXW1iIQCKC3txd2ux1ZWVnweDzweDwoKytjG3ev14v29nYIhUIucPv7+5GWlgaFQgG1Wh3R0IcypNbX1xEIBDg0MyMjAzk5Oejp6eE7nM7TwsJC/nelUslJ6gSCdnd3IzExkW1x4+Pj8etf/xqf//zncezYMdbRnD17NuLnRs5KNpuNEf9gMIiGhgY2C7Lb7QiFQuw+lZSUhCtXrvC9pFAo8MQTT7ClOOVrEJDa1dUFtVqNmZkZ/jkrKysYGRmJqFcSiUTIz8+H0WhkYx+Px4PS0lLcvn0bSUlJGBoagl6vZybCzMwM6uvrMTk5iba2NqbDTU1NobOzE+FwGIFAAGazGcFgEI2Njbh16xZTuMnOmATx/782GklJSXA6nRCJRIiPj4dIJIJEIsF7772HzZs3o7GxEeFwGKurq1hfX2enkvT0dBagGQwGFsa8+OKLePjhh1mcSInBdOFlZmZy7gSFg220VldXP2LnSqK/np4eaDQaFlDSZUVBa2azGQKBADk5OZx8SQ5TNC2g7vL27dts91ZWVgapVMqOVZGsUAGwMw+hYESlSUxMRGZmJjs2WSwWpskMDQ0x55mKCrPZjIGBAeTl5bEgLTk5mR2LdDodF6kxMTH8km204uPj4XA4uAOPiYnBwsICWltbUVdXx0GHVNj6/X6UlpbihRdegEAg4II6JSWFX3aHwwGPx8PIENlnNjY2Ynp6Gl1dXSxyiqRtIVcZCqIipxe/34+bN28iKiqKXc4cDgfcbjeL8ldWVjiZ22w2Y2RkBFarFXv27EFSUhImJyfR2dkJmUzGGiBylKDPNdJ3Gh0dDafTCYVCAaFQyAYJIyMj7KdNlyC9E5s3b8apU6cwMTGBjIwM1NbWQiaT4cSJEygqKoJCoeBCf2FhATdu3EBtbS3TlUiMR/z8jRZpBahpUSqVsNvtuHDhAo4cOYLY2Fh2/SGbv/T0dNhsNv5usrOzOUgtPT0dubm5UCqVMJvNnFhLkzmyoiXRa6QQMOBDrZfX62XeqcPhwMzMDCM4NGClvB16bnw+H4aGhhAOh/n7IsEcFWCE4lKYYmpqKnp7exldj42NjXiOUNIyNY7Jycmw2+24ffs2du/eDZVKhczMTJhMJly/fh2hUAi1tbXMjXc6nYiKikJtbS1rqCgnhH6v7u5uxMfHs5Dc7XbD6/XCbDZHvOwkEgnGx8fZ0YaE6hQgSkJ0MouIj4/ngMNgMIj19XUkJiZCqVTi8uXL/NnRvqanp3Hx4kU0NTVBqVSiuLiYiyayXNxoxcfHc8K8Xq9HYWEhAw3ksgeA31mpVIqSkhJcuHABZrMZOp2OQ+AGBgYwPDwMrVaL3NxcCIVCDA0NYX19HRKJBKFQCCMjI1yIEr850ud2p9VrdHQ0EhMTMTY2xmnmqampTHEheqhGo8H09DRPJ8gh0GazIScnB4uLi+xklZmZyT+PRMPk+BcJsCCBqEgkglAoRDgchslkwo9//GNkZWVBq9XCaDTC5/NBJBJBJpMhOzsbAwMDcLlcGBsb4zTiqqoqDA8Ps/XyysoKJicn0d/fD4lEAr1ez/bP5K5IHPG7LaIyqVQqRufVajXcbjc6Ojo454ZoFGSkIpVKMTMzwzxxSie22+38jovFYp4qp6SkwOl0Qq/Xs407/ftGi/ZCph/BYBBOpxNnz57Fww8/zAYWd2ZPkf7JaDRicXERy8vL/PuT+xIhuuQG1NbWhkceeYR1W/T3IrklEo2U7t+oqChuxJaXl5GRkQG32w2n04n5+XnMzs4iLy+PQ1UJRCQ3OJ/Ph5mZGQSDQaaWkh42EAhwwRcTE4Pi4uKIFrKkXyVdIYmZ19fX+Xkgiuft27fh9Xqxbds2hMNhjiigPLIPPviAUfHV1VWEw2EkJycjIyODAcGUlBTMzc1x4RypeSTL+ISEBJ7uLS8v4/z587DZbMjMzOQ8l6WlJYyPj3PmiUgkYvohTT4mJydZc0nnpc/nw+zsLN544w0UFRVxc0OGOxutpKQkTE9Pc1Evk8nQ2dnJro1k83+n7jQ1NZXBYYvFwqwThUKBsbExFmKHQiGYzWYMDg5CIpGwayLl4JSXl0c834APzRF8/x0foVQqWUcUFxeHbdu2YXx8HJcvX4ZWq0V8fDwaGxths9kwODiI/Px85ObmIhgMshulz+eDVCpFfn4+1tfXUVpaitdff52BKYFAgHA4HBHE/r/Xx9ZorK2tQaFQwOPxsJKf0p2tVis7HCUnJ0Or1cJutyM1NZXtMsnatry8HGKxmN2CKHxIo9Fg79697KLyf8e9RyquqCggJweRSMSjWEKNvV4vFhcXuQjMzc3lv6tSqSCVSpGamvoRq06dTgeVSgWPxwOZTAaLxYK5uTn2vFYqlVhaWopo20aFC9mWESJht9v5AU1KSuK9OhwOdg8hpFEsFiM9PZ1dmGw2G/s0r6ysoLi4mB1d4uPjsbKywpz/SMU88cmJJqZSqSCRSNh2VSqVsgBILBbDbDZz3odQKMT6+joEAgEnW9bW1mJkZIQFlNHR0TAYDGyfSxOM9fV1dvnYaIlEIra8IyR4cXGRC0W9Xs9c5KioKPh8PlRUVCAmJgZpaWncTJKoOT8/H8vLy5zCS9x0ykEg562kpCQoFIqI7h0xMTHIycnhUTvtldK4qfnQ6/XMxyROp0ajQWxsLDt0EOfTbDZDIpGwQLikpARms5kRSHIki+TURYsastXVVcjlcnYGC4VCKCkpQU5ODuLj4+F0OjE5OcnCyvT0dMTHx7OgnTRW5DpCXNSsrCxuYshaUC6XQygURjQfAMDWyYQ2Eiih1WqxsrKC+Ph46HQ6FBYWoqioCGtra6iqqmIBMfmK0+dcU1ODqKgonviIRCIcPHgQAJiWSf79Xq83Ir+aXHHIIUqpVLKL28rKClZXV9ma1+fzcaPhcDi46CE3EqFQiMTERMzMzLCjG01eTSYTX5jJycks+I3kOhUdHc3UKPqH9DUk+CV9j06nw/LyMhd0EomEaUPJyckQi8XYvHkzent7IRAIIBKJWFA/MTHxkf3HxsZGLBAA8JlLFzdNCe6kddLFFxcXB7fbzYm2VGTTVJjQSBIGk5Ui5dzcSQlcWVnhkK6N1uLiIqN4AD5iE2u32xmY0mq1aGxsZB1hYmIiOyu6XC6kpqYiHA4jJyeHCwYqwGlvaWlp8P13Dg6hlpHOXzpvoqKikJCQwDkkdI9RUSsUChEfHw+5XI7S0lLMzc0hNzcXCoUCPp8Pq6urrKVYW1uDSqXiyaRcLmcqLhk50HkaadH06M4mlegwLpeLab7037VaraioqMDCwgL0ej3nK9A0Kzk5md1zyHGQKDYZGRl8HsfGxnJa8kYrJiYGGRkZXFPQFJImCGQdu76+zqYemZmZ8Pl8rG2i6WhSUhLS0tLYRY9E8vX19WyzTY0UndeRXIBIZxcKhdiaXK1WIzo6mqcISUlJ0Ol0KCgowMzMDKRSKQQCAfLy8hj8I8ZAfX09oqKikJeXx+91UVERnx+xsbEcykZWy5E+NwDc7CsUCiQkJEAkErFD1vr6Oj9rDoeDg+/Ky8uh0WgQFRX1kfOfnMISEhKwtraG4uJiBk59Ph/rZygDK9LnplAo2HSEqJWkEVEqlWzqkJ2dzblIKSkpyMjIYKCUaqe8vDyIxWJ2s1tZWWGwgX4uWVFTYnakd0Gn00EgEGB1dZVdFtPT05kuGBMT85HJuUAgwOLiIt+7ZMseHR2NnJwcttgWCARYW1tDVlYWayoIDKSzINLy+XxscUvvFNWJVJssLCywtMHlciEzM5Pzkcjx0Gq1IioqChqNhp9PvV7PGs+FhQWmWy8uLnJDHek9vXN97EZjYWGBGwiysJufn8dDDz3EyDChTllZWVhYWIBYLMb09DSPd00mE7tSPf744wgGg4yM6vV6lJWVYW5uDgUFBVhcXOSJCNmRbrQkEgmKi4shlUo5RyI9PR35+flQq9VIS0vjl4sC/JKSkiAWi7m7J6Q0OjoaUVFRHMoSFRUFh8OBkpIS9pgeHR3lcTchRBut5eVlKBQK1qG4XC4uzMiaj4K+pFIpoqOj2e1Eq9VCqVTyZUMdJE2N6ELS6/WwWCzcHJSWliI/P59fxI1WKBTihoxcH+RyOerq6vj7Jl4moeBCoRBHjhxhp4W6ujo0NzejtLQUVVVV3FnTA5ieng6NRsNJyzqdDpmZmX/zsktMTGRajtvtRmxsLAdBlpSU8HeanJyMoqIiRvbC4TBfdFSIaDQalJeX4/bt2zCbzYiOjkZ2djaSk5MxNTWF2NhYLkbIju9vZXw0NTVBq9UyMrSwsMDC6TspN6WlpUzvUalUeOSRR2AwGDAyMoL5+Xmkp6dDr9ezLog8xHfs2AGbzQaFQgG/3w+BQMC8T/LG32hvVGDeWaQ0NjZifX0deXl5yM7O5nHp4uIiwuEwlEolDh48iLKyMhagUxNLTh9k61pQUICCggKIRCJoNBro9Xq20Iw0QQM+LEqpoZXJZNDpdJzpQO8TiWl37dqF9fV1lJeXs5ZDq9XC5XIxR7umpgaZmZnsElVSUoJPfOITsNvt7N0vk8kYXaImaqPPTqlU8nicLDGrq6shlUphMpngdDoRFxeHjIwM6PV6LvjLy8uRk5ODUCiE3t5evgC7uro4t8DtdqOxsZELfwJhxGIxwuFwxMyFQCCAoqIiaLVaDg+TSCQoLy/nxPaEhATodDo2C7jT7S4QCGBgYACzs7NQq9Vobm5mdy3ypn/66acxPT2NgYEB2Gw2BhLI/W2j5fV6odVqoVAoEAqFMDk5CZvNxs+ETCaDSCTii4++m7W1NRw4cIBF7uFwGFqtFgcOHIBAIMD4+DgXy2R1S8CHVqtFeno66+g2WkSNnZyc5EkaPV9Op5PtVxMSEjg8TiaT8edJGVH79u1DKBTCvffey4UZFQVpaWmclUTTE5p6R0LmiSri9Xp58qNSqfDFL34RycnJbGlMxZFcLudzsbm5mZ3AyMa2ubkZJpMJ4+PjcDqdiI2Nxe7du/kMTkxM5Ok1FfQbrfj4eCQlJXHWiFAo5OBKlUrFTpNkp01W3DMzMygtLeXPpLKyEtHR0Thy5AhKS0vZQlYqlaKwsBAOhwMGgwHj4+OM8tJ3u9GiDCKyeKZ9bN++naeH8/PzDBbW1tay7XBRURG/M2VlZexiR88wAUi1tbVITU3FwMAAA5c6nY6By40WNZ5kU0qTsqKiIuTm5jKrgiZ/RJ2x2+0c1re+vs6g2D333AOVSoXy8nIOnN28eTMbo8TExHDYJpnVbLSUSiVycnKgVCqRlpbGzniUGi0QCODxeJCcnIwDBw5wqJvZbOYznwDG8vJyZgiUl5cjKyuLpx0U1Ed0TwJXI9UiNBkk+g8BEw0NDZw1srKywjRT0tQmJiYy+COVSlFQUIC5uTncd999yMnJYU1bfHw8ysrKkJeXhz179mBgYAAejwfx8fGs/9xoLSwsoLS0FBqNhrVA9K4SjY/YEDSpc7lc8Hq90Ov1bCRBeVGUlA582HTLZDI0NjaySQzRr4qLi9kqfKM1Pz/P4Cm5Q9I0iVy3iKqYmJjINCyXy8VOox6PBzMzM8jLy2P7XwIOyTaawDpydwU+ZLhYrdYN93bn+tiNhkqlQkdHB1t7BYNBpKSk4MUXX8Rjjz3GY+m5uTlYrVZUVVXhtddew7Zt2+B0OpGeno7MzEw89dRTEIvFOH36NDtVkVjqvvvuw/e//30kJiZCKpWyRuKpp56K6J40NzfHwSSEpkVFReHSpUtMsUhLS4NOp0NcXBwqKipw6tQpdkswGAzQarVITk7Go48+ioWFBc62iImJwX333Yfp6Wls3boVFosF+fn5PM6ilO6NFjVhOTk5WF9f506S3LFCoRCys7M5PbS+vh4nTpxAUlIS3nnnHbS1tSEYDOL69evQaDQoLCzEAw88wCK/iooKLC8vs2CQNAuEgNDlcLdFrl+NjY3Iysril+Oll17C3NwcXn31Vba9VCqVePzxx/Hmm2/iqaeeYpvK2NhY3LhxAzU1Nbh27Rq2bNnCF1pWVhauXr2Kxx9/HMvLy8jKykJXVxdOnTrFITcbrdjYWPaEpzCxw4cPY2xsDCkpKWhpaeFR6+rqKhobG5knTNbDYrGYc0ZeeeUVFuUKBAI0NjZCpVLh8OHDsFqtTAMwmUyMSmy0SHCr0+k4Ybu0tBSTk5PYv38/5ubmGAFbWlrC7t270dLSwsnCubm52L9/P9Mturu78alPfYrtJiUSCX7xi1/gmWeeQVRUFLxeLztvREdHY2xsbMO90Qh827ZtMBgMWFhYwNatW3H9+nVs2rQJb731FlNEMjIy2Kr6C1/4AoLBIOx2Ox/q+/fvx+zsLI4cOYK9e/eirq4OqampOHXqFJ555hn09fWx8w2h5ZG+UwCoq6tDd3c3pqen2QLyK1/5Ctra2rB//368/PLLOHbsGFpaWjA+Po57770Xx44dY41Oc3MznnnmGXg8HlRXV+PChQt48sknEQqFMDw8jJSUFH4OiJZDxeiuXbv+5mcnk8mwY8cOZGZmwmg0wuVyobe3l+lQFIyWk5ODLVu24Le//S0LjUOhEAcgLi0tcZqzQqHgKdvLL7+Mp59+Gnv27EFtbS1WV1cZMY9UMK+srGB0dBQVFRU8FUlPT8df/vIXeDweDA0NccaM2WzGnj17MDg4yNxpiUSCsrIyHD9+HD6fD5OTk/jJT34CvV6PQCCAtLQ0nDlzBv/wD/+A+fl5plCFQiHk5eVFRK5SU1MxMTHBphpOpxMPPPAAkpOTcfDgQd4HNZKHDh2CRqNhF5/i4mJ2tVleXuZndXl5GSKRCA0NDThx4gR0Oh0uXbqELVu2oKCgAHK5HNu3b4+o9SIwKTc3l2kU9fX1sFgsyMrKwvnz5wF8KHq+desWrFYrLBYL9uzZg/Lycmzbtg2FhYW4fPkyZDIZurq6kJ6ezhM+og8+++yz6Orqgkwmw/z8PJuGRPpO6VwrKSmBWCzG6OgoF5kNDQ1ISEjgYLIbN25AKpXi1q1bLF69cuUK57dQkUjOZRKJBJs2bcL58+dx7733wmw2s5CZphuR7tTNmzfDarXyXQKAM7DuvfdenD17lt27hoeHcf/99+Phhx9GdnY25ubmkJGRgcrKSrS3t+Ozn/0sTCYT9Ho9T1WIrvPQQw/h4sWLbKepUChw6NChiNa7crkcQ0NDSEhIgEQi4cktpVhPTExAqVSyRmXr1q34y1/+gqamJrzwwgt47bXX4HQ6cfr0aQQCAbz88svQarW4cuUKTpw4AY/Hgxs3buC+++7D9u3bodVqMTk5iY6ODqyurkYEBJKSkjAxMcENZmtrK+s6Dh48yGfKyMgI/vrXv+LQoUOwWCzIycnB4OAgUlJSsG/fPra3J/OOoaEh2Gw26HQ6nD59Gl/4whc4GZ3o0pHyZOj7EwgESE1NxcLCArq6utgSm+otAHyHkq6ksLAQJ0+e5P/77373O2i1WtZ40kSlvLwcMpkM1dXVfN4CwNDQEBQKRcTnLTExEe3t7aiqqkJ6ejp6e3sxNzeH27dvQyqVoquri/Vtly9fxve+9z1cuXIFU1NTaG1tBfBhjXrmzBk8++yzEAgEyM/PR2xsLLKysnDw4EHWmXZ1dbE7W1VVFfbu3RtxErS8vAyj0QitVguNRoOVlRWkpqaitbUVBoMBMzMzMBgMKC0thUKhQEFBASYmJnDo0CEu/ufn53laHA6Hcc8992BychK3bt1CSkoKxsbG8OCDDzKF1m63Y3h4GFlZWaw/vtvSarWQyWTIysqCz+dDb28vmpqakJ+fj7i4OIjFYuzYsYP1b7GxsZiYmMDnP/95BrHJYKWyshIWiwUVFRUMUm3ZsgXr6+vYvn072trakJuby9OjLVu2fMSsI9L62BoNkUiET33qU+jr60N8fDwqKirYZer06dMs9ktISOAAk23btjGP1OFwYGVlBZs2beIvqru7m0fhPp8PX//61/GP//iPEIlEPBWh8VukoK319XUYDAYWbZNguLq6GsFgEN3d3YzeDA4OIisrC5WVlQgEAsyBDIfDnNZrMBhw4cIFiEQifhHJN1qpVKKvrw86nQ4ymexv+gjb7XZkZmYiGAxCq9VCr9djdHSUdQVkS5uYmIjq6moWg2dkZKCsrAxXr17FSy+9BLFYjMLCQvT396OpqQlVVVVwOp0YGxtDamoqduzYgdHRUZhMJs4TWV5ejlgw05TH4XBw2vTMzAzKysqQm5uLnTt3YmpqCufPn0cwGIRSqUR+fj7+8Ic/8HhvamqK0zApiTI6Opq51MeOHUNCQgJkMhl30xqNhvUwG62RkRFkZGRwTkliYiKuX7/Ogsndu3ejt7cXw8PDrFsgZxGHwwG73Y7k5GSUlJSgvb0dNTU1yM3NhcfjYVtatVqN0dFR6PV6Hv1ScnYkygMJokh/Mj8/j5MnT2L//v344x//CKFQiNHRURiNRsjlcqSnp+P8+fNMryFqXiAQYKvi/v5+PohFIhF27tyJ9vZ2pnFlZmayLWEknUE4HMbc3ByGh4eZV3zz5k02HdizZw8sFgs6Ozuh0WiwvLyMv/71r0hPT+epIWXJpKSkcCMUCoXYtEEmk+Ff/uVf0NTUhPfffx9+vx/r6+sYGxv7m3zSixcvMuI+NjaGgYEBXLx4EQaDAdHR0SgvL2e6l06nw1tvvYWLFy+ygPydd96BRqNBY2MjfvjDH+K5557Dr371KxQXF0OlUqGrqwsCgQDp6eloamrC1atXGQ2mUNCNFjnLmM1mprUQSjcxMYG8vDzWNpAmpOH/Ye8/w+M8rzMB+J4ZYAbTB9PRBmXQO0AQJMFOUc1qVrMtS3KaZSdeb6zstXFybeJslNi76zhuiWvkxHZsxZJsSbY6KbGKDSwgQRC9DQbTe++D+X7Q54Tcjxjr+679yeeXLFPEg5n3fZ5z7nOXHTvwwQcf8ATQarWira0NNpuNiyoSSUokErz77rscjkXBcWQFWs6mdWNjA01NTfB4PEzzW1hYwH333Yfl5WW0trZysi452gDXBYxEHY1EIjCZTJBIJFhcXITP58PKygoymQwMBgMsFgubd5w6dQq9vb3o7e3F1atXy9KnyOSB7GLtdjveffddHD9+HM888wwMBgNmZmbgcDjQ1dWFw4cPI5FIcCAcec2Pjo6iVCphZmYGFosFu3fvxvj4OJ5//nncf//9WFpa4iyE9vZ2GAwGnD17tiwyX1lZyc4/ADiMzGAwoFgs4qMf/SgLzzs7O7kp+uCDD9jPPhAIcJAh6UcGBgYQDodx4cIFTExMIJFIYP/+/RgfH8e1a9d4WlEut4WmwuTyQ8/4oUOHsGPHDgDXG6WRkRGMjo6ylqdQKDAtR6vVcsDW6dOnYTabIRaLEY1GGfV+//33YbFY4HA4bhJ+0pl/q/XOO+9gy5YtOH/+PFQqFUwmE9RqNb7yla9geXkZBw8eZG2SSCTC+Pg4PvOZz+CDDz7gjBdCedfX1/H+++9jx44d0Gg0mJubg8PhwJ/+6Z8CAHp7e3HhwgUOAqYA1IGBgVvujZzhSqUSRCIRUz/6+/v5XaNsma6uLjidTng8HkxOTkKv13OIntVq5UKPpk+kw4jH47h06RJPHQFwQG85OgtpPqPRKDe24+Pj2LJlC15++WVkMhkWWx88eBDFYhF33nknlpaWOP+IqNhEZ6QQYdJYUG4JsTFqa2vR2tqKcDhcFmGen5+HXq/H8vIytFoturu7MT09jbNnzwK4zj6Ynp7meujChQvYunUrXnzxRcjlcqYa3X333RCLxfysE6WoWCzC6/Wivb0d27ZtYy0RMUTKiZrpfHO73UyPSqVSOHDgAMxmM1paWvicpYiEbdu24dSpU2hra4PT6cT4+Di0Wi0WFxe5eaRahN6d1tZWKBQKNDU1sWtcOp3+nSwBajAINHrnnXfQ3d2N+fl5dHR0YHZ2FktLS0xFm5ub4+kGBYZSLUsANrmmBYNBuN1uXLx4EZ/85Cdx5coVTgk/c+ZMWTv71dVVDA4OIpFIsA7a7XYjEAigqqoKExMTcLvdTAesrKzEuXPn+Bmk/CWVSsVmIm+//TYKhQIymQyuXLnClG7KnDtz5gzi8Tib3HyY9aEbDZlMhr/7u79DU1MT+1JbLBbk83nIZDLce++9THPR/DZo6fvf/z4EAgE7QBBFoFgsYmRkBPl8nrvrTCaDubk5DAwM8GiShJ7xeLwswlEoFDjqXiQSIZ1OIxKJ8EVH/DhKgSZ6EUXIE4/QYDDg6tWrCAQC2L17N1KpFJxOJ5LJJAKBAPbv349r166hrq4OIpEIXq8XkUhk0wMRAFpaWjA3N4eNjQ2e4BCfeWZmhkf0xPclZw8K+RkcHGSaGTlQHTlyBFu2bIFOp0NnZydnYFitViwtLfGlRXSPzVapVMLx48cxNDTE/HeHw4HOzk6cPHkS9913H+bm5hCPx7F161ZGQffs2cNUCEqaDAaDOH78OA4cOID9+/cjEAhgYmICd9xxB3p6eqDT6XgvpO8ph/ZVV1ezzoMQ2GAwiLm5Odx1113QaDSwWCyIx+M4fPgw7rjjDg5WIgoPjegVCgWOHz+ObDbLuSfUsGi1WlRUVGB6eprHguT4tdmi4KO6ujoYjUZIpVIIBAIsLCwwvcxms7Emwmq14sCBA+zSBlyfhDU1NeG9997DP/zDP2D//v3MLz1//jwWFxfx2c9+FteuXeP3jJxixsbGNt2bRCKBy+XigE1C4NxuN1544QWMjo7yNCoajTLlKJlMor+/n6kk6XSaBc/xeBytra18MP3617/mv3d2dpYviYGBgbKUM/rsgP/ULolEIthsNly4cAFDQ0P49Kc/DY/Hg5MnT+L999/Hnj178LnPfQ7T09P49a9/jb179+LAgQOYmpqCxWLB0aNHIRQKcfjwYfj9fkQiEXR3d7PxQnt7O9LpNC5dusTZJZstsViMl19+Gc3NzbBardBoNNzgXrx4EVarFXa7HXa7Haurq7j33nuZTvb4448zMNLS0oJ3332Xm7LPfvazCIVC+PnPf87J9DTRIZMKj8eDnTt3bro3pVKJ119/Hdu3b2ftGNF9VldXce3aNabvLCwscG6MQCBg1zWyQ71w4QIuXrwIpVKJhx56COl0Gi+++CLW19fx0Y9+FNeuXeOzMJ1Ow2KxcOF7q2WxWOB0OpkTT2m+99xzD1ZXVyGTydDX18fCSIFAgL6+PiwsLODIkSMAwLSUq1evwuPx4IUXXsCjjz7KWUGUcN7c3AypVIqVlRUAYFR3s1VRUQG3281nSTgcZovtfD4Po9GIcDgMkUiEtrY27Ny5E1evXoVEIuECfWBgADU1NXA6nYjH4zh16tRNNBrSBlEYo1QqhVwuR01NTdlivrq6Gv/+7/+OkZERWK1W5PN5DpdbW1tDLpfj37Ozs5PvSbFYzH83WS1/8MEHePXVV/GJT3wCjzzyCCQSCc6fP8/BZvl8nkXgpDEoF0paVVXF9yNZhJ47dw5/8Rd/gY997GMsUk2lUrDb7UgkEnjuuefgdrtx9OhRWK1WDA8PQyKR4Kc//Smam5uh0+nQ2NiIRCKBK1eu4C//8i/xN3/zN9wYEY10ZmamrA6C8h30ej1TsUQiEb72ta/hwQcfZJOFtbU1VFVVwWq18sSbNCxUx7z55puMBO/YsYPPCqLcCgQCTE5OMvWXpkKbrWw2izfffBP9/f1sy0oMju7ubnziE5/g72VhYYFNEEQiEZ/DsVgMjz32GDt9ajQa1vhNT0/D6/VCpVKhvb2daY3RaBSBQKDs3WAwGHDlyhVs27aNmRh6vR5nz56FSCTC6Ogo03umpqZQKpXYCp0CKKlgJVe0lpYWdHV1QSQSYX5+Hna7HaVSCQ6HAx6Ph52LqqqqytII1Wo13nzzTTQ2NjII4HQ6cfnyZfT19eGuu+5i6pLf74ff78fY2BgWFxeZVtrT04OZmRlcuHABKysrGBoaQltbG2cKUaNDNZdIJIJEIkFXV1fZiYZer4fT6WT6OFk1k4ulTCZDW1sb02ibm5vZnZWs1VUqFVpbW3HkyBEGr9rb21FVVQWv14uzZ8+yDoV0tplMBoODg2XrJLPZzHk1pNGJRCIQCoVobGzkvAuHw8HWzmS7/Mtf/hIqlQrbtm1DZ2cn5HI5Xn75ZezZswdqtZqpwzabjaeu165dg9lshtlsZhv6D7M+NHXK4XBg3759nIzY39+PtbU13H333TCbzSy4qays5PCPAwcOsHCLRH5PPPEEF31kF0YI7wMPPMAiZ4fDgZqaGgwNDSGfz+Po0aOb7o18iMkVQalUIhQK4fOf//xNloU9PT3YvXs3d83Ly8vsUJVIJJBOpzl4hlADEknffffdsNvt6Ovr44aGmo1yCAd1jgqFAhKJhC+lPXv2wGAwYMeOHdDr9aisrITJZEI2m+VR1//4H/8D+/btQywWQ7FYRCgUwuDgIEwmE6M4q6ur6OjoYI0MiYLy+Tymp6dZ9H6rFQgE0NraykUjORBR6isJ54HrB4FAIMCzzz6Lbdu23aQ9IF/8Z599FoFAAC6XiwWDf/mXf4krV66wLSKJPCsrK8sKm+n3cDgcLLSUSCTYtm0bJBIJe6ebTCZ89rOfZYEuFUn00oZCIaysrOCee+7B0tISNjY2oNVqUVtbi6eeeoqtg0lvsn37dmSz2bL6EUJfCaXu6upCb28vxsbG0NPTg6GhIbS0tMBqtcJiseCdd97B3r17USqVuAGgJjsajeLJJ59kMVxLSwsaGhqwf/9+LC0tYWBggO1QBwYGOCF7sxWPx6HT6eDxeDA/P89oZ0dHB7q7u/mQIMrKxYsXsWPHDjZEoMRyykihg5AEt4uLi3jqqacgkUgQCoWwdetWHDx4kAGCcoAAAPYZpyZDLBajrq4OX/ziF2GxWHD27FkuKLds2cIo7enTpzmdeXl5GXV1ddBoNKitrWV6zYEDB9Dc3Ay5XA6bzYaFhYWbJgAULLrZCoVC2LFjB4rFIpaWlri4IN6vyWRiSsjevXtx5swZDA8Po66uDpOTk1heXubPQCqV4mMf+xgcDgcOHz6MY8eOIZ/P41Of+hSmp6dx//33s/6JzDDK6dAqKyvxyCOPsLsKiSVbWlpQXV2Nu+++G4VCAYFAABaLBdeuXcMdd9yBuro6jI2NsYh5YGAApVIJe/fu5amnWCyGXq/H4OAg1tfX0dDQAKlUymLRubm5snSRUCiE5uZmpgIZDAYO0CyVSuju7maEu6GhAYVCAd3d3fx5Dw4OQqPRwGg04urVq9i5cycqKipY2EhJ0aQDoYkSIYblplSFQgF9fX1se14oFHgiUF9fzwBPPp+Hx+PB3Nwcfw70PDmdTgYI7r77buTzeTQ0NHAmxMc+9jE291hZWUFFRQVPIMudcaFQiPNwZmZmmDfd39/PxQidLTTdHRsbw8bGBhobGxlg6+zsRKlUwunTpxGJRDA1NYXl5WUkk0l88pOfhN1uh8vlQjqdRqFQYG1fOZezUqmE4eFhNkIgp7D/+T//J7Zt2watVsu5PmNjYxxotri4iG3btqGnpwdmsxnbt2+HwWDAzp07cfnyZeRyOfT39+PAgQM4cOAAjh49CqlUyprDcDiMpaWlsqYNpAV1OBx8xgmFQvzpn/4pWlpasGPHDnR2djK11e/3c3FIAcFer5fvr61btyKfz2NiYgIXL15kgfr58+eZztbR0YHe3l4AKPu8EX2LXPISiQSqq6uxZ88e1rwUCgU2VHn77bcxMjLC00adTseOZp2dnWy5T43i2toaU/9isRjnGpD+qRywGAgEsGfPHjgcDiwuLiKfz2NpaQn33HMPtFotJ4V3dHTgoYceYr0EcN12V6PRsEOX2+3GgQMH2DzD6/UiHo/j6aefxuLiIuvJHn30Ufze7/0edDpd2UbD5XLhwIEDTOdRKpVobm7Gk08+yayUcDjMjo30z6SFy+VySKfT+NjHPoZwOIxvfvOb8Hg8bGBSUVGBz3/+87h48SJPMjKZDNbX13H06NGy5xs1wWRqQ+yT3bt3c3PscrmwuroKoVDITU5FRQUGBwf5jqDvgKZPZGaQSCTw6KOPIhKJcBbV6Ogodu7cifX19bJTA9JvUJ4UcJ022d7ejpmZGVRVVbHmkJqX3bt3Ix6PY+fOnazD0Wg0eOedd/DJT34SFy5cYFvdcDiMkZERLCwssLNgQ0MD7rzzTlgsFg4j/F3rQ080KHeCHCLsdjvC4TDy+TxqamrgcrlY2V9XV4eJiQkW6dLomsJeyBEol8vxAUOi6srKSlbkkxME+UpvtnK5HCNlNNGgePjGxkYWs1AYX09PD5aXlwFcd3OhwDFyStDr9RgaGsLq6iqjkul0mqkNN4pkyIli0w/4t64pxI+MxWJQqVTIZDIcuEMPArnI0KiUOmGyFiW3IJry0FRgbW0N/f39mJychFgsRiKRYDF+OSoLeVaTqwhRPQglI0FhdXU1kskkN3Dk/S2Tyfi7Ic4jddEkRI/H4+zyYLFYGCEj563NlkAgYHoA2RPrdDoWydNDT4hVY2MjgsEg2+1VVVWxS4JMJmN0m54XKnSVSiWPNklz0tvbW9YmMJfLcaNEDl9El5HJZIxsEpVvaGgINpsN6XQa8XgcarUaSqUSV65cwX333cfvEGUuFItFaDQa9PX1YXp6mn276VklB51braqqKnafkMvlkMlkqKqqgtPpRGNjIxwOB9bX12EymdDd3c2/B3GcAbA4eHBwECMjIwiFQlhYWODijlxvKDySUDCiG5VbQqEQ0WgUUqmUswNaW1uRyWRQU1OD5eVlzmIg8ev6+jq2b9+OiooKtkxdXV3FgQMH4HQ6WWRJIj0yb3A6nRzolc1m4XK5MDs7u2n4Eb1PZIdI4koqqsg6lyh6fX19SKfTjJI3NTVx3gZN8MiBhlxwxGIx+vr6GBCJRqOcqO12uzf93KhAFIvF2NjY4M+LdAyEJJJImUI9ycmIKKA2mw3d3d2QSqXo7e1FPB5n+2jS7lDeAekFyEa63HcaiUQ4/BS4jgKurKzAYrGgWCxCoVAgHA7D7/fDarXi6tWr6OnpQWNjI7+zmUwG3d3dWFpa4oaSHKDIje/w4cPQ6/WMeJNz2WaLbE3JaYrAmHQ6zSYOZKBQU1PDRV1DQwOnSTc1NWF1dRXDw8PIZrNQKpUwm82ceu1wONDT08POgYSOVldXl3XayefzkEqlbHlKfvwkNKWzktyQCFgqlUoIhUKora3l83FgYACBQABWq5XR7UwmA4/HwzpJytYhMXI5FFckEmFlZQWpVArJZBLFYhG9vb24dOkSNjY2OAjUYrFArVYjkUggFoth9+7d0Gq13BBTEOr27dsRj8fhcDiYihUMBlmobzKZ2Fae3JU2WzKZjAErAEw9rq2thUwmQzAY5P9vfn6enfTIBU6n06GiogILCwswGo0QCoWs5wgEAuyaSEJ2yjYArovkqXa41SJ9m0aj4TObNFWNjY3sckhieTKpMJlMcDgcMBqNsFgsmJubg1qtZmOJqakpZDIZFv0ePHiQqVDknEQo9GaLDHzI/Cafz6O3txdut5sDjk0mE9ra2lh0TknpdA4A1+ngVLyPjo7CZrOxC2A2m0VTUxM7YpFDHk0pNlsCgYCZJeSiJxQK4Xa7IZPJ2GWutbUV3d3deOedd5DL5TA2NsbZZDKZDJcuXUJ7eztCoRBaW1vZZr5QKHD+Bmk7yYxHoVCUBQTILIaevYqKCpRKJayvr6OzsxOJRIJBbrrbAoEAfx/0LlPjQTUmBfYR02Hnzp1YW1vjZ+1GB7nNVj6f5++IDB5oKkfPFtFHV1dX0dzczKnjPp8Per0eRqMR6+vrLAC3WCwcN9Hd3Y1gMMgBuzRVcjgcZSld//f60BMNovsIhUIWu2WzWUZOyP2BBDvhcBipVIopOeQmMzc3B71ej9XVVRbOBQIB5rwXCgV4vV4IBAL4/X6sr69DLBaXRUrT6TScTidrKujnksiXHHTo8FMoFHA6naioqGBr1+rqakgkEoTDYayvr0Mul3OmgkAgYM41WcrSIVlfX1+2CSoWixzgQ8IgoqMQ5YYeEEovnpubQ7FYxKVLl7C2tsaJ3BaLhRszsj6kB4gue4FAwEXJ73LrEolEzK+kpk8ul2N1dRUKhYIF16VSCS6XC1VVVbDb7YxgkK3iysoKVlZWMDMzww5WwPWi9+LFi2hsbOSgNaFQiEwmwyPicouaKUJDyZtcKpWyFzmFRxJXVyaTwePx8GEPgIuGuro6Pgyj0SjrWxYXF1mbQTav5UTD5CQilUpRLBbhdruRSqUwPz/P9BmhUAiRSIREIoHm5mZcvXoVmUyG7QClUinGx8ehVqs5tTQYDHKAZTgchtlshs/n48+bRublXCgoyV6r1bKFLGk7yHudsiv8fv9NTihUZCmVSh7bZzIZdnoi/dDc3BwHtJGNXyAQYGvJcoss+0qlEmelCIVC9t6n0S7ZcPb09GBubg5Wq5WzCQqFAjweD2sC6L+nxtXn8/G4ny5HovXYbLaynx3lbVRWVjKFLB6Pw2g0sptPNBqF3W5HU1MT7HY7O0ZR4Xbx4kWoVCocO3YM1dXVDLLI5XJ4PB4MDw9jZWWFwY1YLMa5A+UWFVfkVBQOh5nuRw4q9MybzWacPHmS7Slpmkrc4bW1NZ4CO51OdkwijZPH4+HwNaIXbbYqKiqQTqfZqpEcpOLxOFtWUtFMNtDLy8s8eaV9U6G8vLzM2S70bhMFa2lpCclkkjUn9D5vtmhank6n2UkwlUqxjS2h/HRmNTY2sg0xUc90Oh07QNGklsAxpVKJ6elptLa2IpfL8QSAuM+/C4SK/Db8kwre9fV15tDTpIXuHMoFymazWF1dRVVVFTsjmUwmHDt2DF1dXZDJZJyRYLPZOHCPQmepWS43iS+VSvB4PCiVSizwp2wOamJuLLAIPGpqauJskGw2i8uXL7ObI1mMOxwO+Hw++P1+VFdXY2ZmBlKplJ8Zs9lcFuihzJEb7XDJbRK4HpqXSCQQj8exuroKv9/PPHWyrpZIJFhaWoLZbGYaJ9kvk4U8WaQS6EVmD+UciiQSCT9fGo2GqbvBYJAT4EkTQkAdOVuR/a3JZGJ0mwwuKJ9KpVIhmUzCarVyU0qJ8VRLbLYKhQIDhsD1iRqlVuv1ethsNqZzEtuDAIrFxUW2qF1aWmKGhcFg4MkH3WlkCBCLxWC32+HxeKBWq8veDRUVFcjlcswUIUtwqikAMMWUcrLsdjvbUBOIQ2Yuhw4dglarRSwW48bT7XZzHhzdzdFoFAqFgmu9zZ43cn6kZnhtbQ3r6+uoqqri2mZjYwM+nw9KpRILCwucFF5RUcGNDQUhUoYG1cb0nhYKBSQSCbjdbp6SlntPyX6caJz5fB7JZBLr6+swGo189goEAnZqzGazkEgk7Gwml8sxOzuLhoYGOBwOnvhQQDV9f5QLlcvlsLy8zOHGH2Z96IlGOp3mER/ZAabT6ZuEx4RYLS4usl2qQqFAOp1GMplkxNNut+PChQt45plnGA2nYp/4kVqtli/Uqampssp74trSl0Ij2IaGBoRCIXbuIetYsuJbWFhg+z66iMViMSYmJviBz+fzWFxc5C+MuIMej4dR2aWlpU33lkgk2H6SousJsaNiiVDQWCyG1tZWtjyz2Wws2iMbNSqcRCIRkskkfD4fBAIBjh49yimoEomEg3wI0dtsdXV18XQqFouxeC8ajWJ+fp45wZS4Sf78NTU1PCEie0bioVosFm52PB4PmpqaEAwGMTs7y9aEREkrt0iURxOA1dVVjI2NcRK9UqmEw+HAmTNn0N/fz5oeKorq6uo4FIsKZypQkskkj2upuKTJAbmYbbboJaXnwefzMZ85lUqhs7OTf04qlcLq6ipPV8hPnpryX/ziFxxGRNal165dg8fjwfLyMvNCyVmHqIabLTJDoIKPGlJCPGpqapBMJhEMBnHq1ClO/i4UCkxJqqysxMrKCorFIt555x0eEdOhXSqVWPRGiCdd7JFIBPfff/+m+9NoNDzqpwYlGAxCr9cz4JDL5eB2uxl5TqVSHHBJ2qDW1lacOHGC035JpEfIOglKl5aWkEgkIJVKOZio3KLJIhWS1ISSsQVxlcm9jJLOPR4PgwUOhwNmsxmxWIwzBMiyOhQKsa03Fap06ZUDU8i3ns7RXC6H8+fPo7q6Guvr68xPJtvcc+fOsQMf8fLJlvXUqVNsuajValFVVQWtVsu849bWVjgcDnbzkUqlv3OiXF9fz2FPFRUVCIfD2Lt3L7xeLyQSCRfelH+j0Whw/vx5DA0NIRaL8YWbzWbR2tqKWCwGiUTCNNpkMgmn08nIOAmBqfndbGWzWWg0GgSDQaZ0TU1NoampiXnvdKanUils3bqVc2N0Oh3C4TC8Xi/8fj8cDgcaGhpQKpVw7do15PN5+Hw+PocphI2QWY/HU9Zph84cshKurKxEPp9HIBDgnI4bk6gpF8ZmsyGRSHDxcvHiRQahpFIpn6s0ob106RLbCdP5Qe595T43s9nM72kikcD4+DiDPa2trTh//jxOnToFs9kMq9WKsbExfPDBBxwySvbXxWIRJ0+eZCfEmZkZTE1NAQBqamrg8Xj4zKJ9lytK8/k80zyB6+cxId7kjJRKpXiiEAqF4PP50N3djbq6OgYMqXnMZrMYHx9nqmA8Hsfp06fR0NDAulOaftNkfbMlEAig1+sxOzvLOh0CNcmN0+12IxqNMhOEKCpkSRoMBmEymXDx4kXWuBLNlhLpjx8/zo0pveN+v79snUSgQSaT4eeVsqpmZmZQUVHBNEm73Q6fz4exsTGcPn2aRd7V1dWIxWKcwUT3JZkezM3Nce4T/f1GoxEGg6EsHZkMgoiNQU0jZVQQ8ETURDJBOHPmDIDrjbFAIMDIyAhbWc/PzyMajXJNZDQaoVQqucEgi2a73V727CVbfvpdo9Eo1tbWcNddd2FtbY2ZDZQxQfUB3b1ETZ+enuZ33uv1QqPR8FlOQBY5ngLgwOpy5i9EjaIsokQiwWZCFP5IjRv93XNzc3j//feZGkkGApRpNTs7C7FYDJ/Ph9dffx3d3d1s4ED65GKxiNXV1bIT2xvXh240zGYzxsfHoVKpWHRYWVmJ48ePMxJAD0Mmk8H999+Ps2fP4ujRoxgZGeEDh7zQNRoNJiYmGG1ZWVnBu+++i+bmZnR2djLiQrqPclwwSvC9sVgkekVbWxtUKhXTbEZHR6HRaLC2tgadTsdioqqqKnR1dfH0gEREdOmQliMWi7HQqaqqClKpFPX19ZvuTSKR8PgTuH6IplIpTExMwGQywWazoVQqMTpPuRgUYmY2m1FVVcX7IAE6ddM6nQ79/f1YWVnhCxsAi6XLHdharRZHjhyBXq9nNHd2dha1tbUc7EJFLSFRvb29eO+99zA3N4f+/n62YHz66acxPj7OSEQkEoHNZsP09DQaGxthtVqZL59MJpnOtNkyGo0Qi8XcNJCJwMWLF+H3+7Ft2zZOqqQMChrBT0xMIB6PIxgMoqKiAn6/H6VSifNMhEIh62LGx8fR1dXFmhxCA61Wa9nvdG1tDRqNhgtfctyisbTNZuNpANEWKACR9DR33303AHARQGnP5I5EicjEbY1EIohGo2UPHqVSibNnz6KlpYXd3txuN5RKJS5evIje3l7IZDLm1DocDn5miLrT0tKCj3zkIzh69CjEYjE3ILlcDn6/H3V1dWwvSYh8LpdjpLfcInTMbDbDaDRCpVLxAU10nvX1dc48MJlMnIVA6BqFOFIoIU0M6b0nJJHE+Y2NjTzNKJdkStMwmvQlEgksLCzw+VJdXY1IJMKN1vz8PBQKBacfE41ueHgY58+fh06nY+tKgUDARhRTU1Nobm7G2toaJ7gLBAIMDg5uujdqyihHgpD8EydOIJPJQKFQ8ETuRrEj5YHMz88jl8thaGiIqVP0O5GN5Pr6Ou6++24W+ANg+k1/f/+me6uoqOA0WprMxGIxvPHGG/wz4/E4PB4PT2Pb29vR1taGqqoqnoBTwzkwMACfz8d0g3A4zAnG6+vrkEqlfA/dOI241aqsrOS8kFgshomJCahUKqZ21dfXo7OzE3a7HVeuXIFWq0VNTQ1mZmYwOTkJo9GIhoYGBgdogtzY2Mhgx7lz5yAUCrF//35Eo1E+syixuNze7HY71Go1IpEI++5TgUYuYw6Hg2m0nZ2dbMtOCPmePXtYJEx3FE2YKDAxl8sxPYbujnJZFVKplM9Dym2i6cGNWky6869duwatVoudO3fi+eefx9LSEt/ZpVIJ6XQa8/PzTAMhO3FqkhcXF3lCSrzwcntzOBwsBM9kMgiHwzhz5gzrbqhpo8kwABw/fpzpSkQ/pAItn89Dq9VCLBZzUz0zM8PgG53PYrGYtRq3WmQ4QK5Yq6ur7M5FU0+6j2i6Eo1G4fP5eNoklUoxMjLCbID19XVuIBYWFqBQKDA0NMTZTAKBgCf8pAW41dJqtWz9TWyKt956C1KpFDt27EB1dTVWVlaYUUK1EVHh1tfXkU6ncd999+G9997Dr3/9azQ1NbHlaTgcxoEDB9jutba2loHpubk5DA0Nbbo3mUzGFDVq/ilVnGpEmmS6XC4kk0ncddddiMVimJ+f56lOPp/n0EWizlNtmUwm8fnPfx59fX1MhaLJTLmmW6VSwel0MruCqPaHDh3C4OAgh08Tw8Hr9aKtrY1tjK1WK4vmafLvdruZFePxeJBOp9HZ2YlsNov6+noGHwYGBsrWl0RlJnqw1+uFTqfjIN6WlhaeeBcKBbhcLn42CQymxkssFqOlpYVDVCmPZmVlBWq1GnV1dewSSBqy30WVpiUolSO43l631+11e91et9ftdXvdXrfX7XV7/f+xPrRG4/a6vW6v2+v2ur1ur9vr9rq9bq/b68Ou243G7XV73V631+11e91et9ftdXvdXv/P1+1G4/a6vW6v2+v2ur1ur9vr9rq9bq//5+t2o3F73V631+11e91et9ftdXvdXrfX//N1u9G4vW6v2+v2ur1ur9vr9rq9bq/b6//5+tD2tl/72tegUCiQSCSgVCphMBgQCoWQTCbR0NDANqKUL7C4uIiOjg74/X5sbGyguroaOp0OhUIB586dg0KhgNFoRFVVFXK5HFKpFHvYU4okhbBUVVXB4/Hgr/7qr265t+985ztQKpVIJpMcDOVyuTh5mKzcbgwf2rJlC6ampjhDQ6PRQKPRYHZ2lr3lKYeA0pMBwGKxwOFwYGNjg7MKDAYD/uiP/uiWe/viF7+IVCrFCbdkBUnBQFqtlgPMpFIp+1rfaAYmFApRVVXFfvGRSIR/N8pyyOVyHMZCdn1kD/mNb3zjlnv7xje+AbVajXg8DoVCAb1ej3g8jsrKSiSTScjlckQiEU4kjsViaG5uxtTUFIfvicVimEwmrK2tYWNjA2q1GjqdDrlcDsFgkG2K9Xo9+1ZTxko0GsVf//Vf33JvL7zwAud30N9JgYSUraBSqfj5iUQiqKmpwfz8PGdBkI91LpeD0+lEQ0MD5zgEg0H2Q6eUWspOKJVKKJVKeOKJJ265t69+9aucY0Ip0RQuGI/H2aKQQrLI19rpdCKbzUIsFrPX+Pj4OAchGY1G/uwBcEAUvQdkN5fP5/GlL33plnv75je/ydaUwHUrSJFIxO8RhX+RzzxlC9jtdmxsbHDeAtk8ptNpqNVqtkkF/jMkqLa2Fn6/n7NdKA31z/7sz265NwD453/+Zw55Il/ziYkJFItFNDc3o1Qqcc4JPfsUdEmJsfSuUrAZALZ7pfOJEpz9fj/bD0YiERSLRTzzzDO33NuPfvQjtthUqVRsP1ooFGC1Wtl6k961ZDKJkZERDpVKJpPIZrPQ6XRwuVyor69HMpmESCRCRUUFn0EU2kihasB1m8KKigo8+eSTt9zbl770JU6dpoApm82G2tpaLC8vw2Qy8fNLlp0CgQChUIizJMhamQKdtFotn7Vkb1hZWcmhYPT5kxXoV77ylVvu7Qtf+ALEYjGHn9LPr6ys5ADPRCKBTCYDAGzLTZaLZI1O35VKpeJzqFAoIBaLoVgswmQycSAr5ePQef6Tn/xk0+etVCohk8lAJpNBoVBwbhMlcOdyOf7uCoUC20sWi8Wbnh2yXN7Y2OBMF7pTqqqq4PV6odfrOXyM/v2f/Mmf3HJv3/3ud6HX65FOp6HRaKBWq7GysgIAaG5uBgCEw2HOo/L7/WhubmbLaqlUitraWlRXV3PmTjab5RDAmpoazlkyGAwcrkX5UMViER//+Mdvubcvf/nL/N2RXejS0hIHwlG4Jj27fr8fW7duxeLiImdWUS6VSCRi22t69+nciMfjbOlLFsbFYhFyuRzPPvvsLff23HPPoVQq8R1AIZMUKqzRaDi9mT4Ps9kMv9/P95ZUKkVNTQ1OnTqFdDoNlUqFuro6CIVCznChs4/sVmlvoVAI3/72t2+5t7/927/le5fObpfLBblczhk1lDtUKBTg9/s5LZoSzpVKJQc0LiwscIYOvbuU00A5K/TcUg7NP/zDP2z6vNG9qNVqIZPJcPXqVchkMgiFQtTU1CAej3NNRO+r0Wjkd5XyxihHDADbS5dKJT7vFAoF74csyCUSCT7/+c/fcm/PP/88n71UwxWLRbaK1mg0yOfzyGazfB/q9XreB91PlK1F1teUXu3z+djCv7GxEcFgEIVCgUNWpVLppvf9D37wAyiVSs5vUSgUWFlZ4fyjuro6zkaivVAEBGXZqNVqqFQqRCIRzuNRq9Vs2Uvnikwm42A9uVyOfD6PRCKx6Rnyf/7P/+HPRKVSQSaTwe/335QhR7bcFNQoEAggkUhgt9tRUVGBmpoa1NbWYnZ2FvF4nAMR6XlTqVQIh8Oor6/nHCiBQICqqiokk0k899xzt9zbjetDNxp0sIvFYuh0Omg0GmSzWbhcLrS0tHDgCr3gUqkUiUQCVqsVr7/+OrRaLbZs2QKxWIzt27fDYrGw57vf70c8Hkcul0NjYyNqa2uhVCoRi8UQCoU4cXXTX+K3B51Go+EHlJI2FQoFP4xUdHV0dEChUKC9vZ0D7SKRCGcjxONxVFRUcEgZeWA3NjZCoVCgpaWFD4vflf5KRTo1KoVCAXV1dXx519TUIBQKIRAIIJvNwmAwIJ1OQ6fTcUqyXC7n7IM333wTDz/8MIrFIhftOp2OcxwkEgkkEgm/eOUSLzUaDUQiEXQ6HXQ6HSorK7G8vAyRSITe3l5ON5XJZNBoNHjvvfcQi8XQ2dkJs9mMUCjEHu/btm3DpUuXIJFIYDabkU6n4fF4MD09ja6uLkxPT2NwcJBDx5LJZFmPefK1powK+t8ulws6nY4P2VgshnA4jK6uLkQiEWzZsgUej4fTWSkYMJFI3JTzYDQaOWlXrVZDo9FwwUG5EpstqVSKcDgMg8EAtVrNGQfNzc2ccE/Jn7FYDL29vQgEAtDpdIjFYlAoFGhubkZdXR08Hg+i0Siam5uRTCYRjUY51ZwKXLfbDYFAwBkddDjeaqnVauRyOeh0Oi5sBQIB6uvr4fP5EI1GuSBUKBRIJpPQaDQQi8XI5/MolUqoqqpCa2srVlZWoFQqceXKFcjlcv7Mw+Ewp5DS5UK++i0tLWU/u9raWn5uKZOiq6sLs7OzcLlcnKRdXV3NAZv0MwUCAR/ea2tr2L17NxYXF9HU1IREIsGBodeuXUNNTQ2n5dJFmsvl+PK81aqurobP5+Mgt0gkArVaDZfLhUQiwfkVGxsbqKiogE6n42e5srKSQ9ZkMhl8Ph8mJyfR2dnJzfCNXv3U8NJ7HA6Hy4ZrqlQqlEolLuRlMhnMZjNnxVDBTmFK4XAYW7du5XR5emZyuRysViun1VJRQ+cMJdFLpVIGBm4MU7vVouacmiC1Wo1gMAitVotsNsvnHAWFUTigyWTiC9tsNkOn08Hr9aJQKHDQq1QqhU6n49RilUrF4Vj5fB4NDQ1lv9OKigo+++lzVqvVSKVSXJjfGKBI2QY1NTVcMNMZ0tbWhuXlZf7ukskk8vk8IpEI9u7dy58BFUWUq7PZopwTyuOghGu5XA6hUMhnfjqdhl6vR2NjI5aWlhi4ocaXABhK9SUAJRaLwWKxcL5EsVjkQtvtdsNgMGy6NyrQqEjJ5/NQKpWIx+OQSqVobW2F2+3mlHHKxCgUCti1axc/yxqNBl6vF2+99RYX79XV1aitreXClZpSahokEgkWFhY23RvdaVVVVRwUR/dRdXU1Z764XC64XC7U1NTAbrejtraWcweCwSAkEgn27NmD+fl5Dj2jAtHj8UCn06Guro6zr+i8U6vVm+6Nclgo5E8gEKBQKPCZ2dPTg0gkAqfTyecfnQ9isRgNDQ3QarW4cuUKhwa3tLRwqjmBJXTvU4NB70Qikdh0b2azme8nyjtraGhAJBLhrAZqCqPRKJqamjhgsaGhgQE+qvPS6TT8fj9aWlr4TjQajQiHw5y5QuGRdru97L1KYJJCoUBtbS3UajXcbjdcLhe0Wi03SNQcUPq82WyGy+WCRCLhwGKqBaiQp9yZpaUlyGQyqNVqVFVVwW63c1J6MBjcdG9qtZpzKihlWywWw263w2AwIJVKQalUQi6XI5PJoLW1FcViEel0GkqlkvNs3G43VCoV1woul4tBaAp+FYlEqK6u5vussrLyJtD5/14ikYjrcgq7pZ9NQEksFmMghb7HfD4Pi8WCTCaDTCaDaDQKj8fDTR6BjlKplDN4Ojs7MT4+zs0u3YcfZn3oRoMOBUqN1ul0SKfTGBwcRDgchlarRTgcZiTKbDbD4/FgbW0NVqsVmUwGly5dgkqlgtFohNFoxOLiIvR6PSwWC3dLlLJcVVXFXaTX6y0bDJJKpbiYSKVSyOVyfJFmMhkMDAwgEonA5/NhY2MDbW1tOHv2LAwGAyorKxGLxTht1u/3Q61W3xR139PTg2AwiGKxiAsXLqC2tpYbBwpd2WzlcjmYzeabusBMJsM/x+12cwgZpTpLpVKeMiSTSS5cg8EgrFYr7HY7stksBAIBqquruSmTy+Vwu92orKyEQCDgacRmi4qIUqnEScClUolfjo2NDayurmJjYwMdHR1obm6GUqnE9PQ06urqEI1Gsby8zBczpb4TIkXNXCAQ4GkLfW5Op7Ns8RKLxfgFIhRpbW0NIpEIa2traGlpQbFYvAkdJeTdaDQiEokgFotxgSmXy/n3y2aziEaj2NjYYHSIUBdCnsulDRNyT2nG9EJWVVVheXmZJy00xfL7/fB4PBwmtLGxgaWlJaRSKayvr6O5uRl2u50v2lwux83m4uIiF0XUDBCqeKsVjUb50k0kEhAKhTCbzdz4UJFEhZVOp4PP5+PQSkoBLhQKSCQS0Ov1yGQyMJvNMJvN3AzR5Wu1Wvmz8Hq9WF5exv79+zfdXzgchlgs5iYonU4jnU4zegqAvzNCOakw0uv1yOVyiMViKJVK2NjYQE9PD5aWlnhSVFdXB4VCgaWlJRw8eBCpVOqmv5tQnc0+O61WywV2JpPhwLNsNsthWOvr61xAUmFYWVnJ71BPTw8//+l0mhF1QtUpEIvC+orFIrLZbNlzJJPJcHMXjUaRzWZRWVkJrVYLr9fL5wFNXyUSCfx+PwQCARfn9FnT85RMJjlokFC3ZDLJRQ0VMIVCoWygFQWfZTIZnkCJxWJOwFUoFJzSTMhyRUUFlEolamtrudiXy+XI5XI85aVilc5mCuyj0EJKzaZic7O9UZNBjZrP54NGo2EEtLKyEtFoFJFIhBFESpuPRCIIBoNobGyE3+/nz5qabpqu+P1+hMNhTp6niVe5JHoqrmlaQqFfYrEYCwsLkMvl0Gq1/DwD19+NtbU1TlinwquyspJRb6lUilAoBLvdDoVCwX+G7pyKigoEAoGyBYzL5YJMJkM0GkVlZSVPXORyORwOB7MXEokEXC4XQqEQxGIxvF4vB+IlEglotVq43W6kUimo1Wro9XqIRCKedhcKBS50aGpOIYCbLXrW6d2rqKhAKBSCRCKBz+eDw+FANptlwIWeE7/fj+7ubp7IEhBBCfMGg4HBq9raWgbSaOXzeSSTybKNRjQaRVVVFVKpFOLxODM16Ayi8L5UKoXq6mqeACiVSm427XY7B6plMhm43W4GYnO5HBoaGjA3NweRSMSFP52R5cAKv9/PjQTVSnTP3PiZEwhKZ4XH4+F3WygUckI5pZZ7vV5GwOk8IWCFpqQUwLfZojBEAiPoTKCpOd1PxE6hyVMmk4Fer+dzkUIapVIpVldXUSwWodfrodfr+e6nZ4FABKfTyXXJZnurrq7mSRUFttJdLZPJEAqFUFFRAbVaDbvdDplMxiHBgUAAdrsdAoEAdXV1aGxsRKFQ4JqB6o7l5WV0d3dz7UqB0eXSt9PpNANMdL4TMJROp9HS0sKToqqqKgSDQUSjUQaE8vk80uk0/ztqtAlgoBR2CiCmBpre1XIgz43rQzcaRHWhQ42QfBq9KRQKiEQi/iKpOD18+DAAMErW0tKCWCyGy5cvI5lMoq6uji8YQnPlcjmPxWmUUw4ZIqQTuN7h0cidCiaZTAaBQIBAIIBAIIB0Og2TyYT19XXeWz6fZyoTfcl0qMhkMqyurjI6Sl8OUavKIfMVFRU8PiRKikgkwszMDEwmE08MxGIx02K0Wi0f4n6/n5GAiooKNDY2Mi2M9kZdv1gsZrrTxsYGtFptWRSBijixWMwHDSEDhKQlEgkkEglEIhEMDw/DbDZjfX0ds7OzSCQSPBmgaYjFYkFXVxc3UxKJhD9/KoApWbKvr2/TveXzeab2EGJKLwohuJWVlXxp5PN5vkSJrkTocyqVQigUYlpGZWUlcrkcp2hSB0+/az6fZyTmVotQaDoQDAYD6uvrMTMzg42NDVRWVvIlQEUlcL1IIMSWko2pEUylUtBoNEzlOHv2LCwWC7RaLR9WwWAQcrkcGo1m072lUilO+CYqgkgkwvr6OidLUxFExZRAIIDFYsHS0hLTHKVSKfL5PGZmZhi1oPdkfX0d2WwWW7ZsgVwuRzqdhtvthtvtLptiCoBpgkQ9oQPSbrejra0NALjopkurpqaG90KHvMFggFgsxpEjR7jgIlSHpkRVVVU30W/i8XjZ5FyiSNA7TedSJBLh94P+vkgkgubmZkgkEigUCj6EabpJF1IgEEBFRQVUKhV0Oh3W1ta4MJXJZAiHw4wQlWsgacJCSDcVFA6HAyqVCnK5nOl8NIqn1OBr164hn89DJpOhsrISEokE09PTMJvN3EgAgNfrRTAYRHd3NxeH1MxTMvGtFqGbhUIB8XgcxWLxJtCB3ld6JonyUiwW+c/TNEAsFvM7TqjljTQkorXRuRqNRssWzPR9EJVHoVAgn8/zmalQKFBVVYVwOMzTLKJ90LQUuI6eRyIRTn+nJqBQKGB9fR25XA4KhYKpokS7Kfed6vV6hEIhplfQd1MsFhm1JqAlHA5DKBSira0NXq8XAHgySPelz+dj+oRcLkd1dTU3WlSQe71ePvPLFVf0nUqlUi6qTCYTrly5wt9HsVhEMBjkAmlgYADj4+PweDyoqqpi6tD8/DwAoL6+HhaLBalUCpFIBCsrKwyUEZhE53S5M44oHXTvU3Pr9Xq5wY3FYtxo19fXo729nZsvSneuqalBKpXCysoKamtr+WdubGwgEAgwSk+gyI3P6WaL0pflcjmzKwDw+0f/PhaLIRKJQCwWw2g0IpvNMjWN2CCtra149913sW/fPn5uU6kUN3BEESV6ulgsRnV19aZ7I9CDKI30bFG9RoyQbDaLYDDIbAKaZJVKJW7mpFIpvF4vVCoVT3Hz+TzW1tagUqn4uaGCl1gZmy2xWMwAHk3JiZonEokYSCYmSUNDA3+uoVCI/5xQKEQikeB90FnhdrsxMzODgYEBptelUikGd8oB2YVCAXa7HTqdjov+YrEIh8OB+vp6nv5Go1H+HWjSur6+jnA4zEA9TRvod6KG2efzIZPJwOl0oq6ujps1evY2W0KhkM9QOsspFZ2ACzq76d3SarVIJBLweDyorKzkmqlUKiEej/NUhaZ/U1NTkMlkNwG19ByVAwRu2ueH+lP4z5e7srKSx0RisRhXrlzhA45e1nQ6jfX1dfT09ECn0yGVSmFjYwN1dXXYtm0bANz0S0ajUSwuLsLr9SKXy6G7uxuNjY2MNhOFYrNFY7dUKsWHyMbGBiMA9CJWVFQgkUhgfX0d27ZtQyaT4VEkFdsmk4lHjNT9+/1++P1+LCws8GFEFBvqUjdbEokE0WiUEXPqooHrL0ZtbS2MRiP0ej10Oh0qKiqYB3gjxYjGfVQYGo1GNDY2QqPRIBKJMIeS9pfL5WAymcoWflSQC4VCKJVKRr7lcjlsNhu8Xi9fYi6XC5FIBBqNBjqdDjMzMwgEAmhqasLQ0BAfQE1NTWhpaYHBYEA0GuVDiS75VCoFv9+PysrKsjQbiUTChzE1QFTUEr2NivRCocDagoqKCsTjcUaACcEnmoJUKuXinQov0stkMhlGs8shQ1SEE1dUo9EwXUwmk0EqlTIqQNz0+vp6mEwmbsAikQjrOOi5INobFVEulwvd3d1oamri5kgsFpelPBBqls/n+bIvFotwuVzw+/2M/FdWVjLqptVqUVdXx/xc4u6LxWI4HA5oNBoIBALE43GmaiSTSVitViSTSdhsNthsNgQCgbIFAgAGKGi6d+P+KioqGGmm35X404TQ+3w+FAoF6PV6bGxs4LXXXkMymWQ+MNEiqPmgaUEsFkMsFitLwSR9DI3Iq6urecpDxRF97/RsKpVKboQJ5ACARCKBjY0NhMNhRKNRSCQSmEwm/jnU1MTjccRiMf7+y31u6XT6Jl48oY16vR6tra1oa2tjOoVYLEZTUxN0Oh2CwSBPh0gHRFStG98F+myJtkdcdqJVbbZuPKepCUulUlhbW+P9EqJJ002j0YhgMMhNITUiVDBTEV1VVcVnIQC+NOnzBlAW6BEKhQwQEQVCp9PxVI/OAJqQEQJaU1ODSCSCRCLBSGplZSUUCgXTC7RaLerr66FSqSAUCtHc3Iz29naePNbV1aGpqans5xYMBlmjpdVq+Tw2Go2oqanhgoOmPEqlEq2traxDoEKCdGh0dhH4QcCgVCq9qQkibdlmS6PRcFNnMpkYEQ6FQhAKhfxuEG9er9ejr68P9fX1/CzU1dXx5FsikaC2tpZp1xUVFfB4PMjlcujt7eX3mWge5b5TajAI2aazJBqNQiQSQa/XQyqVch0Si8Vwxx13oLOzEx6PBy6XC1KpFP39/SgWi/D7/dBqtXy36XQ6+P1+vh9pskfT63LgnUgk4umZWq1GTU0NI86xWIyfGblczg1mTU0NRCIRbDYba4NIP0STn5qaGtTV1UGtViMQCKCurg41NTWorq7ms+l3faf0eQHX3zODwcDNFFGLqVElEEyr1cJoNDLljhotmm7R70OF6Y2UcHqGNjY2IJVKy74LMpkMkUgEAHgCEo/HEQqFuFag+oHud/r7XS4XAoEAg3w0cSEallQqRTAY5AkW0eKpmZbL5Uw3vNWqqKjA+vo64vE4PwtE3yUQSS6XI5FIYGlpiZ99Ar2IElZdXc1TLnpu6VkOh8OsyQDA06bfBQiQloYmZ6T1o++atKNEzxOJROjp6WEAh6ZONTU13ExnMhmmk6fTaaysrGBjY4PrewKO6L/9MOtDNxpUFFOXRry4Bx54AKFQCMPDwzydMJvNjPZWV1fj4MGDMJlMcDqdjF7u3r2bf4np6Wm43W6Mjo7yCHZ5eRlisRj19fUoFAqMdt5q0eTD4XAwhUgoFGJgYAAGgwFOp5M5vkTxqqqqgsVigcFggE6nQ3t7O/bt24fZ2VkYjUZG5ehL+8xnPgOJRMICUxLuFIvFsgUzicLo8BEKhZidnUVTUxM8Hg9WV1cxPT0Nh8PBKIDZbMbKygpTQdra2jAwMACn08lTI3r4QqEQ6uvrsb6+Dp1Oxw9EXV0d7HY7fD7fpnujac/a2hqP9kj8FgwGUVtbC4vFgtbWVkY58/k8FhYWWGg6NTUFj8eD5eVl3HfffbDZbJicnGStR1VVFZaWlqBSqTA5OYmKigr09fWhoqICx48f33RvarUaBoMBwWAQPp8PsVgMV65cgVarhVQqRSQSYWSvqakJQqEQTU1NcLlcEAgEiEajiEajaGlpgcfjwT333HPTSJeKVaI0ORwOyOVytLS0QCaTleW60p8jUe3CwgIuXryIe++9FwKBAA0NDRCJRDCZTLj//vsRDAYxODiIaDSKjo4OtLS0oKqqivmyxJssFAr8WY6NjTFHkz63pqam34ngVlRUMMVIJBJBq9UimUyio6MDIpEIKpUKBoMBVqsVg4ODyGQyUCqVGB8fZ23PzMwMJicnMTMzg7GxMRgMBqbNZbNZ7N69GyMjI/D7/VhdXeWzgUwiyi1CXAgtl8lkCAaDGBgYgEwmQ2trKwwGA6PohPSLRCLs27cPbW1tjOJeuXIFn/jEJ5BIJGA2m2G1WlFTU4Onn34au3fvhlarZSCB6GzlAAs6OIkCClynem3btg2tra1shkHgwnvvvQepVIr19XXU19ejt7cX1dXVWF1dhd1uR2NjIzY2NmAymbhhuf/+++H1etHc3IxMJgOr1YrR0VEIhcKyBUw2m0VLSwvTGrPZLLxeL3bt2sXcbJfLhUwmg7q6OggEArjdbpw+fRqdnZ2wWCwQiUTo7OzE+vo6HnnkEZRKJeh0OjQ1NcFsNkOtVqOtrY0nYT09Pdi6dSuy2Szm5uY23VuxWIROp+NChShj/f39LK4mikpHRwdMJhOGhoawurrKv08qlUJjYyM360qlko0BSLi5traGlZUVTE5OIhKJcFFH6OatFukCNjY2EI1GsbS0BJfLhdHRUbS2tqKvrw9SqRQqlQp79+6FzWZDoVDA4uIiT6GI3kbNOwFbFy9exFtvvcXFj0qlgtPphFAo5EKD0NZbrYWFBdb9UZFbKBRY4C2RSBCPx6FUKrF9+3YWgVPxb7FY0NnZiZ6eHszNzTHCKBAIYLPZ8Prrr6OyspKbmWKxiPb2dvT09CCZTMJut2+6t3A4zEjw6uoq5ufn8fbbb+POO+8EADQ2NqK+vh59fX246667MDExwQ09AWZEJ2lvb4dCoUAoFOJCJh6Po6OjA3K5HP39/XA6nXw/02R5s5VMJmE2mxm0oGejv78fXq8XSqUSTU1NGB4exr59+9jYxOl0sgaMpjp2ux179uzBysoKbDYbVlZW4HQ6MTIygqtXr6K2thZarRatra3o6upi4HCztbGxgc7OTgaUyHyjo6MDR48exbVr1+Dz+fh+yGQyXKTefffd6OzsRGVlJfbv34/p6Wk8+eSTWFlZgcvlgs/n4zpmaWkJwWCQaV5kUlPucwPApjnBYBCRSASBQADbt2+HXq+HRCKBx+NBKBSCRqPB9PQ0PB4PPB4PamtrYTKZoNPpMDQ0hKmpKezcuZMbT+C64NpqtWJlZQUGg4GLc+A/p6WbrVQqBYvFwpPbWCzGAn+iDxHgPDAwgHg8jtraWqRSKfT396OhoYHB5MXFRezYsQN+vx+hUAgikQjNzc34vd/7PaY6Li0t8SSZdBrlvlOawMtkMtY9kHkJNdzAdS0dgb50f/b29jIQAQA7d+5kCnA4HEY2m8Xw8DBUKhXuuOMO2Gw2fneohtlsEaBMk3W1Wo1IJML1n9PpxNzcHBKJBNrb2xngVCgUXB+XSiWmxu/fvx9SqZQnjJlMBp/85CfhdDrh9/thNBqxZcsWdHR0MAX3w6wP3WjQGPTAgQO499570draikQigampKTz88MN8uRKFYs+ePYjH4xgYGMBzzz2HS5cuob+/H5FIBHfccQeee+451NTUoKKiAiaTCXfeeSey2Sw+97nPwWAwoL+/Hy6XCysrK9i/fz/Onz+/6d5oMmI2mxGJRHDhwgU0NDTgN7/5DYaHh1ln0NTUBKvViq1btyKRSHA3tmfPHtx7772oqKjA/v378e1vfxv79+9ngVVTUxMmJydx9913858vlUpwOp0YGhqC0+ncdG9SqZR1EwqFAiaTCQ8//DB+/vOfY3BwkBsamUyGVCqFe+65BwsLC+jq6sLx48fh8XjQ0NCAeDyO9vZ2zM/P47HHHkMul2Pe+tLSEgvZGhoaeERHmoXNlkajQTKZxJYtW9Dd3Y1gMIiTJ0/iu9/9Lvr7+5n6pFQq4fF4kE6n8e1vfxvDw8O488478dRTT+GJJ55AS0sLOjs78eKLL0Kn0yEQCMBoNOKTn/wkpFIpvvSlL6GpqQn19fXweDzw+/3YvXt32aKPnJOo+56bm0N3dzd/Np2dndDr9Sz+pctiYWEBsVgMra2t6O3t5VHguXPnWKhGIutSqYT6+no4nU54PB7WH5WjTdHzNjExAZ1Oh5aWFphMJgwPD+PkyZM4ePAgstksu52QWxqhw6dPn0YkEoFer8e5c+fw0EMP4fz582hoaEBVVRUaGhowNDSEn/70p3jmmWcgFAqxd+9eSCQSrK6uQiqV4uTJk5vujdDRnTt3oqWlBX6/HysrK/jZz36GxsZGngJVV1ejvr4eHR0dmJmZwejoKPOo9+3bh8HBQbS2tuL06dPY2NjgS1Mmk+HkyZO49957AVxHjEmT09vbW/Z5A4Du7m7E43EW+IvFYmzbtg0CgQDbt2/H1atXceXKFSwsLODKlSsYGxvD+vo6NBoNLl++jEQigf7+fhiNRmQyGZw6dQpKpZJFl5lMBm+//TY6OzsxPz/PBzpd3uUabzKQMJlMLBgmw4Cmpiasrq4ikUigvr4eo6OjeOaZZ7hgIspZZ2cnxGIxHnroIbjdbnz0ox/liWepVMI777yDhx56CAqFgt2NZmZmUF1dzQ5at1pVVVXw+Xzo7u7Gli1b0NLSgqGhIZw7dw7d3d1QKBTszHb69Gk8/vjj7Kx07tw5VFZWYs+ePfD5fFCr1fjEJz4BrVaLjY0N2O12dmFzuVw4deoUnE4nLly4gAsXLrCWYrMlFAoxPj6OQqEAg8EAo9HIE82RkRGsr69DLBZDpVLB5/OxGcbY2Bg3bk1NTXxRHjlyhKdYPp8Ply9fxuTkJJ555hlsbGygq6sL0WgUs7OzjLRvtlQqFRwOBywWCzo6OmC1WrFr1y5cunQJGxsbOHz4MOx2OxKJBGw2G5599lm4XC4MDg6yE09fXx9P+t566y2MjIwwnXdgYACrq6vYuXMnF0M3UkvLGYW0t7djeXmZp2LhcBhbtmzhaRPx4G02G1555RU8++yzmJubQzgcZnqkQCDA0tISPvvZz0KlUmH37t2MSDc2NiKTycBgMODs2bMYGBiAxWKBTCbjQmGzpVAocPXqVT7bVSoVDhw4AJvNBr1ej1gsxjSXc+fO4eMf/zheeeUV6PV6zM/PIxAIQKFQsBkGFWQLCwt8X4lEInzhC1/A1atXYTQame60Z8+esvQkMqIZGxvDli1bWEfpdDqh1+uxvr6OVCqFQCCAixcvYmxsDN/97nd5qjw0NIStW7fC4/Ggq6sLly9fxq5du6BQKKBSqdDd3Y3z58/jqaeeYsH25OQkLly4gJqamrIoLtF7e3p60NbWBrlcjtHRUfzmN7/Bl7/8ZYjFYjQ3N6O+vh4rKytQqVT4xS9+gZ07d6K1tRXDw8Noa2vDuXPn0N/fj4mJCdx3332oq6vjaVapVGIjEZFIhImJCZw5cwZNTU1l71SZTAaRSISxsTF0dXVxkep0OqFQKPDKK68gmUyir68Pra2tePrpp7G8vMxi9IGBAXR1deHtt99GbW0txsfHsW/fPqYVZjIZnDx5Eh//+Mf5u8hkMshms7/zOyXwSafTMWjz2c9+Fg6HAwcOHODzhcwFPv7xj7Oz3+zsLEqlEvbs2YOxsTG0trYiFAphaGgIBoMBbrcbJ06cwI9//GM88MADDHgQsNbZ2Vm2hiOTCIVCwYDDwYMHeRKbTqfR0dGBrVu3QqPRoKenB++++y7cbje+973vweVyYfv27YhEIpBIJJiYmIBer+dJUT6fZx3Y8vIyG7lUVlbiwIEDrM+61aLpekVFBQu09+zZgzfffBNDQ0PYsmULurq62DDpC1/4AqamprC0tIRvfetbsNlsaG1txfj4OFP2m5ubeUI0PDwMr9eLmpoa2Gw2NDQ0IJVKwefzoa2t7SYNU7n1oTUa8XgcGo0GU1NTyGQyjCSMjIzgzTff5NEToXI+nw/9/f1YWVnB//pf/wvhcBh2ux02mw1TU1NoaWlh/mg6ncaxY8fwzjvv4M///M+RSqXgdDp5OrK+vl72oiOrs3A4DKfTyfzNrVu3Ym5uDmNjYzyJSafTyOVymJubYxeTQ4cOcaETjUYxODiIf/qnf8Lw8DCMRiNmZ2fR3NyMUCiE9vZ2XL58mfUDKysrZZFIh8PBbjSxWAw2mw3z8/N46KGHYLPZYLFYcPnyZQiFQmzbtg3f//73EYvFcM8996CzsxMSiYRdIdbX17G+vo433ngDNTU1EAgEcDqdOHjwIJxOJxfKyWSSR73lxOBOpxPbtm1jzYRer8fjjz+OtbU1Fii+8cYbLDytr69Ha2srJBIJZmZmEA6HUVtbi+HhYVy8eBGdnZ14//33MTw8jEAggJmZGdjtdpw5cwYvvvgihoeHGaU7depUWTrG4uIiGhsbWadC42Gz2cyoDiGdcrkc77//PpqamtDW1sZ2fDMzM9Dr9diyZQvMZjNOnTqFQqGAQCCAq1evolgsoq2tjRFpEg0SbWezFQ6HuWifmZlBMBhk96RTp05hYGAACwsLLL5cXV1FNptFTU0No+9kU3nixAm2XYzH41hYWEChUMDu3bvxH//xH9BoNLDZbNzQTExMlG2ESFtw5swZ/s4GBwfh9XoxPz+P7u5uTE5OIhaLsV3ovn378NprrzE3/dKlS6isrMS1a9dQX1/Pjkt+vx+XLl3C1NQUC8hbW1thMplQLBYxNzeH8+fP4/777990f2tra9iyZQtWVlawvLzMh71areYJFyFXOp0Os7Oz2LNnDy5fvszUylAohOPHj6NUKmH79u0sco/H45ifn+fLkxpVojnSuHezRVScuro6LhiDwSAaGhpw7tw5/llerxfXrl1Da2srrly5ApfLxWK72tpadHd3w+Fw4L777sPExASUSiUymQy7YdlsNraVbmlpYcegciNyr9eL1tZWTE1NQalUQiaT4cqVK+ju7kYkEsHJkyeh0Wig1+uxbds2vP766+jt7UUoFMKBAwdQWVnJjZpOp8P3v/99RCIRngaXSiWMjo4ikUgwxUwoFLLWbXJyctO9ERhD9COa8lRWVrKRAFGkpFIpnE4nJicn0dXVhYGBAUZYnU4ngsEg0uk0wuEw9Ho9zGYzqqqqsG3bNhw5coRdXoxGI1viljvjaFJO9LFsNouVlRXU1dXxGULInMFgwOuvv85oLFGFCKyIRqP49Kc/jWg0CqVSiUQigTNnzrAg8/jx46isrOT3gYrxzVY4HEZNTQ07u4TDYXa1yuVyTDklBDIWi8FqtWJ+fh5tbW1sCmAwGPDiiy8ilUphYWEBWq0WwWAQa2traGxshNvtxsDAAE6dOsWTJ+Jrb7bi8TiDQclkEm63Gx988AHa2toQj8eh0+nw9ttvw2azwWAwYG1tjScRe/bsgd1ux8mTJxkgOXjwINxuN9s/m0wmHDp0CIcPH2ZjCovFwuBlufPX6XRiYGCA7zua6NGkgygs2WwWTU1NOHr0KLLZLLuUlUolrK2twWw2QygU4iMf+Qjm5uYQjUbZfp7oNwaDgZ3SstnsTfTpzZ63jo4OXLt2jSdVp0+fxrZt2xCPx9Hd3Y33338f6XQaDzzwACYmJrCysoLjx4/zvelwOFBbW4sTJ07AZDLhjTfegMFggNlsRl1dHYaHh/H1r38d8Xgc/f39DJDMzc2VrUWEQiEaGxuxsrLC7AUSmgcCAfT392N+fh7z8/Nob29nB6O33noLW7duRaFQYCMEopO+8sorSKVSqKmpwcDAAJqamnDu3DmEw2F0dHQwXej8+fPQ6/Wb7o0AHKIU0d+9sbHBU8Jjx45BLBZjdHQUP/vZzyAQCNDX18ff0enTp9Hc3IxcLodLly5xI0dOqSQ293q9cLvdTM+cnZ0ta/5CE1tyaJRKpTh06BCeeOIJvPLKK0z3JDACALZs2YK33noLtbW1cLlcOHToELMTtm3bhtXVVXR2dsJgMCAcDjO4cPbsWRiNRkSjUQiFQqyurmJ1dXXTvVVVVfEUK5lMYnV1FUePHsWWLVvYdY0auO7ubvz0pz+FXq/n72ZjYwOLi4tQq9V455132LgpFouxNICGDMViEa+++ipUKhXUajVCoRA8Hs+me7vp2ftQfwrXO6df//rXuHr1KjsVWCwWfoB27NgBsVgMp9MJl8sFp9PJ//zTn/4Uq6ur6OnpgUgkQqlUQlNTEzY2NuD1ehEIBJBMJhmBJDTe7/djeXkZlZWVzN+71RKLxVhdXYVOp8Ndd92Fj3zkI4z0RCIRRiPOnz+Pq1evwmQy4b777kM0GsX8/DwXim63G7FYDJOTkzyJsNlsfJHTn4vH4+w2pdFofuf4iDirSqUSWq0WarUaiUQCuVwO09PTzK8jwTl15EQPIqS+uroaDQ0NiMViyOVyPML/xje+wWIzq9XKzYlEIsHg4OCm+0qlUnj//fe56DQajfB6vfj5z38OqVSKxx9/HAcOHGAOqcFgwL59+xCLxXDy5El4PB62xMxkMjzRINeNWCyG1dVVZDIZPProo4hGo5iZmWEbxHJCSYFAAI/HA6FQiNbWVmzbtg0tLS04e/bsTe4fYrEYPp8PHR0dPCmgJqu2thadnZ2Qy+V477330NTUhNraWj7I6MAZGBhguh2Jtco5J5H4lxDswcFBWCwWvPjiiyy2JIHj3Nwcfv3rX8Pv97MAe3l5GQ6Hg5uH3t5eFoMWi0Wsra3hrbfeQqlUgsViweDgIAvs9u7di7GxsU33ptPpcP78eXaD8Pl8sNvtePHFF9Hb2wuj0Yg9e/agr68PbrcbZrOZed7k4d/f388ZLUqlkrm3ZGhA6IzJZGIRpsPhYFehciuZTOL06dOQyWRoamqCVqtFbW0tN9iDg4MYGxtju2a1Wo2zZ88iFAqhra0N27dvx969e7F37140NzdjaWkJ9fX1aG5uRnNzM0wmE6qrqxGJRDA6Osq6BmoiyxUwgUCA7ZeJ8kT0T6fTyd9pIBCA1+vFSy+9xN8RIUCUVxAKhTA3N4eVlRV2njOZTBgfH8eJEycwPz8PpVKJixcv4tChQ5DL5bh69eqmeyN/9UQigWg0ilQqhc7OThw+fBjFYhHd3d2Qy+WIRqNIp9M8Rcnn8zh58iQuXLjAYvJisYh//dd/hVAoxK5du7B161aIxWLOZhgcHORCnNyQBgYGNt0biUVjsRjbu1ZXV+PChQtMw6LvhcTvxDU/e/YsbDYbc+WNRiMGBweRy+XYVriiogLXrl3jiRs5zAgEAnR0dLAO5FaLPO9DoRB/blqtFufPn8fGxgZz6NPpNDKZDBfmAHD8+HE+WyorK+HxeDA4OIjp6Wk0NzdjaGiI84LOnDmDhoYGNDQ0sLB1aWmpbGNL2j+ypVxdXWUXQnI89Hg8OHPmDC5cuAC1Ws3I+w9/+EO89dZbzAgIhUJYXV29Se9HvP99+/axOcDi4iJnR5TTQUSjUZw9e5aF7kRlPHnyJKLRKOrr69HW1galUolAIIBwOIx9+/YhFArh3XffRalUwsMPP4yDBw+ivb0dP/vZzzAzMwOr1YrOzk4Eg0G8+eabOHToEIDrVFmj0cgGCeUomPQuxONxnhqTOHV1dRXNzc1Qq9UMPDY3N2P79u1QKBR47bXXMDExwc39+++/z3cpaUTcbjempqZw5coV+P1+1NTUQK/XM22unJaqvr4eU1NTkEgkPEFpaGjA7OwsLl26hLNnzzIi/Jvf/AYdHR349Kc/DafTiW9/+9s4c+YMGhsbsXv3brS1teHll1+GWq1GY2Mj4vE4XnrpJfzt3/4tOjo68Bd/8RfsGEXfL+nAbrWkUimuXLmCqqoqzlugdycYDKK+vh4DAwNQKpW4cOECzpw5g4MHD+LOO+/EtWvXMDc3B5PJhN27d8NgMGBxcRFmsxmPPfYY7r33XshkMhw9ehQ6nY6pfjabDcFgkPWKmy2JRMLNajweh9frRSqVwuzsLGsMiJq/vLzMxgKzs7NYWlqCRCLhKc+2bdvwiU98Avv27UNfXx96enrQ2tqKeDyOK1euYGRkhD83crMrN4mXSqWw2WxwOBxstGMymXDhwgWmBRYKBUxPT+PFF19k22UCaRKJBGQyGcbGxrB9+3a4XC7WWxHIkEqlsLi4iLGxMbb/Jovajo6OTfdGuivK4aIpCZmrkDvk9PQ0/uVf/gWZTAYqlQqLi4tYWlpCIpFgp7uuri4GzEj/Nzc3h1deeQWhUAiPPPIIAyP084hK+bvWh240iGdIBxgJLffv38/OPgqFAj09Pdi1axej4E6nk3nvyWQS27dvR2VlJbZt28aoCh20n/70p/HWW28hmUyy9eCNfvqbrXA4zJ2Yx+Ph0bJWq0WpVMK+ffuwc+dO9PX1wWq1IhgMwm63QyqVwmg0oqOjAyMjI8wvPHjwIJqbm1lsTS4jJOhWKpWoq6tj3/dyBTN5wtM/k0MGORh0dXWhsbGRC3TyKQ8EAti6dStzpjc2NniiolQqYbFYYDQambvu8/k4HIhC9G4MBLvVIq65QCCAXC6HWq3G4uIidu3axVoZk8mEjo4OdHZ2Mq9+bm4OPT09XJTV19cjEAjgC1/4AorFIpxOJ5aXlxEIBDA2NoZYLIa+vj7I5XLU1tbyBVVOd0MiUHIgImHf6Ogo4vE4ampq+P+rrq7mznppaYkdw0gQbrPZ+BBSKBSwWCyQy+WwWCwwmUzo7Oxk/YxGo2HL480WWe/SIS2Xy9m/m+ghxWIRjY2N6OrqQigUgslkQmNjI49X/X4/gOtoya5duwCA9wMAO3bsQDQahUqlglarhclkYr96+m9vtbLZLOsxSNC8uLiI2tpaJBIJBAIBuFwulEolPnwp7K2lpYW9yEkM29DQAJ/PB5/Px8htU1MTlpeXmRtfVVXFyBHxUDdbSqUSVqsVgUAA8/Pz8Hq9EIlEeOKJJ5DJZFjYJ5fL0dHRgdXVVTQ0NMBms6GjowMqlQp+v5+RbpFIBLfbzUI4cukIh8Mwm803OZiUSqWyaCSJcAUCARKJBEqlEtra2lj8S2GBdXV1zIvNZDIsiBSJRHA6nXxJRn4bnEbPVFVVFTo7O+FyuVg43tjYyILZcqYSwPXsl42NDSSTSUbxt27dyhz0G92B5ubmUF1dDZfLBZPJxML6np4eJBIJ7Nixg129qMirra2FWCyG1WplShNRQSic61arUCigpaWFue/pdBo2mw09PT0IhUIIh8Pwer3Y2NhAY2MjRCIRuru7USwWMTg4CI1Gw8VlPB7Hww8/zGF48Xic7WhJr1AqlRiwoXO+3KKCmhxXiN5AVqGa32Y3kJ6Mzs7e3l4oFAq4XC4OlwTARbpcLofJZEJXVxdsNhva2tpgNpvR3t4Oq9XKwtNyiyb7crkcfX19fC4Gg0HmxFdXV8NqtcLlckGhUMBqtaK/vx81NTUMUFAjFwwGmfI1PDyM4eFhzM7Oor6+ns8REnmWE8CSEx3ZnBKtbe/evejt7WXhrdVqxdjYGKqqqng6VVtby85edXV1CIfDeOCBB9gFh4qYJ554AkKhkCl8FEBI0+XNFlk4J5NJPo8kEgk6OjqYUklTaWqE+vv7ecpWKpW4+EulUqyhVCqV0Ol0MBqN+P3f/33OgCE6anV1NWQyGdrb28t+bmQn7ff7GVGmiURHRwcaGxv5PJ+ZmYFQKMT6+jrf7cFgEDKZDFNTU/jjP/5jNgdRKpXo7OzEgQMHkMlk+DMWCoVMISwHekZ+GwpIaL5Wq+WJWX19Perr62E0GlmbRAJi0ijE43EsLy/zXdzW1sZxBWTW0dPTw2c43ZM30lA3WxSRQFoqsrvt6upil6nm5mZYrVbWyoyOjiKbzWJoaAharRaBQAA1NTVwOp3o7OxkkMntdmNhYYGdFck6l2ybtVotOjs7N90bgZYEjBOwWiqVWPNJd8UDDzzArB8AGB0dZXczcgnt6+tj5gRRnvr6+tgmWK1WY2hoCP39/fw+b7bIkp2s4qkm0uv1mJqagsViQUNDAzo6OrBz507WwlVXV2Pfvn1oampCJpNh05CBgQGegOj1esjlctx///2shyKtSi6XY9vjD7M+NHWK/HSJc0oXXXd3N8LhMEKhEBQKBRoaGniUBYBdPnK5HAueqSsymUzQarUsGBobG8NLL73EtmuUkqnX61n4e6tVLBZRX1/PnTN1bRqNhoXR9EIRl/rGIDdKdA6FQixipC+PnAbIivPGl5o44eU+bOr+SDCUz+dhMpnYfjaZTPJLLRQKMTw8zPaG5EBFVDUSy7W0tMBqtXJ2wcDAAM6dO8fFFLkhkVXdZouCoigYkKYuxNUmQaBcLmc+JAC2M6bQLa1WywFwNNIn9JTQJbpEyBr1d9ndkYc7XWg0haDLklw9Kioq2HubXgJymaEkZaKe3RjwRJZuVEzQCJN8ucs9b9Q8kuUpjZPb29tRX1/P/21dXR30ej1WV1chk8mQy+XQ19cHrVaLxcVFTE1NsTmCSqVCY2MjZDIZa5wuXLgAp9PJBSBlG/yuRQgLWfZSOA+592SzWVRXV6Onp4ftV4VCIerr6/miymazMBqNrAsxGo1Qq9XszLa6uoqqqioOKQLArmDlllwu5/eK3vvKykp0dnayC4dQKOTQppmZGd43nQlE6Wlra0MymYTFYmFalEajYTe0xcVFDr6MRqOM/G626H0RCoVcYBgMBjYaoPOBgA+6zMRiMQMhEomEn3uFQgGbzcaTKprUtrS0oLKykjOHbsw42WzdiKLSxUNnBIXykU6LtGVut5udj8iZrampCQsLC9Dr9RwCSA5ahcL10FVC2aurq1EsFrlB3WxRmi8ZVZA2wWKxsJMaWaKScw/pGSiQcnl5mU0aqAggO3BC7emCJ6tLosOVK67o7CAxekVFBfL5PIe1klOa2WzmSbDJZMLa2hqGhobYzUwoFKK7uxsikYiLMYlEwsJUp9PJQm1yO1SpVGUpGfS5ZbNZqNVqtsMlJxr6ueRMmM1mWahLuTYSiQQulws7duxgGindA4Ta3tiYms1mGI1GRkvLfW5WqxWhUIjdJkkL2dDQwC5mVqsVFosFly5dAgAWtefzeRa7VlVVYc+ePfyMUMO4c+dORKNRnuSTMyEBN5stsVjM4BO5RFF+iMViwerqKgvyyZSGrNhramqgVqtZ2Ev6GHK7I4pVVVUVhoeHbwqtpMa73J1KEyyi5d14j5PRCp2nmUwGgUAA165dY5c/cqoiV7m+vj6sr6+zQ59YLGYRNDlNKZVKfo7oTL3VIotouhvoHYxGo0yFIbYJTSycTidCodBNFtpkHW4wGDgslc7n7u5ubGxscL4N3fM3WrnfapEOghwO6QywWq1cO2luCA+meojAOTKNuHGCTRk99HtT0+31evm7J3CqXIYRAcMUZ0BZLwBgtVo56JdqPQqyJJoiOR3SuUx3GwHBZF5DjqI30rzKGQ/Qd0oGHGR9TAAb/f2U59HW1obJyUmk02nU19ez6x1NBSngc2VlBaVSieuN4eFhJJNJ1gTRXZdOp8vepzeuDz3RIGEeoQnAfyZy79q1iw/wZDKJeDyOvr4+BINBtLe3Y21tDU6nEw6HAy+99BLa2tpw6dIlVuNXVFSgo6MDpVIJf/AHf4CamhpGroDrhVM50QkFMrW1taGjowO1tbXYsWMHiwzD4TBsNhv8fj8UCgUj9BTot7y8jEOHDuFnP/sZTCYTI+b0800mE/L5PE9zaLxLYYDlLmEqHsgOsFQqcZZIVVUVp21TDsbjjz/OXvnXrl3jvRCqFAqFmA5EgX00ps1ms2hubkaxWEQkEkFra2vZbpiE2/RQh8Nh9Pf3M7fX5XKxs9KlS5c47Kinp4dDcSwWC/L5PKxWK/7+7/8e27dvR2NjI1QqFWpqarC+vs7TEAp+uXjxIiPRmy0KlaIXkYqNy5cvo7a2FmtrazAYDGhtbWW0JBKJsPCJDkFCsYl3ms/nOSxwbW2N9Rw0GqcU3nKHokKhgFQqZZpQJBLh6YxGo7kpibNUKqGvrw+xWAyLi4vMRa6vr8fZs2cRjUbxwgsvQKlUQiwWc7Om1WoxMjKCmZkZpsgRylnOZUcmk8HhcECpVCKdTmN+fh5msxkbGxusB6CfQ1Q7SuQmL3bKRNm7dy/effddzkZpb2/n4lGlUvEBTjbGFDRWbhWLRVy8eJHtLjW/TSWfmJhAX18fGhsbuRhQKpUYGhri74eCPLu6uuByuWA2m9Hb28tUsmw2i/r6ejYD+M53vgONRsOcd7VaXVZYR/QYymAhK2wA6OzsxOzsLCYnJ+FwOJDL5XgyFwqFOJiutbUVZrMZjY2NPN25MdjR4XDgIx/5CIDrtLalpSVOZC0nSKyqqoLD4WAknWwjDx8+jJ6eHp7opNNpLCwsoK6uDkeOHMH+/fvZ6nbnzp0AwLznkZERKBQKeDwezM7O4tVXX0VNTQ0OHToEnU7HjjvkaFNub06nkzNHfD4fa5hIQNrd3Q2lUonV1VXkcjkEAgEIhUIO2qyrq4PNZkN1dTVefvllAOD3m9y8KEiVplp2u53Rws0W2ZPSxRmLxbhhbGhowMTEBCorK9HU1ASlUoktW7ZwFkN1dTU6OjrQ3t6OcDiM9vZ2phAR/76xsRFKpRK9vb345S9/yQ50s7OzbFe72SKElBpncgyz2WwQi8VYXFzkrAWBQMCUFqKhULPzwQcfoKWlBTU1NRw0Z7PZ4PP5cPLkSQwODuLChQsIhUJ8bnV0dJQtYmjiRvoih8OBUqmE8fFxPkMJMKLp0+LiIurr67kArqurw8TEBJqampBOpzEyMsL2m4RKDw4O4tixYzdlJWg0mrK5LWKxGOvr6wy6UPPpdruZ5kiAplgsxo4dO3DixAmMjIzwRIsmxzKZDHa7Hc3NzWzhHQqF8Ktf/Qr79+/n4DXi0dP5tdmigGESPhMQQZodAo9KpRInrM/NzWFkZARnzpxhhB64PuF+++23YTQa0draymfA1NQUU/SGh4fR3NwMvV7Pk+Jyn5tGo+FgR9IaUXDgkSNH4PV6YTQa2aaYKIu0H5rad3Z2MlV0bW0Ni4uLPPHZsWMHqqqqWAMDAF1dXWX5/AqFAuvr65xvQiwRAoRXVlaQzWaZ5tPU1IRDhw4xZVoikXCTqVQqsbCwgPb2djQ0NDDl1mKxsAmAyWTiiaHRaOSsl1stmnjpdDousmnyPzQ0xEU7Ue62bNkCiUSC5uZmTE5OQigUcuizz+djtgrpRTOZDI4ePcqNrUQi4eyypqamslNRsha3Wq0cJD08PAy3242RkREsLy9jbW2N83p6enpw6tQpiMVinDt3js0lxsfH+ef09fUxgGw0GrG+vs5TmI2NDej1ejQ1NaGmpqYsWHHj+tATDUowBsAIB9mwVlZWoq2tDc8//zyi0SgefvhhLC4uwmQy4c0330RXVxcAcAhOIBDAlStXcOXKFXg8HlgsFrZPbW9vR3t7O7xeLyoqKhAMBnHp0qWynTolzRJPmpAijUaDlZUVDA4OYnx8HNPT07Db7fx3v/nmmxgeHmYudk9PD8RiMebn5zE7O8tpq0qlEj6fDxaLBaVSCVeuXGGnhkKhwKFOt1p2ux2tra3w+/2cgzE7O4vOzk6cPHkSW7duZdtGGu1Rdzw8PMyXUHNzM44dO4a1tTXs3bsXc3Nz8Hg8bLM7OTnJdq0UuLa6ulqWkz4xMYFHHnkEExMTWFhY4DGe1WrlBN2JiQmmXExPT6NYLGJiYgI7d+6Ey+XCb37zG9x7772ora1FU1MTXnzxRdhsNigUCn5xXC4XNBoNLl68CKlUiq1bt/LhsdlaW1vDjh07+ICnxHexWIxf/vKX2L9/P1ZXV7nAIwvWU6dOIRqNore3l4t+SmKdmprixGGavv3oRz8CAL64ye63HEdYIBBAoVBgdnYW6XQaEokES0tLWFpagkAgQDabxblz5yCRSHDvvfeivb0dc3NzWFtbw9raGvR6PbRaLXbu3Invf//7+MY3voFXXnkFZ86c4fBAoVCI733vezhw4AAcDgfcbjcXZeUKq1gsxiPlRCLBSJzP58P4+DgnGKvValitVna9oeLq2rVr3LDlcjk89NBDOHXqFMbHx2+iAx08eBAymYxdnzQaDVZXV8sK/oDrI3KLxcKThkQiwfzuubk5rK6uYmVlBVKpFF1dXfD5fLj77ruRz+fxyiuvIBKJoL29HdlsFv/2b/+GJ598EpFIhKcp6XQa+/btw7Fjx2A0GvHuu+8yrbCurq7s1CAQCKCjo4MnKGQXGo/HoVarsWXLFiwuLiIYDDJync1m8d577zEKLRaLIZFI8Mtf/hIdHR3ciFVXV6OlpQVSqZTf9cXFRUZdc7lc2bAtr9fLIVBEByNKz9TUFE8dA4EA+/CbzWYcP36cw7RoOvH9738fR44cYQe4yspK9Pb2oqKiAjMzMwiFQjhy5AhGRkag0Whw/vz5svSkcDjMFBlCvEl0+c4777B7WaFQwB133IFAIACLxcLUEqIe9Pb2olgs4sEHH0QsFuOJJmk//H4/Wltb+c6h4qRcwbyysgKj0QidTsefA1FsFQoFGhsbMTc3xwLkQqGAnTt3oqamhkGI+fl5du/q7OzEq6++irNnz/IkZefOnXj55ZfR0dHBLoE9PT3w+XxlbTPJGp3ut+rqas7wOHXqFO644w5ks1k4HA6sra1xPopcLsfHP/5xvh9FIhF+8pOfYHR0FK+99hoDJQQ4UAhiVVUVW7ePj49jdHR0073RtIgmHxSGeccdd8BoNMLj8WBmZgZLS0vo6elh4G5paYk1W+SM9ld/9Vd46qmnOENjY2MDq6urKJVK+NnPfsZUTAIx5ubmytK6PB7PTUYpdrsd+Xwera2tyOfzGBsbw+TkJBYWFmA2m/Hmm2/C7/fjK1/5Cvr6+hiMaW1tRbFYxM9//nPcc889fMeoVCo8+uij+P73v4+Ojg5uHIrFIrxeb1kdBNG4acoil8v5PidQZXFxEaFQCB0dHcyg+NWvfsXmHFevXkVVVRWmp6cRCoVYU0HIfrFYxOLiItOKCHAkcG2zFY1GmZ5I00Wfz8dAWG1tLVub19TUoKmpCePj42hoaIBAIMDExAR8Ph8+8pGP4B//8R9RV1eH3/zmNxgbG2PL2O7ubvzqV7/C7t27sbq6ikgkAoFAgGAwWPZ883g8aGtr41BbArXJhIQo71NTU1CpVJifn2eqI52Hcrkce/fuxT/+4z+ip6eHP0cCGsjUw2g04sqVK6yvraioYBDmVmt9fZ3BRQqWTiaT0Gq1uHbtGgfsqVQq7NixA4uLi9i5cydPtCYnJ+FyuaBUKrG2toa77rqLLXCz2SxrG5eXlxGJRFBRUcFgBk0aNlterxcSiQSXL19mQOLs2bOsK+rp6UGpVEJFRQWDBVKpFJOTk3j00Ufh9Xpx4cIFZLNZ5HI5vPrqqyyyt9vteOONN7jRJZfS5uZmbGxsYGpq6ndSpWl96ImGwWDAzMwM3G431Go19Ho9jh07hsOHD+MnP/kJ9Ho9HnjgAfT29sLtdqO2thYdHR08iiHr1i996Uvw+Xxobm7GZz7zGXzzm9/Ef/kv/wUmkwlXr15FXV0damtr8ZGPfASDg4Oorq5GW1sbDh48uOnezGYzjh07hqtXr8JgMGDXrl1Ip9P48Y9/jOPHj+NXv/oVamtr8cgjj6CzsxMmkwn33nsvh6t0dnait7eXR+tf+tKXoFAosG/fPoyMjDAXm14CCoEhCsonP/nJTfdG3D6/34+lpSXYbDZIpVL86le/Ql1dHaPqOp0OV65cwfDwML7whS/A5XLh8uXLmJ6e5kDELVu2MCWEvL3z+TwuX76M3t5etLe3Y2NjA8vLy1hZWeEuf7Ol0+nw4x//mEWG7e3tMBgMjFpRFgE5wnzve99Db28vHA4HNjY20NPTg6GhIXi9Xjz77LNwOp24//77sXv3btZQkIuNVqtFQ0MD6y5oIrLZkslkLCokZIQautbWVkbcSDRKvOnh4WHs3bsXBoMBkd8GGU5MTODYsWM4duwYUwC9Xi9sNhsAYHBwkFGYQqGArVu3crDkrZZGo8Frr72GWCyGrq4ujIyMYGNjAzqdDul0GjU1NXjsscfQ1taGl156CUtLS2hpaeECgIJ+/uiP/gh/8zd/w2L+u+66i53Pzp07h0cffRRPPvkkC+ebmprQ3t5eVgyeSqVw4sQJLjLq6upQLBbx+uuvIxAIYNeuXfjIRz6C5uZmhMNh7N+/H/v37+cDr6OjA11dXVhfX8fy8jIuXryIO++8kwPh7rzzTlitVhiNRi60Ll++jFOnTnEBV27p9XpMT09jdXUVcrkc7e3tSCaTeOKJJ3Du3DlotVpusOmQ++pXv4orV65AoVCw0DaZTOLjH/84jh8/jsuXL6O7uxttbW04ceIE/uM//gPV1dV44okncOedd7KWqb6+vuxFTN/P+fPnsbKywkXCuXPn+ICWSCQcSpXNZnHw4EFYLBYcPXoUhw4dwuzsLIeJkSPS8PAwrFYrlpaW8Nprr2Fubg6ZTIZBAIFAwFqhzZZKpUIgEIBSqWRKDgWpTU9PI5fLobGxEWq1GhcvXsSlS5eYg0sOJL/4xS9gt9vx4IMP4tvf/ja+973vsZZncnISarUaX/va17jQpoyj5uZmPPDAA5vuraKigsftdXV1aGlpgVgsRiwWw7Vr1zA5OclI8vHjx7G4uIi1tTUsLCzwGdrZ2ckhqjU1NYjFYjh06BDeeOMNphaSzo5oMpR03draWvY7tdvtOHv2LBYXF1EoFNgg4YUXXkBTUxMOHDiAjY0NfOc734FcLkd9fT3efvtt/P3f/z3+5V/+BWtra2hra2Pr5I997GP4xCc+gaamJkxMTODv/u7v0NjYiN7eXpjNZqyurmJ8fBy5XK6sGYder8eFCxcQi8VYyDk+Po7//b//N6anpzmnhmzG4/E49u/fj3PnzuG1117D0aNHEYlEsHfvXqjVatxxxx0Qi8UYHBxka9tcLofjx4/jgQce4LP96NGjPMnfbDU2NmJ9fR01NTXo6uqCWq2Gw+HAN7/5Tbz11lvo6OjA9u3bIRKJ8N5772F5eRkvv/wyQqEQmpubkU6nMT09DY1Gg927d+P06dNwu90wmUysl3v++efxe7/3e/joRz+Knp4eaLVa5suXm8Sn02nO3SA9QalUwnvvvQe/34+6ujrs27ePbZABcPghGTkoFArs2bMHAwMD2L17N1KpFAYGBjA2NoZisYh//ud/5jO0trYWBoOB9TjlapHa2lqEw2EOGiX9IDkUdnV1oaOjA263G//4j/8It9uNxx57DA899BDuv/9+tLa2wuVyYXJyEp/85Cdx9epVbpoIbP3BD36A0dFR3HHHHdi5cydTALPZLC5fvrzp3pqbmxksVKlUAK5PVd977z1MTEyguroaY2NjEAgE+Kd/+ifEYjF0dHRAr9dzmOni4iIuXryIO+64AxcuXIDRaGQ6cy6Xw2OPPYYXX3yRrYITiQTbx5ej/apUKng8HqaTkjbs1KlTuHTpEmc/bWxs4Pz586iurkZzczM8Hg9sNhskEgkDrZ/73Ofwy1/+EmfPnkVtbS22bt0KkUiEX/7yl7jrrrvw4IMPoqWlBaFQCAsLCwgEAlhbW9t0b6RrkslkqKmpQU9PD6qrq/HSSy+xmUc8HsfKygr+6q/+CgsLCzyZa2xsZH1XS0sL/uAP/gC/+MUv2IJ937590Ol0mJycxM6dO7F792709/fzNL2pqYk1nLda5LJJIAQFiL7wwgt4+eWX0draitHRUTQ2NjJVfOvWrQgEAnj++edx8uRJtLe347/9t/+G3t5e/PznP8fp06chlUqxb98+PPLII9BoNNixYwdn8XzwwQd46623kM/ny96nN64P3WhMT09zgUOFY29vLz7+8Y+jubmZ4+h37NiB3t5eHDp0iB2Qnn76adxzzz18WMZiMWg0Gpw5cwYnTpyA1+tFT08PPvrRj2JmZgaHDx/Gj370I8zMzLDIqFxRarfb8eSTT2LLli1Mf5HL5XjyySfR2tqKp556CgaDgRHwn/zkJ5idncWdd96J5uZmzgh47LHHcPXqVTz//PPMDSaesNVqxcmTJ6HVahGLxZiy9Prrr5c9sGncRCF7JpMJ4XAYn/jEJ1AsFnHgwAEWCW3fvh2vvvoqvF4vh7jQCFOj0SCVSuGRRx5BdXU1j9bEYjG2bt3KYYhUMIyOjv5Oznwmk8GuXbvYgUBzg0f14cOHMTU1BYFAgM7OTuzfvx9PP/002/xevHgRdrsdBoMBIyMjaGpqwmc+mBHydwABAABJREFU8xksLS1h9+7dGB0dZZevkydP4ty5c7hw4QKH3RENaLNFdDriXlLy/MDAAC5duoR8Ps/oU29vL3PMyUWL9DAWiwWPPPIIHnvsMeYTq9Vq9pfPZDJoampiDjcVmuPj45vuLRKJoLOzk1OQqfChNN4dO3ags7MTY2Nj+NSnPoWzZ89y9seuXbtgMBhw+fJlvPHGGwgEAhgdHYXVakU4HGbnNXLpmpiYwPj4OAeNzc/Ply3midpGArbt27cjEAjgy1/+MqetXrp0CfPz89jY2MBPf/pTfo6I800ivMOHD0MsFuP06dOcAE96KOJVU3r5zp07Ybfby6JWwPXp46c+9SkIhULMz8+zBd8LL7zArkp+vx8ikQhbt26FWq3GF77wBVRWVuKjH/0o7rrrLgwNDeHBBx/EyZMn+dKmw/6ZZ57BQw89xLqYubk5npj++te/xsTExKZ7CwaDbMlK1n46nQ5jY2Nwu93sNEc6qHQ6DalUCp1Ohx/96Ed48sknYTQa2Uwik8kgEolgaWkJq6urqKiowMMPP8wccDJj0Pw2HKucqQTR1IgzLhKJoFAo8OCDD95UlG3btg1f/vKXuXnxer3w+/0MwOzbtw9KpRKPP/44PB4PHA4HJzffd9997AZYX18Pq9UKhUKBubk5HDt2bNO95fN59Pf3o6Kigqkni4uLTFl48MEHOUxxdHQUlZWVsNlsnJtw5coVnDp1is95mUzGgtx77rkHoVAIEokE58+fZ6SdEnOJ877Z8ng82L17N4vQKU+pra0NDz30EOrr67G2tgaTyYQf/vCHSKVSXGA/9NBD2LVrF7q7u9Hf3w+lUol7770XR48exfz8PHQ6HR588EF861vfQqlUwvr6OtxuN1M0yJFts5XL5dgBhwLKLBYL/uZv/gbNzc0wGo1sWrF3715YrVamVZKrks/nYw3OuXPn+Cxubm5GXV0dHnjgAdjtdp62kSmF2WwuO7V1u93MCMjn8xgaGoJer8c3v/lNLni1Wi327NmDT3/601hZWcEXv/hFtmk/cOAArFYri0//7u/+Dmtra0gmk4jFYnA6nfj0pz+NmZkZ2Gw2HDp0iGkcNpsN3d3dm+6NdE3kYmQ0GlFVVYWHH34YLpcL09PTmJmZAQCMjIygWCzi3nvvxcDAAOdUBINB/OIXv0AgEEB9fT1rZEh4/+yzz/LUnswbyF2tHHXK5/OxRpX465cvX8bWrVuRyWSYPdDS0oLnnnuOJ5D/+q//isOHD3PjuX37diwsLOD5559nDcHCwgIWFhbwgx/8gEPYXnvtNaTTabZLv+uuuzbdG4WPRqNRpmRvbGxgy5YtTPHy+Xzo6enBj3/8YzgcDjz55JO4dOkSRkZGMDo6ym5rJ0+exDPPPIPIbwNRSTP5ne98h8ObfT4ftmzZgvvuu4/v4s1WIpFAc3MzW2APDg5yFszy8jK8Xi+OHTsGl8uF3bt349///d+xa9cuDo0eGhpiOtihQ4fw6KOPYmBggDU6+/btw3//7/+dtS8Oh4MFz5cvXy6rNfD7/TxFIYezqakp/PCHP+SzqLKyEjt37sRzzz3HdrwvvPACu5n5/X6YTCa8//77ePTRR7nOmJmZQbFYxB//8R/j5ZdfxtmzZzlIk6x3yzEYqFGiMESVSoVYLIavfvWreOCBB7hpsdvt0Ol0+NWvfoWOjg50d3dj27ZtUKvVsNlsWFtbw/z8PL7zne9wLMXVq1eRTCaxY8cOvPHGG2hsbOQQxYaGBhSLxbJ7u3EJSuXsHW5Yzz//PJRKJaanp2EymdDf349AIIATJ07gD//wD/Hqq69yumN7ezs8Hg8L5S5evIjGxkbm/ZtMJnzrW9/CH//xH2N6ehqRSITFUb29vWyruLy8DIVCwVzxP//zP7/l3r71rW+hWCxyumVFRQU0Gg2OHDmChoYGVFdXsy8xuVpQ/oVarYZKpeJL8urVqwgGg/jUpz7FB0xdXR2HvtTU1MBgMPAlmUgkoFar8eyzz95yb5/73Od4dJVKpdid4JVXXsH+/fvh9/thsVggFAr5wuvp6QEAtrclf+9IJIKqqirs3buXsyVIZExBY6VSCXNzc4xGKJXKTff29a9/HRqNBj6fD/X19ejp6UEmk8GJEyc4L4NEUjR1uXr1Kjo6OhgdogecHHuIO18qlTAxMYEf/vCHeOGFF3D8+HFs3boVWq0WlZWVEAqFOH/+PP7kT/7klnv7zne+A7FYzEgo6YJ+/OMf49FHH0U+n0coFILdbkc0GsVTTz2FqakpaDQa/k7z+TympqbQ2tqK6elpjIyMQCQSMYc5l8uhv78f58+fh9lsZnRX89u8mP/6X//rLff23e9+l+lX5P5RV1eHV155BcPDw1haWoJWq0V/fz+sViuSySRWVlZgNpvxi1/8AtlsFt3d3RgZGUEqlcLRo0fxxBNPYGJiAk6nEwaDAV1dXYyoJ5NJHD9+HH6/Hzt37sTAwMCmo97vfOc7nNbd0NAAs9kMh8OBcDiMe++9FwsLC9zIymQy1NbW4ujRo3jqqafY7pDyXh5//HGcPHkSAwMDXBgWCgVcuHABn//851nzEA6HWc9ks9nwla985ZZ7A4B/+7d/Y55rOBzmIndpaYnpcNQArq+v44477mBqBOXRZLNZeDwe9PX1YXp6mlFEauq7u7uZfplOp3H27FkEAgEMDg7C4/Hgz/7sz265t5deeglCoRAej4c5+uS+VV9fzxbUsVgMBoMB27ZtQz6fh8fj4e9NpVLB6/XC4/Hg/PnzGBkZgV6vZ9cZg8GAK1eusBvO8vIyKioqOARu+/btt9zbn//5n0MoFKKtrQ2pVIrDOt1uN1paWniEL5PJ4PF4oNFokMlkYDQa0dDQwOcphdGRjThw/ZyJRqOwWCwYGhrC4uIiP3s3orNf/OIXb7m3z372s2ywQeJUei+am5vxyiuvoKGhgS3LGxsbcf78eb64LBYLamtrsby8jGAwyKnalZWV7FC0vr6OwcFBnoSR5q1QKEAqleJv//Zvb7m3b3zjGwD+06lIrVaz28+2bdtw4cIFJBIJVFVV3eQoRtTHYrEIqVSK9vZ2BINBeDweNDU1wWazoVQqsSU48bE1Gg3W1tZQKpWwe/duCASCTRHw119/HfF4HMVikTU81dXVmJubw+OPP36TQQl9T/feey8OHz7MlCxqfLdt24bTp0/zfjQaDaPRV69ehUKhQKlU4sKlt7cXL730En8+//f6+te/zvey2Wxm62ShUIiOjg6mgZJmSqvVsoMS3XNEF5qfn8fv//7v49q1a3xvzM/Po1Qq4c477+SUa2oclEolTCYTHnnkkVvu7S/+4i84Ad1sNjOl+dq1a7jzzjvx9ttvQyqVorm5mfVsYrGYKTnkgNbd3c36otbWVpw7d461V7lcDp2dnfj1r3/NIBJNU1Uq1aZ36l//9V9zkja5EjU3N3NOxNraGhvhqFQqTExMwGAwMD2aTFCWl5dRKBRw8OBBfkYCgQDW19eZly8SiRiwIFHzzMwMvvrVr95yb//6r//KttJCoZCFyiaTCVVVVZicnITNZoNKpWImh1AohFAoxOLiIorFIk8wyLq2sbGRw2gpEy0Wi2HPnj2Ym5vjvYfDYVy6dAlf+9rXbrm3559/nl1IpVLpTaYIpJclvWgkEsHTTz+N8+fPQy6XIxAI8ETs7NmzUKlUOHXqFB588EG8/fbbyGQy6OvrQyAQwBNPPAGbzQahUMjnIbluPfbYY7fc2w9+8AOmIlGOEgB+J5aXl2G1WlFfX49cLsep8LlcDqOjoxAIBEwJfvrpp3H8+HGkUim0trZCq9UilUrhvffeg0gkQiqVwv333w+73c5GKLt27dqUYfH1r3+dnwcyJioUCigUChgaGmKzoxt1XiaTCUePHuUsFQK7/v7v/x733nsvHnzwQUQiESwuLjLFsaKigp05c7kc0uk0SqUSBgYG8Oijj95ybzeuDz3RCIVC7AxAjiarq6toa2uDTCZjVx9yHBoYGGCLW3L7oQA5ilePRCIs+ozH4/wlkfVaNpvF6uoq1tbWyooR8/k8W3kajUZUV1ff5KwQDocRi8WwtraGixcvclgW8fvIVvb9999nFxQqoqlosNvtbFlIIU80rivHXya+nVqthua3gVr031FxFo1G2fFBrVZjcnISfr+fvd0pt4M+p1KpxA8Xib9IyCOVSjkluVAolBVc38htJkSNJgPt7e0siA0EAoyir62t4dSpU3C73ey4RMVDOBzmoD4ShO7cuROhUIiRy8XFRdjtdly7do0791utjY0NqNVq5pWnUimkUimebADXaUL08rjdbiwuLnJxFAgEkEql+KJJJBIQiUTsa02/m1wuZ7tjstSjcJrNVjgcZlcrqVSKUqkEv9/PTjaUDZPJZNgFiGz7ZDIZPwM2mw3ZbBbXrl3jz5OsoslVh5y9yDktFAqV/U79fj+USiXkcjlPXEiUTrz99vZ26PV63g/5bpPzWGVlJdrb26HT6VinEPlt8iv972QyySg0hWaJRKKyGggALKykqUAmk4FAIEA+n2fLWqfTyda35ALT3NzMQkBKjSe+MrmbkLWyz+fjZoEcgDY2NthYYLNFOSFyuZyF1WQOEAwGWSdD76vH48Hi4iK72tB+rly5whajZrOZeb10volEIjZsoAP7dzmfkN032RdS8rxMJuOJDWUBkQsaudX5fD5+5pVKJdMfKMelWCyyUFGv17NDFnGIydZ6s0UTlVQqhWg0yqFjAPi7oVwdQvjo/FMqlVCr1fyZG41GuN1ueL1etuMkVxRyXaJ9kftPOTSSCipyHIzFYojH48zHz2QySCaTfG/R303aN7lcjlwuh5WVFQSDQTYVIGDC4/EgEolwcjBx5oVCIex2e1lzBOJ5azQadvUhxzNy3llfX2eEVKPRwG63Q/Nby3WxWIyNjQ0cPXqUjQTofEmlUpxLQA49ZANNzXq56SM5NxJlhp59oq/SdJTynpLJJGuhSCMkEAh4Wn7p0iUGCHw+H4RCIRKJBAqFAuLxOGpra9mNkTILNlvkkkhuPuRgR1NpEp8Tf500UHSe0PMaDAaxvLzMGUepVIrzSBYXFxGLxVjITaF29O5vtsRiMWpqatgtkihDbrcbxWKRDWgymQyHkiaTSSQSCTgcDmZdAMD8/DxPGkirWSgUEIvFUFdXd9N7Tr/b77JTLpVKnB2TSCSg0WiwuLjIE1JybZJKpTxdvjEjB7iue7LZbJwFRmAYnbMVFRXweDx811IuEdG1brVuDIUjl8BAIMDnBbn0UUMaiURw9uxZOJ1OBomJYk5gIWX6iEQivo+DwSAWFha4LqVnptzZS00tAG684vE47HY7xyOk02mcOXMGp06dQm1tLeLxONbW1tja2+12QygU8vNHzaXP58P6+jpPt8k6mZwcKSJis0V5HEqlEpWVlXx+SaVSzjEjXabX64VYLMb7778Pn8/HLmZutxuXLl2CVqvF1NQUv0/BYJCjAnQ6HYrFImpra2/6HstNbG9cH1oM7nA4mHZCdrDr6+sc3tXb2wuxWMzC5u7ubshkMhw/fpwnFOR4QUK66elptLS0QKfTwel0IpPJYHFxkT3rI5EI3G43Tp48WdYFiMLTstks9Ho9i6Orq6shl8sZVXE4HIjFYkwT8vl8EIlEyGQycLvdTHHyer14++23ofmtLzy5f9xzzz1cdCeTSUagyvHUyOWH0ACyP2tubkYymURTUxNcLhfnVtAL5vV62VefLP8ikQhyuRyWl5dZdOvxeCCTySASiZDP5+H3+yEUCllsGykTdBgOh9HU1MQXz9zc3E2WveSutba2hurqatTW1qKlpQW/+MUveERPXuTz8/Ms4CRaiUwmw+joKHMNFxcXGU3xeDxlnUUAsJUefQbBYJCzJMgylLJJlpeXsb6+zkifSCRifvH09DQ3jBqNht07qNjo7u7GysoKF8sVFRVs5XurRU0ZXdoUgEMuV3RYBINBLC0tccDV+Pg4u+dQyKTRaITT6cTExARPP6iB93q9jKQbDAb4/X54PB5YrdZN9xaNRtHR0cE5EOTzXVFRwWFW+XyeL7Wmpiae+PT397Nt5ODgIK5du8bvJU3VqHCbmZmBx+Ph6UQul+Pk4nKLsmgIiZXJZMhms+js7OTEYY/Hw7xmSjO/MW2abDCdTidza81mM3Q6HTeZ6+vr6O/v50OakK9y5wgJCukSosZly5YtuHLlCgwGAxdtkUgEZ86cYSEfXfixWAwymQyhUAgGgwEajQbRaJQDxsgiuFAowOv1cgPo9/vZEnyzReAMpbYmEgm2Cu/r68OZM2fYqjKfz3P4HAkxa2pqoFKp8MYbbyASibCTUiAQwPnz51FTU8Pe6JWVlZy5AqAszYaaPcquCQaDcDgciMfjPGanM4782ukzpWKMrLsBwOVy8USJLECpAG9vb+cGihrBctQpSiZPpVLs/kWNxerqKovxCYX3eDwQi8XIZrOMBFKhSIUCTd0oZIwoniqVCqurq+y+s7CwUNZ6N5lMQqFQ8B1RKpUQDAZZAE4TZbonisUiQqEQBAIB1tbWUFNTw7am77//PjsOUpDlO++8w9lKDQ0NLIAlS9K+vr5N90bTctoTpaNT/gTZwyYSCb5b+/r6cOTIEVRVVbGOUSAQIBQK4Qc/+AFP94DrOgufzweHwwGXy8XFkVAohM/nK/u50YSNbFMpmI/uxo6ODthsNp4sU+gmIcQEWITDYdYW0dSisrIS6+vrnExPdGICa0jYvdmiLAuiAFLSeDgcZhDIbrdzEV0oFNDQ0IC3334bIpGIbeAHBgZw5MgRnD9//v/LMjiVSnE4qcfjwfLyMnK5HDo6OsqavySTSZ4M092QTqc5Z4WCHskU4YMPPsDo6ChPCYrFIt/HNOEgG2s6L+RyOZ+5KysrXMulUqmyd0Mul+OpG+VtzM3Noa6uDsvLy+jt7UUwGITL5UKxWMSbb76JxsZGOJ1ONDY2ch4T1VdSqRTBYBCjo6NIJpOw2+0cBGi32zlPRaFQYGNjo6yhD7EnyLUNuF5g6/V6JBIJtLS04OTJk7h8+TKHAJrNZpw4ceImK97h4WGcPXuWrbOB63U1uW1ZLJabspio+ftdYDGZDNBUmcxs4vE4FAoF8vk8B0/X1dWxpS2BYPRuNzQ04MiRIzh27Bg3/el0mrPKxGIx10f07JYD7m5c/z+JwcViMXp6ejA4OMiWXv/+7//OvGHqqpaWlnD58mWIRCK88MILeOWVV3DixAl+KCgojIKxiEcMXC9+KysrMTExAbvdzjzpcsW8TqeD3W7H+fPnYbfbUSwW4XA4mLvZ2NgIqVQKhUKB5uZmnD59Gl1dXRgaGmK+cD6fx1NPPYVDhw5hcnIS165dw9raGqP8xNsOBoNoaGhAoVBgFX85ZEgkEuHw4cNsqZrNZrG+vg6Xy4VgMAir1cpBS5OTk1hdXb0pBl6n02HHjh0YGxvDK6+8gmPHjmFxcRG5XI6RbrfbzfkRbrcbIpEIKpWKeZibLfJtHxwcRHt7OyorKyGVSvH222/j9ddf5wfY7/djfX0d6XQae/bswdNPP81IKCEsb775JtvnGY1G+P1+vPPOO8hkMrBarXjsscfwh3/4h6ipqWGf6XJpw8RPjMViHDhDDeSRI0cAXEdBVlZW8Oqrr7I9clVVFaNvqVQKNpsNR48ehd1u5wO1vr6e7Rmz2Sz27duHwcFBToulbJLNFo2819fXGX2USCS4ePEi8vk87rvvPnR1dWFtbQ3vvPMO52PQRCeXyzEVbW1tDaOjo5y90NTUhGQyicuXLzNaQe4v+XweQqEQW7Zs2XRvZrOZGyFCYGtqauD3+3Ht2jWm2VHaPE0/yJmGpnn0/fl8PjidTvj9fthsNly9ehWRSATz8/PYsWMHtm7dCr1ej1gshoWFhbLIEAA+dPP5PKqrq1FfX49EIoGmpiZMTU2hqqoKQ0ND6OnpQbFYxIkTJ2A0GrG8vIw33niDdVs6nY4tZyn4SKfTobW1lSk8KysrnOZKhUK5RRazlHFDdqrUbLW1tfG5R6FfNFZuaWmBVqtFVVUVBgcHsbCwwGFLhUIBdrsdJ0+exPvvvw/Nb7M+yLWKpjZkTnCrVVlZeZNDE00IXnvtNTidTtjtduRyOQiFQm5GjEYjotEorl69inA4DIFAwChcU1MTLl++DI1Gg+HhYQZBpqeneQK9urrKNq3lzjiBQIB0Os3hqlQYv/POO+zGRgJ5t9sNgUCA/v5+yOVyzM7O4uzZs1hZWYFSqYTT6WT7RJPJhPr6erS3t0MkEsFgMGD//v1obGxksIton5utVCrF7jcSiQRqtZrfJZfLxanRvb29nPwulUpRVVXFtAGdToeZmRnW0pw5c4YDt4iCQ8hpOp3m6WsmkylrRa1Wq/H+++/zmQ1cn0hOTU2hUCigtbUVe/bsQVtbG5xOJ+bm5tDY2MhofTqd5qTmbDaL06dP44MPPkAymYRMJmMqjdlshtPpRDQaRSwW42lGuTwI4LouMxaLMU2YgENKe66vr4dSqcTVq1f5PD9//jzeeustLC0t3fR9nT59moGtrq4uLqD8fj+8Xi+OHz+Oc+fOsStdOZYAAWtCoZDRfrfbjVdffRXhcJibvlAohO9973tYXV1FOp1GOBzG1atXuYmj/dO71Nvby05r1JASYEfnB+kHNlvpdBoffPABUwD7+vpQU1ODYDDIhZpIJMLs7Cy+/e1vw+fzobq6Glqtlt0T29raYLVa0d7ejjfeeAOXLl1CLpeDxWJBTU0NJicnMT09jba2NthsNoyPj8Nms7Eb1WYrHA5jaWkJk5OTSCQSMJlMCAQCnOkAXLehNRgMuHDhAtbW1hgYIYevZDKJ0dFRtLe347333uM7mujQVNCGw2FmClBWTbnzNxwOQyQSQSAQQK/Xo729HVqtloM0icIuEolw4sQJBAIB/MEf/AHk8v8Pe/8Z3vZ53ovjHwAEwAFiEBskwAVwb0qiKFGStSXbcmwnju2ksuMkzjwZda+m7Unza52cnKZtTo/bpE3S2E3STMeOEw9ZshRrWJI1KIp7D3AABAECBLEBYv1fuPd9pP8lwj7XdV7qftMklaiH3+/zfZ57fEYR3njjDUxNTUGtVjM3ZmFhAZlMBtXV1ejs7ER1dTVGRkZYPemtt97C2NgYiouLWY5/sygoKMDw8DDGxsYwPT0Nt9sNlUqFmpoaDA4OYm1tjaWeScjCaDSitLQUTqeTBYckEgkWFxfxz//8z1AqlbBYLCgvL2d3b+KOLi4uwufzsf9FVVXVpmsjtUGS+DcajVD+lwEqybwrFAp4PB709vaisLAQx44dQ0FBAUZGRrgJ/PjjjwN4r4CcmppCWVkZOjs7OdchzpPT6WS4vE6nQ0dHx6ZruzU+cKHhdDohkUhw8eJFnDx5krs13/ve91gGVqvVoqOjA4888gjsdjt8Ph927tyJz372s9ixYwcfCq+99hoeffRRlJaWIj8/H5FIBHl5efjiF7+IqakphMNhLCws4OjRo3jmmWegUChw+PDhTde2urrKLpWkxT03N4cjR45gZmYGMpkMarUaRqMR3d3daGxs5LEuyVc6HA6Ew2GEQiH89V//NbRaLXbt2oUdO3ZApVKxJFlVVRX6+/tRVlaGgwcPYnV1FRcuXNh0bWtra4ynFwqFsFgsiEajeOqpp1BdXY0HHngA3d3dqK6uRk9PD/sbiMViWCwW+P1+vP322xgeHsbBgwfx+c9/nouS0dFR7vzOzs6ivr4epaWl/OGMjY2xD8CdIhKJoLm5GdPT05ibm+OJ1Kc//Wn84Q9/wODgIMRiMQ4dOoQ//dM/ZWfviYkJLC4uwul0IhKJwGq1wmKxoLu7GzKZDFu2bGE1o89//vN455138J//+Z+4du0a9u/fj6effhqxWCwn4U8kEqGqqoo7jx6PB3a7HX/7t3/LhadCocCOHTvwL//yL5iensazzz6LSCSCQ4cOYf/+/WhqasKuXbuwsbGBL37xi1hcXMTq6ioUCgXuu+8+HD58GDdv3mQt8dbWVmzfvh0ajSan9G48HseePXsQj8d51D0+Po6vfOUrKCsrg9lsZsfU7du3sxdGJBJhgQK/34+DBw/iueeew759+7C6uoqpqSkUFRVxUUZSzL/61a9YFSUej+Odd97ZdG2hUAgajQaZTAbFxcWQyWT49a9/zZLI2WwWTqcTFosF//RP/4TV1VU89NBDMJvNbDhoNpvR1taGYDCI48ePIy8vD/fccw/uueceGI1GPP3005ifn2dIUVFRESuKtbS0bLo22nOkWqfX62GxWNDW1sZworq6OpSUlKChoQGf/exnuesUDodZ197j8aCnpwfXr1/HX/zFX6CmpoYno5FIBHv37kUoFOL9aDKZ0NTUBIvFklPGMBKJoKamhsf3BQUFPFUjPwUylCP8e3t7O1577TXuoL355ptYXl7G/fffj/379zMmmzqbLS0tuHr1KgwGA2KxGLZt24b29nbuBG8WJIVKU9uenh6o1Wo88cQTiEaj6OzsRGlpKaxWK44dO4ZTp05h//79yGaz+MQnPoGqqiosLS2xEs+XvvQlnpKRq313dzdeeuklfOYzn0EwGERzczOefvppGAyGnET1cDjM3S9SbRKJRHj++edx8+ZNyGQyhrVWVFSw6pPP58M3vvENHD9+HFarFffddx+7SDscDi6WE4kEPvGJT3AxRZBLs9kMvV6fU/4xHA5jy5YtkEgkKC8v5w7ngQMHOHmx2+2YmZlBdXU1xsfHoVQqce7cOQwPD+PatWsYHh6GXq/H5cuX8Y1vfAM7d+5EeXk58vLy4HK54Pf78ctf/hJ6vZ4vdpoK5/oeCI9NAiYymQxSqRQ7duxgiCkptFEhIpFI0NfXh/3798PlcuEnP/kJ8+meeOIJbhoQ7vz48ePIZrPsjt3a2gqZTAaPx5PTMHV1dRXHjh1DPB5HXV0dnn76aXi9Xlbhog7snj178OSTT8LlciEQCKCmpgZ/93d/h8bGRpw5cwYzMzMQi8X43ve+hwcffBB5eXm4evUqxsbG8Oyzz2JlZQVf//rXmZNG8Lb9+/dvujbgPV+q2dlZzM7OQiQSoaamBh//+MdZcIbOkuPHjyMUCrG078GDB1FeXs5u9k6nE6WlpfB6vbhy5QomJiYglUpRWlrKfiN0TlVUVLA8/2ahUChw6NAhht/V1NRgbW0NDz/8ME6ePMkCK7t27cJnPvMZ2O127Nu3j9dst9tx9uxZzM3NwWw241//9V/R3t6OgoICntIcOXIEDocDWq0WZrMZn/vc5/C5z30OADblGQDvJYa7d+9Gfn4+xGIxVCoVVldXsW3bNrz++uuYnp7GtWvX2GOHOFXk9k4wX+K6/OQnP8G2bdvQ0tICgUCA/v5+WCwWuN1u1NTUQCgU8gRboVDkLNCkUim0Wi1/CzQ5IVQKcSKPHTuGX/3qV2xut337dnzjG9/g+7ijowOJRAL79u3D5OQkBgcH8e6772JgYACf+cxnsLy8jI997GNoa2uD1WqFXq9HJBJh9Midwufz4emnn8ahQ4ewd+9eHDhwACaTCZ2dnaivr0dVVRWqqqpw4MABfO5zn8OZM2ewc+dOCAQCNDY2wmKxsBiD0WjExYsXcfnyZSwtLWFtbQ1erxddXV2IRqPo7u5mNT69Xg+Hw8F+XXcK4gwNDg7C6XRCoVBgfHwcTz75JHQ6HQ8Furq68Gd/9mf453/+Z8jlclitVnR3d7OHFaEXvv71rzM8sLi4GPv27cPXvvY1bNu2DVu3bmU/k5KSEiwtLeGNN97YdG23xgcmg7/wwgtsRkJeA263Gy0tLaivr8fq6iqTc1dWVnDkyBFs27YNfX19PEqemZnBT3/6U3zjG9/A3/zN3+DZZ59lozCv14tf/epX+OY3v4m3336bE3IiA9HBcaf4/ve/zw+GHHC7u7sxPDyMdDqNw4cP8zg6EAigp6cHyWQSv//979nEx+/3o7e3F4888gheeukl3H///ejr62PDmsXFRXzlK1+B0+lkQyLCAQ4ODuIv/uIv7ri2Z555hrWRCf8ul8tZc5q8OGZmZuBwOPCtb30L//Iv/4LKykr09/dDo9FwV8RiseDll1/G/fffzzKZeXl5ePfdd/G9732PvUtovEpmYt/5znfuuLavf/3rEAgE6Orq4glIIpHAd77zHezcuRM2m41Jl7QOr9eLf/qnf4JQKGTXd4VCgcnJSUQiEXzuc5/DW2+9hZWVFfYkaGlpwSuvvILHHnsMy8vLcDqdqKurYzncO8XPfvYzVpvKZDLcnZiZmUF3dzcuXrzIDptEmvvBD36ArVu34he/+AV0Oh16enoQjUYxMTGB2tpapNPp27TIKysrUVxczMkyjbgNBgMikQiOHDlyx7X94Ac/wB//+Ec0NDRwJ2rr1q2YmJiAQqFAIBBAS0sLpqam8L3vfQ/PPfccXnjhBWzfvh2jo6MwGAxobm7mC+2FF17gaeD6+jocDgdisRi+8pWvMHzC4XBAJBKhsrISr776Kv6//+//u+PafvrTn2JxcRFSqZSN4woLC/H6669jx44dTJzu7e3F8vIyvvGNbyAej2NtbQ2XLl1CY2MjysrKmIcjl8tRXV3NnA8ihB87dgz/63/9L9x3330IhUJYXV1lB/NckIyf//zniMfj2LVrF1ZXV3H58mVoNBpcvXoVzz77LH7xi19wgURdxurqaohEIvz85z+H2WxGdXU1Ll++jE984hP4/ve/jy9+8YsQCARslCgUCiESifD666+jsbER09PTSCQS2LNnDxcsd4p//Md/ZM4YcS7q6+vxk5/8BB/96Ed5kkJcCCp6rFYrBgcHkUgkYDKZGNbwxhtvYM+ePfxtFBcX4w9/+AP+9V//FWfOnMH8/Dx3osrKyuDxePCpT33qjmv7zne+g8nJSWzduhUSiQQOh4P5Gp/85Cfxne98B9u2bYNarca1a9fQ3t4OtVqNgoICfPe730VzczO2bduGM2fOoLGxEXNzc3j66afhcDhu40qNjo7i8OHDrBw4OTmJPXv2YGRkZNM9R3KsRJh2uVzo7OzEX//1XzM5Nj8/Hx6PB6Ojo/j617+OYDCI0dFRVFRUQKPRIB6PY35+nqEpxcXFPAFJJBLcsCAne7FYzCpxfX19ePnll++4tueffx4LCwtoaWnhrqxEIsHExAR6enpw5swZdk8nfs7CwgIOHDiAZ599FgaDATt27EB/fz/+9E//FN/+9rfxhS98AXa7HYODg/B6vfB6vfjWt76Ff/iHf0BtbS3DFbVaLYaGhvCNb3xj07WRog/dg9XV1Zibm8OWLVvw/PPPM1KgoKAA1dXV6OvrwyOPPIJwOMx/56233sLOnTvx5ptvoru7GwUFBVhZWcHc3ByEQiHuvfde9mcisihxrb7whS/ccW1/+Zd/CbvdjgMHDkAsFmNubo6FR3bs2IH5+XlYrVaMjY3hueeeQ09PD8NBFhcXUVpaylOrjY0N/OAHP8CHP/xh5hBsbGygt7cXzz77LJ5++ml8+ctfZmW9ffv2YWJiYlPZ+G9961vcASYi8/z8PA4ePAihUMheAGq1GsXFxTAYDBgYGMDa2hqGh4dhMBi4uCgrK2MzNuock/pZQ0MDvvWtb2HLli3cNd6yZQuSySS+/OUv33FtRKIvKSnB3NwcRkZGUF5ejrm5OXzxi1+Ez+fD5OQkw3uefPJJPPPMM3jooYewuLiI1tZWtLW1YXp6GoFAAD/84Q/xwgsvsHmvSCTC//gf/wOnTp3C6OgohEIhN0nVajXcbje++93v3nFtZChptVp5KmY0GvHrX/8an/70pyEWi6HT6ZiH+eSTT+KrX/0qfvazn8Hr9TLELxgMIpvN4h//8R/xzDPPIJFIMGf0r/7qr/DHP/6Rc7gLFy4gHA7jYx/7GK5evYpPfOITd1wb8Yzo7I5Go3C73byviO8wPDyM0dFRPPXUU1haWoJIJMJvf/tbtLW14ciRI4jFYsjLy8OHP/xhPPXUU+ju7mZ1rdOnT+MTn/gEzpw5g66uLkaudHR0oKSkZFPC9SuvvIJgMIiqqiq+i1UqFU6cOIH/9t/+G/vGOBwORCIRHD9+HOXl5Xj77bdx4sQJbnAkk0k0NjbiU5/6FF544QWEw2FuVAwPD+PBBx+ETqeDUChkLopUKmXxlTvFc889x1N4ErSoqKhAIBDAkSNHcPr0aUakuFwu/Pmf/zm+/vWv4xOf+ARu3rwJs9l8m/v8+vo6LBYLent7EQqFUFhYiJs3b/L3KZfL+Y4sKChAOp3OKb9L8X9l2EcjxeXlZSwtLSEajbLai1qtZoLk8vIy4vE4xsfH8dJLL8FsNrMKSlVVFX7yk5/gs5/9LC5evAiPx8P6vEtLS/j2t7+Nhx9+GCMjI0zGu3DhAlZXV/FXf/VXd1yb3+9nEjjwHob+4sWL2L9/P5aWlrC4uIiFhQX09vZidnYWCoWCx9zvvPMOY7ILCwvxn//5nxAIBDh9+jSrJpB5HflJrKyssHzk++kIk9kdOYkT7ptcd2kaQeoHly9f5t/DZDIhHA5jeHgYS0tL+O1vf4stW7bg1VdfZUjB+vo6Kisr2bSMuDOkMZ7L6DCdTqOkpASTk5MoKiriv3/06FGUlZXB7/djYGAAAoEAbW1tLHW6detWvrSKiopQWVmJvr4+mEwmTE1NobOzEzMzMxgaGkJdXR0mJibwwAMPsJ49weZyFRrhcBgdHR1M6iYogFwuZ5MuIhPSpU57ZceOHQiHw7h+/TqkUil8Ph/sdjuam5uZdEcSfwUFBWy05XK5EAwG4Xa7c3aX5+bm8MADD/DINBwO48c//jEXQqQQEgqFUF1djf7+fpjNZszMzKC8vBwCgQB9fX2IRqOsP07kbDKKq6ysxPnz5/lnUMeQ9s5msbq6Co1Gw2ZeBNXq6upCUVHRbVrlUqkUJ06cgMViwcWLF9HV1YXh4WH87ne/Q21tLfbu3cu66xsbGxgYGMCFCxcYKtTR0cH/Rjweh0wmw4kTJza96ABw4jQ8PAyXy8VGiU888QRWV1dRVVWF0dFRnqY1NDQwjEun08Hv9+PcuXMoKSnB2toatm3bxjJ8lPQlEgkUFxejoqICKpUKXV1d8Hg8OHv2LIRC4aaFRn5+PjulE4fE4XCgpaUFCoWC5Xxp/0qlUlbEWlpaYkOv8+fPw2g0wmq14o033mA3d+pW//GPf4TdbudDm5JsUt25U3i9XtTU1DCno6ysjOVAyexraWkJKysrUKvVWF9fZ3fcgwcPIpvNcmNoeHgY5eXlDCf0er0QiUTw+XxsRnn58mUUFxejuroar7/+ek5cOiVmIpGIDSXn5ubw0Y9+lJ+bw+Hgi5aUtubm5hj6GYlEWJWQkmEyFBsYGOAivLu7G+vr65z4lJSU5OxGktlXKBRi/gm59s7Pz3NxdOnSJWQyGezYsQP19fX40Y9+hIaGBkSjUZw+fRpCoRA/+9nPYDab+bwkeGQ6nWbpXWoIkPpdrue2traGRx99FHNzc1hbW2MVMYJHURODurwmkwnr6+t4/fXXuQtLcJqXXnoJu3fvZjKswWCA2+3G1atXsX//fhQWFnLj7VbS+WZBiTbxZSKRCNxuNwsbtLa2YmRkBHa7HTt27GAFMeK6BINBDAwMMMfk2LFjrCpWWFgItVoNuVyOK1euYM+ePTx9D4fDOHv2LLxe76aFhsPhgEKhYBK+WCxGMBjE0tIS+vr6eDJEMqYTExMYHx9He3s7jEYj33VqtRqTk5M4cuQIfD4fIwTW1taQTCaxsLCAe++9l7kGMpmMoZibBXE36c602WwYHByEwWCAz+djLhMAlJWVYWxsjJX1qDF45coV9jl57rnncO7cOebqXb9+nZUCa2trGZZIE9VcfWOPx8PQZb/fz1Pb7du3M++UuBdkLPyxj30Mp0+f5im50+nE9PQ09u3bh927d+PGjRtsA5DNZnHo0CH89Kc/ZfuCpqYmyGQynD17NidR3W63M0eMFPFI2v7KlSt8XpBR8fz8PJRKJd58800cOHAASqUSV69eZWjok08+CalUigsXLiCTyTCX6T/+4z/Q0NDAfM3q6moMDw9DLBZvWmgQ15XU+MhU8qMf/Sg3Ooj8ns1mcf78eX5fe/fuxcbGBmZmZrgB/NnPfhY3btzgHCqbzcJqtWJ4eBg1NTWIRqNIJBKIx+Pc8NosqOm9vr7O/k7nzp3Dnj17cPbsWZbf1Wq1KCwsxDvvvIPOzk709/fDZrNBIpFgdnYWBQUFGBgYQCqVglqtRiqVYkEigqKTtxRBk41GI1skvF98YOgUKRJIpVI2rjKZTHjzzTf5odEGyWazjAc1mUysFFJZWYnq6mp0dHTgxz/+MbugikQiBINBHp+dPXsWYrGYTX8mJiZyYoSJgES4Tb/fj7W1NfziF7+A3W5nRQOCp8RiMczPz8NkMjERkIqkjY0NDA4OsuqGWq1GSUkJqqqq4Pf7YbVa0dbWBqVSyQ8/F4kzHo8zoZKUOPLy8rC0tIS5uTkmUkqlUkilUng8HkQiEfb2yM/PZ4z0xsYGwuEwDAYD8z0IAkaKVUTmzWQyjDfeLAi7RwZakUgE+fn5uH79OjY2NniSkkwmsbi4iPn5eSaHDQ0N8Ts1Go2IRCLMkyAVBPo4L168yCoNExMTPKbbTMoTeA/3TclXXl4ekskkhoaG8Oqrr2J8fBy7d++GUqlkMpXX60U2m+UPND8/H3V1dbj33ntRW1vLneiamho0NDQw34bMi/x+PyYmJjA5OYloNJoTI0zQA5LRJTURUibZvXs3TCYTK9yQn4ndbsfk5CSTAltbW7GxsYGamhrodDrs2LED9913H2pqajA1NQWtVssXB2HvBwYGmGx+pyClmtnZWVbpikaj6O3txeTkJNbW1pgQt7S0BKFQyLyfqakpxpXTCHd4eJiLIqvVitraWly+fBnDw8MIh8OM+SUcZy7zNACsZJPJZFjKmZI4wr+Swo5CocDc3BxPDwcHBznJkEgkuH79On7zm9/A4XDwpMput/N3Ojc3h3fffZcLIZqsbhYETxCJRFCr1VyYkewjKeQ4HA5MTk5idnYWhYWFGBsbw+zsLHfjBQIBBgYGuAsJvJfwEqlxYWEBEokEGo0G5eXlqKys5Isw19rcbjcGBgbgcDj4TCKlP0puBAIB8vPzoVKpGD5w8+ZNeL1elJSUwGQyQSgUYmJiAl6vFzabjc9oOkMSiQQmJyfR39+P+fl5FBYW5iSDFxYWsqwlqff4/X6oVCqEQiHmRBB3jBKZ5uZmxshT4gO85+FAfJfq6mo8+OCDyGQyWFpagsPhwMrKCk9OZmdnc64tHo+jr68Pbreb4SIEAQHA5qEulwsOh4O798lkEh6PB0VFRdi6dSva29uZ20QEXIfDwUIi4XAY3d3dbJ61urrK/hWbhUwmQ19fHycJ5PZORnxkxDYxMcETiV27duHatWs4ffo0+vr6YLfbAYAFBSgJI+ENiUSC4eFhGI1GtLW1QS6X89QxF+GauptUUBcWFkIul/O5QmaxxBtqbm5Gd3c3xGIxPB4Pd4hpUtDf34/q6mo8/PDD6OzsxPz8PE6fPs0cCGrCJZPJ9+XvaTQa/q4IyigWi3Hp0iWMjY2hrKyMG5fk1F1WVobFxUUUFBSwSMiuXbtQVFSE06dPsyKP2WyG0WjE1atXWc6W9pHX631fwYtYLMYKmuSobjabUVxcjPHxcU4aqWGg1+uh1WrR3t7OOZNAIOCi+JVXXkEmk8H6+jqLoADgSSDlNT6fDwqF4n2/Bbob6Y7Oz8/H7OwsMpkMqqqqWJ6b+AsE5Z6YmMDa2hrMZjNMJhNOnjyJoaEh9rLKz8+H1+vFuXPnYDAY0NnZyVxcmtzOzMxsujbiKZhMJthsNlgsFuj1evzud7/DyMgI+vr6EI/HUVBQgFQqxcmuQqHAwsICK5gKBAJ4vV5YrVbs2LEDjY2NEIvFsNvtmJ2dhdlsxsLCApaXlzE+Po6BgQGWac4VpJKYn5/PkDXKI0tLS9HY2IimpibU19ejsbERPT09MBgMWFxcRDAYhMViQUdHB5xOJwYHB2+7K0goYGlpiWGcdrud1VlzNQQKCwvZl46EVcjclZQkCwoKWJnMbDYjFAohEAgwTxh4z5SwsrKSvysyPq6rq+PmU319PSwWCxflpKT4QeIDFxokSUjyjiSNZbVa4XQ6EQwGmbxaVlYGt9sNvV7P2FOStm1tbWWDOvJJ0Gg0KC0txeHDhxGJRBAKhVheEHiP4a/T6TZdG0mIElmLxu5U/KjVapSWlqKhoQEPP/wwYrEYQ35IK/1Wgzhi79fX18Nms0GhUDB8QC6Xs0a52WzmpDFXEE6xsLCQuy0E+SFFCSIP0UiYyJw0RaJLmuSBqaCj329ubo7VdugwJaO/XM+NpFYLCgpQUlLChCIiORcVFXFn7dq1a7DZbDxSI0100igvLS1l6IpcLodcLofRaOTuPnXbS0tLsbi4mFMmkC64VCqFwsJClJSU8N8ng6LS0lJ2DKVRM8HnAoEA0uk0tFotiouLeapExVlhYSEaGxsRCoX4strY2ODnnGu/SaVSBAIBVuehKp9k9lQqFctV6vV6Hs2T7j9J7VmtVoyMjECv17Nr8a161ysrK+jq6mJZuaqqKk7cckVpaSmy2SwymQxUKhU/p5mZGSY+kgs4HURUoBIZT6/XY2pqii9bkUiEkpIS5iaRUVAsFoNOp4NWq8Xy8nJOYi4APpiIKE3O5STdXF5eDrVaDb1ef9v3SGpd1DnLZDLo7+9HIpGAx+PhBJsKzI2NDTb1ovdeVFSUs/sNgA2INBoNTCYTBAIBjEYji1SQ2IHJZGLfn1QqBYPBwOeGUqnkvZRKpVBUVMQTg2QyCafTCZlMhrW1NYjFYp5q5PpWqRNPv08oFGJCJwlSUPebXHVlMhnsdjubMNLvRnvpVpW1dDrNHXySRyWYHEEqcq1NJBLxtySTydi53O/38/lRVFQEjUYDj8eDeDwOoVDIeO+8vDy0trYyifZWlSgq/EiukngbdAnnInJSx5CKDIIbabVanoQSd4QScYVCgfz8fPYCuVWpiQoqkqJOp9N8/tJ+oeaU2+3OaVJG9xv9jnl5eSyPSxBOIuarVCo4nU72DampqYHZbIZOp4NGo2GO1PLyMivFRCIRdHV1MYdFpVJBr9ezymCuopsm4qR8Q4plHR0dLEdM9xop1HR0dCCdTkMkErFiJKEBZDIZN3CUSiVisRhqa2vhdDphMBh4IqpWq7kpsFlkMhlYLBZWSCQjxltzACJwkzJWV1cX89aA91APpBiYSqVY9ZE4LnSG0DdMhRZNn3MFTXQ8Hg83UOvq6njPS6VSLhi8Xi+6u7tZPZEkY7ds2cL8NFLZA97LJQgeR//WrTkPKTbdKVKpFID3GrNarRYlJSWIx+OsMkX+V0KhEOXl5QgEAmycRwWHUqlEe3s71tbW+Ow2GAwwmUwQi8UoLy+/zbGd5N+FQmHOtcXj8dsaDpRfqtVqAO+dWTKZDBqNBvX19azaRXuZlOl27tyJ1dVV5i1ZLBbodDrk5eVh27ZtWFtbY4i4XC5n1+9cDTLixZFYDuVJdOZptVr2j2tqauLcllSqKL8gPlh+fj4X1/T+rFYr1tbWUFJSws+qqKgIiUQC5eXlm66NZHELCgpYZIVELdLpNJP55XI5mpubGfZEeSzBT0k9iqapFRUVqKqqYmg9+UTRRFSpVCIUCuVUObs1PnChEYlEuIAgbehwOIxHHnkEJSUlEAgEyGazUCqVjGVTKBRwOBys+72wsMCqVMePH+cRkVwuR0VFBVpaWhAMBlFXV8fTE5LJyzU+ymQykMlk3FmuqKiAWq1Ge3s7JBIJpFIp5HI5du7ciY997GOIxWLQarV8WSkUCoaCeDwedHV1sUNoVVUVZDIZmpub4fF4uPNFpoUFBQU5lUWoAibJu6KiIhQWFqKmpoYVEGjDU0eB4FiVlZUsoebxeCCRSHikSpOOvLw8lvGcnZ2F1+tlrB5J1G4WJCva0NCAuro6huQ8+uij0Gq1mJycZLwpuXbm5+ejq6sL+/fvR1FREWZnZ5FIJBgvS6Q2mUwGi8WC7du3M0YyHo+jq6uLC8r3IxIZjUY+4OlCfeyxx3hyIpVKUV9fj3379rH2NvFJkskkEyoTiQTMZjM2NjYwNjaGmZkZqFQqnmzQxIm6KRUVFTnJr6QTTxrjVHRRwkyygQqFAjU1NTzZs9lsaG5uhlwu5xE7QTZmZ2eZ4O/z+WCz2bC4uIiqqip24+7q6oLRaORu7J2CEjaTycQXmtVqxf79+9mRuri4GPX19SywUF9fD7VajfLycvYuITyu1WqFXC5n7fBkMskKFaTjvnv3buzfvx8ikeh9JVoJkkTqMOR18/jjj0Oj0aC6uhqVlZWwWCywWq2QSCTsGXD//fejo6OD/Q9SqRS2bt3K3j5isZgxp8vLy2hvb79NvYQu2M0imUyisLCQGw8k6bxv3z7+joqLi1FbW4utW7cyB6a8vBzbt29HTU0NJwZEJi0uLoZGo4FOp4NUKsX4+Dj/HIKfkglSLoU4v9/PEpSJRAJzc3NwuVwoKyuDUqnk35EgcW63G/Pz88hkMrjvvvvQ1NTEzaCysjLcd999EAqFmJmZgdfrhVqtZqnbkpISqNVqlJWV8dQ312R0/b+MRAUCAZuRkcSp1+uFXq9n52WTyQSPx4PZ2VmWJyZ+XW1tLdbW1rBnzx64XC5WlRodHUUmk4HVaoXNZgMA9iLSaDS3OaPfKWpra6HT6fiblsvl2Lt3LydG1I3fu3cvFAoFqqurUVxcDJ1Ox47fBGGg+0iv1zNRtbu7G06nkyWHKysr0djYyJKRm4VAIEB7e/tte46mDeSxUVtbi87OTnR0dEAikWBwcBBdXV04evQo9u/fj4aGBhgMBthsNpSWlkImk0Gv16OkpARyuRw9PT0s8U1QTeII5gpSCyQH9vLycuj1ehw5cgT19fWctBoMBjQ2NrI0tEgkQnV1NYLBIC5fvoxwOIy5uTns2rWLHYidTify8/Px+OOP4/z58zCZTCy7WllZyd5MmwXh5cljigrlyspKVFVVwev1sgwvTb1ramr4uVB33e12w2AwoKurixsYpJi0a9cueL1elJaWQiwWw2g0orq6mifXmwU1Egn2Qg2BLVu2wGq1ss9EcXExRCIRpqamUFlZibW1NZSVlbGiFpHWjx8/joKCAlbiSqVS2LFjB9bW1rC0tIRsNguLxQKz2cyFTK79JhaLb4OKEzyV1OKCwSD0ej2OHj2KQCCAiooKnnbQtNNoNGLHjh3Yt28f806pafzUU0+xD0osFuPGDCXEm4Xb7Wbo1uzsLAKBAFKpFB5++GHmrZSUlECr1aK7uxsrKysIBoMQCoUwm80Qi8WIx+OorKzkpjjBWpVKJSorK/HhD38Ybreb8zCbzYadO3eycuJmQfBeatjk5eUhkUhAqVQyUZumV9Qg7Ovrw+zsLLZu3QqdTgeHwwGBQID19XX09PSw7HM8HufJPpnnUZOtoqKChUc2i0QiwQImtC+pQavVavk+Ly0tZQ6L2WyG2WzGjh07YLVa2RiRJKpJ2TAYDDJR3e12w+l0wul0srkz+aN8kPjAhYZOp0NfXx/MZjN3nORyOX784x/jT/7kT6DVahkC5XQ60dHRgV//+tfYvXs3E0XLy8vxyU9+EgqFAidOnGClKqVSidXVVTzwwAP41re+xc6vExMTcLvd+OQnP5nz4CkpKWHDNZfLhYWFBXR2duKdd97B0tISRkZGMD4+jrfffhs/+9nPoFKp8Oabb3LB8JGPfAQHDx7EhQsX0NTUhMXFRWzbto39A8rKynDq1Cn85V/+JcN+ent74XQ6sXfv3pyHNh0cSqWS+QGNjY24ceMG2tvbGa5D4/euri4uvoaHhyGRSLBt2zY2nLty5QoOHDgAhUKBvLw8lJaW4uLFi3jooYcgFouxZ88erK2t4eTJkwiHwzkVWRKJBIaGhrg7Rk7nfr8f+/btQzabvU1DmqQCH3nkERiNRnaojkaj+Pa3v43y8nJUVVUxiV+j0eDKlSvYt28fy1YShveBBx7IWQ3L5XKcOnUKSqWS1V16enowNDSE++67D5OTk5icnITH40EoFMJXvvIV1tCur6/HoUOHUFpait///vewWCwIBoPo6uriqpxIdAcOHMDo6Ch3sPV6PfR6PSYmJjZdG2GcScOdlDvefvttPPbYY7BYLGyC19/fj+7ubvT29rJU8fHjx3H48GH09vbiox/9KNbX1/G5z30OlZWVkMvlsNlsOHPmDL74xS/iF7/4BaxWK1KpFHw+H+677z6+5O8UqVQKJ06cQCQS4cMxmUxiYmICe/fu5QKjpqYG77zzDsxmM/74xz/i8ccfh9VqRXNzM9rb25FKpXDPPfdgZGQE1dXVGBoawvj4OPLz8/GjH/0ITz31FGuGT05Owul0orW1FdeuXdt0bcB7I3+BQIC9e/fyBbtjxw5cuHABBoMBr776KoxGI3diVSoVnn/+edTV1aGqqgrbtm1DT08P/H4/HnnkEczNzWHPnj3cYS0qKsKpU6fwoQ99iCFYwHswwW3btuVcW0FBARMuiUxrsVjw+uuvw2w24/r16xgZGcHs7CwmJydhNpuxsrKC9vZ2LC4uwmg04tChQ3wZXrhwAQ899BAMBgObAUajUTzzzDOM8advdPfu3TknfHq9nrG0lIh9+MMfRnFxMY4dO4axsTGeumQyGTz44IMoLS3F8ePHUVtby+N8mnReunSJnc2lUim6u7vx6quvwmw249y5c9i5cyfq6uqgVqtxzz335JQtLikpQTgcZp8lwrGToeHVq1cRCARYfWvr1q1YXFzkDiB5Cv3+97+H2WxGJBKBVqtFaWkptmzZgm3btuHixYs4evQoLly4wA0amhTmwszTXeX1ejE6OspJbygUQldXF8tvTk1N4Q9/+AM0Gg3v5ebmZnR2dkKtVuPFF1/Efffdh4qKCrS3t2NlZYU9M/Ly8vD4449jfHyc3Z2pAXT27NlN1yYWi+Hz+ZgoPzg4CLPZjPPnz0Oj0aC/v5+7tktLS7j//vtRVlaGnTt3oq+vD/39/ayQZrFYcOXKFTzyyCPsbUP7cvfu3XjttdfY1Vkul2Pbtm058fwEDyUIIXGbXnzxRezatYtd0ckn5r777sOPfvQj3HvvvSyB/rnPfQ41NTX467/+a4yPj2PLli1YX1/H6uoqJBIJfv7zn+PBBx/Eiy++yAVzNptFfX19zsLWZDLhN7/5DU+nCRKSzWbR3d2N8fFxTlT9fj8ef/xxTE5OQiAQ8LOqq6vDwsICdu/ezS7KhL/XaDSYnp6GwWBgNceNjQ14PB60trbmzEVKS0s54bVYLGyem0gkcOTIEeZVyWQyFBcXo6GhAf/+7/+OT33qU6ipqcGhQ4dw5MgRDAwM4Gtf+xrcbjfbAOj1erS1teH8+fOorKzElStXUFxcjEgkgpWVFSYSbxZ5eXn883w+H6thBYNBJi+XlpbynXD//ffjueeeQ1NTE/r6+uB0OqFWqxEKhfD5z38e3//+99HQ0IDR0VH2SyOZ9pdffpnldIeHh9lYcrPIz8+H3+9nKDbxFvr6+rBz505cvnyZEQozMzPo6OjAhQsXIJfLUVlZid27d6OyshIvvPACnnjiCQwNDUEkEmFychKpVApbtmzBu+++i69//euMIunt7WX4Xi6DyEwmgxs3brBpHSFbXC4XWltbWXHP4/FgZGQEeXl5GBgYQF1dHQoLC1FfX8/32aOPPorp6Wm0tLRALpdDIBAgHo/j+eefx5NPPokrV64gGo2yr0tLSwsmJyc3XZtUKsW5c+cQjUaZA2O1WtHb24vW1lZu2Pt8Prz11lvMvSH+c2VlJfPSiHtSV1eHwcFBeDwe3HPPPbhx4wY+8pGP4OrVqzAajaz8R3K9HyQ+MBmc5POGh4dZS5pUpk6cOMFEROrWDw0NYffu3ZienmYuQCqVQldXF1KpFPR6PQYGBjj5XF9fx9e+9jV8/etfh1Qq5akIdT0rKys3XZvT6URtbS27MXq9XszOzqK1tRUVFRUQCoXsLmyz2VBUVISPf/zjOHv2LE6ePIn5+XnU1dXB4/Ggrq6OsYako63T6RCLxfDiiy+ivr4eADA1NYWFhQVYrdacvgbkwEhutOl0GsPDw/jQhz6E06dPw+fz3eZXMTg4CJ1Oh9/85jessaxSqXD8+HG8+OKLOHLkCDulU9d827Zt+Jd/+Re0t7djaWmJ9efJuXuzoGJFIBAgEAhgamoKMzMz+PjHPw6v18uVv9PpxMWLF1lS9qc//Sn8fj+kUin0ej10Oh0uX76ML3/5yzhz5gzLuY2OjsLlcsFsNsPpdLJxHLkB5wrqSJM5mUQiYT7MxYsXGYJDH9eNGzdQVlaG6elpDA4OskNufX09hoaGMDc3B6vVCrFYjI2NDdb/Jg8RIpeRoViuAm1tbQ1Wq5VlmaVSKSs0vfHGGwyXqK2tZSfmj3/84/iHf/gH5Ofno6qqio0ju7q68L3vfY9hAOTf0tbWht7eXjQ2NkIikeDatWu833KZ5Oh0OlRVVWFsbIzxziMjIwiHwzh48CBeeeUV7p4KBALMzMzwiPT8+fPs6L5//3688MIL6O7uhtvthkKhgMvlYiWaf/qnf7pNr56Mm3I5SAPA2NgYSwpSsXbt2jV+jw8++CAGBgYwMTEBsViMffv2Men8pZdeQjAYRHFxMTcnCPZls9lQUFCAV199lTkGNKrX6/UIh8P4xS9+AQCbStzSqN7v92NjY4MP0kOHDrFCC0EKSfxCp9NhbGwMKpUKfX19uHnzJlpaWtiMdHFxEX6/n83Zqqqq8Morr2BjYwN1dXVwuVyYn5+HwWDIqZdORHlSy1lcXMSpU6dw/vx5PP3009BqtRgbG4PD4UB9fT1Onz7NqiU0OSADq2w2i7GxMVgsFsb8//jHP8b999+PmZkZhEIhXLhwATU1NdBqtbhy5cr7OoOXlpbC7/ez2SVBJvfv388FQSaTwc2bN+Hz+VBTU8NutMQj+s1vfoMtW7ZgeHiYuRPBYBDr6+uor6/HlStXYLPZEAwGMTIywp3qXAlzJBJBY2PjbU7FN27cQDQaZbUWEuMQCAQ4deoUS3j7fD7m0TzwwAMYGhrC5OQklpeX0d3dzd9WR0cHzp49i2AwCLlcjsnJSSSTSdTW1uY857xeL8rLyxlvT2o/jz/+ODe7+vv7mcR7+vRpVFdXw+fzwWq1IhgM4vz581CpVJiamsLevXtZ4YxI+ePj47h06RI++clPYmZmhvlifX19PB26U4TDYWzbtg2xWAzxeBwzMzP44x//iHvuuQfvvPMO0uk08yfJYyIej+PkyZPMe6PO6enTp9HZ2YmJiQlotVqoVCqetnk8HiauExyNErDNwuFw4ODBg0xOF4lE/B7ouydeqEajwQsvvIBHHnkE/f393Kmlqfv6+jp6e3thNBp5wj02NoZt27YhkUhArVazIk9hYSFcLlfOqejk5CQkEglDh0i1x+/3Y3l5GVu2bEF/fz/fr7FYDAaDAX/1V3/F33hbWxvMZjP+5m/+Bl/4whdw8uRJnuqur69j69at+OlPfwqr1Yrx8XFYrVaYTCaMjo7mxPNTp5vgcCsrK/j2t7/NIikmk4ntALq6urg59eKLLzIndHx8nPki+/fvh1gsRmtrKztcLy0tMdRuZmaGURnj4+M5uYUAsH37dvbrIi+I2dlZnD17FlarFbOzs1hZWQEAzMzMoL29HXNzc5wbFhUV4eGHH2ZeSEdHB8OS33zzTXbBbmlp4XM5Ho9jeHg4J2x1YWEBjY2NbCwZDoexuLjIayovL8epU6dY3a6yspINDzUaDRvjDQwMoKenB1euXOHci7iFWq2WoX1er5ehSgSH2izW1tZw6NAhVkcFgJdffhkqlQoXL17k710oFKKkpATT09OMKgKA6upqyOVyrKyssHeR3W5nR/G8vDwYDAb88Ic/xKOPPgq73c7/DsHBPkh84EKjsLAQ3/zmN1FRUcFOqxaLheEGR48eZe8MMoH6wQ9+wLKTZIBFJCmSiaOkIx6PY2JiAq2trUzMSSaTiEajjHvfLIqLi9HX14eDBw+irq4ODocDfr8fU1NTUCgU7Pz87rvv4sKFC3jwwQdhtVq5q52fn89dcNrot0KDXnvtNSwsLOCLX/wiywrSBU543c2irKwMQqGQiUuRSAQ+nw9vv/02GhoaUFRUhKWlJbz77ruYmZnBo48+iqNHj+LYsWMwmUws/evz+SASiXDixAns2LEDbW1t/DvSAUVOmQ6HAxKJJCc+GHivi7C6uopXX30VNpsNnZ2dSCaTeP7552Gz2bC+vs6E8EuXLuHUqVOs2FNbWwuVSoVUKsXSc2+88Qa6urpY4tFgMGB9fR3Ly8t49NFHcebMGcakr62tobGxcdO1mc1mzM/PMxyEsK1LS0tIJBK4//77Wdrxueeew2OPPYZdu3bx34tGo1AoFKitrcWNGzeg1+sRCASg1WrZdO3atWtQq9WMJZVIJIjFYlhZWck5SlWpVDh58iTa29thNpsZtkV47VQqhd7eXu4ypdNpTE1NYdeuXXj99dehUqlgtVqZw0HqK1qtFnK5nDGZdrudnTvD4TB3wXJhNouKiuD3+xnj7vf7UVBQgNdeew0NDQ0wGo1M/gfA6khXrlxBX18fampqYLFYMDQ0hKKiIly7do2LeJVKxco4Tz/9NC5fvszu5QT/eD/8slKphN1u52fmdrtRVVUFsViM119/nbHJlGwolUo88cQTePzxx/HKK68gPz+f4VMEkyP1mdraWnzqU5/Cj370I8hkMtTW1uLMmTOw2+2QyWR8DmwWRKRvaGhg1a5IJIJ3330XPT093L0bGxvj90l8FoLfyWQyCIVCrK2tYXJyEnv37uU1ktQldfapm61WqxEIBHK+V4vFAqfTyWILJpMJTqcTR44cgd1uR2FhIZqbm9mFXCAQoLm5GVNTU2xwaTabUVRUhKGhIaysrOCXv/wlPvzhD3M3d2xsDB6PB5WVlSgoKOBnpVAocjZ6pFIpJicnodfrAYBhbIcOHcLExASi0Sjeffdddlumi47glPn5+TCbzejp6cGNGzfwy1/+EnV1daipqUFRURE8Hg+uX7+OkpISiMViuFwuuN1uhulevnx507Xl5+ejt7eXJ+rUjCoqKmKuVktLCwoLC9Hb2wvgPULv9u3b8W//9m/sykuOzXl5eZiensaVK1eYm7W+vo6lpSV85jOfwY0bN9gAljDlm0UgEMDExAQOHz4MrVbLsrgvv/wyDAYDc3cIluF0Otmklc4Fmvx4vV5cu3YNPT097GjtdDpRVFSExsZGNpqlRO348eM5E2aNRoNz585Bo9EgPz+fJ9GXL19mUzmbzYZkMonJyUloNBru/p84cYLhcydPnoRUKsUrr7yCj33sY5zgCYVCBINBjI+PQyKRYNeuXTzVcblcOZPSwsJCnDt3DmKxGGq1GjKZDJFIBL/85S/R0dHBZF0ymCP48eTkJEuR19TUYHx8nD0EWlpa4Ha7+X2Pj49DIBCgrq4OYrGYTRhFIlFOb5TCwkKMjo4ilUpx15p8a44cOYKioiIu4EdHR7F161aYzWaegDscDgSDQbS3t2N4eJhVqqRSKQBgfn4ek5OTKCgogM1mg1QqxczMDJux5fLRuNXkUK1WQyqVoq+vD6dOncKxY8cQCASYZ3Tjxg2YzWaoVCrEYjGcPXsWPT096OjowNzcHJvBSiQSVFZWIplMsjjDV7/6VWg0GhZFIBO/XA1ZuVyOc+fOwWw2QygUwm63swTz+fPncd9997EfDE3E+/v7WYkrm82ymIBarUZeXh5GR0fR0dHBPOKf//zn+OxnP4upqSkWEJBKpchkMjmh0t3d3RgZGWGCOilFktE0EfqlUimCwSBefvll7Nq1C1u3buXJSk9PDx5//HG89dZbaGhogFwuR3FxMbLZLOx2O1566SXmAFHOTFzTXF5oCoUCJ0+eZI4IoQx6e3tx7NgxaLVavPTSS7h06RJKSkpw+PBhfOUrX8Gbb74Jr9eLdDqN6upqaDQa3LhxA5lMBj09Pdi/fz9WVlbQ29uLs2fPoqmpCadOnWJlLpoAvR+JnuIDQ6ccDgfuueceJp61tLRgYWEBhw8fhsFgYFIV4aTHx8exb98+5OXlwWKxMAGRcJmJRAIul4uTyEQigWPHjiEYDCIej8PhcMBoNKK9vR3JZDLn2C0SiaCpqQkOhwNOpxN5eXmsk762tsbk2cbGRjz88MN49913IZVKcenSJezduxc2mw1zc3MIhUJwuVyw2Wz8gSwtLUGtVuP48eOw2+1oa2vD+Pg4pqamkEwm39eIhshdU1NTWFlZYaLc1q1bkUqlGPtJRnYE//H7/UilUuweTATyJ554AsvLy3A4HGyO19TUhGg0CoPBgFQqxUSehYWFnByNSCSCjo4OHvH6fD5kMhkcP34cwWAQHR0dCIVC/J8HBgbQ2tqK1tZWxl1qtVp89KMfhVKp5BE1uclOTEzAYrFgdnaWsZEFBQXsTk2mPHeKUCiEsrIySCQSlJWVob6+nmX1JBIJpqenEQwG0dnZiS996UsMsxGLxdi6dSva2tpgsVhgsViQn5/PBVI6nUY4HEYikWAcqsViQSAQ4GdOJLLNIpVK3QYtI4fv7du3QyaTob29neUQt2/fjry8PLS3tyObzeLBBx/Etm3bUFBQgPLycvz0pz/Fk08+yYaHO3bsQFlZGWQyGVZXV7F9+3bWsDcajbh+/XpOHgSZ9hCxXKVSYWVlBf/9v/93LCwsQKlUsuvn0aNHsbKywu//vvvuQ0lJCfr6+gC8h9P+yEc+Aq1Wi4qKCi5M29vbceHCBZSWlrKSEU0m348c5vF4mDyqUqnQ3t6OqqoqNDY2MuadEtKjR4/i3XffxTe/+U0UFhbib/7mb/DUU0+xueDKygruueceOBwOzM7OYnp6movakZERLC8vIz8/H01NTSgvL2dOw2YhEAhQU1MDj8fD05JwOIwdO3awCINUKoXNZkNtbS2mpqbgdDrh9/ths9lQVVWFvLw8hlv+yZ/8CVwuF8upGo1GNrciVZ3i4mKW6n2/zlVlZSUXkFqtFhqNBp2dnchms2hoaGCcudlsRiqVQkNDA9bW1tDd3c1KeTqdDkNDQ9i5cyfy8vL4clteXkZXVxfzQEg2lMi8uTqlPp8Pra2t3CmtqKiA0+nE+Pg43G43kw9NJhPzWKiLb7FYWDlHqVTiypUrOHz4MJtoknP9sWPHYDQa0dzcDKFQiI6ODiZ35jJzzWazaGpqYinu5uZm+Hw+aLVa5o+QwtOxY8fgdrsRCASwsLCARx55BO3t7YjH49BqtfjVr36FUCh0G2espKQE5eXlKCsrw/r6OgKBABoaGmCz2dDX15fzWyV1sMHBQQwMDDDcp62tDXq9niePBNciJauXXnoJOp0OBoMBc3Nz6Ovrg9FoxGOPPYbZ2VnmMC0sLGDLli2sbjQ7O4tQKASfz4cXX3wxJ9crEAiw2z1xQIiXqNFo0NDQgGw2C61Wi8ceewwXLlzAtm3bYLfbcfToUZY6J/L9V7/6VXi9XjQ3N6OrqwsFBQXsKlxWVsYu02KxGKFQKCd/j4Q8aP8QV/JTn/oUF3fksl1fX88wUplMxt1ml8uF7u5uls0XiUQoLy9Ha2srLBYLC2qQBK7f70cymcTIyEhOuIhAIMD+/fuh0WiYJ5Wfn4+vfe1rqKqqwt69e5Gfn4+CggLs378fgUAABoMBwWAQO3fuxIMPPoj29nYIBAJ2VCd+J3lSfeUrX2GzVFLvIy7p8PDwpmsjuPvc3ByWl5dRWFgIlUqFL3/5y0in00zklslkOHz4MMLhMKqrq+H3+/GZz3wGBw8ehNFoxIc+9CE0NjZi7969TKSPRCLQ6XT4/Oc/jzNnziASibBiHTmQ51IocjgcMJvNCIfDzA30eDyorq5maXBqFt1777081SssLITFYuHi8ujRoxgfH0dZWRmi0Sh8Ph/W19eh0Wjwz//8z3C5XGhsbGQhF5r8UZPkTmG325n4TsVVMBiEzWZj8ZjCwkLU1tbiyJEjLLsdjUaZ/3L27Fm8+eabcLlcqK2txdDQEAvKiEQifPOb38TCwgJqamqY90JCRZv5xwFgbs/S0hJPYSORCJuuEizuIx/5CJ588kksLS0hHo9jYGAAXq+XFVrT6TSWl5ehUChYQVEqlaKkpATHjh2D1+vFvn37MDU1xeqdJDr0QeIDTzQIRmCxWCASibgzkUwmYTQaWW+b1JNu3ryJhoYGlhxTKpUoLi7GzMwMjEYjy5mRclImk2GMMUlJ+v1+FBUVoaKiIicON5VKMdbN5/OxNwKRGEkakA4mwuxXVFSwEgo59xYUFMDpdKKiooLNX8rLy9nUz+/3o7S0FBKJhKUcqTt8p8hkMkySTCQSCAaDTO6JxWKYnp6GQCBgmJNCocDNmzfZkEooFEIoFDLZkBzDA4EAfyBE1qaElKBicrmc5cvuFAqFgiXQgPeKIkrE1Go1VldXudtoMBigVqtx8uRJLC4ushJNJpPBzMwM7rnnHpSVlcFmsyGbzSKVSrHMWlNTEy5fvoz6+nrmfNA6NwuxWMzyboQPJzlMmqiREko0GkVZWRkfCPTckskkO4XT319eXkY4HGZMqFar5Wqf/C9ITSLX2iKRCI+ySVWFDKAIElJaWsoqD06nk8fuhBWmBGBubo67g8lkkrs6Gxsb3GEj3gtBmDYLmUyG69ev37ZOk8mEubk5bN++nZ+p0WiEVquFUCjE/Pw8KyCRHwmRtDOZDMrKyuD1em/rrFVVVaGvrw9VVVX8HEQi0fsqsOXl5SEWi8FoNLLaycbGBjY2NrhDRzLOpMwhFotRUlKCWCwGm82G/Px82O12mEwm7lBbLBaIxWJMTU2xROW1a9eg0+kYvpJOp3nsu9naiE9FHTKpVIpYLMbfLxXLtC/JlTgWi7G6yurqKsrKylgNiVT0bDYbysvL0dfXx505l8vFmNxchQbJGxcWFjJ+nRzsCbImk8mYM0S8msbGRpSXlyM/Px/pdBrxeBwNDQ2YmZlBXV0dZmdnWeXE4/GgqqqKdfPJjVoul+eEJwHgJDkWi2FjYwMqlQqLi4vsrE7qJ0KhEBqNBoWFhWwot7GxAbFYzPKTdFeQrwBNTS0WC5aXl7G2tsb7k/bLZiEWi+F2u5lPtb6+zhK0ZrOZzTqpWSYUCjE3NweBQACbzcbuxoFAAFu2bEFnZyerZNHksKWlBbW1tbh06RKrfUmlUpSVleX0RsnPz2deDhHwybWZnhfhvIlvRA2E1dVVVrwLBAIsJZzNZnlfkmlcfX09vz/yupBIJAzdvFOQPxGdAaSvT5MRSliMRiPq6urgdrsRDAb5fqTpq8/nQ11dHU8uFhYWuJtcUFAAnU6HqakpCAQCiEQiFBQUoL6+PidmnpAOpEKXSqXQ1NQE4D3eIfkbpdNppFIp2Gw2eL1eVFZWckEciUSwsLAAuVyO0dFRiMVi+P1+ZLNZ6PV65OXlcbOMnsHGxgYsFkvOM46EKQhORrkPwWfo3RgMBm7W0fdKzQKHw8G8QYVCAbPZzApeBQUFGBwcRFlZGQYHB7F161Z4PB6k02k0Njbm/BYikQira6XTafh8PuZ00RSbvieHw8EKkyT2U1xczBO92tpa2O125saRlwfw3h2UzWZRWFjI0wKbzZazWSGTyRAOh3mvJZNJlJSUcNNmdXWVp7kCgQDRaBQDAwMoLCxkyV0qRqqqqjA5OclNFFJULCoqgtVqxcDAAE8lyIHbbrdj9+7dd1wbwfjoDKZzOBKJQCAQQCgU8poikQiam5sxPT3NRRPlVIuLi2hoaODmH6k/0s+4deJO9xQZJm8WxAGioiQSibBipFarZfQA+cZZrVbcuHEDKpUKyWTytudTX1/PZqu0jurqaphMJlb7I5g/8F6TbGBgAEePHt10fRQfeKJB0ookwTY/P49EIsHVD2mKZzIZhEIhPoSJ35DJZFi/XaPRwG63M4mQJFLpUiHCEmkLU2d2s5BIJAyloERAr9cztpzUk6gQIRWApqYmxunRQe3z+Xi0RodVMpnE+Pg4G1kB/0eaMJFI5CSDi0QiVoiiUSDhcemwI31uSgIJL+x2u1mVhirzqakphvrEYjHmo2g0GoZMkLQhyRHnem4kcUmXaX5+PqsykTwfJbm7d+/GhQsXEAgEeOwmFAo5QZienmZlDJr2rKysoKWlhQ8RUmahSyXXc6MEJBaLYX19nbsOGo2GZWCj0SgCgQCsVisWFhZ4WkKJ3/r6OgwGA8bGxliZh/DmZNpDvgbJZBLr/+XinquwzWQynJgLhULmkND3MTY2xtLBwWAQarWaSb8DAwOYm5vj340KS41GA6/Xi7m5OYTDYS7k7HY7UqkUVlZWsLKywjyBzYK090lij+SSJycnWUKPJHKJlDg0NIRMJnObESV5qoyPj0MkErEShUQiwcrKCsrLy3kCRAnGrd/FZkHuwuQpQ8pCpHZEss2rq6tsvkUGb+SHQU7SGo0Gk5OT3BkmDXXajzTNvFXOkHTx7xS0b+VyObueki8P/Z4kg0zJGkmn+nw+lookEQk6F9bW1vhnUBdybm6O/Yb8fv/7NgWoQBMKhchkMqwgFQqFGBoilUpZOrG+vp59PpLJJAoKCqBUKuH1elFdXY3Z2VmYTCa4XC522CUIFjVmKDGXSCQ5uV7E8aIJXzgcZmnG4uJiBINB5gQtLy/z5MXtdjNXKp1OY3JyElqtlr8jmhDQs9fpdKx04vP5uBHyfoRESgxockO4ZeoKU8JOkyJKLIaHh7nZtba2htraWggEAm6weL1euN1untqQ3PPCwgIXbblCKpWyWiMVeyUlJfB4PPyOxWIxstkswzlJIp1M/khqdn19Hf39/TwdIylZl8vFEIxYLIbV1VUEAgGUlJTkPOOIgEzvlLqvxDMkYzeSbbZarXxn0uS+tLSUlXrOnTsHlUrF/CJytSd1ILFYzPeK0WjMKadM9wjdDR6Ph808ybMlHA7zWUfdZZrUAe8ljcS9uHjxIkNmKWkn/t3k5CT/d2qQ5PoWyNyN7h8qqIisSwkzKVMVFxdjYWGBkSIKhYI9G4jPSPueDEmvXLnC01ahUMiwrvfrLpNSKDV46CxaWFhAKpWCx+NBLBZDIBDA9PQ0CgsLMT09zfDgSCSCRCIBp9PJEzPaf2KxGIlEAjdv3mS4cDKZZNM3UoPaLGQyGcOFqNgQiUQQiUSQSCSw2+3M17Tb7dxgIolYygfojlxcXIRKpeIEmzioFRUV7IFF5zfB1zcLOkOpKU73J5lP0lm8urqK8fFxVFVVYXl5GV6vF8vLy5BIJDCZTNwQdzgczG2gu3x6epr3AqlahUIhlirfLMgjQyqVIi8vj+VyCXFD/ECSW1YqlVhZWWH7AYL6kj0BNeoJcUJ71mq1YmZmhv89OjdzNT1vjQ880YjFYqwaQfrUsVjsNl1x6qZNT08jk8mwDjTpepMM7eLiInp7e/H0009jfX2dO05EqKQOJslgkuHQZkEaz9TtJ2yoUCiEQqFgfgM590qlUoYsUAJAusM3btyAVCrF7Owsb36n0wmPx4N7770XN2/exPz8POttU4c350P+L/k9hUKBTCaDyclJrKysoLi4mBOuWzW8KXGZn5/nzjNdJpRIE7TH7XZjcHAQDQ0NbN5FBxT97M2COilEyqOLPZlMory8HEqlEiMjI7hx4wbq6uqwd+9elg8lJYRbyf8XL17Enj17UFNTg3A4zA7l2WyWiXrU3ZVIJDmJRIlEAtXV1TxVAoCBgQHU1tayPJ/T6eQEh4qIoaEhlJaWMn5aLBZjYGAA/f39qK2tZQIgSWpGIhHs3LmTf//8/HzmEm0WRPpMJBIMU3vppZdYYpBwudR1I2fotbU1DA4OQqlUoq2tDWKxGFeuXOHENZFIYHl5GdPT06iurmbcqM/nQygUgkKheN+OmtvtRmNjI5sckhO9Xq/n5kAgEMD6+jqKi4u5g6XX6xGNRrGyssIO5alUivc6SeXRJDMUCqG9vR2zs7NcuNEUIFcQX4kK4Hg8jmAwCK1Wy4k8YVSvX7+Onp4ePsTp+6UigBK9Xbt2sawgrYMgKHNzc1hYWOAxcK5JFXW9SDaRupzUeSasPPB/pHAnJycRCATYm0GpVCI/P5+N2JqamqDVahGNRjE9PY1Tp06hs7MT0WiUfwYlnLkuO5KlpYYDddT37NnDZxo5ltOkVqlU4vr167eJKhCpk8jElPA4HA5EIhE20qTuuEgkgtvt5unGnYJkjgkfTAZjJKRB0Aq67JVKJcPkpqam2Cna5XKxidXy8jJfnFKplEUQrFYrfD4fHA4HwuEw4vE4F7l3CkrAaNpGE7zS0lK43W6G0VCjzGQyoba2FplMBqdPn4bRaERlZSUcDgcuXLiAVCqF/fv3IxaLcSf32rVrUKlUqK2tZYOuYDDIE+rNQiqVora2lqcDxF2ggp+M3+jPkrvvysoKnw0CgQAqlQozMzMs8UpJDrlfDw4OQiQSMZmVvvlcdxcVQKRak0qlYLfbUV1djaWlJfbzWFxcxM2bN2G1WtHS0oLBwUEu2Mm/6Pr163C73SgqKmKJVHLbDgQCkMvlWF9fh91uRyaT4cnNZkFSqtSNJUSAWq3m3IEgjSTG0dXVxcRikhGlAppc0gnCTecxQVCJ4E1eTisrK3jkkUfuuDaCMhFPkrrKyWQSbrcbhYWFLESQl5fHcMzl5WUolUr26rn33ntx5swZzM7Oori4GGq1GsvLy1hcXOS7r6WlBfPz8yw443K5cnLQSGmI+ElkEkrO9gRbo8S1v78f0WgUJpMJRUVFPBEpLy/H6Ogoe12QXxE1ykhsY2pqCkKhkD1wcvEgyIyPGidFRUWw2+2ora1lUSCpVMqFA/meiUQifp5EuKc9Qjkh5Q80vd22bRufL3K5nBEJmwVNuumcoykY2SjQ5JaKzEwmw2qgxKEh+d53330XY2NjePTRRyGXy1lsYXV1lZ23abpN7ui5EDPU2KPvPS8vDwsLC9xwuHXioVQqMTY2hubmZrz99tuor6+HRqNhFalAIMDPnTiSY2NjmJycRENDA+LxOJtaA+9NW96PB8zP8AP9Kbynl33t2jXI5XLk5eXxSOz8+fNsEkVwlXg8jvvvvx9XrlzB2bNnsWXLFsa4UedSqVRy9Uv28qdOnUJlZSXq6uoYWkW8j1xawplMBplMhs2wqJqmC666uprlb+vr67lzevPmTSj/y1FxYWEBU1NTaGxsxODgIMbHx3HPPfegqqqKHYOnpqawY8cOHv9S4pHL7CWVSmFxcRFarRYFBQUIBAIYHR1Fc3Mz+vv7YbFYeDqgUqkYFkSu26FQCHa7HTabDSMjI9DpdKxmQbrNRUVFPKbNy8tjwj3BczaLjY0NXL9+nbHQ+fn5GBoaglAoxIc+9CEsLS1hdXWVO19vv/02enp6MD09DZfLhZWVFYRCIbS2tqK6uhoul4uhUgsLC/je977HSl9UIS8uLrJUXy5cJE0rUqkUJxQA+JnRBQOAf3ZLSwtEIhEKCwsZ0kTOvTqdjsm3tFepkyyTyXgCkU6n4XA4cnYjSTKWphR0oVy4cAEtLS08Rk6n0/xvj46Owu1248CBAwyZefnllxGLxbC0tMTypMRpIudQMhAjicTl5eX3Na+kwpy6TxsbG6xwo1Qq2RRTJBLxQXHjxg1ks9nbiJOVlZVYXV3FzMwMRCIRXC4Xbt68yVKaoVCIu6eUCL1fh0Or1WJlZYXhcKQulJeXh/n5edx7773cpCAjSJvNhuvXr6OgoICnWQQVGhoagkwmQ2lpKRKJBFKpFF566SWcPXsW3/zmN7mLTTCNXL4GhCcXiUSsokP4/FtFIEgasqKiAr29vdBoNLh8+TIMBgOampqQzWZRUVEBn8/HyQx1mkhlZ3p6mqep1MHLdWjTpFMmk3G3ORgM4vXXX8fGxgba29sRCoX4Ikgmk6ipqWGoGU2ZSUmJlGKoE0YNF/KNoL1HZqi5upFk6khQhfX1de7g+nw+hqEkk0nYbDY2Yy0rK2O5aYLeqdVqhsURfIoSIfJZoLOOoHW5Cm+xWIyFhQWk02n+9gnuMTY2xlCvYDCIcDiM3bt3M1m8oqIChYWFjKNfWlpCeXk5TwDJvMtut6O3t5dV5qqqqthFnryJ7hT0HZJZHZFdqTnW0dGB6elpNvIiBUCBQICFhQXmE+7evRuNjY2sikUNtP7+fmxsbOChhx4CADaPpM5srv0mEAjgdDrh8/m4ibOxsYGlpSUYjUZO7oggvbCwgH379qG1tZXV6fLy8tDU1ITdu3djfn4e165dYwNCvV6PwcFBSCQSfs4k2OHz+XLeDXl5ebDb7dyUS6fTWFlZgd/vx/79+zE+Ps7rcrlczOkh6A8Vl3a7HU1NTTwZSaVS/A2IRCJUVlZCp9Phxo0bAMDTQoJp3SmkUimL29C+p6ktNVupeJbL5SwnT9D0WCyG/Px8NDQ0wO/348aNG9i1axerElI+tLKygrKyMobk0vpyEdWpc11cXMyiAvF4HOvr68hmswxN9ng8mJubY5+uiYkJLhSEQiFDv+ieooktAC4OTCYT85bod8pV2JIjOU12V1ZWUFRUhHfeeQfFxcXYvXs3oyIaGhq42UKTV4LO07mo1WrhdDq54KiqqsLVq1dx+vRpPPLIIzwFIrRMLrd3mUyGQCDAnmfpdJrzxps3b7KXBk3+3njjDRw4cAA7duxAf38/lpaWoNPp2O8ilUphfHwcZrOZIfixWAwtLS0oKirC/Pw873OCYm4WBH0j3iOhC959913U1dXBbDZjcXGRv4Gamhqe1lVXV7Mkr0QiwdLSEhcrwWCQ82ryhrq1cCPkUi50xa0hyL4f+PZu3I27cTfuxt24G3fjbtyNu3E3/i/jA3M07sbduBt3427cjbtxN+7G3bgbd+ODxt1C427cjbtxN+7G3bgbd+Nu3I278f887hYad+Nu3I27cTfuxt24G3fjbtyN/+dxt9C4G3fjbtyNu3E37sbduBt34278P4+7hcbduBt3427cjbtxN+7G3bgbd+P/eXxgedvvf//7kMlkSKfTUKvVUCqVrPnf3t7OsmukfR4IBBAOh6FWq/Haa6+hpKQEnZ2dkEgk7ERJtvSrq6tYXl7GxsYGysvLWdqQTOxIL/vP/uzP7ri27373uygtLUUymWTN40AgwBrCZG3v9XqRTCZRWVkJr9fLf7agoIAl70wmE4aHh5Gfn89yXgDYmXV9fR1+vx8ikYjl2hKJBP7qr/7qjmv793//d5YClkgkLLEYCoVYxzoQCGBtbQ3hcBi1tbWorKzE6OgonE4nCgsLUVpaymZjpJFcWVmJjY0NOJ1O1qQXi8WYnJxkB2mZTIZ4PI7Pfe5zm64tlUpBqVSyZCZJ7el0OpZOI/OY9fV1FBUVIZlMQiwWo6ysDHq9HtlsFjdu3MDq6ipUKhWUSiVrYZO0WyAQYM10ki4WiUSb6pH/6le/glKpZO1/gUCAjY0NGAwGuFwuWCwW2O12ltUk/fSpqSkoFArW489ms6wfv3v3bpYUJQlckjwm/f9wOMyGU5/61KfuuLbnnnsOEokEarUapaWlEIvFuHjxIjt/b926lX0NyJ8gHo+juroaV69ehUQiQW1tLQwGA0ZHR9ntWaPRIJVKYXl5GaFQCHK5HO3t7RgeHmY/Er/fD5VKhS9+8Yubrg14T/K5uLgYMpkMq6urbGqlVCpRUFCA1dVVLC4uory8HJ2dnVhYWMDc3Bw7Y5M2PbmG63Q6NlgkWUT6bkiilb67zb4FAPjbv/1bdogWiURslra4uIjS0lL+3klikhyGSSKRZJiDwSDLjJrNZpZ1DofDCAQCKC8vx8LCArsqk1SfVqvF1772tTuu7Qc/+AF/4wKBgKVAA4EASktL+fmTt0B5eTmSySRcLhdLKpaXl7MGPZ0tZrP5Ng+JbDYLn88Hr9cLq9UKvV7PZpN/8Rd/cce1/d3f/R3rwZPcoNfrxdraGoRCIUtnk8cRnaN9fX3sGZTJZOBwOKDT6VBeXs4OwGR65XQ60dXVhUgkApfLdZtJlUQiwTe/+c1Nnxtpv5MEJ5mUKZVK1rpPJBJ8nthsNqRSKT5LJRIJ+x6Rd0A4HGap3vX1dezZswcul4u9I2h/FhQU4Ktf/eod1/bKK68AAJtgFhQUsLwjST273W42FyQz1IKCAt6HUqkUBQUFuHLlCpqbmzE3NweJRMJnnNfrZRdwt9vN95BYLIZarca+ffvuuLZf/epX7NNC39KtkuHkgE7mpZFIBAaDAeFwmOVGCwoK2FXZbDZjaGgIZWVlyMvLg8Ph4O/eaDSyN0UkEsHq6ioymQz+/M///I5r+853vsOGaQDY52phYQEmk4k9nUhTv6ysDDqdDhcvXkRlZSXy8vJYjjQUCqGgoABisZjzBIlEwqaGCoUC4+Pj7PNAd+Gzzz57x7W9+uqrbDxH5wjdKeRgTb4G4XAYCoUC4XAYkUiEvVTS6TRL3c7PzyObzaK2thaFhYUIhUJYXFxEQUEBbDYb5w4kazo2NoZnnnnmjmv7/ve/j2w2y1KoADjPiUajaGxsxNLSEtbX16HVamEwGFhKdmlpiWXCxWIx5ufneZ+S5HYqlWKPH7PZjNHRUaTTafY6WVtbw9e//vU7ru31119nE9tMJsO5yNDQEHbv3o1EIoG8vDysrKxgdnYWHR0dnHeUlpYiGo3C7/dDrVYDeM8ng/xIyIxOo9FgenoaFRUViMViWFtbYx8opVK56Z36/e9/nz1vSLYWAOdgZA5JksbxeBwejwelpaWYnZ1FLBZDcXExysvL2ZiOnhc5b1NOSAa2ANicVSKR4Iknnrjj2r773e+y7wjlWmTSp9fr2YOLzq9UKoVoNIpYLIbl5WXI5XJUVFTAYDBgZWUFVquVz0mfz8c+SAaDAWq1mg2i/X4/DAYDRCIRPv/5z99xbS+88ALneXl5eSxDTX5qMpkMy8vLSCaTMBgMnGOQHUQ8Hkc6neb8UavVskcK5UOZTAZqtRpyuRwulwvz8/MQCATYsmULdDoddu3adce13RofuNAg04+lpSUEAgGo1WrEYjG0tbXB7/ezdncoFIJYLOaHurCwgOrqasTjcfT19UEul0On00Gn02F6ehoajQYWiwVlZWVYW1uDx+PB+Pg4JxVkdJNLS5j088lFWyAQYG1tDfF4nA2jSM86nU5jbW0NZrOZdZbpEN/Y2IDH40FtbS2mp6fZAIus7QUCAR9qZNxFjqmbRSgUQmlpKSeblGRJpVLWrqeXnc1mEYlEMD09zb4OdDh7vV6EQiEYjUY4nU52PTcajWwaU1xczJrspJmdS7s6EAiw8SK5n5Izst/vR01NDXtpAO9phJN/RzgcxuLiIrxeLyoqKvhilclkSCaTcDgcyGQyrBvtdDqhVCrZXTYcDvNBfKeIRqNQq9WIx+Ns8pfNZtkFnZyeqbj1eDz8sZDLN10M5Lg7MzPDH24oFEJ5eTkGBwfZ0I72UjQazanNn0qlkJeXh6WlJT5AM5kMTCYT3G43pqamWI+dHNzLysqQTqdRXV2NtbU1zM/Psx9DUVER4vE45ufnIRQK2RxqeXkZfX19cLvdvBfI0XiziEQisFgs7JNB/9utOuOTk5MQCAScjJPjsdlsxsLCAiYmJmAwGCCVStHd3Y2zZ89yEWkwGCAQCDA8PIzW1la+3NPpNPx+Pzvv5nqvKpWKNevp73k8HlRWVqKoqOg2Iy65XA6Hw8Hnz+LiIrLZLORyOVZXV9mB2uv1cgJGh39+fj42NjbYWIpchDeLWCwGk8mEcDh828V28+ZNFBUVcZJMJoOUPJGmezQahd1u5wIqFAohGAyyyVQ6nYZWq8XS0hKEQiG0Wi1EIhG7t+YyiaQzlor/SCTC32txcTH7tlBzxW63cyJI3zbwntY+OcdLpVI2IqQzeXFx8TZTL/r+cr1XKjqpYKHvUalUIp1Os3koGUXe6tys0Wiwvr4On8+H8vJyrK6ucuJP3hLRaJSLY7/fj2g0CuD/eBbQf79TrK+vswdFMplkfw/gPQ36ZDKJZDLJjYPV1VVUVVXxs1tdXeXmVH5+Phvp0R7OZDLsIZTJZNi/IRaLsQ/FZoWG2+3mAphc7GUyGUwmE5aWlmCxWJBIJOB2uxEMBlFaWsoa9yaTiQswoVAIj8cDjUbD56tSqYRWq0UymcTGxgYWFhYgFovZlR1Azv0WiUSgVCpv+zOBQAB6vR7BYBAA2MeJijIqzCYmJtgDZWNjA9PT06iqqkI4HAYAiEQiFBUVoaamBh6Ph98JmeqSH9RmQUZ89D7JKNRgMGBhYQFutxvAe3tdLpezgzoViNlsls8K+h3IVDgajSIej6OxsRF9fX1wuVyctJGhXC5HgFgshsLCQmQyGcTjcT4LyJ3e7XZjbW2NvWby8/Oh1WrZRJiKnLy8PDbfpOIzPz+fG4ArKyvsTSYUCpFKpbC6usp5yWbvlLynqCiJx+NQq9XslUXJdE1NDaanp9HZ2Qmfz8deQPRzqGlLjU4yaaR7ViwWQyQS8bdF5ri5oqSkhM9eugdTqRQikQjnUIFAACKRCPF4nM160+k0Fws+nw8qlQoKhQIDAwMoKiqCUCjkYpf2VTabhVqt5mZoRUXFpusSCATIz89HNBplo8uNjQ3IZDI2Y6TnTr5BgUAAoVAIjY2N/MyouVdSUsLfF+3HZDLJa3S5XJy3rKys5PT0IoPbRCJxmzlfVVUVFhYWsLy8zHud7hnKbTQaDTt/+/1+GI1GNuVTKBRQqVRQq9WQyWQYHh7GxsYGiouL2W/Mbrfn9Gm7NT4wdIo6e9RJoeRiaGiIK3bqIjidTiwvL6O2thbhcBiJRALJZBIFBQWoqqpCJpNBf38/IpEIpFIpioqKIJVKEQgE2FRIJpNxt5+q0s2CjHnoIgsEAgDArovxeBwCgQCxWAwLCwt8qZNxExUT1E1eXV3ljiZ1gNfX19m8S6fTsYsyXRSbPuD/MoajpIAu/ZGRESSTSRQVFUGlUkGj0UCn0/HFnJ+fz8ZU5FhaWFjIFT65a1PClZeXB6FQCKPRyJc+faSbhVgs5mdBhz9t9JWVFU54M5kMXC4XpFIpTCYTdDodYrEY1tfXkU6nUVJSwgcDmVDRO6duA7kEp9NpThxyuTRTZyUWi/FzMxqNPOUAwAcHFbcCgQClpaWYn5/naZDRaOQDPJPJIBAIsEv76uoqiouL4ff7kc1m2dRMIpFAqVTm/B4oMQgGg+zKeWsXht5zIBBAMBiE2WxGNptlE0EyMLLZbFhfX0cqlWJzITqoAKC6uhplZWW37YFciVUmk0EsFuMCemVlhQ0PybCKvl0qcv1+P6RSKZxOJ1ZXV7nYIqM6g8EAm80GvV6PWCyGsbExyOVyJJNJ7qasra0hm80iPz8/53OLx+NIJpPcmaPCdXR0FOFwGEVFRTAYDFCpVNypA8CHMe0HtVoNs9nMJoZ0AdB3HI1GUVdXB5vNxsVINptFUVFRzj1HrrgFBQV8sa+srCAYDHICTs+VDAstFgt0Oh2/d0r+aPoRDod5shGPx6FUKlFRUYGGhgaebkokkpxmgvTnMpkMT24ymQwXWBsbG1w80P+Pkn6v18tNgfz8fE5UqBimzl8gEIDT6eSJKJ1B5Oa+WZAbvEgkgkKhgMlk4k4aNVOoQ0YFoEwmg1KpZIMpKgjJpJXOxqqqKjQ3N7MTtEwmg06nY2NAhULBHdY7Bf1utK+o8x6JRPjMMxqNUCgUWFtbY7f5QCAAoVCIaDQKt9vNTR5q4hQXF7Nx3vz8PD9Pi8XCiXdhYWFOZ/BQKMTNMbrA6Zm4XC7e09QYIRPawsJCuFwuNoTUarXIZDIYHx/nbnc6neZClxJ5SpCi0SjEYnHORg8VYHQPUtHg9/uRTqc5SSE35mg0CpfLBZVKxX+Omgr07oXC99INWiN1hfPz87kzHggEeOKUa78JBAK+G6mh0t/fz8aQ9I14vV7E43E27Zybm0MgEGAjyOLiYqysrHAhTROieDwOlUrFZzIANoE1Go2bri0/P59zEL/fz+fG+vo6TCYTIpEINBoNjEYjYrHYbcZxy8vLvI8o0Zybm+PCjs5Cuhep4I1EIlhZWUF+fn7Ob4GmijKZjM/JbDaLubk5uFwuyOVyvgdvbagIhUKsra0hlUqhpKQExcXFkEqlmJ2d5b1C0zuarGm1WsjlcjYEFIlEOe8GKpbo37516iWTydhok3I0Mqkjg12Hw4FwOHzbc5ufn4fb7eYGr9frRTAY5HOAJic08d4sCIVDeWh+fj4XO36/HwUFBdBqtSgsLEQkEkEikYBarYZQKGQzPjprpFIpxsbG4PF4+DzTarWIRCJYXFzk7zORSPB+zJWLiEQirK2tQSQS8b9He5lQN4RsoPwjm81CLBbj+vXrmJ+fh0KhgFarxcbGBi5evIhAIMD3RDAYxOrqKrxeLwwGA/+eyWQSHo+Hp6TvFx+40BAIBIhEIhCLxexGLZFIMDAwAI/HA7lcDpPJxIng0tISGhsboVarEY1G2Za9q6sLwHsfC3WjA4EApqen4Xa7sbGxgYaGBoYgJJNJvsA2CzrwaGpAG4sSTADcjaKXGA6HefNSNVxYWIiNjQ3Y7XYIhUKUlZWhvLwccrkciUQCGxsb3H0gWFc2m83Z/ZZKpQzRSafT3OmgLq3ZbIZer4dOp2MYDjmlbmxs8EjMZDIxlKOoqAiVlZXsIu52u7kAMZlMyMvLY6hJro+bijkamVJ3itw2RSIRtFotV7CxWAw2m+02R/KioiLI5XJ2yvV4PEgkElAoFDwlkEgkaG1t5WdHxWWujhpNfLLZLF/YGo0GjY2Nt23ueDyOSCTC7t5FRUXweDwIhUIoLCyETCaDSCTiDjS5WZLLLr176mQB7xVgdLncKeiSo2dL0BJKMMvKymA2m297vjqdDolEgosKjUaDhoYGNDU1cRFMyYZYLOaksLGxEdXV1ZBKpTyazRUCgQDBYJChZj6fjzvIqVQKMpkMer2eO8R0CNEkLRgMMvwtPz8f8/PzUCqVaG9vZ9jawsICysrKeA+FQiF4vV5IpVKUlJTkXB8A/v7oQtFqtRAIBPB4PAzN1Ov1DHOg76KoqIiT1IqKCtTU1AAAXC4XSkpKUFtbC71ezxekxWKBxWLh/ZlIJFBaWrrpuijRBcAwR+D/OIZTASSRSLip4vP5YDKZoNfrUVhYeFtjgDpz5HhtsViQTCZRWFiIyspKnoTRRVRfX7/p2ijRS6fTPPKmPUfdzoKCAohEIoYulZSUcKJNU0RqCFAjgBxfb4XVFBQU3AY3y8vLy1kE0RSZvlPqhNE7prOJ4ImUqBqNRqyvr/NZTBMiSoTy8/NRUlKCsrIyyOVyCIVCVFZWoqamhqeWpaWlObuReXl5DNmivX9rs0aj0UCv13OCSJACSuipsBAKhchmszzN1ul0KC4uxurqKkZGRuDz+aBUKqFSqbgzr9PpcrpvU3IjEAig0+nYxZ3cymOxGBcEYrGYmxR0xlESS9Aa+vMFBQXIZDLw+Xzw+XxIp9Mwm83QaDS8p0UiUc5Cg/aNSCRiSFM6neZGjU6n47ubpj4CgQAFBQUoLS1Ffn4+J+vUKCkuLobRaITRaGToKqEOqFNPjSoqHu4Ut8KZ6Q7PZDKYm5vDxsYGdDodn3EbGxsQCoUIh8PQarVcANHEhd5hIpHgZ1lYWIjx8XGG0tF0l/ILrVa76dro3iboDCXz1GlWKBSoqqqCzWbjoiwSiXAjl2C9FRUVyGQyWFxc5Kk3na0rKyucS8jlcna2F4vF0Ol0Ofcb3VsSiYSLVLfbjUgkApPJhIKCAvj9fgQCAf5GCgoKoNPpoNVqoVQqoVarUVxcjFQqxXtTJpMxyqW0tBRGo5G/Wbqjc62N1kfnL+VrhP6QSCSQyWTcRAXAz5BgVJFIhGHyVHjc2kRNpVIIBoP8Tmli8n5FEDVShEIhioqKuMtP69JqtVAoFCgsLER+fj6SySRMJhOjEugsKSsr4+cbjUb5nNZoNAx9FQqFXADRXsv1nVKBduudFI/H4fP5EIvFoNfrodfroVQqIZPJEIvF+Myfm5uDw+Hghmc8HofL5YJYLIZer4dUKoXb7cbMzAzy8vLQ3NwMtVrNE5SNjY3/9xMNkUgEk8mEeDyOpaUlLC8vQyqV4tixY1hbW0NHRwdKS0u5c5SXl4fCwkKoVCocOHAAer0eTqcTsVgMi4uL2LVrF2KxGObm5jA6OgqXy4Vt27ZhbW0NEokEs7OzkEgkKCsrQyqVgs1m23Rt9GLpcioqKuIReDAYZCxwcXEx9u7di0AgwNApukCXlpZQV1eHhoYG1NXVcTeVDmy5XM4dRbvdDpPJhJaWFgSDwZz29dFolC8y6iAnk0ncf//9cDqd0Gq1jM0FwIcYdQjoQNmyZQtcLhfy8/N5/BWPxzE+Pg6DwQCHwwGv1wun04l0Os3dyO3bt+dcW0lJCdbW1rC0tMQd2GPHjjHGlC6VgwcPwm63o7q6GsvLyzhw4ADa2tqwsLCA/v5+xjhvbGxwomg0GvHZz36W8dbT09NQKpXQ6/UYGBjgUfqdIhaLob6+npMDs9mMq1evMuyHireioiJUVFRAKBQimUzi0qVL6OnpQVtbG3eg/H4/Ojo6YLfbufM8Pj6OxsZG7lBNTU1BJpOhrq4OoVAoJ8Qmk8lg7969t41IFxYWUFdXh0gkgnA4zFwHo9HIF9/Kygoef/xxbN26lS8Pj8eDXbt2MfYxEAjw4RUKhRg+lZ+fD6vVikQikTMhJbgYQXVKSkpQUlKCVCqFt956C6lUirGz9K6amprw7rvvMremuLgYW7ZswYULF1BbW4vV1VWsrKxgaWkJLpcLjY2NuHnzJgBgbm7uNgjg+4VCoYBAIIDD4cDU1BTm5uawtraGr3zlK/B6vXC5XJiamkIoFILZbIbP50NTUxNcLhfv2aWlJbjdbiwsLEClUqGwsBD19fWoq6tDcXExKisrMTw8jGvXruHChQsMa6Nu7GaRSqV4ikOJ99mzZ/HYY48x5I7Omn379iGRSMDj8eD69esIBAKYm5vDyMgId+IqKytv6y7H43FUVVVhbGwMAoEAFy9exMLCApLJJHfpNotIJAK1Wg2RSMQJXXFxMRc/+/btY25NU1MTQqEQw/taW1vR2dkJtVqNVCqFubk5VFVVwePxQCQScYH70Y9+FCaTCWVlZVhcXEQsFoNcLodAIOCi7k4hlUqZSxUIBDAzM4Pl5WVs27YNVqsVzc3NKCgogFwux549ezA/P49UKoXp6WnI5XL+vaiIom5aOBzGjRs3cOLECUSjUeYtOZ1OLkAlEgnW1tY2XVs4HEY0GsXi4iIWFxfhcrmwuLiI5uZmnvIRnKW1tRWVlZXYuXMn7zcqRAoKCnD+/HkA4MSKGh8HDhxANptFeXk5BgYGMDExgXA4DJfLBa/Xm/O5mc1meDwezM7OYmVlBXa7HUePHkVVVRUqKytRWFiI4uJiVFVVwe/3o729HQsLC6iqqoLRaITf78fi4iLcbjc+9KEP3TZdVCgUUCqVUCgUUCgU2NjYgFwuh8Fg4GnYZiEQCLgB4fV64fP5EAwG0dLSwhMfqVSK5uZmPPTQQ5DL5Thy5Ajsdjvq6urQ0tKC/Px87npTIt3U1ASLxQKfz4ctW7bA4XBgY2MDS0tLSKVSfD7Q9ONOEY/HOcEViUTMPXriiSeQSCS4667T6VBfX49MJoP9+/cjGo2ivr4eIpEIk5OTKCgowPLyMqqrq7npSQ2XeDyOiYkJvlfkcjkqKythMpmwvLy86dqi0Shqa2v57DeZTJicnERVVRV6e3tRW1vLiXF5eTkymQwaGhrg9/vxwAMPoL29nRsxIyMjeOihhxiZQTkVFdYWiwUOhwOVlZVoamrCyMhITlhXQUEBw3ESiQRzQY4fP85TiMLCQmg0GrS0tCCdTqOtrQ1ra2toa2vj6W40GsXg4CC+8IUvIJ1OQyKRIJ1Oc+PupZdewurqKhYWFlBaWoqWlhaEQqGckN9wOAybzcYNNYlEAr/fj3vvvReDg4PczBWLxWhoaGAeXUFBAe69917s2rULZrMZcrkcY2Nj6OzsRHl5OVpaWqBUKuF2u9Hd3Q2pVIrl5WUsLS2huroara2tWFlZyVk8ut1uGAwGFBUV8XedyWTQ2trKd63b7UY2m0VNTc1tXEqbzQatVsswvt7eXrS1tXFREgqF4HQ6sXXrVoYwE8S2rKyMJ/mbxerqKioqKhh6lU6neapH90w8HudJFjXUg8EgnnjiCdhsNoyPjyOVSiEcDuPzn/88mpqaUFJSwmuUyWSora3F6OgoRkZGGGZdUFCQ8+y9NT5woUGJ6759+3D06FFYrVaEw2EMDw/joYcegt1u54Pc5XJh9+7dTGp79tln0dfXh5aWFqyvr2P//v149tlnmfij1+tx8OBBJBIJfOELX4BWq0VLSwuWl5cxNzeHvXv34vr165uuTSqVwm63QyKRcDdtz549UKvVuPfee2Gz2dDW1gaNRgO73Y7u7m688cYb6O3txb//+79jeHgYTU1NqK+v56lHdXU1EzUJR75//3709vYCeA+vSnjAoaGhTdemVCoxOzuLbDYLm82GrVu3orm5Gb///e/x0Y9+FA6HAysrK1hZWcGlS5eg0+ng8XhQXV3N1Xd9fT18Ph8OHjwIAOjo6GCSZHV1NRKJBGpqaqDT6bBv3z6o1Wr4/X7IZDJMTExs/vKFQu7iEQmts7MTTqcTFRUVUCqVWFhYwPXr1xGLxfClL30J/f39qKmpwaVLl7CwsACbzYa8vDzs3bsXGxsbOHLkCPR6PSeeb7/9Nh577DHMzMygvr6eR4Rbt25FZWXlpmsTiURwOp0oLy+HTqdDPB5HSUkJJiYm8OCDD/J42+PxYHFxETabDRqNBlVVVdxBqK6u5gRtcnISO3bsgEajYQzw/Pw8HnnkEe52DQ8PY2hoCIcOHcqJiywuLkZ/fz9aW1uxZ88eVFVVoba2FnNzc3jggQe4qysUCuHz+fCZz3wGCwsLqK+vxy9/+UtMTEygtrYWdrsdAoEAv/nNb2CxWLBnz57biGK1tbVYXFzE9u3bmXAfCAQwMjKy6dpKS0v5IiS4xPbt21FdXY0/+7M/Q15eHoxGI7RaLRYWFrCysoKpqSl8+MMfhlQqZQz43//936O+vp4vC6vViu7ubrS0tOC3v/0t9u3bB4vFgqqqKoZjVVVVcYK2WQQCAbjdbuzatQv79u3jKeh3vvMdHu+63W4sLy8jnU6jpqYGYrEYnZ2dGB8fh0wmw969e+HxeHD48GGcPHkSVqsV58+fx8mTJ5FOp/Hmm2/iwQcfZNgNFXF79+7NeWhTMdLc3Iza2looFArs3LkT77zzDsrKytDb28sFzOuvv45Dhw5xs+Hq1asQCoV48MEH8cgjj+DJJ5+E1+tFIpFAfn4+EokErl69ih/+8Iew2WxQKpV44IEHcP/992PPnj1obm7GmTNnNl2bTCbD7OwszGYzmpqaUFpaioaGBhgMBhiNRly4cIFxtiaTCffddx9PN99++23Y7Xao1WpkMhku9vPy8mC329HX14ebN29iaGgIBw4cwPz8PNrb2xEMBrG4uIh7770XDodj07URj8ZisaC2thbV1dXo6elBX18fMpkMTp8+jcXFRYTDYczPz+OrX/0qlpeX0dbWxnDP5uZmThJPnDiBLVu2MFShtbUVdrsdO3fuRFFREUQiEZaWljA+Ps6Y7s2Cinyr1YrCwkLmpJw4cQI7d+7E9PQ0kskkpFIpxsfHodPp8IMf/ACtra34wx/+gLm5ObS1taGkpIQTWZPJhNdeew1vvfUWJ+M9PT2YmZlBXV0dQ/D27duXE6pns9kQiURw+PBhbN26FfF4HGVlZXjllVe4KLwVMtjY2MhdROI4WK1WOJ1O1NbWor+/H9u3b+c/bzKZcPLkSdTV1SE/Px9LS0uYnJzkQo3EHe4UxNfSarXcSd+yZQvi8Th27tzJ+4VgxlarFW+88QbkcjkTxvft24dUKoXdu3fD4XCgpqYGQqEQGo0GTU1N+Pu//3vs3LkTQ0NDkEgkWF1dRTwex4MPPpgTOkWcxcrKSrS1taGtrQ0HDhzA8PAw8vLysLi4CJlMxnuPigu1Wg2fz8fFZDqdxpYtW+D1elFTU4OrV6/i7bffxsrKCn7zm9/gs5/9LBeUbrcbdrsdZWVl73vGDQ8Po7S0FHV1dSgrK8OOHTuwsbGBLVu2cB4jFAoxMDCAT37ykxgZGcHAwACeeuop/Pa3v4VOp0NDQwO2bdsGpVKJ1tZWVFRUwOVy4ezZs0gkErjnnnuQTqexe/du7lTv2LGDi+E7RUFBARYXF2G1WlFdXc334OLiInp6evDGG2/gtddew9DQEFwuFx577DEIBAKMjo7iypUryGaz0Gq1OHXqFEpLS/GrX/0KJSUlMJvNMBgMyM/Px7Vr13D8+HGGEV+6dAmjo6N4+OGHmTtzpyguLobP58POnTuxa9cuVFRUoLOzEz/5yU8gFAoxOzvLUHulUomnnnoKCwsLyGQyOHPmDAQCAbZv3454PI6HHnoI58+f58JCLpdj165d8Hg8aGpqwssvvwy3242LFy+ir68P99xzT86im8SD6urqUF5ezryI2dlZaLVanD17FsPDw5iensbk5CSefvpphmCfP38efr8ftbW1EIvFeOaZZ3Dq1ClUVlZiamoKMzMz0Ol0mJiY4MaaRCLB2NgYzp8/D6fTiT/+8Y+bro2mszQhJ36WUqnEvn37GAacTqfh8Xhw8OBBLC8vo7y8HG+//TY3WEQiEUpLS/HSSy/xNI4g3lSwX758GRUVFTwZOnbsGO65556c3wLFByaDE+l1eHiYycupVApbtmzBG2+8wdhAws97PB60tLRgbm4O//N//k/uvMzPz2N4eBhVVVVMIozFYjh37hxOnjyJP//zP0c0GoXT6eTpyNLSEkwm06ZrCwaDXLSk02mEw2GMj49jx44dOHfuHB555BHunFZXV2NkZATNzc08TUilUhgfH4fX6+VNsLGxAZvNhmg0yqoiS0tL3B3TaDQoKSnB+vp6zi7C2toaSktLEYvF4PV64fF48PLLL+Oee+5hcqrD4UBRURHa2trwxz/+EVu3boXP52PFDo/Hg+LiYvzwhz8EALS2tqK8vBxCoRBLS0usgrN161bcuHEDS0tLSKfTmJyczImhoyRIr9dDIBDA6XRibGwMDQ0NzD1wOBwMrfrNb36Djo4OXLt2jTtqGo0GCoUCP/3pT3Hw4EFIpVKUlpbC5XLhnXfeQSqVYgWe8+fPo7S0FGazGV6vNyfumwh4i4uLUCgUjCW///770dvby8RTwoteuXIFJSUlkEqlWF1dxeDgIOPFnU4nFAoFZmZmmNBFCef169exurqKdDrN8LDx8XGsr69vqqYwNjaGXbt2YXJyElqtFqWlpRgYGMCePXtw48YNNDU1we/3I5FIoKmpCeFwGFeuXEE6nYbVar2N2EoQGLFYjFOnTiGdTnORMjY2BpVKBZfLxdh0IgFuFjMzM4wFJkWNV199Ffn5+RgcHIRer8fc3Bx8Ph/D4G7evIlMJsOJocViQWtrK3p7e1FVVYVr165xwhMOh/Ef//Ef+MMf/sAdb+pEX758OWdXHgDzK2hMu7y8DIlEgoceeohJ3sD/ISVOTU3BaDSit7cXRqMRwWAQr7/+OiKRCK5fv47du3ezWlskEkF/fz9MJhNPYEpKSiCXy5Gfn4+rV6/mFEcYGhrC1q1b+TunDn1bWxuGh4dRW1uL+fl5mM1mtLe3w+l04sCBA7h27RouXbqEqakpxONxrK6u4vz58wwlqKiowMbGBsbGxrB9+3a89dZbTFSksXkwGGSo4Z0inU7zPtPpdFAoFHjttdfw8MMPY2JiAkVFRfD7/VhZWcHy8jJUKhUaGhoY3kBwAr1ej2vXrjF0hTreEokE1dXVOHfuHE8qiSx65cqVnPBQmkb7fD6GoczNzbESoM/nQzQaZTzya6+9xqT4bDaLtbU1hssEAgF8+tOfZhx9OBzGu+++y1DQ8+fP82ifune54IQ0cWxtbUV+fj6cTicWFxeh0+lw/vx55qSFw2EUFxdz4vHOO+/w+UZ8rBs3buBTn/oU/uM//gOdnZ38LVFCT/Cf0tJSJBIJvPLKKygsLMSBAwfuuDYiXQ4MDPA5oNfrGa8fjUaxtraGvLw8lJWV8fRrYmICJpOJ4VM6nY55Vn19fQzHCYfDePjhh3H16lWGKJHa2OLiYk5IhsfjYU4R/TsrKytobm6G1+tFQ0MDvF4v/H4/zpw5g+3btyOTyWB+fh7Ae5NOnU6H3bt3Y25uDs3Nzejr62NRhitXrqCzsxPRaBQ7d+5Eb28vNBoN1Go1BgcHc54j1I1fWFhghalr166xWIJKpcLMzAwEAgEOHDiAaDTKEL1oNIr+/n6GW7311lvYv38/fD4fq2e6XC488sgj+N//+38jHA7j0UcfRX19PU+ccnE0YrEYrFYrpqam+F6fnZ1FbW0tTp48iY9//OMYHR3F/Pw89Ho9vv/970Ov1/MUyGAwIJvN4sqVKxgYGIDT6WQIDU1WVCoVxsfHmetG2Pn+/v6cd2oymURHRwc8Hg8n4OPj46iurmalKFL7q62txcbGBkZHR1no4p133kEikUBXVxdmZ2fxpS99CfPz8wyBo2c8ODjIMMJDhw4hGo3ixIkTOZ8bnQ+jo6PMidJoNDAYDAyxXV9fZ87FwsIC83o//vGPAwAWFhYQi8WwurqKPXv2YHJykjmsdrsdlZWVSKVS+NjHPobx8XFUVlaioKAAN2/ezJkn2e12bNmyBX19fQy7C4VCGB4exoc//GFUVVWxMmc8Hkd/fz/MZjNCoRA+//nPY3l5Gb/73e845xEIBJxjkGgEAIZG+3w+dHZ2IpPJ8LPcLIiHOzQ0BL1ezw2PhYUFGI1GFjiamZmBz+fj3GFwcJAnt5cvX+Yp7YMPPogf/OAHGBkZYbXTUCiEc+fOYceOHSygJJFIcObMGahUKnz5y1/edH0U/1dk8D/84Q8YGhpicq/FYsH169eh0WjQ3d0NiUTCRHCn08n/+Wc/+xnsdjsaGxtZpo0wiG63G16vF5FIBJOTk4xDJrLu7Owsb7Jca5ubm2NiHuGm/+3f/o3HU3SJnT9/HjqdDiaTCfPz83jxxReZ3Gq1WnH48GEsLi6y9CwpCkxMTECpVKKrqwvd3d0oLi5m5YVciR+NqQg7HwgEsGPHDrzzzju4efMmJ6rBYBDnzp1jdYpLly5hfHwci4uLcDgcWFtbwwMPPIBLly7h1KlTWF5e5rH+5OQkH+D0nPR6PZqamnJODYqKijAzM4P5+XnodDp0d3dz0Udd1+bmZhQWFuLdd9+FyWSC1+uFXq/H9PQ0bty4gcXFRQiFQtTV1eHll1/mjoper4dQKMQf/vAH3Lx5E/X19ejp6UE6ncbS0hKsVmvOi06hUDAuOhwOM6HszJkzjG2nhMvr9UKtVsNqtTIkJ5vNoqWlBa2trdBoNCgsLGTuDhF2/X4/HA4HOjo6+JC/lVS+WVitVt5nsVgMAoEAWq0Wv//975HJZPjfGhkZwYsvvohf//rX6OjoQCgUwu9//3uMjo6iuroahw8fRnNzM2QyGbZu3Ypjx46hrq4OCwsL6O3tRXl5Obq7uxEKhTA+Ps5cBJ/Pt+naiAhJ3ABSonjllVcYnkGX0dWrV7GwsMBkcb1ej9raWtTU1LBE8NzcHCupkPTrt7/9bSSTSSgUCuYd0OX1fhyNRCIBkUiE9fV15Ofno7KykgtegmJUVFQwMZY6k3K5HCdPnoTT6URTUxN2794NgUAAtVrNU5JYLAahUIjR0VGIRCJWbHG5XIyDzqXKQrCcdDoNsViMjY0NhEIhPP/88+jo6GDJ6NXVVZw6dYrhhg6HA5cuXUIikUBzczPKysqYpBmJRLC0tIRgMMgwnePHj+OBBx6ARCJh+WMa9W8WUqkU8/PzCAaDcDgcuHHjBmZmZnDlyhWEQiEIhUIu+mZmZrC+vs6yv9ThIoJ7Z2fnbaRX4mFQ5+xWOWnaSyROcKeQyWTcyAkEAgzJvH79OjKZDIxGI3MP4vE4ysvLuSt8/vx5VvojknpbWxtGR0dRWVmJ9vZ25hq9++67MJvNMJvNkEgk8Hq9mJmZyTmlCofDLNFLHDaVSoXS0lKWLb6VG3Ho0CEmh5OiEfDexRyJRPDMM88gnU6joaEBNTU1MBgMuHLlCne+E4kExsbGMDs7C4vFgo6Ojk3XlpeXh5GREW4KlJSUIBaLwe/3w2azoaWlhUUk3G43J6RNTU1wOp0IhUKwWq2w2WyoqanByMgI8xZkMhlkMhl8Ph96enrQ0dGBRCKB/v5+jIyM8Le3WcRiMea2qNVqVFVVQSqV4vr167Db7dixYwe2bt3KE8n+/n60t7fzvTg7O4vJyUnMzc0hLy8P165dg9VqhclkglwuR3FxMex2O8sLq9VqjI6O4sSJE7xHNouSkhKsrq4im80iGAzC6XTy1FCj0cBms0GtVmNtbY15G4FAAMvLy7hx4wZ8Ph8sFgsqKipQX1/PSpcul4v5TCdOnIBKpUJ7eztGR0f5riOezmZBUDMiCxPmnWRNb968icXFRUxPT+OXv/wlEokE5ufnEY/HYTKZUF5ejurqarS1tTEHU6FQwGq1orKyEsXFxchkMtxwMplMWFxcxOjoKIqLi3M2K6iII2njdDrNjZFkMony8nKUlZXBbrfj17/+NUOkbTYbHA4HDAYDjh49Cq1WC4vFgrm5OVbiFAqFXJh0dHSgs7MTGo0GfX19eOuttyCRSHLC3wmO5fP5uMEsFouxtLTECn4kVx8KhdDc3IzBwUEIBAI8//zz+N3vfge3241oNIobN24gFouhqakJKpWKBS0SiQQcDge0Wi2MRiNCoRDC4TAaGxtzSrQSn5P4DrFYDC6XCwaDAUNDQ6ivr4fBYAAAblbW1NQgkUjg5z//Oa5du8ZcJZLaJ5VR4htWVlZCq9VicnISDocDMzMz8Hq9qK6uzvktUCFFNgNDQ0PMU5bL5VAoFGhqauJposvlwuHDhxGLxXD69Gn4fD7U1dWx+Md3vvMdWK1WHDlyBKWlpSx539PTgwcffJAb0MB7KKfW1tZN13ZrfOBCg6AchKclacq9e/eyrJ9MJkNjYyN6enq4CnU6naiqqmJG/vbt2yEWi9HV1cUQF1LI+fSnP40TJ04wcbq4uJjJdbm6Vul0mlUEiKyeTCbR0tICkUiEubk5lv/r7u5mQk8mk8HOnTthsVh4pLuxsYGOjg4kk0mWLzUajdi/fz8GBwdZSaGwsBBms5kx+JtFLBbjA319fZ0VCdrb27kDqlAoUF1djbq6OiQSCSYs3nfffbBarTzuHx8fx86dO5l8RNKHtbW16O3thcfj4ekGJbu5VCiSySQqKiogFouxuLiIpaUlVFVVYe/evejr68Ps7Cz8fj9zbYh0rVQq0djYCK1Wy9JyqVQKR48e5Y/S7/ejtLQUR44cgcPhYEga+ayMj4/n3G9E3L2VcE57ipIUInppNBo4HA6W2evo6GAcKyXLdOFHIhGoVCrs3LkTLS0tWFlZQVFR0W1KLCRLm2u/USLudrsxNDSEUCiEsrIyhjwkk0nGcJN0JxWZMpkMN2/eZNGE7du38/MWiURobGzEE088genpaS629Ho9LBYL8vLycpJfJRIJS/qFw2H4fD6IRCJs3bqVMchUHPf09CAYDOLgwYNwuVzYu3cvysvLYbfb4fF4+EK+laiZyWSwdetWVgNZW1uDUqlEZWUlRCIRVCpVzvcqlUpZ6jQejzMWn4iA9H9DoRBWV1exvr7O8Jaamhqo1WoUFhbCZrMxnphwwkSMI0gRkcDpwCV1jlzvNZvN8kGq0WjYW2d5eRlXrlzB2toaqqqq0NPTg2w2C51Oh0wmgyNHjsBqtcLv9zOktKqqCo2NjaisrIRCoWC1nNXVVe5CEk56eno6J2fJ5/OhurqalaV0Oh0nXCRwQZwbwlWnUinodDrs378fdXV1EAqFtynEkbY6jdzVajWmp6cBvIeFbm9vR2dnJ8LhcM7CGwBj6gsLC1FSUsJnxMbGBsxmM5RKJXPoZmZmoNfreeJHClVEsAbARWNRURH0ej3q6+sxPz8Pm80Gg8GAmpqa2+AfmwUln8QPVCqVzEUjRReZTMZwx6WlJXi9Xla1IWWZPXv2QCQSoaurC62trcx7crvdsFgsXKyvra0xVIt4ZJsFcURIeYik3EkKO5FIsF+H0WhEIpGASqWCz+fD0tISZmdnme/idDq5C0rnUCgUQn19Pfr7+7mwpLuDBAhyhUqlQjgcRjAYZPnfvLw8+Hw+nnIQRNbn82F+fp5VGcm/RiKR8HRjZmYGc3NzSCaTMBqN6OzsxPT0NAwGA0ujlpWVMZR3syCPH5fLxZLAJOEZjUaxurrKSn9btmzBlStX0NjYCIlEgg996EOoqKjAzMwMIpEIUqkUvyuj0ci+KA888ABDUiQSCSorK2G1WhEIBHLmIpRH0FlCkusmk4kJ8vF4HBUVFfjYxz6GUCiEAwcOQCgU4k/+5E9QUVGBq1evwul0oqamBi0tLYyzp6ZnWVkZJicnb9sjxFmpq6vbdG1Op5OfK71Dgt7Qz9NoNNx4u3nzJkthm0wmpFIpbpqQPO/8/DwymQyKioqg0+nwoQ99CNeuXeNpF30LRCrfLGQyGaxWKysXEj+pp6cHSqWSYd6kfDU+Pg6FQsH+E/Tu6+rqWLX03LlzXIzU1taira2NRRCocVZYWAi/35+T1EwKUGtra+yflEgkWI5bpVIxREmj0WBsbIwLL71ez9/v3r172dMmnU7DZrNxo44sIGhSWlpayvzZXIUtySdTIy0/P58ldgcHB1l5S6/X49ChQ3A6nVhbW0MkEkFHRweKi4uxtraG9vZ2lnUmvgZNUjs6OtDX14fV1dXbCPY0Qfwg8YGhU3l5eSgpKWE8LKkDEJGJHgiRiohMS5wJMocqKChgCVa9Xo+SkhJWhtqxYwdefPFFJheRrB5hJDcLkgAkpjwZHDU2NrK0ZSwWQ15eHkpLS3nkXVZWxrhw8qswm80YHh5mlQPSDu7o6MDs7CxCoRAMBgOCwSBDPXKN3UjGk5SqyAeBksHZ2VkoFAreWDTSpS456Z6TR0F5eTkTrSnhlclkGBsb46KFDjNSW9gsEokEKioqWDnE6/Xy5j9//jz6+vrYHIySXZ1OB6/XC7FYzL9XKpWCXq9HdXU1YrEYS/uJRCJs2bKFx4Ok5ezxeHImBwBYqo0UVW7VL6fJBB0AVGhmMhlWH6IEnYwZCUusVCpZESKZTEKlUiEYDHJxSwTDXJh0kgIFwNM4q9XKhG2SkKMOiF6vx9LSEvue0IEZi8W4OxIOh1lZrKSkBBUVFbh58yZcLhckEsltyhu5LrqioiLu/tBETq1W8/SBuilUoJECBZHqi4qKWKmGlLrUajUX4UqlEi0tLfB6vVxgEmaczPVyBcG5lEolf3NSqZQTeoKHAODuD8lgWiwWVsPKZDKoqKhgQQD6jktKSvhyI4I77aX3e3bUMSNVFtovarWajR9p4lNSUgKHwwGZTAaVSoWqqiqIxWJEo1FMTk5Cp9PxCNxgMMDr9TLJn0yaKMkkicX3mwYVFxezshVBom71VSGTRo1Gwz43xNOgDmYgEODCgpQCE4kEIpEIY/d9Ph/W19dZ+c/v9+c8R2gKSN3NvLw8LgylUim8Xi+USiWr96yvr7NJYXt7OyuCCYVCNDQ0QCQSQafTQS6XQyqVorW1lUmTJMNJ0xa5XJ4zYS4uLobNZuOCm56RWCzmxIa+BzJYXF5ehtFoZGXEiooKyGQyGI1GWCwW9loJhUIoKirihgvBdki9iCCCm8X/v3wzTbxJWYcudSIWE+FUr9czMoDOU7/fz4UZKQcmEglYrVaG0hJMLxaLIZvN5lwbKb7daspJRS75HZAHRlFRERQKBaanp/muB8Bqf7eKtND5SsatTqeTG0j0O4lEovclqt8q30xFFGHRZ2dnEY/HeWpOKoIkc0yQm0gkws3O5uZmWCwWZDIZxruTx8GtMrp0NmwWcrmcJ+40vS0uLkY2m0V1dTXGxsZYda6jowPj4+NsnieTySCXyzE/P4/5+Xk0NjayKp1Go2GJYFIwstvt/F3K5XLo9fr39aaKRqMwGAyIx+Nwu90MLSWII0muyuVyrK+vY3Z2lnlAJNkejUa58UA5EDW2DAYD36XkC0F3CsHq7xQajQYajeY2U2NSlCLoEBUzer2e4ZYKhQI1NTXwer2MVqiqqmLfMYVCgZKSEphMJuYRLiwsMM+Cfodc8HdS3yLfFRJoIJERyrUKCws5J1ldXYXNZoPRaOTJu0KhQHNzMzfFZDIZy3uPjo7CZrPx+Un3FkHENwu6C25VFCP5WXp+JINsMBjg8/kwMjICkUiEqqoquN1ubgpPTk7yHRMMBlkIiBS5bhUkIXGPXO/01vjAEw2pVAqPx3Ob9CclcD09PXy5kDxdc3MzfD4fampqsLCwAKfTCYfDgRdffBE2mw19fX1oampiOa/a2lpks1k89dRT7H1AD1gmk+VM/FQqFY/2VCoV/H7//4+9/wpv87rS/uGbRCdRWUAALGDvvUhUo6wu2ZKLXOSWHqd4ZvJ3PMkkM4knGTuTN8nktZ1uxym2J45bbMdNzeq9sveOShSiEIUgQJD4DpS1Rv4uEfF7XXOodeTJ2NIG8Dx7r73WvX43D+TV1taitLSU3akHBgZQWFiI9957DzU1NVwpSCQSGBkZQUNDA86ePYvMzEwUFhYynjcajaKiogImkwm1tbVQq9V8uSJi1EoPAkmz1Go1G6uMjo5iy5YtjAsNBAJQKpXYvXs37HY7PB4PTp48iVgshoqKCtjtdpYRrFu3DhqNBtnZ2WhtbWUaiVQqhU6n43kQYjmvFESnogrY0tISyxGMRiM8Hg+34ufn59HW1saO29PT08w7t9vtKC4uhtvt5mSO2OWRSATr16+HyWSCUCiEzWbDyMgIhEJh0kF1hUKB8fFxJBIJ7tyIRCK43W7o9Xo+aIj9T9V52oQILTowMIDq6mr87ne/4+FiGlicmppCY2Mje4RQJV0kEmFwcHDFtaWmpjIKlnjmNC9DVYh4PI5YLIaZmRm0trbipZdeQklJCUwmE8RiMVavXs3zMS+++CLKyspQW1vLid/FixeZ9EX865mZGX6OVwrqTl3fHSBX68bGRu5KUmK5d+9eXLp0CXV1dVyd2bZtGwoLCyGRSDA8PIyCggLerHU6HWtWSY8eDofh8XhQXFyc1O+Gfte0tDSUl5dzgqbX69Hf3w+1Wo2xsTG+vNBvTokyGWcCwPT0NL8X5LlBGFYqZgwODiIzMxMulwtTU1M8ELxSEC+dqmnBYBAtLS2IxWIwGo3Q6/UoLi5m2gbtMeq/udDS0LzdbsdDDz2Ec+fOceeXvCSKi4uhVqtx8uRJHtBzuVyoqanh9vuNgi4oubm5UKvV8Hg8SCQSiEQiyMjI4GSbiio1NTXo6upCZmYmzp49i8HBQUQiEXbz9fv9KCws5ARQo9Fg//79DIMgeQdJ7ZLtcZTA0xwPob8jkQjy8/PR2dnJCbtCoUBLSwujJTUaDV/eyCRUKpWyZ4VarYbRaIRCoUBtbS3efPNNjI6O4sqVKxgaGvrYZ79RkLyEJIper5fn8wicQBSlQCCA2tpall3SUKxUKkVXVxcqKiq4kkrGcCQpWrt2LTIzM1FXVwefz8cdhmRByHiDwQCVSsXdj7GxMSaczc/Pc8eO3LbXrl2L+vp6NrgkB+k33ngDaWlpsNvtMJlMAK4VBKmbRUUAsViM6urqpEkCPWOEXyfPj2g0ylVjKtBkZWVh3bp1fIFxu93szzQ9PY26ujp4vV7s3bsX9fX1XEyzWq2oq6vDwMAA1Go1S5jIFXuloGqxwWBg9QLt6+QYn5KSgtnZWYYImEwmxGIxvP/++5ibm2OgCnmNbNu2jS+Hq1evRk9PD4qKirizOzAwwObDyeZHSClBxVTqgFA3jpzLCRe8bds2fPDBB5BIJDh+/Djm5+exZs0alnBdL5GiS+7s7Cz27t3LdKxAIIDl5WVUVlYm/U01Gg1LgLKzs/mimZaWhubmZni9XvT396O/vx9OpxNNTU3w+XwwGAxswJubmwuv14uysjIufFKBxGKx4MyZM9iyZQvy8/ORnp4Op9PJe00ykiN9H0VFRdxJk8vl+PDDD1kFkZ2djeLiYpSUlKC9vZ0v94SRFggEuHz5MmpqatDf388kJ61WC4lEgoGBARQXF+PNN9+EzWbD9PQ0pqam+NKQ7HujSwV9zuzsbExMTDC5EgB36uvr69HT04Pi4mL+zcRiMcbHx1FeXo6qqiomcZHj/EsvvYTc3FycPn2akcZ0ZiTrihIVjQpogUAAZWVl8Pl8WL9+PT+P09PTuHz5MjZu3MjmzjabjQsu9NsSLImMLr1eL/r6+rB161bI5XLU1tbyPHRKSkpSAtv18Yk7GhaLhSVA5Keh0+kwOTkJkUiEsrIyvPDCC5ibm8Ndd92FsbEx5OTk4IMPPmAcJ1VBZ2dn0d3dje7ubjgcDhQUFKC6uhozMzMoLy9HeXk5nE4nt2mvXr2a1NxtYWEBJSUlbNjncDgwPj6OW265BX/5y19QV1cH4H9Y9NPT09i4cSNisRi7uvb19fEw4+23384DNjMzM9zutdlsWLNmDQ4fPszu0IODg0krHNebEMpkMsaQbdu2Dampqbj77ruxf/9+nDx5EuFwGDqdDvn5+XyLJA6zUqnE2bNnodVqcf78ebS1tWF+fh6XLl3iboLP58PIyAi3ziwWS9K2WzAY5IHL+vp6rF+/HhcuXGBTG+B/nCfJ0G10dJRv69Tq9/v9MJvN2Lp1K6xWK3Jzc6HT6biz0dPT8zGkYWNjI6anp5MOh3m9XnR0dHB1u7S0lC8rmZmZ7AJKcpHu7m4oFAoMDg7C6XSyAVpdXR0OHTrEDsRDQ0PcTWloaEB3dzfa29tZ/04442RSEZ/Ph02bNvGlgZy4W1pacPHiRVRUVODkyZOYnp7mi19TUxP6+vr4xfT7/dBoNDh27BjuvfdeHD16FGfOnEFFRQV0Oh1OnjzJXZjBwUEUFxcjJycHfX19SYfDZmZmuN1PRpVerxfZ2dk4ceIEqqur0dvbi6WlJWzZsgXvvPMOjEYj+vv7oVAoWDpD5ohHjhxBU1MTdzgikQjrz+kiFw6HkZGRgYWFBfT19a24NuDawF5LSwtjEikJpwNfIpHgzJkzCAaDKCgo4AOBktLR0VFMTEygsbERLpcLVVVVGB8fZ4wmSYcOHTqERx55BO+99x4AsGv72bNn8c///M83XJvNZsPatWsxNjYGlUqFoqIidHd3Y35+HqOjoygpKcH4+DgGBgaQkpKCK1euoKWlBf39/ejo6OAqXmNjIy5evIg9e/YgLS0N58+fR19fH+9paWlpMBqNCIfDfECMjo5idnYWq1atuuHaXC4X8vLyEAgE2NiQOkFSqZTfQ/LQoYq2xWJhBCxx5v/6179izZo1MJvNXP3y+/3Yt28fHyKULDscDpakrhQkjSFZG/l80CXFaDRieHiYO8XxeBzr1q1j2anX68XIyAhEIhEuXLiAyspKvP322zz7AADr1q3DG2+8gYqKCjgcDuTn5/Mhn6yLZjKZeCifunkTExM82zM8PAyfz8cFl7GxMZYTPPTQQ/z80d91yy23wG63o6amBoFAgPc2utzTZ19aWkJfX1/SgoXT6WR/naWlJaSlpUGpVOL8+fMwGAyYmZlBMBiEx+PhjnAikcCf/vQnzM/PQ6/X87BsIpGA3+/H1atXGRWsUChQWFiIK1eu8OAnVVzpwpRsbVRVFYlEbN4llUoxNDSEoaEhNsWzWq3weDyor6/HmTNn4PP5OPnT6/U4deoUI6qv95XasGEDDh8+jMbGRu60ETI8WYHM7XbzEPT15mG5ubncyaBzICUlhX2x1q1bxxea9957jw3zUlNTcfXqVSY+Dg0NoaOjAz09PUwpzM3NZaPeZAkzwV9o8HxhYQETExPIzc3lZ2tiYoIrxpOTk9i2bRsn7263GxcuXGAzWDrH7XY7dxudTidaW1tRVVXFxSGBQIC3334bCoViRRLQwsICbr/9dvarKSoqwsTEBM/KVlVVQSwWY2pqimcx5XI5/u///b/8no2MjCA/Px9vvfUW+9r4/X6YTCaYTCbk5OSgs7OTJeoikQiLi4uIRqPw+Xwrfm9TU1NoamrC1NQUD6qPjY1h8+bNrKA4ffo0vF4vCgsLWfJ95MgRfPWrX4Xf78eZM2e465GXl8eYaaVSiYKCAuTm5uLcuXNM9KRL44cffojKysoVCwMkLaICDBnaCYVC7N+/HzU1NXA4HPD7/cjKysLs7CwcDgd++tOf4sknn0QkEsGhQ4cYakTPL3lq2Gw2dHR0cKd3fHwcS0tLiMViDDVYKaLRKKqrqxGNRmEymTA3NweNRsMwCplMxnMtVABQq9WYmpr62NwLdS6XlpZw4cIF9k3p6uqCSCRCc3Mz3n77bRQWFrI/j1Ao/N837MvOzsbg4CBmZmbYPO348eM4fPgwXnzxRWRlZWHPnj28oRgMBlRUVPDQ0Pz8PHbu3IknnniCGfNf+tKX8Mwzz+Af/uEfkJOTg97eXr6933rrrWhsbGQW8UrkDuBaZ4V43JmZmSgqKkJRURGOHj0Ku93OlbapqSm88847ePbZZ7kqNjo6ipycHNx///342te+hj/84Q8YGRlBXV0dGhsbUVxcDIPBgC1btuCee+7Bbbfdxj+s3W7nzWClUCqVmJ2dRVdXF28KTqcTP/zhD/HBBx9genoa27Ztw5YtW9hhNiMjAz6fD7/+9a/xyiuvwGq1ori4GM3NzTCZTFhYWMCRI0dw5swZLC0tYXh4GO+//z7a2tqwefNm1iyS6c1KsX79eiaxdHZ2Muni2LFjMJvNWL16NVJTUzE9PQ2v1wuXy4Xt27cjFovh6aefxtGjRxEIBHDy5ElIJBL86U9/YnkBzerEYjF4vV6uFJGZ1ObNm5OagFEVf3R0lPnNhI87ePAg8vPzuVIfCATY4VKtVrPEhqRjmzZtYhlQU1MTysrKMDs7i8uXL6OhoYHxbbW1tawnT2a0RV2ctLQ0JBIJmEwmWK1WjIyMoKuri5PNuro6HoDt6OhAU1MTNmzYwNVCnU6HrVu3YnBwECUlJbjvvvtQXV2NwcFBBINBdHd3Y9WqVfjUpz7FianBYEh6CSKaW3p6OgoKCljXf+rUKXz00Ue8GYfDYfzqV7/i6qDdbsfhw4dhMpmg0+lQUlKCVatWYe3atWwURp0l8vooKCjAjh07oFarMTk5iampqaQDfwD4MujxeCCXy1FUVMTD36Q1JUmV0+nEtm3bsHHjRkgkEpw7dw6JRAI1NTXo7u7G+Pg4/vrXvyIrKwtf+MIXsHHjRnR1deEHP/gBCgoKcPXqVTQ2NqKwsJBd3zdv3rzi2qqrq9Hf3w+32w273c6eLTabjYd2KysrGd7w1a9+FfF4HMXFxXjuuefw9NNP46233sJ7772HlpYWHDx4EG+99RYqKyvR2toKh8MBsViMCxcusC69p6eHE4uNGzeuuDbi1NNlgqqlaWlpMJvNMBqNKC4uRjgcxunTp5GXl4edO3fi8uXLsFgsTLianZ1Fc3Mz3n//fZaWeTwe/PSnP2Xaz759+7BhwwY2FiX6y0qRlZUFs9mM8+fPc0eKsNOvvPIKCgsLsXnzZiwvL+OXv/wl0tPTkZeXh/379+Opp57Cb3/7W0ZlLywsYP/+/bjvvvtw//33s4TwySefhNFoRG1tLXfVLl68iFgshsbGxhXX1tDQgHfffZd58zU1Ndwt1+v1XIm/cOEC+vr64Ha7ceXKFcjlcrz22muwWq2oqanB1NQU8vPz8dxzz+Htt9+G2+0GcO1i/9vf/paluoFAAP39/ejs7MT09HRSTLZIJMLRo0dhtVpZuuX3+/Fv//ZvSE1NxerVq7F+/XpkZ2djZmaG8a8tLS2cFJEX0+joKLxeLzQaDXbv3o3bbruNi1N0Ro2Pj+PkyZO4dOkSzw8kW1tfXx9mZmZ4ppC8pyKRCFavXs10u+vpSqQvT0tLY5PIcDiMf//3f8elS5c4EXQ6nXj11Vchl8uxdu1a5OXlYX5+nosNyaq4JINaWlqC3W7H4OAglpeX8eabb/K7VFxcjFgshueffx4KhQJbtmyB2+3mi/nGjRvx5S9/mZUUBLCJxWKIRqP45je/iampKRw5cgSbN29GTU0NS6yrq6tXXJtcLmcSpU6nQ2ZmJrq6utDZ2Yn3338fmZmZ2LlzJzZt2gTgGgyBjFP/8z//EwcOHEBdXR2+8pWvYNOmTbBYLCzvisViGBoaYv+bpqYm6PV6LgCSR8pK4fP5sH//fly6dInhFKFQiMErGo0GCoUCfr8fly9fZgT1448/Dq/XC6vVCoPBgNbWVshkMrz44ouYn59HVlYWK0nGxsZw6dIl9Pf3IxqNQq/Xw2g0QqVSob6+fsW1paSk4MSJE7BardzRlMlkePnllxmesnHjRtTW1rLHGHm1/PGPf8TPfvYznDt3DtFoFKdPn8a7777LQ8zk/SUWi1FTU8Nd2//6r//Cr3/9a+Tk5KCmpmbFtS0tLeHll1/GyMgIjEYj2tvbEQwGUVhYiMbGRigUCr60Dw0NobOzE3fffTcKCwvx2GOP4Ve/+hUMBgMefPBBRjOTSWdqaiqGhoYQiUTw/vvvc/7R39+PU6dOMbZ3pVCr1RgfH0dGRgZuueUWbNq0CRMTE3j22WcxNjaGTZs2MdhCp9MhNTUVHR0daGxshFKpRE1NDTZv3ozCwkI0NDRgfHwcV65cweLiIucPeXl56OnpQXV1NUOXSJqV7Hu7Pj7xRWNgYABr164FcK09k5GRgdraWuzbtw9FRUVwOp1QKpVYs2YNamtrcejQIXY0/tSnPoWdO3dCIpFw5VKtVuPcuXM4efIknE4nampqcOedd2JwcBCHDx/G7373OwwODjKuNNkgEQ3Q0kYCXKt6bNu2jdGhlNxs2LABqampeOCBB5CbmwuJRIJTp07hl7/8JU6fPg2tVoutW7eiu7sbfX19PNT07LPP4je/+Q0sFgv++Mc/YmZmBlVVVSgtLU2qrQ6FQh/jGVOL93vf+x60Wi2jdNVqNbZs2YKnnnoKQqEQRqMR//Zv/4bW1lau6J47dw4PP/wwVCoVHnzwQTz88MOoqanBQw89xB2fDz74AEVFRdi7dy8cDkdSIsuBAwd4hoEG28lToaioCHl5eSgsLERxcTFKS0tx7NgxrqTdeuutqKurQ3l5OR555BEsLi6iqqoKhw8fxqFDh7hDRJ4T9fX17Nbp8/nw8ssvJzV7IbdLMihqbm6GXq/HN77xDahUKlgsFq4WrlmzBrm5uTwUbjAYkJmZydIeuhDMzMxgfn6eh5arqqrw0UcfweFw4NixY0wvIU3jSkHyQZvNxoPWRqMRe/fuZW0zETl27tyJ4eFhlJaW4vTp07BYLHC5XLDb7fD7/Th27Bh2794Nk8mE/fv34+LFi1CpVPinf/on7kiYTCb4fD4mGCU7TAiPe/LkSRw5cgTDw8O4ePEivv71r6OiooIHnOVyOb761a9icHAQTU1NkEgk2LFjByNEzWYzhoaG8I//+I+wWq18kS8qKsKzzz6LI0eOoLGxEU6nEwaDgUlsyTZFADCbzSguLmZ089zcHAYHB7Fv3z7Mzs6isbERmzdvxvbt29HU1ASLxcIzSY899hi2bNmC3NxcPPLII0hNTcVtt90Gm82Gq1evYmpqCmlpadi1axe6urqwatUqHD9+nJGT4+PjSdc2OjqK9evXIy8vDwaDASUlJYjH4ygvL4f6bzz7YDDIg3SvvfYaUlNT0dXVhUcffRQ7duxATk4Ovv71ryM7OxuPPvooWltbWdNaWVnJ8gytVgu32422tjbs2rULExMTOH369IprI4M6mm8pKytDfn4+ezXodDqe2Wpvb8eRI0eQSCTQ3NyMvXv3oqqqik3VPvroI2zfvh2lpaVM/rnrrruwY8cOXLlyBQcPHsTg4CAn9rOzs0mTUofDgQ0bNsBoNLKMqrCwEGVlZbjjjjsYzZqTk4Pnn3+eTRf1ej3uuOMOrF+/HtXV1aivr4dCocCuXbtw7NgxjIyMIDMzE7fffjueffZZJBIJNo0sKChAW1sbS+xWiv7+fmzatAler5cpgUeOHGEkbXV1NTZt2oS2tjasXr0awWAQ3/72t3HixAn84Ac/wO7du+FwOLB7925YLBZ85zvf4UvwyMgILBYL/umf/olRz/Pz87jrrrvwmc98htHMK0U8Hsfu3buxtLQEs9nMs2d9fX1488030dnZiZ6eHggEAmzevBkjIyNQq9U4duwY9Ho9Wltb0draii1btrDxJgCWvOn1eqxatQq5ubk8C0XULbvdnrTCLBAIUFlZiampKVy4cIG7GO3t7XyWhUIhaDQa7NmzBwsLC6iqqkJxcTE6OjoglUoxMzOD+vp6ZGdn46tf/Srm5uZ4Po+oT4ODg4jH47zvkfRs9erVK66NcPbkwVJfX4/FxUU89thjjJCfmJhAcXExnnzySZw7dw4ajQYmkwm7d+9mPxCBQACfz4fW1laeZyGZ9c9+9jOEQiHcfffduHz5MkZHR7lDmKyKS51qMjkVCATIy8vDqlWrUFlZyTjVs2fPwu/3M5BleXkZv/71r7Fv3z4MDw/jzJkzeP3117mrYDabMTs7i9LSUnzmM5/hrqNIJMKqVauwZs0anq9YKVJSUrBt2zYsLi6yoRzN5XV3dyMSieDixYvwer247bbbsH//fgwPD+PFF1/EnXfeibVr1yIajaK4uBhisRhPPfUUd7QWFxdRVlaG2267DeXl5aisrOT5m5SUFPT29iYtyNLANuGke3t7YbFY8JnPfAa7d++GVCplbLPT6cTVq1fhcDiwY8cO3H333dizZw9Wr16N8vJyWCwW/OY3v4HBYOD8hyr7ZrMZy8vLyM/Px9e+9jV8+9vfRlZWVtJ5JTKmJvNosViMpqYmzik0Gg1LQPfu3csgBKVSiV27dqGwsBBer5dlUF/84hdZuqhUKnHXXXdxMc3r9bIcjFQCySTmJJH+61//ihMnTsDpdGJoaAjvv/8+SktLMTIywp3lrKwsHDp0iHOy64s4Fy9exL//+79znh6NRvn53LRpE+OnCwoKsHfvXuzduxcCgSCpmudjv28i2aTJdfHCCy9AoVBgYGAAOTk5PBB68uRJfP7zn8fbb7+N+fl5FBQUoLy8HA6Hgw/mK1euwGg0wmg0wm63IycnB88++yy+8pWvYGBgAH6/n03Zamtr+UWYmJiAXC7Hxo0bcfDgQXzzm9+84dp+/vOf86S8QqGAQqHgbgPJL+RyOYLBIBswpaSkoKmpiYexRSIRLl++zDIkein8fj8PctbV1SEUCmFiYgI2mw1ZWVnYuHEj3njjDTz99NM3XNsvf/lL/nzxeBzRaBR5eXlwuVw8S1BXV4ecnBzWusfjcZhMJiwtLfGAl9ls5qG8PXv2wGq1ore3F3Nzc1i/fj0UCgUqKytx5coVHnIbHBxEfX09/umf/umGa3vuuec4qSbjtUOHDkEoFOKWW26Bw+GAx+PhKnNLSwvThrq7u1FcXIyGhgYsLy/j3XffRSQSgUKhQEVFxceY5tRqPH/+POOKCwsLYTab8eSTT95wba+99hrMZjNfMFNSUmA0GpGfnw+LxYKsrCzedOLxODZv3ozTp08zaWtxcZG/P9ID03c5Pz+Pvr4+dHd3484772RpETGuadPZuXPnit+bxWKB0WjkwWSNRoPjx49jzZo1mJ6ehkwmY5lIRkYGysrKGB1sNBrZq4T8FcrLyxEMBhGJRFiPXVJSAq/Xy105GswfHh5e8V34/ve/D6lUyh4vMzMzUCgUTGbxeDzQ6/UMYSCdtNvtRlFREaRSKaxWK65evYp9+/ZxIkrEsPT0dB7EJTzkuXPnYLVaccstt0AgEGDHjh03XBsAfOMb32Atd2ZmJl+azpw5g7a2NoyNjSESibAHCkEeyP2YhkjpnwlUIBQK2X1VqVTC5/OxFw05iQsEAmRnZ+NnP/vZDdf285//nGc0AoEAs/XPnTvHdCk68Px+P4qKilBQUACHwwGn04mZmRmWgNx777340Y9+hM985jM8DG6327FhwwacO3eOByhJPpqdnQ2bzYZnn332hmv77ne/i4yMDBQUFHAyVVFRgeXlZR70JkOu0dFRfPrTn8arr76KrKwsliKQxKKrq4tNNs+fPw+Px4OMjAyEw2HU1dVBp9Px56EBynA4zLz6//+gvY8u4NcDKlavXo3Lly8jFApBKpVCrVYzkIN8TWjt5eXl8Hg8cDgcKCwsxPT0NJO9nE4nioqKmCtvMpmQSCSwYcMG9ku4Ubz88sv8npIRJKHNV61aBZlMxhSn+fl5FBUVIRgMwmAwYGBggKlVer0eoVAIR48exYYNG1BTU4NoNIrx8XGe7dmwYQOTBRcWFhip+sADD9xwbT/5yU+QmprKZDQa3qQilkQigdlsZiKYUCjEyMgIWlpa4HQ6kZ+fj8zMTExMTCA9PR1vvPEGHnnkEdjtdoTDYTY77erq4j+bkkLyp/rOd75zw7U988wzmJychFwuR2ZmJrKzs5GTk4M33ngDd911F58xNF/4+OOP48SJE1hcXOROIA3tp6WlobOzEx0dHexQvri4yBfQ5uZmDAwMcOeF3ulHH330hmt75ZVXYLFYUFZWhmg0yiZ8EokEer0e586dQ0FBAQoKChi6Ql3KmZkZ7nTLZDIeXiYZCenmSS7V39+PgoIC/v7cbjdqa2tXlCc9/fTTEIvFyMjI4E4DeUsRmAEA4//37duHgYEBlnuq1WoUFhayweGlS5ewdetWdHZ2YmBggGdTyGh2cXGRLwf19fWIxWJ48MEHb7i2119/HdPT0zx0vbi4CJlMxhjszs5OaLVaqNVqNmEkMMTZs2eh0WhQWlrKfkUbNmxg/Pj8/DyAax11h8MBtVqN06dPQygU8tzF9PQ07rnnnhuu7fe//z13aAmZPDMzg7/+9a/4xje+gbNnz0Kv1yMSiaC/vx933HEHLBYLG8gqlUq0t7cjLS2NHdiVSiXjtikhzsvLg0gkwujoKI4dOwav18sX9Mcee2zF95Sk7HTGkBdWTU0NOjs7EY1GoVarkZ2dDa/Xi0QigaysLHR2dgIAd5z9fj9efPFFPPnkkzh48CDnJYODg7jjjjtw5swZVFVVwefzsbTfYrHgW9/61g3X9uqrryIUCrEUa3l5mY1H9+zZA6/Xi4mJCT4LSdLX1NTEOcDU1BSGhobQ2NgIh8OBrVu3wu/3w+v1wu/3o6urC7fffjv7rRDyPBgMIhgMrpiLXB+fuKPh9XqxvLyMxcVFzM3NYWZmhqUSREGQSqU8J9HQ0MA0CdKz0XAqDVL6/X6oVCo2QInFYkxMysjIQDQaxdTUFEtTVopgMMjO1kqlkq3pZTIZBAIBIpEIFhcXefiWePPxePxjumKBQIC6ujqo/2Yrv7y8zEY0RLXp6uriRA641jn5e3hFpVLJ2FG5XM5EJr1eD4FAgPHxcQwNDWFhYYE1t7FYjBMEgUAAj8fDg89OpxMOh4MTVTKKGR8f58SC7OiTeS6Q7l0ul8PlcmFychJ6vR55eXlMCSNykd1uR0pKCkQiEbKysqDVapmYtLCwwMZ9KSkpfDkLBAIAwNQJIiNEo1E2REv2vVHySEPfRHMCwG6+AHhQjap2pGOn4X+z2cykE6pG5efno7i4GHl5eZzYEvtcIBAkbd2HQiGo1WpGXJJmt6mpCZFIBDk5OYjFYohEItDr9cjNzWUkIxnsxONxKBQKuFwu9PX1QSQS8YZP5pGEfaYBfL/fz+/DSkFGR+QdQtVukmMtLi4y/ICGMkOhEBwOB2ZnZ9lpmS4c9HwTZpjISoS0vF5vurCwgNHR0RXXBvwPJSMQCMDlcjGelWa+yPyPdL8pKSlsaEbvQDweZ8oHuZimpKQgEomwLwhRYwCwZwoNeCd75kKhEEKhELuJEw45Ozsbc3NzH4MQpKam8kAdPTckyxkeHsZdd93FlJ+5uTnMzs5idnaW0ZIymQwSiYTfg2TdR4lEglAoBK/Xi8XFRR7wi8fj8Hg8iEajTI6j+TE6CMjzZX5+HuPj41xlDIVCvD8TNYq0vKTzJ+xqMgABVe/ooAsEAjw8THt6OBzms4EKLuQ5Q5dXMpKMxWJQqVTQ6XQQCASsf15cXPwYyjQ1NZUvViuFWCyGQCDA/Pw85ubm+NmngoLNZmOscCAQwNTUFKampuBwOGC1WvkcGBoawtjYGKNE7XY7+x+4XC42s6TPDYClnCtFLBZjAg2BVBYXF9kvQSwW8+fOz8/H3NwcIpEIQzvoewCuXfIoiScNvtvt5veX9NkA+L+nda60NplMxnQ/on5JJBI2VCNoAxlO0junVCr5XCVgSnZ2NvtZXO/mTnMR5HVAhrvJui0AuOpK75zD4YDb7YZUKmVseVdXF3t+UeGNvsvCwkLuFp88eZLnNaiLTJKv7Oxs9okgClCyTjzh5RUKBROq5HI509fo3wmHw0hJSeHiEUnjIpEIm6w5HA7e12iAWygUIisriyvV9NyR+Vuy95QKYnK5nC9oVKFfXl7G1NQUnE4nU9h8Ph+Wl5c/Rsn0+Xy4cuUK0zDpjCCiHj0TNBPlcDjYfyTZ/jY/Pw+JRMKoeco9KDcjzx/KNeji7fP5OP+jzsnS0hJL0hYXF/m3j0QiiMfj/L3Sb0Rny0pBRtD0rC8vL7PX2vWocJrFpQTeZDLxLBH5xNAeTlAbpVKJqakp/t1UKhWfQcvLyxCJRDxXdaNwu92ci9DeS5dcsjkgiit51DmdToyPj6Orq4uJU9Q4yMnJ4WeJvH1otogKNR6PBz6fj8/iTxKf+KJBXghk7BEMBrmy7Pf7me60vLzMJh90uyQTIr/fD51Oh/7+fnaBFAgE7D0QDoe57UuHi81mw6lTpxiZd6Og/x8lFqQnF4vFCAQC7Mw9NzfHlbu2tjY4HA54vV5MTU1hYmKCDU2I9kBs9UAggLGxMR6EnpmZ4aoMmd2tFMT5pkNVrVbDarUiEAigsLAQlZWV/CI4nU4m+oRCIU58Sa7k/5sDJ1EDysvLUVhYiEgkgoGBAUxOTjLPnXjeyRJmcntXq9Vc7QKuaWCnpqYQj8eRSCQQDodht9vR19fHOLzMzEwEAgHmkXu9Xj6UTCYTRkZGMDY2xnM9/r8ZEUqlUn75k8nhaPOQSqVsTBgMBuH/m/sobfq0IZtMJmRlZWFubo43PzJP6u/vx8zMDDOkyWHVYDCwt4HT6cTY2Bgnl8meNxqKvN7Z3e12MwK5uLgYS0tLcDgcnFTTQHE0GsXMzAzGxsYgFAoxMDAAq9WKiYkJpKWlQS6Xs8RjdHSUZ018Ph+mpqYwPj6elDFPv5nVauVDgxIWmp0Kh8M8mEm/9+DgICYnJ9mllIacbTYbmyNSJUwmk8Fms6GoqAgjIyMIBAIQCAQwmUxJsZQAeEiZKvG0IVOyq9FouJMxPDzMxnH0rNI7XlRUBK/Xi5mZGQwNDcHr9cJsNuPUqVPsVkvVSgBMFkk2OEzYYqfTiVAohGg0iomJCXafJ8QwoSDtdjtisRjGxsbgcrmQnp6OpqYmHigvLy/nxIqMxS5cuPAxY7DFxUVOYrKzs1dcG+25JEdIS0uDxWJhPwEaqp6dnYVKpWKCXjwe572VXG3JD2Vqaoor6ZSE+3w+dHV1cSJ7fdV6pVhYWIBcLud5n3A4DIfDgbm5OUxNTSE3N5eljIlEgosk13sAiMVizMzM8GWS0J0SiQR2ux15eXnw+/1QKpWYmZlhD5XR0dGkSQIATgzI22ZsbIznSihBIFQ3kYXGxsaY/U/zLVarlSu6ly5dQm9vL88VCIVCPsCHh4dhsVgQiUSSSs7i8TgnodQlI807DTJTYYWIfcA1iR/NwJAXgsPhQCKRwOjoKF/wTCYTD4e7XC52e6duXTKZDVF/qOMSDAbR398PsVjM7sxer5cRw6QGuD5HoCTWarUiLy+Pk/fU1FSkpKSgv7+fk0dyoSfsarLnLRgMctEoLS2NO15erxc2mw0FBQXw+Xy4fPkyrFYrd8bNZjOA/0Huy2QyWCwWJqwRLYoM1MgcNBwOw+fzfew8TPabKhQKLjzodDpotVr26qCiE+H2BwcHkZeXB6vVioWFBVYtkLkizSmSVD0nJwcqlQqDg4NsUEzricViSRM/osDRcD/N6lEhgWa4xsbG4PF4cODAAUxPT7PnDCGzE4kE+2pRcYCKJTMzM1xMIXQ35Ux/D7Uvk8l4nyZ5MdkTlJSUALgGxaC9TKPRYHZ2FtnZ2VAoFAgEAuyDc/z4cRw7doy7PmazGR6Ph+WOhAzW6/XcEV8phEIhu2HT5YSkk4FAAHl5ecjKyuLijEgkYpIied9QgXlmZgYtLS04e/YsX8CJrkgyZCrKkBlsMmQxFTaINqZUKll+5vV6uThM2FyRSISamhpMTEzAbDazoeT69ethsVgAAFevXuVinUajgV6vx/T0NGZnZ7lTS141yfaQ6+P/aRichmkaGxuRl5cHiUSCl19+mSsBxCen25JAIMArr7yCt956CydPnoTNZoPRaERpaSm3KkOhEJxOJw/Xke6QHDSJQZzMpIxM2cxmM7euvV4vhoeHEQgE0NDQAIlEgsnJSQwNDcHhcKC2thZOpxPvvfce+vr6mDb0q1/9CgcPHkQgEEBpaSnWrl0Lo9GI06dPIy0tDbm5ufB4PBgdHeXBsmTqM5rwp87K1NQUlpaWcOjQIUZ70mfr7u5mNLDFYuF2vtFoxJ49e/ghLCwsRE5ODoqKilBVVQWn04nl5WUexJ+YmEA8Hmdc6kqh1Wo5wS0vL0dxcTHm5ubw9ttv83wM3fZp4JoSI6IOkJ/H66+/ju7ubkgkEiwuLsLtdsNsNuPy5ctwOBxMLyHSUFFRUVIXU4FAgJmZGfh8Psb41tXVYWZmBolEAvn5+Uz0sFgsuHjxIjOj7XY7e3uQ3C8ej/Nwo8fjQU9PD9566y0MDQ0hkUjg/PnzLO0ixO1KQYk50VZ8Ph98Ph9OnjzJFUjS6f7+97/H2NgYnn/+ea7E00XLarWy3JBakoWFhWhqauJqIA01kqZaqVQmHaIXi8U4ffo0rl69itHRUZjNZq5yWCwWHgKbmJjAhQsX2E+ENnaZTIa2tjYetDtw4ACys7M5yY9Go7hw4QIEAgGam5sZe00gg2SadODaYXj58mVkZ2ejpqYGWq0WwWAQly5dwvDwMKRSKQwGA19qFAoF7rnnHjZbo8upVquFzWbD5OQkLly4wG7Y1Mmcnp6GXC5HXl4e8vPzoVAoEI1Gkw6rE5Vkfn4e+fn5PIA/Pz8Pp9PJDPv5+XkMDg4yBnVkZATvv/8+IpEIWlpamMn/mc98BhcvXuSZK6PRiFgshry8PKhUKu5Qud1u5ObmoqmpacW1JRIJfnfC4TBCoRB72hATXS6X8zPmcrmwa9cuyGQyqFQq5t3v27cPlZWVXDEm/x6/38/o7+HhYaarEMyApAM3ivn5eUxNTcHv90MikbAhHHVCi4qK0N7ejtraWigUCpZKSaVSxjdnZmZicHAQarUaAwMDOHfuHFwuF2uQq6ur+dJN1Wc6d/4eVIKkn+TLQZ4d2dnZ0Ol00Ol0MBqNqKqqgkajwfr16/m3SiQSKCgoQEdHB/Ly8njPB8CmcwsLC8jNzWUX54GBARw9epQ7JisFDeGSNEkul0MqlTLtMBaLobS0FCkpKfjggw9QXl7OZwkZ5NHshM/nw/T0NF9GybzV7/dzV4WY/YlEAsXFxUmHmsmjgySM6r8ZLp4+fRrNzc3cxaOLucvlgkQigcViQWdnJ0ZGRhCPx6HX6/Gb3/yGiZP0Dsnlchw6dIi7GURTogtGsmSeBrYJHkHeKqOjozCZTHC5XCguLkZhYSH6+/vx4Ycfct4yNDSEK1euwG63w2q14tZbb4VUKuVOKM2+tLS0sNJienoaExMTmJ2dhVwuT3o20IA1EYCouEizEGVlZaiurmYPkXfffZdnJOkCZzQaUVFRgfT0dHR1dfFnJd3+hx9+iNbWVqjVaq4w09xLsiIUdbboea2pqWHT2KysLGzbtg1arRYWiwU9PT1ITU3FpUuXGAbj8/mQkpKCNWvWsFlfVlYW8vPzsby8zDmRxWLB8PAwgsEgFxedTuffxSn7/X6e72pqasLc3BwOHjzIF9Pc3FwIhUKcP38eNpuN8cbp6ekIhUJIJBK47bbbGGc+PDwMgUCAxsZGbNy4EeFwGENDQ5y72u12mM1mSKXSpPkl+QMJBAJWzhgMBjzzzDNcTCgpKYFcLmc8M4FgKA+an59HTU0NRCIRz92azWamYwLgSyYpJaggQ9KuGwX5vrndbuTn52P16tWoqKjgM4Lk47W1taisrERpaSluvfVW1NTUoKKigs8ijUaDjo4OvPfeex9TjhBRdnBwEIWFhaiurobb7UZ/fz/PC36S+MQXDZvNxknMgQMHMDo6Crlcjl/84hcQiUTsctvc3Ix7770XU1NT8Hg8WLduHb785S9j7dq13CZ+7733sG/fPuTm5vKAjFAoxD/8wz9gdHSUX9Rdu3bh8ccfh0qlSqr7djqdKCsrYyfxsrIyGAwGPPzww9xJqK6uxu23344HHngALpcLw8PDkMlkuOeee/iHfuyxx/CpT30KpaWl6O/vZxypWq3G17/+dXR2dkKlUmHTpk344he/iN27dyMYDCZtbfn9frS0tHA1be3atUhJScFjjz0Gi8WCkpISHmwjV1KbzYbS0lLs27cPCoUCZ8+eZYfab3/725iamsKBAwfwzjvvoLOzE7t27eLWW1lZGR588EHceeedcLvdSZ1CiXbU2dkJv9+P6upqJBIJfPazn0VFRQV2796Njo4O3HnnnfjXf/1XpKenIz8/H4cOHUJDQwOkUimOHDkCm82Gr3/96/jmN7+J9PR0FBcXQ6lUwuFwsMZdp9NhZGQEjY2N2LVrFzo7O5NWX9xuN9atW4doNIrp6Wm43W709vaitLQU58+fx/T0NOt7f/zjH2N8fBx33XUXU7xI875nzx60tLSgtrYWSqUSeXl53Cr+7ne/i8nJSf4Ndu3aha1bt8JutydNXtLS0lBaWgqJRILKykrccccdMBgMuOWWW5Cdnc2zB8XFxWhpaUFqaip++tOfoqurC0VFRdBqtRAKhWhubsbZs2fx2c9+FktLSxgbG+MkY82aNfD7/ey0++lPfxqPPvooOyavFEtLS2hubsbWrVuxd+9e7NixA3K5HD/84Q+RkpKCjIwMNrjKz8/H4uIiNm/ejLy8POTk5MDhcODo0aMYHBzE0NAQvve978FkMiEtLQ0ejwfnzp3D448/jmAwiLGxMczOzvIhbrfbufK0UpjNZuzbt487nFlZWbhy5Qp+8IMfYGJiAg888ACbGX7uc59DV1cXDh48iL/85S/cih8aGmK2+fPPP48dO3agtrYW27Ztw65du2CxWDA6Oor09HScP38ey8vL0Ol0OHv2LGtmbxRTU1P4/Oc/zwfkpk2bMDw8jJ07d+LKlSuorq5mNOf69evR39+PjIwMuFwubNu2DVarFU8//TTGx8dx8OBB/PrXv2YpZGpqKurr63lmo7W1FX6/HzU1NVi3bh1Lc1YK8vKgChLJmqj4QR2IUCiE/v5+jI6OYmRkhKEYOp2OiYFzc3O4++670d/fD+B/5GwvvPACgsEgOjo6MD09jdTUVO6YJttHQqEQWltbIRaLYTQasWrVKoTDYWzduhUCgYD57OPj4ygpKcHQ0BDUajWOHz+Ovr4+XLx4EX19fcjJycHZs2fxxBNPYN26dTAajRAKhVxweOWVV9iM8Xpj0mQ0G6/XyzN7Pp8ParWaDa4sFgvPRNjtdh7aDwaDOHv2LDZs2ACDwYCTJ0/ixIkTEIlE2LlzJxKJBDo6OiCTyfDnP/8Z9fX1+Mtf/oLFxUX89re/hdfrhdFoxODgYNJCD0l/urq6MDMzw8WAlpYWeL1exppKpVIusjz++OPsN6LT6bjbODExgd27dzMXv6ioCEqlEtu3b8eZM2fw0EMP8b5VV1eH8fHxpFCJ+fl5NDQ0IBKJwGq1slSqvb0dnZ2dqK2tZZjJQw89BKPRiNTUVBw4cAAlJSUMEXn88cexb98+bNq0CX19fdDpdKxmuOeee3Ds2DEGLBQVFfH8ZLIiFElkxWIxk8Tcbjc+9alPwWw2My5YIpEgMzOTZSherxfbtm1jI8mtW7fitddewxe+8AV0dHQwgr2rq4uHYCsrK1FYWIi2tja0t7ejsLAQ5eXlK66NpIKLi4vIzc1FeXk5Ll68+LEZN5I8FxcX86xffX09XnjhBbS1teHEiROoqKjA3NwcHn74YSwsLODSpUtMWbvvvvtw8eJF7qpv3rwZu3fv/rtn6sLCArRaLSYnJ3Hx4kWMjY2xZP2HP/whhoaG+EJFeNNvfetbePXVV7Fx40a0tbVBKBRyQaS9vZ1n6KLRKPr7+7Ft2zY0NTVh586dUP/NaLKgoIBlhSsFdSlPnjyJs2fPYnZ2Fk6nEz/5yU+wvLyM8vJyaDQaNDY24l/+5V8wMTHBPjS33347MjMz0dfXx9CPl19+GUVFRYzWpYvY+Pg4LBYLbDYbVq1ahW3btjFJLdm7oNVqMTQ0hLNnz2J0dBQpKSn42te+xhLtCxcusALh7bffhsPhwD333IPvfve7aGtrY3+Nrq4utntQqVTIy8tDXV0dvv/976OrqwsAMDExAYPBgLVr17Kx30pBs3opKSmwWCyYnJxEZ2cn/uu//gsTExOslgCuURVdLhcKCwuxtLTEe/r58+fR1dUFk8mE73znO2w3IZfLEQ6H8fjjj2NpaQlTU1Po7e1FRUUFbrvtNjYx/STxiYfBf//736Ovrw8tLS1IS0tjw6L6+nrmOZN8weFwYOfOnVi1ahWuXr0KjUaDtLQ0jI+P48UXX8QTTzyB733ve/iP//gPThhmZ2fx5z//GU8++SSOHj2KgoIC1vFT1Xylw+73v/89+vv7edqfnGUvXryIJ598EnNzc1hYWMChQ4fw3HPP4YknnmCXxJdffhltbW249dZbua1133334Stf+Qq7e0YiEZw9exZ79+5FRkYGqqqqcOrUKaYedHZ24h/+4R9uuLa3336bGeipqans0Hz8+HHccsst6O7uxtLSEmvTiXyi0WhYPzg6Oore3l488MADeP311/Hoo4/i9OnT6O7uZh52eXk5SktL8dZbb0EkErGBzeLiIu69994bru25557jyiwlxwKBAB999BGefPJJuN1uliyQ34Lb7UZDQwMOHjyI+fl5VFRUoKOjA5FIBIODg6itrcXFixcBXPO+uHr1Kr785S/j3XffRV1dHXp7e7G4uIhdu3bh8OHD+NrXvnbDtb3++uvsI0GuqSqVChKJhDWJExMTLG0h8EBfXx86OzvR0NCApqYmmEwmiEQi/OY3v8Gjjz7K3hwnT57EgQMH8Mwzz+AnP/kJamtrWYoFXBtqW2lw7bXXXsPIyAiWl5dhs9k42QaARx99FGfOnGEs8Llz57B+/Xq4XC5s2LABAwMDrEe22+3YvHkz3n//fT6c1X9zzKa26tTUFCYnJ1m6SEP0//7v/37DtT3zzDMIh8OoqKjA9PQ0RkdH0dDQgJMnT6K4uJhN8mjD/dGPfoSf//znuOOOO/Dhhx+ioKAAFRUV6Ovrw+c//3k899xzuPXWWxnNnJ2djXfeeQcbNmzg1j35vvT09KCiogJf+MIXbrg2APjnf/5nDA4OorS0lAcym5ub8b3vfQ/btm3jvWJpaQmJRAJr165l2R65Z+fk5DArvLi4GEVFRfjRj36EkydPIjc3F93d3fjSl76EyclJFBcXc4di8+bN+O///u8Vh9def/11dHZ2YtWqVdyl2b59O86fP4/MzExYrVZGjV65cgU//vGPIZVKodVq8fzzz2NhYQGNjY0oKSmBwWDA1NQUdDodOjs74XK5oFKpMDU1hdWrV7PG1el0IjMzEx0dHeju7l5xkPNHP/oRvF4vtmzZwhjJNWvW4J133sG+ffswOjrKXUOCGczNzWHnzp1s6EZJbSgUwl/+8hc89thjyMzMZMPDP/zhD3jsscdgs9nQ09ODYDAIuVyOnJwcjI2NrTio/rvf/Q4mkwn19fXskyAWizE8PIz169fjo48+Qm5uLptApqenw2QyYevWrfiP//gP6HQ6rF27Fl1dXfj617+O//zP/8Sjjz6Kqakp9PT08GzLU089hZ/85CeoqKhg6Wp2djZ6e3vxxBNP3HBtv/rVr6DX61FZWcnDvJmZmfjjH/+I9evXc0UWuCYPpop0XV0dhoeHIRQKufocDodx+fJlPPbYYxgbG2Mp2vPPP48vfvGLrGk+fvw4ZmZmsH37dvz5z3/GT3/60xuu7ZVXXmEKHs1phEIhjI+PY+/evThz5gyqq6tZZvTpT38ak5OTOHXqFP77v/8bd999N+6//35YrVb2q1izZg0mJia4g/Xhhx/ihRdewNjYGNLS0vDBBx/AZDJh79696O/vx+c///kbru1rX/saFAoFX5Si0SgUCgWefvppPPLII2hubobD4YDL5eJ31eFwYGlpCW1tbTwDWV9fD6/Xi9OnT+OOO+5giZjJZMKxY8fw9a9/HZcvX4Zer+eZIprLWQkUcvLkSfaucTqdOH36NOrq6pBIJJCXl4dXXnkFJSUl/L7V19ezpv9b3/oW1q1bhy9+8Yvsbjw8PMyzpoTBf+mll/DUU0/h1VdfhUaj4XNHp9Pxs3ujeP/99wGAZ1Hn5ubYIf2zn/0sX8SkUiksFgtWr16NF198keER1dXV2LBhAw4cOIBvfvOb+Jd/+Rfs3LkTV69eZSDLzMwMCgsL+b3t7e1FNBpFe3s7QqHQims7evQoDh8+jNtvvx2Tk5O4dOkS7rnnHjzzzDN47bXXMDg4iFAohN7eXvT09OAXv/gFzz7++te/Rk1NDTZs2MCd1eHhYbS0tPDlJhQK4Wc/+xlTCe12O0tKm5ubYbPZcPfdd99wbb/5zW9gNpsZFFNWVobU1FT86le/wte+9jV0dXUhOzubZ4RkMhkOHz6MBx98kM+lSCSCN954A4WFhbDZbHjggQdw+fJlDAwMsDE0DYi3t7dzB4W8eVaCv/zqV7/CxYsXUVhYCJ1OB7VaDYPBwE7tL730EtauXYuqqiosLCxg1apVeO6557C8vIw9e/ZAo9FgaGgI3/3ud7Flyxa4XC589rOfxfLyMk6fPo1z586huroat956K+crJF+j+Y4vfelLN1zbK6+8wl5uRqMReXl58Hg8+O1vf4tHHnmEpZLX44DffPNNzhlJcnb27FlIJBJMT0+jra0NBoOBpatLS0uYnp7mIuX09DQTOklt8/fi/8mwT61WY3l5GXa7nWU9Pp8PLpeLB64cDgfsdjsWFhYwNDSEN998k9nACwsLKC4uxh//+Ed8+ctfxunTp+FyubgCYbFY8J//+Z+8CdKgIJmX/eu//usN10YaZdLbLy8vM8/dbrdjYGAA4+PjmJ6eRmVlJQKBAEQiEex2O3bt2gWpVIqBgQEelnzwwQchkUjQ09PDbU2xWIyRkRFs3LgR77zzDkZGRrC0tMTEl5VidHQU1dXVrDdMSUnBCy+8gNWrVyMajWLDhg3o6emB1WqFSqVCT08PM4+J/pCeno76+nocOnQIHo8Hr7zyCutdAfAAWU9PDzweD6+nqKgIAwMDK140ZmZmYDQaIZFImNolFApRXFyMsbExpKSk8BDt1q1bcejQIQDXWNE0JCcQCHD16lX+Lnp6eljulJGRgdWrV+NPf/oTzp8/z6325eVlxt+uFGSiRS1R8hGoq6tj/SINzy8vL+PYsWPIyclhwza5XM4UGYvFgrvvvhtyuRw9PT08mFtQUIC//OUv2LBhA9LS0nh4LCUlJWl1ubOzE6WlpZBKpUyXmp2dZV2tTCZjGlZ1dTUPO9NGHo/HoVKpsGrVKjYMjEQifFEnpOR3v/tdRlOSJpkkbSsFdbZokFqv16Orqwvl5eU8TzU6OorFxUXU1dXh8OHDjOQ1Go08FG4wGPD666+joaEBV65cQU1NDcxmM373u98hGo2ivLwcAoGAZ57IgTjZuwCAk4/09HQ4HA6W+W3YsAENDQ1sypSVlYXa2lrYbDb09vbCbrejsLCQ51syMzNx4sQJtLS08OxFZWUlLBYLPvWpTyESiWDdunW4dOkSoyZDoVDSyhW5jQP/4wZ7/vx53HPPPTh16hQPUMZiMeh0OthsNtjtdqhUKq6ovfPOO9i0aRP6+/vR3t6OkZERJqlcPzhPMxudnZ08o5GsGhkOh1FeXo6ZmRmu8Hd2dqKqqgoDAwMIBoOMmjSZTFi7di0aGxvxox/9CE1NTaioqGD88IcffoiNGzdifHwcVquVZVlCoRC/+MUv0NLSgoKCAia0jY2NJZ2ncrvdKCkpQTAYhMPhwMLCAiQSCYqKijA9Pc00qTNnzmB5eZkP5eeff57fj8OHD/OhnZ+fj5GREaSnp6OqqooJfJcuXeIKu0AggFqthkqlSro2qkaS8aBQKMQHH3yAiooKzM7OYmRkhDn46enpkEqlUKlU+Otf/wqBQACRSMQAC6K/vfjiizyU7vP50NzcjL6+PjQ1NcFut0MoFDLZKpkczuVycUWQBvlpv5+cnOTfnSRQNJPh8/lw1113QavV4ty5czCbzSyrkkqlmJ+fRyKRQCgUQnV1Nb7xjW/g1ltvRTweh8FgQEpKCl5//fWkCG9KMK5HB8/Pz+P/+//+P9brX2+2SUCA5eVlOBwOlJSUQKFQ8ODwxYsXmfgTCoUQi8XQ3NyMJ554At/5zndw7tw5xsEqlcqk7+n4+DhycnLY36eqqoovCaOjo/zPRGiiC2BaWhoef/xxRCIRjI+Ps3O10WjEqVOn+P+enJxkRDTNQtEe4/P5knaUR0dHUVFRwbOS4XCYcwB6T8+fP897ZnZ2Nh566CG88MILDNTp7e1FIpHAK6+8gu3bt2NycpLhIBcvXsS5c+fwrW99i/fB4uJiRCIRDA8PJ50X7ezsxNatW7mwqdPpEAwGeU6U3NQJ1kPJ54kTJ1gyevr0aTgcDtTU1LDR4djYGHQ6HaqqqtDe3o7f/e537HVD8yAnTpxIKlslMiUBNiiR3bBhAy5dugSVSsVAn6qqKqY6HTt2DHv37mVflc2bN+PYsWPw+Xzo7+/nouTly5fh8Xjw6U9/GoFAAJ2dnTxkLpPJkg74+3w+bNmyhYfZw+Ewzz7RxWhubo5x3ENDQ2htbcUzzzwDAKitreUzjgoy58+fh1QqZfm8w+HARx99hFtuuQVDQ0MoLy+HUCjkObCVYnp6GikpKdzhttvt6Orqwh133IFgMIicnBxWEnm9XjidTuzcuRMvvvgiD/DLZDI88MADPEtjt9thMpnYmE8kEvFM8PT0NEuoIpEI0tLSVlzb9fGJLxpyuRxut5sZ2nTD379/P9auXcvmbFTdmJubY243kSeKior4h33hhRdw3333QSAQwGKxwOfzsf7t2LFjMBqNiEQirP0uLS1dcW1Eu5LL5Tw4EwgE2A333Llz/OcXFhZiYWEBa9aswQ9/+EOuLszNzWF6epplUDt37mQPBnIgvd6Zl2gDlESsFPF4HL29vQCu6V6pojcyMoKMjAwe5FlcXGRzrOzsbPj9fng8HqjVaiY6zc3NQafTQaVSMbbV/ze3640bN8JisWDNmjXo7u5mOVmyQ1gqlTJKkpy2bTYbfD4f6uvr0djYCJPJBJvNhvT0dAiFQuh0OvT29mJ4eBjNzc3Yvn37xw5mg8GAwsJCCAQCjI2Nwev1sjdJZmYmBAIBVyOTGc/R0DhpvkUiEWw2G/bv3w+VSsWV/Ugkgry8PCaPOBwOJnXNzMwgEAhw1UqpVGJhYQGBQACLi4uMQhYIBEgkEnxpFIlESY22yLWVqCEFBQWIx+OYnJzEyMgIt+AXFxdZB1tTU4ODBw9CrVazJvnYsWMoKyvDyMgImpqaIJPJ4HA4+GI5NTWFmpoaHlyNx+N8wUv2vNGlhJKxtLQ0poddP1RHSUJJSQnm5+e5G0k0DwI6EMmLMJvAtSE0sVjMml8irfy94bC5uTl4PB5UVVWxbJKGmu12O2vj7XY7otEo1q5di4mJCcZfk9O0TqeD2WzmzhDpZLOysuBwOBAOhxlfGolE+J2li8SNIj09HWazGTqdjjG5YrEY7733HoLBIA+s0hCvQqGAyWRihGdJSQnS09OZIPS73/0Ou3fvZsiE1WrFlStXUFRUhIqKCthsNiiVSnaTTvbdkYZZr9fzM5Ceno6xsTGeV8rJyYFQKMT8/DwaGxuZKELSF0J80+B1aWkpX3AXFxdhNBr5z6NLlcfj4f17pVhYWMDVq1fZo4Ecb/v7+1FRUcGuwDMzM1hcXIRIJMLk5CTPBtG8GcmZaOibnmeSN4VCIaxZs4Y7QURUSkavS0tLw+joKOuQCXvt9Xr58A8GgxgfH2etc2trK+RyOU6cOAG9Xo/i4mLYbDY2qgSuFd5Iiri4uAiTycTO0TTbQdCJlYIuLxkZGUz9AsB7WH5+PlwuF8bHx3nPIk15Tk4ODAYDOy9brVZMTU1hzZo1qKyshNlsZo064WInJycRjUZ5ViPZ/pubm/sxkIJIJILL5cKRI0fQ0NDAA8ASiQQ5OTmsdqA5EfK0oQQ4KysL4+Pj/N9R5yg9PR1Xr17lgt71SdNKkUgkEI/HudNNcAqRSIRgMIi1a9dCLpfzXpOTk4O5uTkolUqMj49DoVCgrKyMyY8kucnJyYFWq0UikcDBgwdx9913o6amhuEK/r85Nyd73gQCARcXiG7l9/tx5coVFBYWYnJykilrNpuNZ25CoRCbB2dnZ/NZXFxczN4lHo8HKSkpaGtrg16vZ7kMFXcIbLBS0CxKTU0Nn2+Tk5P83h0/fhxCoZC/X5r3sdlsCAaDWF5e5hkRrVaLzs5OZGVlcZI/OzuLoaEhNDQ0QCgUsk9ZSkoKXC4XDxvfKFQqFUwmE8+Z+Hw+9oRoa2tDfn4+AoEAZmdnMTc3h5ycHOh0OnR1deHixYuorq6GTqdDaWkphoeHucNGQ9wlJSVITU3F+Pg4Vq1ahd7eXng8HkQiEWRnZyd93uRyOcuiCG9OKPry8nLk5+fzYHxfXx8+/elPw2w2o6WlBVNTU4hEIjybXFlZyW7ver0eUqmUP1N6ejouX74MAEznozN8pSByZGpqKs+sZWVlYWlpCRkZGTxfS7M4KpUKVqsVlZWViEQi3CnTaDQIhUJwu92Qy+VsVZCTk4Ph4WHYbDasW7eO6Z8ejwcymQxr1qxZcW3Xxyee0SDMGbUDCd1WWloKm83GukOFQoG8vDw4nU7k5OR8jLoUi8XQ0NAAlUrF2K2MjAxkZWUhNzcXO3bsYKY1zVYA12Ywkt3UhUIhMjMzkUgkkEgkODlatWoVVyAoOacKeEZGBlJTUyGTyT6GIqUh4vHxcSiVSqSlpWF2dhabNm3iQw+4tvlmZWXx+lcK8lIgrWA0GuXLht1ux9zcHJaWlhjlKpVK+YARCoWMRVUoFDxEB4DpPAUFBaitreXBd7lcjpqaGpSVlSEUCiWtfl+fYIvFYkZoajQaxm8SmpiMxjZu3AiFQsGDTWNjY+xZEA6HYTabodfruSpXWFgIk8nETsZk8uL1etlN80axtLTErtZisRiZmZnIzMzkQ4iSSrFYzMOZlLSRdpXwf263G1VVVYjFYhAKhYylo81WIBAwwYT+jGSzBkShIESkWCxGJBJBXV0dywcICUmyPTr8yUdAr9ejrKwM6enpqKysRFpaGkso0tPT0dbWhnA4zG13ovSQSVWyIOkR/bNEIkFxcTGCwSBUKhUPnhL5aevWrWxqZTQakZOTg6qqKiwvL/PckNvtRiKRQE5ODlpbW9n0j2g3SqUSwWAwKRgBAFeHFxYWoFaruYAgl8thsVigVCqhVquhVCqhUChgsVi44kyITI/HA41Gg6ysLKjVaq40ZWVloaSkBBkZGTz0St3BgoICvhyuFEQNIawgDRp6PB6GNGRkZPClxmKxoLCwEHK5HGVlZfzPdJH0er2MnvT7/exJMT4+Dq1Wi5SUFJSVlaGtrY2LNysFPc92ux0AmPCTmpoKp9PJrr55eXlYv349V7HS09OZEEj4Qhoypu9QLpdDKBRyd4EId3TIUUK9UiwsLLBrMplXLS8vIzs7m+VNWq2WnXSVSiWTiUjPTgOz1HWixJou/5mZmZicnOSKfGZmJjIyMuB0OlFQULDi2lJTU7nbSDMYdCEml2qi29EzQtLGkpISFBcXc6dUKBSivLycB8vVajUUCgWKi4u5uh4Ohz/m0JzsorGwsMDn4vXIdOoAZWRkQCgUcmJst9t5mF4oFHLBRq/XY3Z2Fm1tbXwuy2QyCIXCj8l+qAgoFAqRmpqatEC2vLzMiOSFhQWkpaUxtYb8quj50+v1XFSk54Dkjn6/H93d3aioqIDL5UJKSgo7f9NZQBCB3NxcaLVaxGKxpDMaFIQqpqRSJBKxFl8sFvNz6/P5oNVqYbVakZmZiZycHEgkEk6SiUx0/Z6pVqthsVj4M1GOQvnMSkFocPpstHfRJT8rKws6nQ65ubnIzc2Fz+eD1+tFeno6zyGIxWJUVlYy/jgWi0Gr1fKevW3bNr6w0FlDxL5kVD06+2i/JJmuQqFgCSxJlKuqquDxeFBTU8PnfWFhIXeanU4nFAoF06rIUdxgMDBJ7voiKflArRS011ChjyhOJPWlguD1lL6tW7dyAdTpdPKliy7XoVCI1yGRSNDQ0MBGhdFoFHK5HBqNBh6PJ6nxHBWSyINCJBJxzrS0tMQ+PampqTygPzExwZc2mm+qrq6GQqHg3DMtLY2pijQ3VFpa+jHvMCrsrxR0JlOulpWVhby8PGi1WkbtUnGVCvmZmZmYm5uDRqPh84zmMdra2ljdUFhYCIVCgba2Ni7mEnWLityEBv978YkvGuFwmC8QDoeD+fv33nsvV2QSiQTUajUbPNHtiZIB0p+Nj4/jU5/6FObn59lnorCwEPX19QgEAnzbos1LJpMlZTBfT+egtmxqaip27NjBxjOUCFB70m63o7y8HCUlJbxBrV27FtnZ2di2bRvGxsa4/Wq1WtHU1MRrpQc0MzMTUqmUiVk3Cmqj049Dt8/Vq1ezk6dQKIRKpUJlZSU/sIFAAGVlZcjLywMAxpMRWcnhcHA1f/Pmzbh8+TKj5YqKilBfX88Er5WCLo/AtYea9MqNjY3sDBmJRKDRaJhOUFxczG7kpaWl6Ovrw9GjR5nT7PF4OEmMx+NYvXo1xsbG0NPTw0mSUChkosxKQZ+N0I9isRjV1dVobGxERUUFiouLsWbNGqxdu5Yr8vQ7V1VV8U2/qqoKgUAAt956K2tsqWJtMBj48gFcuzxStTjZpkgJKEm4iL+/du1axskSsWtqagozMzPcuiR9Z319PR588EGkpaVhz549TJIJh8N82SRkKcmhaKNNtjaRSASxWMxIO6JXVFZWQqVSIRwO82WZXJwNBgP6+vqwatUqrFu3DtXV1di5cyeUSiVqamowOTnJrXUa9vd6vZwY6/V6lJeXs99HslAoFMjPz+eBThqOJz+ThYUFyGQylJSUoK2tDbOzs5zIFBUVsTxtcnKSkxoimxCitLKykgeAPR4PCgsLubuRbGNcXFxEZmYm+4XQXENZWRmys7Nx6623Yv369airq+PLfVtbGxoaGtDe3g61Wg2HwwGZTIbZ2VnWU4+NjTEtbtu2bQDAF5qGhgbccsstPB+1UhCueXJykoeaaVjb7XYzVSstLQ0tLS0YGBhARkYGH5LUkd25cyei0Sh2797N3UKlUslIRuoQUPekvr6eHYSTRUVFBbRaLe83SqUSmzZtgkgkgtFoRF1dHdasWYNNmzZBpVKxtIYqyBaLhRM+2vNpFic9PR1r1qzhoc1QKISioiLU1NRAIBAkBTdQZY8kUGKxGDk5OSgrK0NNTQ1L0fR6PXbs2IGysjJO/tetW4empibk5OSgsLAQpaWlLMusrKxEXl4eUlJSUFdXx5Kr+fl5SKVSZGZmcrV9paB3gM6rzMxMKBQKbNiwgXHPmZmZ/N25XC5UVlbyf0c0NJpje+CBB7C0tASTyYRIJIKSkhK0t7ezZ45SqURBQQFKS0tZ+71S0P5MyGSS1d56663QaDTIz89n00mpVIqysjLe98hjgfCmDoeDzeqam5vR1NQEjUbDQ/x0UaSL5fLyMoxGY9LnjfZLUi2QmSah8mdnZxGLxZCVlYXLly+joKAAw8PDWLduHSoqKrgg5ff7GT1rMpkwOjqKQCCA22+/nf1ZbDYbMjMzUVpayhfWlYKkXEqlEm63GyaTCenp6WhoaEBWVhZaWlpQUVGBmpoabNy4kRP9vLw8PPTQQ9i1axeqqqqwceNGLkIQRjWRSLCBZHd3NxKJBDIyMqDRaKBWq6HVapP+pgKBAEVFRYw6Tk1NRVVVFfterFu3DuXl5SgqKsLmzZvh9XpRVVWFgoIC3HfffWhsbER+fj6+9KUvcceY/G5IardlyxbOAejSpVQq2bhvpaD3/vqkODc3F7t27WIvG/rN6MJTWFiIlpYWtLa2spHtxMQEy+7S0tKYbrm4uIja2lrk5uYyXrygoAD5+fnsk7ZS+Hw+1NXVsRpDKpWyR8Xy8jKfGTqdDu3t7TxkT0oQhUIBmUzGnROizVGHZmFhAU1NTZBIJGhvb//Y/kSUvZUiFosxOpzO1pycHAYW0B6q0+mwatUqlJaWIjc3F+Pj45DL5WhsbMSaNWug0+kgk8mwY8cO7kapVCoEAgGeG/J6vRCLxSgqKkJZWRkbin6S+MQXDa1Wi6tXryI/P59RfkqlEi+88AIefvhhTtBIe9zc3IxXX30VHR0dcLvdMBgMMBqN+PznPw+VSoUPP/yQSVVqtRputxu33347nnrqKaSnp0Oj0WB4eBhOpxOf//znkybz5C2h1+uhVqtZRkVDqzSortFo0NDQgLKyMvz85z/nw722thbt7e0sPTp69ChWr14NgUAAo9GIrVu34tFHH8VnP/tZFBUVYc2aNUy5Aq7RalaKaDQKp9PJzHyFQoGNGzciJSUF69at40tbZmYmJBIJRCIRjh8/zqjFkpISVFdXw2azoby8HLFYDC0tLSwbKisrw//5P/8Hn//855FIJFBTU4OBgQG8/vrrkEgkfFG5UaSlpXGb1Ov1IhgMorW1FZcuXcL27duZFEMbcDgcxvT0NHw+H5577jkYDAb8x3/8Bw/k2mw2fOMb34BOp0N2djZ27NiBEydO4Cc/+Qmys7Oxe/durFmzhtGDyRCGQqEQPT09XKEgyQddKk+dOgWLxcLJcmNjI8MAsrKysHr1aqZW1dTU4OTJk/xCUkVyZGQEHR0d8Hq9SEtL402upKQk6fOm1WoxOjqKzMxMNtjJyMhAKBTiTg9JATo6OlBWVoYjR47gi1/8IoxGI1JSUuDxeNDX14f8/Hw23aGLX3t7O5aWlrBnzx4cOXIEmzdvxpYtW1BTU4PW1laMj4+vuDai5pCz8ODgIEt87r//fr6IUPteLpfjqaeewlNPPQWfz4ehoSGYTCa89NJLTEkhDxPgWlJw6dIlVFRUoLOzE9u3b2ePATJxTBZSqRRNTU2or6+Hy+XCmTNnUFxcjCNHjqCqqoo5/DTHQ+7ViUQCIyMjqK+vx969e3H+/HnU1dUhJSUFn/vc56DX67kb+tprr2Hnzp04cuQIcnJy4HQ6MTg4iIaGhqTdIJFIxC1rt9sNp9OJxsZGXLp0CbfddhvC4TDvf6Ojo9iyZQvGx8cxPz+PixcvIj09HXfffTeampp4aLSmpoYlHrm5uXjllVewd+9e/PGPf0Rrayvy8/P5wE+mmVepVNBqtSgrK0NOTg7kcjnvWfT9Ade6OhcuXIDNZoPFYsH27dvR0NCAjo4OVFVV4cSJE8jIyEBXVxcMBgPm5ua4Ct/V1YV//Md/RFdXFz/P09PTPLS/UtB5MDs7y34+JElavXo1zGYzZmdnMTo6ir/+9a/IysqCzWZDQ0MD6urq0NLSgszMTLz++uu47bbbGG/pcDgwNTUFjUYDoVCIBx54AENDQygqKuIKe319PY4dO7bi2oRCIRoaGmAwGDAzM4PR0VE0NjbC6XSiuLgYJ0+ehFwuh1arRW9vL9avX4+BgQG0tbXhBz/4AQ4cOACBQICTJ09i165dCAQC2LJlC1czi4uLYbVasW3bNpbNffDBB3jvvfeQkZGB7u7uFdcml8vR19fHVW2/34/29na8+OKL+MxnPsPJyODgIPbv34/169djbm4OtbW1qK2tRUdHB9asWYPZ2Vl87nOfg9vtRlFREdLT07karFKpcPfddzPpKy0tDTk5Odi9ezdmZmZWXBsZk1VVVaG0tBSpqakIhUKYnZ1FZWUlDhw4AJfLxZ1MrVaLwcFB7kzW1NTgy1/+Mmpra7FlyxaMjo7izjvvRGFhIZRKJSoqKpj8VFdXx55Cdrsdt9xyS9KzQaPRIBgMsr6cHOKnpqag1WoxMjICg8GAkpIS9leyWCxYv349pqamuEjicDiwbds2VFdXo6GhAaWlpaitrWW3502bNuHixYvcyfB6vairq2M60I1CpVIhGAwiMzOTO/xr165FZ2cnD2FTEVYikfBzTt8RwV1+97vf8fNWVlbG0h6DwYBXX30VX/ziF1kFQb4zIyMj+PDDD1dcW1FREcbHx2E2m9l0MTMzE7/97W/R1NSEn/3sZ6xCSEtLQ2VlJQYGBpCVlYVz584xvSgcDqO6uhqHDx/Gli1b2ABQpVKxYz3JS4mKt2PHjqTGmjqdDqFQCKtXr4bBYIDD4UBbWxsPz7vdbp4Likaj0Ol0+Pa3v41169ZBLBajoqICGzZswOXLl9HQ0ID09HTeg+iCTCSojz76CKtXr0ZqaiqGh4e5a7JSyOVyXLp0CRkZGey1o9Vq0dfXhzVr1kAqlXKh7MCBA7jjjjsYyU97a25uLjo7O/HQQw9BLBZDpVLB7XZjeXmZseqrVq3C73//ey4cEF58eHh4xbVRhxy4Vhzo6enh71IkEuHo0aNobm7GPffcw92Ul156CV/60pdw/gvFwVoAAQAASURBVPx5hqxYLBakpqbi3XffRSwWw5/+9CcMDg5i8+bNePbZZyEWi3H58mXU1dUhKyuLc56/N5NJ8YlnNCQSCT796U+jr68PMpkMjY2NPLz64YcfwmKxwOFwsCdDb28vOjo6MDY2BpVKxZrw1atXszypu7sbWVlZmJ+fh9/vx7/8y7/gO9/5Dks9wuEwkwGS3YZJckLVHdok29vb8dxzzzEVa2pqijX7KSkpOHHiBKRSKRsylZSUwG63MwHF4XDwpvW5z30OTz75JFavXs3a4EQigampqaRtt3g8jtLSUnZCJ9NA6oQQl50IV2VlZbjlllvw9ttvo7u7myt6sVgMP/rRj/DNb34TVqsV27dvh1QqxdWrV7F27Vp873vfw7333osPPviAqx6kx10psrOz0dHRgXPnziEQCLBDaX5+Po4fP84O5dSdKCgowBtvvIH09HRkZmbiwIED6O3txerVq/Hhhx/i+eefx/T0NN566y0sLCygpKQEZrMZIyMjWLNmDUZGRtgBnYapk/2mOTk5MJlMyM7ORlZWFo4ePYqNGzfyZWL//v0IhUJYu3YtzwrZbDbo9XomlrW0tOC9994DcK2aHovF2EVWIpHg6NGj3OonygJpGVeKhYUF1NbWwuVyseRiYWEBx48fZ6LT0tISV/3sdjtXpMnsTSKRYMuWLWzYFAqFUFZWhqtXr+LQoUN47LHH8Nprr6G+vh59fX0swTh37lxSHW5vby9LVmheIxaL8dAYPf8Gg4EPO61Wi0996lMoLCxk86Pc3FwsLS3hjTfeQCwWg1qtxtDQEA4dOoS2tjZG0Pb39/PvSBLKZJGamsrzSdQJtdlsaG9v52E1YtWTcdMtt9yCzs5Odi2dn5+Hy+VCd3c3Ojo60NfXBwCsj3/ggQcwMDDAg/Q0LxMIBLCwsID777//hmvzer0sZ6OO7f79+7Fu3TpoNBreE6i6S/NepE++evUq45iLiorQ1NSEw4cPIxqNMjjg6aefxk9/+lOuLKampsLv98Ptdid95qgiT3pd6gDQIXnnnXfCbDZjaGgIlZWVGBwchM/nw+nTp9l/YnZ2FtPT0wgEAhgeHkZeXh7LCS5fvozOzk6EQiFOsPr7+1mClszQKhwOo6amhr+bUCiEK1euYH5+ng0fJyYmYLfbkZKSgoMHD8JgMGB4eBgejwdZWVkwGo24/fbb0dvbi5GREdjtdqxZs4afsebmZhw7doyf35GRESwuLqKioiLp90aGnhkZGWzgSIaYiUSCOyXDw8OIRqNobm6GUqnE+fPncd9997FUatOmTfjDH/6AsrIyXLlyhT0NrFYrDhw4wDQeMkUjd95kB3FGRgYaGhpYxx2NRnHo0CEIBAK89dZbiMfj7IPR3NyM/fv3o7CwELFYDGazGVevXoVUKsW2bdvws5/9DPPz87j77ruh1+vh8Xjw7rvvoqSkhAl0JPMNhUKYmJhgwt6Ngqq1ZNS5sLAAh8OB4uJihkNMTk7iww8/5KHQRx55BG+88QbPy7lcLj4rNRoNrFYrhEIhIpEIa8c3btwInU7H8mKPx4OzZ88m7aD19vayuRzN8l26dIm7DSqVCv39/UypczgceOONN+DxeJCfnw+hUMgy1HPnzrFEur6+nk0n7733XrjdbpZ5hkIhTE1NwWKxoK6ubsW1TU5OorS0FENDQ5BKpejo6GAc8unTp9HY2IjR0VFG0e7btw89PT1MDCLX6q985Sv4wx/+AL1ej40bN6K8vBxXrlzB+fPnkZWVhRdeeIGLU0SG8ng8SddGM4M0RyOXyxGLxfDSSy/h9OnTTIkcGhrCtm3bGMpBjusDAwO4cuUKcnNz0d7ejv3798Nut39svrO0tJQvgEqlkk2IKcdbKRwOByoqKnh+x2AwMIAjFApxVykjIwNerxddXV148MEHMTAwgIsXLyItLY39xY4dOwalUolQKASxWIz+/n5MTU1h+/bt+OMf/4j777+fzY/JEyyZo3owGERTUxNLtoBrHebS0lJcuXKFEdw0x/Xss8/i4YcfxltvvcUX5uXlZczMzOC73/0uuru7sW3bNjZkpnfrwoULaGlpwenTp7G4uAitVguj0Yjp6ekV12YymZgYurS0BLvdjtdffx3xeBw7duxAZWUl3njjDc6F4vE4br31Vly5coVVMmQe2dnZCYFAgPn5ee6qfvTRR8jJycGFCxfQ1NSEDz/8EDk5OYziTlZkvz4+8UUjLS0NTz75JCO+yGhscXERaWlp2LVrF3tn0Mb4m9/8hpObjIwMqFQqHhhvbW3F4uIicnJyWM4xPDzM/gzAtRdjfn6eca8rRU5ODsxmM+ueqZ3U3d3N2llyMlQoFGhpacEzzzyDgwcPYmxsDBKJBEajEXq9nivwX/nKV3DHHXfAbrfj7NmzSElJwUMPPYTXX3+dX1CVSoU1a9YkTfxokJkkNbFYDF1dXVhaWsLq1atRWlqKiYkJ9PT0cHJD/gmxWIxvs7t370Z7ezteeOEF7NmzB4lEAhcuXMClS5fYYby+vh5msxlXrlyBSCTCli1bWCZzo0gkEvj9738Pg8EAg8HACQLRJ/7t3/4Nb775Jg4fPoyysjJs3boVGzZswPDwMDIzM+FyuXgORKfT4aGHHsJDDz2E2tpajI2N4fz581AoFFi1ahXz3Ym3PTIysmLCB1yTcvn9fpauud1uBINB/PKXv0RzczPa29uxfft29Pb24tVXX0VjYyO3VUdGRjA3N8etxFgsBplMhrq6Ot783n33Xfz4xz/GkSNH8PbbbyMjI4N9CkKhUFI2f1paGo4cOYK8vDxkZGTAZDKxy7LVakVHRwccDgcjacltPh6PY3p6GvPz8zAYDBgYGEBfXx8OHTqEaDSKL3zhC6iqqkI8HmfShtfr5QtxcXExamtrk74LZWVl7HSsVCrZLKmnpwdTU1OQyWQwGAywWq0YGxtDXl4eduzYgdbWVvZMWFxcRH5+PoaHh3nGiJ4RqvoDQElJCU6dOoXFxUUUFBSwtjxZkJsz6Z6VSiXMZjNOnDiBL3/5y0gkEuwpQfM3EokESqUS3d3dMBqNKCgogF6vh8FgwPj4OOOM/X4/Ojs7UVBQgJSUFBiNRnakj8fj/OytFHSBJnqH1WrlAfClpSWMjo5Cq9VicXERPT098Pv92LhxI9RqNXp6etibhAzmQqEQGhsbsby8zJKfd999F7W1taiqqsKBAwegUqk4yUjmqk6HGYEOyDWWBqy1Wi0P8JeVlWHdunXo7e2FRCLBkSNHUFBQwB0dGu48c+YM8vPzmbZEF20igZErNqFHVwqpVIrLly9z15rc3omyR0lcWloaDztGIhG0t7fj17/+NVfW6dkUCoW8f5AG2+/3w2Kx4Etf+hKuXLnC5qFyuTzpjFxDQwMbbi0tLSEYDGJoaAjvvvsuG4cSnpmkPgR2IENSKlDs2bMHf/7zn7F3714IhUImQm3YsAFr1qyBXq/HyZMnMTo6iomJCaSmpiZ9VxcWFnDhwgV236W/d3Z2ln1WKioqEAwGcfjwYXaYHh0dxUsvvYTs7GwUFxfjxz/+MVatWgWv14uhoSFOHrRaLc9AkA8H0Y6sVmtSPwgqAFDHWywWQy6Xo7u7G8PDwywdlsvlfIm9dOkSJBIJ3nrrLaxatQodHR0QCoWc6G3bto2rvMeOHWNJ57lz55CWlsZdyXg8nrRrq9fr2dCQZkVIVhSNRrmY5/P5cP78ebS2trJs9OzZs9i4cSN27NiB6elpTE1N8Wwi5Qp9fX18Udm1axfGx8cRi8V41iVZ17agoIApU+TOnpWVxV0qkjeWlZXxmh999FEcP34cGo0GKSkpTDBau3Ytm9Fu2LABd9xxB4qKivDjH/8Y+/btw/z8PCwWC6anpyGVSrFq1aqkM3JWqxUOhwMSiYSd5nt7e3H8+HFWGlRXVyMtLY31+FRgOnr0KFpbW7Fjxw7k5OTgiSeeQDAYxMMPP8zS+OnpaUxPT/OzRp5PNB+bbO5GqVTi1VdfRWtrK+8BFRUVEAqFSEtL49zH6XTiwQcfhFQqxTvvvINoNAqDwcAyUYICRKNRHDx4EOvWrUNraysaGxshFouh0+mg0Wj4mZRKpWhoaEgqncrKysLhw4eRl5fHReqlpSWYzWamUQUCAZ6bzc3Nxcsvv4xoNAq9Xo9IJIITJ06wV9CDDz6ICxcuICcnh+WHSqUSJpMJer0ezc3NLCHOyspK2nkkaSDJ2jIyMjA6OgqHw8EAnNTUVLZPEIlEePHFF5liGAgE4HK5kJGRgZGREWzfvp1pVDS/1NDQgPLycpw5cwZCoRBms5mhCi0tLSuu7fr4xNIpq9WKW265hXX89fX1MJlM2LFjB2ve4/E4RCIR8vPzMTQ0hM2bN/NwlsFgQHFxMR544AGcOHEC0WiU7c9TU1MRjUaxZ88erjoSG7ypqQmLi4tJ2+NTU1Ooq6vj1m4ikYDX60VNTQ2KiorYgKSurg7Z2dk4ffo0pqenEQ6H0d7ezi7DtDn//Oc/Z0IGvXTNzc24cOECjEYjMjIymFJBpJKVgg5Dq9WKUCiErKwsHugaHh7mJMNgMKCiogKZmZnIyspCZ2cnysrKsH79etamK5VKPPLII3C5XJicnIRarcaePXvw0EMPQSqVYmZmBi6XC+vWrWOHzGQHncfjQXt7O+RyOVJSUqDT6VBRUYEdO3ZwgpSfn4+6ujrIZDK8//77nIw88MAD2Lt3L9RqNbxeLwwGA/bu3YuJiQkUFxdj27ZtqK+vx6ZNm9iQanh4GHK5nB2Ck7nmAtdeInI21ul0EIvF2L59O/Ly8rC8vAyfzweFQoEdO3bwYOfY2BjznsnVNz09HXV1dThw4AA6OzthMpmgVCrxhz/8AW63G/fffz/0ej3EYjHTWc6ePbviuoLBIHbs2MEaX6FQyJtwe3s7CgoKGDyQk5MDlUqF7du3IzU1Fa2trdi5cye2b9+O7du3IxwOY+fOnaipqUFubi67p8vlcr6cZmRkoLy8nDF9yQ46+s3C4TBMJhN8Ph8PIBKCNjs7G0ajEVu2bEE8Hkc4HOZ5nry8PDQ2NqK+vp4HBMfHx3H58mWMjY1BIBCgvr4eMzMz7Na6c+dObNy4ES6XK6lUD7imdyUnaqJ+uFwu7N27FzMzM8jMzERZWRk/1/R3isViPPzww2hqakI8HkdeXh5MJhPy8vJQX1/PxloFBQUQCoWYm5tjiRt1UElisVJQ5Z40vEajERqNBjt37oRUKsWuXbt4kLS+vh7hcBiVlZX485//jA0bNqC8vByTk5OMcDYYDBgbG+Pfi3xBaBYnPT0deXl5kMvlPBO2UsTjcdTV1SEajWJ2dhbxeJy7SvRnCIVCLC4u8iHT2NgIi8WCzZs3s7SRHMF37NjBF0qJRAKr1Yr77rsPTqcTy8vLnGgT/joZxjCRSLAPDb1rHo8H2dnZPIBOA7979uyB0+nE3NwcTCYT7r33XjQ1NWFhYQHZ2dn485//jGAwCKVSydV0KgKQp8/c3Byqq6u5A5isMj87O4tgMMjdx7KyMiwsLGDfvn08MyUUCqHVatHS0oLLly/zPN6OHTugVCoxODjI+uwHHniAGfculwszMzNYv3493njjDQDXcJOk1b7eh+JGEYvFUFRUhHA4zCAPkhMRiGFubg6xWIyTrp6eHjidTmzcuBGrV6+G0WjkObji4mJEo1E+Q8joz+v1MilJKBRypySZHA64dkkjQhcBEkgS0tDQwPp3jUbD2nWBQIDHHnsMDQ0NCAQCaG1tRW5uLrZu3foxE1yZTIbt27fjyJEjEIlEsFqtkMlkKC4uZknQSiESiZCTk8O6dLqktbS0MNKWhmTvuOMOmEwm1NXVQSwW47777kNeXh66u7sxOzuLc+fOsdTH5XKx7JouaYlEAlarFVKpFBqNBiaTKSmR0Ov1Qq/Xo7OzE729vQiHwxgfH8fDDz+MRCLBOVRGRgY2bNjAeUF2djbTAWUyGTo6OiASifDQQw8x/Y0SvH/913+F1+tFbW0t6urq8OlPfxr33nsvz2OtFCQ5mp+f5/kfr9eLJ554gglIJSUlKCoqwvz8PJPhwuEwfvrTn+If//EfkZeXh9TUVLS1tWHr1q04c+YMm/xRcXNiYgJFRUUYHBxEOBxmYmky4t/MzAwXRmn/NZlMaGpq4iJba2srU9WuXr2KPXv2IDs7m4eWSYLucDhQX1/PSONAIIBz586hs7OTi35zc3OoqqrCqlWreN51pSDggkAgQGZmJgwGA0ZGRlBbW4tjx47BZrNxgSA7OxvLy8u47777eKhdJpOhsLAQ3/72t+HxeLBq1SoYDAY0NzdDq9XC7/dj06ZNCIfDEIvFjMGnC0qyQorD4cDmzZuRSCQwPj6O2dlZqFQqLupmZ2ejoqICtbW1KCkpwcLCAh588EGo1Wq0t7cjPz8f8/PzkMlkKCsrQ1ZWFs/8JBIJBAIByGQydHV1MaDDaDSipaWFpYafJD5xR0MsFsNsNrMRnNlshs/n4x/BbrcjHo9Dr9ezHq26uhoqlYpb/gqFAuPj49Dr9dwVSCQSiMVinDiKRCJMT08jHo/D5/MhPT2dEXHJYmZmhluckUgEOp0O09PTKCgoQDgcZub63NwcFhcXMTY2xl4dWq0WAoEAPT09LPOiwSTqCPh8Pkbmzc/PIxAIsBwi2UGXnp7OBjf03xHJSqFQMJlmYWGBvwdqEQYCARQWFkKv1+PSpUssgaGLARk8nT9/Hrfccgv8fj9XeFJSUlBQUJCUD030K8Ki0RAeUZqcTicPStHlym6388tBQ4k9PT3YuHEjvF4v2tra+GDPyMjA2rVrOSFaWlpCOBxmhneydiVRqqiitrCwwGQxhULBVailpSWuehIlg4YfCcuakZHB66YkMCsrC0KhkA9SSkQJy5ysMk+tTvp7qfowNzcHvV7PevpYLMYVaOo6paWlMR3E7/ezwRpV5OPxOBQKBfr6+lBUVITJyUnI5XKMj49DIBCgqqrq71403G43E2nIFZueZ8I8arVaqNVqvmSkpKQwuAC4Zu5E+NHKykrI5XLuClCCTQZnsVgMPp+PZVrJgqhr5NESDoeh0WjY5XpmZoZ/D5qhmpyc5A2RHH/n5ubYwFMul/N+JJPJ2JtiYmKCK0qkL07WfaTfPZFIcGensbERbrcbTU1NbLJHVBo6+PPz8yGTyaBWq5lSZTKZoFarUV9fzxcLsVjMsrze3l4mcFESnozPT4P3RPohWhxJbsLhMM+36PV6WK1WvkhQB62wsBBTU1Nobm5mAzai8BF+t6amBunp6dyNAMDyzZVCJBLB6XQyFtnv90Or1SIejyM/Px8ej4eHJanCNjk5ydQtqrjNzc2htbUVLS0tTN3z+XyYn59HfX09KioqcObMGXg8Hk7KSXa4UhARjiiGhCGmYoXVamXpK7nJ095Jcw3p6eks4c3JyUFeXh4PcJN/U3V1NUvviPTk8/n4YnejSElJgcPhgEqlglgshsvlgkgkQjwe54shGbcC4NlFOkMIvhCJRFBQUICCggI2vaNEm0hYPT09XHkOh8N82V8pUlNTec8m/GZGRgYyMzN5rogIRg6HA+Xl5fze0iB7LBZj1CthlskPIRAIsNyD/qxwOMyDqPS+3igEAgEkEgk0Gg1/d8XFxUyrstvtCAQCLDMk2AX9tgSO8Hg8aGtr+9hgL1G+yIOJztREIgGpVIqampqknUexWIy5uTmWnobDYYTDYWRnZ6O8vBx2ux1msxlisRgajQYWi4WhJ6FQiOf+XC4X/H4/BgcHkZaWhr6+Pp7zyszMRGFhIeckdFmmOalkQR0ckhDm5+ezK31aWhrPMVIutri4iLKyMiiVSshkMlaelJeX8xk7NzfHzyQhnaVSKf++5DvkcrlW9NIgcAk9U263GwKBgOVbMzMznLSTRJ4krAsLC3ypTyQS3G0meTqRSGnmiIw26R2i/XuloHOBSH702aamplBdXc05LfnIBINBvnQsLS0xAEYoFKKmpgZzc3MoKSlBLBZjZPbs7CwaGhp4LycIDyHDV4r09HS4XC6eT41Go8jPz+f5QJpRoT2+rKwMly9fhlgsxunTp9nMlP49KnJejxIOBALo6Ohgmtjy8jK/q+Pj41i7dm3SZw74f+ho0ItAQ2HkOTExMYFwOAyr1crJajAY5AOCBkSJtkRM7ampKR44JDMaYpvTg+R2u2GxWPilTLY2v9/Phy/hFqm9SrpuIlOUlJRgcnISZrMZg4ODWFxc/FiLqru7G7m5uZxA0e2aOOzz8/OsMf17JBuRSMQGgUQYoSoHDRIvLi5icXGRCRqEQR0fH2ecHiHShoeHGY1G5oldXV2MLEtNTYXb7YbL5UJ2djZMJtOKayN0JCU5dNmhJNvlcjGGUK1Ws544HA7jzJkzPCjq9/uZe63T6eBwODA5OYlQKIS5uTke3lxeXobT6WQqRbIK7tLSEiNdibQFgKsxtFb6Pa6nOs3Pz7Ovw/j4OKLRKObn57nKJ5FIuIqckpKCvr4+HiqPxWJ/FxNIa6NDC/iftjQZr9F3S1WASCTCulEyqHQ4HKzLzc3NhdvtZgM1r9eLRCLBGmc6oEgik+w3vf7zU4eELvsWiwUejwd+v5+HOefm5iAUCmG1Wpk01tPTw/jQiooKriDS+2kwGBCPx7kDQGv7ezMaAHht9DtKJBJ2uSdU8uzsLCM+Z2ZmeLjabrczDYfoHYuLi7DZbDyLcfXqVUgkEkZU01zO0tLS331XhUIhZDIZJ/P0HaSmprI/B+ETZTIZLl26hNLSUsY8a7Va5ObmsvFVdnY2SzvEYjGuXr3KjsWkSXe5XJDL5UnfBzpEIpEIxGIxP+d0+EUiEX7uAMBoNMLn8/G8ElUwl5aWoFKpMDs7y98N6aIHBgZQWlrK1Wli6ZNUIFmQRI08UNLT0xGJRJCVlcWmT/S/0XcCgIeh09PT4fV6UVFRgZSUFGi1WiwvL2N2dhZOp5NxmrOzs4hEIjCZTHC5XEmrywA4cQSu7WdkRikQCKDRaOD3+5niNzQ0BJVKBZvNhuzsbIyNjcFutyM1NRXBYBB2ux2Tk5MQi8VMJBOJRJiZmUFJSQkcDgejfcmNN5mUhc5K2kOuB16Q3HZ5eZn/HpVKBZVKBbVazfsiebBkZGSwlp4KZNSdJV+TWCzGyQNdWJKtLRQKcUGLCnRkoDo7OwuXy8XOw9nZ2Ux8NJvNWFhYgFQqhc1mQ2pqKlJSUlh2QjRHl8sFsVjMBQLyJxCJREkLFuTyrNFoIBKJmNRHhowkEyEfDa1Wi5mZGRQUFHBRjaR0bW1tiEQiTLADrhVDRCIRD7YLBAL2IcrMzExq5kpdCcI8E0qYLttDQ0O8x12+fJk7lFTUoYLPxYsX4fF4cOLECeh0Ov7OsrKyYLfb+bOQRw35MCTzg6BOFu2ZVCwdGhriBJLm3Aj57PV6kZmZicHBQYyMjMDhcMDpdPIzSXJ1eubIlJPUFEtLS5z0kwnljeJ6OAnlIrT3CoVCdHd3s+3B1NQUVCoVPB4PUlNTWcJNF3GpVMqO9VarFXNzc8jOzkYikYDRaITNZkM0GoXb7YbNZuPL+kpBiTqdf3QJ8Hq9yM/PZ2m/wWBgibTT6UReXh5ftAKBAAYGBpCXl4euri7I5XJ4PB6EQiHI5XJMTEwgLy+PzS8DgQD8fj/jx1cK+qx0mSK5q0AggE6nY/kaADZfHh4ehkKh4JllUsNQ0TORSPAMC/nnUJEhLS0Nfr+fzUU/KXXqE3c0IpEI60ZJZxiJRD7GPPf5fHC73RgbG8Py8jKzkyORCCPoqPp4+fJlPPLII/D7/fwwaTQaNhDJyMhAIpGAz+dDX19fUloMoVlJf0jkHY1Gw/bzlBQqlUomTkgkEtjtdgwPD0Ov10Or1cLj8XB7mYIuV/Rn0ucg7XeySiTd/EQiEXQ6HYRCIS5dusT4WWqj0uZXVlYGp9PJ8ohLly5xBW56ehppaWnweDzIzMxkTn55eTmuXr0Kq9UKj8fDmlpyLl4p6NAViUTIzMxELBZDb28vcnNzmfZCVTMiTtEch9/vR1VVFcrLy7GwsIADBw5AKBTiwoULbGRos9nwi1/8Ah0dHZDL5awRJg1lMuMjv9+P4uJi7nrQQ93S0oJwOAy3282bAx1WhYWFOHXqFLfWl5eXYTKZYLFYsHXrVkxOTkKj0SAtLY1pOzQU6na7GQtLleGVgozfCBFIB5LRaOQ/NxgM8p8PAFevXoXNZuMByfLycqjVatTW1mJwcJCTBpfLhWg0iuLiYvZ5AcBV1LGxsb+bvGRlZSGRSPChTpsBXQRoU/d4PNDpdNx2Pn78OObn59HU1ISSkhLu9iQSCa6ypKSkcCW4urqaO4kpKSkf85lJ9t0RkYUQnQ6HA729vVi1ahV7xSwsLGBycpIH1oFrLWzqAkmlUpw6dQo7d+5EIBBgbj5t0EKhEIFAgD87tYL/Hm6UCErEJu/v70dOTg5f5q832CwqKuKiy+joKCYnJ1FWVsaX6pGREdacE6Kb5tHI5Iou0AqFIukcRDQahVqtZklSVlYW+vr6uONJF1ubzYb5+XlGA8/NzSEzM5MLAXSZzc/PRyKRQH9/Pz934XCYn89QKMSseIInrBTkvSEQCNhlfnp6Grm5uXA6nYjH49wxWVhYYJno8vIyDh8+DL1ej6KiIlitVpw8eRLxeBxbtmzhivry8jIuXrwIjUaDiooKmM3mj3nLJHtXqfPlcDi40i+TyXg9paWlMJvNsNlsvI+43W7GUdJwP/03S0tL8Pl8PPtlNpshl8u5cOZ0OhGJRLgTT123lX5TIn8B/4Mwz8zM5LkAk8nEHVHah8nolaQyer0eQ0ND/E7SBWpwcBBCoZD9X6ijTkSgZBATMuwSCATcoSAIAxkK2mw2TgitVitX3glcIZFIeDCdilEEjyAUMO0ZhKgmmEYy/xEyvqWiotvtht/vx5YtW2C329kYj0iK8/PzWLduHYRCIUNhPB4PG9amp6d/TLFgNpuxefNmOJ1O7iQTZpT26ZWCvBSoIKhQKCAWixEKhdDf389eM+FwGF6vFwUFBXA6nVyZXlhYgMfjgdPpRCwWQ0pKCvLz89nbxmKx8P+u1WphNpvZV8Xj8SQ1YYzFYtzhJB8RqsYrFApYrVaYzWZWL0SjUf6uaBaT/J4sFgtL++h3tFgsGBoa4u4hXebo/U923hNeOxAIcCGHABSRSIQlkDSzQV0FWhsVMwwGA0uNaP+gS5Hb7Wa0q81mY4NnyqtWCjKqJbM+IpAVFxfD7/dDo9HAZrPB4XAAAHc9qXhMg+L03l1/mSaQEalsqIhBnWGPx4PVq1evuDbyZvH5fJBKpUhLS4Pb7UZWVhbPjYTDYbaemJqaQnl5OatUMjMz4f+bzxP59jidTlaOUAG2u7ubL1AEGyIVyCeJT3zR0Ol0uHjxIjOR6YZHg6rUjqQKy+7du3H+/HkcO3aMdZrUhs3IyIBarUZnZye0Wi2EQiEmJydx8OBBFBUVsVQjFovx3EcyfZ9YLMbw8PDHPAZo1oPoMCMjI4hEIqivr+dbbHV1NaNGw+EwWlpa8M477yA7OxsGgwFqtZoraGSqQ90EMo6hdtJKQeaGZKK2tLTEFbXu7m6Ul5fD5/NhYWGBWcZ0QdLpdNwBIBnJ1atX8bWvfY0PDZlMxvhe4i9PTEywrCgZV1ulUmFoaAhyuRw6nY5JP3a7HdnZ2Wz0RVrfyclJ2O12qNVqiMVivkxmZGSgsbERP/jBD3DbbbexLKmgoADNzc0wmUzo7OxkjTV1l5J9b0QVAa7pXokMQZKQlpYWjI+PY2ZmhilJ1C7v7u6Gx+NBeXk5V3vfeecdrtZ6PB5YLBZu01K1gypyf+97o6oAgQzsdjsMBgO6urogEonQ1NTE1YG5uTk0NzezxndgYIAHySYmJhAMBjkZIWlQSkoKa4Gnp6e5bU8bN5mB3Sgo2aEKB21kOTk5CAQCKCoqgk6nY607DX6RLpfmoyhJz8rKQjAY5FYz6T5tNhu8Xi/PT5CzbbLOIwDugtLGvrCwAL/fz3plYnVTRzM3NxcymQynT5+GVqtlopZEIoHb7cbTTz+N6upqHgCXyWTYtm0bFwKoSklywmg0uuLaIpEI3n77bWRlZfHBPzo6irVr1+LYsWOoq6tDIBCAUChEdnY29Ho9vF4vxsbGUFFRwWjRgYEBmM1mTE9Po7W1FXq9nrujVJ1saWlhrTAl03+POkVJXSAQQGdnJ5RKJbKysjA5OYm8vDw2hezu7kZGRgb0ej0GBwfR09MDrVaL/Px8Jp2kpKRgamoKRqOR/RsuXLiA1NRUbNq0iT1iAPxdWZdIJILJZGJuPg2WLy0tYXBwEJFIhKt6oVAIHR0dPCxOKM25uTnodDq+sPf29mJ5eRkymQwZGRmYmprC5cuXUVFRwSZ51HFJNgchFApx4MABNhUjFDoRYq43+SSfmpSUFIyNjfGweGFhIZaXl9Hf388yHTLbys3NhUajwczMDAwGA3fcSJ6SLJknJ2tyhp6fn4darWaMPHVXqTKrVCrZ7VipVHLnk5JMmk2zWCwwmUwMtejq6mJjTDIe8/l8qKqqWnFtaWlpDI8gCZLdbodKpUI0GkV6ejrUajUnpyUlJYjH4/z/9/v93MkNh8Po7OxES0sLFAoFX+boGSX4AvkNFRUVJR2ADQaD3OGioqBGo8GxY8f4YkSeNidPnkRJSQlLqC0WC6+flAmERp+cnITL5WKKVX9/P3bu3Mlu9SSBSeajodPpcPXqVcjlcigUCi40xONxHDt2jKmVdFYtLi4yoYwKJtnZ2aisrGTCVDAYhNVqRTweR3Z2NiQSCfr6+thrhApASqUyqdGsTqdjlDud3eXl5QwoqampQSgUgsPhwNjYGKqrq+Hz+ZCXl4eBgQEu0v31r3/F2rVrkZWVhcrKSgQCAX6XjEYjS0vJy+R6OdFKEYvFMDAwwO/h3NwcTp06hZKSEpZC+/1+logC16r50WgUe/fuxcLCAnp6etDV1YXCwkIMDg4iPT2dZyYcDgfPE+Tl5TFohDwwknUNSDJMUjhylD979iz0ej3PptHvlJmZiatXryKRSHCBnDqjNEtEl0raG+lZKSkpgcFggMfj4aJdMrITScmEQiEXh9LT02EymeD1erF+/XoMDg7C4XCgpKSEwRM9PT1MTSRZ+cDAAF9+mpub4XK50N/fzz5FpDhQqVR8PmZnZ6+4tusjJfFJryQ342bcjJtxM27GzbgZN+Nm3Iyb8QnjE89o3IybcTNuxs24GTfjZtyMm3EzbsYnjZsXjZtxM27GzbgZN+Nm3IybcTNuxv963Lxo3IybcTNuxs24GTfjZtyMm3Ez/tfj5kXjZtyMm3EzbsbNuBk342bcjJvxvx43Lxo342bcjJtxM27GzbgZN+Nm3Iz/9bh50bgZN+Nm3IybcTNuxs24GTfjZvyvxyf20fjlL38JuVzOHgNqtRp2ux1jY2NoampivwuhUIhEIoG5uTmEQiFkZmbivffeQ0ZGBlpaWth/oaCggDnLbreb+eNGoxEGgwEKhQKBQABer5fdtP/5n//5hmv76U9/yqZ3ZPg1NzeHhYUF5OTkQKVSseMxccGDwSAuXLgApVIJsVjMZlMjIyOorq5mIx+NRsOu5WTGQ86SarUa+fn5GBwcxLe//e0bru3NN99kN8n09HReJ7lkFxUVwePx8J8nkUjYuMtqtUIul6OoqAgZGRnw+XwQiUSwWq0IhUL8WUKhEPLy8rC8vIzp6Wk4HA7IZDI0NDQgEAhgz549N1zbgQMH2DGWzAyvd4fUaDQIhUJwOp0Ih8P8XZLHgVgshlarhVqthtPpxODgIAoLC5FIJJhn7vF4kJaWhuLiYjYFEwqFkEgkmJmZwYMPPnjDtb366qvsrkk+DWSEV1ZWBqFQCJfLxU6aGo0GDQ0NGB8fh8lkgkgkglqthlAoRDgcxvz8PDIyMqBUKhEOhzE3N4fS0lL4/X4YDAaEQiF2xgauuX8/9thjN1zbH/7wB8RiMQiFQnZ9TklJQV5eHlwuF5xOJ3u4kLEUuUWTQaBarUZpaSkmJyehUCjQ3d3NPG8yqpRKpfy7k2+NSqVCcXExNm3adMO1Pfnkk9Dr9UhJSYFIJEIikYDFYsHy8jIMBgO0Wi17rKSnp7PnRk5ODsbHxxEKhdgwUyKRYGxsDPn5+ez+CgD5+flQq9X8e0ajUQiFQiiVSszPz+MrX/nKDdcGAC+88AJ/J+np6exsmpKSApfLxeZD0WgUmZmZMBgMEIvFbL5Ee9DU1BS0Wi16e3tRVlYGrVaLeDyOUCjEHHebzYasrCz09vbC7XYz832l3/XJJ59Eeno6u7GS54fP50N1dTWi0Sjkcjk7Eo+MjEAoFLJjslqthkKhgMfjQUVFBTweD8rKytDb2wuXywWRSMQGeoWFhczyJ5NHu92OH/3oRzdc23PPPfcxp1cyH4tEIsjIyIBYLGZXaTJ1SyQSWFxchEqlwuLiIubn59njghydyR2c3gPyHXG5XOx3Q54nX/rSl1Zcm0KhgFKphEKhwNLSEsbGxpCeno78/HykpaVhYmICkUiEjSgnJycRi8XY3T0ej/O75PP5oNFo2KiR9plz585hzZo1MJvN7N5uNpuRnZ2Nu++++4Zr+/Of/8zvkkwmQzweZ/O+hoYGeL1edt+m706n07Ertkgkgkwmg1wuRygUQn5+Pnp7e5GXlwehUAir1QqXywWtVgu9Xs9Gr2Qqury8jG9+85s3XNtLL73EzsFqtRpKpZL9EuLxOMrLyxEKheByuRCJRKDT6dgxPRQKsffG4uIi0tPTUVRUhJGREfYCAa4ZyOl0OsTjcSQSCXZ/DoVC0Ol0uP/++2+4tu9973uIxWKQyWRsckneOwqFAkajEZFIhJ9d4H8ctfPy8qDRaBCPx+HxeNiFeHFxEY2NjVCr1ZiZmcHc3By7XXs8HvZ2CIVCCAQC+O1vf3vDtf385z9nfxGJRAK5XI7FxUVMTExAIBDAaDRCqVQiGo3y90Y+EH19fVhaWoJKpYJIJMKaNWv4+yVfpImJCc5z6DP6fD6IxWIYjUYEg0F8/etfv+HaXn31VWg0GkSjUUgkEojFYlgsFn5O09PT2SB1YWEBRqMRS0tLbH6cmpqKRCIBj8cDpVKJy5cvo62tjY3xyM8mJyeH/WdCoRDnX7FYDI8++ugN1/bjH/8YAKBUKpGeno54PA6r1YrU1FRIpVIUFhbynq7RaFBYWAiJRAKXywWbzcZnhkKhQHp6OvuKmc1mduCWSCTIzc1FIpHA/Pw8G/TSb7xSnvTuu+/yZ1GpVMjIyEAoFGJDvGg0img0ikQiAbFYzF5ewDWfH/I3ov2W/C7oPKV9MhwO8/s8OjoKn8+HsrIyJBIJ3HnnnTdc2y9/+UtYrVbk5uZCr9dDKBTCZrNBJpOxazbtneTf1tfXB5VKBZlMBoVCAa1Wyw71VqsVbrcbGo2Gv6fc3Fx0d3dDqVQiNzeX3egjkQjm5+fx/e9//4Zr+9WvfoVEIgGhUAidTsf5Qk9PD2pra9kJnjzuFhcXEQwGUVdXh+HhYT7rFxcXIZPJ+OzVarVITU3F7OwslpeXodVqoVQqEQwG2fsjFovB6/XiO9/5zg3Xdn184otGPB6HRCKBxWLhwzISiaCxsRE+n48/ILke6nQ6OBwOmEwmlJSUYGFhAVevXoVSqeQvfWxsDFlZWSgoKEBeXh68Xi9cLheGhoYglUqhUCiwuLgIp9OZ1FCFjPB8Ph+7MUciEU5QpFIp5ubmEI/HkZGRAQDQaDScUCQSCTbNSk9PR05ODjugT05OIh6Po6KiAhaLBSkpKQgGg5x0j42NJV3b7OwshEIhIpEIbwb0gsZiMVgsFjZkS0lJYedPcuQWi8WIx+NwuVwwm81YtWoVHA4HVCoVG9ZotVrY7XYsLCxAoVAgkUggGAxiZGQE6enpK67N5XKx+WAsFuMLjkgkgsfj4cRErVazTX1BQQFfwsjQDwA7WE5NTbGzLr18dEDRsyESiXgjWCmmp6dRVFQEAB9zo5+bm+NNmNa8uLgIhUKB2dlZjIyM8He8vLzMDtTk/hqPxz9m6DQ9PQ2dTscuyORWncylmczFvF4vQqEQH+bp6el8YQyFQux+npmZyZciqVSKSCTCh0MoFEJWVhYbNup0Or4IBQIBzM/Po6SkBCqVCpFIBE6nExMTEyteNMioLxAIsGvn3NwcioqKoFar4ff72QTQYDDA5/NBrVYjEomgqqoKk5OTmJyc5OSaDC4dDgcEAgEb/7ndbi4WJBIJJBIJmEwmTrRWimg0ypseHbYLCwt8oSf3WHIuJVPL6x2uI5EI1Go1fD4fJ5z0XZJD7OTkJAKBABslAuDfa6VISUlhczL6d8kccGZmhgsUkUgEAoEAHo8HAoEA4XCYzfYcDgeGhoZQWFiInJwcjI6OQiqVIpFIYHR0FNXV1bBYLJDL5UhNTYVKpWInWKlUuuLayFF8fn6eLwNLS0sfMyqcmpqCz+fjJCI7OxterxelpaV8wBmNRkxMTPB+ToliNBqFz+dDKBRCbm4uhEIhtFotUlJS4PV6OXG9UYhEIiiVSnZ/FovFkMlkEIvFGB0d5QKLXC7H+Pg4gGsJsMlk4gsXXTREIhGys7P5M3m9Xnbfpn/H7Xbz90bP30rhdDqxvLyM5eVleL1eRCIRyOVyGAwGWCwWFBQUIBqNwul0IhAI8GcPBAIwGAzsRp+amsoXYfod1Go1G0iSi7dIJIJEIuE1xWKxFddGRnMej4f/jHg8zn8/ObWnp6ejubkZH374IQQCAZsMzs7OIhwOQ6vVYnFxkQ01KSEDwMZa9Pup1WokEom/64YcCoX42aZ3JjU1FVlZWewQHo/H+bxVKBSQy+VYWFiASCRiN3mBQMDPFXDt/bDZbHA6nZDJZOzWTmcGmajSe3ijmJ+fZyM7KsCQaZlKpWKDV7pU9/f384WBElSRSASBQIDx8XF+r8mYDQBqamrQ1dXFF0p6/vx+f9I9hJJ0iUTCRVcqfJCTO5ksqlQqmM1mpKWlIZFIsEmt2WxmM0ij0Yh4PA6ZTMamyEKhEBMTE6iurmanbDLES3bex+NxFBYWYmZmBsFgkC8NwLVcgFypyQx1eHgYra2tmJ6ehsFgQCAQgMvlwtLSEucgdHbQZSI9PZ1zr9nZWd4HyB1+paA9lQqZs7OzWFhYgFqt5oJHKBTiS8bExAS2b9/O+VckEoHb7YZUKoXNZuMLXCAQYKM/KgbNzs7+/1j7z/A2rytdGL5BFBIgiA6CIMDee6dIiVSjaixblm1ZVmLHKXbKJJlkMsmUOGVyMk53ZjInzXGcuMSxHVfZKrYlq0uURFLsvRcABNGIQhAkAfD94ax15POJiM91ffuXLhdxE3ievde6110QiUQYRKAQ4Y2W3W6HVquF1+vF+vo6p9qbTCZYrVZYrVb4/X4G0BQKBRISErC2tga1Wg2/3w+fzwcAsNlsKCsrw4ULFxjUi4uL47tMp9Mx8EGp3JFIZMO9EWAQDocRDAYhEAi45p2fn4fZbEYgEIDb7cbS0hLi4uKQnJyM2dlZGI1GrKysYH5+Hj6fD0ajkWsyl8uFuLg4DlukYNT5+XkOBnW73Rvu6/9eH5k6RVMKugwEAgEikQh6enp4MkFdmMVigdVqRUFBARdc1DFlZ2cjGo2is7PzQ0UgXXrhcJiR3ZWVFf5nsS46SkyOj4/n9N9IJIKJiQmOradCYGJiAlNTU/D7/ZDL5Rz1Tim08fHx6OjoYGSUpjfU+RUUFPBl5Ha7Ybfbsby8HHNvlMRLaIREIoHFYgHwQeFFn6XT6YTT6YTf7+fDg9Ie19bWAICj4AlNX1pawujoKNrb2/mzJpTM5XIxKni7RfuWy+VQKBR8mFmtVkYRFQoFBAIBp1dSSvDU1BTcbjc3hPS70DSEEPC5uTmkpKTwM0IvPoCYFx0APhwISZdIJFhZWcHs7CykUilPU6RSKV/4MpkMS0tLfLARCk6TGUp+FYvFGBwcRDgcxuDgINxuNxwOBxdKsdK3g8EgbDYbVlZW+HISCoWYnZ1lBCA+Pp4bREqlTk9Ph8/nY2ScnreBgQFGZwh1mJ2d5ZRROrxtNhssFkvM500kEsHv93PBQe8d7YmmPJSKu76+jsLCQrhcLoyOjsLr9XIhplarYbVaIZfLYTQaGeUYHR3l70YqlXJiKk00/97y+/0IhUIfKopCoRAjgCaTCQqFAjMzM/B4PNyUOJ1OzM/Pc+GnUCjgcDi42aQzghAjoVDI7z/93rESruPi4rC0tITV1VX+7NRqNV92QqEQAoGAm7XV1VWUl5dzOnE4HIZEIoFWq0UwGMT58+f5fNBoNMjKyuJnXyAQwOv1YmxsjBFUhUKx4d4ikQisViujx/TeCYVCrK+vQ6PRcCqzxWKBUCiEVqvlotLlcvE0RigUYn5+Hmtra5DL5VAqlYiPj+fGKhqNMshDnz+dP7dbOp0ObrebL+z19XXEx8czAERNvEAggMfjgdfrZZAH+CAdniaEdPFHIhHIZDKkpaUhPz8foVAIUqkUiYmJEIlEsNvtsNlskEgkMc8Rv9/PSHBSUhJ0Oh1UKhV8Ph9sNhuneNOzsra2xunJNpsNLpeL05qj0SgGBwcRFxcHsViMSCTCRUQwGOQibXV1lRvCWN/p8vIygyihUIj/nrW1NaSlpTEaSgBUIBDgJmRubg5WqxVer5eT6kdHRyESiaDVanlSFIlE+BkBwM+3QqGIWTBLJBIsLS1heXkZa2triIuLg0ajQVxcHIRCIadRLy8vY3R0lO92nU7HCHc0GkVCQgKmpqYgFAqh1+s5+RkAF6LRaBQajYbZDdSsbbREIhE/M8FgkBsZej/oO3c6nRgZGYHFYkFOTg7i4uK4nqBiUKlUMrItFoshl8v5ebi1AaRpGxW9G61wOIyZmRlOBF9bW+Nkd2qeKfl6fHwcwWAQWq0Wcrkcs7OzsFqtCIfD/N1RAU53dHx8PKdGWywWbj5u3WOs75RAWPpuqDn0er2QSqVQq9VISEjgZnphYQGrq6sYGxvD5OQkJ74D4PuUEsHT0tKQmJiIUCgErVaLvLw86HQ6CIVCBhA2Wre+p7c2QHSWxcfHQ6lUQiKRYHFxEeFwGJFIBOvr68y4oHdYrVaju7sbo6OjfNff2hRTYR4KhfiMovpwo0UJ51T0E6NHqVRyjeRwODAyMoJgMIjq6moYDAZ4vV5Ogqep1PT0NOLj4xl0FYlE3HACHwCr09PTsFqtnEIea3m9XgD/pw4OBoMIhUJ8V1DDEgqFGAgkwJVYBQKBgOuaQCCA5ORkmM1miMVi+P1+BuF0Oh3C4TC8Xi83ah9lfeRGg4pasVjMozOJRIKuri5+2FJTU7nDnZ2dRUlJCV+6RB/ZtGkTgA+QDfqQvV4vRkdHYbfbsbq6iuLiYmRkZDC6QZ1urL2JxWLIZDJGkyKRCFwuFzweD4APLkOpVAqLxYK5uTmIRCL+2XSxqdVqxMXFcfOkVCpRVFSEgoICRgDNZjMj7URpidUN04FJX4pIJOImYH19HcnJyfxyErWGCi4q4AiFk0gkuH79OhcHNCqdnJzEzMwMTCYTI76E8hKae7slEon4UKeXWywWw+12Q6lUcsFHL2lKSgqEQiHC4TCsVisjS/TPLRYL1tbWeDRL9AGaPsTFxTEaDoCL69stpVIJl8sFt9vNTSQdvoTcqNVq6PV6aLVaJCQkQCQSwWQyMd0gGAzylMHn8/HFQQXU4uIiRCIRhoaG4HK5PjR1ycnJ2XBv0WgUs7OzWFtbQ0JCAhISErgQdDgcWFlZYdSMpiMajQYmk4mbAI1GA71eD4lEgrm5OahUKp6W0d6WlpaQk5ODpaUlTE1NYWpqCk6nMybaJ5FIEAwGGR0lWpnT6eTGJSUlBVKplC8nKuT6+vrgdDphMBhQU1OD+Ph4LqzKysqQm5uLSCSCxcVFnpqpVCoIhUL+LGI9bwB4jE1IL9EWqaBJSkriqaPVakV8fDw3ZLc2njThWlxc5LE5UeNupQMSLYK+p1iXXVxcHNxu94d+l+TkZIhEIr6YiV64tLSESCSC0tJSppkBH9AS6uvrsb6+jsuXL3NhrVKpUFtbi2AwCIVCAb1eD6/Xi5mZGVgsFgQCAb5cb7eoObyVpqZUKuH3++FwOCCTyWA0GrkpBcAFzPT0NJaXl2E0Gpk2sLKygtXVVaZxpaSkcDMgk8k+1LATyrvRouJnZWWFpxdCoZDRM6IUra6uckGZlJSE3NxcbgClUimMRiNWV1dhsVi48dTr9TCbzf8/NKbV1VU+F2O9D1QUCgQCJCcnIy8vj6eK9HdQQyAWi7GwsIBQKMSTAJ/Ph8TERH7O6b+nAtnlcsHlciESiSAtLQ06nY4nUzR92GiFQiGmBlMDTLRao9HI55vb7cb58+eh0WiQlpaGtbU1zM/Pf6gJl0gkPKUgAIbOd+CDyQYVyuvr6zzFjfWdhsNhfu41Gg03tgTe3EorJqqjTCaDw+FAKBSCRqOBwWBgmohOp4PJZEJ6ejqSk5N5+qvVamEwGBAXF4dQKMTnyUZLJpPx3lZXVxnccjqdsFgsUCgUTJ2anJyE3+9HRkYGNBoNT93C4TBUKhXUajXGx8exuLgInU6H1NRUfvbX19f5HlteXubJLZ1Pt1sikQizs7M8bQmHw/D5fJienub3NjExEYFAAGNjYxAKhTCZTJBIJJifn0cgEODP9tbim8CqaDTKU0v6jILBIE8RYjXd1BgKBAL+GXFxcXwWpKen83RRIpEwDVIikcBut/MkQKFQMK3aarVCpVIhMzMTer2eQWGz2czvN/DBOZ+Xl7fh3paXl7GysoJgMMhgDTV71HxQE7S0tMRMjLi4OKYCUzNGU5vR0VGu2QgwpkkRTdXpOYoFLBJdl5gSRO1yuVwQCARISUnhKSw1gRUVFdBoNAyEicVintgPDAwgHA5DqVRyXevz+XiS4fV6Ybfb4fF4EBcXh7S0tA33RvUaPSc0hbyVDhoXF8c1Y1xcHNMNqXlSqVQwm80fkjykpaUhPT2dwTW5XI60tDTk5OTwz5DJZDGn8Leuj9xoCIVCpKamIhQKcecdHx+PO++8E263G9XV1TCZTEhMTERKSgqP9NRqNXbt2gWDwcBo7MzMDJqbm7G8vIyJiQn09/fDZrOhvr4ebrcbEokE4+PjkEgkMJvNCIfDMR9Sn88HrVbLEwEaK3/sYx+DzWbDzMwM06/q6uqgUCiwadMmiEQiVFdXIy0tjXnz0WgUDQ0NWF9fRzgcht/vx9zcHEKhEN5//310dHRgamoKGo0G+fn5TPGItbekpCRGX4LBIOtaQqEQ8vLyPkR1USgUKC8vR39/P/Ly8iCXyzEzM4NoNIru7m7k5ORAr9cjLS2NOX51dXWorq5GeXk51tfXodfrkZOTw/vfaMXHx/O4liYOQ0NDMBqNPBEgXQt99/Hx8RgZGUFVVRVSUlJ4AmCz2VBRUYFQKASJRMJc69zcXHR3d/PBQeia0+nkceLtViQSgUqlYmRZJpMxIkQvyeTkJFPPrl+/DpvNhqmpKUbuqSs/f/48U0xycnIgEonQ39+PmpoaDA4OIjk5GQCQk5OD4uLiD406b7dEIhF0Oh1P8Yj7WVBQwMUFfQeVlZVMabt+/Tpf4AMDA+ju7sbAwAA2b94MvV6PrKwsJCUlYWVlBc3NzaitrYXD4cDk5CS/f0lJSTGL5XA4zHujZ1ogECAQCOCNN96Ay+Xiw4eeO7VajZSUFFRUVCA5OZmf1Z6eHpSWlqKtrY2RDrfbjYaGBlgsFqSnp/OBmZiYiNHR0ZjPG/DBiJ6eASoeQ6EQEhIS+B3t6+vD1NQUKioqGLmPRqPMcSZ0uqurC2VlZRCJRFCr1YzqG41GLhqHhob4mXQ4HMjIyNhwb4TwU1Gam5vLhz49g3Qo5+TkIBgMYnh4GNFoFJmZmUw5pAL/wIEDrHtxuVwYHh7G3r17sba2Br1eD6fTieLiYmzdupWBl42WRCLh6UkkEmFkm3j9NKFKTExEXV0dxsfHGThobm5GZmYm5ubmMDExgenpaWRkZHDDRk2a2WyG1WqFWq2Gx+OBXq9HQUEBAMQs5kdGRpCRkcFFv1arRTgc5qkY6c6SkpLQ0NCAhYUF2Gw2KBQKGAwGpKeno7CwECUlJcwbpgnV1NQU3nrrLf5saWKbn5+PkpISLC0tYWZmZsO9xcfHIy0tDQsLCxgfH8f8/DwmJyexf/9+ZGdnIysrCzKZDElJScjOzobH40FVVRWmp6eRnZ0No9EIj8fDd8jBgwe5QSAahUql4qKBpgVUyDudzpjvA1Fg9Xo908g0Gg1aW1shEokwNTWF4eFhxMXFobi4mCfqdMnTRX/58mWUl5czNYfQfbVajf7+fqyvr2NiYgICgYAnlbGmewsLC8jMzGTNY1JSEqamplBaWgqHw4Hc3FykpKQgPz8fBw4cQGdnJ0ZGRjA1NYXq6mpkZGTw5JYaYNLlxMXFYXl5GQqFgnV4Q0NDkMvlKCoq+ruUjFAoxABYQkICI9SFhYVYWVlBVVUVUlNTWSPk9/sxMzOD5eVl5OTkYH19HfPz88jJycHbb7/Nf9fq6ioj1jqdDsnJyairq4NcLofZbEZeXh6kUilKS0s33BvRr+Pj47lh9/l8yMrKYoo0NQgKhQJut5vvg82bN6O0tBRxcXEMZm7ZsoWZAMSfr66uhkKhQEtLCzMLCDTNzs7ecG9erxfFxcXQaDT87ND0TK1WIykpCdPT05idnUVSUhIWFhag1WphtVpx6NAhNDc3Q61Wo6CggN9PYo3Q90KFsVwux3vvvYeZmRlmQMQ63wQCAfR6PRYXF7nZ93q92LFjB9PiiOJDmorMzEysrq6ioqICMpkMY2NjDNB+9rOfRU5ODtRqNd/HtC8CGHQ6HbRaLebm5mCz2WLurby8nKcfiYmJmJycRFVVFa5evYrp6Wn4/X4oFArk5+dzMd/X18faX9L+0bvjcDhgMBig1+sRCARQUlICn8+HsrIyrK+vIzc3F2VlZfB4PAzM3m4RSGK1WjE5OQmXywWn04mioiI+k+Li4qDX61FXVweBQICqqiqmEs7OzrJWeG1tDVVVVZDL5QgGgwzAZ2VlYXJyEtFoFJOTk5DJZDCZTHC5XDHZFbeuj9xoEM94586d2L9/P3JzcxEIBNDb24tDhw4xqm6z2WCz2bB161YW3H3/+99HR0cHysvLsbi4iJaWFnz/+99nYY3BYMDu3buxsrKCf/iHf4Ber0d5eTmsVismJiawY8cO3LhxY8O9JSUlYWhoCJWVldi+fTtyc3ORn5/PXL35+XnYbDYsLS0hISEB5eXl+PnPf46UlBSmYUilUly9ehVVVVV47LHH0NzcjIKCAkatRkZG8Oijj2JmZoZ5b1arFVlZWcw73uhzW1tbQ3p6OotWCZ3Py8tDZ2cnH8h0yfn9ftTU1ODnP/852tra0NDQgKysLIjFYty8eZMFZsPDw/B4POjt7cXhw4fxwgsvsFh8fHwczc3NMcduer2eD3ij0Qi5XI7s7GzY7XaUlZVBqVQydWd1dRU7d+7kCQJxrzMyMjA6Ooqmpib+HPR6PfLz81FUVIRLly6hoqICExMTPPoVi8XIzs7G4ODghnsj7nRlZSVSU1Nht9uZf3r33XdDqVSioqICmzZtglwuR01NDdRqNcxmM5KSklBSUoLy8nKEQiGkp6dDKpXi4x//OKM4ZrMZZ8+eRV5eHioqKpCfn4/s7GwUFRWhrKwMo6OjG+6NkNstW7YgOzsbDocDExMTeP7555GRkcFoPe2noKAAAwMDqK+vRzAYhFKpxPbt21FZWYnc3FxcuXIF0WiUkUCZTIaLFy9i//79AMCTnMTERJSWlsbUQSQlJcFmszGFcGFhASkpKUhLS8N3vvMdRvtcLhfefvtt7NixA2fOnEFycjKee+45TE5OIj8/H/Pz84hGo7hx4wYOHjyI0tJS5ObmorS0FO+88w5KS0vh8XiQkpICi8UCm82GlpaWv6vRSE1NhVQqhcFgQCgUwujoKJKTk2EymbB//3689tprsNvtiI+Px8zMDJqamiCVStHc3IwrV67A4XBgy5YtKCsrw86dO9Ha2oq8vDxYrVa8//77uHLlCkKhEOrr67G6uor77rsPYrEYY2NjqKmpickTTkxMRHFxMUpKSuDxeHDx4kXIZDJYrVYcPXoUkUgECwsL6OnpwYULF/Dwww9jfHwccrkcv/rVr7C0tIR77rmHJxV/+ctf8O1vfxu1tbVIS0vD3r17YTabUV1djampKeh0OkxMTGBoaAg7d+6MWfiZzWYMDAywJmVubg4ymQzV1dXYtm0brl69yjqP5eVlPPzww5iYmIDZbMbw8DB8Ph+Ki4uRm5uLzZs34/jx4zCZTExlvXHjBubm5nDXXXfh/PnzmJ6eRldXFwYHB1ncudHKz8/nxoYQ15qaGng8HsTHxzNtZGpqCq+99hq+9rWvYWhoCB6Phy94gUCAsbExfP7zn4dCoUBzczNkMhlEIhE3RXq9Hq2traioqEB6ejpkMhkKCgpiTpTz8vKwtLSEvXv3oq6uDqFQCGazGa+//jo3RCSaX15eRklJCVZXVxm9zM/PR25uLiwWCwoKCtDZ2YmGhgb+71NTU3Hq1CkUFhYiISGBz2WLxcL3ykZLpVIxOm+321l7IhAIcPjwYdZgUGGel5eHy5cvQ6vV8hQvJSUFfr8fJSUlOHv2LLMKtFotsrOz4XK5IJVK0dXVhaysLMzOzmJsbAxHjhyByWTacG/JyclwOp3Izs5m9LqpqQl9fX34+Mc/juHhYaYWOxwO/Od//icuX76M2dlZrKysIC0tDXV1daiqqsKDDz6I6elpNDQ0IC4uDt3d3Whra4Pdbsejjz6KN998E+FwmJ8HrVa74b6AD4Aem80Gs9mMsrIyZGVlIT09HcAHIuk33ngDi4uLUCgUWFhYwCOPPAK3242mpiZ4vV4IhULk5OQgEAjwFLuyspKpfWtra+jr68Pi4iKee+45xMfHw2KxYHJyEps2bcL09PSGeyOaM6Hga2tr2LVrF1O2lpeXUVBQgLq6OqhUKpSUlOCdd96BzWbDb37zG1itVjQ0NGBxcRHx8fG4efMmNz1SqRRra2twuVystdJqtQzY7Ny5M2YtkpOTgzfffJOpgHFxcWhoaEBHRwd27NiBqakp6PV66HQ62O12/PGPf0RJSQlMJhN+8IMf4Ny5c8jJyYFWq0VJSQm6urrw0EMPQSgU4ubNm7BarVAoFCgpKcHMzAx2797NtLFdu3bh4sWLG+6NjApSU1MBANPT09yIFRQUwGg0ssEF6RYHBwchkUjw7rvvIi4uDjt37sTAwACSkpLw29/+FmVlZUwBM5lMGBkZYWCVAJrJyUkUFBTEfE+j0ShmZmaYYTM0NASr1YpTp07hnnvuQUtLC5/xAFBYWIiTJ08iJSWFn+fm5masra1hcnISP//5z3Hw4EFUVlYiLy8Pubm5eOONN/C5z32O7xPSj5rNZoyPj2+4t7S0NCQkJKCiogLZ2dnQarU4dOgQhoeHkZeXx5ObmZkZ9Pb24ujRo3j11Vfh9/v5701LS4PH40EkEsHTTz+NvXv3wul0soZ6aGgIubm5GBsbQ1JSEjNRSK/2UdZHFoMTTaK3t5e5beFwGLW1tTh+/DgfDtThLCwsoLy8HBMTE/jhD3/IqNDU1BR6e3uRnZ3NvNvl5WWcO3cOp06dwje/+U0Eg0FYLBaejszOzvIDeLu1uLiI0tJS2O12+P1+noh4PB7U1tZCKpWyyFWhUOCLX/wiAMDhcPBlQGPOiYkJPPTQQ7h58yaPyagLttvtUCgUGBsbg06nQ15eHhYXF2E2mzfc29raGkQiER8ARPHp6+uDRCJBSUkJrl+/zg/Y9evXWTj585//HF6vF4ODg8yfLykpwbFjx7Br1y7k5uZiaGgIdrsdQ0NDaGxsxNLSEj8Ao6OjkEgkTFf7v9fIyAg75UQiEUbPU1JS0N7ejoKCAoyMjEAsFqOsrAwvvPACMjIyUFhYyK5P1PEODw8D+ECcZLFYYLFYsLi4iPvuu495k0Q9CYfDcDqdjJbebhESaLPZEIlEEAqFcPPmTZSUlODcuXMoKCiAxWJhUdvw8DCMRiN/n9RY6nQ6nk48++yzyM/PR3x8PBYXF7F7925YrVZMT0+zwC0cDiMlJSWmwJ9GuVevXmWXlcrKStjtdnYt6+7uhs/ng8FggEAgwPbt2/HGG29ALpcjGo2io6MDYrEYfX193LjSpd3R0YHe3l4WkOfm5sJgMCASiWBoaAg3btzAgQMHbrs3u92OjIwM2Gw2LC8vw+PxoLOzE4cOHUJbWxv0ej1mZ2e5SGptbQXwQZG9b98+LspJ11JWVob29naUlJTA7/ejp6cHubm5jJwQmiaXyzE8PBxT/Ar8nwmfz+dj3dPQ0BAj21VVVeyoQ+Nfg8GA48eP44EHHoDD4cCNGzeg1+sxOTkJnU6H4eFhbN68GSkpKSySPHbsGGQyGW7cuMHI0ZkzZwBgQ6czEnyT6E+r1cLlcuHRRx/FjRs34Ha7odPpkJmZCbVajYmJCW6I7rzzTkSjUbz22mtQKBTo7u5GfX09Ll68CJ/PB6vVinfffRe7d+/GlStXUFpayshmKBTC2NgYc2pvtwYHB1FXV4eenh4oFAqYzWacPn0a27Ztw+TkJGtj6HupqqqCSqXCyy+/zA1uR0cHdDodurq6+Luem5vD6uoqUlNT4ff70dnZyY42ZrMZy8vLXNxutDweD9OyVldX4fF4MD4+zgJY0lbQNNLn8yEnJ4cvQ6/Xi5WVFej1erz00ksIBoMYGRlhegJNYGhqSvuhQiQWXYREq11dXTzZMhgMkEgkTP+kZ9hsNmN6epqLidTUVKZPJScnc+PU0dHBDlWBQAD33HMPrl27hlAohOTkZAYbZmZmYlKnpqenGbkmanJvby/GxsaYnhcIBKDRaKDVavHOO++grKwMx48f/5BGT6/XY2BgAFlZWXjuueewe/dupKWlYXZ2FoWFhUzJI70c8MFZGAv9Jo3i8vIyax8vXboEvV6PmzdvIhwO4/XXX4dQKERRURE6Ojqg1+uZDrq0tASfz4fR0VGcPHkShYWFGBkZgdvthlgsxv79+yGVSnH69GkW0pJ+gwTeGy1C5kk/IBaLcePGDaSlpSEcDuOee+7B7OwsF6t/+ctfsGXLFjidTuTn58PpdDKYQecL3SMrKyssnCVtgFAoRF5eHgKBAN57772YdBFyq1paWmL94LvvvoujR4/itddeY6cios8CQE1NDU6cOIHU1FQ+J8RiMbq7u9lgpbCwEHq9Hh6Ph5/D1tZWJCcnw+v1Ii4uDpOTk5icnNxwb2NjYzwBJrOR48ePo6mpiWm+RA3MyspCZ2cnAoEA0tPT+R69cuUK3n//fYyPj2NmZoaptaQtTUhIYOru2toaDAYDRCIR2traYr6npNUjClhcXBx6e3uhUChQVFT0IYe+lZUV7Nq1C9FoFNevX2djgP7+fpjNZgSDQbS0tMDpdCIhIQFOpxNerxc1NTW4ePEiF9ZqtRpqtRqzs7MxadwWiwUlJSVM71UqlSgsLEROTg7OnDmD1dVV9PT0wOPxID8/n2li8/PzyMvLQygUQmdnJxITE1FRUYGGhgacPHmSRewejwf79u3D6dOnGQwndzwAMacGPT09yMjI4Cmd3W5Ha2srUlJS0NbWhoyMDKbmqtVq/P73v8ehQ4fw3nvvobKyEkKhkEXpRqMRNTU16OrqglQqxcrKCgYGBpCfn4+EhAR+tpKTkyEUClkj91HW/5MY/M0330RPTw+Wl5eh1WqRnp6OGzduQKfTobGxkUXOVquVi02r1Ypnn30Wk5OTKCkpYdpEZmYmW7wSckOFColzHA4HxsfH2bko1t7IXs3tdsPr9TJH8OLFi1Cr1aiuruZC5M9//jOCwSDkcjlOnz6N/v5+FrqQ8MVoNKK9vR1XrlyBz+fD6dOnebogk8l4pE1juFjLYrFAqVQiNzeXKUg3b95kwWRtbS0aGhqQlJSE5uZmVFRUYHx8HAMDA3xwUdHa1dXFmgBy69m/fz/zbZeWlhgFI6rJRosaJOJrkuj18uXLjPREIhHMzs7yhTA5OckvSE5ODurr6yGTyZCXl8eiNZPJxPzMZ555Bjdv3uSDWyaT8aTk76Hfk5OT/DDr9XrYbDZ20iJETygUwuFwsOsViZrj4+ORnZ2NxcVF3i9ZCyuVShQXF+ONN96ARqOBRqOBTCbD7Ows+vv74ff7Y45StVotbty4wd08OYK99NJLKC0tRXJyMrZu3YqysjLYbDbWRBiNRkxMTAAAysvLkZCQgPj4eCQlJbEOgPQohGoZDAZ2VZmbm4NQKIzJJyXBKlk75+TkIBqN4sKFC5BKpRgbG2NhuMPhQEpKCjIyMlj3Qs41Pp8PAoEAN2/e5OdidXUViYmJGBwchNfrZb4mNQYCgQC5ubkxv1ORSMQivWg0ygYT09PTTMMjVJy4+uRi09raikAgAL1ez9TNc+fOYX5+nr83mrCJxWIkJydjYWEBV69e5YYu1vugUCjQ29sLr9fLVJZAIICrV6+yeH9xcRHXrl3Dm2++CbPZjMbGRohEInR3d2NxcRGZmZlITU1lKh0V4Tk5OVheXsZTTz0FhUKBsrIyyGQyOJ1Ofsbr6uo23JtSqcTU1BRUKhXkcjlrA15++WUUFBTgnnvu4dH80tISXnzxReTk5CAtLY25+y0tLWhsbERubi5b8xYWFiIzMxNutxtjY2OwWCxIS0vD+Pg4WltbGVWjgvh2y+FwsN01URqp0Senv/n5eVy9ehVtbW1QKpVoamqCXC7Hk08+iRMnTvBk2+12s3UxURuEQiGmpqawfft2pkSOjo7i8uXLrEeL9bz19fVhaWkJCoUCGo2GL/e8vDyUl5czddZut8NgMKCwsBClpaUMZOTm5iIvLw/5+fno6+tjO1C5XM7fRVNTE6qrq7GysoLOzk709fUhISEh5udGEw2dTsdWmCaTCUtLS4xoFxYWYnV1Fc8++ywSExNx/fp1GAwG1lOZTCaoVCrMzs7i2LFjyMrKgt/vZxfCt99+GyaTCcvLy0hOTobdbkdfXx9EIlFMHZpcLmfKJFkcx8fH49VXX4VIJMLevXuRmZkJi8WCs2fPMqjn8/nw3nvv8c/Q6/UoKSnBtWvXkJiYiLvuugvNzc3w+Xxoa2uDw+FAc3MzG0PI5XJ2NNxo0WTf6/UiGo3y/0uiW7rbA4EALly4wBQaq9WK8+fPY3JykkGvmpoazM7OsrskaSElEgnUajUOHjyIpaWlD9mNxwIEpFIppqam2Co9Go3CYDCgra2NqdbhcBj9/f146aWXGHgk1zxq8DZv3oyGhga2/U9KSuJGnujXmzdvhkajYdF4JBKJCd4JhUK2ryWtbUJCAqanpxEMBpGdnQ2ZTIbe3l788pe/xPPPP48bN24wkl1QUICSkhJkZGSguroajz32GANaRNMkGnZZWRn6+vowNzcHqVTKGolYzxvZwkulUqSkpDATgVzDyDFtYGCAHeSSkpJY5E0TULfbjW9/+9vo7u5Gamoq4uPjcfHiRZw9e5YpTqRfAICUlBRs27Ztw70BYOMiOguj0Sj+67/+i5s8EpzPzs7ihRdeYG0gmZeo1WrU1tbCZDJhbm4ORUVFqKqqYkrdsWPH2BjC7XazAxVRoDday8vLbA2dkpKCvLw8qNVqjI2Nsd5Cp9NBrVZzLTQ8PAy5XM4ax5mZGbjdbszPz6OmpgZWqxVGoxHZ2dnw+Xzo6uoCAJhMJqSkpMBqtfL7FwtIuXV95EaDLF7p4CfV/44dO1gQLZfLUVJSgqamJvbUtlgs/AAvLS2hoaEBYrEYmzZtYjSKLqhHHnkEJ06cYKFlUlISu6TEEoOTep4cHsgStry8HCkpKezRH4lEkJmZyZMJu92OQ4cOoaioiMfP8/PzqK2t5YaHCsTm5ma2rCNHJuKCx5pokB86udIQjzcrK4sbDbvdzoi2xWKBSqVCJBJBcnIyIpEIvF4vCgsLERcXh/vuu48dUKgYm52dhdvthsFgYCs8lUqFtbU11h/c9sv/m3OPy+XC5OQkLBYLZDIZN4E0tUpOTkZjYyMLHp1OJ1Mbzp07xyPz1NRUpKSksNMEIQtLS0ssqvP7/fB6vbBYLDGdxNxuN4qKipCSksL8e4FAwE5OMpkMBoMB+fn5qK6uZiGwTCZDXV0dpFIpbt68ic7OTvT09EAikcBoNPJBPDQ0hJKSEkxPT7PgqbKyEjU1NfD5fDGL+ZWVFdZjxMfHQ61WY3R0lPM4nE4nrFYrcy3JDnl1dRXZ2dmcN2E0GrG4uMgc8luFy5mZmRgfH+dxLF2MEokkZvESiUSg1+u5CZPL5dBqtdi8eTNsNhtT0TQaDQoLC7GwsMAja6LLRSIR6HQ65nN6PB4Wo4dCIezevRvAByNX4qoTr5NQmI0WuayQUJ3QESq66aAE/o8/us/ng16vR25uLn8G9fX10Ol0uP/++9mZKCEhARMTE1Cr1SzSI8MFk8mElZUVNDY2xtxfZmYmO7CRAJ084OkzSUpKQnFxMY4fP46xsTGEQiFUVVUhNzeXP/O8vDxs3rwZ/f39GBwchEqlwt13342Pf/zjcLvdH+IOy+XyD2W43G5RcRMIBJhil5WVheLiYkaKFxcX2d2HMh6IFkluVOFwmDm2o6OjmJycZBMOjUYDnU4HnU7Hji7r6+ucyxFr0YQ6MTERZWVlSEpKQl5eHlwuFwwGA7RaLdRqNXJyctjJLCcnB+Xl5XyOisViGAwGtnyNi4uD0WhEdXU1qqurMTg4CLPZzCJjMh6JNW0hfRTp48gu3Ww2IxKJsHGDTqfjz4l+/uzsLMbHxzE1NYVwOAyLxYKamhpEo1EWmPr9fhQVFaGzs5MnNnq9nu2oY1l4CwQCFj4LBAKmZmzatAmLi4soLi6GWCzmjAeHwwGNRsOFfk5ODrxeLxITE6FQKHDw4EE2mVD9zZLcZDKhu7ubkV6ic9KkKdbzlp6ezlkGhDIXFRWxs5JSqURpaSmys7MRDofZLCIvLw/RaPRDU62tW7ciPj6e9S1LS0soLS1lG1oSXpOX/9atWzfcG9lCk4BYoVBAq9Vi7969MBqNWFpawvT0NORyOe688052r1xaWkJRURGys7NhMplQWFgIiUSCmpoaFtSSpbzJZGIzEbJJpedbp9NtuDc6SwlEJb3B+vo6o/zr6+vIy8vDnXfeyQwRAKivr4fRaGR6rUwmQ1lZGU9uyNWsrKwMdrudNWlVVVUoLy/ndybW80aNDd2fJpMJRUVFGB4exs2bN5lu/qlPfQqhUAh33nknJBIJtm7dygBKSUkJWxCTYY1QKERmZibXdaSd8ng8fC/EatDW19fZon5mZgY+nw9SqRSbN28GAL7bk5KSkJKSgq6uLnR3d8NisbAbJonQ4+LicOjQITbE0ev1KC0tRUlJCbKzs7kWIc0FWVtvtGQyGT8HZLLi8XhQVlbG+1Kr1TAajRyNkJaWhmg0yoyatrY2WCwWqNVq5OfnY3h4GJOTkwiFQtDpdNi1axcikQjXcGTGEx8fH7N5JHCQHDOpgSwqKoJQKGQH1+TkZGRlZbEAnM58MjkqLi7G6uoqjEYjO2cC4OybUCiEwsJCthgmo6CP4jIJ/D9Qp0gwSGIp6gaLi4vh8XjgdrtZma7X67GwsADgA566VqvF6uoqd7c0ZjcYDNBoNIxebN68GS+//DILesgJiTiDGy1yUaHAH7KqlUgkqKysZAEhIcvUdIRCIZhMJnaGoJAdGnlSsUlNFoV/ET+Xxv2xrPjIKpUEQuQMQNaN9HmSTRpxRG8NzyOnJJq0UBPi8/mwtrbGwj2Px8M0Fjq8Y7l3EIqiVqtZQKjX62EymbjYJBTbbDZjZWWFEQwqEFdXV9nmTfW3wEGlUsk2xQUFBew0Q3xJciz4e1xc+u4lEgl8Ph9UKhWMRiNmZ2d5pE0TgOrqanYYI67t3Nwc/H4//5zMzEwIhUIu6OgSVKvViEajLOyPJS699ZkjxwqyBU1PT4dcLudcArVajZKSEs5SIW0I0bIoA2VycpLtekm8ZTAYGHkjVzIAH3IV2ugzI8RVJBIhISEBJSUl7ERDDSjlOFRVVcFqtbLjCKE9aWlp6OjowJYtW9Df38/oYUJCAoxGIxce5MBzq81irEVBivRc0sFH2pGkpCQYDAbMzc1xAz48PMzABr3nJLKrqqpi84H5+XnOOlD9zS+fpnoymYwnJhstiUTyobwGegcUCgUHiUkkEkbAvV4vhoeHkZ6ejtTUVLawpkmBUqlEamoq237T75+Wlgafz8foGgkvY/FdyVuerCej0Sjy8/Oh0WiQnp7ODizUkNbV1aGtrQ2rq6s8maHMkkgkwsjsxMQEC2qpgQ6Hw8zfJ7vEWLQHOttWVlZYgEgW6IQ4x8XFcWAkoXYqlYqzY+Lj42G1WtHY2MhnB9EptFrth6yHAXB4HSG5Gy06H4APihmymZRKpQiFQggEAhyYZzQaPxRgSdN3j8eDhYUFdoGhgpje89zcXMzOzvLzQnRgsm2O9Z0SHYPOTXI7W19fx+LiIlMaCKyor6/H8PAwcnNz+btUKpVMD6S/j6hdaWlpTOmi79/n8zGAtdGKRqMMipEYmXSF0WgU165d44aPHOD6+vqgUChYp0BAjNlshkwm46wtsrUtLS1l1y2yUgfAjmUbLXpPyTqZPvdoNIqCggJ2sqOpJH0elF+jVquZDk3OjHR3EZAokUig0WgwMjLCz/ja2hq//xstsVjMDIS1tTX+3IAPNBL0vRCdkO5TogISLYssRFNTU9m2ms4lsocWCATQarWQSCQsno61yEiCMnMSEhL4eSV9EfDBPVlWVsbTQ7FYjJqaGiwtLWFsbIzNLMbGxtgAiATP1Bi3t7cjEomw5S4J4zdaAoEAiYmJ7GjkdrtZz1lfX8+2tpSJQ3+mKRM5bqlUKoyOjqKgoIBDTT0eD5+zeXl5mJycZGfP1dVVBm82WoT83wociEQiGI1GBtDJUUwoFPK7Sc5rer2encvIaIYmL/Hx8VyfqNVqyGSyDwGFycnJf3fySHRDCjMkhoRGo/mQ6xw5dpEOND4+HgqFgmth+n7S0tIQFxfH+uXa2lp2ZyWji7i4ODYf+SjrI080KCGSbGTpw3Y4HGhqauKEXEKty8rK4HK5kJ+fj+npabaVffnll5GXl4eOjg52WBCJRCgoKMD6+jo+/elPw2g0cvAcfZhzc3Mb7o0caygJ2efzQafTYXp6mi1fdTodcnNzoVarkZeXx+myx44dQ29vL8LhMFpbW5GdnY3JyUkUFxdzx2q1WlkoeO7cObjdbgwODqKrq4sv2I2W6m/BYiaTiUV1QqEQVquV+fsGgwEZGRlsE+d0OjnwkHygr1+/jri4OBw7dgwZGRkwm81cREkkEqSmpuLdd9/ll5MoBbGEREQ5S0lJgdFo5IOUnK+Sk5ORnp4OiUSChYUF1NXVobW1FZFIBK+//jrW1tZwxx13IDs7GxqNBisrKzAYDFzspKenY3Z2FlVVVayHoe+U6AIbLaVSifHxcUaW/X4/u5yQs8PS0hLm5uYwOjrKiaDz8/M4ffo0I9upqak4dOgQbDYbEhMTMTQ0hEAggIqKCkxNTWHr1q2MLi8sLMDtdiMnJydmsyGTyTA3N4ekpCQsLy9jeHiYXWays7MZFaPxJKG41NCQ9e34+Di2bduGd955B+np6SgqKkJ+fj7S09P5EFhZWeHASQpVosvvdoua/FszLcjNp7a2FhMTE+yfH41GsXfvXshkMkSjUVitVohEIp4ckE1sfn4+T/DS0tLQ2tqKkpISOBwOtvMUCoXIzs6OOd0DwF78er2eE75XVlYglUqRlZXFYYyELkejUdarXL58mcf8V65cQSAQ+BAHv7+/n8OsMjMz4fP5WOxst9uRkJCAa9eubbg3sggle+xQKITi4mL4fD4UFRVxg3yrYHdtbQ35+flwuVxYWlriRk+hUGB2dhZHjhxBZWUlbDYb/vrXv+JnP/sZdu7ciZ6eHkxOTrI7U3V1dUx7W5VKhYmJCUaqKSNnenoamzZt4hBUop41NTVxngg5dlGBRDoKcs+hxoJc2cbHx2E2m6FQKNgOM9aiz5cQRAJwpqamIJFIMDo6yqg4TYTa2tq40CeTiEuXLrHTU2pqKtxuN6amprCwsICLFy+isrISbW1tTC1ISUlBQUFBzAKLdAxUsBGqOjo6ytOEYDDIk3CRSIRAIIDNmzejvLwcWVlZSExMxNTUFEQiEf7617+yQQCJgldXV3niTN+hRCJBcXFxTFCAcg0ov0Eul7OTn9lsxl/+8hcAQHV1NeRyOTZv3ozs7GwkJCTwtKC6uppdq9555x22lh8ZGWH62o4dOyAWi1FSUsJobnJyMgYGBjbcG/H4aYJC3y8BCqOjo0wto0bttddeg06nY4caakrIPU+r1SIajXLwn1AoRG1tLev8xGIxVlZWkJKSElM4TBx2nU7HbmRSqRTHjx/nHAyTyQSpVIr5+Xk0NjYiLS0Ny8vLmJubY6rViRMnEI1G0dfXh/z8fMhkMp4ckD7tjTfeQDgc5nqEmqJYe6McCbrfaIJdVVUFs9nMAaC9vb1sI56VlYXu7m7ExcWhpKQELpcLCwsLcDqdqKurY11jKBTC2bNnUV1djWAwyPk3RNuMhTBTzkQgEMDMzAxTTHt6elBSUsJArNvtxujoKLZs2YKBgQEGggoKCjhRurS0FOPj40w/NJlMnEFTWVmJ69evswYiKSmJDRxivQtk/KD6W+Aticw3bdrEWqqFhQU2uYhGoygsLER8fDxSUlKQmZnJTdTs7CwaGxvh8/lw8+ZNjIyMoL+/nzV9NEmm+zgWPUkoFGJ6epozMYh1YrVakZOTg/n5eXi9Xni9XjidTuzZswcXL16E2+1Ga2srpFIp10n9/f04e/YstmzZwkYQNPEoLCxEOBxGeno6lpaWYLFY2FEt1t6IFq7T6bihm5qa4juL4iXW19dRXFzMJg/k+peYmIjW1lZ2ZmxubmYWELk+ZmVl4eTJk8jMzER6ejqSkpKgUChi0uFuXR95ojE7O8uUDeL4paSkYGJiAmKxGHl5eXjqqafg9Xpx6NAhjI6OsoizqKgIwP8J6HE6nejq6kJXVxfm5+eRnp6O4uJi2Gw2LiTJ0szlcqGjoyMmZ3N5eRm5ubmM6oXDYXR1dUEgEHAo04ULF7C+vo6srCy43W6kp6djeXkZtbW1HASWkZHBzgn9/f1cfFIC71tvvYXm5mYMDAyw97zD4Yg5NaDOloKVSO1fWFiIM2fOMN+SLqu6ujpMTU0hKSkJOTk5rHOJi4vD/Pw8RkZGUFBQgOzsbIyOjqKzsxPp6elwOBwwGo0IhUKYnp7mByzW3paXl9HY2AixWMwC10uXLrFgjITJdrsdycnJkMvl2LVrFyYnJzEyMgKlUomkpCQYjUa89NJLbFFZVlbG0xiXy4Xf/OY3qK6u5hCZQCCA2dnZmFaj1BhQIZmYmAiv18spquRHTu4Zly9fBgAMDw9zlgodIqdOncLU1BRGRkZ4JOhwONiZiA548mW3WCwxpy0+nw/19fVYWVlhIRW9mNevX2cURalUcuFGbg3hcJgdTYqKirC6uoqDBw/i8uXLuH79OtvKKRQK7Nq1CzKZDJcvX0ZSUhJUKhULoDdaMzMz3ORPTU0hEolgenoaaWlpOHXqFBobG2Gz2bC+vs7OHSKRCO+99x7uuOMOBAIB9Pf3o6qqisVuxHdOTExkG+nXX38dpaWl6O3tBfCBbuXv0aYAcFEHgP25yYP/woULjOjI5XK2U6R8jbNnz/I0z+Vyoaenh+1SCwoKoFKpMDMzw8/E3NwcN87x8fGMnm60hoaGoFAo+LmlsDMKz/zSl76E8+fP4/Lly9DpdDh9+jRsNhu+8Y1v4NFHH0ViYiJTD86dO8eWm52dnZxjIRAIcPXqVdhsNg5KslgsHFS20SIhdDQahc/nQyAQQHt7O3OqyQji1jDV6upqvPzyyzzRiUQiLFT2er3o7u6G0+nE8vIyo51paWmYm5vD+fPnYTQaoVQqEQgE0N3djdra2tvuLRKJIDs7m89pykwwGo24fPkyWlpasLKygrm5OU6Pl0gkSExMxJEjR/icFwqFeOaZZ1BfX4833niDJ6rU1FPCcEJCAtNKr1+/jvr6+g0/Nzq7aKotk8mgUCjQ2tqK1NRUTkl2uVxM51xfX2cdn9Fo5GkPTRk6Ojp4mpqUlITMzEy0t7djy5YtHOQJfCDgjwWmEKVobm4OTqcTCwsLmJqawtjYGDeI586dQ1ZWFlslt7W1we/3Izk5GUtLS6w/oUC0oaEh7Nu3D8AHlrf9/f1YW1tDSUkJRkdH2e2L7GY3WiQkD4fDPD30+XxMPS4qKmIUmxDmlpYWWK1WVFRUwOFwoKenB+vr63jxxRfxsY99DIODg5DL5Sw8jkajOHv2LLRaLTelNL3Oz8/fcG+Umk70OTJqqaurw3PPPYfy8nK2+tTpdJibm2Pqz65duzA/P4/z589jcXGRBdgqlQpjY2P8DtTU1GBubg46nQ7z8/OMgFOzH+tzIz0XvWdEPezr62MGhUKhQGNjIxf0dN50d3fDarWy1eyePXvYApeCYUlrSjlQ5BhJ9q8bLYvFgqamJg4fpVBOq9UKt9uNiooKNkYoLCzEqVOn4PV6YTKZWGQ8MTGBtLQ0PPfcc9izZw8uX76MxMREeDwe+P1+PPLII7h69SprSfLy8qDVajE4OBiTRkjnOmlDg8EgJ5ITzYiMYUpKSlBTU4OzZ89iZWUF+fn5HMRL9LxPfvKTCAaDKC8vZ+MOhULBGie6E/V6Paanp2M6ifn9fv6uXC4XHA4HZmZm8MADD6C1tRU5OTk4f/48HA4HamtrOTmcXB8vXrwIj8eDvXv34urVq9i9ezeGh4cxNTXFmtaMjAxcvXoVWVlZ6OvrY6teu92Oc+fObWj+Mj09jbKyMsTFxcHv92NxcZHrzdbWVmbeTE9PM/hIxg5NTU1Mn0tMTMTbb7/NIaAUOknvx8svv4zs7GxcuXKFJ8wzMzN/N0yQ1kduNMjZIjk5mTUX586dw8jICFpbW/GNb3wDd955J7q6umCz2ZCdnY3c3Fykp6djYmICKSkp2LdvH6qrq/GLX/wCWVlZ2L17N9RqNVZXVzEwMIB3330XO3bsQGpqKrKzs7GwsIDJyUl2p9lokUOB2WzmUWAkEsHNmzdhMBhw4MABZGRkoKenh1NCt23bhrm5OZw9exYJCQnYsmULysvL8cc//pFF2+Q1bbFYcP78eWi1Wtx1112orq7mpEyfzxdzb+RKQ9Qr8kN/+umn2eOeRMw+nw8DAwPsglBYWAiNRoOcnBxkZ2fjySefxKFDh6BUKjmFl16spaUlZGRkwOFwMA+bCvSNllQqxfj4ONMBKEH9xo0bUCqV2LZtGzIyMtDd3Y2rV6/yoV5UVISXX34Zs7Oz0Gq1OHLkCMrKyvDDH/4QDocD6+vrMBqN8Hq9+PWvf42f/vSnGB8fR3Z2NjeDpaWlMZtHoggR15YamjfffBO1tbWcTUC/b0NDAxISEriRI99olUqFP/zhDygsLOTxLNEIfvCDH+Dzn/88Nm/ejJ6eHgQCAabOxRqPB4NBXLhwga10KYTxrbfegk6nQ1NTEwf4eTwe7NixgwtliUSCgoIC+Hw+plv09/fjwQcfxNzcHIvenE4nN3cA0NnZySmhsVDSSCQCt9vNAZrAB5ffv/3bv3HaMNEILl26hLGxMezduxfp6ek4duwY0tLSsH37dmRlZaGlpQWPP/449u7dy370y8vLKC8vx+bNmzE0NMQ21vPz85ibm4uJWgEfIDDDw8NoaGjgwrm1tRXXr1/nXI7+/n5Eo1EkJydjYmIC3/rWt9DZ2YmlpSWYTCb+3F9//XV2TcrLy8Py8jLcbjeqqqpYLNnV1QWn08kFU1lZ2YZ7UygUKCwsZHoNNVZnz57lqZ1cLkdFRQX7m993330oKSnBzZs3IRAIUF1djdTUVOj1enZpe/zxx7G4uIg//elPKCoqQl5eHh5++GH2Pvf5fIzMbbSID0170mg0GBsbw8mTJ/HQQw8xncFiseDMmTOYm5vDF77wBezduxc9PT2cz9De3g61Wg2v14v8/HwcOXKEU8xpjE56ne7ubszPz6OhoQFVVVUb7k2n06GtrQ2BQIA1Fq2trejt7YVGo2H++OLiIlPY7rvvPvz5z39GT08PtFotMjIysG3bNrS3t/NzR05uY2NjiI+Px/nz5/HAAw+gq6sL169fZ/Hw+Pj4hk2QWCzG+++/j4aGBpjNZqZOfutb3wIAbNq0CQ6HA729vYzwhkIh1NTUIBgMsrFJUVERLl++zPqarVu3MqBFhiHkLjgxMQGpVIr6+vqY5+/S0hL+/Oc/M6Kfm5sLrVaL3t5eLC4uIiMjA729vdwQWCwWfO5zn8M3v/lNxMXFYe/evWhubsb6+jpGR0fxy1/+Ep/5zGfYUl2v16O9vR0CgQDz8/PcwNPEuaGhYcO9paWlYXBwEAUFBQzYLS0t4dixY1hZWcHBgwfh9Xpx+fJlXLhwAUePHuUmlcL3iFIqEokwMTHBeRperxddXV148803cfjwYdxxxx04efIkC5/j4uJiUqUpHygcDvOz43K58K//+q9M76qsrER7ezv+9Kc/4ROf+ATGx8cxNjb2IeHzF7/4RTz//PPYtWsXZ2T09/fj2rVrMBgMGB4eRn19PSYnJ+HxeLC+vo7U1NSYdwOZdxA1mGigv/vd77Bnzx709fVxiOZf/vIXbNu2Dc3NzVCpVMjIyGCta3Z2NrKzs/Hiiy8iEongH//xH6FWq3Hx4kW89dZbuP/++7nhov2VlJTEFOeSwUJ3dzeHzL3yyis4c+YMpqen8e///u/Izs7GzMwMfvWrX6GoqAhyuRx//OMfYbVasWXLFhw9ehTbt29HTk4Ofve737FddldXF1566SVcunQJMpkMDz74IIxGI95//32cPn0aZrMZ991334Z7o+aJrNhpavzOO+/AbDbzvbK2tsbAx5NPPonJyUl85zvfgUajwfbt2xEKhTA0NITOzk58/OMfR1ZWFvR6PZKTk3HmzBnWUVI8g9/vR2FhYUwHNrFYzMCoSqXCwsICRCIRHnjgARw6dIjzWrxeL55//nk2E9HpdOjs7OSAW6VSiR07dqCvrw+FhYUoKytDRkYGJBIJ3n//fVRWVqKqqgqlpaWYn5+Hy+WCWCyO6XKWmZmJnp4e1q4oFAo4nU42q7n77rsxNTWFmzdvwuPx4K677uIzqbu7mzW5BQUF2LlzJ/70pz8hMzMTR48ehcvlwjvvvIP29nakp6ejqakJ3d3dbCu8Y8eOmHS4W9dHpk719/ezMIeK69LSUhw5cgRZWVkssG5sbERpaSneffdd5OTkID4+Hg899BD27dvHFBji21+9ehUXLlyA3W5HSUkJ7r77bgwMDOC9997DH/7wBwwMDLA7TizhWiAQQEtLC1u0+v1+DA0N4Y477kBZWRnS09NhNBpRVVWFI0eOoKenB0tLS7h27RqH35CKXqlUwmw248KFCxgZGUF8fDzq6uqwfft2BAIBnDp1Cm+++SYSEhKwdetW7oo3Wna7HaWlpRyOtb6+juPHj+Ouu+5CfX09du3ahezsbOYrd3R04DOf+Qx0Oh0L706dOoU33niDR5YTExNcdMjlchw6dIgnTsFgkC1o5+fn/y4yr1arEQqFIBKJkJaWhqysLNx7771YXFyEzWZDa2sr/H4/7rnnHly/fh0PPPAA5ufn8eUvfxlHjhxBNBrFxMQE7HY7/uVf/gXl5eVcFBmNRrzzzjtoa2tDU1MT24ESPS0WD9fpdLKol4T0i4uLbBt3q7antrYW7733HjuWPPLII6ipqcH4+Dh39BKJBAkJCaiurubU3z//+c/srELjceAD299Y3vxEQyDhX0NDA5xOJ/7zP/8TkUgEfr8fHR0dHOb27LPPMnWE+OgSiQRFRUV47733IJFIcOXKFWRmZqKqqgqRSARmsxmTk5MQCASwWq0oLi7Gli1bMDMzEzN9Wy6XcyAhTTSmpqbwz//8z7jjjjt45FlcXMzZAkajEWVlZairq4NWq8XQ0BDef/99jI2N4emnn2Z3m6ysLCwtLSErKwvHjx9HMBjkEClCgv9eMrjL5cKuXbvgdDrR29sLq9WKmZkZVFVVYX19Hbt378add96J3bt3Y9euXdBoNHC73Xj11VfR39+Pt956C3/84x/ZPau5uRnFxcWsCwoEApibm8Pg4CCjWeQENTk5GXPqQt/d6OgoQqEQ25seOXIEa2trOH78OM6ePYv5+XlGckkEf/fdd8NgMLCGy2Qy4amnnsLU1BT+9Kc/4eTJk0hISIDBYMAvf/lLPPnkk3jvvffQ09OD0dFRpodttGjS89577+HcuXMQiUTIzMzEJz7xCQwMDGBkZARerxdFRUU4evQoADDPXKfTMaWCLtjHHnsMfX19TB0lDvTNmzf5PMvIyEBmZiYbAWy0VldXUVtby9z0hYUFpKen47vf/S6ysrKQnJyMxMREpKenY9u2bcjJyUFeXh6LWUkDkZmZiXA4jGvXrqG2thaZmZnIysqCyWTCnXfeycGlZFGbkZHBBdNGKxwO48CBA4hEIpiZmeFQsd7eXrzyyiu4efMmuru7IRQKsXPnTgwPD0OlUuHs2bMwGo2ora1FbW0tWlpaoNVqmW42OjqK+fl5GI1G1NfXM1IdHx/PQYUENGy0pFIptm3bhpSUFAZW3n33XRw6dAgVFRXYv38/9u3bh23btmHTpk0cEqnRaLB//34Eg0G88MIL6OzsxNjYGP7whz/wOUO6oJ/85Ccwm834+te/zg5zFHAWCyAj0a/dbkckEkFZWRmkUimOHj0Km80GmUyGmZkZqFQq/PjHP0ZycjL+6Z/+iSkv2dnZ2LZtG+644w4+XyhvhzRglZWVPOGinAK9Xs+TiFjPW0NDAyKRCK5du4bjx4/j5MmTOHr0KGpra1FSUoJwOAyDwYAvfelLHLhJgJXdbsf777+Ps2fPYnZ2FqWlpejq6uKJORllEP1FpVKhubkZu3bt4qnkRsvhcEChUGB4eBjz8/NYX19Hb28vnnzySZ7kicVibNmyBd///vc5b4UysChPyWAw4MyZM7j33nuxurqKGzdusAvlF77wBfz1r39Fa2srU3zJbTDWvUVua0QBIlOJT3/60/j2t7/NFtdarRY7d+5EW1sbamtr8eijj+L5559HfX09B+OSnkuj0cBoNGL37t342te+ht27d2NwcBDV1dU4ffo0kpOT8cADD/CkeKM1NTWFaDTK1s75+fkYGRnB0aNHMT8/zwB3SUkJSktLcfbsWUxMTODUqVP4whe+wLRKclt84okncPHiRdy8eRNXrlzBW2+9hcbGRly+fBl79uyBz+dDYmIisrKymH2w0VIoFFAqlRgYGIDP50Nubi4SEhLw29/+ll28jEYjdu3ahU9/+tMc1jozM4MdO3agpqYG8/PzeOGFF2CxWLBnzx4MDQ0xkHru3Dncf//9fLafOnUKs7OzCAQCbOW+0aKmWSAQsIGRz+fD5z73OSQkJHDiuE6ng16vx8mTJ/kM27p1K3JycuDz+bC6uoqEhAR873vfQ29vL65cuYLx8XHIZDJs2bIFIyMjbFtdUFCAyspKDA0NxaRx37oE67HIabesp556CklJSejv74fBYEB5eTmcTicuXLiAz3zmM3j99dcRDAaRnp7OgV8VFRXw+/1ob29HRkYGU5MMBgP++7//G1/4whfQ39+PxcVFRpnJX5682OVyOfPYv/nNb952b7/97W/ZoeZWm1qv14v9+/fjxo0bmJqawurqKmQyGVJSUlBTU4OTJ09idHQUiYmJHPBmsVgwOjrKCY3ElVxcXMSBAwdw9epVFt/QCN/lcuFTn/rUbff2s5/9jEXIKysrWF5e5iRhvV6P/v5+LC0tQSAQcOI0TTSSk5MZFSKrMfqZHo8HXV1drA9oaWnB9PQ02wKTv3okEsGRI0duuzfiACsUCgwODmJqagp79uzB9u3bceHCBSQnJyMYDGJ2dhZLS0u466674HK5cOPGDR4ZNjQ0sF2q1WpFamoqiyApAO3NN9/E5s2bEQqFMDw8zCFymZmZG1p6/uIXv+DRrtfrZd73ysoK8yJJiExFzMWLFyEQCGAymWAwGDA/P4/f/OY3+OIXv4ihoSE0NDRwEUlcWYfDgcHBQezbt48zCHJycjhg8XbrV7/6Fad1p6WlISUlBXNzc/B4PNi/fz9GRkZgt9tZJJmamoqzZ8/iwQcfhMPhYPvYqakpHD58GBcvXmTNiM/nQzgcRltbG7785S9DqVRibGyMETXgg0P58ccfv+3efvSjHzEXXa1WIzk5GXNzc+ju7v5Q7otarcbKygoyMjL44jx79iyi0Sj/u2AwiH379vFhFxcXB6vVioWFBRQXF6OzsxM1NTXQ6/VsFBAMBvH1r3/9tnsDgO9973tobGzkMS/wAQoYjUah0Wg4j4WMBoLBIIvWqXFaWVnhnIO1tTXO9aDcEcotMZvNsNlssFgsAD6wc3Y4HPjkJz952709/vjj7LRE4j6TycS845/85CfYu3cvCgsL0d/fj7y8POj1eqbRjI+PY2JiAomJiZidnWW6S2VlJYfiTU5OIikpCQ6HA3v37mUaIQm0N9rb73//exaDJicnQ61WY35+npOeX3vtNU6NJhrW8vIyGhoacO3aNUQiEdayKRQKFpLSlIGyXAjhotRzh8OBxMRELC4u4t///d9vu7e33nqLrbBJqK9WqzE0NITDhw/D7/ezHSXwQe7R/v378d5773Fzv7S0hPX1dWzatAlXrlxhK1aVSvUhIw65XI719XUuqkpLS/Hyyy/jF7/4xW339tOf/pRpkQqFgoEVm82GoqIinjiTG5pIJMLw8DBqampgt9uRlpYGrVaL8fFxJCYm4q9//SseffRRTt7OyMhAcnIyOjs7+e+em5vD2toaZ0A99thjt93bz372Mw6AC4VCbD+anp4Ok8nEtu+kWTl58iSam5sxOzvLhgp0xgqFQjz99NOIi4tjJy9qjikjoqamhtPVCwsL4fF4cPDgwdvu7d/+7d/YJIKE1/Hx8fxnnU7HAtG4uDgUFBTg8uXL2Lt3LxYWFthFKi4ujput+fl5xMXFMRXxpZdewj/+4z+iubkZ586dY4vtxMREXLp0CX/6059uu7f/+Z//4ekuOWAqFApcvXoVTU1N+N3vfoeSkhKkp6fD6XTytFAqleLll1/G6uoqMjIy2GgkGAyioaEBarUaFouF9aMU6kZaCbKq1ev1uPfee2+7t9/97ncwGo3Q6XRYWVnhyQxNcsbHx5GTk8POX6FQCA6HA6urq6ivr4dAIMDk5CTneZ0/fx7BYBC5ubnQaDQIBoM4ffo0uzpSQTswMAC5XI6mpqYNc7N+9rOfIS8vDzabjZ8dcoOjiV5CQgJyc3NRWFgIh8PBdslnzpxh44HV1VXodDqEw2E0NTVBqVQy68Pj8bC70ZYtW9DT04OxsTHWjzz66KO33duvf/1rFjKbTCYkJCSgs7OTwz8p34uCPylrJTMzE93d3TCZTEhPT0dvby+6uroYyd+2bRvi4+MxMjKClZUVmM1mTExMQCAQsD6KKPIb1XA//elPmeK/urrKeqPZ2VnU1NRgYGAAKSkpSE1NZQe7hIQEzM3NcTDr8vIyTp48iW9/+9uYmprC7Owsm9qkpqZieXkZDz30EM6dO8cGHESVk8lkeOSRR267t9/+9rdsf280GpGXlwe3241nnnkGDzzwAOeFWCwWdHd34+Mf/zib8zgcDjaMIeCvpqYGJpOJNdfUgAiFQjbDaG9vh8PhYMv2f/qnf7rt3m5dH5k65Xa72ULW6/XCZrNhbm4OeXl5fFHNzMzw4VNRUcEWt+SAQxx44gEuLi6ynabf7+cCdW1tDRqNBgMDA3A4HMjMzIwpBo9EIhCJRIwKzc/PIykpiTmSdAGSXoECW/R6PYc40QtcUFCAhIQEHneR3dqt3EFyFfH5fOzysdGi358CASkUR6lUMm1MIBAgGAyyeHViYoJF1fHx8WyzmZCQgL6+PkaWAfBDTO4oYrGYL3WdThfTUo5cYWif9J2Q1R+Jo0OhEFJSUhitys/PR3t7OwuMyPN9cnISKpXqQ5kQRLdxuVyQyWTsPEWf8UaLDhISatEUgGhiNKm41b2or68PiYmJjBwFg0F2b3C5XPz5k3NFe3s78vPz+dAOBAIsFLRarRs2Gg6HA0lJSTxtIf/r9fV11i4plUrYbDbYbDbk5uZCKpViZmYGSUlJCIfDjNpotVqIxWKsra1hcXGRbSfJWKG3t5edONbX16HRaGJyhEkLsL6+znsDwAJwuVwOv98Pp9OJtbU1zlCgC5VEeWQJeePGDfj9fgiFQqj+ljhMeQN0gJGYOxKJxERwAbDTBwUvSSQSFr8C4AZEKBRCKpWyyQNdClqtlt8Hcjsi8TY1IX6/n3NRHA4H/x0pKSkxNRpkZ02GEoQOr6+vs+NbQkICHA4HO5tYrVYWHE9PT8PtdrNjD/1/NHmj7/FjH/sYTp8+zbx8m80GkUgU87MLBAKMyAJgR67ExER+P8hP3mAw8KVAI3iJRAKlUgmj0chgEdlwktNdMBhEUVERVlZWMDIyAqvVyg3K37Pe1Wg0WFtbY+FiXFwcQqEQn223JkaTlkalUjEg4ff7ce7cOX4OKF/C5/Nhenqas4PW1taYLkn6nlhTtNXVVSgUCrYWXVpaQjQaRVZWFn+Wi4uLEAgESEtLY6QxEonwVJXQdbFYzIUWURAdDgeDRGR4Qc+S0+mMiTDT+aTVarlJFgqFOHv2LFpaWjA/P8/nIN0Fy8vLHOa1tLTECeC32nQPDg6y1adareZif3p6Gqurq+xw6Ha7N9wbOdiQu9bCwgK7rdGUcWZmhsX8FHY6NDTEU6fl5WU++ynJnfQZNG2PRqMcEEYCdHIr2mjReUGfNTX6ZINOJisul4v1mKTXoiaAMkC8Xi+HQpIj1MrKCt566y0GhsiURKFQcJDoRovcIYEP3oG4uDj+GXl5eazTvHr1KkKhEO666y5MTExwuNva2hpsNhvi4uLYrU2v17Otu8fjYTCNfg5NCihOYKNF2iGBQMD0UEruNhgMkEqlGB4e5pyF2tpatLa2soGASqWCWq3G8PAwenp6sG3bNs5mAMCORrRXypCgMzlWLUIBhuSGRs8Z3QMSiYR1VgDYXSovLw9zc3McamkwGPgzlMvl/HeR+5der0dvby/EYjH8fj9rXygg+HaL8iIyMzPhcDiwsLDA361Go2EXpmAwyO8qvaPj4+McBpqSkoLl5WX09fWxMQxFMqysrLDl7q31Df2cjVYoFIJer+cMFovFwlkwRKufnZ1lKjDVswSQkZsZPWMDAwMwmUzcWFBumtFoxMjICLKyshAIBPjOiJU3duv6yI0G8a+VSiUkEgn8fj9mZ2c5WKy0tBQSiQTT09OYmJhAcXExZDIZzp8/zxMKcgppbW2FXC5Hf38/x6ZTOBd11RSOZbPZcPHixZgFAqH3NM6dmpqCyWRiARAJ0DweD/uhKxQKOBwOqFQqiMViLCwsoKuriwWgTqeTOe6hUAgejwc3btxAcnIyC8zJTzkWZ1MmkzGthMRQ9AJKJBKYzWaEQiFMTk7C4XBgamoK5eXlmJmZ4ZRz4hWLRCJYrVY+GMjicWJiAv39/QA+EI16vV6sra1xw7XRoo6VUFKaIHg8Hm44qGiTSCTo7OzkjAqyTiPbzMTERD7cyb2C9uD3+zlEyu/3s7AzFlWE0E8KTqK9AmA/akKaaCRMDSC5i0gkEhw6dAiTk5NcTJPFKeVdkF/11NQUT5YcDgfGxsY2DPHxer0oKChAIBD4kD+6SCTC5OQksrKysLa2hrm5OUxOTiIzMxO5ubno7+9HeXk5HySVlZXo6+vjZ58MA8i1aGBggMXwANhmMpbd3fLy8ocE10tLSywSdDqdKCoqwujoKMbGxiAUCrF161YWMXs8Hs6uUavV8Pv9rJGhpo4oOAkJCWhubsapU6fgdDq5gfp79rYkHCTLX4FAgNHRUbjdbn4G6XOlAreuro6FyxkZGcjPz0dhYSEX+yQMJUEcUQPW19dhs9lgt9shFAr5ko/12VGjQW4efX19SE1Nxc2bN2E2mzE3N4eZmRmMj49jx44daG9vZyofXWo0fRwfH4fX62WEmwpHKh7JB578y2PZtBL6SRaFNPmy2+2w2WxITk7mop7ejYKCAgwNDbG41GQyoby8nN2dqKCRSCTs8GQ2m7kRIMtychvaaC0tLUEul7PbGk2CSQBOTdDS0hIyMzNZR0RBjUajkd37zpw5g6ysLNYtjI2N4dSpU1hfX4fVakVaWhq6urrYoY/cgTZa1JCQNS49w0T3pawVahaoUBsZGcHCwgLi4+PZyYWoMCMjIwwSWa1WzM7OIj09nROfieoZFxfHRdjtViQS4SaHbJU9Hg+745CnPtEoKLCMko69Xi8jyM8++ywKCgq4GBsYGMDi4iLnosjlcvT19TGFyOfzxRTnUuFHTkXEF6eMFaFQiImJCSwsLPBnuX//fhw/fpy1TNQcXbhwAfX19ZDL5cjNzcXq6iqcTicMBgNcLhe6urpgNpvhdDq5cI1ljEChqwTMRKNR+P1+RKNRdhuiQF0y14iPj8fY2Bh0Oh2ysrJYK0Ri7dbWVhQVFbGt/Pz8PCfARyIRtkcli/6NFjkvkTMa8AEooNPpEAgEkJ2djYsXL6Kzs5NdGFNSUnDhwgWmHK+traG6uhqtra2Ynp5mfdTc3BxmZ2ehVquRnp4OoVAIi8XC55tcLo9Z+C0tLcFmszG4ura2hpmZGSwsLGDnzp2orq7GwsICOjo6sLy8jKqqKgSDQQ5NNhgMMBqNCAQCaG1txeLiIi5cuID9+/fznaXVajlKYHBwEH6/HyKRCE6n8+/aUNMUnUBjgUAAmUwGr9fL5geU2UF0JqvVCp/Ph7m5Oa536J1dW1vj8N2hoSFkZ2fD6XQiNzeXp5g+n4/B7I2W1+tlm+hIJAKxWMzUq5GREZSXl2NiYoKBRLKsHRsbw+zsLOtC6+rqcO7cOXZ3JLML0mO+/fbbKCgoYDYE6VFiuZyR6xs56LndbnZuJCMjutuFQiG6u7vZypiadalUCr1eD4FAgLa2Nmi1WnY6Gx0dZaMBl8vFzzg5Zn3U9ZE1Gnq9HhKJBCUlJaisrGTqyXPPPYdgMMjBSGKxGGNjYyxefeGFF/Daa6/hwoUL7MhCIWYCgQCBQAB2u519jAmVv3nzJmZmZnjEH+tBoCJNr9dDo9EgHA5jbW0NL730EmZnZzE4OMgP4/PPP48rV64gNTUV586dwzvvvMNuM9XV1bh69SqefPJJ9i2nRsjlciEUCmHr1q08NaC02eLi4g33JhKJMD8/D6FQyBxMg8HArhcSiQQqlQomk4ktBz/xiU+gqKiIC361Wo2ysjKYTCZkZ2fDYDAgKysL+fn5yMnJYbcK6oZdLhcXWLEaDSpUKJjIZDJxbgB5OhcWFgIAXnjhBczMzGBsbAzXr1+Hy+ViKg9x0rVaLRISEpiHurq6isHBQaSkpMBkMrErjcFgYMeWjdatfuLBYBAul4sLNkJyCZUYHx/H3NwcDh8+jMbGRs4QyczMxB133MEXzerqKoaGhph7WFBQgGAwCLPZDLfbjUAgwNS0WBddSkoKc9FdLhcXig6HA319fWwfOjExgZGREXZnIdccm80Gp9MJh8OBU6dOYWFhgZ2Hpqam0NPTg8XFRQwPD6OxsRF1dXWM7o6MjMREhigoizy8KW+lo6MD/f39zJcnelAgEEBaWhrq6uoYYTGbzdi+fTtbG2dmZjKf2+12Y3x8HOFwmC/m5eVlRnpiCZoBcFAm2WRSQfrd736X/c1JFEc2t5s3b4bf78e1a9fQ2dnJAnBCLuPi4pCZmYn6+npUVVXxpI2KSkr2jUajMZtboVAIr9cLiUSC0tJSlJeXY2BgAO3t7bhw4QKWlpbQ1dWF9957D/Pz82xzOjo6CgDIysris/HSpUt48cUXOQyJ7LbFYjHefPPND2UzEPpM79pGe6MLPyUlhSe2ly5dgtVqhVarhVQqhcViwY0bN7iZJwT5VvOInJwczM7OYm5uDnq9Hps2bUJ9fT1TERUKBbKzszlATq/Xx5waKJVKnDlzhvn3AFhgTUFuW7duRV5eHiwWC4aGhpCRkYHl5WXY7XYOpKOE6CtXruDSpUtsJywQCJieQCg05R4RzS/W3uLi4qDVaj8khu7p6WHAKDc3FwKBAMePH0d+fj7TIsm5bXBwEIODgxxc6ff7YbFYOGBzcXGRp4w+n49pjtnZ2THvBrlcjvb2dg4dy8vLQ3JyMrZv345wOIwtW7agsbGRxcPkoEaUIalUyuGbEokEFy5cgNfrRV1dHWQyGZ5//nk888wzUCgUqKysZO1Cd3c30yQ3WlT4EVuAznOfz/chiotGo8H4+Di6urogEolQX18Pn8+HmZkZbgQ8Hg/++te/wmKxsPYwNTUVdrudJ999fX1oa2vjpjiWliorKwsTExNYXFzE0tISn5svv/wy3nnnHQ4Qnp+fh8Vi4cbabrfzdzk4OMhFFVHUaHpXUFCAL33pSwgEAigpKWEXr46ODszPz8cUDkulUvT29mJgYACjo6Ow2+0c0tbd3Q23242VlRUkJibCaDTC5/PBaDTCZDLBYrFAKBRykODMzAx++ctfQqVSIT09HRkZGZz/oVQqEQqFMDMzw3exTqeLuTcA6OrqQjQaRVlZGbZt24asrCzk5eWhra0NCwsLMJvNSE5ORnd3N65cuYIHHngAExMTcDgciI+Ph9FoRHFxMY4ePQqPx4ORkREMDQ2xZXJqaipyc3NRUFCA4uJizv+hJnWjlZiYiL6+Pg6cJR3l2bNn0d3dDa/XC7FYzHkmWVlZqK+vx/T0NOtEz5w5w5asxH4oKipiK/aJiQlcunSJaetk4rK8vMzOqLdbRNVsa2vDxMQEfD4fRkdHMTg4iEuXLnHIYmdnJ27evInh4WH4fD6uOWw2G+bn55GTk4ORkREONM3MzIRSqeRpw/T0NNucU8O5uroaU2cbjUbR2dkJh8OBvLw81NfXszsdncfEYrh8+TLkcjlUKhX8fj/Onj0Ln8+HjIwMDA4OYnZ2FleuXMHY2Bj0ej3Ky8tRWVnJOUu3RlxQ7g3ptv/e+siNhsVigUQiwaVLl3Dq1CmMjIxALpfjf//v/83Wenq9HtXV1Th8+DDby23ZsoWdfSjk66233sKRI0eYi0eBJ1/60pcwMjKCQCCA6elp7N+/H1//+tehVCqxd+/eDfdGhU5rayvW1ta42Pzzn/8MrVaLPXv2YMuWLbjvvvvw9NNPo6WlBW1tbcjPz8dXvvIVpoN89atfRW5uLq5du4a0tDTYbDbmtB84cICRiszMTHzsYx/D/v37oVKpYoa9rKysQCQSccYDCQV/+MMfQiwWsxhWLBajoaGBrTXz8/Px2c9+lgtAo9GIS5cuQalUQq/X8wXW3t6O++67D6urqygpKYHf70dqaiqam5v/bq6BVCplPjZ5m9tsNggEAuj1euzcuZMdQNLT06HT6VBTUwOBQICWlhbk5eUhISEBmzdvxmuvvYba2loEg0G0tbXhzJkz6OjogNFoRH5+PlJTU5Gfn89FMxVuGy0S81EqNNl33n///cwRJqSVCjkS4ZGrQnt7O9544w0Eg0FUV1djdHQUlZWVSElJwdWrV7F3716sr6/DZDJhcXERJpMJKSkpeOutt2IK1/x+PwdEJSUlQS6X48UXX8S2bdswNDSE9fV1WCwWpKen4xe/+AUcDgcOHTrECdZms5nFkD6fDw899BBEIhG2b9+O7du3w2g04tFHH2V+JwXjpKWlQS6Xo7y8fMO9AcBdd90FqVSK2dlZWK1WxMfH44knnsAjjzyCLVu2oLS0FEVFRdixYwcjYtPT03jooYewY8eOD01DjEYjXnjhBZ5Gut1ufO5zn8Nrr72Gb33rW+ju7kZRURH27NkDjUaD6urqmHtzOBzIyMhAOBzmRr2xsRHf/va3OWWd7D4p7ZumP7/5zW9w4MABeL1eZGRk8Hd25coVHj1PTk7iW9/6Fi5evIiBgQGMjY2xXaVAIMA3vvGNDfe2urqKqqoquFwu+P1+FjI/9thjKCsrw6FDh1BUVIS77roLTzzxBN588018//vf5/NjfX0dRUVFKC0txeLiIv7lX/4FJ0+exPDwMBISElBUVISWlhZ87GMfwze/+U0YjUY8+OCD+OpXv8rUyI2Wz+fDvffey/kxJpMJaWlp+MpXvoIDBw7g/vvvR2lpKdLS0rB582acOHEC09PTkMlkeOihh9DS0sIC9r6+PvzjP/4j7HY7/zOr1Yof//jHOH78OLq6utDW1gaTyYScnBy8++67MZ854orTd0VZBps3b8bo6ChmZmbQ39/PBTzwwaSyo6MDLS0tsNls+NOf/oTc3FzEx8fjk5/8JDfmUqkUZrMZDz30EKP55eXlqKiogFwux8LCQkznE+I5d3Z2wmazQaPRMLfa7XbzO5KQkICysjLYbDZ8/etfh8fjQX5+PufjJCcnY3x8HAcOHIBYLEZubi6ysrKgUCjY5vMTn/gEZDIZcnNzUVZWhrGxMQ593Whv9fX1iI+Px+LiIhYXF+H1ejnbh5KgS0tLsW/fPg5Z8/l8aGpqgtFoxLVr1/D8889jy5Yt+NWvfsUueGlpaaioqEBVVRWefPJJPP/88zh//jyEQiGHr1Io4+1WQkIC8vPzIRaLkZiYiKKiImzatAmZmZno7e3l5PCDBw/i3/7t3/gMpcBMh8OBt99+G8PDw7jnnntw5513oqamBuFwGFeuXEFbWxs++clPwmazobm5mROId+7cCQB4+OGHN9zb3NwcT7nj4+NRUVGBu+++G//5n/+JjIwMFBUVoby8HEeOHMFPfvIThMNhuN1utLS04LOf/SwHXf70pz9FT08PvvzlL7MIu6+vD+3t7cjLy0N7eztTbmpqarBlyxYO/NtouVwuPProo9izZw927NiBXbt2ITU1FTU1NZxKnp2djV27duELX/gCTp8+jS1btkAgELCuhKhndOdfuXIFs7OzcLvdcDqdnCvR2NjI2gNyt4r1vBE4RGG2lLnyqU99CpmZmcjIyEBSUhK2bt2Kn//85/jhD3+IoaEhaDQa1NTUYGpqCn/9618RjUbx/vvv484774TZbGYmAVlqHzt2jG2aU1JSUFxcjLW1NTzwwAMb7g0ACgsLeeoyPz+PCxcu4IEHHkBubi6am5tRUFDATczZs2d5cvjTn/4UDzzwAJxOJ+Lj49HQ0IAdO3ZgaWmJnfPKysqwY8cO9PT0wG63o6urC0ajEZs2beKaa6MlEolw3333oaGhAQUFBcjJyYHJZOIMmEAgAKVSia1bt+Lzn/88TCYTVldXUVFRga9//etobm7md4ro53a7HV6vlyfKlMVB2tPNmzdjx44dWF9fx7FjxzbcG9n49/b2YmpqCkKhEIFAAK+//jrXkAcPHsTDDz+MP/7xjxgcHGT68YMPPgiVSoUrV67wdPHVV19FKBSC0+mE2+3G4uIimpqaMDo6ik996lNITk6GXq+H0WjkieBHWR9ZDP7000+zWEQmk2F6ehp2ux3l5eUoKiqCw+Fg3ub8/Dz27duH+vp6dHR0MDo+NjaGZ555Bt/5znfwve99D9///veZq+Z0OvGXv/wF/+t//S+8//77SE9PZz490Qo2Qvz++7//mztuQmvHx8fxzjvv4I9//COGh4cRFxcHn88Hm82GsrIyPPHEE/ja176G+vp6bmzEYjGEQiG6urqwY8cOTkMm96OVlRW2CZyamuKwrr/85S949tlnb7u3119/Ha+99hqqqqpYBGcwGDAwMICDBw9yuqbVasX777/PhSDZoRkMBhYstre3IzMzk9PFqesldOvMmTPYs2cPC0wpxXEjG8PXXnsN2dnZWF1dhcvlYj/tyclJ/M///A9bpI2MjKCtrQ1Hjx7F73//e/zwhz/EiRMnoFKpmNNNBZ9UKmWdxsLCAlpbW/GDH/wAnZ2dOH/+PDIyMpCamoqZmRkkJiZuePj89re/RWpqKgcm+nw+7Nixg109xsbG2N88OzsbOp0ObrcbkUgEL730EhobG1FfX493330XdXV1uHDhAj71qU9henqacxmOHTuG++67D2trazz1IvqH2WxmJOv/Xs888wy7pJCTi0wmw9tvv43NmzcjNTUVHo+HEbrvfOc7CIVCcLvduHz5MkpKSmA2m3H58mXYbDYoFArk5OSwvoRoMXfeeSeeeOIJ3HHHHfD7/XA4HCw424gu8t3vfhcul4sR0vT0dMzMzKCnpwc6nQ4tLS2wWCzcQMfFxaG1tRVKpZIFrDKZDP39/VAqlXj77bfxD//wD2x6MDk5CaVSiZSUFExNTbFAlLj+ZWVlfLDebv34xz+GxWLhn00o6K9//Wt86UtfQnd3N7toTE9P46677sLbb7+NUCjExeXIyAieffZZZGdnQywWo6Wlhd25iPu9Z88eNpMYGBhgikcoFMJnP/vZ2+7t8ccfx+DgIB566CEAYPTp8ccfxyOPPAKz2YyhoSEMDw9DIpHg0UcfxfT0NAv9RCIRSkpKkJGRgePHj+PChQv44Q9/iNXVVfj9foRCIXR1deG73/0unnjiCfj9fqSlpXHOAwB88YtfvO3ennrqKfh8Puayi0QiqFQqvPTSS/jXf/1XWCwWdiyj5trpdOIrX/kK/vu//xtKpRJNTU3Mu52amsIXv/hF5OTksEufRqPhXA6DwcCT5sTERKyurm74uf3hD39AR0cHamtr+TzPycnBxMQEamtr8Yc//IEn3lKpFDk5Oejo6MDhw4cRCAT4/3n33XexZcsWnDx5Eo2NjRy4NjExgbi4OHzsYx/D2NjYh7jgpN/5h3/4h9vu7YUXXuDcAEJDA4EAxsbGcM899+Dy5cuMvPb19eGTn/wkJiYmcPHiRTz//PO499578cADD2Bubg5GoxEXL15EY2MjxsfHOc/nxIkTeOqppzA6OgqZTIbjx49jenoa99xzD/r6+vCZz3zmtnv7r//6L4yNjWHnzp3svZ+SkoLvfve72LVrF1MIySnr05/+NH73u9+huLiYLcSTkpLYc39iYuJD4AeJOBUKBRYXF6HX69kA4PLly9i9eze2bt1627396Ec/Qm9vL1JSUpiievfdd+PYsWN44okncOrUKUilUvj9fvT397NV9crKCo4fP84ueaFQCGNjY3jqqafw/e9/n9kGVBz98Y9/xEMPPYQjR47gxo0b8Hq9+PSnP43jx4/jJz/5yW339uyzz+L8+fMoKirC+vo63G430tLS8O677+I3v/kNvvvd73KydU9PD+666y52u2xpaeH6oK+vjylvjY2NPLESCATYv38/3n77bVy/fh0lJSUcAEhTzf/4j/+47d5ef/111lpSoKxarcaJEyfw5S9/GXa7HXNzc5ibm8PS0hIeeughZGRk4P3338eJEyeQkZGB+vp6zj757Gc/i6effhqBQAA2mw0ejwe9vb24++67kZyczFNhsmYnE5HbrVdffRWXL19GamoqT+yzs7PR1taG7du3o7u7m63xNRoNTCYTLl68iIKCAoRCIdTW1iIpKQmvvvoqPvGJT+ALX/gCnn76ac6Fqqqq4rv9e9/7HsrKyuD1eiEUCrFt2zZEo1Hs2rXrtnv7/e9/j7GxMWRlZbF9+fr6On7zm9/gP/7jP9DX14fNmzdjbW0NL774IhobG/H6669j165dbFpALoGLi4vYsmULJicnmQopFosxPDyM7du3w2azYdOmTWxRTtOEjUxzHn/8cTZYoRohMzMTN2/exD333IPR0VHOfyF9bTgcRnJyMs6fP4/8/HwcOHAAarUaN27cwNzcHDv0kQajra0NX/3qV9HX1wcAmJiYYPC4p6cHTzzxxG339vbbbzOtlyaOpKd4+OGHMTo6CpVKhcHBQZw+fRo/+9nP8Mwzz2Dr1q146qmnkJ2djebmZkxOTmLz5s348Y9/jMceewyjo6MQCARIT09nl9TTp0+joaEBfX19sNvt2Lp1K4RCIZqamm67t1vX/1Ngn0qlYvRrdnaW7S0XFhag1WrhdrsxPz8Pq9WKUCiEwcFBvPLKKywADIVCyM7Oxp/+9Cd8/vOfx6VLl5jXGolEMDs7i8cff5wPaJlMhsTERFy4cAEOh2ND1xNy7CBBLflsk8dxamoqJ7tu2bIFGo0GDz74IMbGxgCAha/EE6aiipCZs2fPYnFxEenp6WhoaMCZM2eYV9va2hpzBD05OcnJvSS47O/vx/LyMq5du4by8nJGjcheNBQK4erVq1zAAh9QADIyMtiudH5+Hna7nYvriooKmEwmzM7OMgecONIbLRLKzc7Ocrqrz+fjl5L+eUZGBruMlZaW4sKFCxxR39bWxoJr4hvShIfcTSilltx3ZmZm4HQ6Y/KXyTWJdDWUjq7X69HX18ce+sAHwVjFxcW4dOkSTCYTdu7cCZvNhj//+c9ISEjAlStXoNFo2Ep2cXGRfcfff/99FBcXs65iYWGBLes2WpSITUFjFLi4adMmDhn0+/0Qi8WIj4/HiRMnkJ6ejkuXLmHTpk3o7e1ll6AdO3agv78fkUgEq6ur6OrqwoULFyASiZCdnY3q6mr+GaFQCHK5HCdOnMDPf/7z2+6NBNb0Pi0sLCAYDPI0xGaz4fr16wDAmgPidJJoj6gWZ86cwT333MO2lMQbJU50VlYWent7MTw8DIVCgdLSUkxNTcVsNNbW1lBWVsb0i/X1dZw+fRoHDx7ks2R8fJzDDkkr88ILL7DziEwmw7Zt2zAwMIDdu3fj0qVLzLX3er0IBAJ499130dLSgtdffx0SiYQDOmNlkASDQSgUCrS3t0OhUEAoFMJms+HQoUMMRszPz7NV9SuvvIKMjAxcunQJNTU1MBgMbI6g0+nwox/9CGtra1hbW+Npbnp6Op5++mnOZRgeHuZpcazMBXJeI2opidK/+93v4uLFi5wzEQqF2K1LLBbjxRdfhEQiQVZWFjsVud1ubNu2Da+88gonMefl5cHj8fB7SmN20p7Eoj243W4cOXIEExMTcLvdPBkletStnP3l5WWkpqZicXERb7/9Nk9nyIL8lVdewdatWzlkMSUlBXa7HdeuXUNLSws3QmTvTaLzjdbCwgKHSjkcDjidTs7ToXwK0kwtLi4yZ93j8eDQoUNITk7G1atXMTMzw7SqhIQEBINBrK+vIxAIoLi4GN/4xjfwsY99jJPeBQIBXn755ZjodzQaRUVFBdvgikQi3LhxA9u3b4dSqURVVRU6OjrYsnx+fh51dXXw+/2MzGZnZ/Okvby8HDabjdPF29vbIZFIsHPnTjidTvT19bEYmpzyNlqBQABZWVlM1zIYDBxC+eqrr2JoaAhJSUnQaDQcqgaAufykaZmYmEA4HMY///M/QygU8v2iUqlgNpvx/PPPo66uDjdv3uT7/tVXX4VEItlwb/Pz8zCbzZybRMFoR48eRWdnJwoKCjA3N4fx8XHEx8ezQ53NZsOxY8cgk8lgMplw4MABXL58GXq9HmfPnkViYiK7hp0+fRplZWUfurOi0WhMkTrwwfm7uLjIbpb08++//36srq4y/YqayPPnz8NsNiMcDmPHjh1YXV3F2NgYfD4fgsEgPv/5z6O9vZ2b6/X1deTm5qK3txf5+fnsThQKhdihcqNFJh7EAAkEApiamsKuXbswNDQEo9HIwcYHDhzg+2ZkZATbt2/H/Pw8+vr6UF9fj+eeew4VFRV45plnkJ2djUgkgvfeew8lJSWwWCxsNkHURqoNN2o0yImSJqxOpxP9/f3Q6/W4efMmhEIhTpw4gdTUVNx7770cX0DueC6Xi51Ku7q6cPnyZVRWVrIOLSsri8NTzWYzXnnlFX4W1Wp1TNMG0mOQHigcDnOWBH3m09PT/OeJiQl4vV40NDTwHdLa2ors7GycOXOG3QZJZzQ2NsaW7KR70Gg0cLlcuHr1asx3obu7m5PnSbsVFxeHlJQUdHd3w263s943KysLs7OzyMzMxMWLF7F161bWX4TDYfz85z9Hfn4+a5FXVlZw6dIlrK+v48SJE8jPz2fjEpVKhe7ubgD4/2+jIZfLeWoglUo52OzkyZPYvHkzmpqaWDhFFz5Zd5GoMCsri1Mzn3rqKdx///0QCoWYnZ2Fx+NhH+2zZ8/yJUVFdW5u7oZ7I56lXq+HWCzmERRRQpKTkyGTyeDz+eD3+zE8PIyqqipcu3aNg3WSk5PZ/nF5eZlFxpSvQDxDp9PJRXVcXBzy8/Njvtzk2ED8T7LUoy44GAxykCAVRLOzs9BoNExtoKAjmUyGv/zlL2hqaoJWq2WeK9naUvoxub0oFApOc7/dIscFCvCilGuv14urV69CIBAgISEBXq+X9QgkfA+Hw9BoNEhMTMTMzAwXTHv27IFcLsfExARu3rwJjUbDOhvSTtCLEytcCPjAxpUuH+CD8EM6dCnhnZoajUaDlZUVKJVKOJ1OpkSpVCr09/fDZrNh3759nDhK1D4SUCoUCua00/e+0QoGg+wuRLkUMzMzaGtrQ05ODuRyOU/37HY7ysrKcO7cOUxOTvLBm5aWhsbGRvT396O3t5cR6dzcXMzPz+O5555Db28vEhMT4Xa70d3dzc1PrHeBEqCpQCDf+OvXr2P37t1ITk5GRkYGpqencfnyZaysrKCuro45++SiU1dXx6YCZrOZLSsBsEsMCcnpkJ+ZmYHBYIj5nRJvNRKJYHl5GYFAgKehjY2N3OzOzc3B5/OxpaJSqcTVq1ehVqvZFSMjIwMTExPMgRaLxRCLxRgfH+fkXAIE4uPjWSC30SKxJwkXk5KSEAwGYbPZ+HumMTtl7pCz2uTkJADwGTQ3N8fBTOR64vF4sLy8jPz8fPj9fpjNZiiVShY8xiqYpVIpn7vk3DU9PY0f//jHTJGcnJxkPRIFffb398PpdGJ0dJQzgqqrqzE0NISsrCzOH5mYmEBfXx+LrJOSknhK9fdcgORyOTo6OvizXlpa4sJ269atiI+Ph9Pp5Is4JycHzc3N+Kd/+idYrVaUl5czN1ssFrOrFDnpOZ1OSCQSnqhXVlZidHQUQqEQRUVFGBwc3HBv5CBH5xD9HvPz81AoFEhLS8PCwgIXdySQpcwTQn9vNXdobGxEYWEhZmZmMDQ0BKFQyJP0iYkJtq/2eDwx6Uk0YSchPwnJyUVNIBAwXfH69evweDzYunUrO+yR7sZgMOC9995DOBxmd67V1VUkJSUhPT2dCyqFQsHaFqVSGbPplkqlnNZOYBc50VCqvVQq5dCzTZs2oaSkBN/+9rfZ1QsAmx0QUl1UVISJiQncuHGDnceUSiXfW+QqFitxmBznVldX2YVvamoKFy5cwJEjR7BlyxZ0dHTAarUygDI1NQWNRoNz584xvYYK4YGBAX7eKQn+xRdfxMTEBA4fPoyOjg7Mzc1BLBYjJycn5p0KgC2yExIS2H2OGlnSMi4tLSEcDqOkpARlZWUYGhrC5cuXIZPJkJ2dDYlEwq6b+fn5kEqlXLAHAgEsLy9/yDmJBNCxzpDV1VXO3srIyADwwVn+7rvvoqqqClarlfUtV69exdatW7G8vAyZTMZhs0ajEdFoFD6fj3WtcrkcTqcTQ0NDuH79Oj7xiU/g4MGDuHHjBtrb2xGJRNDY2BjzvqfpJYmTiVJEGsDGxkYEAgFMTEzgjTfegMfjwf333880fkL/7733Xs6aIe0aAQNisZhzpvLy8jA0NITFv0UrxNJSCQQC/v7pHBYKhWhra0NHRweysrK4iRaJRCgqKkIwGMT09DRns0QiES7SSVdSVlaG+Ph4DAwMwO/3c3FPjodk+R7rXaAJoUKhYAv6UCjEU6rCwkKsrq5iYWEBDocDXV1d3HDRmaPX61FZWcl5SmR+QWL6qakpVFdXo6ysDO+++y4mJyeZ6fP3AnppfWSNBoV+uN1ueL1eFtzk5ubCYrFw6EdSUhLMZvOHumeiVhBvjS7XWwOlTCYT9u7di6WlJXaQoAPObrfHVN4Tn59yKshelZxV6MEl+1GLxcLuJ8D/EfAlJibC5/NBJpNxl0woaGpqKvPAKRiOUNlYxVU0GmUHBeK5KxQK9nCnSUcoFOImJD09nQ+iQCDASOvQ0BCjGqmpqcjMzOTE9dXVVajVar5IiCoQywqVJjjBYJCR6uXlZS5kMzMzGWEQCARYXFxEZWUlRCIRjEYjjEYjO4REo1EUFBR8yJ+bBPDkEkXWgHK5HDqdLiaCSwE0a2trWF9fZ5oNFfKpqamcmZKeng6Xy4Xi4mKeRiiVShQXF6OsrIwRe+KHS6VSPoDov7VarRCLxUhNTWW0MtYymUzMj6TiV6/XY2xsjN0shEIhCgoKIBKJeOy+vLwMgUDArjIUhEMNrEajgdlsxu7duzEyMsINL3EjrVZrTN1NQkICu4eJRCLo9XrEx8ez5R7lNcjlcrYerKurw8LCAjuJOZ1OnowkJibyBUSuOEajEdPT0+z/T5QYKipiLWrKqaiiwk8qlbIDkUwmY8qH0+mE3W7HwsICO0cplUps2rSJzxFqXMhBrby8nF3hSAtCtqax0CESvlORR2eO0WiE3+/n7zknJwc5OTlYXV3lMb9KpYLX68XExARPlMxmMxcEFKRUWFgIlUrFv6NarYZWq2Ux50aL3htyYjEYDIyyUYFGlzM932VlZfD5fMjLy4NOp8Pi4iJb+FJ2CYEwTqcTWq0Wa2tr0Gq1UCgUTDWKNbEFwOc0ZWiQyYBAIIDH40FKSgpbnJMlcjgchlQqRX5+PtPHdDodT96sViuWl5f5u9u0aRN8Ph8AQK1Ww2AwsBtaLI1GKBTiuychIYFNDfLz8+FyuaDRaCASiZhqSGJkEkHTGWQ0GuF0OlFXV8d3H9HY1Go1A28EtJF7HDkX3m7d6uBGNroymQzx8fEsYl9fX+fvk4SuwWCQJ9gCgQDZ2dkQCoXQ6/V8f8lkMkSjUZSUlHADRcULWYXGOn9DoRAUCgU3jVSQFBYWcpMvEon4vqFJht/vZzCBqGDT09PQ6/Vwu92QyWQQiUScUUJucxqNBgqFguuCWM5OYrGYg/NIuyYUCmEymTA+Ps6AET3ngUAAVVVVrJFMSkriBpr0dORYRK6ahYWFbC9KwAMZE8QyvFhZWYFOp+OfT2AcuXfp9XrOGistLeXfl1yqyPzEZDLx9IwmFvQs5Obmwu12Q6PRIC4ujrNHKBdpo7W2tsY6MAoypRqEEu9zc3MZaBCLxaioqMD8/DykUinb9JOluFAo5AwQoVDIGrvBwUGux7RaLTs2xXoXhELhh/LJKIsiNTWVm93ExEQkJCRwRALZsxYVFUGr1fKzV1paioqKCrhcLpjNZtYkpaenc44LPbP051hnCInQCVwmijPRzsl4IxKJ8ER53759DGAC4CaEXFApRFIqlSIpKQkVFRXsGEggJr3Dsdy6KAeDfnfKQ9Lr9QyQ0QQzIyMDNpsNKpWKzVHIKCQhIQFyuZyplWTTLBaLkZ6ezhM4OtdpQh3LcfXW9ZEbjaWlJW4gyKYxEAjg8OHDjBYRZYn4iUqlEnNzczyyJx3E2NgYHnroIR77kUdxeXk5X8bLy8s8ESGr1I0WBcssLy8zlSgxMRH79+/nfdEXl56eztMLAIzkDQ4OMgqalpaGxb8FWcXFxcHhcCA3N5cfRplMBqlUimg0yrZ/Gy0qRqjYI7rP5s2boVAoGHksLy/Hli1bmCLldrvZ2jYYDDKthr50sskk7Qp18VTgkqtCLGSeQoQI3afcim3btqG0tJRzA5KSkjh9mbiAmZmZUKlUTGsymUyoqKjA9evXMTMzA6FQiJycHCQlJWFqaootA61WK1ssxvKHJu7/0tISFhcXmWNbVVWFiooKfs5qa2tRXl7OTkEulwtSqRTJyclQKpVISkqCTCZDXV0dPB4PPB4PFAoFysvLUVVVxT77hDKQviSWDapIJEJFRQVSU1MRHx/P4tCWlhY+zJOSklBUVMQmBnQYZmRkMBJOepDc3Fwu7Px+P9bW1jjdmShiW7duRUtLC4d6bbRIyC8Wi7mxWFlZQU1NDdRqNaanp+FwOJCQkID09HS+bJaXl1FRUYHc3FysrKygp6cHCoUC6enp6OzsZGs/l8uFpqYmPqwISKB8gb8nDqOCj4opKph37doFh8PBTa1CoeAphcViYROGrKwspKeno7i4GAkJCXxWEOd6dnYWZWVlyMjIwKZNmzjrgFyxKE/kdoucgkjrZTKZIJPJUFtbywh3Tk4OamtrsWvXLiQkJECv17P+gUIiV1ZWkJSUhC9/+ctIS0tDYWEhSktLUVxcjMOHD/NZQrkTVFzEajTIyYgEeOT88aUvfYkLYzKKoMKU3u+dO3dyMCbZ2O7cuRPT09MYGxvjgL/du3cjNTUVJpMJiYmJfAlSoxTrmSMNGhXopCOijI2CggLU1NSguroaEokE3d3d2LRpE/bv34+WlhYUFxczhctkMkEul8NgMHAB2tTUhKKiIvT39zOKT1qtWIvQXioStFotkpKS0NzcjISEBJ62lpWVobGxEQsLCygsLOT/LxgMwm6383T36NGjiEQirM2h0FIKqKN3Jjc3FykpKTEdY/R6PXJzc1lcSS5vKSkpXBS53W4kJSXhjjvuQGZmJk9cCgsL2Z1ofX0dFRUVPE2uqKhAdnY2f04EcszNzfHvSwGGGy2Xy8WBrURvTklJYRqwVqtFOBxmANJut6OzsxP5+flMUwwEAti8eTMLgsfGxjA5OQm/3w+VSoWdO3cy4msymZCXl4fMzExu3DZaRNdSKBTcrMnlctx3331oa2tDZ2cnI+5paWn8LE5PT+OOO+7Apk2bGAmmgstisfCdSvd9QUEBJicnEQqFoNVqkZKSgnA4HPNuoFwRmjxSJpFKpWKhNk2cyV2ro6MD4+PjqKur44BVAvaampogEAh44mMwGFBSUsK27AaDAQUFBWzqUlBQsOHe6M4hwbXdbodEIsHHP/5x2O125Obmor6+HjU1NSgrK4NCoUBFRQU8Hg/XBASWUa7RhQsXMDg4yFTnTZs2YXBwEHa7HWazGUeOHMHhw4chl8tjPm909qpUKm6cpFIpA08ENikUCuzcuZMzrjIyMnDvvfdi8+bNCAQCuHjxIjQaDTQaDZKSkpCcnMw1V2pqKk/JyR1Sr9djbW0tZo4GOWZRU7i+vg673Y7KykoUFRWxZjY+Pp5dH9PS0pCeno6qqirodDp2gVSr1UhLS0MgEGD7WbpXLBYLIpEI7HY7g4ThcBhWqzXm3pRKJWsAqZE/cOAAu0RRM3bo0CEGmGg/AoEAs7OznH9DQJDdbmc6/bZt2+B2u3l6m52djcLCQgZrP8r6yI1GcnIyOjo6kJaWBpPJxCj7U089hQcffJADQHw+HywWC6qrq/Hiiy9i69atLGLNyMjAZz7zGSiVSpw4cYKdqsi56a677sIPfvADJCYmcrKs3W7HZz7zmZjOTuvr6+jt7UV5eTmKi4vZ4vXVV19FZmYmOjo6IJPJUFJSgqSkJOzcuRNvvvkmtm7dyu4FlEj7mc98BjqdDl/96lfZvpPclT71qU9xQNfU1BQGBgaYp7fRKisrw+rqKlZWVuDxeBAIBJCfn49IJMLp5FKpFFVVVVhZWYHRaMTrr7+OgwcPQqFQYPv27WhpacHbb7+Nr371q7BarcjMzEQ4HGY3kF//+tfYt28fc+dXVlZ4OkE8utstsVjMHEYKEjx06BBGR0ehUCjQ2trKQXuRSARNTU3o7e3lRG0KJltaWoJEIsGzzz7L+pa4uDg0NTUhOTkZhw4dgsVigUqlwuLiIlO9Yjk9aLVaTsomtw29Xo8f/ehHyMjI4IC5np4eHDt2jAVNR44cYZR2eXkZr732Gv71X/8Vc3NzyMzMZGcUALhw4QIefPBBLCwsICcnBzdv3sT58+fR0NCA3t7eDfcWDodx4sQJdk0iG9ChoSHs2LGDG4z8/HxcvHgRaWlpOHPmDI4ePcqONFVVVQiHw9i+fTv6+vqQk5ODnp4eDA4OIiEhAU8++SQ+/elPM6pJ1DRq5jZaNLHYsWMHMjIyMDk5CafTiZ6eHqZDTU1Ncdrsli1b8Nvf/hYtLS3weDxYWVlBQkICrl+/zjQhSmamKdYzzzyDz33uc9izZw/q6uo4nIyKmliLpm6EhJC3/fDwMLZt24Z3332XUaFXXnkF1dXVEIlE+MpXvoLe3l6cPn0a58+fx3PPPYfq6mp0dHTgvvvuQ3p6Omug/vCHP+Do0aO4fPkyCgoKGNX0er3o7OyMubennnqKx+OUl/DOO++gsbERVqsVly5dwvvvv4/+/n7s378fPT09WFhYgFQqRV1dHWtHpqamcPz4cWi1WnYLqqysxBtvvIFNmzbh2LFj2LRpE5qbm1FVVYWdO3fG1AXR+1laWgqlUomRkRG28G1sbOTJzuTkJK5cuQK1Wo1r165h3759rPuhnBQqYMkhTKVSYdOmTThz5gwnDefk5HCgJGUObbTEYjFcLhcSEhKwsLCA7u5upKWl4fz589DpdOjs7GTaAiWmm81mprh0dnZyQ5yeno7W1lYcPnyY82OqqqowMzODrVu34q233voQAl5fXx9z+iiXy9Hb2wuPx8MuKg0NDXjmmWfw8MMPs0nIwMAATp48iaamJni9XpSWlqK0tBRbt25FY2MjnE4nPv3pTzOoRSg1NZr33nsvBgcHeVplMBhw4MCBmLkGFPRlMBiwtLTEdAYq2oaGhgCAz9sjR45gZmYGRUVFeOutt/jf/+53v4PZbEYwGMSOHTugVCqhUqlQUVHBjkFSqZT58YODg6w/jPWd9vT0MF2GsnXo3Ghvb/+Q+cGOHTvw5ptvcsK2UqlEWloaCgoK8Itf/AJWq5WDZgn1b21tRXV1Nd544w2o1WokJyez0UGsz02lUuHChQswGAxMm1IoFGxIQKLqu+66CykpKXjwwQfx8ssv495772W3n1AohP/6r/+CUCiETqfDgQMHMDg4CIfDgZaWFty8eRP3338/Ll26hMbGRiQmJmJ2dhbJyckxp6IUBEt5En19fVAoFLDZbCyYX15exsLCAvr6+iASiZjOIpPJ2N3L5/PhyJEjGB0dRXl5ORQKBQQCAUKhEP7whz/g4YcfRmtrKwcIkx0paWU2+k4pO8rlcrEJSDAYxL59+3D16lWeLPz1r39lfdLXvvY1WCwWTE1Nwel04rHHHuNz/vDhwwiFQvx9fe5zn8O3v/1t9PT0YOvWrVhbW0NrayuntG+0tFotjEbjh0xHiouL4XA4oNFo0N3dzcyLiYkJ3H333ZBIJJxnlZ+fjzvvvBNerxfhcBihUAiPPPII1Go1099PnTqFcDiMjo4OFBUVQafTQSqVYvPmzTELZmr0qqqqkJOTg2g0iqKiIjz11FMoLS3lKQ8BBkVFRTh16hS2bduGgwcP4uMf/zjuuOMOfl9ef/11BrGIEv/Tn/4U2dnZ6OnpQUZGBqRSKZRKJY4cORIz2oGmSk1NTUwtv+OOO9DW1oZvfOMbbGFLNfaDDz6I06dPIz8/HxaLhSf977zzDhQKBW7cuIGCggKWHqSmpqK7uxtHjx7l7I2lpSWOCOjq6tpwb7euj6zRINvB3t5eSKVSVFZWMg/9xIkTmJ2dZW/5/Px8ftCIG7ywsMAFPVGdurq6oNPpeHzzL//yL3jsscfYSYjQ7Fs5n7dbMpmMC5fk5GQYDAZW7/f3939ImBYIBDj07JVXXuGCtKurC0tLSzh58iTbnI6NjaGnp4fRRnIVmp6eRlZWFtMyYo2P+vv7UVlZienpaRZ0Wa1WrK6uYs+ePaioqMDU1BQ6Ojrgcrm4Wbpw4QIW/5acXlhYiB07dmBychIZGRk4d+4csrKy4PP5MDQ0BJFIhEuXLiEtLQ1KpRJyuZx9xDdKtwaA4eFhpKenczhTYmIiLl++zELT3bt3o6enh33vCR1QqVRYWFjgnI7S0lK0t7ejtrYWeXl5cLvdaG9vZ2HiyMgIMjMzeRS4vLzMvOmNFo3vKOjI5XJxkJXb7Wbh8fr6Omprazmt9/e//z0OHz6MlZUVWCwWNDU14c9//jPrCsrKyuB2u2G1WrF9+3b8/ve/h9/vx/333w+FQoHJyUkMDg6itLR0w70lJycjOzsbAwMDWFtb42C3QCCA3bt34/XXX2dkVyAQYGxsjJ2Uzp8/j4WFBWRkZKClpQVPP/00GhsbYbfbOU18cnISu3btwi9+8QssLi5yim8kEkF6enpMdDkuLg5qtZrFi/QsGAwGjI+PIz8/HwKBgBEpv9+PxsZGzi0wm83IyclBXl4eX+BxcXEsLI2Pj8c777zD1C9KGc3NzeXpVqy1uLiIuLg4bvYUCgXrfZaWllBRUcF0okgkglOnTrFoc9OmTdBoNIiPj4fVasWVK1cgEAhw7do1DmYjqiAV9OfOnYNKpWLP+lhcXKfTia997Wssrl1cXER3dzfuvfdeAGCHGqPRiB07djCl02q14umnn/7/2Hvv6LbP8178A2ISiwAIgAC4956iSG1Zy7LleDtx7NhJM3vb9JfGSZM0vW3SJLcjtyO3SZq2SZrhDMdO7Nqx7HjLlrUoiXsTJEiAAAGCIPaevz90n6dyjwj7ntM/9Z7TkyZHol4C3+/7Ps/n+Qx29Tlw4ACqqqowOjqK7u5uLm4o34MCRc+cOcMpsQKBAA6HY8e9VVVVoaKigh2IyI7w5Zdfxt69ewFcM4wYHBzE0NAQa2ay2SwSiQQUCgV0Oh08Hg8EAgHOnz8Pk8kEiUSCUCjEttavvfYaJylTRgNlRxT73Gpra1FXV8d5IefOncNDDz0Eh8OBoaEhjI+P82f3yiuvoLGxEdvb22hqakI4HMabb74JrVaLpaUlHDlyBIuLizwVj8fjmJ+fx7lz5/Cxj30My8vLUCgUSCQSGB0dLXrG6XQ69Pb2YnV1ldN2X375ZQiFQjz99NPIZrOcgzEwMIAXX3wRdXV1SKfTcDgcGB0dhUwmw4kTJ/BP//RPiMfjuP/++2E2m7G9vY3nnnsOjY2NaG1tZR0AnVkrKytFp4+Li4vQ6/VYWVmBTqdDR0cHZmdncfHiRQDXENjZ2Vm+L65cuYLdu3fjV7/6FTc6uVwOJ0+ehEQiwcjICGZmZlBSUoJcLsfoaEtLC4aHh1mvQ5PUzc3NHfdGKC5pDH0+HzY2Nlh/VldXh3w+z/fzK6+8AqPRyEYJ9PO//e1vM6JLzwhZWD/77LM4fvw4kskkXn/9dWi1Wm6EA4HAjntzOBzo6+uDx+NhsxeHw8FIv1qtRmlpKVubEhhCZxxRLMmtcGVlhSlgY2NjCAQCaGxsxOuvv476+noUCgUOVNzY2Cg6tbXb7ejs7MTGxgZSqRSi0SgcDgeampqwsrKC2tpavPTSS7Db7ejp6eE64u2332aKC7EXDhw4wM0AUWQo14b0gT6fDy6Xi5H5YsyPeDyO3bt3I5VKsY5LKpXiX/7lX3D48GHU1tYiFApBq9Xi05/+NC5fvozf/OY3WFpaQjab5YZyz549mJycxMWLF5ldQCYi3/jGN/C9730PgUAA7e3tWFhYwNTUFEZHRzE8PLzj3gjQIro6OWFGIhF4PB7EYjGMjIxAIpGgpaWFDXyeffZZyOVyNqGgXInu7m5mL1CoKblgVVZW4uLFi6xZicViRcEK+ryWl5cZlF1aWkJXVxcymQxToNPpNGpqanhiePnyZQ4vJVD1+PHjuOWWWxCNRuH3+zE+Po61tTW+dyKRCBwOB7a2tljEXoy66nQ60dfXh6tXr0Iul2NoaAhXr16FXq/HD3/4Qw5LpZDqn//85xCLxZidnWWXwfr6etx+++144403IJVKmQWztLSEiYkJ9Pf3Y2Zmht99YpsQveq9rPfcaMjlcnz9619nXUA8HmcUUS6X4/bbb2cKDh3a//Iv/8Jx9zqdjukVuVwOg4OD/CVRAb6wsIDe3l7uLgmhJH70TstoNOLll19GfX0900CIa2YwGNj2K5lMvkMnQWF1hNqUlpYiHA6zwv7o0aM4deoUxsbG8Pzzz+Puu+9GMplkpwOya3u3vS0sLHDOQygUQjAYxJkzZ9iSbnV1FVarlb2mz549C7FYjEKhAI/Hwxz+ra0tPP3002y/KRKJUFtbC5lMhs7OTrYntVqtCIVC0Ol08Pv9O+5Nq9WydiEYDMJms2F7exsLCwu49dZbOSgoEonglVdewbFjx9De3s48P+I5arVaKJVKvPnmmyxmolwAQiREIhFmZ2f5IJHJZLj33nt33Jvf7+egMKVSyQURjffm5uYwNzeHcDiM5eVlHmMqFApMTU1xoB9NW7q7u5HL5fjQLCkpwfT0NJLJJI4fP44f/vCHzGO8Xqdyo0Xp1oQyUyr5b3/7W3R0dMBsNsNqtcJutwO4xvFvamrCxYsXMTo6yvQC2ufIyAhaW1v5OSfXnk9+8pM4f/48j6FpslSMYiORSPDUU0+hvr4ejY2NPIo+d+4crl69isbGRjgcDjgcDqyuruL222/nlPD3v//93Nw3NDTgpZde4gnJ7//+78Pv9+PnP/85J79TgCJRCD0eD/bv37/j3gCw1qhQKKC2thY1NTWw2Wx49tln0dnZiXw+z025UqnEXXfdhbm5Obz00ktsryiVSlFfX8+oPlGVUqkUNjc34XK5YDKZWIhJqCJlJ+y0PB4Pp05bLBaYTCbEYjH8zd/8DW699VZ0dnZCKBRicXERY2NjqK2txUMPPYSDBw9Co9HA7XYjHA6jtbWVAxIPHTrEacqTk5N44YUXUFdXh+XlZQwPD7PYMZ1O44477thxb1qtFo8//jgGBwfR2NjINq2NjY2w2+1Ip9PsokR+9JQATBoTsjR+++238cwzz+CDH/wg7rvvPkilUrZdJHML4rCTrqlYuGYoFMLCwgJOnjzJ520mk8FvfvMb/h6IeieRSNiVhvzmibteWVkJn8+HkZERHDhwAHq9HhqNBi6XCwqFAp2dnWxIQYFujz76KAwGw457SyaTuHTpErRaLcxmMwvffT4fBgcHEQwG+d2jYrmpqYktlA0GAxoaGvDNb34TQ0ND8Pv9mJ+fh0gkYu0UOdlRDgcVs/R57rQMBgMmJiYwPDzMNAlC+oVCIYaGhhjFnp6e5hRpjUaDRCKBZDIJrVaLPXv2sPNYQ0MD2tvb+Tl1OBwoFAqcnEyJyDKZrChSKpfLOdWY6CIejwcmkwkTExO44447OOBTIBBgfn4ebW1tCIfDLBQHrjV6mUwGy8vLTBX5r+GfpM0iDZjBYMCxY8d23JtCocDVq1dZQ0WauOeee46d9ba3tyGVSjmIjfRJpIehu+/06dN45plncPjwYQwMDECn0yEajSIajUIikeDjH/84nn32WSwuLkKn07Hmb6e1d+9ets2tqKhARUUF0uk0W0VXVFSwbi4cDuM3v/kNDh48iN27d2NqaortQh966CG8/PLL6OjoYFOPQqGA1dVV/PrXv4ZareaGiezQQ6EQ1tfXd9ybTCbD9PQ04vE4jEYj6urqGGhKpVJwOBwIhUKIx+MQCARob2/nTBISs09NTTFltKamBmfPnsWJEycgEolw6dIlZlB86lOfwuXLl2Gz2WA0Gnk6utMiJ6iysjKm9BYKBfzkJz+BQqHg0Ni1tTV2l7x06RIqKiqwtbWF5eVllJaW4q677sLq6iomJiaYgisWi/nfjkajEIlE2LdvH0ZHR+F2u1FZWVkUvKOmlYKS6c+vrq7iueeeg8/nYzr99vY2axvy+TxWV1eZDhkKhfDLX/4S9fX1MBgMfCYqlUpMTU2xgJ0iAogWVUyT2d3dDZvNhkwmA4/HA7vdDovFgn/6p3/Co48+ihMnTmBiYgLnz5/H9vY2+vr68JGPfARbW1v8TsTjcYyMjDCg6XQ6UVtbi5aWFsRiMWxsbLBJAt0zRLUvpvO6fr1n6pTT6cQtt9zCgr2enh7Y7XacPHmSC3dCNKurqzE/P4+jR49CJBKxx3FDQwMeeughLkjdbjcLbVKpFO68805uCMi3vL+/H5lMBm+88UbRvZ04cQImkwmNjY3Yu3cv4vE42tvbIZfL0d/fz0jtLbfcAofDwYjs0NAQtFotcrkchoaGEIvFcPToUUgkEp4OlJeX4wMf+ACWl5eZbkSCrtHR0aKold1u58yDdDoNpVKJaDSKo0ePQqFQoLe3F8PDw3j/+9+PL33pS/D5fLjrrrsQDofR1NSEaDSKyclJdHV1IZfL4Uc/+hG/6CaTCUKhEBaLhS+akpISDA0N4eTJkyzu3WmRbsXpdCIej8NsNkMqlWJ4eJiFXZTa+/u///ssIJ6ZmWHaGzkw2Ww23HbbbVheXma3BIvFgkceeQR+v58R2aGhIezZs4fpXTstctEgfr5EIkEikYDBYGC6hUajwcGDBzEwMICpqSk4HA52JKEpmUgkwuTkJCOnarUaGo0GMpkMt956K4trm5ub0dHRwQL4Yhxhl8vFbj0SiQRarRYejwd/9md/BrvdztaNu3btwu233w6Px4OamhoIBALccccd0Ol0GB0dBXCtEHrggQdgMBhQV1fHzVl/fz/eeustTt+1WCw8/Ss2uif3plwuh+XlZaytrbGOJxwOo6Kigrmjhw8fxoULFzAwMIDKykpMTk5iZWWFL+PS0lJ84AMfgNPpxCuvvIIzZ84gk8ngwx/+MGZnZ/G+972PvfzJ0KEYogb8p70tIVYkIB4cHGSkhNLZm5qaYLVaOeTwlltuQWVlJSQSCfbt2wev14s9e/Ygm82iqqqKNUUPP/wwYrEYa4U6OzvR19f3jqT7G62KigocOHAAcrkc0WgUJSUlaG1txZ/8yZ+wO5Zer8fw8DAOHDjA/PSlpSWsrKxwk7OwsICZmRn867/+KxYXFxkxlclk+NKXvgSbzYaPf/zjEIlE7E5DSfPFvlearMzNzcHn86GtrQ09PT08saKwQJpS7tu3D/l8ngMS/X4/u2CdP38ewWAQ09PTWFlZQSwWw8MPPwyHw8FC7Gw2y2LzYoYX9LxMTk5iYmKC6Qt9fX2oqKjg6V5dXR36+/uxtbUFh8OBX//61zAajTCZTLDZbBzw+cEPfhArKyv8b9vtdgwODrLgeGVlBZFIBNvb23jyySd5en2jRY5H5IFPDmTHjx9no45QKIR0Os3GDWQLefjwYQwPD7Pex2q1oqGhgUWuJFilKSmdZ6SNCwaDRamEPp8Phw4dgtPpZKBpeXkZt912G3Q6HSeFt7a24u6772YKLgC0trZCo9EgFouxhebRo0fZCGJzcxORSASPPvoorFYrOjo6UFVVhfvvvx8f+chHUF5eXrTRoOeGhPKUYtzY2IiTJ09Cq9Vy+vvFixcxNzeH9vZ2trCnXButVguhUIgTJ05Ao9Ggq6sLFosFhUIBX/7yl7G6ugqpVMoaSbvdjpGREaysrOy4t2w2i+bmZnZ7IyrmqVOnkEwmcfLkSXR0dKCyshINDQ0YHx/HHXfcwXkbcrmcC6VEIoE/+7M/4/M+FAoxdZBchUi3QzoPar5vtFZXV7kQTafTnK/Q3NzMBhdyuRytra247bbbIBAIoNPpEI/H0dHRAaFQiDfeeAMvvvgi3G43WltbMTU1xXQvoVCIr3/967Db7WhpaWHDFalUWjRrjJ5LAj1SqRTr1wYHByGXy9HR0QGFQsEGM6FQCPv27ePzIRKJoKSkBIcPH8bk5CS0Wi2CwSAb5pSUlOCRRx6BTCbDlStXcPXqVWi1WuzatYudt3ZakUiEaXcikQiZTIZpPGTRTt9Zd3c3213rdDocOHAAJpOJrceXl5dx7NgxGAwG7Nq1i0FYr9fL2UU2mw25XI6fh2LFfCaTQX9/P8RiMQKBACKRCEQiEY4fP450Oo0777wT1dXVkEqlqKmpYaCLQngrKyuRSqXQ09ODfD6Pu+++m41ATCYTKioqeOrW29vLgcxNTU3Y2NgoSiNcW1tDbW0th2Z3dnYim83ii1/8Ik8YKe2ebGgzmQw6OjrQ09PDOrhdu3bBarXi6NGjbC6yvr6OkZERyOVyXLlyBfF4HJcvX0YkEmGNVjER/Tuevff0p3ANKXU4HKipqYFQKITD4UAgEGBHjo2NDWSzWZjNZlRWVmJsbIwFxAKBABqNhsOFyK0onU6jUCiwPzF5xK+trTG6Qd1sMBjccW/UtJSWlvL0QiqVQiAQoLa2FgKBgD3uC4UCOjs74fF42LUGuIaKOBwOSCQS5PN57kCj0ShcLhdKSkp4dEouVuQSVIxbDfxnqnqhUEAsFuPMkba2NtjtdkgkEha9k5Us5WCUl5dz8ub29jacTicXJtFoFNlsFkKhEGaz+R1+/UqlEo2NjUWFRAKBgGkVuVwO+XyeHXBI+G+z2SCTydDY2Ija2lrmYhM6RG47crmcbUYB8CErFAqhUqkYzSKrxa6urqKcTZlMxr7m5CBElI7y8nIer5PgTywWY3p6Gk1NTYz4x2IxWK1WdqpqampiXjuN6M1mMxYWFlBVVcUuNmSJuNNSKpW4fPkyAPDUxGKxwGazYc+ePfD7/ezMRZZza2tr8Pv9fCCQmxf9Z1VVFXw+H2KxGF96DQ0NGB0dZbvDYDDIn+dOixxbSEgfjUah0Wig0+k4mI0E3BqNBt3d3TyVIR0LoYLl5eWMNhINgOyJu7u7EY/HoVKpEAqFmHdd7FAEwO8RiYxp4kSuHcFgkItBCitbWVlBQ0MDC/vJLYxAgoaGBh6zi0QijI6OcmNHqF86neZparHP7nq75+tD8ORyOZtaUKBVJBKB0+nkDArSsNCkwefzobW1lSl2lGFQWlqK2dlZiEQipjKRM9ROixxKyGEtmUxic3OTReFkNqFQKGAwGOByubC6usqBZhaLhd/z3t5e+Hw+NDY2IhQKwefzIZlMwuPxsKaOfPkpnKrYiFwmk/GkSKfTsUiQELmSkhLmoJMTHjXpJESngl+r1SISifBZSWeszWbjgDYAXHxIJBKeDt1oCQQCPuslEgmfrdlsFvX19eyIR1N00geSe1NpaSmfkzU1NTzhpT0SnY1SiOkZIRF8sXuLDFWud9jr6uqC2+1GfX091tbWUFFRgebmZuTzeezbtw9bW1ucGUV7plR6Cr9cW1tDMBjk57murg7JZJILUuKTF3sX6C4RiURsEkIaO8rgSKfTfIfSNCcej0Oj0bBZCn2OUqmUKW6BQOAdttM2mw1qtRr5fB4lJSUwGo1FaV302ev1esRiMXi9XqhUKkQiEXR0dLDFs0gkQjAYZO2SWCzmppySzEtKSlBbW8tBsGQP6/F4cPToUYjFYi4aiUJTrNGg94RypshUJhaLQSAQoKSkBAKBAPF4HLFYDN3d3bBarTxBoYLX4XCgo6ODQSwyeaGf0dHRwRNMyoC4/jm+0aKzgGh31AhfuHABBoMBcrmcJyf0DFy+fBkqlQp2ux2lpaVMCxwcHOR6gUABoVDIAB4Z8KjVajZdKFYnERhBJjb0HFFzQZQxCtk7fPgwUx5LS0tZR3bx4kU2h7FYLFheXuYzmyj7lKZ+vRtdMdCT3k+tVotkMon19XUOrTaZTFhdXcX6+jpUKhWqq6u5niWrWMowyufzMBgM2NzchFQqRSQSgVAohMlkYhOi+fl5trmlWqQYeEeW02VlZZw/QlNYq9XKocZmsxmFQoHBW6KN0cR0bW0NHR0dmJ+f5+yiaDQKnU7HGUfnz5/ns5vseIs1j9ev9zzRkEqljPJR0EsqleJu1+l0spVgJBJhaglthg4RohGtrq7ymJJ80v1+P7LZLLt8bG1tYX19nVHjHX+J/1t0UmiJw+FALpdjT/Lt7W32BJ6bm4PZbGZawfXFOl3g2WyWx0gbGxuIRCKw2+2M0pD4l9woioW9SKVS/n3oYtFoNPD5fFAoFFheXmbLYJvNBqFQyP9GPB6HVCpFeXk526GdP3+eqQ3EY83lcujo6OAD2Ov1cuNVrGAGrh3axL2ny4ocH7a3t7G1tcUBjUKhkLtYCnkhTnx1dTXb8tEFFwqFYLVaUVFRAavVytoMn88Hi8UCq9W6477IJpNG9yUlJZxerVKpWJjodrsRjUZhNpvZao6oUUSTqK+v5/Rc4jUnEglOIiW+KzWppaWlRYWSlAtALhR04S4uLrL1YCQS4QKeRqP5fJ4LS6IMymQyzM/Pc/FAhbzH40FtbS0HFpIFM4CiKAI5zFCmBFk+08FALkOE3NXV1cHhcLBjFF2UFFp35swZaLVaBgookHBgYAA2m431HpSYWswYgRZx76mRpPFtaWkppy3L5XJEIhGUlpa+4zwgWsry8jJzlcvLy+HxeHgisLKy8g7XsUwmw5kgxfjV14MUZEdJAYI6nQ7JZBKrq6uw2+1s77iyssIonMViQWNjIzweDwwGAxs9kDucTCZjPvXk5CSUSiVbNJJOY6dFRZNareZE4PX1dd4boYB0flG+TSqVYpvMiooKzM7OcvgaTXzJanhtbY0Fublcjv36CRTaaRGoQzbUpAfxer1cbF5PBSUHF8rb8fv9/DwEg0GMj48z7ZPc9NxuN9NDKHCM6KHvVsxHIhEuwOi8S6fT7KZFUzVqjEhMTbai9LnqdDp2bEsmk2w5arPZUFpaikAgwKGl1DSRJe+NFmn8CDi4HnHX6/VYW1tjauLKygpUKhUXbFarlS1ql5eXkcvlmG5Kk4/S0lKMjIygsbER4XAY4XAYDoeDG69ie7veHjqZTCKRSDBNMRQKccOj0+lQUVHBdqJ0Xul0Om5m4vE4h3B6PB62Nw6Hw9wAmkwm6PV6lJeXs7Nise+U7FJpGrC1tYW5uTkWbVOhR2AUCeCXl5fZdICcLUkgTUsikWBjYwMtLS083XO73UxDK0adIjc5AlDpHiBTFYoB2Nrawvz8PBoaGvhO3NjY4AKZrL+dTidnsVATZLVaufinBoie8WJ7IztTel/pfh8ZGeH6hBoOt9vN9DmimROAefbsWZjNZkgkEhgMBgSDQYRCIYhEIrZfl8lkaGho4CkAWaPutMjRkCyjk8kkFAoF3/VKpRJGoxFisRher5cnyQRQ02fv8/mgVqu5iVpeXuacCnrXpqenOQKBssOKuXPS3om2S0ADpb7Pzs6y22A6nUZ1dTW/B3TfX+88dfnyZa5F0uk06zUMBgO2t7c59JFqxWKAgFwu57sTuEYRLBQKiMfj/I7RGUL3jMPh4KwOijdYX19HdXU107np+SWQhRokYsgkk0l+9t7Les8TjUQiwZxW8h2mkRn9MtRtWq1WFj4qlUr2QydxKoWbffKTn0QwGGQ7L61Wi9HRUYjFYuh0OrZznJ6eLhpaIhaLUVVVha2tLR7h+f1+mM1mFvMlk0n4fD74/X5sb28zAkPjQJPJBK1Wi5dffhkHDhxAKpXi/VMeCKGidJkTalHswyZ+YDQa5eA+olH4fD6IRCLEYjHY7XZEIhF0dnaioaEBTqcTpaWlrM0wGAwwGo3cAJBwjtA02gu5NNF39m6heNSAUVFGUfSU9k4hZhcuXEBPTw/rZujlrqysZJceSo8PBAIIBoPM7yNHHXqhiVO7trZWdF/xeJwLtEKhwNOf1dVVKBQKVFRU8KFE7i9Xr17lYDN67rxeL86ePcu6ItKj5PN5nDp1ClVVVVhfX2fUKZPJFJ0EbW5uorOzk4u7XC7HvHNqwEmLQ4dTOp1GRUUFi/RDoRBnpaytrTESXVZWxtPCSCSC/v5+rKys8GFDYV7FFgmACVmhJo/MGYjfTe5gXq+XrVnpwHc6nTCZTAiHw4zWkOaBCkBChVQqFYRCIaLRaFFAALg2fST70+tzCqLRKE8K6EIOBoOsuxoZGeG/S25nJDik3BNC+/r6+hCLxSASificIs/8YsUV7Z847Pl8HhcvXsTu3bthMpkwPT2NpaUlLo4oC4DOmHQ6DbfbjbGxMXYKCgaDaGxshFgsxvr6OtLpNBKJBDQaDcrLyzkfYWlpCW63G3v27Lnh3ujdcbvdnAdEtox6vR5KpZJBBuAaupnP57G2tsafbTwex9WrV1FTU8Oe/MRzp2ZgdHQUa2tryOfzTGslF7qdllQqRWtrK9bX1zm9vKSkhC/ZSCTCjQqh6QRW0MUlEAig1Wq5aK6vr+cCjHJ5JicnGYghdDmdTr9rroHFYuEGkwCM8vJyLC8vc/ChUCjkyQ1NI8hWXC6Xw2w2Y35+Hk6nk5HmYDCIubk5iEQidHd3898hzjxpy3ZayWSS6Q0UqEUXP/3chYUFBgW8Xi/27dvHuq2WlhZuZra2tmCz2fjfJ9vXhYUFttWkn0+ZPMVQXDKLoAwJKrAHBwf5HaBGjPQGZWVl2NraYgQ+HA7DYDDg4sWL/JwC1xoqOp9WV1fR2tqKcDjMDbnH4yna2KrVagagCEX3+/3IZDKYmJh4B3BG5hg0zXzzzTc5BBe4ZqDgcrkQj8e5caSGf3Z2Fk899RRUKhWUSiWfbcWE6iKRiMEDsnSdnZ1FXV0dysvLmc5JxWQ+n0dlZSXefvttFhxvbm4iFovhwoULmJubw4MPPsharmQyyd81AYJ0B5Gj406L4gdSqRSDQ1evXmV2AdEYvV4vG1bQ3UtZFqlUCufOnYPL5UJlZSVisRjkcjlP7peWlhAOh7GyssK2svS5FJtoUF1FU7dkMgmHw4F9+/bB5/Nxjg0xMC5cuIBcLseiablcjrKyMlgsFly4cIEBA5PJhPX1dSwtLUGtVkOv1/PUhhg17zbRIE0pALbMJWo/6c6Ifj8xMcGNBmkGKbiRqMETExOoq6tDQ0MDFhYW2NAil8sxUEX2x4lEoiiQXSgUYDQa3zH9tVqtbHdLAcx0R1LmRi6XY80raYDm5+eRz+fZrCabzWJsbAyhUAia/xvk6vV6oVQqoVKp4Ha73/EdF1vvudEwmUwsGKHLXiwW480334RMJoNMJuOAp2Qyife97324ePEi3njjDQwODjJv0efzcZrn2NgYI2g2mw0vvfQSR8UTika6j2L+0FqtFouLi2wXRqO/l19+mTMmKAjGaDTymIiCyyhx02g04siRIzhz5gwGBgaYa0xd9ODgIFwuF4Br2R0ikQhNTU1FDx61Wo2JiQl+mEmQ7vF44HQ62b0oGo2isrIS09PTMBqN7GFMjg5WqxUajeYd1C5ysFlZWcHc3BwqKys5eE6r1bIjx06LhIy0fzqMr169iq2tLQwPD/NDRT7eNpsNFRUVGBsbY560SCTC1tYWj7NpekXBRSMjI2hvb4fT6WSaVSqVQmNj4457E4lECAQC0Gq1iMViXAQTwi0UClFdXc0oKBX6ZEtaVlbGFKtkMon+/n709PQwqmIwGNi14p577mEHK5oIFGtsRSIRN7/kbkVIZm1tLXOnaTRK6ZlXr17lzygajWJxcRH19fUsZiOu9djYGNt8RiIRLuypSCtGT6ICjgIbo9EolpaWOCGcCiQaoy4uLkKpVGJsbIwLpEwmg4GBAVy+fBnl5eVYX19HU1MTF41arRbT09Oor6+H3W5HJpPhsX5fX9+OewOuNUGE2lCwWE1NDaxWK2w2G2699VYsLi5icnISoVAIzc3NGB0dhdFoRHNzM2w2GzweDxd+brcbZrMZvb29yGazsFqtLAJMJBLw+Xz8fL/b+J4EehSSRRNRSqomWpnX68X4+DhUKhUX6Pl8Hpubm1zkkQ85JcQSAlYoFHDy5EnI5XKcO3cOpaWl/DkUex/EYjEcDgfKysoQDAYRCARQV1fHxWNdXR2fKRRQ2dbWBpFIhOrqakYKDx06hNnZWWxubnJDR+BMoVDgooyoO+vr6xyctdOiZz2ZTHLDc72N5sDAAKxWK4eMKRQKuFwuCAQC2O12uN1uNDc349ChQ+js7MTU1BSLbwOBAMbHx5FOp9k8giY2VCwUS6clZzVqhImSQVbtRInI5XJwOp1Qq9V80avVam6sRSIRB/wRqGK32xGNRtHT04Px8XE2kCCNDzXCOy2dTsd24UTReuGFF1BaWoq9e/dCq9XCZrPxRI8KCApqW19fRyKRwB133IFXX30Vzz77LOrq6tDc3MyhdEePHmVrWYvFwsDYwsIC+vv7d9ybTCbD+Pg4ysrKuFhRKBRMQert7cXY2BhEIhEOHjyI6elp5svPzMxAIpGgsrISFosF3d3dyGQybF1KlMjR0VGcPHmSwa6pqSmsra3BbDYXve8zmQxcLhfbSlNxvLGxgYGBAT5/7XY7NjY2WHcwPT2ND37wg5zlFQqFUFNTg/X1daZU0TsdjUbR1tbGIuZcLsd3WjHzAaVSyYWZWq1GLpdjy+OxsTHO0qDp6unTp3H8+HHs27cP4+PjbKFLFvjZbBbz8/OcB0I5Fj09PVAoFAzW0fNZrPAjY4za2loGlKqqqqDX6/HEE0/g3nvvZWDK4XBwwX/mzBkMDg7yVHfPnj1wOBxwuVyYm5uD3+9HY2Mjurq6GICSy+UcBEiUpWL3lkAg4AZLo9EwIPDSSy+ht7eX9W/b29vIZrN8RgPXatPt7W3YbDZUVVXB4/EwjbalpQUVFRUMnKXTabhcLgZqMpkMu2LutLRaLTuuUUaVw+GAz+dDU1MTdu3axZMVp9PJ+i6n04n5+XlmMKjVanR1dcHv97+j0STKm9Vq5fOKJpIajeYd07b/umiSSoGtNJman59HKpViAISm8SqViqdet9xyC99V+Xwebreb7wqfzwetVosjR46wre3m5ibq6+u51olGo0X39o7vt/BuEcg31811c91cN9fNdXPdXDfXzXVz3Vz/j+s9azRurpvr5rq5bq6b6+a6uW6um+vmurne67rZaNxcN9fNdXPdXDfXzXVz3Vw31831375uNho31811c91cN9fNdXPdXDfXzXVz/bevm43GzXVz3Vw31811c91cN9fNdXPdXP/t62ajcXPdXDfXzXVz3Vw31811c91cN9d/+7rZaNxcN9fNdXPdXDfXzXVz3Vw31831377ec47Gd7/7XSiVSuRyOZSXl0Oj0WBjYwNWqxX9/f2cNyESiVAoFBAKhRCNRlFeXo7f/va30Ol02LVrF+dA1NTUwOfzcUrmxsYG0uk0amtrYbFY2M/a7/cjnU4jFovh85///A339r3vfY8j7KVSKRQKBRQKBebn51FXVweFQoGtrS1kMhkolUr4/X7OpIjFYhzxLhaLOaDues9kSi+nTAaKlhcIBBw893d/93c33Nu3v/1tZDIZyGQylJaWckhRKpVCbW0thoeH4Xa7MTMzA7/fjyNHjkCn08Fms8Hr9aK+vh6NjY3IZrMYGRlBLpdDW1sbh3eFQiHs2rULVqsV3d3dmJiY4FTRZDKJ6upqPPjggzfc2/e//30A10KslEolxGIxhy01NTVxgJ3f74fP58OePXs49GVycpJzLZLJJNra2lBeXs4BU5TkCQDLy8uwWCzsXR2NRhEKhSAUCvG5z31ux+dNr9dzQJlGo4FUKsX29jZyuRxqamqQyWTg8XiwtbUFi8UCgUDAIV7b29tIp9MwGo1QKpW4cuUKpFIp5HI5ysvLodVq4XQ6IRAIOATMbDZDr9dztsCf//mf33Bvv/zlLyGVSqHT6Th5OJPJwO12Q6/Xo7q6mkPzcrkcBAIBGhoasLy8DK/XC6lUCq1WyzkGFouFU5MFAgGUSiUmJydRVVWF2tpaXLp0CSKRCCaTiZOuT506dcO9/frXv+Y0UUp6p4yLWCwGtVrNeQJyuRxVVVX8fdjtdk7n3dzc5FCz3bt3I5fLIRAI8LtDeTAUikWp8QBw++2333BvAPDcc89hc3MTpaWlUKlUkMlkyGQyWFlZQUVFBRQKBXw+H+dRWCwW1NfXY3V1lVN+FQoFJ5H7fD6oVCro9Xr296b8BovFgng8DqFQyCmwAoEAH/vYx264t7/6q79CNBpFU1MTp8uur6+/I6RNoVAgHo9zkNzKygoqKythMBg4ZXhqagp1dXWQSCQoKytDPB5HIpEAACgUCva6dzgcSCQSkEgkUCgUEAgE+MIXvnDDvf3whz/k0D36jlwuFyYmJtDc3IyGhgYAgNfrhd/vx8DAANbW1rC1tQWTyQSdTodcLsehl8C13BBKJKaMi9raWg4ti8fjHHCqVCrxP//n/7zh3h5//HEA4GdCoVDA7/dzsntTUxPcbjenjC8vL3OIaW9vLwfuaTQabG5u4oUXXoBUKoVGo+FU4Gw2i1QqhZKSEqTTafaYl0qlWFpawp/8yZ/ccG9/+7d/C6FQCKFQCAAcDGu32zlXgoJYAaCqqgpGoxFvv/026uvrIRKJEI1G+T0vLS3l8ESxWAyJRAKv14uGhgaUlZVhfn6e85z8fj+i0Si+9rWv3XBvX/3qV/l3uT7ZNxQKQaVSoba2FolEgsM9gWsBhBRQq9VqOZeGwlYzmQz6+vqg0Wj4fkin09Dr9dje3uYg3Wg0inA4zHfAf13f+c53oNfrORSOsjfkcjkA8PNESe0UGkbnBCXRG41GeL1e/nzr6uo4zDGVSkGlUqGpqQl2ux1LS0tIJBKcufDVr371hnv7x3/8R2g0Gg4KLRQKnMWhUChQV1eHeDzOmUmU0EwBY3TXUYYBhc4pFAp+TkpLS1FWVoZoNIpIJAKv18shoMlkcsd765lnnuHgP4VCwTkFVBc1NDRga2sLsVgMWq0WYrEY29vb8Pv9UKvVEAgEKC0tRXV1NaLRKIRCIZxOJwDwc1xeXo4rV66gubkZhUKBM3v0ej0ikQg+/elP33BvP/rRj/j31mg0kMlkWFxcBAD09fUhl8vBZrMhEonAYDCgqqoKq6urEAgEkMvlXFfQvdzW1gaPx8MZPZRQHggEOBVdKBRyeJ7b7cZnP/vZG+7tl7/8JWQyGWeHUOoCZVNlMhl+/8vKyvicWV1dRWVlJfx+PzY2NlBeXs51hkwm41yKSCSC1tZWzM/PI51Oc85MPp9HY2MjotEoHnrooRvu7cknn4RYLOazmjLebDYbmpqauBaORCL8uer1ely+fBmtra0QCoXY2tpCJBLhPBTKYaMAXrVajcXFRXR3d3P9IRQKOUft/e9//w339rvf/Y6zydRqNYRCIYdgV1RUcL4d5WtQFkwkEoHD4eA7KpPJcO1Bn18sFuPcnfr6es7TiUajnJ2Vz+d3rC+vX++50chms5BKpVhfX0coFEJ5eTkSiQT6+voQCAQ4XCkSiUAsFsNkMsHj8cBut6OxsZEDetRqNYxGI4xGI6xWK/R6PWpqalBVVQW/3w+v14v5+XnIZDIuqjY3N4sG0WxtbcFoNPKhKpPJoNPpkM1muUigBoNSR0UiEcLhMJRKJYRCITcZkUgEVVVVmJubQy6Xg1gsht/vR6FQgFQqhUwmQzgchlqthlQqhcvlQknJzoOhTCaDuro6+P1+ToKWyWQoLy/HxsYGFhYWEAwGUVJSgpqaGqysrHDRqlarEQ6HMTExgUQiwRfK8vIyB+1FIhHEYjFEo1GMj4/D7XZz4UdJyjuteDyOlpYWTtwEriWlejweBINBTrLW6XTQ6XSYnJxEdXU1qqqqUFlZiZWVFXg8HiiVSk7dzmQyUKlUUKvVyGazcDgcnPAdjUYhEomQy+UQj8eLhuJtb29DIpEgHo9DoVBArVbzZUXBPmq1GhKJBHq9HsFgkF9Yo9GIzc1NbG9vo7a2lhMzE4kEvF4vfy4ymYx/vlwuh0qlQklJCXw+H1+oN1rJZBICgYAvrtLSUvj9flgsFm5cC4UC1Go1lEolrl69yn/W5XLxhVFRUYH19XW0trZiaWkJgUAAyWQSIpEIra2t2NjYeEd6ciAQQDgcRjKZLLq3kpISZLNZZLNZTqJOJpNQqVTI5/O8P2oiKeRRJpNBIpGgpKQEMpmMw5MorCiTyXCSNyVsU2MWDAYhkUiKJm8D1wph+jtOpxNCoRBmsxlSqRSxWIzfMSoUkskknE4nAoEAysvL+bmVy+UIBALQaDQc3EXNCz1rlOpMTVY4HOaQpxutaDSKQqHAqfb0blHoGiUw53I5SKVSZDIZ5PN5BINByOVyRCIRuN1uLnBMJhMWFxchFotRKBT4opRKpfwMy2QyfpYUCsWOe6NgwHA4zO+30+mEVCpFPp+H0+nky8loNOLy5ctoamrC4uIiNw1UlK2trXGitEqlgkqlAgA0Nzfj7NmzaGtrg1gs5pCy0tLSou/qxsYG5HI5QqEQxGIxh0wpFAo4nU4GeaLRKDc1lKxeVVXFTaBOp4Pb7UY8HkdZWRn0ej2EQiE3OtlsFslkEqFQiJ81CondadEzfH3SdCgUQkVFBT+rlMJLQBAlBi8sLPDnk06nYbVa0dDQwN+vUCiEQqFAS0sLvF4v4vE4N/YEMBQ7f6mQpPMbABcC8XgcXq8X2WyWzxRKqKbEXwJZhEIhAoEA/wyPxwOXy8UNfSQS4eDJ0tJSfnY1Gs2OeyPQIBKJYHNzk0Md6fOi9wm4FmgWiURgsVg4VTiZTGJjYwNLS0vo7e2FUCjE4uIiSktLEY/HYbVa0dPTw2nE9HsolUpEIpGiae+xWIzvBrlcDplMhu3tbQZA6BmjsMrl5WWUlZVhcnISwWAQdXV1UKlU8Pl8EIvFKC8vx/z8PNRqNUpKSpDP59HR0YHZ2VkOJJNIJEgmk0XPXgAMfCSTSUQiEf7f6cyl70ihUEAmk/HdTyBoKpVigIrud7lczs89nZe0qHkvKSnhxPtinxudHXTfSyQSrKyscOglnUkmkwlXrlxBoVDgn0nPq06nw9LSEvx+P9xuNyQSCYPB1dXV2N7e5saTfjc6A3ZaoVCIQ0MpNV6hUHBAIQEU6XQanZ2dSCQSHM5Hf4/eI/q3Kc0+HA4z4EHPcSKR4O+bAll3WolEAiKRiMNsqcim70ir1UIqlSIajb6jtqC081wux6BnNpuF1+vlzzUajWJzcxMajQaBQIADCfV6PUpKSrCyssKp5Dda4XCY741sNvsO0G97exvV1dXI5/MQCASQSCRYXV2Fw+HglHs6r+g+0ev12NjY4LOwUCigpaUFVqsVXV1dUKvVDD4R8PVe1numTtGUQiAQQCwWQyAQIJfLYWpqiicTVNy6XC5sbGygtbWVP/xMJoPS0lI0NDQgn89jfHycCwuFQgGpVMpJ3IT6pVIp/t8EAsGOe6MvnZBivV4PqVQKp9OJYDDIBzah/LlcDslkkpO6w+EwwuEwMpkMX8gCgYCTovV6PbLZLEQiEerr61FdXQ0AnMhYrAkSCoVwuVyIRCLIZrMQCoV8YVByql6vh16vh8lk4kO3rKwMfr+fEdVAIIDm5mYuRoVCIdRqNTQaDU+OVCoVysrKkE6nkUqlUFFRUbRAyGaz2Nra4s+AEiLpJaW0yWw2C7/fzymnQqGQY+ylUinEYjGUSiUuXboEn88HpVLJ6LTf74dUKuX9JpNJRq2KPaR0OKXTaeTzeT4I1tfXIZFIGFkMBAKYm5tDKBSCTqdDRUUFvF4votEoVCoVKisrUVJSwheRVqtlJD6fz0OpVKK8vBw6nQ6pVIqbrmI5liUlJXz5ZzIZbki9Xi9fgMFgkA/MeDwOsVgMsVjM70goFOJ0bkpJNhqNMBgMEIlE8Pl8/HsSWk7NJqFuN1qUyhyLxSAUCiGXy5HP57mJTyaTkEqlKCkp4fdZpVLxXqkpEQgEjBKmUilsb28jEonwIU3vNQD+70Kh8B0F3U7fK/18SsVOJBKIx+OMlmg0Gp5qEpKdTqfhdrvhcrk4lRS4BjLE43F+P0UiEbxeL/L5PFKpFOLxOAKBAAKBAAqFAk/ZbrQIYbr+PBAIBDCZTIxi0ffp8XiQTCZhMpkQj8extrYGp9PJ6dgajYYvf+Ba0rJSqcTa2ho8Hg+/u+l0mqe21FDeaOXzeb6EqAjN5XJwu92MtEqlUi7istks7HY7mpubsbW1BavViq2tLcjlcmg0GmxtbUEgEPBnbTAYuIH0+XwAwO+qVCot2qCJRCIkEgmUlpby70VAE51FlES8sLAAvV6P3t5eBjXC4TAkEgkKhQIjrFVVVaipqeHUbpvNxgWHUqlENBqFw+GA1+t9V6CHvkeFQsFNQyAQQC6Xg16vh0gkQiAQwMbGBuLxONxuNyekBwIBxONxTu8lVBC4dg5Q80cAV2VlJSQSCU8hijUa1yc901lLqd9UDAoEAiQSCVitVr4/9Xo9HA4HnE4n8vk8ZDIZ1tbWIBQKYTAYIJPJ+Izw+XwoLS1FPp+HTqdjBoFYLC5azFNRTInv+Xyem3b6DEUiETKZDNbW1vi8FQqFmJ2dxcbGBqRSKSQSCbLZLFwuFzeOBFZQ0jkVfzRBe7cmSKFQ8H2aSCQQDAbfMXmigisQCGB0dBTAtSKZ0sYJ9FMoFPyZ0Pkai8WQTCaxsrICh8OBra0thMNhPu/prttp5XI5pFIpBkrpnrJarZxoLhQKIZFI4PF4eFJFzVkymWRGhkAggNPp5Akk1TAEBNFnRMhzMBjkydiNFk3hAPDPIOZIKBRCJpPhemRjY4PrgHw+D+Ba4j1N/7VaLZaXl7kJvb5YTSQSKCsrg1arhUQiQSKRYLR/p0XfY0lJCb+npaWl2NzcxPLyMk+/iNVA56VarYbdbkcoFILJZEJVVRXMZjMmJiaQTqd5qkZnVF1dHbMtKA2bpvc7LbFYjGQyyeCHRqPhqfrW1hbXgZlMBl6vF263G6lUihvvcDgMqVTKdefU1BQymQx0Oh3q6upQV1fH5xgBUZFIBCsrK1hdXS3aaKRSKa6viZlwfco6AUYulwuTk5N8FkokEkQiEfh8Ph4OaLVaLC4uIh6PIxwOIx6PI5/PY3NzE2tra1hfX+eamM41nU63496uX++50SBUkJB/hUIBiUSCiYkJeL1eqNVqWCwWaDQaJBIJrK+vo7OzE+Xl5bzhyspKDA8PAwAjtSKRCKFQCFarlUeAHR0dqK2tZeSFxm87LUK7RSIRDAYDamtrIZPJEAqFuOCgw5IuBuDaeLSkpATRaBSBQADZbJY/YLlcDqPRiJqaGtTV1UEkEiGdTsNgMHDxHgqF3vXDLikpwfr6OiMFRNmSy+Wor69HTU0NKisrodfrodPp+JAxmUwIhUIIhUKQSqW8j0wmA5FIBKPRCLPZjNLSUmQyGcjlcjQ3N6O2tpYPt5qamqKNRqFQgN1u54I/lUpha2sLhUKBi07gWsGxubkJo9GIqqoqxONxRqDq6urQ2NgIoVCIS5cuMapJzZ9arYZKpeLfjcaYJpPpHYjPf100lqMLMxaLwefz8XSrs7MTBoMBwWAQ09PTyOVyaG1thVKp5AbFYDBwQ+P3+6HVatHS0sKfCVGt6AAMhUJczBUrXqgJlcvljICLxWKsr69z45xMJuF2u7nob2lpgUKh4HEmXWgKhQLj4+OMZFZXV3OzJBaLoVAo+IAjZKuiomLHvdEBQtQiogMQzY8OGUKABAIBKisrGelMp9NMJ6EJBjXkdLhTcwCAx6gAuEkvtmQyGbxeLzKZDIxGI6qrqxmkyGazPBLXaDTQ6XQoFApMVbHb7XA6nVz8EPpFvzedUcFgkP+9SCSCYDCIaDSKbDaLurq6op8dUXNogkuN6fUNQy6X49+hpaUFKpWKizKBQICqqip+pqgw1ul0KC8vh8/nw+rqKmpqaqDRaLi5EYvFRc8Roibkcjm+uAmcCYfDMJvNqKqqYoqLyWSC1+tFRUUFAwWxWIwvY6FQCK1Wy81QWVkZFwwSiYQnLUS/u/4z/a+Lmiq1Wo2Kigro9XoUCgX4/X6UlJRwkU70B71ej+7ublRVVfH5XllZicrKSj7vLBYLU3RFIhE3LV1dXdDr9cjn81wAFntXC4UCAzxEjyHKTy6X4yIkFAox6kpNcGVlJWQyGZLJJLRaLaqqqlBSUgKVSgWz2Qyz2Qy5XI5gMIhkMsmTerFYzBOxYu+DVCpFNpvlZkGn03GxnsvloFQquVjz+/1IpVIoLy+HXC5ngIjAla2tLQbZKisrUVNTA6PRiFQqBY1Gg/LyclRUVDD9VygUFkWYaZKSSCQgFothMBj43YxGo5BKpSgvL4dIJOIGjQpwq9UKr9eL8vJy9Pb2IhgMIp/Pw2QyMd1MpVLB4XBw0WcwGCCRSHhPxQo/uVz+DlQ5kUjwZ5LJZGAwGGCxWJDP57G0tAShUAi/34+hoSGeoMViMRgMBmg0Gm5s1Go1UzNpmgqAqY9UZBWrRYiaRGcvAXPxeJxBFqKyUKEnkUhQXV3NoB5RKWnqTNNmtVqNsrIy5PP5d0wiiV5HDXuxd6GsrIz/DQJQqdnK5/MwGAxQKBR8z5rNZt4DNT82m43fo3g8jtLSUlRVVcFisTA9j4BGambD4XBRkCebzSIajSKXy6GsrAwmk4nBZa/Xi2AwCLPZDJPJxJQnsViMkpISpihqtVr09PTAYDAgHA5DKBSit7cXQ0NDMBgMKBQKMBgM0Ol00Ov1SKVSDHS829lL94HBYOAmRSAQIJ1O875pck0/k85jqk2DwSA3dYVCgc+RqqoqbG9vM1VYpVIhGAzC5XIhFosVbWyBa3cwgXX0zBJNmKQKGxsbmJubg1QqZUpoLBbD1tYWT5g1Gg1WVlb472ezWcjlcqyvryMYDGJtbe0dtHihUIiqqqqie6P1nhsNoVAIi8WCZDKJ9fV1RizuvPNO5gRXVlZykSwSiSCXy6HVanH8+HFUVFTA5XIhkUjA4XDg4MGDSCQSsNlsmJ2dhdvtxtDQEP/SKysrkEgkqKqqQjabRXNz8457I0RieXkZk5OTWFhYwNzcHAYHB6HT6TAwMADgGv9VrVZjbm4OEokE0WgUZWVl3KkbjUYsLi5CIpEgGAxCr9ejtLQUTqeTR+lbW1u4evUqYrEYysvLkc/nodfrd9zb9vY2BgYGGDWvqKhALBZDbW0tJiYmMDExgcnJSaajxeNxDAwMwOl0oqWlBe3t7WhqasK+fftw6dIl9Pb2wu/389hqbGwMhUIB58+f52JQq9WirKwMa2trePPNN3f+8ktKYDabIZPJ0NzcjOHhYTgcDtTU1GB5eRlmsxkKhQJarZb1NRqNBiMjI6isrITJZIJWq8WBAwdw4cIFdHZ2wmw2QywWY2FhAefOncPw8DAEAgHq6+sxPj4Oj8fDCCWN7260aGRnNBqh0WiQz+dx6dIlVFRUwOfz8cGhVqtx5513Mv3m1VdfRVVVFY8lFQoFXC4X+vr6eFxJvGqdTsf8YaKs0QVX7DIhpEQsFnMT43a7UVlZiVwuh5aWFpSXlyOVSiGfz2NtbY0PK6PRCLVazb/jxsYGSkpKMDY2Bo/Hw4Xz4cOHsbS0BKvVilAohOrqanR3d0MmkxWldWk0GtTV1SGbzXIBFAgEYDQaYbFYmDJCz/fExARSqRSqqqqwa9cuWCwWRKNR1iYdOnQIDocD5eXlMJvNyOfz6OzsZCohUcI0Gg2mpqbedZRKiPD29jZfGACwZ88eVFVVob+/H4lEAjMzMwgEAojFYnA4HNje3uZCR6vVorOzEx6PBwcOHODRdSwWg9fr5XOqvLycOb10eS4vL++4t0gkwohyKpWCUChEY2MjVCoVlpaWUCgUeMJDU9Pa2lr09fVhYGCAKRMVFRXY3t6GwWDA2toaVCoVNBoNwuEw2tvbsbi4CK/XC5/Ph7a2Nhw4cIB50DutXC6H2tpaRspJ37Nnzx5Gw4PBIAqFAurq6uB0OnHo0CH86Ec/QmVlJaqrq1EoFGA2m7G4uIj9+/fD6/Xi8uXLmJmZQT6fR0tLC9bW1nD06FGkUimo1Wo0NzdDLpcXfR8CgQBEIhHcbjdWV1exuLiIF198ESdOnABwDa2tqqpCd3c3br31VoyNjSGVSiEcDnOxSlSXlpYWprkSAkj8aoVCgZ6eHrhcLkYEo9EoT7dutIh3TEAFUSl6enqwubnJjU13dzfuvfdeqNVq3HbbbVhdXUVbWxt6enogk8n4AqbJfldXF2pqarC9vY3BwUE4nU6k02msr68jm82irKyM0eedltfrRV1dHeRyOZRKJVQqFdbW1tDV1YWtrS00NTXBZDKhpaUF73vf+zA+Po6lpSWsra1hYGAAtbW1SKVSXBAqFArYbDak0+l30EOIorawsAClUon29nb4/f4d9wWA7xg6w4jO2NDQgDfeeIPBvLKyMhw+fBjZbBYmkwmJRAJ33XUX2tvbEQqFUFZWhq2tLdx///04e/YslpaWWJNQX1/PzUehUMAtt9yC+++/n4ujnZbH44Fer2ctgFarZbqv3+/nwsxkMuHEiROw2+2QyWTw+Xxob29HS0sLNBoNa+EaGxuxtrYGi8XCjcjhw4chEAjQ0tLCuh6j0ch0yZ0WUYJXVlYwNTXFFLaHH34YyWQS3d3dDBjs2rULU1NTMJvNiMVi6O/vh9FoxNLSErxeLxYWFjA4OIh4PA6j0Yi6ujqIxWJmPNCzaLFY0NfXx7VMsb2VlJTA6XRifX0d29vbWF5exvve9z5mEIyMjMDtdvO5Qs+/QCDg36W7uxuTk5M4cuQISktLGRRMp9OslayqqmJaFVGZi9FqiWpIkxSxWIzl5WXccccdaG1tZQo90SXp3llcXMTevXshl8tx5coVBiU++clPYnNzkwEhQuwvX74MlUqFzc1NlJWVsfZudXV1x715vV6oVCpuoPP5PM6ePYsjR46gtrYWlZWV0Gq1aGhowP79+5FOp7nxa2pqgkwmw/r6Ok88Tp48yZSyzc1NTE5Ocl1DGrnq6moMDw+jtra2KCCbTqd56imXy1FRUYFUKoWenh4olUpuaCUSCVpaWnjaduHCBQbBgGuN/dLSEvbv38+UPalUCrPZjAMHDkClUqGjowPr6+tIJpPMpCkGQF2/3nOjIZPJUCgUcPToUdx+++1oampCNBrF9PQ07r33XuZ+ud1uuN1uHDp0CJFIBL29vfja176G0dFR9PT0IBgM4tixY/ja174Gs9kMkUiEiooKnDhxAqlUCn/4h38Ig8GAnp4ebGxswGaz4ciRI7h8+fKOe6uoqEAgEMC9996L+++/H21tbWhoaMDk5CQOHz6Mv/qrv4LNZmMe4mc+8xkA1wRQqVSKCwaFQoGBgQH86le/wsDAANxuNyYmJhAOh7G+vo6PfvSjPJmYn5/HzMwMOjs74Xa7d9zbvn37cOXKFbjdbiwuLvKLPTs7i0996lOs8fD5fHjyySfxqU99Ck899RS6u7vx+uuvIxwOo7u7G1tbW3jggQewvr6Ow4cPo6mpCfv378fv/d7vcfceiURw6623oqenB2KxGJ2dnUWboObmZni9Xn5oNjc38anGv3IzAAEAAElEQVRPfQrRaBSVlZX8n7W1tdjY2GDu8Pr6OtxuNyPdV69exe233w6dTof7778fFRUVSCaTiMVieOqpp1jk2dvbi/3792NwcBC9vb0wGAw77k2v1+P8+fPQ6/Xo6OhAe3s7PvjBD6JQKKCqqgo2mw06nQ4WiwUbGxswGAzc8NbX16O3txd79uxhJOipp56CwWDgw3phYQGXLl3Cnj178Nxzz6GmpgZlZWXQ6XT4+Mc/XpQvX11dzc1WdXU1j20JDX3yyScxMTEBl8uFixcv4sSJE2hvb0dnZyc3XX19fZibm0NlZSX279+P9vZ2RKNRqNVq9PX14bXXXkNvby+LJUOhEHw+H2699daiNEKi9vT29sJkMsHlcjGdYs+ePdjc3EQmk4Fer4dWq8Vtt93Gl8vp06chFApx6623QqVSobu7G+Pj46ivr+cLhRq3Rx99lOmRSqUScrkcd9xxx7s2GjQd6O7uRm1tLQQCAXQ6HZ566ikoFAqcPn2akU6RSISOjg4oFAp0dXVBpVJheHgYx48fh0qlQldXF37605/CYDBwwUli3qGhIUZeCC3es2dP0b1R0UearpWVFdjtdpw/fx633347mwcQJ/z48eOYnZ1FKpXCW2+9hXw+jz179jD95hvf+AYeeOABlJSU8PuztbWF/fv348knn4REIsHy8jKmpqbQ19eH+vr6HfdWVVWF9fV17Nu3DwcPHkRpaSmi0Siefvpp3H333UxfC4fDsNlsrAcCrqGJZWVlkEgk+PGPf4xYLIbvfve7XJyKxWKk02l84xvfwNe//nWmx5w/fx4vvPAC65t2WkqlElNTU2hoaEBbWxvUajWOHj2KtbU11uNQYXTp0iU8+OCDePrpp6HX67G4uMh0y3A4jK6uLjacWFpaYj2aUCjEH//xH2NqagpGo5FpC4cOHSr6PlRXV8NutzOSCQCDg4NIJpPYv38/xsbGMDU1xVSypqYmnD59mikZFosFR48eRTabxaFDhxgAoglkV1cXvvnNb2L//v2YmppioWcymcQ999xTlDplNBrh8/nQ0NAArVaLaDSKAwcOYGZmBg8//DAWFxfZvGJrawv/63/9L5w7dw7r6+tIpVKorq7G7t270d/fj0ceeQR2ux179uxBSUkJJicnceXKFWxubuKTn/wknn32WWSzWQQCAbjd7qITAwDcnHd1dcFgMHBBu7y8jPvuuw8CgYDv3R//+Mf4wAc+wEYDIyMjSKVS6O/vh0wmw/79+/HVr34Ve/fuRSgUQjAYxNDQEAMGkUgE6XQaFy9exMjICGuidlotLS0oKyvD8PAwWltbIRKJYDab0dTUhDvvvBNbW1vY2tqC2+1mE5C3334bgUAAFy9eRKFQwNDQENRqNaampjA+Po6jR4/C4/HAZrNBJpPhiSeewMmTJzEzM4N4PI5z587h4sWL6OvrK3rG0bR6cHAQra2tyOVykMvleOWVV1jLsrq6irm5ObjdbgwODjKFhyiPKpUKV65cwdDQEFZWVhCLxXg6qNfrMTY2hqGhIczOzkIul8PhcGB1dRWDg4NFi3mtVguhUIi+vj50dnaiuroap06dgtVqhVgs5mfL7XZjZGQEnZ2dTO1aXV2FQqFAf38/1Go1PvShD+HKlSs4fPgwPB4P3nrrLSwsLOCVV17B3XffjbfffpsBr3Q6jYcffrgorYuMB4ht4PP5UF1dDa/XC5lMhra2NnR3d6OrqwttbW0oKyvD2NgY0xsPHTqEP/zDPwRwrRH97Gc/i6GhITQ2NqK+vh7Dw8NYW1tDfX095ufnmers9/uxd+/eHc1VgGsTNGLaqFQqhEIhHDp0CEtLS2hvb0cmk8HIyAhOnz6NCxcusFi/pqYGyWSSNRcEGgSDQRw+fBharZanRWNjYzh27BguXbrEND2a7hQDUmjSbTKZIJFI4Ha7EQwG8cQTT8BoNLLWV61WMzDX2NiIjY0NvP3228hms2htbWVq+draGr9/Pp8PgUAATz/9NO666y6mxF25cgUXL17E/v37i07Q3rHP9/SnAObYTU9Ps84hm81icHAQp0+f5oOLLiSv14uenh7YbDb89V//NQKBABwOB9bW1jA9PY2GhgZ2KUkkEjhz5gx+97vf4Qtf+ALi8ThcLhdPR9bX12GxWHbc2+LiIjQaDebm5niMSIK5qakpPProo4jH48wzz+Vy8Hg8MBqN6OvrQyQSweLiIn77299Cq9XiueeeQ19fH0wmE3w+H3w+H1pbWxGJRBiNrqioQDQaxfLyctER9OzsLJqamqDValnk/f3vfx9DQ0NwOBy4++67MT09jUgkgj179uB//+//jUQigcnJSajVai7sjUYjFyTPPfccGhoaUF1dDalUit7eXjzzzDPYv38//uRP/gRtbW0wm834j//4j6LFi8vlegfyFgwG8R//8R9obm7G4uIiCoUCJicnWeMSCATw5ptvorm5GWNjY9wE5HI5vPDCC3j00UfhcDiwubkJtVqNI0eOQK/Xw2az4Xe/+x2/OGVlZSwq22mRa8LKygpzNy9duoT3ve99CAaDEAqF+O1vf8vfTSKRwOnTp9He3g6lUon19XWsrq7izJkzmJqa4maYhKfV1dWorq6GQCBAX18fi7Tcbjf8fn/RRmNpaQk9PT3siiaTyVhQXV5eDplMxrx24hxfuXKFaWnEoR4eHmaHlsnJSXR3dyMYDGJpaQktLS2YmJiA0WjE6uoq/14XL15EW1vbjnvz+/2orKxkR6NYLIaVlRU88sgjrG8gdxZyZtu1axfW19fZRcTtdsNoNGJ0dBTt7e3weDyMqhDli8aw5EoRiUTewSfeaRFnm5Am0nZQ0dXZ2YmJiQmEQiHU1NTAbrdDJBJha2sL/f39CIVCeOmll6BWq+FyuSCVSjExMQGTycQUyng8DplMxlMdGlWTRmintbGxwdNMmlqIRCJoNBosLy+jt7cXVquVaXhPPfUU/H4/mpub0dXVhWg0irm5OZSXl6O0tBQPPvggfvrTnyKZTMJisaC5uRmtra1YWFjAyZMnoVQq2WXrrbfe4qL2Rmt2dhbNzc3w+XyMIpnNZnz961/Hj3/8YwwMDPBoXiqVQqlU8kVIXP7y8nK0trZienoaJ06cwBtvvAGNRoOamhpGvSYmJnD27Fns2rULe/bsYTCptrZ2x88tEong4MGDKCsrQywWg9vtxttvv43m5mZEIhGUl5fjxRdfxNraGgwGA+x2OzeGNDE7e/YspFIpVldXcfz4ceY5E+Xn5ZdfxiuvvMIIe01NDe+t2DPn9XpRXV2NZDLJfGmPx4Pu7m74fD50dHTwhfrqq69iz549PIUEAJvNBqPRiEOHDsFms6G7uxujo6PMPb948SJ27dqFeDyO/fv348qVK+wKNjk5WRT9jkajTEOUSqUQCAR4++23YTAYMDY2hmw2i2eeeQZCoRDt7e0YHR2FwWBgmhEJsq1WK1588UW0tbWxSFcsFuP2229HaWkpXn31VUYo6ewh6slOi6gYXq+X3ZPeeustpvF6PB5GaA0GA1599VUEg0EYDAZ8+MMf5omo3+/H2toaHnnkETYYIQOF1dVVNv1QKBQwGo0QCASYnJxELBbb0c1mYWGBGQFEA5NKpTh27Bhef/11dlwjh79EIoGuri6srq6itLSUUVyVSoXe3l4MDAxgdHQUDoeD6TEajQYTExPMyZfJZFhdXcWTTz7JWo8bLTIECIfD/N37/X40NTUx4k+aIJlMxmBOKpXCfffdB4fDgdnZWVRWVuLq1au46667mJIbCoXgdrvR19eHX/7yl0gkEmhubkZdXR0CgQBOnz6NQqGAO++884Z7W1tbQ0VFBQQCASYmJmCz2dDY2Ih8Po9du3bh1KlTTIMTiURM3YnFYtizZw9GR0fxi1/8Ag0NDUwhu3TpEnp6eiCRSLCxsYG9e/fizJkzUKvVGB0dZR0INUwPPPDADfcWCATQ0NDANPvrzSJoKn3lyhXYbDaUl5dDqVTiP/7jP2A2m7GwsMBNeWVlJUZGRvD888/jwoULvAeZTIb6+np85zvfgdls5gltMpnE4uIirFbrjm6E4XAYzc3NCIVCiEQiDMrSs9rc3IzKykqEw2Gsrq5i//790Gg0mJmZweTkJBvq1NTUYHZ2Fn/8x3+MtbU1VFdXM9OltrYWdrudWRWxWAzpdJonPTuttbU1HDp0CMFgEBsbG6yV+tjHPsbvL02FaUrkdDrR1NTEtK6rV6/yZ2Q2m/H6669Do9Hw1P7BBx9kRziFQoHq6mqEw2FcunSpqJb1+vX/JAZ/9tlnMTU1hUQigfLyctTU1ODy5cvQ6/XYu3cvJBIJC8FdLhf//z/96U+xurqKzs5Odj6pq6tjoQnx2BcXF/nDpZHrysoKxGJx0RGNRCLB/Pw8W8a2t7dDKpVifHwco6OjzIMkPh8J5KjA83g8qKiogE6ng9PpxLe//W1oNBpUVlZiYGAAJpMJTz/9NCYmJhj1GBsbw+rqKlN4dlokIKXxNtEIFhcXYbPZcOnSJaytrcFms+HVV1+FxWLBY489htXVVYyOjuKtt97CW2+9hWg0itXVVXzuc59ja1Uq/H/yk5/gL//yL2E0GtHT0wO3242pqSl0dnYWFXEmEgnY7XZMT09jYWGBbe0WFhZYUwBcK/rJNrerqwupVAqTk5Ms9M7n81Cr1XjzzTdhs9mY5z49PY3Z2VkoFAr8wR/8AR/QJNor1mjQOJ3s+9xuN5qbm/Hnf/7nWFxcRENDA+6++24MDg6ymHJoaAhra2v44Q9/CL/fj3vuuQe33XYbW7gmk0m0tLSgq6sL8XgczzzzDE6fPo3Dhw/DZDIxn7pQKBRFrdRqNbxeL9bX11nQJ5VK4ff7MTMzg6qqKohEIoyPj+PFF19kzQQhRUQLCofDeP7553Hp0iV0dnZieXkZ4+PjTMHR6/UYHBxEZWUlVldXsbCwwBf1TkuhUDAKR7xyk8mEM2fOIBAIsC4pkUgglUoxHYq4t36/H3q9HvX19ejo6MD8/DxzZgmRO3PmDI+FS0pKWJiq1+tx66237rg34D81GgqFAq2treju7kZ5eTmbHKyvr7N2hpoEoqmdPn0aMzMzUCgUSCQSrJ8gMSk5E7311ltsPkD/V15eDoPBUFSsTudBNBqFwWBAZWUlAGB8fBwCgYB56uFwGHNzc4hGo7jnnnsQjUaxsrKCeDwOi8WCzs5ONDQ04MqVK9i3bx9OnDiBhoYGeL1evPrqq6yrWlxchMvlYi1TMf0I6YEAcGE9MzODv//7v0cgEEBlZSVzb+fm5rC2tobf//3fh0qlwsWLFxGNRrFnzx4cPnwYzc3NuHz5Mqqrq6HRaPh5v3DhAjweDxobG5nOd/78eaYd7bRCoRAuXryI9fV1FkWbzWacPXsWoVAIVVVVaG5uZqefQCCAW265BX6/Hy+99BIKhQLuvfdeHD9+HC0tLfjZz36Gubk5NDY2oq2tDdvb2zh9+jRefvllANfQdqPRyM5jxSaj9JxTo9XQ0ACpVIrLly9jdXUV+/btw+7du1lfOD4+jv7+ft7rysoKn9UikQgjIyNoamqCxWJh/dnq6ipisRiLSmdnZ/HCCy8gmUwWbdDIdpumw3K5HFKpFL/5zW8gEolw8uRJ1NXVweVy4Y033kAgEGDU+pVXXsHMzAzrEjs7O3Hp0iUoFArcddddOHjwIMLhMAMcBw8eZOEoNbjFKBkGgwGzs7PsJmSz2bC9vc12rKTFdLvdyGaz2LNnD9rb22G1WvHSSy/h9ddfx8zMDFKpFBQKBT7/+c9jY2ODRf4lJSXMVyfdCXCNWllWVoa77rprx72VlpZicXER0WiUp10OhwNf+tKXEI1G2RGHzrC9e/fivvvuYycft9uN6elpzM3NweFw4Ac/+AEEAgEGBgbQ1taGdDqNqakpeL1ezM7OQiaTYWtrC+vr6ygrK2Ow8UarvLyc9aZ0hxAqTXx7oveQq9muXbuQzWbx+OOPY25uDgMDAwwkPvfcc/D5fIxwh0IhPP/889jc3ERHRwcAcBHZ2trKRjU3WmVlZXj11VcRCATQ3t6O4eFh5PN5hEIhmM1mtLe3M6AVj8dx9OhR1NXVYWpqCt/5zncwMTGBpqYm7Nq1CzKZDH/1V3/Fxiikz3vttdcwNzfHtqrZbBYajQa33Xbbjk0GAEbTt7a2+L+ThsTtdnNNSEBePp/HAw88gGg0ihdeeAEbGxvo7+/nM+7v/u7vMDc3B6FQiFAohLfeegtPPPEEZDIZ7rvvPqjVatbAJJPJot9paWkpLl68yLpAcrQkkCyfz8NisbBRg9FoRGlpKcrLyyGVSrFr1y68//3vx/DwMG655RY8/vjjuHLlCqxWK1wuF0KhEE+8b731VkSj0XeYnPT39++4N6oD8/k82tvbsXfvXvh8Prz88suYn5+HRqPhc54oeyS2J32YWq1GTU0NlpaWMDU1hba2Ngb7s9kslpeX+T13u92w2+0IBAIoKysrCmRfv95zo0EjlvLychZu5nI5HDlyBPl8nsVgnZ2dOHDgAPt9u1wuNDQ0QC6Xc2csFosxPDzMPFCyR/vEJz6BF154gf24VSoVj/uKcYQBsJaDLC0J0amrq2MqEQC0trbyWJiKFXJfIvuvuro6xGIxFmPT5IZ4/cSXrqmpgdVqLdpokACJMggIvUkmk+wkUlNTg+7ubuzduxdVVVU8yXn44Ydx8OBBKBQKLg5PnTqFuro6Fn4bjUbcfvvtmJycZLRWKpUy1aWYDkIsFqO+vp5pcbSam5vZZpI45wcPHoTH40F9fT2CwSAGBwcZjW1sbGSHmFgshvr6ejQ0NCCXy6GjowMXL16EQCCAVqtFW1sbUw/ezVnkegctcr8gnmEoFMLy8jKLzPx+P+68804u9JLJJBdOhUIBvb29cDqdGB8fRyAQQEtLCwYHB9m9ZH19HcA1lOvdRM0kFk0mk5DJZLBYLGxbbLVakUgk2Hauu7sbHo8Hra2tUKvVGB4ehsVigdvtxvLyMmKxGBobG5FIJNDT0wOLxcLWynNzcwCujbxbW1tRX1/Ph89Oi9BzEnYD18bSZGNssVig0+mgVqthMBjYxSuVSnGhk8vlkM/nuWkidFkoFEImk3FhRwLbhoYGtqcuRrEBwDbAZBjhdruZukjOIq2trTh48CAOHjyIWCyG0tJSBAIB1NbW8jMmkUiwsLCAhoYG1NXVsc93IBCA2Wxm1414PM7TTLvdXpTDTK4rZCVJ51pbWxsymQzMZjMXAFVVVZzBAgCVlZXsl055FYODgzh37hycTicXeUSnCAaDcDqdbAUuEAiKFqUkKATA349MJsOhQ4dgMBig1WqhVqsxMDCAT33qU/B4PAgEAtjc3MTtt9+OlpYWBn/cbjdMJhPkcjn27t0Li8UCh8OBo0ePIpfLsTNLbW0tOjo6YLFYcPz48R33JhAImCZAzlDE3e/q6mL0vLGxEfv27YNMJkNvby9isRgsFgsymQw2NjZQWVmJQCCAO++8kx3XyOHroYceYlemfD6P0tJS5tS/G6pGtCQSy5IuZHt7m6ccZJ27vb3NkxdqZkpKSiCRSHi6sby8DJvNxs8E5RgRfVKn0zHX/91sWmkyQ/bQJSUlaG9vZ2vwsrIydHV1oaGhAdlsFk1NTcjlcmhubkY+n8fKygq7lx06dAhSqRTJZJLF/11dXdje3uYznnIxiG5S7HkjaiO584nFYpSVlXGGjEajQW1tLRobG3HmzBlYLBbIZDIMDg7ye9rU1ASVSoWPfvSjmJqa4jwVgUCAT37yk9jY2IDRaORzgzI7imlb0uk0Ghoa2ESBzoV9+/ahUCiwzooogJcvX8bm5iZrEukZIoH97t27Wc9HzkZ33nknkskkW85XVlbi8OHDMBgMaGpqKvqdUi4BmbaIRCIolUoGzjKZDNRqNZqamjA1NQUA7BpHZjsWi4U1qqFQiIHPiooKDA0NQSKR8PNiMBg4p6LYdC+fz0OhUMButzP1jiayDoeDhfFkUvLaa6+xS53FYoHJZGIK5ubmJv6//+//43c7EolgdnYWRqORQUpirAiFQszNzRUFK3K5HFtWX6/noBrS5/O9Q48kFovR0NCAQCDAJjkTExOsw1UoFGzqYjAY0NjYyM9iaWkpT69Ik/huQDblKhGAZzAYsHfvXs6CIlvb+vp6eDwedpIifSnRR8mKXS6X82SFDHIymQyqq6vR0NDAUxupVIrZ2dkd90Z1FOVmkfFLT08PT9CoeT958iRWV1f5ORscHGRwihxLq6urkcvl0NDQALVajYWFBZ5KdXZ2QqFQoLm5melXJEd4t/WeqVMikYiLbABcxHR0dHARrVQqUV1dDYPBAK/XC+DaIV9eXo50Og2n08me7KWlpTxFIGRl3759zF2+vvPS6/XY3NzccW902EgkElbTK5VKdHd3s4sKAH5IyVWHuODku7+9vY3y8nJ2EaJmSqfTYffu3fB4PCwKVSgUjCwXcwHK5XKIxWJsu1daWvqOsDjaf1lZGQvFl5aWGGEj27hMJoPe3l6srq6itrYW+XweuVyONRS//e1v2ZWCXjCtVouNjY0d9yaXy9kSFABbFmcymXdY7AFg5CiRSKC6upppEZRB0t7ejpmZGchkMv57NBoku1aiadFBW6yYJ+oL/Znri9qKigp2BiJeJzldkXsHUTPIu31zc5OnQkqlEr29vSgtLWVhOrnTUBhWsakBCUwJraPxPPCfDmhlZWUYGBiAVCrFpUuX+KC2WCwsShaJRBgYGGAuKh2C5NLS1NTE3wVZzV4fZrTTIs0CTXrIjppE00TrojwYQtTpOya9DzUVJMamoCRCWWhfVHCTRWixlUgkoNFoIBAIOOtCpVLxVEOtVjNdiSyb6fsk+1Q6HwgVJeGbz+eDUChkWibRP2gcTy41Oy1Cd+h9vV7ALxAIsLy8zO8c2VOTewhNZEjImkwm0dnZibGxMUa+rncVoTwRmjApFAp2sbvRIuMAtVrNloUVFRVoaWlBZWUlm28YjUb+7ux2OzcyhDBubW2hpKSE3ZGIykFFb319PSorK9kGmfb0bvaPjY2NXNySlTPlGJBPPQESZDlK3zHZ6hKdhApmeg5VKhX279+PUCjEAWJkgU4Bijsten7ouU0mk9xQk5icMjAI3LBarXxnAdfOHqKk0flG5w1l5bhcLgbXaGovFAqLFldUANDUm4Su1ERcunSJDUQICJiZmYFareY8HKINVVVVQS6XM8JJU/uuri62T77eiEIoFBYt5um8JdohBeEB17RMVJhp/m92BfCfIEcul+MzRavVwuVy4dSpU2wDTOBdSUkJWltbOS/r+qyqYu8CFb9kaEE2ouTyQ+G/FOipVCqZNuXxeNjJa2ZmBtXV1RzsSc5oiUQCMpkMjY2N6O/vx9TUFGdDbW1tFWUJxGIx6PV6+Hw+dmyjwo5sUun+kMlkcLlcqK+vh06nw+bmJjtMEZhQVVWFmZkZLlApMG5gYOAdgbDkvlUMkJXJZGhqakIgEOAm7HpnpGw2C61Wy2yTyclJDAwM8PNH+V8UjlxbW8uUeMrIIh2C0+mETqdjobJSqSyqZSXHMWpMyOBidnaWrabJGUskEqGpqQkul4unB/QskxMbFcTkeKdSqeByudDT08OGIKRrA1D0O6X3nEwvaJJO3wW9HxQWSLrj6elpCAQCtiTPZDIIBoNQKpWor6/nz4YyPpqbmzk4lcBUsVhctPYlQTcZQADXQC+j0cjmPGSDTnWvUqlEc3MzAzl0b1EcAgG7wLVp2fLyMoaHh/l5Iat1AvLey3rPEw2pVMqWm4QKEnf6wIED7KlNtn7d3d3Y3t5GS0sL7HY7XC4XnE4nnnzySTQ3N2N0dBRdXV1sr9ja2opCoYCPfvSjMJvNUKlU/MsqlUpOx7zRog6RXIpisRgqKirYanRycpK9w8kKVqfTYWFhAWNjYxwA5Xa7sXfvXjgcDkYi5HI5u3/s378fTz/9NDdN5FNPI8wbrVwuB6fTyV7IFDxGjjZerxc2mw02mw1bW1tQqVR47bXXkM1mcfXqVWSzWVRVVcHlcqG1tRVzc3NQKBRYXFzEwsIC/zlyMCBhoVQqRXV1NSP1O31um5ubUKlUqKio4JfmypUr6OrqYivejY0NjIyMwGKx4PLly9i/fz+6urrQ29uLuro6rK6usvA4n89z6mtDQwP8fj9uueUWuFwu5qaShuPdHAt8Ph/bnEokEhgMBqRSKXbCaWlpQVtbG5sHjI2NQSaTsaMJTdv0ej1++9vfory8nJ05qPC4/fbbsbi4iJqaGuRyOYTDYW5CdloUcqNSqbhZIwrP4OAgADACZTAY0NrayhdIKpVCaWkpIyzk2FJRUYGFhQWmuADAhz70If73JicnceHCBQ483GnJ5XKmFFABGQgEMDExwUnD+XweXq+X9R7EhSfnI/IqHxoaQi6Xg1arZcohjVL7+vq4QCBr6Jqamnd1s5HJZEgkEjAYDKiurmZ0kRK5RSIR7HY7VlZWEIlEUFNTw05Vs7OzHNSUTqexa9cuXL16lXMVyL61vLwcTU1NXPDRtKmlpQWBQKDo93q91R9dej6fD7lcDr/61a84AE8mk+HgwYNsDTszM8NBVTSRdDqd+MAHPvCOidf29jYOHjyItra2d1h9a7VaXLlypejeFhcXuQmiRGn6LuLxOAQCAex2O5544gk0NTWxXeWlS5fYVEKv16O2tpYzcMiWUa/XM7pZVlbGziw2mw2xWIyR1xut6wNPKWCxUChgZGSEswOub8oLhQKsViuqqqr44q2srMTY2Bjq6ur4PSIQhKiwfX19OHPmDBfBhOYVs/DWaDQcXFVRUYFIJIJ4PI5UKoW2tjb2w6cCcf/+/WxlSgnO6XQaa2tr6O7uht/vx3333Yeenh4Gm5xOJ7q7uzE7OwvN/w2QJMEpaT12+k4p/4cSpCnfoKSkhKkVJJbWaDQsoie9GTUlg4OD2NraYidEEg4LhUIMDg4il8vx1I/Eo2fPnt1xb2Q/KxAIGBwiPQtpaIizvrW1hXvuuQerq6swGo146aWX4HK5UFtbC6/Xi8nJSWxsbOD+++9HeXk5O/J897vfxX333feOaR1luxS7GwwGA6dAV1RUcLoyubi9+eabDEy4XC7cfvvtTH28fPkyFhYW4Pf7ceHCBTQ0NODcuXOcVUQ6ym9/+9sYHBxkhD4QCGBhYQElJSW4cOHCjnujZomAKAKQ6EwmUDWVSmFpaYkbA41GA5fLxQ3+8vIyI+gymQznz5/HuXPn4Pf7cfnyZezdu5ebFnJX0uv1RRsNsnfv7e1lJse+ffswNjYGo9HI9J3+/n6YTCY0NjYyQEiWuGVlZczo+M1vfoOmpibMz89jZWWFJ3G1tbWsayBqUn9/f9Finu7PmpoaZsBkMhlcvHgRw8PD0Gg0PDlZX19HY2Mjnn32WaZyG41G7N69m8N9Q6EQdu3axfeaQCDA4uIidu/ejatXr6KqqgperxcTExMcvlxskRaIqFKlpaU4d+4cUxB1Oh3MZjPfWRQkSYU9magoFAq+J+bm5nDlyhUoFArs27cPbW1tePHFF5mxQJP7YnQ4rVbLzQ+dG0TT3bNnD1voJhIJ/PrXv0ZVVRXUajU7GAoEAgabyPa+qamJwYOamhq8/PLL3MwTi2V9fR3l5eVF34Xr13ueaKyvrzMfi1Ark8kEm80GsViM5uZm/OAHP0AoFMK9994Lq9WKiooKFugC/xke5PP52NrV4/GgpqYGHR0dcLvdaGlpQUtLCzY3N3m8PTo6WpRPSjSHdDoNnU6H5uZmlJSUIJfL4cUXX2QXAAA4dOgQXC4XWlpakEgk2AkokUjg3nvvxfT0NNbW1vgyKikpQXl5OVpaWjAyMsK2fhaLBX6/H1evXi36cgsEAhw7dgx2ux0Oh4NHqlSc1dfXY2lpiW0Lm5qacNttt+E73/kODAYDbDYb1tfXYbfb8c///M948MEHsby8zIUw5T6Q2w4VY0KhEFNTU0XpSUSFIt40FVpGoxGTk5OQSCRwOByoqqrC7t27cf78eXR1dWF5eZkNAcjeVSwW44Mf/CD+9V//FfPz84w0UINKlqEdHR0wGo1YWVkp6g9NTlJqtRp6vR6JRAKvvfYa01IikQjeeOMNaLVaPProozh//jze//7349///d/h8/l4rPfAAw/g29/+No4cOYKamhqo1WrMz8/DarXi/vvvx9TUFAqFAlwuFzeDZF+80/J6vfwyEupJFrQbGxvY2NjgScrQ0BCuXLmC3t5edszZvXs3NBoNrl69ijfffJMncDU1NQiHwzh79izMZjNeeeUVGI1GDigSCoWwWq1FOekUFNjY2AiRSIR4PI5YLMZIDqFn2WwWoVAICwsLMJvN+PnPf479+/ezbzYhaKQ3obGv1WplLjbxNUksabPZih6KADiskTI5CMkj5y6/349QKMSJ0tTEyeVyNDY2YnNzE5ubm0w/a2xsxPr6OqO8JBqnIo4SWyORCKqqqth7/kYrEolwqqvb7cbKygrkcjluv/12fP/730djYyMXnTKZDJcvX8bY2BjKysrQ2trKeRWbm5twOp3o7+9nO0+aaAaDQfzyl79ksSx5nZOxQLG97d69m4MX8/k8JiYmcPz4cVy6dAlGoxETExMIBALYs2cP1Go1SktL8eyzzzJgQQX5hQsXsGvXLg7MI5oM8cQfeOABXLhwgal2e/fuxUsvvYR77rlnx++UwtOIAiGRSHDs2DEYjUZ4PB7Mzc1heXkZnZ2daGtrg1wu58Avp9OJhoYG9Pf343/+z/+JRx55hDM08vk8VldXUSgU8LOf/Qx1dXWcZCyVSrGwsFB02kKpuxSuRzlK5Bw4Pz/PXvhOpxPb29vo6enBuXPn2Baa8ojOnj2Lrq4upvsF/2+C/MGDB/HKK6+gr6+PaS70/BWbGpABw/VBruFwGHV1ddjc3ER7eztnVpGA+tixY9jY2EBvby+2trb4/HriiSdw6tQpzM/PM82JJnlvvPEGysvLsba2xt9NOBxGS0vLjntzu91MNUokEtja2sL29ja2t7exsrKC3t5exONxzM/Ps60saTZ2796NpqYmhMNh/PM//zPEYjH++I//GPfffz+nYYdCIej1epw+fZpToEtLSxGLxTA6OorOzs4d90YZXHQXBINBuN1u3H///Th9+jQDiiUlJbBYLPjFL36Buro6/m4JoX7sscfwwx/+ECKRCD09Pdja2oLNZkMqlcLdd9+NN954g3NACGhxOBxFgR69Xs8BupQNQU0TGdUsLy9zUU3T4PHxcRb5Ly4uYnp6GhsbG9xI1tXVcVbQ3r17mZZD9Feaho2MjODIkSM33JvL5WIXNqILklkCgVgkXO7q6oJcLmeDgX379sFqteLSpUvMWBkcHIRAIMCpU6cwMjKC3/zmNzh+/DgbBpGdsFgsxk9/+lNsb2/v+LnNzc2xdofCF8+cOYPPfOYzWFpaYhAqHo9jY2MDW1tbuOuuu/Dv//7v3GytrKygs7MT4+PjuP3223H27FlUV1czVVCpVGJiYgLHjh3D+Pg4388mkwkOh2PHvcXjcXYMJFCCwlEvXLiApqYmXLlyhQHrX/ziF3jssccQjUbR3d3NImy32w2NRoNIJMIhgy6XC3NzczwBIct4ornRdG2nRQ09hWaWlJTwpOfNN9+ETqdjIf7dd9/NtMLHH38cdXV1TFlOp9Ow2+0wm82IRqNs3b20tASz2YznnnsOUqmUrcWNRiNaW1uLxidcv97zRMNgMLAlGwWInTlzBq+88gp+8pOfQK/X48477+SD2GKxoLW1FTU1NbDZbIjH47jtttvwF3/xF/B6vaivr8enPvUpfOtb38KnP/1pVFRUYGpqitOyT506hb6+Pmi1WjQ3NxflCKtUKszMzOCtt97iFMkrV67g5ZdfRj6fh9FoxN13342mpia8+OKL0Ol0yGQyaGhogMvlgsfj4TGYx+PB4OAgmpubsXfvXmg0Gpw9exbf+973UFlZib/9279FbW0tJ/I2NzcXFZh2dXXh+eefRygUwv79+3Hs2DFYrVYAwEsvvYTFxUW8/vrreP7552G323Hu3DmmQ1BCo06nw1133YUvfvGL+OxnPwuHwwGVSoWGhgYuKMk2EwCWl5cxPT0Nt9uNAwcO7Lg3QrbVajW6urrQ398PuVyOf//3f8f29jbbg25tbeFnP/sZrFYrbDYbHA4H3nrrLUQiEQwODqKzsxNvvPEGHnvsMTQ2NuLRRx9FW1sbRkdH4ff7cfLkSZw6dQr9/f2Yn5/H888/j0wmU5STXl5ejqmpKYyNjeHSpUssRv/5z3/OlquHDx+GWCzGz372M8zMzOD8+fOwWCxst7q5uYlvfetbGBoawoULF3DhwgXW/jidTnz961+H1+vFsWPHWJQsEolQUlJSdG8mkwlWqxVra2vY2NhANpvFwMAAzpw5g7GxMUilUjz88MM4cuQIZmZmcPvtt+Opp57CzMwMPv/5z+OrX/0qpqen0dPTw5a6hUIBe/bsQVdXFyYnJ/Hnf/7nfEERVUev1+Pw4cOw2Ww77q2yshJ1dXXI5XIwGAyMnp89exbNzc1YWlpiHnhvby9qamogFAqxa9cuPPHEE3jqqacQDofR2NiIsrIyzo4hC+qPfOQjkMvlaG9vR1VVFU6dOgWTyYTt7W3o9fqiNEIAsFgssFqtWFxcRCaTYS73a6+9hl/84heMShqNRthsNm426V0gARtNGQhRXltbY8eYb37zmywErKurQ19fHyorK7mx3mkRVcdsNqO3txfNzc0oFAo4duwYdu3axQYFFGQnlUrx6U9/GjabjadksVgMqVQKq6ur8Pl8EIvFqKioYC7u5cuXOfyKOO7Ehy0WfkT++gSmtLW14cSJE7h8+TILsEnv8Oqrr3Iz/Rd/8Rf467/+aw6BvPPOO1nTsbW1BYvFgsrKSgSDQQQCAZ5wNTY24vjx4zhy5AiMRiM+8YlP7Li32tparK+vs6C0rKwMTqcT3/rWt/DCCy+gtbUVe/bsgVAoxKuvvoqVlRV27Kqvr0cikeBpwMGDB3H+/Hm43W5UVFTw9/aDH/wAH/nIR3DPPfegs7MTOp2OP4dixbxYLOazsLy8HP39/aiuruZMJ9JMxWIxOJ1OKBQKrKys8L9NeUU0MfvKV76Cy5cvs0vS5uYmnnjiCSiVSuzbt48DTYlqVQywqK6uxvz8POsJ3G43vF4vnnvuObz11lvcaFy4cAHf/OY3sbi4yOnLFFZGgZkikQg2mw3hcJjzFa5cuYK/+Iu/QG1tLR588EE0NDSwUQcVZ8UWaTMaGhqYjlhWVobnnnuO8y+Aa1Sq5eVltLS0QCgUsttfOp3Gt7/9bTQ1NfG50NTUhD179vCUQCwW48qVK0x5jUQi6OvrK3pvCYVCeL1e5s7r9XoMDAzg05/+NKqqqvDwww+jpqYGGxsb8Hq9OHToEFpbW+Hz+bCwsACbzQaJRIKTJ0/i8OHDbK1PLpQtLS1coJlMJqZPkptjMcMLCgMsFAqora1FZ2cn59tQ3o5Go8Hs7CxefPFFPtOGh4exuLiIc+fOIZfL4ZFHHsH4+Dh++MMfQi6Xo7W1lbU6ExMTmJ2dZQOA7e1t1NbW8l2y0+rq6sKTTz6JaDSKvr4+HDp0CPX19bh06RKLrB0OB370ox/hs5/9LLxeL+x2O15//XV861vf4swPq9UKh8OBxx9/HN/97neRzWa5YH/jjTeQyWRgMpmQy+Xwk5/8BN/+9rehUCiK8vk1Gg1nbJF7VG9vL775zW9CoVCgr68PpaWlcLvdiEQi8Hq9WF5expe+9CUMDQ2hvr4earWa88SeeOIJLC8vw2QywWQywe1244033mBXwSNHjqClpQWxWAzb29vo6+vbcW96vR5TU1Ooq6vD7t272ap+cnISZrMZWq2WXUUJ6HE6nVygj42NwWKxwGw2w263Y3V1FW63G8ePH8epU6eQSqWY1bBr1y4YDAYsLi5idnYWdXV12Ldv3457A4AXXniBJxGdnZ247bbb3kFnpMk12RiPjo7iyJEjcDqdmJubg0gkQl1dHVKpFKampvCXf/mXuHLlCoNjKpUKfX19PJmpqKiAyWRCaWkpPve5zxXdG6333GjMzs7yL0zFb1dXFx588EHU19czHWbv3r3o6urCyy+/jMbGRkilUjz66KO47bbbOFo9HA5Do9HgwoULeOutt7C5uYnOzk7cc889mJubwyuvvIIf/vCHbKG5tbVVlMoSjUbx4Q9/mCcZNFI3mUwsZCIaVFtbGzs05HI5HD16FL29vSgvL0d7ezu6urrewQWuqanB8PAw/uzP/oxR1qtXryKfz3PQTjFkfmlpCRUVFbBarWyht76+jsceewz19fUoKyuDwWDAyZMn8ZnPfIZFNsTVdbvdePbZZ7GxsQGfz4fnnnsOAFi0c+bMGdx7771ob29nh5Dq6mo0NjayM8BOS6FQoLGxES6XC+fPn8f4+DgA4Ctf+QpfaouLi+zoQC//2toaizpXVlZw4MABDAwM4LOf/SzS6TSnjlZWVuJjH/sYnnvuOTgcDkamKXG52KHocrnQ3d3N41oaw//DP/wDhEIhLly4gHA4jNbWVuzbt4/D8oLBIPNuW1pa8OlPfxoOhwNf+cpX0NzcjO7ubvT29qKyshKnTp1i68jJyUn+vpeXl4sG+CSTSezbt48TbYnX+Nhjj6GqqgpHjhzB0tIS5ubmcOeddyKVSuHYsWN46KGHcPbsWXzgAx/AzMwM0uk0VldXEY1GodPpMDk5iZGREVRVVeHf/u3f2Elpbm4OhUKBub/FXCgoQ4EuPHLbamtrQywWw+7du9mIIBaL4dVXX2Vnpg9+8IPo7e1FoVBATU0NAoEA9u3bxwJicspqb2/Hk08+iUQigd/97ndwOBycBl0MfQEAu93OAUBEUREKhfjSl77ESPfc3BySySTe9773wWq1Qq1WY319HTU1NTCZTDCbzbj77rvZLlGr1aK9vZ2bzK997WuMYNHFQwYPxfYXj8fZ9IH82+12O37+858jHo/j8OHDsFgssFgsuOOOOwBcKyx6e3tx9OhR5r2qVCqk02kOJI1Go0xt+vznP8+0k8XFRT7Ig8FgUQvvUCiEO+64A1KpFGtra2z7+4lPfIKRceBaQ/LlL38ZhUIB99xzD372s5/BbDbDaDTCarViZWUFw8PDaGhogEwmw8LCAk6fPo25uTl88Ytf5MJndXUVS0tLmJ+fx8LCQtE8CELqSCDd398PvV6Pb33rWzyJ1ul0OHToED7xiU/AZrPhi1/8IkpKSjA8PIyjR4+isbER8XgctbW1+PrXvw673c72rS6XC5/4xCfYTevll1/GwsICAoEA1tbWilJXhUIh2trasLq6ikuXLvEUY8+ePWyaQanCJABub29HQ0MDDh06xNMtShz+gz/4A4RCIXi9XohEItTU1KCurg5zc3PIZrMYGRlBWVkZuru7EQgEmB53oxWLxdDZ2YnNzU3kcjl0d3ejtLQUDz30ENxuN2ckaDQa/O3f/i2MRiMjpaRFOnz4MO644w6YzWZ0d3fzu0p6tb6+PtjtdhaXUzNOwMBOa3t7GyqVCpcuXcLS0hI0Gg0CgQB27dqF6upqOJ1OtqttamrC2NgYTzn279+PhoYGLC8v49y5c1hbW8Of/umfYm1tDYFAABqNBlVVVWhtbcX4+DiGh4cRjUbZaej8+fNFkdJIJAKtVgur1YpkMsn0nS984QvI5XJobGxEZWUlJ5C73W5kMhk0Njbij/7oj9DV1YULFy7gySefhFwuZy3K0tISrl69CpvNxo6NqVQKIyMjiMViqKysZCOQndbm5ib6+/v57rx69Sp8Ph+MRiPGx8ehUCjQ29uL++67Dx/96EexurqKeDyOQqGAL3zhC7j77ruZ1kjADgFgV69exZNPPolHHnkE8Xic9T9lZWU8YSt2309OTuLDH/4w1Go1/H4/gsEgJiYmcOjQIZw9exYPP/wwPv7xj+Oxxx7DD37wA0atRSIR7rvvPs4Z+upXv4qhoSGcPn2aaaEEin384x/HM888g4GBAWQyGdx///147LHHeIK100omk6iqqsL09DR+97vf4cKFC/B6vfi7v/s7WK1WzM3NwW63Q6fTobe3FysrK2hpacHY2Bi0Wi1qa2vR3d2Nj33sY7h69So+9KEPwW634/Lly5ifn4ff70d7ezvfrx6PB52dnbj77ruZ3rXTcrvdOHr0KLa3t7G5uYlCoYCJiQncc889cLvdDBQKhUJ0dHRAIpGw0+P+/fuh1+s548bn8+ErX/kKWweXl5ejs7MT9fX1mJiY4DDXvr4+7N+/H3a7HYuLizvuLRaL4ZZbbkE8HudJ1fb2Nvbu3csOlpWVlejp6cGRI0eQTqdhMpkwNzeH+++/H/v27eMoiJ6eHgwMDGD37t0QCoUYHR3F3NwcTp06heeffx6BQIAp3+l0Gq+99homJyd33Nv1S1B4j0a4P/jBD6BSqTA7O4uKigr09PSw//vHPvYxPPPMM4jH46ipqUFLSws8Hg96e3sRiURw9epV1NbWcvBbRUUF/s//+T/4H//jf2B2dhbBYBBGoxHJZBJdXV3cGRK95vDhw3jppZfwhS984YZ7+5d/+RdIJBL4fD6o1WpUV1cjlUohGo2it7cXL7zwAsRiMXw+H5aWlvDZz36WRWIvv/wyc+VFIhF+/OMfo7+/H319fayfIB4xCcJzuRzGxsZ47JjNZvHlL395x89tY2OD3QAo9GxhYQHNzc0s9qWU1KWlJYhEIkxMTKBQKLCALxqN4tSpU/jyl7+Mhx9+mGkP8Xgcx48fx/nz52E0GqFSqfhhrampwfz8PL7yla/ccG/f//73UVNTA7FYDKfTCafTCalUyiNISnslvqlSqcSZM2dw3333se++RCLB9vY2FAoFi9jIASGXyyEQCGBmZgaHDx9GRUUFp8P29/ejqqoKhw8fvuHe/vzP/5wbEqK2JZNJ5PN5nDhxAhcuXGDnGYlEgvHxcTgcDqbpkcZndXUVe/fuhcfjYV4m8V5Jl9PY2MiFP2kgIpEI/vEf//GGe/vZz36G1tZWeL1eBAIBqFQqtLS0MH2kvr4ebrcb8/PzHMIViUTw61//GslkEoODg0xv8Xg8+Ld/+zccOnQIsVgMWq0WNTU12NzcRD6fx5UrV1BbW4vNzU1IpVIcP36cA4xutJ588knWNpEFJ01AKB2YTAemp6cxNDTEF6/RaGRxKllTer1etLe387NI04vm5mb87ne/Y8EfHbRisRi33XbbDfcGAD/96U+5yYhEIixM9vv96OrqYiErCVwBsK2p2WxmhyaHwwGdTodQKITm5mYEg0HWX5HYnfQ4RDciespOPvNf+cpXkEqloNPpIBQKmRpHYZlTU1PsI07WizabDbW1tVhYWODndXt7m40vALC+KJfL4Tvf+Q4++9nPwuVysesc8cMjkQi++tWv3nBv3/jGN6BQKNDU1ISFhQXMzc3h4MGD2LVrFxYWFph6SgVVJpPh8DSdTodkMol4PI7y8nK+1O12O4xGI9bX12Gz2fChD30Ic3NzmJycxIEDB7C9vY2SkhLs2rULXq8XQ0NDN9zbP/zDP/AZbjKZWNhOYl+73c7pzO3t7dDpdJibm0NJSQkXWaWlpdBoNFhcXMTv/d7vYWZmBtlsFj6fjzN9Tpw4geXlZbaWBMD6svvuu++Ge/vWt77FzTdZHFdUVOCpp57Cvffey2FodEl/7nOfw5tvvsn2wwKBgA0e5HI5xsbGcOjQIX5PMpkMf5YDAwOYnZ1FOByGSCSCQqFAoVDgILH/uv70T/+UKZ5kSkL8/lwuB71ezwF39FmeO3cOJ0+ehNfrZRFqSUkJa488Hg9KSkogl8sRiUTwq1/9Cp/5zGdw8OBBnDlzhh2jFAoF3n77bfz4xz++4d6+/vWvc1aI2WxGVVUV1tbWkEgkcOLECXajIXvk2dlZFAoF3Hbbbfz8kwNTJBKBzWaD2WyGxWJBOp3GysoKXC4X3v/+9+PNN9+EVqvlQp5yI3aaon3nO995hzkEacooo2BjY4MLb6vVikOHDkEoFGJ9fR2JRAKJRIKnOiaTCXq9HsFgEBqNBrFYDDMzM0gmk/jABz4Aj8eDl19+GUKhEL29vejo6IDL5cItt9xyw709/vjjnIWg0+lYe7C6uorh4WHWVzocDrb1JQOQkZER1NfXo729HQ6HA1tbW0in0zh16hQikQgmJyfh8/nw8MMPQ6VSsdEEOTJSbtOnP/3pG+7tG9/4BtM/lUolDAYD8vk8nnrqKdx///3QarWYnJyEy+Xi6eyJEycwMjKCSCTCTptERSVjhXQ6DalUyqF7RNVUKBRsIOPz+RAKhXa8U5944gnMzMygoaGB85U6Ozvx4osvoqOjA319fcjn89jY2MDm5iZ2796NpaUlHDt2DFNTU4jFYhCJRNxM0+TjjjvuQGlpKRYWFtjuncKlSXzf1NTEuUc3Wr/61a+YLkTvJuXBPfDAAygrK+NcGArIJb2nUqlEa2srtFotvF4vT/TpXQgGg7h69So76F26dAlqtZodKTs6OlBSUoL3v//9N9zb3/zN3+DYsWOspXW5XGhqaoJer2eKH4Wb5vN5DA0NweVyweFwYHBwkA1VCoUCHA4Hrly5gtbWVha7EwVzbm6O88iMRiOAa2cvgbrvtt6zRoNCzDKZDAfHOJ1OFuZWVVXB4XDwwdjb28suHDKZDCKRiHUUfr8fIpGIO3Aq7AqFApLJJFsEzs3NYWtrC3V1dUXF4NlslnmuuVyOuc7kVEBcVeCaQ9T8/DxCoRDa2tqYviCVSjE6Ogq1Wo3Lly/DbDZzFDtZURJ3VigUsicxuTXstGKxGBoaGpjukUgk2HWmra2NA2DIJUehUDAVwm63o7S0lJMj6ZCgQBe/38/cXRqf04WayWTYK3mnFYlEIJfLWdxaWlrKTh2EGBHflf5dSkEmwa1AIMD29jYCgQCEQiHb35Lb0fLyMqfR0uFTW1uLWCyGkZGRHRsNQnqpACP0ZmZmBnfeeSdqa2sxNzeHcDiMzs5Onl7RZ0wOGGazGcvLyxxY5fP5WMxFbjI0Gg8EAkx9KRbCSNbHhAqRbWY8HkdjYyPC4TDC4TDzhOPxOILBIF8qJGojbqRcLofL5WJhl9/vx9LSEo8r6bsFrhUSCoVix0aDuOipVIodIcgRxOFwYGlpib3ciUZ2/XtKzhhUROn1es7HicfjPJVSKpVc5LlcLs7VKOYWA4DfdXLnINohPafJZBLBYJDdz8xmM6ftplIpdnAhT//x8XEe+9JnTgUDZdxQg0quMTstcpCinAsSptIBThk/5CMuk8kwPj6Ozc1Ndo/R6/VsV0j2m+QQB1xDiunnkxtINBpFW1tb0ckoifXIEeR6rQEJ1mmioFAooNVqMTo6yt83OaW43W52QvF4PADALk4TExN8JtA5XygUuAjaqdEgH3qxWMxTCBJPXv+d03tAuq2KigourgUCAaxWK5aXl9mVamtrixORyakoEonAYrHwHbS2tlb0XSXRPGnEyLlNKpUyOkluc5SRotFoEAqF2OFMLBZzwCtlsYRCIXbfAa5Nkqj4J30guaoVe97IxjcWizFwRRatfr+fHX0IlMhms1hYWOBQMyqct7e3WYRKxYHH42Fx+MTEBEpLS1k78m5BYMA1B71MJsNOTJS3k81mIZfLWXdYUVGB8vJyzM3NcSAuifWpOHQ6ndi1axeHf9Lkj6hp1BzHYjG2id5pkUMY3cP0HTc1NbF7JOVDpNNpFAoFnn6VlZWx5XsgEGAkuFAowG63I5fLsYNeSUkJlpaWoNfrkU6nEQwGsb29jampqR0bjWw2y7UNPf90d9HzRXcUvWOkzSGgi6zs8/k8pqen0dHRgVAoxGGrFy9eRFdXFzsSSaVSlJSUYHNzs2gWj0AgQDweZ4cz+rupVAoGg4GbeJrYNTY2YnV1FX6/n7V+pC8gcfzx48eRSqXg8/k4LPb6TJhMJsNOUcUmQfS+lZWV8fNJAER9fT2EQiFisRjW19exvr6O3bt3IxwOw263Y25ujuMVqM7s7OzkrCt6ZkioTo5gZDvrcDiKxgCkUilotVp+1ghUI7YFAVJ+vx/z8/MQCASIRCKor6/ns4logaR9pGRyysCwWq2QSCQIBALwer1cF9A9stOi+oieNYVCAZvNBqFQyO/7xsYGHA4HA0CUH0NOdGTQQWc93eGkL4pGo5BKpaisrGQ3S4lEwqYF/62NhtPpRE1NDfsok/p/z549CAaD6OrqgkQigd1uh81mQ0dHB+RyOaNDFBZmMplw8eJFKJVKzM7OsmcwOS5YrVYueknkdfbs2aIHNhV65DKzvb3N/Gi6YKPRKMrKytDQ0ICFhQUolUoW9ZaXl/ND0tXVha9+9asswKJCJZlMYmRkhA8SmUzGPMRiHOFoNMrFEnDthSLBFVkGz87OctiVRqPBrl27OPGVQvEEAgF++ctf8oNN0fahUAgzMzPw+XwYGhqCz+dj/QgFj+20iOPu8/kQjUZ5ckFhTIR20uVLaMurr76KlpYW/nxTqRSHtt1zzz38EhGCVF1djXg8jvHxcVgsFtTW1sLhcBQdV9LvR0U7ZavMzc1hYWGBhaLr6+vI5XKoqqrC8ePH8W//9m8Ih8PQarUwGo3o7e3F3//93/PkhZoPEpIWCgWYzWbE43G23/X5fEXzFpLJJBwOB7vBANeohZT0u729jeXlZaTTaR7TyuVytn5OpVKYnp7m0EiaMBgMBiSTSeaPy+VyDAwMsFU0OZWo1WocPHhwx+80m81CIBBwBsnW1hbbq1IjQ3avAoEAXq+XQ9no8m1oaMDq6ioL8Umf4HK5WGNAXulkn0lFU7FFhTJl2YhEIrjdbnbIyuVyfKAHAgE4nU4MDAzg6tWr73BJampq4qyX7e1tvrjp766srLDtLB2UVLAWW+RSQoV9JpNhy9Oamhqsr69ziChRAejMomJGJpNhcXGRPcoBcDZLd3c3vF4vIpEIB2NRsVhMP0I2mdTwkOVkNBrFwsIC9u/fD6fTyWK92tpaXL58GR6PB/l8nm2wqbGcnp5m20dq+K5cucI5CNPT0wiHw4ziF+Pzx+NxDhTc3t7mi4/eB6lUym5oi4uL7NL0+uuvs7aEQA6/349//dd/xeDgIH++iUQCXq8XTqcTGxsb7NxWUlICr9dbtEGjc40KnkgkgpWVFc4CIBpJOp2GWq3GlStX0NHRwb73hUIBqVQKEokETqeTwz/pdxIIBJiZmeECy+/386V9fQGw07tA07NIJMLNM6GRQqEQNpuNNY2UiXL69GnodDq2SJVKpXjrrbcwNDQEpVLJ1Aafz4eKigpsb29jYmICVVVVnCxPgvFiS6vVshYpk8mwUQPlSY2NjfH3UV9fD61WizfffJMbSJq4XLx4EQ0NDXwGkd18PB5nYxN6j/1+P0+bd1rUzJHpCfHu9Xo9WxNPTk5ycUUuiNSAkI2qTCbD2toawuEwLBbLOwxn3G43RkdHWZNKReDVq1eLmjYQQEbTiEAgAI/HwyF9ABiAIl0OGX6Qpf71VsSTk5OYnp7msL54PI7Lly/D5XKhr6+PJzH5fB4Oh6OolpUKSQJuSJxNIv9IJIJ8Ps/ACIFmxCagpHUSRpMVv8lk4jqE3D17e3vh8Xj4mREIBDuCYwDYcZMaLJFI9I7Gm2rDqakp2Gw2dlI7ffo0gsEgBAIBzGYzenp68OKLL+KDH/wgCoUCO/WR2YDVaoVer2frctKmFXtP4/E467HICYpAQ3qmiG5LIBOFQ1OeVklJCTvyyeVyvscIyLbZbKyJWl9fh8FgYECagpNvtMrKyrCysoJcLscU2atXryKZTEKlUqG7u5uBMpr8h0IheDweLC4ucgCjyWRiQI6AFwqsVCqV2LVrF+ucKCuKwKRieTy0/p/E4BKJBJ2dnejr60NVVRWkUikef/xx9uJVqVSMYo+Pj0MoFOIXv/gFnn76abz11luskG9qakIkEmEv/c3NTUYwSCA2NjbG9nrl5eVFO/VCocBFLRU6ly5dwsrKCqO5paWlHANvMBhgMpn4wSZ7WQp6USgUPAJXKpUwmUxoa2uDSqXC9PQ0u1CRo0ExwV9ZWRleeuklxONxtlikQkav12NoaAjDw8Oor69HOp3G2bNnUVtbi7q6OhZ37du3D5/73OcwOzuL1dVVdqQoKytDXV0d3nzzTfj9fpSXl2NlZQVnzpzB5cuXEY1Gi1rK6fV6rK+vM2pBqHJ/fz/S6TROnDiB6upqbl40Gg0OHDiAP/iDP4BYLIbdbme+OAA8//zznC4OAGNjY/D7/fijP/ojDA8PMwJHxdtOo0oAzCne2NhAKBSCSCRiO9uxsTFMT0+jr68PJpMJ3/ve93Dp0iUumMjOjjJb9Ho91tbWMDU1BZ/Ph6amJgwODrIxAFmqZrNZzpUo1qDJ5XJ4PB6eHimVSg5wo6R1QnWpaaivr8fGxgaWlpbYJjYSiaCyspJ5p9lslu17h4eHodVqYTab0dnZyXaEcrm8KNeVLnZ6HoxGIzcQDQ0NzBO9dOkSnE4ncrkcZmZmMD4+jldeeYUF2C0tLaivr8e5c+eQz+exZ88eTmH/zW9+g9/97ndYXV3FhQsXkE6n2Z3m3RoNmkZQwFEmk8HS0tI7ir3u7m7s27cP7e3tWFhYgMFgYN43IWsUNjQ0NIREIsHPL50/Xq8Xr7/+OpxOJ//+hUKhaFFKmT6Ua0MWyEQTrK6uhkKhQDT6/7P331F2XuW9OP45vfd+pp3pvY9G0qhXWzKShRtu2IRrUxJDgCRALuESkwXcQAKEXALBodgUG2wsN1lGzbKkUZmRNL2XM+20Of3MaXPq7w9nP1f+Lc3B37Xun3rWYi3jMtrzvu/e+ymfEiXOyt13342jR48Sznx2dhbNzc1wuVzw+/10Qc/MzGBxcRFFRUUIBAJ08ba0tNCzK6Rmw+VyCYrHplYM/tLX1wer1QqTyUSXWTgchtfrRVdXF+3xfD6PyspKSCQS9PX1EQTPZrOBy+US9K20tBRTU1MIh8Pg8XhwOp0FuQYA6LxkY3rWZFpYWIDf7yfFr+HhYSQSCbS2tqKvrw8nTpzA7OwsDAYD9uzZg7KyMvT29pIRF3Mqdrlc8Hq98Hg8OH/+PK5evYpgMIjS0tKCXC/WzVapVORNJBKJcPHiRXR0dJC3QTgcJplQkUiE5eVl3Lx5k3g0FosFP/nJT0g5kcGTGPyWTTMYjpsZjhW6G1hRzSYjAoGAus2MX8DOh7m5OQwODoLP56O7u5s6sWz6GAwG8Yc//AEOhwN6vZ44SwyCyfZ5f38/nE4nFAoFJb63C+YJ0NTUhNraWohEIkQiEfzqV7+CUqkkdUfmm8HlcknZ6cKFC+jt7SWe28LCAiEcDh06hH379mF6ehpvvvkmZmdn0dPTg/Pnz+PVV1/FwMAAQqFQweSFucUz1bZcLofJyUn89Kc/JdPPTCYDv98Pr9cLr9dLsJ+JiQn09vZiYWGBCOOs4ffkk0/i4YcfBofDwdmzZ3H9+nWYTCb4fD74fD4kk0lwudwNpxnsubEilUE3y8rKMDg4iJGREUpU2btmPmTxeBw3btzA3NwcSfHu2bMHjz32GIlJFBcXo7GxEa2trSSd3NDQQMk4QzRsFHw+n1QLa2trUVFRgZ6eHly7dg0TExPUlGU8HolEQrzG6elpEr1obm7G4cOHsXv3brI8qKqqQnFxMcGCYrEYLBYLlpaWcOXKFfB4PGzatGnDtUkkEoyOjhK3zmg0kinrzZs3yacoHo9jaWkJQ0NDSKVSJC1rs9lQUlJCz4UpoJaXl8NkMkEikWDfvn2YmppCe3s7TVVZI7hQYcuaLfF4nPx9mNs7MwVmxXtTUxPlEdPT03j77bcxOTlJucA999yDN954Az6fj9bM9ntdXR22bt2Kbdu2kQwug+FtFKwJxqajPB4PZWVlmJqaQui//bjW19eRy+VI4GLbtm3o6uoi8QiWbzK7g7a2NjQ1NWHTpk1ETu/s7ITNZiMJZT6fj0gkggMHDmy4tlvjQxcaDocDQqEQFy9exMmTJ2kk/+///u8QCAQwGAwwGAzo6OjAgw8+CLvdDr/fj23btuHTn/40enp6yMnyjTfewMc+9jEUFRVRdc3n8/FXf/VXmJ6eRjQaxeLiIg4dOoQvfelL5Gq4UbAiguFUOzs7ce+991LXl/EDGCmTQWTm5uZQXV0NPp+P9fV1PPDAA3j11Vfx3HPP4dixY7RhUqkUtm7ditHRUTQ0NMDr9WJlZYU+fkYMvV3EYjHs2bMHy8vLGB8fRyKRwNLSEj7/+c8TRprhRBsbG1FTUwODwYB/+qd/oir++eefh8/nw0c/+lH86Ec/wnvvvYd8Pk8QqocffhgOh4MSxc997nP48pe/jHPnzlE3/HbBOnQlJSVob2/Hpk2bIJFI8Oijj8Lv92NychIA8LGPfQzf/OY30dfXB71ej+vXr6O0tBQSiQThcBhbt27F+vo6/umf/ok2pE6nQ21tLb7zne9gbGwMExMTpMSzurpKCjobhUAgINfiqqoq1NbWEomUFaDLy8tQq9X46le/SiZuLpcLFRUV8Pv9eP311+FyuTA5OYnvfve7aG5uJn+XU6dO4R//8R/h8XhQXFxMsm52u51w/xsFK0T4fD6USiWsViu4XC4efvhhlJSUoKysjOQXeTwekdJefvllCIVC2Gw2tLa24qGHHsLp06fR3t6OK1euYGZmBmNjY7h48SJ0Oh0uXLiA0tJSnD9/HrlcjuQzC2l+Ly8vo7OzkyaOrOvNkoWmpiZUVVWhu7sbTz31FK5fv469e/dCo9Hg8ccfx8GDB6HX6yGXy8kb47333iMjPpvNhr/927+Fx+OhJG/z5s1oaGjAxMREwSkV8H4BWVRURBO8lpYWlJeXY+fOncQRcjgcuHbtGkZGRqDValFUVEQGl4lEAsPDwzh16hQkEgkefPBBlJeXQyaTwePx4ObNmyguLsY999yDz372syThJ5VK0dvbi9ra2g3XxgifXq8Xy8vLpDa0e/duIqhXV1fj6NGjeOKJJ0iadn19nbT5mQTlnj17yDDqgQcewNatW8lf6FZPmlQqBYPBgJs3b2J6enrDtbFuNjM6PXToELZt24Z77rkHOp0Ob731FjweD8xmM7RaLaRSKb785S/jxRdfRE1NDaqqqkixy+Vy4Y9//CMVFwaDAc3NzTh27Bg5rXO5XErWhoaGCibMXq+XiNR1dXV4+umn4fP58LOf/QyLi4vEO9q1axeefPJJIsPX1NTgO9/5DhobG3H69GnMzs5CIBDg3//933Hs2DHw+XxcvXoV4+PjePbZZ+F2u/G1r30N9fX1JPkaiUSwb9++DdcWj8fR2tqKRCKBlZUVgk5t2bIFN2/eRFNTE4xGI8rLy/HYY4+hrKwMXC4XJ0+eRGVlJWw2GyoqKvClL30JH/vYx7Bnzx6MjIwQd21sbAwPPPAAzp07h7W1NcRiMZSXl6OtrQ0cDqegChvzdmFFY319PTZv3gybzUZJaWlpKe6991589atfJXUw5png9Xrx5ptvYmpqCvfddx+OHDmCzs5OZDIZ9Pb2or+/H0888QRcLhd27NiBdDqNtrY27N27FwDw5JNPFvzejEYjhoeHqUPa1NSEX//61zQlZAkhh8PBm2++icOHD2PPnj3YunUrtm/fju7ubhiNRjQ3NyMcDuO3v/0t3nzzTTidTuzatQu//OUvce3aNcjlckgkEnzlK1/Bf/zHf2Dfvn0Fi8eZmRnU1NQQ/KqhoQGbN2/Gt771LdL4LykpwaOPPorPfvazBO1UqVR44oknKJGzWq3Q6XTI5/P4t3/7N/zoRz/Ciy++CJfLhe9973uw2+1ElN20aRPa29sxNzdHhm+3i7m5Oej1eiwtLRGUfGxsDHfffTf0ej3W19cxPz+PyclJuN1uRKNRmhb/wz/8A7RaLY4fPw6z2Yyvf/3rKCoqwuLiIu1Rs9kMoVCIubk59PT04N1334XL5SIltt/85jcbri0QCGDr1q3o7e3F7OwsjEYj/uu//gvf/e53AYAmN9XV1Th8+DBOnDiB5eVlDA0N4cEHHyRvm4qKCvzt3/4tDh06hJKSErS2tmLr1q24//778e1vfxvXr1/H3Nwcfvvb38JqteKpp54iDu5GkU6n0d3dTRMdmUyG8fFxPP7446itrUVbWxuqq6uxfft2EkY4evQoGhoaUF9fj7GxMfzXf/0XotEoHn74YXzve9/D3NwclEolNm3a9AF4bX9/P0KhEB566CE8/vjjWFhYKNggm52dRWVlJTVQtm7dilwuh4cffpgkotPpNEpLS3HPPfegqKgIDz30EJqbm/GNb3wDTU1N5O79r//6r/jhD3+IQCBAPN+2tjY89dRT6O3tRSAQwPj4OJn1CQQCdHZ2brg2t9uN7u5uKmr1ej0UCgX+9m//FhUVFYhEIuSVIZfL8aMf/QhqtRpDQ0P45Cc/iQcffBAajQYKhQIDAwP4x3/8R5Kcbm5uxsGDB1FVVQW3242Kigr87ne/I+uKQCCAkydPbri2W+NDk8F//vOfY2RkBJ2dnYTP9Hg8aGlpQX19PeFpl5aW4Ha7cffdd6O7uxs3btyARqMh3fRf/epX+PrXv45vfOMbePbZZ6HRaAi+87vf/Q7f/OY3cfbsWZSWlhK3grkbbjR6+9nPfoY33ngDR48eRTQaxcTEBLZv344LFy7g0KFDGBkZgV6vJ+wtw2IeOnQIcrkcHA4HAoEAarUaHo8HX/3qV/HjH/8YXq8XbrcbYrEY0WgUR48epY7M8vIyMpkM7rnnHiwuLm5IBv/ud79L6hUMnnP33Xfj9OnT2LVrF1599VUy3Xr66aehUqnwiU98Alu3bkU2m6VK88KFCzh8+DCGhoZIPo2NL8fGxnDXXXfh0KFDePXVVzE5OUm+C0qlEs8+++xt1/aTn/wEV69exX333UeGMsXFxfjZz35G6jjMAG9hYQFHjx7FjRs3UFFRAbfbTZXt0NAQjh49in//93/H008/jVOnTiGRSGD79u2kwPKHP/wB7e3tiEQi1IWIx+PYunXrbdf2z//8z4SNZFV0NpvFt7/9bTzzzDP405/+hLKyMvJk6O7uxunTp/H444/jhRdeAJ/PR0NDA1KpFEpKSvDcc8/hiSeeoI54PB7HyMgI/vmf/xlf/epX0djYSHjJ+vp6XLx4Ef/yL/9y27X98Ic/BJ/Px/79+2G32/GnP/0JjY2NePnll/E//+f/RD6fx9zcHHw+HxlwdXd3o66uDoFAgOTsgsEgrl27hvX1dXR2dtIo1uFwgMfjYffu3YhEIkRkZMV+ZWXlhp2Es2fPYnh4GHq9nqYtrPN94MABgnysra1hYGAAo6Oj+Iu/+AtEo1H8/Oc/h8ViwdGjR2Gz2eDz+fDmm29ix44dNDHg8Xg4fvw47r//fszPzxNx0efzEYfo6NGjt10bALz11lu4evUqiouLCVJZVlYGuVxOSkys686MILu6ukiyk0FGfD4fysvL8f3vfx9Hjx7F8vIy/H4/CQB85jOfwWuvvUawNQYdmp2dxec+97nbro2pHbW3t5NE6ZEjR/D1r38dX/va1zA1NUVTuVAohCNHjiAWiyEWi+E3v/kN6uvrcfDgQWSzWZw/fx7r6+tobm7G6OgoqUQxs6zLly8T7JQVhBaLBV/4whduu7Zf/OIXAECF/vj4OA4ePAi73U5iHACog/jjH/8YsVgMmUwGp06dQlVVFfbt20eGaQxLLxKJsLKygsuXLyOXy+GZZ55BX18fSktLIZPJ4PV6cfHiRZhMJnziE5+47dq++tWvwm63Y//+/RAIBNT5TKVS6OnpIX+g8fFx/PCHP8T27dtJOW1paYlUURg/7Sc/+Qnuv/9+gp0xWNezzz6Lp59+Gp///OcxOzsLp9OJvXv3YnJyEo8++uht1/b5z38eCoWCcMSs4/39738fTz/9NDo6OuB2u7G6ukpwHyb4sWnTJoIitrS0IBAI4OLFi7j33nsJs7+4uEjS3v39/WTaxWBTbrcb3/zmN2+7tu985ztUtKyursLn8+HYsWN4/fXX8a//+q84efIkJBIJ1tbWMDY2htbWVupQvvXWW2hoaMC2bduQTCYxOzuL5557Ds8++ywymQxBxUZGRvCLX/wCH//4x/Gxj30MfX19CIfD+Iu/+Au89dZb+Od//ufbro35EQkEApqqMM8RloQyefHXX38dKpUK09PTsNlscLlcJFPPTPx6e3tRWVmJvXv3IpfL4eLFi5iensY3vvENkldlvDWNRgO3241PfvKTt13b9773PcTjcYKrcblcdHV14Xe/+x2eeeYZmlANDAxgcHAQjz32GP3s2dlZ6PV68qianJxET08PGcIpFApwOBycPn2aYNYNDQ2wWCyIRCIYGhqCXC7fcJ8+//zzdG6xQr6trQ2vvvoqnnnmGbz88stoaWlBUVERwuEwlpaWYLVasbi4iNnZWdTX1xPXpKioCKlUCi6XC6dOnYJGo0F3dzdefvllfP7zn8dbb72F9vZ2aniWlpbizJkzG4q/fPe738Xa2hrq6+vJ0LWhoQHf/e53sXPnTgwNDWHHjh20tvr6ekQiEbz88st48skniZ/E8gHm3TU8PIzZ2VlCFORyOZIDn5mZIfn3EydObPjcfv3rX2NlZeUDsKRUKoWzZ8/ic5/7HKanp0nhUSqVIhKJ4Pvf/z6+8pWvoK+vD/F4HBaLBRaLBX6/n6Sh//SnP0EqlaKxsRH/8i//gmPHjsFoNMLhcGBychJCoRB79uxBOBzGoUOHbru23/3ud1haWkJZWRnxb5LJJEZHR/GZz3wGPp8PkUiEBA6++MUvwuVyUWN5y5YtJKZQXV2N0dFRhEIhCIVCFBUVQa/Xk7LdSy+9hM2bNxN53mq14tKlSxsS/F944QUAIB4PU0wbGBhAT08PXn/9ddTW1kKtVsNut6OzsxNarRanTp3C0tISWltbUV9fT15o7PkODQ1hcnKS4PW7du0idU2Wp5w4cQI2m42MhQvF/yfDPoYFdDqd1PFj5BXGc3C73XA6nUgmk5iYmMDLL7+MkpISqNVqJJNJVFRU4Je//CU+/elP4+LFi1hdXaXx8fLyMr71rW/hvvvuw+joKKRSKWQyGd577z14vd4Nk3k2VZDL5aTc4/P5CCfI5/NJ5aS8vBx1dXVIJBLo7e0l92MmMfr222/j4MGDOHv2LJqammAwGDA9PY21tTWMjo7is5/9LPL5PIqKiggfWoiM6PF4oNVqyeBQIBDgtddeQy6Xg9lsxiOPPIKTJ08iHA6jt7cXBoMBmzdvpg6yUChEWVkZOjs7cebMGUxMTIDH45G5jdPpREVFBRYWFvDSSy/B6XRicXERAoEAe/fuLcgfYaoGzLk8nU7j+vXrOHr0KBkUjY6O0j9va2tDMplEb28vWltbCd+vVCqpezE/P4/S0lLMzc3h9ddfx9atWzE9PY1gMIhgMEhjPLZRNyo0GI41n8/D5XKR78KuXbuIIMfj8SAWi1FSUoLjx48jlUrhxRdfxP79+8kpXSKRYHFxEVu3biXC2ujoKBVOv/nNb7BlyxZSnWHqDYWgUwKBgAywmFFfKBTCs88++wG337W1NdjtdjQ0NKC/vx8zMzPEZeJwOAQn6e/vJ8Ukl8uF/v5+NDY24tSpU7jrrrvIlIp1Pgt1X+bm5kgvn6lBMRnR9957DyqVirCjzG359OnTBJlJp9M4deoUTCYTIpEIDAYDbty4QRKgZWVlOHToEOFIp6am4PF4qPtf6LkB7xMjKyoqiN/B3jGPx4PFYkE+n6dJV0tLC+x2Ozn9lpaWYmVlBZFIBPF4nGBezF2VKWjo9XrMzs5CpVLBbDaTYyojmW8U8XgcdXV1SKfTEIvFyOVyeOGFF/CRj3wEExMTZB7HDuV33nkHLpcLzc3NdAmNjo4iEAggEAigu7sbbrcbdrud/C9OnTqFHTt2YNu2bWQ4qVarEQgEChISZ2ZmqKNtNBqJa8MKFSZ6kEgk0NDQQOZPbrcbyWQSCwsLOHv2LMxmMzweD/bv34/+/n6CwJaVlUEoFOLll1+GwWDAG2+8AZlMBg6HQ/C/jUIikaChoYE8ZWKxGDweD5GmW1tbMTo6Crvdjp6eHpKqZopTkUgEg4ODVLQdOXIEc3Nz8Pv9kEql0Ol0UCqVuHLlCnbt2oXx8XEsLy8jGo3i3LlzpMRzu2C8nls5JvF4HH/9139N3D1WTDBsNTN/dLvdqKyshEKhII4Qk8dkhoupVAodHR1UjDIoBWvyFHpu0WgU5eXl5AdhMpnw7rvvIpPJ4JVXXsHk5CQUCgW0Wi2sVivJXDJvFgbhnJ+fRyaTwd/8zd+QwAVrnBUXF+PXv/41Nm3ahJs3b9Kd+sorrxSEizCvJpZYMjy7x+PBO++8Q2IlDPKXTqdRVVUFHo8Hk8mEiooKaDQajIyMYG5ujjD1zNtKJBJBqVTi0qVLsNlsxA8Si8Wk7rZRMOM9RuTPZrPo7e2FSqXC888/j3vuuQe5XI5UcRgRnolDpFIpKBQK7N69G2tra3A4HNBqtXA6nQRtkslkuHTpEsnEMyy/0+mE2WwuuDbGV2F3HlOuvHDhAqxWK/FkGGmYqWPl83nMz8/TJOtf/uVfUFJSQpCobDaLqakpHDx4EBMTE5BKpRgbGyP4GJNs3SgYVItxQ9iZ1NnZSXyNmzdvYnZ2lhQZrVYrBAIBrl+/jmAwiHA4jMrKSpw4cQJbtmzBxMQEFTvXr1/HfffdR6T+d999l4R9WAGyUTCECFM+ZN9XbW0tkskkKisrcenSJVy9epWSZYVCAbvdTgqDrAE1PDyMV199FY8//jjxuOLxOPbu3UtFwcLCAkEKL168WBDWxXIcxt/0er3QaDQkBZ9MJuHxeEjkgIk3MLjquXPnIJfLqQFZW1uL0dFR1NfXUxHJRBYsFgvee+89xGIxSKVShEIhygVvF0wwhfETuVwuYrEYTCYTpqamCMLF4XDIO2l4eBihUIhyFQb7CgQCGB4eRi6Xg1qthlarhd1ux5YtWwj6q9frqYGyvr5OKKI/Fx8aOiWXy5FIJEh7XCQSwWq14u233yYnZabKwrqPjGTFDqry8nJUVlaio6MDzz33HPR6PSwWCx2ODBt47tw5CAQCMnJi0mQbBVM1SafT4PF4SKfTGB8fJ/WSlpYW1NTUQCwWY3l5GT6fDzt37kQ2m0VfXx9mZ2chEokIF3jhwgXk83lIpVLw+Xysra2R82RJSQm5DbtcLiwvLxfERQoEAohEIqhUKnLrZfi2lZUVXLx4EW63G8FgEP39/Th//jwZlM3OzmJoaAiDg4OENZdKpTCbzUT2FovFROBmRjJlZWUwGAzQarUF8X16vR6BQADJZJLkaKPRKCKRCI0LN23ahLq6OvpZnZ2dRNwdGRnBwMAAkUtZEsfj8SjhyOVy6O3tped67tw5XL16FQMDAwVVTxhJOJ1OQy6Xg8vlYnR0FBcvXsTa2hr0ej1V1hMTE1Cr1eSlwRR9lEolqqurMTk5iQsXLoDL5aKpqYlIkTdu3KDCbHV1FUtLS3R5FiKDM3wnI4omEgnU1dWRTwFzhGUHuUKhQF1dHSQSCaampijpcrlcZK6YSCTogtmzZw8WFxcxPT2Nubk5gqIxIjnTLL9dKBQKLC4uwuVyIRAIwO/3Y3FxEWfOnIHX60VJSQmUSiVdXrfKPcZiMXC5XJhMJpSVlUEikeDMmTNkZsWkYpnzeCgUgkwmI9lZuVz+Z53BGUGdFQtcLhdCoRATExMQCAQ02RgcHMSFCxdI8cZoNOL06dO4du0agPf5FC6Xi4j/bOp562WrUqkIk8+eG5Pm22htTNklHA4TrGFmZgYrKyvkWstkJFkHemBgAKurq5SMAO+TSa9cuYLl5WXqXJnNZkxMTODq1avUvbRYLETAZCphtwsGZTIajbBarWQgJpfLcePGDWi1WqjVaoKzmEwmBINBaDQaCAQC4sPdvHkTExMT+O1vf4uBgQEEg0HijDHvEKb0ZzQaSUxAqVRuuDbm9szhcGhiplQq6dutqqpCOp2G0+lEOp1Gc3Mztm7dSoRDBr/T6XSoqKjAwMAAKisrcd9996GzsxMLCws4deoUqd4lk0na54zvsVEUFRWReh27b7LZLM6cOUMqbC6Xi5J3t9tN3gCsUcAKuKGhISpiL126hJs3b9I+lclkuHHjBvh8PkpKSsjsr9AZx0jQ2WyWiPlM8YhNk1ni4na70d7ejscffxwjIyPw+/008XE4HFheXqbmRU9PD3Q6HQYGBjA8PAyv10tQs2QyiUgkQhOcjUKpVJKaGCPT5/N5pNNpzM3N0YToVrnw4uJiTE9P4+rVqxgeHkY2m8W+ffvQ3t5Oyk4ASORjaGgIQ0NDtC+Z6p5YLC6YMLPOejweJ18uJqTCGh7////MarXSe2RJGfMLOnnyJKlMMUK/w+GAzWaDw+Eg9Z1kMomioqKCZoKMp8ecndmEjCEGGKx2bm4OMzMzkMvlJBrC/gy1Wg2pVIqKigpcuHABHA4HTU1N5NYNvN/AZA7abN/FYrGCTSilUkn+M7FYjLruY2NjyOVyOHLkCKRSKYaHhzE4OEgO3FarlbieZrMZxcXF0Gq1hDTQ6XTo6OjArl27EI/HMTU1RSIXDGYnkUgKenwolUo6awKBAOVyiUQCIyMjkEqlKC0tRS6Xw7Vr10iVkcloM6fuoaEhLC8vo62tDQqFAmKxmAq6ubk5MgWNx+Pwer0QiUTk/r5R8Hg89PX1wW63U06RTCYxNDSERCIBg8FAEr6MWzo6OorS0lK678xmM3bt2oWGhgZcu3YNPB4POp0OKpUKPp8PL730Et0VgUCA7m+RSETwrNuF0WhEKBQitT92p7Jzsrm5GXV1dZDJZCQcIBQKcejQIbS1tZGwk1gsprzPaDRCq9WirKwMdXV1UCqVVAjPzc3B6/XSkKFQ0X1rfOhCI5fLUWIaDodJGaKqqoowkKxTUFxcDI/HA5PJhEQiQQZiqVQKra2thCcTCoX0wRYVFeGuu+4iCTUmkQb8X7zoRpFOp5FMJon4zfCdZWVlCAQCpPXLnGvX19dRVFREBl6sy6HRaEjCMxAIgMfjQSQSgc/nw2QyYWhoCPl8nkhBMpkM6+vrBR0vmdY3UyJhjq719fUYHBwkNSxWKUajUfLXYLKtyWSSVIQqKytpnMXhcMDlcmGxWMhp2GQyoaOjA42NjR8geN/25XO50Ol0hIsUi8WUqLDunsFgQHl5ORH4NRoNHUCh/3Y8bWlpQTqdJv8Ur9dLxOWysjJSpWH/PpMSLNT9Zh1+1nFkGt0MZsPc3V0uF1wuFzQaDfbv309yv36/nzrY0WiUZNkYJp11KpmcIpN6ZXKQhQ5FAFTNMzlRVvCNjY0RHDCTyZDPSllZGUnFMhUPmUyGiYkJ+jOZx4pSqaRuIuuicblcqFQq6t4UCplMRsmLWq3+gHyfwWBAU1MTenp66KCuqKiAXC5HdXU1VCoVyRAnEglotVq6KORyOXg8HiorKwlWEYvFoNfrUV5eTt96oWA+K0y9jckMM6y3TqeDSCSCWCyGTqeDRCJBZWUlyXmyRofBYIDP5yMZWvZn63Q6mEwmeDweUu5gRRr7dwsFaywwnLBCoSB1LL1eTwkhU1phfhpqtZpkZRlZV61WI5vNoqWlBWVlZfD7/WhsbPwAVOdWic9ChQaHwyGVKqbmEwqF6DkwPwHG4WCqNLFYDDabDWq1mtSqlpeXKakFQMRmpVKJUChEbvEMI5zNZgteKOl0GkqlEjweD3K5HBaLBUqlEh0dHaQuxgjDJpMJuVwOHR0dxGFi6oKs2GG/JyueEokEamtr4XA46DxkQgzJZLJgE4op2Nyq+8+I19FolKAy7BxlDTR2VjudToyMjCD03+ZmzD/n1uTuVuUjJgNpNBrpeW8ULJFnim9MZrSuro5InawpxSSoTSYTSXWzZNtsNtN5GggE6Hf0eDwoKSlBIBCg5EGpVNLdWygpZUULAErcmaISI52yyZ/NZqNzlSkAsu4vc55nruGsYRGLxVBbW0v3LdubXC6XztRC71ShUGBtbQ2JROIDanJM4tTj8SAajcJqtVKBwNTX2L3C5GHr6+uxtrZGJr8SiQRGo5HOGTY9Yz5Rhc441vRkfC2dTkfEX2ZoygouZkbMhG6Y0hSHw4FCoUB1dTUlmWzCygi+kUgEZrOZGlpM9OBW5azbBeMR6XQ68nu6dR+JRCKIRCJYLBasrq7C7/cjk8lAq9XSpEEul0OpVJLgCVP6Y1PxRCIBvV5P0Hd2jhZ6bkzBkpHajUYjNU/ZnaFQKGCxWEilkQkM2Gw2UoVLJpMwGAzYuXMnNYGZCpper6eCmXmoVFZWEk+4ULCGza1+LwaDgRRMBQIBpFIpKisrMT8/T1DAoqIiQgIxyWfWYAwEApTfNDY2IhKJgMvlQiKRQK/XQ6vVIpfLFcyTmBCTw+EAh8NBUVERFhYWAICKmZKSElRVVaGsrAzz8/Mk486eB1OEnZmZgc1mI0sDlncyqDUzkA4Gg+Dz+eBwOCgrKyv43Fh86EIjFotRAcEkE6PRKB588MEPmHap1WpUVFSQWc/KygrUajXS6TRBemZnZ/Hxj38c8Xgc6+vrUCqVsNlsaGlpQSQSIWgTO/TZQbRRMFkwNpK12Wxob2/HoUOHqPPMDsBDhw4R7AYAysvLIZfL4fV6acx95MgRgoMws61bCbaJRAKlpaUk4VtIBYhtymg0inA4TMoJmzdvRjKZhMViQXV1Nerr60kBRqPRgMfj4aMf/SgRnsrKyiCTyYjgIxQKIZfLaQokk8lIXs1gMJBkXqHRFnPW5nA4RMg3GAw09mZqYCqVCk1NTZiZmYHX6yVPDI1Gg+rqapSXl5NakMfjoW6byWQiWdxcLoeamhp0dnaiurqa1r9RrK+vo6ysjMawGo0G7e3teOihh6hTEA6HqSDh8/kkD5rNZklbnpG9y8rKEPpvUx0Oh4NNmzZR10yr1UKr1aKurg5NTU1kVrNRSKVSItUmEgnqxtTU1BB0R61WQywWU5eOJdR6vZ46zmazGaFQiMjYarUamUwGTqcT27Zto7Uw7Xkej0cX+kYRi8VQU1MDo9GIoqIiWCwWpFIp7Nixg6BJGo0GNpsNRUVF4PP5KCsrI+nV8vJyuigXFxdx9913U2eJ7TOz2Uz62Uylicl0/rlg2uhsX7DEh+1Jpu60detWfOQjH0FRURFqa2sRDoexfft2bN++nVRCWJefeTQUFRVhy5YtxPliBaRYLCYVqUIXMbuUAJDMKHN7ZZcs43u0t7dDLBZjenoaZWVlqK6uRlFREUpKSlBXVwc+n4/t27dDp9Ohq6sLRqMR8/Pz6OnpQU1NDaRSKaamphAMBiESiQpCbADQM2OKXUKhEIODgwDeT1DYhcMmClNTUzCbzXA6nWhubobFYgGfzyfX4vvuuw/Nzc1QqVR0gdbX15OUKuNAyGQyFBcXF9RKZ9AGhq9m3Km7774b9fX11Owwm81obGwkuUlWtEYiEfT29iIajWJ+fh47duwgNTuHwwGxWIxHHnkE58+fh9VqJX+X8vJy6oJuFEyTnk2gWDPq8OHD0Gg0NOFjXCp2NrGEizWWYrEYTTv4fD46OjrQ3t4OjUaDlpYWAKCGE+NH5HK5ghex3++nd8cgxGazGZs3b4bRaKQmEGvyeTweDAwMoKamhhK6aDSKnp4emM1mNDQ0YHZ2Fna7nRLnvXv3IhwO03dbXV1Nd0whUQl2x0UiEYLfsX3e1NREZqx8Pp+6nlwuF5WVlXjggQfQ0tJCTRI+n499+/ZR80Cj0UAul5PZGYM6s/OcwVM2ing8DoPBQP5PzNjQ6/XCaDSStCiHw0FzczN5U1itVuIesW9ydnYWDz30EBXpbAra3NyMQCCAHTt2IBgMIpPJ0P4rJLDCmkXMn4nlBgwhwTyz1Go1bDYbgsEgiouLCfLIBGjYuXXfffdBoVDA4/FgYWEBTqeTvBUYnIp5ebHO/UbBGqwSiQQ2mw0dHR3g8Xjo7OwkyDBrHN59992QSCTEA2V34traGrxeL/GK3G43lpeXMTg4SEIc7HxbX1+nO5CpIhV6p8wIlfnVWK1W7Nq1C3V1dfB4PPD5fCgtLcWBAwegUCigVqtpD3O5XPh8PlJcY0INrPHBCP2pVAoDAwNQKpVoamqCyWSi37tQ7Nq1i6CBTNBkz549tG/ZhLmpqQlKpZJgUWz6s7Ky8gG1UJ/Ph76+PuL8fPGLX6SmHsuh2d3NrBFuF16vlyZber0elZWVWF1dpaYKy5uLiorQ2tqKiYkJ+P1+uFwukthl0DtmKsk8aFiuz3x9WLHO1CHLysr+bGORxYcuNIxGI27cuEFj4Xg8DqVSieeeew6PP/44DAYDQaAcDgc6Ojrw4osvYufOnTR+Kysrwyc/+UmoVCqcOHGClKrUajW8Xi+OHj1KDrgajQaTk5PweDz45Cc/WfDg0Wq14HK55PAYi8Vw8OBBvPTSSygqKkJvby+pNEWjURw7dgzJZBKPP/44zpw5Q7Cft956C1/+8pdRWVmJHTt2EN+EdfOeffZZnD59mnTOl5aWwOVy6cK/XRQXF9ML9Xq9GBsbQyaTwfnz5/HRj34UJ06cwPXr14kEt3XrVjz33HMoKSnByMgI5HI59uzZAwDo6OhAPp/HXXfdRR2urVu34sc//jGqq6sxODgIr9eLF198EcePH8eWLVv+rGHU0NAQHc7MjffixYtoamqCWCyG0WiEzWZDcXExOjs7cfz4cTz++OMkgcYkjlOpFKampnD33XfDZDLBYDCgvr4ev/zlL3HgwAEsLS1hy5YtBL/p6enByMjIhmtjuuOsG+v3+1FTU4OZmRlUVlbi5MmTiMVi6OzsxLZt22AwGPBf//VfdElXVlaiu7sbZWVlOHDgAAYHB6HRaJBMJmE2m9Hd3Y0XXngB7e3teP3119Ha2opUKoXx8XFUVFRgbm5uw7U1NjZifHwcGo0GgUAAV65coUKUOSoXFRWhqqoKKysr6OrqItjIG2+8QdrsN2/epOkZS2YtFgvuuusuVFZW4lOf+hR8Ph/BC9kFW0j1RK1WY2ZmhjgLiUQC1dXVeO2117Br1y6SfX3llVdw/vx5bN26lbwYrly5AoPBQBjnfD6PCxcu4GMf+xh0Oh2Wl5cxOTmJz372s/iLv/gLaLVa7NixgzrQDHtfKLLZLOn/M+iG2WzG8ePHIZFIcPnyZQgEAhoJ22w2KsYYx4upnH3iE58goQe2x/R6PQYHB3HXXXfhvffew8WLF3H8+HFcuHABxcXFBQ9tm81GkogKhQIrKyvg8/l455134HQ6cf78ecjlctTU1EAoFOLpp59GJBKBUqnEyMgIPB4PMpkMxsbGSIaZFZOpVArV1dWYnp7Gk08+CZPJhJKSEszPz+PGjRs0Bdgoenp64HA4CAbKntuVK1fwkY98hCS0pVIpJicnce+99+Khhx6iRL60tBTt7e24fv06nnrqKSwuLsJms1GCyJoxDz74IM6dO0eYar1ej2PHjhWcGjBPIp1OR9/dnj178Pvf/x47duwgV3kmxnHPPffgP//zP3H48GHs3LkTH//4x/GZz3wGNTU1+Id/+AdMTEygq6sLoVAIXq8XQqEQv/71r3Hs2DH8/ve/R3FxMTlS19fXF+zMSyQSEnioqqoCl8tFNBqFz+dDXV0dTp48SRjstbU1GI1GjI+Pk559Y2MjPv3pT6OpqYlkWY8dO0bPjqm57d+/n5LasbExOJ1O7N69u2CnVCAQYHh4GEKhEBaLBRqNhqbqVVVVuH79Ovh8PsFh9+zZg9deew1btmyBRqOBSqVCSUkJamtr8f3vfx9OpxPl5eVkbqnX63HlyhV0dHTg+PHj5C3E/HRcLteGazMYDPjTn/6EYDAIpVKJ0tJS4tPt2LEDjY2NCIVCOH/+PM6cOYMtW7ZgdHQUOp0OExMT4PP50Ol0eP7559Ha2oqRkRFKiMrLy3Hs2DHyH1laWkJlZSV4PB4ZmhWS3m1ra4PH40FVVRUaGhroPJ+bm0NjYyOGh4eJnzUzM4NDhw7h97//PfL5PE6fPg2PxwOFQoHz58+js7MTP/vZz9DW1gaHw4GrV6/C6/Xi8uXL0Gq10Gg0OHDgAKxWK0lSF3puzEhPqVTC4/FgdnYWBw4cwBe/+EUolUpK8Bg36fDhw5iensbevXsJPm61WomzcvXqVezfv58aHTU1NThx4gQqKyvx9ttvo7GxkVymP/nJT/7ZpmdNTQ06OjoQCoXQ29uLbdu24Ze//CXuueceQlcw369nnnkGTqcTHR0duHjxIoD3c5DBwUE8/PDDuOeee3DkyBGSfLVarbhy5QoOHDiAn/3sZ2hvb4fJZCIZ5kITW8bdLCoqQjqdxvz8PORyOTVDb9y4gXA4TNP8XC5Hoj8TExOkghiLxTA5OYnh4WEcPXqUGpKlpaU4ceIEuru7IZFIUFdXh5GREZw+ffrPwn11Oh1mZ2eRyWSwuLiI4eFhdHd349KlS3jkkUcwODiIyspKNDQ04PLly3jsscewsrKCBx54AJcuXUIwGERjYyMuXLiA+vp6SKVSfPvb34ZKpUIikcCePXsgEAiwZ88enD17Fuvr6xgfH8fQ0BAqKysL+o/U1dUhmUxiz549ZDr80EMP4dKlSySL7fV6SYTnySef/IBM/v79+7Fr1y4S+WHKgeXl5aitrUVdXR2uXr2Kffv2YWFhAZs2bUJlZSV0Oh0pRX6Y+NBkcJFIhCeeeIKMxtra2khlismgMYWXmpoaDA8PY+fOnZiZmYFKpcLq6ioymQw2b96MTCYDk8mEwcFBGrGFQiF8+ctfxte+9jWIRCKairBRYSEzK4bzZYd6NpvFe++9R0TkpaUlAO93ey9fvgyFQoGWlhYMDAygpKSERnJ6vR4//OEPoVQq6VCdn5/HwsICDAYDuFwu2tvboVKpcOXKFSwuLqKurq7gZeLxeMDhcFBdXY3KykosLCxgYmICe/bsQV9fHx544AHcuHEDAwMD1CHbvn07hoeH0d/fj+XlZVKeWlhYwL333ouf//znqK6uhkAgwJkzZ1BXV4ef//zn2LVrF/r7+8nz4+TJkwWnBk6nEwcPHkR/fz/xaXg8HpkYMSJcJBIhCBAzhGlsbKQOGVNC+fu//3v84Ac/QFtbGxktnjlzBrW1tTCbzRgeHqaO7PLyMiYmJjZc262wFC6Xi1QqheHhYSLNbtq0CbOzszh37hwkEgnq6+vR0tKCUChEbroMrvH888/ja1/7GuLxOHVt33jjDTzwwAPw+/3Yv38/GSfx+Xy8+eabaG5u3nBtfX192Lt3LzlB6/V6umTffvttVFRUUNW/efNm+P1+wjTK5XIIhUJyNv6rv/ornDlzBq+88gr0ej1BIphD/aZNm8gs0ufzobm5uSAcjpEyg8EgYT4TiQSam5tx8+ZN1NbWQi6Xkx468w5Qq9Uk8ZvP51FVVUWkuYWFBcjlcrS0tJDXxx//+EcAIPUSprBSCCoC/F9YFzu8+/r6YLFYcODAAXC5XEoULly4AI1Gg82bN5M7N/C+P87k5CR4PB5+8Ytf4J577oHdbkdJSQkSiQT6+voItplOp0mK0O/3E459o7jVeA8AmWCx5LmnpwfBYBCLi4sQCoU4f/48SktLEQqFsHv3bkq8WCf07NmzyGQyaGtrg0QiIWhdX18fYrEYkVUZNKVQw+LkyZPo7OxEX18fEYdVKhW+9a1vYW5uDvv378fy8jJWVlbA4/Fw7do1fOpTn8LFixc/4P4qFAqxvLyMM2fOYOvWrVCr1ZicnMTKygo+//nPA3g/Werv76cO5tLSEmGZbxfRaJT8TJgC0pkzZ7B7925cuHAB2WyWuHYMG59MJnHy5EmCopSUlKCkpASnTp1CZ2cn+adoNBoibq6urhKZnU0NGNRko2DTR6PRCL1ej2QySVKN7Bybn5/HiRMniJv19NNP4w9/+AN5TayurlLnUKPRUAHKpqaRSAS7du2C2WwmxSe/34/e3t6C/k+sMJbJZASfcjqdRMy02WwfgMucOnWKZELZVMvj8eBHP/oR/W5ME5+t87XXXsP+/fuRTCZx9uxZkrLkcrkFp90rKys4dOgQzp8/TwU084UZGhpCRUUFPB4P+SOcOHECa2trZOzL5Kgff/xxvP3223jjjTdQXFxMJnOjo6MYGhrCX//1X5MQQFFREcrLy8l7ZqN47733cM8992B8fBwikQgmkwkrKyu49957weFwUFJSgpmZGZpmzc7OQq1W4/Tp0/joRz9KBOHa2lpMTEygvLwcFy5cQFtbG8RiMcmKKxQK3Lx5E6Ojo6R253K5Ck6UJycnaRrFpkHXr1/Ht7/9bTrbmZiEQqHA5cuXYbVa6bnNzMxgaGgINpsN//mf/4nm5mYsLy8TX2pubg5PPPEEnE4ntmzZAofDQTBMu91ecG3sHM9ms3A4HPB4PJiYmMBdd91FBna5XA6xWAwTExO4du0atm3bhl//+tcA3m9sMO7n//7f/xtXr17F4cOHiQMwMjKCkZERpNNpbNq0ifggHA4HQ0NDAIBHHnnktmsTCoUEMWNCMSsrK9i+fTsaGhogEongcrlw+fJlctyORqN45ZVXMDo6SucImzrv2LEDo6Oj5P/CxDRGR0fB5XJx48YNNDY2orq6Gl6vF9FodMPnNjY2hk2bNpECqUKhwE9+8hN0dXXRFP/EiRPg8/koLS3F+Pg4QqEQBgYG4Ha7YbFYIJFIsHXrVrz99tvkJcTMKy9fvgyj0Yh8Pk8NKLPZjHg8DrfbjVgstuFEeWhoiLyZPB4PwazLy8shFotJoZT5A83OziKdTuPs2bOwWCxoaGigc3737t343Oc+h2eeeYb4irFYjGB1HA4Hvb29xCWtqakpWDzeGh+60JBKpfjmN78Jm81GD4Gp1EilUhw6dIi8M9RqNTo6OvCTn/wEHA4HkUiEMH6MMN7V1YV0Ok14u2QyicnJSbS2tlLXMZ1OE6mrUEeNOWczU6dQKASdTodsNgutVot9+/aRucrQ0BA+85nPwOVykWoRU4Zg5BZGOBYKhbBardi3bx/W1tZw6dIlfPazn8XNmzeRyWQgkUiQz+fR1ta24dqcTifBOhjxc2lpCb/73e9gNptRU1NDY1WGpz98+DBqa2tx7do1LC4uIhgMYvfu3fB6vTh79iwZKUUiEYyNjeHBBx8kZZhUKoWVlRWYTCZ0dXUV7OAyJ1WGn2XTD4bn5nA48Hg8GB4eBofDAY/Hw8MPP4yXXnoJY2NjJGd44MABnDx5Evv378elS5dodHj48GHcuHEDXq8Xi4uLpFnPCFqFxpVlZWXEq2BEVobjzuVy5ErLIEWZTAYTExOQSCRwOBzEOSguLiYJXqbExOA5jHMglUpx9epVGu0yHOtGoVAocPr0aQSDQRr3Tk1N4dq1a3jkkUcgk8nQ39+P+fl5lJSUQCwW00HMZIEbGxuxZ88ejI2NoaqqCjU1NUTguvWbvnUErVarCQu/UcTjcUxPT9P3xDrsbDrEOrCDg4MYGBjAli1b0N3djcXFRcJ6qlQqIrhdv34dBw8eBI/Ho8Lu+eefx+uvv44zZ84QEZtNEguR6gBQd5vBdWw2GxQKBc6cOYPGxkZKFLLZLEGNHA4HQfaYskdpaSkZnrGJH+uasXf6uc99DlevXiV/D61Wi4aGhg3Xlk6nqYtdUVGBTCZDcMfZ2VmUl5eT4hHjlr355ptIJpPo6OhAJpOBWCzGQw89RB1U5kLM4/GQTCZRXFwMg8FA4/toNErKLIW8A8RiMSYmJoi/srKygqtXr+IrX/kKHnroIbS2tlLThhXazz77LFwuF86dO0ciHCKRCM8//zzKy8uh0+lonw0ODuKrX/0q/tf/+l9UCFutVthsNoyPjxf85vR6Pd59913isDAeCVPRq66uJt7Z1NQU9Ho95ubmUF9fjxMnTsBkMkEsFuPkyZMQiUR49dVX8eijj2JpaYlIupFIBBMTExAKhdixYwdWV1cxNDQEl8tVkCzJVLOYQhGDbA4ODpLICJOB5vF4BGkQiUT44x//iO7ubuzcuZMUxex2Ow4cOICuri7EYjGcO3cObrcbdXV1uHz5MqRSKcFfMplMwemjVCrF8vIyZDIZhEIhEYfNZjMGBwdpssj4CxMTE6irq0MkEiGiOADiqzGjSnbvAe97J8zNzWFxcREWi4VktQ0GQ0H/Ea1Wi3/7t3+DRCIhM7aysjJcv34dV69eRWlpKfbu3Ytr167h1KlTeOyxx3D58mUsLi7SZMhkMtGeYHuVnWerq6vYvHkzent7if80NTVFSVehLq7FYiHYVDAYJD7ewMAASktL8fjjj0MqleLGjRs0+ejq6sKrr76K1tZWckLOZrNQqVT48pe/jPvvv5+aUxKJhNS5GhoayHCNcVsKCazE43HY7Xak02kyO3z33Xdx/fp1eL1eNDU10WTH6XSioaGBchHmtVBZWUl5i8PhwOjoKBW2zDvEYDBgbGyMeEy5XA5arbZggdbV1UWolObmZmzfvh1OpxNf/OIXkc/nCV44MzODiYkJfPzjHyf1sVAoRJA2kUiEY8eOwel0Eq/L4/EQuX5+fh7bt29HW1sbotEoYrEYgsEgrly5suHastks/H4/xGIxJdsMbsTMok0mE8HeamtriXNZX18PtVqN6upq6HQ6/OEPf8CFCxfozFYqlaioqKAmmNvtBofDoSQ+Eomgp6dnw7UZDAaMjIygsrISfr8f4+PjKC0txW9+8xsSzYnFYrh+/TrS6TSeeeYZpNNpGI1GErIB3p8osek2M2pkULuTJ0/iiSeegNFoJOGJpaUlGAyGgo1FjUaDmZmZD5h+3rx5k8wSi4uLodPpKD9paGjApk2bcO3aNYyOjsLv96OiooIg9A8++CAV1ozfyuVy8frrr+PYsWNIJBIEfezr68P8/DyOHDmy4fpYfGjo1MrKCnbv3g29Xo+SkhK0tLRgcXERd911F8xmM5RKJTnzlpSUYGJiAnv37qUqz2q1oqKigrC26+vrpPbBWPJHjhwhgtHKygosFgva29uRTqdx7ty5DdcWj8dJHsxqtaKjowO5XI5UIwYHB3H16lVwOBxKftmkYdeuXejq6kJZWRl27tyJ/v5+HD16FHK5HO3t7bBYLOSmGYlEYLfbMTg4CLPZTL4QhWTbGPZ9ZGQEly9fhs/nQz6fx5EjR1BZWYmamhrCNDPyod/vx/Hjx6FUKlFcXIxMJgOHw4FgMIjq6mpkMhksLCzA4XBQV5SRz4LBIGGlmcrGRsEgFW63Gw6HA+l0mvg31dXVMJlMBKkQCoXEAVEoFKipqaELlanLtLW1EcF8dXUVly5dwt69e+FwOLB161b4/X6kUilwuVyMj48T2fB2EY1G0dHRAZ/Ph7m5OfIUYVJrLS0t2LVrF+6++24yGtu3bx9EIhH+4R/+AZs2bSKX0nvuuQeHDh0iAif73S0WC2lf8/l8NDU1UQe9ENY1FAqhs7OTfpZKpUI8HkdHRweCwSDEYjGamppw9OhRHDt2jAo4l8uF2tpa8pDgcrn4/e9/j1Ao9AEpS4b7ZM97YmICmzdvxtGjR7GwsFCweGQ8m2QySVKNbEpWVVVFkqG7du3Cvffei0AgQHKkpaWlZATGDmDmp+L1epHP51FTU4Pf/va3mJqaQmdnJ5GKORwO0ul0QS4V8D6eP5FIEDSNufVaLBYsLCwgn8+T7O6jjz6K2dlZcLlcDA8PY9euXbjrrruIrFtcXExKNwsLCwS7OHToEDweDxYXF+F0OlFcXIyGhgbqZm8UUqkUdXV1iEajCAQCkEgk4HA42LFjB/m+yGQy1NTUoLW1FSKRCI888gi2bNlCClCM/MhUghjMiqmBMMPPzZs3g8vlkpmdTCYr6Iacz+fR0dEBsVhM/KimpiZ84xvfwObNm0lOWSwWo6enB7W1tVhbW8PMzAw2b95MwhJbtmwhp/WBgQEyTdy7dy/27t1LE0ImkBEMBjE7O1swuWKCAlqtFmazmc6OlpYW6PV6NDQ0IJ/Pw2Aw4OGHH8Z7772H7u5u2O12HDp0CFarFdPT0ySi8IUvfIGmd5s3b4ZEIkFHRweA96Gop0+fJpWytbU1vPXWWwW/udbWVmSzWQQCAeRyOTKY7erqQmtrK7kHazQawkTzeDx84QtfQGtrKyKRCLq6ulBUVIT9+/djdHSU8NYSiQQHDx7EmTNnIBAIsLKyAolEgoqKClJQ2igYh4PP50OhUMBms0GlUqGyshJ33XUXyVGOjIzgypUrGB8fR319PUEIORwOnE4ncfoOHDgAtVqNpqYm2iN///d/D7vdDpFIhImJCSo6rl27VhAems1msWfPHtTW1sJms8FqtcLlcmHLli2or6+nDrNIJMLevXsxMzODXbt2obOzkwzGWNPiueeew//8n/+TpIoZ/KW9vZ2aUozrxRKrQg0LDodDKkhGo5FM2x5//HGafDPMf1lZGcRiMbRaLXbv3o3W1lZ4vV5cvXqVlNBeeeUVyOVyVFVVobq6GjKZDFu2bMG1a9eo0cAkzfV6fcG1MQdv1pUfHh6GRCJBd3c3FSkSiQRqtRplZWUYHR3F2toaZmdn0d7eDqvVSrzSGzduoLi4GPl8nuBRmUwGgUCAfCXYVIlN45k/ze3CbrdDoVBgfX0ddrsdw8PDGBgYwKc+9Smk02l0dHSgtrYWd999N771rW9hYWEBnZ2dCAaD5KMwOjqKpaUlpNNp/OVf/iUpULIJG5NRF4lEBJdraWlBXV0d/vIv/7LgXtixYwdNP+12O4qKigjmxTg5Pp8Pi4uL+NOf/oTq6mqEQiF0dHSQ6pvD4UB1dTXEYjGpDTqdTkxPT6Ovrw9vvvkmstks8VmYT1WhPCmZTJKxJpsCrKysEM+WGQo+8MAD+PrXv07w69HRUezfv58mKUzs57HHHiNIKFMYO3LkCN566y24XC7Mzc0RLyIWi/1Z8YGSkhIyf2STDzal8Pl8mJqagkwmw5NPPkkqmXq9nsxrg8EgKisrMTExgUOHDhEvj5HRmW8cm2yUlpaSZ8kDDzyw4dpujQ890RAKhVhaWiJFEtZtYjq+TEPdYrGgqKgIN2/eJNw5h8OBWq2GQqHA7OwsLBYLybqxCy2XyyEYDJKFPOu8yGQywmkXCia1yV5mMpmESqWCSqXC0NAQTCYTdUJSqRTKy8tJf5/P55OsYGdnJzo6OrC4uPiBDjO7kFOpFEQiETHyWWd1o1AqlfT7MSlZdlnU1NSQLN+t7uasE8hcu9lUIZFIwG63o6KiAul0mpLhd999FwDosmZO47FYDA6HY8O1WSwWLC8vkwwtI/MxrepcLkewJ0bIZoo3DD6iVCqxurqKhoYGeDweKhyZFnk4HIbNZkMgEKAObigUQjqdLth94XA4CIVC0Gg0NCZlikJWqxXl5eU02mMk3pWVFeTzeUxOTkKlUqG9vR0LCwuk5iGRSKjzsr6+jpmZGdTX11MXnE00SkpKCnJbmMoOk4RlyWUgEEBdXR2CwSBV/iqVCrW1tYhEIigrK4PRaKRvlMfjQSKREOmTQROY2llRURGSySSCwSDm5ubosmEKK7cLkUiEdDpN5kmpVAqdnZ1wuVwwm82w2+1IJBJQq9U04p+fnyfjQzblW1hYQGNjI9bX16HT6Uh2kI17zWYzNQOSySQCgQAp0RUKoVCISCRCo2OxWIyioiK43W6CITFcPeuGrqyswGq1wuv1ori4GC0tLRgeHkZ1dTWZRzLYDusesWTGYDDQRIGpexV6r8XFxWRKxA7xZDKJkpISLC8vg8vlQiaTIZ1Oo6ysjJTrlEolioqKSDCCEe0bGhoQjUaRy+XIf8FgMMDpdILH44HL5RKMotAUjcfjYX5+nojJ2WwWTU1NuHHjBqnQ6HQ6lJaWQqVSkUw1K5KYvvr58+dRVlaGLVu2ENSFQbH8fj9isRh9D2wKU1JSUrCAZHAhJqGayWSg0+ng9/vpuTHhC0bsjEQiCAaDKCoqglwuh0KhgN/vR11dHU0uFhcXEY/HIRAISAmIacKzvVNfX18QJ8x4StlslgjHTKqbkUjZnnG73aipqUEgEKApC7svpqamiFC9srIClUqFdDpN6j8MpsmUqPh8PoqKigom8+l0mqCHAoGAEo9QKET7K5VKkUpjc3MzXC4X4vE43Rns3UulUohEIlRXVwMAQVaYYMj8/Dz5g3C5XBiNxg94i2y0PgbjYNMgpsfPYIhsgszOFqYy1tjYCKFQiEwmg9LSUiiVShw6dIggGWazme4wBruWy+UkUVuoIcDj8aixxuVySb2L/dypqSnodDpSTGO8im3btlEx63a70dfXR9LEjMjP5XIpuS8vLydeAPuu6+vrC96prGhkSj1ra2soLi6G3W6H1WolGBwzDWSJH+NwsmngzZs30dzcDIPB8AEZW51Oh7m5OVI6ZK7qDJFQiMuaSqVI+SgcDtNEh615YmKCJjpM1IRND5iCWV1dHUHdBAIBCRCwXG14eBgHDx4keV6VSoVkMgm/319QUILJwFutViK4M58Klm8w016Hw4HW1lYSOZicnCQVQ7fbjfLycgwPD5OPEJ/Pp9yDcXG0Wi0py32Y4tHj8ZCRocfjQW1tLQKBAKqrq7G6ukrKqQwtce3aNQiFQiJpM5VAxm8QCAQ0CWJ3iU6nI+6cXq8n816fz7fh2pgyK2vWs/c1Pj4OlUqFqakp8ohTKBTYtGkTIpEIWlpacPHiReJyBQIBKqaam5uJ9F1ZWQmZTEaKU2azGaWlpRCLxfD7/VAoFBuu7db40BMN5h3ByHQLCwuEGYzFYiQtlsvlsLa2hmAwiHg8TqMgdsBNTk5Cr9fDbrcTy97n85F+cCaToWmD1+slnHkh6BSfz6dOaDabRTgcJuk/dukyDOLU1BR1rHK5HMbHx6mbzz4mJmUXiUTo8mX4QXY5rK6uwuVyQavVFtzcrOvGLhIm8cgSdZY0soJHJBIhFApBpVJhbW0NHA4Her2edPaZiRpLvqPRKEZHR0nejo13mfEU6/zcLthIlJHTmNIFk+FbW1uDVCqFXC6nwogpcIyPj5PMI3tfrJvH/AZYoVFZWUnvgnEVVCpVwcuEz+fD7/eTMlE2m6X/Vq/XQyqVIplMIhQKkZmc3+8Hj8fD9evXkUwmUVVVRX8GI9oxnfVMJgOv14vS0lIyv1pdXSXY1Z+TBWbTkXw+T4euy+UiApvL5SJvBXYpsDEzG/WHw2GabohEIkxOTsJut9NeUCqVpPgyNjZGI80/R7hmkCsAlDR7vV5SlwiHwwRHkkqllNCsrKxQYscInXNzc5QQM9Lw9evXoVAoyF+Bz+cTVrNQIg+AlKCYYlQqlYJeryfljkQiQYf5wsIC1Go1FWmrq6uU2LNDFQApBAH4QPLMJGoTiQQikQiR7zYK5r/ClJhYs8Ln80Eul5P8JlM6YqZSbFLHFHUYp4FdcrFYjGQ4GeGS7XsmDSmXy2G32zdcG4PVsCKZSUjPz8/D5/ORvDeDjzE5TMbvYt29gYEB6HQ68Hg81NbWIp1OE4aXGVGNj4+TM+/a2hrMZnPBy45BGxlU7FZ1vWw2S6N9Ho8Hv9+PqqoqUhxk5nZFRUUQiUQQCoV49913SYloaWkJ6+vrJKO8tLQEgUCAcDhMQgmFpM+ZOiKbuDFlI7Zen8+H1dVVMrE0GAwkGbq0tIRkMgmxWAyHw0H7nvFC0uk0UqkUVldXaZp863MTCAQFGxZMLSiTydCkj71fpqLE4/Gg1WphMpnQ2tpKSoJMcpRJysbjcczNzdG56nA4yK9CpVJRc0Cv11NBWgiXDoBgqalUis5WVuR5PB5qLHg8HohEItjtdkqebm3+NDY2YnJykiDXrEvLBDKcTieJOTBoYKHmHYfDoX+fJbHA//WWYF1/xgfk8Xjk7cAksUUiEZ13V69epbuK7eXh4WFUVVUhHA5Dp9PRVJJBbwu9Uy6XS8Uz+6ZZQsd4EOw5GgwGkoVlpnWMFF9aWkqqektLS/B6vcjlcvB4PIQgYXkRe5eFiOpMNpf5u7A/3+VyQSqVwul0IhqN0hTTarXSxIz5j7HidmVlBcvLyySBzc7fCxcuYPPmzXTvuFwujIyMIBgMElf2dsEUkoRCIcn9s2KeSTEzbqFUKkVTUxMVEwzyxiS+gf9baLNpi1QqJQVBBj2UyWT08wrdC0qlEuFwGAKBgM5eBokym830zjKZDOx2O6qqqqi57XQ66W5jZ8HKygrJRDPxJAbzstvt5HcTjUbJxHejYHmsRqMBl8vF2toameGazWa43W7iQC8vL5OQxq2FLYNIM04au+sEAgFUKhXm5+dRXl5O+QKHw0EymaSmwYeJDz3RSCQSpATCXhwz+4pEIqTH6/V6MTMzQ6o1zBSMdX7lcjmWlpbQ39+Pp59+mg4mRrS7ceMGyeix5HlkZAQWi2XDtbEODcP9KxQKIm2ySyyVSmF0dBRzc3M4dOgQxsfHUVNTQ5AQJrs5MTEBh8NBilisqGJ4WCb9ybDDHo8H8/PzG66NqUOxrotGo8H8/Dx11EUiEcLhMLk6Ms3j1157jbSeKyoq0NLSgitXrqC5uZnkTpkOM1NbKCkpwcLCArLZLJLJJPh8fkHPBYfDgaKiIjidTuoasmkGIzrOzMzQIWMymTA3N0fmZ6wAWl5exvz8PMLhMLq7u6lIZJjL9fV1VFVVIRAIEN9Aq9UiEAhsuLZMJgMej4e1tTVks1mIRCJyG2YSnky+lhlE1tbWEhmWuTMzTejBwUGk02lSdWEd1pWVFRiNRrjdboTDYbroCnX75HI5Tery+TwSiQS8Xi9d+DKZDKlUisz7Qv9tSLWwsIB4PE7yrOyweu+99whuw3gVAwMD2L59O8nxssuKcX4KfW9Go5GKqWQySST8YDBIeu7scPJ4PMjlcrhw4QL0ej11nQKBAM6ePQur1YrBwUEUFxfDZrPB5XJhbGwMc3Nz0Gq15Lmh0WjItLNQsOSfvd9kMomlpSUiljHCJLuobj1DZDIZcTUSiQRGR0cxMTFBGu3BYBAejweBQABVVVVYWloi9/pcLkeky42Cz+fD5/ORehMAOkgzmQw2bdqElZUVUo6Znp4muOPa2hpdjOvr62R8xLrArMBkWueskcCmTsxIrdB7NZvNZGQajUZx7do1KuirqqrQ19eHS5cukT58T08PLl68CIFAALPZTFOybDaLCxcuoKamBiUlJRgfHycFOIvFArfbDYFAQEVjJBIpuLZb94FIJKILt7KyEsvLy8RnW1paws2bN1FVVYWWlhYMDQ3RNEShUKCxsZHI/DKZjHxj5ufnIRaLEQ6HqVhnFzLbLxtFIpEgeB+bUDA5WbfbDaVSCYfDQRyelZUVSKVSjIyMEGSB+QqwBkEwGMTo6CjJJjOPCYbjZ4ny7OxsQdhDOp2GQCCAz+ejZo/f70dXVxemp6fp92eyxaxo8Hq9cLvd9F4MBgOuXLlC8p4AyFgzlUrBbrfTVJW5Ibvd7oJ7lXlVrKysEC4/Ho+jsrIS4XCYlCIzmQxJnbKJE7t/1Go1XC4XuFwuLl++DIfDQfhuJveu0+kQDAYpKWe/byFYbT6fp2KdTfeYB8nU1BTKy8vpjAfeT355PB4uXryIyspKqNVqult5PB5isRhJtjK4YSKRwL333kt+G2yyfu3atYJNT3aPs8JMJBJRB53D4cBisVCTh+UFrLEokUgIJVJSUoLh4WHw+XzKY5h/CSNss2Is9N9mmaWlpQUV2DKZDBkxMvnlcDhM3wojYieTSUgkkg8U9IxIbLfbScijpqaGoJYCgQCBQABerxezs7Pg8XiYm5uD0+mEWCwm+OJGwaZydrudODvMiDQcDqO6upr8Idrb25FMJjExMUH8G9YQrqqqwujoKKRSKYmOpFIpOrP9fj/0ej01UXk83p81JI3FYjCbzeByueSYffXqVbS2tmJubg7V1dWIRCJYXFxEJBIhLolKpUIkEqHGi0qlQm9vLxKJBLZt20bEdWaqK5fL4fF4cP78efh8PnC5XGzatKngc2NFO5tC+f1+EtIoLS2F0+mkJtLKygo0Gg2tn+WGDJbPGsRs6s7n8zE7O4vZ2Vk0NTVBr9cjGo1iZmaGRGFWV1dx4MCBDdfH4kMXGmazGdeuXaMOJsO6nj9/HmKxGGKxmMx2kskkPvKRj+DKlSs4d+4c4VvZ+JoRmG7evEnYwvn5ebzzzjsoLy8nvDPr+AgEAsKT3S6y2SympqZIGYoZJc3OzsJms9F4kWlbFxUVIRgM4uLFiwTHYR1uBq9iCalMJkNrayt8Ph9BR9iFxA7uHTt2bLg2nU5HSeut3aHa2lrcvHkTNpuNDnK9Xg8+n4+33noL6XQaRUVFRHJ2Op1IpVLo7e1FR0fHB7oTrGsqEomosmVVbCEdd1ZkMFgFq35jsRhGR0dRW1tLHSCGA2TjWiYBCrzvRcJMFR0OB3XjS0tLEQwGMT4+Thh8gUBAxNY/56MxODiIoqIiknZ1u92orq4mchyrupeWljA3N0cH0NTUFHFFpFIpFAoFent7UVVVRZMSv9+PtrY2DAwMoKKigpR/mPxqobVpNBqsr6+Taznrmnd1dZGRn8FgwOrqKs6dOweTyYTa2lpoNBpUVFQgn8/D5XIR3pR1l0pLSwlyEgwGkcvl0NnZCR6PR5AJqVRasINrMBgwPT1N6jSM96BUKqk7xBxrFQoFGcuxYpvpnjPFKSa9eqv4wa5du6hjPTMzg/LyclitVtKELxSsgGXQIr/fj3A4DJlMBrvdjrKyMpp4MCUOAOTgzjxistks7rvvPvzyl7+kUS4zcotEIhgfH0dLSwtmZ2cpkWMu5hsFg61EIhHIZDJyruZyuejo6IBCoaACjpHt9u7dC5fLBaFQCJfLhenpaaTTaZjNZqhUKlit1g/4jBw/fpxc1MViMfEgWDG+UUgkEoIKsKSUQUNu5caxM5hNv7Zt24bnnnsOs7OzdIayooDBgW793RkZnjnBMnWirq6uDdfGCNd+vx9arZaKxeXlZVgsFsTjcTgcDoIoLi4uYu/evWhtbYXL5aLkoqmpCTt37sTCwgKuXbsGsVj8AbNUoVAIm80GqVQKk8lEHehCSmcseWG4ePYtsSSeGZ2urKyQzGomk6F/zmCerFl28+ZN4iaxxppWq6Xf0+l0UnLNDNE2CrFYjIGBAZqg3boPBAIBWltbcfPmTfD5fOzYsQMjIyPQarXYvn07RkdHyXHearWiubmZCKh+v5+UvW7cuIG77rqLYErDw8NYWFiAxWIpeKdKpVLMz89T918qlSKTyeB73/seDh8+DKVSifHxcWpeMKPezZs3k/kgMyy9evUqqqur6Z4BQMUBm4Qkk0nYbDZotVqS5N4oFAoFRkZG6PvkcrmkjvfRj34UbrebJi2jo6PkA8W4LBwOh7rdwWAQWq0WMpkMdXV1yOfzpLzIJiMej4fuu9XV1YI8CFYQM4EXNtkIhUJQKBRYWloiWGZ/fz9xWFmyx8zkotEo5TBms5kmLqFQCPX19fT7OZ1OMtt0Op0F5W15PB4mJyep6ctM5lixzZrDsViMfNBY7sMQAQKBgPKen/70p4jH49i+fTtqa2thtVrR1dUFu92Ompoa1NXV0RSLydduFBKJBIODgzCZTNBqtUilUohEIuSJZTAY6LtmIjYikQhcLhfnz5+HUChEdXU1TZCYF05FRQXm5+dx+fJlggExSeVIJEJFWqE8iXFkWf4jEomgVqsJycHQH+w8GR8fx7Zt28hsj02BjEYjmpubcenSJbz99tuorKyEUqmEXC4n2G1dXR0VqayptHfv3g3XxkQLstksNbnC4TDxaTgcDsHXkskk3nnnHTQ2NpL8NBOS6O3txdjYGA4fPoyTJ0/CarXCYDBApVKhs7MTV69exVNPPYXR0VESpbHb7X9WZZIFJ//nnEruxJ24E3fiTtyJO3En7sSduBN34v9jfGiOxp24E3fiTtyJO3En7sSduBN34k582LhTaNyJO3En7sSduBN34k7ciTtxJ/6fx51C407ciTtxJ+7EnbgTd+JO3Ik78f887hQad+JO3Ik7cSfuxJ24E3fiTtyJ/+dxp9C4E3fiTtyJO3En7sSduBN34k78P487hcaduBN34k7ciTtxJ+7EnbgTd+L/eXxoH43/83/+D+RyOZmgqNVqOJ1OzMzMoL29nbTcmUt3OBxGNBqFTqfDG2+8Aa1Wi87OTgiFQuRyOZSWlsLn8yGVSsHr9ZJPRFlZGaxWK2n8BwIBpFIpxGIx/M3f/M1t1/biiy+S+REzTGKu5UqlEvl8HiUlJRCLxVhdXUUqlUJxcTEWFhYgk8lIH5q5eM/OzqKqqgpcLhc+nw9erxcSiQRbt25FIBAg+/VQKERa9p/4xCduu7Yf/OAHCIfDKC0tJWdQ9jNLS0sBAMlkEkKhEFKpFKOjoygrK8P6+jqUSiV4PB65RxqNRqytrZF2ukQiQSwWw5kzZ1BVVQW/3w+1Wo1cLodEIkF68J/5zGduu7b/+I//IG8RqVQKtVoNmUyG06dPY8+ePSgpKUEwGCTTMolEAq1WS+7LfD4f2WwWTqcTd999NyYnJyEUCsnyXq/X49y5c2hoaIBAICBN/lgsBj6fD6VSiaeffvq2a/v1r38NjUaDVCoFlUoFuVwOr9eLGzduoKSkBAaDgYzCBAIBlEolEokEzGYz+vv7Sd96cXER7e3tqKiogNvtRjAYhNvthtfrhUwmg81mo+92fX2d3NYlEgkee+yx267tj3/8I5LJJNLpNJl23eqmrdFoyByMx+NBpVKR+SRz52bmOjt27MDMzAxsNhui0ShcLhc8Hg88Hg8sFgsEAgE4HA4MBgPEYjE5hX/hC1+47dqee+45iEQi5HI5ciRnDu6rq6vQ6/Xk26DT6WC1WiEUCpFIJMhDJJvNwm63w2g0Ynh4GNXV1TAajchkMuRWqlar4XA4oNfrMTw8DK/Xi7q6OkQikQ3XBgAvvPAC6XrzeDzIZDJwuVzY7XbYbDaoVCoyFePz3z+eYrEYpqenyeGavXen04mtW7cCAJn4yeVy8Hg8OBwOdHZ2kiEe85UJh8N45plnNtwPwPtmZSqVikwA/X4/ysrKEIvFyASytLQUcrkcfX196O7uJlNPoVAIu90Oq9WK4eFhtLa2kiM1M9lqbm6Gz+dDMBiEWCwml9hcLodHH330tmv76le/Si7CuVyOjO4EAgHy+TyMRiOCwSBCoRAAoLKykjT55+fnsba2Bg6HAz6fD7FYjLa2Nly9ehUVFRXkAcLn87G0tIQdO3ZgenqaHG+Zf8CPfvSj267tO9/5DgB84OdzuVwEAgEIhUK0tLQAeN+/gun+Ly0twWq1gs/nIxKJYH19HVarFXq9HlNTU3SWM9d5t9sNnU6H8vJyRCIRxONxpNNp+t83v/nN267t9ddfRzQahVAoBJfLBY/HI48m5kXE4/Fo76tUKkSjUcRiMSgUCnC5XPoWM5kM+dLU1tZCKpWSa7NEIkF1dTWi0Sj5tqjVaoyPj+NLX/rSbdf2ox/9CBqNhgxJ5XI50uk05ubmwOPxUFZWBqVSifX1dTrbxsbGoNFoMDIy8gEX+61bt2J1dRWJRAImkwmxWAxzc3N0XycSCYTDYQSDQQiFQpSVlWFtbQ1f/OIXb7u273//+1Cr1fR8mOs7cx622WyIx+PkSJ3JZOB2u+FyuciHiH2rzMyTaf7zeDwA798n7Hmvra1hdXWV7oZkMrnhc3vxxRfhcDg+4L7N5/MRCoUgEonIOFQqldKdC7xvthuLxZBOp8Hn8+kOYfef1+ulv888WoqKihAOh8Hlcml/x+NxPPzwwxuujeUTLA9aWVmBQCAAj8dDRUUFPB4PIpEIhEIhwuEwnn76abz22muor69HJpPB+Pg4RkdH0draioWFBZSXl6O6uhrZbBYOhwOpVAoWiwX5fJ5+DvPSWllZwde+9rXbru1Xv/oVmTrKZDIA75tGAkBpaSn8fj+SySQZ77HvWSaTIR6PI5fLQafTobq6GhMTExgeHkZxcTGi0SgUCgWqqqqQzWaxurqKiooKuFwurK+vk29JPp/f8Hz76U9/inQ6DS6XSx5GwWAQqVQKOp2OfML4fD55bHC5XPI7Y/+dVCqF2+3GysoKZDIZvTPmiSKXy1FcXEymhcz3TSqVbpgnvfLKKwDeN+6UyWT0rTGnbbPZDL/fD5/PBz6fT3eqQqEg82qZTIZ0Ok1mvtFolMyi9Xo9ioqKEIvFoFarkU6nsbS0hFgsBpvNBpFIhJ07d952bT//+c+Ry+XIJDCRSGBpaQkLCwtobW2FQqGgXIjL5eL69etk5pzP5yESiWAymVBeXo5Tp06RYXFzczN0Oh3S6TScTicCgQAqKirQ398PvV4Pk8lEpqcbfW+3xocuNDKZDEQiEZaXl8nRM5FIoK2tjUxvgsEg1tbWyInW7XZjcXERlZWVZB6kVCphNBphNBoxMzMDvV6P0tJSFBcXIxAIYHV1FRMTE3QBp9NpeDyeggZq6XSanB2ZuRIAFBcXI51OIxQKweFw0KEZiURgNBqxsrICuVyOeDwOv98PiUQCl8sFjUZDzozModhiscDr9dJHkkwmkc/nsb6+XvC5sY16a/Hl9XqpGNJoNFhbW0M4HIZYLIZEIiEDlrW1NUSjUSQSCWg0GqjVanKP9Pv9ZNJSWVkJv99PhYlOp6NLsZBhlMvlIodrdjA6HA5YrVZkMhl4PB6srq7SO5XJZFhdXYVEIqFEUSqVQqfTweVyQafTYWBggBxRtVotOTQ3NTUhHA5T0RgOh8mF+naRSqWQzWYhkUiQTqfh9XqRTCZRXl4OAFTQsEuDGTPlcjk6hFlRk0gkEAgE4HK5IBAIYDKZyNHX4/FQkcm+j1AoRK6ytwt2Wd/qiMycq5kpETOOZO65XC4XLpcLer2eDkrm0N3Y2IjZ2Vmsr6+TCZdcLsfs7Cz2799PxpHsZzOjxNsFc6ZNp9PI5XJk1KPRaCiZBd53N9dqtRgcHERNTc0HXHoTiQTUajWCwSAkEgm4XC4VGX6/HwKBAPPz84hEImTwB4Cc6gsFMzRMJBLkmJ3JZKhxwZJopVIJhUKBxcVFSKVSxONx5PN5pFIpKlCYg+yFCxcQi8XA4/GQyWToeTLnW5a4LC4uoqKiYsO1MWfyfD6PaDSKbDZLz8vtdlNSw/YyM0iy2+1IJBIIhUIfcNzl8Xjw+/0QCoVIp9PIZrMoLi7G1NQUgPcvrludiUUi0YZrY4aJbF3MeIztQ2bmxePxYDKZMD4+TsVRIpGgs2FxcRFarRYul4uMqNify+PxsLa2hoWFBSSTSSgUCojFYqTT6YLvlSUT7CLl8/kIBAIQiURYXV3FysoK1tfXKWnI5/PkVsuaEIlEAj6fj4pzj8cDg8FAyZTVakUgEPiAs3s6nSZjsY2CuU6n02n6hrLZLMxmMxYXF8lMVSwWQ6lUIhAIQKVSYX5+HkKhkL4FDodDyQMz72Pu042Njbhx4wZcLhd9bxKJBGtrayhkUxWPx8mlmBmoeTwe8Pl8qFQqBAIB+P1+iMViyGQyjI6OUsGgVqshFAopgZ2dnYVarcba2hq59gJAY2MjBgYGUFxcDD6fD7lcjkwmg1AoVPCdxmIxSqpZ8441spLJJCUffD4fJSUlmJ2dhUqlwtDQEEKhEJn4+Xw+CAQC6HQ6TExMQKlUgsvlIpfLoaGhAWNjY/T+hEIhnVeFIhqNkvs6KxxY8cIM9kpLS1FUVAQAmJqagkKhIKO8RCJBTbOVlRXU1taS+3w8Hsfk5CSsVisSiQQ14vh8PjULCjmDM9NHdj9zOBxKeJPJJN1/zEhtYGAAbreb7lV2V1mtVsjlcnC5XHA4HCwsLJDRbElJCS5duoT29nYyfMtkMh9oCm703KRSKVKpFDUpFAoF8vk85ufnyV29qqoKJpMJdrsd+XwepaWlSKfT1Bg2GAxQKBRoamrC8vIypFIpmSZWVFRgbW2NmnrMvFEulxfcp+ydJBIJ5PN5SCQScDgc2pPJZJIaimKxmHI81kxh+RJrshUXFyMWiyGTyUAsFkOr1WJ9fR1ra2tkhsuaXKFQqGAeF4vFqPhkhTYzSF1dXYVYLCZDQ5PJhLGxMRiNRlitVvB4PDL2FIlESCQSUKlU8Pv9UCgUtH+ZUSdr0LBieXZ2FgKBYMNCg+1J9pyZWbPFYoHT6UR1dTU1apgZZU1NDTKZDJLJJOViq6ur0Gg0SKfT0Ov1mJ6epvuwuLgYbrcbBoMB6+vrlC+vrq5CIBBs+NxujQ9daLDLlSWl7OUODw+TAybrDK2uriKXy6G2thanTp0C8P6lIJFIUFFRgUgkgoGBAcRiMRQVFVGVFw6HkclkaGOyDyOTyYDD4Wy4NlahKZVKCIVCrK2tUVeptLSULhf28oD3L252YLDLYG1tjZJmLpcLqVQKoVAIAJibm4PZbKYHm06nqVJkndfbBUvgWUeIw+HAYrEgGAxiZWWFDqJ4PI65uTnodDpKVEQiESKRCNbW1qBSqWgzsa54KpVCIpEgF8lcLgetVksfj1wuJzfcjSISiUAikQAAJVZ2ux1CoRBKpZIupdXVVfrzy8rK4HA4IJFIyM1aIpGQWydLOiKRCF0EbrcbPB6PDvpwOFwwYU6n0/D7/eSCzRxl19fXUVRUREmXz+f7QDFmt9uh0WiooyaRSGA0GjE/Pw+RSASLxYJkMgm3241wOIx4PI6WlhbweDzweDx6noUilUohn8/TNIz9d0tLS+Rou76+jlgsRheVxWJBOp3G+Pg4ucYaDAYIhUKcPXsWMpkMmUyGXE3Z+sRiMQQCATKZDH0L7ALdKNbW1gCAChnmEsxcU4uKipBMJrG0tIRgMEhFic/no4PebDZDqVRiYmICGo0GMpmM9uDKygp1DNfW1sDn86FWqxGLxcDlFkZjCgQCSrzZd5tMJqmIZw2MYDCI5eVlrK2tobGxEUVFRZifn0cmk4HBYEBtbS1EIhGmp6eRy+UQj8chkUigVCoRCoXINTiXy2F+fp66vX/um0smk1RcscQ0EomAw+HAZDJBLBbD6/Vifn4eNpsNXC4XfD4fiUQCXC6X9ix7X3K5HFVVVRCJRFhaWqIDvq6uDgKBAAsLC9RJLeRaLpVK6eeyAod1wlmhDID2KHu2wWAQTqcTpaWlKC0tpYJlcnKSki9WkAKAVqsF8H4RxDrAMpmMOqC3C+ayzM4lLpdLSTPrJkYiEXi9XnA4HBQXF6OmpgaBQABLS0sQiUTQaDTkrj0/Pw+r1UpnF/s2JRIJTQXZmc4SjY2CJXscDucD3+bAwAD4fD516zOZDFKpFHXEJRIJ5ufnodVqoVar6bxihS/wfkeeFbGskGeJXCgUQiwWK/hO2X+bSqVoisfOND6fTx3eaDQKh8OBWCyGTZs2YXp6GuFwGBqNBjqdjiYb09PTyGazUCqVEAgEtMeYezqb4uTzeepQbxQymQxra2uU/LE7NhqN0nnEJtSrq6s0maitrYXdbkcgEKAJBnN6ZokW+2bm5ubg8XhgNpshEoloSiUQCAoW3WxKyefzIRQKIRaLycm8ubkZyWSSGjOBQIDuXp1OB5/Ph3g8Tv/d+vo6rly5Qt1qtp9ZAskaASx3yGazMBgMG64tGo1iYWEBfD4fMpkMQqGQGoLsd9fpdPSzKioqqME4OTlJZ3Yul4NUKsXU1BQqKiqg1WoJqZDNZiGVSpHNZsmJPBwO08/eKFjhbzQaKV8QCoUYHx+HzWZDJpMBj8ej+1EgEECv10OlUsHj8dD3xOPx4HQ6AYAmilKpFBKJBMvLy9S85XK59H2XlpbS73+7yGaziEQiAEB5AgAsLy+Dz+fDbDZjfX2dJhFCoZCav06nEzweD2q1mppirOgQCoX0XtPpNE0pWb4JvN90K1R0CwQCcjdnZ61UKqXzjTVDs9ksNYHY2cT+Hnt27K9ZY0MqlSKTyWBpaQmLi4tUDLDvg7mObxS5XA6rq6tQq9V0nolEIszPz6O8vJwaGS6XC3a7HTKZDNu2bSPkBCucWZExMTFBfy5bezQahcFggFwup2Z8IBCgs+fDxIfmaLDLhFXBbBMNDg5idXUVSqWSLodEIoHl5WU0NjZCp9PRoVZUVITNmzcDAH3IrFMwMzMDj8eDVCqFhoYGlJWVQSQS0QsqVA2zqUUul4NEIqFu0vz8PB3CrNsdCoWg0Wiwvr4OqVSKfD5PHSiBQACLxULdSpaocjgcuFwu+Hw+KhbYGF4kEhVcG+t853I5sn1nxQ+bALH1JZNJJBIJ5HI5qlRFIhGSySRdpul0Gvl8HkVFRaioqIBSqcTq6iolUKxoYYVWVVXVhmtTKBR0IbLERSwWY3JyEktLS9DpdFAoFEgmk1hYWMDMzAwSiQRBe1hiyp7jxMQETasEAgHC4TDcbjeEQiECgQBV3EKhkDqyhSIejxPchEGvWMHCDspQKEQTL6vVSjADliQWFRVBpVIhlUqBy+VSZ5dV+LFYDOXl5VCr1eBwONTtYQnXRnuBJTBisZg2pdPppKQ7m80ilUoRLEmpVEIkEsHr9WJ1dRWZTAZ6vR65XA7Hjx9HLBZDLpfD+vo6wapY8ZHNZunvsynCRsEmLKygZs86kUjQQct+N6fTCZFIRJCkWCxG0zq2P0KhECKRCPh8PrRaLcGJUqkUwbISiQQ9h0KXMAAad7NkgnVmvV4vfX/s//f399P7LioqoikWg31JpVJMTk5SQ0AikUCtVlM3US6XI5/Pw+fzIRQKQSqVFuzAcLlcrK2tIZlMIpPJUDHPvhPWUWMwhWw2i5KSEmg0GvD5fEilUqhUKio8gPcvTb1eD6PRSE0QLpeL2tpaqNVquvDY77RRsDMtm80ik8kQbCWZTCIYDNIEKJvNIhAIQCaTwWg00vSMTVa3bNkCpVJJ56/ZbKbiyefzwWq1QqPRQCKRIB6Pw+v1IpFIFCxu2fnGzkW2L9hUR6/XU8K6vLyMSCSCffv2oa6uDm63G06nExKJBC0tLchms/B6vZTg63Q66HQ6+j54PB5NvXK5HH3bGwV731wulyYDrPhMpVIwGo0wmUyQSCR0RrCLNRgMIh6PQ6FQ0P+8Xi/W19chk8mgVqshlUoxMTEBhUIBnU5HSXMsFkMymSy4H1iSwb4zNiX3+XxwOBxQKpUEnbLb7dTk0Wq1CIVCCAaDNA3UaDSYm5tDKBSCXq+H1WqFVCpFLBajpgibbrHGV6EzTiqVfiA5Yue81+tFOp2GwWCA1WpFLpfD9PQ0eDweAoEAuru7UVxcjFwuR51gBgdRq9X0O8lkMvp+gffP+lu72YXuVABUJMlkMuh0OuoMFxUVobKyEgKBAIFAAIFAAHq9nhqhbN+yBmEmk8H169fpXmUTegazYUgJBr9i399Gwefz4ff7qWElFosp8WWNI61WC7lcDj6fj/r6emi1WphMJpr6sSYHh8PB4uIiJepNTU007S4tLaWz4NbJAZv43y5kMhnBy1gxyva4Xq9HSUkJhEIhPB4PFhcXYTQaCY3C9jb7b5eXl7G8vExTP7FYDL1eD5fLBalUSuekVColGHWh+57D4dC0lhWQ6XQaq6urWF9fR3V1NUwmEz1LmUyG5uZm2kMsX7NYLDQVyOfzBGNmjUqNRgOVSoVMJkONRgYr2ijYxII1aRi82OfzUf4nl8upYWYwGKDX66lRwIpYlUoFhUJBk0V2VgLA7OwsoRxYwcDyRdbsvl0IhUJaG2tWiMVieDweeva3PoNAIACz2YxkMol4PE6NfTbx5HA48Hq9MJlMqK2tJQqD0WiEQqGAUqmkopnlOR8mPnShwePxYLVakUwmsby8TEnKkSNHEAgE0NHRQdMJs9lMH5lGo8H+/fthMpngcDio87tjxw4kEgnMz89jbGwMLpcL3d3dhOudm5uDUChEcXExMpkMdYo3+hDkcjl8Ph+Wl5cRCoUgFotRXFyM+fl5ShQYdMFoNMJgMMDlctEHHY1GYTabcfPmTbS3t1OXjU0r7r//fqysrNC/z4okhvffKBg/g43mZDIZbt68CQ6Hg5qaGpSUlCAej0Mul2P//v0ERUkkEvTP5XI5GhsbYTAYcOjQIUxMTEClUqGoqAj5fB6dnZ3IZrOoq6uD3W5HaWkpmpubMTAwgLGxsQ3XxuFwqHPJfie/348jR44AAPbv308jx9raWoRCIdx3331wuVyQSCTI5/Pw+/3IZDL43e9+B4VCgWAwSEVoOByGyWRCIpFAWVkZjdFtNhsMBkPBEbRKpaIuRiaTgUKhQCqVgkQiwfDwMBVT+XweZWVlhAVWqVTo7Oykyru6uhrDw8Po6urC9evXMTQ0hEQigc7OTjz00EM09ZmamkIgEMDa2hquX7+OsrKyDdfGpmHxeJy6S36/H62trZBKpaiqqiJOhcVioa4zj8fD7t27UV1dTaPbwcFBPPzww/T9VVZWwmKx4OMf/zh27NhBSQXrVDJOyEaxurpKXVo2fUwmk3T4yOVyjI6OEoaTQRRzuRwMBgN1fxQKBQYHB9Hc3Ey8E61WC5VKRdyR1dVVTE5OEsfI6/UWfG5sfQyix6B9brcblZWV1JFiz7CrqwvxeBw8Hg8ulwu7du0imFcymcSf/vQn7Nu3j2AbSqUSKysr6OjoQCQSgV6vh9PpRENDA+6//36Ul5cX7FyxC4Fh2dl319LSAj6fj5WVFbhcLqjVauzYsQMSiQQ7d+7E/Pw8fQ/Ly8uoq6uDw+HA1q1boVQqYbfbEQqF6N2yc/Dq1auQSqWor68nTsBG4ff7YbVaqdFRUlJCk6nZ2VlUV1cThMFkMmFqaoo4HU1NTVCr1YjH4zCbzZifn0d1dTUikQjBQ+bn5wn/HY1GEQqFYDKZYLPZEIvFaLJ4u4jFYjCbzVSAZzIZLC4uoqWlBR6PBwqFAjabDR0dHdi9ezdNSRwOB0FCWQNiaWmJnunCwgKtq6urC8PDw7BarQTJrK+vh0gkos7k7SKZTFJSxZLElZUVPPHEE1hfX4dWq4VCoYDRaER9fT1yuRz27duHeDyO+vp68Hg8TE1NQSKRwOl0UhLLis2ZmRkkk0lMTk5S11SpVKK8vBxWq5U6v4XWxuAdZrMZmUwGdXV1WF9fR3t7O6xWK2QyGUpKSogPkkgkiIPD9s6bb75JP4t1QwFQkbtp0ybCp1dXV0MikaCpqWnDtbndbuJzpVIpaDQauN1uhEIhSsIBwGw248CBA1hcXIRYLIbP50N9fT1qamoI6js0NITKykosLCzAarVSIbJr1y66B9kZYzQaacJaKFhCyZKwc+fOYevWrbh06RI4HA41W9j0u7S0lOBHUqkU5eXlUCqVyOVy+MQnPkF5i0Qiod+dNSn1ej0aGhrQ0NAAAFhYWNhwXVwuFw0NDQSHZE1Sxv+Uy+WYnp6G2+1GQ0MDent7EQ6HkcvlsHv3bjQ2NsJsNuPIkSOIRqN47LHH4HK5EI/HEY/Hsbq6ipqaGoyPjxPSoKSkBBUVFVhcXEQgENhwbaFQCKWlpVQ0MMjxzp07MT4+DoPBQJCybdu2QaVSQSQS4dy5c8RRYRMV1gAwGAyoqamBTCbD0tISnS86nY5gVSqVChcvXvyzjUWGGGFTiFgshs7OTsTjcUq+M5kMNeVYfnb33XejqqoKiUQCer0eUqkU7e3tlDsw/lVjYyNCoRA4HA58Ph81tYaGhgqujcHnWSIfi8UwMDAAtVqNpaUlpFIpOBwOLC8vE49JKpXi6tWrNOXkcDgoLy+HSqWCyWQiLiiDllZXV6O6uhpNTU3EA9ZqtVhYWCi4FzKZDGpqaqhwYCiDBx54AAsLCwiHw3A6nZDL5bj//vtRVlYGi8WCWCyGnTt3oqenh3KU0dFRdHV1weFwYH5+HpOTkxgfH0c2m8WNGzcIstjT04PNmzfDbrfD4XAUfKcsPnShIRaLkc/nsXfvXhw6dAhVVVWIRqMYGRnBRz/6UdjtdiwtLcHlcsHlcmHnzp1YW1tDa2srnn32Wdy4cQMtLS0IhULYt28fnn32WVgsFvD5fJhMJhw4cADr6+v4y7/8SxgMBrS0tMDpdGJ+fh579uxBX1/fhmtjCT+bGrBEIJFIoLGxESqVCjabDcXFxdTdi0ajKC4uxqVLlwAAra2tGB4eRkVFBY4fP4729nYIhUL4/X5Eo1FMT09j8+bNMJvNSKfTcLlchLMr1LUSCoXo7++HSqUCj8eD3W5HTU0NnE4nLBYLdefY5vnMZz4DLpeLkpISTExMIJPJoLa2Fqurq3j++edx6tQpfO5zn0Mul8PS0hJqa2vB5/Nx+PBheDwe6PV69Pb24vLlyzh69ChmZ2c3XJtAIMD6+jq6urrQ3t5OXTmPx4O7774b//mf/4loNIrm5masra3BYDAglUqhsbERYrEYNpsNhw4dwpYtWxCPx9HZ2Yn9+/fDbrfj4sWLGBsbww9+8APC/rNOQzQaRUNDQ8HNzQhHdXV1lMg3NjZiaWkJx44dg8PhoA7U+vo67rvvPtpEP/7xj3H58mUUFxcTRO2b3/wm7r33Xuh0OnC5XJSVlWFychLBYBCTk5NoaGhASUkJ6urq8Mwzz1AH63bR0NCAtbU1aDQagutt3rwZHA4HW7ZswfDwMAYHBzE9PY3BwUH09PRgeXkZarUaAwMDiEajaGlpgdFoRDKZxKVLl4i0xaA1b7/9Nurq6jA1NUVdaYFAgD179mB1dXXDtTGRAJPJhGQyiZmZGRiNRhQVFeHQoUP44x//CI/HQ1Ce7du3QyKRYMeOHejt7YXX68W2bdvQ3NyMvXv34sqVK6iurobT6cTZs2fR29uLZDKJ7u5upFIpPPDAAxAIBJidnUVnZyeRkTeK5uZmOBwO9PT0YMuWLeDxeJTEbdu2DSMjI/D7/eDz+eDz+Thw4ADOnz8Po9GIsbExmEwmHDt2DHw+H9u2bcPf//3f4/7776fJm81mw8mTJ7Fv3z5MT0+jq6sLk5OTeP3111FbW1vwIpbJZFhfX0dbWxuampqg0+lQU1ODkydP4uDBg7BarQiHw7hy5QrefPNNVFdX4ze/+Q1mZmZw/PhxBAIB7NixA21tbXjmmWfwve99D1euXMGJEycwNjaG4uJiuqBnZ2fR1tZGRWNPTw9Bcm4XPB4PKysrRAwVi8XEd3jmmWdw4sQJKvp7e3vp9zeZTAiHw9BqtbDZbLh27RqN4w8dOoS6ujo0NDTgwIEDMJlMUCqVmJ6ehtVqpYnuPffcU/CbU6vVWF9fR09PDzo7O6HVatHY2EhiAcvLy9QYuH79Onp6evDjH/8YxcXFMJlMaG9vx6ZNm+B2u1FfX4+BgQFs376dOsoNDQ3o6+vD448/TrC4oaEh9Pf3w2KxFOz4MfGN8vJytLW1oa2tDfv378fIyAiR3+VyOXK5HE6dOkXFhU6ng9/vR3l5ObZt24ZsNouuri74fD7U1NTg6tWrOHv2LNxuN1566SV8+tOfhlwuh16vh8fjgd1uR3FxcUGOHJ/Ph8vlQnFxMZqbm1FeXk4CISaTCcePH0coFKKp9VNPPYVAIIDt27fTtKiyshLRaJTw421tbeBwOASJHB0dRSgUwgsvvACRSASHwwG73Y7NmzdjcXFxw7XV1NRApVJh8+bNdMdYLBZUVVXhyJEj8Hq98Hq9cLlc6O/vh0gkwsWLFxEMBnHlyhXk83l0d3dDqVRieHgYAwMD2Lt3L9xuN/EhXnzxRdx1110YHR1FPB7HpUuXcOXKFbS1tSEej2+4NqFQiMnJSWoGsqLpD3/4A93brNOeSCSwa9curKysEBZdp9OhrKwMXq8XFRUVOH36NIxGIy5fvoxQKIQjR45gcnIS3d3d6O/vh8fjwezsLGKxGA4dOoTW1tYN18bu5YqKClRUVIDD4UCj0SASiaCsrAyDg4OoqKhAT08PxGIxDh48iEgkgs7OTtTV1WHLli0oLS3FSy+9hMbGRrz44ouorKzEzMwMRkdHYTQaIZPJcO+992Lr1q2ora3F6Ogopqam8MADDxSE/YpEIqyvr5P4hEgkwvbt2zE4OEjNDx6Ph2g0isXFRXR1dSESieDAgQN037a2tkIkEhE3lzVRmQiBTCbDo48+isnJSWzZsoWaV/fee29BHgSbvrH3FovFYDKZsLKygoMHDxI0PpfLYW5uDg0NDQiFQujo6MClS5cIgjc6OgqTyYSLFy9Co9FQY0IoFGJxcRFmsxl2ux1tbW0EUd+9e/eG6wLen4LYbDZUVVURMmHTpk0YHh7GgQMHoFAo0NzcjPb2dqTTaTQ2NkIkEuGRRx7B22+/TYI0fr8f4+PjGBwcJO4EABIHaW1thcfjQXNzMzVEKisr0djYuOHaOBwOTVhLSkpQVVWF7u5uJBIJfOQjH0FxcTEJG42MjODpp5/GtWvXkEwmMT4+DrVajYaGBrhcLrS0tGBoaAif//znsXv3blRWVqKkpARjY2P4+Mc/DgCoqKjAysoKVlZW8MADDxSEdd0aH5qjsba2BrVajZGRESSTSepcdXV14a233qKxPxtjr66uoqWlBfPz8/j2t7+NYDBIbPiRkRFUVFQQHjqRSODdd9/FyZMn8Xd/93eIx+NwOBzUZVheXi5IHHa5XKipqaFkTCqVYnp6Guvr65iamkJtbS1isRiRJRnJzePxwGq1kloGIz5u27YNFy5cQEdHB1QqFXUnBwYGsLi4CKFQSIUBG49uFKurqzThYXCR5eVlWCwWuN1ubNmyBQ6HAwMDAxgcHEQwGMTevXsxNDRExEQGY/gf/+N/4OzZs1hcXEQ6nYbD4cDa2hruuusu/PSnPyXilFQqpQ7YAw88sOHaWEGTz+extLREJEOLxYKrV6+itbUVfr8fCwsLhGv8xS9+gXvuuQc9PT2YmprCK6+8gvr6euzfvx8LCwu4efMmLBYLtm3bhrW1NXi9Xuzfvx+vvfYaysrKIJFI4HA4cOHCBdx3330brm10dJQ6X6wrceHCBfT09ODGjRsEpZJIJJBKpXjnnXdw6NAh5HI5bN26FQsLCxgeHsa2bdvA4XCwa9cuTE9PQyqVgsfjYXJykrCNjGzOYCcLCwuoqanZcG2Li4vo7OzE/Pw85ubmIBKJSOHr/PnzAECdLEaC3LlzJwYGBggeGAgEcP78eeTzeWzZsgXr6+tE5pyamqIJYDgcRmNjI8Ee2Jh4o4hEIjTuFAgE0Gq1mJychMlkQmlpKdrb26nQZt+vyWTCW2+9hYcffhherxd9fX0wGAyw2+2kAtTT0wOz2UyH/Ouvvw6pVIq+vj6YTCYYDAacOXMGADZUFgGA6elpFBUVkVDE6uoqTCYTZmZm8Pvf/x533XUXdfvn5ubwd3/3d1hZWcE777zzASKuWCxGX18fdu7ciePHj6O5uRmJRAJ//OMfodPpCErEmh7ZbBYjIyMF9+ri4iJN3liXMZ1Oo6mpCXa7HS6XCw6HA1qtFocPH0Y4HMbBgwcRi8Xg8XiwsrKCixcvwuPxYG5ujjgc27Ztg0QioWKgoaGBEgf2/fX39yOXy21I+mNQF7fbTROA9957D08++SQGBwdpesWwwidOnCCoAfB+F3Z1dZWmawaDgbDkbreb/nplZQVlZWVwu90wmUwQCoW4ePFiwSmaw+FAa2srCYUw0Qs26YhGoyR2YLPZcO7cOayvryOdTtP5w5IALpeLw4cPY3JykojVTqeT1HsMBgNWV1dp0jo9PV2Qh8YIrouLiyT2cO3aNWi1WggEAmg0GszOzoLD4ZDwAiNKx+NxDAwMENyKTdD8fj8pLbpcLjz44IP4wQ9+gGg0io997GOor6+H2+2G3W4vyNEIh8NoaGggJUSBQIC+vj6UlJQgk8ngvvvuw/LyMhYXF6FUKvG73/0O27Zto2LH5/NR04DtYwYrWV9fJ1Ipg/jyeDxSEjp16lTBvTA5OUmJMRMcEIlE2LdvH86ePYuOjo4PfFOJRIL2CYOXAO/Dc1tbW9HR0YEbN24QJ4dBqgYHB6HX66FWqyEWi2G32/H73/8etbW1G66NcTvFYjHGx8fhdruRTqexsLCA6elpzM7OIhqNQq/Xo6amBufPn0cwGKSp3PLyMt566y3U1dWhuLgYe/bsweTkJEH0XnrpJbS1tSEajWLfvn24ceMG/RmFuEoAiEO2tLQEg8GAoqIiTE5OoqKiAqWlpXC5XJifn8fo6ChNM6enp/HOO+/AYrGguLgYBoMB999/P/74xz9Cr9ejvr4ePp+PVDjZ9H1wcJASf5FIBLvdXnDawmCMbDIQCoUwNjZGghWxWAxXrlxBJpPB7t278dvf/hbNzc1UtOp0OoJ8RSIRbNmyBdeuXSNYZyAQwJ49exAKhdDc3Iz+/n56ZlNTUwU784xMze43LpeLRCKB+vp6XLt2DVarlfK3PXv24PLlyzSlZWdTOp1GWVkZzp07R5Dw8fFxRCIRmnoyHp/P5yPkhd1uL8izXVpaQnFxMeVAPp8PLpcLDQ0NWF5ehkQioWkSn8/HwMAA6uvraYrN4XBgt9sJfupwODA7O0sQMQZPFYlEqKurw8zMDCQSCQwGAxwOB4RCIfbs2XPbtTGYrkajAYfDwczMDF577TVCIuh0OjidTiJuv/766xgZGcHZs2fxV3/1V9Q0Z03gQ4cO4ebNm0QwX1tbw5e+9CWMjIxgbm4Ok5OTKC0tRUlJCU6dOoXOzs6C+4HFh55o5PN5vPbaaxgeHkYikSCuQV9fH/R6PbZu3QqhUAiHwwGn0wmHw0F//fzzz8Nut6OxsZFgHTabDblcjrD+sViMurdsZOn1ejE3NweBQFCwUyqXy6kaZkx+dkkFg0GCBCQSCczNzSEWi2FwcJAOHlYRdnd3o66uDleuXCFFI1b5j42NERehpqaG8OIMH7pR6PV6wsKp1WpYrVbk83n09fUhk8nA6/UiFAqRPJtIJMJbb72FSCSCdDqN5eVlqmyvXr2K/v5+9Pf3U2fGbDbj5MmT4HK5KC8vRzgcxqVLl3D16lWS89wo1tfXce3aNQwMDJC8cCqVwq9+9SuCLbANoFAo0NDQgL/5m7+B1WrFa6+9hnA4jO7ublKzKC4upslWJpOBXC7HyMgIrl27BgCkfuD1ehGJRHDixIkN16bT6eBwOGAwGGA2m4lY1d/fTwQzNp2w2+3YtGkT4WOj0SgsFgsaGxuJSBYOhzEwMADg/QN3fn6eCk2lUolYLIbV1VVK1Lu6ujZcWywWQ29vL6RSKWw2G7RaLaxWK37yk58gEomgra0NPT090Gg0WFhYgEqlwpUrVxAIBFBdXY0tW7Zg165d2LVrF8rLyzE7O4vi4mKUl5ejvLwcJpMJGo0GoVCIuhPr6+uk1lOog8vn8zEzM0PwAybgsLi4SGpZuVyO9gVT83C73fTdGwwGgka+++67cLvdGBsbI4z4zMwMBAIBjEYjVldXcfnyZSoM/9xFzLDYGo0GJSUlMBqN4PP5NCVihy/DU7/77rvg8/koKyvDysoKfWuzs7MoKSmhyymTycBsNlM36N/+7d8wMjJCSe7Q0BAymQz8fv+Ga1OpVLh06RJ4PB5J7bpcLly+fBmZTAZHjhyhqdiVK1cwMTFB3cZ3330XXq8XRUVFpHIkEAhQVFRE34fFYkFfXx+am5tx3333obS0lM7KqqoqtLW1bbg2LpeL8fFxUpwLBoPg8/no7e2Fz+dDWVkZuFwu5ubm0N/fj/Lychw4cIBkvSUSCTKZDMFUmYobw8kzboRGo0FLSwt9EwyqUwgeKpVKibgfCoWI/yQQCGC32wkywL658vJybNmyBXK5HMePHyco6crKCs6cOUNJN5OBdrlcGBkZweDgILxeLywWC/R6PQklFCIOa7VakpFkMuGMBKzX61FdXU2YfMbbYHCD69evw+/3o7S0FDabDfX19aSKyFS74vE4Tpw4AY1Gg/b2doyNjeH69etYWlpCPp8vqJYoEAhw8+ZNKmrX19exvr5O5yOD1EajUbz33nuorKyERqOB0+nE+fPnYbfbiVTa2dmJ5eVlEkyQy+XU2ddoNLj33nsRi8VIUpmJhmwUEokEU1NTJA3M4/GwtLSEr3zlK6Q+xyBIDQ0N2Lp1K+677z7o9XpS2BsZGcH4+DiWlpbw3HPPgcPhoKOjA3V1dUilUhgeHsbq6irGxsaIJ7S8vEwwyI2CqQoxeKpcLkdzczMuX75M0EjGfxoYGKDGIkvG2OR4ZGQE58+fx8svv4yqqioiPhsMBoyPj5OE8KZNm9DR0UHw7EJnnMlkwtzcHMFJGTm5r6+PBCRMJhO8Xi/OnDmD8+fPo76+niBNuVwOCoWCJtJMzUkmk5EAxgsvvIBwOIxPfepT0Ol0uHz5Mt555x1EIhEUFxdvuDb2vv1+PxwOBwKBADgcDvbu3Utojfr6eqRSKfzmN7/B9PQ0Ll26REIDExMTmJ2dxYkTJ3Dz5k3Y7XYiIWu1WpSVleHdd9/F7Ows0uk0Dh8+jNraWsTjcZr8F9oLDKZ3q8Ibm2YXFRXReXLmzBniYzCUCFNIZPcSI+Fv3rwZO3bsAJ/Px+zsLBXb7A5MpVJkp7BRsDuTcRMY32hwcBAzMzM07WWoH6/Xi5WVFczPzyMcDpNiZyKRoGeTzWZhNBrpXHS5XNQcZXn0zMwMLBZLQdjqrQISsViMbCQymQympqaQzWY/oKbZ2NiITCaDffv2weVy4b333sPo6CiA94u9H//4xwR1bWxshFwux3e+8x1cvnwZx44dw4EDB6gRuLCwUBD5cWt86EKDQXh0Oh1VxNlsFnv27CHVF8Yl2L59OzH0HQ4HKioqaDS2ZcsWCAQCbN68mbrvQqEQqVQKTz31FE6cOEF8CoVCAY1G82fJ4ABIhYHhq7PZLI182cEiFAqh1+uxuLgIk8mEGzduUELBcHHr6+vo7u4mDW6z2Qy9Xk9J7fbt20mGUa1Ww+12F8R9MyKVx+Mh1Zu1tTU0NzeTFCSTTzSZTBgYGCBZwLq6OoJllZSUwO/344knniDSMguxWEzdAjaiZTjfQkVQLBZDVVUVkbmMRiPGx8dx9913o7GxETt27KDfv729nTSsr127Bo1GQ4SntbU16uwzqINMJsPc3By2b9+O6elpWCwWpFIpyOVylJSUwGKxoK6ubsO18Xg8KJVKKlaZEkdtbS2pa7GEpLy8HGfOnCFpQ5vNhsrKSuKBMK4GU2oxGAzUAWTa/ZFIhIiJbHS8USgUClRWVsLn82Fqagoejwc8Hg+PPPIIST+yC4mpsJSUlGBhYQG1tbVQKpXwer1ExGUcBCajyJRhgsEgFVmMsJ/P5wt2cOPxOMkbc7lcGI1GkiH2+Xyorq4mbgxTSIpEIiRryC6E7u5u6PV6PPTQQygpKUFNTQ3EYjHxETgcDq2NXQTr6+vka1Eo2H6Xy+Ww2WxQKpWorq4mqUY2jens7MTCwgJNZlpaWkiVymQyQSAQoLa2lnCwTOWGecqwrrVOp4PFYoFSqSyYzOfzeVRVVVGRJpPJsLCwAKlUSl0hBmWprq6G2+2mpJ9h/blcLsmjHjx4kL7RRCIBh8OBmpoaOBwOmqzW/v/Y+++oxu8r/x9/SgIJgVBDQgIhgei9DQzD9D4eT7GdcW9xiuMkTnH2pG+c5mSTrLPZdbyJk43jjZ24xc641/EUpjIMDL13EKigggqSkED6/uG99zPeM8j+/c7+Oa9z9mxy4vIavdvr3vt8Pp4lJaisrORDWKJ1JYo6MzMTZrMZZWVlnClAE8q7776bO9uLi4soLS2FTqdjbwcA7uTT4dpsNmPnzp2syXW73YyjFolE0Ov1a+6Lpsg0aSL2e0lJCcsDCYxA77bq6mrMzs4ylY8KSDJnEl43IyMDmZmZuO+++5g6Q40hlUqF1NTUhNNHIuBdmbNC71sywvp8PhiNRjQ0NODChQuoqKiAWCzGDTfcgLy8PJbNrKysoLCwEIuLi8jKykJaWhqcTicOHz7M7yGxWAyz2YzCwkJ4vd6E3y26V6iRIJfLkZGRgX379rGOenp6GjKZDIcOHcLy8jIjVsvKypCfnw+DwYDS0lKIxWKsW7cObrebjcREmCM8NpF9iMqk0WjW3FskEkF+fj4XjLm5uVCpVNi4cSNP18jMmp6ejra2NtjtdgiFQmRlZTEliSAgjY2NDCQgTPShQ4cQDodhMpkwPj4Og8GAbdu28bso0TWNx+MYHx/HysoKTwoOHTqExcVFNDY2wmAwcKe/u7sbBoMBc3NzyM3N5cKYpoPbt2/nBg7hXvPz87G6usqTMzLwBgKBhMUjZTBRxhQRkiorK3H+/Hk+7CsUCpjNZm6QpqSkoLy8HHK5HFarlSltZrMZCwsLfMYKBAK44447YLFYsLKyAofDgYqKCtTX12NgYCCh8iMWiyE3N5cliTqdjpunFouFD44mkwnl5eVISUnB1q1bIRAIsHfvXphMJrjdbhiNRjZop6amoqKiAmVlZUhNTYVOp8Pp06f5PZ6ZmYn6+np+Vye6pgTrIGw1vXd8Ph97+zQaDXJycrC4uMhno5ycHJ52U3O2qqoKMzMzGBwchM/ng8lkQlNTE8bHxzE3N8dQB2o+fZz8ksicJPWVyWSora3lvBzytpSWliIej6O2thZ+v58Lb/p3kqdmbm6Op3jT09PIysricwRN4yhbI9G0hQoWmvyOj49Do9EgPz+fUclerxdpaWkwGAw4d+4cG/9NJhNPp+jZ3rJlC6xWK2KxGH+f77//fkYMS6VSGAwGnigmknVduT6xdIqqVjKZ0UG3vLwcHo+HDWIUpkZVHiH4IpEILBYLZ1KQjpwC4IAPdcovvvgiV6P0UiLd61qLyEwA+GNEU45t27YxRYowePT3JCUlIT09/SPYWmK7k6yGDkSkrSZcHwDuBiaaGhAyjkgflAVAYWqEZiWKERkJu7q6+K+TSqUQiUSoqKjg7oDdbufsg5SUFDQ2NvKong7ldKBba5EWGQB3PgOBABoaGljbSL8VXXdCHVJIUDQaRSQSYVSmVqtFRUUFBAIBhoeHUV5eDo1Gg9zcXPT29jJ2Ljc3N+HhhUzKhK4jqgOFRdJvSgd1CkAjsz6h44hi4vf7UVRUxCQzut6ZmZlsbqPOKHUF11qUi+L3+/neTU5ORmlp6UdQmtnZ2VAoFBgYGOD7ke5r6nQVFRVhaWmJ0aPULXE6ncjLy8Po6CgyMjL4kKHT6RL6DJKTk1mOA4DRmUR7oW6ZxWKB3W5HIBDA8PDwR+hWJKlKT09nzTMZTyn0j2hRpMFNTU3l3z/RonuZ7hnlFQGT5PVJTk5Gfn4+pFIp7HY75HI5I1sBcAHl8/lQVlb2kY4RYSivu+46PkRS3gCAj+0wq9VqJn/Q/SWXy5GXl4eZmRnEYjGmIVHwFo2oCWtKh/O8vDx+pqn5UVFRAbfbjWg0iry8PKSmpiIcDqOzszPhwY+Q4vTnFwgEHAwll8v5OddoNAy6cDqd/B4AwMQ1pVIJn8/H3TCSVNA0hqho8XgcYrEYarU6YaeUZBc06c7IyIBAIGATLvHhDQYDQ0ICgQAyMzORlZXFptPU1FQGExDRhCRWKSkpqK+v5+wK+j2oObTWoi7xlXRBwjSurq5y95mKbCqaiMZCZnMitqSmpqKqqgomkwmxWIyJdfSMXEkkJLNpot+NCDZkNCUSHRXQIpEIWq0WeXl5/K4lhChhvCORCAMqiFRIk0G6fiMjI3zv0KSeis6rLQrlvJJi5XA4kJOTg+XlZZ6Ak7FWJpOxbMpmszHut6+vD0ajEdnZ2VxEUmc4JSUFBQUFqKurQ09PD1Nt6BC41iLMcTAYhFarhVqt5vwKQuVTUe5yuRAMBllJQIAEegfRGWNxcZE181KpFC6Xi0MZyduxurrKGVtrLZpC0H1HmUDZ2dkfyeCi35i+reRBIGQwTTG0Wi0H1pG812g0MvUpKSkJeXl5nF2R6CxCzRj6bek7SUUWmbe1Wi1nuUgkEjidTuh0OkgkEp5w1dbWQqlU8vuZimZCegcCAW52UTDcx6HPKdTuSvqWTqeDx+PhaYRCoeBwQbqXiAZIhf/KygoMBgMcDgf/d4lEguLiYnR2dvJ1pvsAQMK9Ec6ZMrtIYaNUKlleSpTJSCSC0tJSzg0Jh8NMhaPvAgCWsBFQhpQ4tCfK26JzQqKl1+vhcDhY4lZYWMhn4M7OTpZl5+Xl4dy5c1xMTk1NcdjiyMgItmzZwnARarzr9Xro9XrU1NR8JEiVGkGJpslXrk880SAGM/2LgA+Lj4WFBWzevJkPnUtLS9yxd7lcKC4uxvT0NObm5mCxWPDiiy+iqKgIHR0dqKysZPpBSUkJ4vE4PvOZzyArK4s7C8CHh4Mrg5r+95LJZLDZbPxij0QiUCqV6Onpwbp165CRkQGFQsHTk/Xr18NisaC5uZn56MTfpq5leXk5BwglJSVxENzg4CCKi4thNpsZ6ZbopUg3Dh1g3G43741SQP1+P0KhEBwOB6677jouwi5dugSfz8ea9m3btuGRRx6BTCbD+Pg4ew5CoRC2bNkCv9+P+vp6LC4u4tKlS0xeWmtRimZRURGbEClhWCAQ8MNNyFuS9JAWlxCZ5IGg7hd1HMlgvnfvXh43Dw4O8oSDPn5XW9RZp4eUDqVWq5XHykQ18Xq9uOWWW7jT3d/fz50nq9XK3aQdO3YgEAhgcHAQ0WiUAxvHx8dhNBqZlkIYwbXW6uoq2tvbIZFIGOksFotx+fJlVFVVsSmdcHZ1dXWMWaQwyrKyMszPz0Ov16OyshIbN24E8GGhTNCCwsJC/Od//ieUSiUXvQqFIqHBnzqNWq2WE76JmmE2mznYMSsri4uK3t5eOBwOnD17FtPT0wgGgzh37hwCgQBsNhsUCgW6urrQ39+PpKQk9h/4fD7k5+ezBDIlJQWtra1r7g0AH/AAfIQkQhQs0qfm5eVBKpViz5490Gq1cLlcHG5FB0EqbmtrayGRSBjF297ejltvvZVlgZcvX8bAwABcLlfC54ECu6ihEIvFUFRUhPT0dDQ3N/MhVSgUMmWMJrzUcJifn8fo6ChqamowNDSEnTt3cjikTqfjfBLgw04c/R4UhrTWIi0u5TQQ+a2trQ0NDQ2cQeTz+TA4OIgdO3ZgamqKi0qiWlE3fHh4GDk5OXwgI3LS9ddfj/n5eajVai7iifW/1qJDCk1VKWmXwk8pc4eM283NzWhpaUFDQwNjbHU6HQBwarjZbIZAIGDt98svv4wdO3Zw88Dj8XDx1t7evubeSFdN8ohoNIri4mLI5XJmx9NheXJyEps2bWKk6BtvvAGv14v6+npEIhH4/X42x1Kx2dTUhO7ubpjNZjgcDrhcLvT393NQbSJdOqFPNRoNE/+kUinefPNNyGQySCQSpibZbDY0NzfDaDQiFApxCOLy8jLeeustxGIx9PX1obi4mO8RmpZXVVXhlVde4WeGml2JJBkkn8zIyIBOp2PpKk3aT506xd37ubk57N+/HwKBAFqtFm1tbRgaGoLb7cb58+eRn5+Ps2fPQqvVcqPHarXit7/9LRoaGpCZmYni4mIGcwiFQpw/f37NvZHUlTKcotEoTyyKi4tx/Phx7qbH43E0NDQgKSkJTU1NiEajMJvNuOmmm7h7+95776GgoAA+n4+7ym1tbcjMzGTak91uRzAYRE5Ozsfeb0TnIZ+BUqnE7OwsysvLuVgMhUJMkqMJ1MzMDAcger1eFBcXs+8gEonwZOXtt9/G9ddfD4/Hwxhm8lQNDg6uuTdqxgDg0DWJRAKPx4N169YxHYlyrqqqqnD27FksLy+jo6MD0WgUOTk5GBoaQm1tLeOPaUqj1+vhdDpx3XXX8T4GBgZw8eJFJCcnY3R0dM29UVODDuKER6Z3o91u5/BbjUaD6upqRCIRzt2hfDJ6d4TDYdTV1cFoNHLocyAQ4Hc2EVIpZC9RSKTyf8JrqbAmmen4+Diqq6t5r/Pz85zoTr5iIpjV1NRAqVRCo9FgamoKzc3NiEQi8Hg8MBgMHEpKWW6USUfY47UWTYKoaXGl8d1gMKCrqwtTU1PcMCe1kcFgwNmzZzE7O8tSOQCc9k5IeJFIhFdffRWHDh3ihHu/38/vqkTKjyvXJ55ozM7OMqOZmPKEMktOTkZRURH+9Kc/wev14qabbsLo6CibTMvKygCAQ5ecTie6urrQ1dUFm83GozoydRcXF3NCqsvlQkdHR0J3u9VqhV6v5242mW+2bduGF154AUlJSbDb7dyBorHc2NgYLBYLTzZolAYAHR0djPMkesg//vEPbN26Fa+//jojcp1OZ8IXtsViwXXXXYehoSFYLBaUlZVh586d3Inr7e1lrGY8HkdLSwsMBgMWFxchk8mwvLyM2dlZxONxnDx5EnfddRd36+VyOXJzc6FQKPDkk09CJBJhYGAA5eXlqK6uxtjYWMIuqdvt5ih5h8MBt9sNn8+Hbdu24eTJkzAYDDh69Cj8fj+qq6sxMjICjUaDlpYW+Hw+OJ1OTExMsC+gra2NjU6kaU5PT8dbb73FGsvV1VUu3hK9eEieFo/HMT8/zyn0CoUCL7zwAgDw4a2xsRG9vb1YWlpCX18fIyhnZ2cxMzMDqVSKgoICWK1WzM/P81QlJycHFosFZ8+eRUlJCUwmExYXF3Hu3DkAWFPKQrQNmjQEAgF4PB5s374dQ0NDmJycxMTEBKRSKeu69+3bh2g0in/84x9YXFxEcXExlpeX8dRTT+Guu+7C4uIifwhCoRC2b9+OkydPIjMzE++++y6Pjg0Gw8dKp+ggLxAI+B7KyMhAS0sLd6xkMhm2bNnCh6309HScOHGCR7Eulws9PT3YsGEDHA4H5z6QyVEoFDLumUgcbrc74QSNrisV3Q6HA16vF3Nzc9ixYwdOnjyJlZUVvPPOO4jFYigrK4PX60VRURGPl0mnm5aWhmeffRZarRaNjY0oLS3lgKaqqiq0trZi48aNmJ6ehkgkglAo5EyBtdbY2Bib8ajDPjExgdLSUvzlL39BVVUV+0QyMjJgsViwadMmvPPOO9i8eTN3B4eHh5Gbm4vp6WnMzs6itbUVfX19LL0pLS3FwMAA5ufnuYGSl5eHY8eOrWmuozE8hZfS7y0SiZjPT6FuqampeP311xEKhZCVlYW8vDx+LpOTk2GxWDiQjSaggUAAJpOJu1kFBQV8vS5cuAC5XI6DBw9edW82m41//8XFRczMzHBnLRqNYuPGjeju7sbIyAj0ej3efPNNLCws4Oc//zmz8C0WCwoLC7G6uoq//e1vuO666xAMBhGJRCCXy3HkyBE88cQTKCkpgd1u5w++3W7nIuVqiw4ner2esyEcDgejgQsLC9n7Qt8NMvArlUo4HA7+LSmErKOjg/1JBHro7u7mrACDwcCH1ES0Lgo01el03Eiw2+1obGzEM888g+rqakxOTsLlckGj0cBisWDr1q3Izs7G7t27YbPZcOrUKSwuLjJKV6lUYmxsjDvf69atg8VigUajgc1m42dkYmIi4XuEmkirq6uYnp7G4uIirFYrjhw5gjfffJMlLjS5ffbZZ5GXlweXy8UBqHK5HN/4xjfw5JNPIikpCdXV1Rx2uby8jBtuuAEnTpxATU0NY3OdTidmZmYS8vlJgk0ZSiSZpWur0WgwPj6OmZkZTjSn/IXx8XE+S0gkEkxNTcHtduPMmTMwGo0YHx/HG2+8gZKSEoYgCIVCxuv39vYy5vZqi4raK4NhSb5psVjQ29sLo9GI5uZmlkOdO3cOFosFSqUSc3NzSE9Px8aNG/Hyyy/D6XTi+PHjjL5vaWlBTU0N5ufnEQwGcfnyZZSWlsJoNDKIZa01PT3Nh1mSHg4MDHBBVFxcjIsXLyIej3MBrdPp+HvgcrmwvLwMhUKB3/72t3jggQcAfFj4UaDzzTffjLNnz/JvKZVKORQwEZ1zaWkJCoWCZUBUDNM5qLy8nLOltFoturu7OczZ6/UyXS05OZmnKTMzM0w2pURrAIyB9fl8nL/m8XjW3BspOGgaRJkle/bswdjYGDIzM9kvRZLQeDyOvr4++Hw+zgk6c+YMQxnoG2EwGCAUCjnEmt7LFFpLXuO1VjQahcfj4ZwoUlBQhABJEEla5Xa78YUvfAGXLl3Crl27WIpqNpvx5S9/GVu2bIHb7UZOTg4ikQgGBgZgMBjQ39+PhoYGtLW1wWw2o6SkBBcuXEj47r1yfeJCgwxSmZmZ7Lk4efIkRkZGcOHCBXzzm9/EoUOH0NXVBavVivz8fBQWFsJkMmFiYgJ6vR7XXXcd6uvr8Zvf/IbNijRSGhgYwHvvvYcdO3YgOzsb+fn5cDgcmJycZEnDWqu2thYDAwOQSqVcpebm5uLChQvcESM5T0tLCwQCAfbt24euri709vaiqKgIZWVlnHhM0qO6ujoIhUK0trbiwoULuPPOO/HUU0/h0KFDuHDhAux2O+M911o1NTV45513sLy8zCOtd955BwaDAaurq7j33nvR19cHoVCIxsZG1p2urKzg73//O4qKirBr1y6kpKRgbGwMoVAId999N9atW4fp6Wm0tbXB4/Hg5ptvRiAQgNvtRnd3N4+58/Ly1tzbwsICjh49is2bN7NXw+/34yc/+Ql27dqF6667DkqlEh0dHVhcXITf72cmc2lpKQoKClBTUwO/34/XX38dFosFjzzyCLq7uzE6OorTp09jbGwMt99+O6OHCwoK2JyX6IUdi8XQ1dWF6upqNDU1ITk5GUNDQ/jlL3+Jn//857BYLJifn4fH40F/fz+2bNnCsphXXnkFeXl52LhxIzQaDerr67F582Zs3rwZ3/rWt7CysoInn3wSfr8fhw4dwvz8PIxGI4aGhhCLxfDVr3414YdOo9EwHaaoqAjZ2dmwWq34yle+ggceeABlZWVQ/k82xxtvvIFDhw7hV7/6FfLz85na4XQ6IRQKcdttt7Hh7Qtf+AJ8Ph8ef/xxJCUlYfPmzbjjjjug1Wpx7NgxOJ3OhBIWAPwS27BhA2pqahCLxXDhwgVcvHiR5Vn9/f2IxWKcmP79738fnZ2dTHMhzevRo0f5mSgqKmLNZ11dHUZHR7Fx40Z0dXXxeFokEqGqqupj99fb28tEFpIyPPHEE7jjjjuwceNGnmz4/X44HA60tLTg+uuvx3PPPQe1Wo2bb74Z1dXVsNlsCAaDsFgsOHDgAFOTkpOT0dPTg5KSEhw5cgS9vb0YHBxEZWVlws48dZvy8/ORl5fHmu2zZ88y1s/r9XIRsri4iPHxcTz66KNIS0uDw+FAd3c3pqamMDU1haNHjyIajWLr1q3Q6/Xo6OgA8OFhpLa2lhOwqbuWqGGhUChY2lBWVgaNRoPXXnsNL730EjQaDQMc3n//fajVauzevRuNjY344IMP0N3dDb1ej4aGBhQXF6O7uxsikQhZWVkIhUKMsX399df53g2FQuwHoQ/3Wov8J0ajEXK5HKurq7BarTh27Bi2b9+O8vJyqFQqDA4OYmpqCunp6cjKymJpQzAYRFZWFrZu3Yqenh6eLNXU1CA5ORnd3d14/PHHUVpaioaGBgYAUOGdyNuSmpoKt9vNgZoUBvjSSy9Bq9Vi7969yM/Px/z8PP74xz/iyJEj2LBhA2ZnZ2G1WpGWlobGxkbk5ubi9ddf58YHBbwtLy/jW9/6Fo4cOYKuri58+tOfBgCe/id6x5HfamVlhZGrLpcL3/nOd/hwW1tbi/b2dvz3f/837rrrLoyPj2NsbIwnj/n5+fjSl76Ev/71r9i9ezdnZPT396O1tZUzVdavX8+m2Xg8juzs7ITSKZFIxA0GCrfLysrCgw8+iIceegi7d+/mNOPV1VVs3boVUqkU7e3tGBoaYtncvn37MDMzw4c08mSR2X94eBj79u3jhp1Go2FFQaLf7f3332dkPIE8zp07h7S0NBw5cgSBQADj4+OMG33hhRfg9Xo5n2vDhg246667WM4yOTmJ+vp6mM1myGQyfPDBBygqKoLdbofNZuMAUIPBkDAUj66n1WplSZTBYMCpU6ewadMmSCQS9PT04O9//zump6fxyCOP4IEHHsDTTz+NV155heWLL7/8MsNWsrKyMDY2hng8Dp1Oh4cffhh79+7lbxeF05Lkda1FcBQKBFxeXoZMJsOf//xnTE5OoqysDCkpKYhEIjh9+jRDO6amptDf34/s7GwUFhZiaWkJ5eXlGBsbQ0dHB0pKSqBQKHD58mX8/ve/Z3/npUuXuJFLfr61FkmSaGItkUjQ2dmJlpYWNDc3o76+HmlpabBarfj73/8Os9mM5uZmdHd3s7pBr9cjIyMDsVgMv/jFL/CLX/wC0WgUTqcTLpcLX/va1/CNb3wDmzZtgl6v51yN4eHhhKh9MugTQMJkMuHy5cv45je/ic9+9rMoLS1FTU0NrFYrBgYGsGfPHvT29iI9PR3vv/8+LBYLPvOZz+Azn/kMnn/+ebS1taG0tBTZ2dnIyMhAOBzGhQsXUFxczL41kgpXV1cnDCQtLCyE0+lkT9D09DRGRkbQ1tYGlUqFW2+9lSlTDoeD81GSkpLY80NBxs3NzTh58iQKCwuRlpYGk8mEaDSKu+66C4cOHcLPfvYzGI1GnDx5EmfPnuVMnE+yPrF0qr+/n+UdPp8ParUalZWVuO2222A2m1mr19zcjMrKSh5HSiQS3HPPPbjuuus4HM7n80GpVOL8+fNoaWmB3W5HRUUFbrzxRgwMDOD999/Hk08+iYGBAU5TJjPO1RZ1agYHB2GxWBCLxXDu3Dls27YNJpOJR6eZmZm48cYbeYz87LPPsiZycnKSO6dk4G1ra8OJEyfQ19eH++67D8899xw2bNgAn8+H5uZm3HrrrZzlsNayWCz41Kc+xZKc8vJyXLp0CUKhEKurq9Dr9Yyife+99zA2NsZ40n379qG4uJj12CMjI5DL5bjpppvwX//1X7BYLMyKpq4RodVuvfVWHm2utZRKJb73ve+hoqICubm5WL9+PQoKCnD//ffD4XAwxYTQntFoFA0NDRgYGMCnPvUpFBYWcsc3Fovhlltugd/vR11dHSeK3nLLLVheXsb27du5YKQwn+rq6jX3Fg6HeQpAabQ+nw/f/e53Wbaybt065OXlQSQS4b333mP5wQ9+8AOUlpbi1VdfxczMDF5//XWcPXsWaWlprNvPzMzEV7/6Vfzwhz9kmQelBJ86dYpJWVdbCwsLuPfeeyEUCjE8PIz5+XnMz8/j2WefxezsLAKBAIc1NTY2QqFQ4Otf/zqSk5Nx4403Yu/evairq8Phw4dx+vRplJSU8Ajf7/fj/vvvxw033IDJyUlOv6ap36uvvorLly+vuTeXy4Xdu3fD6XSit7cX8/PzmJmZQV1dHeLxOPbs2YNDhw5hz549HMhI0pT+/n68/vrreOqppzA2NoZYLIYtW7agvLyc01QDgQAsFgsGBwdRXV3NjPHs7GyW0yVaYrEYW7duRSQS4UJfpVLhq1/9Kjo6OjA+Ps6TT5Itffvb30ZSUhJ+9atf4eDBg/jggw/Q3t6O1dVVrK6uoq6ujvNStFotDh48iKWlJRQXF2N4eBgpKSlcECcqNEjnHYvFsLCwAJvNho6ODtx9991IS0vDgQMHcPjwYfatkMb5tddew/DwMKanp3nylpqaiptvvhmrq6vcDaQJg0wmQ1paGgdIDg4O4vTp0wmzAyKRCGpqahCNRtHZ2YnW1lYkJyfjl7/8JQ4dOgS/3w+BQIDKykqsW7eOGybr16/Htm3bsLy8jGPHjqGnpwfnzp3DF7/4RUQiETQ2NqK6uhorKyu4++67MTg4iJqaGiwtLaGyshJ79uzhJPO1FnlkCIqRmZmJlJQU3HTTTZifn0d/fz8GBgYAAA0NDVhdXeU8gvr6ehQVFcHlcuH555/nYlqhUEChUEClUqGgoAAPPfQQNm3axM8aeYgoYHOt5fP5ONwtKysL1dXViEajeOihh+B0OuFwODA+Po78/Hz89Kc/xfnz56FSqTA9PY2DBw+ioaGB8a4ejwcNDQ3cNCosLERmZiYee+wxBAIBHDlyBJcuXWK0+uzsbMICLRKJYMOGDVhdXUVrayvefPNNvP3227jjjjvQ0NDAhBidTocHH3yQu/YymQzp6emw2+04fvw4Tpw4gdnZWVRWVqKrqwter5dTgUtLS3H69GmW7G7ZsgW7d+9GVlZWwsMV5QRRIGFhYSEGBwfxrW99C6urqygoKIDBYOBinL4PBQUF+MpXvsJy1RdffJG9AV6vFyMjI2hvb8fExAQqKipY/nXx4kVudJC/Z61F07KUlBSYTCbs2rULu3btws9//nMolUqo1WpMTEzA5XKhvr4es7OzOHDgAMrKyvDYY4/hu9/9LgNLNBoNHnzwQc5boWnlY489htzcXHzhC1/AjTfeiAceeAB33HEH4vF4Qrw4Ue28Xi8bZycmJnDvvfcyyU+lUuHw4cP44x//yNhnAjJQSObdd9+NrKws3HTTTXj//ffhcrk4Qf4///M/odVq0dDQwM01pVKJ6upqHDhwYM29DQ4Owuv1sgm+tLQUwWAQ//Zv/4bCwkIUFRWxhGjTpk0wGAw4dOgQZmdncffdd7MqYO/evcjMzEReXh6bxm02G+bn5/HLX/4SsVgMAoEA1dXVHLKayNdC15S0/zKZDLm5uVheXsajjz6K3NxcnlxoNBps3rwZly5dQmFhIebn51FfX4/U1FR0d3czAvgXv/gFjh07Bo/Hg+zsbFRWVuJPf/oTAyZOnDiBixcvYmpqCrOzswnDg6kYJWkkybl+97vfwW638+RzaWkJGzduxKlTp7B9+3ZEIhG8++67+PrXv47h4WGmjv3zP/8zI+b9fj+EQiF+/OMfs6QqGAyitLQU69atg9VqTXiGu3jxInsyKaQ6Go3i8OHDKC0tRV1dHdatW4c9e/bg29/+Nk9uL126hK1bt+L6669HY2Mjdu3aBbfbjR/96EcYGxuDw+Fgu8PFixc5N+X48eNIS0vD9u3beVL0SZYgnqjMvGLRRaLQrOrqajidTrS0tOCzn/0sjh49imAwCJPJhOLiYthsNv64U9Jybm4u5ufnodPp8B//8R/44he/iP7+fiwuLjKdpLKyksdB4+PjkMlk2LZtG959911861vfuure/uM//oPNw4QiW1lZgdvthlQqxcWLF6HRaCCVSnksJBKJUFZWxnzjzMxMzM3NQafT4c9//jMOHTr0EVf+mTNnsHfvXrS0tKCxsRH9/f3wer3Ytm0bRCIRmpqarro3wsEC4HChgYEBTE1NYdeuXTzWd7vdGBwcxN69exEOhzlYqaioCPn5+RgeHobH42Ffw80334zMzEyMjo6iv78ft956K/793/+dx8RGoxFbtmzBysoK7r333qvu7de//jXsdjsjELVaLcRiMSwWC66//nrOiaBE70996lOYnp5Gd3c3srKymMxx5swZljQ0Nzez/EIgEHBuAlFlMjIyOLisv78fn/3sZ6+6t9/85jccfEPmumAwiNbWVtx4440YGxuD3+/nkabb7YZer+dEZ6VSyVp4l8sFg8GAUCiE0tJSpKSk8Evj5Zdfxp49exhxubi4yMi5u+6666p7e+qpp5gmQ+NNiUSCsbEx7NixA5OTk9BqtQiHw5idncWuXbtYTqLRaFjOZLPZUFVVxd0ij8cDh8OBWCyG8vJylhCGQiFcuHABTqcTtbW1sNls+MY3vnHVvf3oRz9Cc3Mzo0aBD5GLROoaHh4GADZDkhykrq4OU1NTbMAdGhriJGqiODkcDj7Qbt++nSWFlA6ak5PDRdha691334VYLMbg4CBLGSl9msyEFFoYDAaRl5eHvLw89Pb24vTp04wkpQyG3/72t/jyl7+M6elptLe3Y2VlBTfeeCPL45aWlviZIGnC5z//+avu7Sc/+QlLX0jiZzKZOFNmYGAAubm5SE5OxszMDMrKymA0GtHd3c3dwomJCVy8eBF79+7lAo+SgEnuQ4fLzs5OWK1Wxvc6nU48/PDDV93bb3/7WywtLSEQCEChUMBgMCAWi2FxcRHr1q1DOBzG5OQkRkdH+b6qr6/HmTNnYLVaUVJSgoKCAvaG0bORlZXF1Bmis5HULDc3l3N/Zmdn8d3vfveqe/vOd77DklW9Xg+TycTSgT179uDtt99mjxB10cViMex2OxwOB8ta6dBCuN/W1laeppHJ8tVXX2XohFgshtPphFwux0MPPXTVvVHxX1RUhOXlZQ7hk0gkyMrKwvnz52EymWAymRjSsLCwgPn5ef7IE5BDr9ezB5H8IyTxNBgM6Ovrg8lkgkqlQjgcxsLCAiorK9cMBPvtb3/LkyXyKsnlcpw/fx6bN2/GH/7wB1RUVMBkMrE3QiKRQCqV4sUXX0QkEkFubi40Gg1PiTds2MC+GPJByuVyWCwWniDFYjHOvjly5MhV9/b444+zB4OkkeQ/+frXv86H5sXFRYyOjmLr1q0QiUSYnZ1l+Sdp2olcSAZakriGw2HceuutsNlseO+99yASiVBTU4Py8nLMzc2t+bv913/9FyKRCHQ6HZaWlvgemJ6exhe/+EUkJSVhaGgIw8PDSE1NRU1NDQKBAI4dO4bKyko23RYUFLC3iaSgZMDt7u7GwYMH+QxBkxOfz4eioiI0NjZedW+PPPIIDAYDG/Zzc3Ph9XoxOjqK3bt3w+fzIRaLQaFQQKlUwu12w+FwQCgUor29HU1NTTCbzXj66aeRn5/PjZXS0lIkJSXh8uXLaG1txS9/+Us899xzqK+vx/T0NGeraLVa7Ny586p7e+qppyCRSNjXuLi4CJ1OxyCUY8eOQa/Xszm+rq4Ovb29kMlkuHDhArRaLSorKyGRSFBeXo6hoSH2ftE3OisrC6Ojo6ivr4fNZsPw8DBfn6KiojXzIOhZWFxc5Hvc7XZzsO1bb72F9PR0bNiwAXl5ebhw4QJKSkq44UJ+P4oQAD6UvxMqmRK9Z2dnAYChLVKplOmha31Tjx49yh5fu92O2dlZnvyLRCKmChI9zufzcV4SJZGTnxD40MtCCHqPx8NSQPIDkU/N7/ejuLg4Yb7S7373OwBgfHkwGIRIJMLp06fxi1/8gkP5APC+9Xo92traoFaroVKpsLq6ipGRERQXFzPNjcz009PTuO2225jc+uqrryI7Oxt5eXmw2+3w+/1rnuGuXJ9YOuV2u5kK4PV6YbVaOTqdMhQo9G1xcRE1NTWMV6XRK1XkFGxCh7rV1VX4/X7E43GEw2FEo1Go1WoMDAxgYWEBeXl5Cc3gsVgMGo2GsZurq6usO5TJZEhNTWXiFNGdRkdHsX79ekSjUe5iEKVCLpez/pSC4VJSUjg5k8xSgUCAUZBrreTkZASDQYjFYng8HtaUEilpeXkZfr8fHo8HTqcTCoWCP2RLS0uMVaTKUafTIScnBykpKVhcXOR/Bo2PFQoFd+rm5+cT0rpCoRAjawOBAEtC6DckwotYLIZer8fy8jIcDgdX44QBDQQCMBqNWFhYYKoDFQhOpxMajQa9vb0wGAxMWfF4PAmrYUowJRrS6uoqm5OIvEJp0BaLhT9OoVCITeREFDl9+jTm5+dRVFTEhA23282McDpcLywsYGlpCVqtNmF3mcyoSqWS71kiYOj1ej58U+ANkXPMZjNjloVCIdPQaMROCMr5+XloNBp+QRLNggroRJ1IgiF4vV4sLy/zfUcjTipACNNnt9tZb01aYjqgC4VCSCQSNoLS7+T3+xkRubCwwP8MvV7/sR4N8kER3jUWi8HpdCIcDjMueXp6mpHGZrMZ4+Pj/F5RKBSIRqMYGhpi7CBhWImKNzAwgMLCQr5fiTyVlJSUUCeclJSElJQUeL1eLnhUKhXnDtAhVCAQcL6D0+lkUh09D7m5ucjIyGAsI2Vf0HRu+/btPJ2kKZHVasXk5OSae6N3A10Pn8/Hxk2S8FBAm1Qq5f+NqFKkQ6ZCfHBwEEVFRexPk0qlmJ2dhVqt5n82SZMofTbRNb2S7EXvcoFAwPStqakp2O129mGQH4EOdrFYDC6XC+FwmL0jwWAQPp8Pk5OTTA4iwgv9O+igmGhRIjJNQUieYTabUV1djaGhIbhcLqxbtw4mkwnDw8NMiiOpD310+/v72XNI6HIKJyRstlAohFAoRDgcTvhtoOeSiDcikYjJRmq1mpHELpcLbrcbJpOJfVGUJUL+G6/XC5fLxUZ2wmG//vrruPvuu9m8SoUDBXautQiJS6bUSCQCqVSKwsJCztGgnJ5IJMLvRMoNog4x0aGmp6c5mHF1dZUnREKhkL1/pMknf9hahQYhYclYHQ6HuQi3Wq2QyWSsuddoNFxIXxmWm5qaypIZm82GyspKDmokb+bq6ir/fRqNhuUpia4pHRRJCkQSMbpH6Prk5+cjOzubJ+ACgQC5ublsYCc09dtvv41AIIDz589DrVaz9NbtdnOhS1Mhym1Yq9AAPoSFkDqAyE1EQJJKpXxGEwgEWFxcZH8P4akpq0csFqOrqwv19fVMnJNKpUzs6u/v58M9UdXokH+1FQqFWO1AuRCUcE/AILrfwuEwF3AkGaNvMAXw2e12zgei/43+nBQaSThcel+utQhRHo/HubDweDzIyMiAWCzm77jH42E/xtzcHLKzsxncotfrOaSZZKk0GUtLS+NznsPhgMlk4swbyjpKtDdqVFCWBr1PSCpI3hXKrqLG8vT0NIRCIf855ubm2G/ncrnYz0QKJLfbjfT0dGRnZzNE4JNSpz5xoWGxWGAymaBQKCAWi+H3+zE7O8umrMrKSna7T0xMoLy8HKmpqTh16hRfBOoKXbhwATKZDP39/cjPz+dwtnA4jNHRUQ6YIgPa6dOnEx5g6AGglyMdwslwWlxczMUHZTOkpqayvnx1dZUZzufPn2cpi9FoxOrqKhu0ent7kZubi4mJCe7ye73ehC8ewquS+ZsKFEKSCQQCLgiCwSC/eOgAarPZIJVKkZ6ejvb2dlRWVkIqlbI/YX5+Hqurqzh//jyys7N5vEmJ3olwd+FwGPn5+UyMWVpawsTEBBQKBS5dusQHUOp8v/vuu9i6dSsmJiYAfGjCX1paYkkc4QzJaAZ8qJHOzMxEZmYmvF4v6/zHxsYSGomSkpKwsrLC10oul2NmZgYqlYpDxLKyshgssGXLFpYJ0UtIJpMxRWt6ehrLy8tM1Jqfn2cjZ3JyMpxOJ6e/UsDiWovuJTIXk/64tLSUqRE2mw02mw2RSIQxv3SYI6mJTCbD3NwckxtIY0pFJ8nLaGJA+L5Ez0JaWhr//ZTJMTo6CrfbjczMTKSnpyMQCDC6lbjz3d3dHPxWXFzMpuiSkhI2rAqFQvj9fni9Xg5Bs1qtTGpJSkpKCG0APpSdEbmJ8l8GBgaYWEZeh9HRUYjFYrhcLiwuLnJQW3JyMhPXRkdHkZ+fj/7+fmi1WhQXF8Pr9aKjo4OpWoSEpA8n0dWutkjjQqW+rgABAABJREFUTIQXKgzI+0VUFSou6CU/PT2NyclJLs4bGhp4EriwsMBmPkoMp/uCul1kQv44SYZWq4XX62VtbTgcRjgcxsDAAFNr6DpTE4TIYtPT05iZmeEDKKFtFQoFN0NisRhGR0eRk5PDzwtJQRJhWkOhEAMq/ndI6vj4OEpKSjA1NYWxsTGo1WqkpqayEZVQs1QQT0xM8DuT/Duzs7PweDyMVbbb7ZwpQ4XVWsvv93NBl56ejvT0dHR2dkKhUGBubg5FRUW4cOECZysBH3YHZ2Zm2FuiVCrh9/sxOjrKZKCCggJkZWVhZWUFS0tLmJqawqZNm9Df38/3Mu1vrRUOh6FQKBiJHIvF4Pf7+VDW3NyMqakpDA8Pc2I5TU41Gg3MZjNjpQnBe+HCBZSVlbEPguR/FIRLiGtCza+14vE4N+uoKUCoW0Jud3d3Y2ZmBmlpabDZbHy/ULgZffempqY+QtWhZbVa0dHRAavVyucKj8eD9vb2hN9UkixSLkdubi7a2trYqF9WVsZTTGoqkd9gdnaWJVOU8Ew5ClSgBQIBmM1mTE1NMdGSfk+n05lQKh0KhVjKfeX5gL6NRJeMRCJ8vtDpdDh79iyb6WkydeLECUxNTcFgMLD8krKXjh8/jsLCQvh8Pr5nEzUVgf8XTEyAAgKy5ObmIhwOc6I95eEMDg6irq4OnZ2dyMjIgFqtxsrKCjweD44dOwYA3IyiswidOeh9TZ5ZAvustQKBAHQ6HdMp6YBN2N7U1NSPBO0WFhbyOVSn07GHVK/Xo7OzEwsLC9BqtewLIdxwSkoKDAYDe8OIlJoIb3tloUj5SnNzc8jKykJ3dzcKCwv5utvtdqjVaqSlpaG7uxvLy8vctElLS8P8/Dw3JWQyGTdevV4v7HY74vE4T3hJeZHoe090SiqGhEIhZmZmIJPJOFh3bGwMPp8P5eXlWFpawg033IBgMMjfnPr6ephMJjz11FNQKpV8X1IDa3Z2lolzlBFD57hEGUZXrk/s0aBRdUVFBWpra5GTk8MpldT5Jhbw2NgYOjs7IRKJ8Oyzz+If//gHWlpaODCnsLCQu/aBQIDRZcCHHRBKTJ2ZmYFAIEBGRkZCUzMZYLu6utjw1tPTg7Nnz+Ldd99lCtHExASGh4fZS9LS0sIvvsXFRe6kUrgTccjdbjcXRxUVFZzm6/V64XQ6E9I7fD4fo14J2dnU1MQ6RnroCb349ttvY3FxEYWFhdBqtSwBW1pawszMDILBILq6ulgWEw6HsbKygkgkgry8PDQ1NaGwsBAKhQJpaWkJA1Xkcjny8/NZRuF2u9HR0YHu7m7Mzs6yJIAQsJ2dnWhoaMBNN90ErVaLhYUFTE9Pw+FwYGxsDGlpaTAajbBYLBgZGUFycjJqamqwfft2NDU1saGK/j76M6x1v7W2tuLy5cuIRCLQaDTcHQkEAsjOzmb6EXGoKVuBaGZisRgCgQCHDx/G7Ows4+IoObSoqAhNTU3MDScyxdLSUkLD3+rqKqLRKKLRKFQqFXJychAIBFjik5KSgrq6OlRUVGB1dRUtLS3IzMxkogl5jzIyMlBaWorBwUHGUWZkZKCwsJADoiYmJmCxWLC4uMiHq0SLMlYIr0ikkB/+8IeYnJyERCJBbW0t6urqGHO7ceNG+P1+tLa2orOzkyUE1FEVCoXIy8vD+vXrUVdXx7kExBWn7kssFktI2QHAzzzpXKljPjo6ytCIqqoqRr86HA6Ul5fzM0Afh9TUVAiFQpSXlyMQCHDnkKYsSUlJcDgcnCcQCoUwNTWVcH/JyckYHBxk5DAZZtva2hCJRDg4y+/348KFCxAIBCyDOnPmDB8y4/E4/vznP+PSpUuQSCSoqKhgzX1RURHefPNNDmSjaY5SqUwYUkY6YDLek2mT3qvj4+PctaKPdEFBAfx+P1paWjA8PMzTA7PZjNtuu41/w/n5eVy6dImnv/v27UNZWRk3eQgAsdaiQ4BQKOSmj9VqxdGjR5mIIpfL4Xa78fvf/x6Tk5NMVenp6eGuMUEIaJJRWVmJhoYGzlpZWlpi0hs9C4Q3XmtR0ZicnIylpSVYrVbI5XKMjIzwu4vM/319fXjrrbf4Gzc4OIj29namg11//fUcsBUIBJCVlYWGhgasW7eO/SJTU1NcZMpksoSgELPZjImJCSwuLnJOwtTUFF588UW8++67TCWy2WyYm5vDmTNnkJ+fD7vdjsHBQf6/S5cuISMjgzuaq6urSElJQUlJCR588EEEAgFUVFTguuuuA/ChpMRmsyE/P3/NvVFXk7rtsVgMQ0ND+MMf/vCRabPL5cLCwgIWFhaQm5sLk8mEwcFBnDt3DlNTUzCZTJifn+ccrk9/+tO4/fbbIRAIcPz4cbS3t0On03EBTzkUa00zgA/fcfTbUBAZHeArKiqQnJzMsufjx4/DarWivr6epZNUzDY2NkIulzN5h5op7e3teO+99zAxMYHi4mIuQOk6JQJekCGepkH9/f383I6Pj0OpVCI9PZ3BDQSo6OjowOnTp3HhwgX2mslkMqxbtw47duzAvffei8LCQnzwwQd4++23MTc3x2F4xcXFyMzMhNFoTFg8ikQizM3N8WSXGpaTk5PsA83JyUEsFsPExAQWFhbwqU99CuvWrYNAIEBaWho3e1ZXV1kaJBQKGerzj3/8A1lZWSzZk8vlyMnJgdlsRk1NzZp7S05O5lwiCv6jnIloNIpwOAytVotIJILXX38d4+PjXOiLxWIuaCKRCMsiSRqkUCgQi8Vw+vRpdHV1MVLc5/PxNCKR4VqlUqGnpwdTU1OYmZlh/8/i4iIGBwdhMpk48V2n0yE/Px9arRbT09Ow2WzQarWora2FQCBAXV0dcnNzkZOTwwTJxcVFnDx5EgKBACUlJTwdyszM5D/PWkuhUPC5UCKRICUlBQKBABMTE8jMzITJZML27du5oXLq1Cm0t7dzMTg8PIyRkREkJSXhxIkT/K3csGED9u7dC6lUivPnzyMWiyEjIwMulwutra3o7+9HZmbmx2aj0PrEhcbc3BzEYjHOnDmDd955ByMjI5DJZHj88ceRnJwMrVYLrVaL+vp63HLLLYzl27RpEx544AFs3LiRP0Kvv/46brvtNk4XpPTDBx98ECMjI5zMvH//fvzTP/0TFAoF9u3bt+beVldXmf6Tk5ODrKwsaLVa3Hnnndi6dSvq6+uxe/duHDhwAOvXr0coFMLWrVuh1WqZa5+eno6amhoYjUZ87WtfYwMOdZq+8pWv4Pz581AqlcjJycGRI0ewa9cutLW1fWwyeCAQQH9/P1JTU9HY2Iiuri4cOHAABoMBt9xyC0pLS9HU1IRvfvOb6OvrQ0pKCt577z0IBAIeQ2/ZsgXf+c53cPPNNwMAOjs7MTIyApVKhf3796O3t5cN7OXl5dixYwemp6cTpm/T+I8kGSaTCV/84hexc+dODA4Oor+/nzsUGRkZeOihh9Da2oqRkRE0NzejuroaqampOHDgACNbdTodSkpKEA6HWfN5/PhxruSpk04mz7WW0+lkPTcFGrpcLuzcuRM5OTnYtWsXYrEY8vLy8Mtf/hKvvvoqtmzZgoMHD+Kee+5BQUEBdwB/+ctf4l/+5V+4U0Ds7aqqKrS1tWFpaQknTpxAWloaSktLuUO+1iI/gUajgU6ng8lkQm1tLUsfSktLoVarUV5ejgceeADxeJxDjCgLwOFwYPPmzWhra8N3vvMdFBcX83RvaWkJO3bsgN/vR3NzM2QyGRvaTCYTNm3atObe6INPnVYqqn/wgx+wEdFisbCmlySBtbW1+P3vf4+DBw/C6/UiNzcXi4uLnCZK8rzJyUl8//vfx+nTpzEwMICxsTHGaAoEAnzzm99cc28AON11ZWUFQ0NDGBkZYQmLQCDA3Nwck4nIY0TM/pqaGmRmZiI3Nxff//73uTOvVCphMBjYh1VWVoYPPvgAAoEA09PTTLgrKSlJKIkjXfjc3BxisRgaGhrQ3d2N3/zmNzhx4gRuv/12NDY2Ytu2bXj66aeh1WrR1NSErq4uhEIhnnqQTv/hhx+GXq9HdnY2cnNz0dTUhF/96lfYu3cvNm3axIcJmggmeo9QUBbpeCmIj2QxKpUKEokEWq2WKVckN3rggQcYp3zTTTdhamoKhYWFOH/+PPr7+xlcsHPnTiwtLeHZZ5+F1WpFc3Mz7rnnHpSUlHCw51pLpVJhfHwc4+PjEIlEKC4uxl133cV/Nnou7rnnHjblZ2ZmYs+ePXy/5ufnY25ujjuOFy5cwNDQEOdJ6PV6lJSU8DNHjZtERlMK6xKLxcjLy0NFRQUWFhZwzz33YGZmhu8hKvxJUuR2u7Fnzx4UFxdDIpFg9+7deOGFF/C5z30OW7du5dCwzs5ORKNR7NixA6WlpcjLy0NjYyNryRN1/AjmEY1GIZFIUFNTgxtvvBE/+9nPkJubi7KyMlRXV+O2227Dr371K2467dq1C5/73OdQXFwMtVqNf/3Xf0VPTw++8pWvMMigr68P7e3tKCoqQnt7O3w+H4RCIdatW4dNmzZx4N9aa3R0FMXFxTxNLy8vR1NTE37+85/ztNVoNOLOO+/El770JczNzcHn80GhUODee+/Fpk2bkJaWxmSdeDyOxx57DL/97W/x/PPPw2q14tFHH8Xk5CQHMDY2NqKuro7JWmstoVDIk5zBwUFMTEzAbDZj//796Ovr438f8GExd/bsWYZq3H///Vi/fj3m5uaQkZGBnp4e3HfffVAoFIjH4zCZTNi9ezfWr1+P6elpZGRkcLeZsggSoVDFYjF2797NOSu5ubkYHx/HPffcA5FIhPr6esjlcmi1WtTU1OCHP/whI+9vvvlmSKVS9PT0sJzm4MGDePvttzE6OsoBhW+99RZCoRDncBHS/Epgz9UWRQYQorauro7BD11dXSxDysjIwKZNm5CdnQ2v14vW1lbs378farUaQ0ND2LFjBzZv3ozi4mImQY2Pj0MikeDRRx9l2iiRIefm5nD58uWPzTAqKSnh6S/JiCmgtq6uDiKRCGazGd/+9rc5Y4xwySSPlUgkCAaD3EijDLZTp05xs5xyZoqLi1FQUIChoaGERTdRS4lsVV9fz8/mxo0bmdJHnqauri7k5eUhKysLu3btQklJCdxuN/r7+3Hx4kWMjo6itbUVXV1d6OjowMzMDO69914sLS2hsLCQQ2vFYjGGh4eRnp6+5t4WFxdRWlrKygiZTIaCggI8/PDDsNlsqK2tRU1NDTZv3oyvfvWrWL9+PTdAv/GNb+DWW28F8OFZcO/evfjMZz4DlUqF/v5+jI+PQ61WIxQK4eWXX4bJZEJqaipTYWma9UnWJzaD//nPf0Zvby8Hu0xPT8Nut6O6uhplZWVYWFiA2+3GzMwMbDYbrrvuOqxfvx4dHR1QqVRITU3F2NgY/vKXv+Dhhx/Gj370I/zkJz+BSqVinfZzzz2Hn/70pzh+/DhMJhN/ULOysvhDdbX11FNPYWlpieUSLpeLQ0yi0SiysrKgUqlw4cIFHD16FE899RReeOEFVFRU4JVXXkFNTQ22bNmCgYEBGI1GvPPOO/j85z8PiUSCkZERHD9+HF6vF3fddRekUimCwSDm5+exvLzMZsrrr7/+qnt74oknEIvFUFhYyGba66+/Hr/+9a9x//33o6urC1u3boVKpUJfXx9aWlpw3XXXob+/H8CHYYSUvHnx4kV88MEHuOWWWzAyMsJp4TTWq66uhslkwunTp2G1WrFt2zacO3cOP/zhD6+6t1//+tcIh8Oor69Ha2sr2tra8IUvfAF/+tOf8OUvfxkXL15EWloafD4f5ubm8NBDD0GlUuGll17C3NwcNmzYwLhMvV6Po0ePYvfu3TCbzbBarejt7cWGDRtQWlqKP/3pT9i3bx9nRVC42VpGohdeeIELSDIsORwOdHV14eDBg+jq6mJD3unTp7Fv3z6Mj48jJycHU1NTfIjr6enBxo0bUV1djWeeeYYNcEVFRTh37hzKysowOzsLk8nECcsUVLXWy+evf/0rwuEwtmzZgoWFBZw7dw4ajQatra34yU9+gr/97W9IT0+HTCbjzmxBQQFEIhH++te/wmg0oqCgAOfOncN9992H//zP/8SDDz7I2n8a5YpEIrzxxhuoqKjA6OgolpeXsW3bNi5YrrZ++ctfYm5ujrXfSUlJWL9+PX73u9/hwQcfRHd3N/Lz8/kQfvjwYbzxxhsIh8OoqamBTCbDyMgImxGTk5Oxa9culpvZbDZkZGRg7969DGsYGBiAw+HgoKTPfe5zV90bAPzlL3+B2+1GaWkpwuEwm0pXV1exbds2AB++9GZnZ+F0OjnbJC0tDeXl5QyNOH/+PCorK9Hd3c0EGZfLhba2NrS2tuJf/uVf8Nxzz8FoNDIswG63o7y8HDfccMNV9/a73/2OE4vJxHjnnXfi0Ucfxde//nWWpQ0NDcHv9+PXv/41YrEYnnnmGXR1dUEoFKK2tpYzQTZs2AC5XI6pqSmcPn0anZ2dWFlZwbe//W04HA4YjUbu4ppMJhw/fnxNQ+LPfvYzzM7Osl5cpVJx6NfevXsxOjqKc+fOwWazwWw2o7a2FjKZDEqlEiUlJVheXkZbWxveeust3HfffXjmmWewc+dOpKWl8SQkIyMDWq0WDz/8MB9MqSFQXFyM22677ap7e+SRRzhMUqfTsVyGiFVHjx7lyXR6ejr0ej26urrgdrvR29sLvV7PxUVOTg7C4fBHnj+Sr5WXl+ORRx5BQ0MDYz0bGhoQjUbxta997ap7a2lpQV9fH7Zu3Qq73Y4zZ86gqqoK8XgcOTk5ePbZZ1FQUACFQoHJyUlUV1cjIyMDSUlJ+M53voNNmzbh85//PHsbhoaG2JcYjUYxOTmJp59+Go888gief/55LvjoUDI9PY3du3dfdW9PP/00Tp06hbKyMg5zNRqNeO+99/D73/8eP/zhD1FVVcUBr4cPH2bN9K5du9i4SSZ0h8OB5uZm9koIBALs378fb7zxBi5evIiKigoOAKTD5Y9//OOr7u3RRx9FMBhk6IBQKERDQwOee+45fOUrX+GAys7OTnR1deGuu+5iPxpJu7RaLSYmJjA0NMQHsuTkZKSnp0MgEODYsWPIz8/H0NAQysvLkZWVBZ/Ph+7ubshksjUN/r///e+h/J/keLpvSfYUjUbxwgsvoK6uDpWVlQgGgxxkplKp8Pjjj6OpqQmbN29GZ2cnsrKycPnyZQaVGI1GyGQyHDt2DEeOHMGPf/xjbNy4kXHb27dvh1QqXfOa/tu//Rump6f5r4tGo6ipqcFjjz2G2tpa2O12ZGdn83RBLpfzxIRkvVqtFjk5OQgGg7h06RK2bduGs2fPsnepv78fX/ziF9Ha2so+G8rislgsa8IunnjiCSiVSphMJszMzGBoaIil1ps3b+aE+eHhYfT29mL//v2QSCSQSCTo6OhgQubs7CwKCwuxa9cuvP3220hNTeUg3oKCAmzZsgXd3d1IT0/nJkBeXh66urrw4IMPXnVvf/jDH2CxWJCbmwvgw8M9KTV27NiBnp4e9ny4XC6UlZVhaWmJG2dGo5E9kBqNBo8//jj27t2LlJQUnr5PTU2hoqICTz/9NDZt2sRm7mg0Cr/fz2jq/71efvll2O12lJSUYGFhgc8+ZBCfm5uDTCaDw+FAW1sbDh8+zEG3/f39HPAcDAaRm5uL7u5uWK1WeL1eqNVqpKen47333sN3v/tdPPbYYzhy5AhCoRAGBwexsLCAdevW8bfxf6+//OUvCIfDSEtL46C/kZERzMzM4Ic//CH+5V/+BTKZjCfokUgETzzxBO655x709fXxFLK7uxtHjhzBD3/4Q/z85z9n33AkEuEmxltvvQWFQoH5+XlEIhEUFhaipaWFw/4Srf+fAvtohD0/P4/Z2VkEg0Em5RC1hzBn4XAYg4ODeOmll2A0GqFUKtkT8N///d944IEHcObMGTgcDu4qzc7O4uc//zk+9alPoa+vD6mpqUhLS0NLSwsWFhbwve9976p7oyRwMjQrFAomwYjFYiwtLaG9vR2RSASf//zn0dPTw+Msq9UKj8eDU6dOcVBVRUUF3n77bWRmZnIAVl1dHex2O+rr67GwsMAae7vdjrm5uTULDafTiby8PHR2djIa7YknnoBer8eZM2cwPT2Ns2fPMppMrVZjcnISAwMDzPvWaDTo6OjA8PAwd9KIvDM2Nsa4R+BDqdbMzAzsdjva2toSXtP5+Xk0NTVBKBTyzTg6OopPf/rTUCgUKCsr43EhTSQmJibQ09OD9PR0PowBwPnz53HDDTdgamoKHo8HLpcLY2Nj6Orqwo4dOzg8jeQblKa81vJ4PFCpVLBarXC73QgGgxgbG8O9996LJ5544iNBdouLi2hqakJGRgbOnj3LOnSVSoWVlRW89dZbeOihh3DhwgU267W1tTFy0+VywWKxcFgQpayuVWiQTLC3txdWqxWBQAByuRz33nsvFhYW2DfQ3d3NLP2FhQW888477Bk5efIko2XXr1+Pnp4eltKQwS89PR15eXlQqVRoamqCw+HAiRMnIBQK1yw0otEoqqqqGDsZj8dx7Ngx3HDDDfysjo+PQ6FQoKCgAGNjY9i2bRueffZZTE5OcrDZtm3bmAl+5swZ5qCTx+i9997Drl27cPToUYjFYphMJiZvJFoejwf5+fksT7yS2rGwsMBjeWLkq1QqBjB0d3czA9zj8eC5556DRCKBWq1mAk4sFoNWq8X58+fR1NSECxcucHfJarUmNCQGAgEez5vNZqjVahw/fhxf+9rXoFKp0NXV9ZEu2ZtvvomhoSH09vaitrYWFouFk5Dp8NTf34/Z2Vn2UhmNRnR0dKChoQGXLl1CT08PBAIBGhoaEqJ3nU4nfD4ff7xdLheEQiGUSiUuXbqE2tpaxo7L5XIEg0EoFAr09vayRjw1NRX33XcfTp48CbPZzJ6QYDCIkZERdHR04JZbbsG2bdvQ29vLnH3yuq21LBYLew0IKuDz+TA7O4uOjg6e2lDmydDQEOu/ScdNH8rh4WFcd911bCi12Wxwu90MCSAaXkpKCl+HRBJM8oI5HA7WoVORMDIywv+Zgtbkcjn7SP7pn/4JoVCIZaF2ux25ubk4ffo0//eJiQls2LABH3zwAQMSiLtPRta1ls1mY7AHHdIdDgfuuOMOdHZ2oqSkBBaLhbvFRIKzWq147bXXkJqaCoPBgIMHD3LyNk1mCZJx7NgxVFVVoba2FrFYDDMzMyzbTbQoeC8ejzOQ49y5c1AoFHj66adx4MAB/nMajUaWpJAuPhKJID09Hdu3b4ff78fc3BzUajV/R0jucvbsWRQUFLDEjrT+er1+zb15PB5UVFSwFJL+PGSg3rlzJ4xGIwNnCP/5/vvvQ6fTYWVlBefOncOZM2ewefNmBtCQt47eTZS9s7q6iuLiYgQCAXR2dkIul69ZaAgEAn6/kYLjvffeg8lkgt/vR3JyMstkmpubYTKZ0N7ejpqaGt4vyQLfe+899tqRPHt6ehpFRUU4deoUe9oIBDE2Npbw/UYgju7ubggEAuh0OvT393MqOr1/KIyW5DVqtZpzyEg6JBaL8dhjj/GEhLC5lLNA/hyRSMRnk0SYVioaBAIBkpKSOMSTTOd0bdPS0mA2mzEwMMDfDgpl9Pl8nBGzadMmLCws8P1OsJ/BwUFoNBoEAgHMzMwAAMNw1lqBQAAlJSUQiURQq9UQCAQ4f/48VldXuWlBCh6lUsnwIzobTExM8Hfx8uXLEIvFsFqtUKlU8Pv9HLA5NzcHs9nMQX/klRkZGVmz0CDMPNkP5HI5BAIBwuEwOjs7sX37dpZy0rvtjjvugFgsxo4dO1jOdvnyZRw/fhw5OTlYWlpiszoVRAAY100FKfnfPsn6xNIpmUyGUCjE6DGJRILs7Gy8/fbbaG9v52qJTCREbsnOzubq1Gw2o6CgAPX19fjTn/4EjUbDjnmfz8ej/xMnTvDho7+/H0NDQwnHvGTSWVpaYs2dUqnEBx98gIWFBa7iSfPW09OD4uJiLkAoCIvCaeRyORvSlUolFAoFpqam0NvbC5/Pxzfp/Pw8eyvWWkSxEYlELO0KBAIfSRCmlx99hPPy8rjjT0Y6hULBPHixWMxVOmEoaURNBWAkEsHExAS2bNmS8Lr29vaypGhlZQVTU1Po7OzkAxBpS+kjAHzI2C4uLkZRURHMZjM/WH/9619hs9kQj8chkUiQnp6OnJwc9Pf34x//+Ad3nMbGxnDy5MmEH+Hl5WVMTU1xMSISiaDX6/Hqq6/C6/Vi165dyMvLQygUgs/nQ3t7O2ZmZuByuTA0NIT29nZMT08jLS2Nk5JJ/02G7dzcXIyNjfFHbXZ2lnMNEpnBrwyc0ul0qKiogFAoxNNPP43R0VHY7XY+BCoUCkxMTHB3ubu7mw9mYrEYbW1teOGFF2CxWJhcRT4jyik5f/488+xpOrjWIp0qaZYnJyd56uL3+5mUZbFY0NnZySm9CoUC58+fx8mTJzlNm8AHKpWKc2CIbz4+Po5//OMfAMAHSY1G87GjVGpIZGZmoqysDDqdDmNjY3j55ZcZuReLxTAyMoL+/n4mMZEenAyNdGjp7+/nVNilpSXY7XbMzMywWZ1yTFZWVlBaWprQWBeNRjE+Ps6oRvJ+nDlzBidPnoROp8P8/DwnfY+NjWF8fBwpKSnssaivr4der4der+fwQJ1OB6PRiMzMTFgsFj7EEOUrJSUFgUAgoQ+CQq+MRiOys7OhUCgQDofx7rvvQiKRYGJigqUqdCAiQ+3k5CS6urpw6dIltLS0APjwnSkSiTA9PQ2LxcJkqkuXLjHIIx6PcwGYCCqh0WjY60aIxeTkZJw9exYDAwPIycnhRpLH40FBQQFTCqVSKbKyslBWVoYtW7YgLS0N77//PmO3jUYjsrKy0Nrayjhb4MNin4qvRBJMOiinpKSwvJeMoH6/Hxs3bkR9fT0To3Q6HafxkokyIyOD9em9vb0sn62qqoLZbMa7776LpKQk9uAAYBhJIrITTS6JmEahmC0tLZDJZNi0aRNjd9VqNb8T1Wo1zp49i5aWFkxMTCAQCEClUrEvjlQADQ0N3Byi793k5CTm5+eRkpKS0IdGBTf9Tj6fjxt6BCP43/8bUXaI5kMHZpPJhHfeeYcLL5Kmzc3NMTUrEAhgcXER4XAYBoMBmzdvXnNvycnJTCKTSqUMDDh9+jRaWlq4yO3s7ERHRwcuX74Mp9PJQATKW/jSl74Eo9GI2tpaTpLu6+vDwMAAU+7MZjPcbjcXSEqlMqFHg6hUVxI3w+EwCgoKkJaWhg0bNmDjxo2M9D5//jzuvPNODA4O4pVXXkF3dzdSUlL4fUF+JYlEAoFAAK/Xi5SUFMzMzHAjlDCj1PxcaxECmP75arUafr+fowkI40yAlXg8zvjuo0ePoquri4uegYEBmEwmWK1W9reQ1I4KS5fLxb+Bx+Nho/fVFj3Pi4uLLGUkyhNRD8mI7Ha7IRQK2YczOTnJgB2VSoXh4WGcOHECHo+HKZqE26d3MSHMPR7Px9IIyZ9H9Cz6XYh+R9LzUCgEu92O4eFhLqrm5+cZFnL58mWcO3cO4+PjCIfDjLZVqVSor6/nZ4PgGOnp6SypXmtlZWVhYmICSqUSeXl5kEgksFqt8Pv9eOutt9hXrdFoMDY2hsuXL+P2229ncz4Vsps2bUIgEGBICeHl6bu0sLCAm2++mWmiWq0WeXl5Cf1xV65PXGgQQtbtdvPNLhaLUVhYyPpM6mLk5OTAbrdDp9MxYpawaTU1NTxyEovFUKvV0Gg0MBgM2LdvH3PKY7EYH6jsdnvC1Fwa7a6urkIsFjMalMZSKpUKBoMBOp0Ocrmc6TOXL1/mwDsyxlqtVhQUFDCCk7ptWq0W8/PziMfjjBskPOzH6ZdJ90s4wqSkJH6QdDodZ1IUFBSgs7MTSUlJbFiigCP66Dc2NkIoFPLDF4/HmapVVlbGSY9KpZINxWtefKGQkX7p6ekwGAwAwDhLGnXn5eVh69at6OzshE6n43/30tISkpOTsW7dOgDg60xYWZVKhY0bN3K3injhxNCnLIqrLTrokxkxMzMT2dnZ3Bmg0LPs7GzU1dXxIZxMcqRrpMwOMo+bzWYOFNu6dSsWFxeRlJTESb+kjdRoNGvujQ6r1FWmlxYZQglvSn6VxcVFqFQqNqbRtY/FYujs7OROpkAgQEpKClJSUvhQTEFohAMm8+Nai4pe6l5R51IqlWJ6eprRjiKRCHK5HE6nk/MMiBylUCjQ1NTEzyk1EAh7WF1dzV0N8oJQsZ+ITkSL9ND0fPn9fv5oKpVKZnar1Wp4vV4UFBSweZxe6HT/ymQyLC4uQiaTQafTsTRnYWEBTqeT6TnAh4f1RIZrkUjEkxTCvpIsg2giKSkpUCgUqKysZPS2QqFgMyAd9Ox2O0saDQYDCgoKoFarmYjmdrvhcrmgVqu5+ZCoYZGUlASTycTvUcK8kiyMGiDxeJzvffLZFBcXs3Hyyuklffy1Wi1UKhU2bNjAErOkpCTodDpoNBrGka61YrEYp85TuB01LsiwTQbu1NRUBAIBBmLQXn0+H8tEyYtAXW+xWMzPA+VNEImOQBEft+j5jsfjsNlszN43mUwMjVCr1fB4PFwQ0jNMRTShiIkICHxohFcqlZidnUV2djZ7kMignOj9m5yczMF5BNIQiUQwGAyMEKXUdp1Oh0AggLq6OthsNmRnZ/PUhL4DhKdMT0/nRkZpaSmWl5cZNU73jEAgSNgUoGvj9/sRCoUYxw2AEat0j2dnZ3OBEIvFYDAY+DqTybesrIwbHXq9ns2uNAUh2mJKSgrTsT5u0Xc/KSkJgUAAXq8XcrmcUdOEQaXGIN3rwIffl5ycHL7vUlJSkJaWhng8jmAwyE0FwlsTOUgoFCacthDljc4PV2LxKWHcaDSiuLgYSqUSLpcLWVlZcDgcHIJI/iWXy4XKysqPIKAJPU1nLbvdzt8euvfWWvF4nDNR6P7TarVYXl5mjD2909PT0zlrjCIAwuEwJBIJ9Ho9BgYGEIlEWN4Ti8UgkUiQm5vLU0068BJAI9HvRjAJgqzQe0qv1yMWi3HzlxqsSqUSW7duZfQq7Ver1TLCWyQSIScnh5/d7Oxsnn6mpKQwKS8WiyV8TgntTSjx/w0iobOwXC5HdnY2fD4fVCoVp92TP5OeQ0JXk7ybSFiEfl5cXER6ejpyc3ORmpqasAgiciOdSYhapdFomHhF75fMzEysrKzAZDIxoYqkjSqVCjKZDCUlJawMkkqlEAqFWL9+PUZGRvjcRA3U5eXlhM2KK9cnLjSWlpa4gLDZbIz8uuWWW3icFI/HoVQqkZ+fz4ZZi8XCo8vp6WmmUt1zzz0IBoNYXl6GXC5HXl4eqqur4fP5UFpaytOT7Oxs1jom+rHphazX6/mDf9ttt6GqqgoSiYQrxezsbJ5W9Pb2YufOndi8eTOSk5N5LJSdnY1IJMJUnYyMDGzcuBFyuZzZ/2ToApCwCKKbkxCBYrGYE02zsrJY7kFUH5lMhmAwyON7l8uFgYEBDA8Pw+FwYO/evUhPT4dcLkdaWhrfRKFQiBG99HsWFxfzePBqi5K6U1JSoNPpUF5eDpPJhKqqKh7BURLnnXfeCafTifT0dAwMDGBoaAgDAwPMi1ar1fjMZz7DnGbah8lkgk6nQ2NjI+bn5+F2u/nARcavta4pTaCIvJCeno69e/dCqVTylMVsNmPDhg2oqqriwL76+nrU1tbyQx4Oh3HbbbdBKpWisrISOp0Oc3NzMBqNTFKgQ0Fubi5KSkoSksTI1E4FFBVXd9xxBzQaDQoKCmA2m2EymRjDF41GIRaLcfDgQdTX1zP/fWVlBY2NjZxPk5ycDKPRiKqqKszPz6Ouro5Nn6TD/bjuS0pKCksICRm4e/du/iAIBALI5XIUFxcjOTkZc3NzcLvdyMvL431TeBo9i6QFn52dRVVVFZubqduzsLDAGSuJFr1HHA4HJiYmuLjYt28fzGYzvzDpeSD9PhXPpG0eHByE0WhEUVERAw10Oh3q6+uxd+9epof19PTA4XDwCzwR3lYikaC+vp7fD2azGXq9Hg0NDSw1ysnJwd69e3HHHXdAoVCguLiYddV06Pd4PBgbG2P2OB1UNRoNtm7dyg2AqakprKysICMjA6FQKKFRnbTtNpsNFouFP/rbt2+Hw+FAPB7nj5nRaERjYyNMJhMCgQDKy8sZdFFdXc3NA8pxKCwsRFpaGhobG1lOQXAIo9HIyPC1ls/nQ35+PmdgUN6A2WxGfn4+Jy7TodxisTA4Qq1WsyzSbrdDr9ejqamJi3GapG7ZsgVOpxMGg4EP6AUFBUhNTf1YpLJIJOLmEBXEKSkpyMjI4ERoKlguXboEk8mEoaEhbNq0CSUlJZy6SzRAIhSOjIzA5/Ph8OHDHKRKJmPKcUlUoKnVam5y0btQJpPh5ptvxqVLl9DZ2Qmfzwe5XM7v1aqqKkxPT+PAgQNoamrCysoK4+NjsRiDDMgUXlhYiJKSEjZdZ2RkQK/Xs/RhrUXGY2p+aDQaltIQbYa6xFVVVUhKSoJQKER2djZPYahZNDY2hltvvZUPLxRWV1VVBbfbjS1btvDhUCaTcTjoWosadJT7EIvFmLq0b98+vvdNJhP27duH0tJSBluQX4+mAhaLBUtLS9Dr9SguLuaMgPr6elgsFsTjcWRkZLD3hjJF1lorKytQKBTcDATAOR/z8/OM1Cf1RkFBAV+Xr3zlKzh48CDTg2ZmZrBnzx5IJBIu2ilvSSaTwe/3IxgMwmg0orCwkKE6a61QKIT8/HysrKwwYpvgADRVdblcyMjIwK5du9i3kp2dzWcDOhiLRCL4/X74/X5uNFEjQSaTwWAwIBwOw+VycdZNIiIWSWAJqU/FRGFhIWKxGFQqFU//6DtIDZq6ujoYjUb4/X4u7srKyqDRaGA0Ghn4Qw0oUtvI5XI+fCf6bkkkEmRmZnIoqVwuh9frRX5+Phcq8Xic09ppsrKwsIDa2lqWa9LZOC8vDwUFBcjPz4dKpWJSXF9fH6RSKaamphCLxaBUKrGyspLwmvr9fi6u6F6m5mdVVRVWV1cxPDzMYBkyglPANWWSzc/PIycnhyW89L1SqVTYvHkzTwnp3iNpZiJ895XrExcamZmZ6Ojo4LFTMBiEXC7Hn/70J9x99908eibjcH19PZ5//nls3boVCwsLTF757Gc/C4VCgbfeeotH2UqlEgsLCzh8+DAeeeQRzmEYGhqC3W7HZz/7WcbfXm1R58VoNCI5ORkWi4UnGrm5ubBYLEwoaGlpgVarxYULF7Bu3Tq0traybIU09KTvzszMZDNlNBrFrbfeivn5eaxfvx7bt29HVVXVR7rsay2JRILNmzfz+Io0qZmZmbh48SKCwSCcTifOnj2LJ598EkajET6fD6FQiMkIR48exa5du2Cz2bB+/Xrk5+fDbDajsLAQ3d3duPvuu/H0008zSi47OxsbNmxgH8LVlslkYixsIBDA1NQU8vLy0N7ejq6uLvztb3+D2+2GQqGAQCDAd7/7XfT09KChoYE/RBkZGXj00Udx1113YXBwEM3NzRwYp1Qq0dLSgi9+8YsoLi5GJBLBsWPH0NnZiT179qC7u3vNvVVXVzNpCABaW1t5lLx3714cO3aMk1r/8Y9/oLi4GI899hgOHjyIc+fOMcbvmWeewWuvvYYnnngC8Xgco6Oj6O3tZazfbbfdhlAohLKyMs5ukUqlGB8fX3NveXl5EAgE2LFjB4/WN27ciJaWFuj1erz22mvIyspCU1MTdzeefPJJlJaWIj8/H+vXr8fmzZvh8Xhwyy23YGJiAtu2bWPSRlpaGt59913ccMMNfMAGPpwErF+/PuG9plKp4PP5mNhFxszh4WFs27YN7733HsLhMCKRCF566SXU19cjKSkJX/3qV9Hb24tjx47h1KlTeOaZZ1BfX4+Ojg7cfPPNMJlMnDHy5JNP4o477sDZs2eZAkQdxc7OzoT7MxgMmJqagkQiQTgcxvT0NPLz83Hx4kWUl5fjpZdegt1u5zTj2tpaXL58GRkZGXjppZfYg/LEE0/g4MGDuHTpEnbs2MHhaOQx2blzJ2Ote3p6MDo6iurqapw6dWrNvdEhWCqVsvyzrKwMAoEAO3fuhMVigdvt5knF1772NRw/fhyBQIDpW7fffjsbJYuLi1FfXw+73Y5oNIra2lq88cYb+PznP8+4RjK+5+XlJZTEZWRkYHBwkD9Yq6urTEkiY75Wq0VRUREfzF944QVs27YNjz/+OJ5//nksLCzgrbfegt/vx1/+8hfk5OSgpaUFr732GtxuN86dO4fDhw9j+/+kvk9MTKCjo4M572ut7OxsvPDCC9xBs9vtMBqNiMfjaG5uxuDgIB82PB4P7rjjDg5LW15ehslkQmlpKaanp7F161asrKygrq6OkZWU3aDX6zE+Po6ysjJEIhE4HA7U1NQk/DaQDppwnvF4HFu2bMHk5CQyMzMxPDzM+UPT09PQaDSYnZ3F5s2bMTk5yYWizWbDnj17uGgrLCxEZWUlKioqcPnyZezYsQMXL17kSYbb7UZVVVXC54HejzqdDkqlkmW75OF56623kJubi8OHD0Ov1+Puu+/Giy++iCNHjsBut3N42b//+79DJBJBo9Hg4MGD3GXetWsXLl++jFtvvRVnzpxBc3Mz0tLSMDs7y4jvtRYZlwsLC1FeXg69Xo/169djfHwcFRUV6OnpgUKh4JTj/fv348UXX2RPmN1uR3p6Ok6dOoV169bhv/7rv1BbW4u5uTm0trZiYWGBQ+hUKhX27NmD7OxsCAQClJaWJiQU0dlAqVTy5H/Xrl0sraFwS8KDU+PJ7XZjcXERFRUV2LBhA6xWKw4cOIBgMIiioiLOk0lNTYVCoWA5KZGN7HY7iouL0d7evube6B4j1KzX60VVVRWysrKwZ88e7sjPzMzg+PHjaG5uRjAYxP79++H3+7GyssLBp1//+teRkpKC9evXM8a1qakJer0eZWVl+Nvf/obm5mbU1NRw0ZKosajVanH58mUOIl5eXmbvSXV1Ndra2rCysoKsrCxEo1FoNBrMzMxww1OhUCAzMxOjo6P4t3/7N0SjUdxyyy2ora1FfX09Nm7ciKNHj7J/Z9OmTVAoFLBarYhGo+jr61tzb1cWpiTZIwlwXl4eJ86TtJj8ilu2bOGCsLCwEC6XCxUVFYxfpaZEZWUl2tvbcfPNNyMjIwPJyclYWFiA3++HyWSCzWZbc281NTWsSKHGRFNTE0ZHR7Fnzx6mppEROz09HV6vF/X19ZiamoJUKkVNTQ2sVivi8Thef/11zgibmprC8vIynE4nbr31Vp5yTE5OMtk1UaYMAUoMBgMrBfbt24euri4UFxczPZTym8jfIhaLcfHiRRQUFOAzn/kMw1b6+vqwefNmbqhThsvWrVvhdrthMBgwNDTEeTUXLlxYc29Xrk9sBpdIJLj33nvR29vL7HKiTL311luYnZ3lYJXi4mL09PRg69atGB0dhUKhgMPhwMrKCndhdDodurq6oNFoOIzo29/+Nv75n/8ZEomEpyLUMU40oiFU5+TkJFf8drsdFy5cwLZt25CVlYWxsTGkpqZix44dCIVCKCwsxE9+8hMONpFIJNiwYQPKysowPT3NdKm5uTks/k/SOR1CaAyr0+mgUCgSdjgIxUcfHKVSyYbNpaUlFBQUsBZueXkZDz74IFZWVrB+/XpUVVVhfHycu2tEOBgZGUF+fj5TRyixcfPmzSzLiEQiTLRZa7lcLjasWq1W1t+VlJTAZDJx0Xjy5En87ne/w/79+3HDDTfgzTffZKIOjbwfffRRZlVTCN65c+dQXV2N559/HiKRCKWlpZx6+XFG9ffeew/Nzc2YmZmBTqdDc3Mzzp8/zx+WvXv3YmRkBMFgkA+a+/btw+zsLFQqFTOtd+zYAafTiZMnT2JycpI74ENDQ3jzzTcxMDCAiYkJ1sqTRIhC8q62BgYGoNPpuHuQlJSEixcvcufhxhtvRFdXF4aGhpCcnIydO3eivr4eLpcLL730Ept6qcAmCkxRURGkUilee+01LC8vY3h4mHWtdJgnwsNaiFvimlNHkKQoarUaS0tLqKmpYVLL6uoq3nnnHTaTNjU18eRkfn4e586dg0AgQGtrK3cbqeh55ZVX0NTUhJMnT0KpVDIxpry8POF17e7uRnNzM+x2O4qKiqDVajEzM4NPf/rTiMViKCgoQF9fH/9nKmZ8Ph8+9alP8bj+Jz/5Cdrb2zlckbwwp0+f5veLx+OBTqeDXq/nYLtEfH6v14v09HSWhAYCAZw5c4aLXQrpXFxcxPj4OIxGIxoaGjA1NYUnn3wSRUVFqK+vh8fjwdmzZ5Gbm4vJyUmkpaXxFJC65jTlJRAGGQfXWi6Xi+URJB+am5tDdXU12tvbIRaLMTIyAr/fj7KyMpbRdHd3Q6PRQCgUMgCgtbUVcrkcKpWKfU5EXuno6PiI5IfoQonecRaLBXv27GEEqEgkQl9fH+rr6/keJp+eRqPBn//8Z9xyyy3o7OzklPPV1VWWwV26dAlZWVkoLy9HKBTCwMAA1q9fj+XlZfahpaSkIDU1FVarNeGEr6enh8PZyAPV1tbG0waFQoG+vj6WNNhsNvz973+Hy+ViQzFJHs6fP89yi+rqau6w3nLLLVhYWGAJSiAQwOTkJE//1lozMzOora2FzWZjGejMzAwqKiqgUCggl8shlUrR3d0Nm82GxsZGzmwhTySZjzUaDcbHx+Hz+bC8vIzLly+zH+b48eMwm82M2V5ZWcH8/HzC4rGlpQUHDhzgzB+dTgeLxYIbbrgBAoEARqMRo6OjHChGE7xjx47hpptu4ndQSUkJBgcHYTabcfr0adTW1iIlJQWnT5/mRtfly5fR19fHydcfd00nJydx6NAhjI6OMt761Vdf5SyipKQkTExM4N1338Xk5CTuv/9+1NbWcud9bGwMEokES0tLeP7559HT04ODBw+iqqoK6enpmJ6eRm9vLxobG3Hp0iX+3cgzmmi6d6U3hULORkZGoNPpoFKpEAgEMDc3h/z8fNx11104e/YsWltbMTo6Cq1Wy1Jms9kMh8PB9DiFQoFgMIje3l6cOXMGDz74IA4dOgSxWIy//vWvGBsbQ3V1NUv6rrbsdjtqa2sxPj7OSo3jx49jy5YtmJqawq5duzA+Ps45IEVFRYjFYlhdXcWOHTvgcrnwxhtvQCqV4p133mFZnt/vh8Vi4Wnaa6+9hl27dmFoaIi9LQASSswph4q8daurqxgbG+PUbrVazX6C0tJSltO2trZieXkZoVCI//12ux133303Z7yQrDQ7OxvHjh1DKBTC4uIiS/9IZbHWOnnyJJPHotEo0tLScP78eWzZsgVnz57l/ZOihzKpaArmdDqh0+lw//33czP7gw8+QG1tLQwGAywWC1ZWVtDb28vTDPKVDA0NIScnZ829Wa1WrF+/nqdbFJ5IuHFS0tB0mK5TRkYGKisr8e677+LNN9/E/v378fvf/x6/+MUvMDMzg2g0CqfTyZ7TkpISlJSUcHNAr9djfn5+TSjN/16fuNBITU3FT3/6U+Tl5TGjnbqcqamp2L9//0ec9/X19XjiiScY2alWq/lQvrq6ylhCOrCFw2EMDQ2hpqaGH2TqoPr9/oRmcJ1Oh/fff5/H/CsrK6ipqUFnZyfa29uRmprKH+a2tjbU1dVhZWUFKpWKjY9Eznn77bcxOTmJjIwM3HTTTSgoKMD4+DhmZ2eZc3zq1CkmsXxcaAmZ0mnkT+PN7du3Y3p6GjfffDOGh4fR1taGYDDIXgjSTyuVSqxbtw5qtRpSqRRvvPEGysrKUFhYyB9bMmutX7/+I9p5qVTKuLirLUqvpI/c4uIi3n33XaYdEGM6OTkZX/ziFwEAL730Ep577jkIBAIcOnQIVVVVEIlEmJycxIYNGz6i1c7KyoLX64VAIMCXvvQlfPDBB7hw4QICgQAHASa6pm+88Qa2bt0K4EOjttfrxdNPP41t27YhNzcXkUgEfX19nAb+3HPP4c4770RLSwuOHz+O6upqVFVVwW63QyAQcK4ETdF+97vf4XOf+xxzrru7u7GwsID09PSEJBulUonJyUmmbdjtdkbBvvHGG2hoaGA9rEAggFKpxL333os77rgDR48eRUpKCsunaExJxJ6SkhJ87nOfwx//+EfWTB47dgyTk5OQyWQoLy/nZParLUJwxuNxRtdNTEzg1VdfRUVFBadELy0tQSaT4fDhwxgYGMC7776LhoYGpKenQyKRwGw2Y2RkhNNBKWyRKGt6vZ47KH19fZDL5Vi3bl3C7gvwoW+ACgkKvJRKpXjsscdw3333cTETj8eh1+shEAigUqnw4osv4uLFi2hqasLWrVsRjUZZakP+EiLELS4u4sCBA7DZbBgYGGDfRzAYTHhIKCoqQn9/P0KhEIxGIwQCAUZGRrjojUajWFxcxOzsLAd/7tixA6dOnUJhYSEKCwtZI1tWVsZ0O4PBwJ1Wwic3Nzejvb2dNb7Ah9kFay2Px8P3CqV5i0QiPProozh8+DDjfcnsWVBQgM2bN6O3t5elJfTufvPNN7G4uIienh40NzcjFAqho6MDycnJHKDY3d2NqakprK6uMt52rZWamoqTJ0+yuV0mk3EeBxmtjUYjG/aJ+jI8PAyZTIbGxkbuvtGhk0gq1NkeHBzkbjd1noPBIEQiEaqrq9fcG/nuSNNOEl/yjBBi0uPx4MKFC2hoaIBCocDS0hLOnTuHbdu2Yd++fZiamuLAS4J5hMNh9Pb2cqGyf/9+jI2NsbxD+T98/bVWWloa2tvbWXpLxt/XXnuND9wulwsSiQRlZWUAwGZZyiJSqVSQy+V48803cfToUWzbtg319fVQq9UIBAKcv/K5z30Or776KoaHh6FWq1FVVZUwqZm8I2TkpYCyzs5OmEwm3H333UhNTUVHRwdPPhoaGnD06FHU1NRg3bp10Ov1TJD79re/jSNHjnBYp1QqZapkeXk5hoaGEA6H+Vuc6FBaVVWFEydOQKfTcbKy2WyGxWJBd3c3U4soh+DixYvo7OxEXl4eF6tyuRw2mw0ikQhVVVWoq6vD8PAwQqEQDhw4gGPHjjGqdXBwkL0wlOi91lKr1Zibm0MwGOT0dqlUiosXL6KoqIh9bxaLhaXb69evx5NPPskJ5Js3b0Z1dTVaWlrwve99D3fddRf7izIzM7Flyxb88Y9/xLZt2zAzMwO1Wo26urqPzfhQKpXc1XY6nRgbG0NWVtZHwkfHxsbgcDggl8thtVpRVVWFQCCAlZUVKJVKyGQy5OTk4IMPPsDo6CiOHDnCZxqaptvtdi4AyKORnZ2NkpKSNfdG10oikbCPRCaT4Q9/+AOam5tRVFTE3f+ioiIGPOTk5ODcuXMIBoPIz8/H9u3b0d7eziRA8ibOzMxgenoazc3N6OrqQkVFBRYXF/keqKurW3NvCoWCk+7T0tIYFPHwww/j9ttvh1arhc1mY3/Utm3bcOTIEQYGkaeL7suFhQXcdNNNHHRMjZa8vDwOeST52Md9s7KysvDiiy+ioaGBoRuEJe/u7mYVzNjYGFZWVnDzzTfD6XTi73//Oyt/zGYzvF4vSkpK8Nxzz3ETUCqVckZYV1cX/1lsNhs8Hg9yc3MTPgtXrk8snbJYLNi+fTvr3qqrqzE9PY19+/axBm5lZYU15oODg9i5cycb7ijk44477sCpU6ewvLwMq9XKppTl5WUcOnQIPp+P9V9ZWVmoq6tDNBrFiRMn1tybx+PB9u3bIRKJ4PV6mWqRk5ODgYEBZGVlISsrCzU1Nbj99tu5W0RdM4FAwLSMpKQk3HjjjbDb7ejp6cH4+DiWlpawa9cu9j5s3boV27dvR0FBASc/rrXcbjcaGxuhVCqZVkL6O8LZDQ0NsU707bffRlFREbZv3w6FQsGJ58Ra3rt3Lxv6CJNaVFSEy5cv80NFPG7S1q+1yGRPuuXMzEzWIFK0fU5ODptFL1++DKVSiS984Qv47ne/i7KyMh6Rzs3Noby8nD8WarWak689Hg9Xx8XFxdi8eTMCgUBCY67P50NzczNEIhFTkRQKBXbv3s2m4OzsbGzcuBE33HAD5ufn8YMf/ADt7e3Iz89no5xcLsfg4CDuuOMOFBYWYnV1lQ1QX/3qV/Hyyy/D6XRiZmYGOTk52L17N+rq6tDU1LTm3hwOB3Q6HWtH6+rqkJ+fj4qKCqhUKh5najQa7N+/H+fPn8dPf/pTpKam4kc/+hF7WYqLi2Gz2bB9+3bGWI6OjmJ+fh633XYb+vr6mBBDxCer1Zqww0HSokgkApfLhVgsBpFIhIaGBn55k/+lsLCQr0lOTg62b98Og8EAsViMjRs3wuFwYMOGDWyczMrKQkpKCu68805Oqqcsl9raWqSmpiYMiAQ+7MyTGVStVkOtVmN8fBx33nknpqenkZmZiczMTD6cWq1WJhN99rOfZaPn5s2bkZSUhO9///s4d+4cBgcH2e+1Z88eHDt2DFqtFlNTU9BoNFxETE1Nrbm3/v5+TmYlOk80GsVnPvMZVFdXo6ioCAUFBdi4cSNuvfVWzMzMMA1m69atMJvNUCqVuPXWW+F0OrFu3Toel9Oh47bbbuOQLavVCoPBgOLiYtjtdhw4cGDNvVG3zGKxYHh4GDabDUKhEF/72teQn5+P5uZmlJaWMpWKZAF0KCU6DTVdyBd0+fJltLe3s3+lra2NC8ySkhJUVlYCQMLJaGpqKpNNTCYTqqurIZVK8bnPfQ4ymQwajQYymQxarZb594QJJvY9BQQSxpyoZzU1NTCZTDAYDKyXHx4ehsfjYTnGx/nQyLOXnp7Opvh169axH480/zfccAOmp6cZD33rrbciJyeHdfXnz5+H0WjEysoKHA4HvF4vZDIZy5CI5kZFAMkC11orKysoKiqC3+/H5OQkSx6vv/56ntCWl5fDYDAgPz8fnZ2dOHDgAOdtkKSFCI3f//732ejr9XoxMzODgoICXLp0CR0dHXA4HCgtLWWfRyL/CMnagA9l0+Xl5SgrK8Pdd9/N5EbyB+Tm5iIlJQVqtRrbt29nOVtrayui0Sjm5+fx8ssvQyaTobCwEEVFRUxgunjxIkvAqFClxstai+5jwlGbzWacO3eO0dhk1j148CBOnjyJtLQ0XH/99RCLxdi6dSuEQiEGBwfR1NSEYDCITZs28eGOkpGPHDmC5eVl6PV6OJ1O5OfnY9OmTVhZWUloVKfEeLvdjqmpKQSDQYRCITQ0NDAVkbCt9fX1+POf/8y+qw0bNiAzM5ODTN9991385je/gVqtRn19PUwmE+bm5qBQKJCfnw+lUomhoSGGuQgEgoQT5UAgwGcOKk78fj9qamrQ1NSE2tpalJeXo7y8HBs2bEAoFGIDcHV1NbRaLYN/6urqYDAY8MorryAcDrPX6ODBg8jMzERjYyOT0/bs2YNAIAC73b7m3iKRCPR6PZKSkjiAsKCgAF/+8pdRX1+PTZs2oaysDHl5eTwJz8/Ph8PhQH19PdatW4eMjAwubgBw3lFqaipyc3Nx7733IhKJoLS0FLFYDHV1ddi/fz/DHNZa5I2hAjk7OxvFxcVoaGhgdQJ9ryorK/m7ODMzw01NkjZOTEygtrYWHR0drJDJzMxERkYGOjo62DOSm5uLuro6JCcnJ3z32u12rF+/HuFwGKurqywDKykp4ZDazMxMmM1m1NfX4+9//zuqq6tRX1+Pr3zlK9i7dy97Lvx+Pxes1JyjZkcwGERFRQU8Hg97AKPRKAYHB9fc25XrE080xGIxZmZmYDKZ+Eekl31WVhbm5+dZ32cwGHD58mWUl5ezvp/IAFRFy2QyRCIRZm7HYjF4PB4kJyezSZK6+nl5eQlfPBQmRliwSCQCn8/HujX64CYnJ0MikcBoNGJ8fBzJycmMQFOpVCzn0Wg03J2kUR6NqFtaWvjjRPkJiYzqYrGY9wWATU7z8/MQi8Xc5aObuKqqCn6/HwaDgcPMCJFLhZFarYbP54PX64XRaGQz99zcHBul6GOXCNNK5BqiP1G42+DgIBQKBdxuN5uC9Ho9zGYzowWlUilUKhWSk5MRj8d5EkWmOzpsnz9/nmllAoGAK/usrKyE5lfqjmg0GiQlJSEUCsFsNsPv9zN6lHB1SqUSw8PDEIvFyM7OZmqGRqNBOBzmw2NWVhamp6cRjUY5RIckf9nZ2Uy9IN3wWov2k5WVxaQOMq0XFRUxYpbkcPQRpZTNoqIipKSkYHJyEtnZ2fD7/ZienmYaxMjICGM9L168iMzMTL5Gq6urCX83oVDIf36aoBGEgAzRhKONx+MIh8MYHx/n4iw1NZUPpuvXr4dKpUJ+fj5cLhcbfTs6OmAymZh+Qv+fppWJFlFdiHxFtDQi0LjdbgY7LC0tcZjfnj17YLFYeII6PDzMOlv6XUh2kJeXx9IwKnyIXpXIkAiAjfFkqCeyCIWGEuZUJBJxMnFFRQXS09ORlJSESCQCv9+PvLw8PkgPDQ0hEonwhCs/Px8DAwMshyCQQ6LJaGpqKmOyATDOMjs7mw+c9L8NDw9DKpXC5/MhLS2NAyiTkpKYHiIUCtnPQXQuuVzOCbwUSArgYz1L1HkjotrKygoXKMT4J4kYHa6dTifMZjNqamp4GjU9PQ25XI7+/n5+N1MRkJSUBJ/Ph6GhIaSlpTFpheQ3ay2RSASJRMLEN7FYjPz8fEQiESYLEbkG+NBv0tvby6CNlJQUCIVCuFwuNDY2QiKRsAGUyGTBYBBms5lpMfF4HCkpKaioqEiYWu73+7kIXVpagsPhYL9heXk5FhcXIZFI+HAYiUQwOTnJEx2dTgeDwcByydzcXGbp0+HEZrNh586dSE5O5oPc8vIyJBJJwkJDJBIx+UsoFCIcDjOlTq/XY3h4mLXu9JxZrVZs2rSJQ3htNhsXrmlpaQxiEAqFyMnJQTwe526qQqFgWhNJ/9ZaBG4h+IlEIkFJSQkcDgcaGhrYE0VeiH379mFmZoYlNAqFgqlDFouFEdpJSUkIh8MIh8OIx+Oorq7G1NQUUlNTmWSo1+sT6vnpO04yLdpHNBrlPCWispG37/3330dGRgabwgUCATo6OnDPPfegtraWvXtkzu7v78eNN96I3t5enuACQGlpacJngYph8j4AH551yH9AQAYiiNbW1vK1oSKltLQUOp0OMzMzrKiQyWRMJ6R3EhESiaKoVqsTeqloWkRSLXqfiMViaLVa9qiQty8lJYW9GQStoeeE8jiys7P5u0X3MUEVyLxOz0WiXBm6x68k5BF+NhKJYGhoCJmZmTAYDExSJKnq7Owsy5empqaQnp6OQCCAgoICXLx4EeFwGMXFxVAoFJwGT88VAOj1+oTnS5FIhMLCQszPzzM9UygUori4mJshBNWxWq1MXW1oaOBnIRgMcqMVAIxGI7xeL3+n6XklEh5lGFGT5JOsTzzRoJGxUChk4zDx9JeWlpiEQigzj8eDYDCIzMxMhEIhRtGRTnlycpLHu06nE2KxmHW8JHVZWFjA7OwsxGLxxx78HA4Ho7foZRqLxVhLtrS0xF1EkUgEu92OpaUlDA4OIhaLMVFLKpXCarVyh47G7HSIHx0d5a4EacATaV0J1UkPeSgUgkAgwNTUFIRCIY8d6UFobGzE0tISk3zUajUneUokEvj9fn7I7HY7tFotkpKS2JRMB6RYLMaHskS/WygU4gMCdRXoxT03N8fFII3H6XelyQlNCMxmM3p6epjKsbi4CKvViqGhIRiNRu68ET6T9NBrLUo+jsViTO1ITU3lQ/aVaFW6186dOweDwYCFhQXWoI6MjEChUGB0dJSnbsFgEAKBAHNzc+yRoYfxyj/zWoteqsnJyRAIBPD7/XA4HHA6nVCr1YybXFhY4MAy0sVSHgYVQWTUpsLwyuwSo9HIGGUK9KKPfaJFxDcK7qIXNxkVSWfu9/shlUo/8ryR9ntsbAxarZbDjmw2G/s+xsfHufihHBnKr0n0LNA9R78/BX4qFAqWrPX09PBodnx8nCVWSqWS5SrRaBRnz56FWCzGhQsXGGdN+Mfx8XEuvIno5Ha7uVBea0mlUi4Kr2xK9Pf3IxAIwO/3sxE8FAqxbl0qlbJRfGlpCX19fdBoNJx54PV6YbVa4XQ60d/fj/LycoyNjXGa7OjoKJKSkhJ21aiBQntbXV1FNBrlRgJlEVB3fGFhgUOhiJ4nkUg4N4ZQ4HQQoO4VffRoIrq8vIyUlJSEkrhYLIZQKMSddZKUejwe9n+QFtrv9zNpSqPR8EdUKBSy9+LMmTMIhUJ8zxFKOykpCcPDw/zfqdinguhqizxU1BQhWShp+el39Hq9cLlcyMzMhNVqhclkQjAYZFzv6uoqGhsbmSJI9xEdVmQyGR8qlpeXOQAukRyO3m1E7QsGg1hYWMDAwACbtgkZ7fF4eDpLh0Iy0hOhkUzQV94z8/PzKC4uxvz8PEKhEKxWK2ZmZhjTvtaiphB1VOmvJfQxIYgp20YkEmF4eJjBJtFoFBKJBBaLBcFgEK2trUhLS+PfmaYihYWF8Hq9yMjIgFgs5ndDIqM6Bb5S0KHH40FRUREngLvdboTDYZaMUiOUAshoGk4H6tHRUQiFQg4cXV5exsjICPR6PaxWKxOZqCBNdL+RZJlIYvQ+oZR1IkYGAgEMDQ1h69atGBwcZD8HNaRIfklTecotEYvF3CSlCQzBVz4ueI5+N5raEOjA5/NxkN/8/DxcLhdmZmZQVFTEUr2+vj64XC7I5XIIhUI2F2u1Wg68lEgkcLvdLA0Lh8N8jpFKpQllhPQ9J1pXKBSCx+Nh3wWd4agh7XA4YLPZ2EtL9E2v18tTPWoCk79senqaC+y0tDT2BqelpSUsusk4T00Uh8PBzWEAjO2/8q+j4o+eBQAYGRmBXC5ngz2dRej/K5VKfoaXlpZY8ZOosSgWi7mZEgwG4XA4uAiiAG3y31Fy++joKAQCAVwuFytYPB4PJBIJZmZmkJ2dzed4aqJKpVLYbDYkJSVxkyclJSXhs3Dl+sQTDRr/Lf5PcmVqaio/mD6fjxF7CwsLGB0dZeQXmajoJpHJZJiZmcGlS5dw//33symHPgakFVar1YwQ7e3tRVZW1pp7W11dRXZ2NpKSkvgmotwG6s6SXpbCXQgjFo1GoVAoYDKZkJOTA5VKhYmJCTQ0NHwEE9jb2wu5XI7169fD4/FwkElaWlrC7ovf7+dDJSEsybyt1Wo5i4IuWiAQQFpaGt58800sLS0hPz8fUqkUQ0NDmJ6e5oeWJjU+nw/bt2/nYDqPx8OHUqfTmbBLSnz0UCiElZUVhEIhXL58mX9DeqgmJiYQDAZx4MAB+Hw+XHfddXj99dc5THB0dBQFBQV4/fXXsX37dibpDA4OIj8/HykpKYyV83g87NtI9KFbXV1Fbm4u/7Y03qQ0Sjpwz87OcoFFh3sqAGOxGN5++20O3ALA1IV33nkHfr8fu3fvhlAohMfjgc/n4w9lovE4Mb7p8BUOh+Hz+aDVavmfQd6ktrY2bN68+SOhP1TQ0sfFbrdjy5YtjC8F/l/QWG1tLSYmJvglSSFLa63l5WUuXKnDTgc9krSlpqbC5/MxHlgikeDixYv898ZiMSgUCng8HpSVlSEYDHLnSCgUsrGSNKehUIiLK5/Pt+be6LejDjYFIFGafUlJCR8I6GBMfPvh4WFMTU1xwnUwGORCWafTcceeXrZOpxNDQ0NshKfMgUSLuu10nejwTe+11dVVOBwOzMzMQCwWcyZOS0sLMjMzoVarkZSUxMmq/f392LZtG/Lz82G32zE5OclyqWg0iqWlJSbhXInDvNqKRqNQqVR8z115QBgaGgKAj/gW3G43HA4HS2+oy00TzOXlZVy8eBEajYaLznPnzsFoNLLXTiQSIT09nYvNtRblIUUikY+kylJnjnTrHo8H6enpGB0dRVNTE8bHx3HixAkoFAqW7JHxOhwOs6SWKDQUvEjTS+qS2mw23HLLLVfdG4WH0qF+YWEBi4uL2LVrF+bn55GXl4eJiQkugkhKk5SUhIWFBf4tUlJS2LTt8Xggk8lYnrRz507Y7XaMjY1xEnIoFOKieK1FiHI6LFBBF41G0dXVxR1cym9QqVQ8NTx16hRP14APdeTkDaCOPHVp+/v78fe//x3p6emQyWTctEv0PMTjcSgUCni9Xn5Wl5eXodVqMTw8DLPZzP8uADh9+jREIhHOnDmDgoICll7o9Xp+3hcWFqDX6+H3+zE6OopQKIQbbriB8zaoKL548WLCxiI1SMLhMGZmZjhjp7GxkY29w8PD3EwcGBhAQUEBN7soDDcWiyE/Px/RaJQLYuqoUy6KwWBg2Rl18BPp0sk7AOAjE19qDpJUeXZ2FmNjY5iZmYFCoUBnZyc2bdrEzYr09HQ888wziMViqKio4CwfKjz+8pe/oLKykkMQBQIBOjs7kZqaiiNHjlx1b3QOonMSSdSpSCWVCd2blGU2NzfHwa0kn52cnITFYsHGjRs5kJaC3eigTBPJ1NRUfo8k+t3S0tL4GhBUIRqNcqAoZeGQHM9oNPJfS7J3Cv4TCAQ8daPvHTUukpKSIJfLeSJBxdRai8Kgr8z5oO9fcnIycnJy4Ha7MTs7y5MPouJR2CZNQgn8Q0oF8ijTeyk1NZWDAKkJkqg5Rr8PJdFTg5aodCKRCOPj4+wdi0ajWFhY4OmKQqGAVCpFRkYGxsfHeSKVm5uLYDCI8fFxnrZpNBrMz89zA5MaZJ9kfeJCQ6/X4+LFi1ypx2IxJCcn49SpU9wVowsRDodx8OBBXLhwASdOnEBDQwOHmlHXV6lU4vLly8jMzPwIJcJsNqO0tJRvevJ9JDIS0YudXsjLy8vcESVvBkXB0wtDKBRCp9MhOzsb0WiUU1UdDgc2bdoEi8UCmUzGo8yenh6kpqbygY9Y0ouLi8jLy1tzb8nJyRgcHOQgHvqN9Ho93wjEJD99+jTy8/Oh0+mwurrKRs6srCxs374dr7zyChYWFjAyMsI8/snJSdjtduzcuZMJKwC4y5UowEcmk3G2BUk/fD4fpqam+M9Osi0KK9RoNGhvb8cHH3yA9PR0lJWVsTyIvBM0nvR4PAiHw3jrrbdw33334fz58+jp6eHRYqLDvFwu571RgXhl4rxOp+NAL+rIazQavPnmm9i0aRPrMFNSUtDd3Y3GxkZOnFcqlWhsbERHRwfOnTvHPgEKZyS9+FqLzF9E06D7LSkpCVNTU7j++uu50KZwwqKiIrS1tUEqlUKj0bCUSSaToaenh9njFLT30ksv4cSJE/jpT3/KBmLSc37ch466jKmpqUhNTYXJZMLo6CgmJiawd+9eDA8Po7u7G16vF0VFRawNLSoqwsTEBGw2G7KysjA4OAir1crP0MrKCndD5HI5QqEQnE4nH1jIcJ9oUedXIBDwaJxMaXK5HPv374fFYmGvikQiQXFxMR++UlJSOOCKwi4XFxdRVVUFoVCIU6dOMYKPng/KQfi458Hn8+HChQucH0Am2Ly8PPT29jJ2mrIvFAoFQqEQSktLUVpaCofDgZGREQ6PPHr0KPbu3cvyPo1GA4fDgeHhYezYsYOLTspTSdQUoKkJGcEpWfb8+fPIycmBTCbjLqfH4+FwrFOnTiEpKYm9YRTMFQwGWSZIEk7gQ6IaSQPoAysWi1kKdbVFvzPl56yurvJUateuXRgcHOQCyGq1QigUor6+nt855NGYnJxEZWUlT0aoy0mkLQrbJLwoNWcS7c3v9zNqlf5dKpUKJ06c4MM9mWRbWlpQUFDAZLzZ2VmWgASDQbhcLvYi0beEKFZ9fX247rrrMDs7i8XFRSiVSmRnZyfslEajUczNzcHhcHAGlMPhwPz8POrr66FUKllSRh94vV6P3t5e3H777ZxJ5fV6YTKZMDs7y5Kq9PR0lgqVlpaiuLiY5YRCoRAajSahtzA9PR29vb38TRUKhZiamsLU1BRuuukm2Gw2frf19fVxiKVKpYJIJOKgMDpwqtVqpKWlobS0lK8nSaRpym02m5Geng6Hw5HQnCuRSDA+Po6CggJotVq43W689tprKC0tRUVFBaxWK0tJaOJTUFDAB0qLxcLht+np6ejs7ITb7WaS4+zsLGQyGd5//30UFRUhEokgMzOTvYsWi2XNvWk0Gkar07PodruRnZ2NwsJCjI+PQ6VSsZz4nXfeQXl5OVZWVtDX18fJ1RqNBgsLCzxNIXQ0yZhjsRh6enpQXFyM7u5ujI6Ofuz9JpFIMDk5yfkRaWlpHOI6OzsLk8mE+fl5Do8dGRmBSCRCZmYmOjs7OY1bKpVi9+7d+OY3v4mmpiaWSFGzmOTNlFMDfLzkl/wLJIGiM6bX62WkNRWs1MSYmppCcnIym7CzsrIYcTw0NMREOSrcCgsLMTY2BrFYzA1bmmomaqSQXJygDdRYo3ed0+nkab/JZEJGRgZT3axWK7xeLwNBFhYWsH79ei5UqHARi8WYnZ1FSUkJvF4vcnJy+PokkvvSn5G80WKxGL29vSw/zczMhEQiQV5eHpxOJ86dO4ebb74ZgUAAv/nNb2AymbBr1y72H7ndbvT19cFoNHJuCsE9Ojo6UFJSwoURgIQDgCuXIJ6ojXZtXVvX1rV1bV1b19a1dW1dW9fWtfX/x/rEHo1r69q6tq6ta+vauraurWvr2rq2rq1Puq4VGtfWtXVtXVvX1rV1bV1b19a1dW39n69rhca1dW1dW9fWtXVtXVvX1rV1bV1b/+frWqFxbV1b19a1dW1dW9fWtXVtXVvX1v/5ulZoXFvX1rV1bV1b19a1dW1dW9fWtfV/vj4x3vbRRx+FTCbjuHvCyi0tLcFoNDK6i4J6RkdHUVJSgoWFBcRiMahUKsadtba2QiaTcbw9cdIpy0Gr1TKXn0KlbDYb/vmf//mqe/v1r3+NlJQUrKysQCaTcQhZJBKBUChEWloaM94p9IrC0AQCAYdzUcIuBYRRLgKlYKrVaiQnJ8Pn8zGqlhj43/nOd666t9/85jeceSGTyaDRaOD3+zk8ijI/IpEIxGIxfD4fzGYzent7IZVKIRQKIRaLodPpMD09zRkHGRkZiEQicLlcjALWaDTMNie8qtfrxQ9+8IOr7u0Pf/gDkpOTGbErEAg4jIp+NwpJowA4m82GeDzO/GSJRAKdTgen08mcfuLnh0IhRCIRLC8vQ6VSIRgMwuv1Ih6PIyMjA9FoFJ/+9Kevurd///d/h1Qq5QA4qVTKYUMpKSmMBKXsAK1WC41Gg5mZGcRiMQ6xIXQnBROurKwwM5v2mJ2dzShBABya841vfOOqe/ve977HCZwZGRmQSqUYGRmBVqv9CFqUQnzcbjcMBgNGR0exuroKiUTC6bZisRjhcBgymYwZ4pSpIBaLkZmZyXkjsVgMgUAAKpUK3/3ud6+6t4cffphTjWUyGSeSZmdnc5AdJbBeGZBF6c1KpRJqtZrRlLOzs1Cr1fxMer1expjK5XLG2QqFQsan/vznP7/q3gDg8ccfZzwrZWJQkFMgEODU3KSkJKSkpDASkRJVJRIJB8pZrVbE43EsLS3BYDAgHo/ztVMqlQgGg8yxB8Dvl29/+9tX3dvvfvc7aDQahEIhKJVKKBQKTExMAPgwfwUAs+GJGW82m+FwOGC1WiGVSpGdnQ2VSoXx8XHIZDIsLy9jcXERcrmcsbwCgQBarZaDF4kRv7q6ittuu+2qe/vFL37BHPm0tDRIJBLMzc1xyKNGo+EMk8zMTCwuLiIpKYkTvsViMZRKJfR6PYaGhvg5T09Ph0gkYtwthV0R1nppaQlSqRQpKSlr/m4//vGPOcGa8mwIx+p0OlFYWIilpSUEAgGsrKxgYWEBtbW1WFxc5ITz9PR0ZukTVpMww5QQH41GIZPJOER1ZWUFsVgMy8vL+Nd//dc1rymhVgkTOTc3x8FplAng8/mQkZHBQZR2u51R6ZQp4fF4oFQq4Xa7OUWX3of0rg4EApxhQ/f4F77whavu7Q9/+AMHXqpUKshkMkxMTEAkEiEQCMBgMCAYDGJ5eRlSqRQrKyuM/fT5fEhOToZCoeC0X4vFAqPR+JEcA6VSyXkIs7OzkEgknFcQCATwpS996ap7e+KJJwB8+MwoFAooFAo4HA7E43GYTCYOiQuHw/z75ubmMuo6HA5zJgrdX3RvxmIxOJ1Oxg3L5XL4/X7OHBGJRIhEInjooYeuurd//dd/5SR3yrtJTU1l5HN5eTmmp6cxNzeH5ORkRrBPTU0hGAxCr9ejsLCQEb6EdafsAAraJGwrfZ8J8+tyufDII49cdW8/+9nPOMhVJpNBKBRicnISBQUFsNvtMJlMjMim60bJ206nk5+FYDDIgYRSqRTp6ekcuBiNRpGVlQWVSsXXhO6LcDiMH/3oR1fd269+9SusrKzwt5GStOndShkO9H8UU3Dq1CkoFApEIhE+u7jdbohEIsjlcn7P0bMcj8eh1Wrhcrn4z0Hhlb/4xS+uurcnn3ySMyOkUinnbCUnJyMWi8FgMHBmG2V91NbW4vLly4yfpsBhr9eLQCAAuVzO7wpKPw8EAjCZTJweTxlmPp9vzWfhmWee4QBJmUwGiUSCqakpxONxqNVqFBYWwmazwWaz8VlydXWVz4nRaBQikQjZ2dkYHh6GWq3mrDLg/+Fy6ZmIxWIcLBgKhaBSqXD77bdfdW8/+MEPOAxRoVBArVbD5/MhHo/DZvv/WPvv8LbP81wcvwEQJIhBAiQGQZDg3lOcIrW3ZUnejuN4xHZix5lNczXNOWmTNm1Wj52kTlMnObFrO4njvSRLsvYWKXGKe4HEIAYJggAIgABBgvz+oT7Pkc9PhHOu6/f+1atVjZfA5/O+z3M/93AhJycH8/PznLdCeXFDQ0N895JdO2XSaLVa5ObmIhKJsL391NQU5+oIhUIoFArOpvrud797273duv7qRiM5ORnJyclITExEeno6J2Q6HA7k5+djZWUFOp0OCQkJfPBS1Prhw4eRlpaG+vp6JCYmYuPGjTAajZibm+Mgs0AggGg0ipycHGRmZrIP+Pz8PKcQrrcyMzO5oKBiiPInFhcXkZaWxjkH1LSQrz75DN966QYCAX5oKMwkGAyy/zdw8yJOSUnhAKD1llKphEgkQnp6OhfgJpMJIpEIlZWVXABIpVIolUqcOnUKCwsLKC0tRUZGBubn5zE9PQ2Px4Pm5mZ0dXUhKSkJGRkZnJ47ODiIsrIyDA4Oora2loMAKWRmvUUFG4UlUWFP3wElLtP3mZmZif7+fmzduhWrq6ucPDk1NYXGxkb09fXBYDAgKSmJQ+zS09NRXFyM7u5uLC8v88FDhVq8vUWjUc4xoQCqrKwszM7O8iWXkJDAPs9KpRKJiYlckEkkEhQWFmJychIKhQK9vb2QyWRcrHi9Xj4IqGClJjQ/P3/dvdGho1KpkJGRgeTkZC7eby0WKJcjJSUFZrMZRUVFnMCZkpLCTRglvlISq8ViYd97k8kEn8/Hyd4U9rbeosOVQvukUikyMjLg8Xj4YqcMmFgsBq/Xi8bGRvbwp6I8Go2ioKCAQ/moEExISOAGdHl5mZNZKQE3XigTcLMxFQqF/FsB/ydN12AwcLored5XV1fD6/VywbC2tob5+XlYLBYIhUI4HA40NDRwGJHBYEAkEkFKSgpUKhUXCeTNbrFY1t2bSCTiQ1ehUHBgEuW+SKVSzMzMIBwOQ61WIycnh4OQ1Go1Bz/6fD4IBAJMT08zwALczOkwGo2cfRGLxbhAdTqdcXMNAKC8vJwveLlcDpFIBIvFgqysLG4cYrEYgsEgF/z0m8jlck7WDoVCHDZIabSzs7MwGAyoqamByWRCNBqFSqWCVCpFMBhkD/nbrbS0NOj1eg75EwgEWFlZ4feroqICPp8PdrsdPp+P300CV7Kzs5GWlobe3l4OvMvPz+cAN3oWKDiQGoyVlRUuKtZba2trWF1dhVKp5NyeWCzGIE1FRQV796vVami1WkxOTiItLY0Lc41GA7FYjJmZGczPz3P4K4WREaBAKfJUzIlEos8841QqFacJUyK21WqFRqPB4uIiFAoFB2QVFhYiFoshHA5DoVAwIOF0OjnANC8vDw6HA4uLixAKhZw3IxKJoFKpEAwG+X2K52yv0+k4+I+Skyll3eVycdDd2toap67T95OSksI5OdnZ2QwgUEq4SCRCWloa0tPT0dHRAY1Gw5kc0WiUgbn1VnZ2Nt/ZBKZNT09DLBZz/gvVKYmJiZiYmMDTTz+NDz/8EGVlZVhZWcHQ0BAGBgZQU1MDs9mMvLw8BlMoaE+n0/F3RM0j3avrLQoYFQgEyMzMRHJyMhwOB1wuF0QiEW7cuIHU1FQoFAoEAgG+fwoKCqDVajkEmBqQtrY26PV6pKWlwWq1IhwOIy0tDbW1tejo6IDBYIDT6UQ4HEZeXl7cJHq6p+RyOaRSKTeIdrsd2dnZnM9kt9tht9tRUVGBjz/+GBUVFRw+SfeZSqWCzWbje0apVDLY4/V6YTAYMDc3h1gsBpFIBKlUuu6+gJt3qcvl4ppjeXkZKysrcLlc2Lp1K6LRKAMDHo8HeXl56Ovrg91uR3V1NbRaLX8OgRQEXFB4M4UQEuCQnJzMxX68JZPJOOSQAE+tVguHw8EZJwTY6fV6BoIcDgcGBwe5thsdHUVVVRXsdjtkMhk0Gg18Ph+mpqZgNBqhVqv5MyjMlcCZ9RZ9V2q1mp93SkynrBVqHAgwWl5ehtFoxPDwMGQyGYcGZmdnY3Z2lgFTr9fLAJHBYODawe/3c6AgAXGftf7qRmNlZQVJSUmw2Wzw+/1IT09HOBxGbW0tB/JQKrVYLEZGRgZcLhcsFgsKCgoQiUTQ1dXFYTFarRbj4+NQq9XcZVGa7fDwMCQSCaM9MzMzkMvl6+6NApmWl5extLTEAXKE8lExRCmzy8vLnB7s9Xo5sj0zM5M7/KWlJUbQ6ZKemZnhQ1EikXACLnXYt1tUgK2trWF6eponNXRRrK6uYmpqCqurqygpKeHQosHBQRgMBg6sSUhIgF6v52T1WCzGRXNxcTHm5uaQlJTEQW3AzQMjXuFHnfPq6ip8Ph8WFhbg9/t5KjQ/P4+FhQXEYjGkpKTwxbe8vMwF6fLyMvx+P6amphgNDYVC8Pv98Hg8PPWh6UJKSgpEIhHm5ubiXsJ+v58brWAwCKFQiIyMDMhkMkbe6SWnAmR2dpYP73A4zC9pMBjkdPaMjAxkZGTwHinJu6CggAPYZmZmYDKZsGPHjtvuzefzQaVScZFD35dMJoPVauWClZJNKdHb6/Vy0UoTPHp37HY7wuEwBAIBI2wZGRmMTNOUYX5+Pu6hHYlE+DDx+/1YWlridOaZmRkOfhKLxZBKpUhKSoLb7eYQPkL9KcWemjhC4QhFDYVCXAhS0UcoXbw1OzuLtLQ0Tj6nBo/erbS0NPh8Pj5Drl27hg0bNiAUCjEC7na7+Vmk4p4Q1qWlJUYI8/PzEQ6Hsbq6ysFIS0tL6+6NpjQ+n4/PBQJXxsbGOOVVLpdjYmICwM1JjsViQW5uLmQyGVZWVvj7JUSe3iWr1Qq5XM7/hlJ0ExISMDc395nJ4LdOk8LhMObn56HX66FQKBCJRDA1NQWv1wuVSoWWlhaEQiGIRCIUFhYy4r28vAyNRsMINSGEFJ7Y0dGBjIwM2Gw2KJVKCIVCRu3XW36/n6dPlK5Nl/zq6ipsNhsXmSqVivdFqFgsFoPVauVngpA0Ap+i0Siys7MxMjLC6LhOp+MGjc6d261wOAypVMpor0gkglgs5hReKo6pmZdIJBw8SsWg1WpFQkICBAIBcnNz4XK5+JzRarUcGkahtEKhkCc31EzfbtE7ROgyNS5KpRLRaBRSqRTz8/NISEhAamoqrFYrpFIpB6fOzc3BarVCIBDAYDAgJyeHm3aauCckJMBkMqG8vJxBg0AgwOj5emtubo6fq1uLfmouqLm7FfWnMFpCuAmppbqApg4EZt0a9hUOh3kCkJCQEDdAzWq1ctI3NQYymYzTwm9NqKfUbUqFJvSXQvToMwUCAcxmMzweDzweD7Kzs3H58mVs2LCBUXs6r2kSebsVDAb595ufn+ezPz09Hb29vVzHhMNhPqezs7MxPj6O3NxcBINBTExMYGVlBVlZWUhKSkIwGEQoFEIgEGDAx2w2IxAIYGlpCXq9ntOq4wX2LS4uIiEhgQtIKsp1Oh3cbjfS0tI4JV0oFGJkZISf74yMDJhMJm4u6PeiCdXMzAyDaGq1GuPj47BYLMjMzERqaipsNlvcoFl6nuhdpN9RoVDA5XJBLpfzb7O2tsaBefSuLSwsYGlpCRKJBMPDwygqKuL/O4EfNBHp6+tDeno6T1+CwWDcd4GmShTuR81tamoqg7I+nw8rKyscZmc0GuF0OqHT6fj3FgqFGB0d5TuI3tHc3FyoVCqcOnUKjY2NSEtLY+BSLBbHBVKILULfGU1TCCSPRqP8d9NZ3tTUBJPJBKPRiEAgAJvNBqFQiGg0isbGRg76JKBhbm4Oi4uLKCoqwszMDE/EvF5v3DDBW9dfrdGgL5cuUZoe9PX18WRCJBJhaWkJdrsdDocDJSUlXAwS+pmfn4/V1VX09PTwSJw6QL/fzxcUHVb0v6PC9naLxo6E9NJBNTc3x2NmKizn5+f5y9FoNBgdHYXJZOLCNBQKIRKJfAoxogIPuIlCGQwGhEIhjI+PY35+Pm4TRFQd6vwTEhIY0Z6ZmcHk5CSCwSBmZ2dx7do1TohNSUnB8PAwvF4vp0HTNESj0aCsrAxarZYfWqlUysmxlCZLD0e8vQWDQT6wYrEYVlZWYLPZ+OAiFNxqtSIajaK+vh7Ly8uYnJxEKBSCXq/nA9Lr9WJpaYmj7RMSErjhoGh7mmDRS7beWlxchNPpZIoENX42m41/16SkJKysrDASLxAIeDS9tLTEDUBSUhKGhob40qeikwogOnRoNE1F/3qLLqfk5GSkpKQwOjQ7OwupVMrp1Wq1mql69N7Qcz07O8tjy2AwCKfTyQgQFQc0sqYJxNraGo9I11vUNFKTQwUipW9rNBrodDo+KGQyGTf+MzMzcLlcPFJOSkrC5OQkI9QrKyvc+Hs8Hp7K0NhaKBR+ZlIojbKJLhQMBpGWlsbfCSGVXq8XY2NjiEajXDzNzs7C4/Hw7yeXy/k9TU9Ph8FgQFpaGheXkUgE6enpTGkBEDcZnNJ6qehcW1vjg5sQ9cTERE4N9/v9UKvV/P7T5ZiQkIDk5GRG4qVSKbKzs1FcXIxIJMJNKYEXTqeTp0/xFu2BJrP0O1FxIZPJ+HmhIjYhIYFpKVTE5ubmYmpqisfyWq0WiYmJPGGjyR4h9bf+jbdbKysrcDgciMViPCkmqhOd70Rt9fl8TAlUKBSYmJiAyWSC1+tFMBhEZmYmhoeHGeBRKBQ8HU9LS+Ops9vt5u9NpVKtuzcqgv1+P7xeLxYWFhjlzMzMRCgUglqthl6v5+aaJoyERK+srECpVEImk2FycpIpatTguFwuhMNhBAIBbqRoah6vYKZzNRQK8btFydXUoIpEIgabFhcXkZ6eDrlcDpvNBofDwYnyNOmgaVdKSgqSkpLg8XgQiURgt9v5zKXpf7zmMRaLceFGv4FYLMb4+DgnmhMQ53K5GOXPyclBJBJBJBLhiS5N926dYAoEAgiFQk5mB8Bggs/ni1vABINBmM1m+Hw+psnS9JHO7fT0dKSmpiIWiyE/Px8SiQTJyckYGRmB2Wzmwl0qlWJ0dBRCoRBpaWk8xaP3lp5pv98Ph8PB/+31VkJCAhfDVLOkpaVxs5ucnMzvpcvl4vuBUr0jkQgXhjQhpPuP3sfR0VFMTk7+/wBDNCGM95sS8EifI5VKuSGmcyIYDMJkMsHtdkOpVMJqtXL9Q0nRHo/nU8U9TYF9Ph9kMhny8vKQlpaGxcVFWK1WOByOuDUcFcRUh9BEkCYpPp8PEokEq6urGB8fh0gkQlFREYNeCwsLTNFPTU3lv4n+W1RvEXBENCCz2YyFhYW4DRoAPp/ofLi13jWbzUw1X1xcZLBKIpHA6XTynUp1Lp291AQB4Pudpnqzs7Nwu91ISkri9+N2i/ZEtRvVRcFgEBqNht+DtbU12O12bgYjkQiDy/Q3JSUloa+vj+s+mUyGxMREbuhpqkRTq8XFxbiT7lvXX91oEFeWKEa0id7eXh5LZWZm8ljZZrOhoqIC6enpWFxcZJ5dc3MzADA1iYrR8fFx/pLLy8uRk5PDl5VIJIpbXK2trfHhSRebTqdDOBxmygQ1RjR9SE1NhUaj4QnI6uoqI3L08lPnDIBH0cSNDAQCmJ6e5gtrvUUPOvHaCJWXyWQwm82YmZlhNMbhcMDn80GpVCI9PR1DQ0OYm5tDbm4uNmzYgKSkJMzOziI3Nxf5+fnQaDTw+/2M1FCDtri4CLfbDbFYHJcCFIvFsLi4iIWFBS4KiFtMhbBMJoNYLMbs7Cw3LoRCE6rc2NiI1NRULtCUSiUjGYQ40EiZuJyEHq+3CAldXl6GRCKBRCJBLBaDw+GA2+1m3rlYLObfLS0tDQaDgRsn0hYkJiZienoaSqUSAoEAgUCAKS6hUAgFBQUIhUIwm80wm82Ym5uL+3IT9UoqlUKj0UCj0TBSl5ycDK1Wi/z8fGRlZTG6SPSIhIQE/s4TExP576AGW6lUMup07tw5FBUVMT2DLud4LzchN9SA0ufRWLqwsBBFRUVMQUlMTERubi7S09Ph8XjgdruxsrLCSBYdTGlpaVCr1ZBKpZidneXiiwodQnw+axRNl/eti4o4OpyJOzo9Pc1FsEgk4oJMJpNxc6jX6yEUCvlvMxqNiMViUKvV/MzR+SAWi+M2QlSYLS0t8fSC3l2tVgu9Xo+VlRVEo1FGnRQKBQoLC/l3S05Ohl6vRzQahd1uZ5RJo9EgKyuLG87k5GTWVhE6GO+ZI6SXLijSBgFgDn9OTg7Ky8uRmZnJ2hpqKGm8XlJSAo1GA5fLxWioTqdj9Dc9PZ0nfzR9y8jIQEFBwbp7o4KAtAx6vZ6b44WFBaSlpSErKwsymYy1BXq9HiKRCGazGU6nk1E40iTJ5XLo9XoYDAakpqZibm4OBoOBz9/FxUX4fL7P/N6kUik3w9TY0+SE9D75+fkoKiqCSqViNJyAtUgkAqlUitzcXKyurnLRRc8HcPMuoyKOmm/6O+O9qwkJCbDZbAgEAjwVXFhYgMViQUJCAlJSUiCTyRjlFolEMBgMXNzTNJXuI7qjCHRZXV1lMIFAOPoePquxJcodTQuIAkxankgkwp9FExKiwdGZn5iYyO8fPfdEOU5NTcXq6ipPtYCbqC9NmeMVzFTMU2NFTWwgEODpGk0eExISUFZWhrS0NOh0OmY10FRZIBDAYrEgGo3CaDSisrISBoMBcrmcz5JbJxlLS0us17rdkslkmJ+f58ZeoVDwd0TnpFqtRmJiIk96iJa8trbGtPPExET4/X7+blJSUpgaMzs7y/x7KkrpHY/3LtB3RU0jofOE2FOtRRoa+lyn0wmLxYJIJMLvMn3e3NwcxGIxCgsLUV9fz0VocXEx8vPzIRAI4PP5uIFYb93amBF4QBpeomET/ZFowNnZ2Z/S6kilUqaZAeA7kHQvdHYWFxdzA+/xeOLS9Gg/1NARLZKe8bm5OQbCkpKS+AykhoPqFaJ409SbJm63TgczMjK4waTahP7O9RaBQ7cCM6T7JCoZURvn5+cZyFxZWeH6ld7T5ORk9PT08LT31meXziGtVguhUMgMh5ycnLjfHa2/utEgMUskEmE0JSkpCYcOHcL8/Dzq6uqYY52RkcFjWpVKhd27d0On0zFSbLVasWXLFoTDYUxOTmJwcBBOpxNNTU2Yn59HYmIiTCYTEhMTkZWVhZWVlbjIvFAoRHV1NTcmsVgMLpeLOa0VFRXIyclh2ozZbIZcLkdbWxuam5t5JGSz2WC1WpGfnw+Xy/WpkezmzZsZTR0ZGUFycjIKCgqYb7reWllZgVQqhcVi4TE3HbIejweZmZkwGo0oLCxEeXk5i73GxsZ4ktLf3w+XywWTyYQDBw7AbDbjxo0brPWQSCSYmJhASkoKbty4gYSEBFRVVSEhIQHnz5+P+7uqVCrWtRBKW19fD7vdDpfLBbPZjJWVFRQWFmJqaoqFetu3b4dQKMTVq1eh1WpRW1vL4kXiV66srKC0tBQzMzOIRCKYnJxETU0Ntm/fjqGhobgTDeJME3KQlpaGUCiEkpISRig0Gg0KCgpQW1vLU6hr167xpGNoaAg3btzA0NAQWltbodFomJq2tLSELVu2oKGhAW63m/820gfF48ubzWaIRCKYTCb09PRgZGQEs7Oz2Lp1KyYmJlBeXg7g5sVpNBoZGTKZTHC5XABucqALCgpw/fp1bN++HbW1taiuruZCorKyEhUVFVCpVFAoFMjJyYHRaASAuMjQ0tIS8vPzGelfWlrCzMwMNm/ezFQvh8OBSCQCg8HAouorV66gtLQURqMRIpEIpaWlsNlsuO+++1i8n5ubi4yMDKSmpqKoqIiLtoqKCjQ2NmJpaQkjIyNxnzdC5B0OB1+wpKEJh8MYHh7mS/See+5BLBZDQUEBBgcHGTRITk7Gpk2bIBQKkZ+fj9HRUfT29uL69evo7OxENBrF+Pg49Ho9bty4wQc9NVLrrbGxMeTk5HDxSs0YCbyTkpKYvrBx40YWgaekpECn08FoNKK0tBQVFRUYGRmBWCxmmqHZbMbhw4chFou5mYnFYiguLkZFRQVCoRCsVuu6e7tVUxMIBPi9LywsxMLCAjQaDTcidAES9au2thZisRhutxvFxcWw2+3YtGkTAoEABgcHMTU1hZSUFOzdu5cFxMSH9vv96O/v/0wKZmlpKSKRCNxuNxs1lJSU4OzZsxgYGMDs7CzTNAjtDgaD2LdvH0pLSyEWi7Fjxw4MDg7ikUceweTkJBwOB2ZnZ2E2m7G8vIyJiQlGUuncnJ6ejkstWFxcRElJCZRKJfR6PQsy8/Pz0dHRgZKSEgZWcnJysLq6ivLycni9Xtx1113YsGEDlpaWEAwGMTAwgHvvvRfhcJj1YdSEAOD95OXlobKyEgMDA3GnBqurq4xySqVSSKVSNgMhHQs1CCkpKZifn+dzrbW1FZWVlRAKhVz4btq0iZscmi7X1dUhJSUFu3btgtlsZiqW3++PC0ItLi5Cr9fDZDIxF35mZgZf+MIXEIlEUFVVxc9wfX09+vr6oNfrEQqFsGHDBmi1WoyNjWF2dhYjIyNoaGhgBDQ3N5efR0Jb/X4/MjMzUVtbi9TU1LhNkFAoRHl5OdNUCIgUi8VsujI2NgaXy4Xy8nJcuXKFdYbbt29HRUUFMjIycOjQIQSDQTzyyCNwOp2M7M7OzqK4uBhDQ0PM7yfdnsViYXT/dosAGBLgrq2twWq1Ym5uDuFwGMXFxQBuFsZZWVlwuVxITEzE22+/zVpVer49Hg/y8/N5kpWZmQmtVosHH3yQz6Xp6WlIpVIYjUb4/X6Ulpauu7dIJAKtVsvgZ3JyMkwmEx5++GE4nU4GqbRaLe655x6oVCrk5uYyyBQOhxlUDofDaGpqgkAgYHou0avfe+89uFwuOBwO1NfX4/7770dmZibKysrW3VsoFEJeXh5PzOlM2717N9rb25nCIxaLsWvXLkbgFQoFsrKyIJVKWU9w8uRJ1NXVYWRkBFKpFGq1GrFY7FMmO+Pj48jIyMDGjRu54V1vhcNhfmYlEgnUajVWV1exurqKcDiM3bt3QywWswFGV1cXA8lVVVWoqKiAQqGAVCrFmTNncNddd8Fut2NgYICn4snJyVAqlWhubmZmRmVl5bp7opWUlITS0lLWJhFTQK1Wo6uri0GAUCjE4GFSUhKzUWiyXlNTg+HhYdx///0sM6DmXSAQwO/3MxuFQCAAn8lgoPVXNxokLty5cyf279+PwsJCBINB9Pf3495778XU1BSsViucTiecTie2bt2KQCCAmpoa/OhHP0JXVxeqq6vh8/mwa9cu/OhHP4Jer0dCQgJ0Oh327NmDpaUlfO1rX4NGo0F1dTUcDgcmJyexY8cOXL9+fd29qVQqtLW1MWVqeXkZCwsL6OjoQHFxMdra2jA1NQUAyMrKwve+9z0sLy+jsLAQExMTjIzZbDbk5eXB5/MhLS0NGzZsQF5eHmZmZnD48GF861vfgtfrxcrKCrq6utDb24vi4mL09vauuzelUolQKIT6+nqUl5fD4/Hg4sWL+M///E9UV1fzeIy4iOFwGC+88ALq6uqwZ88ePProo3j44YeRn5+P0tJSvPnmm0wL02q1+MIXvoDk5GT84Ac/QG5uLh9ebrcbW7Zs+UyaTTAYRGVlJcrKyqBQKJCXlwez2Yy9e/di48aNqK2tZdT4wQcfRHt7OxwOB1544QXMzs5ix44dmJiYQE9PD9555x1kZmZCLBbzqHd5eRlbtmyB2Wxmzrzb7UZzczO7Kd1uEaq8adMm5Ofnw+12Y3JyEn/6058YyaHfPisrCyUlJRgaGkJTUxMWFxeRmprKBXxhYSGuXLmC1dVVLoSkUikuXryI/fv3AwDTD2QyGSorK+MKsKqrqyEQCLB3717U1tZCIBCgtrYWL730Epqbm3H69GmmFsRiMXzpS1/iaYvX60VFRQXuv/9+qNVqPPHEE/jnf/5nZGVl4fjx42hra0NTUxNPc/7yl7/A6/UyNYCoEestiUSC2dlZlJeXo76+Hvn5+diwYQPa29tRXl4OuVyO9PR0+Hw+XLlyBQ8++CASEhIgFovR3t4OsViMrVu3YnZ2Fqmpqfj85z+PtLQ0RnM9Hg/S09PhcDhw+fJl2O12dHR0oKOjg3VO8RbRvwoKCpjCtXnzZvz2t79FU1MTtFotGhsbkZKSgsOHDyMnJwfT09Nwu92QyWTYuHEjtmzZwgDAlStXUFBQAJfLhZmZGaSlpeHatWt46KGHMDMzg8bGRuj1ekilUrS0tMTVaBQXF8NkMkGhUDAaXF9fz1MdorSYzWa89957+Pa3v42RkRF4vV44nU4AN5vAiYkJfOUrX0FKSgq2bNkCqVSKhIQEppVoNBq0tbWhpqYGRqMRUqkUJSUl7Mxzu1VSUsJCW6/Xi0uXLrG2JTMzk6dOpAV75JFHYLPZ4HK52GBCLpfjrbfeQkZGBk6cOMETjqamJrS0tGB1dRX9/f2shSAqilgsjtsEkUCzoqICRUVFkMlkaGpqwkcffYQf//jHSExMRF5eHrKyspjy8cYbb2DTpk0oLCxEXV0dioqK0N7ejurqanR3d+PAgQMwGAw8MVpbW4PBYGBRbXd3N65evYrc3Ny4ZxwA9Pf3w2AwoLS0FFlZWWhtbUU0GkVDQwPfK0KhEL29vXjqqacwMDCA3t5ePPnkk3j77beh1WpRXl6OpqYmKJVK1NTUIDc3F06nE2fPnsXS0hK2b9+OWCyGrVu3Qi6XIxwOo7W1NS7QQ9ohuVzO7/fu3buZLhwOh1FSUoLGxkYolUpUVFTgk08+gdPpxIsvvgiHw4GNGzeyBq67u5sF7YRQezwe5OTkwGQysbGGWCzGzp07WWe03m8aCoXQ0NCAkpISRoxPnjzJE5SpqSkMDQ3B6XSioaEBq6urcDqdGB8fx8rKChQKBTo6OpgTTgJXmkB2d3ejqamJBbNWqxVTU1NoaGhgetp63xsBgoSaq1Qqdo7q7e1Ffn4+WltbIZFIsHfvXiwsLKC+vh6lpaVsRvPmm2+ioqICb7zxBgoKCjA+Po6BgQFotVrIZDLcfffdaGlpQUlJCQYGBjA6OooHHnggLs0mISEBfX19rCEcGBjAoUOHEIvF8Mwzz2BsbAx+vx8zMzO4cOECnn/+eQgEAtTU1ODw4cMYGxtDc3Mztm3bhg0bNvD7evXqVVy8eBEikQijo6PYvHkzent7kZiYiHA4jLm5OUil0ri/6eLiIq5cucLU66ysLLS0tODKlSvIycnBpUuXmErT1dWFr3zlKxgfH2fKM6H1KSkpePLJJ/HSSy+xQUd3dzfOnz8Pk8mE73//+3jvvfewsrKCtrY2nDx5EllZWXGNOFZXV+FyudDS0oKqqiq43W5MTEzg5Zdfxj333AOXy8VnrVwuZwoZmQTV1taiubkZw8PDaGxsxNe+9jXs3buXDWgqKyvhcrmwb98+HD16lCdIkUgE+/bti9s8pqenw+v1orKyElqtFhaLBXK5HAUFBbj//vuZQi8QCBAMBvG5z30OZ86cgUgkQl9fHywWC3w+H9rb27F161Y4HA7s3r2bAfienh6cPn0aeXl5UKlU3BgJhUKUlpZ+psD/3LlzSExMRH5+PrMOPvjgAzz88MNsRkIT9/vuuw82mw3hcBiXL18GcLMmfvvtt/H000/j6NGj0Gq1mJ2dhVarxeOPPw6z2YympibWjwQCATbfeeedd9bd263rrxaDBwIBKJVK9Pf3s43pysoKGhoa8PHHHzNvkTiSs7OzqK6uxuTkJH7605/C6/XCarXCbDajv78f+fn5zFcOh8M4d+4cjh8/ju9+97tYXFxkZT4JE+MVMG63G0VFRXxRZ2Vlwe/3o6ysDB0dHaitrWXHhqWlJXzwwQcoLy9Hb28v2+9mZmZi06ZNePvtt3HHHXcgGAxidHSUeX6pqan4+OOPYTKZUFBQgIaGBvj9fvT29sZF5u12O5qbm1kzoVar8eCDD/LD5/V6ceTIEUYFsrKyUFhYyLoCr9eLzMxM1NXVobOzE6WlpTh9+jTq6uowNzeHoaEhWK1WXL16FW+++Sbq6uoYeb18+XJcKgsJ0ebm5qBSqaBSqdDZ2YnKykqMjIywQxfZiv7hD3/APffcA6fTifvuu49dx1QqFTweD+6++25kZ2djaWkJbrcbbrcbFosFBoMBUqkUU1NT7DbkcrnYIvd2iziPV69e5e+ltrYWMzMzGB0dRXl5OW7cuIGFhQWmfmzfvh0ffPAB5HI5VldX0dXVBbFYjIGBAWRlZSEYDCI9PR1utxtdXV3o7+9nAXlhYSF0Oh1isRhGRkZw/fp1HDx48LZ7czgcyMrKwsTEBLsvzM7Oorm5GUKhEKmpqZiYmIDf72e3CqJr3HnnnYhGozx5UavV2LNnD95//30oFAr4/X68/fbb2LlzJ86fP48nnngCb775Jjv4zMzM4Pz589i3b99t9zYzM4PCwkL09/czitLb24vy8nL4fD5cvHgRSqUSarUazc3NOHz4MCorKzE/P4+dO3dCLBajp6eHv6vf/va3PPoOBAJYW1tDU1MTgsHgp2yeSWty48aNdX9TAHxABYNBpkk4HA784z/+I4LBIKanp9HW1ob09HRs374dY2NjSExMxKFDh+ByuTA8PIzk5GS2nlWpVBgZGWFKFV3aV65cYZvV6elpRKNRRCKRuAWz1+uFXq9nEZ3X64XJZGJxJ9ETifa0sLCAgoICjI6OoqioiMXoGo0Gb775JhYXFzE2Noa0tDR4PB5YLBbk5OTA6XSipqYGly9fRnp6OqPV8VDc/v5+VFVVYXBwkA0uiMc8MjKC+fl5Fg+Wl5fjyJEjePjhh3HlyhWm8CUnJ6OyshI3btzAPffcg8HBQQwODqK/v58tVsl97OTJk8jNzeXJXjzXP6fTycUYTYOuXLmC5uZmBAIBlJeX4/Tp0wiHwzh06BC6u7sxOTmJ8+fP8zk2PT2NzMxMXLhwATqdDkeOHIFGo0FGRgYMBgPq6urwi1/8AoFAgKfXXq8XIyMjcfVU4XAYhYWFGBsbw/T0NGQyGUwmE0pKSnD8+HE88sgjGBwchNlshk6nw29+8xvodDqUlpaiuroaGRkZWFtbQ1tbG3p7e5kOp9FosLy8zA48w8PDPMnNyMiARqNBT09PXFCArMDpnUhOTsaJEyfw8MMP47333mNdEtFAAaC+vp4LJYfDgRMnTkAsFuPGjRtobm7G1NQUSktLP+Uck5SUhLa2Nmi1WrannJqaYvDtdouEw8R9B24KdQsLCxGNRpGZmckUT4lEgqqqKvT09GBpaQn33XcfrFYrG5p0dnbirrvuYhcvv98Pp9OJ2tpa/OUvf0E4HEZRURFyc3Ph9Xrx8ccfY21tDYcOHbrt3lZWVqDRaNidy2AwYGRkBPn5+SzCnZycxMDAAKRSKcrKyjA2NoZPPvkEer0eWVlZ0Gg0uP/++/Hee+9BrVajrKwMc3Nz7HQ5OjqK+fl59Pb2oqamhnVHU1NTMJvN635vfr8fd911F8bHx5mSdOzYMRQXF8PpdLKmk1wJ33zzTXZ23LdvHwu9l5eXcfbsWUQiEYyNjTFd5cqVK5DL5bBYLHwGqFQqrKysYGBgIC7CTAAPAC4Wx8fH4fP5kJWVhb179yIQCCAcDmPDhg24ceMGXC4Xgwc2mw0TExN49dVXIZPJGMlfWFhgne6tTpoSiQQFBQWIRqMYHBxES0vLunsjvU5nZyfkcjmqqqqwvLzM9vREnwXAJghbtmzB/Pw8+vr64HA4UFxcjLy8PNy4cQPPPfccLBYLXC4XP7/9/f3QaDTYtWsX9Ho9I/Umk4mBotstmgSNjY0hNTUVhYWFMJvNrO+x2+0MFFgsFrz77rsoKipiChRR03NycnD9+nVs27aNDRGEQiHKysoQjUZx9epVPqMJlDSZTHGpXdPT06irq8Pi4iJPt/x+P+699144HA5cvHgRcrmc9RpExyRpgMViwdTUFDQaDf7rv/4Le/bsQW9vL8rKyhCLxXDu3Dls2rQJ165dg1qtxszMDEseSAfy16z/JzH4hx9+iL6+PoTDYaSnp8NoNOL69etQq9VoaWlBYmIiC8HJIs3hcOC1117D1NQUKioquOgizuvMzAyPdkZHRxGNRrmwd7vdMJlM7BKy3qIfRSqVIjk5GQsLC7Db7XjuuedgNBpRXFyM2tparK6u4siRIxgZGcHU1BTy8/MZxd64cSPuvfdebN68GTabjZ2wyP5tdHQUc3Nz2LNnD5KSknh0V1JSwrkJt1uLi4s4ffo0F8Qkuv3zn/+M5ORkPPjgg9i5cydzljUaDbZv346FhQVcvHgRLpfrU3kWNNEgl5eFhQVMTU0hEong/vvvh9/vx9DQEGw2G7sErbdWV1fR3t4Ot9vNTY/RaMTJkycRjUZRVFSEhoYGpKenw2azQa1Ws4Xgm2++iQsXLiA1NRU1NTXMsSY3maqqKmzevBlmsxkJCQmorKyEUqmEz+djnnM8Olx6ejquX7+OSCQC4GbjarVa8eabbzKysHXrVlRVVcHpdLLNrF6vZ8u16upqFqcqFAr2qKYsC0IDdTod2zNOT09DJBLF/U2Xl5cxODjI/HUSf33wwQefMgdITExERkYG+vr6sGvXLkQiEZw6dQqXLl3CyMgIrl27hqmpKZw7d44pBSRuM5vNaG5uRnt7O6LRKKanp+FwOFBeXo6SkpJ190Z++VTILy4uorS0FCdPnkQsFkN5eTlbyIbDYRgMBv6bLl68iI6ODnaQicViePnllyEUCrF582Y0NjYiMTGRMyOILre8vMwuTTU1NevuDQBreUgfYDQaMTIygg8//BACgQClpaUoLCxkNEyr1eLee+/FqVOncOTIEXR0dLA9K9myFhQUwGg0IiMjAxKJBOfOnfuUzSjpEOLlVADg5piE0VNTU3A6nawNSUtLg8vlwtWrV9HR0YHU1FRs3rwZcrkcv//973H06FGe0M7Pz2NqagparZatB0mTsH37diiVSnbeu3z5MvOH11symQzd3d1ITU1FZWUlCgoKGPihbJ1IJILu7m58+OGHkEql/PcT8jw3N8cX7JEjRxj9pgt3bm4OVVVV2LVrFwwGAywWC8bGxpCZmRlXAJuVlYX+/n4kJSUhKysLOp0O2dnZGB4eRldXF9ra2piS8tFHH6GkpARf/vKXYbfb8cILL+Dq1avIycnBli1bUFRUhLfffhupqanIyclBIBDAW2+9hX/+539GSUkJvve977HAn75Dcna53QqFQpibm2MdidPpZJeowsJCdHd3w2q1Ynx8HK+//jqLOyORCDIzM5GTk8P0zIyMDJ6WFhYWMg2T0FiDwYDMzEwushUKRdy9JScnw2w2M1d6dXUVOp2OJ/HkdDQ4OIg333wTEomE+dXBYJCzD1pbW7Fx40Y4HA7WDVGzvLi4iPHxcbS2tiItLY1F47FYLO45QrkHMzMzDOCRm1soFGL9AyHNCoUC9fX1WFlZwR//+EcMDQ2hrq4O+fn5yM7OxkcffcSGLUTVPXLkCGZmZphqStq9kpKSuLx0nU4Hk8nEOSUEdFy/fh2Tk5PIzc1lJ6XTp0/j/PnzKCsrg9FoZO0K/TakNTGbzZDJZCgpKUFSUhL++Mc/wu/345lnnkF6ejquXr2KTz75BAsLC5yhst73durUKQBAaWkp9Ho9xsbGOHeDNG52u51F3tu2beO8HQKqUlJSsHPnTsjlckQiEWRnZ6OoqAhyuRwul4vt/z0eD1tu6/X6uJx5ejbJXUssFvOU1ePxIDc3FwqFAiMjI/jP//xPvP766/D7/Whra8Pzzz+PkZERNDY2oqioCNXV1UzXJO1uamoqPvjgA1y4cAHp6emYn5+H3W7nJiQeIFBQUMA1XiQSgcPhYGpcLBZDc3MzO5m2tbUxqCEUCnHjxg127lxZWUFxcTGOHTvG5y4J/I8fP86uku3t7bh48SJ6e3sRCATi6m6Wl5cZUPR6vRgfH8fo6CjOnj2L9PR07Ny5E8XFxVheXobD4cDMzAwzKTQaDYqKirBz505s374d5eXlsNvt7KRJAA4xLurr6yEUCjE2Nobu7m5MTU3FpUoTZZCMSMiY44MPPoDZbGaQaXp6GocPH8ZPf/pTTE1N8cRIKpWioaEBer0eNTU1GBgYgFAoxLVr13Dy5EkMDw/j7Nmz/O6Tba7L5YLdbl8XDPi/11/daBDnli5MclPasWMHC6nlcjkqKiqwefNmRujtdjvy8/OZQ7dx40aIxWI0NzczikcX+5e//GUcPXoUoVAIsVgMCoUCKpXqM8Xg9FlkhSgUCpGUlISmpiasra0xOpGTk4PW1lbeZyAQQFNTE2QyGdrb22GxWODxeLBv3z4OpyFBeXV1NfMpU1NToVarWWsRD5kn60HSe6SmpmJ8fBybN29mPYpOp0NJSQlKS0vZQWBkZAQVFRWciZGVlYW5uTn8zd/8DWKxGOx2O0wmE+bm5tDa2oqFhQVUVVWxu1NRUREUCkXcYp4OarL8JQeQBx54gPn8ZJe5Y8cOzlGYn5/Hjh07UFJSwkJU8h6/du0aIpEIX3A1NTX8QhFnVaFQICkpKa5bzNLSEusx6N/SGJdcxhwOB9bW1lBYWIhAIMBhdPn5+cwX1ev18Pl87BFNIneJRILc3FyYTCa+1EioSFSP9dby8jK0Wi3m5+dZKBUIBFBQUMBmAmlpaWyfuLi4yHoastmlTAWaNkSjUVRXV6O5uRlpaWn8Qufm5rJNolKpxPT09GdmVZDlIAVUAkBjYyMHRJL7kE6nw8jICFQqFRwOB3Q6HY/iKyoqEAwG0dLSwsKv1NRUaLVaZGZmIjExEQUFBQgGgxAIBEyf+Sy7u5WVFej1eg6VpJDJlJQUjI6OMipPHvxkwbqysoJdu3ahtLQUc3NzjCpu3boVwE0NA00NHnnkEQA3+asulwsZGRmsg/gsmg1NWmUyGaqqqvgd8ng80Ol0fHEVFBTA4XDwCL26uhp6vR5isRhisRg6nY4nfWQiUVdXh7q6OgwPDyMrK4sFqmSgEa+Yp+Z4cXGRHcWi0SgqKirg8Xg4WC8nJwdVVVUcxhiLxRgho/wdEiJGIhEWXZKxwfLyMkpKSlgITCYMs7Oz6+7tVmtkt9vNOQ40kSgpKUFOTg50Oh3kcjnrs2w2G4qLi6FQKODxeCCVStHf349nn30WCQkJ7GBXWlqKnTt3sgONw+Fg+g2FJ6636N+R8JXQvMzMTBaaRiIR5Obm4gtf+AICgQB2794NoVCIRx99FLm5uWhvb4fdbkdxcTHTJmkvpCMcHR1lpyay8RaLxXE58zRJJ8CNwCQyLyDXuKKiIhw6dIjZBADQ1NTEOVGkaaiqqsLs7Cw7nyUlJaGqqoophampqdiwYQOqq6v5uVxv0buyvLyMhIQEaLVaFrSSaJkmOoWFhejr6wMA/lwyjaGzglgGlLeg0+nQ1NSExMREbhbIdpncxNZbVPCQ3Si5wVVWVuLq1avsgJWamoq8vDyEQiG2pC8vL2eB88LCAoduErOB3q+HH34Y09PT7GpYUVGBuro6DA0NxWVXLC8vs/YlISGBDWfKy8sxOjoKtVoNg8EAo9GIoqIirnUMBgM2bdoEtVqN2dlZzM/P4/r169i/fz/nM9A7TBOr++67j+m0FKYbj/JLug5yllteXobL5UJ9fT2ysrKQnZ3NuT5GoxFpaWmoq6vD8vIyWlpaIJVKMTg4yI5m1dXVGB4eZkBzbW0Nd9xxB/x+P2pra7G8vAyFQoH8/Hy2wl1vzczMcOYKNbLRaJR1u1qtFgaDAYWFhdi8eTNnCIlEIjQ3N0Or1cJut2NpaQlTU1PQ6/X8TJKF/1e+8hW2pV1aWuKzfHp6mrUzt1t+v5/Nj6j+MxgMaGhoQF9fH0cAlJeX45FHHuEah8TSZLBBdHKyfibqOuXSEPi3vLzMxhwSiQSbNm1ad2+ktaF3ge5VuucaGxvZUKOgoIA1wG63GwcPHmTdETkQtrS0wGKxQKPRQKvVYmVlBc888wwUCgW2b9/O4ZEZGRlQKBRxrZ5vXX81dYq8/Mk5gAoYEs4RkpudnQ2NRsMXE3HECJUl1Dk5ORk6nY45XwDQ2tqKt956i+lK5BZAI5v1Vjgc5oeJUBMK1KKkz5SUFGRnZyMvL4/FNteuXWOXomAwiMHBQaSmpsJoNHJTIZfLOUmULhytVssoLrnKrLfI9m1paYlpNAqFgnnuFCQok8mYkgPcRD6Iu0vfEx30RNMgBCQvLw+pqamcTGyz2VjjEI+OQS4JhF5Ho1Gm8+j1eszNzXG6KjnrWK1WLC4ucvARiZD0ej0fzPQSra6uorGxEQAwNTXF1BZCb+MFHQJgRI9eSAqaoWwH8pCuqKjgVE2hUIisrCwuPpaWlqDVajE1NcWOUOSGpdPpMDU1xe5EdImT1e16i8TpXq+XOc/U2JAglOhvbrcbOTk56OnpgVgsZgcXunyHh4dZy0TaG5FIBJfLhdLSUnaVIW0FJe/G+01pUSFBThNEiyGbZQo8czqdMBgMyM3NZRQ8NzcXY2NjHKC0traGhYUF9gsnxJAcQigo7rMOHsrvICoSCQ6XlpaQkZHBhzmFcNH/rNPpoNPpkJyczBNQEpHSOUHOJdu3b4fJZOIcFrKWJprKeosACsriILSIUFC6KEiUubS0xAFuZDRBQU0tLS1M56J0aULib20AMzIyoNVqGWWOtzdywaP0eKJvURFIKH9CQgJKS0vR39/PBT5lBdFZQ00EiY7VajVCoRDntUilUqSmpnIWRrysIBIskyaAnjeymiRbT61Wi0gkgrm5OQwMDLAjHDlVkUNaVVUVbDYbu80lJiYiMzPzU05TBFYQpW+9lZKSAuV/p2OTrTI9KwUFBZyQm5eXx03g6uoqh4pS2KbZbEZFRQULQEkQSkGOWVlZfMbR5+p0urjOemKxGOnp6UytIK97AEw5oQZSJpNxMBtRIAgNpfeYnP5IyEmTKrVaDYFAwK5F5JAXb5HtL+W73NpAEIJK6dcSiQR2u50tTYleQU3q6uoqsrKy2BSAXJXkcjnq6ur42SdnyM+y8L7V8YpQbHK9jEajnAEAgLOC6JlMSUnh7B2aYhCt5FYEPTs7GzabjXMjcnNzOcg33ne3trYGnU7H1tbATeCHrJlpiqBWq+FwOKBQKGCz2bC6uors7GxmUJBtaVFR0afObuBmTZGWlgaJRMLFN1Fk44FQ5GhFzxk5+Wk0Gs5UICtuMgRJTU2FXC6HTqdjS1NKrSbgiiiDiYmJaGlpYbSeHIqocYhHv6TcKLq3ydmM7goCtIj54fF4+DuiOoGee5lMhtzcXMzOzrJOlET6ZL1Pjnp0dtHzcrtFpjxkzkC0Yq1Wi2AwiKGhIW4qtFotmpubPyWopmeOhPSkJ7oVzKH3mp5/qpPJsXW9RWAW/Q3kGEgNLwU0arVa+P47BJLqqc2bN38KIJyfn0dVVRUKCwtRWFjITqk5OTmYnZ1lvQjlXBHY8Nesv3qiQdaqVCgAN5sPt9uNzZs3IyEhgbMXAoEAqqqq4PF4UFxcDIvFArvdjunpabz11lsoKipCV1cXu2YkJCSgpKQEa2trePLJJzmEihw75HI5pqen190bFbVSqZT998nNIjs7G1KplEeTNGIUCoWcRqnX67FlyxamflC4DBUPer0eSqUS9fX1cDgcPAWgQjMeikvFIx3wXq8X1dXVzNemEaHNZkNXVxfKy8shFotRUVHBgShGo5HTQ//1X/8VGzduRE5ODlJSUqDX62Gz2XgakpaWBrPZjM7OTohEorjcw/T0dE4Npsh6n8+H7u5uRiRI9NfW1ob8/HwcP34cYrGYHQ3UajW8Xi8KCgqg1+vR0NDAaEl1dTWSk5ORl5eH3t5eTgAm15bh4eF19yaVSjE9Pc1uRKOjo8jIyMDq6ioLAOmykkqljDCrVCpGuCh3ZNu2bfjkk09gNBpRVlaG4uJiGI1GzjMhL3CyCqaAtvUWWdTRxR6NRmEwGOBwOFBWVsaFi9FoRGJiIjZt2oT5+Xn211cqlSgrK8Pi4iJqa2vx1ltvsQbh8uXLWFhYwNDQEFJSUjA0NISamhouFqurq9fdF3BzKjM9Pc0aFwqFPHnyJCoqKngqFQ6HMTY2BoPBgDNnzmDHjh1sdUsIyurqKux2OxoaGnhkPzw8jPfffx96vR4nTpxgN5S0tDRusuItypegVGSJRIKamhpIJBLceeedSElJQUpKCtxuN9566y0ublJSUnD58mWYzWZkZ2fzlPDNN9+EXq9HdnY2SktL0dDQgHA4jKqqKqSlpXGy+OTkJDek6y1ClomKQc5cZrMZiYmJfMhSlgaJXSORCILBICN9ly5dQn5+Prsczc/Pw2w2Y3Z2FhcvXuRUX8r0IdQ/XgFD3ujUVCUnJyMrKwvnzp1Da2sr1Go15xuYzWZUV1ejra0NJSUlfAbodDo4HA5kZGTwu0vhcxqNBr29vUw9pOKLrFLjnXHkFEMjfrJPjcVibMpht9u50CksLGQnoqtXr2JpaQkbNmwAcNO56dixY9BqtSgsLOTnub+/H9nZ2UhMTERdXR3y8vKgVqtRUFAQ9zelM47ALUI0zWYzioqK+HeUyWSIxWLYs2cPPv74YyQlJeHcuXNYXFzkppFsookiZTQakZmZibm5Odx3330YHx/naSU5ccUDLGiiQl73ABiw2LBhAwe2OZ1O9Pf3o76+HklJScxDFwqFPNGiANrGxkZ2HYtEIjh79ixzuOkZ8vl8nAy/3qIMmVtzfxQKBYLBIGcuADeBHLpvgZvmJ7faOpPZCoUhXrlyBZcvX2bEvqWlhZsWmuoQW2C9Rbk4pA8Lh8NQKpWw2WwoLy/naV84HGZnR7KypfDF7Oxs+P1+pryQwQgBgseOHcOdd94Jr9fLeheXywWdThf33hKJRBwgDNzUExBaXllZyfQwtVoNhULBmqmxsTGe1Gg0Gs6s8ng8aG1t5ecgGo3yb0up3cXFxXz+xqPZ0NlLjatQKERKSgo6OjqwvLyMM2fOMK0duJnA7vF4IBAI2M2vpqYGUqkUy8vLOHbsGDZs2MAhvmQ1W11djYGBAWRnZ8PlcqG7uxsSiSQuICCVSuHxeHhaQ81Wb28vFAoFRkdHmWZvNptRW1sLjUaDmZkZdHR08PNJjpsZGRnYtm0bNyqJiYl49913UVZWxuDR8PAwJiYmkJmZiUuXLq27N6VSieHhYW6uSCc3OzvL5jl0xg8MDKC5uZlBYApNpsmJWq1GcXEx62pkMhlycnLQ19eHxsZGBjlJv6xUKnH8+PF196bRaDA5OcmNlsfj4ck61V0ExHu9XjQ0NCAYDGJ1dRUTExNITk5Ga2srCgoKYLPZ0NbWhs997nPcFBcXF+Ott97iXCkCvykQ+7MYArT+6okGOTIBYE5eRkYGJicnIRaLUVRUhD/84Q8sRBkfH4dOp8PHH3/MtmZzc3OMSPb29qK3txculwtGo5GtXYuLi1FcXIyZmRm2pKSidr21trbG3RvFuE9PT8PpdOKOO+5AYWEhOjs7MTs7i1AohP7+fj5kLRYLFhYW+GB/7rnnUFhYyDQkm82Gzs5OuN1uFtxR0UidfTz9SHd3N+677z50d3djbGyMkaGCggIkJCRgdnYW3d3dTFcZHBxELBZDd3c3Nm3aBIfDgY8++gj79+9HZmYmcnNz8eabb7JFL10iDocDSqUSnZ2dSE5ORmNjI7RabdyIeArBomKZJlPbtm1DV1cX+5HLZDKo1WoWMBHKTfxIi8UClUqFb3zjG3jvvffgdDohkUgwPz/PbkGtra24evUqampqUFNTg3PnzsV1elhYWEBTUxNbS9LhQMGGbrcbXq+XR4LkFkSe1wMDA/D5fCy0uvvuu3H58mVcu3aNkdGUlBTs3r0bUqkUly9fhkKhgFKpxNTUVNxgt8XFRRgMBh7Npqenw+/3o6CgAG+99RaHAVKo2Je//GUsLS3hk08+wZ133gmlUslpomfPnsU999yDkydPMl+0qKgIBw8eRHt7O1ZWVlBQUMDp60RJeeqpp267NxpBEwUjFArB4XBAq9Wiv7+fJ2cUDkjv8fnz5/kCoenEb3/7W5w5c4ZdzMRiMSorK5GQkIChoSHMz8/jzJkzaGhogFKpxPXr1+NaPQPA4OAgNm/ejOnpaU6nvXbtGh5++GFuJIjy8MQTT0AikWBsbAxXrlxBRkYG5ubmcPnyZVRVVaG0tBQ+nw+jo6Ocrj03N8fNNvmlU74OTTbXWxTuRecNOYDo9XpcvnwZu3btwtLSEqanp2GxWD6VFfDQQw/xeSUSifDqq6+iqakJH3zwAds0U+NMIUiULB2LxXDt2jU0NTWtu7e5uTls3rwZPT09nFJeUVGB7du3Y3BwkPVPfr8fa2tr/D3/9Kc/xec//3kYDAbY7XZcvnwZXq8XRUVFGBsbw/z8PKOja2trbMFL1BehUAiTyRT3fSCggRoTmUzGSDxN78iZpaSkBOFwGNXV1Xj33XfZyKGvrw8SiQSDg4OMHlL4JT2T4+PjKCsrY3RTpVJBo9FwYXS7NT8/D4PBwOhnJBKByWSCwWDA+Pg4FAoFTCYT0x8mJyexZ88e1pC53W60t7dziBplXxB9izjyDQ0NKCsrg9vtZrovGTxs3779tnuz2WwcKkrJ9jRVGhgYgMFgYES0paUF4+Pj2LRpE0+Nbty4wai4xWLB3r172QKXpjeU6Ozz+ZCQkAC9Xs+gQTxnJ7VazcgtaULonqO8i4mJCbbwFggEEIlE6Onp4UnV6Ogo+vv74XA4UFRUxFRQatRbWlowOzsLpVIJjUYDm83GaPu1a9ewY8eO2+6NxOhEwaRMrvT09E81peSkRm5LlKVkt9uhUCjQ2tqKd999F3Nzczhz5gx2794NiUSCCxcuoKamhimA3d3dKC0tRXZ2Nrq7u+MKrp1OJ4RCIU+NafK4urqKN954A+Xl5Tzd3rBhA9NlZmZmcPToUaYS9vb2wuFwoKurC9u2bcP4+DhcLhfTZC5fvgytVovh4WGYzWaEw2EOhltvLS4uoqamBlarlYMRFxYWkJKSgjNnzjAThRghS0tLaGhowF/+8he0traybS+BTFKpFD09PVCr1dDpdPD7/ejr68Px48exc+dOnDhxAgaDgXV78UBPyrWhbAlyvItEIqitrUVycjK6u7vh8XiQlZXFMQN03iwsLMDlcqGqqgovv/wynnnmGVy9epVd6xYWFrBr1y7OyqGEbmrq4jk70USdqIxyuRw+n4+bnpKSEmi1WtYtffTRR+wuRVRv0j5OTk4iIyMDsVgMKSkpsNvt8Pv92Lp1K/+GpNVaW1tDX19fXBZDf38/MjMzeW8k4DYYDHjnnXewfft29Pf3w+12o6ysjHN1kpKSYLFYGJDU6XRQKBRMhQsGg0w3HRoagl6vx5///GcMDQ3BYDAwze0nP/kJPvnkk3X3R+uvnmhoNBq2siONwrlz53Dy5Em8+uqrUKvVOHToECorK+F0OpGZmYmSkhIYjUZMTk5icXERd9xxB37wgx9wJ/jMM8/gV7/6Fb7+9a9Dp9Ohr6+P0aI777wTtbW1UKlUKCoqwu7du9fdW0pKCkZGRuByuZjL7nK54HK58Kc//Qkmkwm7du1CWVkZPvroI5SXl7MDh0wmw8zMDHp6epCSkoJvf/vbWFxc5MkM+Q7/6le/Qnp6OjZt2sRcSbFYDKVSGTckJz09Ha+88goLR4uLi6HRaHDt2jW2fiVXGrvdjhdffBGVlZXMxa+oqMCGDRswMzODb3/727Db7Th48CC2bNkCvV7PhTgFY2VnZzMK9VkR8SkpKXz5EAdVKpXiySefhNPpREFBAXJycuD3+3HixAl8/PHH2L17N+tKotEoZDIZvv71r+Pw4cN4/vnn0drain379qG6upoFrAUFBVhZWcHu3buRlZXFtK4HHnhg3b0tLi7iwoULXJwZDAbEYjEcPnyYi64777wTeXl58Hq92LFjB3bs2MEXbElJCcrKymCz2WAymdDZ2Yk9e/ZwYN2ePXtQUFDABwQA9PT04PLly3x5rbdycnKYdtfQ0ICsrCykpKTgt7/9LSQSCbKysqBUKrG8vIz5+XlkZWXhq1/9Kr71rW9BIpHAZDLh0qVLeOmll9DU1ISJiQkMDAzgzjvvRGlpKV5++WV8+OGHWFlZwcMPP4zOzk689tprmJiYwL59+3DPPffE/U3n5uagUCiQkpLC2QVqtRqDg4OIRqPIyclBamoqOjs70dXVhe3btyM3N5cndm+88QasVivuuusuvPDCC3jxxRdZK3Pjxg2kpqbiueeew6ZNmxgBHxwcRF5e3meKw3Jzc3H58mWm6s3Pz2Pjxo34+c9/DrfbjdbWVszNzeH48ePo6uriS/2rX/0qqqursW3bNjz44IMoLCzE9evXMTo6ildffZU96qnp+7u/+zsOtbxVFBjPL12tVqOjo4PpSFqtFteuXcPPfvYzDA4Oct4KWVIHAgHs2LED7e3t+OCDD3D27Fn4fD5s27YNqamp2LVrFxITE1FbW8uiy2g0ivPnz+PQoUN8DpC7jMlkWndvycnJ6OjogFQqxd133427774b58+fx9tvvw25XI6ZmRnWBrhcLnz00Uf44he/iNzcXPzqV7/Ce++9x3xfg8GAq1evoqGhAd/+9rdx8OBBDsnLz89Hamoqamtr4Xa7MTw8jMrKyrjj+8zMTHi9XoTDYaYGUOrx0NAQysrKeLLy/PPPw+l04oEHHsDdd9+NgwcPorCwEA6HAzdu3MAXvvAF9PX1McpLANPvfvc7NDU1YdeuXdi0aROSkpI4QK2np2fdvVFRsLa2hoyMDKSnp6Onpwfd3d04cuQI0tPTcccdd3BRe/78ebjdbigUCvzkJz/B8ePHUVVVhWeffRY7duyAzWZjbWE0GsXw8DBnhGzYsAF6vR52ux1jY2NISUmJaypB+hwKGqPcnLfeegudnZ04ceIEAoEAJicn8Q//8A/sPqRUKpmmQRz4J598Em+88QaOHTuG0tJSbN++Henp6bhx4wY2bdqELVu2oLq6GsFgEDabDbm5udi8efO6eyON19raGnJyclBRUcHfY21tLerq6qBUKjE4OIhjx46xjqq5uRmjo6P8jj/66KPo6enBSy+9xDbOlZWVyM/PR29vLwYHB1FRUYH29na24k1NTY0LWCj/OyjU6XRibm4OQqEQBoMB7e3tqKqqQk1NDaanp/Hb3/4W3//+95GUlISvfOUrKC0tRVtbG5/t7777Lvx+P5qamlBUVMQ5LTqdDj/4wQ9w+PBhFBUV8ZSJBPvx7G2p2KuqqkJ5eTlbyV68eJEZGsRtf+ONNzAwMIAHHngATz75JGeNBAIB3HXXXdi1axc6OzvR29uLDRs2ICMjAx9//DGAm9OGO++8E6urq+jo6IDVasX+/fvxxBNPrLu3ubk5HD58GHK5HDk5OXw/TU1NwWAwID8/HzqdjvVTXV1d0Gg0OHjwIKexU14V6b4uXbrEDI9z586hoKAAd911F4xGI9dJlKRNxiO3WwkJCRgfH8fk5CS7NRJdVK1Ws027SCRCe3s7YrEY9u7di5ycHJ6cAjeNKUZGRvD666/DZrNx/lQ0GkVPTw9efPFFLC8v8zNcWlrK2or1lkKhYN0kNcWRSARHjx5FamoqU7VNJhPeeOMNFBUVYWRkBD09Pfiv//ovjI6OorW1Fd/85jexuLjIIJPBYIBKpYLb7cbbb7+Nzs5OtsceHBxEZ2cnBAIBHnvssXX35nQ6YbPZGDAkDejbb7+NhYUFdnDcsGEDmx489thjbCBAYaxra2twu90Ih8MwmUzYuHEj8vPzcenSJcRiMfh8PtTU1KC0tBSBQAADAwOIRqP4+c9/vu7ebl1/daMxODiI1tZWAOCitrKyEg899BBnTRDyUllZiRMnTqCgoABJSUl47LHHcMcdd/DlTCmPV69exYULFzAzM4OKigrcc889GBoawsmTJ/HSSy9haGiIee7xCuZwOMxjPjoYQ6EQDh06BLVaDaFQiK6uLgSDQTz66KOw2Wyor69n9IjEOu3t7XjttdcQi8WY1kCBJl/84hfZQ//y5ctYW1uDVqvl0fB6KxKJYPPmzRgfH8fExASUSiVmZmbw0EMP4eTJk+jv72e3nR07duCxxx5DXl4e5ufn0dnZyRZ+DQ0NyM3NxTPPPIOJiQls2bIFTU1NuH79Ovr6+nDx4kW0t7ejo6MDMpkM+fn5fGiut5xOJ+rr6+Hz+TiIyOl04vXXX+ekaHJ0+tznPgeNRoPNmzdzansgEEBPTw9OnTqFffv24ZFHHsGvfvUr9PX1YWVlBR6PB2q1mmlTf/nLX2Cz2aDX65GamgqHw7Hu3og+RoLJjRs3Ym5uDj/+8Y8Ri8UQCATQ1dXFAuLXXnuNaS3ElU9MTERZWRlOnjyJxMREXLlyhVPWKTCJXB3I0WnTpk2wWq1xhepmsxk1NTWw2+2YnJzkkepLL72E5eVlVFRUoLW1FQ8//DCeeuop/PrXv0Z3dzfm5uawceNGPmD+7u/+DtFoFI8//jii0Sja29sxOzuLlpYW7Nu3D2+99RaGh4cRjUbx7LPP4gtf+AJef/31uN7VRAMj9JZyN+66665PiT+bm5vx4x//GAKBADqdDjMzM3C73fwbb9++HQqFAg8++CBcLhemp6fh9/uRlJSEAwcOsGtcVlYWCgoKIJfLMTIygnPnzq27N+CmNTCl0pI1ZU9PDw4cOID29nYUFxdj69atuOOOO/ClL32JqY4nTpyASqXC8vIyTzDy8/Pxne98B3v37kVGRgbsdjuGh4fR3NyMV155BY2NjZxHkpiYiIsXL8al2VC2AvHmZ2dnYTQa8cMf/hB5eXnsr280GrFt2zYUFBSgqKiIp4Jerxezs7OsdWlvb+f3Ni8vDwaDAYcOHYLVamV6n06n4zDReFaBRHOwWCy4du0aLBYL8vPz8W//9m8wGAxYWlriaRdlyUxNTaGxsRF33nknJBIJWzKePXsWjz32GI4fP46jR4/ixo0bXED29vZCIpFgdHQU2dnZKCkp4WnXemt2dpbRUJpS9vT0oLGxkZ2wxsbGkJ+fjx/96Ec8TXv55Zdx8uRJbu42btyIsbEx/OEPf4BAIEAoFMLY2BjGxsbwu9/9DiMjIwgGg/jggw8QDoeRl5eH2dlZ7N27d929+Xw+vswpgyMrKwtNTU0oLS1lpPTKlSvw+XxYXl7mZ/vFF1/EQw89hJGREVy+fBlvvfUWC9ophK2wsBBf/OIX2epVLBZzLgnxo9dbbrebTRBcLhfW1tbQ39+P3//+9zwtE4vF2LRpE370ox/BbrdDKBTi9ddf5+Rut9sNnU6H06dP4/7770c0GsX169cxNDSEWCyGZ599Fm+//Tba2trY2nl1dRXDw8Nxc1tmZma4sBsdHUVnZydnN/X09EAmk6Gmpgb33XcfnnzySZ62rq2t4bvf/S7uvvtuNjDYs2cPvvjFLyI9PR0JCQno7OzEW2+9hUcffRSLi4v8TqampiIpKYmplest4qT7/X4kJyfDYDBgcnISjz/+OAcJq1Qq3HXXXfj973+PiYkJTgTPy8uDTqdDbm4uHn30Uej1etx77704efIkPB4P65F+85vf8L1Lv6FSqUR1dTUOHDiw7t6SkpKQk5ODjz/+GDdu3IDBYMDw8DCeeuopqNVq1NfXo7GxEbW1tSgtLWU61LFjx1g3Z7VaOfH7zjvvhE6n47Dg2tpaZGdns9tneno6a2A/+eQTXLhwYd29ZWRk4J577kEkEoHP50NOTg6Ki4uxf/9+DvsrLCxEdXU17rrrLqYtfvTRRyzGTk9PZ6prSUkJDAYD53gQHdpisWDjxo3sCpeRkcFp0+utubk5bN26lXWm27Ztg8FgwNatW3kKS6L0v/u7v8Po6CiH4B44cAB79uxhuvs3vvENPP7441AqlZzrplQq8S//8i/Ytm0bvF4v2tvboVAoYDQaMTAwEBcsnpqaYtrb3Nwc57y1tLTA7XbzBM5qtWLnzp147733eO8vv/wy7rjjDgwMDGB4eBglJSWfsu4tKirCXXfdhSeeeAJ5eXn8HjU1NWH//v3IycmJG1FA0oDh4WF0dnYiEAjg4MGD+OIXv8h5ORSOKZVK4fP5OCiwpqaGaaQUvnnvvfdy8G0kEkFjYyMeeughjiog91RKh/8sqjQtwVq86NJb1h/+8AcoFAoMDg5Cp9Ohuroac3NzuHDhAp566im8//77LIYtLi6Gy+VCTU0NAoEAOjs7kZOTw5oEnU6Hf//3f8ezzz6LwcFB+Hw+FgpWVlay0NpkMkEulzPH/rvf/e5t9/aLX/yCxbwkwBIKhWhpacHly5chEAg4sKq0tJRFSadOnYLf74der0dubi7zpU+ePMm8R9JP+Hw+tLS0wOFwIC8vDwsLC/D7/ZBKpRAIBOvaZv7iF7+AUqnE7OwssrKyUFFRgUgkggsXLrBgmASqWq0W6enp6OvrQ0lJCXNt6WAlgRXxrCko5/e//z1ef/11nD9/nkNVxGIxhEIhrl+/jq9+9au33dvLL7+MgoICTExMQKVSscvP1NQUamtrcfLkSWRmZqK+vh46nQ4TExMwGo3sfmIwGCASidDR0QGVSoVDhw7h5MmTbIFLCPBDDz3EVCbimRcUFMBiseCZZ5657d5+85vfcFo3HSDT09Pwer3Yv38/xsbGMDMzg6WlJUilUmRmZuLs2bN49NFH4Xa7sbS0hIWFBZjNZjz44IO4ePEiampqYDabGX3r6OjAN77xDc69IFcs4GYz8ZOf/OS2e/vbv/1bJCYmori4mIVcYrEY7777Lvbt28fiKpfLhevXr6OkpAQdHR343//7f+PEiRNMt7l+/To2b96M3/3ud/iP//gPmEwmFpRu2LABEokES0tL6Ovrw8DAAAQCARoaGjA8PIxf//rXt93bd7/7XQiFQs6VcTqdUKvVcDqdyM/PZ0qGVCplvQj5hGdnZ/N753A4UFtby3bTAJiaYzQasWHDBoyPjzNN5lZE++///u9vuzcAeP7557kBpunC8vIy/H4/qquroVQqmSLndrtx5513oqOjg5tH4Oa0y2KxsGMMZfFQVsbU1BRryShJXCwWc/H2+c9//rZ7O3z4MNsYEnJJOR0PPvggAoEA5ufnmXfv8/mwf/9+nDx5kv8WQoGbm5tx5coVtrNWKpUoKSmBWq1GX18f66Ko4KusrMRbb72FX/7yl+t+b8Rzps+Qy+Xw+/2or6/nMTdZXqelpWFsbIxFnDShoXfGbrejpaWFxd6EnGdlZeHixYucmEvC+La2Nrz44ou33ds//uM/wuVycQgWFXTBYJCDusj8IyUlBd3d3dBoNFhZWUFeXh57v5tMJp580u8wNzfH+QvksCORSCAQCJCQkACBQIChoSH827/922339stf/hKJiYlIS0vjSUNSUhJPzolCS3bsDz30EAYHB1FbW4sTJ05AqVQiNzeXnWmuX7+O3bt3o7u7myeEmZmZePDBB9mw4tq1a5ifn0d1dTWi0Si+8IUv3HZvv/vd76DX65mmQoYnZD9NmU1ZWVn8bLvdbnbjEQgEmJqawuTkJB577DGcP38ei4uLKCwsRFpaGhYXF3Hq1CmIRCIsLi7i4MGDsFqtGBoaglwux+bNm9Hc3Hzbvf3xj3/E8vIyC4/pHZuamkJzczNkMhlGR0dhtVqRnp7O1vI+nw/Xrl1DXl4eysrKYLVaec933nknAoEAbty4gbm5OXzhC1+AQqFAd3c3gsEg1tbW+Jmbn5/H17/+9dvu7V//9V9hMBiYekZT9/HxcezevZs1MqmpqUz3IWFwZ2cnmpubkZeXh9deew35+fk4ePAgTp8+jdLSUiQkJKC7uxvt7e34+c9/jr/85S+oq6uDxWJhvYVGo8HOnTtvu7fvfe97iEajKC4uZkCOKEX5+fm4ePEi8vLy2I1q586dePfdd3HPPffg3Llz7MonFArhdrsxMDCARx99lKmfPT09mJ2dRV1dHWpqauDxePD+++9jeXkZ3/zmN9HZ2Ylvfetbt93bc889x3buSqWSjRKuX7+OvXv3or29naeEYrGYQyhlMhlOnToFo9GIpqYmSKVSjIyM4NKlS6irq2Mh/9LSEs6dO4fGxkYUFhbi6tWriEQikEgkEIvF8Hq9+NnPfnbbvf3Hf/wHvF7vp+ICSHdUUVEB4CZNaG1tDRs2bGBKn1AoxEcffcThg0lJSXjjjTcQjUbR2trKekSfz4dAIIDa2lqeVOt0OshkMgwODmJ1dXXdadCf/vQnyGQyDvgjPQrZ2pNRQzAYRGpqKjIzM6HRaNhMRSqVMjDa0tKCjo4OlJWVIRQKYWFhAaFQCFarFV//+tfZOGRmZoaF+svLywzy/9/r61//OtLT06FWqzmmoKWlBcPDw5wvs7q6ipycHJSXl2NiYgLl5eW4ePEi1Go1u5B1dXVxaCQZIxQXF6Ourg7vv/8+xwikpqZiZWXlU6L79e7TW9dfrdGYn59nhwFyqpmenkZRURG7blitVnZDqqmp4QeW/L8JLSAnGEIuCKG+9YdMS0vD0NAQ3G43H/TrrXA4zOFkCwsLrLy32WzM30tISGAnmWg0ipmZGeTm5mJxcZGty+jCJdEbuSOlpqYyYkeoAwnHAMTt1MnSDwCjpLf6PTudTszMzLAtWXp6OhdR5IlNRXNTUxPz6+fn55Geno6cnBwWG7vdbp4eyOVyBIPBuAFl5JpC7iQkrCbhI00thoeH4fV6oVQqMTQ0hOnpaZSVlSElJYV924mzqFar4fP54HA4WBNDadAymQw+n49pHvF0N0RfEAgEWFlZ4eyFtbU1HhOSFaLT6URhYSGSk5NhtVrZWo7ETOnp6RCLxVheXobP52PHIzIv6O/v56nE2toa0tLS4iIc5BZBhSz9HbW1tex7npSUxG46s7Oz8Pv9nGxqMBjYrnBkZISLeXKDoEZp9+7dGB0dhUAgwPLyMrtYxMMGKIOGLAzJBEEqlcJoNGJ1dRU+n4+RNKKWkIUp2XMqFAqMj4/D7Xbj/vvvRygUgsfj4bGvWq3GyMgIB3cSoh4PfQHwKc4y/RaU3pqRkcGCUNIaUMDn8PAwGhoaANycipDvO+lPHA4HO+0MDQ2hsbER09PTbDXp9Xr589ZbNOEklxLaL4lVFxcX2dEtOzubXdioWSOb43Pnzn3KPU6tVmNhYQEWi4WdeMhicGVlhamO8aZowWAQWq2Wm4nl5WW2vna73dzAk0NLfX09N9Ver5ftuqPRKNP7iPpJ6Cm5KdG543Q62VAjHvc7MTERer2ebZJnZ2c5mJBS2YlvnZ2dzRfZ8vIypqen+d0EgNHRUWzatAmJiYn8vNPzVVhYCIvFArVazZa+KpUqrqiZ7gaFQsEOVnK5nKlXGRkZCIfDbPkrl8u5cCCKHH0/KSkp7Jqn0+kwOzuLmZkZqNVqjI6OQiQSISUlhfUM4XAYwWBw3b0tLy+zeFcoFLITn8fjYbehQCDABdtdd92FyclJDlFbXl5mTQA5olEDNzs7y2YlZJwA/B93HbKeX2+RaYTP5/tUUBoAdi6am5tjO06/38/ZDmSvS0JtSpwvLy/nvJZAIIC2tjZUVlZyhlFSUhKEQiHfzeutxcVFPntJT0RnZygUgsViYZtzcmEkXQ9Rzsjq2Ofz4dixYwgGg7h69SrS0tKYa0/25VR8Ly0tMbVnvUaDdAZ0DgYCASwtLaGsrAxqtRrJycnwer0IBoNYWlqC1WpFe3s7Kisr+VlSqVSIRCLskrW6ugqbzca6VafTiezsbNZZkDve6upqXBtqsqIn84q1tTUWUZPlqs1mw9raGlvLU/NHVFdyFhwdHYVOp/uUzsFisXDBbbVa+Vmj5yeeMQI5SkajUba6Xl1d/ZSrVSAQYD1qeno6rFYrG3CQccLk5CRCoRDnpFGQKeU3kcsnnYUk3o/niEVn061CezJZiUQiCIVC/JyHw2EIBAJ4PJ5PTSVJL3lrzpbD4eBE8Vgshs7OTn5GPB4PAoEAn1PrrdXVVWg0GsRiMQ5KJNt/lUoFoVCIubk5diUlEfza2hrrJClThiytMzIyMDY2xvUz0SilUimL1emZGBoaWndvt66/utGYnp6G0WhEamoqF4k2m41DzygR0mKxYHJyEuXl5ZBKpTh//jxPKGhi0NbWxjy0/Px8pKens1PF+Pg4JBIJoyNOpxMXL16MWyCQrdetTUAwGGRRL9nyErrg8XiQl5cHiUTCX1gsFmMEmAJmFAoFpzeq1Wr09PSwcw01IqFQKC5n0+v1Ijc3l4sJSjGmPZNTFgmqMzMzkZ+fjzfeeINpF1KpFIuLixgdHeVJChXvUqkUTU1NzLsdHx9n3io5aq23aFxGnbnVauXvyGKxIDMzE3a7HVevXsXq6iruu+8+ppUB4CJGLBZzQmcsFoNEIkE0GuVgN3KM8vv97O9vtVrjjsf9fj9KSkr4QKSCNCEhAVNTU8jLy+NCZWpqCrm5uSgsLMTg4CCqq6v55a+trcXAwAA/X7fyP8nVyeVy8V6i0SgnPq+3iJNK4rxQKIRgMMiTrszMTH4em5qacOzYMWzcuBFtbW0sugVu2jk/99xzaG5uxuXLlyGXyyEQCDAxMQGr1cp8X+JxUx5HvEMRABcICwsLXOyQ605VVRWuXr3K7jvkuOJ0OuHxeKBSqaDX65GSkoIjR47w4VxcXIy5uTlcv34der0eLpcLkUgEYrGYM00AfGZSKNE1qClYWVlhxyKXy4VQKIQbN27A4/EgOzsb/f39WFpawpEjRzjThmgzkUgEo6OjnHYcCoXgdDrR29uL1tZWtvQTiUQIBAIwm81xA63IhpEczdbW1uDxeFgATtNHCgykd0EgEMBisUCv17Pl6unTp5GXl4dYLIa8vDxMTEzg+PHjnFaenZ2N3t5eRsyDwSCqqqrifm+BQAACgYDtmYnL7na7OS2ckG/KoZmenkYsFoPD4UAwGERRURGGh4dRUVHBbiXkLmaxWFBaWsp5DGRXnZeXF7eBpOlKcnIyF4Bk3U2AAVGNaLKUnZ2NY8eOsQ98ZmYmampqcObMGVy/fh1KpRLp6els+7q4uMi0F5fLBZPJhGg0ipKSkriZCysrK1AoFEwjzMjIgEwmQ2FhITo6OlBfXw+TycTP1tDQEFuxRiIRPqtJC7K6uoqxsTEUFxejsrISwE3KD7nEKRQKnlBTgNZ6izjyRNMFwMVmMBhkBLynp4f56hkZGbhw4QKUSiU3rHV1dWhra4PFYmH3runpadhsNqhUKhiNRohEItjtdszMzDCd8rPEuQqFgqcR5LpEIX20fwKqnE4ntFotB8HRPazVapGSkoIbN26gv7+fw/oWFxdx/fp12O121NbWwufzsQ2x1WqNq8kk0SzpJP1+P2dSDQwMIBwOc55IdnY2W8RfvnwZubm58Hg8WFxcxMaNG3H27FmYzWYYDAYGUij488yZMywkBm42WPFs9gEw0ERT82g0Cp/Px5b5TU1NGBkZgclkgt/vx8LCAiQSCU6ePAmVSsV3C7kgEuBDUwOyyHc4HEhJSWGL2ZWVFVy7di2uwH9lZQVZWVlwu90cdup0OlFRUYGOjg52DqVJwcTEBLKzs2Eymbgp6e/vR3JyMjo7O9HQ0IDJyUkUFxcjGAxiZGQE8/PzfB6QwJuymeLdWysrK6ioqOBg0bS0NAZfCLCm/6bH42EHwOHhYaSmprLlLf22YrEYk5OTmJqaQlJSEsrKylgH4vF4MD8/D5FIhNXVVSwsLMQFeUKhEDNuADAoTWC70WhksDwcDmNwcJCtl6kREAqFKC4u5twbAuTp91MoFLhy5QpKSkqY+kx23/HE4AkJCVyLkI3t0NAQu0JRfs3MzAwmJiaYmkc1HLGJ8vLyWAOYkJCAzMxMzM7Oor29Hfn5+WzH73a74ff7EYvFIBKJ4gIpt67/JzF4YmIiKioqUFtbyx3kH//4R+aDkyXWxMQEenp6IBKJ8Prrr+O9997DhQsXYLfbkZOTwwFrAoEAwWCQ+eEAuHClxFby/46HcAgEAnY1oZec4t8nJyd5v2azGSdOnOAAvOHhYVy/fh0LCwvsmJKdnY3Lly8jMTGRvdIpvVepVHIYSyAQgMfj4QtxvUVoQ21tLYqLiyEWi5GcnIxjx47h8OHD7Mridrths9kQDoexdetWPPbYY5xmvLKygoWFBXz88cfctWq1Wrjdbhw/fhyRSAQFBQV44IEH8NRTT0Gv17Pn+mclNd+4cQMCgYATgvv7+/Gb3/yGDyylUgmJRMIFXEtLC0QiEY4cOYK2tjbWdQBAX18fLBYL0tPT+fmoqanByMgIcnNzodfruXCgvI71VkZGBk+APB4PotEo9Ho9j5Pp8pucnMTY2BhbVJKjDzWM9B1RGqvb7YbZbEZfXx87FrW0tKCxsZGR57Gxsbh5C1qtFr29vZiamoLL5WJ627/+67/CYrEgFothYGCAD+WGhgYWCAM3pzXT09OclVJQUICenh4kJCSgpqYGVVVVHGyWkZHBlnwOhwOxWCzuu0D/jlwoiErzwQcfwG63M/9XKBRyM0LGCORwQTaQlFbe09MDpVKJuro61ikMDg7ypHJqagrDw8NIS0uLe2ADN5tbyjzJyspCcXEx8vLyIBKJ0NbW9qnpT39/PzZu3IgDBw6guLgYnZ2dnI+xYcMGvP766+jt7UUkEkFLSwu2bdvGl292djY2btyI+fl52Gw2dpF6+OGH191bamoqTp8+DafTyeCB2+1Gf38/VlZWUFhYiK1bt6KoqAh2ux0jIyPIyclh1xhCurds2YKlpSVcuXIFly5dYjSIpi2kJ6FC49YLdb0lkUjQ2dmJ5eVlFBUVobCwEDMzM/y3NTQ0IDU1FYFAAH6/n5NmN2zYwNzrwsJCdjB75ZVXYDabcfz4cVy6dAmBQACnT59mlEur1UIsFvP05LM0cpcuXYLH44HBYEBVVRX0ej08Hg8SExMhEokgEokwPDyMF154AbOzs1CpVIweU3puQUEBiouLceTIEXR1dSEajcJoNEKv1+PGjRsYHBxEUVERzGYzrl27BrPZzG5U6y2aTlssFqa6UnMnk8lQVFSE8vJyyOVy2O12fPTRR2x/SpPmnJwclJSUQCaToaenhwsfykc4evQoO6/RtC0hIYEttNdbycnJ6O/vx9DQEKdwq1QqFBcX48aNG5ifn2fjDL1ej4WFBZ6E2u12zjpITEyE1WrFCy+8AKVSCaPRiJycHA49o9R4q9XKOkYS2K63SKdELACpVIqcnBz09vaiv7+fQ/AoKJfytBYXF9HV1QWTyQSBQACNRoMdO3bgkUcegU6nY/FsRUUFampqIBaLYTabUV5ejuzsbM7siKdtoUJXJpNBJBJhcHCQaTYmk4nzEsxmM06ePInKykpIJBJ0dXXh4sWLaGtrw/T0NDQaDeRyOerr67Fjxw48/vjjKCwsxOnTp3Hs2DHOzKqrq0NxcTHTS+O9CzQponeb6MN///d/j9deew2ZmZkQiURckDscDjzyyCOck0SUKTp3PB4PhoaGsGfPHtx///3IysqCRqNhUbter8f8/DzOnj2L/v7+uCJ6iUQCn8/HNsdTU1NITEzE5cuXGYjT6XQsuB4cHGQKDoFck5OTbI367//+7zz1I6v9iooKVFVVISsri0FU+m/Hm2ioVCoEg0EkJSVx2nggEGAtGhkJUACm3W5HRkYGhEIhg9HDw8Oora2FxWJBeXk5gyOkQSVknlxSi4qKkJeXx+/KekuhULCGijJZyG3O7XajoqKCxfUWiwU2mw12ux03btzAc889h9OnT3MYHr0nlDWyuroKr9fLRgp0f83PzzMFOl7oslAoZI0bTW1tNhtOnDjB1sAEyp04cYIzVwYGBjhQkXRYHR0d+POf/wyBQIDW1lZUVVVBLBZDo9HgwIEDyMvLg9PpxMDAAGw2G5RKZVx93Kf2+Vf9q//+YRMTE3Hp0iUcP34cY2NjkMvl+I//+A/ejEajQV1dHR588EFMTU3B4/Fg06ZN+MpXvoLW1lZOAD18+DAeeughGAwGSCQShEIhJCQk4Otf/zr7SVssFuzfvx/f+c53kJqain379sXdX15eHtLT01FbW4tDhw7BaDTiy1/+MrsODQ4OYnl5GTU1NdzFORwOSKVSLqJ1Oh0++OAD/PjHP+bcC5pYPP7446xRoAaIbAXjFX6hUAhVVVXsqEBTny9/+cv48MMPcePGDYjFYuzduxd/+7d/y8neIyMjsFqtsNvtCIVCKCwshNFoREtLC+RyORoaGthp6atf/SouXryIP/7xj7h27Rp27dqFp59+GuFwGOXl5evubXFxEQqFAp2dnXA6nSgvL0dBQQHOnDkDrVaLnTt3orS0FHfeeSd+/etfY2xsjMfLW7ZsYY74rl27kJGRgYMHD2J5eRlisRh6vR6FhYV8MAQCAczMzLA4dW1tDUajcd29kc//6uoqFAoF5HI53njjDWzbtg0jIyNYW1uD3W6H0WjEL3/5S7jdbtx7773Izs5GcXExJ53W1tZiYWEBjz32GBISErB9+3Zs374der0eTz/9NMxmM1MxZDIZ087i5VWQlshms3FQlt1ux1e/+lU0NTXh/vvvx86dO7lAkcvlKCsrw/LyMhoaGqDT6di2tri4GM3NzWw0QAfAd7/7XbjdbuzcuRNDQ0OorKzE5z73OYyOjsYdV5JF69LSEtRqNTZv3oz09HQ8/vjjWFxcRH19PYc8HTp0CJ988gl27dqFtbU1PPHEE8jPz4fNZkNZWRn8fj+++c1vQqPRwO12c1BjS0sL3nnnHTzzzDOcSP/0008jIyMDYrF43b0BYH94osSkpaVhenoaX/3qV7G8vIwNGzZg69at+NrXvoYf/vCHeOWVV2A0GpGcnIzNmzczMkX2q4899hgDG2tra7j33ntx6tQpCIVC3k9TUxMOHDjAuRvx3oeDBw+y7oYoNq2trRgfH4fVasXg4CBrh4Cb6FZXVxd27doFp9OJV155hbVXjz/+ODe/lHvx2GOPYW1tDXNzc6iurkZNTQ3kcjlmZ2fjUjAjkQgefPBBTExMoLu7m9GuZ599Flu2bMHGjRtZoHj//ffDZDKhsbER7e3tmJiYwI0bN7jAUigU+PWvf42VlRUcOHAAW7duhUQiwfnz59HW1gahUIju7m40Nzdj//79OHXqFFPBbrdSU1Oxd+9eLC0tYXV1FcXFxZifn8d9992H48ePs+HFli1b8Mwzz2Bqago7d+5kHcbU1BTOnj2LyclJZGdn4z//8z+xYcMGJCcnw2w2Y2JiAnfccQcXh9nZ2Xj22Wfx7LPPAkBc9zqa8CwvL8NgMKC4uJgtrim8kOg+9Ozn5OSguroaf/jDH9DY2Ijz589zzsmjjz6KSCSC69ev49q1a4hGo/jc5z6Ha9eu8XOzc+dOHDx4EN3d3XEnGh6PB08//TT27t2LHTt2YPfu3ayJKysrQ35+PvLz87F79248++yzOHXqFDZt2gSBQICKigqmQhJ17dKlS7hy5Qo7Y83NzXF4WEtLC8bGxrC0tASdTofp6em4jlhkaWy1WpkSPTg4iDvuuIM1JZOTk+z2SBbka2tr+Md//EekpaXhgw8+QEZGBn7wgx/AYDBw4jDlHCQmJsJkMqG1tRXnzp1jdD0tLQ1//vOf191bYmIidu/ezSBfTk4OTCYTHnvsMYhEItTV1SElJQUajQY1NTX44Q9/iLKyMuzcuRMPPPAAkpOT0dfXx/qygwcP4tixYxgfH2dDhaNHjyIcDmNlZQVDQ0NYXFxELBb7lCnO7Zbf72etm1wuxx133IF7770XN27cwL59+1BfX4/a2lrce++9eO655+D3+1FYWIicnBzOhsnPz8eBAweg0Whw7733YnR0FDabDUNDQ+jt7cVPf/pTtji/ePEiMjIy8KUvfQkpKSnrUroA8NRuZWUFGRkZnBpdUFCAqakplJaWoqWlBbt378aePXswPT0Ns9kMi8WC+++/H1u2bIFWq8X27duhUqnw9ttvo6GhAUeOHMHbb78NqVSK73znOzh79ixnsOTk5KCgoAAajWZdrShwkx0xPz8Pl8uF9PR0lJSUYGhoiB3LCKhoa2uDSCTCjh07UFBQgK1bt+Kpp55CY2Mj5+Wsrq5yLlp2djYDiGKxGFarlXWlRDOzWq1x096pXiS6p1gs5jMsISEBSqUSoVAIOp0OX/3qV6HT6fD000/jsccew+9+9ztIJBL80z/9Ez7++GMcP34cu3btgt1uR0lJCfLy8hAIBLBnzx60tbXBZDLB6/WiqqoKKpUKv/71r+M2QaTXKikpQXJyMlZWVlBZWYk333wTJSUleOihh7BlyxZkZ2dzNMKOHTtQXl6OnTt3oqqqCkajEQcPHoTNZsNPf/pTdHR0wGQyMVWtqakJv/nNbzjegJz/2tvb4xoh3br+ajH4yy+/zKFBUqkUFosFMzMzqK6uZv/w+fl5WK1WuFwu3HHHHWhqakJXVxdUKhWkUikmJibw6quv4gc/+AH+6Z/+CT/60Y84YG1ubg5/+ctf8C//8i84c+YMjEYjpyLq9XrEYjEuWv/v9dprr2F2dpYF1FarlekO+/fvZz9sckj60Y9+xAjn4cOHYTQasWfPHubgnj9/nvclkUh49L59+3b09fUhMzMT169fh9vtxrZt22A2m/HFL37xtnv7h3/4BwgEAu5WibP585//HJs2bUJRUREHl6nValRVVWFubg6//OUvOVSQAp1GR0cRCoXw7LPP4sSJE3C5XFzIVldX4/3338fnP/95OBwO2O12lJaWsh3u7daf/vQn9Pb2svtEQkICzp07h7a2Nrzzzjt45513+Lebmppi9Pqb3/wmTp8+jYSEBJSVlbEY8KWXXsLevXtx9uxZhMNhNDY2Mh//L3/5C/7mb/4GYrEYgUCA8xHuv//+2+7t1VdfZSpXRkYGcnJyIJVKceTIEbS2trKlZkdHBxwOB37wgx8gEolgfn4ely9fRkVFBbKysnD58mU4nU6kpKSgoKCAx8w02j506BB+8Ytf4MCBAwgEAnC73cjMzERiYuK6VBZ6Ril4Jzk5GaWlpTylAICGhgZEo1FcvHgRTU1NaG9vx549e5g2RkjplStXYDAYUFdXh66uLggEAuTl5eHChQtobGxkPcmpU6dgMplQV1cHiUSCH/zgB7fd289//nOMjo6isbERiYmJmJ6eZr3GU089hZ///OdoampCeno6rl27xsK65ORkPP/886iqqkJTUxNOnTrF9Jqnn34a09PTn9L7DA4OYt++fewwNzo6im3btmFgYAA//OEPb7s34KboLzk5mWlJALB161YMDw9j+/btWFpags1mQ09PDzweD374wx+y+9r169eRn5+PjRs3Ym1tDT09PUhKSsLOnTthsVgwODjItMYnnngCS0tLcLlc8Hg8fHldunQJX/rSl267t5deegldXV1oaGjg97+goACTk5NoaGjASy+9xJPb5ORkFBQUoKuri4XA9P9z4sQJbNq0CceOHUNLSwsbA0xOTkIoFOLOO+/ExMQEQqEQ03NIp/C1r33ttnt78cUXMT4+zvxxgUAApVIJt9uNJ598Eq+88grT3gQCAUpKSvD222/jgQcewOjoKFQqFVQqFfr6+tDZ2cmWnx6Ph12hpqensWvXLhQUFEAgEGBwcBBer5dTYtfLbvnFL34Bn8+HtLQ0TE5OYmBgADk5OZicnMTXv/51eDwejI6OMpXyi1/8Ir7zne/g3nvvhdVqRU1NDWprazE+Pg6/34/f/e53ePnllzEwMIBAIACRSIQf//jH+OSTTzA4OAihUIjp6WnOeZiZmcHzzz9/270dOXIEAFgbSJflwsICnnjiCRw/fhwFBQWMBDY3N+PVV1/Fgw8+iJ///OcoLy/Hli1bcPz4cXz3u9/F3//93+OOO+5AV1cXxGIx6uvr4XQ6kZubC5VKhVgshr6+PiwtLWHjxo0IBoPr0oDef/99LCwsID8/H6FQCNPT01CpVDh69Ci+8Y1vYGZmBtPT0zxleuyxx5CTk4MzZ87g6NGjyMnJQVNTEzvdfelLX8LLL7+MYDAIp9MJr9eL/v5+3HPPPZzSTInKSUlJbIZxu/Xaa69hamqKUWORSITa2lq8//77+MY3voF33nkH1dXVMBgM8Pv9XKxZLBZMTEygrKwMhYWFCAaDMBgMTKUlilBTUxPeeecdfOtb38LHH3+MDRs2wOv1IhKJwGg04vTp0+ueI7/4xS9gsViwfft21trV1NTghRdeQG1tLWZmZnhyYLfbOaUdAE9sNRoNsrKysLi4iI6ODmzbtg2XL19GIBCAXq/H4OAgnn32WbS3t7O1c1JSEmQyGaanp/HlL3/5tnv78Y9/jPb2dhQUFCAYDCIYDGLfvn345JNPcODAAXbL83q96Onpwfe+9z188sknyMnJwYkTJ5CcnIyysjLEYjFcvnwZKpUK9fX1bKtN4aW1tbV48cUXcejQIczMzGB+fh7FxcWYnJzEd77zndvu7Wc/+xn8fj/bTy8sLOCOO+7A0aNH8Z3vfAe/+tWvsGXLFvh8Przwwgv41a9+BQBcoK+trTH1VyqVYnR0FNXV1fD5fLDb7QgEAigrK8PevXsxNDSEUCiE06dPY2VlBU888QSOHj26rsHKf/3Xf7GYWiwWc5Dd8ePH0dzcjJ6eHnbuS0xMZAMJmUyGiYkJ/m6IWjk5OYmNGzdidHQUkUgEqampmJubwz/+4z/ixIkTkEgkmJiYAABs3LgRfX1961rcvvfee6xFIV2GTCbDmTNn8PTTT+PDDz9Eamoq2xmT1lMikeB3v/sdDAYDmpqaMDU1hQMHDuD555/H97//fVitVqysrECn02FwcBBVVVXo7u5GUVERxsfHMTY2BqPRCLFYvK5t/P/4H/+Ds56IVuzxeHDx4kUEAgF4vV5s3bqVwdSqqioYDAYolUr8/ve/R1paGls7Hzp0CN///vfxs5/9DA6HAyMjI3A6nbBYLHjkkUdw9913o7+/nxPVu7u7UVlZGZfmSOv/KbCPOJQOhwM2m435Y7Ozs0hPT+eO1OFwsNXZO++8w8LJSCSC/Px8vPLKK/jKV76CS5cusQA1FovBZrPhJz/5Ce677z4MDAyw+OTChQtwu934n//zf952bzR6slqtCIVCmJ2d5aCjxMRE5iOura2hsbGRXZbW1tawf/9+DmUqLCzE/v37kZycjLGxMdjtdszOzrLwjYKSCA0jJxOifd1uxWIxpKWlYXR0FDKZjAWj+/fvZ4uw3t5eCAQC1NbWsg1rY2MjFyIymQx5eXno6upCZmYmxsbGUF9fj4mJCfT19aG0tBQjIyO46667YLVa0dbWxtS0eI2G779D6wKBAMbGxtiHv6GhgdMvJycnEQ6HIRKJsHHjRsRiMZw6dYrFdxcvXoRMJmO7O6fTye4ws7OzzGcuKSnBm2+++akUXq1Wu+735na7oVarOQRtfHwc0WiUHU8o0ZhyTo4ePQqj0YhLly6hubkZ/f39eO+991BSUoIdO3ZwEGI0GkVvby8uXLjAh35dXR1/RiQSgVwux9GjR9ctXlwuF/bu3QupVMrTiQ8++IAbGYlEwlxhsViM/v5+9Pf3w2q1oqioiK1Mp6en8ec//xkPP/ww6wg8Hg96e3uZ075p0ya4XC5oNBq2qYs3CSJnFBLNk6+41+vlgDSbzcbIkc/n499oz5497HhRXV2N/v5+5OTkwGq1Ynh4GHNzc8yFJeHslStXoFAoUFBQgCNHjsSlFQD/h+/qdDoZ6W9vb0diYiKmpqbg8/lgs9kwOzuLQCCAEydOoKOjA0VFRdixYwdzdI1GI2ZmZqDX63H9+nVkZ2dDKBRibGwMjz/+OKamppCamopQKASTyYTx8XGIRKK4Go35+Xk89NBDmJycxPz8PLt1ET2KqD6EXGdmZsLn8+HIkSMIhUIwGAxM9XnnnXewdetWFiuS0L29vR27du2CVCplzjql9BIH+HaLrE1JrzM/P49r167hoYceYlEk6ePIqWRychL/63/9LwBASUkJcnNz4fP5oFAo4HA4cP36dYyMjCAUCnGCdigUgs/nY142hbvFs8mmqS+dYUVFRbhx4wYyMjKYRkRUxKysLAwNDWHXrl1s3DA/P4+2tjbOEvn3f/93FtSvrKzg+vXr7CpXUlKCiYkJrK6usmgyHlY2NjbGWi+aUI+NjaG8vByDg4MsSg6FQohGo9BoNHjkkUfwhz/8AVlZWZBIJOjr68Pa2hpef/117N27F5OTk5ibm0NiYiKuXbuGq1ev4nvf+x7Gx8eRnp6O/Px8hMNhjIyMxD3jyKiB3NuSkpIwMjKCz33uc4hGo+jq6uJpwdraGs6fP8/fyY4dOxCNRjExMcEalq985Svo7Ozke2NtbQ2FhYXo7+9nfvjS0hKLgeMJ/EnHqFKp4PV6mabq8/lw8eJFTkT3+XwQCAQcQDo+Po61tTUW5RqNRjz//PPIzs5mSlQsFsPo6Cj27t2L4eFhSKXST/HaXS4XOw3dbgkEAuTn5/MZIhQKceLECRiNRr4Tzp07x6GARqMRnZ2dqKmpYf0aGYucOHGCqZxkAGKxWFBUVITz589z0UtOWBMTE7DZbOvuDQD27dvHBhLhcBhnzpzBfffdx0F9169fZ73i+fPnMTU1hVOnTiEjI4Pzoubn5zE0NMQMDJvNxvrLhYUFPPTQQ9i1axcikQhqamrY3jYedUooFGLDhg2wWq1ITExEeno6hoaGWJuRk5PDxi/l5eWYnJxEUlISrFYrh4uSJsbpdGLXrl149dVXkZWVBYVCgXA4jNHRUWRlZSE5ORnDw8PIz8+HQCDAmTNn4rIEiHZKhkH0bGk0GkxMTCAnJwcpKSlwu90sip+fn+c7gSjMycnJqK+vR0lJCaamppiKFwgEIJfLcfnyZQ6/JZ0gOZOutxwOB9RqNQfZzs3NQSqVorGxEW1tbUhISIDFYsHo6CjS0tKwc+dODl2ORqNM7SsuLsaf/vQnPPDAA2hvb4fL5YJQKMTk5CSDj2S6lJ6ejsrKSoyPj8elIxNoSlbZJAGor6/n37Cvrw8mkwk5OTlc9166dImzgEhf9eGHH6KiogLHjh1Deno6T+0VCgV6enpQVlbGdRgJ7kUi0f9/Gw25XM58vOTkZA7GO3bsGFpbW7F582Z2z1lbW2PP/czMTFba5+XlsWjwD3/4Az73uc9BJBLBZrPB6/UiKSkJGo0GZ8+e5cvdbDbD5XKhsLBw3b3RZSORSCCXy9mxZHFxkRMOk5OTodFo2JrXYrFgZGSEHaoKCgo4y2B0dJQL0HA4zGmXDzzwAEQiESwWC1wuF1ZWVthDer2VmprKRZNKpUJOTg4kEgmuX78OrVaLqqoqttC1Wq1IS0tDU1MT/vjHPwIA8vPzkZeXB71ej1AohJKSEtaJkNvT+Pg4J7FbrVYuLDUaDTZu3Lju3kQiERf0iYmJLPQfHR3FxMQE8vLyOFyIftvW1lYcOXIEJpMJRqOR7XQpCFGtVqOxsRFSqRShUIibJnKK8vv98Pv97P6x3lpcXGTHCAqfs1qt6Ojo4NwGmqDNzMygqqoK586dw9TUFPPdKR12cHAQ/f39sNvt2LBhAwoLC+FyufDHP/6R07Ln5+dx48YNLojiPW8AWNQYCoUgkUiwYcMGHD9+HPn5+bj//vvR3d2NkZERSCQSGI1GlJSUsCidKGWJiYlobGzE7OwsJBIJaxVsNhtfiKurq4xABQIB6HS6uJcJCRYnJyeRm5vLFAz63onaJBAIIJFIoFKpUFlZiTNnzsBqtSI7OxvV1dWc0jwyMoKsrCwUFRUhMTGRHSuUSiWWlpYwOjrK3FtyWIq39Ho9AoEAu6Y4nU7meZ85c4YvJnJAoRRpciUh1xp6Pnbs2MG0BrVaDYPBgFdeeQV33XUXP7sUGNXf37+unSdw84yjqZJcLkcoFMLc3BwGBgawdetWdgChIq2goABbtmzB3/7t38LhcKC6uprzQagpJJcwr9fLxSlNhgnFF4lEKCsrw/DwcNznzfff6c4ymQwCgQDl5eXo7+/n78bj8bADlcvlQnFxMbxeL8xmM4c6yWQyDh+jSa9IJGKqKOnPRCIRpqenkZCQwAYY6y2yFpZKpex+kp2dDZvNhuHhYZ5EU/Or0+nYPKGrqwtCoZDtliORCN5//31kZWWxWJKe2dzcXKjVanZDEwgEPMFZbxH/WCqVMn3A5/Ohs7MTubm5mJycZLcuu93Ol2cwGGR7YI1Gw4GqNFFLSkrihruxsZH3YbPZ2A76Vvey9dba2hrfXampqWzy4fP5OLcgFAqxWLaqqopzPaRSKQs1yaGxuLgYycnJXBiTaQlNkkmgK5VK4za2ZHCQmprK56PL5YJMJoPdbseWLVvgcDgwMTGBtbU15OXlIRqNIisri6ly1BTn5+fjzJkz2LVrFyorK1kfBoCLRgK9KEcjHmBB74JSqYRMJoNYLIbH40FBQQE8Hg9KS0uRkpICp9OJUCiEq1ev4oknnsC77777KepvdnY2tFotrFYra9XIgTEvLw8mk4nTy8lFSCaTxaWyUIo93T8qlQpmsxmvv/46B3dOTU2xRiMWizHl7MKFCzCZTNiwYQN0Oh3y8/NhMpmg0+mwe/duFBcX45133kF3dzc/ZzTdW15ehkQiids8JiQkYGJigmsHcmwcGRlBd3c3xGIxu+PR95GbmwubzYaKigrIZDIWJpP+qqKiAlu2bEFSUhLa29tx7tw5Nv5JS0vDzMwMu/3FO0MA8F0D3AR9YrEYXC4XNm7cCLVaDYlEgsnJSZw5cwb79u2DWq3GBx98gFgsxvlLXq8XMzMzKC4uZtMQOrfp82UyGQwGAxwOB+sZ49FWgZvgot/vh0aj4ZiGa9euYfPmzXA6ncwaIH0cmeyMj48jOzubKb82mw2XLl1CbW0tmxGR/onAHjJxofoinsEKmQ2QKQM5SJGFtUgkgkKhYIqly+VCa2srent7GQzIy8vjTA1y+iTr79XVVQwMDCA5OZk1My6XC+FwmEHJv2b91RqN1dVVqNVqHj+TVWBhYSHsdjvzXBUKBbKysjAzMwOdTsdcZ+pU6Q+SSqXsb04Fwr59+1gERD8AcPMwileUkhtRYmIiO1bJZDKkpaVhfHycUY+EhASo1WooFAps376dLTWVSiXEYjEyMzNx8uRJdsKRSCTsn9za2orZ2VlotVosLS1BoVBAo9FwSNF6iz6TGrS0tDQW9xHqQXuVy+W4du0aioqKEAgE2GqWCpSZmRkYDAaEQiF2TSIRFlkGUuNgMBg+5S6w3t4KCwv5sCbHnLKyMvj+O/wuNTUVubm5aGhoYDHi6uoqDAYDxGIxfD4fd/v0meQxHQ6HUVlZCavVCrlczmJh+v0/axkMBrbXU6lUMBgMjHCQyJlSwCkvgdymBAIB1Go1dDodT2sIkU9LS0NWVhb27NmDsbExtmnUarXQaDRwOBxxOelCoZCLNRIvZ2Zmoq6ujikk5B1OAr+UlBR+5skAgVxAyOmBhH4CgYBRoZKSEm6k09LS4jrsAGBr3WAwCK/Xi0AggGAwCJ1Ox5Z3hMpLJBJOhCZ3DnIFA8AOaEQ3SUhIQCwWQ3l5ORYXF6FWq/l3IPpPvPcUAI/do9EoBAIBUlJSAIDfM/qeSLRL3t0kEiTXtbS0NHi9XpSWlvI4mxyZSEe1uLiI6elpyOVyptrFK0rpmaQMjYSEBLbF9Hq97EaTkJAAlUoFu92OlZUVJCcno7i4mAsXtVrNWh+Hw8HudKFQCM3NzWy0oFKpOACO8mDWW6R7IhtKmUyGjIwMiEQitmyUy+XIzs5GaWkpHA4H2xtS8Un23GKxGBs3boRAIOB9Ly8vo7y8HBaLhd3EKIdAIBDE/d7odyXzBjJ7KC0tRSQSgVAoZIcg4ObUraWlhd1tgsEghyXSFOXWgi4WizEFjT4rOTmZL8V4jljkgkfNlEKhQGpqKuRyOevACEk2GAzwer2MKhI1MjExEaWlpYhGo+wKpNVq+V7bs2cPNyxUZAoEgs9MoicdFRmE0N1A002NRsO5VJWVlXx+kEsV2ZAbDAZMTU1x7g5NM5KTk1FYWIj5+XmkpaVBKBTyVH1paSnudI9cdMjNKT09HXNzc8jNzf0UhQYAh+qSVpGcpqjwKSoqQnFxMQBwcUUgysLCAjIyMvgZoKY8nvU5aYFWVlY4V4Wm7i6XC7FYjLV6SqUSHo+HbcYLCgpYG0ouWZWVlWyQQXsSCARcz8zMzCAWi0GhULCQfr1Fe6L3NDExEVqtlqctubm5yMjIQF5eHhobGxEIBPhMJ5vSubk5KJVKZGRksGsQUaYkEgmam5uxurqKkpIStt+lZjKes9Pq6irW1tawsrICqVTKJjaVlZWYm5uDQqHgmqyiooIBMHJdo5yppqYmhMNhJCcnQyQSferd1mg0bM5AUQMEjlADfrtFxT7d90TPIQT+1vuhvLwcDocDycnJ7KqXmpoKnU6HyspKjjNYWFjgs4eKeZvNxsW30Whk0COeqymBWOR8SfeXRCLhhkyv1yMvLw+VlZVcAywtLaGpqQnV1dVMfwdu5oOYzWYGjFUqFcrLy+FyuZCWlgaBQICkpCQ+D+JNNOiZJTtqciszGo3wer0IhULsPkWNJIFvDQ0NDGAXFBQgNTUVu3fvxtLSEkshaMpLTAR6FnU6HU/C/pr1Vzca5NQ0MzMDl8vF1JkHH3yQvxxCOolzmpqaiunpaSiVSlbkkyvVY489xqPclJQU5Obmorq6GgsLCygtLUU4HOaJCPEw11tJSUl8mYTDYT4oVCoVv0BUzNEPTSKYAwcOoLCwEOFwGEVFRfD7/di6dStbt1IT9fjjj2NoaIhzBOrr69Hc3Mwv5npraWmJY+5LS0tZdPTQQw9Bo9FgdHSUH8y8vDxu4pqbm7Fr1y7IZDKYTCYOztLpdCyilMvlMBqN2LhxIzcAkUgEzc3N3LR9/PHH6+5NIBBg27ZtyMrK4sCdUCiE/fv3c27I4uIisrOzUVVVxY2PVqvF5z//eeTl5WF0dBTBYBCZmZnIzc1l5y+fz4fp6Wns2LGDQ+PIhpQsIC0Wy7p7IwemzMxMPsgKCwuxa9cuRm8UCgXKysrYKID0Ijk5OZzPQlalhYWFSElJgVAoRCAQwPLyMjsQLS0tQS6XY+vWrdi1axfTvtZbQqGQDx0ATBt58sknUVBQwHbIZWVlaGhoYOtESlXNy8vjIlYul0On03EgnUwmg0ajgVKp5GAlk8mEpKQklJSUQCgUxkVJvV4vsrKyPiXY/P/Y++/ots/zbBy/QBAgNohBAAQBgntvSpRESdRetuUVO45348RJ3oy2SZvkTdOVuOlKm7xpM5rYTZzETuo6dmxHsmzJtmQtapHi3gMAMUkABEASAAEQ/P3h3vcrnZ8I+3vO+6fuc3qaprb0wWc8z3Nf9zW8Xi8sFgvy8/Oh1Wr5fubl5cHv98NutyOTyeDOO+/khZrQyTvvvJOtDgOBAHQ6HVvdarVadhgjBOTDqFO0iRIKVFpayqgpLXwSiQQmkwnt7e1YWFjAyMgIRCIRay5IsEm5M4SY0aTh05/+NB8Yya2sqqoKYrE462YnEAjQ2trKyDyBFzR11Gq1qK6uRnt7O9ra2iAWi9Hf38+i6X379qGurg4mkwmVlZUoKiri56vVaqFSqVjQPjw8jFgsxiF2xBfeqHJzc1FXV8cudSQGr6urQ2VlJaPrNMUiP3q/34/6+no+IKZSKcjlctxxxx0wGAxobm7mJOzOzk54vV620SQnqOXl5ay5MiSaJ0oONbebNm1CRUUFMpkMcnNz2eFkYmICpaWlCIVC3GgLhUIcOHAANpsNjz/+OKRSKYqLi6HVapFOp9HZ2ckOYmQkYbVauZHZqBKJBGf+ULotpVpTUnN1dTXq6+uxa9cuNhmwWCx49NFHceTIEdTW1mLXrl2orKzE5s2b2WZ8fX2dm4W+vj4GAzQaDTsDZnMkXF5eZhSfnunq6iofMgHwhIicb8jRafPmzTAYDHC5XBAIBAiHw9ixYwcEAgGvwUajEfX19VCr1SgqKoLRaGQKXSwWQ3V19YbXJhQKWSifTqf5cE1AAVHRyMGL1h0yIKEDDDV6999/Px/a7XY7PB4PZ2cRnYrsgImKtVGl02m2eKZmh2hxHo8HfX19CAQCzJAoLy9HIpGATqfDF7/4Rdx11118ZnA6nThw4ADy8vJ4+kgHY2pGaQ+sqKhg45ps900kErEOiJqbr33ta/xnSyQSFBYWorm5GfF4nDNQHnnkEWzfvh3r6+sQCAT8TYjFYvT19WF4eBh6vR579+7lZ0mBttTUZ1t/6V0FwN9XUVER7rnnHgafZDIZamtrcffddyMQCMDhcCAWi2FoaAhDQ0M8WaOwP7fbzTlH4XAYHR0dbDdMdtbUeGaz2s/JyUFZWRkDISaTCRqNBi0tLTwZpino008/zTkkpaWlqKurg1AoZM0G2eBSNhSFlJrNZszOziISiXCjUlJSwhkpG1Umk0FFRQW7t5ExyrZt2xCLxVBSUoK2tjZs27YNHR0dKC0tRWdnJ4RCIR5//HFs2bKFafcymQyVlZWYmJjgCS41LTMzM9ws5ufno7CwEDabLSuQTeuVSqWCRqOBXC6H3+/H/v372QyF9HlE07148SIEAgEeeeQRHDx4kAOrQ6EQ2tvbkUgkMDU1hdHRUfj9ftTU1ECn07Eetba2Fp2dnQwmfJT6yI2GwWBAT08PrFYr252pVCo8++yzeOyxx1BQUMAfl9vtRltbG37729+iq6uLBbY2mw1PPfUU1Go1jh8/zk5VJGq8++678cwzz3AXNzY2Br/fj6eeeiqrDoIs5QgJEQgEzDF/4IEHUFpaiv3792PHjh0IhUKQSCQ4duwYOjs7EQwGoVQq+cH80R/9EeRyOfbs2cNcuS1btmB6ehpPPfUU/vCHP3AIFYmas20mlOxMyCFRGxYXF7F3717e/MlP/cc//jFmZ2fx4IMP8mK0fft2xGIxfOc734HNZkNZWRkL5fV6Pbq7u7F371643W5GmdfX13H33XdnRcCJE2y1WmE2mxGPx9HY2IiZmRkcOXKE4+b1ej0SiQQ7I2zevJk9+Y8cOcLjwb6+PjzxxBPcgefn5+P73/8+Pve5zzEq3NPTg56eHgiFwqy2lOl0GsePH8fKygpvxqlUCmNjY9izZw83GFVVVTh79iysViveeecdPPzww6ioqEBjYyNaW1uRTqexe/duDA0Noby8HAMDAxgdHYVEIsFPf/pTfPKTn+QNa3x8HG63G83Nzbh8+fKG16ZSqTA/P8+NgVqtRmdnJ7q7u7F//3709fWxxd2lS5ewZ88ezsNYXV1FY2MjDhw4gOXlZRw4cAA+nw8ajYYnP/fccw8uXbqEoqIifOtb3+KJkcvlQmNjY1Yk0mg0Ynp6mukTFLinVCpx9OhRjIyMsIV0JpPBvffei6KiIjz++ON84NqxYwdbEZ8/fx5btmxBKpVCXl4etm3bhtdffx1WqxWnT5/G9u3beSHavXt3Vltg4AMUnyxeaUq3b98+DAwMcO6EWCyG2WxGa2srHn30UVy8eJE34+LiYuh0Os6CINc3SiYWCAT453/+Z9x1112IRCL8++bn51mfslERBUMikWB+fh79/f2wWq04c+YM5+iQn/vc3BzuuusuWCwWbN++HT09Pbh+/Toj2sXFxeju7saDDz7ISBbxo7u6uvDGG2+wPapKpUJHR0fWDUWhUGB6eppR4ytXrrC3/dGjRzE7O8tBWpQ0PDc3h4qKCs54OHz4MJaXlzE3N4e33noLJpMJo6OjHAB2/PhxfP7zn4darUZpaSlrrD4sG6WoqAiJRAKlpaUoLi7mfJTV1VUcPnyYqZwKhQJKpRJ1dXX42c9+hk996lOoqqrCwYMHcfjwYfT19eFrX/sa56rQJK6lpQVnzpxBaWkpuru7Gajw+Xwsct6oyPKXKEhOpxOdnZ3o7e3lADwCxfLy8tDa2gqfz4d9+/ZhYmICIpEIpaWleO6553hNrKysvGnq99vf/haf/vSnb0IUZ2dnMT4+juPHj294bZlMBteuXYNOp2NLbKL8NDc3cwDY/Pw8hoaGkJubi76+PtTU1PCBkCZkDz30ECYnJ9HU1MQHgEQigeeeew5PPvkkuru7EYvFmELc1NSE8fHxDa+NgvRUKhV78B84cABf/vKX+RqJJh2NRnHHHXdgYmICe/fu5YmW2WzG1atXIZFIcOnSJezfvx9qtRoSiQRVVVU4fvw4ysvL8eabb6K+vh6FhYUwm8146qmnsk4NyEyCrGYjkQhbKpPOLJVKwel04t133+UD4ZEjR5g+ZjKZkEwm8Sd/8ieQSCTo6OjggLUtW7awrfgLL7yAbdu2obm5mZuWbCG4BoOB3SmtVitPD9fW1vC5z30OU1NTcLvd6O3txU9/+lMcPXoUDocD+/btQ2lpKSoqKiCRSHDixAkGV5qbm1kk/8gjj2Bqagqtra34u7/7O9hsNlgsFqYYZlvfMpkMBgYG2FiFvu1z585xkBtNB9RqNb785S9jdnYWTU1N/NwoZJB0KG1tbTCbzRz0KRAI8JWvfAUnT57Enj17eB1JJBJZAdmysjKcP3/+JgS+qamJ4wjOnj3LVOpIJIIvfvGLzCIwm82oqKhgW3+iNn784x/nbCPSnzzwwAPw+Xy4cuUKRkdHkU6n0dHRkXXyWFBQgNHRUQ7+JZ2C3+/HI488Ar/fzxlzL7zwAqqrq+F2u7F3715MTEzA7/dDKpXi3Llz2LRpE8rKynD06FG2JT5z5gxefPFFfOpTn2Ljh6mpKczMzEChUOCNN97Y8NoSiQTW1tbQ1dUFi8UCj8eD5uZm9PT0oK2tDW1tbchkMpiZmUEwGERbWxtMJhPuvPNOTE9Pw2w24/7778fY2Bhef/11LCwsoKurC21tbaisrITVakUymcRXvvIVvPXWW+yQ2dfXB4VC8aF0OKqPrNEgu0bKBmhpaWGO/PHjx1lgKpPJUFVVhYGBAXR1dWFycpJ1CuQVTEr7vr4+PhiHw2F87Wtfwze/+U3k5eXxVIRGrKWlpRteGyVU5+fnIy8vD9FoFO+88w6USiWuXr0KALh06RKEQiF7Ay8sLGB0dJQ5rOQcRONx6gYHBgbYx9put3MWRnd3N9xuN3Q6HSKRyIbXplar2SUmEolgYmICU1NTePTRR1m8RLzXc+fOoaSkBGq1Gs8//zzrVoxGIwwGAy5cuIA//uM/xqlTp9jacHh4mA8KbrebaWREEchWqVSKR/GECL/xxhs4cuQIfv7zn0MsFmNiYgKzs7PQ6XQwm8145513oNVqIRKJOJF2aWkJDQ0NOHDgAIaGhrgpzMvLw759+zjxkpwkiIebTQdhMBhQVlaGkZERpFIpxONxDA0N8eH81VdfZdSZQu6I33/mzBlOJ963bx/+8z//E9u2bYPf7+c08dnZWezfvx/f+973mB9MvFAK4NmohEIhNBoNampqsLS0hLGxMYyOjkIgEHDCMonjtVotLl++jI9//OP46U9/CrVajcnJSfbBf+GFFzhNmoTfPT09aG1tZTSegh0VCgXsdjt6enpwzz333PLaFhYWUFZWhqKiIgQCATidTrz11ls4c+YMnn76aRQUFNyU7n7y5El2oaHQtmAwyAffkZERFBcXY+fOnbh8+TKeffZZ3HXXXZiamsLS0hLef/99VFVVsbd7tvsGfOBWVVFRgfn5eR63//znP4dGo8Fvf/tbPPbYY8hkMoyQqdVqlJWV4eWXX8bevXvR1dWFxsZGjI+PY2ZmBq+//jqMRiPzyClgTyAQoLGxEX19fYjFYqzfmp2d3fDaAoEAbDYbawEcDgfOnz+Phx9+GE6nEx0dHbh+/Trm5+dRWFiIkydPMi+cgr3IsW5iYgJ79uxhpzi1Wo1YLIbR0VGcP38eTz31FKampjhptaen50P90isqKhCJRJhudfnyZbS3t+O///u/kUgkEIvFYLPZsH//fqytreHAgQOYmppi0SE1MoFAAHv37uV7EY1GOXwqEokwV502cJrsblTj4+NsJEEOdL29vVhcXITH48GmTZtw/fp1Xu/i8ThMJhO+8Y1v8Pva0tICq9WKv/mbv8HnP/95nDhxgtencDiMzZs34/nnn0dFRQVGR0dRUVEBs9mM4eHhrFqDmZkZ/nckEgm6urqwvr6Orq4u5kpPTEywFe1DDz2E/v5+PsD5fD4IBAJ87nOfw89//nMUFhZi165dnOvS3d0NvV6PZ599ls1SiMpALi8blcPhQH19PTweD1ZXVzlRuaKigsWbb731FhwOB5qamlBaWop0Oo1z584x5YvMLXbs2IHu7m7eb4ivT5M/0iu53W42C8jGEiAhO+Vp+Hw+XLt2DX//93/PaemhUIgR1YsXL8JsNnOA2eTkJPr7+1FSUoKf/vSnaGxsxNzcHFOPp6en8cQTT8Dj8WDr1q1svEJ2x9ma21QqxWJuohqRnobyGEhP8+ijj+L8+fO4dOkSJicnUVBQgHA4zOj4/Pw8WlpaoFAo+BsdHBzEuXPn8IUvfAFHjx6FWCzGr3/9a0xNTbEOa6MKBAKwWCx8rkilUujr62PrbplMhl27dnEw6MzMDGw2G373u9+hsLCQp9vl5eWIx+Po7u7Gzp07YbFY4HK50N3djXfeeQdbt27F/fffj6WlJQ70Kykp4eDUW9XKygoKCwsRDodZzKxQKDhQubq6GqlUCuFwGMePH8frr78OqVQKjUbDTWc0GmXHzOeeew5NTU2Ym5vjqXJHRwe6u7vR3t6OVCrFMQeVlZW4ePEi9u3bd8trI52lzWZDOBzG1NQUCgoKsH//fiwvL3N+1jvvvIN4PI6nnnoK5eXlcDgccLvdGB0d5SBIYjOQxX00GsXx48eRk5ODuro61NXVYW5uDi6X60PNVQAw5Q0A9Ho9T6ApdJq0vWKxGLt378bi4iJ0Oh1cLhfvI6urq/jSl74Eh8OBn/zkJ/jUpz6F+++/n01ozGYzzp8/z9pd0v+KRCLs3Llzw2tbXV1FMplkB6tgMIiCggL4/X68+eabsNlsvL8bDAa+l7/97W9RVlYGiUSCxcVFOJ1O3H333Th69Ci6urrQ0dGB1dVVTExMoK2tDW+99RbW19fZBIpomNmaxxvrIzcaMpkM3/72t5ljGIvFUFxczJy1I0eO8EtF4V4/+clPIBAIEI1GodVqoVarWVS8adMmpFIpjj5PJBIYGxtDc3Mz8wxTqRQn4mbjqel0OhaOAmBV/OjoKNrb2zkQyuVy4fXXX8f27dtRX1+PsbExvPHGG5z/MTs7y2N+r9eLjo4ObNq0CTk5OXjllVewZcsWtLW1AfhAAEl/X21t7YbXJpFIsLCwgNdffx2VlZX8AT733HOorKxEOBxmNOb8+fN46623sGfPHgAfOMVoNBqk02k4HA4sLi7i2LFj2LJlC6qrq5FMJmEymVjY+dBDD+HUqVPMrw6FQlndOxQKBa5du4aioiIYDAZGhCcmJpBIJCAWi2G327G6uoqGhgaUl5dj7969N6VVFhQUoKSkBKdOncI///M/Y8+ePewIcuXKFUxOTuKzn/0shoaG+H2JRCLQ6/VZ/chJeBaLxRCPx1lg9sYbb/BofnJykulXlNvR3d2Nnp4eVFVVobi4mP3SL1++zJxWjUbDjkJPP/00Lly4AL/fzzQO4mRmq0QiwSm4crkcY2NjUKlU2L9/PwQCAd544w28+eabqKurw7Zt23DixAkWki0tLbGVpclkwtWrVxlRITSpqqoK0WgUTU1NuHjxInN95XJ51tyW4uJiuN1u5n9Sxsfhw4cxOzsLmUyGxsZGRCIRzM3N8YF8YmIC7777LgDAarVCLpdjYGAAPp8PL774Ij72sY+htbUVcrkcIyMjmJ+fR2lpKaRSKWZmZvjaswECAFBZWYm8vDxerPR6PbRaLafwdnd380H33Llz+M1vfoNXXnkFP/jBD9ilh2hzBw8eZNc2m82GUCiEyclJaLVaRsCJIkDNaDY+PwUFHjp0CAUFBUyP+93vfgeTycSjc5q6uN1uGI1Gtu8kXj01eSQW1Ov1TDOQy+Wor6/n5HNKWX788cezHq5WV1dx7NgxNDU1Qa/XIx6P82S4rq4On/jEJ3DlyhW4XC5MTEywoF8oFLIHfDQaxQMPPMAOgjSeJ8DC7/dDpVKhqqoKfr8fsViMxffZvlVyDUqn04yoUwbL4cOHWe9QUFCA4eFhbN68mdFop9MJl8uFaDSK1tZWDA4OsksVhd3Z7XaMj49DKpXy+zM1NcVJttlyNIqLi9llKplMwuFwQK/X4/nnn8eTTz7J2pjKykqmQn3+85/H6dOnodFoGCAaHx9HZ2cn5ubmcPnyZezcuRP33HMPSktL8U//9E946KGHEIvFMDc3B7vdzih5tinVtm3beF00Go0wGo2MlC4sLLAQncCz3/3ud9i5cyc2b96MgYEBCIVC7NixAw8//DDefvtt1NXVcTr5+vo6Zmdn8fLLLzMQR3txOp3m73+jisVimJ2dRSqVQkNDA6qrq3H69Glcu3YNCwsLaGhoYPqnx+NBXV0dB10ODw8DAMrLy6HVajk3gKYyBOyRznF4eBharZYbNa1Wm5W6Sm6PpBOj75z0jaQBdLlcGBwcxN69e9HR0YHnnnsOPp+PKYxNTU14//338Y1vfAOPPvooC/MNBgN27tyJn/70p9i1axebtLS2tkKhUGRNapZKpWxIIhQKmUJMB16bzcU5YpIAAQAASURBVIaf/exn6Ovrw/bt23HgwAHWaRw+fBipVAp2ux0CgQAnTpzA3NwcBgcHcdddd6G5uRn5+fmYmJjgnAPSzWYyGaYwblQdHR340Y9+hIaGBnaQonWOzj3BYBAulwuLi4uQyWTcmCqVSmi1WpjNZjQ1NeHb3/42rFYrXnvtNezdu5ev62c/+xkOHz6MhoYGrK+vw+VycX5WNv1ebm4uRkdHkZeXx8YKFy9exJkzZ/Bnf/ZnqK2tZTBseHgYU1NTOHbsGK+9lFBtNBoRDAZht9sBfOCsubi4yFoOoiHKZDLWDdvt9qzXFgwGmd6oVquRyWTgdrvx2muv4TOf+Qw7FBL1fMeOHYjFYhgfH4ff70ckEuHm32g0oqSkBFKpFJlMBiKRCCKRCMePH8euXbtw5MgRnD59GkKhkAOis30LLS0t+Na3vgW1Wo3W1lYO9iSqL/DBOe9G0yBy2RweHkZVVRVqampQXl4OvV6P73//+3jppZfYFVSj0bC5A2VeURDgXXfdlXUSdGN9ZOqUy+XC7t27WehIzk2HDh2CyWSCSqViQajVasXo6CgHmhQXF7Ow9OGHH8aZM2ewurrKjjOEjB89epQFRC6XC4WFhWhtbUUqlcJ777234bX5/X40NDRwUqnJZIJcLsdjjz0GuVyOlpYW5OTkwGKxsK87Bb594hOfwIMPPoiKigpoNBpG4IjjTJOYw4cP8wF7ZWUFmzdvZurVh6EIbW1tfNgJBoPIZDJ4/PHHEY1G0dbWhqWlJf7PfX19aG5uRnNzMx+4CgoK8PGPfxz5+fl4+OGHmSMcjUYxNjaG4uJiTE9P84hOKpVycna2aQsh12KxGHl5eaitrUVDQwM6OztRX1+P1tZWlJWVoby8HMXFxThx4gR27dqF9fV1ToqnZjESieDRRx+FSCTi0CGr1Yo9e/ZgamoKzc3NWFpaYocDt9sNl8u14bW53W6o1WoeZ5Pz0l/8xV/A4XAgPz8fFosF7e3tOHLkCHw+H9/jO++8E1qtFj09PQA+aAoeeOABboqo+WttbcX777+PoqIiXkRpwpaNckbBdeQwQi5DBoMBAwMDWFhYYLFhTk4ORkdHYbPZMDk5yZQlot3Z7XYcPXoUmzZtQkdHB+fHJJNJuFyum5JyxWIxIpFI1kYjFAqhtLSUG7SCggLmoq+vr6Ourg55eXlMPUin06irq0MoFMK2bdvQ0tLC/PKBgQFs374dubm5fFjxeDzYsmUL60AowCg3N5dH5NmKmjMCF+LxOEZGRpiuoFAoOPyLEmbD4TCampqwadMm+Hw+XL9+nUMtGxsbWWNDh/6qqipMTU2hrq6OBfv0vWzbtm3DayMXrv7+fvT19SESiSCdTqOlpQVGo5EnaCUlJWhtbcXCwgKcTidefvllGAwGmEwmzMzMoKenB4WFhfjEJz6B6elpDo1zOBzYtGkTi+nosBAMBvHSSy9haGhow2tbXl7GwYMHEQ6HeRKr0WjQ1dUFhULBrjjkHPfmm29i06ZNCIfDMBqN0Ol0iMfjCIVCqKmp4U2D0EyHw4GtW7dibm4O0WgUHo+Hn0NeXl5WPZVAIMC+ffug1+uZgy+RSPC1r30NZWVl2LNnD5t07Nu3D5FIBCaTCdFoFNu3b8e9996L1tZWCAQCzpYhd0LKCPqTP/kT9oLPZDLsyqNQKDA4OJj1eygsLERvby8GBgaYlvDYY49hfX2d9zRyIqJ8joKCAshkMqyurkIqlaKrqwsikQiPPvoogsEggsEgnE4npqam8I1vfAOhUAgNDQ1obGzEE088gQcffJCduDaq2dlZtjOlSTpRs4RCIRsjVFdX4/Dhw7zZkzZHKBTivffew5tvvgmv14vq6moMDAwgEokgkUhAKBTi29/+NhwOB6qqqlg/QOnGG+VSAeAE70QigaGhIQwMDEAqlfJ+Qdk7+fn5sNlsnHlCtB6z2cyW8j09PbBYLFhfX2d6VDqdRigUYvqO3+/npjg3Nxetra1Zv4XCwkJ+12it27RpE6efu1wuiMVitLW14T//8z/h8/ngcrmwdetWGAwGjI2NYWJiAm+99Ra+973vQavV8j5Ne09ZWRny8/MxNjaG5eVlNh/IFoIrl8v5XaZUdtJRulwuLC0tQSqVorS0FAUFBbh27RoefPBBtpGen5+HXC5HbW0t4vE47rvvPj47UbTAX//1X8PhcMBqtWJ8fBzpdJqNaTZv3rzhtc3MzODw4cMAwNbnly5dYip0eXk5gA+AAwrkbWtrQ3l5ObZv3w6ZTIaZmRmsra3BZrPhk5/8JNra2hhcpL+fgu26u7thsVjQ0dGBkZGRrEAKBdjGYjGkUik2Sfirv/ordgclSuW2bdsQDofxmc98Bn6/H21tbSgrK4NQKGTg5ODBg2ymQGszUZOkUimi0Siv9xTml+2Zki5paWkJcrkc27Ztw+c+9zlMT0/D4/FgfX0dVVVV2L59OwYGBhCLxeD1etHa2ootW7agtrYWlZWVOHnyJJtHDAwMYGpqCmKxGE8++SRb8JeVlcFms8FkMsFsNmfVtjidTtx7771sp00hi9XV1cwGisfjKCkpwWc/+1nWgCqVStTU1EAqlbLO5MyZM7DZbCgqKkI0GmXa3tNPP41kMonGxkamfXV1dSEYDKK9vX3Da7uxPvJEQywWw+l0ori4GEKhEE6nE4uLi0ilUigsLITH40E6nUZhYSGKiorQ29uLuro6qNVqtrpTKpWYmppCYWEhFAoFu0xQR03ccbvdjnQ6jcXFRUZwyQ7vVkWIv0wm45EwBYKRPV0oFOLNQyQS4fLlyzc9SOpSCT2y2WwsVs/Ly4NOp8P6+jouXLiA+vp6KJVKHs9lE+eq1Wo4nU4WkC0vL7OjkU6n40Op1WqFyWSCTqfDiRMn4HQ6+fCayWQwNTWF3bt3s9UouUeoVCoEAgE0NDTgwoULfAAOhUKQyWRZ+cvJZBIikYhF3qlUCpWVlbDb7ZDJZJienmY7xLW1NbS2tsJutyMej2NpaYnt0Pr6+nDnnXfyu+B2u5nDm5+fj8bGRkY8yU6TmrqNSqFQ4MqVKwDAB1Oz2cxBPKFQCLm5uSgsLERBQQFycnJgt9t5nF9QUAC5XI5MJsP/22KxIBAI3ISWlpWVoaenh20iw+Ewu1Jk+xaoGSE7Sp1Oh1gshsLCQkxMTHBeCLkhzc7Oori4GPF4HEqlkt2+qLHZu3cvgsEg88mnp6chFosxMDAAhULBCExubm5WhwyyjiTNAvDBuHdmZoYpYYTKLSwssG6lvr6erZfX1taQSCRQV1eHqakp1NTUYHp6msel8/PzKCsrw8mTJ6HX69lvW6VSZUVwAbDlZU5ODi/wTU1N/Jtoo83NzeVMg6mpKRQVFTFfndxPiCrX0NAAqVTKgmlCn6anp6FQKNiZo7CwMCuKK5FI2KWNhO7kHkbiU+LHU6YFNcIUGCWXyzkUi8SulDNBgXa1tbV8n8hwQSwWY2pqakP7XbFYjHQ6jfz8fP6GCgsL4XA4YLPZ4Ha7ObCPHMyIZuVyudj5ZmxsDGq1GgMDA1CpVBgcHGShrEgkwv79+zE+Ps66IPrOP8wxJhKJMJ2F1nui9tDvN5lMDJ7Qu0fPy+VysdhVrVbDarXC6/WywUB/fz8sFgv6+/vZEnptbQ319fVZvwdqzulAT+5fBQUFqKqqgsfj4VwBjUaDubk5WCwWdm4jt5j5+XmEw2GMjIxwBopCoWALWhJEKxQK5Ofns1g/G4eZhJT0zdJ3u7KyAoFAwJaVsVgMKysraGxsxOTkJJaXlyEWi3kfcTqdqKuruymIjQwqiC5CU0fSllBI7EZF9FvSIi4tLcFisWB2dpbzY0gjWFBQwC5LREsmnWBvby8aGxtRUFBwk4sbrXGUO0SNpVAoRDKZ/NBsKnIYpEMt/U9dXR0WFhYgFAqRyWQQCoXQ2dmJkydPQqfT8bsuEAjQ09ODxx9/HC0tLeyKRG5Mw8PDuPfeezE4OAiZTMbnj5qamg9FccmtaWVlhZkfTqcTUqkUIyMjUKvVMJvNHHBItBv6XXQfDh48CKPRCJvNxkYGZMazf/9+TE1NsXie9qFs0xYKByV7W8oCI1rlzMwMJBIJKioqeDpGZjSRSIRp2SKRiC3sN2/ezE5hYrGY7YVHRkZgt9sZeNTpdDx1uFUlk0k2AIpGo0yXT6VSaGtr4/OmVqvlvCIytiBggNbI1tZW1lrStIemhZSnkZOTw/sqUaezfQukB6N/Tq1WY2xsDCUlJXA4HCwwpyDiYDCIkpISJJNJNofo6+u7iRYVDodZJkCxBmSwQC6LZBawUdHeSzk85MxF1x2NRvmdFovFqK2txcTEBJsN0DdOQC2dZYjOSbbYLS0tGB0d5Sk/hUnTRP7D6iNPNGjhJTtCotRMT09zqmkikWC0lmgvBoMB8XicLcvGxsag1+sxOzsLrVbLASikFSBrWdqM5ubmeBPYqGgxJktKcj8hi1273c6LJY0lg8Egd7PxeJyRp7y8PHg8HpSVlfF/RwnL1Bj4fD4WX5pMpqwuFLTRCYVCzl6gVEqNRsNoK4m7u7q68P777zNlgbyyp6enYbVamRpCjYFarYbP50NTUxOWl5e5g11eXoZQKMzqQkGboVQqxdraGrxeL4/8SB9DqbDEaR0YGEAikeAXmEbWarUaU1NTPH6lIEayBZ2fn0cqlYLf72eKRraFh7yvydKR0KHx8XG2bFxaWuLFVaFQYGBggAOfBAIB0/IkEglGR0chFAq5CSVPaZvNxoGP5DQGICsSKRKJ2O2G7k9RURGPryORyE3NNdFmSAyem5sLg8EAsVgMsVgMh8MBhUIBr9cLn8/HH7dUKmVL3kQigUQiwbSUbM80Ho/fZNVIC6xer2e+Jgn7amtrMT09zU06oZSBQADl5eUsGPN6vWwNSBQsSrdeXFxkO+RsiyIA3uDIOx4AH1TW1tYYLV5cXOSmfGZmBna7ne1kU6kUU/6I1kCHDtI8lJaWYnZ2lhuQnJwcmM3mrFkVeXl5vGnfyEGdn5/ne0nX7fP5oNfrWTtDIX/koBUOh3H9+nVotVqEQiHEYjFotVp4vV6mrsTjcSwsLLBdbzYwhehmYrEY+fn5bFMcDAb5N5LziEwmQ0lJCYaHh5GXl8f2t0ajEeFwmHVFZGoQCoWgUqnY5tDv90MkEvFBWSAQZEX8iNJG3upk10rBpgsLC1hfX2dnKqVSyRQmq9XK+RE0vVMqlUz3Iv/77u5uSKVS3oNisRg39tmKphLkvELvQjAYhFwu54CyYDCIq1evYnV1FYFAAKlUCvPz8+zAdfnyZQSDQZw5cwYmkwmpVArJZBJ6vR4ej4cnkbTmkQMOHew3ujZaj+gbpWRfmUzG3y7pCcvKyuDxeBAIBODxeNg0gQ6bLpcLRUVFAMCTxcnJSb7f5GpF09RgMLjhtZHdKwn4qXkn56J0Oo1MJsMHFprqFhcXM/WG3PuKi4uRTqextLQEp9OJhYUFZDIZ3gfS6TTv77QneL3eDa+NLKVVKhXv9/S+FhUVsQvP8vIyxsbG0NXVxUJnckKijIfa2tqb9iP6xqanpyESiVgnlEwmEYlEbqLpbHRt9E3S9aVSKfh8Ps5uIA1obm4uU2dpT6UzjM/nQ3l5Ofr7+znglPItLly4wHaoarWamzOlUplVSyUSiZiCDIA1U4FAgNcCep5erxcFBQU8eaJQQ61Wy4DX2NgYTCYT7HY7HA4H8vLyEIlEoFar2RWKzjc0KdqoKHOG7I+j0SjkcjlmZmZQVFSEnJwc/jYowHZgYAAajYbXTaIv5ebmcmNH0y6BQID+/n62uSctSvh/Akw/rCh4mdz3wv8Tomg0GlFZWYlMJoPJyUmMjY0BAAPqDoeDDVKuXLkCo9EIh8PBFrbkGDc5OclMBUqiX1tbY3r+RkVuofSdCoVCpNNptuinBtzv9+P69esoKyuD0+lkC21ad0dHR2EwGNiQQiKRYHl5GQ6HAxcvXkRpaSlT6kl7pFQqPzS8kuojTzRoNBn+n1RNmUzGOoVoNMoj+IWFBUxOTrJns0KhYB95OohR8NrTTz+NcDjMoT0ajQY9PT28EFCnPDg4mHVDIZ45APYjf+edd1BaWoq5uTkWvQUCAczNzTFaOzg4yH/3jRSuG1GX9fV1eDweDA8PQy6Xo6amBouLi5yNQCO4jYqQu0wmw6gYoQM2mw35+fkYGhrCtWvXUFNTgz179rCdLLmC3CiwP3fuHIsRyUGGNqu2tjaMj49zDolYLOaN51ZFTk40sZifn2eeZCwW498aDoeZs5ufn49IJMIvNTWXv/3tb2Gz2WC322GxWKBSqTA0NASfz8fIMlkQAuD7vVGRLSct1Gtra8yJpyY3EonwQkECRaPRiFgsxgE7FHZlt9t5bEhTJnIpa21txfT0NKO2JDLcqFKpFCPLcrkcFouFuc8LCwswGAxYXFxEMpnkCaBCoWB+ZCQSYQSBGtZz585heXkZy8vL7FkNgIWFhCyTs8RGRba0wWAQ4f8JeFtcXMSuXbvg9/uRl5fHzmwlJSWQy+XIz8/HlStX0Nrayo13JpPB6uoqi5zpkEiuGG63G1u3buXFSigUMv0hWxEam5OTw45zlHhMmw0luiqVSpw4cQI5OTk4ffo0duzYwYdF0mA5HA4OEM1kMnwAJQolHQpovcrWCJGFMK0R9J1T47q0tMToOVlnEhp0YxCiRqPB1NQU1tbWUFpayodDcrHr7++HUChk1Ije3Wz8asqFGR0d5Rwet9uN3Nxcdvnzer2IRCI8Yfb7/VhcXIRKpUImk0EwGITRaMS1a9eYr0+0R6LpnTlzBjk5OZibm+Ow04WFhazrLyGGRFcpKChgfr/f74dMJkMsFuPrDYVCDOhQhlF+fj7uuOMOnDp1CtPT01AqlYysUTKx2+1GU1MT7HY7o8Zer5fR+lsVTZ7GxsbYWpuybIaGhtj+cWVlBaFQiBPnaXJHWiy/3886B6vVyqLaubk5/u8p/I0odsFgMOv6m5uby5kLCoUCKpUKw8PDKCkpgU6n4wMBNXKUX3Tu3DnWqfj9fg6lGxkZwUMPPcQZRmTFSRkfZBNPgXfZ6HDk8hYMBjkrwe12s5U9vX80ySNLc7VaDalUymwHq9WKgYEBTk+mPBOaOJAFLvDB4U0gEKC4uDgrQEYHUvou6P5LJBKe3lEI7tTUFJxOJ9RqNa5fv47t27cjFAphZWUFSqUSv/rVr5DJZHgydiMQ9vzzz6OhoYHXAYFAgOvXr0Mmk+FjH/vYLa+NLI5pfVOpVKwhI0MUOgtQrphWq8Xbb7/NjQmBYLFYDD/5yU+g1+s5k4RYAXa7ndf6WCyGdDqN0tLSrM0j8MHUmZLHyWJepVJxaG8gEMDY2BiCwSBaWlrYPY4mLel0GvPz87h48SKMRiOUSiUWFhb42RmNRohEIlRXV/N+RrqGbPsWMQ7okBuNRlmX5XK50NTUxKyFkZER1iBRZhWJ0TOZDN59910GeAk4ou+kubmZE+7Hx8chFApRWVn5oaGfJSUlzGCg31JQUACv14u2tjZMTk7C6XSyC2U0GmXtLO0VqVQKXq8X8XgcZrMZJpOJRe7xeByHDh2CXC7H0tIST+ri8Thn0NyqKESQfmc6ncbw8DBkMhna29vZzIfu/cMPP8wGRATirK6uwuVyMcC5f/9+zu2hBp0YQ3SGIlfAj2pv+5EbDZPJhMuXL/PHQEKWM2fOcPgXhawkEgncdddd6O7uxnvvvcdZAoQoUGfY29vLnMyZmRm89dZbKC0tRU1NDVOrSPeRzfMbACYmJhgZkUqlUKvVmJ2dxdDQEKxWK3fJhJKur6+zrSB1lh6Phzm/xJmlAy1xe61WK2QyGWZnZ7GysgKdTpdVOJxMJnHlyhUOb5JIJBgYGEBOTg7uuecezM3N8YcKAO+++y527NiByclJRriXlpbQ3NyM8vJyeL1epko5HA78+7//O0pLS1FZWclImtPpZGtIo9G44bUR95pGdbTp0tSHhFKJRAIWiwULCwtIJBLQaDQ3ZadQjgXlMZD1pNvtRiKR4DRp6tip2crmOpWbm8sN5urqKtxuN6eLUoNG4Y5CoZCdI65du8ahSsvLyxgfH0dpaSkWFhYwNTXFaE1vby9bkFIwVSwW40UhG6K2trYGu93OotxgMIhYLAaFQoGxsTHU1tbyhkDhhWKxmJ1NaPwtlUpZJLi6usobUzgcRjQaZe2EWCxmNPf69etZp3u5ubn8HtNiEI1G8Yc//AHJZBKtra1YWlqCz+fjMWhVVRUqKyshkUh4GhkMBqHX69Hc3MxWvtS0kN/83NzcTcgdoZzZKpFIIBQKsVhycXERU1NTvJnRAYJocTT9IfqNx+NhodvJkychFAqxe/duPgjT711aWsL27dtx9epV/q1Eqduo6H1KJBJQKBRYX19HIBCAWq1ma8DJyUkOQCO3OEpR9nq9qKysRFdXF+rr6zEwMIDx8XEOF7x+/TqSySTuu+8+AOCkYUK0s7mf0L26UehLzim0odLkgCgOkUgE8/PzNznrbdq0CdeuXePwKrpvExMTUCgUbAlNFCdKG/8wwII0NzSuX1pawvz8PANMNNEg5JioFk6nk13a6urqsLi4iGvXrmHnzp1Ip9NM9xCJRPD5fLBYLEzNBD6guzU1NW14bSaTCT09PYz4EfCQTqfx3nvvYcuWLVhYWEA0GuW8JxIbR6NRbpxqamrYYYrMHIgbn5eXh8HBQbadJgRVpVIx7/1WRdNPWiOIakquXZSlQdS0Y8eOYf/+/ejs7MT169fZxamqqgpra2tIp9MYHR2F1WqFWCzm8NKmpibI5XIWyFITlC3hmmx6KTeD7h8BO4QYr62t4erVq6zFJJt5otDRPjQ1NcXaSQKIamtr2eLa4/FAJpNBpVLB4/Fk3VP1ej03rQRwhkIhdkmbnp5mw49QKIQTJ06grq4O6XQaQ0NDMBqNsFqt0Ov1TF0mBJ4aepPJxHawVVVV6O/vZ3egbGAKZfnQe0AuhsvLyzh27Bg2b94Mh8PB9JXm5mZuIrZt2wan08nI8+LiIg4ePIiZmRnEYjHodDrU19djfHyc8zlIj0MWsNms9sPhMEKhEHQ6Het/tFotkskkpqensWXLFigUCqbn5uTkwGq1wmg0sn6Emsfdu3fD6/VCpVKxAcTU1BQWFhaYhlZeXo5Lly7hnXfewezsbFbBNaHjWq0WMpkMi4uL8Hq9KCsrY1piZWUltFotZmdn4fP5sGvXLpw9e5ZNOGjS/eSTT+Ly5cswm80MlBA9jIxSKKQ4k8lAIpFkfd+Ki4vR39/P9CsSh9NUm+4pCfnJsKSvr48n6SaTiYNulUolSktL4XQ6kUgkUFVVhdXVVUxNTaGqqgqFhYWIRCJYXl5m+vVGRc2fSCTiCUhBQQG0Wi2OHz/O9CiFQoELFy6gpKQE+fn5kEgkrMFKpVJsekDfH01FaN+9cuUKNm3aBI/Hw7R8rVablc1zYwnWP4xUfbtu1+26Xbfrdt2u23W7btftul3/H+sjazRu1+26Xbfrdt2u23W7btftul2366PW7Ubjdt2u23W7btftul2363bdrtv1/7xuNxq363bdrtt1u27X7bpdt+t23a7/53W70bhdt+t23a7bdbtu1+26Xbfrdv0/r9uNxu26Xbfrdt2u23W7btftul236/95fWR72+9+97tQKBRYXl6GUqlEQUEBezFbrVYOZqJchsnJSVRXV3M4j0ajgU6nQzqdxqVLl6BQKGAwGCCRSJBMJtkLmuy51tbWOFhNIpHA5/Phm9/85i2v7d/+7d+gVqs5bEcikbAHulgshs1m40AvCrSjgCyxWMz2XwKBAIlEArOzs5yUS97Eq6ur/OdRboVIJOIE0K997Wu3vLbvfOc77PNNRaE4MpkMBQUFsNvtnFabyWTgdDpRUFDA9zgajaKoqAi5ubkcqkdhQpTkKBQK///C3MjS89vf/vYtr+0//uM/2Ko4NzcXubm5WFpaYlvWwsJCLC4usvUoBeMJBAKIxWLO67BYLOju7mbLUoPBwDamZCGcSCQgkUg4UIYSMJ944olbXtv3vvc95OTkcPq5XC6Hy+WC2WzGysoK1Go1IpEIYrEY2yi2tLTA4XAgHo9z8JdIJMLo6CgqKirYRpPC+ZaXlzmvhezh6F4sLi7ib/7mb255bc899xyHuqlUKohEIjidTqTTaZSXlyMnJ4dzNMj6c9OmTRgZGYFcLsfKygpWV1c5J8BisbCNXG5uLtsGUyji6uoqp8/Tc3r00UdveW3PPPMM5HI50uk0CgsLkZ+fj56eHhQXF2NlZQUqlYqfsUAgYOvKhYUFzM/PQyaTcUL9xMQEB7lRIBeltq6srMBoNN4U3Li+vo5wOIw//uM/vuW1AcA//uM/Ij8/H0tLS1AqldBoNHA6nfwMDAYDQqEQEokEW/epVCq2eDQajbBYLJxCGwwGOR9BIpHwd0aJ8LOzs5BIJJBKpUgmk5DL5Xj66adveW3PPvssW/6SBeTo6Cjy8/PZ+pTsUXNycpBIJFBZWcmhaGtra5zuSzkkZJ1NAZgAYDAY+LdRUKFIJIJYLMZTTz11y2v7yU9+guXlZb5PQqEQ8XgcJSUlmJmZQWFhIYd+0fqwf/9+XLlyhcMlJRIJtFot4vE4W/SqVCqkUiksLy/ztz03NweRSMTvYjKZhNPpxPe///1bXtuPfvQjXo/InnJgYIAT1AsLC7G0tMT3g+wa6Vmn02nk5eVBLpez7TM9QwA3pasrFAq+txRmmpeXhy9+8Yu3vLbnnnuOrS3NZjPkcjkuXrzImQYymYyDzNxuNwwGAyorKzEzM8N2p/RsKCldqVRyVkI4HMb8/DykUik0Gg0ikQhUKhXbEK+vr+PIkSO3vDbaU2+023Q6nRyuWF5ejuHhYbaapjwqCqkja2lKpq+vr0coFOKwTsp9WVtbg0Kh4MwoSrtPJpP48pe/fMtr+973vgeBQMDheJSmLBKJsLq6ysFqNwZ4UVioQCDgf4fWWwqqpeA1SoFOJpNYXV3l+5VMJvm+/fmf//ktr+1nP/sZUqkUMpkMdDodW6PSvaS/I5VK8XdSXV2NcDjMOU60xq6uriKVSsFgMGBtbY2txCl5XiaTIZFIYG1tjZPWE4nEhmvcP/3TPyGdTnP2SG5uLlsCLy8vQyaT8TpB++Jdd92FM2fOQK1Wc3CjSCRCKBTifV2hUHDYIlliFxQUsLW6RCKBTqfD5OQk/uEf/mHDZ0p7Kr37CwsLkMlkcDqdqKio4L8jmUxCKBSitbUVV65c4ZDGG+2qJRIJZDIZiouLEQ6HMTU1xbkWtG8JBAKkUikOO/3KV75yy2v75S9/ifz8fESjUT6P2e12KBQKxGIxDsmkjBqyA+/v70deXh5bo8tkMrhcLuTl5bEN/MrKCme9iUQiVFZWIhgMcpK9UqlEIBDAww8/vOF3ajabef/Jz8/HtWvXkE6nUVNTwwGtkUgEq6uriMfjqK+vx9zcHHw+H1QqFduFk7V7KBQCAA7CpPeZAmlpvabk809/+tO3vLbvf//7N4VWklWzXC7nrDaPx8Prezgchl6v5zw8oVAIuVzOFvIUeGw2m/kc7ff7OSKCvhnah5aXl/FXf/VXt7y2G+sjNxoU2CIWi6HT6TjlmFK00+k0jEYjcnNzsb6+zr7/5eXleOONN6DVatHe3g6xWIytW7eiuLgYgUCAk0/p5bbZbDCbzVAqlYhGowiFQkgmk1lD8egFo4BACoiJRCJQKBQoKiril4Aakng8DrFYjPHxcQ7n8/v9UKvVWFlZQU1NDSepUi5EeXk5pFIpgsEg52zodDpEo9ENr81gMHD4USaTQTKZRDKZhMfjQWlpKZaWljikJx6Pw2g0Qi6XY2RkhFOxTSYTGhoa0NfXxwchhUIBm80GuVyOyclJFBQU8GFqbW0NQqEQEokkawgYZTrQwY3C1FZXV2EymThUKZFIIJlMQqfTobm5GVNTU3A4HBy0RV7QlH5Mh1MAKC8vRzgchtlsxvLyMge+0Ue3UeXl5XGQFf3nnJwcjI+PQ61Wc9YBJR9XVlYiPz8fTqcTJpMJYrGYD3ItLS18iKEUXZ1Oh+LiYkxOTuLQoUNwuVyYmJhgP/1sQTSU6F5YWIhMJsNBVfRB63Q6iMViblp0Oh03uiKRCHq9HgqFAjKZDPPz8+jv70dNTQ033XRgoxRoCk6jELlsz5Q2aaPRCJlMxjkjQ0NDqKurY6CguLgYIpGIAx7Jb5s8tp1OJ5qbmxEMBtHe3o6BgQG43W6IRCJEIhHe4E0mE2KxGGfNZAtlAsDhcRQaJJFIeMO0Wq18kJdKpSguLoZCocCVK1ewd+9e2O12XoNmZ2dhNptht9vR3NzMWQMUANbY2IhAIMAZPwqFgp/JRkXPZGVlBbFYDAsLCygpKUFPTw+2bdvGqarhcBgGg4ET1tvb2zE8PMxNUV5eHm8olNtB4ZRut5sPFJQlIRKJIBKJUFtbu+G1yWQyPphR7oLL5eKEawonzGQyEAqFMJlMiEajHOxJqcfkc0/hnh6Ph9/1ZDIJk8mE4uJiDqaKxWJIpVLYsmXLhtdG31soFOIcJavVinA4DIvFAgCcXhyJRFBSUgKr1YqRkRFYrVbE43HexOmwurCwgLKyMgY4KASTAAHy5Kccjo3KaDRyA+z3+yGRSFBTU4OBgQF0dXVxkjDwQcaLXq/H2toa5HI5r2mLi4ucTzAxMQGNRgORSIR0Oo38/HxUVFRgcnISRUVFnATvcDiwtLTEScK3Ksp2UqlUDHJRdpTf7+eAPcptqK2tRV5eHubn5+F2u28C46qqqjgp3Ol0cr5KXl4eioqK+JtaWVnhxmZpaWnDa6MgxUwmw2sCBci2tLRgbW0NMzMznGFgsVgwOzsLpVIJmUx20zrr9XqxefNmbiJvTCinb4DWILVaDbVanTXHSCwWIxqNQq/Xc1gg3efi4mIEg0F+H+fn51FZWQm32w25XA6BQIBMJgOtVovKykqMjo5iYGCAD2hKpRJ6vR7FxcWYn59HWVkZvF4vH5oFAkHW/BHag2ktoX/H7XbDarXCarUC+GAdcLvdqK+vx7Fjx1BfX8+ZBnK5HCqVChqNBnNzc3w+yM/PR1FREQcZFxUVIRAI8H4vk8k2vC7gg/wRv9/PoZXUMAaDQZSXlyMej2N9fR2JRALz8/NoaGjgLC4K3KXzSCaTQU5ODk6cOIF0Og2tVsv5aJOTk9i+fTvGx8c5XJEAtmzPlDJr6HvU6/W8fwJgoCsUCqGqqgqXLl2CSCRCUVERAzoUvkzPOJlMcnNNoJjX64VSqYRarWbwINt3qtPp+HtxOBwIhUKoq6tDX18fjEYjA5ZqtRqLi4tYXFzE0NAQr6VisRgCgQDxeBxFRUU4f/48qqqqGJilxjuZTKKwsBAqlQoAEI1GbwqDvVUplUpkMhkGq3JzcznQ1ePxQKPRwO12M7heWlrKz0GtVoPSLejvnp2d5WdOf55areamgt6PVCoFsVicNaftxvrIjQahTnNzc3zQiMfjaGlpweLiIodSUTduMpng8/ngcDhQXl6ORCKBnp4e7u4MBgMmJyf5o7ZYLLwwjI6OQiKRQKlUcrpsto87kUgw2kAbajqdhlgsxuLiIqMbdNhLJBKc1FtWVsZIPh0gNRoNfD4fjEYjJw43NDRgdHSUD5e0oIyMjGQNySG0SyAQYG1tjSPh6QAQi8X4gJ+fn49kMsmbMP3/CI0bHx+H1WrlzZf+WZvNBq/XC5PJhLW1NUSjUQgEAj78blR2ux2lpaUAcFPieyQSQTAYhEqlYoSFIucDgQDGx8chEok45XRlZYURSQp0k0gkyM/Ph8/ng91u58VJLBZz8ni2jW5paYlDrDweDxKJBAoLC5GTk4NAIIDy8nLk5uZCKpXCYDBgZmYGOp0O4XAYRUVFiMVicLlcSCQSUKvVUCqVnJq8trbGgVUrKyuw2+0chiaVSrnB2agikQi0Wi1isRgSiQQSiQTW19eh0WiwurrK4WZzc3N8iKMNRCQSweVyYW1tDfX19ZDL5VAoFDct8olEAjKZDH6/nxEhoVCItbU1bnw3KoFAwItmKBTiAKm8vDx4vV6eJsTjcQiFQk7+pSkRAPh8PoyOjqKkpARGoxETExOQSCRYX1/HxMQE6urqGEHMycmBWq3mKVA2QAAAhyKur69zA0WHf5/Pxynv6+vriEQiSKVSWF1dxezsLOLxOCckE5BAv4FQobW1NVgsFj4U5eXl8e9bWFjI2qRRkjrdx9XVVXi9XkgkEt4IAoEAI5RSqRQjIyMoLS1FcXExp2nX1tbC7/dzIvXa2hon0vt8Pj4E5eTkQKvVYnV1FZOTk9BqtRteG4WRBoNBpFIpPpBSQN7Kygrm5+e5iaCwJppYBAIBTpxPpVKMOBI6BYDTsOmdzc/Px/r6Oux2e9ZDzMLCAjcSOTk5DOTodDoOcQVwEwIei8Xg8/l42pKTk8MJ5ZRa7vf7ORg0Go0inU5zI04NGq13GxWBGnQPaVpGawW9IxqNBlVVVZicnER7ezuCweBN92ZlZQWBQACpVAputxsajYbRR61Wi6mpKZ400fVRQGC2Z1pSUgKv18tItVKpBPDBviEWi5Gbm4u8vDwsLCxgbGwMmzZtgt1uh9lsRjQaZeTd5/NBrVYjPz+fkVqa2hCoFggEuFEPBoP8+ze6bzk5OXwQAj44DE5PT3Ow5MrKCiQSCUwmE65evcohuMAHwXVCoRBarRYTExMIhULwer0Qi8VIJpPMhAgGg4ym0rtCzIiNiiYD9C4QKr2+vs6TqIKCAlRUVMBoNGJ2dhbr6+soLi7mbyGZTKKgoABKpRINDQ2Ym5vjCZzdbkdZWRkHmy4uLnKCMgGZG1UsFuO1cGVlBTKZDEKhEEajEQsLC9BqtcxEyMnJwdjYGAwGA5+ZpqenubmIx+PM/MhkMvD7/Qw06fV6TE5OwuFwwGw2Q61WY25ujhuZW5Xf7+cJ3vLyMjeJJpOJwxnpXubk5PAUlt6BaDSKtbU1qNVqXlvo91Egp81mw8DAAIaHh/mbDgaDH3ogTSaTN03GhEIhT+mcTiezQHJycvjsSZMdWt8jkQikUik8Hg82b96MUCjE+xYB2YFAgBtdjUbD0+Zsh3la1wjES6VSvFetrKwwuLuyssKTErFYjJmZGdTU1GBxcRF+v58T3jUaDWKxGP+5wAdrAZ3p8vPzb3pPCKzZ6FsQCoV8PqDJ8Y37JgEOarUa4+Pj3ETrdDpIJBJu8ijYNplMYmJiggMny8vLMT09jYKCAgZ4M5kM7ykfpT6yRoM2fhol08F5YGCAP1yhUMgpzh6Ph2Po6YdLpVKUlZUhk8ng+vXrNx1Q8/LyEIlEeKxHo0L677IhzCKRCH6/n/85opkQxWl5eZkfptfrRTgchkKh4AMZPSh6USlR9cbxJz1I6trT6TS/vMlkcsNrI5oKLYo0IiOUhH4/fdixWIwTx0OhENO7KIGcxtBEL3A6nYhEItyVm0wm5ObmckOVLQkZ+GCzWF5eZjoDIR2U+mwwGJCfnw+pVMrpm4T60mZFo/nl5WX4/X5OECXaEiXWhkIhLCwsMIpFybe3KkLwacQMgKlXhAQSSjA9PX3TgTMQCPAhVCaTIZPJYGxsDKlUip9HOp3mj4lSrZeWlrj5oft7qyIEgUaccrmcUbnFxUX+7el0GvPz8xCJRMjLy+O0e6L8AOAJYCAQgM/nYzqAw+HgtGeagLlcLn7fNyp6j4iSkEqloNFokJ+fj1gsBqFQCIFAgHA4DLvdjmQyiaamJr4H1KDrdDrEYjGcOXMGfr//JkSEDigCgQCRSARTU1OYnp7G2toaozEbFU0eqCmgQ2k0GkUgEIBMJoNOp+NDw/LyMnJycnjjpQ0tlUoxBY5AAp1Ox+PnRCKBkpISFBcXI5FIwOVyIRqNZk0yXV1dZWBCIBDwty8Wi/nPkMlknLLs9Xq5UfP5fPxMiU7gdDrh8/n4sLi8vIxIJAKPx4OioiKeQng8HmQymayHK2qyiMJJFClK6pVKpVAqlUx9Wl5e5ibE5XLB4/EgEokgNzcXBQUFmJycZLRQrVYjJyeHkdEbD9fJZJLTvLNdG9Eob6TzzM/PIxqNIi8vj1FBSrHVaDRQKpVYWlpiSguBMkQ/IlQ4Ly8PgUCAKTk0sabDRrZ1JBaL3TQZpVT7mZkZTjamg8iN9zYnJ4dpXVqtlhvg6elpfo9FIhF0Oh2Wl5dhtVpRUFAAlUqFRCLBB7RsjTc1x/F4nBOKCTmmNV6j0UAikSCRSCAWi2F+fh7JZBJTU1OYnZ1FOBzm94bWXpVKBZvNBqvVysnCOp0OlZWV0Ov1EAqFEIlEWfcGek4AmMZBLAP6fs1mM/R6PScI0xQMAB9ACVWdmpridYT2W5fLhXg8DrVaDY1Gw1P9D0OYaYpNZxECsKanpxlYEAqFSCQSjAjTYZyAG6I30bXT3kyIMAEpsViM6VKrq6uMkm9UNwIn1FQTRYn2CKIqTk9PY2FhgSfxNOFXKBRQKBQIBoOIRqM8eaApcjgchlwuR2lpKQNeTqfzpunkrWp1dZX/DmJYEBhL7z4dpunMQsnpfr8fsViMm7GlpSVcuHABZrP5Jpo5AZtqtZpT4O12O9xud9ZrI/BlcXGRzyJEsyaKL/0dBLY1NDRAq9Vibm4OsVgMFouFAU0CBZaWlvjdDAaDPDGTSqW8F66trWWdoIlEIgY+aX9LpVIIBoOw2+0Qi8XcjBFgUFtbexPAQ5NFonPTuUQoFEKj0SA3NxclJSVQKBTQarVMPQeQdWJL01paQ2j66ff7EQ6Hee1VKpXcEOn1euj1eoyOjsLhcPDUQqvVore3l89AtL/SmVyj0fAUiADwD5uiUX3kRuPGzk+pVPLhqq+vjxc3s9nMiMrc3Bzq6+v5wJLJZFBUVMQjeFoAcnNzEYlEMDk5Cb/fj2Qyibq6OthsNj5Q0Eh1o5LJZIxUEdJOB1LSEwDgjwkAGhoaoNPpkEgkIBQKkZ+fz/xqQmro5aaXirizhLTTiDTb4eVGTuGNB2eHw4FkMgmZTAaNRgOpVMpNBm3YwAcvuUKhYF7w+vo65ufnmaueSCSwsLCAlZUV5pLfiEjU1NRseG1qtRrBYBChUAhra2vIy8vjAwdNfIj+Qd1vbm4uioqKsLy8jPn5ecRiMcjlcqyuriIajTLimkqlsLi4yCjx2NgYgsEgN0UikQjl5eUbXhs1rXRIMBqN3KTRQkcL4vT0NPMPlUolFhcXEYvFGKWk5pMO2ISqz8/PQygUwmaz3bSZf5TGlnQWhIYS6h2LxXjzJ50FXRdxHGmjB8B0l8XFRUYeCP2hTZSQimg0ys3vRkUHpFQqBYlEAo1GA4PBwFociUTCuhKiGdGCTX+uSqVCR0cH1tfXcf78eb62/Px8bNq0CbFYDCqVCgUFBYhEInA6nXC73Uw9yFb07BKJBNLpNNMJiYcvEomgUqm4WV5bW4PVauXFWCaT8QSFFmChUAi9Xg+DwcCbeU5ODqqrq3lKSNTJbNNH4v7TN0hghFKpxOrqKk/B6Lux2+18bx0OB6LRKOsz1Go1L/TFxcVMycnJyUEwGITFYmFOcjQaRUFBAW8StyraeG5EwRKJBDKZDAoLC/k7DYVCOHPmDLRaLaxWK6Nv1ADT4YKmFAQkEOUAACOFsVgM6+vrvHlvVDRNBsCABE1dl5eXeYOWyWS8Fmq1WtaqEFpIU1i5XA6tVguLxcLPnaYhpDuig5JUKkVJSUnWZyoQCG5C7goKCuD3+7GysgKz2czXGolE+JsjkKWgoAD5+fm8WRM9jtZlQleLioqY9kAHU5ogZbtv1NQqFApoNBrk5ORgdXWVOejUnNGEhHQEfr+faTNEs6GmNT8/HyUlJSgoKOC1zGKxoKKigg/wYrEYlZWVG17b+vo61Go1/90EctFBO5PJoKCgAHK5nBkHhYWFfPgnmtLMzAzkcjmDClKpFBaLBWazGcFgEJlMhunCtP5Eo9Gs94048Xl5efwsiBao1+thtVr5HjkcDhgMBmZV0LtK/+7c3Bzm5uYgFotZd6DX6+H1eiGTyXgtkslk0Gq1jEpvVNRcSqVSnrhRI3sjFY4Ow8AH663X64XD4UAikeB3n77ZQCAAkUiEiooKtLe3M3WqqqoKZWVlvK+RbinbtZFWgZraeDwOr9fLSDWtX4lEgpsuAnxpmkHfj9PpZBo9sUmI7dDc3AyNRsOT52AwmBUgIyYBTXpUKhVTe+hdpEM+7eE2m43fe6IN2mw2vp+0b9Ieu7q6isLCQphMJgZDaEqSjV0hlUr5+SkUCn6/V1dX4ff7GawRi8WsV6S9Jx6P8/9dUVHBzQ3tY7Qu0jpG32gikcDS0hI3gtmeKa3VmUyG6fbAB4wGs9nM1FaadObl5aGkpASRSISBTHq/x8bGkE6nYbVaYbFYIJFIsLq6CpvNxnRnAr2EQmHWacuN9ZEbDaFQCLPZjEQigbm5OXg8HuTl5eHo0aMIhUJoa2tDUVERI0z0cWo0Guzfvx9Go5G5Yk6nEzt37kQ8HsfMzAyGh4fh9XrR0dGBUCjE6IRYLIbFYkE6nc66KCaTSej1ej7U3agTCQaDjBx6vV5+WWQyGebm5lBeXs6H6N27d2NychK1tbV8gCCklaYm4XAYwWCQ+em08WxU6XQaNpuNUXg6oO7YsYMpPCsrK9BqtWhubkY6nca2bdswPDyMrVu3orS0FLOzszh37hxefvllNDU1QavVQi6Xo7y8HF1dXSgvL0c0GsXCwgKuXbsGALDZbBAIBFlf0rW1Ndba0KTiRiFfMpnE7Ows5ubmsL6+jsuXL8Pr9cJutyMvLw/5+fkwGAywWCw4c+YMiouLIRAImNY0PDyM9vZ2jI6O8sZRXl6Ouro6PjhvVLFYDAUFBcyhNhqNCIfD0Ol0SKVSKCsrA/CBXuLuu++G1+vl6ZXFYoFUKkUikYDJZILdbsejjz6K9fV1KJVKGI1GaDQadHZ2ss5FIBCgtbUV27dvZ4rGRkXUCKIKAsDi4iK2bNmCiooKnu4IBAIUFRXh1KlTkEqlmJubg8ViQUNDAzQaDWZnZ+F0Ovn9MBqN3LDcdddd8Pv9KC0tRSKRQHl5OTo6OljomO3aSNRrMBhQUVGBSCQCv9/Pzzg/Px8WiwXl5eWIxWIYHx9HJpNBSUkJRCIRgsEgdDodFAoF7rrrLkxNTWF5eRnBYBDj4+M4dOgQUqkUCgoKEAgEUFdXh66urg/lkwLgCV4kEmGanVKp5KmKy+ViitfOnTshlUrR1dWFmZkZHjnPzc2hpqYGbrcb27Ztg0qlYnS3vLwchYWFvNZcunQJMpkMtbW1WFlZydoIEcWJAInc3FykUik0NDQgnU7DYDBgenoaDocDpaWlCAaD6OrqwuLiIsrLy1FWVsZ8+7feegsHDhxAa2srI+nUuJO+grRVmzZtYsF7tlKr1axpKSkp4QN5d3c3cnNzYbfbMT4+jpycHNTV1aG6uhpFRUWQyWSsVQGA8+fPo6mpiQEaQnU1Gg2Gh4cZ8acxv8fj4UZioyIaAU05A4EAtm7dCr1ej7y8PPh8PoRCIeTn52N4eBg+n49Fh0ajETqdDq2trRgcHMT27duZWgZ8QCMqLy/HzMwMCgoKMDc3x9+d3+/nSdKtijZHn8/HlBqRSITHH3+cpxAymQx6vR5NTU1YW1tDS0sLQqEQWlpaYDAY4PP5EIvF0N/fj89//vN82F9bW4Pb7YZUKsXLL7+MhYUFOBwOFBUVoampCUtLS9Dr9RteWyQSQV1dHQtoJRIJT6ho4uNwODA3NwelUsl0B4/Hg/vuuw87d+6ERqNBdXU1UqkUH+zp3hAtzWq1QqFQ4OTJk3A6nUwPyvatEpLvcrkwNzeHYDCIqakp3HXXXUxvov1g69atSKVSaGpqYkMGt9sNv9+PxsZG9Pf3Y8+ePYwiE7hgNpu5CSJalcFggN/vz7o3hMNh3mto2jk0NISuri6MjIxwo6vVarF9+3ao1Wrk5eXhvffeQ05ODqRSKdNCdDoddDodCgoKUFVVBblczjo/ouMSrUqtVuPcuXNZG41EIsHCcqFQCKlUiunpaTz88MPwer3cXBoMBtx7773QaDQoKSnhJiYejzNwG4/H0dHRwTROAmFisRheeeUV+Hw+eDwetLe342Mf+xjMZnNWndfq6ioqKyt5UqlQKJgK53K50NnZiZKSEuj1emzZsgVSqRStra24evUqT4Pm5+eh1+sxPz+Pr3/960zDJSokTSLVajVkMhlsNhtaWlpQXl6ede1NJBJseEPrGhnNjIyMYHFxkRsQEjrTVHPPnj0wGo08TTGZTKivr2f6rEAggM/nQ2lpKdxuNxKJBLxeL/x+P5vnNDY2bnhtqVQKer0egUCAxeoymQwHDx6EXC5nLeGNGla9Xg+dToempibU1dXBZDKxoY1KpYLP5+Nmpbe3F2tra2wq0t3dDafTiby8vA+dPMbjcTQ0NDB1lQYClZWVPHl0u90IhUKQy+UYHx/HwMAAQqEQ7r//fjQ2NvIZtLe3F0888QTy8/NRUFDAIAyxI8bHxzE9Pc2TcGoKP0p95EaDuG579+7FkSNHUFFRgeXlZQwODuK+++7jQ5PX64XX60VXVxeWlpbQ3NyMb33rW+jp6UFTUxPC4TD27duHb33rWygsLORD5IEDB7C6uorPf/7zKCgoQFNTEzweD2ZmZrBnzx5cuXJlw2vTarWMphIal5+fj/7+fhw9epQpAzKZDB6PB0tLS/jNb34DpVKJ5557Dr/5zW8wODiIq1evMjeOuME0dtVoNKipqcH6+jqsVivz6WtqarLy+XNycnDp0iVe8EtLS9HW1oZkMslTDHpB8vPzcfDgQUSjUdTX12NsbAxGoxF33XUXFhYW0NjYiNzcXNxxxx2orKzk6cbk5CR27tyJgoICVFdXsxjo4x//OObm5ja8tuLiYhZLm81m+P1+yOVyAMC9994LtVqN5uZmbNmyBQqFAu3t7dBoNLBYLFAqlaivr0dTUxMSiQSKi4shlUrxyCOPMDJnsVjw3nvvobKyEs3NzYzA1NbWorGxEZOTk1mfqcFgwObNmwEAV65cgUajwalTp1BbW8toTyqVgtfrRVNTE+LxOCKRCORyORoaGtDU1ASxWIyHHnoIP/zhD1FdXY1oNIqBgQG4XC4MDw+jqqoK3/ve9+B0OnH9+nUMDg6iuro6KzJEo3uj0cjmArRBlZSUYHZ2lhuejo4OPP3001haWkJ1dTU7sNXU1EAsFuOee+6B1+vFvffey9qO9fV1nDhxAvfccw8UCgVvxCMjI9BoNFm5/HK5HHV1daivr8fi4iLOnj3L7/3DDz+MtbU1zM/PY2BgAO+//z6efPJJngj98Ic/xMrKCu6//36eVPzmN7/BX/7lX2LTpk2wWq04dOgQLBYL2traYLfbodfrMTMzg7GxMezdu/dDFx5aXFtaWniqWFVVhRMnTuDgwYMwm82IRCLo7u7GH/7wB1RWVuKFF17A5OQkfv/73yMUCmHnzp1oaWnBF7/4RXz3u99Fd3c3jh8/juHhYVgsFj48TE1NoaWlhaehnZ2dWUe9IpEI8/PzaGtrQ0VFBdP83n//fRw4cACzs7OQy+WMTBUWFmJychKpVArXrl3D4uIirFYrVCoVWltbEYvFYDAYGA0HgJGRERw9ehQikQharRaXL1/G7373O6yurmL//v0bXlt+fj6LbP1+PxYWFnjzf/DBB3lET41oZWUlzp8/z2YVubm5MJlMWFpaQn19Pd577z2eQOt0OpSVlSEYDEIqlaKvrw+lpaWYm5vD1NQUHnroIX43b1VEk+ns7ERtbS2SySQDSwqFAq+88gpWVlbQ2NiIiooKPP7445ienkZZWRk0Gg2am5tRW1uLN998E2azGZcvX8bu3bshkUgQDoeRSCRw9uxZPPTQQ3C73dDr9Uxl6erqynrwk0ql7KhTXl7OglGn04kdO3bg2LFjeOONNzAwMACv14tPfOITEAgEGB4eRnd3N7v7vPXWWygqKsJvfvMbnhaZTCZIJBJcvnwZjz/+OPOVz58/j+HhYdx///3w+/0bXlt5eTlee+01nrLk5ORg69at6OnpwZ49e2C321FQUMAi3p///Oeor69HUVERnnnmGZw+fZqBsvr6evT19eHxxx+HUChEb28vPB4PVCoV89wPHDiAtbU1uFwu7N+/H2fPnt3w2kio2tLSgvr6elitVtxxxx2YnJyESCTC+fPnMTc3B6/Xi8uXL6O+vp5/P30nra2tUKlUePTRR3H16lXs2rULPp8P77//PsbGxnDy5Encc889OHfuHEpKShjgeeSRR7JSV8kchETNeXl52LFjB/r6+hhgEAqFWF5ehsPhwKZNmxCNRnHgwAG43W6sr6+jubkZeXl5rDGlvfBGQ5ZHHnkEY2Nj2Lp1K09v7rnnHqyurm54bbFYDBcuXEBBQQFqa2thsViwbds2XLhwATabDefOnWPaY09PDz772c9icnISZrOZJ7uEdH/yk5/Ec889h9raWqyvr6O3txdnzpzB9PQ0/uIv/gKvvPIK0uk0uru7cfLkSVgsFjgcjg2vTafTYWhoiO9tNBrFvffei5qaGjz++OPw+XyYnZ3FzMwM+vv7cfjwYXYIpXNQXl4eXnrpJXzqU59COByGyWSCyWRCTU0NamtrMTQ0hIMHD+K5556Dz+dj2tCePXs2vC4ADAx2dHRAqVSir68PAoEAP/zhD7F//352AyO966FDhzA/P4+lpSW89957TLsdHh7Gvn37MD09ja1bt/Je2NbWBpfLhQMHDmBoaIgP76TjyHYWkclkWFxcRHNzM9ra2ljb8/rrr6OpqQmTk5Po6+vD6dOn8eqrr2LPnj14++23kZOTgzfffBNXrlxBMBjEtWvXUFdXh3g8zmZIcrmcz3w7d+5kcJ0mOVu3bs269orFYpw9exbFxcUoLi5mM5twOIzdu3ez+6RKpYLD4cCBAwegVCphs9kwOjoK4ANzBwIohUIhKisrGVQvLy9nkyWpVMri99XVVezcuTPrfbuxPrIYnHiTg4ODTAFKp9PYtGkTjh07xpQEQlvn5+fR1NSEmZkZ/P3f/z0WFxfhdDpht9sxODiIsrIytuiLx+M4ffo0Tpw4ga9+9auIxWLsEmEymTA3Nwez2bzhtdGYk8Zgq6ur/MIR4jQ6OopUKoXm5mZ0d3ejvr4eXq8Xjz/+OCKRCJaXl2EymbCysoLp6WlEIhHs3r0bCoUC/f39zMO9evUq0zpWVlYwNTXF4rhb3uDcXOj1eubRCoVCjI2NIZPJMKVgbm6Oee+E2F+4cAFKpfKmsbzdbseVK1cQDofR0NAAsVjM7ibXrl3D1atXUV1djW3btgEAXnvttayUMxrd0pgxkUigt7cX9fX1OH36NKqrq+F2u5l3OD4+zoJsqVQKr9eLlZUVniYBH9jUVVVVIS8vD+FwGAcOHIDH44HD4WDRYjqdhslkytqgpVIpjI+P8wYsFosxODgIs9kMr9fLUwValCORCMrLy/Hqq6+yPS8JAk+dOoUjR45gdXWVuYVkg+xyuVBeXs5ITCaTYYR3oyKKS1FREYRCISN+VqsVly5dwtatW3msOjQ0hIqKCvT19TEyLJFIYDabUVdXB5fLhTvvvBO9vb1QKpVIJBIYGhri561Wq7G0tMQjctIJbFQ0DSBxn06nQzAYxNNPP40rV64gFApBr9ejpKQEGo0GMzMzyMvLg9PpxNGjR5HJZPDKK69ApVKhv78fHR0dOHv2LKLRKDweD95++20cOHAAFy5cQENDA7/XiUQCU1NTHyoOczgcsNlsrKMhbUJDQwNmZ2fh9Xrhdruh1Wpxxx13IBKJ4ODBg1hZWYHf74fL5cK5c+fg9/sxPT2NkpIS5OTkYPv27ZBKpTh+/DiCwSDq6uowOTmJoaEhtqa9evUqMpkMurq6bnlt8XgcgUCAqVmZTAbV1dWYmprC1atXUVhYCIfDAa/Xy+vdpUuXUFlZiY9//OPweDx46623WFfi8XgQDAZRWloKi8UChUKBu+++G36/H2fOnEFdXR0efPBBeDwevPLKKyguLt6Q6khTFOD/0lgHBwdZhExaK61WC51Oh7feeguNjY04duwY29SSTTEJ2H/1q1/hwIEDsFqtPCWihsjlcrH24Ze//CVPEG9VOTk5sNlsPAUh16ClpSUEAgE0NTVhfHwc4+PjqKqqYq7w8ePHsXnzZnaHIyoHIbaxWAyFhYVobm5GSUkJLl26hMXFRVRXV0Oj0WBpaYmtNzeqVCqFtrY2zM/PM11mdHQU5eXlmJycRElJCdMZq6urkUwmMTw8jNzcXMRiMZw9exarq6vYsmULpqen8aUvfQl2u50boHA4jOXlZfT39yM/Px8ajQYHDx5ELBbD8ePHUVhYuOG1TU1NMXgUiUQQjUZx7Ngx7Nixg+lARHcoLS3F9evXsby8zFS8aDSKCxcu4N1338X09DScTidGR0fZMGNpaQkSieQmjSA5e129ejXrOmK322E0GiEQCNDX14eZmRmUl5cjk8mgvb0dd9xxBzweD4uf3W4374nULL344ovsHKZWq3Hp0iUGfzweD7Zt24bTp09DpVKhp6eHdSDT09NYWVnBAw88cMtrI6okcfjD4TDbAIvFYqysrKC7uxvpdBq7d+/Giy++iMbGRganaFpLjIWtW7fi8uXLTJEJhULYs2cPwuEwGhsbcfXqVTbzoOnvRhWPx5kSTPqAyclJdmA7ePAgTwFaW1vR398Pn8+HyspK1oZMTU3h+eefh1wux/79+9ltibSwtPeTG1h5eTm/t7T3b3RtdXV1bPMtEAjwgx/8AEajEW1tbRCJRJiammJDlRdeeAElJSWQyWQYGxtjSl5jYyNeeeUVfPWrX8Xp06cxNTUFk8kErVaLo0eP4tVXX8XOnTuxtrbG+rtLly5hYGBgQ5tWOje63W62yFcqlXjqqafgcDgwNjaGvLw8mEwmmM1mTE9PQy6XQ61Wo7q6GjMzMzhx4gTbrKdSKQwMDNxENfV6vfjRj36EsrIyNr4hvWy2xpaE/UNDQwxydHd342Mf+xiuX78O4AOHrEOHDkEmk6G3txetra3weDzMkCBWzPPPP4/Dhw9DKpViZmYGg4OD7N4qFouZuk7TvgsXLkCr1W74XN1uNxobG5mxAHwgS+jq6uIz2cLCAtPjMpkMlEolLl++zGeLixcvwmKxIBAIoLS0lE1oYrEYQqEQ4vE4FhcX4fV6sWvXLlgsFjidTrz77rtZm6Ab6/+TGPy1117DwMAAO4oUFxfzQr9t2zaIxWIWgpN9m8fjwS9/+UvMzs6ivr6eOWUlJSXspEBo3Pj4OG9CarUaCwsLmJ6ehkgkyuqQQc4oPp+PxTXhcBgDAwMAPhA1lpeXQyQS4cyZM3jyySdx8OBBCIVCdHd349KlS5iamoLdbkd+fj6uXLkCoVDIgsGcnBycPXsWOp0ODz30EPP+qfvLttHl5uZifn6eOYyjo6P8kWi1Whw6dAh79+7l3ILu7m7U1NRwxyuRSGCxWLC0tISHHnoIs7OzUKlULBTXarVIp9MoKCjA0aNHEQwG8dJLL+HNN9+ETqfjZmKjosMdoWperxfj4+OQy+WMhJIDRDAYxJYtWxAMBjE5OYm8vDyUlZWxuJ4Ew+T0VFdXh9///vdsfUd0teHhYSwtLWUVYKnVahZjEleWeJxSqRQNDQ0QiUTo6enBH/7wB6avEYeQRKZTU1MQCAT4wx/+wHxxcsvKZDIYHBzkDAafzwev14uCgoKsB6tAIICCggLm8pOVLFnJEZc6EAjA7/fjpZdeYteTQCDA/OZAIIBQKISxsTHMzMywvajRaMTly5fx/vvvY3x8HEqlEteuXcPbb78NuVzO7/WtSqVSYXBwEJFIhCk2y8vLuHjxIqMS4XAYly5dwmuvvcaIW25uLvr7+xEOh1FSUsJ5JdXV1VhcXERhYSFbID777LNQqVRobGyETCZDIBDgd4gmUNme6/nz5yEUClFSUsKH0osXLyKdTuPo0aO45557oNPp0N3dfdOh8PTp01hYWGBRHQlyyVbUbDajsLAQV65cQWNjI+6//34UFxfzelRRUYGWlpas947EeoSULi0tYXh4GHl5eTCbzSguLkZhYSEMBgNaWlrwyU9+Ej09Pfja176G3t5e7N69G3fccQeUSiV6e3tRWlrKlr7A/3W26ujoQF5eHi5evIj+/n60t7dnbdJooqHX61nMWFRUxM4weXl5qKmpQTKZxC9/+UvI5XJcvnwZRqMRQ0NDCAQCKCoqQn5+Pubm5vD666+zvTa5uv3hD39AUVERUzuoUc7Nzc2qp6IpCOnaFhYWGPUlPUpzczOUSiWuXr2KixcvYv/+/Ywu0uSWprKTk5MwmUx44IEHcOTIEchkMrz33nvQ6XTYunUr5ufnYbfbEQwGkUgksgrVdTod+vv7Oc+JABVyNrLZbGzN+tvf/hYejwf79+9HZWUlN1tHjhxBQUEBiouLuZkiUIsOeG1tbWhvb4der0dPTw/efvvtD9VBCIVCzM/P36R9lEgkcDgciMViKCsrg0wmw+DgIH7wgx/g17/+Na5cuYKpqSkolUpUV1ejvr4eNpsNbW1t+OY3vwmv14t4PM7vcSAQQCAQQGNjI4aGhuByuSCVSplbv1Gp1WqcOnUKi4uLqK2txZYtW5hvX1hYiNraWtTW1rJgeu/evSgpKcHAwAD+/d//HX19fawpkEgk+M53vnOT4YlQKMQ777yDkZERztcgu+DDhw9v2GQA4O8kGAwyLUQgEGDv3r3YsmULTCYTT9ZeeOEFTExM4Pz581Cr1ZiYmMDo6CimpqZw/Phx9Pb2YnZ2lnVKWq0WNpuND9CpVAp33HEHqqurWZtGHPhbFdkL0/spEolgs9mQSCQQDAZRUlICpVKJsbEx/OhHP8KLL77IE9x/+Zd/wdjYGDZv3ozKyko0NTWx3Su9I2q1Gr///e/x/vvvs2GM2+3mJiQbrVapVDK1kZyRyBpWr9ejtraWbf0HBwcxOzsLh8OB+fl55Obm8jRdLBbDarXiT//0T5GXl4edO3fCZDLh4sWL+NGPfoT19XUUFRXdlN+TSCSyrr1yuRwej+emKAOaYms0Gmzfvp1ZKq+88gqfMYRCIf7rv/4Lw8PD2L17Nz7zmc8gPz8fzz77LOsxaMK9sLCA6upqdHZ2srGI2Wxmd8Vs75vdbsfc3BwcDgcCgQAKCwshEolQVlaG++67D6lUCi+++CJ+9atfQaVSwWKxoKysDO+99x56e3vZXIQO+dFoFFu2bMG+fftgMBhw8eJF5Obm4vDhw2hubkZjYyNqa2uh0+myguykR1leXubnH4vFcOnSJQSDQchkMlgsFuTm5sLn87HFvdVqhcvlglKpxP79+1FSUsKNDa0/NMGgpu8LX/gCa2+Jwvv/3HUqnU6jurqaw+HS6TTW1tawZ88e9ltXKBSor6/Hjh07mFPodrt5wSS0QyQSYcuWLQiFQixeSSaT+PSnP43jx4+zCJQCvT5MDE7hYVKplEXaRH+Kx+M85q6qqsL+/fuxtLSESCSC6elp7uZ37dqFXbt2wel04uDBg8yLJF77wYMH0dfXx+JNuVwOs9nMFKts10b2hOQARGgT+dt7vV4kk0kUFRUxz7e4uBilpaXw+Xzo7u7mIL+nnnqKOagLCwsYGRlBc3Mzu2RQ00b8xYqKig2vLRQKoba2FiaTiRcREk5TeJvRaERVVRXa2to4I0Mmk2Hz5s2QSqXo7e3F9evXMTAwALFYjMLCQlRXVwMAxsbGUF9fD4fDwSKolpYWtLe3IxqNZnWLWV5ehs1mY+0JieirqqoQCoVgtVpRVVWFpqYmtLe349KlS2wvS/xQgUAAk8mEsrIyHDp0CIuLi3yQTyQSaGho4OYjEAhAq9XCaDRiZmaG0eNbVU5ODgfw0ISksrKScxWkUil7npNF8o0iP6FQyBxmEo+rVCr2rievf4/Hw8Jxm83GeTAfhiKUlJRwY0aBgXq9noO/BAIBlEol6urqcOzYMUxNTSGRSKC1tZVpQQqFApWVlejs7MTw8DAH191777145JFHOCeCROEKhQIulysrIAB8AApUVFQwx5YcnIjeNT8/z/ejsrKS3ZzIQpuMDtRqNWZmZnDw4EEOpozH43C73aiqqmJkTCwWo7q6Gg0NDRAIBGhra9vw2uiddLvdnDcRCoWwY8cOOBwOyGQyFBYWori4GCUlJSyiW1xcxKZNm6BQKOBwOFg0XFFRwfk0s7OznI1z/fp1mEwm5Ofno7KyEpWVlYzibVR0uCUuPhllbNmyBeFwGHV1dXwPbrTSzM3NxaFDh1BeXs60QpVKhXvuuQdarZbFzhKJBEVFRejv74fFYmEzBYvFwjTPjSocDjNwQO+5XC5n4a/FYoHBYIBKpWI9HAmKyZ9+enqaTUQqKyvZBp3Wzvr6eng8HuZJE0pOtMWNyu12s8g4GAzyukYZMuTC0tbWhqamJvT29jINw2w2I51OY25ujvV/sVgMdrudr91gMOCee+7B5cuXEQgEmFJZUVFxk6nHRs+UDDZorS0qKkJtbS3Gx8fR29sLt9sNm82GP/qjP0IikcDRo0chFovR1dXFYEB9fT1SqRQbmlC4YUlJCe+zKysrcDqdbJRBtJaNin6fw+HA6uoqrFYrQqEQKisr4XQ6eUqYSCQgl8vxzjvvsP6LBKgkkPX7/fjSl76ETCbDDj7Dw8MwGAysiyB2g1AoxMjISFaALJPJwGazQaFQQKVSMdCzvr4Ol8sFu92OdDqN4uJi1NXVQSKRMMXu4MGDKC4u5j2EdAsymQz19fWora3lZ3H27FkoFAoWpxPqT/vbrSqdTsNsNrOpBJkxtLe3w2KxsENcTk4OiouLodVq0dbWhlQqhW3btjH9h/J4mpqa2LmRXJcOHz6MSCTClBelUomysjK2wt2oSIdFzyAUCvFeSWu3RCJBfX09tm/fDoFAgMOHD8NgMGDr1q0Qi8UYGRlBOp3GwMAAu1AC4GlUU1MTTxSEQiGKi4ths9mwvr6OpqamDa8tFouhvLycJ7NEj+vo6OAQS9q71Wo1O/gRTb+4uBhutxvRaBRDQ0P4xje+AY/Hc5P4/siRI7xO3WjkE41GOdzvVkV0eXKDo/OeXq/HyMgIT5JbWlqwZcsWTExMYGBggCemVquVD/yFhYWQy+UYGxvjnLfq6mp89atfxdjYGO9/RPGkxnyjIgtqyk6qr6+HWq1GRUUFXC4XZmdnMTo6CrFYzAwfs9mMcDiMlpYWaLVaeDwezr0zGo0MCBCTZd++faxzIoZLbm4ur50fpT4ydYq6feKuk1i2rq4Oi4uLCIVCUCgULLCbn58HALadTCaTjKYUFhZyt6TValm81tnZiZdeeomFduQrThzVjUogELAIkwTWBoMB8XgcJpOJk5Up1XFmZga5ubmcE6HT6VBSUsKuJOXl5QgGg5yEKZFI0NDQgP7+fhavUlIoOQltVNShi0Qifino99ColA6C1dXVbHMql8uRm5uLQCDAftpOpxMdHR1obGzE+Pg4j62tViv/JgqzA8CUo2xF95dCkPL/JxF8bm6OrQLJkaqtrY3F5SQ0d7lcWFpaYipJSUkJhEIhHzjJ9YccKMi+zel0Zr0uQrfIcpXcDwQCAQoLC/m/VyqVMBgMWFpaYl96crCRyWSc/WGz2RjFiMfjHGDV2toKk8kEv98Pg8EAuVwOt9udVWtA13Kj7SFRvEpKSnjcSA1yTU0NI0jUMJPDV2lpKaegkksJTfTKysp4U7jR6SIbEikWi6FQKNhthhYDsvEkhw7SGUUiEfbWpnEtCSxramqgVqthNpvZBQz4QMtgtVrZWpYmAfPz8/zubVSkTVhfX2fTBnLCKikpgdPp5JAwCtGkyRqtI+FwmO20CYlRqVRYXFzkfBJy3qLRfyKRwPXr17MujDTJIDCFchTID5+yKyjQiuwTW1pa2HyBwvdsNhuHrtFYW6PRcC4QOePRpkJr3kZFIlLKRCFhuEajYYtDkUjEKdxGoxEdHR0YHx9HRUUF013VajWMRiMKCgr4z7sx14DErJTnQt75JJq8VZH9JrmI0W+LRCI8maQpNk0sCImmCSP98zeK3ZeXlxl0qqurQyaTYXSVko3JxWqjisVinCycSCQ4m4PyRSh8lQ6t4XAY09PTqKio4FA82uxv1FCRu9SNlFtyuSEBOnnqb1S0WZOtMjkeUcox6euIrkLmGyKRiCdghLoXFBRgamqKDVlIr0jN57Vr13jPIlvKbNQpiUSCiooKtpFVKpWwWCzsiEW5LsRM6O/vR1tbG4xG4012rOS6aLPZmD5N1ssE/rlcLga5aK/LNu2ma6f1KJVKQaVScfYIibdpGkCHpEAgwMAPrYMtLS3Iz8+H3+/nZ6pWqxGNRtnUI51OszMardsbFZnQkEUyZTzQRGxpaQkymQxWqxVCoZDXWHJWpEbwxvwfEj+TA9i2bduYnmkwGPhgSEnsGxU5sJGz5vLyMmsjyMmSpqY0MTYajaisrGRgJRwOsz6VnNzItZIsoIuKiqBSqfi8SBTebIAs5XGRDo0yj/R6PQOydA4gkTRlD5WXl0OhUDBQBXygO3jnnXfYShYAa4LJbY7Wjg+bUlHUAqWTLy0tcQq9VCqF2+0GAAZIZmZmsLKywkCfTqfjBrC+vh7JZBJXr15FOBzmybTJZEJfXx/GxsbYjpfWt2zPlPZQAiqVSiVKSkr4faEYBQKYyD1ybm6Oz8YrKytsxEDBijTRBz4AfSsrK3lPpTUEQFa96I31kScaZAd644PLzc3FwsICduzYwQ4t1AmRmr2qqgoOhwNutxsulwsvvfQSKisr0dPTg4aGBj7UVFdXY319HZ/85CdRWFjIATz0oClD4FZF/t75+fnIZDIIhUKorq7G0NAQUzAo8Is2Ar1ej+3bt2NoaAj9/f3w+/04efIkKisr4fF4UFlZyYfplZUVzM7OorW1FRqNBhUVFWwdSO5C2Wp1dRWlpaUQiUT8Z1N2BdmjUVLkpk2b2F3H5/MhJyeHXbzS6TR+/OMfM0KQm5uL5uZmrKys4L777oNCoUBDQwMH4qlUqqxiHbVajenpaf4glpaWePGvqqriBYwcciorKzEwMACfz4dTp06xE5DZbMZ9990Hr9fL3fry8jKam5tht9vR1dXF6Pf8/DxCoRDKy8uzNhsUelRYWIjS0lL+WIeGhrBlyxaMj49jaGgIk5OTmJmZwaFDhzA3N4dt27YhHo8zxWphYQHj4+NMSfN6vZiamsLq6ioGBwdx4MABDhekvIO6ujr4fL4Nr00kEjFNjxBrOhTU1NRgdHQU/f39cLlcSCaTqKyshFKpRCgUYivIiooKmEwm2Gw2np7c6IXtcrlwxx13APhA7zQ1NQWfz8eowkZF1qXr6+tME6mrq0M0GmXnJXJQISFxKvVBgCS5T9DUgPRDDz30EFpaWuD1evHf//3f+O53v4u9e/diYGAAs7OzjOK1tbV9qL0tccYpVyGTyfD92bZtGzuekQ1sc3MzH/wJUfJ4PJicnERzczOL0AndNhqNbF8IgB3IbnSU2qjoIEUOejQlJLc3Gp9PTk7izJkzEAqFCIVCuPPOO3mBb2lpgVAoxObNmzl4FAAKCgpQX1+PiYkJWK1WbkgGBwcxMDCAwsJCDA8PZ32uRBUjn32TyYTFxUVYLBb85je/AQC0tbVBoVCgs7MTZWVlkEgkmJ6eRn5+Ptra2vgg/9Zbb/EEYWJigvVWe/bsgUgk4jBJsmgdGRnJem2kiaJsFZpGr6ys4N133+VG3mKxIJVKYWhoiJG64uJi1NbWQqVSoaamhqmFDocDk5OT0Gg0kMlk2LZtGwfE0WGvtrY267dKdEZqrqgRlslkaGtrQygUwtDQEIaGhuD3+9Ha2orFxUWYzWa4XC62ZSc0n+hWtMHOzc3h/Pnz2LdvH+dWkJCypKSEDz63KqFQyJNtp9PJQuuBgQHU19fDZDJBKpUiFApx2vLIyAjr+6qrq9HY2IixsTE0NDRgenqaKTdFRUVIpVLsnnX58mVoNBp2s2pubs6qLSRrcLIpTafT6OzsRG9vLwwGAzweD/R6PQM15eXlrG+iAzF57ttsNvzud79DRUUFU4dra2vZkTEajfJ+S5PVbN8p0WOBDxrJYDDIBh3t7e08zSZzkMbGRpw/fx6rq6vo6elBKpWCxWLB2NgYTwXoUA984FoUCARw+PBhFsyOjIzg8uXLfMDdqIiaSs0mraOk83j33Xe56QbAoYUCgQDXrl1DMplEc3MzZDIZUqkU3nzzTbS2tvLBn0CmpqYmDA0NwWq1wufzobe3FxKJJKtNK+VhEM2XAFAykcjLy+MJ3Pr6OrZv345AIID8/HwEAgE2bsjLy0NlZSXOnDmD/P8JgKV8F7vdjs7OTuh0OkilUtjtdjidThQVFWFsbGzDa8vLy2MNpkwmY2r2yMgIKioqWKMXDAZ52jo8PAybzcbfP9EUW1pacPr0aXYPpQbd7XajoaEBBoMBRqORpzqkF96oKMi5tbUVFouFdXdzc3PYsWMHIpEI3G43HA4HXC4X2traUF9fj4aGBjidTt5TPR4PLBYLB7yS5e76+jquX7+O++67j51JJyYmMDExgfz/Ce/bqGhf0Ol0SKfTnNF08eJFBn+Li4uRl5eHc+fOobi4mPNQfD4fpFIp02Kbm5sZ+CspKUFhYSHm5+fxi1/8AlarFX6/n9diSiLPNrG9sT7yRGNubo4PRMQXJAs4kUiEyspKPPvss4hEIrjvvvswOTkJo9GIY8eOseUaJZMGAgH09fWhr68PPp+PR5xer5cPuX6/H7m5uQgGg+jp6cn6AVHXT4gmLQa0IKbTaUxOTiKdTmPz5s04efIkdu7ciVdffRUNDQ1YXV3F22+/DeCDQ11xcTEMBgMUCgVmZ2f54OzxeDhMkJC+goKCrNdGB01Kwk6lUpidnUV1dTWPeFdWPkhlrqqqwtWrV7FlyxYkk0keG4bDYbS3t6O3txeXL19Ge3s7TCYTFhYWcPr0aWzbtg11dXUIh8Pwer0wGo08ss7GX6bGgFJlyXaUxtCENhJaf/78eQDA+Pg4pzeTL/+JEydgt9sxMTHBfPSFhQV0dHQgmUyiv7+fve+TySTcbnfWTINQKITCwkL2Bw+FQggGg1hfX8eVK1f4gyLkkLQ0L7/8Mk+rKOmSbDEdDgcjRbOzs1hYWMAvfvELzlloaGiAXq9np5GNKhAIoLq6mpPFKWtlaWkJarUa7e3tmJycvCldfXV1FadOneIpGCEGL7/8MqqrqyGRSBCJRKDRaFhAOTMzg+LiYkxOTrL1H02HNqqxsTEODQsGg2yzKJPJsLS0hC984Qs4c+YMzp8/D71ej1OnTsHr9eLP//zP8fTTT/NEh3jK6XQaNTU1uH79OuLxOHvlX7x4kVOzyc5yYWHhQ+1tp6amUFNTwxuJTCbjBNXnn38ejY2NcLlcWF5ehk6ng8vlwvbt23HixAns2LGDm9Xx8XHYbDa2/7x06RKGhoaYslRTU4ORkZGbgg5LSkpw6tQptLe33/La5ufn0dDQgO7ubkxMTHDq6u7duzE2Nobp6WlGiol2IRAI8Morr6CwsJCT6OmfWV1dxYULF2C1WjE/P4/Lly/D6XTimWeegd/vx8TEBGQyGZqbmxm02aiSySS6urrgcrkQCARYpzA1NcX6g9OnT6O0tJQtia9evYqlpSVOXh4aGuL3taCgAGNjYzh8+DCA/+uUlEqlUF9fj8nJSYyNjWFxcRFyuTyr9S7pgSj0jiYPjY2NiEajMJvNGBkZQX9/PwoLC1FSUoLLly/DarVCIBCgt7cX8/PzuOOOO/Av//IvKCoqwuuvv47Ozk5IpVJMTU2hrq4Ov/vd77Bz5062MhYIBAgGg1m/h0QigbvvvptzDUpLSzE9Pc36wNraWojFYszOzjLirVAo8K//+q+Yn5+HUqnE+Pg4rFYrXnnlFZ5ChsNhOBwOOBwOGI1G9Pb2Ytu2bTxFoGTebPbibrcbO3bsYM0CHSY9Hg9CoRCam5sRCoV4unjixAmmTvr9fnR3d2NmZgZWqxW/+tWvcPDgQZw/f573wqWlJXz605/GxYsXkUwmEYvFUFlZCZ1Oh9HR0ay5LW63G1arlfNhiE5rtVqZTx6LxaDVatHQ0ACZTIaamhpMTEygs7MTk5OTuHTpErMbNm3aBIFAgDvuuIOd1vbv38/mMoFAALW1tRCJRPjlL3+Z1bKY0rCJ2qHRaDAyMsKOa1VVVbh8+TLW19exZcsW9Pf3M9ecwlKJYvtv//Zv+OxnPwsAPPWNRCJ44IEHcP78eVitVgbjiDqUzW40FouhubmZ9+1wOIxoNAqVSoV3332X7wdZpq6urmLTpk34zW9+g87OTuTn5/Pe9/bbbzPVUq/Xw2g0IhKJYGBgACdOnMDevXvx9ttvo6ioCM3NzQCQdRJEe6RUKmUHup6eHni9XjQ0NCAYDGJubg6BQABCoRB1dXX4+Mc/jn/4h3+4adLY3NyMa9eu4ROf+ASADw7iNCUqKirCyZMnGTV3Op0cMpxtYuv3+3H33XfzZLC8vJwRdLvdjrq6OgYXtm3bxvb6L7zwAsRiMYeAVlVVsRZWpVLBZDJxIN/6+jrOnDmDnJwcOJ1OqNVqGAwG9PX1IR6PY+fOnbe8toWFBbS3t/OknahuFM9gMpng8/kQiURQWVkJrVaLnp4evPPOO7BarZicnITT6eTvgkJJPR4Pp7mTZoLOJ5OTk1hcXERJSQni8fiG+pZYLMYaDprK9Pb2oqKigidCdN/UajWDJx6PB5s2bYLb7calS5eYzrewsMCuof39/Xj55ZfR1dWF/v5+1jrSpIOMWj5KfeRGg9xKDAYDay5Onz7NAuY///M/x9GjR9HX1wev14uysjJUVFSwgM5kMuHw4cNoa2vD9773PZSWluLAgQPQaDRIJpMYGRnB22+/jT179sBsNqOsrAzz8/OYnZ1lukW2ayMaEYUcjY6OMvrr8/mQn58Pl8uFS5cuQa1WY3V1Fbt370YoFOI025ycHExMTMDj8WBxcRGNjY2wWq0AgIsXL2LPnj1MdyLHAkIZN6qamhpcuHABTU1NMBqNSKfTzEmnAxGJdX/5y1/iySefhEaj4SaH+LFWqxUGgwHf+ta3MDg4CADs8f6d73wHf/jDH/DlL38ZkUgEPT098Pv9KCoqyrrRETLkcDg4FLCwsBCvvfYaNm3axNkJCwsLSKfT2Lp1K6MmFEm/efNm5Ofn47nnnkNNTQ0EAgFzlxOJBJ555hl89rOfRWdnJwYGBph/mZ+fz/f2VkVTKrL7s1gsEIlEuHTpEkpLS7Fv3z6Mj4+zYDOZTMLhcKCkpASjo6NwuVwoLi7G3XffjfHxcfT19WF2dhb3338/mpub8c477yAYDOLQoUO4du0acnNzMT09DY/Hg7q6OtTV1W14bXq9HslkEleuXOFFmz7YgwcP8oSIgo9EIhH2798Pu92OX/7yl5ienkZOTg6ampo4/Zu0Qjk5OXj33Xdx8uRJ1NbWoqCgAGKxmKlQlDmwUREqTAm+crkcOp0O7733HsrKyqDVaqFQKNDc3IzFxUUsLCzggQceQH19PYvW2traYDabUVBQgBdffBE2mw3f+c53EA6H8Ytf/AK1tbWorKzEk08+CY/Hg9nZWUSjUdTU1GQNE6R7Z7fbUVZWxnRFpVKJ8+fPo6ysDC6Xi4MLZTIZU1m++93vQi6XY35+Hv39/bDb7bDb7Xj11VeRSqXQ1dUFk8mEnp4eAB8czFtaWjAzMwOz2cxNaTYOs8FgwN/+7d/izjvvxJ49e+B2u/HOO+/AYrHg8OHDOHLkCF555RVcu3aNqZmdnZ0wGAwYHBxkEWo8Hsff/d3fQaVSobS0FB6Ph4PVWlpaYLfbeVKSm5uLpqYmVFRU4MCBAxte28rKCl544QWsra2htLQUFRUV0Ol0GBwcRDgchs1mw+DgIDcEbrcbn/nMZ/DVr34VOTk5OHToEHbu3In19XVMTk7iBz/4AZ566inObigoKMC1a9fYc550N0Rry9Z4l5aW4vLlyyx8D4fDmJ+fx6lTpyASidDU1ITCwkKcPXsW//Zv/4YvfOELqK6uhkwmQzQaZcT+2rVr2LdvH37961/j0KFDTL0YHx9ncfCRI0c4h4ToLdloD4uLixgZGYHVamUh7vLyMqeLE52NnIs0Gg12796N0tJSfOYzn0EkEsHmzZuxadMmDAwM4Kc//Sm+/vWvc3bR9evXEY1GuQmVSCQsFCWQJtv7ptfr0d/fD5PJhIKCArz88st455134HA48I1vfANlZWVwOp344Q9/yOLrn//85+xo8/DDD2P37t0oLy/Hf/zHf6CiogKdnZ3o6+vDf/3Xf+HcuXOQyWR47LHHUFhYiHfffRenTp2CxWLJKrhuaGjASy+9hJqaGrS0tEAmk6Gnpwevv/46HnzwQayurmJsbAyvvfYaMpkMnnzySTgcDrz77rs4fvw4myaMjo4iEomgt7cXly5dwmOPPYaOjg4sLi7ivffewxNPPMHU1eeffx7xeJwbto2qvr4eubm5yMnJuSn5/T//8z8xOzuL2tpa1gGdPXsW8XgcGo0Gdrsdw8PDMJvNjJLX1dVhamoKPT09qK6uhlqtRm9vL3784x+jvr4eO3fuxNWrV+FyuZjulY0qHQgE8MYbb2DHjh1MZwuFQmybTcYudMhMpVI4cuQI7rrrLm5OjEYjlEolg6Dnzp3DAw88wO6cHR0daG9vh1ar5UyrG88/G1V5eTk7Ay4tLSGTyUCv13MD9OlPf5qBn1AohD/6oz/CqVOnUFdXx/QbsVjMACLlaxw9epRpl3a7HcAHE5dr164hHA6jqqoKe/bsYYemW1Vubi7nTRGVjChsJpOJJw8KhQLvvfceOjo6cM899+D111/HsWPHYLFY0NLSgtbWVni9Xrz66quQSCTweDzsKnflyhXWymzevJk1HqlUKut9W15e5uwJpVIJlUqFiYkJDA4OYvPmzbjjjjug1Wpx8uRJ9PX1obGxEXfccQcWFhZYX1NaWgqxWIzR0VG8//772L9/Px5//HGsrq7ixIkT+P3vf4+//uu/5rgAhULB09BsjBmi+BMjIxQKMbAqEAiwY8cOjI+PswFPQUEBdu/ejffffx/PP/88DAYDjhw5gs2bN+Pf//3fIRKJsHXrVj4HNzQ0MPtEJpNBLpfj6tWrmJmZQVNTE7xeb1aRP9VHpk4NDw+js7MTwAeiSUIyHnroIbbEUqlU2LZtGxoaGvD222+jvLwceXl5ePzxx3H48GGm5xDN6eLFi3j//ffh9/tRX1+Pe++9FyMjIzh58iSee+45jIyMsBgw24jGbrczT9bn88Hv92N8fBz19fXIZDLo6OhAUVERioqKsHXrVqanPP/88+jr68O5c+dw8uRJxONxTExM4O6774ZarUYymcTo6CguXryI9vZ2XLx4EZOTkzh+/DiLTb1eb9bAPpfLhfb2dtaxaLVaiEQinvbEYjF4vV5YLBZ85zvfwfnz51FeXo5wOIyHHnqIxXyzs7OYnZ1la1SFQoGlpSUIhUJ8//vfR01NDbZu3QqdTodPfvKT+M53voPKyko88sgjG14bjUNJAE5WgSTCvVE/s2nTJpw8eRIKhQJyuRyf/vSn0d7eziLO1dVVtoxta2vjQ+ELL7yAq1evwuv1sqYCACYmJrKKnObn55HJZODz+dhRg6hRhKjNz8+z6NVut+PBBx+Ey+XC448/jnvuuQc2mw0VFRXYvn07vvjFL7LQ7vr161hZWUFrayteffVVaLVadseQSCRMcdqoyE0kkUhwsq9Op0NnZye8Xi9TkMgLm9JBdTodnnvuOTz66KMwGAzYv38/B/KRQ9bs7Cxyc3PZyYImBuQYNDw8nLWxJZ745OQkEokEzGYz5ufn8dBDDyGVSuHYsWN47733WAdEuplYLIZ7770XRqMRAwMDSKVSKCoqwrPPPgu73Y5f/OIXePPNNyGRSGA0GvGDH/wAP/3pT3Hy5EkWvg0ODmaligCA0+lkm8yFhQX4fD709PTgscceg1wux5133om7774bra2tPBZWqVR4/fXXMT4+DofDwQiPTCbDAw88wLos4sQXFhbye3rt2jX09/djdHQUZ8+ezfrOTU9P43//7/8NvV7PTmpmsxn3338/ent7EYlEUFFRgf379+PIkSNM96R3kKZUu3btwqFDh/DEE09w7gYJJz/xiU9wBsZdd92FZ555Bk888QSmpqY+NJ12165dHPwUj8fx9ttv47777kNzczOOHDmCw4cPY9euXdiyZQuHMWq1Whw5cgSxWAwvvvgirl+/jqmpKTz33HMIh8Ocjru6uop/+qd/gsViwVe+8hWm9RGNIxvQQ2GVkUgES0tLKCwsZBtU0ibNz8+jvr4ev/jFL+ByufDoo4+ip6cHmzZtQkdHB0pLS9Ha2oqzZ8/i6aefRjgcZn63Wq3GD3/4Q6a0zc/Po729HXfeeeeHHhIEAgEOHDjAbimkK5DL5YxkXr58mSlwb775JsbGxvD888/j3nvvRWdnJ1ZXV1FWVgaxWIxnnnkGy8vLcLlcSKVSqKysxJ133omqqioOkaRJ18DAAHJzN8byyDaSEnoXFhaQSCTwyU9+En/5l3/JqL1Op8PevXtx9epVbNq0CU8//TR+/etfo6Ojg0PzVldXUV1dDa1Wi8LCQhw4cAB/+qd/igMHDmB0dBRtbW04deoUDAYDPvGJT/DUc6Pq7+/HE088cZPDYV9fH7q6unD27Fk88sgj+NSnPoUvf/nLePbZZ2EwGFBdXY3c3Fzcf//9rI36m7/5G3R0dODYsWMsrif93qc+9Sm8+uqrjBB/7GMfY7Asm5aKmhcCEGtqahCLxfCv//qvqKioQGVlJf+27du3o6ioCEePHsXc3Bwee+wxlJeXw+v14uDBgzAYDCgpKWHROIXg/eM//iNrGpqamrBr1y5UVVV9qIujyWTCvffey2u6zWZDVVUVjhw5gkgkgpqaGlRUVKCpqQl33303W6y//vrrMBgMKCoq4uA3iUTCoZtkvZ1IJDA6OgqHw4GtW7cyLchkMiEcDmfV3ZAA3u/3Y3Z2FisrK0in03jmmWc4nHFoaAhyuRyHDx/GSy+9hMbGRmi1Wtx5551obW2FQqFAeXk5lpeXeV0hOuz4+DhTkXNyctDZ2YkHH3wQdXV1uHDhQlYRfSqVwoEDB9h212AwQCaTYevWrXjttdfY3S0Wi+HRRx/F8PAwN7ubN29GWVkZ4vE4U/z/4R/+ASsrK7BYLGhvb0dzczMOHjyIxcVFKJVKnuTQmTQbQLawsIDW1lbOA9u5cyc2b96Mb37zm7zHRCIRbNu2DQ8++CAHF6bTadx5553YtGkTTCYT53B8/etfZwZDJBKBVCrFpz71KZw+fRp+vx92ux25ubkMUmxkeQ78X71SNBpFPB6HXq9Hc3Mz7r//fphMJpw/fx69vb2Ix+PIzc3FxMQEA7h/+7d/i7vuuosjJ9RqNfbv3w+hUIht27ahra2NDQ3ISKG0tBRHjhzBn/3Zn7FRzEcpwXq29vyGevbZZ6FUKjE8PAyj0YimpiYEAgG8//77eOqppzi/oLi4GFVVVfD5fGhubuaMB5vNBpvNBo/HA6PRiP/zf/4PPve5z2F4eBjhcJgRcPLmX1xc5CCxXbt24a233sJXv/rVW17bT37yE8jlcubHEg1mamoK+/fv58OkWq1mwdgDDzyAl156CWVlZWwTd/nyZbS2tvKfRfaRIyMj7FwQCAQ4MTcnJ4cPVl/60pdueW1f//rXWQhFEfEVFRWYn5/nVGgaL5IbBnWh7777Lt/P0dFRDoUrLy/H2NgYh7NNTk6ivLwcJSUl8Pl80Ov1kMvlPAY7cuTILa/te9/7Ho/rI5EIp3Ourq6ivLyck56Jb1pcXIyzZ8/ywddoNMLn8+HHP/4x/tf/+l9MOaJDrsVi4UZxdHQUhw8fRjAYRCAQQHl5OYqLize0f/zud78LgUDAkxZKJSVUngIaiaYhFotZtxKPx6FWq9nb+yc/+QkKCgrQ3NzMnFSiltx5552YnZ1lIXIoFELx/4+9/wyPtDzPgOFzetUUzYxGGpVR79JKWmm1TdsLLB1MNQZcE2MH45rkcHAOO8ENE8eJHRxsbDCxscHGdAML2/tKWq16L1Ok6X1Goxlp5v2xua4XvmM15nuP/NzrX+JFuvU8z33fVzlLWRkqKyvXNVD7wx/+AKFQyJ4ger2eYV7kZE9dfpPJhO7ublYfcTqdMJlM0Gg0cLvdcLlcuHDhAjo7O2E0GmEymbi7OTAwwGR2cuTs7Ozk6dLV4vHHH+cChyBkhDFva2vDD37wAxw8eBD19fUYGRlBTU0NTCYTk/5nZmYwOzvLkzuC4bS1tSGTyeDChQuYm5tDXl4evF4vDh48yByVtbU15Ofn44EHHrjq2gDg29/+Nn8/xCEpKyvDuXPnsGHDBoyOjsJqtUIikcBms6GhoQGlpaW4fPkydzJnZ2dx/vx5HDhwADabDe3t7ZicnITL5WIC/ObNm7G2toZLly5haWmJCak+nw+PPfbYVdf2/e9/HyaTCUVFRbDZbFhYWEBlZSX6+/tx7733oqSkhCFPgUAAVquVIR4VFRXMpXI4HNx4IblTKiBbW1vR3t6OP/3pT9yxF4lEMJvNCIVC6+rMP/HEE+ju7sbCwgKSySRLo5aVlaG4uJjV5oqKimCxWPDWW2+hp6cHdrudFYLovBCJRHjmmWd4qlZUVMRFKMFRNm7cyBCT+vp6BINB3HLLLVdd2zPPPAOtVgufz8eYdCpI5XI5T6A0Gg06OzuZUCsUCjE1NYW1tTUUFBQgPz+fIWFWq5VhHuS1FIlEsGPHDoyPjyMajWJtbQ3BYBB9fX144okn1t2rZCxJhatCoUBpaSnEYjFzDnQ6HQQCAaqrq1kC9PTp08zJi8fjuHTpEnp6ethvh4rWyspKTnpPnjwJsViMiooK7qKvNzl44oknUFNTw11Vmj7q9XoMDQ1hamqKSdn19fXwer0sSfzee+8xuT+VSsFoNGJ1dRXbt2+HVquF0+nEsWPHEAwGUVFRgVQqhW3btmFwcBDT09MoLy+HWCzGZz/72auu7V/+5V/Yn0GtVsNkMiGTyeDFF1/EHXfcAb1ej8uXL7NPVTwex/79+3H+/HlEo1FYrVaUlZUhFoshGo0imUyyoAJBOihJW11dZUEASqjD4TD+7d/+7apr+9WvfgWZTAa1Wo1wOMxTAIPBAJlMhsOHD6OwsBB5eXkIhULsOK9Wq3H27FmYTCbmMjY2NjIBl7hpJDgyNTWFjo4OuFwuTExMwOfzsRreegZ0TzzxBK9Hp9MxSffChQs4cOAAzp07B5/Px6qcu3btYuGbw4cPo6ysDJs2bWLvipMnT6Kjo4OJ/CsrKzh69Ci6urpQXV2NM2fOMDFbIpEgGAzie9/73lXX9oMf/ACLi4vYvHkzhEIhFhYWkMlkUFZWhpKSEiwvL8PtdsPj8WBtbQ1btmzBCy+8wLA3oVAIiUTCd3FBQQHGx8fR0NCARCKBhYUFlqN2Op2Qy+VQqVQIhULo7e1FR0fHurnIb3/7W25AEz/X5XIxPD0UCsFisbBUbnl5OV566SUcOnQIZ8+ehdVqRXNzMwDgL3/5C9LpNG677TbEYjEsLy/zRLihoQG1tbWsrEiiAq+99hq+//3vX3Vtf/jDH9iEsqysDFqtFtPT0xgbG8PDDz+M3/zmN1hZWYHZbEZVVRWam5shl8sRiUTw7rvvcrOJYP1k3QCAc4GBgQFEo1GUlZVheHiYcxiyPfiHf/iHq67t8ccfZxigQqFgmNqWLVtYGGRycpJFIz75yU9iZWWFFVQzmQzEYjF0Oh1kMhnW1tZgNpvhcDgwPT3NsOP7778fgUAA09PTyGQyKCgoQEVFBfr6+vCVr3zlqmv7YHxk6BRZmFNSurS0BIfDwdJwZOJBG3/Dhg28mUjCj/D5hDsNhUJMGItGo6xmQNK0o6Oj8Hq9KC8vz0kGJ3xe6H/Nk4hkHY1GkUqlWA6RpiLkZkwygERiJ6ldUuch5RVSPLBaraykQrrFRJRaL2KxGGOk19bWGAdHkmgSiYSxiJWVlTCZTAgGg3y4i0QiNl5JpVK4dOkSJBIJf1xkxLWyssJ+BgQ9IenV9YLkBulvlslkkEqlSCaTiMfjPKkgCbtUKsUdDyL80chWJBLxGC+RSLBPRW9vL2/sRCLBrpJkUrNeofFBZQ/CQ/r9flbmcLvdnMgEAgHIZDLMzMygtLQU0WgU5eXl0Ol0mJubg9vtxuzsLG88+v0k00YcGVJ9Aq50zdYrNNbW1rj4oudE3QQiq5GkpEgkgsvlQiAQYFdNkqEcGBhg74fCwkIkEgmEw2H2IqHkn3TP1Wo1v+/1gtaWzWZZkpPeL12gcrkcXq8XU1NTqKurw+LiIv/chYUFBAIBZDIZaLVa/u+o206kyUOHDuHw4cPMF6BkPhdUDwCraJBBGRVqcrmcJ0terxcCgQCRSARLS0vw+Xzwer1obGxkWVyr1cqu1x6Ph4mC6XQaXq8Xu3btwuTkJHeWSEZ6bm5u3bVJJBJOyjOZDEsD63Q6hhukUinmYND6l5eXmQslEAi42ZFOp1nlifD6ZHoJXJl2EqmbIEzrBe0zg8HAjrgikQhHjhzB3r174XK5eD+TMgupq5GUKTmAf1BuemxsDAKBACUlJdw8CQaDWFhYQCqVYkPMQCCQ871ms1kUFRV9yPx0amoKbW1t7LFDZNNIJMKGWQR/AoDZ2Vn4fD4+W9fW1nj6TBeiy+ViFRo6Y3JNlAkeRRNgkrgmOOLc3Byy2Swr5gSDQYaUEJk3GAxicHDwQxKqdOcQ+ZnUnegcFolE/C2tF0R4pm+dcOwOh4Pl2snoUK1Wo7OzE2fPnuVzhwwCJyYmMDg4iJ07d/IkDrgCt0smk8yvcLlczHkjM7n1ggy76P50u90sOUqQIL/fj3A4DI/Hg6qqKszNzSEQCCCRSPB+WVhYQDAYxPj4OPbt28fPJxAIoLi4mJtvJNdM7/mvkUzz8/ORTqcRiUTg9/tZsUskEkGhUHCuIRAIEAqF4HA4GPJKxmgzMzOQSqUYGBjg7i1NnxUKBfx+P0ZGRlh5jL5HEv5Y752SkhPdv8T1Iedxu92ObDaLuro69hUjj5dUKoVYLAafz4eJiQk+O0wmE8ODyTfBZrPx5Ib4NrlUzmhfktLQysoKwy9JtYqU30pLS7G4uMj+E5QfAGCfHOLE9vf3QygUIi8vD9PT07BarVhaWoLFYmHzSJ/Pl5PnRT4q9G5SqRTnPyaTiQsG+jbp3CSuLPGiSNGPIJL0zii/I0g23bekgJYrVldXIZPJkEqluOEnkUiYm7a2tsYqZl6vl1VJSY2L7rtjx46hoaGBm8NECCerhS1btiAej8Pj8SCbzUKlUiGbzebk3dDEn6SmSVr49OnTuOOOO1hNlFBIq6urOHbsGPOrZTIZ8vPz4XK5GMXgcDh4DZTfLi0tsZGxXC7H8vIyZmZmcvJuPhgfudAgzDtpY0ejUdjtdmzevBmh/x+n6tnZWTQ2NkKpVOLYsWM8oSA8+9mzZ6FWqzEyMsITBafTiWQyyV0cUm9YWlrCiRMnckoYUpDeu0gkYhIb/bwPSiWWlJQgHo+z8Vw2m4VQKGRsGo0fVSoVEokEw55IVpGSXqFQiJKSkpwj6EQiwX8/KQGRFjN9wKQ8QgmfWCyG2+3mLrnNZmNZSJLGJWwyJdnxeJxdkt1uN0sQ5sL30caVSqWMVabn7HQ6WW2BJHbpHVJi7XA4IJVKcdttt/El43A4EAwGWY2JlEiKi4sxPz+PeDzOvITp6Wns3Llz3fURBpcO4XA4jFQqhb6+PpSXl2NqagorKyswGo1s6kgXHI37qUqnjotQKOQLKhQKMbE0EomwJBxNd9YLEh+gpG5tbQ1OpxMbN27EwMAAHz6E/T5z5gzrXKvVapYNVSqVCAQC7GUQDodht9uZVE6Hr9vtRjabhUQigdfrzQlPoouScKnLy8sYHh6GxWJBf38/SkpK4HA4YLPZMDMzg927d6O3t5ehcuQoXllZydjzcDjMUBFKaCmppa5mKpVCUVHRXz14KHEkgjwVBlS4keILFRf0cxcWFjA3N8fvsrOzEzMzM/zv6LIgx3ASAiDSsEAgwNzcXM7E74MmfeQrQSpDpCAVj8dZMpPI8HTokkJRZWUlTp06xX47JPUZDAYxNzcHpVKJsrIyDA0NwWAw8KQlF9eACk66ZFdXVxEMBjEyMsJEUfJ8oGI2m81ydzgcDnN3+7nnnkNdXR1WVlawtraG0dFRhEIhGI1GGI1GqNVqDA8PI51Os/BFLuIwjdeNRiOy2SwbR42NjaGkpARGoxEGg4GT+5MnT2LTpk04e/YsNBrNh+RzacKxtraGsrIyNp0jkQLgSkFCdwTp7+faD6WlpeywTUk6nQFGoxE+n48LpbNnz6Kmpob9DqLRKDfBSEVrdnaWjROBKyRbInRTkk1uvLm4BvF4nL1HaNpis9ng8XiwZ88edjTv6+tjJ+lEIsEmtmazGUVFRYjFYjh79ixCoRCOHz+O66+/nidLJDFbWFiIsbExbq74fL6ce5Ukk+mbp8kdQRQJ409nM3FWlpeXWS6VJhder5eVdAoLCxEOhzE8PMziERs2bOBmDN2NueAixBegc1AsFmNqaoqN8UiJjJKisbExtLe349KlSzAYDJxwBYNBHD58GABYdSkYDGJxcRHz8/MMaSPvA5lMxgI168Xq6ipKSkrg9Xo/1IRpamrCxYsXWZ2TJHOnp6eZcE5FydDQEBQKBXp7e9HZ2YnZ2VkmOY+Pj7NcNJ0D1CBcXl7OKYVKRPnFxUVW6qL9MDExgfr6ep7Mmc1m/OlPf2LCORXfZARJDanKykrOCxQKBY4ePYrm5mbem5FIBIuLi9w8WC/Ic0ahUDAciFT9SDL7gw2wcDiMzs5OnD59+kM+PDTZ0ev1zKWTSqUIh8PsTUKFt8fjwcrKCgoKCnKa4nk8HubWhP7XM4jOMuJHkM8R7fuKigpu4ggEAiwuLmJycpKbywsLC3zH2+12BAIByOVy2Gy2DzVA6fvLFdT8k0qlnDf39vaiu7ubRWBIBGRsbAzDw8PIy8tDOBxGQUEB537z8/Pc1FEoFFwwGY1GTE9Pw2w28/0di8UwOTmZky/6wfjIHA0ipTY1NaGtrY1hMb/5zW+QSCRYbYVs7C9dugSRSITf/va3+NOf/oTjx4+zmk11dTWi0Sgbnrndbh4lBYNBSCQS9Pf3w2azMf6d5OCuFjKZDIODg6y+UF5eDqvVymYq5GBIsqOZTAabN29GUVERRkdH4XA4UFBQgOrqaiwuLuL1119nrWCdToeGhgY4nU6cOnUKUqmU3Z01Gg37i6wXWq2W8f7pdJqN6Obn5zE8PAy73Q6NRoNEIoHf/e53mJ+fZyLlc889h8uXL7Nrt1Qqxac+9SnuVhIRjhIcqVSKgwcPoqCgAKlUCg0NDTkLDTqUaArh9/s5oYzFYpxM00Z2OBy48847WWaSZPpuuOEGTupTqRQr9CiVSnZULSkpQSAQQCwWg1wuZz34XM+NDHXUajV3q4AretgHDhxATU0NQzCItEjiAydOnOBEx2AwoKenBy0tLbjvvvtw2223QSgU4tKlS7wmekfUUSc+0nrfm9PpRCaTYUgewaKSySRqamp4f9B0jKBMBP2Sy+Voa2vD5OQkF62rq6uw2Ww4ceIE3nvvPe4mUFJOiTkR7q4W5EcglUrR3NyM1tZWjI6Oore3F8ePH0c8HsfAwADeffdduFwuNsojycaKigre3ydPnsQLL7zAEAOj0YiFhQVIJBK88sorCAaD8Hg8rLVfUlKSM0EAwIddYWEhmpubYbFYUFpaigsXLiCVSrGCUjQaxdmzZyEQCBgGdfLkSYyMjDA07JlnnsHFixchk8nQ1NSEzs5ONDU1oaamBm+88QaKiooQj8cZ26zT6XIaWAqFQoyOjrIHi16vx759+zAxMcGE7rW1NdhsNpw9exZisRgTExOorKyEQCBg4ueGDRtYGIFUi8bGxpBIJDiRVqlU2Lx5M8MC/pq5m1qtRm9vLzvP19TUoKCgALt27cLq6iq2bduGLVu2sGmgVCqFRqNBUVERd3nJYFQqleL48eNMdFYqlXj++efx7LPPMmG9qakJq6uruHz5Mo4cOcJKcleLYDCI6elpXL58mU2fqMFAXXsSNrh48eKHLlmZTMYNgk2bNqG2thaHDx/G2NgYIpEI+w/Nz88jFAohGAzC7/fDZrOx9GauqS2peREstKmpibvKRqMR+/fvR0FBAex2OyvjXbhwAfPz8wgEAvxtb9myhc36jEYjSktLkclkWBrYbrczpEupVLKUaK4OMwAMDAwgk8mgpaUFO3fuREVFBWpqanDx4kVOcAoKCnD58mWcPn0a99xzD2ZnZ9lnoKioCI2Njbj33nsRDAYxOTnJUCBKoKqrq1FXV4fGxkYIBAL4fD5IJJKcPAhK3k0mE+rq6lBZWYmtW7fi/PnzGBsbg16vZ9ELMpCtqalBS0sLJicnMTs7C6VSycTYXbt2sTx+dXU1SkpKMDU1xXuU4Ipnz55leej1ggxPCceuUChYlEIgEMBisaCkpASZTIaf1e23346NGzeyWAkR9smjJRQKQSgUsjgNKck1NzdDo9Gw23NFRQUrPF0t5HI534Vk1CmVSnHq1Cm+H8xmM0QiEc6dO8dmqMXFxVxEzs7OIpPJYHp6Gv/+7//O75r2c1NTE1paWlBSUsKGofSzc31vSqWSfYqEQiFP+Lq6utDV1cViBqFQiM838kGZnZ1FKpXCxo0bceONN7L08tatW3Hw4EFs3rwZMpkMr732GsuCk9Q1+djs2LFj3bXRNEer1bIh6srKCn7605+yLxVNFN544w2eChEkqb+/H/39/SwG8x//8R+YmZnB+fPnce7cOczMzKCvrw+BQAASiYRzL+CKalauqQb5djQ1NeHgwYPYtWsXXC4Xc4VMJhNKS0uhVquZT6tUKlFcXMycZGo2Wa1W9PX1scrU6dOnsbi4yB5lNJFMpVJYXFxEOBzO+dwsFgtmZ2ehUCjQ2dnJdwApa0UiEZSXlyM/Px8jIyNIJpM4ePAgZmdnYbfbIRQKUVNTg127dmHTpk3o7+/n90DFntVqZVU5MpclFM5fmwZRfORCw+l0QiqV4uTJk/jLX/6CyclJqNVqZqqbTCaYTCZ0dHTgzjvvxNzcHPx+P7Zt28aqQzQWe+2113D33XejuLj4Q13CL3zhCywrSXi/r3zlK9BqtTh48OC6a6Ox0+joKIaHhyGRSJBIJPDYY49xMlVXV4dDhw7hwQcfZDOVWCyGHTt2sHKSQqHAxo0b8cMf/hAAGEMei8XwwAMPoLOzEz09PWhubkZjYyO7SC8uLq67NnItdrlcbOs+MTGBG2+8EclkEvv370dhYSEKCgpw3333oampiSFUd955J0uf7t+/H4ODgyyJV19fj5KSEsRiMVx33XWYnZ3FmTNn8MILL2BtbQ3l5eWYnJzMeSiSiSFxCWh0d9ddd3ElSyRpSjQ1Gg0mJia4U9fb24s///nPSCQS6OjoYLhEYWEhzpw5g4MHDyKbzaK4uBihUIjNaV577bWcUwNSuvJ4PPD7/VxwSCQSiMVivPPOOwgEAkzs/PWvf439+/ejuLgYX/rSl3DLLbfwxIdk/c6dO4djx45hbm4OGzZswPe+9z3IZDJ84xvfwPnz5xH6gMNyrko9Ho+jtraWzbZoihYOh1FXV4fa2lou3urq6lhv/rXXXuOR/FtvvYXFxUXceOON2Lt3LxYWFqBQKCAWi7G4uIjW1lacO3cOhYWFWF5exqZNm9De3o73338/Z9cqlUqhvb2du44VFRUoKCjAN7/5TbS0tOC2225DQ0MDbr75Zjz55JN45ZVX8O1vf5vN8bLZLBoaGtDc3IxQKIRvfOMbeOuttzAxMQG5XI6Ghgbs3bsXhw4dwte//nUUFRXh/vvvx5e+9CXuIOYKp9OJXbt2caHW2dmJy5cv49/+7d9w5MgR3HPPPejq6sLOnTvx3HPPMceFiLs09SgrK4Ner8djjz2GwsJCWCwWWK1WdHd34wc/+AEOHDiAbdu2sXrSByeH60Umk8GGDRuYdE4NjwceeIDhmL29vfD7/di8eTOi0Si+/vWvo7q6Gh//+Mexfft2XL58Gc888wyi0SjL1t555524/fbbYTAY8OSTTzJ+2W638wTmnXfeyalQRC65lJiHQiG+hOiilMlkaG5uxnXXXccGcJFIBNu3b0dRURHOnTuH559/Htu2bcNPf/pT5OXlseHnhg0b0N7ejv/+7//G888/zz4h5C2Sy42+oKAAO3bs+FA3kZRpXn/9dUxNTeH8+fNwOp3o7OxkjDIZZ62srLCPBem2b9q0iVXZLl26hLKyMrjdbvb3IRiqVqvNqXpCBoTE65mammII3He/+132dfD5fCgoKIDJZMLf//3f44UXXsDOnTvR1dUFsViM9vZ2AMDmzZuZU0Pr3r9/P9rb23Hdddex4ekH+QnrxerqKhoaGtj4lAqghx56iJtleXl52LFjB370ox/hu9/9LsbHx5Gfn4+NGzdifn4eL774IjKZDN5//33cdNNN7BuzvLyMtbU1dHR04NVXX2Up5MLCQoYgkjzp1SIQCGDLli04ffo0pqenUVBQgF/+8pd8NxKyoaamBocOHcKbb77Jxdqdd96Jzs5Obqx87Wtfw/XXX8/f2ZYtW3DHHXfgu9/9Lnp7ezEzM4Pf/va3sFgs+MxnPsN8zfWCpO8JPtze3s5+UgMDAwxFMhgM2LZtGywWC8LhMM6dO4frr78e+fn5GB8fx+7du7F9+3Z2J19aWsLMzAxkMhmeeOIJVs10OBws7dnf358TykKk29XVVRQWFqKrqwvFxcUMLauvr8eWLVuwb98+7N+/n53MFxYWcMcdd6Cnp4cbCHq9Hi+++CI6Ozvx+uuv48UXX4RSqcRXvvIVHDlyhHkqVqsVVVVVMJlM+PznP5/zuVGjobGxEdu3b8fw8DA2bdqEEydOoL+/H3a7nadKHR0d3ETct28fZDIZ/vCHP+DVV19FX18f4vE4Tp48ieHhYRw5cgRHjx7Fk08+CalUiu3bt+PcuXOYnZ1liHUuIY5oNIotW7ZgamoKFy5cgMfjgVAoxLe//W2IRCJs2LABTU1N2L17Nz71qU/hzJkzPI3p6OhAe3s7F8QtLS148sknWbCopKQEGo0Gf/jDH1BYWIjdu3ejuroad9xxB2699Vb2EVkvaKpw5swZvPPOO5iamkIqlcLNN9+Mt956C9u2beOGTnd3N8rKymCz2fD73/8eu3fv5pzvkUcegc1mw+233w6TyYS8vDzE43FWkHO73ZyzlJaWoquri6V714vZ2Vl0dXXB5/NhcHAQbrcbwWAQt912G8Owyc27vb0dAwMDSKfTKCgoYN7H9773PfYke+yxxxgaStLACoUCIyMjOH36NPr6+hiCa7fb/2pjkeIjk8GfeeYZDA0NsSHOwsIC3G43Wltb0dDQAK/Xi0AgAJvNBpfLheuuuw6bNm1CX18fm3xMT0/j2WefxWOPPYZ//ud/xre//W12jPb5fPjd736H73znO0yCJtgMQTLW+6N+9atfYXl5GTqdjnkaZChz0003YXh4GAAY2/vII49AKpXil7/8JUpKStDU1ASdTsdj0x/84Af4r//6L8zOzrLjNilD7du3D3a7nRWg6urqMD09va660w9/+EMevZIJz/DwMA4fPow77rgDsVgMGo0Gfr8fbrcb3/nOd3DixAk4HA7Y7XZYrVY289q2bRu76ZKTsMvlwpkzZ1BZWcnmSZcvX4bX60VXVxeOHDmCf/zHf7zq2p566ilYLBZoNBosLi4iEolg9+7d8Hq90Gg0HzKJq6yshNFoZNzh73//e2zZsgWbNm3CO++8g66uLhw/fhwPPfQQFhYWkE6nkZeXh1dffRUf+9jHkE6nuUtJTsgkG3q1+MEPfsDmbTRO/PjHP45EIoHKykr8+c9/xoEDB7C6uoonn3wSZrMZn/jEJ1hl4dChQ6irq8PTTz+NPXv2YHx8nHG4pJLx6KOPYnh4GC+99BJKSkoYRlVZWQm/349Pf/rTV13bE088Aa1WyzjQdDqNhoYG/PrXv8Zdd93FbqCEr1QqlfB4PKiursbly5exsrICi8WCiooKVh/buXMnJiYmmJT4yiuv4Gc/+xkOHz6M+fl5JBIJqFQqlJSUwOPxrLu2xx9/HGNjY/jEJz4B4Iq6V0tLCx5//HF85jOfYaOqiYkJSKVSfPazn2Ulp7feegtisRhNTU2wWq144403cPz4cXz3u99FKpViUufAwAC+9a1v4cknn0Q0GmXpZepe57rsfvazn/GImwiW9913H5544gl86UtfYg8A6g7/6Ec/QiaTwW9+8xsMDAxAKBSira0Nu3fvxtGjR7F582ZoNBrMz8/jxIkTuHTpElZXV/GNb3yDdf9polFWVob3338fX/7yl6+6tueffx5erxdCoZB11++//35885vfxOc+9zm8++67qK2t5WK0oKAADocDXq8XOp2O5V2dTideeuklNDc3o6am5kMKYGSG9OSTT6Knp4flZOPxOPLz8/Hwww9fdW0//vGPMT09/SFzwsLCQnzrW9/Cvn37eGqWTCYhFArxyU9+Ej//+c/R2NjIsCDCUEulUu6a5uXlsYytSCRieVqTyQSDwYBQKIRTp05h//7963bWXnrpJUxOTqK6upoJpUVFRXjhhRfwmc98hp/VwsICTp06hQcffBCPPvoonnvuORbXICxxNpvFE088ga985StYWVlh7P4//uM/4r333uO74fjx44jFYrjvvvtw7tw5PPTQQ1ddG0lF33zzzZidncWFCxfwsY99DD/+8Y/x+9//nqfeg4ODuHz5Mv7zP/8TmUyGzVFJ4jSRSGBtbQ3j4+PYuHEjVldXAVy5U37yk5/g3//93/Hee++xO7FKpUJHRwecTifuuOOOq67tj3/8I06dOgWLxcLJRGVlJS5evIhdu3bh8uXLWF1dZdWv4uJinDhxAnV1dUgmk+js7EReXh7++Mc/4uMf/zj+9m//Fs888wyGh4eZBG2xWGCz2fDP//zPaGlpQTgchkgkws6dO5HJZNblof3whz9ENBplnxGxWIzGxkb88Ic/ZF39np4eFBcXIxwOo6GhAZFIBC+99BIefPBB5hQEAgHMz8/zZIbI6OQJROpk5H21urqKjo4OvPnmm3j00UevurannnoKOp2OE7rx8XFWktq+fTvDhicmJjA0NITrr78eMpkMMpkMfX19KCwsRGlpKex2O6qrq7F371689dZbUCqVGBsbw+TkJKqqqtDT04PLly+zIAdwxYtnYGAAX/jCF666tu9973vsh+RyuRCJRHDdddfhzTffxFe+8hX8+Mc/Rk9PD0KhEH7yk5/gxz/+MQCw+AUZppEz9MTEBFpbWxEKhThhbGhowIEDBzA6Oop4PI733nsPq6ureOihh/Dmm2/i8ccfv+rann/+eS7QV1dXoVAoUFlZiWeffRaf+9zn8Kc//QkbNmxAXl4ezp49yyIft99+O86dOwe1Ws13Q3NzM1555RUolUosLS1xcf3GG2+wMAx9r8PDw6iurkYgEMC3vvWtq67t+PHjOHnyJCtXqlQqWK1WDA4O4tZbb8XZs2dZXpZMKS9dusRIDL1ej/z8fCwvL+PcuXMMsaqrq4Pf78f09DS2b9+O9vZ2HD58GAsLC9BoNKitrUVpaSmbPV4tfvazn+HixYv42Mc+BrFYDLvdjqamJvzrv/4rnn32WVZppDw3FAqho6MDS0tL6O3thVqtRjqdxmuvvYYbb7wRp06dwje/+U1oNBpWiBweHsb999+P7373u8jPz4dGo0F5eTmqqqpgt9vX9fj48Y9/jPn5eVabMxgMkEgkOHbsGP7mb/4GP/zhD1FYWIi6ujoWJ3n88cfR1dUFmUyGqqoqSKVSHDt2DF1dXTAajWhubsbp06dZsp4c5N99913s2LGDRZUGBwdRU1ODe++996pr+2D8/2XYR87bpAFN/AWPx8PKTSQPRzJsL730EkpLS6HT6ZBMJlFZWYlf//rX+Ju/+RucPHmScXJra2uw2+14/PHHcfvtt2N4eJh1e48fPw6v17tuwkwjdOrqkYtkZ2cn+wqMjo6yogoVEJFIBE6nk42aGhsb4ff78fGPfxxvv/02CgoKeAROnhShUAjz8/PcqXK5XDkJsH6/n43wSHFpbW0NXV1dSCaTmJmZYeOY2tpa/OpXv8K2bduY+EwdTwD43e9+x+Z1u3btQiaTwejoKDZt2oTR0VFUVVXh0qVLfIC/8cYbMJvN664tHo9Dr9cjGAwyofH8+fMwmUwYHh5mfXLgCjm6sbERJ0+eRHFxMfbs2YOlpSX8z//8D+RyOU6fPs1a0lKplGEOTU1NeP/999HY2IiKigqk02nGR+YiI9IEg+Br2WwWzz33HFKpFBvr2Ww2nqTk5+djdnYWNpsNIpGIzRBlMhn+8z//E4888gjeeOMN6HQ6FBcXQyAQYO/evXjqqafQ3t6O6elpLgbJ+G29kMvlqKqqYnIUEURbW1uZoL+4uMjwQJlMBpvNhnQ6zepMbrcbx44dQ1FREaqrq/ldlZaWQiaTYevWrXjvvfcwNzcHjUYDrVbLOuW5uvLkCN/b2wuNRsMSzKTAsbCwAJfLhWQyiZWVFbz00kuwWq04efIkNm7cCLPZzPhso9GI733ve0in00in0zyRLCsrwzPPPIOGhgacOnUKExMTPPHMhUkHwFDGVCqFiooK5Ofn4/3338cjjzwCvV6PgYEBaDQaqNVqOBwOvPHGGxgfH8fQ0BDa2trgcDi4sB4fH0djYyNGRkZgt9sZ3lRaWsrSqRcvXsTg4CAEAgE6OztzTlzcbjd27tyJZDIJu90Op9OJV199FXl5eexMnU6n+W8lIYWnnnoKhYWFaGlp4SIkmUzi2LFjDNMgnxWSSt2/fz970dTX1yMej3+kaQt5/IjFYly4cAG7du3irm5fXx8SiQS75XZ1dSEajXKnq7KyEoFAAIFAgDXQyV28t7cXUqkUe/bsgc/nw/DwMIqKimA0GlFYWJhTjMPj8aCoqAhLS0sIBoM85du8eTMXUQQZMxqNGBsbw3333Yd3330XRqMRmUwGTqcTU1NT2LNnD3bs2MHeNgQ/PHDgAJ599lmWRW9ubmZN/Vzj+/7+fuzbt49hb2RUVl9fj0AgwFAi4uCdPn0aMpkMx44dg1KpRCKRwMmTJ+FyudDU1AShUIi+vj5MTU2xb8rmzZvxy1/+EoWFhez7RJf9emIX9L2ZzWYsLy+zQMj8/Dz27duH8fFxFBUV4fjx48hmszwBV6vVmJycZPgGdaR/85vfYMOGDXj22WdRWVmJtbU1vPvuu2hqamJndIvFAr1ez3eIy+Vat9BIpVIMtVpaWkIqleJJCnEU+vv7uXAl8q9EIkFvby/L9lZVVeHNN9/E5s2bmStB39vtt9/OQixHjx5lERi6v9aLlZUVhvWRt9DIyAjzRGiPl5WVwWAwMNciPz8fTU1NEAgEsNls3G3/yU9+whMSks0tLS3F0aNHoVAouAgn6G4uvpJQKER7eztsNhukUikMBgNGR0eZm0F8r2AwiMbGRibk2my2D0GKSXBn7969ePbZZ1FSUoK8vDwsLy9jYmKCJ1djY2MM3Xz//ffR2tq67trm5uZ4MphIJCAQCDAzM4OVlRX86le/QmdnJyQSCex2O1KpFIu8XL58mRu/xOeanJxkTlk2m8WZM2fw4osvYnFxkSH0ly5dgsvl4mlBLm+qYDCI0tJSlsYl7t7u3bsRDAZZKCSTycDhcGBoaIi5CSS0kkgk4HK5YDAYUFNTg97eXhw7dgxGoxHd3d1QKBT47W9/i7a2NiYyh0IhNmJer9AIBAJsJBsMBrnJ8+CDD2JiYoL5Pg6HA6urq8zPdDqdzOMoKCjAV7/6Vbz77rt46KGHcPHiRfZTs1qtPDHs7u7GiRMnOPciy4b1Cg1yKU+lUmzKmUwmUVRUxM7xZMS4fft2ZDIZ7Ny5E0KhENXV1RCLxRAIBLj77rvx6quvsllgU1MTstksXn/9dRQWFjIfzW63Y2pqinPHjzin+OiFhlqtZqwgYTN1Oh3eeustbN26lTsJpH1OykcWiwWBQABqtRoVFRVQq9VQKBT4xS9+gbvuugsikQh2ux3BYJDxnkeOHIHVasXy8jLm5+fhcrlyYqtJ6UUoFPLmFolE6OvrY7lLUoIhlRC73c7JIimikALK888/j46ODmSzWRgMBjQ3N8PhcEChUMDpdDJGWKlUwmQy5YQVkAwi6foTH+DChQu455570NLSAolEwpg8j8fDqkB0GJAiis1mw1133QW32422tjYmsR85cgSLi4u4//77MTo6isnJSayursJkMuX0gwDAHzsliDKZDL29vYjH46ioqEAgEGD+TH5+Po+riUD5QX+HpaUlXHfddYxlJvgcEU9pEkMKLbmSea1Wy+/FYDCwsgxtPJlMxuIBIyMjuP/++6FWqzmh0Wg03P1Ip9MYGBhg0jupKJEpJElnJpNJZDIZhEKhnCNBhUKB+fl59sZYXl6Gy+XC4OAgFwvRaBQOhwOJRAJmsxlKpZL5QCaTCQUFBYhEIhgYGEAymeQLkxQ9mpubOfkjEl4qlWK1l/UiLy+P3aPX1taQl5fHXi1kNkSu4bQfSPWNFJlMJhMkEgnrqtfX17OKVTAYZFJhNBpFSUkJ4zn/GokeuMJTmpmZgdVqZY6WRCLByZMnuSkwMzODy5cvw+FwoKmpCTMzM5DL5dDpdHzpFxYWcgJMxFgiWTocDpSWlmJsbIxhcKQwQj4uV4uVlRUcP34cOp2OL4xQKISqqipuLNhsNk66P/axjyGRSDCsZmVlBfPz82xqOjIyArfbzdKQOp0OWq0Wk5OTqKmpgU6ng81mw8TEBJRKZc6klBS4CgoKWOZydXWVBSYEAgEbW50/fx7BYBA7duxgpbhQKMTf4rvvvovV1VUWMiAlrbKyMuZFaDQaVoeivbhe0Pcrl8uRl5fHXcmZmRnk5+czYXR8fBwOhwMbNmxgns34+DhzHuLxOP7yl78glUqhvr4e+fn5EAgEsNvtOHr0KL70pS+hqamJJ5w+nw+JRCKnOIJcLsfCwgJDUgHwNId+rlgsRjabxerqKqLRKJxOJ3ePiYf15ptvoqCgAP39/Swlu7a2Bp/Ph7GxMYbJFRcXQyaTsQRoLoWiVCoFlUrF8BfgCmfjnXfeQXt7OxYXF/n+OHPmDHbs2IHl5WUolUocOXIEUqmUPUvIfbqjowNqtRo+nw/j4+M4f/48Pv7xj+OWW27BhQsX0Nvby9KludS6NBoNxsfH2Z/K5/NhdHQUIyMjqKiowE033YRz586xV0hdXR0kEglD4YArnhIlJSXIz89n8rzBYIDFYmGhldnZWe5EE8mWRGdyrY3cwQsKChCLxTA8PIyXX34Zn/3sZ2EwGNh3aWJigiWyz5w5gzNnzqCqqgpWqxU6nQ6jo6Psf6TX62E2m1FbWwuFQsEFKHkNSaVSBIPBnM0UsViM6elp5OXlIS8vD0tLS1haWsL4+Dj6+/shkUgQiURYrIZ8PKhLThNd4EoSSbzYnp4eyGQynDt3DkePHmUCdn5+PtxuN0KhEFKpVE4FzEwmw34J1IQiPolMJkNpaSkbvs3MzDBvwGazcbOL1NSOHj0KiUTC5rDAFTXPgoICtiR47bXXuHAgHsF6QQI7xcXFWF1dZR6s2+1GWVkZ5ufn+ZsIBoMs8z40NMTO5ARx1Gq1mJ2dRXd3N+x2OzweD8bHx9m4dXh4GOXl5SyNTyJC64VOp+MChu4SkhXu7u7mwvaDzYxwOIyJiQkkk0nmSGk0GlitVjz//PMwmUxoamqCVCrF3Nwc+3ssLS0x9JoKwlzJvEAgYKEh+hsWFxfR39+PlpYWRsvQ/iXJ5ZWVFRZnaGpqQmlpKWpqanD69Gl0dHQgFoshFAqxkTFNaC9cuMD8OyraP0p8ZI4GqQ6QMoRcLodUKkV1dTWcTicikQhfWKQ3T90a0h1PpVLYsGEDJyZSqZQPoeLiYhw8ePBDrpWk/uR2u3M6+gJgWVaSBaPLkrB+AFjKy+PxoL6+niVJFQoFG87ZbDYUFBSwSQkRkRsaGgCATW7ow6aDKOdDFgqxsrLCEnyrq6usiFNYWIjV1VX+OYSjpGSTHMUJimMwGJCXl8dJuk6ng0gkYtWbeDzOyTld+uuFQCBAKpVCOp3mokUsFqOqqorNynQ6HYxGI8rKyuD3+9HY2MjTCK1Wi8bGRrS0tMDv9/PhBYCTcOLgaLVaLC4u8mX01zZQIpFgNYRsNsvE+6KiIoYS0EdOZPpsNsuyn6T2YDab+d+SrwbxJxoaGlixY2VlhZ9FOp3OaS5E6wOuHK4Wi4WlY0nMgIyULBYL+8MQbpcw9TqdDqlUCiKR6ENa8iTT6nQ6mWBG+ttEal4vpFIpysvLOfmkfUPSvnq9njHDpJNP3TJSvqJOHym0kWLP2toaj1N1Oh2TxfR6PX+fuQimwJVuIF1YYrEYeXl5nBjQty+Xy6HVatHc3Mzy1lqtFuXl5TCbzZBIJLxXSE2O/qb8/HyWoQ4EAvD7/cjPz2c+Uy7VKeo+JZNJmEwmho1UVVUhEAiwxr5SqWQN+VAoBIPBgNbWVsYC0+/fs2cPJ+BUwBYVFbFizAflv6lDtF5Q114ul0Ov18NoNLIsKBGr6bIhCVyCCG7YsIGneJWVlSwZScpnSqUSmUwGTU1NrL5GBHXaG7mSKzqLyAmb5JlJmYXclIVCIT+btrY2nloqlUrodDq0t7ez7Cld2NQlt1qtLOVoMBhYWU0oFP7VxI/gV8CV81IikSAvLw/z8/NM4tRqtWhoaIDf70dTUxPy8vLQ1NSE8vJyhhO43W5Wa6Hin/T9iXxP5yVJQ9NE+GpBhpwA2EhQJpOhoKAAgUCAPTzI0fyDCk3UaScZZlKZI6U2kUgEmUwGvV6PsbEx5pQYDAZ2cc6ltAOA1WoMBgMqKiqwtLQEg8GAZDLJyR0R0olLt7q6ygIZcrkcarWai1aaohJvsaysjO9ggkmTNGmuIojuA5JbJY7oysrKh747MogViURoaGhgkZNkMgmZTIbCwkKMjo6yMS8JUchkMv7eJBIJlpaWOCeJRqMoLCxcd22ZTIaLVqVSyVy/5uZm+Hw+vpNpukL7lJ4NJY2bNm1iBUF6lwSvIwVKAPxcSREzl5IYrY2alwaDgT0R6DwhQjyphtLkTCgUshSvRCLB3Nwc31WExqisrMT27dsZxi4UClFZWYm6urq/qlyXSqU4QQ/9r/GgUqlk1APxecViMXN4qIlMyAfgipgJTY2FQiEjBIhTTHKzxPGj5nSus5c8Nyg/IJNTtVqNYDCIoqIiFgwg5c/y8nLOfcViMUNaSb2OVAxJ6r6mpoahhnTuAeAzZ72g9ZPYDKmiktQuFXqrq6s8Payvr2fIM8n9ptNplJaWwmw2w+/3s78TvUPyLgmHw5zjR6PRnPfph9b5kf4VwC6LZDKWyWQQi8Vw5513ciJHMJfKykpOLh0OB+uJE8Fyenoan/jEJ1g1iV5Qa2srJzLUzbZYLFAoFDn/IIJzUIcunU4zGVOv1/PhVVxczFjS2tpazM/Pc3dZoVBAo9Fgbm4On/nMZ/iApEOguLj4Q2NUchr3eDwI/a/T6dWCuo6UEAFX1B927tyJVCoFh8OB2dlZdtQViUTo7OyExWJBd3c3mpqa2GeAYAeTk5O4ePEi+vv7EQqFWDllYWEBsVgMZWVlaGtrYxOW9YLIZPF4nGXbIpEIk+voXXZ2dqK1tZWVjPx+PxQKBQoKCpiroFQq0dXVhWAwiGAwCI1Gw+ZkqVSKu1CZTIZhbrmIksFgEAaDAbFYDIFAACsrK4jFYpwk0eYuLy/Hvn37GFdPCmVUkKyurjJ5njpVRJQ0m83QaDSQSCSMT6cLNJeSWDqdhlKpZEUOvV4Pi8WCPXv2cHGcl5eHuro6dHV1scuy1WrF5s2bUVtbyxMuIsfn5eVxR0gmk2FsbIx/DsEUSV88l9wdSdOSdGFxcTGUSiXDhshUqLOzE/v27YNcLofJZIJWq8X27dvZhHFlZQV5eXn44he/iNLSUtTX17MIwp133onq6mrmHNGlnkgk/mqhIZPJ0NHRwfKkZHTX2dnJ0LqSkhIcOHAA9957L7RaLWprayEQCGAymVgSmJSOdDodSzyTPOuOHTs4iSUIHU2echEShUIhNm3ahKqqKlaYIUdvSm4qKytx3XXX4b777kPof92rSee+pqYGdXV1KCoqQiAQwN69e1FcXMznI4lGNDY2QiqVwul08lifzrn1wmQyscM44aGlUikT44VCIWOYb7jhBpSXl7Ncan19Perr6/lCIpdctVqNDRs2oLKykqcdZL7mcDhY8e+veVUIBAKGC8nlciiVSkgkEjYHjMfjiEQiMJvN7JBMHcXKykpWpyoqKsLWrVuxZ88eiEQihqbl5+fjk5/8JObn55k0TYU8mT+uFyKRCBUVFfB4PCxv3dDQwFPVbdu2oba2FhUVFdizZw8CgQAaGhpQVlaGu+66C21tbSgtLcXnPvc5VsYiM1CSzt67dy83WiiRocZIRUXFumujZgDJ3BIZ9L777oPb7UZ1dTU2bdqEjRs3cndyw4YNCAaD2LlzJ5qbm9n7gZLi48ePY2xsjAvX7u5ujI2Nwe12o6SkBHfffTfuvPNOvt/WCxIAUCgUKC8vR0dHB0QiETZu3AipVIrFxUVks1m0trbiuuuug0KhYIK/QqFgCXyv14vCwkL28bDb7RgYGGCp7draWjZNoyI6m83mbJAtLy+jsrKSPWJIDWnbtm0sZe/3+2EwGLB3715O+C0WCxobG1FWVvYhXhJJGMfjcfaXUCqVUKvVKC4u/tAEmLgNuZ4bnYEKhYINNW+55Rb+nUqlkgU5fD4fFhYWkEgkMDw8zLDdpqYmLlydTieGhoYwNjaGUCiETZs2cSOFFMQKCgpYlSrXXigsLOSChYoX4l8SV6qhoQHXX389tFotNm/eDKVSiaqqKuh0ug954OzYsQNKpRKlpaWora1FcXExSkpKMDMzw9Cq+vp6NDQ0wGw2o6ysbN21pdNplJeXfyjpNRqN7DBP8Oi8vDy0tLRgbGwM8/PzWF5e5sYiADbhJKNVKubpbiKOi1ar5XubBEPWC4L5kmeTTCZDV1cXuru7sby8zKZ7er2eCx1K4skAmqS5/X4/HnzwQaysrGBiYoLznLq6OjazpAl9RUUFlEolT2LXe6fEv5PJZNy06e7uhsfj4WYR+bLp9XrU1dUhk8lgy5YtKC4uxsjICKanp6FWq1lEiIp+gUCAnp4eWK1W9o2jMykYDOaEv38wPnKhUVBQwLJcJMWm0Wjwi1/8Avfff/+HzOWcTic6OjrwwgsvYMeOHfB6vawK86lPfQparRZvvvkmK1XpdDp4vV7cfPPN+Jd/+ReoVCro9XqMj4/D7XbjU5/6FMN3rhZFRUUQCoWsvEA4y9/+9rfYtm0bTp8+Db/fz+SaHTt24KWXXkJNTQ0mJiZgNBrR09ODpaUl7Ny5E2NjY9i6dSuTw2UyGf7jP/4D119/PfR6PRKJBKanp9m5PBfMJpFIsDdHIBDA4OAg2traYDQaceutt+Kdd96BTqfD7t27odPpsH//fkxMTKClpQX/9V//hZdffhkrKyuIRCK45557mAhGeFvaND/4wQ8QiUSYyHz58mUAwIsvvrju2gwGA+bn5xGJRPjQMplM+N73vger1crGLYODg3j11VfR09ODubk53H333SgoKIBSqcTy8jL+9Kc/4e///u/hcDhQXl6OUCjEcIbjx4/j/vvvZ1On/v5+HDt2DJs3b8bQ0NC6a6uoqEB/fz9Dm9bW1phMLZPJMD09zT4rZ86cYUGCmpoaPtxISengwYOw2WxobW1lN3G1Wo1Lly6huLgYR48ehdVqRWlpKfLz87l7t16Qvj6Z8c3OzqKsrAyvv/46S7UODw9jZmYGExMTKC0thcvlYuwuyfOS0tfx48dx2223scqDTCZDIpHAV77yFahUKjQ0NCAWi7Ezci7zNL1ej1/84hesST45OQm9Xo+3334bW7ZsweLiIk6ePIn3338fIyMjuP766zE4OAiPxwOFQsGKT6Ojo5ifn8cbb7zBHhAkyfvnP/8Z3d3dePXVV9Hd3Y2enh60t7djz549f/Xg0el0DEMkiCXJMO/ZswcOh4Mlh+PxOB555BG8//77iMVirDl/zz33MImztrYWHR0dcLvdSKfTaGtrw+uvv47PfOYzPJkIBoOw2+18aa0XlCDo9Xr09fXhV7/6FaqqqvCzn/0MDQ0NSCaTMBgMnDx84QtfgMFgwI4dO9jHQKvV4o033sA3vvENLC8vw2q1srrbzTffDJlMhjvvvBMXLlzA6uoq5ufnMTY2BqPRmBNmU1hYCKFQCLPZzC7VlKTSWQlcSR68Xi/uvvtudlZ/7bXX+H//+c9/jpKSEiQSCezevRtarRY6nQ4bNmxgDL5CoWDsPq0t1/lLvj8CgQB+vx+BQAC33347IpEIrFYr3n//fRQXF6O0tBTvvfcebrzxRvz7v/87mpub0dfXB6fTCYPBgGg0is9//vP46U9/ytwb8mESCoWoqKjAH//4Ry4yh4aGkMlkcOTIkXXXVlFRgenpadhsNjZxNRgMePrpp9He3o6f/OQnmJ2d5W+jvr6evUnOnDnD/Lh4PI7Gxka8++672Lt3L0+kSL68tLQUq6urPKGhCf3k5OS6ayOPEK1WC7/fj7NnzzLR/7rrrmP/HZFIhBdffBEFBQU4c+YMHn30UTidTszPz8Pn8+Gb3/wmJ7933nknkskkn1+f+9zn8E//9E8YHBzEjh07kE6ncfbsWZSVleWE2ajVat5bof91Sd+2bRt+/etf44YbbkAsFoNWq2UD0C9+8Yss03ny5EkAQEdHBwYGBnDPPffghhtuwE033QSZTAaRSASLxYKzZ89i//79/C7MZjNsNhvD/NYLk8nEJnFkXks+E62trby3yPGe+HxKpRJ+v585kVNTU3jyySeRTqdx5513oq2tDR0dHdi6dStefvll3HDDDchkMti2bRv75pCYy3qRyWQwODjIkqCTk5MwmUw4efIkG2SSo7tWq8WXv/xlzM3NMb9PLpdjZWUFNpsNBw8eRDKZREdHBywWC4xGI0NOv/KVr+Ddd9/F7t27uZBOJpM5G2RWqxUzMzMsg7u8vIx9+/bBYrGgpaWFJdTHx8fx2muvoauri4UUdu7ciZ07d2Lbtm3Iz8/HPffcA6vViv379+P48eM4evQoCgoKcOrUKajVarz88suoq6uDUCjk35OLL6pSqfDee+/BbDYzl2x4eBinTp1Cb28vBgYGsLKywuqON9xwA6vcWSwWtLa2wmKxMAn9woUL8Hq9sNvtUCqVqK2txZ///Gfccsst7BdBaISCgoKczWKTycRcSYKGGQwG/OY3vwEADA4OIhwO853W0dHBLvJOp5MnU36/H+3t7Zibm8Ojjz6KYDCIS5cuIZvN4lvf+hbuuusuVFRUwGAwoLCwEG1tbbjzzjtzToLy8/OxtLTEE754PM5+O7feeivcbjdMJhMqKysRDodRU1ODlZUV3H777RgfH0c4HIbVasXp06dhsVhw/PhxHDp0CGNjY7DZbOjp6cGJEydw8OBBvPnmm0wOv3DhAtRqdU4Y9wfjI3M0ZDIZHnjgATaTaWtrY5UpkrYjKdna2lo+2Kamppilvrq6iu7ubr54BwYGeGxKUprf/OY3WeWIOu2ZTCZnZ8jn86G8vJwNtCorKyGVSnH33XfD7XbjlltuQX9/P+P3s9ksGhsbcfr0aQBXOkuLi4tMYm5vb8epU6fYGMntdkOn0+Hw4cNMilIoFAw1OXz48Lq+C0S2FovFqK6uRlFREZOsjhw5wl1ZUloYHx/H5OQkamtrceutt+LYsWP43e9+h8bGRszMzGBgYAD/9E//xFhKlUqF1tZW/PKXv8QXv/hFXL58meUv4/F4TgnDUCjEBzF1geiSDQQCaG5uxvz8PLLZLDo7OxnL+PTTT+POO+/EysoKnE4ntm/fjv/5n//BwMAAFAoFWlpaEAgEsLi4iF27duHpp59GNBrFXXfdxVOjsbExNDc3r7u2mZkZ7Nu3j8nt9MwKCwtRVFQEqVSKkZERCIVCNDU14Y033sDJkyfxyU9+En6/H4ODgygoKEBNTQ2Ghobwta99jT1PqJs8PDyMu+66C5FIhGUGqXP9/vvvr0uUJB5QMBhkp1mbzYYDBw6w4g6NdkkkoaCgAKOjo5zE9vf3o7W1lU0rbTYbdwjUajUqKyvx8ssvM159aWkJ8/PzKCwszNnh8Pl8ePTRR5n0GwqFcPnyZVa+KSsrY/Lu7t27GZa4uLiIZ555htWGtm/fjpKSEvT19aGlpYU5TjR+n5+f5yJteHiYuS82m23dtQHg0SvBLmOxGE6ePMkyzNShC4VC7PTe2dmJ+fl5/PKXv0RNTQ1jh0+dOgWr1Yq5uTmoVCqMjo4y5v/ixYs8SSWxCXLUXS/IfXtxcRFisRhtbW0oLi7GM888g4WFBSwsLGBmZoa5al1dXSgoKGDI1NzcHAoLC3H33XfjRz/6EW666SY8/fTT6OjoQCKRwBtvvIFYLIaHHnqIuS4qlYpNKHMVQdQQId4DJeJnz54FcEVPfWRkBIuLi0ilUrh48SK6urrw+9//niF5a2trOHjwIKRSKc6fP4/h4WGWiiUeW21tLbq7uzEyMvIhRahcXC8ywSI4gcvlwuOPP46WlhbEYjFYLBbumHV3d7O06B/+8AdkMhmsrKxwtzYUCmHv3r0MEyKeA2nzFxUVYXp6GhUVFTzhra2tXXdt6XQaTU1NLOBBEIjnnnsOJ0+eRHV1Nfr6+jA2Nob9+/cjEAhwYSWXyzEyMoLe3l4UFxdj8+bNLEstlUrh9/vhdDp50kRTcbfbjbm5Ob7Xcn1vXV1drK6Vn58PmUyGp556Cjt37mSYmV6vxxe+8AVcuHABf/zjH5mDZ7VasXv3bmzevBmXL1/G2bNneRIdjUah0WjwL//yL/iv//ovBINBNDQ0YHx8HIODg+jr60N3d/e6a5ufn2eoidPphNvtxtjYGA4ePMjyrQTVHRsbw/nz57Ft2zY8//zzAK4UxgSj+/73v49z587h0KFDqK+vh0qlwtDQEIaGhpBOp9HV1YXx8XH2LKEm2XpqNsRRJJdmg8GA999/Hz09PZifn8fevXsxMzOD3t5eqFQq1NTUsLHg7t274ff78frrr0OhUOAvf/kL6uvr2S3d4XCw9Parr76KvXv3Ynx8nIU/APxV6XMy2qUpP6kOBYNB1NXVMTTxzTffxKuvvgqFQgG9Xv8hbtT3v/99bNu2Db/85S/R2toKu93OPAwyu9y4cSPS6TRzIWtqanDmzBns3bv3qmsbGBhAUVERTzAXFhYQjUZx8uRJ9nBZWlpCJpNBc3MzhoaGeLJEfjHhcBjd3d0YHBzEd7/7XXzsYx/DrbfeisnJSfzrv/4rVCoVvvGNb2B8fJxNUsnHZmpqCv/93/991bWp1WrcdNNN8Pv9LHpA/FiSnyXfq+LiYlRUVGD79u148cUXOe8UCAS4ePEio2I6OzuRn5/PhOfnnnsOY2NjKCgowDvvvMMw4rW1NQwPD6/73BKJBHbs2AGXy8Uw0SNHjuDTn/40xGIxMpkMhoeHsbi4CLVajeuuu45ROU6nkw0GJyYmUFdXxw1FInin02ls27YNP/7xj1FdXQ2hUIjz588jFoux59x6EY1GYTabuUhNp9OYnJxEKBTiqR5x2bRaLd59912GzCeTSZ5W3Xjjjbj77rvxwAMPwOfzwWq1MvdJIpHgL3/5Cx5++GG88MILnAcGg8GcU6oPxkcuNJRKJb7zne+gvLyc7dvLysoYRnL99dfzB6/T6dDR0YGnnnoKAoGA7c+1Wi0Txjs7O5FOp7lDl0wmMT4+jg0bNjBuNZ1OI5FIMLZ8vdDr9bh06RJMJhOrD7jdbvznf/4nJ/Z0oQ0NDWHLli2sN+z3+xkKdujQIbz11ls4c+YMtmzZgvb2dkxOTvIo2mg0oqqqCps3b2Yyl0qlyklcs1qt3KFoaWlBRUUFXn/9dVy4cAF/+7d/y6PJiYkJ2O12bNmyBTfccAO+9rWvQSAQYNOmTdi3bx/6+/tRU1PDhjVkqjU/P4/BwUGUlZVBIBBgYWGBMbU+n4+JWlcLcqXW6/VMdqNkUq/XY3R0FKOjo4hEIpienkZbWxsaGhqgUqkwODjIXBhypWxpacHa2hpfhEKhEENDQ0gmk9i3bx8rsxDRPxfBnzgdBNEbHx+HWCzG0NAQPB4PPvGJT3ARMTs7i61bt2Lr1q1sclNYWAiVSoWzZ89ifn4eDQ0NmJ6e5i6ITCZDeXk5k8+I47O8vMw41PUikUjAbrejsbGRnWLj8TjOnDmD7du3cxJIxPyenh52byb4DJnqBAIBTExMYPfu3TAajQiFQhgeHobVamVfDb/fz4Y/1IFYL1wuFwYGBqDT6WCxWJjM+b3vfQ8HDhxAU1MTRCIRJiYm0N/fD6vVinvvvRc9PT3Q6XRYWlpCJBJBXV0d+vr6MDIygh07dsBoNGJtbQ2XL1/Gm2++ifLyckxPT6O7uxsajYYNlW644YZ11wYANTU1GBkZYcdmgUCAyclJvPPOO6zqFAqFmMip0Wiwe/duHDt2DNXV1aiurkY2m4Xb7UZDQwMGBwdZkIEcj0lYYcuWLejt7WWTSACMbb5akHqOUqlkBZKnnnoKs7Oz2L17NxtTDQ0NYXR0FKWlpYhEIpifn0dtbS2WlpYwODjI77avrw9FRUXcRUqn0+ju7kYwGERPTw/Onj0Lt9vNHLfdu3evuzaTyYSBgQF0d3fDZDJhdXUVRqORDc42bdoEgUCAZDKJoaEhZLNZFBYWQqfTsVuzXq/H5s2bceLECW7mNDQ08PdA8poOhwMul4tV3UiSd70gLC8V4CQj+vbbb+Omm25COBxmHlNvby9KS0uh1+uxvLyMI0eOYPv27ejo6MDs7Cz8fj8X7wSlI3+DRx99lDluXq+XTfzWU4sBwH8LceqcTicGBwdx9OhRbNmyBZWVlYxHJwKvTqfD2NgY3n//fXR2duLgwYMwm8147LHHEI1Gcf/993MROz8/j/n5ebS0tGBpaQnLy8vs72E2m3N2ceVyOYaGhpBIJJgUTH4CdGeQ4ZlAIEBDQwMaGhrQ2tqK5eVlhEIhDA4OMvyxrKwMJ06cwP79+yEWi3Hu3DlO2j/3uc/hwoULmJ2dZUx9roS5s7OTEQwtLS3Yvn07FhcX8eUvfxnZbJYn4FNTUyynbbPZmLfi8/m4IL/11luxuLjI0snk10Pmqtu3b0dbWxtPMYPBIBfQVwsi59IdNz09za7uZPA5PT0Nj8cDjUaDpaUlLnrJyVmtVqOkpATvvfcepqamcMcdd7Cfl16vRyQSgdvtZqU44mhYLJac/L1NmzbhZz/7GZqbm1lBilT2MpkMn+ekYKRUKuF0OtlXIT8/nzv03/nOd1BaWopXXnkFe/bsgU6nw+TkJJ5++mlcd911aG5u5v3qdru58bFe5OfnczPFYrFAKBRifHwcHo8Hvb29EAqFyM/PRzabRV9fH8RiMaxWK5qamliWvKamBsFgEP39/dixYwc0Gg2efvppBINBRpTQGi5fvsyE6PLycpSWlq67NmrEEGeKhEeWlpZYwpzysZmZGaRSKWSzWRbFoLOUBAdmZ2exZ88e5hLMz8/j+PHjuOuuuxCLxdDc3Ay5XI5AIICpqSl0dHSsu7ZsNouLFy8yd5B4Lm63G5OTk9i3bx9qamqQl5fHE6Hx8XFEIhGsra2xJ1hXVxempqYwMzODPXv2YG1tDSMjIywSIJVKodVqUVpaCpVKxbLpuaChpBxG0vckbxsIBHDx4kV2PlepVKwa2dHRgX/913/laeTMzAwGBweh0WiQSqVw6dIlKJVKGI1G1NTUwGw241e/+hUbBZOACMFDP0p8ZOiUw+HArl27WCGktbUVCwsLOHjwIAoLC6HRaFgilRRf9uzZA7FYjLKyMlgsFlRWVuLee+/FsWPHsLKygqWlJXZ+XVlZwU033cTkJofDgaKiIrS3tyOdTuccjxOJlDqhoVAIVqsV9913H1QqFfbt24dNmzahq6sLN954I0KhEFeBd955J/bt28djTr1ej9LSUpbDNRqN6Orqwje/+U14vV7u9BUWFqK2thZSqTQnD4JkUwlCNTo6CrVajb/7u7+Dx+OB1WpFTU0Nrr/+enz961/H/Pw83nnnHSwvL2Pv3r3YuHEjysvLsXv3bqytreHLX/4yhoeHMTQ0hOnpabjdbmzatAnxeBzj4+NcvBFGsKmpad21EabP6XQiEAhAKpUy5rCvrw+XLl2CTqdDT08POjo6MDg4CJvNxh0G6tiJxWJcvnwZ1dXVrH5CU5oDBw4wgbampgaNjY1sspfruRFxilQxLBYLQqEQPv7xj6O6upol9ggPef78ef73LS0tAK4klVarFYlEAp/97GcZWkb43u3bt+PIkSMwGo2Ym5tDJBKBUChEOBzGtm3b1l2bQCBAbW0tPB4PvF4vmz9u3bqVCV0ymYwx+5OTk3A6nQgGg6ipqUFlZSXEYjHC4TBKSkpw//33Y2lpCTKZDBUVFSgqKmKzMur00yVEjpzrhdlsxvbt26FUKhGLxSAUClFXV4evfe1riMVi8Hq9zAsguTuj0cgHOCW+4+PjGB4exs9//nNMTExgZmaGpSP//u//HrOzs9zRIe13wrDmipGREdTV1WF5eRlzc3NYXFxEOp3GJz/5SbS2tqKmpgZVVVXYunUr7rrrLthsNlbx2bFjByoqKqDT6XDXXXfB5/Nh48aN7HlhNBpRUVGBu+++G6Ojo6y2VVxcjNraWrjd7pyFEPnHOJ1O2O12xONxKJVKfP7zn8fg4CD7dEilUvT09DDBkCR7CbNP3J9NmzZBIpGgpKQExcXFWF5ehsPhQG9vL5PcqZNEjYP1wufzYceOHXA4HJiamkI6ncb09DSuu+465Ofns1N4XV0dbrnlFnaTB4C6ujrodDrE43GWO96zZw/y8vKQTqcZUvSJT3wCU1NTaGxsRElJCe644w48+OCDzHtaLwhGS+al5Kr+yCOPYG1tjYm1arUaBw8eRCwWQ1VVFYLBID73uc9h//79KCoqwi233MKmXCSQQQ7Xn//853H48GHE43FWyiMH8lQqte7aVlZWuDH2wQnjY489xhdtVVUVKioqWAWJSJM/+tGP8MUvfhElJSUQCoXo6urCvn37eNpN0zfqoFdUVLC3Aak05kpKxWIxF/BEkvZ6vejs7IRSqURjYyMbVF533XUIh8PYunUrZmZmWDRFKBRi586duHz5MvR6PUKhEMNihEIh7r//fsjlcly8eBG9vb3Q6/XYuHEjstlsTiWxubk5Tpzm5uYwODiIS5cu4XOf+xzS6TQ6OjpQV1eH6667Do8//jjm5+cZt71z506W1yRZ74cffhhisZg5EFKpFN3d3Th+/DhkMhmGhoYgFovR2tqK+vr6df1kgCsS2WKxGKFQCPn5+Whvb2dOZnd3N9ra2tDY2IjGxkZs3rwZy8vLLFXf2toKk8nExX17ezuKi4vx5z//mQnyGo0GN954IwoKCngisW3bNuzfv59VI9eL2dlZ9oaivX/u3Dl0dXXxdw9cadz29PRArVajo6MDVVVV2LZtG5RKJWZnZ3nffPKTn0RHRwd2797Nk4iuri7E43GGwZWUlLDEfS7TT2oY2O12LCwsALgC9aqrq+ME3ePxIBaLobKykt3G33nnHdxzzz04dOgQT1OHh4fR0tIChUKBuro6VFdXQyQSIZPJ4O2338bi4iIWFxdRVlbGsrm5JvFisZgFd7RaLVpbW1FaWoq9e/fC4XAwjJuUriQSCbq7u7G0tMSTM71ej71796KhoQEf+9jHsLS0xA7YJpMJL7/8MsxmM3bs2MFGeXSn5eLdpNNphq4CVwpdEm8gNS6hUMicw8XFRZjNZqytreHQoUPYvXs38vLy2F2dbALC4TDMZjNaW1vx8MMP49ChQ2hpacH4+Djm5+e5oMsFqSWZ2mg0iv7+fly8eJGNQP1+PytIqlQqlJeXo6+vD16vFwaDAbt27UJtbS00Gg22bdvGil1UiAJXJKwzmQySySRef/11OJ1O5uUUFRXl/N4+9H4/0r8CGB5SVlYGkUjEUI90Oo2ioiIsLi4yLrK4uBj9/f1obGxkXwGdTseGUUVFRTzCzmazXO2TYg8ROElKjnD/6wXJZFJySh8GFRzU6UwkEgydIWMgqVTKVfjU1BQbyZFSFhHtHA4HOjs7WSmKxscikSinNj8ptnxQfYDcc8mkJ5FIYGRkhOVII5EIWltbkZ+fD4PBgEgkwgeKRCLhTgZhPfv6+lgnnaptIqjmGrsRXCuZTLLJETmhGgwGCIVCJnaXlpZCIpFgaGgI1dXVjGcnjXxSqqKOMxn2CYVCFBUVYXx8nJ8rCQBQInS1EIvFmJubY64BdZPInIfUv0gpi+RWm5qaGC9JpNTa2loUFBSgpaWFO+9E9M3Ly4PH44HFYmFiYDKZzAkBIhgX+XsQr4KwmOQjQBMJSvBqa2t5YiIUCuH1ehl2A4C7ZjU1NbBarejr62P4ytLSEkP2chUapBKk1+sRDofhdDrR3t6OtbU1JorRFDI/P5/hAtSpKioqQlFREV+KPp8PdXV1GB0d5b1OSm0jIyMQi8VwuVwQCASoqan5q2RwAFzYyuVyyGQyNDY2IhaLsTEneU+IRCIEg0GMjY2xEhAZfUWjUZSXl7Ns9vj4OHdZSF1pdHSUMe8kC5rLAVapVCIYDLJBGRWUIpGIddTp76OGCL3X6elpRKNRhtvQJVRbW8sJaVtbG5LJJDZv3gyfzwej0QiZTAahUMgQ0vWCJnsfVIprbm7G0tISKioqMD8/D7PZzDCRrVu3wuv1QiQSfUjdiLrJYrEYmzZt4qkAfTfl5eV8RpGqkMlkyqlmQ1r2pFTm9/tZGaysrAyDg4OQyWQsflFTU4NoNMokeXqvU1NTqKurYyjc2toaMpkMJwGkLkMqWRKJBDU1NX9VUnlubo7xyz6fD6WlpVhaWkJ+fj4ThSUSCd8/6XQaNTU10Gg0UCgUPG0nB2mZTIZwOMyqXaSGJZfLGRZBKjoej2dd2WKaDBC0LRQKQaFQ4MyZMyx8kJeXx4o66XQaFy5cQF5eHhYWFqBQKBj22tnZyW7Y1PGlu+nAgQPslk3rJH7PepFKpXi/hMNhvmtIKY64BgqFApFIhCdNdXV1zFmpr6/HwMAA4vE4iwNMT0/zvT44OIgDBw4w4ZVU9fx+f05FLBI1SKVSXCyRH4vRaOQGHClhtrW1sZQ3FSn19fXMCSGCNvFrSHaYpHpVKhXkcjnkcjny8/Nz8pWSySSrXdF3TR4Q2WwWs7OzkMvlqK6uRmNjIzQaDVQqFWZmZhAOh5FOpyEWiyGRSNDV1cXwq2w2y2eT3+9nb7D5+XlIJBIYjUYWT1kvSIKcpkYCgQAajQbhcJglqEkUJS8vDz09PXA4HHwWFhUVQSaT8YRILBbDZDLBaDQyF468efx+P9/3wJXikAROrhZisRjBYBCxWAzhcBgqlYpzMo1G8yG1T7lcjsbGRpZjD4VCKC4uRlFREcNkl5eXWQ2OoKFUcBKHVKPRsKJnrjOEpqEEI8tkMjCbzXA6nSgqKmL/CgAM70yn09DpdJiamkJJSQl7phBsOJPJQCQSQSqVQqlUYm5ujsnwJA5BogG5pvDUpFcoFBAIBFAoFNyAIYVVWn88HsfGjRtx4sQJ9ssg88pMJsMiGAS7ontzYGAAN998M/txGAwGmM1mJJPJ/3syuEwm42SejIVWVla4u+JwONiHIBqNsrFJQUEBlpeXkclkeFRHHeT8/HzEYjH4fD62Xif9YIFAwGSevyY/RmMnOnxIymtkZARKpRJutxtLS0vw+Xxs7re8vIz8/Hw2lKKuNH00hPlbW1tDMBjEW2+9xXJowBWsuc/ng0gkypkwk8wqmcGRGY3T6eREi+AX8/PzMBqNyGazKCkp4RF0OBzG6OgomxqWlpZCoVCw5Bx1MkjJKBgMwuVyQSKR5CTrkMToB8dqWq2WiwQivhO2nTYVyeuSbNrs7CyTLkn6jTY24anJhTiVSiESibBKyXpB74E8RkhSkjCkJOlHF6bZbGbOBGlwk0un2WyG3W5nrhDhrynZc7lcjLGmv4sgTlcL+r4oESG3VTI+jEQiSCQS/DfQt0nvgyQYk8kkwuEwpqenAYDVlFZXV+H3+5HJZDA7O8ukaSr6cr1Ter5EuoxGo/B4PIztTyaTmJubw8LCAktizszMsOSuxWJBVVUVP5O33nqLCz1S1KFR/+XLl6FWq3kPEE8jV1DiRipFpN8+MjKCWCyGaDTKEIrl5WWYzWYmjxNRPB6PY3h4GEajkV1RyeDK5/NhZGSEkxq1Wg2Px4OpqSl2dV0vaC+SXCppoLtcLtTV1XEBkkgk4HA4oFKpGNZGF9Xa2houXLgAmUyGxcVFGAwG2Gw2zM/P8xlQWVmJoqIi7qDPzc2xkWWub45c4+lbKS0t5RH3/Pw80uk0MpkMZmZmkJeXx3tjamqKE6bp6Wmsra0xbJImHwqFAufPn2fTs0gkApvNBpfLBa1WmxOCSUUQNXDo211YWMDq6ioLOoTDYUxNTUGpVGJqagqlpaVYWVnhLjd1UBcWFqDVahmaubKygv7+fhQUFLCqoNfrxfLyMp8tuZ5bKBTic5c6mGNjY9BoNOxTQYovBKcgo7WJiQm4XC643W5WwiK1q2QyiWQyiVOnTrEmv16vZ/8VuVyO2dnZnM8tkUjwxM7v98Pr9eL8+fMQCATIZDJccFDiYbfbmQciFAqhVqtx4sQJ5q2ZTCaEQiGEw2HmG5WUlDB3kQoqUkJcL6gApHuQ1P5oHYuLi4jFYkzMt1gsWFxchF6vh91uh8/n43PD4XDAbrd/aM8DwIkTJ9Dd3c3f39LSEoaGhti7Yb2ge48m8DRBjkQibOS3uLjIflc1NTWsEDc8PMwKYUKhEDMzM3zWpdNp/g4CgQCUSiXnNV6vl6fXuSZB9HypOKbnQORgj8fDz5O68TS98/l8XJgQzIq8TObn57GwsMBFLhVNKysr8Hg8iEQiKCsrY3WjqwUVtplMBgKBgPlrer2ep36Uk5CpJTUhBwcH2Q+N+CDhcJgbsbQ3iHcWCoW4qUjCAbnON4LJU3OXOKbEb6RGCVkDEIxMqVQiGo2yrPfs7CzzISinAa6cy2NjYxAKhayol8lkuJGR67nR7wXAuQeplJlMJvYai8fjWFtbQ0FBAU+sL126hNnZWf7eVldXGcqbTqc5Tzp69Cii0ShmZ2dZ/GdlZQVmsznnxJb+Dq1WC6PRyF5lpOJH+TCdvxs2bEAsFkNBQQH7d8hkMoyPj0Ov17OCVzAY5Jytt7cX9fX1sFgsMBgMUCqV3EzONQD4YHzkicby8jJ39EnejEa0JEkWDAbh9XoxNTXFpFG1Wo3l5WXE43FW+7HZbLh48SI++9nPMmaTsJF9fX2QSCSMFQwGgxgaGsop20bkJafTiXQ6DZlMxh/44uIiVlZWIBaLWUYvPz8fTqeTZbwcDge8Xi9/CL29vSwbRlh9p9OJkydPckW/vLzMRLRc3T6SMKQutEgkYoy/2+3mDlNlZSVqamoYTiKRSPiCJpk5qvZJ3YQc0OkjnpycZMk0uiByJX60uSmBzGazWFhYYBMZlUoFs9nMkxiSQe3t7WXTRnq3Ho8HJ06cYO5OKBRiY6xDhw6hpKSE4Sj0PHIl86TzTG7DQqGQTSI/mNTQZX/p0iXuBjY2NvLPJ04QqYfRBTUyMoJsNguTycSQlrW1NdZYz6XeQYcpQa3ocqEkZW1tjbsfND2YmJhg/gsprcnlcvT19SEUCqG5uRkmkwmJRAJTU1N4++23sXHjRiQSCf4ZlJDkSpZjsRgXiKSzf/bsWZbZHRoawuTkJLRaLZN8yUlbLpcjlUphaWkJ/f39rGAU+l/Tug+6xpLEKJkYeTweJgxu3rx53fWtrq6ipqYGXq+X9wa9Y/r/eTwedtYtLy+HWCzG8ePHUVBQwOQ++o5GRkawc+dOVFZWMgmX4FIEgSFSPHUV14tUKsU8MrqQ6+vrmRNAeuLRaBQymQx5eXmYmppCKpVCeXk5AoEAlpaWoFarMT4+jlgshvLycn5u/f396OjowKVLl3Do0CH85S9/YYda8hZaL5LJJOvHU7G8srLCbr2UlITDYdhsNng8HmzduhWnT59mkjdhz71eL2ZnZ5FIJBCJRBhnOz4+zhAE+vkFBQUwmUw5k3ky/CQ5RDp/qeiSSCQ8KZTJZLh06RISiQQbt9E7slqtGBkZYY8mMjj1+XyYnZ1lOOPk5CSEQiEMBgNL6+Z6p6WlpZzQU8ebJCEdDgdsNhvy8/NRXV3NUCG/349YLMbwUjKWValUzJsg49exsTFubFCTZXV1lZsi6wXJwRN/JxKJoLe3lz0XCgoKeCpC4gt0TotEIr7PTp06BafTieLiYob7mUwmvhMikQgrJFLQHlovVldX2dyTEjKSVF1eXubJVzKZhEKhYDdrn8/HqoBzc3MwGAyYnp5GbW0tjhw5wv+WkrPp6WludiwuLkIul2PDhg1/dYJGEukikYih1jKZDD6fj9ESALiYJIjw7Ows9Ho9w+bm5ubgcDiwdetWzM7OQq1WM+xNq9Vy958maaRamCtI5piaIgsLC9BoNDAajZBKpWym6Pf70dbWxhLsNAWn4vzMmTMwm83Iy8vjc5i8oSQSCerq6hCLxTiRz2QyOe9UOgNJ9UsgEODIkSO455572GMlkUjA5/MxJDovLw8DAwOM8CBpYBLqISI2udtT/kRcTfJ9IljsekEwokQiwQIZxKclbxmv14twOAydTofZ2Vm+R6jIDAQCPFV2uVzcACNPk3g8zmIkZrMZU1NTsNlsKC4uZnW5qwU1n2KxGDcWL126hFtvvRVCoRBzc3PweDwsxS0UCuHz+dDZ2cmke51OB5/Pxx4XsViMp5IEmzx9+jRPVQUCARtW55LeJahuKBRCJBJBLBbD7OwsYrEYF7L0e8nTa+vWrZBIJEgmk4jFYpiZmeHJHgk/kBxuJpPBpUuXMD4+zo37aDTK045cCIEPxkcuNAoLC3H+/HkmgNDo+tixYzxWpK5RMpnEjTfeiLNnz+LIkSPo7Oxkdj+NdHQ6HXepxGIxZmdn8fbbb6OiogL19fV8WBDvIxfWlZQXFAoFd8kuX77M1R5w5XCiSnhubo4nGTSOl0qlLD968eJFNl6h7gOZhQHgDzibzbJ+8XpBRU1hYSHS6TTC4TB3YOngIvm9t99+m2FRJHkYDAbZryGVSuG9997D/fffj7y8PDaBoc4BjVnJkZi6w+u+/P8dVxIUyeVy8TicOnhETAqFQnA6nay1T8kqQaySySTa29vR2trKv5Okhl955RXceuutrOpEWv25iketVovh4WF22rXZbEgmkzx2rqurw8DAABsylpSUsLeHVCrlA8Hj8SCRSHBBSF2rD5ppCYVC9Pf3sxFYMBjMiUnX6/U8+sxms3zhmkwmTvRlMhlLfZaXl+PixYswGo04ffo0CgsL+T2Xl5ezAyx9UwqFAh6PB4WFhZiamkIwGOQLn+Bt64XJZOLn9EH3aLoECG7k8Xhw6dIlhup90ASPkk+3281TGVIocTgcyGazOHjwIJRKJU6dOsUwNVKwyBUEAzSZTNw5IenpoaEhJliT9wW5UpMXBBU0RG5++eWXceDAAZjNZjZL83g8TLAnNSWCNOQ6GPV6PX/3BH/p6+tDLBZDS0vLhy7hTCaDiYkJpFIpXLhwAYcPH0ZjYyNaW1s5kdi2bRsKCgp4z5BUtMFgwOXLl7kZEwqFsLq6mhMukp+fj6GhIchkMh6Tv/nmm1AoFNiyZQv0ej1mZ2d5Gjw1NcXwJKFQCLvdjuXlZdxwww04fPgwXnnlFZSXlzOJMRgMYs+ePVhcXERFRQVzoojcSO7nVwuhUMjGUmazmb0HQqEQstksWlpaGKJITtBUGFGhQAkQ+YJQ8UEuyXNzc6irq4PFYuFp7/LyMndg1wtySye4VTabZQ+lRCKBpqYmxGIxuFwu5qcEg0GUlJRgZGQEer0eSqUSr7zyCrZu3Qqj0chuyOFwmA0aCZZInBGa7OeSkM1msyz8QJCzkpISGI1GvPDCC7jtttuQyWQ4IZLJZEgmkzh69Cgr6hAUz2azwel0YnR0FIFAAFVVVWhubmavKjKZJNSA2WzOKeEtEokwPj7ODUKJRAKlUskcJnqW5D2h1WqxYcMGFiAhP4BIJIJMJoOf//znSCQS2L59O7/Hzs5OzM3Noba2FvX19dxVX1lZyZnMy2SyD8mNqlQqLCwsoKioCHa7HWVlZVhcXOTpweTkJEQiEQoKCnDp0iX23SIZ56997Wvo7u5miBQ1PaPRKPN6qKO9traWE+IYCoV4IqbRaBiimkqlMDMzg+7uboZRp1IpNpUj/yKPx8M+Ert27WLp0tbWVmg0GkxPT8Pr9TK0sKqqCufOncN7772Hubm5nGRwUgojIjO9JyqIaKoTDocRDocBXBEFKC8v5wSYppL5+fls1kdGnVarFS6XC1arld24abJBxp3rBRnMUQFMcOO+vj7s3bsXYrGYG8Z1dXUMiybzSbvdznxgaqBaLBbYbDaextJkgQxE5XI5N8tziTbQv/9gs1wmk+Gtt97CzTffDJVKBavVypK8Bw8exMLCAl5//XXodDpUVVUxgqKjowPPPvssT/KoKWOz2VBSUoKGhgaIxWJMTk5ykzHXBI0aTXl5eVzcer1etLW1cQODpn7UVKGiMpvNsprazp078dJLL0GlUrGR8MTEBADga1/7Gvr7+7nxHw6HuRGUSwjpgyHI5mrxXYtrcS2uxbW4FtfiWlyLa3EtrsX/h/jIHI1rcS2uxbW4FtfiWlyLa3EtrsW1+KhxrdC4FtfiWlyLa3EtrsW1uBbX4lr8n8e1QuNaXItrcS2uxbW4FtfiWlyLa/F/HtcKjWtxLa7FtbgW1+JaXItrcS2uxf95XCs0rsW1uBbX4lpci2txLa7FtbgW/+fxkeVtf/rTn0KtVmNtbQ0GgwE6nQ6Li4uYmppCe3s7y9CKxWI2GIvFYjAYDHjttdeQn5+PjRs3sitiWVkZSwF6vV72PLBarbBYLCy7GQgEkEqlEI/H8dWvfvWqa/vd734HlUrFnhNqtZpdJpPJJFpaWhAIBGC325FOp1FdXY2JiQl2hVxbW2NdfrPZjBMnTrBuuE6nQ2VlJVKpFMxmMztrAmCN9bm5OTzyyCNXXdsPf/hDdi9OJBJwuVwsTafRaFBWVgav14ulpSWsrq5CLBYjFoux5GVeXh77B4yMjMDhcKChoYHlSIErDt8Wi4XlXBUKBVQqFVZWVuDz+fD5z3/+qmv74x//CADsUk7eE6FQCGVlZezSHAgEMD8/j61bt7JspkqlYg1ocgotKiqC2+2GVCpFIpFAIBCA0WhEMplEa2srPB4PLly4AL/fz0ZbDz/88FXX9txzzyEajbIDN+mmh0IhJBIJNDY2wm63IxKJsFmf2+1GZWUl8vPz2XE5mUxiZmYGy8vLiEajaG5uhkKhYIfxZDKJ2dlZlJaWwu/3Y3l5GcXFxVCpVNi/f/9V1/aDH/wAarWapYVJBz4ejyORSKCrqwt5eXnw+Xzwer2ora1lKTmxWAyxWMyeHUVFRbh48SK0Wi1MJhNWVlYQCoVYB5807GOxGHvEOJ1O/Md//MdV1/azn/0My8vL7BlCP0+v17OMKUneJZNJ1NbWori4GA6HA8AVwymVSsWyzOSALZPJ2IOirKwM09PT0Ol0kEgkiMVi7HieTCbx2GOPXXVtAPDLX/6SderJaZe04MvKyiCXy+H3+9mZmCRMyV2Y/Emi0SgMBgMikQjLkZJM88TEBMtKkpEiudGvrq7i05/+9FXX9qMf/YjlLEnLfX5+no0tyQMAAO89iUQCvV6PxcVFCAQCNmKUSCRQKBQsU037ymw2Q6/Xs045mZmRz853vvOdq67t8ccf5/OA1iASiTA3N4fu7m6WexWJRHy+jI2NQaFQsJcH6d5XVlayBCRJPZN2v9FoZLM8+kZI5nu98/fZZ59FLBaDWq1GaWkptFotlpaWWAZTrVYjkUjwGavX65Gfn4+VlRX2t1GpVGzwGgqF2MOA9sv09DQ75ZIcJBkYxmIxfPGLX7zq2n76058ikUigqKjoQ94RJI+5trbG3jqkLe/xeFhbf3l5GXl5eSzZqdPp+HkJBAJ2zhYIBNBqtSwJSWaeUqkUDzzwwFXX9uqrr/I9qdVqeS/Qfl1ZWWHJa6lUCp1Ox98/eQeIxWLU1NRgcHCQJUFJsp1MacmQVq1WY3JyEsFgEDU1Nchms7j11luvurbf/e53kMvl7AlCe9/n88FqtbLZG5m80p0zNzeH4uJiBAIBNqyku0Uul7OnSjQaRV1dHcbGxtgElkzCqqqqEIvFcO+99151bT//+c+RTqchFArZ1JHMWg0GA+LxOLshk4GpUChkfyH675RKJVwuF5tv0vsiAz21Wo2SkhKWFifpfqVSib/927+96tr+9Kc/weVyobi4GBqNBul0Gm63Gy6XCzt27GBfp3A4zPfg+Pg4FhYWWMKWPFLIC2J1dZUl5j0eD6qrqxEKhVBaWor5+Xk23QUAr9e77n3/wgsvQKfTQSAQsHcOOaV/UCI6EokgGo2itrYWvb29kEgksFqt7OMkFov5vqipqUEqlUIymWSjOXJXz8vL428oGo1CKpXi+uuvv+rajh8/zsa3UqmU75tgMAidTsfeDeR4X1RUxO+EvMJEIhGEQiGGh4dZtnpxcRHpdBp6vR5msxnj4+MwGAxsTpfNZtm88tChQ1dd23PPPcemwCqVio3qlpaWYLVa2deLDHirq6vh9XqRTqf5njIajbw/VSoVXC4XWlpaoNFoeD94PB6WGBYKhYhGo1hYWIBKpVr3nb7++ussjU97kEyUKQ9yOp1szLdhwwZotVr4fD64XC6WWCazxc7OTvYmEYlESKVSCIVCbMNA51MqleIcaM+ePVdd2wfjIxcaZP9ut9sRDodhMBiwvLyMtrY2BINB5OfnIxgMIhqNQiKRoLCwEC6XCwsLC6iqqkIymURfXx9rX5O9vNFoRFlZGUpKShAIBODxeDA2Nga5XI68vDzeqLlMS/x+P/sWkLMnJZqTk5MwGo1seiYUCjE0NASNRsPOvqFQCBKJBJWVlXC5XCgpKcHs7Cx/iMvLyzAajZidnYXBYOBNQ6ZMuYyPIpEISkpK2OE7kUiwkR4l5ZQkAFcuEHIx/eBhGQqFIJPJUFRUBJvNBoPBAJFIhFgsxo7TVqsVDoeD/1673Y7i4uJ11xYKhdglnRJxj8eDuro6rKysQCKRsH+FxWLBwsICBAIBfD4fLBYLjEYjJ/j0cZI5TSaTwcrKCtRqNV5//XW+zFtbW5FIJNggZr0IBoMQiUT8Dshsp66uDqOjoxgaGkIgEIBYLIZcLofD4eBnQi6rKysrsFgs7Hw6NDSE2dlZyOVyXs/Y2BgnN3Tg2u12GI3GdddG75sSFXL4pe+VkhVyAA0Gg5z80TdHfirnzp2DTqfjwjAej7MHwcrKCmvCk/O70+mESCRad22BQAA6nQ7pdJoLgFQqBb/fj3g8jvr6eiwuLrLR1sTEBLuQ0gFPf1symYTb7YbZbIZAIGDnbvqO19bWOHEg07m/ZuBDBQmZLQYCAaTTaXa0l8lknMTTniAvD3JdJsfS6elpNDU1sZlVJpOBUChETU0Npqam2KCO1rW0tMSJ2tUinU5zUkSGbfQsVldXIZVKOaHRarUQCATskqxUKhGJRGC32xGLxdDa2orl5WXEYjHodDqYzWZOdiYmJlBbW8s651qtlg2Qcj03MnqiIpWaH2RoSe+cvvvCwkKMj4+jrKwMwWAQdrudL56ioiJEIhEubmOxGDo7O/HWW29h27Zt7KpLTt9Wq3XdtcXjcb5oPR4PGwlaLBZ4vV420SL/D6FQyAUsnVXhcJhNXxUKBebm5rC2tgaj0Qij0YhsNssuyF6vl4s/p9OZ0+gQACfwH0xkyGmdTO7C4TCbv4XDYWSzWfYUkkql3JjQarUYGBiASqXiZEChUHDils1muVC12+05/XioMKRvyefzscEZuSRTEqPT6TAzM4MDBw5gamqKjXCpYHc6nezHEYlE2FmZvj+fz8f7VaFQcMNmvQiHw2xwSWcImSsuLy9DrVZjenoaqVQKTU1NWF5eRklJCVwuF/939PsLCgoQj8eRzWbh9/sRiUSgUChQXV3Npl/Ly8tsPEueP7meGzl0k++QQCCARqNBIBDgQp/McGlfZbNZCAQCTnzprCkpKUE8Hsfq6io3P1ZWVhCNRrn4ofdN5/J6QWcGmYTSPs3Ly4PL5WJnbb/fz98Y+cisrq7yWUzu9TU1NR/ymSEjt0gkwp48KpWKk2UqOK4WZLSaTqfZ7NDr9aKpqQk2m409w8gMk4xpySCS9olCocDi4iK6uroQCATYRDIajSKVSrHDObmOCwQCbhTlWptUKuWGC70nuicSiQQ3MwoKCuDz+aDVapGXl4dUKgW32414PI6ysjIYDAZUVFTg2LFjkEgk0Ol0kMlk7EdGuaJCoUA2m4XP54PJZFp3bfR3xeNxRKNRbgIHg0FUV1ejsrISfr8fmUwGpaWlEIlEqK2txeTkJEpKSrgZSybHfr+fz7FAIICFhQUYjUY4nU6sra1xPgqAn8F6EYlE2LeLmuPZbBalpaUIhUIwGAzsvZSfn4/Z2VludpWXl2NxcZG91BKJBDweD6anp6FQKDjnoMJIr9cjlUrx30G530cpND4ydIqmFGSwRB/C4OAgTyaog+p0OrG4uMjOlXShKhQKVFZWstsgdR5UKhVkMhm7XqtUKjaho/9fLodr6qSSoRlV2NPT0+zSmMlk2CE6kUhwB8vlcmFxcZEdTx0OByKRCHQ6HYqLi9kAZ25uDrOzs5zYhsNhOBwOhEKhnBddXl4em+d9sJPidDr5uZBFPB3Aer0eBoOBk/VsNguVSgWNRgPgikswGVmZzWZ2laWOJ3Wx5XI5m9VcLZaXl+F2uzkxD4VCUKlUbHq2urrK7sr0c2prayGXy3m6otPpUFRUBJ1Oh7Nnz/IEiAwKbTYbT33C4TAbtFG3dL34YGKuVCohFovZqTY/P5+nVzSRUqvV3Ckml2lyAyYjOpFIxB0Wr9fLSQp1xRKJBJvdkbPs1WJlZYUnbXK5HEajkV1NrVYrfD4f4vE4J6w+n4/3DLmjy+Vy7mSSGZJcLmfTqIWFBe6+0d/t8Xi4079eUPGTzWaRyWQgFothNBrh9/vZxVqlUkGv1/PzKi0t5e+KDKeomzo3N4dMJsOFW1FRERch2WwW6XQa6XSaTb1yXSYAuFsoEom4GSAUCnkCFovFeJLj9Xp5TVSc0/fg9XrZwfqDRny0N6jTKhAIOKmMRqM5ncGpYFxeXuaiRSgUQqfTcddHp9Nx4ULPjrqO9DvLysqgUql4ykXTECp2UqnUh8wKycXdYDCsuzZKMighSafT0Gg0yM/P/5BTeDgcxszMDF+k1LkXCARQKBQoKSnhn0VdQ6FQCLlcDrfbjWw2y4UdFZXkkL1eUCL+QRM3crsViURsarq8vAyPx8PFYjqdhtPp5OIEuJKojY+PAwCbwy4tLWF0dJSLXZ1Ox3cNgJxNKHpe0WiUmydSqRQejwdqtZr33OrqKhYXF5HJZGA2m7G2tgaHwwGHw8G/U6VSYXZ2FvPz82wCSfdLJBJBYWEhSkpKeHJC3/F6QV1auhfy8vIAgJsJMpkMWq2Wm1Grq6u8tymxEolEiEaj0Ov1uHz5Mqamprhgpu8+nU6jsLCQk7a1tTVuDKwXy8vL7CRNayMj1+npaaysrPB7pQYfJfcLCwsIh8P8PIqKijAwMMCFMZn0ksM4JY+ZTAZLS0twu9059wIVU3TukIs4FflGoxFyuZwTVKlUyoXy4uIiF130fqg5SkkVFe15eXn8/VBzhQq89YKS+ZWVFZ6WUQFLnXC5XM5GjCKRCDU1NTzBI4NDKmq9Xi+kUin/LI1GA7fbzQWwVqtFOBzG/Pw8IpFIziaUVCplE0y6e+nsoqaJRCLhoj4ej6O5uRn5+fmw2+1IJBIoKSlBYWEh3wnUtf9gIanVannKRWfN2tpaToNISpLT6TQ3omjyrlQqkc1m+exdXV3lQplyJ3q+1PCj78JoNMJkMrEBpVarZSQKAG4g5covxWIxF3o0yaNn6fF4eIojEAiwsLCAZDIJkUgEg8HAptFSqfRD/zehLejvtdvt7LiuVCo5jzWZTDn36crKCjdlqQCgwi6dTvMdRU7wH8yFqbDW6XRYW1uDUqnkxiM1TgKBACYmJrC6ugqfz4dsNsvIJTpHPkp85ELjg1VpXl4eVCoVpFIpuzNrNBpYLBbukNvtdjQ1NbGtfSaTQXFxMbq7uwEALpeLF0xuk+RE3NjYyKO6dDrNXZ/1glyQqQhIp9PweDxYWFiAyWTiAzGZTMLlcrGrISX/dNhEIhH4fD6GEJBDLrmLUvdIqVSyQ6hAIMjpcJ2fn8/FjVKphMFggFqt5s6HwWCAUqnkDrtAIEBLSwssFguPr9RqNYqLiyEQCJBIJPjDz8vLQ0VFBbtCCgQCWCwWTjYLCgpyFhr0N1B3USKRwGg0IpVKMZSNNiQVRiaTiR1Y5+fnIRQK0djYCLPZjFgsBovFAovFAq1Wi0gkApvNhoaGBt6ki4uLnETnuoQJJkDJJY0B3W43dDodSktLUVtbC6vViry8POTn50Or1TLMIC8vDyaTCUajEcvLy5iZmYFMJkNVVRUMBgNCoRDC4TCqqqrY6ZOSK3K5z7UX6DCWSqUoLCzkLhh9//Q+4/E4vF4vTCYTO3QDV+Bq6XQaZWVlXGTqdDqUlZXxpUNwRLPZjGQyCafTiXQ6nXPaolarkUql+BAhmCNB8nw+HyfJMpkM+fn5aGpqglQqhUgk4skWJcaUOEajUQBAUVERrFYrF78E96PLM9eBTUHwAalUyskeFXlUVNJhSG6stEfy8vL4QC4uLmb3X41Gww60DoeDIXcEbaGJR66JBn2jVByYTCZOTOmZ5ufn8/OXyWTsSE4NlaKiIuzYsYMvEkq46RJ0Op2Qy+U87qZEPpvN5nyvYrGYCzmCVpSVlUEqlbLzK3XsCKKpVqv5ZwqFQuTn56Oqqoovbkr26LnOzc0hLy+PIYrhcJgd1XN1cZVKJUKhEID/t9CNRqMIBAI8KSb4VDQa5a69UCjkxI8KVWrgqNVqWCwWhjn6fD6GbVgsFl67SqXKmZQCV5IYahzI5XIu2GQyGX+DCoWCk6+amhqGVXo8HsTjcZSWliIWi3HhQUUU7YFIJAKDwYDCwkKemFBjY72ghJSSYZrI+v1+TvD1ej1DB5VKJcMqaEKjVquh0WhQXl7OcDWZTIaSkhLe+wKBAMXFxZxAk0N0Lqfm1dVV/h1arRaFhYW8tz0eD0KhEIqKilBYWMhdf+q8U8NAr9ejtbUVJpOJk+ANGzZg06ZNfB6aTCbeUysrK3C5XIhEIsjPz193bVRw0vdOnXAqWGtqamA2m7nAVKlUaGlp4QSOCrGioiJebzab/dCzBsDJ/gcLjb92N9DUBLiS2Ov1ehQUFHDhQ+e8RqOB3++HWCxGaWkpdDodAHDCV1RUxM+A9h4VfDQFqq2thUgkYhgWndHrBUGd6b/XaDR8Z2azWWi1Wm5mUiJvtVqh1+t5smaxWGC1WqHRaLC0tMR7PRaLcWOUvguNRsP3Ck3/cj03+t/FYjEEAgFSqRS/X4KQq9VqRCIRhuGFQiGGxtE0JxQKwefzce6kUCiwtrb2odyP0C30XHLtU7lczvebRCKBVquFTqeDWq2Gz+eD3W7ne8LhcMDlcjE0Pp1O8xTAYrEgHo8zrJIKR4vFwrBRrVYLvV7Pz4D2x3pB328ikeCpBcFg6d6k30cFcnFxMSMc8vLy+MyiZgHRI+jvoVyVpr4CgQDpdBpra2s5HdU/tM6P9K9w5RK2WCxs907Y2ptuugmBQAAdHR2MbS8sLIRYLIZSqYRer8e+fftgNpvhdDqxvLwMm82Gnp4eLC8vY3Z2FiMjI1haWsKmTZsQCAQglUoxMzMDqVSKkpISrK6uoqamZt210QdJSUIoFILdbufDhTDyq6urqK+vZ5zp1NQUWltbUVFR8SFoRGFhIY/6M5kMQqEQ8vPzIRaLGXtvsVjQ3t7O3cr1wuPxwGAwwOPx8Mt3uVxoa2tj/K9CoUBxcTG2b98Or9eL8vJyZDIZ1NXV8fp0Oh2mpqZgNpuRyWTQ0tICs9mM0dFRKJVKnio5HA7uhC0sLPzVD6GlpQUymQw6nQ7l5eWw2+0YGRnh6RUd5iqVCm+//TbGx8chlUpx9913M1a0srISTqcTX/jCFzA1NYWxsTH09vZicnISFosFfr8ftbW1UCqV6Orqwt69ezlhWC98Ph9/M7Th3njjDe4q0sQHuJL8LiwsQKFQ4NKlSygrK0NpaemHOuCFhYXclZRIJFhZWWG8JRWDdElcunQpZ9dKLBajrq4O0WgUU1NTGB0dRV9fH1paWtDb24uCggIkk0nE43FYrVZEo1GeeHR0dPAY3Ww249ixY9i5cyfGx8eRzWaRn58PqVSKtrY2OJ1OhloVFxejubkZAoEAGzZsWHdtNCXz+/0QCATQ6XSYmJjA1q1bsWPHDuTn58PtdjME8sKFC4jFYojH4/xsCJokl8tx6623IhaLoba2FiqVCseOHePnrlAo4PV6IRAIoFarEQgEcsIIAXDS4/f7P9RBNxqNkEqlqKys5M5tc3MzVlZW0NLSwkl2LBbD6uoqOjs7eTRtsVigUqm4o15VVQWPx4NsNouFhQWsrq6ioKAAeXl50Ov1665NJBIhPz+fE2KJRAKlUonZ2VkeMdtsNiwsLMDn83HxHQgEEIlEMDc3h4mJCYjFYthsNuzfvx9erxcul4sbBDU1NQiFQjx1IyhpMBjM+ewIM05wDIlEArvdDrfbDb1ej5KSEqjVapSXl+P666+HQCBAa2srIpEIHA4H5ufnOTGw2+2IRqOw2WwMQUqn09i1axcCgQCqq6sRCASgUqlQVVX1IUz21SKRSKCsrIzPykgkAo/HA5lMBp/PB5VKxc2iDRs2IBqN8uXa2tqK0tJSnr5NTU1hy5YtDCsQiUSoqKjAgw8+yJfp9PQ0T8PpslwvYrEYampqeHpGZ/yhQ4dw+fJl/m8lEgkaGxt5eqZQKHDo0CH09PSgtLQUGo0Go6Oj2LhxI6xWK1pbW6HT6eB2u7FlyxbIZDIsLi7CbrejqqoKGzZsgMvlygnJoHONsPcE29q9ezfcbjfEYjEn3sSpKC8vRyqVwoYNG6BUKjE9Pc0d709/+tOoqqqCXq+HRqOByWRCaWkpNx8IAmwwGOBwOHJ2mKlxtbi4yA3G6elp3HDDDairq2NIMyV5S0tLUKvVmJiYwJYtW6BUKnHx4kX+Gz772c/C7XbD4/Fwoq/X63HhwgWe+mu1WjQ2NkKv12Nubm7dtQFAYWEhJ3cE6dq4cSPvM+rUU6OCkrfrrruOIVtGoxFKpRLt7e1YWVnhgjoWi6GpqYm/BZ/Px82By5cv52ymxONxVFRUfOj5mEwm7Nu3D+fOnWOojkQiwd69e3nqnpeXh5KSEiiVSoacvfvuu+jo6MD4+DiUSiWMRiPW1tZQUVGBoaEhKBQKTE1NobCwEJs3b+Zze71IJpMoLi7m+zw/P5+hY6OjozwJJlg5TYyWl5exe/dumM1mLC4u8vNvampiTpJAIIDL5UJFRQWcTieSySRPp8LhMIArucZ6QRMwmtTI5XI+J2ZmZmCxWBi+pVKpUFlZidraWgSDQcjlci4gLRYLLl269CFezfLyMsRiMfbt24dgMAiDwQC73Q6/349gMIiBgQEu9K4W9L4cDgfS6TTzMu69917k5+ejpKQEmUwGRUVFuPnmmzE6OoqCggJ4vV6UlpYimUwy3HFoaIhhZfF4nPk7VVVVcLvdKC0txdjYGDcbCA2zXlBzzG63M8JicHCQm2QCgQDz8/MYHR3lgq2qqgqTk5MoKCjgiUl9fT2Gh4dxww03IJFIIBwO8+++//774Xa7UVZWxvd1JBLB0tJSzgHAB+MjFxqEw9uzZw+uv/56VFdXIxaLYWhoCLfddhvm5uZgs9mwtLSEpaUl7NixA9FoFBs2bMC3v/1t9PX1obW1FaFQCHv37sW3v/1tJuiZzWbs378fKysrePjhh2EymdDa2orFxUXMzs5i9+7duHDhwrprW11dxcjICFQqFSoqKmC1WrFp0yaG7Zw4cQJut5u7nbt27YJYLMbWrVvx05/+FEeOHIHVauVuvkwmQ3FxMWw2G1wuF2ONH330Ufh8PhgMBkxOTqKvrw+1tbW8ka4W1FWqr69nrLvVasV///d/85jZ5XLh3Llz+MlPfoKmpiYcPnwYRqMRR48eRW9vLwQCAeRyOaxWK0QiEbq7uxGJRBCJRFBfXw+VSoVvf/vbCAQCyMvLw+joKE6ePMkQr/WCRoybN28GcIUAuLS0hDvuuANf+MIXYDQaUV1djaKiIoRCITzwwAN44403GNa2ceNGdHZ24uWXX0Ymk8Gzzz6LsrIyKJVKdHZ24pFHHoFCocDOnTsxMjKC+fl5DAwMYGlpCT09PTlhLEajEXa7HTqdDpFIBF6vF5/97Gc50SYSE/EPHnzwQfT398NiseCNN97AyMgIjEYjMpkMCgoKUFNTw1ANh8OBYDCI48eP48CBAzh9+jTDxex2O7Zv356zCEokElwQFhQUYHV1FU1NTThy5Ag6OzsxMTHBcACXy4X7778fIpEIhYWFXDhVVFQAAKqqqiAUCtHZ2YmVlRUMDw9jZmYG09PT6OrqQjAYhNlsht/vx/DwMJaWlvDmm2+uuzaCUphMJni9Xpw5cwYtLS2YnJyETqeDzWbjAresrIyfW2NjI2QyGRoaGtDV1YWZmRkUFxfjzJkzqKqqgkqlgslkQktLC+bn53H33XcjFosx/8rlcqG+vp47Yrm+OepsNjQ0QKfTwWKxQCqVYufOnThz5gxDMXp7e1FSUoKxsTGYzWYu9gOBAKanp2G1WjE7O4vKykomDM7Pz8PpdEKv1+P8+fMQCoUYHx9Hb28vysvLc3a/iSRI30pfXx8kEgmmpqbQ2NiIhoYGLpgjkQh6enowNzcHlUqFSCQCi8WCDRs2YHJyEgqFAv/wD/+AlpYW/hsPHTqEkpIS6HQ6hEIhFBcXw2w2MwQqV+eKLreGhgZoNBpMTU0hGAwiEAgAAHepo9EolpaWYDab8eSTT8Lr9cJgMPC7OX78OG644QYsLCzgwIEDMBqN0Ov1aGtrw8LCAr761a+iv78fcrkcAwMDOHz4ML+z9YLwvQaDASUlJWhubsbf/M3fwOFwYM+ePTh//jxWV1c5Mb/77ruxuLjI7zabzWLHjh3YunUrFznt7e0wmUxYWlrC8ePH8etf/xo33XQTRCIR6urqIJPJuHHkdDpzfm9+vx/btm1DT08PysvLsXHjRvz617+GUChkeBs1Wz75yU9iYWEBmUwGhw8fhkAgwObNm5FMJnHbbbfh2LFjXFhoNBr09PTA4/GgubkZf/zjH+F2u3Hy5En09fVh165d8Pl8666N4I101iwsLDDPoK6ujrva2WwWiUQCVVVVGBsbg1QqxTvvvMNEzNHRUeTl5eGpp55CS0sLQ4aKi4uZo+jz+aDRaOBwODA3N4e6urqcDTKj0QiVSsXTep/Ph9LSUng8HsjlctTX16OlpQXNzc2or6+HVqtFf38/UqkUgsEgduzYwUIfLpcLjz76KDZt2oSqqipUVFSgu7sb8/PzqKiowNjYGCQSCTweDwKBALZs2bIuMRcAJ+NSqZQnrmazGQ6HAwcOHGCYbSaTwczMDBeQHR0dOHXqFNxuNwoLCzE8PAyz2YyTJ09Cr9fDYrFwo2dhYQGFhYWYm5tDW1sbQ3t27dq17roAcCNxy5YtaGlpgdfrxfT0NJ555hnceuutcLlcGB8fRzAYhFqthkQiwcTEBHfU29ra0N3djbGxMXR1deHhhx/GgQMHeCLa3NwMl8uFgwcP4s0334TFYmHo+MGDB/k8uFrQ1G7Tpk3Iy8vDwMAABAIBfvrTn2Lfvn2QSqVQKBTMeTl48CA8Hg+i0SiOHDkCn88HpVKJkZER7N27FzMzM9i8eTMaGxvR1NSEjo4OOBwO7N+/H8PDw1xkE49jampq3bVR44QI3wQbmpmZQU9PD3NcA4EABgcHWXigpaUFL730Ei5evAilUgm/34+/+7u/g8/nQ1VVFUpLS2E0GqFWqyGVSnHrrbcimUxi+/btKCkpgVQqxbZt23gie7WgPXDLLbdg06ZNWF1dRW1tLV5//XVuqtJzm5+fxwMPPIDLly/DZrNBr9fj5ptvxoMPPgiz2czv9sCBAwwBraurg06nwy233AKn04menh40NDRgbW2NIanrhVQqRXl5OYqKihjKddNNNzF/yu/3w2g0YsOGDbBarejp6UEsFoPVasWvf/1r2O12WK1WJBIJPPTQQ5iYmMADDzyAgoIChMNhpFIp/OpXv0JFRQVTB4Ar58MNN9zwVxsCFB+ZDB6NRqHT6TA0NMQ4T+osvvHGG3yBUyfY4/GgtbUVs7Oz+O53v8vdwPn5eQwNDaGyspIJqcvLyzh69Cj+8pe/4Otf/zpDDGg6YrfbcyZ+0WgUnZ2dsNlsDA2x2+3Q6/U4ffo0Pv3pT2NgYABzc3PQaDQYHx9Hf38/tmzZgoceeggejwfBYBBnz579EEm3qqoKAoEAc3NzqK+vh0Qi4UMduNK9GBgY4KTxauH1elFdXc0EYMJy33HHHVzZTk5OIpPJoKenB08//TR/eAcOHIBAIEA4HGb4B40g9Xo9XC4XLly4wDyDXbt28cVNMCviclwtwuEwampqMDk5CY1Gg0996lNYWFjA/Pw8kskk3nzzTYaeGY1GrK6u4mMf+xgGBgZQXl6Ot99+G9FoFBs3bsTZs2fx0EMPYWBgADabjYuAc+fOoa6uDkajEQUFBazgdfTo0ZxdBFJroqS6YK+5AAEAAElEQVSTMK+lpaV45513sGfPHpw+fRqZTAb79u3DCy+8wFMStVoNh8OB8fFxxk9nMhkYjUYmSjc1NeHQoUP4wx/+ALfbzetTKpWMeVwv1tbWmGCpVCqhUChw8eJFVFRUIJVKob29HZOTk4jFYjCZTLh8+TImJiYYJ+52u7mzH4/HodVqIZFI4PV6EY/HodfrodPp4PF4MDc3x2NUglnk4o8sLy8zpCYejyMej+PChQuoq6tjUi81BGgdmzdvxvvvv4+2tjaEw2EsLi6itLQUQ0ND/JwpsSJ4xFtvvcWj4NXVVSQSCfT39+fE8gNXirRsNsskbSKaZTIZnDlzBnV1dZidnUU6nUZ7eztDFi5duoQtW7YgGAzyJMPhcODMmTNQq9X8PgQCAbq7u/H222/DaDRi27ZtyGazmJmZwfHjx2GxWLBz586rrs3j8bAwgFwuR1VVFVZWVrBv3z4sLCzg4sWLrFSn1+vxzjvvoKamBn6/HzfccAOCwSCrhADAV7/6VUxMTEAgEMDhcGBqaoovz7y8PJ42Eqwz16FNSmRutxsrKytoamrC1q1bcfHiRYZNEVE0k8ng1ltvhcViwfnz52E0GrG0tASPxwOtVouzZ89iw4YNOH/+PHfDieP1i1/8gi/Wzs5OiEQiTE9Po6SkZN21xWIxlJSUsDpVIpHAn/70J2QyGTidTgiFQhw9ehRSqRSbNm3C888/zxBRk8kEj8eD06dP8/7p6+vD7OwsQy9ojE/iBEtLS9yxHBsby7lXCVc/MjLCHTqj0YjCwkLmbIRCIeZcLCwsMF/w4x//OIArBQARr3fu3ImJiQloNBp4/x/K3ju68fJM+78sWbaKLcmSJduyLEu23Hu3pzLjqQwzAwwQQksgkLYsv2Q3dVM25c1udpPsbsKmQhJIKAFCyVAGmN7sGffem2zLqpatZkuWZf/+IPf9DueMBe9zzp4FMuWx9P0+5b6v63O5XJienuaO+H333Yfh4WGYTCZIJBJ0dXXFNHKS/46M/jeCSoqKipjE5fV6+Tnc2Njg79Tv92NwcBB6vR4rKytobm7m6jsdzGpqanDp0iVkZWWx6TMlJQVzc3MxZYRLS0vIyclhiWtCQgIcDgd/zwqFAu3t7dxdTkpKwuuvv46MjAyMjIww+S0zMxPXr1/Hm2++iZaWFnR2drIUxWQy4cknn0RGRgZXRkOhEEZHRzE+Po5HHnnkpnPz+Xx80SQp2erqKoqKinD9+nXodDosLy9DKBRiz549aGlpYYM9/T1UmT537hwb54eGhuDz+bg4J5FIIJfLuSuXkJDA6/FWg/xOHR0dSEpKQllZGSKRCPtsSEoEfHB5I3UHHaAXFhaQn58Pk8mE3t5e/OQnP+FiJB04+/v7odFo0NzcjIyMDD4jTE5OxuxS0dmMPEDU5aV9f2RkBImJiUhPT+dOgkwmg0Kh4HX51KlTbKKORCLo6+vjoh8A2Gw2/PKXv0ROTg7C4TCkUil7b2JdbDc3N5GZmYlQKMSUQ5/Ph46ODlbTEEk0OzsbMzMzqKurw9jYGAoLC6HT6aDRaDAzM4Pr16+z327v3r0QCASYn5/Htm3b0NfXB71ej/7+fgYCUHd6q0skeVAmJycRCAQwNTXFn2dqair27t3Le6pUKsWFCxdY2k6XYbfbjfHxcd733nnnHT4/5ObmQqPR4Nvf/jbUajXUajUqKiqQn5+PK1euxOygkfSVPFwul4s7VgQuom77wsIC9uzZg6ysLPh8PpSXl3NnRyaTYWxsDJubm0w2o45VcXExuru7ccstt8DlcrGk/qOAEjeO/ycz+BtvvIG+vj6srq5CrVbDYDCgra0NqampaGpqQkJCAhvBrVYr//Ozzz6L6elplJSUMEWI5EEOh4PNs6Ojo6zfJiPU5OQkbwZbDZFIhK6uLiQkJPABS6fTYWJignW/Wq2Wq+Tj4+PIycnB5cuX8fzzz2NpaQmHDh3Cfffdh+PHj/PCQ+akjY0NtLa2clWYfCPkByHD+M1GXFwc3nvvPbjdbhiNRhQUFGBpaYlxtqTTczqdeOutt1BQUMAXnBdeeAEnT56E1+tFZmYmG5wJd0s6yDfeeAMA0NraCrlcjrKyMlRVVaG4uDjmYT41NRWXL19mWcXo6Cj6+vpw9uxZ9Pb24rbbbkNBQQGsViuuXr0Kl8v1oeoVADQ1NUGpVOLIkSN46qmn+GUzGAzQaDQ4deoUlpaWUFVVhY2NDZw5cwYnT55EOByG2Wzecm43IvKSkpIQCoXQ39+Pb3zjG4hEItxenJqawksvvYTZ2Vns3bsXMzMzaG9v58PyoUOHsL6+jj//+c8sqyH8HCFi9Xo9m4ZJfherEkmyNDJzEXXp8uXL6OnpwdLSElMphoaGWE5BlAyTyYT9+/fjrrvugkqlwq9//WuWWalUKthsNvzlL3+B1+tFaWkpG9Wj0ShUKhUqKyu3nJtQKMTo6Cjr3EtLS5GZmcmoVp/PxyhHWigWFxehVCrR19eHkZGRDwEZ3n33XUxPT7M3y+Px4PTp0xAIBNx6zczMhE6ng1wuj6n7Bj7QGS8sLEAulyMrK4sxgxcvXoRarYZGo4HBYMD6+jq6urqQnZ2NPXv2QKVSYXx8HFKpFE1NTVCpVCgvL2ffhMFgQHFxMcsKaLOemZnB2NgYyy9jUdgAYHx8nOWbRN1YWVlBfn4+6urqsLGxgYmJCTgcDqysrKC2thYymQwvvvgi2tvbkZ6eDrPZjGg0ivHxce6ALC0tYXJyEqdPn0ZRUREaGxuRmpqK5eVlliOQKf9mIxAIoKOjAx6Phw8C7e3taG9vh0AgQFlZGeN+R0ZG0NPTg3379kGpVOL06dPwer1oaGhgEtX3v/99Xi9JRtHR0YGioiL2NFy+fBnvv/8+XzC3GkSbIyoZfTZUfReLxSyrnZychF6vh8FgwPDwMCYmJriDbLfb0dDQgHvvvRe33HILysrKUFJSArPZDL/fj56eHtTW1kKtVrN/iCSxW42VlRX09vbyZkqSG9JV+/1+9lz5/X6UlZWxPObpp5/Gq6++yj9PR0cHVldXUVpaipSUFDbSh8NhzM/Ps3+NNOslJSXYuXPnlnNLSkriC45EIkF6ejoMBgMkEgkGBgawuroK5d9xukNDQwwsSU5OZrMneZo8Hg++/e1vo7e3l/19ly5dwrlz5+D3+xmbSkWK9PT0LS/cwAceuZmZGfaUKZVKLjaRBI9gHKRfv+uuuxAIBPD2229jYWEBVVVVaGxsxO7du/GTn/wEQ0ND7Cm4ePEiXnzxRYjFYtx5552Qy+VcIAuFQjE7ozfibAkpnZycjAsXLkCr1SIzMxMikQjT09M4c+YM+zEIB01kM5FIBK1Wy5fthoYG7Ny5k3HKZNTe2Njgv4/Q+1uN3NxcPquEQiGWnrlcLv47iMjZ2tqKgYEBxoT29vYyfIOq5u+88w7L3VJSUiCVSnHq1CkGuVy7dg2XLl1CT08P/H5/zKInrb0ksQE+WO9ef/11pKSkYPv27VAqlejt7cWrr76KUCgEhUIBoVCIv/zlLxgcHMQtt9yCz372s1AqlVyUSE9P5wOyy+VCQUEBtm3bxlQwnU4Ho9EYU8ZNP3NcXBx7ZSmqID8/n32Adrsdb7zxBmZnZzE9PQ2Px8MXw6SkJCgUCqyuruLChQvsOVGpVNBoNBgfH8fy8jIikQjcbjempqYYRbxjx44t55acnIzOzk5YLBbEx8dDpVIhGo2ipKQEOTk5DEqyWCzo7+9HXV0d7r33XmxubuJHP/oRnnzySYyNjfFe9+qrr7JcLhqNwmq1oqWlBUajEY2NjQgGg2htbcXw8DBycnJiksSysrK4g01+qa6uLr6Uzs7OQiKRQKvVsjSbCqU+n499bgaDAbt27cLKygquXLnCBZesrCxMT09zsUckEmF4eBiXLl1CMBjEkSNHtpzbjeNjXzTW19dRUFDAhjXSUe/Zs4dvj0lJSSgpKcGOHTu4WmS1WpGTk8PtzsbGRohEIjQ0NMDj8UAqlbIR5dFHH8Xbb7+NYDCIaDTKmuqPMoMDH7TvZTIZtFot0tPTsbm5iZqaGmi1WjZeBQIBZGVlIRwOo7GxEQKBANu2bUNCQgJaWlpYt07sdpI7LS0tsa68srISCQkJyMzMRFFREVdlY31u27dvZ4oOVQJqa2thsVigUCiQm5uLkpISlJeXs3/A7/dj27ZtMBgMTBiRyWTYvn07rFYrZmZmsLS0BIVCgcOHD2NgYIApWhqNhnXqhYWFW86NDvtkKCUjmtlsxsjICBuytVotmpubmQ1NLVzCrUokElgsFpSUlGBqaorZ/ImJifjf//1fxnLS56bT6Zh2sNVYXV3F+vo6pqenmdQTDAbx8MMPc55LamoqysrKsG3bNmRlZSE7Oxvr6+tobm6GTqfD7Ows1tfX+SUiqgeZUbdv3w4AKC8vZyMoaf1jHfro15Kvh0yEJSUlyMjIgNFohPLvGRNU/SK2vMlkgkwmg8VigcfjgUKhQHV1NcbGxtgjEB8fj7179yIQCLC8UCQSsc48VgWXuih0EKPqoUajQXx8PB8M5HI5cnNzmUxEcjhCG9JBw2w2o7q6GrW1tdDr9UhMTGR0K1FziNZC/oFYgy7Nbrcbo6OjsFqtCAQCyMzMRHJyMhQKBXfo1Go1y0BIP0yfeWlpKYLBIB555BGuRBMyksgeVFiQSqUwGo1sattqEGaXiiGU8aPX61neQBQThUIBv9/P/o3y8nKkp6dztdVmsyEzM5MZ5ETb2bNnD5uarVYrYx3dbnfMblA0GkV6ejpWV1dZh+52u3ltSU9PR1paGjIzM5Gbm8safPJ9kAm7vLwcKSkpuO222xgXSQbwEydO8LtNMrDKykquNm41CG9O8hKStRQVFfGaazKZkJubi6KiIiQmJqK+vh7hcBhVVVVQqVRwu93IyMiA1WpFYWEhG6VtNhvGxsY4u4W6ekQAU6lUMde4pKQkmM1mfhdMJhOkUil27NjBhnSqBqpUKgwPDzOZhgzQKysrLFFNSkrC+fPn+TJSUFCAyspKLhhJpVIolUq+XMa6oG1ubrLnb3Z2lqmD27ZtAwD+88iw2dPTg97eXlitVgacxMXFcZfhjjvu4HdEo9GgtLSUD0PkYyBSDPkHYj1vCQkJ8Pl8H/q1tKcThS4pKYlJdDk5OVhaWoLRaEQkEkFPTw+Wl5dZ80/dfNqfzGYzexRnZ2eRmJgInU7HrP6txo2EJCoMkTSV/EEEV9Dr9dwNBD4oKpGhmrTtZWVlmJ2dxfDwMHw+HwwGAxoaGjA5OQmr1cpEN0I9x+oEkc6efG/kjyH/KV2EzGYzduzYAaFQCIvFwpJorVbL3q/p6WlkZGSgr68PAJig+LnPfQ5erxcKhQLhcJgvIfPz88jPz99ybiS/Iw9IYmIiwuEwy8zj4+N5HVEoFAgEAvB6vSyFNxgMnK00MDCAb37zm5xTIRQKsbGxgcOHD7N8k7IW4uPjGcMa6zul7Bjgg2Le8vIyDh8+zDhjAkTk5+ezUZ3k/CkpKZidnWWpIckwyTfmcrkQCARw5coV9gTm5+cjKyuLpZNbDZ/Pxwd6QqQTSpfoWunp6WhqasLx48fR0dHBsid6xgko4fF48KUvfQkqlQpNTU2oqamBVCpFRUUFxsfHuRhSXl4Os9mMYDAYExJCZ0BSLxAsRKPRYGFhASaTibumarUa09PTfO5qamriIqRWq4VAIEBBQQE0Gg2fF+gysrCwwF4YuVzO8t329vYt53bj+NjSqRtvcgC4MlJcXMxaYUJpUTscAKNayagskUiQkZEBiUTCumsyu2zbtg0vvfQSVxiIspCamgqHw7Hl3Ki1JhQK+UBGC6VWq+X/Trd4hULBvOzCwkK4XC44HA4sLi4iFAohKyuLWeFEoDGbzcwpJ68FoT1jbSaEOaODvNfr5Y2BHgqqAlM+ic1m4yoMYf/I8U9GdblczgtPWloaByESMYY027EuQQKBgGkmRIwgrTKZNclIRV0KCmEiNCb9f5KIkWnR5XKxCZv+XNKl0sUmVtttY2MDc3NzbOii31tQUMB/L5lYfT4fX6wIo0kHVjK10kGPzMRFRUXQ6/WsdafDpUAggF6vj3mxJeTp8vIyAoHAh1B2+fn58Hg8WF9f5+CswsJC+Hw+7kqQ0W51dRVLS0soLS3FxMQEVldXERcXh6ysLMhkMkxMTPCvvxFBGEuzubGxwVUdeuYyMjIYfazRaBCNRvlSQc8V8EHVUqvVIiMjgxGARUVFnKFjs9mwsrLCGzhJEUnGQBkwsQbROADw4gh88J6kpKSwCbuwsJAD+0gaRNJDYqEvLS1xcBN9vkTaWlxcBPB/iSFk5oz1zBGVh4gnJFkhWQAhPQmDrVKp0N7ejlAoxN81ISdXVlaYDkXPsFgsRnp6OhPo6JJHG1esgx/NHwBLz0QiETIzM/n7JG16OBxGXFwc+4ii0Sg/ayKRiE3iN2bA0GZO8kahUMh5HQsLCzENifTzEeGILgK5ubnc9aXDN6EUibiWlpaG5eVlRl6TnEin0zFiWSwWo7q6GsPDw3A4HHz4JklhLBM95XBQZ0UsFjNRijZcKlClpaXB4/HA7/dDoVAgPz+fpbjRaBQ5OTlwu938v6tUKqYS5uTkME2PpA7UcdlqEKyCTK4ejweJiYkQiUSor69nuQ2FdtI/096VkJDAn+n4+DgKCgoQHx8Pl8uFpaUlyOVyJCcnIy8vD9PT00zZWltbQyAQ4G7FzQaRnIiOQxKlwcFB5umvra3xnmM2m2G1WqHVaplYB4Bx5DKZjGlelJFltVpRXl6OiYkJAODcGeD/kpa2GrSGEtVNIBBwUZC6EYSaXlxc5OeQELwymQxutxvr6+tMr6N/T0xMRH5+Prq7u3m9oL0aQEzqFGGOCbl8Y8gffe5EqiTCWCAQ4IINGdKpq2w0GuF0Oj9EDFMqlVyUuRHHS5/3VmNjY+ND+FTKvqIgNtobxWIxP4cUukl0xoWFBT7bVVZWcseI6JQ3BmMSHW5tbY1lkFsN+nxlMhmjt4VCIbKzs+FyuXh9p3MRZUdQ3oPT6YTD4YBOp2NTfU5ODl+WCDNMHTNaC+jnjLX2UpHd5/NxF1ir1bKPx+/3M1SGzjuk+JBIJEzvE4lE7AtRq9V8SQEAo9GIvLw8Ph+q1eqPjHUA8CHyGvB/vVX0nGdkZGBmZgYbGxvIyclBRkYGB1HTmcVut2NqagrhcJiVF9QpvVGtQ+cJIjyura2hp6cHDz74YMw5Av8PHY3ExEQ4nc4PPVS0qO3YseND1CdqQRNtyGKxsAn3pZdeQl5eHjo7O1FaWgqBQMAUn83NTTz88MPIyMhAcnIyL9Kkud9qkKyDTHPr6+tIS0vD1NQUt5PS0tKg0WhgsViQlZXFxrvR0VEkJydj586dbIwk46vb7eZ0a6FQCL1ej2vXrjFViw7TsR4GMlVmZ2cjLS0Nfr8fJSUl6Onp4Zs5EQicTicqKio4V6C7uxtCoRBFRUUsc3G73SgqKkJFRQXLzzo7O1FfX88mt8nJSXR0dLCJeKuhUChw4cIF7i6Qj2RychK33norB77Nzc3h/Pnz2LNnD1cXW1tbWSvu9Xq5dX3rrbfys7K8vIx//ud/Rn19PUu/LBYL0zzGxsa2nJtEIuHAK6p4yuVyXLt2DRqNhi9QfX19ePrpp5GamornnnsOAoEATz31FBwOB7Zt2waPx8N4zMLCQm7Bmkwm7iKcO3fuQxphg8EQ82B1YxWYvCAqlYrxx11dXby5paSksE5aLBZzGE59fT2USiXGxsYQDAZRWVnJ4ZG5ubnsfbHZbEhOTmakND17Ww3S+9MCTxUPquxTWvX6+jqjVimEiAzslPFxY+rr4OAgV1MXFxfR1NQEq9XKmk632w2DwfCR1KmkpCROVDUYDADA4WaEy45Go/y/U+VWq9VidnaW80+ee+45hMNhvPHGG8jMzERWVhZjDOkyu7i4yNQPSo2PRRMjnwx17QhFOTMzg5KSEgDgy1lXVxfKyspYKtje3g6Xy4Xs7GxIpVIO8qPqPElkurq6uDNcXV0NlUrF9KHZ2dkt5yaVSmGz2dhASKjGoaEhGAwGJlutra3B4/EgMzMTCoWCu6SEK52YmGB5WXFxMdxuNywWC4LBIF566SWW5a2srGB6epoleN3d3TG/U/LEUXUbAG9UtIkRUcZoNOK9995DQUEBlv8eRGowGBivOzY2xtXGvLw8NDY2wmAwoK6ujmlt5JXRarVMvrnZoDwek8nEmM6kpCS8/fbbiI+PZ8RqTk4OcnNz0djYyCFqWq0WBoMBQqEQ7e3tKCkpwcDAAOrq6lBQUACtVovExEQMDg4iJycHr7zyCneb6Wf5KCNnfHw8srOzofw7IlkoFKKrqwsNDQ2cLeN0OjE1NcV0m8LCQu64GI1GZunPzc2hqakJPp8PXV1dGBsbYyjG6OgoSwHJcxHrEkTeSYPBwIqESCSC1tZWNDQ0cMfW7/ezNPSNN95gUpxWq0VdXR3nP5FfhA43cXFxGB0dRV1dHUMfnE4nenp6uHix1aALPR386QAdjUbZx0T5OqmpqSgvL+dsmKmpKT7oz87OwmQyIRQKoaqqCllZWUyLCgQCqKiogFqtZtkf4axjHebJkFxYWAiNRsMd256eHiQnJ2N0dJTl4jMzM6isrOQ5t7e3w+l08t5SVlbGEje6qCQkJOCvf/0rioqKmCpEEkSdTofLly9vObfExET09/dDp9NBKpUyCnloaIir51RwpYDUwcFBZGdnw263AwDTPysrK3H+/HkUFRVBq9VykcpqtaK0tJQv7oS5Jk/uVkMoFHIRSyqVckgwkbJI8m632xEKhVBUVMTkU5vNxlkx0WgUhYWFvFbSZZzwzgcPHmTC3/z8PMLhMF/mthoikYjXa1IkVFVV4cLfAwGHhoYwPj6Onp4eXLt2DTU1Nbh48SL7gUkpMzs7i4qKCnz3u99lGpTVamWS6Kc+9SmW9TkcDu5oxSqyq1Qq+Hw+pKWlcWG6uroawWCQwx4TExOhVquRnZ2NmpoadHd3s8qHLv1PPfUUlpaW2OdKHT+dTgetVouKigqOMxAIBJBKpSguLo6JBb5xfOyOxtzcHOv/qEWTnp6OqakpiEQi5OXl4amnnoLX68Udd9zBKNa33noLRUVFAD4wP0kkErjdbvT09KCnpwd2u5311TabDfn5+cjPz2e83+LiIjo7O2Me/ILBIN/MiNA0OjqKEydOMCXk8uXLsFqtyM/PR0VFBYegTUxMcHrn7Owsb/YJCQnMp56fn4ff74fdbkd8fDwWFhZYMkOav62Gw+FgApfNZoPP50Nvby9uueUWvP/++9i3bx8GBgZYRnXhwgWYTCa88sorqKmpgcvlQn9/PyN5W1tbubVMFSqv14vnn38eBoMBXV1d7AXo6uqKmdo4OTmJvLw8pKenY35+HlarldvR169fx5EjR5iMQuZzqVSKX//614hEIoyVHRgYgMvlwgsvvMAVcalUiqGhIeZ0Dw0NcQVMIpHAYDDENDVvbGzg1ltv5VYtYWe//OUv45133mFdtV6vZzmWUqmE1WpFMBjE9PQ0H1odDge+973vYXh4mM3xc3NzePvtt6HVamE2m3mRC4fD6O/vjylPogR12hAWFxcxNzeHuro6vPvuu7j33nvR2dkJm80GvV6P3t5eeL1eroYMDw9jdHSUqxqrq6t4/fXXoVaroVKpmDTR09PD5kLl37Mw3G53TAlQIBCA0WiEy+WCUqnE+vo6XnvtNUYxO51OvrBOTU3hq1/9Ki5cuICpqSnmvK+srGB5eRnPP/88HnjgAcbbkiRp165d+MMf/oCKigoIhULMzc0xiz5WmxcAY39nZma4RTsxMYFDhw7h/PnzHOo2Pz8Po9GIqakpNDQ0cAdwcnISCwsLfPEgoxv5E0ZHRxEKhZhCY7fbmU8+OTkZ83DldrsZW0zPJhmsW1tbUVdXh0AgALPZjN27d8NoNKKlpQXXrl1DYWEhwuEwLl++zIc4s9mM7u5uWK1W9taQZ4Cwm5SGbLPZeDO/2Zifn0dZWRn6+vrg8Xi4y2gymXDp0iXs3buXq/tKpZIrzt/97nf5s6P2+W9/+1vk5eWhp6eHJZkpKSmor6/H3/72NxQWFuLs2bMscyPE8lbDbrcjLy+PcYxUkCLNdm5uLhYXF9noTDK0iYkJpgbJZDLs3r0bP/3pT1FSUoK+vj7W/lPS7bVr16DVahlHSbIMkkDebExPT6Oqqoor+tTy37t3L2MrL1++DI/HA6PRCKPRCJFIhDNnzuALX/gClpeXceXKFe566PV6jIyMIBAIQC6Xw2AwfIjORt3VjY0NvP322ygsLERNTc1N50ZZR16vl2WIMzMz0Ol0GB0dZdko6cFrampw7tw5hMNh5OfnMyqWigsPPfQQI4NTUlLYUH/9+nWkpaVxkY0KbhaLZcvPjS6wVAgMBAI4f/48nnjiCYyNjcFsNnNmy8LCAlwuF44dO4bf//73iIuLg8PhwOTkJEpKStDd3Y3Dhw+zKV0ikXCHuqenB83Nzeju7uYCTHp6esxLNwE0KPiPZI5ULCkuLmZPDsE4/H4/52EMDAxg+e/py1TFn52d5Wo8yfMAcPeR0tbJr7HVoI4I7U/UuQ6FQqisrGRIwOLiIvR6PSKRCEZGRri75fP5YLfbUVZWht///vf47Gc/i5aWFiQnJ8NsNsPn86G5uZm7SqOjo5xvtba2FpPs5HA4cOzYMb6U5ebmwuPxIBKJYGZmBsXFxdytIbKaUqnEc889x/r8lJQU5OfnIxAIYHh4mH15FMi3ubnJZujZ2Vn+nnp6erC6urqlZ4k6IEtLS7Db7fB6vZienmapW3V1Nbq6urC5uYn6+nqMjIywBJeQ7DQGBgYQHx+PoaEhlJeXM6DA6XQiPT0dLS0t7BeZn5/nAL2txsLCAkQiEYN3TCYTpqenGelNFyLquLtcLpSXl2N0dBSPPfYY/H4/Ojs78eqrr8JkMqG+vh69vb1csPd6vaitrcVLL73EFD2SABNAYKthtVo59PdGWIxcLsf09DQHylJuHEFVTp8+jcrKSs6foXflxRdfZIhBIBDA2NgYTCYTxGIxbDYbamtrmVI1ODgYE9994/jYHQ2NRoOhoSFm5xJ+9f3338czzzyD1NRUHD16FKWlpbDZbNDpdCgoKIDBYMDU1BRWVlZw6NAhfOc734HT6YTJZMJnP/tZ/Pd//zf+4R/+AWlpaejr6+ND46233orKykrGTe7bt2/LuZnNZvT19aGoqAjl5eXMCv63f/s3dHR0YGpqis3JbW1taG9vx8TEBFdvgQ/Qlvv27UN1dTUGBwfhdru5YkQa+c3NTV6Y1tfXoVAoUFNTE/PATJUTq9XKCaXr6+s4dOgQFAoFa7kLCgq4bdrQ0IDc3Fxcu3YNHR0d0Gg0eOyxx6BWq/kzIXNebm4u7rjjDtx1111obm5GamoqBAIBdDod9u3bh+Hh4S3nRhItQuJlZ2fzfGtra9m4nJ+fj3A4jN7eXlRXVyM/P58rsXq9Hl/4whdgt9tx7733cjDjrl27sHfvXkbjXb9+naVLIyMj2NjYQG5u7pZzM5lMOHv2LHQ6HWpqatg4fPjwYdhsNuzfvx8ikQg9PT28KR89ehQ7d+5EdXU16+t37NgBu92OM2fOcFVXKBSiuLgYu3fv5gMRhTctLS3B6XR+JPpRIpGgpKQEu3bt4myAkydPoqqqCs899xzTS772ta/h7Nmz+MxnPgMAuHbtGiYmJpCRkYHbbrsNQqEQr732Gr8vycnJ8Hq9mJub43eKWpVkyIpluDYYDJicnMTa2hqys7PZS9Pd3Q2dTge1Ws1yA5lMhpaWFszMzDAWc2BgANPT03zhb29vZ1pLZmYmkpKSMDY2hlAoBLVazYt8QkICcnNzsWvXri3nBnxQjbRarfB4PBwwmZOTg6mpKWRlZXGVjirqbW1t8Hq93FovKSnBoUOHUFBQAIVCgebmZqyursJut7NkZG1tDVNTU6ivr+eujMVigd/vR3l5+ZZzo7TgwsJCVFRUICMjg7HC1PqfmZlBS0sLTp06hTfeeANf//rXuYpLPhJaM0ZHR+F0OrFjxw7U19djYmICJ0+eZHwrGWBJevLEE0/EfObOnDmDzc1NlJeXo7GxETqdDlarFU1NTRgYGEBrayssFgubk999911oNBoUFRUxhjQrKws9PT2wWq3Y2NjAnXfeiV27dmF+fv5DeS2HDx9GWVkZUlJSYDQacfTo0S3nJpfLYbfbWfYmFouxuLiIK1euoLOzkzX5GxsbaGtrQ0pKCkwmE+x2O2ZmZli+Ybfb8cUvfhGvvPIKWltbodPpUFdXB6FQiFdeeQUHDhzAsWPHkJOTA4/Hg7GxMe7IbDXi4uJw4cIFzM/PQywWs2z3T3/6EwYHB5Gbm4vdu3czdEGtVqO8vBxisRh//OMf8fOf/xwtLS18ifzb3/4GoVCIHTt2IDc3l4tOdBGwWq34yU9+gl/96ldIS0vjTtjNBnWfKbQtLy+PSTOBQAAGg4GLNoFAAL29vfjtb3+LBx98EG+99RZXVEOhEEZGRvDjH/8YbrcbGo0G1dXVaGxsxMLCAkuZzGYzZ+wUFhaiurp6y7kplUoOHqOKcEVFBf7jP/4DMpmMD802m40LLxMTE/j617+O+vp6mEwmyOVyXL16FaFQCC+++CImJiaQnp6O9PR02Gw2nDt3juVhe/bsQX5+PoLBIHshtxq0H9vtdg4ra29vx9NPP41Lly4xuntxcREvv/wy1tbW0NTUxNVZkrhUVFSgsbERf/zjH/l9ksvlEIvFeOKJJzA4OAidTofi4mI0NzfjlltuYXBCrLmNj49jamqK/Z7kl0tNTUVOTg6qqqogFApx7do1RKNRHDhwgGXZJOGhC+3zzz/PCFIqznV3d+NXv/oVIpEIKisrUV1djcLCQhQXFzMpbau5nT59Gt3d3VzBp+T59PR07jzk5OTg/PnzWFhYwPHjx5kcNjMzg+zsbFRVVcFsNjPqeGFhAXa7HZFIhC9FBQUFOHHiBOrq6jjAkjrYNxs6nY4LxWazGQ0NDdBoNPj5z3+O3/72t+jr62NZlNVqhVqtxuzsLJRKJZ555hm0trZyQC+dewYGBhhosLq6yvSuhx56CDU1NQwmEIvFmJmZ2XJuNTU1uHbtGpKTk1FUVISkpCRWxLjdbtTX1+POO+/E9u3bsba2BqfTybktly9fhsViQXl5OY4dO4bZ2Vl4PB6sra2hpKSElRXf/OY3WcpGF9ONjQ0GEmw1cnJy2Iuam5uL2tparK2toaWlBR6PB11dXfB4PLDZbHj//fdhsVhgMBhQWVmJf/qnf8Lly5exf/9+fPrTn0ZZWRnOnz8PgUCAnJwclJeXIy0tDR0dHcjLy2Nv1rlz57ggHotGeOP42BeNwcFBNqlRcmdpaSk+8YlPwGQyweFwQC6Xo6mpCaWlpXjvvfeQm5uLxMREPPjggzh06BASExMZt6VUKtHS0oKLFy/C4XCgpKQEt99+O4aGhvD+++/j6aefxtDQEBITE+FyuWIe/Oj3UzgWJRx/8YtfxPbt27Fnzx5kZGRAo9HgwIEDHAZ2+vRp5OXlQSgU4vTp0+jp6cH4+DhjBCl3Y2RkBHv37oVcLsc//uM/MqKU2saxqCdkJqXkTErQfu6555CcnMypwNnZ2aitreVbpFgsRlFRETQaDet3W1tbYTAYYDAYUFpaColEgv7+fjidTly5cgVvvPEGJ08Gg0FcuXIl5udmNBphMpkwMjKCxcVFVFdXo7m5GceOHcOLL76Iv/zlLzh79iwkEgnuueceDA4OsnaypKQEa2trePvtt3H+/Hk4nU7U19dzpSwcDiMzMxPf+c534PF4kJubC7vdjoyMDFRUVLA2datBRrBLly7h+vXr0Ov1MJvNOHnyJJvLFQoFqqqqmJZAeSoFBQVoaGhgM7Db7UZ1dTXeffddRKNRuN1u/O1vf0NtbS1EIhFqamqYDV9eXg6XyxUzf4QgBtevX2cWuVQqxa5du3D+/HnU19fDaDRi9+7d+N3vfsfaYJ/Ph4qKCiQkJKCrqwsOhwMOhwPHjx/HxMQEVCoVjEYjEhIScNddd+H1119HX18fhoaGMDMzw1CAWIflG+U1hPYtLCxEbm4url+/zlAGqVSKffv2YXx8nHGcJ06cQENDA/t19uzZg8OHD0Or1SIrKwuZmZlQq9VcWSsoKGC/ilgs5kt8rBGNRnHixAkIBAJMTEygv78fFy9eRF1dHaxWK3bv3o2srCyIxWKWzcXFxeGNN96AzWZDb28vLly4gKSkJCwtLfHlLCsri6vL9fX1bOAjeklRURHi4uJidqqII9/e3o6rV68y7vbBBx+EWCyGTCZDaWkpDhw4gKNHj3JFLTk5GeXl5SgtLYXJZILBYIBIJOL3mTYfm82G++67D6Ojo7h48SJ3Q202Gy5duoSOjo4t5xYMBrF7927GvCoUCigUCuzYsQNvvvkmMjIy+AJZU1ODzc1NHDp0CCMjI2zWJDlQbW0tp7HPzc2ho6MDbW1tyMjIwIULF/Diiy+is7OTuz8XL16M+T4EAgGYTCZ4vV5IJBJUVlayJGhychIOh4MPLjt37sSf/vQn7NixgwNfq6qqGNH53nvv4cSJE6ioqOCuzS233IKvfOUrXEGfn5+HWq1Gamoquru7Y2YHkGE7NTWVEaJzc3P41Kc+hdtuuw1isZhBEw6HA52dnZxTcOLECRw9ehQNDQ3Iz8/H3Nwcfv3rX0On03EXm1Cfs7OzDJF44okn8I1vfAOpqakx5SKknSY6Yn5+PsbGxvDJT36SO3E5OTkoKSlBaWkpzp07x4jRz3/+86isrGSPEOWmXLp0CV1dXbh69SpOnjyJpqYmXLlyBQcOHGDinMlkYknlViMUCjEG9NSpU2hpaYHT6cRPfvITDim1WCxQqVSoqKjA5OQk8vPz0dXVhZSUFGRnZ6OsrAyPPPIIOjo6cP/998NisaCtrQ3Dw8PweDyMo9Xr9bDb7SgpKcHx48dZR7/VINoWeaOys7MRDofxk5/8hAt5Xq8Xqamp2LFjB9rb22E2m7GwsIDq6mpIpVL09vbC5/PB4/Hg3//933H69GksLS1Bp9OhtLQUTz31FJKTk6FWq7lQNjMzwxLBrYbb7cauXbs4CXr37t1ceLty5QqWlpYQiUTQ1NSEr3zlKxgdHYVSqcTk5CSOHDmC/fv3IysrC+np6Xj88cfx0EMPQalUshFZqVTiBz/4AXbv3o2lpSU+ABsMBgwMDMQErEQiEezfv599EIRzb2xsxBtvvIG1tTUMDg5iZWUF999/PwYHBxEOhzEyMoK6ujqWnpI0+t///d8RDAah1+tRU1ODiooKHDhwAEtLS0xqstlsfO6LJYcLhUJcfKBuYVxcHL75zW/CaDSioqKCuxzJycmw2WzYsWMHxsfH8eUvfxnl5eW4cOEC7HY7Lly4gLvuuouhG+SVuueeezAxMYGVlRX09/cjKysLDQ0NnFm11bh06RIOHjyI9PR0SKVSpKWlISUlBRUVFRy0Ozo6iuHhYc4rKi8vx5kzZ7jrtry8zGe2xx57DJmZmdwpczqd2LdvH8OC3G43TCYTysrK2DO21aDuyeDgIHe3RkZGcMstt0CtVqO5uZkvLffccw96e3sxMTGBjo4O/OM//iM2Nzfxl7/8BWq1Gg888ABOnTqFcDjMsrR9+/bhX//1XzEwMID7778fSUlJqK6uxvbt2+FwONiL9FEjbjOWluCGQS/e4OAg0tLSUF5eDrfbjYsXL+KRRx7Ba6+9xgmx+fn5sNvtXOnt6OhAdnY2srOzsbCwgLS0NPzP//wPPv/5z2NwcBDLy8ucplxaWsrm38nJSSQlJWH37t1499138dWvfvWmc/vFL36BhIQE5Ofnw+/3Y3JyEomJiVxFI70/kV0MBgObNYeHh5GVlcVkGSIukJ7RZrNBKBQiMzMT+fn56OjoQGFhIW/UpPn+xCc+cdO5/eQnP+H2l9FoRHp6OsbGxjAwMIA777wTra2tKCwsxNraGoaGhvDlL38ZP/vZz3DgwAGMjIwwoq23txculwsPPfQQIpEIrl69ykQXhUKBjIwMnDp1Crm5uUw8sdlsMBgMePTRR286tz/96U9YX19HdnY2nE4nZmdnYTQaWYZEt30yx+p0Opw/fx6JiYnIyMhgA3o0GuVKNeEeyZB09uxZNDU1IRgMMi9er9cjKysLY2NjuPvuu286t1deeQWTk5NMCbNarWy+P3bsGCQSCevR/X4/duzYgeeeew7V1dWwWq1MqEhOTmbz9/j4OIRCIeN61Wo17rzzTkxPT6O7uxtLS0swmUyora3FCy+8gO9///s3ndu3v/1t+Hw+ro5RJ2R4eJhJSSUlJUw66ujowN69ezE4OAgA/HyRVpSMuWq1+kMYyh07dvBBzWaz8XO0Z88eHDt27KZz++Uvf4nZ2VkO26JMhI6ODt5UKHuhrKwMmZmZmJycZNydx+NBKBRi0+jy8jLuuOMOlgwQMe769esAwCGIZIB3Op34+c9/ftO5AcB//dd/QalUYmNjg9GUwAdytOLiYrS3t/PnQ6a1lZUVDt5LT0/nQ21ubi5LaILBIGZnZzmjIj8/H6urq3zxoYPp6uoqHnrooZvO7T//8z8hlUrZNCiTyaDX63Hq1Cns378fMzMzsNvtSEpKQm5uLnd4jx8/ztU1sVjMmFvSYJMG12q1or+/nyvUt912G4LBIHcXFhYW8L3vfe+mc/vRj37EJBgCZLhcLpw+fRqHDx9GQUEBBgcHMT09zeSiiYkJLgqRqY+Sy69cuYL777+foQ8zMzOYnJxETk4OXn75ZVRXVyMpKQmZmZmoqqrCH//4R/zqV7+66dyeeuopvnwTNpRkdhkZGSyPmpubw/LyMh588EG0tbWxIbekpAQqlYrx3FeuXMGxY8fwzjvvIBQKoaysDG63G5/85CdZJ0wSOolEAr1ej7vuuuumc/v9738Pr9cLqVTK65rNZsMbb7yBr3zlK7h69SoyMjKwurqKgYEBHD9+HHNzcygvL8fLL78MuVyOxsZG9uEUFBSw52BlZYWfX71eD5FIhLGxMZw7dw4ejwelpaUAgC996Us3ndsvf/lLRrNmZmZCLBbzOlRTU4Pp6WkmX9GlRCQSwWg0ore3F5mZmTAYDOjv70dPTw9uv/12zMzMYPfu3UhMTMTY2BjC4TD0ej17E240RDudTnz605++6dxefPFFDAwMICcnB5FIhHG977zzDoqLiznEbmFhAQ6HgzMNmpub0dfXxxAVoklR5+PIkSOQSCQYGRnhRHsK+3U4HBCJRDCbzZBIJFvuqb/4xS84O4AMwR6PB+Xl5dBqtXj77beRnJyMxsZGGI1GtLa2oqCgANeuXWMSoFAoZOgJAHR2dvJeG41G+XkFwJ4SiUTCnZ4vf/nLN53bk08+yQUQ6jyTh426W/39/djc3ERVVRWbfgUCAf72t79Br9ezbOnFF1/E2toatm3bhvj4eKyurmJ5eRl+vx+VlZXQarUsi5PJZBgcHMTGxsaW3+nzzz/PRV7ytFLXihLqiQgZCoVgNBrxyiuv4NZbb0Vrayt3yAHg1KlTiEQiuOOOO9jLGQwG8dxzz3GwqVAoRFJSEqRSKcLhME6ePIkf//jHN53b+fPnERcXxwUkq9UKg8HAkka73Q673c5QE/IqyWQy9PX1wWAwcKgpZVrk5eWxVJgoZRqNBnl5eVhfX4fFYmFVx9TUFL7whS/cdG4/+9nPAIC9KBRoePLkSdx33338LPb19WFqagonTpzAa6+9hrvuugvXr19Heno66urqoFAo8Nxzz2F6ehpf+MIXsLi4CKfTyXI9uVwOpVKJrq4uVhFotVp0d3fj//yf/3PTub300kvIzc1l2l8kEsHy8jLi4+ORkpLC9CiPx8NrP3XZ6O8kOeXq6ireeust3HHHHbxXut1uxMfHc5YbFSkCgQDS09PhcDhidrtpfGyPBtF7IpEII8/m5+eRl5cHqVQKvV6P2dlZDparqKhgxK1YLEZ8fDzz2UljvLy8DIVCgWg0Cr/fzwmX5OAfGhpickosMzjwAd2KCA9UEfF6vbzJB4NBTv2mLgSFDBKRYHNzkyve9GUQ5Yn48JT9kZSUxMSCWDdO0tiTRIm0osSmJ5IX/XciDQwNDbG2mnSblAyr1+s5sGxiYoIDugCw8TkQCMDn832k1pUeRIlEAplMhlAohKWlJfj9fv7cCHl64zxIrpCQkIDExER4PB6UlJRgeHiYEZEZGRm4du0a7rzzTpw7d441n2R+j0Xros8jJSWFw+WSkpKQnp6OSCTCJmUynVutVszNzWHbtm3sK6GfcXFxkTWZFDCnUqmYGkUBhUKhkNvcsTjua2trbC6lgwaZfymciXI4CP9IQXVk9lIoFFhbW2OML4X3BYNBzkcBwDADsVjMWN9YtBjSbNNzSTrwjY0NZGdn8yEgGo1ygjktTtSZo0wbvV7PSEcKw1QqlVAoFAiFQtyNAz6o7BJdKNYgah1pv0UiEZNEaB7kGaCFLhKJoKysDFarlS8CCwsLiIuL+5BxnzoWQ0NDKCsrw8DAAHdv6HARy08VCARY+0wBkV6vlyWFtPZRx2dtbY3Nu16vFzqdjjd9m82GmZkZ7N+/H4FAgENOu7q60NzcDIlEgvHxcfaHicXimEb6G/W3hBGOi4tjQ978/Dw2Nzd5PV1bW8P09DT7gogAs7y8jMLCQuj1et5kaROenZ1FUVER51RQZZgOI1sNsVjMayfNk4AcJKvz+/1MllpeXkZrayuH0t24JtIaERcXB6VSCZfLBbfbjVAohMXFRYyNjaGsrIwLIERs2mqsrKwgMTGRw+9I+krmZsroCYVCnFdgs9l4DdRoNIiLi0NfXx+i0Sh7/uLi4uDxeBhZTe9JXFwckpOTsbKywn6kWN8prf9er5c590KhkI3z9BwCYLpUXl4eU8/UajXS0tK4M5aUlMR/ls/nY5xsf38/rwkk0Y2VFeT1eiGXy6FQKOD1ermYJJFIYDKZ+HOfm5tjf5rP54PFYsHQ0BBTlGjfLykpgdPp5MM7PSPZ2dlwu928ZtOeFatrsLq6yj97IBDgoE6SLtLfSbSf7Oxs/kw2NjaYOkQBfARqIMoaYayFQiFflMgcS4fMrQbR1CiVmahzdHZaX19nPw4RHGdnZ/nZJ4T41NQUew1JKkhEweXlZTbukp9MKBTC4XDENOfeiAenQF2NRsM4VLowAOBDPyGDCZBAZnkiggYCAabX0VqbnJzM9C1652IBTOhdkEql3PWgvYVklUNDQ/wZzs7OIi0tDYODg7xGUMGkv7+fyYf07tA5CgDL90hOSO/aR5EciWxGnU+SiBPdz+12c5DdxsYGm+wpGgEA/xr6nEgVA3zgKykoKGCzPsmHA4FAzI4teYKIWAZ8IIGfmppihL5er+fMsYKCAoRCIfj9fj7jer1efleJYEWZYvROikQiLu7S930jJe6jxse+aMzPzzM3mDaOubk5NDY2clsoISEBFosFU1NTKC4u5pRE6lAQb7i1tZXTWnNycqBWq2G1WhEKhTA+Pg6xWMyBYiQriOWDiEajbFahaizd4Cg1nEzo0WgUAwMDqK2tZQMsHSzoMDI3N4dgMIhdu3Yx0g0AV7quX7/OzOmUlJSY0qmNjQ3mtdNDQ9hLu93OVJvFxUVIJBKMjo6ivLycKRXU9iJE46VLl9Dc3MxGpL6+Ply9ehVGoxFSqRRWqxXRaJSrsrGMa5FIBPHx8cyGzs3NZc3z/Pw8TCYTY1dzcnIgkUgQCASwuLgIi8XCtKny8nJ0d3ejoKCAZSKRSAQpKSlskKKKR3x8PEuGYmVVEMaPbulULTabzWhra+NcCupU0cZL5m/KYFlZWcG5c+d4oyHtpFqtxvz8PC5cuAC5XM4LD+kaqROw1fNGJm6/389GMrVajbGxMRw+fBhDQ0MYGhpCXl4eG+IdDgcvGoTIo6RNSgqnjZZwnpQXQRWKYDAYE9FKL35CQgLi4uJ4I1hcXOTvmkhnDoeDUXuk2aVFmTxJIpEIHR0dfMienJzkhOuUlBRGm9IFOTs7e8u5AeDDSjgc5t9LIZSjo6MQiUS8GAMfyEtycnKYSU9tXULrUauZSCqRSARDQ0OYmpriTZgoZZSzsdUIhUIQCASc3UE4P61Wi6WlJWi1WpYwWK1Wzjm4fv06JBIJ4uPjkZqaisrKSjz//PNwuVxYWFjA4uIiPB4PYyzX19eh1+vR19fHVVlKrd9qkHSJDgtra2ssz7BarUz9W1tbQyQSweDgIEQiEZxOJ68B5C2iA9DIyAh3Le12O4aHh1FUVASDwcByB4/HA7fbjZycnC3nRrz8zc1NpsGRWXNychKlpaVYXFzEwsICotEo3nrrLWRnZ8NqtSI7OxsOh4MNi5QgvLi4iPr6eu5UUaFndnaWn00iKMVa4+gwT4d/p9OJUCgEmUzGOMfZ2Vles4i2Q14HOjTNzc1hc3OTgQUNDQ186JHL5fy503dJBYtYB6wbL2aU5ksyTAqZpeID5XQoFAosLCzA5/Mx7IDIbPT8E3hkZGSEkbxmsxmzs7NMU6MC3lbD5XIxypkQtk6nE1lZWVwQHB8f5ypubW0tf7dEFczIyEB5eTneeecdDjAbHR1l8z9dtlNTUxEfH4/19XU+RH9UVhBd6AFw0YOALVSMmJqagsPh4Kwov9/POGW/34/09HR0d3fD5XJBo9Hw2kDdOAqadLvdWFlZYapmLOPw+vo6SkpKMDQ0BLfbDZVKhXA4zBkRIpGID4WLi4scIkfFOULeEvmJqu0EMygqKmIfCK0rlGHxUVkVpO4grDZ9TnSJkclkH7rQkEn56tWrrBRZXV3lwmlKSgo6Ojo49JAup4Q7dzqdcDqdTCGj7tHNBj0Ti4uL8Pl83KWm7gUpTegdpm5JV1cX79tUgKJLMMn55XI5f979/f1M7KLDNtEctxp0pqSuEqkrCgoKYLFYeN1aXl5GRkYGxsfHUV5ejuHhYe7m0LpG8tLZ2VlWaExNTWFkZIQhRvfeey/i4uKwuLjIRchYzxt1M27Em5PMLDs7my+HVFhNSkqC3W6HRCJhZY/RaMT09DRDI2hNEQgEmJqagtFoRFJSEndGaY2OVbi7cfw/mcHJ9FZZWck37z/96U9cvUlOToZIJMLExASjWZ9//nm8+uqruHjxIm8slPRKByHC0QFgeUZXVxdmZ2cRFxcHtVodc1GkQwcFVBGzvq2tDS+99BLEYjFXtCmFXK1W88IXiUQ4TTQ/P5+rltnZ2cjKymIdKG1Y1EqidnAsHwShytRqNYqKipCVlYX19XW8/fbbmJ6eht/vx+XLl3Hq1CmWrlRVVeHo0aNYWlrC1NQUpFIpjhw5gp6eHpw/f56rUXK5HLt27cLa2hra2tpQXl7O+SHZ2dkoKiqK+QJptVpMT09jcXERAoGAq9JkGNLpdEhKSsLs7Cxee+01BINB2Gw2DguixYpM0c888wyqq6tRWVnJAVG1tbUYHBxEU1MT1tbWmErzUWx+OlQS3rWqqgpJSUl444030N7ezodIj8eDnp4evPXWWygrK2Oz3NjYGJaWlpgnf+bMGYyOjjKCc2NjAyMjI5ifn0ddXR3jdjs6OjA2NhazapWQkIALFy7g2rVr6O/vR3d3N959911YrVasr6+jsbERKpUKVqsVs7OzTDSjSxtVCrKysjgsamxsjFnlp06dQltbG5utqZJAi0KsbgsZliORCMxmMyoqKuB0OtHR0YH+/n4+eAwODnJVWK1WQyqVcmVw+/bt+OIXv8iYZZfLBZPJhJycHGbBLy0tYXl5GQKBgC+REokkZmouzY8ukYmJiYhEIlAoFHj55Zf53fd6vRgcHERHRwdCoRCTY0hCtri4iJycHK400kK7ubnJ0rju7m7umhFDnyrVWw0ifimVShgMBqjVakQiETz33HP4y1/+whU/Iq3p9Xp4vV5oNBpmssvl8g9t9q2trYx1bm1txfbt25mCRQni1HVsamracm5EXzIajSgrK0Nubi6EQiG+//3vw+l0clbR6Ogorl69yuFdOTk5cDqduHr1Krq7u6FUKllfv7GxgZmZGfT29rL23Ol0QiqVQqFQ8OUyISFhS3ISAD4YxsXFcRCWSqVCYmIixGIxS08pAd7tduPhhx+GTCbDW2+9hbGxMb7sejwenltubi5qamqQm5uLgYEBmM1mKJVKvPfeexgaGkJycjKys7Njvg+Ud7OxsQGj0Yiqqip4vV68++67EAgEfMGPj49Ha2srrFYrSy5lMhlXVo8cOQKn04np6WmMjIxAKBSisrISu3fvRjAY5E5zYmIiFhYWMDs7yz6jrYZMJsPAwABLV2QyGZxOJ86dO8ekOpFIxDkJRKyhz2dqagpnzpxhUy75d4qKitizODU1hcuXL8NoNHL3mjqpRIO82aB0cqJD0WFyZGQEXV1dWFpa4gv57Owsent7sba2Br/fz14zgjvMzMwwkdJkMiEtLQ0SiQTNzc0YHR1FVVUV6/4ppPSjMhcoRJA084RHJVUDUZhOnjyJyclJzhJKSEhgVC0pHDIyMqBSqZCSkgKFQoGNjQ1O2yZkNsE9iCK11SCEMuU8Ee7fYrEwJrigoIBDJq1WK9LT05k8d+nSJQwPD6OyshIWiwXFxcV8oVpeXubvnta2vLw85OXlwWQyITs7O6ZmnjqdlC9CaOT//d//5dT5tbU1TE5O4q233uLLNa1TXV1d6OrqgtFoxPDwMH7xi19gcnIS169fx7Vr1zA5OYnOzk54PB6IRCIMDAxwB4sKfFuNjY0NRsTabDYA4NDD1dVVmM1mNoPX19cjPj4eBw4cQHl5OZxOJ4LBILKzs3H8+HEcOHAAra2tUCgULBcMh8M4e/YsF8KWl5cxNDQEp9OJzMzMmIVFlUrFZ0qq5hcXF+P06dNYXFxEamoqJ5r7/X7uXPzLv/wL6urq4PV62WxOXdA33ngDKysr0Ol0SE1Nxc6dO7Fnzx4+Hy4sLCAQCKCoqAgHDhzYcm4CgQBdXV2YmJiAQCBgCR1FSqSkpHDxjIqBlPFlt9uxtraGnJwcpKenIzU1FRcuXGDKKV2sz549yxf5a9euYXR0FKurq5y6/nHGx75oEDWJDsVjY2NISkrCk08+ydo3ol3cfffdfIDdvn07Pve5z3ECt0AgwMmTJ/GJT3yCdal0G/uHf/gHjI2NIRAIwGKx4PDhw/inf/onKBQKHDx4MOaHrdVqodFoUFdXh4MHDyI/Px9f//rXYTQaYbFYsLCwwKFBFRUVHJzyrW99C3V1dZibm0NmZib+/d//HSdOnIBEIsHMzAyGh4cxPj7OqEa9Xo+UlBROQxaLxTEJGW63G42NjZicnITFYkFaWhpEIhFuu+02lJSU4MiRIzh27BgOHjyIoqIi1s0lJyfjG9/4Bv7hH/4BtbW1KCoqQm1tLX7wgx+gvb0dvb296OrqwsLCAlpbW7Fz507W2u3Zs4cTxEdGRrac2/LyMvbt24ekpCSMjIxgaGgIRqMRt956K3w+H1fPMzIycOzYMSQlJeHrX/86Y4gzMzM5cddms+Ezn/kMnnnmGZSVlaGxsRFerxc//elPYbFYYDKZoFarUVtbi6qqKjbexfrclEolVldXIRQK+dCxa9cupKWloa6uDqWlpbjzzjvx1FNP8YVmZmYGR48eZb1oRkYGjh8/joceeojpTZSr8K1vfQt+v58lNgcPHsRtt93GLcetRjgcxqFDh9DQ0IDt27fj8OHD2LZtGz75yU+ysY9SWCsrK2Gz2XDXXXdBqVQiOzsbWq0WqampMJlMOH36NG677TYMDQ2hu7sbIpGIzcZ//etf2Uy8e/du7N+/nzsgWw2Px8OfxaVLl2C1WmGz2bBnzx7GCmdkZECv1yM1NZWxqsPDw/jEJz4Bg8GAixcvYnFxEb29vdBqtRxap9FoUFBQgKNHjyIhIQHl5eVoaWmBQCBAaWkprFYrhoaGtpwbAPafXL9+HQMDAywd27VrF+bm5lBbW4u6ujo0NjZi//79HIZmNptx9OhRFBQUQCaTobGxEZubm6iurkZmZib7TYxGI9544w1IJBJkZWVBrVazlnxmZibmYd7lcqGoqAidnZ1oa2vjC9S//Mu/oKCgAJmZmcjLy0NTUxNuvfVW7i6SUXx8fBxnz55l2cQPf/hDLC0tobi4GJ/5zGfwpS99CbfffjvsdjtkMhkuX74MjUaDvXv3wuFw4Ny5c1vObWNjg31GhOtMTEzEz3/+c3R2dqKzsxPhcBgHDhzAv/3bvyE+Pp6xq4WFhSgrK0NhYSHuu+8+2O12/H//3/+HhIQE1NfX49ChQ9i/fz9effVVJmFZLBYUFRWhsrISp06d+kg+P1226OIHgDOJqLV/9OhRvPDCC7wxNzY24jvf+Q52796NUCiE6upqhMNh7N27F6Ojo+jt7UVLSwt6enrw2c9+FgsLC7jvvvtQWVkJs9mMtLQ0BIPBmBU/yjm5ePEirl69CrfbDYfDgf/8z//ExsYG8vPzkZKSgsrKSnzta1/D5OQkY4KPHTsGtVqN/v5+mM1mTExM4E9/+hNMJhMfXuLj4zm1fG5uDlarFfX19di/fz+sVmvMQykAFBYWcgHHbrfj4sWLuPfee2E2m7Fz504UFBTAbDajoKAA586dY3ntf/7nf+Lee+9l32FjYyP27NmDYDCI3t5eRqTu2bMHfX19cDgc6OnpQUZGBhoaGliKttWIRCKor6/n7plMJsPQ0BAeeOABTkPPy8vDjh078OMf/xharRbHjh1DcXExioqKMDg4iKeffhqBQAD33nsvfvKTn2BychJyuRx1dXV8yPX5fGhvb8fy8jLuuecePPDAA5iZmYlZvBOLxRxOCPzfrBQKXCSqk8lkwte+9jW0t7dzF2dqaoqD1xITE7GyssIEOMrrunDhAhdWKb8iPz8fubm53CXaalCx0G63Q61Wo6CggNf2p59+mn2hra2tEAqF2LNnD9P6HnnkEdTV1bG3aWNjAzt37kRCQgKysrKQmprKMpbZ2VnOd6Fu9OzsbMw91e/3o6mpCePj42hra+MgwO9///sQCoWoqKhASUkJ9uzZg0ceeQQtLS1cMKyurkZVVRUKCgqQk5ODsrIy/OxnP2MokF6vh1wux0svvYT09HTs2bMHZrMZJ06cwO233/6RnSAq9paWlmLHjh3Ytm0bhEIhtm/fjvfeew+BQADj4+OsXHj//fexvr4OkUiEJ554AlqtFu+//z4Dau6++26WPlIn4X/+538gl8uxtLTEHqfy8nIEg8GY3yl109vb27GxscEE1E9+8pNISEhAR0cHNjY2YDabYTKZoFAosG3bNrS3t3PnWSQSMQn0hz/8IUNnlpeXUVZWhrvvvhvz8/M4cuQIxsbGUFJSgt27d+PNN9+MKesKh8MoKytjpDrJ6T7/+c/jq1/9Kvbt2weFQoHi4mJ8/etfRzQaxf79+7F7927cf//9XJwViUS4cOECfvCDH+Dq1atcMAsGg/jxj3/M5xm6mOt0Oni93phUvRvHxzaD//73v0d/fz9HplssFjgcDpSXl6OoqAgulwsejwezs7Ow2+04dOgQ6uvr0dnZiZSUFEilUkxMTOCZZ57Bd77zHfzrv/4rvv/977P8wu1244UXXsAPfvADnD17lg3b9NJREMvNxksvvcTSrNnZWczNzXG1/oEHHsDq6irj4p577jl88YtfhN/vh1KpZK2zVqtFQUEBJBIJfvnLX3KmwdzcHFebH3zwQXR1dTGXnSgxdrsdP/rRj7b83AYGBrhCGA6HkZOTA5fLhSeeeIIRbKOjo5ifn8fnPvc5dHR0YGFhgQ2MlJhsMBjwi1/8At/97nexubmJK1euYGxsDI8//jgSEhIwMDCAzs5OxMfHQ6FQQCQSITs7G7feeutN5/biiy/yRkVJpFVVVfjzn/+Mz3/+85zXQXIXm80Go9EIhUKBv/zlL9BoNBxaNj09jaSkJFRVVTF6Nz4+HmfPnkVVVRUmJyfhdruRmpqKxcVFnD17FsePH8ehQ4duOrdnn30WPp+Pcyp8Ph+ys7Px7rvv4tlnn8WlS5fYSNfZ2Qmj0Yjf/va3+NnPfoYXXngBCQkJMJvNWFxcZJwiBdiEw2EsLy/D6XTiwIED+Otf/4ra2lpOKJZIJAgGg/jKV75y07n96le/YnkWVW21Wi1nn5SXl0OtVmN0dBTvv/8+Hn30UbhcLjidTvT19SEvLw87d+7k9vRvf/tbmEwmvP766wgEAqivr8fy8jLTPKiFSQvWyZMn8ctf/vKmc/vZz36GgYEBzrMQCATYtm0bo0upjR0MBtHR0YGEhASkpaVhYWEBSqUSmZmZbBTMy8vD4uIi/H4/Ll68CL/fD5lMhq6uLnzlK19BX18fkpOT0dPTg7W1Nezbtw9Xr17d8l2gz87j8aCgoACzs7OYn5/H3r178ec//xmf+9zn8Oabb6KkpATr6+vo6+vDD37wA6yvr+P06dMIh8NMpZqZmcGzzz6L9fV1HD9+HElJSZiYmEBnZycMBgMaGhpw/vx5ZGVlYWlpCevr68jJycHi4uKWSabf+973GE1IemHSBefk5OD69et84SouLsauXbtw6tQpDkYkve65c+ewc+dO7oh2dXVheXmZq3rHjh3DxYsXGQO7vr4Oo9GIzc1NHD9+/KZz++pXv4qMjAykpaXBarVyGOD09DQb96jbKhAIcPjwYZhMJnz1q1/loDYKkHzppZe4m0b+jomJCcTHx+Ouu+7iyt2pU6fgdrtx6NAh9PT04F//9V9vOrdz584xCIIqyw6HAzt27EBVVRUsFguWlpbQ39+PwcFBPPzww5ibm4NQKMTLL7+MyspKHDp0CKurq4iPj8eJEyfw8MMPo6mpiQ9c77//Pj796U/j9OnTaGhoQG9vLwYGBjj0sKGh4aZz+/Wvf81yK0rfFQgE+OUvf4knnngC3d3d0Gg0SExMhMPhgEQiwfvvv4/77ruPzZSrq6t4+eWXYTQaYbVa8clPfhLt7e0YHBxkTwAZxElKvLGxgYKCAt4LbzZ+97vfcYAi5QVsbm7iV7/6Fb73ve9hYGAA27ZtQyQSwYsvvoimpia89tpr2LdvHxYWFrjrTD617du3Y3p6GlarFcAHlf/R0VHccsstsNlsaGhowNLSElwuF2QyGSwWy5aG6z//+c+Yn5/nwzZ1Vc+ePYt//Md/xNjYGDQaDeN3fT4f/uu//gtf//rX0dbWxgUqQkQXFRUhOTkZ7733HnfBf/rTn+L222/noLeRkRGGXXi9Xhw+fPimc/vNb36D+fl5rk4TiGJtbY0vVmTopr87GAwiJSUF8/PzyMrKYl9NamoqnnzySRw4cIC9RpQrUVJSgmeffRbbt2+HXq9nj5ff78enPvWpm87tD3/4AxMRRSIRZDIZsrOzcerUKTQ0NKC7uxsmkwmZmZn82Xq9XshkMkxMTMDlckEkEiEtLY0TmxsbGzkfSKFQwO1249vf/jbee+89zqMBgMbGRvT19W2JuL148SLn/FBxhHx7t99+O1pbW+HxeDhNniTRKysrWFxcREpKClQqFVZXV3Ht2jWWWBUUFGBxcRETExP8zp8+fZoRsBS+SZCDm41Lly7BZrOhpKQEkUgEc3NzvFcnJibyvrW4uIjh4WE8+uijcDqd0Gg07ItcXV3F0NAQdu7cibvvvhs///nPEQ6H4XK5GBVLIbBOpxPd3d0sg/R4PFtmfBDJLSUlBZcvX0ZnZyf+7d/+jQljb731Fmpra+Hz+fD000/j2LFjHDpaXl6O1dVVeDweJCUlMUlVoVAwyjgUCuH8+fP41a9+hXfeeQeZmZm4ePEiBAIBHn74Yfz1r3/dEijxt7/9jbv21Pl0Op04deoUfvzjH/NlnjocJ06cwHe/+1089thjbF0oLCyESCRCQkICDh8+jNtvvx3l5eWc69LW1obHH3+cscgZGRmQyWRYXl7G9PQ0HnvssZvO7cbx/xTYR7SYhYUFzM3NYWVlhXMH1Go13+QXFhYQCoUwPDyMV155BVlZWXyoz8nJwR//+Ed87nOfw+XLl1nDF41GMTc3hx/96Ee48847MTAwAKlUCplMhosXL8LlcuGb3/zmTec2PT3NBrXc3FyOWSd3PAWmkM9EpVJBpVLh0qVL0Ol02NjYwMrKCsRiMebm5thImpSUxKhci8WC/v5+JCUl4b333mO9MaVPbjUsFgtfWsigHggEUFNTg46ODojFYvh8PoTDYWRlZWFhYYEDZ+iCERcXh/z8fDz//PM4fPgwzp8/j4SEBASDQZboXL58GWVlZVCr1XC73XA6ncym32rMzMwgJSUF0WgUycnJUCqVmJ6ehlKpxPXr11l7TzrtqqoqnDx5krGYNTU18Hg8uHz5MvR6PaLRKC5evMh0FMI+zs3NcUoqmbgJRbvVJjw+Ps6ShuzsbAiFQiwtLeGOO+5gvOX169fhdruh1WqxvLyMEydO4KmnnsLy8jLy8/OZEPHaa6/h/vvvZ6kNaexJk11UVITV1VUOZXK5XDFbgsQTd7vdLBuh8LFQKIQzZ86w7MBoNOL111/HoUOHcOXKFQSDQVgsFm7jX758GZ///Odx9epVHDt2jFO5e3p6sGfPHiQmJuLcuXNQqVRccYuVP+JwOLBv3z54PB6WFohEIszPzzPud2pqii+RJpMJL7zwAlfeSBeclpbGFf3c3Fw2hwWDQTQ3N+PMmTPstyEJ3fXr1z+ygkuJr/TzE6Xptttuw8rKChoaGtiwn5+fzyFtpGltaWlhRv7Kygry8/M5bPLGoCeNRsOdRNLodnZ2sgzhZoNaynK5nGUwVPnyeDwQi8VYWlriIM/4+Hh0d3cjGo2yJCUcDrO3gBj/KSkpiIuLY1rM008/jePHj6Ovrw8qlYoPcNR9u9kgudrm5ib8fj/C4TDm5uZgMBhgNptZuuJ2u2Gz2RAOh1FUVMT/rtFokJ6eDr/fz9klPT097FWgytsvf/lLfOELX8Dzzz+PuLg4KBQKvPvuuyw9vdmgBFtqxUejUb60t7a2Ij4+nnXIlJ2hVCrxzjvvYN++fVAqlbh27RpmZ2eh1WrxqU99ComJibyW0EHjD3/4A4qLi9nblpubyybnrS4a5EERCASwWq2wWq0Qi8XYuXMn2trauABEOHHKOzh37hzuvPNOrK+vIxqNYu/evTh37hyWlpYYI6pSqdDe3o7FxUU89NBDnMhNJnOiIW01KEBMJpNxsY1CsLq6uiAUCvH2229Dp9PhxIkTGBkZwZEjR7CysoLR0VEsLi4ynbGnpwdXrlzhDqpAIIDJZEJhYSG6u7uh1+vxyiuvcEWc/Gtbjfn5efYlKRQKiMViWCwWNpPm5ubiypUrnIRMsuLp6WmEw2EoFAokJSXB7Xajr68Pr732Gh544AEIBAImAVFoos/nY6pWNBrF5cuXY+JG6dJA+FKpVMqXInqmCSphMpkwNDTEeGe63Pt8PmxubkKn02H79u1wuVyMKKb08uHhYaSmpjLJCABLR7caq6urDEGg93F5eZmDSUnq53K5OJTY4/Fgz549WF9fh91u52DjmpoaFBQUYHp6+kPV6qSkJFy5coXXOpLEXLlyJabkd2lpidHhdCh1uVzYs2cPlpaWmKC4sbGB+fl5Dq6ltSwtLQ0rKyvcrcnLy0NHRwcuXLiA1NRUNDQ0QCKR4Pnnn0dlZSUmJyd5LSBFyVYXDZ/PB4PBgLm5OZY0trS0oLm5GUqlEh6PB8PDw4hGoygvL8fc3BzC4TCcTic2NjY42oD8up/+9Kdx7do15ObmYnl5GRcuXIBCocDu3buxvr7O3h2iiMXCd1PQqF6vR3NzM9LS0vDqq69CpVIhNzcXc3NzbI4mCMHq6iomJiYwNDQEjUYDvV4Pj8eDjo4O6HQ6GI1Gzp+h5+DUqVNMSiRT9rvvvhtT/r68vIzs7Gx4PB4sLi4iGAwiMTERhw8fht1u5yR6l8uFcDiMqakpPPjggzh//jy0Wi0H75F5nzzYhH+mUFnK/ggEAgydIIroxxkfWzpFfzDh5BITE6HT6fDOO++go6ODqU5Ep/B6vfD5fNDpdFxxMJlMyM3NRXV1NZ566imkpqYiIyMDQqEQPp+PW/Dnzp3jJNvBwUFG4W011tbWWAtIpI7l5WWcPn0akUiEw95UKhWysrL4ISguLsa1a9cwMjKCUCiEa9eucaolaWKbmppQVlb2odsn+UDIzBdrqFQqloaJxWLGqp0+fRpWqxXt7e2YnZ3lF4S48YR5Iz17UlISCgoKWAtIn9Xm5iaeffZZpKWlYW1tDWlpaVAqlZzYGesFIhILmYOpRUecamotk7Y9JSUFO3fuhEqlQlNTE3JychCNRlnbfeXKFfj9fmi1WgYH0GE0Go2yhpRa34TcvNmgihBF3hOOrb29HQMDAx/63ImTXVlZySZXu93OpqiMjAx4PB5MTEywR4PoIJFIBL29vRgbG+MLMkm8thrhcJhJQwqFgjMmpFIpVldXUVFRgfr6ejQ1NaGiooIXTpFIhMXFRdjtdjgcDqZhtbe3IxAIoLm5GYcPH4bBYOCDuFarhVqtZiIKEZS2GmKxGP39/aytj0QizDO32+0oKipCJBJBd3c3rl69ioSEBCQlJUEkEnEVqbCwkMlSy8vLWFtbg8FgQG5uLhtS6Z+FQiHDED6KOAWAD+Ner5cPssFgEH19fZicnERKSgrrZ0dGRtDT08OXVpLI0KVDq9Wyd0Kv16O0tBRms5kvpUS3c7vdcLlcH6J53WxQVfRGmo5Wq8X58+cxPDzM8IdwOIyJiQm89957SElJwcLCAoaHh9nwV1BQwNQfIk4R8WZiYgI9PT1crCEjMiXBbjWSk5OxuLiItbU1poNEIhGcOXMGly9fRkpKCleufD4fZzMQlYSIKcS4J7M8BUEmJiZieXkZdrsdL7/8MoqLi5GVlcXgg1ifW1JSEuOv8/LyYDAYeDOmLmsoFIJEIsH6+jri4+ORlJQEhUIBi8XC9MG4uDg2Lm/btg0lJSUQiUSYnp5m1DXJYIeHh9HT0wOLxRKTtEPQBrVaDZ1OB4lEAqfTifb2dohEIk6qdrvdTJ9LT0/H3Nwcrl+/DovFwiFihMe12+0fAmjQIbK2tpZNnC6Xi+lCWw2JRMKhcwSESEhIgFwux+rqKsrKyhgR//rrr6OlpYWDc2m9kslkOHHiBPLz86HX67GwsMCBdKQIIKJMXl4er/kikQjFxcVbzo1MpaRUoCyW1dVVRjQTkOT69esYGxtDZWUlvF4vBAIB7x3k/6msrERycjL7wshcrFQqsXPnTvaCJSYmoqKigslBNxuhUIgP8NFoFImJiezPIvoZwRk8Hg8EAgEMBgPi4+MxPT3NaM6UlBSMjo7yBVIqlTJNLC4uDmlpaby2R6NRJj19VBryyMgIQx8oLI1CP1NTUxkw8/777yMajSI1NRWvv/46uru7YTAYcOedd6KxsREejweZmZlITEzkgEWj0chkJplMhrKyMqSlpTFwJdZYXV1ludz6+jrLCC9evAiLxYLh4WH2287NzaGwsBAmk4khKuQ7qaqqwrZt2+D3+9HQ0MDPEXkeMzMzMTAwwOoH8l7FkgBRdAJlRRHBqbW1FQDYIxgIBHDx4kX2yLlcLjzzzDP8HVosFvzhD39gVQLRu+hnEYvFcDqd0Ol0LCckiMNWIyEhgT3EtKeST6atrQ3bt2+HyWRiUE5FRQXuuusuKBQKPleRHzkpKQk5OTmMKKd9VaFQYGJi4kM5HWtra4yo3mosLS3xO19QUIDc3Fy+KASDQVRVVbGSg77/zs5ONDY2sh+HoDB//etfGWJBlEvy2K2trUGv16OoqAgSiQShUAhyuTym/PLG8bEvGkQ+8ng8XCUieYrVaoXP58Pa2hqSk5Oh1+vhcDi4Kkp0kLW1NVRUVLCROCEhASqVCqmpqcjMzMTBgwfZPLWxscER7Q6HI+ahlA6hpDWjqnBlZSVCoRASExP58KvX6xEMBmE2m9k0JhAIsLy8zBVjOphQlS83NxcNDQ0YHR3FzMwMnE4nm3oDgQBjcm82CGFL5mXKCKDFlrBxycnJvPFSlZMqebTwra+vY3Nzk1FwAoGAzeikV6SFlz67WPSO9fV17ujExcVBLpcjFArxYY30qTKZDMXFxdw2i0QikMvl2NjYgEAgwC233MLp5mSkpQ4WoTIJP5yUlMSynlg3dQDcpqbPenl5mX8uqjpFIhGmo+Tm5nJaulgsZuJVUlISzGYzY/lUKhU2NzfZGJ2ZmckIx6SkpI8MdqNcEYFAwJzwxcVFNnbHxcWxuYqeezq4l5SUMIiAtMpUQaIKpc/nw7Zt2zAzM4O4uDh++dfX1/nwv9WgS3s4HGZzttvthlqt5qpVeno6srOzUVdXxxItev4o4IgoGzk5OQiHw0hISOBEep1Oh+XlZaZTEX2JKCGxxvr6OlcupVIptFotIwaDwSAnBRONyel0clWMckvEYjEMBgPy8vL4faaQq/X1ddTW1jJogII3iYgUS/sdiUSg0+k4RCkajUImk/FGLpVKmf1PFB9qTWu1WqSkpEAikXDKb3V1NRswhUIhEhIS+B0lAh6htT/qoCASibi6GolEoFarIZFIuCOq0WiwsrKCaDTKWnKBQAC5XM5yAfo+yWNFCGrq+FCoKf25QqHwQ+jtrQaZgpeWlhAOh3lvoHWRoBKpqancZSH6DoEtBAIBV5bNZjPnHVHgVX19PePBycdGRRWz2RzzfVAqlbzOErZUo9FwJ5ckMVTQ2LdvH2QyGRdJ5ubm+Lsigzgx7+lgTIWYcDiMpKQkpKSkYHFxMWbFj757IhylpqZCLBZDp9Px+ks+QAJfCIVC7lap1Wr26pSWlnIQKuHPw+EwDAYDBAIB028IZ0x7SKznLSUlhSvplCFQVFSEaDTKnx3J+YLBICPtKXR0fn6e91jyYCUlJSEtLY0P2JFIhC/Z6enpvHbHwgIThpyoPbQOp6encwX2RhmUUqnErl27PpQhQc8AFfOEQiH71mhfpHVWLBbzGkcXt60GhQlSYYHmSZ+lTCZjfHpxcTFnwczPz/OlPi0tDaWlpSwdJkwx7evZ2dksPaSwPgoCjuXfW1tb4+Ij7aW0bgNgzyx93yQrp32RVPYmk4kl4AKBAFlZWZzbQgh2WqPJjEz7x1aD9nK60CoUCi6AeDweJmXR50P5QHa7HZmZmVzEbmhoYD/x2toanwGysrKwfft2lhlS8RYAk6diPW8KhYIBKDKZDFqtlj1pVABLSkrCrl27WCni8/n48xMKhSgsLEQgEEBeXh7vlQTNoAw46vZTAYg6uluNzc1NlpxqtVpkZ2dz15JIXkqlEjk5OWhoaGACGhUx6QKUnJzM7/CN+OjExET+TKlIRP4NiUQSswB14/jYFw1KgHQ4HLDb7djY2EAgEMDdd9/N6b2bm5v8QwWDQSgUCszPz0OpVDLNiKhUDz74IFfS5XI5jEYjysvLOcmYuidUhYqF0ZLJZFAoFPwyqtVqpKSk4NOf/jRz0YluI5fLIRAIUFhYiJ6eHuzduxelpaVsDNRoNPD5fIhEIpifn4fH44FcLsfevXs5GdThcPCmDiBmRPzKygpnJFCCK5EiaJHQ6XTc5p6YmGDDGiE7KUSNWlbUSaED5Y4dOxAIBPi2TaZ7l8sVEzdKOlrSklKiOP29fr+fD19NTU0sVyEjIQXoFBUVoa6uDp/61KdQUVHBRBQKXExNTYVKpcL8/Dy/tA6HI+ZNnUzCdEikrsDu3buRl5fHiwRlfNBmk5ycjB07drBhkxDBZrOZ8ZGkq6ZW9a5du7iSSJs86V5vNkiip9Vqsbm5iYWFBYyOjiIvL4/JP3Nzc5iZmUF3dzdTSNbX13HrrbeitLSUOwcAUFlZiZWVFZw6dQqvvvoqJiYmsGvXLsYM0wFcKBTC5XLFJCdR6582T6JakCbY5/NBrVbjwIEDuP/++/kgkJKSgtLSUigUCs4tEYlEDE0gghcdxkZHR/lgmZKSwp2Ij7poEDWLOiNyuZy9QXK5HH6/n+dUW1vLVZOVlRWWSkgkEn6PPvvZz/JzFAqF4PF40NzcDKFQyLQQkleQbG+rERcXh6ysLCY80YHn2LFjfPCIi4tDYWEh9uzZA6VSyZSmAwcOoLKykg+WRFwjZDb5PdLS0mA2m/mCRiGN9Hu2GpFIBNnZ2dwRompTU1MTTCYT07zkcjlKS0u560IXazr8FhYWwufz4dixYzCbzUhPT2eZT3l5OfLz83HgwAEMDg7yBr+wsBBTAkRVyPHxcUxOTvLmfuedd6KiogKVlZVQqVTQaDRoamqC3W7nwxORiSgZmApa1LVSKpUwmUw4ceIEeygI6rF9+3bU1dVx4WKrz02n0yEUCnEnODMzE4cPH2apA0mnjEYjUlNTYTQaUVNTg9raWshkMoyMjDDaE/ggM4foSZFIBKWlpcjMzITNZsP6+joMBgOysrK4eLXVoGKHUqmETCbjizD5HojQR/sP7TPZ2dk4ceIEtm3bhkAggEuXLrEcODk5GVqtlv9eujgT1p1wz5FIJGaOxsrKChuQCbRCSdeFhYVwOBxwu90wGAzYv38/S29TUlKQlZUFgUDAnryioiLG2VOukEgkQl1dHVMC6bklv1gs62hiYiJUKhXnOtBzYjabsbGxwYdbKtZR8GYgEEBVVRWysrLg9/shFApZMpeamsrFClIDuFwuVmbI5XJotVqsr6/HlE4JBALk5ORwRhB1TiorK+Fyufj3m0wmPPbYYwgEAgiHwzCZTCguLoZQKGTPBmFwo9EoS2zC4TB0Oh2mp6fh9Xr5okLm+lh7aiQSgdFo5PMNfb+Exie/U3JyMsrKyjA8PIyZmRkGslDHNT4+Hl1dXcjJyWGpIAUYp6amwu/3o6ioCAqFAikpKdDpdOwT22rQ5flGgp9Wq0VtbS3C4TBWV1exubmJ4uJi3HHHHfx9rqys4JOf/CRuv/121NTUYP/+/aivr4dMJuOLGMlaVSoVF4lJNldQUMAd363G2toaMjMzmZRpNBrZ10BI+qmpKWRlZeHhhx/G9evXMTU1heW/B+fRpZOe+9TUVJZ8UQGZ9t6NjQ02+ptMJi5gbTXoOaEuC3W6d+3ahcTERNjtdja7b9++Hevr66ivr8fIyAjUajX0ej2AD85bpaWl2LVrF+Lj4/kzEQgEKC8vZ5gKKYcUCgVn6nyc8bEvGlqtFp2dndxKXFlZgVwux1NPPYUHHniAg198Ph+sViuqq6vx4osvYteuXRyokp2djUceeQQKhQJvv/02k6oodOXYsWP44Q9/yIFdIyMjcDgceOSRR2KGlFELl5jB09PTOHjwIDo7O3Hw4EH09PRgZmYGbrcbw8PDyMjI4NTXkZERmM1mNDc349SpU/jCF74Ao9GIRx99FGVlZVCpVIxh/MIXvoCEhAQUFxdDr9cjLS0NDQ0NMW91EokEHR0dUKvVbELSarX4zW9+gwMHDmBmZgYrKysoKChARUUFGhoa+IZLVb7jx4/DarXiscce+1AlRiwWIzc3F7/5zW/wwAMPwOVyoaKigjNMAGypXQY+2LBeeeUV1pgTT3x2dhZLS0tsqotEIhgeHsYdd9yBcDiMRx99FHv27IFOp0MgEOCXymazobS0FJ2dnbh+/To0Gg0WFxdRXl7OPOhAIMDYNkptvtkwm81ITU3lqr9YLEZzczOmpqag0+nw3HPPITs7G0ePHoXX68Xu3bvR0tKC4uJirsBSymlWVhbOnj2LvXv3cuU1MzMTL774IqRSKS5fvswVXIVCgX379sVs89KlR6/XY2VlBdPT09i+fTuDDoxGI3eb5ubmUFxcjJMnT6K5uZmxkJFIBL/73e8wPj6O2dlZfPrTn2bJhEgkwk9/+lOoVCqcPn0aJpMJZWVlyMrKgsFgiLlgU4WKpCBLS0vYvn07zpw5g+rqarz55ptoaWnB9evX8dZbb+HAgQP8We3duxef//zn8cADD2BhYQGlpaW4dOkS7rjjDgiFQr74fO9738OePXswMTHByM+1tTUcOnSIJYZbDdJNG41GrKysYGRkBHK5HG1tbUhNTcXly5ehVqtRU1MDpVKJe+65B3Nzc/zZdXZ2or29HV1dXVD+PeVZIBCgt7cXkUiEU0qbmpq4ImmxWDAzM4P09PSYrV6lUokLFy4gKysLcrkcPT09cDqduHbtGtLS0jAyMgKr1YqxsTFcvXoVx48fR1ZWFg4ePAipVMoSlXfeeYfD5fR6PXchCe+6f/9+zgvp7u7G6OgoampqYtKTZDIZOjo6UF1dDZ1Oh76+Pvh8Ply/fh0pKSno7u7GxsYG/H4/00MuXryImZkZtLW1AfhgDT916hQef/xxCAQCFBQUQCQSIScnB0ePHoXNZsOBAwfQ3d3N3b/q6mocPHgwpqyLvCsmk4mTwM1mMzo7O7F9+3ZcvXqVEa4TExOorq7GxYsXIZfLYTKZsGvXLphMJvz+97/HQw89hL6+PgiFQoyOjnKHqqWlBd/61regVCqhVqvR3t6O999/n/Nwthrp6ekIBAJoaGiATqeD3W5HXV0dTp06hdzcXLhcLs6aCYfDSE9Pxze+8Q1s374dCQkJKCgowM6dO9He3o6KigrIZDLs3LmTGfy33nor7HY77rrrLjaqU4o9dU22Gmq1GhkZGWxynZ6eRnFxMXvEent7Gdk6NTWF22+/necUCASQn5/P6x91Ox999FHOdkpOTsapU6fYn0QHakqOjyU5I8lHZmYm//1JSUnw+XwoKChAZ2cnS0eBDwocw8PD2LFjB4aHh+FwOGAwGBAMBjEyMoK+vj4cO3aMq8wGgwFvv/026uvrIZFIUFhYiP7+fpw+fTpmvhK9C9RhI+w3IXuNRiPm5ua4Cz49Pc3I4J07d3KwJYFCSkpKMDg4CK1Wy8GhpaWl6OjowF133cWIcZLCGQyGmL7HnJwc9uKlpqZCr9ejvLwc7733HoLBIC5dusQyIq/Xi8cffxw6nY7fa7PZjIyMDNhsNjQ2NiIxMRH33HMPMjIykJiYiOzsbAwPD+Ouu+6C3W5HW1sbhoeH+QAZq9Ajk8lw5swZpKWlcQjgwMAArly5go6ODvT09PA65fV6ceTIEc7M0Ol0KC8vh06n44yctrY2uFwuzM3NQSqVIj8/H6+//jqOHz+OmZkZCIVCpKamQqfTsY9yqyGVSqFSqVBVVYXNzU1cvXoVubm5DJVYXV3lz0coFCIvLw///M//jMbGRly4cIEDfQcGBvDggw8iGo2ipqaGYTrRaBT/+7//i3vvvZe7NRaLBRaLBQKBgLPSbjZEIhHnw9DldNu2bRxq29HRwUUTqVSKL33pS7Db7fjEJz7B6OG1tTX813/9F370ox8hLi4OOp0O4XAYGRkZOHz4MDweD+644w5OvQfAXqhYeyqpFhISEjA0NISLFy/i0KFDSEtLQ3FxMVsZHA4HLl26hI2NDZw7dw5JSUloa2tDfHw8CgsL+ZzR2tqKvXv3wm63w2KxsG/4wIEDrLgYHR2F2+1GWlpazGLFjeNjm8ETExPx0EMPob+/HxKJBJWVlUyZevvttzE3N8dhOfn5+ejr68OuXbswPj7OOr319XU0NDRwamxPTw9SU1OxsrKC5eVlfO1rX8O3vvUtJCYmcleECB6xbk59fX3IyMjA7Owst5GGh4ehUqnQ0tLC5A6xWMzJxna7HZubm5iamsLa2hrnP5w/fx7AByQLqu6Oj49DIBDg17/+NQwGA/Lz8+F2uzE3N4f29nbs3r17y7kFg0E0NTWxpEYikeDSpUt44okncOnSJSQnJ2NhYQGvv/46X7xKSkrw2muv8SWkqKgIy8vL+NWvfoUvfvGLWF9fZ401dTrefPNNZGVl4eTJk2xSl8vl+Mtf/rIlsWBjYwO33nora/cmJychEAg4J2XPnj2cD1FWVoYzZ85gZGQEkUiEjcuUGj04OIjW1lbs3r2bedd2ux3t7e148MEHcfnyZYYFUAcq1k19aGgIOTk56OjogFKphFQqxUsvvYRdu3bB4/Hg1ltvhcvlQltbG6anp7GysgK1Ws0GWdJhq1QqtLa24sknn8S7776LnJwc+Hw+tLa2shfD4XDgzTff5MDEq1evorGxccu5ORwO3sToktbZ2YnHH38cL7/8MkpKSrC5uclVK71ejzfffBOnTp3iCjbJHsrKyvCjH/0Ijz32GG92Ho+HNcV6vZ6T3klq2NfXt+XcJBIJKioquOLY3d2NiYkJ1NXV4cqVK5BKpXz4UalUmJqaQk5ODl577TXs3r2b+d0HDhzgQzeZxcLhMEsFr169ir1792J6epoDsxYWFlBWVrbl3ABwKvHU1BQSEhIYvNDU1MQGVeqwkfmbwi1HRka4SriwsICnnnoKer2eDztjY2O4ePEiJBIJdu7cibS0NCwuLkIulyMajWJsbCxmpyoajfLFgNaKnp4eFBQUAADy8/M5Y6W5uZnT5E+fPs2/ni6sKSkpePLJJ9Hc3Ay5XM7maKlUiqmpKczNzTHSVafTob+/P2bXgHIgbDYby6PIUJueno6cnBxcvnyZMZInT55EQ0MDrly5gry8PFitVly/fh0qlQrj4+N45plnmKQTDoextLSE2dlZmM1mJCUlwWg08gZNMIxYo7GxkQ94SqWSTcrnzp2D2WzG5OQkH9AmJiZQVVWFqakpXtdlMhnuvPNO+P1+zMzMoLq6GmlpaRgfH8c777yDhIQE2Gw2lJeXcyEkFAqhv78/pjfIbrejoKAAGxsbSE5Ohk6nw6VLl/hZtlqtDOvweDzo7u7Gfffdh8HBQVy/fh1SqRRGoxFpaWk4d+4c5HI5v4sDAwOYnp7GgQMH8Mc//hH33nsvlv+e2i4QCLC4uBjTI7e4uMjEHsowIMobVSKvX7+OhIQE5Ofns7fpjTfe4E43yTO7u7tRVlbGuUGUOVNWVgav14vMzEy0trayFJZyZ7YaCQkJcLvdMBqNCIVCGBgYwPz8PHbs2IHi4mIkJibCZrOhpaWFu8eBQAB//etfORuEJExSqRQ7d+7EwMAAEhMT4fP5GPYxMDAAgUCAzs5OlJSUIC8vjy9/W41AIACj0QiXy8WejImJCU7tVqlUcLvdfIjKzc1FNBrFtWvXuDputVqxsbEBh8OBBx54gH015KvS6XQ4ffo0J9k7HA4O3I0FCnE4HCgrK0N2djaWl5cxMTEBjUaDffv2IRAIwOVyYWBgAGfOnMHq6ioeeeQR5ObmMhVoeHgYXq8XKysrUCgUvCZlZWXB5/Ph7bffhkAgQHFxMYqLizE3N4f5+Xm4XC4YDIYt5wWAEdOLi4sIhUIsz6FQWIFAgO7ubvaGmEwm7NixAy+//DKf7eLi4hgXXFhYiNraWqhUKsTHxyMuLg7PPvsshoeHodVq2cNGHpeBgQE0NzffdG4UbruysgKJRIKioiKMjY1Bp9Ohs7OT31+RSISNjQ1oNBp861vfwsTEBHfQSA7W2dmJgoICLC8vQyKRQCgUYn5+HlNTU3juuecglUoxOTkJjUYDqVTKkJ+txurqKm655RbMzc1xF9/v9+Puu+/+kExufn4eDocDY2NjsFqtcLvdSElJgcVi4S7Pz3/+c5adp6amcvAimbhPnDjBlCzKxenr68Pdd99907nZ7XaYTCb278nlcs5Loi7OyMgIXC4XqqurUVJSgpaWFoyOjrI8SqPR4MiRI5icnERmZiZ6enpYJvbee+/h1KlTuPvuu2E0GjEzM4OEhATuvMSSnN04PvZFQyqV4gc/+AGMRiMbzchMK5VKcfjwYc7OUCqVqK6uxq9//Ws2L6tUKigUCjaM19bWIhKJsL4zFAphZGQEFRUVXGkhKpDf7495KKXqWVlZGb+cbrcbZ86c4YXX5XJhfn4e4XAYn/rUp3DixAlcv36dZRI+nw8ymQzXrl3D3NwcHn74YSiVSv7f+vr68C//8i9ITk6Gw+HgQLCampqY7UqhUMipj/Hx8XyLJYJHaWkpZDIZp5GHQiHo9Xqsrq7i0KFDmJycxJkzZ3D06FG0trbi0Ucfxd69e7k619nZieXlZbz11ltcgaE02fX1dezdu3fLuZERrKqqClqtFj6fD6+//jpOnTqFAwcOoKCgABkZGRw8l5mZifr6evz2t7/Fc889B4PBgOrqaq5OEQWLwl4EAgFvhISJXFtb425BrAouBR2aTCZmrQcCAb7QlJSUwOfzwefzccbC3NwckylIe05ymhdeeAHhcJgvYNu2bUM4HEZHRwfKysqQnp7O0pWcnByUl5dvObfs7Gz87W9/w/79+1FcXIzs7GwsLCzgf/7nf1BWVoa2tjYIBAL4fD48+eSTqK+vx969exEOh/HSSy+huroat912G8RiMYaGhtDQ0ACHw8E63qGhIchkMq6kbW5uYmVlhTtdsYzqTqcTXq8XKpUKCQkJcDgcmJmZgdfrxZ49e9DQ0IDZ2VlYLBbY7XakpKRg//79WFpawsjICJRKJTIyMlh7SjhIhUKB6elp/n1FRUUQCASoqanB9PQ0XC7XRx5eAHBmC3lOSNNPF3ydToeEhARMT08jGAziE5/4BM6ePcvaWmrBJyUlwe/34/3330dlZSWOHDnCIIDR0VFucaekpGBiYoLXq1gVv83NTcYg5+TkIDs7m7sEc3NzDJWYn5/HK6+8ArPZjJWVFZjNZtYYp6SkoKqqCu+88w5MJhOWl5exa9cuiMVi9PX1oaOjA9PT0zAYDPy9UGhfrGKKQqHgRG2qlFMwYVlZGQ4cOIDMzEysrq7C5XLB5XJh27ZtGB8fh9/vR3FxMScWt7e3Y2pqio2Cfr8fHR0dcDgcAMBGTJJ8FRUVxexoyOVyRgkLBAJMT0+zrOXChQs4cuQIJ7jHxcVhz5496O7uxvr6Otra2viwSN6T+Ph4DA4Oorq6mj2AhD8eGxvjy2hiYiIT7mLN7cUXX0RtbS10Oh3i4+M5g0EqlaKxsRFPPfUUHA4H7rvvPojFYrz++ussUSG/nNVqZdzmu+++i+3bt6O2tpblciSRocuHWCxGRUVFzAMMkaBuDFDb3NzEM888A5lMBqPRCJVKhZmZGVy+fBmZmZncXXO5XHy4PnbsGKanp9HT08NeO5FIxB6ZQCCA+Ph4bNu2jcEQdPjbakSjUSwuLnLYbVZWFpOIKGOC0rnX19dRUFDAEpuioiIolUrk5eVBrVbj5ZdfxqVLlxjlK5fLkZOTwyQzu92OuLg4vlyRR22rkZCQgNHRUQ7IpJyW3/zmN2hqakJeXh5mZmYQDoeRl5eH9fV1iMVi6PV6XL16FSsrK8jJycEtt9zCQaaVlZWQSCSwWCy8PjY1NaGnpwclJSUMShAKhaiqqtpyblTkpLwBoidduHAB//zP/4yioiIkJibCarVicHAQExMTeOutt5CZmfkhohUVSSjobfnvienk5UhISEBycjL7RtbW1jAzMxPTy7qysoK33nqLQ4h9Ph+Wlpa4eyKXy1FVVYWxsTFMTk5ibW2Nu7Hr6+tobW2Fw+Fg/8TU1BT27t0LvV7PBYKLFy/innvuQSAQQGlpKcRiMTweD8bHx1FdXb3l3PLy8njviEQiWFpaQjAYxMTEBEsUyatESFqfz4fFxUWWx09OTrIsOxgM4sCBA5wFRplV1KEiJUJiYiL7dbcacXFxmJ2dZcQ8Ef+SkpKwuLiInTt3Ii4ujrvdEokEO3bsQG9vL8uLPB4PLl26hLKyMvT29iI9PZ2BCQ6HA+np6RgaGsKBAweYzkmm+FjZT2lpabh8+TK2b9/O0sFoNIonnngCP/zhD9lUPjU1BZfLBYlEgrq6OiwsLODatWuwWq04evQoGhsbIZVK0dbWxmRPIqC9/vrrjLTd3NzE5cuX0d7eDqPRiGg0ittuu23L+dH42NKp+fl53HLLLaxlLC8vh8ViwcGDB1nXSIbkrKwsDA8PY+/evRxoREjFT37yk7hw4QKnPhNZKBwO4+jRo/D5fAiFQpifn0dGRgaqqqoQiURihlm5XC7s2LEDXq8X8/PznKZ8yy23QKFQoKqqCkVFRdi2bRs+8YlPoKOjg2UOFRUVSEtLQ3x8PEfTf/3rX2euvMlkQlZWFo4ePYqBgQFoNBqmLezbtw8WiyWmHGN9fR0ajYa1luQ3oZwP6u7QAWJzcxOLi4tsOF5bW8Py8jIvrL/97W/ZbKXValFYWIj/+I//YMJRMBiEUChktCAdHrYa27dvRzgcRigUQkFBAfbt24cvf/nLTNWZmppiys2pU6c40Esmk8HtdnNKrM1mw5NPPgmHwwGHw8E3//vuuw/PP/88m7nj4uLYRBlrEwbA2LalpSVoNBpUVFRgZWWFQ8BoHiqVCi+++CJUKhVXWbKysqDT6XDkyBEYjUY4nU4oFAomx1DFbX5+nnXfycnJUKvVnMmw1YhEIrjllls42Iy6AIcOHYLD4eBAscrKSpw4cQI2mw0rKyvsCaIuQXd3N/70pz/hnnvuYZ46ABQUFGDbtm2wWCwcRFdcXIzMzExMTEzEDGWirAR6DnQ6HRITE1FXV8fSo76+PjidTsTFxaGrqwt2ux09PT1cRAgEAqx1vuOOO9gzRKbSe++9l7sOtPlQIncsIhbwAcbwRhMooT137doFiUQCs9nMfP6ysjKMjIzAZDIhNTUVBw8eZIoXJYjfcccdzFYfGBiAxWLBtm3b2Kjd09ODpKQklJWVQSQSxUy4FggEbJYkw7JMJkNDQwNWVlY4bV6tVqOoqAgWiwVHjhxBS0sL5ufn4fP5+H0mZrvH40FfXx/a2towPz+P22+/nb1kVBWemJjgA/1WY2FhAXv37mV4RXJyMkwmE+6//35+tpaWlmAwGPi7NhgMbAanDfqee+7B0tIS/vu//5sPdmT4ffzxx9HR0cGdDKLPnDt3LmZlni6jVOk3m81wOp3Izc1lo2hSUhKKi4tx6623YnFxEWazmclFGo0GWVlZOHz4MCdsE7t/eXkZqamp+PnPf86c/VAoBJFIBIVCgZWVlZjoXZvNxt0n8lBYLBZUVVVBLBYjJycHtbW1aGpqQmFhITo7O3H06FFoNBoYjUYkJyez78lut6O8vBwqlYqzI1paWtDV1cXvuNfrRVFREerr62G322Ma/P1+PxobGzlfJRKJoK2tDQUFBYyAXVxchEgkQllZGa5du4bm5maoVCrs2LED6enpsNvtCIVCmJiYQHNzMzQaDWpqajiPwOl0wu/3IysrC1NTU4hGoyzxIH32zQaFxUWjUQwODmJ6ehqZmZmorq7mCz9RwiwWC9577z3k5eVheXkZ1dXVkMlk6OzshNVqRV5eHuvuw+EwFhYWMDY2hra2Nrz55puIRqMMR/H5fEy32mqQqTk+Pv5DAIQvfvGLqK6uxvbt21FUVMS5KRqNBjk5OXA6naiurkZNTQ3UajVXpoEPiEl+v5/N1g899BDW1tZQWFiIjY0NVFVV4fDhw1AqlaxTv9lYX1/nbmMkEkFqaipEIhG+853vMN3S4XAgLi6O5Z2f/exn4XA4UF1djZycHAiFQs6POXDgADQaDYMR0tPT8ZnPfIb3LZ/Px2s+mYC3GnTGAT4oXJSXlyMrKwvNzc2Yn5/H/Pw8nE4n+wcJG014bEpPb25uRlFREe666y7YbDbu0Go0Grz22mtIS0vDrl27EAwGsbCwwHOMBeKYmZnhwpNcLkd5eTmys7NRWFjI/04BuAcPHoTFYuGMCjpz1tTUsDKlvr4efX193FW0WCy48847YbPZGDhAPiy73R7T50XwB5lMhvz8fFRVVWFxcZHXtrW1NbS3t/NF/+rVq8jPz0c0GkVlZSWfGYnu+PDDD3NWCRnak5OTMT8/j/X1dVYPEDUr1nC5XMjPz8fIyAg6OzsxNzeHgYEB7Nu3j8Ovi4uL8fDDD+OLX/wifD4fVlZW4PF4cPToUeh0Oj7DXbt2DfX19ejo6EBXVxfGx8cRCoXw+OOP43e/+x3UajV8Ph+Ki4v5XBWLXPehZ+9j/Sp8UEWYnZ2FwWCAUCjk9nUkEkFGRgYWFhaYXJKZmYmuri4UFxcztpBoDxMTE8jIyGAk4ubmJle5l5aWIBKJ+KFbWlriyk4sfV98fDxmZ2fZKBkMBplAIZFI0NbWBplMxlSiyclJfunD4TDzoycnJ7Fjxw5uzw8MDLARmSgkdIMOBoMIBAKQSqUx9X1UcSBjc3x8PJt1VSoVp0oC4DCw2dlZPvDQpkbVeLfbzQ8sIc3olh4MBiGRSJjoE8ukDoBbzRqNBmq1GjKZDFlZWXxApRaqSqVCOBzGwYMHOaSKcKM6nQ4WiwX33nsvQqEQy0vW19chkUjQ2tqKjIwMJCQkQKFQsMaRjHWxvlMyOJKvg2hkxcXFsNlsCIVCkMlkkEqlqKio4LCjpaUlrKyscIv+8OHDcDgcEIvFvPB5vV6moLW3t3NFkoJ/YklFyECqUCjg8Xh4cyWz8tzcHJvyqWJy/vx5Tj81GAwIBAIYGhrCvn37kJqairy8PAwPD2N1dRVSqRSzs7MoKChAf38/TCYTvF4vvF4vUlNTY14eqSpESGlKBaXW+sjICH83IpEIZrMZc3NzXA2h6vLMzAxuvfVWzM7OchdAJpOx5MtgMGBsbAyJiYlMTVpfX4fNZov5zFH1LTU1FQKBgLM6UlNTOeGUEL5kSCeTP5FH1tbW4HK5kJWVBavVyjpSIgvZbDZUV1ezOZoISHK5PKamlDZCel99Ph9SUlL4nSO+PXVna2pqMDw8zN4hqsq3t7ejuroaNpsNVVVVTOzIyMjA2toaGhsb8e677/JnTtSPWJr5uLg4bqdTIUUgEHDyNV1yzGYziouLOUiQMJREmOns7ER+fj48Hg/MZjNTyei7C4fDLPFaWVlhJGMsQlFSUhJfTilvRaVSwev1MoJyc3MTycnJiIuL44uRVCrlggBdRnJycjA6Ogq5XM5VvdXVVchkMpjNZvT09DDKOxwOIy0tDdPT09i1a9dN50bPZmJiIucGCIVClh3ZbDb2LdABkDoAoVCI5Vabm5vo7e3F8vIyxGIx/0y0vhcVFWFlZYWrzJRMH+twRbJLjUbDh2alUsmXi0gkwn48l8uF3bt3MzGHiC8ulwutra0oLy9HQkICdDodJiYmWGlAF1/CZNL7JxaLY/rQyBSs0+mYnFZRUcHFAYvFwuoBq9WKiooKeL1e7irSXkfSjr6+PjanErmGyJBWq5VJgGSWjbXf03yoeksyo4SEBC7q0dobCoU4lJLoVhQmu/z3EE3SzJNsjWAqWVlZnFdFxK709PSY3T260JNEjGTfkUgE1dXVfG5SqVS8F0QiEaaZUbdofX2d146Kigr2ddCzTwfXG8mHy8vLMf2i8fHxnC1GhnOtVovx8XEGwdC7IhaLUVxcjOnpaQAfdFQyMzORkZHBuWfkm0hISGAS2erqKvsxCGSQlpYGtVods2uQkpICm83GMkiKAyB0+eTkJFJTUzkF/kZst9Vq5TOM3W6HWCxGR0cHcnJysLS0xGGmcXFxqKio4PUJABctYtG66Pugn1GhUECv1/N54v3338fm5ibLwQkJTJ2SuLg43kNDoRCHTxMMhS45IyMjaGtrw+rqKqOu09LSOMPlZoOw5PQ+jYyMsC8mNTWVA5nj4uIY9R8MBtnblpyczN658vJypk91dXUhEAigoKAAGo0GWq0WTqeTz8pEQJucnNxybjeOj93RSExMRCAQgEAgQCAQ4Nbk5OQkgsEgo+xIakCHPa1Wy3QPMsmlpqZienqaD5FutxsJCQl8OKIFn4xGH4VGE4lEsNvt/MMTbtHr9UIikWBwcJB1cBQ2Q5UgIlLFx8ejv78fqampbBb2eDxMwCD0mNVq5erm1NQUMjMzuSpy0w/4hgwFqvbSIpSeno7Z2VlMTk5iYWGBX37q8ni9XsTHxyMzMxMKhYLNfUScIqrG5OQky02ADxY7OtTGmtvm5ianmBIOdmVlBVarFdnZ2dDr9YyRpeoOpb4SfpIkXEVFRbh8+TKnwRIJ4dq1a1wll8lkTMb6KJxnQkICv7xarZZ1xZReTZ8j/XmZmZkcpkZtQr/fj76+PqSnp0Or1bLWn3DMy8vLSE9PZ2xkQkIC/18sWUFcXBwvXiKRiJGjCwsLnCni8/ng9/vhcDig1+sxOjoKhULBBzIiAZWWlmJoaIj1/dSqnpubYykGbSyhUAgZGRncTt/qO6VNmC7dEokEKysr0Gg0jHsmvB8ZNlNSUphoRVKt+Ph49PX1sYeKaB29vb0AwO1kOmDTzxdrxMXFIRwOMxWE5BZra2tQKpUfIkjNzc1hfX2dCR2U6UGHRwoJpbBI2kTtdjvLD0kPS8ndsYhd9K4STpCeT7fbzXpWOnBvbGwgKysLV69eZakISVbI0zU2NsbmaMLjDg0NIT09nTGJRCUhOtpWg8gpNyJR19fXWWYH4EMkOJVKxRpav9/PyGI6IL733ntQqVQsPxAKhUy1o8M4HUiSkpJiYlpJhkHoYiKe0Ts1PT0Nn8+H1dVVTE9Pw+/3w2KxcL5IQkICS2fIa5eSksLPh9frxdjY2Ic6k7Q+kOx0qyGTybC2tsaHslAoBJVKxSFZPT09XMCYnp6GQqHA4uIiB8vRupWSksIM/mg0ivn5eXi9Xu5CZ2dnw2q1cvaE1WrldWKrQfIkynygwklycjJSUlKQlJTEvi0KYB0bG+PnGfigkOV2u9kHJJFIOCjM7/czRrO/vx/x8fF8saWMla2GVCrlYolCoUBqaiqHGyYkJEAikfABVyqVorS0lC8TZK4nWRMAfv/o0E7SvOLiYq54U9GI8oi2GlTUoILX6uoqlpaW2HexsLCAYDDIn5PT6YTdbud1MC4uDjKZDF6vl+cnl8sRiUT4wGmxWLiIQtkj9M7GQp/T3kYXOZJk01mBiiv0rmo0GvT19TG1DwBSU1MRDoe5gErzpgNob28ve2hIKfBxiH8kRadwueXlZQ7jI1wrfb60Ho+NjTGBUiKRsGTK6/VidHSUL2X0PA8PD0MgEGB+fp6LI1TwpcT6mw3aSyn0loApRNWam5uDzWaDzWZDf38/AHBRi7C+4XAYFosFwWAQ/f39SE5O5u+DJJAajYZzzZxOJ+bm5iCXy2N2bGntIB9SNBqFTqdDJBKB1+vlIjed26qqquByuZCcnMw5SYR1ttlsuHLlCjY3Nz9UqAc+gENMTEzwuYGKIbH2LMLCp6SkQKFQcPGDLlN+vx+BQADz8/Po6urigjT5WcnXQV4ir9fLmXPUxXE6nSgsLMT4+Dh/R8FgkDNnPs742B2N1dVV1NbWYnl5mReK1dVVTlQmXZ3L5cL4+DhXRynoj178pKQkzM7Oor29HY899hgbrSgJu7Ozk6v19GX09/fzl3GzQZUtYstTYBU95FQ9HRgYgM1mQ0VFBcxmM1pbW5nWQZQOips3m81shKMHioyTfX19TNLKyMiIudF5vV7k5uayPCExMZHNy9SlWFlZ4UPO8vIy6urq8OyzzyI9PZ0X0aKiIk60JpMQ6WOJUuL3++H1evlFFQqFMWVd1DFZXV3llMrZ2VnGEZaWlqKtrY2rxqOjo3w5KSwsRDQaxeTkJGvlZ2ZmkJmZyQvl2NgYZDIZhoeHOUODUlutViuEQiEOHjx407mJxWI24FHFgUIfidJFlwUy9mdkZLBumW7dBQUFaG1tZZQwsa/pwD0zM4Nt27bhypUrmJiYgFwuh0QiiVmJJDSpQCDgzaO3txdpaWnIzMxkpB7wwaJttVo5KXd8fBxOpxNZWVnQaDRobW3F0tISmpqaWHIyOTnJRlgyrxO28qNebsoEIK07BRJSZZmMXJT+bbVakZaWhpaWFgDgLJz8/HxcunQJCwsLfJmgQKzx8XEYDAbWrtKFkOQzHzXowE+bukajYXwjXUrn5uYYX0jaZfJHEFRhfn6eW7fU1iUYAOlLKcnUYrF8JFKZZHn0bMTHx2NhYYHTwpVKJXp7ezkNvaenB8FgEKdPn4ZGo4FcLucN5b333sP8/DyGhoa4ijw8PIyFhQUIhULU19cjGo1ySCThULcaVJkOBAJceaT/Hh8fzxshSR2pJU/fK1Vza2trMTU1xfQQKi4QTjI5OZkvGISypoP/VoPC+Px+P1cdp6enUVBQwEAPOrQRrKOgoABCoZALGxQ8BYC7WjcWZwhUUV9fj4WFBa6K0ya+1SCSEEly6HIHfLCnud1uJkhJpVKIxWKmk1EAF1F3qPK4urrK7wP5qEjWaLVa+XmZmZmJ2Qki3DL5Teiz3rZtG9xuNzIyMuB2u7mj09LSgmg0CpfLxTJGkoO2tLRAoVBAoVBw4ODY2BhXTJOTk7kIQbCDWB0Nn88HqVSK6elpxMfHIzU1FZ2dndypIh8EAFRVVSEUCmF4eBhmsxm1tbVc2DObzRgYGGAIhVqtxtraGkMNFhcXGYlKlw+Xy/WRwZoEi9jY2GAYBSHpaQ2kHI2FhQXGVodCIX4GKfgvLi4Oo6OjXEjw+Xyw2+1wOp38nCUnJ7OMOVYRamNjA3q9no3wPp8Po6OjvF6Vl5dDIBBgZmYGQ0ND2Nzc5KKrWCzmYsrGxgbOnj2LhIQEDjclehDlHRDtcXR0lElMsbotXq+X073pAt7f34+mpiZ+/wnpq1QqMTU1xeZ8Ko5Slo1YLGbPCnk4iHjo9XqRnJzMexj5O2NVv5eWlpCTk8PnFgqQpUDL7OxsvkAsLi6iqKgIp0+fBvDBJcXj8fDl1Gazcb4SFTc3NzehVqsxOzsLqVTKh29SkcRCiwsEAj6nEUKY3qNoNIqSkhIsLi7CYrEwzGdycpJzUCjLp6GhAX/+858xNjbGyGTqGuv1emRnZ3P4MP0dCwsLH+krpPdHLBajsLCQvwMKoSSIARHg6PxIYbkTExNYW1tDT08PVldXUVRUhMrKSi5UDg8Pc/gg7cX0ecbaF24cH/uikZ6ejuvXr0Mul3NlRCQS4cKFCxCLxR+KtQ+FQrjtttvQ2tqKc+fOoba2ls1nVB1UKpXo6uriQKapqSm8++67nCdB0iryfRD55WaDqsfBYBBarRYSiYQTj3t6epCXl4elpSU4HA74/X4MDg6yzIJ0sQMDA8zxJpmYXq+HSqXidvnU1BS3UEn76XQ6Y2qEyZAsEAjYzDQzM4Pi4mJYrVbo9Xo+dOfk5GB2dhYbGxt8qKTuEC2OHR0duP/++zksjVr3NpsNOp2OTaZUcaOshpuN5ORk+P3/P3v/Hd3med6N4x+QIEAMYoMECYIgwb3Fpb2nZVvyHmni2E7ixEmz036bNKnbtEne9E3cNNtZb2P7dRI3URzb8pIla4uSKC5xb4IEQALE3gQI4PeHe12Vf0eE/Z7TP3Wd03Ni1aZuPs9z3/c1PiPE/gX+/zJFW15eRlVVFcLhMHQ6HSKRCJvQbN68GVVVVejv74fb7ebERCwWs8wadTQVCgV3MYiwFQ6HuTuc7cCmS+5GTfiysjLMzc3xBIUwjNFoFJ2dnbh8+TJUKhUndxaLBXV1dRCLxdzJLS4u5ktDKpXyhIrgK6lUClarNWthm5uby3hi6sBRl5pwwOl0mh2EDQYDDAYDb854PM6EUYInkE+FUCiEyWRCIpFAc3MzZmZm+HekYjSb/CN1+iiJoEQ0Ho/zdxSLxSASiWCxWLgDT/wUmjTRf5dIJNhZnQ7j5uZmHkvTYSYSiWC329/XHTyTyfDIlmAGpAJHBkak7kHd2MrKSpw/fx4rKyucgPp8PgwMDKCtrQ0bN26ExWKBxWJhIuPq6ip0Oh2Ki4ths9mwtLTEJmjrBZHdqLNH7r5Xr15FfX098zDC4TCP8slxlThJhJM2GAzslEyja4fDgbm5OTQ2NgIA++rEYjFMTU1lxQkTRJPgL6lUirG8dIaS8zd1dA8ePMiJTjQa5Q4o+abEYjGYzWY+eyORCD772c+iubmZFaJSqRR7Ca0XpHJEniekLEeqejt37uRJXkNDA+vL03oFAgEni2Q+SA0VqVQKi8WCy5cv48SJE3jggQcQjUYRjUZ50l1ZWbnu2hKJBEZGRlgNLBAI4Ny5c9z8ocYHdeiAd5scq6uruPfeexGPxzE4OIj+/n6Ul5fzBEmv1/Meb2pqAgD2nMnPz2coarYiSCAQcOKoUqmQm5sLoVCIN998k5Xj7HY7PB4Pd8mpUKZiaHZ2lhX+qGCqqalBUVERbDYbDAbDe8js5DtBE6L1QiKRMEyNusHkxxGLxVi6nBKdqakphrKcOXMGIpEI1dXVPHUmKJPFYsHs7CwuXbqEyspKyGQyGI1GLCwscONIIBBk9X/S6XSYnZ1lCBTlI4FAADMzM6ivr0dubi4ikQg3Xebn5xn5QEaDNEkaHx+HUqnE8PAwnzlVVVWYnp5mV2j6d6lYWi8KCgqwuLjIkGfiL1osFpSXl8Pn8zE2n+6yXbt24dy5cwiFQuxxcu3aNTz66KO4cuUKw9eowMxkMrDb7fz9khITmT6uF/S70N4jHl9vby/27dvHYjXUCKCz1mQy8YSZOLc0HSkpKeF3BwCzs7PsQ0JnKd052fKkvLw8DA4OcgENgO9Oq9WKxsZGzM7OIicnB5s2bWIYz8WLFxGPx2E2m2GxWFiEhniYZAq6vLwMm83GHJhMJsMO79FoNKuSGDXDCVZ64+SwsLCQ/5x8O+bn55Gfnw+j0QiXy8Wk7nQ6jU996lO4fPkyQ5CIj3z8+HE8/fTTEIvFzMclCNN6sFAAPLkuKiqC3+9nc16BQIDu7m7U1dVxAU9FDN3rlCvIZDLk5OSgp6cHJSUlmJ+fZ8is2WyG0WjE4OAgCgsLWU6eEEjZeF43hiDzfjIxt+JW3IpbcStuxa24FbfiVtyKW/H/GB+Yo3ErbsWtuBW34lbciltxK27FrbgVHzRuFRq34lbciltxK27FrbgVt+JW3Ir/8bhVaNyKW3ErbsWtuBW34lbciltxK/7H41ahcStuxa24FbfiVtyKW3ErbsWt+B+PW4XGrbgVt+JW3IpbcStuxa24FbfifzxuFRq34lbciltxK27FrbgVt+JW3Ir/8fjAPho/+clPIJfLWUdapVLB4XBgamoKbW1t7HdBTreBQADhcBharRavvPIKNBoNOjo62DCsrKwMbrcbiUQCKysrcDgcSCQSMJvNKCkpQUFBARvEJBIJRCIRfOUrX7np2n76059CIBBAKpWyljcZslgsFoTDYUgkEnYmzcvLQ3l5OaxWK2QyGevfp9NpNDU1IRAIYHBwEM3NzZDL5ZiZmcHi4iJKS0vZ2C+dTiMajSKZTCI3Nxdf/vKXb7q2N954g83J8vPz2T2YXHHVajXC4TDrqRcVFUGpVLIzukgkQmFhIVQqFZxOJ0ZHR1FeXo5MJoPc3Fzk5+fD4/Gw3jzpNpM54NLSEv7qr/7qpmv7+c9/Do/HA7VajeLiYjalcbvdrJUcCoUgk8lQWlqK+fl56HQ6eDwedh0uLCwE8K4Odnd3N3bv3g2/34+1tTX2VJidnUVRUREbLJHRjUQiwaOPPnrTtf3zP/8ziouL2VU9k8lgcXER6XQaJSUlKCwsZJdOmUwGp9OJTCaDoqIiTE9Ps9GfRqOBWCzG1NQUTCYTa/YDgMlkgkql4mdGGuJkpPPkk0/edG3f+973WI+6oKAAUqkUXq+XnanJuTg3N5d9Obq6uhCJROByuSAQCNjRvKysDFarFVKpFAUFBVhbW2M3YjIjI2OzZDLJvg3/63/9r5uu7ZlnnmGjx5ycHAiFQqysrCAWi0Gj0bCmPe0pMsYkZ2/SRyenXq/Xy+ZCZFhVUlKCvLw8AIDL5WK3WdI//+QnP3nTtQHAV7/6Vej1etZ+X1tb498rk8mgsLAQPp+PzevI70AgEGB2dhahUAgCgQBCoRD5+fnYsGEDLl++DIvFApFIhKWlJXbU3bFjByYnJ+Hz+Vjj3OVy4Uc/+tFN1/bss88ilUqxK65CoYDNZmMn4pqaGoTDYbhcLsRiMXacJ7dlMhhMJpOQyWSoqKjAxMQEkskkezTk5OTAYDBgbW0NmUyGz5FwOAyDwYCHH374pms7efIku3OTAy2dcWazmR3IyTyLvG2i0ShsNhv7HgGAxWJh40ByuF1YWIBSqUQqlUJ+fj77HUmlUtaR/8QnPnHTtf3+97+HWq3G6uoq+yYsLi5iYWEBer2e3a7JMM1sNrMfA32nmUwGHo8HCoUCPT096OrqYtNAch4uKiqCVCrlu4XunEQigc985jM3Xdv3v/99dntXq9WQyWQIBoNIJBIoKiqCy+WC3+9HTk4OlEol1tbW2IXZ4XBAoVCgvLwcBoOB/YXI4NXj8cDpdEIsFsNgMECr1SKZTGJsbAw+nw8GgwG5ubn49Kc/fdO1/eQnP4HNZoPRaERxcTGEQiHsdjskEgn7BNBeCwaDiMfjGBoaglKphEQiYRNP0re32WxYWVmBWq1GJpNBNBqF0WjEwMAAFAoFjEYjG6zFYjFEo1H80z/9003X9uKLL/JZQ55UiUQCs7OzqKqq4vs/FApBpVKxh8PVq1fZjHFlZQWhUAgmk+k9Hg50/isUCkxMTKC5uZmd48ncLBQK4YEHHrjp2v70pz8BAMRiMRsw+v1+vqPJY8TtdkMoFKKkpAQikQgFBQVsJkyGf2RcFw6HsbKywr4gRqOR/U1of0QiEZSXl0MsFq/rbXDs2DE2aZNIJOzdQg7X9fX1sNvt/FxkMhnEYjEcDgdGRkYglUqhUqng8/nQ3NwMu90OhUIBvV4Pv9+PyclJlJWV8bMiZ2iv14twOIz8/Hx87nOfu+na/s//+T9s5JlMJlFQUACDwYCBgQHs2bMH4XCYzVnJIykUCsFgMCCVSkEkErHbvUajwYULF1BTUwO1Ws1mnKFQiL2XyHuHDBDz8vLWvRueffZZzn/o3fj9fiwtLcFsNiMcDrPRXCAQQFVVFVZWVpBMJhEKhaDVatnzi4w5l5eX0dzczD5hZEpMXiU5OTkIhUKcB663T5955hnk5OSwD1NOTg6WlpYwOTmJjRs3orq6GtPT03C5XJBIJPB4PNi0aROb1974nQ4MDLA5Id23oVAI6XQa1dXVbLpMOaDVasXa2ho++9nP3nRtP/rRj+D1ejkfkclkUCgUmJqaQmdnJ5szCoVCdpafnJzE+Pg4jhw5wk719fX1uHr1KpxOJ9RqNRQKBbRaLfLz8zE6OgqxWMyecgA4j/D5fPjiF79407XdGB+40FhbW4NYLMbi4iICgQC0Wi1isRg2bNgAn88HjUbDHyYZVi0vL8NqtaKyshLxeBy9vb1QKBR8OJJJVVlZGUpLS+H1euFyuTA2NsZmZslkEk6nM6vxEZmTkTMkJTFGoxEAOBHKz8/nJD8Wi7GJFJmv5eTkYGVlBdFoFFKpFNFolC3cyV2WkmdK8ilxXi9cLhdbwicSCYRCIV6jx+PhpI4O66WlJXY2lslkCIVC7DxOH/Dc3BybCNFF5Pf7EQgE+PmT1Xxubu66awsGg5DL5YhGo3A4HIjFYsjPz0cqleKkSSQSIScnBzabDcvLy5DL5cjLy4NKpeLEuaysDHa7HcXFxQgEAkgmk3xY0bNJJBIoLCxENBqF0+mE0+nMurZMJsMurfQzAoEAKioqoFKp4Pf7MT8/j0wmw4Z3KpWKN83s7Cw7IDc0NKClpQV2u53dTIuKihAMBrGyssJFLxna0OZeL+LxOPLz89kxnkwcNRoNG/tQgUTF78rKCgQCARQKBZvi0WUvl8sRiUQQDAY5eSLXTrVazQUaJbzZzNMikQji8Tii0SgnlqlUip28JRIJ5ubm4PP52Fler9fD6/WiqqqKExaz2YyZmRne94FAgIsxKqiMRiOEQuF7HMuzOdMC754jEomEi8Tc3Fwu/uhS9vv9/I5GR0fZFTYWi0GhUCA3NxdWqxUajQZLS0vsvi4WiwH8t6Hi/Pw84vE4CgoK+H1lcy5fW1uDWq2Gx+Ph/UrGhcFgkL93mUyG9vZ2vPbaa2xMmUwm4Xa72TQ0mUxCKpWykyx9w1RA0nNSqVTIZDLv6yJNz4qc0qkRoVKp2DjS6/VidXUVCoUCc3NznERZLBbEYjG+7CUSCaLRKCd0iUSC/3xgYABNTU1cmGQyGSwtLUEmk2X95tRqNbup095VqVRsjOn1eiEUCqFUKtmdl5I6t9uNhYUFCAQCGI1GmM1m/k7ICFYoFGJmZgYNDQ3s4h0KheB2u7OuTSAQID8/n5+bWCxGIpGAXC7H3NwckskkF3AejwdCoZDPUTJWjEQiWFlZwcLCAjQaDSeg5DhMyYJEIsHS0hJUKhUnOhaLZd21OZ1OaLVaBAIBZDIZKJVKLg4cDgcn8mKxGOl0mt2mk8kk1Go131vAu+Zmzc3NOHv2LDdZcnJy4PP5IJFIoNPpkE6nkZubi9zcXDZ9XC/IIJYaAdRQo3Od3nc4HOYGDRnSxuNxLtgLCwuxtrYGl8vFhqXUWKOEmgwJdTodcnJyMDMzw6aZ631ver2e3xOZ1plMJrhcLnbkFovFKCoqwsjICAoLC1FSUoLc3Fz4fD52uybDUI/Hg4KCAuTl5XGiOjc3h5ycHDYrTSQSmJ6eRl5e3rqFBn0HZO5HTVelUsnnEjXi6LsqKyvD0tISG9pR8T0xMcEGjbQPysvLoVar8fbbb6Orq4vNFGm/ZjvfEokEF/X03ZKBIf1+qVQKkUgEHo+Hi8zZ2VnU1dWx6bFOp4NAIIBareZ9Rc2AtbU1bqyqVCpoNBokk0nEYrGs5m50DkUikfc8I5/Ph6qqKlgsFng8HqTTaZhMJuTm5qKmpgaTk5MoLS3F6uoqlpaWIJFIIBaL4fF44HK5sLKyAq/XC6vVCp1OB7vdjlQqBb/f/577Mdt973a7IZVKIZFI2B07mUyioqKCG0uxWIxzMErsqWlHBa3JZOK9QqaM1FyLRqMoKCiA2+3m/IH2k9vtXndtfr8fCoWC89Z0Oo10Os0mvdRcy2QyKC4uRigUQmlpKWZnZ+F0Ojmv0Ol0fNfb7XYEAgEsLy8jJycHRUVFmJmZYZf1G99ZtjPkxvjA0CnaMJRACQQCpFIpXL9+nScT1C2x2+1wOByora3lgyiZTEIikcBisSCdTqO/vx+RSISrPUpm1tbW2FF1dXWV/yxbApNKpbC6uopUKoVUKsVJwsLCAicA0WgU8XicXWlv7PiTVb1MJoNUKsXg4CAEAgH/s0wmQ3l5OWQyGYqKiviSoZeULaj4kcvlUCgUfHk6HA6eVigUCggEAiQSCRQUFPBUZn5+Hl6vl4suAFhdXeVpCCVo5ABL74EcVgFkTV7ImTYQCLCjbzKZZKdylUoFtVqNdDrNHVCqvnNzczkRcTqdAMCJe35+Phcw5LJNzpQOhwNOpxMajSari6lQKEQoFOJEjb6fdDoNsVgMlUoFoVCIaDTKDrV1dXXweDyYmppCIBCAXC5HSUkJ1Go1HA4H5HI5iouLUVhYiJycHExNTSESifDEiw5ZmsytF+l0Gn6/H+FwGMlkkpNIm83G3aeioiKIxWJO6Km4djqd/Jzz8vIgFosxOzsLgUDAhQQV19ThTSQS7znQs7mWp1IpdoYGwN8OPX+NRgOlUsn7NDc3F1qtlpNdj8cDuVzOXSpaq1wuh1Kp5OSCih9qBvh8Pk5IsgUVj5SQeL1eeDwexONxJBIJJBIJ/j1CoRB/Z+R4nUgkoNfrYTQaodFoMD4+jvz8fEgkEgDvFoHxeJydXqmr7/F4ePq1XtBlQXssGAzyRWEymbiLLBAIsLi4iHA4zEWIzWaDw+FAIBCAUCiEXq/H1NQUhEIhtFotlEoldwvpXQDgC16hULxvkrCysoJ4PM4XWDweh9vtRjgcRiKR4MTQ5XLxe5VKpbBarfB4PFCpVOxEbrVakZ+fD41Gg8LCQk5EtVot8vLyUFpayh1UurzWi7W1Ne740vebSqVgs9m4uKUicmZmBtFoFFqtFnK5HIuLi3A4HOxSS5OO3NxcPjMpcYjH47Db7Zx0UcGVrdFz4zlNl3dubi7i8Tgn4Xq9HlKplAtprVaLnJwcLm4p8RSLxRgdHYXL5eJiRa/XIxKJYGFhgfcpJYcSiYQbU9ne641JP00KlEol3wsrKyuYnJxENBpFe3s7ioqKEAgEsLKygmAwyJMfq9UKsVjMjSaa7NEd4Pf7YbVa+SzPdo5QQ06pVEKn03HxRGf8jfvd5XJhaWkJq6urKCkpgcvl4kQ/lUohHo/j+vXrSCaT0Gg0KC8vR3l5OVwuF3JycpCTkwOxWIxQKISZmRnMzc1lLTTy8vL4OWcyGW7oUGIskUggl8u5aUYFEk1SKEdYW1vj/01oA3J0X1hYgNVq5UYGJeEikSjrPgXAjRhysL8xN5qfn2c35mg0CoVCAb/fz01GOm/pe5ubm+PCgxpzdH44nU4kEglOqOlezPbcQqEQFAoFN6uSySQ8Hg/m5+d5Ck+TCZlMhvr6+vc0UcRiMYxGIxdlUqmU8wG1Wg2hUIjy8nLI5XJoNBo+44H/zoVuFkKhkH/HZDKJ1dVVdp+m70kqlXKXnxpT5IKdTqchEone88/kvE139OLiIjf1pFIpv1e9Xg+lUrnu2qiBlZeXh8LCQpSVlfG3RYUR5Upra2tc6N7owE3nN90h9Dvn5+fzt2+z2SCVSnmqA7x7fmVbG51rcrmciwuaOtEkO5FIwGq14uLFi9xoraysRCqVQjqd5nOWJsahUIinttFo9D3NE3IsX1xcxMzMzPs2Fik+cKFxI+yooKCAq5uBgQG4XC4oFAqUlJRwV3lxcRGNjY3QarVcaRmNRmzatAkAuLtGHaSpqSneOA0NDTCbzRCLxdxJyfawKekmKAIdHsvLy4jFYrwRcnJyOFFVKBR8udDGLi4u5gqQOs1FRUXQ6/X8IRUWFjKchTrTlOTcLIRCISeUBCHIy8uD1+uFUqmE0WjkLnckEuFx+9raGhwOB6LRKHQ6Hf+53W5HMpmEQqGATCbjrgglpDk5OXwZAHhPBfr/HzKZjBMrgUDAxU4wGIRIJEJNTQ13q2nUSh9fJpPh7kEgEIBEIoHX6+UEnzZ8MBhk+JLD4YDNZkMoFIJOp0NFRcW6axOJRHxIUxKiVqvhdrths9mg1WphMBggkUg44aipqUE8Hsfw8DDcbjeKiorQ0dEBsViMsbEx5OTkoLm5GVVVVdzVyGQyfFDQIZefnw+1Wp11L8RiMU4oqOBZXl6GTqdDVVUVqqurodVqsba2BpFIhPLycmi1Wng8HqysrGBtbQ2FhYWcRNNoUqfTQSqVwuVyMfyMfn/qlGVLlmktN8LAlEolQqEQVlZWIJVKUVxcDJlMxgc/JX1WqxWxWAzFxcV8WVMHR6vVMoSERuhSqZT3kMfj4eeRLSQSCXdC6FsKh8Oc+CkUChQUFCCVSsHr9XKRRt8lwco2b94MhULB54jBYEB+fj5WVlbgdru5wKTuPcHHaMp5s6BL8cYOXTweRzqdRnFxMdRqNU9/zpw5A41GA5PJxGcNJV906dGUgqCPdBYA7+49grdkMhkYDIasyXwymUQkEuFGD/1fIBDgqZ9SqYRGo8Hq6irkcjnMZjP0ej0cDgd8Ph+USiVDGOlc1Gg00Gq1/O3X1NRAKpVCq9UiFArB4XBAJpPx81jvm1tcXOQmxNraGoLBIKxWK3+DMpkM4XAY09PTyM3NhdFohEgkwvLyMsLhMBe3oVCI4VJ0JqXTaS7YqZAleJNIJHrfSRAATu7oWyeIFyUZUqmUpwUlJSXQaDRwOBzwer2QSqUoLS2FRCKBz+dDNBrliYtOp4NEIkEgEODJC00V9Xp91ukjQZHz8vIgk8n4/Xk8HggEAhgMBuj1eggEAi60WltbodFoGJaUl5fHxdTo6CjW1tagVCr5viRIMBV6TqcTPp8POTk5MJlMWd8pdTj1ej1DBOmOoIkkNZuWl5cRDAa5u0yFr9/v52Iok8mgoKAAxcXFKC0t5QkSQaX9fj/sdjsikUhWBAMlcvQO6Dm63W6IxWIolUrI5XLE43GsrKxAr9dDp9MxXIbuYqVSiYKCAgiFQsjlcv7mAGB6epr3FUGhqGmQ7U4ViUScF6ytrXHesba2BrfbzWsWi8XvmWIpFAo4HA6Gl8nlcp44077PZDJc5BgMBgSDQXg8Hvj9foZLZ3undPbSM6PvbnV1ladrBPulDn5tbS3ndPTPVVVVPImWSqVcFNN/U15ezvC6eDyOUCjEMJts75QQIjTRV6lUkMvlcLvdWFxc5G+d0BUEv6PGMiE8IpEIN3Vp4lxSUoJoNIrCwkIolUouigQCAfR6PTembhYqlYqbnCqVCmVlZVxEUDNMq9WiuLgYGo2Gob05OTnQ6XTQ6XTIy8vDysoKw5ej0SgaGhrQ2dnJhYXX60VJSQnDzqjZWVJSsu7aAPB7oN8XADfRY7EYI2pGRka4wW00GiEWi9+zvzOZDE9jqIlSUFDAcCqdTofV1VWEw2H4fD7YbLb3bSxSfOBCg/C88Xicu1BisRhHjhyB1+tFe3s7jEYjZDIZDAYDj7fVajX279+PoqIi2O12xGIxxk/HYjHMzs5iZGQES0tL2LhxI7xeL0QiEWZmZiASiZgXUV1dnXV9NJajZCeTyeDIkSOIRCLYu3cvcz+2bdvG0Kjl5WVUVFQgnU5jcnIS+fn5mJiYwL59+xgiQgklwZzW1tawvLyMeDzOHYBsI0GxWMzwAJo4jI+Po7i4mA8J2sT0fMViMSYnJ9HW1gaDwQCfz8f489bWVv67M5kMjxYHBwc5YaCOh9vt5tH6zSKdTmPLli3QarUwm81ob2+Hy+VCfX09vF4vd4MJ3uXz+ThxycvL464Qvbf29nbuQOfl5SE/Px9VVVWYnZ0FAIyNjUGpVKK0tBTnz5/H8PDwumujcV4ikUAmk4FKpYJAIEA4HMZLL70Ej8fDCSF1ydVqNQwGA1pbWzk5jUajuH79OpqamtDT04OlpSWEw2F4vV5s3rwZdrsdZWVlfEHLZDJMTU0xxO1msbq6CovFgoKCAuTk5PBBvX37dsaPOxwOxONxGI1GCAQCLC0t4eLFi6irq0NZWRlyc3NRV1eHxcVF3HvvvQwroGReqVSiurqasfyNjY3o6urC6uoqxsfH112bSCRCS0sLj+6p4058A+ruy2QydHV1YWZmhovfHTt2oLy8HDabDbOzs7BarTCbzYjH41y4aDQalJaWwuFwQK1W8zdRW1sLAO/bwfV4PMwLEAgEMJlMMBqNiMfjmJ6eRnV1NeNgi4qKMDExwZyOpqYm5hEZDAbMzs6iurqa8d3BYBCzs7Ow2+2cDPn9fhQVFaG8vJxhTdlCqVQiPz8fer2ep5gajQbd3d0QCoWYn5/HxMQEcnJy0NDQgNraWhiNRk5UqRN74cIFtLS0MGyIpkxqtRojIyPIZDI8yaKJW7ZkPh6Po62tjeEzhJ/t7OxEOBzmqWQsFoNarcbS0hLS6TRisRjuv/9+bNy4kSemOp0OO3fu5Oma1WplfDjB+5aWlqDVatHa2vq+UzTCvIvFYi4+g8EgKioqkMlkOIEHAIVCAa/Xi7W1NYyOjmLr1q1oampCTk4ONx62bdvGhaXP58Pq6ira29uhUCiwb98+nvRSk+r94EkGg4ELHXoura2tnEARv6umpobPVLVajerqauj1eu529/T0YMOGDfyuQ6EQ7HY7urq6UFtby1N+vV6P0tJShqesFwKBAC0tLdxlJ7hOW1sbLl26BKvVyh3ompoaTpKHh4eZcySTyaDT6bCyssK4dWqOhcNhNDY2IhgMorm5GZlMBlVVVWhubmb40HrhcrmY0+ByuZBOp3Hu3Dns2bMHZrMZRqMRarUaFouF3xcVvVVVVcjPz8fi4iJPPA4dOoScnBye1g4ODqKoqAhutxtmsxmZTAYmkwmbNm2C2Wx+3/OXEj/qKvf390OlUvFkyW63898fCoUglUpx+fJlTqIFAgEqKiq4+CaOlcvlgs1mQ3V1Naqrq9HU1MSoAo1Gg/n5+awNgVgshvLycr7/aFJIe3H//v3Iy8vjAr63txcymQzz8/Nobm5GY2Mj8/5OnTqFo0ePwm63Y3h4GIFAgAtblUqFTZs2QSAQoKysDE1NTeuuiSKZTHIuEgwG+e85ePAgZDIZysrKuEnY2tqK6elp6HQ6aLVatLS0oKGhgfcSTe8J4r26uoq+vj6kUilMTU2hoKAA3d3dWFhY4CmbXq9fd21utxt5eXmcvBIv40Mf+hDfOdTwOXr0KEZHR1FYWIiVlRWYTCbE43FMTU1BLpdjaGgIQqEQq6uriEQifM5VVlbC6XTCZDJhbGwMHo8HOTk5cDqdWb+3UCgEoVCIiYkJXLx4EdevX8fS0hJ27dqFZDLJua3dbkdFRQVCoRBaW1uxsrICi8WCoqIi5hYS52diYoLzLrVajcbGRoZP9/f3M4/HarUyOmW9tRUWFsLtdvPE0Gaz4fDhw1wQ6/V67NixAw8//DASiQQ6Ojp4UkY8mHg8jj/84Q9QKpUMM5RIJMwjpZxjamoK+fn5aG5uRlFREWw22/t+d8D/Q6GRn5+PTCaDvXv34vDhw6iqqkI4HMbQ0BDuuecezM3NYWFhAUtLS1haWsLOnTv5gX/zm99Eb28vWlpa4Pf7sW/fPnzzm99kAlxRUREOHDiA1dVVfOYzn4Fer0dLSwscDgdmZ2exZ88eXL16dd21Ee5y48aN6OrqglarhUKhwPXr15GXlwen08nwjtHRUU7ESkpKUF9fj4aGBmi1WiwuLmLHjh04ffo0du3axWTBxcVF+P1+lJeXY2RkhOEjgUAAZrMZVqt13bXp9XqMj48zbEcul8NiscDpdKK5uRlKpZLHtYlEAnv37oVer4dWq8Xk5CRkMhnMZjOmpqawfft2TE9P88+tqalBfX09zp8/j9bWVszOzjLUIC8vDxaLBWNjY+uuTSgUYm5ujqcPU1NTUCqV6O/vh8lkQk5ODhobG1FdXY2FhQU0Nzfj/PnzmJiYwKuvvgqbzYampiZUVlbCZrMhlUqhq6uLu2vj4+OIRCIQiUSIRCJoamqCTqeDUCjkAm+9KCgowNLSEgQCAVZXV+FyuWAwGGAymfAP//APPP71eDx49dVXsWfPHpw8eRKFhYV47rnnMDc3h5qaGiwvLyOdTuPq1au466670NTUhKqqKjQ1NeHNN99EU1MTEzftdjuWlpawb9++rJC4/Px8uFwuNDQ0oKOjAxaLBW1tbbh8+TIaGhr4sPb7/bh48SIeeOAB7mZfvnyZMb4ulwtKpRIPP/wwNBoN0uk0FhYW4PF4oNVq4XA4cOHCBdjtdvT09KCnpwcFBQVZOxylpaUYHR2FRCJhbo1UKkV7ezt27dqFS5cuwefzIT8/H7FYDI8++ihmZ2dRWlqKiYkJBINBNDQ0oKqqClu3bsXx48dhNBoZ8nj16lXYbDYcPXoUZ86cgdVqxcDAAMbGxmA0GrPCWIB396rNZkNFRQWqq6s5ac5kMvjsZz+L1157jTHbFy9exL59+zA5OclwEYJeXLlyhZO9w4cPo66uDg0NDThw4ACKioqgUCgwOTnJHCaJRII77rgDLpdr3bWpVCoeqTudTqysrDAp74EHHmD4ABHoqqurceHCBWi1Wh6VGwwGxve/8847POXVarWMMyYuREVFBRYXFzE9PY2HHnoo67SFBA1u5F9JpVKEw2F89KMf5Wfrdrtx9uxZ7N+/H8899xyqq6vx8ssvY3BwEAUFBZidncXQ0BD+9Kc/obS0lGEs1dXVUCqVsFgsuHDhAsPBAoEAWlpacO3atXXXlk6nGV5H0+T9+/cz7DUWi6G2thZdXV1QqVRobGzEm2++iaWlJfzsZz+Dw+HA5s2b4ff7IRaL0dfXB51Oh8LCQr7sPB4P84bovMrLy8PevXv5TLxZVFRUwO12o66uDmazGdFoFAKBADMzM9Dr9XjnnXcwNDSEqakpTExM4IknnuDGzpkzZ+Dz+VBbW4u8vDx8+ctfxptvvomKigpMTk5ienoahYWFGB8fh8lkgsfjgUgkwujoKM6cOQO73Y6TJ09mfW6UiCWTSYyPj8PhcOCNN97Avffei3379qGzs5O71HV1dXj99ddhMBi4ENyxYweSySTm5ubw/e9/H3fddRc2bNiA6upqVFVV4aWXXsInP/lJzMzMQC6XY3l5GfPz8ygtLcXMzMy6a5NKpYwuKCgoQCAQwM6dOzE5OclwmitXruD48eO4dOkStFotenp6UFZWxrALmsTX1NTA7/dj165dUKvV/O76+vqwb98+XL58mZEFqVQKSqUyKzxJrVZz15zuu66uLly/fh0HDhxAQUEBmpub0dbWhmQyicbGRojFYnzoQx/C66+/DqvVira2Nng8HoyOjmJgYADV1dWIRqMAgObmZrjdbrS2tvI9nU6nYbPZUFlZydydm4VWq4XP50NTUxMKCwthtVohl8tRWVmJ++67D1NTU8xlC4fDePDBB3Hq1Cnk5ubi+vXrnFhevnwZO3fuhMPhwP79+7lZ29/fj5MnT6KiooK7zKlUCjk5Oairq8PU1FTWd+rz+dDa2or29nbuWL/88stoaWnB1NQUBgYGcPr0afz5z3/Gnj178NZbbyEnJwevv/46rl69Co/Hg2vXrqGhoQGxWIz5jTKZDAcPHkQwGMSOHTu4SUx8kM2bN2c936iRcdddd2Hjxo0svvHqq69ibGwM165dg0QigUQiwfz8PD760Y9icHAQCwsLUKvVOHr0KB599FEUFRVh06ZNGBsbw8GDBxmpQJOZu+66C3a7HTt27EB9fT1SqRRzP9aLkpISxGIxbN68GY2NjZDJZLjttttw6tQpfOxjH8Mbb7wBm82GnJwczM3NYefOnZDL5Whra0N3dzemp6dhMBigVquxadMmaDQa3HfffZiZmcHw8DBycnJw/PhxfOELX8CGDRtgsVgwMDCAwcFBHDlyBG1tbeuuraCgANevX0dNTQ22bNkCk8mE4uJiLC4uMryU+JVisRgVFRV48803sbq6io0bN+Khhx7CgQMHEAqF8OlPfxpra2u4//77mQe7urqKl156CY8//jjOnz+PTCYDm80Gl8uF/fv3M6zs/eIDk8EJXjI0NMRkr7W1NXR2duL48ePQarUA/huH53K50NLSgtnZWXznO9+Bz+fDwsIC5ufnMTQ0BIvFgng8DolEglgshtOnT+ONN97A3/7t3yIajcJut/N0ZHFxMWty5fP5UFdXh/7+fsbgLywsAADDTYhI6XA4IBAIsGvXLiwvL+PMmTMoLi5GUVERFhYW0N3djb1797KqC41ASdXC6XRix44d3AUhnPR6MTk5CZVKxXCQlZUVzM3NwWAw4Nq1a6itrcXk5CTy8vLQ3NyMF154AWazGXV1dRAKhXC5XEilUqxgAwDhcJgraL/fj/vvvx+hUAhqtZq7bzSqpU7zzYJUG4gHQePXDRs2YHR0lP9MJpPh3nvvxezsLDZu3MhqYCsrKzh27BhEIhHUajVOnDiB2267jTGd+fn5aGpqgtVqxZUrV1BeXs7P1GAwZOVoOJ1OmM1mHv/5fD709/fjnnvuQU9PD/R6PRYXFxGLxVBSUoLu7m4A4EMgLy8P09PTkMvlCIfDaG5uxrVr19DY2IhQKITr16+jqqqK1UmoQyqXyzExMcFcgfXWVlVVhaGhIe4KDQwMoKGhAX6/H+fOnYNKpYJOp8OmTZvwyiuvcGds7969yMvLQ39/P6uy/fznP+dNHQqFkMlksHHjRoTDYQSDQcakk5rQ4ODgumsbGxvjS1ehUKC0tBRvv/02du3ahbm5OR7H0u/d1tYGlUqFF198ERs3bkQ0GkVvby90Oh0GBgb4WdpsNiQSCZSUlCAUCqG/v58xnaWlpYjFYpx0ZwviiSwvL0OlUiEnJwdnz57Fo48+ioGBAU5CKOl47bXXUFRUxIXf/Pw8XC7Xe7pk8/PzEAqFnEQJhUImtC8vL6OoqAgikQjnz5/PCsG0Wq3cVSeo6NDQEBNAic9DcKM333wTzc3NOH78OI/hiag6OjqKiooKPPfcczhw4ABMJhMWFxdRV1fHGF3iVgHvqq5k68y7XC40NjYyv0mtVsNut2P79u04e/YsK4hZLBbU1NRgdHQUd911F3p7e1FUVASNRsME3dHRUdTV1aGmpgbz8/OYn59HMBjEhQsXsH37djQ0NCCTyaCiogJra2sYHx/POm0hJUIiSUskErz11lv40Ic+hGPHjrGK0urqKkMUOjo68Nprr6GkpAQOhwNvvfUW8vLyMDg4yIIXdXV10Ov1rIAjFovR3d2NwsJChirNzc1hbm5u3bXNzc2hs7MTvb29TLgMhUIYGhrCfffdB4vFwvsuHo9zk4UuXofDgWPHjsHtdkMul0MgEGBsbIwn0Xa7HQBYtcrj8aCjowPpdBqDg4NZJ3x2ux2NjY2IxWKs+lZXV4fKykqcPHkSiUQC169fh8/nQ01NDUQiEaqrq7G8vIzq6mper0wmQ2trKzZv3ozXX38dcrmcz8zbbrsNb7/9NiKRCPLz83kiD2THzAeDQVRXVzPhmgjcAoEAg4ODqK6uhtFoRDAYxNzcHLZt2waVSoXh4WEMDg4iGo1Co9GgrKwMIyMj+MIXvoD5+XmYTCZotVpoNBpu0iWTSf5+iAuRDfu9sLCA0tJSTqTcbjeWlpbQ0NCAxcVFSCQSWK1WPtP7+/tRX18Pv9+PyspKCAQCzM3NceJrt9sxPT0NoVDI6lixWAxisZiTd+Ly2O12iEQi7Nmz56Zri8fjKCwsxOTkJJRKJaqqqhhCOTExwWTkZDIJq9WKP/3pT6iurmZ0QzAY5I7+1atXsWvXLgQCAYZg1dfXI5FI4NKlS5BIJGhqaoLRaITP58PMzEzWzrzP50MkEsHw8DB0Oh3i8Ti6u7tx3333ob+/HwCg0+lw6NAhSKVS9PX1oa2tDQ6HA9u2beOcIi8vD7/97W9x2223QSKRcPNidXWVoTnEtyopKYFAIMDFixeh0WiwZcuWm64tFAohEolgZmYG4XCYURCxWAw6nQ579+7lRrZUKsWZM2eQk5ODgoICRne43W5MTU0hGAxCqVTi9ddfZyJ3ZWUl9Ho9vvGNb0Cr1fLUpqamBhcuXMj6vY2Pj/NecDgccLvdUCqV2LlzJ2w2Gz72sY/h7Nmz6O/vR2FhIYLBIP71X/8VDQ0NyMnJ4XtJq9VidHSU1UNJmc3r9eLxxx/H8ePHMTc3xzwsu92O1157DXq9Hps3b77p2ugcjEQiPI0NhUKYnZ2FyWRirhyJTBw6dOg9zTGr1YqFhQWkUikEAgE89thj+PGPf8wQW6vViq1bt+KXv/wlUqkUqqurodFoEA6HceXKFdTU1Kz73G6M/ycy+F/+8hdcv34dsVgMWq0WZWVluHr1KnQ6HbZs2QKRSMREcEqEHQ4Hnn32WczNzaGxsZFhPeXl5Uin03A6ndwppASPuhorKyuYmZlhVaX1QqPRsDKE0+nkw4sIkx0dHSgpKYHX68XU1BTEYjGuXLmC+vp6JlRLJBKWRqVqUiQSoaSkBGKxGF6vFzk5OXj88ccRCAQQCAQglUqRl5eHurq6dddGsCrCIBP048KFC3yIp1IpLC4u4u2330YoFMLc3BxfFpWVldi4cSOkUimqq6uZJGk0GhkH+dvf/hZ9fX2sMkW4SeIIrBdra2vQarUoKipiTLvdbsfvfvc71NbWMk5+dnYW165d405tMplEf38/MpkM7rvvPpY3q6ysRE1NDaspqVQqVkv58Ic/DJFIxCM+s9mcdXOn02mMjY0hHo+jqKgIlZWVSKfTOHv2LCQSCaanp5lvs7KyAoPBALPZzN0VUvwJBoMQCATo6+vjZ59IJCCTyTA2NoZAIIDKykrk5uby+FwgEKCqqmrdtUmlUiYDExGzrq4OJ06cQCqVQkNDA2QyGQKBwHt4AclkEufOnUNPTw9LaqZSKfzmN79BTk4Otm/fjq6uLobJSSQSbNiwAUajkSdyIpEIra2t665NqVRifn6e8a0ejwcejwcvvvgiamtrce+99zLUIhKJ4Pe//z0qKythMpn4Qty3bx+2bNmCqqoqTExMQCgUoq6uDuXl5fB6vZienobdbofJZMLMzAy6u7u5S5qNdwO8m5DRYevxeODz+SAUCnHx4kWGUZDyTE9PDyoqKjhRJ9L32toawy2JIE1FPxXzarUaLS0tyMvLw/LyMhYWFhCLxbLCRWiiodPpWEKUZC6p215XV4dEIoFnn30WMpkMV65cQVFREfOCjEYjVCoVFhcX8fLLL/MonaB0r776KoxGI2KxGIsDDA8PQygUorKyMut7nZ2d5UYFwRrOnj3LkFC9Xo/h4WG8+uqrPFkZGBjAiRMncOXKFW5GVFVVIRqNMhSjrq4OxcXFzK0rLy/nhGthYQGhUChroUFdRpvNBr/fj3Q6jaKiIvT09KCmpgZyuZzf2R/+8Afk5+czB4D2nFQqxdatW7F582Ym/RcUFDAHKxqNYmpqClu3buWiKRaLIZVKZW2myGQy2O12FBUVMRF+aWkJBoMB169fR319PRd79DNramqwurqK559/HleuXIFarWa4IImOZDIZyOVylJeXo6KiAnq9HhMTE7DZbJienobb7UZlZSXMZnPW/UCdRto76XQaP/jBDziByMvLw9raGhYXF/HCCy+wtPLy8jL8fj/UajU6OzthNBphs9lQX1+PtrY2hq29/PLL3MTwer3MKyFIarZ32t3dzcIjtF/D4TCrVpWUlMBisbxn+qTVaiEWi9HR0YEHHngAmzZtwu7du/Hcc8+hp6cHU1NTrGrjcDig0+lw8OBBlpcleHK2Li5xUohrlEwmoVKpMDAwwFAv4L9RGCsrKwwHDQQCzE2IxWKYnp7mpgbh930+H5aWljA/P4/r169zXjM1NcWNzPWCoFqkzkWTsnfeeQdarRZ79+5FTU0NkskkC6Ns2LABVVVV0Ov1qK6uxt69e7F79240NDTAbrez7CzBMi9evIh0Oo2Ojg7k5ORgcnISfX19mJuby3qnRiIRzM/PY3FxEVarFW63G8XFxYx+uOeee5BMJvHCCy/gueee40aVxWLBO++8g76+PpaKLigowJUrVxAMBrFp0ybs27cPhYWFuHTpEoRCIW677Ta0traiubkZ9fX10Gq1WZvFBQUF6O3tZV4X7fHGxkZYLBYWI7JarRgaGkJXVxcefvhhZDIZfPvb38aPf/xjTE5OsojJsWPHeJ2pVAp2ux2XLl1CeXk5Nm/ejEgkgu7uboyNjcFisWTlPcrlcvT19SEcDqOzsxN33303pFIp/vznP+PMmTNwOp3MF8vNzYXH48HBgwcZumQ2m3HgwAHcfvvtfGesrq6is7OTp2a/+MUvkMlksGvXLly8eBEDAwNYXV1lYZ31gmDGpKqq0WgYJeTz+VBUVIRMJgOv1wun04m+vj7s378fBoMBv/jFLzA9PY2DBw9i7969KCwsxB//+Ee+72QyGaOQVldX0dTUBI/Hw+/JZDJl5QTdGB+40FhbW0NtbS3zAEixYc+ePawFL5fL0djYiO3bt7Pkp91uh8ViYdLo5s2bkZeXh02bNjHRjnT9P/GJT+C1115DJBJBKpVCQUEBfzjZOpFCoZDxmkSaJa8MUmWyWq0QiUSor6+Hw+GASqXiEWpRURHi8Ti2bt0KkUiEoqIiJmARAVSpVLLCBSX0JNNJsnQ3fcA5OQzxmZubg91uh1Qq5UKLJkOFhYXYsmULUqkUTCYT3G43duzYAalUitOnT+PChQs82aFpAE1p9u3bx5MHUkEIBAKw2+1ZD55MJsOjT+JkpNNpHDp0CPPz8wxdMplMaG1tZTWdSCSCjo4O6HQ6jI2NYWFhAZlMBhs2bOCErKCggHkupNDi9XqhVqtRUVHBneH1grDONI0iONLWrVuxtLSEDRs2MGmzrq6OccQlJSUMSUulUizF19bWBp/Pxx3ceDyOAwcOAHjXT4O0qEkti7p+6wWRI6kLB4C9MqjQIZWf8fFxxuFTdz0Wi6GxsRHhcBhbtmxhcuONGEmRSITKykoufsgzJZv4AKlN0cUdj8dRUVGBhoYGTE5Owuv1snwsPV+bzcbQPlKjWltbg1QqhdFoxNTUFObm5lisgUjrNLan8Th11d8vCI5EKh4VFRWsL0+kTJPJhI985COsfuL3+1FXV4eioiL+JoF3YRTT09OYnZ1FIpFARUUF9u7di9HRUS6UA4EAK79QUnmzEAgETIqmCzWZTGLTpk3w+/1oaGhgb4GioiIm9wmFQhw6dAiVlZUsb6hQKHDXXXdBo9GwmER+fj6MRiMGBwdRWlqKaDTKSSyRGbOtTSQS8XlB+P+Ojg6Mjo7yd1hSUoL9+/ezMEFubi46OztRVVUFmUyGyspKJvT39fVheXmZGw5Hjx7lgi2dTnPiSPCX9YL2HTWOXC4XJ6gkBZnJZFBdXY0jR47wVBwANm7ciOLiYkSjUSZlNzc3s7ITqQM2NzfzNEepVKKtrQ0tLS2Ms14viLNH55vZbMbq6irMZjMXpGKxmBPw0dFR7pQXFRVBp9OhuLgYe/bsQWFhIYqLi7mzV1ZWhmQyyfLtHR0dKCoqYkU0r9f7vkmCTCbjSRPJeZL2P3EcaeLu9/thMpmQTqfR2dkJiUSCnp4e2O12qNVq1NTUYGJiAnNzc4jH49DpdNi/fz9SqRRPBUmARCwWZy3QRCIRKyLRd6rX67FlyxaWiybeVkVFBZaXl1lJihpM1FkmfwESoiBxilAoxIpuFovlPUT9kZGRdddGhGoAfA/L5XJs2LABqVSKp2p0N9DdRCIkOTk5sNvtWFlZgcvlgslkgt1u5z1KSokkwR4Oh9/jrZFNkZD2P/kM0PPu7OzE9evXEQwGuRn14Q9/mAnya2trMJvN0Gq1zIkj/h8JN4TDYZZMpiYWTcKIs7Vt27Z110Y8GPKioNyAvnu6kzds2IBNmzZhcnIS169fx9TUFMrLy2EymXiCTYIi4+PjLABTW1uLv/3bv8X4+Dj70xAxOhKJMDTtZhEMBmEymRh6T3DrsrIybvoYDAZs2bIFd911F65du8awp6qqKpbopbv8i1/8Ik9QOjo6IJVK0draiqmpKUbKtLS0oKqqCpFIJCu6gp6R3+9ndT0SLSHSPgnT+P1+eDweiMViuN1uho9NT08jGo3i5MmTDC8XCASsulZbW4vV1VX2AqECg87n9UIikaC6uhqLi4vc7CLVRbovtFotamtrUVlZiYGBAf6GjUYjnE4nTp8+jZWVFS5+aP+SIhU1y5ubm1FWVoaOjg60trayL8cHiQ8MnbqxygTAyVVDQwN8Ph8fqiaTCXq9nrsKarUaWq0WiUQCNpsNEomEVW1opE/jvq1bt+LFF1/kB0QqTTqdjiVUbxYk00WMezoASYGCFFEI4rO6uorc3Fz4/X7U19ezBBj9PmtrawgEAnxYkMpHbW0twwiowqbLcL2grp1arWZSKMlzUiJMHfYb9aCp45mbm4tEIsGShyqVin8vkgKura1lPW3yUCAMdzYoS35+PiKRCCfHEokECoUCGo2GO/WUVJJ8HUGiaOJC5L66ujrGUxNJmszCWlpamLyYn5+PoqIiJo2vF/QzqPuZn5+PxsZGVvAhUhUlpjTiLSsr40QvNzcXJpMJvb292LZtG0ZGRhAIBLj6Ly4u5jEv4fhvlM5cL27EJVKSQ8Rrg8EAl8sFkUjEXdtAIIClpSUYjUaUl5ezGkl5eTkmJyeh0+mgUCiQyWRYmnhtbY2TWfpuyd8kG1RPIBDwO6KEsaamhmEMpLxCELuuri709PQgkUgwtI0kYlOpFPsGzM7OIj8/HwaDAaurq6itrcXa2hpPa1KpFIssZAuSxqZnJxAIuKgi5TTg3YNdr9djaWkJbreblZwA8LtV/ZeHBD2X3NxcTghIPY6SctrH2YQbSE0smUzyHiMFskwmw9rrpEdeVFSEjRs3YmJigo3caBpLhFz6eaQRTxd9YWEhP2f6HQKBQNZnR6Rq6uTS2Qq8C4WjM5jkpElRxWQyQaPRQKFQIBaLQaVS8XfodruhVqvZnI8mJ7Qv6BzNdqHk5eXxKD6ZTLInEfDulJN+d4lEAplMxucbcemoU0t7paSkhCWYSb6Rpi8CgYCVfyhher/vjS51IgKTeSCJb9AUmIqSlZUVVFdXs2EoqfE0Nzfzc5HL5UxWHxkZ4WmzUChkSCAVW+sFmSGShC7w7j1bXFzMjTlS7crNzeVvjBIJavqQHCnxhKioom9DrVZDKpW+p3FTWFiYdYJGaj00cSQFNzLvI4UakkOmLurQ0BBLdZM0tN/vh1wuR0VFBe8xiUTC6nxer5fV0mjikO2+pylPMplk7pvf7+fGDuUPJJddV1fH8tzxeJwVJKn5AOA9jTtqchIfis4VEh/JlviRn9CNvj1arZYNZkkZzGw2o7CwEJs2bWKVQPJIysnJYSVPtVrNymHUbKK9QxM4yqlI3XO9IBVBg8HAkxd6lxKJhGGA1dXVUCgUmJ2d5X1L3xNNcxobG5FIJHg6T9NfMgAkfiZ56tAdvl5QI5sUL0n632q18nSWyM50B/X19QEAq77F43GW5iapWypSADAXLRAIoLS0lBEf75csZzIZlh8npIzBYIDf70d1dTWrpBUWFmJ+fh75+fmMWKCGGk0hqdiOxWKsoicSidDe3s5T5OrqasRisfeoH64XCoWCn4/f74fP52M1QjLKLCwsREFBATQaDfOyjEYjKisrMTMzw9zeqqoqGAwGNDU1sUcTCTzV1NSwsBHljYTs+SDxgScaYrEYLpeLtZMBsOvw9u3b2YSEOurNzc3weDyoqamB1WqF3W6HzWbDiy++iOrqavT29rLaiFAoRG1tLTKZDB5//HGWmaVDWi6XZ2W3k+49JbokEUfwDqqqqetaX18Pq9XKkqxarRalpaXo7u6GxWJBYWEhj7xJFzkcDqO9vR3Xrl1jx+yFhQVWk1gviDxjMBhQXFzMH5ZCoUBLSwvrMotEIrhcLnR1daG7uxupVAp//vOfkUwmcccdd8BisfBhXFRUxIliWVkZFhcX0dbWhpWVFS6CADAEZL1QKBSYmZlBbm4uO1VrNBqcP38ebW1tKCws5EKwv78fWq0Wx44dg9FoxOzsLDKZDFpbW2EwGNDe3g6Hw8HyboRpnZ2d5QvGYrGw3jOZ3awXVKze6GnR2NiI8fFxdHZ2YnZ2lgmDNIUhTW+HwwGhUMhkQZqqkFxvcXExTCYTuru70djYiJWVFd5Aubm5sFgsWRPS/Px8Jn+R9Gc6ncaJEyfQ2NjIJm+xWAyTk5MwGo04deoU9uzZw1K31HlKp9Ow2+3o7OzkDsbY2Bj+/Oc/o7i4GG+99RaPnalYyLYXVCoVZmdnuTtH3RWr1YpNmzaxnwfBD7Zv386Owul0mhNT4N1mAhEbAXBhIRAI+JAqLS3lBPaDdDfouRDZlCBiV69eRWdnJxOIg8EgxsbGsGfPHszPz7PCRSQS4e5VfX09JiYmUFpaylOSeDyO8fFx3H777fw93vhes112tAfy8vIgl8shl8tZ9a20tBS/+93vAADt7e2Qy+XYunUr/8yZmRmoVCq0t7ezatWbb77JcKTJyUn27NmzZw/y8vKYWEhqWKOjo+uujfa1UqlkTXbyXGhvb0csFoPL5cLc3BwcDgeqqqr43KFpj0AgwKlTpyCRSNDe3s6/B72X+fl5VFVVobe3l6GoNF3OFlTU0sUOvHu2RKNRtLW1obS0lOUWh4aGWHK6oqICg4ODLDpBJltutxtdXV1cCMXjcbzzzjtob29HNBplMQ4S6MjW6CEfIIJtLC4uQq/XY2ZmBo2NjdwQo8u5paUFg4ODsFgsKCsr4yJ4enqaxTdaWlrYLDCVSuHZZ5+F0WjE+fPnsbCwwKpadCGvF2Q8SZ4Y1FV3OByorKzE8vIyX+ZutxsHDx7EuXPn4PV60d3dzQIHFosFIyMjeOedd7Bt2zYWNKCJR11dHdbW1lBWVoZIJAK73c7u9NmCijCCSkkkEly4cIGNZDUaDRfdZWVlrMZFE2yCrclkMng8Hv7Ge3p6IJPJsHXrVia401pIPCQbJEP1X0Z/9E0TXHJmZgYtLS08aXY4HBgeHkZrayujAWjSR6pjOp0O8/Pz2LJlC593xD0hPhqhOOjuzSbjrVKpMDY2htzcXEQiEYyMjCAYDMLlcqGiooIV0+bn5zE8PMzTUpqypNNpqNVq2Gw26HQ6dt6mpqLZbMb169fR1dXF8C/KfVQqFd54442se8HlcvGe9Hg8LEhBiol2ux1WqxU2mw3t7e1obGxEU1MTd/ILCgrgcDgY6ldWVoZ0Os2eJsSjJIPTyclJ5qlmm3ZTs6K9vR0VFRWQyWRoa2vDmTNnkJeXh9HRUSarX758GR0dHTh79iwUCgXGx8fZSHphYQGtra146qmnIBAIMD8/z9DJdDqNRx99lP1znE4n5ubmeHq1XlCzuKysjJXoqIjX6/UYHBxEMBiEVquFWq1GZWUlvF4vCgoKMDMzA7FYjIaGBoRCIdx+++2Ynp5GWVkZc2rFYjFP5Kanp7Fjxw6W+i4rK8t6/pIfy5YtW9DS0gKBQICCggLYbDZUVVVxk4EmJ+3t7SgsLIRer+dmXWlpKWQyGTZu3IhLly6hra0NjY2NfP7JZDLceeed8Hg8cDqduHbtGnp7e5FOp7M2PW+MDzzRWFxcZPw1+WnQJZaXl4fq6mr86le/QiAQwD333IOpqSkUFRXh+PHjqK+vB/CuhJlEIoHb7cbAwAAGBgawvLyMsrIyNDQ0YGlpCTU1NaipqYHT6YRQKGRMWDaSE3WTiZBH6id+vx/j4+Pw+Xzv0Yw/efIkE40zmQyuXbuGdDoNnU6Hn/zkJ/ja176Gl19+mbv7UqkUR44cwfnz51mxRKfTsTdETk4ODh06dNO1xWIxbNmyhaFcarUa58+fR3NzMxdqvb29cDqd7NGxf/9+zM3NMaGMtMf/8Ic/oK6uDo2NjWhubmZTJY/Hg5/97Gdob29HPB5n0uri4mJWjLDD4UBHRwe6u7vZREsoFGL37t14++23UVpaCpfLBY1Gg82bN8PpdGJ2dhZdXV2oqqrCyMgI+vr6YDaboVKpYLFYMD09zbhWMm+an5/H3Nwcenp6GIN/6tSpdYlhAFjlisyEUqkU4wLfeOMNbNmyhd9fZWUlxsbGIBQKceLECdxxxx0Ih8MYGRlBW1sbkyuJGE6dOLfbjT//+c9oamrC0NAQgHdVQ94PNkUSedSxpEutsLAQQ0NDkMlkTBojrXmDwYAzZ86wpjV14X/+85/j1KlTWF5exsrKCsu4CoVCjI6Owuv14tSpUzzSvHr1atYDmySQCboQDodx7do1SKVSDA0NYcuWLbx/6ZJub2/Hiy++yJCvVCoFs9nMihWDg4Nwu93sFkzwPpvNxmIKpBQzODiIzs7OdddH8AXqJBH/iXT+8/PzWUtfKpXilVdeYW+P8vJyJm4SQZ2SnuLiYoYXlJWVYXp6Gl6vl7u2brcb3d3dUCgUuPPOO2+6tkQiwQQ/t9vN3+709DQXYqdPn0ZFRQXuvPNOTE9Po6enhyUGiWhJz5cU52677TYA70rejoyMsBLO1NQUn08ymYwJkDcLmsqk02lu9DidTuTl5bGePE1hSktL8Ze//IWnTSKRCHNzc1hcXMTy8jIymQzuvfdejI2NsVvtyMgI5ubmsGfPHqjValYXKigowPj4OHbs2LHu2sgIi/gUpHKi0WgwPDwMo9HI3dotW7ZgamoK27ZtYxPFwcFBOBwOFBQUwGq14uDBg5ys0/TZYDBgZmaGDVdJwY/8G9YLr9eLtrY22O12LCws8DcmFArx+uuvo7GxkfkOJP25vLyM73//+/jnf/5nxGIxvPXWW5yokgcOeWrY7Xbs3LmTZZSnp6e5mz49PZ21+A6FQvw8yF9nYWEBDz/8MLq7u1FZWYkzZ85gZWUFnZ2d7LlAKnznzp2Dz+fDoUOHcOnSJRw4cAATExOYn59nHp/ZbMalS5dQUVGB4eFhTE9PQyQSMWRivb1A/k1kCkhckmg0ikuXLqGqqgo9PT08+X/hhRfwpS99iYU3SIyDzL5IqpOS2dHRUZ6AUBFKMsmxWCxrQ4Dk3WkKRW7KBw4cYCUwMv1raWnB4uIiMpkMhoeHEQwG4XA4IJFIcP78eYjFYuTm5mJiYoJle8mLioj2oVCIyfVkyLleBAIBJtISFNfv98PtdmN+fh61tbV8zwuFQrz88susLlVSUoJwOMxTgtnZWRgMBt7fxG3ZuXMnd8iJD5XJZFg4YL1YWVlhoQJqitB0lP4uKm6J9Nvb24uTJ0/CZDJhamoKCwsL2Lp1KxPkiWuzuLjImH2bzcbPdWpqCj6fD+Xl5YjFYtiwYcNN10aS+VevXoVUKkVFRQXm5ua4kKHpIzWiVlZW0NLSwkpxoVAIvb29OHbsGCoqKrBx40YMDg7yWRkIBNDZ2YkXX3wRdXV1sNvt7L/m9/uzeqMQodzn8/Gki4rEkZER5qMlEgls2rQJqv/yTPrNb36DsrIyLmwaGhrwm9/8Bv39/XjwwQcxMTHB8rF5eXl46qmn8PDDD2NycpLh9FQ8rRdjY2Ooq6vDhQsXIBKJYDAYMDExgaamJm4U0H7zeDwMU3zmmWdYUMLj8WDnzp146aWX8POf/5yRNktLSzh16hQqKiowMjICn8+Hl156CUqlkr/jbMiPG+MDFxqkpFJYWMici9OnT2NychLd3d34m7/5Gxw5cgQDAwNYWlqCxWJBVVUVysrK+CO+7bbb0N7ejn/7t39jkieNMEdHR/HWW29hz549TDKjDl1eXl7W5EoqlfJhlUgkkJeXh6WlJUxOTmLPnj1s0Ga1WnH58mXuctbW1uLf/u3fUFlZyR3x1tZW+Hw+njIIBALY7XZe2+HDh5kgTdOQbMm8RCLBzMwMd7+FQiGUSiWuXr0KpVKJXbt2wWw2Y3BwEJcuXYJKpUIikUB9fT1efPFFLC4uQqvV4qGHHkJzczO+853vYGVlhS3lA4EAfvrTn+J//+//jZmZGVgsFobHNDU1ZS3QCJJGihw2m415DC0tLbh69Sp3SiYnJ5FMJvHNb34Tp0+fhkqlQmVlJYxGI5qamvD000/DYrGww7bT6YTD4WDln6qqKkxPTzMRjZLZ9YLw9WS2A7yb0Hz1q19ll+ZEIoHFxUWcP38e09PTOHToEMrKyvDyyy/DZDJh9+7dqKiowL59+/Dtb38bhw4dYkWZWCyGlpYWbN26FePj4yzHvLy8DJvNhrKysnXXplAomEgHvIsvJe7AyMgIE9OTySROnDiBZDKJJ598EpcuXYLJZEJfXx9Onz6N1tZWHD16FD/84Q/h8Xjw8MMPQywWY3BwEIWFhfje976Hr3zlK5ifn8fs7Cx3gXfu3Lnu2lQqFcMOSd1lenoar7/+Oh555BEkk0lWTzl58iRsNhuefPJJHDp0CNevX2ffiGvXrkGtViMQCKCmpgYPPfQQotEozpw5A4VCwYoizc3NGBwcxPLyMjZv3pyVxAmAoYxSqRT19fXQ6XR4+eWX8cc//hE6nQ6//e1vMT8/jxMnTkCj0WD//v3o6urCyZMnMTg4CIPBgM7OTtTU1GBwcJAdjmOxGMPfXnnlFdTX17NPAo2vw+Fw1i5uJBLB//2//5dV3qqqqqDVajE0NAS/3w+z2YyhoSEuCOx2Oz75yU/ib//2b7nZsGPHDmQyGUxNTeGHP/whPvaxjzGMQq/X49q1axAIBFheXoZer2cHY4VCsa6yCAAukEnWmdyo+/r6sH37dhw9epTlWn/wgx/AYrEgLy+PvTry8vJQVlaGw4cP4+c//zm++c1v4qGHHmJys8FgwPT0NBwOB3bs2MEk7ZWVFXabX0/0oqioiCfdJC5hs9nwzDPP4ODBgxgeHmZDyN/97nfYtWsXduzYAZVKxZKzJPttsVjw+9//HqlUCp///OehVqtx7tw5vPLKK3jwwQc56Z2bm4PP50NjY2NWU7xUKoXnnnsOpaWlqKurg1Qq5YkteWTEYjF4vV6MjY3B4XDgIx/5CE6cOIEvfvGLKC0txc6dO7Fjxw6cPHmSoWaUBIyNjaGjowOvvvoq9u7dC7fbjeHhYeb5ZHuneXl53AxSqVRwuVwQCoV4+OGHcc8990Amk8FkMiEQCOD5559HIBBg87n+/n42HFUqldizZw+Gh4dRV1eH5uZmmM1miEQinDp1Chs2bEBbWxuampqwvLwMj8fDPg/rhU6nw/Xr17FlyxaGyUokEvzxj3/E9u3b+WwgIZfNmzfDZrOhuLgYL7/8MiwWCzccxsfHMTc3B7PZjEOHDsFut+O3v/0tw0NLS0sRDAZx7tw5lqvOpsCWTqeZA6jT6VBWVoa+vj78zd/8DT72sY+hrq4Ora2tWFpawujoKA4cOMAKgSdOnIDNZsPjjz+Oxx9/HL///e9x9epV1NXVoaSkhDkS3d3dqKmpYaM4MmZraWnJimAgyK9EImFzQ4/Hg9deew2bNm1iWM/MzAz6+vpw5MgRjI+Po7+/H2+99RZ2796Nj3/846isrMS3v/1teDweGAwGGI1G+P1+TE9P8+Tqr/7qr3Dt2jWMjIzwpO+RRx5Zd23hcBjXr1/nbjtJgBO5+vbbb4dGo8GJEycwMDCA5uZm3H777VhZWcHY2BjW1tZQUVEBkUiEsbExltF+5JFHsLq6ijfeeAMvvfQSnnrqKbS0tCAej0Mul/PUMFvR3dHRgePHj7OgjFAoxPj4ON+1O3bswIYNGzA5OckS7fX19RAKhTh//jyLfxQXF+O1116D1+uFSqVCV1cXotEozp07h2PHjqGtrY35BcC7SB2hUJjVp621tRXz8/N8FhKs8/vf/z46Ojq4wJqbm8OFCxeYBP+hD30IJ0+ehFAohE6ng91uZ+8u4j8Eg0H09fXhP/7jP3D48GGsrq6isLCQpxgkuLRelJWV4U9/+hM31HJycnDHHXfg+PHjuHLlCg4fPgyz2Qyfz8fF4OjoKIRCIYaGhtgy4NSpU1hYWGDbioaGBm6kU848NjaGBx98EFarFcFgkKXRP0h8YOjUyMgItm7dCgDs9tzU1ISHHnoIFRUVcDqd3LFqamrCW2+9hcrKSv74b7vtNrZaDwaDUKlUuHTpEs6ePQun04nGxkbcfffdGB0dxYkTJ/DrX/8ao6OjrCqUjSjp9Xqh1+u5G0KJ2Yc//GHMzs4inU7D5XK9hxRG3dtnnnkGn/70p1l5aXl5GbOzs3A4HPwgRSIR9u/fj7GxMSaQGQwGfhnZcLhkJBePx9m5s6KiAvfddx+bpXR3dyMUCuHee+/FlStX8PDDD2N5eRmf/exn8dBDDyGdTmN2dhZOpxP/3//3/6GlpYUTyuLiYrz55pvo6enB9u3bOSG1WCyYm5vLivGLRCJoaGgAAMbM5ufno76+Hjk5OThw4ABqa2vZBZMUed555x309/fDZrOxYsz27dvx4IMPYnV1FVVVVTCZTEgkErjtttvg8XigVquxb98+tLe3o6SkhPkn6wV1fEZGRniiMT8/j6985Su44447+KBsaGjAoUOH0NXVheLiYjQ3N7OXyvj4OE6dOoXp6Wn85je/YVWgiooKRCIRVFRU4Pjx44hGo2wMRg6s2ZzBSQ2CVM2IrH706NH3PO9NmzbhW9/6FgQCAYqKitibQa/XY/v27di9ezcKCgrwwAMPcIETCAQgFotxxx13sGBAaWkpKisrIZfLMT4+jtOnT6+7NsJinzhxAqdPn4ZQKER5eTk+/OEPY3R0FJOTkwgEAqivr8eHPvQhAGD+AnmcAOAD5+tf/zqGh4fx8ssvY2hoCCKRCH6/H319fWzAZDabUV5ezkT7bJFIJNDa2srKZeQr8t3vfhdHjhxhY6OmpiZ0dHQgkUggnU5j48aN2LVrF1ZXV/H222/j+vXruHjxIp588kkkEgl0dXWhpaUFa2tr+MhHPoKxsTG0trayf8uBAweYdLdeSCQS7Nq1i02pqJt9zz33oLW1FYcPH8Ztt92GXbt2YdOmTaisrGQe0+HDhxGNRvHCCy+gv78f09PT+PWvf83dMoJe/Ou//itKS0vx5S9/mQvS1tZWSKXSrM0UgUCAuro6eL1eFl8oLCzEl7/8ZeTl5aG3txfT09OoqKjAgw8+CADYu3cvtm3bhubmZsZ6E9frwx/+ME6fPo1QKMTqPF1dXQwnnZqaglQqRWNjI0PT1ouVlRUoFApMTEzwxGRoaAi/+MUvsLa2BpFIhLy8PGzbtg3f/OY3YbfbkZOTgxdeeIE5FGQ0d/LkSdx3331IJBK4evUqk/qffPJJ/Od//iebgAH/rUyXDYJJprJk/CoSidDW1oZMJoOlpSWo1Wqo1WpWZCOYgUKhwOHDh1lpjWBQn/jEJ9hDQKFQ4J577sGuXbt4MkcCBxqNhmF864VCoYBSqcTo6Oh7jO5+/vOfw+VyIRKJoLi4GPv378fjjz8OuVyOO++8EwsLC9izZw86OjqwvLyMF154AXa7HQcPHsT4+DhisRisVitOnz6NBx98kM+CN954g9Xyrl69mtVAbWlpCXv37mW4RCaTwcDAAO6++24sLS1Bo9Egk8kgNzcXDQ0N3E09ffo0tm3bBp1OB6vVis2bN8PtduOpp57iSaVWq0VjYyMqKiowMDCAiYkJuFwubNiwAdu2bYPVamUp95sFcStcLhdPxfR6PX7605/C6XRy0RaJRLB161acOXMGu3fvRiKRwJtvvokvfOELmJiYwOzsLMrLy/H1r3+dC2tSWPunf/onhlSRqmBHRweWlpay3ltzc3Pc4HO73ewJtmXLFoY2X7lyBQsLC9i7dy9DkXfu3Inf/OY3uO222zA8PIyxsTHU1taiubmZ4dbV1dU4evQoHnvsMe74R6NRbNy4kRPKbOpJKysraGtrQ3V1NVpbW7Fjxw50dXXh61//OmQyGa5du4ZAIIAtW7bwnURS+XfccQc6OzvZELe9vR1/93d/h/z8fIb3SSQSfPzjH8fp06fhdDpZapy+s2zqnOfOncOhQ4dgMBgglUpRVFQEtVqN1tZWjI+PI5PJYGJigjv4BD0n7yxyoW9qaoJEIsETTzzBRqoEXdu/fz83ad1uNyoqKtDc3Ayv15u1IWu329HU1MRTePIa+8pXvoLa2lrIZDJUVVWhpaUFBoMBCwsLkEqlmJycxP79+6FQKDA9PY1Nmzbh2rVr+OpXv8r+HoTGueOOO3DlyhVUV1fD6XSyZ8jbb7+dVa2LpKXNZjOam5vR0dHBSrD5+fmQyWQYHR2FzWZjIRJStiotLWXe1Wc+8xlkMhl85StfgclkwtraGmw2GxwOBxoaGnDixAmUl5fD4XCguLiYG/LZxC5ujA9caFCRQaQl4N0E5dy5czhy5AhOnz6NkydPYm5uDiKRCLt27cL8/DwOHTqE4eFh+P1+aDQaTE5O4hOf+ARGR0exb98+yOVyTE5OYnx8HBKJBLt370Z7eztjF91uNxvdrBdarRazs7NsZkY684uLi7jnnnswNDSEwsJCbNy4EUqlEtu3b8e1a9dQUlKC06dPY3R0FAKBADabDbfffjtEIhH0ej1/WCqVCufOnWNvA3LeHRsbY7LoekG8EUrEnn/+eayurkImk3E1WFhYiLW1NVy7dg3bt29njOelS5fg9/tRWlrKhHaSaEun06weJJFIUFpaivz8fDQ0NCAajWJkZISJhOtFLBbjzrVarWa4g9PpRFtbG6vYkGHNxo0bce7cOXR0dLAM3549e1gPvqioCDt27EBxcTG2bduGj370ozh16hSam5tx7tw5+P1+xpMSln29yMnJYTd6nU6HgoICNDY2YmBgAMC7xaVSqWR3V6VSyeorJJkcj8fhcDiwsLDAePV4PM4E5O7ubpZwFYlEKCsrQ21tLW+obEF4XcLYi0QinD17lk3FPB4PbDYbLl68iPr6ejz//POoqKhgEmAoFMLvfvc7VFRUYGhoCHfffTcbxSmVSgwNDeEb3/gGk9oIy1laWpqVRE+yip2dnfwuJBIJ1Go1br/9dlYR8Xq98Hg8qK2txV/+8hcmIZKCG+l8v/jii4x79vv9KC4uhl6vZ8lojUbD43epVIorV65kfW56vR6BQIDfXWtrK3bt2gWBQIAPf/jDKCgoQH19PaqrqxEOh5lTs7y8jN7eXlgsFjz88MPweDyorq7GlStXkMlkWCoymUzi9OnT2L17N5xOJ4xGIxQKBWNQs2FKqctE0Aev14uuri4kk0ns2rULwWAQer0eTU1NrIQVDodx8OBBmEwmVFVVYd++fdi/fz8effRRXLhwgSeURqMRt99+O5577jk4nU788Ic/hEQi4ckEGcutF1VVVbh48SJWV1ehUqkgEomgUqkwPz8Pi8WC3t5eKJVK1obfvXs3gsEgEyTLyspgMpkQDofR1NSEoqIiNDY2QiAQsK77+Pg4WlpaIJPJOFmbnZ1FLBbLmjATHLCtrQ0ajQYOh4MNwAivLBKJuHi788478cYbb6CzsxP19fXYtm0bKioq8PLLL+PTn/40lpaWsGXLFpjNZuh0OlRUVLAvTCQSYf4YEUSzCRAQ94IIpgBYdamrqwuDg4OYn59nbk5TUxPGxsZQXV0Nh8PBIiIrKytob2/Hd7/7XXR0dAB4t4kkl8tx9uxZbNu2DaOjo2xCKxaLcdttt2WFsgDA7OwsC1iQn9KJEydw+PBhbmrl5eXBZDJh8+bN3Mkl9+WamhpcvnwZn/rUp6BQKLBp0ybYbDYsLCwwjOLb3/42S1+Gw2GWvcy2tsLCQthsNlbAIxW9xcVFdHV1IZVKcSOODLzefPNNVFVVMR9u48aNWFxcxJe+9CXMzs7i7rvvZmw7TSLuv/9+xGIxlr3u7e1lMu16QfxNEnLp7u5mY8j9+/cjkUhAr9ejpKSEIW0vv/wyNmzYgMHBQQQCAXR0dDBxuKCggKHPMzMzmJycxOzsLI4ePYr8/Hx0dHTA7/czTCZb866iogIzMzMQiUTMJctkMrBYLKivr0c4HIZCoWDp8bvuuovJ7STlbjQace7cOdTU1GB6eppRAT09Pbh27RquXLmCzZs3o7i4mBWMXC4XLBZL1oltZ2cnRkZGkE6nGXadk5ODM2fO4IknnkBvby/Onj2L0dFRxGIxfPzjH0cymcTRo0exsLDAKJCenh6sra0hLy8PRqMRoVAISqWSp6otLS0MpbXb7VhdXWWjuPWipKQEVquVVf9CoRBaWlpw/Phx3H777QwTzWQyuHTpEvbv348f//jHePrpp9kos6urCz6fDwKBAC+++CK2bt2KVCoFiUSC++67D2azGQ888ACrmV6/fp2h32fPnl13bQKBAFarlbmY8XgcTU1NcLvdjHywWq2YmppCMpnEwYMH8frrr+Pw4cMoKChAa2srjhw5wnzMiYkJKBQKFBcXc05AKovHjx9HMBhkt+6DBw9m9QkiNSxqnJN1xOTkJO677z4WnlldXcXMzAzuuOMOOBwO/Nu//RvDbDds2ICJiQl0dHTgF7/4BeRyOfLy8tDZ2YkvfOELMJlMaGhogMvlgsvlQl9fH8bGxqBWq/H222+vu7Yb4wNDp7xeL7PkSUXHZrOhurqaJc8IA+v3+9Ha2soSt4SJJXUnMtLx+/0sQ0pGZfF4nEnJo6OjWFlZ4cNsvaCxIWG06XKdmppCdXU1RCIRAoEAVCoVyzAaDAa2nics6/LyMq5cuYJkMgmj0QiHw8GdL7fbzRhh6o7FYjFWe1kvBAIBK0EQsdnv98Pr9SIajcLlcsHr9SIej8NgMLB6Tk1NDXNHSPFGLBZjbm6OMYAkj0tQII/HA6lUyspT73dgE6/F4/EgGo0iHA4jGo1Cr9cjk8kgFoshEAggHA4jPz8f+fn5OH/+PDQaDaamppjzsry8jIKCAgwMDLDkJ2FIE4kElpeXuUpPp9Ms29ff34+HHnropmsjXH4mk2HDRABMAJfL5dw1okLO6/XypUeqT6RkcvXqVR5/q/7LqdnpdHLnhA5Egn5lu4RvHJ9GIhGsrq7C7/dDKpUyppSmVUSM9Pv9SKVSbFxGF9HU1BRWVlZw3333IRKJsO9Ffn4+dDodxsfHWdJ0dXUV0Wg0a9cqHA5DLpezKZzT6WTYTSaTgVgsZhdeSjbIVIgObKVSieLiYr5Yac/4fD5W3aivr8fq6iomJyfhcDiQSqXe93sDwGZHNAkkn5NYLIZMJsN6/6R2Qv+/G/X7ycBtbW2Nk0KhUMi+C4uLi9BoNPyzM5kMk9izETmj0SiEQiG0Wi1cLhd/L++88w727dvHmGg608RiMRt6Li8vc7fWYrHwpUkGbwKBAKWlpVCr1cjJyYHP54PVakUikYBSqYRCoch6jlA3nqYPRIb1eDzYs2cPy3IC78LTTCYTG8qRok4mk4FQKITVakVTUxPjqOncXFtbY0UoOrdIMSobD4JgQgCYdBgIBLgYFAqF3CSJx+M4evQoZmdn2XgumUxiaWmJz4WcnBxu9LhcLk4e6B0CYHlcklBfL4hTRB4s5MZNylAikYh/d9pX8/PziEQiWFtbY3WakZERKJVKNiotLi5mszpSXqHkKhwOsxpStu8tEAhAoVCgvLyciwp6fqSuQ+p99M3RtzYzMwOhUIjS0lIYDAbEYjEMDw8z+Z14YHRGpVIpVqiiZ/x+ssCk0EieGyQaQupNdJfT9x0KhVBRUYFkMsnfQigUYnnbvLw8fp/pdBpTU1MQiUTw+XwsZyyVSjkXWC8IHk1rysvLg8/nYzWyQCDAvkPEx7Db7SgpKWEXeRJnWV5e5u+d1k3NI/r+iETv8/nYs2e9oJ9xI0zoxgSV3gGJyAgEAng8nvdM/oj3d6M8ssPhYEfxVCqFa9euQafTQSKRwOPxIBQKsaTqerG2tgaxWIxEIsHfDxXier2eeYNUWJMHhdfr5T0UCARw5swZ1NfXo6GhAVNTU3znBQIBzMzMMA+QUCR09ywtLa27NjpryHzO6XQinU7znl9ZWYHb7eZvhDy0RkdHmcAPgP8d4vhRjgcAw8PDqK2txdDQEBsJUt6T7eyl391sNnPSbjAY2N+HmsmkDLa8vIyBgQF0dnZiYmICWq0WKpWK74jTp09j7969/J3QHVdcXIzLly/znqfp2vtNDUgZzefzcTOwpKQEubm5jLggBExeXh5Onz4Nk8kEq9XK8t7nz59nQaf5+Xnm2BYVFWFychIVFRVwOBysehiPxznH+SDxgQsNwq0rlUpWXVpcXMTmzZt5ZCUSiWC1WjE7O4uGhgZ2cKRNT1rI3d3dkMvlGBkZYf1su92OeDyOqakpvhToFzl37lxW0gkpQ8RiMU6iyDhlZGSESSvz8/NQq9VQKpVoaWlBT08Pj8JoRDg0NMQJmdfr5Z8pEokwNTXFBn7UjQ8Gg1nXRtKWAoGAZcboeVDBEQqF2E+kv7+fIQwajYax5fF4HDKZjA8m0m1OJpNYXl5mXXLqwpFjJuEjbxZisZg3YyqVYilduVyO6elpJBIJJqARWZWUYC5evMi8EzKum52dhUwmQ1NTE/x+PwYHB5kMRfwUOkhJVWi9INM/wipGIhEmfrrdbu7qTU9PIzc3Fzt37mSVEyJt0aSGiIgk70eyiMC7ijk7duzAG2+8AbfbzZrm70dyooIxGAxyIkzTg+bmZly6dIk792RQuLS0xDCy4uJiKBQKvPrqqyxLV1NTA7fbjatXr/IlSEWz2+3mKUs2yANJHBPUg/w5nE4nlpaWUFhYyAcsqZCQbDMRco1GI1paWnD+/HlYLBZOAkUiESumlJaWQqVSscoOyQxnm1IB78qw0lSDDnmS7KTJIv1MkoSkhCmdTrOTKRV4dDkqlUrk5eWxxOPU1BRKS0thtVqxuroKj8cDAFlJfyQgIRAI2GSO5Et1Oh17ERD8RKVS8Rie4AM+nw8VFRV49tlnWRs9lUphdHSUCcc6nQ5yuZyx/ITVzQbJoOcBvLsX4vE4k2DX1tZQU1PDHTqtVovHHnsMOTk57H1CZG2JRILl5WUsLS3xdJYKGDLVo4kJJV83yoDeLGj6Q4ZwwLsFrk6nQzgchsViwblz59Df38/qWQaDAWfPnuULMJlMor29Hd3d3exvBLx77ywuLkKtVqOsrIzJjeRxIJfLs152JOdKhT15fTidThgMBnaYpvdP8sWnTp1ieVG5XA6fz4doNIqOjg5cvHgRzc3NrDhDcqwlJSVwOp1stmez2bJOlEmCkmSR8/LyEAwGebrR0tKC2dlZLCwsoKCggCVGiesmk8lQWFiIrq4unD59mtX2SIrV7XajsLAQr776Kmpra+F0Ovn8o2nhehGNRtlQku4/qVTKiS110akgp6TI6/Xy70ASvzabjUUmiO9AZGeaIpIaGMlSZ2umUGEciUSYQG6321FcXIzBwUFUVVVxo4u8V2QyGQYHB98DjyXRjkgkwnwpEpIJBAIMGaOkNz8/nwui9YIU5KgQJgEHasySihA1B0dGRrioJ9W/nJwc1NTUsLcMNW9Jhr6goAAXL15EbW0tQ3iFQiFDgNcLl8uF0tJSfv40rVtbW8Pa2hpUKhV7Ca2srGBpaQkVFRVcSAoEAjgcDkxOTnJuYrVaoVQqkZOTg8XFRYYZktEneWhQcb5eEMGfVAntdjuSySRqa2thtVpZFpsS6ampKbS0tGBsbIxFAUjBkGBcCwsLnBjPzs5ifHwc+fn5mJiYwMMPP/yeIo8KlZsF5UYajYYVtsRiMYaHh/neTyQSDJE9c+YMMpkMBgcH2SBWp9NBKpWygAUJaND0c2hoiKV4CcYdjUZx/fr1dQn0wLv3PTWBg8EglpeXWWp6enqaVclI2Wp8fBwej4cVEKmAvtHAldBFq6urLCpEPkyq/zI9pgb1/zh0Sq/XQyQSobGxERs2bGDJwueee46x6sTIn56eRn9/P3Jzc/HCCy/g2LFjOHv2LOx2O8xmM6qqqhiLHQ6HGbsOgHWA+/r6sLCwwDrm2Ugn1DnKzc2FVqtlt0saJ9HIL5VK4T//8z9ZwYUe4rVr1/jSP3jwIB8u1Bkh9YjS0lJs3bqVNYWpG5nNzIqSPOpEG41G9k0g6THCLr7wwgtYWFjA9PQ0rly5Ao/Hw50dmrIQjIa6H4lEAmNjY0wYI4WuoqIi1gNfL3Jzc7G8vIycnBz2HygrK4PVauWkj/wdJicnce3aNTQ0NCA3NxdutxsTExMYGhpinDWp7ZDeNMFxOjo6UF5ezhhSUuRpbm5ed23pdBozMzOsGV9YWIhgMIje3l4uHqlTIhQKEQ6HYTKZeJScTCZRWlqK3bt3M7G/vLyccfBerxczMzNYW1tDfX09S7RSZ/FGr4ybfW9U3WcyGUSjUQSDQbz00kusbkPdGSpGiDxKyiCUUBcWFqK8vBz9/f2sBEK/28jICE8D5+bmMDY2xrDAbO90ZmaGla4KCgqQSCRw/vx5OBwOaLVa7vBdvXqVC1JK0v3/ZdAlFotRWVmJxcVF2Gw26PV6bNq0CRs3bmTStUKhgMViYWM7vV6fdW0AGJLjdrtZAjSRSPD5QGsnXlM4HEZlZSVCoRDOnj2LiYkJfrYVFRV46KGHGFfvcDjQ09MDuVwOqVSKQ4cOob6+npsVDocj68Eol8tx7do12O12FBQUoLq6GoWFhdi9ezfW1tawbds2JseSASiNwAnCSHAygtIFAgF0dXVBKpXi+eefx29/+1soFAps2LCBpZAHBwfxzjvvZNXAp7E77VWTycT7NRaLobS0lHlAx44dQ29vL3etrVYrPB4PIpEIBgYGUF9fj9OnT7PqCgDmX+n1eiQSCcYQU7depVKtuzaJRIKhoSGWoHQ6nWwgNzg4CK/Xy3BRmgQUFxfDaDTCbrcjNzcXdXV1EIlEWFhYwA9/+EOoVCqUlZXBbDZDLpfzWRuPx7GwsMBno06ny0ocpsQpNzcXYrGYITU/+MEPWMue+E9jY2PIy8tDc3MznwULCwuIRqNobGxkOMH8/DwWFhbeI8NK0BeNRsNTh2AwyNLQN4tMJoOxsTH09PRgdnYWwWAQU1NTGBsbw/nz59kbob+/H319fZiYmEAwGOS7Z2lpCcvLy6isrMTk5CSam5tRUFCA8vJyTlBlMhmsViva29u5Q03FYzaPJalUir6+Pj4LyDQ3HA7j0qVLrNQll8sZv07k4tdffx3j4+OIx+Ow2+2444478Morr8DtdrPxG/kl1NXVYcuWLdi2bRvL4CaTyaxrI9gLvYfZ2Vl4PB74/X6MjY2hrKwM5eXl7AZvsVig1+thtVpZhGHDhg1s5Go2m1FaWgqj0QiVSgW/34/Tp09DIBCgtraWifDUsMx2NxQUFDBPSSwWs6kc5TiNjY0sFGK1WrG4uAi73Y7BwUF873vfw8mTJ6FWq7Flyxb2KCNncJrmrq2tYdOmTQwl9Hq9yMnJQXV1dVZSs1gsZlntQ4cOYffu3SynTlMNk8nE5rpzc3Ns2krc2vz8fBbA6e3tZZWpixcvstQ2NVFp3zocDlbLWi8Igpufn8++aA0NDXj77bfh8XjYQycYDDKSIZFI4O///u/R1dWFQCDA/EBqevzlL39BNBpl+PWOHTuwZ88e5iA4HA6Ew2HU19fj4MGD664NAMP7V1dXUVJSgoKCArz66qt8Fi0vL3Pzzu12Y+PGjXxHGQwGqFQqdHZ2orm5mc1zbTYbCgoKYDab8c4772BiYoJNTgsKCpgrSFOymwUhT5RKJf89yWQSo6OjrHRGhQU1ce6//352SE+lUhgZGWGhCCoQCZJaXl6OxsZGFp2gs5IUN6uqqrI+N4oPXGjY7XaIRCKcP38eb7zxBiYnJyGXy/HjH/+YE0e9Xo/29nY88MADmJubg8fjwbZt2/CpT32KXbdzcnLwyiuv4KGHHoLRaOTOq1AoxF//9V9jcnIS4XAYVqsVhw8fxpe//GUolcp15WPpYcvlch6j0uV65MgRXLp0iSE1lZWV+Lu/+zsMDQ1Bq9UiEAigtbUVXq8Xb775JhQKBZN1iEDa1tYGmUyGI0eO8OTgnXfegcFgwMGDB/lCWi8kEgnKy8u5CiTZMDLT2rt3L3cey8rKoNPp0NHRAYFAgH379qG6uhr5+fnYunUrjh07hs7OTkSjUfT09ODkyZPo7e1FcXExampqUFJSgpqaGnR1dUGn0+Hs2bNZddwTiQR0Oh1CoRASiQQnuI8//ji7vnd1dWHnzp148skn2SSsra0N//7v/47NmzdjeXkZra2t0Ov1OHz4MJtCUaK7fft27swTsdPv9+NXv/pVVnIYABw9epShMA6HA2KxGE8//TQ+8YlPYNu2bWhqakJ9fT3zRJxOJ6xWKx555BHs2bPnPdOQ4uJivPDCCzxV83q9+OQnP4ljx47h7//+7zE4OMgHjkajQXt7+7rrSqVSsFgsrHqyfft2aLVafPSjH+Wup9FoL/s/kgABAABJREFURFVVFY4cOYI333wT+/btQyaTwWOPPQaLxYLFxUXU19cjEAjgc5/7HPR6PVZWVhiWtGXLFvzxj3/EJz/5SQSDQTQ3N+OJJ56AwWDIytEIBoO47777eARtNBphMpnwuc99DnfeeScefPBBNDU1wWQyYevWrXjttddgtVohlUrxyCOPYN++fZDJZEin0xgeHsbnP/95OJ1O/jOHw4Hvfve7OH78OAYGBtDT08PmP2+99RZaWlqyvlMazUejUZ4+iEQifOQjH3mPU7Ner8fOnTthMBj4svjUpz6FmpoarK6u4p577mHfh0uXLmFkZAQrKyvIzc3F3r17EYlE8MILLzDe/5FHHkFtbW3WBGZ1dRUbN26EWCyG3++H3+/nC1KtVnP3qampifH31Enavn07j76ff/55bNu2DT/5yU9QUFDAIhCtra1oa2vDL37xCzz//PM4c+YMcnNz2agzW1fN6/WitrYWoVAIqVQKRqORJX3feecdxoLfcccdrFa3urqK5uZmPPjgg0yCP3DgAEZHR9HZ2YlgMMimaiKRCDt27EB/fz9/40VFRWhqamLy7nrh8XjwxBNP4ODBg9izZw/279+PkpISdHR0oL6+ntWk9u/fjyeffBJvv/02tm3bBoFAwHrt9B0UFxfj/PnzuHjxIndI3W43G5tt2bIFk5OTzJEi7tV6QZ35sbExXLx4EZOTkxAIBPj85z/Pk9jLly+z1v+f//xnLC8v4/7778c3vvENTmJaWlrQ39/PUu2Eb25ubsY//dM/8XObmZlBSUkJtm7d+r6wB6FQiPvvvx+bN29m515ykb5xUrdz50586lOfgtFoZDGFL3/5yyw5TD5Pubm575moiEQilitdXl5GXl4etm7dij179iCTyeDll19ed23T09OorKzEysoKpFIptmzZgnQ6jYcffpgbayTNeccdd8BoNOLBBx9Ec3Mz/vEf/xFNTU3s7v3000/j3//93+H1etHX18fE70984hO4ePEivF4vRkdH2awvLy+PeTA3C6/Xi8bGRqhUKvZwIn+TrVu3stpcOByGRCLBwMAAysvLUVxcjH379qG2thZerxcjIyO4cuUKpqamcPnyZQwMDKC3txcLCwv46Ec/ikgkgqqqKoauiUQiTExMZG3e+f1+lrom3s/c3Bz27t3LZo6RSARFRUUsQvPEE0/gkUcewTPPPIP8/Hz84z/+I44fP4433ngD+/btg91uR21tLSoqKhAKhXDgwAF0d3djZmaGneTVajV+9KMfZf3eaKpw6dIlvPXWW5iamkIikcDRo0fx+uuvY9u2bdw02bRpE8rKyrCwsIA//OEP2LNnDyQSCSYmJvD5z38eCwsLuPfee3kKRRLv27Ztg9Pp5Ok3Nf+ombVekOldT08P0uk09u/fDwD40Ic+BJFIxDDyqqoq5mVu3bqVZWUJmldSUoL9+/fjX/7lX9DS0oKVlRX4/X40NzfjgQcegM1mwx133IHJyUk0NjZi165dePXVV7M2eUQiEZqamt4DeX7ttdfw9NNPo7KyEnv27MGBAwfQ2tqK0tJSbNmyBYWFhXA4HAgGgzhz5gx+9rOfobu7G8lkEps2bYJcLkdJSQkmJyfxu9/9Dl/96ldx7tw57Ny5k6fc5ESfrclDRdjS0hJMJhPnqffccw+cTic2bdqEqqoqbN68GV/72tdQUlICs9mMH//4x1haWmIILzUKn3jiCWzatImbATMzM7jvvvug0WhgtVoRj8fR1dWFjo4OPp8/SAgy2cCQN8RvfvMbNluSSqWwWq1wOp1oaWlBfX09VlZWWBFleXkZt912GzZu3Ije3l7u3E9PT+O3v/0t/uEf/gH/+I//iG9+85uMKXW73fjd736Hf/7nf8apU6dQVlbGiUhxcTFSqdS6iemzzz7LkrCEy1coFPjP//xPPProo/jWt76FyspKdHZ2st48dU2efvppVtAaGxvD9u3bWZFobm6OCVqkRETyY93d3XA6ndi7dy8EAgE2btx407UdO3YMFouF8dSEtZybm8OPfvQjXL16FfF4HJOTk+jp6cGHPvQh/PKXv8R3vvMdvPbaa1CpVIyHNxqNuHjxIkvOCoVCuFwudHd341/+5V/Q39+PM2fOwGw2o6SkBAsLC5DJZHj44Ydvurbvfe977PZIXXmtVovTp0/js5/9LN58802ukgOBAJqamnD69GksLy+jr68Pd999N2677TYMDg7C6XTiZz/7GX72s5/htddew9zcHHdOrl69CpFIBLPZDLVazXjeRCKBu++++6Zre+qpp+DxeLizTIfe9evXodPp+AAmzkxOTg66u7sZqkTqFYStfvXVV/GZz3wGhYWF8Hg8mJub4y7A/Pw8jxapa9Tc3Lyud8B3v/tdTExM8AFqs9kYC/2xj30M3/3ud7Fx40ZotVpcuXIFbW1tPEn4/ve/j+bmZmzcuBFvv/02GhsbMTs7iyeeeIJJnBQjIyM4dOgQq7hNTExg165dGB4exlNPPXXTtf3qV79CMBiERCKBUCjky+0Pf/gD/u7v/g52ux1tbW1IpVJ46aWXEI1G4Xa78bnPfQ7//u//zmIJPp8Pc3NzmJ+fx6c//WmeKoyPj7O3zNDQ0HvUtGQyGRKJBD7+8Y/fdG0A8K1vfQuLi4v83anVauYXHTx4EFNTU7h48SKWl5dRUVGBDRs2QC6XQ6VSMRTp6tWreO211/DYY4/hueeew969e3lSRm7Zer0e//AP/8Au1WSkRFK9N4sf/OAHmJ6ext69e9mzwGAw4KmnnsL+/fsRDoeZR5aTk4PHH38czzzzDBoaGlhuuqCggL0KCBpSUFDAMrbkuOz3+9n4ye/348KFCzhw4MC6Xb9z585henqaJ0iJRAJra2v45S9/iR//+Mfo7u5mo0zqwFICMDc3B5PJBKlUypr4brcbR44cYbytWCyGwWDg0Tq5TstkMlgsFgwPD+Pw4cM3Xduf//xnBINBWCwWRCIR2Gw2qNVqvPbaa/jsZz8Lp9MJm83GhouPPPIIzGYzTp06hddeew1msxkbN25kf5GPf/zj+M1vfoNwOMyXIQkmFBYW8tg+HA5DLBajp6cHn/3sZ2+6tp/+9Ke4cuUKysvL+SwjzlFOTg6effZZbN26FfX19YjH49i4cSOeeeYZpNNpHDlyBGq1GmNjY/jGN76Bffv2weVy4bHHHkM6ncb58+dx6dIlNDQ04Pbbb4fVamVuIMFbtVotPvnJT950bd/+9rehVCoZ4igQCFBeXo6+vj7ce++9zGEIBoOYnZ1l6F5hYSHOnDmDmpoa3HnnnVCr1bh69SpsNhs7tNM0q6enB1/4whcwPDwMAGze2NjYiOvXr+Ppp5++6dp+97vfYWFhAWazmWGi8Xgcw8PDePLJJ+F2uzkRsdvt+NKXvsRysrFYDJs3b0ZpaSnm5+dRXV3NgjAikYin+ktLS2hpacEf/vAHbNq0iSGIJSUluHDhAv76r//6pmv705/+BKfTidraWjY63Lx5M5RKJaRSKex2O8uqXr16FUePHsXy8jKUSiVGRkZQU1PDhrEkLb+0tIRAIACNRoOCggK89dZb+OpXv4of/vCHuO+++xCLxVh1sqOjA7t27brp2o4dO4bV1VXo9XrmZchkMpw6dQpPPPEE/vKXv0CpVKKhoYH3WCaTQX5+Pp555hkYjUZs3LgRc3NzuOOOO/D9738ff//3f4+FhQWWAx4ZGUFzczP6+vpQXV2NqakpTE5Osh/DkSNH1t0LPT09uP/++yEUCrG4uIjGxkZ861vfwm9/+1tcv34dyWSS8zW/34/29nYsLS3h2rVrkMvlSCaTeOWVV3DnnXfiwoUL+PrXvw6FQoFQKISpqSkMDw/jIx/5CL7zne9Ao9EwB4mm4+vdqefOnUNfXx/7jPX29uI73/kO3G43du7ciePHj3Nz5Ne//jWOHj2KyclJ1NTUsOQwQXhJrVSpVDK3LB6P4/Tp0/jZz36G119/HUajEWfPnuVz/E9/+hO++MUv3nRtv/zlL2G321FRUQGv1wuXy4WGhga8/PLL+NKXvgSVSgWVSgW3243Lly9j9+7d+I//+A9s2LABPT09LM4hFotx/PhxLC4u4sknn8TExAQ3nL/3ve/hZz/7GT73uc/hoYcewujoKCYmJlBTU4Pq6mrcf//9N13b1772NRiNRiiVSly+fBlzc3P46le/ik9/+tOsbEkc5wsXLmDnzp04f/48zGYzZmdn0djYiNbWVkxNTTGSY8+ePSzcMTk5iZdeegn3338/rl27xs3oiooKVFRU4I9//CO+8pWv3HRtN8b/k2GfSqXirubi4iJjAl0uF7RaLbxeL5aXl+FwOBCPxzE2NoY//vGPMJlMUKlUiMfjsFgs+I//+A986lOfwvnz55kcm0qlsLi4iG9/+9u49957MTw8DKlUCplMhrNnz2JlZQVf+9rXbro2cgIfHR1lEo1UKsWBAwdw7NgxVFVVwev14vr169i5cyfje/Pz83HPPfdgeXkZXq8XpaWlbAhD+tnxeBz9/f2YmpriF0AuwFqtFlevXoVQKFy30PB6vezeTZCEYDCIvLw8TExM8J+bzWa0tLTA7XajqamJN4Hf70dPTw9jjQl7S1OU1dVV1NXVsfPwhg0bmPDodrvfF0MnEokYorKysgKr1Yrm5mZcv34dMzMzuHLlChoaGnD33XcjlUphy5YtuHz5MsNB3G43dDodlpeXsW/fPoyPj8NkMqGgoADRaBRnz56Fx+PBhg0bEAgEmDwZj8fh9XrXLTSoe0DfhcvlQjQa5dHu0tISKxyVlpbCZrPB4/FAoVBgdXUVoVCIISonT57EvffeC7vdjqWlJcYpp9NpJjAODQ2xGkRTUxPm5+fXPRTdbjdqamqQyWRYuYSM1/70pz+hqKiIzdEokaRD8cCBAwzNa2lpwdDQEMxmMxYWFjA2Nga32838IuIKXLx4EQUFBaisrMSrr76aVeqZni1BEPPz87GysoKnnnoK586dg1gsxpkzZ5jMSd263//+9xCJRKioqODxq9frxa5du/DHP/6RCXDV1dXw+Xz8rV24cAFyuRwajYYxntmCEpSCggK43W54PB7k5ORApVKhp6cHGzZsYPlscpcmFS5SL5JKpXjsscfYPI9Gy9FoFJOTk+jt7cUDDzyAXbt2YWhoCKurq5DL5chkMlk5S+l0Gq2trXA4HPD5fBAKhbh69Sp2794NpVKJtrY29Pb2coKyvLyMrq4uhEIhXL9+HU6nExaLhUf3LS0tWFpaYnfxa9euQSQSvcdvobi4GDqdDgaDIavgxeLiIoB3ZUdJpYY6/DabDfF4nDH4pD5Fikh0/up0Ojz22GO4evUq47EJfunxeNDf3w+DwQCHw8E/Z21tDRMTE1mdc0kMYWpqCjKZDGKxGOPj43jwwQeRSCTQ29uL5eVlLtTOnDnDghp79uxhczuasHzqU5/CtWvXmItCI/qhoSHGrq+urvK5lQ1a4PP5sG/fPv53IpEIent7efpdXl6OQCDApM2xsTF0dnbiBz/4AQAwLGjHjh2wWq1oaWlBd3c3QzyIS/X2229j9+7dGBsbYx+A69evZ5XeJS4DSTiTuAHBegleQ/97dnYWgUAAmzdvRm5uLsMhLBYLTp48iUOHDvEUhDwXbDYbeyAA70JUPB4PLl26lJWv5HK5oFAoIBAI+L4huVEqtoloK5FIWE1pbm4OmUwG77zzDu+5mZkZ1NbWYnh4mKGMJ06cgEAgwNraGoqLi3n6TgluNtW/cDiM2tpaxs0LBAJcunQJqVQKzc3NyGQyjKhQqVQMPyVeEimpAUBfXx9EIhFLHYdCITZdpOSSjP4ikQjy8/MxOTm5bqHhcDig0+l4IkVmb11dXeju7mYxhomJCWg0GuzduxepVAp9fX1IJBIYGRmBy+VCTU0Nnn/+edx///24fPkyQ5yJs0P3J/klNTU1YWpqKit01ev1orm5mf2yqJHy6KOPYmJigjlpNpsNa2trDLOz2+3M4ygsLMRXvvIVnDhxAo899hh6enowPz8PqVTKioq//vWvsWnTJpw7d47PXuJYrnenDg0NQaFQoLS0FPv27UNRURGOHTsGjUbDRQoR6Il4HovFMD09jdHRUej1epSWlsLr9bKiaHl5OYsRkAH0G2+8gVgsxjmVSCTihup6QfxJuo+SySQmJydx5MgRDA0NobS0lEUGmpqa0NPTg6amJggEAphMJgiFQtjtduTn5+Mvf/kLtm/fjlOnTmF+fh45OTkoLCxEaWkpXn31VTz88MMYHR1FJBLhSffIyMi6hUYsFoNYLGY4tlKpxMDAAL7xjW/A7/cjnU5jZGQEEokEd955JyYmJpgPRM/d7Xajuroay8vLKCkpwfPPP4+qqir4fD709vYCAHp6et4DpywtLWVz1w8SH7jQkMvlWFlZgVgshkQigVgshkqlwuuvv46tW7di+/btSCQSSKVSyGQy7AdQUlLClWZFRQWTEn/1q1/hwQcfRG5uLhYXF+Hz+Rgy8c4778BsNrMnBhm+ZYtAIIB0Os028GTsIpPJYDabEQwGkUwmsbi4CKfTia6uLoyNjTHOXqvVorCwEAsLC9z1oIO4oKAAOp0OmUwGJpMJLpcLk5OTEAqFkMlkWZVFSC2IDI7C4TArPVy6dAkCgYDVHEgX/EbcLxHZFhYWMDExwXrpcrkcs7Oz6Ovr48OW+BvEQQGQlYxIXAYiz+v1ejidTpw6dQr33nsv8vLyGI9HpMeSkhIsLS2hv78fyWQSlZWVbEJXUlKC6upq6PV6LCws4MKFC0xsUigU6O/vZ3IygKzmbkQeJBx8QUEBnE4nrly5ggMHDqCwsBBmsxlWqxUXLlzA6uoqurq6mFOwsrICgUDA0AfibJASBgAMDAxAIpEwkdzj8cDn82FhYQFFRUXrrk0ikbBLenl5OcNDiBBMI2J6t2q1Gk1NTWyKYzKZ0NLSgvz8fIyMjGB8fBylpaWskDY2NoZMJgOVSoXV1VVMTEzwxUhY+mxro/0pEomQSCRgtVrx3e9+FxaLBaWlpZibm2MVMzrIR0ZG4Ha7MTU1xbCQ9vZ2jI+P86h6bW0Ns7OzGB4ehkqlQnl5ORNsfT4fUqlUVlMm4F24iMFgYDJhNBrFysoK3nzzTTz44IMsKHCjpw1houfm5hCLxd6jchSJRJCbmwur1Yp0Os0qSj09PXj44YeZ50P/XTalHZIVJMI8Ea0zmQz/bgS7u3LlCnw+H3bu3IlEIsEd22g0iqKiIpw4cYI5KOQUXVBQgLKyMm40KBQKBINBTuqzGYHJ5XJ23K2trYVGo0FfXx8TAcntlwiHANgMLRAIsDuyWq2GTqfD1NQUHA4Hn18LCwsYHR2FSqWC0WhEIBBg35TW1tb3LSAzmQx3ZpVKJWOH/X4/c8tIyaexsRHNzc0YHx/HhQsXIJVKYbFYIBKJWGmwpqaGCYlerxfhcJhNGUOhECvokMpetudGsCjiAxERt6amBiaTCZFIBLOzsxgaGsJHP/pRLCwsoKOjg7834hXW1dVxJ7i4uBj5+fnchZfJZOjp6QHwLnyGZCWzwR5IUp04TxKJBLm5uejp6UFvby8qKiqQm5uLYDAIoVCI+vp6RKNRdtimM3JpaYkT2HQ6jebmZlbkIdf61dVVBINBTj40Gk1Wcm5ubi6uXr2K2tpalJWVQaVSwWazYXp6mqGyk5OTcLvdfD+FQiGUlZVhamqK/ZRqamqQTCZx6dIl5lDGYjG43W6cOnWKk1Mqzun7zMZ7TCQS7EdEqowmkwkzMzOsIknqTtFolBtIAJgvQM0Yu92OsrIyVoITi8Us1kG8CIlEwlNMs9n8vmccwdX0ej3MZjMcDgeuXLmC7du3c6MrEokgEAjg4sWL7Pw9NTUFk8nEyAwyo92wYQPvcUrotVot0uk0i5EEAgEmta8XpHyk1Wqh1WrZ5T0ej2PTpk0s5JFKpdjEk4rweDyOqqoq1NbWQqFQwGw24/nnn4der0djYyNEIhGbaBYUFGBpaQk1NTXv4ahmA8+Q0hUZTVIjMBKJ4OrVqwy1vHr1KjweD1pbW9HZ2YlLly7BZrOx4hOpLlosFm565Ofns+DA9PQ0qqqquJj3+XwMo1ovSHSAoP+kZHnp0iUcPnwYxcXFsNvtLJOdyWRQVFTEqokk/kOc10AgALVazUJKo6OjkEgk0Ov10Gg0kEgkfEdky0MAoLi4GDMzMzw1J+PLSCSCjo4OVi91uVyoqKjA2toaysvLWWm0pqYGO3bsYD4IQQPpHolEIjh//jxkMhn+6q/+ClKpFA6Hg2GZ2RpQN8YH5mik02nodDp4vV4EAgEmJFdVVcFut7P6UkFBAUpLS1k6NBaLMSSF8KU04rzRKMxoNOLQoUOIRCIIhUK8qYB3FUyyKWRQQkpycbQJSS1CpVJBLpdDJpNBJpOx5vTS0hLDLJRKJUpKSlghamlpiXXo6XIkFSGJRIK1tTXGYGYz2hKLxawgQJsnFotx4VReXs74d4FAAL/fjw0bNkAoFKK4uBjFxcWsa5xOp1FbW4tkMsmdDOrMkYdEOBxmJQ+dTpdVvQP4b535VCoFrVYLsVjMU4GmpiZUVVVBIpFgZWUFs7OzTOQtKSnhDQGA+Th08d6oH05FDMk+kgxhtsskPz+fFbrI9IcODeq2aDQalo6lQsPlcnESS6o8pOHvcDi4c+jz+VBcXAyr1crYQ5JHtdvtWZVFCGpCB1UoFEI4HH4PxI6gMvn5+azOMDc3xwUAdTpLSkq4SMnNzWU5TvJDuRHWRtrl2fYCvXuBQMDGR9Q1Jelowm9LJBJotVo0NzcjGAyiurqadblJf7yoqAjpdBqF/z/2/jO+zfO8F4D/AAiA2HsQAEFw7yFSokRqL1uyJe/t2M6ok8Zp0jRpe86vTdombZM0PW3SJK2bODtxM+x427JkS7KtvTjFPUES4MAgiD2J94N7XbXPa8J+39/5qOtLWtmmbj7Pc9/3Nf7DbOYJlsFgYOghEf7p2/6wKCoqYkw+KXXkcjnI5XJWrKHON2Ghy8rKEAqFUFNTg5KSEmSzWca/l5SUsKoZkdG3bduGYDDIXSyLxcJdxkLqSe9VIqOERy6X8xieFJPoua2srLDqS2trK0sxvvebD4fDkMvlLMnY2NjITQ9K+iQSCZOlNwrqjgmFQlbvyefzsNlsnMAXFRWxpwxNishYLpVKYWlpiUUzqqqqMDs7y89yfX0dFRUVWF1dhVqt5iYDfTuFgrhKlDBTc0QkEiESicBkMsHpdKKmpgZNTU189pNKFU1n7HY7ZmZmUFxczBMLet40ldbr9ax2R0VSWVnZhmsjOUyS7qZzlL67srIyFBcX81TN6/Wy9Gc+n+eipqGhASqViv9+uVzOzs8kDkJnJb0vasptFMQnJHEDhUKBbDbLUFsSasjlcjyBPHToEDduAHARQt4M5CVD0LnW1lYmqQaDQb5XSRq9UOh0OoYj0rdtMpkwOjrKd5JcLkdlZSWmp6dhMBg40Sf0Awk1LC4uYnJykovGXC7He4GctI1GIyv7FEqYqbFHktj0dzQ0NLAUOzWobDYbm+aSCpdSqUQ0GmWZX71eD4vFwspTxcXFsNvtcLvdzCkk0i4Zom4UuVyOVb+IUEvKQCQKQv4JpM6o1+uZH9bS0gKDwcAGmSsrK5idneXzUKfToaGhAUtLS9xgpIaRQqH4UKEQImgTL4v+jO5DtVrNcCehUAiXy8U5XFFRERdcdM+Rlxipx1VXV7PssdVqZbO55eXlDxWm0Wg0TCQnRbXi4mIkEgnmrymVSuzatYsVRAlySGd7XV0dotEoqqurEYvFeM9KpVLU1NQgEonAZrMhm81CqVRyrliI4J/JZJhvQdLhNFmngqyyspIVOAmVQvklJezpdBo6nQ5VVVUQCoVwOp2oqKiAXC5HVVUVlpaWeE+SITGAgj5BZLiXTCah0+nQ2NjIjUbyzqHGE+URBw8e5Gflcrn4rCMRm0QiwcqI8XgcFRUVUCqVLBSi0WhYcZLuzA+Lj1xo0ChneXmZ9eSj0Sjuvfde/uCpC0tYXY1Gg4WFBYZiEIZ1cnISjzzyCI/A6cNuaWlBOBxGXV0dj4QooS00Hs9kMjAajcz18Pv9nNCShCYZuOl0Ov6o8/k8HA4HS8qRvFpnZydPZihBc7lcmJ+fRygUgk6nQ3NzM2pra9lFdqNQKBQMGQoEAkwq2717N5qamrgzplKp0NDQwN3kdDoNl8sFrVb7PnOc1tZWdhYViUSorKyESqXC7OwsxGIxQ9eIUFSog0ujNvLToORzx44dyOfzqKmpQWVlJSvpJBIJNkM6evQoWlpaEIlEsLy8zMXY2NgYzp07x7KzdXV1qKurg1Qqhd1uh8vlYmnUQpMgutQIJkISpx0dHdDpdHC73fD5fCguLmaVEfI1aG1tRVVVFVKpFBt9OZ1O9Pb2spRkIBDAjh07oNVqsbq6yoepRqNBOp1mtZ0PitXVVTgcDhiNRqRSKUxPT2NxcZF/LzrYxGIxpFIpu6Sur6/j1ltvRVNTExfmDocDt956K4RCISYnJzmRJ6lb8ipxOByw2WwoKioqmLysrq6yzwJNV8xmMz73uc+x6oxGo4HJZOKEmb7Rffv2YcuWLQDAMrb79u2D2+3G5OQkfD4fxGIxDh48CJvNBrvdDoVCwUmNWCz+0ORFLBazMgtBfqRSKfbs2cPTJkoCiEjodDoRjUbR0NCA1tZWnggR9IXeB0nwbdmyhbHMUqmUzeoUCkVB+V2TyYSqqiqYTCaUlJSwWpnVauWuZzAYhEqlYuf2TCaDubk5/s4lEgny+TxaW1t58tja2oqKigqedpBR38LCwvsaI4XOkVgshubmZtjtdua00LslOBpNMom75ff7sbq6+j4VIoFAgGQyyW7kADjZ2rFjB5RKJTdUWltb0djYyMTajYJgM1REE0+OyLrA/zSD6N1fu3YNU1NT2LJlC5vDUaNlx44dEAgE7C9E5oIajQZ2ux0Wi4WNNePxeEG3d+JbEeyU/E8o2abv32q1Ytu2bZiYmEAkEoHH42EpUeo2EoxPKBSyzHYymcSmTZsglUqxbds2WCwWVFdXo7GxESKRqOCUSiwWczFL383y8jLa2tpQX1/PpqXkETU1NYXS0lI4nU5s2rQJRqORla50Oh0bMq6srDCZv7KyEh6PB7lcjgtv8ij5MFPS3bt3o6qqCiKRiCVA9+7dy3Bns9mMuro6NDU1Qa1WMyzK4XBAr9djYWEBg4OD7EZP0t39/f1QKpX4sz/7MzYRpLyBpEIL7VOpVAqz2cyTTeoSU9JGUw6TycRu1jqdDj6fj58tGRBWVFQwh6CiooKNa3O5HK5fvw6ZTMZnN93FhZIrIiyTQhoJfJDDuMvlQnt7O7q6utDZ2Yny8nJ0d3dDJBLhkUcewdatW9ntXC6Xo7q6GuPj4yyoQs9menqaobFarRYlJSUoKyv7UP+R8vJydsom08qtW7cikUjAZDKxGmZ5eTmfwSqVikVxyLMmEAjgscce44k7+ZjU1tayMhd15MvLy3lqWWht5F0WiUTgcrlQV1fHeQ/xlEpLS/GJT3wCly5dwvT0NEKhECfKxOelfbWyssIN3Gw2i6amJmg0GoaWi0QilJeXw2w2FyzQaHIZCoWg1WrR0NAAl8uFO+64g33giAup0+mQzWa5AW+z2VBaWgqpVAqhUIiSkhLs2LGDPX2kUin/rn19fey9ReqpBF/bKBKJBJtIGo1GdHd3Y/fu3bj33nuZz0aQZ4IZV1dXw2KxoKOjgzmFk5OTuHz5MqtvUqNgfn4e+/fvR0dHB0+3iHdIE/GPEh+50DCbzSxnZrfbEY/HoVar8dRTT+FjH/sYTCYTj3k9Hg/a29vxm9/8Brt27YLP52O2+yc/+UloNBq8+uqrrFSl1Wrh8/lw22234e///u+5Mh8dHcXy8jI++clPFlR2okKnqqoK+XwefX19jG/fuXMnUqkUKisrIRaL8eKLL2Lbtm148cUXoVQq8cILL+DKlSuIx+M4e/Ys7HY76uvrmWkvl8vR1tbGBkjPP/888ylo9E5u1R8UYrGYfQji8TgSiQTuvPNOTExMQK1W48KFC2y0l8vlsGPHDgwODkIqlcLn82F9fZ0JgxKJBL/4xS+Y0CwUCrFjxw6YzWbceeed8Hg8LM9H+tbkH/BBQXCQXbt2oby8HLFYDDt37sTZs2exdetWPPfcc4jH42hra4PT6WRZ4yeeeALxeJzVJbLZLA4fPoylpSXcdddduPnmm9HZ2QmLxYKXX34Zf/zHf4zBwUFWNCJXWzIY22hter0ee/fuRVlZGWZmZuD3+zEwMPA+Obt4PI6qqips374dTz75JPbv389wreLiYly6dAnJZJJdsI1GI0+Kfv7zn+PTn/40brrpJna79fv9nAxuFBaLBVNTUwztIMM9lUqFo0ePsokQdU/vuOMO2O12Vj5qbGxklaJMJsPPO5PJQCqVoqurCy+++CJKS0tx+vRpbN++HXV1dTAYDNizZ09BJTH6xuhQHR8fRz6fRyaTQVdXF3fYZmZmcO7cOeh0Oly8eBGHDh1CX18f3n77bR5/U2JNClxarRZbt27Fm2++iSNHjmBubo4J1jTdKLRPATAOni56mqSRw/bU1BRMJhNzQXbu3Inf/va32L17N77//e/jN7/5DXw+H1599VVEIhH8/Oc/h8PhwNtvv40XX3wRwWAQ586dw2233YY9e/bA4XBgenoa165dY338jYIM0iwWC2KxGHp7eyEQCBCLxfg8AsB78/7778fc3Bzq6+vx0ksv8T//z//8TzgcDsTjcezdu5c7QK2trdDr9ejo6IBMJmNllZGRERiNxoLPLpPJYGZmhmU4iRh6+fJlNlMqLy9Hc3MzjEYj6urqMDU1hTvuuIMlp6PRKKamptiE7ZZbbsH09DQuXrzI0I177rmHoYPLy8sYHR3lBHCjWF9fx9WrV2EwGDhBU6vVWFxcRGtrK09xV1ZWcP36dRQVFaGvrw91dXWQy+Wor6/H1q1bEQ6Hcf/997NGPnEEkskkfvzjH+Oxxx7DhQsX2CSLXIjHxsY2XJtSqcTly5e5+He73TCbzRgcHERXVxeKi4vhcDiQSCRw7Ngx3H777ZygxmIxqNVq2O129PT04OGHH2bDOjqb7XY7zp8/j87OTvzkJz95HxT2wxzVqZjatGkTKisrsb6+jvr6ejz11FNoamriRIQK3/r6ehw7dgy7d+/G7bffjoceegi33norioqK0NjYiOeeew52u525jXK5HN/+9rdRUVGBgYEBlJWVQSaTQaPR4P777y8oGU9+HdlsFm63GwMDA+js7MTZs2fx4IMPoq+vD5WVlWhoaMD58+fx8MMPY2FhAffccw9DZhsbG/HOO+9wYv+Nb3yDIYJ79+6FWCzG3r17cfLkSaRSKQwPD6O/vx+VlZUFFQnpmzIajezrtHXrVkxMTODgwYPsPSQSiTA+Pg6VSoW1tTW0t7djdnYWMpkMra2tWFxcRD6fx0svvYRsNsvwl1QqBb/fj/vuu4+nHDMzM6y0WUhpx2QyYWRkBAaDgYUB0uk0lpeX8dBDD7G6pEajwa9//WvU1tbC4/Fg3759GB8fZ2PbM2fOYPPmzaioqMDRo0cRDAZx6tQpvPXWW3j66afxqU99isUVJicnMT09DaVSiZdeeqng2mZmZqBWq3myYzAY8Mtf/hIAMDAwwB4U+Xwe7e3t8Pv9UKvV8Hg8LDIRCASwadMmzMzM4Itf/CJWV1fR29uLfD6Pv/mbv8F9992H8vJyGAwGWK1WtLW14d577y04TRaLxXjllVcQCoWYP9vd3Y2xsTE4HA5cvXoVQqGQRS2++MUvYmlpCffffz8n0el0Gv/6r/+Kf/zHf4RAIIDNZkMqlUJJSQkOHz6MYDCIO++8E9/73vd4LclkEpWVlQUNSUk22G63w+fzYWxsjH3Ydu/ejf/6r//Cs88+i/HxcaytrfEdSWewUqnEli1bEIvFcNtttyEcDrPdAnkK9fb2orW1FSdOnEBJSQlP6bq7uwtO0DKZDPr6+niK6PF4YLfbcfbsWRw7dgxTU1P8vVosFuzdu5fh9s8//zwCgQA6OzthNBrxjW98Aw888AA+97nPoaSkBE1NTfjyl78Mk8nE6lxbtmzBtm3b4HQ6UV5ejnPnzm24tvfGR+ZoSKVSPProoxgcHIRMJkNbWxurTL366qtMfpXL5WwetWvXLsZ8r6yssP4zXZJ9fX0MDQmFQvjLv/xL/PVf/zWkUilPRQheUF5evuHalpeXUVZWxiQVsmAnzgPJ0ioUCnR0dDAZ3Ol0oqWlBe+88w5+9rOfQaPRoL6+HtevX8fu3bvR3t4On8+HiYkJfknj4+Nwu91IJBIMgSiUzI+NjcHpdCKVSvF48+zZs0zSPXjwIAYGBjA6Osp4VVLZIRKTSqVCU1MTrl69is2bN6O6uppJT0Q2HR8fh8vl4o5OIpGA1+stCOtKp9MIh8OseZ5IJHD+/HmGKdx0002Yn59HT08P7HY7MpkMnn32WdhsNq6ySR5NrVZz0ZVKpZjgr9fr8Xd/93fYvXs3jh8/jrW1NeTzeUxMTBTEfZNS0NzcHMOBqCM8NTWFmpoaxj8Sbrurqwtnzpzh6VtlZSWqq6sxOzvLEB8i5EqlUrz++utsxDY3N8cdbZogbRQ+nw8VFRWw2+3w+/2Ym5vD66+/jrfeeguPP/44TCYThoeHsbCwgPr6epw4cYIVcjweD/sadHZ2Ip/PY3h4GE6nEzt37sSlS5fw1FNP4ciRI5icnGT/iJqaGphMJly4cKHg1ID8FGjEbbFYEAgEcPz4cXR1dQF4V5pz8+bN6OzsZE4KOd0rFAro9XosLS1BIBDg3LlzsFqt7LhL0sxvvvkmnE4nFhYWeOI1OzvLLqwbBSmJkasvXfAtLS38bsbHxxGJRFBfXw+Px4OlpSX09/fDaDRCKBSynjclyDqdjkf8mUwGkUgE165d4ykV8O6Fsra2VhAGNDY2BqPRiKmpKej1ejQ0NGBoaAgXLlwA8C7MbWhoCF6vF+l0GleuXMGWLVvw29/+lqFCuVwON998MyQSCS5duoTr168z3Im6yjU1Ndi6dSvzYgQCASQSSUG8KzljZ7NZLpaPHTuGhoYGjI2Noba2FiMjI5icnGS41+joKE83qMtN5+ns7CzL9RqNRtZav3r1Kh566CHurtGZ4HQ6N1yb2+1GY2Mjk8ij0Sjm5uZQVVWFqakplJWV4fXXX2cyNWGFz5w5w0T4dDqNvr4+7Nixg9XjioqKUFxczNKL1Enz+/3weDwwmUzMMdsoIpEINm3a9D4nbJ/Ph6qqKly9epVJwmRM+t3vfhcf+9jH8Ic//IELejJc+8pXvoK+vj6GHpDR5NLSEi5evIiOjg6cOXMGmUyGOWSk7f9BQcZfk5OT3IgaHx9HU1MTMpkMu/Km02k4nU6efF2+fBn5fB6BQIDvngMHDmDPnj3sctzb24vZ2Vl88YtfZFPYubk5+Hw+1vcvBHUcGhrCli1bsLS0xBP3J598Eps3b+Yk7dVXX2UoJBlS9vb2Ymlpic1Lu7q68Nprr2FgYACJRIIJuOfPn4fZbGY8e2lpKUtZk4MywW7+7zh9+jS6u7sxMTHBMOLz589zkwwAwy+1Wi0ikQguXrzI0tnUTHr88cdx7NgxmM1mvPnmm2hra4Pdbmcy9ODgIE8zqCgkPl2h740KOPLLmpycZIPidDqNixcvQiKRYM+ePTwJWFhYgNFoZIPRz3/+83C73XjyySfxqU99CnfddRcCgQCbBp89exaXL1+G2Wzms1ssFm9ItgbebULt2rULS0tLzJM8deoUPvWpT6GoqIglzclv6NChQ4wu8Xg8PBWl88ZqtSKXy/HfmclksH37dnznO99heNClS5cQjUbZO22jSCQS2LNnD/N1SdDl3nvvhVwu54nFwsIClpeXMT4+Do/HA7/fzwgHujP+7d/+DWq1motRUoGLxWKQSqW4++67uVFAsKOBgQHce++9H7g2UtHz+XzMp5mZmYFOp+PznCBFu3btwvj4OCQSCXO2lpaW0NbWhp07d+Lf/u3f8KUvfQlPPfUUKwyePn2aoX979+5lFAZBSQsVaAR5FQqF8Hg8zIe22Wx4+OGHAYANNtfW1tDf3893/OLiIl566SVcu3aNoZ/33HMPnn76aZaU1+l0GB4exsjICJqamhAMBjE4OAjg3TupkBfae+MjFxpyuRxf//rXWSYwHo/D6XQyDvHw4cPvU3pob2/Hk08+ySYrer2eYSm5XA6bN2/mw5TURUZHR9Ha2spj00wmwypShUZbFRUVGB0dZWwn4fmj0SiGh4cZp0s4w2AwiFAohOXlZczPz6OtrY2hXKRAdfLkSXR0dMBgMKCuro7NWiorK/lisFgs/JFvFDqdDmq1GiKRiN1QA4EARkdHcdNNN7ExVSQSwYkTJ7B//34eQRO8iHCySqUSb731FhP7SAXD5/NBr9ejqKgIQ0NDCAQCSCQSrKq1UUilUni9XjZjpI7v4uIinn76aXR2drLhHEn/kUZ4S0sLVCoVUqkUEokEE7IjkQiqqqpQVlaGSCSCF154gX/uyMgI1tfX4XK50NraWhDWJZFI8Pvf/x7l5eWorKyEVqvlIu3q1auorKzE3Nwc5ubmMDMzg8OHDzNc69577+UitaKiAq+//joXPZ/5zGcQDAbx61//mt3VFxYWmGNAhen27ds3XJvT6YTH42HcrM1mg8fjwaFDh9jkqLm5mUm4tCHHx8dx8uRJAGAoz8DAAJaWlvD000/j7rvv5hH18PAwE7hkMhmmp6cBvFskFCq6dTodfvnLX2Lz5s2orKxkF+DKykq43W6k02n+WXV1dbz3yMOAXGMdDgfOnDmD5557Dg888ADuuusuSKVSltEkkieRwOkwLITDBcDeE0ajkd28RSIR/vmf/xm33XYbk/LdbjeKi4tRWVnJUz7iitAZRB2wgYEBdHV1IZFI4Nq1azxuFwgE6O/vx+zsLHK5HE9fNgqTyYS+vj5s3bqV4SVGoxEXLlyASCRCZ2cnd9gHBwe5G0W6/QTN3LZtGyt8VVRUoL6+HiKRCGNjY5ibm0M+n8fCwgIbJMXjcRQXFxfsMBuNRpbtTCaT7KdBikAEsyD4ICWSXq8XPp+PFdmqqqpw8uRJLtprampQXFyM5eVlXLhwgSFCxJ1IJpNoa2srOOHr6urC9evX4XQ62SSNurg+n4/x71KpFOFwGM8++yx27tyJLVu2YGBgACKRCDt27MCDDz6I48ePo6GhgQUgSEHomWeegVqtZg4dcZZojxV6bidOnIDD4eAJSS6Xw9zcHKtREYaf4IC//OUvuROaSCTw1ltvsZTzQw89hIsXL8JisXDzQq1Ww+12o6SkBO3t7Ugmk8w5KORaLpPJ0N/fz/ASu92OXC6HmZkZvPjii6xYRF1kUglbX1/HzMwMw/rW1tbwX//1XygvL4fJZGJ4qFKpxMDAAPR6PbxeL8uii8VihugW2guDg4OorKxEIBDgZsivf/1r9tiIxWK4evUqMpkM/uRP/oQLrPdCdolDl06n2YBOJBJBoVDg2LFjePTRR2E2m5FIJNDf388NH0qEPyhIwlwmk0GhUKCoqAiTk5P46le/igceeAAmkwlLS0tYWlqCSqXC7t27cffdd2Nubo5hxQR3kcvl8Pl8uPPOO7GyssLqZ7lcDi6XCyKRCENDQ1zQ0l7dKAKBAEMICabj8Xjwwgsv4NOf/jSCwSBDbmOxGHbs2MGEdfJAoQLbYrHA5XIxzIY8Rl599VXs3r0bhw8fxunTp1l04MNU9fL5PK5cufI+wQaBQMCJ+4EDB1BdXc33+ujoKEZHR/l5+Hw+ZLNZbNmyBRMTE5iammLVrKGhIfT09DA0nLgMCoUCPp+PRSY2CjKwJb5OKpVi9+xAIICdO3dCIBDA4/FgfHycId79/f2QyWQsPfvOO++gubkZ/f39sFqtkEqljL6wWq0YHh7GTTfdxDxM4k5QE+6DIplMYmhoiCGK5IL+1FNPwWQyoby8HEajETMzM/j5z3+OBx54gO9EImtPTU3hk5/8JLRaLU6ePPk+Rb/e3l7s3r2buYsOh4Ph88ePH8e2bds2XJvD4cDFixfh8/mYm0lQ9r6+PpYsHxkZ4ebzV77yFfzDP/wDOjo60NnZCbPZjIGBAVy8eBEjIyNwuVzcrL948SIef/xxvPLKK/j4xz+OV199FXK5nIusDxMJofjI0KmFhQXs2bOHXWlbWlrgdrtx8803w2q1Qq1Wcye4tLQUIyMjbFLjdDqZ9Prggw9yskxSjcTkP3r0KBOwFxYWUFJSgk2bNiGTyeDUqVMbro3kJpVKJaRSKasN7Nq1CyaTCV1dXUxWtFgsSKVS2Lt3L1paWvBXf/VX2LNnD2+mYDCItrY21iz3eDyYmZlBbW0t81CI5JbJZDA0NFTQjZP+3YWFBcTjcZSUlEAqlWLr1q2scEE4u8985jN8MF+/fp2Lpng8jmAwiOnpaRw6dAiTk5OsHGKz2fCxj32M5XktFgs6Ozuxbds2pFKpgl2rSCQCg8GApaUljI2NcRe7trYWDQ0NsFqtsFqtDPe5evUqm9EQuXBxcZEVYyhhIbLyxMQEPvaxj0EqlSIYDGLLli04cOAAF5MfJsXX1dWFXC6HyclJThbr6uoQDodhsVgYq7x7926cP38e7e3tsNvt6O/vx9TUFP8dMpkM9913HxYWFnDixAmcPn0amUwGjz76KIaGhnDkyBH2QCBhgkJd0mAwyDrshG0lo8V8Po+GhgZIpVJWQyEyKf1ObW1t0Gq1vMG3b9/OhE5yUt26dSvzQGhqQ93pQio7wWAQd999NwCwO2hdXR1aWlo4uayvr0dTUxNP2rq7u7G+vo6ysjJks1kEg0HU1dUhn8/j3LlzCIVCGBwcxNTUFGKxGB566CG+tBOJBLLZLHOZPkwlg7qMCwsL/M0JhUJ84QtfQEVFBbq6ulBXV4fS0lLYbDb4fD5Oqsncc3l5GXK5nC+9TCaDnp4eXL16FaFQCE6nE5cvX2bYWG1tLTs0F3p2pNm+sLDA3dLJyUkcOnQIer2encJra2tx++23I51OM8+otrYW2v824yLp0X379rGgAUmBPvLII5iYmEBDQwMcDgfuvvtuPPbYYzAYDAULDfIXIMUTms7u3LmTpRy9Xi9mZmZYBnPTpk0oKipCW1sb7w36PUm+kEj50WgUd999N0KhEHvcdHZ2Yvv27Zifny84RZuZmeEkmQj+JC5AviFyuRy1tbU4dOgQBAIB9Ho94vE4GhoaIBKJcOrUKbz22mtYXFxEbW0twziSySREIhG+/vWvw+12o6amhgUwyHm5EMwmk8mwCZnBYIDNZsPY2Biamppw6tQpeDweTE9Pw+/389TkvvvuY5w3Ga7+7//9v3kKabPZ0N7eDrPZjFAohL179zK0dXh4mBWy3nrrrYIXcSaTwaZNmyAWi1lUoqioCAcOHEA6ncbRo0cZ303NDavViqKiIuzZswd2ux2pVAotLS1YX1/H7bffDpFIBJ1OB6vVCovFwpBQUopqaGhAVVUVvF5vwSIomUyipqYGYrEYJSUlaGhowMLCArZu3YpkMona2lq0tbXhnnvuwVe/+lUMDw+z38qBAwdgt9tx/fp1iEQiRKNRPPzww/D5fJBIJKyOdvToUbzyyitYXFzE1NQU8vk89Ho9Q9Y2inw+z6IZMpmMjWrJZ0GlUrHLdVNTE+PxqYjR/rfrvdFoxPT0NNra2nDt2jUsLCywA7jBYMC1a9d4YlBWVsbvqtAZQpC1aDTKKIquri788R//MaampuD1epn/uH37dpZAXlxcxKZNm7B161bU19ejuroaJ06cwMGDBzE9PY2BgQH253nssccwNDTExrFlZWVMvC4EcSR/KSoCiVPgcrmgVquZf0SJu9frhcViQS6Xwy233IK9e/dCpVKxF0tDQwPOnDmDtbU1WCwWtLS04IknnsAtt9zCqnKzs7NYXV1FT09PwYYA8bwUCgVqamqwadMmVuAk36ArV64wLP7cuXOoqalBLpdDW1sbTCYTIpEIixR84hOfYA8umtCrVCqeVpHaHynOFQqFQoGtW7cil8thaWmJ+bMPPfQQT4mVSiVP6Kjgqq+vx6233orbb78d27Ztg91uZz4s+T5ZLBa0tbXBarW+T8LX7XZjZWWFeZ8bxfj4OCorKyEQCBAIBBj69rGPfYyLzubmZnzqU5/C1772NWi1WqTTaezbtw+33XYbRCIRzp07x6pgjz76KEZGRhCPx1FeXo6KigqG6ZOYBkmiU/P5o8RHnmhIJBLMzc3B6XTypl1dXeWD3Ov1siY2YVqJ3CwQCKDVapl4QkpK6XSaCdckkSkWi1kNhdRkXC4XQqHQxr/Efx86tInC4TDUajXj76gTRCNGnU6HmZkZAOBOH0myknIITVKIIE1j//7+fpZGI8J7octEIBAwJCWXyzEBkYjoJJNK3duysjIEAgGWxSSyF8kZ6nQ67srSpS4SiaBSqRgeQdJoTU1N8Pv9G66tuLgYcrmcN7hcLkdxcTE8Hg/KysqwsLCA+fl5WCwWNDQ0sMoIYeqBd7tLIpEIbW1t2Lx5M4LBIMbHxzkxpoSDTBCpI0xwqELfm0wmY8UGIgzq9Xo2jiNojFarRXNzMxKJBOuAu1wu9tswGAzccSRoBxFnm5ubEY/HGcdLCjqFLmHyN6FuIwC+tJxOJ3cTV1dX4fP5UFlZiYGBATQ2NrLKDSnJNDQ0YHJykvGcdAiRMtqJEydgNBp5YqBWqwsS/qjT53Q6EQgEWO+eiMOkRkEqTVRI5/N5BINB2Gw2/lZbW1vh9/tRWVmJtbU1Jr8uLS0x94q8DMhs7MOkH+VyOStsAeDL2GazQS6XIxAI8D8bGxtjZTNSDTMYDCgqKsL4+DiT3YjP4ff7kc/noVarsWnTJohEIobzAO92kKempjZcGwlckJpQJpNh9ZDy8nLMzs4y2Xd9fR3d3d3sRp7JZLjTuby8zMlQZ2cnZmdn2awslUrB5XIxjlwqlTIOv1AyT0IP9AyJADk/P89KKySvS5eN3+9nsiK9eyo8QqEQF7HJZJI9kbZv387wDXompDi2UdA7p31BeyMWi0EgEEAoFEIgECAejzOpfWJigl3iqbM+NzeHhoYG6HQ6LC0tIRwOQyQS8c9oaGjgpIBU23Q63YcShwOBAEtTFhcXw2q1YmZmhvceqe2Qrw4VHblcDtr/NvgjHsTa2hoqKyv5opVIJPD7/WhtbeXEhe4Ygh4Uem6kRJNMJjE/P89muLTG+fl5qFQqlJaW8j1JijfkeUOT/OXlZUilUkQiEYhEIlitVoRCIW78kcwtCbAUaqZoNBosLy+z1DEZ5AWDQVRXV2NlZYWbMhKJBKWlpbh06RLfjaR4GAqFUFlZyZwFQhJkMhmWaXW73XC5XAzhI0jMRkGiEzQ9DYfDLDSQTqcxOjoKs9kMu93O0xOCXM7Pz0MkEkEikWB2dhYqlYonvsTnq6mpgUajgUKhQCwW47UD7/K4Cj23oqIiLC0twWw2c7Gk0WgwOjoKl8vF0sRer5c9OQKBAFwuF38LWq0WfX1974NFkRx5RUUF9Ho9Ojs7kUwmodVqeV8nEomCAiv075H0/fr6OiwWCzweD5PwaV+Rol8mk4FWq8XExAQcDgfvQVJEI0lx+iZnZmbgcrkYcbG4uMg8rImJiQ3XRlxPgpjSxK24uBherxcnTpxAPp+HRqNhsjr56ySTSQgEAv4eaKJXV1cHjUYDtVrNOdro6CguX77MCqUSiYSh0xuFQqFglS1SRSwuLsbevXsxMTHBsLeSkhJYrVaIRCImrtP7X1lZwenTp1FbWwuxWMyQbr/fzwbLfr8fbrcbOp2OqQaVlZUFp1R0dpPzOjVhqChYXV3l5q/L5cKOHTuQy+UYIkhcFpL8FggEKCsr4/xNrVZjYWGB977L5WIIJp05HyU+8kSDoEhCoRDRaJRJU9TlJAUZOqzJEIbGouTEOjo6ymMmUpzw+/0sd0i6ywKBAD6fD/Pz85BIJAUP7Fwux/KERHwkGA9pGdPhFAgE2GAtl8vh2rVrcLvdrKPtdDq5+CEpUIFAwJ3CUCjEKgbpdPpDFbEAcMFCXW7y36CRuM/nYxNEwq3L5XLGUlJFW1payjKQlLSsra0xh2RiYoLHYyRBW2hzy2QyyOVy6PV6NkiTSqUs50dmNwS/qK6u5hE+wY1UKhWbOSaTSTgcDvj9fubBjI6OsrkdyW36/X6WMi20tlgsxhU0QbRIF55UkNbW1jA3N8cbgBIASn6uXr0KtVqN06dP8yGuUqmgUCiwtLSE9vZ2TE9PM9+D5PIKqezQoU6yoKQgFYlEYDQamY9BMp319fWYmpriKRhBGCiJn5qaYn8S+gYIgjU5OYlYLMayf/TeC60tFAox4Y/GvMQ7KCoq4uQym82yBn4qlWJpUVJsslgsOH36NJM5yVdidnYWZrOZC3zq4FLzoFCQtwe9V1LVIKiEx+PhbuDMzAx8Ph+baZHUMeHaCSNM07Ti4mKWpqZkgQp9EgcoROTMZrMsiQi8Ox0i8qHRaGSddFIAUqlUTBYn7wCFQsGGX8FgkCUBU6kUZDIZLl26xOTDcDiMubk5di0utB+II0NKRMlkEm63G/Pz8yzPShCLlZUVqFQqhhgQNl4ul7/PbJDkYel8pveazWbZlZskwgu9V/qu6dykS4tELmh/+Hw+jIyMoKKigs8o0mQnyUk6W2j6Qt3j9wpIkKpVJBJh+eiNgrDn/3ejid4twXJtNhvi8TjfPw6Hgyc04XAYQ0NDcDgc6O3tZTgHafZPTU3B4XAgEolwo4sKuULNFFJYU6lUXCBEIhHGhA8NDbEkcTqdRmlpKVZXV5HNZnn6/F7lqcuXL3PjiaZU9A0GAgE2VqSmTaHClpSc6OwlaAap2BAEKpvNYmZmBlVVVQwDo+LebDbzd7OwsMDJKQnGTE5Oory8HDMzM+yiHY1GIZfLCzZ6aE1SqRTZbBYrKyusFAW8KwkrFArf9+9Ro9Lv9/N9PT4+DrVajbm5OZ6Q0jcQj8f5jCYxFkJgFDJhBP5HyITu7VAoxA07alJMTEywUAAVu263m3lBly9fhsVigdvtZglbUmWbmJjg6SchFnK5XEGFMwo6Z+l5RyIR9t8IBoPw+Xz8LM1mM3w+H1wuF3p7ezE9Pc1wQTojiENFf//p06cRiUQwPT3NEO9UKsVwyo2Cvi2C+uZyOeaC0h1P94ZQKMSmTZvg8/mgUqkwNzeHlZUVKJVKWCwWLC4u4uzZs6y+SM1w4H9EDkgtk6Bsq6urG65NKBQiFotBq9XCZDLxZJ0EU+ibIWsH8vwSiURYXl5mufjJyUk+c6lB4fV62ZCTphw0dchms7znNwpSpKOJFN2BAwMDfE5NTU3xRKy6upqV03p6euDz+Vj5lPJzaoQTp5FghNSAeq8EcaH88r3xkScaiUQCmzdvZs8CuVyORCLxPlI0dXAnJiZYYk6pVCKRSLDpiVKpxNzcHK5cuYLHH3+cOxxFRUXQ6XSMsdbr9fyhDA4OFhwJRqNR7vjQC6Iu8dLSEux2O3e3w+EwE5WIwEokVpKJpA6ZSCRCLBbDysoKBAIBTp06xe6QUqkUIpGITbQKBRFUNRoNVCoVZmZm0N3dzY7qNNI7f/48WlpaGAtKH5jdbmdDJ0rq6WXHYjF4vV7s27ePE3M6KMPhcEEyIunxU7JMhRVVxSUlJYjFYggEAjh79iw7f2ezWUxNTcFut0MsFmN6ehq5XA7Hjh1jKEc4HEYgEGCdc8JK0nSCuABHjhzZcH2UGFAnjwopEhkgXDyN9lZWVmCxWFiPOhAIYGFhAVarFeFwmLuDhE8NBoMssUxEUxr1FypsSZY2EAiwvN7q6ip27979vq4idfkVCgW0Wi0uX76MTZs2scnS+vo6G1uFw2FIpVKGDcZiMXg8Hmzbtg2RSITJ03SAbRT0/klRRSwWI5PJsIP7/+2QrdPpOMmkSz4ej+Pq1atcdBOcAABPy65du8ZESTr8SUmtUGQyGU70ADBuP5vN8sUbj8cRj8chEokQDAaxsrKChoYG2O12nlRS1yuVSuHSpUssExqJRHDu3DmUlpYyZ4wmfjRN3CiSySTMZjOSySRWVlbg8/nYA2d4eBhFRUUYHR3li29lZQXd3d04d+4ckwKp60W+M7QPSRJzdHSUsb70881mM8N2NopcLseNBuJMud1u3HTTTXC73ZwokdqRVCplXDmR0dfW1jA0NMTfyPLyMnfE0+k0F/CEWwbApqmFDFOLioogEonYBVitVmNoaAgulwsGg4GhK5ToklrTmTNnuGhcXl5GLBbD+fPnMTw8jPvvv5+lFkkmlMwcqblDsIVCHLl4PM5GpqQRHwwGUVFRwVLlJDgA/I/JYSaTYWgoOSMrlUpEIhGGDohEIk6OJiYmWNqXzuZAIICtW7duuDbi0QHvcptI5pdkLanBlsvl0NfXx0lHKpXC8vIyY+2lUimUSiX6+vrgcrmYs3jt2jVUV1czP2lpaYk7/2Rmt1HEYjFGCNAk+eLFi2htbcXU1BSqq6sRDofhdrsZyqpQKLhgJtibRqPBuXPnkEgksH37dp6iEw+LvD/eeust+P1+CIVChnttFGTkRs8+k8kgFouhqKgIYrGYHaIJ8keTjpWVFSbCksQxyd9funQJZrOZOaM+n48nc7lcjuGbBGPdKEQiEVwuF8sVE0/KZDJhcXER7e3tmJiYwNzc3PueV+i/3dBpupnJZFiMwWazwWq1wufz4fr160gkErj55puhUCi4UMhkMmxCuVHQZDgajUImkyGXy6G3txd33HEHhEIhZmZmsLKywv5mQqEQfr8fmzdvZqNIKr4SiQRPr9xuN/Mq0uk0zp07x+gUavTMzs4WLLrpO6P7mH4WmaU2NjbyN0MmgVNTU3y2Ek9l69at+NWvfoXx8XFGttA7djgcLAGcz+f57/B6vQW5hbFYDHq9ngtIs9mM6elp9Pf3QyqVMl+LCmhSUvR4POw5QipaV69eZZ+RXC6HbDbLd0tNTQ1aWlo4tyE/jEJyytQEoxwuHo9zs4MmSzT1GBoagva/PeWoeSASiXgfFBUV4eLFiwwbTSaTCIfDKCsr4zxzaGiIp0d+v5/PzQ+Lj1xoWK1WXLp0CWq1+n1dorfeeou7ibTxk8kkjhw5ggsXLuDUqVPYvHkzY3jJG0Cr1aKnp4c7o9PT03j99ddRXl6Ouro67o4S76OQVrpUKmWmPgB+4D09PbBYLGx4Q5MD8sVYW1uDw+GA1Wp9n8srVaHULTQYDGhpacH09DQniQCYyF2oE2k2myGRSLhooArx6tWr8Pl82Lp1K5RKJVQqFevGT09Pw2KxoKenB5FIBIFAAEVFRfD5fMjn8/xhkXayWCzGpUuXUF9fj4WFBYZZEXl9o1CpVLhw4QIqKip4vLe4uAiVSoWrV6+yuRbhyxcWFvh3p45TRUUFbrnlFpw6dQoSiYQLEOpg2u12ljOliUE6nWY+wkZBEx26zKPRKEsVLi8vQ6fTIRQKcSEzNjYGpVKJnp4eTlwzmQza29tx+fJlGAwGzM/Po6qqCgKBgDuZg4ODKC8vh9vtZod0gUCAtra2DddWVFTEyhw0/QiHw3j55ZeRTqexadMmRCIRxnNmMhnU1NSguroaxcXFPPELBAIwGo1obW1lXXNKVMiRen5+HjKZjPddNpstmJCKxWLMzc1Bo9EgFAqxjwIltS6Xiz0sCDZIjselpaWQyWQoKirCrl27MDQ0xHwI+u7fa9ZEuuyzs7O8VzZSiqEg8j0Rwcmh9/z583A4HIyxpcYF+TC89dZbKCoqgsPh4G4cGR9mMhno9XpIJBLuXA4PDzOkghITiUTCXI0PCr1ez9LSNAl99dVXWT1Hp9NhenqaJ67kuUCX8vz8PBKJBG699Va88cYbeOGFF+ByuZhgubq6in379rEUrc1m48bN6OgoNm3atOHaSGKSJqM6nQ4CgQDHjx9HW1sbezFQArG8vIzq6mr4/X6MjY2xT0B9fT1PFBcXF1mNZWlpCYlEAnV1dUilUnA4HFystra2FiQOk/eGVquFWq1myJFcLkdPTw+/Q+pOvvLKKzhw4AC6u7vR29uL+fl5mM1mxlxns1mMjIywMRbBkVpaWqBQKLh5UlRUxGaYG0U2m2UFPOLPlJSU4Ny5cygpKUEymeSEbWFhgbH5+Xyem1ter5e7uB0dHcyToiaISqVCLBZjzyGCLAJgiO4HhU6n43OEPIPm5uaYQ9PR0cGQYlLb2b17NxYWFjAyMgKBQMBJDinCvLeYI1jZxMQEK/PRZE2r1bIp3EbPjQow8h7RarX87dM5RlO84eFhbN++nWFcNF0zm81obm7G2bNn8dprr6GyspLNBe12OyKRCOrq6rghIBaLkUgksG/fvg3XJhaLMTMzw82PRCLBTTPKMaiRQmpd5Ai+uLiItbU1xsv7fD50dnZyoUKFi0Qiwfz8PGprazlPUCgUnFRvFE6nkyc76XSayeFLS0tseEyGpwSpLC4uRl9fH4RCIRcVZNiqUqlQXl6Oubk5hnWlUilMTk6ygena2hqi0SgUCkVB/ojVakU+n39f01cqleK1115jIY6ysjIEg0Fcv34dN998M9xuN15++WVotVpUVlZyntTe3o6f//zn2Lp1K0+NUqkU5ubm4HA42N18fHycz+hCExdqOKdSKW7gyWQyTE5OMuTvvU2j2dlZNlakiRY1JD/zmc/g4sWLfC5T8+6VV17Bv/zLv0AqlWJsbAxSqRSxWAw9PT2sAPVBkUqlMDIywkgIKmxINp4g6uSzlsvloNPp2GmezrS5uTkMDg5icnISjz32GAv8kOfLwMAAVldXOd+hgqPQ2RuPx9Hf38+TFvp7FAoF2tramEfm9Xr5Z3d1dXHuSf4eAoEAlZWV+M///E88/PDDrKIoEAiwtLQEj8eDvXv3YmRkhH30RCIR6uvrN1zbe0OQLwT4vhE34kbciBtxI27EjbgRN+JG3Ij/P+IjczRuxI24ETfiRtyIG3EjbsSNuBE34qPGjULjRtyIG3EjbsSNuBE34kbciBvx/zxuFBo34kbciBtxI27EjbgRN+JG3Ij/53Gj0LgRN+JG3IgbcSNuxI24ETfiRvw/jxuFxo24ETfiRtyIG3EjbsSNuBE34v953Cg0bsSNuBE34kbciBtxI27EjbgR/8/jI/to/OAHP4BSqWQzEq1WC6/Xi4mJCWzatIn9LoqKithVMBqNwmAw4KWXXoJer0dHRwfr2judTvj9fvZb8Hq9SKfTbMeuUqkQDocRDAaRTqcRi8Xw5S9/+QPX9tOf/pT9FMhXYmhoiM1campqoNVqIRAIkMvlIBKJ2LwmEAhgbW0NEokEpaWlbNpnMplgsViwvr6OhYUFtlwnwz2fz8cuqbFYDF/4whc+cG3PPPMM624LhUJ2MSYnTXIjjsVibIRYU1MDuVyOlZUVhMNhiEQiaDQayGQyDA4OorKyEh6PB+FwGEqlEi6Xi41tSBucnCzz+TzuuOOOD1zbT37yE4hEIiiVSmi1WhQVFbFnRlFREfR6PRsNlpeXs4+HXC7HmTNnYDKZ0NTUhKKiIgSDQczMzECtVkOhULBzaDqdhlAohMlkgkgkYrfdaDSKqakpfPWrX/3Atf3ud7+D0WjEysoKO4Lmcjmo1Wqsrq5Cq9Wyb0sqlWIPlaKiIqytraGoqAhGoxFqtRqvv/46e1WQW20wGERdXR1OnTqFvXv3vs8llbTIP/WpT33g2n784x/D4/FApVKhsrISJpMJHo8HY2NjqKiogN/vZ/fmtbU11NbWQqVSwWQyobe3F6lUCiqVivcLOR2TO3kymWSvkZaWFkxNTfF3Sz9zo+/tRz/6EftuiMVixGIxTE1Nwe/3o6Kigo2AAGBtbQ2rq6t45JFHMDg4yN4oMpmMTcnW19ehUChQXV2NfD7PJkSLi4uor6/HyMgIJBIJgHddvXU6Hf78z//8A9dGz66oqAjFxcXsLB4OhzExMYGysjJUVFSwD4pKpUIoFIJUKoXRaMTa2hoikQii0Sji8ThMJhMUCgX8fj8kEgkUCgUkEgkCgQD7G6ytrfHvQ87NTzzxxAeu7ec//zmWlpbgcrlQVlbGBn1CoRBSqZTdacmfZPv27WxQt7CwgHg8Dp1Oh6qqKqyvr2NwcBByuRzJZBKhUAjpdBolJSWIx+NQKBSQSqXsASCXy+HxeDZ8dn/4wx8wNzeHXC6HkpISdsAdGhpCdXU1fD4fpFIpFAoFgHf9epqbm5FIJDA1NYX19XXYbDaUl5djZGQENpsNp0+f5me4trbGZqW7du1CNBpFb28vwuEwampqoFQqcdddd33g2v75n/8Z+XweEokEEokE2WwWwWAQFouFTSD9fj9isRgUCgVKS0uRSCTYNyGZTCKfz8NgMKCnpwdSqZS9mUQiEZu/ms1m1NXV4dKlSwgEAiguLobJZEIkEsEXv/jFD1zbT37yEyiVSiSTSXaVT6fTfEYolUp4vV5kMhlYrVYYjUb2qqG9mMvl2LXXZDJhaGgIxcXFWF9fRzQaZSNItVqNxcVFzM7OQiAQYPPmzTCbzdi5c+cHru3f//3fkc/nUVRUBKvVCr1ej9XVVfT396OpqQmhUAhCofB9xnSRSATNzc0YHR1lY9tMJgOZTMZmfmazmc3WyMtCrVazYzsZ2waDQfz1X//1B67t2LFj7MekVqshEonY68BisbBvlkAgeJ+nUyQSwdzcHCQSCTQaDTKZDAQCwfv8cWKxGPvHlJeXQywWc75AXkbr6+u4//77N3yn6+vrbBKYSCQwNzeH2dlZtLa2QqVSsdeHUCjE1atX+f3n83k2WSsvL8eJEyf4e2tubmaPC6/Xy8aOV65cgdFohMViwcrKCpaWljZ8bt/+9rfZ5dlgMEAikWBychIymQxerxeRSIQNOmUyGYqLiyGRSNirQaFQwGg0su/PysoKysvLkU6nsbq6ilgshpKSEt5nANjBWS6XY2pqCk8++eQHru3HP/4x1tfXEY/HIRaL2dSzpKQEXq8XTU1NuH79Ovx+P+x2OxQKBWKxGILBIIqLi0FOCFqtFkajETMzMxCLxbDZbBCLxWwgLBaLEY1Gkc/n3+d6LZfL8elPf/oD1/byyy/zc5DJZJxDTE9Ps8Gix+Nhb4rW1lZoNBo2jaM8g3xYNm/ezGeiSCTic1ulUsFoNCIajfKZTK7kG3m3fO9730MwGOS9qlAooFarMTExwWaG5K+RSCQgEAgwPj6O0dFRHD16FEKhEIlEAvX19bh8+TL7gKnVajYTHR4ehlQqhV6vZy8oOk9XV1c3PN/+z//5PzCZTPB6vWxiK5VKsbi4CJPJBLvdjmw2C6/Xi5WVFWzevJl9SrLZLNLpNCQSCaxWK5aXlwGAjSMtFgt0Oh1mZ2ehVqshEAgAAAKBgM0o4/H4hjnce+MjFxrZbBZSqRTz8/NYW1uDwWBAIpFAW1sbVldX+ZAk51Sr1YqlpSW43W5UVlYimUzi2rVrUKvVMJvNMJvNmJiYgNFohNPpZDfPlZUVjIyMoLi4GCqVCplMBsvLywVNmeLxOCcYy8vLWF9fRzab5QSIkgSJRAKXy4WRkRGEw2HU1ta+z8jIaDSyE/Di4iJkMhnC4TCmpqZgMBgwOzvLF7lMJkM0GsXs7GxB46O1tTUoFAo29VMqlZBIJBAKhZidnUVZWRmEQiHS6TQnkQqFAmazGVKplJNFhUIBn88HlUrFhikqlYo/KplMBp1Oh3g8Do1GwwlYIYfVSCSC0tJSCAQCxGIxNvOhzUpGV+TaW15ejkwmw4ZwiUQCvb29fOiR0YxCoYBSqYRIJGKHcyqo6D3Oz8/z+/mg8Pl87DhNFy29Y4PBwOZpoVCILxylUgmPx8MJSi6XQzweRy6Xw8LCAkKhEOLxOBskxmIx3oRkyEVJTSHjIzKdpOdFh0F1dTWWl5fZ7IYS/rW1NS5olUolm3CJxWKEw2F0dnZidHQUuVwOdrsd1dXVmJiYYOddMn2igi4Wi224NnI7Jrd0iUTCz5z+fipKybU1EonAYDDwc1pbW0NZWRmkUikbOU5NTbEp0+rqKuLxOEKhENbW1qBWq6FSqSCXyxGNRjdcG/CumSYlJGKxmBNKvV6PRCKBiYkJyOVyKBQKpFIpeDwelJeXY3p6ml1efT4fBAIBhEIhmxPRM6V9IRAIuGAjl2UqlDaK+fl5NoKk9wq86/a7urqKoqIiTqgUCgV/w6WlpbBYLAiFQgiFQhgaGkIymYTBYOBGikAggM1mY3O7cDjMLsMymQyJRIJNPT8olpeXodFoEI1Gsbq6ykU3mZ2R4y8lrceOHeMk0GQy8e+Ry+W4qBWJRCgqKoJKpWLn9PHxcS7uS0pKIJPJ+JzaKMjhWiQSIZFIIJFIQCwWY319HYlEAhUVFcjn8xAKhSguLn7fXqBkNJFI8J+RIWwymYRAIOCmx+zsLJLJJBt3SSQSdr7dKOLxOAQCAf/OdI9VVFTA7XbD6/UiGo1CIBBwIkcuwUajkRs3q6urbPC3tLQEjUYDnU4Hg8EApVKJwcFBLm6NRiNyuRwnYhtFNBqF1WpFNpvlddKzXFpagsPhQDQa5T0vFAphNpsxPz+PkpISpFIpLC0tIRwOo6SkBCKRCAB476+vryMSiSASicBms2FpaQl6vR4ymQzBYHDDdQH/4yINgM92kUiEbDaLQCCA0tJSrK+vQyAQQCKRYGZmBnNzc9DpdAD+xy1eJBJhYWEBRqMRXq8XMpmMHZ9ramowMTGBpqYmqNVqBINBBAIBdireKAKBADtUp1IpCIVCxONxTpirq6sRj8extLSEfD4PkUiEmpoaZLNZJJNJxGIxrK2tYWVlBTqdDplMBkajkc3lpFIpHA4HlpaWYDKZOCcIBAJYWVkp+E5TqRSfIclkkg3baJ+TO304HIZQKMTS0hI6OzsxNTUFp9OJSCSC+fl5zgm2bNmCcDiMaDTKBbnf70c8Huf7RiQSQSqVYnV1taCZYDQa5XdCbuepVIqbdTMzM1hbW+Nzf2xsDE6nEysrK5wQU/JLyX06ncb4+Dg3TCorKzE1NQWTyYRkMsn3HTUaCn1vOp0OUqkU6+vrWFtbQz6fR2lpKUKhEAwGAxup6vV6NjXW6XRwuVxsSEc50MrKChd4JpMJSqUSCoUCS0tLbDZLzturq6tYXl7esNAIhUJQq9Xw+XyIx+NYX1/H+vo6ioqKkEwmoVKpsLKywiafkUgEDoeDzV0pH6UmBhVNa2trWFpaglAohMViwdTUFDfLKMiRvtA7dTgcnG9otVq+48i8MxKJAHjXTPLChQtoaWnhZxwOh7mAyOfz6OjowOLiIt+nyWQSTU1NuHr1KhdodG98mHnwe+MjQ6doSiEQCNhaPpfLYWBggCcTIpGIkwOv14va2lpEo1H+mGUyGSoqKrC+vo7e3l7EYjHuwkmlUqytrSGbzXLSmEql+M/oYXxQUGd2dXUV6XQaxcXFkMvlWFtbe99HlUqlMDg4iGw2y86Nw8PDCAaD3G0hq3ZKQqmztbq6yt0Zu92O9fV1hEIhnlBsFFRJA+AECgCGhoYQj8ffV9WmUil+tpS008GnVCr5wltbW2P3SYPBgJmZGXg8Hp4qxeNx7tgVColEguXlZUSjUcRiMcTjce4+isViTlLX19cxPz+P5eVlBAIBdrbOZDIA3k28DQYDpqamEA6HufKnRCAUCvElGI1GeVIjlUoLPrf19XXI5XLupsTjcSwsLHDRSK7yc3Nz3FXLZDKc1NC7q6+v5wkCXeqRSAQ+nw8ymQyRSITdM6mqFwo33hrJZPJ9E4h0Os3JZ3FxMcrKyiCTybgbRBf/xMQEZmZm3uf8vb6+jtHRUYTDYe5wBQIB9PX1cbeXvi/6OwtFJpPhyRM5Yq+vryMQCCCbzUKtVkOn06GoqIjdcGUyGe/vdDrNSbrD4eBvgQ47sVgMv98PtVqNlZUVyOVyvnj1en3BvQAAuVyOndQpIaUuIk1PFAoF0uk0f9P03QiFQmg0GqjVaqyvr0MikWBhYYG7lAqFgt8n/f8mk4kTEUqqCwUVJrFYDOFwGBqNBhMTE4hGo7DZbOzGS0WcXq/ngiaVSvE793q9WFpawtraGp99Op2O3W9pShmLxbCwsIBkMlmwYZFKpTjxfm8Svri4CK/XC61WC51Oh3w+j6WlJe7EU2JOTuAjIyOw2+2YmJjgzmFxcTHEYjGCwSD/XCoKVCrVhyamQqHwfZMm6mAmEgl2Ky8pKYFOp+OOn16vRzqdxtLSEif6uVwO+XwekUiEv3sqOAcHB7lpkM/nkUqlOPmic+iDQiQSIRgMQiQSQSgUIhaL8b9PCZpKpeICnBIGsViMy5cvcyPJZDIhnU7jzJkzWFtb42cWDofh8/ng9/thtVphMpkgl8uRyWSwsrLC3+9Gsba2xmuhxgglw/Tn9L7pZ2WzWX42xcXFEAgE3ByLRqMwm81wOBzcRBMIBNyFzmazWFtb48S30PdG93Y0GuW7n5pOdCZ4PB709/fz3SiRSBCJROD3+7mZoNPpMDY2hng8jnA4zMna8vIyZmdnMT8/j3w+D5lMxneWXq/fcG3r6+tYWVlBPB5HIpHghH5ubo6nnzqdDuFwGAMDA/D5fHC5XDy5oyRWp9PBZrPxd0UO6HRGUYIqk8l4UkWNwo2C9gC5Qa+vr0MqlfLPMxgM0Gg03PSgDj1NPen50L0/MDDA5/R7J7aEDFCr1cjn89z8Ibf0D4p8Po9QKIREIsHOzsC7TQyaHBsMBqhUKj4HjUYjjEYjRkZG4Ha7UVRUBI1GA71ej56eHk5is9ksT6IVCgV0Oh00Gg0/S0JCFPreVldXOWGWSqXcAM1kMlAqlSgtLYXNZoNAIOBpHT3n4uJiaLVang6PjY0xogUAgsEgxsbGkM1m4ff7eY/TVL1QMk/5JzVB6IxMJBJYWFjA+vo60uk03G43zp07h6WlJYhEIlRWViKXy7ELu1qthsVigVwu5zOO8pHFxUVotVrOhWOxGObn5zE1NVXwzhcIBAgGg1zYJxIJFBUVYWZmhouutbU1nsIHAgGEw2E4HA6ebqpUKuj1ekaLSKVSfn/kDK7X66FUKmG1Wnl6tbq6+qH5CMVHLjTopYvFYrZil0gk6Ovrw8rKCtRqNWw2G7RaLRKJBObn59HY2AiDwcAHi91ux9atWwEAS0tL/KLX1tYwMTGB5eVlpNNpNDQ0cEeVRqmFLuFMJoNsNotIJMLQI7VajaKiIojFYu5CZDIZDA4OQiqVYvPmzfB4PFhcXOTxmVKpRDgcRjgc5kRXq9XC4XBwh8XpdEKr1XJSSBCejYIuIxrT0+80PT3No3ilUslJKyXQABguo1Kp+LnSlEEkEkEul3OXhLq5NLYMBAJ8wWwUNCWhbkM+n0dxcTEnp3TI0OicCjDqPufzeSgUCh7Zeb1e7i6+FxLk9/u5I08XGH3MhZ4bHfoEXSFoABUWJpOJO+6hUIiTVepAUcLjcrlQW1sLi8UCiUSCRCLBxY9QKOTJGV1CmUymYEJKHVJaG0EYaOz73k6o2+1GJpNBW1sbIpEI3G43VldXUVxczJ2ImZkZZLNZmM1m5HI59Pb2YnJykpN+GsHTXig03aPuO+1ToVCIZDKJ1dVVKJVKOBwOqFQqhu7J5XLugtC6rFYr1Go15HI5X0o0+qZElyaaOp0OxcXFyGazKCoqKvi90fpisRjD/IqKihgWIhKJUF5eDq1WyxMwmnpQ8qvX6+F0OmEymXjfZ7NZTmYJmpTL5VBaWgqj0cjFD617o1CpVEgkEgDACbJEIoHP50M6nYZOp+OfFwqFYDKZ0NHRwY0IKuacTid/SwRhNJlMyOfzPHVVKBTQ6/XcOYpGoygtLS34zdEURCKRQCqVIhwOY25uDtFolM8NgUAAt9sNm82GsrIyiMViBAIBLC0t8T4xGo08BdFqtTw1DYVCEAgE3OUNh8Pc0CjUjaREKR6P83dQVFTE7ywSiXBDhhpSjY2NDGOi36mkpISnbVR40bk3PT3N8C+CeWazWf5vNwqaVEokEm6EUfGVSCRgsVhgsVig1WqhVCqRSCT4W5+ensbCwgKkUimqq6uRTCaxuLgIsVgMi8UCqVSK5eVlTE5OoqioiKE3dD4Q5HOjoOIuHo/zxIDOXoJ7CIVCnvzQOahUKv+/7qf3wpVLS0vhdDohEok4QSotLUVlZSX/HdTAKRTRaBSJRIKLH/ozKijW19fh9XoZ9lFeXs4NDJ/Ph2AwCIlEAq1Wi6mpKf7vs9ks5HI55ufnEQqFMDs7y9Mss9kMkUgEh8Ox4bqogUWJPBUJy8vLkMvl0Ov1fC5R4W+1Wt/3jSoUCjgcDv5OfT4fLBYLw1zD4TDMZjNUKhXUajVP3SiZ3Sio4UBNOpVKBYlEgmQyyXe2Tqfjd0/nYDab5ak7wUBlMhl6e3sRCoUgl8t5UhuJRHiCSTA5eh6FJo8ikYgbdtTEo70TDAZhs9lgtVr5jqQ72OVyYW1tjc9Besajo6PIZrMoLS2Fw+HgZik12qjZQHdtoXcqFAp5L9DUIpvNYmVlhdeqVqt5+gsAdrsdgUCAp5C09vdCrenuXFhY4Gn72toa741MJoNcLgeLxbLh2oB3z1/Kt6hAIzQJ3aOZTAZDQ0Pwer2QSCSw2+2QSqWQSqUwmUwwm82cM1E+SHB8glMRqoYm1wsLCwUbKXQP0JlDU/H5+XmIxWKGfNKZp1AoIBQK+X9lMhkMBgOsVivkcjkmJycBAGazmRtqk5OTUKlUMJvNDJOjqSA9iw+Lj1xoiEQi2Gw2JJNJzM/PM47r6NGjCAaDaG9vZ1yf1WpFUVERb6oDBw7AYrHA4/EwnnLnzp18gQwNDWFxcRGdnZ18OE1NTUEikcDhcCCbzaK6unrDtalUKtTX1zPuNJlMIhqN4t5778X169cxMTGBN954AyMjIwzDKC0txejoKA4cOAC73Q6Px4OSkhJ4PB488sgjCIfDWFxchEQiQXV1NcrKyjA7OwuZTIb+/n60t7fj3nvvxdLSErxe74Zro4OWNura2hpmZ2exZcsWSKVSaLVaRKNRLC8vQyaTYWFhATqdDlevXkV5eTnKy8s5KczlctixYwcnx3S5bNu2DTqdDkqlEouLi9BoNHA4HPyzNopcLsdTJ+oCezweNDU1YXFxEWazGTKZjJO4eDyOtrY2WK1W7N27FyKRCOfPn4fb7cZrr72GxsZGyOVyCIVCFBUVQa1Wo7m5GeFwGBKJBGtrazCZTGhubobT6URFRcWGa6OJRH9/PwKBAKxWK0KhEDo7O9Hc3Ix0Oo3JyUmEw2FUV1djaWmJu5KxWAxerxexWAzV1dUYGhriDR2JRHg83NDQAJ/PB6vVCplMBofDAZvNhkAgUDB5IY5RNptlCNLFixd5OkHdP+pgVFVVYdu2bTAajaioqIBOp4NOp0NHRwempqbQ3NzMXS46KD75yU+ip6eHL2M6fObm5goWGgBgtVq5k1lRUQGxWIytW7fC4XCgvLwcwWCQcZeUMAwNDaGxsRENDQ2oqqrC/v37cfnyZdx0002Ymprionx+fh52ux2hUAh6vZ4nWCKRCIuLiwW7VgCYn6HT6aDVahGPx3H27Fm4XC5IJBLE43GGCtbU1HDxQ7C3UCgEsViMrq4ujI+PY9OmTTzBoo6wxWLB0tISSktL4fF4UFRUxO91dnZ2w7VRURAMBhGJRLC+vo6ZmRns2bMHfr8fQ0NDGBsbw/LyMoqLi5FMJuF0OrGwsAAAnER3dXXB4/Fg69atUCgUqKysZDhFfX09zp49C4FAgJ6eHt4jwWDwQ6e2xDOjRMTr9eLw4cNwuVzvwxwXFxejoqICJSUlCIfDKC8vh9FohMlkwu23346JiQns3LkTWq2WeVzhcJh/Vj6fR19fH3w+H5LJJMbGxgruh3Q6zYk3JcWhUAh2ux1zc3PweDwYHR1FNBpFTU0NQqEQZDIZlEolWltbuQirra1FeXk59u7dC5lMhlwux+f5Qw89BI/HA5/PB7PZjI6ODtTW1jIUcqOgbjbxG4gTlMlkMDw8zNO7VCrF3Wq5XI5wOIxHH30U1dXVGBkZYc7ZZz/7WTQ1NUGv13OyrlQqUVtbi6GhIVy/fh0CgQClpaUfOgkSi8VQq9Xwer2YmZlBIBCA3+9HfX09NBoNFxcmkwlbtmyBQCDApk2bGGZGU2aCtG7atAlKpZL3SS6XQ3l5OWZmZvhblsvlnKBRwrbRO6WurVwuh8ViQSqVQktLCzfGqICsqalBKpVCcXExzp8/z007ADAYDBgfH8f27dsZWi2VSlFSUoIdO3ZApVKhoaEB8/PzSCaTfB8SCuCDIpvNoqamhgsH6hDfc889XLR4vV4olUrcfffdKCsrQ0lJCWKxGHbt2oXu7m6UlJRAKpXi+vXr3HScnp7G6OgohoeHkcvlcO3aNUSjURQXF6O7uxtbt25l9MBGIZVKUVdXx+eqSCRiaNa1a9d42hOLxWA2mzmZJ+4FJaGtra0YGRnB3XffjXg8jlQqxc1UgUDA02efzwelUomSkhIA4P/9oEgkEmhqauIilprH1dXV3GDzeDwIBoNQKBQYGxvDwMAAgsEg7rrrLjQ3N3Ni39PTg0cffZSbKDabjZueqVQKY2NjmJqaQlFRETelCjVk6TnNz8/zGTYwMACTycQoj9nZWQwPD0MikTA/cnx8nDkvGo0GdXV1uH79Om699Vbu5hPv9GMf+xiWl5fhdDqRz+eRTqc5zyvUyCZejd/vRyaTgV6vx8LCAg4fPsznoslkws6dO/HAAw8gnU6jo6MDMzMzXGgtLi4imUzit7/9LTQaDSftMpkMmUwGTqcTo6OjAICJiQnmDFksFr5fPiiI25ZOp/k5Xrt2DbW1tbh48SIqKipgt9tRUlKCzs5OqFQqbN68GSdOnIBSqURxcTEWFhYwPj6OyclJVFdXc2M+FAqht7cXdrsd165dg0wmQ09PD/x+P6MgPuy+p/jIhQbBV/bt24fDhw+jqqoK0WgUg4ODuPPOOxmjubi4iMXFRezatQuRSAStra342te+hmvXrqGlpQWhUAj79+/H1772NZSUlKCoqAgWiwUHDx5EKpXCE088AZPJhJaWFni9XkxPT2Pv3r24fPlywQ9hbm4O27ZtQ319Paanp9Hf349f/vKXaG9vh9/vR0tLC2677TbU19fjlltuwalTp6DT6XDs2DHEYjHs27ePuxwvvfQSHn74YWzZsgU+nw9vvvkm+vv7cfjwYfzqV79CPp/HK6+8ghdeeAFHjhzB0aNHN1wbYbsFAgGkUinMZjN27NgBv9+Prq4u/rgMBgMikQg+97nPIZPJ4Oabb8bnPvc5/Nd//RcTjp1OJ55//nkei1OC9cYbb+CBBx7A+vo6NBoNenp6cPbsWdhsNq5QPyjq6uqgVCqxadMmtLa2wmKxoLKyErOzs3jssccwMDCAyclJJuEePXoUy8vLEAqF6Ovrg0wmw1133YV77rkHX/rSl5hsRFjy2dlZvPzyy/jsZz+LRCKBkpISLC4uor+/H+l0Gu+8886Ga5PJZJienobZbIZCocDKygpsNhvcbjeqqqq4Y6JQKPiyou68yWRCe3s7ysrKcP78eayvr+OHP/whqqursXXrVuzbtw/bt2/Hq6++yiRnOmhkMhk6OjowPz+/4dqampowOzvLRLPFxUWEQiGcOHEChw8fht/vh8ViwbZt29DQ0IDu7m688847zG+pqKjgS+sTn/gE/vVf/xXl5eWw2Wxobm7Gtm3b8OSTT2Lnzp2IRCLI5/OYn59HJBLB/v374fP5NlxbaWkp5ufnGaaQSqWwe/du7m6cOnUKu3fvxsMPP4zp6WncddddsNls3N2zWCxM9NuyZQuGhoZw33334ejRo2htbYVcLkcgEIDFYkEymYRer2chg+7ubhiNxg3XBrx7IKvVapSWlkKv18PhcOCzn/0sRkdHkc/nsby8DIFAwLCaJ554gnHFCoUC5eXlKCkpwcLCArRaLYqLi/GJT3wCFRUVGBoawokTJ/D2228DAM6ePctwE71ej1tuuQV2u73g+vx+P7RaLUpLS1FaWgqXy4WvfvWr3Dm12+1obW3F9u3bceuttyIQCCAQCOCFF16ATqfD3XffjUAggKNHj+Izn/kM2trasLi4iIWFBZSWluLcuXN4+OGHYbVa0d3dzdjh++67ryBHg8jGlZWVkMvl8Pl8UCgUmJub4++msbGRfz+aqDz00EM8LaisrMTJkyeRz+cxNDQEvV7PY3WBQICTJ09icHAQg4ODaGtrg1AohM/nw9atW7Fjx44N10aFCpGtDQYDdu3ahVdeeQWbNm1CR0cH6uvrecr3p3/6pxgcHMTk5CS++93vYnZ2FlVVVbh06RKTm8vLyxke0d7ejuXlZZSUlGB2dhalpaWMw66uri54ERO0jyZl8XicC6x9+/Yx7juXy2FlZQUHDx6E1+tFWVkZTp48iWAwiNbWVohEItjtdjzzzDPvg+wUFxfDbDajvr4e586dg8vlYhjJ0aNHsWfPng3XVlpaiuLiYrS2tqKiogIGgwF33nknxsbGUF1dzVDQubk5DA4O4sEHH8Szzz6LSCTCE4LS0lLm7PzkJz/BzTffDL/fz/zH0dFRVFVVcVeSEiOj0ViQ20LTZ+oS0xn3m9/8BmazGYuLi1Cr1VCr1VhaWsKOHTtQWVkJr9eLM2fOIJvNora2lvkUs7OzqK+v5z22urqKP/zhD7jtttuQSCSg0Whw5coVXLhwAdu3by9Y2FL3loQFqqqq0NnZiUQigSNHjsDhcKCqqgpCoRCDg4N4/PHHcenSJSSTSQwPD0Or1aKhoQGLi4toaWlBf38/vvCFL2DPnj2orKxEaWkphoaG8MgjjwAAKioqsLCwgIWFBdxzzz0MF/qgkMlkOH36NCQSCSoqKmA0GiGXy/H888/jwQcfZLgzTeXuuusuzM/PI5FI4OzZswAAh8OB3//+93j88cfx6quvwmw2s5DJo48+itnZWXR2dmLLli0s2EJNkmeeeWbDtUkkErzzzjtwOp1wOp0Ih8PQarUIhULYs2cPstksSkpKoFar4Xa7cfDgQahUKpSVlWFkZAQA0NbWxlAmkUjEPJFQKITKykrk83mGm5EYRCqVws6dOzExMVFwbS6XCyUlJczVPXr0KE/2A4EAjEYjWltbUVZWhp07dyIajaKsrAw/+9nPMD8/j7KyMsTjcXz84x/H2NgYHn30UZjNZi7KfvrTn3KjmSBVRqMRt956K2ZmZjZcm0qlwsDAAGpqatDV1YXS0lKUlJS8j0tD02ua7L3++utIpVLo7OzE/fffj4MHDyISieCzn/0sstks7rnnHn72qVQKzz//PD7xiU/gzJkzyOfzWFhYwMrKCg4cOFAQYi6Tyfi8aGpqgs1mwwMPPMConcXFRc4hgsEgDAYDXnvtNdTW1nKjMZvNYnR0FKlUCvv27UN9fT0UCgVsNhssFgu+9rWv4fHHH8fU1BR27NjB08LHHnvsQ+97io9MBo9EItBqtRgcHGT8bzabxebNm/HKK68wJIG6JCsrK2hpacH09DS+8Y1vYHV1lZUhBgcHUVFRgWQyyaOw06dP49ixY/iLv/gLxONxeDweno7Mz8/DZrNtuLZ8Ps8jwXw+D4FAgKqqKiYT19XVAQDcbjeTsc6cOYOWlhZWRRgaGsLs7Cy0Wi06Ojpw9uxZmM1mKJVKbNu2DTKZDL///e+ZeHrw4EEIBAK89dZbyGazDAn7v4PgYQRNSqVS6O3txSOPPILjx4+jpaUFHo8HsVgMVVVVOHv2LCorK3H69Gk88MADfEjLZDIcP36c1U4kEgmCwSCWl5eRy+WYJO3z+TjZoA7IRjE6Ogqn0wmv1wufz8eJQHl5OZ5//nnceeedOHv2LK5fvw6n04nJyUnuOAsEAt4MW7duxQ9/+EOoVCpcvXoVu3fvRiaTwbVr1zgpP3HiBO644w7eXIU6VgAwNTUFu92ORCLBI9W1tTUuOmtra3nNLpcLy8vLWFtbQyqVQiAQwPz8PMxmM0PwNm/ejFOnTiESiXDiSu+diF4ulwsymQx9fX0FO0Pj4+Ow2WyMiUwmkzCZTOju7mZVqYsXL0IoFKKmpgaBQAD5fB6ZTAbt7e18qVDB+MADD+CXv/wlWltbUV5eDqFQiC984QvcTR8fH0dlZSXUajWrXW0U4XCYccAymQw+nw8DAwPve88nT56ExWLB/fffj+npaVy6dAmJRAJ9fX0wGAwwGAyIxWIYGRnBbbfdhlgshsXFRSYeHj16FC+++CLKysrQ09MDs9kMi8WCwcHBgpNH4F1FC51Ox1ylmZkZPP/885BIJBAIBFAoFFxslJaW4s0334REImE1FLrEbDYbfD4fbrvtNkxNTUEoFKKjo4MT8nPnzmF0dBTV1dWwWCxYXV1FKBSC1WrdcG0kvkAKOx6PB36/H3/6p3/K3IDjx49DrVZj165dGB8fh0ajQWtrKzZt2oRcLocXXniBIV8tLS349re/zUVLPp9HXV0d1tbW4Pf7YTQaOWl7+eWXC2Lml5eXMT4+joqKCphMJuj1ev7+lpeXuTO6vr4Ol8sFjUYDq9WK73//+zzNIDLp9evXsb6+zmRXGq1Ho1HIZDJ0dXVhdnYWFouFVasmJyfR3d39gWsrLi5mWEIsFsPMzAxOnTqFjo4OeL1eeL1exONxGAwGNDQ04Be/+AWMRiNqa2uh0+mwvr6OiYkJaDQaHDt2jMVDiMdDCkIEq3ruueegVquh0WgQDAaxtLS04XMjCN7AwAAsFgt27NiByclJuN1ulJSUIJ1OM3E0EAjw2dnf34/t27djcXER586dg1gshlKpxB133IEnn3wS169fh91uZzWn06dPo7u7Gx6Phwmzb7zxBnQ63YYKcQMDAygrK2NezfLyMi5cuACr1YorV66grKwMHo8H0WgUOp0OP/rRj3DnnXfixIkTaGtrg0gkQjQahVgsRklJCTo6OrgBlEqlMDw8jJqaGhQXF3MiRdAkmtZvFLOzs9i1axdz16LRKFQqFT75yU9ykUdKfWKxGJOTk1hYWEBVVRXDxq5evcpiDyUlJTh58iRzRaRSKe6//34mElPREA6HcfHiRVY4+qAgdR+dTgeBQICJiQm88MILaG5u5g46qeyIxWK8+OKLGBwcxMmTJ7mRt7i4yHCdw4cPo6enhwnmkUgEX/rSlzA4OIipqSm+J0tLS3HixAl0dHRsuLaFhQW0t7czn5C4onfeeSe8Xi/eeecdhszQWUXwlnw+D7fbjZmZGZhMJvz0pz/FwYMH0dfXh/r6euRyOZw+fRrbt2/HpUuXYDQaee8XFRUxD2Sj8Hg8aG5uxvLyMsNxlpaWsGvXLhar8fl83H0nYY1Lly5Bo9EgEong/PnzzN0rLy/nszoejyMYDLKoxeLiInbv3g2Hw4G5uTmcPHmyYJOHVOhIDYnudlLvikQiCIfDmJmZgdfrxd69e/l7aWlpwfz8PEODxsfHmW9VVVWFxsZGjI2NoaGhAb29vdizZ8/7YGA0Ld8o1tbWmN9FqINIJMLIGBJRicfjsNvtuPnmm3H27FkYDAYWdCHFwLW1NXz84x/H97//fVgsFmQyGbjdbnR3d+NHP/oRcrkcqqurodfrEY1GcenSJdTU1Gy4ttXVVUYpxGIxpNNp9Pf3w2Aw4O2338bHP/5xpNNpzM3NIR6PY2pqClu2bEFvby8GBgZQXl6O2tpaZDIZ9PT0YG5uDn6/n6dc0WgU3/zmN/HLX/4SCoWCleuSySROnjyJysrKDdf23vj/iQz+wgsvYGBgAIlEAgaDAU6nE5cvX4bRaERXVxckEgkTwT0eD//fv/jFLzAzM4PGxkbGCbpcLiaE0ShxbGyMSUgajQY+nw9TU1M8xtnwl/hvmb3i4mJYLBbGOufzeVy6dAkWiwUymQzXrl3Dd7/7XfT19WH//v3Q6/W4dOkSLl26hHA4jMrKSgwPD+P555+H1WrF2NgYzp8/j8nJSTzzzDOYnZ1FeXk51Go1pqamcP36dRQXFxfcQEqlEoFAgMnVKpUK27dvx3e+8x2WbCSMXm9vL+RyOctK+v1+CAQClizbsmULSktLIRKJ0Nraivb2dgDgDsCzzz7LzyIcDkMsFhfEfYfDYbzzzjvvw1lqtVo8//zzjK2matrtdqOjowO5XA6bNm2C1WpFS0sLtmzZgmQyCbvdzjhUIhllMhmcPXuWq/yrV6+ir6+PIVuF8MulpaWIxWIwGo2MtbdYLJwwXLp0CfPz85iZmcGxY8eQTqdRWVnJo2m/3w+v14vJyUmEQiF8/etfx+zsLNLpNI8uBwYGGN7gcDjQ09ODZ599lsleGwXha2UyGTZt2oSdO3cil8vh2LFjuHjxIiwWC7Zv3w6z2Yzz58/j7bffht/vR2NjIzweD9xuN0KhECYnJyESiXDlyhW0tbUx9DCTyeAHP/gBcwbEYjEGBgZw8uRJZDIZtLa2brg2nU7Ham+xWAxqtZolHsvKyhhe8fbbb+Ob3/wmRCIRNm/ezFMrv98Pn88HtVqNsrIyHD9+nDHN1Ez41a9+hc2bN6O7u5sTHlICogtro9BqtXC73Qz7iMfjqKyshNFohFAohN1uZ8EIghZptVqYzWaWxFUoFCzx+vTTT2N4eBhisRhms5nH9FVVVdx9iUQi8Hg8rJSyUWg0Gr5wSA1PoVBgZmYGS0tLcDqdqK6uRiKRwK9+9St85zvfYRLnL37xC1y9epVhJVqtlrll1OVXKBT41re+xWu4ePEi3nnnHQwNDTEsaKMgTodYLOZEjoQQBAIBmpqauClz7Ngx9Pf34+mnn+bzNB6Pc1JqtVpx/vx59PT0cAeSoA3d3d1YXV1FPp/H8PAwRkdHUVdXx93oDwriDJBaHkEu5+bmUF1dzQTXoaEh/OhHP0IymWR5yMnJSS5wJBIJ6uvrmYhOykGjo6P4wx/+wBAOUsOiv+/gwYMbro2aCBaLBfl8HgMDA0gmk7wGjUaDpqYm1NTUsGzzzTffjEQigRMnTiAQCKCurg4ajQbFxcX41re+haqqKhw6dAh2ux3T09MQiUTYsWMH7rjjDj63AfC0YqNIJBIIBAJYX1+H1WpFdXU1dDodT5Gj0SgTm0OhEJRKJcbGxqBUKjlBmpub42KLCruSkhKWie7r6wPwLp7darXC6/Wip6eH1eI2ClKRXF9fR319Pbq6uuD3+3H8+HGMjIzw1E+r1TJfjabLxJ1Qq9VwOp0YHx/HwMAA6urquImYzWYxOTmJxcVFZLNZLC4uMn+N5NQ3CiIYU95A0vnZbBZjY2PI5XJQqVScuDY2NiKbzWL//v1YXFzE22+/jevXrwN49w7893//d6hUKrhcLjQ2NkKpVOKb3/wmzp8/jzvuuAMHDx5EIpHAxYsXMTs7W5Dgr9FouHBPp9Osavj8889jdnYWDoeDIdIvvfQSvvGNb7Bc/uTkJORyOTZv3oySkhK0trbi+vXrEAqFuHTpEk6cOIGRkRGcOnWKUQMymYybBh6PpyC6guSXSXae+DcXL15ktS+abC8tLaGhoYE5PwsLC1CpVDhw4ABcLhcEAgHOnTsHt9vNvAqLxcL5DBV0BH01mUwFeV6lpaVYXFyExWKB1WrF6uoqenp6WNVobm4OMpmMeQ56vR65XA5arZYbEgqFAk6nE7t27WJIrtvthlQqRWlpKWZmZpizIRaLMTIygnfeeQexWAy33nrrhmsjOPj6+jorJNL0f3V1lc8Wavz29PTgwIEDsFqt+OEPf4jJyUncdNNN2LdvH8xmM5555hnmxCkUCkb6pFIpNDU1IRAI4Nq1a3C73dyk2ihSqRQWFxcRCAQwMzODgYEBLCws4NKlS1Cr1RgdHWWZ58uXL6OzsxOlpaVQq9UoKSmB1WpFeXk5ampqUFFRgTfeeANyuRwmkwkOhwMSiQT/8R//AaFQiL179zLMGwDzQD9KfORCg0ahBoOBZe5yuRz27t3LerpKpRKNjY3YsWMHKyh5PB5UVFRALpcjFoth27ZtjBkPBoNMSEqn0/ijP/ojvPrqq0wWValU0Ol0H0oGJ1lHwg+Sbv7+/fuh0+mwadMmyGQyqFQqHDx4kMe5yWQSjY2NUCgUWFxc5KLh0KFDcLvdTCJcX19nCMB7R9UikYhhWYWeGxGTaNqSy+XQ3d3NREWtVou6ujo0NTVhamoKVVVVKCoqQmdnJyQSCa5duwav18vYzeHhYSbvFhUV4ciRI1hYWEBJSQkrHJFSBqmafFDQZiE9/1gsxs+gubmZ+QcE24rH41wA6HQ61oc2m82IRqO4++67WWNaJpMhmUyyVO7+/fuZ0KXT6bg7vVHQBbKysoLl5WUkk0ksLCxAo9HA4/GgtLQU9fX1aGtrw6ZNm+D1ellSk8iAhEVdWlrC7t27IZVKUVZWxpCRzs5Oxu8TZtblcnFyv1EIBAKoVCqenhDkhYiq1dXVTOBvbGyEz+djdS+73Q6hUMi+MQMDA7jjjju4K0Xa/TfddBMAsDSy0WjktaVSqQ3X5vf7UVJSwupjRIwWiUTweDzQarWQy+V8qYTDYSbC1dXVsVKF2WyG3W5niU1SGTObzejs7ERfXx8XMxqNBkqlEisrKwW7pABYGpDIeUSuJAUgoVDIqirV1dXw+/0oKytjjwMAPKYuLS3lzgup86ysrMBisTBEhn4WdXELPbu1tTVoNBommJMYARU1JSUlDPfasmULk4JtNhtuueUW6PV6TE5OcpJ+6623cjeXnv/999/PogE+nw96vZ45FoWmtul0mn1Q5ufnGUtN/APC96vVanR3d0Ov16O7uxtyuRypVAqRSAQymQxbtmzB2toaOjo6uLAlWGdFRQWrnFABWFZWhv7+/oKwrrW1NZZ7pu+TmgSDg4PcDa6trcX27dsRj8dRVlYGnU6HPXv2wOVysRxwIBBAa2srT0BIze3IkSN8dpIyC+3tQhAg8up4r9hFIBBAUVER+vv7MTs7y94QN910Ez/XWCyG9vZ2qFQqBINBbNq0ie8L4msUFRXB4XCgvb0d165dg8/n4yROJBJxYbhREOmc1ATp4q6vr2eoIBGkyUuBmnOUxBLXjOSICW4GgEUokskk6urqWDSAJjGFVAmFQiHvTeJgCQQCtLS0IJ1Ow2azMVT35ptvxszMDE8EN2/ezMRwlUoFjUbDnd+Kior3JUAqlYrv4OrqaoZfOZ3ODdeWy+VYcdDr9WJqaor5bzQBJ5l4u92Oc+fOccPK6XSiqKiI5VS7u7uxc+dOlvQkX6rHH3+c+UEymQx2ux12ux3FxcVobGws+L2RehaJGtBzAoAtW7bAbrdDo9GgsrISi4uLrBR45MgRll2miU1XVxfcbjeTibPZLD796U9DpVJhz549nNdYrVaoVCoucjd6pzTNt1qtaGxshEajQVVVFRYWFjAzM8O+SPv374fH44HNZkMoFEJbWxv0ej1LFEejUVgsFu70E0F9//793FzxeDzsyUUKVhsFFZjkKUaTZfKIKC8v5/OFlDZJeKSrqwt2ux3pdJrJ8bW1tTCZTDCZTPy+E4kEvF4vi5cQ9G9iYgJXrlzZcG0ymQzV1dWYn5/nyTrBhiUSCZPOa2trUVlZib6+PlYKs9vtWF5exunTp+Hz+ZBKpbB582ZotVq+C0mkBQDzVzs6OtDa2soT3Y2C1LkSiQSUSiWcTidLsOt0Os5RZDIZ55f0nAk6TxAwarpkMhmEQiH2T3nsscdw8eJFVmKUy+UsWFQIcvbe+MjQKdIyJ8UF6so1NDRgdXUVwWCQ1S1MJhNWVlYAgDGJ6XQaCwsLTCymCpgwhgDQ3d2N3/3ud/zyqENCI8KNghQaQqEQa8DTuI1k7Ugthw5+IvhIpVKGXhUXF7PuMo2a1Go17HY7mwFmMpn3aYp/mOSoRCKByWRiiUIib23fvp3JRevr63w4UZFEnW1Sr/D7/dxdI8leKgLIOI2kyihpI3jKRiEQCGC1WtmYh0jSpFhD8q9VVVUs7VZcXMxkZ3oWpHhBCSwdZhqNBk6nk383l8uFYDDIv2+hSRBdcNS5JFOnWCyGsrIyLiioG0smfhqNhmUIqdtBhH4ypyGvDJp+UWeP+AYTExMFlR4ocZHL5SwrqNVqWd6SRvukZtbW1sbmPvTdkAb97Ows2tvb2bgqFAohGo0yBEGv18NqtTLB3u/3F/SqoL+TFEBWV1dZipY6cSTtR0XF8vIyTCYTdDod5HI5d8vpZ5CGvFgshtPpZBKl2+2GXC5neT+z2Vwwkaf3qtPp+BmQjLXb7YbT6WQCMPEhpqam+DnQv19UVMQmSXTB0r6XyWSQy+Ww2Wx83sjlcjZwLNRVI2IddbzT6TRP9KxWK0QiEatyGY1GTE1NYW5uDpFIBHa7nb8fkgBWKBTYsmULLBYLfD4fX9gku00wNerEFSo0CBpVVFTEBqb0jiorK3n/0r6iZLC8vBxLS0uc9AkEAt73TU1NLPe4vr7OUsykoKXX66FQKPjPNwoyzCQiMvlDEFSUpJyNRiOqq6vR39+PRCIBh8MBqVTKBlLkCeF0OjE9Pc1wQ+JpxGIxlo6kxI0Sy42CvDNov5H0eSaTgU6nY/iPTqeD1WpFIBDA9evXIRKJUFFRgeXlZayursJut2NsbIz9YkiRiyZtwLvNEWrEkYdOIcI14dzpPKKJN921dFeRjC2ZicrlckilUqjVak5miZRZWloKoVCI0H+b/dG0krwA6CwhmEmhtdGkiki8drsdZrMZjY2NKCkpQS6XY+4GnTvV1dWQyWSsVCWRSLhIT6VSDImKRCKYnJzE1q1boVKp4HA4WIabpNYLhdVqxcrKCjdAqqqq2LeB4KtarRYulwvnzp1jNbjZ2VmGgY2Pj2Pnzp1IJpOYnJzkxMpqtcJqtaK1tRVjY2MQi8VsvEneVRsFeRgB4HPEarUik8mgoqKCn5PZbEYoFIJGo2Hjxx07dnAhTJP25uZmVFVVMeeEOBMrKyuoq6vDxMQEC2TQftkoyLCQpsQ0xSH1PmrEULOQBCjm5+dhtVq5gUAKd5Q3kAoe8G7Tt7q6GsFgkCWP6Z0Xkiwm9a33cojo3qfu++zsLNbX11nogtZHSm9LS0uYnp5GKpVCZWUlNBoNn/3vlf0lYR06s9LpNPr6+piT838H7RuSwaemwNraGkpLS/l5kEws8afsdjv7ipAPVFVVFZ+9Pp8PWq2WGz41NTUsz0z7nbh2GwVJ9svl8vfxZshzjb5Xg8EAh8PBIk4Oh4Nz72AwiKmpKWj/W02Vprck0btt2zb09PSwPwrl0RqNpiA/7r3xkScaUqmUMY90qBGmf8eOHdyNpeqWFApqamrgdrvh8XiwsLCA3/3ud6iurmb8PiVQtbW1yOfz+MQnPoGSkhJWVQLePfQK/UIulws6nY4TjqGhISgUCvzoRz9i86NUKoXl5WW89NJLsNlsGBkZ4cPN5XJh165dEIlE8Hq96OvrQ2VlJRs42e129Pb2Mn5crVbzpjMYDBgaGtpwbTqdjpPFdDqN+fl51onetGkThEIhPB4PBgcHMTw8jD179rBh4RtvvIFsNouuri7k83nGTu7YsYOfEUlVqlQq9Pf3c7dEJpOhvb29oKQc6aSTtCMVTeSVsrKywpKipJBFqheUjMvlcia/vfDCC4x/9fv9cLlcXL339PSwFj3xQAolVjTmra2txdatW9HW1oYdO3ZgamoK27Ztw/DwMLxeLwKBAPr7+5mEXlxcjMHBQYb/UEFJhjMrKyvMZyFlIFJUEwqFSKVScLlcBaF6UqmUVU1IvpiKZjpACL50/fp17N+/H+vr66iqqkImk4HBYEBbWxvUajWy2Syee+45NDY2cgG5vr6Ovr4+TmxJCjQUCnEncaMgvxXg3cSZJA/pmdPkijDwRUVFeOONN1ihSSqVoqamBmtra2zISJ0M6qJeuXIFdXV1WF9fR3V1Nf/zbdu2FUxIaU02m40VV/L5PJxOJ/x+P0NESPovl8vB4XBgcHCQJZYbGxuxbds2CAQCTg5cLheuXr2KgYEBuFwuxONx1NfXY3x8HDU1NVyMdHZ2FiyE6GIzGAzIZrMsQ+v1epnwTCR7v9+P5uZm/OIXv0Bvby/eeecdqNVqHDp0CC6XC3V1dXj99dexe/dunhhJpVKMj4+joaEBCoUCDQ0NbN61e/fugrh0jUaDxcVFlkAE3p0kXLp0Ca2trdwhCwQCbJBGnA6JRAKDwQC5XI5Tp06x6tDmzZvZkCyZTKKvrw82m42hHDMzMwiFQmhpaSm4NpLsrqyshM1mg1wuR3t7OxYXF7F582ZMTU3B7XYjGAwiHA6jsbERZ8+ehUQiwcWLFzE6OorV1VVcunSJu+ykuKLX69mkrrm5meUuacJHCe9GQYRqKkxIpW51dRU7duyAwWDA+vo6ZmdnceXKFezevRtzc3NQqVTweDwQi8Xs9G632zEzM4Ouri42mAsGgxgcHMSBAwegVCrR1NTEIicCgaCgIiHBTPV6PZOG5XI5E6cp4aZCpKGhAX6/n6cCZFR64cIFlmHeuXMnBAIBVlZWeGJQXl6O1157DS6XC06nkyVbqSP8QUFeO0T8FAqFqKiowMrKCrZt2waVSoWSkhIkEgk888wzcDgcDLekRpTNZuPkMZ/PczEAvCsTf/z4cdhsNqysrLDB7Pz8PAwGA86fP7/h2khqlJpw1Fl2u92w2+3o6+tjmKxSqWSEhd1ux9mzZ1nZ6Ne//jWAd7kyxDsgKdYXXngBR48excLCAvuRUAHgdrs3XJvJZML09DQ3IQOBAE+Yy8vL0dfXh0QiwXLtmzdvZnd5Mpjr7u5GZWUl5ufnceHCBdx3333sA1VTU4Pf/e53zEelczKZTMJoNBZEfpBEOp1vXq8Xdrsd58+fZ1gsNZLOnDnDKAa5XI6lpSXIZDLG5Le2tnJzlvbhysoKfvazn6G0tJTlWuVy+fsMCzcKUjCkotRsNnNzobq6mhstBoMBZWVl6OjoQG9vL9RqNQKBAH/rTz31FCM9bDYbEokEMpkM3zmtra2YnZ1lI2C5XI6GhoaCUs8KhQLxeBxdXV1oaWlhRANxkuj3pJ/Z3t7OSlgikYglqBUKBTo7O3H+/Hls2rQJjY2N3GCiqS3Jn1+9ehXXrl1js8ONglAyVquVm0Xt7e0IBoPcbHY4HLBardBoNDh06BByuRz27NnDz0ytVqO/vx9qtRpnzpxhSwOLxYL6+nrE43E88MADjAQihENFRUXBd/re+MgTjfn5ecZNUtfOarWyH0R1dTWeeuopJj5NTEzAYrHglVdeeZ/ahEwmg9/vR19fH/r6+hj7TCoQNTU1qKmpwfLyMsOhSBZuo4hEIigrK8PMzAyPvd944w0YDAb09PRgy5YtiEajcDqdqKysRHNzM37/+9+zwRURmcjR+e6778bg4CCamprYNKevr48NfwYHB1FfX4/a2lqsrq4WxKVT14e6r9lslsm5g4ODAIDJyUn4/X7s2rWLyV1zc3MQi8Xo7+9nl2jS3yYzt0QigaWlJTZ/2blzJxO4JBIJenp6CkJZSPKRIDWNjY2Ix+OoqanBCy+8AK1Wi+npaZ4WGI1GbNmyBb/97W/54KBu5fj4OHclT548CblcDqfTiZ6eHj4sqOucyWRw5coV2O32DWFn5IEyMjLC/92ZM2ewbds2dpinb5Eq93PnzuGnP/0puwkXFRWhvLycTXSOHj0Kp9MJn8+HQCCAAwcOYHZ2FhUVFTh37hwbPhUa2wPv8lV2796NdDrNmNeZmRlOCkpKSnDt2jXMzMxg+/bt8Pl8aG1txQ9+8AOk02lYrVa0tbVh9+7dmJmZgc1mw7FjxxhioNVqMTQ0hHPnznFBOz09zfCWQskLSQGTSyol8SSNSsTZhoYGlJeXQyAQYPPmzejt7eXviKaRGo0GW7duZfNNq9WKeDwOkUiEwcFBxGIxNtIrLi7G8PDwh8rdUSFCGvAAcPLkSdY6n5qaYk5AcXExTpw4gerqaqjVajZ7dLvdiEQieOmll/CTn/zkfQlqb28vpFIpXnvtNVitVkxMTKC+vh4GgwGTk5MFyeAzMzM8RaNve3h4GLt378b09DSqqqrw0ksvYX5+HrfeeivsdjtsNhv7xPT29mJ+fh56vR4vvvgiTCYTnn/+eR7nGwwGCIVCvPnmm2hubsbs7Cx3lqVSaUGFOBJhAMAXfDwex/bt29HT04NwOIz+/n6IRCLs2bMHWq0Wi4uLOH78OEMQCNPf2NiIxsZGvPHGG9i6dSv27NmDq1evoqGhAaWlpZiYmOAphVgsxtTUVEGY4/LyMv/+ROq/cOECSkpKYDQa0djYyCaeZLBIMuF33303lpeXceXKFeaePPfccxAIBGhubsbc3BxefvllmM1mVFVVQavVIhaLoby8HOvr6xgcHCyI50+lUmhoaOCpGU2TUqkUnnvuOZZpJANPUu+ZmZnhpgqtl+DCFy9e5ESit7cXYrEY7e3teO655+ByuViJjPwRNgq3243m5mbmw4X+2016bW0NFy5cYBia2+1maKVAIMCZM2ewY8cOWCwW7oa+/PLL8Hq9WFtbw9zcHGKxGGw2G7xeL373u9/xGUff4NzcXEHBC4/Hw5LutMc9Hg/MZjPeeust7tzGYjHcfvvt0Ov1EAqF+OUvfwmXy4WysjJ+n0S8pzs4EAhgfHwcJSUlePHFFxlmQ9DM2tpavPXWWxuujbD/5C+h0WgwPDwMl8sFk8mEqqoqAGBoVTAYxKc//WlcuXIF+/fvZznj8vJyPPHEE9i5cyfz9NLpNIaHh2G32zE0NITNmzfj8uXLTJq9cOFCQc+FwcFB2Gw2No4lAjcplu3Zs4cbYfX19VhdXeX973a72ePAYrFApVK9T1qZpvzDw8MoKSnBr3/9a15raWkpotEo/vEf/xGvv/76B64tHo9zc4/ygp6eHlRVVXEnnaZX1K0mrxSSAL548SJkMhkr0hGEur+/H8888wx27dqF/v5+5unSpGNtbY3hwBt9bxqNhr2y4vE4GxLOzMwwN5UIyb29vejq6sIbb7zBSlhUsJpMJvzmN7+BUqlEc3MzotEoxsfHUV5ejuLiYm6AkEoVyd9vFCMjI6irq+PmCPF3m5qamJfq8XgwPDyMQCCA0tJSVFRU4D//8z/ZfycQCGDXrl14/vnn8eSTTzKUbHFxESdPnkR5eTmGhoawurqK559/HhqNBrW1tTCbzQV5halUihshJCxw4cIFJuYPDw/jwoULsNvt6OjowHe+8x3moIXDYd5HarWaOa8DAwO4cOECNzgOHz6M5557DnfccQf/naFQCD09PR86eaT4yIWGyWTC8PAwzGYzcy5Onz6N8fFxXLhwAX/+53+Oo0ePoq+vD4uLi6ioqEBVVRWPwa1WKw4dOoT29naW8zx48CB3cYeHh3H8+HHs3bsXNpuNuyczMzNsiLZRGAwG/Pa3v8Xhw4fR0NDAo6uxsTF8+ctfxsDAAAYHB7G4uMgqK//yL/+CM2fOcFXc2dmJyspK/NM//RO+973vQa1W48iRIzAYDCzfZ7fbMTo6yoZsHo8HgUAAt9xyy4Zro7FmcXExDAYDHyhf+9rXcOedd6K2thZdXV24fv06hoaGUFdXh8bGRnR3d+Opp55CIpFAR0cHHA4HAoEAfvKTn6Czs5OJ2TRhamho4NEpOU+7XK6CiVVVVRXb3tOIsa+vDydOnGDCLRUf5AvR1NSE/fv3s+oKEZWKi4vR09ODzZs3w2KxsMU9QTYsFgsWFxfZgJC8QjYKmUyGgYEBlht0OBwIBoO4evUqPv/5z6O+vh7z8/Pw+XysPrN7926MjY3h2rVrTDKtqKjA6uoqtmzZAqFQiJKSEobVPPvsszCbzbh48SL6+vrgcDhgNpsxODiIv/iLvyi4NiJENzc3s/zuysoKQ9zUajVMJhNGRkYglUrx0EMPYXJyknHOer0e165dw5/8yZ/g85//PJRKJVpaWqBSqTA9PY23334b+/fvRyaTQWNjI5O039ud/aAg3XfCrpNhm8lkwqlTp9DY2Aij0YihoSGcPn0abW1tuOOOO/Dyyy9jfHwcdXV1DHuRyWScMJKL/PDwMKanp3HkyBG+CMlUiOQHC0V9fT2LKpCcbWtrK9555x1WBpmYmMDx48exvr6OPXv2YG1tDSMjI8zbIbjcl770Jbz55ptMgiwuLsbx48fx4x//GLfddhtPYN544w0IBAK4XK6CKhnkZEtcDBoRX716FZWVlZiZmcHOnTsxNjaGc+fO4a/+6q9w4sQJPProo9wdJVnSI0eOYHl5GUtLS9izZw8kEgleeeUVvPHGGzh48CArjlH3U6lU4sCBAxuuTalUsg8CmVA2Njbi5ZdfRiaTQV1dHVQqFSuiZLNZBINBlJeX46c//SlSqRRuvfVWWK1W+P1+bgCRzOnbb7+NoaEhHDp0iFWtXnrpJdby37x584Zro8uUZC/JAfzpp5/G3Nwc7r33XoaUTExMoKWlhQuTp556ClqtFq2trTh48CBOnDiB733ve6ioqEBnZyf27NkDsViM69evo6uri8Ukzpw5w7Akl8u14dq0Wi0mJydZ0S0cDuPtt9/Gd7/7XTz66KP40z/9U1y7dg1TU1MMK9q1axcbgTqdTlYGUygU+OEPf4hMJoODBw+yRCj5/TQ0NOD69esI/bfLMuHqNwqXy4WBgQHG2KvVavj9fly+fBnJZBJ33HEHZmdn0dPTg9XVVdx2220Md+rv74dcLmcz0n379uFnP/sZXC4XHnzwQQQCAbz++uu4evUqnE4nduzYgf7+fsbg792790ObAq+++ipqa2tZTry5uZknDeTGncvlMDExgR07duDatWvYu3cvzpw5w3xMm82GkydPYmBgAG+88QZP/LLZLFQqFdra2jA/P8/JNgm3fOlLX9pwXVVVVfD7/Yz9d7vdGB8fx+XLl6HT6XDfffexytTKygq2b98OjUbDkEsqnNRqNbq6unD69GlUVVUxhDGTyeDhhx/G0aNH8Q//8A8oLS3F6dOnWTK+EEpgcXGRzUJJGnhhYQG///3vsX//fly4cAFlZWVwOBzsjP6tb30L/+t//S+WrzabzVz4kurb7bffjsXFRfz85z9nIYDdu3dzIXD9+nXYbDZ861vf2nBtZChK0rrBYBDT09MwmUwQCATYsWMHxsbGMDQ0hEgkApPJhD179uDtt9/Gz3/+c5jNZhw+fBhbtmzB97//fYjFYmzbtg3j4+Pwer1oampCZWUl5ubm+Jy6cuUKpqen0dLSgsXFRbS1tX3g2ioqKnDhwgXmOUilUoyNjeHSpUsMBTKZTAgEAhgeHkZbWxs6OjrQ1taGL33pS3jggQfw6KOPMszsz//8z3H77bezV9fa2hquXr2KT3ziE7h69SpcLhfvsw/bC06nE88++yzfHUKhELfeeiteeeUVXLp0CYcPH0ZZWRlWV1cxPz+P0tJSDA8Po6ioCIODg7BarSgtLcXJkycxNzfH1hANDQ2Mjrh48SL0ej1GRkZw3333we12IxwOw+VyFTzfKPfJZDI89SkuLmZLhl/96lcYHh7Gq6++iieffBJ/9Ed/hGvXrkGhUODNN99EPp9Hd3c3HnvsMRw7dowbM11dXfB6vXjjjTfw+9//nkUMCHZJvnOFOGjvjY8MnRoaGmJ5w3A4zDCR+++/n2XOaOM2NTXh+PHj/ME88sgjOHToEKRSKcuUabVaVuRZXl5GY2Mj7rjjDgwPD+PEiRP48Y9/zI6jxBXYKGZmZvDggw/C4/FgYGCAMePd3d145plnoNPp0NbWhj179uDBBx+ExWJhMqDRaITBYGA5y8XFRTzxxBOsznHhwgW8+eabPOqsrKxkHKfL5eLDq1Co1Wr09PRgenoaGo0GmUwGTzzxBP9zg8GAHTt24KabbsJvfvMbuFwu9PX14bHHHsOhQ4e4i/vWW2/hT/7kTzA0NIRXXnkFg4ODbAv/0ksvcbeDsOUXLlxAT0/Phuuan5/H9u3b2dSPyLiUaFZWVkKpVCIajbK0HHURd+/ezWNiuVwOt9vN3SIa4fX19cHv92NgYAArKys4d+4cLly4gDfeeAO//vWvC5IRx8bGcNtttzH+nORA7733XoaIuVwumM1mxONxfP7zn8evfvUrlibcunUrm1nZbDZUVlYyTCwajeLs2bM4cuQI1tbWsHXrVvYi6Orqgs1mK8gJKi8vx5YtWzA9PY1r166xGaNer2flGODd4stms2F8fBzRaBRut5s5LgS9euedd/CVr3wFa2trGB4exvj4OOLxOD73uc8hFAph27ZtuHDhAqRSKerr6zE8PFyw0NDr9aisrOQJBk1EampqmKztdruh1+vx5S9/GfF4HNPT0yx1SoZwR44cwdjYGDo6Oni/22w2lJSU4FOf+hSeffZZOJ1OVqjQarU8kSgUIyMjcDqdmJmZwfj4OHw+H06dOoX77ruPVVloP3zlK1/BzMwMbrrpJsRiMTzwwAPYvXs37HY7Dh8+jCtXrqCrq4uhNAQv+Pa3v80Jh9FohM1mQ2lpKUKhUEGYI/BuETkyMoKenh5MTExgZWUFDz74IOP0hUIhysvL8eCDD+LJJ5/En/3Zn+HKlSv4zGc+g02bNrFUcG1tLQ4dOoSqqiosLi7i8uXLCIfD+Pu//3tMT09Dq9XizJkz/LvSVGCjWF9fZ+14mu6+8sor6OzsxMrKCrtoW61WOJ1O/Md//AduvfVWSKVSPPHEE1AqlXjllVeg0+kQjUbx13/914hEIohGowx1evrppzE2Nobu7m4sLy/jrrvuwr/9279Bp9PxlOuDYnx8nAts4N3zLhwO45/+6Z9w9OhRlhGfm5uDwWDAs88+i9raWjQ0NGDr1q3Mq3K73RgbG8MPfvADlkYfGBhALBZDV1cXXn75Zcank5IMKWptFARFfOGFF/DWW29heXkZIyMjePnll1FVVYWxsTE2+zQajTh+/DhUKhV6e3vhcrnQ09ODr3/967h06RL+5m/+hu8x6uplMhns3bsXV65cYaWyu+66C3fddRdEIlHBiXI8HkdFRQX7xpCZ4Kc//WkUFxdzEUPyxK+99hqampoAALt27UJlZSVDGouLi/G3f/u3GBwcxLlz5zA1NQW5XI7t27djfHwcEokEsVgMtbW1aGtrw+joKMbHxzdcWywWw549exCPxznpCQQC6OrqQm9vL1QqFU+k9+7dy5Pa4eFh3H333eju7uZGR0tLC9rb27FlyxY2FBseHsYtt9yCl19+me/VqqoqpNNpTpI2ikuXLkGr1SKTyaC4uBgOhwOZTAa33XYb6urqWLjl4MGD+Mu//EuWYL1y5Qp27dqFW265BVu2bMH+/fsRDAbxt3/7t5icnMTKygpDvC9duoS2tjasrq7i5MmTUCgU2LNnD/uLbBSlpaWQy+UYGRnB1atXEYlEcOTIETz22GMQi8XYt28fxGIxfD4fFwzXr1/H2toaWltbeS+Qktedd96Jy5cv4+rVq0gmk9iyZQvuv/9++Hw+fi8WiwUOh4OdpDcKguYRMZoaQ3fddResVivOnj2Lnp4eJBIJFBUVYXx8nA1Z/+7v/g5HjhxhewKNRoMDBw6wQSnBnIiXcdNNN6G8vByHDx/Gl7/8ZXR3dxc8e30+H1paWtgUNZVKYXR0FHv27IHBYHhfbnHfffehv78fk5OT3HjM5/P47W9/C4PBgI997GOcNBMn4cCBA/jbv/1bXL9+HQ8//DCUSiXa29uxfft2LC8vF5zYEjKhrKwMzc3N6OjoYLXV4uJiKBQKDA8PY2FhAXa7Hf39/di9ezfOnTvHCbpEIsETTzyBfD6PL3/5yygtLWU3eK/Xi4aGBpw4cQIul4uV41pbW7G6ulpwbcRL9Pl8zFmanZ3F9773PayurmJwcBAXL14EAHz2s599n/nnP/3TP+HRRx9FLpfju3Hbtm3MeyJ0zDe/+U309fXxOnbs2MGNxg+Ts6cQ5AuBb98TTz31FFQqFYaGhmCxWNDS0gK/34+3334bn/zkJ/Hcc88hHo/D6XSipqYGS0tLaG1tRSQSYWnNsrIyeL1eWCwWfPe738Uf//EfY2hoCKFQCGazGclkklnvq6urPLLfvXs3Xn/99Q27zF/7/7T3pvFt31Xa9yVLli1bsmUttmVb3hd5jWPHdhwnjrMvTdMtpQtNCy1lGcrAlB0GGGAK3DcMFO4Z2oEbJkBpKS0tSZMmadLsTpx43/dNsrUvtiVZq+XnReacJ51PLPo8H176vIGZT2n+kf76Ledc1/f67neh0+lYUkCjLIPBgH379rHePxwOM3L0iSeeYK61TqeDTqfDwsICXn75ZezZswfT09Pcjfd4PDh//jx+8pOf4KWXXoJarYZKpeKEzvLycuzbt++uz3by5En+s6VSKevyFQoFBAIBJ2pnZWXBarVyeFRZWRksFgsTHHp6erC4uIjdu3fD6/WitbUVCwsLyMvLQ05ODhsGiWCysLDAQS3PPffcXZ/tjTfeYC2+RqOBVqtFKBTC2bNnsWfPHjZbBQIBTExMcJI28aFJJ0vUH/J2SCQSOBwOGI1G7t5fu3aNvQYkD6D09rvV7373O8TGxkKtVsPn8zHp4datW3jggQdY0ysUCpGamorR0VHodDoYjUaEQiHeDMjQev78eTz++OPIzs7G4uIi5ufnodPpuONC3WvCNqpUKs5f+Z/10ksv8Y+baBt1dXV48803ceDAAczMzCAUCrE+9J577oHb7Ybb7WaChlKpxNTUFGQyGbxeL4RCIaNbw+EwWltbeTFMTU1l0yuN0B999NG7PtvPfvYzZGVlcbaK2+2GTCZjio9IJGI9v9Vq5YMaXa5qa2t5Cjk8PMzoPsIKEmHo/fff5+69zWZj6lwwGMQ//dM/3fXZAOC3v/0tCgsL4XK5YLVaWT9rMpk4UIjIYADQ0dGB3bt3o7W1lU3YgUAAt27d4gnA4OAgNBoN4uPj4XK5MDg4iMrKSgQCAe7A0GQxJSUFjz/++F2f7Uc/+hGWl5eRlZXFvoWGhgbMzs6iuLgYFy5cgEwmg06nY/05kapef/111rSOjo4yUUSn07HhTiwWo7a2FpcuXeKJ5NLSEqxWK5xOJ1ZXV/G5z33urs/2y1/+krX8S0tLbLrs7e1liUhKSgoyMjJ4WiCVSqHT6TAxMcEGY/pPk8nEmmcAjFfU6/VIS0tDcnIyLBYLE+YsFgu++tWv3vXZ/u3f/o19GmSAJXLXxo0bmXZEsiqacF64cIHx3ZmZmZDL5fj+97+PAwcO4PDhw1hYWMD4+DhL2sjMSiFZPp8Pq6ur2LBhAx566KG7Pttrr70Gj8eDcDjMKNDh4WGkpqbi3nvvZSMkYWY9Hg+Wl5exceNGNksSjae6uhpmsxm7d+9mKgul51JTZHx8nA3U9Jtfa9966aWXEBsby2GEZKI9duwYHn30UZY/zs/Po7e3F48//jgUCgX73AgGYLfb0d/fzyQx8kvSBUQoFMLlcjHm2mazoaCgAD6fb83f6g9/+EPs2rULYrGYZTCFhYVQqVTsiVlaWoLNZkMkEkF9fT3m5+eh1+uxadMmhoSsrq5Cr9ejvb0dJSUlfOgiKt7Q0BD6+vrwwAMP8PRHJpNBq9Wu6eH7j//4DwBgORvJOa9cuYIf/vCHHMoH3G6mEdr31q1bTCRcWVlhD9fq6irnNlFT75FHHmGQwF//+ldkZGRwXpPb7cbTTz9912f77Gc/y7Q0AkA0NjZieHiYFRGRSAQ5OTkoKyvjffXKlSvcFAkEAujs7MSGDRtw4sQJzjUqLi5miV5lZSUnWtNvjSada+0NL7zwAgKBAHs4w+EwFhcX0djYCI1GA7fbzQnRoVAIH//4xxEIBNDT08OXXto/aJpFydWUowIATzzxBJxOJyYmJhCJRJia1tnZueak6vXXX0dBQQEDX4h8RD4hgqw4nU6Mjo6yNJp8lRQCS+eFkydP4oEHHoBKpeLzA63Xi4uLyMvLg8vlYnCFxWJZEw38/e9/H06nE/n5+ZDJZEwxPHPmDD75yU/C6/Vy3MH8/Dw+/elPs1Swo6MDCoWC37Pe3l7OSEtKSuLf5/j4OE/DbDYb/H4/UlJSUFJSApPJhG9961t3fbaXXnqJfV4WiwVGo5Ex5wSZ8Xg8cDgcDGwRiUR444034Ha7kZeXx9LiwsJCnvbQND4SiUCr1TK0ZHZ2Fr29vfD7/SgrK4PX68UXvvCFuz7bnfWhpVPkYQiFQlhcXOS026KiIkZl6vV6Jlxs2LCBEbfEv6dJg9PphEgkYurCysoKH4ppBK9QKDA0NMRBatFu6qSF9fl8iEQijNIFbofSZWVlwWaz8Wa4srKCwcFB7jKToZDIWYR4NBqNbFYHbntB6PBNJACadKxVfr+fcbORSIQX/PT0dAgEAt7oCdNH0i6bzfaBoKv09HQmGQUCAZSWlsLhcHD3ISkpiZ+XzMhk9FyrSOITCoX4YOz3+zktm4KDwuEwrFYry9yysrL4gkLEhWAwiJs3byInJwdLS0uYm5uD3W5HWVkZVlZWEAqFWGfu8Xjg8/miElkWFxehUqmYNETm9MzMTHg8HkxOTnI3Kz4+Hnl5eRgaGkJSUhL8fj9fvOh2bjabYbfb+c/1er0YHBxk4zSRpAAw9jXas8nlctbq+nw+WK1WKBQK7kStrq5CJpOxyd5ms2FoaAjLy8sIh8NwuVwwGo2orq5mkhSN5ElfeadkY2lpiWlMMzMzaz4b4QHJ2BwMBmE0GpnRvbCwgLm5OYTDYSbe0OSKcMgUrkm+IJp0kOyAkHq00QkEAkilUiiVyqhGSQB8CCWNqM/nQ0JCAtra2rB582ao1WrMzs6ir68PIpEIOTk5uHz5MkQiEVwuF+P+kpOTMTg4yN8/hSEZjUb4/X7+58nIRyjdaO9cIBBgUx+R5CwWC1O66CJ/J8Gpp6eH8dwAeDJKAAuZTIaF/07eJgMheaOIMBcMBtm8ulbR2ubxeCAUCiGVSvlCq1arWdJClLf09HSWzUxNTSEnJwdqtRozMzPsPygpKWFPGwV1lZaWwmKx8KWEfrvRJsrBYJCJfJRxEAgEIJVKObBKoVAw7UmpVOL8+fOMLqbk6cnJSSgUCvT392PHjh1swCWvBFGgSEZJ32W06aPNZoNGo0EkEmFogUKhYP04NUvo77mysgKLxYKJiQkGA2RmZjJJLC0tDQsLC/D5fJxRMz4+zllIRqORPWJerzcquIGCPj0eDzweD783RPHx+/0wGAyMarXb7dDr9WwyJaIXrdGk16eLxfT0NKRSKTQaDWvU70RVm0ymNZ+N1AdCoRDAbUMsZYYQRdBoNEKv17N3cWhoCHq9ng80dxIdFxYWeM/0eDxM14uLi+M1nfYT8iutddEglC/h5b1eL3sLFhcXMTk5+YHGApGBUlJS2AdBeQqUr6PT6eBwODA5OfkB1YXT6YRMJuMLvMvl+puTR7VajZWVFYjFYsjlcoZJkJ/BbrfzYV8gEGB4eJjToMlzmJaWxujw9PR0jI2NscSM3mWSJ9HasLKygqGhoTWfLRQK8X5P4Z2hUAitra146KGHIBaLsbS0xL+RcDiMS5cusRc3Li4OCoUCZrMZoVAImZmZHNq7urrKgYcmkwlGo5Fx+ST/igZtIFSzVCplX1NOTg6mpqYYJEHy8ampKZSUlDAxji4ci4uLTCUkCEE4HObuPGWLSSQSniBGIhH4fL6oJEcAjNulJi5JsIVCIfs0CAcdGxuLixcvQqvVYnZ2FikpKWywpybAzMwMxGIxp2/T75MapfQdLSwsRP2dkr+C9iDKSqKcL7Ie0N/TZDKhoKCAVSv0uZGsnVLUSTpNciy9Xs//m9TUVPYgRUOL31kf+qIxNzeH7OxsJCcnQywWw+12w2AwYPPmzVhYWEBFRQXLMqamplBWVoaEhARcunSJJxS0Ad64cQNSqRSDg4PIz89nApDf78f4+DhjZulDvnLlStRNmDINPB4Pd8sXFxcRCoXQ1taGLVu28Fg/Pj6e3fUUGpSYmMjSD7fbzVhbwnq63W4kJydzIjThemmji0YsoOTxQCDAaNW5uTlOhaytreVxFyVgNzQ0sBaZSDPl5eWcwOr1etHQ0MBEnkAgAKfTyYsudYoJM7tWuVwuvnkDtw8zfX19UCgUmJ6ehs/ng16vZ3xsX18fp4ja7XbeRDIzM3njSk5OhlKpZDb42NgYj5O7u7s5g4RyLtYqQtAtLCxArVYzorWmpoY335WVFb643elLIA1/OByGSqXC9evXIRKJ0NbWBrlczvp1vV6PrKwslJeXY2FhgX+MZrM5ahrn8vIy3G43X7ICgQDGx8eRmJjIITzUQRCLxWhvb0dKSgpu3ryJzMxMPsyoVCo4nU7Mzs7y+ymTyT6AI5XJZEzYoolcNNwdHWLpv1P3kw55sbGxmJ6eRjgcRmVlJex2OyoqKmAymWAymbjbSd2fmZkZyOVynlwZDAYOaIqLi2NoA4EKoqGegdtdUEpxpYtfIBDA0tISd6Y6OjowPDwMhUIBjUYDo9HIUw+iiRQWFuK1115DbW0tUzfosyTCG4Xb0YVGoVBEXRhp6kidcuD2tCQjIwPj4+Osw52YmOBuYHp6Oo4fP476+npeT+jfYbVaMTIywrpZyoShgzh9toQjjaZ3pU3aaDSyROzq1av8nsr/OyDQ4XDwBIEQnkajEYmJidzBGxoagkqlgtls/kCq7+zsLHJycljKQ1M2ujSsVXTxpsOcSCRi9r/b7YZUKuXNdWFhgalXC/+d1B4MBvlQS1rmixcvsteLGgMpKSkQi8X8XIR2jPbO0dpN3ycZJoeHh7nhRYcIulCWl5cz2CAYDKK0tBRbt27Ff/zHf0CpVKKzsxPZ2dmMpCb0ZmJiImORI5EIwuFwVI+c2+3mzzkUCjFGmNa6lZUVzksSCoXo7e1FfHw8DAYDsrKyuIFFjYD29nYolUrU1dUhHA5jfHwcUqkUZrOZ1xciBUYzqQO3td90OCQKEMl3ZDIZKisredJRVFTEFwmz2YzR0VEUFhaitrYW6enpLHmhTCkiQ0qlUtTW1qKkpIQ9aERQEgqFaG5uvuuzLS0tscwqNjaWze1SqRTj4+O8Ty4tLXHX9b777sPy8jKmp6cRCoVQU1OD7Oxs/Pa3v+UpNjWIAoEADAYDVlZWGGBDyHKn0xl1byD8NMnxUlNTMTQ0xFQoatLRZTYcDnPoIgBWd+Tl5aG9vZ29k0TnamtrY5IcNVBpL6Sk+GglFAoZSUtnrI6ODjQ0NCAhIQFut5thP8PDwxgYGIBMJsPi4iJSU1ORkpLCv2VqVFGWBxmcJyYm+LdLzzQ2NsZN1bsVraeE7Say5+LiIqanp5GTk4NQKASLxcKXVHq3CacsFAqRm5uL6elppKWlYXZ2lvGuMTExmJqaQm5uLqRSKaampiAQCFh9EQ0LTJNBkjPS/pyWloaJiQlu0NJFaWRkBA6HAyaTiRt/hMGnBurIyAgkEgmfWw0GA/uE5HI5lpeXeRofbe0luiYpAih2gIKAaWKysrKClZUVxMfHY+PGjcjPz2fzPyH96VwTDoeRlpbG+9jS0hLa2trQ1NSEmJgYZGVlwe/3w2g0RiXX3Vkf2qOhVqshFotRXl6O6upqZqD//ve/Z2Y2bYATExPo7u6GUCjEH//4R/zlL3/B5cuXmWRRWFjIXSqPxwOLxcKdZWIUUxw6df2jGWJUKhUMBgPUajX/KGJiYjA6OspaXpFIBJPJhMHBQebdE4qvt7cXHR0diEQiaG9vx/T0NIRCISorK6HRaDAxMcFItMOHD2PXrl2orKzk3APSzd6txGIxpqamuKshkUgwMTGBa9euMT2KpBDEmSc2O0XKBwIBnlCcOnUKaWlpUCqVjM4dGhpiqcT169eZykKyq7VKLpdjcnIS09PTWFxc5DH3T37yE7jdbtbgk3GONgGz2YwbN25gdHSUDVDhcBiFhYXQaDSorq7Grl27kJubi5GREV4U6MdA5IZoP25KJXU4HNxtHRwchNlsZkObxWLB5cuXcfHiRcjlcuzbt48Pw4ODg3A4HKirq+MLIpmQq6urEQgE0N3dDQBsqE9MTGSZWDSSDR2krFYrlEolysvLEQgEcO7cOZ5A0YXhypUr3OWoqqpCRUUFUlJSIJfLsWvXLnR1deHYsWOclUJTKQog1Ol0fOF0Op2Qy+XIycmJ+p0GAgFEIhEm2dBImfCiJOuhS5lAIGAaDKFNqUPT29vLFJDFxUX+Z7Zu3Yr8/HwsLCxwHgzxxqMVjYnpMCyVSpGcnIyDBw/C5XLh1q1bkMvlqKqqgkQiQXd3Nw4ePAiJRIITJ07AZDIhOzsbZrMZFRUVGB0dZf9AUlISB+rR34vkOiKRCOnp6VHNuSKRCJOTkzyto03v2rVruHTpEpN8VlZWuCN/6NAhTrWlDv2DDz4Ik8nEi35GRgaSkpLYx1ZUVMTdv7GxMTZtRzOqJyUlQSAQIDU1FZFIBLOzs7x+ut1u7NmzB0qlEr29vThx4gTcbje2bt2KgYEBvmQSOrSpqYn5/HQoTktLg9VqhdlshkKhgFKphMvlYrJMNB8EIYAp20Sj0UAulzORr7CwkM3VdHi69957IZFIMDAwAIPBwCZmABgfH8fY2BiysrJQW1vLHjfq1hKEAwBjMNcqrVbLlB+tVouGhgaUlJTwZJXkHxUVFdDpdCgsLMTBgwdRXl6OkpISZGVlITk5GSkpKWhubsaJEye4gwuAiYtEPSJ56cDAACQSSdSNOBKJoLu7GzabDUVFRaivr8fExATa29u5wyiVSuF2u3Ht2jXOBnK73bhw4QKWlpaQk5OD4eFhGAwGtLa2YmJiAmq1GlVVVaiurmY/4Z14+tTUVA52XKuSk5MxPj7O+R5CoRA5OTkYHR3lpgytM0S/ampqwqZNmzA1NcXPlpubC4lEAovFgurqalRUVKCurg61tbVQq9Wora1Fbm4uo1kpXC1a2ntycjLnXNElXiAQYGpqCqmpqcjOzkZLSwuKiopw48YNXLp0CR0dHeylGx0dxdjYGEQiES5cuACxWAyfz4fNmzdj7969kEgkuH79OiKRCIdItrW1YXBwkAlya1VMTAza29uxtLQEmUzGF8OzZ8+ip6eHccN+vx9nz55FYWEhEhISMDAwgFAoxDlYo6OjaG9vxyuvvAKBQIAtW7bwmUOtVuOee+5BXl4eTCYT/4bkcnlUslNGRgampqYgkUiwadMmNDY2QiqV8kWNzMcKhQKDg4Pw+/3Yt28fpqamONStqKgILS0tqK+vR1dXF4LBIOdV0DuSkJAAnU6HoqIiCIVCNiv/rYDIrq4uTExMMHlrcHCQoxFoGkQTcZK10WWDAk1JXnvp0iX09/fz3uT1evH+++/zetnW1obR0VGeTEaDDVFQIE2XyB80NDQEu93O9ECTyYQbN25gdnYWR44cQW5uLjZv3swKmoqKCpjNZs5eoks/gT1WV1c5yFEqlfLUiShqdyuSXk5PT3OIIMF9zGYz0tLSkJaWxs1QmkqZTCaG01DMQ0tLC/r6+pheR43eF198kZvFtGcNDAxgcnISJSUlaz7bB77fD/VP4fbiLhaLcfXqVZw+fRpjY2OQSqVMH6AUxpqaGjz88MOYnp6Gw+FAU1MTPvWpT2HLli0Qi8WIiYnBiRMn8Mgjj3DSJm3on/3sZz9gnD1w4ACef/55Th9dq8bHx1FWVobp6WmYTCZ2+X/5y19GUlISzGYzYmJioNPpUFNTg3fffZeTPl9++WU8//zzKCsrw7Zt29Dc3IyXXnqJQwDvv/9+vPTSS9i2bRuuXLkCl8uFV199lTuVY2NjuHnz5prP5vf7ceDAAWZxq9VqNDc34/DhwygtLUVdXR3Ky8tRU1ODBx98EFeuXAFw+6D9zDPP4NFHH0VdXR20Wi1Onz6NQ4cOoaenBxMTE+jv78fly5dRV1cHh8OB8vJylJWVoampCeXl5fB4PFEPzGRGXFlZ4VFsXV0dXnnlFf68yOi7ceNGlJaWoqCgACkpKfj85z+PQ4cOQS6Xo6SkBH/+859x9OhRzM7Oor+/n8PctmzZgunpaTQ0NCAYDGLTpk18qIx2Qevr6+OuGh3wxsbG8PDDD8Pj8eDBBx/Eww8/jM2bNyM9PR1nz57FwMAAFAoFDh48iOzsbExOTmJ0dBQpKSnYsWPHB2RHDz30EE6ePAmtVguZTIbOzk5ODr9y5Qp++tOfrvlsEomEc1doIUtJScEnPvEJCAQCqFQqVFVV4dFHH8XXv/517Ny5k/n2FAZFAXnz8/N48cUXsby8jP3793PuwBNPPMEaeaPRiMbGRtx3332YmZmJSsjw+/2Mu6PUXZlMxpd7o9HIGwEhL8PhMDo6OlBVVcU+mtTUVAiFQjz33HMYGxvDzMwMFhcXER8fj6NHj+LnP/85MjMzoVKpUFNTA51Oh8HBQTbCr1XBYBA1NTUIBAIciubxeLBr1y4AwObNm5Gfn4+6ujo8+eSTTHkbGRnBRz7yESQmJuLcuXPIyMjAwMAA7rvvPvYMCQQClJWVQSAQYGxsDLt27YJer0dMTAy0Wi2npkd7NgrUS0pKws6dO7GysoLnn38ebrcbmzdvRkJCAmJjY7F//340NTXB6XQiNTUVDz74ILKzs2EymSCRSJjAY7VaOSCMQkTT09NRW1vLCasU1PS3pgYikYgN/TU1NSgoKEBTUxOHMoZCIeTl5eG5557D8PAw8vLykJSUhCeeeAJNTU3cdBkeHuZpQF1dHW/qL7zwAuNxu7u7WSM9MDCAbdu2rflslEje29vLmMrh4WE89dRTSE1N5cZUQ0MDvvjFL+LnP/85h1c1NjYyips0zt/85jexuLjIspWdO3fiK1/5Curr6xnCQCGOBoMBJ0+eXPPZrFYrSkpKIBAIYDAYMDU1ha6uLvz4xz/G5OTkBwAGZWVlsFqtyM3NxcrKCnu0bty4ge7ubszOzuKb3/wm49ilUim8Xi+ef/55rKysMBqypKQE99xzD8uW1qrV1VWGa1CH2OPx4K233kJxcTGeeeYZ3HfffXjqqafw29/+FsPDw0wufOKJJyCXy9Ha2oq0tDQ4HA68+eab8Pv9sNvt7B/ZunUrxsfH8bGPfYyRvBqNBi6XK+pk1Gw2o76+ng+RBFv40pe+hPz8fP69ZWRkQCqV4he/+AU3Jp5++mkmjZGx/l/+5V9w8uRJBINBVFZWYu/evSgsLITZbEZ+fj5effVVRuI7nU6cPn16zWdbWFiATqfD/Pw8ZmdnIZVKUVBQgG9961swm82orq7Ghg0bsHXrVnzuc59DfX09ysvLsbKygn/6p3/CRz7yEQC3m5p79+7Fxz/+caSkpGBwcJDlez6f7wPACyJhxsTERG3eBYNB1NfXsx8lHA6joqICf/rTn1BSUoJHHnkE27Ztg1arRW5uLv70pz9hx44dKCsrw86dOzkZ+tChQzAYDPjBD36A9vZ2lnQtLy+jvr4e//7v/87eyF27dqGpqQltbW1R85+mpqZQV1fHgBYKo3zggQfYe0dp3hs3bkRPTw8jh7/2ta8BuO3dGRgYwPj4OL71rW9xzkh6ejpWVlYgkUgYzd7Z2QmlUon8/HwYDIY1PY/AbdlqZWUlZ66IRCK43W58+tOfxpe//GXs3r0bycnJKCsrw1e/+lWsrKxgz5492L59Oz760Y+isrKSJ1yXLl3C9773PbS2tiIpKQkpKSnwer3swSNIEcmfyGe7VjkcDkbRarVa3HvvvTCbzXjggQdgsVjQ0NCAwsJCbN68GV//+tc58Pj//J//A5PJBJfLhdnZWZa5Pvvss2hoaIBWq8XS0hImJyfx0EMPQaFQYHZ2lk3/tbW1MBgMUQNJSZJXXFzMyPqUlBR8+ctfhtVq5Sa5WCzGli1bOLLh7bffhlarhV6vx40bN/D888/j7Nmz+NjHPsY+HpIkf/e734XH40FmZibnZ7S0tCAhIeFvwl+oPrQZ/De/+Q0bzogyZLFYUFVVhdLSUthsNpajmM1m7N+/H/X19ejs7ORAk4mJCRw7dgzf+ta38J3vfAff/e53kZKSwqEkr776Kr73ve/h/fff57AYyiW4c+H/n/Wzn/2MDUQajQZqtRq9vb04duwY3nzzTXR2dqKyspI7aKFQCOfPn0dWVhbC4TAeffRR1NTUwOPxcCdjeXkZb731FoLBIHbu3IlNmzZheXkZer0eO3fuxOzsLLq6uiASiTAzM4N/+7d/u+uzXblyBZcuXeKDG+Eng8EgvvGNb7DGcGBgAL/73e/wjW98g1NBX3nlFZSVlTHGLi8vD9/5znewfft2TvQOBAI4c+YMHn74YRw7dgx1dXWcFFtaWoqpqSns2LHjrs927tw51k+HQiFOZTWZTHjwwQc5lt5ms+HGjRu47777kJubi1/+8pcwGo3QarUoLCyEQCCATqfDiy++iGeeeYZH0NSZ2bdvH86ePYv6+npMT08z8i03NxeNjY13fbbf//73cDgc3BURiUSoqKjA2NgYZDIZzp49i4MHD8Lv9+PYsWP49re/DYFAgNHRUczOzmLjxo1QqVQ4d+4cpFIp6urqIJFIWA7W3t4On8+HDRs2fMCgSKPv8+fP45//+Z/v+mz//u//jvj4eB7xJyQksGmuvLwcBQUFLFeQyWSoqanBF7/4Rezdu5cxofHx8SguLsZ7772HT3/605wVsLCwgJ6eHly8eBHf+c53AIATqJeXlyGTyZCfn49Dhw7d9dl++ctfYmxsDA0NDRAIBHA4HMjOzsYrr7yC5557Dq+99hqT08bGxnD48GF4PB5kZGTg+PHjjNN75513sGvXLoYlUFecTLXUFW5vb8fIyAhycnLw8Y9/HKdPn8YnPvGJuz4bcNvkbzAYsHXrVu66jIyMIBgM4gtf+AJeeOEF9jAlJiaysdTn8+EnP/kJ9u3bhwMHDvDFqL+/n0k4S0tLSE9Px+nTp3H06FG0t7dj9+7deP/99zE5OYmjR4+iv79/TdPfL3/5S5jNZh7T+3w+VFRU4OrVq0hNTYVKpYLH42Ea1XPPPYdf/OIX2Lp1K5qamhAMBjn/wePx4NVXX8WuXbtw8eJFzM7OIhwO449//CMGBwcxPT2NQCCAgYEBAEBjYyPefPNNfPvb317z2axWK19w5+fnERMTg8uXL2Pnzp2w2+1oamqCQCDAlStXUFFRwX9mfHw8Nm/eDJVKhWvXrmF2dhaRSAQPP/wwJiYmYDAYIBQK0dPTgz179kAqlcJoNDIGVCKRwOVy4amnnrrrs7344ouYn59HJBJBfHw85P+dyLy4uIj9+/fjvffeg81mg8FggMlkwpe//GV885vfxMc+9jF0dXVBq9WisrISBoMB8fHxWFhYQHZ2Ntrb2+F2u5GQkICuri784z/+IyYmJpCUlITk5GTIZDKGZ2zduvWuz/bHP/4RQ0NDEIvFjBV1OBz41a9+hWeffZanfEqlElVVVYiPj8cbb7yBvLw8FBcXQyaTYWlpCa2trYiLi8PMzAzq6uo4ZItMsTMzM1AoFJBIJJiZmcH8/Dzi4+ORk5Oz5vv2zjvvoKuri/MblpaW2E/x1FNPYXx8HHK5HMPDwzh37hx+/OMf49ixY2hubsavf/1r5OfnY9u2bZiensaWLVvwox/9CN/85jcxPj7OU8qTJ0/i8OHDOHfuHDZv3oyBgQFYLBZ+j9b63H7/+98DuH35WlxcxNTUFMrLy9Hd3Y0tW7bg+PHjKCkp4cyR2tpaKBQKvPfee9Dr9diwYQNKS0vhdDpZXpaQkIDe3l6MjIywPn779u2cGk4yllOnTiE3Nxcf/ehH7/psx44dYx8eJbGPjY1Br9fj29/+Nn7wgx/wJY9UAS+99BKOHj2KgYEB5OTkIDs7m3Ncvv3tb+OFF16A2+2Gy+VCMBhEX18fnnvuOZw6dQrJyckwGo0IBoMoLCzE5cuXGWf9P+trX/saVCoVhzb6fD44HA5cuXKF//3Nzc0ctFtZWckghP/8z/9kcuHExATuvfdefOMb38APf/hDGI1GjIyMwGQyYXZ2Fh/96Edx3333ob+/n0Mpu7q6UFFRsSYm+2c/+xm/pxqNBkqlkg/mn/rUp/C///f/Rnp6OkpKSpCUlASVSoUXXngBdXV1iIuLQ0FBAcRiMS5duoS6ujqoVCpUVFSgtbUVS0tL0Ol08Hg8kEgkeO+99z6Aie7r60NRURFPLf9nHT9+nH1w9PexWq04ffo0fvSjH7EEliYc9L09++yzLMHX6XSIjY2FWCzGgQMHcP/996Oqqoo9jrdu3cJzzz2H7u5uWK1WaDQaljtPT0/j2Wefveuzff3rX+cg5La2NkxPT+NrX/saPvOZz2Dr1q1oaWlhH/G1a9fQ3NyMq1evssekvLwcGzZs4H0jLi4OO3bsYH/N2NgY3n77bRw5cgQdHR2cup6Xl4e8vDy88cYb+OIXv3jXZ3vxxRcRGxuLyspKjI+Pcy7K66+/zmCEmZkZ2Gw2xMbG4plnnsGlS5ewZcsWXLhwAcnJyYiNjcVbb72Fp556CpcuXcJ9992HtLQ0xMTEwOVycUBvf38/6uvruUlBe/SBAwfu+mx31v+nwD6i1RiNRtZqEz1GqVTC6XTCbDazIXN4eBhvvPEGh5D5/X7k5+fjv/7rv/CpT30KV69ehdVqZQOtwWDACy+8gAcffJBH/omJibh8+TJsNhu+/vWv3/XZ/H4/4uPj2cBIHT7q2onFYvzqV7/CysoKtm/fzrru3t5ehMNh5tOPjo6io6ODF6/du3ezyfTdd9/FAw88AL/fj9dee42JNqSbX6tI261Wq1liRt6G7u5u+Hw+DAwMwOl0oqqqiiUD58+fx86dO5Gfnw+fz8cMb4VCgd7eXvT09ECn06GsrAxbtmzB3NwcJ1qTvGlsbAxut3vNi8bY2Bjne7jdbtjtds5JGRsbQ2JiIuuGyYBmNpuRnJyM3NxcTExM4J133oFSqcR7772H3bt34+zZsxCLxUhOToZQKGS0Z3FxMaamppgzD9w+QK910dDr9di2bRsHzAmFQrz88svMmy8sLOSE69raWvj9fszMzMBoNMJms7HGOy0tDceOHcPg4CCqqqo4GZygAxkZGbh27RrnftAlLZo8yev1su6c0nvj4uI+oCsFbstdZDIZX5j0ej1WV1c5Q+HWrVuorKzEc889h2vXrvH/nmQev/jFL3DkyBF4vV6kpaVhaWkJ4+PjUb0tHo8HhYWFWF5eRiAQgNvtxvXr19HY2Ijl5WVs3rwZBoMB8/PzWF1dxfXr1zkvIzU1FTk5OZBIJB8IWFxdXeXxN/mM3n33XdZ6b9iwAWKxGG+99RanVq9VpOMdGRnh5HKRSITCwkKcOXMGDoeDR9VyuRwajYaNtvQet7a2YnV1lYMh//CHPzBwwul0orGxkQ/yb7/9NoRCITIzM3H+/Pmoev7V1VXk5OQgPj6ewRVXr16FUChEYWEhOjs7AQBpaWnQarXo6uqCw+GA1WrFxYsXeQLgdDpx8+ZNxMTE4NixY3wAtVqt+NznPoff/va3qK6uRnd3NxsV//jHP0adPsbFxaGkpATT09Msp3Q6naisrMTNmzchlUoxPDyM3NxcbN26ldfhzZs3Y2pqCu3t7bx+UDYDeSPoErt161a0tbVBo9Hwdy0UCjE3N8cTirvVwsICpFIpS2r8fj8uXryI7du348KFC7wGq9VqJCQk4MqVK5zsW1RUBLFYjMnJSUgkEvT09CAcDnN6MWUJkHRwamoKiYmJnB6v0Wjg9/vXPDDPzMxAIBAwXc1oNKK7uxv33Xcf3G430tLSeNLudDphsViwf/9+HDt2jJ9ZIpHgscceYwmn0WhkX5XD4UBsbCxkMhmKi4sxMzPDEioCHaxVlMa7vLwMu93Okt/09HT09vbCYrGwzCIvL48lZleuXEFzczPLRsLhMH7yk5+guLgYt27dYt/Y1atXsbq6ilOnTqG4uJg9TjR5ALDm50ZNO/IXkpY7LS0No6OjSEpKwtjYGAQCAVJSUmCz2dDX18e/AYLCSKVSOJ1Oxs7L5XL2AG7evBnj4+OYnZ2FSqWCUCiE2WxGIBCIClgZHh5GVVUVS65JVuj3+9Hd3Y2WlhZMTk7ypE2hUOCxxx7j/BDK+unq6sL777+PrKwseL1eljoSFAMAy4Ipk8ftdnNY7N0qHA4zjSspKQlKpRICgQC1tbUoKytjZPPk5CRycnI4NPjq1avsCTKZTEhNTcVf//pXlJeX49133+UzCQCeEpWWljLlzOv1suR7rYuGw+FAXl4egsEggsEgn0c0Gg0GBgag1WrhcDi4EUQXwZiYGBQWFnJg5COPPILjx4+zDJZkP++88w4nUNNnNz4+zv6qaD3thYUF5OTk8HnD6/UiLi4OBw4cYN8Pfd+BQABTU1M4evQoLl68yIj7wcFBbjaRl7i3t5flwklJSXA6nWyaNplM7EOIhqG+M8mdQCQ9PT3453/+Z17zBgcHIZFIGAtP8mQALOEqKiqC2WxGRkYG/vCHPzB9kfYVkpVSEF5WVhZ27NgRVSWg1+uhUChw9uxZ+Hw+xMTEYGZmBk1NTUzDEgqFSElJYZ9tYWEh3nvvPZSXl8PlcsHlcuGzn/0sAoEANm7cyETKnp4eXLt2jYEwwWAQV65cgVKp5EZLNBDHnfWhpVP0hcTFxbERNCMjA++++y46Ojq4c0Dd7MXFRSwtLSEjIwNOpxPhcJh1yDU1Nfj1r3/NN3+hUIilpSUOObpw4QInXw8ODmJkZCTqh03po7RQU6epoaEBY2NjKC8vx6ZNm6BWq2EymdDT04Ps7GyWbs3NzbGrPicnhzGpRBdoaGhAdnY237pTUlIQDodht9uxvLwc9XBFmxhtRqQFJGKCzWbD6uoqEhMTOZlcIpFAo9Ggv78fN27c4PFkX18fG7CIcNTR0YG2tjY2qdM4nrB/0QxYCwsLTMQRCoXc7X/11VfhcrmQm5uLuLg4nkIsLCzA5XJBoVDg0qVLrOGsq6tDbm4uLly4AKVSidraWjZjDQwMYG5ujvMcnE4nm5aiPRtNQ9xuN3w+H5ve2tvbeUI0OTmJkydPorOzk7uyIpEIarWaNcQkiXvttdfQ09ODgoICbNmyBQUFBcjMzMTk5CR3xzweD2w2G+Lj46NeHuk7TU9P564QLTYGg4H9EfPz8xgbG0NbWxuOHDmCDRs2oLq6mjvSdFhWKBSIj49HZWUliouLuQOoVCr54kMLI5nQ1yqiklBGAkEIVldX0dfXh1u3bmFlZYXfQ5VKhaamJp7K9PX14b333sPVq1dx69YtWCwWZGZmMjkmEAggLi6OtekAmJLhdDqjEjLovSevElGNYmNjce3aNQwNDUGpVCI1NZW9S3a7HVKplGk2RJQqLy9HW1sbrl69irS0NJ6ahsNhHD9+HDdv3kRLSwtvUna7nfGMaxVRg0ifGhcXx9QYr9eLnTt3sj+kq6uLQQUikQhKpZJleAMDAzh9+jR3uhobG9Hc3MybPOmIc3Jy4PF4MDU1heXl5b+Zx0M64NXVVajVavbYxMfHo7m5GYuLi7h06RIGBgbg9XqxdetWpp0tLS0xOtloNLIZOykpibMVaKrgdrvZZBsMBiESiVBfX7/mcxGbXy6X83tE4XN6vR4ikQgSiQR+vx8WiwVarZYJfvQswO0LXF5eHsMMampqsH37duh0OiablZaWsp9ocHCQKSlrFe0FcXFxfKAnRKtCoeDPYX5+Hrdu3WIqm06ng8/nQ2dnJ27evMk+OkJPkhyOqHrz8/Msh7Rarejs7MT09HRUUAgRcSiLJiUlBX6/H1euXIHVauXwTJ/Ph4mJCfT09LB8gRomsbGxqK6u5qksZSUQXndmZgZJSUmMQx0cHMTU1BR/92sVTVeJ1EXmVMI+V1ZWQqfTcUeYYAAHDhxAdXU1A2PIQDsxMYHU1FQoFAreY5OSktgHMjk5yZhsl8sV1eul0Wg4i4b2KFobT506xV5SMiZ3dXXh0UcfhUgkYgiFSCRCU1MTPB4PgsEgN+iIUCeVSmGz2XDkyBE2E6vVauTm5q4ZOgeAKX93KhfS0tKg1+uh1+s5sJZIQWazGRqNBuFwGFNTU+jt7cXMzAxEIhF7AuisVVxczGnvdrsdbrcbs7Oz6O7uRm9vL5vM1yqBQMBACDr0G41GXLt2DYmJifw+2O12DA0NYX5+nhu25CVLT09HQUEBioqKYDKZGCZDF0ySK1dVVWF4eBjt7e2Yn5/nS8paRRRGhULBoX10UfB6vdi4cSOKior4XOhwONDZ2YnNmzcjGAxicnISFosFYrEYb775JmJiYjjugNZIoVDI1MzS0lJek5KSkngNWut9I+kaETCvXLmC3//+95ifn0d6ejocDge6urowOjrKYckE98nOzsbOnTtRV1eHlpYWJlImJCRAq9VCp9PBZrNhbm4OmzZtwoYNG7jhZzabo36n9P1kZmZiw4YNKC8v5/ysmZkZDsAUiURQqVRYWFhgmuTQ0BAGBwc51fzmzZs4ffo0uru70dfXB5/Px35CmlQZjUaGq5Cf5sPUh75oUHCQ0+lkrbZYLEZhYSHm5+c5OEgmk3HqZVpaGnw+H6RSKXcIKZSGsILEhs/MzMS+ffvg9XrhdrsZUwvcPgBEO/jRAYJ03wkJCXC5XLzR0sKRlZWFpKQkdHd38+ZXWlrKkfYajYb/ObodUheDskEI80idmUAgEHUzIbc/dUiIgEJhQAkJCdBoNMjJyWECCy0CZGzX6/XQaDRYXl5GUVERU35ICqRUKjnk507DX3x8PMrKytb+8v+7S2W32/nv5HK5OG2SZAExMTFIS0uD2WxGamoqByjSCJZ0f4SoIyyaSCRCTU0Nm2tXVlaQmprKmLRoUwPCHJImVi6XIzExEVKp9APGbYlEguLiYty4cYMD5XJzc/niEIlEkJmZicbGRggEAmi1WmbNK5VKtLW1cfJofHw8j9ujTQ2I7EQXJiJZFRYW8vu8urrKON/JyUnk5eVheXmZqSYrKyvIysrC+Pg4gxVkMhkzyhMTE/lHTMnshHFWq9VRv1NikJO5UCAQsGGX5F6EfRQIBHw4IRwgHeSXl5dRV1fHBznq1mVkZHBSK31OoVCIcZXRikK8gNvvN3Wj6FCrUqmQlJTE0wzS/UYiEd5oqSsbGxvLci76d4bDYSQnJ2Nqagrx8fH8d6N3JjMzc81nows6gREIgVlSUgK9Xo/k5GRuBtDlr6KiAkajEampqWxCj42NhcFgAAA29CqVSqSlpSEuLg4ej4c3Xrlczk2PaB4NQqrSupiUlMRSLUIQEqaWCHQ5OTno6elhtG44HMb8/DwEAgH/eaQVp3G91+tlfPed5LdoBz/690kkEr50EoVmZWUFycnJDJSorKxk2RN9z3QAoINITk4OfD4fcnNzOdCuoaGBD/MrKytMZ/lbF+/V1VXebOPj45n8Q39fIpHd2ehSKpVYXFxk2AatO16vF3V1dYiJiWGjs0wmQ11dHbKyspgSRxMxouOsVZSD4Xa7ERcXh6ysLHg8Hm6IyWQyqFQqboCZTCZeo0neu7CwwIcpCp6l0LTY2FhkZ2fzwdLj8fCFLT4+Hlqtds1no273ne8L+duEQiFfrAsLC1kiQgcoQh0TaXJ8fBy5ubn8e/X7/bzuuN1uPrS6XC7+XUTbG+6cONJ3TBS/QCAAgUDAa15qairC4TCys7P58jw1NcXNS6lUipKSEl67KP24vr4eY2NjvI/K5XIkJycjEAhEnTzGxMTwOkLf7+LiIrKzs+FyueD1evkdvLOZsry8jE2bNvH+U1BQwKF4NOWmbrxarYbX62UCYWxsLIcQRwOsxMTEfMADQdIdsVjM+x+deWhaTzlA1ERZWlpCKBSCVqtlbxARr2JiYpCfnw+/38/SSToPkvl5raIptkgk4sk64WgJ1y6Xy5Gfn4+GhgYEAgGm0KWmpjIYg3KpNmzYwKRB8jXRGkLfDfk3JBJJ1Es3XeIp26K8vJyb7aSkobV7eXmZYQZCoRA6nY6DnQm2Q8RSlUoFqVTKXlmpVMqTAwpgpUbNWkVnQTorkfxMqVRCr9cjOzub85rIx0eNCdqrgP+3WZSens4AH/r9bNu2DYuLi3xhIcy61Wr9m/Q6fvc+1D+F23IRukBQkAcFrlH4HLGU8/PzmQE9NzfHLv3Z2VmmUh09epTlHUlJScjNzUVVVRVr/Wh6kpGRwRvYmn+JmBiUlJQgLS0NiYmJvDmSyZIIAyKRiD9ku93OPwzgNr6XGPBJSUkoLi5GeXk5vF4vLl++DLFYjA0bNmB1dZVv+hRYFo0xv7S0xBcN+iHJZDI2QtMYig4bLpcLfr8fNpsN1dXVKCgowOrqKoe7EK6N8HRpaWnYtGkTLl26hMXFRQ5Oy8jIYOPfWkVkHqvVCpFIhLy8PIjFYjz55JNs0ifqzLZt27CyssJ5Hc3NzWwwFovFcLlcqK6uhsfj4S5jZmYmjhw58oHvT6fTYcOGDYiNjUViYuKazxYXF4e8vDwkJiZyoFdGRgZ0Oh22bt3Kh72Ghgbcf//9nACalJTEl5lIJMKBX08//TQ2bNjAKEnqzNntdpSUlHB6bkVFBV/Y1irqzM7MzGBpaYm53Pfffz8aGxv5sE2EB4lEgsXFRcYFBgIBlrFIpVLk5ubyAZdQokqlkjcBj8fD9Jjk5OSoB1JCLhPDny7rpMUsLy9nSUxFRQVnZhB9orCwEE1NTXjiiSeQm5uLQ4cOwWaz8XtO74DP58Py8jIHYNHnGu0gD4D58vR8RFlpbGxEdnY2pylTx18ikbCGnqhmq6urCAaDKCkp4YO+3W7nMfZDDz3EnrCsrCw2WFJjY61KSUnhwwStZWKxGJs2beIL48LCAgoLC3HvvfeyRtlsNn/gEpyXl4eysjL09PRAr9d/IIApNTUVBoMBMzMzTMlpbGxEYWFh1AskLfw0QSNeelJSEiKRCGZmZhhmcOdI2263c2fc6XTCaDSiuLiYJwN0maKgPboAUEYPAA64XKsCgQCKi4s5DwkANx7UajVnJWVmZmL//v3w+XzQarXQarXYsmUL+7wI/V1ZWcmcdjqINTQ0wGKxYH5+HvPz84yl/ltZFcFgkD8zCoJLS0tjRClNw9LT01FfX4/CwkJkZmYypri6uhqNjY1IT0+HRCLBvn37IBKJWLNNCNVt27YxUS8vLw9FRUXMn1+rYmNjkZycDLfbzZNLkUiEQ4cOMSWKMpMeeOABRoYSXYYM7hQCR3AJi8WCYDAIuVyO7du3w+l0YmRkhOELOp0OSqUyaoOM8mKIhlhQUACr1crPSes5dVKHh4d54kZTSvpOCQZC8lQ6Q6yurkKhUCAzM5MvDklJScjJyYl68COoCiVhU45GZmYmKisrsbKywtPwxsZGNoLT/hkMBmEwGGA0GpGVlYVNmzbxJVSlUiElJQVbt25lFDf9tv1+P/950b5T+g2lpKTwb3H37t1IT09nRQSFBiYmJuL69esQCAR4/PHHsXfvXm6WOZ1OlgVPTExgeHgYFouFvz86JJeWlmLLli0sIVurhEIhS1EpQ0Yul6OhoQFWqxWRSIRJgDRhKykpQSQSQWNjIzIzMzE4OMi/jdzcXIyNjSESiSAxMRECgQDbtm1DTk4Ov3/19fWM8I/2udFnTFJSujQ1NzcjLi4OZrMZXq8X+fn5aGpqQjgcRn19PUZGRqBUKjmDinx1zc3NEIlEfACPiYlBVVUVjEYjZmZmuBFEeSbRLo8+n489CyqVClu2bMH27dvx8MMPM4UsPT0dRUVFyMvLQygUQlFREdLS0lBbWwuZTIaJiQlMTEzg1q1bvH+SFcFgMGDXrl2ora3F4uIiS69kMhmjyaMVXRinpqYwNTUFhUKBwsJCbvhQzkhCQgLi4uKQm5uLQCCATZs2oby8HKmpqaioqIBKpcLHPvYx9tpJJBIIhUJs2rQJ4XCYJeVSqZT38WjZKHfWh75opKamorOzkzvClDXx61//Gk888QR3N5eWljA/P4+amhq89tpraG5uhs1mYyf+008/jeTkZJw6dYpJVXK5HDabDYcPH8b3v/99JCYmIiUlBSMjI7BYLHj66acZf3u3SklJQWFhIXbs2MEhZ2VlZThx4gQyMjKY8U9x7d/+9rfR1taGyspKvP7665DL5Th69ChOnTqFrKwsKJVKfPazn0VZWRnf4ChDgoKxaNOjl2vND/i/uwiZmZnMHm5qasK7776L/fv3IxKJcAfG4XBwZ7mgoABXr17F8vIyqqurYbVakZSUhEcffRTZ2dnIzc2F3+9Ha2sr/vKXv+ALX/gCNBoNp2G6XC4e669VdNsnsoZQKERBQQFu3LiBT3/609yV0uv1uHnzJqqrq/HjH/8YNTU1uH79OhITE1FdXc2mW0p9z8rKQnZ2NqRSKW7cuIH9+/fz6M1kMsFisfAkKdp3euPGDQ45pM3q2rVrHLRkNBpx/fp1/OUvf8EnPvEJ/OpXv2L+eW1tLQ4ePAir1Qq73Y5XXnkF9fX1mJqaYr3/2NgYnnjiCSwuLnJSbigUwtGjR9HR0bHms6Wnp2NxcREVFRVITU3lbJObN28iLi4OFy9exOTkJILBIBISEvD444/j1KlTTPiprKxEXV0dZmZmsH37dgwMDPBInQ6vmzdvxuHDh5Geno6qqioerW7fvh2jo6NrPtvq6iojO91uN4aGhlBaWsomMbpcUseKQg2Lioqg1WqRlJSEyclJvPbaaygsLMSpU6eg1WoRHx+PtLQ0VFdXo7e3F1/96lfR3d2Nmpoalj82NDTAaDSu+WwAuKNPZvTW1lbs27cPHo8HBw8eZLKHWq1mbPFf//pXyGQyvP766+jr62M/GHVna2trMT8/z8Qgg8GAp59+GgMDA8jIyOADLGGD1yryyFAAmtlsRlNTE37wgx9g+/bt6OnpgdfrRW5uLgoLC/HMM89gfn4eBw8eRFpaGnJyclBdXY2kpCQ8//zzkMlk3MmkS+zIyAgeeugh/OUvf8Hq6ir6+/tx5swZhEIhjI+Pr/lsWVlZUCgUjBTu6+vD9u3bUVJSgvj4eCQnJ2PHjh1skI+NjcXk5CQ+85nPQK1Ws/QjKysLGzduhMFgYMyzUChEU1MTVldX0dLSgvb2dhQVFfEm1dTUFPXATO/88vIyd98KCwvR3t7OncW4uDg4HA6cPXsWKpUKw8PDPGXKy8vDli1bUFpairi4OIyMjECn06G3txdWqxUtLS3o6OjAkSNH2EMSDAah1+shFoujPlt+fj4mJiYA3CZQ9fb2Ij09nS9q77//PmpqanDkyBGWtv7ud7/DJz/5Sdy4cYPNtoT3PH78OILBIF555RUMDQ1h586dePHFFzkvp7KykqEBCoUi6vorFAphs9mwdetWxMXFoaOjA/fccw/a29vxpS99iRG2tD8+8cQTOHfuHIqLizE/P8+XrDNnziApKQm3bt1CSUkJyy0yMjLQ29uLxx57jLM36MKckJCAnp6eNZ9Np9PB7/fznupyufCRj3wE165dg0KhYKnT0tIS3G43nnrqKT6MqFQq7N69G9u3b0dnZyd3uXU6HfLy8lBSUgKdToe2tjbs2rWLDfYFBQVQKpVobm6OGtBLmTB02VtdXcW+ffvQ09OD4uJiDA8PM0HKbDZzmKtYLMbNmzdRUFCAj3/840hMTERZWRkGBgawdetW9p9RzktzczOcTicnend2dqK0tBQ3btxY89n8fj9WVlbQ3NyMrKwsGI1GbNiwAZ2dnaipqUFNTQ0ikQimpqbgcDhQU1OD9PR03HPPPZicnERGRgYefPBBjIyM4Pjx47DZbGhubkZNTQ2v0cFgEM8//zzOnDnD0I+enh5GpK9VhDVNSkriCejGjRuxvLyM+++/HxaLBWq1Gvn5+VhcXERRURECgQA/z+LiInJyctDa2oqMjAxcvnwZBw8exPDwMHsqr1y5gn379uHUqVNsDr916xakUmlUeRJlcYjFYgwNDeHy5cvYv38/0tLS+BxDk6srV64gEongwoULkEqluHXrFkQiEXQ6Hc6dO4e8vDzcuHEDO3fuhNlsxuzsLLRaLYxGI/bu3QuHwwGXy8WS2rS0tKifWygUYtmiXC7H/Pw8MjMzce3aNZw+fRqTk5NMhkpLS8OOHTvQ1dUFhUKBt99+Gw6HA/X19VCpVPjBD36ARx99FJ/97GdZDfLFL34RarUa27dvxzvvvIO6ujps3ryZs3paW1vXfDadToe5uTlUVVVBo9FwSPLc3BwOHjyIubk57Nq1Cx/72MeQmZmJrKwsfPWrX0VGRgZmZ2dZtv/mm29yKvl9990Hs9kMm83GAImamhqoVCo0NDQw2KG0tPRvUiapPrQZPC4uDk8++ST6+/shkUhQXV3NlKlTp07BYDDAbDYjISEBxcXFfDAfHx9n8kA4HEZDQwMHgvT09EClUvEI+Ctf+Qq++c1vIi4ujqci1KWMdpinEa/NZmM++vT0NMtVVCoVTwlOnjwJg8EAqVSKU6dOITY2FgqFgrtiMTEx+Nd//Vfk5uZyiqff78fIyAi2bNmCkpISXLlyBXNzc0zfOXnyJDZu3HjXZxMIBCgsLGQtqlwux/nz5yEWi3HixAnIZDLWOEYiEVRWVuLUqVMwm80oLy9HXl4eE2+6u7uxceNGhEIh9PX1cZhfY2Mj3njjDU5BpjH09PQ0srKy1qR1EZqTUrBJ/5iWlob+/n5YLBbuBBQWFmJhYQF79uyB1WpFcnIyRkdHuQv11ltvoa6uDgAwNDTE0qLY2FicP3+eU5LJJD45ORlV1qXX61FUVMQmPzI3FhYWIj09nROCSUrhdDqxceNGzsAYHh7G0NAQ8vLyWGv7yiuvYHR0FEKhEOXl5Thw4AAuXryIuro6lJaWcpfZ4/FEzW3x+/1oaGjgQzOhSxcWFnD58mW0tLSwtlksFmN0dBQymQwdHR0QCoWsq6QQvJWVFUilUnR2dnLAT2trKxobGyGRSHgSqFKpsLi4GHVRpFAlovOo1WomCI2MjGD37t1snCbp1Pz8PBQKBYxGI3tolpaWMDg4iEAggPLycuTn58NsNmN8fBzz8/PsFSADHnm0/lYy+MzMDF9cKAvl97//PWQyGdra2pgNn5iYiPvvv583Epp4AuBwT61Wi5s3b0IulyMrKwsLCwu8qZw4cYLlJpQmTkbTtYpCA7OzszE/P4/u7m7Y7Xb84z/+I86fPw+9Xo+cnBzcunUL58+fx+OPP45AIIChoSHemOPj4zmJ/ZVXXsFjjz0Gi8WCwcFBxMTEYN++fThz5gxLIsh3YjKZogb2TU9P88RQKpXy52S32xEfH4+uri5eDzZv3ozY2Fi0tbVheXkZOTk5nFuUlJSEd999F0tLS3j33XcRDofh9/vR09MDuVzO8qaFhQVcv36dsb7RDsxOpxN79+7FwMAA+0zefPNNpKSk4OrVq9xxi4mJgUKh+ECoGgAUFBQwhjw3NxdpaWmYnp7mTBfKQHn55ZfxyCOPYHp6mv+c5eXlqFO02dlZKJVKxm8ajUa8/vrrCIfD2LdvH3Q6Hf785z8jEomgtrYW4XAYBw8e5N8qkYNEIhG6urogFAqxvLyMbdu2IT4+HufOnUNaWhra2tqwceNGzjki9O709PSazzY3N4fq6mp0dHQgISEB9fX16OjogEqlwv/9v/+XD4M0QX/llVcQGxvLWVA0PTtw4AAuXLjAUyTgNuijp6cHGzduxMDAAJuyCaNM8qq1qre3F5mZmWyQp3yfvLw8pphRKjQleYdCIbz//vvQaDQoKytjn0ZLSws+97nP4bnnnkMwGORgWZryCQQCtLa2wuv1Ijs7G8XFxVH9SiaTCfX19Yx7JaNuSkoKm19JIkSem/n5eSiVSlRUVODMmTM4efIkDhw4gF/+8pf44Q9/CL1ej1AoBLvdjvHxcVgsFpSUlKCkpATXr1+HQqFAeno6jEbjmnAVAPyb1uv1CIfDcDgcUKvVsFgsePfdd9mXRQhx8v289tpryM/P58aPXq/H4cOHce+996K5uRn19fUIBAIYGxtDTU0Nzpw5g9XVVYbykDc12sSW4AeELg2FQhgbG8PCwgIcDgeUSiXLuJKTk/Hee+9xeCdNsgoKCnDo0CE88sgjePLJJ1mi6fF4MDAwgNjYWJw+fRr/8A//gNdee42buy6XC9nZ2Ws+G10IKeiWJO5yuRzBYBAKhQIjIyOw2WyoqalBeXk5rl+/jtHRUW6YUr7I5OQkMjMz0dPTw0neZ8+exenTp/Hwww8jNzeXka+RSITT7deqSCTCaOP5+Xk4nU5WjRAZjYA5lMtG34XJZMKJEycYn19YWIgjR47gj3/8I5aXl7kRPjQ0hOHhYVRUVMDpdKK/vx8AmGK4Vl28eBFZWVk4e/YsFAoF6urqcO7cOUbIl5WVsfJCpVJBLBbjyJEjOHXqFIccJiYmcvjjhQsX8JGPfARisRjXr1/Hz372M+zduxdmsxkFBQUM5/B6vYzkPnLkyJrPR/WhJxoJCQn43ve+h+7ubphMJk5CJjPbgQMH0NLSgoKCAsjlcuzZswe//e1vce3aNUZg2mw2NozT2Kaqqop1uyMjIygtLUVZWRn/ECitMNrNKS8vD2+++SZPTtRqNSc8z8zMIDs7m+Pil5aW4HA4IJfLeeHo6uqCzWZDQ0MDDAYD2tracOLECYyOjkIsFqOsrAxbt27F3NwcH9wpZTTaiBe43UVYWFjgBZ428vn5eTYB0lSjoKCANYjNzc3w+/24desW+vv72SshFosxNzcHtVqNwsJC+Hw+/OY3v0FGRgb6+/s5+ZM26GjelszMTIyOjmJ6eprlJ4TtfOeddzA7O8thUd/73vf4ABoOh/Hee+9hYGAAi4uL6OrqYnQw6dWzs7MRDAYxPDwMnU7H3/H8/Dx3R+igcbeibA86HKlUKsTHx3OK55YtW5CRkQGHwwGn04nh4WEcPnwYIpEI//mf/4n5+Xns37+fx7j/+q//CqlUis985jP4zGc+g+rqatY/z8/P4/LlywiHwyyli6aLJMwdhUuS/rm1tRXj4+NMkrh48SI6Ozuh1+vZrH/lyhX8+c9/xtmzZ9ng7nA4IBQKOUjLYrHgT3/6E2JiYjiAkP65kZGRqMm0CwsLGB0dRXx8PJKSkrC6ugqpVMq0FXofRkZGcPr0aU5atVgs6O3txfDwMORyOT7/+c9zvsuNGzdgsVh4lLq0tASLxYL6+nr4/X7Mzs5iYmICJpMpamAUAEZexsfHY8OGDWhpaUF2djZ8Ph/m5uYQCoUwOTmJCxcu4Pjx4xgeHsbXv/51eL1e9PX1IT4+HnV1ddi0aRPGxsawsrKCyclJTkQmX4JarUZpaSm0Wi1/rjQOXquSkpKQn5+P2NhYFBQUoL6+Hnq9HkePHsWhQ4eQmJgIk8nETZXLly+juroaOp2OTdZWqxWhUIjZ7EQVmp6extWrV/HnP/+Zc35cLtcHzPXRjHXp6emsnaYJKB0QtFotU3horbjTB/LGG2/g+PHjMJlMyMjIQF1dHdrb25Gens4BrNTBzs7ORmFhIUv1ioqKkJWVFXUjTk5OxunTpyEQCFBcXAydTofi4mIYDAZs2LABjY2NcDgcePvtt/H222/D7/fj85//PHJzczkAivT5HR0deOeddyAWi7Fr1y60tLQgLi4OFy5cgEAgwJkzZ/hCROFd0SRA1KAxmUwsZSQJDGGVyThKoWLHjh3DzZs3EQqFsLS0xL630dFRZGVlsZ9OKpUiMTERGzZswPbt2zE5OQmRSAS9Xo9bt27B4/GgtrZ2zWerrKzE1NQU07Q6OzsRiUTw85//nJs6iYmJaG1txSuvvIL5+XkcOnQImZmZ7PNaXl7GzZs3+bc+NzcHjUaDXbt2YefOnfD5fEhMTERJSQkqKyv5fQwGg1G/05SUFCZCkQeqq6sLGo0Ger0esbGxHOp4/PhxOJ1O1NXVMbL56tWrmJ6e5gyThx9+GAMDA7Db7fx+RiIRHD9+HMBteQp5Jm7duoVz586t+WwajQavv/46rFYr5HI5h36mpKSgt7cXWq0WEokEBoMBo6OjaGxsREtLC2OC7XY7UlJSsLi4iJKSErz66qv4y1/+guHhYQBAdnY257XcuHEDoVAIZrMZvb293DBcq6qrq3H69GlMT09DpVKhsrKSD2bkJZJKpRCJRBgfH8dvfvMbCIVCOBwOvPPOO+jt7eUDfXFxMX72s5/xmYmaSP39/ejt7cXc3ByGhobw17/+FefOnUM4HIZMJlvz2QQCAZu1CSQQGxsLp9OJ9vZ2BAIBpKamQqvVMqjn4MGDDJeIRCKYnJzEW2+9xR6x7u5uTE9PY3V1FUVFRWhsbMTk5CT7eVZWVvjiEG1PTUtLw9WrVyGXyzkHpbq6Gj/96U/ZVxgMBjE1NYWzZ89yJgg1qN555x3uwNNnvnfvXuzZswc7duzAli1bYLPZoNFokJ2dzb6id999l8MY16qsrCxMT09zQB9NVpxOJ3p6ehjRfvLkSfz0pz/FH/7wB55eiMViNDU1ob6+HgKBAG1tbfj+978PuVzOuT2nT5/G3r17MTMzwxeEmJgYKJVKbrJEezaDwYC4uDisrq7CZrNBJpPha1/7Gs6ePYuOjg6YTCYGQZAvhIIDV1ZW+HdHjeKJiQkkJCSgsrISGzduZGkzTQgTEhJQUlKC8vJybN++fc1nu7M+9EVjbm4OLS0tUKlUvLHNzs5i3759jBylBYlCoXbu3AmRSMQH/fz8fDz22GO4dOkSAoEAd/GIZnHvvfdiaWmJNwKNRsMd/AsXLqz5bBMTE9i+fTsEAgGWlpaYulFXV4fl5WWkpaVhZmYGXq8X999/P86fP8+hatXV1fD7/RgfH4dGo0F7eztefvll7Nq1C83NzTxGnJubw3/9138hKSmJA8Yee+wxOJ1OxuXdrSgIhzo4q6urGB4eRk5ODktX7sxwmJ2dRV5eHlQqFRobG5GRkcE+AJFIhCeffBJGoxEWiwWBQAAymQyPPvooLBYLHnjgAaZn0ecRzSi5sLCAjRs3cqI6eTr+1//6X3wIbGpqwqFDh/Dkk09iamqKw2++8IUvYPfu3RAIBCgvL8fs7Cz7E2w2G2w2G5KTk/HQQw9henoaoVCIiTsUEhgtXIg6Q4ODgxgfH2dKzT333IObN2+is7MTfX19kEql2Lp1K2uTu7u78cADD6CqqgpOp5NNc9XV1Tzqn52dRVtbG8RiMSwWCyoqKhAfH8+64r8VJmixWFBXV8dgBI1GA7fbjerqajQ0NLBHpKSkhDWadIjcuXMnSktLoVar0dLSglAohKqqKhgMBk4JLS0txQsvvIC33nqLFw+NRoPNmzdDoVBENVx7PB4olUpYrVb26xiNRpbwBINBmM1mLCwsICEhgb0tfr8fmzZtQnZ2NiwWC6amplBaWspkHiLJWa1WHD16FCKRCH19ffw9azQaCASCqNMW4LZ8paCgAJOTk+jt7WXvwbZt25Cfn4/du3ejpaUFDQ0N2LBhA8bGxqBUKmEwGKBWqzExMYHTp0/DbrfD4XCguroaCwsLMBqN6OzsxNWrV6HVajE5OQmr1YqhoSFOXDWbzVENsCTvu3HjBrq6uvhy/8wzz/DGlpubC41Gg02bNsFms8HlcmFqaoq78haLhWEBjzzyCIaGhiAQCFBRUYFt27ahpaUFV65c4SZITk4OampqYDKZoqaskqnYZDKxjFQul6O4uBhDQ0P8/obDYZ5SbNu2DW63G01NTcjNzUUoFIJcLsfp06fx+OOPo729nbGRLpeLL2/0/Wu1WuzZswfZ2dlRn83pdPKGNzIywnjKT37yk1haWuKU6CNHjuCpp56CwWDgKYrdbofL5YLT6eSJw51BnXQ5uPfee2G327Fz506MjY1hdXUVxcXFTOhbq8xmM3bu3InV1VVMTEzAbrcjOTkZhYWFLJMjr09BQQH8fj8ef/xxyOVybN68GVqtFsvLy4x8Jg2/XC5nDwkl2JNRNicnB7W1tSgsLIy6jtD3T4G35eXlCIfD+MpXvoLU1FTGtpeUlDCGNhQKoaysDFVVVTw5qa2txfj4OHbu3MnYboPBgJs3byIhIQHt7e1YXl7GrVu34Ha7GR0cDb27srICrVYLs9nMOTsAeEpht9sxOjqKxMREPPXUUwgEAmxwpXfF5XKhoKAAw8PDOHDgAHdOSUGwa9cufhdSUlKQnZ0NjUYDjUYTtUt6Z5NjZWWFSXAlJSXYs2cPSkpKkJqairy8PNTU1ODPf/4zqqqqUFNTg+eeew579+5lzwWF01HqukAggMlkgsPhwPLyMiNAtVot6urqEAqF+EJyt9Lr9bj//vvh8/kwMjKCubk5zMzMoKSkhFULBDr41Kc+hcTERN5/dTodJBIJlpeXkZubi0uXLiEnJweZmZk8bd2wYQOeffZZDj7Mz8/nqYfD4Yh6sSVMrdvtRldXF9rb2xEOh1FaWsqTF/JO5ubmorOzEzabDUqlEi0tLSguLkZSUhKampqwceNGTnKnd6Orq4s9RO+88w43U7VaLeebrVU2mw3FxcUsUTMYDBgYGMDu3bs5xLmsrAwf//jH8Q//8A9MQnM6nbj33nuRkZGBrq4uuFwutLW18XSwq6sL4+Pj8Pv9eO655/CrX/2KA17Lyspwzz33MI56rRobG0NBQQHL28kY/cQTT7CXs7KyEs888wy++93v8hRm586dOHz4MIRCIVpbWxkG8+STT2J4eBjLy8vIy8tDfn4+HA4HYmJisLy8zBfAYDDI58e1ymg0spd1ZWUFIpEI+fn5rAYgBUhJSQlaWlpw/fp1XrOam5uxceNGpKWlQaPRsOLhxo0bGBkZgdfrZe/H8ePHce+997KnOCUlhSWKH6Y+tHSKtLBk2NTr9XC5XMzbNhqNCIfD0Gg0yMzMRFdXF8rKypCcnMx0FTLFEN2JCEvBYJC1+LGxsZiZmUE4HIbL5eKXPtqhlIwpMpkMUqkUq6uryMjIgM/nQyAQwF//+lcYDAbG6O3evZvNSz6fD0KhEHFxcfzjVSgUzLwOBoNMjlCr1bBardBqtVhdXWXMYbROZEJCApxOJ7Kzs2G32zEwMMCG2tjYWExPT7MsJhAI8CGFqD5isRgCgQBerxc1NTVwu92orKxkWhQZQgkfu7Kywl0j2ijWKpL+qNVqPtAShWN+fh4ul4vJQMnJyZidnYXL5WIjEwD+v0mGk5qaCrvdzsmxRFQZGhrizYH0tdE6HHdihMViMRNgIpEIcnNzGSMXFxeH+fl5lJeXo6+vD42NjQgEAowrtNlsPLkqLS3lzbusrAxnzpzh4LmioiKkpKRAJBIhNjY2qidIIBDAYrHw4k/SJ+LMW61Wpm3Q4g2AqR2EEpyfn2dMYUZGBm+cKpUKOp0Ot27dgsPhYE36wsICP+9aJRQKWSIRGxvLm2lPTw8TPBQKBcLhMFZXV5kARgjbjIwM1nKT6ZgM2PT9LiwsoLa2lnMEfD4f4uPjmUYVrYg8Q8ZWj8eD1NRURCIRzmJITExkFJ9UKsX4+DibcUlaptfrUVNTw5hSeufJhyASiTAyMsKfOX3+IyMja4YdRiIRmM1m+Hw+7qLR9HJ1dZWnuBqNhkkkXq+XDetyuZylakRhKikpYeMhyXcomyE9PZ0Je/SdrFWhUAg2mw1CoRCxsbFYWVlh/GpWVhbTZwQCActG09LSUFFRAavVCpVKxUZ0MoDT5TMuLg5lZWVwOByMks3MzOQpUzTJA/0e6LdIk8i0tDSMjY1BrVZzpgkhtAsLC9HR0YGUlBRuQNCmVlpaCpPJhNraWkxNTQG4La3KyMiAXC7naTQ1UGQyGXp6etYMjUpMTITVauXmUyAQgFarxezsLMrLyzkwNRKJwGQyoaioCO3t7RCLxbh69So0Gg2Td0QiEV/eyaROGPfm5maIxWI253u9XohEIkxMTGDLli13fTYKcE1OTubQWtLIj4+Pw2w2c4Lx6uoqEhISMD8/z9MIkpXOzMygrKwMw8PDLCsljwgd3ltbW5mO53a7kZqaymb/uxVdQugzC4fDKCkpwdDQEMtm6c8g8tbS0hKqqqpw9epVBAIBplQSsruyspJN3wUFBUhMTGTiVHp6OrKzs3lqHa0zT7k2RqORTcMxMTEoLi6G1WrF4uIiUlNTOZMiKSkJCwsL2LRpEzweD5KTkzl8l0zEWq2WYSr0rmRlZWFychJisZjJXkVFRVG9hUThI6wygVyA21NyaoTSb7C0tJTzSMinR0GmNOlVKpUsx4yNjeWm1vDwMILBIP/9U1JSou739F1KJBIIBAKGydjtdigUCpbBiUQieL1e1NbW4sqVK5yXQZPYSCTCMAmaqi4vL8Pn86GnpweHDx/mPA6i7fn9/qj7VlxcHCOXSearUqmQkZEBlUoFu92OuLg4CAQCbu55vV4olUqWZVMjqqqqiulTXV1d8Hg8KCkpgVqtRmpqKqxWKxQKBRMt/1bCNWGPqSFMazBdClwuF5NRKceI4Bdzc3MQCATIyMhgVD9R1ai5nJSUhLm5OZSUlMDpdCI3N5chIiS7i/a+ORwOJCcnw+PxMBEsPz8fMpmM6ZtEtaT3QKvVIhQKQaVSQaFQMFJ7amqKqaEymQyZmZkIhUJISkpiKIzD4YBUKkVBQQHTVP9WfeiJBh1KY2Ji4PF4GKdKnae5uTn4/X5m+LtcLiwvL/OCFolEEBMTwy/Q9PQ0FAoFPB4P7HY7xGIx521YLBYIBAJOk6WD5lpFB3Gi3zgcDiQmJmJ6ehqpqamYmZlBTEwM/H4/rl27ho0bN7JJiEaaIpEIRqMRKpUKs7OzzNE2GAzMbc7OzsbU1BT/2OlHHW3hoUMpdRwpi4A+G8KdUSeFONwUDAXcNkpRZ8FkMiEnJ4f/zpSgmZiYCIfDwSPNpaUlKBSKv7nw3DlVITQgSdamp6dZs0kHqZWVFcjlchgMBjidTgiFQv7/zc/PQyqVIhwOw+PxwOPxQK/XQy6XY2lpCfHx8Rx+pFQqo47uaQqgVqt5oSZKg1qtZuoJGezpIkTG5/7+fuaMy2QyDm+ibAkysMbHx2N+fp5ReX6/HzExMVGpUzQ6pUvynV38SCSCkZERWK1WOJ1OxkOKRCK+WCUlJSExMRFTU1P8Z1E6sNFohNlshslkQn5+Phv0KGyMQoeivW+0IRHqNhKJYGJighPnifBBRBmS+pAOmzxNlMWRlpYGj8fDh+q+vj6o1WqWI4bDYZa4RXs2ANx1J+oNLVROp5N9RXQAcrvdSE9Px+DgIP/+Ce1KcAT6zdKiTZ6noqIi9r8sLCxwFyraxIWaHSSJuZOwJZFIMDY2xp6txcVF5ObmclYA0eJIXiaXyzE3N8fP7Xa74XA4mDHv9XqhVqvhdDphMA2CkVgAAA2hSURBVBjYFxLtcyMZDMlKvV4vDAYDa71pI6ZROr3noVCIc3qGh4eh1Wr52UjGQub35ORkhEIhfh8plT7a50Y8ejqkUE7K2NgYAyXIs0RyF7PZjKSkJCQlJUEqlSImJoYzWxwOB+u6FxcXIRKJYLPZUFhYyO+x3+9n+Ue07Jb4+HhYrVY+qEskEkYUU0YFNYoIN04X1OnpaV63aPpOU2lqZMTGxvLBRalUIiEhAQsLC7Db7RAIBFGN6gkJCTwtAW5PmAmLTShp+v0SzlOv12NpaekD+4jBYIBWq0VfXx8SExN5XaJGGh1WqOFBWTTR/EqU15SSksKZHImJifD7/UhPT4fZbGZvJRnNCTlNeRg+nw8dHR2QyWQwm80MoiDa1tTUFHvoEhIS+OL0P9fU/1lEzaNsFKvVyo1GCg2mhh11oykt3eFw8FpFl2ySttLZhfYoyjGgiyg1HaN9brT+SqVSJuqRrDIrK4vxzzR9z8/P53wNt9vNYb7Dw8NITU3FwMAAyz49Hg9mZ2dx/fp15OXlYWxsDH6/nyEmMpmMmxprPVskEkFycjJjxGnfp2ZWOByGz+fD4uIiNmzYwI2g6elp/rwIDSwUClkBQJSkjo4O6HQ6lgonJCSwLDFas5iaSET+o99rKBSCQqHgvWhubg5dXV3cNKNpN8mzJiYmoFarP4Cnpv2WsmnII7a6usry7Gj7VlxcHAf+kb9FIBCwosLhcGBychJ9fX2YmJhAUVERI3RJlq/RaBgfTKQsUgmQNC43Nxezs7McWElNt2iQELIv0ETR4/GwH4/eB5KX0xoWCoWgVCoxMTGBubk5hMNhWK1WxMfHs6wwLi4OQqGQ/67FxcUYHx/nc4LT6UROTk7UGIA760NPNHw+HzZt2sQLXEJCAnw+H3w+Hy+ILpcLNpsN4+PjiEQirA8njSJ1LfV6Pdrb2/Hss8+yPpx4wJ2dnWzQXl1dhcvlQn9/f9Tx+J3BVXNzc+jt7YVMJoPNZsNjjz2GmJgYdHR0oL29HQaDAdeuXcPExATnFlA3aWBgAMPDw2xMysjIQDgc5lA3iUSChIQEnnSQTCGaHIP8GbOzs9xFJBwhbTAkaaFRI3BbOkSfIV0caAPW6/WIj49HSkoKlpaW+NJGUwWRSMTd1mgGUwpGokOCRqPB3NwcBgYGWK9M+j4akdKY7dy5cxCLxdDpdMjKykJnZyfLduhAT1ONUCjEKLxAIMCdnWiGP+r4U4YCEW2qqqr4u6Mgw4KCAtbm9vb2YmxsDFqtlrM2aNTf39/PGSsUmNfR0QGNRsObPBnfoo15aYEjbF4oFOLOr8PhgMVi4e5FKBRCSUkJysrK4HQ6+TOMjY1l2QfpWOPi4pgURRhSen56F8iMtlZRQBRtkDTZuHNq5ff7+fBNsqPu7m7OL7BYLNx9psMVdfsoUbavrw+VlZVQKpVMJ6LOVrQipCN5TojtT9kTeXl5vNHTe0xmf7pQ0AZ39uxZxMfHQ61WIzMzE1arFRMTE0yxobBQqVQKiUQCt9sdtTtP0006AIdCIQwMDLC0jC78gUCAg6UWFxc5LZgQ2RkZGZxiS74Qr9fLMqqWlhZcvnwZMpmMAxXvzN64W1GjQiwWc6iYUChEdnY2DAYDfy7UTQ8GgxgZGcH58+dRU1PDF2oAvMENDw9DLBbDarXixIkTKCsrY7lHZmYmNxGmp6ejNgVozbLb7ZzaPTs7C4VCwXQwmnjI5XIMDQ2hsrIS77//PkpLS6FSqdhAu7i4yE0iwnUODQ1hdHQUZWVl8Pv9MJvNnKFAkpu1yu12M4WPclVsNhtUKhUHV3m9XgZ1TE9Po7i4GFKplC8PC/8dbKpQKCASibjJQAfJSCTCieYkaVWpVEhLS4uq519dXUVqairn9pBRnnC3sbGx/Luj8DG32824dDr8yGQyDA8Pc6JvTk4OwuEwurq6sLi4yMF2VquVD8CUirxWeb1eJCUl8bSO/GFqtRrZ2dnsRyIaHh3q2traOBmapuIxMTE8KaaAS8J9ElbT4/FgfHwc4XCY8eJ79uy567PRemsymbgJSevW4uIiw0YmJia442+z2TAzM8NAEolEAqVSicnJSSQmJjIVc3l5mT1ffr8fKpWKEaMksY42iSflA+H+w+EwBgcHkZCQgNraWoyNjbEnEgAee+wxvvza7XbY7XYEAgHe29xuN3bv3o2KigokJCRgeHgYKysrmJ2dZWob/Xm0lq5VRCOk0DYKC6UwWLVaDbvdDp/PxxP2LVu28B7g8Xi48UDricViYRxuJBJBd3c3RkZG+O9Mhv07L/Rr/RZUKhU3bnQ6HbxeLxYXF7kB4PF4WA49MjLCjcHk5GR4vV7+vnt6eng6SmQ9r9eL4eFhxpdTOjjlJUVrZBNJLRKJfCC8cmlpCbm5uSz3p3Ry8g1R/gmh+OkM09bWxioWyvAhKb3RaMTg4CBfFOx2e9RmsVgs5qkUZcdQ018kEmF6epob6SMjI0hMTOSL+ODgIOflaLVaxsqPj48zTc5qtWJ+fp7XOofDwf5PakJ/mPrQF4309HQ2ndENPzY2FpcuXUJ8fDzf1iiQ59ChQ7hx4wYuXLiATZs2ITMzk2U9CoUCcrkcXV1dSE1NhUgkwtTUFM6cOYO8vDzodDqWVpHvI5pGWK1WQ6/X8yiRbtkJCQmwWq2Ym5vD5OQkYmNjUVZWhsHBQTQ2NvKGRQclo9HIWuuRkRGkp6cjLy+P+cM0RQgGgxAKhXzjo/HrWs9mMBhYRhQOh7F582YMDg7iT3/6E3OyyUtAHSk6MNCGIpPJcOPGDTz88MMsvwFuHyyJKEKd4JWVFV6sohmHRSIRB9jQ6DYYDKK/vx9arRabNm1ighX9qCnlm8bLNKYuLCxEV1cXZDIZs5ZNJhNrhOvq6hAfH4+BgQHu8pP56G5Fn4lIJOJul8lkwksvvYQDBw7AarXC7Xaz3Oyee+7BrVu30NnZidraWp6Q0cXwwIEDTN+iyY9Wq0VFRQW6u7uRkJDA8pXFxUX4/f41n42ehzrfS0tLGBgY4CkSGeqI2FJcXMyBVpOTkxgaGmK98uzsLNNd6BCg0WiQlpaGyclJbNq0iZnwMTEx8Pl8UQlsdDCg9OPl5WWeRE1NTfFGThd/SuLNy8vDzMwMy/wSEhIY+0jj0nA4zJKAnJwcTE5OciefQpuiPRtwe2GcmJhAQUEB0tPTYTKZmMxDgZ40EQ2FQhgdHUVxcTH/OX6/H2KxGLm5uejv78f+/fv5YhwKhfhSYTQakZGRwZsQrSPREIsCgQDLy8u86RDzXyKRYGZmhvNOKGzM6/UyUtDhcLAfYW5uDrm5uUhISMDmzZvh8Xj4MhEXF4fJyUls3rwZly9fht1uh0QiweDgYNQ8CLqcp6SkIBAIwGKxQKlUYmpqClarFfn5+fD5fDwVNhqNkMlkfNihjXVxcRFisRj5+fmcx0Mwg6mpKc6smZ+fx+zsLHvcoh1KY2NjEYlEWA63tLQEl8uF69evQ6fTQavVQq/XIyYmBjU1NSguLubfNuUzDAwMQCwWc24B/X0jkQg3qBITE5GQkMDTK+pAR/utSiQSbmSRATkxMRGzs7NwOp3YunUrhoaGmKqSkpICjUaD3t5eRogT4GJwcJDX/pqaGn5uajSRjIKCHa1Wa9SGBU1tSPpCHefh4WEEAgGW8fr9fkxPT0Mmk/H729LSwpcekn2RCZq6kjt27OBpg8Vi4WR6u93OEqK1ig6xKysr3DElycrAwAAEAgGSk5P5N3nmzBmUl5dzEKpCoUAoFEJraysGBwdx8OBBnD59mmEtycnJqK2tRVtbGz7xiU9gYGCATf7T09NIS0tb89mSk5OZcEQHpP7+fgwODnJYLeUF2O12tLa24siRI/B4PPjpT3+K7Oxs7Nq1i/MYnE4nf4+EZScpbGdnJ8tVSRYarekpkUh4rSF5o1qthkKhwKlTp1geJZVK0draitzcXMjlcj5HkMdm+/bt0Ov1PLWl3zRdSm/duoVNmzZxd5n2r2iXoOXlZQwNDXGYptPp5Lwu2kdpDZmbm4Ner0cgEODuO/3527dvZ8ol+REIuf6lL30JXV1d3CSm9YbysNYqhUIBvV7PFyG73Q6lUgmBQIAbN25Ap9Mxgp+mPiKRiM+IRN2KiYlBe3s7MjIyMDMzw5Q+8rr09vZyBALBdchjFu1z6+3thVqtZlUGkaqqq6tZTkyJ8DExMWhsbGTAEGV5EPDn5Zdfxkc/+lGerBIRcX5+Hjt27OBmdyQSgVAoRGlpadTPbWpqij2XpCqgTJTS0lIsLi5ibGwMQ0ND2LZtGzerCTNPTa5IJIL+/n5kZGQgJiaG31WCbcTFxSElJYUb3xSc+2FKsBqt5bJe67Ve67Ve67Ve67Ve67Ve6/X/oz60R2O91mu91mu91mu91mu91mu91uvD1vpFY73Wa73Wa73Wa73Wa73Wa73+7rV+0Viv9Vqv9Vqv9Vqv9Vqv9Vqvv3utXzTWa73Wa73Wa73Wa73Wa73W6+9e6xeN9Vqv9Vqv9Vqv9Vqv9Vqv9fq71/pFY73Wa73Wa73Wa73Wa73Wa73+7rV+0Viv9Vqv9Vqv9Vqv9Vqv9Vqvv3utXzTWa73Wa73Wa73Wa73Wa73W6+9e6xeN9Vqv9Vqv9Vqv9Vqv9Vqv9fq71/8DbOLqncNBWNAAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 42, + "id": "30a86c4e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "plot_number = 400\n", "generated_images = generate_images(generate_latent_points(number=plot_number,\n", @@ -3657,8 +3736,10 @@ }, { "cell_type": "markdown", - "id": "dbe15f11", - "metadata": {}, + "id": "2b84d5f3", + "metadata": { + "editable": true + }, "source": [ "A pretty cool result! We see that our generator indeed has learned a\n", "distribution which qualitatively looks a whole lot like the MNIST dataset." @@ -3666,8 +3747,10 @@ }, { "cell_type": "markdown", - "id": "f54ccceb", - "metadata": {}, + "id": "d0d717c0", + "metadata": { + "editable": true + }, "source": [ "## Interpolating Between MNIST Digits\n", "Another interesting way to explore the latent space of our generator model is by\n", @@ -3681,9 +3764,12 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "1ab8363e", - "metadata": {}, + "execution_count": 43, + "id": "2cccf1dc", + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def interpolation(point_1, point_2, n_steps=10):\n", @@ -3697,30 +3783,23 @@ }, { "cell_type": "markdown", - "id": "dc71fa17", - "metadata": {}, + "id": "77e397c1", + "metadata": { + "editable": true + }, "source": [ "Now we have all we need to do our interpolation analysis." ] }, { "cell_type": "code", - "execution_count": 32, - "id": "f72c1f5a", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'numpy.float64' object cannot be interpreted as an integer", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m latent_points \u001b[38;5;241m=\u001b[39m generate_latent_points(number\u001b[38;5;241m=\u001b[39mplot_number)\n\u001b[1;32m 3\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mplot_number\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 5\u001b[0m interpolated \u001b[38;5;241m=\u001b[39m interpolation(latent_points[i], latent_points[i\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 6\u001b[0m generated_images \u001b[38;5;241m=\u001b[39m generate_images(interpolated)\n", - "\u001b[0;31mTypeError\u001b[0m: 'numpy.float64' object cannot be interpreted as an integer" - ] - } - ], + "execution_count": 44, + "id": "27662070", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "plot_number = 100\n", "latent_points = generate_latent_points(number=plot_number)\n", @@ -3736,1532 +3815,9 @@ "\n", "plot_results(results, plot_number)" ] - }, - { - "cell_type": "markdown", - "id": "f56bc158", - "metadata": {}, - "source": [ - "## Basic ideas of the Principal Component Analysis (PCA)\n", - "\n", - "The principal component analysis deals with the problem of fitting a\n", - "low-dimensional affine subspace $S$ of dimension $d$ much smaller than\n", - "the total dimension $D$ of the problem at hand (our data\n", - "set). Mathematically it can be formulated as a statistical problem or\n", - "a geometric problem. In our discussion of the theorem for the\n", - "classical PCA, we will stay with a statistical approach. \n", - "Historically, the PCA was first formulated in a statistical setting in order to estimate the principal component of a multivariate random variable.\n", - "\n", - "We have a data set defined by a design/feature matrix $\\boldsymbol{X}$ (see below for its definition) \n", - "* Each data point is determined by $p$ extrinsic (measurement) variables\n", - "\n", - "* We may want to ask the following question: Are there fewer intrinsic variables (say $d << p$) that still approximately describe the data?\n", - "\n", - "* If so, these intrinsic variables may tell us something important and finding these intrinsic variables is what dimension reduction methods do. \n", - "\n", - "A good read is for example [Vidal, Ma and Sastry](https://www.springer.com/gp/book/9780387878102)." - ] - }, - { - "cell_type": "markdown", - "id": "b26e3e1a", - "metadata": {}, - "source": [ - "## Introducing the Covariance and Correlation functions\n", - "\n", - "Before we discuss the PCA theorem, we need to remind ourselves about\n", - "the definition of the covariance and the correlation function. These are quantities \n", - "\n", - "Suppose we have defined two vectors\n", - "$\\hat{x}$ and $\\hat{y}$ with $n$ elements each. The covariance matrix $\\boldsymbol{C}$ is defined as" - ] - }, - { - "cell_type": "markdown", - "id": "54cb45d4", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", - " \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{y}] \\\\\n", - " \\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "623caca2", - "metadata": {}, - "source": [ - "where for example" - ] - }, - { - "cell_type": "markdown", - "id": "f7ef812c", - "metadata": {}, - "source": [ - "$$\n", - "\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] =\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})(y_i- \\overline{y}).\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "2602eb49", - "metadata": {}, - "source": [ - "With this definition and recalling that the variance is defined as" - ] - }, - { - "cell_type": "markdown", - "id": "7f9ea215", - "metadata": {}, - "source": [ - "$$\n", - "\\mathrm{var}[\\boldsymbol{x}]=\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})^2,\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "b8b6670e", - "metadata": {}, - "source": [ - "we can rewrite the covariance matrix as" - ] - }, - { - "cell_type": "markdown", - "id": "a66ac744", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", - " \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] & \\mathrm{var}[\\boldsymbol{y}] \\\\\n", - " \\end{bmatrix}.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "abf06e7c", - "metadata": {}, - "source": [ - "## More on the covariance\n", - "The covariance takes values between zero and infinity and may thus\n", - "lead to problems with loss of numerical precision for particularly\n", - "large values. It is common to scale the covariance matrix by\n", - "introducing instead the correlation matrix defined via the so-called\n", - "correlation function" - ] - }, - { - "cell_type": "markdown", - "id": "a9361290", - "metadata": {}, - "source": [ - "$$\n", - "\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]=\\frac{\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}]}{\\sqrt{\\mathrm{var}[\\boldsymbol{x}] \\mathrm{var}[\\boldsymbol{y}]}}.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "c9eb215d", - "metadata": {}, - "source": [ - "The correlation function is then given by values $\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]\n", - "\\in [-1,1]$. This avoids eventual problems with too large values. We\n", - "can then define the correlation matrix for the two vectors $\\boldsymbol{x}$\n", - "and $\\boldsymbol{y}$ as" - ] - }, - { - "cell_type": "markdown", - "id": "5bea480d", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{K}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} 1 & \\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n", - " \\mathrm{corr}[\\boldsymbol{y},\\boldsymbol{x}] & 1 \\\\\n", - " \\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "4adcb1e9", - "metadata": {}, - "source": [ - "In the above example this is the function we constructed using **pandas**." - ] - }, - { - "cell_type": "markdown", - "id": "0cf211dd", - "metadata": {}, - "source": [ - "## Reminding ourselves about Linear Regression\n", - "In our derivation of the various regression algorithms like **Ordinary Least Squares** or **Ridge regression**\n", - "we defined the design/feature matrix $\\boldsymbol{X}$ as" - ] - }, - { - "cell_type": "markdown", - "id": "f5fbd136", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{X}=\\begin{bmatrix}\n", - "x_{0,0} & x_{0,1} & x_{0,2}& \\dots & \\dots x_{0,p-1}\\\\\n", - "x_{1,0} & x_{1,1} & x_{1,2}& \\dots & \\dots x_{1,p-1}\\\\\n", - "x_{2,0} & x_{2,1} & x_{2,2}& \\dots & \\dots x_{2,p-1}\\\\\n", - "\\dots & \\dots & \\dots & \\dots \\dots & \\dots \\\\\n", - "x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \\dots & \\dots x_{n-2,p-1}\\\\\n", - "x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \\dots & \\dots x_{n-1,p-1}\\\\\n", - "\\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "40560425", - "metadata": {}, - "source": [ - "with $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors/features $p$ refering to the column numbers and the\n", - "entries $n$ being the row elements.\n", - "We can rewrite the design/feature matrix in terms of its column vectors as" - ] - }, - { - "cell_type": "markdown", - "id": "fd4c3c5c", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{X}=\\begin{bmatrix} \\boldsymbol{x}_0 & \\boldsymbol{x}_1 & \\boldsymbol{x}_2 & \\dots & \\dots & \\boldsymbol{x}_{p-1}\\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "657b5492", - "metadata": {}, - "source": [ - "with a given vector" - ] - }, - { - "cell_type": "markdown", - "id": "f1966092", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{x}_i^T = \\begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \\dots & \\dots x_{n-1,i}\\end{bmatrix}.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "15ddbf77", - "metadata": {}, - "source": [ - "## Simple Example\n", - "With these definitions, we can now rewrite our $2\\times 2$\n", - "correlation/covariance matrix in terms of a moe general design/feature\n", - "matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. This leads to a $p\\times p$\n", - "covariance matrix for the vectors $\\boldsymbol{x}_i$ with $i=0,1,\\dots,p-1$" - ] - }, - { - "cell_type": "markdown", - "id": "7166e7db", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{x}] = \\begin{bmatrix}\n", - "\\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n", - "\\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n", - "\\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & \\mathrm{var}[\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n", - "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", - "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", - "\\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & \\mathrm{var}[\\boldsymbol{x}_{p-1}]\\\\\n", - "\\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "4a9e0c63", - "metadata": {}, - "source": [ - "## The Correlation Matrix\n", - "\n", - "and the correlation matrix" - ] - }, - { - "cell_type": "markdown", - "id": "88cfcdae", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{K}[\\boldsymbol{x}] = \\begin{bmatrix}\n", - "1 & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n", - "\\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & 1 & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n", - "\\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & 1 & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n", - "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", - "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n", - "\\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & 1\\\\\n", - "\\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "6bde42d6", - "metadata": {}, - "source": [ - "## Numpy Functionality\n", - "\n", - "The Numpy function **np.cov** calculates the covariance elements using\n", - "the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have\n", - "the exact mean values. The following simple function uses the\n", - "**np.vstack** function which takes each vector of dimension $1\\times n$\n", - "and produces a $2\\times n$ matrix $\\boldsymbol{W}$" - ] - }, - { - "cell_type": "markdown", - "id": "d00d6e18", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{W}^T = \\begin{bmatrix} x_0 & y_0 \\\\\n", - " x_1 & y_1 \\\\\n", - " x_2 & y_2\\\\\n", - " \\dots & \\dots \\\\\n", - " x_{n-2} & y_{n-2}\\\\\n", - " x_{n-1} & y_{n-1} & \n", - " \\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "7033f0c9", - "metadata": {}, - "source": [ - "which in turn is converted into into the $2\\times 2$ covariance matrix\n", - "$\\boldsymbol{C}$ via the Numpy function **np.cov()**. We note that we can also calculate\n", - "the mean value of each set of samples $\\boldsymbol{x}$ etc using the Numpy\n", - "function **np.mean(x)**. We can also extract the eigenvalues of the\n", - "covariance matrix through the **np.linalg.eig()** function." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "9b694c59", - "metadata": {}, - "outputs": [], - "source": [ - "# Importing various packages\n", - "import numpy as np\n", - "n = 100\n", - "x = np.random.normal(size=n)\n", - "print(np.mean(x))\n", - "y = 4+3*x+np.random.normal(size=n)\n", - "print(np.mean(y))\n", - "W = np.vstack((x, y))\n", - "C = np.cov(W)\n", - "print(C)" - ] - }, - { - "cell_type": "markdown", - "id": "3575ce25", - "metadata": {}, - "source": [ - "## Correlation Matrix again\n", - "\n", - "The previous example can be converted into the correlation matrix by\n", - "simply scaling the matrix elements with the variances. We should also\n", - "subtract the mean values for each column. This leads to the following\n", - "code which sets up the correlations matrix for the previous example in\n", - "a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the $2\\times 2$ correlation matrix (since we have only two vectors)." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "888d8df2", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "n = 100\n", - "# define two vectors \n", - "x = np.random.random(size=n)\n", - "y = 4+3*x+np.random.normal(size=n)\n", - "#scaling the x and y vectors \n", - "x = x - np.mean(x)\n", - "y = y - np.mean(y)\n", - "variance_x = np.sum(x@x)/n\n", - "variance_y = np.sum(y@y)/n\n", - "print(variance_x)\n", - "print(variance_y)\n", - "cov_xy = np.sum(x@y)/n\n", - "cov_xx = np.sum(x@x)/n\n", - "cov_yy = np.sum(y@y)/n\n", - "C = np.zeros((2,2))\n", - "C[0,0]= cov_xx/variance_x\n", - "C[1,1]= cov_yy/variance_y\n", - "C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)\n", - "C[1,0]= C[0,1]\n", - "print(C)" - ] - }, - { - "cell_type": "markdown", - "id": "419750af", - "metadata": {}, - "source": [ - "We see that the matrix elements along the diagonal are one as they\n", - "should be and that the matrix is symmetric. Furthermore, diagonalizing\n", - "this matrix we easily see that it is a positive definite matrix.\n", - "\n", - "The above procedure with **numpy** can be made more compact if we use **pandas**." - ] - }, - { - "cell_type": "markdown", - "id": "ee3269dd", - "metadata": {}, - "source": [ - "## Using Pandas\n", - "\n", - "We whow here how we can set up the correlation matrix using **pandas**, as done in this simple code" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "7bdb4c28", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "n = 10\n", - "x = np.random.normal(size=n)\n", - "x = x - np.mean(x)\n", - "y = 4+3*x+np.random.normal(size=n)\n", - "y = y - np.mean(y)\n", - "X = (np.vstack((x, y))).T\n", - "print(X)\n", - "Xpd = pd.DataFrame(X)\n", - "print(Xpd)\n", - "correlation_matrix = Xpd.corr()\n", - "print(correlation_matrix)" - ] - }, - { - "cell_type": "markdown", - "id": "85dd2b2a", - "metadata": {}, - "source": [ - "## And then the Franke Function\n", - "\n", - "We expand this model to the Franke function discussed above." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "dad17c14", - "metadata": {}, - "outputs": [], - "source": [ - "# Common imports\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "\n", - "def FrankeFunction(x,y):\n", - "\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n", - "\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n", - "\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n", - "\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n", - "\treturn term1 + term2 + term3 + term4\n", - "\n", - "\n", - "def create_X(x, y, n ):\n", - "\tif len(x.shape) > 1:\n", - "\t\tx = np.ravel(x)\n", - "\t\ty = np.ravel(y)\n", - "\n", - "\tN = len(x)\n", - "\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n", - "\tX = np.ones((N,l))\n", - "\n", - "\tfor i in range(1,n+1):\n", - "\t\tq = int((i)*(i+1)/2)\n", - "\t\tfor k in range(i+1):\n", - "\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n", - "\n", - "\treturn X\n", - "\n", - "\n", - "# Making meshgrid of datapoints and compute Franke's function\n", - "n = 4\n", - "N = 100\n", - "x = np.sort(np.random.uniform(0, 1, N))\n", - "y = np.sort(np.random.uniform(0, 1, N))\n", - "z = FrankeFunction(x, y)\n", - "X = create_X(x, y, n=n) \n", - "\n", - "Xpd = pd.DataFrame(X)\n", - "# subtract the mean values and set up the covariance matrix\n", - "Xpd = Xpd - Xpd.mean()\n", - "covariance_matrix = Xpd.cov()\n", - "print(covariance_matrix)" - ] - }, - { - "cell_type": "markdown", - "id": "0f414e1a", - "metadata": {}, - "source": [ - "We note here that the covariance is zero for the first rows and\n", - "columns since all matrix elements in the design matrix were set to one\n", - "(we are fitting the function in terms of a polynomial of degree $n$). We would however not include the intercept\n", - "and wee can simply\n", - "drop these elements and construct a correlation\n", - "matrix without them by centering our matrix elements by subtracting the mean of each column." - ] - }, - { - "cell_type": "markdown", - "id": "5d24b749", - "metadata": {}, - "source": [ - "## Lnks with the Design Matrix\n", - "\n", - "We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix $\\boldsymbol{X}$ as" - ] - }, - { - "cell_type": "markdown", - "id": "1fa2b9f6", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}= \\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}].\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "1eafde32", - "metadata": {}, - "source": [ - "To see this let us simply look at a design matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{2\\times 2}$" - ] - }, - { - "cell_type": "markdown", - "id": "0f8aec31", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{X}=\\begin{bmatrix}\n", - "x_{00} & x_{01}\\\\\n", - "x_{10} & x_{11}\\\\\n", - "\\end{bmatrix}=\\begin{bmatrix}\n", - "\\boldsymbol{x}_{0} & \\boldsymbol{x}_{1}\\\\\n", - "\\end{bmatrix}.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "36ca3c7b", - "metadata": {}, - "source": [ - "## Computing the Expectation Values\n", - "\n", - "If we then compute the expectation value" - ] - }, - { - "cell_type": "markdown", - "id": "0557c3d0", - "metadata": {}, - "source": [ - "$$\n", - "\\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}=\\begin{bmatrix}\n", - "x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\\\\n", - "x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\\\\n", - "\\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "2b1149ce", - "metadata": {}, - "source": [ - "which is just" - ] - }, - { - "cell_type": "markdown", - "id": "b0b887e0", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]=\\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] \\\\\n", - " \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] \\\\\n", - " \\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "b43bb2a3", - "metadata": {}, - "source": [ - "where we wrote $$\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]$$ to indicate that this the covariance of the vectors $\\boldsymbol{x}$ of the design/feature matrix $\\boldsymbol{X}$.\n", - "\n", - "It is easy to generalize this to a matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$." - ] - }, - { - "cell_type": "markdown", - "id": "e72ab391", - "metadata": {}, - "source": [ - "## Towards the PCA theorem\n", - "\n", - "We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as" - ] - }, - { - "cell_type": "markdown", - "id": "4dc2f372", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}= \\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}].\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "18b25871", - "metadata": {}, - "source": [ - "Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices $\\boldsymbol{S}$.\n", - "These matrices are defined as $\\boldsymbol{S}\\in {\\mathbb{R}}^{p\\times p}$ and obey the orthogonality requirements $\\boldsymbol{S}\\boldsymbol{S}^T=\\boldsymbol{S}^T\\boldsymbol{S}=\\boldsymbol{I}$. The matrix can be written out in terms of the column vectors $\\boldsymbol{s}_i$ as $\\boldsymbol{S}=[\\boldsymbol{s}_0,\\boldsymbol{s}_1,\\dots,\\boldsymbol{s}_{p-1}]$ and $\\boldsymbol{s}_i \\in {\\mathbb{R}}^{p}$.\n", - "\n", - "Assume also that there is a transformation $\\boldsymbol{S}^T\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}=\\boldsymbol{C}[\\boldsymbol{y}]$ such that the new matrix $\\boldsymbol{C}[\\boldsymbol{y}]$ is diagonal with elements $[\\lambda_0,\\lambda_1,\\lambda_2,\\dots,\\lambda_{p-1}]$. \n", - "\n", - "That is we have" - ] - }, - { - "cell_type": "markdown", - "id": "e7c81445", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{y}] = \\mathbb{E}[\\boldsymbol{S}^T\\boldsymbol{X}^T\\boldsymbol{X}T\\boldsymbol{S}]=\\boldsymbol{S}^T\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "3f09f7bd", - "metadata": {}, - "source": [ - "since the matrix $\\boldsymbol{S}$ is not a data dependent matrix. Multiplying with $\\boldsymbol{S}$ from the left we have" - ] - }, - { - "cell_type": "markdown", - "id": "af9e8912", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{S}\\boldsymbol{C}[\\boldsymbol{y}] = \\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "b40079b8", - "metadata": {}, - "source": [ - "and since $\\boldsymbol{C}[\\boldsymbol{y}]$ is diagonal we have for a given eigenvalue $i$ of the covariance matrix that" - ] - }, - { - "cell_type": "markdown", - "id": "25352fed", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{S}_i\\lambda_i = \\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}_i.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "dc56caec", - "metadata": {}, - "source": [ - "## More on the PCA Theorem\n", - "\n", - "In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is\n", - "$\\lambda_0 > \\lambda_1 > \\dots > \\lambda_{p-1}$. \n", - "\n", - "The eigenvalues tell us then how much we need to stretch the\n", - "corresponding eigenvectors. Dimensions with large eigenvalues have\n", - "thus large variations (large variance) and define therefore useful\n", - "dimensions. The data points are more spread out in the direction of\n", - "these eigenvectors. Smaller eigenvalues mean on the other hand that\n", - "the corresponding eigenvectors are shrunk accordingly and the data\n", - "points are tightly bunched together and there is not much variation in\n", - "these specific directions. Hopefully then we could leave it out\n", - "dimensions where the eigenvalues are very small. If $p$ is very large,\n", - "we could then aim at reducing $p$ to $l << p$ and handle only $l$\n", - "features/predictors." - ] - }, - { - "cell_type": "markdown", - "id": "96038157", - "metadata": {}, - "source": [ - "## The Algorithm before theorem\n", - "\n", - "Here's how we would proceed in setting up the algorithm for the PCA, see also discussion below here. \n", - "* Set up the datapoints for the design/feature matrix $\\boldsymbol{X}$ with $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors/features $p$ referring to the column numbers and the entries $n$ being the row elements." - ] - }, - { - "cell_type": "markdown", - "id": "da0c829e", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{X}=\\begin{bmatrix}\n", - "x_{0,0} & x_{0,1} & x_{0,2}& \\dots & \\dots x_{0,p-1}\\\\\n", - "x_{1,0} & x_{1,1} & x_{1,2}& \\dots & \\dots x_{1,p-1}\\\\\n", - "x_{2,0} & x_{2,1} & x_{2,2}& \\dots & \\dots x_{2,p-1}\\\\\n", - "\\dots & \\dots & \\dots & \\dots \\dots & \\dots \\\\\n", - "x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \\dots & \\dots x_{n-2,p-1}\\\\\n", - "x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \\dots & \\dots x_{n-1,p-1}\\\\\n", - "\\end{bmatrix},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "e30713b6", - "metadata": {}, - "source": [ - "* Center the data by subtracting the mean value for each column. This leads to a new matrix $\\boldsymbol{X}\\rightarrow \\overline{\\boldsymbol{X}}$.\n", - "\n", - "* Compute then the covariance/correlation matrix $\\mathbb{E}[\\overline{\\boldsymbol{X}}^T\\overline{\\boldsymbol{X}}]$.\n", - "\n", - "* Find the eigenpairs of $\\boldsymbol{C}$ with eigenvalues $[\\lambda_0,\\lambda_1,\\dots,\\lambda_{p-1}]$ and eigenvectors $[\\boldsymbol{s}_0,\\boldsymbol{s}_1,\\dots,\\boldsymbol{s}_{p-1}]$.\n", - "\n", - "* Order the eigenvalue (and the eigenvectors accordingly) in order of decreasing eigenvalues.\n", - "\n", - "* Keep only those $l$ eigenvalues larger than a selected threshold value, discarding thus $p-l$ features since we expect small variations in the data here." - ] - }, - { - "cell_type": "markdown", - "id": "9d5288c9", - "metadata": {}, - "source": [ - "## Writing our own PCA code\n", - "\n", - "We will use a simple example first with two-dimensional data\n", - "drawn from a multivariate normal distribution with the following mean and covariance matrix (we have fixed these quantities but will play around with them below):" - ] - }, - { - "cell_type": "markdown", - "id": "21135db4", - "metadata": {}, - "source": [ - "$$\n", - "\\mu = (-1,2) \\qquad \\Sigma = \\begin{bmatrix} 4 & 2 \\\\\n", - "2 & 2\n", - "\\end{bmatrix}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "6d729452", - "metadata": {}, - "source": [ - "Note that the mean refers to each column of data. \n", - "We will generate $n = 10000$ points $X = \\{ x_1, \\ldots, x_N \\}$ from\n", - "this distribution, and store them in the $1000 \\times 2$ matrix $\\boldsymbol{X}$. This is our design matrix where we have forced the covariance and mean values to take specific values." - ] - }, - { - "cell_type": "markdown", - "id": "378a5b62", - "metadata": {}, - "source": [ - "## Implementing it\n", - "The following Python code aids in setting up the data and writing out the design matrix.\n", - "Note that the function **multivariate** returns also the covariance discussed above and that it is defined by dividing by $n-1$ instead of $n$." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "f71d343b", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import display\n", - "n = 10000\n", - "mean = (-1, 2)\n", - "cov = [[4, 2], [2, 2]]\n", - "X = np.random.multivariate_normal(mean, cov, n)" - ] - }, - { - "cell_type": "markdown", - "id": "5a59e528", - "metadata": {}, - "source": [ - "Now we are going to implement the PCA algorithm. We will break it down into various substeps." - ] - }, - { - "cell_type": "markdown", - "id": "577128ba", - "metadata": {}, - "source": [ - "## First Step\n", - "\n", - "The first step of PCA is to compute the sample mean of the data and use it to center the data. Recall that the sample mean is" - ] - }, - { - "cell_type": "markdown", - "id": "3666d3f7", - "metadata": {}, - "source": [ - "$$\n", - "\\mu_n = \\frac{1}{n} \\sum_{i=1}^n x_i\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "1446f097", - "metadata": {}, - "source": [ - "and the mean-centered data $\\bar{X} = \\{ \\bar{x}_1, \\ldots, \\bar{x}_n \\}$ takes the form" - ] - }, - { - "cell_type": "markdown", - "id": "8c16b19e", - "metadata": {}, - "source": [ - "$$\n", - "\\bar{x}_i = x_i - \\mu_n.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "d77314b2", - "metadata": {}, - "source": [ - "When you are done with these steps, print out $\\mu_n$ to verify it is\n", - "close to $\\mu$ and plot your mean centered data to verify it is\n", - "centered at the origin! \n", - "The following code elements perform these operations using **pandas** or using our own functionality for doing so. The latter, using **numpy** is rather simple through the **mean()** function." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "ca1b9dab", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame(X)\n", - "# Pandas does the centering for us\n", - "df = df -df.mean()\n", - "# we center it ourselves\n", - "X_centered = X - X.mean(axis=0)" - ] - }, - { - "cell_type": "markdown", - "id": "0de9f78f", - "metadata": {}, - "source": [ - "## Scaling\n", - "Alternatively, we could use the functions we discussed\n", - "earlier for scaling the data set. That is, we could have used the\n", - "**StandardScaler** function in **Scikit-Learn**, a function which ensures\n", - "that for each feature/predictor we study the mean value is zero and\n", - "the variance is one (every column in the design/feature matrix). You\n", - "would then not get the same results, since we divide by the\n", - "variance. The diagonal covariance matrix elements will then be one,\n", - "while the non-diagonal ones need to be divided by $2\\sqrt{2}$ for our\n", - "specific case." - ] - }, - { - "cell_type": "markdown", - "id": "0ac61e42", - "metadata": {}, - "source": [ - "## Centered Data\n", - "\n", - "Now we are going to use the mean centered data to compute the sample covariance of the data by using the following equation" - ] - }, - { - "cell_type": "markdown", - "id": "1f8093d8", - "metadata": {}, - "source": [ - "$$\n", - "\\Sigma_n = \\frac{1}{n-1} \\sum_{i=1}^n \\bar{x}_i^T \\bar{x}_i = \\frac{1}{n-1} \\sum_{i=1}^n (x_i - \\mu_n)^T (x_i - \\mu_n)\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "cb0c7d4a", - "metadata": {}, - "source": [ - "where the data points $x_i \\in \\mathbb{R}^p$ (here in this example $p = 2$) are column vectors and $x^T$ is the transpose of $x$.\n", - "We can write our own code or simply use either the functionaly of **numpy** or that of **pandas**, as follows" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "86bec00a", - "metadata": {}, - "outputs": [], - "source": [ - "print(df.cov())\n", - "print(np.cov(X_centered.T))" - ] - }, - { - "cell_type": "markdown", - "id": "f7e06369", - "metadata": {}, - "source": [ - "Note that the way we define the covariance matrix here has a factor $n-1$ instead of $n$. This is included in the **cov()** function by **numpy** and **pandas**. \n", - "Our own code here is not very elegant and asks for obvious improvements. It is tailored to this specific $2\\times 2$ covariance matrix." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "03b6567c", - "metadata": {}, - "outputs": [], - "source": [ - "# extract the relevant columns from the centered design matrix of dim n x 2\n", - "x = X_centered[:,0]\n", - "y = X_centered[:,1]\n", - "Cov = np.zeros((2,2))\n", - "Cov[0,1] = np.sum(x.T@y)/(n-1.0)\n", - "Cov[0,0] = np.sum(x.T@x)/(n-1.0)\n", - "Cov[1,1] = np.sum(y.T@y)/(n-1.0)\n", - "Cov[1,0]= Cov[0,1]\n", - "print(\"Centered covariance using own code\")\n", - "print(Cov)\n", - "plt.plot(x, y, 'x')\n", - "plt.axis('equal')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f02610ea", - "metadata": {}, - "source": [ - "## Exploring\n", - "\n", - "Depending on the number of points $n$, we will get results that are close to the covariance values defined above.\n", - "The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed." - ] - }, - { - "cell_type": "markdown", - "id": "2222d34b", - "metadata": {}, - "source": [ - "## Diagonalize the sample covariance matrix to obtain the principal components\n", - "\n", - "Now we are ready to solve for the principal components! To do so we\n", - "diagonalize the sample covariance matrix $\\Sigma$. We can use the\n", - "function **np.linalg.eig** to do so. It will return the eigenvalues and\n", - "eigenvectors of $\\Sigma$. Once we have these we can perform the \n", - "following tasks:\n", - "\n", - "* We compute the percentage of the total variance captured by the first principal component\n", - "\n", - "* We plot the mean centered data and lines along the first and second principal components\n", - "\n", - "* Then we project the mean centered data onto the first and second principal components, and plot the projected data. \n", - "\n", - "* Finally, we approximate the data as" - ] - }, - { - "cell_type": "markdown", - "id": "d1bb9dac", - "metadata": {}, - "source": [ - "$$\n", - "x_i \\approx \\tilde{x}_i = \\mu_n + \\langle x_i, v_0 \\rangle v_0\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "08ae6f5a", - "metadata": {}, - "source": [ - "where $v_0$ is the first principal component." - ] - }, - { - "cell_type": "markdown", - "id": "208bdfd1", - "metadata": {}, - "source": [ - "## Collecting all Steps\n", - "\n", - "Collecting all these steps we can write our own PCA function and\n", - "compare this with the functionality included in **Scikit-Learn**. \n", - "\n", - "The code here outlines some of the elements we could include in the\n", - "analysis. Feel free to extend upon this in order to address the above\n", - "questions." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "730093a6", - "metadata": {}, - "outputs": [], - "source": [ - "# diagonalize and obtain eigenvalues, not necessarily sorted\n", - "EigValues, EigVectors = np.linalg.eig(Cov)\n", - "# sort eigenvectors and eigenvalues\n", - "#permute = EigValues.argsort()\n", - "#EigValues = EigValues[permute]\n", - "#EigVectors = EigVectors[:,permute]\n", - "print(\"Eigenvalues of Covariance matrix\")\n", - "for i in range(2):\n", - " print(EigValues[i])\n", - "FirstEigvector = EigVectors[:,0]\n", - "SecondEigvector = EigVectors[:,1]\n", - "print(\"First eigenvector\")\n", - "print(FirstEigvector)\n", - "print(\"Second eigenvector\")\n", - "print(SecondEigvector)\n", - "#thereafter we do a PCA with Scikit-learn\n", - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components = 2)\n", - "X2Dsl = pca.fit_transform(X)\n", - "print(\"Eigenvector of largest eigenvalue\")\n", - "print(pca.components_.T[:, 0])" - ] - }, - { - "cell_type": "markdown", - "id": "7ec0ad20", - "metadata": {}, - "source": [ - "This code does not contain all the above elements, but it shows how we can use **Scikit-Learn** to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?" - ] - }, - { - "cell_type": "markdown", - "id": "d3ae832e", - "metadata": {}, - "source": [ - "## Classical PCA Theorem\n", - "\n", - "We assume now that we have a design matrix $\\boldsymbol{X}$ which has been\n", - "centered as discussed above. For the sake of simplicity we skip the\n", - "overline symbol. The matrix is defined in terms of the various column\n", - "vectors $[\\boldsymbol{x}_0,\\boldsymbol{x}_1,\\dots, \\boldsymbol{x}_{p-1}]$ each with dimension\n", - "$\\boldsymbol{x}\\in {\\mathbb{R}}^{n}$.\n", - "\n", - "The PCA theorem states that minimizing the above reconstruction error\n", - "corresponds to setting $\\boldsymbol{W}=\\boldsymbol{S}$, the orthogonal matrix which\n", - "diagonalizes the empirical covariance(correlation) matrix. The optimal\n", - "low-dimensional encoding of the data is then given by a set of vectors\n", - "$\\boldsymbol{z}_i$ with at most $l$ vectors, with $l << p$, defined by the\n", - "orthogonal projection of the data onto the columns spanned by the\n", - "eigenvectors of the covariance(correlations matrix)." - ] - }, - { - "cell_type": "markdown", - "id": "8c8b6bb8", - "metadata": {}, - "source": [ - "## The PCA Theorem\n", - "\n", - "To show the PCA theorem let us start with the assumption that there is one vector $\\boldsymbol{s}_0$ which corresponds to a solution which minimized the reconstruction error $J$. This is an orthogonal vector. It means that we now approximate the reconstruction error in terms of $\\boldsymbol{w}_0$ and $\\boldsymbol{z}_0$ as\n", - "\n", - "We are almost there, we have obtained a relation between minimizing\n", - "the reconstruction error and the variance and the covariance\n", - "matrix. Minimizing the error is equivalent to maximizing the variance\n", - "of the projected data.\n", - "\n", - "We could trivially maximize the variance of the projection (and\n", - "thereby minimize the error in the reconstruction function) by letting\n", - "the norm-2 of $\\boldsymbol{w}_0$ go to infinity. However, this norm since we\n", - "want the matrix $\\boldsymbol{W}$ to be an orthogonal matrix, is constrained by\n", - "$\\vert\\vert \\boldsymbol{w}_0 \\vert\\vert_2^2=1$. Imposing this condition via a\n", - "Lagrange multiplier we can then in turn maximize" - ] - }, - { - "cell_type": "markdown", - "id": "129557ad", - "metadata": {}, - "source": [ - "$$\n", - "J(\\boldsymbol{w}_0)= \\boldsymbol{w}_0^T\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{w}_0+\\lambda_0(1-\\boldsymbol{w}_0^T\\boldsymbol{w}_0).\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "ddc91069", - "metadata": {}, - "source": [ - "Taking the derivative with respect to $\\boldsymbol{w}_0$ we obtain" - ] - }, - { - "cell_type": "markdown", - "id": "4dcaca82", - "metadata": {}, - "source": [ - "$$\n", - "\\frac{\\partial J(\\boldsymbol{w}_0)}{\\partial \\boldsymbol{w}_0}= 2\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{w}_0-2\\lambda_0\\boldsymbol{w}_0=0,\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "07127bb6", - "metadata": {}, - "source": [ - "meaning that" - ] - }, - { - "cell_type": "markdown", - "id": "045b6dd8", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{w}_0=\\lambda_0\\boldsymbol{w}_0.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "d6c69b78", - "metadata": {}, - "source": [ - "**The direction that maximizes the variance (or minimizes the construction error) is an eigenvector of the covariance matrix**! If we left multiply with $\\boldsymbol{w}_0^T$ we have the variance of the projected data is" - ] - }, - { - "cell_type": "markdown", - "id": "9d45048b", - "metadata": {}, - "source": [ - "$$\n", - "\\boldsymbol{w}_0^T\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{w}_0=\\lambda_0.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "b3698ab8", - "metadata": {}, - "source": [ - "If we want to maximize the variance (minimize the construction error)\n", - "we simply pick the eigenvector of the covariance matrix with the\n", - "largest eigenvalue. This establishes the link between the minimization\n", - "of the reconstruction function $J$ in terms of an orthogonal matrix\n", - "and the maximization of the variance and thereby the covariance of our\n", - "observations encoded in the design/feature matrix $\\boldsymbol{X}$.\n", - "\n", - "The proof\n", - "for the other eigenvectors $\\boldsymbol{w}_1,\\boldsymbol{w}_2,\\dots$ can be\n", - "established by applying the above arguments and using the fact that\n", - "our basis of eigenvectors is orthogonal, see [Murphy chapter\n", - "12.2](https://mitpress.mit.edu/books/machine-learning-1). The\n", - "discussion in chapter 12.2 of Murphy's text has also a nice link with\n", - "the Singular Value Decomposition theorem. For categorical data, see\n", - "chapter 12.4 and discussion therein.\n", - "\n", - "For more details, see for example [Vidal, Ma and Sastry, chapter 2](https://www.springer.com/gp/book/9780387878102)." - ] - }, - { - "cell_type": "markdown", - "id": "1b734d66", - "metadata": {}, - "source": [ - "## Geometric Interpretation and link with Singular Value Decomposition\n", - "\n", - "For a detailed demonstration of the geometric interpretation, see [Vidal, Ma and Sastry, section 2.1.2](https://www.springer.com/gp/book/9780387878102).\n", - "\n", - "Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm.\n", - "First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it.\n", - "\n", - "The following Python code uses NumPy’s **svd()** function to obtain all the principal components of the\n", - "training set, then extracts the first two principal components. First we center the data using either **pandas** or our own code" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "d2853e5f", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from IPython.display import display\n", - "np.random.seed(100)\n", - "# setting up a 10 x 5 vanilla matrix \n", - "rows = 10\n", - "cols = 5\n", - "X = np.random.randn(rows,cols)\n", - "df = pd.DataFrame(X)\n", - "# Pandas does the centering for us\n", - "df = df -df.mean()\n", - "display(df)\n", - "\n", - "# we center it ourselves\n", - "X_centered = X - X.mean(axis=0)\n", - "# Then check the difference between pandas and our own set up\n", - "print(X_centered-df)\n", - "#Now we do an SVD\n", - "U, s, V = np.linalg.svd(X_centered)\n", - "c1 = V.T[:, 0]\n", - "c2 = V.T[:, 1]\n", - "W2 = V.T[:, :2]\n", - "X2D = X_centered.dot(W2)\n", - "print(X2D)" - ] - }, - { - "cell_type": "markdown", - "id": "7db2f1d3", - "metadata": {}, - "source": [ - "PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering\n", - "the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t\n", - "forget to center the data first.\n", - "\n", - "Once you have identified all the principal components, you can reduce the dimensionality of the dataset\n", - "down to $d$ dimensions by projecting it onto the hyperplane defined by the first $d$ principal components.\n", - "Selecting this hyperplane ensures that the projection will preserve as much variance as possible." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "b0202a4e", - "metadata": {}, - "outputs": [], - "source": [ - "W2 = V.T[:, :2]\n", - "X2D = X_centered.dot(W2)" - ] - }, - { - "cell_type": "markdown", - "id": "df76a6cd", - "metadata": {}, - "source": [ - "## PCA and scikit-learn\n", - "\n", - "Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The\n", - "following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note\n", - "that it automatically takes care of centering the data):" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "addac10b", - "metadata": {}, - "outputs": [], - "source": [ - "#thereafter we do a PCA with Scikit-learn\n", - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components = 2)\n", - "X2D = pca.fit_transform(X)\n", - "print(X2D)" - ] - }, - { - "cell_type": "markdown", - "id": "365b1a6a", - "metadata": {}, - "source": [ - "After fitting the PCA transformer to the dataset, you can access the principal components using the\n", - "components variable (note that it contains the PCs as horizontal vectors, so, for example, the first\n", - "principal component is equal to" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "34e14cc8", - "metadata": {}, - "outputs": [], - "source": [ - "pca.components_.T[:, 0]" - ] - }, - { - "cell_type": "markdown", - "id": "7e6a56cf", - "metadata": {}, - "source": [ - "Another very useful piece of information is the explained variance ratio of each principal component,\n", - "available via the $explained\\_variance\\_ratio$ variable. It indicates the proportion of the dataset’s\n", - "variance that lies along the axis of each principal component." - ] - }, - { - "cell_type": "markdown", - "id": "78eef1a8", - "metadata": {}, - "source": [ - "## Back to the Cancer Data\n", - "We can now repeat the above but applied to real data, in this case our breast cancer data.\n", - "Here we compute performance scores on the training data using logistic regression." - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "79e42917", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split \n", - "from sklearn.datasets import load_breast_cancer\n", - "from sklearn.linear_model import LogisticRegression\n", - "cancer = load_breast_cancer()\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", - "\n", - "logreg = LogisticRegression()\n", - "logreg.fit(X_train, y_train)\n", - "print(\"Train set accuracy from Logistic Regression: {:.2f}\".format(logreg.score(X_train,y_train)))\n", - "# We scale the data\n", - "from sklearn.preprocessing import StandardScaler\n", - "scaler = StandardScaler()\n", - "scaler.fit(X_train)\n", - "X_train_scaled = scaler.transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)\n", - "# Then perform again a log reg fit\n", - "logreg.fit(X_train_scaled, y_train)\n", - "print(\"Train set accuracy scaled data: {:.2f}\".format(logreg.score(X_train_scaled,y_train)))\n", - "#thereafter we do a PCA with Scikit-learn\n", - "from sklearn.decomposition import PCA\n", - "pca = PCA(n_components = 2)\n", - "X2D_train = pca.fit_transform(X_train_scaled)\n", - "# and finally compute the log reg fit and the score on the training data\t\n", - "logreg.fit(X2D_train,y_train)\n", - "print(\"Train set accuracy scaled and PCA data: {:.2f}\".format(logreg.score(X2D_train,y_train)))" - ] - }, - { - "cell_type": "markdown", - "id": "e52a6e2f", - "metadata": {}, - "source": [ - "We see that our training data after the PCA decomposition has a performance similar to the non-scaled data. \n", - "\n", - "Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to\n", - "choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%).\n", - "Unless, of course, you are reducing dimensionality for data visualization — in that case you will\n", - "generally want to reduce the dimensionality down to 2 or 3.\n", - "The following code computes PCA without reducing dimensionality, then computes the minimum number\n", - "of dimensions required to preserve 95% of the training set’s variance:" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "b96ea604", - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA()\n", - "pca.fit(X)\n", - "cumsum = np.cumsum(pca.explained_variance_ratio_)\n", - "d = np.argmax(cumsum >= 0.95) + 1" - ] - }, - { - "cell_type": "markdown", - "id": "788a3fad", - "metadata": {}, - "source": [ - "You could then set $n\\_components=d$ and run PCA again. However, there is a much better option: instead\n", - "of specifying the number of principal components you want to preserve, you can set $n\\_components$ to be\n", - "a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve:" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c7f8c08c", - "metadata": {}, - "outputs": [], - "source": [ - "pca = PCA(n_components=0.95)\n", - "X_reduced = pca.fit_transform(X)" - ] - }, - { - "cell_type": "markdown", - "id": "10115c96", - "metadata": {}, - "source": [ - "## Incremental PCA\n", - "\n", - "One problem with the preceding implementation of PCA is that it requires the whole training set to fit in\n", - "memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have\n", - "been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch\n", - "at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new\n", - "instances arrive)." - ] - }, - { - "cell_type": "markdown", - "id": "0b8def9f", - "metadata": {}, - "source": [ - "### Randomized PCA\n", - "\n", - "Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic\n", - "algorithm that quickly finds an approximation of the first d principal components. Its computational\n", - "complexity is $O(m \\times d^2)+O(d^3)$, instead of $O(m \\times n^2) + O(n^3)$, so it is dramatically faster than the\n", - "previous algorithms when $d$ is much smaller than $n$." - ] - }, - { - "cell_type": "markdown", - "id": "bec0254b", - "metadata": {}, - "source": [ - "### Kernel PCA\n", - "\n", - "The kernel trick is a mathematical technique that implicitly maps instances into a\n", - "very high-dimensional space (called the feature space), enabling nonlinear classification and regression\n", - "with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature\n", - "space corresponds to a complex nonlinear decision boundary in the original space.\n", - "It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear\n", - "projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at\n", - "preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a\n", - "twisted manifold.\n", - "For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "a65304d1", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.decomposition import KernelPCA\n", - "rbf_pca = KernelPCA(n_components = 2, kernel=\"rbf\", gamma=0.04)\n", - "X_reduced = rbf_pca.fit_transform(X)" - ] - }, - { - "cell_type": "markdown", - "id": "37a30bcd", - "metadata": {}, - "source": [ - "## Other techniques\n", - "\n", - "There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.\n", - "\n", - "Here are some of the most popular:\n", - "* **Multidimensional Scaling (MDS)** reduces dimensionality while trying to preserve the distances between the instances.\n", - "\n", - "* **Isomap** creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances.\n", - "\n", - "* **t-Distributed Stochastic Neighbor Embedding** (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).\n", - "\n", - "* Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures." - ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/src/week43/version2021.do.txt b/doc/src/week43/version2021.do.txt new file mode 100644 index 000000000..f0c8fd270 --- /dev/null +++ b/doc/src/week43/version2021.do.txt @@ -0,0 +1,2216 @@ +ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis +AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University +DATE: today + +!split +===== Plans for week 43 ===== + +* Thursday: Convolutional Neural Networks, basic elements and +* Friday: Recurrent Neural Networks and other Deep learning methods, Generalized Adversarial Neural Networ and autoencoders + + + +!bblock Excellent lectures on CNNs and RNNs +* "Video on Convolutional Neural Networks from MIT":"https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini" +* "Video on Recurrent Neural Networks from MIT":"https://www.youtube.com/watch?v=SEnXr6v2ifU&ab_channel=AlexanderAmini" +* "Video on Deep Learning":"https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" +!eblock + +!bblock More resources +* "IN5400 at UiO Lecture":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/v20/material/week10/in5400_2020_week10_recurrent_neural_network.pdf" +* "CS231 at Stanford Lecture":"https://www.youtube.com/watch?v=6niqTuYFZLQ&list=PLzUTmXVwsnXod6WNdg57Yc3zFx_f-RYsq&index=10&ab_channel=StanfordUniversitySchoolofEngineering" +!eblock + + +!split +===== Reading Recommendations ===== + +* Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended. +* Aurelien Geron, chapter 14 on RNNs. + + + +neural nets will be very large: impractical to write down gradient formula +by hand for all parameters +● backpropagation = recursive application of the chain rule along a +computational graph to compute the gradients of all +inputs/parameters/intermediates +● implementations maintain a graph structure, where the nodes implement +the forward() / backward() API +● forward: compute result of an operation and save any intermediates +needed for gradient computation in memory +● backward: apply the chain rule to compute the gradient of the loss +function with respect to the inputs + + + + +!split +===== CNNs in brief ===== + +In summary: + +* A CNN architecture is in the simplest case a list of Layers that transform the image volume into an output volume (e.g. holding the class scores) +* There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far the most popular) +* Each Layer accepts an input 3D volume and transforms it to an output 3D volume through a differentiable function +* Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL don’t) +* Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesn’t) + +For more material on convolutional networks, we strongly recommend +the course +"IN5400 – Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html" +and the slides of "CS231":"http://cs231n.github.io/convolutional-networks/" which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). "Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs":"http://neuralnetworksanddeeplearning.com/chap6.html". + + +However, both standard feed forwards networks and CNNs perform well on data with unknown length. + +This is where recurrent nueral networks (RNNs) come to our rescue. + +!split +===== Recurrent neural networks: Overarching view ===== + +Till now our focus has been, including convolutional neural networks +as well, on feedforward neural networks. The output or the activations +flow only in one direction, from the input layer to the output layer. + +A recurrent neural network (RNN) looks very much like a feedforward +neural network, except that it also has connections pointing +backward. + +RNNs are used to analyze time series data such as stock prices, and +tell you when to buy or sell. In autonomous driving systems, they can +anticipate car trajectories and help avoid accidents. More generally, +they can work on sequences of arbitrary lengths, rather than on +fixed-sized inputs like all the nets we have discussed so far. For +example, they can take sentences, documents, or audio samples as +input, making them extremely useful for natural language processing +systems such as automatic translation and speech-to-text. + + +!split +===== Set up of an RNN ===== + +More to text to be added + +!split +===== A simple example ===== + +!bc pycod +# Start importing packages +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import tensorflow as tf +from tensorflow.keras import datasets, layers, models +from tensorflow.keras.layers import Input +from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.layers import Dense, SimpleRNN, LSTM, GRU +from tensorflow.keras import optimizers +from tensorflow.keras import regularizers +from tensorflow.keras.utils import to_categorical + + + +# convert into dataset matrix +def convertToMatrix(data, step): + X, Y =[], [] + for i in range(len(data)-step): + d=i+step + X.append(data[i:d,]) + Y.append(data[d,]) + return np.array(X), np.array(Y) + +step = 4 +N = 1000 +Tp = 800 + +t=np.arange(0,N) +x=np.sin(0.02*t)+2*np.random.rand(N) +df = pd.DataFrame(x) +df.head() + +plt.plot(df) +plt.show() + +values=df.values +train,test = values[0:Tp,:], values[Tp:N,:] + +# add step elements into train and test +test = np.append(test,np.repeat(test[-1,],step)) +train = np.append(train,np.repeat(train[-1,],step)) + +trainX,trainY =convertToMatrix(train,step) +testX,testY =convertToMatrix(test,step) +trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) +testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) + +model = Sequential() +model.add(SimpleRNN(units=32, input_shape=(1,step), activation="relu")) +model.add(Dense(8, activation="relu")) +model.add(Dense(1)) +model.compile(loss='mean_squared_error', optimizer='rmsprop') +model.summary() + +model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2) +trainPredict = model.predict(trainX) +testPredict= model.predict(testX) +predicted=np.concatenate((trainPredict,testPredict),axis=0) + +trainScore = model.evaluate(trainX, trainY, verbose=0) +print(trainScore) + +index = df.index.values +plt.plot(index,df) +plt.plot(index,predicted) +plt.axvline(df.index[Tp], c="r") +plt.show() +!ec + + +!split +===== An extrapolation example ===== + +The following code provides an example of how recurrent neural +networks can be used to extrapolate to unknown values of physics data +sets. Specifically, the data sets used in this program come from +a quantum mechanical many-body calculation of energies as functions of the number of particles. + + +!bc pycod + +# For matrices and calculations +import numpy as np +# For machine learning (backend for keras) +import tensorflow as tf +# User-friendly machine learning library +# Front end for TensorFlow +import tensorflow.keras +# Different methods from Keras needed to create an RNN +# This is not necessary but it shortened function calls +# that need to be used in the code. +from tensorflow.keras import datasets, layers, models +from tensorflow.keras.layers import Input +from tensorflow.keras import regularizers +from tensorflow.keras.models import Model, Sequential +from tensorflow.keras.layers import Dense, SimpleRNN, LSTM, GRU +# For timing the code +from timeit import default_timer as timer +# For plotting +import matplotlib.pyplot as plt + + +# The data set +datatype='VaryDimension' +X_tot = np.arange(2, 42, 2) +y_tot = np.array([-0.03077640549, -0.08336233266, -0.1446729567, -0.2116753732, -0.2830637392, -0.3581341341, -0.436462435, -0.5177783846, + -0.6019067271, -0.6887363571, -0.7782028952, -0.8702784034, -0.9649652536, -1.062292565, -1.16231451, + -1.265109911, -1.370782966, -1.479465113, -1.591317992, -1.70653767]) + +!ec + +!split +===== Formatting the Data ===== + +The way the recurrent neural networks are trained in this program +differs from how machine learning algorithms are usually trained. +Typically a machine learning algorithm is trained by learning the +relationship between the x data and the y data. In this program, the +recurrent neural network will be trained to recognize the relationship +in a sequence of y values. This is type of data formatting is +typically used time series forcasting, but it can also be used in any +extrapolation (time series forecasting is just a specific type of +extrapolation along the time axis). This method of data formatting +does not use the x data and assumes that the y data are evenly spaced. + +For a standard machine learning algorithm, the training data has the +form of (x,y) so the machine learning algorithm learns to assiciate a +y value with a given x value. This is useful when the test data has x +values within the same range as the training data. However, for this +application, the x values of the test data are outside of the x values +of the training data and the traditional method of training a machine +learning algorithm does not work as well. For this reason, the +recurrent neural network is trained on sequences of y values of the +form ((y1, y2), y3), so that the network is concerned with learning +the pattern of the y data and not the relation between the x and y +data. As long as the pattern of y data outside of the training region +stays relatively stable compared to what was inside the training +region, this method of training can produce accurate extrapolations to +y values far removed from the training data set. + + +# +# The idea behind formatting the data in this way comes from [this resource](https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/) and [this one](https://fairyonice.github.io/Understand-Keras%27s-RNN-behind-the-scenes-with-a-sin-wave-example.html). +# +# The following method takes in a y data set and formats it so the "x data" are of the form (y1, y2) and the "y data" are of the form y3, with extra brackets added in to make the resulting arrays compatable with both Keras and Tensorflow. +# +# Note: Using a sequence length of two is not required for time series forecasting so any lenght of sequence could be used (for example instead of ((y1, y2) y3) you could change the length of sequence to be 4 and the resulting data points would have the form ((y1, y2, y3, y4), y5)). While the following method can be used to create a data set of any sequence length, the remainder of the code expects the length of sequence to be 2. This is because the data sets are very small and the higher the lenght of the sequence the less resulting data points. + +!bc pycod +# FORMAT_DATA +def format_data(data, length_of_sequence = 2): + """ + Inputs: + data(a numpy array): the data that will be the inputs to the recurrent neural + network + length_of_sequence (an int): the number of elements in one iteration of the + sequence patter. For a function approximator use length_of_sequence = 2. + Returns: + rnn_input (a 3D numpy array): the input data for the recurrent neural network. Its + dimensions are length of data - length of sequence, length of sequence, + dimnsion of data + rnn_output (a numpy array): the training data for the neural network + Formats data to be used in a recurrent neural network. + """ + + X, Y = [], [] + for i in range(len(data)-length_of_sequence): + # Get the next length_of_sequence elements + a = data[i:i+length_of_sequence] + # Get the element that immediately follows that + b = data[i+length_of_sequence] + # Reshape so that each data point is contained in its own array + a = np.reshape (a, (len(a), 1)) + X.append(a) + Y.append(b) + rnn_input = np.array(X) + rnn_output = np.array(Y) + + return rnn_input, rnn_output + + +# ## Defining the Recurrent Neural Network Using Keras +# +# The following method defines a simple recurrent neural network in keras consisting of one input layer, one hidden layer, and one output layer. + +def rnn(length_of_sequences, batch_size = None, stateful = False): + """ + Inputs: + length_of_sequences (an int): the number of y values in "x data". This is determined + when the data is formatted + batch_size (an int): Default value is None. See Keras documentation of SimpleRNN. + stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN. + Returns: + model (a Keras model): The recurrent neural network that is built and compiled by this + method + Builds and compiles a recurrent neural network with one hidden layer and returns the model. + """ + # Number of neurons in the input and output layers + in_out_neurons = 1 + # Number of neurons in the hidden layer + hidden_neurons = 200 + # Define the input layer + inp = Input(batch_shape=(batch_size, + length_of_sequences, + in_out_neurons)) + # Define the hidden layer as a simple RNN layer with a set number of neurons and add it to + # the network immediately after the input layer + rnn = SimpleRNN(hidden_neurons, + return_sequences=False, + stateful = stateful, + name="RNN")(inp) + # Define the output layer as a dense neural network layer (standard neural network layer) + #and add it to the network immediately after the hidden layer. + dens = Dense(in_out_neurons,name="dense")(rnn) + # Create the machine learning model starting with the input layer and ending with the + # output layer + model = Model(inputs=[inp],outputs=[dens]) + # Compile the machine learning model using the mean squared error function as the loss + # function and an Adams optimizer. + model.compile(loss="mean_squared_error", optimizer="adam") + return model + +!ec + +!split +===== Predicting New Points With A Trained Recurrent Neural Network ===== + +!bc pycod +def test_rnn (x1, y_test, plot_min, plot_max): + """ + Inputs: + x1 (a list or numpy array): The complete x component of the data set + y_test (a list or numpy array): The complete y component of the data set + plot_min (an int or float): the smallest x value used in the training data + plot_max (an int or float): the largest x valye used in the training data + Returns: + None. + Uses a trained recurrent neural network model to predict future points in the + series. Computes the MSE of the predicted data set from the true data set, saves + the predicted data set to a csv file, and plots the predicted and true data sets w + while also displaying the data range used for training. + """ + # Add the training data as the first dim points in the predicted data array as these + # are known values. + y_pred = y_test[:dim].tolist() + # Generate the first input to the trained recurrent neural network using the last two + # points of the training data. Based on how the network was trained this means that it + # will predict the first point in the data set after the training data. All of the + # brackets are necessary for Tensorflow. + next_input = np.array([[[y_test[dim-2]], [y_test[dim-1]]]]) + # Save the very last point in the training data set. This will be used later. + last = [y_test[dim-1]] + + # Iterate until the complete data set is created. + for i in range (dim, len(y_test)): + # Predict the next point in the data set using the previous two points. + next = model.predict(next_input) + # Append just the number of the predicted data set + y_pred.append(next[0][0]) + # Create the input that will be used to predict the next data point in the data set. + next_input = np.array([[last, next[0]]], dtype=np.float64) + last = next + + # Print the mean squared error between the known data set and the predicted data set. + print('MSE: ', np.square(np.subtract(y_test, y_pred)).mean()) + # Save the predicted data set as a csv file for later use + name = datatype + 'Predicted'+str(dim)+'.csv' + np.savetxt(name, y_pred, delimiter=',') + # Plot the known data set and the predicted data set. The red box represents the region that was used + # for the training data. + fig, ax = plt.subplots() + ax.plot(x1, y_test, label="true", linewidth=3) + ax.plot(x1, y_pred, 'g-.',label="predicted", linewidth=4) + ax.legend() + # Created a red region to represent the points used in the training data. + ax.axvspan(plot_min, plot_max, alpha=0.25, color='red') + plt.show() + +# Check to make sure the data set is complete +assert len(X_tot) == len(y_tot) + +# This is the number of points that will be used in as the training data +dim=12 + +# Separate the training data from the whole data set +X_train = X_tot[:dim] +y_train = y_tot[:dim] + + +# Generate the training data for the RNN, using a sequence of 2 +rnn_input, rnn_training = format_data(y_train, 2) + + +# Create a recurrent neural network in Keras and produce a summary of the +# machine learning model +model = rnn(length_of_sequences = rnn_input.shape[1]) +model.summary() + +# Start the timer. Want to time training+testing +start = timer() +# Fit the model using the training data genenerated above using 150 training iterations and a 5% +# validation split. Setting verbose to True prints information about each training iteration. +hist = model.fit(rnn_input, rnn_training, batch_size=None, epochs=150, + verbose=True,validation_split=0.05) + +for label in ["loss","val_loss"]: + plt.plot(hist.history[label],label=label) + +plt.ylabel("loss") +plt.xlabel("epoch") +plt.title("The final validation loss: {}".format(hist.history["val_loss"][-1])) +plt.legend() +plt.show() + +# Use the trained neural network to predict more points of the data set +test_rnn(X_tot, y_tot, X_tot[0], X_tot[dim-1]) +# Stop the timer and calculate the total time needed. +end = timer() +print('Time: ', end-start) +!ec + +!split +===== Other Things to Try ===== + + +Changing the size of the recurrent neural network and its parameters +can drastically change the results you get from the model. The below +code takes the simple recurrent neural network from above and adds a +second hidden layer, changes the number of neurons in the hidden +layer, and explicitly declares the activation function of the hidden +layers to be a sigmoid function. The loss function and optimizer can +also be changed but are kept the same as the above network. These +parameters can be tuned to provide the optimal result from the +network. For some ideas on how to improve the performance of a +"recurrent neural network":"https://danijar.com/tips-for-training-recurrent-neural-networks". + +!bc pycod +def rnn_2layers(length_of_sequences, batch_size = None, stateful = False): + """ + Inputs: + length_of_sequences (an int): the number of y values in "x data". This is determined + when the data is formatted + batch_size (an int): Default value is None. See Keras documentation of SimpleRNN. + stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN. + Returns: + model (a Keras model): The recurrent neural network that is built and compiled by this + method + Builds and compiles a recurrent neural network with two hidden layers and returns the model. + """ + # Number of neurons in the input and output layers + in_out_neurons = 1 + # Number of neurons in the hidden layer, increased from the first network + hidden_neurons = 500 + # Define the input layer + inp = Input(batch_shape=(batch_size, + length_of_sequences, + in_out_neurons)) + # Create two hidden layers instead of one hidden layer. Explicitly set the activation + # function to be the sigmoid function (the default value is hyperbolic tangent) + rnn1 = SimpleRNN(hidden_neurons, + return_sequences=True, # This needs to be True if another hidden layer is to follow + stateful = stateful, activation = 'sigmoid', + name="RNN1")(inp) + rnn2 = SimpleRNN(hidden_neurons, + return_sequences=False, activation = 'sigmoid', + stateful = stateful, + name="RNN2")(rnn1) + # Define the output layer as a dense neural network layer (standard neural network layer) + #and add it to the network immediately after the hidden layer. + dens = Dense(in_out_neurons,name="dense")(rnn2) + # Create the machine learning model starting with the input layer and ending with the + # output layer + model = Model(inputs=[inp],outputs=[dens]) + # Compile the machine learning model using the mean squared error function as the loss + # function and an Adams optimizer. + model.compile(loss="mean_squared_error", optimizer="adam") + return model + +# Check to make sure the data set is complete +assert len(X_tot) == len(y_tot) + +# This is the number of points that will be used in as the training data +dim=12 + +# Separate the training data from the whole data set +X_train = X_tot[:dim] +y_train = y_tot[:dim] + + +# Generate the training data for the RNN, using a sequence of 2 +rnn_input, rnn_training = format_data(y_train, 2) + + +# Create a recurrent neural network in Keras and produce a summary of the +# machine learning model +model = rnn_2layers(length_of_sequences = 2) +model.summary() + +# Start the timer. Want to time training+testing +start = timer() +# Fit the model using the training data genenerated above using 150 training iterations and a 5% +# validation split. Setting verbose to True prints information about each training iteration. +hist = model.fit(rnn_input, rnn_training, batch_size=None, epochs=150, + verbose=True,validation_split=0.05) + + +# This section plots the training loss and the validation loss as a function of training iteration. +# This is not required for analyzing the couple cluster data but can help determine if the network is +# being overtrained. +for label in ["loss","val_loss"]: + plt.plot(hist.history[label],label=label) + +plt.ylabel("loss") +plt.xlabel("epoch") +plt.title("The final validation loss: {}".format(hist.history["val_loss"][-1])) +plt.legend() +plt.show() + +# Use the trained neural network to predict more points of the data set +test_rnn(X_tot, y_tot, X_tot[0], X_tot[dim-1]) +# Stop the timer and calculate the total time needed. +end = timer() +print('Time: ', end-start) +!ec + +!split +===== Other Types of Recurrent Neural Networks ===== + +Besides a simple recurrent neural network layer, there are two other +commonly used types of recurrent neural network layers: Long Short +Term Memory (LSTM) and Gated Recurrent Unit (GRU). For a short +introduction to these layers see URL:"https://medium.com/mindboard/lstm-vs-gru-experimental-comparison-955820c21e8b" +and URL:"https://medium.com/mindboard/lstm-vs-gru-experimental-comparison-955820c21e8b". + +The first network created below is similar to the previous network, +but it replaces the SimpleRNN layers with LSTM layers. The second +network below has two hidden layers made up of GRUs, which are +preceeded by two dense (feeddorward) neural network layers. These +dense layers "preprocess" the data before it reaches the recurrent +layers. This architecture has been shown to improve the performance +of recurrent neural networks (see the link above and also +URL:"https://arxiv.org/pdf/1807.02857.pdf". + +!bc pycod +def lstm_2layers(length_of_sequences, batch_size = None, stateful = False): + """ + Inputs: + length_of_sequences (an int): the number of y values in "x data". This is determined + when the data is formatted + batch_size (an int): Default value is None. See Keras documentation of SimpleRNN. + stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN. + Returns: + model (a Keras model): The recurrent neural network that is built and compiled by this + method + Builds and compiles a recurrent neural network with two LSTM hidden layers and returns the model. + """ + # Number of neurons on the input/output layer and the number of neurons in the hidden layer + in_out_neurons = 1 + hidden_neurons = 250 + # Input Layer + inp = Input(batch_shape=(batch_size, + length_of_sequences, + in_out_neurons)) + # Hidden layers (in this case they are LSTM layers instead if SimpleRNN layers) + rnn= LSTM(hidden_neurons, + return_sequences=True, + stateful = stateful, + name="RNN", use_bias=True, activation='tanh')(inp) + rnn1 = LSTM(hidden_neurons, + return_sequences=False, + stateful = stateful, + name="RNN1", use_bias=True, activation='tanh')(rnn) + # Output layer + dens = Dense(in_out_neurons,name="dense")(rnn1) + # Define the midel + model = Model(inputs=[inp],outputs=[dens]) + # Compile the model + model.compile(loss='mean_squared_error', optimizer='adam') + # Return the model + return model + +def dnn2_gru2(length_of_sequences, batch_size = None, stateful = False): + """ + Inputs: + length_of_sequences (an int): the number of y values in "x data". This is determined + when the data is formatted + batch_size (an int): Default value is None. See Keras documentation of SimpleRNN. + stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN. + Returns: + model (a Keras model): The recurrent neural network that is built and compiled by this + method + Builds and compiles a recurrent neural network with four hidden layers (two dense followed by + two GRU layers) and returns the model. + """ + # Number of neurons on the input/output layers and hidden layers + in_out_neurons = 1 + hidden_neurons = 250 + # Input layer + inp = Input(batch_shape=(batch_size, + length_of_sequences, + in_out_neurons)) + # Hidden Dense (feedforward) layers + dnn = Dense(hidden_neurons/2, activation='relu', name='dnn')(inp) + dnn1 = Dense(hidden_neurons/2, activation='relu', name='dnn1')(dnn) + # Hidden GRU layers + rnn1 = GRU(hidden_neurons, + return_sequences=True, + stateful = stateful, + name="RNN1", use_bias=True)(dnn1) + rnn = GRU(hidden_neurons, + return_sequences=False, + stateful = stateful, + name="RNN", use_bias=True)(rnn1) + # Output layer + dens = Dense(in_out_neurons,name="dense")(rnn) + # Define the model + model = Model(inputs=[inp],outputs=[dens]) + # Compile the mdoel + model.compile(loss='mean_squared_error', optimizer='adam') + # Return the model + return model + +# Check to make sure the data set is complete +assert len(X_tot) == len(y_tot) + +# This is the number of points that will be used in as the training data +dim=12 + +# Separate the training data from the whole data set +X_train = X_tot[:dim] +y_train = y_tot[:dim] + + +# Generate the training data for the RNN, using a sequence of 2 +rnn_input, rnn_training = format_data(y_train, 2) + + +# Create a recurrent neural network in Keras and produce a summary of the +# machine learning model +# Change the method name to reflect which network you want to use +model = dnn2_gru2(length_of_sequences = 2) +model.summary() + +# Start the timer. Want to time training+testing +start = timer() +# Fit the model using the training data genenerated above using 150 training iterations and a 5% +# validation split. Setting verbose to True prints information about each training iteration. +hist = model.fit(rnn_input, rnn_training, batch_size=None, epochs=150, + verbose=True,validation_split=0.05) + + +# This section plots the training loss and the validation loss as a function of training iteration. +# This is not required for analyzing the couple cluster data but can help determine if the network is +# being overtrained. +for label in ["loss","val_loss"]: + plt.plot(hist.history[label],label=label) + +plt.ylabel("loss") +plt.xlabel("epoch") +plt.title("The final validation loss: {}".format(hist.history["val_loss"][-1])) +plt.legend() +plt.show() + +# Use the trained neural network to predict more points of the data set +test_rnn(X_tot, y_tot, X_tot[0], X_tot[dim-1]) +# Stop the timer and calculate the total time needed. +end = timer() +print('Time: ', end-start) + + +# ### Training Recurrent Neural Networks in the Standard Way (i.e. learning the relationship between the X and Y data) +# +# Finally, comparing the performace of a recurrent neural network using the standard data formatting to the performance of the network with time sequence data formatting shows the benefit of this type of data formatting with extrapolation. + +# Check to make sure the data set is complete +assert len(X_tot) == len(y_tot) + +# This is the number of points that will be used in as the training data +dim=12 + +# Separate the training data from the whole data set +X_train = X_tot[:dim] +y_train = y_tot[:dim] + +# Reshape the data for Keras specifications +X_train = X_train.reshape((dim, 1)) +y_train = y_train.reshape((dim, 1)) + + +# Create a recurrent neural network in Keras and produce a summary of the +# machine learning model +# Set the sequence length to 1 for regular data formatting +model = rnn(length_of_sequences = 1) +model.summary() + +# Start the timer. Want to time training+testing +start = timer() +# Fit the model using the training data genenerated above using 150 training iterations and a 5% +# validation split. Setting verbose to True prints information about each training iteration. +hist = model.fit(X_train, y_train, batch_size=None, epochs=150, + verbose=True,validation_split=0.05) + + +# This section plots the training loss and the validation loss as a function of training iteration. +# This is not required for analyzing the couple cluster data but can help determine if the network is +# being overtrained. +for label in ["loss","val_loss"]: + plt.plot(hist.history[label],label=label) + +plt.ylabel("loss") +plt.xlabel("epoch") +plt.title("The final validation loss: {}".format(hist.history["val_loss"][-1])) +plt.legend() +plt.show() + +# Use the trained neural network to predict the remaining data points +X_pred = X_tot[dim:] +X_pred = X_pred.reshape((len(X_pred), 1)) +y_model = model.predict(X_pred) +y_pred = np.concatenate((y_tot[:dim], y_model.flatten())) + +# Plot the known data set and the predicted data set. The red box represents the region that was used +# for the training data. +fig, ax = plt.subplots() +ax.plot(X_tot, y_tot, label="true", linewidth=3) +ax.plot(X_tot, y_pred, 'g-.',label="predicted", linewidth=4) +ax.legend() +# Created a red region to represent the points used in the training data. +ax.axvspan(X_tot[0], X_tot[dim], alpha=0.25, color='red') +plt.show() + +# Stop the timer and calculate the total time needed. +end = timer() +print('Time: ', end-start) + +!ec + + + + + +!split +===== Generative Models ===== + +_Generative models_ describe a class of statistical models that are a contrast +to _discriminative models_. Informally we say that generative models can +generate new data instances while discriminative models discriminate between +different kinds of data instances. A generative model could generate new photos +of animals that look like 'real' animals while a discriminative model could tell +a dog from a cat. More formally, given a data set $x$ and a set of labels / +targets $y$. Generative models capture the joint probability $p(x, y)$, or +just $p(x)$ if there are no labels, while discriminative models capture the +conditional probability $p(y | x)$. Discriminative models generally try to draw +boundaries in the data space (often high dimensional), while generative models +try to model how data is placed throughout the space. + +_Note_: this material is thanks to Linus Ekstrøm. + +!split +===== Generative Adversarial Networks ===== + +_Generative Adversarial Networks_ are a type of unsupervised machine learning +algorithm proposed by "Goodfellow et. al": "https://arxiv.org/pdf/1406.2661.pdf" +in 2014 (short and good article). + +The simplest formulation of +the model is based on a game theoretic approach, *zero sum game*, where we pit +two neural networks against one another. We define two rival networks, one +generator $g$, and one discriminator $d$. The generator directly produces +samples +!bt +\begin{equation} + x = g(z; \theta^{(g)}) +\end{equation} +!et + + +!split +===== Discriminator ===== +The discriminator attempts to distinguish between samples drawn from the +training data and samples drawn from the generator. In other words, it tries to +tell the difference between the fake data produced by $g$ and the actual data +samples we want to do prediction on. The discriminator outputs a probability +value given by + +!bt +\begin{equation} + d(x; \theta^{(d)}) +\end{equation} +!et + +indicating the probability that $x$ is a real training example rather than a +fake sample the generator has generated. The simplest way to formulate the +learning process in a generative adversarial network is a zero-sum game, in +which a function + +!bt +\begin{equation} + v(\theta^{(g)}, \theta^{(d)}) +\end{equation} +!et + +determines the reward for the discriminator, while the generator gets the +conjugate reward + +!bt +\begin{equation} + -v(\theta^{(g)}, \theta^{(d)}) +\end{equation} +!et + + +!split +===== Learning Process ===== + +During learning both of the networks maximize their own reward function, so that +the generator gets better and better at tricking the discriminator, while the +discriminator gets better and better at telling the difference between the fake +and real data. The generator and discriminator alternate on which one trains at +one time (i.e. for one epoch). In other words, we keep the generator constant +and train the discriminator, then we keep the discriminator constant to train +the generator and repeat. It is this back and forth dynamic which lets GANs +tackle otherwise intractable generative problems. As the generator improves with + training, the discriminator's performance gets worse because it cannot easily + tell the difference between real and fake. If the generator ends up succeeding + perfectly, the the discriminator will do no better than random guessing i.e. + 50\%. This progression in the training poses a problem for the convergence + criteria for GANs. The discriminator feedback gets less meaningful over time, + if we continue training after this point then the generator is effectively + training on junk data which can undo the learning up to that point. Therefore, + we stop training when the discriminator starts outputting $1/2$ everywhere. + + +!split +===== More about the Learning Process ===== + +At convergence we have + +!bt +\begin{equation} + g^* = \underset{g}{\mathrm{argmin}}\hspace{2pt} + \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) +\end{equation} +!et +The default choice for $v$ is +!bt +\begin{equation} + v(\theta^{(g)}, \theta^{(d)}) = \mathbb{E}_{x\sim p_\mathrm{data}}\log d(x) + + \mathbb{E}_{x\sim p_\mathrm{model}} + \log (1 - d(x)) +\end{equation} +!et +The main motivation for the design of GANs is that the learning process requires +neither approximate inference (variational autoencoders for example) nor +approximation of a partition function. In the case where +!bt +\begin{equation} + \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) +\end{equation} +!et +is convex in $\theta^{(g)} then the procedure is guaranteed to converge and is +asymptotically consistent +( "Seth Lloyd on QuGANs": "https://arxiv.org/pdf/1804.09139.pdf" ). + +!split +===== Additional References ===== +This is in +general not the case and it is possible to get situations where the training +process never converges because the generator and discriminator chase one +another around in the parameter space indefinitely. A much deeper discussion on +the currently open research problem of GAN convergence is available +"here": "https://www.deeplearningbook.org/contents/generative_models.html". To +anyone interested in learning more about GANs it is a highly recommended read. +Direct quote: "In this best-performing formulation, the generator aims to +increase the log probability that the discriminator makes a mistake, rather than +aiming to decrease the log probability that the discriminator makes the correct +prediction." "Another interesting read": "https://arxiv.org/abs/1701.00160" + + +!split +===== Writing Our First Generative Adversarial Network ===== +Let us now move on to actually implementing a GAN in tensorflow. We will study +the performance of our GAN on the MNIST dataset. This code is based on and +adapted from the +"google tutorial": "https://www.tensorflow.org/tutorials/generative/dcgan" + +First we import our libraries + +!bc pycod +import os +import time +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt +from tensorflow.keras import layers +from tensorflow.keras.utils import plot_model +!ec + +Next we define our hyperparameters and import our data the usual way + +!bc pycod +BUFFER_SIZE = 60000 +BATCH_SIZE = 256 +EPOCHS = 30 + +data = tf.keras.datasets.mnist.load_data() +(train_images, train_labels), (test_images, test_labels) = data +train_images = np.reshape(train_images, (train_images.shape[0], + 28, + 28, + 1)).astype('float32') + +# we normalize between -1 and 1 +train_images = (train_images - 127.5) / 127.5 +training_dataset = tf.data.Dataset.from_tensor_slices( + train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE) +!ec + +!split +===== MNIST and GANs ===== + +Let's have a quick look + +!bc pycod +plt.imshow(train_images[0], cmap='Greys') +plt.show() +!ec + +Now we define our two models. This is where the 'magic' happens. There are a +huge amount of possible formulations for both models. A lot of engineering and +trial and error can be done here to try to produce better performing models. For +more advanced GANs this is by far the step where you can 'make or break' a +model. + +We start with the generator. As stated in the introductory text the generator +$g$ upsamples from a random sample to the shape of what we want to predict. In +our case we are trying to predict MNIST images ($28\times 28$ pixels). + +!bc pycod +def generator_model(): + """ + The generator uses upsampling layers tf.keras.layers.Conv2DTranspose() to + produce an image from a random seed. We start with a Dense layer taking this + random sample as an input and subsequently upsample through multiple + convolutional layers. + """ + + # we define our model + model = tf.keras.Sequential() + + + # adding our input layer. Dense means that every neuron is connected and + # the input shape is the shape of our random noise. The units need to match + # in some sense the upsampling strides to reach our desired output shape. + # we are using 100 random numbers as our seed + model.add(layers.Dense(units=7*7*BATCH_SIZE, + use_bias=False, + input_shape=(100, ))) + # we normalize the output form the Dense layer + model.add(layers.BatchNormalization()) + # and add an activation function to our 'layer'. LeakyReLU avoids vanishing + # gradient problem + model.add(layers.LeakyReLU()) + model.add(layers.Reshape((7, 7, BATCH_SIZE))) + assert model.output_shape == (None, 7, 7, BATCH_SIZE) + # even though we just added four keras layers we think of everything above + # as 'one' layer + + # next we add our upscaling convolutional layers + model.add(layers.Conv2DTranspose(filters=128, + kernel_size=(5, 5), + strides=(1, 1), + padding='same', + use_bias=False)) + model.add(layers.BatchNormalization()) + model.add(layers.LeakyReLU()) + assert model.output_shape == (None, 7, 7, 128) + + model.add(layers.Conv2DTranspose(filters=64, + kernel_size=(5, 5), + strides=(2, 2), + padding='same', + use_bias=False)) + model.add(layers.BatchNormalization()) + model.add(layers.LeakyReLU()) + assert model.output_shape == (None, 14, 14, 64) + + model.add(layers.Conv2DTranspose(filters=1, + kernel_size=(5, 5), + strides=(2, 2), + padding='same', + use_bias=False, + activation='tanh')) + assert model.output_shape == (None, 28, 28, 1) + + return model + +!ec + +And there we have our 'simple' generator model. Now we move on to defining our +discriminator model $d$, which is a convolutional neural network based image +classifier. + +!bc pycod +def discriminator_model(): + """ + The discriminator is a convolutional neural network based image classifier + """ + + # we define our model + model = tf.keras.Sequential() + model.add(layers.Conv2D(filters=64, + kernel_size=(5, 5), + strides=(2, 2), + padding='same', + input_shape=[28, 28, 1])) + model.add(layers.LeakyReLU()) + # adding a dropout layer as you do in conv-nets + model.add(layers.Dropout(0.3)) + + + model.add(layers.Conv2D(filters=128, + kernel_size=(5, 5), + strides=(2, 2), + padding='same')) + model.add(layers.LeakyReLU()) + # adding a dropout layer as you do in conv-nets + model.add(layers.Dropout(0.3)) + + model.add(layers.Flatten()) + model.add(layers.Dense(1)) + + return model +!ec + +!split +===== Other Models ===== +Let us take a look at our models. _Note_: double click images for bigger view. + +!bc pycod +generator = generator_model() +plot_model(generator, show_shapes=True, rankdir='LR') +!ec + +!bc pycod +discriminator = discriminator_model() +plot_model(discriminator, show_shapes=True, rankdir='LR') +!ec + +Next we need a few helper objects we will use in training + +!bc pycod +cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True) +generator_optimizer = tf.keras.optimizers.Adam(1e-4) +discriminator_optimizer = tf.keras.optimizers.Adam(1e-4) +!ec + +The first object, *cross_entropy* is our loss function and the two others are +our optimizers. Notice we use the same learning rate for both $g$ and $d$. This +is because they need to improve their accuracy at approximately equal speeds to +get convergence (not necessarily exactly equal). Now we define our loss +functions + +!bc pycod +def generator_loss(fake_output): + loss = cross_entropy(tf.ones_like(fake_output), fake_output) + + return loss +!ec + +!bc pycod +def discriminator_loss(real_output, fake_output): + real_loss = cross_entropy(tf.ones_like(real_output), real_output) + fake_loss = cross_entropy(tf.zeros_liks(fake_output), fake_output) + total_loss = real_loss + fake_loss + + return total_loss +!ec + +Next we define a kind of seed to help us compare the learning process over +multiple training epochs. + +!bc pycod +noise_dimension = 100 +n_examples_to_generate = 16 +seed_images = tf.random.normal([n_examples_to_generate, noise_dimension]) +!ec + +!split +===== Training Step ===== + +Now we have everything we need to define our training step, which we will apply +for every step in our training loop. Notice the @tf.function flag signifying +that the function is tensorflow 'compiled'. Removing this flag doubles the +computation time. + +!bc pycod +@tf.function +def train_step(images): + noise = tf.random.normal([BATCH_SIZE, noise_dimension]) + + with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape: + generated_images = generator(noise, training=True) + + real_output = discriminator(images, training=True) + fake_output = discriminator(generated_images, training=True) + + gen_loss = generator_loss(fake_output) + disc_loss = discriminator_loss(real_output, fake_output) + + gradients_of_generator = gen_tape.gradient(gen_loss, + generator.trainable_variables) + gradients_of_discriminator = disc_tape.gradient(disc_loss, + discriminator.trainable_variables) + generator_optimizer.apply_gradients(zip(gradients_of_generator, + generator.trainable_variables)) + discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, + discriminator.trainable_variables)) + + return gen_loss, disc_loss +!ec + + +Next we define a helper function to produce an output over our training epochs +to see the predictive progression of our generator model. _Note_: I am including +this code here, but comment it out in the training loop. +!bc pycod +def generate_and_save_images(model, epoch, test_input): + # we're making inferences here + predictions = model(test_input, training=False) + + fig = plt.figure(figsize=(4, 4)) + + for i in range(predictions.shape[0]): + plt.subplot(4, 4, i+1) + plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray') + plt.axis('off') + + plt.savefig(f'./images_from_seed_images/image_at_epoch_{str(epoch).zfill(3)}.png') + plt.close() + #plt.show() +!ec + + +!split +===== Checkpoints ===== +Setting up checkpoints to periodically save our model during training so that +everything is not lost even if the program were to somehow terminate while +training. + +!bc pycod +# Setting up checkpoints to save model during training +checkpoint_dir = './training_checkpoints' +checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt') +checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer, + discriminator_optimizer=discriminator_optimizer, + generator=generator, + discriminator=discriminator) +!ec + +Now we define our training loop + +!bc pycod +def train(dataset, epochs): + generator_loss_list = [] + discriminator_loss_list = [] + + for epoch in range(epochs): + start = time.time() + + for image_batch in dataset: + gen_loss, disc_loss = train_step(image_batch) + generator_loss_list.append(gen_loss.numpy()) + discriminator_loss_list.append(disc_loss.numpy()) + + #generate_and_save_images(generator, epoch + 1, seed_images) + + if (epoch + 1) % 15 == 0: + checkpoint.save(file_prefix=checkpoint_prefix) + + print(f'Time for epoch {epoch} is {time.time() - start}') + + #generate_and_save_images(generator, epochs, seed_images) + + loss_file = './data/lossfile.txt' + with open(loss_file, 'w') as outfile: + outfile.write(str(generator_loss_list)) + outfile.write('\n') + outfile.write('\n') + outfile.write(str(discriminator_loss_list)) + outfile.write('\n') + outfile.write('\n') +!ec + + +To train simply call this function. _Warning_: this might take a long time so +there is a folder of a pretrained network already included in the repository. + +!bc pycod +train(train_dataset, EPOCHS) +!ec + +And here is the result of training our model for 100 epochs + +MOVIE: [images_from_seed_images/generation.gif] + +Now to avoid having to train and everything, which will take a while depending +on your computer setup we now load in the model which produced the above gif. + +!bc pycod +checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir)) +restored_generator = checkpoint.generator +restored_discriminator = checkpoint.discriminator + +print(restored_generator) +print(restored_discriminator) +!ec + + +!split +===== Exploring the Latent Space ===== + +We have successfully loaded in our latest model. Let us now play around a bit +and see what kind of things we can learn about this model. Our generator takes +an array of 100 numbers. One idea can be to try to systematically change our +input. Let us try and see what we get + +!bc pycod +def generate_latent_points(number=100, scale_means=1, scale_stds=1): + latent_dim = 100 + means = scale_means * tf.linspace(-1, 1, num=latent_dim) + stds = scale_stds * tf.linspace(-1, 1, num=latent_dim) + latent_space_value_range = tf.random.normal([number, latent_dim], + means, + stds, + dtype=tf.float64) + + return latent_space_value_range + +def generate_images(latent_points): + # notice we set training to false because we are making inferences + generated_images = restored_generator.predict(latent_points) + + return generated_images +!ec + +!bc pycod +def plot_result(generated_images, number=100): + # obviously this assumes sqrt number is an int + fig, axs = plt.subplots(int(np.sqrt(number)), int(np.sqrt(number)), + figsize=(10, 10)) + + for i in range(int(np.sqrt(number))): + for j in range(int(np.sqrt(number))): + axs[i, j].imshow(generated_images[i*j], cmap='Greys') + axs[i, j].axis('off') + + plt.show() +!ec + +!bc pycod +generated_images = generate_images(generate_latent_points()) +plot_result(generated_images) +!ec + +!split +===== Getting Results ===== +We see that the generator generates images that look like MNIST +numbers: $1, 4, 7, 9$. Let's try to tweak it a bit more to see if we are able +to generate a similar plot where we generate every MNIST number. Let us now try +to 'move' a bit around in the latent space. _Note_: decrease the plot number if +these following cells take too long to run on your computer. + +!bc pycod +plot_number = 225 + +generated_images = generate_images(generate_latent_points(number=plot_number, + scale_means=5, + scale_stds=1)) +plot_result(generated_images, number=plot_number) + +generated_images = generate_images(generate_latent_points(number=plot_number, + scale_means=-5, + scale_stds=1)) +plot_result(generated_images, number=plot_number) + +generated_images = generate_images(generate_latent_points(number=plot_number, + scale_means=1, + scale_stds=5)) +plot_result(generated_images, number=plot_number) +!ec + +Again, we have found something interesting. *Moving* around using our means +takes us from digit to digit, while *moving* around using our standard +deviations seem to increase the number of different digits! In the last image +above, we can barely make out every MNIST digit. Let us make on last plot using +this information by upping the standard deviation of our Gaussian noises. + +!bc pycod +plot_number = 400 +generated_images = generate_images(generate_latent_points(number=plot_number, + scale_means=1, + scale_stds=10)) +plot_result(generated_images, number=plot_number) +!ec +A pretty cool result! We see that our generator indeed has learned a +distribution which qualitatively looks a whole lot like the MNIST dataset. + +!split +===== Interpolating Between MNIST Digits ===== +Another interesting way to explore the latent space of our generator model is by +interpolating between the MNIST digits. This section is largely based on +"this excellent blogpost": "https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/" +by Jason Brownlee. + +So let us start by defining a function to interpolate between two points in the +latent space. + +!bc pycod +def interpolation(point_1, point_2, n_steps=10): + ratios = np.linspace(0, 1, num=n_steps) + vectors = [] + for i, ratio in enumerate(ratios): + vectors.append(((1.0 - ratio) * point_1 + ratio * point_2)) + + return tf.stack(vectors) +!ec + +Now we have all we need to do our interpolation analysis. + +!bc pycod +plot_number = 100 +latent_points = generate_latent_points(number=plot_number) +results = None +for i in range(0, 2*np.sqrt(plot_number), 2): + interpolated = interpolation(latent_points[i], latent_points[i+1]) + generated_images = generate_images(interpolated) + + if results is None: + results = generated_images + else: + results = tf.stack((results, generated_images)) + +plot_results(results, plot_number) +!ec + +!split +===== Basic ideas of the Principal Component Analysis (PCA) ===== + +The principal component analysis deals with the problem of fitting a +low-dimensional affine subspace $S$ of dimension $d$ much smaller than +the total dimension $D$ of the problem at hand (our data +set). Mathematically it can be formulated as a statistical problem or +a geometric problem. In our discussion of the theorem for the +classical PCA, we will stay with a statistical approach. +Historically, the PCA was first formulated in a statistical setting in order to estimate the principal component of a multivariate random variable. + +We have a data set defined by a design/feature matrix $\bm{X}$ (see below for its definition) +* Each data point is determined by $p$ extrinsic (measurement) variables +* We may want to ask the following question: Are there fewer intrinsic variables (say $d << p$) that still approximately describe the data? +* If so, these intrinsic variables may tell us something important and finding these intrinsic variables is what dimension reduction methods do. + +A good read is for example "Vidal, Ma and Sastry":"https://www.springer.com/gp/book/9780387878102". + + +!split +===== Introducing the Covariance and Correlation functions ===== + +Before we discuss the PCA theorem, we need to remind ourselves about +the definition of the covariance and the correlation function. These are quantities + +Suppose we have defined two vectors +$\hat{x}$ and $\hat{y}$ with $n$ elements each. The covariance matrix $\bm{C}$ is defined as +!bt +\[ +\bm{C}[\bm{x},\bm{y}] = \begin{bmatrix} \mathrm{cov}[\bm{x},\bm{x}] & \mathrm{cov}[\bm{x},\bm{y}] \\ + \mathrm{cov}[\bm{y},\bm{x}] & \mathrm{cov}[\bm{y},\bm{y}] \\ + \end{bmatrix}, +\] +!et +where for example +!bt +\[ +\mathrm{cov}[\bm{x},\bm{y}] =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). +\] +!et +With this definition and recalling that the variance is defined as +!bt +\[ +\mathrm{var}[\bm{x}]=\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})^2, +\] +!et +we can rewrite the covariance matrix as +!bt +\[ +\bm{C}[\bm{x},\bm{y}] = \begin{bmatrix} \mathrm{var}[\bm{x}] & \mathrm{cov}[\bm{x},\bm{y}] \\ + \mathrm{cov}[\bm{x},\bm{y}] & \mathrm{var}[\bm{y}] \\ + \end{bmatrix}. +\] +!et + +!split +===== More on the covariance ===== +The covariance takes values between zero and infinity and may thus +lead to problems with loss of numerical precision for particularly +large values. It is common to scale the covariance matrix by +introducing instead the correlation matrix defined via the so-called +correlation function + +!bt +\[ +\mathrm{corr}[\bm{x},\bm{y}]=\frac{\mathrm{cov}[\bm{x},\bm{y}]}{\sqrt{\mathrm{var}[\bm{x}] \mathrm{var}[\bm{y}]}}. +\] +!et + +The correlation function is then given by values $\mathrm{corr}[\bm{x},\bm{y}] +\in [-1,1]$. This avoids eventual problems with too large values. We +can then define the correlation matrix for the two vectors $\bm{x}$ +and $\bm{y}$ as + +!bt +\[ +\bm{K}[\bm{x},\bm{y}] = \begin{bmatrix} 1 & \mathrm{corr}[\bm{x},\bm{y}] \\ + \mathrm{corr}[\bm{y},\bm{x}] & 1 \\ + \end{bmatrix}, +\] +!et + +In the above example this is the function we constructed using _pandas_. + +!split +===== Reminding ourselves about Linear Regression ===== +In our derivation of the various regression algorithms like _Ordinary Least Squares_ or _Ridge regression_ +we defined the design/feature matrix $\bm{X}$ as + +!bt +\[ +\bm{X}=\begin{bmatrix} +x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ +x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ +x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ +\dots & \dots & \dots & \dots \dots & \dots \\ +x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ +x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ +\end{bmatrix}, +\] +!et +with $\bm{X}\in {\mathbb{R}}^{n\times p}$, with the predictors/features $p$ refering to the column numbers and the +entries $n$ being the row elements. +We can rewrite the design/feature matrix in terms of its column vectors as +!bt +\[ +\bm{X}=\begin{bmatrix} \bm{x}_0 & \bm{x}_1 & \bm{x}_2 & \dots & \dots & \bm{x}_{p-1}\end{bmatrix}, +\] +!et +with a given vector +!bt +\[ +\bm{x}_i^T = \begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \dots & \dots x_{n-1,i}\end{bmatrix}. +\] +!et + +!split +===== Simple Example ===== +With these definitions, we can now rewrite our $2\times 2$ +correlation/covariance matrix in terms of a moe general design/feature +matrix $\bm{X}\in {\mathbb{R}}^{n\times p}$. This leads to a $p\times p$ +covariance matrix for the vectors $\bm{x}_i$ with $i=0,1,\dots,p-1$ + +!bt +\[ +\bm{C}[\bm{x}] = \begin{bmatrix} +\mathrm{var}[\bm{x}_0] & \mathrm{cov}[\bm{x}_0,\bm{x}_1] & \mathrm{cov}[\bm{x}_0,\bm{x}_2] & \dots & \dots & \mathrm{cov}[\bm{x}_0,\bm{x}_{p-1}]\\ +\mathrm{cov}[\bm{x}_1,\bm{x}_0] & \mathrm{var}[\bm{x}_1] & \mathrm{cov}[\bm{x}_1,\bm{x}_2] & \dots & \dots & \mathrm{cov}[\bm{x}_1,\bm{x}_{p-1}]\\ +\mathrm{cov}[\bm{x}_2,\bm{x}_0] & \mathrm{cov}[\bm{x}_2,\bm{x}_1] & \mathrm{var}[\bm{x}_2] & \dots & \dots & \mathrm{cov}[\bm{x}_2,\bm{x}_{p-1}]\\ +\dots & \dots & \dots & \dots & \dots & \dots \\ +\dots & \dots & \dots & \dots & \dots & \dots \\ +\mathrm{cov}[\bm{x}_{p-1},\bm{x}_0] & \mathrm{cov}[\bm{x}_{p-1},\bm{x}_1] & \mathrm{cov}[\bm{x}_{p-1},\bm{x}_{2}] & \dots & \dots & \mathrm{var}[\bm{x}_{p-1}]\\ +\end{bmatrix}, +\] +!et + +!split +===== The Correlation Matrix ===== + +and the correlation matrix +!bt +\[ +\bm{K}[\bm{x}] = \begin{bmatrix} +1 & \mathrm{corr}[\bm{x}_0,\bm{x}_1] & \mathrm{corr}[\bm{x}_0,\bm{x}_2] & \dots & \dots & \mathrm{corr}[\bm{x}_0,\bm{x}_{p-1}]\\ +\mathrm{corr}[\bm{x}_1,\bm{x}_0] & 1 & \mathrm{corr}[\bm{x}_1,\bm{x}_2] & \dots & \dots & \mathrm{corr}[\bm{x}_1,\bm{x}_{p-1}]\\ +\mathrm{corr}[\bm{x}_2,\bm{x}_0] & \mathrm{corr}[\bm{x}_2,\bm{x}_1] & 1 & \dots & \dots & \mathrm{corr}[\bm{x}_2,\bm{x}_{p-1}]\\ +\dots & \dots & \dots & \dots & \dots & \dots \\ +\dots & \dots & \dots & \dots & \dots & \dots \\ +\mathrm{corr}[\bm{x}_{p-1},\bm{x}_0] & \mathrm{corr}[\bm{x}_{p-1},\bm{x}_1] & \mathrm{corr}[\bm{x}_{p-1},\bm{x}_{2}] & \dots & \dots & 1\\ +\end{bmatrix}, +\] +!et + + +!split +===== Numpy Functionality ===== + +The Numpy function _np.cov_ calculates the covariance elements using +the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have +the exact mean values. The following simple function uses the +_np.vstack_ function which takes each vector of dimension $1\times n$ +and produces a $2\times n$ matrix $\bm{W}$ + + +!bt +\[ +\bm{W}^T = \begin{bmatrix} x_0 & y_0 \\ + x_1 & y_1 \\ + x_2 & y_2\\ + \dots & \dots \\ + x_{n-2} & y_{n-2}\\ + x_{n-1} & y_{n-1} & + \end{bmatrix}, +\] +!et + +which in turn is converted into into the $2\times 2$ covariance matrix +$\bm{C}$ via the Numpy function _np.cov()_. We note that we can also calculate +the mean value of each set of samples $\bm{x}$ etc using the Numpy +function _np.mean(x)_. We can also extract the eigenvalues of the +covariance matrix through the _np.linalg.eig()_ function. + +!bc pycod +# Importing various packages +import numpy as np +n = 100 +x = np.random.normal(size=n) +print(np.mean(x)) +y = 4+3*x+np.random.normal(size=n) +print(np.mean(y)) +W = np.vstack((x, y)) +C = np.cov(W) +print(C) +!ec + + +!split +===== Correlation Matrix again ===== + +The previous example can be converted into the correlation matrix by +simply scaling the matrix elements with the variances. We should also +subtract the mean values for each column. This leads to the following +code which sets up the correlations matrix for the previous example in +a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the $2\times 2$ correlation matrix (since we have only two vectors). + +!bc pycod +import numpy as np +n = 100 +# define two vectors +x = np.random.random(size=n) +y = 4+3*x+np.random.normal(size=n) +#scaling the x and y vectors +x = x - np.mean(x) +y = y - np.mean(y) +variance_x = np.sum(x@x)/n +variance_y = np.sum(y@y)/n +print(variance_x) +print(variance_y) +cov_xy = np.sum(x@y)/n +cov_xx = np.sum(x@x)/n +cov_yy = np.sum(y@y)/n +C = np.zeros((2,2)) +C[0,0]= cov_xx/variance_x +C[1,1]= cov_yy/variance_y +C[0,1]= cov_xy/np.sqrt(variance_y*variance_x) +C[1,0]= C[0,1] +print(C) +!ec + +We see that the matrix elements along the diagonal are one as they +should be and that the matrix is symmetric. Furthermore, diagonalizing +this matrix we easily see that it is a positive definite matrix. + +The above procedure with _numpy_ can be made more compact if we use _pandas_. + +!split +===== Using Pandas ===== + +We whow here how we can set up the correlation matrix using _pandas_, as done in this simple code +!bc pycod +import numpy as np +import pandas as pd +n = 10 +x = np.random.normal(size=n) +x = x - np.mean(x) +y = 4+3*x+np.random.normal(size=n) +y = y - np.mean(y) +X = (np.vstack((x, y))).T +print(X) +Xpd = pd.DataFrame(X) +print(Xpd) +correlation_matrix = Xpd.corr() +print(correlation_matrix) +!ec + +!split +===== And then the Franke Function ===== + +We expand this model to the Franke function discussed above. + + +!bc pycod +# Common imports +import numpy as np +import pandas as pd + + +def FrankeFunction(x,y): + term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2)) + term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1)) + term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2)) + term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2) + return term1 + term2 + term3 + term4 + + +def create_X(x, y, n ): + if len(x.shape) > 1: + x = np.ravel(x) + y = np.ravel(y) + + N = len(x) + l = int((n+1)*(n+2)/2) # Number of elements in beta + X = np.ones((N,l)) + + for i in range(1,n+1): + q = int((i)*(i+1)/2) + for k in range(i+1): + X[:,q+k] = (x**(i-k))*(y**k) + + return X + + +# Making meshgrid of datapoints and compute Franke's function +n = 4 +N = 100 +x = np.sort(np.random.uniform(0, 1, N)) +y = np.sort(np.random.uniform(0, 1, N)) +z = FrankeFunction(x, y) +X = create_X(x, y, n=n) + +Xpd = pd.DataFrame(X) +# subtract the mean values and set up the covariance matrix +Xpd = Xpd - Xpd.mean() +covariance_matrix = Xpd.cov() +print(covariance_matrix) +!ec + +We note here that the covariance is zero for the first rows and +columns since all matrix elements in the design matrix were set to one +(we are fitting the function in terms of a polynomial of degree $n$). We would however not include the intercept +and wee can simply +drop these elements and construct a correlation +matrix without them by centering our matrix elements by subtracting the mean of each column. + +!split +===== Lnks with the Design Matrix ===== + +We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix $\bm{X}$ as +!bt +\[ +\bm{C}[\bm{x}] = \frac{1}{n}\bm{X}^T\bm{X}= \mathbb{E}[\bm{X}^T\bm{X}]. +\] +!et + +To see this let us simply look at a design matrix $\bm{X}\in {\mathbb{R}}^{2\times 2}$ +!bt +\[ +\bm{X}=\begin{bmatrix} +x_{00} & x_{01}\\ +x_{10} & x_{11}\\ +\end{bmatrix}=\begin{bmatrix} +\bm{x}_{0} & \bm{x}_{1}\\ +\end{bmatrix}. +\] +!et + +!split +===== Computing the Expectation Values ===== + +If we then compute the expectation value +!bt +\[ +\mathbb{E}[\bm{X}^T\bm{X}] = \frac{1}{n}\bm{X}^T\bm{X}=\begin{bmatrix} +x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\ +x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\ +\end{bmatrix}, +\] +!et +which is just +!bt +\[ +\bm{C}[\bm{x}_0,\bm{x}_1] = \bm{C}[\bm{x}]=\begin{bmatrix} \mathrm{var}[\bm{x}_0] & \mathrm{cov}[\bm{x}_0,\bm{x}_1] \\ + \mathrm{cov}[\bm{x}_1,\bm{x}_0] & \mathrm{var}[\bm{x}_1] \\ + \end{bmatrix}, +\] +!et +where we wrote $$\bm{C}[\bm{x}_0,\bm{x}_1] = \bm{C}[\bm{x}]$$ to indicate that this the covariance of the vectors $\bm{x}$ of the design/feature matrix $\bm{X}$. + +It is easy to generalize this to a matrix $\bm{X}\in {\mathbb{R}}^{n\times p}$. + + +!split +===== Towards the PCA theorem ===== + +We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as +!bt +\[ +\bm{C}[\bm{x}] = \frac{1}{n}\bm{X}^T\bm{X}= \mathbb{E}[\bm{X}^T\bm{X}]. +\] +!et +Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices $\bm{S}$. +These matrices are defined as $\bm{S}\in {\mathbb{R}}^{p\times p}$ and obey the orthogonality requirements $\bm{S}\bm{S}^T=\bm{S}^T\bm{S}=\bm{I}$. The matrix can be written out in terms of the column vectors $\bm{s}_i$ as $\bm{S}=[\bm{s}_0,\bm{s}_1,\dots,\bm{s}_{p-1}]$ and $\bm{s}_i \in {\mathbb{R}}^{p}$. + +Assume also that there is a transformation $\bm{S}^T\bm{C}[\bm{x}]\bm{S}=\bm{C}[\bm{y}]$ such that the new matrix $\bm{C}[\bm{y}]$ is diagonal with elements $[\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}]$. + +That is we have +!bt +\[ +\bm{C}[\bm{y}] = \mathbb{E}[\bm{S}^T\bm{X}^T\bm{X}T\bm{S}]=\bm{S}^T\bm{C}[\bm{x}]\bm{S}, +\] +!et +since the matrix $\bm{S}$ is not a data dependent matrix. Multiplying with $\bm{S}$ from the left we have +!bt +\[ +\bm{S}\bm{C}[\bm{y}] = \bm{C}[\bm{x}]\bm{S}, +\] +!et +and since $\bm{C}[\bm{y}]$ is diagonal we have for a given eigenvalue $i$ of the covariance matrix that + +!bt +\[ +\bm{S}_i\lambda_i = \bm{C}[\bm{x}]\bm{S}_i. +\] +!et + +!split +===== More on the PCA Theorem ===== + +In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is +$\lambda_0 > \lambda_1 > \dots > \lambda_{p-1}$. + + +The eigenvalues tell us then how much we need to stretch the +corresponding eigenvectors. Dimensions with large eigenvalues have +thus large variations (large variance) and define therefore useful +dimensions. The data points are more spread out in the direction of +these eigenvectors. Smaller eigenvalues mean on the other hand that +the corresponding eigenvectors are shrunk accordingly and the data +points are tightly bunched together and there is not much variation in +these specific directions. Hopefully then we could leave it out +dimensions where the eigenvalues are very small. If $p$ is very large, +we could then aim at reducing $p$ to $l << p$ and handle only $l$ +features/predictors. + +!split +===== The Algorithm before the Theorem ===== + +Here's how we would proceed in setting up the algorithm for the PCA, see also discussion below here. +* Set up the datapoints for the design/feature matrix $\bm{X}$ with $\bm{X}\in {\mathbb{R}}^{n\times p}$, with the predictors/features $p$ referring to the column numbers and the entries $n$ being the row elements. +!bt +\[ +\bm{X}=\begin{bmatrix} +x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ +x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ +x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ +\dots & \dots & \dots & \dots \dots & \dots \\ +x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ +x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ +\end{bmatrix}, +\] +!et +* Center the data by subtracting the mean value for each column. This leads to a new matrix $\bm{X}\rightarrow \overline{\bm{X}}$. +* Compute then the covariance/correlation matrix $\mathbb{E}[\overline{\bm{X}}^T\overline{\bm{X}}]$. +* Find the eigenpairs of $\bm{C}$ with eigenvalues $[\lambda_0,\lambda_1,\dots,\lambda_{p-1}]$ and eigenvectors $[\bm{s}_0,\bm{s}_1,\dots,\bm{s}_{p-1}]$. +* Order the eigenvalue (and the eigenvectors accordingly) in order of decreasing eigenvalues. +* Keep only those $l$ eigenvalues larger than a selected threshold value, discarding thus $p-l$ features since we expect small variations in the data here. + +!split +===== Writing our own PCA code ===== + +We will use a simple example first with two-dimensional data +drawn from a multivariate normal distribution with the following mean and covariance matrix (we have fixed these quantities but will play around with them below): +!bt +\[ +\mu = (-1,2) \qquad \Sigma = \begin{bmatrix} 4 & 2 \\ +2 & 2 +\end{bmatrix} +\] +!et +Note that the mean refers to each column of data. +We will generate $n = 10000$ points $X = \{ x_1, \ldots, x_N \}$ from +this distribution, and store them in the $1000 \times 2$ matrix $\bm{X}$. This is our design matrix where we have forced the covariance and mean values to take specific values. + +!split +===== Implementing it ===== +The following Python code aids in setting up the data and writing out the design matrix. +Note that the function _multivariate_ returns also the covariance discussed above and that it is defined by dividing by $n-1$ instead of $n$. +!bc pycod +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from IPython.display import display +n = 10000 +mean = (-1, 2) +cov = [[4, 2], [2, 2]] +X = np.random.multivariate_normal(mean, cov, n) +!ec + +Now we are going to implement the PCA algorithm. We will break it down into various substeps. + +!split +===== First Step ===== + +The first step of PCA is to compute the sample mean of the data and use it to center the data. Recall that the sample mean is +!bt +\[ +\mu_n = \frac{1}{n} \sum_{i=1}^n x_i +\] +!et +and the mean-centered data $\bar{X} = \{ \bar{x}_1, \ldots, \bar{x}_n \}$ takes the form +!bt +\[ +\bar{x}_i = x_i - \mu_n. +\] +!et +When you are done with these steps, print out $\mu_n$ to verify it is +close to $\mu$ and plot your mean centered data to verify it is +centered at the origin! +The following code elements perform these operations using _pandas_ or using our own functionality for doing so. The latter, using _numpy_ is rather simple through the _mean()_ function. +!bc pycod +df = pd.DataFrame(X) +# Pandas does the centering for us +df = df -df.mean() +# we center it ourselves +X_centered = X - X.mean(axis=0) +!ec + +!split +===== Scaling ===== +Alternatively, we could use the functions we discussed +earlier for scaling the data set. That is, we could have used the +_StandardScaler_ function in _Scikit-Learn_, a function which ensures +that for each feature/predictor we study the mean value is zero and +the variance is one (every column in the design/feature matrix). You +would then not get the same results, since we divide by the +variance. The diagonal covariance matrix elements will then be one, +while the non-diagonal ones need to be divided by $2\sqrt{2}$ for our +specific case. + +!split +===== Centered Data ===== + +Now we are going to use the mean centered data to compute the sample covariance of the data by using the following equation +!bt +\begin{equation*} +\Sigma_n = \frac{1}{n-1} \sum_{i=1}^n \bar{x}_i^T \bar{x}_i = \frac{1}{n-1} \sum_{i=1}^n (x_i - \mu_n)^T (x_i - \mu_n) +\end{equation*} +!et +where the data points $x_i \in \mathbb{R}^p$ (here in this example $p = 2$) are column vectors and $x^T$ is the transpose of $x$. +We can write our own code or simply use either the functionaly of _numpy_ or that of _pandas_, as follows +!bc pycod +print(df.cov()) +print(np.cov(X_centered.T)) +!ec +Note that the way we define the covariance matrix here has a factor $n-1$ instead of $n$. This is included in the _cov()_ function by _numpy_ and _pandas_. +Our own code here is not very elegant and asks for obvious improvements. It is tailored to this specific $2\times 2$ covariance matrix. +!bc pycod +# extract the relevant columns from the centered design matrix of dim n x 2 +x = X_centered[:,0] +y = X_centered[:,1] +Cov = np.zeros((2,2)) +Cov[0,1] = np.sum(x.T@y)/(n-1.0) +Cov[0,0] = np.sum(x.T@x)/(n-1.0) +Cov[1,1] = np.sum(y.T@y)/(n-1.0) +Cov[1,0]= Cov[0,1] +print("Centered covariance using own code") +print(Cov) +plt.plot(x, y, 'x') +plt.axis('equal') +plt.show() +!ec + +!split +===== Exploring ===== + +Depending on the number of points $n$, we will get results that are close to the covariance values defined above. +The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed. + +!split +===== Diagonalize the sample covariance matrix to obtain the principal components ===== + +Now we are ready to solve for the principal components! To do so we +diagonalize the sample covariance matrix $\Sigma$. We can use the +function _np.linalg.eig_ to do so. It will return the eigenvalues and +eigenvectors of $\Sigma$. Once we have these we can perform the +following tasks: + +* We compute the percentage of the total variance captured by the first principal component +* We plot the mean centered data and lines along the first and second principal components +* Then we project the mean centered data onto the first and second principal components, and plot the projected data. +* Finally, we approximate the data as + +!bt +\begin{equation*} +x_i \approx \tilde{x}_i = \mu_n + \langle x_i, v_0 \rangle v_0 +\end{equation*} +!et +where $v_0$ is the first principal component. + +!split +===== Collecting all Steps ===== + +Collecting all these steps we can write our own PCA function and +compare this with the functionality included in _Scikit-Learn_. + +The code here outlines some of the elements we could include in the +analysis. Feel free to extend upon this in order to address the above +questions. + +!bc pycod +# diagonalize and obtain eigenvalues, not necessarily sorted +EigValues, EigVectors = np.linalg.eig(Cov) +# sort eigenvectors and eigenvalues +#permute = EigValues.argsort() +#EigValues = EigValues[permute] +#EigVectors = EigVectors[:,permute] +print("Eigenvalues of Covariance matrix") +for i in range(2): + print(EigValues[i]) +FirstEigvector = EigVectors[:,0] +SecondEigvector = EigVectors[:,1] +print("First eigenvector") +print(FirstEigvector) +print("Second eigenvector") +print(SecondEigvector) +#thereafter we do a PCA with Scikit-learn +from sklearn.decomposition import PCA +pca = PCA(n_components = 2) +X2Dsl = pca.fit_transform(X) +print("Eigenvector of largest eigenvalue") +print(pca.components_.T[:, 0]) + +!ec +This code does not contain all the above elements, but it shows how we can use _Scikit-Learn_ to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then? + +!split +===== Classical PCA Theorem ===== + +We assume now that we have a design matrix $\bm{X}$ which has been +centered as discussed above. For the sake of simplicity we skip the +overline symbol. The matrix is defined in terms of the various column +vectors $[\bm{x}_0,\bm{x}_1,\dots, \bm{x}_{p-1}]$ each with dimension +$\bm{x}\in {\mathbb{R}}^{n}$. + + + +The PCA theorem states that minimizing the above reconstruction error +corresponds to setting $\bm{W}=\bm{S}$, the orthogonal matrix which +diagonalizes the empirical covariance(correlation) matrix. The optimal +low-dimensional encoding of the data is then given by a set of vectors +$\bm{z}_i$ with at most $l$ vectors, with $l << p$, defined by the +orthogonal projection of the data onto the columns spanned by the +eigenvectors of the covariance(correlations matrix). + + + +!split +===== The PCA Theorem ===== + +To show the PCA theorem let us start with the assumption that there is one vector $\bm{s}_0$ which corresponds to a solution which minimized the reconstruction error $J$. This is an orthogonal vector. It means that we now approximate the reconstruction error in terms of $\bm{w}_0$ and $\bm{z}_0$ as + + + +We are almost there, we have obtained a relation between minimizing +the reconstruction error and the variance and the covariance +matrix. Minimizing the error is equivalent to maximizing the variance +of the projected data. + + +We could trivially maximize the variance of the projection (and +thereby minimize the error in the reconstruction function) by letting +the norm-2 of $\bm{w}_0$ go to infinity. However, this norm since we +want the matrix $\bm{W}$ to be an orthogonal matrix, is constrained by +$\vert\vert \bm{w}_0 \vert\vert_2^2=1$. Imposing this condition via a +Lagrange multiplier we can then in turn maximize + +!bt +\[ +J(\bm{w}_0)= \bm{w}_0^T\bm{C}[\bm{x}]\bm{w}_0+\lambda_0(1-\bm{w}_0^T\bm{w}_0). +\] +!et +Taking the derivative with respect to $\bm{w}_0$ we obtain + +!bt +\[ +\frac{\partial J(\bm{w}_0)}{\partial \bm{w}_0}= 2\bm{C}[\bm{x}]\bm{w}_0-2\lambda_0\bm{w}_0=0, +\] +!et +meaning that +!bt +\[ +\bm{C}[\bm{x}]\bm{w}_0=\lambda_0\bm{w}_0. +\] +!et +_The direction that maximizes the variance (or minimizes the construction error) is an eigenvector of the covariance matrix_! If we left multiply with $\bm{w}_0^T$ we have the variance of the projected data is +!bt +\[ +\bm{w}_0^T\bm{C}[\bm{x}]\bm{w}_0=\lambda_0. +\] +!et + +If we want to maximize the variance (minimize the construction error) +we simply pick the eigenvector of the covariance matrix with the +largest eigenvalue. This establishes the link between the minimization +of the reconstruction function $J$ in terms of an orthogonal matrix +and the maximization of the variance and thereby the covariance of our +observations encoded in the design/feature matrix $\bm{X}$. + +The proof +for the other eigenvectors $\bm{w}_1,\bm{w}_2,\dots$ can be +established by applying the above arguments and using the fact that +our basis of eigenvectors is orthogonal, see "Murphy chapter +12.2":"https://mitpress.mit.edu/books/machine-learning-1". The +discussion in chapter 12.2 of Murphy's text has also a nice link with +the Singular Value Decomposition theorem. For categorical data, see +chapter 12.4 and discussion therein. + +For more details, see for example "Vidal, Ma and Sastry, chapter 2":"https://www.springer.com/gp/book/9780387878102". + +!split +===== Geometric Interpretation and link with Singular Value Decomposition ===== + +For a detailed demonstration of the geometric interpretation, see "Vidal, Ma and Sastry, section 2.1.2":"https://www.springer.com/gp/book/9780387878102". + + +Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm. +First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. + +The following Python code uses NumPy’s _svd()_ function to obtain all the principal components of the +training set, then extracts the first two principal components. First we center the data using either _pandas_ or our own code +!bc pycod +import numpy as np +import pandas as pd +from IPython.display import display +np.random.seed(100) +# setting up a 10 x 5 vanilla matrix +rows = 10 +cols = 5 +X = np.random.randn(rows,cols) +df = pd.DataFrame(X) +# Pandas does the centering for us +df = df -df.mean() +display(df) + +# we center it ourselves +X_centered = X - X.mean(axis=0) +# Then check the difference between pandas and our own set up +print(X_centered-df) +#Now we do an SVD +U, s, V = np.linalg.svd(X_centered) +c1 = V.T[:, 0] +c2 = V.T[:, 1] +W2 = V.T[:, :2] +X2D = X_centered.dot(W2) +print(X2D) +!ec + +PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering +the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t +forget to center the data first. + +Once you have identified all the principal components, you can reduce the dimensionality of the dataset +down to $d$ dimensions by projecting it onto the hyperplane defined by the first $d$ principal components. +Selecting this hyperplane ensures that the projection will preserve as much variance as possible. +!bc pycod +W2 = V.T[:, :2] +X2D = X_centered.dot(W2) +!ec + +!split +===== PCA and scikit-learn ===== + +Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The +following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note +that it automatically takes care of centering the data): +!bc pycod +#thereafter we do a PCA with Scikit-learn +from sklearn.decomposition import PCA +pca = PCA(n_components = 2) +X2D = pca.fit_transform(X) +print(X2D) +!ec +After fitting the PCA transformer to the dataset, you can access the principal components using the +components variable (note that it contains the PCs as horizontal vectors, so, for example, the first +principal component is equal to +!bc pycod +pca.components_.T[:, 0] +!ec +Another very useful piece of information is the explained variance ratio of each principal component, +available via the $explained\_variance\_ratio$ variable. It indicates the proportion of the dataset’s +variance that lies along the axis of each principal component. + +!split +===== Back to the Cancer Data ===== +We can now repeat the above but applied to real data, in this case our breast cancer data. +Here we compute performance scores on the training data using logistic regression. +!bc pycod +import matplotlib.pyplot as plt +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.datasets import load_breast_cancer +from sklearn.linear_model import LogisticRegression +cancer = load_breast_cancer() + +X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0) + +logreg = LogisticRegression() +logreg.fit(X_train, y_train) +print("Train set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_train,y_train))) +# We scale the data +from sklearn.preprocessing import StandardScaler +scaler = StandardScaler() +scaler.fit(X_train) +X_train_scaled = scaler.transform(X_train) +X_test_scaled = scaler.transform(X_test) +# Then perform again a log reg fit +logreg.fit(X_train_scaled, y_train) +print("Train set accuracy scaled data: {:.2f}".format(logreg.score(X_train_scaled,y_train))) +#thereafter we do a PCA with Scikit-learn +from sklearn.decomposition import PCA +pca = PCA(n_components = 2) +X2D_train = pca.fit_transform(X_train_scaled) +# and finally compute the log reg fit and the score on the training data +logreg.fit(X2D_train,y_train) +print("Train set accuracy scaled and PCA data: {:.2f}".format(logreg.score(X2D_train,y_train))) + +!ec + +We see that our training data after the PCA decomposition has a performance similar to the non-scaled data. + + +Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to +choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). +Unless, of course, you are reducing dimensionality for data visualization — in that case you will +generally want to reduce the dimensionality down to 2 or 3. +The following code computes PCA without reducing dimensionality, then computes the minimum number +of dimensions required to preserve 95% of the training set’s variance: +!bc pycod +pca = PCA() +pca.fit(X) +cumsum = np.cumsum(pca.explained_variance_ratio_) +d = np.argmax(cumsum >= 0.95) + 1 +!ec +You could then set $n\_components=d$ and run PCA again. However, there is a much better option: instead +of specifying the number of principal components you want to preserve, you can set $n\_components$ to be +a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: +!bc pycod +pca = PCA(n_components=0.95) +X_reduced = pca.fit_transform(X) +!ec + +!split +===== Incremental PCA ===== + +One problem with the preceding implementation of PCA is that it requires the whole training set to fit in +memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have +been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch +at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new +instances arrive). + + +=== Randomized PCA === + +Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic +algorithm that quickly finds an approximation of the first d principal components. Its computational +complexity is $O(m \times d^2)+O(d^3)$, instead of $O(m \times n^2) + O(n^3)$, so it is dramatically faster than the +previous algorithms when $d$ is much smaller than $n$. + + +=== Kernel PCA === + + +The kernel trick is a mathematical technique that implicitly maps instances into a +very high-dimensional space (called the feature space), enabling nonlinear classification and regression +with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature +space corresponds to a complex nonlinear decision boundary in the original space. +It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear +projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at +preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a +twisted manifold. +For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an +!bc pycod +from sklearn.decomposition import KernelPCA +rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04) +X_reduced = rbf_pca.fit_transform(X) +!ec + +!split +===== Other techniques ===== + + +There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn. + +Here are some of the most popular: +* _Multidimensional Scaling (MDS)_ reduces dimensionality while trying to preserve the distances between the instances. +* _Isomap_ creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances. +* _t-Distributed Stochastic Neighbor Embedding_ (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D). +* Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures. + + + diff --git a/doc/src/week43/week43.do.txt b/doc/src/week43/week43.do.txt index f0c8fd270..a6c601853 100644 --- a/doc/src/week43/week43.do.txt +++ b/doc/src/week43/week43.do.txt @@ -1,12 +1,12 @@ -ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis +ATITLE: Week 43: Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University DATE: today !split ===== Plans for week 43 ===== -* Thursday: Convolutional Neural Networks, basic elements and -* Friday: Recurrent Neural Networks and other Deep learning methods, Generalized Adversarial Neural Networ and autoencoders +* Thursday: Convolutional Neural Networks (CNN) +* Friday: Recurrent Neural Networks (RNN) @@ -25,24 +25,179 @@ DATE: today !split ===== Reading Recommendations ===== -* Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended. -* Aurelien Geron, chapter 14 on RNNs. +!bblock CNN readings +o "Goodfellow, Bengio, Courville, chapter 9":"https://www.deeplearningbook.org/contents/convnets.html" +o "Lectures from CS231 at Stanford":"http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture5.pdf" +o "Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs":"http://neuralnetworksanddeeplearning.com/chap6.html". +!eblock + + +!bblock RNN readings +o "Goodfellow et al":"https://www.deeplearningbook.org/contents/rnn.html", chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended. +o "Lectures from CS231 at Stanford":"http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf" +o Aurelien Geron, chapter 14 on RNNs. +!eblock + + +!split +===== Convolutional Neural Networks (recognizing images) ===== + + +Convolutional neural networks (CNNs) were developed during the last +decade of the previous century, with a focus on character recognition +tasks. Nowadays, CNNs are a central element in the spectacular success +of deep learning methods. The success in for example image +classifications have made them a central tool for most machine +learning practitioners. + +CNNs are very similar to ordinary Neural Networks. +They are made up of neurons that have learnable weights and +biases. Each neuron receives some inputs, performs a dot product and +optionally follows it with a non-linearity. The whole network still +expresses a single differentiable score function: from the raw image +pixels on one end to class scores at the other. And they still have a +loss function (for example Softmax) on the last (fully-connected) layer +and all the tips/tricks we developed for learning regular Neural +Networks still apply (back propagation, gradient descent etc etc). + +!split +===== What is the Difference ===== + +_CNN architectures make the explicit assumption that +the inputs are images, which allows us to encode certain properties +into the architecture. These then make the forward function more +efficient to implement and vastly reduce the amount of parameters in +the network._ + +Here we provide only a superficial overview, for the more interested, we recommend highly the course +"IN5400 – Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html" +and the slides of "CS231":"http://cs231n.github.io/convolutional-networks/". + +Another good read is the article here URL:"https://arxiv.org/pdf/1603.07285.pdf". -neural nets will be very large: impractical to write down gradient formula -by hand for all parameters -● backpropagation = recursive application of the chain rule along a -computational graph to compute the gradients of all -inputs/parameters/intermediates -● implementations maintain a graph structure, where the nodes implement -the forward() / backward() API -● forward: compute result of an operation and save any intermediates -needed for gradient computation in memory -● backward: apply the chain rule to compute the gradient of the loss -function with respect to the inputs + +!split +===== Neural Networks vs CNNs ===== + +Neural networks are defined as _affine transformations_, that is +a vector is received as input and is multiplied with a matrix of so-called weights (our unknown paramters) to produce an +output (to which a bias vector is usually added before passing the result +through a nonlinear activation function). This is applicable to any type of input, be it an +image, a sound clip or an unordered collection of features: whatever their +dimensionality, their representation can always be flattened into a vector +before the transformation. +!split +===== Why CNNS for images, sound files, medical images from CT scans etc? ===== + +However, when we consider images, sound clips and many other similar kinds of data, these data have an intrinsic +structure. More formally, they share these important properties: +* They are stored as multi-dimensional arrays (think of the pixels of a figure) . +* They feature one or more axes for which ordering matters (e.g., width and height axes for an image, time axis for a sound clip). +* One axis, called the channel axis, is used to access different views of the data (e.g., the red, green and blue channels of a color image, or the left and right channels of a stereo audio track). + +These properties are not exploited when an affine transformation is applied; in +fact, all the axes are treated in the same way and the topological information +is not taken into account. Still, taking advantage of the implicit structure of +the data may prove very handy in solving some tasks, like computer vision and +speech recognition, and in these cases it would be best to preserve it. This is +where discrete convolutions come into play. + +A discrete convolution is a linear transformation that preserves this notion of +ordering. It is sparse (only a few input units contribute to a given output +unit) and reuses parameters (the same weights are applied to multiple locations +in the input). + + + + +!split +===== Regular NNs don’t scale well to full images ===== + +As an example, consider +an image of size $32\times 32\times 3$ (32 wide, 32 high, 3 color channels), so a +single fully-connected neuron in a first hidden layer of a regular +Neural Network would have $32\times 32\times 3 = 3072$ weights. This amount still +seems manageable, but clearly this fully-connected structure does not +scale to larger images. For example, an image of more respectable +size, say $200\times 200\times 3$, would lead to neurons that have +$200\times 200\times 3 = 120,000$ weights. + +We could have +several such neurons, and the parameters would add up quickly! Clearly, +this full connectivity is wasteful and the huge number of parameters +would quickly lead to possible overfitting. + +FIGURE: [figslides/nn.jpeg, width=500 frac=0.6] A regular 3-layer Neural Network. + +!split +===== 3D volumes of neurons ===== + +Convolutional Neural Networks take advantage of the fact that the +input consists of images and they constrain the architecture in a more +sensible way. + +In particular, unlike a regular Neural Network, the +layers of a CNN have neurons arranged in 3 dimensions: width, +height, depth. (Note that the word depth here refers to the third +dimension of an activation volume, not to the depth of a full Neural +Network, which can refer to the total number of layers in a network.) + +To understand it better, the above example of an image +with an input volume of +activations has dimensions $32\times 32\times 3$ (width, height, +depth respectively). + +The neurons in a layer will +only be connected to a small region of the layer before it, instead of +all of the neurons in a fully-connected manner. Moreover, the final +output layer could for this specific image have dimensions $1\times 1 \times 10$, +because by the +end of the CNN architecture we will reduce the full image into a +single vector of class scores, arranged along the depth +dimension. + +FIGURE: [figslides/cnn.jpeg, width=500 frac=0.6] A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels). + + + +!split +===== Layers used to build CNNs ===== + + +A simple CNN is a sequence of layers, and every layer of a CNN +transforms one volume of activations to another through a +differentiable function. We use three main types of layers to build +CNN architectures: Convolutional Layer, Pooling Layer, and +Fully-Connected Layer (exactly as seen in regular Neural Networks). We +will stack these layers to form a full CNN architecture. + +A simple CNN for image classification could have the architecture: + +* _INPUT_ ($32\times 32 \times 3$) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B. +* _CONV_ (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as $[32\times 32\times 12]$ if we decided to use 12 filters. +* _RELU_ layer will apply an elementwise activation function, such as the $max(0,x)$ thresholding at zero. This leaves the size of the volume unchanged ($[32\times 32\times 12]$). +* _POOL_ (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as $[16\times 16\times 12]$. +* _FC_ (i.e. fully-connected) layer will compute the class scores, resulting in volume of size $[1\times 1\times 10]$, where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume. + + +!split +===== Transforming images ===== + +CNNs transform the original image layer by layer from the original +pixel values to the final class scores. + +Observe that some layers contain +parameters and other don’t. In particular, the CNN layers perform +transformations that are a function of not only the activations in the +input volume, but also of the parameters (the weights and biases of +the neurons). On the other hand, the RELU/POOL layers will implement a +fixed function. The parameters in the CONV/FC layers will be trained +with gradient descent so that the class scores that the CNN computes +are consistent with the labels in the training set for each image. !split @@ -61,10 +216,843 @@ the course "IN5400 – Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html" and the slides of "CS231":"http://cs231n.github.io/convolutional-networks/" which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). "Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs":"http://neuralnetworksanddeeplearning.com/chap6.html". +The textbook by Goodfellow et al, see chapter 9 contains an in depth discussion as well. -However, both standard feed forwards networks and CNNs perform well on data with unknown length. +!split +===== Key Idea ===== + +A dense neural network is representd by an affine operation (like matrix-matrix multiplication) where all parameters are included. + +The key idea in CNNs for say imaging is that in images neighbor pixels tend to be related! So we connect +only neighboring neurons in the input instead of connecting all with the first hidden layer. + +We say we perform a filtering (convolution is the mathematical operation). + + +!split +===== Mathematics of CNNs ===== + +The mathematics of CNNs is based on the mathematical operation of +_convolution_. In mathematics (in particular in functional analysis), +convolution is represented by mathematical operation (integration, +summation etc) on two function in order to produce a third function +that expresses how the shape of one gets modified by the other. +Convolution has a plethora of applications in a variety of disciplines, spanning from statistics to signal processing, computer vision, solutions of differential equations,linear algebra, engineering, and yes, machine learning. + +Mathematically, convolution is defined as follows (one-dimensional example): +Let us define a continuous function $y(t)$ given by +!bt +\[ +y(t) = \int x(a) w(t-a) da, +\] +!et +where $x(a)$ represents a so-called input and $w(t-a)$ is normally called the weight function or kernel. + +The above integral is written in a more compact form as +!bt +\[ +y(t) = \left(x * w\right)(t). +\] +!et + +The discretized version reads +!bt +\[ +y(t) = \sum_{a=-\infty}^{a=\infty}x(a)w(t-a). +\] +!et +Computing the inverse of the above convolution operations is known as deconvolution. + +How can we use this? And what does it mean? Let us study some familiar examples first. + + +!split +===== Convolution Examples: Polynomial multiplication ===== + +We have already met such an example in project 1 when we tried to set +up the design matrix for a two-dimensional function. This was an +example of polynomial multiplication. Let us recast such a problem in terms of the convolution operation. +Let us look a the following polynomials to second and third order, respectively: +!bt +\[ +p(t) = \alpha_0+\alpha_1 t+\alpha_2 t^2, +\] +!et +and +!bt +\[ +s(t) = \beta_0+\beta_1 t+\beta_2 t^2+\beta_3 t^3. +\] +!et + +The polynomial multiplication gives us a new polynomial of degree $5$ +!bt +\[ +z(t) = \delta_0+\delta_1 t+\delta_2 t^2+\delta_3 t^3+\delta_4 t^4+\delta_5 t^5. +\] +!et + +!split +===== Efficient Polynomial Multiplication ===== + +Computing polynomial products can be implemented efficiently if we rewrite the more brute force multiplications using convolution. +We note first that the new coefficients are given as + +!bt +\begin{split} +\delta_0=&\alpha_0\beta_0\\ +\delta_1=&\alpha_1\beta_0+\alpha_1\beta_0\\ +\delta_2=&\alpha_0\beta_2+\alpha_1\beta_1+\alpha_2\beta_0\\ +\delta_3=&\alpha_1\beta_2+\alpha_2\beta_1+\alpha_0\beta_3\\ +\delta_4=&\alpha_2\beta_2+\alpha_1\beta_3\\ +\delta_5=&\alpha_2\beta_3.\\ +\end{split} +!et + + +We note that $\alpha_i=0$ except for $i\in \left\{0,1,2\right\}$ and $\beta_i=0$ except for $i\in\left\{0,1,2,3\right\}$. + +We can then rewrite the coefficients $\delta_j$ using a discrete convolution as +!bt +\[ +\delta_j = \sum_{i=-\infty}^{i=\infty}\alpha_i\beta_{j-i}=(\alpha * \beta)_j, +\] +!et +or as a double sum with restriction $l=i+j$ +!bt +\[ +\delta_l = \sum_{ij}\alpha_i\beta_{j}. +\] +!et + +Do you see a potential drawback with these equations? + +!split +===== A more efficient way of coding the above Convolution ===== + +Since we only have a finite number of $\alpha$ and $\beta$ values +which are non-zero, we can rewrite the above convolution expressions +as a matrix-vector multiplication + +!bt +\[ +\bm{\delta}=\begin{bmatrix}\alpha_0 & 0 & 0 & 0 \\ + \alpha_1 & \alpha_0 & 0 & 0 \\ + \alpha_2 & \alpha_1 & \alpha_0 & 0 \\ + 0 & \alpha_2 & \alpha_1 & \alpha_0 \\ + 0 & 0 & \alpha_2 & \alpha_1 \\ + 0 & 0 & 0 & \alpha_2 + \end{bmatrix}\begin{bmatrix} \beta_0 \\ \beta_1 \\ \beta_2 \\ \beta_3\end{bmatrix}. +\] +!et + +The process is commutative and we can easily see that we can rewrite the multiplication in terms of a matrix holding $\beta$ and a vector holding $\alpha$. +In this case we have +!bt +\[ +\bm{\delta}=\begin{bmatrix}\beta_0 & 0 & 0 \\ + \beta_1 & \beta_0 & 0 \\ + \beta_2 & \beta_1 & \beta_0 \\ + \beta_3 & \beta_2 & \beta_1 \\ + 0 & \beta_3 & \beta_2 \\ + 0 & 0 & \beta_3 + \end{bmatrix}\begin{bmatrix} \alpha_0 \\ \alpha_1 \\ \alpha_2\end{bmatrix}. +\] +!et + +Note that the use of these matrices is for mathematical purposes only and not implementation purposes. +When implementing the above equation we do not encode (and allocate memory) the matrices explicitely. +We rather code the convolutions in the minimal memory footprint that they require. + +Does the number of floating point operations change here when we use the commutative property? + +!split +===== Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms) ===== + +For problems with so-called harmonic oscillations, given by for example the following differential equation +!bt +\[ +m\frac{d^2x}{dt^2}+\eta\frac{dx}{dt}+x(t)=F(t), +\] +!et +where $F(t)$ is an applied external force acting on the system (often called a driving force), one can use the theory of Fourier transformations to find the solutions of this type of equations. + +If one has several driving forces, $F(t)=\sum_n F_n(t)$, one can find +the particular solution to each $F_n$, $x_{pn}(t)$, and the particular +solution for the entire driving force is then given by a series like + +!bt +\begin{equation} +x_p(t)=\sum_nx_{pn}(t). +\end{equation} +!et + +!split +===== Principle of Superposition ===== + +This is known as the principle of superposition. It only applies when +the homogenous equation is linear. If there were an anharmonic term +such as $x^3$ in the homogenous equation, then when one summed various +solutions, $x=(\sum_n x_n)^2$, one would get cross +terms. Superposition is especially useful when $F(t)$ can be written +as a sum of sinusoidal terms, because the solutions for each +sinusoidal (sine or cosine) term is analytic. + +Driving forces are often periodic, even when they are not +sinusoidal. Periodicity implies that for some time $\tau$ + +!bt +\begin{eqnarray} +F(t+\tau)=F(t). +\end{eqnarray} +!et + +One example of a non-sinusoidal periodic force is a square wave. Many +components in electric circuits are non-linear, e.g. diodes, which +makes many wave forms non-sinusoidal even when the circuits are being +driven by purely sinusoidal sources. + +!split +===== Simple Code Example ===== + +The code here shows a typical example of such a square wave generated using the functionality included in the _scipy_ Python package. We have used a period of $\tau=0.2$. + +!bc pycod +import numpy as np +import math +from scipy import signal +import matplotlib.pyplot as plt + +# number of points +n = 500 +# start and final times +t0 = 0.0 +tn = 1.0 +# Period +t = np.linspace(t0, tn, n, endpoint=False) +SqrSignal = np.zeros(n) +SqrSignal = 1.0+signal.square(2*np.pi*5*t) +plt.plot(t, SqrSignal) +plt.ylim(-0.5, 2.5) +plt.show() +!ec + + +For the sinusoidal example the +period is $\tau=2\pi/\omega$. However, higher harmonics can also +satisfy the periodicity requirement. In general, any force that +satisfies the periodicity requirement can be expressed as a sum over +harmonics, + +!bt +\begin{equation} +F(t)=\frac{f_0}{2}+\sum_{n>0} f_n\cos(2n\pi t/\tau)+g_n\sin(2n\pi t/\tau). +\end{equation} +!et + +!split +===== Wrapping up Fourier transforms ===== + +We can write down the answer for +$x_{pn}(t)$, by substituting $f_n/m$ or $g_n/m$ for $F_0/m$. By +writing each factor $2n\pi t/\tau$ as $n\omega t$, with $\omega\equiv +2\pi/\tau$, + +!bt +\begin{equation} +label{eq:fourierdef1} +F(t)=\frac{f_0}{2}+\sum_{n>0}f_n\cos(n\omega t)+g_n\sin(n\omega t). +\end{equation} +!et + +The solutions for $x(t)$ then come from replacing $\omega$ with +$n\omega$ for each term in the particular solution, + +!bt +\begin{eqnarray} +x_p(t)&=&\frac{f_0}{2k}+\sum_{n>0} \alpha_n\cos(n\omega t-\delta_n)+\beta_n\sin(n\omega t-\delta_n),\\ +\nonumber +\alpha_n&=&\frac{f_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\beta_n&=&\frac{g_n/m}{\sqrt{((n\omega)^2-\omega_0^2)+4\beta^2n^2\omega^2}},\\ +\nonumber +\delta_n&=&\tan^{-1}\left(\frac{2\beta n\omega}{\omega_0^2-n^2\omega^2}\right). +\end{eqnarray} +!et + +!split +===== Finding the Coefficients ===== + +Because the forces have been applied for a long time, any non-zero +damping eliminates the homogenous parts of the solution, so one need +only consider the particular solution for each $n$. + +The problem is considered solved if one can find expressions for the +coefficients $f_n$ and $g_n$, even though the solutions are expressed +as an infinite sum. The coefficients can be extracted from the +function $F(t)$ by + +!bt +\begin{eqnarray} +label{eq:fourierdef2} +f_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\cos(2n\pi t/\tau),\\ +\nonumber +g_n&=&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~F(t)\sin(2n\pi t/\tau). +\end{eqnarray} +!et + +To check the consistency of these expressions and to verify +Eq. (ref{eq:fourierdef2}), one can insert the expansion of $F(t)$ in +Eq. (ref{eq:fourierdef1}) into the expression for the coefficients in +Eq. (ref{eq:fourierdef2}) and see whether + +!bt +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~\left\{ +\frac{f_0}{2}+\sum_{m>0}f_m\cos(m\omega t)+g_m\sin(m\omega t) +\right\}\cos(n\omega t). +\end{eqnarray} +!et + +Immediately, one can throw away all the terms with $g_m$ because they +convolute an even and an odd function. The term with $f_0/2$ +disappears because $\cos(n\omega t)$ is equally positive and negative +over the interval and will integrate to zero. For all the terms +$f_m\cos(m\omega t)$ appearing in the sum, one can use angle addition +formulas to see that $\cos(m\omega t)\cos(n\omega +t)=(1/2)(\cos[(m+n)\omega t]+\cos[(m-n)\omega t]$. This will integrate +to zero unless $m=n$. In that case the $m=n$ term gives + +!bt +\begin{equation} +\int_{-\tau/2}^{\tau/2}dt~\cos^2(m\omega t)=\frac{\tau}{2}, +\end{equation} +!et + +and + +!bt +\begin{eqnarray} +f_n&=?&\frac{2}{\tau}\int_{-\tau/2}^{\tau/2} dt~f_n/2\\ +\nonumber +&=&f_n~\checkmark. +\end{eqnarray} +!et + +The same method can be used to check for the consistency of $g_n$. + + + +!split +===== Final words on Fourier Transforms ===== + +The code here uses the Fourier series applied to a +square wave signal. The code here +visualizes the various approximations given by Fourier series compared +with a square wave with period $T=0.2$ (dimensionless time), width $0.1$ and max value of the force $F=2$. We +see that when we increase the number of components in the Fourier +series, the Fourier series approximation gets closer and closer to the +square wave signal. + +!bc pycod +import numpy as np +import math +from scipy import signal +import matplotlib.pyplot as plt + +# number of points +n = 500 +# start and final times +t0 = 0.0 +tn = 1.0 +# Period +T =0.2 +# Max value of square signal +Fmax= 2.0 +# Width of signal +Width = 0.1 +t = np.linspace(t0, tn, n, endpoint=False) +SqrSignal = np.zeros(n) +FourierSeriesSignal = np.zeros(n) +SqrSignal = 1.0+signal.square(2*np.pi*5*t+np.pi*Width/T) +a0 = Fmax*Width/T +FourierSeriesSignal = a0 +Factor = 2.0*Fmax/np.pi +for i in range(1,500): + FourierSeriesSignal += Factor/(i)*np.sin(np.pi*i*Width/T)*np.cos(i*t*2*np.pi/T) +plt.plot(t, SqrSignal) +plt.plot(t, FourierSeriesSignal) +plt.ylim(-0.5, 2.5) +plt.show() +!ec + + +!split +===== Two-dimensional Objects ===== + +We often use convolutions over more than one dimension at a time. If +we have a two-dimensional image $I$ as input, we can have a _filter_ +defined by a two-dimensional _kernel_ $K$. This leads to an output $S$ + +!bt +\[ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(m,n)K(i-m,j-n). +\] +!et + +Convolution is a commutatitave process, which means we can rewrite this equation as +!bt +\[ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i-m,j-n)K(m,n). +\] +!et + +Normally the latter is more straightforward to implement in a machine elarning library since there is less variation in the range of values of $m$ and $n$. + +!split +===== Cross-Correlation ===== + + + +Many deep learning libraries implement cross-correlation instead of convolution +!bt +\[ +S_(i,j)=(I * K)(i,j) = \sum_m\sum_n I(i+m,j-+)K(m,n). +\] +!et + + +!split +===== More on Dimensionalities ===== + +In feilds like signal processing (and imaging as well), one designs +so-called filters. These filters are defined by the convolutions and +are often hand-crafted. One may specify filters for smoothing, edge +detection, frequency reshaping, and similar operations. However with +neural networks the idea is to automatically learn the filters and use +many of them in conjunction with non-linear operations (activation +functions). + +As an example consider a neural network operating on sound sequence +data. Assume that we an input vector $\bm{x}$ of length $d=10^6$. We +construct then a neural network with onle hidden layer only with +$10^4$ nodes. This means that we will have a weight matrix with +$10^4\times 10^6=10^{10}$ weights to be determined, together with $10^4$ biases. + +Assume furthermore that we have an output layer which is meant to train whether the sound sequence represents a human voice (true) or something else (false). +It means that we have only one output node. But since this output node connects to $10^4$ nodes in the hidden layer, there are in total $10^4$ weights to be determined for the output layer, plus one bias. In total we have + +!bt +\[ +\mathrm{NumberParameters}=10^{10}+10^4+10^4+1 \approx 10^{10}, +\] +!et +that is ten billion parameters to determine. + + +!split +===== Further Dimensionality Remarks ===== + +In today’s architecture one can train such neural networks, however +this is a huge number of parameters for the task at hand. In general, +it is a very wasteful and inefficient use of dense matrices as +parameters. Just as importantly, such trained network parameters are +very specific for the type of input data on which they were trained +and the network is not likely to generalize easily to variations in +the input. + + +The main principles that justify convolutions is locality of +information and repetion of patterns within the signal. Sound samples +of the input in adjacent spots are much more likely to affect each +other than those that are very far away. Similarly, sounds are +repeated in multiple times in the signal. While slightly simplistic, +reasoning about such a sound example demonstrates this. The same +principles then apply to images and other similar data. + + +!split +===== CNNs in more detail, Lecture from IN5400 ===== + +* "Lectures from IN5400 spring 2019":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/v19/material/week5/in5400_2019_week5_convolutional_nerual_networks.pdf" + + +!split +===== CNNs in more detail, building convolutional neural networks in Tensorflow and Keras ===== + + +As discussed above, CNNs are neural networks built from the assumption that the inputs +to the network are 2D images. This is important because the number of features or pixels in images +grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network. + +As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks +are the _convolutional_ and _pooling_ layers stacked in pairs between the input and the hidden layer. +In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D +matrices, typically 1 for each color dimension (Red, Green, Blue). + + +!split +===== Setting it up ===== + +It means that to represent the entire +dataset of images, we require a 4D matrix or _tensor_. This tensor has the dimensions: +!bt +\[ +(n_{inputs},\, n_{pixels, width},\, n_{pixels, height},\, depth) . +\] +!et + +!split +===== The MNIST dataset again ===== + +The MNIST dataset consists of grayscale images with a pixel size of +$28\times 28$, meaning we require $28 \times 28 = 724$ weights to each +neuron in the first hidden layer. + +If we were to analyze images of size $128\times 128$ we would require +$128 \times 128 = 16384$ weights to each neuron. Even worse if we were +dealing with color images, as most images are, we have an image matrix +of size $128\times 128$ for each color dimension (Red, Green, Blue), +meaning 3 times the number of weights $= 49152$ are required for every +single neuron in the first hidden layer. + + +!split +===== Strong correlations ===== + +Images typically have strong local correlations, meaning that a small +part of the image varies little from its neighboring regions. If for +example we have an image of a blue car, we can roughly assume that a +small blue part of the image is surrounded by other blue regions. + +Therefore, instead of connecting every single pixel to a neuron in the +first hidden layer, as we have previously done with deep neural +networks, we can instead connect each neuron to a small part of the +image (in all 3 RGB depth dimensions). The size of each small area is +fixed, and known as a "receptive":"https://en.wikipedia.org/wiki/Receptive_field". + + +!split +===== Layers of a CNN ===== +The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. +The input image is typically a square matrix of depth 3. + +A _convolution_ is performed on the image which outputs +a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as _filters_. + + +Each filter slides along the input image, taking the dot product +between each small part of the image and the filter, in all depth +dimensions. This is then passed through a non-linear function, +typically the _Rectified Linear (ReLu)_ function, which serves as the +activation of the neurons in the first convolutional layer. This is +further passed through a _pooling layer_, which reduces the size of the +convolutional layer, e.g. by taking the maximum or average across some +small regions, and this serves as input to the next convolutional +layer. + + +!split +===== Systematic reduction ===== + +By systematically reducing the size of the input volume, through +convolution and pooling, the network should create representations of +small parts of the input, and then from them assemble representations +of larger areas. The final pooling layer is flattened to serve as +input to a hidden layer, such that each neuron in the final pooling +layer is connected to every single neuron in the hidden layer. This +then serves as input to the output layer, e.g. a softmax output for +classification. + + +!split +===== Prerequisites: Collect and pre-process data ===== +!bc pycod +# import necessary packages +import numpy as np +import matplotlib.pyplot as plt +from sklearn import datasets + + +# ensure the same random numbers appear every time +np.random.seed(0) + +# display images in notebook +%matplotlib inline +plt.rcParams['figure.figsize'] = (12,12) + + +# download MNIST dataset +digits = datasets.load_digits() + +# define inputs and labels +inputs = digits.images +labels = digits.target + +# RGB images have a depth of 3 +# our images are grayscale so they should have a depth of 1 +inputs = inputs[:,:,:,np.newaxis] + +print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape)) +print("labels = (n_inputs) = " + str(labels.shape)) + + +# choose some random images to display +n_inputs = len(inputs) +indices = np.arange(n_inputs) +random_indices = np.random.choice(indices, size=5) + +for i, image in enumerate(digits.images[random_indices]): + plt.subplot(1, 5, i+1) + plt.axis('off') + plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') + plt.title("Label: %d" % digits.target[random_indices[i]]) +plt.show() +!ec + + +!split +===== Importing Keras and Tensorflow ===== +!bc pycod +from tensorflow.keras import datasets, layers, models +from tensorflow.keras.layers import Input +from tensorflow.keras.models import Sequential #This allows appending layers to existing models +from tensorflow.keras.layers import Dense #This allows defining the characteristics of a particular layer +from tensorflow.keras import optimizers #This allows using whichever optimiser we want (sgd,adam,RMSprop) +from tensorflow.keras import regularizers #This allows using whichever regularizer we want (l1,l2,l1_l2) +from tensorflow.keras.utils import to_categorical #This allows using categorical cross entropy as the cost function +#from tensorflow.keras import Conv2D +#from tensorflow.keras import MaxPooling2D +#from tensorflow.keras import Flatten + +from sklearn.model_selection import train_test_split + +# representation of labels +labels = to_categorical(labels) + +# split into train and test data +# one-liner from scikit-learn library +train_size = 0.8 +test_size = 1 - train_size +X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size, + test_size=test_size) +!ec + +!split +===== Running with Keras ===== + +!bc pycod +def create_convolutional_neural_network_keras(input_shape, receptive_field, + n_filters, n_neurons_connected, n_categories, + eta, lmbd): + model = Sequential() + model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same', + activation='relu', kernel_regularizer=regularizers.l2(lmbd))) + model.add(layers.MaxPooling2D(pool_size=(2, 2))) + model.add(layers.Flatten()) + model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd))) + model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd))) + + sgd = optimizers.SGD(lr=eta) + model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) + + return model + +epochs = 100 +batch_size = 100 +input_shape = X_train.shape[1:4] +receptive_field = 3 +n_filters = 10 +n_neurons_connected = 50 +n_categories = 10 + +eta_vals = np.logspace(-5, 1, 7) +lmbd_vals = np.logspace(-5, 1, 7) +!ec + +!split +===== Final part ===== + +!bc pycod +CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object) + +for i, eta in enumerate(eta_vals): + for j, lmbd in enumerate(lmbd_vals): + CNN = create_convolutional_neural_network_keras(input_shape, receptive_field, + n_filters, n_neurons_connected, n_categories, + eta, lmbd) + CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0) + scores = CNN.evaluate(X_test, Y_test) + + CNN_keras[i][j] = CNN + + print("Learning rate = ", eta) + print("Lambda = ", lmbd) + print("Test accuracy: %.3f" % scores[1]) + print() +!ec + +!split +===== Final visualization ===== + +!bc pycod +# visual representation of grid search +# uses seaborn heatmap, could probably do this in matplotlib +import seaborn as sns + +sns.set() + +train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals))) +test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals))) + +for i in range(len(eta_vals)): + for j in range(len(lmbd_vals)): + CNN = CNN_keras[i][j] + + train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1] + test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1] + + +fig, ax = plt.subplots(figsize = (10, 10)) +sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis") +ax.set_title("Training Accuracy") +ax.set_ylabel("$\eta$") +ax.set_xlabel("$\lambda$") +plt.show() + +fig, ax = plt.subplots(figsize = (10, 10)) +sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis") +ax.set_title("Test Accuracy") +ax.set_ylabel("$\eta$") +ax.set_xlabel("$\lambda$") +plt.show() +!ec + + + +!split +===== The CIFAR01 data set ===== + +The CIFAR10 dataset contains 60,000 color images in 10 classes, with +6,000 images in each class. The dataset is divided into 50,000 +training images and 10,000 testing images. The classes are mutually +exclusive and there is no overlap between them. + +!bc pycod +import tensorflow as tf + +from tensorflow.keras import datasets, layers, models +import matplotlib.pyplot as plt + +# We import the data set +(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() + +# Normalize pixel values to be between 0 and 1 by dividing by 255. +train_images, test_images = train_images / 255.0, test_images / 255.0 + +!ec + + + +!split +===== Verifying the data set ===== + +To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image. + +!bc pycod +class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck'] +​ +plt.figure(figsize=(10,10)) +for i in range(25): + plt.subplot(5,5,i+1) + plt.xticks([]) + plt.yticks([]) + plt.grid(False) + plt.imshow(train_images[i], cmap=plt.cm.binary) + # The CIFAR labels happen to be arrays, + # which is why you need the extra index + plt.xlabel(class_names[train_labels[i][0]]) +plt.show() +!ec + +!split +===== Set up the model ===== + +The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers. + +As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer. + +!bc pycod +model = models.Sequential() +model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) +model.add(layers.MaxPooling2D((2, 2))) +model.add(layers.Conv2D(64, (3, 3), activation='relu')) +model.add(layers.MaxPooling2D((2, 2))) +model.add(layers.Conv2D(64, (3, 3), activation='relu')) + +# Let's display the architecture of our model so far. + +model.summary() +!ec + +You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer. + + + + +!split +===== Add Dense layers on top ===== + +To complete our model, you will feed the last output tensor from the +convolutional base (of shape (4, 4, 64)) into one or more Dense layers +to perform classification. Dense layers take vectors as input (which +are 1D), while the current output is a 3D tensor. First, you will +flatten (or unroll) the 3D output to 1D, then add one or more Dense +layers on top. CIFAR has 10 output classes, so you use a final Dense +layer with 10 outputs and a softmax activation. + +!bc pycod +model.add(layers.Flatten()) +model.add(layers.Dense(64, activation='relu')) +model.add(layers.Dense(10)) +Here's the complete architecture of our model. + +model.summary() +!ec +As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers. + +!split +===== Compile and train the model ===== + +!bc pycod +model.compile(optimizer='adam', + loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + metrics=['accuracy']) +​ +history = model.fit(train_images, train_labels, epochs=10, + validation_data=(test_images, test_labels)) + +!ec + + +!split +===== Finally, evaluate the model ===== + +!bc pycod +plt.plot(history.history['accuracy'], label='accuracy') +plt.plot(history.history['val_accuracy'], label = 'val_accuracy') +plt.xlabel('Epoch') +plt.ylabel('Accuracy') +plt.ylim([0.5, 1]) +plt.legend(loc='lower right') + +test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) + +print(test_acc) + +!ec -This is where recurrent nueral networks (RNNs) come to our rescue. !split ===== Recurrent neural networks: Overarching view ===== @@ -90,7 +1078,7 @@ systems such as automatic translation and speech-to-text. !split ===== Set up of an RNN ===== -More to text to be added +See handwritten notes for week 43 and "Lectures from CS231 at Stanford":"http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf" !split ===== A simple example ===== @@ -1343,874 +2331,13 @@ for i in range(0, 2*np.sqrt(plot_number), 2): plot_results(results, plot_number) !ec -!split -===== Basic ideas of the Principal Component Analysis (PCA) ===== -The principal component analysis deals with the problem of fitting a -low-dimensional affine subspace $S$ of dimension $d$ much smaller than -the total dimension $D$ of the problem at hand (our data -set). Mathematically it can be formulated as a statistical problem or -a geometric problem. In our discussion of the theorem for the -classical PCA, we will stay with a statistical approach. -Historically, the PCA was first formulated in a statistical setting in order to estimate the principal component of a multivariate random variable. -We have a data set defined by a design/feature matrix $\bm{X}$ (see below for its definition) -* Each data point is determined by $p$ extrinsic (measurement) variables -* We may want to ask the following question: Are there fewer intrinsic variables (say $d << p$) that still approximately describe the data? -* If so, these intrinsic variables may tell us something important and finding these intrinsic variables is what dimension reduction methods do. -A good read is for example "Vidal, Ma and Sastry":"https://www.springer.com/gp/book/9780387878102". -!split -===== Introducing the Covariance and Correlation functions ===== -Before we discuss the PCA theorem, we need to remind ourselves about -the definition of the covariance and the correlation function. These are quantities -Suppose we have defined two vectors -$\hat{x}$ and $\hat{y}$ with $n$ elements each. The covariance matrix $\bm{C}$ is defined as -!bt -\[ -\bm{C}[\bm{x},\bm{y}] = \begin{bmatrix} \mathrm{cov}[\bm{x},\bm{x}] & \mathrm{cov}[\bm{x},\bm{y}] \\ - \mathrm{cov}[\bm{y},\bm{x}] & \mathrm{cov}[\bm{y},\bm{y}] \\ - \end{bmatrix}, -\] -!et -where for example -!bt -\[ -\mathrm{cov}[\bm{x},\bm{y}] =\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})(y_i- \overline{y}). -\] -!et -With this definition and recalling that the variance is defined as -!bt -\[ -\mathrm{var}[\bm{x}]=\frac{1}{n} \sum_{i=0}^{n-1}(x_i- \overline{x})^2, -\] -!et -we can rewrite the covariance matrix as -!bt -\[ -\bm{C}[\bm{x},\bm{y}] = \begin{bmatrix} \mathrm{var}[\bm{x}] & \mathrm{cov}[\bm{x},\bm{y}] \\ - \mathrm{cov}[\bm{x},\bm{y}] & \mathrm{var}[\bm{y}] \\ - \end{bmatrix}. -\] -!et - -!split -===== More on the covariance ===== -The covariance takes values between zero and infinity and may thus -lead to problems with loss of numerical precision for particularly -large values. It is common to scale the covariance matrix by -introducing instead the correlation matrix defined via the so-called -correlation function - -!bt -\[ -\mathrm{corr}[\bm{x},\bm{y}]=\frac{\mathrm{cov}[\bm{x},\bm{y}]}{\sqrt{\mathrm{var}[\bm{x}] \mathrm{var}[\bm{y}]}}. -\] -!et - -The correlation function is then given by values $\mathrm{corr}[\bm{x},\bm{y}] -\in [-1,1]$. This avoids eventual problems with too large values. We -can then define the correlation matrix for the two vectors $\bm{x}$ -and $\bm{y}$ as - -!bt -\[ -\bm{K}[\bm{x},\bm{y}] = \begin{bmatrix} 1 & \mathrm{corr}[\bm{x},\bm{y}] \\ - \mathrm{corr}[\bm{y},\bm{x}] & 1 \\ - \end{bmatrix}, -\] -!et - -In the above example this is the function we constructed using _pandas_. - -!split -===== Reminding ourselves about Linear Regression ===== -In our derivation of the various regression algorithms like _Ordinary Least Squares_ or _Ridge regression_ -we defined the design/feature matrix $\bm{X}$ as - -!bt -\[ -\bm{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -\] -!et -with $\bm{X}\in {\mathbb{R}}^{n\times p}$, with the predictors/features $p$ refering to the column numbers and the -entries $n$ being the row elements. -We can rewrite the design/feature matrix in terms of its column vectors as -!bt -\[ -\bm{X}=\begin{bmatrix} \bm{x}_0 & \bm{x}_1 & \bm{x}_2 & \dots & \dots & \bm{x}_{p-1}\end{bmatrix}, -\] -!et -with a given vector -!bt -\[ -\bm{x}_i^T = \begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \dots & \dots x_{n-1,i}\end{bmatrix}. -\] -!et - -!split -===== Simple Example ===== -With these definitions, we can now rewrite our $2\times 2$ -correlation/covariance matrix in terms of a moe general design/feature -matrix $\bm{X}\in {\mathbb{R}}^{n\times p}$. This leads to a $p\times p$ -covariance matrix for the vectors $\bm{x}_i$ with $i=0,1,\dots,p-1$ - -!bt -\[ -\bm{C}[\bm{x}] = \begin{bmatrix} -\mathrm{var}[\bm{x}_0] & \mathrm{cov}[\bm{x}_0,\bm{x}_1] & \mathrm{cov}[\bm{x}_0,\bm{x}_2] & \dots & \dots & \mathrm{cov}[\bm{x}_0,\bm{x}_{p-1}]\\ -\mathrm{cov}[\bm{x}_1,\bm{x}_0] & \mathrm{var}[\bm{x}_1] & \mathrm{cov}[\bm{x}_1,\bm{x}_2] & \dots & \dots & \mathrm{cov}[\bm{x}_1,\bm{x}_{p-1}]\\ -\mathrm{cov}[\bm{x}_2,\bm{x}_0] & \mathrm{cov}[\bm{x}_2,\bm{x}_1] & \mathrm{var}[\bm{x}_2] & \dots & \dots & \mathrm{cov}[\bm{x}_2,\bm{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{cov}[\bm{x}_{p-1},\bm{x}_0] & \mathrm{cov}[\bm{x}_{p-1},\bm{x}_1] & \mathrm{cov}[\bm{x}_{p-1},\bm{x}_{2}] & \dots & \dots & \mathrm{var}[\bm{x}_{p-1}]\\ -\end{bmatrix}, -\] -!et - -!split -===== The Correlation Matrix ===== - -and the correlation matrix -!bt -\[ -\bm{K}[\bm{x}] = \begin{bmatrix} -1 & \mathrm{corr}[\bm{x}_0,\bm{x}_1] & \mathrm{corr}[\bm{x}_0,\bm{x}_2] & \dots & \dots & \mathrm{corr}[\bm{x}_0,\bm{x}_{p-1}]\\ -\mathrm{corr}[\bm{x}_1,\bm{x}_0] & 1 & \mathrm{corr}[\bm{x}_1,\bm{x}_2] & \dots & \dots & \mathrm{corr}[\bm{x}_1,\bm{x}_{p-1}]\\ -\mathrm{corr}[\bm{x}_2,\bm{x}_0] & \mathrm{corr}[\bm{x}_2,\bm{x}_1] & 1 & \dots & \dots & \mathrm{corr}[\bm{x}_2,\bm{x}_{p-1}]\\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\dots & \dots & \dots & \dots & \dots & \dots \\ -\mathrm{corr}[\bm{x}_{p-1},\bm{x}_0] & \mathrm{corr}[\bm{x}_{p-1},\bm{x}_1] & \mathrm{corr}[\bm{x}_{p-1},\bm{x}_{2}] & \dots & \dots & 1\\ -\end{bmatrix}, -\] -!et - - -!split -===== Numpy Functionality ===== - -The Numpy function _np.cov_ calculates the covariance elements using -the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have -the exact mean values. The following simple function uses the -_np.vstack_ function which takes each vector of dimension $1\times n$ -and produces a $2\times n$ matrix $\bm{W}$ - - -!bt -\[ -\bm{W}^T = \begin{bmatrix} x_0 & y_0 \\ - x_1 & y_1 \\ - x_2 & y_2\\ - \dots & \dots \\ - x_{n-2} & y_{n-2}\\ - x_{n-1} & y_{n-1} & - \end{bmatrix}, -\] -!et - -which in turn is converted into into the $2\times 2$ covariance matrix -$\bm{C}$ via the Numpy function _np.cov()_. We note that we can also calculate -the mean value of each set of samples $\bm{x}$ etc using the Numpy -function _np.mean(x)_. We can also extract the eigenvalues of the -covariance matrix through the _np.linalg.eig()_ function. - -!bc pycod -# Importing various packages -import numpy as np -n = 100 -x = np.random.normal(size=n) -print(np.mean(x)) -y = 4+3*x+np.random.normal(size=n) -print(np.mean(y)) -W = np.vstack((x, y)) -C = np.cov(W) -print(C) -!ec - - -!split -===== Correlation Matrix again ===== - -The previous example can be converted into the correlation matrix by -simply scaling the matrix elements with the variances. We should also -subtract the mean values for each column. This leads to the following -code which sets up the correlations matrix for the previous example in -a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the $2\times 2$ correlation matrix (since we have only two vectors). - -!bc pycod -import numpy as np -n = 100 -# define two vectors -x = np.random.random(size=n) -y = 4+3*x+np.random.normal(size=n) -#scaling the x and y vectors -x = x - np.mean(x) -y = y - np.mean(y) -variance_x = np.sum(x@x)/n -variance_y = np.sum(y@y)/n -print(variance_x) -print(variance_y) -cov_xy = np.sum(x@y)/n -cov_xx = np.sum(x@x)/n -cov_yy = np.sum(y@y)/n -C = np.zeros((2,2)) -C[0,0]= cov_xx/variance_x -C[1,1]= cov_yy/variance_y -C[0,1]= cov_xy/np.sqrt(variance_y*variance_x) -C[1,0]= C[0,1] -print(C) -!ec - -We see that the matrix elements along the diagonal are one as they -should be and that the matrix is symmetric. Furthermore, diagonalizing -this matrix we easily see that it is a positive definite matrix. - -The above procedure with _numpy_ can be made more compact if we use _pandas_. - -!split -===== Using Pandas ===== - -We whow here how we can set up the correlation matrix using _pandas_, as done in this simple code -!bc pycod -import numpy as np -import pandas as pd -n = 10 -x = np.random.normal(size=n) -x = x - np.mean(x) -y = 4+3*x+np.random.normal(size=n) -y = y - np.mean(y) -X = (np.vstack((x, y))).T -print(X) -Xpd = pd.DataFrame(X) -print(Xpd) -correlation_matrix = Xpd.corr() -print(correlation_matrix) -!ec - -!split -===== And then the Franke Function ===== - -We expand this model to the Franke function discussed above. - - -!bc pycod -# Common imports -import numpy as np -import pandas as pd - - -def FrankeFunction(x,y): - term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2)) - term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1)) - term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2)) - term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2) - return term1 + term2 + term3 + term4 - - -def create_X(x, y, n ): - if len(x.shape) > 1: - x = np.ravel(x) - y = np.ravel(y) - - N = len(x) - l = int((n+1)*(n+2)/2) # Number of elements in beta - X = np.ones((N,l)) - - for i in range(1,n+1): - q = int((i)*(i+1)/2) - for k in range(i+1): - X[:,q+k] = (x**(i-k))*(y**k) - - return X - - -# Making meshgrid of datapoints and compute Franke's function -n = 4 -N = 100 -x = np.sort(np.random.uniform(0, 1, N)) -y = np.sort(np.random.uniform(0, 1, N)) -z = FrankeFunction(x, y) -X = create_X(x, y, n=n) - -Xpd = pd.DataFrame(X) -# subtract the mean values and set up the covariance matrix -Xpd = Xpd - Xpd.mean() -covariance_matrix = Xpd.cov() -print(covariance_matrix) -!ec - -We note here that the covariance is zero for the first rows and -columns since all matrix elements in the design matrix were set to one -(we are fitting the function in terms of a polynomial of degree $n$). We would however not include the intercept -and wee can simply -drop these elements and construct a correlation -matrix without them by centering our matrix elements by subtracting the mean of each column. - -!split -===== Lnks with the Design Matrix ===== - -We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix $\bm{X}$ as -!bt -\[ -\bm{C}[\bm{x}] = \frac{1}{n}\bm{X}^T\bm{X}= \mathbb{E}[\bm{X}^T\bm{X}]. -\] -!et - -To see this let us simply look at a design matrix $\bm{X}\in {\mathbb{R}}^{2\times 2}$ -!bt -\[ -\bm{X}=\begin{bmatrix} -x_{00} & x_{01}\\ -x_{10} & x_{11}\\ -\end{bmatrix}=\begin{bmatrix} -\bm{x}_{0} & \bm{x}_{1}\\ -\end{bmatrix}. -\] -!et - -!split -===== Computing the Expectation Values ===== - -If we then compute the expectation value -!bt -\[ -\mathbb{E}[\bm{X}^T\bm{X}] = \frac{1}{n}\bm{X}^T\bm{X}=\begin{bmatrix} -x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\ -x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\ -\end{bmatrix}, -\] -!et -which is just -!bt -\[ -\bm{C}[\bm{x}_0,\bm{x}_1] = \bm{C}[\bm{x}]=\begin{bmatrix} \mathrm{var}[\bm{x}_0] & \mathrm{cov}[\bm{x}_0,\bm{x}_1] \\ - \mathrm{cov}[\bm{x}_1,\bm{x}_0] & \mathrm{var}[\bm{x}_1] \\ - \end{bmatrix}, -\] -!et -where we wrote $$\bm{C}[\bm{x}_0,\bm{x}_1] = \bm{C}[\bm{x}]$$ to indicate that this the covariance of the vectors $\bm{x}$ of the design/feature matrix $\bm{X}$. - -It is easy to generalize this to a matrix $\bm{X}\in {\mathbb{R}}^{n\times p}$. - - -!split -===== Towards the PCA theorem ===== - -We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as -!bt -\[ -\bm{C}[\bm{x}] = \frac{1}{n}\bm{X}^T\bm{X}= \mathbb{E}[\bm{X}^T\bm{X}]. -\] -!et -Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices $\bm{S}$. -These matrices are defined as $\bm{S}\in {\mathbb{R}}^{p\times p}$ and obey the orthogonality requirements $\bm{S}\bm{S}^T=\bm{S}^T\bm{S}=\bm{I}$. The matrix can be written out in terms of the column vectors $\bm{s}_i$ as $\bm{S}=[\bm{s}_0,\bm{s}_1,\dots,\bm{s}_{p-1}]$ and $\bm{s}_i \in {\mathbb{R}}^{p}$. - -Assume also that there is a transformation $\bm{S}^T\bm{C}[\bm{x}]\bm{S}=\bm{C}[\bm{y}]$ such that the new matrix $\bm{C}[\bm{y}]$ is diagonal with elements $[\lambda_0,\lambda_1,\lambda_2,\dots,\lambda_{p-1}]$. - -That is we have -!bt -\[ -\bm{C}[\bm{y}] = \mathbb{E}[\bm{S}^T\bm{X}^T\bm{X}T\bm{S}]=\bm{S}^T\bm{C}[\bm{x}]\bm{S}, -\] -!et -since the matrix $\bm{S}$ is not a data dependent matrix. Multiplying with $\bm{S}$ from the left we have -!bt -\[ -\bm{S}\bm{C}[\bm{y}] = \bm{C}[\bm{x}]\bm{S}, -\] -!et -and since $\bm{C}[\bm{y}]$ is diagonal we have for a given eigenvalue $i$ of the covariance matrix that - -!bt -\[ -\bm{S}_i\lambda_i = \bm{C}[\bm{x}]\bm{S}_i. -\] -!et - -!split -===== More on the PCA Theorem ===== - -In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is -$\lambda_0 > \lambda_1 > \dots > \lambda_{p-1}$. - - -The eigenvalues tell us then how much we need to stretch the -corresponding eigenvectors. Dimensions with large eigenvalues have -thus large variations (large variance) and define therefore useful -dimensions. The data points are more spread out in the direction of -these eigenvectors. Smaller eigenvalues mean on the other hand that -the corresponding eigenvectors are shrunk accordingly and the data -points are tightly bunched together and there is not much variation in -these specific directions. Hopefully then we could leave it out -dimensions where the eigenvalues are very small. If $p$ is very large, -we could then aim at reducing $p$ to $l << p$ and handle only $l$ -features/predictors. - -!split -===== The Algorithm before the Theorem ===== - -Here's how we would proceed in setting up the algorithm for the PCA, see also discussion below here. -* Set up the datapoints for the design/feature matrix $\bm{X}$ with $\bm{X}\in {\mathbb{R}}^{n\times p}$, with the predictors/features $p$ referring to the column numbers and the entries $n$ being the row elements. -!bt -\[ -\bm{X}=\begin{bmatrix} -x_{0,0} & x_{0,1} & x_{0,2}& \dots & \dots x_{0,p-1}\\ -x_{1,0} & x_{1,1} & x_{1,2}& \dots & \dots x_{1,p-1}\\ -x_{2,0} & x_{2,1} & x_{2,2}& \dots & \dots x_{2,p-1}\\ -\dots & \dots & \dots & \dots \dots & \dots \\ -x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \dots & \dots x_{n-2,p-1}\\ -x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ -\end{bmatrix}, -\] -!et -* Center the data by subtracting the mean value for each column. This leads to a new matrix $\bm{X}\rightarrow \overline{\bm{X}}$. -* Compute then the covariance/correlation matrix $\mathbb{E}[\overline{\bm{X}}^T\overline{\bm{X}}]$. -* Find the eigenpairs of $\bm{C}$ with eigenvalues $[\lambda_0,\lambda_1,\dots,\lambda_{p-1}]$ and eigenvectors $[\bm{s}_0,\bm{s}_1,\dots,\bm{s}_{p-1}]$. -* Order the eigenvalue (and the eigenvectors accordingly) in order of decreasing eigenvalues. -* Keep only those $l$ eigenvalues larger than a selected threshold value, discarding thus $p-l$ features since we expect small variations in the data here. - -!split -===== Writing our own PCA code ===== - -We will use a simple example first with two-dimensional data -drawn from a multivariate normal distribution with the following mean and covariance matrix (we have fixed these quantities but will play around with them below): -!bt -\[ -\mu = (-1,2) \qquad \Sigma = \begin{bmatrix} 4 & 2 \\ -2 & 2 -\end{bmatrix} -\] -!et -Note that the mean refers to each column of data. -We will generate $n = 10000$ points $X = \{ x_1, \ldots, x_N \}$ from -this distribution, and store them in the $1000 \times 2$ matrix $\bm{X}$. This is our design matrix where we have forced the covariance and mean values to take specific values. - -!split -===== Implementing it ===== -The following Python code aids in setting up the data and writing out the design matrix. -Note that the function _multivariate_ returns also the covariance discussed above and that it is defined by dividing by $n-1$ instead of $n$. -!bc pycod -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from IPython.display import display -n = 10000 -mean = (-1, 2) -cov = [[4, 2], [2, 2]] -X = np.random.multivariate_normal(mean, cov, n) -!ec - -Now we are going to implement the PCA algorithm. We will break it down into various substeps. - -!split -===== First Step ===== - -The first step of PCA is to compute the sample mean of the data and use it to center the data. Recall that the sample mean is -!bt -\[ -\mu_n = \frac{1}{n} \sum_{i=1}^n x_i -\] -!et -and the mean-centered data $\bar{X} = \{ \bar{x}_1, \ldots, \bar{x}_n \}$ takes the form -!bt -\[ -\bar{x}_i = x_i - \mu_n. -\] -!et -When you are done with these steps, print out $\mu_n$ to verify it is -close to $\mu$ and plot your mean centered data to verify it is -centered at the origin! -The following code elements perform these operations using _pandas_ or using our own functionality for doing so. The latter, using _numpy_ is rather simple through the _mean()_ function. -!bc pycod -df = pd.DataFrame(X) -# Pandas does the centering for us -df = df -df.mean() -# we center it ourselves -X_centered = X - X.mean(axis=0) -!ec - -!split -===== Scaling ===== -Alternatively, we could use the functions we discussed -earlier for scaling the data set. That is, we could have used the -_StandardScaler_ function in _Scikit-Learn_, a function which ensures -that for each feature/predictor we study the mean value is zero and -the variance is one (every column in the design/feature matrix). You -would then not get the same results, since we divide by the -variance. The diagonal covariance matrix elements will then be one, -while the non-diagonal ones need to be divided by $2\sqrt{2}$ for our -specific case. - -!split -===== Centered Data ===== - -Now we are going to use the mean centered data to compute the sample covariance of the data by using the following equation -!bt -\begin{equation*} -\Sigma_n = \frac{1}{n-1} \sum_{i=1}^n \bar{x}_i^T \bar{x}_i = \frac{1}{n-1} \sum_{i=1}^n (x_i - \mu_n)^T (x_i - \mu_n) -\end{equation*} -!et -where the data points $x_i \in \mathbb{R}^p$ (here in this example $p = 2$) are column vectors and $x^T$ is the transpose of $x$. -We can write our own code or simply use either the functionaly of _numpy_ or that of _pandas_, as follows -!bc pycod -print(df.cov()) -print(np.cov(X_centered.T)) -!ec -Note that the way we define the covariance matrix here has a factor $n-1$ instead of $n$. This is included in the _cov()_ function by _numpy_ and _pandas_. -Our own code here is not very elegant and asks for obvious improvements. It is tailored to this specific $2\times 2$ covariance matrix. -!bc pycod -# extract the relevant columns from the centered design matrix of dim n x 2 -x = X_centered[:,0] -y = X_centered[:,1] -Cov = np.zeros((2,2)) -Cov[0,1] = np.sum(x.T@y)/(n-1.0) -Cov[0,0] = np.sum(x.T@x)/(n-1.0) -Cov[1,1] = np.sum(y.T@y)/(n-1.0) -Cov[1,0]= Cov[0,1] -print("Centered covariance using own code") -print(Cov) -plt.plot(x, y, 'x') -plt.axis('equal') -plt.show() -!ec - -!split -===== Exploring ===== - -Depending on the number of points $n$, we will get results that are close to the covariance values defined above. -The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed. - -!split -===== Diagonalize the sample covariance matrix to obtain the principal components ===== - -Now we are ready to solve for the principal components! To do so we -diagonalize the sample covariance matrix $\Sigma$. We can use the -function _np.linalg.eig_ to do so. It will return the eigenvalues and -eigenvectors of $\Sigma$. Once we have these we can perform the -following tasks: - -* We compute the percentage of the total variance captured by the first principal component -* We plot the mean centered data and lines along the first and second principal components -* Then we project the mean centered data onto the first and second principal components, and plot the projected data. -* Finally, we approximate the data as - -!bt -\begin{equation*} -x_i \approx \tilde{x}_i = \mu_n + \langle x_i, v_0 \rangle v_0 -\end{equation*} -!et -where $v_0$ is the first principal component. - -!split -===== Collecting all Steps ===== - -Collecting all these steps we can write our own PCA function and -compare this with the functionality included in _Scikit-Learn_. - -The code here outlines some of the elements we could include in the -analysis. Feel free to extend upon this in order to address the above -questions. - -!bc pycod -# diagonalize and obtain eigenvalues, not necessarily sorted -EigValues, EigVectors = np.linalg.eig(Cov) -# sort eigenvectors and eigenvalues -#permute = EigValues.argsort() -#EigValues = EigValues[permute] -#EigVectors = EigVectors[:,permute] -print("Eigenvalues of Covariance matrix") -for i in range(2): - print(EigValues[i]) -FirstEigvector = EigVectors[:,0] -SecondEigvector = EigVectors[:,1] -print("First eigenvector") -print(FirstEigvector) -print("Second eigenvector") -print(SecondEigvector) -#thereafter we do a PCA with Scikit-learn -from sklearn.decomposition import PCA -pca = PCA(n_components = 2) -X2Dsl = pca.fit_transform(X) -print("Eigenvector of largest eigenvalue") -print(pca.components_.T[:, 0]) - -!ec -This code does not contain all the above elements, but it shows how we can use _Scikit-Learn_ to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then? - -!split -===== Classical PCA Theorem ===== - -We assume now that we have a design matrix $\bm{X}$ which has been -centered as discussed above. For the sake of simplicity we skip the -overline symbol. The matrix is defined in terms of the various column -vectors $[\bm{x}_0,\bm{x}_1,\dots, \bm{x}_{p-1}]$ each with dimension -$\bm{x}\in {\mathbb{R}}^{n}$. - - - -The PCA theorem states that minimizing the above reconstruction error -corresponds to setting $\bm{W}=\bm{S}$, the orthogonal matrix which -diagonalizes the empirical covariance(correlation) matrix. The optimal -low-dimensional encoding of the data is then given by a set of vectors -$\bm{z}_i$ with at most $l$ vectors, with $l << p$, defined by the -orthogonal projection of the data onto the columns spanned by the -eigenvectors of the covariance(correlations matrix). - - - -!split -===== The PCA Theorem ===== - -To show the PCA theorem let us start with the assumption that there is one vector $\bm{s}_0$ which corresponds to a solution which minimized the reconstruction error $J$. This is an orthogonal vector. It means that we now approximate the reconstruction error in terms of $\bm{w}_0$ and $\bm{z}_0$ as - - - -We are almost there, we have obtained a relation between minimizing -the reconstruction error and the variance and the covariance -matrix. Minimizing the error is equivalent to maximizing the variance -of the projected data. - - -We could trivially maximize the variance of the projection (and -thereby minimize the error in the reconstruction function) by letting -the norm-2 of $\bm{w}_0$ go to infinity. However, this norm since we -want the matrix $\bm{W}$ to be an orthogonal matrix, is constrained by -$\vert\vert \bm{w}_0 \vert\vert_2^2=1$. Imposing this condition via a -Lagrange multiplier we can then in turn maximize - -!bt -\[ -J(\bm{w}_0)= \bm{w}_0^T\bm{C}[\bm{x}]\bm{w}_0+\lambda_0(1-\bm{w}_0^T\bm{w}_0). -\] -!et -Taking the derivative with respect to $\bm{w}_0$ we obtain - -!bt -\[ -\frac{\partial J(\bm{w}_0)}{\partial \bm{w}_0}= 2\bm{C}[\bm{x}]\bm{w}_0-2\lambda_0\bm{w}_0=0, -\] -!et -meaning that -!bt -\[ -\bm{C}[\bm{x}]\bm{w}_0=\lambda_0\bm{w}_0. -\] -!et -_The direction that maximizes the variance (or minimizes the construction error) is an eigenvector of the covariance matrix_! If we left multiply with $\bm{w}_0^T$ we have the variance of the projected data is -!bt -\[ -\bm{w}_0^T\bm{C}[\bm{x}]\bm{w}_0=\lambda_0. -\] -!et - -If we want to maximize the variance (minimize the construction error) -we simply pick the eigenvector of the covariance matrix with the -largest eigenvalue. This establishes the link between the minimization -of the reconstruction function $J$ in terms of an orthogonal matrix -and the maximization of the variance and thereby the covariance of our -observations encoded in the design/feature matrix $\bm{X}$. - -The proof -for the other eigenvectors $\bm{w}_1,\bm{w}_2,\dots$ can be -established by applying the above arguments and using the fact that -our basis of eigenvectors is orthogonal, see "Murphy chapter -12.2":"https://mitpress.mit.edu/books/machine-learning-1". The -discussion in chapter 12.2 of Murphy's text has also a nice link with -the Singular Value Decomposition theorem. For categorical data, see -chapter 12.4 and discussion therein. - -For more details, see for example "Vidal, Ma and Sastry, chapter 2":"https://www.springer.com/gp/book/9780387878102". - -!split -===== Geometric Interpretation and link with Singular Value Decomposition ===== - -For a detailed demonstration of the geometric interpretation, see "Vidal, Ma and Sastry, section 2.1.2":"https://www.springer.com/gp/book/9780387878102". - - -Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm. -First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. - -The following Python code uses NumPy’s _svd()_ function to obtain all the principal components of the -training set, then extracts the first two principal components. First we center the data using either _pandas_ or our own code -!bc pycod -import numpy as np -import pandas as pd -from IPython.display import display -np.random.seed(100) -# setting up a 10 x 5 vanilla matrix -rows = 10 -cols = 5 -X = np.random.randn(rows,cols) -df = pd.DataFrame(X) -# Pandas does the centering for us -df = df -df.mean() -display(df) - -# we center it ourselves -X_centered = X - X.mean(axis=0) -# Then check the difference between pandas and our own set up -print(X_centered-df) -#Now we do an SVD -U, s, V = np.linalg.svd(X_centered) -c1 = V.T[:, 0] -c2 = V.T[:, 1] -W2 = V.T[:, :2] -X2D = X_centered.dot(W2) -print(X2D) -!ec - -PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering -the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t -forget to center the data first. - -Once you have identified all the principal components, you can reduce the dimensionality of the dataset -down to $d$ dimensions by projecting it onto the hyperplane defined by the first $d$ principal components. -Selecting this hyperplane ensures that the projection will preserve as much variance as possible. -!bc pycod -W2 = V.T[:, :2] -X2D = X_centered.dot(W2) -!ec - -!split -===== PCA and scikit-learn ===== - -Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The -following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note -that it automatically takes care of centering the data): -!bc pycod -#thereafter we do a PCA with Scikit-learn -from sklearn.decomposition import PCA -pca = PCA(n_components = 2) -X2D = pca.fit_transform(X) -print(X2D) -!ec -After fitting the PCA transformer to the dataset, you can access the principal components using the -components variable (note that it contains the PCs as horizontal vectors, so, for example, the first -principal component is equal to -!bc pycod -pca.components_.T[:, 0] -!ec -Another very useful piece of information is the explained variance ratio of each principal component, -available via the $explained\_variance\_ratio$ variable. It indicates the proportion of the dataset’s -variance that lies along the axis of each principal component. - -!split -===== Back to the Cancer Data ===== -We can now repeat the above but applied to real data, in this case our breast cancer data. -Here we compute performance scores on the training data using logistic regression. -!bc pycod -import matplotlib.pyplot as plt -import numpy as np -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_breast_cancer -from sklearn.linear_model import LogisticRegression -cancer = load_breast_cancer() - -X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0) - -logreg = LogisticRegression() -logreg.fit(X_train, y_train) -print("Train set accuracy from Logistic Regression: {:.2f}".format(logreg.score(X_train,y_train))) -# We scale the data -from sklearn.preprocessing import StandardScaler -scaler = StandardScaler() -scaler.fit(X_train) -X_train_scaled = scaler.transform(X_train) -X_test_scaled = scaler.transform(X_test) -# Then perform again a log reg fit -logreg.fit(X_train_scaled, y_train) -print("Train set accuracy scaled data: {:.2f}".format(logreg.score(X_train_scaled,y_train))) -#thereafter we do a PCA with Scikit-learn -from sklearn.decomposition import PCA -pca = PCA(n_components = 2) -X2D_train = pca.fit_transform(X_train_scaled) -# and finally compute the log reg fit and the score on the training data -logreg.fit(X2D_train,y_train) -print("Train set accuracy scaled and PCA data: {:.2f}".format(logreg.score(X2D_train,y_train))) - -!ec - -We see that our training data after the PCA decomposition has a performance similar to the non-scaled data. - - -Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to -choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). -Unless, of course, you are reducing dimensionality for data visualization — in that case you will -generally want to reduce the dimensionality down to 2 or 3. -The following code computes PCA without reducing dimensionality, then computes the minimum number -of dimensions required to preserve 95% of the training set’s variance: -!bc pycod -pca = PCA() -pca.fit(X) -cumsum = np.cumsum(pca.explained_variance_ratio_) -d = np.argmax(cumsum >= 0.95) + 1 -!ec -You could then set $n\_components=d$ and run PCA again. However, there is a much better option: instead -of specifying the number of principal components you want to preserve, you can set $n\_components$ to be -a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: -!bc pycod -pca = PCA(n_components=0.95) -X_reduced = pca.fit_transform(X) -!ec - -!split -===== Incremental PCA ===== - -One problem with the preceding implementation of PCA is that it requires the whole training set to fit in -memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have -been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch -at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new -instances arrive). - - -=== Randomized PCA === - -Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic -algorithm that quickly finds an approximation of the first d principal components. Its computational -complexity is $O(m \times d^2)+O(d^3)$, instead of $O(m \times n^2) + O(n^3)$, so it is dramatically faster than the -previous algorithms when $d$ is much smaller than $n$. - - -=== Kernel PCA === - - -The kernel trick is a mathematical technique that implicitly maps instances into a -very high-dimensional space (called the feature space), enabling nonlinear classification and regression -with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature -space corresponds to a complex nonlinear decision boundary in the original space. -It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear -projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at -preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a -twisted manifold. -For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an -!bc pycod -from sklearn.decomposition import KernelPCA -rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04) -X_reduced = rbf_pca.fit_transform(X) -!ec - -!split -===== Other techniques ===== - - -There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn. - -Here are some of the most popular: -* _Multidimensional Scaling (MDS)_ reduces dimensionality while trying to preserve the distances between the instances. -* _Isomap_ creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances. -* _t-Distributed Stochastic Neighbor Embedding_ (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D). -* Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures.