From 56128797fc965da6ee06779228bfc4e06eaa7189 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Mon, 7 Sep 2020 22:26:14 +0200 Subject: [PATCH] update on reg --- .../Regression/html/._Regression-bs000.html | 2 +- .../Regression/html/._Regression-bs011.html | 4 +- .../Regression/html/._Regression-bs017.html | 6 +- .../Regression/html/._Regression-bs018.html | 6 +- .../Regression/html/._Regression-bs026.html | 20 +- .../Regression/html/._Regression-bs027.html | 22 +- .../Regression/html/._Regression-bs029.html | 28 +- .../Regression/html/._Regression-bs042.html | 14 +- .../Regression/html/._Regression-bs057.html | 14 +- .../Regression/html/._Regression-bs083.html | 6 +- .../Regression/html/._Regression-bs089.html | 8 +- .../Regression/html/._Regression-bs095.html | 16 +- .../Regression/html/._Regression-bs096.html | 18 +- .../Regression/html/._Regression-bs098.html | 6 +- .../Regression/html/._Regression-bs099.html | 10 +- .../Regression/html/._Regression-bs100.html | 2 +- .../Regression/html/._Regression-bs102.html | 2 +- .../Regression/html/._Regression-bs104.html | 2 +- .../Regression/html/._Regression-bs105.html | 2 +- .../Regression/html/._Regression-bs106.html | 2 +- .../Regression/html/._Regression-bs110.html | 4 +- doc/pub/Regression/html/Regression-bs.html | 2 +- .../Regression/html/Regression-reveal.html | 184 ++-- .../Regression/html/Regression-solarized.html | 184 ++-- doc/pub/Regression/html/Regression.html | 194 ++-- doc/pub/Regression/ipynb/Regression.ipynb | 992 ++++-------------- .../ipynb/ipynb-Regression-src.tar.gz | Bin 193 -> 198 bytes doc/pub/Regression/pdf/Regression-minted.pdf | Bin 573535 -> 572376 bytes doc/src/Regression/make.sh | 2 +- 29 files changed, 578 insertions(+), 1174 deletions(-) diff --git a/doc/pub/Regression/html/._Regression-bs000.html b/doc/pub/Regression/html/._Regression-bs000.html index aebb349e2..38599e970 100644 --- a/doc/pub/Regression/html/._Regression-bs000.html +++ b/doc/pub/Regression/html/._Regression-bs000.html @@ -425,7 +425,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 23, 2020

+

Sep 7, 2020


diff --git a/doc/pub/Regression/html/._Regression-bs011.html b/doc/pub/Regression/html/._Regression-bs011.html index 08e710277..d603884e0 100644 --- a/doc/pub/Regression/html/._Regression-bs011.html +++ b/doc/pub/Regression/html/._Regression-bs011.html @@ -444,7 +444,7 @@ DATA_ID = " return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("MassEval2016.dat"),'r') @@ -454,7 +454,7 @@ Masses = pd.=('N', 'Z', 'A', 'Element', 'Ebinding'), widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), header=39, - index_col=False) + index_col=False) # Extrapolated values are indicated by '#' in place of the decimal place, so # the Ebinding column won't be numeric. Coerce to float and drop these entries. diff --git a/doc/pub/Regression/html/._Regression-bs017.html b/doc/pub/Regression/html/._Regression-bs017.html index 2ead03df8..831acc659 100644 --- a/doc/pub/Regression/html/._Regression-bs017.html +++ b/doc/pub/Regression/html/._Regression-bs017.html @@ -417,14 +417,14 @@ write 

# matrix inversion to find beta
 beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)
 # and then make the prediction
-ytilde = X @ beta
+ytilde = X @ beta

Alternatively, you can use the least squares functionality in Numpy as

-

fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
+
fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
 ytildenp = np.dot(fit,X.T)
 

 

@@ -436,7 +436,7 @@ And finally we plot our fit with and compare with data
 # Generate a plot comparing the experimental with the fitted values values.
 fig, ax = plt.subplots()
 ax.set_xlabel(r'$A = N + Z$')
-ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
+ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
 ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,
             label='Ame2016')
 ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',
diff --git a/doc/pub/Regression/html/._Regression-bs018.html b/doc/pub/Regression/html/._Regression-bs018.html
index 747f32972..a7b97d613 100644
--- a/doc/pub/Regression/html/._Regression-bs018.html
+++ b/doc/pub/Regression/html/._Regression-bs018.html
@@ -422,7 +422,7 @@ and we would be using it as
 

 
 
-
print(R2(Energies,ytilde))
+
print(R2(Energies,ytilde))
 

 

 We can easily add our MSE score as
@@ -433,7 +433,7 @@ We can easily add our MSE score as
     n = np.size(y_model)
     return np.sum((y_data-y_model)**2)/n
 
-print(MSE(Energies,ytilde))
+print(MSE(Energies,ytilde))
 

 

 and finally the relative error as
@@ -442,7 +442,7 @@ and finally the relative error as
 
 
def RelativeError(y_data,y_model):
     return abs((y_data-y_model)/y_data)
-print(RelativeError(Energies, ytilde))
+print(RelativeError(Energies, ytilde))
 

 

 

diff --git a/doc/pub/Regression/html/._Regression-bs026.html b/doc/pub/Regression/html/._Regression-bs026.html
index 0a61ca46a..151f4a402 100644
--- a/doc/pub/Regression/html/._Regression-bs026.html
+++ b/doc/pub/Regression/html/._Regression-bs026.html
@@ -441,7 +441,7 @@ DATA_ID = "
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 infile = open(data_path("EoS.csv"),'r')
 
@@ -464,12 +464,12 @@ clf = skl.= clf.predict(X)
 EoS['Eols']  = ytilde
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, ytilde))
+print('Variance score: %.2f' % r2_score(Energies, ytilde))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
-print(clf.coef_, clf.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
+print(clf.coef_, clf.intercept_)
 
 # The Ridge regression with a hyperparameter lambda = 0.1
 _lambda = 0.1
@@ -477,15 +477,15 @@ clf_ridge = skl
 yridge = clf_ridge.predict(X)
 EoS['Eridge']  = yridge
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, yridge))
+print('Variance score: %.2f' % r2_score(Energies, yridge))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
-print(clf_ridge.coef_, clf_ridge.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
+print(clf_ridge.coef_, clf_ridge.intercept_)
 
 fig, ax = plt.subplots()
-ax.set_xlabel(r'$\rho[\mathrm{fm}^{-3}]$')
+ax.set_xlabel(r'$\rho[\mathrm{fm}^{-3}]$')
 ax.set_ylabel(r'Energy per particle')
 ax.plot(EoS['Density'], EoS['Energy'], alpha=0.7, lw=2,
             label='Theoretical data')
diff --git a/doc/pub/Regression/html/._Regression-bs027.html b/doc/pub/Regression/html/._Regression-bs027.html
index 5042b0d49..a04765bab 100644
--- a/doc/pub/Regression/html/._Regression-bs027.html
+++ b/doc/pub/Regression/html/._Regression-bs027.html
@@ -449,7 +449,7 @@ DATA_ID = "
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 def R2(y_data, y_model):
     return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
@@ -477,16 +477,16 @@ X_train, X_test, y_train, y_test = train_tes
 # matrix inversion to find beta
 beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)
 # and then make the prediction
-ytilde = X_train @ beta
-print("Training R2")
-print(R2(y_train,ytilde))
-print("Training MSE")
-print(MSE(y_train,ytilde))
-ypredict = X_test @ beta
-print("Test R2")
-print(R2(y_test,ypredict))
-print("Test MSE")
-print(MSE(y_test,ypredict))
+ytilde = X_train @ beta
+print("Training R2")
+print(R2(y_train,ytilde))
+print("Training MSE")
+print(MSE(y_train,ytilde))
+ypredict = X_test @ beta
+print("Test R2")
+print(R2(y_test,ypredict))
+print("Test MSE")
+print(MSE(y_test,ypredict))

diff --git a/doc/pub/Regression/html/._Regression-bs029.html b/doc/pub/Regression/html/._Regression-bs029.html index 32033a071..4feec208c 100644 --- a/doc/pub/Regression/html/._Regression-bs029.html +++ b/doc/pub/Regression/html/._Regression-bs029.html @@ -469,7 +469,7 @@ It is now useful to look at the correlation matrix correlation_matrix = boston.corr().round(2) # use the heatmap function from seaborn to plot the correlation matrix # annot = True to print the values inside the square -sns.heatmap(data=correlation_matrix, annot=True) +sns.heatmap(data=correlation_matrix, annot=True)

From the above coorelation plot we can see that MEDV is strongly correlated to LSTAT and RM. We see also that RAD and TAX are stronly correlated, but we don't include this in our features together to avoid multi-colinearity @@ -510,10 +510,10 @@ We split the data into training and test sets # splits the training and test data set in 80% : 20% # assign random_state to any value.This ensures consistency. X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5) -print(X_train.shape) -print(X_test.shape) -print(Y_train.shape) -print(Y_test.shape) +print(X_train.shape) +print(X_test.shape) +print(Y_train.shape) +print(Y_test.shape)

Then we use the linear regression functionality from Scikit-Learn @@ -532,11 +532,11 @@ y_train_predict = lin_model= (np.sqrt(mean_squared_error(Y_train, y_train_predict))) r2 = r2_score(Y_train, y_train_predict) -print("The model performance for training set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) -print("\n") +print("The model performance for training set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2)) +print("\n") # model evaluation for testing set @@ -547,10 +547,10 @@ rmse = (np.# r-squared score of the model r2 = r2_score(Y_test, y_test_predict) -print("The model performance for testing set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) +print("The model performance for testing set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2))

diff --git a/doc/pub/Regression/html/._Regression-bs042.html b/doc/pub/Regression/html/._Regression-bs042.html index 5cfe07209..f04e76545 100644 --- a/doc/pub/Regression/html/._Regression-bs042.html +++ b/doc/pub/Regression/html/._Regression-bs042.html @@ -423,9 +423,9 @@ MathJax.Hub.Config({ # print( (np.transpose(U) @ U - U @np.transpose(U))) # print('test VT') # print( (np.transpose(VT) @ VT - VT @np.transpose(VT))) - print(U) - print(s) - print(VT) + print(U) + print(s) + print(VT) D = np.zeros((len(U),len(VT))) for i in range(0,len(VT)): @@ -435,14 +435,14 @@ MathJax.Hub.Config({ X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ]) -print(X) -A = np.transpose(X) @ X -print(A) +print(X) +A = np.transpose(X) @ X +print(A) # Brute force inversion of super-collinear matrix #B = np.linalg.inv(A) #print(B) C = SVDinv(A) -print(C) +print(C)

The matrix \( \boldsymbol{X} \) has columns that are linearly dependent. The first diff --git a/doc/pub/Regression/html/._Regression-bs057.html b/doc/pub/Regression/html/._Regression-bs057.html index 88933f9e3..d4c28da77 100644 --- a/doc/pub/Regression/html/._Regression-bs057.html +++ b/doc/pub/Regression/html/._Regression-bs057.html @@ -416,16 +416,16 @@ MathJax.Hub.Config({ n = 100 x = np.random.normal(size=n) -print(np.mean(x)) +print(np.mean(x)) y = 4+3*x+np.random.normal(size=n) -print(np.mean(y)) +print(np.mean(y)) z = x**3+np.random.normal(size=n) -print(np.mean(z)) +print(np.mean(z)) W = np.vstack((x, y, z)) Sigma = np.cov(W) -print(Sigma) +print(Sigma) Eigvals, Eigvecs = np.linalg.eig(Sigma) -print(Eigvals) +print(Eigvals)

@@ -434,9 +434,9 @@ Eigvals, Eigvecs = npimport matplotlib.pyplot as plt from scipy import sparse eye = np.eye(4) -print(eye) +print(eye) sparse_mtx = sparse.csr_matrix(eye) -print(sparse_mtx) +print(sparse_mtx) x = np.linspace(-10,10,100) y = np.sin(x) plt.plot(x,y,marker='x') diff --git a/doc/pub/Regression/html/._Regression-bs083.html b/doc/pub/Regression/html/._Regression-bs083.html index ca16866fc..7c36714c2 100644 --- a/doc/pub/Regression/html/._Regression-bs083.html +++ b/doc/pub/Regression/html/._Regression-bs083.html @@ -421,9 +421,9 @@ MathJax.Hub.Config({ t[i] = stat(delete(data,i) ) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") - print("original bias std. error") - print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) + print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") + print("original bias std. error") + print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) return t diff --git a/doc/pub/Regression/html/._Regression-bs089.html b/doc/pub/Regression/html/._Regression-bs089.html index 1c8409656..6b7880006 100644 --- a/doc/pub/Regression/html/._Regression-bs089.html +++ b/doc/pub/Regression/html/._Regression-bs089.html @@ -443,9 +443,9 @@ theorem. t[i] = statistic(data[randint(0,n,n)]) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") - print("original bias std. error") - print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) + print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") + print("original bias std. error") + print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) return t @@ -463,7 +463,7 @@ lt = plt..xlabel('Smarts') plt.ylabel('Probability') plt.axis([99.5, 100.6, 0, 3.0]) -plt.grid(True) +plt.grid(True) plt.show() diff --git a/doc/pub/Regression/html/._Regression-bs095.html b/doc/pub/Regression/html/._Regression-bs095.html index 2b955367b..4caa22b32 100644 --- a/doc/pub/Regression/html/._Regression-bs095.html +++ b/doc/pub/Regression/html/._Regression-bs095.html @@ -434,7 +434,7 @@ x_train, x_test, y_train, y_test = train_tes # Combine x transformation and model into one operation. # Not neccesary, but convenient. -model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) +model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) # The following (m x n_bootstraps) matrix holds the column vectors y_pred # for each bootstrap iteration. @@ -451,13 +451,13 @@ y_pred = np.# calculated per data point in the test set. # Note 2: The use of keepdims=True is important in the calculation of bias as this # maintains the column vector form. Dropping this yields very unexpected results. -error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) -bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) -variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) -print('Error:', error) -print('Bias^2:', bias) -print('Var:', variance) -print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) +error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) +bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) +variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) +print('Error:', error) +print('Bias^2:', bias) +print('Var:', variance) +print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) plt.plot(x[::5, :], y[::5, :], label='f(x)') plt.scatter(x_test, y_test, label='Data points') diff --git a/doc/pub/Regression/html/._Regression-bs096.html b/doc/pub/Regression/html/._Regression-bs096.html index 33abbc1ea..28f71446a 100644 --- a/doc/pub/Regression/html/._Regression-bs096.html +++ b/doc/pub/Regression/html/._Regression-bs096.html @@ -435,21 +435,21 @@ polydegree = np x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2) for degree in range(maxdegree): - model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) + model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) y_pred = np.empty((y_test.shape[0], n_boostraps)) for i in range(n_boostraps): x_, y_ = resample(x_train, y_train) y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel() polydegree[degree] = degree - error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) - bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) - variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) - print('Polynomial degree:', degree) - print('Error:', error[degree]) - print('Bias^2:', bias[degree]) - print('Var:', variance[degree]) - print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) + error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) + bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) + variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) + print('Polynomial degree:', degree) + print('Error:', error[degree]) + print('Bias^2:', bias[degree]) + print('Var:', variance[degree]) + print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) plt.plot(polydegree, error, label='Error') plt.plot(polydegree, bias, label='bias') diff --git a/doc/pub/Regression/html/._Regression-bs098.html b/doc/pub/Regression/html/._Regression-bs098.html index b5ca48531..b0b972733 100644 --- a/doc/pub/Regression/html/._Regression-bs098.html +++ b/doc/pub/Regression/html/._Regression-bs098.html @@ -432,7 +432,7 @@ MathJax.Hub.Config({ training data. """ -print(__doc__) +print(__doc__) import numpy as np import matplotlib.pyplot as plt @@ -459,7 +459,7 @@ plt.figure(figsize.setp(ax, xticks=(), yticks=()) polynomial_features = PolynomialFeatures(degree=degrees[i], - include_bias=False) + include_bias=False) linear_regression = LinearRegression() pipeline = Pipeline([("polynomial_features", polynomial_features), ("linear_regression", linear_regression)]) @@ -478,7 +478,7 @@ plt.figure(figsize.xlim((0, 1)) plt.ylim((-2, 2)) plt.legend(loc="best") - plt.title("Degree {}\nMSE = {:.2e}(+/- {:.2e})".format( + plt.title("Degree {}\nMSE = {:.2e}(+/- {:.2e})".format( degrees[i], -scores.mean(), scores.std())) plt.show() diff --git a/doc/pub/Regression/html/._Regression-bs099.html b/doc/pub/Regression/html/._Regression-bs099.html index 0e0ce4eaa..64aebc988 100644 --- a/doc/pub/Regression/html/._Regression-bs099.html +++ b/doc/pub/Regression/html/._Regression-bs099.html @@ -441,7 +441,7 @@ DATA_ID = " return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') @@ -471,7 +471,7 @@ trials = 100= 0.0 for samples in range(trials): x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2) - model = LinearRegression(fit_intercept=True).fit(x_train, y_train) + model = LinearRegression(fit_intercept=True).fit(x_train, y_train) ypred = model.predict(x_train) ytilde = model.predict(x_test) testerror[polydegree] += mean_squared_error(y_test, ytilde) @@ -479,9 +479,9 @@ trials = 100/= trials trainingerror[polydegree] /= trials - print("Degree of polynomial: %3d"% polynomial[polydegree]) - print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) - print("Mean squared error on test data: %.8f" % testerror[polydegree]) + print("Degree of polynomial: %3d"% polynomial[polydegree]) + print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) + print("Mean squared error on test data: %.8f" % testerror[polydegree]) plt.plot(polynomial, np.log10(trainingerror), label='Training Error') plt.plot(polynomial, np.log10(testerror), label='Test Error') diff --git a/doc/pub/Regression/html/._Regression-bs100.html b/doc/pub/Regression/html/._Regression-bs100.html index 4d9d3877c..8910ba75d 100644 --- a/doc/pub/Regression/html/._Regression-bs100.html +++ b/doc/pub/Regression/html/._Regression-bs100.html @@ -443,7 +443,7 @@ DATA_ID = " return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') diff --git a/doc/pub/Regression/html/._Regression-bs102.html b/doc/pub/Regression/html/._Regression-bs102.html index 85c8cb11c..1a9a7bdd0 100644 --- a/doc/pub/Regression/html/._Regression-bs102.html +++ b/doc/pub/Regression/html/._Regression-bs102.html @@ -439,7 +439,7 @@ with their respective energies. import scipy.linalg as scl from sklearn.model_selection import train_test_split import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 diff --git a/doc/pub/Regression/html/._Regression-bs104.html b/doc/pub/Regression/html/._Regression-bs104.html index 6c809493d..b5ccabc96 100644 --- a/doc/pub/Regression/html/._Regression-bs104.html +++ b/doc/pub/Regression/html/._Regression-bs104.html @@ -448,7 +448,7 @@ X_test_own = np 

def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:
-    return scl.inv(x.T @ x) @ (x.T @ y)
+    return scl.inv(x.T @ x) @ (x.T @ y)
 beta = ols_inv(X_train_own, y_train)

diff --git a/doc/pub/Regression/html/._Regression-bs105.html b/doc/pub/Regression/html/._Regression-bs105.html index f40df4994..bcc834276 100644 --- a/doc/pub/Regression/html/._Regression-bs105.html +++ b/doc/pub/Regression/html/._Regression-bs105.html @@ -450,7 +450,7 @@ linear system as an equation would reduce this down to 

def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:
     u, s, v = scl.svd(x)
-    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y
+    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y

diff --git a/doc/pub/Regression/html/._Regression-bs106.html b/doc/pub/Regression/html/._Regression-bs106.html index e7c535630..c393477b2 100644 --- a/doc/pub/Regression/html/._Regression-bs106.html +++ b/doc/pub/Regression/html/._Regression-bs106.html @@ -439,7 +439,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J from sklearn.model_selection import train_test_split import sklearn.linear_model as skl import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 diff --git a/doc/pub/Regression/html/._Regression-bs110.html b/doc/pub/Regression/html/._Regression-bs110.html index 135d1f39d..edf67ae2c 100644 --- a/doc/pub/Regression/html/._Regression-bs110.html +++ b/doc/pub/Regression/html/._Regression-bs110.html @@ -432,7 +432,7 @@ colors = { lambdas, train_errors[key], colors[key], - label="Train {0}".format(key), + label="Train {0}".format(key), linewidth=4.0 ) @@ -441,7 +441,7 @@ colors = { lambdas, test_errors[key], colors[key] + "--", - label="Test {0}".format(key), + label="Test {0}".format(key), linewidth=4.0 ) plt.legend(loc="best", fontsize=18) diff --git a/doc/pub/Regression/html/Regression-bs.html b/doc/pub/Regression/html/Regression-bs.html index aebb349e2..38599e970 100644 --- a/doc/pub/Regression/html/Regression-bs.html +++ b/doc/pub/Regression/html/Regression-bs.html @@ -425,7 +425,7 @@ MathJax.Hub.Config({

[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 23, 2020

+

Sep 7, 2020


diff --git a/doc/pub/Regression/html/Regression-reveal.html b/doc/pub/Regression/html/Regression-reveal.html index c22a8b69f..1f35937d3 100644 --- a/doc/pub/Regression/html/Regression-reveal.html +++ b/doc/pub/Regression/html/Regression-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({

[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

 
-

Aug 23, 2020

+

Sep 7, 2020


@@ -491,7 +491,7 @@ DATA_ID = "DataFiles/" return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("MassEval2016.dat"),'r') @@ -501,7 +501,7 @@ Masses = pd.read_fwf(infile, usecols=(2,'N', 'Z', 'A', 'Element', 'Ebinding'), widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), header=39, - index_col=False) + index_col=False) # Extrapolated values are indicated by '#' in place of the decimal place, so # the Ebinding column won't be numeric. Coerce to float and drop these entries. @@ -782,14 +782,14 @@ write 

# matrix inversion to find beta
 beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)
 # and then make the prediction
-ytilde = X @ beta
+ytilde = X @ beta

Alternatively, you can use the least squares functionality in Numpy as

-

fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
+
fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
 ytildenp = np.dot(fit,X.T)
 

 

@@ -830,7 +830,7 @@ and we would be using it as
 

 
 
-
print(R2(Energies,ytilde))
+
print(R2(Energies,ytilde))
 

 

 We can easily add our MSE score as
@@ -841,7 +841,7 @@ We can easily add our MSE score as
     n = np.size(y_model)
     return np.sum((y_data-y_model)**2)/n
 
-print(MSE(Energies,ytilde))
+print(MSE(Energies,ytilde))
 

 

 and finally the relative error as
@@ -850,7 +850,7 @@ and finally the relative error as
 
 
def RelativeError(y_data,y_model):
     return abs((y_data-y_model)/y_data)
-print(RelativeError(Energies, ytilde))
+print(RelativeError(Energies, ytilde))
 

 
 
@@ -1136,7 +1136,7 @@ DATA_ID = "DataFiles/"
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 infile = open(data_path("EoS.csv"),'r')
 
@@ -1159,12 +1159,12 @@ clf = skl.LinearRegression().fit(X, Energies)
 ytilde = clf.predict(X)
 EoS['Eols']  = ytilde
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, ytilde))
+print('Variance score: %.2f' % r2_score(Energies, ytilde))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
-print(clf.coef_, clf.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
+print(clf.coef_, clf.intercept_)
 
 # The Ridge regression with a hyperparameter lambda = 0.1
 _lambda = 0.1
@@ -1172,12 +1172,12 @@ clf_ridge = skl.Ridge(alpha=_lambda).fit(X, Energies)
 yridge = clf_ridge.predict(X)
 EoS['Eridge']  = yridge
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, yridge))
+print('Variance score: %.2f' % r2_score(Energies, yridge))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
-print(clf_ridge.coef_, clf_ridge.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
+print(clf_ridge.coef_, clf_ridge.intercept_)
 
 fig, ax = plt.subplots()
 ax.set_xlabel(r'$\rho[\mathrm{fm}^{-3}]$')
@@ -1247,7 +1247,7 @@ DATA_ID = "DataFiles/"
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 def R2(y_data, y_model):
     return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
@@ -1275,16 +1275,16 @@ X_train, X_test, y_train, y_test = train_test_split(X, Energies, test_size=# matrix inversion to find beta
 beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)
 # and then make the prediction
-ytilde = X_train @ beta
-print("Training R2")
-print(R2(y_train,ytilde))
-print("Training MSE")
-print(MSE(y_train,ytilde))
-ypredict = X_test @ beta
-print("Test R2")
-print(R2(y_test,ypredict))
-print("Test MSE")
-print(MSE(y_test,ypredict))
+ytilde = X_train @ beta
+print("Training R2")
+print(R2(y_train,ytilde))
+print("Training MSE")
+print(MSE(y_train,ytilde))
+ypredict = X_test @ beta
+print("Test R2")
+print(R2(y_test,ypredict))
+print("Test MSE")
+print(MSE(y_test,ypredict))
@@ -1385,7 +1385,7 @@ It is now useful to look at the correlation matrix correlation_matrix = boston.corr().round(2) # use the heatmap function from seaborn to plot the correlation matrix # annot = True to print the values inside the square -sns.heatmap(data=correlation_matrix, annot=True) +sns.heatmap(data=correlation_matrix, annot=True)

From the above coorelation plot we can see that MEDV is strongly correlated to LSTAT and RM. We see also that RAD and TAX are stronly correlated, but we don't include this in our features together to avoid multi-colinearity @@ -1426,10 +1426,10 @@ We split the data into training and test sets # splits the training and test data set in 80% : 20% # assign random_state to any value.This ensures consistency. X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5) -print(X_train.shape) -print(X_test.shape) -print(Y_train.shape) -print(Y_test.shape) +print(X_train.shape) +print(X_test.shape) +print(Y_train.shape) +print(Y_test.shape)

Then we use the linear regression functionality from Scikit-Learn @@ -1448,11 +1448,11 @@ y_train_predict = lin_model.predict(X_train) rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict))) r2 = r2_score(Y_train, y_train_predict) -print("The model performance for training set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) -print("\n") +print("The model performance for training set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2)) +print("\n") # model evaluation for testing set @@ -1463,10 +1463,10 @@ rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict))) # r-squared score of the model r2 = r2_score(Y_test, y_test_predict) -print("The model performance for testing set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) +print("The model performance for testing set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2))

@@ -2039,9 +2039,9 @@ Similarly, Mehta et a # print( (np.transpose(U) @ U - U @np.transpose(U))) # print('test VT') # print( (np.transpose(VT) @ VT - VT @np.transpose(VT))) - print(U) - print(s) - print(VT) + print(U) + print(s) + print(VT) D = np.zeros((len(U),len(VT))) for i in range(0,len(VT)): @@ -2051,14 +2051,14 @@ Similarly, Mehta et a X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ]) -print(X) -A = np.transpose(X) @ X -print(A) +print(X) +A = np.transpose(X) @ X +print(A) # Brute force inversion of super-collinear matrix #B = np.linalg.inv(A) #print(B) C = SVDinv(A) -print(C) +print(C)

The matrix \( \boldsymbol{X} \) has columns that are linearly dependent. The first @@ -2465,16 +2465,16 @@ function. n = 100 x = np.random.normal(size=n) -print(np.mean(x)) +print(np.mean(x)) y = 4+3*x+np.random.normal(size=n) -print(np.mean(y)) +print(np.mean(y)) z = x**3+np.random.normal(size=n) -print(np.mean(z)) +print(np.mean(z)) W = np.vstack((x, y, z)) Sigma = np.cov(W) -print(Sigma) +print(Sigma) Eigvals, Eigvecs = np.linalg.eig(Sigma) -print(Eigvals) +print(Eigvals)

@@ -2483,9 +2483,9 @@ Eigvals, Eigvecs = np.linalg.eig(Sigma) import matplotlib.pyplot as plt from scipy import sparse eye = np.eye(4) -print(eye) +print(eye) sparse_mtx = sparse.csr_matrix(eye) -print(sparse_mtx) +print(sparse_mtx) x = np.linspace(-10,10,100) y = np.sin(x) plt.plot(x,y,marker='x') @@ -3329,9 +3329,9 @@ number \( i \) is left out. Using this notation, define t[i] = stat(delete(data,i) ) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") - print("original bias std. error") - print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) + print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") + print("original bias std. error") + print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) return t @@ -3496,9 +3496,9 @@ theorem. t[i] = statistic(data[randint(0,n,n)]) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") - print("original bias std. error") - print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) + print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") + print("original bias std. error") + print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) return t @@ -3516,7 +3516,7 @@ lt = plt.plot(binsboot, y, 'r--', li plt.xlabel('Smarts') plt.ylabel('Probability') plt.axis([99.5, 100.6, 0, 3.0]) -plt.grid(True) +plt.grid(True) plt.show() @@ -3808,7 +3808,7 @@ x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=# Combine x transformation and model into one operation. # Not neccesary, but convenient. -model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) +model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) # The following (m x n_bootstraps) matrix holds the column vectors y_pred # for each bootstrap iteration. @@ -3825,13 +3825,13 @@ y_pred = np.empty((y_test.shape[0], n_boostr # calculated per data point in the test set. # Note 2: The use of keepdims=True is important in the calculation of bias as this # maintains the column vector form. Dropping this yields very unexpected results. -error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) -bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) -variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) -print('Error:', error) -print('Bias^2:', bias) -print('Var:', variance) -print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) +error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) +bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) +variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) +print('Error:', error) +print('Bias^2:', bias) +print('Var:', variance) +print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) plt.plot(x[::5, :], y[::5, :], label='f(x)') plt.scatter(x_test, y_test, label='Data points') @@ -3872,21 +3872,21 @@ polydegree = np.zeros(maxdegree) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2) for degree in range(maxdegree): - model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) + model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) y_pred = np.empty((y_test.shape[0], n_boostraps)) for i in range(n_boostraps): x_, y_ = resample(x_train, y_train) y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel() polydegree[degree] = degree - error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) - bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) - variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) - print('Polynomial degree:', degree) - print('Error:', error[degree]) - print('Bias^2:', bias[degree]) - print('Var:', variance[degree]) - print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) + error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) + bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) + variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) + print('Polynomial degree:', degree) + print('Error:', error[degree]) + print('Bias^2:', bias[degree]) + print('Var:', variance[degree]) + print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) plt.plot(polydegree, error, label='Error') plt.plot(polydegree, bias, label='bias') @@ -3961,7 +3961,7 @@ You may also find this recent training data. """ -print(__doc__) +print(__doc__) import numpy as np import matplotlib.pyplot as plt @@ -3988,7 +3988,7 @@ plt.figure(figsize=(14, False) + include_bias=False) linear_regression = LinearRegression() pipeline = Pipeline([("polynomial_features", polynomial_features), ("linear_regression", linear_regression)]) @@ -4050,7 +4050,7 @@ DATA_ID = "DataFiles/" return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') @@ -4080,7 +4080,7 @@ trials = 100 trainingerror[polydegree] = 0.0 for samples in range(trials): x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2) - model = LinearRegression(fit_intercept=True).fit(x_train, y_train) + model = LinearRegression(fit_intercept=True).fit(x_train, y_train) ypred = model.predict(x_train) ytilde = model.predict(x_test) testerror[polydegree] += mean_squared_error(y_test, ytilde) @@ -4088,9 +4088,9 @@ trials = 100 testerror[polydegree] /= trials trainingerror[polydegree] /= trials - print("Degree of polynomial: %3d"% polynomial[polydegree]) - print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) - print("Mean squared error on test data: %.8f" % testerror[polydegree]) + print("Degree of polynomial: %3d"% polynomial[polydegree]) + print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) + print("Mean squared error on test data: %.8f" % testerror[polydegree]) plt.plot(polynomial, np.log10(trainingerror), label='Training Error') plt.plot(polynomial, np.log10(testerror), label='Test Error') @@ -4140,7 +4140,7 @@ DATA_ID = "DataFiles/" return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') @@ -4259,7 +4259,7 @@ with their respective energies. import scipy.linalg as scl from sklearn.model_selection import train_test_split import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 @@ -4386,7 +4386,7 @@ X_test_own = np.concatenate( 

def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:
-    return scl.inv(x.T @ x) @ (x.T @ y)
+    return scl.inv(x.T @ x) @ (x.T @ y)
 beta = ols_inv(X_train_own, y_train)
@@ -4443,7 +4443,7 @@ linear system as an equation would reduce this down to 
def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:
     u, s, v = scl.svd(x)
-    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y
+    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y

@@ -4521,7 +4521,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J from sklearn.model_selection import train_test_split import sklearn.linear_model as skl import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 diff --git a/doc/pub/Regression/html/Regression-solarized.html b/doc/pub/Regression/html/Regression-solarized.html index ba7c4eff6..677e04649 100644 --- a/doc/pub/Regression/html/Regression-solarized.html +++ b/doc/pub/Regression/html/Regression-solarized.html @@ -305,7 +305,7 @@ MathJax.Hub.Config({

[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 23, 2020

+

Sep 7, 2020












@@ -631,7 +631,7 @@ DATA_ID = "DataFiles/" return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("MassEval2016.dat"),'r') @@ -641,7 +641,7 @@ Masses = pd.read_fwf(infile, usecols=(2,'N', 'Z', 'A', 'Element', 'Ebinding'), widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), header=39, - index_col=False) + index_col=False) # Extrapolated values are indicated by '#' in place of the decimal place, so # the Ebinding column won't be numeric. Coerce to float and drop these entries. @@ -886,14 +886,14 @@ write 

# matrix inversion to find beta
 beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)
 # and then make the prediction
-ytilde = X @ beta
+ytilde = X @ beta

Alternatively, you can use the least squares functionality in Numpy as

-

fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
+
fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
 ytildenp = np.dot(fit,X.T)
 

 

@@ -933,7 +933,7 @@ and we would be using it as
 

 
 
-
print(R2(Energies,ytilde))
+
print(R2(Energies,ytilde))
 

 

 We can easily add our MSE score as
@@ -944,7 +944,7 @@ We can easily add our MSE score as
     n = np.size(y_model)
     return np.sum((y_data-y_model)**2)/n
 
-print(MSE(Energies,ytilde))
+print(MSE(Energies,ytilde))
 

 

 and finally the relative error as
@@ -953,7 +953,7 @@ and finally the relative error as
 
 
def RelativeError(y_data,y_model):
     return abs((y_data-y_model)/y_data)
-print(RelativeError(Energies, ytilde))
+print(RelativeError(Energies, ytilde))
 

 

 










@@ -1217,7 +1217,7 @@ DATA_ID = "DataFiles/"
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 infile = open(data_path("EoS.csv"),'r')
 
@@ -1240,12 +1240,12 @@ clf = skl.LinearRegression().fit(X, Energies)
 ytilde = clf.predict(X)
 EoS['Eols']  = ytilde
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, ytilde))
+print('Variance score: %.2f' % r2_score(Energies, ytilde))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
-print(clf.coef_, clf.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
+print(clf.coef_, clf.intercept_)
 
 # The Ridge regression with a hyperparameter lambda = 0.1
 _lambda = 0.1
@@ -1253,12 +1253,12 @@ clf_ridge = skl.Ridge(alpha=_lambda).fit(X, Energies)
 yridge = clf_ridge.predict(X)
 EoS['Eridge']  = yridge
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, yridge))
+print('Variance score: %.2f' % r2_score(Energies, yridge))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
-print(clf_ridge.coef_, clf_ridge.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
+print(clf_ridge.coef_, clf_ridge.intercept_)
 
 fig, ax = plt.subplots()
 ax.set_xlabel(r'$\rho[\mathrm{fm}^{-3}]$')
@@ -1328,7 +1328,7 @@ DATA_ID = "DataFiles/"
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 def R2(y_data, y_model):
     return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
@@ -1356,16 +1356,16 @@ X_train, X_test, y_train, y_test = train_test_split(X, Energies, test_size=# matrix inversion to find beta
 beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)
 # and then make the prediction
-ytilde = X_train @ beta
-print("Training R2")
-print(R2(y_train,ytilde))
-print("Training MSE")
-print(MSE(y_train,ytilde))
-ypredict = X_test @ beta
-print("Test R2")
-print(R2(y_test,ypredict))
-print("Test MSE")
-print(MSE(y_test,ypredict))
+ytilde = X_train @ beta
+print("Training R2")
+print(R2(y_train,ytilde))
+print("Training MSE")
+print(MSE(y_train,ytilde))
+ypredict = X_test @ beta
+print("Test R2")
+print(R2(y_test,ypredict))
+print("Test MSE")
+print(MSE(y_test,ypredict))

@@ -1464,7 +1464,7 @@ It is now useful to look at the correlation matrix correlation_matrix = boston.corr().round(2) # use the heatmap function from seaborn to plot the correlation matrix # annot = True to print the values inside the square -sns.heatmap(data=correlation_matrix, annot=True) +sns.heatmap(data=correlation_matrix, annot=True)

From the above coorelation plot we can see that MEDV is strongly correlated to LSTAT and RM. We see also that RAD and TAX are stronly correlated, but we don't include this in our features together to avoid multi-colinearity @@ -1505,10 +1505,10 @@ We split the data into training and test sets # splits the training and test data set in 80% : 20% # assign random_state to any value.This ensures consistency. X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5) -print(X_train.shape) -print(X_test.shape) -print(Y_train.shape) -print(Y_test.shape) +print(X_train.shape) +print(X_test.shape) +print(Y_train.shape) +print(Y_test.shape)

Then we use the linear regression functionality from Scikit-Learn @@ -1527,11 +1527,11 @@ y_train_predict = lin_model.predict(X_train) rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict))) r2 = r2_score(Y_train, y_train_predict) -print("The model performance for training set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) -print("\n") +print("The model performance for training set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2)) +print("\n") # model evaluation for testing set @@ -1542,10 +1542,10 @@ rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict))) # r-squared score of the model r2 = r2_score(Y_test, y_test_predict) -print("The model performance for testing set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) +print("The model performance for testing set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2))

@@ -2052,9 +2052,9 @@ Similarly, Mehta et a # print( (np.transpose(U) @ U - U @np.transpose(U))) # print('test VT') # print( (np.transpose(VT) @ VT - VT @np.transpose(VT))) - print(U) - print(s) - print(VT) + print(U) + print(s) + print(VT) D = np.zeros((len(U),len(VT))) for i in range(0,len(VT)): @@ -2064,14 +2064,14 @@ Similarly, Mehta et a X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ]) -print(X) -A = np.transpose(X) @ X -print(A) +print(X) +A = np.transpose(X) @ X +print(A) # Brute force inversion of super-collinear matrix #B = np.linalg.inv(A) #print(B) C = SVDinv(A) -print(C) +print(C)

The matrix \( \boldsymbol{X} \) has columns that are linearly dependent. The first @@ -2464,16 +2464,16 @@ function. n = 100 x = np.random.normal(size=n) -print(np.mean(x)) +print(np.mean(x)) y = 4+3*x+np.random.normal(size=n) -print(np.mean(y)) +print(np.mean(y)) z = x**3+np.random.normal(size=n) -print(np.mean(z)) +print(np.mean(z)) W = np.vstack((x, y, z)) Sigma = np.cov(W) -print(Sigma) +print(Sigma) Eigvals, Eigvecs = np.linalg.eig(Sigma) -print(Eigvals) +print(Eigvals)

@@ -2482,9 +2482,9 @@ Eigvals, Eigvecs = np.linalg.eig(Sigma) import matplotlib.pyplot as plt from scipy import sparse eye = np.eye(4) -print(eye) +print(eye) sparse_mtx = sparse.csr_matrix(eye) -print(sparse_mtx) +print(sparse_mtx) x = np.linspace(-10,10,100) y = np.sin(x) plt.plot(x,y,marker='x') @@ -3262,9 +3262,9 @@ number \( i \) is left out. Using this notation, define t[i] = stat(delete(data,i) ) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") - print("original bias std. error") - print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) + print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") + print("original bias std. error") + print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) return t @@ -3425,9 +3425,9 @@ theorem. t[i] = statistic(data[randint(0,n,n)]) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") - print("original bias std. error") - print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) + print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") + print("original bias std. error") + print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) return t @@ -3445,7 +3445,7 @@ lt = plt.plot(binsboot, y, 'r--', li plt.xlabel('Smarts') plt.ylabel('Probability') plt.axis([99.5, 100.6, 0, 3.0]) -plt.grid(True) +plt.grid(True) plt.show() @@ -3721,7 +3721,7 @@ x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=# Combine x transformation and model into one operation. # Not neccesary, but convenient. -model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) +model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) # The following (m x n_bootstraps) matrix holds the column vectors y_pred # for each bootstrap iteration. @@ -3738,13 +3738,13 @@ y_pred = np.empty((y_test.shape[0], n_boostr # calculated per data point in the test set. # Note 2: The use of keepdims=True is important in the calculation of bias as this # maintains the column vector form. Dropping this yields very unexpected results. -error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) -bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) -variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) -print('Error:', error) -print('Bias^2:', bias) -print('Var:', variance) -print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) +error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) +bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) +variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) +print('Error:', error) +print('Bias^2:', bias) +print('Var:', variance) +print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) plt.plot(x[::5, :], y[::5, :], label='f(x)') plt.scatter(x_test, y_test, label='Data points') @@ -3784,21 +3784,21 @@ polydegree = np.zeros(maxdegree) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2) for degree in range(maxdegree): - model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) + model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) y_pred = np.empty((y_test.shape[0], n_boostraps)) for i in range(n_boostraps): x_, y_ = resample(x_train, y_train) y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel() polydegree[degree] = degree - error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) - bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) - variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) - print('Polynomial degree:', degree) - print('Error:', error[degree]) - print('Bias^2:', bias[degree]) - print('Var:', variance[degree]) - print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) + error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) + bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) + variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) + print('Polynomial degree:', degree) + print('Error:', error[degree]) + print('Bias^2:', bias[degree]) + print('Var:', variance[degree]) + print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) plt.plot(polydegree, error, label='Error') plt.plot(polydegree, bias, label='bias') @@ -3872,7 +3872,7 @@ You may also find this recent training data. """ -print(__doc__) +print(__doc__) import numpy as np import matplotlib.pyplot as plt @@ -3899,7 +3899,7 @@ plt.figure(figsize=(14, False) + include_bias=False) linear_regression = LinearRegression() pipeline = Pipeline([("polynomial_features", polynomial_features), ("linear_regression", linear_regression)]) @@ -3960,7 +3960,7 @@ DATA_ID = "DataFiles/" return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') @@ -3990,7 +3990,7 @@ trials = 100 trainingerror[polydegree] = 0.0 for samples in range(trials): x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2) - model = LinearRegression(fit_intercept=True).fit(x_train, y_train) + model = LinearRegression(fit_intercept=True).fit(x_train, y_train) ypred = model.predict(x_train) ytilde = model.predict(x_test) testerror[polydegree] += mean_squared_error(y_test, ytilde) @@ -3998,9 +3998,9 @@ trials = 100 testerror[polydegree] /= trials trainingerror[polydegree] /= trials - print("Degree of polynomial: %3d"% polynomial[polydegree]) - print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) - print("Mean squared error on test data: %.8f" % testerror[polydegree]) + print("Degree of polynomial: %3d"% polynomial[polydegree]) + print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) + print("Mean squared error on test data: %.8f" % testerror[polydegree]) plt.plot(polynomial, np.log10(trainingerror), label='Training Error') plt.plot(polynomial, np.log10(testerror), label='Test Error') @@ -4049,7 +4049,7 @@ DATA_ID = "DataFiles/" return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') @@ -4164,7 +4164,7 @@ with their respective energies. import scipy.linalg as scl from sklearn.model_selection import train_test_split import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 @@ -4280,7 +4280,7 @@ X_test_own = np.concatenate( 

def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:
-    return scl.inv(x.T @ x) @ (x.T @ y)
+    return scl.inv(x.T @ x) @ (x.T @ y)
 beta = ols_inv(X_train_own, y_train)

@@ -4330,7 +4330,7 @@ linear system as an equation would reduce this down to 

def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:
     u, s, v = scl.svd(x)
-    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y
+    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y

@@ -4406,7 +4406,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J from sklearn.model_selection import train_test_split import sklearn.linear_model as skl import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 diff --git a/doc/pub/Regression/html/Regression.html b/doc/pub/Regression/html/Regression.html index 3050dfd3b..c79a83e81 100644 --- a/doc/pub/Regression/html/Regression.html +++ b/doc/pub/Regression/html/Regression.html @@ -310,7 +310,7 @@ MathJax.Hub.Config({

[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Aug 23, 2020

+

Sep 7, 2020












@@ -636,7 +636,7 @@ DATA_ID = " return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("MassEval2016.dat"),'r') @@ -646,7 +646,7 @@ Masses = pd.=('N', 'Z', 'A', 'Element', 'Ebinding'), widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), header=39, - index_col=False) + index_col=False) # Extrapolated values are indicated by '#' in place of the decimal place, so # the Ebinding column won't be numeric. Coerce to float and drop these entries. @@ -891,14 +891,14 @@ write 

# matrix inversion to find beta
 beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)
 # and then make the prediction
-ytilde = X @ beta
+ytilde = X @ beta

Alternatively, you can use the least squares functionality in Numpy as

-

fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
+
fit = np.linalg.lstsq(X, Energies, rcond =None)[0]
 ytildenp = np.dot(fit,X.T)
 

 

@@ -910,7 +910,7 @@ And finally we plot our fit with and compare with data
 # Generate a plot comparing the experimental with the fitted values values.
 fig, ax = plt.subplots()
 ax.set_xlabel(r'$A = N + Z$')
-ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
+ax.set_ylabel(r'$E_\mathrm{bind}\,/\mathrm{MeV}$')
 ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,
             label='Ame2016')
 ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',
@@ -938,7 +938,7 @@ and we would be using it as
 

 
 
-
print(R2(Energies,ytilde))
+
print(R2(Energies,ytilde))
 

 

 We can easily add our MSE score as
@@ -949,7 +949,7 @@ We can easily add our MSE score as
     n = np.size(y_model)
     return np.sum((y_data-y_model)**2)/n
 
-print(MSE(Energies,ytilde))
+print(MSE(Energies,ytilde))
 

 

 and finally the relative error as
@@ -958,7 +958,7 @@ and finally the relative error as
 
 
def RelativeError(y_data,y_model):
     return abs((y_data-y_model)/y_data)
-print(RelativeError(Energies, ytilde))
+print(RelativeError(Energies, ytilde))
 

 

 










@@ -1222,7 +1222,7 @@ DATA_ID = "
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 infile = open(data_path("EoS.csv"),'r')
 
@@ -1245,12 +1245,12 @@ clf = skl.= clf.predict(X)
 EoS['Eols']  = ytilde
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, ytilde))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, ytilde))
+print('Variance score: %.2f' % r2_score(Energies, ytilde))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
-print(clf.coef_, clf.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))
+print(clf.coef_, clf.intercept_)
 
 # The Ridge regression with a hyperparameter lambda = 0.1
 _lambda = 0.1
@@ -1258,15 +1258,15 @@ clf_ridge = skl
 yridge = clf_ridge.predict(X)
 EoS['Eridge']  = yridge
 # The mean squared error                               
-print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
+print("Mean squared error: %.2f" % mean_squared_error(Energies, yridge))
 # Explained variance score: 1 is perfect prediction                                 
-print('Variance score: %.2f' % r2_score(Energies, yridge))
+print('Variance score: %.2f' % r2_score(Energies, yridge))
 # Mean absolute error                                                           
-print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
-print(clf_ridge.coef_, clf_ridge.intercept_)
+print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))
+print(clf_ridge.coef_, clf_ridge.intercept_)
 
 fig, ax = plt.subplots()
-ax.set_xlabel(r'$\rho[\mathrm{fm}^{-3}]$')
+ax.set_xlabel(r'$\rho[\mathrm{fm}^{-3}]$')
 ax.set_ylabel(r'Energy per particle')
 ax.plot(EoS['Density'], EoS['Energy'], alpha=0.7, lw=2,
             label='Theoretical data')
@@ -1333,7 +1333,7 @@ DATA_ID = "
     return os.path.join(DATA_ID, dat_id)
 
 def save_fig(fig_id):
-    plt.savefig(image_path(fig_id) + ".png", format='png')
+    plt.savefig(image_path(fig_id) + ".png", format='png')
 
 def R2(y_data, y_model):
     return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
@@ -1361,16 +1361,16 @@ X_train, X_test, y_train, y_test = train_tes
 # matrix inversion to find beta
 beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)
 # and then make the prediction
-ytilde = X_train @ beta
-print("Training R2")
-print(R2(y_train,ytilde))
-print("Training MSE")
-print(MSE(y_train,ytilde))
-ypredict = X_test @ beta
-print("Test R2")
-print(R2(y_test,ypredict))
-print("Test MSE")
-print(MSE(y_test,ypredict))
+ytilde = X_train @ beta
+print("Training R2")
+print(R2(y_train,ytilde))
+print("Training MSE")
+print(MSE(y_train,ytilde))
+ypredict = X_test @ beta
+print("Test R2")
+print(R2(y_test,ypredict))
+print("Test MSE")
+print(MSE(y_test,ypredict))

@@ -1469,7 +1469,7 @@ It is now useful to look at the correlation matrix correlation_matrix = boston.corr().round(2) # use the heatmap function from seaborn to plot the correlation matrix # annot = True to print the values inside the square -sns.heatmap(data=correlation_matrix, annot=True) +sns.heatmap(data=correlation_matrix, annot=True)

From the above coorelation plot we can see that MEDV is strongly correlated to LSTAT and RM. We see also that RAD and TAX are stronly correlated, but we don't include this in our features together to avoid multi-colinearity @@ -1510,10 +1510,10 @@ We split the data into training and test sets # splits the training and test data set in 80% : 20% # assign random_state to any value.This ensures consistency. X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5) -print(X_train.shape) -print(X_test.shape) -print(Y_train.shape) -print(Y_test.shape) +print(X_train.shape) +print(X_test.shape) +print(Y_train.shape) +print(Y_test.shape)

Then we use the linear regression functionality from Scikit-Learn @@ -1532,11 +1532,11 @@ y_train_predict = lin_model= (np.sqrt(mean_squared_error(Y_train, y_train_predict))) r2 = r2_score(Y_train, y_train_predict) -print("The model performance for training set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) -print("\n") +print("The model performance for training set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2)) +print("\n") # model evaluation for testing set @@ -1547,10 +1547,10 @@ rmse = (np.# r-squared score of the model r2 = r2_score(Y_test, y_test_predict) -print("The model performance for testing set") -print("--------------------------------------") -print('RMSE is {}'.format(rmse)) -print('R2 score is {}'.format(r2)) +print("The model performance for testing set") +print("--------------------------------------") +print('RMSE is {}'.format(rmse)) +print('R2 score is {}'.format(r2))

@@ -2057,9 +2057,9 @@ Similarly, Mehta et a # print( (np.transpose(U) @ U - U @np.transpose(U))) # print('test VT') # print( (np.transpose(VT) @ VT - VT @np.transpose(VT))) - print(U) - print(s) - print(VT) + print(U) + print(s) + print(VT) D = np.zeros((len(U),len(VT))) for i in range(0,len(VT)): @@ -2069,14 +2069,14 @@ Similarly, Mehta et a X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ]) -print(X) -A = np.transpose(X) @ X -print(A) +print(X) +A = np.transpose(X) @ X +print(A) # Brute force inversion of super-collinear matrix #B = np.linalg.inv(A) #print(B) C = SVDinv(A) -print(C) +print(C)

The matrix \( \boldsymbol{X} \) has columns that are linearly dependent. The first @@ -2469,16 +2469,16 @@ function. n = 100 x = np.random.normal(size=n) -print(np.mean(x)) +print(np.mean(x)) y = 4+3*x+np.random.normal(size=n) -print(np.mean(y)) +print(np.mean(y)) z = x**3+np.random.normal(size=n) -print(np.mean(z)) +print(np.mean(z)) W = np.vstack((x, y, z)) Sigma = np.cov(W) -print(Sigma) +print(Sigma) Eigvals, Eigvecs = np.linalg.eig(Sigma) -print(Eigvals) +print(Eigvals)

@@ -2487,9 +2487,9 @@ Eigvals, Eigvecs = npimport matplotlib.pyplot as plt from scipy import sparse eye = np.eye(4) -print(eye) +print(eye) sparse_mtx = sparse.csr_matrix(eye) -print(sparse_mtx) +print(sparse_mtx) x = np.linspace(-10,10,100) y = np.sin(x) plt.plot(x,y,marker='x') @@ -3267,9 +3267,9 @@ number \( i \) is left out. Using this notation, define t[i] = stat(delete(data,i) ) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") - print("original bias std. error") - print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) + print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :") + print("original bias std. error") + print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5)) return t @@ -3430,9 +3430,9 @@ theorem. t[i] = statistic(data[randint(0,n,n)]) # analysis - print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") - print("original bias std. error") - print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) + print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :") + print("original bias std. error") + print("%8g %8g %14g %15g" % (statistic(data), std(data),mean(t),std(t))) return t @@ -3450,7 +3450,7 @@ lt = plt..xlabel('Smarts') plt.ylabel('Probability') plt.axis([99.5, 100.6, 0, 3.0]) -plt.grid(True) +plt.grid(True) plt.show() @@ -3726,7 +3726,7 @@ x_train, x_test, y_train, y_test = train_tes # Combine x transformation and model into one operation. # Not neccesary, but convenient. -model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) +model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) # The following (m x n_bootstraps) matrix holds the column vectors y_pred # for each bootstrap iteration. @@ -3743,13 +3743,13 @@ y_pred = np.# calculated per data point in the test set. # Note 2: The use of keepdims=True is important in the calculation of bias as this # maintains the column vector form. Dropping this yields very unexpected results. -error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) -bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) -variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) -print('Error:', error) -print('Bias^2:', bias) -print('Var:', variance) -print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) +error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) +bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) +variance = np.mean( np.var(y_pred, axis=1, keepdims=True) ) +print('Error:', error) +print('Bias^2:', bias) +print('Var:', variance) +print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance)) plt.plot(x[::5, :], y[::5, :], label='f(x)') plt.scatter(x_test, y_test, label='Data points') @@ -3789,21 +3789,21 @@ polydegree = np x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2) for degree in range(maxdegree): - model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) + model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) y_pred = np.empty((y_test.shape[0], n_boostraps)) for i in range(n_boostraps): x_, y_ = resample(x_train, y_train) y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel() polydegree[degree] = degree - error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) - bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) - variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) - print('Polynomial degree:', degree) - print('Error:', error[degree]) - print('Bias^2:', bias[degree]) - print('Var:', variance[degree]) - print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) + error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) ) + bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 ) + variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) ) + print('Polynomial degree:', degree) + print('Error:', error[degree]) + print('Bias^2:', bias[degree]) + print('Var:', variance[degree]) + print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree])) plt.plot(polydegree, error, label='Error') plt.plot(polydegree, bias, label='bias') @@ -3877,7 +3877,7 @@ You may also find this recent training data. """ -print(__doc__) +print(__doc__) import numpy as np import matplotlib.pyplot as plt @@ -3904,7 +3904,7 @@ plt.figure(figsize.setp(ax, xticks=(), yticks=()) polynomial_features = PolynomialFeatures(degree=degrees[i], - include_bias=False) + include_bias=False) linear_regression = LinearRegression() pipeline = Pipeline([("polynomial_features", polynomial_features), ("linear_regression", linear_regression)]) @@ -3923,7 +3923,7 @@ plt.figure(figsize.xlim((0, 1)) plt.ylim((-2, 2)) plt.legend(loc="best") - plt.title("Degree {}\nMSE = {:.2e}(+/- {:.2e})".format( + plt.title("Degree {}\nMSE = {:.2e}(+/- {:.2e})".format( degrees[i], -scores.mean(), scores.std())) plt.show() @@ -3965,7 +3965,7 @@ DATA_ID = " return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') @@ -3995,7 +3995,7 @@ trials = 100= 0.0 for samples in range(trials): x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2) - model = LinearRegression(fit_intercept=True).fit(x_train, y_train) + model = LinearRegression(fit_intercept=True).fit(x_train, y_train) ypred = model.predict(x_train) ytilde = model.predict(x_test) testerror[polydegree] += mean_squared_error(y_test, ytilde) @@ -4003,9 +4003,9 @@ trials = 100/= trials trainingerror[polydegree] /= trials - print("Degree of polynomial: %3d"% polynomial[polydegree]) - print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) - print("Mean squared error on test data: %.8f" % testerror[polydegree]) + print("Degree of polynomial: %3d"% polynomial[polydegree]) + print("Mean squared error on training data: %.8f" % trainingerror[polydegree]) + print("Mean squared error on test data: %.8f" % testerror[polydegree]) plt.plot(polynomial, np.log10(trainingerror), label='Training Error') plt.plot(polynomial, np.log10(testerror), label='Test Error') @@ -4054,7 +4054,7 @@ DATA_ID = " return os.path.join(DATA_ID, dat_id) def save_fig(fig_id): - plt.savefig(image_path(fig_id) + ".png", format='png') + plt.savefig(image_path(fig_id) + ".png", format='png') infile = open(data_path("EoS.csv"),'r') @@ -4169,7 +4169,7 @@ with their respective energies. import scipy.linalg as scl from sklearn.model_selection import train_test_split import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 @@ -4285,7 +4285,7 @@ X_test_own = np 

def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:
-    return scl.inv(x.T @ x) @ (x.T @ y)
+    return scl.inv(x.T @ x) @ (x.T @ y)
 beta = ols_inv(X_train_own, y_train)

@@ -4335,7 +4335,7 @@ linear system as an equation would reduce this down to 

def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:
     u, s, v = scl.svd(x)
-    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y
+    return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y

@@ -4411,7 +4411,7 @@ We will look at a system of \( L = 40 \) spins with a coupling constant of \( J from sklearn.model_selection import train_test_split import sklearn.linear_model as skl import tqdm -sns.set(color_codes=True) +sns.set(color_codes=True) cmap_args=dict(vmin=-1., vmax=1., cmap='seismic') L = 40 @@ -4665,7 +4665,7 @@ colors = { lambdas, train_errors[key], colors[key], - label="Train {0}".format(key), + label="Train {0}".format(key), linewidth=4.0 ) @@ -4674,7 +4674,7 @@ colors = { lambdas, test_errors[key], colors[key] + "--", - label="Test {0}".format(key), + label="Test {0}".format(key), linewidth=4.0 ) plt.legend(loc="best", fontsize=18) diff --git a/doc/pub/Regression/ipynb/Regression.ipynb b/doc/pub/Regression/ipynb/Regression.ipynb index 3b7248be4..452d9d097 100644 --- a/doc/pub/Regression/ipynb/Regression.ipynb +++ b/doc/pub/Regression/ipynb/Regression.ipynb @@ -10,9 +10,11 @@ " \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: **Dec 24, 2019**\n", + "Date: **Sep 7, 2020**\n", + "\n", + "Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", + "\n", "\n", - "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", "\n", "\n", @@ -378,161 +380,11 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
1AA^(2/3)A^(-1/3)1/A
A
11.01.01.0000001.0000001.000000
21.02.01.5874010.7937010.500000
31.03.02.0800840.6933610.333333
41.04.02.5198420.6299610.250000
51.05.02.9240180.5848040.200000
..................
2641.0264.041.1531060.1558830.003788
2651.0265.041.2569620.1556870.003774
2661.0266.041.3606880.1554910.003759
2691.0269.041.6710890.1549110.003717
2701.0270.041.7743000.1547200.003704
\n", - "

267 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " 1 A A^(2/3) A^(-1/3) 1/A\n", - "A \n", - "1 1.0 1.0 1.000000 1.000000 1.000000\n", - "2 1.0 2.0 1.587401 0.793701 0.500000\n", - "3 1.0 3.0 2.080084 0.693361 0.333333\n", - "4 1.0 4.0 2.519842 0.629961 0.250000\n", - "5 1.0 5.0 2.924018 0.584804 0.200000\n", - ".. ... ... ... ... ...\n", - "264 1.0 264.0 41.153106 0.155883 0.003788\n", - "265 1.0 265.0 41.256962 0.155687 0.003774\n", - "266 1.0 266.0 41.360688 0.155491 0.003759\n", - "269 1.0 269.0 41.671089 0.154911 0.003717\n", - "270 1.0 270.0 41.774300 0.154720 0.003704\n", - "\n", - "[267 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -671,7 +523,7 @@ "metadata": {}, "source": [ "$$\n", - "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}^T\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}^T\\boldsymbol{\\beta}\\right)\\right\\}.\n", + "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n", "$$" ] }, @@ -684,7 +536,7 @@ "\n", "\n", "It is also common to define\n", - "the function $Q$ as" + "the function $C$ as" ] }, { @@ -1035,13 +887,14 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 2, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "# matrix inversion to find beta\n", - "#beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)\n", - "beta = np.linalg.inv(X.T @ X) @ (X.T @ Energies)\n", + "beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)\n", "# and then make the prediction\n", "ytilde = X @ beta" ] @@ -1056,7 +909,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n", @@ -1073,21 +928,10 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "Masses['Eapprox'] = ytilde\n", "# Generate a plot comparing the experimental with the fitted values values.\n", @@ -1109,14 +953,16 @@ "source": [ "## Adding error analysis and training set up\n", "\n", - "We can easily test our fit by computing the $R2$ score that we discussed in connection with the functionality of _Scikit_Learn_ in the introductory slides.\n", - "Since we are not using _Scikit-Learn here we can define our own $R2$ function as" + "We can easily test our fit by computing the $R2$ score that we discussed in connection with the functionality of **Scikit-Learn** in the introductory slides.\n", + "Since we are not using **Scikit-Learn** here we can define our own $R2$ function as" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "execution_count": 5, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "def R2(y_data, y_model):\n", @@ -1132,17 +978,11 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.9547578478889096\n" - ] - } - ], + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "print(R2(Energies,ytilde))" ] @@ -1157,16 +997,10 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.037875961483052376\n" - ] - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "def MSE(y_data,y_model):\n", " n = np.size(y_model)\n", @@ -1185,28 +1019,10 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A \n", - "1 0 inf\n", - "2 1 1.123190\n", - "3 2 0.327631\n", - "4 6 0.344172\n", - "5 9 0.044402\n", - " ... \n", - "264 3304 0.009911\n", - "265 3310 0.009154\n", - "266 3317 0.007824\n", - "269 3338 0.011347\n", - "270 3344 0.009790\n", - "Name: Ebinding, Length: 267, dtype: float64\n" - ] - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "def RelativeError(y_data,y_model):\n", " return abs((y_data-y_model)/y_data)\n", @@ -1217,25 +1033,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We could also add the so-called Huber norm, which we defined as" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\n", - "H_{\\delta}(a)={\\begin{cases}{\\frac {1}{2}}{a^{2}}&{\\text{for }}|a|\\leq \\delta ,\\\\\\delta (|a|-{\\frac {1}{2}}\\delta ),&{\\text{otherwise.}}\\end{cases}}},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "with $a=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", - "\n", - "\n", "## The $\\chi^2$ function\n", "\n", "Normally, the response (dependent or outcome) variable $y_i$ is the\n", @@ -1601,35 +1398,10 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean squared error: 12.36\n", - "Variance score: 1.00\n", - "Mean absolute error: 2.83\n", - "[ 0. 618.32047562 -861.13519106 1404.91549644] -11.057088709963296\n", - "Mean squared error: 197.93\n", - "Variance score: 1.00\n", - "Mean absolute error: 11.69\n", - "[ 0. 28.18220995 282.79902342 842.30879705] 12.946893955209475\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "# Common imports\n", "import os\n", @@ -1746,24 +1518,11 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training R2\n", - "0.9999855660203318\n", - "Training MSE\n", - "6.9544863467435345\n", - "Test R2\n", - "0.9999823980708721\n", - "Test MSE\n", - "6.925606384101718\n" - ] - } - ], + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "import os\n", "import numpy as np\n", @@ -1815,7 +1574,7 @@ "X[:,3] = Density**(4.0/3.0)\n", "X[:,4] = Density**(5.0/3.0)\n", "# We split the data in test and training data\n", - "X_train, X_test, y_train, y_test = train_test_split(X, Energies, test_size=0.5)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n", "# matrix inversion to find beta\n", "beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)\n", "# and then make the prediction\n", @@ -1831,209 +1590,6 @@ "print(MSE(y_test,ypredict))" ] }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:72: RuntimeWarning: divide by zero encountered in log10\n", - "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:73: RuntimeWarning: divide by zero encountered in log10\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEICAYAAABS0fM3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3dd3hUVfrA8e+ZSS+kU0MJgVBDkSIgvSigLhYsKArqrl1X3VV31d111y3+VtddG6vYUEFFRQW7sjRBepHeCb2EACG9zJzfH2eCSUjPTO5M5v08T57J3Ln3zjsMmXfuKe9RWmuEEEL4L5vVAQghhLCWJAIhhPBzkgiEEMLPSSIQQgg/J4lACCH8nCQCIYTwc5YmAqVUtFLqY6XUdqXUNqXUQCvjEUIIfxRg8fM/D3yjtZ6olAoCwqraOT4+Xrdr165BAhNCiMZi7dq1J7XWCZU9blkiUEo1AYYCUwG01oVAYVXHtGvXjjVr1ng+OCGEaESUUvuretzKpqH2QDrwllJqvVLqdaVUuIXxCCGEX7IyEQQAFwD/1Vr3BnKA35XfSSl1u1JqjVJqTXp6ekPHKIQQjZ6VieAQcEhrvdJ1/2NMYihDaz1da91Xa903IaHSJi4hhBB1ZFki0FofAw4qpTq5No0CtloVjxBC+CurRw3dB8xyjRjaC9xicTxCCOF3LE0EWusNQF8rYxBCCH8nM4uFEMLP+UUi0AdXk7foX1aHIYQQXsnqPoIGsWbef+mXPof80BhCLrzV6nCEEMKr+MUVQfGYv7HE2YPAr3+DY+d8q8MRQgiv4heJYGBKC45c/Crbna0p/uAmOLrR6pCEEMJr+EUiALh+cFe+7vE8GY4QcmdcDZmHrA5JCCG8gt8kAoAHrhzGi83/gSM/i9y3roL8TKtDEkIIy/lVIgiw2/jdlKt5MvT3BJ7ZTf6syVBcZcFTIYRo9PwqEQBEhQVyz2238WfuIOTgEorm3gdaWx2WEEJYxu8SAUD7hAjG3vgb/l08kcBNH+Bc9LTVIQkhhGX8MhEADO4YT/z4J/ioeCi2xU/D+llWhySEEJbw20QAcNOgJDb3eYoljlSc8+6HPQutDkkIIRqcXycCgCd+0YN3Wv+Znc6WZo7B8S1WhySEEA3K7xNBoN3GvyYP5Q/hfyKjKJDidyfC2SNWhyWEEA3G7xMBmJFET98ylnv4HQXZp3F8eIuMJBJC+A1JBC7JCRH8+sareKb4OuyHVsDBldUfJIQQjYAkglKGdEygycCpnNYR5C58zupwhBCiQUgiKGfykC7Mcl5MyL7v4OQuq8MRQgiPk0RQTtMmIZzochNFOoCCH16wOhwhhPA4SQQVuG54H+Y4hmDf9AFkp1sdjhBCeJQkggp0axnF6paTsDmLcK581epwhBDCoyQRVGL88GHMd1xA8YrpUJhrdThCCOExkggqMapzUz4Pv5qgokzYIHWIhBCNlySCSthsin5Dx7PO2cF0GjsdVockhHdLWyZ/Jz5KEkEVru7Tmpm2XxCcdQC2fW51OEJ4rz0LYMZ4+OkDqyMRdSCJoArhwQE07X81aboZhUv+I2UnhKjMunfN7Y6vrI1D1IkkgmrcPCiZNx3jCTq+Hg4stzocIbxP7inY/gXYAk0p96J8qyMStSSJoBoto0PJ7nIdp3UkxUuftzocIbzPpo/BUQgjHoOiHEhbanVEopYkEdTAzUO78I5jDAG7voH0nVaHI4R3Wf8utOgJA+6GwDDY+Y3VEVVv1Wsw6xpY/E/YuxgKc6yOyFIBVgfgC3q1jub5FtdQkP4FgctfwvYLKT0hBABHf4JjG2H8sxAYAu1HmEQw/hlQyuroKuYohkVPQ3EB7Poe0KDs0Lw7tB4AbS40t1GtrI60wUgiqKFrhvbmo9lDmLThfRj5BEQ0tTokIay3fhbYg6H71eZ+yiWw40s4sRWadbM2tsrsXwa5J+Gat6H9cDi02pSdP7DCXN2sclUTaJL4c1JInQhhsVZG7VGSCGro4q7NuDHsKm4oXACrpptkIIQ/K8qHjbOhy2U/f0imXGJud3ztvYlg62emCavjxRAUBh3HmB8ARxEc3wwHVsLBFbB/OWyeA7vnw40fWhu3B0kfQQ0F2G2MGTKI7xx9KV75mt+3KQrBjq8g/wz0nvzztsjm0LI37PzWuriq4nSYOUEpl5gkUJ490MQ/4E64Zgb8ZhtceBfsXQgFWQ0ebkORRFAL1/ZrzbvqcgIKzphLYiH82fqZENUakoaV3Z4yzjS3eGPl3v3LICcdul5R82M6X2pGRe1Z6Lm4LCaJoBaahASS0m80a50pFC970XQ6CeGPMg+Z2cS9bgCbvexjncYCGnZ/b0loVdpSqlmoptoMgJAo3xgNVUeSCGrplkFJTHdcSsDZA7Bdyk4IP7XhPUCbRFBe8x4Q2dL0E3iTkmahkr6BmrIHQocxprmrkdZSsjwRKKXsSqn1SqkvrI6lJtrEhaE6jWM/LXAufUHKTgj/43SaZqGkoRDT7vzHlTJt8HsWQHFhg4dXqf0/Qs4J6FaLZqESncaZkUaH17o/Li9geSIAfg1sszqI2rh1SEemF43DdnSd+c8lhD/ZvxTO7IfeN1W+T8pYKMw2+3qLrZ9BQGjtmoVKdBhl5hp421WOm1iaCJRSicClwOtWxlFb/drFsL3ZZZxRTdArX7E6HCEa1vqZEBwFXS6vfJ/2w8yH7g4vaVd3OmDrPEi5GILCa398aAy0HdRo+wmsviL4D/AI4KxsB6XU7UqpNUqpNenp3jEKQSnFTUM682nRQJw7v23Uw8qEKCM/E7bOhdSrITC08v0CQ00y2PmNdzSfHlhumoVqM1qovJSxZqLc6f3ui8tLWJYIlFKXASe01lU2ummtp2ut+2qt+yYkJDRQdNUbn9qCH0OGYncUNNrLRSHOs3kOFOeXnTtQmZSxpgkpfbvn46rOFlezUMmEt7roNM7cNsKrAiuvCC4CfqGUSgM+AEYqpWZaGE+tBAXYaNtzOEd1LEU/fWR1OEI0jPUzoWlXaHlB9fuWfOha/cHpdMC2eWb2cF2ahUrEJUNcx0b5xc+yRKC1/r3WOlFr3Q64Hligta7B1wzvMb5nKz53DMS+dwHknbY6HCE86/hWM2qm9+SaFZRr0tJUJbW6n+DAcsg+XrfRQuWlXGLKbDey5mCr+wh8Wu/W0awMHYpNF8P2L60ORwjP2jALbAHQ47qaH5MyFg6tgpwMz8VVnS2fQUAIdKxHs1CJTuPAWWSGxjYiXpEItNaLtNaXWR1HbSmlaNdjCAd0U4o3zrE6HCE8p7jQrEfcaRyEx9f8uJSxoJ3WzTIu3SwUHFH/87UeACHR1l/luJlXJAJfNr5HS75wDMCWttjabz1CeNKub82EqqrmDlSkRS+IaG5dP8GBFaZZqD6jhUqzB5iksqtxzTKWRFBPvVtHszx0GDbtgG1zrQ5HCM9YPxMiW0DyqNodZ7OZsfu7/2fNLOOtrmahlLHuO2fKWMjNgENr3HdOi0kiqCebTdExdSB7dUtpHhKN09mjsOs76DnJfCOurZRxUHDWdNo2JKfTTCLrMNo9zUIlOow2fSU7G8/oIUkEbnBpzxZ87hiA/cCPkHXc6nCEcK+NH5h2/prMHahI+2FmFbOGbh46uAKyj0G3K9173tBoaDOwUfUTSCJwg96tY1geOgyF08y6FKKx0No0C7UZZMbR10VQuEkGO76u/SzjonwzbLUutnxmElB9JpFVptM4SN8Gp9Pcf24LSCJwA5tN0aVHP7brNhRv+tjqcIRwn4MrIWN33a8GSqRcAqf3wcldNT8m7wy88wv470BY81btns/pLDVaKLJ2x9ZESZ9DI7kqkETgJpemtuDz4gEEHFppFu0QojFY/y4ERUDXCfU7T8kHZ03b1XNOwtuXw+F10KoPfPGgaw2EGjq4ErKOum+0UHkls4wbqrkrY49HTy+JwE0uaBPDyjDXkn1bPrM2GCHcoTAHNn9q2tjr29kalQjNUmu2lvHZI/DWeDi5EyZ9AFO/gvbDYe49UNMr7q2uZqFObhwtVF6nsWaWcf5Zzz0HwNq34aV+sM1zS7ZIInATm02R2qM3m3USjk0yekg0ArvnQ1EOpF7jnvN1GmvG9eeeqnyfU/vgzbEmGUz+BDqOhsAQuP4900/xye1mJFBVnK6+ug6jPdMsVCKlAWYZL/0PfH4/JI80Px4iicCNSpqH7EfXNZpOJOHHtn0OobHQ9iL3nC9lLGiHmVNQkfQd8JZrqOmUudCu1PMGhcENsyGxL3x8a9Vt84dWmWYhd48WKq/1hWaWsSeah7SG7/8I8/8E3SeaRFib5TVrSRKBG13QJoZVJc1Dmz+xNhgh6qO4wDTjdB5ft7kDFWl5AYQnVNxPcGSDSQJOh2kKatXn/H2CI+DGj6B5Knx4U+UJZUsDNAuBa5bxxWaOhTtnGTsd5ipg2fPQ75dw1WsQEOS+81dAEoEb2WyKnqk9WK874tgkiUD4sL2LzTfzLvXsJC7NZjOF33bPB0fRz9sPrDAdw4FhcOs30Kxr5ecIiYKbPoGETvDBDbDvh7KPN1SzUIlOJbOMV7vnfMUF8NFUWPcODH0Yxj9r/t08TBKBm13aw9U8dGITnNxtdThC1M22uRDcxIz/d6dOY80qZwdWmPt7FsC7V0JEU5MEajJXITQGbpoLMUnw3nU/nwvMB3LWEfeUnK6JklnG7lijoCAb3rvWDHu95B8w8omalft2A0kEbtanTQyrwobgRMEWuSoQPshRDNu/MmP/A4Lde+72I8AeZNrVt39pPshjkuCWr83IopoKj4Mp88yaBzMnwiHXQoclo4XcWVuoKiFR7lnLOPeUmTOx7we44hUYeLd74qshSQRuZrMp+qZ2Z43ujEMmlwlftH8Z5J2qenH6ugqOgHZDzGzl2TdB8x4w9QtzRVBbEU1NMgiPg5lXmn6GLZ9Bh1EQ0sT9sVcmZZxZjvPUvrodf/aI6R85thmumwm9Jrk3vhqQROAB41NbMK94APaTO+o+PV4Iq2z73Kzv22G0Z87faRzknzHfpG/+DMJi636uJi1hyuemGeutcaZZyFOTyCpzbknOGsyRKC9jD7xxCWQehslzTOe8BSQReEDftjGsCR2ME5s0Dwnf4nSaRNBxdP3W961K75vgiv+aEUDu6NCNbmOuDEKiG2a0UHlxyRCfUvtqpEc3wpuXmLkaUz+HpCGeia8GJBF4gM2muDC1MyucXXFu+qT2hbaEsMrhNaZiZ5dfeO45AkOg1w0QGOq+c8a2h1/9z/Q1hES577w1lTIW0pbVbJbxmQPwzWNm4pw9GG75Blr29nyMVZBE4CHjU1swzzEA2+k9cGyj1eEIUTNb54It0DMVOz2tSUtIrGD+QUM4t5ZxJXMbwNRN+vhWeL4XrHzFNAPd9i0kpDRcnJWQROAhfdvFsiZ0MA7ssFlKTggfoLVpFmo/3Jpv1b4ssb8Z1lp+xrPTaUZgvTUeXhsBu743I4Ie2AhXv167kVIe5KYpg6I8u00xKLUjS9emMmTzp9hG/7nBxgQLUSfHNsKZ/TD0t1ZH4nvKzzJ2FMJP78Pyl00Z76jWcMnfTf9IQ45oqiG5IvCgktFDtswDcHit1eEIUbVtn4OyQSdrRq74vJSxZtjt3Hvh391M6ezgSLj6Dbh/Awy8xyuTAEgi8Kh+7WJZEzqIIgKl9pDwflvnmQJz4fFWR+KbOowyk+V+et8UpJv6FfxqIaROdF+9Jg/x7uh8nN2mGJqazOL1PRm55RNsF/+1QeqGCFFr6Tvg5A5T5EzUTUiUa9RSNMR3sDqaWpFPJQ8bn9qCeUUDsGUdNYtpC+GNtrlq/He5zNo4fF1iX59LAiCJwOP6J8WyPnQAhSrItMEK4Y22zoPEfmYIpvA7kgg8zG5TDE9NYpWzM87K6qcLYaXTaWbEkCcnkQmvJomgAYxPbcGi4lRsJ3eYmiJCuNvp/XD2aN2OLblS9USROeETJBE0gP5JsfwU5JpCvnehtcGIxufkbnh1CEwfbipZ1ta2z82qX7FJbg9N+AZJBA3AblMkJPfmJNHoPZIIhBvlnYb3rzOLoxRmw/uToDC35sefPQoHV0qzkJ+rcvioUuqCGpyjSGu9yU3xNFoDk+NZvKM7V+xegN3plGGkov4cRWZZw9P7TSnm/DMmEcy9Gya+VbOZ7Nu/MLeSCPxadfMIFgOrgar+RyUB7dwVUGM1MDmOlxw9uDp/qemYa9nL6pCEr/vm97B3EUx4GdoONNtGPwnz/wQJXWD4o9WfY9s8U0K5aWcPBiq8XXWJYLXWemRVOyilFrgxnkYrOSGC7WEXQDGmn0ASgaiP1a/D6tdg4L3Qe/LP2y/6tVkta9HfTVXLbldWfo6cDFM6efADno9XeLUq2yeqSwI13UeAUoqU5A7spC16j+ROUQ97F8FXj0DHS2DMX8o+phRc9h9TDfPTu+DI+srPs+Mr0A5pFhJVJwKl1ORSv19U7rF7PRVUYzUwOY6Fxd1h/woozLE6HOGLMvbAh1NMc87Vr4PNfv4+gSFw/SxTM+j9GyDrWMXn2va5Wd2rRU/Pxiy8XnU9lg+V+v3Fco/dWp8nVkq1VkotVEptU0ptUUr9uj7n8wWDkuP4wdkD5SyE/T9aHY7wNXln4L3rTIXQSe9XXckyoqnZJ/8MfHADFOWVfTz/rGmi7PILKY8uqk0EqpLfK7pfW8XAb7TWXYABwD1Kqa71PKdXaxMbxqGIHqbchAwjFbXhKIaPbzGzgK+bWbMx/81T4arppgT63HvLLpm66ztTM18mkQmqTwS6kt8rul8rWuujWut1rt+zgG1Aq/qc09sppbigQ0vW6s7STyBq57vHYc8CuOw5aHdR9fuX6HI5jPwDbP4Yfnj25+1b50JEM9OXIPxedYmgs1Jqo1JqU6nfS+53clcQSql2QG9gpbvO6a0GJcezoKg7Kn1b3WaBCv+z5i2zxu2Ae+CCm2t//JDfQOq1sOCvprhcYS7sng+dL5P5LAKofvhoF08HoJSKAOYAD2itz1bw+O3A7QBt2rTxdDgeNzA5jtedPYD3zOiPXjdYHZLwZvuWwFe/hQ5j4OKn6nYOpeAXL8KpPfDpHTDofijKha4yWkgY1Q0f3V/6B8gGLgDiXffrRSkViEkCs7TWFS7hpbWerrXuq7Xum5CQUN+ntFyr6FDyYjqRaY8xl/pCVCZjD3x4M8Qmw8Q3Kh4hVFOBIXD9e2aB9cVPm9u2tWhiEo1adcNHv1BKdXf93gLYjBkt9K5Sql6zUJRSCngD2Ka1fq4+5/I1A5PjWeLobuoOOZ1WhyO81RcPmtsbPjCrX9VXZHOTDALDzEQze2D9zykaheoaCJO01ptdv98CfK+1vhy4kHoOHwUuAm4CRiqlNrh+/GLV7IHJcSwo7I7KPQnHpUyTqMCxzbBvsZkpHNvefedt2Qt+/ROMfdp95xQ+r7o+gqJSv48CXgMzykcpVa+vslrrpdR/CKpPGtg+jr86u5s7exbKhB5xvhX/Nd/cL5ji/nNHNHX/OYVPq+6K4KBS6j6l1JWYvoFvAJRSoYBcV9ZR0yYhNElI5GBgkvQTiPNln4BNH0LPSRAWa3U0wg9UlwhuA7oBU4HrtNZnXNsHAG95MK5Gb2ByHPMLu6EPLK9d/XjR+K1+w0z2GnCX1ZEIP1HdqKETWus7tdYTtNbfldq+UGv9bFXHiqqdm0/gKIQDUm5CuBTlw5o3TEG5+I5WRyP8RHUL08yr6nGttQxErqMB7eN40NmZYhVEwJ6F0GG01SEJb7D5Y8hJh4F3Wx2J8CPVdRYPBA4C72Nm/fpl564nxIYHkdQ8jm153UiVfgIBphbQ8mnQtBskDbM6GuFHqusjaA48BnQHngfGACe11ou11os9HVxjNzA5jq9zu8KJrZWXChb+Y98SOLHF9A1IRVDRgKrrI3Borb/RWk/BdBDvBhYppe5rkOgauYHt41hU3M3ckWqkYsU0CE+A1GusjkT4mWorTimlgpVSVwEzgXuAF4AKy0GI2rmwfRw7aENOYKwMI/V3J3fDzm+g722mHIQQDai6zuK3Mc1CXwN/LjXLWLhBVGggXVvGsC6nJ0P2uspNSDVI/7Tyv2APgn63WR2J8EPVfercBKQAvwZ+VEqddf1kKaXOqxQqam9gchzzsjubkSIntlgdjrBC3mnY8J5pEpJZv8IC1fUR2LTWka6fJqV+IrXWVayTJ2pqYHIci4tLyk1I85BfWvu2KQstE8iERaQdwmL92sWSYYslPTRZEoE/chTBqumQNNQsLSmEBaorQ72uuhPUZB9RuYjgAHokRvEjPWD/8vMXGReN27Z5cPYwDJAJZMI61a5QppTaWMXjCnBDoXT/Nig5js+WpDAh8FPY/yN0GGV1SKKhLJ9mFp7peInVkQg/Vl0i6FyDczjcEYg/G9g+njcWdsYZHIhtzwJJBP7i4Co4vAbGPyujxYSlqkwEJctRKqWaAa0ADRzRWh9vgNj8Rp+2MTjsIeyP6EmSTCzzH8tfNiuP9ZxkdSTCz1U3j6AX8Aqm+eewa3OiUuoMcLfWWvoH3CA0yE7vNjEsykwl6cRbptxEZHOrwxKedOaA6R8YeC8ER1gdjfBz1V2PzgB+rbXuorUe7frpDDyArEfgVgPbxzHnjKvs8N5F1R+gNRzfAhveN6WLhW9ZNR1QcOEdVkciRLV9BOFa65XlN2qtVyilwj0Uk18amBzHC/9rQ2FwLEF7FkDP68/fKe+MSRK758Pu/0HWEbN913dw9RvSzuwrCrJh7TvQdQJEJVodjRDVJoKvlVJfAu9gylEDtAZuxrVspXCP3m2iCQoIYHt4X3rsWWi+8WsNxzbC7u/NB//BVaAdEBwFycPNGgaZh2Dx/0FcMox8wuqX0Xgc2QCzroHJc6BFD/eee8MsKMiEgfe497xC1FF1ncX3K6XGARMwncUKOAS8rLX+qgHi8xvBAXb6tovh+4wu9Mj7DmZPNh/8OSfMDi16wuAHzYd/Yj+wu946reHsEVjyDMS2h143WPciGpPVr5l/+wV/hRs/dN95nU6zMH1iP0js677zClEP1V0RoLX+GlN0TnjYoOR4ZuxO4aGIENT+ZZA8ynzwdxhVeQ0apeCyf5vOx3n3Q1RrSBrSsIE3NgXZsOUzCImGXd/CoTXu+9De+Q2c3gej/uie8wnhBnVuVFZKTXdnIMIsX5lODN+PWwIP74GJb0CvSdUXIrMHwrXvQGySuZI4ubthAm6sts6FwmzT7xIWBwv/7p7zOophwVMQ3Ra6yCqvwntUV2IitpKfOGB8A8XoN3okRhEWZGfJwUKw2Wt3cGg03PAh2ALgvWsgJ8MzQfqDDbPMbN8Oo+CiB2DP/+DAivqfd93bZjW6i5/6uWlPCC9Q3RVBOrAGWFvqZ43rR+rlulmg3Ub/pFiW76njh3hsElz/HmQeNlcGxQXuDdAfZOyB/cug942m2a3fLyG8KSz8W/3Om3fGnKPtYLkaEF6nukSwFxiutU4q9dNea50EyOxiDxjYPo496TmcOFvHuQFtLoQrpsGBH2HefaYzWdTchvdA2X6e7RsUZjrp9y2BfT/U/byL/wm5p2DsP2Q9YuF1qrs+/Q8QAxyo4LF/uj8cMTA5DoCR/1pMeLCdkEA7oYHmNiTQRmigndAgOyEBdkKC7DSNDOZXQ9oTHlzqrUydCKf2wcK/miaO4Y9a9Gp8jNMBP70PySOhScuft/e9BZY9D4v+Ae0G1/6D/OQuWPUqXHCz+4eiCuEG1Q0ffbmKx150fzgitVUUj4/vwuEzeeQXOcgrcrhuneQXOjiZXXhuW36RuZ9b6OCx8V3Knmjob+HUHlj0dzOstIcsiF6tvYtMSehLyjUDBYbCkN/A1w/DvsXQfnjtzvvt4xAQKvM8hNeqUY+Va/H68jKBTVrrE+4Nyb8ppfjV0PY13v/hj35ixrI0bhrQltaxYaVPBJc/b4aVzr0boltDmwEeiLgR2TALQmOgUwXjIC64GZb9x4wgShpW86uC3fPNENQxf5FlKIXXqunw0duA14EbXT+vAQ8By5RSN3koNlEDv72kE3ab4ulvtp//YEAwXDfTzC344AY4tbfhA/QVeadh2xdm3eCA4PMfDwwxVwUHV5pZ3jXhKIZvHoOYJLjwTvfGK4Qb1TQROIEuWuurtdZXA12BAuBCQBqgLdSsSQi3D23PlxuPsnb/6fN3CIuFGz8C7YRZ10JhbsMH6Qs2fQyOAuh1Y+X79L4JotqY0T816YRf8yac3GGamipKLkJ4iZomgnbl1iA4AaRorU8BRe4PS9TGHcPa0zQymL9+uRVd0QdUXDJc9Tpk7IJNbiyX0JhsmAXNUk0pj8oEBMGwh+HIOtj5bdXnyz1l+meShlXc1CSEF6lpIvhBKfWFUmqKUmoKMA9Y4qpAesZz4YmaCAsK4DcXp7D+wBm+3HS04p06jIJm3WHldBlSWt7xLXBk/c9zB6rScxLEtKv+qmDR05CfKcNFhU+oaSK4B7P+QC+gN/A2cI/WOkdrPcJTwYmam9inNZ2bR/J/32ynoLiC1UOVgv63w4ktZsKU+Nn6WWALhNRrq9/XHgjDHjVVYbd/WfE+J7bD6tehz1Ro1s2toQrhCTVKBNq0NywFFgDzgSW6wjYIYRW7TfH4pV04eCqPt39Mq3in1GtMIbWVrzZobF6tuBA2zoZOYyE8rmbHpF5r5mcs/LupJlqa1vDtYxAUASMed3+8QnhAjRKBUupaYBUwEbgWWKmUmujJwETtDemYwPBOCby4YDencgrP3yEozAyD3P6lWcdAmKGduSdNR3BN2QNg+O/M1dW2ueXO952pTTT8UQiPd2+sQnhITZuGHgf6aa2naK1vBvoDf6jvkyulxiqldiildiulflff8wl4bHwXcgqKeeF/uyreod8vAQ2r32jQuLzW+lkQ0dyU/K6N7ldDfIrpC3C6muIcReZqIK4D9PuV+2MVwkNqmghs5SaOZdTi2AoppezAy8A4zHDUSUqprvU5p4CUZpFc378NM1fsZ2969vk7xLSFlHGmEqa/r3Wcddx8g+95Xe2rgdrs5qogfTts+dRsW/UaZOyGS/5uRhgJ4SNq+mH+je0a/FYAACAASURBVFLqW6XUVKXUVOBLoL4rlPUHdmut92qtC4EPMCuhiXp6cHQKwQE2nv66gklmABfeDrkZsOWThg3M22ycbZb+7DW5bsd3vRKadjU1iLJPwOKnzZVFx4vdG6cQHlbTzuKHgelAD6AnMF1rXd+JZK34eR1kMEtgtqrnOQWQEBnM3SM68N3W46zYW0FJ66RhkNDZdBr7a5+/1rB+JiT2h4SUup3DZoPhvzdXATMuNSubXfJ3GS4qfE6Nm3e01nO01g9prR/UWn/qhueu6K/lvE8lpdTtSqk1Sqk16enpbnha/3DrRUm0iArhb19uw+ks98+qFPT/FRzdAIdWWxOg1Q6vNbN+e1cxk7gmOl8GzVPh5E7odxs07eye+IRoQNWtUJallDpbwU+WUupsPZ/7ENC61P1E4Ej5nbTW07XWfbXWfRMSEur5lP4jNMjOw5d0YtPhTOb+dPj8HXpcD8FR/juUdP1MUxG0W0X1FGvBZoNx/zRrSw//vXtiE6KBVZkItNaRWusmFfxEaq2b1PO5VwMdlVJJSqkg4HrMjGXhJlf0akVqqyie+WYH+UXlJpkFR5hvw1s/g6xj1gRolcJc2DwHuk6AkPr+NwbaDoLJc0xdJyF8UL1G/tSH1roYuBf4FtgGfKi13mJVPI2RzTXJ7EhmPm8s3Xf+Dv1+aYY+rnmr4YOz0vYvoOBs/ZuFhGgkLEsEAFrrr7TWKVrrZK11PReFFRUZ0D6OMV2bMW3hbtKzyq1hHJcMHcfA2rfMDFt/sX4mRLcx6wcLIaxNBKJh/H5cZwqKnfx7/s7zH+x/O2Qfh61zz3+sMTq936wy1utG074vhJBE4A/aJ0QweUBbPlh1gD3lJ5kljzJ1c1ZNtya4hvbT+4CCXjdYHYkQXkMSgZ+4d2QHggJsvLxwd9kHbDYzlPTQKlOK2ZO++T18OAUy9nj2eSrjdJp1B5KGmqYhIQQgicBvxEcEM/nCtszdcIS0kzllH+x1AwSGm7UKPGXTx7BiGmz7HF6+EL7/IxRkee75KrLiZbOGc58pDfu8Qng5SQR+5Pah7QmwKaYtKndVEBIFvSaZIZU5J93/xGePwJcPQWI/eGAT9LgWlj0PL/YxRd/Kl3L2hAMrYf6T0OUX9Z87IEQjI4nAjzRtEsKk/m34ZN1hDp4qt3Zx/9vNmr1rZ7j3SbWGufeYypxXvgpRreCKafCrBaZ5Zu7d8PooOOjBGc45GfDxLRCVCBNekhIQQpQjicDP3DksGZtSTFtUrp0+oRO0H24WXHcUu+8JV78OexbAxX81w1VLtOoDt35nksPZI/DGaPjkdjhbyVKbdeV0wqd3QE46XPO2ufoRQpQhicDPNI8K4bp+rfl47UEOn8kr+2D/O+DsYTPhyh1O7oLv/gAdxkDfW89/3GaDntfDfWth8EOmnPOLfWDJs+4rkb3sP7D7e1MMrmUv95xTiEZGEoEfunO4+Wb+SvmrgpRLTHONO4aSOorMN/zAkOqbY4IjYPSf4J6VkDwCFjwFL/c3VxL1sf9HWPBX0yfQ75f1O5cQjZgkAj/UKjqUiX1aM3v1QY5llvrmbbOblbX2L4Njm+v3JD/8C46sg8v+DZHNa3ZMbHu4fhbc9BkEBMO7V8GSZ+rWmZydDh/fCjHt4PLnpV9AiCpIIvBTdw9Pxqk1rywud1XQe7Kpylmfq4LDa2HxP80i792urP3xySPg9kWQOtF8o589GfIza3680wmf3g65p+CaGe4pLCdEIyaJwE+1jg3jqgta8f6qA5w4W+qqICzWLN24YZb5MK9tDaLCXPjkDnMVMP6ZugcYFA5XvQZjnzYLzL82Ek5UsuJaeT/8yzQrjfs/aNGj7jEI4SckEfixe0Z0oNipmb5kb9kHRj9pSjQv/BtMHwaH1tT8pPOfhIxdZohoaHT9AlQKBtwFUz6H/LMmGWypZk2kfUtg0d8h9RroM7V+zy+En5BE4MfaxoUzoVdLZq7cz8nsUpVJQ2Ng4pswabZpknl9NHz9O7MUY1X2LIBVr8KFd5mhqG4LdBDcsQSadYOPpsJ3T1Q8xDXrOHx8m6mddNl/pF9AiBqSRODn7hnRgcJiJ6/9sPf8BzuNhbtXmBE3K/8L0wbC7vkVnyjvNHx2D8R3MiOA3K1JC5j6pYnlxxfh3SvKzoJ2OmDObaZsxbVvm5FIQogakUTg55ITIri8Z0veXb6fUzkV9AeENIFLn4VbvzVDQWdebfoAcjLK7vflbyHnBFz1KgSGeibYgCC49F9wxX/NWsuvDjMd0wCL/w/SfjD9Es26eeb5hWikJBEI7h3RgbwiB28sreCqoESbAXDHDzD0Edj8MbzcDzZ+ZEpIbJ5jtg17FFr29nzAvW6A274zE9LeHGuqmi7+J/ScZEY9CSFqRWmtrY6hxvr27avXrKlFx6WosXveW8fiHeksfXQE0WFBVe98fAvMu898G+8w2nQmx3UwVw32gIYJGMzw0Dm3mb6JhM6mflFQeMM9vxA+Qim1Vmvdt7LH5YpAAHDfyA5kFxTz5rK06ndu1g1u+x4u+YeZvesohKumN2wSADPU9caPYcI0uPEjSQJC1FED/+UKb9W5eRPGdmvOW8v2cdvgJKJCA6s+wGaHgXebYaYFWWULyjUkm10WoReinuSKQJxz36gOZOUX8/aPaTU/KKoVNO3ssZiEEJ4niUCc061lFKO7NOONpfvIyi+yOhwhRAORRCDKuH9UBzLzinhn+X6rQxFCNBBJBKKMHonRjOiUwCuL9jB79QGcTt8ZVSaEqBtJBOI8f5nQnU7NI3l0ziYmvLyMtftPWR2SEMKDJBGI87SODeOjOwfy/PW9SM8q4Or/LueBD9aXXbtACNFoSCIQFVJKMaFXKxb8dhj3jujAV5uPMeLZRby0YBf5RQ6rwxNCuJEkAlGlsKAAfntJJ+Y/OIyhKfE8+91Oxvx7Md9sPoYvzUoXQlROEoGokTZxYbx6U19m3nYhoYF27py5lslvrGTn8SyrQxNC1JPUGhK1VuxwMnPFfp77fic5hQ5Gd2lKfEQwESEBRAYHEBEcQERIIBHBAUSGmB+zLYC48GDsNlknQIiGVF2tISkxIWotwG5j6kVJXN6zJf+ev5Mfdp0kO/80WQXFFBZXvdB8XHgQl/VowYTerejdOholi8cIYTm5IhBuVVDsIKfAQXZ+MVkFRWTnF5NdUExWfjFZ+UWs2HeK+VuPU1DspG1cGBN6teKKXi1pnyALyQjhKdVdEUgiEA3ubH4R32w+xtwNh/lxTwZaQ8/EKK7o3YrLerQkITLY6hCFaFQkEQivdiwzn89/OsKn6w+z9ehZ7DbF4A7xXNG7JWO7tSA0yG51iEL4PEkEwmfsPJ7FZ+sPM3fDEQ6fyaNVdCh/urwrF3drbnVoQvg0r0wESqlngMuBQmAPcIvW+kx1x0ki8A9Op+bHPRk89cVWdhzPYnSXpvzp8m60jg2zOjQhfJK3rlD2PdBda90D2An83qI4hBey2RSDO8bzxf2DeWx8Z37ck8GYfy/m5YW7qx2VJISoPUsSgdb6O611sevuCiDRijiEdwu027h9aDLzHxrGsJQEnvl2B+Nf+IHlezKsDk2IRsUb5hHcCsyu68FFRUUcOnSI/HwpiOZNQkJCSExMJDCwmiUva6BldCiv3tSXBduP88e5W5j02gqu6t2Kxy7tQnyEjDASor481keglJoPVNTL97jWeq5rn8eBvsBVupJAlFK3A7cDtGnTps/+/WUXTNm3bx+RkZHExcXJ5CQvobUmIyODrKwskpKS3HruvEIHLy/czatL9hAaaOeRsZ2Z1L+NzFYWogpe2VkMoJSaAtwJjNJa59bkmIo6i7dt20bnzp0lCXgZrTXbt2+nS5cuHjn/7hPZ/OGzzSzfm0HP1tGM6tyU0EA7IUF2QgPthLluQwLthAb9fD88OIDY8CCPxCSEt/LKEhNKqbHAo8CwmiaBas5X/6CEW3n6PenQNIL3fnUh8346wj++2s5z3++s8bF928Zw2+AkLu7WXK4khMC6PoKXgGDge9cHxgqt9Z0WxVIvGRkZjBo1CoBjx45ht9tJSEgAYNWqVQQFVf7tc82aNbzzzju88MILVT7HoEGD+PHHH+sd66JFi5gwYUKZ5ppnn32W0aNH1/vcVihZM2FCr1YUOZzkFznIK3KQX+gkz/V7bmGx2e7advxsPh+sPsBds9aRGBPK1EHtuK5fayJD6t+XIYSv8vkJZdu2bfNY80NtPfnkk0RERPDb3/723Lbi4mICAryhT94kgmeffZYvvvii0n201mitsdlsFd6vjMPhwG4vOwvYm96b0hxOzfdbj/HG0n2sTjtNRHAA1/ZtzS0XtZO5CqJR8tZ5BI3a1KlTeeihhxgxYgSPPvooq1atYtCgQfTu3ZtBgwaxY8cOwHwwX3bZZYBJIrfeeivDhw+nffv2Za4SIiIizu0/fPhwJk6cSOfOnbnxxhvPLQ7z1Vdf0blzZwYPHsz9999/7rw1kZaWRpcuXbj77ru54IIL+OGHH8rcP3jwIA8//DDdu3cnNTWV2bNnn4tnxIgR3HDDDaSmprrl364h2G2Ksd1b8NGdg5h370WM6tKUd5anMeyZhdzx7hpW7Tsli+4Iv+IdX1Xd5M+fb2HrkbNuPWfXlk340+Xdan3czp07mT9/Pna7nbNnz7JkyRICAgKYP38+jz32GHPmzDnvmO3bt7Nw4UKysrLo1KkTd91113nDL9evX8+WLVto2bIlF110EcuWLaNv377ccccdLFmyhKSkJCZNmlRpXD/88AO9evU6d3/OnDnY7XZ27NjBW2+9xbRp00hLSytzf86cOWzYsIGffvqJkydP0q9fP4YOHQqY5q/Nmze7fXRQQ+mRGM3z1/fm9+O68M7yNN5bdYBvtxwntVUUNw9sS6fmkTSNDCEuIohAu3xvEo1To0oE3uSaa64511SSmZnJlClT2LVrF0opioqKKjzm0ksvJTg4mODgYJo2bcrx48dJTCw7165///7ntvXq1Yu0tDQiIiJo3779uQ/jSZMmMX369AqfY8iQIec1DaWlpdG2bVsGDBhwblvp+0uXLmXSpEnY7XaaNWvGsGHDWL16NU2aNKF///4+mwRKax4VwiNjO3PfyI7MWXeIN5ft4+GPN5bZJzY8iISIYBIig2kaaW5LflpEhdK9VRPCguRPSvieRvW/ti7f3D0lPDz83O9/+MMfGDFiBJ9++ilpaWkMHz68wmOCg3+eHGW32ykuLq7RPu5oxigdb/n7VZ2//HG+LjTIzuQBbbmhfxu2Hj3L0cx80rMKSM8q4ESW6/fsAlal5XAiq6BMyYsAm6Jbyyb0aRtL33Yx9G0bQ9MmIRa+GiFqplElAm+VmZlJq1atAJgxY4bbz9+5c2f27t1LWloa7dq1O9eG7y5Dhw7l1VdfZcqUKZw6dYolS5bwzDPPsH37drc+jzex2RTdW0XRvVVUpftorTmbX0x6VgEHTuWwdv9pVqedZtbK/by5bB8AbWLD6Ns2hj7tYujXLpYOCRHYZMiq8DKSCBrAI488wpQpU3juuecYOXKk288fGhrKtGnTGDt2LPHx8fTv37/Sfcv3ETzxxBP07VvpYAIArrzySpYvX07Pnj1RSvHPf/6T5s2bN+pEUBNKKaJCA4kKDaRD0whGdm4GQGGxky1HMlmTdpo1+0+xZFc6n6w/DEBUaCB928YwqEM8F3WIo1OzSJkHIywnw0cbiezsbCIiItBac88999CxY0cefPBBS2OS98bQWrM/I5fVaadYu/80K/edYt/JHADiI4IYlGySwkUd4kmMkeGrwv28cmaxcL/XXnuNt99+m8LCQnr37s0dd9xhdUjCRSlFu/hw2sWHc03f1gAcPpPHst0n+XH3SZbuzmDeT0cAaBsXxqDkeAZ3iGdgcpyUwxANQq4IhMfIe1MzWmt2nchm2e6TLNt9khV7T5FdYAYKtI4NJTYsiKiwIKJDA4kOCyQ6NLDs/bBAokKDSIwJJSRQlvYU55MrAiG8nFKKlGaRpDSL5JaLkih2OPnpUCY/7j7JrhPZZOYVcSaviAMZOZzJKyIzr4iKvr+FBdkZ0akpY7s3Z0TnpkQEy5+3qBn5nyKElwmw2+jTNoY+bWMqfNzp1GTlF3M6t5AzeUWcyS3kdG4hq9NO892WY3y56ShBATaGdoznkm7NGdO1GdFh0sQkKieJQAgfY7MposICiQorO+v8yt6JPDWhO2v3n+brzUf5dvMx5m87gd2mGNg+jrHdm3Nxt2Y0jZS5DaIsSQRCNCJ2m6J/Uiz9k2L542Vd2XQ4k683H+Obzcd44rPN/GHuZvq2jaF3mxhaRYeanxjz00QqsPotSQT1VJ8y1GAKtwUFBTFo0KDzHpsxYwYPP/zwucloAO+99x5du3Z14ysQjZVSih6J0fRIjOaRSzqx60Q2X286xndbjzHjx7Qys6IBIoMDTFIoSQ6u29YxYbSJDSM6LFDmPDRSkgjqKS4ujg0bNgAVl6GuzqJFi4iIiKgwEQBcd911vPTSS5UeX778c0XloCviTeWxheeV7pD+9eiOaK05mV3I4TN5HD6dx+Ezua7bPA6dzmNV2imy8suWOIkMDqB1bBitY0NpE2uSQ2vXT2JMKMEBMmLJV8kngQesXbuWhx56iOzsbOLj45kxYwYtWrTghRde4JVXXiEgIICuXbvy9NNP88orr2C325k5cyYvvvgiQ4YMqfb8ixYt4s9//jMtWrRgw4YNTJs2rcz9devWcdddd7FmzRoCAgJ47rnnGDFiBDNmzODLL78kPz+fnJwcFixY0AD/GsIbKaXOFczr1Tq6wn3O5hdx+HQeB0/lcuBULodO53HgVC570nNYtCOdglJXFEpBiyYhdG7RhO6tokhtFUWPxCiaSa0ln9C4EsHXv4Njm9x7zuapMO7pGu+utea+++5j7ty5JCQkMHv2bB5//HHefPNNnn76afbt20dwcDBnzpwhOjqaO++8s8qriNmzZ7N06dJz95cvXw6ULf+8aNGiMvf/9a9/AbBp0ya2b9/OxRdfzM6dO88dv3HjRmJjY+v6LyL8RJOQQJq0CKRLiybnPeZ0ak5mF3DAlSQOnMol7WQOW46cZdGOEzhdw1sTIoNJddVsSnX9NGsSLE1MXqZxJQIvUFBQwObNmxkzZgxgmmpatGgBQI8ePbjxxhu54ooruOKKK2p0vsqahsqXfy59f+nSpdx3332AKUjXtm3bc4lgzJgxkgREvdlsiqZNQmjaJIS+7cr+f8otLGbrkbNsOpzJpsOZbD6cWSY5xEcE07VlE5pGBhMXHkRMeBCx4UHEhgURGxF0bltkcIAkjAbSuBJBLb65e4rWmm7dup375l7al19+yZIlS5g3bx5PPfUUW7ZsqfPzSNlo4a3CggLo2y62TILILSxm29GzbDqUyabDZ9lx/Cy7jmeRkVN4Xqd1iSC7jZjwQKJDgwgNshMaaD93GxJoJzTIZrYF2gkptT3Apgi02wiwKwJsNgLtP98PtNvOPR4SaCMsKIDw4ADCg+wE+PHCQ40rEXiB4OBg0tPTWb58OQMHDqSoqIidO3fSpUsXDh48yIgRIxg8eDDvvfce2dnZREZGcvase1dVGzp0KLNmzWLkyJHs3LmTAwcO0KlTJ9atW+fW5xGipsKCAujTNpY+bctePWityS10cCqnkIycQk67bk/lFHAqp4hTOQWcyS0ir8hBfpGDE1lF5BU6yC9yklfkIK/QQV6Rwy0xBgfYiAg2iSEsyH7u9/BgO1GhQSREBBEfGUxceDDxrt/jw4NpEur7Vy6SCNzMZrPx8ccfc//995OZmUlxcTEPPPAAKSkpTJ48mczMTLTWPPjgg0RHR3P55ZczceJE5s6dW2Fncfk+gmnTplUbw913382dd95JamoqAQEBzJgxo8yCNkJ4C6WU68PWjEiqC601BcVOkyCKHRQ7NEUOJ8VO163rfpFDU+z8+X5+sZPcgmKyC4rJKXCQW1jyezE5hQ5yCoo5k1fE4TN5nMkt5FRO4bnmrdKC7DbiIoKIiwgiPiKY2LAgosOCiA4LJCbM1IWKCTNXNiW1oSK8rNlLis4Jj5H3RjQmDqfmdG4hJ7MLOJlVSEaOWbnuZHYhGdkFZnu2KfdxJrfoXOHAigTYzFoWAXaFTSkUJikqhblfcosZkaWU4h9XpdKvXd3696TonBBCuIHdpoiPCCY+IhiaV79/kcPJmdwiMvMKOZ1bxJncIk7nFpLpuj2TV4TDodFonBq0Nlc3GnBqjdauW8z2sCDPzdOQRCCEEB4QaLedm6vh7fy3m1wIIQTQSBKBL/Vz+At5T4TwHT6fCEJCQsjIyJAPHi+itSYjI4OQECkvIIQv8Pk+gsTERA4dOkR6errVoYhSQkJCSExMtDoMIUQN+HwiCAwMLFNqQQghRO34fNOQEEKI+pFEIIQQfk4SgRBC+DmfKjGhlEoH9pfaFA+ctCgcT2usr01el+9prK/Nn15XW611QmUH+FQiKE8ptaaq+hm+rLG+NnldvqexvjZ5XT+TpiEhhPBzkgiEEMLP+XoimG51AB7UWF+bvC7f01hfm7wuF5/uIxBCCFF/vn5FIIQQop58NhEopcYqpXYopXYrpX5ndTzuopRKU0ptUkptUEqtqf4I76WUelMpdUIptbnUtlil1PdKqV2u2xgrY6yLSl7Xk0qpw673bYNSaryVMdaFUqq1UmqhUmqbUmqLUurXru0+/Z5V8boaw3sWopRapZT6yfXa/uzaXqv3zCebhpRSdmAnMAY4BKwGJmmtt1oamBsopdKAvlprnx/frJQaCmQD72itu7u2/RM4pbV+2pXAY7TWj1oZZ21V8rqeBLK11s9aGVt9KKVaAC201uuUUpHAWuAKYCo+/J5V8bquxfffMwWEa62zlVKBwFLg18BV1OI989Urgv7Abq31Xq11IfABMMHimEQ5WuslwKlymycAb7t+fxvzB+lTKnldPk9rfVRrvc71exawDWiFj79nVbwun6eNbNfdQNePppbvma8mglbAwVL3D9FI3ljMm/idUmqtUup2q4PxgGZa66Ng/kCBphbH4073KqU2upqOfKr5pDylVDugN7CSRvSelXtd0AjeM6WUXSm1ATgBfK+1rvV75quJQFWwzffauCp2kdb6AmAccI+rGUJ4v/8CyUAv4CjwL2vDqTulVAQwB3hAa33W6njcpYLX1SjeM621Q2vdC0gE+iulutf2HL6aCA4BrUvdTwSOWBSLW2mtj7huTwCfYprBGpPjrjbbkrbbExbH4xZa6+OuP0gn8Bo++r652pnnALO01p+4Nvv8e1bR62os71kJrfUZYBEwllq+Z76aCFYDHZVSSUqpIOB6YJ7FMdWbUirc1ZmFUiocuBjYXPVRPmceMMX1+xRgroWxuE3JH53Llfjg++bqeHwD2Ka1fq7UQz79nlX2uhrJe5aglIp2/R4KjAa2U8v3zCdHDQG4hnr9B7ADb2qt/2ZxSPWmlGqPuQoAs3rce778upRS7wPDMdUQjwN/Aj4DPgTaAAeAa7TWPtXxWsnrGo5pYtBAGnBHSRutr1BKDQZ+ADYBTtfmxzDt6T77nlXxuibh++9ZD0xnsB3zxf5DrfVflFJx1OI989lEIIQQwj18tWlICCGEm0giEEIIPyeJQAgh/JwkAiGE8HOSCIQQws9JIhBeRynlcFWD3KyU+kgpFVbFvlOVUi81ZHylnvsvSqnR1ewzQyk1sZp92pWuZCpEQ5NEILxRnta6l6uyZyFwp9UBVURr/Uet9Xyr4yjNVZlXiFqRRCC83Q9AB1d99c9cBcJWuCbSnKOUilRK7XOVEkAp1USZtR0ClVKLlFL/56rbvlMpNcS1T4hS6i1l1n9Yr5Qa4do+1fVcn7vOea9S6iHXPiuUUrGu/c5921dK/VEptdp1FTPdNZu1UkqpPq4a8suBe0pttyulnnGda6NS6g7XdptSapoyNee/UEp9Veq501zPvxS4Ril1sVJquVJqneuKKqLUcy52FTT8ttzMWuHHJBEIr6WUCsAU39sE/BlYr7XugZkV+k7pfV3lhRcBl7o2XQ/M0VoXue4HaK37Aw9gZgKD6wNYa52KmWX6tlIqxPVYd+AGTP2ZvwG5WuvewHLg5grCfUlr3c91FRMKXFbNy3sLuF9rPbDc9tuATK11P6Af8CulVBKmvnw7IBX4JVD+uHyt9WBgPvAEMNpVvHAN8JArQb4ITNRa9wHedL0uIQiwOgAhKhDqKqsL5orgDUyZg6sBtNYLlFJxSqmocse9DjyCKWNxC/CrUo+VFFBbi/lABRiM+XBEa71dKbUfSHE9ttCVXLKUUpnA567tm4AyVyMuI5RSjwBhQCywpdQxZbjijtZaL3ZteheT8MDUl+pRql8hCujoivUjV4G0Y0qpheVOO9t1OwDoCixzXZQEYZJXJ0xy+9613Y6puCmEJALhlfJcZXXPqaSppUx9FK31MlfH6zDArrUu3QFb4Lp18PP/+6qabwpK/e4sdd9Jub8b11XENMzKcgeVWa0shMqp8rGXe+w+rfW35Z7j0kr2L5FT6vjvtdaTyh2fCmyp4ApECGkaEj5jCXAjgFJqOHCyklr57wDvY5peanPOFEyBrh11iK3kQ/+kqz2+ylFCrnLBma5iaJTE4PItcFepvo4UZSrRLgWudvUVNMMUuavICuAipVQH1/Fhrte2A0hQSg10bQ9USnWr7QsVjZNcEQhf8STwllJqI5DLzyV2y5sF/BWTDKozDXhFKbUJKAamaq0LqunnPY/W+oxS6jVMs1Eapkx6dW4B3lRK5WI+/Eu8jmm6Wue6CkrHLDM4BxiFKZW8E9NUlllBLOlKqanA+0qpYNfmJ7TWO13NTS+4mqYCMNV7t9TqxYpGSaqPikbF9WE3QWt9k9WxuJtSKsK1SHkcsAqzj8WbtAAAAFZJREFUmt0xq+MSvk+uCESjoZR6EdPpOt7qWDzkC2UWIQkCnpIkINxFrgiEEMLPSWexEEL4OUkEQgjh5yQRCCGEn5NEIIQQfk4SgRBC+DlJBEII4ef+H+TjJHSqHa0qAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Common imports \n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.utils import resample\n", - "from sklearn.metrics import mean_squared_error\n", - "# Where to save the figures and data files \n", - "PROJECT_ROOT_DIR = \"Results\"\n", - "FIGURE_ID = \"Results/FigureFiles\"\n", - "DATA_ID = \"DataFiles/\"\n", - "\n", - "if not os.path.exists(PROJECT_ROOT_DIR):\n", - " os.mkdir(PROJECT_ROOT_DIR)\n", - "\n", - "if not os.path.exists(FIGURE_ID):\n", - " os.makedirs(FIGURE_ID)\n", - "\n", - "if not os.path.exists(DATA_ID):\n", - " os.makedirs(DATA_ID)\n", - "\n", - "def image_path(fig_id):\n", - " return os.path.join(FIGURE_ID, fig_id)\n", - "\n", - "def data_path(dat_id):\n", - " return os.path.join(DATA_ID, dat_id)\n", - "\n", - "def save_fig(fig_id):\n", - " plt.savefig(image_path(fig_id) + \".png\", format='png')\n", - "\n", - "infile = open(data_path(\"EoS.csv\"),'r')\n", - "\n", - "# Read the EoS data as csv file and organize the data into two arrays with density and energies \n", - "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n", - "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n", - "EoS = EoS.dropna()\n", - "Energies = EoS['Energy']\n", - "Density = EoS['Density']\n", - "# The design matrix now as function of various polytrops \n", - "\n", - "Maxpolydegree = 30\n", - "X = np.zeros((len(Density),Maxpolydegree))\n", - "X[:,0] = 1.0\n", - "testerror = np.zeros(Maxpolydegree)\n", - "trainingerror = np.zeros(Maxpolydegree)\n", - "polynomial = np.zeros(Maxpolydegree)\n", - "trials = 100\n", - "for polydegree in range(1, Maxpolydegree):\n", - " polynomial[polydegree] = polydegree\n", - " for degree in range(polydegree):\n", - " X[:,degree] = Density**(degree/3.0)\n", - "\n", - "# loop over trials in order to estimate the expectation value of the MSE \n", - " testerror[polydegree] = 0.0\n", - " trainingerror[polydegree] = 0.0\n", - " for samples in range(trials):\n", - " x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n", - " model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n", - " ypred = model.predict(x_train)\n", - " ytilde = model.predict(x_test)\n", - " testerror[polydegree] += mean_squared_error(y_test, ytilde)\n", - " trainingerror[polydegree] += mean_squared_error(y_train, ypred)\n", - "\n", - " testerror[polydegree] /= trials\n", - " trainingerror[polydegree] /= trials\n", - "# print(\"Degree of polynomial: %3d\"% polynomial[polydegree])\n", - "# print(\"Mean squared error on training data: %.8f\" % trainingerror[polydegree])\n", - "# print(\"Mean squared error on test data: %.8f\" % testerror[polydegree])\n", - "\n", - "plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n", - "plt.plot(polynomial, np.log10(testerror), label='Test Error')\n", - "plt.xlabel('Polynomial degree')\n", - "plt.ylabel('log10[MSE]')\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.3155976363078281 0.23757342963159728\n", - "0.0787018918847104 0.06930516292030015\n", - "0.10722101293279243 0.04511656503567851\n", - "0.06201683426091631 0.028151617239178105\n", - "0.061553763561113806 0.028147674082776217\n", - "0.05180713869664486 0.02060255094046228\n", - "0.03998787703985385 0.015790440504624886\n", - "0.025488444196422547 0.011449129463020355\n", - "0.01102027119693583 0.006966589766559406\n", - "0.011604235570598458 0.006794797700077043\n", - "0.010565611128562757 0.005640710472162157\n", - "0.012017287100241886 0.005510647881654117\n", - "0.012434158910648174 0.005494366141070369\n", - "0.016448736882635217 0.004851325080751917\n", - "0.016527602132524744 0.00485116638755085\n", - "0.01700730692292449 0.004643005417799459\n", - "0.016363523213895396 0.0044421082615854045\n", - "0.02589431662771564 0.003955552768646823\n", - "0.0345227493961171 0.0036008586465254297\n", - "0.030648702911583114 0.003575160894214332\n", - "0.024075703870136637 0.0020989162180337336\n", - "0.02313833139471145 0.0020604302851563927\n", - "0.022843671413936378 0.0017142543146276665\n", - "0.0290322923187248 0.0015508854597766758\n", - "0.02002383737785974 0.0014734766087485915\n", - "0.015172806063794947 0.0014540292316795322\n", - "0.016705582402610115 0.0013171827913206382\n", - "0.4103173112183884 0.0009008366033628069\n", - "0.0684274290287695 0.0008539361078357445\n", - "1.721345521028988 0.0007853710802684171\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de5hcVZnv8e9b1bf0JdfuXEgnJCHhEkgI0EYlSJJREBQGnBkG0BnwNhlUmBGPZ1B8Rhk9c8bR8cyIoDmMwzD6CKIigkcUBIkQ0SEd5JIACTF0SBNIOkknqUpS1XV5zx9V1al0+lLdqa5b/z7PU8++rr3Xrp28e/Xaq9Yyd0dERCpfoNgZEBGRwlDAFxEZIxTwRUTGCAV8EZExQgFfRGSMqCp2BvrT3Nzsc+bMKXY2RETKxvr163e7e8tg+5RkwJ8zZw7t7e3FzoaISNkws21D7aMqHRGRMUIBX0RkjBiySsfM7gQuAXa5+xn9bP+fwAeyjnca0OLue82sAwgBCSDu7m35yriIiAxPLnX4dwG3Ad/pb6O7fxX4KoCZXQrc6O57s3ZZ6e67jzOfxGIxOjs7iUQix3soyVJXV0drayvV1dXFzoqIjLIhA767P2Fmc3I83tXAPceToYF0dnbS1NTEnDlzMLPROMWY4+7s2bOHzs5O5s6dW+zsiMgoy1sdvpnVAxcB92WtduARM1tvZquGSL/KzNrNrL2rq+uY7ZFIhClTpijY55GZMWXKFP3VJDJG5POl7aXAb/pU5yxz97OBi4FPmNn5AyV29zvcvc3d21pa+m9KqmCff/pORcaOfAb8q+hTnePuO9LTXcD9wNI8nk9EpGL88sWdrP71H0b1HHkJ+GY2AVgOPJC1rsHMmjLzwIXAhnycr9D27NnDkiVLWLJkCdOnT2fmzJm9yz09PUOmX7NmDU899VS/2+666y5aWlp6j7dkyRJefPHFfF+CiJS4R1/cyV2/6RjVc+TSLPMeYAXQbGadwBeAagB3X53e7X3AI+5+MCvpNOD+dJVBFXC3u/8if1kvnClTpvDss88CcMstt9DY2MinP/3pnNOvWbOGxsZGzj333H63X3nlldx2220Dpk8kEgSDwQGXBxKPx6mqKskfU4tIH6FojMa60f3/OmQJ392vdvcZ7l7t7q3u/h/uvjor2OPud7n7VX3SbXX3M9Of0939H0fjAopl/fr1LF++nHPOOYd3v/vdvPHGGwDceuutLFy4kMWLF3PVVVfR0dHB6tWr+dd//VeWLFnCk08+mdPx16xZw8qVK3n/+9/PokWLjlmORCJ86EMfYtGiRZx11lk8/vjjQOovhiuuuIJLL72UCy+8cNSuX0TyKxSJ01g7ugG/LIt///DTjby440Bej7nwhPF84dLTc9rX3bnhhht44IEHaGlp4d577+Vzn/scd955J1/+8pd59dVXqa2tZd++fUycOJHrrrtu0L8K7r33XtauXdu7/Nvf/haAp59+mg0bNjB37lzWrFlz1PLXvvY1AF544QVefvllLrzwQjZv3tyb/vnnn2fy5MnH85WISAGFo3GaRrmEX5YBv9ii0SgbNmzgggsuAFJVLDNmzABg8eLFfOADH+Dyyy/n8ssvz+l4A1XpLF269Kj28dnLa9eu5YYbbgDg1FNP5cQTT+wN+BdccIGCvUiZCUfizJhQN6rnKMuAn2tJfLS4O6effnpvSTzbz372M5544gkefPBBvvSlL7Fx48YRn6ehoWHA5cEGn++bTkRKXzg6+lU66jxtBGpra+nq6uoN+LFYjI0bN5JMJtm+fTsrV67kK1/5Cvv27SMcDtPU1EQoFMprHs4//3y+973vAbB582Zee+01TjnllLyeQ0QKJxyJ01g7ul2cKOCPQCAQ4Ec/+hE33XQTZ555JkuWLOGpp54ikUjwF3/xF70vUm+88UYmTpzIpZdeyv333z/gS9t77733qGaZAzXhzPbxj3+cRCLBokWLuPLKK7nrrruora0djcsVkVGWTDrhnviot9KxwaoGiqWtrc37DoDy0ksvcdpppxUpR5VN361IcYUiMRbd8gife89p/NX580Z0DDNbP1SPxCrhi4gUWTgaByh+O3wRERld4Ugq4I92s0wFfBGRIgtlSvhqpSMiUtlUwhcRGSN66/DVLFNEpLJlSvij/dK2LH9pW2h79uzhne98JwBvvvkmwWCQzCAtTz/9NDU1NQOmbW9v5zvf+Q633nprzuebM2cOTU1NvT1inn/++cNKLyLl5UAkBox+Hb4Cfg6G6h55sG6I29raaGsbtGlsvx5//HGam5sH3N73nLl2hZxr18oiUjhhvbQtbR/84Af51Kc+xcqVK7npppt4+umnOffccznrrLM499xz2bRpE5Dq5viSSy4BUg+LD3/4w6xYsYJ58+YNu9S+YsUKbr75ZpYvX87Xv/71Y5Yfe+wxzjrrLBYtWsSHP/xhotEokPqL4Ytf/CLnnXceP/zhD/P7RYjIcQtH4jTUBAkGRnfI0fIs4f/8M/DmC/k95vRFcPGXh5Vk8+bNPProowSDQQ4cOMATTzxBVVUVjz76KDfffDP33XffMWlefvllHn/8cUKhEKeccgof+9jHqK4+9kXNypUre0vi1157LTfeeCMA+/bt49e//jUAP/3pT3uXI5EICxYs4LHHHuPkk0/mmmuu4Vvf+haf/OQnAairqzuqC2YRKR3h6Oh3qwDlGvBLxBVXXNEblPfv38+1117LK6+8gpkRi8X6TfPe976X2tpaamtrmTp1Kjt37qS1tfWY/Qaq0rnyyiv7Xd60aRNz587l5JNPBlIPidtvv7034PdNJyKlI1SAnjKhXAP+MEvioyW7G+K///u/Z+XKldx///10dHSwYsWKftNkd3AWDAaJx+MjPmf28lB9IqnLZJHSFY7Eaawb3SaZoDr8vNm/fz8zZ84EUsMMFtqpp55KR0cHW7ZsAeC73/0uy5cvL3g+RGT4QpEYTQUo4Svg58nf/d3f8dnPfpZly5aRSCSO+3grV67s7S75mmuuGXL/uro6/vM//5MrrriCRYsWEQgEuO666447HyIy+gox+Ank0D2ymd0JXALscvcz+tm+AngAeDW96sfu/sX0touArwNB4NvunlNdjLpHLix9tyLFde4/Pca585v5lyvOHPEx8tU98l3ARUPs86S7L0l/MsE+CNwOXAwsBK42s4U5nE9EZEwJFWAAc8gh4Lv7E8DeERx7KbDF3be6ew/wfeCyERxHRKRiuTvhaLys6vDfbmbPmdnPzSwzwvhMYHvWPp3pdf0ys1Vm1m5m7V1dXf3uU4qjc5U7facixXWoJ4H76PejA/kJ+M8AJ7r7mcA3gJ+k1/f3k7EBo4u73+Hube7elumnJltdXR179uxRgMojd2fPnj3U1dUVOysiY1ahesqEPLTDd/cDWfMPmdk3zayZVIl+VtaurcCOkZ6ntbWVzs5OBir9y8jU1dX1+8MvESmMUKbjtHL4pa2ZTQd2urub2VJSfzXsAfYBC8xsLvA6cBXw/pGep7q6mrlz5x5vdkVESkooM/hJKfzS1szuAVYAzWbWCXwBqAZw99XAnwEfM7M4cBi4ylP1LnEzux54mFSzzDvdfeOoXIWISJkq1ADmkEPAd/erh9h+G3DbANseAh4aWdZERCpfoYY3BP3SVkSkqAo1gDko4IuIFFVvCb8ArXQU8EVEiihTh99QO/oj0Sngi4gUUSgSY1x1kKrg6IdjBXwRkSIq1GhXoIAvIlJUoUhh+tEBBXwRkaIKF6inTFDAFxEpqtTwhgr4IiIVr1CjXYECvohIUYUi8YL0lAkK+CIiRRWKxFSHLyJS6TKjXalKR0Skwh2OJUh6YTpOAwV8EZGiyfSjo1Y6IiIVrpA9ZYICvohI0RSyL3xQwBcRKZrM8IZqlikiUuHC0fQA5qrSERGpbKFSq9IxszvNbJeZbRhg+wfM7Pn05ykzOzNrW4eZvWBmz5pZez4zLiJS7jKDn5RMwAfuAi4aZPurwHJ3Xwx8Cbijz/aV7r7E3dtGlkURkcqUeWnbUKAqnSHP4u5PmNmcQbY/lbX4O6D1+LMlIlL5wtE4ddUBqgsw2hXkvw7/I8DPs5YdeMTM1pvZqsESmtkqM2s3s/aurq48Z0tEpPSEooXrOA1yKOHnysxWkgr452WtXubuO8xsKvBLM3vZ3Z/oL72730G6Oqitrc3zlS8RkVIVihRu8BPIUwnfzBYD3wYuc/c9mfXuviM93QXcDyzNx/lERCpBOBIrWJNMyEPAN7PZwI+Bv3T3zVnrG8ysKTMPXAj029JHRGQsKmRPmZBDlY6Z3QOsAJrNrBP4AlAN4O6rgc8DU4BvmhlAPN0iZxpwf3pdFXC3u/9iFK5BRKQshSJxZk+uL9j5cmmlc/UQ2z8KfLSf9VuBM49NISIikC7hl1sdvoiIDF84GqepnOrwRURk+NydcEQlfBGRiheJJYknvaDt8BXwRUSKIJTpKVMlfBGRytY7+Inq8EVEKluhe8oEBXwRkaLoHcBcJXwRkcrWO4C5SvgiIpXtSB2+WumIiFS0UEStdERExoTMS9uG2mDBzqmALyJSBKFonJqqALVVCvgiIhUtHIkzvoDVOaCALyJSFIXuCx8U8EVEiqLQHaeBAr6ISFGEIirhi4iMCaFovKA9ZYICvohIUYSjsYL2owMK+CIiRRFWlY6ISOVz99TwhqVWwjezO81sl5ltGGC7mdmtZrbFzJ43s7Oztl1kZpvS2z6Tz4yLiJSraDxJLOEl2UrnLuCiQbZfDCxIf1YB3wIwsyBwe3r7QuBqM1t4PJkVEakEvX3hl1qVjrs/AewdZJfLgO94yu+AiWY2A1gKbHH3re7eA3w/va+IyJgWihS+a2TITx3+TGB71nJnet1A6/tlZqvMrN3M2ru6uvKQLRGR0nRk8JPya5Zp/azzQdb3y93vcPc2d29raWnJQ7ZEREpT7wDmBa7SycfZOoFZWcutwA6gZoD1IiJjWu/gJ2VYpfMgcE26tc7bgP3u/gawDlhgZnPNrAa4Kr2viMiYVowBzCGHEr6Z3QOsAJrNrBP4AlAN4O6rgYeA9wBbgEPAh9Lb4mZ2PfAwEATudPeNo3ANIiJlJRPwS65Kx92vHmK7A58YYNtDpB4IIiKSVs6tdEREZBhCkTg1wcKOdgUK+CIiBReOxgpeugcFfBGRgitGx2mggC8iUnDF6DgNFPBFRAquGKNdgQK+iEjBqYQvIjJGhKMq4YuIjAmhSFytdERExoJUK53C9pQJCvgiIgUVjSfoSSRVhy8iUumK1VMmKOCLiBRUsTpOAwV8EZGC6u04TQFfRKSyFaunTFDAFxEpqN7BT9RKR0SksoUz49mqhC8iUtnCqsMXERkbQkUazxYU8EVECiociVMdNGqrCh9+FfBFRAoo03GamRX83DkFfDO7yMw2mdkWM/tMP9v/p5k9m/5sMLOEmU1Ob+swsxfS29rzfQEiIuWkWB2nAQx5VjMLArcDFwCdwDoze9DdX8zs4+5fBb6a3v9S4EZ335t1mJXuvjuvORcRKUOhInWcBrmV8JcCW9x9q7v3AN8HLhtk/6uBe/KRORGRShOOxmgqQgsdyC3gzwS2Zy13ptcdw8zqgYuA+7JWO/CIma03s1UDncTMVplZu5m1d3V15ZAtEZHyE44Wr0onl4Df35sFH2DfS4Hf9KnOWebuZwMXA58ws/P7S+jud7h7m7u3tbS05JAtEZHyE44UZ3hDyC3gdwKzspZbgR0D7HsVfapz3H1HeroLuJ9UFZGIyJhUrOENIbeAvw5YYGZzzayGVFB/sO9OZjYBWA48kLWuwcyaMvPAhcCGfGRcRKQclXQrHXePm9n1wMNAELjT3Tea2XXp7avTu74PeMTdD2Ylnwbcn25vWgXc7e6/yOcFiIiUi554kmg8WbSXtjmd1d0fAh7qs251n+W7gLv6rNsKnHlcORQRqRDFHPwE9EtbEZGC6e04ra502+GLiEgehDJdI6uELyJS2TIl/PEl3CxTRETyoLcOXwFfRKSy6aWtiMgYcaCIA5iDAr6ISMFk6vCLMYA5KOCLiBRMOBojGDDqqosTehXwRUQKJBwp3mhXoIAvIlIwoWjxesoEBXwRkYLJlPCLRQFfRKRAQkXsCx8U8EVECqaYfeGDAr6ISMGkhjcsTpNMUMAXESmYkOrwRUTGhnA0VrSO00ABX0SkIGKJJJFYUiV8EZFKd7DIPWWCAr6ISEGEIsXtKRNyDPhmdpGZbTKzLWb2mX62rzCz/Wb2bPrz+VzTioiMBZmAX8x2+EOe2cyCwO3ABUAnsM7MHnT3F/vs+qS7XzLCtCIiFe1IX/il3SxzKbDF3be6ew/wfeCyHI9/PGlFRCpGODOebYnX4c8Etmctd6bX9fV2M3vOzH5uZqcPM62ISEUriyodoL9+PL3P8jPAie4eNrP3AD8BFuSYNnUSs1XAKoDZs2fnkC0RkfKRqdJpKvGXtp3ArKzlVmBH9g7ufsDdw+n5h4BqM2vOJW3WMe5w9zZ3b2tpaRnGJYiIlL5wkYc3hNwC/jpggZnNNbMa4CrgwewdzGy6pXv0N7Ol6ePuySWtiMhYEIrECRiMqw4WLQ9DPmrcPW5m1wMPA0HgTnffaGbXpbevBv4M+JiZxYHDwFXu7kC/aUfpWkRESlamp8xijXYFudXhZ6ppHuqzbnXW/G3AbbmmFREZa1J94RevSSbol7YiIgURjsaK+itbUMAXESmIcJHHswUFfBGRgghH4kVtoQMK+CIiBVHswU9AAV9EZNjuW9/JJ7//+2GlCalKR0Sk/Nzbvp2fPLuDHfsO55wmrBK+iEh56YkneW77PgDWbtmdU5p4IsnhWKKoPWWCAr6IyLBs3LGfaDwJwG9yDPgHowmguN0qgAK+iMiwtHd0A7Bs/hR+s2U3yWS//UEeJZTuGll1+CIiZaR9215OnFLP+85qZXe4h007Q0OmKYWeMkEBX0QkZ+5Oe0c355w4iWXzpwC5VeuESqCnTFDAFxHJWceeQ+w52EPbiZOZMWEcJ7U08OQrQwf8cAkMYA4K+CIiOVvXsReAt8yZBMA7FrTw9Kt7icYTg6YLRYs/2hUo4IuI5Gx9RzcTxlVzUksjAMvmN3M4luCZbfsGTXekhK9mmXkTjSd6X46IiORb+7a9tJ04iUAg1af92+ZNJhiwIevxS2EAc6iggH8wGufcf/oV//7E1mJnRUQq0N6DPfyh6yDnpKtzAJrqqlkya+KQP8AKR+KYQUNN8Ua7ggoK+A21VSw8YTw/Wt9JIod2sSIiw7F+W6r9/VvmTD5q/bL5zTzfuY/9h2IDpg2VwGhXUEEBH+DKt8zi9X2Hc/71m4hIrto79lITDLBo5oSj1r9jQTNJh99u3TNg2lAkXvQ2+FBhAf+ChdOYWF/ND9q3FzsrIlJh2rd1c8bM8dT1GYR8yayJNNQEWbula8C0pdAXPlRYwK+tCnL5kpk8snEn3Qd7ip0dEakQkViCFzr3H1OdA1AdDPDWeVP4zZaBS/iZAcyLLaeAb2YXmdkmM9tiZp/pZ/sHzOz59OcpMzsza1uHmb1gZs+aWXs+M9+fK98yi55Ekp88+/pon0pExogXXt9PTyLJOSdO6nf7efObeXX3QTq7D/W7PRSN01jkAcwhh4BvZkHgduBiYCFwtZkt7LPbq8Byd18MfAm4o8/2le6+xN3b8pDnQZ02YzyLWydw77rtuOvlrYgcv0yHaQMG/AXNwMDdLIQjsbKpw18KbHH3re7eA3wfuCx7B3d/yt2704u/A1rzm83huaJtFi+/GWLD6weKmQ0RqRDtHXuZ19LAlMbafrcvmNrI1KZa1g5QrVMKA5hDbgF/JpD9FrQzvW4gHwF+nrXswCNmtt7MVg2UyMxWmVm7mbV3dQ388mNQh/dB5AB/fOYJ1FYFuLf9tZEdR0QkLZl01r/WTdsApXsAM+O8+c08NUB3yaUw2hXkFvD7azjab12Jma0kFfBvylq9zN3PJlUl9AkzO7+/tO5+h7u3uXtbS0tLDtnq4/A++MbZ8OTXmDCumvcsmsEDz+4gEhu8jwsRkcFs3R1m36EYbf28sM22bH4zew728NKbR9csJJLOwZ5E2bTS6QRmZS23Ajv67mRmi4FvA5e5e+/fNe6+Iz3dBdxPqooo/8ZNhPnvgv9eDfs7uaKtlVAkzi82vDkqpxORsWFduv5+sBI+DFyPn+nupVxK+OuABWY218xqgKuAB7N3MLPZwI+Bv3T3zVnrG8ysKTMPXAhsyFfmj7Hyc+BJePyfeNvcKcyeXM+969QmX0RGrr2jmykNNcxtbhh0v2nj61gwtfGY7pLDJdJTJuQQ8N09DlwPPAy8BPzA3Tea2XVmdl16t88DU4Bv9ml+OQ1Ya2bPAU8DP3P3X+T9KjImnQhLV8FzdxPoeok/b2vlt1v3sG3PwVE7pYhUtvZteznnxEk5dYtw3oJm1nXsPaoquVR6yoQc2+G7+0PufrK7n+Tu/5het9rdV6fnP+ruk9JNL3ubX6Zb9pyZ/pyeSTuq3vE/oKYJHr2FPz2nlYDBj9Z3jvppRaTy7ApF2LbnUL8/uOrPefObicSSPPNad++6cImMZwsV9ktbAOonwztuhFceZkb3es4/uUUdqonIiKzPtL+fM3j9fcZb502hKmCszarWKZXhDaESAz7AW6+D8TPhl5/nynNaeWN/hCdeGWFTTxEZs9q3dVNbFeCMEyYMvTOpF7NnzZ541IvbTMAvlx9elZ/qcbDyZnh9Pe/id0xuqOEHenkrIsPUvq2bM2dNpKYq91C5bH4zz7++n32HUv159bbSUQl/FJ15NUxdSPWaL/GnZ07j0Zd2siccLXauRKRMHO5JsPH1/UM2x+zrHQuacYff/iHVOr1UBjCHSg74gSC86xbYu5WPNvyaWMK5//fqUE1EcvPs9n3Ek57zC9uMxa0Taayt6h0FKzOAeUONAv7oWnAhnHge09b/G2+bWcMP2tWhmojkpr1jLwBnzx5eCb86GOBt8yb3BvxMtwqZcXCLqbIDvhlc8EU4tJvPTXqUzTvDPNe5v9i5EpEy0L6tm5OnNTKhfvjt58+b38y2PYfYvvcQ4WisJJpkQqUHfIDWc2Dh5Zyx7bvMqg7pl7ciMqRE0nnmte4h+88ZSKabhbVbdpfM4CcwFgI+wDs/jyWi/HPzQ/z0uR0c6okXO0ciUsI27wwRisSH/cI246SWRqaPr2Ptlt2ESmR4QxgrAX/KSXDOh3j7vv/H1J7XeOiF3DtU66+rUxGpbO3bUj+4Gu4L2wwzY1m6u+QDh2Mq4Rfc8pugehy31N835CDnu8NR7l33Gh+5ax2nfv4XXPqNtfzyxZ164SsyRrR37GVqUy2tk8aN+BjnLZhC96EYL75xoGTq8EsjF4XQ2IKd+zecv+Z/828dv+PV3YuP6v2uY/dBfvniTh558U3at3XjDq2TxvFn57Sy9pXd/NV32jn9hPH8zTsXcOHCaTl1pCQi5am9o5u2Obl1mDaQZfNT9fixhJdMCb80clEob/8Eiaf/nc8m7+Hepy/iPYtn8MjGVJDfvDMMwMIZ4/nbdy7gwoXTOW1GE2ZGLJHkJ79/ndse38Jff3c9p80Yz9/80Xzeffr0kmhqJSL588b+w7y+7zAfOW/ucR1nalMdp0xrYtPOUEn0lAljLeDXNhJc+Vne8rNP8X/X/pA/fuIcggFj6ZzJfOHS2bzrtGnMmlx/TLLqYIAr2mbxvrNm8uBzO7jtV1v42Pee4dTpTdzwRwu4+AwFfpFKkRmwvC3HDtMGs2x+M5t2hlSlUzRnX0N07W3886G7ee3UccxbegkTWk9NtdkfQlUwwJ+c3cplS2by0+d28I1fvcIn7n6GBVMbueGdC3jvohkEFfhFytr6bd3U1wRZOGP8cR/rHQuaufM3r5ZMwLdSfBHZ1tbm7e3tQ+84Uq8+CT/5OOxPD3I+vhXmrYB5y2HucmialtNhEknnZy+8wTcee4VXdoVpbqxhXE2QgBlG6k19anpkPmCGGUysr2b6+DqmTahjxvg6pk+oY9r4OmZMGEdzYw1VwbHzPl2klLz31ieZMK6au//qbcd9rEM9cT76X+18+t2nDPsXu8NlZuszY5EMuM+YDPgA7rB3K2xdA6/+Grb+GiL7UtumLkw9AOYuhznLoLZp0EMlk85DG97g8Ze7SLqTdMc9NdK7986npw4Jd7oP9vDmgQg7D0SIJY6+BwGDlqZapk8Yx4zxdcyaPI5Zk+uZNameWZPraZ00jrrq4Gh8KyJjWjgaZ/EtD3P9Hy3gUxecXOzsDEsuAb80/s4oBrNU+/wpJ8FbPgLJBLz5fOoBsHUNtN8Jv/smBKpg6mkwbRFMXwTTz4BpZ6QGWkkLBIxLFp/AJYtPGHY2kkln76Ee3twfSX3SD4E39qemm3eF+NWmXfTEk0elm9pUm34IjGP25HpaJ9czr7mBk1oamdRQc5xfjsjY9PvXukn60AOWl6uxG/D7CgThhLNSn/NuhFgEtv93qvS/41n4w2Pw3N1H9p8wKxX4Mw+B6Ytg4hwIDK8qJhAwmhtraW6s5YyZ/Q+ykEw6XeEo2/ceYnv3IbbvPdw7v66jmwef20H278OmNNRwUksjJ01tZP7URk5qaWD+1EZOmDBOL5fLUCSWoCsUZVcoSlcoQlcomvqEo3SFeggY1NcEGVdTRX1NMD0fpL46SH1NVWq+JkhDbRUT66uZVF/DhHHVJfdXorsTiSVJuNNQEyxK0+f2jm4CBmfNnljwcxeCAv5AqutSdfrzlh9ZF94Fb76Q+uzckJq+8gh4esBiC4LlGPBrm6ChGeqboWFKetp8ZJqZr59MoLaJaY0NTBtf12/fHrFEkte7D/Pq7oNs2RXmD11htuwK8/MNb7DvUKx3v3HVQea1pP4KSD0IUtM5zfXUVpXWf/6xIBPI3zyQ+utuZ/qvuzcPRNl14Ehgz3Svm80MpjTU0txYgzscisU53JPgUE+Cw7EEudTUjqsOMrG+mgnjUg+BifXVTEw/DGqqAgTNCFiqUBIMpOfNCNiRZUu/kzIy0yPtH4zUikzYPhiNs+9wjP2HY+w/lJ4ejryR9EMAAAjaSURBVB1ZdzjW+5fsuOog08bXMrWpjpbxtUxtSs1n1k1Nr2uqq85rQ4n127o5dfp4mupKoxllvuVUh29mFwFfB4LAt939y322W3r7e4BDwAfd/Zlc0vanIHX4+RI7DLteSj0AujvI6X8aDpEDcGg3HNyTnu6Gw3vBkwOkMahpTD0oajPTpvS68al1NY1Q09A79ZoGQslaOg8G2BYK8If9sGlfkpd3J9myP4mnf2gdMJg9uf7IgyDzMGhppL42iDtZ7yWcZPZy1vuKoBmBAOlgYL3TTGA45ltwJ550Esn0NOHEk0kSSSeWdJJJP3Lu9P6paeo7TG1L5SkSS3Ko50jQO9QTT08THO5JcDC9LRo/9vvt7/9A5jxO6vz08x1krh9S15e6ziNBMTOfmQLsPdjTG9i7sx7GGbVVgdQL/HSga2mspaUp69NYy9TxtUyuH/jFvrsTjSd7v4fMdxKOxtl/OEb3oR72HYqxLz3tPhRj/+Eeug/F2Jee7/teKZ+aaqsYPy71oMk8cCbWVzN+XDUTx9UQMOgKRdkZOvLg2xWK9o4c1Vd9TZDG2qrUpy41baitoik9bayrorYq0PvvLJ5IHvXvLnv54Y1v8udts/jiZWeM2vWPlry8tDWzILAZuADoBNYBV7v7i1n7vAe4gVTAfyvwdXd/ay5p+1NWAT+fkgk43J0K/tkPgWgYoiHoCUP0QJ/lUHr5QGo5mXvHcImqemLBeg7bOEJey/54LXtiNYS8loNex0HGEU8/FLy3nHYkcHvv1HAgQYAkAeIeJEmARNYnSQACqfVJD5DIBO+sY/RnoPXH7OdGEsPJngZIYpgZ1VVVVFcF00Hy2GMOVHsQMMPNUqXV9I4BS11vKoin1ifhyEOgz3Iykz93GuuqmVhfx6SGGiY31DKxsZbJDXVMbqhlSmMdDXVV6Ydj+umRmVrg2HXZ01xY6h4QqMr69F2ugkCg90Ge8FQgzMwnPfUgTjrp9Z71cPTe8k52g4WMhtoqxtdVjbgF2sFonF3ph8Cu9EPgwOEYB6NxwtE4oWg8NR9JLfd+InHiSccMqgMBggGjKmBUBY1gINA7XxUw6qqD/K/LzxhxL5nFlK+XtkuBLe6+NX3Q7wOXAdlB+zLgO556evzOzCaa2QxgTg5pJSMQPFKdM1LxnlTg7wlDz8H0Jz0fDR+1LdhzkGA0RF3PQSb1hCEaxnvCxA/vIhENE+g5iHmCTFg+Oqyk12VKuDjmiXToL1GJ9KeYeoADRc7DkAyzQKoZMTBk5cZAD6F+p6njZ0+O+pfVd58sDWbMBY7+/esgDzsD6sDrMofu59hHPekN4sB9ffMdSCcZ7JqGyNOA7yP6rK+fAh/++cDXdJxyCfgzgezexjpJleKH2mdmjmkBMLNVwCqA2bNn55At6VdVDVRNPqoV0XAYqf/gI67BdE/9pZKMp95tJBNHppl5P1Ku701z5ABHHyv3E6eqwzJtXz159IesdcO9nt7j98njUdsGuibvZz4rr73z9LO+73SgbbleS/LIven9ZC/Hsu5drsftL5998pZZ7vu9HLU80Lo+245alVsebajz9c1Xb/6zlnv/DfW5xpzyNEA++9u37vh/7DWYXAJ+f4+mvjkdaJ9c0qZWut8B3AGpKp0c8iWlyAyCVamPiJSUXP5XdgKzspZbgR057lOTQ1oRESmAXN6erAMWmNlcM6sBrgIe7LPPg8A1lvI2YL+7v5FjWhERKYAhS/juHjez64GHSTWtvNPdN5rZdentq4GHSLXQ2UKqWeaHBks7KlciIiKDGrt96YiIVJBcmmWqS0YRkTFCAV9EZIxQwBcRGSMU8EVExoiSfGlrZl3AthEmbwZ25zE7xVZp1wOVd02Vdj1QeddUadcDx17Tie7eMliCkgz4x8PM2od6U11OKu16oPKuqdKuByrvmirtemBk16QqHRGRMUIBX0RkjKjEgH9HsTOQZ5V2PVB511Rp1wOVd02Vdj0wgmuquDp8ERHpXyWW8EVEpB8K+CIiY0TFBHwzu8jMNpnZFjP7TLHzkw9m1mFmL5jZs2ZWdr3JmdmdZrbLzDZkrZtsZr80s1fS00nFzONwDXBNt5jZ6+n79Gx6jOeyYGazzOxxM3vJzDaa2d+m15ftfRrkmsryPplZnZk9bWbPpa/nH9Lrh32PKqIOf6SDpZc6M+sA2ty9LH8wYmbnA2FS4x2fkV73FWCvu385/WCe5O43FTOfwzHANd0ChN39X4qZt5FIjz09w92fMbMmYD1wOfBByvQ+DXJNf04Z3idLDcbb4O5hM6sG1gJ/C/wJw7xHlVLC7x1o3d17gMxg6VJE7v4EsLfP6suA/0rP/xep/4hlY4BrKlvu/oa7P5OeDwEvkRqLumzv0yDXVJY8JZxezAw57YzgHlVKwB9oEPVy58AjZrY+Pch7JZiWHg2N9HRqkfOTL9eb2fPpKp+yqf7IZmZzgLOA/6ZC7lOfa4IyvU9mFjSzZ4FdwC/dfUT3qFICfs6DpZeZZe5+NnAx8Il0dYKUnm8BJwFLgDeArxU3O8NnZo3AfcAn3f1AsfOTD/1cU9neJ3dPuPsSUuOCLzWzM0ZynEoJ+LkMtF523H1HeroLuJ9U1VW525muY83Ute4qcn6Om7vvTP+HTAL/Tpndp3S98H3A99z9x+nVZX2f+rumcr9PAO6+D1gDXMQI7lGlBPyKGyzdzBrSL5wwswbgQmDD4KnKwoPAten5a4EHipiXvMj8p0t7H2V0n9IvBP8DeMnd/0/WprK9TwNdU7neJzNrMbOJ6flxwLuAlxnBPaqIVjoA6SZW/8aRwdL/schZOi5mNo9UqR5Sg83fXW7XZGb3ACtIdeO6E/gC8BPgB8Bs4DXgCncvm5egA1zTClLVBA50AH+dqVstdWZ2HvAk8AKQTK++mVSdd1nep0Gu6WrK8D6Z2WJSL2WDpArpP3D3L5rZFIZ5jyom4IuIyOAqpUpHRESGoIAvIjJGKOCLiIwRCvgiImOEAr6IyBihgC8iMkYo4IuIjBH/H1Vv80tTi7vvAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.pipeline import make_pipeline\n", - "\n", - "\n", - "np.random.seed(2018)\n", - "n =40\n", - "maxdegree = 30\n", - "# Make data set.\n", - "x = np.linspace(-3, 3, n).reshape(-1, 1)\n", - "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n", - "TestError = np.zeros(maxdegree)\n", - "TrainError = np.zeros(maxdegree)\n", - "polydegree = np.zeros(maxdegree)\n", - "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\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", - "#df = x_train-x_train.mean()\n", - "\n", - "for degree in range(maxdegree):\n", - " model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", - " clf = model.fit(x_train_scaled,y_train)\n", - " y_fit = clf.predict(x_train_scaled)\n", - " y_pred = clf.predict(x_test_scaled) \n", - " polydegree[degree] = degree\n", - " TestError[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n", - " TrainError[degree] = np.mean( np.mean((y_train - y_fit)**2) )\n", - " print(TestError[degree],TrainError[degree])\n", - "\n", - "plt.plot(polydegree, TestError, label='Test Error')\n", - "plt.plot(polydegree, TrainError, label='Train Error')\n", - "plt.legend()\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -2082,7 +1638,9 @@ { "cell_type": "code", "execution_count": 11, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import numpy as np\n", @@ -2102,19 +1660,10 @@ { "cell_type": "code", "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "from sklearn.datasets import load_boston\n", "\n", @@ -2135,7 +1684,9 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n", @@ -2153,33 +1704,10 @@ { "cell_type": "code", "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CRIM 0\n", - "ZN 0\n", - "INDUS 0\n", - "CHAS 0\n", - "NOX 0\n", - "RM 0\n", - "AGE 0\n", - "DIS 0\n", - "RAD 0\n", - "TAX 0\n", - "PTRATIO 0\n", - "B 0\n", - "LSTAT 0\n", - "MEDV 0\n", - "dtype: int64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "# check for missing values in all the columns\n", "boston.isnull().sum()" @@ -2195,19 +1723,10 @@ { "cell_type": "code", "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "# set the size of the figure\n", "sns.set(rc={'figure.figsize':(11.7,8.27)})\n", @@ -2227,29 +1746,10 @@ { "cell_type": "code", "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "# compute the pair wise correlation for all columns \n", "correlation_matrix = boston.corr().round(2)\n", @@ -2268,19 +1768,10 @@ { "cell_type": "code", "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "plt.figure(figsize=(20, 5))\n", "\n", @@ -2307,7 +1798,9 @@ { "cell_type": "code", "execution_count": 18, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n", @@ -2324,19 +1817,10 @@ { "cell_type": "code", "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(404, 2)\n", - "(102, 2)\n", - "(404,)\n", - "(102,)\n" - ] - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -2359,25 +1843,10 @@ { "cell_type": "code", "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model performance for training set\n", - "--------------------------------------\n", - "RMSE is 5.6371293350711955\n", - "R2 score is 0.6300745149331701\n", - "\n", - "\n", - "The model performance for testing set\n", - "--------------------------------------\n", - "RMSE is 5.137400784702911\n", - "R2 score is 0.6628996975186953\n" - ] - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, r2_score\n", @@ -2415,19 +1884,10 @@ { "cell_type": "code", "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "# plotting the y_test vs y_pred\n", "# ideally should have been a straight line\n", @@ -3205,7 +2665,9 @@ { "cell_type": "code", "execution_count": 22, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import numpy as np\n", @@ -3715,7 +3177,7 @@ "source": [ "## Covariance example\n", "\n", - "Suppose we have defined three vectors $\\hat{x}, \\hat{y}, \\hat{z}$ with\n", + "Suppose we have defined three vectors $\\boldsymbol{x}, \\boldsymbol{y}, \\boldsymbol{z}$ with\n", "$n$ elements each. The covariance matrix is defined as" ] }, @@ -3724,7 +3186,7 @@ "metadata": {}, "source": [ "$$\n", - "\\hat{\\Sigma} = \\begin{bmatrix} \\sigma_{xx} & \\sigma_{xy} & \\sigma_{xz} \\\\\n", + "\\boldsymbol{\\Sigma} = \\begin{bmatrix} \\sigma_{xx} & \\sigma_{xy} & \\sigma_{xz} \\\\\n", " \\sigma_{yx} & \\sigma_{yy} & \\sigma_{yz} \\\\\n", " \\sigma_{zx} & \\sigma_{zy} & \\sigma_{zz}\n", " \\end{bmatrix},\n", @@ -3757,7 +3219,7 @@ "\n", "The following simple function uses the **np.vstack** function which\n", "takes each vector of dimension $1\\times n$ and produces a $3\\times n$\n", - "matrix $\\hat{W}$" + "matrix $\\boldsymbol{W}$" ] }, { @@ -3765,7 +3227,7 @@ "metadata": {}, "source": [ "$$\n", - "\\hat{W} = \\begin{bmatrix} x_0 & y_0 & z_0 \\\\\n", + "\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 & z_0 \\\\\n", " x_1 & y_1 & z_1 \\\\\n", " x_2 & y_2 & z_2 \\\\\n", " \\dots & \\dots & \\dots \\\\\n", @@ -3780,8 +3242,8 @@ "metadata": {}, "source": [ "which in turn is converted into into the $3\\times 3$ covariance matrix\n", - "$\\hat{\\Sigma}$ via the Numpy function **np.cov()**. We note that we can\n", - "also calculate the mean value of each set of samples $\\hat{x}$ etc\n", + "$\\boldsymbol{\\Sigma}$ via the Numpy function **np.cov()**. We note that we can\n", + "also calculate the mean value of each set of samples $\\boldsymbol{x}$ etc\n", "using the Numpy function **np.mean(x)**. We can also extract the\n", "eigenvalues of the covariance matrix through the **np.linalg.eig()**\n", "function.\n", @@ -3793,7 +3255,9 @@ { "cell_type": "code", "execution_count": 23, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "# Importing various packages\n", @@ -3816,7 +3280,9 @@ { "cell_type": "code", "execution_count": 24, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import numpy as np\n", @@ -3910,7 +3376,7 @@ "source": [ "1\n", "1\n", - "3\n", + "2\n", " \n", "<\n", "<\n", @@ -4839,7 +4305,7 @@ "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n", "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n", "variance of the estimate of the $j$-th regression coefficient:\n", - "$\\hat{\\sigma}^2 (\\hat{\\beta}_j ) = \\hat{\\sigma}^2 \\sqrt{\n", + "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 \\sqrt{\n", "[(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} }$. This may be used to\n", "construct a confidence interval for the estimates.\n", "\n", @@ -4979,7 +4445,9 @@ { "cell_type": "code", "execution_count": 25, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "from numpy import *\n", @@ -5119,37 +4587,11 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Runtime: 2.15685 sec\n", - "Bootstrap Statistics :\n", - "original bias std. error\n", - " 100.212 15.1357 100.213 0.149893\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:34: MatplotlibDeprecationWarning: scipy.stats.norm.pdf\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "from numpy import *\n", "from numpy.random import randint, randn\n", @@ -5294,7 +4736,9 @@ { "cell_type": "code", "execution_count": 27, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import numpy as np\n", @@ -5523,7 +4967,9 @@ { "cell_type": "code", "execution_count": 28, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -5591,63 +5037,11 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 0\n", - "Error: 0.2532788185411167\n", - "Bias^2: 0.2526964484949394\n", - "Var: 0.0005823700461773749\n", - "0.2532788185411167 >= 0.2526964484949394 + 0.0005823700461773749 = 0.2532788185411168\n", - "Polynomial degree: 1\n", - "Error: 0.08455386893059441\n", - "Bias^2: 0.08422251109115914\n", - "Var: 0.0003313578394352724\n", - "0.08455386893059441 >= 0.08422251109115914 + 0.0003313578394352724 = 0.08455386893059441\n", - "Polynomial degree: 2\n", - "Error: 0.059233304118678526\n", - "Bias^2: 0.0586320532355154\n", - "Var: 0.0006012508831631393\n", - "0.059233304118678526 >= 0.0586320532355154 + 0.0006012508831631393 = 0.05923330411867854\n", - "Polynomial degree: 3\n", - "Error: 0.029801715226919333\n", - "Bias^2: 0.029452192264365906\n", - "Var: 0.000349522962553423\n", - "0.029801715226919333 >= 0.029452192264365906 + 0.000349522962553423 = 0.02980171522691933\n", - "Polynomial degree: 4\n", - "Error: 0.029794287278949182\n", - "Bias^2: 0.029348037991086092\n", - "Var: 0.00044624928786308614\n", - "0.029794287278949182 >= 0.029348037991086092 + 0.00044624928786308614 = 0.02979428727894918\n", - "Polynomial degree: 5\n", - "Error: 0.024440674046123795\n", - "Bias^2: 0.023999783622663227\n", - "Var: 0.0004408904234605626\n", - "0.024440674046123795 >= 0.023999783622663227 + 0.0004408904234605626 = 0.024440674046123788\n", - "Polynomial degree: 6\n", - "Error: 0.02059388133830918\n", - "Bias^2: 0.02004411755832111\n", - "Var: 0.0005497637799880787\n", - "0.02059388133830918 >= 0.02004411755832111 + 0.0005497637799880787 = 0.020593881338309188\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -5659,9 +5053,9 @@ "\n", "np.random.seed(2018)\n", "\n", - "n = 500\n", + "n = 40\n", "n_boostraps = 100\n", - "maxdegree =7\n", + "maxdegree = 14\n", "\n", "\n", "# Make data set.\n", @@ -5673,7 +5067,6 @@ "polydegree = np.zeros(maxdegree)\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", "\n", - "# complexity\n", "for degree in range(maxdegree):\n", " model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", " y_pred = np.empty((y_test.shape[0], n_boostraps))\n", @@ -5746,7 +5139,9 @@ { "cell_type": "code", "execution_count": 30, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "\"\"\"\n", @@ -5832,7 +5227,9 @@ { "cell_type": "code", "execution_count": 31, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "# Common imports\n", @@ -5926,7 +5323,9 @@ { "cell_type": "code", "execution_count": 32, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "# Common imports\n", @@ -6007,22 +5406,11 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -6102,7 +5490,9 @@ { "cell_type": "code", "execution_count": 34, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import numpy as np\n", @@ -6215,7 +5605,9 @@ { "cell_type": "code", "execution_count": 35, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "X = np.zeros((n, L ** 2))\n", @@ -6279,7 +5671,9 @@ { "cell_type": "code", "execution_count": 36, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "X_train_own = np.concatenate(\n", @@ -6295,7 +5689,9 @@ { "cell_type": "code", "execution_count": 37, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n", @@ -6380,7 +5776,9 @@ { "cell_type": "code", "execution_count": 38, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n", @@ -6391,7 +5789,9 @@ { "cell_type": "code", "execution_count": 39, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "beta = ols_svd(X_train_own,y_train)" @@ -6407,7 +5807,9 @@ { "cell_type": "code", "execution_count": 40, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "J = beta[1:].reshape(L, L)" @@ -6423,7 +5825,9 @@ { "cell_type": "code", "execution_count": 41, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", @@ -6489,7 +5893,9 @@ { "cell_type": "code", "execution_count": 42, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import numpy as np\n", @@ -6595,7 +6001,9 @@ { "cell_type": "code", "execution_count": 43, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "X = np.zeros((n, L ** 2))\n", @@ -6625,7 +6033,9 @@ { "cell_type": "code", "execution_count": 44, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "clf = skl.LinearRegression().fit(X_train, y_train)" @@ -6641,7 +6051,9 @@ { "cell_type": "code", "execution_count": 45, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "J_sk = clf.coef_.reshape(L, L)" @@ -6657,7 +6069,9 @@ { "cell_type": "code", "execution_count": 46, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", @@ -6691,7 +6105,7 @@ "source": [ "1\n", "7\n", - "7\n", + "6\n", " \n", "<\n", "<\n", @@ -6713,7 +6127,9 @@ { "cell_type": "code", "execution_count": 47, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "_lambda = 0.1\n", @@ -6764,7 +6180,9 @@ { "cell_type": "code", "execution_count": 48, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "clf_lasso = skl.Lasso(alpha=_lambda).fit(X_train, y_train)\n", @@ -6798,7 +6216,9 @@ { "cell_type": "code", "execution_count": 49, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "lambdas = np.logspace(-4, 5, 10)\n", @@ -6861,7 +6281,9 @@ { "cell_type": "code", "execution_count": 50, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", @@ -6906,25 +6328,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.8" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 2 } diff --git a/doc/pub/Regression/ipynb/ipynb-Regression-src.tar.gz b/doc/pub/Regression/ipynb/ipynb-Regression-src.tar.gz index 5cbbcd30edf182d20f543b9999033450c00aaf22..43dc171f37a138420b69c030cf0bf3b0dfe27a43 100644 GIT binary patch literal 198 zcmb2|=3r2o92U>O{Pz68u0sX_$3D)f?W%0LRC2pY?8}2h$5ZcfQB2ZGmuP$RPibbkZw-O9l-F1^`m#Uj_gG literal 193 zcmV;y06za8iwFP%(?VYW1MSaC3WG2Z24L5oVoo55iMlS-MHhu4y+FiK(??>H2=(^$ z0qv@#8!3f+o1ZX0VW!A8+kGCny9-uB2uV4IDYG=0lf=t=Mq>(;Wid$-$_WEP8RIMf zvffHBy>-HjtF$Ln7S-F{SgY#~|177#GylY~l@@k~!L?d}(hf_l&ro1v&|0ZgB)dVC vP-(Q&7!2NeX%K`OKvb1?D;DxB4L;m zsgAC1BcU^9ausE1%CyX*>ni#X|I3bclPHtMFIw9?4)s7^wykJ!Wy_5c@vd%v9T6^_ z?c2KDez*Dg`iCgdOyyaWNW)YSWL^TV?TP!X+xBi4>aIPJyYKoS@CC}RK5a4uyQQnV zTvITfi6GaI+*4Vz#&F;>2Y~f5Dp--p42_qPWg6sZ!!%bU@2cxZhq7`N|NDRNu}gk% z?EoYY2;!NU@w3A(-yo_TwH5kVQO#VOquAE&cvyheU8! zIb0Cszis^qX|5mY(QmF#{5Uk-Sp_q}1jamP0I9VlUqwG65K^>^Ukqb^-?iO-BXd$w zPJ1eTV@H64ZHWBtaKA~sMP=7khjIj%($34rvguF^9lNwQz?jEfZ^y2G+~jF|L5x>* ziTCX4t!;zQ(T?t{$55Sf(EA*U!YrXEQYH)Zi~yP-*SvF}x>71O7xXK+bT+0Whr8iQ9kTq^?(60eX9CK$6hhNnr1xK4K zw)6urL!SbDBTanh4g=9|4{bS;0g&_$^|&K?Gnol%z?I#*(e?FzZ}!Mrx3mlNeGrCl z^Q4{gMu}4`b5K1kNXuF3KqhLvFfkP@&C@BBKXReZ_yH1M%=Lh+(DMA15(~~mD~2IX zL=F`%ou!(SLo4yPQj_>AH|{zQ>xv-cQwx3x(Oq34jFpaWyPh^aa-~BY@Y4qN&IiE9 zArEmt`V@z6o-~7hRKnsVCTxGn3eJk$OmRqamYFnC=ngE@7>C!Gc6sc)B6;jHQ$ZU3 zTDqp$h}aQuh)QaYJUf=e{9mr*%{TnwXGzZ9x zLjplnqgq9gf7pO+U4J*6u}Wg6C?@b;oY5j-}+j!F-z15JTk?^CTai zEI(3k#&vdDfBw)ODS;mvG1>Fy6Y~Vm66&Kgl8~^pP#V3ka3F#=5f=dZu^wjDG`{b< zdta+uKh|iWXPY7EzxEC@9ao<=DYOYp^@GFlMdmqw(oAgo8fV7gVqMH9WwU}Qu*fS5 zWexpXxt8jnnkt3sgZP1>K*I&>`w{gk*SaSW(!0ivGd`%(p+?}W8?fEp)Qxu{QD^nR z=_8%P-2Sr)|9uE3AQl;U^?N(O_#k08!pb9IUYhD}XhjF~Htyfp0X0V!2u11c$ALB$ zL5ID6o7xV^GPXf_Z!6b902PKm0~vicOpHxkdHeqW1z5y1sSEJ5`}^kD@}^k#ZhL6# zaeVN3>8NwI^)ClEg9Dsg(Ry#&X(q*P3uzn@`s$Jx#_k@+*{>Us{B96o&1p6wYC2r{3b%BWm@q@q_x^@c&K41|B6JqF*EJJwjr4eJx%~K1g9x&3ka_llZ=Ll4P}^a4AIow$sx0zc1SLvd!~PX z30W(L9IN00<$8Lyqlcz($iEF1Wg<+{*l#5dsCWgn1ZAk8Fu#3Lh_=!L&GmEhWa4JeUsg?HfP_g{w&LrHhYVcxuj2|B-?9 z>OV5*IV0gU+d$H}ia0QtHVF;6@cHs3{l3^Jn1jc@zBypYO5SwsOSH8wYhPM`MKeyp zgja^li}ldjz(Wb{TzTdD>u0|MfJW|>lL5yQv#SSmE&?<;mvMp!DSz!+TW{hx6n^io zi0A{%Y;ohH2~cSt*xuBU+Kv!ATa~SlOAKTi;>Zbw&aYoPaRLdXB^OFNofndr`1qXj zovR&L8cDKru_uRDAj^^rH2%Zx=txS8bT0P(>h2w$Lnup4(9-oIsXLUQrUD&8N!J=c zQ8lS+NWGdlaR~Lk+<$k!CCRfN7$+Z&F!EBbt|?lLxQUsH7P6s>18LF3XVAU;g2s=Y-FL1KNH$$sDJDYD1Q8jv602ZN(Z}B z%X2F+0wH&Lz}@-f#owK?{^jYLvvqjxEBc(Pb3Kbffv4?m`%M`oRuJ?}Bhp*mG_a&) zioGedU$-LVy;#wI#I$1$c@Wh~LN)mz)*xs!;kF^>^?zF`!tBrA2nPrYuRVyW-VcH= zFXa2o95-;_1XPaY71A7EnXSMVTEvm~vRO;lKXR9@z_CY_B!{815;{SdWX)4T*Q$(# z>KYiJNIL71vCW&Rq6Plzwiz~<4z^Rm0E*BP+-xLr76QtuuV$F77MgujXtrKxHiW4jBIz1v z$!e^pu+apU2FzyucU?^raN}rPy(@u6y963%7JudrT?VBG(>BbM0DrJ1yUt9*8^5hj z!5#XP_F-qk4;zV!aSqjz1KC?nZ|zWV%4Zc^-KDkeRO|57s4w#MnAKRA7HKs$Lt?!d zBvKH|Sa{xNrCkwzHfb(u=g4w#sbzaTEQXfbB~iSe%{_`7Vj&NUBA#_1)9BQQ65G?vq*h_z25KV?LD8On)zMCf z{e-D-i6JGE;O-Ish&#>|{sA(aa_`m`4)dfBeFG^-^O8WC92@_}n!hgk{ zSoHb?&CSVFI94p3VH?xYGuX|(SqYFL$FY_H&3+sJWff?8bUbRI$%BD1oRfIRLfO4y zyY^9;ktGqWY)!m94{MHG=V79_U)p6?b)docCR)2hE9w>W==(Kw`rXd>yL*{Gp}*^$ z@prwE{=_cd&bUn5Wn9ZE4}@l^fq$*!+9Q7z5bx5i$xI{)ksVwtim#R8z`(9Kvcnw0 z+L3v1f14p++?JwI+bI;2equ^o5tTMi6+9iRKbUJTAD`vh3*{72YX95baVFYw$*o(K zYW1a0IkU!m1-TNQh-vr#VH#|od0W_D;EdnZk-C0|Y z+r|}s-(NwXFBED`=1LAz1gV22K>{N|>P2h7_5)XDtc*vSIugb;y>z}`W`hmrRS1Cy}u+o49oJAyJ(Fw~ASiW8?C1>H^s>PDW zD!g0pB&-f@({A(fL$}P_BKw@~_J!TZ@VMYQ^gnDW;LRyX zVxcWOC7iK9SS?r`Ue92S2#=#+Ie>aIqt}6qq~s*xV;OzBSSrTDe{P`!E7GYv@f6(K zz=zx@NC**D8}pRme!;?jldN#ygbIjG;}~gk$g8DpK=s zQx!#J)O>VG*8Frk*6wyJoGJL*v9GftweDqi$K1LdRyW3cLyx?F=Lu_P=Odh(4q4k# z1BTjj=B{EzSmJeQl@dw+W+cK@b*P(+K*cf+|FHlrOKju7(%OTi#nCX5ntylx$77wh zZ3gX@9@w8dR;psXNmtq0of%o_@aX))9j(&FBQG-G>8}Krb5^DzLAdm4u%cdNjkBH9 zKM#9vL$%xc8^jEMNus}%cx%(67^#R-3MR*h5Z>he>H)<7P!SE1r~@!@XvPNx(Xs4c zZf0B=>d}eyWc4Ud_E6~zJrNO94d`J8=IZOz}v6Oe5k)Vw$6yB@mJV`C3Q^ z_03$2q(mW(O+=|j7a37{fWKHfTE-f7V^yiYq$blDO*-*ELX+`GlP_mQBC(7g(52#p zF(`m0gwcz-l#DQWNtF>2I!ywZBqWZXqf0K~uX3zQc%MEeRWK?N)?s`(Ckkq9oGPf7-Qbl1~ z&+FeL_=hdBzvnxYUwU4E$ei(mae)#lqCjd!q+%E20!&0i7vln|2;h)0?2|{2G^RPq zxiZfCn79z1#Bg8ziB;*0`t?QicM$~lOFq@yePW@1VA^w2RJRTUwf0D>is=sKUo*^b zcuzbL$q0or$Sg;izoXP60)}Bk=De3Co1qMxMadbTADNJIwI`d<*K@@zl92!ip2&M()I$DLnnD#N z3Ch!d*4Bv4q>`=nZNAI@?Fvq_KlqY%pVsLvvo+NHiq^%(B}0zZ{wi0+q6R|Un6D3M z(ahy3M${?Yy`wCiL9PiCy?kA@2(-B`@Y_ROx&rJr|744O|HF;h+|67HOG^@$tp%0S ztEb+kdvANVT)3NcB^eDk>?}yKN%B#>E&gYJ=AI=Wl6Z2@h}4A8Q+#?jf}JB(%#eCE z3%*$_t$#5&SKy+h;bN4aCW>8}4&NHHRfV_av}^8;dAptCLr~jlE}gNDTF(3J>w^h7 zMs*oao%6xU>?xX_ijSWapExT%H!d*I%m+0(BXQ1M{dGy;kexQNgJnlvcEc8VYr8Lh z=SOq0M@)${dy?$sgAK<3>VhBg#~b&BVa9!m&RK)}-&lkABdtLsm(0P;+TuwIEGSB| zZrHZV($ZxD5u=?q12Q4BZ|rf@RK>v$8n81XgY!ctj@~*(2pe5+;`S_IJeGv=$$NRz z(Uduos?w8m+~zA|y<4~4<)ukyu8SXkP~Imcf`V4j@KuV;c5fztAkB4xvclaubQC{T zLE0(o(9x!%ZQcw*mp}94fnEAJ-`T<8&d&z&4;EjMZ!0@eaQ}VqOZWS_N*yb9d@!7h zGO0-n_d=70df&_BRc6$~qwvtUXX)o5c7uHH5+&Alyl7?>Hn$r{GX?bnO?z;E8d+k~ zjV#^Hg=Z|9e77&|9Hy10J@XfA{US9(kp!IK-zlivo4fExAsi)+kL=^YRVL&SGrC`o zr7KSi1t@LSeqy)jCUL*kU?i>Y-0oGDHmzH}#xMwj z5O>}%D_$BCiQ?H(KCR=%MK;~&TZV0I49Tl?d zMq+JO7oHze%fvSg=cmbnDA9+*lV3_GlZBmOVq0eYy_I*(I&ZC*u8Rk~-9KQNoB#_mA=3V2|7K6dQxB?8!UQZb zA;6A3%()~njU8t}oduh#-)8WPzLkz8(PKrOW711M0GT835p3OmBUvOI^&_>N_JvUm zQZPMDZ^tN9!YtbyR&oIaMTiz?EAF8%Z3k+<=b-2qc^Ji*kxP}KB=IHW z=1^KMN{hU8lMIW0Aom~5N8j7cI8L70gI^B2{X+1rEgA74AuHNv@3gCZMsiu2*{T^c zj*{FaZ~spcP|>u_M;o$VxR}+&OuwyHmHR;W`+4;qsZNeMIdP=@V)~W|btZ7XeF}ec zis8NM7e+DMMc60F{tr{3&r~p|7hf!Cp3=(~tsLACerWb_8Y<$nPaa}sTn z;AIq(*IX0=I5LyL2PuE$8eNauxbb~|g>XF}_BH16OA^&04=Jz-Qe3WRvS^DYL7=s` zyFz8jC&}5}7T4e28IqDkt?aD5N$;+GutiejaAr8)6b-Hd8oWHA?)iuFlhYS04ib_? zJPOV)111%T84sc`CR~KUc^15zJukEQLI^Q?PlYVgqPVl&n>l}vXWF*cMb+9?wW+tW z&6u3ssd*2F1wV%GM+`m)(}{l_bo=qPNH27OdmJK$wFl~< zvit4xxnq}k{9qrywU57ldrzjn9@TgMO{&j6zGp(N()PMu{p%txv!Ag4^mH)#TL0yz z&(j}&5&BW6kn&@!&-43s$0^NGwhWm8ioX+RTCA_rLx_DgI-v(G0_4UBxc!Tpuf93I zzcrqC7!iM|lqdE^WFLdi1CMy6Ic3pqVO$Jx`=1lZUGDY&S|Y8V{`#`Fdh$!th8KDJ zKu!2E&Z*E>y3D3rzSFcO{iHuata7OD`o?Mls`j70fBVe?UG1Y|nCt3hNX+RA&H~0r z5{4W@umwwqk|J1eh}IJqtj`6TWtj;GMGSFZT~~j#ZKe>r-!*yD^)lON>ohO>`QYDe zW3zT~xR)Og$^knX03JdR7?cTQ49MOUZ2s4|;+PRg8O@eyi4DvU*eq>w%o?!ULXgR3 zt?ig(0cX2p0hki4(QPL$y*v|51xd9o;~z)*(KALBi}+DB<63>jl8*^i!wzN7(i-G;2S<+pXuF4 ztSvZieD`rxWhRATzdM>N-?h}b-B{TYVuyd#Aj5rP;37gITPxqf6yy>i~j zG4s$we-f#|Q5*{IyYG%=58vg%4*`F2`m5pbBavF^wA^BZtontV*2thOVn!k^Aufsu zlU;JvRUB{7+G`PT|i(On~U?0!K1p|h#SUEPjQ1{Dw4oA@r*D-b)qiDbUB6ZKl+K>F7 z+M+zRsz>{`Xs3VM@4J3ROL)%;9_eL{vssV!F84f3FIz{bPRK~Cn6U#1M1@BiAkd8i zEszvMs2#?5bd_XN2CzG|F4BKC|1{s4<1T@HoCTRO!(SotwAkx2gY&~J$=3=$9|Uxd z3a1y%6csbXIc?SuZN;;yY}^p?DZ?t!+J<4J>6BQ>XzdJR0PwkKm(|Mf&klT= zhY+3BIhL3%ioL}lh+P!Kh907KZb1@6z%Lv%H_Zw|NFozyIg#&Yfdn?jap*%fRW?Ad zr`w^vtLnpL1jjDy;_81A)<}hyy+X#86PuN;^CjNp;*h>|yuvHqA+unWVHU0~;Y9@q zxV|k7S>|gu^>PR5@e7cKi)2KJD1t*O61{+Q0i-^(LtJaPigp?^Zm3Aa8Ouc_CMFj5}H?}nQ<7JL@ zHypSyZ{16`P}Xr-wB1|H;zWVN@-W<`gQ3;IaIX&b z#7hpQEAb(xjYZIXk-|XC(`9Sw`qU$M{QKlSjFx%_ZT_~q(F+~ zZ3fOkOz=4*Q6dhpb%HaK-Fd(*g8*NhE|I2d+gm|mxv6z;Q1-|LKvi&R9@H;mn+14$ zHo_NOQpmQSFJX1QE6|RG0~LzJl0=~sj?xxwt!PZ&M+k7fOy2j)@ODc|L^wp_SPu<7 zx9m|8lQ@3~7K2HHlB@}$Lt25|Y5Ri379sD#gF^Te#$HizgtLUio^=zRO*(x^xauRr zR@W=%hWo&L1QL>VP)3Li7H|lvQ{=tufZ&`7KS~iEl8{F%29vsLywev_4iA8+c)))q zCt$(Wj1NLLhH9v6PHv)lIPi$Xa#)8wJ3smHq`!Yg0E-ig2>1fHOBF0vC+|McAcL29 znIRGt+?c5=P%~3#FM_uxf3_dpd5r2I2|$XAK{a6{{(u!ugIG<)-YmgAB}wo@vyL)K zr`gTN@*t#lcoYKS2;xw{-Nm7Qn?MO>Ne;6Z#E*%mlHPt`_gSAMJ)=`HDhUnIL~d>V z_H=)J^%|b;LEZyoaSJ8{*$milH#&(IC6Vk|_3tS;hZ*X#2+1jgyqzHK2Zi%6>QjUv zFOmKb&`0ODRh5x&LOv6Q>>pLj-KI}=|z2mZttD;ffR66DcS;vqADT>>)j z1t3O~-Q0lODuH#0xiGR~V^x!bq_O>~i7tPT0<_1gW)EpXH9dVn)r1rP44q;7QC z{?T}EyVsO*o)I#cX^rp)k20=7rJ#~INL3j*l zbUbj)-Ff-Z2-vrzXOBq+F%EGbk(+t|ghY`4ViIh!W*B!{Za+Dn2uV?@qGS-feI{<+ zlb=N9N_{XXZ9LKqP8ie|eURLL4zVt+Z!vI*A*43IS&(KYos4>uK!yGTy!`n}3T19& zb98cLVQmU!Ze(v_Y6>wjFqc6a0TTi>Ig`N$DSz!--)|c?5`OnzvABDYAHc-wWd1!DN|I z27gmkqkiMk#$m!1mU+uX7-gB1s!;gX*8`~@l`iZV30=%P!)_i|SKH-!73Z`V;tiQF zm^1Igx{#(23KC0E80naUG#B@H_Q%!vTuMD#Z?4Nt{c*dl|3XZ4E@rFi_%n2?KD=LS z7Wd`0+&o0_TxUTM7b8c^K$~Q--?&im8Gm0G=q+7%Mq?y25=?njYqUI|3VFpm_v!3U z=j3Kl#;4`oT`P7}@qW2lLZAEP|H__55AQeSBC5H*8PQ5<$)u2dt(xt`;GI?mZXpZF zjL_+B$^oU6QH%@IpIr0$Ii+#}8Oxm2gJvFF%PMBw{#qhq_-3uvt!TqyE>Or~^zymP_(uu1GX+x+E*3A^)Ize?X7&)M&%n{oZA~|6uTYwlz+xi5_ zjHaPNkO91TljtZjA$GuGZ4L~q#{4~#41u;5Xe*F9rD-U_oUF^j8-G%}@~w$qWk}*& zY7X`qGK+^AF=ss8F0TSXybT0Vf*?LT-fbW1FUwVyBWh&wIf1Jh|Mhc3@H?a2X*eR{ zT#|eNj+meo0#FN+2|$@3u~M?bBPqutJ<|w=2_`u}tJ;~m23cjopr%z6_B17hX^1*O zp_)gpnFGCju`Y$uv~W{4`GU8D6>GHE7}N%In9k`&y)7s@$iz>SaLlBB7UT3HyK ze5dKx6CJEb(E-uH(FNppQuX|y`tv$7D>?9=cG@@DPM87CC`Q^F{XE&?zy8ar^IcyB1k>dn%f_a~=`X+yr&`L@^NZNbo^~fTU zV`E^6^#-)9AphaiY>wz?coig_D z5gt%KAeOF5A!TCoWKo|m%EnLCtb_FXZe&dNEcG(843bsr6wT(!LmO(~#pHU^_R0dX zCZ}T$(SLblmTKhWC8??p^tpSBB~$^vXD4rvI=Df5^Re6s^gJBoC>+j}OSduI9NQ3weNVUeazK_S4| zu&t+}5b`ey@q7xA<<%q!&=R_Tpm-uS_8NYirhj7@a1so>j<4riG)3ha96O^f^LE#! zpU_nw811L*?*VDLW_N>T&zm$>>4R($9XAySIUT-Nnjs*LJVjA}G#gyS%*HiM=D0az`p{^GnRma`v#kd)z^88lF_~ zkAKj=x1>g6AHZsT&lRNF&ylso#==W69hg;T;JDw&Nq!@fse0|kFjXp;$o4vPP5F$L z&u)11y;7ye0Z2$Ds=fXU9KcQE0PfQ`z>hbzBR{3yz3K+Y zcC$tnabOoH z96vPdJD&e)pu|h-W+KnDa_PlD@~T%XlW#5vhX1Q}IDEKS-`%alxTkQFt@^3_@P1Q1 zJOnSRp1KKLsN|tZ*CM5bDyXm%`6geBd^@f?931S;_9xf8m9Z&sg9c4Km6ifnrxDvFh1Hf3KKx z7NwOegvVbRt>!!;Vyu>HW^zF;_h|Fos3!Iu8H|b=ind@CfPt@K7xS61*2$-+N zj#Rj)2*`1o_O3iQ%3ZMS$2|8Z3wnF9pcm7kUVTYX{}tZ9sPDc8J83oESAYD3R^P-X z^SeWDg&^DGS7AKpyseY{&*x4xZ&EyL7u)uwitX+CaU0(Pi3E9l(|ehs%ap?RoAstQ z0(2dv=OPmA_5}hR&;6eJs!4bU!i2;1t_nwSs(HBNr-kbEsbd8l7*)4)xGNh@>2}pb zU2d!Pp{CVXekeCr%ZGA+n|}ykr!%3pM_}y1RO;@?-5t6<(r3)Jxq8m!teSqlKf;Bu zmbZH(tlQ5^5z;cDU5FIbgvco`rhakp|IE$o0k?#yPzz~mhtK*^U$_tpRd4UWT_{;! zg>q9b725snPjfc1VYRLZ6^M{Wy)7cc%>eAE2wh^A$oMgzYc1J>S1}=Ub?{wXgfLUD z+K$}nsT(uh6j;@#?Tdr`a)_gX4!0g8AWO$x6e4*92K_%89IrBy@Ng*sGLs=TDSzc! zO>ZMN621FZ^lT0*A=?aD{LW&4MX);yiVYo zak7~|e7M^#R_~{^lyb6IO@F@r_J6B4$;DmI>{`FD^~{0mTd3TZVO;qfoTFPs>rq2yb)yd=Acwk$)rF*@%Kq zwct!zgIi!q`)Kz=!K0ctcTf{ft6a4Et*{|(f$xI*x)gpPn8gbC<(-Unx5XR4CFK`T zYZv#d;4~Ql=rONse0x=lkSBeK2w4X(5FB>F(>?Rd|;wY6DFOq zX;zF68IuKQ@ms-r%PdvGF@K#x{c2hZWB1_>&{Xzv$TOEf9!I)T4!pZ!V6{FNSVO1c zuGtccDbT{DU}r7?JGd%wo)91IQy7)iJG z1+Y2B`E%kO#CB;3q#UG$AFOJ2r1P=3i1D4WN4KOSIyQzGC4X9*S7i>h@{_pQtG;|r z!i&OI5@n-(QGFda6mTOB-a)4*G`*rQXFgtV3)=zl9iuM``-Q*ci0$$kwB&g#dpkzQ z1=I}%^y@;VLT1E^#1fz?(0G$s-#mrH;gh%P<^6}Y1>h}hh)Kv-FSk|kvGi`_$sZ2ILLhU+3IG=C2EsUGJl~!jc)$~HGtWN5Re&10s0am zrM|Lb^-OC@?W46$U~_WtLi9=a>R;Da|GC=1kcV1GR>~^0T-Dp<)sH{(>IUz93z?K? z3?CcIWrfC>cp$C2>ietjVr!nogK!xJHLo%XZGg^0QoMK+Ax#5tfa3%@a;%FhCzlCZ zM!bDe<$t_bi&?dkM=BJOk-nP(G1c6EDK4YYl7ym}yzM}?7h4-T8#5xdI&|Ozk!=_1 zMs`(TUdLSM7*Nr*Mf@jjR88y{{BWZh8wL`7xKnlgh9NKUW2Z`*IZmzbM1{m`nt&R` zFYgsfJ_~|#MUl!trLmOARiE=c3QMO7dcS_i1%Lg_9Y<-pJyFtgyd&OVL^T5MP%GO^ z#r7c{JdCbMmI`1k4Rn-XLd4fc^POODElaONt$wr*(JY)6cp_Px(<}(L-u;^j!1+FY5iYf)D^zh`Ck= zl?%&{kuE(-x<=~A@x9@>%Js*0qdn;grGK0qm0}~b3t0$B%93pGyQUys;(&O$C|+(@ zymZtqSI{mwM!S4}I66|hzAle6Er*IX6HQ^{|L8;$eI@kwvWw3A{yPSSAX5cmw#9D z`AvMWnULa@#VYioae%SeW*#kP+s)$Rt5?N77cDYtyX1e)gKMiWDJfj4GR+*lrtm$8 z-KD55!bRB^lg8}pov43cQl%-)I(2GQYmRCzB!9O<*RNkdC8yx|u#>RxZ2>&IsR)gRvC_R7#Ii+^T|%M+*a zXo*O1nUagojOU9n>1EotFoB{>p3nQH%3P?;u*?8Yg!)S8VN!Mtv<485D4|X#gx1NC z6SDKG0pBz~1x{8-0eLKnTakMzqnKl>j@kcXJ+MQu9yQ9=W4>=azqCL@2CpL4hcFzd z?GT0uU7xVTguM)g?FbA@<9}XYxIcLn4rPH48f=(uzfO66RzpqJzf8qsJ1ahIDsXke zh9?*`(_Ef5UFp>*B#Sjl|Dh@z%7mxNP85dqH|o8mCc7NI&!px4z?C8cypn)kKB#WQf{?_XkvE;h+db{wAIyYjov4`}>9%GPlzjph z^j}TTPSTU`a1@hHdK3XOmyxCjDu3--S##XR5q{^dKq(IatGLh%W^kp-53(t$q;evM z+}ID2ivMUG(p^vrQP!Dq5X zt_PN&IgBmt75QSE^IOGQ*vWGkzeB38>}GU4c)dRO<$!}I%;J_CMvfna0F>|WAheR= z;KR?%N@4Jw#T)@cS7vHqd4H}W1Fb@CogMsh3G_%ho(G@{-wC}b0xQqIOG?h1(2r0| zT5^58U}Fuf46Vtudm4I9xqyT2P8bk1-37^3473r}%5#L|cf$dKF`!%IxKV(!{)V%7 zFqy@i2uz7K=Q2c^au}C@O#zG6+pIZU3E|oCZdX@va_ENk7JEK2kAJ1vM<#@-RjCJB z9LTd$#WgJ}Q*4w~xtb2tYE!GG$*L0e;n>%7xEmc=U9_;TXShh8tS5KiC& zOBhf+LV-UWu0W8gH8O_gHL}$qx3hAiyCS;-iMXxmRJE!uK%x|ngv%hl7Cc20tK4*Nk(>QmJlK>q#rnekTWeNF)3P(NyN5n8Pvf< zvZ20zbV-i>1%EGpj}2zG=>fpI`(_g$5A+(leXC$^!sg0&0k#v626O^hb!VrJusc_6 z4?EETr`46QMA{{^{^rmS2`9xRxM)u)bsZPGTxn~91O24wL2Ke(kWGaq(mPi@IfQI` zD&Nw`^_)-!s1Z3Uw~b_70&tO?pihmKhBsqrcs68dXn%GM5rYBpO_#R~_M4DoT19L& zzF8sor1%XECUKqs_w^Bt;Y$cXRrPW+u`f6mP8bmsQ`uaS%^hWPM|_~YjmNl!Jw=>S zi-tTPSeD11@_q>f?jxGf;PV2E)0N5XQ&Zqs)*c!lLt!8FhtfK7FYU|a5UBTcW8egW zKuJ}dk$x0=F2?EpOH0{F=Be#Rzc3Ug8qRfKP=VmedNGMyPkfVa&MTu(Y zaI8p8@HK&_&#MB2EhuylO@(20PVIi$4!B3j1b-u%B*-8};R9$Kcs>{oPd>@*2O&r4MTB9DF#fI(#QY+7uRq@D*< ztrj2~ssj#zEb!-o?ljWP9jU)x=s`{o_%(nY*5ZyAdG@a$|G`Ua9L=yFOYp zzad(d$iWIKB%0S!%B<>5TxP!#f2)pCY=0WJ@!fQiuETvkr4u;GSX`PMB6SgIvNygM zc1X=ELj>pO@osgh?KjFkaNhN+-3 z(MU^NHm!=&A^6LE3C@^@UGSn7uApfrn#hnI{k9k{0r;^{Dc?H1;oiv6H9c})z^y=#E486mqW3t3-~1OQq|yijGbGPzIS*=y#u!`)KEMv7wvGF z6g-;A|B<=gSJXCa5wlfPMwu{RWPfU_W&dP8+~vGkr0N}Xm&g4T<#wU_g$k6endop( zGwQnwlq{*jtfggy+a|*wojK<{kU&9K7Fm!vd|w)6BPhGzb_!8TGP<7r1uojIXMa}} zg@#tQftpp_&XXephv+wFblj{5kK=4|94KO5=rUw>UD_0W_hDx4-Lp$os^vv{H6 z^1Oj-cCFHLRo7L0gc+)K&wrZ)izCBSJum*$RC(7bocQ1zx$0$D@c{0_VwNR;(sy!f z>e|N`C;r^`Y^iaPZMs?)F$Q&g<^#@)A#S!=f`hP zNjGUO%JgG&oAr!_|7H&{{Xt$AI_AeLob`+gU!3A6l^64S^4!5r-haON*Xir?x5v*e z*2@!P=o4+uWf94M$FJ6}o>jnOz}krQ^2JkBQBx*n7kfJb4(9mLjb}ZRTB7>0^9dJ9 zP~Q7qUvT{6esU+<)46;0EeCYuZNNGWO}OFA(O>C14o16~onMiuEJqE-uro zzUx%l`TiKL;7Sp?(SQFtl0&HYeIbl56@YmA#6RJC9DEc;pAEdpc~1*nsxIzb3*9PJ za9`F}nej%QkG38a(uzPqaGs@u-JakwU8}aM%dfM@{cmB>zpJdAIpon$E0Jj)4zb6b zAmqO{#JYaF6JZ#9DujdoafHchkA|vuhVQ6KF#FdZ7Xk^?+!IiHdy@Y2%EfHS>-E8Z z06<-Uo0D9GDw82LDwl5g0SbT87Ph-u)zy*%^krC9V8~D)3AO@hCNL5u6V+{$oWw2q z-$#m+Ey=QBTC$t9d68&JJUo~09G*kbiWEqS^!`LC@3NvuifqygY2iR>^jy9_`Stqb z?eD6pNVYtkObqGTmsC@iC#ot<3|rPFQ|a21ZiXK8N4M91G-;EITBm*Z}BQ5oeJ|>UG1sOIn4Rba~Ht3AyrQo5~b6F6S zn={av$qaNN6oCLbDadE_X$9zyP0bws(ol}2lQ&d;DF)VhRs&DvG!9Oi9KIP95B&s3 zKuFeid+;$F5Ece4W+;EOAdR`%#;PSxb)A_2#Xbt5sFvQfL1-&H`M@NFaT`uNkb|Z5 zI5#_qe0rW-$!>Ok+9viJ5lRERQFVlbW|s1pT*6upcamwvNtC*xUoa$GLg2AhK7*`5 zBw!7y1l9uCXsi-V42fhMLDWj@S+jr4y8RXXKb4s4xM zntjo4-+qTUv+qU_Y-DTUh-DTUhZR6~jb31vH`44ifc;byvC;sz2;>xQT2iK)a z?Maxe>O-e3-1+2Wk6>dJi%V?IDd+Tw7L!S-z!yg(!O$Y?Q0QbT6oZ!lB^QzwqQFbxDc3dF4kHpJ z_NYB}mxr`wn2}HIK#it6le_U0Qp6Ua17Ie^@-iHH{a}a1_uG3iu_G(jv;Q}6)#K$QMak=a5T-G8Uq>I!X_!A_ZIT}XFM!?^ zGxUQYLw(Fxe|q(W;-V;f;eG7!h-H4W-k#nW&AT4cF2c$!=N?TIu_NYv`n<)kFe5~( z?V@~aR{OZ7!v0onu9Db014;tvZj0g510lkkMlJ46U%@4>p`jvNxqI7(O^?H^YD)lZ z=l2MR;&|sbZ@TX}b==9wL2LvUcTkRakIBk&81r=^q!neXGOVvyZxB+oH z``1uF7B(SQ$O<1aIr!hk(RGygCQ+RwWXM6j29@zNHJBh75WB2;sXH!^2EYh-%S@VU zcK?@i1ilq!Q8K>BAmnN%p@a}ZQ#^(iofzY2h@M|vUOe~A{Pb;}Jtz+a^JFIt0Q`^sZFOSL`7UfsE6P`p0>qreK;)d_%3%?6_3i{{} zq0F)RpkjR6+iS1B@|YS88W8G6#V4FTBUI)>0ZN8M|2*jMb7PURh@~6^ zA-J#p-P){HQY|IysfShik=AAcq&+3Ef{uAW$0Jq{FIJQ{-OlA6V0==s(tc0NS@afp z%!2FVLUQ-#ooD6WcJJ1+kPM;-V=NULFIJ=Ow}yd7JnjJLxSUh-YGldZGVz_54}J>O zPW`qZ$JMy(#+Lnf+c(F%@s<@;&qVG7_{~}`58#DCLP%0I)Nx}dJmGepo{nQEhm=J^ zgxng!Rex*8#*()?08uI_iWeZA4;={j=k9#lUw~6Z46jcrPq>3{;#On3((APQ<|GOvVj5l>k;d*8fbM)! zOk4Zu+Nm1t)M~4d%DKfB36}HT9HrJAiPPtd9kuZe`hg$y@|H|jDk0U{$aPIdc*l22C)+9qdT7Z_z<`}a7 z>;LTIAeHf`C3=^K!B(cs9W&c=AUK*>{IAFhg=QXqU}1Oly%j6;S@ zi>s@vF{ak2(@Q(OEbs5H`&Cssv~wh+4|WE)0+yx7;sm~|?6OT;b5L|Tk3D_$_=>pZ z8<5|t`ezq~s~&iYv(egr0lNGORY4NZY_;b${MTBYR9-dJp5*+MbPB2;i4l_6tBqsl zlYVs$;hp6^cb_4GB1iC6C+f>ixkF|mpS!=lqs5QKuTF2MQZo;{vB+-)CtbNl;ba1f zj!%Jd6nu2U#b|0R$pL7NPZim6;?jp=M=8}MB`FWtfkN z5UWo3i&9P@r}dZ}-q@4A9a!>BA7BTXhGLo0j$H>k&R~;F*Rl9LR4jPAB%-~8*ZN}Z z-o$Sr(D4hJg4Zjc>TKALho%TwN9 zj6@_%RiyIoFFBOR+!{F0+c^H&a5EOGuN*26`l@>%AAC>@z_uGP&~`cf$F`<_qMO0_ zx(D5;qp0j<(r5f&KL4Xd{xd(RGsrY7-{3RWhE)sOa1zM`qbL2XSf8 zCm(d&Qh{uhrNL@b8E%{rh&Ui$LC^pbaALV+F4k1ms4#?SYUa{3xY0-E&c);b-{|lU z!~aqhh7i^b?3@)5C%UL+{7J_^cbXtaVMk?7>kaAep;M6f$TH~qBpF#7rb z#v>*92%@#N@2E~I)~NYC15a6?hCwUw4Kc)fSWP_?RZW)yNI(SDCmi>*nzSn9UbM`e z=!p)-%CfKbHVbUldUsE39&;U|2{(wzfH*B3-tMs}00}lgbJzSc{&lW$n^1UFJ&?`Y zlVc5hgK)eX?LN&;wu|*&ga2K#0gY=rhx|qGeXztGpnAff?#y5f!X7D^Z@fIIhbHF@ z&ov5QOZ=29-SMP*(v`ZnU_2ul`Iu_cpefEq0;P}|Oi>qol=ngT)aFc;E76Eu`g3E5 zykO{nbVIwU@^t7{Xt}V<_`1r~TBd-)Q(nBm%OGxKQ<8FBL^g_@bm8b9tjZ{cWW>kmbHPm(J=kQ{P3Gl~xT zfP)k%LN${?w_rY>D(b$n?*QI96(rA~kEBw-7jV^Eu9A3R9K4Puk$)Z|ZdF8lpw?Ng zwLVZ#7kp*=k4yHn%8$FC1;;xJ?BNN!pk^czLs!J#lS$Tnt0aiT%s}HBRM(Ynyq)r1 z;|*IDyQUbIYVE?PW7+^vPi%spzVd9#oQ!@QTL*eoSYX9nHBSkNGae;MKhKXplx7?N z+KEgl4tN&iAi^s7&J5s%jqqLzQ9^aQ%?=H!tE*(G)4jaeru73yWJ?!a{AqXh=8b;t zrQChw{ErXs*+d6ePP7*-?WKxtPIFj1KTy=-=L!x_s^}}tm*p}vTpdzcl!REK444ba zxaL6&E+VV+eeZ=k(!tOI>4B;k_JI%pTj3xSIq9a&na@-MyXg2>%*xpd?!cwdeOfNc zrG|B(Qv|dsHGv=pt11EC*m(C4Pa)E9awMoV#hDLHWlv{|)BEb)w<6~+RxHHrE~4b> zEkt!@G!L7gOv}pDM7p565)_`M(S6*;TBep=H&%po?#i-ww2)-yria5Xe>*myr|aj! zSZ|v~bhnVZEqd%s?;m#WdnL%hxk03xo0B7$Ycy%OF}NdvE0;3AD^*Zp)HjWo4y(;@ z!5X1bEPUCYs>rg^px!av&Uubs$a1jfCo+(Ors@kA#3#Mi(ct&pvk<$EFz}q?PTpK9 zj~D8MV}@EuPA)%MHW4m|?>tgKha9>A#=u(=5E3W%5A?3pofFQt^;`oC7XlfukZCYV&+WVuvDS%P`DNp32CcwAnDrJpr zRP;(qBEMGaAJx#>ziTlpfuBew;wj}%@Ac;&;k#i8f3TakAfOwe7CvtPz7_Wo9A5l9 zOUdo@p-(QaOCvfnlkf z_}N72pqz{Y^0TX1ik1GSwZIR2)aIL#SonCaurOWh$2rF}nk5{x@P*I)aFc11+xtsQ z)Q*qAwKuT>7UWaf$hkj&T|{k4w8oYVrysJX$?fS}x}|Bs9F^2OcS&aojpSc+GG|Vq ze?pa8c@Y1Ml7R);89=X3bx?Ft5Ue`d8DRX3P=JI_2ihlu5t$ipwmaUx~ zH@Y^M$7D0ahVN=8)5D(ML&5EIhv2(mg3?@ONVw1cbfOX16QZ3-4BUBVnEwXlky^0> zM0r@2WSP#P;7U3@k7u^-kDVwg4}Hwtr-OaZGHreSTc-YHMB?rLDk>8{= zW1ci&$fFm|>T@`bS}ftu+jOU!*nhJ-+Dd^Ha96$h5M0{>Fi4cvJztMO4aUcX=-W(A z-!1gj>CB;3$renCF2=7CG2PVj4?KV|l3HJRd>VabFucQhZMcrK& zlM%wC;Ellr4s7cTusRAxMg9gE8CC@A5&y%4e@FqpXatt;ibF(}UU;8q*a=(&*II_Xv@P&e(5l~pkS_V+!WW(kZKx~7u&K4$!(6Q+ks$)Uoh=J0w zM_@&w2k*=S8XgSgT@!o)*K#ALl#%+;;IV`;rVz9xO3h9|u6^$hLa7+V->(m@MUv}= zc;kq?NhhL9dr%DgLi3^@uInbXlk2MD+?4&B(%YW9^*~uM=q5Oyy`9=Jmu^wRy~F9ph|^!E5697%+i=H| zTAOqy$7b)doV$Pl^3^_IpR!5|Bgx>q^n9#@ zS;2|`a8Lw}5t^tfBCi^lg}yq1&7XYRu<)p_qJwX$#)rt3sEym4Xxkr*mK6!*O{O`C$j}s$D=cYt zQ%TS25@}_OE5g{Uqu`9>*MC1lLN1daewxiggbZU^@`%ZKv~h@?@_ZB0PV8_ei~pVi z;^Qpyd`(Nq%{SJhf2x<{ASzI0I>Z)$$zkqrqo5ISND*^$UvF-2kG)rx9y?ZlH&$cwJBonjRx`1diwP5 zAnpi@oRK5B7P~4C@wO3;%_zW{TFgWlCM6Fsge3KD^NF-+1<>~J=iEH&?r;in%5oIa z8obqfX|RXmw84ITpSN0TOb^D-#Wq6oqkVs;IR07juGUN@pH$11&uG+RmH`X_qTF0l zcoLrC$OsaDoXE4J>;Xddf(cvxCS`(ghlY(u{KX0qJUiA&X%hD<_&;uCNqE@49o;`} z+fyqKCW|D3ljh1ln$xveUf-g6H7CUfVl6aJ^DL zEPg!~F!hp?5qD8F&wnOI245Qiy>>s^y9`-stX1|%{g=;*zdF_SxAv7i-%|c~pQs0G zc}SxsjYsv1!WkLl(N2S7Bpehr^r3<(;}x~xeU=C2M>)G!#BulG^h+~E)a;%(rBC{egt zj}m4Hc5rAD3D?fG(~JFB;cM=S7o$!N_=2owkVEs84Vb3B3X*v`$$5=Ia*#37sMtvl z*hv!IX84^-V@bnzD_y5FpO9*Dgm1Z^pEKvYgxrGjnyGRm*V$X!Br#??{;k)%9g2l6 zRWt%bJFlLZCr$!kvu^Kz+^d@_Cg~j4(t3qNm0UV4O6gHKBly2|r4u7o*jGs^b$}xm z*M=L_I>5<#*=fSIg+eWND}_Ay`u?~eB2R+ZIaq2*!t-z;!JlNxDO|1I!U%^{p3@jlxGa)*KY-0B$+6tLkElw(@bLvBgJR1x5qwQzHZo%&SU>j*OUn>lvrI%^l zgVcOQb#r(%o2r(Tf?)|ZX(mfz;W(=MUHuGdOJto6%V>+Q{q4`);+p@$#pGo%sEng8)Sy)fPUZd=+=&Vel*JT=CGkf^-7(&_?!2+ko2m=`4;t1~Hl0ycJ+1e~ z_sux*Dr|=E89_%WV1|`Me*eEUDhJJpzi%86=u&8Myz7aio*k967|vCq>huAhf2-4w zM4e3u%EH3>zue2VmYu_Yy28(mf$)|em0vx_mR@fuSH8m4kuIB+4Q+eAJ<{C5#zq*4 z8yULJ`*Y4VwhUR#SZndqLa>ol2jio_>`X+i%c1AYC1~BdIf)}5hUc5|fUU>L*K>dL zet(n3mTY!9xzQQ#piMYrVrVw65fU>os0Eih!!{uz7PhN7t-ZmNz9UZE3Kirwa@wkP0=K5JD5aB-_FJ5vT&%T&TkmeC z?bWU8Xz|@bO??ujj-mW+@vlMM(jlKTo%$wV_kv;3D|7ow3`EYv1O(KPQe`klAuhU; zTnf;|w}m>s?Yf!96rJMVHR7`r`HC(fztnj7UhGZ9NxQ6R()Re~Xt2bCTh#;RyNQYS ziQupt`D7k|;W3q`)d;Y$PzZ2<24DN&TXGCmmU&QAncR^tFwqLJEXdflxhY2ClFlaS))e z@5tc4<_K(GfGWKj7Hap02a`U6XQ26(_&#MB6R1c{0R54?q_W%mE1LPPptgq2C0+Co-zbeqbxi}A! zhCfHbD2jcib97LhrMt*Ue(m00zw7`C#?P)>f;joIHPi23Y9hwJ-*)|6TaZ|Q>8)9P zXd$$jL?WbARx+ymK_M}^lP7|KUO>F^UN2SUhHJJtAN5H}&)+G~p9MpS3x=E-me67^ zFz)OSlIA+DbV{mF?muckuDw9tz;I!Uta$+#|RHvgV zo+Ia-2;Lu#^fRuElrG(a*A|&eFPD)a?qLt*jkDIpJVa1SGT9t`voL^KX!abR`a}7t zKK5tKsA;lyVWig~gwd8^3FhCpcAb2{Z&5zHc-}!bh5N}W_Tv^B_e();{05QnS?PK< zfnD;(wD(f^&Q!nhjRKXXQQbJ=L1?6L%Y_XZG=>Uz8@dXy>=^F2T0Lox|L!zGE2M*|%?E4G^L$rMUlHji&&%3o0^V1vw(gfl`5 z>bf<8`;q+kVA3E~`DxCNTUs_KbdG%QRNQ#bF1}jzJJU7i3Z}2dOY&7r{!E&pvVXcNnhZ+D&LAui- zU%%qz@N2*J4-hNiUnN|C!&HdQ=Yiy#3?nX3$QANOvvakSdn?>#dxU?BZCML~@@8`5 z(Xd^8;*ml=4w3s*p0A7(tzEse;~7aW%8W4-ruj3O$ON#m&feSdB-Hn1UgS8hy&pS$ zzFLN80fha;0l8WzRD`_dBd`dMGb2Bpk1MM`?WUg8S7Pss>txW$ci;28pi+fgm-2E{ zJ-%!(om25NJ>=coAI}`%k{O2sCuuLhrS(yzBUKASgGJk}SYsYt0Ub9+UXW#|Y}PVg zJ?@=Hm!djkrqv^c5YNyf3a;)U&55zAm+d$U^h6PS|Kqm8 z5Db0NekA7!hRT?$dQ9<8g?G@7Ao2#ARARCBzaha>etNWkNH(#46m|o1_B&*h%z+TL zKt$IKCAUqB3b;lR>eVy+gTg7Fzz8|{CkGVKlEKiS&xa{WTzoEyn9^Dj0WgP+( zW<|#`REx<<6~31ddM;Y4w3Bvp)ls$hy+=nTJwCt_9XR8}v(k~0B&HI_^zClVS;kuk zA#);7N)wM$TyJgNm51+*(Xsj>;{V`#Ia!mOLePM@(&p(wr~y6yGv;t0{p=VJ8xoR$ zGRsDJ)6XH&FSz1r8(MbT3u%DG46TWnj-!^CuGjze7R5S?ty>+WJ&V#s5qphxh0ks) zFJgP6%NlT%47qE7@r~e0J{WsMh$3f1}x%9SAktWqO<*hDeGcE?G zybccAqPV^pU3S0C4u)&1Rkbc8NKaH=UD`TFER^ND$9e{p*Gh2an2{%Bsc1oH{IySgH1&&jc{1z{pO8iVlmbC8t!(wt`z)1aX`W@Knr#!D{t(`XK zTK7U$2PX{UU;ax7a?w0y_I8SYmEt+Fp%P@tJlYY9Ef*cE7w=E{CgIAI5G6p&`N@*d zh*(tUfW(mSa`Xr=t5kz2k-NdGlFwYlGhRl=N%xMjsW%fPf#?IB6TZbUE`71L}jKJG>=9qdEvsS&|U$4{u$c4Dr1_$zCHMO)|z+q)` zSu8bVJI71MbjMy%?wa@t>O@FKSnsZSWd)opK;RC)I~#acPfApI)vblC`W)Uw`5>V$ zPAE-bNj!7+X!vq9Kgl`UOoN-?kc&R4=FYaZ33j`e# zpm}oVb5_T`d(q@zUt8owcXASiqsCh)2B^0Qz%fi$JQh~J33JCNAqIvfp8kF=rpZrr z0QT@OOKHMw5EoQPx`Byu&dt!E%B8#Ptaf=p?5&e`}_ypRZ{9j|hRfArd{Uw2d}KQ+&~pX_=&?LYYLnZg*_* zlo9WENweBMsYDn5X~Rn?gXDJB=`h9XK=8K7D_3Z@Kc`v#SalPCgjnm>vF7~PXs)wu z_Pcx{Liw_wZ0(ar@wAk+y&Itg)XXtl2ssh_#pK>L3rzRKg|!^|_DOb)&~ZGI=`(t0 z8r$!lCZ5A~q^s%;*egw&=F}#WQjvcSc>fac+tI1DaD^-xh1# z1=-YJn6^R?mynDH23oVOOk%3nV%AD)<{cO4XHbf}w&mHYW>P15uueBT;n_yN7+B}F zY_FHGw`47s89ZbTmR8^cKx7~i*Lp!QI5bgjb1wdqlV(zLRV;VBz6NnItM0oB~z71+ZHUsGyL^$;94;~dT5@QGY- zFD3ci66|wxO8GK+7(mwRFncWuUJS3s&3N-^mk(;Dk?FB&fUEvsf1^mnz}3P&@;>A%d25U*|*g9Y{)up4<8-NKXF(1uxON zAQtkuLL?kh4=ka8OlA^rhor&<$~q>+=}^>I$#>40%0j$mZH_OnEQ=J|d$o z#(m}2{^dYW=yt0#iM5gngpKq6|5MvqoAyN#SiQ5V`F~RE*v*X~;PfjnYdEwjWV{nJ z?Fwx}BKVz@m^{OsaN91rUh_b)nB1rOmrD~s2GieX!yO<*vuwg%-iMT4^C&3^8RQ0+ zb^Z8ov^I{US?#Lh`5baDf@%>ZSTFYybd&jz`_ixr|k*k&*m?5T)$rmxp9*m6I!r$+XcJRex3RyC9d ztV)~}lf~8L>D?NC@ZjpQE=bDJ%!^BVCbPyBlXe)~5DSRYUux@GBlwCdA2gkcOQ*_Q z26*NlnH4ngmKR?xz7`#podF=w!H+HP!%7Ee<^f@-o9=M)oAiIy!yOBTg61q=Dd z!r#j6-j>{*`r&O; z>?66K4Kr_HSM}rjPE>*{YbBKewgYiB&vj2waK5W zo&j4wdBMaZ7+Z#NZvd=U)CdKN@R$!_G%=TVm2RALKbp~KPo@xZRuI(!V=cOJlkFC! zr%2={XtT;qg+vQ(8?#$Qfk(#je@F$W^g8pI-mmhVPV6t9N|75O#^^9!YftOTB;I*g zRoYGX>;A9s169rxB%-6O&{n`MQrFo2gza3iy=GX&Bp@jxdUS6l& zIP(W`2^4W+VmhmsdlqRUn7%A9Z#Mlu!u0Lw;sfVXBWvTbQocCn(Vh|_jKstljYPF^ zTWB4I4KdW4h`f>}+d%~&ZUedmZLyR!bo8M5N>6sf4NOJ#2V9aK=_m{Vd$*>o&)CkEKDq z{W9KrRnEOCG{|nmty0?=j@)}|`+u}tE1FOWn+(aDwE!#NT?B2@+aNUg*GR8ov0zKU z2ubzknl!39mxP2cuhQ2Qqckmlm`puFa|PzGq{fIs5<*+0LFr)jt_2XrK^buo~4^>Wu#sanDnUMnxx$QM3 z>V4lvx1d6(qJB2+QtA4NE(r`u*aJ!Z2e)HYmPe=(X8BFjheRv6NIqy;4fK3uF6P_* z)bFk{3=cbU+kP3*+W#B(Fv`KR-?1pIwj zWlEk>Z0~q3JwH^-h2YDp4n@lI6Ph$eSrKA!41YFq2l6SaGNCqFG?ht^!Qh|R^76-B zD!@ipZzs1#mT}AW*Jqx7()P5w)#*G*W12>?FHLV2)0rMA`cfe*;ilY~9?p65g=&~m zXFXL;MZ9;lj=6jC)9{e`pU+DvEpi2xL5XqP*=DSyBJ18#y`0FdvN-9i%Aan zdI2$Z{hKcdIDbvtd`IU4Sn>kECid_!#w3lt{_v1(?HpbS^7?3CS-R9}z{RnESceuV z&tr;`kEwgvI(!rXHTiMSz@`c6^iiygj!5~QxY)@?S$2k!pC%;%fQ6Kusgt%FsDL@s z8qR=e0LxlhaYwDl|7E5Vb0MmOE;f5g6%ste4@shMIX3dTb3_CS6`i=kR;6wvdYfYk z|M)~zlekrzkBl`4ofr*e4cArn(ljQWVUf>s)}j2GNu!X{wfyEU5f-UUUOMQ1f4nSX z8o1<&Wuj}97H)nZ+_fAKkIqqWS?v^2vV=eJ!yx~=HX!tk&~*dv|%#%4^1 z7O?Y<0EN$ga057j{5fW~nF7c_pLx%(zks<}gWvsWjq-NyzFWbc_jlvL5Ng_{-&6Jf z6plR^+rOc#sq-*Pd5|Uv@3#d>02)^?Y`5j-697VzW+0ggqO=SwgEmre@ft|vDcv!S z;y3KWZYeG$Cw@;j;H}HefKCvu_j(~~@WPW(78LZfRrYfQQk7#G669e?FAFqP@fdX6 z(Lye|Br)u-pBAAanYwD*i55MjY?b90fZ|8~FPs{8Y-tb_} z;k4p*(e^sy@9=7nWOfsYeCQ$*W4byDTTYXlGM}R>y-OeblaJj5fem7p+>n_|$xLuX z=Og$8-%Xfzbr%Nbp4Xo~V6l^Y(@+SlL@0MPl z!=`EqJ;sH5Vm_g=tYfVs6Ex(cYcfb!DAk?e3~}+4$=yh=f7~1udvMt2xi%AhYSV6F zZ4{M>I716ABEf*T>0#UI;z%Vn0M!fQ+M;Mjpa6rhR|+Pz_Qj&BM<(g*v!A;?dBZ!A ze;Pc&L7;iNg8}!7^_J0$_sY}O4oDJ_i|-711K}AM_tx?6eaJP#&8B;h9H?pVa`LDb zJgXuwYGYob+l2-jHjE&J3No|wUE_`avV(tTDk%&M`xS&N6#|xQgRi|6eCk%H6l7P>iWiJdCjtdw< z#lb`HO)k|xL{~sw5<+^X%uztzrk(MNKIwtr)beLW+CQ2hzVyd zGJs;@WbOIclV_7A?pCNA^kol<05Nt@&~Vtg@0lmLpuYg_8GB;xgL#6>dH?}}9iopG zQ4=_8cd1%Mj_SMwFcJ>|o7umRt#Y5|p-ocA;$NKwAsftG?%OqtmP5wP8-pdCmq<0u zHiq1f)2^Krj3#G^Z%hbLK=R`5^x&R<>dr(x_dI0>xn2>$XeL+|T6t4-nehd$F1&LW zv*+HF^l?+meA-gO2I@45{N;(OoWR7zLA;Rz-PH*`${$(-_@i6c;I2`bgsk(AXUT3o zNf3Awfiw`|Y71ASr8^*mJaIqdT#_NxlH$?#+W5z(dcIQUR)F2)15M$_G;i%%L*_we4K-*lZ1sNS(BCEIr6% z$^{W&>2JF;VD=};?fG>TqRW=&3IWV)ZFB3{tURA+M!wGhp57hb(mcGXx9EOJei4y9 z#2A`F0GsgDY6qiV2oDS?W5SsbGRKFCAp)xbZ);awfqY*5j3SG`(%7yEZkXKIIEw;d zXgMC#j@vO`9Cv-1W7bgfGYFj<(~%v8HICtyI(0$|@KK~IqU0kzuY<>sbBclkiIOuW zeWRi0U>Hf*YpUbL_hwR=ruEgPUl(8Xk3;f~#@M#}cfP3EE{{+v4e0XWqjiz&;F$4svycUOdtN>BKg>>Gussb8}dqe zpS!+HTbMKo>_)bjs#zeW803%W*Cs*4n0>v8yGDq%!;SoIkpMS$OTG<~;|%(({0NQB zzfBjZZsKbz$0~0ziD0rJVrmgIP=A&e^W&()3OvrDhB5ZZ1>gQ}=hW>AZY*Mrei?&S zz^$)}je~+3f02#2bRaox)GYBnHRR<9Jg{!Km|c1QVPqSRGgo2zISKPUPLC%SMGl{DJV%p3D4CFwry+ACjHzXp^{!k^fyILR;6ZZL8`YP!B>F8(%WIk8!@x;ueeo% z40+KEoKYf`?2H1~7Q}UL6{xIcAV6zBl9zlI){@RQKDp9^yDC)`E3jr;V&haqDgQM6 z+k{_f(vjb!|CrpK?!Ch2Khq?T^FF+!1W9s(dL_G%8yjc_0dP!{5=sY3&6uCP1 zo>2(Qc*-`;!u+#In+1z_Ti!L~(F6yR_g&$v_6bg2C~Xs=tz z7|}01qNsq)z`Zxs)+@#a=+dM{e;V`}rtTb6z=`NLpp-7l?r9!Pnjh{5THE{y3b}@_3a-)C&Pci_?Hgap{CkJ zneazJdT)f5?i3vLrzWAC(}2HAz|GC)UIV58ESPu1IZ5%JHFTE{aI-$3GU5xc3ER)_ zx7%Xv`Bj!2Y=HofOq@5!@k+rUj<;XocAgM_y(KF$sep98dC`~iGzrF?Brv3hbQPlh z#f)w+*zWznlx~rs{6BPdZgwIjqW?+M3lryupaJ3|2Wnp0_`riY?n3RD*1nAM0 zP1^k5B34U>KQ-W}j35!T;nU_hZk6tcJJJW0o`}su*bO8JO=!pWN2?B;1Z2X(3vvfX zBqC%1_G};bZlLc6_6dx=J_(hrc-Hy1oOZvKwTymA5M%y?|1`Dp;$Z$ItUyH-l|Y8?xMFPECX zAO3OfGE$AN`wEMolx};+=ZgeQAfsumx1aWL`ko^!%lhgNHHpp!o4p_dex(eDYINk* zf*IUgJixfGCT422EjV=qJ3n-9ZascxTQ7WVRIi0I{WMvY>tyE3mxyOTGN{w5CCeOXnMEYr+=ih_R}Y^r&cd&JdlJM86YoL0?58*!9rFTPKI)i{F!$dE-iDTEgmJmlLY za6vk&pccsUJvIGj+*m?is?KthTZ;m>3qA6CEe=X!-wZh?umHRy@kIyth4v z)<&{QTzHg#MNBwO^UFrsJA8^U!UcM~%2R8SzL+PDc|%jlW&m6k{Xq zjN8W_;IW`1I0|C%2*fI^|J2~XB|P%^0-(Cna$WzgY!h89j#i5Axlus|>#HN7?@ zZ0b^8TfAkK5Jjn|6o#>7G;VS33{S$Q*QY z8jeW7KrZ65{pp91wyA-it7@m<@u=Q$Z&8u6%k6EPpJ&JOx3#SQQ=$b5T>%(%5Tv~W zW1q~^I*Oqm+ra>Xvx<_J8zyDX4A|d56Y$vo$_}3NTmy9Nq+arS7;v-ZUT*6Cg7;=M z`B-kH>2=h7Oz?o6%Qf;mvA+?mrOo3d#;4%|BD`EFx+A}{u&#wg?B{b0Ed0E3NFYxO z4?2h=q}Qgi`o71`wfyLbpcg~aR8Co+S{taNCXa-V-MdoaQkF+F!0hYDVDJ_w9WCd- zG*D5%{fi?;vzPI#*~>f=PtZ=kHE=wgau~}NK%5vZ_rT>L2Qf1*c5A(Q-_b1=ZIK(I=%7Dm1ltH7NSW;Z!EI`&7YxDA zi`=Hb$&eX{Js?_P%0zJ3aoi@&q{KNoSjh0sw3AJCerRE3lFO1V64L%!%76 z(enRtJ6W>(LgrJ*bRqgKMJAYMvfWU`lTk{u#ejR3*t*0dSG|>Zz@Vc&vjIX+ysw`i z>fy|(=wht@2m!&$B0S z{6Re(9s)c;_u&0V%eI}}&F|2E?tpzQdyd}yu4`GioVBf`V|cS5ypJ&19h7D~DzV@l{Q^~?ULh~O(ih7`6JDm6zb;Q_8-2K)6C5v&GEw%)O;?n- zz;va$v2RYQge#+>j%swF_U*(tOtM3RUEZiq&v>`G%8p7T1e|WE_V7jk_8aJxE(nY6 zO@0V66LXSqA=?lH5|6MF3WyRU+b++D<4+&J8nBgPpjAD)#MmD@&ys@DI*B?z=+i)E z7{@7O#G=k=a3cs{5e^sx7Mv5lK#;tiP44;Y&&46!%(ySZm&>*AM40FpEXjb})^dUR6&H`OokB{pRJ0@p_S_+RG`S zL%ripi;p1+$$bIjnZ7&jE2HT?+*0e-wi;nq3H-_%KuQ1XRd$iY-p|oMS3JyVHwA%G z@$H@_eLoWwrZGC*7a%?iFLn_$YOX_g577h=fvNHh4q z;Mj6i$I^rGiBMOGv_|{jZKP;Wr2l}=WTQ5m-Cz{F-*jO{=h>~6UcX?$H}J!lbLoOT zAa#?$i!;bhp)c7!z7%4CTcSpp`L-dtsOOo!<6hV!!%cP&A z6IrR$`mfg=#P%*_@u;(Nrnx)QQ}v|JmX|~PCzvM-So4_Y+?_}=XQ=v_53z-!o?*u* zO5DOCl<9x{CC-mjlNEmSOQKCd&jpwH^!Krz|J0ykKWax0NTo)!||Zqb<%5LHpKpQWWvf7^cV6cR4wX*7YlA6PtYz727dVoMth2j%Mx3 z^+-oHs<9~x>bzq~kqP7ljWmM&v2kTR&0-0{usH!RrJmbWLUJG%$qagNgdF8z`T;G@ z4?hOSL5%bBgCh+j${oRg)6DQK-y}^hV}Yo*{hIQt@=19_>SM6uilAa)S$`~BN5*SO=>}RK-IZQvT*vd$-iA~7gOS$Ym8SbeS*7ocPLplydLvTOriZ1P9bcK zcT9_II~*stf41;W?5-uePxV6iF>kM51Dx&!gb&l7Is=(9I+Ck|bWzZiViR{*1ea53;+*T5A z4ufUq3~~yH_FDT|A?{B{OLGT&ph0|Kh?my1BltpRm%;>TZQqS&Jvk&w8b#;3uOFe({ z?)w+vX%T*YxOIPOzS!MLK5rL?wqAzj?=}znrf%2U+n?|M@y!psoVmgoVTb}NoGU?| z&G?a;w1rjpyalzt?{9^jKhvM;SO2#mqLK)$!b7{RZ~6QXUg|^FNL)%eKeSD?duZ3; zZT~U(tmbbp^xgMy#*WdR0ulTaMm|)#s#$Epm+hi$>fe9DdXyThhsU)j1Bs2-@;)N~ zUNA1k0uZLKj)y@@Z~>ZZ{-4`BT7Gz0Rm=VM5iR9>f2dZWv5r04s@Zxm@VH*)cZi17 zi--L#tb%o`@Et*g&sX8OjUva~q2s=3*Ly$ko2OmfE}BoY652dFW}U0T$+%uW|FSw1 zw|7bkuQ-1gc0(grp=Qm)tb6>|k2kXqSWG#8Q|}(0HsEE^-1>%hw-ySvd8)$e_tg>~ z!`dCz-q!maeKGT&m}XE({P55fd%SO|ZKN5L+*2`7ibY!;x^9a$%&-Uz-A?<>5;n8D z{c4$j2;xRX^_F}vz@ZyPc)R(DF_D?mq6_c(?DBsm(o5OU%b4zzE^;Rd0VPXDy}wLJ zFkNuWmW=VLC`#*9(ao#y@(hz{EC0oL6{N6x2=QFatKWYX z+eg~X>ct4MEJCRF$Y&_SRrRr2w)L?|4MMo=kb)c{1$jgYwnqw}kaG$OtJlPISxES? z%X?vyy_mtkX~Cu_RcPiu?7q6c`Q@f}HqcF@;mt6C6tm^_=EKiyw!$}p5)yKK_C2>S z#6rOI*=#d=bMw!DOcNbGPQ?n24f1~^A9ExzMwh;ygoarT(R30 zQ6Cxx&X_7E+`gG|``7q=HRbnR#_uOl6R?PkV$kc`Tyzjd6qYF;^CT0Y=X2eqs8x*w zB)EPZGM__+R;dah1{g%j*vWqkxYluV3guL?`J;Um$k+y*UIuI~a-ao)jk_Ue$zTwN zkbVtoB4JIOVXe%t#&WD_bSp=o>%ek&v=}Q0h~vXa-EAP!s{p%WWW5l?z>6T^QWmpPBmRHPhc^PtO_9R zijA2BM*UQSjgjYp(U)!uPqrn-7=j&T=c~rBLwu!3H$-LGh$lu7F98vVc(VK)g5oSj zyGltA%qXf1+rM1>uYz^_i!h)5Ql0k+m{ImT1T+lcntu)DK2Co%P;Px5k1?!BT#Lte zxAvetDknzuRdiThhW+MhBxe7QkeIp(iN#)j%AdXWNAD_xd1)k6%Z0O_`?bGKQ02V) zUF2fpU=}*7LOe}ok+~i02iQU>lMWc1$(*4hm@Tv#(GizdC}3G!^K?v)GO)HG$`~y9 z^rnG6?!s?((|~_Rf777Ml!z3AKLl*JT0{!CjzMuhQ$$MWf=iL5b52Bx&i|lgIT2mD z>Va~~kzn6WP$~_KX~f-&5WPo-krLt65d7T^PYH8Uj=2>(AN-&JPG^q($vnZ3Q@H-R9p2&rtS=7n`mNoEFCKrO&D+8*c#(aq@sj9!NcXL&Z^?A2ZH zLX!axxgCEbc`l!S#&A|=v=XLZ)ojpd5Qps90Q0#dlk8Irv)DiKiMu4D3utPx;E($z zSZl$U8e1$3v9vN;?B?P{q9Syt(;;=_Y%gojmis2}14lQBkWcr567LjL{b7$XsvB*mrSm~b&hzY|XgM@hD!h9FmZ;FXr!6xawGkc_Zqj@ZJ1Zs7Tz4&lqm-H7kL zqb?M0=)m*pq*8^#az+c)>=MUxzsX0NwEH&Pb3IOQPB?wM!9IVk zTsD@HdF-AH8-^)$CFYXwi1K8)|0B$GMg~edBw{HKYzjHS@s*?XhmhmLzaCqiflaVz zqrk%58y*ng<^_hQQUw68J5pMe3@EP9q~eVh$FZA zLwjkTN`z@srt3K4pWJxunrv5`>d1c(!-x09;qbJL3oHj&P(DT(Dx-1f{IOc=&*3&@ zfHms)!@Mcx~Ju)S~O>UF^rwL zt{w-A_mz8225(6Zel~ijetBBNia-~NJ?Yn1`*l6*IJp1at*UA~Plw$YVYh#J6I(s^ zNx#x zes&XwT$CAO1CtrBkwzKP`a|R=XuYE&9beZ+k0g*o_#EmbsS%sD3Qs;eCkComxk)br zC8o)at>_azxgk7=F9!&Y3uVIyg&;s6u@x`eLS6K z0s>Jb2-Wl^V}j6#HZnecOeRFsay)FRk8OBL6Ld6$YMy1gZz8`@;ga#b=Jnb&DR>`l zM{CNV5eh)i%+X~=e~{5vqBV3e`LHGhZDUdOz(2|15{lddWB z)7_!|{a8&tp}kQ-xWZ=_^YGW?$}K&W!zM<;+>Px z=?vsLoC-B>o{iuhEx!Vj3*+yLlKCc16X|5a@R4Lfre}G~^hoV4Cwq)84DDDHxpYsP z>2#0kU@qh`$*C&`_a=WO46me}jTUX`@Pby*UQxWsC1S5qmvS+eyN*lC9|=rdmtSze zBI8DM2M)+iI3O6Q7yEGdqyyrcLe~k^UQiU{E@6bw|irbQLH(>GbOU=D!sw*L{-# z#}t#nqZ0!%Ha3@Wf(R*p-CA3(+cpw@&#&;K4a6+(ls4!-EV3!G=%Ph)k_OoJLC4my zh1!xU$vNlrVSoD$$E(HWaDnGnPHOe;gUP_w)J>|gV{ZN6BFRL%2MRaX0d1yAPB%cl4Pd)Lst zKP;Bq%&Vqoi>mbfb-u3_(D}(O$QvZ_)Ych}yU|A+sA~URRlDPY&G%*3|KPuW5X^FPT&far%Y`vUQ8EHx+Q=O1jo_*2arbTx~ zVxFy5$2wboJ^NpsQuo8Xg(luY;VkV@;>pltV*DyKL~76U?SsE#(9?)!DOkYMU_oht zAesNwe-1GX8M90TTung0Spu6sI@Iv(-`x4*3;R&#BrNZ64zrs-f@TQ4>4A!6)pTi&nGZP)GID0Ba970PS`ctC z;hK?u_KGXo3rKFPCXSbXxZ>HkBH+ftHCc_@v>?F#KQ$YaT!ZqJs(mHq;hI%u9kuo1MdQEKy$chI>IlKs zQW|5y-#J`6TIX$Mg90hRXKrms=Gza)-OkT=_u)0h1#e6L-Q;CnXE@yY!;7MWW<;f^ zCX?vaa}u0c`nEymyF6PrzS&lOZ0BOc#{YQp{SQCB^Wzu|P>28uE2nJ&{U)z5X0bGX zw#Z`W+YXLNA=ZyDZ=4DGoTG|Zn1JfuTk?K>&p60 z5VmfMyt$%^Kntu=F|cN)`y{~GajMavK`ooyM@BUW>li^FTorV+Fd24U!obG&??aJ* z%)yCdy}VuTq;fMKx5diR*!pRY{`_En;NImJC`6s_fV;9Wo{OfsD^A5^6QJlqGrx&P zL}4`wFS`9eV^j4K!!LKm@Ld;;yAF5Dwtq+~0JsJ0v|QylcJDXTTI!xf$FRwqX8CDV zy>^_k8aqI~Bg`{M-S_w^m#XB7O zsaS9FhynluDSJq(^NkI=i!M+cTMx#G(m-^o@MwRwnraV*Y5?2?PciTFJ(!eHH3xBh z7p4ZZJ5U%qWOcUBL3bKI-~!q4gk4eQE=hyV?BJsFbpBLWwQ>PD?Jg4!!%Gqid*_GXjN!29LT1( zo8N~B5Tp<+jguH1kvRc>Y1HS7{60b|g<Y9EfG5wl&QJ^|z zcgy~lTWjLeYDu*U9bY^x@xN$FPy0!$%CDLjSE{+6NnoFUT3awsGm`Y z^h~(s!j%f5nYwzVqgfxXh-@@Tru>IXK3l>|Y*MenWj@1!Lv)sxc*}!hySH)Qvv2nY zJ=FV{^wHdSv@SMVpFO*NSA{@p?4C``^oA4NKU(!DVuQd-wM1rY+-HKtALGzAFoZ-` zr#x8Gzp-F{=_DB|lC1?GcG>3`cS_t0QxQk-$ACJ1F#?}0B#C7Hw#6qPqz38vGjgt? zkOuB@cwB3?l`jBbbyrc>W4fLe0BgwE&momf_`v{gMJg4hk1^%ult|!lidO;SQ@px> zMsgBbO~4jrd`7hLegT`6=g`OuTYPoxS*g1fDIvIjGXWeyAW$?feX`QO9-$hU5gtum zc&3Ha;f+WMfD9S(x(Y{z0LlbQ!fQLr&4B-s;)ENEVOftNj7XWd?2Fp!;eNRJ3r}D0 zOHCMIrL2E;<0r3Sc&r#KKzo-z;Hi%tJ?5`4?I~_fwiuQ0vDFW$fpnq&aAW?lK1avj zKR5J$vzu(6N1_MJ_`j?A)Z;!|+3WuepHC~yL%Q78m#!>(K$0%w6(=b}|6UwP88obA3Vo08*%C^#c<+ZqpAaP-c;7%UtZSv{0g6*faMn@ z>eKj7sv8n15mJ$0=HZ3>-zmhzrA-y$7DxJjXO>e=ZYjN%?rLJBnPk^dnD;XOZ`9cN z>sSQ~{59wN>(orGKOxF6z*WF?miU=JFA~wgGQJ`CH|Tr>_Qr#~=)u12b4mQ)(E#)K zb|eG9caK92F7Yobt~q%=>HmE<0|KxeTUJ-zuUc6-4<`Nn?!|uq2Mu2b3T19&b98c( z2*(VQ_umx)GB=aK2PuEuT1%7LHWI$iui%lYwGg}sCgsCs%h|1zU8(g3Pt#wUSfv)O2LfB3o~n%pNe`R5h&zhB>6een%v31cKz ziYGVk5+(%6v`Mte2p1~3StdWGZ>!~fIh%`2rNwsX2Cx5qef@u(8@?^?o3d@IdOQ2+ z=G!m6VOf%sTyt#|63XFYG+}$)!Vn|j=>~MaE<4a7WV&nJZ&Pk6`wZH=Dy{z4NbitI`e1j}N^S_WE7jOeES4tS=g``%PI`NtIrI*v(j4H02WQk6O$% zF*$PbQt*+a zA|lh^7AwQOKpqO{ycc-u1!w}UGfX%E3QmMn$+;7Hh?uU3$qXhGSIkK zUl^_w(OQ2dDu-Cp$zpT$<4-hM!n1D^NIADL2wSP`d`GJIY^kZ2FaK~!IuOPkeh);1H|c55XYks z$0rc?($NAA3&lWNCXYt8fH-9E_R}MK&=h}(>|ikf_%fWM5gq{EzmFg(E}1+B$MPv~ z9B2#SxVI=+3$#eV`Z6X*YXPqym}ObGK4Z^Fur!CX!0p2deHBG#MFFXjxu8U59z!2j zWm69L?3txZR|Q`DJlqij*A#p5>&a#Xybhq7r8wxRa{bN;7w%_UH=8+kkDS&;S2uq# zsuyvbfVh*$-EgPnXI6CBU_VAwfg82DU^-I~bk&x_{St?01(15_&JyIN- zq||_kl!dQ(B6Xy&ls3JE6J6B1wM|=w_S)Q}X?@of)z;lDhTmfSsjC*n+R1-}r7!DC zU)pCsyKvgL@$-pM7fc(#5}-GDi-5PKN&ho5JoP4c7D>}$d9==!%s?7?&mkIt@0#*{ zw=SA}2?_Ux>BRD2RAf=u>H?j&l&jOT;aWnTg>T*A8h&!0?r{`@6|cRO+w##3eDw~a zQS}a2w&;gAcI}JO-SowA33Y#TtZU*S+pUu7>Y9IOi4K;Mb&7cc55IZ>{cBtsXu4>9CHVCPK;F}RP96Asv zgEIQ;I;dw^2P74mse^UEWDW3x9dI-5HVz+9GEQ!go_QO($ch5`*{rt$FUD?-l##%= z@{`(d$^)*so~EbA*bjdN{D;M?I=^IFT(aF}=FuIN&o@zE+q&<^uta|4F%Zh8wp*mK zBnY36_LWGTnGx8KLTvv>l!#8wDusd#SP)nm(K@B97BN;>5Uqqxw5Hj`j38Ay;0Au zc%})X&%x91H#6dM=4lsnkUj&R7D?M_6~@Gf=1A%YL91cODNWE+8_0I0_D7|W`?SBhL@TfhBjd3!L z%`fARiQ95rF7}X6KX__Tbag}A>yUq#;O_X{`Q=|1cKG_U<7u1E_U`;SZ4qtI+3i%@ zev;vTdV`KVu|bzZ(5utEG9B%eg-$r^mA#Lo5QkOGCLVv7d=8sw=Z+f=y8CCp<-2L& zUo&8z5*DK75N1YtU2p2f|24x#8vY$)|8I?x|I7fK0Ujp2o4#*dOJg5QBHa{SQ+=Gf z)*JSK{q7dDn?&Dk67d@7MP#zt_yfXc;@c~xAvF6-9{bDjE_xGPJq{b{d?a;AH2ong;rAZ_ty>5>@K6( z-><0u|LgVDcdwYv3^4^Svg_N75>vt>nz4+@l4!=WLTSQ<%C2|Wuldb_m-*vjDJaW_ z>i1@Uf9Knq8XG)c*9(?+&4-05@@jy8Zt}W!&mNj#yFca7L2mGnKzG?UF z_I=mhY*A}Fyf*p6rdgqk8~6CO?c9Bwbl*U~p>{Vw$11P32UIWiY;T=9VD5wpwIBj_ zGZ5|_UhJ;jIR(wN1OI~i&FBF@hi%n0 zf5V6?A-qC%J%N#=%70mu(ry?$@*9>w3gtvAlPv)$hGp052q4g>AV!N4yF!bxOcKU~ z>-ST$zN>vl5sZnTV=!dU^?TP%#8VT+imW8GFyq9OCS;UE6-BlTI==O@nm{}lj>A3$ zlU^srDhSs_B--1=3K%C6R83uv7YTS^e@l!Vj(CXXQlw;j?DJzn1JI$+GX*f;j|WTh z3SgvAVy>y#7ICh}8Ci1q43xGkhy=}laaBGh6a~E|MgBQqG)s+w#f z+jC0YvDZf2c>(?eoD4n{L=;P*7;nZYzWGA5Vz21TO?{fk`M>L@3q_e*_xr9cC^j3*<5-oO5bk{vnBY1|bCr!ji+JrJf@` z8ZSotkQff#@}#iJVa0{Kp>`;_w{fTK8%rx(5Qp0qNQWK%dPC`mjXVi1qhQ;V7B2We zU{N>-FLd75zG0zgTP`fvW>?<t;+pbf?;BU5w60AP(=+X*cgtR>aN{aTT~7OZqV(p1uSO0uGjwN zC2qxqBoYFCH0T9yDxg>Kq&E$Ey}eA*c)`GYXY*s|7c{>{r-6$>r%}H@AWPb>>i#9} zvqC~;*LPoaL$g92;d)!je}oU(gTJ$e9fMI0ZVywmL>f(s!XpEiROqy1euKQL>X(jk zbxj3Q_%207vc+$|NzMy(KsSqm)zEt3@gDLkClL z$O+r7e(6STyigN?h4ulu^!3cccBrQQupPWa&MI1b?L+XcCBA}Nf5`NnUfudz<4~`E z{=qfYxRh19bFAIkpjYIQfq$YVUc@`9s8zld0MPl^H~l0&;N4%W+r6L3?pdUBYbJPS zR}UM{ykT7S@>C6*YMo*~u(c(4A@tSgTPDd8u!Wv_ik@#5OJJ}m8{iZOl}Ip(XyQ{w zfpVXjb&95MQcGaaf5S{+BuRWjNBw32+aU8FQXU;V;Bkq6x@?rAzmyyvL#N1J3Ze5= zWzkdm&2_F0ul@Gvar?mewc7)trFWN&e{q7H_OR3AyGV8Jtf?O%yBgZg_r@KZW?8}D zyL~_E_tdPBjRB=en{{V1JWoT!l#RHnDZTP2bhIY11E4FZf7xss3QW9RU zgsF(rZ~-}+(od!i+NBQiNuF0ckvfR4o;iq!Q&Xlt#)MrD7H0#D1q8Q9i~{OD`Vzk( zkPsZ#WC%Crf2sWdDARJbAKw}e`k8nrJ{1o$XFbEib0Wl7-K6sA_@ZTbCcZ>EcQXGm zD7Jy^c z%36Q8GcwO{M*9E28O`IjQU-aK;qn_6j*3flBSCpwH~LRvI-srds!{9-IH6Q8r*uqJSXeky z;1b|*e_&EhL;YR-?fUBd)yVrHJ0k-0i$^~)TkWoX{f%a8c!Y%%0gHXG9e3z!6vcFB zo4vmJw>t@oq@PCO5*$GB9QmbS&y&B5l_sJB9hDh}R84B8ElLejTb@8CaY_r0tsBtQj0X$26 zDQ80uIzA^}W|p}k2X_^JihIeRp-EZ#J^gXN5O_q_-FNliB-6@E291%8Koh9QVLSAx zMBJ9Vh1d7%28&IJk}9B%$x|3a^^+$WZohmxX;*CER#XG4=v?9h>$4LwJjob(?DJdbEL+)TH(dRglX)K0P z$mcl{{_!jLa*jmAOajYBfg=e{49=s7C1#6Ibfy4xQh{Oyf<3In7H}pmuV!3c*`oq1 z8LfMF?R|j)D7unmw{G^;Q2WP8r*Qn-e*jYy7SH|ogup_87oanNM_;LeFCMTEJ$ND8 znXqj)Y}$R>?W*m$uM1d-M9SC6Zz`;-o?i)52EbIrFTJ^Z=FbX1H2B8o9k5#nh}P&O zl~!P^&)JK9(>43w`{NU62&Z=)>Kz~7pshcq7~7~u>EO*u5RU8dLxss&w1bgwe}F=M zE^MR=%|+0l6PODGIbf8=oVR!IPQ(bDwIHInXz$DrWusYW?-9?P#)6qgL%z5IZVzBC zuYlPlFnOLiLPUd*!NCf7Z1HiVWk(}@#`hme5e)-GSRjjKQ55-4^{U!#$6c?6+5X$3 z1H7|w_0E&hEaI#*zBG#!UtjKZe^pnkDEBUG0EFr(mNfhE4BQfT9n)sQl!fJo!xG1`rXpuCrIZ%J>n7)e}Qr~^vACF z974G&!Lo~ZaLY|V&XJo)H9|mIgf@OjtJbb!=$f19h(gMdSE8eCceU%fg<#`a&2Bj= zkDWMYiDv6D?a!;bp>nfWFj9wHn%`C~3Q+vu3#Y<98+XT_u23t9c0`Hhhu#?hb#a3m z2rBEt%0KlngdWes_4s<{e;&Bw4+=H|q0<@{3^Crkgp zGdIU>r%u2V{s&2M0vdpm0ml;oGM7Q(0TTo=GB-7oaYiYBT5WIJI1>JzUm@T=M8?Hj zzKT+d0_|O|iyn4yxUJJIkmiG8E4Hq($J$L$>r>yKfGhwi%1mk070lXWg!t^WS762gu*XMKoH!oR|qA9U&UW0 zJoH_!xVDe0biK*ad0OMMTU>Z%CuB)&XKX7w5LeZ9?N-;TxE{bSk}6&1c5ofnW%^$` zp1S2lS+6F{FY=;X$JrTLVe~dsSiKumv&dK}WyeZ?hBgY}O}QWuvr9hNc|7t05@`{d zd6aO*JxLf7cGheJEg)P6-n28T!4h4W9AG0*)XaD_nQ|TapQ)l_`bi={FZzIH>@k|@ zhc&ZUP8z7h>?FZE0tE@R>dN~}sZ$+LcJn|a^c@x(k%qz9*Xh+51C%J%;=Fe_VP}RS zVW69T2`@%MdCI9K0nC{)O(Nlhz0aGtv*9HDImA+2CAPDR@jA{Iak;R)fBp2qHp-;h zX0;1M*i!qNM@V`I(jy{t%N3KOTt$5!qL>l?ghbQuK+2vnoNq^ziU?(T7tN!>l!{E) zb7`k-M&b&@P`!)2J7Y=@VoG#Xo`NuekuHdT4DPm{5-oAlGoZmGxO7AuCm{=4(?<6v zCo;m^tDA(g9T6809V*Zb=DJz`8NmVqqR{J1x*KZfILvwhQ98IA-8vK04F?qI?JyGhJgQq?IGNLZiYlggZbWLZ+#u3I7P;e{i;`O?3#g<5|yoM|w6GvSe+ zl$Dc-B_2MEZTC8!Bgnqzx@JK&3d~E*h@jG1WdcKw1_o=EDM}a?8r^lfIJuKV1-yp? zaaevzYSNTx*t1Z%S{d-cn*4I^%TLcGxO#KzE zyxoAA$leCsQO4^8if%h`Ss;NR(PVGdp$ZFkiK$aI5l}QW#%C&I-l?jIP5mT_qG1S0 z4S7#kfM0R0t7^WcS+*;7inSZQisuvX#}`-n6!v>U594a+l6QfGl_*+ox*DH+B|kv#d)0F zR?Yoj6PL9mi4n?&9Km4%Tg`&Q^@f}4jg*12TT10G(rUh~Dr+qcpb|L6?L8baQH714 z;5Jj($7QmF`ViNFe&$|sg2E}0Ub*y|<}W{ep8j_&;P~4HUO!Cvp$+P!ElmMOpCenF^DnkeP!S7YNTxGYmT_*Ely-xB4I(R`Q zQ!TXrAzi0gT*4gC?;$%B_kT`S==M6cn0VbY!n0E+u~B10!$zq%0~W`kK}3-)IndCZ zx(nj6Nbe2|1rgUIY_6|=f5(t!c%ig+;A^wVwM%!{`0(+q)d~&(uu9R~2S1Y$Tu8tPF z8LZ^0k)Yhl-iT!aj>{brK)dN!7XC6ZxJa#Or1=dTCAcdNwQb0MxRax;oJDedu+6A> z*!5vZLf+QCpfV^s2vbwjyumu;g$oskMv7&F`7mO7BM%aEdU|S^nZ;i)G@bt(S(|O` zh9;e`FwN#|2)5t2NtlPQQ6}}a%v+S3R%<(Q1y@`*6C7c_t2AG3VV~RHO`L5L+g#u- z+Z0ub3EvJO8nA@C)1+dfV;x^*t_?*|-n5coXjw9{ zWl^T}YVCkGWpR@(8oRGVcN?qH;rckQh}G;NGpz zimypYzEw4hsV>AY(EBbpKXby}_s5wFZE145`!I5QBfG4B@oJdNyQ$<+Fg_V>G)LmM zKvXx2UEo`w!_(Q+Wraaf57(yn;QM zVE=r#uU|VA?ANr{dp)|Y`G2&&`e8;s&i-slKF;mhmkrePP4%;t(`Q&UfXwM;kRAr0zdWFyF_x zJo`?6B+w_oV9&@N{)__SyOHoe3XDr@&gBzk-D67cPy3a32l36bJA_+(Pgwi%uiE#=eS=Fl?Z-CR&An9+CimRlc~S-U1%s&qyK)y{~5C+EWlqSZ!zC z2e660(3Ng zk;3@#`|i!&yNJQBpF9Eyp!pOumol^~?eC=R5{QC70RM;)9tve{WOH}`<*#wD2|r9iIG0EFXHg<%*?sp6t8xxc=f}x*gus?SFuWC{^Uz8SIr`JpIyHZ3^s+yVH7{?wek04K{Z#iu z)3yBC-STrd-PQei(;W^?TUR{;kJZ>Wzg+$E`o}M#6Nqa^qLA~FPy31=?VZ9M)nTVV z%7-|Ozrm0G! z*->%qiH?8e%mDJ*(Wc{vvWw4SXf+ z?sRP3OBRj$YGiQL3rTF0hbtX@X27<-yIPwfs@s2U-Ckias;jlN<(Z2Qabo*n*o~RGt;9w8)h$b0%)&lT6fiS0<0{*Z0jJkl}RX1$hrW1viO0ta^bF zS@*QHV}LN}2u(bpIKFB>%4RvO;#blGIEE_RE9rKkNf;7F9`KQ;DzzeHI^UVZI!R07 z-aUW$BrLYgv2HmY(5s^X&*84x)YGzU>n*?B5LcvxEXEliujcgzOD|-geImZ=&DizC zZ<=_1@k#jQyB=d8gIO1WBCPR->DG=9YtA2?6+kbz_Xehc+k5`tIKoqG=(Fr^*@W_$ z3Go+E({p;ufamY=H*R3G%r;*Bbs9!Ce3O67J>|@j%{BbJ3|TS@&Jyg&fP1k=EeQ)- z(^RW0(Tw!|sxYh)8x6HAg!U|nZu{=&VIHe#cPH{J3i<^2bd}nuI-F|RbHA@!{?eb? z7T64)4yah%&PiD94BvDPzOBgsAj#MCO->+AC^*l6L>L`GmFl&){a{gGdh@!0hzqj`MmJ+?g&?vgS_j=dmG!ksBsnJV+7a2t@N5LGE7 zOZ>iGI5txThpcB zF_~GF$9Z_dhuEY~-%I(dh_i4TXD+(=UL1ke`%0{Cf{-2N(b$WJoHi6)<%55Fj?96M zUN_sFY@+xNY4OWX)i89tq#Sy_@^J9!-e`C5PQF zaZym=V3p<~-oMtpWb1nuab(>v#b#ciAUhEeNQob%1Q77P<0^tH3MnKiRL`G(t%K6T zAd%z11b4O@f!w$w9otfifC?AP=liDUD(OXECS!|dW<(i(|LF~Fi=%(~-zTSik{WJt zmPv3qH}jS%q=rI}vyMVOQVu^1<;xoa?v;1RG?; zOtf$sTzs)wQWh;xtn*?@8Ze5iv=lk|x`O%lNdFe9pZpletT|eNvX8tXr*wHXI3EU6 z_e~|(WV8OWm+>XVap`~T$5DLKRKprs819BcM4OORUv2A-bWHEsS2+i^5UKD0$2Eg& z>28L)=aamn#$rYKtSA`3*bHRZ7vgnjUMZgXxz!8xNr~)|=0RQ}FT<`^Em0Yh-@|t} zf6%-42wHDo7R|PnFO$X7i=&YiU?M+@!{kg?fHH-a;fY*sO{#xNtwWMO#M&;WV9%*d zQe`s}ZX*fN#g+5B%~)?2V_8z9$P`|;zI=vlZD_r(+TE0Ur#9lr#D!|WCdIksTTE5B zQ)`d2n5Wm;pcJuXn6AI85oYgPvhIc{Gc#dWu=``d{*Hxrb1a>kU#)c{uKtj!HTAv) zt}ax9`qtfuk~M#cgzqU;O1MNGIZ4H_t%qjU&a00lHA6P})D25rh1z(IfpV?%7*dBP zbNM1c#uLKpg)%Q!x=dY_C-W95Q4%vlvZ|zsh1XGR77l{=%|z_og1FE!$0?VJsn;Zu zmyO<2#mb4$n~aNTw78#1Z!6lQ`g{=Blqi?{WKHvgGI4)T@{_q7!wx5q<5x?%fUZ&D z3dUQ((1E1&*+wCwY~hpw)w1Pe_paqU4ZM- zV3Dg5>U5ZobO*+$41x;T3r$U{A}{_2O|4yw7Z&=Ro7(dQjb$n>iiaAzWIMLz|HQ+f zXOjhNQ(%8vFy>Mvxw){z%fJ$6=^ti0o3nkl$hMC2-@$hFrGe#UhLsHBiK5%%~4fDf)xG6@&8c;8em)S7?f(RSSAc;?cMPom_g+)F%19eUt^ zeGceJ*h?-5Mnk2sxrj=EZE(>2;u*!;r5q3hxJPq9ekNy)_`66I3Ib!6Mkm3AB_c}_ zkoDV%=hcEeKnCY9_5}`qAxHzO`eH%8TTZ5Nmi_)@`VJXA%@(PGAk#AcJ%Y6V1wo?9 z%<_Myt4w5PRZ_5-SDA0P!bH*BJ1a-=I2E3T3QuWQW<#N%1YL0Pu&Df7=l$s1i_PZ- zH{9}z&uzCn;DCQo&;GdyuUoTIPbaD|5bfJ~e5{h2d zw@f;k-pTQ?zUz5Pv8kz_-#(u(a*xs0jtPIP9FxH9>v8WoRfj6^D%=##rU)W!r5h9A zbxWMMX0C9}9KpCIr@p@0af`Clh3gC+l1Y$rB&D&NdAK?I^bbTFZL6^&e$w#_2cnpN0cz)LBs*T>4%EBzein zZIH;be##9?Jk3j-wD8O52?ZQ_tVL!(~S&Nf%0x|ZWPW0h(~uuo5ZFK7^ew9l6< zC4g>0!XwU18Iis(2I%xXgNaV{)#->Udu-p#?8&iWk`^WT*E|HR$?+VYP3|8!*2 zwKMjMfxjYBXjID}sa94M6O4lfWst^*R&3UOHS3-pv$lNspGr0_I84i$-e_LEMt#4> zD4G2&T=MPjG;&_gTj6-QT%{QH?0{LZOR`3LB%C@GAN%Y8=_~ot*d>50%+yJiS}fP+ zh*u$ln}u;;GXTrQv8e>Cd*Quq3i=_tUU)ub*6jAh^J8|-x7&A6Qz`arov;acAWpt> z4uFxh{$WqDFhX<{7mj-}@*Ezlu}5;SdA*FYdUAu`(j5NaPzvGCOTRPl6ge#Nd=JiF zW9$6#d zSS!q7CKzAt3zQ%x6f@Xq&?H1aI*aD`g&|r&)NJ;jEX1g*cc#Ibx(a7Q3nzMe8GX^H zLE|n@CY$*`NrD5^bQLkP$06S#(iEzHt-J-?>?L8 zK`-H)Xyu(Ubkk{IVtiiS_8xav0GY)&y;jio#c?ZIZ_Tx$zUfNi=4?2j2!AcuipFd>CnqktB3s zXtQ_`yrO>glvt7{*6IiG{M)~a!PZtFS zGg}vcehM&W#Nm{@Pp>*yZ}fc6g5|s$)}tvk4%#c&kJJmY*GM(5STVj<&cAbpRZN1x zot&f#%4qXw2>h6+d52Gief@@;v{n$L&Q?bmE=&wK-SB9W5<#2Gg5u|avr)KudeV<1 zfqc&lEN1{=jqw(wp519ImdSA(nop|VKI82s_Fj0w{xqSg2us<$T#NYRC(6gAefH0| zciBG+1^af>)C1S#WoJV{9AZoBN~vrpI$OT5)*8sY?dw1}7>J+bx?hoR?AACr{Cf)9 zgux1+dC#2|zgl9a#(sw^t3c6d!1c4_{n_3k5Alx`W|eP?KsaeuKftP#)Plez&@pI) ze{l8PHd%)EXZ|;2u9n53m8F$LlMs@8?3H70Oe0jZ{1P_oKY|nqG*@SiJiWaZc-yd$ zZotKNX~;T+aB3N5M*328F+!`PNI&Tpw=n>M=hZ0&v@ru6b^43Ev^gP0Hic2tewFfuBJ`poE)g$A(V);2RioGxGN9$HnB1{ZBeQqqzZ^kc_pM4F@4_lwu))7h%35% zGzCN%`FTZ;(UP4&$RePGadtH)2^eyN(dmaB zOuNqlClPSi7#&17MFL1CK=1k0+-b#s1?LHhiZL&Iy5-{j5WDm>D+C1ZemdCoTw33> zc1v%YrBw}<-wG=8=~-+=d5huQgbK?alAm%a^U-HgL!1txe~O6C4M$1&UyuO&*K@Et zWI2N>S!w7*O$=j2N`uT%0vUosWyDI=SoMsjiCpeZXE-q%6?1zS%%^eneugKtBoU$f^82DDb z3O~Vwfj@t?mHQV)HG+LOI-?+e)l|V(d)*S2(%pJ!gCsJvNy^fOac)V+K#7XU(0Q%GW)NTKLhB~gHrgP&hXyO>`G!2tJ8~Ryv&E`}Pk==HYrXD$Kh zJU5a3n;S_JHVtS|V;3;H@W~RAR`0Qo`N$ULi|cl^46jlnk=TmErE*eWD`!W=2W_9e zToR&gmRO@kj<7PPp_)J#Q7^CFRKbqk9P@k>4vIV2>_Ffe-hx@8{o zamCMK_R16ENUx`sshHQc`1Zk&)9*{As<7fI`wkejPm=vu~ zpo+F?W6E}O1$3@PS*MD1j@}A94xH9A{yz1;8L+=#FQ2$S`r?Q{B_!2Pb=Rm<&)pAc@72fVQI>BSVBiePHHma&P(^SGyd4A9n&!t_*;W?>l$@msWEAmsWzav9SIx)!VK8uSD2}`0rMAty*K_I+#w> z*c@J*BuiOVJQx1!{8E&$YF*B}f?SS${wn|!EQPZ9{Bl+9z%-FVG=b!L2YhdHo@wjj zK)mbq^X7A4WLo#qC6StLbyN7VcuazBk*U5yW$p4ZzB8}L$Tag3=>A%#c)Dp+I1(e% zVj;TP2b9(nC@Q)~^F%*CH7=0X233Ejs~!wnIxR3qW8v||6FowwN@BW4wOAYKyBG5s zo$W~MiE9sITIHUGnU-&=q)Uneh=kQCQU-foj7#_K$VCkM>zgYCHwtw}i&L?fbVaf8 zQ_O%JaJuxCp4_lgA(`%+4R!7hL-gN z)F*hh8l|j8>pkPoFub$aVAM^*IGLv4*0#LAu0<#B_m{$-6+}oOKS6Ee5)6C-gKbf6^uzM1FI*POy z0Xk>t*PgSS-(~T6nCDVa8@dNq(zuBuCztuZM(vca z@27v{m2mNdcDm1_VX;ABjfbWkZgS_GrAAgy{ZKnOsBCWeth`wdlI<@r^eyQu(mCpX zGAch>-@`qe^)`+$pZ~$|;YwHcWI?<;4@iL{58drxwO+2$frM(zl8(OT zX@#lSI0(>cWIJb2^m&bM4Hn?k!GEr_u_5Tz-lt80Gdd+;FcR6#rz~m42PH{~-!+`vK!H#s#>`+BrAo@siAx_2Cwhvgwsbg3 zlhtkyg!07nY>AXE7PTe*bbgPkme z!JNIWCFb=q=pBOk_e`5|&&)*?l>W24vH7+4dDT~(rrxcD@(UK}kTi(ecRM~|F{~Ua z%Wa&kz+{S`r2x32mXn!N#6je$|A}Ihqk#CB;54Ilo_hak7VN%4M&!EF&_x5c7tzDI#_w_L96!W^+JinxT(*`W8+St0ktD)h_cBK; z7o3KVYI9^Qcv*;e0dW#pb0c|*60ejNB0A{v!UAF2O8&~+y`)ksf9{qM{O3wGm5hm! ze#iAV46X{0cSamP(!FVfw|`Va>;@^SkrrL;eq6p9J*9$a8k=J=(l4}vq>Cbh#Uepw zuM+94IdED|NV@t2-X0Tid$h#k#lyi*GpH=Az+UZ#D^x}hYvD^% z!5m1E@%L(%f)~a87ru!=T`u+XqL~;;RTw|LGk9^8L&e;i`C&Twn%f$Smh+G=*K#NRELFmp3!J_kkP%})M4hxmaI z^9Y3oSaAPcAHMW<{#ki|1d;9MHPcuhLNZLS0Mvs(3*dC9EE9N--q+Xs}RZ4#OK z{D3v_fg0_^-L1!=eH+kd&Bf03fnv1Py7=dVN{E>|y5%9>qoX^rcCB-jz^LlLXBM$? z>#6pgwAJ13jYZwEV`bx;{d3F;`x2zMTpS8lL^E-+}HDE0pbTH>j+bTmN;_%N5%#+?8O(&Q7QBZ#sLW z?oh?w8wMu)@+{rNbq;$rRj1N)<_5oZmnHE%yH_2!^nfVOi;1=r<5jiEv9WCN`ItEP z9$Er96kGG(dv6YbS=N&P8kdWR8KEprva^c8iUR7`ka=#5uDEne&Zi9T+3-XPW*CbF zDb#5E5bPHn!IZGcs@sZLT*fbVzdk7W37v>M6H;041@ z;;FgiwX;K)g)QOBt0O8)mX~kC4B@q>xVZ4|j8^WS*u7F8{!1#dB|@y$Tja-(6#lN^ZmDA zVb1d6UONCh-no%m`Of>+&JXUD0hr%T&Fj;Ih&`b*5Q?@n!eUi4p0=e^Ue&7rAtK8VC(7EKqJ+9^;{XhEuD1cbp%r@v4yWe}=W zv(?&AO9+~^#R;0jC21gYvax>=YndYVZ6{9(^DCKxeYe9Hd&Stvq78E-gSO>^`5W*R z^T8ORbb}!v0ISb>oT7$iuY>01VumDI_J#KDq=5~PnxqNc!~RVtRNzG2QZOB~=Wag1 z@*_-eT4!#zT|x#K0wHwM{cs^hH5GA%Ge;8r$^PQJ%JTz^revq0kvBJEHm7(w;639{ zp_LPAJxB>%-aP8E#v6feYryq;B^R-!7HK6yMn)MZC>G?d*49w2(O+MYCc>WA@RnQA zI=~W81uK2hd%j5^@tQtdxc~dG1o(d&`77?R`pe^-H)()GMLewHc0hpP=R;xGSK#FE zp0)YAgj~8ZaqQ9$S5lZU%k1c-3JCgHyDq<4T8k#5qSo;v7lLekL!lEsC@-7gjP73^KG0xP*FPJ|vhN3DKm0 zzhu`|Z7n2f7fpLIh*^RzoqN+CT#jTYs0t{(ZUXvXUb%#--nwmrUxM^ZX+E9K-n8Zz z686fNCv3>r)-{TJ}vZhA1TlfZnxc842_NXD)#^x(Sn%? zJToQ{2_AO@H1mXzvSqAQzeIp@M)?$Qv+k!APzC?YC?94f{o+rjfT27ydL)~5*TAz; zIW^Uv+&uS@;K^DXoWKk%;Ow2*LNPw~4HHGKQ=*2FiY``Q8qLP#*1cYXL8(d@W8Fiw zlV?qh08`*N!?>HfLt`YjRz{=cIeWi=5n1L_oHkwQ_e05DA^-hLswtb7EAa87BY5iNUeH zKb*!mE(4;f8X`e>VWpK*K?yaGLFT2P=d-4`ZgeSbGAPU~&ib<8{x6m^Ftfr zPlNJ~VfGj)JDyy_oak2DSSgtyk<%hr zaApb<$llwpzm|Ms!%W>-lk;+&w{zT^U&FTfaBS^?X)51K|F9r7QZ{8+=+CbGvB{}v z*JD~)yISK<%=p+O0RMibVqCl%>?;}tCgPf$TzYrV z>Voy>vFD0k`tsQpI{}Nf1$)P@jz1JG&sd2QyR|3rnI(kgO?RX1)tyn2$a>#EzJ4pX zMDZT&Y`wEw2JxK15E}N1G+Dbv|LT1mdGnYnKq_ZYyfZehB9mD>ljng;1qLO*JeO(r zdLZ5)&*7@BV@P=cXyi53J`=xJ`PZ0LCh{C0uFY0MeDn88&K~+Yq_2xp`fB>M{xHto zAFuP?rM-N^TFJ|>z%8x(SFcYcA9EVn-Ic7BpH8~ufJMIT_JWzBhAw1d!xt5lmpUXs zD10J?WOVeOA(gu|n`CQ(DqB?bufnV!3pG}KEVF5rI4b`*Ao?JA{-`~iZB0j&&DZu) zY^nPeAtiEfW~0;0cqkqLN^yPJW9X1oy2cPlh5Grv?s?_}UMsEq<#fh*5wIs(svc(W zG~uaT;|T>Lw);5ONxn+Q=hY9@5t%x0LbS3?ZdP3XuCf#ALbep;tp3uCba|Ly?c zraiOgo|rEH$oSlwcy4gBLJBnhG3rq@Jrj4Wh3_xH&p#imYM5>(gxFGP9JzcjL#j#` zejLu5y?FAm3Bk2_`KD04VwJ??c8v5b)|8)0KP?;5>7)kH!XGcQvQ6=o<93GnhR!gi zq2xzNgsW3Rw5LIrVKJ)!uG_IRk_(;*lVqMlNh_rifO^<)0rjm{i|7Elm0>qx@2o83 zoV88BT)c;tf^WGMft?72Hm3=XN3ViNdZhzr~&pV^UsukU_wvjJ)t ztG)Dq$zG__y9htW-gya9x#-CcMb~Xx1|+!86ePuhP|o8S6|5YEui|7nyPil-kloC6 zky~$u;E%r)0cOc<5tyf}B@b!{&_b(a3(t8NdEcL2z?!@Us*h8c+TgO1pc(3hb4rxK zRp&ko(?<0$X@lF`6ARySdFC)7?|vpSk)h5 z{(V@~A6PAasx^I44+nMQ4dV59B2>B6xNdA%u=lk0ydp1R;5+@_s$TjD6(|cbH_!hc zfMVt0;QC*%Sm&=JJ~vvxvQgj27JPd4&hQmrjFo{Y|ceaW&n>p{% z=hKI!{ZQvzWr0@BdiYS%M*P0hVb)H<*^d}@!3Rg~9>_FRKCC9RmdtM?W{J z!H8Dw-DV{zi8H&xJ9grbNKqARba*L9uKJ~9S)DUr)qIYxRJMGNmdG7dy>N-OY#A2a zyg-6I?>bemRK^SE&Lb~m9!n$GT3%IlM$lGrL>?D8^f5D*c}*~yw2aJ(;ZNkGpHf)Y zydf@RDjq#mt!cV34aCJMLL^h0mpJV)wHq@L8HO7(opMlI_P9D692g^plxWZtzNLW` z=|BO14w^@XCCwz#C{)EjBLaky^6=X`(?qnGhy3s`{TYZk1p^NQN0F}5yEZo9u-imJ zb6l)#AAyoeViCbtL5+@P3qPrC3?0lkB-$)d9L9jdTa<6A!iEO3rxIun*~s1tYXF;; zTrF1!<|=L$Dp~|?5-}z*t&Q#TPl(ocj@kfBMI=Y=VrHwNe1u-2{v?sXHS0|ZkuI=? zAC?6{8;+v2m1h}q$8j2iwg=5L?#3mcY0 z+-;In=`9Zx6~#J6$wL=m4^E-2LJ}QMVPArjFZ*Y4rpb%aMHwGgS_Fe0WwMD5+%yK@ zO?IpGDGz)Df5^fj^Z%Keviu`|wXSu3s@pacHU=^?D>Tc7o@%ys8_|xL=P5aM)`bz%PLO>0*`yMyN8(*=U9j>Fo)Aygmf_a~jZ%5I&_8s_yZCPO#HFPp!Nhep3H zCv6v1?``mXLcSl`-v8LhQ2@6XCdVLt!Xyr5=xRioL}M^jq|6BUqectmTTjFUXh4>d zrO}uC8?5kgBjl;GsKLMm9eTB?gI94qc=bcVLQ;i|Q*j+n-3H-I7O2P)(3&EY1gI=&&jR%d@9|1&N)#yAW&HQCkS28#;GB>0&ZSwhyF zQ?v>^WH(cuFoSX(K~u1#-G=LPU>1Ra{P&PGmhdjd=sa(%gn)TOL5yUVa-tI*Vbu9- z!L_QJwIM?~J;Fl=zgRhgPw>ps)B>Tyl3%m#Z`a}t{pRi^C3ne z{4w5Wv7beK=)N1Zd=0rp1MwFz91`KP2^lKfk_(l`% zeU4P*UkT5}iBh^A$TOd4c#Lb4vd?-~)Sr~#sgrtajCd1ph0&a;lP{Ayk65U!=Jv;h zoaYBQ#F@iSFS4bB^<=op5jKWe*vbx5Gcl zfAr#59C6!kM|;DpWMPr)p2F3Bi1&EV{Ct#UxjujpxT-g9c(~1dpwZHa&+xnj>Auj? zxo_z7P0miic5>GGQiO2P`e$-(0oCy}cI2J8`QGQ}-}=0A0D#}+Ip8rPdAr@zD=5$M zN0F(+`UD2HcMV=r;QG~P&*zCsTq3o8CP$FUr0WZ&My#F~SW#`s7SJsE-lDu^iU>kk zw)va;JXV(z%e(~u#>|2k<(^C+!&h8oK0G6W$VW~o;ZG^kYL1KlPKC*>*AX8>2q>=_ zVMehVM=iKG0Eb!w?%NIW=N($#XgOO78spp%9a?ShleR&5{^pkz-BJJ6LHs9i_Gr5b z0A3&RadCF3T8B~Wr$*N|ifka~zD;7cH~P2KnPrPrb9^$=K1~HS42Q>gDab!^Nwa2} zdT{&w98iDT59s2{l+#~T4yRC}tRbj}l*8LAY&F>zuOO4C_8eBqs6`+3mnA9G8R~8+OX11 z`K@3z^WENeOR<8f<0=rQYeVtJF{(58M2qjMLzPQs1+hS!#t4tcGizTW8`;p6KXcgOyTpF*Lo>stOhlPa#mQ81W{g7C61N+)R<@N5Ece&;^A_7;kwnlCwgY zd2X_8?OCi~V5*tW?5>`O1)?=E&QDUP)FjNKh7-t=G}InIjKCumY$Q13O^B@v=XxQN zJ$M%eiE>RMZkw)fCC#C(523Gr|0K&b!+~bVWRu1~JBt|v{paK68IxVZ9b^(cm#*HM z0%nt}ZsO#@y50zu5a%jr)I!aVC-FA|vL%vA2NH@f$%~K?;USE*oj#Uyd9)`S=6Uqk zhJ3Jl7{_ZgnbLQXn+^XO0)sJs$R_rECpfm)yz3OK1vImo^YVL;dOM6Rus#DVr+@jZ zsz7K>0fv*JlCkbM08z(&cAH>!39CT#aJAk6BZ z)c2kl3;E3s+aahEI&UbFCcquuz+I1cNnQt&ZIZSB*h zsR1Gcj9CR*Zs6(8Qhm|S^&h~dm-;PU*M_DD+)<&|6_zzU($T7ylh=7*PEa}#5c=TH zVmUnpPFS`;qEv0j`@=s?m--YIZVWA61+0pV%c8gLD@zpH_roSdaezTROI;h_V^M?KjsZP4(L4~ifvaGn{)b@z`Q`7DbgpX- zMtXBr8z=O1Z}iUJi@A6ag^DI-ig)3EKAfriEI&>}`AQNUZCOcIonjCD2SgP$uqjbb z=ydic2xP9TJP^;V{AP`Q&RhFrt!O`~2yTl7?v}pZHPy+g-+0ctd~w)xK7eE5d!4Ud zO!GC4N9050uWQ8W3}!6=;c}9 z5^m^xTT+4yds4^Wn7(}6L2O<8zh58D7So#VmEkV#odh&@gqP7SV<=b~j`RU5n_I8D z2Jm%lOa9&Qwwes%HO0&RvqPE>sO(!Sh;#H`7M&{^o63Kkdkh?b4g(35BZbr;?WJ*y zc>Nb;GE!gvFCp+ma-}!H&=H2vZahTLV}q6X?~kkw2_SChO;=|ZyBeaB&JviiBOF$T zvtX)rbF3!t2JMV)^gk=J*OwuG{u34r9rO~hVT0fW#}{j)ESc%h^meqYgN0k6+@}JJ zArk2E5Meg*K{Mb$5Z+@aX!{@48QaN|J|x<`k*}K{hr+kj`S#yu znRy7YVq)12!o$13cej5h3&z!76Wv{xb1;WIr37*R+m>2KCfAb{1ZUX{ZXSZxK~Vc8 zWts`jC-HjJOF)r#SASoI;NNhNwVPUU8PwBoEI3mZQ(+AQFb`6+@TE0kB=$*@L)BPH z_0i7^vQmA*q*;P(DTxk@*XjjaVlS4u;x3=XI#S3h%kj#DuiW7n&+1mBuc_`zfY?Vk z6*?HCrjyyw7w2PsHtH!e2efrPUrAm^XaorlEm4Prp!%>G8NA=K=j!(<>!DiIdHwCI zq3<1Dlul0ukg>l`k@o*>8$-*Q*7Z9KQ|YPAD)1rB(47ofFTnQ;;aw;_`Bt!W(g&pRXdGMoFh+a6rTy70Fzk`CmD0<)Nd138bJH{aSFLu#gg! zEKg+%Y0k?{Ysm4?o)o01AVHa>{bcBy1ozP@IbabX8>orqe;}G27(YVWnn0OOvHsun-Yx!RbPf}doxoO zqnKW;#B3@%g%qv{Hsfc^cebb+11a}&oNy;r1fdD=U}&Uqi9MP$7-EE~EDya@rbqW9 z4kaZ8(&3gvj-T@yG7z?ITtJRjy)Ojhd_A6vFu|ue( z`o}+(o9-=-(Rt8qdH4oOMp#~ zfMLJ#1YweDr0kFnX3AOhR3v^3$240}960TslUW^bF`S4-kl&H&N=r-)sKY37#3Snq zS{h5LPBj_}TA0WVPxb$l1=RitqRlZf$ylpM7Y1qA@*vL;GO3MA%!R_`UZXUCxo^cu zmK6}`LGE;ut@F5v$bYYObS!%mz4-DBDxvtuMe0JiCbQT@AxZYzy_u1tai(L4pfXBG>X%q9Xqa@3` zN!PE=a1cm2XXaSet&x`V6C}oDdUB7=@k#oq#vENADv(s4pzhvspapUOY98r)L>}ML zPyCA~f58^gCUsQJK7YX2KarQGy-Dm0T0&4@q1|Cj25|Qu&JLVZU3zl(@iKy%)`GLZ zI#*+HH8wtr%pK`gcJ`b6{_#H3a+I*&{`a84AY6I#PzvYh%6p|{0@k4H6D}Bd7;6w$ zA#{OP)0X6a?AVa;^_`If^z8iysD&!6H*0Py*vOh&AQ~G@|+Qkj@2CQLhc{!6MMBi&iUmk1pj*G8igZ zfjW^355n0zySX~^8mh38v+i5fp4`zNqb8Ql zcD)t}7}CZ|4G!Z6h+thxI6>Wq2bT&ICVD7|>w(9$i^1|{4sY&M7;P zLCtiEkN!*1;rT%^!B{wxjRhEh_;m@4fUDZNL2%iM@OB?d5q}T}m{62pMo3Vxt%a-( z3TJcQ)~rvTDgErhEoW0|c~8-a%5r&Siw@* z-%w2u#v~*o6C&794Z5NNviwvPa5Fa)(79wM1PuN(e4i}N*r)e2$Xs3qCYsyrN&$Vp zHwbb%vKR;tVajXU`1fbdIx1~xxD$$5qbwLfS{s z)o>RJW`*aPSt14QXS-mn5otPYWz_b^7o1esWM{uf9jjg)OYj0%b5hW7+vfmz6~^P6 zB_S5T7tRPz44>N6{xIfpl z3hmePZeQ5^5$GnP@X6mMpp_h!h@czi(qG=G3^cY{4r#MA|ds8%dp2Q-_iT)Cms23g%cn>vG}Y<*QM3SL^ps{>xJPmj%NtZRbl*ryYquK z<^?_-=Q|t$^@C3GB!66dW3I6;iQ7{1_9d@7yUaAe;^3qcAK?UN3+>AcyIE?Z5ZO_f z;5g7VwaO6N(f`VE%oDkO?~o=_t~lj37RXuh);waLAM2Pu`?iYbFh&u=?;B>&$;~|< zx|V!7@dM!32`eTcM}(UhJVYtR90j9fC(90Elo><|ZsTP(0;_VY>C&)qME$M>6Vxz-5DO0{nEBAiJhrSwkjcLZO9 zO|d3oa}P%Vw|}BSB!8gF2h%oamEnBXUilegGst3F_4*!Yk2622Q777hO1)Y-^b}`* zi^3jB@og{TK}T6~06auC&S@D3%0XXwyt>1%EnXfe15}zr>Odn1EXOzxQ$6q|R^F9A zmjFNmr_5J?w3t zVE=3++rt9|JJ%P8MNcT5rV=r5tVavh2cwz!6G}m0cX#U@A>m6st-vv0(T7F5;b8n}3K|RVmn5DGHL$$qr#uR!YT{gkFEd z62wVC6cp-~;Z>y3Isve6bsfiOpWBz>nVv zHFt+l1QJ!Z(eY5eoetX9CwQ9+@eP3hh z_vOs`Qm7pyU-gb@hkg#0Ks=0<#W8TZ3X%EIOwK7A7-kqSca(uNa)(KH)BJ;B7Al;GVgdvIORRs$N}Sg`g8 z&AMUn<#$`^<47`}&^zZV31!2exv)X#k~adW2Ws|M*oFUkP4W-AL~DbvFVJS4aoKM; z2IXwNevDMNSbG%J3^LX<=HutDg{&gBWnSy8mcB)6I+BAskGBf;N7LG`)#X<;e=IKs z%!5{F|HxrNMIGn+070SkN(9`=_UH50L8FH?hc9+RC)d%*O%<3BT1^mx2ftGhFDer( z_4_h~nk1XT5Y{SQ;X+x!+ChoPSQF8n@DFNZSuF83vE-Q4$*-KrZx%o%)=5Jb*b{zk z{e1XUFj3B%+wgU3)&z^&vFt16$LV-CZtz$C+e6SV^i(`qxpjshV^g35_OGAALD8Ca z5BcB3ZuB2o$s%cXzi^Lymdu#K3Pb~O&Z5lB)IB`-N}Ff(o2aj@Q1w!H&DqGLXyHaZ zg7_pGzyN@gBSLnM3FpAnI22XI~rz|Lry;k&3@Y!;HJXC z1!}!+-M!HCy0=Ucs^vi_V8YuK@s<)epAR|CLADhzOBFMQLHlhZg^nzJV=eEQdt;6A+@7cF&8O@8{L456WWLe5A z7rOAaY7PB=Pl5d6zcLOI*v52zy`Rk^1`-7KPs+6DM>3PafW=A)_XNa5ottTy1D$mJ zchlV8jJk{X8VBi08?x48l7jon$ck_evYoPMD`5B7I#;cP4*?s`W&A!x!`4sUElr*} z;Kd2(HsPynabJB_UUw;fKi;W{O3(7c;nzlvuGyJ3;Enm$#d-E)lr(&Fe<`Sd64rb) znJ?J%@HgT-(7xE}O;L;6R^|qvJ>nH?E!Ze2OorJvyP0|Zo}b>DEt9!s-*wqk3bH_k z$AeErbag%$5k&K>cs%1iC&uM@;2#nK{0=P7M+2OO8}Y);4$C3HTghoBcdj3DYr#ta z?%88eUd4Lr=ck_s_$#Y`#pX8;UDA6~@b#x|X7lWH$DWsL6cY9(ht3+3P4 z=bv)+cQwLfh39--lWzQlrC(g+xRp+)05fMZ#e?1ayMaX# zmtw#y$Kux%$APPY`f%wBhvmG*3>HUn6k4U&Oh8+ef5uRo`aZIIh-kv?#=o@E;6murC5+cLpN(yJhbR%DZ#%a^QY6r^V zkT8MLT+F>Qt7ZYBzbGLf(yqzWaLdiS4?Ek6Cau?B>I!>Y zT4`>>XD1yJf5d!)V8AW&SC1|BkL9>Zn`+)c<#AglRw0{ja|m?UGiqTsK|af;8PJ+x zYNNm~{X%d(n`4PglV)4SxL2zo>XW)l9<>8%a3BRbsdj>$blVbcBV8H9aLKL5eF_{jb zhR{a2`s}$p`&DZd)Q2p-81nbH3t{|_G}pBL0$d>G@=B#Ve!$f1>i4;P8`OJd&-Pm7 zk68PcW+QLlueh`D_N>!eZ%wnU2k8>qFRBsP%UcUHBSA^4I#p5$2so1Rfu5iJhy?0! zu97_>Q0@|8?O0YXNQGWT01pH#c=JWenPy?sb;JA-cN*di88UyfS-*7mY%IcU{;}WXQKipGVq^>vJE+r(d5di4TjCmeHZyC-qis%RibI+fG+0nix%v$D@| z4;`zZh3DOH+Wj5pc;O~5I(hSd-#mz`13z)nlF4_(7-pwrqG?6Eu^K;njOk3e;pXW7 z9{{vKOTX@|H*Pze%4q}?Oah!eBJOjraM5*jC=0+#$b^Ip|qOE*J_;j@=d=df(roxoE7~ zFbnjt9bSieJh((*@bBgNWZ_{JKQ+#VO8oN}=A2`~(-~nsRuj=*DsW?|P5uv;5NkQr zjp3^wg|@>v`_ctbtDCeJtVSy$jg9w^IZx4Xe}9A*k}!ZZx5_8VP@B$q%66QyC~?d= z@Kw$ohtfa1$U>7p80VS79uL;Qbi}|cc4?=grM*zab47^+bC2i*E_^ZX6M2sp@Up9% zvT$bSXUb6X-*!#of3z4S*yM z|LlR<)#xdN<#2U1ZLE_viZv1(@7S2+^U^+TD%bI1DAwKA$VL@Wf*sfVY6E4Kf`36Q zp#~PK-(aCLrNR&cN_duy>gU<0`JHT(#|{3%#5Q_DgB*}Zz6@qQB~%CzgUT3Zihm-_ zn6zr4>sQ9UmF+?4pZZ~HB~R|q`04K)C$sO6c2XmVWdX-)?C>zf^Wo=%%UYQdd1&-@ zqkr|4ROM1mTzWFt&D9$>E-#8s8b-)-z%3!$++pE`Y5eCpgj*JROl)dH;rEk$u*Ff3 zRuBUzcytt&6u|M#B|b$#34Ow6Kz}G?1+ac>S4QOfnodQc)iv6?SfLRkE$doCQr@;syjf0+{{;+4_C8&V%H5P4{$eXlV zEB$(S!=HsW>JNc8C*+{PChW_OY!Y&S)K$|z4DQbVHMMel3w{F5w1I?BP7HfEi*|s| z{1-QQ$|RTSP5~(aHIpGWDSx#Z%W~t!@vg66ZdBE3jCl|2^1-QiT~4K3NvtY6W%~e; zkccb^XaR7wo3BszbPpaRD9Nu2G3c4;>FIYjn5`aG+3K6CjGuqKz548}k}D}iZmn9~ z-mj$A!n?3?)(fTW>UO*OY5jd)^i|hao9@l^jrMlk6^F-tN#hUKPJgY7wkqlkLyCG! zgH3a-)qKG(w}1cat@Nv0RPV%MN998qOQx9mvjV+Cn8{pK3q^S&adAK8vLBu z3@N)pqB3Dt?ZYY!|NYGskmPupqz(YsA{kpZqwr7`-LYi~ntOU8NqF;2tzanEZE2@5NbgW zViizY(tG5VP6C8MSH3^OeSxIDD)t1Mu?PV~J+deuKXPJG+L_e`Wq`QunH9>f(R@Ubt^R)JMS9b9lX8rzH(1}%FMfM|* z$!6lr`duBh^{J-U^5Y{dK>k#bWUxYFLX@Xl7yILYZCin?bhHEw&IN7cp+=b5@Y(xM zG*=j-cz_r1QFI4lg;v#wN^+rB4W;{?8qMweNCQIg^B0% znQ#`l5S{eTnR2`+UpbL`9dMBoS*BMvQkdM(!XFo$uou>w)nw5dQm>i&A>?|p%>dnC z{Y(Lqet&%<2cL98vGl??lh*&ZfDu#>qRwz7a8paf%6vGK1RYI_t@=5FK}GDyvb5zU zTqHOMZR|+!jneBc;DXMt|FeKzfn8N1~s?>bh#SOi%vVf;CTd$=P3vi z(Nf_&Sj3JW&Wkf0kY0AQfr2=W3ZCuL0bTV!MwPHP;9zMgxo?~Uh;`12jfue=SF9pG8*#i?8l17$F?abCY9ZZfA8Dlbi;!35d;9rq1&+lo37*X(37g(>NPWY7QC`$#fMb& zr83K85Z(MBq6QrhW-NA6NK~ot!JC<|{sAL=Nvn#;4&~Sh4bBUuD%u3Fj(@~Ou=u3N z2?L0fG>GgRXQ*L-6+6EscZM>Or-_9Q0U{wAOW&3Idn8ro>u;lQAR&T0D7uoO-4bA1 zeyCD0h}^Csi#dTJ9;-YSoG?2IUa^AV&2|jvkj}O&w)6QR_9#-*?#(3~(;iII&B`a^ zMp+x5$O9OtdX3xR;9eKVBD_LM))g3zbz!OFF)o1UW{={eTM zF#)~o-4%Fu2l%HL@%HB~62qi^*+)*vRVqMDsbf%{|omoCMtMQ;=^Gwq*05fSYp270xwaN~SRq$nl72mqgAq$34gXwtRoA+SBc+^|3Fv4?IWIn4xiY z<|*LvS)OwVG9*jrhJUbCHR}xRs(Q=C_|vZ9_Sl&lK6XbiL-y<2hMv^y0BP(B?v4Sz z#ey??q0~H(KgBw;KJNe%e_vHuqV)#FPVygRv_eTS$7C_rYC8t@#XD~2;afqEJ?UEW zj-fqme40RQ>ZcyN`ziHTU*BH6 zzZyj9r3jGuDxV7}&1!SF`stT!wS`IKr3QO@ic1f%k%qLK%*Dmlr0ERQ+o zbM#E?BWGRwd)a8T;>Q?Vh zh@ItAT7OsvDIzs#TvZl_`&P04)b8vxC?3|yofS{+}?C7+~# ze;ALwwU>`WPh?*7L?@Y&#@l+}Zy=-jit@V@4q>oqIKRWLy>^?5OWuyAskVYaQg#`a zLPfb>GT2F?4za;GH$PrJ=aGX4hWh!2FYA}e0e`U5?BtT1@7|^>Qhf&lh^Xgk=(LcS znq*e!-{nym(Ll;`hGk#$Ka)#p?}ZH~mwZFQpbBJ5_q(I&{+a%a#{P_+V|qMi3a01y z`CsOML#yZ50*L%6Tc)=iQ9*e3YQ`3wVfQ#dtcZ;~M0A5yg1_u!{gfE`7)u z_J4TcPL=HA3+I4f!9ScHBVXuN;paIM=G&@nu^^Da3P_PpzDaD$4$6(~SR7DccJ`h5 zq`lnzx)lF)f60*emyA?zN5e8I)_!-z9mFvH=n{dTk=@W|LI6@6pLcyx)$yA#z83Vr zchJO=w#J`y+!+#c0sU-H;2I~hFT|s! zG_sSvB}?w(;B(|1z839W)wf0aiC!sd#4qa4)dSK9>Fw;qj|=hYoGD9Q8xk@%3;g_9 zbP1!(goL7Ric)!@fwI1#d8om55Z*;??q{|9<24iO4vZe(+Ga%Ev{3T19&Z(?c+ zGcq?IFd%PYY6?6&3NK7$ZfA68ATv0ZAv*ype_Kg&Bexa4>sK(hR%HldU+nTBE_+<@ zB~GkVT(N!NP(4z6C{pG!n)&znuynK0LN}?AoQkV*5uqmf0dN1_14w5#GdlbBf~L=} zt}Z@%&CJXZr@5Y8y`3>3h|zYYl_6ZH+12&zr)qy&U(SUP)m^<<`;n|RZ{w5ht6jan zfBfa@yU$(=)+1*PF_vSkK<90o!EJAbB`bnm8k@pP-0#ayDl~;v>vz3JVD$ zn4a|}-NZ>5bCw7y+O?WpT1@oWr$j5(PWhQ~UD)y*lZ1HMp4Wx#C?c_VZ_?+b#TfH+ z?QG5kagN2A_kQNZH&++GUHIM78JjUBe+XrAriCUH^OtuQKm9^y*ZA_g870Em*+ZCm zH#3-^FN%Xvg;DB%#$iP z%D@<@9XQIQLhR!}LI<1Blbc|Lj5!zFCleErq2&pl{AGO|F-WtHpZAOHf4*5BR*P)} zD-W04RtcvpwskidPxtk<-tFt_II$(r?00eS{!%Hta>=S@v0l~%W8<7bGPY$7FKE2b z2Cad?7)G3AGmVta_+*N9go;UfX-QE;iB=Fl7PK1)JtGWe3dmA4Nrae>LQW|jRK%4g ziegC)1j!OC_+h`;H#;b0fAr26^{dTd64_3`xvyVey%FVXED=ZID^A_&F z5ezI;X%1j9ySl?Fe+2;vKmceLK5y!EjRjU8$)ABi>tRtnOV^4xIe{~X+^+YKo4X^Zh z8OgdGUlLJ;o9vYjr&e*qa5h|pO{-aeTZQTUx4f5ng)QR8a<$oc2lnH)o9$iFee?d3 zo2qW-1{q`qlqwx1EzybcV*93nNp3%S@3+6|ENc_uGws{-FWYJ4Ct78gf!pe^ysPDwq=uF&GuzZ0_!Z z#x~piBB*g6XCV(AKw_2t4SUAt5&;rVVc>y%$Hv&lR)x#A9Q9&&wY&tSdcEKJ&lqvp zQCUOQDNwB965>3P!F!wV_Q|-cO~6il0$w0mhVU#5Mb2HVJa^hvhh2JfyV%Dki>au~ z5e=AkjUMq+e};_xSex(XW)pp;U3?RlZq`3jA=eNJzWjJ;dG%^C1Ol>{jBY6bNQLDQ zB@n%pd|m?RNU=~*zhDA@ScT*nFQ*a!;xQkn`BVacCY-gyRZb-Ug5gR1 z+aY)phSAsjV0$=^#FTMKlmpJgi{5s7-Kf4kW%?6NMgI4})Uxf3X$GV;7>-3u}wOVRKJrI-q8(8_;*y z)N|s5>Ol`E%P|;rEPh!a1N|M_^(7(s%GUX=b!bzBX?4-bJX(Q7sU7yW1{N(j$F`Im z7_{k4f+-&>@T0UdLMYdq9T+n(=5Yd8m$J}p>o0@^EcN5lfovgopUk}`kuJGq&V{vj zfAKh})p*#u!(ZAr&H>KQ-VZ>|0RFnkUX(!DBKg1{`Wq1lGg7+XTcYjg^7ofM%Wg-+ z%MetKvEav|>>Mu`bCwG&M<>!yPS1r*dqErI$+$(&+} z5<%0w)j0|oDn&lG+*jhts}wV_;&hYYR9mT|l-7J+SEvidg+FAk zdBrkWHKaymM4|%rty*@AyZcod*V}C}ihS-O3!$UoLPx{@`^H0}A|UF_!k-v|BNzbjYC6e~B#em@3@M|> z6BdKDNfu6TM4LkbB$#ZAa^Dp|R*@7S0g>rB_M1qxET3V&>YT2qDq@^j==~ah`hsM3 zOsa#>47Pl!Bq=C~DefRXA9p9Zf4+&7aGWqY$R+tH49IfeH=C(5Lk>U$z_|>Vn2!qz z7UTs5Mmb9?HoulMj}0;9L$gsySj&yK(D#>fJ6cTovQrI{Mt^xrxRRNknj%p3>7qC3 zi+C_mqKJ)ZK&3v(UyEm}3=$lwzEHjB0Hb@@gUK#$59&|6ffAKCmfAD_fATE`AF4F3 zc6A!8LpSLm{?oQacs!ZF=Vp8Dquw@szlop7Xa2ZjU$?KNLH%K|Oa$6k=xMwiO!Ez~ zHf-YGhh~3!oRQL!8x+#MXW)TKsK?*L##+>Od)A^Wzta!*DCBmiuwTVz554and_3D6 zR@d?0bsf)PRNGM0Z842Uf4-QWY{*LuWwC5uPpz0UM1xR=kk|)pO*w|U4uZk!9M5(T zugU0^Ayw_X${>NI7sKts1C{tiijd9X&8nW_vy;Cxp-~r>IpI2TeE%$Tr(3)O5QjjP zk@ihr;i{`kD=Keo&{%9qb_T~o19+E)+xoWyaM7;)A{a164{h@lf8%#YBj;`R*bkzz zF}m~8Ijnr$pZX5Bkyh3WZ}flcLJXsXS6^)Zfa zHr;CJJ9yRZu&wXBf2YRt(6s30ZHGtoP^5qr)FnPP>p;mR+!fg32_)lb3=H*dAOAgW z6dn+-lDX=76F(!rHnHcbRh`~SyR}Db=V$MC_3CZ5N;)A)WU**-7wtwQTS-S11EAgU zMDRdfy0x`|ZZJ(_9G3+fe+ptV{$|_yjZVEDq#TI0)scRvF>_?RImxxjjlY>@S#R zGSb#ULv9^t56ly0Wk2k8w-aTsoN$a%JhP_huv!gE$PvdLoz{M`E{kOw5M)bY` zs%bfMhP-1`e@^JXl)ZSrHpSZ69->c%guEW4=A$#lx&{ z=Z{IaHRBhx<`SC@I&{{JS2)lfFo7?yWZM{jSM0k=+KK;5R=x>nK%w7wJx@*VinlOz zx>~Bbe>xtO{g~4R^U+gIJNL~9E!k&7`|T3@Bp^W5_VVz3U8Uvw8Zj~Gc-K4i5w?r)m+{cCLN!hHaZBqqBicb9(( z*SH+9qU-ZNy34-|@Vu)?*2}tozfM-)U|YYDe>m=1I^M-7k+bd1tR4R2+Y9fpGJPL^ zbCBP$71>pH?TE4-3fXX1`1fcd$aC5V@=0w3c}^Qaj-8UR$ERe2Ff7Kx@XK9|Zbq(+ z4=#LCY@PFBe#kG~KJSMdnG%{2DX9Kg!iXdcyvm*0lPGC1+mpYN%YR={l$L#DJk1#f zf5D0}?TM?Oa4|APGxLp2!OyTW`7(CK9!a;@T#k3BpZ?pN;i0v7Dcg~sXgj|vcjuUc zJetD^#ePxD<_=lX>O4~W8*HmsdZuhqVAKCz!QefI_-RHV9Ye(+@do1#YGBU*bw^6b zNX$&aIt9po7OdkZVVw$X-VD~QO4lM#e_soY;uTVKt0{j&h*3En9{l=&rJon7(_XAz^7n&JJ)U}jXr^*| z&0{q;{0||Af(gbn2#iJBQ`tX_{RGr6+M5?o`}`>>V<(GIznvFwz>ZMC|3r3Ie;RJ* zozRH!O)+15v6t9YN9pmlGGSv|kRJ1={&wnk(XdDEKIgJEzDJrd=3K8=FXQ#F!+wmUQwD?re;>R1B{(g6N^X(6u6^s$56ff@X3nm0H+7?FN4DKHbc*MKBBMCnMk~MO^{=e~5J~8gVFJ zCJDmJhr-YCN1T+sj-u=~$wI$qUU^@~2ZO+mi@J+$<#x}l%ql{yf<_@y|4vJw7I8*c zzr+%*WHDhxIvH0rX_eR$G;xY>UQ9S8lBacP^VAaFV07IW!P4jbK8ou z+j@7H{cW2jI^#Dgl3T{M=aM*Tu4DC)(Z@k!l&DbCX%W{WI!!3iLcC6=)u>LAXG%5a zqW<@hE;Qk2JyQBSN6%w6u6&v|vG8KeHC?RE>cKZ2%zq6XVU`NHe;aWHipN?Abwo&e z-Qp1!6^}V8o?{nkqG9d`p(APZKXU{MOCws$5pGSMByvBz z66cB!F1EEVyA?SJe-DKmkbSs5r*(R7og|VD@%g~UOMpTle}GysIG?_dn(B3B@OdPL zXnail1!^SNTNAE)Fa=)>p>6GJF9KWh_?HSC{z8?Vj>VDsx$^1 z<>KO^e}wXWisimT6r!9)IN%41>2{IufJi7oW+*`nXZ&!Sf7flhZb!`E6luVQgA2>X zavddzs33`rlvxsI<|4w@sbX){ff};xJTB_Nk1UsUH5NTbPs$^OprOMyNu!s*9 zteOB?8Q_8gXX#^SNt8vZKR%)xEhd^Mqw|(3Pm z0BBQ4{AHS-e`<)>M@o01B@VKz0hX|N(do?J&NwJ+jm-?t8loWr19VR`{ukS!AWrDe z4S8P}%^%}uLufvX7?qg8G!)R|?Zm!#$XPh{QiP~Z9qs)gr~2c4!n7Hc2t~E!naDAF zkTfn6`NFw5@u5Iik(Pu}edhcGc+&3l0V=4&u~A5^f6rF1y?PGyU|Bw18U z7IuYf@vY8>e<<}aPY8#;!Y*;e87MJG8uNt6Pf+7MV}*QG4y)IlPdn~Xz*Qb3oK6Yr zkd5v-Inm2{V^U6By=my&1vtb|A}`FZ~_+|z?uxr>A{mIr~u6S zdl;PDlA=j_pDqsx9Z!Ugm#g(=-mE^wA0M}yI7o1bN4Vtq+VTOTMVj>%o1=>zUe21w zW)?{ygbkNFd-B4K&vxnm{TOa{>NY-zK#hAqf7Wf=dUABH_QLFhr=^H9ok*xeG{+kwyWv7ZR=QC{e`0)e2Tt91r7-V9%-VsKBww0+a`u~kzBlZKEU#CznbgmvO_ZiY{)i$)m7Qi zMQU%C@FW07OD4K%ljfOASUQ&cgD3Cav}hiZK4kcg{>0eqxdAx~z#}F@_36WG^_@hg zJeW1rr}fH*+`m~b>MVbE`9BEnyNXi#e>KK>Q+WBGx?K|lTFUZn9?O}PpAb54NS8@t z<^6iG2u1re6nhOTd&)LiY(Oi9CI=q#sA{hx$jI)-c#A<_M(abLCyrgnTI5;QV&21A zPTb;19x{nLO1QP}LL7rEi|o|m{V-Ht4~FvK{OU z?HRO*excA0uMzt}*0o}Po`NW1>|yN3tAlHmh5rBjMdQr9;P{ob)&Vg(o@|W=bn0yZ z4UE~&1F)SixLum7HKY6rCI2Y1YexJuN4}&x83Yds_|5_>|Gw_j%R;lK)?O_`ubQCh zZ3+5nGK$t_l{j;ug*Xz5HgkdZf6vkKUecKFPcn@^#}gyh#_09JW<%Kq!tzOm1BQ4VdWa={kHT_v20d5FZcF3$ybacVTAR)#;7zzmWJsZ~Kkc0-0X4 zpNf9?uODxG{8V}#Kk6q-@w(;;Xeo(~SJGh^@J*&_e@X1^=*A597>QF#e@uc%c-7mh zW%c;wR4#G(X*ojuecUm`z}i zQn*N@{bZ9?r=ZkrGX)12gWF8|FV!;YU4MN@?6?`i@XbI}5+{-ALO*$|LMr(GR}nE> zcyAFI+WPYWb*mPk+^Rs%Q)nJ=dZt~{7oNnCU~EV7N)ldO94!;$zlf)HDe`hd-~2D6 znK-tWpK<{x0yH(3ae@dbf9+b|j~llUe!stB`?iNz41bf`)-NsW06|fp%>fCJ9MkUL9bM~*|nSNiS*^V zYr61iy=sE^uG`g%#i#JL+brhWD6!iFf$e=0UNkUyS#RUG^=+_Js*$B4TB&8lI9I8a zP20vHwy>;n%J)Mge}GYiVpc?WWR$8#QoNX0rc4zie!2jX)|mDPqgTP~yZMJ}0MV>S z#opfiHc88TCBlezB={)|dvh(y_bfdB!VRa#0INNlHk9umC_ zg3v+6GVc9BhVzUbdfG$&;@fjDEYU<8RSj}~%EXO{G?F<_CrdMH;(2(upHB(6`xyG( zBS_{>rE@qV8e=81+@C%U+%ql+Sfk~A~OiG*=2M3XLBy_@oVVLQ2OcatfV-sMoN{W6+Eufr?q+%Sy=t_>_n9TTYWf=Z@8Rs2z{XXwa+!7~G8d`yt zr=1d;!Nsvt;-r^;H4A)v=J@Ygu2-}8If7xzdOBV20(*`_Z^ME|BljTKu-qxtK|yA= ze~U7Kdq;rP7@ykWwoQ8nS5ROlneB4~Q|G|uUtf5tie zdCsXWb58%#sb4=O_3O(i&%9*H(@#lx`Xc8vJ?GS?IVW9s*}ysV==$JjqiKBv1iVMt zVN#thAB~q-0tbV%r>kzd^LY`E-fQ|&J^FDKl|&DLgP&GWAG1uG93!)pnY*I2s!LX< zSp}X040IJ~{U0(;kG&H_$J5<@e`&w}%;l{zDNL}0#o?s_?W?Gypj`0dwFmv8G$_k| z?fT4wC(xB0a*-={i*Rd-cU`loTm0&?oEr!l9*_z^a2C6!%kAah+)^@!44zDWYT3Cv z)l-W?LS)vORB;WiamE&7kc;G}itN-yI^_=QI2tu=*WDlr zj5_v+Sn@5)Xf47DVC2C76#}?FJ z%b$L0!pmSi-fr6UyG4I$!tZwk&!-^Nw|BO|98$CiB5|xga%<8W$Teg}T??9|ZnyJ# zk!8u!8s>Fm$=z-_G@Nb+n29BaXYBIwkC{_lp*T&vr7RF09H89hFrkOL`{8te)psX#X_G z%rek;#>d05pK9M)o*Lu@8p(2;;pD4r*6Mgt*$Qx<&q(l_2eNBns22B>h%$_N&P^W?X2;!FhY%AdRGy>!ocN<{;m$v~g&e+fR|sl%#`et!P7r}D2jk$;^x7xORDuk+~dV-YpI;(s2Lz=fXkVSXia=rEhT-X#s@^PqyZz~h_fn15N()JWRZkbf+}FxVVYn63S#KZ~t4KsP zPg-0yP5pWN-bK-M52>Tw$BtFG*;I+?Ud+%&2E#d&LmLL-!-nOd+b@jBSM;-Q8hTWl zva9zytZHn2ci64M+YHY%Bxn_cUNC>-Wt92{F7=Fh7-sdHc1(C`nHDM=4SF}TtK>`< z1(vpH&{t^V&0lYCKHVI3TQRSpA4)I*9bav4e*KMSYkd4E<4k#%J%yp$Oj^bj32u}9 zeDlwUOOvAyYe~Tz3^453>*Z3MRXdOjZR2tLoSqq0_+h{qrQ*!{F)C_k6*qs(XcdM0 z^iZ$J=pP_bW%7H{CqO_OUk~Y7_OI35VH4Zis%#HU6<_v*AF9Y77a}j~=BaM0*s)p! z)k$iTR%^Sm>FV8m98zvDCIC-KKhJII?q(<+Q=?%Vq*@bluCYKDhBtpF1$I z?dnwDg7ywvElGd*~utdVns4c^>eEHV|wneZC_2!k1V#AFJCf(q9B7H?b{jAuQbO9HTJO{tB(2bfpalKn#OjbC+$1BhZElm zkKRgg!+G}nokS?fdl+AoTcu$ zx}Mn&=A?)H-if(rKw^l@gkx5!%khA{77s`W*?9jNwYeG(^lMEN=er3UlmYOy7Xhlc zorX^n2?+}(I88foW&33`pEYjhh-ssdgDHl`9K^w?xmW3$d4;YOf3&XIZ_zbSB@kqg z3wOUS+MS#qiKKrvq`3x25=Q-L&LIdUwF<;aZ_i@CmgG2q*j6KdG2Z}r!%Bq)72-1X z7XkCscUNyoS}TeWD|xTZX9pylv3H>G>Aa`ZA%l^Bgr`)rr@iPP-2ak?n1qfJ8IUaG zXcQNI;Qma_MMG^pV%L~M?$#7a*<;#@b_ygd^3>TVe*k}74tOD;9ItqcvHZ&zYiw7) zTq}n_m=ubmAzRiFr^N?!HVJF^U#aZU&mn3D%NCtxYoN7dyq=zDh&o zx(Qj=eUNb>N1tqSL1FsiNkp6ymOPn9S^@|jYFD%T8R#F%O3k=#MR^1si8r?kr}KT1O?AH^bzF55rFGHMk^oL@ z!i`+)8F6?3QZe1~`az|4tSC-S`c%g-0>{~$)~A7^<)KZd-5-)lwe>O3n}mVAwVfIp zQWohGf>dx9Ta3nZ8SMCcU@ROQ6t{mT^Dh-26)Qw>rTD0diL0KZC`0Yh>SOjK=8Kd@ z6sJkb3H=m%8@S5J`LErSQUn9og=-4%xhSR=cb zOd=Il(vPayc-bJ4pcW~6z;(i$hxD;)4yo1BMM_ep;EtPp6X1>~xC@swNnwA^WD*p; z0?I~E?)$4jH2UH`nn}^U9;H1rbRXXKbJvEW^gu(yiekFkJ>q1Zho37~y|NI@a5bF{ zN(-3KKXxZ)-lLVy4CbxhP#z(UpAq-34g*+t^^)kj11L@G684l|TtUP1=<<`upCXV< zL4FXxW8_a8Zp+Y$@{E3{Sk-^kj&8r6)7WPJaZ_#M!1^vN)r+zX;Q=j{zKoqxOsc}X z_C%GXsUpAWjB9$ zHKW>siV{5CQ@^iql+E+PNxCBkLxx~XTM6nzLqR8NFeA{0HAkJu zg?batO&t?L&;dzC!B-dG|;*TRR(&d zn=WKXK{==^u)aSL-j6(2f0PYd=y|}1FmxRY4pEKD`_vs*viW)+g^jKawvI@BjDLp; zVqt;mHiL6jMfy8}vXilhJN^q6^IRym8i)bJ441Ah0SuR1w+IlIPKyW&4>&hC3NK7$ zZfA68ATc*Fmk|>JDSypb%WhjY5Z&u5xNJ8Rhw~r_1Of7>(*i9J#96d8vTz-t2pUMK zBLnH*_so^$T*)^U{Ya3FMbd}E;qc6Zyt$;1LM{|U@smSs_~Ae-_?1A#`}kM(zJ+Yw zFM-MXHL#*@5+uniR|=MZsT@io{7NV;wrVI1t5ge>y*AmM_Bp@9{1D}gj)*;dggKGFgiY2bwEW(f@lED1}sjE1HeqeIQG zkpx-EG8#2xg8H1WB~rp3p%Xx8kQ)Fau8MDii9;6X&j}5~A11e-(2U94GFY98n*ys- z^*#GYa}6+(eSf!xk?carM_dE-5to&GR(2~3Mz9`hoiWaU1&6~K3mcT;+|ck@zD)61 zj8Qi_GjIi#Wt=2e{{z!=Ej|K)&4s~^+^j*e;snXXg9zDt!5V~NUKR#5pg2PnQG--S z7lXA}3=Q>>f(Fik1+xl{iKU@Y%ZxPzsq7=(aOPlwCV#g)w*bM=8MfA8PuPiy|4S{X z`UbV2fgbiB)DjJZQxlsp&Q;B)8>ScE98OV-Z&1;wzCrB7lG_h@8;M&>f;sWnYB0%I z3yd}?ZnhLm36jw z@?pVSFjtEeXWIuhjRrFHfu%*kadzzw6pRUO+y@qog-V|qR3v)18-v6A+3Nl3{P@G^ z*&^^}&dbyFdU3uCdDH#r)B45n`gFYrGz`qZjQ?eML0tzSQGe2H-oolt5sss+zGZ8X^QES zV3kM+9XEruU3zkhId@{S)3MWX(lm1WCfptKb%r8Q$Xgw&gmfwT;nv_ z$jNdqemA+s@0Ppx%_;BmyKz6icd-ZfJ%0t}hToDq!G&@Deb`LULOQ|7!uTyWgHcvR z9!kO*E9I}oLX=2%HuoUH1@=i_u+2Z>|S1mYudh-WxcDHWqC+16V*s=O?a2TC*b-TQ*nznc;6u> zBiZ+|Z2exgi@l_GlV}sytZDFY8Pc1<85dJSg@yZgobt#i!Gd}@)!GU6-+y#KWkEC( z-0i!-S4B|O*r?NTxGqLz`C9pFrx@w+vMaCovS)oUuZ=71f_1Ni`U3aMcyjG7=}$X% z^uRHf|utlpwZ z)!S65dYdYB2i$V>&na3xK(p~tBX*BL1q>+sRa6KXlAO568eyG8wLlO=77{-vf53`0)7oQ%O&c~mR1ogM(Y zFM_CoyI^abIx{BuSE@cRg0?`3^nuB%=Jw6sWX4{*^A%*|{W~|ru{V!u*ZV)zm3GaW zOSzRdk1SkjKR0)=mw~#Zn57TPl;KDt9{&$ThB+~}Bb)(883HmelOZ-KmzA{vAb-h$ zk|2pOA6h=fp8Wdl2AUMf;mD(heG$PX(CGg93CMG{j_3UL%A@Bm_pjdnD!9Wrix8yU;D?Xdb`)Ui zd|D`>+@j6*k8_KU%0!FPd|y`0WVMgex*xN8M6nuq(LkffXQ33Z+-UReWND@dEr{X5 zkgkLgtCC_p&*My}rZ8|GvLKQj4!B=hG`BZqJ-!uZ`^=)_Z6FCRhonh!{eK;yh+v6O z9TJDl&TPV?d<#Q2qGkZKGy`9E}oMUh5L7NnIuH~M^|Nyd<(_8{09ie-1^7O_z0=77svmglij76I>SnM$_2eCAw%xoGnpZh(V z;v6N$V)={t_tUrTW->q2YtG4S)ZkucHQzKOl0_x=B~HeZPA>6UY4g=0HF{KWU4;yKk3Ox=kT2 zL8B1P1Isk2@qnN_$P;Tu8NI5?>e{+mv43bBUmU+!8Tv->ZFGA4p0artnB4zC`>4zF zQNS6GdxWl^jQZL|z2U7Xoad*d7cSyQeE&rZ-c1?gIw?$cJ%5$c`zfd5wlCVJA@M|} zhotI7I$6PY>>Oa#9I%%qmKaK+ACCKvN;hp)98E1!uvqn#v^l^1p&Gvs0gK{z)c<@m zVo>ow51>|hW1r~MbS^WOGS}{tu7A$Na6EQ&yF1&Af31YkIIn(5AcNR{! zd2;EQ_r(jj=YLIsyFGPtK8c=*wL$fL2^F>ekxEU~8q4(cDhHUnJ(bPBX3s$<8vJR( z9A3h`j0Kxx(R->j*ELT0;VAF?J#J&GVH#?7dOrIcjkd8_uX91uONnrNo!n62g6Wm_ zqPu(+A&Vl4=a1TmZ!RwIf;-pEj;_4xJK*Vg?l1T*+<&sEn=08`&OC(XE)GOsv}17u zl0g=P1D1rQhdWY9<_F5T&tVT_Afk@J-tHx&n_}xMgqzscqAd1Fm4F0Qx}q=peN`r_ z8(6)wlbX_gsk^fUw&I3a0ruixtYW2U>JGB48PzR(S;Fjvy|^35OMe4MpEJd+xJ}zY zErE_3Z-4*uecRBj4HC?)4G!|M(sl$oty7$Vk_Z{NYuF4#tI}nAAj4_CuG@T%6k-mp z+TEGu2NSyRJF!}tApWTkY=KRqdRRogejSNd*&Ft$1VF3mDs z(d-S)TR+?=b7ch|n`Eo&@e$tPg7GjkjuZ>}&wqqZViYT(=rm9f3qs{cFJ^(X3fyA= zN$@9>A#_Iy;0JEq0w(;gO&glKMbj3#O0ru^NHTEKcDu805R?^6pvg%^gSyPMWw}af zOK*(Rg3KAVbT#^lSm0D}uT7rJ)EJwyTq*CL}k?+1B zUj3)>`o#Y|!8|!YTteQqro{#!18}S;h%D0Tv~vbU;w!|y`%F%wo(Yh_A}m3f{%T*) zi5IIf%k;|Bq&a+f42Oh}@ZD!gL4OW74St`3A8iX!mICB{=jEm};b4`ubpv6y9=f=R zbT->!2%p6sR0ye9q|R!n z6`7H$ha(c^{Wu=@w}OKU7k?m6M6Dnw9dt^Yeo#SkWqLe{>y%RKE*Un0wn9YidP7MR zQ)O2yyJ}@2$9|)j0jEb3#PaZ;!J%0D87~>?B4uR$(;qGRhBIP&9>u|UP4lgd{J`Yw z=|+712s2m*bZ@e*2bjuwlIG*)xo-oW>1$1a9+w%~&ksU4dxQ=IoDl4eO#b)^^*{Ho z{tqVUdSsU|%K;O&J-z`Q4FWhelaUrEf6W@(a@)A|-Cw~;Cu6y_umnKxlFq)Qv&pni zZQ_|eIC-EYSmF#tYDp@#`t>~r2cSrr(pnd1_C*9h;M}jEd~f0T-sdadK0n@F{ql)N zUc!=4gx=kw=d&c1@Xq(R$E9RZ7<-|Ln2^f5TY2}Bc~&=7_ONNPvbdQ_nM}4feRL)-6JE=nDM$wD5weZHi9Jymx2mr^hlB|ZK@(26S%blvE5 zPMm;jBa$slHnr|aLOPw6RqX`K`B1{}l)~D|tgI?suWzOZwU}qc0@VcCe=gw(S|q@7 zXAv`6H!6T-w2%TxYBa!U&@@2FpKf9?DJ$BlpVQSk*SGe-BdzZEfJs_eELnj%Bv^Xh zt{GN_YzWR|L|j;|E%iVP|3C)$WRC1b%2_E*9*yn7fVhXj$D1ji(225v@S3h^eWjbFe+4u`9){|U*@A>r zq*$Ohne83PHUfNkUYf9IO*jI_1fNWZJeY#}RZUIfrwj}TM)4%2h0p1xt}_bj(TngO zw+A91*X?T6Iie@$YC#}DhN4e2Xe_j6T@eVnTTlj*-Jn0_;!lfTgxH?R?5CQXj-am`sFo`Dr zy@|=+fddNJFlZpD;V33ogO!CYbVd2tytjjS+N87*&!VB3NmdN@J)nfqMlrBj8{Ly+ zmEy)lNDVPi#o)J$c0rH&1s=tQhcmw+APz?jWhur zggp^i2@t+oJHSR>ut=fA$_wUMz1LfOcna$*d5zwxf5?i}F7{%U57lI*Z8c)rkN)pBcZ??L+|L%Kpc!vQ7qLTMycCI`{;U@RKUj2uf zus)qFAWI)2EXHNoN<)AYAw2Y%x}_5MX$eK|0f7h^GYAv?vww&XI+}2J$ONUfw1|XO%57$WC<5MqOCn7o&X&aJYF_51FZh*tqwtiJRq~3BPDff-r)1 zqAPBQmroS3ppt%aeJZT#XQEBd8XLB&ay6*DXBaLhtW?MwzRa^#*0lS|O0P`id*ws= ze*g(9{DD!m@CGYiKI44?_q`Y_CJe}ySsX}jicL^peaDbt0J{4K0s}w157?~$VDEma zgqjqa)p|R2_#jpGaJE{PRWmpp;5PSTPWM)bCqM4+6bBGKglmlcdNegd8BHy~DK149 zk`b%Grp95MD5{|tIEMfbw=E>%%ZdgR&3x&I<^5K0l!G3y=C|dx!M=kBpFUk&4(k*GWdKb+q8;PmsZaWT7J($f9Lw~ zXu-UQCozjf;+zQY1bZM&ji!7|yqIG@oa|wJ%BjUX=-pD7n+i)9O1MjirQ>*QPN7hU zpfVZL>Bqj((tH8sN3&f0>D9KgwmrJD`Juh?wev5nP6v)}jo=k&+B({=1I%Xef4VBM zH1iFfzZ1^lJkUpkja(OpY}wM2e--okBHX5seHR)r6dmx5e-YrA-4OIyw%<2HYxG4~ zOxHj=H0Tw63+P9$%Ub>;U5f87ZosPB(k8Qff=&7iTArPA^j1cYCM@9XWqnqh!bG2} z0J?3_DY+JU%&OT8^5nE`z*je+vtpag=1cr$}JxI{k#xM zY8;EO?M35d?Fv6w9pmx?Jgp>MbGBsqYU#2`?r2JcLjs^3#(JJytg%7S=?cJlh9DE%JHU^^Y z>q%Xg?{EfpF;(~G#$3tcfIcnk?rILY*IP8pEtM#a=T5_ZyCd+eB-Bcw^S8Y2ZC3lf zC31~lLOD=Mf_sIqe=Nqrvh$D?HA1t~b=u69`ZA4pd+B$gQKAq`2tPU`sgbB=o9t>u z-_>7B)hSVE!uOzmOlNvl=C1obje=KISl5-FU+&sRM=TrS5;z`P>fZ zas8m{#!5SDu4$BK#W8_odzt*uUi_bE|7fi1`J*YW$9%P4fA92aqNZE%3_L$?{uJJg zA0m*{0aM%CBN_?Pcr~5wGj&w_Q#NmwKi}o|NdH{1J%d&0{Md%;`s)h+<#_!^L`Ih( za(fz)|E|i1^dZZ$W_$gU#F;*4=4Sl<7$`}?)cIwz@3Zi1DCd{&lD&Y@xpv&e%kDp6 zB$q7lL;U+M5sTxPR1wJDk>BpF{tx^!Pdt|~%K;OY2-X1>m(VQ&Du2})%W~Yt@y@Tn z5nJ(&Fz*?wd`M!;QOZ@W$SfySk`B-=NW$3#)_7P}zCPXaz+ymfN!m&(xe!2aPft(3 zyN5+~n-$r&S4DXK>H6x$D_WS$5UZ5Pu5U6bIZ>2mN@~Km%&zy@+x&DGHt(5m&L^0KSjvEOXDmU%x^r|?=g z{<-Vg9zZnT{<2X#FAw#;9O}05n^2uu5Ij>(@T6eCPm#gSoPQGlO3MKTDmcP!f4{2& z&Ec-{FI{!o07rQ>)IUQ z;E9}ZmEV;8;C}(I5uWm=wyOufr31TD0pf7Cq3+=D?hf9k^r{-p-TC*SE9=I~1u1la zLKU|HagYZOq%5H{Xyb=*hpgP8D0toj2WQdnNi^=`&V7J-j8_j>LF1pJlgNkiP&T`2 z?_o8NYTgxGjuikstPS|nNpOqS31wd${P%~kKJ4MhG=IMhi@JCa(8A&*;M!FAgPgi* zU+`(EJ+Bp!GNoBe}A5t6V%_k?09T)>8fMkaDf;f1gi7sTi=VS8VR1wJ*i;isucf?dzJv}o@m<{3!rXljKJ;W#?LSzx~S3U&@5#l9)KHk-b zYJoO3!3HAv)bnBFe|(?b-&^F^q$^EfJ$yREt=iqOgSXpqE)x6NQgqTLCWY zf`628QU!P!eg?X;^Z<~Lh*WkE@15cH2|7e43~nJn0RWN?cvj_cqt^l4pV)d;PMdmv znT{bB#}HN%Zh|s=z_$4Zh-{$s0ZDL^#B3XE5WztwHtNJecmf;pfwuFIApT%d&K4!h ze;De+VS#`y+z0j9vk&{N?y3vUEVxEkdJL8|XPyfHT!Lt$(nXhK+5s(pt7a)nISqtj z0uu4)y&PVhm-|2mUmrt7qH>=LeMV|~)YZe{nogs+3-5DrG5$b3Bm{gc)_iqo6RC)@ zG+ppm(WMwsda(xNq>Tx35<=#uvK#8%cqlvk7WuBkP=s$0qeI8Nsk#p3ga9-nL@0az zJXEnF02yx~x^3HW2-ISKQj=+^IFlYQS@29KN+dO&NsOf5egFNRf5ezc*6LRZ8B~Vq z)%fc5FYN{r!N*V;wu^Gm6=n01F>^kXhJZ5F8u>c{G{<5m(LOU|!d zkpX=1pcV$DtFPo452|Cn?hUbvB^57zx#-EJk~dkmYmcX{?gOrJp?l-&31?BMvSJ)# zkcX5?IlWo&=Gp0g1Q&!0ovxu5BdjG10$;M=B^Z>_`Iq=m{-NXJr3W@Ej57m8yp1LU zM}@kgS=oEb3)BJX(7BPvuUA3{1adjqAPRUzS|B9NBF@dqc3}I4Mud8W1jHoH&TQXj z11U@hUj*Z?`e4XA*C4_R_H#VHY`g*rZxgLoKG}Nh(s~ttG9A?Qs&>Anql;+>A?RII z+4;{7^Hm>Hu=Wj(2@MX%oP>2<_^m3N-oJXMhUFph04A7reVqCM9lcwhOOp$xrV2ut zPI-R^P562~x6?9<4STYxZtfM;Q7{mtg_&2NjJgpCz7_fCeGq}uud^bExe^aG%z{JD zFbi|?D~GItFa*TB7u-SvrnSNIl08n7avc*1!?2LdNo&sWnkSW4s%sRwL&w%Z?8 z6q(e2NM}VRXG29!>F4zdzmgO-wZGG8g&{*UC@wWFx^g%)E;(25OiXGaCKdUjt4e`F zVml-Bpgqz5b$j}>O2#m!filj7KIxnD z%n%ZaEJBHjFY^hb`}WOc2Wk1}(g;9(aUPa`^O|s$8L~5OX zF1V0d`ET>F3mi)jmE}tYRx|CyG!|S!F4wGP-uYGvst9@StnyMv#guV%)uRFx?&tq{ zJ@;fW8z?yGGsSEfz@9H=3&~Bdi2416_LvuvNV#z8+skt(4(Uqr#hJ)vYfX`V-$6Yo zrI@-QS=bm3%_7p8b4~wH^DeQUURc3@Pb;EhM61c|pA}+~eW6@Y*{YwVWif}AyO1hv zy+41nxu1;_-u}aNcrp&h>oJTL(0xv$$4AVVT1^bPaJpvIjOTl?0z8$`dwbCfhQbQ% zB%Q4J>5&mP!?LRo>7p;r6ZO#bAIdnI@IO^JKLxu@F{3fNMg7lX*;U~lAOa_U#5XKp z?ZVxd$#2HSy9K>G`IuxYd?CvOxT4Tg`gnGUG++NF-qyQIFBy*}HO5uZ^%K;O?ExPD zpFrY0*L17ma6FAL($EneZd=?h?j}s*O_=)(n;E?63K(t%AgAk%r15v$GXp&{3v-HT zSUO#SwNSi0eG2-FInr{F#BdaUr-B&&xS^EBTb}Z^O77Wsh=HK!IRBYuxl_RzuG|(^ zbUZmC5(s!@0M10sSsd3)f1-nAVX+KWlb@9iboUvVSofIBx}P z{Ce=+HC{I$68H_UcCSK$xw3st_B;riWQz{W9uIlQIp_-E5j|B(Qktl2V!6lV(RkAXvT;xAlY zhkM;RD0?675ccWVChfWvF-qWsm?$o0z3J6A*H=GZO+k}E(+u4?UJp`}?T%M(-xt{) zenUZXkn~4~d4&Ar#D2(sez^LZ|Cg*Z`vyYJ9m9UjTARy>R0fC5%;*G-C+QUw3OQ{H zRZzTUS<9`^4GYAg&^y~Sjm?Wc5bJrI{uN!Tp`f_1cp0T*D*rw06us0=;hk#y{q1TU zW2kwojuF4PRWkaa%6Uz|7}MzIX_c16%M&X3|LZB^BXoXWBQqN^6J2g(o;N1CFqoc3 zK%Y}c?^g?OC6<>+4|~{`m-4h>el&%{q{zMy$qI;u{{|}Y5zv=0%K;O&K;i)c7Xdhz z5EB9{e_3zaMi74YuOQF|%ZbJC5Oo1A&@@hhA}xZ{$cvK-D{3WeLvop2%9dL6zjsfH zOC2#ON^ycb*rZ5tcIKOJjvdOeW|m{UI&;+V`OVq0m!506u003>&$^jfuJ7BuZr|z# zJ=^mG>tV8JR1mX{l z?_Yep`R&ZH9mjI)fd6A>WM-AFynDrTa$U#j+k|1lfYYb^tU=bkkH492!v=!7yV!e?2NV1G~dc6*w%fqGXgonvG~0q3jT7q~OL` zgHp&N?Uc`QQXj6(OGE_RDgZ3|AQHf0Qh& zAXwmhRdUdS9}FDFRLU&Z$!Lr*Wl@{bld0tHP)X`@$PblUVwPkMo7dZ3;(uE@}I z1_g$9UE3W5n&3alqWOzFd|sdo%F{WT&E?r0h!(oI{=J}?Jj<{=(mB4j_l8tkt!Q`> zBYHa29NRq@wo*(e+ItS^uf`hv+g%>!sdJG(c%k(f@pfTa1;3}Y z6TBVNee}sA^%)niJbEx4>bA$jmS#NHKwp_)5`(C!|DA)gTDe@W^ZW9!e?D1dcI}_0 znt<0hn#eP}P~8#5vFZ!>?y}q=WOJadA*u=&yn{rZE+|yeP@=xhx2Y(ZJ*cZted%%} zp3=C9O|JT|GH$KJtw!oYc{?ovUoUgHhGRt0KUJE)T8$rL(Z1NM?|aYi{jWG4^Nq5i zr^X?nQyx5GoFT`3iE^qOf0!x%>Lwa%2{?sBJY`pISW5&@`u%}3&9E?Jn^~eAG1gQK zadHO}1d~c#YhmtpAZv_)1E9EVTK9SDJCT>r`08N*RVpoE8!;)gldDDE33jP+Zsdr^JP)TuiPfbX}01% z;eVC0^Ny-_dr$@ccv;}Znid6)R-X`>&gEX+JC^I)eJ>wsBeg1X9_tnS!t~I%vSrnQ z-Es5T2dX}5-q5RKf4g4(c7o;o*|unQO27@}IMMU%!0(yEfKgtS8{wj!Uy4#(uKWDc z5%SD69~c2WN6OPf0`E5}pstSjmzcJD`lfZrXDEhM%9RZ7+o3WZeQc(5RV;#=!8ui3 z(%S6@PcXQlC{$uOiigicz#WWKMygMiwu<3xlwn;894huKe^XXFME<6w*6B{Y6xW4^ zH4chD_cpOv`y%Orrc+2*J7YG_4fRvTn2##2&7pWzZ!j4w5xsJiPJb@O6nnzh-RuOGs4_!U^3Agk-Rw_XF%W98yK8{XmdK8_mpGE5_*n^-sIft z(K6$c3TQ$we|a`WfIidCZOE_?MByT0Sx9jw)mE(?m|~Qatlt9m#x(b!UR;UyhNYH_ zZTb}0Q+fOZVLCxEy}BU_Xzmr|TP(0scWrvU_s}vq&KLT}2W#In19;3h!g=!Lt}yT8 zVig@P2u{69e&2ENxto zhMZsXe~w{zz5`IvPaI^9F$?IO$SD46NSvj%J- zugmL;@A~$@>00ex&-Mb9Bs=D>MXztp{s+tS=kk{^%K;OYLjeL512H)^mk|>JDt}wu zZ`(EyfA3!*U=NYonv+DymI`za1=<3`1`KW1m$Wk&i?)gC3nHD^9rnNP_#xX;>@1dK zHyQd6OCrVl`0?(@J28$mw;b#JGsj%tTt55xo#$Gvi=rU#tjn3@VvNS4&>96}E97kI=mm8bXA(6`@F4Fm zoSqb%2>Ebpz!4>3*+ft-CV#w0nCJqHCfvK#u!2fYJ0&J~gaQ{=n55(@oz&U30>n`5 z8<3@72|pzoo`-PcR%n2~khDk?5TKOl9J*r^VXR-N!ZGqCzv@ScQEO3R*r>)`a)3p@ zf~6%1PHFtv37lhyc#a!3=>QE^De5#dJc6*o`E_H09z4)@>N3?=>wj!=o#z~!w%})z z$aVIMG_Jg570bnlo=+gZG13bvTMx|0T8nm0C)%-bP{A9aFp9JeKk0SzcW``t7ZXAB z9j5}_>n`;PT6IIAvA=2Q4G_0@L8kOrj~KR?5gRwTzdRO#Jrpzwl{dDJER;j)a2_1o z&N#UDMWY=XTR#RHqkpTS8>~kGey2V(oG+jh@H^;&c^`w?1}dHEP3;(_=xvHgH46fNfCz_oQukWR9^TkP zQLriHvEvIpcck+Xq~x1ion&*mZ^f=z*(;sO`(xyc>-CzQQf5aJs?|`W1b3@&vh`?( zyu;1j>Qz-g8-E2-$!EsIgk^%3Q(A~F7WRN3IR^sx(N+Yqo@lzTt~*c1m@Yu}HM@A~I={RVEv`8|vRJgQmY zYO+~LdNIYSaHv*~=29h}4yCus8?IiNm)Vzo0@9J8?SHN<&&)!&g{gCWLMqCphOBh9 z?iCMmLlfh`t|{3n^R2CU+r7}0zVw6fHkvo4RPD)oWz{jxrIe_3VbCYBRxwivFx;o2 z_Ji+~_!=80E5=X0!8?>Aex%LBStJcu#=qVJlsfkY(MwnN}OmakXpRDM^m8-ae}z#k$s_6}i}^ zk%OHr$E%A22-iz1?Y`q1iFaM)WL=)wWapDR-_pd;=@zU*19Ob{E#Cr`#uU zc;1>3_UYN>-~VV$etE60)$@z` zeScZ#aqifPKrU~jYI4Yt6*H|-ohMa zY|1E4;=3VCwi{?E)hRyY=62;NSUl1>(kW+gCO zp-p!Z{hUzFhX!fgqA-mVrt0~YiG?1rkOnkg&{98|7>niiKK;)aT8u+x&*WVz5{ z?_ny9F|_Fv29hN|>;>3EUQ|tm)Hd)nbGTB%nW7K(3Tb(jW~6Vm?FCq&t(!RbBNOaK zTYqh!DvWFUgCSYYxOoA*ott)F9~M@33nGc!Yec{@Y-jW*tz za%J{dnJuTrGh{5}DM=EgBRDWbr%@H1WRPq`P%L@MgYXz^dTjom#*b<*y}f+)FK6i5 z*_ScP0TY*-2?7=YGLs=TDUzfFf8$0He)q2s=4C0v>fwDX5G)d4CX+?78_ak&StJXA zCRJ$eAB)Iw7hw(W+LWrI`SYGqN-i_WZU)0S3LNh?*I%bJ$jik>^QbgWYC)Nf3-CBD8TzwCiAu*+JR z8y*c)lyzOzH_QLt{#`4Q7)?A+_|78q1L4M`#{a=%7N=pd z8q^1^h9hj-9jOu6$s&ZO=dwW#%K(f~tF#1>Gp&(lrDaY7LkcFYb*IusELXnoTmPt9 z-r8>A9Led8h!G{%QVqRPL)cC6*ol2NJch4N6ZV#m}^8*j~sVg&RabJp&< z2^`up$q`5=Fnex{8%femp_hQsx=h^_tf&r4(VIS)C72+IgdY-UKe0U$E3fD|m@R=2 z)0p};UUVXz%({IFFSPPa6zx|#+%6fd%JS@1sGRCPs8Q#zBD_sl|VM%$OL3<4Z~X(d;` z+MXwC3>~9~wosj_0nR={`r}ma|tV|UA$B||&9jTr+j2DS8X2a}I`YyxYE#bh4v8B9-Ls^dS?ZHJzwe#54hicf& z*_KVEbiBtD@6o(UV{9}$Q1@HfI?p39RY?*85ZD2dPZhGj4MgO!g-9uX9eiMC1ji>M zyC!4eV9*I(v%|nT)ejidl3iqUx=8DLK0h#NXko^IQ$&m=)a5_6zT+MDvXpgN+?*_C zfdkLTVBbQ#e6ev4-jrDiPOG=>V)Hq1Pam0aYPaX zRBHZ%9Yod9kzGnng=PtVodCKnO)$Vs|I=d2?)Dl; zm~855N25=f`mEa3_n3tK($LyRxksB>RioMp0!<4hz&y5a_y=`=nXZ6JP`jaJjG=gk zzU~wf5&Is<76{*VNTxS$-b@sI4qe!|(o~7WSAXz#DeZria6j0nw$(ub0cIUA>~ZkV z!|>)@F43Q)ffI?ubBXw^_Cu6d8k6HBmpiyAK?1<;Qm^Qk#lgO=AjEdOj6R3Y?hzEa z!_iv~C*E9$$+mibAX*PBmCi|8kcL+ZRf3z+xR&=23=Q1=_>_XYN!nD!v8;9qa|F&e zGHtuMSGr<}_n`F`Do7Xl4BQCDl5M&&)3;Lgo7*1d0!tW_z(yqVghr&0>{r*<vd3tH?3WtVzRFQyIv;~zy~kmZ7Nef>zQv9++DWnq<& ztc39mea)JqQdY_omvMG`RUbmB#H9e4_?3sW==3EQ3TOD)t5w((A0*zlCSZ{l(&F3G zE=sGH+7JRW4vm57rAokMZ(gv->F2AL8xaC2f5c>k?gNfJx(}E})gzMoT9xgDC|)4q z(E-;JaDAVDY6RYnW|H7tDk0vJ6@9(3*6X~=*6Z25s{hh>mI^BtX`+bQ?W?*y1rPi< zQ`%;QZh$%mb-VrX#n|%Uv^ksY{^dNUE^(RKP^7RSF4UdaA9ZW|e}`aSw3lL_!}d(} zf6ZrIlf+)_CALoI0&v0(^FVUEPy|l@(f-E?$8*BK5FBTnSHYr|(86SRvwKvFUKNlK z&iF|WNZ*~kH@)@w&@k!Ob`$EZ*HlyfL}Y?Mc)n|p`CfDVINY|+$At;MHZB^FbtCbA zsE=#^>pD#${ILI*m2P#`&~>bv3_n?3f8a4hcjbm6A=IF&dl$zGz#4l0 z6XnqU8EfKS&sJc50fzu8{}4#e6-m&$n5jg<*B?juM{}O6P*oRm1+uCcbq3F(;fP>{ z?sJY&3qAi#JFcZ?YOq%?IR3O0Kby{Gu?id!#m=-#xlqVDEprpBXOd{7%wK9MG5e|Z zZT{J_T$^u7FU0bzb6vrk$~JQL$$zjNPb7A@Sa}f~=kN}uN8jCE{SI6}o*$Pn%K;OY z`Vs;b0W^~#HZ6bKHW2=vUm;{4>a3NbSKBJE4_y-%En2j2J*+?)2((N)L}W=7>D1oP z|2~SOY`rj>o&BbY-FF%9}G?{QrG&b~!Tip)%Cme&1FmbH#8h zt#24g-x+A89fi zfYLKc2)!3gb+Dxo45tinn-0(~sxi=9%j6t=>4cpyZbFY%)(l4p`Kmj*BuoOrS+taf zV48RsEaL!T`o&29GVW5B8*u72olr-T74+t-FqQfxlhg$D`4J4#5tCts%BGY1wE)}T zL0u~?!pwi~EpEIxAX3wtqs1zL>HO#wMt{L3Xk|y}r8tcOjC_1>YIHDiCeqUN38WUKD|GHVI73no-%*lU$PLRnwI8Rg94Rc-ASa8b$Ps+ z#z90H`|3VdJ%l@^_|yEoxL`LTx!Boccdc7JHG70K=a9tdT)|pQaZQkakEMf6j$ox6D};!?mbO8U@p$&<|EK z=uLkSkYMk?5HbP*hI@TZE$AnrMu0*H5-&-of-#E1PAGrRP{&))%=C4>k7AfA(T%AE zSSs%(7lH;B-yYZpLoJMO_Reb@@Vpw9c$BQ($`?yIL6>e?j$NR+u`U(6?9-%jW2(pP z&geDDw4l5$wP7*-%gsxnh-5Mu!tY>>@NIt13Yn2*ARK*q+^Dl95m2e&Lwc z4`7CKNe%X+u3{Hc!lPPv3^Zjt zvz{R8Xa*25jq=zZ+Jz;Xs*=(q5zY=<1R-2_O9*If@NESWIP+kVM9IFD;6KP{bXb3u zqCXN{Go}iKvqvB%|Ay+Bx(tqi51S@tx_pm89zoYnlu2c&E4Nz~?7N7UG4)^M=fJyO zx4C3308l((LS)tu5)mAG|ni}(j)C6z=dFMWe{Nsm*Dcz4w<=X_205)EFSG)pmv#H8v!7WRDpeEk0R`>LVwPRJ|v|=iC?>a!mWR`E>?dl$j zmOYN!ZDMm5eb2XyE=Q(8D^bmL95eL^)o`_;Z7Hfb(1ydI@C`b}3)LR#J-FukGXF+P zclLjJp;~4yqMt@DBA6r~=0AUcaK@VH|6Ls6Vv^3WivT0R{TQ@7R z$rz4i7y1>iqvbNyfHkd`ag-3;=HTLy;}?!v2TDJTB4{?UbKa=AI;E&5CqG;*<4ub` zDSH^;)?(w<)9!@!n0h0?r=NE``?=<(VK^-U12om*O?vecC!V>c6WBFhj3^Jo0Q!|y zZpW1MX}&@)1!Z)TK_Tl)csLFak)TEZ=a*9#)T{^!@G%K7P0ezR?MXT0uX#uiApkq~ z5l9l`AC(Hr(1!Xpm#u#`wp(WqY6hJrMI;e_-oYe0@rXn=2p-C3ApAuYaR|b)o%DjH zKp^`)fcTIj5aF7i#vk8Zzn*yGj~^%A)!VUL2mG*dhCO&bhIkc_a!;lhgGX=Q{4u`v z-d>$tt;-Q3dW5QNqoRseuO_cfs^As1HB;;Iyo)3=fBc5#CB%QH-`~knt?2P3Wtp@l z*anwKuEL1{xsw8bYlI1VxuER+xi@#jn?w1J{R1M1 zeR>LIZe(+Ga%Gn}%K;0Q;u!)K0yH?2VLU8;;V zCLgLuJGnz>6T-?UOL!!Yv@uyuDY{GGz*zvIM}aVT@|R*L3)h*sHn~zS*y9SaVlzZ;^b^orOC$lxaCHMJicC0{{3fidFIb$JL)V zli82Uv(tg08U0i>jWshcRM{G-(m=I;57Q{A)Kq(Yd)}T+-{w1dzODAur{DUqO?xYKkT&g$ykg^S-<%D#qSef0S0H#BT<6tGIxPl4nG9G%!#D=YEL55{Av) zkQ-|)tGZ;m?d?W}>QpH2y-AG6rqD>YJ}$;r0>^zVW=pnsI0R55e}+~|h~0UNL1&_I z%YYdBIDGvGJ@+A=!}1{gI{s9T&aUol><0+MdM~pyAy%~KwmY-UX_UL;`|w#7e+wSk zB$ul&?*t*K7yiiv}4U1a3>jZohwDPE55c?r_D5Y|RPZoN`1r#?eTJ;D@ z-;*Bh?k~>j2%&VG7iugdoya~&eH_22&D>#V&m(3=<2axhZY?g*ye?^Kn8lxkF9l5jh;n zstPJjQT!Z(oqSV2cfYISk-Mnf8P$`+QCn! zX72!hA#OuURxA;>IGuF~R{c`3|M_#_aHDSZ(?4$zW%|0TbUMHo=$3367-^9)oh9tDG!+~h#q9~rj1g&QBmHM?QUco(Kxml&VV34l!fA$mqDk;@B0fu%b zI<1RN!I2qZ#hqtGC}TQuJsTb**TikgTDmSW&6i>!AV9P5-xVON%;+95Hya_QP?eSF zv$H%?lfA5!#%dJLr1V^4)`kAgOX6bE^Szd0UfQ>Vnm@oKdhJcEnl2l zUKL8Ov?;nMntM}4e;8Ss$(;gmRF!lRmYMK(qT97c>HE|5ag+3PQr58T_JZwB-_Mg!@s7xI9|4hg=wM95*d|PfF{j zqHxd-c}+9uA5RVy`MCV-5_NMZTZZ*(*7PsnqO7rE4l) z?f*dhkscH9W69XH-iql>Go~rb_t&?Rhqp3V4>}OpMW^f?X(D{|pR?q%DaVq=WFyU_ z(-c*g$BHQp5-bwH`5(m$v3Z|NkNyMWr!f$hG0OoHmsKDF7L(SaDu3l#TXUN@6n@XI zVA2OW-B{sbjOolibQ>p=ZaUd?7N|TwSXc{mELusHJK-F|fvz31`KIa@NvMCLLIT-X~4oxuZYf?|qK|f*d-BC|h_3kI)`+VJa?D_$rAxns) zy9TnHE-edUxhDxRMP4H&OTG9eA(X7TZBhQC1v(FT3I@Po(d zEF_a5t4%hjz3o=S9zbkE_VW7GxW3AQaSAAg0^owDZ?m8O89tf!*#5yTezzJL_j7NV5U20{@_uNzV;LW#7;V#q_& zEPOqWa0Vvm^Pv#VpJ&9g8^}Yf*1D3smOSEQ!dgO3d`c*)okwJnQ3qgXnz>x!pF6~t zj)x&ht_&{ICwF2nbDagZ?&OAtnPkB88<_s@A+~dGd4Grh*ZAu5=+nd+UtLYC(~Gg# zTR+-F`V;Rh_aqbM?*$YLjlZFYpLk;KNjC9tjM%~p*8{MHxKhSjTLg9JY~bDh4n$O+^1$VMVSbG5O3yb5GVXF0$L8G?l0O zN@`DOT7SBixVj)O{u`cN@hrc|0_eeIA^TU><)ZrYXUd1@buO4bP}F>+Ysj}hw_RFB zwSl`Gf}v{u>PW6!MG1sPI}pM-;vjBPYzaQ~SA|cw(H8@6jb6LZ*=SZd&)G;nl6B>@ zCW8*LGFfLEiZ;R%0?V~ma%<9Z5K^WnTu}V97JsR4MT`C;F3uU`@hVpmqNxwDt^i}G z2Ewh$it z=zj@r_S*8{hol%6x<5neI+4u$= zHoroHfRV+Zvb;J=aPe7>qAp9V?&wm0< z99|YUX*(y8WtLo*kh)|cUI~cz7;-&$G9Ii)y)W<~nrBC+HjT54y(en&uBPU7E8wTc zur)2}2cm=tgOc~o@cBe`SjY>2 z5ZRW`kR^^(-W1Fmm~1PD_zRf$E`RiXI@aeep$T1mmk!Kl*|}ab!Nn(p(ay2O0?!TG ziE8skwS2*^&e9{@vNOJ1g}&i}pYFI8>5EoE+O@-VPG|BQCLK_*AcfdLJ96#4)I%w$ zzBd$DG+_riFlDX81aGn_Ev3Ij5Sli+_R#@t)r4yXf4;N+a`11(t#1I?(0_8qABD0u z!K$7ncHh6>+SsU?`E*a}ct#D#Pn8rxYpyQaDJhDd*v z%yfyUXlYPxq%3W8q#Y4^^|xqvq?tjXNrD~%G!0>v@-CsbK>6%<_41dmqkWuhc|sCJ zrgnlz;bV@EVuW^6UGac|NP1(grFo%gPV*P={F$)VRk^((gHlE z2Uv$Pa%!IDxI*ApV*?F7ebJ-D4r;a9fyU2|vuyG>blc39T- zU#$Sdq3CvYN|8vxrWi-SyrEzrm2B5#Ef)YnDeOtIcoz^*s&z=b+y~>i?P=hw-tjDm$tGC-B_dTV#c@(}n%( z#Yv3@0)_)l$7p&Wn2|l^h_l=;c(0Y3#ViR(kWiE*f4z*oq7Aj6;yqa`o;TY~Rn|*8 zIdAGt6}48KIz}oJh~_=1*FjPV#|NYa4V#1E=X-5ynI|YW$i1mC9Hj&!Dy*f=CK=FW z$y-WCr>vXpa@DJ`tQ}7*UKGml`l$JGs`hHrC~Daj%FW|MhhS?(3T8XMT&;RzDqA~j zWw)`OO*L2LgGC;6lOzWooTuvDmZj_rt8WsNz*@xh4~*JnbTISFkNPOXNtA)eQf5S! zqYV6iiZg^W;!c_MrfHQ$=?_DT2!8MwwVwgh(TA78FajDU8Emf$wW%6amA7cK)1%iH z!KPA|%HXbT)@Ms#Q{AYhsqRbFq2jZb0x<$Ce?P-QI$7K{ZTVZ>pxfnS(-3Lihd*oyD_uD-X#48n8`HMxvar ze~Zmc(Judbq88;`1&B%>Am=`5^Xb9vFM`5P^zwFMtl?o@e7=PD%XZh|zhrko;VpJQ z252;6fxhcx*{#d@cgJi3ZrM2{gVgLR|jqL6xCqK zUMgREpSg&)gADY{*aX+bg+FD^L@{4Af8|_iVSP9S&v{P#)Qe6O;u8#?oFjJS7kR!T zI&-NVvY)l$p9#3vujX5D>^*TVV};aG;xBT42SfZK+om%j^kO?JTR8BuyQV#!v}7nF z{oTEE+k&P{@Z2X7qnW_A_IB*ioStSqp^G6*`sxQb=~4#GP0Q*BTje1XaVkcZf9Cc3 zq5Sk}Ha88~#>poCkH5ge7L7^6Oy~uPI8VJael}G##(HTy8b*$%B~)W&!yW+5b0iXC zD>FaYDj~^rp-9ATytT!_ByQRT+=kknorViSr|hu%eOt7)P?W^2E}$;qQP(5wlxpm{ zO)E_jXC7P)GcjfcFw32NyfZ~kf49ID!?J^V63qk>%vHl`sW*4E##)xkl`cfx+ESz| znEE>Dph!yN|8{+eY;7x-aM|(eT?x}Zwy^N69>PJ7BzoHy!hGp%v9a5gwsPoejjc`2 zp49UU4}v3+JINPqv#l4V)SK$!DzjBO-dE|kR|AXTN(vm?$#x`ZW+e^te;*UdlpVU# z-@}B%xZ1k%HoUNchC$EAuoTJzrBJ{gs_xO0qRiJOi@63^mGWUwYgDVU^9AL1R|G&! zEt$BGOF*0Mvh<4tvepxd<84(aON;O%!eG-jx0R_P>)FQsciU3g(Lf$g?SGmeDl$_x zq*-qF`hCUP{4grdeBN6c7`x0OAy!=G0sMncFKmrVsLAwpd0W$C#zor*f^prg;f|4 z?3Vpzjf5i%wm}#oa5P=I4Fd;aW*f%b>2x2GC4@LGjCFZQzNk6&e_H?-9H9_*28&*V z={|@lhX^}ZbR85VC+vWzDbIokGn9@I{t$}xi9mGU0@0@oA$?96Xa^IPLe{{CM*+j4 zKMEMmAbwPhcI!v`Xgor%DNVOcf!CNaQDjWoACWO*PEyp($WWBnDb$j%hOSmgZP^=8qZdyNd`+H z1S!)rgoX0+W*=%bnxeKZTyN1nm~a8Obo4TfK$t5Wwjvlkb*q{9-pIO8ZTWc$QO=%6 zTU+vYcg5JKWu&j^)*rAq5-}(QJbDl@#+XJCb`?_r3G6wJ{(i6$PnSjyy%(UHh4OmU=B7y`?`Q2IAKN**I(HsCFUE35sYykk(ks&U}w~J>Ty-hwJ)yhqxQbCQ*OQK%fr7r z?IAUUqu%*$e|O~L3pmKYrudkM!SOfbRVR-5Vc+(IK>VB_m`Fi(`IrV2ic<{!Qn+Ng z)Xznml=xI5rS1a-0tsa4a-h_w9WZO|Wji}IiC$+HxNz6r$+@cBd)Kx#;Qq@7+V4c- zttzYXi-}Y)Z@+>dKE(`$E6h2t@A%uNTW@`8Ikxv>u;c#gOnxDj8q66jUPl&dO*FftWye;efDASk@@x zST}#`8*i|Ib|CPS#{-76a0rAK9Ri5t3a_zXaKecoq62{i*+SO@4l_6g3nqq)(aP#1 z9IvdEy$sIhWG@py$XlZHNCbkb}KF9tH$$2+5(^8o}*?7e>hfuB4K>K%+q4jSUmk=Vy!ohYh0u^LY4 zy%?OZ1)hW*W8u9OV+ltLAV4_Al*czUM&Uli7XcvE2 z5#vnQ2ZWdx2%;$F4OlRWdEw5WVK&c31tUj+X@Y~VJO>ci2imCsTofLs4J1YYGDCw6 z93U_p;(_kK5cJ@NBwN7ftl=3S0c9;_0p{6?S%6h`z?TQw*%3S#1BfK&hA_!Vfe#L1 zKOmliU<{B4`a}lnFjn=+AV_3)7$Sda3NVsU(ZI$8%tw=`I6Es;^?Gx&*!fb*;|MRCNwq0e+3xxggFSXoN-B(=ecY z-~)Q@Kuc5|XvBd%{NUt-dsD{CI<=?1F$!?8azx?_e^tC0!?X0Opc3I!w z++2;zx<)pzDGd5k*19sdOf7%Vhm;|vOkvMu z0zUGDO#r4dEr<53G=ZND)9CK_0Az!5&Ghg^3iwbWZ9%zHD_ddk2=Te_-D%i{uXKr1 z;9I?1uQyogvT0#Om!mD4-;3(iX8U2aZPwCP)%Vrg>NUQ=Mpv@?5^6i961zBtlY{h< z0DjY0COADo?Bf3S8cTmxKU}Z>s$QO*H5*hfm-Y2#U0qZ^zk4fxU;RSs6E-l&WLd&! z%}Qx=^Moyx>|LI)jjoi@C{NhClGtVnBNxb2lP9e4X){+gj8A`b(N7q$qwVQn$Q^}f zE82%EOt)%1R;0P1YkS?M3~xE1EAZ@A9+?QUQEzD&^&Fqg8)M0Ew3BRmqvgGJACQ%k z=Q?MnVaGX6&vlM7`OfLe?wx~NAk$}-Fh*~L_tSc^ZQRjTZzSN_F6)eO?O$C7F1V$*2`B#-fpWn3JA0uTej*(d@iNd3o(Av(J%n27B+> zw&@pSnq81-LP4g}U;lK9b1OrSUh}kv_mP{wvFzTtq-%d=^e?`!L%8-mmgaga3P5B=#ZgY(q`wXKZZth`K;_m}@F zjIL~dOK-~bv<15Bq0x(qj_W4gT8!ODJodOVy-pncmrp+P=IVvBb;ax$)!SInI*XBul=#$lUi|t}cH6cPx_v6@rN5>L$S` zBhZy4IKeDO5|c!5i3sJ%)jD~X{&$v>RL$m$(6rmEs@^{KcczV{%d#(Re^XVvuC}b| zs=l>vWxaIGs&jMqQ_9Y@RNZ3Rb!s`Gbmtb{wCZ!!Y`fX}s~><-$1bHviaZgFBa&Ng z)kC@7tdt%Ha%OpwgP?*LK?{^Kn#^&IG7$1=3B#}Tz>5qqpg8<%Z<9&OPv2c6@8+EF z^sSxXwCNh_%?rq+XhI_We@(Sb_e;hz{i)x~49y>3%Go#H1ax08lS~-`KCu2c7BW49 z>wlRe6tn;#P&Lj*s?L(Rd9g~qY1&|g1H61lzK{RS`{PwN}-o@yt1)h*KFzfBmj{|2247f3xjXtNHCVRowwo z&e8nq$lzbLKwExRE9b`8s>4~)Wfyeq zOxIc1F=P$74+bK&7Owp=?%VcOzp0lf`Ry0%=`=&p9c?j@0B&Mba89&~&pjv-i6pfO z{IRN4+0N!v^0ZaAf351es;M2Kj!%xv7?>7z+V_Cjlse3&l%-9(oMG69WxKNtD^=Yi4tjr%T&#KZ9pSWuc!O8sWn2DD=s8;zkTmjl9FN0!2%e|wDmh(AO~FvMWSuncAq zRs^7F;NY;X|B;|XOHF>h$6Vmg)zc%niBW|6T$v@9YTM>&WF`!$o# zYMSjeJqPjkf6AhGtnva;fZRb_S&W>18jQP(s+_PQP`MpbXk$p4@2wAq0(Lv)Do#j> zWIh^ooG+b2Lh_7+{7^u8sPwUIFpfC6L`()Yyo`VYI1YiQ(S9O)I2iN^&L=@9%LSaB zYzR83MF65Vdu^Y7$tbIrL#usI05T7KtJdYpHf#)(e>4yPz*leEnw!{WbsW$P{Fbwj zgKr};joGGHfQfN9>7!n9=NK%1H8F6&>{7MW=Nac|*~7O-Ig;L2{hfURRNNS_Q;R+t z?J~&_mxW!hZz8S$yB+5&>gVcWlr z>5?K*&=J~RM}WbgK7s8cApRU9LqN(Q!)Vm*5V>iV0V0EyVOJCY%}+o#5x`mzf_I28 zo)*<&(r5W6u>JFA`5%pno`4tv5eb8H%7Rlce+(T!I64IY5Qyf|I`;itWlw%>Zk|@_ zwch8;s_fNj=iA&)?`$B!(ym!6+t;d3+X^s2SrBmjJ~{|3>@_dIUGfPV*U#`ybBqKW zdBG&RnYi8#tqWMznvJcERUmqOsO%$y+p!H{!B7Vq9gb55o^NPcG=n`@q^dPx5W@qi ze?{d12!K!{r1Gfo@iERT1Lu82HG>!=(`R52GN9pui}d>&EkNb~10)Fm13HEX5T47{ z4O+V(QLQYnLel~!fJ%A@zdPGf*7m!y9jaZZjgJ3K8&b$UmR{|}f!Z?y=bNzn0{yNp z_a}+a6xVO;h^gFWz5_ha`|H?DkG=bSXzuH;;WiF>UjUZX@bU3sGQKT~ecW{Ff4#p} zonPXpGziRDsXCVFa60tqp?8<=YI`DwwYo0>5#R5x?0%Iuk^8+K`s!C`9P$`TWDLRV zLHa>o^#AW>C%GA@@b$gSEfP4H{7RfmgiiLQS=ccGF4aveB?|v=NYc0b^jxa8S#=+( z`g6>N50~dL+Ollh(x=dNXC?Z=e}t!5dH%;&*rbl1%QL0hTh(vd`pJwtC2-*#?o{1k zbrS#J0>+%7m!eGLz!*yg2A z@IcKU7WfqRp@buVvXe-7e?iXZfs}$|l7_0^jjE?oAP#HJF)ZMtxR0tY&vLVH!Q1Fw zr!OsGrSv6b!=J1AcDwS0!~e_*mX@onvad_EXmq+xr{cS+f25vhYfQV-O zZEW5LW0yX`#HD#`Zn|~(BdimJty=UZQwW1}Jk;5aX4|{A#y0pwf3j&TU0bO2jl z>7Q$@Ck!U-Ra=j$7l1}-x!JT$>GQR^MJH+W@V>F%+s+Nu%Cqv5SXv7>(fr03(K;7k zX)*W}mV3X#$N3e5e=Q|L`sh)0=yVlvzk>RAXMB%PXMbXfIi?_Gmo;6@jqPzmaQY7| ze~~t=n*Y;e3+vVtv5Bcufm^BVvKex_xhZC~et=JcYC?;$c8zV-s|h3O(-!8HUi^bh z8rIoDmz<85VlWTupU0JqVx=dFC>Q33FaEU3ut zB6W*#c{EnBnCpBx2H=JVa2`kL-lzCS#jj0hR0+UjEbVF#(X=j{aS+u`WDS+Ce_1q) z@YT0lw-0Me+|?f=MKKAdmIzB?&uEkd8l9L<@1FT+RMdU~9P6)!1@T~6=kp{+q5rrd%D<#7?r0R8+A&Z2d;mpexS zCjv7ulOQ%Kf882uZ`(%lyMKkQpX4^$a$j6BhujO-7BPY=n!0I$HV9~ume^3FLQ;0z zUw<<@OG-=8l3h8t;9x)|m%Fp`-kI46CW}ch`R*)m|NnJ)_Vu^iOkx&?BAi@aPdEz% zdYeIj`ivyc*DVYhQNvJdvkzjB{LbnZktMf*>4$ z88HFqf4Vtu9HQ|gWU-ME0TFRDku2mPO}eB>Ly-kxhg}#lBYf+FO&9>7iYaeLVJ7yg9au3Yi3G-Y!6kC9A62Wwofvr1&k+!KTdH zq`rekPM=X}K!x&o*3iHfJwB#FT(B_ocG}ABe_jNY05*i&TQ&z>2PJQlymULaOUr)G z#5!ey|57;Z+uD_s2JK2&yRuNCDN?zrYBCEPNHf@@a9^*i`D8_fd*31(7#L3&?@Avq z+$UVhfQ82Rm~^%idMX*n(5j>+mHg_kfk_2QYz8F0ID| zfAa|F_c5BO7!iYSik5sef>#Kns>y7J;-5w=)vzdvciD1}rS=%dA+Tk@Z!4>4db!M< zIO{CkIL$R%{TV8c;7l5VkbT%Bxq_f5Cw(MK8GLNVvwj+N zy6CxgCS8t#5^!qlg1Q^k9nt&u=VJ;X1Q5SNn+`;1otJYIfgT*j+8Cy#Q`i4gt#tx$ zv@VmDo|>w-u}3rwlO}ObF*vvha1VF%oXp!zQWWkjt#%eW;A=RI`E-My&INiTe>}YF z#(hPkZZlGi9J4W@ZE-^XL`dV4iwEp;UKI3{R?d&ZbGN3f+>(U; zEh-8y;5UY#gXY@c!%ax+!k8k74obDQ^Rv3+35+%Kq(C~|d{}4YCC85m&NwKIN7nZv z+P}jJ#bX9$?5{!Q9q@%pNb3u|e>LTZ`fKLm1m}I^5nMnlI+A<1@DUyLmKi1o!nDyv zaP)jp;4<_m9mCCHGt?aogbB5yJ5qJZsi*do-K_BU%Bn(E(~RnxCS*mn%IGY1Yx9!6 zB=ow-N>GG?#wf^S6p_9xn>I_*XEcvY;{b^PMgg&p?+>q!P>E_`BIB=`e_exBR(1Xd z>1XE*_N+>Q9}eSf+-2ho@&KU8i*4LtABw!pD69Z2zXQ@%E+le3A}Q(J_Z7Ej^VOzE z>6MP84sz#S(h6tYx+UG2F>X6rFb*f5hy3^B=X+1Ff}lV~+Fx|cctVgz>@RtU?TBNz z7{!d*LuJf%hDWs)AZ+f0e_XAdzFKBC4A-9puo38o#vgd97n8xr>+djS;{lv_aTqGQ z3mn)=PG%g2fM82Rnq3pTF5@F8@WXw$-3Y-2B6r|MEE4_MT@}r{<|g%^Iw5>~em>Iz zY;o;qPzbsEv3q$VukA`U1XZR1{OBJt@Bdx!R51XgpjRr|^QjgEYCdG_I~PiG_taU6tC$05#3AWTZ1^QW57 zIxla~hE9c(=E=gDW9K6_r`h&0S!cgK21GCf@%B$Rt51Une>$k%NqrJ)S0R=R_)}Z^ z7VM|6c7z_p;uLE~5F+NM*s3x(&zuCJBjgFqT^Y?JO!Zj5`CLyM6)Q;o#gpq_5B8Jj zA3*2UqyBmQ0}xW4bll+X3gZ2fKsZ94Q2$^WlS=!nx0R&HOzMCciB-0M3FE3vS6Q1p zNm%H@c|1#ge|gu|NnXCIZp&wP^n71Fvwu6J085PPFr)c-nPCQK%ryH5VCF5=l1$Xy zh%-kiBMZ#vT86i+&X(vk^HWf`+CGYzwX4-7(YzJFg zn=;W36a#_ls@j&oy2;~ZS3`dQYn|qo-{xWss1K zBzhN5q7A54k2-$&(Cs~cdy?0zGaCY+I=*rd#jAUiaHd!k$A0nopTsyTtE4Y(N>>Du z)54Vr-@EFjB=K$wNpd=;DS(0u=lO2MNTT>gR5y&6?$hIo@j|+F19n0uW~+R#@w%|3 zOdRP@f3V8y>is+Vu}T)UFv-SrV#bXG%hA48T<41?(TjD_o+MVl1FQ!)gI~tqcPnW(Th`A z^s&w7?;C4H|97B8-^g^^(1OwuAN_7oWf@)5E+fn>@{G2aay$s&;v>10`ZnWPH3XM(*b! zxe|~SaU1nm=KX%Irhol?v{?mjTwMV66+e6_DnrA#zx|5?_gIh*Fcl6K{1ybkkzFFq z#9-B-TPz8=K2|-v1<#M{VYA8DU!#xQf9(c%!!}@l-z+tL!3GhhfkgO064Uoe3=(@^ zV&P7&M=&DQkO|RC^@vcFGAxLqhl^Ed5UYwGD^@H{@i=<8T(KY)nD|k0RR-j$%){h5 z%tu8eY!QzHYjcYjyUQS9D)N+G`|Mdpd-vw?J?%t&2CuP8FZB1hID(uAg}r0+e-`2G zyOz_JB_UF6YT7@|oB5_`+|4xI_s7)i7DMW1=g_x!y2P=&uFYeO`Fx$MvevpJoZwV@ z)H7nOQo4%5>pWe!^vvC`%jv76X*%KZsZm9#Nrg!($MV%e{)v0wT}uAU1!2NsoUaiv76C|$OX~MQ z1i>l$M9GdQpos19$d!GtCsPT>BP%Hpb_BrzJ5OwT5R8%Zgx=~F4ASv(V;NODfx@um z95w8OyM0OU*7XrL`BjnG_6+Qk>-MnyOgDM8aoc~`WX-m7V#pG_;xUdTe-Ga1;Qvshz|&t8WL>KLR3GdtgC zkV`g8b1*yPiET-N!IzCIqiGcW47)9|V!SlQhrKgAioh*NqV-i~d#B8FhRUvYctty(0u(d3x}w{h@G>IR_~r(rm~q>&^|^R^=p z#;-A$u=1S~W@fr=dKtFLas%#PM!s<><8&LeqSIq#cJ!D&S=CZqVX!DrkK$e_s+Id>Ye}kO4h&o+O+KeFw7_P6nxkP~EZCY7e>t&q@`YVj#D)|N zz2mW^Ybz6jvG!)|*mO;k`fs*)+k@M#1#@scbxE5b+0ua4xh7&XtX;)JXIb=3M{;V$_W_rmoJInM&YVwSi#vU|@L%WcC}ME@}OMQjs-HtGSm0tatI*QmW&7kyc9xOQ;ZMQ~_Kb*$zmhc&-NP7b^5 z9EPn>dLg_}lEvUyXBwIAvI1?qN#9(a{TIpFR%@5OQUWOgH8zux7ASws8ryE;w(@`4mRw2M-R#$&;l;M3Fivc5lfr;5hwnnrL+uz+g70TWV5;EiD8heW5EsLbB!n@b%al^-#mayuah%pOs?yuF zynLF`<5<_puCc3swH zG0PZZz>(_oE@B%gkk0AW2En|1OA`b&b#uF1?dGwpgHk+Q6n#+klT|Ft^>*GYiQ2S9 zAK(Pr^y}TI2WYoN?^io-DqH{9nr`C*Yu=Y1J@J2Npm2>e3sGvhElblmU0-2X1wdh8 zFYweZlL_R%zrsF-5De=@E+uz1%aP`=)Dr;Fup=nfo2KnO$?ZWd{C}1xadx1yE&dGPBLo=^)=L97yYJc`l`G_n>+jT{3h5`{mC|v-Uiog zvp#>>0-NfxZ<^|+>^oF^Hr?g2Ef?feJ4B`foEPh%^U55yv%4MIy0|fyj&aq@AyBU> zb8=i{TiQeGoaZ5(DXyBfety5`>h5J@qnnqTI|NHZQ7vCU{dx70VbR@Grf6%lZWgAx z>`Y}7d{aMPShyAJ5>}u7vV--x|4K3Obv%C~7zcaq zmqok$`-NJR^FAUJ{s?D}qnKwC?oS#;?#;{hGv^H-*Tv^^bU%-~HUBx>;RJ3E`~H98 z28|$o&^MhayLCDL=8#Vig>ps*N0=vyAtX%D@(~k4v8Wjl>8NVGA2IB6ehs-)-KLiz7mJRUin`rHQ(mwbhTl+C>=>OpUVV zc7ETLAD+$UvY2li?egFL4IZ{=+&0XmUa&}r)N}J^Q&nTC=jNkf=9pC6>>Pi)iY6r} zC)!W)zkK~~-Lz(36q^xnnouNz7+8Cp>qr?EFnuL zJPI+)l8h5@-2ChYos2Z-M*TO2w4zVzzp+RAZvrtcomD9bXn*=@pg=_GgmOf&KAVkg z^YPH;(@$&ju_Kyue@5GUB-ei@xA_h>4BtczDJY)^l?fdssF55K{4rKzc78=iO+g)P z3UZAUp$Qcr1E}okwZFOM0Vix%KDN(ARW-J;^2xutpx}hsv<=YSw1uk}bRK%;Is@j; z5@_m$ALCms@L-S8HrTp;`(7?r`>svXc7dlKNp0=kzPo4*1#>MYHD`ay8IqiZj@qKw z>?+g{fcdEIpW8xcTYk2^7QxetmG6?|X;t*SY5lYLylG8m!#fZt!b8x_zqm93ueUJN zcY<TJ8=b@G2dG9h0^XwwdFDSw)AE&qn6G=WvB=_3R z`$Y1`EK?esmhgD@SrC6DxEwiJzTd#XEKMAZXaxe$C$^uJLYR!l#4?0NRImf_Gc>?Z zCfiD66PXS+H?ee|9E=9MOp+PR!ayfV!Omi$(_`&rK%JWT%_D4tf;=>_gbSi=N7X)f z$M?^+(N=bSX;;pr2id0V2;Wo&7F5@Z>khQtIfZ>ARb%D{K1qMc4}FsINbY^e0300O zX4sJoE}bx+PNx1J;+-hl3|1cBZ!xHHO-Hyx!fngmP^V>@EsMTprawog_>MZ;3> z&m_^s)rJ@zeR0F^F6U*8N-)!8w>j*eH|?^h?WuX3+4oJ`V%xgjtzPhNxd?xTCDN{z z`v&ZOCk!}{4Z-pZgX@QHzJwkj{e1E2{~oxc`wC@lWOKKfTLNzn0yH+0krpVIGHU`p z1>c<9ne~@;YXWJ1jq-GC2=B}XbW=Sa=1yopBF>PVe5Drgx57Y@tNHnZC={w(#v3Dq z7nc7QH(2M$`X;L$ChqOUCd;*Cy+i0wuTSXoQ2EQ76imN{bDreeb)||n*V>|k<1n4B z%4L3~c{Y}FF!U!+ZYX^fZ_eXlQT7r0$3a}BRi;`>3OG!Er}kl}bb63V(V?>EqS4SI zgHE;=)jhBX0uJ?B0Y~~ogdr?9oIfrPM$wO85fN0>^sJRUdPBL$;?>109uK(leQ^7f z(8zCa6ES}-c@me8uF`6$iV>{jzI(M)nnxZ0Q^1+RcDhc+ zMqZ`5gCz#q5TWRjzlEn3Dh4MSpZeWpm6m>}b)ffOX)N1BKbK@Y5h8S7rn5zjuC%k@ z#DQFYlvR9@*-@jROT$coqcIL-5a+QgaEo}oFswDqP|iux{vog0k$1?;WfGcB(@G!02 zIOOPB01McmB$ATpEhj!B9z;B_Zcvv!NuH&D>sh*90I5KNwJj z55u_?&3>k`vgQ`lZ|t%a^{i=%KoZ6f&8ASZG^vaV1sH~*C^YFJPS;I$yq?)^9Oov5 zXogEwfqMlYQHY|{wNupljR}P+sd|gAxzS&mn|jzrp0E$u=in5zk2labjr88w(5|oF zTCkB3C4Bt7O)J}6G@${8XAqaP+c05&-3BJ4b4zGxSG0riZiPi#!pt8(1w6ESQ@{_N!H463=aF|^DhP5 zCw4|<-6dXI+p=*F^rmC?8;=V+^V-NvEDhXgZGP!Y8E@BnBx)TOsU@10gTYaM@Q83} z)rlMPj){X&tUD&&GB}O~XM3#>g!L=#CRtwAOY(YB*NcF!UE}uc8N6*{**Vu{FEho$x_+W;X_X7~HhkxYIMRv8Kdp zLh1VMT?L`6IWd|LW~+P8Fb{@Q0tkNN0uj_U!xbG(a|lJ2=jbhJ?!ke??p&B zbXffiHvsRApM04sfL3w+(f>!nyDur>mw&SFLA1fKuCJ1J=bN~QSEUILTG0P3b=4Y4Of>}> zc!gnznFMIPrB*h=nEd@^=P$Fa(;&r`~@nOz*2jwatz4RdczH)fT!26*-{Wqg zIYIY4h3QhbK9sPCgkgwt{O;}G&(o8C0om^K#0q6@WOH~aE!>~aH#>~aK$>~aOS z>~aRZ!4fevISMaKWo~D5Xfhx&H8(Xdm%+6L6a_LjFfuWh(TD{ofBI!q8}70`4CAiF zwZWmdySuwX(I7>F1b26L_fp)7Q(TKfi@OweipxX0|7Y*t+3)%KE>=jc(dz(DK(N1_ znR~KU5-Ktkbw*KhkSS0KWDjO!Wn$q2C@3m|>_G}(Ru)DvkgYj@jfsVY1Br@C+zDs` zwg%Ztn1F$N02Ur5Ru(2U7Jn9ABr1S3&>raYCN>9{dH@uGU=s}w2Ouke*5ofx0Xc&i zO--EN)IfVnYkMHwn~ONe!NbYg(h3ZCi^0yw_$SgIZ!so-oQatY$j#Zt8en2?4v=F~ zWCAFG+}?!N09ueez!Yd@Vru~aSpYPEIsi>|Ni}tVw3@P}iaH(BTYq467Y7HB)Bocl zuCAde%>a-PRnm|I0JRtZ(wgcTfBtI#?M;DBmJ9$TjW_+DJa2}7+!ZA?L^X6(Bw3mN zoB_ZJa0NO!TmMP>U)-qQm;wGn?akG~31s&-0f5#D40hmSW_ELPW3qH{1~Y-2ESVf^ z|MJtYvUUczft+jrZ-38DKwIG7!noL*1Dyb1E8xEZ{OJZj!P*RH?+pCoBnA4H((bL4 zZ%%J=@V|+@6$1PxrtQDX0nR|+|4L(J;{4ZFK}AIYU}s`&4+h$s*qgl>f=$3K&H&@T zvbP7&oaSEyfdFwAC#OF-6#qx%^zStPLtPB?b}|E7FCP=P|9|e7iM@-n=Ra!mzmIJO zvUj$220Q;NA`oCb^i~~vCa^pBul1jJ zq7n*x0B#-*02?b8faPsGCGE|{L3Va;teuhmluyF?txjN&lLzyE544Rv$j#pC|3_?L zZEtSzr|#x14u8y=_STLrKv{|ZXZ$8Y`j5;K2nMhKfQ|s5yO|a9pJab$<&T*4kNB+! zK3)zW2Y`i%tuxTa+5-6YgyiLH;tB+Som_xEUjJ15Z-m6k4KTMh1HX;=+f_mO8(r4k z0tDdwm-vmz|ET`o0MP!upmcAy(;Q@P>j5waS|Bkifq%en<3RiWpIi1{xujfdZIw*y zfVBTr(f>9xv9q@I_%Fl%ve5?qAxryzcw0M5S-S(xRjk2gR)355FR?7xXkp8rin;pO``$zN_@vyul`#0j{1~8la2k`)yP5*<~-q2gKoNY~< zt^OnB1u&ca8?myyg){#LWCbt-{|(>hGXJgiKYt9~f?E8?;EkGvwd+4T{-{ANPXBOt zGqL;!R{R46Wn63W--;QJR5BPQ-+ke0}>URHutZ&rq{{i2qgZ>RU-fThkz<;RU zm^u6#vcDyA06P6$lK*z$`YW>r{i7bNZ|oia0pHmF<7}*N?4ADs-vaz69OoM!u$2?= zAAiMpBMf!}{lnpnh0A|B^A_#zj(0W#IsJn<+nbyFKj2$jkH7GL&!WnoJ^Xjcu>5B@ z|KA(bU#t#x0@(nyt$ALV2-Y|G+u#4ZGW=f&sQ$Cu{^2bq26Fde zR4GL_t*yltfb)~)0RYcsZ!?K}4c48m_j3sc`9!LRq&anUBdqKhA9jSE9@ zN9Eugdb%+5=+g={bTbzcbZ?8xdi0RsRcm)F4HCrD4mK{k?v|9HW{gdpJq*ddU3DHt zOa7=xeL6+GF)h7Q?EryfzDp;+M1Omw9;ko#wGhx_j9ePfa{yXEJgn#roGGCfaL}tt z*?f}x++i7e4S|c^!Ud~g71esE8B;_b2S3S3 z-Tm$fOm!v7?Ixqt7q5^f6p^Rl;7-CFvUOxuFMZ2=P%Q-`-NkV*QQs3AcwQ!Ti; zTe-)F%h4z9in}%|K*mf3j?B9b=A;0v!(#`nJLR)3oel|2VK%vchbGfF%$B%L z{KwiLtEJ9?NmQLM;yW1aC8DVH)*J*0f_-+Hbzx;EqGs#_<0#rZ-DOBy&tDGxihl>~p~q>4>Ji|!AD&3h1sA$S zi6~}{fL*xdP}h(9y5eDn@}laBqVN$n_~&7RxQ}#RzZ$MYzV+v$Q5<~cmo`+P!aHn= zS=4FzpF2~1DFVq!3w}})O4*(o?B-?d9oY!&Qc>20uukZ7eye_PaG}SCdu+5_aK574*1lARhQ*SX&MFP-m0O7mAK zDCW^^1~bNsX~2gRgG^JDzrWb#U>CY^TN)>KCcqr(F0J*zCE^W1xO@R+fq>U!Ki%qU+&aigLrr1eGfa7nnX(GV%tF3Ft$+PXdb{TrzWXn8t7jsxv8m zet)oURW)g#yaIY`)w-{i+tBp0rv@o;xnMz5DLk_oa{EGi@{X6x$B41JYo$dFVlv=z29Lf|&JS{K|rbSaSHONsj$S@UPO%RcYgMse9c? zeglE;)uiHAL@KtoK4}ORyu!(?E*`V0^JFq*YHp@vRCUBUik8t76?%ZV&pKMQGJkRf z21^!0T_a*NPP{0a3<wF&n*HI;j$``tw2E*sM#!W|1_u~&x~j-!cNGJ!Jh;wLx|7Z z!(OfT5tSBIp)Kr}auf_JC}hU^xB)R4O;=l(d&YNtgzqj1BTUC!CH{{IC3#-Lw^@T3$+D&)T754*S^PUDY)^Hmy_KdG6{Is>#itUNd9zsPv zgFZ=9GQPfhQ|*YkkBAd){(8SmsWgy)k=X_5a1KNXRT}A%>6cYfFMr!}k*Elv6_LSE z*zlPmj+Sf;F9~Dc;SE+KkhN=V9ID)JWH` zG5Uz_k(&#zYhQv3ZNbhxTBn@O8svCjZOe48btZAHrd6n9vpV4^1b~`?f=AV)KD5!g9 zEycR$NyrBIir2=m%`2Dzd(H;W@>soJMmtqA7Zi^*wi0YDcQ+-+#3l*E7EMf!|NT6?)k{ zAsf^e!z!&rd(!M^rxYkAv~+*1C4WF!sI8LeN^5mWEC`(YdC@|+(fZAE&)SZ);l!vM zC@LEMk4tko{W%DWj!`%!uz2rBHqw5`Rn>6u}joV zdY3JKdQ0=*k$-djiqw0kVx{XPW6A)QyZ1^fVMJZFqi3Q)KC8s?BWFdO8*@Ua{l6~N zKO@uQdwwQk5vIvvlsH|%HviBNST>5>_jpmvRvqK$bhYEi`*JZO$ee_9(O|MwI@*S! z@f(uE;)SzZr>yCzrF9ikKzC>7o1~9}TW7;3ru0?!{eMtV83xs*0*`hZ`Crtx0${93 zu+;Qa0olR^u+3)@0=dM|P73AZMPrlH$RPMQI`r|pQN+{~%T#z>GOLII`&v)m$-3SN2dYs!G(Vo?W zp;R1CT7Sl6u2tWTfB&vJP%nv$ zvOdpKLj_3``1NnR?1pq}tMXv%@HRRaMug2WwC5Nx6sm+4SrRVQqy!JAXUwZY%``qRGPg`0 z4NPA48AO{6f^#vtR-wVR+a%|jp_0`83FK%EfEd1>^B+;{>tEh=wv5z1H(U>l(rP<> zLVrW89c!y}+D&p2?I$PJqXqJ_dWLLcmOJDrS zqVt|uWLA4pgeWDB&_-?UmH9pG8rDuD6{UV)M!3r^WaK#}Zhh9b+rEw8-JWrNDhxO% zKDe;O4eV^nW+m_7D-V4(`PQX)g&^Gxtp-M)G@_ZW=*@O|^NS4u7)D zr!g99)j5A3d|T)%{=&ed8w9m0%OmzXq4l|$Whr#zJ3y9<M%P~IhbF#Ms^OxGevE)*K~)`j*XiA6n)WLwtia@!AxwRNaq|cHioP8+ z-AQB;xxS|g7YSG}7fA>&z2Nf)Bv5Q1+NjY^<>%rgd^EJ(q0@U`+ERk}k$=TSi+fdA z?ZYy7!q-JMSAO|XZn7qBc&f3>HaL=f8O`G~nHTUqHiY2AAP)_5kl9zRf!Yhd@$97N zZO5q0yz*O`%LQ5!w^N$7DUm^7xCGX%nH1@~c_YirSG$Fs$0i6NQHv#c{7MPK@I_!& zK!FD@&6O@|qV-e`)}9%YTz|xgxRG)&-x2E8RG8FeqyrXb-M0`{G=+f0dEWJK#ZI!s zdGF--2rmu}D6Dr;BR`9jQR{bOMO`Dm;PBeAo)^o!JA(E%&JeL>hRcjeuFyE$X7G=K1GnU+jKd$4_#eg#DAA{3Cb#xM1H}2fYCmqHXq%gsEXG@E@UoS3+ZJl1RRD?&@=v&k9W3d}@da4xU^#|Ie# zhx8n0SQMhG3+l*^#eb@X24u<5YTr_Aqs47Su%tnnefjKYkE7dxGJe@S80(NusA89y zp2{QcpJNXXuDWPC6-*J$*SpQvJ~*q!C%1-;|9o)t$f=tC+-WCEf<^PK{V^f4dN&XA z{!ZST7~$bp>K=FYnG&Xl1$yTC3XDyjcQgx|fbWJa=~umWwtqS0$8Bl7eyE1EzL0?= ze$WaN-nAkvEuQ{WP!-%IWqWooPjgu7*BU>Nl1-kwo?5ATNgYu7m36tL4|yB~{WeP} zUDkl7Uzg5L2(_o<7lOU3=7Ryxp{_MJ2u+24fLdtyA{*vbN7xu4Fj+SSOmF^rowTZFMkmVT-Ya zs8#8=(1g`!uCEsSb_U=^hzFA^RNANpso%Zyh0^iDk(>HW%!+Juoyo1) zs&`HgxpL&cykLd=(3%Qa&l_%T6&SZuri6z`tIA6KM1PQ^+-pw}t}c&e&IfBQ6d45V zPq)u@j$8UHqk3u4Co5C4o-k4JEY2^5uH6Mf()oHI11<5*B>ryk=T_8K9BF};GU?#e zP)iP59c*aDQM#XFv^nkMpv=>GXm!zH@$?zFxc(vrH^)C3j1NpKy*tE;L9;p|TAyejLH(OgqTJLy*st)cI zECeC4lyaYkF_rUm1)ASNv)kvI-)7|k_g^n+i%M#*qi4F3tn7A{77~TF{FLDQ=R$&Y zsJj{rmKi*wboWckb0ovFf6O%%VJ|$oA`LB%eV*drH0ZD?91HBXX{0>OTk~f z@ztNzm!kC4% ze8j~b>>v12+wQN-7|LQ2G&5ga#=hdy?b|yO=vsrm5FU2q&T`cJh|RRAcjo3^9spY^Re$E%>rg&2{fKfmb{n|R-N7sLQ@Z3yqSpXrZoStyX3=W|Vz=Pk;Ojlw10} z=Jc@(uVH|X1GjlmHVnR7DFNpP<}o`hiiy*S>w<& z1vWNE!71aSFd2>$O~!dTK@z@X_)FK#o;&V3*u|#y%o#~(Ewkuo8v4q0yL3TPxsYy? z_%l8lxb&M=3RF5-rKS5koPXOM++{N+ZiaI5dFeXiaP4d|<9c8O{CMTl^`{yZYsyGo zc#hvohlh2o5f zA~jqUjNvXWrjYPe!5&}}exT>dLHGH#{hOORq;$>zgVV)q{TG5!6?c5c$j6Wf*`KS{ zF*qy^>#5jiak6H=34i6zVziE0TG}X$(*ea}O+WOOvzhM}LT<2U=gm!j+q${Y}2l zH*RiAlnaSf(4ClK;m6HP=DHji$Sy@j&{vZ|#D(=Wq>bKeOWU#!bD+bXj@(cfkHvz{ z2KPg<=pbzW=q&kaF!AOllN3;ESy91Yd^^LK@&4EHK zWGkjBtQ?N2ZL`!mdX5(%^bC6UsDmLL_a4zy>KdL$n_ll_w&)>+*|il?zRvPA=kLq_ zUlZ36;1xj~1E3mf2L_wody-ryh4!Dkfm<(ulTL+x`F~nd_h_}1UJ)1DpXONBW=uRS zdv2DCR50s_1#nDFPBCk}n*yTh9IA-dGrS*{^2WBv(ibd@!eNRpgOMW!)g8m3O za*;9oQ(nqAT?MIz1M-7m$(i2#1>JACIx&0{;wRBO+%Wz>ANjJQTTPrKCE~ohT}xv3 z>)Db;I|LI}#NkOsN^wf@3}i2D8Ar4EX{1$D@bt*>_RtB`NF)Iithd{O?0kPiM&-9+ z3xB?IMsLIvZgJH-4mIFETwJ@qyi+5l!}Kwl8H<)HoZlxIX6$ECmGpp5fya~82aMtg zw<2C#_-!l%K6W0eogl_*M>&?c4pw0prrKX=i^)3gj5>UOHKB3}b_P3(d&9M`hg2qG z_WkgQ5dQG8V&&fSvs{P22%a#Q%2+~%^?w}lw7p8c&7e^9))l5|_j?$k$)I8GzR3*< zL_B-dyAop61{`qxL!@I{#7Z0p*V`MKrSC}k#p?TBdb+z&k@0dd>ezb^$*0~SR4z>g zx|7*8UDmOruOXRaHC3m}U*hh^FhoB;O>d!88n-#c?&yv_(prD*LAaLv>ei_i&3|Qi zZa*NMg+STjEq$`%Vu9x4(ae_V_ot#D&OIDtS5Oj!@=B&GF*j_fmP z8KA|M35OQ))YrdU(q9aKmF*PL@*@A}7JAX`*Ej=gCpnN3J-nGHu#FIh4~#w>f=2%& zN>k)F@7>6;r>uLUI&tP}&(qO26n}dTt$e5bdjJM!9nr7SPcQuh9Y?(KeN;b9l-Yhs zaf7|-P5C-NHE`Qa@8)EZ@?C8i&sxi?k5uSnmM&P8dE@fc-S|vrRdB$!tjx3KTs=Xl zcp0)q$cPrqOGRAXGCH(=sVl|uD9ZiQ$Q2=+!2d({@tRb8NJn2m!hm{^KYtxlS|@d5 zK)OdJNy2aXE(hV99P&M96zWnG#7Ej!RiIa7V)hinHVOI2M%R!=uzQ-b$u_a(RR@(tDZ&_#j@n%fi z+&!>6m&)%?i(*wLBrAXt0a$m{9oh}U$?7TixLdft0MLHdmBOOJxRoV zgOz$Md*jwztsX@^EgHQii;kQa{-GiMHp>GvkghUOSU!jo$)6|r6MxEYo)R{ujAkhu zqn(oTE3=Eecbe09FCR1XhJ|`aTHu(N?VMf}t0x8vP=1ktLP%;Ad9|On5!VG|dQ_SiU(`&zYuhoW<+yLndR3W3 z91*L7WnYZBD52=1#DA2P7iB(N0*#2$%GXK*lCNyCoY&x!QB$N<$BOGF}t&23=0HPwJL%kbyGy!ifj;V{uPx*pX=Ws!u3?C`BhX^3D!$Xy&5+HxPMb*ulO7k*J)JM)V0g99){ip#vab-d%*58GPTjFHUWREA4D%Ma~U7(RXc+XRVX)I*Mi`0uY@jwE*q8+;OlaLu5 zWVM-nGE_|o`1r}QtpY3Gig1~kv&3y_`Y|&Dx;40vI)B!@Jz@=~l58U^Hl=*11?w_T z5*~{c$_))2V>{|0(gu@eHBV*D(YsBB!~ve|u{a$&KI6e+&sc7Tp%e9saFxVHYGy;( z0_B7@G9<|uR7(NKr&;c1p}81xoqDke_I63?>|8|cJWstgtZ0{3o>(*_9ii9T=f3$7*LPe!_FTyPcbaBa>#k+4quKF_Vy*oyCfsT3?LD|@9F=3p}BDGT*fe7BJUpa9G@P- zth#C&W_p}QzToOhPF9_=fM7xI=SCEcAAdV!OJ_Z#ozTB`+LQOiB~**qx5jtF2ujn> zDiQ8T9oVC_8k#SRE62d~bC-@!j{SLBW+tS+X%I9fBe3HTjUVpLU zJKVP>hsgoCFC&p}&VWU|yD zsT|wB&f zxgRb)V7AVDK<5yeA+{a^&jUi(%@*Io`#``aCLv~RX7)UpOXhD4L~L8$p-{E(Wv=0r zF#6#G2vLwsfuWg*nhXSahs;BUMTSEG{SOax3p4S$6(1e!KQ=esnxMs=hJVzQN~$c4 za|d;Bwx<5z-A1tf(#2v4;TDGdWxm`sLoNHbz4|(I7WD&u-I;Omlvq2MUID>HF%P15 zOEa?92Q;IL=000C_DaCRGAC>&eKg%N7fUlE27QbRK)!oa zNE-J#dPDQpJp=8gv@$Hq#ee*HkT~A?@zJi%;9$$+m|tsWqEAtW)wHT>NbO-_*_*G*nldgsTjb9Fb1@uy{wN7I86=!dxru>T0s+m49;wXDFdTrTVTv zQ!XOKB3PNg581St>gK*p^Nzpz;*Zx#jz-o(ZJ>=_j!MeidJ-IK8-w&F$7~Rc3f)X1 z-+#*rjEXf}!vr&Yc6pZ?zKKK7$F70Y&#dvWj8~kkUmzVyTy7$egqjPk2;3wGxn+>; z<@j^c=J)tR4J+1E#DDzoM;+ZuubBRU1+yM0cL39z0$1#1`H1@I^RjLhF@%_;bYt+l zV_1GyIs;R%fW0~L4K<+DjuzRHN+76#%4};OubP&;EDo?nOu@KYM0Py!F}uH!DGebl zek$(e&<9&^%=eUPJX@i*d&Ik=YfLu{@RXfj_*EPzgNRBa=YQQ;$%9M;fUA%CX-U-^ zCIQdhNy&k#jlM6kiH!r*Ll&*+CQvju?MFrPZhS|QhZ`hpchDFc2mt5k{2&)>&coj= zhk`)o)g%_2c!?@zWwvs_PF>gEG==;M0}q)=+msPcLc&!xYgdITpR&96B4`Rf_=6 zjXds<>5CavHV}`tSGPYAQDT66*2vQBrB5h1D>nXh-Q*)fldOL~PD4hocuvExd$p7K zTqRC~F=sAoF?Z&G^E^a{@$TwAOSRGEGa+>n8ur6#kbjF>8k0CU1Z5|l+`${VHrb$~ zrq5?4>>+jA#@bpntwd|Xpx=1O=&mi_0!CP3x`w^zN5DAm(AVm3CDW6yHTLb>yXP$) zsXx`LiG_zf9DgzH`}}|-kmxh}jup}s+Jl-T4t-MhySB(3uR?SjM;)Yl6)?0m+g0bo z(H-Ae^?zX|gX>^oh_^rlfQLRe^v$?jb5g?P$3PUdV1e2#Nq-A5PhpenDhVZXP4mn$ z>x4+Nv{%@}c)$Gr@hQx^t%cU7tJS>Y92wXQL}+-j{V_u}3BS#IRXrD)Ro88mOZ&r? zkENA0V2>N`yRs-mX6VwJd!1%w1$;SQ^AU5+bbs|vNO2qIq_ncBZtGY|H-ld_dgR4r zazD;5%6-zt?iy8zn(#~7bxXT%8(76Yq{(a%Cu%SM7z*pA?H@$GPG$xC5w$~!0J+%` z{F$>@5w*i3|vQjFu}pVnro?KDklh4@vP+><{U10c!uPU>@>sGYs1k{l{#S_C9CDo zBSoS-JWrYI97TO)eh+IQD3*xWu4y;_KJ5SavI-$UfwfoR-YNjXvY21+JBmhHbRY@) z7CTBR@a$2gFAY=U)Q>ZQ-4A_hrKcMIK-|M005g;-1VR6$1a!KDCxr> z-@RIZyfH1uEmhK+aDRR={WQI|pqD-#@}(CEaL`$?|-Qk zHUdYdwbR0()hksVd>Pz$z3cV*!UExH1b5ZLX3Q2Ph67(|AQrVo0l57EU&qj)RLhfk z#jr^~9RK2OL9UIYB0{LeEv2%Q2%?rMWV1Hl(>-qAa^IeL~$zW-;vS!uoI zv;=0ComRnlGb4b{+vp0?1z|m%Y{R4Ypl%=0qglqQaMaxxe+%g&v}QR+Fz|J}w5kii z{tM@Q=0>#`39^pBmX%aKT|I#j9e1{T_yFqpdtE%U3()2do%?$^3w&EV7k@}ef!P<% zKzP9g5R7=JKvhjoC0?y~z@Upb&4V|vx{{DqfIC>o5*>Ptz6@`hR#D^TT{q*8LFyHc z)btM7#l;_4*{x#P1!-(V8d_9{d~IL4@~I?6i(aJP)lE5P9qPk|o(yg{LF~J7=G%CK zn=?rBA`oh00|QA7uKG;oFMsV48yUhe)%VF`*w0#pVSROh5$UjuapaCVhF*(M*N%(1 zGq_ipSa~O0ZHx6naMV7TwK8Z6m*PXEvbYdYeLL{E34h9DmAN9v0wM}gpY zjOLq^RoNT&qTF{4S$`C1Q^-x~y-u9h3{nChQd3pt(CtNsTx&s?imehS_%1ve|2omj zP3xHh`MEt=TtG&s?9>9MQ3vF}Tz`Fzm8`XcOv#81M!TI| zHMD-TX~b(xs)qXL(`?T#gdjKzItHZ~k4wzIZ+Fst6$GoWA3%o+P!L+ItjbX6OCjU)*LBoS;7W%tNS@O z?rThFb8V;H=vq`^o)Ib8LM4%CxymVHl)%tN98B(MoW-V+qEKYL9@bM$o3U(h@SzREWK5KJ zo_`L`k{j^)*sYE=IlYsaBxfTwljLZ^XJ&orp|_iN#{XJ zwTA*^7!G4Xs9`_j=Wzq-Vz+4|Ridk|Br20Rg%rnh056!Fr5N*z_(f4Jnl+SLlK!U* zFMo`w4sn3U4aBaAg13$d&w#;jj3pp>1Gek5A28x9&7&A=tjlO_RUNf~h+lKE!ERx^xu z^{D;T#G!2h+zWk3J0EDqpM#+~qR61xgLk*|emowleg2b#1^0Z4Dw_U-^hbZyA%7O? zy5E!Abu67U>x& z(kLI6dLL{oA-n2O-&J&op@ryw0uVvmnK>^ z)j{8G+-Kd7FKjGDm4Q>Ch>INJ`G5NUdth=o`z}v<4ejI)O$a*HioohssuUPMJ+ZIRGE1MpRzAB6e%;^dm5CeM4@v59O4DW_0k;H^RM|bMyl-c>jpm$;1pn1 z+KJy^FzLViq+-?DF13-IND7L-%#`FcSdm$-6LiJDe*OgaN%6`kvJWlHvZi?K}5QE;jczo zb;LsiiBvm#&C6s5W~@=izlVNMImFyHP){0#?dcUEF&P(I z&W&5Ge~3q>m(WAub91*I{D1n{0^XU)@86?oQxu~@P|dueMI;;D3w3p}m17AJjPKL{ zc+-l(d{n3dja?8pQ9!_d_gcQ(t`&VAc%gjiG*tMzQA;Zr-ft@f~EE z`H!u;wgv4STL!SfJh2xO>?iCGRIv!(IjC?6jC;4A{UYsD94nhn=VJKs1t#S39jyo{ zdR>=YW%ZqL(ES(LZ+{D^W%W1LtQ@-L^T%?oU$a|8#UJZs%FPAV>11SQdoR{E#&}qw zY*q6yBk@^RRckX$)&>lccNPQlmNH4H%08w=w5?cM-rH*;La%29B&4b1;oX_L6DEqU_(K4O^k&65Ges<0%PcEOpj}YpqA*~gR9L`_$^8;3L zOi+g0WT&4#jKsJe#XrLrW=P8v4Gp(^d5)lx4SyLVNiI^{o^UfGnQidDo^}(^hgcvc zayslAOD}Uitb5EE6$ZV=HvUG8?>A_DM2!&kxT1g&pT@$PJ8I=TUP7iw^0UP>W#$l9 zUyS?cF|AmKbprHuRf4xa4WKn@7Z?v+9-ty&eTBdI#KxMB&N}y8{X7J#o^K(4P zE`Pe6{QOh5$o$h~3uk=&MJ1mwac+#1nu5md0m?L!#L1nEH!k(TH6Y`9@$5luE-|#O z8!p`ywft$Et;Gp5dj8Ao{C|BP-3;T}!}lJXNjs5`wg*FO<;B^X@IzQd zW1B+yhX-dd&iaqiIL%v&aKp1w7{zERoiCw$GN3+Lz7H~0RHUm796#dp2dr$9HJ=!6#=ZR&#wW|R472#zoA?7(Wre6m2 zTi}CmJL{cm=LQ4|9cRhMuFZ)AF{dw=59&pf2+Kv!yAs~%uCN%kL*kgw&dZr)rVfrb z(m&_95onaO@y{gDgBwc+s!>ks^?x^AQ#_%Pt$n6AoP+F7 z!cxcS&B3Piq|sGW`kY)_&vWCQKYXvCKhl20_7y3t3VeoPntkJQtgqk-N`iS`j@nLO zgG7!T%a`}v%pX4qVrStPtGiKbStWzG-cDjYKgCDhZ__c_jDgqjw_2!YQ79Z@$i(1A zPYUr|6^gtqoSgHHpQ7ho{(s8-9oMut=yg&Ij{Jz@>-H+6&AgA;j|+3#8KQPCZj5cw z%=3d^b6E!^dDB{3Se=a~ad+PbvhqKmlmeMg>ckWBV}v;1-X}#J(o6MQ^@~5MP$HbL zITjv04%4srmJ1Nf8X}r}B-en4jWwqi=AoN(S%25>y6brx(9Rsvz<)Az{Nvubg4U`b zVP1nGp=_rFGvg5atGAB*wkzkQE zom8E#t_RAys4}fkW@n7Jo_y{*Pb7vdKWov=Y2f=bG~D`+6&}Q6!6XB4eOcQbhS05z@Lalv$Sh=vy=XT#222 zP>AW4{D^wKxPNQjuQb9YraT0{@(W!sBw&suDqgOX8#fDL61Xm8Q*j0FQpnpCPc{63 z^;hpND#_zmR*G_7X2lcjo2E3WSMO1&2ehaSQt2<{uWUh?wiST#UywczzlSk9$Dsz& zut@5O!ivQy#%g=A*$*rFL}+O)1{UueN_kV2VCTj@-qR|qZHgx%Azo=YF_FXh_X8khJW;B>-TlO7#vKmzakqkAbtcksk653 z9mnHQZN}?HvTn2~_@nm9PfpuBwgf4mMRy7e%ZBlbM-JJ2%NII%u7Wvt^*S&%A@i7} z>NILsRi#~Gm~F{W-GwDDeD5|88((jdkqblk{jrcHTL+h8FQTLex#xf*e5KbJTgZiv zJ%3eUn%4N9)j_Z+3;VcC=}Mb`XVaz3B23!`34?W4U47{YyK}VUwrJF-Y)K>&GDjd* zE}<=nwep+b$69J1$n|1wxlls)0G5WKXm)C^GhzLZ*b#^&vV!YHJnoN&K1?zvJuH2gaJ%2s7y{r=H#}r(xG+u{RV-dx_zB{7}xP{B1 zSB6|-21I%I7PCVwOXxZn0v6$(yh-V1Kc(--+&>FV>=mr2JO=t>W?i_up(H6vLBDXT zAxoU(Na3ht3clJqU7el71HO5{1z;g`a@zq~BhHVRLV|pf+%<;yUm6-puA9tOrGMpp z$%N;O#((kC>nxuMP=un18=leK)C|9n*Be}T_vi$_^F1ynp=V@(ZOYY5&D>L%Eh4d3 zgSoOwNLa#CB$nl$tTIJ(;D>9Z|8dj=a^LF5`T@ID2+;qE(Xh$teJM?@G@19z-P*oa zDLuWQ*s~mJx8xmoW^Zm=Q;i7|Q9 zA~|A=d=3eTU*^`_$>}eSP|kb5JshO%xEA!<9uM3>RT)yGU^a3F246X@?NV+T-L|T$`B7H4CU~g)Uit4XcxfgZ)IHh(V}_0te|5 zXRh$2Hp@ULP1yR(Gw+xnXowAy!Tv{lXWUo^!;vDrI;n0jOCc@4j+Mg;(9jIwo!vHr ztiq=%T%L^EBP9~zkCTOqRGa+sbFP1Y#vhBwg!gGKa~!VY^%WO81p+(VlDJ}kJuY0g z`7zVyLvgWakVhXI2NTY%KEmgz*nvoqBjWYvR>*q+7=^Hg@st{Ceo69#zJmfcQkL{! zkIR)Y^*wS9q91Z}kVCXIj!%?M+puFlyBQ(jyqPM}9bv=eMQvj!pKz}KzW{#*DEZf3 zSz*+{8q~#U%P9F{??T2G_PBju(oaL%W%7HzRk@VggB5iqE9K68DCa8@o|JmkleCLH z&|L}Eor|K#_^3+Q(!wm2?YBV%6*=}|W z`S&m=HY8%~Q`!LQwyz&2mHU5AP0LURVR3pHhg;~~rC#(K?B#<7-LJ+~;1MOy`<|rB zB#c&9b6VO;tFJA6Z)YF*-|G7PBgWj|@37Es=I>MuV!KkVtY-T*XZ}WfguVrZFPs2E z8CWevl$(q?c?YK@v}99wU2f{8tD&`xI!23egJxKL4>KI1Z}w5y_w9dmkdX#1?J98^{h~SljFmle72^6J&&fcXPTd?MOE3S7r$~ zQ;Qlr7i=080<=`K@B~aK?uu+HPX0+P;T|xhc7cY9#4l;CM0%JibKeKaTKabL4jL}Y zL8##uGG=*HhF8?tEz^GpPFRnD&8!I+-W(c8#qW`vYW-aT$0wcih6zz8e_r?#T*;0c z@T5}chVNIRL7zM}eM)@dxTtnHY-x ztVBmwL`^5X+he*h`E9*@uilEvgiX^*F2VK1$8Z?{gK|c`6&-)!kRK;R6=!T0p*`{+ zhF89gbr#uJRJ5~q)NKY%HCt2s-=ET$3&!cGuQk9d_sC84Sqxr2ivUo*jW#nr-?6KTd+2{{w_Rd8y*0`VdF{e?je7461+UIom`Ayj!Hz-D&F?@pJHGD7VIM+& zv1|Xk>vM$;A(^<*0m?dIBnlc`F3EKmzd2~d74p`vdT_rb*ClauM9AX9KBBUJ7Z_ji z$&%E7-ZuG*gwx&zZITT>+I#!VsOg#mTPDz6%NAKVyHS4~6vp+T{zmA9h#vy(=Mdvn zekoa@+61R}wUUU9IPK<-`#%1IfTt<^-Fm7qgSu_Vho&l-{4ag^4L;ZrV}d2^4u&jE zUJf#1AIRgXCQZYJz-}Li`JVABZI2rh^*krT-()yS&a#duGmtWg$kJ-;=ZD>p#;$s- z^A%B;vnhZ3Mm>pT6({DQ^c$`{g7*Oy2ljgjsA+APEg@S&rzZklTO;Z1{X#63oBO!L zdI@!d?L#LDUx1^MdBytOA7ZVl7e%ht8QL4w9dDNggoD?8C6x!ZU?*-p4jC>UMDKbQ z<`UlqM=`qoo}M{%*|N!`=LtrJ$Pgdg2CKQgTx@@)pW&Y!{;R=Zyjay>Oscwn`p7tM zb&7=|zN@g3Qn8JsFiRU_sFBXyrjrL~{%ZbhD8qeaf=^QlZTm=XU0tz}Cr^8YXoN?rlz`v@TpU*kUIvYa0-8+%X;PYKceMtgvCmUyu0oS9;BwrB~pBDN=l&vFviL} zOiF(d=-Gr8*K(Lih{Pd~QKBJBJN+4o#1s;~d?{1z6@a)Sug@=*7$>a&<~bh73+0%! zKi!4>)sH9!zFWheat*75TR^*&{#ua%W`;k+ArlS6GA0R$517<6S=cAw0Hm?Iiz0@N z_1>Z<*kkqmnulfdi5M{E*qAimW$SYjLmht+2x({TFrX_$k1zQg0h?6?r~2lu>D{Gb zQy#sekWA+asrRQ%tKjrtm3Hks1jl>1;KMc;_ikJY_m^}^2^O6@9Xlh<>6Te%P%e~j zu`(c&Ke89cv>kF4+p5A-`?qZpov2YkAAKnI8~r8r`>m3e-5Y5L5WRdxZ(l?1)3$#> z6$B&T!!~Q4G&(FC(r#UVn}to9@zxs~xf&>fWRZ}#CK2kheJqx%X8T?;u`TR5yfG&G&ByA3*p0@5mFI3)jvtlPcXzel`y1C92@U;DNVQHANV0`!|hRv1U zNr0)>h%u*c$N>El78+q{J4AL5?m`%R+baw?k+Ww>C^(AdBPyU$8O+1DHUxjNN(3<^ z{$R<+kPo~C)A4w5ulP1b*EDyOEREKQOu^X<2l|Y3md;9GC0){6a#fd)B`cUw{v)%o(S}l$vQsB zV5AN!u`8HSjc1WJt{wWGpBcoPMjfDY+#BSs-2*2Wz(jK!ZMXn;Nzs3{1@o6dJ^63g zzS&zq12XGo)kO@w=oAFtuTLCIA zto$$5K7Hx1?OU2Z`FnpeKf>X6cru9%4wY}pUeA1zVZ>oz-X9U|b5<@w$25Clvvmpd zb$W2B&C%rS*W-$9^iW-Hm)F{3f`4YH;%kLgn5@mb{AR`!^aS+chVUBZ6*Ow@zyfF5 zschkSHy>g&#cW3Wm_C`(e~~uYy?;KoXe`n5km$cd^Ej9XcrYl5#J)xZY5yc;4v~-T851DRPx5FgOb^Vj=sPfJneh*|o0sxf+g7(c z-1p@h+IP^TtSf)W&8^zc%ngAyneK>1c_2F~@>vt@{k7EGjNFLgg?j$PekA}+py6j9 zoJ$y(Fz$^M_6!S$-PD2FM~f$kBJJ7bf5id=?rz4_0ZnR_ohDUuA}F3Yb>UBd_@zYr z^{~#kF*Z6xW3xk#J=dMX#0aazZ_H*ZuqPQ@v&l^R!nuE;2Dws2LSd1sJ80_M!+t-~ z$s$tw1J4_eW@yN@RFs!lnypUdQAQ~gX!D8_elL@Z< zi8aH2X+VEm{lo#l6N%QwTc*YK>BK$-XsIaA#1WEk79!Y^gC3g{U|J@yt$MD-_k z^fYvO@)8VBOX)tjiigO9nssC!jly~|-caEBKpOE#( z_HaEO^{h|zRoFIb@7R%pOH_KVrNp3Gavg#pb|w~nHk!s)B;e(9H16Q18T1}lkeKiN zb?AQqe59>U={oyZ;vtNH#f~)JKA>@ne2y(;5wKYeVIT_}-tBn11Sppr$$^i_N7$EB z0pnZneTQx5)DGZOO;nP@Kci6gczV1j=jpN}@pmkomNKOm2R%-X93WMQz1G|;0&zSa z+n(Cim@M^M$J_?6G|=x@8Oavi3EaQ~YsG&BvCnuISi4RKM8GBnY-Ckly=W^cSbsQ0 ziA12|Rr!gg)X>y{!%kw=;%EqsN3|fAZ2TecDJ5-xD0BFeLC11EagO0#F&9@%L#U|G zcToRpyu7OjGVcdIH3h@#q6)(#-nC~x#(EUS`4i@5!hZ-VMhx`c1ig&5U_-VSTK|h*T_@N$v0`O_-B%$>HB}zW&yE zO@mRcZe>}i#K2g~Pp?t8GV7bxxDtP)c1x)Q%B9l!u>WN&w$^1F7+;`3!A60)*I(^v zI;|TeG@isgqk$TO@#RLq%N9#w(YgU0QE`eveX5sp%hB8%RHwSssO_J%#n#)B^1xV6 zF4j`y&pduNvfFxllgbZEseKH%o^x-npM!bb5@{(GZBi{8_D^nrwYW~16)S)IfqWyz zB*GT>(Z85U5bAQKiGNm>!KcqtNqll^kX>F4hOe$@*k|;Jr2x%>s(~*G6!_=Pj2>Fr zjRnwH>PG-(^KL-DnXchlSOtVc8b{#`6Ryl`XEFR+moKZ`iRlHF@F2DGImrIt;Z4u(u7nyYa;X2YZY;u zC%?YTXWsZ5|H!|GdQJss+M0Qfe+DNA#Uf{cM(E}h(@E`jn|N|EU|Z`rG;TAW_|vfq z_x?Z`VI0PCd3h{Zzyf~*e-=!6OJMdiJCgCXehZ74+}G>0;Ex8t6w(+|l=FEh?p3fd zrFE1V=||pHAknhIR9`s2LdJoSoWGa%0F>C%VZ|57!x-03NVTKOhFNc~p|;AWx5`G5U@tY)*<->nd`?&F42{Xa|Jw~e0!Ts?&9fEZrEwSTzR?=rq@tQJ5l&!P1jU-4v zR*wEd<=lmet8jnJFD$Aa)u0f|ig_n*Y?fP0<*(9FMpXmI>X{VK>(&nlH^LsC5ift_n=?N>U zu!ByJ7A1e`hO@W-(rJ#iWq}$&=c^#c5&2v7(pG<9S?s^&RZ)_6a)3RP6oH$u zOnpz%#O4Ki<@LoHBp?EEVp0VFa!}9u*^SY|t36U*ZKnz|EMa0f_vmuk4XPn$yeT;C z#}rcFw`DT%g>)v9FBI)SWju+R)6#48xTzYz7jR zKq@KR@W6lP?o#YpiVC@LMq23 z6`7vNp~t%;B|eHP`)QL}*2VbDmiF2G_SYt&FJBMGZJmT|N)oJWNT8Eq}} zu#QIEI}fZ^db!pA>VWVmeXr@f*ExJ?j%R za@km66T9Bd+d-19nUP6)k|_t{Q5$v>0AIL**e_s5dymIu3Q*(+vF@G28< z_6#^SS3sUlqY5iOqo~YTK1Y8mSJN~pDbBDSwre26Bn1a}@W|Suz60|RnHku^tjX^o z+{-%FRG@&Z4le0yT7hee$KppPHw3R<{!+J)Vpf<_vfg{~b}#}pZK_Ht3+=I3WJHq# zvBFJR7UHJ$g;>fbPMtt;dwa(2`F<#;8IZ}@*=G4s1y|~+2O9Y-+xLGoICY8{W)p$c z+~owaX8bSn#{ySQ!J`S9@#?c>BADj(=y`a=kcu}aK)Beeblnc+q)H z-nOH~i;#&{V3*bPrDFdX!xhd>4C2B0QPH%~gErBlN0Z@m&@%JZ74GXKmA-A?d~pWG zZ$9>@pPg>zfUUjowAz0vflU!ld;h?8OZ7Dd(tT9dy&IH^8_Z5> z@#SFU>qS%?voH72N6UrHG(t?_H7UFTZpshtedx{acDI*C-;p0xXqosKr~TWIFbc)S zfm7$)Myz$@0~j__YM=Q05uDkG6r|=Z7c7F%weRJRXJ(xbFN=R=Z`^-9N+82#iT|B- zKlGTB6yaf#A|_JmyI1u0aWie0Tnsh~FIrf4{Q$7bZ7t>yz-0MV1jSnmGrE!;J^)j6 zbw=xfeFdbwWG(vGxf}4g?W}2tVV8B`>e0bC!o9R}YG4X3zmjB_ouZXcK>6 zOQ-Ru8{TbzT9HP(hz1v}ywO z1-7IxT-SefHtrdUNhU=aS7N?M_4z|vps3O+_FIjP$sGT;op})OIdf=T z*?$9T)(uWR>hk@i+J;OFhxU~1DL3Q7?~daF+9iL-w;-+gBllyd^XHzd)x)kK=-}nu z94!VG!+^CerBz9--c$u!k2g}oy9T)9G@XeiOXS>2ecs238C`X6Pacj`I@4DW@6SwJY`p8*gw(F;9U5Q)E(AK>RdI;i zC=XZ;i^ui8vZIC4kZ)To@j}AM9?-t%VhM~1o*l-Dzo?3yOz0$_a;o=Wx_!9_6i?Qq zono}ANPKeiR*9OhkSl>C-z|8~~F5gZ3I?N4GhuUK;w{zaPw`5Ca>R=mT8uYf{@RO^tbjVjGV1$29G!LW3 ztR!w;;&w(XD%y0hZX;_K1jyFXYceL>UEaUeXbax`NAjEqH73W95a~B8ZRoUK972Dk z4cncc_lpb7FKmeqEwb|o$yTM|n zJAg$5MG08T!~K5t(6xp#4u#K0Z3o-1#hc)(_v*C#jmj`8{<476uzt{XfOY%mhy?V! zyMWs7M7=ybHu}7nn8{6p?ZDFG`hS1QeuY^7-T?Z`pBp%XLa+Pc#HqNSV8?Bj;Fjae za}%Sk1_9e+@CxC1qSppJT_B?ZwH&(QLxRgX2ewExtkbu!AUfqbeTGjgo<~fh5w}qW z+~p@NSOXIWmmoY8BO~7rhGy2Fu}_RMYipo!S;!S9xlt@Y-N`ISn&TVEse^yIjU>1r z(*K!8A3B|=P;GIhS$eo{Or5bdz=$LKc*$<M!$5WJW$74~XBO_cW1edofYo2cLBxDkvlPSpb zXnn}LoQ6XCz=cK}A6y9ZB@uG~8pv@OD9@#IU0`dzbS=%smj%9*q)El=hULuV(A(!Y z2uGE=lZq(&7yDw*?Fcp8%eUJ&pF8lzWRMblDhW@FHQQNofwu3_Z2EsqUP?GXl%d#4 zn4%ljobyTLM`luOs@urTIR~v=mQ!Ck9UFfZd+zBJV&2kvCUSq&dQOI};ue%5J^m*D zm*Y@Fw$ai_9%1HJ$CoH&C%p!ID;hY?c=4hW>L1$jEXS#JDE2#w6pCW(1s(7*jkGLI z8TT0t(!phobuyAm9(RBFWpdf`sRoTQtlRiH2e;E@VT!3Q(L8&uV`n9Qf3tM^4Eyoga5l z5Zi*VF;r99v4E38s(t+-E(8C^U#>*FCWi@~oclQ-26DWIR^EME$t>j0@YHNU6-k#Q z*trEG=IOMg7>_9KeYx^>9lw=?E-ZoIiYwxeFzL*DKMLdDsm>FVTSTD{L)*@f5W}+p z!WoD-`ooKJTb6(7E17|#_P6Zxeu9csINqc4iU47tqYnXHnuy1>#Ud*|4%w!oi?f`d zDm4WDx$N|KeDlK5yf>s5XYKfMMXlV?=}j;05)6B$1zM!E8$6`lTOI7538rJZgR31^ zfCOCHzuFRFOLp~!)hlMEPf~CPM$S@ndf~cBx&a2795jDhOM)nR_3+GrGdWkWC;;P) z`n=(zA7Bb04|j#TF?BQ%kKvFlO-45; zIi=UOy3K#vc+v)B-P4U2x6e_%QLvc=QP!mVS5795nlS4@_s6Z`6B{YJj+fR2f0smM z&ot*A>EFn5o;?AG;M7#8WQmUH_&+6qW;j6kU$|NV6!LK`qmN&JDZBq)cmXBSXoxiG z9s2eUJ=u!Y`w;ry1hn7Fst7U=mv(11H4y}rO1pn1KbYNwjw5-PKN6waSDXc&6Sv%K zRP6JB9$YLJ0uCDK;G5}Cr1q7L^vaGE0bIHYkL0mD_f?z(=gHwcRe7@%tkQG~*<{i^6^yK8b%Cf^qK5)Aho(^qW$I8lc!w7;Xd4pt2d6 z+TK4Y%AjRq+~Lg_-!KOO+zSecnj5zMS4(Nu&$=I0`{3T0p^=)ZvRoo!Y*{{i*RGhN z{}%K!baE#Ym?L3HD;AAirKL5ghmhskN@tuP;+oP1*E9Cn0_}i#KZGT9ohbdXGai3^ zJIa_#u@d&(0^Qop9QrypEVA{dE8azx>*fj#4E)*ZxnYG8n9EsYU|4XhVcp{@I9QYP zc;Kq;t}@-ddE-Z!RGTY>Jfnc*pJA=$rBZ~gc8;rF!_PYwv&acauJ_%_BFJ?4dY}n1 z=y(_?#Y-QL=P`5*NtoxzH*RY@IV^wEUf;>Wb5qJUrq@hL04*A^%MGPanbIu(o1w8j ziow>Za{%@$9V29U+?*G#@wvavAI!OkQ_R8ZXz^#a<|$DlJCWwBnDG?%BPPT4P|sk? zhG14azlf*p#$cJ4>gV12bUQJrvU>JRg>Bk@K$Zm`)i;D$E+uMQaC8{m#(@HNkA%k!dJvogNCswDTs4h%xhkx%26(FQS1?!PR+EW%5FVtG%q(eCyb&vJh`qarR zaOD{}#Ac(klIp?ZR&Im8Hpj)DHoBrlm=zJramxxY;8S{P*b3ZhbbJQ763e{RERyNU z!eFXrf%^!(kI3`fi)00R{}|o=AzXDH1rCAmTci?c^Y-|tyn%#@2ycJ>^wG0=-iN?C zEzx6yL-3mvp-X;CwZ|~^pINupOg2<1-hZ{oR7mX;aC;m`G2)Et*_`h5MZ+;3Gi$L+M{Yc7KMn0z}QFSQT&$ z$kkXxuMM?t4U$F;$jE;|6DOf{=aGkT0`*1e)~*8OM@=w%{n z`y7h#XQy&m0{Qc?3*C+@_S2YkqaKMS!m9||E2O!43DNG10q672Pk*=nZtZLZX)XbX{V@F|zoQtV0`Xpd=}%a;@*X(y(Rf)+dh7oA`v(xk!>f=4A#J?GDD?F8== zO(zMmPTKwoUQ2a1lCUYk(UD{ggD{w@o!4u;W@UuZT7MA^lde&6G*1U&Y2v_z$G4E) zXvLjLSCs!at4x_YKrnYaiUVY-c=eJR3Zfi3U(~TTQ6%C(0enyC1n`T5 z`P_Ao%g2ldbB6dSHV6hjH2nZLug`>>+(C}ctF0CYC29l8LYTX>Tan;^IoJN_P5Ea_ z&*WAT`2s)F)hJ`1h)AFT;Rv{QInLLIS9}-R*RJVqd#Zmt`LOP#l?#g0`Xvn6ZWEn7X2(5evwIiw{{{uCtk@@N-NtU_Z2Z{9A{!8ybja zbU1EJjpc!p*50g#mC^aumhG+hG3vHy{a=gnUXyiQ6olNjusrW|LLVJE zuk~D$NVR{=r^y)U#?h(3Gy5YAnC2}WOh!zwhmO|)FLLRBie*dooO?u4poWFgF%!;2 zX&VF;EWblQiueH?oB|V9rJ-(=9M?{rP{j}Mo9Oq5H6%&^wzVvHD^8#jRQ2^Ea8#A* z^;e4b%!PsextWb~IU0e*^o8^9>0~{pHGLBIVlICvi_E<`g#rxvw+s(N4 zAO?SIw)7k>EZo~vW+ox->QeV*H5|R#1wST8->2FKPa@EBs4OkagH7QyJh+>;?djPo;kz7fI=I@IOmJCahK?pQ4bCX?SV z5eMNjH~e_9GX+qzO-?}vBSGtzV^j_9`LMkHaV79@h}<+vJ|=cAVXS`qt4IMDhiQLA zfJA_$DxtpLNiGGnb~CR9m?LA1@C#8c9lWyKu}Bz#bzq)sEpLuM5NM6wX4wgHpH_qg zObgN)6~%qCo9=SAw!}61etnAie7m8rkY*68q5E~P z*{G}DppQ2K$nfV77y6s8pGE8_6aBp2jrGL9-C%ZH!k;>UFo6U=t|n?P)~_Dy&q?WcGhE5W>HNJv6Mjid-df4*uo@%23V4I(qV|VMu(6-0zFQ z=afwr$aAVW=#B%1Kc;xX4Fo0#nw`^IGZ{&fZS`s%DsgRUo&#&L_sP3-jCDPJlv%nx$ zvAVjh?>?%?m(Gg_BY!M>LZ67EL*caDv(gp8V|yr~hjtkYDX^0gP_YqOY#}8qX_dky zuwn{nbbt!HG67alu8>y2a_}duS9?~$ew@S>2^HLth6#g*Q5xon9_G=yPy+kF#+AT6 zDi%r#Y;lFs3j2gY8HuCdm2oh$5rwkY$4IDHoY_JpVhcygE2o*Fv9wGgu21dgTg$9xEPj900jCUrCN1wYb6sgxdMSFfhtX zqz}yM0eeF0xPNB>g;1lHKoH?0pvxn%3=qh|mZcI%at}074w31RNd!XWBi4{~0mz~q zjs-}7tiVqe+JUZ4>{$R)0>)uW0ZHtTGXl&4og*=lbON4jgb_|54WdDMBkM&N;RTWd znbW&q0TsvuNMR>nL_jzkFtQAY1V9wj0zd?l0*~OjPJhCP=m0xTBHsjn26q9BLv27` z01`$3k0{|`H3ysvOa!!cz_CgiXZ9RmL`fI$EsTI^fGohGO2K;&1Q?-^!yNF0(vpM& zMrey8h!=fCib6jfsRj&Yj7O3~Kaf7P_scJPd)uZbPRk?kH3DO zKRcQC=M~5UHtdy8C*yhX%P&Rw6a-SHm7W5Mw5sdCGA`9Mh+fkU0;20WGD)g~(kOs2 z(*vN?*Kbe^>k)uvQ?F)_NK^+v%A@IIa8k{SuYa)ngQrD#T791v4MI+T+*AnGWwi(3 zj_1{QJ_B7PaQI$%T+Jr8(?K;OMMq7C)%mc$KlxsK#XiVPKC0hfnf?^ULO+5n!#AqZ z2H#RE!nYT;2R#>Qv(Q&lUl6<=i1)_6H61~f_7q@#tBI8jJ?H;zMDNh5L&cnM9oEQL zpnoM|m$)64$yf=kidkZxpJo8R!~i_w&oDr}$88uO?^_cU^i)sBJ32Q+RibDAZ8XiP z(xH|tf>}kCoVuA%=TuxT;AmNwdFE$ns`L^~8J3@+X}$YxXqxwJXlis%2)kWp@Hlz~ zEcaHOl`=gKqOetGFn+xVpAX0`>jZk8;D6@A-O>BGDp|`S_t*7WF0)1aS+>w(d$#EI zW$D%0_Q3Dj+_|B&6+Io~gUb3^o!v3#xvtI0o$2*;nqoB@{50{F^|bzJJ*{`p(|U#K zbzb)5f)lmWxlsTjfSB6tX4X;n;)3IMUop*SwH0mG+2$?8y6d&A_U(R_1B_YX0Dq&l z;{f9qIl$=cIiTA&xoR@@FjW5QC31}vN({eSdggMN+t#i9rM4)F^5*j5W_o#6d~M(< z52|m&L3R9W|2LfKD{Nl(^ly7#T@XE2L@J#GF)30DxS$w*D&GsKNlK3oQcH(V{K|j@ ztV?Gsy@M{J#=?rmw&!@8Pm4DCWA(X0Tja@A!>v51ivt_ znFw)eaF!Z?p>*nLqUk|!84(Z(;VQgX;H1l|#8m6*{0m^<4F zl>vFTgW?=H9Rg4bPRyiiYX-tdo4rq?sMXWXYvd5UXkgQsJJe)@&?MEHcYnx72FAfB zSZ#W?pmH(!5OHzfHCPM)^O$AnaZ<~IhXY_?T!d+*9wjYslTssAH8PnKpp!B6f_;Gd zz?l_@Ssbq|lE%SKOc7zET2U&`e+Ks99-yq}^G-Hxc^w>o*qsOY_25QAO@Pl21k{CB z$W=}wCOmMsfmVrV2Yf}sr+)(G(S>skcMsTvF~J}a4f2T7L{M*$Uogd(iqI?iDl zNFd`VLvyPhrJGQmp&fHw_%<(dJQ*o)7od*}KmfZ^lBg7rZkDJQa7N<@`wo;UC@km= zDp+Dpl4vd^EE|geTL7j81i&j|r@WNfAtdR8+q@@1BGGatLfva5ynn-75QHy?Mvwzw z5{F4eEJGkR4a8moB~yW_4Fs7dR!&n`+%Y-;nlK^L?t^iNPo+pZK@jdyi~$I0!cR?T zcaBy=#ssrOb%PT|T+^w5?CG(^TV!=)1`v!Gh&Ayo_xFHi^h0n<-b-R_d<&~VZ@`(r zb%H~J1G}IwL=+SsS%04hnFgDPXbmKPQh@@pt3NbECHUto%GdqNdOp|*K~9aG2PBdQ zezK(DP)kHbgb_x;mZL!GddrHDm1}UsO@c-v$6=BawWvh_k;)lrvq-#1^az6H@;a$e z6bXlTlCU69jrDIJ#k^d1BqB}|OHi2C7McFF`oeqcB-%1-MYUty4gkAElVer2}8VRi4v9I3X=F5 zEsj?xb+NIAMmxd+j*LXQ+6>YM8%)xG0XuR%IEDJ15FfAyh=xxKCzF|WK_!bZ@7!he zMW`?-&yzB3Qh!~cxoD}$tvQXIa7_!-B8_Z%a}VPYc2t8d(I$VJIE(6BCJv!YJs><0 z0&OA$3jg{0%l`g@@&91;|1p-?H{lZ95MMj>6oqlA1AD_FJ#)Wk&;v;0xji4592`R` zFO?FXrP7iC;xcd7q==1`;APs&T<<*;)+oMV_cqYxuwHPIS*uIj#nPrDUfR^ z{^ODX_J7n{3o0#11=W)ozDJqnq%z5lYwP*y8113MLLQGKhsgqEeLy$EQZzzN1ozVc zqd1gwPw|vfQ!lPRS&_0aWb=YNPJ$SX8}4RqaGm(u3K(&(l#!oPtWTdO25IK^o=u#S0ykQ4N(#HC@7-34R&RHn^H>N4h`FN@K#f&kM(cz zwYzURL%VHWATGJwFi0z=I*)OYA`y_N|Nx>qO84$d7l%6D; za zAdu3LTt)`irnL}83fzB;1)N_k}lrnQM73cE1*#u=Fn5-l~nQ`%_E1@bz9A^O?|t&Jh!q!6J>AlHY7}NE>3m zlx@r4bYOlWIeavaZ}a?Vc2>tJ`d29_%+8ov*r#2~_!pWXcMJ*%-_{8jHz$On;4sTFyMt zlBJJ~4srl;k07;*DqIu2@tP?fb=bsxQlLWv6&0xyk(}#vvU0DI=ixFaM+j|8D-dX| z`)G;iiY$vyrxlzp2h?*8S`!(mrzT=pLq4Ix#I>ycB>XT-s8B#r9BHR*Ec$Ktn zWOE&5r@;{?<;X~N)=|`_gMTWbv-L*O z5f}3f9N~6B!ZpfU}OIl$#dG zM=*3`%2o&_aHOtr3zWtWycDT@$41!PnJdUqmW<0%Sz|v}YF*XN<6GrTb`j5D@dgXu z{wKs4_MFH?z>C?|p@bWW`6S;Qhj`(W84GpapeTZr%D4!`xl z@~S1tTC8;+AVXrf?#Uaazg&hJ#UYYU*74>x`tmJDYdYZjzjVEqwdUX4^=<)31FiTw zyWZ&HP#O$lVOW<_VuYmolspfYK2pkmCo2tyuiWAV$uRk`M$vT(;B$J%iV@G`ZR&?d z-JZsK`W6{k5k;~>H)ZIm1E7l18>oWl&PIrIG%wOv3@1$kK2^g0hu}B`4`xheSZUptzV6qh+l?t^mf9h6t_b@)NotmRYh1ML6$;*TD=0 z6`^G@L!eQK(WFkLR+Q4FaLMvLjWuO3Y*fhUNp~JdFfaz>yfLSP05x=fz7Oqbb}Fq7 zgkp+BU2N)YzZ4ZxnO11$pxZ!crj7gDrp))nB|)Ltng=>;@(E(}nOzO56DhF67@n@x z1MgSFrdQPR|=hr8gkJe(Gw#4C7A_4M4w@L84>?Jz3QOX^}X$43X(uAZ3b z)z)%KV{1--j?=k?X+z_Gn!YKHPq90@VUCnqb7uo{hA0c2-2TSPL*$R5*A`@x{ayL-ZQnXdC>T1^zaIg9)?WFffAhQ=US7@7mD($D8GQFh;>#EF{%AOOJiZ)N z1x$Z3udY8}0J~RyN`t@&hdHkLQ~W}~f0ZZ0%jyehAL++0)TgHR#nFUkw}H|%1(#rI@f zt;0EgBDhiCec3;L4!A$;&#!*{KCi|zSQB{txVpR@^{1=J zpbT=m!+Tp6(=sxXxw2lo zX@CvSyL`BlCEb5{@Z#WY;zMSVyO>a`WKHn5aW-2My{$D7YZ;MM0jgY70Y~LK5dU0% z)slrBTn!uPKd(k;b5~j2%kr|1oi3+UA4D6J1MA`6`!_dz&<>!c&(8axB(85+Ph3}* zebyB7Arc<6N>a){DEp{^TFHTNhf8Os> zN!>vuSyM^52cnW)uP%@PzbE{4;oQl8@}Io^czXJ>1E<{xrf3|S_&`7gvlhrt#4clC zi0?U!PV8iL4?mo~dUe!+(QhVYO-bqH0|DvPS|DG8kv&b`Yb{c>x02*zRLy2&NpCv5 zzr^HD>|_!jzk2ra^j+rmZA6OENPGMdEQ8 zlyIbW+C%`K%P*{>T65JgE-O42TD`3Pxb2Tx#}S$ryZo^Wo#CPc{7R4)`ey#@~Q*hQ!3)8~&izf{#!yMD|YXWC6ZBef<9E zNe$c`iqQm(z$yGi9wM%}H*h{~(2RAcmqe9>6lIeIaabm@qbzZpmZSiG2)@Mgaz~@_ z>eajb8ZF@tv`~5q(PH8Q(cHTYs__XqXQ&E~n|Q2=#1JAPHn~`Qs$$BEbm| z2=4CgB-jQEHx9uyKp;U*?&mqD&in1Ht7>Yds^|A%dOl27Pj!=9>tHY9{~CTtL09L6 zq)8wbqSBUPt%81;#dQ1jSQwj@OR+nGnu5p!qlA2~gBi#p?W_Rthu_8P`jv*>!q zMZbIKy8<77+k-nhFmP&bXT~uiUTzpnDhvJK=*aLe1EO#kH;;a~ET(f8g6M%=)UK7K z&kQ0ucM!y4_VhcxFAXacD{KB|A6$wduDoyi#|UCI@+~KO@a6g5I&wWHjC42GpMEA0jVkm=5{T!O{3gjd=*pCVtT3yK4|V_ z$TLPQ!9}Xl8oR!0EyTneX_kETJTBar*RQgLSywrQhG zjIkFS5QKw8ZJFTn=>eyIL1i_x)1T8k;9`FO%C#_%azD_d{s=cGdKi~}paqOfC5VB; zz+B_b6RhH`(~Znm+Il+v5FhKMRxX%3c)&%4`kjP;-vpR~_b<4k*!( zbI7GFR!s}<)_llW0^vEcrG*aey*JHdPdYsKN8M17jk<_MQ!eR~%?z`b$ze^=Sntj= zmKQFw9OSqcZLo_v7it-@6=5v5N~CGR%Z!*kV-@Qa)PZ~&kXT?&7jEh3;269dGift) zyR><}(ywTJ-wxuPOaeAFPn*oVNLvw9ZN3Lf2(HS-H#tjC z?8*uI^oS zbC-~~Gdl*rWNMUWXB5o}RDzV!CwJWm;KENs2YyO+YodDi8wYKZutOFw^L z&qxq7VN7nMoJ36CXsq|nr}a=-ewgaB=Cz_){ZPZ4!L!C>sks*eH#s1!WAfHS=zpjN zGj>|d#*hTh#AAa1LqP^G*~tSq>T}yUzWv{r6OsPI8$_ILQ4Eqm7f(r|;sUzNU~iWt zsDc)1*0=n9nwh#oGj$y|Tb*+j-_yx@^O1gO0|Rp!`k5Es zJXN4YV#yB$bo@r_`|`>}9*3!=d^JWeRCK$cg??hQq0&~kR@rx?dHYP1#Wcah*LP}# zIaOnys}cMlYFOLVho2ufgv6L$IKQW=xTiR?Na$gwz$EwN)JZv9a(+f0YrwZmO>KXCp4jc3 zM(AXzBky|$<>!t~eY{qtE~6O9L9H7fwxXDv45am)-cgpID^`rt2&$#9pi6(D+*c^z<)HJd;W_J5E{@7!qM4KMNRG_CVg)^5Tz21gtCvd zD^~P^ZoVi!spZ>1ZRSDMXSSde7P>Iy&=n$B)J7epmzQNPnkQCqATQpLJ9%n+RBfsYUPy81E;e2`x(;ss zxcarb_B@6VHCF?)Y+X6_f8{{!*v;!u`B>m1XL+s=8(US}f?Dz&8}{*;7=p|k_2ahuN||cxUi59Vxa-bY4YWl0JD_e$ z_5<4=`m;>mYJ<2RmO9&LYD!{nUrAU76^r*eY+d=wIL`yI9pkj#hNdHybqgSxYY*v< z(tlfn87mKxD4xxbQ5x&N24Ze{d2wm9HLPjSCzk9;v{2&2xRQlz0mG zU)7Y9M4$S};eSocnzNJcG8ar^L&k`Fp{v(Rued@@iMcoo>JFcO%t=_>x%9vHQx!Q> zM!mjxxISB%$wsZOoV;;wpY+Q09T}K!yYb#Vm8b9Oy35STV}zX0cO16wve0$j9OPXd zE#Cu>AYad$_NyM-OExv-lUyye)9#f&GaD5@x6&VFq=On-8K(U1PX~sM{BiMTRGEDt zcx+z^jvYQ24p;RsETUq!cGIVtdsQH{EFzJ|*c{jBM`IItmKJw8Lf6G@ZJeL^YctzH z7ViG6BS)Ca`yJ_m0Q4XD+xux>#cV@vF?WF6$rCbN{ZUR{HlQK&2X20*wG*h5U#x?~ zDIIK_ZhV?dBWIcDh#DhyiliuQC&^kOXl$5hC`6HmdADZ>>dprpqTXR-Hg=6(*Awbm z<@`UGsRN2|B<%j&8OZODVtcl#0B83DWZ zHzVztHKnbzNYdljAtW}v-WV_C_LHT(v!R*unL|*Wy*ufe@|bWLp{sC*S&(e+*3a0K ziUIn3>gjcVDUZ4L)6QKaA-kOEV6dP_h(DUulZZ)stm3)&CERY54_MIZqy5bh8uV(H zyyxmq@2|_-v7y7SEYgpje>{ZEi2!?30}{a`iu?l%qeQOEj}|$WT>-h6^cmL7QD^L^ ziaY3ZkW|3=^|oq)J3Kg&)=qGOE}-w%_QB?DbPD~NLIKM-tyVFby-0@*2^uWI&!iT-0w#vMu0*S49 zaxAyv2uK=7h`^LG)!zX=^Cti2O#z>*7*T#5Z)*YrHr$R?jMpsvI`JQ!NIHWmTVLV! zdtDZ6z2)7cYytYBU7R|pozNSG!#sA)tO;kIkh#Cr}HKvSoM&HOrt_vX$` zM}8fr9e6-l*2?87^Ic>aDQy+o_E3+J9x4I|3zBxZvQmWk#*6lqgW0*K+u^@%rw{tt zQt=dCIHIFG*EdDtna8$7gRxu&_7(AKIoFPrns<#h92?ShrGChG7BIeqbkEgu;UtA0 ztJe&-NKHPD%-8@Ir$4WJ+n}Z2&ng}~I^ErSwl4n}9v{{p*nCd#eX%k=M74wpPdkU# zTcj(uTGq1V7~kobz)OKZUb)!1u51XY>ZriS_xFbfrkN8@ldlN^{C)ngdCnb9U9Kqy zV$qn}U&oFfDxyNyj)7^ISiE9hi^?@6jt(5PgTuFGz%HQ&dy1 zWO;I*L?)HAiV<@2VJgRr_fKPv)BhD1-ahld9~&TjvQZ**XK@2#m@i$kE;fke(#XvH z9-Hx2L>4#~aOo!JQ`{HhXDX?2?za1)kO}Ln)|P$3ZTlg4iRh@gk-W?ZB$ucKR2n( zIz@xS1(k7Q3WgGb>5q1>l;j#~F)`8q3MpeI0ZiS4vDnqBZCFeKX63WQn=fb-G(?(R z6Lf5^dbwl_KI4vr@Y!ouYP_f}n(niRN{E*B_EhJVe}|@*msBDRK_j|f65~H>7Mv~{ z^;d0A$;7^eE8Dw->XsAIMHt3@!}!c+eDK#Je@|-fz~Pe_!QiZYZ+=?cpgX@w6Ep}1 zP+cve(y-wMO zgP++uCmZ@-x9xRnFNKB_CjpXgjXKijxbZJU+tj`4~ z${90tE0vfF8zi{w5E9g>ejJ#IB2z~=I5o>JJ3^n}r_>i3{8&Q7h%I{T&rZWR>rF zS`k9DnAfj3`GU@x3`ltFT3po-qb`})H1Q<`g~|fjj?Y+Q$5I6j9Duh@v#L#zb0sjG zHmzbtqI#p|Ymia%1t?iBkJ-%&tML^F=_L>hZgk<~YSme$?CtKxY;9lmh=+qeXAXQJ z5LHp6*n7oy{Z*GmIGWN5naMn!O;Qm3x3vWqg>qUoGhOxpld!UE5%-trhnTvIK3hb448J7N0WEG zJH&8YykEec*#PAWgz@KKo`pTu_U!JoBI#}w(9oZ+20Ni`+!P_gw#^O*wPG-!2l(s+ zHwCTws!mb(%0zuWxVhJuXUblNlR>32I>P*aa{~4*q;BILp-$#l=A|nCgal*!M}+A+ zn6E&FJ0AEuQHhrMY(XJ&^o_tkY3S3f!X2i;YA_nKoZ)M%6rR0KW{@)fbB-qc>Cv!9 z{kc*lK*{;}39qM)-=9!)QkLgVSR34h5!0!{N+$SMM09c0@x_zG$dAqs`P<4cm?TdA zuWCAU{eT4qy2fs#Iag3Y>a^dfKl3h+JlBQ35n%a_jqM|tmRl)zk-=&a;pMi7zp$dH zyy~}N#jt$<Ne$W0*t1mIe4F zfGqm+Q@?sMoP~I3yLkd!ULvrp|HhKf2qY!=lIJoTtWA0GH3$gYJ&@yCoz^_*NXFTx z8mf=*jI}XUYE`YnDp(Hl*~O{n_w8}rEtli^&ZW>%TW3YlIl7La7dU@qIL{es1uNSc*ELmN_9(z;fa)x^mFU%t^Mh!!s2L8n{wUB4 z)8{P^-ojnPBhu$AsG4s%cBoqm@n6IYu!fKaPx4Q56;yovZ}M{fp7fCh=Z`K!OEfFsz-%kBKwixL;ZChH)gHBtco8u$PIj z4~tM+DggD1I@EwVJVl7O1Wv`l{uKC=E%-5tCL;`$GpwfO2-oc=;O};a#@?!0AkQx3 zT}9PZUJ3jzrS@dA=-z^iEgK?notf+lbqjS_5IkcEkciC)wNkp-LXle4{wpT-ii`%3n(cV_C@_;T{w z)W_<$qPW0HSU>Q-rcj7gy--GWoH2N_$R>Bm9JP1Yo=-PA;~!cbSN+$>%5GoWO2{D1Ou5Zvszm$%>n|L=-57W$CyOB<6YUrL!VG%WlON1z3NFxcj zN5jSJR4hAAV~v%%HG&EO3w&&S&_q5cODbx-N0dXrZTSkxx)s0+4)aV^XjI!?#>iub{L9YALxCjo zfS|Of)Q3iZu~hTF(sZGosefr?FjIB!*;Fr(XD5|oy4a0)T}c0#{xg{zi>G6k9x^dG zku2WZ9{A96jW>&8+}oMBX<>t~(;la={e&}!bBlBL?IYQE%>u)UQj1cv5Jt%t9Z*GU zEvSru=3LS1Lf+53x#8TJ<-i5rrs1X`BcP_0GQ*phUUVHLSirkuJ-CMU{%M>|zx0B} zk0IXfcqf}`w&RIA)~R9*Wu$4TFFBqmUdtxeDJdInE3QZi7CuPjtD zNQ|W{{US(=r)*j8)=B3ANB(j~bL>z4p!!zwuNqEDPBz$OZXyRe?7S{fSJQtaTr@Ws zpb+|sGWq0pa`S7J0lmObq9TMoI+!{{kvfcA zh>%qX-}VjJur&t928B+a*q4yU$c#wojA&7$o1`gWa@zwc#Fc-TQtPftI-ehian6D85xSIHNC|8n!{yEjTk zG8amwCc3$cq{&2H=qO85VN?;_)X~rJU+rX9Yn`X@WoO%`u^IWxb~P`qZMD)e-hVco zW5SiQL)UbhKdt_C^Fy>YLKj)IR=Ln+ka-1OJ23Zqw;9|xHhuR@?yet#z)O-}e_CZ4 zyOpcG+`JS?CWG9Zqd|u5a+Wf6J@Ro?>wo5F@Gx3B9kc}mw~N3V$)bj-0vCwd`6BEI?HAT#SCc{ zrC`XtqHAdkZqitkqLAw3t~7}Xn3VZ=8X;3yq(zaGA(-yL4$}?>s4|{ln9*^6$G?By z5)tmI#tWwWqia49%?)ES`OVW4KaOpGR>4dPNii=3Aa!cVSJ96Nj1;5d%YShi`mW!t zRKY5$c~8gh>;yR2mdQWuA69E`QPp?#zM242-g*60wLOX9V^{VTj78iJcItsTc! z2_Ql$gFQ`E0Erd`@mOa-Tz?vZ@L@}t1Q(gqEJ;Y~-pako|Hg3gQGZ-@VcD zh;xmwQi#{GK-o(dsrj=*C*oYgP5C{6S zfxY(u@Npsj5YEJ{`y)Ox<23RvHh+*nkOcjm|zJ_3f`W^T~zXM_Jcg%n1 z?6eokZ8MJ@r zrZo=(Doc<+DEeHhIP#Ax*wzpC;~A?IwTp5F@5LD{GLaO;%zy&Vu0pfszWvWtnZ>S} zJer}4A6JwQ-+#ILBy2FHndP~udJ)Gm&xB0WA4BpZO%rKNsa~e{p zd}2P!#}~_wl8H!6_PqZ5rael5AKjSk`TdFc49^PnQRRw}aBQK<&5?y86TFGE0O+S? zTv*fiVdx)xt#ZTEkcFOYfu#T1Im&ceeOjex6O`(EM}ISnDhi_Iu^k#39LI~(Vm>RI zl|sWJuPn+M`L}i**Fim33O5AtBS(QJ4D9vMbClb^SKQ^yAFfHnq3^H&8%D%&@wEr`)*_!NZ>o8&0+sOC zx?MPH7kTJl9-il-Ad6g-DxJfyIVpup7`nN)`1M3uJ+Az;en0+yQ?bR12Nyk#hvNKs zNAgd|rRF+v^_j+IL@pPmEo>Xtv!RCxV1eK~rEP%l=4g^Jh}c+#=_Y5IUOG84>t%-& z3V+=*{X@t)amb+x9--XKZ+G(0JP!G{!BLqAlQi{Pl>}72p?8*V209BeXjM03wO<;B zPQ5c|<9$B_Eq%rTA@6Apu3?bilb}!JQw_q zKR%%UG?dmG*L{2HRFXg{phs2h`3Hl9gMU>T2Gu5@2XDx2`!rXUs^8(^Tf15yPYhmH z4KBmu$qwMbAoB$qd_{3v4-CsuZ~+ar{Wjcqq~fvmzE6aakE&NY_Gh#@#YA34NYy-% zgo#{aNsj5E6xsans~GbbNe^smB{V%=5`k%P23yLEC-N{Is&aV|p>UOJdg;=m7gA5n z<=-+`pZr^fJeMoHW?LkktC@q6X|?7d7oV?R^Y7(KV-B8%=5|jdYyGb8UX!gu)%el^ z79$c4CSDn;C{Ksh77r!ddE%AtZ(jWuGo9~vvz7;kE&??%mvJ8gDSz!+ZEvGE5dPj@ zfzl7j?lmrkK$1%PA=fu`rM9amw^ub+MQ(5st^^}Pn)H7C889ReXnAYXZud(72am_| zcxIll5rwHB3g>%ba0Q|$h(K|F*d3h;v6lY%-k*cL!!rm)p#!?A+7bp60V*<3Arw@l z4J26+2AVKz=yQuu=YPxH;Coy=`+-sMK?@Tv44aCiG>9GRDJg*(3hx&;A!r@ZU2`v486j|7xShpk>ByCi-+ivF$$~Q}b^$coR<Spej zu7$8&4r1PDX{9`zvN?|?%ZcaW-ooSIabe>zF>*!jZ%NB2j{qsnyEFBcOZ9G*>aCXQ z4PmN>2&w|Qq8zD0Of<)p0h7)DuIgzM%pAY#w|^DL=v5%&)WEJoS0U+MH#I#az#k;! z>(K5TFJVIxsF&7oXzAbZR1-5nZE`m7U|cez@3YIXQx)aUtn#yA$H z#iM>!B$AgwECsQQg;yg+?aJtrzf^9uf#{}Delmj$8$ZjJB^fAkJ9xho*>6Hw1q#dD zk$)dt>kH~^EH8;J^0_hS_T^PE@Tp`B{V!{ zMYOT;BB$Cm$$cmo19d+(R6tDpCkFOn&J|n|v+nRo@R!ZSoYgpE;6EJ`Ovka4#EC(W z6F)Sh#0p2XgL27qsE%!ryA&GcVxy_N>woS&lvEBGag(}St=jqV6@w`y)Gp(}*%TRj ziy?jgA{yr^tZH+M+J?bhpeAx5D7#g#?P$lue8iNy#DtQ$fA@fYMjhu8{{%UX(|79& zhefiE*At4O_)6b3oKv!}V$sbz!=|hVjs|0MF*YzU!=QGLDR-A%eh}WvDxSLYzkh$q zU9k5m-!R+uf-~KW@0U@sv{TZbHB0(Mq2$xMM}q88$JUK7rkAw^aZs zvMgg0(EMZyii{np;W?~<=Jy)PaUkOyabpk59S?*wBTpjQ+R=D>9TLuVS3#n<+t{2B zc;iNN=vBCih9y1vE`i?P?o7Yi=YRF%_Pg4depi|7$2SLere)gZWTLKaAk-@jY!!2l z;w&IqrCpIFPZT^m*qD>AEpnh?Tc4Ui4q@fU%D=zKkuPp4v8ZX4ib?-rN^BmL#2lxb z4I}wHueW*XR%{E^m5{>yZ-z&?WQ!G-Zh6@1bKi{SodE`2)i4z>ux^4A6MW^K*&WZFWPsV}Ks@#cOd)M*P%smrb%e@f9W z4o(Mq{{zh@8deHrZe(+Ga?gTQ z5F`%j1PP1;tq;uswjX#Bbz)7CGD-P-edyob*_|Op@+jS1&gny6#NE|$cIG#qJLJJl zz=NMII6Z&3y!h%zDT9P1k&J@NO(3*lu@XUKVkVUdF4w`^@XanQip5e19)3JzO`BKc z;{D}+FJJvA;=!w&B^IdiKm<}mEE3U?$_}V}y;y1~!oO9ErHoB@w~$F#9dOca^YTMC z%y5u>PIvplPjq;@kT#@0wyB$Za_`eh+IIhxYZ_%Wz4?wC-qS4In}=1ArL~hF5R6OW zStyV^vO%N`lNMRN&dYUP-hk*vhFO`_H+j~706~E(tF*)c&|BlEt?;?8tLq}$H8{)5 z{)wE&VOF<2WzU`)?9#T*KO?qU57k0UYGGI+lM$0wxZka^Bv#h%=S&I_D6a*t!^;_} z5tDHgEC;);X7oD1NNdR=ImYO_#nK2F{=Nkzcu1#s!c$4Ife*P;kWwnFHtwmz{X&F) zN3380L4_Ti%H25oyaxrsbk%-G;=&1nCB{f^djY7I#a5ERRy+41DE!c5n?r$PLf4PI zp}4`Pg%XZzRpT5^s&POyi9kI6K1_WwPnY)P%6A9_kD<#tO zW~9PZb*P(+fr@n;{&NAeJhKgMOKaMe7Pn52)cxGipSN}1wi#%*q|LOBqEy9tldiHg zt~so1c#D34ORKaY;zb5@`XfQgIV&@fVp5YDJk+bKLEA~`dDxQ;)i|dU)C^01sy~&H zwb@aOG|U(SCMTFuWOCorMm5-Im<5(NZg6pE#s?L%vF`R<&$u$E$0pvB&7(Y-cBM1? zL`-or;77PU7q2caK3;Tq$~XaURe@14iy|4Ub{B8o^I#2s{sIwRfu-E~t-C;h^pP{C zB6xH0cgiJ0oTRlZ0aDTeOz{YRrV;ZanCAEv34mmPeXBI?`gSfxa%LcoO`x=+gB+B! z;Tvy9%h-b5cvadjsmXLklL-GKG#QUH`EnK#4a4{WT^h-R00r=b34T_WS}>u{sWO6u z&XYhV35(_Gk?? zGw~kpV}o5C>@$X7rWn{$en@r*X~alSyo53eGB*LdG*Q^r^ZIuQ{$Y#k&-nq$FFOw) zWX|}(xWE}#QJ}40+K97p0Zc?iXX64?5kN!7V4pI2q%qBLE{${E$HayB2*Z8#Ctjs9 z>bGar-$f8OU(2cH?lTL23g*2uMRkK)pw=E~)d<_|`PYmv61*p#Kr$kMraYuDQDg)p zKwH{?b(~cr2<&+|sm2(B6r@3|HPf*OAX${0@cEGmp{qUFgub3DW+53V*aA<;dmyOa`E%(CRVYb+pge7Tjo3^o*=pbB zyZql+aJu<{O4@x|r@PG8Q1>fZjEzeHIoi@uSj9pO1a)J+KBPr6m!||X$8`6Oi+F~( zWkU7%Vzmflb1LxLLtSD4c9Vbd#XkLU?Iw3Khk~Ug31(}>_4Meex7ps?9xfN|W?e}~ z0|`3|(qfW)OmB;S|Cu>w2|JQ_a?Xg=gz;m1dN_ieVpYwsdNK=MEtcNDgq|yK;YuUb zC_zmWyL2~vZ_Hj5WX)-Jxx3BV?VKEn`>W;>jeT_GynlUtFd@fKUB*-Ae6TWmil(RV z$&>J@lkj1?z(q3(YJ3K9DY5=K2RLA79d=;Z5n(sHB5(bF?F;>A4tt0xkrhwEUO#x@ z80mTl|3YJ}EIMXblfvr;yq1^#mYDOPfGhfu{}~g}N$`b_(Bg^r>i@H-pec zYt$atrJwViZyfHZH;{ku^oo32`IZ9yQt(Un`?^X|6yH7=c1DG^EC%<2OCHqwgvsm7 zsYgcPp}}WK{SZHae2<9|#=gDidKEsmyPd8JN)1hazi@0>!lY|i;tazx44QnmFYb`i zig?fHfGrJD*Az)WGWaQOCb*H=M#<#BB=o$)eWP?7VxTEagD6n)t zp-xo49gYu@5C6*013JZkFqO)qUN2$hxTs zc-Q8|I{PB_ef3l--_KF$-0fZg4cJLn|3X#JwHaO(5c^8VS(xD<^jHXFcfjPeTVuAOF@X%M3L)gaHNV689NJekGy1e)=6pgwLlL%cX12Q!-m!WF{ zDSuj7Z{#)-e)q2svH_+$nWlN^T3{Y#0ncE9Oct0ic7bF9LvGQwuxhCxseSAs|2%YSfYTzj3Djcb*g>UOpr)3d9rxp0F?OKVxK z)=5RH+xL2wCQUN``S?5Fi$`7rj0GAQ(=3W(Zy^E}tH81KF{x`)|Aey-I181>nV_Ev zAER}`eKA*iAky!ZBpWQTBo_`vm zC4E@rbN6Fu4Mu<#3K{o=bgyKZwq6V3J7 zRN2ZDO_IA2r{uw;v#e=M>e`n{zA<&b%?2WlPlTAI!#K;%UlWJd`{)u^OsaC7H+#^T zvGIx4n^RO${RIvS{sg`s@w^wO6Ms9H=Hxn0PEC$`>|=)2fYd{6_v^QFQkQ-F;2z() z$B$oc+4NZI@Y$rk`7NbBI{6}$UM0;%wVI+}`Str0+7Wld1n(KGkdmobz z9^=8^Zu@R>JdcV8XaL|m?x*O`Vc(37=0S^qxiJH@e}47io6GI3@!E@TmC(p5ZjC1z=AvuYJWGF??=<(df;KsR+(x4qOgnkvbPZaySQuCZBDF*7K# z7?;2v_ka@KyhzOJjo5nJSUCjQSBBDN1#F5gV&3MSsCz`LOg-}p53MGeYtikzyJPMI^f{i^43MtOQ-EpsPlq zWeXt{Kq0g#mZ(selYit{L(0Q9V{-D3;hxChYQZ36NusR3sX(7W^VvCQe)u<8g?iHHh)k%SH%vC*3xHuwAVcr zt(9bfKU#B-MQe>R1;aIHSJv?C;fznlkC`v&+>@Hz9Oo@az{$-GMy84y@?aJmAt#-h zvr)9+L_iGbwj&cYf{0ion8#gD~eB~%zfdLNv3*7u`j)EzIm*A=mdBp+1!CCl`1Ct^yDd-m$Vl=yzx3n?vK2i#+q(Y{p_mpdgv@Or zLbxjG9)GU_7Qd%k>73S-A02&LbLl_0lgi{tlU>elJ!xBvKFu-wMoFXN-W*w;AMPMt zDs$XF(hP5Aa#~MG@%vu2!)-ckM)3&a?;$;RN7VkY4sOA`_Z@V27ajn+9Jk1Xki0(` z=0LgMeP7`!q-k)!;HrjD-NCGo!F};G{1Q1iwSRHUr0_AschmYc+76^fD_iPIDe<){ z+I}djmA$i(M2u2Jysg_~u)lf@2~apIi<*W|Qz;@jYnK@nwo_oW+qfC_V&ikuEX$SE zpc}ZH`;ahI8NxuP`Az@_QjG}GwE?3yv>^82x`!ya{iGgYR>yQq&M3R5!UZ*kQ1lNd zTYn@B80_RG*6)HNq(ILRy2a%gtkDsE)mA!mn%JyNl`Zk3H4d3;(iMKo?kfvcS!Lns z0$!BB0QDVwRF#$=*abHIX?_DtUO5MVs2p&T3=Yl>^8eYG69N*^P+MdL42ft zovjjro+3SKN_^56c-yEp)PfU6aDQXie18!LKKszPam!F-0inW;B)J7h5@hhErm>~{ zM}Q(jYzzlZ?OS?D3r*{VC)tle#$!SwOx+;g<~0Is3e?x&=34Suo~<(bKLgl7aqyYA zN8Ny^u+p9$4p6AFO`_;}=NQr87>H{p*bYGf$Sy7Utvw5fMN+5YZ`v$pzNrT&i)_rWK?Y)%vig z^IjKp>52O{(VS5Fp-M5`+wl>nFTPzS-M%eLXePX+vtwkUP0>>+tb|P=y=3@ zKOSxd!9h*v*=Iql_VIN}3R{c~h&u%Vzb09tOc$;Pg?zoKOy^K`L6L$*A{tVC9^b4W zl8X_)7?ON(3#TH2-lC{efTU0m9YZpX!Xc~9G^W29C`cr_Y&?l9TI^Arzs(yfb~MqwtO(@==k7Qo(2;I3T19&b98cLVQmU!Ze(v_Y6>wi zmr;lT69Y0gHIvblDSz!-OLHT)5x(nJOgR_oKp2n4D_g4arYec;TPmuut85=AiCS?j zk!y;I);{F7=fk`RK$szEQOa)OlZI3e(B0_A*WDnm-c`K%`w4IV{^k7S%fCridFG8Y z)%kTL95LsqGRiUOb#;DK{V@5FD|`CW`M01om0{j07ibNWN`F}2r8_Ft%cmuXmL7~)=ka=DL7|NE6~_>BN4tn`Rn<~FDC*o z@k&%u60-_25NFnz>hkX7ho5+Lg@1lqai+Yh9&6ION;7=m)ou09lYbQS)UTujBO{s} zmo3jeQWCfVnSbz1I+su>8Br1QQg0>WysU0%I~vh0602}38mXody!(dk`vOa#A_*$N zq-e<&h5aOR_;`+kYKw~%aRh&GMeFpMZ6m? z^2M27)o|hObMHEq(woOL4FPPIe+(&t7OKh1=CvWW1NA)+cEPr zh8dq)C%jk{&5CRAN3h4liDP9zT}r!Z3KQghSQ}Eoj6J?EqTRD~O$kdAOK?qHNsg12y^2aP%p!)tVS>4n+Zn}lW>$#>!&QuW>gXg=^MwLZ>rHmS zaX4>t>wirnWgJd=lk}NMQf!jvB~Ma_TN(y#l^8xcqoG2(&(YQEne^}y+55Fp%jKq3 zgG@rJgt8H)tRK!l7P_<$)@%J4pPYJ{DpPChT|4#!&jb>5Vz+M^G6b!NDd{;A7HL{) zW|VCxA8+RC`RP4|lW8dhxL!xOGg~D1S9~=auXu5Z`>Sp2dB$<}Cc7$<^Xi zOlI#+r(&{NhR56H`*zEk^l`DdDTpb8RT;nCy;nwT?-0$aCZOvige5#8Y=S%@n%yU& z*F4R^-|hTvzC4w7vbo16)@~?*kT5mb=v8P0KK!+ah^F;2=(5>X@|YMjL?)74B*b+LF~DPoUu;f(g72w zT&fWOtzaB_IDo}Mk%242lCs5%0j+1q*amzIsCjTNOLa8Q_Yx(~?(9dwevGTMCU~1` z-nj@$|F!X)b#R?u9v17x@?A-ZO!$CbCDWn}LtmXvwHZ<%fCaav)}R?JK@c6L5r5S_ zIbfs)5qW@4^Px~MJp;prPkX?sk$i|OC%G8`GlC?ua0LyJZMaKyQgju>0kSR$bni=< z9ViMp*RW1cXvpaoZ65!*nv=<5yEv6)ekL8(h$idBS0b`BV~IKBTN09P$H-hytzru zIa#o7%cA%Xz%J1IsgMj2ZBbcI1yZLn9Y&}-t|c2oYNu^!TdXsc=76suv$$<)dAwO% z284Li)LHBK{lo31ExH!VB7aB(yu=!uY4_P~VZ>NGo=XR=ACiZzOceouM-{gDDT3&wm4#v=_J(f1u0y z=~(E}9#J~l1up5uq?6B(PQ5m^v^}%eLDb<)ha@iG&YUG$^s%c(v{UVckcZ#ZCF?zv{N`<))*$Uajw1x?%&GdmMzU zc_v4PVMx6fhSaZtA%7()SWiVEgNtV&NU3EFdxiI+~nFEDZGwd)~aaN4e(wG9i9?>ij)-o{cK%ZPk*5zsp%^xSh{T4tR#@u?7S*u5Lpvcc~-XUsG2^gxt-m&%I+nN9uVz3peO5< z2i{?NLt3>HZ++Z}x7%;x?RHMQVQs)FA^$tpjkUfzb$@Eo-g3B{ExTUS<9e}~m!Llo zghsCSOmO|V0|+Ht9bArqAXLCsM*e{y{(A_be8oqFnR;mY`8p1DcUubCScNbN_}#eI z;TlbYauXXPs*AGTwaN22<#Eyep7Y(oO{{h|sP?i-L+MoCuL2icd$7-0i|Zz3Ann?GtGxk>Ksf8o;^KD8_7+pBEvB@S z{Bqd+>h@t9aZ~q{HT(!Yd!=a9_Ypp<&rCtl(-i4Kk8sSpi0Oc3? zscA1`@7SzYNI`CL^>8_QGcvv00N^K6)Bn1B(^hQ*&Y(%Y;D$qTKyKD7V^N%0a^ZWZ?W%XOvp#hb= zw13w{NM#`ks_TTm-9!3xbT=eB3>?tS=8J3I^Vk))L4vLp@@*~jic&7R!byL(tSGVZ2Qcd>SR;qdwLBUIf(e>3sxxIFN z*b(IL+vs@Xvw`YMgyO)<*1j{)+yy|*jXP)}^K(H?6y6fyW)C?S{xiIF(Oh;7dg_Ynp`N1N=~~;iT=#30@dmR_ zJgfT^aGo+z{|AjI1App%^(B%w;FJFWf@gq@lg@G}0y#IAaqR&pf8AQ!ZsW)iefL*z zl7~o0*xUEZ1_K0(WER-BAO;d(cOMj+9y4o6j>H+;f1lIrrp%^VA}!0#Vqa{DDt1@b zSdCBc;eG|Snw^6Ekyx&yYZM|NF?>FnsK;p~l zs@gP*dNs%WjsNo$eyCRi$@|4-v8+5me_&}vALiTy3yIlXweV;7 z>i@(`&28NZ%iT?jRrp^$R-3DOTVWBFI}=mHY6A@z5l)1Sf1*M}S=~E}CR7!YFc!sl zlo7r4B-D3B*^~0xXEqkZ8VPeonS;jD!FVb$-e61SOwki+k6LnLXCE_*GDDcM1>;1j zsNyP!3EmTvf5dV1tRJOWgK%y-nP^=h`+kKxjfMX?an=R7e`R!nl;n_NgtI7CXZGX7 z;^(;E$DCDhDyuZSN{mt`IkT@;Nr`}|N==l}iCW1iHIWt$fE4StG5M zs8B6apV-9M@K_1sI2$$ma}H2ASA@bIrJ!(LlmtwExohgx=Ku}$DzmI+%z(ym8 zy*nPLe@S>?kK#e%pAHY;bD_H%B+YDZVDoSk9^~$`xWTA72W|+&YAIm8!?1z;^e*tY zA!W>|Uo!i_2~-}(4FHC;ML(==vkowFVE5;e4c-IAk2&Q~T-eTF zhaAT;p34EZAvSH~I@MSkA4>61+N{SjEGkejT|C7lI`~iqIqgexq>j|jZWktspngp+6SSyP*gT%4TyL(cP59+Lu(rFWf7QASb)rWEjkbk^KRL3XU2$12?*7zqnne>h zO&vMSLyz%1Z0e@Uh9pAHgC$bWv6y;KnP{_w(&yA04es0$NIFy(I@;IlVCONrh<3fk zr($g$2pl%*gI-&x^E5H;e@HWG zVLOC;Pw0=C{ro2}VY`&l=QIzEZ%^oD2DQfw`fVmvQE1p+W=V?DC_-j^*UmiX>}tKd z|I(2F+TjM9`GjS&`s-ZD*`lfO`D;6K*ao@v%@IIBZw+iT>4wi>JFKz5T0t~Z3DzDw z4R;*DJ6OC!9_mSrhZtyj)r6A(eO@b|5naLYS{9eAzF1u!Q^-kTtvyj8 zHlmQE7SZF<)wj28`FM!tia6WG!nVp|G-$SOLBn}A5raS1VFtAdF#Yc^S=&FV@e@Da zG@(OHnSVAy0A>^&=zfUgm}O7u8P=pc1ZzFRrr_Wh8-j2;Y2bb;;9X07e-Gu3O&Y+R zIXyOM;7k&YwpTdx(NrFohUnom#Eya-11ZP4@Hw|hWlNBEh^mxkyD=ko^^s@CJ|4gO zE#mL8%P4BvzL`7Si`jv3jpsIcigA@^;YUmxS4vE~%3!Z0*0TmQb=JHKTh)Oz*yC(f zOk=BdNE*^MKd@DsT7{$Hu;(_JWU$eT^Uca_SBbHi2>)2*H8C z%B7RdNT_<@{`+hPu3AV-NM<{y%MxJ)gYLpUKlIeL#rkCEnXkSEe>vvejn?9v70FC3 zNOD3!;$sC#q0rkxD6n-P-s=hyhcmI3grs9r3zEqu%o`OX2PQ9}f$2}j2PRQ29An%h zV`ZdUsc(=~CJRCt;ohe#t%4~gSh%y4L|O4G8Bx8-X!I1WlV}eUAgYk-N{$vTEI*}k zsY#V&sY01L;M;DeLFMs1HteB&&%#!e}jO~^?LP(CR|=s)pfYo z&PZ`3yz3Dr7@H>1Xt`)M_2Y*RS)$9WkLoy#{A=ZPTlrZ@#;a1dv-FbtXCZc%qbeIO z((KI|2hWmy0c>ANkiUfjG3WuTk2*Vms3ERE3>d{VdNQ2)*pqy3Eq4r8y0sL}*u8D= zHn=i)IFDk5fA5;HUYlB!fJ(=Fc3aSXxfd!a2K%Sc{RbwGd2${Lh?*8u;yo#@dmD;7 zNI!5Lodne@N}3}KsL4?gls2yny|@+>|L9(lF;tef-aa1P=lQq%#S%7g);?ZF9~P)D+1E<&^Opw@l$~`^QcOn ziDnq05l_C&;^O#=eYOp)V=wkGBA}M=Qx$QZfFExLTuXk2ki2_Kde~bFG@hV9r9v-m zu5lSX2X3h(Rk!CpW*&xx_sxlIR4n1kM zHn^ENFA~N?6N45r%#=_v8JqKZ);Do~I>T!3#p&K3dlG~VJm~=+0h=PS#hkIk-ouS@#4Ql!(Bujx3xJe*x#~ z#9M$XgY<4wq(a5`9!oy*Z$t$48GG9#LIai;VJx&si1KPbG``(mC>8nj`+ezucUe)* zxek0M6A7EO(1F?nL^Pur7+q;HI!8`06AxfZMgG&%aF`w|5>P4z1U@tdncMaCtf91u z#%Dg59phBVi3R~!V!|y5W>1CDHdr63zzioX0!$8%m46ER5bzMiv8RoJD7hE-8d$6N z9^i**Bs3%}od6aSITCK6d@5!`rHy231`x#tV(TXl&aSbp?DR&9yyo<0I?y4*0s^c~h4zgi$s=>>hjSQ z;9EpRMbGZi(x9(CRBpo+uPkdPfdy6KWx&8e? z5{nqxrwMoAY$;vHZfyzaNe56o6L$V{P$(o5Ccf>!5HzuYLgJu*01pQe)U&2%-&&ry z zf?7_4yl9tw+miDXW5ceqtDn72?hf_Pr9tL zG(4{x8XVfH<+*?K%N4~Ky!<_LPP+{d@O-#yHwb@&1q$u}^c&)X_hf@07d4p( zIoAU^RMZVvzNL|h-G#+z#PKS9Ct1G)cd?yd&TcIYZ_d*2Y}wK<>{|v#0G@CARoBwI zOG|E4W{&aAf}GEa-_YVRTP*?nctq>)Vj!{FLGD293LfG$(vaX|*+P~r+{zY?ZVI7) znQc=S&6HVx&LdvZ0I*_}yA3#Ff%`TP@L zT4m=!6fJGbhtoF=mwjF08EUHnVM|UO3{&Nhz2fmewg&8RJ)w*ivCAMw(F0&n_KQT&I0jas=|-(+KUkrma!EOu3l-8p*{RLl~oh?>4J~NHsw;J@{lob&rlnxzqa?6PL(K;MtaJB zVA~n6WK;@jmyJ;3s_kr+Z<2qs>w^eCRdEM4Bwn^)=#M&0%U5CUX~9 zR7*}84u~%Kg#HmG@V-G8m-VIsjB7~?z50g@^Lx+<{!==g3Qn!kto7t>`>sN`rIX})oX21Chos=&J-)Spls>@TF6ZsShMWdbKvSI>v2gfyMac!PniCAU<#1b#&jI-nmcp3@nqp`dJ#dQd##IC9#i!r-TANL)~V4~hL> z;JzJd|EIbv(YZJVbks_Q>McV^#LeT z{rTby=Os8&YiuYoDgQM?Y zf>rl@A^$&agRg>v_5)r=D?y)s$j-_-hg$Z{%>Yt-sTqbZbzfoxD7;Ovd~`OL@dOqq zGcHgHd%w-s@zQ41SqnGq#^z_XY3k;HDpV7mwM*)c98!@kd*9ZpzOzL5=o+~gWLV(e zoJviqoDSj<*gRV3$3tuXb*qyLOm=`tFzGH?Lp( zbb5CD=FREZ;j3fTEpm5d{&BiZX10cZw&OAUE{~m#`*9m5Guw?%j_Iq)lj&7?nq-b% zz5Lhli?dgU-(0MxN6yekXwG>N*?@=7PoIBN0T02~POPU-zD5@{Mq+Wk!x8-8j^Bpy zWM=grDa1*!cf4_wt8-uaS%eoh}`C>4J+1Z(AW}aErsxnnn<<)@-$6qH0&t7Wk zNExBAWet>xr)WbR4F-y34v}t{%EVPJkHYx)YVzBGiUz8Zf3T@ix^nS<>VV&k#LX}S zzc9leh)Y@gUNzO1uV4Lnac;kU!M|uARZSl^(+?G-7OrrqTuDt`Q?jp(&`2Ni;}DGv zU1&+Y!oVuBhFpMVDB4PH;Yy}$-_*^cAWRC6mTh9_Xl!cyCmGg=aB|a00J)=ak{R>G z^T~5Cn2J$nr z|57r}&=8d<`^X+FT*)6iL{Tt3ENggD+j$_y3)u`OB_(QE=d6Y1M?J1|uflN}l&Oju zKm(8Zq^Vucsz6uAAYnv;Er|oK{0-XFn)2a!_!c?wr^BjgpEQxwV_QwfMd;>oJ|@ht za0S9@T+CzZhJGY}62S$*t~g6>h&bUhSS1N8&TxF(*SIB)_{^3C!iU>&00Kfb#R2_H zO5$=UG)O}D@fJ=nV|WpZ^IOVOop^)AgOb?ThE zjF+YTiR^6x;RdzFd4!68<0wdFh_+H%x6F%=N0(c;aM<5AGQl|6yx2?89!KA7PKMY| z6d}d`B;;+m45+7*G@XV?DzQ^k>j;_fzz=g%lEm%`6Tmq>h$N?`d+(F6h3bkb;t3i{ z0EW1?QNq4S4!EpT5O<{;T#?*CZY>7~vFfWijGQC`*foI}f1}3gM8jX0IRyUcnp%=* zjQc9NI$dyieEbuiq&=q~Wj~aW-{L(^6{nsUUF$)x@N$zF4`U;UWcK(0o|z_GBl9d3 z0@<<{a=nG$Gk^05A7(|6ta9RgIgiNp2hc~H0nSgwECBzi={!50lE{UiT}f-aoSSq{ zFQ3ZD4U_V91kYxIY#|*&bM~5-G9M-QnhUd&J?)1%NYkBB_t3H`!k<;*vJ0$efYL+% z%kZBT8--@e)?_mxLqN*l9(up{UOb=B^Bxo1Q-H96m{nZc&pbBys1{`Ry$r+_~1Cz10)z zv(AAkSUD(hA%F{KY{@b1V-zV4B@bNSuCf6m?V83E&`#a!(gEe)*-&UO5}6Dk;@A>Ivu`8MfoVozNwW}N!H?l=@lfucBc+bk#=v@LdFZbs(705 zYBi6(bto#z97J5#ri3p5*IO?<_+n!)xoN-Xs_O>-5>#TjD;*T|bq?X_g9+^MZGc8N-SNwE ziX+cj;xWQLDxhq2x8nP~gs|cpbj4Bb3EM0Yvg$|Z=|$?veBdhR;^$bc4@N4kviHfp ziZMtb1*#7$D!aL_n|r)i6@Z1jH?d*6;Lf=F$#XwM_S@$K5qMi_DH>VIWbf@Y>s6lg zSIef?-D{5p+`7D1CV?eahcPR3gAK8&uJ|gZL5HRSGd&v?y0jR?;+Ny*VGlZ`6kh!L+>6#t^|o_lBV8?zAGszyh~kgymz!Zcj8wjK75pUYoDGa{y;4m#r0lx{oyGJpF3JbX=$n zw||BMSnjsfV9_n{nF&rS@dS7$PH>eCmV!@96FKuDQ%jj;zXd{C$SVdFR7bcA!(K|Xko`85IJHnd2KJw-hu?x9 zfZP5H($0eHPxUpE)6hZEu)CyOn;<< zq2trosN~V9d=b@6%3$~Vh%ag?^jY+7zT`pdj}hlRF9Pf*(&9g7-MI93>MYNSlUB4g zb(IadNX<1{dQ%70)@*W-Nl4*krxoOw-85zOMB}1C#aW}x#}0c_ksU@fPZ|$Rb6#b1 zIc^y#NJ*>qu{T~Wd;?$(6yp%S^TQ=kr4=ZspEbr@I>d9IABQ^L2_K$hjm|W)4x`w9 zH9-guw?>Nf{F6S>f;Hv^euDnA!)z>}eJf_X&H$=G$%1CzxMn!e(n$3$eubteLy0Gw zbU=i<;20|0T*ovx|C{tMIRrg4JgRz+T**9~+WaAzjHJ1Pspj2>^k6hnPSH<(HVJ+s2RjBI!$1Vi9@A<;AG2T}uCvHc;TTgJ5&8*#3wXUPj#wOV1a_0PZ>*_zj6}+w^FH$y z>A*YoJ#Z}rBNX5jC0xZ!p2?qI|GBTp^aYvJ`pZyMWqtm)r&!sQ;W55xA%}je`b9l2b42;$E6~gA+K30I|(dzYL1>z7U zw|%273<|*4KJOl_wiQ~OW6r}PyoBd$8;yruy)PWNXJ^@y3ab|`=uGUVtI|P4n;-O( z!_;r3$^^|Hh7B$Y9;mR<#KU(HCKbFwQd19J)ljykSjV$s>3n^yGP{lru;LJ*UF_IO zn~nksDlm|@mYYWAia7v_j*@yNqJ%Vxdta_I(*wZQK?)qZQXlz|+&Ghk!ageKhfy~meXZwZqwRfPy*dhUqov|2DgQoK~)rh`Z6i$PMcl8=imI8hr2P)Ifm8Cm&y6tg`6$% zWeC3)kH=?i@L|(Fl*^}>5Y_@iJYjU+z~qABAe+r%r~sv_HR{bwYxD>cfCx9GBq!JUMyS)~ zyWq<)3{Bo@*c~RY!uIiN`iFvRe)xi=t3k}(Q6~fPQpiV6+;hmej{FDK+_0DZ? zEKYEPoQ4N)D_nE51NDV@<{qK=r*2fy4+hIE*>SacU5UJFcMN|v_yss^Cm=&GHKqZu z^~c!jH9cZth{x8jwsTi73XrW>iYT?8SFP}U51sSGxyc^ywn4Dj`F(x9no8g5cklmX zbY{(>`gyEI_>doRBrqE?&nwssm~uY~c7$!~Vh)a*E$C|CQ5A$~edTR9A>Q3c5PU}7 z)qp|pr8w`N;Nd7u%lu{x0&tubLN}{dq|qXjz6>FbWkuXZT|%OIf0h5s|A?5A{JT$K zWiP&CuF|{C#K$Yq`!u#4QR(ws7wCP!4hjYpNTQn1$yc8xe!D3^xduM3m z8!2KG^pmU`klFH8on05pl~ujJl7&;r0h#I~-ZMKbDhgT=l;pGq8mg~2iPq_ zlJ>EvX5K8cP+xex|7v7X7V>*}3KKU4IVsg4c8^nrrYS=Nu^UlPe(ZuW7nLN2&f$)+ z666R-bR0lb`&gcs4FDVhKj0M{r7 zl8(4NGs0S#5*aOTo)Bvz^o~C}`VLf%eKQ6KwI-Uxi|;O1z68w8xN)c*8Of{Kk}1y5 ziKNJ|zx_3;pe+)n%yhfNm?st}MFBJvOqI<5MUJzdeLxMB_wND8nFY=yFXDQ$30sme z00?2+=5;Jg0V}W(k)8D43`J-gK)&+^r&JFGwGWTBQ~-n~&)SX*zh6px7R3$n!N>g_ z96pAHGyVbev~Maj>WcFi%~wDSY`^{`#0&lnl9BSv3NHbE;;q7_B@6E&Y3}Ihj zRxEUIOXN_Vr$)pg9Tf&8kojwV24eqsH3F-w7k#*OYEK%id6+AS)Pp=CzGDzo*E=}Z zba-7iC4*Ksm9SFtTBhB+Qlt;cijjlvSmo~8mZfP6rv7p;b|I=Cu)`PWNp}$BBXw#M z{!Qx!*1>7Qh#3Ph)Gb;MxDRdk0I+XmHzsGI8*)}7u2kVF=%eqU?_uukgHsvaJw9l3 zH>H$>paSQZ8KU-i8c|mI1#X}0d|;Sj2rkFu$D}hFxUr%!p4sovMNb{(JNBOSG5+52 z==g4=np_e6im}rmjJf~n1k%ESY?c9p1uUttC6+knh;@U(a$}hTBCjrqglC%6V<%WHriLOh-A!M3KDEOGHx~22BHHp&@qWJj8qJ;`rEGEjWasCIGQ}yAPRU#d1kx2 zIQkQ|I0P=5?NJ*6HM_0Ii5AtP;Eg#OGw6vyoUQ}0hfh_Rh8j*pZUA@U=(ZJ6-|?l8 zd8Ruiwpc-Oj=Vq);w}G=BHbTwdUh1|v>H_wnp|?*%qfo32_M?xsU=KzrO|2U0ad&f(?wzyYGb_XW)N`+vM|zrPKBpSibW4CSGp{(3lVT@Mh}u)BPdSEsw|Y*(8A zA0&|l52Gnq+5=Qh^{s0@J?(t_iRz9KRdYjI|9y%@(rt>KTr*sC_|s1$2DfoF+`!lg z%}_ILMwCqns9tyYclxB{Zm@|Y`2LlxGq}GE1+y@_2K6r?o{YqgC{R2qJe0q3dEv|~ z%fTC2Eye4lWlS&!rS}{N3b*L`Q+M+eUTlbOFY_b)Gt`Oegz;<2 zq%Fs*$1`UTd_?8)+l%zP?Q_;`ix)o~m>M`L!iNHSKE4bvW>P z(^u>PUu_p2+I1J+-XTy1b%1xnrp}%&YQ$&KH|HzGNK=3a9^ypOkOJh?G@W)VE=2VD zubP1yhOAf1Q9r?O#rPkC=?CRu2R{LQ=mZ%nauEJ+5N1yW8z{-Hawg{&w0jT5GX|RH znOo3@Q1Lx=ROhHur1XkLr>H+b=@m_;PKIuPu6^_8$s2$>-bZsT%L&N7h+mZ&(6rCym8`V0%=|=h4_zf%N+NXlN z4~@Pmwx=I_KdW4c9#4>q_4zW%xRFU-z4Og^zmMK}k{pj;RY~)0%0i1e0{1hNzB*~)`KbL=&Y#KnGPIrx{Em>+sw1j5G0R+jRi0(k|$f~tj077JwwHETv$ zksByv+0Y=348BTUJ$p_Wi)3pw;_;Ftw;{e92c)`vsFZg%7H@Lybox=K#RCc;YgfOY zFHh-ihySwZPd6*gte`S?jdhCJqgSGc#Dvv#d5>qkur;Qgj7%Zf+k>MbU1Rg%rDH@Y zVG>Qy8FQ%M6bu52ugF0IXjEpAM1=*6u0Q~<7J;B84&laZY%S&oGH0hgPDjE|N`!FM zpfXVsFEx^HJkgIfLa0RAdV~~Gt}jtZuD~coZuE>Jz*!yapYDK`{E>qS#>~aW`oAC? zUB@jBw1Bxfq{WDF@tVj^9ekJ?`K8}OS@2OY;8W2;4cOV)blzzAYhO>i$K28MBhuIF z)*-!GWa4?3o(w7awniPRUHIvFbzQuWj+t8xJPIh{K`T9tTIamTrW$B&c#JYN4tqRp z;sN9=yYI_i?qlvCYgs?zK`UC^-hejK+%{3hX2Zjv=<=fr{K9YNKSuWE0rgl9-5wZX zuUqgt0ac4J&Q;+)B8$TUITnwmk*gYT%DyiZT<$(6Pd6x4b^v@CUl&Jgc#?y#Q~_dZ zEnQ%g&FzWc4*lvG6UsY(w#TM4=6-0!4=xqac+hzu$W16NnJJCzAac8TIDlqs`d@to zFc@wr*SoMKwc{$HNmZN@m9i$3G1bEG;qbFkr6vgWJXda{?wXAPXyW+^IVE!XSBd_@ z5oZ8AOY72O5?Hc9U?H0u((FftN?tsTzXs`-3pU%@;5=^gH9c8vh#2X|ZsPNp9W@!P z)~?6co6g|wz~PdrQxy%vJzyeIc40|iJ)`28x5JiL9}rEk#beZ ztDJXz#_UuyHAQlFxtOqS46UC*xe6NUCJRGjCJ0`QD&lOb$(dg_GQg|PNjoKUhsLvR zF>ih2em1nbeO;l3Osieylp-T9w@)jkwd*9MXH;0jatEkC%cp*fn*UO==j-#0zZ5_Y zlXoQ0U-mp8itrZy(UNp8l;D+clL%jU`y=!VIsj@MOz@ZFVEI9!d{#)i$IfT6PKZkm z^h&$uY+ZIR5AcZW3ZNZ>CNo;^I3$eJmJJN^l#6|b3G|&UbH!k0y1I6M^cVXvL+m0B zh~;qEh3?DQg+Yo_$nG&oz0QC~_t;-|wTCB@Daw0WhN*0fx_1^Qa+*Ga`xQQEr!2>x z-W`34dp2Aha!8 zS;b?_qpQF0>Hx9FUDM8gS4}ZY6;Q(SW|!^LxN`g3HVv-ac<@UcdxPGbt@;>bO1lX7 z2{4iuhs*Me+Nvc#U3qYh00v6nCbYoJJ>#FEr|kq#XFC1!FS&u*0sTr*3-HNQd8|=9 zuyXt0%t0qfw0c>s-Rpi{OUWY9K{rN?*f%oH-Z?U1uz+{n9D_HnR{~6d5`K9jDD=<` zytRO%N;c^+u+C(+B=Ewc3ez`%_8{hz_s|4gdqk-r_o`o64TPrGk6+^iI15H6i*|>d zNf+)DlH#x!zn>6qkMg$Zkf89PK@HO`V>GW(ODmf z*I#u0p-L$u3MXlk$i%FCBeV>n)S~HJy-L&8DP=cx`pJ<2TI)q!=uaIAFO4S}wELMI~nop)} z8qlFWqqio&=lyd4uJSgNp;e^T<3=~n-n)}*B1ST*SXN;qHt||p9~yW0fjaTTa2SpAo`^z$CxMg4 zo%!1yS!(JTC=;{hi_HP!%Hh{tc0zeI6(9-ITUP{>SSn&FDxM(mAt_YeR{|%?TI4QH zQG>;G9tp|C9z|mwo>u`aBJIovfs7_(d&tnB@Ho+*OOCuWY+L{tmLkND8$0gNFMP@S zGo%@NOs{g`8)c7mGF|gEG!5~HwJ1kl?p}a73B5S~S9Hu2cxZ~<8Y$xNi?&$<7+|uM zu;5cw`QAIV4;tu{J7D^v3_AV&=GAlN`wD#XRVz>g#$_19Ljd%d3MIN*k||WG{q8rk zYej@?xu8;UjrrNh*wlTowUWHOD6VGEWidd?E?!Rk00GeJ=i6)v(`!H#4K%V>-K zS7jk2$Z3dgSWG(1i%foi=i?I)k!py7s}nuU@?}sDrKz&{P}B{!nEAw1)HoV6ojCet z#*2S8x$u!|$t^QwEPpjT{jYqml-Huovf`WBAGkG>g#RZq%<-ShaJmB_2pynDTibDq z3n}2X4rxAOQfMT)WESY-7m0jZ42Bll?hS~w@;r+@>Von4_`=Bd3x0G8$z)YFm&1dR zGTOu&*BGbhXv=DvZIAzHd|utzoI&ou1G|Fz3I(OpyyswSY-4?uY z^`Fb4su;Z%?^b!(w`+!@x7bjR+IQ49igX5A)F@kStbSB@wmh)+yGJ=t|8&N;S{n>n z@Cfq2dXyOKR&g2|>PH8tj7@S~n6#<|TyD7=)@DaClGR}RAQwI~$#sBO_>Q!dYh}k0 zfUVp0ZMCG*mHz=WyEIN?p_Q(cRkEt4MmGqDs7t*&Q5bJGym{4h* zZS?^JmIG^X{1@l|h7zrlG4Z?$Z&bydg%ZN?D}&3?`mYCtCX#yf% z290KQ0#!*dZ!G}ZfJdBGKsu}T9LrbLEV`Co_n1GdB0Tm$#uqaKs(Ae+$_)*&8z%Nd ziBLSCX8+rOEp>d2iS_ZCym~`+Ma?%tf}{gI1e=m?&UxSh`ASfPZ4tH#Zi%*~#<$p( z18jJs=clEpQ1KGSjueBO7JN-EPVX<aMEO28S zpG^|OdIiALqxA|397bqnZ)or-@9+Kz9Mo;FZM06Rc$sxF+>))_uiG$vc-O>8T4ml_ z?#mQcAaQQ{Kj6@+Dwc9=?jAr0i<{~+$i&hI=yix5>(J%Ee}(Q}_L<=e0*i7*N+$al zQo_yc_ucm5h}1xPZM<}x^U(Ibnk!=SeAW7{OVj}8cu;BNwweP2o%q9$+Roo8}$mk0Tki} ztPQxd;gxdGkX!u(>1@yFebwPIgpkz7Mu#E@6iWX4Qc?Mi?ePQI-%1;4jDHsiSYxV% z0lI*pr)}O{t+s&dqoIT6_(L81H-ZX$Y1%XLsdlvmNu_vRW{DDwzxJ9n`8TS4mLNqs;a9FM#1+7)*kMH$(nzF6jWAR3T{{8?^ zwJK$c{r#1-eh`;@b8}VbwC_d`kLmrE0Zt<3f$@vrWlxbRVv}^g{&fQ?vW=aL=Nd2j zuq@$jm+|<-g&HHS(>BWuf5t)7!OKhiL5(x%^?3Hi^@$lJQeg@9E(PzaUwS$BJ}~NY zJ--Ywj*IE9fXssu+L#56PdTEd$exDa$%T%rI|sTUd*z#AgxZdWFntBX*QN5JOmyS% zJHhYkajf>WTIkfl1{xQCSUaF&iN~27Y%0goxo^lvjN_Q(`5Gz^F0TKuCsuU+uTkTH zYiP(orJhKFc`OuV?4Q3PX72>kHlpIlhFTdR|I@l#|I>F4IOU&uT<%LRpe z2V%+M`5RIYUJ@7p4bjA6NC&QtcxZr#Aw9W#ZaZ1&oy?p?PC^gMe%G*kOQvmMFp=gx z5W$(ex5lSAEyix4XGunghRI&E#D`4xRfa*@$CKDG9-_nQq;#fp^ES9Cueh702bBGh zj8g!CJ+Wcvus_Hs5RF8eKGI#Z90p^pQrXb31(5||G^Gcy4VcK*=_`*KPYzqXY0Ox0 zo0-&$R@2Pp)Q^ZY|M^ zTM90J_{WtyGas~qggqVIMPx&yf1DILjgsn|_gS!LYURvb8F6Q_#_agL5M7O_%{qBn zubYhG41Et^rc#ZPt~z6`JcJU@^WM_p?MCBa?iL+Yxu=R3^%`@p58+1)I~vFC!g><` zYr$LX!Itu&tPy~X|m}_~JV9ej3+3hQcJ$f`Sb-`k7yuFEnMY)fU5mb^P z*huN@k2Z*1OOAQQ%Rw?M3HI?!qZT!c|H&O2sgiBm97e1d6zyF};88gXuf+6>L)YWi z5<&@3f{b?FKKW~~Ps->}Ar@YvCj^4Z6K})L45p2uhQWjfcO=KJiGN(!A zkiPiNKlOfQ?p#&AWRgaIrp6!XYo5B&?9-$doRf(I2>sh?rq)M1$qQpJF`e8=Y1>D) z)R8q=U2aaXh-?(yTmUUA&fcr@(`z=1$59SM&xuXC(@FU8b4@tgJh69{^qG(~vX#+AynIaOIOy^28njpLC% zj1U*<^}baERG%NqvFUt=WZ^V*!DOv6^}88oiQ(#NQEA!OvMM4uYy6|;;P(Izz2K^Q ze^oj^L6Ptgl%(zq9dhs_$fD8TmCQ;&$U5a@Jn+#+Sk}E;dgoxkER9OygdB9$rU~+x z!yUb>gY|Q>Z_TPN9^pdUG9xKtaHLEQ@<1R8>BtMAIk;Uf#}qu`U)@2reH1$BnCAAa zVa*O2W1!dr;b$r?9MQaaoW83OZ;XfH<1d)19ZtH5(m!Z1M_KGkm!gf%ywB}`g?JyR z5e_sL*VRS*j{zo)kBL^V!xc}cx~x1Yy<>2!ZIIJQhbWPS^V(d;#M05Xo2Q(sIjK6( z0#F6Zi}X%UpTd=cp^&Q4#%EEWBR zj=Z$2DhZj9syS-8Y^5F27fW`p-wt4Ti1rqnx$B`1j|#Vgft4)zB3GDk9u_J!HvDpI ziRPY9a%ys!3^N>- zh*_B)bbY!WSM^9Kt=LqY>aJjZTeix7#~}mx{hEN|%qY7{Gv;js5iWKqVc*rZ-J4nj zGGRTLJfHMqZmKL7T1pfTO>oB*Zmb(NI8yR6YuKvZy=|t?@k1H%(tJwaE>g_O_R2dW z(f8G<>lVbx^J0pezA_I0Uvyt7(FW|nr>a`7BR+20E9fnXM-4?`iNa@4rz_dgML^^&8C zjI(=vtKj&Km*q0%m#`OL({i#TaKMB{$+F*XZLWY;?8U?PKA9W>6-K-8x5?2GO3j7R zo3xKVMrZ>_A_2D)Za%`zK~sEN=`vM{`v#vw=b`I3-cs)F8S+jbcJ`QC&^e#dkFFBMH1!9K&#=uBw;D=g|_TSbjaN9IV^TE4F!P-@7#i5?x zYBBQej9bNcI6KCGYIqKa6-4>AA%H^NDTLN8X_+Ys-|5}z?_Pc^b#`2xsQGt7&#=tl z%nAJGGjIfjb&ehSs;~QEsY3s-H9$^R1bfiW z4|`b#PeI#A78)_DU31&uW#Jhn@a|vT@)cO7qKDquYFQ?Rp?pr)61|54xW6L?9=K2F z&#x$LJy#N_SB$QOPch$Jv4?Nq=m6iZ7tcM?difta4cUCNmPb2{DO0I-0no3Pmnn

)MQ$(NjKV7$I_Cd=x?PXcuT4J zWV1N1Hm+<`U}-R(l0pwV3h@on0h}&aDc;5;+AJ5gh;>gP{2Pux*8s#E@L1u292ZMt z4T&(RVD`DB>oXPM{wIy4LlceUn~L;?(dOHpMTHR!vGTakOeXz;uv(46J$F zRK<-z`iDRY=+IzbutcFGSX&8@&IDC+E&wa_gq{d=MiHFK((COyuc#z4yC)lXlCOoC zDyJ;GzgxDS4!MgS{ebz#xT~=*Tk~bF<#uOF(Xoy+{Ec;-1&*wnk|6AP38(5i9cVpT zS`c6;3zCtT4kxK3zECZ;8A~vUNNA>MMn*5Szi$^WW|b`CMJ$RioP>uVcm2JC|EtH|MEdy3ImnGC zT8z%}=MWQr&-O8;;%#&a4TWFmK-!MJros?m;mGcZ^tqqhs3SU!t;X5Gouc-^HCGNg@r(J3@{@0;X1c&5owV_ z7g9~Ro6ow*{WuKyxC{sf1$c*3 z+Z0b{<>go)`?3FsXRLgVJPr5t=^e(gtE@pSm^N{;38q zDZu*^Ol^I)2m;;s1lUh|YjQFYzG^6-mYr#7Ofto1waJO(Qhc}GkF1kq(!R!`zj9a8 z(WVSi3JtF-AF-Evk#e_Mx=)21jpafl zRZ`iF0wB$CTHBBi~o&eh|8^Z!g`gN_4ZBg;kIyK0vR5gg4GcdTz{7ULk z1HN4$iVcQfey*ql@kceqc@>&BW0?j>QDYrAUC>rripN~XrV~ar{Jcx!I=y%CSqz^y^5`Mbg=#ZQ zQtcMHt1P6ycC1*8YC-!c@%?z1<9AhAa`++icq<{4+Y2SIh&q9gXq2AU;G=Zph&Woh z2>{#VyUTXInwL-j^{XwSn&x|tmzS@EU0zJWd52-?I%oDyMp=NG8G@3Bw}M`Jd+qLJ2UusZjd(Vz*+ zf{Sb=TPB*|;0kEm8d~wlK@orYJl}jp8PNtq`0fBkpFd`9^}!L9bc{FnPjcHo<^0c@ z4WzPoEc}-tpUm0ULzJiUzQ0VIU>Be5Oz#MzI^XJW+7aT+Xel8U_XY`e6K<<|+kzw{ zr-^w)Zl#^Ao0L_y@*OQ&JD=rCYiOHup%l9`HAb4T(8r0EDlu@RYUY8QW91iYDtAP` zT$5Yx8tciUm%y3Oy*Rg_8KIGyG9b!Znq(G@@22u}6lao~o$0dE=DDiLX#6?a2JZ;A za(SSKU|~FsBUYHU2#W>APPRgHDmgHt_tmcPLFKY<3Ap@Ec#@UGE!QtcH{Wg4^#>Mg z)w2gH*ztJ0#Ywf5v;OBbRFb%RzTDc4gcXP!sY-7%g@*pH)L&CDjVPT2764@g58|6^ z8j=QDyo_#?Ld#_h?2u72wD}wYp?*$DKW1v%$|swdg46J%@{!^!;0-t!qzF<%RxHmS zG2xlQw4n{T>HEQNeQTMIiIRF$Rk~3Ya9#-yLVteSJ-KX)!R6o!;3?IVtTCN`)oxgK zunpU`xi`p)a&i&PZrGuml4Uq*(E?B^b>-jxcnOl^Bk|KZKDkkH>$v*~OhF7?^Z)42 z>HFj$Xke^N>}>xt;qB3pO*s0uUGlb0$pNLYXyeubgwT%cvL;CHO1L{P7gX{v9!hpO zw1ltg`{}KUkStp?jxRiUs$Tl3xU=)3x~_`SkiX`Bh12o=@8|o?4CZZyHoA6Uhry1& z9y2jZ@wE(XvU8z^=f_IIrT<2Hb}f>L|DTJ~0nP+>)l~YH6F_Gz!DiJ~y}M?9wp~-I zN^ReTUti2&;`0d)K))x1UOlC`$zg*m?KApg?VtwGaVwy zJsXM};m)bqr^8okHFmz$8k_sJH{^F{-L9#&Fl5JN^SNgaU$;C-$0$lG8wJClbZ#rx(X+J+z+CaT577pF+ zn0DZ1%d1|0P4Qp9Jgl$=lA?n@+<$by;gU;y05EY9^mBC=lNUX{5@5zYSF3HXP~5^b znvc-bhB`sGO28D?dQ8TYHGn_qnx)3LzKmKf1k>-&v-xwYCL}X+@P|15zU<<9@ojIdmsz-6P|i?vRaUT1l%+K7Am?1ZVG~5lpq#d1AS9j~cbo z0&>5itv0P4e6QGK(P!y~~yGxq%d{iM zJzSU1Qx@z4^>FO#tmaPhap4+kNfY3yn%@t_tR=DuMoOCo9h0G9ST z9~ORoQH_g%;QaIBLuqSyZYt0zDv`?QMkw&_c1FI513jC_kGndwP7psmeKnB4Df@kW z7vH|1nGd=}u(&sUlykJ&T7S43T-*2}>W({M=5p|TZ_VB7Q*yYh>-si|EB%aDZ7h*-!v00_7`DLDLc zyr0p2URb#wFfrt@$)LH@gxN=x;rczABiB6;`I55fy6&jd08613&*@N{JJ}T4GM8aQ zVsW&U{gkrChTJ9zJ`NKezoe7~vE`H*a$GR|bj${sn&5LMN4nhUp)582RZiofbX!#F z#&D$7;U_yROMv+69x<;k;LXiQHS@4Cyzh~+>0Hw}V>vT3A$vaCwmQgq;xKE1z7DKx zq}IDtCBT&29=}vRydja2zRp~T)%ES7g)VnS4x90}DiOcsVasN6$E3>>&Gp2#g+{NA zZ<6)=miRSz`H1M~@47*#l{FwoE5$eSG{S^$q3a~-d!jO=f z&1|L^9~>8JT=6CTPBO|e$hUbW#`9(In?-woH4{<2Ok5l~JvE$w_zA_fuTzf@*}A?d zU^1{i`@Imx$62@YU%un!PoNxPKH{HKK<%b0#&k@rU%_cJ7kU$nGEpS%uR|p*A5Fl$ z4HFGTvke!8nQQP{Nq~bn5O;}2B)kKLB1z(%{-#vE-qT~Dw*i>En#OVF?24o2hTR-7 zXW7VR^Dk!l^Jr{CjLWfSI0!!-Bq*?m5N`WQ1PVJiI=d)q0D5L#5=X^~LEIh->WE#s zEH`{<+P#GW>z}6s7*Mn19M3>K0d1t%56@kmj9s9t7M1qi7=-zl9OAY9M!Q?kQJgJ%ZX3UC1g;ng+Ce9X+t?D=X++{ z?>yb|=Bta9E({MS`*EqFhA78eHlCzjKKQTt^n}_szim;ps}u`3;+>;{%7zzX^}C|` zK3r-50FUo0#07{V6`%wHXb&jSis>uFS0(m_3l=ZIY*Y*fq~n(E0j+Rl96wfk*Xu<~ zEE89x&kd0P%eQwWLA9Q5hdh4N+zh z+2=e~z%G&f`c8$5Yj=vE8Z-__`z$!#mA;~$^D z2FcC~W@O=>5WeSTEy6o-=jQPgTYc`%R%-K4v=H(z6F$4#no(@ zt7QbcQakG0hCllxfk!FXiGnwm4xWEV)b7YVNTa7oj>%d4G;fQ043hP+AOe&Av{P%n z^AGR5Qg(YnG6{s!Dt0a_sLvu%6{c3W+-g}ID&6FR`SF!(ewnsk2w~Jv!+OjDBE?q1 zG%;Y`EyS+Vy$JL}fHP3BC0w)r{nybV)qsIA;U1)n0yoBrDRoC9NGDbPfQcRoabFPW zBE9F|%>oeA;gnOGex;@efaNgy`|QNvwYj;{lYMw+odWbdq;m9K5LJ0z)B5uAj=%PP zE*;zctqG*m8g;!K@s%`xc=ihh{E8gbN4zj-_5Hy}c0y>PPe5Gp?{Xf<+J!t4@5|3- zy<4UHiK!cNLhful*o_M4mbM5*#}*YxhVnkTPoN3Ty1^+k^-v2cJh=shbY)})Tj@J& zt|K#yMVC?;x(o7lEL{*NEexl;TqYSMjdSKW?tGQ>+=Z|m{QS3VfG@ck@NA5pz32a6 z@l<@dnX`o&CTf$Ah5b#6Fh%M^jbW?jz-*}KI+MV7p*)!5)tNa$74peUSR_Zcy7ibp z7To}Jzax^t%*DblPoE8rfau+>*FMeo+xhuaLV}{wMZ1JI7Mjx9tv!uQRQ{0%&8uYs z?G%zEQtSHId-3Jg)A1V?EaoA*KY3}A5||~uE)|6KALUcl6}RI*5POGW8syQBhTpX* zsCAsP#OhsX=$hUyOlcB_YU73Jtc^9F?`#CDVBuIv_cp4PdR&Y^J63+@-<`sG+vx4D zcW@Dxh75BC-3Q%F<5Dy+Q(M7I;VH{uXfVnY(No!nrf&y+g>0YSK5s9Wjuq5oEYa3m zIVON%YDTOivG>;WLi8Ajk#Q>+5NBZ+l1yALwEA#`y$^*-3Q{kYp}g-jM>J`XHqTImYt_`|mZkPa z1moFqf3~+Qvk20OjZGz##{?hVl@{OD7-_w!9BJXxpL$chA>KqoUY;=gne!w#SPPV6 z>3Y*Z-vPNQARa@ffypUzrrXYowwbkoW&CX?z2~XUr#c#1)3)UBo*D3>O~mz;o`0T@ z$(h=o`$mUZX>ZN8{A!D>lUSH!BPm*rK&kzCn(%6SWZ<~yc?pSdRH-2keWYRd7kFt4 zvUng2IlMwPw-dOGlY>Yr)Qvu_UbjJXRXHH zm6R8E&7i&(gAssZZ>U8+!H#?%*!*A`(TTk321U_!b1UnJLri;bz2fJx>6~m-(0{RY zj?I|>+uDw8Yhv5BZQGpKd}7;~*tTukw(U%uGke!t@0W8v^&jZ!>bmc>u1imxB$twf z1@t!fZSBk(3juG@h9YuqUF=8_)^~tBuk?2yT1J@Css2n~D7EPjL1QvtciMv$|BZmL zXAWtq>HeF-gPG5x0hCt|medeA@rEEaf)&DuQE%bA!@7G)0bk{N-)Qs6_g4?={>JL2 z>#2Qr(9-doXXABRCJ$^Q=3m=Ijm^j+ZAqCVMh5%wH_5h>wOFvcy5P9LH0*f)fv_(L0(AzK%T|Xd2p$U|ph{Y>KVcB;Z#~9)^2Pa`=K;g7jFZW~2GA(^MpT6u;RwRc z57~V)GeFl=<8%nxjLqRRjiw$WOFxYj?(tRwUPJ|g4))@Xu%r_JkR%|oaOvRcMSVul zKM<>WzS_&F8xHzCkzT&f ze=AQ6ND7Nmw_h{mco3HLcHQ-SlgxMm2U4VJt&dwj$>Wu?DT?t*WU=-Tw+KD5O0W4- znK<|TY1kk@a-Q1&OTzd_wTYbZC(wd>o}zG!Gtouy2ZhRZ$;SIF&@x}+EHLR)I1uwI ze>;AgpnSdPHfL2~uFm~xY0{}B=iJ2*JWwhjInrFyT}!L@Js`0Q{2C0nVvn*BEDjsV zKnKeZsqdx<-OL5>((rfSb3EJnZgUujmz7hJ$6!D9^-H}0S`ZV1B3+mH-Z{R=p=t*? zzZzgJwC+v>CjzcaN`!qjnmDFq@Al^W(guD>P5FkB$~(CqsMa!NdM!pbbua0VFF5UI z)}_aLUN$s5SI$mudixJtU{1B^siP$mg-EbJ0|#r;^xJ_n6!s%}$7Jm)7WlY7zYpxH zeLtLRIpD4Uu&F*OE;DU@-j^@zK(h?nXFylW&T*9U#`|YKr#-Q9p}a+H8M?j&UB2Xc zn=;f-3I5o5Xnnl7eYM!#5+0F_YrRfQy3ajdxMDfZIVT%}^S|fBk1uapANS%NYw8Nu zaAiol#q?E zaPAH+bV0k+|26}B^bB+$PfqL%Z|s~0G0&YKfr<)OYp7`N&2?%iJE^C+H8}(-1jR&-t$5Xrky`xp0XhZ9L=$cdLYq2zE2{it}Svvm7$uK8l z_$=>Z+GnP8FvMHI0oOyzt&Ifqvwl3A*#}6ZL{`RFn)F9ptCx*&y$1&n{MzhBX8dg> zHj{egm3-Ba?iK=Xhs|KagPL_XH3es;elz32{KD;SXdA!c&y%V}kBav2LKj8=YAH!1 ztSaJGB{YFEWe8Y=?2zl{^7(vY436^dcz~>u z7yX-@*D6Jrebiv)Dhs2LD!|E=mLYqzE1Wr8Q>*KUh5tI|5*4G$E>xpH1u-!TXHFUX zj@Uqxyrvx^*A}hrw8W1Ax_LREh<>=yD9#84-8|d923JzJ>k*Ux>MQn*OYibV9K44BLc)h25KRSBR4*8yD@eAbjot`YDTV#mE53D^LEjO%qjz zayNn=oryA8uk&>Tm@Z08(DJm~-$mVM5ka9zoKghEwf zI@w^lR{VJ_s9Ag`C6G zy=w83g!km#Uzf(lseIshN$F{GP~o1kd}4&z#a5;LUu9(8DI`l+c0N3jYQHtgL#7t4bMFq8KI^dc>`%8!U? zbhhZo+9v@`pX2;^J}t04Iq9!&&HWiP56--Ui689WIGF`X(bX4GE@-Jb{q;fOS(8m7 zsu>vxBK6{d3P*E*L6)Mr`m3MQM1GP*|Mp>7l0OgM0YP7D@0SjHK&>}@0h1==2UvK- zieX7|LjfB`KsTszL*;_O^Ho_~sE#@iV9Cd_E@yp^edFYzz5vn0MEY{scU{vsHfR<@ z#*}1#t(6I)?t#bo*`I#bUZAlP7=JF=w>#!!j?=0^e;6YG*ZNq}kNa9o7V}H1uTSde z2&fSTLa^t#rY)c9bOk@j23C=q>&)Lb5Zc}BxS8a-B+(OUfyn5EeA~UJP8OD*-*Kg> zIY&S1IZF9Yx)XwwEb)jiR}+vsSEN>;_16L*w)uSLDtO9 z!o`x1g@x(=hxD7VM=k$B??0I!HjqTQGmk(>BJr&C1;Bf|nGj||+P0dDOB#$MdReR@ zzP)4-Q#F?<$#e_FQz)S^3>(*9WEDIt4^kU^9ar-5|G4~JUfpGCbjn!Nt-ZPVCzzt; zKc;ChUN8^6Pf;g76t2E`sRUI0c&zPg!I-aHn_vTA1bRQ5DSuB+JN~#hPhP*TsoJ{o zDt7Dj&Z_Nf)w#NQa9&4pvwnLNK6R{RPe9~qm3f!(?>My5XWnV6BOdQV6y{e$+uDD7 z{#~u@J#-^vj9QSMVNQus-T2#%R3E2vK80;m9yig1TB(yNMWNxY!&)Uw(+@#%N!#kg zX88o*Ked()NU8%sy?{l=yG$ zAd@2Guv@csZSFE&G|n1|_fke4EM>05j4EE*=wX696%``fv!nj;FxSWW=MMPgPI1Kk z+Qcy(Ig$s0@0=>Xt&I@Po!z96zt$*!1W@%h-~|@6(k9!{10xK%C}CuoA4!X*u0N9E zSt*doYptKodlA_I_rbP9!wVjl`L3fy&}gUg_UbZl69NstI~TD?3=5}LsAe*I~Ddou@#g)rn$J?q`Sn63{ys(zwvQs9e%yK<3XW{md)+yW2 z22jd$h&I<_ds@1DIZ3T17~jtRb4TWwd(6%*pqTW6wR18XcC3XUkSxnrOq9eljHznM zGOD$D!gw1dTEkMQ$9-RKM-YcN3IN3>i0JvPq;4S{<&xYA%fi#9M)Geh4eutel1pZy zo_}!BPa%oOU+aym!b{s>J<#l>p@beQ$%#CPyS{kR%QPHDdaP)%)3ZFiUh776fvm$0 zn(uwIh5Ep5L5CPTlUY3ItXz3MUx1GR3XV*LL@1~7#nGGwT-AzbpiYJl8Gs(DJ}67B zJg-k7uj!DKF`xc%!MKA@MCLXhy;AnTWwqbLCBt!g2d^8zo_(&HtPMM4%9UdaOahez z>QU4kswAZkT?3YthsEFueO6{um*N#rO;0IEod&!MFE*A2grb`*g-Jjh9rtAIGes+p zaL{YNI{NCX^h?;v0}SiU7@!)$41UIc1>3$UG)PjWm?noSJ^F!g^TaY|G+}2~e014~ zl|jM|6|&|G7{X%7G(}2kmRfb}Cu+anh#5M7ps zT{o?#i22UHS>M4>@Z4^)vf{WCdq`5h&X*~Edr^a2=W}dWc(L2gr|L~oyWQl5{SyF; zp8IRd3s!FoYcu#R+{hia21I%bLm^-p{{^8i{&h526!Z~(;oY-82XF3xgA}dLANmJd z8tA!ExAc&A9x0SP5r7}&NjRJ}3Fj}t48pD~Bd5MA>KV9f(st;pgD;$bwh%nKv*Sa? zZi_1o7S{!I4FB(%F*6$DTEgsDn(6P6$jsbv6>oVK9{6-~ z-N<_@INX8IwBc296?%*=IASm&o}%5kY*0|foIuo*co#Tr0-&2{ZZ{RBtvIRq7l0`_ zuIpnT#Diqs?dL8p*rebm%p`37fDoej8fKu;2wo*4I1+Mq#|{z-E~8(skziZm^22{6 zVbK#sK}7#5ATs)0)QP?&>k!V`-@7@-8KFdv0xYR#{no_x6%~o0arfp1wkl$50|bJB)~Z) z$SQGvhu(oZpm9pM8cg0o>&V5_@v#Zvsb7*uc@V8$#VcbC+V0eHGJXv>%I2e^2YbdCHK6n$A z1G$0A@Ek-@C92om=x{ko!n9%mpd6g93gT7MOY3CrlOu57oa-N?NnFLHrrsN5=iK2> zY;Z-Tc0A#5gt!B4p%(~ClYvH~0$)Mep4|z1)6?O*roR2P&(2$Whd}Oy;~46QmR33j zKf2~-2?$p4fq>e5%DOk+5Qj0obi(4!BIZkU4s7an7PigBW(*plzPLm&5&*NueUN7z zDs7=ZaQ}Jc@`EB$^dt!Y3CdRd^7RN^HV_m_Jl%6v%Kpn{idP{ow&AV3vgpow&6KC{23UYv+Jnny7X-A^&(~C)YPy$FWYMI~ zW>G*y)4!wLIy%WB#I9MO?Z_bt7q1Zu6EA$M&j`3raG z5WPMrRTS8_TYWbxAlNs_mn-EwB<|TZ^wo4W;wBW@>-*&NXW@ekWG7GxIdKnkz?KJC zpNnE4Jfi#qI{Vc!ABvq2o;)^p`2s&EE(R<+T&>+0Uf4D!b1MibED_!Hq6+1ML}?2s z?_(;R^Kn0bdBG_wWsgxdZUqniqeCZq$6*f>Jh%TRmUoT#n`1;T;s;uYM*H5uc!>Wn z8q$=7mxfe-P4yP~Lj*pTT&pYD&q!nw{cKP-C zu2c5eB2ma4L6fS?@Dt}GR3KK?w`NJ*u66Un-A4D1B$X5a& zp_{}X7y5Et{bHvKyIjde)S0%+xZO@@i9Mjyt4UEEVivNErL?F|H)G1M*{fcBYf>(g ztKy?^NvX&?z;+SuRy(xkNC{t}#YCSyS1HhlqfhfsJFz5x!Y=1exN>taB(V+mAVi=f zGCP!Kz|@>??S;bDFq|d<-fWv-RxPouJ9@^uFX|T(W=2r#=@;ZrFHWOAv$=Z+(#lsxttWwi0qzRPOWPby9KOD~wh%oI^3z}&!Yb5|EUKf~~Qw`aaqWRXh; zyxcHCD!151Fv((XQz{KAX)u7f{l{d2v^OfR0g-@mw5rkQ3sCqitF)T_hrOy)pCRl) zaI$LBL*q4{r;_}(d=<-=DG#e=XmXclNGsT5Rk+*>F{~9o?4>s{sMCn{+~Lzd{A?iF zE#$EXX1>+k;0tn73v4P0Hd=c7NCJ#BcUwTuQ5M6BTsQROwq7^S&;0{n zlcC3)*IbY-Aq!nbT8a!b=x;GCUo{dk9fde7)Pdy=xVc)sWO2LWl=J{$G+}Zip>${r zv23OBKi|wGw7KW}KC7fCq9iHgpWc%;MrCR?MV}0#0B2G))Ht#v>-*BaiJ@Q!8GfXm zC$tDAX=Q951B|A;EYxb*{B&~NvjZnU4BrfxjQsJydKe6^)n|c~edaV5OO(M??ul3H z^~`y}-=$5&j7+gGrhErUuN7d_1*0fh=wt5f$6zTgCB+4%ByrW-*zhWJrpHGL5&~r! z7DXGXMO#$%~(w zyLwGvB1+;ktt{C-`eQ-WrQWLFfcYI(RqC?9a;ReY;`S|bnnG+9r4pe$`*J2OAg4FD z`{zhTnf7AYckzRvs~|l`@+0^5o{{k^tg=I4jrPmjsRwO~_PguRpeaB{)I=F75uUz__ zFHJ&q3<*%2_3J1IMjJZP1bN6q54;&f2Bg2@4XFt~1TTl@3243aH>Z?XM3pGsGrcmN ziYZc*TH$c~U}zttQan?^&84fzCsUv*qa=#*?r);BT}L$ooyVVTdy%5f(#?+DwaO}y!;?MLvnv?LhaZVv(vcqQ)3cVtqIjvgAmMim;C(Iu}U7jPdW(6GPnRhJ| z8gqw=7#4Ln2?UcU;haj)=dI@uZRIrIL9*^U9!rFYc}1*+aZCq8ZUkzvcLo4gz>Vs| zmcAno>?Qh)nQGZGY1e`|O^!;`8paa5N#&60$Ib55e+u#1`dH&%vxBdccP!`ajGN;=ZM!Bn5M#NIiujn)BML{aw1ta$ z^o!jF+X)||_Q%2TzT2P&8~enWBa=)ifLa8e?flaGRIK;*>!(JmmA@V)^Wfe5cv1be zqer;N#s{@UuAM_EQqyP6jV00#^8Awi?i<0jU2lANc=j(~Y_kRe5q>0RNEpRTxc;Z& zlGo-?__;GmLu5L7;l0MSq!X4v!WnC8cKLM+Ea=a$++)v?F=`Lu`Bop|d$xgZ+paF+ z+ZNpM$egJm)6oZiynW~IdEzSo`%7;>e9N|6XL+Z@k5z~~@jo>uN&e%og|}2PmWoN8 zotq_YYTE{2I;u>~p|XX54m0+x&1UslprGT(q_ta{?J|L%!_6>QhX9Pa0?F4Q5>8hO ztR*BVa4`}%GBlD*=(mfFmnb9#R7J923}1hypdtu4wFz<`O0sT0PX%@O6B9|tzd#UI zUUmWMX;xn^=gWsZW!-^-3`R*^v~Z_WKlkg-L~9GMH))Ryz>9e3PpIjiecrA-IJjc) zdrID$9fGD)MY2hfs~VYzO>d#I5oH^64T(0coKya@n4|nST2(U6R9b#`m@e)^uauQvTp8_NYrIoj^0=v0iB#N7YN_z74-c|ZX`OuH5(MN(8+SOZ1%8d*XS#~hH{)xbP+HwsoQjTW>#APu=53aZ&H|}2S z`p33q+iUz&V{wZ~=Cy9+-5~^-LwO&aN(Dp3m*N3k8?isQ8t!-4UP<;# zqqA10Lp455p{2`tj)IB&$9ewt_Iirw=^DUgn&^|QZevE%uc{O0T4wQCLADi==Et(i|>Bz z+Wo+4wJQ_W)0(AEoaJVf`MfS$hH3|2G-R8k_oA1==r+-hAr!1AoEr6hVwelnJeU~L_P5V%(xSje}$i;uwP^X;$&iBqZ%_x+{6TU#c> zDs4j!fWUESsP<>en?VUaAX;%yp7J9_b5s+?W|b1^c8iEJp1(pz(Z>O|%=IZ~3M!1# zFg7CVx+8(4?7gUxzLVh}L#1(9N7b+v-YFbidcZt=Op2m@dAVmIqX zcvN-2!`J`7;g^oqMx}1UM5H?;16HC(t|+0 zu3>ld7&AV6N59^A4SA+8Ln$P+S_g5+VNzRT=UjBseic{xZ6*6nP-lEAqYBAIUIC_3 z+d@YN3sL=|5*P?eF(nxqYq!BzKIB;j6UVGRr_zC!g<-Az`>qVmF_N*D6eyR=>!zDM z5%v(XI8nk5eopDmYC(Knw19rl^72dFyoi0%I(D@rL}wyK4eVx&x3NBS(u(T}n;DT! zdDnq@KQY^Zx?%p0P@d_(s1`70cDDaVLz~u?N!tAHtbAVrZPZc`*kM&S-%KSVCpoQ>q8aS8^=Il2vlpY8sj5E=oS2QzSu+z{i;aH|MGz1HSDkv` z3<7PJCq1>T&w$AuJ2&|WUj3ifYnG89E!ZR)@0e5QGKk5Y@mJWLQf?J3YT2X z_Nf?l>|(B@B`Kxkj`z*y_xGTES9}I-3PH=Dsd0j#(nN_3?}Bz-@{zic#AqR@8w>&+47SG%E55UEyF1B* zC%t7!_&9dYwy|Z+B^ks4j!E9m?+s!FlAH44idfF^2GLw4MV%l z#&w`q(S@?#I2M)&@Hdjf+`)qXTY6Kc>VtA;)Ywe|56SxlEs_`Df&x}UM2fyV5K?%Y z=yOYvY0fIh8#MZ1O}o@Z&MJcfT`tXafqPzE(J{-%S8PCioX}y-2p$3i{7`9>Ei9u4V zkq3|wbI2H8Vj?Hu!@a5H6)3N7E|&07V1`L_rl6gWRUH}>6U5%n5_p4S1oi5Hi+(ck z>E6kJfBJV~6DM4eD}8X}VnO&rMt>Fpp~!0+N8-$7U26znJc7USq7ggUAYSOL91e$$ zW+qSlbd>1qpYCy~@|)X=7lPUVOgW8nQDYwp^E#fuhL06NE`Sn>u#$Jo7lb0c&#waZEN}MO53O3<6h2h+|CoVnBcsJA+Ri?0l8q) zcAT~d5jR4yy-dcGN(Z1Lov@47*B)Ct_Oceb9ZVv?+rTQu(|vm}5!)D%#sFlVDHjcg z_Y#9Ndn=V9esVq&PlJam|fKEQ~)9-Zm)u)?+%|RT`<@b=*X?Cfd zV8T7E9BX6tE&GX<8_3nbq7&7IYue1@#-FJeth4l1tpP14Q#UQanjrMWLPo{$ya$Kt zhjT|jS>f*dfEb3rWV-N?_ojh?Dy!1O`O#lk0 zII=|nb+~btA8k`v_L;RF(S$^-aD1C2KlDIO@%h8%fhZyilCQ+yxqnUrEa=*Z?z%lk ztm}{}pHUG!*OC>dW~>}LVj(7IB7r?M;u!#}Qf#cd@7v&pm<$tXLD3Z;2NAIX&ibNQ zv_j)AtEojWRD*>)tFDf4FH+I@+f8rR80s+XCdp6l1^NTJj4qE^?12_6${phao$NiG zI?J_uEDUpx4zC?n`Dc+Ie2lD(#@iBEjdvk#eLHGUacwqAlma?A}h~tYAR7C8ERPt0+`5E#o>U}Ij z+r^u#dDC;V1sLHGUTS zThwTS&E@9;cZuist+{xpSx>=LMF2LRv%80poU#u21^aQwWIfEeWEy{6C`16Pl2Zxn zL;$=fc=3FQ7emE3?&2r=ku_C=%;LZjj=kK;=Tx-aaFLUjO}0SQ+56FOeF-3Qf}*g6 zM(weD`5vzrd0BE2z&Mjg4o3BtIN$^IP~Ey{7g|wsgOzC8=m=KxNNCYjgKxk`oUZ;U zqRWPCx>*_21NU13jm^E0=F7z8-)v=cjf$?bs-&Aw)kweSY3+JR01pu9_IM7u4cJ#tonA?B!wu3;NWY<^I*>D*wn(wZ1loC< zTi$N-iMeLx_}y4Zo*kof$}aoczUX2#$HQd%DvyPZr0#1qiF!4rB9nNvHPpZznpjm~WR9wd#JYZ!LBy2ytBfunh|ZC^=&XbE>y7oHNhpQEF;Hgq`LeI z75N&p#xF!tHJn~FlhAEtsQ$qj{-9%3MtQYk`7a2t}=Gllbz)=3@h z^a{8}c4`P8&?ImM`z7RCBOu%7b%2;=8eVHn>dfD3X(WG?RtSi#{!HpxBQHoRUmGwP znT@M%gV+bEmVMwH)q#0FlNyhQgit-KhxgZ-@&!C#&L>+ZLqbXJX~x!B+w4cBvvS8aH$Yh zc!0vW@B`_r?^j718rLo+pkt6)5|J=ZQTL~1+!0kZgt|6kjiiLYR3M!$ej zL7CaP{tJDo){=4lCkN`e)X1|&B&XRo&FLegBIxRLoG349Sy`#kJ3aymK!V0aga9gb z0DQU4f({AhnUb}K2MHCHcAj3AS~f*lJ`JL~_5yl)-cQ&pkWEO{A|+N^kx>Il>tl^m zkmyxix?}ijc35rt)kWribr@4-ZhMiSG~>L4=yd_;T6tQpBEL1^zus3b33@%J6Di58 zrc6v_3*i?<&z2f|G}t|j#@lk@@6#2@12z=DgxecL1LeY5m89O|L!+SK3q|YKQ0KuT zNTt{w!3=^vg{vlj0ml{e4JEBQgZ#Kx)b^JmE9g3{yV?u?9=jP|pWYKMwY6t{okvqw z3wHp1#dsO96^<<@39Gq2G7+8?-Y}_Lu(&c~cqCXvMkm~j!IxWz^g_6pu1Y~lwDCwpGVR1=WnAxqB`gCV+Fzui&|5?sluD^DLR{_X=3*Ct zvY*dWUlj?${0juW$Z&Sm(5(_WpZS1PY6}oyK(~gWaR3cg+$a1`2tSq}&mTeD;-Kwc zV4voXh*`Z2&sZ)s;8D~>EE|Zx=5w?yk@s=c7xdm>d&PpKTC-lU{_?Wkfk*2EL?Bps z-C~vmAL6~ev+-D*Rk1gN;ZTZ~#mD*a6x=6FD!Q1__xHH&@52iui6pS{Nf2s7HZ)+< zZLr7oUr0;MOAj*TY>$L#rz&ier1j0AW$+SbIiLz-7&#dC-IzE2l!t``4+FOAzbkE* zt*>ka24S)p=C_+Nefs?gfibYbm$C87U^lNe7ekS6LtopY63nMx%kh9VLM)-qW5E$* zN7xF*BPtJ-_zt6{Ag1H&0x4aR`VJt70OYr{tR=1bXG+(%VrYyeHbU|UR0BZC7mP`r z6Q3i7yJp1v^lY^j9~{9d{N{OfWrd`(wk|L`;|C2POqB=}1O#E)9dYRK+%nc!@qn2F z-&hIwy9?OJ_Fd7znq`9PqAHO+(n{_u=syFrs#~Y1NeHI8szjVojj$@SzW_KI1vU$2 zqs(oRs-~%+yFkTDn^W24#--#RVo@M0B+u%NEgGZU+1Rp)9jOUvP%}DoC^3o2fYc`~ zg;~d2-;IlWZW8*3oeS2}cY2LwIAFr8*is^S!)f`Kr0>XV8IGT|o$L5DC7^@bWf0>Wa`wu{)_RV_NaNk&?klqIm=fboX$Xy(5K)o9l`zH>bn_9UZ%A z9%u_!*YY`RZjL6JXfOL~I*57f26t$Q<1gu*t@Q45Dfjs%F;`os*#KYQ_L)yRCNrE_ z5DdRZjFuK#xzq*tAvAENxZ!fOEA)q%Fp|9e;8(`T00^xVg376sPR(zx1RzqU6j!qq z4=aM{PG0afQf}U-L9Zmk-oH{SLNxQvQ13)f$6 zE*j84EN_!n8?8uHdjMB9{W&vxtbXBqqdo_H0evWDmB|qW)xZt)K&$j$106EXQ>@gk z87F3qc$5nsaKT7tWFAA_8oiPPF8LL+7a;GMA3riPC3-uOm=7Ece*qP`Uj<%zD;1Kr>vq`a|Fw+{oS_Bo$5{el`WIpz4=t0GI>SOQUnwUmuY(|LX zbz5w1jWdK*U;HJ!-h|01NY=V$Z_W3v$+))rch8Ipq4bg5Fi>;4f>g!=Z*@mWigki6 z!GS}oG^?;%K!7@_Xtim{XQT4My*{_0MVV+T9K+(XL?4ub1&PoZ5jHzz#niE>$nG+HE8bbE|Xm5j*8gm0TcBIaSH&?T@7 zO{Q2@yc=nalw<$mDh8i`V|<`p>MvG=KvlnZo=T(ufC)SmRxbvD3V| zl}DJz)WDaZ5GY&!qwI^uz*`fcDvYa$llbF-k#sS{%ho*??H){Q{y4TbCR553BUuTt z{T^dNpByZ)n%6o|GO2R=8h9SBmo!oleoWPz@O#B6c7m(h@|?Ln zdGxpLfn*LY=m@M)-I33H#heypc`nv|fmSam0brb=N&A7el21Sr^0=P3+%(|4M<=Iw z+9D{Bnz)GO&=z>GnOO~Sm7Zlk6c_hEe;$!cRZ=r=KXa$3q2lbi@I!dxDuf5P8{5FB z7y~8E&I?!%J^9a#Y}NgpnK?oIrA=o@W@ZKW7cj;Jm79JIA7h^+Mu20^z0yD*LEtUD zDqweU5p7LxHg@n`MH#hZYJl8*d@k6qJlp$^CV06lPj&~gUn>h;iOV+i=XdSd)`gz| zYif{*Z2n^igMwyv;WL8Aubx51U>=?xx>QIhT@ef?}Dn z%9)5B-IS3F7fuSBr@L6S(sun+f+hC$8UiMObmp(aWF8ErfDq5(#%dB1zfQ);5da3U zR8)MM{$a9JKv#@_gE`_plG8t*fqHU69t-E=u7@^HYfJY}mUF;Y6E6C7xew_QtIqw#4Y;12LF&Fm{2xzjb81 z^k;DBye0t#1<11a4zWsc+5xgDXhCqNi?Su33=dM0%Jx%uwHTi>it-i%S*s}#3Yll~p2JmTX`}${| zi+*XNzCQB3W0#UI}%2^a4{9!pK69O>LT{O0>t-FaRapc;E0l z-6Sx362htYJqnYd%B-ew7V4i}b4D}laIa+rRWrSqAbjsVg=9c7II0FKR*%K09k;lJ zha&^K`vB!L8o++Ypjx`F@$WO zetr75to#Ah$w5mqIs->dV+RJt0Ab-uLe8cE{3DZ!WBC28MXEzA28znYwWkY|Tln3n zhejDcAq7N%*u~PnMtYh> zzNiKwYSLV&Uy~j2t4bW7qDvV5X3wUg3T~xo>=MV=U23Tq_p;c`9D-on_`kIMXjT75oN zSsKsFzu6!-95OiGof1}M@~ETd{0^R+Tq)RncFj=ermD|Lc2UGF)0=ZGb>e4r3 zH$$-LAt^6r2~yiAb7k{ugzNkXFfW-Or7(N4J;a>>z7S^&eUI}F9r1lb)kjD-`2-Qd zPvMaRfM=7OX+3W9reVV^x)F$}=PtS5e_~(`^Z>(ofIctGL1O%7CixWVdlkI!c@@z< zci)!paNZkEqN%v$e&5xpI5ko3_I|RN8Ki%c;E+RmFl(=+gU=t-Uu($#)S0EAbIC=? zeBYb)HXi?0`kgnn*;FJ}reOAR-jBJPrt-qzy;LebaB99cT&TOZY4bcu;xy=%$FPGK z7r3~))5WcRihO@BB7&YAu}7U0OzqO~Dh2W}Bs$42t@xphppqC1EJX%a zMkGE=7*`PAdbwq*btzxBvi7C-QQR);0~aQ)DpuL>YR`etoP#X`V)9^Wg9zRjRb;Fp z{eWWn(d{C8Y|V^AG!ydixuN%CaTD<+bNxscgaS7@DU<^HnKw7G3h+whAw1MOU5qx3 zayH52=oM*Pgemivi;9M@pw~nxA}J_>yMqGkt(Hc%yen2lUa>5cLpjs4%ic-O6JdAGAf_NVUUv2kUwMqnjj{2yn={QDqLG(I_%;2eL*qRfF$*3mWtZ{(VLRHLYc_YFpk|k8m=j zDB%k%t#b|Fa9yT8$+2V(OU&0J+8+frG1b7RWZqU*FSl(E!O=-FFfwX)SBDHnYwBAM zcQ_CnXs7x(YDD%=2=->t%_=vq7>g5IV0CBt1&)=3Q&3W)r#UcyKTzY6L$-qKiuFoc zeVFVEQ6KqvqbLLq|0+o-0QXN##3Jq4iT2bqmUsk+mWaY0N)LZW+kr*jn#R1;q8<2V zs?Z&K`vVF*dYP=WT0@9Ha>mIe`~o{w^Etq0vmoeI+jty_X!H$fGk=uUQ~`ry49{1$ z^&3HsU62wVNC-HUBp(;58b~aRa|}yvn9L<;;c!{3V_h@0I~gAovb{N|`j;2wC!yQw zT&WL0R>Q4sQmudOqaA;^GBQtyX^Sjg69;9&O=b&WPjufhdHGixj{Wro8V594J$N?G zOu*mNPB=jyq0S2a{A#%jY=2+d)*4aMvAdJo7~ex7k>Y{5I21d=Z!4Lal~ww~J5G`a zwN{aUi(p|vsn0PwAck7l3*xfEJ#pfYoYxaT<8?DW3=9|2QXh-K-9!k<`O=9#Sdqhc zmv_x{jjsMt9abe!Ud=jo@Am^PK5EX_?2acvHH0o6vt#yoBt*ySg5L(y9JQCkn{*zJ zU4zlA-|g`YU|*nUy2fO867$T}n3ymW8=xd(R0C5h zwjgS_eF)LDOb~`L8J)a1NBT^93lXS&1jLm9xnYUOsEJh3a%k0l%0x&BV4#aCVN=AZ z%N90ytD@V2o&^1YZc^p$j$kE-Qok91l5~J0V!g;S=&79Oo(FK3h(JOE!-F@`v!p((>B$`g2g8M{J$_yH>&(7iJ==F)Mbl9}8DQJ=aXM&i zocQe6;ir3hB>Cb{0dHzFS9U`HT?aD^p;|SU;YknsdXr+|HNlZ%Ry zwhra!!I1C0Op~{9mdo{8@BY)(>+dPdJsW?%Th7B@4Hj0OeUxjIc=;$Ity_MPzJs`d zvB;p)lBojn91rO`q*l)JAP0&r_bty>6aPrm=A)XG-F{yZI0 z`UNUZN<4-D|G4G5aUayrVLJL;)#q;vl>HQ9YgXp}E|sOd zI0GYqvM~QwC{78e)|5#)Z21rHZ7!ig+Y$QCSF+eT>9=v3Ud@F33gjlFdqxIt4iB4%9-(=-;DeZ0F*#$ zzdl*0^K7$Q%<@~stJA_FFZ^MDx}{GPrnrb%ot;w2O|4WBYOQamwK}j2qaR%RtpnCEPj(9%Tq&Nlo~H1mta68X9?`ZR`p5z|e7EiDT--TLM9EA9_0sgt}XNooeJ6 zHK!!gJP-9iUP%)63ehw>7$Caunx>=Uo`lpd4324a)6f}n5~k=^aMTWzQ)&rE6|Uzj z^Kx2*e_5Vx(x_lSl1>eyqkAM~&;e;e{{uP9jP)?q2l5ihfTS~{zaMLt&2$FewTEV^ zaMv_R1%~HV^>CQjEly0qNb-|4BKf)n5t(MPbQJBCMeVL_Ul!`C^Z4RXH<6~Rjdva^Vji-*OZG|P^J5iei zf8~hMr7!Vn4_kt&TWcJki8@?&k_nzo8)g~e*ZB?+f|cp3d4e^)DAYXIeQ!i5P?R|L zMXW5Uozh2-01|Gr)y+sv@UL+13@z_b-erzKueX_Xy@Ut}RvN@u1zKpK9 zuzQM-V)C}g*H!b(!m4)&cw!47aQ3>`Lg&8kPTZo4OcUaQy1mS`ql~%`z<6(6e*zlk zDTe=M`$C7AV4c z9<|?Cb;s_P+&QaH`?+|+Kb%lgf4#*M+zU$D%y;1JecjX}Ip&#{xRAmG!(AbYTag{d zs9JN$!HazDBGXw!3dZSzK~g-$WCFvhSipkI%B;63g1_k|E%i-!MZiqy&4IxT#Kf6MfK7?(%EiO2dt;-d!^s-G)!6p}`kE}`XW&S7dKz@Uw@bKoKG zU#TWPPZwhf<|q0;qdqaqk(2Rop^$W$>)=HsK4@8e}}ixBekQ-KoX^TsgsTu z#9OjXBy&1ZV`e@lz1B&`IqO7qy3>go^cYMFai~3*X|OzXq*~GK#C5it?ux-8u&>oI zDk;M_&KB8|sWpk^@)G!gA7#F?tmNujEiNeOv_cZTDZ~yfKS0i|LC|Xp=wx4WW*}v_YAsEetF0Ly**qi z7W7R8q!YLDH|4ZoHpU2FoSaw&^0DM{*5VZZ;rd3nDxHRj|6H}g0ud>FO8I@N`_&g0 zNL`)hMi-%=)pc^gj0f8iQ8}<<0^(Sz7A2V&a{$$2AiW3cF=(!z^`dc-E9RU*rGB7bWmvQX@D1W_JOK;>h628x`=v&l)TcZ~x zDnPKSxrw1ky^c?G`9bJzG4--CAmH0T-`>4@e;XH@GA#2X-`qWHqOjC4)}l?kiPJPJa=pn_5hkhH+;yAZgCCFEU7E$g zcxWuo>VIvbgF|&1&2Yn%N1_;>?;BfBR<@3`fYmbSiI4>SgXbpOyWWn%w(q+B*go^u zWX+yugRxb2YMbLnIbp^FxPkHB-faJS_tPd0qhzf@rou$C2KCTeyZf|NDyZ6KUyV&q zI;l9Qj&z5LL>x@+9b;l&1pR2op*n@zor?0{+kbvM@F_1q0Ri|_H5guMx)UEA#_G7m zWEB3wv9+@Ac)6D}ATO*nz?dfDgws&fV^dcxj)6~gf<0=l2r+)(v7=kd;F-Vdh%I(@ zt~J$gWJBWXG_V|s3F%&2?Gs8gg(ca2hIt9@hoY*t*u^~^< z&WeYR%_kS2M_!+s!t!wYkA14!i0rwFD$%m!Gq1KVO|GUYlR9VP*$=+4=(l6lc5SGcjDs6VL4pUt+$A2$?Ohesc z)jr$CI&sd`?fq>U&zQtnSY{bGfkYJI^dX9i)tN;(q*;muxA~W85Gytv8GC9SoQbI& zp3&rtXY?^5%p4NY2S@Sy+j1e60#4@IUs7+8YkhZ`Mi);CoSsfZGed(o9)E?!QpzkW zRN*-|zZURoW9QDxf>SlXW)ma!F3IvRMnT!J=lE<}4c@(@kBhppqRcrMf9^S*;CWW|ya$P6;6b3H># z<1x%hfF+dDDtxnr-x%)m(&y#OAg7kn_fM);xzxCvpuXkFT2NTgQvK4GX3*^)WYP-E z{K0h;6c<|yR`q!97INP~OhzPdtr}0{n04^n`3m^L-f0}d98L{};L5&f?5O1%tAd&9e2$Eh9K0vUfdrYtr=5>R{FRNwyQC2&u4U>E7;_G5Ed|WyPeC+9Tie8mpkL~l- zkWW+^YTw#ljOGhB8{>5M^lYCwb+9@-Q81|9CIqXchhX{Vj(03*rJikao%P-bPTR z;^EAjjiu)iaDC|R7PHzeN6{e2}5)`I_Z==1-I!BC6-$v0&f9tQKj$sB9h z=(@i3BaBo)Uzuw?S3w7db8FxmV*lRtWuHsZ%8jK8qcrg#`r8%z$4Qu8`geJQq8s`8 z3bzFIhJRaj7|U1^^;*#3()fJoD**=70D8!tr>jhI7Z{#NOhBcBZ+H;vzrsADW>T%x zUT_Eg=;&NszPXkG5qjkOuSs}4ywlYqJEm%=lG#7a`J29mX7a>oxQFQ{`6J*1biN(1 z!;?Li03&;k{V4QiKs(DXD|(HS<(hh2v8n2Ds(;$^Uj>Bq;$H#Yx#4R+sA7~nT^3#) zl&s3t)co=<7W2KKT0}oFgdkVva^2-FN0cUZw#`a~mS4V98+d`+^P#Dv9nPiXV!7y4QCzwqB>tdF9&|Xjk?6^Se_q=UhMtX-Mc>l%XPrn3T19&b98cLVQmU!Ze(v_ zYL{Le0Tu)@IWaPqp=$yue_KhD+qf0J_pi{As1Lk-3a%UQ*TIQB8C7;A=sUQIoMd}C9<_onYz z)Td}b&K>S4kX2ne284i1wxL=THp$_@5fyS++t2#vM)| z4L3v=E9R~7e`W()sJtyWfK!?8Gf^&)6G|lzI)YaTedWY3(|0KT=$CSv7>X${&%WcE zgbKh^<}gy#2A()Q#HWAn+YXU>L4mYp3y=T-aCPIN*iKj;7sT*k zE53&qYqud(9JcV9a+$JUN)*fcPQN5Z0TCGbv|&>Gf0Tsld>93c+-dvsFryp~29~G{ z=rK4M<_Z24Mg%h>RttmOkU#*pz%Pi=yo;425*D}OlrXI#_Dt7IExc3RGdrqX_e_GXVuYDVuAhgBy4n|nC-rnKW8`;6$fv71To3aJ{hOb@g+q`bW19X?p*24!x zK>T&R_Ye5@)~};!y>Sz3^>jNbcX5T=&Kiy7e%E?~g0-~%x4QFYb&F$u^3MUV5Kz{o z?{?Sh9{f|gdkAfg72q4z6@m?PE;s(eKmPQhfA8ym0gk)=sV6SCNCAF#7gpr~I%t(W0Eg$)0%|HYblMokdw>w0;9cg3#)>fI-*9llhz1ZxD>aA&t+i>pq?3_gkEk94#yK3hPOwrkK#PNtTxz=$ zvmB{niu;i=Uxxs=?cFtm8r!Xhe;s)O#dAiaW&H@?!WF~x;&>*=@-h1k9n*qgkOb!! z!(&lzdKcaG@jT>$yRKjH?I_DRK=ZcjAUR3RE-cYzWc@r(=MzJior2vJGmyRH0KAxJ z+Al1;$Uad859@t?Hr^k0H1QK!kGdq$+K=;ev`%3x45n%Rtw2^yK{k%Le^yvf5RhQ3 z02BqxE87QF*y4*K4W{%C!?>ZLN?}9A0U82o zSQp-7L)DIZ;J_HX2HM%cO8|^XJz3p)sdxslSA)N9!_;0Fj#1@wAR6u(H_3;^)y`Yt z(>j1(6DkQ)tP}!L>^{+$f5u!JjIBp;5#%+=Fvvp8MbR7q?Cr!H#VL)O(aKqq)x=UW z3_>Yvl=GzdgPE8i7W^#xkCrpCfpKu;xAirmH=V)*5rZ|2@!>V zGXzSQ(W97?YdyrA_~P8KR~DD8W8~xB*8!y(k8XNo!fhaJ!hp99e+YN|kX=)~2TXLx zL9_DRK3PT_QZ-L)leAe>t54?44&tN>ma>DmD?4CI#+XG*$Wh$uht(TP9Exa z7Rz52>-FPey*?E0=Fa|%OW~sh{BrK>5AqbvW18!z(#?m%e}Z})T84^4-5*-crcwl1|tK6O2j0@iYf>?2 zPaz>-fs7)e2yB_^fmEP@Tuk?O=DZCLcaoAjecZ-j!|G_7GiW9?7^Ip^^=z&J=D>)% zL|qGcR$OfCf9>YwrF0(2D`q`VK#o5;4|u*@Sth#eCJFY&_ z|BGlap9@=fs_p=yv6E=9$5^y z`T;R$N|nJ8ay?dw|8c{@yE(>=*CNgv79&iv*s$2i3gG>vsbxNQymG0}aPWJ=c9;(- z=-h}Sf8+TA+LjsuVq|Im%M$G9ddYV*nv_$I(;^DkN?9Z#R55v0#XwZdd?O8$7ipM0 z*09sA-nw(EuK`Lif2Af$s)^rFRQ3E#9OyBnptBht=;bzR-|byp1fLs!^J9l6U)q;~ zvAOH}`uiL{gS$SHLP`L`XvR^nf9li^lI~nhf5?*l`*3HJgGq5PZ%FR&)cKI5U;X*! z>fIF^-4oT20%AV{{nigxuintb20miWB_OT&;JQ966eI5b)OPXm>Suo|Hxl1rsD6!e zA-(*)hV&f%nzP0;>UHKQR%k=C60zjnQ7mZPI-CmjsyJIIlENP8dfOHC1~P0#vrWo2 zf3R4F2Pdq*)3GVvSN@cD{-7LIEn$Zd?QB_zSw;k~CZae~R$?|M!myZzELR4S*uSEs z<|434Wb18NGq(3aSK1$& z(x1Ho>ATT2HxsV8(QtQ#iVLm;DGJcT<1Mt0?o6t52UDArl%@;-_~&X?5%A3U@+&#v zqP{;T??SZ3NK7$ZfA68ATc;LIG1tl0VsdXSy68rHxho=ui#_vg-gzm9P(fkNSn)D zfxDuJdq8_Z5U7qWC)AM)St#_KHNAVac> z!1IhwaS)avrxXA)cIN`md=@mazLgDm1`wO3{?&p8>*|U#6!3;G!BBz%e2Ioc1s2E= zXv`PLk)k6WdgC%9tdhMhwLpVJ`lx>x4j3UUj-yJ22u=*CE~0@QkaJKs>4+FEP%b$r z-bIM_&^!drpb<2J?jWI|6M8ir;dLKPi2Az6qnLD01bg8*`D{XlB8ecUK>Y+=N9PqR zXhf`;qKS-QB3KoIpa5W!Vu(!?Xq^b)67=JVRDjXXCHFN}$6T}l#ETa1Y<3%zggYuGfUog33ogJi? z0!0!GBL$Qv{IUb2E$q6wxx9Qk`}*qX7`}VwhxvB9*sKiQ zb^ms|{rmZLzFipK56rq(>($nrotf^H$6i2^9`_2ofU57>q%7XG!D@frwc)j4*9Nb5 z&5l@W^=K)?n6pH^smMH<@}rVt$Cw5N?vV4Z!yh$gLkpKei;L0rV|vO4i(Q5 zEW?YNz$DQM9J37^ljVQVDw&fFpko`@16&#<`y4DHLL)c@tD&M_#>j^488Mlb`MTP1sMLu!;9rC2>Q+Q6~c z6r~YN_}_D($1<$uP8H4x^d-lbN*efk}%$$)w$j)oKkhnvg!=G~|H<;~*V z`^Clj8kGQOjBs{ZHw1qoKU61}n#`o$j+!44r7; zfR)n)Z*n-|$e*2YvUBA_=SmYZ0lmCwH>n&+VV$#jE7JsKf~)!TZ29g;EF8^uE3?226Vllu1LZV!9!rG0;#316Zr8Fk3IS~;reac9FhU5;6g zizz!iJ5vtXG1+?M(0Vf2diezF9jhq=S-q_M64t9UU0Y^M(_=O0fCZIR2Vt{jM`hK| zmY-@pN(a`X&}2Pg-pf_!eouO<+W!+jDre#1EXpTCFlvG}B-ydeqto-)r}Bs-MJXzJTkdSqj}|f6%zDS|vDEx$SR$kT%*wa>C590DJnE^I zm>pY}Gx1)wM6unIhuHf|DiqmTBR7FVVM}{KdlZbr*$O@Bz0>IZ<=DMbeK%E~$(5fE z;(~t9DaXQh9eApMF<$6b_W@I51|P-@J}rM{R2JeBc)~}l$sBAcu?_6O6Ccx;xzrxL z4jWjuX2M96A7jy2Qhq!im}>2CSj`4E)!N~N8Y=jad+yMNjalVVBUqF3BVS#Pg0*Na zp)`VhvBZs%xnz{%z70&mmPH!DdTPc-ZK$6l%8g(wL%;VvQ_j^=wjQrjrZPSr#TFl;Z0|!D zUHd1@_lB~fg~9qAh67GKfHVC_VAsXn{IKyD#D|o4u6$M z8{+ZEfA*03-QW+v#JI>7uwC&MGhSJUY)=ms`wqcA24}A}Rb4JDA?%p^U0znc>9Av* zXLYgj^aT?3=o^57p@GOb3y#ig{7jFdvw>dhmVwYQEWSi&efW7BHCj-n(+Q z^exXzXsoTI?N__96^31S^8+mR*Vm`$Vki<+#E|_ECa^SNIL3%!evHkDeSbLSAP6#^ za~~$ip1cSoK?a<>1WQ4ZV#mF>gfTCsGa^;?+&-j#sOIs)citz2lPaVci*~ChW?gA~rEf@GTyLG7WPvX$H*0%!!? zv=)dl5E?5hcT;CvTu1xDNq-aLeG%84h{FqzF4>CsvlO#Am}1Pv%t6fFG6A|oVAcEl z&8tOfKZBS$*ZPE&-lr&qh>5m)=1F3Y1dI5>TKUfELQh`%5jUIFYU3zcKTinGjDmy_ zBA@}sA<=&IxtsA}I@5z%qY>DY$01fG*6GY@rzaNd)QTXPjMEgYB7c;QXw?aSWYK|z zyk*T>Oo^2#kf)S-!Oo@|nxqIrTD1g8sq(SiNS!kR0o5YlF$TaWZ2^ne0J}2v^j(kG zP$sBgG2}fekO4|4q_}w;Oz)HYvFH0|B}Ho6Bk?(l6iJ%)1R}M_j9`VpzB=6lE9ytS z3`_AoZWsw7EsAhJxqngrO>AgeR^z%+Kr!Nvn5HFtE3t(&|17aZ1TttnrUzRLY6HKF zHjr=X@P*akEK-PLb%+46tZc`m{5PPJKEuWyN!2I$X43q2&KU$Ml%)@(VG1o$=z$&3 z%8cSC!J72EEPpIE0{n-I+K7+j3g4c0NgwzX;yHSlOVU~BZ^Dp}x3GY?i9WU=HlJmO zt@GWsyg!vf5F9D6R1tiy5+sFCh{hW7J{@egs53>7!Uw3o^x2hOVuu3xqah7yAq~^5 zXC#nP#e+vP7Z{>Z9?uyGZTUGN8Glqr#)m_)K875mP=9f8gdrIbGS0X&%F(=MUIO4; zAz4v1KiUi?HU}w84ys__Tym2aW&Uncm7d|6e`<~n>1T^07>z!`}k;U0f zzAjf~zV*NQBAU)Sw2}xXr;NcE5=0VnsvMwegCYaE5Y6JX4`Z3jDv5&lsUrC5|Dd`=)Q1hgQB? zHCxPz{^Evt87 za^;`vvRZ_h%dqLHtO}obZ|;L^ugi;uVSQO`5ukvTVjh9w{??JBnA?MYQ+ZYL61>{G zTYniY!mW&D$JVD2gTZ45&V{0Jm z%A>67fGT@G91n*c61A^oLTT|qz!Br*0}gx&TB%q+2v6sG1-P!@u=sqhx@^9ee?9qL zm&<*bxSwP}na-r8A|;N$`j)Lz9ZnsMeRk^dLcYz{EcdX1_j*aE>6p z{CtM$I3b?Wc7-8$ic)0y52@^5nMVAkx_^vnYI|zSHTYMr{f|F2CBnM(v&PNCkblbR z|3mwrN9_Yw5NXrCeK6nb!&h)(ezIV&KMty2#y_f=z?(y)|LKegIKFGp-e40g!)s~ z97NyNU7m-3Owf<^`X;+D*MI$@7iPU*^kYI*>v@0Dq0UA6-q}T>UG2 zZWc%UekEcsQkur4JMoo@X3|N3R?#R%d!xYZqrL2On@jB%Bf&w|oph;EGph5_*-q^}nCs@_!teL%+vX~ri|l4!fk!%b7vp|gnsCP`#AOnk zqG-wL7TJP1>Pu*b;jx~SWG0?G&sX!kttF^ESfP4BbgmZfxqn(PAa>+z&%X8*1mV-k zP@i#F_(X*1Y5{I?>tm;GpnDS~$gi#O?N1~pd{rVr6v2W-6JCyN0<eB$WB^PWLJ{ zn~D`^z_C6Lqt-=U9mN&`A{2-#;DFctTB_CFmz@;#aivkC&rGlS!T}O0X7nXVs!_dBBzH z@@uI(ZRN^^I}M^^x3noCG~h6jWHn6papNAetGvK)WANFa;o|h#(0VtQtECMTk z?ONS$<2Dk1-@n4ZJyhPVS$==C*oR)x7A=ZCTywW~Xb*+rD2}%Nf+S~?{p&jvDO;2& zTXwubP!x*|Wr{PL`OR>K)DVwmBRu;47542vuU>ue9p@v1jHGgObvvS*BCX{}5(OtmFcWb7!fiL@vf7>5y;|o zbwterye3j1g0)xEjr(#uAyTfb#`_&{Iao`$(-*n&?g9PFO1X8C7^f4+>*M?6)=%7FoWrJ$m z-Oa&OeG!bvVT|};qYV>CTwb7mBS!lK0tHDD}AnY1H+5%cCDZJLIF zfuTe=9!yS;Ov8Z+;%ID|KCqa84VW<@&2;4;5F$y7JY7yRr}iSPirlYp9+R6hfv~1~ zg3H4~fY3;R;|65+dFpNC;2mLPbYo)1kdk^NghmD%NAUAzu^v+mYkoqtOl~*JX_c@1 zQUdh%#iba66XqeC1Lkp=*A@R&x_0rm)1-YXf~eB>UIm3>Y`;Og^*oK zn59!YdA%NVQf8HV0CH$mQl^XbJS)Aec?>*a5O{yc^`Qr=MifeaA@tCWB-PZ-y(#nM z%spD0*K)LsYWoKml}Q3qh;R-tszuCN^x=R>8!{esHnc%RN;fzUO9l~mqHkN?026Bg$QXrpS z|1q8jDv}=->s3+JPG^7KgAA)p>3*!!=^yFLt$^(T79)&*o4K%`J?L?>6Wp2|RJK2S zCd}(8V@%->37IxMz;yI$Ez}aQcsyAmPn?!#PrD8%ExtO`5H=66ehv{khv;<|y;HorBWE4! z|Mu?Z@$Sc%cgxnP@X3+tBu{w#`q^CeZ>QULI`uqG_wj(!y?Y7i@xH8Jqvbc5_~DJ) zd_DU1kodpkvqkz6RG6;nlj6Hi;s>>sJqUc8&+g%W;Nh{dzp`oh5)g5xhbOV{Cyzk= zivsJxJBfE>YYzuSiT^@-`!f>f)6|B8>w3Pb=J_>RKia3~hhROgo{GV3(IlkPr!S}Z z`G${x-#wmbjufNKSK)G<7Nz^3Ht}!T>m_^4UP1%`G3+8eg$IwU^2=`qonWzO?124! zQC;SL*txUQ`H874es|x6I+9;BknZ&E0p{U_q$Ch@da_qd&e`x1+@1c-DRVxV9QQ4S z%O0vPW0^7!Kg)JkViTOtWN%Z7+gd)pS)|1uS#kbuA49l=x5WRciGm(S^x4B_#|&^G z<;uI&hwuY!__=k`eEMvEbp54DtGul8sogn$y05J_*%}^6lEG8`&9!oG?#GhWUuLhj zwSc}d2;mH{fYunKv~pjPf4GemMH>o15f?n9_-~{>su2}oeKbEJiCQMj8et$$5qrnEMBTQk-HB>d&+cHp%JvB)MW}yPx;*d%Fuvu0eC>K&-Z7(8! z1XJ8av}H)EZIyjku|AKoVW4em&@fi-BPsyuCSa9EYMd=J88wJu7OML8U@6rgyAc|M zjW`RNVBS4!^4MewDXveOtPeds8on@?;s|%aua-EQXY`F2&<2a?!IobAYeeOuQ+ z?L({vYWr=m)V8qK%hIA5yRcq)oi9^clq|!MrMmFp<8s&r3v>D~5pd}Iu} zqkQV#fg5me1L)fGg!+AREuaTLZm*0mexgsLQ2?owmjTe;XF%PeevczSE>>H^9u@pL zdiRYg8C+OAEV$?w++7sg&NXpYYZznJ%6eyI#v3a$f58IUa+=QJ9BU1K$VKwaY6&(i z^BXuV)CB%M3fsmO``n2wUqOdc9#{|->!;q{r>vuoA*>_mRQA9c72;s&WTngQ&JPg zWp=&Je#yUoS}Zx|d4FHwK;)acsTMRZ+qz#cf1QWNyW80`ob%;jv1BH%R(;vmyT;G> zypS?4H~VVw>-FEidC&NigMt&oMYd#|aH%~9tK9;Kd|oU~{-KaXSZ1cn4BXya1;S4(6Z9luN@Yt*gblOm1Tl~hXm2vSM3G;Fs8(*JZ~ zeMGvP&{<4?z=u^&|yoaEzyj{TLM7^1aVo!AG0X=8Gpxg zJ+1+FusmLKO!dO+Nye(@N5sVo1JEO$vEzJGd*2-S;7k$=#urT@wMDN{rG8*_}2~uxO%%jn@U+d_-Xe^Si71qgLuo zlp_33H8eyLsT7=DdD3AycM$|k(v4TUEk==>8V$aLjVPQK=rt+z6qHCy4>+F+AVf(u z%t%6dK_qabiO}57`Y{bPIBN-4f1&7c)e2Z)AOKinULDUb0@X;%&q%#Zh-xVd=gmtd zh$sU416+U_j`;{kPKi@G(8Pc=E{(6^mE)^?kv=Z*+Lw_!9g*bg##izB*h4Qr%@M4z zg7B#w6qF4!5r^0WD>$G~0V}ojQ1M-}UMvMSIlzLuI_wbK<{hS#8dA!-f86*-tFo*7 z=<&Xar<=U%cP<58p}g-)4D0>@7`ku}cC)EEf2(Z6qJ6X4wQaR2`)Un%C?JtUf+>I# z*1$=G=X&>{;C#8R%e!5J+#$YIn`#SO!R~$M6XRK?%tvF0F_n9M+@SNJhS!7E^q}IpdFF>XSToQ_361>Z_2F~h-u%TS<# zS}ovb$WZXXLmM7c9VR$5?-D90h_!mwa^L_bN{6EBJHH63rX!1_aF7%&LY^e-JhCGV zu!FbYJ_A-^1YDirhoOlG6_c(*7F2qvx4jUzctl0lDu z9?4S_0gEV$BRRW(1K`#!816-rD!2pER~@7wd2-;99N;o4`b(he^v#)TyN*j-|=dIDgCf+sF1X; zkjB;0`88Yuhi>`Um%?$q0lFLiXEhS`pbly>YzFMiXkz&R;^u{65-ATrBZ!) zKtQb88nvFr!;t`EkeA4q3v2O}NkL@sexJQvbb(4jyf3hv%81OSZ!MRlf3!CztpZU;2Vc+4_aQeWmwR3irK+_CP z6I^9@)#Nnc+y?AZ2ob>n$Z5Z=hBy-%j-eKxe_(<-V8U31vwRKVyRC2bLBK$5wc54o zP-cVM?t+)1BnDB>S(gm(HAwo$zq~)ugQijw4yuZlJS4MO%*vLGXfexq`OFYUZ^qj; zrhKL&PBk7t#cRHt*+*j@{%jk1#7%}(20%*cco}*Dli1*Ct5Yv1AiA*Gjz6E?&N(I2 ze-P0cm!vT2U%0?%z+|{}yKA@3dSTo>ZPC(hoaOB{e&(&uNa7eV-Fb~bEc+; z9+aQ@c)#n88;^h_V92%6cpy6z$mg9A5y_@UsU3HT-ldxrbB)uX?&*phmw2&j|p}^_ zXH*etZS-Ds@+vrPfncs+4{cD+lU}DRJIgm_3oM&VN|u{Tz%M~XC_2r#qH{>8rZ2T! zC;W6C7%EH`L0AaEKcKm2EY~mt^r0R8c7JuhcX>kR|CjTfg@;*uv~f06y3a|%J?EHk zbw*hC)j;(33fx#~lmEjd!&(jnV|eOEq2X}KzH~v<>?F+@tI>)`sOO_KWK2_Z+!?|6 zZB?jxug9-q5}(9~L(YMJ<=AQ{{l$wcwD*H?o~Y<~0hj?yM+}T&mv*REnlt4)SAUd9 zDPuZ;3tz?iB;Ml%{8?8{S$Jpd@06j~zuj#%{&hw7_T&c);1K{<6x>o#^UUX`dR&6~ zIG-2c_GA&>BuEuM_1EjG->$~51`6Z}^iLgDueMje{90sd zc!mbT0n{Je%q_6y5Pp;WboDR)9bi@r9)S>}M4OoRxV_IoOxAtSWORJQX;XA5z-b*m zj&G?`y+V*^;T!fsn7H&;(HBLc7<-Q2rurJ#FcsQ?li`=>$89%$pZ4tIGzbWVa^LD` z8M({|D!n9Bh}teE}1f+)n`(0x>X`K{f&@e_C6Q+tv|&-(SJLQGj*Ab0<^y zp+%D*K~c2L0&x-NftI){))KiUDX-mM-&Uqqh!oVk2+p|a#Q$&&A` zGJgK~`s%B1rB4b`IOUS-n?!0Yj7*&MLTQ^^Zs___<@$%OzAfZrrpkozIcyW= z^yT4he_yTE+UDu|va8Exv$8Vfg|j2P7MXQ%VcmaCbCjNLu7%2cvQ}33Leb3kE9=s- zTdigKI8Yq^z>p*IHOK;lw8Nq9r^!lbKj zuCLx*Nm!dDGEs#T&Z)!!OABq1&Ee|jU$SHie}g|Hnb1X^+=rS z-D})Q8R4>mPHdR00;njw&PjU0$s&=3P9KohV5MEGe$HH*4upgkih>fel?$O8jhm6GSF1tZExV^Gh~WWCE1 zm%$q7y*{8>4gG4ZoKE|9$Fi&N-K1R$&pd4hYQ71rs%~y+>ab|%larLrLE+8(q<$!y zEtsWM=@#}WOuDV!*X2;R4K{7s-p~`be-Gn~6^~v@dn0n^=0}Ou+5pAZPtj>IDdYn> z8KICAz$TrxBTlJBoRVk!wFMZx(ahgP*eY^?b|sC-qM2^m6_EUXwazc17A!LM_eCvT zT!t;+yaCSIJQH61CfJHQc~JVxXq1awyF@yniu^JdfefT(F!Isz*UF^(WxIC%fAay3 zCfl};1CC23lYTeAvCjbtsDd7nB02!KL4W1JDKB9v|R|>O;Mf=^jHkY#+*|sXCt0Q(rg{ zJC^9f2{rHe%{xIaQJ=4nCNOC+(A#W5v?bgpIJXB?Wf9#k7bX)jj zuejuKZ3d@Umei*0x@sT&Uao_CEz)XO!S6jffvO54COvhv)X@**nK#(C116QIZ!`ux z@PP+!_7!U2$(6!jNj{u?3?4`Q+v;_uO;c@&l+pLWP;^MVE34(w^-P|FHOT1jfE^&z zk{-mW6-M}>9k`{V0O6&tf8HJ8zChAYmwW1)u?Raj0vV8^`#^rhBmHr+sj6*=O*EJh zi{^k^=IPK+Peu6!-8`q?J7JBRx_=Sxdb+HEm!A+( z$IfZwgzdLYP}lp0n$?Fpnt%+d1I=KD#0A57%>Fp;wyi-}I+%h6e{?}3xoxn|Z20W` zM;g27+QTI?G*$sz@@*M}fN!||*bmfdT7JO`_#iuvw$|Jn`zoxE0RgC=WC<^5xnQ<| z3YgK9hl;l+?s(=74d1jKFAG}jQLBBrBb7dvlZ80R7y$a5SNbbN~Hne<>#w7Gmznq!*-MGXQ$%S&S7@k8bQ!6Vz8r5v0hpeHiMU5i3u= zlO!FCi|zU`f*oA+#IjF~O<#dHb^1rRp!4a!meO{xtSob%)=o>z~M^*aPdre~L`H5epD>HJ%q>J2WV% z(eXS5jv|;UoClK_`n#S+AVnIgo)(ZB$G4J4hj>6=|A)7VySD(bG&CkJD!uK3*zV)< zmZsEAfc!du>Bm)WL5G)6QRiA@e76%?$B>4gopG$ZyX)GLoKn@F0sPP%rwbNzr@%sw zex7}*kXT-{e@}=Pb1==xtTTsW*mt&l^dNu#%ePA@+S*}aSpXt{YCFlHY<*a&e}EF& zWDQx4KP#rFS^1+-qlNroG40BQgdHaPeBpo>&MRh7_{$~nKnO0~bERXPzAT-!ERd-{ z7yy7OCS}8ADgdEFIm`kd zi0O7Q7&1?rhQdtT7N5m5$aPI7i3bq^hd5fns+>*#r+`D+X}mp!0`T5c!LwsAsKuvA z1E|2&w$u!{0gi`w5S}}Ve5i3t4xfTq-YBpQp9m&6_XBbdL%;<~84EbM$TKsi_zP>r zgkeT0e^^}1kB=7y#w-|9Txpz8&~=IMUx9oRvTOldIg=x#LDpEzwJyYk8sI0>GRB1{ zfsa(DpJyGMa%fwOd5}zF?20ZIe14E6tc)hG9q02vU=s8MtAKXXQv2BRw}D~op!S$= zVrO~r4jC}Z^Nmkg+9dC&V@gnU7<9KC)(RmMfBF;kPNMk803CgCc&qk%-r&g39eG)5 zkd5s3(-wza5f%c!?zqIdJ#J39QO$?c&819B0e(s~%BV#I!jQ#IQi(d|K6o>er$3>K zuV_}N@M`54g+}y(p_&#!Ur+L)zVIa92`}I)=_Ro9G(t54Oxn2_@eG9|&m#+!D2t`a*MMyef^dx!3Du!3vaYv7Y zc2&8ZkB{C*k)rl5E@_#9gyJLw+{U<*tc{PB}7(A>vCfK+9+GTfAp4RnlGe>NmBv|fuwLBs#1e9*YrL^!|gUX1y%J!g*1 z!DZPgcQAFWns#8XM{17AaR@*!qq_ohcNoo0x5Vo&e9!_vshsed zA_XMgb>#-ouHyXiWX~qb7~a~3((W&!{C&>2L_-8Gfcrc! z?tFPHe(bC5Esqg3W~`k(KniU_UP(#F7%d?TVYO;j9NN{*mb3BuUCkA;2QYl>k6?z3 z>)V!|RPF#_>`E?>!G4PwXZA#{e|aE0#YD4tSOg~iJ}b49Ip{urk@{;D%utfcFM3@5zxt&kn*Y;#<@6J}u2*bdVFH&qmF_z2f{Gk5M{` zT?8=MtQ2a_L@K=#UfKD#Hm&nhx*9yPVjIkfF*o)pHcR_q^iCk%y?-H`HFI9TXQ6k$Lyc4+4me})f_OvMM8}&a?+o35;9cp zC!cY(_Eh1e`l5djxB*uvcGG@VKYN5Z%3|qycGl8YH-skrEY|qZwJ8Lu>Bd3uSpc0-jch9RCq;_#Y(5^(iyvXqRIMbad3QubV1-*$Jh{P}^(?`g$LlOp zHoYWg_l*Ar=}d-#iG)TL1gzd`x@KMAHlSM061d>rD?TyN{ySG@=SVpG6YpieuSAR8Eyn4%|S+&jfO4#anh@Y4HH1F5b;YcGMrv7$XuCIQ1^TSu)30dAdoYo$!}rX%skzkC-5yLSZb#b4jN`0Yiv ztu(Uck}r(WwUTbJ+PwJr7q+;?#~&6940MZ!Fn@Ie48S0a){CEB{3G%{qofDbA#)34 z{*_1ujrr8_=FxQOL?9+vPAXup$ZNuk$Dhmi zIKZ7G3g{{>h`oc_ADT=L$%#V@)~YW*(CZiKih^JF!xd1^gJ_0U`(w++(gMnmA}>1s@YyiULhS_ z#~15%(;njxDdle8Y=53s+$m}=XcP0)zfsA8gdkKkc)oA84R%<6jz71%^!+~cjdoPN z9om~vzTxe0M^9fhpYBQG&0)465wLkMKHLAkw2h#L<*9U`c`?)ZV;r$22QZcU2+Dn|%A0GG-9aR< zPK{BEo-U9rrL97{^R%JAH=$~8_Oo3|xM9I_ke8a2&exzZUqekw0$J;bub09BWZ|8Aw9ln*U@12@`LbYp%N0rLu1V^&jN<%$ zc{&{08Q+g2UUYD7k)-U~n-keC8J(>j?hskxFg3rZTN(T?1}Q&`$zgcYgyW?!j5bGu z{po2S<`j(FnWuzfi0`05HPj6Q!<8}GsOe|}mw#t!juqGN@jMmogjWFxU&DEvOED=6 zDLi)7$Pg(<$?Co#X3#?ry|^~DG}D+-4?ryj=)a8VI2>bi+|BApu^of;=U1hCmVqK_ znYmhZ=)lR95JLV8^U$HimK8r@cn(y1(O3U}Dwa6F8Gr_G3f3W4D&yO z5`Wm(J=gxvExO;)b$Mosj_Ol8+b(R18Y96$Mpy;)h; z!9wBA6rXGRgV6)SC^uHvxg9a85Fe|NI(H^Y{jshx95?(_CIql4bO`vP4#Ci3Qy)sy z%#8X%S){?JC-HRw3*RDUMAq+d4BaOC$ zNzmnUS$^hr1_&jbcOz{^x_O=e(v>Q7;D#F^3@h{a#30)U=BIMANuW!1nN#7MUJNF6 z5)Zh0{AGNoo#P7v3Z;`>NDn6=W*ZI?b&L`tix^@e4ScEc!Mb?Ypb{UiDAMjH_N_~=zA2Y)2fYq!-pmZ{k#dc8D7n)n}F*flIZ9_s3(x5YR=UKlC-tcF@KA*fd1f6 z=XFdAX4|ELngj?elCn>Djpt}^4=U08-)NVf6P-n93fsY{3@G|pdU#l_^kYKzUO&i>AqMzwbNt%%&lONU70)OG!CaAYT)bWe$F5#Ys+5T_gNUs&Wj9#czwon1nKHiq4#E|`v<#($rOhINit#rVTn$wk*qra=@oMjPVJX-&m{>YBuzeDx!C4%ZAtx_5?&%k{d~ z(noBf_SxWi(+p!Z3MrPanBuFv$kz-PgOt|hkcq9Db-i^;VSl=ob-G1q`yTdb8~6nl zZj&^v56y%7mAO+!ESSTF8QQOg1ckx$z>n^C2;1nHr z4*7yksac)sQ)9Y5=tq!W(z!`bc({j2IFfmU!To+mZ8abK@;=_*OvKFiRhR|0{%JI* zFQFk+tw!gNk9Ygs>HXcAy8*+XpGi|YH7(s{6pVJte1C6$bx{TN2`4MhzJ1Jsqt6RP zZYY0F=TLt??W0>CYN4LzwBQInR_B2JMS-qQ=q9hF8UVTqqL9ZI=^hC=HJEIkBImtHO7oHIo2o)zJe@msk-F zN-=Kp3M(rksF?F~U?QBIzQuK|fOL?dcYeCeflKt54po-wTI3&!4Oqo`_P^%kYl8F- zO2ZE9)5P?lctz8sd#Adu=272I8EsG>lW9l)Utr^`ll-(s6(;GNHmRgk*%CP3!o$dY54j3;}4O=+Iv8J{MQdRV9#XxJ-SBFs$v}<9g8tbgzWA7bH-kla3#T?NT(7@Bzy!BSad-2^({m)S&oboZCS#9^0&;EBw@}D zS%$4oxUuCot;Y-v$P8hsH{e5tp&%BH(tBrCAD%|-#6=>!BHCrukqD#UzNzSxLUhi1 z!4e~2Rbeh)b=+rng&oUWLWe+r zC(IFUO+qH*aOaX31O`gKv+i)gfmA>qUOQ5@+~yf>Fgg>u;2x;B&So;_9=$tuHd1+L@(7Os_{WU^ioFsRB*7XzYW zF|5vt;p0Lrvym=;xk!b&N-mSByQZ0%8~&%zL&gPT5d^}bq79(Gb@R&^aZjfp(7ph~Fkt)$Nq=M=gsGL0<>tM$A zVtvA8=XrZ(F=lMYtLuck8tdx%{~`Ycg#5Ov@*SZ?^KvwQR;55O!r~XlMbGF|w8E$0 zni|m|&ueX80Qfiy!J-roiWdS~!r~L532h7TPn&8pYc{iK1H5KbSuOA5LsKmt=XHGk zc&m9o1lR|HakkN6HiR^sM{x^ZDDD-(p5`))h? z(1Y2BE}xozI^i>r5np!)71&A>zr^3G?I89x^Lz63x^+Wclhzy8qN#bd2z;nWMiilx7p=6KruD1G|u@P1#Jf@ zuxuh%7j>h^JW}$s8-0G5#lUO3(I!S`_2?51-v1sl!hIXaO|H#Q#9H6$A_v>+29FpO zJZ3L=a`Oc(2ZI`s8?26jt{l}%8(T<;awpjZ^|0rJTBDgc+B<4?wU!$`2@U0&N1gPT zlJ@q0`G(~~fZD5rO*IB={;{!n0Ayvdclf6bhC{lMM00)ybV*{{GNNrV@Mfwb1)714 zD8SnU5KMFvlz)WqevaY(JUBC%R?F^TK7E?EctG?gEJKta_D@CX^?J3=%}OcUfc1q7 zWcl*~F0w~P=_wf{7Wy7g?T8`E#^a(6{K#^DQCHc{(Bjd)o~tBM@43p~a#+M03%iHJ~Tw&0m)PHW8JkAaXibrxS0KxFAX7o z_b_7A%es=W08x%PaFgQ>+<3zB(1DvdFbx`}+=(^BYVN&~Fse?JPZfroCq_RGRih9J zpnHX9@!lj3wOHApv}{Wstb;6I!;UxVc3^3hDu-p7aqTw z1kCAPm58W-%eyg*FWlIYlIF0Rc&+&4(+B~#Jj=X&Pw22QbTH+Bc`F3r8@rb8+ z`!;{Ug_rlUkF)#O6M|Vk+Zpf+1D~gb|NCti?$qn}A>uUd@mTY;t|xnAC4c#e`&px} zOnyU)IxyQb>E)X3_$Z(}lAGrN`f$`QyEn=EYn<9WEvKvXx}LWUF5bkm*(L`$3FSKz zJdVWcM>j#tze6rbb(Bj|Q#Ox(J`v5J5Vg;JwAa|>;EvmVw4pJRs#V~AA5{%ff{fi=u5U4tIO)&w{h9vA zu0@>eTJ-C5EnM>8NzBrJaDnF%%+d2Qlo)C*$D!A4T4Zcm)T`VQ{1Td$UASpaz)gW- ziF9}k)AzitWqNhW^okg}Yx;a_aIF%{U->*kjyccR>n#sY1@i-Itpj58$A;stPEj6! z?S%OW?Q0EC(j%F@RCK6C9+R8kOdJCEy3N#O6P2D9=iw&vXXueZ&+t%ro`=4;8y$1p z4LcI*v&?DjpK8lHPC4BlZ8Tm-5t*SeQoXQApJ6)E?LG8(LjpLHw7xA=LICys4_{mz znU}$R0TY++a{(0tH8nVwaqR&qe_BhE+r|;T@2@~^+_Dy)_alifNlC7_a!I^Zc9rD= zL}ImwK!O5Dt=7Lj-#kEKutTm^+BkNVFT?=o8T4zqzn&&3-WH_z_JYLg->xsdenoX* zORJbFu5Suj5{6f#patc;6tqxMmyFBedR_dqxVx?U`tpifvS>EA8oSutf5rzxz3eut zINsIU@cQnO=|!Ztt=!FrOJ>F=`=O3AUcN8Ggx5rp%b%~myKr2PQWX8$VjTYa+Y2y3 zWqcomz!S`&n68*yT#xM3ec$zA*xk%ot=8Lilw)wK%>~!W6iUdFnIJf(I(d~?{AWMx z!l>B|VKD5f&8q5G;ciube`u;*(?$AK^PySQ@!cDzfcr2G+_>rQ_f;RqeRp{Uk}iJ2 zb3UcrxCQoW_xr|E*8PDpib42X4qQ2l7F^fWuzrU?DgZ-1td+;U33f6=np}_V?iq#=Y6MVSLl|Yxi>BR$<27f32IobRW14Bu(T6 z4BnqK38N*~(Iilnpc-thxBFcU_B2t8f9%53YUyP2qwyLpuL&3ZFj8%|&^UPb*sPqfP3*SypY}e9ktp*hqqx=H$b6QBnOC?2+ z|J9{|IDM4zxLDOjj`w{~P&d?3h;9>=vLE(U+unz#!?q1ue-PvSHV_7xP-;y6ji9wYHs`#hFHi8&`}lC!y=WPP(g#LA>bo8UQJRIa=6KH5c7RsC+r zXx5lgLP1vye;GvDpog;x6(yzO5rs;v6OE>}Bqc|(Vx>?#P<<1WhV?0uSgGUk)JyyF z`r`e?s8A-|;)UeIJL__N@zc+wSmE7w&=a?&xbu|jLYBB8MO%D-@vo3Pksppsj0+)1 z2@=h}$uO%+s+|%Hk4$~Ilxk7+qp@boQthQ8f%ao&e?bXH5F>#zANCJpi?4zTG=x{@ zRlo@>AfG`6<3gXJ0y$NIlYJS3+?83JPz8fI+0!)PX&}>)MwP^vBMl*$b=>Hn@@F^C zk+EbYXDB<;{UsAOu9#${Hg0EO@4Ill@}R6)I>p__$Okyl=60n5bU&5!We|ko$`u_#b4W; zfq??-AC8xCyc2pzqU)(o^5m0kMh|cb&3;E4Fi9ZN0uI?pDk9X3q`r2X75@Sl6ViK# ze|bIDh=&TwSB^CbyGi}VlUg#4s(G0?7Uyo-CV2-azj1|OtWtd8>}t_!s3Rr~Ck395 zN3pFwaZ+$7WS!g&jnA+(>b#Q6W68Yn0w^$9dAa4tt~wrjxYtn`66NQz+fPbtJ4mB$O|@*I>f8IQPPs(1V|F~dsy4~ign_V*w^5nHi4+B?bzl<-^M6;MXfxJm zD1aq@q(&3EB*?R19yP<4)M(>EkWP|WNKF8zEAo+G5iyNq21;8mLX9qJwJNcX<;6CHg&TAs_wWBl)A`rkw zb-Lu{aQo-*=)*$4S&x2LLM73IV&kVJ)W9+gQ9mC=|pJ(a-aiYqi}!9I6Z7f z9Trcm{oH>4h2m{88EkfDj)$CZU&i=U@WZ8H{(xDtv5|ohz)EN-?Avf+e~M>Ay{&rO z`6Q=Dc&6PMzXZH#_w|so<>=fD^%tY*$4`l!J5N0&6!KxIluQ?{fHltWA{FwP$?|gY zGfIJ$r*i^=g9_>ozNytm)kHLelfviC~;rfbj@K4sA6}7C?@5 z6IcR*lS)UZj#rbze)oWSe+=Rflfm$|+rPc_Hw^HD29)k-x^RsRrnpkoF83p{(GG`( z=g}_!Cp_TOWb8yaEM;b}glWKPBM&XL2x=ZSM8b=q@uff{J2pr3Vp9i%rQwNf7#xRs8sg7VuALW?MTK3&Al0rwJHIm>eX8{GI!w=<9U&AHFvLrIXf3x72&wHN^jKxFr z2-J_jY==cxV>_dFM(XzN@w~(FP;GhSj7r8lf}ZdCYP)SVZ^NB$rr=8RZb`)qA~Say z8M|`-tR0^Y);hsqfC@&qCm@J=2O#^U<0ORf1gTW4HC7oI^Y`f>mYRj z%l(1WA0qWHIibty;WXs@D(fpwQ8QmjJ48m>Pt~OQ|JOXTCJ{P5@mM1M+hqEOg2M?FriC*E_YV}h@PC=Odyu|3pvYa1*j;Z9?c>8IhFK>*J!%I6 z6o;PN=LgX%e@38fC%bQQvD1wL*XhK96j!F~mmDaJDh~I8c=E~fEr8#Ac!0=L$%_ z+N4`xl@0P#==0|a3}dju|-jb4_X?<;OSpz5NQ4jqG7zgm%)7j zC;~S(mvJ8gDSypcTaVkg6@H&zp}yH@tQGHMfV>oF7F`rA+8v+^Z1zF1H8W~`nU&;B z=HK@_BqfU)*`9PaMP4k5;^pvMzjNp!yU&X3`9V%3){ene;?Z}@Q|2!PJiNf3Ldqv=e5ux+DlVI^3|I;4)sE0 zM8RYsOxBn@-IQ~gL+#VC9|~1mU<&6p*M&>xe7-C)E@UnW@nRo#MIVLDxl-vgD4T+) z|8hrckkDM#UsLKSvOC0TZImatEU(rS3|&Ux_W;fhLl^3@s6jYpXWAqcQYp*pGLw)NVSsV zTw{UMD!0<4lP(949H%U>wm*-=c?8se>sZ#XNzrwoQhsORu z!hZ@cgaShx!pl=rcU={S)$!R?&8`j2pnK>dTY3lpSO^FAg*H6ciaU=p>3_>A#DS?~ znS`lDuQxb2AqEHcc1GHq=Z?c~A?Q1rOA-RZ;z&JmIE(`ci;Mz}2;=ySTPm^@T&2-r z#7}c!1%3zS%R#Fc`9QxubmZjq*h_!LT;cUyIw;viccyZ$n`38|SVg~(<+;|8+Wo9G zFoSrKTk%yr-imX+_+#J$Vt=L_dw>3t4+Me~FxqeC!?4f{AH+DqI;=Wo!-t|^Gbe)< zS=OKID+Y&2yIPr;`6|~n_1$)6kU8>}X z82~mQIs=ACX;L_#6PlPD_It~XghBD!idRBim28;Mk1SFuQd}isy$z4hfNI2uzK-lR*nB)%J`d#8AbHEt#VtlaQ zQh-QsBNxyz%}1jO<_7N16kKGXyh9BM_>3+Ia3xO~RlZ_XTSk&}I$nX+zbrp{?0-yktJq8U4H-A0@FvfJJpKt=yhQy|* z>a;q?KH-p-aNe_T9m}L$y{p<#)-6Yc({*1rRg1fF-CfLLxDoIdI^Cf+f(wj7n4;V@ znHF+JLpdYSQm)sBV^Wo+gvyh3xzKBUq1Q(BsP?nqTv!$WHtjqv0)y*RE0t0%{QA2eqgQ0)GkBZFm_o%tQOYFyD>^#7Sv{BTrK&le=0CAtB8$)0$k{!Oq^!h-k$$ zVw%x0m$0dWnwSUG@FqFt_1H@VLpVtvedRY8eeM4{`d%tS@}TLXVdVCBF~ZPkt}ZYU z8h|*&&m`(}{UJ#EJ}pJaPtm!j_HjqQFb3*x!Uvzw;(xgw7f$AL`bkPpl_ut5Rpb5A zVy!0VNJ$Tqnj!VyHRO(|Rl!(3Kl*IBYHDv`ZqZ#T=yRyH1yV_NJ>ybF!~(74g-mBv zzaGnN8`YVNTJS)BbJ&Gm?@6ff=pDzQ4)ldS;ut+Y)z#Cu$AnS^b-bcf;pR#e-X{_D zGA*KU7k}_qaEQxOxgtgpN0Ea2IDz=lg>Pl}(&D(0zqK#l2$xF6moH*y>6aVn&}p-# z+8o!XIYTMrA=W@V3D7ZY^)ZMRQf`Jbsfn-U#^u~bne>ZIeQsZ(p;}#Xtm`pIS)ZXN z+gwnXo;j@_4hF(p zj8Xd;F9xt^T;h5_E)(;xo=SCB3f&lG==NfOv-_D6HUp?cRN)$fvE=`bWd*<_;}XXl z)t=7F%}p$YDkW6c@^!|gm5W0ZPIbQ}eOz~m*4hwiNdgn2u%j4zMjj3xNvB(0CzLyj zTz`B*=~EqW1jyM=tJ83#<*|*_9*$9`+WLg_5**l@+tk{)&NX65@PLRHJB-G3?oOBY zbI6!c?Yc0N`IkzMBG*h^DLo362J0kA8A^{vp7JL)U!zn^Pt%kM0|Jbg=ui+uD=;Xqp$6w88zL> zQQ|{G5B}{icWXFG5F}I#yCl2a11T2^|8wn65^zqc5b1QBRKte;zMs&1Kr7M==6`M8 zP$t1npMm_>$H7}T@$%4jN1QaWihi4bDZ=~d=Fk6k2znm= z7Dhuiq2v(MsJxHeVI{NJ9D=aXwZ+!KQ=bs1@ibWF8mpj zm%)7j6Sq)^0kI4LHj|u`CzmOe0Sy8+IG1tl0VsdXSxs*nHxRw+SMbVH4x)Ha zXx`$G*_#q_^)_U}&jhl!YY;#Q|EU!`bFiFeR4JGm+8j#CcuFYZZ4IS)+d^e;mrzT? zvxa|0=(D!a3~h{^Q-%WIoV3lTl#p|B&0vxX8nTFk4Fc5EfKjdxDun793fZUzh7q|X zg#c|z2?DxEA~S}T5(ef3gE9*Q%p@1rpf4iVU>Jg7oH1$6u5m$KgAUa)1kB5dK~P@^ zo^c*#T%ajFC4%6_iEG?$SjXh1Ff^2XJjZ_wKi6Ow7N`lG3YLpqD>cp7r=k7*W(ghy8_X~jMv$8|Us1eX*fn^)RM%**>JBj%ifbyO-Yo7b{K!~LoVNi zb+(g{Gj{L7cX=}tm^*)Yv3hg5Scfy%`1~ZyUoAeb!{xMJ{rh3z#{adLVN%QWV!8f^ zR5=VWoBz7_xcYSQX5p%`QT?)b`|kYd>T@{r6l@6B(&#mYIlsWW$TNc#<5ewn90@c; zLP;8t!mfE>AG?VfD>>%MonU`Wu_A%q3f}ovi^K{_boHz$N2Jp}FcM#^()!GhEV0vK zZY^fDPH>e-nT~+9ZMoSZS?vU?L{Ir$Yr&*ZxIH9;#?d}z)0ioH%o;Og#Ih@<958Og zGHf7}cXAHcfVLQ<%AJfsv)qeuO|CJnQ6J-4J7iqre#Y%%ccJc4R{MXzgaPtYEKGuP zMriUw#=_V?W8dy&%5f}=d_Ingu|IE$sj>gBe0~s*sK3f(12tH=>=kuHxmRN5M2BJG z#ogV)#Ouu7Fgd_(b8Sx1QtJb2OOdGD4u+A@ixn+3dRgDqGm65PGIxTlM4QOslVI%o zk+gGyC`7TDl&@AOjMIPCD4f5nAh@ZpxNAJ-YiY}m1YZ)5^C!#Y3SFHIhZ48tV94SB z+5Gg=pX;Ih^SkBWv-#82#oNWj&~DKl=5+tzdsazS!Itbbv3DezPblo#0|I75dgY#2yn;XvcPln~s}%JIA>AEq?so z{ozL3Z%>hYjYr(>x~FVs@<4lvsG`=*IAu~43?_)e1e%#ygf+)Dl`Mz5illy;?_;-Y zw1myAbb=*C6}=ry9{$y-i$mm8WRJm@=Rq!*T;ZTAb$>W)GnP2q*TBzyX(<*fss}UmGU+Ij zbj+jxnm}d0`&p8$M!$@eI^34z3YM4-)WTm!EwP8QL|9|f;pRG4+B_Q~ZJ=2>Zb~^! zFZu;Vt4`IS4Fv%i1QS{Fink!Nf_jN2dh9&B>!Y`zaTOXKIVoIeGjHl68 zD{%KR!3}p*dk4H53g2;dxqne>hfV-(cA0!ma7KiWpr2uDqSX@>+h>nHF&c6aKmcy?mB zMxH9E$Kc(LFOorEIsz7n?>m5mCV;a>u~31ogJA+EBpnYb$IdK$g-l50+%KI^ z?gyRRNbY|5Qf904W+6l6CKWunyQJ6jn=ZC_dh_S~hgTl+JjY`Se_&LvoeAaRZ?G)q zo)fWuI0$^RY|J8GIt#9tZ`WMaRbj>TN?RfD!^dKK3W#++EtF7h*%jNTIp(7}5pz~- ztGblh`^C-*1}kd;2*graiOY0# zvgHTvidZD#UdO+5LODN_3m-mv%3JDIlOLN_w?N`|I1lCwW3>10_cMr){P|oL7uSPg&bLnSNMKUvbl(+#;rKLXXhR9$Nax+KCwlh%*A%_<_Ss@e`xoooo* zi;Jf>t`V@r(V7KGu&Y?KI0I&naXHAP5oKo4nEBk_i7C%9Vyu?WxDbO%x}MF;a-WF{ zA(@iF$YbrN?9Z!26D7U3O!Kr&cNHOjGtp&cToh*u>xEH;d(+2`&YzmBxfs!LEN^Sr zndT5B8$xOQZGMdW5yimezW&&im}hqr<;+xrtQs=+l$r1e6_Y&b-TihwC-A5?$K$6- zlh-FVIpq4cbMb&k%~Y6|2z3`z*jppQmcW;8lBCCV-1uR)^%`n zvjTs)s*4D!gFmWD*U`JGvvmgf0cwWOmp^2>!A4zqkgrQdm9?&``a%PKMCx3eGE%@f zW*6aaqtkQdl-2uW;Ql|fj=H=C1f21B*MnZa0PHpeYPxRCj&y!0cp(!%63j0W@NO!g zNOfr{+NqM>PbHPNyWQ|KBo1_cderq!rIQVO$Iby(%>jE-r8qdc#IOzd;do=O)3&S2 zqfOa3x7+jk3dLC%ap;W#7RB+XvG{1zpc0P}Kt1ZpJ~622z+-MZnrro?!H+t?VLBc= z6WyKd#&Jh|vj6(@>J_o=>YQteu0LdE>q1|8n0zrK_q-`^x2G=7C)qQ9@iv>zh45 z{z;>zW{q|Fc9oMs(VZ&hU$X~{6CHk7Fo(BWFB8G$MC>Bfy6YOp=WtYa{u(!%^{@=J zn{>YV9L=_|Td#BTvC|Ua_%^xvL5AsF@}j@h6(Nfv%IA-%QQzDM;LUN7wi~)jZtg&) z=efV&yKt+vX=}Z;l6eS!=jk{Qfw7Locrb!02!|R8O%HdZlFSd3b6>z3C_qHLfUR9i zNH@b?SO{0!*Rm?Ny4IjUoh9^TyR9po+yM8+PHHRrrRh%q*jpQ6CHRYjv&tl;sT-)a zX4G`>0AF@Sy@@jCy>kn~0fGgk_yJUEVfubntzD z9$6{f>a!=q*rx5eq`)NH1oAv08xTI?J1h zxeLG~lw(N53?#u+a2T@Ck10XYM0btR)k96pb7`SL%NwhIatXt#wc{G;fKW|b%Spr` zZ4_}xFh_!JZ#GV`&$6luo##&$1a{y-5hxbMgu`yF6arWVu|2_>LX7T-JXow*9#RnFDoYoG$q6P?Pp4%x_?R2A!YKWIH6?27hreDF|uR!~N9cO>jD5E575Z{^L zks+t(q1{1FCJn1q?+vI7F{gz39gG0u%|iGDfJN+94aQCOfC?cM>!wgXR#dd}$-Wug z@v|)mlVsfU$$h=0Rla8JhQ93f4}2TOVKbjn0=J}ph-$5^(O7(}SA38F3PSw~5-4;8 zaYa5%>&$xN))Lak-gD%PeJM539Qp1G^3^}g*Ms=?1oPwoaS3(Xx)vLR9Kf-vAhJkn zvOX9Tsjm?G{xb!QdL}^zOt=N*={Ng=LA*$+JWms|C(Yr@V>l#)gzq-jB?aI#`F%=$ z^etq6Sq6~%eUzK62?t5uH7%swav0(!)7fl`A$%5lutP}2B6W6)VWvIczudz8vaYKR z)H!TBhHj#*l1rZjRHgpNuoEzB(jT(*Jb3vM#K03W5m4cHAgiJQ!vb%9Vz7+`f=v4C z2|+XEGa3akRQ+(I(a*=Ui98m;M)QNTi4+rm(HNipKJfjo{=7ml6aI+fzu#FTj+qZ} zFj<5jcb)J;#(6yAGKm!^)3^kIyXs0C?uKZ(6`;~-!qA{^!MdJlsCUin5twBFdF+Q5 z!u*^qGW4K93dXjyO`5N)ngx~!NGNLnZ89TM_eUhG`*A#oZv_VzE~|{*Irban3^+S9K`jseavQ3(or#j6 zEmB72&+-w|cY+by^C%9+Jni=u@&i+|rwj4i=WSR>+qj;sF!4H@*QK4FNZov6TWTf6ZD; zliRovzR#~KPKiBLIgEYah>*l8iqw*nXOv%`ZZtrV97?N~ zr}iQOAkZ&-{QxM}Svaoq`NlQRj}JG$eDXpkW^up+=V9)+ERF=ca~;p|gkWJ1Ie`or z7t(oHIggVxtD7o&+B8{N+)ague<#~J9!<&(b!LefRdtiCl15PvMmf%$3g4a7P0|3B z?9Q9aY8q;m3VM|)=j?9kOELMUA+xTsVwSDpWv*!I7AaYFh5CWqRYl#jO0fHU_x<5t zj>lX+)WnyJN2Ca{oC;vI1X;>Om8_^+&gp61`yf>g@)93p8jBH=}9nj-KZobO~AGx*%msRTJJf)}s|cC53rr|y4mUupkcnsZN#ll3}BGzuTY1FI=&tEQ$6e;W}i1}Fr=(%B;s zmywWwv!$LLea0e2QG2OtmLHuFqan1` z#Mfl58VGy1?Y<9|%n$!k zxp9`fe@1VWgkrgiy_n?#IhiR_4e450*KFVw74*tYVY+SlhqjSNhNe_G9FT>CIzBU4>DN9}$j43~T!)027nMCap-For*N6c2F3ePyE<}r#+vjADIT( zp$HyAMFQnrZ=sIhU}lh>N(Qf2!!Z|Xh>&@qe<`|k_`CV-;pX?7U1A8C8$ybTW9IqJ zY<2Va-E~rUhXDqnlJl%*t{h3>CU?Hx{D%%caTG z80}Jk!^yjPNOvv8#=dVMu7`UNeswhQgAk4rU2{Xce5Q~EmGrYcr^2XuA=%WdF=4wZ zSA)uXhT($3N`<=N%RF0UO*^lw)Jj*re>X0q53sPrA81tzXE5^RGu|h7t`h-b0-vzV zB40RDJOug1ceEIqpuLYEGVtT|3EL4M>^x2-my=?%T5rb|AC$^0&Q|NPY6h!)oMwOY z>fQ+HRrk1ug$rWG&b&dLorifq(m(~(HW@mwcXmvFo#qH6$Lq)gF%O=mB*eXw+ z&NV|)pDn;yT-}~OT+IaQEwv6ahmlq*>Yd;Pa_$(<*jtTD>%Af4Mz`rfPn&VP3_Ph($cMPK0-YJ(9XclP)G+%&{NN z_OQO>bi_R9-Gb|z3L_X=xGRW}AN!k@A>*~2#!e8#?gKqaMpwWQ&owjnXCEyop=_Pkv<`8z> zGNUJ8(q(Yu**QmVWQ1wVe6PK%&x&)D=!@mUX`6RSu7w%{R>zyB_yG!z^*x_@%)Va* z3#}JhG(Vau5c?-^n7BhNf9{KWjot5;g;?6gu?X8j{5RqwFAp{PAlxR; z>&KZ_$qT{TmZxcN)E51X?wPlZ4KF95e&ntL5EllQok|)?_V!4@0~W>RGx%y_mC6>Y z2UWw?tZfww<{q7RvpnLs{Q)nwCn&YK&629LF z__Hk3TA}lQx9&Aj`@R)&jh`4jkdzE}gs}|A!l?6<6*W>bf8BM`%$DjpC*1Zn?MzUj z5p)PYIV8!EsAf*D)rvk?A1l>4QE1}rVLwl1YF6g<0M1S{6kSZG|s-OMZK}2Khgh=PRI>A>siPmk!ne6#_Ij zmq9iHDSsNta@@xC&ac1`Q}MLWd$(0SB=HiZT;+<)a#AJf0L_3ToEcz@#j^7Cd9N1? z8U%-=tfZ0)0rb3n{r0U{WVcz7eRoxa=bx^xUVKXnlNn-_64~`lMkObT(o9KB7?;`g zK6{g&4#Vc%^;jO=GKOk@Mlo#JJ zno&xulniOvQcH~B*_LTyjP-~9w6P*DySg3w&6aDK_d|6GuXW>}yRPkFiRRm1H;U)w zp?}_&L)|w15UMi^{4(YEB?SX|iVRNXoWP>A9M(X6M>y^8cU8bS+*SUit4OdsecMFrWwUROhmRY>@-JY?=)5nFr$ZGW zk0qY?(J5s?yH+NX1{Sraz1;2UJ&+IZ1AidgaxU|#+54CANCZoFI&Ll-bq@hA`nd5|Gx38g_B zKaM*jpvp6H>Z*Ocb0!HAKtWQVRe~B0 zqq9`9*$P4PYczw_!<5L2W2mk7erdP^1M-ez<~AKpW?;!y^W@}zuKU15Ewh%0#h&d= zdk7~w&jPQKYUWk$mwxER-7t1e8-I>AXB~w%`v-+0Bw>Z{=S&V1x%VjO%mYvkT|`{} z3icfc10{fDhW3Iuc%g|dWP0Xf^59ev$rhWAZ5Jr>#!Pdh4(JTp{ADZ;-s51c{#T^2 z`t7b9`=NHqc5ws6QV?sFQo%?hY;a@evee|q4YT<;MBK1a{kA)dJq97`tbZJq`td!u zQ|~0G@>i%F3jRNKjsG3vZ8?PPByo99>clDuKfQ?PC!H9y-4nW5` zo}-V5^6_xHwgGhv%Dm#ex&T#t#9HJ8jw-BIU0g?`QR^?%%*TK5y*m_k?hkAdR*pLf0gw=$bpbQ_d zZT=o28)$t%6x<{++XfrN<)9NAbz&hrferaU+xe9s{$Nth7A4Dn93$$(VF7?H+z0j9 zvk&{D?y3vUEV#zCl$SMUo(lk6f@q`CO_x#H0WAT$mw(y;6@NlSqH>=LeMV|~)YZe{ znz+&2h4;C*7=NH15&}LJd%n7Kh*U&bnr?Wk=u(U*z1RbC(#8Zi2_f@S*$wq>Jd_=N zi+opND8jc0(V^qsR9%O1!UZ%V1Sos|JXEnF02yx~x^3HW2-sp$lWD0qlO7OR@Jv7z zEUEECVkG_khkqab{1e7ZvR1!B$*g3kUX8C_|I%(C5qt=hVY?^?T~RhK86#zifq_Am zIcHq>@x~<|czIp#Z^H&KwHI;u`_dP|?V{l4bgl|PtPoIY8}JAltA47NO~PD#_{L+4 z@O1@slW0OPF8u>c{_oU2Zb`Y(?aY$lg@*?PjT zZLqlV6kg`pR~j%ynQ*U-yRC#gd8_zg+ZWQ^}j` z+qK72SN8!{xzN4w^@OviR9P{OG3%PTgwmT8Z=P`{xFB5UbPv54VJ%@0_>v7T!Jw4R zzr=^~4}TpWFa2V(LO3%}#7|u%unKiWvvT&97pMc&p>rb-U$2A^2;_3IK@{+cv_MFj zMVOnFbFe6sc0 zrS&Q#I;iX0+WDG}E~X)bpm$Ye=RZ5lSA9&u+J84VCNwx4a}w5d;kT-6djIO38n%bX z1CU_a^>OM4bo6e0E=?|&nkoooI;H(BG~w&{+)mppHtfl+y17?WN5Mdp7G_?7GU~=F z_*Uej_d#5oew`IT%$0bkVHO;E23dd`caz%NWpz)he5Gzg@jePJ0+sDf>=VDD@Ov@Ia6 z*leo+cuEFE5zyHgBtS5|aGfOda0nVYV?<)rA7)9#iVbjLv^diqb)Mjo#89@JNiFOm z!4o{O>{q0h%*o~E+LmCO9&nOKjF$`2@?r}z(a1H%e zaSmnwSmA*dgaKXwk(!5L;kt!raHcJaMm<9&FIR9$LS1PdvG5hT1(61``9tafo1UHahZRL8HPTs;32&&#DgC@!p;wZ^ruKI_tq^302F0buMSoYA zL*tTj1<%B!7Gjzv#&lIFP)KZNh#s^j`oC^ZA6M}hC<~rwPbzWy2eUha))RyDmKD%W z@~5Tud7l)r7&B1Dnb0SFbDkMOLXkx%QSoIyVRYZVx$Gbq>n&^eSbgAte4$p+@*yJwaKnSd%ITEF=C{Ctg4$L-S?05 zxYiO1Eo*Y{n@cm*f{_A@C)x0E#Wn;OkxY!JUXAyQyzp|}-OiT3=4u!Kb$ZsP2Dh1P z7-UETkuG6%jlM}X9GXb2(+w9=EB`bfyFjr7QCYquU^UZDL}S4vEsHs{+=WzW>wo?Eqs{$noY3|kuEUdYI9`uow1Dn&8a+N@#?)$J(1p`I zt7beuixuFhjNUtoUN96^Xea4p&(DvHxEYpRg-ADjah|A$uK!TR(S-l0!ucuKZ3-EU z*=_278OyE;_W*Hm0({K^)GpkOnfzvKyj#%AlaEQZ!WXhkfGY|;rGJlSmq_#duj6gK zyY!NgHK{SKimsnl{n#GXX`3`;|bND=;Fv|_I6VFxnL-azG zuVuJ?_ZP5nOb#!uM7Lo`2j8p1C*07+jqbh)-onNG7!cjVe1BgeZZ!%c>}ve8X{Y~4 z07}+unL>&)1@&y;(46=SmsjClw+_nQhdYFQI<`r>Zbgg|I3XsAi&<}a_09FwFIQ90 zWY9E2caGPC)MUHk)th%kwuj$P&>STF!9gA&KRK}r-uUXb|D|EvGp(ymuHceym;t#}n9;bgr*J>yzE-YR~ z>6prYPdh~~wNrSf8h?MYTE`e_9;;)-C$~yQKU6ud2^eD<{XDJGvUqtyCI5dtWqbtB z?`vdcW1`EA%=5-X7Y5VQ2D_7puEg>Z>0uB1@)c5^Hq4KvaF`Uy7b4jK(eU3A zZytrWA>siQ7Xdk!a32CHlY4~=f3Sx*ZOrO!%RmOKYuaJxhN27ll6D4d(>7O0R7E+l zH|)QUq)6G6FO4pCvThHtXfeOVi=F* z`7DJKL_V61bMfis=Q9(SreOk)f3Q(HGfGF^zhOFAmTB}s;QOx7DFA)fF}juq+*~0X zB_z#qfoe^;81`Z5g4Gcw*tqR_w%3VZmV_h=@dzX-;|cbwE-b}wJ{e>5dg-P!5q5WzGCA&%MiFd`Ea*2Q24{EW9|f0MVK2OQUC z=Y-~O#jNkqEJc)J5?@{jLGSTohUEAyq?FY5ldhU@lO}E8m82+1$(XAh&yES0&+c&p z1=u?hu3{2l7`{d@TQJxfKqE!BO(nshAw?k?XUwKY1t$P58%l6ke_X}!FhevO(j>%L z6KHb5t+NIvh!q0S&qUaf}hb)pSBhtQfk|PEHWI@W1?Z(zzHmc2J@FZZL@pW zqU6c0)dPX!*$3|u~+`VeYpM}!(3pv}%_fbm4ZQd0y)X!6nY%ql``)9RPyFd3Fu zs99QJ1;GO6tCE2pe|)27nz~YYx{ikdF~4OA<^^hnfkj#;}!qLgMxdiDDkw$12Z$Srr0jTSAV43SsrbF`{#r{9ACa4u$Xyj;)>o$Ivoq25UuU={$Q@$13(d zOsnAcymo*GUfo-tJyDx+0n4L1Q&Y1&9k#S%xfbfmn4}R5tJ>de9M#I{dL7@F&FW+s z*_D0TDgu5Yf8it_k%g>|D2ZfUz;~DB3L%?Ac??sTxX>L+^WlObDGkT+a=uN)@$5lW zg{(`LLvfeFO>A=IkCk?6B~G=HAIjTl;rMEr%QYNBivO%q{M9P_7>f@1W_{b+n(coi z(TH!9B|Q}mF`aVf5$%k;-4{!v<_$AxUsXgCC4nR;e-(Fu$^~nQ08+nSNv0S!9<$AC zsthqwR2`G}4#gP7m8#Z_zF$MuXblHWneCPpKU#UCwKbdEt8o&jR$-}|$mf~jY{(3Q z(lJW1CM2!iG;u8#*sia(X0KsL(Ls~g4)C1fdcLqm4DY&55A^!>&LDR)mlU0nFeLZ9 zi79?^f6O9{{$iJ&<=b?jk9uH`VtK`!1x%_!AyPC97m;+9J2cKnN>zYLQWg>Gx`VH+ zLOf~V0|du%ds`?goif|$frq2*rjCzXWJgK1;x^%bm1Fy!oO#DKERdh~pT*u*cyt!mvo{t#DCwKSd%hR}K8v5sIm6-fn`b3sr=N7~OB>PnBDXiMHTDvP1Ic9;AR3KRI3Sbc#||4C$qJOVyax zB&5=6b0i-0YfOeqOs_1d({GCrMPC+$QW1j1>Qc0|95_x1EvMI#L$s4k*&_6v3P~qw z8&j0FqYSFkYWqVn7;g8Li;8rFSU@ije^Fbcd)1g`e3A~GrX+na!jQgD#%*a~V;GJX zA&WwiJBeD=+JFhcacT8Cz~1TwH>%{NczaMP$=H@rf<4W@KO>Y(a73?e(go6Yn)x#@ zxL0?z^kV0tWOAG@)T;|?+q46C#5lrv^7X2)sN*8(9j{W7Bt<#8=F1ABqRM2Of9sWm zDn=qiD9f@Gw+J&@B^wCZJ%w72#{touX;WCOx=|?5-L=F#bv{xr?s6SWkKS(B*RoQs zc-pO`V_LxOsmfABfrl5!>rGqnIHBv+KWATwk3(`S}fYE!Rbn?_=wHVY!5$U=msr zKR}rH*7@AJu>VNstPmwl=RCa{jY;6!H%ot7>eiCxIka4xrDF8?{MRQAavaM+zWfKR z;>v3DynhR9UDvTf6ip_cVjH2*Bi7jUk*9g`B+rUc6RgD6WZ<7$CtURaVe4Y-W8Y3_ zna5cf^GlR(Wa|*2J0&!PsOTy}9(Q2sSYrjs5E!JZB;TA~?=6j{=hyZC<+ zGg_o5<|$mKNj7J3R}2oo4|eN>`*!R5hzl z5LstG8CMMV&149?@`B|>Hj`q5i{B$)6PV*g6=1(-@g~g@PUAO>mMeg*2Q-HC(59qp zXewCDW+klYy@C@V51Iy?vY;sJ^Jh;{HoJmi+PfW-J`7Wt2N$3|g zt+VX}h@m<(Aghwc;y%&97?H@W&;Wm-NggX8Kq>QZ>;@Yoy$)}zF* zQH{Ig0GoaVOGgrdvH2&*ciIrajvF@V01a0u>NGTLL0IAZvavwlJ<)UOGSz=~>vVdV zWdfWw7bla*bqC4^qJWC|6&rKaPx;q2Xm&dCLlC(uEVob08>!u*iWbMx;Shy*TTH515cmT`II@?z z*Ln`{CKyHjwv@-)7kX|<=OIYRx4Ak^uh^j-yJlrCbSm!!$O-E8n!QqHPZO%uP^1L6 zt8lWnwL{+GX7BW>s-J(20;%M);N_I3B`anuFZ)<*dvo((iLf`Rmgo(Q1-sMWFhM>E zs_{~>ZD<&KBtSt(YSDNz%?mbv6gfeXmm3MU{A(SLj%@Q6y^buOt6XwdIN3+EYXI3V z>3v81*p?P~=g1KuFr?E5js|WfbSM*@)U0ql-R?t!m||6SRC|9zr?pJA( zuP;jkcBy~;el$1jm|nIkg1l_K+}rKt>|5_b1oy1CT+P$!EjgZj=PMxhrgmj}r&ax< z;d-i7kJ%MV=e0uDu=t8)cC$~u`3c0h`WOKRC=5f*^usDiwjV#Nay`!tAm&9AJWydvCNrL&IR&|#JowMwdLx{c?zz+s| zWkTLNc3I6|tkOA6SUSRiUCP6$nk~P*aBcZ^vbyKyn$VdXw>0m{Hf59_dblAhHiT^@ zrFw}K)C3D7gxEPPNt>(1IN&%V?nO3C>~{$nHgafl|V z^pJm(W|^!YuQ?10?TvnzE%Y<0FG;2-nd+|PHYJ~g$en1xX=-FK^odQAY?VsdWu3OF zPwSP0JN+)9e4a8kuM{dXBP@&s*^TJDM1ZR2*&n!Cw)%WX_*74}C@WoKp72uUgvy3d z6gCy`CkQ(Rdbwl-yKwxYGXP!E#~66(UPXT$t`&87v=VHwigmXV7_QK#A5wiEGa<$X zY2BkRjTENp{)U%JJ!L5k=xWIdeQRPYkv}KvZ+)~F$IQs{kb(N$|nC6Vjx6MEPeu!9JRwEPf7yc8^NEMEbb?@YNed!o!%Fyk3A z7K((%vCIG1tl0VsdHS?h1(#u5MSze2b#r3`D2??-{4NPxRsilhzh+$Kd*2sF8}7DtgP zAMz=GeP(uM`LZ0?l2d;WcZa*PGrxH*Z7=Q@_Tt+so1fp@UcG(qM2kcup&Kr4Hw#-N zF}$)D&cg9y5jpN63?kur!Qyte_{F;2%H_&)0xP{+uH1jvs+RC`Sh|VDbosw7ZB(R(J)6@ z*HwM9{O|4GwIYep#PfvjEJ8mJZcKVSpxnsP3$0RWPB7BPdy&;hIKF@*XqwX7rUlKK zR%K~HghGF&b*ajGn(bt}tu!cx8)=IC)D9u)iZ(4})iuSD8D~h|DtXZ2G|X0m`k>Wt zgl)ScH3B{)|t zMmj}dya-)KcoC_@(gWbYqBgcc>1m|+nHCM(3(rU41`$xtz_mU|(By^|jZNN2Iki#9 z9tMNn!w^t%GT04H26MLO51-zVb>>>w2{;;CCQD03hs}#V?)g@s7$AxuIWqU#_u!vH zFvfo&V1j4d=D_dYu-pa6YhLM%Rywh;L(i=I)eh_v>^u=i{;u(H8;kMBx$G6OTcJdrtS(>REMSLO&`n>Oprvv4+*rN*dB?MSM(grmOzMUOnn=$ z0}CB#%x5R)zR}oy6LuwB&)$<*)rMcuV-}Z-3j!v82Gb3aTeB(MRUO3)&F?VSjx>JQ zstl)FnpRy+gB^+&S-C8D9WJW6BBv?sNy~daLTyINWN#CH=E*?>xyDaODafyCmvSg1 zmTC#puIXV)iIS^dZO@Z6hK|ugTc}RefN7q#ydFbJwmMdQ>ysk0jV=1rMo0@p>Kc&h zRKC}LN`#IG61EKG_7I%f^Er(Bkbiz==Ul>yYFF+@urmI}0g-+Y9QGB$TW*d;E zs%fnK3kPHW!g;eL=R!VZ#oW1Y9BJ0lmg;GL!+4PhV>ZkVrSCHQ-4YI*7+cDVIF#iG z-yU3qR6CDteW-@ToNd`uO2>Ol@gB{qG{#2519iWprSm)zQ!3TCmaC|bdYceJd2A$wFI}FgNe!!rX>>{JxMOxqU{^5{@2(uG7MZ{=AUH)T# z>pNa?FH2dc#m&iL7C7*XJT_8Qb20~?3*?s}xzAz;PD5pTkhy=@rkHQW?E%6~C2`t! zP@Qmmkw76@IgUtzfJ)7Ou!E>N+OkWjsnAD4CxEU?6AW>&P{R&Xf=^t8Y(*2$Go!i_{6$E3v9 z5HqBA6j<-Rq`SQa5+<9v+R^A!rar57^*tt`zcfVqDEDYHt7=qRL7-{D1enJb4*#Go zQwpdAwHsQ-7>al3>rNpNvG0Lwf$(jIWP0=F%|y}X@P&md&6QYu^#^~K^8QzU3HO7I zYFiy75Mb5;!yX6!JPdEnJtg{+G;ku3crG!&)qaQ)OJj1J53(P-hrxO`lafM$40oNF;eEQv~EM_&Xdj#&0# z(`_~?Q*s^%8`Zv=`wSh@$W40#8UtM31Gv@U*Z~FSQ4STDNboVb6ev}dB z>7}i^lZhrRc2&dh?Lm5GnYG1{MgLGe$b)Wf^}dQtYr3hCmtE>Py_h-xjDHk`L6!^7 z_4OmI#@51qmW5S9vJ%EO^fhaaN?9pWT*le$RecDh5|;vG;#VHhqSKcsoZ)A$R$)_o zka*vkfJI(N#J8til&F^w5CSs}kKxcum4M6MykL>j MD5CSQG#e~B5fp~NuFpa85 zEcdl4+u5zB|DFg3IGR&}7p8;`m{9Z`%381UDqF8luyL5pv*ZNMPs*uw`>JkFX%9cn zl&hi81y84#Znrd}x^UYr6?G&uc0feO%v+`JfpZtT`@kC;Wi4_FX;<% zFnRN)1xnAXAoO0emBCR)Fx@J|Ex*CO=;i=;wv}@9xf6E6xD7p^DjA6q`o(Y!MVJJJ z^Jt|EAtdn$T*Uz*?2F0&GVZv^4N~g1olr-UHS!nhkdOK~8>tQI^CKvvBPPQdm2D^Y za|yP^iw3ATe_>|#cB;HMpi0wQ;N?0&e17bT;J;8Cw5lQWI8LhoqaGjJ1{utPiVXgS z%RdYWRJy}}SPV9eLJXGP(N76pL`i9cTErfLq2;lk#M4-g%6yAy&eqI))Y6@XQ9a<8 zZVnSx?Bz+bD9hyl)3P!^X2nbN9c?|1&z=uZ*aRV$f2WN1mmPvK4o{y2XxF2sXaEq1n9U7Nh8VU1Ac9MPE16>j7dHx$qAiE^+h zkhbj(u}DHO$%vz18%8S(1Al=aWr4Gsr0ipkJCd+FyHJ{tG_4zaD~we$J)4=NSyC+y zXTf@ofBY$i3hW&cX!|BGy*%cPU_j7!L#+%c^7&jzx-}Sp0b3A@u_nMFf{CAS0tmrT z*omUv3Dj{8ni))!$KV)oA-XXT3C72}DFm*8oh~ZkL!g%Kq>4IkFx&HbFiT@={n6CL z_yj$+)VWTH=GL;*^s-Nq+Jjt<+nv%IggJonf2M>;tPG!TUP?g}Q_v87g&Rz6vnW+a zO)NL_4_5+~?#M;mE2k#KYe-T0jo!(NRWwHdNw3!ghs*SGmPCZGH}D+N z+%NWcTsajYv#6_xCxq~s6utoEjAz=DL>)~5G7>T#`$MPXvRo8SlR^-DniGU*>8}uE ze-*`_YnY(9kCG%x_IbkWK}Djc% zhH;|&D@%A&y=B3^i+C9``^B>Yc-MYgNbv$VMZxji^F0ro=Z|Toe?-lcnknLk)3Y#P zGNc1CL1wgRsXkV*FoTq$L`;4cwZ(rle_x_m6l68|5$FGG`fN`;CPxp)ma#7>I*sZ- zrH!srxxhwDt92^5IhB0d!{pnZ%3n4CUdc-2M=ya*ZRXCjaM&#MgFH}~8ta7A1ZauU zet!_njvpSTY&|-aZBtAG)Y|o~3ayj!fhh?0-80ze_WO? zrtO5SX>RH=+nFHmKV4rm8~Eaq=0y1Qz!lR#S7gT8a=d0Q=ar&6sG^D7rJfr zBKB$aB1&iiwBRViu{DElzWpxK?B@kZ^%sVpdJ{2ylpQ+fBG^c2M-$` zJnc?skC`<>a{774^B=2j7K3?x9pEX5xBTh{P!Ld6-7k{t3`6_j$$fxe5(3#y7d_!a zNodeT{rr*(Y$s2ds{18!Z5d-$#>?w>kdWx$y?Q+xuxq{qQ9eNdn$^1G9aGl#fYZ-& z5VpwRP_%q}5(k(nP$Pkhf9});HEW6ka!f)j3)r5SA6-D{xf=x{Bw*(@f@p$gN2S6x z!O+OJNb7qB2WJp^23;gYCJ}$!p(NW(iexqjAFA6+>B%9vza+99e!)^8nEf6g@|4{r zX__C$AKqU7Hu1+FK1}?pH)FNUg{M`D;>GhZBI|%w+g;@tJbLr`fA{gV|K{rCVqJ~| z(IZ^%$dyIBdNp}5Z)uE#QKQ; zt4~dRGK`)~d4=(+A$$!qj}2+{MW@PhfiW0py3J~SQ-=J?E7y~=e*h-1&s~=x;sFzv z{TKok0yi+1aqR&qe_CyCqc{-$-d};Ts+#ULUS0wrmHJ^%%~rd*N~bm_Rq9odFE}YD zVmL$EWPkk`+XPG?g+SX>x-SW@I(B5t%EwD>qk1 zzm1Q+e%CZrvBA)?`pS5wXr=)yO;fDC4RmX$j2-2^>!8``f5Z4^UN-&FWZB6TDw_&c z?W|xM*!qyZ&Lp}EI7gEJMvszUdgm|IRA=sd;yUz5JLk76*pfFyReJ1%k4I3nBv=|N zub`oWp{XYd`k?@kKyJSnxgQUa{IHRwD>qDOkhdbWY)=9I_%4oJ@%_iupEsk)kIS>u zo~fJN)HQp1re|tkV1H{$PaA-tVZ0W^4vb(a!u00<2y=ulzm318_@<6IbbLejgeEi` zD@@=7ssVp9@S;YZi$|s-!~>-~A`e?bK6Cq*oO1>SeL@pI>_zEH`eFwJc;3ltcvcxa zA!g>21&ne^RPxTn9-4PgsM03|z%jqkz~Mmi0MZ3xJ*uMR!7naDRmtcp__?4rm#@DBBs?)=CSNd{ltmj20@1(MURLd zPzyFB0r?ROPkS2mM3-8YBnVGD4zUp6lR3ohFO(CPN^x=Ii<{5l01^V%AwPb0Db?{; z5DwjFos*9+B7buig*(YtX6x9LMxi7w22LX;A=Jqe>N-XxEWiZ~W4J)I^20?@u-1yI zOYYl~GA^l1NhhNik9A>?Zsxem$P|wITFRCT@o)&BM*ajZ7l^pi7=zYK6On-k@kxWw zsH(=)M`Vi1N&0o>Q$0D`zITWpU>N(oEYc*dXs&I0VSk(RsF;rLqh}cxJTyr%8RI^Q ztTEo?0u;J)%#UdyLfn<$LH{{TG%UR=@icOM@u)xJ|4)#({!%;=_})6Aaqwb3$Y#B3 zxQgF%(43&gRYv6(4ORBA?4`R_z{f$O8TAVhKNJ&6tx;BXz2X8&gchrMB&Y944|n(H zXLXEFTYt_AHD07^mtBxXD2y0`9~87(I22$I5$IwU&p(*JLdEjBev5`_1hU+eHVULG1;dL5MAGZS``9CE*aw=v>D&-V zr@@YTyHZeS+jTZ`|5ixw4o1&1!B90yg8DvvrGKRoI1j?!F)*ke1IDIx*9?BvXL|?u z3vufYfI5(Ii_=+~VAU@byPrQ7HaF^4Ki#kUw2Ta(w%Wkx83SOM!@@R(=lg-E6`v3h zO$JV=qV3QL6cCUuP$ z)qj%urKk!x2nF9JJsD}&c@cP<<>MaQ6o;z)_T0o{KEq2aM@oKJO&Gc&579Fkh~i`| z-CDC-txy*e%fwB>z;mNm)jKgvvQ!tR{Gqsw*r4}OZTSH7m)ag;IxKt#wNp|4?qUSV zO+1;xkWvfZPKYKhj^aK{5H+#7{tVG<1Amydm4wxpEK!XTuI3KNc7Z(Ok+8~#U3`{q z6X>cA`kE;P@j4*LX$6tG*cNQjQB~Y&){>Id$vSd92OXr=bkdbA2;0b0Uy6-D5KqE? zmyoJ5VPnMFtR+TbE-Ue8YgwwMdf8x&-6-#98M$R$8~*!R!m7!N@wFWD(zw-{M1Q$$ zSwhLK={82^lGm_OIhHPz&Wn2C%sewIGr3a4jD`hFZBq{%8&2)^`tB6o*(YTkmo4J9 z*N3(C_G!qPw5Ty;l5Gd6YdVcMK4LCqHaG-+_%y8&XxOIvhRt!*)dBT-iX*UR&4HA% zU}xVF_qeoDDw?`A@ziybeTDL_cz?#_V~bd7whi=Q|Kng;ME#k%CV?oME}DL{5_o=; zG$}YKBwNkRdYPU-*Hpg3$OI_=sDZD*;otQtlKu88#}Y@3VkJ&0R`G}L+9Y1y!L6IA zTFs?aJ1$)@^J;&?@JHI_7u$;Q)OstW*ZV2WV7|Y;9X-5NzGD7p*WX76rx&UsDGD%kU6n=?NzpW5^aoO3H-M_?(#V$X zx}yhlbfOu0mqAaA&=d50SKNG`2k`)tBTeZ(#y5{nfAA2G#*{F2vN-CiUFbSC3><=w z?nhq^WT5Jj40JjEpK~IxT6dm+p@!lPs=&IiROgxX% zjmD1W6B@9XNwR7n%L!>&Fv~oNnJMxbGg&%`Zem6;)on!ikCG*tJmEy8yGSNdU7Uo! z(FT7!fA{=lbrIspkkw}E*IsrjYWE?wA>04DefxFc_F&+{@vMod@p$(KGNUv|C zR+JK2kHwIS#%cI^8Rhg%(Bng)oIfszYd6q`pw_yPyp}$qWWriN&U{QLs+~uqno;{; zV49g+;_nBR$A=Ql9<-$iWa+OiJ+&*9CN^Uot| ze|U2=5kJwy%#(CtXNcI`4Ok~bAm@rNt}g!? zo?BOEdl&2UB2x54sLq`to_!j9+N*$P9P3D|(@qTpPIC0T`&}ua4x(RTMyIv;!fWAr9gu$Cltze^vN|8{HdtYy8@U&PJ2U zdCo@q5!02EnhZ{nmFhZOP`D7D5Lk}A(p!_3gU~WX!JOjftw{Y@tmt3j;LL_Re_rKE zLN)av))in3l&o`YAL1~7D@2(8*%re6H;%B_wa2Th_l@8iCh`_H#}XSj2i2;dQB!jv zXU-vHl=_h@+p4MSZ?#ib;Nxj0FX3@%@C2C&p&rb4c}i)gPSV{yY&Ll0Go47h8P2Jm+_!x$IFS4|ZuV^l9duE&OfeBalDsmS2g8L>xX3bpQiKsDJ#R9~f zsW|lIi3oL`gdZf2X`qS1f6E-ljp{hEOycVTQisgND?V|T0k?x^Tj0JyDZ)wLx!N0Y5pot+}IqAWE3nP;!N|vg>Y7poD2fSx-jkJHo^5-G}2Ui;!E6 z3l%2v+GQ)G!tfCbc*zhT+wvH)#39U21@i_b+X^WD0w$gV-8U!te{3I?(A9U@z-*G9 z1U6G#bfy@t99ztHouHklHfvP<=ltp{9H*PtmfvrosuH|0nd7I zQ16Qa%>aGf%m^!*CN3eq$V?ZAik1fDo0KJuj-(@Eul^Pdk0dk5HA&EukH!H^Q{Dx1 z=O~-~u3rB7?YxY$ElxU9L+*KIdwp7p)r(U|2nQ=Zk_#efW@y3@S;sF!4 z;3Wd=1_CoMmvQX@D1Xfw+m73|@;zUnx5{nIh&NH{K^{(lZreq19ts1eKoTspHrG~H zmRyP6jq~*#-ek$kIBUDz^u?wq&T#HS&M1kNQ4;<1Dslfm%wD~@!XnC1o{}`0-9;EB z1YRW(jxfuRz$8k!K!oyWwvOJ#k1N^A$%onXn=6tH;XIJY5PwU=7^rgqd6c4D&`bj` zl!|DI5zARLTfm~yE~0pVH3b4_G+0C?Q_66B_dZDyhm?k?Czv444+z0WC?{B;6lfa( z{O1ds5Q3644Q?`Gq~C-HgxLXAOAGKhJ-|AYk%LW&dU1>w4j_4Yh8F(k7Nvtl0wp{H zyk^07_zNP*Fn>xDKHmE6F%r}03cs?o@0vUN-)+jev%|8s|7ryw4n?=?lw7;_hVZ>NP>MtfHpMss<_!f4 zsbsq@Yq7raq zAiD0#rY3?PH6>X>m74OMn@x0k?s3KE4z*LtW3EoA1v~qne$uH7Cs76>OPLW_jxzB7Db5hih|4nTP17oi(kDZU2!8MwwVwgh z(Yu$vF9I4T9c-@)wW%6amA7cK)1#Lc!KPA|%HXbT)@Ms#Q{AYhsqRbFq2i;L|1Sb8 zf4{>*I$7K{ZT<3g(be78^^I;`ZgvPVLs2ci0{ipoYq~{uU&*4a(Yjg4>ZX&G4)9I= za&BQV*bQVKb<;t5R{tiSP=7AgM|m0Rp|@nXKSlc=v%_e{>=2_7Y7H;d`-CU+rfS-o zIVj{LbZ<}A*`Wk;W^Oi4F|&3nk1H~Re@jh&o&n0SJw{@0!PuHaA7P{D(Qx zhx2l-4h5^F`Tz&^lQy3o?EWk${6wj*C&rW>*2Tw5c)x6SE&fY(7Zl!N_hW!YGZyHZ zPL|!eoPQG#zcmeT7r3J5lnl=O&m_sPagI<7puIWlX-Y=MSzk)W^TT5uw9Qjge}g4^ zu6*rf=px<@j<071Hn?jpe2O{~#eCJ2bE$>(?i4)dIq_33I#GyEFnn;1*p;8;`Htw! zrFO`E)rub~e_+$sFXmg2GoLt@u|jGo@wdQ#gCTyBZPOVMda<3AC=dMXu4zw5l%?Ai zG-ZP4{uDBr3FT*Vwf1Q0bMpO|e=f-Pq%WO-lP+b@+_bE|x4E5A#Hkorn%D2U^23YS z+%#kxC!74Y|A2)p8gqs-p%*0LJoVD}*;LgS>!tB%7&)HSUyYRw8wfPdkw}Ow>-=m> zj3n1pCK0>w)>amixM>$~m1=i(8g3VzvcvArZPD5)Qxdnj`nrTiUCgvof2y(THmx*= zIP>5NpouXvfLZSBMm()IPVwJm$XP06o!^-ufQ!ot^j2#Fp^^t!L2`MTX=W49}9+0hp&TYETrQVcXa z2#!SVBww`6wqBSzajGk`f6SKncwge!y``B~^>4^<{WNj#+beP`ZDnRS9oZz5@{`nWuzDQwHSuQs~d2Mh-^VzSr|M z)X9RRfYbfGt?$DP+e#ReB?xSb7-yq0J!L~-F}QM5&<%K>lhyA5Y#dPr7eay&!EV_f z)<`(QU>k%X0;%cJe{C2z5Hs5_=Cad$NR|-dxG>h`CHbt*+8+U2kU}Bu3>G~L(|r(A z4iR>+=sGA!PS^oaQ=SD8W+)va{5}-z6M^Wyf1jWoAAv%*VvaZGy)cX!v;$s1S!*TgoX0sW*=%bnxeKZTw~Hcm~a8O zbo4S^M3^fawjvlkb)TB}#>%=-ZTWEuQO@S0tu1-HxngY8Z_?Lv>kn8Qi5L_D9zBQ{ zV@#un@q5Un5}=OkyJVRA#{1aX7?6_kk+sod)`mq6ap;sU7l4xyP$*7hi11@OUi6v9 zICwf_umk)@8{=9m?0}1lLX9{#)O`Na1c>mU>=B7y`ZR$c$S@;_>o4q*67vDX2*$XN zNX*feH3AfWY3jqj%l2?IgjDZ*w>$FX1|%}DDLy7*kp70e>Vso`*z`Ri5Pwb(Or#*Y zd`trh#VH1VDO@t$(a%Mjl=!1YO5N8D1P+j;I|HTu*a5TVUbeGibI|MT0vGPuJ2_XC zd+&O_2HbxZLHnIZyj5jYeln2?=ItjG#2+z3;RfD99N;LFyZWr^U;zYXRrPOa#pr)m%)7j6PF1#0yhCSmq9iHDu3H<8#fSr z->=|f`?AZ+kV6(mfL!V}KnnzM0<<;!;3`7u){qQYM$*6UnU!R5wc3`ui3Rk*UM@8x zhiA@Q$aTz0D;K@8!Qsiu`G{v!q%EGLh|rg`3XpNhDvpp(PDM+2l8PbW=~XgRxfE3L z^3KX=4S|@@An8HFvd6SW8GpyRo!^kb0@|U0QG@};Yw7W5kmwN5NUkD{34@cKh(>g1 zU_!RgHKB(YJq8mdhK$k5>Lfj+td+G4_UB|R6VQ;g3@eKTD>4xclAeSHHbfS~y+e;R zXb1$>0v;hk2rvQGyy)CKA8{hRaB8Ohse`Bk7S%0#PE-NtPflJCU9s>W0>S&`Elr zoU_uCWEoMTofQ8(UVKUwc}y4t1P`590QZQ+FoJ_vSp_Y7f|kLEy#P&c0FpSM zAwaQJ0k*0LI)xx0fSDnHf`r||O0ok+LvY3huq`Zs7uLX-BB-nd7OclsU;$Uz!R7?E zvkShW7!(VeNOQ=h^M7@a%Mi`Yx<|2Vz9GwW%$m_4 zr$}vVVM$1b!@#W>oIv z%FGS+6rW1pjfOdWrAiz_-{$#ZvBXrDZ3!#794y)XUNtY4s}J*4JD0v{er#ShFYyA5 zu4MHY)JB0+V-?3RGU^VLnYC^U%LJn*h+RDVzJJD)%}+OrznbS~XYB&b^V#}lxo9q$ z_itaz-#34(*LU~dHO<{>`6nixKg@1#KGe(A$7b8SUfwsTaRbU)@9nqHcFzte1WSGJ zKz8ecn&By_OxR-qgGg2+jM5CQVhV+=ubEt#u#K*kqfwbKx!SWW6hCQmiaM#GMAoSteNXUdJ!mE9T#vA_jeCXAyu-23ZVvIA}o z`(WKS;C;FMI%m4vvvN-4o_O?}IN89lZ-3x0*}y!)z+7JU9T&y|a{pB(Y)q|7)$j?z z#}d?0;gC>q73z#MU58Sgy$eVqrNUMta1{y@j!BUVH%P(M-o%p}lQMTP7FE^bq3KD3 zobLxYKRL+xvLN?lp=h8GpNvgEJNwDm*^kK1{`7Z2olKoY=)pUm{3Jhc4LB6tJAXHJ zSw#Qh+dH^x-(qR1#X|YL(1Gk}EH?H$IvLcy8-zkPVBG_Cgc}4uB269>ww7x7g7bg3 z08`HbY)4rjJK=9#Z0ZT$a;(|gO&*m`b2v3boDRLl$(DKi5&zStPxam6`o2Bg&o+C; z(LOZJHpWLv2Z#F|t|biW%5HOdgnz=PKXi78)1k*eI-5+E)F%s1bytofz>Y`m5q+rL zLA7<7^nX0LO$9afJuup-phkC+GU-&&DfW+1F-*p0#4#%J>62~;x?zOB7}V6i)J4HDvYXZdu4Cx^mP;TaBLk;-6L-;hHfPGH6ZRptNZ?! zC@TCC<#p&;J&myXv2Q?L*V<;5{0ENlMcbEQa{?2W4?O}E12Q!>mq9iHDt}pTBoYoP2kN-S1x(XTSeD$&vyUlFDRpl@Np}OyPtigy$$DG?5}hl!;`qNiNg> z&T^Wn*_>0BcH4E;+sFRKjDPWTRraMFs%kgYmQ`KV*Y>TfS8iB!Ztr1B*}0LbTkg6} zttOOi+`-#ceW{vVH~X;o0T^}cGKMmlCz1<9bIYx|D>vJ<((6D@oF_R5D!CD~Ksjg0 zoaBgt1{Nz=exVnhVZ?#r@YP-?laZgkJ4-I-f{OIL-Qcw82J7t!WPegLA(8&3+NS#j z3#>o&yP2W+pW_9IGl^KvHO#7!hH5iGUS|CouK&GCk<4ID4d~rl5^#t3pzE%-U;aaQe z+<4}kG9*}LrS~p&S$}z?5?||2i`G^A2dsFKX@Fm{@f<%Z{C4 z|0=prwQ8>`W$^%q`u=F}$4Lio06XqwA587d)T^*#$Qp7V4Mb`!T=-+$ubr)aQ>Q5T z{WD@Z#V8ul785DpCP9S|RJ-`xgCflUN`=+nk5#S8b~b03NPk;(-Kwsun%Y6?`0BvM z!mO~{z6a1|%z-vzJZ;+5j6fik?cNToRjIE4y{P-VD}k_m*{J@enaydQuI#|=44xg8 zrG7TkS=w}N(4E23W#cJc?doMe@Co<9fzLcc!1tWNH9?$=-@0XNv@An8V?Nxq3GEur zM{5|$vjJl+B7bKQ&^{r4#UCOhIO4ElIEL^DD-soij#wdD{M@>O!AmfoM44B;4cxyi z&OV>{%aLXj36Tgw5d-0uo3qOgI9b6Q1Ps_LPj1c1O(IaC*;yw)pZ&|`2Z2DI5T>Xo zm_5%anT^E)rkK>2SztEEh9j~Gl!I&#vRD?6>Y5*PJ%0xU9Mr|gSm^~~0K0>}aEzRO z8qB+oDxLBoP`X`HXmeyNKiD5G2I7QV#0h0GnU7XI&6{3BLi&h=;#fd-tn|P*ghc#2 zLp%$ucpgCr@ErfHz%r+wuj7-8!pY)M?$6)!Z$%F%D zSE{YP%!EkG9)3O4(e$?JZ|oC5@JW^(<4h>FwXqyL4_@o-n_-TxS zz=on_Ohu^92^L5S$UP{yD0u2aXj?#PIRyUum@XNTfPH|tJVgF6G#KQk5J1vjGK&Z) zhYh1u`(yBC7y~GSl_4q^mk*&gmB3mN;&+HKo)#;h(j$-ZPXPe(NBJMEir#_*(vb`n z=YNa`H({7Rm>XpR02J&75oH|v_NKDezc#l>tIbBo`Kl^=wch(N7wMhNCV1L48)fHO z^?6qTCMXL6Zr-N|p@qHXCAdpIVdLTv-dRqN#5~+&gGuOcXkFH_(QNE&!U8$$U1c9R z)W|lG1w$PLy7oOGElrDN5Q9akT9XTLGJl|2R33l;NH;<%_ZpuV=aml|KeNs&1LIFs zGl)U4z6Fbr0SzBQrr%#_0k8uMkR$*Mm>AMPcrIJFXyYx9Q3{fEUV$;GsI+EGk<4?yy>OYf2}%y#FLsKuxG96Sii&R&^L)L&s~B0 zLJn(nTLL0}I27*wly~DQ-{Fp4`qc|*67m>pXAH^gQ4>O!`~N3yCf}%=feOFAm$^gU zMXdPNob0*1*f9dG)K#o83ST57=|_HgtYO=%yN^}CH{>QHnq>i8Smp0vT8`7s{hsUSDBh_xx$CWXIXhkfZO`zM1@=Spl`q zdZ+B`N-Y~*z3I+OSM~Swn15||a-0%UsChY*_9i9#Yh%j*LR>aYsLT3otD<$~1krV4 ztCqbf9AS|TkIqgsyWWj8cEI-)%wXxp%9W$OQf{cL>ZO{Ty(#;)x_cmL=bRvtiBO8y z>?d42tVN!|3zToc`BgU*pi^ZN4xq#Bvd)=Pr|po2|GCzBf--6i^?y)3YG%k*x^>*al8e|Mc*s+DKur?DyrIMMvZ7|}YHU}-t{70C{MMV{tYJd-F3>7#qq zfhiK)Y3<*g@jc3%{i*5gnf|0b)=WV;WoFzGlKw->U!+Z|=KnOsW8IoRHmGS^f?KKG zsu@cC-1IhEzrfeTnt#x;tleN+^=iV1`n-dEr5FFGup&@A3f<7(9R8x6x~vF0;!kTK z##)gKF(J+0qXl_kD-O6Oa{3R9JdPXPcW}L@-$mm_!t{K!Y9HxijT`2%4%S@vR|s6E z@BrT9Q18~ad`8XCZD`dDkja?Z^`xR@J_SiKI*~P0zW%>?SY6?}vDYpTYfIeqqa#hy z4CSVoH)@fdq|u4#n7)@rtD^RkC@1;zTB*|}sXzr6#=AuE(k7{+1t37Pr!`57CpAfn zQ<|jb(|j$oHR!{%-^idmA%h*DpZ@{(YDH6*VRHf#mjOot6a+FjHa3@W?Exr%-5T3+ z+cx$+U!m=j+*l}aC$p2i&2BsKWHz&@lXj-*WN49=*ifWOQg+<0-vb;#A}Ct2YIioX zFA@m?2j`A+K{Q!RqRDs9BKQA~%V*zwOZ6lniDGJUc|D;dV(=-N&CbLQT!nrc4`ue7iZ5a+;Q#+`Q^(c1EZ9yk4!(W-Ol8Z2|9HQCIl& z_6!DdTsTiFe8~KK4f7qCZfW4)^D6C{;`7hp>=>NG(Hzf5Zihj}s|W z0&7&b=H-D+DCb10c%pLMSzBE;E?$A&S%p%$P7h*UfoqT9q<%dW{o2uy2<_;byel|)2Dpbt#76*3d z(J>XGj3^cCw3FSv2nq&&Y)~{Ye@!m4{KnHA2XL z-K6MdIp@=B(35?Fq$COVUgu9EXeK1qiJ#EbBi5`bQ4*K}J0~>uK8}hJDJ7s*TVSJ( zH6oI#ffpROQsV`G_grZpRdl5SoURDeGT6$AvN!A9sM9r`yEo}_6cmF~qYvtCRCn0c zzrPq$2*!Z;9&IvW$~rISC?YvHjHT8@a;L8Ut~=`l;AmZ@9X_>nd1H@g97au|fnrc_ z6W|{1@Hw4#o3t$5TUPfLJK$?Ljp=k_KAkb+k<8)UwC<~aLHc%C%+2W1;vEqyuD*^+ zCE=J&5N(T-@lS}fd2;c9eJ;uppR(Hdad`G?s@g3{@o!OMfC0Y=1RXfn1|M!hY8NIL zNq8vr+Ro1#&l4DH=V@u^bn{`Il@}jBCMcnxGyz#}Mzp`j3PBSBW*n|D%sb!<1>@Ei z25YJj^_Rqd#R<;)@FTc@SY*uiaG@hQ8Z6U<4}@u>i|FY2BEV(HQ96d3C3>hk5(s0W zr#oDD<&;x<%5GNX_sXh5-r$V-8YkpszRK||c590YzohuO$SY8U62~yeBp8vts@g73 zvvZt>rg4D8$V352i0^;i9--2xg^qQ&YIY4)SvSRhZ>XQ%8SGh=06(mfUEF2k4Dy?A=hiC zua?<=Eivn#MX(X*hcrL%)GQ{0k+8iaxDf9zht$XmOT3_(>{ z+BE6iIRd+TQlMTy4}@cPp8aJ2>2TxZ~N2tqUo4ngJRj|=e_c$YoWM21f9!0_BseJs~AHm0UQQa6uJ{C?^qzfk# zuPB?x33!>V^Pf%u5e-57{5v%Ek3qy}>F=@iQIT)H0JXDe#bgGFypCz;sUJUfO)Y!54xjHk+x9Dk=4dx5i#^_cSmq`i(0ZDG1~~H$6AK;tqTc-{ zHxni`7a_ch!#FVDPy#7gux93Jh87JQ$mEDZWg`7NbNJLaVU>Q`PSMkL)8svTZK|T< z9uy6JY*p_vB0qUNOBVusqeH8}a5Qg!=J~oi$6P)2!D)-{SzYD$w2Q0<$MeXsfAJ*x zU=-^lJETM1fARC9OsC1MFE9%E+PRk3_oyMH;Bk_KrHH>H@by+u8X=9jFtnQJ+Nx1Isk$!$$AfI<1tt89zDle@# z|F;M&_7O6S`|@1G6M0eZ@sF2ueR#?e3E>OF`1Dn0+K5;#xBos~gUWX4VK z!Ze$jVxw(E*g;#>H&5V|@nK$9ZIR`T1BZ3mx?3LXN5&+Mb%@u+NVf()GXdd_je0Ee zemN7oAwjGnIaRDeNkWtO;c^uk8lD6sW%Po zpB3$V)3&Zpf!&`3uRbJ@`q?@7EtxLO*qv|stxY=Lq^rEMF3C)Asy*r%O|u(Nw8*?I zvV}{}T)%}+U#D&BvbZK+fR8jz(xD|x5}M#u4dGjxu%^v(pY5gFywCTx)=hl{AWH1d z2)rR>rnm-|-wWn{tn5V(tUvfZ)jDmu@@}*U!DK=-Q$fQB(&7rczXm~Y${|tQ69p8J zJs!Ej4+Fc>+VRLriYZSJ9I*4$Hj7~lrN`t>w_p&DmmABd#t9ULE$6U(tNLAPc=NZ) z+TyCrZL`kYn(%wr?)FVlZ`}63Zt`~5M;o$)OA=GZk_zvC&4iVh+pUPUN&C{t0H|2z z@MCVb&7Jhz6`W;(?CsSQzJLGEzdDnF{V7SLWUe^Q@csy1H?Z@y1i9q1tN^n!Jh2T* z7<|>bI+`@XpPAlUUXGW>=&+MY_dgj9;5z!pe7!nCa=R z1vWpa6wP)QM&GLcjxEJ<>?Ox~xz%|2dU03jO?5`whQ zz{OF#^%RT@DKLK$L~SWPV2lHsp*)2Y-*LGJ1X&_crkHkID3Vy(8yDP@y$izlfX|$R z+BBI{lvsggPH%J}2FKqRKgfaoYr zazw;2-(vMWiRlfHYcUk|v!{Z27>1JJw|yi-TC;JdXAQZC*bo>Y&W7WmvpH8Jy&0I($SU{BQ;d;q9VtQ%Xil*} z3afF4pPvu{h!ZBFLdMRp>mU#%6RlRUNg4>D7OM%QdA&R|7A}yW= z!_~C@kLJeTyG>Dd_K~DT?VI{-=kC7iO%?v^y!#A%M@gLs4Qgl73tyVvv{g}?U%Rd_5_hi~|JMen)_E8B^=%G(df)`j$c=d}AdG|la~m*)@JjvHY7;nq zF3QqBmB16m+}wI*dGDKZf7{~e^uCGN423+v?bidfc)DorHf2$-eB+|2yFRb&)b)o{RVv6t)u>Giu=#91+ZJ;S znM_XNw8UT(!PG8i8Wi*TtxQnV)Xm*$y_?6P4uj(9GVjA+Kih$Yx!%s3Wl@_p??XGm zHvD=s>Y=sUlK1PKH5ILYY)!ZEjKD!(;XPH@>Qz)`PDbFyE| zx4ehwoM$1O$*-HXetEm->h5)Yqgxc4djzQ=FITTH{6+biZqePACU0w0HA_=ob*8id zzNuf%EnEh>g6z{@b&#L?Z{!o-#xj2bIA9xazj7G8f`#0kZ6W8u(Eo2)9h}m!u$@E0 zPB+NIuJU&ECy&T(Qi?@C1=gba2{tZA37=1-KdTpcFsXk(%$zBG zs`4-AjQu?67XEXj!}i^t6!?qlG-~mkzUxfURmI}F2>jkOz+H$!8KZ;!&ofnW!#D*L z*U|5w_BxHTXnn38&(9Kd)HaWq8e;g9__E=q@T{>-R`DkWHrR77x0(7x7K?RLEQ|%~ z{V{w_bK-|ybR>umID8C_*cE?Y&FPL9%%u*@-r0#CPW~VlUA&lYLC$>OTth3|ZifE~ zhWORAP3Iuo!gf-koW!x4radB27C|gT9~V6LtB_GkEI%Vo#EwmUO1>ZK1^J%tj*!4f zS0-w17OS6pZl??hrbf|oJHPLXk1u9(^O$WMZSsHn6D(}mxHX&$y?}p6@YHkTXH%AA zuII+1VdR(`*V#FC$Dx#q81L%#t}LV7S@Y)jb)h?v09f3~5E5SG;3S7ViXNTsl-K z31olxqeOv-)CuK?Vts$M7+WLcp+=~mS0iLcRQCRe)(A?{Qf3bb{Cy~E9zA8Jj3B84@SE0GW0rS|P>1odcaevA zaS`Je6k(N(L)?F(fh3y*k_R>GeIWULHdh*qmhgD#S`Z|-92r`+U%twXYjP6sQaSi0{OMjc)!$c$!TFeXX?XE7PmQ?+Rz zox*(agczY94o#47PP8q@+6Qm={@GUPimoqg=3MzEThV_V(VNmhLUo0_?m*3+Q&=}r zH5Pv0dzS3b_bgAu-p2soVE8t|j&yM0g!ynXwg2GnMA=HR^7MX*L6xgm!W{x`Tl6kQ z*~WftR~i1p0OZ;?J{Gk%X3>}7e_!_V9E@+;OSgPuEX&Du#iy+FilpQ!{U3>uJYRMn#YlS zU-8Yib?;sFf`5x;_%ozPn=SX%+x4y)-gdP$7^YYFA0q@4K=$B!0 z0u#4BTmoSa0yi|5p=$zW26KEf+@0~43~T~vf6e9T*bv^C59p?PKFpoafJB_3IQdE~ z;%`NOBv?gxr{eP2`{YtFK&p=QS?n#Jxtx(i%phm&3cE>p7n|UHz}BY z&CYp}Z`YM7+EQyo2geaQU6srHO3Q4Jb1>^qp4>3{D&CyO#iHzU>>r2YDy=frGE%@{ ze>$}fL#5M$REiFjJr|9J78&kjdr{p3iy+`ouN82lPed5PawGZU@?b9d5iBBtikhB9 z$)h)vi!5GU%;NEYJKu+IpAs7R4c|n}AIv!oNyws_JE_@SVM%OOa2y_T3j(W(fHKwE~~WmLu~`S|4Q?+ zP4sg~#uFhz_hmX;)ZCSJ7MwUxe~Pk-FETr7EOcpvDR4B#fehk2b_H$`uNOwNMi|OD z3H;EJ@UEExgTidyH8gx{INVeVn27nT4jQ-UL6VTtR!r37F1Ff?T(}C^Uh8MIjk9cr z6XLAQ?auKGnGhhAK_Wa%Yc~!#x)!hn;!qMv$@G>JpAiot9-te{Wlxf4f9ZObt`|Tm zkl-x}8~LV6S8;|Pmt&t9sqL6WH)UR%It_q2_dg-FHot6XS^%`efY88ddO8jT8eh6I z1rctQt`*98wM-ERI10A#Yp!7QPM>2kBEHAFTkXLi^C8Ue9*e2f`-K52oNAGuYqt?5 zL}M>%R9h)Ywnd!mup0p-f9hd4w<7FkDl2Pg!Tbi7t*B?ADFR6tLvA*Onx#o)Oeg>t zhN94l6D&=f2`ZUgmi8RE$xby z(%~!b4>v04uwm>A?{@6L9v$=}Yv`hhv7#@kqy#X7k}eS~00fm~wz(}a9NB+U2Rnd- zbg?vkkCQCQa10Ln=JPKF(kFICW!)uSTWs052X@o3`;Etiop}v16H5bkTEj1$DdX*W zk3_BGBDF-*axgdwe;yGotvPXH-hnuPV%>pw3ve6(XM3#@g!L=#CRtwAOY(YB*NZ@| zUE}uc8N7|H8rSIKy*o7t1w=yX6B$MrvF7RGC|A&jo`QgnE#9D^Ri^)_L#XvRau2;2 zvWH&`sTg2_-gW^_@+>dPqiFua{+BCzuy3EMM|oDBm+wY>f99VZaAh-WjEd21I^2Us z_oh55^l|4nZ0(M~R;{ZnF3bElY$ZJ!lL6a&v{XHF`_m84;-k|ua4GDe&7Rnr-R{2d z!R^d$4E8X5(`MsN&%nl-60ZrR>$`UqgtF$uXhN9H?mfeMAe`SLIA$F)hf=Cln&pf4 zb%3x8x3!Kpf70@FARp4^(PSV`2hu#~+Bb(A@)s(*e{kD3A2QXf9w)gVYZHC&K0Z9R z&@uXbQVRqBj=WgKl}#+@h>LoMlN}sMx@m+A4%S!ctXe+A0(&hI#2fH%-hoQ}cet02 zHxc8z^YrU9{(z(VU)9lr;Y!1hBe(qg;DppYTpE8Hf0N1Z@9ZlD8GR2tZsPU3i!tCS zw1b^*B*ggL5Ys(i9O-YGsfAk4v{JG>`TTIZ0Jx2AslUwgb@i}o!@f17aOj->8Hy+G zjT3*F>Pf3E@#z1f;oX#3Z7ifz=;F%p{kf zRabxXk`#YPx8#a>K#7oD7Ozvqj1No3LpYqmKc%zmxlXrnTWf!M#+l=jDBxkI9OQv- zNC69d5(HtrpJ&&=M%Qb6!dOuM%-P+l7L=1HfAYHw#hA_1MIu{bD6O*bWveB-+Y--| zQ^GvUWUE>hnrfNO$9M8`GQcdL^sQNS0~6-Z!kn{Y!kuE4RO)OeP~2gA9v61vw%EjF zsmdOyT$it~*-fA8#kn1cZ8fZ&!IT~tibjMDKmSc1X! ze9X+QuC7cL&Q4$^kfQ~Yz0F_z)Ge)?0IncMYrxy9BhUu;moUzDWg#Wmc~wh<;p87%L8nU zt?a-+J7YW3w?MEl*x3nS^q1}J7idQPFM>dTn6snfpB@VT>vH^eoByRQ3VK_azKy51 zvFm^Dn6aI+lgB@_`OjsWg6y2EoWM^1st5#_TiF2ruy=p@vu9Rzf7uj76lEkO)YKW} z-$u@kQ33R(jvW)&4g6R1pL!zV@_Ybp9u5E-D;I#}Z9FCH%)~&pwr{MRkp9RgZuO=U z806^A{NDp@Z3l9-^Zfsq&8_Ur%>U?a=4{WbVQ1yw43rW7pWrtW(%&``06GAG zZl;#Zf0}>&H7kG2tbfdJB6xe+gX{t3#x_nsZ!2@)+Y6GXld%gB0Csc+dVBuU@t+8Z zl^bAYWeR>9^S7gd^jCEmJ97|#_h05WCjaaDp8=rx>wwa}olY~5osB!d3}}wTtOx?X zjRVd9Kd0=!dPzFl*eDv?0%`tB(fd%D*U}WWD|1aO$IGS490iB!x zJZyitfNvuF7vDGK|F8wTO^S%Nh&cU!56oXt5_YB_Gb=j_02?P4z}V5z*d2-GZ3@^p zIRSs3tZ(CN26X#tOaRPGb|COu3c%hO>|uV4PaLI z8}R^`75|NR-tv_HMjQZU^}i7pfLZh3i1)3E@!#mJipk%I?G3$cxs#2tljXmytZZ*I zOdO3(-!3O}@ITC)|6~5wME$!6FM!$f--v(nt$``X=1ui~=drwTHTwr-1uz5uj(;Ny z{EvF9EN`sL{|1HS2D{SWxY*XAGa zZ85fgL)JIy|5yp@8+FjXA;()R$PV}q_Zu_&e?#`SM)p9*zwYXPr*QpcvjY7?57vJ- z_74AmZ|wiEHr6-xPXB;!1^zC_`Njuq=?MIXIB$f(uAqM;ys>cpd#~QA{q?AEG6gyQ zgE`wRn%72~@e_ehoe;=Cvd2s%P)xeG*YoL~u8QAil zAqvJ|M=LiymbaUW_09hF^S}QY{6~KSs=qJYe`Je_g4{eAIXF20jO=gw$IZ_1X9jq^ z|0C7(UyrcAF3q>Y{lECn^8)|`x&cj*7Uw{w9|J$9GzORYNaRnJ!BX)u9arE8YRksK zET&Jk6XJ>IZjl3p3jCVCWPB(9$xHJY_$2w+6=_oiV%xl|wIo(fT$`x~ZySI6DEJVf zNr;T(XfkQ|XDBRuDeEAoJ&?`G(<$bd&S;HmB>`xR9E$O_ciu;_jlW`CCX?%zwXS@H zbuIkK+Jxn3^WJR%o!~p+!sK^I@asKROr&v-$Rgd0QC=|4h%B6acPF|oeM+9XPWnQe z&TTNQs~2$xo-KHzvh*D(xVU z^be`Tmq^d#12wu|3x3^3$i;r$2cQMS!}2cw=|Xybd)>;U%_oVXc8ln12%LA#T(Ihv z5iN&0L8(|Zz*=!j{tuCh4O+W3TGd!+M%{$p13UdD^DxUHYbcAXLk)jmoiv9ehxELC zo)}V-?`dz-#o;+~4KnmQ{7cB{?pWH`+^$cVKrxjqX2P?GQmCiz9-vhrI=YZ4RCtzq zDGH$G!MwrHNIec7-4Njq!?2^?d~lf{F^Eazd*6eZj;ZyFn5)*M_m?AfSzk^gDo9h8 zNF?HKvfbKvlFhQcCbNI?s&P%`MAq>D3P?g^KsRHKgS3xZU2(fHhaRmZTu7M#9qWFQovS_WLBbT;^eDJwcE!EE0W2o>?!k@|sE57ES?FjNw zE>y@T!NKy~W0GLdg$%{j+4WY>@|cT#k8he!P5dHzFiS?_=6ionAg6rQRg}aS;qWqF zuQWd$gBm-c;4jMCnAltCKOK`|yix1++kZ!kWuLRJR6uCOeCkUQ1%j#*L>*?ask(Vk z+o7#?P#$0+7_vijP%VXGj#lh2jW?6bGeFOJ)jUE~3%WLoOI)v9j59Lh8;h>^QyPBw z(d}!Bo{0VHvEP4weNUI={$7kVQ~Jl!;ZYT{V%iYarHk=&1T%QC*L$lhB)rf382g?p zp(fC?mpvV7R#LBITITq!3mB*v+PuB~Oj6f`^>MoCs)0>iO5NUE4&E_W+%*|~(x%FA zWL^~+td&<{3scGf@C_gQ81dofsM08676>?1XIf^|A8UVj-rcdUmtJ~bXEOWNn=IPC z+@BG$Y8~8j!)!_r_LuM~rv2#6Y_U#L-cx20!xPWo)tpt)q;_i0vq)|;;#uoS&cLw1 z)Cc3rHt@<~qzzTTKKstK0GbtHP)vsiGReQRYcP$$Y>8>deyRzuT}J}6XUP9W&Oak@y(~>7@H&KJe7Yg-AYOZH*{-nIHJ}P*mz>a+9tZ8 zJ3gzzYQNrqnjTSW&TxkLN*=U_ZpUe=M}X^oXgoa^T<{hpqNo`HR^FCgLV3Y&_r`cC*s9_DqB}$q1`FSjTs?zN@})aKXoiWnXU*p+%78eKyiW?oWijk5%`eMC=}~ zUjXrJo%*TuhE1cwodg7RV2@1Y+`^xzX$Sdfr_VlWs{zrJHnIEAB!ReyLcCcE8bTv^BtL$c?q@~8H4vwRB=7t4gDR6ySi3q zVc6`GH>(2U0yc`$lV2UZmpqg^nX|);e*`9x8j`O9q8Q%1O~(V@NBLVUJrlu zP?Q-=#3^4-~;{+Hltm(sX+rgv6KKTZT@I{}OicNt986w{(h(f`t*%N^?)Z0b^#O}mN>xf28aKc6nIRM8bpY3b+daOlVB| zT+38B$IkV*Ii1BN#4GJZ5}-X>j!FXsR;5w3u)vc+#Aogy&zAeJ3iHa~X7)>23WgOF zG9x`4zo@jvt1XN@qq|;$_m>1=CZjIm|Hp(9ljg_y-@2+#+(Z*sIw*ghpDN|xgj{o> za$8>SJ`bmtQB_)!*U@JVA&Ij;BJPvNJFiZZz(aWh?TE@lwgr^J?Rqj2z%8#^ zZ1ADnj>ePW>({;rPW;a(xG2gvxgQWhhYqb}FT&jNBD1hH;6be9ZX;XNwi(b7*hG8l z3!PRqn{Fg4ZU;hU-Sud!q1K-4Y2Q#j($rilw8cNW3+8(dcqe~MO8fZiO|~IsKO&C1 ze$oA9LZyxb4A15X%y`MPtD z)*-9C207+m(>#CGW0g*n?V+hySd-S3xMabQ1p`od`~=6J6BZ(2{O)0<0!v`{M`Uep z0tr{y7kIfbx7N*CDm~0}Ipvy!*+|?#+Z}HQvm)ePiVrdC}xdt$=FVb%A^X zDj>06Jzt*JSQ5O;GkTvpE40E@_3m1X>KI>p!K9OLd7idU$oh2!u!<{@9@INpNnaJ> zn!CQ$kUyX-)Kp4$rnET5fAyc6y=W%bX!-83XJyM;e_~h$@)R|BrbsSm_>Cv7Yd)Tt z8bEKd^!b0=5hJ18AWvG*h>|WFG{OB~B_VI{ncQ{VlISJsCcX347kUe`z~OVf^5lD{ z0>$ekBT7G(J5t4!5W-HIku#A1?^PnX;j{dXjX6QozF(JWMacAc9z|pc1g5%wIUmv`ZVGnp;*e z_;q%6zDs!9yLQxnW=dUk+Yc6zW>8uB>fUB8_v^zgKNxcYEIBp#m26=H*y=q2fn4Zd zD~WRQqCQ5x=bWgXt(}c9o9^*~{9$=givi@Vl&(MNVsg=|qGO`f0nFr=AcAc@8hS_5 zSmuARYVz|rq*5^A(_DZS>`PNDRre34yWKE*-#YBu9g*(UxWQy>4;sdZ0aZDzhzW+G zp3~naWgLW#?{xR3g|*kE`OSQ-!~pwkE|uSpfB&vLP%8|NusY9CMFoj}_3hhu*$wK_ zQsF`0;cc)t2n(5EXv>nHpYvhYpZYoS0bhRr?pE#T<6=&E@I%r1T^()c@7KSHzh^a%~MX0)}!aW}zHq>r3P zmj?Ke)g$CvRtV<01+5pvEJ8E!>cw^Uh#p|Y4ADTGEOl{~MVpjJct&eNm@p}Zz*=?g zm6?=g4Rfb~ic-%%E!24zGW;9^r!Ifv`)%*W?=FuRUu6bt6mJ~Zf_io~CDX$9@D+#N zn|$k%yn?VkCB0i*?=d!O%<>vCVh>XonturD#IV2|r{lIa9MvNIRk9qqhe?Du~wqzAuq=TkjQgJVuzWzMBVD4e|U|V z4(}z=Vguc2NcKpdcZ*2m(dn<`yG%q-qz3W9or9qZWy?bJ>Q^g6Tof&z-)SxNK@(jn zR&&utK88WEpsEbN@9=6hN%<8J^40j3Aw+F~aq}nnik|HU+LQ1Eay@?!WiDc{KsJ&f zZtB+}IwVlEKiY`lPDN2c0v;OL?%?UY4^1(C?C|2E`MnCP)?q0;!RsQM%SX8pZn8#h zc&gFMRyg8)X^rC)=@;-l7KFgV01q{Dfay1`{+bKlvCM?XZHI{ToU&W$%LN)^*Hh}& zN#Oxts5s`WsU*p~Sp$E|^f%juoySH9K@syMIlKySgV04_hTm6rUg|3y)_AMQEX+Mq zCfTqPF+-(5z9ZDF$q>oSaC=P7+V4TEX!3rG^StY!3LRwe^InOuVV)cuP?+x{hG+AY zQ0sQ1MO?y5uz77*&kLmAA3=XHN)xtWhD(oVJgTvjQ$cT~}$$&EPB?`Nq>gXi6cFd{%)s}1w-&5YxIWcO(xt^?vFNrXO zhMT!xaVGOjC_aCF6?wGB{8yN!bdm8H?h4F!USKxwmroDU`1Yw;POvD1R~H|`KNYAL z=#wQvtA0pszzZqKaOodngUN6-6H&Ty;`+$eSRV zt#_HN(K)HaCboo(%|1A|XH`xWb=b-fV^V)_dyGr3+RcB#xWAM0B0_lhmAuEDd8UZr zZvHNPeFerk$19SBjo)X(hUA-W8{3@HYb&v>BYM>=f8`qul*Xo;wxwK$NjgoZtj>ky2>eaeX{jfQoZXbl zMMb_H1|wk?%~N(#8Jp)T7cy);%oB~zx>J?F)>;_Zkj3ac)XLObXo4yg z4Te8v!o)kL5Dg@jE4NY&e0cxT8%)a!M{eRfJ|lm;(Rn7jW~0_IHR!^T`|^Sr^iy*( zXgz1BsfB;cR*4cGBBe4T`7?fkQjZ-)sG1y_86T{fV0ZxZ7utQcbDZL5X_ZU!UK#1? z^|_ z7?6K{IuFjQyO4|@i;gqE$kpGZEZAo^GDCl^PVRhN9s)<#Oxvn@xB!=DCnk9lct&86 zua#CL(Kwl*OpkYJu!7NdzC7Y!s=p@yU=UKoMc?!9cVl@b6rYBdgRpDngP<@1TJINf zq3yX_*@)opsB6u{n6Xb1=suZk?cfeHDg=Ml=6ErBVF}XsyyBc|;XhoyGuoZfGeufb z6P+BVqBjn#Wq5T_afcjngMFZ`B4A3N=wfS4OX(SlQ_;q`f`uSJmQ?C>H==U7E=TiS zXmTy8{%u;ufB)^GCcm)eI&!)*!P0hTX(3*4%U2QZ%Un>P_J_`T{bdG^2%Y`nvMhgz z(9EB6jbF2Qs+i%WFu9e=xNyG;AcnH_N4F`d--|6VUn-IN(@bTCsBj9&2<`MZ?t~mn z&J)tZ&C7YG-QUx8y(+hQ+oC0lh~>swnBaScZkMzQ|%A@il#Ff$MT4|3+x@dOq;fQ~dVwApRt2i^D&Sz>=`%cp|&#KBfLf2#tclMrA zWtHL!3y=B}7UqY-L+jXBsLPlK@TI!q4(!X^N|Dtf*`>fQ-q@-lcnKYKF&6Vk0#Bh# zcEu0y{^lo)KLRZz`R=!&D2AiHj0lfEyQ}(GcO1TV+d`j#wRptA8tCgUscC=vqQn@? zVjM6%UscM!;@#!bGwttE{ca&N*x^Dapcs_EU4?gl7dBOE5FEaj>n{^pAG%E;{lDt^M3bm z4iQXtYLYBJp3C8+;r*UP10#Q8Y@2PgIOHNpo|1be{2EG+_Z253M)~%_{MI#^5+Hl# z>Q?3lTP#`X(qmsXJoSijH+t*8(ACZ>I4f27B;KQsvbRZFIv>5TF+(VkC~4|5hoo_0 zSkDwGGD~@`RtVY|GdEKHrXfE*uePi@R}y^BZMILsnOMR1+m)~)K$d_1hWR#tr{{+g zS(pCsOH}%lEzBBv2=%iU1nB_(weM<0MnhWZCsLwkpzPA`HOEh#xb^*f95_vjG9mC? zigDOKF`mJdu7tYuHog>FL^A24o_e1NxMjLI=&m7BWsHJT!QG#KY; z1&H|);V)e_yYIMbVHbZJ+tQ~cq%=(}=r3F4i0%3SXouG1~! zz_}=WmHrgC*e)ml-@;1^1*tA0D&4mQ2&cw5ZkAK$i*a!$92=N*4y7Ev*w@a2_N7z3 zB_SamQS9+(3Omn$9}(=qpEmZ7V((znXiO4*=(HC)24jzkAk|-e9mQE(Od{s1ggw9_ zprhx?dguLp`!|0#cTn-1K02qf>H04O!%FVh_Tf)KVKTF;R#Dh2_Up-5XfZOTzX@c| zqBM`1n_DT3QUL{{b?Z{3hS$8#A*Z%!b7L+7+KtV$c6bWsFmJyOUt4cqrcsbmfS!Hm&Kgc{n7?HU`be!`7pGs9ul}PQU z+f1rR72><<9@-XF679vOK%@r2qnSS3-TuqHx`t?>CV#{{|k-)4V!nsRrhfv@rF2=EG^_I^;cl|6%X z&pmOrqkP*eum9GI|Ab?nZ?5L#Jz7nLXV}H|=Q)uPh)pN-q?vwW9UWhQ5HsvFS{Co z^9tdb4RfiStj2Xy`Y?&E1d5+g5j_#oORKVK$dCG8-aSGNU!)Dq%1IifDkD{MK++i$ zp6SkC(EgUK6~#j#dJ@UO3HkWvEnoI`mg6T0@!0Qg*Am!$ySHS}4#5QFF}M=plAM2% zJpGwVTSk#=zUnFENd8_T#09&8mkP*2pSOV{z-ZfwdHM?jW2kU=4 zTwJ@qyi+Bj#qc(q9*vaEo8KoMV(ep4k#L7kg2$E71B~DbwIE(x_--utKXx3do*>3* zML3kY3{;{UB->qSiOM+bjM)EpHKu=Z40HlJiFv^_vj4iln#S+R6$oGsJ- zm=8}7NM$50&3X=b+Eyvos-Guv>jG1``y&L=c)%cg-}r_YB9^`KeIb!bJvO-RA>5%g zY$XPS}Axkz5w%cKuQr2$aoUQYSmk=4jsT zO>F7DpPMMEPK-L4FtpEA^EMWQ;_x-Wly#sE5*i=q@Lt2_ei|(4P-sC9J-y2%y+uD* znGQisPx4Q$!53Y=4b#9j;sbw4k;9wuuQp*~@cxm9gV66ji%{qL&U-a*>?!HosEnWa z*zvUY4o06tE8S`R?uWr%NA#`m)lEHlhb>k?8qr4`VY**fP;X~)Q??FJ@!xjUy*Zhn zd|y+_v)26TEg3wKp#xT7-ne{qGdj~=73lXVE%m5ASBq0DScYsCG^Bq4^HLF&HIEFg zU+PG*Jc@A78oD5a;(wv*I$o2E4QlWG8rQED@P(EsrQ<_`U#fdLaolhEPJ5xOEb=`k zln=!yh>tX{DnQTh_{>R$ZQ^%7nW50%;hg0f@{;%S`6-sUA)fEQbQnx_OW>fFCE1a; z&z4)b`p}$S-Yi`>$Qgf=MJPDkmAbmQ_XpL+Pu_GconU)K8Jn)+>E&?Xvafo_#wOh> z#aWv*_-&{`iMi+u*S#A0~y=0v5#+op2viHDlT*|-Gp?xYbsobj^ zxoo| zoGdzUqJIev`eMD@PYvlJ9f9eMIG$KE@iDIK<|%Gt(r|{tA<{81w<5E^Yo{rN_wq4K zcSx|CL~etArH2&;fusyjRnUcdbZ?8xiO%*4$HC-V`XYCfg4So~p$U$P+Ro}xwtQkR z2j%AL%LgS_l2?EEdKq$EK&D2di1I~D$GWs0b6Sk~RIgW-ipLPL+FSHSnTZgHJc>_R zdQ#@X#Ze0@u6(P|C;rAJ!+8xp88Jaxbtu0M)V)vE@xdV0>nS}B+qy`>@gvMPJk-5W zL*w`IfrPcJS7Ceou_z2?8>PhmmH&~4i&YWR@<9ic?c3PmQ-K-=Y%21`V)Gj zbc}?QOz3}!;bn|+My$;=`RHd&Xv= zxJ;p{B(GhTb~E%WFm`iB-UD`*kv|x&YT0AwM&GmE(9Mxd9;>e z=2{XgGjkTYE=@hAr$M&_=6#4ZYYSV$t{~e8iB2jTY{tCIk$}f!g>prEhrS*05N?e@ zy_%!E=HS(;Ol%L&_E?aL6`S^8zGoym&Cr2bB2+29k(^#%x?o4DcCTY5s1wNotJCwJmu*8AjSGmY3ve+~sWx(s0On zWDi}A5%lyX$T=q>MD-&Pk#_g}xX^!CICm;#7%P->i+YYt4PsVVwFxmf&LLlL@gXOx zN?JfLr}uRw48@Bcw4t>c)QaodJMGRXaSqmG_Nn&SFoe?ZwTy>5QUi8t?s#O9yi10X zr#{4T7Fv?4>8A4Cve7l%k;ymdWiExkqJJ#zxWc{{cg#jHlwIQ6@94}m^3sU=yJpPu;^oQ>wAaNfZG?q(=gLCB`NbcfkG^9G$mXaw1~ z_doXwVl|on2<-&{pO^%hwV2s+q%WDj*AudBc?Cn&z?Zs&Qo`tk_9K5pKr;CUrz2`G z;O87N4;thf4EpswJkTyo$Lf@SvbX!x)NpHz7JV92T`ZxzG{zm!&e@XulXn}zs-%;} z0>U)}t7N{+B~3N+xUK3scm|aYulCHSU{bUVOfQe%tdIlIGyIBRr~M<*7K(8AS}3eY z#=?~B^#RTBqN&%0jlF*Y@UYAY+d&^myUfMX#E4EGxOGcIyD6>+ z$#6D%9w3T!dVI94)j!yBKmMq>Gv2G9&1%(WvX{pxV%lo*5zCtMcvNWHQY>NarXY)j zA=knFyo)Z=L2RCUqAS?dfgdty($&m- z8s{B;^~N5r6&?+*1zSTKz8n>nxpv1n)HDR>PK;V380NVehm(HK@Q;W#Si=A_6gj_7 z4&B7Y?`2m<>SKRa|5VB=#@5H53MD2x?oa%I3oakrC=0ozpXuo^yJ`JilJ(cNJe@+z*d1(w_jfjGAH=pcy{8MIM157*HA!m0H%Tp@IjQ5CB&fF>6896Cw`J-a*NMs`YMPcoPd7 zs+%lQ!<9dOV9J+@`u*6B1P?bz$o8Ni+8+SU(*8*<(3FF>TLuO3j#q<7VEiSbfR)+O z9xHiWZ_|GS@*501WI9b_S}Y*}$Scb#(6%JTUT}_@M%GOT0yiu`Nu`KlVBF|~2RN=q z+Rm$hI>=pe`f75E2{Q76HZO8H`GzU1L1Hv=zbSYow6g{Qo*Q}0KHUc+qO?C2ZLfBJ zJgiV3`K*DZ%Ttd)Vn%fA+q&^5hDMn$ec1JBJz{@Z^+Rq|j%ITe*kMMT*{lWJ>HSXg z5bZ|0tNScfhL_I-9}>{89##XKRa2P6z(FWGvE=q%&^3wr?bW^B(;*MZ+tyZADk+7U z8~S}lONMu?x#loJ;#1Y^`9J-}cn7~#eJ`Avc&)Z;+ul8Ic2AyFt0EE_a(DQ}xbOWF z4qtz~*YpQwP-k#AYJwQ_N$u~Nd^g;3kuhvFkj|Cg;Mz=Qtv5$kY)2*CbQ;&e_#p3B zVF2#Cxxw#7Wf~LW&OiGjJ_vkOy(R8zCgRC!lvyRFWUg+SUS=H^Zj$m0c^K=H`#;`= zdB3&L@^rPDbDSj&n}!GtPqsg5z$Wgyd9Qz>>rB1svaNh+ci8-?xS|^De&cml8iB|R zU3_z|-J~RsC+lN2Y^IT__8BQ=CfRizQ}Jftn|imLm~{51`9;~!T3DSU$`Rwf z3A?T-_pSY_SclZ<&0>UYWuJm!UA4XhkgtGUOg1L4(TUMVU0=bQfP2s&eI zbj-Qt3Z^prU}cXQ&LPgB!iQ(bkKrArIJ&La>MD{a%p+tq9J(Y(l!xa@6CER{ugs*d z<^rPeh;16S^Q0mF$A?u2e)6n6^7nt1eh?M~9|eA(sHa5w6SHrzqa;JleVs1@9i@Wp zM^3GAMsD|OTh_1(N#?LN1|J96HT?i!v+}#X6TRr=GHXRW805QGbC4H?#h8V1Y9r2U ziOJ`wy#?LWu_%XD$RFSt(PHIL1bSm4#!p+|0?fkd%c9 zG`S^}m*PQ`6WtXKjw`}BW}Dp6u&ZT>Aw z$z0kxd_!99Ou5j0)N@iDT+<8C=1=YWds%Zl8(e2d3I3TEPJeiT1rUr_Fn?urcLi>Z znBRc281;h}u&RQ9hMzl7(Bd8R8ht75HjRS%&HFCKp93FO+>=w=Wfm8IW@NUAW`0d! zBUIOCB~)5Xpakk$PV{>6CG(2OE4cu;B=?@4}gD?FDX1BgqLvsEPLXC(*y^ zHJ-n;jc;HG#ZcQPk77S-5rXy60fwc*GRBZQXd8GgLR~v7W>4c>X<+7@aJ4Sh3BrBw zPOp(hTeuV(ESAB6z?D7qDq$0;iBlHZszEb!cCWfl7f_a>cKSDaV2utxw zYFyl$@S=VkCFvsvubE&QRTHlodyaM6U(|?a?R;h331-^~0o4x#8O4pCLm+ii8g)}* zp#8!$b;m;8eM*&Azh_}8)N8{zM&2k}QQ%?LmSr3nDMu$?`P9$izi3^;8^sgo1|5l8@K%I_w^YR zDU-;JYCVpe*9?;UAQBT5=HTr``)n%#nDVVcNBB-$>Myk-mz!482Xb?JGB|*=V40`2 z&7E!??_y8)oqQ`ntx*H>7j`gqof- zoI6{$qB?&lkc44{o*S!{e*j$kJjV^F9w~vaS=*^=rCyd<9;mt6DNGPf5Uq>aZ5F-`p(rZ)iMFQ%oB-Z5z-VcV^PQkbgiA88}l(Du)em{YH-OfH%kkb zXr>a6w^-$rHjHCvB?=_>Fv?(4PExHT_WJdMze|7p+0iY#m1EQ>rx0-<)Vi&Ak3~EB zB9NuQVQNkL2}GXX|1)hlXJ52)L~dbb8_ASXFvq)tf=FPS-akJ)qfD1x#5>L81fe*Y z#5v&XN~WPq%?K#;hlYiXj|-Op zkCuOIePz-q0!$I!n$Rv?`v*C6Oq+8TbWj;DS2Rj>-@9WrJjI&`FOBHTzCOf_!V1)< zv(>NC!_6fA8YI-B65Xii=sYizkI=R4sggHwbm7w0xFBjPnwRRF5bNG{eHlY{Q!{3$ z_Z%T7WQ!81_4Ode>j`lVtAqQ>^^tP%Zij#4^+?&LFospuWM~2YXzj5{F*UIhZ7{(q z2hH+>U^5|pRoiE6+_E%kg?lYBBw-AR%7-4xB;gPnk|~yp_@h~e<+Zz@hHS`y;Hqn+ z?rik5J=Y;S{76m(0u|v6c%i`Nz-^~@xlTpV#f=^H3lmNQ7OD?lkzqKD2%v_1jh=tU z^gk51P9dofUUeo=8PCZlIiv!3!Q?Cj7$ssC`PpbzP_7AjpVK@kCJ0sina6X}`rOUu zQ4l+DjUu$MMkb36G2vQqCi;JR*>0KDBsv$#3;e3A;9-h3W5( z$4^&6&S|O~p07c)c)T<|PVE1l^i(COIS;m1UP#pZZ?8_xpdmFH}!*l90^!6B~xbd$O1mk z>~aMh1Iw4~H15%GfO{MbYbjO)c&tmofyr43cs<<|9Q58&isR@#V2FPMMyUS_FSHMfhc zCB_p1VlUGrc=cDLmum%F@UEXf!+ln`G7Rs93&LEFc_)6r?yMnLf)5-LvMJJN51Y`X z)a5OH*7(UJ^t!`4jdm4XfG2=b7GG^4$o6q}P5_ZMR_L3dW-ZYmemvFAUehw!fhlXm z@$bRk;Oew>n=51a_x69AsQC&2A4U#Q*A3K@1|d6o1xO6W#pZLP7OS6P;i-l6PBv)d%-S_HnJ>1051qCkN{$!RYsXx9V%3{0*n^kM(qI|InWpJ6`mru+-&?vQ-g z=?b47(I9@ulMb?^On&o@gIUv`H-h>l62YEJuz9nd!*`Hw`ek(8r8Qvh*uswu=83%k ze?M-&zmi4h&R&0+i+{|k?d%sxhr(#-R4NxkNeN8Q<$D@ol6SS8wiQ)(MgjLFGT#@H zOY3f~Svhpf=8t7vzGXIxh&|RxmznXe(@M+C^jxfOjPkHV*r?=UgyXTUs??+zul4IE z?kxJ}ETxlBm3~SIYhAIjxVO_lgkI0^i%U_%#l_uWm}q~DXJ9k{tqH)Y{iuUpWfYW1 zfJ(aQxOjJ)MIH0RuhKzKOdZ3TUR5;MBfmM&6$S6jfjUFzkLFD~5`21$ywYviLOEnC z_A#R49z&Gw`_Uz0@w6SQ?KA8ods7?aDsrOLFnUw*a#W!iHVY>)SlHlnx74ZU3w&d*`UudC(rmgzv}*zD@Ns`X%-jlL>4@3g=G_OMC|^Z-e?)eQ zRHEJXB;~Tnnd>!8PEatTh1qlVR}Rh`Dc`%~;HrH7wr<&2e0;>}s?zV{ZVs!EwSy3a zbNx+ItcR;?OCku z8_iSYt1B(FgGl!pJEZZ4o*$)^PqvL6std-C}WCsFQZ0T;orzy$xi`}q9KV*L@ zMyOEklLeUX$S#D@Q+s&QF``*Ll30;$PJ(}$E9hCAzv|}uEoGUY47kZoKhq6Ixg5nl z!{?<*N#_p^HJ3bxQON`i5GUp&H|63^6sxt?<6*MnFfB6K|L9ZfBDI;?%n8W94$ zMmPLMjP28Jc|;8pa=)U05u3uqoI7gaJYGVkNbt46Fk$8pQ(KJr$NtF>K=>3tsN0B4*@~W~ExDZ@@=ouSANYRTW}z=cM&@ILgewohX{s$v69a*~}SR zcTvG7M3fyRsVc92dw?>Y+Xa{Ef?D=8#@1{A+Nin_ z9#<|DfXi3IH4zxKL^L%*$8;N|QC@$bD+{+o^NL+@K#@V5h2Uz7Jk~N7#l!0nY+cVC z*cyD;YLs#UES;0~(oQq3J^bj#p0E}EWOFdcR#uR?2|tLLKe{QXcX)6X<)rs0h269z zq3}*82Kk1tIChyB(bP8TMdntQ^w774=R3=hiwI&*&7He$XeFqf$v(+)S95)^mAJp61ZSDs4SpbG&V)*# z$g1z2s;;q}tG+8O!O~8PwyZZIkn3ivtQ=-o4vqF~YehqEQ!G66=I4J2Nqr89YAI)% zcW8})?TwdDeDL_n5CA}i-S$`Nxd___&Kqr zj_1ZJcj#VTZ@BGv?a{pBH8}(}ZoF-00gP>E{Q(<}wZnbEY)6FgqHI zWA1+RXXMhM6a$%0YQ^GmqXaqNNE0Fs=_UIt`@|lVDG^TC9P)pT9*5{xe9HLoXABUH zKas1$!$zCY3-QoSIIq9&bJ_K{^=o4es%M!z{&{azPGeagH?K*o;aLk1;{0lYc+v=^ zP&h&A)@RT5d!L|O{6tPk!sl5o1G!^HSOfryq3146-6EqO!BCU<$$mm{Thmf3BA08I zH$CN7Lyd9DfNp=-hSOS|dTj+4k$k0rs?YUQC+xAjJmb4h)y|{V>V%miN&z{*_7ibw zUE&#E(C+u0451Smh!ZKV&Z)Aqc!a?M+2Q+1siOw;aG>y-cCz+2a=ZM8janI@ONE=X z2>c#yE_g+9>n95qN&=C*s@gTf)-cA0aj(`y*pY8c7T15Kp&}qf@V8;HR4f#Xnwf>( z86tFnVuVXxzDbq2FF}=L!~TP$XbZ*t|HrpsT7(?>PK+C%cG2@h9Auf+u$U)(k8QygX! zRT_j}`Sng9$Zw7%B38DT8z%!|0=O<{U48}coX6W4OEpBt`m5&`mBev0D@7SEv%-ni zO=F7WtJjF+16sreiBw71D_cOiO*x?K7o_*Y?;(GTjxnhI6inhe!jJ+niqV?xO!mX_ zUSS&QOa8L5FVoF6k6$E|pZ9EJCu;EhBGKX;H+1-A*TrmQr|3|UKa5sg8L zYeIjcuj)p#y9IqkQ<)M6@$YY zo~}CKkTD?4uVg~gOQiRe>6$BL=)`5#d$6xs%F?4Q5zQBZmw5ljH)Z)eyQ4os;;)K>Wfizj zFX7`;T?s4F7uwqGzLGlAd_4Ry?tJX{gE|*Qv&A%Y)Sx*7Z}ih;4vIOQA-qLqah!i{ zuTAzw@XwQ=K&azj1!+=_PZLU-Ymgq9n|!gcN_ik7M7k%{QG-i*q9%ut@%(QzCdk_y zrwsv4DRIK`F3N>mORR|xgqJ_)QQC#j#$ziqc6fe0L0`X|!wf^6k?wDfo4P}=pPm&; z-O(>z%Mc!Q2hK`bxoz3+eJZb_Us%bg(~_S>%8 zFte7sg+|}}1gav(uRnm~aO@1jfk;S1D`6v3Gfbl*BY;)IaPZ<2c1QUoY`PR%3&9N2 zU*xe#^!l=r8R?vKzEgD5@$?Jns7rihXhgZ-s~NNFLLY!9Wi-RlXD}m=16F?qO}M9v zrzC4ZT0ss&t`7An`7)sx+)uLv913&qmJqNhiPW?{a?TIl#VQ#O)-m!R_BU56Gg)X9 zc!$=9AL;Q+@RtBb(e5v47S{Xds~xjSLzS`S(j1^55oRcJ9jh}H0#l7y>#be7ogJQ7 zTraDROkTm*?8iOhl4F`yXAysWA!i#D(QPNaBRY$5^Qaz$eddonzrUMnDGjJ^Y3Obt zVe|fy|DCV2FZPn{U)`ipopJi{C?ow$ROoIHNq3M_H_B~(WEpm|O;O&(bfyy&c+rqt zD9m}|*qT&5=!{VOd^98_>82ePK3kHrhFifzVC$XXDf(DdmZiCe!SR314@jQt?tP-E zZ-6(!Wa7V=9;l)jS@3fEbAEC(yUe;sp2TR8tQC`>o?zbo;(Q648i{7&icE|_hx{{vGSAUOGOtPyvD1BkQTI+Q%3rQj##F0?t^Bm%+)?m8^G4 zB$JOV?`xqlwud)2+2k=pmkL`1GkJvM#2{1gbaRY$xX_>2<^NwB=ddJy3ZUT7wr%^i zZQHhO+qP}nwr$(CZTGy{%x2yfq$-=7s-*I7iwHoEu#dQ<*mosNCX*+iVe#iqdQwN; z0pc2H1qL>32n%ta7KI`{_q6)S_(@)!DlTRsz&+4=>@qU>RkLpS#b zr3vULfwFjkT>zEm+;_Ksj9h=<&v3>OLIU_eawj1C0S<&ii zKs^8w2LpQ}qrxpjM^-l=p zhPwd0gklYQk{6N_q=e}mE{uJyU;q_t(qs5W>pYL0x7m+4KJ~{mtHz*=fNJvEND&@( zFF6)1;ysw%!C&!zfIUQGDT0Dbknn2Uwj8EPKM#~Bg|iq^Qnqxebo$HQE1jmhW zeNudPIx-0JI_`&97cT+~K%b!JV%4C98uFI{Mb7cZM=UGFhRD9vN)F{eT`yJwbi-z6 z&lWSu(v+AKVGL{iG{3G>5hqZSR41+Nvpxiu#O@>bh{}*H+FaKzzP#^Em2C)|i)!e0SA?dn0y>Bp zoHTnN^Qc&YDIZJc+U{0c+yEL7Q{g&iWn)PB)!gchNWAAC))au&{)1MJU@-Cl#a={Q z58SY}ju^RvNCdJ;^~sEyh)NS0bzv<$4`azNfpPXzNI7A^>(2< z=Q%{Pp|mUJ*9ImjBbF#sN&!`XoHKQn#)#sb6V`QkrnI+zCJYVUVUX7w@?}zr88;|T zKG&vNW90>=JBvqIB}pFAZOQv8a#Pc5vfsFA@U_ZE_n7zXsjB^&W_+Hbp^1Zag5Gk^1JjM4yKl3i9oFIb z|FOp5_L)QpuHsEdqjmY8<WrLVz~9+@gkmrS926de2-%ulhuYAKM z34ucoC60c`X6CU7jw?|RA3~l#M+O%d&0SNU1SRz7Nai1@m~mQqN(PUwS7skx0xZUV zh&`7T?rEl1j6w`^h2qOpyAPP(@WUa;eMaFYY)L!>+!yG=gZ#h-vK|yiY28b93hRBSA5TI?=K#?? zrwKnjtm`gEQ25JNII_Qolb!A6SGA#k8|C-t&=`P7pXqW5=`(LR!z`MyV71jE_}>^> zrKa~MA`;vK#x-zOzbDLeEaw0s=f8xr99|fQmEHX~(Q(xQV*uqYHQ*#fHN_xWgz6ou zmSwUVvWa|qjk!0u>ImW_Xx>Vlxe6H;5>%GPQr{i|kHwQjEI$umsS|V+$C=Q7J`B}O zVb14QW~t9=#C=PjjV_zL`Ip72PUeISbnR?IH@e5OnenmOkT=)Das32&W~zC@%vIe7 zdyj1_rg4Ha=ZAP~9&KAlhvSEvGmXJS4|pwZmsP^0ZFWyH03rJf-Gc368e5t10P=J2 z(Cv9W8vF5b#j?115 zfi5e7F#C8@f#$X`IuIpDR@_hlXDv#oNY}%DSrZ6x%aLke|C_;j2As!JZE<}eu@IpQ zH>A^`b<2o_S-Sy^|C`)@*c4<8DYOPjh5gz9A6*DOHL?D+p~NyDi}v&=1nSZRK@n3= zGZ*3J>F2p6{};m5hg>(WAsWjm!=w(dI#Yre=H`p?dWZ7O!)++)u}i$a<5Y%i3kRpq zv29Dg_+HC8o4#RQ8luqJ!gC!G1YFk)deKCp#5e3*Zf2IHru6)OWb2ryUCJpiC>3Lz z0$lIlX`i2;>IRIULFW>EWv6J(YrA46XPUif;!HaqD=J?U1h78Z1>S~UdRFVca;Nka zJ+XlwRMt>-8vZ^!p!|r&P%o!Fus?c4zV-s3J1H3uauJ*2wwt&_cQ$Te{mLv7?<6Vuy%~F442NJoPfoFhSzX+T;aLHFOXiXCNMaCm3 z5R7Na{={`0Ja-CYR)*CRHb{}K?Rk4?Rmvgt&wl*K`~|)8qEZ+8)KZJ%4Mz?nBT3%5 zrKLy=rsPS;pjPG63L<5!y9D8vk0+mYgk4%zUj27kM;9M|6lxw8bMESGJY&kH{|V+P z&Ye1WdI{VhhB3X7n+$JVCc6MGIa}!#83AHi`zsq;Y>^S9&gcw9HT!7KYb)lJ_8?kR~$l8&sH^CjGg8@q<+Z$3qe8@PmcW{hqO#=NFETS)e=WWbM(8I{6thF(DmUOEFJ!e!P z;&CPgvktrp;P^aLd7TA=9ZgLK-UHoDEDmr8D_j7Lky%^DZN7DX0Ma1Ys^X zKdJ10_aap>>^u@X^CEFZY;9{m7K87gbn6_fD4;_BXV4vF{hmK=F*;I@;<4hN1fzix zkyyDJYyc2>2lkbvg+aGEDenlXo6w(eo(p|eU+?jh&En4=r0IP%^iE~^t5pnF`{`>B zByGH{Gs(1JWv)T7qk8^Cdqw=&WWw_dL|(3c)L0hX$mJQQ4vRvHYkm z@@$M4i{kgDxE#<6OPB8}&pmJp7y{sgmeWoc$zj@^ZJLeqwO8%RD;^JJ-(wB_4h;3b zj2{LOoc_X=Rxu5uN$ND2Z~7nHa`9a5SpE*c6e2d81%Noe?l2P2HVhu(kV+ZnV`tQV zAkZ8y6P$33{x8Yy`g>&ro#6zH9z>NxuvHJddeVANNTJB!=W@}yUCe8j}Q z->nnj3WcDHe0bu%3@5YfftR`?5zawWr2Q{%<4N_2-|=~0HIa53L1QRJk%iju0F*mc3Yi&_WneZ8_l+`kC&J=0Az8dLz6Xe89s zqkYp*)@nN~aAVR0$7y@fI`Mn7t@qWa1T$2_G=_H=Yeoum>sGXf)MFtVYIk!V-BDNz zGWwyERdkdU6>ax?N+7DM`ARpFen(XCO9b%=>q*#yICiOt;SJL$S-zxy8$&K5pUBq* zmJ1HngUrLPR_{9~D5S&bi_SFz8=eR(^miQ=JAsHw=B*j)EZ*iC7%l{)$RoMyO>cKv zh$xy7$hV#>EScB1=~e=tu*vNsvm7Cfvg+pyn8vd#xV!~CEtI3L1w-#E3Q@AsvxQFg z0BQ?As2_ler$83e|EL8)1s8yR=giMh=uF(pE-np|{&zrR%+M0--2?FaSS4ESq4 ziiB{D;M`YW`zr)Tu|jZc&F0v2e=32v#m(euH3*OmPygl1!J90hJi)P{fK#0KRD>-; zn(aaA`_1=oKZ)Q3O$wB`VOtQ#Rxu{YmWIHr&_*J1MS*xVQ3Qotqzk# z(B>yLyekvbTdfw5z~~@DOP3Kx%ScKw^XLJKF$$<^MABJ*?Kd2)8EYv>HMzjQ|O7G8>a}2Mi)R^qvV#xV0~LXS_6-pplo!y<`xK@FZld z)m3BuF*IUkjbxwKB)=@C3g3H4V4+1`0NW{YstX^4D92qA{wi03T$=h}2VOv9XJt3E z!)CFCa5D3M&_{&N4VI1jdv~X3x|i#GkTYQjF}ZH(3IZG}r6-Ksh=So`%J;a!vKFcP z(;(tO1a`eOx?OR*@uMutVy`XJ06T3GVP-pO>lR;wJO;qW|{=RFa(a`4O-25o(^bN>|F2~%u>h_9zNG}sy$-9eO@hv6s;wEtu25ZwkLPK0&~ z;&vR2J`y;qiN`u*pdvtc37C1voT>jPH)ETBtOhU#6X;{cX9sxxXDoa5RmMToLZc~V zi>7_AVjm6Qkn(3(iv{ka{VIBr_#a802&2A%fp@a3g34}2PpGuKF|YskyH5=j!G77 zjFFY+MCM#ljoCjZft*A3x~EYYiuM)Wd1~crL-`mWD>uh###I{vJc$;Nftqlkc5qmR z({KIvu~h5|Bg08IJf4mnFl023J`XH^b4WB1#Y6g8ii9)+vJwj-7SOqBZX9siu5Fn6ZZw{8sF>x4_ zZj8|RUjBEYPQ2+P4rHv=R~Q;&mm_vMjI;q4nk;P~Wl*sq87g%4^$$VwI6oVIZfl9} zqfGNsSiwSM4=%O_OVf3Pz?lZjTAHZrHuKs&gs{Q12}EeKr@CP0Fa^Xr5Uq-J4;jb-+`Pb-65yAe!|YF2qr!Byiz4vfQpw8S&J4O zOotgX=>s>zv;AKnveZ+$W|q*XSg^mE@4%|+#uATY5D9U$$xPX1SVhGSTunV`9p@N8 zhS=JkrO@1UgZJus)taC?OHUYKJTXk!kDXewAS@o1N43KF*y;8^D!MIuIpvBCjEW}&YS z`m`3x!DR@-RJ2|JRV9vp4gXaM8Won>LwUWw*(7XzL|vwt`C&9$K@tn*9f1DWM~r$# z(#IJ425X)yr}wbVW8LBEs+4r0=|gPsr2$za8{ zx>~R@SG*~d;8h`qG2w2X$FiN{25*Zv0>z3^0T7ly9z;2_%NZem8jj0 z_QNVd*5|lsO^yV06evRN$D2?9_7k;9tUo;tBC7Ex;MzEXoK5#fGL)~Jb69q!-D`L8 zwf@eKN9HM(tx4d?gUs}IME>~0rMxU!H0_MKOf8Y&IC-9ZsjEl0gJ1{DeflNP%)Tnr zPr=7W<5|DkbxXj1>NvD zEQkX;N{Q#+7oWC^1_nTBy*1S5~*b_=%137RA)@2A#f ze{u)J>jXvm&ioI#ng}=Lsed?Htz@>3@_02Mh&5o=#+9dI)3=`_}GP47>#^giiy2|8%eea%;;`&inM1on`OKDhi1&5o;iX)dUMG4Svq`@ z`^Qg|Q@+A!$xfiIzfZ$#W!+%bqVCa?6~^U%4#y!`q2GIGtk-u&CI$G!6Yfd&-k6rs z#~9AR@wkIbodp)BbIQvOIoiVisebh(zGpU@MYifE6AMsSeB)lP;A!#YD+)d@eqC$% zN&CLGx@3OQcJ=2_8X{PFxp2jt(6YjP>B7x9l8N9;JU8Hlb^+1lgFIU`w8-%OOxZ$z zp@iAIa)^+Atp>qfUaQZy9#0}QlqI7@@19ZgbI${t!zJD?z^hvMg!SXCoE|639)yY> zqZ9hA01?+I6mvsBdF)gem zQu0&jNUl9qkxQ;GmB#LP{7oExzD`YOyBVl88jA?!3A~FKVFGnfE7+OoUgP_A$Z^qu zw=?x|Nf(XCr76POS27?O*L;hg6QYXfjM|oABX!W5HIeC~ft9X|4?QMNPu0)V{T_KL zivlS}7V(O7lSzX{u>KifYIAS;$%j=?n{P68T|gt~5w%0stV!4ETicL-*Ukt)GyAb& zRqXKZhG>bAXA5UpuCF|-s38!@9~-oJG!hKQ<}9ahlE`YFaizZ#a8cf#zg*Gf2}TQ{ z1r_5^8x&I7Tm6c!ynE}R2 zqcp>LpV6WMUu=zeLe8Flbyw{Md8weupGS}@3s!a%=Ad?&YSN%FZXlfZW|f_RV8Kdk z(&wy*>1-DLKAf@5vwGlUtRDJ&^PJ_CP?IuEAy1Fu&z_BW)J={5pLnF8xomGeL<<$k zX24ZcH8T;yO8t$B{BVl^WI@Z4h4~wJ=+d^_z#&x7Uab>e4X{yvS=Tk&QpvleFsIkt z)9~eX!UY(9ZtMFX*1wpAo4nC68PN~w42cNQOd|m;8|dc-xx8r7&RRXr)-J?Ny{gU8 zg}^IV4%P^n9#NZw^n9GdPxr=z4#n^23;#1sV(17cZpiBPBA;m@oO1w_ckmJpkwg4u z4+$MHQNkp`W4w}o&tPj%BD2(W>t_LNE%z{BMn|=c-iB_WSwoZ1AGbstj~AC`3^=!S zUI6+Goj&ociAl)=>s&4PFbi(+rWP~DElxY?^5A2Jy9Ha&mu#%qkQbM3Jl9Vua%iba z<0wbc@IS|8?{XjDnyy@wLH+g15eF1kHkQ;M;1e&L@}s|$|)`J5p0eeXJ>yQ>)1w=@EYTlu@6jE6%}o?$yD20P8D64-zYsM$Tu4H#-{Es&fq)UljR$ z=#F`^6ow8puh(IP;M(XPrxLc!SXoPkGxI&7me8lff&j~UG!A6ig!AOM^qS}ewv1XU zgvTj=04P|bC}Nt-ToM3Fxv9KmNXb!}S-NID%}xILqNw*xqiG!O2)$*-2SZ!U?YfFG zeZd8Gu_%fM_1GE9BZcTqI1<|i-9ZdKoXuy*tzsyagQBO!(Y$}i(R{r>0*r}{qdJKj z=!acR5u5!}0Gfepv?oodg^zA*4l-**Qf>Kvn%X_Wo0Uo>s*N)BVX@w0dvT=rjIV=eC&OcR%8=jH*uhKdT zD+^aK6+Kh5)%kC$T!wK5+ZjH`n>6jOn8WSZ1{VU67ib_>^$~94eCuz=dq*z;xZ#(7 zBn|Hd9!2gmM@hPJ_X)IifQ>NrhXPS&8HF(0HuuLxAV&_AJe>C@-!`uy;F8#}stkK| zWe{Dsj~|Is4XeM;_=5;g7Erarhaka&5IxtbIPziSthOJUGHYpeuJuk+625E{XF4_} z6}RkemD1CMTkEFr=;!x3jAC;pPW4=W;^2pWJYavcG*`G|)*YZ`n%pfD@|skqNmHh% z8fSOyaH=hXdl(($PXX(DNWC%=G0FW|v2Y^P5TbfdNrMz@J5RCRbC1*ui22ZX33GpyKY@x}cPetCwasx#B>_yOjqT|IFyWa;McmOs zA5FXbe-lW(X&J2)g--arhchdG=ULUDnOPy%Me4(NS5gCdLE!F3saU=`t?wGC@#$%1 z+OoRt8~cmTbLpi4))ib0ucN4D+AZk8MN<|T*kfZ;X+mky${}}buwR`L+u(xa zNAlqqE*5Q#GrH>9b^)6TP5hK$A8zpf|I9d|!U`=kLFb&j+LV)`F(L&5ZWE6;&m+d``NZ}ebB z$&fU8;aEX>#pssz$3bS1-pAnc#rjS6_x-=2T?=BX9ym@-dzP(A?;ndCYx4oXrdXwz zc(ZbtN9ft8F%?NeoKF0I1zLLo?S^f|bBN_6;qoYeNGc%(Y$qWWg8;s&5A&)BI3GDC z-&K7(Xi30HR0iZd=zrcg1qM<}UoqyK191W^*J{iGjEgEKrZf^#-wp8e-tZdo(0U*F zl_EV*UZe`Z<$k5C9HfD9iA5$CIxnZ*f~;!icy2?fz@kZr3+^y~T6*?Udix-Ug_VMv z3E7pH64hx!Q*i{HQ>ziA3iv3(i&g7rCu)D4tsf2#$%OAj^Y2!Y?Lus_p{gvIlNqqytVCYS96$$lYD`$B*=3B?^o#yWoyJT>v|`NH8*E}7*r;s|8RJ814T00;Z4Q< z+DoQO>*vz#Hb?^glfg77zA6?K?D;GvueK8>e!JgKug z26x-T4}3~8o4DnXpMJ))+>{v=jIQTy#5y%CN=Iy-%Dhu5h{Yh;Gy^^_m_;r|FRcrc z6~zThX$V?>%?Z^KqTSC30flKF3m>MkM-9P9&@URyyphhWcX42bCYetr9Q>+rC}aOpGGbpPzof|$^fH9m-ZH&E%`Pri zkxA7*4zG;N+zBeIH^;h9z4YG&FGZ3=D>fY?js>XXInPuuxggY$%#!IJaJt1~bFAvZ z>ew~R#Rbs{qqT;};Mh+7%FCZ0Kg>FNs(vkhy6o)#VvcpHpv1*RJfG3GiaTrL^+tK? zK>FLbeWAyeywZQllo&!Gq<(jLMX+baDn?ek8Nlt)5?<7n0W+oM5jw72;)n2gA}Dx} z9+mtHY+%Tx2yH1p)GxFGgGVRFbYzNX_Ms5!}lqR?q?L}u*9-9Dq9wK%C5F}53 zU2!NWmCdlA{d!3f-aHwkpHAA7y!)PYG_|)a;s$NrG)yM1NFTylh&1KdN98R-s4bIW@1(vQAtSBit%LcjfLU>Z&lE3S)AZdta}j ztSH$}kuAGZ6!>|U&aAIT*ucsZ-)%I1qv(vVzuWM@*k3Gglu(*iZxAP#NO6bYx1gq5 zjY%W@)%|7X(~pHPddPS4LL*5FC@>Q8@wHs>hrQ?X8<;8GE6VBtJ%{dpmZiDB zX`2NKz1hNSm)b;I%?JoH)+EiLhPcsRQM8B9;g9BHd<`(uGu8RW4!f$e6o17%jTP3w zu#sy@>tM3$-=rvT3ied|$2b=Qi;S90vL0MKE}NLGggj`6W~8~7ieW{xgFjgtFXmG~ zn~B)v({;pfZ*Dc7cydVIhRQ8}qkk`>X@mxFuL>)Q@dnZowSPBFo@40SiT2skf;|D}021xW0^Wacf5? zgkWL82{B}L9%8x4Eoe!7uTn$Si25?X$iWj<7XZO(fq#*AdtAuZf@U(HURzRXID%=Z z#G94`t&a$&(g2N&4Oe8 zy(|7C!Ci%nj)iObR6rkphLLc6x`dB{8=j*X$9i~|%r3Nf;v{54P7{?iSU#11&JF{q z>hKz0hzKOoU2Y;1bW4Jo*|QCPTvx*%CaH07tD%H>C=xWq{Zhx-$z_kzgr1}amMLyM^rlJfU zg@XTMr=n>AD3UCjb1?e;!WMR|!xzf}8N)bh|XwXCu;2;+pWByDbpNea) zRf766=Decb!hnl^rzQX9~D-(;{ z%=(&3H$0p=fz$(z>?4FY( zEH$Z7)G+~sPVgi34bNcrk>Sa3QgLGZ>ggY|e{%*xcDNaT@v*ZakD3;Cmy3WH$ZVzU zCoNY6EobxL_(x?19-RAe2nJAbFoePTuv3X*afkal*~ap%o1T(arqw18KiY$5i%?-~ zdsc>hAmDL^xoRfUWeWh*G!`8eI@13;2HK+gtNKkR66l?F6^*tm>93ld+W#%*ph&Ad z4a{Ou(PVgk&1sbV!>=KUoYlsMIZIT^4L*_Z8o8LbYBYBo1YsQyO$^M3s!z#mjb`TT zfW5bSJ{JT3_V(TX3ajeqPmgarSIk>AOJwUXiT zx^k@p1If1>_UfOCS1~DPBds~IMnP)Cy>d;$#=&kcuU(YC!Yx@AVLz|_T9=82V7!Zr z6~SJAGiJnIR378!H6N8JMHCiKJm){@IkG((etK12KMhA0e(lLsdeq+SsgEgdo`^2D zr?2u+ERxEQIuj{etw-hX03^z@pH2b)mI1P73xF~3ISROtN-Rk`p1|1fC&L}`; z)bN}05>Cv%={0w3jsGX^-Yi0`mrg$9EdJboE1eb{ajTNruM5eW2HO=Zr8IT|h9GQc z7#^rWk)ehgKnJPENxpm7k%zy^u4fqmSP}HveeFaqY4a4>GMS(Dq?nmO{+2Y16nTy7 zw6&ugo;JsS({9%(GI!7?WPVFOW0O?R3H`~ON7F)vu+HlrO( ztcr4HmZkE>8~K6emqPqjfKyU1SoD>?KkfXgTP9s!0c8lE%^E-I!;^kzRf%96YRQ9f z$Gv9FXgE3r#33i8olEr9uA)Sc+LcKg(79u5G}&4@#*Wm~+1bkN*x!vfxj&?_+lEum zx`wA^RZ|7TO(#e*@Y~@R7w*RmM^warXGf2LwD7rxU&X{uD+56dL+Rowpw<=(b-efV zJRjo>oXz5;*kK3NQqBME?SV+o=rQ9)c&mKg{JIN`LbAMhzIR`B6S_`A^4(&{Q^0}? zA zzkbeA-CfO1q@|k)20^T1b?s+gs>zo=hX^Bo9DKrZ1AL@U*kuBH34hN19~j>2f&Ag5y8Q}uz?P^g0)bgE>{>$ z6%RNLfM6S-f>=d-3B-c~l)LEnEYQLU2h#yOX8>W=nZkKQnsbFCgh00kq(LPVK^Pny zivTApERZA$2eZ5c`XH`6z#z1BdpHAsYSak?5kbNMJQB+QW-LG>0jY30IJ{KOU@tO> zz@x*qP>Zm60J3P0bAv>(0*(+SpsR=D2Ur;p4Mz${Vy9u{0IS#l6he@J$iRU%!VKs{ z<}yfcByWTnURbQb4DSV^0+~PpVI4flfN(hAEg5hpKoryhK;(eFGUC3lJk%V2!0IIO zO@Q^CM1GOf2J{7t!3@BS5*~JQz`4Lgzy=OrQ2>bCbFia=!(cDWkP#qv$OcLQL=IR} z5eGToO=tmvIOIv~fPWnEVz7WA2jh&O8Zeka=CrUQu%nTCKmWW}9u!{;EG>$6Mfvg5 zXXJ{W2db*5dO-W}?P&Dd-oO5TwRxg=e+69 z=hbu!d{2+Re49T#nfK=v$OAU)m4}n@y!iR&qC5no%e2!Wph&B_4lLtR9gwD)b`TI< z*O5t59h61^Y>*y+puX-wl~B(BG@E)igG8b_08+l0P6j8{y!ZmAKR7IZ%G2uGyl4<| z`u(Otur8}T0CzmE#`78IDuL7Y%6HXlayuPVGg5TabX=Ve`}>n`#TOid%;cl`4Yuh| zVJ?g#*fM;hDsAvBwIX~2KlfndB5fANY8nfI*V8_39$XU=lxhzIb*qJy4Lu*-e~RcG zT6L(H6RyJ=84I*T>=L(s!!j8wp-whS?DNA6;FlPHSNstMsL!|!1LR|Cnu4C{>3GNB zhNw#R&=UWLh?~@2}<>(nO-CGS-r~*J1wi*n!xEJB`0oi4pM6Z+FT)I1dia*ySYgy#}%3jN5 zwunE<7Ful27TvKdzFOO!1SfOj;D*vx^mLF9D(h>NcE_IQ$~Gr=rr6hMiq&lJ!^B%w z)cS`NwcbHd>lM1!dD)W-UewayMga)0zS3^DvW~hJ)q`sFoK{=WcAatFLae)P+iK_T zM>)WlB@QrZI}R{^evt!=-kt-xW0S8YV-I8Hzg{BONTI~|yQOC?hq-Ot+F@#oq9|`J zFK(unXT_HWp7NmjIviB*p6>sKOMQXE>z@8??~4nf=ZZ+BlOQHVY5^Y<<4@&#AvH;e z@p>4NNQY0{WxxW~r8AZ~qVK7C4nK4TL%iAZsjNrkYk7!&rRo*=T=yvR^Kn_j1c$B5 z)Wleb+F(7wFAN+eLRbr7D>VQ^=`_+r(}RdJA|O&o><^kT6+$njQT5^&8mFoQW(3|5 zX_uJM0GK;F3Y7tQw}avwIUNE}3ogv0Y-3*^$4hw8nLR8$(#V4 zj7gXtZuY5Ay55jf9#I z08s#`_X@enX~cvF4mZ#$5$%AlNcdF1Ji2hs;pqW?n=mIBB%(ncaheF~EfUNiJ)BU4 z72L;BmPjDuC_`&2JdFz|&(My^F6_CaGUoeNF-X$M5ud>gm)e_gdsG7 z8~~GlILsyYrARwL6z)-s0SIcs zPfcidj&?)F1hYhSg9}F7)2V>$>9NIIWOZZ)5R4dzHL;h+dq6Y#A$TP3C9yX4!fr4c za3*k{;E>?JE+`BU1;t0!CqkylCL&q`$)8kzpn&Y^4-HWX{yB^Cb^o$n4^BdmQ)A}= ziR6KwENM8@5>XLhgi&zhD3H3|vSMWA8XR$xpxMZAQUH*kMJ);lCN-(eBJm>8BM4f{ z>!e0eBpl*N!iGRK*1v%i^K#vhh&W9wL1j|FRBDNwr!BEQ=FAuVg2R3*TfWVApwz5? zV7f>e$?f&3f&{iUEI68L`6z=*G?lbtz5*KTR9|(vb$@AgbBc6YmQE@YhWN}9B`U!c zB=I#`9IsI7Vq*=Bc7z3-8HseY8Ke(3n4|#%4&-`p3iUf7KHvxt4WBknCNrIaN)}^2 zxy$N{P+?Y{7iHR{x%=Cg7`pO-=4*_KPGIKbtPKsZVz8e=pIjQ5RYU_-V=nZ~EJn?2Zw@xFvh@T)0? zW3&@uQ&KvN@LiMWU6(x+wc)r%n}&$@^Z^99bU?LAS-jc^4v~= zTubpFmkh9{(OOVxNh+wG%itfFwCk*5Ydc=yq6&PROa? zaXMgBhm!6ozH*A3%b2aic%b=d2Sp)?BbnwMcqElE;yQT?M%B8;1fVa~Y&$c@l5v9m z+9&R9+j3lBtFo3NUBU#ND#skHr%*nbQd-jpUC(xoID))H1K2=UzI8Ew?O)(x*70*) zx2cw%cXTmr<6`>syw1V&8{A9ZIF}y(FuaM1utz}>)orjV>)Vu4@^NU|u9LS~I(@9~ z$@lK|bf$LOyg*!XxnYu4Om&{)BFSr{Bp1GdQGZYh+BizCxn@j4hn5sPQken4dq?R> zvPr%aMzJAq28$R{lC%SV&jIy42K%@^$ao+f>|2{9HAKk3(V7&szyLZ3O3r9Y(Xu2) z#f$6fNk0fVWdD#Y0^V2yerBKgH}^1Xg0V{5;9#!hquBjgbiz_!cDz*wTlc4yPT}jbH0Cp*2c07(EQ3WFF(t#<=#VzV zaVh(j!Rf&KL~{6O9pC2t)9S2_Q#L@QBr!WYq|uh%!1VaGUys{hq`@Q^X8VW+l(Hn#p%ks`sFxIw3PdxHZ-C-x zN`S9$icb}PlL8$YsHjMTh~!+So0fZ(ybhN^IYMY#T7f`w z-A7AAS7cdyy1n51IiQ|%(wfLfBQ+7j8uAGhCbFH=WCzK5(wMX|t&r*g6XTYU!n>q> zBb)0eI}MIFDMv=CvyP%M9aJ&4IpE0ZS`h`aF$vj!-0m=~osbmh@`}df<9Upcn7ucW zuDqCUu@?>69Ws@N@SDqAIqfiQi$$qe-CFOrpk)?^<$>$Up^8%EIC4$6jILO2*m*=| zsB>o^Ph>>Y1I{{&JfBT!mD@K&Vu9UEbDXRaVeSu!q5WsUt@sdZI5&u^7C*+smQ#Wz~` z>i~o}!=4kl2>7P9btvIRVm`??*CSr|WX3{0HYkc9CAkEoz7$`ikfN4~%s`+EXM&ug zK!pkrV>VRuHqAGR<6gQ7#Ps+=(Mjy@aLuuQcq!0c%Ct2{=Aw@ONYl}MEqbfyLkoKQ zf!UGrW&!=(I^v^CD7w}5DJKH9D&m`7fN&1UNe$A~SD-6i7tycht89oy=?ao%hBRj6 zXwMsRvQ!iQrNI^6o*JYS;w2(=*zbp zt?9z=|I+nd)|!8F*SiHEO|;_g?0Tb%LuoLKg<)S#i4l_SQ}Q}o`ba6CtTY_Ha*G!v z!{o;rMXz4~pHm+zM!ci9X&hdKTk^+$aW?wAio8-)ufy~59W6OkUe>GgI4Y&Eln>9E z94(%sA=R0fG^T?jk6#EQolP~=9rS&jrZ=G%+YE|6KZoX6?MUu7LQ$v#8a0xJX?hp{ zu2YG&rnO}F1hO=okf3ZOV#!H*%|jv}UQk?2iqW!GAXfn6 zNkfEI^fDB>A(mOP2}L;Xg|CMh3MxX&VunDY5~E3-O06iRP2rN|_cqp)!LU&wrzgDw zL4x6ZdQRyeKn>mZLwj1CN~;5*m?BXZn|eDgMTJzR9ojYM?Vz;M#&d3eQ|9O5lAzFR z%>!LF`2;cg%&vyji4@pj3{S7w1G`8mS=Qy2Pm^L)88Jptx6uYw<=18eE?{Hww z5M`m0+u!&y5&5I&wFTLKWdB#a^kX-?^kcJ^es=eS>oUFGla&wg+RtBgC)3`*kay@} zgPXs_oeb|yu@-4>W!amfbRU9>3Zi9V~Bz8WZ_eaCQ>uqen=Zno4>tK#$A)@}PWHJ}-YQ-}?&tU0YVv(2^s^7uFMnP$!a&UGVc8(SxR|Bw05YA(6dmAK9-?T`; zm`h!PG@jms`5Txd>aDs|l(-79J{8`#QY6l9H>M{DY~%Sa+G`vCZQ}189!j$pP6yJH zfdpVlb%&%uWyruHO+t@TH37DU7=T~cQsR?~ma2GNhuKo){myEPS=jY5S)+!&&HS*$ zs2mCJK8K^!uUg$a##D)=WJB=iXQ{Nq5E17nk9aUrmoqUky@M~&{S`JB)T|8wSMylm zP00U5iZ=z+Bu?6WF&ZU~M=Sji0Dt8lo;hN#2V-|{k$pA2cqy_ikI)k*1LgRlBpkUd zSpqWbjzSX|lw>W*p>!16$;WKztwRvUVkk5u^iuYtJ;uc3{06baqsj1OZpN+;_-@WG zb60J;;r_F~mc(>n;~}q=635#R@|}!64|kv7?aJE=iO^jgA&Sw`q0ayq5Kmu@yj?YX z%n?gtSmFd4|IRtI-_7AVsqr6WfiNG&{dM~^poV5nTZ29q1<3fKC+F*`N#Lj6&WoVg zaL_M>V+-l>I6gx9C3BU_`We|WYgt|NA~y~67N*s2H@hWCtcPp%b043)`J~fegZm~) z$$k`Iq4Ha*g=`9a(9;Ug-UJWwK;rZBSiz)o^&9fpXl_1)7Nme;PqTI6G~39&X{MN{ zsB^`zRZe%e0EQ!#r|LzFZMvwEH_T4e@!^2Ko6qIvb|pG@%tR{{y6tyiiXN@ak|_6? zr?Vkn8HND2%k|64516VjLgjHm{Qf zWnS#{O{xCPqz;iq)Dlg&aZ69RXdK?omqeL!?ym|faavum3!jORA17IPdVa85$<1#kbu7&JdmTc4{wS@`sF18Bxx* zN^PTv`J&>nmJdIRn%|uxY^U`EI~;Gyb%OLJOE$7eEL)cxn4gSQ7>vc|2du>W?hsdL zrVirsWt8vMIoic0I>IJ4E1MLykB8|s8|xqwm9A=xrXj#7Q_iNt>&J}9&O*Ht3CO*E z_sMZUR+rhmhM?_7<-^u>kPq7p)|Ir?NssEQwTtylQ;?ed+Oc}%jc*uIdY9=Q^+u{M z>h31SuzskIg0{j(4SoZHQ0w<1gG7Cw6>tKcD|0zKdEaoDwk=nV6CF#w$S)*dqZcO_ zZe7fNlNAGe{a$ivO#G8CmUN>aFINz2h>y55JD6*NWN2<-APyo7x!xIuy)it)cl?za z&RKxs7~*Tm@*`?axF5F2zhJyw^}1|G=2|FrU9NcG1>H@py(wPK5r1Zs~Cm#(RN z14N&{Q%>-WIhfx=Vm(M>^{$Bw=GzC)6pS}F&MN@L;@%$M+vey#wKmf%6*;I#-kH8= zu9=a=_i1Z$19ZG=D%N{)Y+8M2~}#@{{rEn0PU_0yx5{IGhW577(7@f)n2_i zvJ(ed$ox3^2*h#(Iz^xkMMv(56y?H)s~y9y$`QjU?><>i19&`VBK|{5gE=Mdf0P20^s|*jl+Kq zf{xxR?JdEau$|&0_uC7^7xiHVffuZkA0`3k@$SM&+pM3>cFo`-gjX40QKOy|P* zN23_s)NboX?lc9~fWJUaJ-+EE1C8W}^vgv$`!pXzSK=C-#?;AnY{LsX_d+E1SZjXS zVtafL_gwgGt}viqkl(|I&Btg|&|A?x+3Jta44k88@Mk_ZJHHi6 zz;VN@tz^LZwim;`3l&6V>urCjXU!l3m(_m4V7C$F=IQ-1hSl zeMB%4;hp=sI399p>Zl&>7*a7V7AUQoaR!|Na~N8XHf=G3rZ%haZ+wS`L|!t%K84@* zDhpeOlHv_YS{`^Z^0^}4f$S(|?xArUEy9N%Ugf@(4}Iq$?hck?62pgoT@1$!${If# zw(J=;YL>wwsy67M@C5Uty5;$E_wDoi_gH-kFpd3L`{~5>iSq4@2RokZDvR!3+q=0| zF5||{LzC+;pyr&^j#Jcg@m8mlkfrmwDrIspbx()%z=bWzZpFSCU zgXacqQ!5y*EAR5hpy4|@A(Ym25V#ce=O5OMz0u6|5IARw(t2p!nxSFn)k?WRCE(W6 zwKT@^n>D=+{eaUx+?$Es_v^<1a@*k0Y%+2^261wK4weA2K(gx*G9%FX>E!ETBjH0u z`!~vil}pKc3>)E$@Xev#@QfP^hUiH+<_BGh;ejNUfmx-e+hbH}^sOJ4bcJwbi#Qg7 zyVEK!Sb&vE(f;5<^8~+kr}oG%MP>tpz8*MyLN}JRZandKDY!ZgT$)%yyZpw5<-@4z zQ0#LGb?T&6jH|yc&;;DUB&m$&Ywr2VPRztC)&ps}iRW7~ntjY@IvR!<6yrM6KCdH zgF|4T)S^U9tD|E1F+P_jYLw-Y&L~&hfyZ}|1Ow_Ynv_>=qAxG5j$G3#+ zQK~0#Aj@?DG}eGi_9W?qDM~i-&kmQ;ZTWQKd#dZ~_R37lOv((e5~ro2A0Pk4Hr}{N zl=rB4#`j(w9^24H+hmEWBu~Nyz&a1mR}D~DXkud)yKTh5z@@VarE$b!D8k1>;_3Y# z7Vf{gK>JGcY!-Gy%HO1T1h}U0sm3TCbarMgo;8v2dh)ryt3UbZI7hiMS5}3t)tk}Y zVK-s9O0Vo*I$h}EJf!92=H1=)#y>}Vc*tVtfbAz!ixwmHM`-#o#AMRv6%?ihh0Wha zq>L0c1jlTcE~&WuIhfyY=A*0>pL|1bapofiq$$rv+zpDjY9gpi!e_C_&Rm5u$4awi zhK=|2B2S!8FAu;O;)vTh$h~WeEa|xWxx;3Vv;u42EEKylpNepHGc65%Zkh+&Z@MOB z2xcuWMHhq4;KbOWjc?VGB1CZAZ||(c+vxHM|5@5+cKn8kLfDRXK`cbC&Q{@W)UNf&>AX3$+}{d0BLqIq~?y|5Kq z2zNKWC6#)~E9tbn)~gl&(1~5Hso^HAq!rmK&qX+Eq1a>ZDg%_Km~V$SHIjH*P#@8}xL@BC7VBb^-}us!x)HC~;&VNDmu32J zdU<);S|(qN7zgtij2zEK&|&}1!M*r?2;H`Sxa1j5B@d5xZDS~;aTS7@04A}2|4dz` zwX};tJTG^6z5j`%!z#WSzFs-2a<6DxM%;nqT{r;h-NL?5k-m4$$NBbnCVs$`n$Pvo z<4A;|L0DwhvY9`(P@mwBMoi&I?HgF^hva*HzWe%h`^(7IUn`<96Wop!Hi0b0 zE?bR$W+Hz7&bK*2$sKbjQ>}nnGA-%&wt#|v$VdD07f`4Gxt%LgD*5;C9gETzcL5&W zuI}Df6)EGJS6EtzCzq>Fn$ZG~lLuphr-IB2_qzmzFh9O`)0e_eyC zYMP!U4#daeDHqz@=Bsejv@-ThuTO5q4@Ekf@z?pF8B~ido6K~~k5U5otD>0PB^Um; zeqybL8m@d))ciHQV>p5aaeX9PA~oad5qX<=vt$-mW&;tzOH~X<&Em9aSOzIrfV1T^p`~D zjo%VxTj8^$iOZ`21dzP=GXi1HI8@@}6`p{k@;Vz49S0xig-^p~odQsK%BBd~EPPa! zFq=WP94nbEc6sC}&s_8K4%W4;YrbyoXZPI-$>y~-8iHlRIl2ZPzUAe;W$vFgEO=b{_R z;F6W$5`$M1Z4H;dhcA*wFz z>BgIpFF-(TpUUfa;fhpNLp~oZKHprxPi=R2r1i(}2mU%@W6Q5j^%Au|HavY}g#VeR z27XNQo4Y?e-+ffO!NOy!AuLgx<~AjtNti;sX_6k(H*}3?t=ObqJObx zw2zWqY=}Jhh2Y$cPbH=QK9Zwzf=|4;wu)i(NLpZ58}0!8zAL&T{MG!O9%a*X4J*d4 zpd%%q@=t@{$kWic#d*WUl~wqgzYn$|EDZDLgSMhh8ZY;V6|>u(YcGlC{3p zPXF8}JbwPjarvy-sV|$4G6nV1O~v6v*M?FAO@ve6RaO545x!kcU5A{$8a!TCKEFInk4^3AdHLm=b;++#x%)0F(Gqs9xi9XQ8TVkPgb|7i}D zb^E>)OG#)sMWN86y9RSn4rcy2#!@l)%~r}pK88@iHqlm!+XQbv7u~=cRDKxz+pJUR zJ2SIF5|uZHDQ-` z*ttNXl=UNV))Hs9#@y zRio2hbaqhL#h*3FY`jcRaEhh~J&Ka)Ne#`Pyj_p|zM0fTzC#;3inHvyKVyQ~boD!E zmYtuPPy>Q8h;e(X?BykpJ;XKCL8bRVhh)IBo-s+@uwAd}j&htO)gD8q#&%8rc zqAt5EoD21FIe`OfD(lH@wOcLP%tXZ*z%ry7^dOnt}*un>4vwxBN_ zl&iuvrcV`Bw}LlB32~|;e>DRwF=QvGszFELn%Q_6p}uWwQ%@xe9{RS&2@C8zsMi5; z%EmgmhatJ;qYKrem1x~E2JR;aesawLxd*N-|Feha)OHTcnl}R9>I;f}E z>40quRZlKmjvnhu)$FtxaGFNNZKhQ8pvtCWMJ@hG#~NSK!i|E5LuT+N8F|16)F9qa z?$OO%>eW?{3LD zrkA4~&|n)j*ib8*ur9e#9G+Kq^zyHFYB$J!vB@6o$m~(SN<|H^aGX&w|L*8m64Zrz zG#33U2Rjw-#a9MQU~DnS;{f@Rw1gj*-pH=otiWygi)i*dXQ~$5X+B$X+sRMj5&vNV zAD@cptCeb;DAYh+r-j$(i zcR{Lm$Ty!iO?lEk|1;&uog|bT{>GMp`tc92u&N}^z9lC_AlWhHEWsN9hID06HMwO- zDRi#oRdA+us`ZAL=lo*EO7B|kg)9NO2&vf6Tt)i07_R|d&3xf%k+xd+ z{z4T=1uW=$%TRN>uwCh0cB71Z{vkWnM_9sgr{sLTmD!iDY<-a6NA)tynBU4Ar}ZVE zAHpxSng39y=op6;|Jh&YU@iTgv#aTeX*mJ^aU8m=c_GCW>a3Xbm1;Xq1`VoAPPG-M zlQ#yqe*Q&5JO3y;rF$ktC;*Rv4ZSWYWM5SOGVqES&8Q|3=OM|lSh|hwX}>Tef4Z5H zAFNqp4T9@B8b~*h7$y-o)4@JrF6-8a2*fzi-C4uR7lLx5P-;ti<~W$^tGoGis`vk_ z|BX~TlbI=5yP|)awY(VJQUe8GTh-FR7pqi6k&YJ{u;2UhayA6 z7HYP$wP_E7v$aGvCXk5%*G?x(=^HUGYr1Gm`HJ@X^YZcx%7uAvf}v}xcRe*-V?p{! zXMVyihbZz_v&KBgv75~9G4D(WMe?`rh|1d1Ty4dKOLY%G78#b zU<|}Lt-cS=3^xn@SpAL4qJibEG=5I;xpKfdak#yyUub%J z(;*pJ%#%hLI}h=@4<*d>JiZu0guXR3s5268`--pyjg+jofae<|nhsdyoG&aJAalp( zh}a=auH$izFN5)iDyw8RNi4~r03+Sk$yYr>lK^S!g(%ymP_b^kem=NANL1d9w{j)L1_U&eyI-v;#8vlUlC&*4rOa+Td*A!7eC*BfAHyM zl^nHoNIT9hmPfz^`5;9lCN^}%9MGpvjF(b|pKkrz3or3P>~q<9t2oDlA+e2A=H|Jq zf3Zby!`GxO#`dL?QyaPDj7e#vi23O4X$k2t^Dds65!-*oc2`DPFS0}$Muh&EZqZVF zV=jtLj}UQnft-+H%f=vMyRxs+TL1(WCmGH7v!6fH$e5lXB)=j$B$$~8!-UY>>cdf*TPM(gy2$R|f;X^E%4kyusg zHMslpUZD9Nal>sL+i@r|=~h3w*{n5|jhXDyB*^KE5&CMFG_o5_x$C8J_xry;V5IZT zUt_*`3!=WL!re7fSQ^@__aXsi%+N>989YMV#zox@7bEeBhdBCqh?{`uLG1jZR|~gv65&I`8xDwO=WagJHkUIVB}V5vVnW9QA(4 z)3G7%3vTL~V0wVf%9g4e+ZfJs+!xGAF>_JaIL0|-e&gh+{+z_a+A z7@M5P@to+BoTyMvknI}9Z&`K`kq#8rE+W=$oU*PrbADkFMfLK+sijx^!l2h?GPyHB zj#|vX_@&>T9JG~|l8%z@xqaV-M}&%%Gzz0U(|>@t)$-`;bmzPK|&r{Zr%G;TE`?v!vDfgLvmUFz502% zY>Iqd!uUT5ZYS|$OouY^qcy{?e~8wr$qmR4$cM{e(#4si zk)kM05vu+fiGw8Aft7R1%G>2s6jcZb<3{R-hQ84`R;~;i=3KZQvx3HnO4TVk3x#{mr30Bw;KBdQ=sXMu8CNYFU;B z$G7en$0~s{D2QP0aDEGZCG)^8ln`{B7h9yvv_8jbhSD}Uaq-oCjEjJ7cmGJ7h$dCB zq-JLYvN3wei=}=P1@t%^2qvmAdNU4~WRLloq{tP01LwcZjCTyPjp=09bqq7cXg}|B zc=kBEKvKnAGFRzoZc&B7{lSGCPvP>|fE-T={A5TT_3`pVl{`xE&x>n1Y|FT_4uAcw zMk~=Ki#+UbfX~G^P9gm2(K(MPJilVm?vur97Drwh*M?a+C{b2f{djs$Wt&Z2LJv?c z+}&cO>OQ|0X|>`zDfc1{QM4=<5!Y{8Hy%f;=<8U^8ve|%*Ze-5xN^}>he%+xW;~Vl zpJSb?ZXAbr?YNMF$>*B!W?C0E5%C7$qEMPKq$smd0M?g{9UUaLzW|bZF^B3*o*wM6 z7oaMLZajup*qJHd)0xSAYmRg%3UXUd8d*oXdYb5h%Q@vV{gby7JsrZ|#OLR5*3uK&z|0O~ WawQDw-3u;$Zf+bpx)0Aaw*Ljr+tX+O diff --git a/doc/src/Regression/make.sh b/doc/src/Regression/make.sh index 9d16b4645..d908b90b9 100755 --- a/doc/src/Regression/make.sh +++ b/doc/src/Regression/make.sh @@ -47,7 +47,7 @@ system doconce format html $name --html_style=bootstrap --pygments_html_style=de system doconce split_html $html.html --method=split --pagination --nav_button=bottom # IPython notebook -#system doconce format ipynb $name $opt +system doconce format ipynb $name $opt # Ordinary plain LaTeX document