From 6f55e215deb5289518c4618dd36ca6105e202285 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Fri, 27 Aug 2021 07:36:50 +0200 Subject: [PATCH] update week34 --- doc/BookChapters/chapter1.dlog | 9 + doc/BookChapters/chapter1.do.txt | 39 ++-- .../_build/.doctrees/chapter1.doctree | Bin 819089 -> 816384 bytes .../_build/.doctrees/environment.pickle | Bin 184696 -> 184724 bytes .../_build/html/_images/chapter1_11_0.png | Bin 6624 -> 6573 bytes .../_build/html/_images/chapter1_13_1.png | Bin 9441 -> 9329 bytes .../_build/html/_images/chapter1_27_0.png | Bin 13644 -> 14031 bytes .../_build/html/_images/chapter1_3_0.png | Bin 9599 -> 9959 bytes .../_build/html/_images/chapter1_61_10.png | Bin 26084 -> 25483 bytes .../_build/html/_sources/chapter1.ipynb | 37 +-- doc/LectureNotes/_build/html/chapter1.html | 186 +++++++-------- doc/LectureNotes/_build/html/searchindex.js | 2 +- .../_build/jupyter_execute/chapter1.ipynb | 212 +++++++++--------- .../_build/jupyter_execute/chapter1.py | 37 +-- .../_build/jupyter_execute/chapter1_11_0.png | Bin 6624 -> 6573 bytes .../_build/jupyter_execute/chapter1_13_1.png | Bin 9441 -> 9329 bytes .../_build/jupyter_execute/chapter1_27_0.png | Bin 13644 -> 14031 bytes .../_build/jupyter_execute/chapter1_3_0.png | Bin 9599 -> 9959 bytes .../_build/jupyter_execute/chapter1_61_10.png | Bin 26084 -> 25483 bytes doc/LectureNotes/chapter1.ipynb | 37 +-- doc/pub/week34/html/._week34-bs038.html | 13 +- doc/pub/week34/html/._week34-bs066.html | 21 -- doc/pub/week34/html/week34-reveal.html | 19 +- doc/pub/week34/html/week34-solarized.html | 19 +- doc/pub/week34/html/week34.html | 19 +- doc/pub/week34/ipynb/ipynb-week34-src.tar.gz | Bin 103349 -> 103349 bytes doc/pub/week34/ipynb/week34.ipynb | 19 +- doc/src/week34/week34.do.txt | 17 +- 28 files changed, 342 insertions(+), 344 deletions(-) diff --git a/doc/BookChapters/chapter1.dlog b/doc/BookChapters/chapter1.dlog index 875fd7507..4f6d8fde4 100644 --- a/doc/BookChapters/chapter1.dlog +++ b/doc/BookChapters/chapter1.dlog @@ -43,3 +43,12 @@ Translating doconce text in chapter1.do.txt to ipynb Failed to remove ans_at_end environment Failed to remove sol_at_end environment output in chapter1.ipynb +Translating doconce text in chapter1.do.txt to ipynb +*** replacing \bm{...} by \boldsymbol{...} (\bm is not supported by MathJax) + +*** warning: latex envir \begin{bmatrix} does not work well in Markdown. Stick to \[ ... \], equation, equation*, align, or align* environments in math environments. + +*** warning: latex envir \begin{bmatrix} does not work well in Markdown. Stick to \[ ... \], equation, equation*, align, or align* environments in math environments. +Failed to remove ans_at_end environment +Failed to remove sol_at_end environment +output in chapter1.ipynb diff --git a/doc/BookChapters/chapter1.do.txt b/doc/BookChapters/chapter1.do.txt index 5e738504e..5c3670c10 100644 --- a/doc/BookChapters/chapter1.do.txt +++ b/doc/BookChapters/chapter1.do.txt @@ -495,11 +495,13 @@ ways of dealing with outliers. The Huber cost function is defined as !bt \[ -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\del\ -ta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\bm{a})=\left\{\begin{array}{cc}\frac{1}{2} \bm{a}^{2}& \text{for }|\bm{a}|\leq \delta\\ \delta (|\b\ +m{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. \] !et -Here $a=\bm{y} - \bm{\tilde{y}}$. +Here $\bm{a}=\bm{y} - \bm{\tilde{y}}$. + + We will discuss in more detail these and other functions in the various lectures. We conclude this part with another example. Instead of a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn. @@ -2181,7 +2183,7 @@ It also common to split the data in a _training_ set and a _testing_ set. A typi for testing. This can be done as follows with our design matrix $\bm{X}$ and data $\bm{y}$ (remember to import _scikit-learn_) !bc pycod # split in training and test data -X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2) +# X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2) !ec Then we can use the standard scaler to scale our data as !bc pycod @@ -2211,16 +2213,27 @@ x = np.linspace(-3, 3, n).reshape(-1, 1) y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape) !ec where $y$ is the function we want to fit with a given polynomial. -!bsubex -Write a first code which sets up a design matrix $X$ defined by a fifth-order polynomial. Scale your data and split it in training and test data. -!esubex -!bsubex -Perform an ordinary least squares and compute the means squared error and the $R2$ factor for the training data and the test data, with and without scaling. -!esubex -!bsubex -Add now a model which allows you to make polynomials up to degree $15$. Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)? +Write a first code which sets up a design matrix $X$ defined by a +fifth-order polynomial. Scale your data and split it in training and +test data. + + + +Perform an ordinary least squares and compute the means squared error +and the $R2$ factor for the training data and the test data, with and +without scaling. + + + +Add now a model which allows you to make polynomials up to degree +$15$. Perform a standard OLS fitting of the training data and compute +the MSE and $R2$ for the training and test data and plot both test and +training data MSE and $R2$ as functions of the polynomial +degree. Compare what you see with Figure 2.11 of Hastie et al. Comment +your results. For which polynomial degree do you find an optimal MSE +(smallest value)? + -!esubex diff --git a/doc/LectureNotes/_build/.doctrees/chapter1.doctree b/doc/LectureNotes/_build/.doctrees/chapter1.doctree index 754a7576ce22f7b40de1a753ea0e57b93cedad21..30c0bee2e7bc312316606c03b8ec5ccdac4b28a2 100644 GIT binary patch delta 115021 zcmcHhxvuO`(t>Az6KcWBi|A_wA|DS*QkN@dE{U835 zfBG-}v;W}#hyTm}?mzl}{6GJt{h$5QfBWl0sgvCBNenh#l-U`k6|yrX6cawznEgk{ z8$tGE`6+&NzgL2(?=w7PTL;2A>gnOne4F|r#{_<~uFXLM@3YCM*vh^xl*K9Nb>m`jcKSzqYvm9< zwwn9<=gPa_rH-tAi_?gg&QOt(`fTKnV>I8wU2f#|7*$5JK7mqVkR zlj@mx5;H1{zCCjV#aQsjWX-mdg(L3PidOUGY?*dP7C70)zhVo|5g>|Qk+wa03=53p_P4>@%enNU> z(iYsWZDhh5-#_ts2o)y7Ua&=*3QY$?Z*`MN@6j#35-is49{qeZCB?R}XH(^s?Pc&Q z!fjAwq1n%nBrS4J4X{Z0NWeIAmf#EUyF7Fg+s(2hA`{)atSG>0Ef7f$dtY>7+GOHw z5o#_|XFjToWQseN+>a2n82b5xd*sslR9-uPp*$;9@ z$65Dehnksn;sc#!O0xHCtE*LJ1RHZlWJapsn_bbO&t}PRh3`e&Pl}_UV2FLEdqJcj z`c7BoTo8(c*xSr@;{krv^gsB(kU9|vf@>WDQjW1bMK3J zhzpN#Z7smV?j#MzWrF%tDghIyKE@W}Ff5v@x#F-1!Q?nj&4peGd;B#Q3x5sVS&M|o zaWU*a#M`7LMnE}{a1vhN_Cp~Is9-@@O!Q80zpAck*LP;{O-%1{CAV_UVV)n}7w&iv zX)?g~>XUQT;UDIDLD19VGO|gpg3RU`oT|yITzW_=&d^Zv;1K#o=>xC*L+DF8VueNg zyG2?Amn$a`9p+Y?>GCBrW+!*>KbX(Bp;qztUElZS;+)!%E8@1b0h!zXTIjfexU@a2e$h@3hH(t9b=n5%zX;+fZ#h!oNbHW|mf%JQX*=IQHOIRvc zhc7hvv7Iwb0FECHRl0DBbv*6mULF&hk`2B?K3+FNeTQRo(&MVJU-L(AJL6)3=m`pc zydjL0*Wr=lf=ZS}7D8mxxB`SIGI<_qaAHDl02_tkUXWnlPK7*n2PcHtY7E_H@o~_|O5{_b#OEn4 z!Z)sxk5q#{Xf7eBQte%YU22x~_yOxwTV3b+gNeb@38|ob$X@T1M?R2W=|cN;-u)Is z7@ky37VwyFtIb0`hJy9@)7657HpS*~KNxh0AsDI#e!XFfiA}^x*Q^d-)WG5Oabqha zl>Em$*Vza4$7XO7dOy?bYo|pHk-y?a9kH5oknu`Y*I+ipK?Ih(D_1}`xpuzVmckLk zRo18z5H^Lrk!ZaNm7`A@lj(aIUym23v02%D4~bA=&^7^rCO;lO9SGrjk{LHXmPd2YKL zUMD84_I3G@D#&-z;o$vqrxjr$Ri1oD~;w^wlqq#}*B_lE30o5QtCBUEuz2 z10$pGA#oO?F4joXJ1i8zH&!j($lqa3!V#LA@(f~UCStl7N|18rs_ti4;pj#xp(WXV zCJQ|XKIH^rH`Ssxzx>MSiO_R3nXGZ8~#Fal^B#ABsz&z3bfrc0P%xCx30?VKSBySVH~oNuqdo}z`wlsQ>3h>k=3m=$R<4>O zTbGBDi!A35^i}IR>`vV)uMJ*8TI=v2^DRB6w`;f3!~^4|UBbFO6LWMufOK=Pmpxxh=nF zi8FukS}K{wcov*<*`&-0(F4*xy8C7%vG&6pcI*cIXur2s_)jJTzeqj-u#*6!`^wx? z@H+tPq$hxMb-+uy9ROeluDqpq0FC~y`|R)kt^@yXJnrwGuPzjXGXGv8g<8FJ|hv>-WYFpcEOYVBEs(MOl<7 zne`VDBGo0@NApU7I~LaDcgebvOpV+0{MpO5YY~d)YeL()??)?5jnM8oI{2<%-!})l zyloj~s=dzmDftgct!vHc)^^`K72w&7+ zZL2}2@>syngX_hs_fcmXoO~l%JUCxR(%9Jkmcng+s#O%-fe6ZzWeA>fdT@U~Qk{eo zwa|{_KAr-&b(3GGvxWw*UY^pCo$ z9`P}c^7G+I2WRr0Yb4Z)uhfbptorx9%X};M*~{Hv;)@OdG&g))(qY(HAG>rRTXaSt z*!iBHPgM_NV=2%4`}96PuEv7#k~JPg&Do_N=iZfsy1bX?C#E90QxgdWbeTsIXOf^9 z=KXvJrY^{W>W5O^*c=7|RETX>`$~_5d42DuZe2}MLHnAeO2+OEOLJnaANI=6D8Q7G z>L^x_KIylo{8S$RQvl5welgL-OgK4L8M*n3cAY=@`}|lQLA@;5b0027k#&Zi(&`7N zo%Q6_7~fPY{ju*{KW}6O*6S6eHg_5m+N_NihK)tlOag9tlp@AX`_L5kn!fxcdCO`JTUQ9P&Vn&hVB*$L(BdAYR#)hB;ImWnQwVFqbrI=6^nxiAE5k74^!ah5teTUsooIMDH*6XmeK%VmYCS@m01yu$JzSNyF#|G%oB6z_n2LmN6R7B-XD9dq)}9s(}IGk=?kGD%t!JU-YBu$wy$2wr%zyO#Ub+&S_zDE4nv)?oH$d4tDjsQmQM zU}ugGjpMOAS42+S_GZ6m4^N*M-}|7W`h7OvAf8XXa@~(M29NCv|K;Aznym_SzYzL5 zLEZB|6Xw>M(QBlT^A1S6pyR^!xLOlXGAGyS*r$^8u1fHPm~p|OFcNb^$mH@*^V!Bi zAaYO8WnY}-@{rhbTJ4zJz=^;5A=;VR`|rQn4I0K=3J$^4{MPe4*n_pR1x?Yw-8H0% zhJHOf@vZi;FbxgH>B7OR_5xaKP&aV$k)RJW@`@d>Ml2GrCc}-o0eh1MplE$hR2!yA zn+^8(LR?9;KEVt5ecE9P#wlDC2>`3eq`DGfR*D#}s{wJh_Z{PD$eAVN+@i`EYuh{K zFIL~HFJ)$cYG?E`@Dz`o5&Mqz4mf1$_j?2r2?NbpFC< z^E>V3fHWKc5=l>u0P~`On4G$&-jL|=K3Jmii&&zYe|`6QqJ$<2H=Li#3V57XpBlG) zUtbH5FS!qr0_Ux@XJSG-z9o7)#Ld{tC8rVVYSRG)5ZpcNLfgCf>y+Q$!gj6K;(`u6 zazx=w^(+0(JrBHj@HCo-Y-_6||&c(G=Jp@Qm z?%XI!e8S<$hj{EO_2XBmr?sI3@7Es>9uka+-Pq|VtNmonu7Kfl%_*Tl?XLClPa74s zf@L}t$AaBHE@tJ$*YTBNPoqF`(Bt<*y_2*cKMmc~;~G$sae*e|PayZPE>`M!U#}g8t*sHqqV$dq<-3@*64!?@2 zax;dk*02zt0^%Evfq9*zjcz{;wB}aBakusZ9aOJxkKoK}X7GztWi^F1(PBeqfD% zua==Z19p;6f<%UlavVwDu+9&OcL4IhH)IR>Fw!WFvB>d`csfs+rW;KWRRRA^AWLHF ztc57?&gji@>}6R77^YRCG?NFoV&kgaN-N5hiBbe)u21)Eh}Yc5Y&9k<)hGT=SkKN> z);U2L;=r%F*>wZQbX?u{jPDiEa~y|+7#1Zjwj8;zV-3yX(9ae93j0eXTzFhd!)<9z z2VM7lQ?o%|B@jWs9AX7~)RDN3J0?qZ9t;OpUl4bf3)*?d_WbM9z>!xLlCCkX(Lf2HuPTEQ zBQnBE(F71Yz8eK1O$tOy%mawdi(l*=CFXAlSazwKLy2O}v()FCkX~K7lE}lg@F03$ z{?xSE6WQTC@nQp`A2MiNt1H?zE!|$1L=~}JY%D}?-6X!(kRO)tY5Gq`rmC;SmV+n~ z>M$@6#?bKL?x|L>2Z1u@{deI%%#gM>0-TA4|lL0N>ZddMSNq+wHxb7nOhm*4-eEO0yHI3 z|LM4|8~_e3H{dW*ys+y@%>#62NaN18wD<5s--9|B;WWJsiekN#3Q1?*pPbxv!)t-K z-_%)q_8N2yq$#jx7$9O$#Z2rli6+*D5ff`ca}ovHh<9aqXNTeXa)N#X$-gESd%v=c zghBhn4AQ40RFutncu7`)6J!LHTB01Ytso(VZgyuA0eb~967Gd~JPe&n2tU^WgsVUJ z;pe@%6zd z?Aof33Bx>3GKh(Ld)4)X9mO76U;sg(CZrFiJL04P^o3Zo?OX)?S3VH&_^F;LnDHG}25PPf7Bapj_6IA<+A z74*e5Lvyi-hZFqnlRSdvxqAiB{RKOpkmS#;h5PL|W#w6aT!5uvvD$T3kBT!&KRZ#a zid&C^fsVCMsxRqHrO&j}9^3E}s3_2&Ru$850_VnNxs7Ed^@Vm2C%)+T)}%NUU=-%3 zP0lM0mC=^pW88v+C9fXyy+MmG*S|)?Wx|(+O}H39Ful!DSlMmIY&opoFdfAY$H|PW z1Oz$xh~l{30eY7^6w zHg=()53YiOcYCJ(r&H13X^!gF?7A}fw=-$lTo7n6`)Dm$I~-$ z!Dy0cO8uuT_0`p#-&Nivxq0zV0Nl`QT`MILn<^?5WSfdy#;h@p&X4hqH`IF#fjPi=^EafPa(0$^NiAsIT4}Q8WC-a!E`qjr195`oC#LlI1ot)l zwAI(;=$lTuk6L9n z6Y}#E;rSL7k)%|D^EvK707l{FPeF#PILnnlAyF=WA}9nYVsQOed0d4nRs~% z??LV>Z`>C3`-uSGc%K*3(K7rN#|YmL&-$?@24n?luF6bBp@#Pr zTy{VQKF+1i5f-RO^xN()7(Eko-V!Im+V2*(cbV5KNse8+<8(^<@Kp&>rNpF zxjdOQG4?%pK7dN+B>5ZE(hSB#PqgsSPb*s%ciN zcnoXc#AFzbfPJERe7&phPDkP%*e&0`*8Mmi``es=z|C)wm97FVw_}MA3S7urqek){V0MIAzfJKIJSOc=OmeA z@j*WE-AdKtxX~1VB z5<^f#X6*l#ta zQ4C6#-=y%fKBMr<(O>bnblP*6LOwm*=V!CSFYyRyhV%Rv z2$i!bkrCh@XvT~e!2vD9t3I)2J)^%AkLJ5Cfa zgHz>%MKH%gV`lXtAy`Mq6~Wrz4Q|B~*u<{xnm^9Eer=@*_9$K+m{r-P#e9KgEU@gS zIOiEUX_kd5$6*=va_mt-HRfQ8>+PrW?CyWT2g?aY(S~-9UIivY%y(kG>z)vw8iqq#? zc)WrGa27^b7K|LuKIMP{A$7Fr+Pwx_{Ne%6L>Eqk0%Bmx7Bya$Uy1twv#-h!j)VUho;r_A>MO4({N|9yE2cQ8 z9%gXiRa)%SFw|OPaQrLkj;@0JCzgybE!Ti^DQ%+p^4|Jru%+7o2M2au@5LV2esT{H zeKPNFJWt&JAH1WxE9qqIU%JUJrRFv!F^uH zYBWHYVOTPv9cSBKfq8KnL2x?QwQ|Lt=OKe)nHJdg1=%mJix(a6Da}+PThRwAt|?-; z1wHMvO{_)S;X3of?lP`v)*LXRMRK6om6>5+8G9zI+p!7Lf7><>#kKwxFJAmeW(QnQ z@;i4sRGP8~Z`)72y}ET6f*7MoXcZ-(APW$6$yoPdG3sT00d9OVYTwsYicqWT3DAZt z&F~0>`Zq8X3a41M-6_4C5FyTh{4I8WZ9RkU+R6tY9SxA_^R7${G}HR$S?%dYtpM8O zs)!MAiB~XOq1B4iTT}24JwaaAr}_ro2)eJ=4(T8+0O$gO14OK)8M}S^#bA~WrIEW>&KFCwnXs>nmAQO~tvCJW9-a!4G zvrkOliBnLs3=6t{aB*x4HYAl62zB~dYQRQohI*Jy*2%Z_50OBrg!2>>9S{r*Lu`W8h8y1^6 z03$~&e`II>+z$G~;ET)wvzc9+gq*k4c`M}#RGnFx5zkq?Vt=VHYH)DP$Hh(+|N9cEu`oTAK?Sa5ihtqER zoH-WxfMkTj7T5r=th`y&4Fzj{;bs__qfHD$tCS84{v7W&=Faf0HlRaVt51WB`4+wf zsMbHSzPt5^0qXG-5f=ug#Q=e|4#V31pZf<>$Q*Z4&ns)XY2Hz1hzPq#CqeFi495HvAMI+Aw$}A(byhFpRfy~FgTNMi07WL2n#`&QV z+l;ZFF~=E)ks$O2hpgW<17G>T<@*5&*x?G%6U8^qsZ{;oa-kGLZebnH0j3)+tyN7V z$23XNCqR9!Sn5U}kP^YMhe<4qHQ&IH8v&;LeeldR9UCAl!@Pss-yk7Z3$Jf=J@ZB# zgL}^fV5#K@A?Ma_k#4#WE*G-_(bs42;XXELxCF3Hr39j(=QvaLQYLP`Mu6;C>YxUjcng z)vHn|$cRj33S+{&|IFjySmfjoL``m=OQl zIiI>6@BFrb=R``>J4lmW045ZH5lxOnzycV*=F6=)Q*cHem0E7zTR_^$S2&}m0sOvV z-T|V)FiH?yx;ap{Zwz0mu?65PvSD>c$v23m#@`d{-3er#zT(<6fU-r`_QAJpK|F(X z7DDV@umrDpPLK8%q`n-i3SU6tv;qHOpf2CH#R4+wt-_fZ_v0J`Z@e zzv7y^^B0+#&+4Nd|J5%E=u}zzI+QYTrB;&m8G~PY6Q2d)q%lXK7T6kSqHek2R_^dn5YW)SQVT1rv?L9RkQiyXp)1W# zSSE{jt_9JiMTp>%hZyU)#?}vla~;jzM1X$4E&xHx*AFO)l~+N3fF;T6{q+4ZJ`XSd zhmpzreMbrSn@0T*>g26mAJC5hk#_ip3ksx>Kljr=J}hfWcIGUwy~7A_8?)QT7;P;k zpNK!X3<_dpABUAMfdH)r_=4`9ciYA@AKP?+xv}~Zb?HTuxirK(N5!NMj{LCsU&cAL zjy$+d-~Ti}Zx=SYaeaCjod^O3{YLPDKWj}J0TBS$a1Z{_feD=uD8Gy`WPBQD2b$l1 z7#I8eAJ;M>Tn(=1`tyTmpMG!!V0j+22mg01uY%JAa7BN|1lRp9SeW6@yz>b>qZ4=e zm;e2L^B?|~|6fb~r+*>*2mjT7{?Btd_y6mk{@*{P{WoJe70u@N!sn6hw^+4vf^O*Ym9* z#PQWflGA|pm41NVy8+rf02&5w{WyL*IWeyR%Yrs6WrU!=Di^e*OSmZa5ICFh%E6+_ zsrHiGpz^-&j=rbY)C$j0du`EH^E$E}w;??B#yt!#)S3f%c&Pa~TEYFti{VkZ9%acjM_8JU*EMG0C7?KBaF7DNN930Eg~mXGP z?2zsiBYo)d8odRd%Jy}x04lNyRsVdj_U_9*xszcSGoDW z3K)Y`7FSEG^kzKYY2?0In`Tw^2}vQge#!Iwje_;c2X4#7jozOCl$E@7m;+}pI?xYW z>Hq;#)hq#|{`+|Ry)OoGuya-R1oJxq3OXEmU!H_gdV`hRG!k=YfzSKF{FtGoG|=!I z(jca-T^!CXW{1eAXl=n_eAP2M-1P$tJJ7QaQw zY0m85zt@5H&8k0iX|8q0Ve#4gFa8^`!rt+xz%+2ggEES{R49*ldz#c-gum1 zXXvGZ9aI@IZ@{o0Q;Bcy9R4fH2&+D{TOWuvAO6OtS4EaU9IsKr_76;_^|NcBPl_D>m({Hg*aS0TuE@Sw`*U@J`JyWwMRs6+Ust$chzIcgxBCsa!WX05bppk5jI*G;IVo1qYk^z1 z%&N{O)qYhB8<4ObVqB~p)Gk04mK8=RYGAz#fgxc|X#bpyP;9BrbJtr}Wc@KbLsR=) zeg~#^u*+t^%m4<)5>id{e7f4b8;Et7H=2C{WLy|e?Jag&pySpKuJdh*hV1l$N1g&Y zJ<$#^7!`cs{!@{AFkJ=ST&FK3$fuYdWveTF+M9`f2B7WZ><7D=WELne2`Z+(^BrNu zMMej?zO>Oc<&$kw1c88j)g8fSX zSSYv*!PUjTm;aTb^g}vNV=SRveQnXX*7|#qdQdo4J@`1HJO1xD*#CiJ{r^51i? z@vTK(PU?BTd3exn(Mv2C(hAxI|L3I#bBV`g=&y}`5b_Dan18n|B4TgE*}0jZAJ4f3 zoq78HHlN%6ad;S_5gGu3h2ZZu?9xNYufX6sNV?Td?$Z~{#jo{^CWjIyaXdl#k_W=7(b7FI0IfT} z7X5g@Yjj+MlfpgZ8lF#8z~J8k2Z9BVi%tgzW9W|s)II601B`p-VB*uBCvdC7DhLP# z@K9hds=K^#k3iu9#8J^zVFuz#GHS&(AlwaO)@9%}4|waJ_dRfg+S2LQzkd!a@xIs{ zRXqFvNS^|bq6xol&|{qZmyGVjbW%YNdhrI>o}R!6#q=$C8UR1rcCB@rUV{a`kOF=p z6cI?T{j&960w_c*sY>qCy2N#*!~Wvw_pbvA3EuCve1={@`+G%HdS`o!9{b72 z0j~m@qcDbC9k@Nc@YP(G2PGJ(*#9!KgS_VVD%%gDhvi~CS z*al%xaX{0arVW6Z-(1gMAfg2Q>+wQWx$PjZRSzKs)$oEoG*X)2yJqD2WZxp;9jJl8 z1SOGtw9rfS-ru$Z6s@^k&j$%L>zWI9EkjwTDBftbCpAdXOo{hG75jIvj^X2v50Rgu zzbg?w#r+EfM+GC~VE_JIC13g*S2O(1J(;SydvBCm_vG=Y=t=P4)_w|v4JLS%1;`hmzC|yy?8NdUBvf?>G-?M=So1_~TPQ%o;rOzS zKGY0j0g##XdUzFjUw3OU0G>k-b^8z|Y5?s3iMBxs5?Z14hHd~|K=>Kk4Mh4UfgLGG zo4(!OK4k#w4J?L-1*loixE(44RJNC|NrJuuG-br*D75^O9quM}5(UKa<|#h+FBZxn z%kPc(R;DL-xWIRcP8(zyU{~6FTB0u?CR1R0^;Zh!kaWM5kYCGqFA%+8d%uy$L&4DL zVGNuOEOolRQ~iM0n0$u*kwLuLWTOrCFVOUwRF%P0C1?u)SgHvyx+)F;p?s&7jZhyi zKx@UfRN?+5r7`NIGiY;rLde7UTUEIf2kovXo(!vKeH3o%J%K~Xv_BIt4jA=o<1nyoyJu)!CFz(Q ziAP{7pM+hB4KT|&i)v_uS7=^urv2m$)t-HT!b5gWQ8`1KfCT2t{i`i-#YU5(0QQK_ z91bx=ub&g;Lmip5AKHPXL~R@h1sisVc;N!&p59QCLr(-L46ro^12w;S6rL3IQtDm> z8f1!t9Rp4`zYfeO_SFG9g^SkP5R*kw5C{Yr56Jw*Xg)&sq1l}$Uu=PqAUi(i9?5MZAn>gPz_XMY!m(P6XX(=X_}8CzpO0%<91~M@374*4{WW~pe0xv( zTxP-ovRdzwTAu*O@dq9yO#XN3^}i{x69O(zXpiE7wC)O1uwCvt=cN-tf(C}MJszCg zdELz7r~na3c+S*};h*5qDNh@R0X+HpUGSU&@XKI)kkk}lQ&tU_U0uR#MS$CdZih8@ z)Q~)1U_%7vD{x}jX9RV^%fT#kkEKASs}&p!X6QK31PrD%q7N9!{|d|=Z*OYl_!8Jn zbPW$r(*(Bgx%M_bPXK$6q`Fn*_z&YeMx5W|NIU_P0ek@_m>567lQaIx?EL!LfX3xW zILD^Pmkr~RKLxg?a#ATo5zG(O@cfTkOycG$LY1>{x7Vgj%VZQtgd znv1zSc>x6WG#7Ip=7q6lkhezhcCpGwC=mpvtM*l+{grL*B9ytdHqIx5(9eg>zxC4P z_38r{pG}YtkpFyKyq`ctt~vq0VlV*(tjW`hkY@-=3v_T;zL~O-N}KBQyr@@H_yLny zAOjzr4epuFJs>9!wEYde+;+-<2|q%{;$XQ{hwd1QZ|7x+JYPCfWX_|^GHR>&H~_E1Bh(j|K8RR4RjlMRMW})Ki})S zCyM5BPU`xxm4CJ0|K08055HbH{f}w(4}ubmWI6J(1P@n1DZyR2Bat_!{z$4P^hLtj zGYf4SRivna0JAg$7vvQz6auj@3a6HrquMuva_0S^d* z1;AlBi}Sj(vOv@Le1PW!h)5@mVHO9LcR$Dl%U*1`#if-I=F1w=xOKM-IE9wC0Y+pP z1RKKF4(L*lblB}CSssCLDM74$Fug6Cz6s01H1O^YDJXGG7$GGaQ?_BhP}}D>_zfOc zB2QD5u-FCyRwRM`@nN?7sx$1WQ5H{(4o*p~ln>jttL3T! z{Y`y9>b{qVaYOKxS%}e<@O&CDP2Vgt#+4u>PBw$t(;2_JQe>YN0mdT-Oo*ZB|4-e! z{LId->0SM;bV%p~64dMjsiUx?M4=PY?ehsm5W5bpa#h(b+hx0)9f;3$uw5=6tGu&S z3<3&Lq$onLV1kqx2nG>S)FXtVFv&1e2K*11KxW~3zuh4n2q;rN>D5}_dJk1^mHj-= zec#vhyOt`7NU7d`R@(=?@f>y!YD-%s3LC@T|^va*Q(V7W{Nb;7<_*tUTpfeUd$1Xel>2B$9kU_x^$K)oa z(#^;!qF#Qy4MQ5K`cTvUe4)h#nh7-(HRs+DB{4yy#AMS7)TVpaB=sgmrRgI@8$HZ@niSZ8p{*a2Ve* z%|{o!d!72-=-v5R_TFymrt>;TDKWuLQ65l@2{JS=eNr-C_m)( z3McBb75)C^tHc=GSk8hZNNE^SoC|VkorVtAcQ(9V1x@Vn8;y@VcOk*VFLR&C4JBGc zI|MJ3#B#)pY}-O86jA+WSI4?Vp^8c+bYMXv$_T1~K0W zMCeoMaFf;7%T)LC8J4rpgMe&j=8KewpqhPIW_zAjxAs5=%bcHw+k+0x zi)_XH4f#pIT5dZ#u4d5^AHlMRW|+991~HSgK`^3!IGn~M zydPKbq@%e#N|Nx|+(&tHo5gfIdF!QkUJ)^kF1#cVqA_Ojdg6&qCO$+dtIUB|7qDy|#2i9EA! zcIQ8m7jRoJBKKmu=@dq=PT&!U*%(8T+aIF!eh9zpta0724;j-1{y^;o+~Ta=YJ_&bx`uN2mzxd)NbvbDuYq&nPzD5yPP!PcI~(&ICG+8A$|Cu0$!>2;LU z8yfH0wLCcQ`ysm#@4hfmDk$lJhB|1$7@#iKaI0K`su&-(_j1PU;0b@Z_^Y7tvvsi|MIVW|A+tc5C7(` zQoqeG-~Zm<|3Ux83g7>OU;ouV@%^tbzxo^BUw{3F|K$5W_+NhbE8qTW>i6(nZ|v_8 zj%4B`O#|>c@}%A#R}TE?D?3+`akaJgXjI91`Rq@R{BeGpJwP?}fF7X^erw}Y#Fb9r# zSO$5R)p99{Fw>haZ1g3t$t-|VT@RVAFeK;)( z8?AA+K7#*A*NUlXVYSRs<*XfU!r6D?Cr(s`p-{E&1fOzN5?iVTSJ%zAC!Y+REJsIA zLUN-&ItiwJP51A!q)Jz=>Amvp@0*RO%89!CI`Lvy)LpI1rOf zZfIL8&Rf44l(#-ePww(marlR6%a>Ar4FZwVeH4(o^1Y@c6du*0;KA|-aY84udc$v> zc7~HTu%AKndBMpCJzdLootSXaopx8|HnS)=Rq^>dX_LCkU@*tbddfOcx8s6-*P1# zfa1C%Rg055ya1=q+q#N<_zECZv4HYCuiyy1U!p}jnT!&5k;W2BNF}8;)V{&jkB3}t zT-1=cC<&)*oJiJd2#$Hc3wS|VlPwm`5S+EQoocU5pzsoyO9z|fA&NFtz4gsRO;JK< zvz)_9@{ykoF@hzS(CtVvWKkyz(H|soQx{aC*x0_5(#fUs%I$>ZYill@ zSi~GuG&gCJFN>>K-BQ;ef@GL2u!ZQ>9xJ>x#wy=5UR@1p%O6%9u&@~EmHEecT#dqB zp@@r@Qc`W1t^M9#iC1YD)u5zQzOroX1-IO&Z#8fxOle^KgTnzcw>!-3qYf3^|6Xh7 za7~em8H-d-b;&wv{mdcgf)xSBb(Mu9Nf@*qu&+uOL`EdOXh8HSn+huxL<{!OI?S{q43hitJrw= zvJS~9174R&tv4vTejj^duwv&rb>sF8u8-FQm?Bel7j4AP@>I<@0IDTM<+p7ie;sV- z{W=yayS9y~jcjH@6A)&}lvu1`Z-&vsVAcCek}Y@H1F%58$8*6QGE1_iFx&ZeUDAM& zmJ}B+X6@wG(Z|)Vh+GfRkc~#?aFk!`RrMYslKM!L{9xic2L~;t2Fs|^!>uW7aEdLL zuOw5zu@sL&UCn{9ML*A-Z#>Qy_QWA9X&{>w>PI2Z+_DXes5)Z%VN9l+PgZuf zttu8W-1{PZ$Bw<%dv(`sCYxA%EGP~0JuTg|6LGW@^IqX(cF*?jQy2gXN2u^-&gjf% zFqnhc{iI4jFM-@7o)l<1wPba7u!=+wp_Xg`854XHp0=19oQnOm{|8j~12 zsbxwl)RiBN#{2AIyKxTne#XFZy^6JxCHX2WBdNowG$`7HyJA}{VQ)H1w`@V{o>;8? zk%6Rk)deCi(JoBpYuv2Ke7KC+TuYnf5`CVrJ~pr>hQyhea~JOPqb{24!`{;mSY9QG zHBPTo`?%{xb4?*uy}Yl(I4+Z`ciXL~=BfFkDYME++P?QhlV-$|&c@rF!LY}3_BV6B zJAedeHG8UR!i^ayc=?@jUyW#AF3&H)O|DI<*;*^{K##o#_MH30K zZbl2=Om5xS_cAmctk`qJ3;f_5^-;Uvc;XFlFN;*SEe<N-PFUHDCr8F@$xd^fe@s@6L)#$Do$$(Q z@_4P9QZG8CAkJqM^W$@6`=y6*Ua?BDBB!0WGg|s$3midKFrZ>2zNgT`%IgzlDHqgR zOJ&}HS#V1Ba#VEX^PX^rn;GkMB^02HESSr&y#PjcX7{f9N&2M%1{D1g_JEB3_<(0- zsjiaT0%u7zUNFaBZ?|In<%7}M-GCugQWRW#;>h#5s2tgyaoo-wvq`e57oeLyZ>D2a zXdTXl(ehn9l$C_bAJ@!i>?;*R*fiRKwpSdhk6zO$nS(9vD{E(j3!6BDCA^(??_lS5 z8cK@@mw#BbcJ(m&u@)bNvcoQmUB8DbX|svX(?GkLvV;}rOAjDr`XQ zhfUtuZk4ZtXzYiIB;D84J~M!OzV+j$B~I;*SX5irXq)FVD$;iEi2#6B-55-j=bo&K z_RvD()JF$dp=lg-mbyddW&m`npIF>!S%j6*R-v(pN@2$wBK+GE0B5I3 zo(G_I4lj*@%8uD@%PeXJgAG2X9faLb;;2GKctILzslxJDWT%858WVy9lN;r57W97S?0S$ zem!ZsuWR?!8(wK_JpVJkomRcS^H;x0s&-#_X*Zpjn8WadH*qg#HKqtovB8zUx9n!= z(cXSl#q~*HHr|*#qbA!bO?11g z%&+D>P9s=Lo;d$#RT`uQxZK7PL}$ewhBwnJP(dHo3*}zhrM;UnzITRIcmu_zmZYtx za%(W2z$!MoL~gMykGn^v$XaT=uP+c{c3b#Fue;;u%AU(sd>)GEQY33WG|nJ#(1dZ-Oi0)cY`L*Bc)#{1U!G3mK6D2* zJ8q4wM&`#S{LL%%WX#u2S^*vdf6JBA56UWEPqy(2j<8UY!5d?ryz3VVjS~}Td`%+-znrzBW zsJ!JNL{q0^-g*6Ll?3IO>`*^~QrfO1lD!HS92@oJt?Phty9BGX$4&R^;{%ZsjueGp z#4JB;b73El&(d$FZrUNJ0u3!8Bl%~#&%YGFskUEt49U;?@*2d9yx)~UlrZUiyUXi{ z$jj!Cx#L26@3t9fH^=D_5hvX$0Mhok^$ZH;#;e7QT7>>-4L0^6`}k?3JTY@BOaTXL zmG__G@@*>7b2FI57H?y-XGkVpzxFr{9o<8_QoXsVUCUoSeOZn)#RcdFnF3GDZH>g8 ztIo$^O}`YtG0L9js>`7D$ktYwdf+*yt^h$^ttrq7zDW?ZnDG|rT_OV15l=A@bKo zvVungXK8hRo9Aw`gJ~R*FozXgE)(E^eUCd(1@~@mEHmnmb+P0`xyNxWqqo(x7p&J| zvYi(&Wofwyh?iYa<)w|Pp z)BO{Dz9swpx!nry`Z|MIQ!@AVk_E0S&gnzv`0mo1Le(w?Slrg(Ciiv{I6LhLDF%Mt z%ksVhjOU;jsxK!C=@1;SjSn%vp4<5a17^H=+!ID zgHQV9dh!{|$Kp(%?xGXFFpnqYD|AWQtk-s(CbCUHRB>}4*HFn8c=m_#+Rd%raj)yi zJB)Y;b&jy3MYH(ySBstk-+SHk#+Bv`>+Z95lp3G{VAwZ2{1;W_&46PjT_#s$dh%wc z5p5R(NJhoUHmA+Ie|m@yfoR{?x=1z3@vNt z|FnrkdKp>WXqDY*n`IgM<~1{8=f}p8=LExIW}i{eIuDsQh`NFMXOX4GhR9FZS$o#j z6@7y1X1fm8vHn(~EG@ffV4hzi#nybRQ!!5gj_^i};asR*)=8Qv@ZMMVcko<%M$$!p z0|V%m*}z=b&X@AermkDumn*^;UsiZ&A2;l5YyU!X4BV2txivVJ4T&*(G`RY>x++Z7 zw}Ur(!vLyJ+v{*?+c!<%PhFEH+CGlfjXQjC^<&S?R+xI2qXmKh%YKBlv9hJ@tG=zp zEWd`$0Z7lCM*+9cvR`9vFD$%xu(YcNx?w?8Z+{HC3ay$9XTCE7sS3hF!1D^2oM+zT z-nb2M4q{d)bOUp`0rggCggVm6t0^bx&e%bFX=vQShhyfC~rG$DZvn% z>u=^2oVV`?K*j6%Rxj5*eXs}IHTO%npw1num-2C?=$|Xeqvq4aaCf?2&10{YXFM5} z?-4?E++t0ACCSq=4su_uoSYtH=M zS2ASBz;%J-A3%$QmWf@+s?4HwIF`ABD|i>858Q)3N%1R7O}Islwbvn^>~(cT|8z^E z^8pFTu5?b`TX<{KGuj4iLvb#Q?eq&noK~+|pNiYN|EBHT7LGxa?!j`{0B&|6G<22V zgrXYr%B`L!fW>a9M%?I@CrQXr-loTXJTX`2L{w*9+V>7DA$vK!R>x-4njN)S7vLE7 zG0RHJY?H2X>?H?@v;75A&h}LZSLGWW7LE(p_)?bkeu2)Q;nN#eXoGE%s$*U`1*j^9 zBS#s~S7@p7~efnU1JKg&UxFhd^HHUGF1M)&1JW`KZZA)_}wJ-{vr;FDhNE1@jPSd<< ztD~kcOJw{s2kBT1@$vXZ|EmgH702t2*2?AaA(EDIUqc4t&{qL!5;A9ZRlG+|8FC&p zD)4Dl{a_*SGiAPUoOYc2C{^w2E(YL0owVs3p(^V2U@_DO#m5G6)6C~}RYp2yr>`?u zzYxDwOdl!RrL^NtUXSAkE*mFogXHRY#ZTfIEn%k6!757})I z`$_#qN8(G`yrrH{N_sq1`17J%GRK`SWsm)+hh#noRkAgrp&1{mV*3cZm|eWJ3Ra|O}lBhH_JDN);k;#R^HA{^IA)-&RyEZZAC`I zV770M(X^9EaM6>~h7acj;3DSxCcw149ri1Trvm!}M#9QXfynzB4Dj;_3wjty`eAj; z$rz~eiqn$I*X?r6HitER_voJ39$uxCX23Z(uouBS znOyg=DbQL7^qJnoy?q`XM!xQSy(nFolt0LvP;M9aW)_=ua5U$IyRsKDB;VNSqI(r! zT{2(S+w&dcfZRAxF7hP5u)_JY0NYWQ#MuK{CiMC8%jdU6op>eq>*a}u3|Z$o7x!)j z#BDWIY--pi>#J1vua$ZwY{o^kmU=I|2WeKlnRmpWQ$!DhI5O>DUG%xAj6ckwDlQ@c^96`8J?sKJ6BXXWU# zK)`hT5VOJ-wYrSvwi;kne1Gws!#)ZWZdDrOv-3N3y(Ufo zsHbpwQFoQwRd;?Hr)UzQP*(+GP>Kjfv zh5`5HU`X2_k?v7>ZswVP5|QgEsATan?DG{Vt18g7=!Ak^;efdV= zCQpwdTA%_%udwFE?L|0#dd206$QkW}V>!1Q%bRuZ7V_=1rhojVrx+}f$+4kkr#TcA zPgd+tL0~`m%<{CiI69emV21mt*{@OC?1v%(8Wxea>$lx?k^m3gJx177^xv$ z19o}nKULRP576YCdx^ng6woA2-&e_kYUg&7li`Oh!_x`{# zw(F**js`~K>cY~d%m6XNhf`gk>$VEk`$d*O$Rkw*Yfp$(jA+#4ogwp2 z%E{R)9;(^9X2$i+-%j(L<`BWO596KAAJ=zsnxhS`V!C0LhJ_sl@~872Q#S4lntOp% zh&LrC+jlF#Yc#{BfP&aKzMy+GBh-@|#|A((>jsV6LZmq3%OC}X`bvvW@%sZmZ*>x? zL-X9|M)jHRI1g1{&_yM;(n`)PMY!r!ev3b?%>Y z%IQGK1IyJvnmyQpTmi~l+Cc;!CUD6vepkW z;of(3F;s*CIx|&n<_|D%tM2%=2O9B*xZ8K{?6YH@}J(|9ozZP$Pn_Zw#?uPY1XEv`|)y?$|IyHF?~`2KZSURub{^at#tT* z_dQ}xBX-5QI5yph>bXjm9DXVyt~ar9UJl!}N*ypK^b@Um;w^})(%rSkG+U?=X;o&6X?d9eG%{o`l9$I;MZru+AY1Z$1=|@J~A?nYRw`#XuUzQCNubFWQOjnEiEG+Gs&gHHE6Tb#_4wA%l)LcJ9c+diP zC1vwiMv`M_d2>sy9j6yAd;ohw`1h@E%U%aaD9OY1N2r zvX3gyzuj>}ww2i7o1C40#Wx*NVEvmL4P<^(30xJy0UC|X?~qnym%MC{`D@6N5Cocy?X zaGXCeM)(Z&mbmjkKzeM>$IO()yRcKxdZlQ=^B>CEkME23B&D({>=iT9)!feW9?irha^oXVmB{L(EwqI$QL5G-A3g2 zqRj{+U}SDdN)zE$>>MHaK%n|_73^~hIXd{c>-AgRo;J$8v>?ASGj%#n;0D-Iw$5t0 zLK2GCp#zElf}A^@&f9)2n+WMO?I7x0&nmu+A;<>FsGtws23CB=(=C(`bG3Uus93JT z3t||4+{nJlIlmX#WB~*6Mz){=x{Q*!c@pboARt<$QyM4Q>mjxNY!oS31v~U2?{T=J zA{5td^_eZb*V$NR$WEzub@Slu6?9a#k#QDx^KOt*Vv!DR{d=1n*(Em9DX=qTU=|mOL z!+2kH2<5HL{CdxG=%Pr<>JlDOs20p;!Is9M(#w#8JZwfqKR>{0+Rc|A2hb`Mya@o3 z=ITd{fYu3^`@XyXm#_W&e|~Y>I_0iNE|HT?XE`XJ+dFL4w6_+vTCO6oBMy-HoL+0; zaN`E`VwE>8E9~V^Z_av}Rkz4CV8$Sp4K3|+Of1sNhU%9sfo&M)1w5(GFMosfDX5(F zej*6{HeSwpz@>7&_~iqH#HBqYFm9ojT1MIMHS0gT2hhhsjZ6%0T@?4xo1I&Tx2v`p zEHI)YK4A~yy!%6VKJ;t(-mH4^Y2Jl*AbZA+)ep3fUd>zn>^pyx53*nC+p2#K1zQal z1jxX@!nBP=4WQln`Z*z~w1V0Z&))AzFtj{+W1>$dlsjA0*Gw$M+&zz=b_=+t5SQ}u z^{|_mP+lk-Cf!sUr1C-VCd&2QmG&FtQAz9l^9^nRSi|e9q?WBuc92eM>2nnUUEFoMTt@f1L1RQu=2@I!5d?2;F*nwfS z;Z(eJSZ#_>3S=zy~IkpiVfRwhiC%p7rnUDur;>BgV~U zS|h7u*1Eyvkrv0b5;Sxvfz{`VcJbYZ%v_qe+gY&{TjuTL)q+E51F^9CqIR3=Ks~xZ zxZ`Z87a1p~K~YPAV?!iAU4{IkX6R&LrUKoaA-<#+(<5z1gm7@+*12+y{8L-LknQKg zQ)sLRgiXrylX|wLb=zLZU5jk>{p;GT|a1fYo1G6S;JI6;x&As8m6*fjc zy6(s&pv&hQ_t?3)dgEFlzHS)|7$zrFJ=b5D=uoBOOA6tR5^jPcU(H6WsJ02*@?9VL zyLEh@m+W+XT)5A3=e$eUm3p^0e~I*9l{0jc$=8(JpwzTj>4dTS$(Bx+6$$0Z)j3YC z-bt|V(UY1e+hMnvl+}GDZ|PISiw<<{Sm>=HO7#rTn8tB+B=?GPdq0~ymL_dUWjWpl z^I`|q>Bql6xXpY$=t^%z%i@Kv^H${IarJNwoUr6VDM;`TlOA&W?$5rD*}>KV8cA&y zQ~@T1V4R&def2FPzZ%1$RCJxVzK_Rv!CK{*7AE_D-ph&oE)HEjYeY_pEUTFg} zk2bjNSoaZ0Y|59;b_@WGQ%F1*AQR0TJcha0%KPlv>m1B=yMTr&VqZHpr+A;%es70F zaVAJ6;1MvxoeNyM8YD`XpCumgtT?H@-conD9*_W(yg}P!I!?v9)o~&Ee2{9*1y){j zzSNlOE{)dwQn>u?*S>`nO{04Y)pNcj$`RUgmx(VN1x|gA;rWNXvQ3vPUz6b&;y$jv z9;oA|dwzx#^dSYd+)eYF0v>RHxDZ-Z%bQ50GDmHNb({D7Ej5Ip)cRorsOiBQ>T{GnlGM04yr7Ax8i$AKHrWpf9{r;m`zJkH>M;Vsu zC%;@`56EV?hQ^n&9!jVQ>g$6_X2*7gh4S$Qr2H>`=YRX(fB1`k@L&Bee*drh@GD>c zegBjH2Knze)ek>9^%`3rIRjhplRzhl4sANZ!f&3&VPA3pK@AN@Dq ze*OC&`D;ILzw;;fZ@>6gzWt4#eE)mvKP-Ox&wlb<`TqOUTmIVjfBYA|Mc;n1{n6d>>$rmzWhpF_GyYVg3KW9x|Kzv+(9b`N zwfbkM)GWBTUfwEFChJr|LA*DHdxQ*x$RCT5xU|1*xoh#b3H$3odq2pYU2Ey#Y@@!q zup1>v(#Z=!1q?68qkpnjMzLD2PEYk*mHUP09$|9h$zbE&TTq5rwk6n4mEj2h1t6Ns z+>vzxecQR*MI3cJK5pl8(Xa}4JRS=tQ#Z-wvc0HCSzROBT5|#=K!5YBAJ*vtV(`0S zv)i+?J)S8o6@nA?#U|eYK=4dUiT9l9#jr8{KkV}N5~s{6HQMCE?a%2|TdmN#wAMH9 z(iEOMuplqCElVdP&P-oX+q7R3EDa)NMMd{!=RR)R=@84@^`fc<`#2lg#5b*Hq+=RB z5+jXTH1WguBDfA(iWt!_IR{wl;Z~5C5s(8gvDzwvisy5JMR?4@aUQ9|b|tsud33vq z-#t(C?rD3wr)x><9xp*rtu zF7VPy{0cRi_bM*(S!nR$oURXVXCOEN-DCEwVRUsKHGz0OuiIMZ^0!oP$?AGZoYU*j zKuNUk-1Y11i@Ou1^4gsm76US215;gpcBd2CYz3Z*$0=YvF^*yvOKbN&J&S!et(`s% zA<;-i{q3xH6V%3=f><2!d|mBbf32SVdt(Ay|qQNn5^>gR#k^F2yrKM z%TxJynBr=sugdh~a_NFSFQ^yth%Cq{Tzy}#oVipB`t^F*fKda^x`dJw!j0RjT|-X* zq8o48_e8rKxf}*y38}LD_!-Bly0RG;-l7;?QK0V2GGr2<0FDpL-RF+KvoR8a%(uB( z?8Rk8A=M?xqv$qSE0%0o&GcyqUslKtpWOhhV_t;fVVWk8;q)x9p^M!=J5if$8m!Hc zhK$6i_A`(iborz|Aq1uzj<>Z7OmONBtJP|A(tGQH{(HOILUOZuL!=~X65ht^dDuT+ z{z;v<1Y-vjd7B_W-T+~3ZV?|^71Y!;Et6vw^AAoUoDo_D8_H7kiZ_Tt{T&@NcP0q) zpoqqkzOo-U-NH;&rsSPjDmwj8sd}g~Tl)cEKP3RB-TMy@x{+|!;l7a(4}03K-UCJo z%dN9N)Eb#x*Jq%fK#NE=WJ}qsDD)}6M)Xxf3HNeIi}2L5I6tA}l&;6E%l88fF;6w8 z!>2j1EcfVb3ncLl5Yxlsdor{lT}1h+9&jpcB?QMd^%{MxU%XwBKXbi z@(KkpxbvDLq-MRaU-k`;2ltXy;`VYwiW`?bmUA+Hnom0nX=^M=mjG#6}A8 zmB`&e&d9_(as9Ps-{kRrB%2fy&D=YBf{f`iwcYxV;eGyPHx+x2HgD1GcZkt$)8%F2 zsjruruW`Hjo?<=C=kozIl90QAZsAgvGFTF95I;w_Qetj1pqmrI&DAIF(0 zKBBEJ>^wcO5=YBzRKgzV+81K-ybc zpi(A9(HbN1!RSmxAmbwEO<*Rtu##vWJs4aAzC>F4z4etO`aa%uWpOND@28 zi>=yCZw^hld3c&a-Xnn(%`i4#C|f-%Fc(X?9n;&_@J*4=ih)oU?3h8`#`EkJH)^rP zN|7&5Ct4u)PEubWETSugsrdWr%*FdOM$)7tN=SS{99utLKhQpKau~;ikNZ-*2+v_` zF{q{mK5cecO^JHx2xNTMnRJeE&&yXio71`uJbw;vFWTney3nHYIaf7>R~HxE;KQn#@xkYrM`)8$Q6s=ru6f4w4V<-Nwd- zMc2>rbZwUtY{kp{yY7+MC10;)T5OhuCV%Aky(Q#$v22iUhM)`!$>1`^Y*kea1LwhS z(&VuStrmiFWC+jfU_E!;m=D(4o{kihyX<4Qs7r!3-ZkbP%x>pp-l_4@i4qlZNa?P8 z?YF(I*>}8=(5syB8qL`o6roHxd^nzeP57;wRbF$1vcdv|*LM(eKjS+(l42SaqqV`3 zck%#apw;7a*sWi!ogh^T6M2Hz#3R6GP+2?{>%}X*!V?>gLC5-D8P=QqZWntQZ~^dG z-3TH}+xp%|_TyT;A8~ z&&YXQkHdg?M+5{?LSh{%_kcFQjM0HwC}x(XFPN3$8i}V+3*2AMiMFsVKv|1B?M%*` zy7RMcp>OnOdGT4&mNJ?k~{{Df|QXm z_vUsS)_T)kOFQUV%zp5S29Ha$+a9;(3c-2j6ZYK za0rOrdS;coWvBC9cOZzfa24>RRW2|ETW|ya)HaCM7V811p$bA)bwPhlMPZ+G5wSMO z*lwgZz3vh1x6GWLjG5!D86Z9fn+rB}N-T*i%O;^W(mG^995gTY!{+urPv^r1gf&Ae z7M07B=GUJ8bxa8@WHTT$nyMPTZX%_4Y5T+X&9TdUX(u^jRx)7lVY zow#f~KEU&1UfS_3rC91^?|^D2saG%ELH%VA(D>`P8YlZGPA-K&8>}4gIY1nNINfgG zc}%$qoDCO>(#yQCW3ZSU&r5Z}-HqGU2oVpANO5J4q60Ci3V)X~5s!n)Yugl(Hg1u> zgzG|HxFVfx1vQ_x0kQt7B|b(`4`07Q@!krB*;$1~H4qtUC zTYU;@(1zZd^fmH5@HnK5?BIUA zm+L3SrH>H}u?3Fa2nF_7EaSQ2jMJ?$?0vHsdYgl^l06_Ak4`Nw!#ObdIt#W)Dmcee3NtZ>GAEb4tBqbSYQ z>WtGKbp-dwp#@7yjNzms15Tr$2HjpnZVg1xYh+i^w~?`yGs9FK?7buqlu z#q0kh>|LOpSI_#+ZtpKSA(sL-y@|`##VA`TaW>o^ggJ)MRxV#&kDZq@I(V^I-?|DJ^U-af660 zV5@}Mt_e;CpaNrP+wd5Jz9{I}jm}>3Rtwl`RwIdTZ>Cu5-YUDZXTmT85{N3+``F-sW6Ai`j8q~&26WRvewvT7`J=MaB0_mf|^^_glVRt&qFHmN0ELG zsA`2_1A(Vrq%S5c&r3(%pYKD;>yIO!8UqG3j;u+`xJ7dcZ>iKPF~78EC7z5Ec_K+h zvzV{s4d@XqufCPatmR`u(C`t083^pMUrN3R&W~u` z-j8<<-MZB+orOWU16`&+*=4&9vqCsa?Vw%OFgU@_bT1y3<`I=#w4?40uB*Vix-0B( z6!1XN~I3s9Q1Q=YQ ztIt?f=~|6F2Wvszn${^x+MwcZrpWjOtP&@Ir9&MmJZP&P7$ zkLT>6(-A#X|AoxN> zJwU-)2-tLdmLJYyQhj_E^p zQSWmJ5670~C~lKY6_qk23B>^lkQ5zH4#@ar=V+Kr(~tYRc23sQtqcxo(vs^TW;%!Y z9_0medg4bW3pNr#!uPgVifjR=n#Q&$Cu?+=43p8Bz^VFhPa(I=(;LhUD zJFY`zlYvd>>f)e#WGna7b8h9uird0Zn8zARZi2TS9Yx9?5JgJwGLlonNrDolw$*^a z>5gkvTOM?0D9(T-=63XI-iLRRT&-b$0N^hV#~Y%<)61c@uR@AE`hc`KC8{|)CUQ*Z z7V0juB3%YJcRG^&jjb_fuU;Y+mGFhHw0znuxHumae6kyG0BxN2*19r@S%s`oeNEYY zb}>E%AXVbh4?2x#XF0jk?qMy=*`TRt-fTc2{>G{TS#aUl8o~5tIdw~iCJCpD;(;8)tFa%CLTCuS_pM`^lujj)sIibXSR0JE<(Uxt|+_s@w)u{kQ zLwFY3{W;UHLUJ1J0Q93N)ccns1lQMlr*5i zrfg>ypJrTpaRa!~#^lPSg^5DuGw+hO@USo6GgS!l~_g8I~9|AU1T)7V;r&^*mT?Gl6E; zt;2qU)rVddWEVb3164{CWj0fkRttvD)$){WGb1|(6Y4PL=sD`F2fU&Q^niLdSkGvR0f{J4fLe6W72rRh^&$yi>K9Ep^^eQ<3#pvs`a0xHUEh9=nkO_B@XJfZoOF z7FvJCENI>S_V9lyetwy zvWB9JmONU_*{pMdz#osmo~F&U42JY@J$1bEyxdi%l#S%|c7kA1XQH3YrR8o;-0aNp zqxNaCoOXMVkIg`G7!#Oyo(4$Ks78Z{?e4Knw~bbX3C=|wr^i^|I{7wQ^VRHhR`vG;RP~31gTE<-9|{{sYz%?e2{w0 zMegYkv3P0hWZG@g2+6bEm5#JMlq?BJeH0Z){lWrXrnzLK@{rJ(ty7Y8Y&MhilsyOm z&rBkIdsK@>cd@y}eS44*ps9QyiYp1d+6slb+0xy^zZ!b|YXw0jzGT1A8#4KC$w~azPJs#_8@G+}2w)R3T+qD{jLp zdfAgu`C3=f4kDIJHPQ}cYoE1^x`bJ}v`Efc2U(U0-IjO#1_|qWVNDe4Rv+($iX>)2 z-4NDHRy0bI-J0|=U7xedvYX{y-7*18F8bXzB(BHv`>RY4mymmyd z(U7-jSdKI~h8<+1v}foLneg$hi<-WkR{;%2Qz4nh;ub#lQKQ@D45mSGi|))5b;I=V zqJ}HcFweB(hP&7t6g3uaB4Kx|=-lKW(VGHr6L@QB5}#*LK(2r?Yxhb^NH?RDV9E2j zqc>Mwm0I?S;r7(UiV5+|2~ZvY^(m(_v!M@i>Q9^~05XQPU@oc!63GCYRFELY3n_;*?saUf;2l`xsDKtRWHq_lqe^O9h z-4apQUI@#AD-js9k~OeNgw|f32CUlcDF~luWw(>7-PL9bFQaVfB?8hpI*MFuR}LNT z;IA*jCdV{kNN6-`n^3cb23MLgs*2MsLXM3B&;=~Hm{vY?+nJ3F`$9bLTM-9e1D-qx z@=~`XWyFzihdzfe^3?rOz#zT+(eCI=r(4skX%9C11mrcPMvd# zY;fG_)Es1fTk(p`oa~5+qS~OSpysbw)<-PHK+4$j$7&&<9;u$snngZb>?KN|c68Hh z+O}~}Vf3!E`3lw6$lLX~3NbcV!Rj;54a<(a?LoV+Maee|dKziv2E z42$iUFnwrhJxjbP8%Dm_2=;v03~6_eg$2hErOq6dvPUSZ6*6!zCk>|zvPn#*vBC@~ z7U|Bk5vtzc=?rwEft!~_p<0Dp^Y0?%(EoIaODIuPb zJVD(StAfQGLFyp<-NZr7GQnn*I++$GW1X#N-;l=Ru|gC(2i`*TMJcIs7Nx}(q$B~Dtdd6suN0fkSOU=BQi{&2KOC?5?k4v&A zAh#xmGH8udyhw}FaxAva%~Nb!9Rnw%Pn{U${f)SFR4-z}`TGs>yzXQMAU(A{Z>Nm0 z+_XYu^hQI4ijpA7jH-tue5=l;1k@0fM+|q)lvu-0MqY-|9*)$_X5RpwUW^>Gbqhdr zGfPjmrqOet7hc3$nA7wy>9dE&c7S+7i}t9*9Ca?4;wb9D8xWMSG2i7YnFm&5Ca`=iV>}Z&1=|u%OBK`PRknx#>5$32 zgKR;zYEKhlo6CrLbdL;`3@HhgsQVJ6sY*+>j|CG^FTT05`hf`mD5&2iot5aKj)$54{WpcoG@#D{$D+@B1`{ zOn5(#1)Z)co7gQovTnmri_KHTZs= zu``QQC~?qTPsx1ha1!aHywPJ_ByGsVjQ1&<&o!KotA?fB<__*@JSQ@GS~m@TRQe68 zl4$D$Pw={UNlppu7@S)*S&NZ--4gNHRZDKTE1~t`Mha{s-1mK87imkHi2RgY218## zo<8<-7Nt6TLJUNa=%T_JS|6$dtbIqG+J~++Tjs6X+~GWdzrEDc0jG(gYF?&k+(e4{ za!%`(?3?4hO-BID8a}-*8vxa2@Et;o#zIt_3<)7Oo053TULg>ihPOD80rx*{+0c)b zs{0MRyA}_vS|p#UWCA z7bmm2#W^#{Z0(GJa868thl@3-jNzxdT};I>zoJ;aTtq18;07oga6#Z1&8HV6_Jd)A zXF3QtH+phwvDb-)G8#(dy0g!dxL=SuXX`v??gZWMPSfL=&BBx(j>747M~2mrZuf0_ z2FcnS=}5W_+aqQ_F@-^Qmn@(YnM(aoUUNyjX9hqj?7N3lX*YE5Y?y^cUe2>TKGTqb z%{$1B8WudNq5I~y1s`#SEzJ-yRmzO%dlT7)b_fOYE&|p$a)9JXIJbtY(m%EtjUDM; zak3B62%w5%rBk$4(Whf`*a;=pnPF+{2D6|!n4o5D>!6}i-p}fb48%zlA*7a{62d6O zq#S_H^(|P~Ic?4Q7J6x73TfKn(2u#wE}G~xjF=QK!^km0bLC;v?epbec?+8CnXbSJ zb#C85uIaFmk%{m3&hzmOh$w7uyfo@fp)q9#zHPZO*{-vT#JpI2Glj2ZjzW+aerOv~ zr4g3Uw_HpeIxNYy6jGQ%T4T%#!?MAU?>jSEHHy)InzWEWuy8q+jfSjK3vs=HS|u6p z^(Dc(VDiexF07)Y`DVrNT4=N8GLia{fF$L%6Ly)Zfa1J7&$m;VOIQ0$ROh#jbX<53LbpO;^YzR|imvEcaX9Hc^5S_+W+BH}4ahWi- z0$_4?1XLFSt$S20oR7B{lPaFLRijw!DmtTR#}|ytnayM2w5Qqr9tyt$?tddUF(P!M zG^gNj!!zv&>wl`{+ja|;X(hi0Zpup3-Kw0gmbXj0@2b#G1!pV89n2*HbHiqn&i@zd92SQtd8(BBoQ-Rwf2dA zJoM6J(bD1ZdWcR37D)q5ZDFvZsDp5cPb=tXiV@5~^-7PQZvEsA~s zl%R=83nnF)f_b-D()P0NbYOI#jgD%kKFp`eG9J*ddlE8^Bh}e9CbUI2@drvs%{Ju( zIyaN&kEX6ea+KlZ3aRA|PTgJ4kTx6#{+a636 zrSOWj(i&3qMc~ioZhY+4n?schkO(m*?gUPeY?DFtT4yzpvRPr`ozO%$Udz(JS{7vH zrsO~xEDr=~Z={W>?n1!UnjISJ*pwGGR$~>EdS|&8RVSAxdNgiB$n5Q=L~9Z|D{bQQ zn66+xpI^4Ife4`(%}0Zj1J};;;eeY;Y7PLLfgW#`#CBte5kVzC-Uvn;m!7J8XI-z}R^jKju6w@RDkN_I4v9*RhX9y0Ueq&RJ| z&R|F)=yEbEQOij=#^9xPLDsU%UQ*G#H#?L}SZG2%+9@g>XdVi<28@%34OEQwE-ME< zpj2+D#TEpVDI#RIB%_aY;>k9)Zm*elkO`b}Ske|;VwKzN`aA*ST4o_8H=)R{uh$k* zGWuat9-**n>yFnn<`y(Lb!*b!wYGd6)1eZ@TfMUrgHPHagk;!gA}7yK<;6(&vn1xU z-I)1{y1C_%$=&FiR7)%Kz-6|Dnx2_XCNTHe*x zTfw-y;X0EB?5&g_LGWH=>oHCQ9gxWxOpCnD&H}pwKvah_y8cDzhNFCOV@}O;h&{2!GAq87 zWd&Bh8-&iN1~5m>0qoXL6OLJ*mhUzYkj*m71XY$y_2X%SYnJ5-(qRpTiVrR_37@iKIeN?b6rd) z@nU;Ya^nP#J>}>iQ$oRE^G0E>SKEI5GMMN>BLQ-=O*9RdONdg?w&ua8z2GZV3W%;c zpUbdfo3s$$($5bExoyivdp?l+YpZN@Y!)s&A)?qBoEBP*iP_rZb{^px4d<5Mg_%Ls z7)Gmfn5{^fZJw1aC{OCdBiCs*xTHPxyk3}*Q$>f>5%#HFz>CX8s4c|tj_RB@(_Noy z&SDuJ><|#bb3H|t8;a#XFL#~AR33KJ zhFu*X)`(3mP4r3ZwFa0_H6^(ja$xB0gMD2X_{DbILw4KCu*j4JbwoR!v8Nlms5NKI zm3LGOksisqbyC&Kylk7{Fp#=Cv%l)5`hYo}7R!8Nm8r!Ge$ZrhttFM@N4S+NqRk3E zM0vQQ^mBh#9B)Njp9IA-u1Gh6{gv=&Jw zRyAjlFq%GNFJuocW263hQw?4C{W)%f=HPsu0Q|A`s7A-Ceq!*buQH{2rZSfd`~d853F+KCO4D@0NHQc5Xle5}(Z z-RCy}q}qrOWS))3cyQoh%{&n%Lr1^U;9SuW1v+#f9ZLe<{EZ{Cv0l9ne& z>q5I=;w&v=w1FIC<#OJdZu%_@{Q@@#^CK8ldlfyrND`_cK6v-^~ob1x|=;F0G zo{HM3ZJ`^&W5|++1KzTEgpEa;>-zica_IF|?S2_`ki`cleTyu-q3-5NRF=WIbYOH* zSqLaJxgL4klarCXI#{wMSjSK$M^I2m>Eyhx_5g#*9uF?$0+mzj(hE^EDPNYbRBJhr zvp>b>mfkxdy2W6cziZgca-ZHfNL^8g3#C2oS(-R%s`hZQEibe7IO7uJJvr&N(LoR;GP^;go04E*V4vvdfLi#GF?Ti=1UygLfq zE_6(d0ol77fa5|$bkWq?6KXu4wdgIu?g#7kA?;JWKFSYL)L(=yQbzW$f6`=j-&pff zd!hBL)-h@o?v&AIdwOyJzoX0X>_9NeLc987+Sg5>@s3iMozcmtL%6y zQbN?zC+bpl#h8H60i4G9`83!xK`l>3i$6BZl+spdCki$w+C5mDR>%u<`(ab{cJovH zD6T;rrV~_&4*S5q>>_Z1=3~ZJIZt03#0YT0e6!3i^f&~YQnKpSNz#bP(ifAh);orA z)C=KCv~$i(%%$44T*h*D)jJWf)SOKEuHPJwHTA@-QwUG>nL~3yKj#{zm!Wh*?}|Ya z5DtRfF4F>OHqjjHC~~_E_9g-g^!Da?+%isOeqkLI%PkDW>3DNOR&Y0*%xTYWA1BJF zURuhAOeLdYL?$+Okuf-Wb@9FW#_wFmJr zKd_{DL`e;-BE2ZY+7ntRkzac zi=gg&v5LCAPU6(}+pV~pA{e&?=K7>~;wn_b=>$I1`!zM<60Alxb#4USW)x66t!%VG z@f!z=nLzYNN1ZaLq%8*}H-_vQX$t?+?4PzLy#v8-mvYW^_EbzFGMf#W74ZK&B`h1H z2Radhw^x`)0Hy+bN04!qBTk_@lQBah5H(TFGu*f5`$Ltqr)*d7+5z}!35XkG3W>!= zS#9ejCJsTdcj%>IpUX`Q(eQY6N%jXmiR`Kq=Azu_`?1$_!(su$h2DrOQAj5nFd93p zg~gs88Y;|%`)Z}&_2|}pNff6W6i4UAz^t2IZ_`rsR@mCp)=qF}mUcHQT81LlTV{Ga z6Z<{|$IPQtmOOc8iJ057#-%A@HpDFYdo!5(c|Re`Q)ezqgS5R^jbkUES4GYpq~SW; z?t5{fDUE8ImNFvKnGj4nTDI*$fkC0LyQ#QVmY8APTev>Qm$oqrhsK~$uHt0GnZRgQ z^E$GJ;Q+I|nIa7|OlrW#ah4~RLlPKw!%c5J(nm_YDXU2; z+oyGpLnM^%z{=6xVeH~siBl0pr%vT4BrtiUa2RRrbg`6k2951TmV|8;HK|@Or`wA# z*>B8s?;O;}zT_(xrtfSfMn~#2BfQuf3H(~vcnVk3OgxTUHnx`T7y|{+&ez=>a73^#|rPj%EFbF!wyj=CO0#uyRYlPluKZ};#a&>3~k=;t@ zL*oz~E@cp(*OMj6>auEOfE>F?P=!P%1u|zwU+ToUXD`9CgO1uYj)>o`R`&Fmb~f6u zTw=B5*>))`wao#pxZOZqV=&k!jD#Y%@h}unn{cj6Z6b{A#nD9fOS=mP8jQ&%J#9QU zo?NQlcI;_s$7MmFKe?4L0On{+XmJtrCakg%j0NSV&AM+8Ddzs6GkoVB8Q~gNey3_Em_fx=j`%z_>?LtUL ziwNMWWJ>qp#W6B^%zDv@^u$Xn#C9-M5|4-3ay37SC#;B8>RLSRXPcFF?3_;P`Gxi9 zr7Cuq>FI2n-gum%T-eYLLFbG-z>u18_SV`?ItxRomXsowYJ=(Rp%rSuf8VE)rK~ zW=kk1ZiNpvyyW(E>X@}lYiQ&sDuGM<)H4LIA}}-_+?$SQH0azClsor=z&P?fx0;+s zvH57X>7!CLh&0cnOpWJMlCLb9BWCjT=%2PNl#6AbSayRQ3`oa#K zK5kfh(QJ^kV+R>TcB$ex(B$grsF=-?+3r>m(PnA>270J=LA{{kZ}1KUgIIS;`#7E(G4C~PVu69zH3l#d zm~`{)IYYZKsxSNE1WNGj43IEZ_7jIRw}7Q4+k`G%)D}54 zs87c`lHfTjK5f?VNn1DIcdK;j7T>DZQG|4wi$2Iz79(->O~$C_2zd+P3%8piGrsF5j;9>jbS5R+Bl3E!q8OyH-*D8b(AjD{ zTUf9=3pjq^K^8A`w?U952zvWgn=htIiUNe{a;xL8+x^1BrGn3%!RU6{)WtMiO*v6o zcTh@VmB?H)B*A7ek7Md>xWgp+W{jLO@cIIZgo>hVaAW}>^kA(>KGH90T^X=_5^HAY z1SKkj=YpiQL>G>eFV%`f-nf}vzyIBkb$pH;+=pi5>oWks;{K} zk<4?V=VNR!Ie}`?lNRPQrjvtXHJ^u=vh{%>Yah)dUH8MYgV&(U&iBD`SM`-rA^NAl z$J(Bc`_)apYq3dba?vXU2^7yAZioc+tLc$8lSOAqclDj(J#7elT#14m~cGxEMY=?q-Oa!5J&g339-%=DZ?gXXZd&I{<%B-g>p6 zG?I55R#TxFJ^5=34wQeaIR zB2lezF?rd9Pa1dA=#X3BQU>x=K?IwmMtW-km$Q18vCJ9o=6j(p9J`X2CLy~5jZ?AV zUw(3WX9Z;mJVBqcDe^W7Y&duOu!FK7jb{pnZAbEa<4N$$@=WN+n~v*@+Qt#WJ`|+a z!=iuk;$~Li>F#s1ol9WZf-?+I%m6F^Lq$DLyVV|jF5?F{23KwE+aTb6=5YF7kEUi* zVN~$1$E>xadsi5Wf5mn3?31@%{{?sd%DXSVe&kQpo_)f<^3P`1x86M|U)_5N#`K{t zzJB6QzMQ%H#;1ScPuKpy{oOA-P@eGPfBB}`_r362?H3-wPCxMO+8Y19zVCyzPVHZR z@%8RoYhQ;Z-}KEj`Ey!+!Fy`_?OUJtfN=MDSL$18??8jU^PXDc_Vs_ad-e0z*L_Rv zO?Pj8@uTQlYKxaX`t{pKU-QC8Pv7~iwbmWJeS<|^fAqbzpMAyXbs$i^*&EzieAV?w zzOD8RcZ=(fd`GQ)&A+|o*6i0k?%{X79h(Vnjo9nteYIEJ*;oC2wdw8i_x-7t*RP*^ zU+o{?{h8YrzwZ^_`MAmJ<0d~=yMOuhTi;)c?mqmu-PWJ2eeElrKDPPs7k2w+YyaTx zr=R}u57hj-+CO~V`@eAg#;4C*YhQ48_s?$)zVh1o^ELPGA6|d=yK2wl$KU(&wRfZ0 z!=9gh!*|xc_pbKauY3PYecRn{U&nv3_NQ;Zz3+X`-PNzZ`4?+%uD#~<57eyd*L@d` z8!f)8)_k$W^Vj$O8d`kGXScZR=Ai}nTlEjt{(F4s+do+A)*7GP?2Es3Af2sD5?snG?eW>>K+UfPz*3Z0*ebxW{vKJ{{ zyX*7cQ`9zo+(=+Bf_+8a?@5+}YjhA5L|5{rc~%UGdxF&IaFC`)joizp%4+ z{$*UCAAkLoGVh+d{>EQM2m7gqLuaq=`f%-)cRzkTe7Huw)ZiyRj0T_h>;`ZD{@Ne7 zHMrf}ryuzK+V|hpK7|8%W$nwZ@A@ma!k>EmLwEeo*Dw76bjMF!-}M8v){8CfUjP0d z`mML!e*4I4?w`AU-q*kEi>`0}!CDhf&fQ0Do$R;oUU7Z*58`Hg>iU5ntX*y`A39P0 zf2{re8u^CnM?Unj_I3F`)&5@X9dCHp&G3aUK399i_3M8KUwp^)JOAfef{(A?^P$@F zSK^0iZ@Jt2*NwjUhid2BHf~+x9oMh;;o1T}jGwAePk;D_YqPt%KlT&{^lYv6zBfEx zD*8ujZ@v2qkK6gNOwx^l^Ot8z0X0`lUbpu(|ht!GHMj>yP}SC$D(A z`RUq+?rQIR*M9i!owtpB@bjEEJ`6eAH{PC%w_KI~_^MZ2 z|H$9PPK~Ggzl-sw7C&wyzJB4));{v-->{7@divWxTf4iviysc_`ZYg?V=&(M{=fah z&pwrYuC~6fy&oIBb-H)F_x|f@-}T1pul&Dv%xixB(?^fbzmTeZ$=&-OHt_1t_{6w= zqIUm>p8nE5sD1q9wZHksM`qqnyz!Bl_vTMNJT}TN)dqJ49)tVmo__NuYhP8XeG31IX^tG@4)hExq*ol7Y`kDLtv0tj)*J{tb>EZG|cm3zTQu~J6Z+I!xzUEDjckbtZ z6mB+ofe+MZ9#h&t0`&ugz|oz|;1yCG7WK{Ce&CKIcz=O}e}xnK&g*yntJo0%DGjDtP zUH`iFZ(dRRJ1<+HYO ze&EGl?%s4YzWV-)e*6#Jf5A%)?jEoHH{RWU?XB7E%H6%~`YW%!XRq(9-M{MYcdwJ$ z{Qy61SL4;MtzJ4{<0(4h_rJW>e)B``z^L_+&$}Pg-u~w6?3ZeNJSgabAFI{==$jvU z!ymo={@2{U`|gikf8;gyqr1~>2k*ddfAII-6Q6Eky!QUt>j!`D{daxNpQ6vd|EACR z6ZHl6-~Bm%dgcr7zX_ZC_Ak8e-~E+`9o!oH?t6aUXSMsX$76W@`mR6r@~^x5=@$>= ztxpZ=9+@4x)6_KS}v^NZJi|3&w++i$n|&cC^BrkB6%=^uH+{WEtp>bb|K^J|`a=u_8!`o{ZL)d=+7TW|X1 zFMsmd>(77l%U}8Q9dEk-JGENxxrf=`?eovG9PeeTmQo9o~H()+Kx{=}E!!r`0md-cm^cYpf!rFY!9*FW*r`!B^` zA8x&S{h7Dk%eOWU?|SBK_r%?Y9va^M_STO+^HsN>50k|I>uvXMy8F==H`4#Id*h{# ze&);Y(a*g2(OZAt{XfG;57+;ny!glB%kTg2t^Msq{c82o2mLUZz5VIr_uqfrUF|=; zaJl}U$IJESz5-Y4-#zz2pS=y6Z@wPC;y$lE`~2g@xc^E#7|%WbFiO8V-oA7tp1oea z^8N$2W)Hjjj87hScYW`FcaMqG^N$Zl^ZHJ3O<(gT-}BPWsw&XafC=}+JF)wgbkzd!8cJO1iRUw_!i3uDV2dHX1j>Km><^u5n~(bNBfyWcEoA9((;GvM=2cZN4V{k3noU8j$~aQ+W(sh@v5 z|LfoQHgxZgUw`Vo_x4K-THkixt^Mj}H~5)vyZ@3~gNLL3tH0;-UhF=fdDO7DU-9%eulLV7Pd+fl19aTH;!Tf>oQEFW`rZ3a)^5w2 zKUsU-TOQ8@U;m6Vxpr$${&B7Umd6|U2k)PdHTf+MH}d-3_fKAR*T4SY{gWSkslmgU zTA$tEx1KzCKN>vrldW|=4k~lfAacQ)V}5Mv}*5u%i}}&Tfg_o zt7_l)me0Nque|=HfAq{NpT6t!pS=Fb|NsB_XKKHB_w+~J^yJ^(UEhhd-Dmyw<{KdQ z*K0rdryj7lSCe`2-pd|gjoxgjvZ2gu?af(gIOI^+LV2I8xzZbZvD#Hf?jl|6>ig2; zmh+y!SOtZ8SWHB^KcVOQ!FkaL+=%OPkY*_oS&~TT>zD zIc=c0jEo8%n^3ZgO;ik>2|Sm9O>a^}PQkep|B0()W^=9i?cbAzr=8Xhe|I-pyAIh* zPwTC-({8`U$9;D?oEIwo2p`g*JHCA<7y;Q=}?7#$Y*C}jrn~A=W%*B4k zowe85W!QG<95F>B0CRBy<{}`IXnQ;oo9Up}L7$ku!pVftN-)ElIfNkr z1uCu-WEAK9Mp$Q7g*p^Lo~s+EPDP**;6fJ~tw0;s4QXg><)8}#DvH|xn6w5^u-m=t zR@@vOui0_l?;MsTxV`Y=Q--}BuSV4R1jmz8$xA!f?c|xfjTo~|c8@qzFzv*Qd2 zCvUlLK%v%}cw6&epuiP}e4t@t$ioR(UoMGo5iR9_hcv`!4=FBPc(NB6-HH?ricCf{ zG?ga`tiD)Hg5~5G$sP?S^MnY`*mh*e@K`i8I5;{y4fCLruE$getlsD{1dBA&4iLS- zm7{BH?QD4CWT9A2Ji;oMU$-qKt^<_Dar2b{LlqH$H_5gk@2T9Xu{*=>_XwoAnauS( zyTPB50bpS8tvjr}k!qknY(x-PO7_rMPJFLT^Zgw`5JhFi@o9W<*O`t0b-GGUOVpTH zIxzQSC3|k2e;|kQP_k(i%Z{XJ=wW|}4Fs|}gYEISzb~ONn~!@;h$7Q1%3>RVU{Cs_ zqG=jaambXx39Zw*-HuK3nE689Kf*1`K&_v&Mvt&32!YvBf^Z_7UA4t>a$@*84_hy$ z(Uc^`b{L8RPGY^A0i_abCi#|CL?bT{u_gtWmqV3)BTW)EIxo9dJlX*nbdjK7#NuT4 z$b=*?7A=r3%aIk1&gTXyqpCISXZSt}oMpzj%+mn+B5)nM(+&aF+B%^vr3=bp6MCBc z$b(J1eCyR^aI2v*9L*l%uB7J;ZW|*cz2T5=Gd$J67AeL^?aZ-;`BFq-#45{k)o5`X z$9sER`at6t#j)+P(7IuAl{qdXB$y2g-eJJYz|g5~f~?tT4Lm3JV73G37&~v7q*b&=q(1pjjmr4`dLAumF<`OcP$kdv$yY&1RloNP2a7bww7igXV&Px<6ueV4?a~ZiTh8(hb z=b+eD1E9Jz%RF!hSMkQ*0|qV5SqBOg0nimY8|~MDI|uQFfp1T~oTsbP%E#{=b~xjv zRYigsdl`z$`LWkufyvnii!n-cGlPONTLF8!!9d!WFXY}R1TPNGIzYKJrniA5T7phI z7D%WfGl|OPO-mi9;X`MfFZ^*~FVCCJiQuL4Wmc@?EqExqzFokCw4~lEujHu|s zOk&U=qdp+LfIvfPW=gwZQLhUml7B8&vafxpQerODV_8a%gwXm6iV33|Kpb;5_ zACLFqd@x_GN2ua!;Pp=rb8?1_94LbeK%VG8>sD2PUV~ej0A_XA&m~|2qs`R`t$|GSDm1%o_fi! zU8vhFnH=Mo>>8jusRK6_L)`(6*=23fOi`%pwn*ft3~7m%13PT-xP;8)#_02qk2x-E zWteEaQbLuHnp>s^$!oP$=1t@^f@&mkgfKfoDfO664T_1vj8p?3DbJ&6W%F{evRR-jTlmS6#0+5%v3)4Fx6mFb_y8pfq5 zlKiJwgd0ub_NL2LlZF>=UrRa;%#}_oIa@XfCZF*mqa(U{6_JA8mV3)CUWd+x^(fL! zb#n@Do~k~f!1R^|-O!`ZeNkrX49m;FVhQiKAFZdL>}cVtjBp9d7p{aRfK1t#ZiiaE zZ6&$yv+ZEnn919A*cG$KnNA!G=q0N5HnViqz214YH1OU&2~ea})UY zjVk2iXO`oPK{@4tA005F0B&e~uQ9trrXlA-lbiUUhwalL&PgEb!Xby(_iVdC(iEPY z4HJrqc~2Z*c)9FHrKa{ojIyH$e#1&CcQ(*t;=gjEKF;sNaH5!54r}Yqi*q{=zR+a6 zKVzCRsdlqoFM6B+cFqAj5^g6u<>n={sA0S3;#b&{$^Pv-m@cN%+3_}S9J@eZvEFP` z|F6GnAjiu$UN`_uFkd*RbscDC$FQK8y6r;UKVBtR7%TiDAqaSt1=xqX)>^^pwzohU zqv-o~6faC#50CpQ$10@argHHIo$;;Vs#lDC^n?A_gKBe5EZC8MJKD9^^H;a46HG*J zev^c_)n|NXJGtE@bucm3?hOCvX+ysrz#vYOfx(&8?T}u(et;O|cUf4RO6sm1=ifxL$_Z`5>~kT4LK?%n5zx z;t(Wu<^k-LEsgPJz!IHk@=jWvu zo;Hc(jk8|Z##uyzb!!aAMv#}tiz*(<4Cng&Ta8;iiB7GN= zG!0iiW|~>X;?M@eMV>FB&M>FC1F0Zg2g+KQbV5I{3kEHM@H(61HdpNQE@lL$u{tzv z!~(dPT#IxTMaXA|TteM_)OzkjITkPjtag=qrly=QSR8utYPeV-BRZ7rgRW}?VYJe- zl99CDBTX!KT7{E33h<#8XYE!t*Cz(B;^wl9+UOpz@Y@*E@Fq+U{L3nqfs5!24L-y( z*^xN{959gJV?RE_>6#;elANdPdA({(15=B!$g z6T~I%=uWaGm@aHu9hBaq)`XZ7?!0n?wJ;6ZG$GGXZ#y|GI`k#stY+31#%H~oOBKmf z4ybqJ#@c!=t+DR7V;aVAuk6npsN*fRm6o#(Ls!&-^E)mdF*L+uQmHX@xi7$l1=HF% z#OjiD0)6NCG&6-992(FjJ&WAMs6*d$eP+I#Sqs2;veRWD@ho{eOUwZpy%RraVq*fh zc<`x@juMq0MVf3Rs88D_TS??L^Aj9Yfn;S8Gjm*Eag;bd)W?>XrKduhY#DAm$V@q0 zz~nZpqkb2Yq7<)Tr1x(|0-FRb6ac=$Pd1AP{{q3YfYnY>Zez_CSqTrSUYbx}kTWL% ze~s}>Ht$C;<5ZmF>+MWU;oSa&AVa+oZ7LX%fZm;ytFq#q2y zN7=-gG;*FK^Mej;ka!FTr8&^%{2DXy__E(jyU}KQwhmw;uG(z2YKX!SwbX>nEhhzB zAW&YJaB2sTjC??Dg;8BTR10|CBmuwhJsvmC(_RL+`R<^Zma65tX?p=y0ia3%Fb+CQ7!A%55xs^btxjCqM#xI zcT!t`NDVi>`sqfo|H#b9bN-XjTDFMTE7nq+Oy(G)_pkTq8X}xW+{@6MUX4RE%Y%~L zJo_|0C`~<78Dw405HD8h;LP21F)5rq;Sjp*X^yjW|I!u7Rz-Jch{o9r16jZXsPcZW zXJtf82=pVY@76@VLD7EMs&0IO>~^>bY`3n#7}@O2=fj1{Ua5D>nOZXX((E8sl*7mZ zAha%yUAN=>H8Bj$aSztQJTuL@V=MW|H6`N8azyCXH>Cja3S~~(WbR1Lkth~16M&;# zlr4)eO*T{JhlyjYT75!Ch@2-dSo>#nLdcwQ5PPAp7+T*c9VGW=cVT!YVo6A;-po#i zJuou$b|E3F+}S?uV1jA0yX8KezH!yY@4@8G2I7shC!h>dURH>Kx|OG=bE!z&Oy=z( z84@Gih)cC3KJ(KF24|!_;!AHV3n3A!+7wPx)XTfs2N#CueEaRPAd%PJ*p*rCNh4xP z6a6u;B%R9r`guZde1lYHiUs4;%LxtcVI_y@5B$RXqUk~vdZ!6l*)y_f>@p-_DTdPj zHA$aIV%8+_vdu(%KK%G3q)hWj-VLaCO66C4I`Gy3R^W!MBNKEwF1?TPnm1ry4OfZA z16OYD(|OvL_oBGOFLX>=IjPpkeqM7NXDd*<8u`@!$)D-p^Kb4vuAiqAB&S2>|& zkNZPko6 z)bk^!hjVO}>ai`Mujw7qp`-Kc`IK<4n1zf8Jg!R9P#ePm@#_r1`XSnQ{U&Znq@kwa z)p;kDv-I#-UZGoccXkAnHNfux09d{xq`b6t1x^=9@Yf~^?OGo`$6h_pw@|Tnp2a>M z20tp7KxIi`NWOKBNL**_Y@B-;`&?)Z@~g>+-rJPCE$ z(cpMGz{n3$?y^RyQ}W8c?`%65(5cW&5uk`Z}+T>Vw)GY^%%7yPJ^Y55l^+ z&rHmX*rj-~M#8X%RM}J|;7~X|>=!CxX?I=u1}p%hFPcgk1G7~!Q+$YwIYf6RL|#J| z-x5iW9DkF&U^@zsex*5PCPnH)=I5r==Y<%-i#I=B9{RS*L>|`U6vRq6!d*C-CJ078GVlJZpbV23YtL^oIO7-VAk>~pRO)6s$jI)eW^x+ZNCSobS zCv0PbWoA-Fy!}zFt`LF%MCXsAxAkZatyB%iHU6kWxcIwi9Xda&otqz(9kxUmTlnvx zAN9{aRnD@orTmr(M>uf)Ik$fg^!js}9nQ&q78Ex;OMVs^Syi6BZ^+5d?}W~Hc01$u z0N`%kD(~!m)(t2ve%BIJJ8ykPJr3%KqZzeYBFP0-J*Efhu=?P2of$q>q!zE{KVA&K>~GO1 zf!qGOe*0tSaNy7ZK@obYV86b`-jC1iUu#0YAxP>5AN9Y~gtqy*&!EDfMnwIKUw?e2 zbA)Qq<-Q-#apgC^7L(?b_faseXx?gf{hre%=+Mu_1xZp1?MG=&Z8cT4$h7>%kIAu!ziJf)1H@4)%i81{m2cC{ZUP(kCSxguD(AqELNq{vb$2v4wiZ2 zKvuj7dxT_h3spgc#kbWaZYKU*bEZ610I~*V=SfoD(VQ zXHlC^K52&zgNu*IQp3Dwn|U=-$tOl307$S5U8jcW#)J8G%Q@7mB%)(rQ|d0JgW#F` zgt7%~H~w6^=mCZ;C17xXFh13M!VBBzEA@RXswW8>gTMPxIinUrGMR<%#fv8NtosX^ z9EjsEa^6B?QU2z+y*@1Zb@j|JMUQS~nretSSGTECq*wn%CH5j;qy{>A8!M-*8~UtD zhPz*tAji z<=i>QL6Oi$2Cb(&;DK{_Yg^=(MP?g!`@uql$!WR3CEtGkZ~Yx=ApLLRy(RB$1|jxw z|2L*;z8`xv|J7tDS!<}Z?c=2Ag%kApHx6YHWZ%F(a5)e87L{yQdUb=uq^MbN6bXMG z!6|EXJv~}{jDRGXvA3-)k2W~@<) z)CPIrm7SOC?d@t8Bx$}0BxD2G-U(if1$Xi67K0kyGqa1N}^6W6#*-?gSGY>kQ`$;ZjN-Qs!K_thQEqbJ26>3{$N}_@^lvTmrYop_U&22>9_@t3fj_{^qSfY5cmPeD&2lQ^y|W?Mwxx!`E2w$*YZr` zB~sR!`A7(_+FlP)(bPT&H&Q>@SeNy3cWj@hvJ32)<<|{yj+qM%wbC5d2;@G*aOML+ z+&p`}WUd);7UOWy4YXvSUn4sADY<`kphGvnmhx6?sIOCDg#3ZI(&Hqs5?CYp&?nKj zid4%^;#9pbgP^NYl^UWNrs83a9@)0N5L{zhlyq!Fe~Ls4aRI=htmK>-yii3BmY^+$ zxUJW;35Za}pCgYUxv)nrR9oVWck?@;jISFif%sk&nO4(U7ap}Sn8N6>Sp2`7(EyjM% zuga#JAw7hv#>rnq$)a`a4X$ zsGnm(*WdOvAI{zt&w(};yVSq>ba}(rrGHjb@kq-W0Vm7o*s*N4&3Ss)X^+u-#Q|;f zZ^8ikr03(?ly?>3)!{-qeUHs@9pmG-5p^o4fWV5nx@wW&*Hp&3V0TF&lZuP}%Y`M^ zB#z0iOW9Asa}e3p2HBw|f_0GDd+sAwm8aM#oTe?@+Dp89{!AU&U{pM)uh!VMuaqT; z(_2*1Jw^(Oy=vBag=-$YcEP5`wu!?na#!PBN^Svq(imgGznFO>AI*0+upXKC$u z4T9T>%aaU`MA9JG<(?p+{P=cp{(JR%<%JME*3KSF;IW4yX{P%ni|o9*05*^3>Y8Xp z-R7rlbJ^0!O2dS_aW*Orcrf_w zaXyZTMVej(+KlX95Z;n#geIRSA_7!eyvh5Tlz9b-T|xb0O5Q+w;C=VwUL|_LI?k8i zF2-+~Ebe;XJ;fYdw1OMSs#qIMLQwS!1Nz&t#LDRwOa92Lm(dlHk362Lc*EqpiDrU65tZA%=^- zlFa$;-x{6WVOr+_Sfu962Y{u~++{E(=S(SW^4_saXChyRb5;#QAnFb=)=%=xGj5Ib zp-~B2^%cL?VaXjZC=5@BYg;59;ftH1Gb^#W*0Vv%`Ta$A{YtMr43Ectlh|Ai0w?j# zLlY627DY03(dAfwOX70!O~4LGjJ_%gzAy7jTd8Te97XMIaPv)yYaIuMyCxCF0;d}x z;^)eb3dEbD;WqK?P`-8>aV*Vkbv2^Mp3!wgBGS1x$zaQWd%>k5LXL79Eme{(l-!N{ zD>zKvC-#Q7Zl)o;{H_=p681zTwOfPj${blRacsO8cVP*ZJblhDp^U`2`O~gJws?h&O5xPshmF{lBc>a5z|553( zDoSzvzm?)-rQucnw^E#{$gT7VD2&IxC$~SUQ?Wi^%Kw*H-a0b(e|tw~^PX1L@0$L9 zO8OGcuWh`#{Sv4BQ&n5Sx5)jD@qW~Qf6o4p^zm=AJG?mb0F+((Yb1-g{T9Q_@2n69 z$o=Z@`d*dleq0G=BeBwI+Z@hXmp!l}3Q#6>M{mi(xcPIO8{kDq6i)+CX^pZ&wxX zzp&SDTqcNeeqE+NG4-ZS@h{+Ac=jH^cb^NRgTf8n1A>gSKK>wmlS zmV5i_I93>FYDun>|GYA2-Y-y}{xLuPDNr;vG?-o>H$0GteF|?;@qy?2i=@82-W!hJ zXvPUO+hVzRZhQ{~JEG%{t`-{yFxTNWZPZ$JHiE3*uKP=?f18a=7_tX+5W*+Be8+wtq3lwXS%PK-!5jcPG=n^@XBNE6Z~q$78KpXVXjjcreEO+nNAS#lpMI(VmYgQ$|@Oyte+ zv*(oS94|>iA5tR}d>Vkos3EXidWmf|&!z-jW7VNG^S|ux3{5S^#N$FNT zz81k!P+FJBXvWTg7QOS!@ldHdX_xJ@Y%hFnXvyk>G^XD_1xJ^n?bl}+VOmu{bp&Xce*UUeU_|1M>8|BsD5y9@;akY znB#f9s#Y|I*2k z$}mIvbcqbPgv!OoN*7WaBSF(11dxSCfFkvi;Hsk4W#hEZc&8#Xh7p#_D`qzD=RhoP zk{lp@PRyIj^^x8Ym-lj!^==46QhM2ZuLhCY56*mel&>u&)iKD*xPll#GwEUvmG$D| zWaPus+WAn15KC#-wMcPk99EgsJSuolfHTuR_8 zL|t_eBShk9%5fFwRqr0#LX1xcC|)W*wp;Z0_?!vx2#4Mi0Y5e3Bs%uoVPOC|Lm=1& zXaqUw5Z*&we+Ug)K}yb(k7!GG%T0>VjJK9j=S1D4NISn?whyUSKaq|rx!LC}B_32! zg$i>0%}KiJ7!lgo(2YUf+B#v3$s_Otexj6W*dYv@YAACwhZ5d!v)j?ph7nx>M+U;AFdnzs$`0_+-a+%t{2SMu&UIMNo^4u!KN3)PS zq=0iBUl}6A8~Q$N&`CC&H@z;-PvwZD`do2V!|sEl!ftT|c!vqja0bMfaD|dQ&hv-? z4Q1sZ!vDCu2Od!Xl3VQ1Q=h7H4LTbXr-)WRfA1de^z0(UO83kAlRSkZ-s0`DO{V9` zGT0y#`<$MM%!zF(aD`sFgkqHM&fDSoVUd-UmQ_Rq=H`?VI6k0*_?S;>Qd5T%d zseTqF7t1Tlcz_Z_zhZ3S`t7@#HzH}18wvY@4}Pv#H=e!Z9-FlTCq|q$`>|*sMH>)O zs4BMu(BZ-+g5dZ)1m(x6Kun4-B{*Hd;jecLd>b1i+V>BWFPx{+yM3Neq(1Jv#Svg( zcUws^GH@YYG!OyDZnzYWs}5c3i;uk=s-Zmjp5EGEX4r0twM3~rb^X1dG`#IUsc6uWxQZWZd}X90p~?OiCio@N{^#coEL*x#botnj2IcAd<{1xWlmX6#Z!2Nzj% zKTQZ#odMAw8ar)2KLI(b_b0TxOM`QdXF+)eLk6X7?fSNgfyq;7(K3tgNU^`lzS*I2 z&aLS}lJa~ppq^6S(bZBZBt7qSHoNmdxEA{!F?Hh5048SpklXj!^I0Eo6wR(5a-+E& z-#^3mO%QW*8DXcA9eHnk2-b*Pr;9x@bmVr>GD5|PH(@(tNY7~J9K;N1@-AK zE${DQwDYO&2=|XG_xF15f66__SWfBo^Di=4oGQWxfTqUu{L8y3UiS0PCd5BRwETrg zF8o_>cf}O_e+t>(^}c5d=lSv18uIg=pFghlzbl)+^Zxmd0`hlTg`$hqx9a})ubo#a}DAW(<7tA7P z;}7woxvm34+zSh8CKlGnESCs96Q#9FhdY#^Nlhs${dkx9J#h_RPZJxbxWdlq)Uqt) zhsgQ(i{9qeQx$JLUh;$`Yqk#)hlMle%Xugf8St%{!yYC5u97!WdAELioY!yqNDfy% zZ649ag%*cg?D{*m-qe^6X@w6EowGqT#D?6b zj*H85uH_IIxA;T^>Z>cz3N;abWivZteIw5vzXwj0yx2uHYRkqd8%9+uQOz$TD1?-9 zew6gZIHc3IMQoVWd7`}!6{hdmnu4_db#&ARF5VDzT^9Ncb!v$Y1d{Zu076!fy(RC);eVoK%-n9pT=&Sc zgccm~b54dOGGT+wtu~-a!qlZgh@Fl!X{QA_KDve)GL_8FVWSd~sj{b4qanAZP9Ax5w=?Xu`YsO!0k( zxk>fME_dncia8=WV$ugfWnb1XB7!qR3VO;q9*)gb%Fo)3QIBP;RH>LN`fIE@`2WasJmUa@Y#+f!JnS zDqrg@f+w!~om#~T_83HPWAJdESdhb!s)VfDP(wF6$zGQFc5mbQi7NSahAYr%Z3w5+ z;JKfHm7k=o<;k1gm??dOoVu;M=FqAE&tIL@q;Pbn#Ydf0jZKx7k@?4mG(K}l^x+Vi zPLF|HA>J)m#Fr0_My2AW!&^y&FeXXW?P;1g03hAah`JQetx2Si+h^o}yNv_!F^|FP zDA_#%?~E^C=QjXthd<;tRJgw%WVP%%0(W7_Qs*zm z8XmHboH^Oo4q07LvXg8wXSopS$;$Bv)#eV-%F{>qCy(ZKX*u% zP2>aj7HSvDj|WQ#_CcU)(q**02f@8fXh{MN<+pT9}$tB*gWaPYypMP3_>s-y(t zSP!967?R&VOA%7!*U(G78+=XumpnRkdAH zXVv$JgoH%Zu3Av%@#`6y&WL#KV=BLo|29J}z!M4XY;BAU4{6nFDzSjz zl%P{X7y8qA8jPvbmX9rn!|sbNoC8jV=SN$L``dVU>FTjw4REhxGTT7|h|7L>&=g*^ zU-*dWKu(|fOeyVxL6702u6Ktc+|CKC*e1yjT8Im1G@vvYX%m5ceK-+XO9 zhinE}W?WHBt*do-=7w5H#b(%IhSLPFvuN_cg*gAl&xPx!U23z}(}-dp9Go&xQ6>#a0i!m7SgpA15XJGfV^+)xZ?S7wt6rm~S^-tH_lQ-@9wWE{%_)_o* zfk#@MryMwj;~g>Q%FBSt5M3%nfMTBO!ogwWBO;vQCAEaUkMeiLVQso^YOQ9RWwB#W zd=+f5-7{0NmLp71F4JKZp{m&HZpQRKGBvqV$`X&F((^;Z~{#(`ZwORemGy3m_)8AFjfBX6?Ms}aSnN0s% z8THgr;Qca|qF(!T%R*?r{=E$!T57?rze2BnAIDSEku5PkO5*&&r28Bpl` zF5kD5yp|(-l{1%RxE;-q@?aZBPAj(ML3+R#?~}}cG9gIb#zmzx?MADQ&l?hrsP+cq z?%gr*B%gSVwL#d_Hh{@+z08B5lH8O2eO|xiB&<^5{+#ZKXD7)zz3$He{}7a^ax>9v z2O=AG@K*C2y+=~Xqdcx+L_4845mGQz5OTb&j%<&>z>6q@8KN|wiBzWgNra*QY%R(Z zYadp`fD~76UtX*tt1$1vocASk-V%D> zTue|!E@m;jcnp7@(MvkEft2!MOovA@9T~Eo9uIWzcJv2Y>DOU9Fw-2g*|B}=g*0XB zG#s{Z&FPz%x*+8h<>DPSCYI%F9Zhe!pr^j3FB2v;HaV=-JG$o?jYGroTqN)}3SPxr zgVtj)_`jUc%*^kN;olvFZA&t^)+ZIoSsvi(<|lj37^6{`3{cyC@YL5EZdj`IhQp$} zM*~udSdHTfoqXPwytC%)RVY%RWO%0C-){8w%?h)rYd@>488oEI0X~n>#rUw%eYF`#sqS~kL ze8d?r1^V9}RM+({OMBR#PU{b8QWq`Gc;mLzj{aq=(b*L8pqZ|q6!;-hdl;R9j(b&# zDI-3l3pV_XxOt223noTkIiZsvP)`~hfLzi{`=sCEoj~tS@v~xuMNZesBvQGhBLy@9@9f_*~VJP!$2NlmidlbB=NoU4Eb_}Gv})eEG4p5uBm#m$ga!1hfehtE4I#RYnbbELd#7fN%-5qb{b!IKl+b9rBl+m37bQZ>L)$)0w%-zAriKXeh6tgEjxsr(b@YG}lmAl+R zWN>3v)d{*%+)7?pizL`7%e-@pO-Esf-sj@AO^@|5h#_AWv#`YA;44GNMwE6mN%(t)f8W^E4_U49YhhKW?n)h?L{wf;eBYe$ykxO_5?Z%2FMlOuw# zuVK>H{Vrq4f4;f};Pg&gLe_|0+QGOD7c1e~=Y6ymm4ECb8!g7qanxPz=A^ zPs?NaEhzLslB9*wGBtco&<7YWA+6Rb)cEkIC9VEFIp61bS}xGPI4nWuaSlBzldt#o zc3TTp(5KypJt$o7^03hGUzgzix)&r11whe4TUv+vJ^Wlj7;u(^bhFyh(4T22Q33#JkXZdofTADcjZNaucYR~dH`1b4gaB> z%&GaDj;3Z1R{ytBi?Y9O=0~01L5PH>&K6jc12VU=dGS4><1gRZVSye% z@EACM57MIENe)XIQZA%YFcWdCdVLGzCt8G$_uKoNaw?%l$+$q~{} zejnu#j>+1bR`bIyP<$){yDVo#$~ie{mtaLnX!XcLq<%k+gECn5xHHNrP+s_fBa|$I z#3e?)P;asGdbwNax>-%oyL^5KUPzR!tNYit`L$9+kv8p!pK!U;Ry=kdJhC8~{Q}1b zR4fP0WvzWH+1Xq+zss!$SV>RwkoREyr2YFgGddg0!V40C5aN}|2$!Y}jm*y?7R(Tp zBtML?hqgYig6TDX%dF$d)nFQsWUF-GS3}C>Ny}3MkPhhpbMy-L=t}vrVy^pdDGwAB zkFTsWkZb&JDS?&tfWyXh2OY(hvOu-G`WCoo)vn=ynRfj9CpV^l;$VROQ+j#2w_y)C&$Wf{VuiYNiM|=nkHS~arNfe2 zV8?#yw$4~WLs@mOh-e3Dw1(n;6y6k^Z zmKCaYA2an(YY&J#z=3jn#m2pnKk%v`PGg~t3-!gQlB=jYg$Lw*A6MzBhS3^+6*dh6 zHkCeYC-jgz;JAu28S=k=mSbm|z?tBfT(jwGsr=uJQ~rxKG)UN*fcN1KK+Vq<&Cj~< z^OJnzQ&UX;r&@70RR5xk{5?X!e&7g7_^_kbKW5W^=6%SY{_AduL-v1)QPF?Gbjp{c zKX$p_z?luQ*8atE`TOepd$B6~9!65*GXFj!3LviHzsfbEuTu7pI`H$WQd$20)^*O` zhm-)btZ(DLRMNF=HE4m~(eFM3Om{>ush#N?E%nHV`*`=4w47JYD>?bcCn-xxj@E94 z07js`Zo!Li{S5+kCj!l6{S`kK8T6A0fyt8$WaZ^@+tqR;%8C_2p+xCuu`g({Lg=HZ zrvO&eq3HLoRDm<*yO_7kL6ghjXNhe=gx1W|3VWv;}Ql$qe-5>-s7_uWOe z&))K-dx?NH)GVheZKK64r?1nrgRB!b@D1Fr&V_3%8#cd1i}kWos2Omk?qrhCV*$0p zT{&dz5v)^cAOMl^7g|m#>X8 zQ>SLLksd85XvCxjG_iboJJo$`nPBS?cG_o6EDn-$T5lIDj%ON%cx&%Q7>38O!vBzM9KY7CxWAR{K5vpQ`7f0nfcz0+K7X^dJApY?f3Jj6`*WN8<;8} zN6Y00l#tDtgSPFhxG)O8H(K-#TDjapPj%fOIej9IH?qF;7&tFJp}I3C%1i@fEq7n5)-;kj+G&h;H9TL<(Ir&d`HCvZXl3=fQ_oN);=}v!`-QP2 zAAR?H3(FD3Sc|?O$62)OvHz@8NNrd7NC`5BoFMpP9cve+u5f&&Z#5%STEjrXTQ9@0we z0;*DAsz(N^8cN15^pddEkE*hz1o$217KTy4QWn0&$~PWTfkKy=$UaI^@p_CJZ&N{5 zR9a6SWDEXc$WpLCzjxmq;@Faw#DP_Zn?YW$Na}41^4>AVeD3aqrOxh*8p%~+sgEyy zDu$e$BFZVac@Os!KUz{SE{%aUG{pyk0d$gX+}FJCb(%?DFo3RzT-wJ9vewBann7^` zJE9fquWoqvcC$%o&@m9|;OY^{xnBs*^3=V$poOek$TBMzy%w#DU5fxLoR;oJTW@1W z&10x1oe#UNdiFHH5at;#!uaA;&%zOAwuI*_B@=aDevAVXITbYDhLqZeV55w7Pmr(O zDL=BS?l|O6vSR0;R@twv2~qdQO4#4yigu<7&;4#LSGvI1mT$>XOHaWZP?S}o110sIQ1Go z!d{9HGE)HVh@#m>X7X82cQq`#&U91vBmJKB5kJ)smUqrWTZ*h#wV%X2mJs+?2mSEW zXUQf7KT3&BiblRELtX92VTiY7k5S>&B-ef!PItQ6?2)w2tu%4OVz3Ui_XcFPUvGNy z*qtiBIbFW3c~X+j;N)3GF$et*DDfP;Xgmn;Ie7K*vR{J@MRCK28Ock0=Wf1#bFDj( zr-akfVMAUyX<*;iXbh|{=_J)0##`r+H5;v$xS5M#@ZXy;zc~w1yS*aka=uNfJ)Ph3 zGD@-Yq!}Ta!PtY967-WnhAFb*E^3HC$zX3>VB2I) z0tGD*VPHTK+QCy2Bhj9b(wSa80Jh3WdMW`gfxnTTN~Ar9JOu)3Y1B+~B>NJBG)%Qh z6C~jT+`NVXy+=BsrJ#l;jg;N^c)07CXGBP}yDp#cdB1`nFKdv@HeAVdIOg}g$l67B z59h($ZDz5c>=hS2%fr48`g3A!oN@1he`SROsm^2P%c3qVG+svbx!b*=Bu2;f#hlH0 zqIQu^pUdLzlLk;Ucci9ga-`7+W1WN>&lY74?EopvCr6R`ZvbcVUIrPP5!Ynrlz9SM zg23`Lq@CNI@W>hHPbu)Gp)5ab+rt@W;~yT38jNCcJ@r1x6AF}u)RTkSi6Y>L=vkdV zmy=cR+cK!$8zXm)60B#^63RXLTodMgw;Y5dSE7jZtaity!MI{V<9%$zcp5fIt@J=~ zI?v5NjSd;^@K4W9l!fskN~fitNk(*DYjxu8f0mR_4h>X`?Ar?UQOaeWq}^InE4SQ6v+3wkbp1y=h^n&T2vvHvV`KE=k; z#+okvQ|||>{_-Vak$eH74{wAGDroNtx(SfR9Npa4mjI`46=-oPbZ)j9xGPX&!)jP-q`$Ywn;nEX-LTWs9pk@{TmpH@_C1?3n$< z^Yy3Y>+>I;FRwIXq4+{c^L1V6ezCiuu^e^C3D`NdQzh=^gy%Xc?udp0clBCb_c|nE zDVX-aEP>0-+`bd=@34KxlrYO;=<$_SACwmpuJNcrCqe~7_RD~8KOj|Kngyc4X}1@_ zegp*`B=D}}_CK488E;FXQouIzbv1eHE$+tuUC-x>V!y`vP4S_=VM2n0;Tg&+Lpd~; zl_I4Mn{(D@p;#3Up@CmPuXEa{yG8*PN3~#E`{8^r?=RvX9Lse7_W3YWG7vm30nI|a zY673vW!vgH5ed}=vn=Q?_P9jPAFz>58vxc4h3i3qgMy*k9cV-_%=UI!6#Rhxzk_sm zF${Mj3Dh;Hy}uUR%G(o;sVooFes9rMaP!}2`n}%5%LJ2vNcr}OXT-TN*CPn{h4I=h zGF##7uehuNnmxQ{(Y#%9s?|Lqg&qg8QISWbX9s;F43g^?mt)-1m7B7x>g9v^aYKcQDC7vHAk4>tJyXD_H@<6g;d+pPhF0TH!VbHHFY z?FBBZlMTV{bwH!)g*Si;U1qcJ`L)vf(uPC)C4KPBK{A&K*b(t0 z`v%%xL-$Q!fR+E3`sJ&b{rM~abofj@i4_{n|MvW_;DY^|lJ+ydcdLp1Q|bC^OVuBU zi~Y;i2?{a}v;U$0=Bp#Nw_gn5KOS6`OZ|PzHXcYvmB5M z9~0D~1|vE&KMi_skWa>UNM+4j_Rv(P(|hmU#Y=vVl;N!nTbUemsB5ib1)kC>xg#=F zo^Ry%nj1#{x)HSla>2p>UVepF=Pk|(fiyn&VI_(K=Eow+z9C8MQw0=bEy81((x@ct zCR={i@N1|6$FHAf1VXr>d#*0dwK%zZ1{_$uwb0UZgzkiQp?ht*W8S1SxuG!ik67Kl zC6A7@QLtD*C*<9=zu0Vv=r*ub3j)keG{t*mW zJDy~f5FeEno4!aTyO>n(^?a4QH^zrStjuClX^NRCIE;cOGwlghXb*nB+0Yw=0Kgo2 zqs2%vjWV_Y&U(;tU_aQ`-7#_>LwtUo zW}_stxH_lI^)%xmJu}~wsrVMt1!NFHBFo7KtESq<1DlLj+e{0e9_!>JK5yeGcdEr* z)n~7`h1qkzk;H;_Nph#xKm|V&mc9_=1J#Omm|y6MfN3iv=Jo~w#jGx@63;uh{icCR z9oUx9hIdc9$CMUgW7PrJzmekj^XoHimv22_#A+5N|2~#Nlwu)0lksvdQ29sU?m*Z) zUxb7RC*yKdwVFWD2dQO$&gY7^xL1j1pqlk zPC^NXjT5}gkYqINS)6Drb%VtUFU!YHG=jmh)T>acuPuz%>NsukIY381+L#Q5qhtrs zNXu*E`TRkfh;gpk<9f9ZZ$Ta;5TAw2&ckD8NsC#P;SwP5EvAoz;QKCjiOALJ<*vM48O!2pDBF zb#LdHM7{n>*w@}X#(CkgZOH5aBj^_=B`0S<%R)h&)@jFYNG@fI7rWU#4E2Wne^qkt zYFNF9bb6H-vw3e=EN6fA45Z5JhovljZM#QJkJN5|Z_ZuKkU9DvOM90j^bwXIN4nIH8FCkyOhS2QAO*+i`q^3=B{d6n~8lviK@v-c5 z7m3uEp0E5b*3nkKa?I12$G~|;6JbQU)rk4q8oa~b+$NPK) zM?V;?%A&v{n1ukXFR_dwJ&qGwLNp3{6gD&L`i@al0}O_&CmIGFa*z*?c!rFEqRzw; z&z3Au^7A%>v6N#)8NvtXUgjgWcFz%Smd>Z!VW04pvz@9}t+Sn2IfQB#O))8ktNHqP zoqQoGqNqg*1tRiG76C%|0d91aOHLS2Bx7yGYMr)hB}{4ZBx4JXL^vumOaO z042jsWoQ~s-|*%iUrf+r{?U)3X?@hg*-Q7=Yjx-jmcXDj^s7&uR1N-!>2?Azc@_oFP;yBC>N^Dw#*Q`E;<>M7vv znEPEf9u`B==N@oFz~ffnRDB)+E3Ql1J01%gxmXKW#7PGja(UAsIc=o20)KY7&>&Mo zHEwkpEnGn0I7FpWtuQLrF2AAcojjszEWV9deWoXlJY5nfE|C<&av99OHfwKUKTglV zdqpPCUdD57oSx3xIeZpjm4sKKItQspyu%~+zUw1$Tl}-mUci34ObDJy@M>(C7#GKu zVAiZDUNxS)bdbKUgD#uGQP`_|tyJU4Ct_JwWH>hO#9WosJ)H@gv>(sa_Oc^mu>mEj z<4s@<1S+61`txco6@{D7GS8@Nb|SeO;x~0Y{!px|7;pbzeL^dEJ$|bWU?rhw2f{KW zKz6llCF1S2l7-Ehh&A#0{SpY#SItc5b4LMo)hUnG6&iBOm8_taZ*sAgjf1q}?@Vww z?mkw~7UtQ%KCdb$mIL*+qjmRa9D}>(5E1O3T!2p1%-Yv|Hp@ga+fdl97s-BL9+kC+ z?jq11rnEZtWT`Gsw;P>WT=wD#PXiz-Fmkey`;&$!o?W_3VCnTaur3OjWy~~n^bb?t zkYNy#q_T5w@W*yr`S`rnXY-j@-8xDak&-vGy}lkt9gVhQacj4^vPnjyA+Ut=e#<2p zPS6$9{u!Qi^=^qX@{wZcobV~m9%5a*cdZ>B6`LQtqgCCU(p?u`9UC!dC7)PKws!|e zQc5N_0lSZ{X-~}Xc}tuX$Ll+@GM=T_Plv4rCzuVF%`%nn8YE3|=OJaVyUp8zf;xea z+48o2ZPvFQeTaM}Xp%cuV{}eTt37e`vFgL|vCsB&z7bAv`MY-k`g3{fO3$n^pF_5M zv4jsCBC!eNObrj1B!SljM!b`QyJs6nKK(4-tlPA{~O-g^&%q$el6 z7sP<&s-Q>_MidnEDmFwwJr)$uiy|rl_6pcg5fr@YwQ&94nLRc5UwEGE+4;(RWoCBj zH|5x9Ued0!WEm4mthHtBnPrJtw&vX4Zev9ePI>8Y%AhJEwhMtKv)T*OYZKBE5JkPg zo^3KCY-me)y|JLYt=o!inpw7@VjG&H^vI%atlO64r}o9*PK6w-)z(-nJ&yeH%;vIW z1nDX&Zmno+Yt~g|^fub-ZHA=U$|gsB9`3!u2ez|4A>Lt$%xuZaMASfgQ7K!HG88uD z$0p(&ngjC#+40T!9YyR$z4R`OHBDJq+G{B9ZSU$u*x%X;T+UO9T7hyO*OyUHlxOR0 zx5mXc$HJ6g(ei*x(po6{;SWmWC@4Xwop@LAZ{+=k1n?8Pw%BwEv5hYkZmChF|g=F)OU zRZ3f0nhB@W5K*l@DWN7SDXk($SJ>)|))nRF=3vNHi}?0!wJ9Y%^_9sD6|GfO*33*} zeYv5c7PHbedtGEwRaKHH1v3`a#*VgTOHyB6PbvBqu{foz@3d8A6q-}wuol>Y8I1I# zDuf}9jZMn7#73GBOT5$2mYN?|YR`;~FYB#Di1^Byx}M6E)P$D&Vq;}`oZZsXUEJDT zu1DB=EJ5bCWatxdvcQ28Gl`M5(jIF=eo3l1HPxP&Wv_}a?96T{D(SN~rDy6ej4Uz6 zwKNwx+OeI_TvA--EYz1KRTk9i5ZIwICM`C%EjG11uLL(>wwqhZD$5d@47~`G*kZTg z!;;uukYJ9>=s_L`eOFpkZ?{$_86%TYbE=w*IGvYTP;QTF$8j1nZfQ)6FKVpRArL`D zOIEwBuU!|1+tr+Han821ZhIc?!0f;#C!?`2y}mX&y4z-sHb%E&(h31R3!8fljghzv zvpyD!gYCGRtP*XJH9o(?hT9j5@+@`O=~i8F?g z@rtaZ1p*{dIZRIB=#D0*s$A* z?Ugormjk;2o6T9-O>H=w*pU*Ca5|AVi-(I&daYHBHiRjvPsPUURC|@(TALYPUu#Qh zZSBfKV^vsR*plB%u-Vh& ztIBYmEFJNkda&y?BPrij&{BXf4c4~Y#diY)op;>A+VDGYp+^xSX&fC%LesD&Jg{kl$TrHq>+)N=+$|skoCX zwY;jmq6{%W>zWY+B0JTLO}1t2xgF(=af!GYDW_KGF<`TQ5F{iG# zr?NN`xM4%y`^#WxV@p+TF@1j z6RRuF$-ulqVVkuq+fivw$<4JCrLj&+m054CD#1!=Wm0R4E}^Y3)>)Zb+uLn0+3U;l zTH@Pu7E2SNYQ@zg8qM*}bX!4IK}BDIrK!zP)ty#b9+%o&ncac-rtyhgF~)+{Hhg%n zD2)5TiVAG8IF5%&lcZW}t-UC(D6coayDc*f$A!(h?nq~6TV$azwWhGU66;%Kw#@F9 zniRz2*3}iYLFm}tnp#_3LqVt2ZtrT3tg5ezOsurya=zk3OJ9Y($6T9f?#*g1ipKS9 z+1cGy9a*h4S$0!RRz);U(%3t)5{mNJnb#KdzI&=#YKz)%=D63K(OzU}$B|TBT3lla zW`!#&>k11SvoRE95rs-?5l9y!9Q4_;vkPKzGiXeav8g;M-x8hJWa@~qCh6^ox+28? zFUAcj4G5&wlTmEXY$(rkX6s73%-DN}3H7FAYzlTbag$|Qy1lh1J;9!C%&IijVI7;@ zIns-M3(kk>@(OS+p|ZEP0z;*$oVa3>BP%tfE**1-Ep5Gtwjz6O63$UuTI&i6lADsF ztudLsx$PJu_O|t9=H%h7i0&RIF8FZd>r4oGrHf<-%gm)%6KG0nceH1)uxq6_pq8A6 z>oXHGjg9RUy_SrW#%$b4>!`37*Z1Jsp5px0)V9>7?3`#gK2jIk#7De7*k$wmaEm^fWq zY)6*agd?2{=;5$LnbS=v^S z7t>u5o7jOE$T7CMnn-7}Ij#;pn3fJlXIF1&l_LwWHtg7mY^i9*ew&>7=ERH!M@)|i zL4lms_F@cR3UEs+qJEY)py7A%Z zvnbJlxFJ~o%fu>qN@Jucttc@Pn|GVqjI9OMqQA9b1fzeqcg0L)fOD%&t+Gx z=n>exC9Ah5t+)phk+CUBrRCW7-Bw(0Ol>NNv6NN3`*RFpY0h%}n)I%jo^JwB~D(roF?Ye!(K zG+ULat+TKk;nFLcE9}n1B7I9y0dBIfMd~d^o868GOmQ}AvIFDBtoS-3Zm6l$wHvx( zaJ5kvBChnL8*=k7y=tvW=rX4i*TpqfTI)+Pvr}!o<<|6)3SE!Ap|9PUWpos`_cmv> zx7H!NVO3gBURDb3LNht*O}4aFV->F3F}3wz^FfgvZB;Xm2VYRrn`vq&s41`Rh{SeU z9OVr_ChTQe^2#zIw9bLyp0FdI5EOjVBP zs!Tg>;N_ti{UWAVSd0-itKP?$_=ikki;jP{R*Og!ZQ1pI8!6-O_j|UrwBn}BOIJLx z;K{djs$p6B;{QG_3(kaR)L$A-B^?)cl++~_c{ZZ@#qzLJ$h51dMn-LRymyuMf(Juo|#d**A{f|4t+5I2>mxil* zvjao#7_dOX+=QzXy*pj=vHFVG`Zz1h;sP0(^)1;cY;7>ED5p0v)5b!kVb!rA7b~Zk zF=Yz?+U~&(!&wdO*alc#jmsfofr%rtuO_>zqN6j?U}TpFCv?;{=n$OV zR)M*ek^;=TVn3KQw#?j!I|3s0hD2<%M*Py;6fC!7G+XR;oXlu7STgE*N-}V#gT52v zZA4QuTbkRkM<3gMa;?~U326|mJ~6+sprt-P&XkuM$?gVE*Rx6Bz67kXAp#~WR9S{u zmrmTIjm_nS=Bm8Hs%TwSXIZBSK{uN6GfFy>s=L{+s<{#Ok+k3f6N?S;1Ztu)^oE{f zEU(q;8e6gXyus=0>`2DirZW~dbhpRi?r?PR>bpv;iDi~v+=f!yjdMk@)o^HHOj#eU z2&*Zs4CZMy>f}j@->`E0}&t7P9U|G;;K&a!yB!u6_ZF%-cCq{wIb+tLk zO$i-{Hiv^CNW)oVOqy3#vd!jIi8ieGW1gWiqq{yM3p;snrre0L{}q`{DV=o%R&0Z^ zmRS1iy3+hqLuDpgzhze}=jd>KN;a->M+?1*otq6%&e}?Pm6A^u8qVZZ!C80cPBTd<3{5+ z6rIw11e-A;d}vRht`8y6>SJ-0hl7PsDMyJkH&!BkEAD&870Lw$hoinU7v~5pHd|aG zLIc#t;$kR6w1fK^=C&utR=3vH=Hf6WmX527i!%}<)0j1qIxsDsi`v~$hw2+y8C#N9 zT9$@0A$9q2*v5&GAwsmLb|L<2Txtxv#{*@+#+O}d?KlQw$}-y%`IyBj(tt+}MGuMP)0v3S>md!T!4SVYE5S2v>QC)k}eZA~dHY|lql zmJK0ha1^l`4HdRBcR1^@C7{z()@ZX>a>@~Fw!EV^2{&_>S0K`uzR;XdX6cS(hiVWC zF((5DQgUk8-h!$u#DXw)6{qK>Wh2OQ8M{Fp`)^?UR$RK+qBCWun-XwUNkg1^X&4kr z#)gsHq-9WZIXTja?`R&BsC~V2vDZXfCO$ z%8HAL;Wyah+$Q#uv>3`e6P-9(lp2HTnpTmPW=9Z#sm;}$Sp{9jz7)*mmDaP62w7QW z#l_a#hUU(+>gp~>kJ+p@#k4k;m=SFUVc8oq^BPL>vDc(I7jcbJvBRe~(q4{@1eQD< zwiP5GijK9Sy#XFZ7}-7I6`43Eg4+l>ofs&<1;)PC zf>t}GvGW|M-NpI}+&Pqqi(_E9-rlZk)P|I@hJ5T`EGda)(GlWvItsf=lle`%{`Y(R zj}HuJ{{Qcb)7+O9+tgCq*xAuhj5>k%BM2;)6JOp?z&|?d8qAJ{MuccW3L=o8HA>CJ zg$*Ui_zinuySi`B;T2wdx!N?B<*J z>V~oeYX!nLq7T3>Yxl*1_l99ZP4=96S0iQoFVnNGgfk&Bq*|AZ5FqIp@nz znn8D#1eel#p4W^dTf^wF>m(ETM;H~3X){RaDE1ufD%h)W3PHohUv^o|80V1jbk{l( zMzemA2hty2(8xjv{o@7AFJ$6)I^kMTN5l4MR+6ysbf|%hXV0}aNP7CmK8|?cIWg2V zX1~TF2ysA5XWH3nqHB-BfsTs`)Q*s$Jh`=Fl5o((EDS$RkPAQ|HS7 zETKnV*F-44Z4SDuW=4$;vMr*9H#JjT@4u|65rn05!9mT9PFO|N=Y6HgxV!vdSnU)4-uSX@lUA+YXN zrTL@3H1xqknwNyPRBb;{wGFxG!+LA{`XN70rxAxWitq;KL2D0#pQ;Ppd|2~C(66fL ze^#|u{_;^@U=$7yvf&NQil9N^+)qllYN|7Dz_3B#^w{f~S&Fd5XDb(M^>2YpQt?6t z_(0)qY`-M8W&ib-CRreJ+^nOHfwf&lragG^$nn@QO&7@xSB)HfTyrJ4DqQu#=HtN1 zr4Jp~T+a}=`4+tmHd*dGwLUT27C8L2COT+^s#Kqvvl8UPx`t-Gqj8W$%#2g$6Ypq- zqd@X<_Q5+Swq#TI`k}?6=%V*Di^+W;XGOI41I-m1-`9*2Hqo&6H47O|cFsh+-Sa+J zxhYIQqQM_%V#$6VnFUBse1KB32?~8B8|aJ=HNk3z!(B-qYMv+LW2hA**vXgSYRUfX zQ>gqgz4jB$vkYb^6UNf~Pc=>C7e53B;T7g%Rg{lu%4eENkUjC4CPx@bkA1C~MfE2% z!;lnvLNlG=^CGb zDMz@D!|@jPH3})GoU#7ilhcF^9>CDkn#2H>4|yP?ztI#k&a8+i!~KOpbI>L{MTho^ zV}#df^tYNQ;Z?fgTa6A6F5BU6!SquOA+Jv0)$5w?Gz$6G1YQeh!grbx^x2m+Vf4H2 zG{=SCRjTFX!gyf-HT(#BM0}4T`TGR=mRX$Ufpe~x2f!KYkd(`P!u?7YoJ9#bFoEuR zS#zap+gS|}ghSLY0;1X&X_R2v_=9Ex+M{DX!-{);&@2;@-2mG1qh`7X!gIUI16goh z)8K);eqJ*MmRFh4sTVX;JP_vv&3M?{4LN*4Q%6h_xdB72@X4s3$gLn;K@-2z7+8%N zNI(7wWz_BF-+$7)CkWe_fH}ewjGH-Rw2yrIL~8i2e5Hz@dh=zDr1&Gb#{Y^!Mu5!y z=Lhms&i_De;S-k2SmJb>=qt{M*8dJ^KB3*eYvR2aZh!w8fMNebVIgp1hG-^IxBZ zcK%l*4IrtLSX~aI?+T($$YZ2YLMn|W;v9h1|M<*UdKa=5s_CwQ;v_tNB4Q+|p0s}L zy*fHXD-H*8`2cY}9vmT+j?suF#&z3pC0n@7!|-#BxX_!r)suQm1W%Qc<+gVGa5_s8 zQ$0Cvl*DvlFGHS1vj&L6fI3+#TKZ9}(~9|C)*pIUFCFNQNK3yF!|0Gkr2Xi@a0ZDD zjE*;DKL?4~s5Zh-WHls&XLVDs*x<`bB8s$wEJYQ_8uJ?6>$P6*C=9+VD%yM z%uwjO!Y2D*K>sX6T8>4lQIEpc4Zg_Z*c(qW;D42ti2a7t+kF~?=28%ZdJps&0 zhQLj3%I!nMV&PT~W%N)noKf-;J;7Ha+$DO?P%%g#4@{<|_2MM@#6BrpcwFUt@<~A( zGRsE*9NyOB$$9yK+`OI~E-n_xE0fo+T|3s(e!S&lM(15F%EBx3wh>~q2g_YSPF;$X ze7RT)EVW$y)DO-8;iu*Cj1Bt+zCu@x6!)-HUhJE%Kt^t%)?6Vj7lcqcbTrCdD7B0h zO?U{CMFYKSG)F7lDUPHsj22grnNwIVF-HEEx4*%e40mlEBW@GO+$rmiZLkaRbj<`f zWiH)2PE151UAu*hqZ7l#D}_Q2=$0_i!9Xok8b?osp*?nT5KChX5q~mY-acNOA}sTe z{X8DEa;_@tTpAy)GT|k5bGR6Z&$v5lWrCOt9^BGy8$RZN9i1TF>t@2TK~)$}%@YUA z5MJ_NjGrXVVxYAOYQeiamAx|ef)KWu>uFZACyO@^5YDO+ou&VsD%!}|DZFozHBI~n z`D=>WHyJQpEC*Wkba4^jsztt-4vU1+YzvyQ`7>bZv#yjGth52)Ko9KFsuRZwQ~eR> zTRQP62Inf@H&e6;vjgCxXNf-{AMU=W2#=SCyB0@?Cke^DQYE~~AXW-jF~TS%_eyju zg_;2!Wi^-6$Bp8h^vp?3Fx_bs@AAhFp|?efqv>jsI3J%WMu`qzh*>oFknn=ngeMWjOX~^T;%wJKLX{#zH_C2X(4o5vN)4I zF_sK;v!M^qgWN0!)^{1}F9EM1bLNY0lhIRI4T)AJ4h!}SU(|X=TcX6P$c(A#*X3*! z%CX5Wi}VGMbOtS6AeJ*EUQ5rhG&l92XmK-11AtYf*P>By@_p2b#o^5@jp}2>WgL}_ z&`!i~9@|pIkuDM|jvF8}((nX)M%;X_OAzM`Tt1bLEv{u84NA^UUS=VbYc?iP>?60T zn$b@aeNtVUERGp?k4n8+rEYzpcM-kvP+2nn6WD|cD8m_^1-Q!Dq0Y%v}HwfV=B z7my1fidQ5yavAR0m?L6jW}CKtf^!nxnJ3oaH$6WPgNo#7>Y(DSV&rC{8}h|WH=Zz! zfB41~p#K4EUhO*z#7U$gkmMtO1XCfp99@A3+Y7}hUJlK*~e`dBU=Iu5lW|CEw+)j0A-`~XRa34llQ=w zwFb^5Vi-9AjuTLOUt9vuy+>bLBHj%I-otw04`CqPR4U3KUvKzsG)CYDO3@;@OLJ(M zxQYnVdAm)5r*or*gkI&%AD|!hh!Z&qtHgew5XvQgv|QXp$fW72_wTLpd4FG(NP{fX zc}+1*S4ZKuS93Tov#i(%(R4|Tw{p2&su7z6l098@Tc%Tt6-wyxIy6Sv^lhh@%|O{Z z(a<*?(99JYJfQt`_&#ORCG}#Qu-udQRXsjQz~^Nv=RaPR+?rQ3h^xsax90b$n$zA! zh_{LUqY?cH24fAvZ!FENEZj6qa78qWBL2>-*C$f1RS$iFMCJ>>b3*Ro44|}lQZBDm13#8z=l4xfH zuuJVhv%%2NS5m*=WON`#L$8?5GUR%F-|KPJaF@JP%o2!k2Cu1|*WhIi4Zjwx`A^q~ zje?PuEkh644Gy|i45ecpAr2vn;q(H0_qF2v0IPhP`#gL@>6$)qRZznWUIgl9@FJje zC-~Xx*m7x}Ys+%cIhgd#pa&8Zn{W+{UV~mx572K$_b2Tw?MRxXXa~8ZwcGy z&H>IR=R(HQ>|@$7bmJEBA+mRdT5_(sN9-U6XRzXImhbT9x0epPSA3RXaSL3$mrKeE zWB69_cHuMn#8z)%q{Fs}$w5D=!k=>szx#79GqoJKp4=wt1@fmmV{*S3FNidHx93ZE z&Hc#ZPkQtHD7--O72YfQAu%YgYIt(#4*xSyI%3eN?eqQzJni#v*Jlr4>_DOcsYhK& z@#A@BJIJG5zivmDfFbZgmbDjO@~bDl9Ya+gQ(P@1tzyufBXc6)l3p>*ADaXI-&$7|h3)V2qGkB@Zpe6=!; zuHPe$7QXl8tX%%4Hru^GW=q>;^KPAo-$cUM$ z@os-om_)*6s$ZuWF6cLcT3rwY2waqPE^#z51R|Vup%V-QE`V{&V4r2V{W_l!X9cCr zQh+bjjqC!ZBxBt$v*K9@}S#XWo!Jv z{G0CDkIGG+cAIPL%V^v8(FL#I@ig81ve*I;7vyPb{1+M>AbRD-WStvPJSdjB5!r(^ zjQ1WyM=$^zlTa$#<#_n8>4Z)>l>YV#x`%339u7xd#rNEsq@w+FNKB;P9O8m7u{^k) zuc1hLak&5qhsCK#@`+1qvsn8)ABvqh3=0FsP1foPv`Hv^`gQRP$(p5B;$r&an~)c; zA8(VY*pt`F$KFH*ujeo{y!s>P5CFs(F7;#RI(0<+Kp;2HQmf&gM}5_B>@l$}h|c20 zXu~YE80|miDMlk*UmO!p3FLlIqW>t*9MZ28e*1uA33^H;-=mT%yK{Y5u5Is#FABl| zy5v1y8~W0F@aQo&_VI0g*e&miWdb>=%Bg&Q$Oq!H)^yPmk|F4Vn%$3XX}5mJr3L9u zy6_`3S^wqaG4jZv{e-=5w=^nfNCek)P=xy788+V!i#Pfs_eg{2;z807dN@-YPBXp` zhtP<@(l8n&OTjeu6S0DfjZh8w-6uXn&UsP_r)gh+V&11HhchGSu_q-gUHpWU@J3Nf zzxY&)AeIQVH)Xuln=LSJcuFc`6lzuXf!uA)oTsH}bn6K*ir)9M6v=+2lZ*(pQt$Ed z;VsW_SLR9a?g3;mz};d5z2~GjiXQ65kdy!VqgxzIssUpa{p2^O=!@yoUy2Sqe*98= zjPwSAKK%-sfEDk$F8W%$lHBHpz#x2fWN`$qoQrADY4op=$}4C2X*4Pi`QZ;*%uMed zBi|Z9qfbG=i>Jl;AaOHT@{O26_W993uruF?Z@MuU?_FP_qF(q`T<1$eO$((jekZQ+ zOFESoN^d@c!MDnrJy?UV@OyM^85h=P)|?f?$uAM=;!E*a_;N9=Jd5s^2jO35#YPew z8Gz7s4!sC}fydLcPf8*E9X&D9XJp2Y&uQVQwvQhLdM%Wie-zJ=*^#PT3(lik812sX z+4GQVw(H<|aiqX7{-#u65S#TA+RE8<%}i+F5ovze>iz5kXjO2E1q&NKn zB^up$5x;uzXvVKF$v-0L(66LPbp5Xwx!{F!`_2z5_&?%oat~*Q2R(C93>)}hB(GQZ z&~3koBj}pn#36$FciMkN<$r+h1~};FaVoVcpqr3E26`a<`A%~S;K}8Nzr^AFlCpjm zhXp2W2$STXTZN=MupX+)?KLI|GgPkW-t^5G+j1u(CrO9ZsX7^3E!i5XtK(m+B}N-Abe>#HHuh%5|B;`hAQ1 zU35gHITGNcBdU}5BhX2K5Pv6m8C~M0KpO6*KxFQyz@z|I1%UjW6$tQiS0I2nEFk6Y zTo!;3;Iu%9zoP;n{;oQr8lS$X<*wo%_fuNwW0Gc2Ki_{1^!adnkko>?+_gc{5>jd4 zAL)_zZSi{TDgz3NXL@&Wuyh5@50)mg$={KZfj%1S%i&0{bdg-^&S8+^%OOIMrUb2F ze8xCe8`PfGtty<0wo{Szk^FJNo$)<` zeHp(vSh^q_rn`nnCFE@bJ$8>|mT&g5KTOkxNv&;jGsD7-zfOtLU4Znd&juk=2vO`uDz zmHr@$O{%XJnz*k*S~q!di%fwYi(W3J23=#~2JAJd2E1;+7oD?SMOM2Ru5vS6ciM|_ zGl*`+a?;5unql-a>wsWtyH2_jGYUtqlgtEAfAi6n>!k*A(4^Yw&+Da0!VwyN1GGCx zm)syl;<4riPw|`Yl5do>0y*c-{EUf~&XngWmJuHJesf9QXa6B>BE+ov4&Z>yr`;?S z4xDP{x?=5VgEW(7O(ml8(p6rLT;KW3iV_`oi}W*bm{n6+&8jJnU+={=nSGkOj;xfP z6G*x{XMkAFs78ZslPWcB$dg7Kg7>a5v!*YMF8@=ImCqmX@@k`B+$QOSHad8`Pl8 zJvEemxL&%$FNsk;c!!jZB)?og^W@sHLCWoieQ^Uc0yZy?U;313rp+6rzsO}4wk{ek z1ktT`N>S|fKU7M&PccT_I(-jX(m%F0wM!_UC7XA?mxp2(ugl026MaXda z-WJ%Z+bzSIdtmex^eB6D(;@f53IK7%?(n3Zxfg17vz+D<50mS*O2^3#3!gq9PeWO@ zk?_bih_Qoyv`wmJFs|g)_rd0`1tOfiPqG1ziLQvh`2=JNjCA$fFNF~DxvBtt?E$ol zo?Q8Sz`rfLh+nX%)p=s#<(>+|Eils6`=Io*KrXY=mWNcqt= zCd?S;Ep8PnzVNBY4ErRTz6hpkcS(;6lrGo}w|Gp$XD%LA$+x@7uQQAcG%Ll-#R2k^ z+e%}e^vfQT6L+9(<3_@0HJ==@@&cjU*XLz&kY4|kR89_Z@eQ&MpgN6W7UBzXTIC+H z`#~?AN5&N{DL3dhRmNXc8EMEfo?<_e>YtJBC!$R)^~e9+pWbDCR+0o^1YP@@G+Est zf*rT};K)tSgUJZ`(sNQK9up41(fW}jgwB0lnkJw>c+$ZyNwWrSv+;6pFXJ|oHoqhdL5sWMB`HrJJ8Y~@#3(0xEZvQQ z{a?~);RUAtSQ`Ebwr9SgsmZ8$3#vpk`4ykp>tB&>A)mR;o-=EdS5!CO zqF1Fu!a2q_M)*}_x#%^JpQHag1YOUm>JNNP3MavKwPr1TO$sLGuw{>Jb>j$eWSk!i zX_p_C3K(~)#m_RD*H1eWm0cn6l7qJ1e!p>XcrAMU+3(u)74% z^GBtn@CZyoKm zHE%cRv+qc2$*XR<+urpn7CYXRrUrees{X!P^^o^`jIok`kbLE4JdBL#$9UoUsQ5p- z8DISYXT1Kv&f%`U52T|4mYybjD@`JzgLZr*ZFRrmaq4pkYbGB{a~O!P6@}9k-$}D5 z-;-s`@@50@kWZu~{U{&)M56tYdOnpFAu0O|z8ik@%{25gG%X?H*Pq`$nQr+^>I*7z za6cC~m^a-U=2K5V<6Kwa326fnD!9;i4EmIU!S#t~w1<=Q{XT+x6&asY4LnRP%h3#(yh4?0Z87!`JWseF%Az^G_1KW~`%t zaNs-Xc09muE?9A|-FHT+!RK+dD#^67QUdxgOMYN|nA!A+v*_GkHe2n(%%-8|q=$U3 zyrlnp4$cN99|YNcKqDFHrQY^~bVEP%#Xm}s3>^g^jPCdmvleNy`Ntt7(Wf`yq35Ln z&X&zBZa%MSUbAyM8(McuQ)o>*#W=T_7K+h#%2e*JI}X3qwrS<{TDIqY z(DV{X%iVS6~3M*(fyZQ3bb&{qu9!$BYY)#c2J0gn_-8XCnu*=6*PGoCMr1HVSS=>K!(wvi%~q4iVyD7Q zQZIetew5fR=cpy~Fg<@iN~Y|J{--un2nwCc%fzs`zA~Y-e(x=JgI#ML(7FT?K3Dzt zOrNWMeDKZ}EKY9Mt`oxPfk#km!YO@7OBt%SjF5!6>S%r6!zdu(^!0~19zzEnFSS!2 z(Qa~c@Fe|qY0~SDYL_w2{JY!x81xwo&TLR|*JIk$pk=BSOXsRz*RUPPZ7`j&L;Dt4 z>rT@>4x-g_)gv+2_%vMQYI$5cN+1ur@%HWn-h*zuYyI#j@TSt~PiXg%18&^WyMX(m z8+Z97xHs+6{y{!eah2eM-CBpRO67Rp9<76x?9pCN&b!$TeiCfYscdO7UOcGR zJgHqrg6648E_e!9(meHaLoZ%DSUaB5zD_2%u^xFESYaxbaRC=e2W$mGtEEsOl?d_+GRE0P)?e^?S8rg)JV?YkRfZ7>N6S^9zvkTDP2Qd>OfQ z3VKnyhwO9fbnZpq?sem?y99UPKDhIJH*VT~;J%~chTP$YJC_|Z3U%JXO4;-jzrr(^ zzOi3>GU%^)yb@iU=c`2Y;{&kF@AUKmZCy~vd=8iAtDal+5(kxDHBg-ir0=|>{ZJTB zTaIY$WbAx(Lgc#PV?Dj0QSjY%ibRDYBgBs((dWBnV|{c2}#}(bYT4OCRc4dhPK2lSM*_RRM5Sus$1NuK6^M&RTp_3Er75~&HQnm`CPjB z4Q()aG*CJz@TPn9VRc^}h_q-CQy3nI z%ax(~jzZs`-D-U7Q==g$EQ&j8Y!r8va$)i~k00Fji#o1dEf9T_+AWwBrTUAcdGTg| z)W35O?7H_IZP`CaT$DP01~{L^a@+0xg#&wnI@Mg;-MJ2H^K$#$1+VoGa!-bHgh4&nEmvV!S&k0 z;3h#hN{22E9!Con1z#zAxbf=XD5SiwC^%OIBnW5SX?HFTp6)?Du{b!K2n(oib?|(o zEx0;3)&tshb?_uILIshdQR=wLokz+Nz@~f29$ylChN1BK{d#FIt2$77zH3BT@Ir#q zQY{t1c9O6_wROxlUhBjzV769gn0HqOuir@)El{lth@Xb^^awdOXypR#+nYgz6QxSd z;IIJSx~t>f%jFZoW;M%A?kvl!Uet{X0;=PRE99o2=TyeeaK<SOPc-+*E~w`jF)1&FkM}s{an}>oaxcNYskQa;Bs?;m@($nIggOZ8h2brf{!u4y z=$G_by}YSk(%lX6-Tji98s%ED&uzcpCV1#^m>Hc6*Ni5)P!KdU`Z{?M(Zta0&GJL; zS3Fv7My@xv$e9epjUUv?iTS8!daL|{fYX|c7LR%Das!zX!#AHEY?rO*MstXbhIhy& zVvE5}=4o_Uha3as{T=cgA1TXc{a*%&&hC_HU@{Hvl5g*qbi7NRB~01aEzgA>T$Jo? zIgALMYKEQkj$Zj;-y7GWbE&+gUsBpNu$`BNV}&h~*Y->LW*OA#RMijp(9eC*u5II$ zl|JBIH7->rIQ$Y0`6pmCOquT(bl%VqpOpip4O-~Iz?2PQp#Q$MUs zATX`4E7-6~R)<1v#TMNH<8x>jefSpWkZ=jg)Rpq|emUH-5>|xd*ww6QnRwY{^vHS5 z6+$^9jRI=RDtRiv>fTD%DtWZf!9gr-!Yt)V_T=TS?p9y8WG#IJ#;z z>_xdO^vBimFkv+va66c2~@?E|pj&<=aIMmCAC%t;NKOs-LX|p^PSdiO5Pi|HX z%jSEAZjo;vm=VWYuQY1dBac_(BfPammNm&pr#sI}HrI%ICNGm2!qLJwVEwP zyqfT8GUKi)tJ#28dvLfR!eF-=^;SK5w}u-d*jUnNwy;+VphlC~WVajim9q>6Ky79N z2AM`1UiHD@)(E|wU6pA8SnmL~$zn8E`wTXgmksE478=dWzy=GjG15e+N!*h;%V1_% zn{n@>-HclvW*Lm(rU=}%=&l(rjAot}UM&uj-i#nKfI2v_)r>%G7KUvD zvC(31=#55z^#*IWEdo)B^eh4pz)TOT-sAwj6|bgXpj)hv)aGE!jr8HCWNRPB$~KcN zoEg)CAk2<%{u3|A)SYUvnH>BlgoJRxZVZzV;;VEfvt4BYA@HB2+AT1ck^hA57<8+Q zEqVjQQCZ-oI>%Ki3M__b?wp++V{;f^&BSiUCGz~I*u{@_ENiP37an)pvW z9BAa3*eq5viol{^x@MO=fqwF|Jgm95M{8wxLuq$-C;i?fPwqotD67M2F|dMRG8ruRkJ5?~VKkxm zwp)OeL`023QP#nV< z0?M(;j6%Rz*d0(Od?8P_BM3Y=GIlnH+2UaQOc9utv0}!?z=|{`X{9?tr`)2TRT$ki!Az-=dJy zKGKgNFCvoAWiLVa58`;d1p35Fa_&GSo;Rbxbl0o0x_f}meHlpFcuo=Wt&hnEacnfM zhA?;nIFR`KX*tw&^kvyWaKiu8A$-Rs(}-8(WxhAVA$-Tad_`VMCdaE^oAs~C<1h$9 zeo;U>|EfGD0DEV^TPPj%8oC2s=A7M`OOswWEKgxMu#d2E#wRluak$I$I{FF-B*R5q zO82~ho~-wc^W5|%Isi-EA}%=rO*S$S&Gechum%8phYz0oGyf?PL!M!t;!DBKm2>Iz zujL3j=`HyWY{d83GV3U)_o%j{E7{8v{N>8w?m3NL`^!qdI4b`s9HS?X$@#)ZstnHK zu=X+9d0f5?52ge;7S9Vo&^z)%y5(*8HSDHAuG8t7cjP1nFhT8- z?0#3ak)e2D5qrYkLmmU^jQ3<6L*Rz|^Ah?8-;-O2F2Ow@f|-cfO@G__P!13l#KVhm z@_if`PwI!kcP;~yTXNF}o`N*o^~?vdK_I0Gd`iBQ4*Ce)@=|wRSAPUfZEjA_eFUOX z`pQT0DmOZNu&MZ!AIs5Pz$}L$y@0* z@+7aj{2=b4mG-H;Q6NvNLO)HlC*(PJ^E1#PRp^nDkLlAaZ}%pGjVI-4^w$#@@qvnq zz3AlMi+$xOc{cs@qW@9|)i7({G z+^>i$G4y-X)_;8|ukV+%<|~Y`fQ=wLb5QNk4vnRbujN~aEm3WMe)w9BL?Ie~S`No! z!D;j~^@(bm^XO?=reUY$VEWBX0^w>I3Q8@8txt60)&Utek}#7bU72C-CG3bt2JE>M$&M$#;?+ZqYl=$@9s> zT-Ygm`xHHK4on_&i$CfIh`;BYJe4Ce7`;)wj{N^Uy{BKV9 z(7)v^WP2Q63a$HDK8Vl+hF@f(Fxge{3wCfKw8zljz>Xi=kD@iKYw|XQ-ew zA%~~p?VjI2&hg!Q0xCU_&wiJSJrE^G7$Q$ZRqzz zNg;nFsRg-wfYKoRLXQnlUISds`3()2{*B}$k?mrKe424Wh7XndsM$!g`;p#^I2iu~d+!q`#6iFVx>*JnK=_i)URgOwdXv@FzLSGU7`YxVY#-NK zgB4lmrK3ZXc?{uz6S7|uqD*Hmd{dWoC`l&w`eAdlp`^r%;TT3Hklp<-HV-3ahT+{A zM-KMG$Q(|t^m5=_Hyzt z!Kqai>4ya4357#qioJLMv5&hT&~2j>gFnP$(XcB>51Ew0EgF)u+)Ft*iO=rbG(x$b z5K{{OZkb$}S0JJgYzl63(Q|>fhST$-l+B(xsu`n9pzn-Ua!CPa>E5i6IR-AsbuApD zyeg1Nm02ZC9H%_$dpi}Tgi#kt)pTCQvBcx4qKw<8PB!ZF#HqBbUr zXP{63vUi1i1AA^;kG%;gwCXzfAi|T4 zF1^)*vo=;Suv8v?>_$&&L7bAvQhAl-Uy?fmGiJc-z0$~FXMwusk1=NZXKsb$C^h`+ zC^$Fnlqa%SlgU zrlCxaalXxDnMwCA$6oU9@|0jtelBCaGDg5m9cTKbo9Vi-6Fe>TsQ{*mCJb>S9McRR=Z-#|1>;PBGLM0iU*L0?`fu6le6@fSM$|B`? zVoT>ST)tnVv|)CC$zr954b2xS)9L4n6@+z3SNnx{a+__vTD2Kxu>Web(BKYN$J z=k<)#Og~$KGEq*yTcQ*)IRBC@DOJLRz5w{XQpLvLjC07Nv0mNXK5?~_DLn$Y2Pz@_ z1i3q1-TLqJ;@z1ZV0eTqIaYzirc|N?tsniLapXC$W+9Xi{*Otus0LC!N0(G7w{Z|F z18>y&^byw4Wz~v-rd29KgkXBEMu{I9+|IVLRFt-tM%=-gsA;sPTIs}j_aCbjyYMAv z9Di`FSEP^A;db{h<<^k!ipr|m#>$HDvYzl#U&GcPfO7INcWkJKykMyMCc%*+SCsFO~Cp7HM{E216-<(Q3RG)*#nBcD3_L`=S z_VBjy(t7OmaT|b(#P9+|Vkumt!5OTG4jG)G8p5Lq?N*e@WN?OBJSWgob@0f=Rz)7< z!Q|RHXhgl@@Mo>2_^}2PrmbVK zz+-45e-t$;4!r%<#^08;DRFoUYf?s|fH4Ap>C_Z1T`X6e#BvqK(U@jsEaCw$Jx3k$ z6_8lBUAVR}^hkpe$|J38NDkv~Z8a)}GnbEDvgmIU*d-i&eAl; znNe9uF(`g+%cWO$pfTF+`mh7tedX35Sfv~nHI~=i;TqGeOwdTj;8^Do8gq>@vo9Fo zkeyoghp<0?h2hUYY-n_f>`xZ9-8$3RpUc?m#o73yV7rbpi2eB$`*WQCW`7>VVzcuh z_UE6NHgj%ce>Sr}o7kV%SbndtKNB!!bB3`$St2WRsru(Kb8!7-W)rUeU}g@k|L@Ek oT>rSjJ3$XRgb7EqYr22lagcwEzGB delta 117879 zcmcG$$*%O=wk4!PALwM0CLLr6hikh7auKe=wH>Z{uES-xt~!ylQ#6o}?nBTx;dvXTm9!;)<=6!_PZL>g@HJ6|u0EaTDMG@d zCfQo4TNf8uM|W<%vTJ1|JcVN&IzIR+sukk273XQwnVPuZmJn1(%f%8CSmS$?_4mu>(6do`wX2 z{!3-v=x##GYA-9hhlT6-bG;sJUA?%Qwb^#1t1W_C_i|uoP*hfJVl_z7Op-reK80>H zMaA1n<37Q9WB7m_s5#G~y0JBcIAZSXDva5*`nBtIWL@JKMfhIr(>uAHAo-!H-;|V; zmY#xL;irG+_!gJ>pwFg`P2e>~Q;akHBnp0M*yn?%CeUuoy45qS z%kofy1}wNXj8bIRQ%#BoKRH7rk_M)qjxDd^)dKgCa|y=ZeTd%C$`}Ghl^A_N;^-hv z+%dO)NL@kPI=o?D?fUcKi>^Xptmu4I9`T`3)%cUmpm!b-KEMTUa!>Y^u^UUVCCi3v zT!Gz6SpKmEmhG{oRwZ=yW{Gi+pYeM(G^=6rL&VuH8y#}S20Vx%v_`xRmvgJ z0%4ckC*Cq1;%jz2o{<2t7^@KuJ;3l!2+LZotcpB%C=EcUbT};19L917`+YG5o1F`1 zoKWG;cyaO|kkm-f@;$7|ssu}WZd{pG_90~4ht)|Ir66CcoFktFrUEwwKFqVj7UoT! zikk>Rtte#GQ_Mat>mTAT5?^;CRxAwesjBqu&@yA%t4{@!EIl47uHu~LA_S~PEPRAm zFdY67qXXLor>9tKb~zclb6>Dthj{SO9iUv#F(t9mkXtHpt5=-Hmf+w#CpS1`n$rdF z={u|uFUqxkctZ6KQjR_nR#YX7FN>>DuYh=Rd=MjbK6q9jjI7$#)#jj2@LJ*LmmRJb zQEg7as`T?A8ecKy=?-#l)hgVb3)$Dfzqwg1Tlm=99pRc3N(69p#Y#wVE4@uz8@xj~ zhTrBHs+`GXDVA)=akMoP-~&~9=v|LC^NU}Zs&L83SVFd}&dZ@?qHKvu0TKSO7xZAN zZX8wzQw7>+xr4)5%x3#`Jym*>UrqdpeT3f{Ldd}JMJjRCuxepvUM9p?qkgP$2obAh znW~uICtLAP^4Sr5CWu8d98I-0GfdBgiy_cN@1@etP^rRQedWwt?S4pB@sZ~Iy0~w= zc$4E2TsPd!7le~8J-h`-zEx(#g-B+R9OMoTH#U$eI1=6ku;f1t`GyfQLypDrTfWn7 z{gqk}wa8+G)TWR};ZI!oBU8Gt~Jv-F> zHE1)dA5HNV)aY*16mepso@Z7(oh8E<*TOf6g>Yda`m*N^eWhPHqb$6DASuHpn;mVm zd4#FF`ez!;t$V+5p=9$Ml&e>dqTd;#e&eAP8C)vewNJJ}@Yuvje;S&E{1zYqHiy%gyYroBM?d7rP^8SKs%+_e44DC2FHwUwHC=26!HP}9}G!6FEH#mg(id9c2 z0$;h85ResxJn!OsZY+O@nS?w^5Uvr(JS1Dd`fR5Id#YL!yI6%Uj|%PQLpq+2L{^`f@TodDdmjH9l@M`$ zHkaF+8HmNo>|MPI0fF%ydvT9Dc8Ye}D{i7;{!`FMCF4)y#7a)c>T-!Ulic4waMNOW z&u++)~D226dmH5{YZkk3-r0@;WsYi?%vY zaU)VtJh>F)1sOMBPg8}~ztr4*Jgfd`Tp4tv&r@>7=`*`e@0mS%gcp-fuZ}234}>2l zgv^txP#aPsH0Q)qC7Kt& zX_css3S9`MKpoNbsK6==g=0OLdIK=-`ILW4#q&gHUDQSa?B+__+p51c-n1h7c{=yi z0)Y3yz3+b*-cvS(;QqkBj2@p#trSm^v;VBp@ddxWiuc9Z&r-_RucXvw_U3;8SX!?f z#F4Q4)40!a^oz`Jp-gbK9Gq9-5L;d2R0P(i_%Skvb z0|1j?OY$M!=6M0k_l>IsOY`ad*2Jg7d+49fNRQ-b3+LA`anzVG+{&tYpI^F^vEuym zX6@6}aFb>S$pDy3&NxWe@8T{vEtTyr9o`rwvN~J6seEn*h2+3izuY|7Lu1eB_V?Fj z!M)OLeiI|Hw*Yq6&nCFzMHV=wfYNty3ukIPjnL5 zfAPZP__T@l?5+3bQIQCU(*Li5_V;&=m_vNWt*IZCX0QknZcR_}n)_WpDJMnft=~{2 zCE6Waai2fl%$+c!`seyuYx~NfSnXgg7XwhE1{g(KGQE09ib0uq$hIf)hM0SanAc5!UM{eB$;Z)_1&Ehs zc5$)Q2Kod#6n6&%dNAoDXa-x?FyFm5|@CNrlX_|3sT{-Fkc=!3ui zyU!}zYw)JAONg(V{>h5EJGST?_fz@|{$#g7o>p;0(+fI^CbTh#fnL=Iw*fRzpX3z3 z7x~Szb{(%18dBd*@IJ56{z0!=bLWKXCW^=H=Tx zLNro(RHNZ$AOF0;$5u*F{vHUR0cu_=V{m*rWtxdV+!Pr0+Qpke>q=D?dmqmXL2Kd# zs?;!4wHjzSmi#=5A8mM>cTXF;_DnIXT}-(<>GH4V?q2ifQo?&#kWAbY!!hi}!D?us z5D0U_2pae5!Q*1aO)N)un=ALIViOq@A9(f>vjCPfTnAOfgxj&xlGBZi1@k+8MRIC; zAioFIAEmg*U&t8@>jmHGBGnpU>VwCYphI}V2BoJJwpgy~O|*dM?!$Zv!w`JHE`cXO*pnWt64`H{!aUh$~rY`o1rF9_44=C@$ieFT+4xR1V1 zcw4F0#7XbxTM1TK|B!IMr_O6D;J72KeT4`c`HsD{|E}>(?iNOB{T_J^N-kbWq~Mxu z72D_BZ$0IgMxkoVMej(UNFPx@A*kPT(ah@Y$@g9dG1vqf;Rz4l+FG1ZxUoVn)MO$k zDlD976(rbh-b!El+T!ot$BV@fVGbz>yI+vcVHn3bk7~Q-NAw;;st_3}foRp0cao zVX7D2ZJxtEagG*1V4Br)t=IV3`skV0=j~x-qn!pp@EF5@5PsfN`|=|8kX6C8%RdYW z*Rvv~Vy5~;Mnr9Xi5v(pY!;mIwqC=zT&IAQ^K@Z>I{7PHdW{r$PsSl#A!bCKeX!EP zH81Kyw>K*Sd~C|$gghH>njGGL^;r^Ccj-C%lyqQ4m`@+SOB4^}Ae7=`#z)J5R1AN= z65e18?I&|lqo;f!gsho3un3O1=SQ-j8|rf^3~z!a)PE!XgjT3SE?mXl(xFr)&6Cpd z=6IV697ORsBmb`aHz3ahL8NZgXbs6c9DY}ILRWy>=qfH7tTTNuyenxe-X^%YX2H+i ztHx2hW4v1&uC`S(OMEAw)tCm+ia@vgW_gKKt7pjJ4-A2)tQp7V8;fnGT+IVPBIr-@ zdJ+1gTl{8<#pFb$j0hOa!b7~ActelWCx`EX1+wLHv<`OdNL|25=3y{-i!9A%|fbFn9&7C!Soe zo%Iz=>(F*G32b8wMwuPd z8oC#+qe4gmIg*!@gZdJGX#bUbz@{sR95!6*6m@Ts^n0wvy;xS7NHF%f1+20JY_xOP*F|bu95$`Lm(8p`9)|f5_0hPP*6ZI4$X+mBm_qy!KhNq z@hYLGca8#cMmt6&lik7yBJ^D@AFG-Jis(arNOtx&E%YXFcx)j%uh_;EvH%zy?;F7G z<>SNp%&%?wsb3oK5AB}U7G76jIM;JXAHC&In{wfF7A9~Zp7a$TLIF!5-F+A7oZg(> zAOIiiZKv1eOL+zqgMEunZ%2FFk zQskHi)O=bp*Ne-u&acR6?Mvx*OsJX$4GJ!VaThY~p^^*efrX!jJJdmD`ON6LLo8r& z9tn7VmQ}v)+({6eQKNVKXs5?;@1Je0_qIT+VV6FNqe6R#SRVG1JDFrd-P5pP{MHsp+1EL zi%fH>2`a4L!gMW)BaYe&nUf~o<)Pt2O6G<9;OBAs4Xq}6kkHNDviEJyzYW)QV?_3o z&5S-|{w{Cm$UqEX){T((u6gR*db>9a-L{YpgZAv|wjV$WP4_iD+c|%G@q9Af0C$=O zWC*bxE$Gj2G`2rMNNr!fX)4tOlnR}ozcSm*1OxPkhahY~x(fb^YBB^YHov4&Q-rzs z1?TupgTru|QWH$OxDDfx;l!uHIGk>sUVj#`+1VbV`osrW$TvC1hbY^)x;{9}fGvME&cy+`))1@`B4`EuH3rb&snGw;_6wXG=>$LnbD#gKS6T z3+OrvXLYmWQS{qwa*%_)zL!gtR)Qo=02v+Sk>|S@+WR`T1^^>n18=YK9ISWN^<^A^bVk*WVLaXA54_$9Iw+f+5LwjH!B_ z5EW!9gutut#-mcz!b>X|m$>fF8MYThGo&b7j$tZ1L^amU1DQ*f>lcHXwf_3lWfaGM!`I z=ivSW=#Fs`d^{+GO}u@3N%Bo_&YjV-lrzh%L)V zT<9V~Q;qcx|GZ}i&XbK|U;E>uG2j-K(X4nH|AgBY2&gA)68DFFFzRoA9B z>^bSi$0U~!P?_Tz=|rV04ip{@aicVMCtQR==< zx%(phv;tvIap0pU221zcvU)%CcFRIwdWQy&L(|n$>uUZW>HrvS{KFysgQQksoY#11 zh;Gkfe`1PoLQ}58j}AibpB3IfGwW}ozpJ#nK~X@RFQHGn4RBP4bi%N(G?%kN#0T7r zmY1eV{TLz>fC1u2JXTd-TOj$p&`le(Y_qgMXl4b7xd(1TUI^3V79PYFCTp)WRy0;JeM{BdBFpOo0%=i8jUDrzXI)ocGAyLKunYZagqt> zKJQwRRVxbAXMCzv*@g+|eAHI1q+VIt!1kX6)b-ym`N(gq${J_n@0c(Jj>t-ZEHb+z z2sH%wivk%Ds8i`mmW)t4<}fgFYlSnfpNsTm%Qim!m>4vm7MiSR4qk(tUK&6cX#I6m zzO3z&PPi|#(;95~%oIiG!#i}}^#r?2`XCDet&5ixk2>Jp)BF7Z!T3o)_A$&j`Ss#+ z@+U35x#~|^tU&;g6cmJOgwvY=n3`s_KS}x#AH%| z_3TqSSiIRE@`sCRM-1uj9ms7vmyme0@%}^Jv2D+BK=W)!Y0B2YVc4(m6FUiASt}gk z;{y=~kcfuy_&N{_06rEmI4wKS3bH$ISOz=dI9N$>r%rEN59}s4D!$2uPprazAh~n`=v| z9)W+jA>s7Zwo8qz@0UDZSD?fW{zy!1Nc61u;XD;gsaIMoB)WuT*3LtH#No5RWKMvc zPQXtJp433_Y-Jek^O#3u80rC{2z#Vj2gZg$40iJKq2?q!D<}l0wPU!rq}-rne&=}@^-0IRMo&eat`9n|I@#e--g;Gt_8>z422``e zz%LT+5Ng~i%!|$D!5oGtZ&2?$`Ro9&oY2WJe^AToi@tG_-akLR98BHLFrY7PB`77} zd85FBZUV@O&-=z4@AdnfS8eSKv~3(Hmjx<8FuB7eW+PcK^=Z5gvH~O+-4zhOSitJb zg5@&nI|A8la%LHkA1w-(LIc_*YcOsiTe7^RS%GWo*X|Ek*B5zdR(2W4vw&;`69C`B z@(9nA4qD_#-JS`ElJJ?h@sj|()ag;6%>qj-!mwmrEkU>U4V8n?->?GI&jYW#doxM{ zrT|{p)lar6L%B{fTo_i4iMt6e*r7wux^xgsAu?=!P$XzG$59i%%%i|!`yx_jPZ6+t zKk_LLl2mv&UHF~3J2pKbyBa`bUEZ8PH~xg62gx~O$s`CQOup*_Z-v*WwH)zwnA{%# zD!ryGO?{*ZiX-TCL&X9T0;oxfLIDIpZZhy}e=~jwijaU(V_=Ugdx$5o)W2lj3P0B3 z(&_~N{l4-2CGRvHx;r>6mofpYLU<>Ri}PG}J=NTZZ%K$A=o0qPfVySm76unF=vUqd z_EkTRc7XbZ3GlmD+%HJU$wHQBWF)VHN2mD zifxV|eq%d77H(37^Ceyj%E$jf_dly6@hy)1f7GNc77;w@IV!bF* zzTt)z(}orR{}wL)fR4<%jaiL`vGw=*ho;cPU=$zgDYfcot8DM1^uRSYp<7nl6!~W* z$Rup(tnh3+165uSKI3y39v1|dx3;4g9o`d+V3&Oqtzg$bliPC{a2-h0odd7b88AfO zP9ws$#^bD-Y8YHA$3=Jyh2N%$V=!Fsg2#`j^-184 zN<-p7Oh`c~U7t>gdfsT`ygK^^~W|fwzW*c0n!=9@`>O*sXKUO*?hy#tv z8Iy3t0xeQx=grxWhgAVhhWyBFF>^Tapspn=6%(S{nF0)sj?5R3h3k8TAG~l)FYBl9 z7u{O09@F0^&@*7jNYtF9T0nxih*ynySadK{=+NU0ELfnA{fPbeu=~gRP&214;0vLv zUbx~?&!|f>E!EB}ZKJ+V*HU~6eE~vaqQ5wGJ+o2;$4)P_{N=iHdG)eT|3bO>Vm+Z+ zPOyfWxr@&p3jpoM##Z(;TSH=zH(p&P&@h2C;f;T2eKJ1|+A2>bOWx3GXRAxum;EX& z(ZU~6;H~3RxP*$Ki0hmuvXFTv+Fk9NmgI^EoA1slMCyQvR_>oU<+k`HlIISMnFh5+s=R&@3#K%~PF#gdrAuZ-or!cr zPaFw^DOH$WGxUyVAUB>RzYpkEUxixd&xhpi&=kJxA78!sDZw5t)q$RmE#X6aP> z1ABV$@rsUdM&|mgX-#zh$ksh}YbXEp*Pg^yDnXsq31vU^fJ4Cq0+tp6YvlT#P$Zv$ zOtFzRRu5Lpow5M|vx@&(x6tonIjBVCWQ`xhMK3%V-%#!qU6=PCVi#rGhe#6I`p93ZdvF*3#jeSy5KQ`Wa#$2JlI z_0@MaU}T!7Ts1&`17?y#%D4hJ&$uE%^XEFdhlV<`e@z1McI|cgeiTksBIO1aDNzSt z`^AF8A>$%2pMVtXp7B=xR8-WzpLadXnM)mB0Z-q)d;Z8-P%q% zuon^|5T@0rV?g}FH=0+LlyJ;DahP{#KQCWeVIaFN-x%C{cE|^C3Q5uor5y3auume; z@5KMN|9XGWM1<~izozAdf44I|a5SX;SzncVD)47#1wp3}{gT+ZVh=?DngNjLYx#*a z{_mMhP`ul~?|ASeYXECr*9#@FlsVOswUvlp7_mngasK>6tQT~dP6l|fy8WDa;2!_Z z{Xo8f|8X=-RP5)Qzk$JEx6rYFAj&tcRi087t2eqohJ-qv>0^QL2k8I$=VF+^%Fq#U z8{HR*O_BOxq96rNQA&DaQN^lu-g8nY`tt@}P9JQ~38^IgijYfPWSwe&gAwS(tDXWm zG%k57^`DJSeG3ufnG~u3+JXmw$i60JOUqfkbYSP|fOM0|`wyX6TwTXbcsCHFK3LrZ zTr_+YKK;D#2TyZ6>wx8V(>;4Rc&G!X%roP;Bd29RtZbt7$2iD^zzkLj0fzvR*~~(S z78e#+b7Rbd|}I*sp=vRpN4BEVMY9@qzia4J%+o zicVvcJ(ZvJ-Q6EREcdK0o>EHR^j<9zC83D!LyB-z=_|GCnQplY+SBj$S^z8lS}qvk zGER*5=urXLY7?gT+xzYU6>sYKJ5D76Hnlgf)mAKS6zDbTvd#gomaK5v8|cM;bxGuj zlR`rJkjeue(|WnYB)Us4vL@aa$ri&l&BAEcFOLQt0bun;E~qa-Upal(P@}IPDJejE z_MXS~6Hoj>p9OHnO5phm8&iE7jkO>VA-x(mn|?m;;J`wdYOC?Eq$nb_5%>fYt(OTN zw8VI%33BVj9ZUlyZf9*l1&BN1n(Q+~P%^C1Fuz$FDD5g|gIymem^*kl7Ff4!bc4Zz zShnFxm@DA_Fmfn*3EW=uL|52&yBdo31*Ye_mt%bcowgtKCajdSgaBi0j|1CW-9>$% zC+)`oZT}yOaRm-IU*opMjt^h$&qHCUVI3O&fcQ@wEBVKqE&)M8h|&P=|6>1H1Wj{+NlfZ1O9bAI0}^T$%B z57`=z0`*;IHoV2#z%2vgX!niJO#|`(&H1bziP5ECz);7tyt9AKr70Yf9gm-|urfD- z)$VigPuBV<8vv*OA>RIf=Jk3$^%E%{=>c7!ZYbbLKic*+jt{SPGYJ@&QVDoqJ>c*( z!Ds;&!(=wr?JovPXXM8lLxDe6O@XTE)X_wKT8Gy)qha3%yND}iTdD*qbz2oc;2n@7 z2g=K^>FEKiVSl_}BzRIBSg|;WOWJy`($OFyYU-ZfJ~%7jWCLC?@gGfR|HQrn{d-Mk z`wkz<`{Y}t!nfx?nE%IL{+s{pzm@;;U-EzXAO7LL`!|32Uw+>E-wkhMxzpw$R?@(| zFCuYnE7+2J!YIZEPI|r63-DX(6IBj-?m6M~1B^$K*-ht3MkYb57qxPA|LH}{DhMRL zy1nffZ{$Y4^T&DVXPS9J+SpUJvOXHTPx7n&`1Q4HmzKkrJY-Q^$0lqoI&A4QbW?do zW-&y9rNS^1D|>KE3C!CuZ~DI6_$38t_~zcV8;{IKd3RInf0VWZ((3qX{cj?IlJ7^Z zsDfTT0uCFOPz^PlWj67$7V=3KiGFL|-QFy)%W6J=`2*IN`vai+C3sW?*2R z)-m<7JU}8kD3HOO1$|k?8`vA(Ie!D`hM$km^j>2CaDYhm+GKf9()X!p?&9ZwJ~P+R zu1w(9U=(`JRjQ>f<}sMmPsrDH$p$D{+WZ36Pak@14M9 zukv!#%i%QqjFuk`_d1>1Az>_o3p-o+v@CyGf|5fPf&slmGqkFi@SEo6vpO*7m+=FH z#5Vx0$GK3~y-hlHAQ|XR;Tm{T*3|tN0?eKi@Q^&>!^k8+w>5xbqdr~6aX3%h1Iqy` z7zsb`O0fjc0Ei_QQ=UE(+LF@8aOWQ1oN1vPVN-9Unq}@-k&o&6Eq2Rq^zB1TjSOU_r{%*fn-eZBl7H)umk~Pns=5Gjb!jX@2!-jAS6lR!~sHy>HNdiWT z*SjyQfCEe>x4YKs9@4}DKlyh|;2r$SgcKYfYiY@T(;FYB2>}H*YOq&f7p@!(?5TUh zQJFxqg~b>uoP>`OX5bM9Os<4Ijr(ogR`H?WGX5I0fv9D_v-m`FAQ~WF46!@{x0@jx zb1tB4R1mk{kII^15#|E?XN)F#!GJxs%aho+ArpqmrP-3nV)a5_zR^;hYa21m@a^hM zuU>Kp^Jv%0i-*FktuF){`Tzm?cE-5)K{*jnJh1EBI6qUeA`>2*Dw6`9q@@5s3m&Hz z2U??r@qvD{c5v%gH^Ep5Fe-LO( z`wiCGWYb<#+K$&N1qX5fvr3n&ZW+fIK;JE(z9k8zZ2~GD7BI+Xuc`#}vd|BZB)>;X zG17MRUW*FG?L1{spZzMZtNN~Y!5+2uHS@L#erUX-&Vb5uePuo#FBkdg)6w!SqSr_3 zCJ&u|bnc5bz+V4HyxD)md;R{!JNuhAL;L3^R_~SoxLM0x7f&7=K>!X$9sA*}zJcfj zYP+&@6D|X*ipjlkMQKMk9`o}C%rOg&GoiMPTO_5ucvX)2OtAXl`)6-lgaaoJg&kJG z?`Op~zS=I)(WETSrZtmbpE71H3!u;>6IH(qyr@yt#Z3Xbn^6oH>g80-2N0NuY-y1fOM*R@F4_tM%XIRVs z|8em9e~r+;iG%;d0F}N|1jeW+0IO7^&xx1vgBlp3RPhfiYFqd!)d_HsE}=a7Z!bHs z1~fykpWsLztj`=9pG?De`QAe;xcz9_A^a13nCFnCG%SCqFEH7?028u4OeJpmw~~yq#>vD}j5+5AzOkcw8PUBjh@u z-@XY^tzslxS)S0d512rOL4D){kjyvN(8f_V`F@&kVcwQFe+(h)=f)RUdLE0W->kT~ zxwib%T=ScNlSf(lxt%X6gK5~CqSnn{i(_ARu0nS?NM$Rw!s@;*do|k}Xsztvc17?Br zk*PWq2O`J;YI}JLT)#8UdVos9@AvqAv}E+XvxTfD;V>|N^?i_d{LCt!)A&=`J9eGo zjMefJ;0oJ5cHsDX>^ji9Uu_rk8RmmF+hj0!#jQ|C9N;Wab+l}kS3ON&?AjhIlLJb&&j>MOzkmR5X{=a9dOoNHDf z8om8?aGnyFf|Wr7A`)uG8n-8fWq$IL2bS@tXMp^sP>YquX$KrOQ#gq`zPc|6s&)XN zQ-=guJat;wYjDN9oUiTA3!iVBH~{TXfemM;ZK4htN@G!SN29&mu4(~-20rDtnN>QS zDhlGpYJc;MtO36S%p!+S3bLF2C>;_Z0zj-aB9YJA_hHsMIDl&61B`KZ4+#GQECg-< zQ|}xoT0`|X&B=+C$Cpel^BPq24f@7Zp{~d<$#%c`+9};#BUFyDbYKX5#dhm?_|i@X zuyK|q`^H7B{ai^4EDpfEn@-z2R01txO*(EMxW2?H-GE7qE*;?nla-Fo@PJJ0H7`WNA;H9()=&+$` zT-r|tm>?eeG1x{=Gng<=k_nxGXQ7-&vCdV8;?DU1rb{rDu3~RvV5*ooI2E8;fKvZd zKpg+1Uw|Qj=^7k4XXD>0pACU+f>~A2Lytl`WjqDIz)W^4$Q3=v6$y}6eIK*)45Vla zWQQNUyvEHP)Uh3OAok&=%z`Og9ax#RBhrNx%t^kYvnRh$4l&z{7%<+?85&BQ59*Fe z5Kp$1wUZae;=kPcyQ-QDOe^}WR2Kw6z^LJhV!@WBe< z-^Jpxfc|q09lu)6U*qVPih$f`8c@ZW!0$dw0DQrw-T`PH2Z4B^L2%^TLpVt!NAU~H z$6?V`P1+r=AM{S59yePkUH|P-EZSpE&K(ST0xXsYDvwTG9^5T(gnp!deB!@-;NRbU z;(tb+{8>KFeJ=nU=Y$%y8yKfY%<>E$_XUc~AJ&BaNAPm$IRkpDiE_zi}y93jB zDNh~mO-fw`&^2dM7!yqbBJBEYQ@3!y`K%3WPA4e=Ksgo^#SMYzZa{Q##N z{+3q$&jhh|wqSMuS|t5-5${%uU;jKx|7nnN?GEU)0b_Zb1Gugm9|HiaZG?3>T+P!A zYQ;*P9p>gGBlRodfpQ+NFJQ-dZZ6=a{+6iv{T-(2Uyjzl-MhcLb@c-OKZ3QyWH(kM zpaua+7LBQV{aj}RWKcH#VY4oWdvv*`P#tfpS^IOOdbrpH2LS~@515vzaWG%~v4Mk@ zWEh0AZ(%*ajQ{Kw;~Yn&h|O5D7FYpf6W~BLZXc}TTnhP{TTnyWVrP6lHWG}vq*=Ug z+h*f{q4+Agk7Pi4CB&i%@LUgJS}$x>SA^455we|k-EgowD|?oK%2gsD`rU+m}QL_>tNuG z1=Y_%PoRZHw+iVinp+FGj_|tbSR^~(SWBIsnOGAdRKQpx1}YFB(e>*lhv*I|X4U)O zonhxCY&!)L8#dgyfp|Fh+8A?G3Dfb%zrMRCe}l)?lY_)?_t0M)rEB@@^l!!!&tWPLnig&deN20*j3Dl@ye7q)Ic!^1FsrY%s3V{KE5JSMw#+|YQ|7JXL@$Uc* zw<6rZJOzvHBM2vCbHNV4=z*XOSpLzU4$`ooB!c(%;w((vDab*g+auuU;8TOIaiQd< z0T$r}sJ5B;Y0?8IW4lCAi!j0+vlSjPnDxajxR_Q3#ya7nD3AY(xp(_@ zp6}1R`rIw0R4tl;dBs2xQwfCHx~=y+jb>xVi4!~C#BpLLdf7OR?Zmq{j(71pFBw4M zU`{H8s{07RKQMCuNIeG8e}MxI7*6H{gan)*5D2dIv_q$8ggE5UyS?wVvewG7{rhgN z>vQE}7K^~4ESc^Frvt8~L|p26`Z-s69fCBJU2;ckJhqr~@p*Y-;3+P*Eew8gzJ=V@ zK54pkNvGWtj)b-%`C8b9{o!HI1AZk$P~4Q6l1M=-nVYmm6`7{GDnn`Z3ODfo-W|?e zZ=8T;qC?#~nS42(TA9f5k1tu;lVLQ*z0QKn5>Nc(gPiEziQ$M*$HIk`M8-~wB`L0D znS>8EgK%x$oK`Qj<;($dQx$qNoVAJkl$P!N9+R@fp*@u*4Bavm%(|+xJ~awU3Tzaj zWl3LCDsJD4mHwf9*Tm{U9!L4&(mks+GV|Iw`DKUa)a%@(4`-s>!;A@fo9496#CKPE z1&c0+u#rE79E*+hHeHG9HF_c?vTfNLnae4fL+2H@$$2jojk#``-MHzGg>#=BSY>*h z9T(CgN#>Xmx|8W|o|8nxMJGPnK)7CuTgM8Qz@8ub0E9BFFeSwGG3IN`~ES=GjsFT-~hG z*Ystx;IHU|nVjyRbrfN(Vi)CUZQ)*hU}d7!Tryxb<`VMtW%lCKUKsUy49NLL&kLE< z3J#DiVi+eBsg8sF46z7PR@c};p7PL?ctUs61uYJ#+*`y%%&_e7Ir(XLhgyix$i|87 z$jqB4Yxo`>I`AQzSBqutl4Z*HM#rZdeYqA+0BqbTXTNm-oifi;f0F*Jv~mX63rcTX zI7-he#eDLbn-$pOqBQ(Mc;EVyB0q%0&e2)1e(z4?hkNoZMdm9^D6$G3r{}Z(LYb}Y zT{0g>bF;g1R-__hOWu@;WCM?e+>XSzcn8JKX^nS=#Ap6A9bh6j9se z(t#1SMNJQ;33Cq(lM2BZWft;qEk6tlDFJ?rzyC-adu z+-6<8c$O?+((ul#UpT&L*yOXe+(7)0%YxVw*J~QWcO|!VQZ8n+lk|f!LiqEYE3b!Y zLTcvRv9>H2Cb9L~A(o(~(KB@&B3zU`>k-X&hghjwP#3JB$#oTF?--kw``EeCo305*m z0Jsca)~<>Ht?KcZM&=W>xqcBKM?xE{_eWVtRdHN_r$J$twm`q>vr<4nDL6cw-RdYu z(u4M2uZJL*%8gAwy*q9>;^w!N@Oqj>A2g9wgN+641zXd=+De-{X_dP$e_rA_L$>EAtIPr|j?pJ9Gp}apdWVL&l z{0tNg)G12^Grko@O&4C)B&RQ600=sXe%}P@x$ic&D)p0tp??ZpSO0N?fqzV^wP*Ho zC6%t(&7?#dWZ&)S_pSpck;v#SI(OVMgtd{1RX_8NtlYjGJYB%PWA4UUwquBPEexo8 zs=MZ*cVZQ)++f~-vfuQ%^hSGMl~P8?oJlrI_E-|D<&)Erzoxun)3g%It%l7XRe$$~KltZ<_`Bc# zkN@(Azx=Dr@6$ZR!2KnE|0{p|!$1G4-0w>=1hEn=eOLbYhkxM*;rmbj_y^+$?)%^T ztG_6H|NDRZ!$0`;d-Olb1@((Rla{0x?|P-#T1w+OI+&NP^^3<*L30>1tJ|U|l=p!@ z-IVDR98DjSY{C@Ufc9Rsr?1ATOpN@%`KxVsdM@V2SZ1XPE&b_EAG7o29KMgHzNbp# z5hbD8EaeUL{G6H=<9TZ6uP%Aw`EHtJa5ih!WuWX&X}V$gD=*LR!e7~BPx=gc6p|%K z?y$s=j7g^G5XRbUyaFm#FYetuku#Wgz~cJVV>@yDCXF3g!WRL1{E zSM|6Imy4Gwa7vo{%Z|s65{E65IlXDYNN{^aP~M;P7q{ON{#7SkY*tHHPWG4cCMoOv zvC(aD4S__ndX<>Nz{_b6*p^KlRSV~PvI4)XAmw>xtYCj>e2U>WiE4Au)-j)}FUDeK zTg3&EX7247YyLuA4mG$tNvQ z800Yl=`7rL?S%-b{+;F`vA>PWQ~^d%Hi>d&i2R;H1z?`%hFtfVL^cL!b)b97r`o(Y z_gW#O#c&I`CH8u~iiIIGgPF$VlG1Y^JT$^?-Vnyb8BolAMwiswF+45Td)-@4V$Ad5 zI7^EZO{*PbY5^5)I=&bClyK+E_uWgJ#i|8c zU{D64jfn{Z@L)gO9^Q4tvHKF-yCs_m_DI8-p23A_;{`K6$o0q+I6+{qtX~&P$VmyC zhI%x4NUp{xR*g{~)Gb$;J2L0)>!gtOOYgdc)|cJf#TGIYl5(9x$(seFG)z?8nSKd^ zYT94#+l<@{CoQEQ%N^a5Qjg=N63qJ|{@l6jxldj3tblsf@NBp}T$aaJ3o|dJYd0nC zZhpOM*?xJF%fU_gBIA^tU&riPdTt)LlJ3p89W?#!-ov|{ZAtAiGQ9{5jJ%w-%(Vi< zr8&>yV_HoKk?6#}$*z=#BhyKstWd}eo)Zc#NS?Y0aj}zD>M9lTk}x3@S)SsYYpt_+ z?s(3oyq6!}Ax0cFS-F&rxn}Veri#I>v~0%peA*_qa?(SQ=fy!AH}!RkJubYS&GO(} zb@4QVzN%8%ekP+L-*L+P!LU}wqN5-jx#fgSL6w4blvef-Nbs|obP|a| zOyx!dB;}&eVl0b;Rj|`uC2!b;FXpeiBB3|^0Q#LG-W;lpi(${^Xa|$i61Ocd5fkLf zqWfC1Y=50k=l3Ui<`Pg+6S)*LSkARP>9j9^Ql;Gm%bxV>(uMANfuoAL z$uv83(`ilX3rI&t+ZNNB*O{so)8^`W(yp~+k-ST4i+XyIW)-K`H{H`P6TJz-(Z+({ zfV-(OK1*`Nre?y`3qOL135!;(3~WWpX7C^v`V>Dws9{Kp79NSoj#mO!^{q48LyqU; zk~bW~s(4jY+=u3J#C0gU{jjDT{$X2q# z#ko58#w|^I|F7Hu(4x}`q5}<6pP{CJ#PX%Zxf()I*k&Bm?ZX*pBvidMPMV?-T zB9)X{=Bdh#389Dv=k&IE@p-JFx&n|2-N#t-ViMe9ot@r3pA`26s3giIhJIvAw#)P8 za=o^Ll;r>*dbtMQcVpSaI=sPI4)=PCC!d#c>e-~0F&-SDye&GH{J5@4ZU|E^w^wc} zRSHqD$`M}S>xny>_l=`;J$Br$rfr>Vn#nC{h=( zB1a=8CP2Btw=!fVJU|pV)DPNqWPZBH49SY8*Z7HpggF6l0BjHIV2Q$dCo;vC7_gUQ zVF_VlU=wVO@|WuCybFTo<3NCF*(+=4g3-DQVv@Q*l^xsTJM667-gbetzSsq{`x0i| z=L)odR2$rxiKL4x26fGYdR6mZyHU~pu=DdkYVY!ms>$in+w3~Z5)TLubL{JVD5Hx> z+gNwgQnbqOQ>)D_%>_P=ugQ>BUEAs(hA6O#lQO`$r%;#&vdcH|D!-g{M(L1oq?oAQM-MNrIq^ zurb>@7b)~shP8Z&VWtC;2-Eqi7R?J`c1t?ytBYOr9(RcRa<^Z=hN381=2i6FzxpJc zg!dgg_Cif=KVEiNhS9m1&n{bap;5&z8D$?GN6PjQ1tEJ7#@xcsH}+suxlrW}!&Ev> z|Kj7g9#q&|J&>rcW%giavwa)gVfLEDTRa?vIZR)*pz+9~3FiOsevZyPRN@0X&ap## zoYH@8s1D7M_XY+EK_YE7%U}hARyCb4?R?hdm?-dU){EVuMJMOOI_6*o9l%t>l`C?| zJa(|!Z<&x~X{xupFGh^&@85Wl^+~_jRt2}&eRF*I(QO!GB|jAEIn><|1#85c^k)&& zx#FfNOJZ-Wkj(CD9HC>DpY9AlBi85zZ%;z1X=oZ60-f zyAyuBKi{ctdylpw^fj??W}~Z-L*Js~3bJ#EXvir2&^LD(bcC zNUvnuugPkAK1{G$Zc9EJE$Q{X7^~>YjM)s*(%#n|+GwoGN4RmQk9s||$KzLx9*MGQ zo^ACm@8Dvml~;Z&+xgg@bp6I`FL3yF-q*)A+{J2bMsJp*{w~_BhN;x`>PVPA?x6vD zzOAW3$A?G9lJLDm_Jc5a-5%w!>}cSOXV~r}8U!Aw`>O0CMsaPtpnnbXuk)&vc7p%) zYiKU1xw5X$*)WBqA)=@XPMmB{+4_lHe6~#%!BJ`l0D6@4@Z_&L1oD^=e9TnA#XFw<;TLL=QzO8&YXwk;0E10z1l**rk&B-OTuqnk_pHtZycn?PpVkjiX7YGJa)$=zhJdmFDM16G$e zzPctDOW(tHx5N+AvBf%(liqLX&oXR;p!7RqIch6y3Fa4DFD~zAakGTZNonTFK8^mt zz`4P)5>*U0N0!edq!^8KV5@8FA+Ug!ZXW-phr>3JxN$Q*NbRxML*hfg`r*8fvJXtk zv%Udj(Vtw~ddtLkt{072eMVkj7Zomkt)*fw}VodsNymfW}ZMk3wy zUMZI6?sU({?Za9LPI?7-&AH!YmjS7LwCNu|@9-QBG5Ele#V*`twuD>Ep)#!b)Oruz zL;K7Ed{fNg&7aoUE?7OE=`wE(K&gT`k1z{k)Mo1_+Nif#AgLwlfF0Tr*gqC8?sVy3 z+{?E}@3^+yXe{&h{saWP!7jyFAba<7GR&Hd3(aj*MRI}k3JsB@6J62hZ|<_=g;=Jz0bkk z1osVq(Qx2S$vPkHgLEim3P~(_LSha<+3dm^U(B}DcL|2uwm=suRuFXc+-fnDjJOL5 z;DRFXU6`oOqGtq#Eab4=y9v5FY0hc}E`w-NSInW`h6ohol#K92rWGkf&nHgCjLn(` zCMMIXEsG|PQxPw$8RaT1i=gIo+Y4xkbk^veU#)na&TJr@vhGRG2x>4`ifmZ>v|yk4 zuHAt%m9H!I6ugBF%>nA6EbdG>x{F}Wv;=u9T3+A`s@!eOw0QG;fT(K7C@@LBr_C1^PAdIa<+N(LCwoO!<@&U zNPRZvacFc~GC+{>MYedBpW)w{ctt7IZA6 zas)xp!$!MfF@=(G36J=u-_A{GpKylAn!adW5*I)TxGq|t9xj-7+0&(e%(lhM+pp_n z$>0sNov*Xwv8X@(ZWkXh-YJK3%&0aM#0zDt5N)y$-1okCY1=&9y(kbKXC~itNxY~==?YcI1CG=hkK9xwI2+pbE?#ww#kC$N%IjFBX%N_V$*Op?1i-*$|<#fWGA@nYB+lZz#j_PPaVAPZQ zGyziAb}HsA?AqPjxz%^i6IthNAY0+dVr`V-R>ALYk+pqB`~LX##;y$5V;9dI z|G`REb<=A}@+Xp_p$t4Y{t-zyNnqTqGm88tWya&v_R}ytnpmTE{k9|9)r6_$b2<~< zgO2!Z{*Z1nswXDzhTg0t)|>)7cq5Qf9#)6a32Zc@VGt-DJ|Svexy~n;QlI714*XL)T*FxnMjW zPnBqglmz2lDH?^-V&Lk%jp!j|eG=?qb9`J4q06i%ulqD1UWKczuA#c|jDxi*Ucwez z;ey?nXl}>%w4SPHt`ku^oy%=tH>=9=J>sQhcd;KN+}oU5-@H}wkZ;?vQsyTUA-SePBAZ`eJuSEUg3AK#TZX5RhM zgSEfA;He5DmB{R%?7Y34a(|U5=7O!&ZYRCgS^H;y4^Cw}ytj-l@$F$%VVET+Y5RUi zofoWK;<|M(9dqaqD{~g%Mj)7LpT*c~&BJi}cvg&=n~Wooru&1<&kSCSs#$&Fs)$~m z%B@Al>RLEUcvsQ#-h!K?2haJ?1V>4tO@%yS*)jSv_nu&z+XZgdF=K?bT#~!G1yiU_ zTV=6ZdHcw*ho!~XFID*cayvqQ1~v?xkNQAXIgyQG;e`%XY=MZ0Gyx(7P}- z*o}GZgV&2&u5w^yq&kW;%vpVxMT&u)iDQ#rk3B_<4Fj_HINGXV_9f{{=B>%gd-HKu zFuh$*OK96>;obCFh6UnC_|5V%9+NFm$|Sl)bms(D7Gea^or2x}ShNzHEfe4Ak&taTfy+ zMc@+eLQ-YR`!4lmz?gzI@fZ$HP?D(b(I*{nj`T?lo3Up1S9$#zMW1XZn*M4+m%ps+m5qE7RBD`S*7R065;9@I-J4T4o77D5S9dsTnB>NY1k$ zL5-k{5$Bn}ygGL%Lu#RYe?XK~*w90cMg^K9rd4XYs$e6I+^uyk9$RIZIHUDE@)zHj z7neJ#qt*NR*p6FDc~O^Hw{(>Kwj}$&+nb3B^ns=oc3lWfLoJFh$sOM*HB&$`Y~%G%cBaFxrlp zsO7wP`#8(LRCjZAL~ozJl*%r4C|o^yyY5b1s56JL@&zi*k*F3NL|fZ=4Ya^;HZBTQ zw|8D*UC#ajqmi{AqFovlP1!2fixrm^C9QyQ#l?J*V-Oqm;qEM4GMN$Y&2O3JA(BD8AA=!s@co(FKt2Ppvzc(e@FOJ*Umq8*hG>C#ty?MMN z(XZ5pdGBZn?QW}3Oh>pwxg5m%iL}wc*jsbgV}SLRl*VQhmHbe|IrRwPmbcc$0ZlL7 zt#HP1xq4K29w|5*#n4h;^Ll-l#a6B7zQ0z3jX7)y&q|98k$MahVsog?rf@Mu^ zoEk|vLMWSp=jZr!a`ac9*|w9=t>i_zcs<{rIB_4*$gjbh}|^DMp>h zq2k9XEM&m_o>BOI1u~t2O$XRv$W?v|^xzVeHD`xIy~SqoBEOLqwF!)VTNoyZn7J(w zrQQ*-TbCM|Vb;#CX%MBgXhA=ELj7$eXQIDuCWQjF0KVG6gO@yj^Q@coMeLm~9RftC zmu)ya8N3Sd`eZM({C=4NPX+$@0Tq}b`9N^E?`#0q^E4hg=* zOHk#l-D>;ee0!Sr4?K%nyl7*zA;n{@Tes6Y_w3WP7p*V@q>YuPFz??=zn&)Sh+2TK zxJT>p0|JNISt87p24dfUBN)C%XV)RKW34XNhDu>Om`X|E#T6OAcr8okeu~27NDvW! zg>N|?Sc3Yv>(t}M0UU&CR$izuy5`NU^}OAY58&YI^*j%(FyE@2*T4L=nc&0sO6+rW zg*nC&nXxO@l?swrD3W};#Yu8}n5Rk_Q*iDD9?J#+EqYaC z(WhkXsJ|Qpe21u|8h(dm^H@AI5xSd>_*`mk zZ>9T_@x^?lffO|MoTcU|~J_FQA_gb8GFA?qvt8UrBKY~x3GZD`^H;U5?yQ_7~ z^|v!HxsdS)ve**i>-|zYYmSKAaftPK{s5Ccm|{B5p)^O3M@S=9J!lxqCH>k@%VV>Y zM4~A%9reY|+kLH5HdG3a@@7mnGVpVj9t99^SQd%o7^Q8CYOzBqhkWsyT1@D8u!5&? zeMal`y6lK}W@%)Kl4RXR7I%ZQ(>Ld`y16-}Mwo!#C3J&6VNsSe9LS|MmTmmpL0A-ZRuIE9=xRaL@vYxz01T`E^ zfZ(=)FQuyDI_IC~JBwU6+uL1d0?j|wEOuJ1X8Hp%eQYU+C^)>}*HL0j?j<74^MXi^ z0^jNa(94+p?`M!PhaH#r<~hre`WMD;uuxL5YAPGMW5B02ucyB&jP!Mo<>xfA=#Ju1 z&|3tVL|(n$ej1g6Mf3tO$*ozrR3nG7>{$RpE{?`Baxx>7;x5c7t|hSJ4$jprDybJDc7(6(h|%rW z-RmBG&Pfrbpf$H%;X{Wn4tpu(Y2Sih0vAi=>%rHFW{d_hRk`;@Bv0h}w!Ul_0}_Eb zdFgA7GpwWQ!+aruA~V^Q{Wv^v7eSY|TxJM}1K-f_>Aaw=$-4l^G2Jim3FO4`4lX2t z0954&&fnX;=V6&xFh}4&qh^2E-EUztC4xZFy|{}GY+z1jL>*~Zp$T3A6IsZ?=Bsiy z9KQ}t)<;bGh|^ayjc8ZPoU8c#x<${Eogx8ag$;&;{*)ANr zB_3HGSw?H;*v41?L<^?(HAcm+;Mxpej$)4webSG@(-vpk#wm|QFYIN7HZD}hlQ2}b zJ>oVSr$99fLMCJ?n-_~KUZW~z{~-%;AF3>R5_62?^*k8F;fsiN?VGHwUN<3~oq=O7 zMhYxvz`XVkk=#sBOzyR&e2HYix^1M#N5lQvlrj6EX{UbS%YJ{2zP`%sV7vNFKc<)( zVS@$?nioLKqSu|v)~@t1Lt6we9V(G9pm0;*T|{U55?RPt;Md$@8R4i9<)?~v3E6~; z5JDuap|h6^-O!a-SsCr ze3RRkl3>3nNDC5_T<+cn{OTz|jxH}~FSG^r5n;<)#Smj6t%BvSCAAVfI@R=1JAJsw1D9ky=`R!*=wFSXtW49H}?Q(l^E6b8-+bEPjpra zJ5rmDtI--NC58a{q;GKnzqF)%F0Uh!Zrz#}u5A815Ey7qhRw+8yKCSuh5k`_733aY z+|LF?N)1n)hOpt(`Pn{Mu#-tu!kz>z%Jisl9^8S0sN+oS;{0(Vl|k?fO-1R71y7$S z&vAL@23L9QcPn)2UR!r&D?S!bE~p~3f5X`bigAP$aT4c3%pR|g$hzYZjP(abb#^6e zgPn}WbN2Ss)2f|EO@r;qwt-?jVD6;Wt730)}# z-%26w8mSTx^^IU|(zE=745d@}=)TtTp=`^>2XNt`?l$h%7eDbES8-nA3BZ5Mzf^oe zY3Nv9jazVgxkU;4AJ2PHl2?zX*0hif}O8 z<081kRF7PXeKbGX{86t8LPP-AHYpCA5-POGzPhe1!GW%vLoL|Ls>%85YFa>bDB(_7 za(0y*G9j#$XRO>*SRw7Nry&6F*E)@Mz?)Kn?GK7>&3ioNF?oR?Rdq8S$NDPW$j73R zCKR%-=POXT{KuaFW0T6LZ3oL6lvjY^tJyifHPlwKm1$&Cb+8NwRzWv#4#O z_s@Ttwo&{u`sY7mejfen_>X_}XFu(ruK!v3Xn*tPKmS|N-@+$7fAddXFe{Pb75=r@1+cmMP?e(h(-+fC3_zLMYn`S<_v_rLwd|L?w| zzx4gTBEL1i77BRt)=8WE=D!ks|0(tDcYgTQKl&x++pqud{ST;b^0(jq@ehCN`~OLO z`<4Iu|NM3O+u#2FZ?fP1(qCYH@(aIk`{DMBx8JdEzYRRZ_kWuGrvAWu-?88R^{_1FK4 zo{Pib{(O6zK)`UaY0}Bf z#BRSY4w;tlrs)NkyJc;2+yU^g=*!i{Gqo1ce%gcGR`dQKL$`Lo7TSDzQ2kyjCIJU~ z#a^DUh>DGnl*JhYhE;#NndUXP->q8@(=N4*oWlXJ*ZBgFaUx$OPdZk(D$T2nIq?uwE9OTVE6!3(^@ZP{Nopn`9f^3L_dBg~)g+Rm7u z8ZE07Fa$=f-dc0ouXn^-60Y|JmW)Lc-MRo@68YZg-FS%|O;Mt=RmH|MP3&j=hP6Q5C$2mkTfvRjd5^h~+f%!ezCcd{nI$Ftdz z#4y;7x-Ekk_vvEa$44x@F$E5=EzpR!OcL>{Dk-a3?KW3S7!l{CYu;)Y6k+zNw%@pm z_^wjNqLBJGJfcMgInbWkF^-YcD7W~x_^g#OXF$N@I1aq#&k5{)9@JcK(1Fd(gKhaX zylWg=AYCHqWk&1{&x7eX2n0d!`P)#CoA{vbc(c0Ef^UG2zed;qgG0{t;tAqC>;Czr zFOXBAYwP?*CuLMZ{|&sGQOq|vXpdlP6!8jMG`9E%0^YNo(TR)g!&RU6rNGv&(`%6y zw*fz%t8kL$u^V%OKAYZhL9t7eQ6Tl_Cw4QfPjs~31l3AO+#_OCzF%L%+VLjx zYqmP!Ose*?+%ZHN_JG!$FRS}>8aLRGA-_fxMgDRo;isi%wMO(vr2&`%6cy?wBqg}a z;m9X`an{vLm5IT-z0aG{gMK7Iddh6PSdq_V^BFFV>nU{f>+ztF=V-04NSfalp~ni6 z|GehQvttb|AGBUm2vfRvF*?bE{YbL++fSXj-5XQ*jD?V z1)Qpj`r9?{4*N&2XhphgKAAc6NS)Gz-TB0wG#hTD`|`f;M=1gW`Er^~8~Z90$ijLv z3STND;V!iR>|NZNl?%pC)6{aGbTl;VRrdN?~_%+4XMdn8il{P>*L2=cp@=3${)(OLK0$KnZf+|=9`xV+Qq z{3#ouAc2z!x2uAaqBf3Kh|c7Q}?d`SGc)mnWr?g*P?Ruk|~spu6QoLqPD(HDS zwBqH`_BMc!2vx0pqcZZ7RO11w`~J)~bj!W1*)`_1e_@Y@2EiDg2EnT^EHN03P3PB~ zBUKL@k@FeE+ZjxF1eEG3o@|d50tAL3-aihJyLnr;jDNk)(83;Qy9|S4ah~yywt-3a zcE^+k(Nqo_l`(GV(O%!kcol3X&bm64?+@>EsNXyWx99F-k|`7d^fO2Ck4bJ>!%*YY>pk1oDVcTo- z>H-lg{T`|;tf^~Acb1Y1{{`v3-vl|^#}Lq)qWZvT((z7!Qea0vMx^jKu+(AcE9oB$E-(9n>(4?d}JNf7-UK^n?)jiLi+rB-3P#d{BtB_C*W85`VpoO&=|9w<|#$ z4@EPJIlu8Kxn?iOvNEw(CjokrmxcH5yLnhW)hLJw)z_XNIlWm)WTaIGo;NW-7KGOx) zZBMlKpia~26K?=pZ=cV=u41yiuq3aaz~{@x*gbdm@f6N@D8(w~frmrCHlJ@e04W9| z77Cs_;TR32q$6X(#7tS^HrxzV`kxf1_}#^>#1y@&4c6r!K2M6xpZ}Q8&YLTmKj>E> zK#XeeYJYY^^f;8 zWvZ$MbXu-p=Vofo8aWXn5XN^Jd9Lu57)Q_S{F^G|tpBIy`~tX|n59Am8HT5Q1@5Gz zGACo_^1Hqv+#8zd`7}DXMd|7*x)e2*SmycANA3rf_U?T=WR!X6rE|z`dt?h1I>g9L zeJ-9iQCTHen2Yt+Y&kjE5%{<>yP+;qdj^_r_MHoq-% zJY(;Zk_YXoz_MrLW87v*e!xjoAY571)}Qw3@7v=Z>T?;P1<0S~mLhT_BofSq?Xwas zFGzbp_CB?yuHG@wGdd z$98bL@VN-z!bz^<*P+t@#O zxH&z=$%1%=Y|h8|#5C)%d3=_%8x;tJ<~+;@)$KzgPf(06gwp1W@p(ZYB*nk%Tle(@ zyM6u69uRaQ!!7Pr9hcVH;#Lg3uG_~ZA{f*!^s>++V>@_XMGKh1z`M{ZL+z23aML`^ z9H|^KQKL|%Cbu^&2>gU1jEX4HQwK$M#F7Y!*c-T>N*VyYd?m`&w7fZ6=$}wBEzx$V zyJ2v(MG@50@@DxaNIFb#$y`;`oV~_R;BOel+eEDmXu-yD7eOimkfY z{P^&Z?%rm}yp~z^Cm-DCj&8?~m%8@u`9a&E8a*5u!3uZ#s|RZI0|uDKrl6HO@*Twj zO{c$6gYw6Zty;9{L#b76@S>8h)_hAW^%IzLd%R3=a5?XcdPL8}&!6wv2;BS0wC25H zA0Pp2&65VTL>p1;SZAUwb-Tq2KLXcsx-&;X5E#}OxyH;gL(sVow|vAZEuN6q8@i+B z?FK2=G^JtQF2yL&bw2NWXvlX3w8?LcW|Q=jtzv}Izs=})cuxkt;Xx`wht$gaDYal4 zm}6LgAB{MKTH|V{71zeHS-#-Jy_(*EL5UmgS(jfSd5kv+WgX_{KFCPi1n7=MfO^I| zL>|41e1|F^tS_DX*^G+Dz3nwLI3KBzMu80Xp+!03ee>2XnbKPuj+Zv`RPMmnGZ9K0 zF94M%()%5eS&x;+=X6pbk0v{^OTU0=7*lFrhlTOT-=QR_O;S`4AYZ547Q=8~bB-s-ls3l`>A)vFvA zBt7=jTie@?W<57&>>h*a{c4RxxI;;;wZVAbwJ$p{{f7_T=(5enmlp)R^~QVT_ww#| zcs93(f1VEjCjL0G`E2aP#kmwP)$d3SSV2!4`?)f7xtXDXYWC*t$Ey|f`j6)Xf+%ka za!NJ@F52Tkn0B{u1hMCZEB$&9yf_;bo`2vWXLbj!>s&(Ue!S>Fh3ruqmfeG7kI?$a zEH2pX@zdZ2BcmK8H3AI-mU6|GMgDtFo6qxUzyq$FqfiZTM!c41PQ1K1TSUiR-q{5< zFLvXXKfci6%wvGO+rmgh%Ud0>!92^BQxwzqM!eBWd&hH`^6QyEukD5t^23j>^C=4p z*@rBmz>(9JT$jAL*j(1exxA0}H{}%-=^!)D;qTTx-?Rpv-JDl=x@xx5*O}o>r86YQ zZcdRgJD}Ntjomy^BYE0{vm0Y>W{|VBvpH(G-qER{#p_&yzn*4tg_uU>a|aL#Sd3I} zm5nLf8O)G!l}H~ePB%lG-xmIzWniL9lzsl{h{z3&TuUc-`z`924>+`x0-sIp5Hz09 zZ|2RO`X^;C&xkK9A3$~_mx?GAtNFhOd-tgK@w>kBJbr#NlVp-^0%0b}%!Cq1NGgJ? zy99wmvMkBAY|D4qDHlt!Y|FN6>uRZI*X6L`QVK~Jg0~bJpg@}~mkkttU6vNY;c&PF z&Zg`sTPWqSK!DIf=%xj_E&KNK`~7An{bM)hWS-yiNS0(>zWRPY-}n3Vp7o0cwSpDr zB#72}+w(@#De|+1r#9K{h_j8pYZoJ`IUuzgiTR-ip*FFZ9uCt+FsTDxa6&pa z*|yxX5DbiivW6r?TsGDslt_xFT<4t?n$@`Feu}4qnW`XvdLRxLN_or|Bi8nW~nxTK6Cbyn87%fFK|j89Y{l|odR9IF1odWVhkU= z;cQ~K(M+=pFpZR#Ml-ILM>OG$sem zq!)fYKnDz}I$hO`bOlyuSHYPx0_Yu%iM-6{<5Em`M z*it@hMT~Sr(W$O>l5YQscn?};##rguBp2)0NpoT;nPV+fXEX{_urp$;Af2>Xx7*9sv+tQW)W%-)X5-E|gZPfxanPSv+yzMT}l z(WgA>q$WiDDi&wZ^iWE_Gdr`D?0Pf(;B8h%7J^~nx?I`o$}va$-U zrQD7?ohqaZY4Ul0b++MM?I3rsPYr!o3iDPp+-uDZxlVhi;Ot1G!bu_xUs-KILlX*5 z?|PH8&`0F;30^>w)RhB=WBaQX;@0=ms^jU=h}0>~qioQ!ja~x8Dg`8EQM<=U@3J)l*J}LSf@K># zO^@x(hiEo8WB;%hg3W2@go9&*DYiBmTgPPm&fXcC!p^88&J{@z5!f0wymzEHZJyiL zS*})=SEs|0>PE-~oP@TyS_;|$I`z=q*)any9A422*J6>cv^r@31U6zS1NFC+s}uOs z6Ko8U7NWX0RP^;%2O&JD2ov*@@s*$}sN)<#-@XTmsLfm}#6yeCSY+SQ=kcgc^E*#r z>Lx~K-cGvfyRG^X>FxCxvU$o2q3@v9#csbJDvUK=spQEXHEekA){5@7=iO#5?{s(0 z6Wi;^XKV#0u3Q$Ye!4u#0t2-&#Wl;p&aPwUb{Ztqtg9#0YTg!BXJe9PGY8Z=*dAR_ z8R5;@*lm|F>&iOSO+>GAaprkO(;sXWz`aj53u6I-ph)bbNQ54T0dzBLa410*ipWgi znO*1+-A9<7v_5pLl5d`PR$ReUzw0{wsyjqsOO)?MMU6f3)BO$d>hWUeJ7ca&E)&=k z^M)@7{)xw=-EIhN*@Od+IUj>E;x4NMhEw_xY#jjthDpRW_EDp@Fc^b$3%oX)0>FMX zo3YFJs3W4jUfzld|3`^Me76S(JFC{0oX0vKjv^0X0R$NFA+bn|P z)MMG{TERT;vEcFQRS*)m)^R>Hc&3JlCUDhg#H`r9yL3WKmrTi|srbG|HA|vbXAXO) zp>@FfDH67svdd1UsH2ZYo2o^{_4zt-S4hTMslu7p3459Yfg}+pFxyXATTI46YAaKq z^EkdQ59pxR(n@XiMGb zN4jzM?NdV=ksO)l2oGxv1%Y*E=Qi^1+%;_{#;&G;X2)sX!6uDDXrxk&C_Bc8XJJ9d z#6z!Tl6FZ}F%E5V!nD)d7wuF90>VmAVmigrf{D>}K+0LR+i!d!Bh;M<|86HG31b=z zk5YHMOQhNm_Xx7^k#l09Kiy1L1BTpPq5~=5$7HPqEDL0a;>P>8gC(V zUAQJk$j*}OwY@%i$b(jwb4y^|Tuk*by}4~Snt=%A_D1FPHOEbRj0P98)UmjoQ=Sqz zQQfk6jy6Lzsj^F;QL@RXEw=QA-c=4$5g6@Oe40g4N%l}Tk>}p1zNZG`wgFJVmK$r6 z*|G}$hpcXz`<4Sp*wV8sZbXp(Y|XjoiEV@@uQ5b4*CHKq{T_&C*63gk+G}TuLq~GDNKUw1JrgpH z8A#*Q!$1a*mfCi#sAFowohDsUz&HD%#nOSni+;ljsBnkv~IKJVU7dj(z9+1;~L7%D&_g5 zox=aFf@Va(!&A%|j-o8nvbqNW?Hc%Cd5UbY<8j}s^U3}+;J08=H>-0wkL`R@WpZDT z5I(+Rj4Gqh?EwK)70+N8mFv2s2i7u8y5Pn!O@r`y^EAX@@Qh*Ewyp^z^uva(^YlSr z(S0LHLIYm}ItQ@>z6&F!>&!d?J=^KBnimjk-*+NfyS|LZ=BiW?X`(a2EeusjHScm?4-TTeA{HK>@c$SJE@f}T(75k z5cX{GM$R!5#`9u(9E3{(TADT0S|bc;>aV~VUu8H|h(=|F;j}w)9d$?&Xc2SZl%^-# z4`g*Dr(JryB#eF*)MZfMMlFvV3sKTPnzC%TXJRi-X|-9;f?)^=?+`Vs-eJk8Qa3ZI zF=H%bA2T1|tu;m?4%6G4lo=B%cCIQ7LPc$7H$cW-;|QuB$WO?6Tq=T|iQ9>0g}%Sr zdVOmE{^ zI3jdtX|^kPBUq>E*l@aVP6IMiyL^~a8L=FkJeFPVrtPU!1MI=dgh_KN2;v&fey4LH z*ikL*hCRR98wpWEYrsOKht>5uAJgZ9+wrDVYcleI9`%WnWwMK8PAezXE($Ge#%|m7 z+)k%6Zo8q_HN%wIvc!1W9ZTc<iyAxsRXgBKt8SOFKM`WhT zR#C9ftBnK7g2iE)DOD$%?*d3fdEaUc>e-qk*)HKPbgva4K}AM9z$%IMRbiuto}>$ZuSxxAMptyW5gglQ%l+K?Vj8J85qfJMHMGC7KGmV|r@b ztGac1Hk$9MRP%_K3&uOAEA4=16RMH~*bN4(R6+yi-$!95BH30X=Qb6jCw$Jo(=^i$^zNbh8-T}$mP+pa-rl_ zbaI66xy!-fn_>K2FkummXuvE+3%SK2*Wi@D%8ZA4kdvJ*Ew$IRN~%0KxkI$E(-KPmF40QV5VyK@V&jET`6 z7Yll3`C)k8h}JOP^{icu%c_mluw^h9S7K!DZv6(lNn_O~fFPOgG~=+X<}9KhS{rX1 zvPh)FoE&Ep!bDsw!+jP7WY9JC+a^%kzG0!t+2bS)9ykf%mWKhU%n$U$BzA~JBLJm= zX#cRp^OvwkONV`Z06YFqhE2^WOxHXx!I;OTGHM)}yoKb7qt@hRNA=4brp{0gk6WYiqYHfn5%7|)$K9Fs1 zkPf0KU65!N-MxNI#8Njtuo!dR0gy|mA0WJ$Bc1!CI4NW)#nz(|m1a7poxeWUkBr(w zw=`*(@J$1xIRl9_3yf{O-^EO%I6IHyZMWn}DiAk8WXOg1`@3T=;*vm=H|6N2*w@LE#R zudc#HiYY;%-rElw5qFA*jmc&J5Jit$mUVxZZpT zH)V@UayrM{=}h*)wL*50j$+LQRJpzPmnl+Q(7LfH6a)pq&Cyjp2AIxjO*^3 z@rjGj+dDvo|6rC&Z0$Yh1nP+D&YP1iJ0I}IsWvAgrM`4Hnbx5jPHf}QulvJHEjwWz z*mm4*_X`e_!$M=<;y|hBkE_ngH@VqH+S$#)*<=pPJjtrao*(6%q=(7CC_U_pW8nec zr8nDVxS_j~erp*7z`39zoitSB(Ta`wiQCi}9_i^G1`^9tHV^U{LrqXmHd{KP0QoOjgs@oo%ny30TiG_u3guzX&W8KNlijSlE_?3Cx{Vv}-JR+(OIN zM-`Hcd-18WCJlhG>?LLe))Zq>l5Fh+XjG5cWMXu(`DU`z`}>v<3Dxb&SWYH2b53Ji zJrTOT3J8;~3|(ZcjN8Zp2}hLFn{J298N&PMSW1?%f>iSWcu+1cGG2lvX&#U*Kq6~1 zFWg?6;jIMg?q<-*%UX^)2N~K+q?~{dkBXjvBkG-kk;&j@-T8c>kHuXUZLQ;6fpt0- zU4O@D>97uKD_c#n-M9-Doq`11$v}0bt-ow-*6_(wGht)yx}n;}Y=vr*odFRYPNG6h zymTdY=gv5ewjsEy%$hu4UQny2qhLVoX6|xpVkUTEk^Av%jBnRn_DZ%K63BOiu}(i& zd$u*51OGB0OT8g@VF9nM6vHP=2F{>LZH|6#&gX0RK1vDImg7-`5Z$T`0aP|QjxC~H zmMQ&71klymSGL{RVAMlc{T0lM&J5pm(*Yr9n5NSvPCzyK&O{BCn^UNwotehvB2)&8 zpxGZSS{7`2e)kw4hjxBujkGZMkWFW^5>>`zC~rr-(aK#_r4_^Oy;7yu>M^)0%dMIx zMLF>M7Km7N!~x|-7IIaSm29KYJPiqQB}+p^FslTU6TpWVV|1;}{SF7=F4rIl1|^i4 z?6wP3N%a&nx_ktuht8)*x#@>_GnRrK1*#{hR%b&gnGYgal|~H}Q!3JUL@v?h&TU9C z5agFp5J+0P2F*Ac+MBhsq9oV=tIVj;V%T`P>(Ffw(7SQ3oZ9k{%cbsY)SvfxBnIQ% z%kool83OZk-Z>}`^9gsDbBA$NQ_Nr&?JTp^opepU4MjNS4v>||dAf~CDs;bry8}hV zq&1aW?L?5u>*F*(HYQR8ia^p4JA${USsXbGOQp-VTPtW*X1+4;tiWz`I-bL&MLiqU z+6N>I*k@M+wqxzAYnFV? zGY|7&Y7L=^HDcckh6L~B)N<=}TNGJ~w+YPzi-h;|bUK~$RMcAG77(&#;vBY45r%dx zLrz4_9VZY(C9R$=v}S&jz?H!8DrdrELa#@eT6FTXE`s(DG1Kaf3nCc`#rC8e)>-N6QZy-6V>3)z8`ct${i=t<)uRZ?L!2tsoKF#A~_n@1}p z_9GPW8e)COQt)*-4I?iM@L$I*aEnl89*>96{$x&7M;`<|GjRj8bTFo^KHm!9$ES^= z6U3+WIBsl*^+*i5hAGMINvERTtc^}qtE$e(ewRy~D*PK&exo>j z6m!$1jB0+^5rN@1J<$zTM+9^h4#JVuHFQAQn4Rn3h1o7Gk)s;)KPj7fRO;mG?NmXg zy$|Pee`8hKO@shAWq%2T$QFqKasTMRI@4lij2n2PP~CaJk*Sd6Z(H+&1nlcN9GZQp z9MA{gJC|8)kteV!j|I6`ip*i?5@x$~;yT-SSso`RCYUCXdR}x7V1F%)I+{ipp*<5s zo7@|ZYdICt*;?cSs5-57n*jSww=GJgQBgU8aI{}^l-6|I*DzPl@r<^ShDF~SQc%PX z*uHUqTR>}&h+ICU1gfF2I9*Yi(GCrOBPYa0*62N^)X&u|M~rIjfW{PF+sZJRTcBf= zL%}s~HCElYITv_<7Afs)dMFp+EE~+sqGB8Z_fW zHh@zS%!l@z(d;s{r-9ZmP;w0t(-kSLb9PChgL-;A>_v&y?*d25&i1Ud^%$rbEj2Pp zJPb3UKN%s9ab3Fg`X+>joLms$?1bSO47{DfD95WYFGhRano&dAaCS;NC%p(pT_oU6 zeX6NjVvB}Nb(#2H-`Mzs81rJ7qW;gP$OG;n-QL zeR4@u)!szvA*@B89$Zhu0MlD-)rJN6x#Ny~+=SKnv>hYyYmT{GG9B1yf6|9;HAk_%@{BWi%aP7Vr+O@=#Ki{R?eMT zwN~HEWHxNuZMJ$8{l1lQQEEm9d#uJMYesL(V%=4dveDkEhSfjiD_Wm+s#cc?qwq8& zt6Z%wOv+}8yHe52Fse$5@0@84&Dw_cV9p~?)84*2=9=WOaTIgP-WdG;nw1KH(WrUq ztRi09xjmj(%Y1Ka%oU4;m8k_@4MU!!gVM7hd@UquJvL;^Q&qzgM$37%9xt;2Rdwh= z!66wO&Xl0p8_L`=G6d-Vh-uS30hI&T6Oa6?9r8jLb)+4u0dI~oUf(0NQ4>e)gODZR zdKV8`Rz$a^2Via)8-WYa4h5{-Dckii8M>xHx1!^OOS(eVkQEJ-xUE2FNxK=RfCwS1 z84%Vf0^KHbN2o84=aItfn~6*k@a|SgU^Vr8wl~9gwgt6T-qrJUmv<$B{ zM#-GfzT}PdhTn`h0{DM~(gep=E$aiU*qX4-1EQm%>4+Ni2eUe{No(`wG=&Mq?(zNk zSlf0E0U%5W9~mTPqcszTeGDLdv}nM)%%^S_kYzMW!$B|DPqv&gwd*r6#t3JF*fk^( zg3z{9(qM_+h+u*B;-sxZM;H!9)GpYoamjlaH3>=AiM^V$KaSc_KRNE5)p5P(Ma{m8 zM=_nQ)BSuMr|0PE@v$}D#sm*|_jIce_lNC~9gRi!9O1z1mWQ6xAc7*TbHeEa7!no3 zaJpqJoQhF+WHW*YJofC>$tbf;cnCv8p{dI9QvaZ6)O{}u?N&NOl!wE z3uy7WIz_CH2p-%`28}}h)YJq^B?t5KoKB-0}RJ!azZ8k@K^QfG*NkbtYL{cG?zEK&vgejMxt#Cx zW1B!>S^^?J^<3Z+i2O9tIxAaR&|(Vo8z0p9@|13>qu_WbYR7a?w32Sa5yfzCwwm%e zN{U&3a?+Y}!^Nyexa`vX_Wp?JBi9(Ylt0Dvbx!rw*l+h(A@*m-X-bWo>ZV(D_}$i; zck`mhLvC%4s(4k0jlq0QH>r+R=5Yb3ou*zef!a+;YfmHldrjw&%~7 z87R{UH7Au6dd9;Bz8{Jmwp_kkP6Da7fc)|h4nog}jd@8^=+5L;Bk7Z4mSB=$41{dF zsd}}sl97jTeVr_v1!E278OzQ}rEhNtFyvG8 zhD&xE3T;M~>#dZAwqbeDyCRF-=hFw+%Fo;?sj?3Kz( znPs3N&XowNSYLb&{jwNjS|Reqjs()k-asJ-UjUM zgegGFJS2T-+&mz1X*1|{2Mtv75QL_5^#RCU^!w#Q;!>9zul-t%r> z>TB+RISzU&%sPNMorVm& z9LtKkqr7-uTRX{|S*7c%+&0fMTx=7$~*(k-N)v~Uer7J(Ai&Xswty)Q*OZ}?r3Kf zymA=!>YmdYI;V{a3rG?zT1|o0(V}yc6yp*eqR9rFxM(&~?!1@8DZi;!tMxsYY<$?= zOO)RY569RTgj04#>gx@L%iFfF=5d8vZcm90%K^SvRy9lz{_RWC9;z( z>qZx2DjIfj%Li7*a4=z@*;#8t?5jPzi}s_1VBx!ZtH5snX{^JQbR~R$5#(W zZ;AfiRi%7d^l4W{RBpKC%3K3fzsiGPU8NSBH6Y+(i^ejd06XB~gZXS9t!i|&TMUi1 zJy>tXNGH0p@;;yy^au6PT1}~D3!3`QS=j6k+O&SBh9Ap!YeIHc-HCIj@o>-k~wrF`UyF`yZAc5*A5upp#Z`X!VL^Buj>V~?C(niR_yz_zJ z0EI?NSU2;m0g-B9O#8bZs z`vUu~yQMfB7&U_2&>I{8!@*f-uE43Wb7h7=Z>DaD2_YJh?DE}20t|h^Em6}JS;OyY zqNNyfEln_=pZUWjK*h}^83C+j$~~&6TYLXuAfPz%i+BMGqkgDTY+=Yx~GggHToz`P1n zU6UJ-)3d1c6bjd7IK5DzgxX7Sjc9PacsdpHBkDDfg0aZ72u!Ui8}&ta-j$A^nohvT z)*KK*iN-X?wHmIgtDYK8n}ak~3n0$CA+YqAd>I8Zo)i({vqcxm7wf>=z#|MNt3iOX zbUbX7W9*bj9;&vptv@2ecH}$QXcgm`-n(bu`{6`uHJYPDSHK{v z_Lpr_8T5_WitCepa8PvQ+!F!}0SU_N$Zwk(td12ekvF_bjZp!t7l7-UzuUMS29&@BX?p~6%h3e@e>mgk~8xL(_wX+83jY}IU=Qf*%ND{YSZJ^ zH6Y>DWt%ThsYzuQi3PKO@FYHy{UhFx#3AkmzKn9aT?iy!7nuxG+gnmBBXJ4WQh$zk zQZe3Nn^HN4%<})IDgAF+Qr>4aAwA(I^o+MI$d-QVc~Rll%W zFc@7(`eUtjq@D>om#WBTC7>F-wl;fue$ z>l-V(%11xz@|r(;{JG0Vzp?U`n~y)>_`Ldkl_#+p^G%iR!?m&6l?QbB-@XY1AN>1c z9}p|IPhGxhal>35uDyG2g}ZKz`VaS>yL{xml{ZvA>(f_w_I;Jn3oE?ueOTeGpSHsN zPrR=p-(1(YLkE5NH&=fC<+Z24D0(K4pSryNU*4-;zVi9X|4`{Zbvgd_WAf#5zNNCN ze8p1_cKDCK1>0La*xoDu?8jd~3w_1q_5+pn%~#y-eW3F4o61)`{OospYi0iO=O1kR z{SP-TTq;rJgAZ5u<4fg>U;g0-EBw$?m)~AJMqTFrQ|0F?|NL$R<$n10DsQ`~{L)j; zzh?US`#=0`l~>hiIFQ2AW^_|!kZh5OV~&%giu&))ov%fI`0<(Zo&E`R&GD(}25`ye<@ z50^cCX@4h{z5Z_5%Rcpz?cc3|tMlvMS@{b5`z05Ye6*pT`mRdj;VvJot$cUo&G^&x zkS>4kyDQ&T={^16#Ag4n@_*dSFYo{U%Cnc{_f-CRC3yP5mHvtE!F@5m{M`3c){mBV zz8A~?`%ho~L*I)FJ%9T7UjI#4?QP#zx!}h~zOVA}$_F3r;S>J|XXA$+?BV_YxT046 z+1(b{%fI2Q4efh^fRQY@?$2O_^@A{uB-*;2_-3Pn--OH2T zUwO~X?_M`8RsQ3{I^o4rr_SwrH{y^omm(}?(>E^$? z%zvWt_RDY0Zk~AM2js^eA*z1&@~$7OoUxJXJ};mA!OBM~Z+qR{>H60H1$Tw?y6Yox zqg+4CUf%Y@*q?M!KU|5iht z`uAr)Qt92?WY?9y;PUFveC)CNzwu8izjJf*4VRz#vC5a;e9MEa&_52gmT@0XMCBVE zeDGC2{@xc3`0HMG`K=#+;_WZ}(|ca`ycNIszW3vm58qUN>~)vdcsI{oSnT8GC$C$4 z=gp7Z|E`~?q?PNk>v6p5Ctlp#k38JmOa64Xxwl+?^e1s)fBSB0&t87~rz-WEKe{`< zSN@9^KmFU6cYUPti`TX89_=sr>B^IpfBSHYFIny30eS6*&@rt;-CpLO}l zpTVKM^8R=JOywu9fBnjzt-Ryr<(H5BTIK1>SN=5q^NT-=t-a@Umk&Sr%D3GAuRmM) zM>mx(di~wa_eGaq__@k=Jp2tmF2@eG_aA<~Qonrs=PSeOU+#`)_Y0Nn%f9mUcTLTg zUw-=+D)h~}@4xmJD_?z6**@6P_VN=St$g_5Z`U7a4DWy6FI8@?YdqM2!`b?>upzVY(v zU#YwUpS`^1^^bq?{jdL(%FjN2^P|_}`0UI6*D9~C{4kCj$h24f^pCx8d4K470`%)= zpVUjF2F@Y(sl&M}8lz*Hf3T{k_UpSDt#q z_4cp4^$ic&i7)vBeC(;qdw#$2#V@Yd`y)JJfARa3&$+IBZR;MiXO-Hgf9L}r$AkND zjR&n^W%TK5H2$DMV2uY4?%#Rr_6sV@H{6}uSN<9C#G~i+smove0Un|k4)bpBU;l@A z{k`;0AH;8;{G+>be|O!KPgL@ImFHi&?Qegu?Wh0Z#cefnX*zCW#qm#_WP zihA?>byL*kzx?S7r}ml4cmB!~pL1XPWaUp^R{4oH-0|;j{@2UlFLC954BP!XH}AMS zJAavcpa0L5|8np0r(gH@t3J@UeX{buzv01K9P3=);*YNKkACX*6E`=1{@~32`Q@?O z+Ydeb?a4pKA${QA-LVw!Zq1M1-oEA~tKNP4{$saKUyR3YKd27ee=!DuRi#qzk2zO*WLCizxvGG6Y+=7Ja{6$ z?DaUgzj|4}{_vX^iFZ8tCodfCoA1B>x!Y%MEAM>v!QJ&{tnre& z>wf!lZk5W7b9vwA-hTPb=DO2|ufhIPFMIS@IT!NtZr51pBX7oKv_5|CRhJL$AA9S~ z`SRhn+`hBoJbTxo;pbiZ>s*d+zU@9(+qn6*yLIqe^DVb;f3%4Yyyf-LaQ{LSU6oJp0mn^_j~vUvQh=yz27)FSxbQBv8-avX?i$9hdA? zm%s7$+YjIeMc-1dio6$2#Dk~e9hWcq!rKp4_~#xx+RWvX((OAgT@rh!KKJQ+_(}5i z=PLc@E+77{_uh5$?&~hz_WW-?_VUY@e&~t6hQHrEwVzF0pW6O)XOCV751WvGLf^jf ziOS!4xS!K?KYAs7?geb-o9@O2_rLjzZr?qsycbvIItak>mt5dy?!WhaI3IY|<6Ay= zIs8lXB!2FC9Ph-R*57=)c(mk?zxh_Z`8SW29G}1a?CX-}|Jy@)@>_1d@A1n2^YD=V z;_j=z>Zd>d<&SQa|M=q3z54QHUwr#N-v89!zrDY>{OupWJNVIX<`wrpb#w1mD=+`R z-+aV@z4@Cj%U}7D&Aj^3ee51v!PZ{-T`qt67(Vu{H$Hd?{NCgD$V%gl58AQD6Zf9H zdDmt1#JwMRxWdCX@Rxo13g7#(d(UHqyBqagmoIzyy?0($zq^Zm`Q`T>wR;cG|I@Fy z_qvx=zUGY&o~^Hb_1?J8hF!+&41q%Qhi z+SykJ^HrnKs*g0i*tc589USP+coL~3+mA%lRfZ_ zN`HLZj)GQ zyB#e}GlrG+bA5G);JI$B$(UI3lUh^jkrvdYn=8!|vnzJ#jje+7)fJ?i5yc7-W|tOS zf)F>k>!0Bnw}*X@S%-~kzqT6mA%Ji1$A*F8Z)eBM#colSjJYWkec@Y3nhu?KU||P~ zaB%1);F7Ug&^2Sg`G{#Zn?&~TNHw-sN?1P&DFI5afbfAr%XP%lNg)M{o!kMggB1@W z-Z}S<<94_UJQ#Wk&ds&Fs}yZLn6$1qu@!&-*=%9k;StJinh(v6v_84nVXFsyR7p<0 zk*0)HKmkErp=?(cqCz46XG6KR0>abo1QE|EWt{z0XAna}q`ZPF>&+U+o zX32n_?V4+x>6UUmPxTWDH9*Cx5NpIP*|p6a@T`Wx zkmAON;2r?883Dm4qHr54REemT(j2$y$h@-c0+_kO5M&F#ECvhTZMQq9FDtmuO+ZHz zV^Cirih+>ynd* zqy`ioD5%@Ac?46n*nzb-!Y8^&9d*sI9+tD)NRPd5VF${V1a0AMh?{^@fO829<_jbJRF#InsGJc zOeeVI_mtd??5K+3U^*$*0+CG4f;ULR{sGlUK(11xG;*Ab+n|R3uqI%o%Qmnlt=jeR z+O)O#LMHT-Y7g`{igQ@8iv&g*2$`fOZHA5h2}hm@sZ4NfKk2NWfbr zpo37SVJj|teNq$?w`BEmQR9Iootkouw_8Fw?TVeFW%~`%ts4ecTZ9|l)YetGanj7e zC|qH4WV680Suq6%HH8o&ZYwQPdzjgA3_!eAFr@^XinPdP#IUi!0_H#qR|+5+9 ze+S@Po%hvwyCyFD?%oDXQro9m3C4#^svvTb{VOI?EI6inphl6l?i-B2$_PrZ04m9V zE6j{dSAtk9e!RuHz z@YGZf&C%F17Psn&1ktDog1|e2f{;2Lzqo!*MD%<>TBxXs<;>lW>@Z8t9e3U} zM#*xDsfWWRUqM{4&)NyH%{9#q!>#6;wR{)2scNj)g%LU$f%8n^ZMF^Pz=f*4x*`t& zYivNREICV0jc~QytWkw>Py?AXKNLZY0e0h#n!M!>( zlPW*!7%ByXBp&dkp?l7;wq)D0j=n~W5{mjjdq^h=O*FUVkc7OLPNDngwU+rlhE0zq zAX8^k3+m3O+LC!=S)lQ1w(i~^@y<3O7RNC$IosAm5n|*(IRp88NHR#5Y~zjtQ z-CtkxL9^3_I}p79vctEuLD~qW3_zt>I(7A$0+n#Eo^xx&Z>$Yb@U3NDzqUnS99qS6 zf0ZWUG@XiqW>SkGqlVoP@-)tRiz5*s_ITCl<*9!>KwkkW*}PYeso8SvF%xSNmM3V& zm;>dzJeoevp+r2>)l~;huhOdJaeylVHUq)&_;#r|nb5@6w?Zgs`!PLhqFDv*4%b`T z&EUQ_1$mR;YYbXw00DIa-GM17v|2VOwEQ{r+Z=F>&2%7S^;Nx}U+>O5O#*+ndf|gD z&P&0h&^Qn6eK-K;7eReSjpDq`Xja?$(@z4P+yl1_x=m4ee&0>1m8P4{IjM_O{9Z{k9dx0RB~8b+F+nWsc{biP`^)w9h_s|T2o=re>Q&5s;*<^Rv-&Z=>j~DVEZmHt?hKUc zS*n8)hq*8;(oO|o_?(ExPHDtgg6YlnAxDo2GhYl)Rd72!+8l7o9tnpE{PG>e6a&td(eSpxwED#+)&<4u1B?Q??zK}YZ3P7boW`$tv>kn{d%!e z>UU?vb&;+G)2tY*9(~&`kX+YqTR-il(?)-Uf7IyEOb~VR(l3=xRu(y%F6_>+hSVAD zc$(;zdLGT^{C>)nayOI~Z6%w~P-C#uePqZdwtJwL4NyDidS8&X>sdFp_X}1YD*-3% zf`|kvH|Vco-;|fTRvxc`FK9!b2|!A?mS*TRR;y5kiO#A$YR%ju%UcqH%qM}H9jBWe z!VR4>4`-kmj7=jVggsP9GihhE^RRj>-P%yglR}#VD`_UYtFd|xH)8j$V~st`ePusXf%lBx%RBaqSoe2L?% zgFsB7Xd$k6c!U#m;FYtbFQjc7?7+25Re^%&4!70nl|4RSI_9+WCx?cVF%gB=hGohl zpzS6lTyrS}Y?W_zTl2Z-viqjPAOWGx*qHiQH245=3&HL9t>ZL4fK_xA^6 zCT?bsM3G~Hq++-^_){C9&8@+LaM{@P8|HyYddL^fiUPytr2zZnm{xA2So#C zm+~+oJ5cx-EjUQwF;rcw?qk?dBW!fOK|CXNjs1%2##AdxSl~R@ zB4O03NCZ}MuHB?Ubhy`MyU?n+7Bo4r271+2D+n>{f-k6rkztV%)b{($y1va0czYh1 zbh|7>0)PQ3fqXGeYmrGKXr(CvpHPFwh1g75OE(O=9xoop_M-GgLOzH(N^fB_5vC+| z+E#>MJXVm}D!e1RsKij#u!{j%oK{_nSCiW%RR@9X6(-Lk>M5Qgf+4 zb%rGkEL4^qH2s9!EXKyU^?Uv_75!G8g-WK5gur&~18K-TZU>k-J#pKdf#|q7v|;Etd39u#HVd6gtO-_9lCpAY7nRN12?IYZgW)z|eH@0<;1b#}|%?LBv7{f+!oMz5& zK!OcDI84j%l1#>=)iIm#q%$eAf=P}Y z!yNaz5-A1K*l-)Bz?~E99^Ps)p3o&M+>>_ALpcKGR_ZNf$U(%^UmZ@Ul>bvDg&sRVJKdf6s1{99j&{ zi*52}pBRaQx@*z=s<#}$E(9@DD?r3Z;jYCv^@Jwk9+}7qm1QDY6LaVsiEJy_)eR}d zKpqIPIAe4!Q>})Jl=rEF4)my5Ak8S_hr@^s^6~uzWqk*UJ)I+YGtLV;=_GkQAXO1sWh_OQU;;BEPR(3IXllfl5HBRr; z{^K2ElF*`4RV00I>pEeo5xvdXYSSw>>DIa7X|EK{nlz$hI`hdrch{+|jFo(hs90G! zNJ)J-P((6GN%x{DHxg@cJweNy{KOFESnyux$Pbdt0Nql8xLs)nM-<+0lt^a%$q-^} zpD_+ZZ`_s07Y?n_>uQ3y9|^)dq|_TJPF#NzE4^>Amm%UM@j@}8iIYInb=veyjP#=8 zxu{3?l7@aDyVoPzQOM}Z$EZRe`s;M&R*?A${c=~mMct9YU zL|3ZncaBbW#HH-Makrc7n!!m-Laa6ZJ#PPBEEm`zhXi@2`m10t1_+&l~2|=tB5= zE5gq}@)!OPPf2+l-d23BHb0B!CEq-~?<&=yTqk$l7V4Y7KkN53tTgJw8_)K?o~->b ze9O&&Hjrf-MKR)xn6to%v5Oi2$NS=TX!Ne$^DGF4jS?(ALN^ocI7)Veuyim{4H(Co zs**X{x^QhBo+*iZLthV&vl=ggo(#WF1^+-LnIAT&7S`9#)d5AobbU;bFLpmJ*HdV1 z*C<7LABt_dNe4F4aFg3E6&Oava<81uBVH?%UR)35D3WVP`v>o5W$P?=@Pe6saPQkx ztreLx#Mkat#H~}H%+~xidkkBYsobB-Z>@;sI~97w(RvRU2S>#Rph3r6-+KUtC&Y@X z1lj{`fL4ve5`PxJ?55}VxUu4HNB_Owz03^ZTVj0v0$BkTcL`lnNCh%bowFVf=c9qq z?;vBmT$rWvb~|9R8jFl(%~=iKk0?B*2oiJB!v5%a=iZcb8S4-3bWUM_ch|8b+Wm?j z?itU*`>}J47~!q0d}{7?wOrTqGFFyc+!RnY&oW{dTw&&L&;Zm}z$-O|W#^)TTQ8F{ zhs}PMA1h|p-bSENHGZMa|GqL`oT!ODeQVnSs>UMN0{aD$xkQHC7-G__P86;pOdgG~ z99q#ocOly2fOW08eHpv2oAv;Pba0;FR?0d^;!VCB0HbfRWrpz=&+qhdF{!r5b>SX8 zIyJuU;aT0C?iE&!!*a7aKhw|8)zQC?Vo9tZuKivpoG#-~yXLP4s-V71#~saE*BAb- ze31GbzV4=A|Cp94c!r0EMimO3l+)u2$KeWmEDgD(XDlPE+26|hVX(GSmkj)EX4r-3 zE_{1TPKiWUD|Pizj+*DnRhHVYa|;vSw}lY&3DU<9I^;P!oJr>%6%vm&N^$hTzrZ~d`&x&6#5{yKCN0?aJ>wxvelPWvwOkU@mb-Udr z?hO?1GmIMH3ifs-UoSfYR}eq32i47ELy!wVI?9dc-(C#gF;Ca#)^8&EjtAF{hc_B+ zzi$x^7OQFx_-f54TR|+J+iFF9QH<>Ov!N&X`MMxaYKphv3sxMzC+cT?46(c*C(J(v zkISzU3TM>rvi|wu`x(mf*}h%6Unl4qmGobH(8!+)-5c!P<-d>Cx3wl2dy(<$Uq9pb zlP_ds@ioyuza22f=jU&Pi+d}a^Z0d=fm%GXzfQNH00h|mesUom^84#+hubFoK4}eV z<|>o6*}p&Z@1IF==vhMZ_|LKX=O^{~UU9{5&nnXJfGE!Rg#%@mzp)`4GzYyi|2oWn zo&RFH3WC8yrHQp~3O8B;49l)|I!MNL-W)KQA!ng%g6g*JKIfjR8coZM|=CH z_&R>>RJ&=s9Wx;K5xWtS&xSy9YgFq1SeS>!?nUESaIG)qq{G}_1$jX0n}>Oui?vrA z@#v8U+%~fl?8OU85UT1KH=gP&cZ9Vnf_BS?z+(`>?1w8Ne%0D|seRRmCVvkz@&`R< z8X4Sz3oupytr~1gm5LmGUbDCDc0ANta$TvJubMU2v?MMz2v>AB@F zc87b^4~M0>Lak3n`K)eL73(*0y@!r^ zO^WIjUxEBY$n`r<{QgM=HRBGqV84IdEOZ4SYVi^XQIyED($2@#>GP;+SnhYo9y<@D zJPTws0~T#BjTE4eIdp5$m;$@8y=@MO`>Jb%H*3O=UJK2tfbHsb*@2*`gZp{pOg2pq z^A;T2${~|okRCiEFb2ltsOjn{Cy!Ola%485C_T`HNUUi81I{PyuRUr>#oR8vo=k-N zj67q~)6-rmiV#p>O3k{w4>cM|71otkOQFa*<->A2pIv|)a{f&Gr(#c*B=EiFeY(K}vclxi%7 zf=1k1fpDYTt)-5paVH5eNz`}=IU4rl!$88Mg3Iu65HEZC%oit~8G071q)Dh`zi6+h z7uq0n%rKKvlfZl_QDVIsLyZbtLp(j+B>EjB-DP9#jKFcmW*Yaphw)svjs~vfgx(Pi ze{x=7zvpf>$S$_AncUjEH{<@4Kkto6)}(CX2bK`gPmTiaYMLsycCZ2MNJAx zlaT}|oNs>Gy32gLOD34FnpKfOlumpY&xYl4Jo^`a^ z|Lk6Zx(#K0qKCC?q?PXXU{6Y<`QBn(R(4WyMsPC9L}j!MtQS3kehbz);PY@9S@imp zYt)}ewQQ2~UNT;d=4S((`g{ESes#_{I>CO}yfW$Xa@-x0UH8+PP3=4}X51H-pCw1k zD3>|YO3F_UTRv*{M*@Av0^!`RV1!BTMef;P%YH9D(FA>NhDpLr;QT}E44RChLV^D> z*gNPwL^hZMz*I!I79-ami`(wq)eoud@zP^tXOkjKOE^z>AP`871H&?LJAY5M6IRgPX@n+U4j=Y2j75yHY(u9o{~nsqMVo9gIYvd_3@TyAvjX2#X?A5*jSMQ zv2yV>N?xL6kUrf-LrTJqf@eQAQfK?31hpcwTVFQ^Ll9dMgfo(SYk`h5qPimOInb6- zzP%qu_I_-bv|g%LR4B!piG>R+O|+P`$f+YmTMxE}PhU^Q2_V=dKq9wsiuTh*C(uhO zJBYV`${IQ@Ohrog$qE!BHv27kSe5U^xyx_Qv%d-(;KxjG z@&8j~cpdyFD71=(yxjs@j>@6?^5_D4FP9(+#

DV()59+mPmrj*m@^q#w)B#Z$fLyD4MVauJb3y1W^~}-t~PA<-FZmjk*KsD4jCdj*m&KgHCK^4T^$PtY_EiSv1#*iwE;}CMzAtyT?1**s>lJGY3Du^2b7f#A4sI=m9DbWv;)H67 zK4mcQ2~GTVrLtu75) zc3(Rwy=*G&M6yml@B+%dO3h;pJ^^RBK(~s&$EhHHv7$%0ut^16?8`B)_t$q-+B!|BmX}vl;FZM&G~of#VMA47Q;|Tb}*uk0^-p zHYjcX>-+wKe7^V#5v^Q)yevS8E>pSAC(Ohy!#_ zP;qVBfvAD@sw)JB9R_0ihmZd^Igi2_0=Lb_?(Q1Xy_mVxmIk#4xJh#{b}Zh)6-m z$czsFezAjy#u-{TcW54_L{Mj7H*d%C#aW+7KT>svRXxNrW1|w{7+aab*X~jRuQ_~I zT|}#5;`>=5Pmjrh>h08`>k#4ZRjj60hOsUXPhN%_WSxG4kKc`f;B;N0>6Z>_5(Aoh z4GnyZp9|6arI_8Gt{ZIP!Q<1u8uq!(Zrb-pVEBX_wp>Yg<+1&wDWY$_60H1wHq>{VPJO{c zY@FdUifE+(YR&z9fc)`&L<#0v19Wyj-z`*Fex-QV2kHlBz>b1-%J%{37I-f6?v};7fG|Q)(xa3e2Z-jI%-_7f} zE|X^J7zf3^uR&Zzmt|}}t{nu6;&nPcKC~h@-F}RuVKtLC?Y>lciP&cxD7gzC$b9b} zjAml}!~_w#I*CblQyc|q4^&xP*F%it#hsPx_2(qsyIC+7Tl=t02vgq-zTQ0}sZGvH zv@A{jlUC5>U4}fH_^@`Xb)&xTpJNHlqFYqno-O_0QN^8D(_wU33o#YE-V8cAML zsX%$DA$;m|r57G{E1`{)N+dGmqh&Ihc916t$Rl@@E(XEK&Ym~3?z16uK__(d8_gjC zn>G$f%KSd52U>p_mi;LmRO7KcIq;9ugmF9H#Bk|NG)X!LQXj#t(~IM_rQLaP@>&Ss?3vj!K$D`*ae!f@LTAH8Emz zM5Zn%*l#;HX(P^)7$ZW0@YjDI5kh(BuM)t9PFi0agnpkD^^9;-PEQ*fEmkcQbi1$q zn6jUzezl_QJNXn2>{tvITAmdFoq)`Eiz=wdW352Yl}{oP3X&5i7jg58^_SmVn9k~Y z%!o>^eDgJW8RRc{&9J4<+Q=%&v1t4y1}cqYne9r}l^5 zUnnRz!rs=~kM6i9Uu}Hxi@hM6`d~DOU^-RW;u50nz2UYS{ceLe%F7cY$bFsT1=;^XX%gq{{mMgT)b-0HzMXrT$%A=sN{+!>Uy$RB&MM8vE zk`TTRd7D?!?RhcOWlr&fFe&2maHK6}9@PL0u0B9IdY>U#uq>MDcZN;(f%wKn3GnQm zprH5))iH6;1c}{Uc}gj|a1Vt|%`m0kawn%G7+>QIRSdRw6cq^ z*O=^Ab={vtXnuHI>PaYeZFzf>ajKY)>Rw1A$NNnG+DZtL<$4us=NZE`?;0u-bbHjG zbk=US{X! ze##Tl9r57#s)w)5q@TqiXl7U-U-4386%za>2ZXJ8xje;kU7!31Xq z45DI6e+CX7WT4Spkp!rFNb*Exh-hvM6+!az`@moGL!S?eMB@{P!hCs)GifglicIU3 zrP(2p+L&jr_QUN-tv<{5dXvzmm;{J5xi7{bPj1yZ;6?pzr%QwZrF-M(U;`qjJz_Z% zf63MTk8DAnx3ZK7H^FLlk0(i&oigPcTugn12Sh&lh*#n@`3O9tQkNC z|H*LwNl=QyTK{GJwGV6%N%xmzx8qNh{fEH>wE15X&i^rV#M?ssP4N2pSMmB~vHvuT zbj|_j*nh4~-)G0{H=$wvJ_G!s=)<$PeKx-{D8H!M4CPJ7)z?F|?fw~XKL24PGqSSj z412onoCjaHP}Z~>nZSY(S*4EVvThD6pP{T z1c_?(^`%4!AUROzpqtj&DDB62mbAumEYENOMZ)m%(G-b5C6EJo>Em!gQRwPYP;c0% zC@|H_a!JD2usTrtEy&NuNma0dCvZC*dFc}V+7Wf57{i-$Uf_H1e<8{mz?cq=u@ZZV z2iO>~a3>|SkJ$FdlF{M)0eXX8@+B-HAHSg$d3l%N=@9l127wGX@KN zd04o`G4{Z&wXAw?M4<|IKM#(5glp(vluoZRX*OR2kwGGJKZLZMm=Ul=RPR-aJ0rCK zuNj@bz90zdl)GnEp*@KDXSoa%h-Sh!WJI9$g~D61CmmwK^Aq`7KO7)5ldub9!u}0@ z3hc69cpcS2EpIv++8^8<_uPB+nYFO<@u){*koJr7;ESvP=!6Org9k%mDv)p3PHVoL z4TC!TT2emp;ac8V6`4POlgn5kVZ&lGBjfjA=xO%ynL^EVNEkq!3gd%LK7$r%GK${R|FF3M56TlGn6*8IHd^3M6cObbTslZi>&^P_L`%7M* zi2hraZmhrJM}1y(L$ZD<>G#zG*GWxB=6gDI2c`E9=r`!p)qepCB|L45{<8l~GXIa~ z6O1{s3bRc-erebHFQnu*5sO64qB>um=?}q-hx!fRAnh*U`$GHW9RQ2^`CCSl;K0zI zeu@~z9y%%QX3PJ6EbH~5?3e@JyfdBKVeg4t@NO&eOy4`+rHJ~7@ zYE5e$P3E-fYBwFVWp$pBbC3J^3PoG@{TV6Q{vBCa&`7*FU9$V*C99M*koGt8dNsY> zmU+KfWP=LBMU~v1rhRzrP~2IN7esG+E%DXFpBI$2e#WXYl4HVBnf#G=IHf!gJQm}TJ`u`3Cf za2y*XC`~8#Sn*j){RGs`1t5Xebg54tMNhyGtxoVKIDkr0nwL=0r&;+$?S+esu3Loz zxT?Kp$F=cTAOLEeyb6@%q!E>*n>abHvVuem^vJ$=n9!K268w<&CSC+O7k?3t$DZPWCk%6R$W<@jGT1WI6u72Ji2 z5Le}><0jVm0~^{)L4Hift(MEV3@>5b8!n#%`B&e(MD=%N$z+3m8^aYH`_&!Uyh-~g zO7uG~w8uti-dsYoOk+I)exF;oVRVXJA{?A-?~e|wcM4$;!0u{4~xvDP!>tn&hjpCCX$)o6SzkQYhqpQK8;(E)rS@qv}L zPfJOrZPK2_UD?4XAOg{Y0o*3&Rg}@@CTl7*h|%}_9Q1n|-9gt8x3sb|@;h#3xc^yz ztj}+yiWTs#72n>_`Mhvm6dS#9XeN#3&Ra@&lE4FrJ!RG9V)1Oa2puRN_~~Y4#EC1b zBx$d4cdjY(c7}iaxtn{sSj!Jo|5oJ zK6D`wuX;(z>@~KHXD+^O=&9?5>a>8@AZ{*xZi=*Kek=`6d=%(Km~+6o!g3jcMOp}W zH5l$I7*oGa83gU_H4-*!NtAy!8Zy7%2ht+O3ggTHaJ-6Cofo{aKVsBgu9p!m&+p` z+OFB~*^RHkc}w0US}ePH?cH^OA31oEuG1JV!$qGGFE`{a6176jCcnJ%_GLNS=Xm7eTA6O2*WAO3Au#v zO{OdVOgQh@sdmddKBk}gSc$QHxLZntiEXi7j}-AfHiqRh+fJWZu!nDcCwsSso}G4~ zkn{^3%2%{g*awQX*AA(lH{)ZNpQ*@Q@6+oFkzr=V;$nzr4i3`SmYTBnEO=wV*vS#>&l+!UAda@YIj<}_ zza(dkoQeUi5MXMvOQPm?#nj@GoIr4;%rG{)8a+*~H=sJ^fsZvEi4x1O4Cqr6l$W|S zF>6`yUttJ#GgwETobdHbJM|s~H^~sav*V$OaX6$p49;XyFP4Pj`wWWZh{VG1zmE6$ zV<@BGAmyV8ZOLUn#;rWvNIJf-M8%61Ww8`>M#}({z^!>Fx_v_x-kfgExqZ0W)W5iiQv)O=dLwsqZirGnoN>B_$z=tSWCxRp}ZE-QCLk!62eOd{>WZ*=4rTeepAl%Ua}y8FiIop()Y=@0SImzm{xW&T5kzM6*nF+#Rr%(p}8s(bi9`k55i##D*%@U9cNnWYz z_sY5}Yo8iLw}l%jfXVI66DJg{02ExKr_gT6g_g<+D`4iTRMkf$nQMw+DNlsG?%}q|Gg+ zljn!#vL{=vaf4nSWe(8fo6gh#40$NzS{Ub6Wk%EN~3(NhV)A$(fjm* zn|qS9I`SlbS+9k<5DCr7PFJ&_ZZDXPHm7v~(ewQbB5PP)Y(XS0Cj`apE`^yLHb^ti zSy8{({u}-3GlCjg_r17`K~zz{WYK*KGO_!$>7*Nkcialll(*s?bDo-BF5vFaqu@Pw zkc3cF58Hw0k1u4Gtd!$DA??1OW!o=$U;`?}w^wd z3{GU{!4~N15(yOFLcs9Yp1dPU-hK$|vO~wmU&?t^egYx+T%Jb8!W*__c-Q?u#NsbJ zac7ti(-1;r9tp5Ic{TsV7)0cW%;e+dz8m*Bwf)vB7JiFE|1ZPBxj2R>!oEA}!Ti3R z-SrY#%*`ye_X`U61iY=7(dMm)eoMvj1=0Es#Or}NXA zeao=LOOc(rZ|H!05LAF38|BUx8i1U;HsGS5Pp`0ZLT{&fCBjMYen1W$fZWVu<$l@i zvZ}~WujK<2<-9~`_weC3iEPRQhXVeLiEz5bzqa8{;8@=qzRIev&Yy#v%Ad}XeP!^l z%};>D(tu+>+zqHLfTX>04>)ae`72OTDqh1C-i}*ZxVFpVoeK+x9`(##5HK0Bt6XQ+ z#UfEZi}hE-ZOl8b@O)Q_zuG7LwfkM!|3h>j?4N%S9gNPsJCDDu z?SDNP_BABXbpKfPe>bmuB1(Phe*zFo*=+x?cK_0Y|3NA7KR4n30KDYTVs8;W8oW+ii_Lq&%8jgO~&D#3hgMzx4$9(rRc`iXays1gsxucvdp$;o# zVQkz}x7sklp*XU0Pf6B83*sL#3Ld$H>L;JlrGU7Thmd_Pr>LytXT!^@_$I;+wxS@> zW^`KdgY-UXXFis-X4%^V&R5e1{=%;U2W>ND$jafSK7lUErP8OB8T6ED=~!gX|M zkFFOqfANRPcs=wz54tNj-vv3e61z|GhL{hpBS5ROqPIQzoyt6mla#v0&%MaLPmB$z zh#notp}!^ba`#w0L4T}H>E|Y%e4TSIT1ooId3s4!I$rh~6uFf9%$SwSC(64oIEHWg zL68R$%#*7Qi=$t(l6$*e(d|vtnshMB7;b56x)1>2zuR$w$|XeAzbRvZ3>mH?Q;4_r zwy<2vTq94|p!(TvnbvLg%0E{LGodwo905rN9QOn53AJ3`7IrrQ7w?F_DxFrin-9%^A^O)J((%z<(3fc^-raaSCw~H;>^G~`v z%w1On%HQUtO_OB)kO`7s5p4qOSP=9XD%oDupByW%xa-O>>=i5x*HdYbC&qv`o$4VC zmC7VQ$$3kT(_%1}Pl9M@mW(MBo~P5Jp7EhoitzfO>)qBn!Sd-Ks>?S-;&HnE%7Q}L zQK~ZhN`cU7xw7+1yoAK*til{8b7Z~D1!=|XOfHvNW>D_xo?McQr0~&ZKV7k1Vdvuy zZQM;*G>x{^ZMQ|kAUPDmDmJm zwES^T{);QCy?XQb^QWEuO|*dl?R%OZAFvYr$Xxa_Y?u0<@ZIMr=KsIIi9i2K#VFw( z{-+1<#@l%_Eq))he>tKUeUBou@ZfAKDmy)$SK%bi1Bou~&!_d9=65&@#PI>)HENkZ6N% z8p8?mX}QiY@h&#bdU_nNH0Scbtt()q1hjfTbN40#iDPaYme4B&(>BI#h!L^>gYgfJ zfa)?k7f}ZURZaU%v%=f3bGGP=Gr7=`m*tB*<50JKl*%F{4Pj)31HX#EE#Aa0tspYw zDEK)5r@D&rrsO&WMw>rqJ@EAOaBk*=e{=ctShz`@0d%eJ{1ljW|{OS@x`D~{o z$v4vmzD(n>Qeo>gQ3&%RQv8&==Oo$3^nrxZ< zGWwC)o?fT_3k#-&8`t}1HG}76TvmxPTp?L+N?euQ%l+^~OryQ<@4%?6ZOC|Jhs#nK z#YCf^CEb0K%S@CB#mMNxrM7CYR>9Q&R=?^yqE5h+P02}z*gd!oQY?-s z{GN5Csy4AT-<@4=zmhyXbK9^2Gbi*PuHgFb;J}EZM5B^>_dw3p_q8#R{f zh!nD5qYP0Ige&eh=c9lKvwirK4)?zw zZctL#ii-4?5aILNv)Y^CpTP%=N(mYB4V~T3sY}!n;H;fJM_{ff;-JI9r?~_fj}saw?4olQh5^7$blT@ z0F-@)u{NPHLRIfX;;m5W=(G+m*9J z6a~|hg$oN`7B<>}Lc_ZOU~qEt0#Q5l8r8B2ZuRcC%SYDq(DB#&=AuTL}q!hKEVIOv=dx= z^SKiR>on~xg5SN;FnHeITD}xA@maN9d7YMG1l&ydL@ui8tPH# z^EAwPlhl zShT`XK2AUl1D9gMX2!Snj!~i6snKPK_7{th23Bn2`dg<&T$bsPXQW5R?uTo$&r*NU zJrdPKi1)qoVz&n#PdY`Efz z>#$~nV(41#b(ud+<#;p3aGZ*T7MiEtiRBr-&_KDJTbitAPI zZIdIQor^{|il7P>UiYF$@+iNStHlg{0-WFGHh@ETBzps|>w8h?a!*YA#h7IL)e8gQ z*?TF!8CYm?EPjb?j~BvE4YV|qg>(C)-gda2-oehGX&#kX(4rO0=CFz&5OyTah%UKh ziv-j)%)XC~l^OW^lQ=HJ{c~k(-;@PmRfuA&1p@#PtR6CFr5jSfU_mz@tBEP%O>&&) z+Zyp&?^wHYBzgCGCEs~cJebdq6t85CSnCf`LHgI~BI{yfK6_gNUH_aJ+0FKC+ur&B zw=RY&|0d|S#5vu5u8yx5i58L6y4j6tXdziH%)>0Q)6D{^jSUvQ;DHpnB_TKU9h4k?hWfXPOUBkor=&c`4r1 z$EBF(PfdnC+hwkJIfw!AemgxOSdulx7rS2U5J>9ZB0gFV^K$GxnUO7o)XywjazW_a z+lA3XFS97r3R)8$S&93jmZDJ5*|vaHiE55u|5Uh)RQb1R9;k>kjJnwHG91NX3lQWJ zYcBu3Le7j?7xF5w&V3@4O6tOpvQxp~bL%7IQ`PW@yBn1Xc7834VSh{whg8H70;!j{ zPRVW;Zh4!jevmI?d(uV0LBC{hzX^l(t zw6?mj*rR3~3NCo&azd{Aq?{Jq@g>I2D*$J=e>`6lVTT;)qPkmhgSadbh&m0F$S&f5 zKshhc?(V0h%5t9kX28%Nm4Y5yIMb6po z@?3ZdwZ@IZ9djPZqvzFV!-9l1Rljeab7_|er7<#CYOtF777`6;f$N)&ba~-DCq-0q z#G&KKPH*Fv9uLCGqLO_q)U}fut~S$grIWL{l|vKIm6Y+FqokD!m7FEzUj6LC}7HDPpwP- z;XO=or0Gr5Syf!~eD8(L_=V_IzR_cP5v8}shP#KDW4EW5oi0?%42~XwuFLO4pC$i< znLJB~`ff+(U(Dh2PmFHOWU;Om)gQ!U|5MAm{2!vUp9%M`K&#&d+%_9F%}=(~UlG2m z?DOA<$5xO3pmc*pvLtCi`J2nzHQ$ipzkvp1p$dDB5+WY4^bBfh_t_CfU39}M2qPa5 z?es$K)nLLkhhc=5v|;5>qIW_51h}}01u1s(zKUI+Sw+9BcFx-V@py;@w7*Erf$vY# zzhZOlS(7YVHMl*e-x7(32|eopET4X#|18%X$R*!;(3Rqs{C?pXh_%A-Ax8P9mHMcD zw!h?&&n*wW8suGddJdqrE()|`o+|^mR4+=aLZ7+2M1k5Dzg8E+0mYj7h2hmm< ze4-STUhY7p;m^LC)?J(=M63KVsj0{IgXH=P45uO}KsY6z*X%L@1E`n{e2jRt@ll_1 zrVkfCrcpgFd_7NT*ob(6W9V=JC3bWH7Y?5mc^c|p?ncC`HJ-5$HNM#C%P;5m1tP?L z!FCd3eP0dBpIH+f9K>hh5Fm9?zmnNIcs^CD&+nzWJUy!2cMHe%hL^IYOeWW?*4)mCV2% zEi&$-Q~hy8RHVNCV!-uYVMOj2J8Y{@4TY4&K(s z!+FGLeff5KR6r6$K59Wms?`cseZ(`QMutLcZy`K1wWl|KIA1usNg;r@&? zyhV~N+C+@r{GuGBS^G~=!Vk;n0>9ti6)gWcUK4wh4c%|&$G=hT>qGTDiG-rq{HFu@ z^Aoopi4QRKlr`h#RR`2CEar;q`%R1e@M;yUmnrV|57wa9B?3I zdj7Yxg4zF(O8uV#=Klf3D2oU#%g-Mu)^UdTD_YhBkm;Y$@%44}ZxH$B{MC`Dqk~u| z@lW#WFPmBs?zG(2Yph;&90iT9>URoUpCD!zuMI67I za3L~R=~a7#uM#HLnA$Z5!~uQWG(eQrv`B@oS)|%c-0r#0xm}}a zXrY=|M6%tYETh>(Zn1(;JZ+wU5jy{E(T4T@t~EBegub^mQ4lL!Jo z2VN*hK;W{dk;+?Gl6k*xMA)fk+JhY69v!YP*G*+k_o(J3_uKw<&cKqosj#UCE{*%iNVrWDC5H+MPuP@e%t7HUK2h zy)bCmOoku9jiB9shHASF?0fh{N+|mhhdn;!Hu~Am+SDg0n58Na2bQ(j zH?mNuQ#^?4#buxNT^HJHxV&8K=v*D2fM{5^AHrKJnh2i3a#k|b)@LX~7m1acn0Ncg z6bXmSZm1d#%_bzYyC#5tygsvw2l%Pq8e{+`R1Del8N8#~xl+mXa4&dK+V&1wxVv_? zEWd@mV~nw<0cJI!E{6W}x*}Pr*4^`lf4i$LIR;`fa`t7*sIj#2eZ#*4m(@DKj0xLe zo`_Oz#GW*xPy8g~XYo#-2b_DkEs3vAl`m~GZ>QUQk7lYa9$nSHnqfcNUH}9)AObo( z)+bbQDOAM1#QGS z>$}K*Z0K%H#W&?BAEiJ@Hg8F;nU#8R`p#X%@k54K6r#WT!kIM|FDq3_H)n#z?40wN zzO-W=x7zxgjKq?$o3ebl{mY62XNk~m%J>CrY`@X0-*IevD_-xzvhf7mz!n^rUOu>$ zH)-$|37T-5+`c`p3_`CiN_*)Xq^p%a^ZiI`86txZ!E<_Sn{bfSX}G>zr1EO5eZ6@iITIPo0#=jU|JM3!R{7ziD_#Io5_FJ9`6FLVlYINp z{cVwV<~gvl&Op(CAf%F%*)TvH&{eqYTLPe`Fp&!QgpKjKGHr4p(giBNE_|Ey6jxRa zE!P_nnRKJtg9%jx3s1DH$@j*C`x$Kr{VRL7r0m`Ki0wq($qkzL?78@yHR&P|o44YZ z-PFP#(_z1Zh>KD})<1I={UGOKVqjB_-D1cn`jwH*2K=3!J{j`cM}`X?INGNhq`m<38?BH@C2|nYYXsvF2`bi2 z$ng?x(it@9&}M*qU?+uU{(gAg>LSlqf?`Qvg+J>k%xIxcn0>u{kCwv4%fY!_ zOkJn!LAGqxX7L1D+lE5wdFt18fV#q);bq!JgipPib#MR zIj~Q^#a3891l~s0l=o6asoN&rfF-%>xF~jecJSO|HHb#{lJ){ES3RACg0Wz30;|`` zUF@7kuQDht$~9m7I7lwAj9mNmE--x{@hNBX1 z8PqA|d!#cM%y62ZThGZ-!TDPA`*U6JBI}f}>eQjTZIF^plnZaqH1zb&d8i)YJL8c5 z)N@9L3O1=2FO@4VQ3Zrbr#QXz{sURM^71Pjd_-~YF-5xCcM)ei0)d&F3lrN` z5X%@9hz1yYO<9@~vT5%Z#7hhpa^=~{y=;X;nX0$_u*AA`#8uc8Zv;0SRn}_dnoFyHO?=%5!}jYBk2Euv4m;TAc_--MqD}}O(zs?dM(_p zw-%YS%TQc5#jCe6UerYQlsK!(9{|oH^CAL(^S+Qn@DlR(yZjvJK)B#m`8*kRe&4QD zc`VEr89cHyvRA}w@~f>`r1_GyTbjuOb`xB=>K@wN#r8sj&6+j*4*jzYn6m?iI6O8U zst~m0zLB@#1R6!$h}t=DJy}Lz&oa5U<%J1>ctLhU1fLN-Y$%hnWQs$~Mkn}vrP=3; z;@0u7$#78Eo2$Rr5JYq?5#mOY>JA?J)4DOhj>q3n+B@{tyHa^AvM&U?<`cVO_HLi< z3uUMQML3gl$Q`H7MR{Gh(p7yUcWve$x5Bad4{s3mdwgFlTHV?A9U^vuDU45-vzLX0 zGjM28d05L8DO@fH&CFl3zPBF=wUw;HD>kmo{8GLW_>}5ucuOJaYQ)Na^W8<7{mS(% zeKjrrpVqzux~d|N+k1KUK9YOsp$H^-^b(T1Ug+t)kshSHBrhZ{dFkB}`&v*y(6O$f zyK7fq#V0BPHpGI0y#ns8y@2Shipuw!nfpotd-ir@A}Y=xWZbXiuy! zu0dGpb?N2pIlcMj4tri(~?{) zswtuyRn^&x2ZiIywDxvZ*7xQ4R-~rkTtXEB=V$eF6j{7QiS8EMf>1D!ZN^9R=KA{5 zjH*Ikmm|YdU64{b#_Ar?zG5tw6Ci%wHm8St9sgs zQ$2}3Yt2AsLsE5-J2M08x<(IzY_7{LORwzBuZ*j!bSEX{q*h}|uQbVxuF_eZY)f(# zrg{@?#T8ZUxa-5wv#zYohM)@t8Hq{mbv0$!lvjW&V%o}EXlQB&qByo=fY4K2SAk%S z?WXkfjEXW3LJuUmD?55K^UAXN25?{cju7-59Ab?@Ft-8WjS7SmGP}uG3TH&&o zFl2WYx3-w-aUEM_RhiwKYRz-Hd(6dUD>@2V``wuXMnDR z$(`F&=cvxM6?P5eBC1cHC(qS^OLOXN8I9OASJKzg)ty|J>FLib$!W3{mf@nHmWtZK zbQf+uz#>vjqpKv(m)xC&5QjzhkcN}-m9BMI7R>8OvO?mN7MCTrBcrPVo3KpH1^pNi zr=}<2NM=EIQ(t9o61IKzC*dPxL8Sxd@4MQ|lTA5@m*Qw@aawW14{qJ7t*(me>_^Ot ziq1xFMKZqOCS}krH#slGoL|z}V)Ar&3Y!vpvJvvUIj0&`+-&R4v~^mWy5j8Jm8Q}%|Y@HxhYRCjhaMnYMbHsz&waDbsSaYd_nAkCRL;c)7vX?*Z|?;QC;gha9?DLkseyNwc}rReny%j zyR2~UbKkXh|Sbkvj-H|knZ74Cqs^mnYVIn0ILWNS~h#oFzuM|t$brK~XKxT~GT z<@F`~o|aluMX$XX_UouZ{AwKEY|U}>nLFHVr8ZxBW1lCrydoES2l^X2+RAaXvOBk> zyg#+J2b(V7f9TTEOWoz`@(|wF(VpgX*H$G~mvoyEm4%MmDly#g_B58evG2dW(23yA zRo=4V+PJ=gE`*XrxVFs1zMQuF)SUhduQfNd3ipV16ju8(as6PIt+lB)KcmSL*VJup zZftEbR^rn&t}m$U=+7-oOmWqC%@)jo5dmi zGTM;bj&p;l`Ej14e6)t8RsBv2&XGF2x!H~ikHeAPkrY>#w8B%9h@B=a<)+L=ONQN^ ziTQFtnbX;aU9u@ygtR0g?ia3aY{zb=mij_VVL>jIk-F2prFEtn+`UrfE^f|DwG|FD zRP;5iKn$G%2UbA6HR!LqGrOI=Wp$la#8Szw#|MSDwDh=^g8JHax*(iZj9XI@3o4uY z8nB_RG?(t0>dL~lE4KYUtq{@FhMw`KE1#hw{@!L&a!P%x7B^rZJj|jEYe@g)Y7LLg zch-9X9bKI%EoTctU~{dYhY^Z{X9%=KNo%Vk*H&$=EAl3{;`7OXuO!=7TxDs(-49tf zC)?AXj2#GZxt?l7EY8a)%{SJj7So-I9k{y273aw4uX5sx2u?!Li-bF9yohyOU8VR8 zPqi#NxXZPXO4J@&jxN>?cjkUOMv&l!pZPdB2@zR-8OiQHG z)az-^Dk$@{(r+iNZT#|y44kPiqnjkLCDZJsaXip9deFAJ^2~Jwt@Oi4cYkm1KrfE( z;>KBXT5?BqN^g0ajfRhG!J>UdI<`kA_Pc0D+Khi4?5IycDq>xkd#qjErL8pl3D!w7Y)#lJQBl=p!@*Ra z$J38PLA5p;Zqo9l*7s&%AF89ZKO1ZIapi56a(bJtw)YcTKN|-s@_LIZ(|Yq$aY`z$ zyWZ}ouI(^m8fWXN&o3-YN~>%uE2qI=D(MM#%&u3^k0Dj5PBYGRrBk76aA!rCyQ|!s zSk}T1p{&TkPJ*tkEHu>!C`JR8;EM=5O=Zo-HjOrVD=n`5t<6bGFCOSDx8i(l9AFDw zh!E44h;W(ZUV4-cTWtI5JF%Y#>q_R-44f;+qT_G#G2+jZ*H-pytAreprFQGURdO% zfumS3$+X0pvwe(6qeoF}-RtuDja^-}7W#n#H=bZ$Zk?^VzPY;B+}>K=Y{NE*iqf*| z;Au)*Zem}1X6f0!mb7)H;e1HnK;sz~3uR)bE3V)>TL2LpGw2WBAINq@Vkw?&;nZ%- zKveI#?zrxnM&4LuXu{wsqY~}2TiCTeRRd>B{QsX;R+@xS52{e5xd~VJc2(BkX7p-n zmv^AagFP9I#W?$hn`o1&mXQUMS3S5?n33q2XqAXREbmIrEK&dx6@I}3`J+U1( zs>In`E}NrapweDY)lrNO^|(;DenmYl#jy0GI}webuQFvto)1yFGc$Yh5VfcnLE+3+ zTUAwcS7Co&pT$y}pM$OxW71rQ13`^(IZ=0+snpTm)rM=^*@(Fq7tYrg=k}K3UOBJD zh8v6uy9#iJxTT-sT5YYJ-2-uj7`>Q$adoLp<}RCsM#E*HQ6062by{mQw!3`TO4o_+ ze%OxW?e4~H1tr~ih$PgWj>B0FeD94b>@TavjxPsx&?go;@mZK%=Z?L6W$tXW83@pn zo08m$OV;CX{c`ELqE<}4vnmi1A-%e80AXG+Dztf;=~kjnH1Ca#2xePb0yE~gdn-FC z^03{ehW2vJ{k7>i^nu?RIr?0QCAF=dvQn0}o0jLn80g>2obG_B< zvzoC7FtGvmv9^?9JcOcZ%CA7{UQ&aTv3=`m;|A~<6k$kAah9r%9#3v>O*U?-F=fzG zi`eRhtJC{yvvU&d)N~D5=6rhTI0E-(Bzv=Qkhi$28`pPN^w;J#V2ih_w<0Y&uQ|J~ z67i$3qtT14)|CZyXh3WojTIi;gV|hcGFp0k#a&%pDVPm9Ob*w8BO9N>Tgr2r`dfN> zt85kKSbwiVqL1i`^I&xq$&-0H5c>?o$U+dBsm zQRE%Y%<}&3yy|*H6vH)1)i?{*ooU6oejGlk;eLC2ZH}+o*H@O_g!rHhy+u_SHMqUU z=;^~!MRT^TF3Vb-gJ>`~s!>tWZfY^1w?q_CTq6f3%0$J+J+DSAeD&bmM}1393aWKM z3Ya4zse7QyS&8cr%5Xt!cXku)7U98QtC(O;wbu{~Y_@-eg?rCjlY{wO2 zh@W3tmEr7MQG$3ISy@<$rgw$m7{h+)X$iT-%A|o+t**!+T^fwH5-l1&Hb(B z)(S^l9ILaqv8Jxyh&vzLjWqg2RYqA;4OT-sSv0viYKr0&=}k2@L`u%)=LjlrK?WNW z{+EtN>thDOFPS@Q55)j8TX6rAI~{FCwUb^pPMsPd^>F3}CmmAz%r#wQIp{1p5Zn?y ziKR9@Ib%guVo)LV0*C$4+z>S}9FOU%x1#>Z&{|=xCDQ>CJJ8b)DG=T+v@!k&g?>T59Mf6KiWG+c{IxpXj1jskJtx zn5)yN1GhKX``p<$A8w=jh_dsYxP&X)N>5pO>YAIH`kLx+l?B=gUz5$3k&W?xUn)I| z(N|py?W$NkW$|0qrF3;8YAn6Sv&`i*7dIB;=u8fxpyk>+uq&nn$KPD(*p!s)EAIC6 zwYAy#SifeSwH((nr)syPmjzEv*qf6AH>U^Bnc17IXG=UY4L8f%;Xoc=UQ26M8Y%dX za1Al+)=d;{B?XesDqb8zlCGEY#g*rh?N2FF{O|A9H3$)E>^bMuPw_^^lI{OdW~)Ek z9&t|n{Q5W~>B)tAbpIBo#*#zt>*B~=dv(`{lVZuf)4Evpd_**o3-{?1K~JWQlcM~0 z?9~Q#AUJM5|3aR@pFhO@)-sCq!ji(r&C0IEV*ls;v(|%y2r#- zv1HR>osoR=JaVdH$0H`)kUt_uPevn=zd+TctcngkT&14#Sp~RAJ9!EpS+|?6aN+l1*#83HsCl zh|{i#b83h3~zH`-X^ae z(RpYp^XY=4I-96W(J~ld;itNF z6hQbwS0rXkBl|wqEd^oQKXh->WL88A{~1!L@}IiD2;$mlT>2@W>1xD=X{=h4jb8$6 zE!p##u7d*Xlcgf@g28z&ey(!~Yj65Ow?fn9bc`2w-#j zm%2*E2F=EZaUnMdG#2l+J1qVyR&w!ox=5N> zWS8dBCo5sfKp0KjKkKYy+qa;AjO~g#i|pMg$!bXO6B?J}-vzn&XMC>{1>srp=To{V zf{D{u{v*sBPcA*KyHSjv&Ux5Q=q?meLisd<2@wEbLX?F7)cEOP78g#UMgRE+oda}@ zCv{GtAq0t}`%j|b110DhO};&;ixDmul%M;ft`T6zPNSju64HtDr!#|%m=x0PDW>#n z^2dv!o~-&Qd@jYsd=e6+H z0GTL%)vvl)g80t#&6_q&4H)bQm-4$)0DFi0dJ0Xt2EhG7I57y|$=^`H06-gpcSzLl zsHKDHspR6*sKD=#%s(K-J7n7*x+F1X#^!zAxtotj5oGE8`jHf+?S^PlGE7QiK2VP=1q6Tkw?c$;{fn4owOMb29QSXkR%Idu|oV4r;gWJSy~xL zy)~FxtOrewDW-vD>^SnMUdo}rk3TV$Od2lbiH}oN&E%QUk_ynj4VP>hhLCnsgwzlq z`eu;m?-4-|N{HHJ+tcz$a%T(b$5%nHZz3fhM~)^v3R;cdgIN(O%c2SeDC|oJZUNYib>F>aS zSel1N`NvL{E*8X*vlzrk;-4Z}@W#88sZ$|~o*ddKO(e^vN|y+;$-b#lUZCTg9xZJZ z1UnI?!y-pxpvyAio{0jtlcljz2G5Bm_rywbgrY!>Vvt;coN9wKPpD-%qJ0+Gc3PQ3 ziA9q!)1+BKPXMK7niPk8CZV0|pB5mHi;;lF@A~OdmL`el)!L1}1u|F6knYs*>4Q4> z$Fqhl5S|VIY@I2^2V^SzTsML{@<(*_W4&XTSgCVtKZ{G41o zPjb*VI-EH?Pr6e0g%X-aE}bvc37?ZM=S!>bV0zZZ!89Yun0OfNbN{PxQiKr5I=KM$ zMCZh^NKw4BGmtYgL2?MzK+f$6(n;iOObit1IRDW^>8OYi+&;5(sZd4%trI7Qq|Tci zYPyKgD&0(cpJIGuvP!pdr1|9ER;dEr3&m8!G06s-WTHSeDIeX-nfZlwlqlmIO^97u zBy2bf&^U**1VBX&hHIHqcIMF!@1>10QWbfJ(=jH#^LQAH>CFH4nT zh6(jlf@C#$Z4j8ZIz*Q#Es5x#gTkNU?VZEtYyFua`R~Y-281gaHd&Ju6zHZLX-dS+ z9PK8KrnbKwMw~Sd|IU)`75>7Jk7f@>ew-serXk-jLkXy%S*|BXdI9Yqm75IY29e2~ zxze(Tw>b7;4SPr1VC)HgSDtj2C>-VJQwu}riwmXVh#xrmaSi?Op26r7$=8L_!@?gN z`@^CT_NgN2pg3|atH%FaAr*=f=kiIlyI4v?25%MUlX~DU#R1H5{z)a$kAi5L%Ni^b zF_lS;cq1o0kQ$rp?*D1(NHPW@>skwBVIgbxa@TAKW zC7Mhto54PZlr~|M6aZjrE9(XWB>T`YQGg$yc-+VG zEU|wuz`AA(a{xert$?#Swi2^XAoZlKU3W{`` z;NR9JjUOiN)J#(yJ7SR4$NA@UNux#ag?ZGG@!*=q_MOUL3#7=v$mF^Uq(VVlFrQW4pD#q^waw?1x9cLQSy({+d685a$p7+TF^bd=NG>s3 z13EZ>?ri~yzF4{kk9`*hy4EN%_7dsZh;{Rsvv}sSHdO6R53}3obLFYzt~d45{IQox z-ce%Td{Xp*=n%W-leS;QMdb7*xNFCJB3y%|ql0hgCz9pW!$m5i7qoP2B?FGiX#{u}I3GbsQSFZXXl#EXmL;yoKNa)|S-y%%!^aZy}Y?MT%q z`|byoMgG3~F*cwO@VG@{`JhZP=|So5G;h4hE=v2z3hptZp4PhOws| zn*YOxq&VHt9kNz$pK4(NN02Zj(9$ zai>Ok+l~Oeb~GWc*HP zvhW!(?nK}67HQpyi5RjrB}yhGoD4L3M?9@@{_61{XMdz$S|A9c$R*FCl;a*3W(bqW ziQQN%dFTlMjUrDxfgzigJ#LpYMS#)6vRigx$U6#Eg|<`XDWe7zWfb{&mlQudeF3XA zsT6SmU$05ybwK^o+P-0riVyC3y%ffBDlGQVG>u(zPBTR%oOY zmm$gYjMOJqFJShmCa*su{XPipPXV~>y|7$0mzM0>8jAY@H zix(XZlu{J=^I@rR_@;Q~W|()p8SKgZo;Rgqf^Y{%x%%x8O5?q8w)$*;ST?JcsqaZ`!Yds4@Yb+gh8+36lp_cqaO6*43d?55N4CqR z;ooopz9J_+2+B-=cr*FMN{_8r&?g%>eh@5Lt?PgM zg!G+gP2_T&{6QK+-uwaUK8rNoi%vo~t3TtUG*O^nY>3wKxIAJg+XBvZ;;Ik_Ya%=| z@b#tOCyW)-f0AsJ3iG9he`3Bwec%7uPtrsY^VJhSqxkAc#LrTfSg*lt`#A_l-uPKE z2p4lO_83Rz{vwSPFHS_yFXOY>FM*L?6j}d^v{1O2!?MRXGH_Z_hu=lTn&Q2gxPQgc z5_^m%2mf2njnT3?{QGDPg~`WJ24{rPx^b60F2olZZNk0ViElR<)ubU(2$J0ZaqCr4t&@r9}Sa(<+Yt@O#hA}Hk~|H+^!+?=|TwMh4T_apnnG! z&P$=f;XlI*XJ}@4DFg@(DV%T=UN|AZxkBjo;KB({4=tWEf%{PV`kR|w*d;S7G~49L=nAoC;1o+!I1KxhSNw3+vr8o;tiauVP!nS|Ei zNAlN6ay$iR9q90ojCwz2_;rHlG_!d#>wnE41Wd1h3=jCvAUgg6~5Up`EHn3EG90AIQY@yiSdaW5poB7}d{O!<=%ai5tj zJqY)bwm!K)(EG9SfbT-z05e*|T;_avs`wK(m|EQ)T6 zrvvgSvBJXFSV}ESJ+=LopnNOHnoH!2K(Rb`iJUXM+rkXjNqH?GN3&2<_6FfYwwq2i zT`K2^S824b&}iTAc@Xwevi36hgm}A!FWOvc8MJ6~@^aZH>>=(eiTcw&#F24o_v{<|;o26FigavUI-H*Fwq-5@`z zz0n69pg+A?-Vg$4-6C%f14x<^MONM@ZwX7HICF18@dntwcQ&>sZ<33LAPo@c3M5)> zM}*1fh4>kmS;;rcUyHxu;~Z_kN8KXN6NlM&D-(Mg>Kt1gosCtPZ*086ysrVkOcVUS-7Ehj zh`(xPGB-~MRA&%3y(e8RM%o$Hh~yi?vnVcU+9BUT{13`c2<#9O59wPbW8VbOi{Z@ZSD1s%7e+b7Y&Agx{ z!*kgvXXn^Nm&-eQ4a37tE2FdZIXkR+?3sr zXB0_&S}vdftRS9w8U+zI7+}tBc@CpMJ-cZ)%5`ZN!1Pg$P}#2+=7r`I^ab_@Y3A~u zPMH+6GB=_>{TbOIh~*AeyUE@?Av5}q?vdjKab39Ft(>bWdV_@4kReJ@)AM3SCjPC7ebPf zCtr|17yUrRCQWg#W|`6ZVOu|$x*s(Kd918oL7LXEO(5Uxm#-Gz1HFgfKu^6C=DQ!f zB)=qn0mZ4gSsQyoBYNaz5dDJ8ei^MC1)xpQ6)(%P1RXIQ#E@a{Bk1Cez6@H(WgFlE zQ07+vHE{?ONZtI3T#m=_SLDS3$^5CW%Ge`kBqv^nWZ%6O(v*I0AD--Frdm#Eg>bGo zC^rg1KE(;TuD|#Vd8{C=c9QLX=#~rXD8{_dMz_^Y*7W~=NS;Pvn1u=thgqoOuzbF7 z2@_+)j){|p_zo#~Q~nbwFq1OotuVZ#x8&Qf#gApgz8#j4{Ii9IS0M67VJk}Y(xz5C>~qj0u7 z|NBSf8%2EXKlU%oK30>>|BxTl-o(`|CS=b)VO9=+B>o{F^kzCaLu<(LnAhso72Yz#)J{OTg+@8zY)qDs=& zo4%Lt3%zo49fu(`6_b(*-9!Fn3{1UxE*byels!Q~Wa6f6dM4y94+xJlK ze)9e$`b7+d4o6q~IapeOlBv5?KU2U5K?eR7dGs><+uED>)S5x9MXYX3SVWGUfXCl`jsC_^ z8e`IWt=@(tHb)dqi`Yz9Juf8g1d_a7?+GDs@3vg0&mi-glt^N}PM;IX-F)cpQN(|p z{;E(CzLR5gs>R=+PYT1N#O}M{3}WYR z)?Y`7F$Y_?1>#DJNXWr1+oJz$r1G_XS3nbcqjh5B1=3SorV?WNMi!MugrlRM5i?};O4(85~n;umnNB*k+ zIb!5u<~NwNvXe^f-vrSj{A2Ic`vq|_ve0pfGZqitp*?vqa~=2n`psyL+aA=I~7GO!7D!1*eqRr_l!<)ZeU;2&P@|PqCdFT=S>*BRq+QW|m>8iziSN#Pcv94Im4kJw7 zrvE{_M}u9u9k6$4uos;H``~u{9`Pv+_VyiseL{o1_zc*Hzw1wlZ*tfXmxi+6;25O! zF`#QQ%-pGWk&hnJUnYD_*6!3V7QfJdUfCG}`eUd51~j&t9@j4x&LKyh)F+VN9uH+F z`t`4%qd~TbEO-KDn8aX4Toq!ial7RS{R`Nl#UMBC0;G{sdl|n268*kQ{}=3)qA=?E z5QR(%=?VZXe+ob~9O(KG&{|He@aYh_%bwO>A)dd459?;{)-S?tIMCLnt>CF5Vz|4p2~AkWN=W@- zW^|wgek4>7|FduEuM@=i*hD{1abhn$)@m0kxk0Fz{zGr;cU&nhUB(;Snac+8n6+wn zUqmbC(X8m*j%0u_S)@+CH3)YB0hgt;xK?vTV~A-64!5jY zG;A)}kf5ymQ*`xke{^-k`d`JHG)XQPB#A#~RU~#szPyZoUVd+NP^Cqv2oEOA%uZ=W_r-FIUu8m9y=FMakBD~D88uCdrqTw>E%IsdzbSe0f|xm zDt@3zjl^)NKZ#Xd7JtVC6YKV(oW$$7Ynq}Uk=1kL@p}GXkgjIB52q=Y>&2N#+;r2D z$R!8#iKq`^y(7MS|$oFjpt>1?|`bB~n;PfV70UnICSJ z;2hR@jzCVV96p&8Oi+9ycKi~g-=)!A$?1weqB*38qkmkd3=<+APGTkTU=km4JZp#z zl*E00bA!?^io23Fn;w`!4mBzdYrlmgHU#iDDc6Nk=(_PmK4nWVg%2lko0XfxlJbS| zB(_DlC78r0ooG=S&@H&9DJHVM6-K=;iO-cDYE{Yw@sA`nWd0+G%xhQf3%xSj$PT4I z_=DqK+reUiz!)Fc_S_k znPT3>N{=W!M7Cb4tQ7yA%$@g+D^NuqB1C|NqDVWCeJaDehT>D5XY7?VwZ4N8k^hyBMzH!D`6DR<-=C7$PC!}zt(0F6Lc_Wjo?^FrjQ{cw(U zz0yRbq}!+)$<}yvim-|zVUE9E8B0yXnom60PEyx(%4DHsFu+|2>TC+g%HiAVLgg^x zdgYiPUX{XIdUYeZtxa5@zKwuF#wL@xRJe+axdlFZ75Q?b@=ECK&<#oy`F4}CXHe1$ zgWljKo0YU7NqJk8eM6EmZd6tx$@FK%q<$Udr~aciDp8_%EQJUzwMzJkO1BKFzngAW z2JoPA(afZU>V4WP9!#)_w<@;{N&5O$z1joywH)VpS?{fXh>PhjKV0*wU4x z(hi_pte8Zu->Fz7Q-aYLNxr>TnNAA)ibTHKsf-%1Sxq*x%WCw*8!ZM?g2ibz(oje2 z)t+Fr;f6nxgXY>J4VDC($!v2t?H2kDXqU-|1QUg(uXd}+Zg*Ntcy$__2`;n4Vm3K# zcy&b@tO-t=7552Rtn}SrPQY)MWx#A?`N%do%oeND?C`{!OvpCdoi3vdmyY2z62#4j zr(-rc?JV1#;4r$ZcB>1Ln9LB&ZgoO1GtD&v*l0IeO;(!|ua-!IEx}<2fXfKUOjbaf z5H8APa#6YQZUQlz*jV2rW>9AWc$!0%6$m|3i3T(AmZDs@eX>*#KRucy{x-4dn=fwQlVPUDT zp2fyftwy8OYULzsW`}*H%g#XTHk%2?qLiufPR`KiG6$HLO(;JuoP`?SsfppRfITN+ z1h|8fFgvXl8(H{-VjHkqa6LC-a~k4d9lOnFwVRyux6$HsAXFGjH$r}!!)~JKCW{p& zHJc+1@x}xv77MVtU}wKk;x_2de%q}UyWQwuzg;G)%V{;x-*z~K!)RnM4xPBRM8Wy4Pg0P#xk97f1#G%?pVLI9`L4p%h0Om^sG zpg)Zc2NqdC0v>8MJ6F*n!*3(?30%Ag8ZH|g*5NMUr6#wQiK%-RILoI_%tLTlZe%7<%cJlH@ng=~8Y zZS#Gk^=akzh`;f6=`XxpBDe2W6x~+ZA_+(37;<2@@_@Kg%e(Cvbi_Nzz0W9rk9d)z zJ)g?Q#h*0?+o&D>JNGCbi{g8!YzRZ1Rr17Rsr;1AnCFz$$Y7ryh4;v&=g>&Loyy}o z{PrAVJ)FvxR1%-Z&|6MpEv7Dw&wQVH9{D)@%|g(88RB=C2rnouaYrf}xfIafVoVwx zhY2y{;r+^m+8ce)u}JMp%0~R=UEA0LN;I~o7!N4RXg)o#7R23lKzUSqqws9a>6Vu< zNaPT~w8mG$(^yBp_*G>V&1b`_uS2r#z$pp(LHO^lDpiP=!=>yZ=7Siq@;5wq_kQl{ z=-%dqYu1?4X6hz9V}t$12+QN&>n24ds-$ zO*7%^hk*GoHzA2QjF$)4OK^SSKWB(aY7T?IE1Db^ys0?Fw=_Avd=u{Q3i;_x46a{c zs?Y~L9evf?%1SczZRLRYO&TxwvF|8plnM*I!2<96awe;L-cc~WB>O(Z<|^a6;H0E$ zBfxjTNhJ5btHg<7I=AX?XX4L(PiYl!nw#P?izR6I>ibZ2RyqsH!2x2(+V@dJ$wL6x zCvgC>a{J>94u0nc3Jy)JB!@o2uxBN??nA|b2N)DT$B@s8LGta*%oq7HUT}!ZXZBFN_kYPz1SyHeF!*T-N_Ub1X zCF3LrlX@Gu@+d~3+rrHBEEj#E9OAQ$N)XtQFmOznOS+FKR&kpq{YS^nF8yCWRTh%- zK2@#|KjpHk5h0pKNbEmQIX@aKZx}#8-h2OnftK()lUMvbovi+s@=)lN*)rubW%H1v zQJ*UX6q;3sji0lo3N6u8^2F!L)uJhbJ7V(}N+Q19{P_#TAjW5K;NxGwJ?3ZdW@!GG zc$vdqc)S#KbV!9A{?;#*OYqG-gS9W^WWv{IBlsJA(2oG8zgBJyCGlqC&2P{|1d%p= zi!o|B8U3A-E?%C&UHsbbz_uI$(THC3iHRBc5{YPV16st8#P31pUQLxBzgL!$dyXTv z0I+Ri)%4rQb^`I!gL8w*Q`x`IF5RHfGc!>96h1juf5R+{T}k{56XriN!OfI zrXh(-`SD3G4p963Bsz?f8NAtg;YYec{$i;=e^v&Ra74p5T&X}r`96o1hk(=j_A}@Bo#{P0r5CzMRH_N z`ZkkykmBxOoADLdB&&zS=qy?&(d6q*!>au%-+ z3nEknvnx=T<6je@zAuPbS-d~?Dqxq+!Bja!=PR;JQRf6!yu;*x8Q6}geWLiTSewOn ztxOpK`Z2^EMLl4IdQ|Mp;@|lej#OjB3$yrlzP+OW)k$s{sV)HkE0a!gYNR@kf@6=A zN4#Y)IZ2JxZ60QZhoZ5ppd;-0~IRb$ivEialdISvm7C?Bi3AkL<- zFqEd1|LAxTJ8r(_YN=+kA+S2dl-n(yf-p2DmoA&BG$Oe&2!SlxmkXz;=}qA|0h7(2 zp!SK;*=)Oua&ZumksjxlCaQlC#rSMymw5ldbJZdNZJc2W7ms~rXq%$m7N}j9O;x9n z;;Cw}SeVT}I2@b``{wySn5ym<#5J7I8uDU{`e5kIJr6~6*G0;vP?8X@jwg)+7^tkt zX6`=1dv8#Kt(4MHZH$6&YXssmh>W?boFOQ5?%cnl0;X3 zkiZ{!Wu#*jC4)90ZGvWV@3zhl`;R^ zp3A$#M{@aMSNp+W1M(o`z!$~=$p`p38tJBc!Zf|f`H+eb?Jq?}pNUUjT?r7-f^fY%Gn!9|}p7styOP!$u!KeYW z(6G{EG#VEe@ORag^%0%z?ix?EyQXo=sx5QZ4=*9LB=z zTinpv*ID1v9M9BaZn)vlU4Yn|KVcG;FvvQWmrpzXfJzLwgqCJ#+sWU=~aZse4e zrbQOB533LOXPTl=43-c1Q6AFT@+tzKWqN>3iQ9i8V;2 zjMj8C^n1c`88f^uvek|IEv@u)MF*24tc1;2NTU+*!p2{`&=N!0 zba+@nvl1!kYvr{iO!|P?D5vR&#gE%bY7MRKc2uxVS~*(X9UY$9g@#!j(-{Xw+3K4W zc=2^~wx5NZPm>f`181|}|F&hod!d^>z%1b0^gZa6a7o;EjwD(&nFVMzw;BC4aHbnI zG}n51X|q7{879me_ItkeJB*43^}pq&1jA%^P8k4?fCvG)gUilElY^2suhCi~ma6d$ zRe?2Vw!$GafPnaCio9YaCB(qs(ZSzV->R_R|4Xke6)k7@byp{Di%(=8M?! z;Zh7O?Ln;tlPOSl>ROmzvx>u-lt2b=?hLG4gX)BtGTf|T$=c75-_o|!6>LqZaulSd z-c!@KCe%x#PqW6e#Hds%p?vxlO{r{#S`|(152x6bnrW*qF5*9xR%(ww*~f zltVhTWx$@=PpVxQNh>3XKUCBp$d-yf|3CQBe>V&z9%v-bbSv8+y?kBG4Q*YXj&Q-? zI|ICm`kGrcDJji_fW#apEGJL|K3+JC8nP+U|2BKbJ~nP2*j#I)%)W*?%7)n+w9m#e zOo4Eg+&is4J~UuCb|0%Y8q~rtVrTq>{wO)tXRtorM!S<>drF;xbtQ(-z-I3B|NCJU zt<>Soqrq(BO}l~I^RzH&V0vgAHmiNQVU}TKLv!ccrg;X`MMD$qDK&eqnm5SLY_#=1 zvyI)dezm4WNNftY&~SFqE&umJL=WFA8#Y#bA(ww+!I+hPW5G0q@BN;WrCt&76CHC; z@t(}(-%2k1DLBN<^JBbRA&7cJ&7{%9ba{MHXipAi5g1L=v&R~($^L{~74seh=UuJR zjZk&@>ciTbI3tfuy>biGzv6f3~Mnd2D6ByI5T+HstZO)MLeJJuh0oX zc{Es84CyUZ6g#2~{>5&lJ9t5lOBBBxb%bX9wLBgux9YLZ|ehgPa-WYpi3 z$fyKExk+|H!CPVV3v-gr_Hy{-GK2*-b%1en1|rQm;b@AZjPe2YGyA zRX;z!eqKI1Agk9HhNk4}gGulQjtCC4?6AuRQklqk=c!4QDciI0^m*!JQJ>F>a85X9 zjThsx+EDAMYiRb=8mjvY?l7&UB!n^kuTXLVJ!LjXmTCHcF@ke72(H%gQ3+tw3;rlxBabFpX6Km=j4+B=aKK~R68@l__L7@ z%O4fWA4>nm|4M&1iK$Uz2OBoVFt@{pNjW_{fw66uXWp_fh0caC=`0vr2DTnDx;j)j zOrhGtm+6A<;l&rKV2|YU4}S+kzKN!pt>b>^RXvErk1r-mM9F&yJ&XSupE^w^A4XYw zUnkq!)CB`NTv*~2=%3$W@CQ3A&+&>l>Ek`U5P!09Qq7w||NKf{|3m-C*q`nlM*p0^ zR&?)o^v_?D@aJy&=g;`A?7fZtxrzSSO#i%0`R%8Frr@K6cM|=RosB;k*_7S0G-nU~ zbghq|%Ql^ugm%z)Lf*rqzg>-9NoCAu^5rwx@|j%uOs0G$Pd<|+pUIKWWXNay^BM1a z#ur%Nna|kech$#G2CtJJ+SRxLrvJSW5QFKz63sf-Ur!Cg^!Hlvi|L<7&B64SsGXSp x5!4(^{~xJ2nEv-sb1?nst}U)V-7UfOr{CY9KmNpIQpQw&Z=V0J9qLNq{{Z{|HT(bo diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle index 313d5601234b15dfd9231d0576c30f8b5da271f3..141148dec1ad44f47ad5194bf008cd9699181ff7 100644 GIT binary patch literal 184724 zcmd?S37B0+buMaK^E^+MEN|)9R!i>PtZNnuFzdSOucQCkD;u?);ZN)Sg7Wkb=xjD^-lYt{)ZcOz0p5bEZLQY zUFkn>TX|3&?LR4g+9zzQUZKD7R{se*-)z&Tw0qPrS@nHHJX!U zn^+TV)oCv*Hpj|C&7wVqrz`mB&~UYS!F#qcs4!HmHJxgO$uDc5CGy3gVXHpWv@fEm zSDh12jAqek7(}A4udmZyTDDphs@G~Zy?0ku&|=~vPNiwrN31+PJ}+Ok8Vx58Nr#J~ zr`GBx_AmC)uV~ZIhlcAGngXpqW;MV#nryUM9WC2KR>dk$Hu1$Fr$F2-MuXO^p^7z* z=Iyi(o~cgQb-Q2;Pa5}FMzL;>9Ng;h>$ZYuaJ0+TdmrLvF%AKOd-|<=J07`_Dw@4c z^p_W_WA;!D$}!|r&@B1R<@TCZ!yXDVD*!`NuBHNXk0!ssK;roddhRqDExX~O+qu$e zuM+xV6$(Q&yFO->oyrAtg&Y3hBc1kIQPilm>Uo=<4>ejNBhE!)Xl|u7Mp}Nky}o8u z>~dp>C_tuW0}W9d$~PMH#YU@EtJ{sn&;<9wM&}7gH#XU5qT7vG4s_G^2qtfIkJpRCrKcExzGgr7z8Az=$Tq*S%Z zx-(jYw$I+ZbN4Ph5;B#mqt#A(KKNqz|U4BK`3+U53IYVaYuVhuxeiL**Wm<1AX5&B)VGBjd?46I9`S}t2q zgC?|o9%1f`Oaie+Yq$ZrEixw%Z@C}{PKO$3m@#~xNTHWFq0?-64GmXtOmK3qrI!GX06qP^@CjjzOx&}>IC{`P`&Al*{IYR z+}vK`_NbveOukKxx50bD_3a_gegVAM&>8T!At2?#6GFbdxNN~jqRB>`3YukptuP{< zbhy`ry6_M`zf8P^ASd(HX&=%aNBT)|`X*tJTJ1RvyV(*WUktlduK90PM`4uQe(nzn zr2F2pc>=Hsbab)TG-Q7WSuw8A-~lt-UgrMkDIYAJ`y?zKECWC7!_z)7B=Fz_&Cn|W_v_^-D7`0)Ep`u2?8SjI6Ec2hCa&ww;SH}Yatn*ASmcdE!gvvG!kV=govy=L*o7lbz6q(epM_1D-B+g%F2^9$tha>sS77gFR^M0w zHjX-mNgZP@Tl!A%ms{;6Jh}~yRLK+y?`&R!zXKb2j(;<5g+OyL2*RTTp<~WypLjH< zNp=>EdY*oYM+@`Sf^80$t9i7`!V_o%7-lMIh0F$=8`y6PhI3s)pX;+~4!&{@l}@6L z??ldF4~a&HIp~S?YNz;)_WUE&d`tM#qF(X6r(mD)eBSwL{ybb~e!Yhu&OZhWj>Gl} zO(26_Xo3G%bY9jeezZNmNe(sXjr(8$H6IxdeuBYZU>JG|FVZ&ha1|PM=kh~8!hct; zTyA!TiywmbF23~9_VPy4YLf8q)Wopr(Ois?#ww479@_0gXYFyvo-nE-#yS5rV*(DI zan^2FWAGBthUe{kvEn=hmqxeJVkj)Cz}@`j+UMzd<1gL4FT#dt(m9?!jLOw08hA<7}H?+iV* z+vX8F?}#rE;Ei76D5ghaq>v|V_)G1v^+zx) z3-1DsWOEV%7tra*X<(h`4#iy|I{3gc522l*=XQI|5!gC$csj-vdgKb8wsD1Cxq_!LTw!NWLDn6vvNx#WHI=6n zTw#AuK~fK{agSdEUeiz=vyo2mxA5DT-LzoE+l$3`Moc6|9dxV_eRqmgmpa(hhjrKAgKf#hP=EBPcZwoPtF2W}PKb_)% zE_0val_sWEbH}YRY$K0g_)H0=27}faL7>x~h0sgq)UzG(u}VYKc=#Q@7J2Y+J@Hq2 z9%2+^M&MR^HQMv)@Rl%D2|t_DtQM@v@RJ4N$crW!m5}t&BB-l76aPkhWgbO7wi!Wvo5F`M$JIH z^)w+KK`4dHAN=7u!a10^0gTK3yUVH0Se3?G#13d46_nl7tUF^+)oPO_FPP|$z_-C9 zZKzSB@XHmS$YSAN@k|(c<2@&@x-`s~wpSK02`vA55ydtO;gpNFd?WHt7 zBb98>ESdDar~OELhJ*L|5gkFdDOh{@%g%5=YwMoA{r%+OnBe{b;uwv7*U6y{$gwNT zdAtvF+Ar28T$?N)c7S%G=%P&8_FH9{_r0NEgyAUg<`*rwfPp56S_G|%DU07AYXO}I z9g#1MslpIq9TXpVqT|z92={ZyhM!#>4e5q>itZSFx>6vH-i{V-4q?JM8rFxUHRywd z=obf0U`%S00dk8g2#6wD$Wg*mr>N^L=Vvr?b0_?lI-+3lm{p-zQbR=20+pSQ$xhA5 zUzqG%5%re3e-AmM6$*AdAzsJS%x{5JuA>a+7{3h_>>Bi9@OrPj_?F_YdcU`qkx6yq z5)=mrRJFZA6xmOq`8?wsaJ9&LPsE#iatoUXI>9(mfFfXM5j{m5z^)@u6VOL!GWj}LFUqv5lU!RI({Kf>%bLe zd6+VELhrdVK0h2+yw87YnfMeY)Rm?);!u=zUaNAUg2{G3nc3lBQ>XO*ghiY$UT+}| z%g^0mtj)6JDaPK}?zb{O9Yu9J^wed%Zu|35{{`j_p=b0+cdqJ{72CyE`BHF8dy&3) zRkz(R33j{4ERRT=MWYhLcmhS{hNf9F;yBQ}DejMsbt3`8+!E+Hs zLlaJcx@7P!%PPhU1n_7I%dSk&*jz&dizzM+rabhQ3*uO?$k!dQv=GCB%VGOr$q;%% zI3pYEDtg_DnMT2}3x=e%tgm!!5j#owzBmZ~AioD+u zFQ-)!2w86qA#RC*IaJ6?D7>)ZTYL&Hd9PD^$^ZS;#n<@1zqa@~?|1JLG&Rk8eD$;+ z)*rUK@hTL|>qh{T=y%%Zu9|KT4h*Os*Qe?R6a!kP^l5pLQw%a7_@b*O=tVIDn&;y* z&#NvMl4A7(Z?Wd#q7(8cgx+Dq#bS`_Bl!xmMwDUAtU1)G!9%g(9tz*sbJ958)qrWj zB9iboy%K&E&Y}oLVpTzm-sF*bHFy<=T?0fS3l1#`T=t47s_OmT=15z(dwWFOUW3R= zlW3spU-94KGE$-;_KO4RwBEqBX*-tOZ z=XX)J%Mo$8jOhxqMojXekKX`ZZ>312>mvnayck1P3&4q~(uzoI7;YpcO-l}!;Tlog zhCYG`NYJ6+_aT-FOGEEt`C+46TV&V;#L%>p@5rc6__>%t8bI)v-!ZuNeHW4-1oJxM4m0kM-@i{k|t} zdFE2j_Mdy~@h2XC{E7b2USr$ikKc0Jw(SRu{(j?@+l(>Wsx(9haiWN6lmVBI;*c(} zg_jmCM7_gs8iz4^FeZvv>a!4StW6p$tbrxMJmF&(`iw`Na@iQRjT)AxL4yL56$~}_ z#cCQ8mS2--0?Sre)TtPwb!%AECLBdL(G|j|IX3zeg(5|fAjEH0wl(Qf(C32TR16N- zFZ7#>8}cw1Jr9~a^!8!Wvr4m9u+Q9|5$Z!VdLalJ7kXkGG)Am)!|pXmMTi2|Izjq@ zLPWDs#+tz;-c!Ou05Xn9C@b9o|8LQ-2e=&K=j%2Mkz0#&8|YGo1XO}F+HC|{s0Gjl z>87CMj2Jy0qaOUM0xs(n1a4g=+4f}5Hskh0Y4Ld5_MW~S+qZ2OWG@+5GqPQEnucCP z5d$R;nsU2Doux)UZ!}$?yvBU&iJ*m8@?+H&R-9c7xDZ$xJ^VH`gr^NIo_z@TIL)4I zk5{&B4>yTua1f$b`oteSj0uZOQc`P*lJI*}5M_lB7tKUP^dG$!QGodSREafG2-;U` zHH!SD$96vve4naGO-~Q$Kk0oEB=)Y)RU#E)b4!8}r{Z?Xud&kj%6xnT{aiG3gVzbD z=evqb=@`a{02NOwMhW3->a_-(Ppt7;WqwakB!)vxALcXEfq`kbZA!=uVDwpAV6v$B z(8taS>kWGar@( zIY+dhi0SeWt?`ANk2ZTR93ntYf;V=zZ|E}u60Q%yIZFQgG8p^a@tw;TxCeUsg3k4es?yLySA9hf%mh6kXNjNjKU?_72fp2hRgP3b7XsEc1x{n3ollFH}m6zI?gbqEv&r-G|ofw-9co z7#i~1_^tlkeRuZV*)M`{&Aw9OKG-9KMGl^~Uu+JXJZGLfd1C0n^CwSK>-a5bToHPq zXm&!w`H^mS8b~3EAeQY*>yvOp+ai>4!{c61+pAYPM)TAM=pPT6WurFoR4z6tNJjbr z|7D*0NJKA|d*u}W<+Y;HOK;$C!(y+jMHBoh{_~aIbLu&6)=e;XzR@oYJm#iV$5V>H z__OGBUNzy9qjy#sXxJ(O5F3s?eBy+0YT)EiPXVbpxt$5q>=4W8xzlxPG%*E8F8XrpLUF z4Kstd9tC$+bA|XULX^2Qg@es;gBvPU4kxbS*F+IbOc2FGL_)lUWI{kcgyjN}%LL~d zxis)OXd$xm;x|EjVTQ<3-b%~7SPg}f<<&3{phoW#o)vrTW+;oq6ts<7eG10okk}0` zKXwADROx*HYo8aMc&3Hb#JWgVv>TWF`l0I2v?|EN!WupGv!KSU24XB^u|*6)jQAK* z5R?gMU{cnsK2B~E!u*KcjZ*CEna5!f>{XboPE1w8S_5_%)(c--PQ z(!(+90;b8_jcFZCBocVh`&E={Shjg??gbS}y|AplXX?iGbhgb;eISCD9p ze8z)jY0vl8${6%D9)SwP!}_mNh6!r9c8T6cb|JSM83)8c=j}LX{`}V4UlI%uOGGW# zTchkHSc!{4@hVYFOL)F-LvD{3q$LK#H#ikD>h^e*7G6Z;1op1k46kA=%OkIN2$`rN zj0$xPjan-y+6eQ~rFd!H&9Ma0q47PT$ng5SSkD^5I+e)6gfryU33y(Yuhls8BQI%r zEUyrCy0N#KN~dCQIaPpj)fyctW9bh;?v7s^sGg#T+JE1mfz=?oeETAp^h8!8M z-9l{gYJRMIS@0b(q#}dRHF^&Mwm{sCK|LCl33v1eS|LI|2{;P1zh8~lP0?XIp4JAm zg!q_&xY4ryR-@jJ9SZ$L2ieJ3e(+uiwU|$r-&x_m;}ryN8Nr9(B>TzMZW?$MYb`;8 zFNa#OrbZV0XiZ)`7fQNSbm6C&|K{<_2o)rovR?D8k$V^=( z!iYaaEO(1VdIFrQ@FMwQw@eJ}=pB_RHb4Zkj@9AfgsDgIVsX;@PU$#}OAmzxr_vz) z2kT?L4N75J7*laU?abNE>9qXp=D)I z0n7e~Q9$hx?e|PDWytd@L$z`X@r&5{;Od}U3_@ijyJK3jKqR|jn&bGSC|lYmyIL&j zkk^XcSXTW4hKe%03BP=uC@+wY><%*6vO{U!!2rTr4Z>A8T_va?6Dc49gZ2rktP;WtU>iODM=87jp{= zm&+L&mCIT9(3T6iO1I4##TWTFFpr*%X-s16R3x4F>cQ<7+MZE-)PLD!5gsz!Lu_PX z7d8l7EEO*c5!|#n|JC)}tX@izm@$hLsSr5a^x^#?5zS}TQ4ANr&q$|`!}ZT}+Sgy| z*xtvGrtklH+W)n^7`A5EhI8U(<@F%d(@))>+b2qqU+z8QqN#_C2N$uB#KS;7>e6 z8FoE*pKPy-Zmoh3*Mu7x;9*S}KAQhi0zS1Y`zu--gu@iv&Lv(mwiWc%O zmoTJa!5c2t;Pte0MIgIRXx|btx@qeff)}(6>r{Il945|aT*~2rKL5eIGj5TeDDQ3V zEH0p2!1jEMpqz5|8ff2qH{JM7#3#JWV+?432?_Q7qq~A%cO!8@tp18@_Tc&6;MYC) z)lWmlRx~%|d?y|wQp7p+?S(-iHHHC<&`5)aT0(RR7zD)b2!7oMLrzwmbMSrU_IF;3 zX7tlFFF~s1ZmaMV3em;R;0@k>9B=G_)2Tkhl-CGqdm#*T!?*fIugi%tB$ zdT@WMz1nrj$=ab=c?lh>uqz|a5t)RAxB~yH%G(ktQiVpLAUyRaG$yj=F=Y~IBgk$- zdtJdgEV{vkA)7Y7(FRCL?nn2#jP0w)9wd*7_o^CRly_ocqK_q_uwXx&y?)ee^uuJ) z8-9`}Y4_s#NIpaZacSOE+pNNs>~rkqh@e51n%<|{k@uI1{YMyJ;Hi>%vO7?a4xZ56MY@};0{{_h`K7A3g@NpKH=sT z7iYDn%VTXX@uK~J2MIx9LV<>s~8^D$=)wz`D7%>x5pdhq=l=KRuk!hK9U zyyfl>{I1_W_H@LvTgTt_DgW6%esqkNsgP&dePyZehx{KcIU~MQjd6is4POF_SRx56RkIf;AsFQgDVRtjUJpoqQ%rKX$8b zM%DIgx5-5NlosL<8I*;HDpD3v%|?FL(Py^03o9ZKeCyrDR(E2&XKSyq)$1bkob3la zrSU6xvS%x7R?wL!uGeY*$uqH9^qy<^=vAa;_wA!+W7xRUL=c)D(&*BE(F{IB9dpc~ zO}oN}K%IarHgeH!gecuTmq5}#!$3?wh_G8%h*TMdH73^qBRg$_vg=eMASMhB7~$My zDud;Oh^^8h(SR=iQ5&VjFC0;qyl_Na`oa-)*$YS1ADC_h#7^L*wZRjqmPoEF)5oPYVc7HG~ZZIw2QZ0~8bGCViK-^nKq3RJOE){EXHcg#jDwv* zE7-)rEC&mQ2o3PbVc-$J&*2~(ew&XCP{ALxz$+4oP_n>ZU&kARi>wy*Vp~`VrhUM! zl&xVgEhs&WPxEncf>W&W{5+mO#Fr7@w~52$5DYOa;}|wf8po=2oJ>_PPT|-kz;8rC0pVXFNeoO{( z#;1`Xe-t6U@G&8G8{*^=%Qy!}urq^B6QRWAlU8L?{J{r_97S{l;YdOQSqs>4LOVy0Id}&9 zW%5o9mgX>WVXH_iZu%D1=s9zEV0*M}G-z#<8C{ zXP!GhxM$bCef@j!kAWRdq-YOU91-Nh5j;@6(~WYq*EmIE-DFhUtUt!Bw(Kffjpks} z=-+`8$V&`Pe(E(IMB*)$(?&`DfqEU`SusqGVPk-CMno*(0f=0(n66$GC6@OlWqC`k z4{$?Fuh&d3d5^b+)$l^?y!nN)eXymjN_**WN&De z?>XwGi4$g6za6%Qv0nx%e7V%rG1$<^387Kxu8tH-e7Ca19*Wn;P+T{? z?5hQeNHZ2vK2R6g8Bv*%t1z#}m3fV#vwnIG7tr*8*PJ7Y6nf6QJbIP9(A<0)O<5T_ z0WQ(0NUFfx*-`FhXikDdMTPGknvT%Cdm1rTov4l?jR;;sH@`ix1`F~`RFF~ye`BIi z;~#bY(c~ZF{NtkdF^E%hd7|+&{>DUurp?oEy23TD-S|u>XD*);pYW#@Gt}I(JP9Vz z$gSa4dm+xS9gWS1#Wa*>g)}MS`4mlaN@9jWQ-hN5>dC<_3E!9;#FFss$ayLWzlWTZ zlJGBR@GprGoCdU#7?sG>mxPrj16dMQjtoah^m*!*CDFI2QvrJ25&Cwi%^gB>BX2`NL=fHHV5;V=$8Ffi~+9&ZR znk2&Ak(v2pL21VEC*R0OkNEV}ncE*I%bl`YwzuR|P`yD$^?S;!NvM8Dm&Cv0#Z++i z==SH;kMiM2#_3kwI6YB8pa3~hVdL3OuEK0>nVVJLT$6K;Z8C9oqKU(~OwQKgi3|0> zSu$3emD!Q7+9*hLwWu@Rp%$}2EZcP&Hh@Znv%gEabk#tw0fSrtt?#<_`v}%C4nt^*KY~xgGj8TFoFZb7J zYqPj5<)%xkNk*>8{F;_Y@edOl58<2O3Ge)U%CIHtBxUR+dgs64d*>m)?cK^> zmGteM+}hn5x@YEF`o<%4RsjVZ;oso2BOEW|odOjdMW(9At^OHh`BS!fdt*+Vr~od7 zZ%tg9K9~Lovm|MJ`IItK5-9&o7ZhD*>b9d7>7D@lZUdVxGbDkvOqWC)ERAt8 z7;3q%Ymf-I_~g_oqdg`L9HG;1so^;%`&(n7U1hr~*7XU3R|Ahtk8>{ZGfNr*kCOCpY#Mtmj+ z@$h_IgG9i^G$9_{iT#@6W0=a1lUc^8x^RugkQfx3RdlJtTJv$`22QUvj>nrB<}IW( z33G^#Df1)g%Gj`7-og1B)(9CJ;{hB$0Qnm2Z<#0V~0pz1-S zMQ~lt65N?_?n0e1Va76FnIQ?VIl3e=2W-hBPNRi8oRgfQSQ*2!Q(2U>@GQ-ZM|cUL zk}%;gmHCiR*`Z4!b5u4q^3DaPY2x->+_Z#|gf~niIIdIHWW*j;7Bekkn{r`3%^05{ z{1WCGXO+2<@H?$bB6Iv=JtyMF#>yC@rk<}CT@smN zvD4oR9$)jra>99>I#sQYl&cdWN;8N}$+57S`sTC?umC~l zXt6{hf`hZiAW;bZgqg})lv$PFf3q%$%;CR{G6xRf^fs&+9j56)UF;|8#T8N$yy}|( zdfJy5>Q5*OpBC!dM0UZj;NHv&?#05A3FdkER>F6oJQMx6GJg`jAJZj~IleoNVMD0d z#ElKos=g8T^CEUw&lIg7U$b@bpMSjMXXm(<*hD~!Ty>ulM>ip)g_TR*xMd0 zI{BhOcXf{oXUNY5@-BEhPM5vi7n?$+eT!jUx-hE=$hO=B@oxtF`x!$f&Xvanm8OGx zCvY}uFkCN|Azq}+pak)JT@sl?yq@g!*`p^O4xk;e#vGhaAL+f_t1*aslm$%NS2qZo zT(`@u06dq&5Nu0i*mf$jB*A9tlE@skRfp&%S2}xOtjlaNu?B=)gwt7F5}D&P?+8-8a29^FH~CK)k4wtzr^RD_uHb$@guyZygQt}_ zkT96gC6PG>E5x>cIuiz0P4lG!5!;qf&bYiyS){bMtju9oaKQmjNI!OY3L&&yM(C}| zyhsSWMVCb82yGw0ff_ZWO=4pn!n|~JGdvDlB3toJ5c85Kqy0$XJx25om75?fqC0Yy z>B&GV5S|Pas*X(?!|uik8R<_b^C%(xaa|IbBfakgUDzRfNpX5pqlLCY91>^7iQpT} zDkx7DSK-+;WDRrbY!r1(8ms~Tq}()VHQ&@HJf$ac$68lMI$MdFzk{iGYjW(A_UVTzDt{b=$j?!PcderwEqb>u(IZ zZPCokh3JgK#EoGePTCljQ~s)?V}>Ac4U^2pj(fU^lX6T9S}COKGwIxoYJ=}iij48o zS7-BZRJkdV&4UDhyFuUM*c2R_QI35NU`|rH`5|T2B$V#cB@y(&dl=}MPB-5yHXJ>2 zq?b+*wQx_8IKF4VKanB==5&=A%966MX`$TAn~=r}*z+;sF`IOJ#+;-C^igHLBpeI6 zBr?Zw`BB_HQ5~B!8%Vx1&OLG@I&{m$jMZzD1xkz6iX4oB&ym>}$7Lg4v}aDz*$QoC zRwQ_?=#t1Bp743K1Lw}2?s`DhR23Pr4=D?o7P8n9+ML0Jtd8GO{E)H$YJ2RT}&mK*o8xmF^=zxr7=d#V*1w@!M{*$fV2p1$7F~lFe4I>R~(Zd)EN_U zLHNahE1L#w891``rc{--j#k}CX%E{V)lVL8qLyg+Io z=DzZxy2bx7uqW@nH3!Ij5RiFCghhG%B)E6+@wn)4v)qpGnl9HOh$u5z{O3v zdgjhk#W{-CZl0=e`g?f1DgC}bZGA!+xnxs9f+tQ}Kan(T{aNL&N-FoLAfYjB)!rq) z{y|&@SFhpXIh)U<#r=yg{#I;-pnEm$`9G&Dd&(AXZ^-d7zTi+;Yj6l74i5dXdS24} zbW)im3661HICL}G#j+-1jAjAX^x^()T(n*`^0;3#I&JA*jnViuWzo~40ms0Mafs9u za2Q4-36Eb)Xmuq}8q4p<6-UGoG~M!L-RN5KsO&Q{X9tP{OXHwaUCm z2(8j3kvT#ekp4-x^NJa6g_KxaFoEj`q6U15dW_h;$|9!iY&YhRhJb9Z9}ldFh*RDF)pqA)Wg+)oa^=ml)N& zviNCH-IgnO{F`BX#{~aogW?o|`w+wl5vi9bGblm)gf5B9AzpDFQK+$MwbC%$L-L}X zGNGJdyR0lyTG&>KwHCpX>G2doC?Qbv3T0j-gkG*oB6EaHY~3!}*ddKQ+hxN&eSkxR zhz^UJzk>t&V!=YicNy9DC^tn~Wcz5Nw=atsmWATPEK5>&a9+|`3hz?pQ$qb6x+LPL zYq-IIC(R)V4H5wt=XL!vH~u7U@3?mHr|B>3i-es%?T4NIUKy}t-6VMv!%lyi6n6SM z<*!Pb_P2tBM%YO=&aqsazAiGZEQ4-QjM{a%m^=U6r8DCdOAUK|LLbbHtHv z)#oQgGiE8XBDMN;x+H?0^+N`3rlT3lAFwOfI9Bd*b(&nv@Z7E}P)hv=JfqB!IpSUB z7J?^XR@tM>iUiNCx+GG=qaC!}=6x5+tsnQCo7-5^H8j7A;oBEnim_GHuH*g)c+AB-paVTP3Q>oJh@G z)+G^i7l#p>X=}FmT;6r-Lg^)R@nGIANT*OvSDBIfC1qjLB6l53j8^222!_bgwW(|ESy~ zX(77BKj?%kG^U)w-;ALac0Xq)IiP>9%%23=-|3PFdf4AG>N9P!bEnZPbp!Vpi#dXK zFpP&ZX>=@@km?OacGHWReA7%K#J7)gXgy^9;Ory|zF3(*3E2g@B;v?w znBTyHvq{h(5pa=}1@G}wxf|kEfOE)iB<*U|g4sXWlbDvJe~ne`eq|ie8WE&4ddcVw zamGIm8z%|O7WS>#d53Zbgk6c~TS5T&pfZb+w%sjATtm};>v_sf95{R4@Ebv(7#);8#hDRp4{VO_f#!?#(rXtR`QZ z$QS4|tC)yeIGUxxXD^AnSy5_=+iH2Jicy>JEYmf-I$jY<3 z=_u`Ds7(r;BujJr$ezGWh#H$|>ax1suso~j!sZ;37l>Dp*a2Q~Ble!aEeQJn3G2!~ z($$3E+Up+(64%giI}kd8&A=^Nupj<8Y)4$LRm4&_A4klljFWb=SS`dn@vEZ5z}}J> zu=nJ`hqVbggCpCb-noFDXN@>3SI8L}ZRRG;{I@9cEm@Jxx+H@ByHSwnx=USmWzZO? zFzRk{MK5LXxH%xAA)`Hb`s$4JabX3 zNyPEgFzLalz~)4QM8HK>qrxVH``M--k3+XQ;eyY+}l+`A=8&Q)brYe0UDOKtJR{pBgW&VdC z(G{hczdiKkfr@c@oDPMuCuqle9JHuEGHq?PV%M*jnK^~j{B3ea%&N;TjUL!WCX~#S z+{*}+cLvuK9bD%`~KLnAZY<8 zr_6!WO$=QUsWH%AJV$b$XawUZ-+LZ&zvN>N$IB2)5c zrF5%^!Mkyqt^O=GpSEymKbh`GWsLUJ{a58S zN(+`L)^s2X?zv|Z@2!gSIU-X)nbg^TUzt$}%>S%QBIxYDtxF>4?5t8hmYw~kU|M4t zhn#vrY~?#TX&aBg1CI_u8{>7cf6uSUy7f)v#!9PO$ZmEgV92r^2BX-sXHVKl`T?6E zqmYnu@h{3uNqY4E3KCt7!pzTu4j#t=VA$D(V~~$IBGsffoK6dyXq$4`TQckRxf0cEMm`YRJzjvnfITs4j^(744B}GX7IN%TV>O~{lP5@Te5`IgI6jGAu07`T@snA({^7c4*3Cg zFH}W=k#0$d3h%Khd{DUw(yGGt1XXa?)5VHnQcmK6q+RUqS7uQH`aQZN{(k{F=Bl{3 zN3B8<&|ThRpueQt1Xlxe%t3K+5BKPVD?q=f%%TMJ=ev`5!|dy>zmEQUqEmV`7KZTS zwfy7t;)hEsbHum4_yN(U??&vI*U+TC9)&v13ykp(yG!3i=(HGb&0QQ71|8vP!E|7) zJv$!;H{pWM?N3zMQv7nRwS z5N_#`2)cHim1L$D^jC(@q{mTIbtkr+B&hlgjd?#cO@8$^416 z;94GMmBf$UTbPsv@CV8qNud1~LE;+P&Q0gBtnWwRaIFWeVkYA;byWuC@0FV%EhsmM z{dB<=N#-;WnIxTL=D-aeaja1~)15A(q6C|U zHC98SXFj-Sg@+5u`m7E&X@3k=tNC6fZBZ0nWh@I~Mk%v;>{4#Vw0-*oGOhh~5Js2V z0UF|8%fjt|D@;vp2e!>#JD{fD4iSw?I7+HdSq@2~Zr3Fd*C>tYQ82^e!9s&Xz(q!G z*cuLh(Gj3ziZI5=!+V!HtZT!{4V+fj*5)`K2HwmrLwiEj&X6)clFmG?OCoc$4nJU5 zkqlhNdK_+i!sQ=zn{U7sE3_>CMY@No1}!tEq2Oo<}3^TyUCv ziEt#G(N38rD0N*^!|74P6qcq0*mn9kOu61iT1x zA3~dMqj0V3R*Mn)oU(jr5nGQ;`1}PL-N7Hiq_|?PE_T<4=gA2>+&-hskp$YO1c_@n zp3e^riIFBxnoE5{nHQwBsfurKW*A|`mwY*KoYz%b>Lel~P+O>Q0wx_Pj4gZJAO^~wT+c)J#LY$h+ zDSo($;CK1ONgmWEl-ZJy`M55LptF69!JTOjYSZCrWxQI((Qe|lO1F!g;+Bc}Q&U%E z2>(f0(DV?}9WFknF$@DYcyUr(`s>PUNf3Tbmqg|eZa8b_TUfG0vci;IaEf{i-ioy| z;~GnbuYDu(YFr*;z=d|}E>2peTdK^Hgxn%s5}6~n&UJ@yiLb~(@eNXRX|-D|M(qw| z@zQp=^`3J?{LuzmpTCg7Oo+ekQRYa(Y^N@X)R<`pXV!{4KFii599f*2n73;<1+EhH zCfeO9F=US^OP3b1b-6LRfmCp3hPewNmJs=Tu`)vvVh;-v*U%F-1Cm4fcY?NNxsR`${b3- zP3V#cx>}R5pK0G@^?=_H#)!KWG1kwLt1xI_U0$2PnM zaXGB%3u;CLm2;#P4z?iW9{a6~4BS-AIGwvO(LyKN? z;$#g6g9@Fblqhj?ffcqmf!BqDPYZTA>VV7)Nmkw4)@3zcJDS7Q_Cj*8#3z>fLXwMQ zFmdmJ#dpQW{Y{f44|y*M%Yiv%_9YEA1c|P;YvvTyXmJ`5QoZV} zax8^!y2^~=gUZd2)&SkiC^EOgvlQ#K#H}f~4davKT^?0tO+x69E{VX#+{Yl#w0F7h z5c1K)$ypALU#yPe{ARn}Ym7K`Y&Mqqx{V_vy;&Ch9z|v)g_U+I^2BBL273Vcx37NtrLmKOk0~Fe!a4| zX(2XpBgAhEP23>l^BX}kVY%})%DhQvwslElj^_5zl~81&55>oBKQ}!(JS)7%2>-Tn z6Qo6WM=o?J6d7)*;|4NBuuh1`eMp&03G3g|B@xG3!_y0#MIP`qNCaH)%u|Czzy&X~ zX^;rGc&qMm0&#)ek}I{=PIc#O>#$1Hqy+}}IS;jPzY1cX*o%E3(s@er{f&t~R92Sc zUO`gEmQY>A3>Hqmuly}Zx4)}PBEa+8x+GjYS83s?9VU5lpjIn8$eKE0HLYNeD|xgh zt$fkJ?EHjn)hqCjdW~a0bIv?>esIsOef#?N;vd7}^Qs@_xw_Mwqz$yE8|7-Raf%j$ zC!>0?*4K-k^)oYLq1KvCFK$)-mZTS31&M1oy;zKt?s~P*N;q{bTEv6T1Ii*McQdIu z+e?sV7~(|gbVroGEH&ruZ&H_66leN()(Et`W$B{>BG(h>jPQg)$g~EE0a7 zQ)W%V?=!k2f-d+eR)Luw&6ax(oi~C+v6PD$uK!RLC@owou=XunOx}Y87qcTF^6$!D zmk{|kT@rCbH0F81RFV6&28n=+X}lb5)4b*^4W~Hr*Emo4*wu1%)QP(~UFxuk^lZq; zn{Tg0z8Ldnip*?}H%U)!Rc1lbldXcpHMG1d+`Cwv3d~lyj0M4Hk4z}%_CBsGY}zil zGUwiz8sbT6>to8Fm)iQUE{UKw+|O{$w3S@*uB7Xa>vvl5pdy}&i5by(Vo|==5Yd_ z-Fd#V{bij?ou1MqL=lQxOFxevIOXp$e$dnB_|G=~NILMaJ(n(6s5CnzF>#(p?kuUw z6P=z@?fH#-(Js(h;4gOYSJTE=M_ru$TF}HHfH?LKdk=oGW49M$FJHM;ut%ISzg>De z8ou-n`sbba$8OIbvo02pU|8%t^dtP&o|mUXnsFd|d-=dHCW5^DD16mUdtu$0@XAZ? z!~5;oqvh&w=Sq8S(J2({O6PKWiTg?S!Ik#>N^5Kw)0IMJ5D}H8(*!HkN_nz#nYAKl zw)WfxK9sj1$82o3#!psyw~gO~4`$h}3wBlJ zf5q06Yt+{Bv7%i=jCU97u>05U$X|ORf9;L@wGV$KsrQ#jsISmJe@Fj(jsE!u{4NLKV5BjmuSNUp4Hgdr3vq(CcJB!CR}p_=e_%j5B^P<(e}IV znzWyW>k;x^#Pm~xMBu&LtUH5s*OXSnh3Pu4>6+K6J)b-f9U6O$2N6xeD2%$b2eWb4+Gx-?#|pgocg-cDN$3z z7L3H2dFgh1N^+r}RQ{^uLO&)*bZz~a&rFN14@HtyqDAbry;NDm_xNrFs+BV`EX3c6*3n)!nax8w|N~)Ub*nU!Se` zE`$1Z<)%mrD(=$pWO2_#72#Eu1z~cQ1@UoLmJsR*iDYk6=2Jrbt-2&KM|}++EbStm zXyDpi>M)9bq%2xm6xZV59=ZU>-Fr0R8Q&0E35jBVsLYRq)+cmHWRBMIQ?0Sur0AHI z7@{I0QClu%jQ&+wptKmR$YDlan-ty45T;3y7boW9Ix$bWfb0m`5l3MN%wEDSd})tABlbEQ3CEPK?pFO(;Zb6yxe+>ULjlx@$iC<~ghQQOON6M`SpgIiwf zev|UYrB;5!)U7=8v(44i%Eq33#z1Qnfiq+G9&`7d5tVHxSK;>lJ!O&8w)Yy*-sYZt zrX@HtnKMbXe@B@EsnI{8OCm5{A7tESdg6H#qjQIc&Yqp-Ijkbtw9;ejiNZ8ZUB>In z%A%&lYqN{j9Uflbwc+tBr*qXEdzI_5x{kaxoc+>v2K+Zs@a=n z#^aU7hxR%bsyjmf3GSrMb-gkV5=Q^)#}=b?jM4rOM!WWyyJFU_Yb{2pN4bU4cBu6( zO8Y}7F@F+Fw<mj_OuKiQyZ1)>QrB7x(+OqqRAIU+1QYWo!E{`i z2??fSx+F4(>1NTJ_U;VBbf;;Y#+g1eSnZ0!HEnH%t)?t)TG(#p-nBP8NbSDUL}m%G zYwnWAt1)F>B=kzUBr->D6{EMy$7`>-KZ;ml4MwS>ELK{SR=X(e@_`|~q>lAEWdZ(Em^tO%czKwATlSd6NM8p>FGb+7CE=UzbGY0C9w2?>=lg9p}02E;DA~lB+O0w`2y7 z8-&=q59j(ZXC`qbp|eGq1F8FL)+LcSI_}_dM}W?Lb62u`i`QX%9#j@h)xO;k;3IgG z06MD7f&|bZT@slC#BqeZcLo6M+Gp;H8Mtnh7^1SWaA_fOgAIG{3?O3eB#azo9wdxL zbV*z*jP?f@?cQzfz9Tvaxfn^67^BxJ3#W?F{s1H9PQvI_$~;IIJ*P_|bBx?c>0Kd= z?lNi3l*go)$(yz|qxK`K7j|z6p-SRN?Tgtph=)G5$L>xVhwZdSz zk(Ws{NCaHGUN9~wqUSwF%i;Y0kP4m;9&PvWH~#boRK=HEKqL$8||$4%NXVs6^r?9S}$c5;u9{D$82aJ&L;EG;q~o6X&~H zjaa|CN?Hv5v&v1C7W{*W;0x1A2MLm_IKr1@b-OW(ipxA9YDh^E@=bq1nQci)UZzVT zb0xV?RY}l{O{dbTwi*d)a@EvWQQocGTxk{MK2=2t#FbPfA>H+z%8W~@@*BD&GFO!w z;k+987Va8APV%@-Cxd%_ZFB&bsv-mYIb|W!0=$t=GxE8lgMWQ4L;XDAYJtxvGbMrd zDP0nogSYYw&T+TvP58i+!hGh)vDm1cRDr?zjfcgkMgr*@x+F3OX;-j+3S%)gPz{S~>VlghaP#1p5iW{$JdO7m;*B?FHP6`<+D8>; z+`kioL^8uA;n6tZKGOBdtV#;7Mwdk93b4lC{1Iu9he^uYM})VK zOwA2_JYkA&9Ux+F43YyHuSHC%QAe+VI*NTWmm>t2m9vy}x*i`j;p9dOvSlP#2C z8&+mXf^A5bMCP!q1Z+4*gOB?gx60D3%t;j(s^^r2N(_e-N$(S z;8GdX?%-g0#$x z_!UG8rkreD6(!b-qH;5()r$iujJO!4SclG30S^iwP!O|+r{b`g%2yFTSch#2F zvbKCdnR7{7KBr3}b8R`8Mq8jP#;{YVj!hlTy-HfFCqGbbsNId(_*Y)~=7uqaJxS$;y0o^%DY1%d*^Pp^A66D3El`Vc$^Gp{8H^7oGa-TTK3x)-gRwoSzx5heIjPp`Hc|s|y_P*r zYi?1j6y9Sf|5CXL(n8sj*yq9$n63n&PPb9U`OlR(lyLrnE{V)>?!h_1IG2$wOQg(% zI^FZo-=L4y`v&mwbAu&T8e6y?`$CR0t)Z?%&iOkjGuJr1G+s=&YaKo;T^WLe5 zVZRDGtQr;NCQ7RsccoVih-ijI3@OIVvSM6NW?52MCimf;_L>RB09DV0s0i$S8}-n+QqNkdhd(lDt-#ZAnR9rAs1nCAlxXl6V<+bT@zq zp5WG_vFIG&s;RM}d{nu)(kjY*=@rGxt`p)?JVuBcQk5;Ts(e_PaYkfz#E@|(;d+a!A;b#u*UF8QRzVKr==M^U zl6EY4lU_&}ZQsSTyPs)&TY?YPcFO`{=l;h8JNxYDhdRYnNxYCyKB3%5X$?Jv748h9e5;J| zapkW|C?C@$kvYnP`onqe)RTo7rOX5VQ_9VlRu2ZRV!)qjB5|9nP8DS#Bz3x=OCoc1 zx*Df05{I}NLaZ!rRBoiS%0fnCf7objlTm(N`RfwOuhk`yIm-JFR|`n!uVQ&%tk%Lw zG88!oR`0wuS$M{=lIT29p9~}XyUGoe7U8?F3RtksW)+^lkTA9q?=9Ox!dOXIEqY|N z_^2|=l3ILNmqg}jvGgq3qdI0Fn1D+IVo~RKAw&3OWnt1nxGYx}+?W-Yo6xjuy9~@< zD>EVi^Ow3LG6!aN6qsIP*lyxBHlt#V*>@YYhTSSu4Q?*OYp#ej)*-+E&$%V5Y1pn9 zz$Oc2l29hgWOi499kL4Cpv!J*ZOU9p_}!vQBJ(c1a|--KxQ*8Ct2nmMsJ5CIlVjFb z{dGqAv~q)_MLO1fqe$~m3+|68H$z&uX;yq!c!=(oar|B7uS+<7 zRF_2NIBuD;+nyab6}#^;MSbp~UsV=4EoNJ$w8(Xv@xsmz~*?^C)YGRL$JLo$$UMzFtO3K>jS zhR`^?Uq*VqGLI6{b96~$jsoWe&T{n_Hd6x{Rsr-2f&>gxYG6!`1 zKnr&mg%YPn+yhCYJ+*r^hUj5s0n${_x#0Y-g2Ol3+WnOCodFj)ZgF zJ;=n`+eFdH7o!X4!3t}`XcViha=|zbs~npR%?wFaho_YrF0DEoMK%m?`VQy6iv=4Y zc~=kGWXVkjwmdSGIv}gggfjb*>NIspWUe~v9u*pbp*f-q_eDE=*IEqlTb0F23otGv zCjMyH?F!jFGGuR2W=MkU&AKEqhiuD()d@;*gXW4Pwv)(cvMUuk=-5&4Qq*S{KcOsg zS{S$Hiq#29S_97_Ee-qP1<%2xHf+BS%7}hknKud1kLi-g9ML6@7AKA2ay5SeH&4d8 zUaX9f`ns|xX^~oj4mW9fyo5oyR|e&4%4|rWd{vjkj~^&Yw`DaQ5kZ2;mXG^nFcv9) zUIJsjE{V*+IPB&11`{$iemEW^FYbfmO@EZ`8}kqi&moFh#TlWX9nv!(5gNBnHx zkldWt8^}%5Qra~flRMEfzF*dxoyy!xdSmL6$Xst$JX*AxhSTU~T@%U~(-$j?lorz! zu&`FsbQ+0Pc0dB?4Yki*C;lrvtjv>y)LC5;KfXv^QWhyKQo`&;_Fx^7&?!Bw{CNqK z30)FDzM#BKStKP;B70m8%b>hf`STJeZ_y=@IVekyQ+Sn5!%En@9xr5T_J_*Cq=jP{ zU9TACCKL`mA_MaYWkw`mKCVk5b71-g_;{meix?A5vnbY^d-=}mWPe2UWd`=2lp7;0 zuzflA6r-p#CP^a}p!rg3UziZ?M`hfm<-iYWp*T}7U`169I7QgOf^ikqZq}?7@Rwl zMM>Mimgan51TP^}9*|Mlqs)hd%1&JpnWM5Mf=aJZxARUd7Le{zhf#V=S+uk$t%;&! zx~zr3dQb-I#mekRU_Go$B15oxN-vvz-F59{F|?w+NGfH7UMl2a0<-1hD}EHNYL_nK zW0yKTrRVVTiB74_KVHp0UdumTFMgy2b5#t?cplYp8Olq#I0VO~JgrM2dngw@WaTeh zs5m2buMveYQo^{rD?Nl$WgY65m+*ISYw72?C%&^wK~L#@ zT}nzH>Q*4k%tj|K=jCFy z-=J;ryLay16}8i8Ut$bsY+~|wA)44;deR@Ft|%jutWhM`bi?5~WZ6MDkk5U4U1@7xX+_amL}KIL%2qbo6wiT#XuNEedQG z{!is*Nr_^6Z>~&oQAUO*7rbd9mx;A0T1{;Ea?$!?mE@9a$Zsk$C_(>rT@rEVH4K2; zdpAtgd%J3swqLvQtS!!Wrh)#X2o=VC~WxcHH*03>0o z8<#GkerXc_FeL2-b$i6F+v2$CXQ@TLsQhu4(h5463c=%{npL+j{?v*c*5}U)ir3KV zU3|RKtXB(gdyyU(?UvCZ=I?vT5~pnL_7ZrwAx=c&KhfzKoIQBGH)s6^e4N?%jxrl= zuliS?#J4m_v=@}&cn&#D9GuO}d_z#`+6%PjBWLX~r&54Ri@Xh-$K1qcoqV(-wAXg2 z#GKu{BRU_MvCe_{2o4Sx+?hC48IHKeT?n@uWZX6>GbG`*L6^jOyqF5G88KVDju1mD zY}dRB%+e8MsnYfnk3qbhFj&b-5es*=UP1mJa8$dw=J$j0XrEP z&Ml8vI8CdC)cvMiYtS`=`Fgd{z+Dk#r;rfSNl~9w=}pSbmsXXw=I91X@C;K5o=w~> zDDfQX!n5Tr{03#-Bn5lDE{V($y^&UrhaHPBtUCxjBC!#ZcDp(<67Bs{Rb&i*Us=es z7;fa1_}&t(X_IWly?_h9rLB;CXMu~l)o-D_i|klK`&Y&NX)p`HTRI! zkmexXLvH;8%FL%!^7g#kFn`@QrgH}q<{0~x8ISxe1RqGcR~EG4-;7dGg%B1V8v4%KS)Z{kkrR z%+VS+U2Edny{E+tv{!6P^{(SX6u3zy!m3OfC&d!6ZJe;JdW8buR(Z6FbDWyR*hPIa zK##THv&v1DRtpAl)sW1*DGNFIuxKWUOf~=_BTJfA#ci|fifo2N4t4Lj^2F)W%B)KY z^WSwzWUesV&f-=&teTM$(6z58ykS3>X&8B{5laG1{T74&ZDsM(g5QG>vBy770k0=o zbiscJ<9Ra1-&E#M!uVfwNo0=kTJq=Yr`)TaX%V$iEsw__?N*5q+|ZlVbZ4FL=zQ*I zDU`Vj0XAO-Y^^dw5@4%zNn{S#wsXLTdL$pxbjqG+VgckVR`43t%2!_c=0@V)hFhn4 zYn%TL1A4Er@M*i`Z8^dZ+`Gp!RhWBPe+2ht!{7Z7xahj<-3$Qldyb;E{V*s%pJYhv@4XAPOi-u zuHVMI$HLvyIEqIs5r zbxC9n+XC`OoPuqYqunX=oPilo79njvS}6Pw<{{L9mdi-otIUIh!~tCrnIo~Ckih9{ z6nz=T8R?A%h3A@hC^ieg;3XSAh4&cA5#=UGi{uW0WJCt<6eLfEBvZLUMmn#|qlENJ zbV+26^zy^i%D8nVD^Q?%YU`ZtGXsndsxvb2J1kWpUNn{Ss zvKTy3bfi)S=l#m!qy=Yr9GoyVt7L56qs)nf&AW6-WRA^dU{ipB#0nJo8mzY6I?{BN z8M!Yi3!4_Xn+Z9WTS{4GZj(FNY8lASD>Ehm`B_~OnFHzjfm1`+y&8k}ePsdDf)}`p zQ^B@IhV8q`EJ?6^TbD%Uu&q9a0Exk;@Ef=u1#{A|Si*U76^85PzO20aH3&1~8fW)@ z0H3pvKe<+hYLhZM5>y*>Nn{Sys$+Ge9#0zQ>>Bos;C^*u_rB;*mRN&E0sBsu(_;DB6Dok$D@TtB1qk!s*n0%|wPsJ&mABMG(l=#uzJ zLG4S*0;WZcvaxZ8W3X1eQAX&C%3qfd`n)cQ%n@2~%&8zb)V_$dF|qL==KUp$A%r%`2z_6f7YU*7>XOJDA=#w4`LEI8se3iXYioa2uKEVq@_8JF zV7p0%?Pg_`B-l3TlE@sk4Tr0hQft&|hT^?RZp9S!7`O+NMNHe#Zlrhy^Jx0S zsA;j<9N*p~cr|@qLnz)RqgYnvOG44nC6PIb>-|`~n~RAMl|@(JL;>txjj`(}3z!zW z4MB9?iS}+GzHdae$Ja z+7X2@{c8;C?l(#?vFW-$M!0tmF0p%Z<5Q4c!hWR7PEJ`qcP?tpJFs~$- zAEbaW67g}ydEL1f1vIGwL-#kzLZyXnm4L3uTwz17;3@>tb{V9)hqXhhcuhI6vQST&?S*!f8eX6Zj`)nw4HI*DU8Pa z@u?~@pqG_}Obh5n*&`=}k(~xEL&J?JgZBz$rX*wba$ORcgLn5Cb^v_)g_~cbJt%yA zra-jYDTM8-A;jRnN4b&Gf`1_AIu?EphLpjorZ+_s*J=ul2%QNPR*pVdIo_qrw4@yG z&?S+%a@_tfj(!@I)q$6R8@)JkY|PypSE?v3Y~RSd~=3cTUlbl1I%Iy6((WH`U8EM{6bHzB3O<5R*k z++BCd_XOJDzx9W4=|hYABL!D5u@f1qvA)^88Y8!9cUJSf4Oox!Ii%o8 zz+tG{?UG^Jpv;m4+ge=`nZvf|+yh6v;WygNA|(vYfU+2AyVc@cW3=E6kl}8$TgKyF zWiBK<4(O7|9FG+gzw$Iwobet-C839R665>BtsC6PH!%O5S; z6{Ao!Ca{gHS*_g{?MQMlBlLb{fzl$hJU0Q3%tFGK)#8}5+t z`@Aw&5`Lf6C6PIPH+xfenz9Eju|IT0F`TwG*v&d$kF8@xWj@kJJ28zaPcp?S1!1BSbTmKu8in*+d|hv{k89{T7erJ z5w7kQJ1V`-zrkYfp6J};d2y>$=?(njm-xq<`Nyw_AIa)R0>*E!_*K*|{Tlvp5&0nf zE#k<#l)vhdc&|^QPmt)^rZazo#bUL4_0B1an6hD|d-bT9=_0UM!Q~Y}OP^6@Lu%=h zx+H$n2kRjQtJ~$#+Iqg>(IRdnbsEwc+cC#yih8V&7nNm9i`E8sK)7Ah!BMQSL$N!h z06G8GivYQnGD{M2b#C@jwoWFYa69A=mQFmy@X?w6E#l)&rgk_fup>lpuT-A?x& z%QarFvy4%?{9>$Hu*n{tw^Ka*x*_5pXf{w%T3Azts&Q;1*``O@doMSb-83@>9OU zYV{3P_eT&f&SnAsKXfsE7%a`y;{C4h;{D0*Zi~p?ed(SXM@2Kz_PO0&6o!EfE#-0V zOI*DVhCcrap`bhdvu-;F?%_YOE_56Ey1LxDHgVPI`lwZLo~GqHy4pe9jS^0wXeZWS z@NV6kkrUotovZqMi7OlU&X!Q?CyicPmHCm(;mx`v0zTKqh+s&J3JNF4JX zh7E+5WA$>~MtUh;6oX7@9{nbAyT0$@3QrbDHnYOA zQMx$E7PclyZu|R`IhO3nyLCy#^+BbRxLgF|6k9b75&;*}j8nRPP4Br{+0{#OYmU$? z9=Fq-uUih^Wrobd^WxZex(qIC&)RCQvJQS#8IH6%XyzK8B)%hrdmZRPXCaB$pNt&Y zrhBbjV2b=}?E*veWo0%c#rtbr5}8AOGYxo$u)_vNz!q_XIk~jtHYTS`Ok10Qoxd+D z7uOI=^}~W+Y}Vtmh|I{m&Pfp03eVOro1dch)`a2LqxnQH9k6r%c50x>Fw zL=8iq6uEdxnQ;mHiY|%3I9$*r5qN~Gf?0Zm+9qnj(TjFH?=)fvaxUG|#UdpYHFxfXCamfH(Z6m=EwK)i6AYiFjJof+qVUE_foa5lD} zl_?8?CDFtXuZSVBv{FzQG-^tX(U>APq7pDxtW;^4Xo&g$zps0HW&tA`kShB9zMk&y zec$W%zW4g|+irZ$$+jV{O7wi%(1`{QAGZxhREVvj;+WCk#6LxwW*6}3c)hgV6J`?f zbuDz}qnCB1!-xUF*y;%WTJ0z^MaO@-UapspyQseE<(k%Wx$s_Uy%EeL~&L4_di0+H$d_z{P^9ag^ug zhmPCmBdW%MzN$FsRb!-6HI~Ga9p31dJ#qM72kS+ypMA3K%tksFIp!GKbN8J;y;IqC zl*d3d$8FgWSJK^A6}_)yA9`Y^GuIxZ4=Nlhbb6-`DjX|x#Z(_uI95#V>w`I3k@02B zTKplA2cvxt9&q4;wyE4Lxn{$KnenzV!_8!*QN^UfsAl3aYM3+{wM^=aIwrM7y(SGz zii}1kE@J?ba$_KqdSeiiHe)c8{>Bg{#l}!3<;E#Y<{DhG*4CIYYiLs{=+~PNG0Ajs zW)e$SB7Tv>O>IT%a>m4wIHRboXnoFDqF4+}DV6{?DfS2MR4fJVS1bdTj}i^#z|#~f zfR`#(0#_@#fLj#Zz}<>f!1ooafv(Y_tOht$u@-oRVjZwcu^#xKVgqoGVk7XN;s9Xv zsiJHkuvKvoaE0Pv;LVCdfDb7S1^zlQEX5JPYZONUZ&Mrv{J!F7 z;46x!0{^bq1Z*55%EkcaDvkxNR2&DqLvcKCo8koEtBTFQe<)4_4jL=UCIQb-oD94{ z@igE%#TMXWic^5UQk)9>SaBL~=r~a}9eB24E6}ev1DI8u34Bs<7Vu5Q*}zW~=KzO~ z7iDvS3l--9+Z9g--mQ2BaJ%A}z_%680)D1=HgMDgQ8pjANO1u$qPP&aLGc{m4#jhU zdleS}4=bJrY-$!|=K~ijE(S&wF96=Jcp-4N;zjFo8M6$J3%$`0+c+9fF&fZ)r$AG& zG@xe`*?|6_$OhDJm>j?cG)|EXXt9NDMR!4Mh4mJL>6YRWC=MVnaRDR_?pCDY$1UV} z#v8U!3}FCfUvWIMoNb{T6jFitKWL%s-(w3r`C}Dm*-(rWpodNSY(+M0zapFVItxY1 zGb*s1@3m0&AGQT*Z$g&^mFN-koo%(8?GI;NSd83;2V_t)O_9znSENg8EEMIBsz8@s zSA^kCDbBDImqPKCZYVAl#oth*;_oU_@r#Po^?@P_p|(m+V{Db8rT9iGG_aT_DYBTCDzaqLij1I}6&XQyDKdh- ztH=miflom}86#+mA|vQ!ij1IZ6d6GuP-FysUXc;>fFdKP8!xSgGDgs8ij1Jk6&XQq zQe*_(s>lfXf+8d6Aw@>e8oU`3${0asC^CXxrN{_+iy|ZF!-|ZcFDWvDeyG?4tjEiD zpll3qw&GaewTk0_YZb=>A5okD{G}oz=tqi-paZa$3Cb8j=P5FRrWF}MH!CuN?owm~ zeOHkYv;uz>01b?wEsBhwmnkxWu2EzJeL#^B^m#=_&;yE$pl&wnvNl9er^d~)YFs&L z+&xwJyC>F&6QTmL9C0D6j>LSDzYx76uo$o#>(`vn-U;udxN5ezyv2Z%w(2ha2jlu~HnKqezFZQ>4SU zS}6HEr2>8YwS}_3xIwhgt+5u${tIn^cExOgZf#Y8p1hz)dk-lxKGZadO2&s7iii)6 z4|6RaT=2o3xzKsT4z0#)9o0vyViu5+Uqd@hPp0G9D?82{H&C~}MO)n$S-zEzRNUtyu- zv|a_6=oG~~OIsDR?PY#dqV2FEwKWZrz0|f?kv>Kh>Ei|qC667pKyThtf!>!57A@!t zuz!vUEQ+fwl>N8b0#E*)EwCtFwgpt=j9F<938q=rSHt?h(7S4}zNlPLl#aIrDqW!h z{pzw%4tmfQc+ehOV9p0^fla8oLKGLbl|pE>g)#^$Y@r;&&9+bh;UQb7gz%;cEX+?8 zS(w8sMJ)?+p(2Z{U6G}9w<4?dc12e0w-wp-eWu8+ZUr-O|3+&GUVIkuVFVvq=))7ZoFktTjK^aituso+1RpKBU5Ly&s21SbGs3dS04y3v#n1RG)U9 zoH0xMiG-Z+gE^>X2a%nz4OCbl#^czv#CU>Rvr?R2np~QI$2Ir_WFm^COv_LT_+lr@ z%HFdu8G>y5NKSo9wp^IE#Mln*HlD^7ivo8@lJOZEQ1W@+l;ZjYJM&#B?>%vXvpI2s zgcB!7IB|l66DLSGae{;sCrCJPf`k(%NZ3-4aN-0BCr*%X;sgmNPLOco1PLcjkZ|Gz z2`5fgG87aWEWT#rr!XG{TaG`r$e?9nU(fZ>C6RdtRpWg(9(xU#p3eE7tayn+%hXdK`fEt^)o($_HdI~ol4+g zedm;D-0#J^lg)Y$)=|Y$s5wxBKQ8X zt;4JFf>n2;wm>9ofk@Z_k+20KVGBgU7Knr`5DD8H5;is@Y+gv%ypXVYZOo|+i|0SL zng70n)>anjK+&agrrWAUnREm-pzeJ1hK#q%W04ydMf_=9fV?q>7#fI5SBddGJTt2o z;rU85?hWvalpba_;3-mw^E4h&@5v-1bh45G%wu&PrW=vcgU?f?gPm*^rn{pnmF5nN z17Lwj32zdwrJTY(Cui1N5e`CY7of96lYH$*dE24uyamrINiZ=3pu+Q{PAc! z=4lVc{CUdjRfz}1hjp$JGs2geHGxQq=Abu@rD3XZ53aoF&LpFr6zW@amUuGV^Mcjs z(-7lBXv2&M1?G)QJhny6oBzrBm1A7r+SkiG_{8{8aMJj9+TOB!s{HrQU)=xN?p`N# z8y)b>yym1hGt2UY@vOkC2~RZq(K4%I!S3rH+OzEIG;6kH7UOGySBI3%kcPKb%(wHmP>q+xJ`QX`Ee_p<} zmoGSpeM#X-v9a5%lRo;_YrlV^=okfX)rqYxI@@o#&z`NosL9W5ool(@xc{_oU%u>( zUY=NW__`Bol=B?VGK_sK3^Pl6LeE7bS}(b;AoNu3ONE~dfAh;iPhk`}ISsGXGMVE7 zmgOyIV@*q)?#Fw-)N2jD{6h`zYH!M)j3m-BzUfR$?8XhDjC`i1 zR);2cc3?zRsd54{Yo<%tNCY8NbNn-v!<7I=QutA1-uQLe0&W8_{y_! znQ0k#@TNV2VN_CwaGst)<-q*Ck=Sb7=Eak|LotWt|H5%z0Mjgazc(bjG2C)^{3-mm vO0L@+?u*A)d9a4KBZv}AVQ~$XmFE3k&!I??!@d-T0T?YMa^c4^zA66&K+A`E literal 184696 zcmd?S37lLeQ*KS1x$_Raaeo75&#)-K-Z&)r&)Jb*j{; zRVUqQtJ~>6*e+EHN2Et@>u!5h_fmJDGta5lyC*w+g<8JVaNR0dP zWw+XNtHWnpCl9Ivet5&!=q#LrQsouT(jF**lgql z)w_(Tf=Iqr9WPCEJIg)s{J$3-;(|b)CABHH(I^$%rVwv&#i>rToe38*uBcB>PUR2yt;x!0tLRSR=_-CYI##Ql_n)l}D~#6atx~PZ9vf{pD&5Z7Vyjhe-aR}#In5m=zqqB{Syp#i#nD!66zDX&#Vwr`4YyuvmRhyOG=Jm~|0DU@WW8RT1}Dv-iBb#H zhf1~7@Kb9I6Z;p3=vTC<^r5kagQh_1PdZI7jwYL^)g~(Ls8e++(=B{)v{WGOmZ3o# z&S=${Li2Vz`yZ-Za2syH8k@FW;8?|mJHCI5&#%`CqQTKFTkd&)o5eZ^2wpJk+|%{R zjn~lZ4Wi#)tWCP3btuPZsfuRFcc1R8Yd76dnOOlCnsO}_pnJ6V1qKq&SJCrQv)Oi= z9=hFU+MP8*Uz|c=wC*-0ol2>C9$n#@-~ME`vtAT6YwbqfrRSr~_V{?|A~CeE+MXmW zf4Z}=?o{1MbCf7RrdAUTQ6J4WoAkwIyIya&&F1I@?uE_nIY>7--E5)TO*$p$rsJ!0 zsYQyu{Xo55DWT~OJ1xgLK<(UwdOFpD<@IRG@6y1FU^MzUb!9*K1IJG|rNt!>312LlwdAk~78jPSd3xga%#-wPp+8*$=XSeZ}c|4Lt@e0wR3? z+$9Z#0RfQf3Es(erG-wC9|b;RZiBw|bZ0#^_^4ZT#-O^yS+yz50*Sa3{jOFW9d|(n z)}>IZR2-;53tGR3FfWZy1F>d%tO>epGA9tPUl0VRqfIo-B)(6i&`U~{7TR?_sXF?$ z(0Ei|?_PBC;@wfFQmI`Sh1sh$go1=6q}T3b9dh-fy=(1Oz1@QKgIxi>^P9!m1@z6Z zdaE?)qEdI{hR$-YM~&uT@?C1YP5u+E?~eNR3*gO%&Va{FAt?_Y3Hi>liUS*oCYvZ# z(JYJWg>mtu%e_wO!lV5BY2rRtT=8J`*+gWtcWu%G|PZx{DQfHmqYNJiJ0^fj4$?p$w!Jswd z6eerc66yRZkAwj--bUPKWjg=Yw*&|-4W~c-BaD- zC3^I@`sgOCM~&TDXP7rF;ugZ94^$%>5cbb2{j)X4}OBd;J_I46keoj<>4wc z-R{#5{0IJf=9#Bk-Lc|-g7+@I^qZajX3J@j@bJ{cu0{vam{2i@CTzEcUg_S30h+ zR;}QxA6M9@R`3;(D_o~m@Kus4SZW1dIl02kY6V}%xWa&1!PhpfFsN4WHHItP9#&9w zhpX%ktN2ajD+O2B8&*)%gKNAXr~$8Ow1L@3xA<%LZPINyu;QI%V!ZLqdS_92%oc7? zd#uyPQ#`V!-QveOE64>dI~OrZirG9~UIfFp6OY7j&{wL${=gK&%{wQ&-)qheU}!zN zt?N3SrG-+nUU8-)#V@7ebE3H3sKIG(o^r>@30~vqp>K9p^7sjsgfSOhHhAm9 zEV>Av1pIW1`+CfMiqEt#wOTmkRA3u<1jA>_Ff|yo9ufq)op}hobWgsaOFmY4lo}7e z!`C7Y9MTNxg3JiqYQIKjQ3Ku*rYiEY1+7}anUV{D>6mKIEPXdsxw)m@fNWInn#6Y*S8v_NvLYA zMUxjy^vB`bV3Ic4tW)^qnSjVL;a~Ag7<%JBC$G9Z#+Y_i7cmL0&_p5^nRf8Xncl)_l6N2LANP5yM`;Jv0>KMT|;|^$-}Y1{dvSO zn!}!xLmiM~SD5qoALw>oWKOs~T|n#r?L^T1ec=9B5-F|*XGdFL-f2k`9mQ6ZUiX}Bg zBrR0g#hC2WOZoHD-DgC-72e;YrHLv9JI;yMF*OTXV2$S}!#T!pqXoAPy%@RLFE9RF z@xA`;vdE)xa589w&H#Ugrs zRcAgu7dE<2)Zj^2`A_hk_=nzI9hOMJ;Go!hQAFOv2ZNHO;zcjKhQWcT5<%wDmk~!Nn`d=-=JkTSEw!KQBc zo3e4q;8*VTk4PIdG5PC+B^yaF5S+P_6 znLr9|X+P2zuj;iMCc$18ndcMfuxM0*7*D9k+|V>@MjQv4_YBGG%f?K>)QrL;pdU+J zX{~6ENq8=zX!JsI4mZ7|8n&z%4zASDZ?~0!4!Y6S`7_0fct@DFF1r$SCC(UX3l2Z&aB>3=U z6ZE5)A+n!qxQD_w z_MJ42cQs*}u!to5O}~U+g|jGvkyup_qc?e^ehps5Vb=hW$U=!01)lbcDXQxK-r-1_ z+`WAwUavu9rA0K*^`8me;xbaAQTB^N>a^a+z3qZmhN;o{u$R`qDm ztP;p|Hc6El?j(jWuLc#alM30)^Lhgojq1}j2mObdQxgc^OEtq7_bLin2fn@qw>2sc z$34aDrTe1qEV$|_e;#AO^gDGIajYX3>jMa>!j5mVp6avkUjwTYjjF{Dhp;eNfg3h3 z{L-QAx83{r%}-q#*!J?5o;`Q=?788IL2K*Tvp3(mb=y8`c-Xr6R%_CAs!b6>yimk6 z%7V*BaY&C?-=)4j)H?*HaSXEu>p~GreGa0H^=XTRwXj5(Cw$z(ko8!pQnAKdtB&Pq z(4fF%6+;bvaaz^|C#Xp@fn~cQ>Qt?XhBGE=6OJOB=m}xfOD_5og(5|fAjEH0x3=h0 z(C30xs#+Yd?+coX8}bksJp-Bp^!6drvubNlu+Q8-B-Dp$3_=hzF7(9OZ;d;ZraNen ziVy{^b)57Ag@|UOj5UKx{HKJ60Avc0P*%Eq!QY}`_i;JI&o^8cBCi(dHqfOE38(~V zwATo1BMGTcZY|3p4b(R|ayxH=A@*DG|=fW0Z$&c3BSaJ3+;6h+&4e;C45WY5e zcn%@pQ)&%tJzL$nO>Pp=;2=b=42eGm7!wwmq@>mqCGvYz5M_lB7tKUP^dG$!QGodS zREafG2s%`6H;eqGm+m|lexIr-O-~Q$KjnQAB=&B=RU#E)^QHtPPQ~qvUt^^Sl==Q~ z^mEbBOuZ9vXS1c6u(jQGFk17-&T>lO zvIQbs7J46?b3_Y@m@bdf8lU8Rbl7|05CL)${IR=p&5#w6@O%i)Q3~dl;n?qu?_4eg zr+nXqS@3e4FlTDe{Bw(#KznH{6l^Sbvm!LV?4V#RIS~0e;mpv2omj7fG*!1+^AoX( z?++^S7twKx7}_cS3119j3&N?`JxX&OJP-XS#9lbC%p>+o{j8vTp;~SZDpx^w8xVG)FD4V9bs!X6c3W+yaU5b5@&ffS+$W7&bU0SPa(EkYUBob`j+e!cQhnx`J4e|%(CPPoWZ zx!9s08R-Z7mqp$q5xwa5%PIcLYenT(Uc=#rWqw(kCiu?;&sY1;spoiEH{sm*+MqP_ zm|IQ*PbmTu%%V5=)r3!u-dSy;VQUCLY&!bL@#EIX11FAHFF11I!2QQxc;N8iQ%6pp zzNZ_our7St3r=HUX%eYO#}Ax5_Q-)_c=h=47gkD?`xd@dbc+{ADa(&i6Fr81go^fY zq+g^ZdBHn=f|N(-mhk2|`qtUZ1>`~VZzAOfSv>8@su#EO)(xo2MfmZEkBN_L;`*&x zz2Z)}m>%;wHp~p-dKBDQ%N63Y2vO$I6b?4W4X&xyIGnhKUlT<%F+mg$5ee}Yk_iF* zD3%LEE)$$<LeX06W?J6>{utrb)EUa;hg%}H2 zY!O2cBR+-{1Z6@Rn3T0@XUT0sm>;pb35tC^bru%Ey+HF%B(6Pm7QK{wz9vQXE-590S%PHs}JK0?>~P&8h1u!YEVFd zNiQ7T*%Ur*xReRd8ZEjG>@iKD1;tfz4a`|E!5Xj_+B=Dp6Ybe*S;_}?w4=R)gVOayyyb7JOR2>&twvi=9 zpC=I%Mi}?2c&l^cgYa1(q>FwTL;zXEyoJT(CrV99K0|LuRuVSps zBd>TAnW!R+3U!r6t<@B5gn8*vydv-ASc2&2)GjG9yfH7Lbs!$)r z`fSyq`6;s4^XD;IH;a;h>!4oHcehf9M^G(zYpPwL!<{8Tp>R6Z1b_N<;a{9Aubsz! z$J(&C;7(EK2G$)Jt!~wZUI*1ShSj_;`pYz(t9OLeR)p0=zHj1p&W}-mhtg#NOJIj2 z`a=?^NIAlZL})sQ#o}oBhVWfpt@Q2IGl3wR!YY#@5w|fpTESojD;s`CYO^Ms2-w8} z+Tv^E$oTChvB|6XvGSGScf^p23_{Q7-3Qo0aW{wcXjmrP(IaSu2>m4FNNWF}8n2t8 z!}vU{4`~VUF$3|UWy9@eV;DOWhKnU+Cu8}+e?@AsfG)qYDtN~)2;Z{855Y+elC8Zo z@EX=y!Upe$TCt`^7W`;UUOX2{x{7Rfv{q*cj}{_LMRXf6aT1l1gQj6QjZeN63MW=8 zpTNjWT_(avFhulw#Uecc&Xv4KzRW8VLpyp$wT2B4;jCk=T%0iVC|)d1``;$-V~(#F#T~#dP2#;zM-ZB;ox^9 z3i8%QErC|oZ+2B_>ADvCsm~XIF(VhXs7;2QlK}n=?{s{6*$Gkb?=y@0U^$U3d z84fM0!wOjTM~nh$k7&Pdf+<6uUm2}e+K6Ao)(6*yj zSY?g)4lk0x#}V^oshGbJCTDv)8ze?o3j$v95bt}CN>@o@D24oOXA%qikg`WH<|P#5 zP>Z<*h09f%oKVYIcxlUpTBX-!jpD-r4$Px(W17=gI~7SMfqHQJN!v4uj|DG#EW$&E zdx(up?81hDixuK!A%d4S7reTWo7GQA5;JD8A|-*tO)u{kiD*8vj$*g~K}I@-9Ik$< z+qwEu*Y!V!H2vV$2ZLWb%V2B9TsS9QR^9+oJ%iN!g+rng`Q`pIPRd838$2D_-QZRA zQtvU)4smQN40+L>0^e@XK7oLV`@(*(M%wZ~8QXQ5LQCCqgIq2)r67ug?0ZZhJXc4q z5KKHo8FoGRpX_XiZmoh3hc$umNC-7u*B##V3nz2tIY4 z^r>-LjKAPEY0H_IyGea#lb_UIsW}u(n*b8UKSYNQ zq-Z1maydgP7QEqNja*GjR|K;6g!U~Vqnoy#A$URCuugUs!C~Tz#uXeM7z!RNddMpZ z66O8Poy8@T3)oqV5tLKzeiqs%W&U;+d#T5J5v%Ziy%V<6`*p|dNLdK8pTjzP!e4iX zzwX4a2(Xcs9}v3>zXs{aorM^ck@^mzVli1H$A@*XVa?~^4*ZG;6X)V%^CgEA!t!0+ zH=`MYbj?eUs^4oBojl_nxuPFEunOl0kF7}o;IYvqR`?Rqm;W4(1f!ksW z|F0d{+wQFOTynB@Xf$3z$13dV$a6#{VIiJ@|5f8{i4>_qqfii@`V$%x+4Gn(iL?=9 zH=(_r!8$Cu!TC{_HonmYNJ{QU_j?-KSCKtP9u@CZwfrdWg$oykSRx7w4#U|SM$P6h zOcuQnBzcl{FKdkFB^roJ^QPKX4X)%+$!(1b8f2;IeX1RKe}&k8gaHPgDjBHq1$gS6 z6_j5{88M?5$uXgZplL8Ha`b6m(LqkD2o%C>b=C{#68kmL*P##IuvLJlYvfcoKZW<4 zms?z%*O@JkwX@uh_5&Uy1c?a+_N7s3-l(6O*X}IFoHg9)BKN>!2flLO$2To_$G^&b zR6M--?qC1kg8s9sE1uml_5R-vp6%ghw|?s{ei%HvgP(2v=P!OHcy=d08~EQ}^{wF9 zUVgUi_g?&-;MraLZ2PwF+!Xdu@!9R$M~?WGfFBH=*%x$ve$v`FE?EYCcJUYfC1M%G z)7yS$``<~%fS-L~$9rWn#t(n*;SZI+hB+<%df@sUm+t)M@>l%7cAommiSnNZfBnq> z_B|n&nZnIb8H$k=--bkXPHsj7nzmGNCL{U;f+6$BWV{VKcxaoIZ>08gf0p_Ut=-ce zBKjk+S@cI>Po+P0iE3WSUG#@!jq#U^HU9JsCe;gVGXC<3-$5i~YburPrLw?w;&;zD z|15T5EiP5X7GNY2Zk~_%!VUO`r0Xol$`0HmxV#sf>89Y8JR?dn_Nrcos-5{>BZ(F% z_u&zljXuNH~rnUWzgE1{0wFQ@pO+>vsO=saP#~&$DmzDw3~<_RzCQY|m*S)JzX)W-xrw4nIWQZ?Z&NbA_jXIw4tX z*P=ZKQM$V>fn;z}ftX|vVXv+bsX7LmOAZ3YZ`$nSHmF8OOqdlg!Wqg`2KxvR+vTOA z0iOq=Hp|PNKcX&w{)oEb`6KGe=Z~oU&mU1&J%2=9`}`4g{qsju@_e5c-cnPz_0JPg zY0mQe5VhS|C?@4103l`+V){;NRm~wvX~r~cfvCX;I?#M$NyE9|CFmnohB*<=IKxr_ ztsuA1^E>m;z}u#&+?p_m6&$l9&SLR^GaA2 zJV(%Fww*ORh6CZ+}D zDSVm_g%g}&E$2x*fryug?_0$YatL`?j&&5D}F7%4gWwM^8II# z=h{=WIhD`1K#SHaWLA5}NJM3rsUq|Ji{hgud@qRcO^*C5d~fSaaA2 zknvhW`fw|-u-3pshYoCuwv8bTmiFt$()PuqC3k-oX;*p{w8|DV)^C+k=J31594?D- zxO#RDHyq-15!#INfOM{6w8eYYVvc{P&2iMGubG|C#}A}F=5WwD`r^~}=`$m{cI??R zyc_>m*vCYQ_DHoPf_yk=2dejAvr-$hPSRL69Thk0k8!Il#Xd2;F)`X|GzXhS|Mne6 z_F;Iw)1Y-9(rdARHbL?qXfzO>6~p8t(*Lc8M8pyvfXEn&>FQ-sVtF@d%Ukw*fNNrU zy>52N2mFnymLFW_EiRPPgN<`F+A)W-5U_`uj_C@9()6#fOat07MKPqwBCX96R|0; zm}l~|k7ivwVWN3VSxiC5_b&^#ojmBWaDK@HEDPt2yrZ&kY{&~K3kQNm_p%tGX~Zgv zVTWvaS(st6fMsFE$TE~gr>1^a7M+K*UM#4VNyj`Dpx;CziG~#|tBm7y!h|QbgJ??lhGkV_hpvx_rXvmkadQ0$b2c~ZJHF>wQ(D+ zs(qfNv9ZGLof~N*>&cVG8&L;FmX2JF6OrCZfPWh7F{8h$&8SD>w*nHsV@N{oj?Bz& z3rcg2KRG{EdclbF#H`nF7)0b=*8+@;}glRjp6xYo(`B%=gBSnaQ~)@E_b+D(^M zldN2e`L!LJ0vtB>75e-ty?DObizl=>Q&g*9N+OP>fjXSSx4DVgJ2M~=a`A4{p@X>a zSD|{jhdqs%@om@cRO8XLH z^-b+&NsHC3IhVP&nR(2BWae$3c^S4O;J%^Fp#t33O-Y1y^Di0uxt{*6rRmS<+IS1+ zmiFjx$yFG=zD0B6E-Ut}vrbHNn#@^p9~()S+$_>&NC9iUDTz2(2IFKn)N)@pAQ5u$ z>6ul=cuYKSm=3Y2(cU{aa5Rg-`CbjHfOEL$ge{Tj;jdHjT8TJ25d95j)bOSzdARJ% z0X^2necH&SwIv5(_kEe+1d_})WVVIOHp`43BDo(H`N%a%Bfv|v8B?%%%#=jfozF15b3FoVID^CSaHuEFETYrz1$NkA z6kCPrS&N~2wYGR^q1%|l=F?JD07v`@!~}nGe@Yq(UZKsCg4)YXNo0=N^}<`majmdK z6#<^e>S{s9=~Apn-k$YN0vKP=WQSw98UNE*Kil5Fo7_DA=eRD7pkzX(PmG9wcnIP z=CEG(AfuNe>N9gyhH{^_plLhg^*Il*StCE!j4Vvq%{e1h)-zqqv*`X zO-W>q*486**CgF0!LvcBS#X6#IIE9(NU7gqM8B#nep*Baa<0d}H$#;8w`)~9nDxsp zaKa?zE7}|?7=PK6MCKT;KTr@CCy3LkDcKOGi74~NUX>WZtCnQt=x)e4f;&6Tozy84 zW-LE6ZS63*A*;ErX(`Q;`U&(cYMtc%L!+2 ztW>QrUa4IWQJN9#I*x_a^j~L0yV?zs7SSC!aa$}+@#8CcSb(5|vREP!!NK8TkVt|* zVW#pHZB`Zd-(*T6bNFwi%z=YAo(*e8hiH1x5F5q@aTC-9UiD1?J?%>j^{2FjPYd;} zBD-KraBt@Y_hMnm2J^gptKi$G&O|?{&7XqrCrn9Xj_>xP*zsw%aPfk&s&B>pyoepv zd69Acnsy_k#rd`zU1Q@(5$n}6d8)j)%uRlXeN$N8pIlaJXKkLVT4r zg9^ky{E>xtBiZXyM~*)dLObqEmT=5`r1$o&#vl%8w^7=@x=GmNhFfWe;CUQMuq{_% zyG5HN1-31wBr@-rYYx(dtaP}*WRKZoVhslCgtk~|!CH&W72pfwrOcOv(+U-*2ei3S za5`p6B6FM;9Y(4bjVrr;^#(a>f;Egp+=1@HS31}jwzYT6tq7*tG2WRAfqv9F&F zeZh^5PFL#iOdn&b^s@4 z)R8ucjd=+3(%H=LIB*wi)juN4Poj+WBaQbM(a&f%L0Uw&=N#LYfmR@V87Nd8n>Nbs z#wr!*Pigb0ApJ>G5}6~t=Q!QFA$&=3JX5ocwn7{dhr)^A8_g;xPZl@Yxpia>bLwmq zbwe7g0e_|4G-);9j-0qtgk_^`)}d@1SfmJo$N?tVg0x^+8&<2@@HK6I6>a#cDT%l? z7_3Q#%bL7($bdx1#d}QmOAr_S(SE)5E@iNFDdjPN<&Oj#!!|FSo4F7jVwkuw?AMYu zhHcdTs-k1-1c@t{WG;5x(}kLpV`9-tA>D#W=Wf)Rd<9ZujGw(an}@r#n#(r!}{VISPVK+ko$`3+*j(W8e4v32CAgX@mO;Wr0@ z6DcBK&Q_VB9Mu*!EtEI#CZwqX_I!-{%qAV5u^=e{{j4@$3XU%}C6PIf{YP-UL~U}~ zZX)^8I{oP3=+LbeGghzF7AP%Nt8y?30Y`Ro3b%>)(VhiKXDhr)n-v9~pE4zpIXv=# zvO224IB6E}m4p?Ju3yB;SZq3D1 zqJ>>J7q?FLAT;5JN#SOPmD0eQtS38c=LkPGtr+AJzC|E?*C z%wgVrKixxz3x)~X7Q(QHYU0K~ESibc(L=Qc!k*E%du+ATj0D5~&)N->7XCdsal;-< z=Ix00B@?Tp`B1VTs4K0SE*MEjK>2Ul>?*489a9pSt3p3cRy71)kNWB;xQGOftiHD$isLNQ7LhHq|q4o+{2!ymIqY zjpNg zSzMzC?vEkvnj{R_<}=Wt-&FTI5-T(>P1QO)2cR03LNK6;V{i; z7t5N6G1>*(!-s3QabJ4H%Hx{Q=(MGGHAdrY+M=gN1CD_m;}EGS;7~>*36D2xGo|41 zGo~a$TlhwXbgttCs{$7pw>>v%xP>>0SVB2N^_$uvrG;vB==26WMKDS@-r+xMbE9DN z>!u{)7#R$C;i$fmA5)ka;nyecBNIq z9w;SkUgt&Dt-sTTBCT%S2Hg@;VEV*T(7IA3kRsSDMN&vgm!NWgqs^qEa(`_~B6G0O z?dfcw;6SSQ{@;XhhHl03x$&eG52izAfp`kenF3D|LJ7N)mTL2&Ahg(&MCJ%>Li#6N zzbj_ARZ?Pcg9Ppyh#K%2>M>%wv_(wY*{;nY%aeE%-cOWA3Au!*)a}|lDahHTBr->C z;BaYt92YMmkHTvof#C_afh6B=yz*VR^@1*pP|kgu>9wulON{D^w8c-0>egJr=iip` zofQ1rO^Qvqw_4r%P!u2|mb0~{hmbXZ*N9Uj;h3l?g= z%gDZ8yD8ElJ474316gcY7K#(IEJ@+PMM-BV{Jb`w3hF;+N+OQBfg2op(j1a7AQ5tL zzo~!b#h=9O9ak>?H2aNwk+9S624Sba)CMeBH%Z>au+x{4!cKpo{Z&QN{!EZC2s@d^ zIr_!v>muXIvFMt_s9jf!x%2-IZBbJ;FyhPNN{S}+bR^CZM}bveoD|LY@7k;=t^O~j zB*LEcPYm2#M>G15xm9c&tMs@VO)X}4tQB)JX;Jha@JujA_PBprn*>k7tnzwoRup)y zH6@W69^;_x=3q}F_ntvohfTckb;g>E+7WFz({`%O;Z8;FQ?%IXvnk=1Fy}m|&6I-Q zy@JFQG-}IFIAYB#$)d%|xaB9bg-qM>E8xozFA26R@m7g~HYZARJEkPU?(#SzHrLkd zhSPb^t&`GA=w`vZTTo7+oUJk=*U=U>Epj(vPrb*j)LZz-TEVT{Z<5lvp3!Daf%5gH zB;rsSc#Was$^F-WM99S~!=thNU3UOmH{?^=#TCd=AErkg)}PO6w{TkhSu>;(C^ zHvi}WPXwbzgn9=1=)X}D`XAKZ)m~UBp8qg^(!k2 zJ`kjGH^r?0r;*=C+SRUyvwyNDF)d5~8mrnKZ79>dS#y<`lCkgB}_N}@3 z66FpEyAsj2gaGnRZ59=68x|z4py|Km3}q)CICaJf8bP2K9h9TCKjk}&+hf{|k`^~U z(j)IQaLB(8pujIWXv^k5area;Z4MRK9yTQrcF&WH{9K#=n|k1TDx5ybA-GP8`i$$# zwM9;g>lU#tkbrCQls9oF#B zaW@OgI)l%ixJ%@7+MFwT@|-D&xSkleWua%sBfbHNkc+H5yX%k8E{6KF&`Gj1_aELB zx(QKZGfQ1ow;yP?cv^M4A&2Az;#DMefLGjzy(@GJWFH`5UHSXkd@0KGJwf6MI&S-< zBiJn5vIYAQoWpk5^IAnLh4XR5Y|1*}wu-ev%oD#XN(}6#{;cMKFUZM$Uk-fL~)uBJ98Yf<(_<>ZU7$)`2Rc?j=_YQWlSw10otS z+Jk4W&RE~0EpXaSY~>v0*iQ!$&I2ciFvAf%6V|HsY4fJwx!06L98Uw29*zoZP7Fwd zTx2yWT!(N!+Z5z+h?tUyAq~>qpfSrbb5&NSns$4qRj2E6#F4NH#3@CQ1gEqD5Shv+ z?0KqabEPO!Ns#C{BFuSrUw*jMbP7|nX?-FdY>Aa|%fCfi#lwgx0ZE;t9pfJE39e`=agL0sTGmX(yrbLH}k@<*yo zcM>-f`yS5S*3m|75+_caMj#K5DF8`RtrJdlTJnmHT!clK#HqHpwTb4_gLvOpyF^QChHUv8DrA zaL+xPcz;!#&k>mc%B0Tz>)MPeVE(VBB*M=AtEMEv&dw_JBiY%n52rPbb+FVZh^>4_ zrd{h%c;L}NXmh+S_U{EXS-1X1yRp*h7P6bY2^g|$$G|A|?77n}l77IZWE2u|F8*4Z zDMgR|A3>t0QJDLA(Ej^z02p?5;TYtjC6Q_}D5ulHCR(STXrrqYY`4*M%E3!bFaRvQrOi6@R zdT8cc9>iz8lpG-IrMrZ0*KK+r@_x6d{_YBLcNf!4ak)ihM$( zf4erP3i4Y`No0=vATY%}*{~13UA)`!VIG7l(JbYyAnPuQyWXpe`YG*(NQ*iSx9}vP z`GN?s**?VZBOpi88tsyi#MF=nwb@i4KW<7Q4!Oa2AG!=Y;24kyxtL{sV7g>D65OZY z!*w^So9C~dZsB}$xayeBHuG37AM9H3KHNq8Wm%%udA)Zh!E~WJ{KidhkkZArz&)WJ)4)b=nr_#K9oI?uV)rL0VPVmY@pWdb(IqOv*`IlC+Ecm$X?_fPTLziT_`Kj=3rx?oq3d1ay!0 z80bIHZi34JI_98wxXV2{;R?{-(`Hct`oEZx=mqrKues{#tLVQ=-GTBn){}}`%1`3Q zxvuF*vE@a_M4!GJv1eXGQ~G)o>NGDf#{Z-(eOipS8_81p@He)9VD5Z?nhBaBu9s6^kagCjexMU>Za?Z8doG93= zG9?km#=yA?{X<@uH6Rgk@rR}hhr|We@B3jKdPYQgE~yxYrT6fhrc}V?vi^$6Lyb~3 zU#i2iq#$aoN~cpDz#*Q~%~I1Ec<9i9Z4m=g{u^TXHT2KJ+9)O)5Nd?P75VR=z|);i zFYv#5Qv0il`i%$@J==ZmBT;6Dj7K(%n2}(h>e@|~5~$AQo>ShJ6m6Q+W>-PDY)T^R z+7qlKa~*A3EuTq`qo^9C*mjbn3Jl}hwS`Ix;~FeF1SjR&I8=(b3If7?Nh|(u)n-Nk z>1Rzzqz1{@P;NL=yWlhm;%q$&Hjvhq#nE<=KH0Mt&1S$aEl~! znutu2)%y!=#uRM+%#=jfq5hQNoolPN@v#Q36hp#>pUgphDqkzaeBa*H7`&_3&W#hS zcpNyh{Q`4n`-vCSs|1IV9h{eB3IE-+)qgASY5s>PiOhi;Ib3ok=uCIIjEWL$n$Bb$ ziJtlJq7@!4DC@H})}s9}RIMI(m9#}sc$Kj%h&iRq>Tzpk>Tw*I)fFdrZ55(~cwSa~ux?Z+4HNJt1r7j5a@t&OB^NB6GA39dm0)25w+I z4mUpG@{fkgH{gnuS-NM(yY>e|jJVD6QE{6kk^!mHCEaE@Clpx|UasA2X*J<6&JgtE z#$H}oZn20CviP<-jkk~P;&Ap;GQ>G*`*PFG}w<*t~nJ=9$wfGX@NI0XD zT!oQ+pSECWkzJRgo-NWcWX`04Az=;cS#5R{sNQ2rA~jUzQ?7#!u9$!qLGFXJ={5@2 zhF-N8vCnJEmlm;&NV(3Rr_mk!Axw%Z<{Dymy*y7&*x~j$ZH^Sso)aXl;FN3S{nb3} z*TEJyF+4^gP)fP!zoji`+NSTvWq1KUG$clvJZUcVO>JJ3M*oH>iLj@9opGD%xs;ba z42QfxzDpFGcp+o6d|g)aCO@~BxKW`=NGR&lW<`N!ktvDHq3PqRIaFBW0)}L#wh(E% z%Tn+14Hb@rsm8E28wwnQrX=EU7|avGsRkQK0}>$@8BI0%AH;~!uv%`uSS>x#j_(;( zi&;VP+Rd6)K~{NZ{z)83jz_{$`^&UhQIzDIDTz2d1_*~r!V5nJBtkCkGY!>vD}JkJ zR#d?Pz z(X&P8el731qb`Oz1fgkx0PWhlc{?*#<%a)^b`zv*_|EmYafwr#ImHi85rQtiEXjlV zlr~!mGM_Xh5q7puFt~H=L0xyKR-LL8N$EP7BoGCbcaj8 zX$-^A4PKTMm;RbITMC3)PyIIoWbSpNuQ)Y?BJ8yVoDgu6>iW z2ZDyM#=WhGv3{0Zh0%M5wqR+|TbB#!gb z0f~@{S=?D;>rBg+R%in)$wXTNCyqZ9&EV|JUY&L6ceUF+tuEcbS5hWfZF{m(mmS-L zb&%iD=1Wnj-xef#8t$H%x29?QFA&FMAq|2SI40?EEHPR&`K-ux6?+gHO-$3pwdWU0 zu#=dYBqupt9w|ol@3mVnEwV4jjpINtT&;~OLTbn)tWM*OZFmjha#%YM)Q$+M=146Y zNjyjNZ?*YWkpHiyB*Jd|SEeLFW5W9PBWX+yA9fq1DgSUCTJ)k5C+j#EROlq7M2VXV z9NFT8UKb8NEx47a12Q)xS#>vW$ZEcJB!{c*h2&z1Pb~LIl8a<8aqoe{cg4s3O`9c` zyqARKz>V7MD;m5`kmzZ<=04(egU|@TKZO|^t)|loElwjss#l|3iKXz(R+&-UuiXr3 z4bY8@B6BM|OR-){+}eU$8J{HY@@{R`6ol?HB@x<~-3;bmRgATJxibf% zMbsf-y>e8WVMQIznv%#|9d0~YLTYCWcx34YtrLmK%vzfveuK8SX(6_97J0xoAQ5uGGfx8&As4*RW2!B# ze_3hVJ4{K0oo*N7GuJbNRle$Di&hJ1^YJvLgmT8?W!fU8#bb4@;&n%C%JO(pKsl%V zc?Fc0n3BjG6kP1#HsIXiZp@Z9_a=vEqETyC3lV$MnYk)M^;&H~(?WGU?f{WE74vS1 zQw6nGX>+2W_EV-LQln-((1-wg9vcapu2sZI3&^;!=ol|b#KJMP@$-OZug>s&NL%8x z@ZH2$K?;s-aBPEP+vC_~jui-hNt+u5!uJajS8%AYPt%Gc&c~yDUygss1vvmUoC7#n zdQyZtXkahEMTmAV&375HFK9PXTF8cS%}^HfH$JdKbUaxW%3u((DENI|n>7W$&zX`4 zyWn%I0&_i@_4^K;KY~QDRErs|?`R8@7OquT`xY)H??Hl#*;Nqvd+o0)i2SW7i8vw# z^Sp4X$o<-YM99S~UXHP8UU!OyQylqgouPc}TBSBoin}^J>adDjzbPYczOx?rV$7Q@ zGP8Z&6g|0Cn*~KrHV6_|(DJVG?qV%fVYVt2EC@z>WI{Q&_dVLertOlebKae)5>HB7 z@6-Og($;%TNrb&&55qOrR&v85O?SMFEnd^O#<6NuN@ESDp>&6ywHTl=ZSm3qv@zEf z{P}m=hWsfojcPNYz;xD>L>wjqyBC^H?wAH7LM}Yh`F;c-I*VGhJWk+qyKn1mdsSCG z!S1z)DbUl`<0lR&e42l}k$-gf$D72D&O*9iq1x(}#l(3LxwE7y=eh$YJByq7qFbP~ zz}I*2SIfm%M_ru$TGGNHfH?LKdkPe8ovB<^v|>S$L%bh zbS@T1OayuPQTVFePG7^h;Fp&_fcHD|Cn~kE?lYZ* z#ZsZ*R=ZDkmV2M{9z4@oTy0N|VY*W2jv%7aDz(5$ty-DxKFwMYHd|+56CcXEkYhf! zTjM7yz1PM+gAZoeu6==z$8EU~JRktxqai;{ZTOj4+VJ2h*S{AmaEAR`GF)+}1ugxH z!BK2h5(~b^A-dC`Q+QmMh*+!twsxzZ_}h6&&0hW$65v3rWXBB5UY$ zyI!v~TGpeS`sq2i%@`Pe4a$05;71yaJ0eYdXOAZSS8CJ$K1-Y0k*lGQc@@7FPiQ&< z{NniZShN27Pgb+;70udrsM2mi<7qOIth=kOMZ>PaKQk%o#=|9S6>o~YG!zy;g^_?` zxl!x933PsfRP!#;hD&^_aeI#@9Hb^3nxzTX9maX@!Qw-36K1sic3+Y9GjKg5??p^M z4M>FE%UaVJthc7L7T!wFc}>r}PUHFHzUa_6Xx)cs5=Lp9bT)1sXf&K@PMMmdL+d<0 z`>5-*+PH};G6O1G!Gh7F0J=Tqlr@~Q#o~Av_y%xy7B=A2H%i?yHAQT}NUWKcx8hTZ z3w>JqtBMQ#kRZ{s_2)h_ExJAwNmhv#vDent7BP9MqBLxL=_p7|xF#vXtf9?@($Y0k z5@AQHFj#Xvr?l_KiA#+ZuE)lrD(&_V!>YGm1veOS=cr>9L%lv*^IZn@-P%o&7F67& zAqh9R z3gfh7b4LC{XI*Z>cFvFPd6nWlbiJN^jWBpe?YrMx$-KO0(X}i?6T%}DH1BWt*T@9WHv@D2i46%d0ufc_2SG!*8 zhskCG+Ds|{-eO83>}*?1NrclAR-?jyMWKVD=)r@a5rPM)ZU>_Q~CZ4|YJ@LGr(YeD%XV>laX{;i-w9;ekio!HYUB>In+M=e#>jn?6JAAyr zYt!e|-lg`IKhb7I0q#qtBr*qXJp*^A6~c9gz2o*MV!bLcRR6EGaA~31;6Zh#1d!lP z>0JM+&4Yr`Kbw-s9HR}4(OwCo9lPuuG3(c}7Nd0ib#pT>QAY93Mh~UE5=zXU0@JnH zOeipIFeP!NV7g0!Y3J?s&fU?z-BLIf@QaaY_wHZ*Ld5tNF%%NGw z(CiGL*|Edku_p>nk2(y`N3}&u3(tBFo}B?a%$owxN3>Z`0Q#UQi7N%rt`MNT_RgJ& z0LAMtK!2<)nl3=QLVyHs3P69T&4L2Z@0*giQUL7^0NT07-g#FdK=C>Z(7$MlrVG&S z03hZ~0qCE!Sx^A_M^h4+1H=)A-FvX3q+CdEwN zthE`n4{M8?7BzngdzXaTU9>zZ*tK`6!_+Tp^P-^li>4&v=ozdPhRcn-OkzMHq_MN+tIYYKz!Hs7T@{A5KaQ{kHa3mA>#TL89l9=-e;XZ8}_=#F>Ab zeT$RdyyjPSx6M$GjneWP=Eh@Inj9Ad6qrYm_Y~zZNU6CxDI=p#ntj&r7 z(4Z-a%mG?^8oLT{N(7Eku*B`hW3-hof>Cl6hUqbF!P3IC4re0xoZ%`|WRwWbB&ZTH zJ73E%CMG3`KR3#zZ^;vDk6;*kUDT&Ng>LCtNM?Ic=sC@SZazkvVv)AHq5A zZleVsm{OP@I(#%XYA01-u)eJ=R9djs;N&o$E6Pf)7sjPaMXnx6I79tg+RP{*ebbag z<{<3|_fKIg#s;b}aZO!#Qv_}voV4VkXvZ^npCRtQF{^pb4rw2i%y@7o1c_wJCGu#T za3AS%ZB`Wp=rbjexdN;UHh)A~B;sE8sKY?+(H1RjUtJ$=|A_D=+0-TK_JN(+{3vJ* zo07;Jt&K-6)^XVh{2_#JB8?IOtamlW>?PU)rp0Vi&J8*2*~$7;*dEhnNrCN*DT&Ns zTMgK7js_q1H|126TbYw8FjTM57Ah@NYX~ZzE6iU7S5n_vs)F=#ZDtgZrcFs?4$_@E z^O8vy+vSpwG^WCEe?Yr=(!zbG&OC&~OHhers!F_1n_)#Io;4+rxk}uxqZ0m+tCKj* zxatQuL|6z>fT-6rhjdwOzM$QNY1QUF9kub#aP^u%#65x*kW9;RRgXTeErFs(pED(q zxgKr7RLGwaT8AY++d7(j=VF={82RsN3!N7E98Hyct|LQv(tV6qkE~EZ{kAr93aH;Q zCGq17>KZGn$(~Ceg4%LZ5}Bj6i$Y4RA`U6E#+^xuDeCr3BMRJE z_i8sw+RnOL1fhgnHp?Z4p;GkTepL_lXmhLR!A?^Wnd`y6*tY-(%{KB(PXY4N@{tvLwA?P(I$sH*Y-ZN?QAwKh3(IQKGXv7UTSyQ$LZ$^JBY0#R*W)SgPRPF0fcYO}2<$+t~O z{Aei2L$y*9=Y@acmG5d=GuV?gY5*B>e#rEuXwtL)K)hHmX>i)8LsQmGRJCb5>|9=D<5!2HhKbg=*43G zso!EeU#Bg8T0FNV;b|jXk=ZBX%j`=5`%NmuuhwQzf%p}sBr=D%?@-VQqkT{=V4yyt zEks(NmgbWC+qEhfAJk?-0pkOvBr*qMTT*`;w6JngYcyP>2I6`xcZ$~BqF8CX$54Jr zy9v@lIgr@rWC?6f0;$t&R&oBKHirt%Uoa(+InDz(Cm824(q)O1nb4qn9$LJkh9*i< zXNV-bq<@W3{=Rktq(ylfoh!@|*pdW6Qn)061oL$&%-_>yQGxlprX+q+VBWMPtH}&G ztGi_f=z0~w_1a%o5L{zQB69@yrR}JLmXmL_(Vuala9d$}kU4 zOfhhKr;ri{jrdYZ%CSXNjxT95ttiJAO-VeTl;a25jg(e7XezQt9xZYz%HP-ix`Oie zOi4VSP`)mg<%IJN8ReT*ls9RAT|s%hDT(J3$_KO?NvAL0DWiOgit;_$Usq7xXG$V- zlt;{m^TC-X3v)`D2mEpEW=yLGBbPDY&oq&^RaK|Fwh)Rsz08zE=IV4gPF*ApaXExo zS$$s2Afdm8<$=k18z;$7`8z?QpcVQK<;M%PkJbxi!Y$e`bwuOYT5?L(C}FxQ)%KI+O0*R=w%A3pIL9v#O}T_f1J;t^%7q6<~O+3AgGtoK~%2ojQ3eYL#cK$)MhNb5^si>!P6A z0iSlGIw9NE+f@9n)8KIc7qsp z-zd_2TBawF?MozC@Ie*+1KO-A@ZV!fB6Ik+&jx=9NgJ#J(cY}{B12x%ZiKXu-!@a{ z7LY><+mk{9+g8CI*Je@yJ8w!NbFgol0qjAGce}V3oyod#Ca2b$4ELL~n_+I?<~=Nd z7!uq=D%?M<&7=bN)21YTvf%!Nb~B`fn`Xs#$wTz8isQ$%zpmi;QBx9`<9O4I-S*Uh zld=0QQ`F}!`W0=F(_(hhj25}!RBx97yQ=XW7;BX^|Z(~ z{#`0)hqXVifOf!?MCPE~I_r3c_;4Y%BPZ=kJn~hvg-;9Lt+S4N2>pV2$&&6?@hxfd zr{FtoN+NT7hq0R)lQI7+;nQvf2Nz=7hX*@Hd97o9BqP8IBb+LXjkCib7yZjQ9rixD=m*Yyq+&`)T8UIFyu zrX+qc0sX3Wb7%n?*$;B33g}m~Kd%7#Wm6KF1G?=r&i|;mR>7UDRdI+opL2_hOa%0s zR$JVzQ=e`XYt?8#P~$ze$XDH(mEXHP*Ay}!cT$ppY%_xWOjvWOrMSX_xQ`#b@g>g%+ zSi3+;Yv5U=rD0#Z;5nSs%JzG|is&b`c~cPmgei&45ncXRaoQTI)bi(X^JJ{+#mX3| zuW5^t7OCavaMQNWiww#=DkxvoW1uyo0p%^GBr*qO#r+gsrPHty_O8bZ*_wSuTbQ(P ztfcD|Wp1Q!=wTI@PiZrv0P{&x5}5-tbbyaHinfS#q0}mh_2xmo^E%ld(S4bL{VVOp zNDJ&x&O5~@DveFjhy`fA)H)DG!u^Pf``5JjRB->QDT&N+UwZ~#=fPU7MT;GEYtqHq zMj`5LCRbq$7Y<}KZ&)X;ff@5TvpvovsE(>o&C_N_f$Az#5}8A_Jb%78W>3M#jllE@sDbrDnst%jQ~)nfta9(5R{hqOgYi_*F% zO18(E1lD~jSSPgEQNVh@lthML4U{ioHqlubLo3>glu|~hE#zSW^VQ@lek50Q%df@9 zE_DaWugA}G-SX4?^uAK)5nj_E}ca;2u!n`#ICL58->4DTz3II?dtPgL4c0B!F*S zPfmgXiI9tL3KEV+zmm9UkCiu|ua)10w1jQ1>QcX)#^1#)dEtov64iJpx)Q0~>5rj5D&e#*pY=V@PUsY+MgC(IptRpKW8p|;p5o4B(fR|>ep z$@3w1O4I+FHV;bE|G<<)*pa@^=*=}ow+e-al3NFwP239W&~evCoeIv&#caPx+v0cL zzH>*^PN#i|F}!Kp+)Oo;ZqnJBYf*Ov_qG#okGm%;6{NE=_qO0(!Sn`go)kU-+tpNWv-khCi5sUZ_+9IZG*v@je zvJxkvna_0xM&^xN?N3g_mi{|!Hk6kB8&eYR4Z!+q2CJ7hYdnA3`KU8qawFLn?2zbSVtS~ z6K$*;Vsc?Qb!<9=)HJpgoR))hVW%>Uc}H~kO8pjNwM)BU()O%@oMVrN{M$bNG=>rX zNc#2om!V7QcDHNusDNjik_fxqcE-O~w=TJR@5}4e23NQ8CLfeO(+&36jj6O+uOQQB(bxsnJ=9$wb%rA z{jj141xAY^n~0)C{H?c{!u=M!SpL;bVDY};M{o^Ib9k~?cB{x#9`<81!`^7-4`#!K zT7LLAwjZ|}?#WuqZ4T#)P93qD9Ydx1bakv-{wV03_`%jlGmuopEwUg8qzdng+et@B z(ssX{{+>3*7>>X1e{x6kcC|%OAUBcymwDf3_E74dGp5o|ss;Y6|EN8H?NZWEC@G5*ZQ;zuzy`Yb$) z;PvA;HN{)b6622lFv(C~F9?RKX{jB8&3I7^GUfN9k;L%uz&Ad!&YOLEe2UL6>5;lF zJ(q>k=|5?8X^qGrLetngrF`;mRG6RSVE3CgO*k$rHzg5Tgg!xH&Le_*j+R<28a|w= zb>s=Y#*iX^xKs$ojbrT!w+g{yz){nAnoL?J96HcAs+fi}80-_;O_LVvJ96VJ8|G3r zW{P5oP#j1?ve}Rg`{5xQ4z3om(OKgpn`}*zJopE+Iach+F;fz8ebDJ79v9&_#a7LL zM99S~G?63SYz&R`8O!Qs(+m38o^wBbmrgLbaz zOACdyMFtBIL_5d#A>;4S~Gl&g_UlGN?)Yd{moP1=h2s zB*K3FV%ChgUTV85#DCJElRK^h=>u@NLbc;tv1t&YP`ynMCPh-L#P@jDc8C((~84v zl^Nt;)fP4_$Tx%_S3_ol*|!|tSu&Ea4D?}b&J-kn*_1>aNrRC%9L#wHF(46g!QmYP z5+N6yJY_&4avtC1#9U4q^&5)y?`W4 zOo@9fwE!P^5hiN=qxM%74gUv0;tF~v(Y&|FfDz!Ve7^XY4g-fab*o4#YxU&(U z!|oHGyI>%npA_5QrOl86*zKkyQUhi@hmtK5Z=R2#l~jR|da1TlX_2DqjBqE1AAOsj zG$y}D`|AoUj|dVyE!Ld(jwQji;@EsOe9kRQ>WRRg#7uHorJ78UYij9SkH|)onRKSd}wZ*WfC!Q7V@^FSxBGuR~6-2BuMle z)8{@$I%}(T5^Hc5AJi5&xr-~!+=;nR3zG);ZQ5T~n)_B$5@9dOF=BHayIgpXau{RI ztpAW(|6y(B)3*MioJdE+HAcPzP<7jy#N*v9}bA6o9Zr0&Ss7 za@+#>zkZ9z>BwjiBoR`J*g=a(#vs8hey6r5ih8}>ltkw0weGP}GmpEHpg)JmDdq&e zm@!DM!$3c$Em~Tj*XJ(yyxDodTP*Qz;YflX{8?>&6tq5VN+NT#4m?pm2g4?t#8l@@o|6H zKNW1jvhq$dcHm*^w;24s;f&@aodHCKeg4I&V3I`_{7V=oWCSkK=1{?SzA1^!FXDo|ebT)^iOHv&da+rne&dzzY9^lJd&^93Z4KUGK+k9kpBB(tbA%tb_cm=-nR}c* zOx)Ye;H2Mhf|ZbT^{_U73ce>zNo0=iZ3i1AcN}hbxa5`+<88CO8OM2AyAjgjygfHY zQowa)DQIaf1=f9Pg58uhj|$dJQxchDy*y%kquy1ljC=6%9G}$Dk|^M z=0ic{ou(u*N99H@U3$W;VePbmEweZq$88{^m=>2~#~yWNtgn#UnkWY zjOn+u#ZHUq%?ML?e3R2dd}H#0-HqUzm!ODm3D@N+uHV$=PQmpXrX(`Qwf}U>b?ahd zpxex2N*6VrYB3|W{PwIS8>@0yI29aWj|p=m!Lvezr%#&|1)fEwBr=C*;|c0%B3o!C zn0i-ZxOQp_n6`s$%85QDGKPA?wo-*{SeqpUwn0-8nZve({Ef zEiiq;4`Cjp4%DwAaYma51&N1ENo0=1HbMgD_fYg@%B^C{7KP_pcqk6Wg~7v?y#&Lj z@g5`ja_uHai{y5JWJCsvw?PKmmm$eiu2PYn*5*+`ddieU=1BJ+s#T}li{X+?wCkzG zjNALP1xkzCD%{;j9ED3FvZJwDh38pqRup*NV@e`(cvi;XiK3&FGB}^t7AGw@{c&() zY}TmQd`_Da1)JwgNo0=A4Zx-V1Bn$V@-B5))qD`ayJrk9=8+@VQ!N< z*;*CIZ)r280QpT*5}5-T_<=J+*t;5o*S{kx?>=-FXM$~=3fppRmK50fOi5%8+uG9z zkXTJ|(Vm5!<(QLB#!_>Vt1w)9v;|As*VZA-Jn7oZnH_K@`IGBasCH_zqd+xmN+NTp z)*Njh^>`W^esRX*+6C zbjFlK<~VJ__L^$JX%r}Te1-tW4D}eZS7?iv7PD&+>0llySG;G)W21`P%e8q@kefCo zkvVcJ#f>R6G4pP#jBH&~N*SdOXp55;rGCCa1^gtescllRd7m~X3O3K0lE@sJjqzxq zl?YPrYK+zwv;|Cy)8>EOA2f|O-W=9+onUc zYPmf@C$svo(!>#KhI$O#Ic*Ws0(ULNGnmIPp6j_j5^^`F$h}0HCk463OiBFsAopr* z5z`_^!`3c&*t${0=oQ*uS1@|HDTyB+j6SF>q7Fv8WsEEpqYr3*UBT#mrX+rRF#4jl zh&mWWf)zKZ7=1ze>k3AnHzkodMgu)ZDRG2k2}wvIVJhYorhkpcu)MFsm@5;*Sry=bQ*<9vSjMiv>UBRf| zl*Eq@M*FlyOxxKAqdR4cZdNhctNnEaqdlf1eta-`nYM^J810oYx<$q4oc7lhj9y|& zB6Ezck8ODxFEw!aKoP4+wEZnX09{^w{Z&jqnz=UuQgl46&#=xq;^DMO#d3g`X%iKNDJ#Wk@6Nw;OD!M z1lUiGBcS|^_cF`_D$HNhW>JCp3#KG8hj}%@d>@@ck3@W2#nUKVi~^cefuZ}pwoqxI z;|rsT%oSY_L|jQGbejs&_q3T&K>DsJiOfM-5!@M2Lj*Fm^E+P1;B4BRm3zO^-{OK) z4#ABC%yt!+_1cUmz^pMPkvTBopmmrNfGzKgk7yt2U5&xIM_a(OJ&FAMyX3BOn~Kmr z?XN2c?KLHlAwu4jep?RU;1mb>hz%@3)hX)_+mF0ejl>&i)4ag2<%QJcx8erAZ0?{v zZ`!PLc?a!o`3~9#c0RP(yJXOlzV|J(TtVmfx3pS4oTIZ}9HebZp!!bRj91Vu92rzQ zz{^aVIlOA{oGFP6I{@E3dadGqqwTCyrNTsPuiZ=)S>NBIEo53iH>)l=`Rj`ADI--G0> zO*aQ|#@J+ucS*;B{#tJ`{NL1WhP3eCmg9Tjd^rNZy|c1$Fjy#tWDAB>xWA#zqyqQX zO-W=9_vX{B8m@(>)84IPz6tQ$&ZxUNV@-y$Z%J2sKSfWRB1pFIweKveipy z6KgO^&uWX67Nxa*+{)uCcK__ck=-gz@6qN)!Rei*Br?aT|FNQrTjpxk1?(GZ)#~>~ zJCa(=2z^dlptK0}=PrOFyHK+)V80kRl6%n}6`kj_IZ@F0tSO1i(b;^uQkp2XD$`b> zG(PS&Xu{{zF;l|vm{Fi+tjU;tOIysen8Dri__PB)?SN0oZ@5Fn@0;3ODfoTkfA)4R zKvGm!7~c2$!S3=Dc`c7!0a-zLB@jRm#9aX&C=p>YkKUbW=Ed|3?3%I)Lxfq<2q`8+ zglIIHSVqef(JIuKn3%-Ugp`z8s1ZXnNkyZDp&}7uB>#U-_x9|nmC_QevhR0#Zl8P3 z?R(F?ed+D{Syi0%`!~jYbmt?xHzn>Hy87S4|C?LyW9_W7HEkc;_Yls9Z_nPoNnn0o zoc+;zS@o$c`^dd4|Ewwo-pfz3GA14D|chA)vPKhC}=M;tC@^8YnW7+wM?qaIwnnKJ(EG^ASU%@gC>nk3e6@aRpwwO z<>nA34dzfL?dC8hCFXD@Mdqv6Py))h2^(^9--Gr!8M}@)Sw>K|?-rBns=OV(U}Ctr zz3}FoIeIj%C~Pm>mNOSC76HSG#lW44CBTDvr7)rwPrHz=MDyhCvs z@YjlMz&8{x0M<vpHiF!d`odQuwjxYn**GsI2X89 z@k_uhieCoaqc{)vjN*LY3B|7f2e*i_1;DwA3xVqu7Xh~_UJSffaWU|);u7FV#Y=$0 zT1DAX;5@};K)>Rpz;7uo2R^KL*|uE9F2k0U_{25)I66=fI?%Rrps7eY(2o__fu2=l z2YOeL9q62ql9wH5o`da$+o84sYeo1o?$mTgaWNGC7h8I^7mMO%{G%0+ikCRZ>&%EN z6hU}U1r~Y4LCI7!MJ{Ifa~+iP>s^5t?^1!5J*3F4{gNWP_J@k>+GDZJ53H#WEmx?( zer`D^=WlfdYJc1niu0YF;W%3YXPdDHQakpCgqmfFbT+C;mu_)Tl>b5ny7U)C81B{L zg^uD8`WCR_F zsV$T-f-Y2K1T_>HL3b!JfQGtBj{;GM$pliTR|BjsMq3VIxVgoE$$m^k>%3j z?sibR#nZ09KKH%~Y>Q)RB|qEZ5=FMfh$7qKEsAW5KUM74+GjasSD@^gI_N^zN7m_z zEa4hOmeB2>SoA$tphbUBfn~qrpkx|SFDkLwC^9WofmVebluSEafte1v0^*5z)D=n~ zR16Yb^klMwa`{SE;N@FXV1K*IL1ywQd$!|nB^)kqfW}I3c&Z{DzCw`>TMkM&cdI}j zf9s%}|F;TsYeb_cL`;GyUvUN66?6r+rt3dB|D$=2YinR2oA}u}bpr{-@ zL{yfG%Ehigm1z~=P{yuW9L?av7%9B;;TT^H<6kMowbf#LQJEl(Z&9T2%N>-GzODjH z^r~X6qpb$o4zs)((RNai+J+64v(z?Ekv{qr>El)hrHp%Bffe+U3iSRz4oaq}!$c*k zVugcp-f{(Ae5)(4Djs(QG~|q3=}rkQaICL|_0QA0TCx6=gQ9d~xt^E$?NEV!#T}GP zdt8AfA94kje9RTtg^DUfS5bQ@gcesQgV5m$BE-+Dz3eOnbd^xdn-q3^IF zhrW}F82U2K<5Fj&%X?h12jK}SoiYqxBp!@r@TxKHf;^V3!_(buBMJ)413cW$!=pU> zjE5I^ILgCIJp7S|V?3PV;XNK2Fda0Tc$mt=G#m`e9@H6$8kR4e@TH7ECK3(trb{`y zkn(YGw-o3}EZLK2f+x`yPohztMC&|>W_l9s^&}eZN%X*z=!z%NCr_etoET^N+4I+aMIeO6B_kcj5Y zzZPSQ#j>kZ+LLo$_S%XnTw&K(kpMTHUld4Srw&iP*oY=JLoUVVQ}IRQ6?S#V2uA%W z{c8CNyF8Q##{Dr|Vm<)RSJ?Ft>q;X(Pp@6+!&W%a5Z3;3>vDFj&mT|37kg(i0lPdA zFpXeZn$qjAvb{jNdA!T8jI_8?j$Tg@g6U?d2s$qYFo$y%;i2zsfg4epE1~&x^5TqL z984zVg1xiR%>Itz%KmOvU$5)dWRgPQq1pzd(As>T$eL{AW0@*?m*4wc~i>$(7W?pDId6Sf~&c3f`khv zNVsr+kZ|Dy2^UU~aNz_A7fz6H;RFd6PLOcn1PK>T z)-eNLK*Z-oa?(EB{6?GoS&%{|IqL2+iOLB;rdTjyvT|ZS@9t& zE@Z`ntT>Pr`?F$xuUOwJ#%IO$UNJo@miLO`y<&G(%+89{*-W2dMaiB#v&A0Jk?8+=!%{Ba8{2WqgY+TD}egX;VTa@+O3sQ-o@ANI|X3Lg*;)r&Yu8*Z zw{q+!YkxmadzoeUQ&_w=3+B+zk5^fJm1G*b%8xhSKF;1kTN5_USXjMOxaIXS!T@(^MnWc=+m^SF`Ju0Y7gU#=LbTzIfukJmC{iFnMP z+Tg=|>&f-njUfgMpFfH*20cJJQ`Tmx=a#Q8$PNr{oOdz z;18h?`G6~yo&DU>px3U2Rhg9Gov6_w`ZClZkc%nanUK*L#U9AsHTB_ijP=&#ooWmw zqKUY#)4)E3-q{9K;*;$bEQuN6E9|;Z#G*MEjpJ#UYVO3Hx2rR$sLw)Mi{T!HueIyL z2HuK{cVUbSh7&1{E|pR)-Xink(TCSGbz)GDC*Zck3-J}6*IJnX-l|HYp)gFD`hT|u>BbaH(FIJ|rGtv$FFg*$H z5AWM;Hz5lf2=2b++swCHaW{G~L!nY5C>FTQNmu-(Q^l}Vppjd)=*3HaGW^4*ueZxn znYf->SatLKEhB&bq+RBG?ZqzfXVM9@Oe+%%8kkRnatP-f`#1z$iv})g#=kXZh`EN+ z!b{HGzy8D<9p+adnb+~K76(i|j8rO-@?oF0oV~VfHCn0F77LsB47w3-yV{Ul`nuYj z4GKFDwGhU;+R*FUIxr(at671Xw%U9$_iW?6XiFJMY+h)!^*x6m#&@9)Ga~deZ({Pk z{c7HVcekw@U$x`-0Q2Az<43=XCjGhnImf5U4}SHV*A721;G$l09XzwIJ1fr2bbMhv z>t|NW{f)0UW>qYFV*Q;@uKFU)n&p_q_}b5`*3C1vI%XBjdi?N1Wd&cPS+gCp81MU; zb>2;9I^5ctpMA0U(7>CBJI~Rf-$j$hU$adwTJY|hhi1OIddmP`a1qCn{ujk3{K&cJ z#OcE?KU??-6|m;>Z7#an@3_yAt)Ed-9=T(V<9^d?Q}e&IFIBod)&(|pL-|-y7 zIM%{2yR80il-Ds5Z`eLYR=@N0Isb*zF2{y3god_fEUJ2ls9pBqRdHu%z< jjGzWBY|k3R%8U1V17|cT&K|La2q#_1T=>3BVA}rxb9zc& diff --git a/doc/LectureNotes/_build/html/_images/chapter1_11_0.png b/doc/LectureNotes/_build/html/_images/chapter1_11_0.png index f67778338ad1c1425a8c249bf89dc29001782663..d56e1d6d04f8fa9dce774c4eeca197848bcd591a 100644 GIT binary patch literal 6573 zcmd6McUV*Dwthm83`iMe1VxHv1C9iv(!1h}vPFu@rbrVJ5ioQJ5UPx*g9vVH7^;F` zsL~;n2qKe_q67>OFi{zh03i|~)Z`cEoHM_3&OP_J&;9Q{$$s{iwO97e`qsPNRdUnb z_JoX-k`w}gkO8fJI)gwUQ$>fucM>AWgY9Gm(a9+MXXo&9`GtR?ded))-@9JDigP!5`OCf|2rUUa*N@+yy>j?@ zmRNaO!J8xZer@zg{QlG_8}|9FyLQ*A;E&Y)fTnJ@qTSl3k$YrUq0A)=`oclgErhyNy0f#BHvR4s*QrVQAYcX6?C2ELTzRp6sZ%>^tz zS2VWYnTP{b%?rAX2=UFNC1|L#lr0FcxUV}Sy;<9-{Tw8R&J{D=7S%a$Uq=PJ?WYqo z=woFgx)uN}%CL-$3y?=(Utom@;MG^ifUit*5ZtZ4@)}^;hWf%Ppz28L+y+I0wRe0= zPmub^!$Md&bHY<&WT>IVNpobRRgeKBWL)%wgEXR@;OjC3RWM$gstvXgCz^pUvjzxe zS}km2X@Pwwt1YNAnX#y4wefC(FIQFBP%O~zPzf@z`IRHQ#9|b&nI2g}>@I9`kNMvOtJ7t(a=_74zJ=(5Qy;c(2nc)rfvLW2?w5%VqS7$%@I% zW$__alNqfaxg;Gh-}Smg@r1&;id-kCo942`9O+EYu|T&}ktNv`03In6k2)n3gH4U8 zdLYMYK`z`;Ftf_gX8E%^IE#wN1V@PfW`|1Iroh=}ANt8WbQVf*){Zg%Mlw4yW_0X5 zJ`V*gcIHlZixCJ<`H@pH0ob&cRd1>tp@dB>a?8n_GRE{RjOs|3Q3WYJfiljtoI1Of zNn&dzD$-m)Vbm8HIiVtD6|gIiSK<-#IpXn%bvHYi^pZZ=m7{^^_}0qF>(CGrV{a|| zN}v9vuSI4&8kd(^yQ|Yu>xD^@ujWd$@sngvn}uwkG}E+}oXnp<@=bF4CdK~J7Q5C_tMAe*baqrOC&ae+a127HYVFMf6)Ah$c~xq>*s{&6j~yAdiDn) zt&Ne8-Y2zO0kj)Ue5jP@lQ4IK_=`OWW;r<=I^G~8PI)4|^ zd?nds`;m+Kb%|z#JK8>_8Lh$fVGWH3)hTOG6`G6p8*&;y*(P+z=N?Q7RrmX&`OB-F z^JOG2^bJ`(E#JKL@3={%)zh#ixh*?6aM=}ulLGRtjk#p>zJ>a<`aBFY5>J=qo z0VhA~YJIdOerm%RiX)Ta2XoegDAQ+4tl_}{-HwkEtXiO0!v*Drn4QIC6!KflrjQM% z3J!p0tmT=~5X4t+df1m`;e?tmClNE^D&iH$KD9R-d{Tp$MYg34T)5@b zUW~0+;C$A5W)9yQ%$3MK1F5-RJ_^0L3J5e@JW$mEY;zO+mW*;7R9z$f`pBMxJ z5;rI2@C+P>#-X|jlD^~6aaIhgg*2*nZ4>~tPnlv!Y;)EHmN83#bqtCKgI{N68%%dU z092a1pMKc|MQ8`(m@ThUYRv+a^uBp4xJ;r#yl;~}Qc?E1R-w5A)xA4iZ zD6(W(cXOvR$-I;%iKzF$=0Abs6@m&r2cOL>LmogxF6mo^p6j2eM0jRdy2FL{HQM?g zlQ`C)hk{kAl%j&5@$8Av^G?v5_yOBc{a`HDomG@sAp+-n-N@jx5M7ME)Jra#S}lEg z`a0kja_JJf0q~3b%W-e5yzWdu;CouDKuzN;4EVjfWn3MleEs$-^Z1@REBMl2iPvdL zCv3VUYF3`hffdZOOL z6C}yOz2KvxPg1(}Ceae*6Wi4YdT?QuF8fkR142#wy*ng0atBaRKbMYy*S%Q@sDPxf zOjG;?2!t>FzHe`;d}3Z954Vq7Y^Oe5@;2t;X-EypDI=AV*!x)(Hz9TK(U0XP6u|gR zjRz&ek1Jvy&!4yjfLgSD#DaH7i_d~MMB{}$+eA0BovA7zmBM2ur#{csQEC!Bi4I;O z5FcWPC{*V2YKns%hXv;X)B6tkJIBgL?aS%4PghMaf(u=A*+-*I5|b%GNHw*;34U)V zh>`0c%cj?46h3(+`jLyuQa8>!TMeA{zMH&I@ip|*bqZ7mUpsp{I}GKa($Q9-8eB`(AMzr^=pH-1 zs_y{BNi6sL;xF=a$+IiACqr#v*UVYlh5Y1IxJ85d03N$LArG2@1we1c&FeqILzH`> z%1ndR0T5Ro#mNr4L3R=X@hC>&3=Gs6q}}P-K@mkjIZ0NGj+o->myqlnhs?&QGfA1n z_M$*5^82L=R5mU*q6j!GULLZsSKi3{69+m!78Sxei`#|)tb{%)NG+TI;YvxpP+j!K z>LN|s7QTJqN$yVnLmY(dbe4IO$^1dEPvXF)_~)c}-D{#i*S7J#_;0ba;J_NEL_>?^ zKMn$*(ukoH+e4Kioq5qJWT*$*S^znh#1AEGQf{q>4+%Am@Yc=pwS=rxowrFW#Ona{bHZOmd z`iDxJ%%2oxf4mSZpdp3 zjA?W}U^BKLx7kj0A1L3j-ub8UOKoA*-&KSRdlnU??$>c&_?zY5zq88@)(G4$BBgAg zL1QFm0!B7i=unv&O<3yYbJ!?BVoNKbfc*v;Es+AG4gFgJj_X}QVo?>UejijbG{Lin z{saEZpa^*`#md?oMlFoRs)OY=|34EOR0FQ??8)jGN)js7Vpo5!6RZpS)FqQi_0Z5H zX|yGNW3@{yV`@?hRHFV9ci$enAom+D_KZl|Px&tXPx7Ar<>e+y>Z|&%UCJ2^UdMcC zw0x0|(OJvC{RhAy!1&$Nc-A!vIdjbP?PJrtyFl8-{UT~01tbNmO}f7e+mJ9O7C-fl z2STTda%}8&>W5$LIGbWMa763Vn0@Mpl|tZIozEg}A2={@@W1UC|ChDY!+I5y^Pi48 z*O7Sm?%PEbqJ*!~+J3j;hsfDPZC6$!oNU$Kw1a$ zs$lNMhkr-=qv(__!&`?hG=APQT!U7P%IDGZGis%`D|}+zI65HZ>-?3yu{zTnkz-AJ z5ys<3Q>~3@^9jNQijCeX^GrK(BEBsmcSt5P%Fk`8jQu0Z;9%aW6AYfaM524$jR+20`Bjmy<44S@XO(M1Jpn0q zZUj>s>Up`SA#RO(aJs#xw=N-YV{&rT>^P&F_(}@z+1WghGLkeS7*QvFpGpKB&%lb>z};DufrG~M6Nk?Z|wPbURC=u&~h z*RMgPvWtJL->9GawRA4P+gYvB!))37M73B>`@Err^x}aUp)h9ld9r6JuRo}OPrIq< zSni^;9%o{~=6EXhFyGa;$IQ$#&pfwZd;bS+JZ3qrA#kpCPWb)7&#j|8Lmd?biV>xp z63F_-RN~9$`Ahv`JBjy4j4`7Xg=?M^Mgw+iC0SbPsa}AB;Dmm8VSQj6_wbrvB{il^ zR9M?+jnzL87=#lua5WtspJ_7}RzF%f26sAcXEexp?$%z4c8c)W*{QF!MAu~pB)pkv zw7F5Y60HAugVt`zao^G4GI^&ZPoY^ktcRQ7Su<(>@GG{4zu0EKOJ=oQ=G^-#rP0{o zX+m~HusmL$almup!1uwt0v0MwbqMjt*j) zw=gFe4RL}eElWfX102U>U$#W~sdTf*^#W$(S?)d|F=WVqiOr1ZkHFfcs6oA#oU!$P7llY>m|JRq;;f~ghT>0@R7B4L#>$}nYKJFcZN5>U5Uwe1Qd?|mb^-bs$!|r1pb5IkfzmaxWDObqSyGdsn9-uC>iNbc9 z<&|7nNN=?{_T!0Q&1ht>E!6thWMeC98j1+9BVr>Nv-7qIb&ScrHXj_a+2Uxod=ggQ zOkiy2h3YB#5YR#R5PkPHis3gy(guX7Av*{0q~q~w zn9@9`oP?w3Cjw;uE!WE`e_HC={nr~*h{!GcmXiL_C!!dDkwla@ckahQ4bb09>90qZcB&8I*HCcaYt){je@{D?Cq>=zKaEGMV7bU3;T1T|+J4fZ z_H!y7SUCd|;n+RYu``24GB$B$`VbB zu(C}XO_c6LL_fluQQPC9=8}z^tspT->|Mb!!SS9%UQ%f@T`0c%2at6kjW#f>2rW`c zkA&L_W~P;C)m!VL-4W{txQ?vrG)&;HqhCAQkrid6c`=8fhNo_yYHO!c#o`|mHnvdc z#m-CzXsFm_86z`=N^n!5qtZ_;JiK<_XvQmKYcQeztEf;;!hLJm16ePhIT*O(Rv*(} zPsfHj-&9(AY*J=kL^3z-?RZ+&8}RW9+V|}azj$M+X#7`Egz>-W z_FYRQNpO&w6@Ggz<^p6^HF!-MU1MI&b=ZBqc6u|ME#oGY?F3moI)kIV8&vG38NHn- zhGz?EY$B(m#;D_yde7B{T_C>W5Iqq~lT5$&)W)+#dHMSzqP8PyUsd4#q264_nM{uw zRfXLB6B)1CtiILj;a4vd1rk$I+Db^|Qh#>{S4K(;Ut)?LlxmzbDjmQREbACQk%G&f z)VQ$ge|%@Cja$8-#(ga+tM1!6D?jwVSL3b8t!!rg8Y2;mhc-4W@eika?e&_@Uc;DZ zuYH`ObSBB+`CsNsNxb`IpC$c96tz5F$2ZC@`+{xaV{$DOB*(Ge$UdGe3Q=~j6K!N< zqk7xw3m8z#=k4}#$E)=c6TOinwtM)}i0OmA%FOqu^_f>b6>r)^?fl!apcEMBMg05t z{$Go+!b8&n$Br5@otZt+j)|}tXzcmAWo+OuPlL4pq>JTBl{G$@iuk(lU7+7Bczs*W z*@|okV!EM zT4DJts}73ToRb$8M;mZdPkXTTtyENY`5z`ZZ-r>d(8X&6Qw&b@*9ijr+4iRj%kx+N EAH*K)I{*Lx literal 6624 zcmd5>cT`i^zD^NP5SSU2-qgcjp@=l;mJv8qTj+=k0)`S02)%?+!5KjGARt`@K@YtK z2uc(Iqf(?wNr-~9kPzuYATQo~=dOEa-S^&l|GjnAUT5!p_Bnh1+V}hRKEI!{HWe0> z5=0;n!l2nN7Z3ztmdYeknM$!vNzHQ0HBn zUH)k0srs*#4}MZVzsG&_jA*%v`PCn_tbzvr`1DJGy5H+ooaT|ho_)g}>T|!Ht}Ri0 zLyR2@A9H(E*Z z_T46tPH$-ck+aTIVfCjI59WU)T|L{1X)~JxE6DZNv5H{dMZf zwWXK=9RdS;8chJY%@&Cu%X)DQ?3&l{gF2MHl>niNjT)3yAZ6ZQ)#wVE{mEK%ikRwa zc>%m*zF3huKc6{Xk~%*#7Hb9>TUP5(bW)w~O}gF4Glg7j0)wChpwWopJmj7>sgoMT zw{8w;q|RF`ZUEy5TAu?=KQ-!kP^1@>5L^^{Zj-OnTlN!AA|KFtM;9_AG~Nbbgld)y z+sk$$b1kc1j8Bpm&?)E`{u8^_T?lZ5dS!;SXc0^s)LgFRhJsmKBzs5J z51WVSJLnLy`;!4Zln}eCRA8GwEY_xG8f7D3&D_mR?OMu4*CWKy-80Gy({X)qIw4l$ zqnD*@FyTw}r8u%5d5y4u;){|AyEcLgLu?G%BCz(B**{Sq#8(NxgQ4e46oSQA^go2YjW#T%ocw;pBI*~ zhW59tHlpZdO&qjK(G|riw4Q!<8N(FVzB#r2l1b(uC$=>6M_f(0Ya6ksJpQpWLFYK# z5pvw!+&rgwc1M-Mpsi)WZk|V}4ve&YtTcI?5?f#Pl+boS1sxZXNk~PyUIxKt=4AkvgBD zT#VMtjt_C3U*=Nf`Dv~gW6MQpGKQ=XDUBJ>z9LO7vdZ?J?#g{`1%PLO;Xc~j%c3gj zIgu=(+D`o{4ORy{R}OEud9hp2#a540!QR_NKZ@y~iUD0+iWEKSifQN#fI7s~6>kU^ z#DkED=2z-D4BizI!j^Lyr5dVXoO$5k${IKX!QD@3Bu8tCpO2g^WI#YNZaD8HQ(Xv@ za)n$lb=a|4gBwmz|J%yDK;3a=04&=r1YS7W%G2Zjv#z3_#K11=KnsWps!H{;!oeey zB5c1RDAoD8^#H}29!Qs=U!wm86t7}KW!OjP8mZ+Vq?w0ZR<;%hu^TAE;D2ZkJ5gan z8b{LvIadU=K&bs#a^(qR!*ic7SFf=Y#c-*B3z+DuACn+ouv&xCwY9mX7V`uue38yU zghCjyK4GZ7t_=3W)IEc>%UoMx7TZ9GFnhC2mJGVIDuuLn>WhT^#HIj5WH%xt?5-2! z*`fLBvkCn^prqu0&Jmq|5L*eep22ubT@|d2(m_ny4s;#v{~45e`MUKn%tEW~C>Ox- z8`)!Y6dN@ujj5Mk0JCgat>jym2JmFO;e@JW&+2<@KLAQmA$fW_&}sDl&`|$$puYqn zmjt+)Ej9vNCCanDrxuWA*6#^tp#kz2vM%`OLp;k;H6z4| z(WH{OL0E66vrx_u^&z}n<-?YESadD}!2V)thbaE23gjT7dtRpi za3ZK6q_utRmrpB#Tb=KbEV!u|mJ@B33!5NlkHX#;F@BCMF5dij9v$z=eymn%{VfW`Uaz~sfX29?tBbW}&Kdei!(*ZlHhdk)pEv2I@ zw<`x_Ah@1zw!#F`Ol_wMuZYpnWD@*V{t#4n@bw$RM1Z?@eVpt_&jGqK?J-bc=5g!p z&*#ZDJyFtvoY{+@-mghxHBVttbg1nB27})hu=W~;fX@5(BE`U%eGLk`G@mDm_bweY zfeO>GIn;9u!b0daP1ITY-X8#{;Wb(L6fS|rP6w^7I?^-A6*llOo*pFD3=-{nIPvOMgpKnfHTSS)sY5aIPEe;*6a}) zRK&h&|E7J3z>D6qdrG~6i*xz)Lsjwnpx|F5rUT{ECdDCV#8{tXDE0M`RG>5IdhFo^ zcd}Gg#Rt``a2|1dc1<`D$S;RA=pEvF3`EwS!5j0hOL!x1o)-x)-l0IzN5FF=a%}e1 z^HAaNadUfs;!Y69iv*$#e1GG?mgoCJ9m+HWM>s&3-2uLMzwX+Y2B^4~xt+(vA`OrV z6iKoWA;YFYm1lEF#^^cB3YOlt2R;jF78F(MAEpe0IIvoua>IJgVlfMFHDCM&$lDy( z7e9O-urgAHO8HdfMRi=f@JwPS$; zpA$v-+M0QfzCwk3P7ZEWp$lOXir_&9Ue&Gj&+7GIkIE1xx}(fxG${j6`S?~au`}Ck zRQo~pjlyrFzq#n_v9ePCGpF|Kj=uq@z-a?DcLA%L|LykWKZ{|ds0`)gPNoB-ROkNb zudbGhp&-j*k@NQi^w+GbIV#k=x52Y_#SCiKNiONi6PYiEosbX@w*{iGFHT{ogW>br zhx-qLQhk~sXL1o^G_B?e4V*7n4-98so!;Ks6n}C8yg>566G;E!5>Hf3p*s?NI}`N@ zsZH@V^GT_I${t=*I_@HrlXF!#z^Kw)@EtDzp#NxhB*ZxlMe_oJw^hRSey2)0Y2);A zm_cAfw7c};xh7fEi%_9tZK+6r1(Y8Df7H=mH-dj^@~H8@S%0eog!r@Gs(n$(WRAMM z6FU*`P?A<7aC!B(8J+&}%tXB}mgP@t*2J6lCOHioCMK#G?cKY?dcJn<+y5h$rp_BGbxQF=Dk1SJl2#| zNxKKjqv;I=De+o9a$~F8o1%PYjae`hyu)eOttwo@ca%RU$g0~7Cqi42Ygjb5e;&d4 zmR#dx0omTYG;m?4QXyO05646m2XT+rYz2*(zI4`}g@tL@%3~672Suz(N7xD zpP}xnvmcjWPz^(dTsr-FOz`)q*qW=GAq}7Z5U8j?3(zqYj#*jhiCBv8zo-FJ?y2KD^tQVfV;CnLTNKX1_0!qav{_Maj?ec=C zk%jGPCJ`xF?(;3h447AUztlUbY%5vXU<qwIuMY8xWG6*PFK?{jPR4h- ze>CyT$(!Z)d?cwe*PJ6mDZ-;~L`Tvh=Bq?=OcxmZoeb@+_8MZkdkT{~A|K14%P~K_ zh@Y7Emi5Z6lpqC|W_GA|EXU?`$Y{{Ml33)~ZC6~lp_iUAp(f6WKqa4zmW$oVj_aDp zM{3u1*6^$rS;a58>M4a^BR5DVv1U!fHBy?gOExdgpJqsUY$esW$qb?;w8z^76IqT^ z+{_J?$omg-{rk3FIGUa8wlSX34M})8V7E3dA&*Mw1DCpWeswRZ6WN zVW*O%=nHWdBS)HlYIq#PjiRIdmSiQwgwzjRF`*hBo3T1RlV38v+={~eL~4v5(0E&I z8r>@DWb8x~$q%VQ7x^5gpP%Y@5Hn z8go)gbNqa(=SLr{uBx{*4>h3h(d?7mI~yHD5;X`6Qv5ig`B8D_3RyCD(-axn+jxNW zaP)C}n?l0qEtgXNW#vJo+L(NkkvZKQ529=-PXBhxUg>A&wM!G6+(+9BBF_xkzN=gr zHEgNZPTe3MDHnWdj>$c)cXbjF(7%}u)+t~bZj3{U;a2#h?&>$`n zBliMPV7%Z`Swd>BU*#LH<(|O5SBn*~meX#jXqA<|GEwQV@SDq4yJ+NAyVl#K{CGol z9cyi?p}T5RD~tGmTbx=e^ui9)k*rUx-f$RzNBDiwX4N%f0amMNZ}8V|Gd}+?)V9Ao z@eHIqlRzlC-k9=d)?ehRUACr?|SkP*x>mR~Q) zTHaO%d*A&0LK=oy7J>UaPt6aI(=hmpkOfwxb{?@Uz5=Fr|t;ZgNwEL8E(< zHM!h-)5>Ra`|eJ@1;*!cS_c=t0^ihf@hjf|8=u_S(_NF@H=F zjYj|#iPoeEf(=p!k)7VeQjD<4K}CtJ|K7ze$lx`d#YYKcxtql(q6(h6$63B*r-AVL zXF^9lI_mz^4vH5dADca0y7uo!0>z!-WK8a6tRTl6QZzmjjB*@0i-~>=iv;ugKh!Pu zP?Y)-jbulXo#NyzYZ?wqc^Gn>=!@`%$Vp7J&s#0)cZV2{2??PrW7PV_UPd+yZ|rTY z*n3@$tu)Yp47&<-i16l2Xxl|*y0mWUO>ERZgEtA0go)w{)ZpZ5)u!h;Q>m@y{Y|QR z)VWl6?qHv??^}~_8v|t6X^5LW84q~blr_ytdAM*?*^hxu0)r51oZIW!+c28?1>Y!| z{4*tyU`19_!K+y8ryQZnk}WbC?mGnR?JR$)fsM$@H(#8Dn<5VSFVI`6~pWLL} z7sD#ikLNLKwgW>1wZ5Aj<{EFC!gSfE2=Im```i`kB{mxn#4$KgA2;sSgzrYT7(}=i~;ta)Ir$kQ&k$b=eMcw0zp|b5ocKn*~%9>3-bm-M8@)0v3wD z-K{JSN}94Fe?VaHe4Xmq(>)x$StQ+~Y{~f%!yKx;?HR3eAHecU{N=>|Ponpap8zUQ zm%pMxNf5Ua+-6)D0#wp7jczzOdeU|{XH9+}UyR!qy4rllIq#*4%3~nbDu|M%Q8Gue za7n4P9}`wofdCtRIUyO%L$$pUV!a1uIB!3OWVtGA(ge5f?U#MDTWI!}qJt1el05=9 wWll{ykJr{>z{Gd%-xsy|zn$96anVYGhdK&dj=tgjZ9#x%t$!&$edV|R0+IOeOaK4? diff --git a/doc/LectureNotes/_build/html/_images/chapter1_13_1.png b/doc/LectureNotes/_build/html/_images/chapter1_13_1.png index a2e04aaa5a1664872badf85045c9a8c5102757d6..3fd1831e1bca9c9308fbc17dc003d0fecd2c8c18 100644 GIT binary patch literal 9329 zcmbt)cT`i|vvvXjRHQ@&lr97m=_T~4w1{*86S_bULNC%w;8jti7S=ndrIkVf@GtWG89$T0hv7O;P0{{TnV8;4Z z001z7esP~-rq`g@(kJPEY9R)9LahCfAz`jT9spCS+{-HJeH^}biOLd%~m^Y6Lx zf4?VSU#IwrmW6%Q$+{|cVO=HD@?(Hy(B?=!LYpFyeqVYL&F(;ke^e zG_v_~A)mN2jyme^>HbqWvwqNWRYXpaIFGm;3Zzg72zC$>$+n>Df*cO`ABt|GOAj!9 zgl7IhU1})C$$=awbY)2arXdz%L5<7adK_5PC7!IAN@f=GQrr*{nZ?=Uh$Z8L(c9!3 zWItLYjgYkGn)Ra$iw^^KF;=Bko*ZIQ8DfD_kCUdjE(~0o zV{~cp%q?nMys?deN-bH5{Y4f};Z z$-_Hb7#Xjl;|Z~tc7fZ7ku{Hs_1&DU%doIICm~F2+yXQwBpV-vXdk$A=|sB-XvR1# zz66BHW`tDYQz{%~P%)&c?PLGl=1OI)nnFC~;}0mg7d->WVJu}7$_UZikf6;l;Nt7Z zotV_rq(|}g@jMLepkuAKmQ-ue*|p(7XTYJ>hN=s!ioqChrj>n^`R6^~(pD9$_p3Ih_a3Ky05r6vrGq9W5{ z&C2bA)xF8a*oW+agBHX_Ll=VsWBo}k@C5qgMvx-o9hQ6^u$_KKVNzo}Uh4!DpR{*( zB{)iS_ByQyy%5H~TbZ;sku)j*Ztqv!MM0)m4zY9d$4204d+PW2475&K5{u}pAk9C1 zq+W+5T2*B2-qfv`oS>fr`W>MaeDl5NKJc$4+lWf$wp-`O06}1C*4FE!Q83sp)JKOu zHzmnPWcJ{asWK`)%&0>PT+Ov73qdEPes66FeQJVciGQV7`FMHf&)*%D!w6HY`U6q0AXSLCz`bC ziru2mp@+4>p?)|fr_b9hgv$|DEa6ZXZA@e-O2AY`2;j%uCbMJ<^YlCvGzZj&9JUtM zRruPEoR3}zRbJ^ey6GDsN00ob#zf9cd1ZEBl!Vt7M`;$^`IKeyCkpfagv1>(tBSi39%QbF?Ll zSynB6JF*ZzgQ>ku<%OlCCK<&aph*=oD;HqqHq?L9ELmQK%a1dXJBfbQO4(%b(mtA9fro0ZNb&7qX36#?^$)% z$%VI(zvC5%+K>WJw3G<4TqYR~b`X_Y5m_SJ&YT=U1c3RBhc||PC`jJ?hSsHishAd8 z9XY}rw=QmCQ*+Zd+2m#a#q*{ zX)ENBCK@gcvuk8R9JP)BrJUQMhvd8Xt&vR>%EMh^(Yp^EvXgBg5}KQN`)R}&4WBWF zD2+(4ECQ4kr#U$!AL8D5CbwWF>|2jyRjZ1Kt-?Q4>_fyRX_84(ROGmLq^;bpD%BLH zoMk1WNPH%Z0D0|Nc^#XQie9DOWYCkHX!zhJm=Sc%@e;Uvv&g4N0r$|1p>mlAM4^cH zQz$MI#L4^iJ5kODh;u$x3FOy3XF+kd42kJ^WBLgP8TMSU+UR~h{Qz<-KDd7_}gvN9}YstxWQ z30=WV>V*Nqzw>3pOdm}Dn&V@pZU2?2iPic!4vm|rblm6<>z^4O)1T>=BsdpsiD2_! z31s2@hd?#X+&r#gyD*D*BSG|$mEQ-{@P)|l>+>a-6^Spmyhn-(OgR|10)*uhC2v+) zzCZG26)Q7RRZzrHpLkFwsgs08QcrSzJrNS|e35psx>eXjRcXG$W-NJuNr9yA$u zviK^M^kF7DAX^WVL6PV>8HtMIOv)7kiN~YpE;zC04B-5~Ev?jf z=JDpxmx%yXy2kCW-KnvZEPUVXYMQ(u#W0dx;P?9QQ??#LQGKKMk#zv_pi zB#l04R7n{YJ>F|qZzqG2bv~VCdgB|t1E>i?@iTNJbL4DhUG$GX+duMk9x$TwQYw+A z0&ikBaa#!T$CQnqo;h}@1SYR*4zE9=$rQsuzva;yv4}E1mC&ye7mWjG=%w{6$6>kz zyH;(D%Q78myn5qm-PL{*#k_2LAFkqar_rL1THM{t#KmOI!l1}!%7#-PC242@+?gII z^fn{=-aqhW*S;ti;eH$|#(ec(5nF2`8`STcZ{52OeZk%3G9HdZW@Z0}q^OLliH6d3 zJFEC0VL9R#JT&xnwYWch9tJ#p>uk4kYVMb->e9Dr>>d@X-s6xFwm$t+9#|D=L+Zg6 z&jN~Hf8$&pD!1z@F~yb1Jj@%KQtD$ z;l=|!nsrf+5JZ>%E16$d-Z5HgMQLDLe06?Su}a}?&G zNt#^EhTpJ}*PDF)DsAv1=n5&!6fBuml+CJAC=GZvsKTqx9Gg zfdV7^14{F2QDSuBn zBd0MDTUmI5Es)-6&INS88ULdAH|J>c0XiWG9R+J#W_;pyrM5q z+ERF~@a}sb#M~0w?QJ~j)fRJE?**#$Cp6iI_bip~^%FJUepNSDK=_gg%L?1=5ea;0 zyr&Qp$NYhP^x6kimIo=SHx`u2V`@ezXO#PQHl0jwC7v~uylZjifntb^MJKs6K^5+! z%!Wi#QJ}>7qOhQzij{}P~K#iOI?|Q5}NtJ`3VT(y6cU!we zrNpP@X)#se;)^qfN~WxxTUH-R8ZEI8=?<;@m0pqR#Dt-*j!?i$Dy~-nJy&=Z7(1m zi>r|-@St0T@(cps@=u>%zF$awx%89eMe@MU!CIHSxHUDFKGQAdGQP*}F}$y%pf0|D zS|J^T-dQN~%P9Kq$Dt@%?H~s2mL9FOT6;GN?-1_#=aY+Z`?LC}UP`~rSwS1|)KY?p zXI{RPa(PD0$PZIi0Fk&~^5%8Wk(>SBaeK-4G`GGo1CxC1gWLutRHl7@TYo^431$yh z_c09|M|SO4ExpMXkJxR8)vu)b3e)Yj4J;~QAQJ0T`+7Gcwh;Rd=t_Z7T#@BGb6N20 zDDx~o8qQS{1S~DcrpSz!>%<}YpW8*njPFR>f1LPdaO0grD2*RIhVPC{Xy2mlYdMdT z9yRZ-^j~tH`Z}1P+p#zCODh-klD_CRFGf;`!3spew8~UbRpgU6^;+ds=mK zZ3OjX5mhA}`(zQuM}Zo!4x}YAqM`OSD*v>q^tT>3#>M=(@CCf_)bpv+bga0ST#l!N z<5dywT|}L?O)i(;_m4>)z2j`+-6%R}h=MN^6{MtNs^AN&4bGnX6SBR^8o1a#q-VLz zm~p(R{G8SZ=^>#>oK$Ku9vU0D7As1gLQ($_>NH9<9Dn;+P z&Xszet74qS<5EkWn965!Mlt1&U`>}p8oXvA77lk;-ntw@#GJ24S34#spg^*ZlNQd} zhsyJ{&}H1EFA{@1wt)a~*?o1`<5f&1Ml`+l(j5Wt?)=z{8iQotq(jiFKE(+nSG{i{ zmn3zj+yu9SUqF;}Tvbb`VR$DSFQ5xpFz85NAHWO)R{%&l%JW@n@GBpy?L~+_uaHq0 zmoRNJ!@`WYPt5c+c~npDDZ*rzr!R~2rzH)6t0RB(WQlS05*|`ZlAM}DzG+`6a&XFv z?8esB`ZaO)-o7cEl%!$rybe4DhR4z!313SsezEafF^6{U6yM@@Rndu)zJi+L(CS8T z@Y~>p_p2t^Uu8$`=AH59#nkfOxd7I%pNV4(57Uo^t|<0{??@N-U2JxT1trGreT$iR z2)C4y1k?`I322u)JQ85uoxgc{JohWfYcY__0XYKg#gTQk&oChCvWBA;o*B)v05AGX zP1DgwSgV^WF3Pb>)4d?>U?-^8RdlRs@w|RV1(}$vod*BL+_bbavHQ%3yZ+#Y70wvP zcMmCk)ZMatf3G>amwEPCCo+O9DA>OC)bv)#)+9Pp5G359Y&jy&5h|U-)`2EVRn_9A zF=9(9+geeyS3(6kpS)Mcj~%aHCefwzEdG_s=+9#l@dfZgOm3}>DUMbnJy-Go^^b4w zx>t{FEjnoVY|hUuKvB$ms&#>GRwe<&Cqw@*qQ_LvVs!2;0451{q2F{}l-VPO&-$%U zfcPOf%FX$|6{unS04H|-^NlN>JxCOYg@I^U%yvfOa09~g+0o=q^M$qcxS$fBdP6!) z!{7HI zBriU6mTP-m%@|rx@KrpaH1};}*@wde_$I?p;OUR*4zlH$^tvT$x|XW$Z_BEkHKpZm z!K$2hw79ZvuU?V7G-*46EC2s>YzO~n_#~)F0(>S_)l}@v?ObnMi+-R$d{OIvcgk9O zx49{_N@CJXE=*9?w0-S)+l%qc;1sf<-EEZty`lh?)$4g-7P({p)+zq94hR1h&*e}* zB^Vv>EtVZHOb5xM_sTAQ%CuqZ&Tz@*W=7W{bcYhhC^zSL(J3WA{R&Cm!hFh~sNE9(Dr@O30!YvT`AQ;6*t&g_-hb;%Y(TFx>+bq?*!nxTGWv{wh4>O5rcjIW zt^(L2_~~mEGd0g;y2itm^Havz%1AjJy!-duscH6TV`{0GfKv$C=Y6JDx`e;r^OkPU z-L+-UQ_%7^ZD?VU$|p%VfE<8xR!AzIF{S9~-%o8{e7S70gbQ$Sfa8 zhX7R=gqWro4vBc{WT{+u8G-lHcwN9drp?`oiL)<|&>e;{&%6z9DVl^<#W0o+H?S3E3hQ9oeP14pByas zCmV|=ZPLLmI)eY5(xM=M2^JLm_5P|sGIe4!`A)8w`OFc$=Tw1!PnSC#8HuOm!K{PE z2&pA+SWaeW|HrG#-6V=%Ch5;w+??oGpY9`mO!PW5s_&ugyu%Go29?b4w)v~xfZKUf zSBVKGp!|sW_r6n5E)F_SM@Z0P-)E5hf6Oxj+dfMCN0FCa>BMTo6`-)R(5#*d@`hx& z;l^c}uP*XK4L%g33>&WvT^Ytafr>qe4w6N^y`!;6N6p8vApQvV`&RpN8Cs_(KE@AN z7+|G=*$G@$UF;)(>eiqa!0Sm# zqN1}`X?5g=C?;UJ#aME6^d@eMgr+6{w){3_-9F>SF8n^@airu?TvlTQHVK%0yxH(0 zkMF9v)!39_AG4J=-88Qoc1U;>217h%_{(qG>KxWeyp(N`I++WY+E15SQ})irp+F=h zuXO6`qm6^1{K_`>`L~{Mc2a}Y1MTo8= zf{R1NMC)B`C&;vqkKvN`vhHW|DIVmTL;_!={qW44JR%Nj+Js0G%?c<7F5_gA>G2Inu(*yi;Prv7Zyhy^0jE@PqY~5&bqzUe?Ta#4*)UqjtQ$*AH z{saxC7f+D*vZdgSq~&}n7Mds)F>w$nl>`@OVX(i)PeL!df2O$(%lYxv#89zlr|q(C zb_V2d4kKDj92k%mISYyp|c?;M~MQZGbz(1hS%+1Wu6PpQzL+<~2CkIZ4d*S`Bfsz2X;Y4(qtRV985tZDSCiM*us{nEyF zA=$~e{F7LN?EnSh`=REMbUb5xZ~PIA`~AL^e*WN!VhUYX6eHKKb=%t{DucDtn zN!{Dn_*%Q`jsTe7xot$dra z`9vwPS|UA!9ErV^6giF>*0z2o)jz?w(7H)u^S`sVzte6~z#Sc^Fh;2N*6RERpM_!f ziis1Z6XBaL2Q+LyC_S2kH(SypLUqqm1vvZ0!T@{QmY8RzDrVw1(_)24HUV}AVJ~?Y zZU4HygVp=vByvVurqD-U9x0Dz^b~6oWt$?THOCK5gDc*}kItvp z3aU;;N?*tbJtRd9ChFR*z8I-y_BH%{%^hj|=4r``Ze^cfXGF1tU*o%LuHt z1_#61zSU1)_#$jMKV4gS+h>d#-uvjU>`_u>D7WUm^p^POn8vcCC;T#wb1`gs%`cyn zplb`?gb@1(mqcFPud*q(?X*z#dwQpyFh#?(W=osYr`@Ha`~Sq?>){0p{_P(lrpLy% zf0CGDdCz7BJ+6!rSis@0&4%q|HYIkr$}d(K z<47*w=}DazD}uVnVFgF@b4uxCZ4-K}rD_!LS-BFohlRpEf8x`m_*^r!+x@bWLYS%u zLW-Ye_}=<%DpFGOhXiXwP5~-V#Y~YeW2p>G-YW{4n6jLJ#;*xmgea!x!QYjX8_M~4 zSzoIa2p5h%{Qe`So8m+HyJ&=3^u6_PqexD`-gcGNUJs=t0R>9ep1MB%n&wv+J4Pv? z(|mvL1^;uy2;7RT8Nlj&O{}8$+*IY%y!-aP>EI>%EfK&QEybUeCdF z4ym#Cr_YHSdkw(s8C@*UK@n#h?utE|7J1?4q*|Q)!EPU7(%|O2#Ii@af#VYd;3I2q zE}TX7*5E2`=9b@Lfu2iO>b}ac<*>{}cVPZY?%qoyfm5yS!{LKmr$jISy7xS&FNpH1 zIZD4Vzpl|U&Xfpx_E)MaaKakpGp68B*g+|IzXeJdh|`WJTfG2W$j@jyxtiQ^_MH9` zgp?tQ;yJAJL7eAFG+U-r+d|=k(=!L_11I~gzLU9-?BY386=YTTfZOJw zl2zn;2VV1i3PDLp0nSg4keQ2)ezy7Q?4QEIVO78RC_}M1QpY`Z807Q&oZ;(H z8DyHUT&}`MxH54ZOVEAmYfmkV``z6=U><2zqf4PYjcPmVmZ|wT&om{HC857Eg+%}) zY5Oy3y{sJbV6Oe}$FHquFK?Bez^Y$9$n&LbY6%T(&=?X;=kUYa{ETwy{k7V<$2roQ zgVlMaPc4H$f8mZf=$Jj6pOd@~{`JkN2u_L&^=aOnj#k;TG(6Ry=H-n%U%A#jJ4J?r z1Em5OHasz@s|RmdMLoOZ-&RuA?@%?e?E>`A z2*rmX6DyS+av?x7z@R5;1>%dT^;#LO*!SP~zPLZ@mx&y9DHcl>697eXjBk+46uzG95>~EQf+#}7dgyXXU1)EZUaVpsHsQi z6nkt8Q)x4RQik>`OW5I+xpVy}BvS1{TX>BAA;1_eKTV-5KcBX~m753u_LryT?Wfbm zg#S~qvbOjd%Vn=C0d7M-UO(m{lWcfiGDi#S{Hh!68o68aN_3Xys(oiK5_F?Ea8zuh zBn5BU#@)tV5H0;z#EANN<)Ha5xBj0&R92#Chh?l5qHWx#ogc;gN62##W!dXqotKwY zT1hAH6(t|jsc`rQ8}3sOk^?mbyP16@ouk9TX?i8#MeJ-l1RBtv*ffHrZ< z0<-Jd;@mQ-3{dTf=-zlCV0pPr*n>D8bH++$!NEWK)iyaT?@@u;8y|;vyBKivM)b&c z19C7Lr8uo~Y440M4Mckq+eIid+=!;lCpAJv1euQo!#zf{t3WdMVot6yeyuR)tYRj6;pC-j!Bf4;X+6?;@LQg`<Tx-_q~-df|bA=+pt`7TaPM2t3nS-%G`APX++iBx{E=~dSD7Kfp=}- zub&*sZ{#KIJ;%>5YfCOQCXL#Mz7yB8fhK66uiQV3c1PJSYh!jq->msc7XRB@`j&!= z*2a^h@Ynd7tcsaB)r+d^dzJV;$Z8Rfxz~HuftgMLawqtgvHl@L<70^2Eu7}a>^7MP zATs-i41}DgLMYFDiR;k>0w2bDm}gPzZ834Ku3-qSFF#7F8P<*0OT`HPpxf#K}>n2y8kF;DG3K& zMA6eqL_vr&qYOj;lO#Ul>~77{ajU$PoZx@IkNKN!`|vlz2hIQY*#FPPqc}OC>Fd>( Tz0~QS{{UbHX8M)aT@wBarNs}p literal 9441 zcmaKS2UJsC*JeUTYE(c#5Fvno6b(&^fFcljZ_*SAMhLxk?OZdVUw1F0(P^6+nk8ql!a`52Kl=tC7=REI& z`3HHU*8U4Kq5b2F{{F~w<&MqG!_$lFpp4A%HESb(N*u1-j+qJugY-vYajsntSTi#5 z^0X8X4CkV%159@QCSAD{GR%@->)YVSrPXXp=+lj75}Y47(N)+1B_BmrUfPcC+K6nKt=9n92k zmV68}zjjDhz7OU}-X1gqc~VsKHUf``S7)9gC-yF1GB@U{(2cpe5LFypd%_kM@7|a`9hkh!^lel+?469!J+yFg+3BU1ti@u!N9s7=SOWB)5@>0z_ zD7MWpXi$tGf)PvE^I+($v^@m$5E@j4PR;2y_ag|gXq^Z{JHR5%Kutms7?JEF;K3SD zh<1iH+Y!Q`{!oREqP9i9u(z5DoBMzIxA&2i>t`3If}!=?rV67Qnu@b3P{OI;^z3m^ z2U>XF&KsJpJu!J4D$#RvdVI$Nw8aih&^|GOZl=@@2KG8Dg!3*bT_Dv)hYK}Fgheo) zvRPWM99fVwNY6=LP)WcA;3xqrL85RdLPG@RJV_`l3*{x_8Ha-!ffKa7-XCbsct*cv z>M;C)M$4M%?Cuudmf_--W@HBiMyDBeG?HUK`|u*>XgWszDk#CJ_3#+U z3`j_tnhF=?f3FoJ>LIb!6?2pxGsbWxyEU<~8o{F8*_MM^t}4hr%?Eh~FGe4WI{2%H zpozIpBUA+F9RP157qx=C)RnG->{lLyiAr&R-U`fNI7Lu_q-oG+IsOCSCO^IUUiRX2 zj&}$}z;$`Zd{&0|yee7g#oSF63Spo2JW7dm^b=+ux3K|0JFO~V-~b)Z3>XmX+4rAL zo^PvqACn9XVzQ@}-ETbzJWW3bgnjK zV4lfcnU%NiW@*FJ2(gL~_xI_0*O2<)o+jFAmJg7l<0&W?H^?6tPT5)j>G$B-dI&X0 zt9?Z1NZu33DY9p3RU^QW`ZO~NzxPHdW+j&&LCX~EFYO2z!HP6#$z^nmJYxul<=UBD zRhmyUvh(~K_S$s!$=N-ROf(a6BsuQMU9^(B@u|Y*CGKiJg6WT{!~6*qbffKbzu(C2 zCa6Vg=in0c0&vI;rtI;dv7Dt1;XPvRA+5$!*B;A7h$HoHb1K>C)wGjtlN`avL0Yee ze6a|uIJjdKgyi;}*xaGpas)dv9wwu|h4BO!FN$zbvP^N2k+Ds$viA4od z!9u}2qnW4SacE+MKVWodS^`s(!b=Cv8P7cbd45ufmP`y`)91q#X@lXK18R^k=Avbf|i9>xgN&k zI?KGfJ6^?oSXY#nOZ7U8+-0y_$co9>V0RptR>= zC#%i%EbL*EXVp{xy)n=3_=Ab22Z>0Px73L(oBa28AdoDteT=1d6!i9Sluz^Md_?JyYZ%~-Q9 zJct&V*Wf08(N>SpA!u6h2BK5=0BqE+^-E@bLE9c|o6a!E^X~WHhW6)Ab{60Be9sLx zY%YAcySsHVDyedD&>##X{j+nWxA~+vv(Dr)Vl7wsm?#_QRNx}(cK6SjEeI;5qi6?A z&yd2vdQL?;-qq?^H^cB*zXOcW3u=6yYWM(TKRE)PHaHGG0Ly*dC_1ZkFnlrr%N|z= ztgy+<#*FTdT5tiHxvWBmR_SoCq3Wi+`G*bA_A@8>)$)aI;Z}|YC!Wa6afken-YR~C zi8MTrwOF1hrHkx&vjkjNW!D2T;iym0KBDSd=3!ny^G!Nj`2&sf6cv>`bR;`RvI>fw zD(s{voi^gIRhS-7W|P|_j~>_hhn^B|9LCu->$OFK;m82U?o!o%u#}5cFad8Qu*smvbA7}>$lqn!6w?KmOv_ld*6}bu8 z7lj3{0(&KL<^G|J%+za8TF$L?0QTV9{YQ)^<~>nlWL8OQW;6*eF;e+4G?uN1LA0ObwTy`BPs$Da_9yrD4xZsIgE~eRejA`&?$e*F>qR zQ$d$J&1tiDOnA!fYcH>a!AsH{6ZE=(D>IvX;-oZF#4vdccgmjUk&NQKzs*$NZj+zt zP4(yz)MS_)Hlwebi&*{g9Mb1L7INCWHkL${-?FJ6L@`-kFk8)XD|)z$HcxC*)jP9m zESebg9cCU6B}hG4)yr|6&YC)-EztdIfQXwPHh7s+6wIXK$qTlXt^4)s=k?Ul-tni=ad;|u$FI-8?I zkpnF{WA%vT*rw8rUwf-|vr6;4)E;Ehm=IpQ7TmeR=W*oWXZxXW&!c9-{Hy6tY4Qx1 zSInr*?5`$Q8F3U96zCJwvG*Qdy~Q@;f$A-}4Jp$Px6PUPZ~!hTS}QK0pwG(uvYMTLn7Cz42k$~qKRzg(R96VJ5M^!!@Eu#*;3_X? z_bdI)AUCFlhPwnypU08xNHZ~wCA+)bTJ+@Y%OAC~BxUzLbW*l)>C@hMR9QCD&|gW> zHceL0CF7L9GOqulcZ6bZm_0=7C%N~=2L}uimb_H#?4@~E6?$IHr}fL0zTk;OV3{76DILb6ram>%b9w2YepaNW zhQ(Ps81gE+zgI>m1Hfu%gc`tFdGbeHG|ts0su#>6_OaF5xsIq-MwlDdsFZmD%kMK0 zda(W(F2RsgXIV*&iJuaX!fZ2^PK;lUQFGj7p=g-3(_<}u1!xdyX^8(qMhP(kGpE}% zrdAT4=I}4x{pO9#WZV4p_H6>C={7WU5a7nSIZJ3``rFO(1y0)HmE@oiRm9;rthjoL zZ-Z<<_XveiZqr%0G^MXfokmpGds8cNR;*}fDKIs)KB>z;axc%;?6ASJsEe%9LTeZk zsWA0(1lm~&A8x@(M$+Cvz0kFBOl|!|?X&~>g-M28;0UFu*_e2lP3H#+f8vS1OhiMe zwAka3w79^AXRgh)yx6%pk{6-vl|*;q$;G+#f&cK))oU+q**KmmxI^J8HOqUS=s)8?iP$8MiaorW_F~VI3CQg2?;=&vo+TlR~Lq zdo{?BG?7RuF|Z9PfA1M_5xH6Z#@-gv%GIbv-q*>+UVejo^Zi&TH|^1|eT6%pe8pQC z9HZXu-PqXvOugrm^?UbT7Y@iM9GDKPN;ryVj!8nZ(s>4@cLdLZ(|!ogn((wW2h@0dtBJGsB% zTu^lw@oSS?uJw!v-o$Tk{?06bhy{z`q{yNVsXfPVhgfC@yVUk~J{TOH?EE2(@(RpwotTjR53LIM+r!#*GT^VC6OG+Ny zm|iBT@c_!zQyJNH6vX*Al6|gtC~x(Fwlm8YgR942#v`&xum{p%)$;rst7lCdjWhv9 zY@044SKv;!4>o*Rt9$=Ox;&E0og0VP1fae6X(*mcQXHUq6(H+c+^#H(DP007u^nZj zB`GffXJ+bWzSQK`buW(+#(~&2K$+Td)OXOfIO*7_Bf&~6;3k&q2T>DWnwUn@yrWGE zbfI!LgPRwYhXQT_%GJn_AogwUQ#Y_&hP;aJgue8q>LmDD+--2mi-M1KwFztg9!j(_ zA_M`1bLk9}JZoRt_~%i2=zVDRIm}(EBpuBDOAgns1y8Tm5LI19gHYXt+astxa<(e_ z?=zxW8{TpEX!8XpIU`aq&DqsMF`o=R?xTJEU(abNTsrsaACfn`rRWFZpcad>(8qxT&t1q?$jvL{Vss+~qv8QN zaqd#}o_zJ@WeDdhjB#q}E?%2*|J91?wSlX*qbn6Nl`PMR$>VvnQo>D68}?6n7`=%h^RgVu$hz5)3Xh{Lg5z73gfyBYhvCwK z?vquDI(R)4HSwy>o3Hv{Igg1bK07FBzy%g(^6BDSR(>aomxY@=t?{WyGm}mq?h{`bZ$v&S#vG@)Qp|Ig*B0@aGDaR_8V`!7phAZE?%+ys^m+w z?oy_3NjHLhoAP+9-11N0N?~0b`7?B8#-*;d&avqvnAPc_i`=aH{>}()Kp6hv`}f}i zGkl4%%v*(WW-SKQe;XN@sF0QEduO6tj%lI>Yr|_6zn4L+YylnU z1TBTSHJVKEd#+WH-M8G`pUGpQZ*bA^&*^}+y-3!iD#5nL1kVLIymiSE*HKjAb8U~c z&pNmIu3QN|Xq4@+inej2LQ+jppYd+>VMhgREn(lvpY2SM`o=`=y7W3-UMHltzVLV^ zUy{Xx!&%=MxbY<(8;W(OECO;+N>bW^;lY{lD7fB)t>`9Gd)yn-z(N}h?CfRePUu8j zPVLp}`W%_(sE>8g;B$b7CR)jeFv>qS{zUH89~UMWgnvo-SGV)n#$NJZ>()L_=acJI zyUAuLmBr-2xEzqykFT-iB=6y3jz-3We@N{{@@>X9hz7cEIn^qPFJAsk`1*unK}C~T zS!6~3YO=H@j?dw`{U-;4w8>0rO(2J{-hCeF=e2xN+ATPknY!r?bvczAu#)FsN)ILb zT^EH~t&Frik&%--X=48m+y&+Sg*&Hg$7i?UMJZ?Fm)Xx2n>U*GWQ6XojTxoJ|f~t7;rZNqf*dAyYnp&faPfnsHlKomEPn|Mkj1VOsTfxdaNA#q} zX07(CY*wE>6ib`<7p$dP2&vcy{0e{-7suycy&tTUY?%SDRC3V7w>*p`$5(-ayZq0C)m`7m;JIa8@3dH9 z@_$4U_($knCuzE!?&?;RDimjm8$Efo^fqD_yiRjL74QcA=E}_Ihd_8CgG59(@6#aH zXJ3HWa*b3sJIQ4yEK1pO$~c)(sYs7n`7)$&rIg%l7J z@{ji6-S;sDz`W8O`!b_1iXv}P;kX8rU&D<%Uso9g-o%#gPYGlQIk_?>3sxvBRWvBj zWz;27evuYw4w1QQAj2fea(=d$aQP5s&Q1Foj`W|6XeHDauPu54FeBc48A4CovB`Eb{`u8={+tpv&pD}6i@6})b%!Bs@=#tm);y^01-^D=MfaV=HA%QP} zi{i?%{qm|Ko4cf_g@TyuclTXt|Iobt)L?tFa0N?r343^~-qDzGHqK2&=zX*{jgkbQ zrz2?3A3E)Yt=w3>he?u_W4+$CJ||^HoBNDF0RYPpnYg1n9V|b}9OS<%-E^=EDc>lji4umG)4NUXi|pgp)(*m#im0HIq#ZCmi99 zOv#K=RhCIJej>}jOsm_4-qnrFQO43{iD|TvMqR)ll{#mHQuyKYRwPT@$Z6(plhB;M z!xZeP@-16JFZq`HkZ$RFl}Y+i43zQYz_qN3w}N9Z?@Dit=ll}90ZKzarFlUx3;dsk zBvrBWIhz(hIiNF35WY|k#m~?GA7oh$6Vh)VDTlr{h_Yz+THYKv?&;^Y2pyS^~aYI<=qc zT?{3b30vmX=75asy3c+;{+gxV2F{LUizED=onj5eukCKh&?>sdxpzPRoZWiZdUo{j zaqgkJ?04;30*SRi`I{hGFb}N_?OBkz4Qf9NKM(?2v$oNFcwA|Vx;fPmUaRE)bDm-T zcMpaFi>{gQn)1xAU|C9+S-hpRIa~fK>!F5+s7DlfGC~p=2YNvRZv$3J*Wo-7Q4Y6ikg&BJ{KHxGTfB)bGZ3 zpIRub2p^a8erfiFI&k{Z=;pH3z?A{TZc!P~V72LcuP^ondsBNxKR(t>2pmbY%9n1_ zG>kKHzTy-wi47=wLCa(lzNk13{~$`aj*e~kv-nHk7WB2#XtS@!PLEC$pwTXI{5sr# z??xS1?dNiGIf~5QZPgZuE8C6ACOx^ng35bK%PW7PsI-OsEg}qYsf86rn^)@6#J0TC zeOzvz_wYlro2?g-8{KfncEa~`ujp5SWKL_m9;AZd6j%6TuORx*AA-v8NSlO17;pbW zMP)j5U1wInD8qe}SNG6#gsIEuqj1iZ3z6h$U!at=!EG^ol5eXEp@tPP($?;qz9fq= zmWIfr)ZhB@!Aey)JMognYzVf^)anM4Z7GzLNr4U8wd9xc@(SXU>Ul}Zo1Uyvm=Z#+ zYNG$hU^P5G5H?~HjPR2r)%0n&G-O(Rs}q)y)?QwK_TH9fdI1xc<}UeIYp~PCkQWq> zVR}vuu}EsPKnN;mot~;X#Ne0F;f$U)0oq52&2$z5k{aiQ5T7%x=Ig{*_qd$|5tm&5 z^hp}n?M`n>gxXO}1v;};$U$8GIfw)4@(&z6qO6L_w6eq8(~$aC?Wy8SU{O>=Ccbu@ zjtb83gAdA-69}tA+#~Zd%gj`u@n9-!045fPE5z;5P>8h28d|VDA+AUuondL;_NZTD z`mJXej@F!?J$qK8aGt-yY?xqt@fBB2Lxi|2^(+1wA0S82m9qmfXZZ3*$vCc8FwPYt zna_9C&6AWiSDau>#El_CGN~n?xxzvr6_DTv!O*~+`HsG+*zf`>n}p~AViqIB!myd) zKAiO@+merDFCFHsyl>igc@Z=LiSggRk4bxJkf3VKOU1uK4K$dShV%`cscxk;+%=$7 z-qE`7jA$BN{7QIs?!#9w@t?RMLw}VBVPz3|z&s7G*qy7WC?U&AM;gL<^I`Vc@zH$p;_zx# z*Ya{cY~(7Qk*bZESGUmNy-SKM?w#rAX;x-@ab^^G%alli1y8RZPJI66y@$ssaJ4WIr@0?Schrz~H!QIbif&P#l%EeXq5h6t1~?<7Aw9c_D>dr8X(BNmP+MpzZB{4J>~T;f&li0=tB!9HlU6BB3Y6V4wxyRA4SX&<-+Uo;uG} zAT;=nZTb5~Gx2GQT7jHpxz+qWCCt28!{8Qm7+oVPIplh(XuoK_)qWgze@n^L^(!V5 zSN^6nLuRij#!&stK|Oo;_(Xkt6E%5Ue0a6H`vGvUb7ta**=LTONGVlDqYV3`LG_TivmGxw2YlhrF?lD2#bpp?z$jTL^uw*pRwU*zl~Ef30@;x!anTavfRdx8jiNa(nr#zaxH9@Z4w^QLg>|Qh-}< znr}I+|1#No^`$c#_&gHbhHTH;ZUmp}FIrCvEx&nezy(ST2Z}|yQw&gQR&z^zX0oiw#htw9!#aOdxrii<{ zZ8?r51OLVdV83CTwXH2Gltw>|4kW3c!ln1DeVUsFVq!WnZlTj9h`p z``b{Kwm5Y95hrvYHiptywp2)kGDMo6e1t*}n~Kck2F3cyl{2lZfTn1b@)sA2{NEQm z)Fj*kPq)#E!ENhyY#6}jcdFUsCHZ-)iVh#^9|boygsKF1CXmHSMs9R#I{6w1Hj5CFzZ@bZ7TlrwWIO=wU3xcaut3u%Fa;d)g5wS z|3r}Ft_7nyBeTyE%hTA(m@)d040P}z6EZSsbmZqq%|a`C2QV9O26t{f+lY~`s@F&d zFVC)AwefH18#I2`uE!9g!@6RYgbsO@KL2 zy>kKV2wtR^1u#>jGb%5p*oi?PeLZ5a`2TxX|34uh?Qzk_xtQW?hXrACWLgOGsc|XW z>ft6}Mtu1?i5fdpu~t96koq0=0D2+~ElEg!(kbBE&|Q8rvJp1*%<(9kxe<6uXsQ&jOIR6cY f{-g26B_)gSl3~&;yBGPm5diu`U%m2?!|VS6Sy^FH diff --git a/doc/LectureNotes/_build/html/_images/chapter1_27_0.png b/doc/LectureNotes/_build/html/_images/chapter1_27_0.png index c1228b4868b174624cc29412fa859cddf9048e3e..ab7dda31033a8f66d0dc767e838a8f9340b9945a 100644 GIT binary patch literal 14031 zcmZ|01z40(_b*C!cb7i>X`_BEJ z^PKzKhhYZh-M!Xczu#JW?N4ttl<{$BaFCFY@Kse5!AMBRP~h(yEDYcsl8&hce29A} z8F*;BSbIP%K3E~CTX?uSx_CI+y<_sS`rvNo;w;Dq{O~Z@dU&|HOYrkM{Z9ctmk&1l zeW|sXKqJ_$Du(VzNVud=zsN}6GO3V|M5I&|<#fC=50-qqwRLaWjuEO8_#6&z=6KMN zt@xuIS=RXk#6IIZ+o_A8<&v#+d+pIfoSbg4=t}9fNN(k(Z%syIsHiA{j7|&+d1v!Pqvh*v$OLE{#i3SA^;%{a)>38kD7gl14RuZl1Ix%gEEIj z$&oCE6R}{Zx>CwVQL(n0U@^Z-`0s;Q3@B`>5cJ0%HYJj8b(##$ps#g=O4{lWtK1^ z_2meBS^~RbZIfoWt?LY=R0m{L=c~>M3s!BLdk}i@>{;mf-pp+!HglNmha|AyQuCI} z$UPpDro(;oWQ8;6k2>fNIp~kz-C_%oJQkz(*A%PZK?5g&#oIz5d-}oLzq7s~Q=v)~B^99Tx9Jpn ze&NR-?C)n57f%NkCg!w;g{TTnMnUm4F;St`d8uQGj#)9m8_X*>H+> zB6-E+Y0|Z^T65l)hx(d$M%t`(qN}Uzp~i0A<*%Dyp}#u@SVN;y|jXtV|bm$%aZk3&U#Z%#bsR?VY!m zA|wskTeM7pf5Rqaa~&3*Ia>BZARl!S$|j_yDwmq_6-v?ydG6|z!Zkslcc)v!)hWe> zl^9~gi}`Ble?29`h~%c&CU?pmxH+ParPT7a4|`5F2R}te7m_Do643Qg+hn%Sm<0`A z9j`m2Xl97S0{=8`UH^=i0X;t4Q;RrKHF_POnD@j`OFdkzBkj*uN4t+or-q zfj8C5N+p5+CjUGqG@Y6`*juP^vg}J>mT_v-E!1;dYDgAc@lTXQG$oG8+!n$2^i6%9 zPv&ddbcA6irKVceF6=q|EGmi_8d5`Q@Z9T(WDGPooi8%1^u9ap-E-2%BDPkwb6p;A ztwo9Yy2{|hrkQQ*9yDVDo%Sux%L_xrBzR%bf!0=++efZ%N6j2|q@#m&8R0wIK2hsp zM!^f&I1!HS1{c?&;KU#Co63y8?rtqbgkUl- ziHfE=E!2ETNO)yx%D}BxbYy1)^l7#8Hg&T^K8j7n_t3QNV%{NL!jr2rl5m?p==x2u zg+KDm`JS?umk5$e>%B;41pYHY!Nd%g7V3;)JqIIT{hRJi2BiQ=va+%wC8VVEyOwyps|P)m4lH2asXMk}x}Y%z0ao;{uqXgd$auQPGD4__&6I@Q2B!}^q- zel(?R+R6)`jAffb^mJx%VH7+Y7fXZBNWw=B|N3X+;TsE_y%WJ!Zr6q&p6e&lFyx9sn-H4yq=REFCW{4i+$r z@Dth8JKhV7143+oB1YoqCGDGAJpN65oBN40Uu8#HHcBC{82LK15$+2Eb9HtenBX<# zSC=4NaKOBfJA+*UTv%kz%R*s3r(44DOZfMN)mz^jO4iICZ&DWy&UVH_18y(0jE(mz zE)S)2Ton{>8h!b~h#oc;^EqKP957Q|kS-Ko67@C<-tWc}tx8Qz?YLM+92`q4T46KF zfHWe$O$Y2x#lS!rBqb%K{iViQxL<~_L(ln)*(g19QA;~O`|aDdj_{r;V>>%L$CVcP z#wAk+3bx>IT*`N~3vD`D7n*y-#9C)FlqsO)~qnLQjS&@;Gj{_DW+wZXc+o#IgvwxjK>4M^qcc;`Z%#pUU zpbYi(_2RMTi#tA3pZaGR$9Ldyz_n?OQX~qNAJ+gFD;#A(HY^k z7EUQ(Gm`0BDSm(8610A_*7>y80n=LHe|^FffGQuQvN@j68do)BGtI68;I&x#XZVd- z+)N9SwQVu2+F)No>v#5*QlfGGRfPvGSZ3o_(qN~!-W()-4j2i5slnN85@s?ASj=Gv zDb8?zN->$^#6;Brcn@%Ff6hER{KdQKcG^^N_+W}@@8_^_kG_A2J7?Z z(|ETTIXR# z2QZB35Fc>48)F++>Z?y#*?tLwpOJt~6E|%AOjb4r*iM!2`$aqizY=^j+Cyht{MQt!x^HMA{le_ z?)heW*vu@736syilSsBp_gN_+0a~0kY&~D=GO9o*tq6h8$le{HdNnpLubNC-k7gf? z`^sYxtCT0Y6Mit%l9NN$&eyO4HZTWF%=e6gUec@Uy}&VGXQtJDre;qC`pmGI6#v`un&RpgtAD~Da#LVQ1oANDegzl zu4bpDsaRNiNBe;^3DGt%;CP-c-&Ge??2ed#;fjlkEADN~ec^fe@(Z6yLs%mFRwK^qr^Z>Wz*()tdHx+- zP;KcG0WJW(nRCk^?WzIP(6uu$`*yEwR$?#(>Anh%8l2S@piz1NhuBCsYoku80R(*6 zR;bRQSRX~nGbP}U=lNAb5qe&U1{O1m3e$(fAhd_Qv!)ENeInRC9K4GMbpeBx@{%B> zNf7?(ePGfCf#?9uH$&z=inG+>ODL&kKwHEg(gsy~oSPP(8w1E&d_Fccr4#fG=;{YS zPG}=|7h*2u3iFB~&aEyPgHTE^xFC6h#1QGw6YIk-f|d;0=A7 z5rAmW)PJ^{I7rWR|J7eCx|KO0i=~iP7HWNT*bt{);CvM7;2>4!TVAG^lq*c$Xuz zf3-$UIL`~fTxOir!*+&7uqhJQ6o1^UNZclf^E?J@p9;2*2HU%q(eQ^6bqH%t3Y4N+ z?(t+0@JvE3(Ne^-QH`I6VQKYX;i~$jOB;4g;ds(tlN8 zRRyORGi|Xcf&2c3``X&u+cP~WR!4izYPNosv)kgfWC|ma#s^4gv}HQrtr8ux*9D;% zuY3FBAC*s@6w>PoJ`4V-@sAd$#JsAN1!&{{s1nu?3IzwO1oHwVk*<&*qX)S$7`)nm zRBMREtoTd{=Mb{j?`$g3+ucA@H=Oeta59hL@no68h}KT_D~~o)<>soM9AB)+diS-9 zg|%2w8nYS9|M1MITm5FOdUCGcqi@!Jbv)MGIPRyZimiw1@FhJB zm;$MKC9ALyWuc^5PykC=&^dXP3**M?Y$j=-*NsnITr@60Rz(KLt@pehdlV;a1QYI* z@`^up?`mqY`&=R$BN4-;G34AaG#4c%og}kzzJNNuJxv<&Ckar*liSu*>QT|$tc6a(2 z{P%Oitj?l(EL`KwC1+7huX(@y_P^>CU@|rzDH68a z_<+4NgfOOl@UJn-cK+#ijgO$$X70Xkp_kBWKg$fN5ZdPWnOJ*r6`brLCMxz*p3CxY z-VqHjThrFSfVvDoZ^Cxz{EE;|8ccn$o$Jvts=_4W3F}D6?wP% zWWV0*0neWTFVd6D)Zc=f#vp{A=ikl%oeCl6z5Wn1l#LHvank8gGsR}hC|~Tx{yPEU z7L;8XX+m7M*+;%#uD3wT_fR45{V#XPuZl<)Ttfs!4)RArmqW<68|YKaPamEw3I_?! zh4!RtKacmhI4K71ifBHXwRAn7J{sUQFRzozd23gFI#gliy|!oKVVmA3DT-$^)&HIP zMG@rHheHsjpeqs;fiWiCBt-V`-|h2lKE)&>fh|Qubp$ofFf#pp{bd1H-SKP7mPsS$ zknL>L#JzW&2ld^H&8wI-+Z68C34C|=KV+)ZVsJr~@?YF#F=vabmkX1E*s;QhwEC>* z8;9%6u~}^Vsm$nP2@=_;Ngbumh@_5?>Z08BGP3FmE7F?ZvloB;>vO&PJ#&WLAG7im z`XzTTG%H(E8)SlCg!zzHEkl<(N-VF)vVEeg&*I~s7C}Zqqi|7Tgz3K3HZ0m`%v-oh z6$H6L&A6bqFn%N$YRZ#!%D}X7*3rBUAWj5xFO69(so z!70he>L5y3#JmNOxGzMZ#<^1+b@36$7K?MWgsTk$ijRZK$x54OMNvoolymM52-ibSVSEoU{exATy1#(`s8lHls39h1RPK-9_ zHRm+Y?w+><@+?L0C_r~IY>T00>spA%0({Df%C4{nJoZ2zWeyvw10s{8U{;Mj~mjPwT z$&$EK zxWp$wT_irBJX9>Pzj3jl(JRdLtuXZymex0AeJwexf|mR`D%A7jl8Vtfar=qwOlwLy z3*BV2{F^B(buyHy+-ddW#;HPwL<@ixf1E z``o&YpCM3?>@|N;{lM8DSnC+i@{o4x=*Hh6jb;6gItOJe_JvG zpTYswTGxsIX(ACz9YvK8il6Ws0X43R{pJt}WS={+yW{vWb7Zw_yvJ)oA%`B(Mgq6T zNTWF;gMm$jhNBs`W;;@31x#;{^*a)Iv=mXfnM9qi#sl=fTr>NQbsHqJ73d5BWQX;U zKPI@W`DTYOX$cd?OQ=tssYZS?e5ZGjo_zv`rtRTR8v2Qrx0TW^{`b{dji$QjIYDs@ zk|~5D4CY(gvRYtgt9e7|G%qhgl684ONifG#kh*TWMEL-ysp-2CQo=G$W9p53ScmF+ z5f(4Y*63vOa5Dh)uPjVx|5toH14ES;NVTi$!`5XE^E=dt@b&vq`Eq2T6OUumVPUV} z_X}(FZzA}7O$h}q@S{6qQc9`nue`VMCw)UIpcI~rM68_Dt1&5iVo8y9O^Q!Cu=J1L{bH}Dr%Dajh ztPz{nFaM+vv*;CPW1&05_#S`MJ+k8;<-SkRK`W+61E0eGbFZ+?QHy{;Kj=?Qn?Dn39|oIUG2!j4p*$8p-Ez#a z%|CPA3m(QpzG0G~_U6U@fgc+as;rM?A=me#mdE&)Cp%q}J4+3Cdi-`3>~Exre`nts zLiW`1zq99~4pzaXL#*Pq0p9dsI^&#`@vvk>DoncznQSB=wxR`fOw#M;qIo7&;pxz8G^Pl$}#*whAOM=WBthJ(sNRujJ%S;Mo#L}dH zU8BUQd5)N8RinpB{Ju}UU_k4_Ko4KQ>H5PO5ytJcZ zaEbKk_}7;$(=b2x&EJyF_J2j4Mwu^>CD-9jZ22Z!k_hQfxERH#*!wc4l7O>HD0T>( zCo!UQ21~(ol#4w}zy;3|5Pt)n&`zy#+XXliAc#FL3s5gb``cxgH99gfY&9NQ>Uu3m z_+m$ePjVEL->ZB3;<;h&u*#!M;iuERumW&FJ(#Qh=B2Al8U7VoT~GPEa9i(#e;iQ< zZfr6ehA)qoiB^e$(2=eLX zmFI+mSpUuCGtCWeP+K^<zILkM@+Wh9_@1%oikTvNBL-cPO~n;D|fkz&e()$ z+2h^v{cWttI;TtNDn2Q-(Q7O%GsA2xfq$K>(|)+|?!A5)RF`KUuuMRXu#3ieNAcjA zOOV3-Q7ANoUS5zkN%WSMS^r4n^^<3CXQS~-*65~%C*njtH-_H z-M8P?E)vDP{D?t=7WQ|8gv6-`k?3W_Fr791{5CI*)1B=q{p4+^dpzNOlhZv>4s ziu#|EXL_at7PO|8(*#kniQJc>S6P8c1&H^(z@hL=e)et`i$oV=Ewh5Vn2}*dy%9+mv*@%V`^NU@$oCpYfqO!zv7FD z_44$DEc`_FPRsake)M%v*k)^|9KcYG`HOg2(=Ui|PgGL`HG zwXz>ZR6AI#w)LsB*O6FUBU}L-QWQgHF+0>`a(6T5D6J@Wy${SbYsm=h-ozWU9gNPe zwdl+2HGlBmnI$&$K`*Ii`%q~9?#1w5-c}3(Trz4gH6grTS>n;db{1`lkf z+Ya822(qK6Ad5Tb^5`U8^eBc4JejRHodA0G$#nTS#m7A?5#DHeo9zMYslEuUty;#QWHs(-ka$ViYY ztVT7JZWK6~bx5n%sU+I=OMNV#(EkY8_=D;s!S3{rx;!u-vhQG%>A#uoE`q-UU8IA3 zOpApbyeI{rRutS`6bWQDTLWt*^%fo&CL)2jsp_HAh2M=ty-(HC1fGB3Ht%5m{DupN zH4}4Xr);A$jXG4%UHd!m@s|#~TL^gvhOoMhUN_g-drK6zn&PxJxBu2fOko4mrlghO z?|Q&uITc0s^JHN7Q9^+mKJoenPqS`n@Fm2IOB$E17O&(x7=4Z^`L*s!kjfq1aS_X; zu_Kzl!*fPX{^Pb&bl2_|6-RBfCnd@|W**IOuS=?hA8sxofWr=FD}&cY3^yibBZqSY zlbDFkb3uy*k)3f=(*W|4hJo6b<}BsDtRxF$$o>R}pg)Phq9H=BP~EHHn%ppJBvBOf zI9xw{( zKJ8ah1C2rac%v*`)F?5_JM=%7wzUo|!(1C#h>Mf%cpuwh@meU&OxAKN@zQ**UP+iL zw5^wscA8J$3~>4J^RsCZMoZZaO@;G@-DdS7`zj~3Z@;j$!iK_{WHXYFPz+dvycHJ+UK z*YOB$^cs6TURHjg=BT!n+CBMKw`+1#o>|l1ncoSZKVqwbtGLDIiF~G`NHRb)(7{$x zfGaPGW;y2X&Zeqfu`ulg!xk`)<7>DvSFu-wms4l*vx)jLGi4I!fe^|Kt;;LbPcN4T zoocC1li8l5TS1?FPLhI8j{Q!M0cMeevW)b(3J#UvMeOwT{!D7Xr7Edz`SINeu-ICb za&O8~rc6_OBlcdcuZ85+HGPg3p` z%VmGklM^Os`d=EtC9F;>ErCWIFDd-589>?$s%JzN=i05Q(TiA>kwXvL7WVPEHwpCI@XhFZh~Uh38{|g}G|s{Toy(|C&s5 zA+)_PhotPj*lUtV?jn3Q~#e8$MbDQN~v1UV%RLe51(>>c_r$)1!NUWNi zMlmEBfjnC`Uia>7zjAGWJCYTeHZbm1Gwz$yeX~|jRyk2gayB|jMS0?=x*u9$zwGQ6Z(ms#Tl(9>C;kEDm zPN9VD)HX_#^P|cT?XAH3&{E7WHgBdcz={xJm7+qxA8unSLBIN!R z`|Lmis9W7w4@Bs~5Ft58_qdskk*yEYJFuJ&pZ7-H9-7UG_Nv0+r4M(7)cEx zH0wP&-Ut}MMA}Bkf(S847M0FUeu?U(C?EXgt339X8|+!x!)k~;sl58}4kfyht-|XX z?C7O_IkmOu>CDfkUOj1b{kXdP?`T-=ne;Mtok2T#y6b5~Pp`L|Cv|`!Ls1hIPOGT3 zi@hXSkN0s6nQsm||wu2{xU+FaBi zy6@jnF~VpX=qCPnTOQNx_b}Wbtu!m_@upnxJO+=inm+G#9`@T$CUQ6<*69L47<4b_ zVS>RMBwj9UhKYbk6Mq&lHwL{!)@~XXaS2sojcffoh}=74#sqY|gtir;Vi|5RF;J$}i>7S-S7iFE;5-`~`2FgQ7AUnREVW zfzt0@3WdNHH?YSPN18A$n_94GvwUTI2mwXQ8PEMe?9GR^<8#U=c7!{0DoF|;-J_}46LoEj()_+S7V|aqmTJd*ZLpn%PLS#P2DdBmvR-nVI7 zYQ#r{GcQVJI;6t(=aHg-}3IuelK7Qh4WHbxA3zU3yZB*>{XOqo2KT4cAe@wQ++9wpD&Fc*d4w79eb64}x z&gQ?-YMpF%q<_>fZ7#ydz+DvE43-KZ<$46uJx0p_hA(1X4eXV2oV_)?_8#2^qtA%djgNE@ z^ou=3A5(XNR*`s5=unet>@3yfL=W_+1Tir1KW^I0Fc9HM;eO5DXx+n**B6HdT`fdA z|F-$(yzs>!WHQY)lO2l`P{F%hj9wsSP}1$GUaPJ8Q$1tWbS|TM%jWtp79n0{C%JTa zr?OQlEBMR>SK9rO*v3<*FAqj7=RZV61*artl^6p8%4qT3S{{gaaB`Il))W2c83und*`aXR83~9%kl&h0~9aUP*b)Kkhw~@4Z3qQ8<@4tSC z%Y`Ud$)WL{_><-S6*EVoC)!uEOO7qBS9OwWhRx3HqEkbNzsCQx*q?tGO6f|myq~ja z$eHk@jk@`-ZC5%#qeqpDHYhy3Z!;?%+ni3Xrs3n&`V(Z-&gE`#IuS7yEsfP8~hVgVL3t zzRGpjR4C?rIQgwGA@IYZWDDI>DZPDh(M^7G>m9n_jzDUh(4gC2j2$q|pKvsnm58SsQtcFcDeSV9)MKdo_;dFc7=@l`@MQJp zNN(1}CgF8Uai$%wOjyjSVusw?Xe=7|t=~!^J z#_XiRap!#s|w?w?@hI+n_gw8 z0VuF&}DZzFYU(T3LvOdQ(~JCSR#y8?b1nimBU`2p`2K2;%+1pcw2clAq&=_O8N4tpNNa%#h2nxd&!a@xm`HDXP;(3!2>tLaGZg}bxynS z%#N*Mv9zhUB8sfJ$sf3wkY8}L;|+@5o%AGy(Xjh7wMEU*E=OP1efBx9)fvPD=9+{) zb{@nlmXEUguz_(Y?gDN0*UwoW)r4wJu(#wHG)a5k-ki3S*C?Izuh>#_RT=fUKOL8q zk%|%awWlRoSHN%B_4jTw-Jxpl-x`*Ex=*&-FSjns=SeR5tg-)Rza#r3Zo|+ae%7WP z6j>)P(Yrk<{yi!#MGDLtX~!58qvI}fz#FNm&he94ncl_L`po$yH8OcyCRWV{LdJ#I z?^aH6MzD3g&9l1iR@1ctxr=UyySkf}A8Vzc47u931{vv`E)=83d8~0l4su%W90HwE zIv(0Vl# zmREe&fb>_UU)jf774r!A4NC9nEm9Ty@k4dXX#4%J#nH_rd(S3ZL$ueua7i(pHw5g* zVpW4S;Dnttr@5#<$D13AJD)7F>^SjFtf;Gy>+f$C25vV4)iK?!g~F&xePWwhi*lov z3kPYAUPE4z1HWF*2@RCA;|xR)UrMRih$Qai zyPhbPlA!43?;h9nJTYfBW$}vaqPNz@%vhNqO}o7C#;{Iri$tP}oTm2borJ;3j%|Ow zr9ZKWq(|QAReY3S1`Q!k35s%t+6=pb!*tEeAD>bqQba#vSfOd)(kQlm89r;!-YKks zbt`N=%-+Vlb@G(X+&k;_|B9|2F67hQ5sT=$jQ!g>Tl}%lS}5?9Q&Q{oc2%67d1n~8 zpdOEo#V?%bV*{fyu1dG~^z~n?Tsl&EiRlI2xFU(^T#3|${U4{_3mgtx7pPGJUq;)|A`^! zIYm*G5MzGvCWjO6^D1G+HM~LVdBZrG!69YUSV{TFFae1)cU$X{qMXE1>@fb&xnr1c z%8EyFX|bQUEY-jKq%Z6riwc76a+a z!H{yu9xdJIBNE;WMAIFRzBE+7x=+q}k`3fIEcWiruUYuwm6Ew;-jws51V=Tymc3@~ zP__!J#(bKhXx@=dsw$kz$%{wOLf#TgL+V9_1KW@5rKzm=Vq3r}r~5UxKt)LRi+KTy zTykPAmz@RQvpF3Qt~+P{);enjNL3{Dt9iwDu<|rPwrB4f?AP(bCYq9DK8q@UNC~|( zXD4s`|7D3%muIB7WlK>t4Tbw%&8}oNg2#NB(*7<2Vfm-&B7bSTa~|I4AGqJ>|L}uH zjOExbwoBwwF1E63t$F5KX-u(TH+fGWKT=tLZ%|1M$O~&u*j}uL>%pdkR&is%efmr2 zPd6$;D668=s^HNf-R<*F7acCuVoQPB6D;#8|KCd*PbWDn7)o95=|O)`%w3)p!pvh> zmvw|U?^6R@8or%8_*EcGs^Nc1OrxS!53ylWzR>O)bvfm%axUY)T)^O zoyzosvu+e0irP`ciD=#To19L%TqzBR{|01d0LfcQ+D}Q?VUa9+Pg!1Eus}V~0T`qX zJ@)+wwr^QKMWu zBNs}zGdsOIHLxv4-Ii2*Ji zKD1Hc%4LPr65KBmw|&sLbV!7FG+w3XU6*;~RkuK&8^DN)hf!1S6F_%l7Sk`MjI`p@ zaGIPj2&T>-L1#Kczx>6T8NNLB#hoOVHx;55?7%5*%<86Zyp+^OKDg zhE?L#l~+C-w3P3&_itm{P9b}pNRYidqS}{fQgcE0RUT5#=yx<77wEBY-{)_q+PElD z|KF^tpX>{9RjY^g?+!)|;o}gake{y_6RP;?q~@SDGnRDYmP$Ww5m&0dU*R{WZ$)^} zq^@fb=x1P1z|SN|VG<2(1spwx{ZnA%7#nJ-LvfDl(D+>nxF82U z&u|!_Q~<2TQ+Af`a(DUArbJvl#0WW@+0Mk(rvkwN8$o}-xzlhkA^uSlE^m38gLj77 zq6f0F++dXb^d+$%`)*+yDaW?BunO+?VvFOD`o{%&2K#N0bQ#hshkM?HsFxR@qEimO zIug6~d%xU#a6tx;Zm}NrkKGe~4IJnkUwv5RJ`g)4+5{FIHpR61Jf@A|jkvz;`^Rdy zay6V2vWHjfiP0k-xnfsrX<_ZD`LGfcHtwLznf&p~uYMd0+R*;Q`xVSg)ZC=qSmH%o z`T2n(;G}3?OIQ#^te|@2d?#y7#%Oea+dOSxzl@vhqy%qxtaU#$gQ1M3#( za_<8v`_Kzs1ZJ5Ks-gdqxNnq2T$$GRo(7qg0M{R)sL1S|u6Yps=cx66u3!CkXFnZz i|6ljA{`=rw)Zr16u-@Y5 z!!PCa-XbvSRJL!CEyU?z85W=QdA%hjcqc|-zVKymjdYU;1C4J$83V z)<7j{vdM^v!4Y=f`4+r1Arq#OBm9>%NrRBnK2#(>SWNQFSDSTk7;HLkQ^P&v!#VNz zlD<#);aOxZ&s+y@sP~FyPD|3TZ$DAOsGL{3QHzx_dR+!+1_qN}WC*%27&JRN9Pt7V zw7IoKQ-51pl55@*f@w^*XjPw-l;pBmYd6aYjiW&Ct}<%#+Dg&Q^mwgM`jZ8Q0(I=* z_t^Ch3U>&#{Z0W&e`7;5YY4`x?m$qYPzg9PGBZaa?`{OV1M98EY$p-N$H(?_H3j2! ze0DP^a-la|fmb6h)1+BQ(P(%pDBEg7Weg6{D}H6RMTs&qV9Q#38L69S6WdqtJc$!z=gk3V z@DqI;C11?`Vlz6o=Yiq&SQd6SVt-NSX0M)t-=6%%yExN<1RCR%QlmBxl+g^{$mw$3 zFG)!~(gb3gyQQo}XbyV}e?%$25?8`&W6=WSooo*#&=b++Tydc3CcitMC1D4dr|o-^ zgI?ciiK85NS@W*!vmL`ZDdxnY7rCf6UG9pNv&e;9E$R_Vo&LU^cL;-L?0Vht0?UJK zhn)u2cci+8J=sFw2g#9=k_u8MfDr1&#wzdLVW9xpHyirQBsa6R+b$7l13An;N;$GxbiXm6=4?fsXR zs55x72Q7*tuZohJ{l*}P=&o@*i-xJ^gh}U;kBpxGKZv6beMvA>bQ8gArM5$aR;}6E2FBRHyK4b7+S-D8J+E^)9 zG@~aJo7elqB7%U<7+xP99*#>RoGeG{-R}?zkLBtdEKC$1{#hVR!E1$!jZghiV?F|( zCT7eEG*@G`kYASUsu;%;?_{wBTj7J^dgyzIJD2lpyQNvONCxv1zmvH}D-WiJriVlz z@-i}Pc2s9-Ek~ZIh#vPBXUE#2-36rBUddQg^1Pf{5_W2R+lk|ckNY&qo-pIUkT1NI zg(7Gw$SK?C1sQrh<&!-CQfx6?$kCj9MwHHL{4%pFolu-EjoFKmV^RuzV zuG}Lp0$9>C%C@F5+pu$l-GyH_jldvao7pNFK(VJu=N@f2e?G2sb}l-$#oygr)Z0wG zGzWo(hKAO@sEXUnbiU^09U668T+(Kl#<3aecnr7&v_ijL@M`l5WGmHR2Y2HMx7U%W z$NmClw<1?Z|9ADm!NEb(?a_?iz|Xv{zCOi-=qYZZ7=paEwid;0e<5Y)3p2W0Dbuo>dTo0k zu>1%~OV9HHoi?Clh;C$j+-fY7Z_b{7dpu{?Ba6eJ>F9d5`gGnpkC2$S?|jx|VuY(} zbXwQM-y#bDlP{^M?=(Iy7I;!N0MOWZy~F0QH`kM4o$GBedilX~*&Ci@7TH6IFU2z5 z+7P!&`;N2g*43WhAat=puQsI7Zq{}3Ju~Xe+@4XZ+pz(~OVwN@EiDp20fkO0oqqRs z7w$7$CY`r~d+I1OA|7XpwJ91L(j$)!#=e_jD=aoSB(@{ooK^Eg*i}Iyd=DT48vzLt zNUwnFzsw2>3b2`FqevXbkMd@ohbbBg-~(}aRaNVyR(F&NgXRRfz+1wg5a#j1JZcmgozZx_mrj-0X2^zV$@JoblEISiZysoky2(DWKU z65>aO0+Xr%!&R_w$xE(ui!$17+BAo{oRI{2 z23{FPMVUHRFxdaqJ7>4Qli^pV@KZoXpg6PHDx)|mfds-A@3_tTAI((gqjv+5Nw33) zkAi|i^z5fJZL_9AqsGI`V8MO47zY?}r&p+}t;KuYcW9$R!TS7BjrfXQ` zd_H>&UGS!B#J-}kG9ESo?KfbW>;_G7EiHmg4vWy=rJ6&-!|ThwhnCCj-f~cVE;crB zTaHFq%1*l3^jy4Heus4?DJ-5x?z0TT3GPwV>ArD(+DNfj&InpHLStYq(Rc^LxvF<( z$JS~hcPs*rs%GBaeyMf3WItJ>vVTr?Kt~2WU%$slrj-I@E7zNe@f-2Yo61X8h8Ck0 z{Vu55kF!W1#JRK{Fl2jvD!>!h02$`n%~sjNS_mWiTt-)o8fkt0&{sDBk4+?%$=LP_ z2tCzk)+jl$j8rW&g3>atYbIZ}1!bT>)5jRf49t6-eX>BFbfEz>DOEF z_#*6O!q(T#fw=Y_@N%y9quwN&Gk^fHaBzs72&pcz6lp!@qXeE*bBR5mDR}=7>i|)t zHbxYrm~(RC`ug?j?^RVVg@rTAt6F2S99tg;?N)c?0w9@;`Q({1@(NCu(GU9bvj9LZ zKnTzVn<-Jpl4OnGZ_>ynjq5ws>65>ox`2o>{Lxugq4W{SOph3~#=9i>F5MyqlR2yl zKp#L@;Cp(N!4q+N>$m^8R4dHxfTj#}w%%H`uD;&pu{3eR8y6Rsv%PuV-#^q8Fx7xw z>FMOxXMc%z%wGZaPts&VQIxDPR?=g{QpE7-!GTT~<%#VuLjFIO-#`(3!|WJt=JAsM z!c}gZ5k`)jwAilRvGBSM z1H=0Ral5z-r?{avP>bSk-!3(90b%8F@19Q@j^Q6LyvA!mY3UV&_wquuC_yJV)w5_K zLCAntB^nBta_Q$jHdR)bo`gblEtr=Op5jqU@0h5$?CKk|yPzE8tM5P~**jydee9;e zH}L{S^d^|%*jQ2WO;JkNwAe(!mglXaxmbQL7^s4NbhCq$q)cM~X`-ri%|2-WItcQz z(x(9ArLL~-tr0LS;hA1Wwwl=(m9QbE0=C2_g!*=ILvGC~hicjir4eECWAB&$t(EW| z*JpicFOo*G7HLWM?rk$V*^K?SA;d^NfKsJq7%uN>G++)|yCn2|xL#iYQ<)Z)9ppyO zeXz?$6^@4c*5(JDerVd>9yj=S@9tzH0W=@c$r2Gj6hgpG*G0`gk-N*2fGw0+#vZ}E z!8Q*)?Fn;5oHzk!FHPm>1;fXRgdTdS=YqC=KM~O5JFAUl5cga*`~U{pQWFMjBuV`1 zM!n#*!}qV3G?i+{wze}_5?p38Kg!-!Kb4(fM8+cP6jBI=L=6@))EUWi5+)G_v$g~1 z^b4w4?QH-Kq!sL1t`q9U0VzqChM$UcW8~+Z@}PT7UYN5Mk;qZ0GN8iBSQl1tsXUriR9? z`h(h7|EmEJ3~W2aF%AzM-|`JM=3G2?9ZOLNaEIAQTG6&s0&vSD?I&|@zT2RvcARWfw`WQAs~)SyrUigSd7yvE z$?C#Z!e~Ob>~0L;tk+QBbKLt1ie!BhW`!TqP6OTMB|}VL{Z24~uimo-W-(i@p)3)9dvv;zc0}o$3hW9lD3;*aP4Maf~kX zl?@oL7d0aj(>wh?ZH*KR@CY2-{D&9h6Y$fS{t#0=-CMjcqY`|okDzz^OLVkh`*2&7 zD|=sBy4!OpZFIFNGXhL1>|BZ}dKd0G$6W9{#?KIJs?Zeu%H4^%4PU>Mw zS4&OV8sgb5Y@VGrkES;!=&$dJKBbfYe*N(SppV;+sz@^()hoQ_3crlHC!7ps$GcCd zsLotJKDgPfg^4i>)k~%A))X$yvyt-mdUb+~!8HHi(PVRzr0z8CvEk*p@8&JiO1S+@ zR3?&B=)wDncI?*KRlFr=R#*Q_QPg|mNh}?!wu-w-L}8TH9Q+gGbtZSL=Gsh7a;|6u zYFl&7X>H&a#DzhKp4vo%lr}KJsq3?n&*v*@d`LqL{Ir_=t62)SX(lN)b5O%JfGp?U zJh4*+s^Xz8C1&Mi7Q1$bVcMqH|D5h?w6}BKo7e8kYr14a;BkGDAN}?FU3A>$8m2E{ zN8tdK)L1JJh0oa>ueZ?}nPg!D7~p**Io7ag^Nbnvs_jC2{oaR-s*NE3tWh?b-^1B- zc(u@K7A9*E6lL%1og6h7&+uAT2GPo%_h$l)t%yMj6rXOq-GdHwEV-TKeSbMZEiSmG zh2M1{HHYPKMBhRM3gCuX0AU~~N$-mpmrGT^caIz%=>4f25YkjZJ&UoTUiEybCoC)l zuDugcfe4E2#0;eyffduepKtDIBv5Wsz;pzA+|WJ56cS({%AlUMB+(XC%2wOC`p6v~ zP_2JM_99nH@EVuIP&zW0u5MNrISf%nHFo!wFp#Ojstb~!1mWxkO3GNF@S;pk_TG@G zv5;bb$Y`>_PV5AST)9=0GnIYf$JScUQe3CF4%Y=dC(OM*9KG{63nvHpEe6X|nbL6& zl7D%@U=%FcjpB*66vs2m*N`|ms`Mo#WlK{CNKbv7w~?b#XIdz$#r{e-z2t(w61EY; zHe@rEI^Wg)1=5TRp%+`cxt|k%n$!qQSMuDwO-ju`so;ffqL71tya6AS9Qz2DdzfHA zO`-8!kFAWm@)ctqTDeN_`!APO(gammSFb!*_?h<5d_fOgV-ywMJuonY$chRY|J53~ zLg~zR(7PqdMhPl)qiGL+QMX6M%Ao=W@Jjj(Z@=L@FUJ`{A0}pu?H1HcO#qaex`n!x z(tcb-%VX($u+r^tS76x^^Yp%qxcppre=OH>X+B!44C6)WR&=hi8t00~a7McX3mFGp zWrHuUAN=n2D@5ozQ6gf5tE8?hlY$s2f0Y408s*DqNKAY@dVI_H69^c|I0VXLzo^hv zMT3Rl)Hn-G@OG99VqO=tC&h`jqBR0=FR5>U&yWUWiXvc zAd!UDiR(jbaztdH)BEEPCBUayZ{L`So(s2k{IZ$lBo}#>>mA|w(s*nFx-!ZKC)fxc zi6jJ_GO*7eFG(uU4bWtoPw(Tk#V3RN0K+eV{KfY~514j3W52;O6rhS+8ef`S8ap-O zz3hs`=C#x!b}V+I>1W4)csM;o&v3dxkKhWtem)Pc9ZkKu%ot*9@N}5OvC&(y+zxXL zYuu0P&S+-YcGX1yPt&?Qh6}z9r^@fgzd*g`+svNKa3Ndzagea2FKFjBg)S22Ue%F0 z0|f20fhn(@u_Q1xtQIw9EtVw|Qg>lQoi>>SuM&%+_8^@A z=<|k`EkgE|7s09`As#(YB^v&%C4GA(Y;%Gx59NWp#fV;Aar+CmpFf+1tFAV~SGpr_ z_HTU*>9&;w2@R6<7BRsdCC{gTmbR_jo%^}k<*PAl&zU1(fv}OSTISkAQ>F_O{^Su} z-|3|$%r|lTY5d<=i!uYI)WA0d*q@j1!SLP;>$iTQCS3?x>v>*5E$zN?UKMfv8tK@N zyO0@9i>GlW7zK`Gs_A`BU zCL{RUOClTN!ViZ)OVV0I1Su2|ebu3V)hFfH<@`KRW$2?|jaIjoSRBtTKux4l;H1A@Rq0|VkdQ32U)Q!LzE#VL9!QnbTy?@`gn(J%&`_1({v(*xG!g#q)74asw z)0Q!?4T*2Q;C||Ozcv@oi{b~6);dL*PSPYihR<7u|C>7<%4#b#;#9>qTK1VbV%~0X zIUoN>u3YTS8}V`Nv!fEE=S#Y9Bp>h&)yQhPcix3kJV%y6@@(RUoHIt8@N0U>rwH`c z{Hl{<+>r9#{hr~xI^8n{tJh9uiz;4MmYso{6amPc3wFb{$a!np2|HdDo8@j3LcfjA z<&s5~oM^_*odKvVf|3L33YZ$N4h+YJ>s)u6m$1?dLqMmV?VrRWVsrJRUA+0YlqE8< zCa5GE1bAOUi1C*ML%ZoZEOCztqR3ZmK%;*7ImJK@mq*x0!=u*DECn=PW7_2onlLKJ~l@6)T4hYxTc zk3*$#UoTSqHXMlWq$!ejpK%4f4X{@%{k+gN9;@optLHTs7o`QFSiSRdeyn1G{eRWNv zPs#Wwy?&6Nc1HQ(v6CeI?RGS+OMKAR7rT9u?$p&T5j_FNu$5aZ@q21$LO65Yx9?7Y z$m0XgThArqYj>N0uIeER=R^cjD7ULIhy~F-01(Xi$GG5nQ9AO&k+1Az~*AChW+NqI8*e(0~`0CI;)%(!<+c%#PJSnii zUy__6mZq0(PH_**G91ry->ToX>?qja9Z`_!5YgpSo+(_V<>+m?0Krr>ouUBEDqXCu zOTH_K=9e^A#b(_MD87{UOnRDd|EnT*;>n^k0DS!} z>D%x47Z*G?cHo`s*J-m!)F~5zi8n7U&iq0WntE$Gj+s9L^tIF(z5xDVG`^&H&Gb|g z4%t3dyy+7uJ({`CS0wXVf1J(}+N*#~(A5~7)_EJ<JR;iQN%`cnRO*28K+qE~{QU4OK^JAwHIm-^pj9%dY#- zPXDzWRzdw$)Kyk0yF!&!GEa$a_X+FZzVZ55i@McTb$9f(>JNjGMr-fv4iqqBoB4hg zW7SJ8;hTK0#UlmqMNJJw;)de(E>X=-WD&9Dx^lq6&NluH>_+d2@X9nf<0=@7X1g;< zc=lRNw*}Zb)96`u_=(^D##uQ-y|m$X<|kpXrB*_lvyYcMY+e^eh;xZ>gBZGv_Cxrw z_g_im+_|hc#gSdr&KbT#%xG_QAVj_8&y-f&UTC3p=t`0 zn0&xVl)yjTrl~Al2@uJ4Bf5zl@jj_kZbwYhqEv}B5_F|Vd@?*ypR)U{JXUaXI{b}v z>yWR7ZObW_Ql*W=msyU0M!bie&chle;n49ZX;^8qIhe4@2q6QI15 zYP#Q5u<<+fhgoDL|7E_IpFT>hx^pfbXH@98WyPW^v#S4{Na~%M43z%iH zS!`FhYhCQK*S{hp0M<5~Hw^Ap$Ap@ViDZgS1~&Fx>tYl`cf=;uIL^MMtsa0)k3HtJ z(Q|?zV1Q^++c6&xC2EUa_5JX3_j*;jyvB6eH$BRZJsnp4%iN>Gk*sG-_t3%9dt5Sl;GG04aEhh^2aswyOR{Pxh zJp~UPM85B+l;bXc9*fb;PXr8R6+q7ldn3{kE6-($I>R`RmF^VhEhDpy)&;e#LcNUT z*%mW-Ql_PM{07>}=5(^ZC8X0VGrP=|+Y0yo!e!j{jMf{&*cY#?yz(GU0ES|nH>QfY z#eV5UR&Bjs(9k z%1OV?LjN@VhDd#!aTNAS-AgR}n;|^?3wPWAy$yZaaeNu45kG*x~n% zixDfiE~2`*lUctJE^-%yQP3X?Z!~*^%a>5G-KeVyg|IS!)!9n4yl@Q;IW7!C30)zG zy<)+?AZub2#R}zHQBgds!?$^2OO16EhFOIOt#Dr+wfnGAH&r3>uC#Vpj=3k?pmO%$ z_fn4@}kZMp{JkLF8Qk5jF4lPn8KZ|F(H3E_J zpB$hcYovzM8f8oSW4!}P#e(C<)w>w-RGWAa9EFvJKI(*LhXlOVy?nDZeS{-fyBBq@ z*s{=>NZlOCMz%7`HYJ>y^`b-L4lPl=yo|cBPPPjY|@}_!N}* z#7ITXV3WHT7-7lLLGz6__|BN$itGj2#&yxrh`LRjMC}tEey#xwH?kl@s#v3u*`EYi zZ40gCGkRbrTiJXHN}PkvvsA)WebZPwQ?ew-aX-;+MpI32Vu9A6SD8ECITn+uXSq*n z{n?S3-Pga^4mGH|*B2P3wi3gRcOjPZTL3tuP16O)#P@O67!U>KcrlRS?-~oF9dLL` zlmF%d_Xt~G4_f+VUT=vP2MgiY9}jH948A-~1Z^lfWoQPM2RQ+C4d8i`pi+j>10{tk4z>n|1@ZD;8WZ{>JaEOY&}ah4_8dZDPF;rdu{%q>;on$%L8 z@9fXhiMXk2SC@ALSl_M%St8m{w7N#kMLdApO4y%EY&dZ6eJ=TFSHbXf6?~x>T2>7d zsPw96PI2XW4MVEiNpvbh8>5dbg_4us!pEKGEt8FWghoE!)ViHY={SnX%Ql84Jm$%k z5KM{X^>Nnld9AL#o>=deC;a^$U8jY1FLEb<(=r>L?W>8V{ZxaDernEhJrYdtF|_Of z-OQMuN|lu1=v0LXEZ5LpUH$A7Gjx6YL!v8;Koh&}?8Z-gDOFB8K9n=MHe29%^kcrn z*D;>aDzpn{&@m`lrNANgCZu}&uiw)3MWvpl=&d{RVVwvV53+f z#3et(>P|GEayu1LWQ2*)vRwrld9{_mRfrAjhdOmRZ2d~(iX2w>1Is9jS7&fla<# ze{^)z2ZXA!X&7Z$A8K9z%EV~MM7>!FIXnSCYxkGH$Aa! zPW`KJz@J@PsM^9`!8!KFyDb&J-*gs8&gdm#sK)#7@ek7BMskLdvT@tZJlED1wC6{h zQT6ULn6)PfC&wEl%6%=QnDd}}0jeiYsfpGk>UtsM?(7^|-qq^ZWEDSs+jlf9Nu{Md zvgUL8Nd$Uu_Z0Hei?#_-KdsjcbHJ>p7$tbBT}wDOSY>>7Y0;1A2LnGcxu6bXWa1I6 zM7KUu+1@@&usGY1G>{YbYem87dOUUn;ow-IPN2+khGjh->WjkiBsHd_VqP*D>$sCF z$2#EjG|1{@(ZhbU8Gu#&PWp`d6O~K*ui)YHl!mKX%B-czqDk&z zD*2l&GCuA@X7aZ>5zxbLfo-DZG_uq~12?-f*OHhE5eTaH-dB>@il7_ZzC+ci*JYN@ zIfj|9qEu>9+p_-joJ&mietQwz(Mvmq%xhI^6+^8j{v5C%b>Y7sRax~7T{63-X(0NR z`@4YbKqIr&x|2|qCk{x17{XdaB{3EvLX7iIP6`HP1@vKQMG2D|=N|^R3#N!1? z@TQXBKF0(f$*jaBjBlp@XVug%A@r%WOhJUMr`1Fve%(D7eg29xnbhO!SP#Z$Psfsr z&YW!LBf0Tcu*fkz?cL!&`w{TQ-Z|h$klQgqwkf?7Kq; zcE(kWyqg~#=_-~*8qw{%H`3{FBHgt=T|lNCpZ`Jek$gv2GXJhZpuJhUe($t=;adVkV0THrJDfCVS3>11voRqMM&?$Y(d^9yfJqE@$I z=uEiaUjvUkC_>#t_u*B3n)fPiuIIxD6>~epFcFB&#lj^IB|JvKklEi`=g&NUHujP8!1h`>YB02s|DGWTmNq)WJFsh7kVkw=u5af zOZ)PaKNX*MaJV^H)NOGOdeRm$VRUcf3>>y~nx^r{4@|8tXL@_bddByueP~GhI^9+9 z-IPG~SeoVF#yZB1=+!lXB}M_Kau&hJmOiW3(>_XQ0(Y)Ou(qadPqnoNp^|!2r*(xl z^xC(Q(J~h0fAV13a&Ho-eO@Z*nV%(jzp)Be?fvy9vt{ z$tEv=c)Vy2zg1Ef`zq7^-MabX>&0agiT|_XO6o$HF;5lfBqdro8_qdFJCE{LyPqyP1QjtlFPhUyE=nscZ{cY@;{Xn8+w$2I(<7 zlrbaaKyjL(aoRJD_6?etkztn9DiYlRO+zLU5n(m^#KPe}&;h1=0;7S}J}vOb!a=(VV4H3N;7o71cC9i#W2qu+H{u z#9A5Ou65D-5W8WS8#X06%H^zKz+A{$Ix={^^OLvkvY7<3pTYQoP6rr-A9KPM&LnpZ z4UjMRp3|Iys4SP7pAOR1=)9(umcNF?4qkP?2*SH;=Gk7Bq+w%kc^{}nb|-0eE04np zdi4sXS|ApUHkI+#mjSbBIazDK0M$U_Rartw3u`7IXsryah#*X&LY3$GG4C7UQ)9Jx z1=F%_Wh@Mk)-e3(qK0Z=nVT4C#uU1j1z$rhPZSoYs~B2GgQ>A>>2e)08d2X%`oh5z zK8GS^(@+svsh#(7zJtF5upxO;ZPzsYIkqMM=Du53yCX^Ta`%?%3N{E zKM$6x(07UEh72>}><=aD^lN;enJ6gp!k;I8RUWh9>`pz6;Gd)BlM$ig!567T*qZNa zGYaAV>MTx1B=j)~4}a9veAkxgN@U9#)vWteS5vlP$)};TiCEKSKu29#t4NB~*(BD3 z8TI4t+OP4gM!O@&k=oMF*E_Re!{Cb54-#s=r;uW|5;tFLJF&(_<0H{n^*dwn<==Zd zvlUK&e-!W3Fu$3`{HCkrL!ah()el+d zX9**Pa%bYL%|k`LhAcTu130kd&6Psp1bho^&24W3RjKBHyc6hkj`XRkN5KJ@rgZ5t z**1oI7=q2(A_wC8o`8)o=lhPFB(I+bybkXGSe0K5h-Cd;D&?>ldVF?}EP1%u#@3_Qf{S8$m^`edrgR}mjtYj*JD+*O;sXK55UVRSmD>vDEml#&PX#_3s-Zio?7@?_mfLRm)q zSnhd|U9v8#O_fub>!0Me>2N3H31jP%R=_VN$ovhu9U^!on+_kKG}m ze#q0{6+sgM43F?uSb*@wR1jhJOM+cyG(}WY71Yi%kCinYFde2nkg<6K)j|cJXL@m) zVHFd;`=i^7FzI()a%q~h3*d*LtgYC z8G~!UPX#isJE$!8)!4%1t&VZbV*cpg0MIR|W zaZ!KEM9Ibn#+o5rWF@39k7L2HEC~6?deqLdjY>#?*ZztDs8~x-BV#uVHyHEh`Z==Y zuL^(Nyci|_W`^lQfSLlC-g34GgDd}$?kXIbKKwj|gPc<2UdEKWMnlHKY>RFs;nO>9 zC**#8aC1D*G&G$qxKgi2oX87|oC*#H$`~ht1uKFZ8FF-8dUBkf9GIZr$$U$UYUr`$ z_HSF}cS4^PG%>;Qv&iNk;4mzNb29V=m(-&9GHw|bB%KOCNvJ@=RG`Utp1*ny_!2-- zQh0Goz}DExU6x*>hs-x#olGn1rz7O@fKXJsy-)~+2-#ZTSdGaA$NbL!ReuC*wdv;J zy4%%8&a74HRl)<^Id8CM0xfEg>SU;k!PP;X1)Sig6aGS<(CwYYm+{>*LcNjaO-Ap- z&Qgz0R*Y*VHC9kp&@|r~Z9mVumt5to$bCZ<1eP|klE}Fz?+(bP+}*Jk=6;gCb#jOd z1MIkoCGb@EHV8Nal<)lSgv1Ftu$}#Xodx=T`1^IBH}kw>>U?e@aIy*Iy{w8%)mzh$ F{{_+0^+^B# diff --git a/doc/LectureNotes/_build/html/_images/chapter1_3_0.png b/doc/LectureNotes/_build/html/_images/chapter1_3_0.png index c7183345f781ecf29d609868da1420e360f3e99f..4ad4c869074debc3f8a40d49c92707310239bac1 100644 GIT binary patch literal 9959 zcmdUVXH-+sv+qghy*H%^K}4lUC<;;q=^#ZA=_)}=AksTQrAd#p&=nC>dgwKZ^iTv5 zLk&n#S}4*>Ui{y6-*Z-A4xGhpQ8i}3LB^>BS8?C}N$i~9ck zuQm*Q@QVFvFDPAWFoBPaPYdLHb0u_BXn1lXj5%c1eR^#-ylsCiSEYQftnMJZ-Cdbxc7&xIBb880<3-< zr+UHf79^0K5)4%y!sSCT2RM({Xcc$ZV*t5mh$@I{P7pxz4X$I8l6yY1+80j!xnG)t zTi1gsUs?4|kALEYGANRYp)`pUUc!}0BYS{p>OwW-CBPG?67BaEOGNugY<*9Jgmi99 z$OD`7&t^oNO@&j~0QDVJR5?Tqa%6>y2VrqTP)rk=Cq!q#0g8#p#&bhYBGCebJkwS$ zScf8pnph{Eg?EFt{J<$>;k}_b4@!k${>|vNC$OBhB>~Gn-JDm5UG_nWIe2Lt&O2Xi z4rdR=1PrdzLEUjdo7_;FCHRCUR9SVUiHOGEfhlz2W@!-i)!wP6tXLu_q2IdO5gPIn z_ag@{3vIc+(j*Amx2d$PMC0XQ^-3#8OR#-Os12iI4%+Vq=Gkoclm|>Y2Tv!kKVU|L z+GHH@GOsryXG|zCQzKf=NUt*g>GPofB;C2FAGQWE$tW)8B%_8B4D0q5|&; zrziro!RbREVD&D1#9MsWr@$}3mcmg9=>x3OI6{!pE3NyY0QDCEwgLvnW^`EDFYw?x zEf7G_NQ(u40C1KwZ06%k6@Li@F9kscaQNHL0#)X~)&Z(4j;zQIP^0up7z5$z<~a~f zL(qV(_ALa)t1IbbY?r@`I;>~}J@DS!y7Dsg1k{1D@A6BAa@ zKB$}GP#SO;3<|%FjKGHdPy{z#QzYoOO0d4;?>hkT(;$IxI>$Ejpe&{>zIEd+iul?V zOyB|{#G29;Lx6KeLM(G7Nvcl(QV{v6tUb#G{Ti6o^KK7-?btLfe|T00zXT9^Ad0VT z=^Pbmbe4E14%zx9bnAh`OGr~R28@F0dP4A;-WQObfcJ}^yAUBU{IbCIicdNkPF+Ri zsTR={63U3wfOMF6mrQVz3;>sq;RrS?i(b2dx4f@3t*5U|K^jtQnH?Vv)dN_1R0jl4PiO%4 zBN>cQ09mgpMQ$fAwj*@@z5q?zE=sO>pOa*U&t`8O%!xHguAI|*9SO*T8%N!L{9M1E zf5H_AFPhb}j}w9R5vQOY(9fPJlEZU`Q1`)glZ=hp_U@@=9-K!UI&eYh-P+DXZk$| z5JI6v59<&{@_Z*tHXbqXriRX);MCLVhI4Vo`)mg!@HrjgG{g=g!>gG6oE3s?%{NVN6?Gz z#?nybLEJH?wdV`8-*A&(MU`y;a9EEP0H3zp`O7ft69&`*4_FH0(Pv&j*fra5WqY4N z8^oZaH1Ko5?{HrRGXzr@ZFOov2n@_AU{nS@Xpsg6*Tboi!`Wo*sAEs+nL1rAsyh95_`6M z_q-J>44qt}10o{V3>`p@%Kh&2SC>iJUqf00QqnR3jsWRwu=U!%`me-4aZ&#uz70a00g7+?jjcCN_jb0I9Z;E2z@f5yQ zntc$>MH`)B`xq7i3S`GVLHW?PwSV{!H#j&LnQ5T)&OQtO#qGr5j_U=HrtXsy=IP#; zqAw3BV`I~}1BJI>vHN|eECeWmBS1?FLj8qTwOJ9V)0$9g2b?A?I%xi<y(Ku55|HGyx110PS9%nCy>G|@o=~YgiHMj* z-vdXYHy8+VKnMNfe}wj`AzDvr1QW7eQ(6Y22m9dx8(f>HWcl!bj4c9%WiVr));r!# z)f1_?#x-$I#tJoFJeSIdjSc*AQ}iY`mXEys@S|v-xF{EXsr;PzmQpCO%UV{lBA@|L4>yTrM5sMQY#G#q*(D zKEF5suzM?ojMcqLrWXf5I-43Ct%HN)SxW0!PN` z@>*7ut#7kRu*Sx|boxEgFUXSmAM$%+s^EPO(u3jr&F$F#`nsUyc{JZxO!^eT}OwJyfQDeE6Fg5JyeB!R$add5qfYT%Iq@ z+FI{kVktm+;Ge5A8>=(Ce}l=C+i(7|Wm&sGL+G%|KtAM_+OYd&v^54TU+K`Hn(!#@ zQ2aggu6940zOXP}->DhF*LEJ8LC{05|EdYZ{eS5IK!u-f=#iMl)XD z&YPk?X|Nvu;1tMK=<{RpX@T&QKPMU_Wy@KlNH8@oQ}2B>FgNGO$;l~_iq;KMf~8YV zCzy6zX7F2Gyjq-uL)O<%W;!GlHr$YkX7Wjo&`C_h?kqWFz88Vjr#vy2CywJDr9Pxm zc`V0#%Wc8ilD(E!JemPTUUZ?$#=bfUaQJ3rfllAEUenhZ^s>wvieP$5k!L(H{PM0J zgnzO6iWaI1qbN12T=AzXaEgjXr&g>%QBq9F$2mZH6;stiSoZ19{ z-i%R)$Mt(kY?`#BDDt_5OWq1uo~yY_`UA*n*{*G*`d1dc)td=6Vj1tkGELztM>sSV z8(C4%g*y|9*%0Gvnfm>$NCL$Rg{7#3pem+DW`g&9lY2~a`!AN%Sh#gF)>Nm;`9v6` z2a%iZ6Z=#2DNIc59@0pdrS-~3%!7B9H`Z@ncU?A|5U=tsR_4f3lFv?BfNZw7aZOrc zGdIeMF%Kfu-&sEErAo7KoeYSo=h|`(k$2OD=s$D!F+s^~v^<*P+}^*+8vFgKB*yba z%KI=yzpEp4Yn#c6a$4vmuGsafF$G z8(%FB%ya66QHqG9(>Es*YyeLMj6)7yb-YYVOKaiN6Jg<|*hO*BZEAuv%)c#Q!-?z# zD4h4$ve2MI8srqnkb27mdZM2~#le<}NModg?O#XA1L4e$w8&i`7a&}L<_JESCmZm< zrCc^cu*}OvJxGVa&JC0oDwmOPh#U|XK{2x}+g>e6cBErqkyIL7*@=yvLuGw0x73?j zj+OQ`AG-}k8Jt~>jioFcs(5!l1LEoP!Ci}z|7V)J?z{YAi4Qh+*&CW33>AkECD&eg z_sc^>j*7QM)ZmQw?>zaU>dPZYhYEaL15KQoGFHN@A6#7Z zS2IkX=0qOafBGke^?4IGQdh;m+^iu%bE?(V{Cpzjo1vxg>+fIVDp-oj2hRpW?_Q$x z@#BuyRni)d%jN5`c#CORl-0L2HO}i25h>OhH{{lI@hT^KB8b3Sx67aEFp4jO*xW9# zv(cHA8<{z`I~d&&5qWms{dHP&@~hbIS620&)G6q>9+n?1*QFj|_Ik>MpAr%;jW@B%@oj7T#5>-p=si#sD|SV< z{o!ww)$9A#?5`1Vhi@LpHq_T|4R91b2;hEgYv7%FK;i}}KimB={DSKK`q-?!G>7Sh zZNvp1$@`bStCCFtHGvk`-My{Yhsz!;3pzXAbK?GV{-;McN^8c0_wVsomivbbkULGF z-%JzN{~noD3aA9jb?N+Oiv=%)j-r#y_V z6tn)mF*i<&!I>&v+$Rd19=|>mptY$hVPBdB{>3QV6oGv$MpyO)<(cB7Q9mKv{^+9V ze`8`|YEfkH+gP3Z$5G1e_VfaMd2%5t2ON1Qvbs-n(=uHyog86+uOUI{(w{~YAlu6K zDgx%jgu>EV*JXD`?zq!a+KDK!vC-MH#|%c>EnhiFqKBKmVd&)?y)hR7TQsfq`+B5& zuQx*yJKcm`$79V+K=Is7RX>Vo5bM?0Ig-{m0f zSH@NOdz_+{>RnGv_e*n(PTe{b2RZ~`eXF8Bt6$_oV_N5_y-yE4NO~(ni{Er!PuLr% z9zmMmHnR_J&-INr6X@ zPLgk4d-82f&q<5%1+oGD$Agee&vbB3HlSMVVGG%mrDjAllnw)vlf72hS##5;{`oQ5>-lhuxa5V_IQE57BD9y=S7cR~LK2_gNqSm^+iIa-tY1 zj=$K$0-PO##B)>14_R(Oc3!Fd&|A@hB(qAXWzmK^NEiNCg3mJIvZ+FUUEDNWLmAkz z0=BHbC%Lj@yHxgjMX}DAAW@yWf|PES@D<(C1!p zDJQ$vIL~&)^I&r`qj0|No=dfbp%F*dpJS%IKbH}lE3S=2A6od*_r%`FVPIz|?L!HhlxYb>6D zjyzXIX*Ircd|bG`YTc8rYp!O}4u`|V970o1@x#4iCgLaigW>iu{F=u3hUfDvi`|Lm zHEF5{oOL#Ka+-F5X1%D`p(hv~c(Q+HZhXnBHPyrOvdzQE4(T~B*&gVCaA*}DXpStaLV7L zhAR}-Z-i|5V{DG!2r^%IUh8-5D6=DF?wI=)%W5cAzU=FIiBt+uY72@HT&59#s8?r1Q!C%$UOZS5)vQ}o{6PHYddv$}Ixt)bSl`s^iiT-pML8Q$bsw7mG7dENX_HKUw|s23{}D`B4?BfdiVC;=I_hXwVns>$CCb( zObKZpY%=ABNNwd20ih$^CtYa^X<-&x{W{I=8_V_~2OAADK6hf8jfqFQb`I*%qllh- zsrwQlUF8p{rhSrgs+B#maz2O5M@iAHcc_hYMC^OVSGG^8(gGF4_2#uNQhqxSU$c+@ z(96RH=0;MuBJk)C5%4ZzvkCh+8T7at=s2X>nsmn+DK=TR<|s5lqtdSh5N!vyLM$va zuMIxm8HDA_Nw7^7zrU0kT=h(J3kSt7=UX}WB_C`sBnTA<3`G2XQ?W33MSfN_vtSqU zw*9w3-K#w&?b{t%Jnz}`at>xiNryT+udel)7S%vqL-s^WRMDq>CcQlN!T}ps+jGsJ zi1@@;AD!il*bMg{S zMoZb*PDZ;|`f-uI=}ctQzZuc$xABy)f8!7^vx)JC3iK~gK8G0hUz0Bi{X$9Dp9+Ww z|MHU1zxY~4DL#UNkjDB$6lL4u-(Wrc3T|Y?UfZqc|UZv^i8s`LL`8N0E0kw z470$)qa=N*=TRVAGgN2YB0HYjc z9HQ*>MbyTeZ9*{SvBdTOeM6PlW2Wq^NlrU3vXi zq5&Q<_I}^>V#f7n|ITIT0ta67yp!3zpk4z4?pIwc2{w2sw!vkM_{zrb1Ys*P4kx`M`eM-2 zrwECQvaT1CRFy)$MtnC-FNUWCej1x=k}~|28S4|Ji}hazg8%laU1vd95&GZ3e}5C5 zuF(s8dzwdcbJ5c1PWT7E$O;AD71kVndSaVz5A%3V@{Uugj3b2? zu2E1`SZF&8Pjz_k1x?s3evMlse8$740vvX(Ib4c3bN4>lDK(HN{<;lZ^XMAV5>XW` zP3GiyhU;7?F&i6Pb3K_Tb1kU91by|mvTd>iesfEmrm zncFlp*YXRvzI|WmhQ7yr^ev*@=B?CybobcoBb~QX^{FPRdiEibw>NE$61x5c|7>F<0ki1TLBc3rjL+4Ir%TPhXyPOq&8$8&?m z-ZvGUYieK_pL$YzwzA#av!IGB?D`Xu`Dr9&3Bv4LVUg>To^A%uF-vLC+G9)E=53pw zU>&VjP_*6sVbeDpHpKJ`cb)iH$i1LL+!#LQ${9nn)6o;`xBV1*0xz5|GTY0n`Y&*w zq}=-t+`Ct+Z7H^>g$9T+y)+7wag&du-q1^(SFgWr#bspm&re z%AO^&2(C{aLN`u3qn8}4khjLQ*(JZsMJvS@2kMOb4A+@VRe7L8JVy%2rX!lgYLYtj zrI}0hBJuY6XqR2OdoL>?*9RFRM9r+m-?(QGFbi1i1G1ByutN+A8wqRBqbI%5=rE6R?1GOgPeo~{@> zo}YSjkxqB z@*GxO%f5$0iY5VslpWvvh-ALRn1+OL^RQ-`UWLOK zQDU2JHZFPE`^18BMx6v<>E>R$V)vQmr~Nz`jiG|Bg<*M0WKVUxaXra9jh^mw%>j>Z z!ML>CI6;rPj6T#nHcIf^5L)Wcuy z22&|sIR6Sr;+*lkY1;QUY0L$Qd$-AV4K830bo19|ND4s8sp*>o3ohEp$>}aj+1HKv zLDYeyu!HGKF;A|YZ~wyRY=mF=TRf2dQo8W+CA~IUi7A)quPUskG)QyZa}(mZ6_6kn zUY*V3z2ntvna`bPY}2l}a5 zMI`}k;ts55`b0PoZ?Lh&MaqCbgMrRLj|iwcfKWhM|9AakjJ1y&+QNzDKy>O5FE_pW zI`y8$u7dJgJEB&ordImj^Kq7^I``gX5s`UQf4Tp@z0uT5F3sdUf{|n}ii7JBOF`{9 zXcdAA`BFo)_C4Iav})-J8I0ASM>YCP4eFloHrMG&owT?8ff_CWX4!CuW&qCv+t((x zRDZ>V^Z4_1nss|UDMb$|V0hZ*`d(h?=c9(UOyOp4U|N03aZTe>R^*e>i()9>zmQos zO;AcCQ4xv|9BTH|2t>?8C=*s-mgGVaDEz9X`-wv+ySfRHWH8z)-8!)clA*!g28>KI zmA??X5P&rJ=U7XJ)%T?&go7asBJP0n8OYGF+XN1J!~uRfLOOWZ1lV!`s2%x{N5vU@ z<9we@fmsN-@NWzpP~60Hx(`uq*$_!FKxluNl=R+xZ-z!W z?i}|V?FZh10xR0KX^p?Z6SsZA)7A_8Q0276*!ZC+zzlXwl|j*oqp`>(#%|F5L>KR0#xT5S)+gGeSfFjH@(UdKA5`5M2w zJwb8Uj^3amr>y-HF*c(H(0J|sC$M2`@$t>b#f;Jc(FGoIN;vH^j+v^81(`<=HuEAU zGxfJBHsv@r`%P@t4#6@gGjhE|JImSxhyV~`eG>$!*Fj;m#q8Ue$N#A&yO0Yc5+&-w jA}DWi$N#B$Kmv93ng8-Xabh4}Is^2yjkPK?9iRU%w$9Y8 literal 9599 zcmd6NcT`hPyY5ax6Qnm0ga8372qGd)K$KpBh=6ni2~tDHNDClJ?=5r{MMRoNZ@~gY zf~eF$fPev%PEZ1ba`OAmckViO-L=lTYu$hD%HC^FX5N|X%sg+I_nCxSaD8^x)2sjh zup1ia+y($p4E+{hVWQV~v8NO157i*uyFnI6L{R9x05`z&UXY(RGRWJ*1sdWO5a@yQ zRg|NDWT6j&g8Ty2T+g+q~yQ{$ttK#J7(h@Ib}I?`zD``s_^QEAp1tC);K=mQeeu zOF`q6kJ{VXA~z^i&B-twolj1&KjhHy@#V(R*L6MxvNA|WlvqHyxVMVH(ed$1d@MRT z*D9_m_ylD!LvSy()fqP^*Q6ph^A>ghjF_Ij}{|18q+z6k90@a4X&yx(yB4V z3;>K~&?UZh+3K=l0pZV=rG`5*&~IxT@Yh2a!qDJq|N8-c0bB;m`jrDmLMb?U3GaOc zEe6_^Kq~;!pq3NZnkxgN9(ZrRlsrx202ndSfs^!=q4^?DA~+h)0A$WWThqwKuqeHN z6F@#e3bEsNZ>~)GXb7<*s5pf{H0KlMw3Fr;6=}H`cJ^G2L43*8hO`0#Leb6#(b>rA zf1XwaSUNt zs@2=ZAUik(6M@Gw<*WxP(wfUU=!3t#RGN1`794#bgzX}g2j*hVoSKiCVIk=N6+jCI zHXINH<%69C&}_!J8Vp#KmeX6GfyF`scg}??4L?CdMiMLF3L=7>>Ff1;RM~2N5P*X( z31j!P_wFHoDy}+9y%`yA*}#iK8TM-j#`4)P(TDIt91n7y%~s+ zsPnsFTxc=y-IL8oM`lt4VyBnnik|Aukz?=+sXL4X5jjb`+9)j+Qaj?R z=ERO5P^25chqeaY<$L>60MLT?LD4wBiyu9^Se%c`u2p~^%Ak#n9iybsT0IeCrHp>= z9@rdj3+Sc@P7vrg4S>QKvC@im&Iso5z2>3J@M!X7z!-FpOg75210676xA+H+Pbny$z#Qv~sqR^!eH?wrHWoLr)17e*s0lgdNsKNR#(qkq(#-kHN_a1fIr-SV z914cy+tqhjL-M&+kDEU-cCjNuy7`%_YuRSD#qi!3jDwY%`cL3wG;Ni23_4*Jokvx` zo$0B4*%y639~_-6z6V3M&w+37ahzDx9CvDD*k^X3w3L9wZLL2&P+| z3`9sZ#+2iz1UybXFnCPNRo(RhZ%ea z^%3-S-lO*_!ey7UA4t6ESU z?_68V={@U~++OFFvI&SYzkM~W%5T~X@KZ!r#Eu`y6U%}IvmG;6NKCa_tb!e5pj|Fv zXgC)!fhN?lUTXoe=Ra66^ea6K0oVY0i=UIxRiF+QXDRg4Ii{fvPfDk;9>&*1(*MK{ z!439a(Q-O+_|a?zB?Tciu&5CUE*w;`&>0csj);v3n(_jLYpn$sYDxv@KF3r)S&5lo zc9zGf8hTro$Fj$@=g!{(>;NJ&X?9pPoop@Pa8nzae={I(RhXAM{PY-F)| zmR}IUjN7x!Z55HL=8)7&R@v&8R7Yg$ac7KZ>`b^88m1x_qOw>SvJ7gJ8)AV25$q^T z>`miAk!oOz-`t0Z0V#=0LB!78s-Lw9LF^g!Vh2Q)ygk1Fo+*Crj9wQ`h93UsFIc-F z4)5OF=|7lCtNg{0kMXrb{5Pg|923Kbonl4C4h<_4gF^GIjzMnY^*aM_In0#Vb zyMzQ!YKnsTu{#o zUnCi@U11M){I&Q?;>Ys|JNlC1@kh>xq(QoYpXQ@p?JWOCCMoj)_bPak3TrMTZ~^pK z#>_x76Wet7>|c#8NbYY0(F0Z3!%ORHkMx!G1v3PsF5mU0gQtlFs|RSw>^;bFlT&Vp zLp1pfzcMxCyWkWQ7{u*iE)k6;Yi4Qy(hwB_9${&N(8Q_2mXgJ^G!tAFl6+I;BrY0V z@rox4DAM@RiXd6jfGNjsgWq+_84d5Vrzkf-VrHojKPI%y&zXO8FH0xM8H^6Yzh#r# z3{N7b@%)!YLR;x!fj?4TN5}TXIcc48gW>Flh88CA@h?1#I$jDy8xYlA=2ri^T%=a{ zf0nO9c`D#<+`I~SkaR`TLlk5vN~=~gh#AK5D(%hYB<#fP~%IGTMSIJEu!IDD~Z3yDa}C>s89 ze0BA-#GVRn7<9mbH5***CCz;NVW`%@X~FYt4pI{Qd$;z&$f;jXumUP*OCn!-C##} z9sf8xNUdT~mvY-KkZ#6>EVXsYZ#*4xk}SR{n4NIn@;{5w&=aUmTZ{RerJ%3Za6MGJ z=rYTDTH&T^(CRONYVM9?GOhahw?u?yxYEW;OB8$wpHvjy&Cg={9sjg;vGcuA>joh& zYomGn))-yrpwgmhX!iMH=i~2FRlHHT3-sOs+7}9bw_9#I!ny+}hgQ%Bx{6|51yo(t z{u-`xS$P|wRom8md%xmo+~wD(5Bm~4Yr@t-Gv0jbJ!Bb?c#tFa842ej@JB>o-STqW zy&OVpf-WX?G~T7`M8dAJ%M=^ON*512JL`M$vaiVIY6GNHOkH52qqUP_310$Q*1O=| zi;%rAtsl44v%a3=GPwM_g_dKpHa@A5x*ZZ))6psBadwQLKjnt7z^_^(nuz#Z3_-~s zX>s}N3V~l0#~fudGh@~;a&ash*2}U3ZpkHo2jHMht-;k_bn|S(qZ{&hqJB_gHo3!e zDZQCGoLpo=OD~^*fN<jTM%EA>4tioz#IC#l3G`l+nrI{ zrr7(B>P`W8Zx-|h_?H(S0}Nzy#^4UG!}D*i?cUmso4pBLCc#d*4txe-!zCvVw01L>dKHoKK*2dw!b!J*tp= zmH+F$jfmvgKjT)KbTzz?btU(+Yh{LQpKZ|sGE^&WH?k~6Ax4Ke>7yne3r~7u^ruUZ z@Jx;3Q`oBFY_>k9>Wv%ivw0y@i};PF;OL+(+0t9@1gA)fHE;V}fHXBf{VmZco4zI=a*A^1%_n!9`qIVko2AjkbObzS za@s-0Go!bl; z=K@Irpd`WeQnPEI)yaiiVKqLLox5cf%qr_D)W98Y=i|e|^9Xgp>^=YO{VvT=Zro&} zoYupcYlApBNL*)pf1YZXnz@K_mVD8L<>H&3gUBG96n3Wb^HOhWu*^QSqwjF_Hz-xb zg(PBoVjv3oI(a0HO#xtpoU>kmT@I1G6i|Mir`UO>V_9iC1QLow?zwllr&W#0+mb~a zZ8O8D;L=^4t#U1YmqVIoIqcZsW`VW-haV1|6!B$IbhTZQgIZjvaUQrmw0-<3d*C!Y zuG?7ZVafM1UzQVI4Bes_PsblgLARhv0N3V2M z2K%mj+Um)-V@M$8>*-vU@S<|C{3N(XJ00=t5|_}SUBkh1$s#9;{J?$$H>`E{avSPs zODuW{$t;Rum}nX-hE*AEcL`S~?um}^D0vG;RmPs59~y6O<_fPy~VDoSMI=K#2Get8KxMtu)-bfrk34*Bn?~CKnQ16p@^b(9Wd5?ntCK}u73rzx zD}S8Qo_XAwc*{XQ^1(>$tmpXm)H4giX9e3rSXYFuol@CzhiFMHjIJ17Uh{SN(6izZtc7`SZ zrOuXK`+J8{;ML#0E+H0GL~K>uYM$gfa{+RlDqF*~kZ?H#B9|y1Re|&JOS;kNS~r$5 z3ZkSSVVs%|NeTh$t^e4W*<;OCPs?c<36Yta^M;S*yl(JwlWnxu!hloDvqv&xxs+4H z`S(7;d+oCsuDv513>q3_sl4>&X+6zd=pHf9BTwzLn$?@z(AOisbcC!rA##IRJ}&}- zU3|{H8Incr({O)RaI_7Xr9sK1uTt(`-_G)!x|-ZUTYqmXDt6|AWg{;O1*61gIc8d& z%#M8-_$p_{M!xgU6Ja&Qp9YxQ>qkvd`xdBz#pk-%SEircI5dKs$G##Xe_GF@>F+y< zJ8jgt|eQn zuOil2@*UJ?H*vghsC;|!!3V+qr=k+g-iGZX1kzMJ56_6w9bG<|XpV081Z4CV0%`I?)LRHDB7f*SB*+<)C%sO$f=yF0k59jN;AxI3t_ z{hS)mG0(r=)r^er+KcehPxD2#ODNn*D$CW|KD=T#Y~qC;Sc+~uJEQ*FH~F?|3gt`A zXSd!INUB!X0V`25)z5k<5vCK!{#iT~*ELyeP?O`!Q7TjC6>@Yq-9CM7FULWBNXzM< z`}Z(2V$CGKtmceiaodx5u3&3;dvk$TW1-}t9nsCU6ZNRcTZpc`P58gt5Z^^Sp&`08 zaG*UzFV*DD9|@dCaJK3y1nU4?1>Zfr6D5H=?P)z$T^HT1Dd)BEA zoNi1ESp(*OJ7Y4qs0;p_Qs!)bZr*gtul`1rRT+to>-h@DpWtM=Ex@O?$3dgmQ>e97F%ELpvA)l)=+m= zT6o3pl?yXR$jK{IWs+xqm38~%;PPnkG|xnb(C%k>v>lSMrp1Mn^DT_pMt%5Qw`O$v zsTL4Ay`X`v1(7Hm@C`YD^j`3#gQ96vziq7 zDDmz?AIcNq{)QoG8B@5tV?qxD$&gKDKsmFcVdnU8 zTbXPcb#-k8BG)xBt9LPw!||04>{MMyv8v`XuwLSGA8;~O94A;{cqvg>pG z8$oXVqC~%&he9`hiXOkti8TewlwR|zDRwV8ceIgxZe1s82KCwY=-Jb@NvBhqPKT?7 zZyib{ff0%RRK3ufN$nx_Rj&|Mvg4$l8n24?xlu%a>_q)mjxnX8*mwF`$O(O&i!s+; zE6KyHt*>d5kWm+E7slIsV}l548XDN|`t84SbSJ-0O-*jSz3|j^81wsuVP#IQ&{Nw= zB$YTkquqrpU1{&$&?N1zL@N#jS)3OMh^HOWzsw$sP9VFNk7uXZ?H^SZA_#rZbg}~U zujPv7bp1ySc^`ASdX{^Mng7W`&T?Ga(%Bmv%9HgcTmvrMkFOdnMIq?mjb-&A8Y-~e-4K3q*PvA2p?_g;Ww@*E{IZ9vt;Y8?!c93ly`8@q zV*6aEULo@C)`FYbpIek_q!h9(;h=}Jf3(V>?iZg&)L7%LT8^QW+g}x2d@1_iGY0f5LeQPTl|Z4Exq*VQ;U&9l|W~%^vkmx~g@-=3?GZ zw$dF7P5HB72U|h4*OMs{6}B?jTR)?{YR)0n=`L-nQNfjIhKHX0bI^*Kx&PPG!r_7k_;f^B=#K3z%%E*l`|vWs2?7WD<#NQUC4~?(YwTQtn5s#}@9mJ0!^k@tWOl z*4;d!hC2GJ#tA0=XN1?L?rs^l*@G=TKA9|9eE(d7z7jH{sc~4Me?6F?vvXbN+w_`e z@ANc5RUsYQ7L4VLVj%`3723{ievCe(GCR_2Nu3*vk(-dbT-4uDtfe)3k*oWzpE6^^ zhBnZ@<6g|<&<-{9q8qxe`qI9RPIY0*tq&DYLmFh-tXWBdU(MRG>Tk z%L$HsnUY0Ng zqdA$lR3e<;*bv@oJjXzMsuXs0bEjF*Y`;&GE%{W2BPL>OdY}I`qejJwOs|_gI@jLUnHqVeS=b7zvyR^hiPpKQKFgqyN{HfJFK*-bZ0lh zSsfgHNA3O8{H|B;q(1l9rt(0cE z<@1zO^?rRZDW`CS`RQ}8s;F~MIi&hhQx?ft-)%<+)V6dAgHzyTLK;0B>EF?#aA^Oc zbG?utyhw#^*FJJ`Mv2*bm{n^J++I7Ges24-Rtl4ix(C;_*w~r9fz!*s=RW8r{g8Vy zyF7if^-h({dNRV%0KZ&(-BR^|@zJaNTQ!mU9}s2l3k+X)NF9t!t-zNL57*s)FFCA) zRBO&k$%_+CGGumnI(_kEV)u-YD|#fuxRrLExzZe(zI?tr+qKWCTku0yL`{-RLc;ee zLUu=$0qLxa;L7Sh#;qvDO|fH8P%yiFjh}*P5vwaJ-NDo+efVs^$|T{lEzkAZiqJMQ zGf5COw-2^6>#S1MEx#Da2p&B@W$neKy8fGezGz4LQ{aDAz_}waO+~sl43K`hhVt z%37dLD%JKSlY*4#X=*0Ru5#W@B=Wt|OL(VYJURBf1P`d&mcjoU^i{bb;SNu<4xQ#M z_CQ2kZm7Vo?!%fer`TPL3hr=cu+VeW^sFN`4^+fRdH@I(5WZimmSrPwkA5lJGlyM# z6K}wfc}h}62x|ee2BR_4%p`kYpwQM>z&E@&?sEK{#79Kq4&Jc+*O)^HTG6oU!Y=I$ z;AlwE;2&_{fT~ov2%ulStQ}%XJp(>jZGSB4OywaZ_+#@Eu;t>|ZyMNjcl6)F=%wf3 z@+n`q7MhHbA$6*+f|aEbBJ88K2@{+`WfA7(5#~V*IP>ZQSH!y~249&kiP4iH_p0{l zP%k3p9%%7-IQ?^BYz73Kq!jLUh+?xdyJeV%65Ld6k;1&QI^Y0A( zjFL`3EvAzh3jx{yqHcgqY*e9GXscTMPTY&bTl62KUn7*RezN^dI%6W@U?clqIqR1K zET>J??;&b0H6ZCElP6;M_F06)jnyR1d;*<2a6xomYFNMW1@pLWI3u5cLg*R+)Rw(H~Q}=(sD3|3cDp8=TZWsd+_U*8&VKW?|WAe2q%`EQ65G0 zXLCk|(no0FD@RcPM{N6bF9r0A3%PslKr?f<6F@5T$X1f1%?AM=V`pnAC?|%26!0b& zS|7k0wiC~Z3Mv8z+}P-zS>~4x>wMJD;ea(`w-~!~@Z>_Oe^iG6_5_eS_VCaYhy-JS zA~5N@-ik?;?k43uFDw*t)u&J187O-UhA*xPGAj#rz0u;6>~#m3h-7X2F;b_w?3r;!z+d zJmsq-z;UVJ#NRQD6*Qm0*#S3e#yn){@Py7}+kuiz%Y$&N4e~~Zy!P*hUOb^mJXnN_ zqoOhyjkWWCx=5oj^xUgr*eCo~lehm8JYvM^B6jE}Fd!{znCEV{81JPix zQ{hktPq#{f;@|Vi%ex|C=L*7xq;I#~k1L4zh7wL0NgC(6qB-eaauvim`wF`F2rO4 zBOu2SdCUxS-@n9@smM-^!<^wHakN0w$0MD~db*&kIb@a0VFP%qu)ONi`vX}%Zg=xH z`lEUpnVbT?C5WyKLrAu4lbZGfUYyid9_j~xMy6dVSmJ2TsmzY z1RyUpEC4%n3Kd6eEV#LzL?=8y<7YUP{lX357gFi?$65ltjII*O;8S1#hI}e4iQ+|9 z0IPtX$H(BsASb#48nnH;`#&7c`7foSGZT-w@WD}fn=~2xYLZ8uA?$oX5FAz{3?kAw zXON2M_4S%nfvDeKTr$$}tK)ta5k-0dc9RR-35jh_bZ%T~a0QtFb#~g(h`0uBM{@V-vZ=5IX Z7#pX+dILH?M?VY&80x}xYHm2k{0Hb-JJA3D diff --git a/doc/LectureNotes/_build/html/_images/chapter1_61_10.png b/doc/LectureNotes/_build/html/_images/chapter1_61_10.png index 97a6c443403b87faca299a1d78272a88508b193d..16b5dca787afa007f7e6e74f4863fb236c504308 100644 GIT binary patch literal 25483 zcmb6B1z1#FzdsIx5+Y@QG$;}h64EUKDy@Kk(jW~(cZ`CRluEa#fJk>Ugn~2@0uCwN zox{xh*P!?PJmUGC^S*e^v7!=drM`h~@9gs$pTB(FQM* zb9mr4y>Po?@SljYoVK&Notd+nk)tV=vXQgBjh(ZNrSVl)Q%5IDI|v^a_{VwG!r9s0 zNtB!0_TL6vc8=!UU*js0!9fV@?>~0J!Xmtkd0}HECR1Wz86U{YN^7_$ua0>nhtHy0 zH-{dZwTQOdGReE}jgkDO8Zl#wWXyeejVCr2U*BmTR@+m!R;-?o&Y6B8`+-3z`|B~H zPeomhu@ek#W}Q8iUA+6rd>x2I-pC-QG`{(I$@%&opZx9md<1>c(DE)L<|noYzZx6! z<}h4T8}t7ab$mrxS=rCVONJrA!NJ-}betE#PiVG3Gx)19ulZY=t7@-^86=Z$>Xc&@ zdhJ8lHFNK$-wD9duCzD$f##mg&Pp|Ic!sT!qq+JnGJ(hFjFz4Yxh=fB%eAIwaioNG z!e^gu`Fq}b`>Cc_iGtw!`sV#Eg|d!8cf+Z9jhb+*20xK6WIk>C_Wqt_eDTVO*0t*UATPf8`Ese%;7ecRk;BR`Z>rat$d3v;)%SP97Y4E~ zw?;DHhjov1r-&Wy!R;1%)6XIi`&pnH{C4BXcJ+sN&z_&fzvXk}j0L{%%Ln+cR>XRhj$T3p;+8oVwlnn)^j{AwGPV>?lw z;=20dTd}F)yZ7%$YCTF8x2OfI?0mVL%{7|87|A;ofmqaf6vp2dh@1fW2)J5 zD4(t?Nr;u5{UzuSw*AqLrM~APbE2TF!uDL3$8uhUu+tn-#kePj*WQY}*BX~zb)i`o zHHYBq;IDGvLc9s36L){btQgDVx}q(KSmA?IFS13a56%=a9=D+g(EPDk^51KffIB^s9OwEoO0Q z7d`_6W&VRJuH(x)xloI-Y5_1RKMyviZH5c6pA8IXRJ*OK?5~X-f=_rLcZaBWjd9r6 z*b=z*-NiEt8rNay?j*bVw!RF-!^Nz$;K)exsipu!E@_^x+~9?^J^l&$UHq7-5>Yj#u_q!N`T%a zi#P@DE)Vr*D%0G#a|g>}qF$oJyr+KBZ75$av~_xFt!BMt(YdeI!^x~GQSRwerW)7P zCP+p9Tj{e{kjg2%*qb^ns#3`4#VtiPgL-jt4o#fNxg@(x*^o<-Mq=Q>9SFa}4v?Den<1r(nh`h`TMvu=e z;WUe*ReWHoW;ieQd4p>X?hnPGi&RvYt1K@s-#F`Nn&`VaQW6CEgBkseFBArtSE0l7 z-KFn&9l4K7K0Ya97fcFQP7=i4=vS5cR^>DwikULe?2lf8>wl)ub9X7U+zwnJew*P} zVD75rXx{vu`#5TkdLTzD5*&1L)BV1@e7aVFzNNW^>&ozJUXzvwHLkYpu{W(3tyvh~ zP%&#WddPyQNyTSI1g?o7Nszw|q$1rzNh8QYxXj1tLoQyw%VX9_>F4Jc&u@9Dv9Xat57o|5u<^sX z;3743dy3n*yt})5=ISzLaXH$3|0qXOn3e={zVm6&h}P-!dy({?KY#88lWr6u1a5d$ z#bIheo39)NwReI@=s11qWM#ST##vr?%}*l)nE|(0sbxPNPkul7rAv3vDCF7E;*~=b zVx3CdohQxrXc4zL8e9`;uprAj?JPXRBcc7eIo;||Gd2K*Nz1?|=I57C{A$ZMTd?|i z?~l2K(Mu%4AvHI9yDq*ybLV6oZ`;PqWU=*H87nLHNGX(Xr1;kT+wk`yn6>{+7M+A= zw8M1EW*ckT1zK7q(8)^V9$Xpit*!EFTW}2j{8{$W<;&dK^wiV}78Vwnux&8uom%PK zE?l{ScN<*3$5oEZU?9J?WhS<7)8)R9AwN6z;#rY;A?vc}d3PkP55lW=CD4zZd^-nC zfip!;a^&KZdVT1?R@~9o!D?SbgjS{Rbkw=0rIsJ5*KR$b-o*(KmHs!8(yS77-cA%E;KXlzAwt zR1K}$xCd*Jklh?u1d}DaVtb^}$zE$!tv|IIg%k?G_Q#~kp;ON3bF?#6 z)JlI{Kp+O6!TS}BT`lih+xG3D#y+NfKgFm~LRVzsjK00)n;JB06)Qu&=OX@NrVqzy zzB*beeHYTf_8I(zU-kwV+RSK5gbG3I$#0@hHrDJ8iJF3|>VCg=RWNhG=GvZ8!7Y5@ z!buNgIWuuE8?n+N=bZ_*#!H~C&q$2(dF_nQCN7<6U**YUNLPQEU_FW|0E z*2$4R8=tiO9E}<%Io-&m(EC&)9qibiQ%8FIqW)BW!*BK~e|7q&*>9v^8TQ$U#S(fSOxLR#r+8P32hTjE5y*GoD}>%^Y&OK5Y0Z=1%ac+}vWMQwOiHO0?nOLGE_iw%KKY!2D+x3A!5j7hps z|GaF&spA~mm+{sT-t;0)9!f`Vo2F30vUZzap!Ik&D_n>L&ta*QIEYSvDXFPUAS_YL zh47jPZZewg;XCS@#eqihhVM_-Bc*I>MR?=YvuuY~_3=#f7P~s z{L$WH#-_U5(p2s_m?@|@&awDg76h}rk5v1op~rA!a?>9ftVepy~Y8^g_9 z3UPk0y^$OGqaDeX^4Jj5Zg~YidZY~Hs8qm_o4YpbTlZwVq2v)yLW%iQ!!?>5lc+vX z?L%d?OFK&j!Ep#yv=+=io%b5&5l={hlfSUzKzA*bJdTLbfKH<~?feHlXVl@_n_m&I z5eF$qLeyFORqumFs_x|H=G7EaiA(hPHJV!Cm!w<^UDwCE!0sdLICIXmCIxJOYxM{S z09msIEKlJDH7f&o(=3dm2botqDnh@R$lO;Bh)Ki$HkBS-%AaE}!K9mgnA5tx_iTtT zOJxL$+q~z;xkK)BMPW0OW)^(A2!)@ox#Z6L=uY07@@8djt{p{B2o2InjW`TXjYwW& zV);ZSqj2K({)C@|0A=<6UelUK323_U1oKf)N1|R$~8X$f6$={{kuN<93w=ZPl z+*fVNu7dyKN{|;#)q!_e)N;NufqhP17EXtoZwU2VcZ%Xfy$|-sM>$t85CqE~pVX`+ zj0$Dp$DE(DXa*-GiJ;|mEuH1?zpq@jY4~@TqWS1Hl49<)5uPSS)tIVLyEbZ=ik{y?mX>V4TLXM zCh&+RxUn*Au3o_pjh!jDVLst2mZ8|VY~ts>vUs6-pRffvajtif@4>v8(0hGlqwh6s zv?5j}?Fi{P)%eyGxZQhaHtyzDoc_bb3!Zy~@;T@u>#^L!zP5ytSBdA-3S1mlWKqXF z?7=6qL2_J0R`!UOn{iaeTT*FbcI#kPZ(}Nm;SvpvBEWupoa=oVgx|h>b6)*1$k6q* zJju#y;Yj8gsth{>@!tosUZf>zz8A1DzGLXQ|5a+@sVmyj zq$NnY6WKt?sJl}XB`tF&Uf?Ey4(eFE>h{X5dFZ0tk#w1@qL88Y4?O(l=2czJqt9*4 zq;qLF&9i~Z6F)rm^PWN~yE~4;T|G&Oj7I#J7?-_*bJHZ5`et8i=+jbn5=xvIHVmyA z-Po-l8TaK#pvmV8Jcl6SlpXae5#K8et;ia2FZR0jl48HF@_CXJsxytD7}o(UB(`Sm zYNqF#=;bCES@Dc^=H^TsNo@b(=h^CwuGr{k*#{5SP?=)3cX?=Fg2ih(5LYKCT;oYO zkCli+Q%fo7RerMe=sm|CJ;=)|0=@;7CKL?b@%!eLFsbV|@=!kbEBr5=GnB2MZ}UP! zz$#%BsmMK2l`m-sv2+)JIkMFKKyw^ex-0cE4uODd*`H z7)6X8MB6cH)c17|4mRO`NCAO>Ug_B0)O~9#mSzcUbeXGGX zT=-4;&~e;*r0R@&wKcn{n=-?Bd2vgnx%$n(F#KMr{ozALfB%QgYWH0l?ZJMyzwqgWi$00tJB=E4__U&d5~TkXNxZ3^C^$Yo{l1LY^s%0N#yTD zAW(;J^Z~)bhWk|M5mdMUHJd|gbJWsE)2uF>gtJW{=E1~}EdBV^K023ve?5PFu7q5A z0M_3%Qc}1Vdi7|xEiDQufZyRJSTM94C(j3fpebN>pIcj}cRkqvvH-*M-e#nD#B&_r zqnlPn-@R*@f>n+E!S}V$jP9(aDBTa{B{lk}J^>uHupD<5zIh!}Xf;1v9h&%DapB+d)E0-wrOYFOw1gMpc9XY}^&H6Ze4YH! z`3jd=CYVd1<4c&Bc9r~9QSGV_%sXZ22^4D0P=0M{okSBdvrh6o)+Yw@k%G}>@R1MR zqI9Ylz-GU7pdkxDQk$h{Xs_F!sOW!zP+l>u2MPu8{{_t0BDW~h7RfK40Io|I@q;&` zga6=9^v0GVw;)z$3_u$YOinOdrg)UU1Nh zp}(+^`jO6y`hE@;4F2N&tkyb^UJSK z{=$rwB(du|FMsJbzhU5iAv_PsMs_no66ecIx5eGhjNuSm#m7a^l);yYoiCrs48GX| zb)FSDGU@rZTRy}WK!4OBdiWiuzR2VJL2_$H`fq&sk7NW`BRCs{s8OG+KhGXDTym;k zTMLmAh+j}O_{%RK$=21lb?OvT#XaeAhljOWBIpzs`!cXCdf#GWzL^7h#Ll63L)CR_*e*$QXC^N7;22mbv1gJQ56qk9RcL zhN~JIGvbrbR!*|=@_w6lT_3HA0v&u(0a1qeA`bu?mseH>Jo+ZT?GLhN815Ovp>?JZ zGA8<@%nJ85_`^t{(b??m?6}>;U(#At3t-8W7}4S&LBKk;r-z)LUZp2hA_Vl&`*5pv z?}zmZT-wwU~MlcX5ioFjV8-)8efS8AU_13L7U`VlIxeZjng&;V8zB+doUJFzZgegw< zNb@7i)V)BOdRs`E$#B#I(VKFUBx)+z@14aV?6|uAwUp34yjs5+Qf1mx=CZ75Y;1gA zLE)X_Yw{Z7+C_C$`KkvmY zZTG_$es}($ga5;%r!SZ@JXH9zRmFd=O}QOAU$g}ABAtsZJ*)y}Ls+Lm##QuvzIE*i zY|k^8Akk%B;yhCl_y#^E%@BVMjz$jd${XZYhFzbm=2fKRKAyi?T3Mu5P~F$DkiG~< z&&a#KbK@zfk8gK5bcUbxK8#7ZcHRHL%ER$Tam1LPyh`9(bi|E^%A?L}lOzuaodQSp z(j;3O&2wlgM|*2Sg0dLgUQeLtH!H!h_13|$9T1;5#KUM4+lND>yeRLrH2tJwhGa8yi1JT2a5h?{Jbp(sl!eLO8UP&+-2GGP}yc?b1yeF%woX z5oabS!;3w-LcKy{PinwfeB0b`MU`d4*?Sal6b-DX#4N?XYCXfSLS~=BKfAiS+lN?! z!XvZRa$c3h7%xi=5g6vOyBsr~F6{idMZM0?V>g2gD#YE2>G}3yoNH`>t9^6Xp>>kt zo<(r)J@cr^SxQ!JXE#0jiQ`DRTGX4+jXrFPcn00vy__8P#qF@9NyF(q7lB$@$wZha zH5=Ood|gmdci{u_a^nuorO#9E7x#P6!;OV;YTK*j_$CP0rOVEZd+1FqF}d?H>(8ad z2bklFqEeV#a^mn$4kmZd>@Jdr->!byS4vi;bVJ3?rUy z(MM9ze7?S_JMb|Uc4;(p6@djsnVNj}W*rkXa5wP%*|=7bcFc&@_{rKl;k;qUK{}YD z?~dhxAf_lQ=rO>L-PX(9`!VEIwMT98_@}(PU2F==>%-+F-6jW`it(T6^XgnzloZpy zyU-ui(}wNC;*8sAvJXAsXFu0ceMaVuQrMvR%-^nc(YFvOn(o!G(S}s;F4i5BnW21fBBA-7SNAf*gCP)do^)jHym6pT;E)I2}FJ|N@TbDYg=Ot2A z6&ttr?QmJ(llFMWhi};c4~KT)MV7faO}@H0K}wKXa^w5+=BW(ZZ9*yW>&sr0C`a}$ zFR2MshVJQlSMfrbh0Wj-N%F8CsXg`8nw;yVO&_C6j~Hy%e@>#|=y~YO2d8@!5yxeV z%KJzoJDhJ*z7&JE4x3UYD(eJc$QcUX!$gc)UIG+rurY3+VDDddnVEoH1!d7<>fsgW z?~)WoOizXbU|R2G6u65y3S#>1hmB?uG3aUuuNyB@YN^8#{|@bi&mX=WI=!noXyK;IJ=~t} zFEY6oNJ6KOBIMWGuApl%F4h*2@IhmM&lF^jmBR zvOH#?>6digLcJjKOXe{zib7D&B2`>9E1)xa^y5j3GtXlov?SW)ebPB*9?aW3BMlojy!$ida3ZCF=tP~ z_yG-z2NNCXkm~_+UUs_Ej9ac_rV}jM|wL+F{ftIKldod_ePHm{-fSu_OgtUbElQWztJo`0LG_LFaYGIWJ(Mm z{|93KscH75wQr`JpzePGa&_;=({d#(=f9HZZ-h=5ex&jzjK`qJQwIX5_#dtEH!A-7 zKtR9whdi8sy?+&4e%XZ@@rlOsMYy=t1dNQRtRAt~x;J~=PUK>9Ht=n8za7)x$Nu|F zFuU8I3?bU^@D#CN);=Y&0t9fX*IVq3TM55U?H^nA7yMvD7MY(OnZLOUX43p~j{o5c z|L4evobFjWAPj_C^nad_xCKdU!9U0PrPdFzD8)AbhqVvF%EHo@ry~T=r2EY4djK$E zU1eqtKp_#A$;suMo%w6r*6{$cyydxLktkq&7f|Mra$7j_Q1&m}z2#}^izw=Ai(;kX zx46J(*7>#2`}kmUAWtU_IC8!DdSU?NVZ}>$2?h{Sm;t;?M9m)zY~C}4R)g#qS#oQ( zBjJ|m`Lp;WpJyEt@^yaoPXHJiBKDIFxKbe@A(B!w&!4zQ4116b-@SCUBuJX{%HBF>_>;1EpncwPGo*%JC=A^ic83}YDXuboma^^1Um2k1!!BRD4?|m= zc@Y;_N$OZXrrD-~WMjL)cd`DE{^gZGJhwZMT!uX|2D8Q6C0IoJK*NB4B+b zid7Y`iAZ{hg^`jh)t*6Mr9C5R_QwJ~&5NL*v%pUL%|pHx(q0u8PriOH0x%Rn&p+dK zbba)X3Av1J=y-3qM)pycprxMBP9-4Shfi`Xh+(QL-F};xDlScg5JB3QDSWQozrbE| z8GqOdYuuf6T0pHHfgKCQPE+!KWw>y7zVndD&Y7n~2hma2kA{Lx<;Uf_#O<4AIxIE2 zswroKBAWV0!?-DATAPlw-DiBEk6uZP_^kIJKD$R`@HI~JmZRIb)8$O0$X_Wx&PLl@ z693k-U?V$@Z7p;MJyq!KdSs&Mjol#`L{qTsMG)Jz<|WN#@PCjg&VnJ|*LHrcHFS0? zH2cPpsx?v_*4>!`*57%&_G|#ri8DF`1~78)1t+`|7xK}vHXDl`ayBi$?O;w`{<#6_ z85QAMTdf6w;R8eL;X^_)HliM)XWt`fCT8BeLF%`db|@eXX)W-Hg0RQd94v;>E&u#vA0d^dsb8mXGpWBYVXO-nq71$VWQBjK7|^(wK^KjynR)8-m9^F z)b#39o>uq|wET;xtUmSp+N&J9c0bN&Y4VF|VTCVcvF>Bl>^JS;Zs?jr%S6q!JvhIT z*6O2?F9HMAz|@bX@^<$XqbVhHfUwqoh-64Rue};CwhT`BF=Tf50~)x8%`Q+NYK`N9mKSGYQT~v ztLFoEs1!-X1d&fo$b?6M_3iOz!SopmP&8fTbl{r-DmbT3Yt*-OSyG<&ZIj`}fab zmI6R#jYf)1foYC9TISwc8&k}CQl>OC1yJkJ?s9^lEj7j+7_aqEisyMw&Jp%}snfXP z_O|;i17BZi?QHQ{A-qqz1cEfqt5^t<#>VR{Le;zllqS2^u5IkRZ#nSRe;so)e-k_F zEWL%p)U+guh=lezaT=vmOnY-Yn@GQA4wxNIW;X+~MCbCVgkB+!(pJt5erjm$kshkI z?^@dQ`aAovB&xw+0EJ{V;;jzov6uLN8BZNjwpe??=!vzbr$=6rcu$-3$LZYL|o|IZhD=86~w1i>5 zeOzLuQJ|j!LNME-PCXYdUzP!xAOX@>X18YKtL>!(dlP;+UyT1Vb6=^Pv0Bvhha+n+Pnab2z$ z2{S6DNaLfgIVLm_W-Iu*=7mP2Jh(;lN!O@${(Vy8hIsM1%=e|p8@A3|)@UKv(s+&G z2IcgA`6|z){KuhF6SBe{)YWws&iA*eIl!#&I!3o`f}5chu~tJN&c(#^0;HJ?3P;UK zcnuq{@Q5g1&AJ>hH(Gtqr2-JyxRqpjAaQ9hhY!S`QlY3r!nvojc)NB1t5P(VDX_0_ z38-ankJ#`SGtvs%jHWUW3gibmUTo>@rLm*26Re;y%* z7k;wyyeNi;c=Yzgbe>wnv^j3)0~9Zam>f9)A~gS=s=x0$uyfg3-6m z^1)Q+dPsTeV{{*W6TZCCY7idMp_oZ?@t~t_Y9y{8^|z+x=Sch#2Mss2eJwr9O)Fe^RFn&}x#r=Cmy31K$SxxUckhzJ zd;f3w%>1^Kk+C}0!4zi|Yx*kRyGKw6pNxIdo26rDy(Oo)M9NJZWjtBtV9(O#9;xar zv924)oRcej<$j~t_FRkn1IZ2ADwbYlAc1ajcvb27>P_rA6dc*=NtJC*R6DBCS0ASV z<>&tRtDsE%Q1IsJ%xY17!-GF$TXw0Gv3c%)K-XWu@)Rm`j=?*P;{F0(Dp7^rzY+rf zYRf<_It3;%3ex`|uKodwofQ6api=^FoaM%!5c`sb7K7 z>G=nm{sQU!k6)Z>)1LYN=g0ok?6c(8P95;?FID$DWfaUgr$792ICxF}8X;a_E&pd+ zml?m?20E-uE3glIJl~9eyXYMiLy6~EB^1_*>$#hF1+a5knwix&UZTt3Rn^{IaMvz3N=Y>in!vjccJ=J)2d_z+$TY~N91pG&~g*7h{Y%>vAfM5*$R=Y!tRuJ zRe56>#Ed?FZDZp(mO~G|q652FGPyz1Tm8if=&^v^kijeKpp`*V`z4|sua*Nr(hZb& z{qEoi-kr;E>}dnBfrjh+CV8HXwv1ATUc&2LtXB$SU^kh=BB{Bqrqv zQhp#|h%*Ti9~l7e%F4+d4$mZOp*4q5d@B-$qxzL()Pp?-@yq$oZ^FY#A3iIi3AfvF zv8FLsPf5ZVtrw6LXcuEBd{uUYjglZhkWk~tJ3;EN{oBVv9uk%=9u#y1$9spZv{uL* zo~&N%MEjMLA^)HL;aCrReE1uX7j7ihTUNCqEz1sC>s5M*WnD%zuJMQhdD0- z+IXKsssxJFq#EkaUeKrZ+ghMa>Lfe4qusm)gmXR(PPfcVtVPP?StIoV1e|Fffcp(a zIP<=39%a1k$?fX<$_OMIkB^oMjzQ4%x&h?Kwzs10a46r0}sTcfac#0)JA( zdEwP!wZN~m;<3k-Dd0xNi+gP0c6jY9ycYZ%_Xy;FaScK4o+_3C2%lGOKIyG=Fq!K~ z0|u&(cA+8m|Dpms09ZftU%BPq@TQE64EK>32dE#QnG?$b5izi!^7XgEfYoV zVFKzR9$QZ{Bg^1?3@bImTTTNfQAj8KqzO%OYHF(=3GGT68f1WyKK9IQoiZppMy}Ai@74AOKu+={t8$Y7(#r2naxv`22eyV-9F*Lm_-M zSHDhlb)BuXr9}-yi_cSVDSs0O<;kl)f{XP5lNiM^C7a+xj>Wt?(-_Vh?KF9e_(scpI3el((LqyZNN_s<5!| z>ZX|uur*a-HXsO#gHab5eNDM5ItrF^1U-`{zVEg^ll&htr8}*urv%yLOQ&EJZ^qxi z@h=ec7aqxUzLNg0=J-3+`H#@p^=th9pV&;jTFL+4&;9~ge;t1g$eyQQDJD8?FsPw6Jh-ozWsl-!GtaTM3w*R3ICeFMe)BDhv=KV z#9#gY{CGc;TUxfi%f)5E=F0u6%;Z)Y0aIt+YJb~7m}oWSep#$gP4#l-wwzR)3(-cKRuA8B+y_HfXKktZ)`$T8B zL#ACuqVR&cg2MoNT3-N#KJ<(iZ9PDqH#Hi#dgF0Z8u@()ShwpustuMz9K&aOJbp4* z3{uUhJa3ONT&b}?A|hH%I-FM5>&<4By(f)ohwcNU7sXRNOj`r{ z3W=vM)PYH%>vbi%)wI1Qc~^(hVt%NrO~y%uajV+a+$m_ZU-;-S(KonpoAA98Ztt{% z`%fcS?A0oz2-1u7JAH~2oQR*H+?ebKHl>K;Qx%~`^Lszjy=9M9i>K_DO2hljYbic| z0frhMMN?~kU)&c4|BF$R$f81vY=cCb?3M59j?F19>FAPI(Lns;H{R=BJ?=ZU`Bu(d z38ybMfXY(E^N(^XcU=TqYVH6%lokS`I&$R;#53o7M(-asWVr78(LunO0S7;_LEw>* zVPwO=o9kOU+FFJ)?tbJ~uC##l>*M1RmWnY~8q5pqkn#v9WM=be{b*`iPMHN2e>EXH zazBrG5Z5QDQBmUBJLm2)URKjA-)BKj8awK)2~*w)ZJs7=Oe}EB%+u^I$Dp|3J)kIU zSNrKgHLVIX=*MZ!KFY^Q>WVk0Tz~Z~-LY!2qagfRqiQDA_P4;=skCy%wdiTOToaRqC#QvyWsmT!oMymy03GhX}jH9Yq7meHzF9t7~Z6ilWH0 zo@Lyu=KJew)#OczG%3XkHx4FLQI<6Z?#P2E)dN!!%{tHBcu^O2jF1KP{z|9A4`D|Y zO?zoZ>)6wKO&Pr(Q2~*NeWn$$c!%QYE+I6rR?JCGmCV$Y%P}&eVnC@ydU3k}7nw~eetR5D%6dG2Yv!EtehCPRc zk8tOX8+IQ8XcHuVQF`c*Feb1FH;MAaAP}I;6$n9KS zjD)*29F$**4ysQ#mvdcSM+ryYfG9aa`f(xk^gqgWtO??j;q{XcuiicGue;_Md)A+* zWhQ&-pNgAxY4NxQI~X({Z4CQwXxmvFQXa40bI&uFTq;UDVL^95feMJ#nH>8h1SWYN zt=}R>Cq+wf^}mX&hhlV(h_C$*l?)f`e&=6)E&1)LPVbN9PTmK9`E$if4m{^`O1Qwp z<$t&RwGxUj|KHTT@2_%%znONi2f5Ly2#pBte-8Z%Oc?;AL_;dRfo1!nG~H>wn^xTY z!ZT3j0`8W(09t?wnR5UrV1d%PUKkV#ysO}d2s2P+gymkRe>aj|xx}o?V!T!aUT#}6 zQ+*Phx~MK#fcDKD0L_6Rg$bE~D@ep3N&5`c>!?>i^fYs|r9qh%D9OS$+*=tT z1{RM+?^O^;I@bUKTElf-LV%)~w@0%9>~JBj!?gN;#C|*Cpt=o%>VaQ&<^cfuBn*;7 z4WR25po|g=K>er9fy96h6l0QS6%gWvyo&Mm2`IV?;)BboKmXx_B>#qup6c~c>pq_7J?An_VyN^ znwr8APEmPD&e0Nbne`SY{y9?8`LD09gUBmLQaUs`@||c)_wzyFmpQ1EJSiaZ{5FA! z*8u?BS(y6^{zXMcN0(l!%L4UI?|O_X%q2gXSHL&T9O()}B~Y{{7wmJi6nfan!s4T) zIXfQ!et>4_6&~*__KPD{N&v#!>=s+UKt_ghTUHha)D6AYt#Yi?`V zNc+J@TG+2%y}~pBkXSkC92Diqmv~Y@bf^XhO4ZV*9L${uioPs6u3x_%5+06gc(^r- zu^QjLy$U!Jh~=F%1OWQQix&Xh;;vQA(*uhz5WYHB07DrA>0;p1qYme>Un-d{eQoao zC^G8ASc{r@)EFooHnRlcd|Ce&IE)8XZdkp!k3kS^__ZLmBnwO6oq+QR`4e+pHio>U zo2Ru&+mwkdcy)c!;1Fhi{4LT+%^S{A{5m~j-V>N*{P)e$_m>$SS5dG)_HQ}TDb=!V z>`xQ96IkGXigN0Ls&92(?cexay!0DC{Uxs{44roBoj~~Xl!^w*K>ru`b_(h-xt)?v zvp_uH6yW^-0Zc~!C8&x-GIARHt6S!7=fXh*%|BY><3sX5(r3nUKG4xA_k|74&niK0 zjHsad4My%8Z1I9uARiOn&uX)96m%@G6n=S>8lODt>mUky1zVQkuNqn zX?D|YJigSGV=X2KSN|HwHq^^0>SgrpoNgYs^BVa$CKCbu z=7tsq6Z8}{msotTf+B_wM7xvH{bQ1e^p2YBYK*=g)P8GhUs~>)dQ90%2ULu!U8PEz zOUOl`1%b~Cw<);sOvVN2`EnV&>#$-)z8K>;)%PL@ z{M&MH*PAhy#$$kL})FE?8D*$$7G)7AcokN>H+%4clOl;T=!zF#YxrUHa&{mXWJeqVY9Uz{L1(fvR{J;QdT zX&N7l>F4i1xl0jFs{4$_iZUfET0^^NgH4pjNd6 z3|2AMyLxyZd+7a)Y+eGU|##z|8-CZ`w&{@8Fs1PWg9B6!F8Wi4jgYnSv1BnBn zZ}D-jwVqa>PC#_HhK2}`CS*!pwabxKy@84i5_u(k+bI}n9fPI(~6K#w=?{ji?`863=`9=Z|*w(niO zHMmS*a^Jemd78KgX>Rh9g)&6_{SgI91*ucASGi5RWZC&xq#}Rg);7tLtC(4 zI98d?b^!LY`dbywfp6Bsl99OX(Us$#fYu*+-a2XA%(wu4QK}^i3fiRzu#2=7m|sL4 z&Zhz0&#_-wsK^#BsFEr!52VH7-DJ%axpOVg9Y(!Ck)DN^4AfMOh zVSae+24|Dgnl0(&z}b|uc4oWu!qJ-okB>xZrAWmgCgUv|*dF^dx|&NV420Xp;n7^R zrM!)0Y98-RHrH&lH*qT^?DbrxSE@r(=2B@)vru4V@vhyxma^n$Mm%|#6{g~@?~r{o zkz(u2Sa1m4jx?(^^)5{HvaO|`mf-Qe?Kyv)`)m82)cmB8#TfJS;}`w&&8f(iS@|u| z65EtSD<*gKd(9P64WZ4p8U;VQcsNBpFX%eCbeDEbm|qmM@qArU;n)zDg9l2*MdW|8 zl7Yr{Ie_a3g2m~+-~^&HK*2aDy#B=`*mTBImH&fbPW;GDy&1+*M~DB*qf~!( z+NS&4*&HGJkCJtY<^WQ23UVZY@|V;8<7pq1Hw){(T)qYj^(3ZEP#?Rgk#6z=ETZq@CR7g2J}|z$YH$>xy9I*Q|+R z9<>6hX}}{COaS!8JP-oVq|2cCMM2>_H|+S96zbr5z_LJ*)w;`F4zi_FlefC$q@l(vedr~pM< z*mZ?+wmqhK*3n^UfUT*i3EZStuU&hdlV5!vQ!WacD8WDcdJqNoE~x4Pe|Sl7nFTzE zf{3!MAH-uGmsycx0WE$YUk_u9T&ALux3S^G0yH@bGFFX)iwmAeA`j*P7WiV|6n=Jg zzi@I(e9f3? zm76#8YlVTIgZmbo4-wU^7eK=Y{Yl<)|4nD>oq#cfaam104M0uj<3htm+zz1HxC84A zP~1zGnijsj_jv+45qtmhNC(x~Q}u*@3m)C?aXPgASfVK<4Am+t~>S`aJ`Uw~8QqVP2$aYUy_$QA%RHB=0&d(&r|k#vRPL zJw&qDv2H_b+T3sFhM^`iHZ|N@n#*;=)8O$lyAFlhTGMsfh$obz_~!!}3ZMMs#v7H4vtCQplse{GOYgpuO$NzX z$D4%e&~_rOo{OZh5p;?n)=sQo3G|Aek>em_y}dz-I*$W7b*b zm~p=KY12oSf<9u*%w(sfKpQy^!AjJE1=3fHku-#qn0w5I3alXK(`@p%Sv%36yWeHp zU4Y8Tamyg8*!22_*YAb7ZA=#k*6M$B$%~WjsbM3#G>M0n+Lv?`T0JVm<7P^cpIaU0 z8!lVzA*6Rxt?_iG?jxz_=qftP8b6Hm`MFP5v$7jVMv}RX=uumpYad>5%_L549&*s> z`@Z|NgA{9Hy(mh_-HaU{E$le5y!EI=tRO>OL$6k!Tb_+;Jb9ZLwj$v<(nE1+WhN$n z-=XVKi?5`|H?3Us_;3%+ge$S$6B@)JjJ4;M;~e!K1G4qyEvC4>3sZ8*2l)E*xR>>V z$erG*;kr9n%|(vYQB?0dk1;UQtv6=?4=-Tu?}s{C&$H+3CQ7W0jZ8YqtPlk!8F_Ei z3bj@iAUZY-3AF_;v(%F`kNdrt_GRz>+=vvE^yZG&y34}nYITWJ@=g1&R>^)_U%~ znW!jP?x;=4Jw_KYX4bLi={2Dr+0Vu{D&%z~^5$Xu5cRlXmGPGw>L1QE(+JFkR5J6Y zi6uRP9yjI?Q=5AEXd(`U;>&uwgQD~}6Bd8Aztqp{lY`)j8=gmJRr*=yz zEvbeiFD5^`LES`@_Xc~QIb^Y?Ha?Q-$N^1AuT}I!Bo$gg!%G@DpOAzou~*`hxdMBY z=i=tMd#yWF#-}@0q~K5)r)f#JSb~O-zywEv1iyIXMZH~ zroD{vN8Pqcq&>}nv+^9=SDzEuFC~Fgap;Zf!YjR1LZypm)Xn_hXd}7Ur)Byjtz%DU zQay-PTLB4xb?4wq-J)fo_@1^z138Kec?{n^WMh-7_Jm zrXG%vN~MuSo1g!-zIZ2${!&wj36&TPp*e1mz2<{J(+P-UUu6~HvyEbXt09N&T~B=Q zB$Ppp1P*qZ9>t#lQyK+mj|Aw((Zp~! zgH)<1i;iJ-EH*FXMk4RrijS9s=Y&3Yb8+V2r=pFl6f9a%k2eK-NV(9ZA^Vk1Du@?a zk0ebrpxNxbvBeEE6SHZ-S6X{LVr{`8xlLo>8f~`P0G(wmM;)$Mp|VzrtT%Jv4vILk zOEQzQa%8uY*yUo4I6m8upI5{7n2X5#L5p%t4ZH>eMUZAO4SzY+-dI9!TBQ$ThPrd)wptYH_ z1E|Nx(I_Tb;fOIK)}Np3FGZ%chivi1EID50GZAP-OAt43dx5;M?89V|X_F$w9Qc9# z@bylQ)raE?gNe6{sP?zBete(KE(dSEiVgQ%#ZW48Q)+ru` zs0*(Mw#SgnPN-MeNl{oIX84m|zT6DVZp@S2K&*gMr#uYoc}1SPR)M4pYmqDmEBD2t zBk#lX1Zh=L${j?bN{u$Q7gK_^t48h3_1w|K@e+XJ82O|IAV{%&sNOoo*((cZp9R_F#UodLcR&&bclqIv~=N`6DxGJ zh|fyll1UbeLx(M7+=JubO^=88_}#_eom9)kTlx{K!vr_6cWE;B3n@B@-gz^Vnsy-8 zY2Z1_^;h?X47d&}R^`+h`>ZL)+>Av+=FXe^PvV~NUcl8|*Q$(q5~_n{auGtYPKyPo^LJLmP>&)?VU zH`jIjuIu_OpYP}M9-T_+7hH0G6|&uycQVHD5aPC8xsnPyEChAOQYq! z^2(EY8E0_on$ssi^@I;u3qD;aB=O}o0ZdrJ9LxxlC%*+k&IEY%KB>fb?_GATWbei+3yGY zO5LanBXSO_ma`z+FASDugW3%Y8iMLUCG`Qn=~#8N*#NAsqLC~m$Vcr9$7m`KVtekD ztdyl<0#XX!9`5W|*0%0{!hK@W>5tcZ4mSgQ7Eg3N9}=CBxq>+yNFpnm@!R^A46Q{5 zATcHKBjuV*UPFqpagmMD{J&^!aSPCsPQ-@EnyZ9JPEFKXHYEy+%&&b3LLpjxkNhF> z<`kc@#JXR&-FjHrctG+HcYb_}bNgWOT^dUT?d_t;dr3Vu3Uf^pUNwt}31b?zPll zx@t2{r|Qg-XNst#SV$ojHA-0UAHC%2dLdl-V_E($&d&Ut_u<3N2q6bqPd8Gf%*7wZ zPP}8vAxvW3mz&#nh{jrD}|r*CHG&N+1G#(_HOABR8II1 zz8`B zDpRx7-rQ=kV&d=_;k^H9w6?#FH(SkatH%1tQIQ5B{p$2Fy)>T6(xpuFMy!7BU2$V1 zk?ZZJ8+6i@G5(__rcp#Z-eslwK*c#?lNv%`DVP(vr8V>|xvD2y)`v@Is$4f*i*~%T zWN^OKtR^TLe`0O**X)3f^ac!V?5s5siBP3r9KT^O;}oq+SjlEGT{&b_JpAQcAxm@M zflQ;!lfJ9ZU#9w(xF63ZNa#tWv)~sw=P@a^8?vl_$;`-YR{_|Eb-^=Bf=_z;wr}NoRv4}(cE+iuSdfBcxn_q(B@}j; ziChaQo>1iwPJTjyv%tG`wZ+^0wvw}U0%W2(*{8S_*3THVaxSY+Jw?(-Mg>qT5$!!{1~(?d)%^F3xtz@PW-mC(z4NV5 zd;tt-tBs-iu?9s!6`o&O;Hq?a|Dp58?NHv5{eK|Mlr%n87+8c7TxUyO3{?~;7jirUL&+T*Dl+m3F)X39A&cGiRA#>*u)OM z9GAOFOb&=L;o$Dt8gs5&x|pnC;}wNyuEaXVR5Y@>l&Cy?sAgzQrLd|*HQ#;5a$B^I zy^vvedQb#)Yi4t4ya$CHCLnz2teu>ZRMm7Fxmj@aH7xk=6C4+tEa%k^+r|7$yG`2V z)cm{>4~1&t7S%TQRi_jU!Vee@AK_`=*h>emLP+yAB+L-QUe<3?dfmsn?9P}Ld?oT> z4+AYu`slly(|dW@kTf=Ms47cL`PkbuwEUKI_2DQfl1nR({i(wJsnnC&1VUu7(Neu| z@2kT}s`2Dv9t4r;%(u4%$jGTvKD=Q(ij(=K=7)V2s8R}>b-P3e3KF^IhT^2YsSY_U z9$Bt%JJy%p)%zWPF`{^p4cGz{uon-{ugNqL`XwA|@HqD=^&hm7DvNqlbk5x~wanU-x#2L(h!& zppX|i_K&32WzIQ@(xK5F&3md7AjgL!(NXF6n za1P#H8yiWekaz#q7hiorfiF*;MU4Fh0PpmXx18JV6mx-W?Rio4!7%qq~rBv`@%m zvfj6Kpxk_$OE0x_b$W!gmOY!M)TZKRSG?)(bKH=CPo6bhk;8Un_Bf#{>DnGkgj0l~ z&FScis&Vh*V4t*<-^*kcC5Y5&jpz~Ea;ix^$hF%_W6CGjP-%N*TYH2pv3TWE^NORP$EPl4EqLzfyX$C*WUh3bqg1TDq|_z*3jAJ&MFU zOVR`4v9{R(@gkJUfcQa5S8cQNpVJl-cV{goFy*(;KL%N9{F1jy`0kLQy9%nHE64b1 z{MEMvTW&fpU z-uyS|7ATSbFxLEMkVo5pzW-+p=x*Z9pH4E-^H(wYFX#Q6I{d$7jKIZe!2AM~HBsIl z*s34W^LO{a50U(P#s0s*#{XMmX#wzDFk<5NT^YMU>f}VbZ!N;shmmT4kT*+(6b1n% z6-Evb#5_Lv->*EwkvzfS{mgrH^40CmhkMj1zjn0k4uJ_v9unXhh`J?VpLsb(1JHMG z1Alz5)Oim&ZfiFDc+Ci~c(WnL2u>c=jPWK9Cfj;l@18{izDT`n73_74LIYOz~aOx>;y;6*%MDKmUfO zK#qU{ROwq4XsKh^aHwT5B+j0^8_aMJ(Bkr!6J*kWh5i5)X6{{WM%!A|0T*|0x8bt7 z`q~na06BESDla)$>}5e;Fe2$6K77zXTy;AnehWJ@SBRgCQd#^JWLnX4VI8mR^==lJ z@K67~(FWyDK4r8dyz21F#Gv3{=n4%8T>x=u3 zGrYY|l+Mn^W(Bf0#9ab_*sc4Ce^ftu#8SH2V8aX3FjPYRe z^m8?#&cNtU3UtUWxWmvRDGV9<5vRZk+z4xhVeDn8+KMY?h3gM(6>+jmvN0MPVAPob zlsfZHj%Q%Ae}IZ2!Zn41cgmfzYzez7p6N+SJwypy zA&&&?DF#x#2BO6oVj>Loa3o^b0&r!FY6Z}(Sr{h<*KZ==tXp1MH+G>K8ygRP7%hJ2 z05ud<8iiD~{Ei*8eAWsj%xl2zV>TuZGJ!$8)o=ZCBhapSM{aI3LJGMbwtisHvV`98 zCD$K>>WgSc+X@bd1;vb)e$cG2RHLo_s9qKaT8>l}d}18YX6EQ7`kJ~kI7 z;|XL;OpGG{`hg)a=5mSwOJ}pWQ1YuoUy5xfHP}#JWi(z$Th%t z$Ti2SgC$Vo8BVLerBlbtyB3ejN;DlodYjCZKLGN*LfZWJAZDJ?ka}BdSG&Ml9%8h; z3^ue{2vh)o6fO% literal 26084 zcma%j1z1#FyY>JgjVL8;0s_*4w4#8tqSDi# zolz<~MP8JC?t5L9v3hjDH95+@sMu&LIex?Iu+o{4BDQl;{66@SGdOU>Lca*1Ay>Tu zz8D|-UI#z)x&Qlri;t@?u3R5|e*Wsw;kY%%NUb~n;HRfouU$LiBN0krgz~x^A#!-p zALrcCZYPHr)PPYw;eI&vEz{{bPv3D*$$Bru(NdzdYQ2|8dU|?hZQk?e^~EcKs#hfL z?Jaf3^O{{aSR34Pe7w83J= zc)sDY9NXR~?zLO^az4dlS6a|%;{iC%>bDF6sR(-Pu(^$31~1+9pMYt zE9ArmKQ^(lo~(*4vmE~h4n9_9DQj%ZcvUhi?OrhP=hoH>q@>MdG@a{l$6lJ9k;0K~zt4x3iho1OW# zy>_Nln`R>Z0@JTohH^Ee9z4Ls!^3k;DYjo$aNA#LEHdh>Kq2Arf{x}XZHC6iO6lU* z6zrN`7TTj+!C~6Je9>)>ynedhub87*85u}GS>%mE+Lv`&l^470=%d>VuBU+8J~Iu! z)tO7Itf8WwCw*Cp49a<$?b-Qt9nbP>C5?@j1Y(7f70SnFVAI~qpm8S0yNl>!USnb^ z1`SirRhIx)hsU@JqPx4$ZfIy&U_Qtab5o<$dD^q>oY?6d_~E8nks%%g{G5*vL#C+} z8`E#%oIkHafyk~rWv%o>`E0A>$M-w)d=oi zU3e+9)nHtFo`$Bg#&yqhtmFoGW0QHQ-Ey)srp;#QaFKoszSqfWmOXp@t4~j}m2%Z@ zLDq)z6)Wu)i$d#jHOiH$9LykfCu^)J-bhzWOiWx{+?iU~6xO9HSNy;TjLS-K3jx!^ z7KMP3l91Rm9)vt63&3Z4eI@6~N2SsF2payE$wJO{)1ENf&YR`B<>LawWF5s!tr~1G z_M&IMu)k%aAb_yPvR20lHJgh#e?c_jFuY1hQ3$2D3AuV+R$6)%TzIBSmoC9?^^TN2 z8+MHy&eIYaF6!#)(k$^wF7i2xq7QAbgcAQvB$9;#DY&FZ&=J>h2-+( z%LAXDhE_SQ7gYMWTKDOvh%_G$jp0Z z)}P6yQ3mizEQI|UgOHsop9@&{c;a_VuE)WTl-6fQx`g{ zDkUi}#mC3*L#M^ygxx*KLOf0zs(JNDw<}k#%1;_!xw8EBjO=H{{nh^9LW8zlu*_T_ zA!!KbpZZeg376Aq12?1-%&5CyjNN$piEJ=irPuyA@SNz8Pc^hIQI?~YcX$`PbN6UI zVh@a69-|I2ZTHPvtuN_hb#*U2&Xl{%%)C<5spGLDq3yaz-qh4&I#IzjJ3G5~c720xk&=1_lPNX`j!nXG1d%f~(`@99N}Yz6S&MHZ3t&dWuQQjJp$d=0Z8pYr^C5 z*ymr4SGb9;L|XP|%F~Lty>Z$c!)SG^^7Hqn;x&C0p`pwPMg<*MF^m>FqFsY-NZiNw z$7eg=?sT-RH#a|zo|?qL{;c^mk?3U#O!t#2GfK#)3~3%tbjC`6_x=jk^`G0eA9~xiGK;MK=goNN`(2Si5bmb%{pIuapUpW@=}me3{r&pf z-#0*bfuJ-1*W@Z#Q$``i>%7PIkn8FAe6PYT!JRk!$JJmc&+Q)P;6uC+8x1e%RluumnCHKm}i zNiNLIZLP44eKtBSC=HzVfKSi*#9L$UA<7`M#00kI3@?ml9vyghGSWbM37Srqfvbsuw(!3$))Ma=gWF|at-T%?m@H*6CV)VzT zdl!+rH;j7}-nlt4*&E})D_WL1zW5AF1#&Jd_RG00la=$9@pzg)I7F-uja(EiAZvR` z(JS_`T74vbx93J?jeQ87b;?^0Y|2`=(Zlq&p;XpE=(Y~~&ogF1=S#jIO)SY7Z3*|bY!?v$pV_1g~&mPAr@R*(4u(C2VX{|@-)DN}l z;N-}q*oH_$ljhp!Ff682J?g4s_YHK|Q-+cFPSC0DwZUv@85yvLq9-n$^Tgd+w}YC& z08F@5V)&;=3RZP3#CP7cI(>S=b%8dEw21qrp`p(petbNIpI?Y#NFidnZrU#UE37qq z^9iCHx-Lp^O^`8^7sT?hjlhg z%rrT*ig)qJ2hXrRx*G*4j$1Hhk!qU2#3sdO>JiSjZyLVWC@`VgbJ%bS8FZf>m6zdJ^UZ9JCMuq^ujbcBQk--P6b%y15+5XDcW}0s10xX=ICm&|!!fh{ajFR42 zC`6(ko_d|+)~Tq?Hu~OIV<|76(olnd?!=cDsG3KrRG9Z zC;H66Q8@#jHfLTih4uy$+%aVq7T`=afyZ89>k910Fkni>Oj9zVwrB0C=?KZ6tHT@qmYij|{2Fd9sE(ZSqhFz9);1FA(H< z_5KnBGF>$+q~c!gY$u=W1rR>AfG1&yLe za?d#Q{i8y^P=0L=QBRza>b8!iLgCJoSvw8);%g3-nDge2B@i{r`GgBY=<(IP zdh|~5;-2M+n0CnPX7S2dMw~MJ!^`tlD4}H6`caKg6;GpmRIP4iw~WEom3OFjI(#Sf zHeMu)9n~(*?b62HHjzS#ZSwq2FOB5^jW#uR3ho; zA#WI>5lh+XnSO7|z|-n9!%KrApW)xMh zjx>unTX^$1zvrKpP^e0_dbpVIxAsB<<(|{>vUgH9pAf@P2KtQ?m+FqoQQn9wRH0>Y z+<`JDlhRF#QNwpC(}b8 zF1K|F&wBwtE_^WXc*?X%Vtq`+KW{Qz?2|zooAA&(!g^?todg||*ajhl;K|3ego`vPGT+sYcAFSY=ym0666+u3om$A*6S@&J9g01NWfc82Qw(9pZcUVRe~*9isn+R_6BDg_*^9&*hhp zDS>RL;-P$&w#Yn@iL3afPEtRcs!QD8T6lU>GVKcnwgJkDcT-8$d0zXdM}{_Umy)vVOMUr^wPzf`+6Oi#Ds`?^2Y0 zM`rB2Yk!`&s_(-e4c;9VP#rI~UM?NhZni3aT<776rY?Z1N}8Lq0A|Gohe5OSTSDZL zZol5%w&CXCah(mMJlbhv0BV8W>Cuj+)2I<;sTFEMA6%I$l$@r0mn2`(N{fqQT8@?I zeSUr(g8t}PBI;LhaSwut=`IS`U4!f`_cHPFzWMz5eh?v*xQEB7M83v%gbI41R&yAY zkkH|ag|mD5J~Pv8U@f?~wBSmpFcF`o~!c-NiN5({=mn!yME3q-13G0bhsM z2qw!V3q}JjN8mOT~p^{>2>4DTq- z0VJ&}Stu6t2vD%X@l9dm(fqO}QTD-vr||SH!Okx>Q&7NJ-ZD`6`SWKIQqm|BS9Nu2 zMd6(@i=DB{x#d$=I5|mtoMH@;AL?tW>PD-r zU}IHo1EYo=>!+uCy+G=+Jzk78D5k=40%N}`j?2Fug-kp;ayf)fD~=W!QHgm811imQ z^{T&KbI=YTjTgwsz<9Y@v6S$P-5kME@Yyn&O)856pZte0fvNrn zJp#hi@~=hXXRE2Nkbzc}(^6mAkNf@O3R;qg$mJJW z*>hpUz^Jn@njNXQ;dB0-xayv?`eBipz5mfE0gZAtP^6Lw-5C96E-~;Gy)p%t{E4pX zN>(c7#gjR+yjnizoq`#}C1Qp$Zg*vBKAM^xNlrXG-_I>Z#~(75WEF$&_6BmVj5=7= z4mM_X^GUuWoSk%SrP&U5faS4i@;i)_;^$U?QCH+K9-s7=n9zjE-!Mi zC5bp>Yblt$Oe<#qAIkON=CMl-Q9^vP!U#ZmIa1n9c6{z=9%CbHmHhioq(9L4DBn zFwM?n;`!RkSFtvn=3#02aoQNS3*9!eDwst@cKiTBolu>=+nYkwm1G-|oo}~2*Y{A9 zy<&|8MNRzm zjmI#ti{ne(&X}v)4{xw2+t@Zcwq!Ta!X!C2#|&*py-%iQ7;%VbZdc?Rn2k-TBt$XY zEP3{BUxrZoBZQbkU$!7H7^>n9{i;n^qfiC{k!iIt~tKCx+{e$W5E=%KcdEC$b3{967|uVJhd*hd$!00+}Z7p;hZ)G?<64O1Bt-5GrD@EK$R^= zqr8WvyMnoR`OMCsN~>|mr?rK{@y!y`*L%TBc2v_-3P}aq4OsEQI{{zod+Q4x-RwY# z+4OFCn|}(F<*+sG<*GvP3C*|R*sr{broMGrpO3n=o>QliySDHW9OksQ?p#4*4+G{% z{d7m19wtppu5$mb96-y>uN$lWA%#fT9hR7GXiDj0Gr3&>7mm!7FPJ>CM%J9 zg{W~D_(3Hmm>jM%W@=lWj3$kxmIfwG@8{HXUo6+`@@)f_-B)RE5_FECN1lIjA^d4V zrW8!h&4K?)f*7*koA;d?YlE`y%%4)?bz*(g8kcsd2qkC5I(7>UKYvvrDuq}!=FHcR z?@&@gCNR_p7r|sz>{7Xw^4gGXk%1J1BJ`05nN);R@4{Dac-`_jR>avib5($Q7A^W} zDvwLwuLzBZT}XQL=ICQM5e-&oi&{X$!I!y0de>kf;_GTI74FP?$m9V;bA)mV19cN? zGk3+o(i{)(zDoofJ*pfv4K3N7W4()BH+L6x55^$o!w!$~l58zJ3b}l~a+pLK@iTgs z*Nme=y8tVT&bBx`Dvsv5%2S}Od!SCkKL~@)r3F*h1 zon;TIGd)djX*JyGqE!jvu)r#^ zAerfG5D5t+nzi~Of76OiZ=PCMBH8_Ase1+Evw4}sW2L)L%x2PaVsR~>Ki+ll);aem~a=jT?TLTAE(*QCT=-sq;vSDmnjUwmj6!(c^f3Z;9h7 zyt%6_>tvX{*y=Ke6u4=I^|M0T>*ZURwlOnxspshrVpWFuXW%%1g-IWXZI4yqKjwX0ww=EFB-4?K>*Vq{Q>?m1XTY=&08r40idp? z`)28AIS&sHFkVQwxVQlHEp&%jK{VMZ4O+u43koKTjHs4bPkVh`fAT|YKi;Fw>CaN! z>tM})6&)R2Yk@pH4hRa0jEnnHKJ6vz;UO#=!=?uu?TxA0#HZQHJIkrw-?CLWfb&~8 zD$Ei4{enJt{rX=#Nqb;DoL(dWSjda&WnKt3;J^~xX$~S1@H)MXbB^8+yaXn(eyRG$ zr`Z&M%Xk1wk+M|#r;5){nga|JF2}V2;Z9(4$pX7w*e%y_edskX3mqIB@)m)e5)d40 zGEIE$9Mg>(FMhZpyKMCrzhC$FUslFSS#51?U1Nd+1CwC_FJ8O=PB_bO5wHyHcNe6W zdXf|K57cv&HQ9#+Vs!|=^H(LqfWW}Yo0&b=85x7(;^H(rW7yQ1fDh-ok{(de>#`5r&^EASc4U>_OP5U>>{C&%(7Qk-Xy0!cakvP z5X9w}sv>tD6h9~KSUEd zXJeIlM8gn)Q*fEX9gd}4b>C8_P4%VT)ZRMJN-;-@o9YX>j0@vS*5L!`#*Xuc{+qgs zV-u9>Xj9#XGkhAwT~P4_#f zberu0+v(ljnL6c%xu0ySt@-D>M;OQ)rm<>Z=fm9Sn^@g*M>B8yYM{5PyvrdTE^&>5;N=(u)YTwMInlYo+aUy@Wf@(tnB zt9DjWLCx*F1ZG)wF`JX;qFI{=3}%qpee+ZWLUvo{N~oqi)SZH4$Y(RxYle}29g3)^ zh^Ctdv#Z*z{1xl@L3Q`S#9r3I8)tn)?}hQFsW6GJDrrMoCAbWxdoSV~kteNaCxDY&(s{V-=AJBU80Qpbwy=-OZA%18uui%gP4vhSS-;QBe1GL zDnfKC*BnyZD-UvVjhq`=^Jjhew!t>JUEAkgu(P=oW|kk{-JdIXdb(T2W3DG ztF2-FNR4+x%HE1W6mzkoikUp2nC)%k4vrIIuywQAieD)afiqjpFcX1j-MvHeS%PMd z`&7c~1T}8;Ks6ce%1$|gdFd_p#1u&rE0wjJE!VcUm}}N1_R~I3C=|a6D%(`(osOtK z5OUd;Hf6B%XlM4bwq4GE)kS)qhE{@RNk~ccXUI}IZSgAPR?LO4#(^t`g+tuM!<5Zj zk$ZXLi0Y7@(~3jgs*C6LI9+)|>b02ItcOo+ z-n+Yyg`yy+;iDp^<+k!JAY9Wv`}9hh(PM+RupRF(xajzbn8`PChiQ1V-xfU5W}|u> zHI2eW1W}uGAFeD+mL=YWdR~VgIfK<`nT?$z{Hx_?5fI04*{e5-HjKcQp?|nBTC?3m zK#8aV83bcqjtC+hnyfWrqsHrlVf)zACm&Z{eDM1Wg-C7nZ*_e-|PBJ+t??FA0wx=4|F8tKl zY|BqefZ%mZJsQ0Jo|MxCY36}pqKpk3a`)Aqz z9%WF(i)s6{mHeJs`1juQ9Y4&abcTvC$|xvgs^n{zSWl;*BPTNr7!ak$k5>n?DFGG} z-tW8j{{4H%tN8f#w7dS#U%a@?&i)c4<8=0`o}%*_4+{4u?0diFsPTYUnIzC{F9KZ> zLdvA@9tfq^7#Y8TkRA}U@fuE#_wa~_u5)p{zI-o;gn_~P>-rGwkMKnzCAGY~{PkP6 zZuRa0q8z{kgXY)}vhLYy3Q!qzvY>r+2` zU`8wTV8Y9Q3>z?xvBT1vfkF?mSu}vL%#IWqRK{Qd$QJeH&HL|LExs>h@0E>TFs)iP zAD~I9Tjo#ljDTJ|0}y7WPQ4d;767Ib0CZZgn|SvO7pV7&JX`bV(5MyKh z)TN(|f)vj4(9m}v1LMO*c3b~5E(9Q*+ja}MPd`0rv?@mk1z8R0nVCxI>b^9^yq{=DR)aP z_iD}4cz0B(NiRK`?}F_IBsK}(NkwFWFx4pnHvPG+t=Jpo)e&>E6LDJW7HbsV>$659xaWpaWMJ`>#2J8&wH`YT;y8 zJp9drNzv&YIwblPkNFLbxueD2-?#@%?v#@6df;!rD?f#&t{sE8064=h{|NTx&wP#h z-(;x>XrBK}qaTuR)|o#iU%y%a{jWIB?}k#A^wvLr`9Tpl_KesbH%P0m@mFI|3BrFx%rnI8+n3`ECqA1{Mchn~^Q;PT8j^Gb$(sKoOR zg!gdVa#u4ud&^v)S5*th#Qo9_GdTBeFf%MA9#RYho>XrmeE1^D<~DZS5ebV* zK&$0tZsBDleR5j2$ka3MyW|Ta&bMaEK(%t1tBjdy|U{?`2KyhOcotb6JcM_qQzw8!y2*-7kOU zh1K(q^6VDhEc3$g*YOXu7LUe#ph}c#9VDH9lru_sQw$-Z5kptC?_Y6oWrPAbf}<#W zE|q=TyjcEmpMkg8+d`B{EwKWngVzOmPm;HZ$2qd}H&&2h%{r*V?lrN7bM*~Avjw^= zPr{k^GZWvYAtJnqFfnJMO&Vtd>NpzwH`CP&_QuU~i#ks!9p4ST83y}`*onyecW0I8 z{MKZ3Y;*8AtOCRK3t$d3f+<*2>K0*vq{yrNc7%oHQb3oZNnjHDmN?iJZkS*TA27ou z?x^%$cP?0^SsCeZ-()8|9;Fc5^poVLY9VhBk)H|853}|`GJ<}Mcfrz?0+Y_gLcIeZ ztPXUL&8p^-%GSa;4t%!_p(Go@$q`t5d@D}hs5hmA)?Jv`5p24S3V~yQ5);Eufv$_S zkdxJ*z_FcfhFxK0CQ0D)yG2vef7oPf>k7@PsF)#{-54zf(Pb8pa0CnG+E8vH0=R#4 zpH%X_8l%w;-0|M>yn|rn!H@>pPJ~U^pJVXcKkt3IgX4L!&y~SrEG^T6$Rwd6X6k2q zpKNC&)si7gHEmA6VL%utklf0D%V**0_83_l7d-_B%nRQACe`v0l*QorcK)>w$}JDL zii+4ylh>}ijkF%~EZps%=6Q)CvZrj6;<1kzrR%Hkf7MUcb1|}Rn?R6LK-71xO$Hi#AGe+!&DJs7j|H9B6gj*|R zSTEnj<{%|*Et_G*RMBLRU$vWb94-V)8n7B$tm_f2Agy=|7CH#J!0G`}91Ff*eo=&;fN-l(&H&z7|}(V1(q=vnR5tO0H6aBZE4I%a6{(JNS> zb2%|`sbla`(M*!{DwAklpKZ5lO(M-6jB^H*OCpV-u5cl%-h@pXquV-o?Wui?NFU=e zEbY?DA*2iS#T!4zj}|U#LiIu`&~AH?`^y*3pa)BAep__4Ur+p4zaV>y;3&}D2o%rK zO|GE!Q~A4au_Nh=aEgg>(k51Fow7Pqq0QKuV^w5z8~f3D)l506t(rXru;+pGal)Zr zzH&>}H^o2iS+U>bFRUb|Wkal-uU)HJWZ3Qs z1uq1<6Dm_1$KFa1(0d)}(GueTIDj<+Kt={gJ%c^jWn`hrS>xWw4$p>&?u2#;Z34<9 zZhlR0dy{xxo=kU=aXwMbe?7cdI;Mx)OR($~W7SC~&4%})iN-Ys-#je)rG=UU_L8d3 z`W{-Lt6~W3qvZhGvL}dV_8ch0xj63eBOmEr&YqmGohZDihXn@p;;t1Z`1AfaVfUB! zx@{1H+Rwav$qiA%^+P{8=x=K=VQ0{O3fFm~dy4Sif$hx7n0l(rmZ^p05qOo;i@5Qs zsi}DYn|Q*ujZV{X@aX_hZu5QiEd6kimJjg9>^Uq=k+Ic~N$IqpZZ zG<_e$C=|m7}2!i$wc{aC4exRpav)t!H3tLieZf=+xaSNwFXH8Hm+!ge9FR z)C$v!mgE=j?Wx<0V#2RNA(E*Os68Jp%6zdHP~+8fdH$=lTm9W}@6_4I-nB1J z*)?|Zq7j;oHzIo43m%UCM!yENElRa+j^K`N<~pUQ=xJu&y?_5b^WLEmwYj1PaJ9#H zwuOgVws|N|k&f-xV{Opiy&?iCGQy5)WYtct&dX>WCxW?gcSHhU4Nw6v0V+5c`O&eK z_b$xFJhHWgq`itd!X3Zrk_-)4M$fYu8u6Adx5Q91svW`M-75 z?^Ni@R%OkR!CUDl#;>2AvR=4w!Sw4}Y!FC?=>A7ueRcUBsNccS{g1ZFWjV(CBa8uJ zEZ?QF@4~8#xw-lG_y&+DUH8?&Y7)hyh-q`OZw5pQ2)Ygl>ntH84MZ1c#k`Why)6QA z0m$hWs^_9BBEYUwheEo8Vw@FQu^*R7V@f#GW~#;&giQcX0Lb|p7}62i&j9#GR~dmF zDGH=m-b#c*9w;a<3JVLvZ9$E|wd>bkCnceiK`Fn)PBJR3YNzY$?CiaE&Z|%L|CKEP z(XefxDcis%or~QL%ufzCJ3%U>_n2Ban>?1)`np-E_Jjq?PwiE+>!Gfk93db{l`)_i zDJ(AT;#M8p3Zhnh(d^VadKV*w~v0sHjgpo-3Q5s~C?8M5eN3P_d1YEyw;v+3X`^1FBM zhS3R8_^hm$I&Doz16daXB#I0VKf6Q>Lj{L@-Z9uR4uWVoxL!3K>PEn?P=Q6`fvN)Q z=9FEu82ddB^e>xWWAuL-j-aCLN5lRe#Q#p#|4-ohe`mITf9el8H2&=W!i)d@^bcPD zO)cOL_4eYNgo{utn{ykY!bX0}m_2B#Ye`Q?U6Bf3($W@mM zy$Ix7aJY-ptSm2bUF>+%Q|UdL#c3_wvq{P{YlH8xbNNXgZuq#kp-T@!MDT8+J6geG zX-9Chd*U3_ry+1|zadOc@i8h+6L-z$t8XYlO~M#-hpjGhQ7NRHc?PLDCDeRODXHLR zDP&LYd>gxo^==a_^zd%T+0f3#-Sw&gpu+DD3m@{Atl|@7-tAuFL|Q%&!fs9#h0Pal z^kp!7Zn@J|w%sP`GUs~a;JYfn7ZN#vikxtMh$0yC=EQ$>!+u6LGBE#wrq@$lGn7sR zDyu)e#b0uEa9>%q98WuyID2W?E4QAwu^BPb{m5pJe5rg846$R5wblbHCKpRBAx6P;IIxi9m{-;hu+J;7qBh>3KfR(iXddnG1$z$ zadum#U;a@fTqhDvyykrup6c=Ws3BRjRcP~8;vp&!UVp4nQIhaT6N9&&h_U7ktTgDQ zf8IGV1>5V$^uA!+RBz<5aHlBh)E;@f%m4v(PfthWw^fz6pM-y@r2Bydj7&zUo1Zc!PytoxUn8vSwhK=mWKHV`Z`XhA}=(hj(A=P4;wMzZUfpI*JZ zXxg*cES09IMwubLC(5@&V1R5Ky`W4pZP8;>*{~#)Mfzp|8M{#Df1=ZegbQt=2w-Aw zFAP)-Fy}$GswOXDS0Tybw$29fFtN)o4EO;XLs2mH~7&0V%iJ zAxCMamPC4Oy#+8ITlcSa21$r|qyPp`b5Dksw2^QIPNT9Nsn`?@f*5hR;wI~IwLiKY z2={KgX~T)=zT98g|3VPI|QYd-?^us zAXn%0W;jg;v7I)3mroiZ>cuDC2}F!LH{8bN-xizhJ8|&0GYXeZ*{r&Uc$RkWS-w;H zoxe`ZppBOq7NEj-C((-2jYZCj+|NH8juM6pemcq_2yC6(ol>+-@8kum{t=b6PUdWo z*HpD!GfMsjeXJR?@pL^Je>iy2wHyT@Dbc~yUrU5Wi<#A%@?RxOzpp4D!up3$ks-bH zf39MGix2+=zWlYW#k6GL{$W5(*t_mzk;o|+H9_Vvnv;r4=z$Q;hO)UfV?0Z3kmqkVCeLw`V7?{N%ec4iC)(@~rn^k$W zR{2ly4+xn8fX}3)4h%er(0K;ZuLD5r2jqNNy$)^imY8AP0aePQ6}DFalFreoPXWpp zW?zBcy_XDPJz!pp7U*FW78a`JDw_{}k^oBKv^T1D-0|7>t-E5en;^U=kw4<&@4+D7yN}ufC5^u zC&~nLQV=s;R9CKcvP7G|P+-iLP1KMLez-u$0g))yfmXndHXT8J6k+z3|9cCb=?RLM^n zcl(u3ch9L1G7+#=4HXtCNLMyn%G0#^*Y)XOa6Z#eWv5f2r?r&;F-W|5KK?vM;E)`}^i9 z1Cplz8hk#tS(>`-Na~TPe&<7e;*5P`ot2N~s~?x}`<5>7?{dDaW7w^BPw}6J`U9r_ zs5kr9^mIp`QBW<;vi|BDqO5M!1 zImu^d>XwHE#CxN7#_sNTjDnkk(@8}};C+mk zwvZXAIw&2SM*AXBly=}}SehGIji?CB06AjfkygV)9Mj`B6#*>ZR?q%vfB#^;W7;E| zv+rpG&&9Y_Q&PsHdTFoNhQ>5cVM*#xDh4}?1tixm2=iaXN}<6}J4`a#d>3w6<`A=! z)ImPm;wzC=(E)WDt);9I!%p>j?S8y_tH4?8T9Xm-6$zgfE!4$Fd<$jDDwbW{*6uxm8!-~)h`^_3uS#k@WveW6p@?c3^D=YR8s0uFj zo-Q&X0B(RWC}h|eDQu?6hK&Hv+kh&U?H~zdOeNaK6)6<;k?Dy z<-x%6)aRw8JakH;6YAvZO$FenL-9l>ox<&8D_Vm1cu89-+5jCpFbsNkZ*8l9%4x!qOi_J_Bw zEIS$APLeBr<-}s5R{F`t z^^7~wsEOD&`loe<*GD$D?J(~XZzOg;F?Y5A5Lljy<#)bu(C;18Whi;)vaFk|dcOd@aOs7!+^4<{>`4Ssy0QkS05O9DyLX?d_Wb z)l;*Z#uOoD#kmeH1Cqf~mU4-n$led#)2a_d^-r}wk$vWM-tElO7sLTnZAy`AHhL{n zJP;pk=b;c#@6qN!@C1}}K>Y!za({cc zdM%8DuUU3JeE!-dS(gT(<<^cid~y|K+H-#)&|0e^b;iqm!jZMoCrqtP@`6P z^DD3IRlg+1K$Rd{jwNAeDro_fGgR%OdyEM6R~=Vhl~FqVFZ_`Dov4;0sgwh`1lV zN=U%JxcU@@L{#qe2-SW)6Y=CHqBodZx<078Pja-Qa`6N@`h;w&gke4stWZ30k;@tGbre4)i!CR~;%J$pvH?%4{59(*}OTUBUr!rPBK&N3wYu z3G1LrG(1WDA;4Ivox1bAR{TXDzMG-8`7SB6Y9IbjV1W52WKjh2#~=74^j~lKgPUA) z{=@X_1j6dZkK#S09PrqOAFKg@NPZ9vL_cQ6VPoXAj@L1^54viNi26QI*gz~2frXvNH+aeg2XBz!2MhbjE zb~Q(twbXp@3JXgJv>ufbL`0L~Jo&Tb94Ys2TTcmRDkOvRfM+$W4(F#zK-TDv;l+z{f*rT(T1O(!@x3@RopvDeV`CkG51$SAvbMnXIHzXv%!)IQEhISTD zXt_m$EbNCz@9b`71C|Ty4H2k4I#1j7(g*}AYT#Sd%brPt<}Hpd(`B;<5p1kK0wFOi zp~H<;ap?^}%mi#=7vWMp*#kNv03J;Ot%I|JN_%V&67T~l1i;%d?to%BpmE-pl#B#l zh*O@<$*F3uqnP4F+?||kvC&ZkASgpd$}DLb8yoLSNPK-SO$wfO*C_Z?Q2A(m5P8R^WcOF!`fsh6U z7!StFJ;^1O;}kxxuS$Wl%sGir3|F3|Ks^6@29d0RIzvsHK@pHL4b{-u8LJxd5b63C zt+4v=b&%brybqL2X4!>Vw}c{_@|#oPVWyexUfj;|Tv40sknkt1|zIA^aXt z_&Y=Ux7Pqm0=y>UH(n$1f5Z{cReyi8ArY;=a!d9Co|BmM?|vTq`(*e>R91{N+y4E6 zz#QVwMnr$Xe1@Qi+a@+NzB@U_sn7Z5Mlz~RfR@vk+TvvBsMcL-WuV8UX9{b3&h$u# zTqdw-$p9jl) zpKsulVZ@~JvHDq^!T2&AVq17cl@W*0T)5}z(O~C$;|#6+lN?o-xm_pT52~>cSx6|% zyRpMx1F6+^UD#E^T|DQm>P=;eO?Xmia5+d-;de%6@QKP4=oQ{6WoEjU(*0gPa3X?A znC*E-v?d|-tHne~ink)}IdaJt#MKi5A*_TRC%lepx7$1XK0X#dnhl0Ptm;O43~IS? zakp*#dky+FL{E46MMXV41IOELSq;BhUOLF|JkG;b*Re4rQS5c`>s%lHtY6B_gA}Qg zIim1kq@&*HBWO)Th)F4MeuOz7Pjr-ghnOU!UeH_t@^B3&HDfLQ3+UYgW*5&YlL(EH z3Fd1ty}m8|vyn77tob&!i*_PX1nF_Y$RrzSPDw_cdx+pTp zEUDZBQX7q}x!Wx&E!n74Pdpt@0)}2c^1VwUbW7fG{isfJ@g~|M)3YG(7{f@Rfa17T zKW+lkUO|$1oU~Nxm@L5yJWce<_UE1wM>aEuLeiHh#nQvuZAAgrcj*__}cY9-W)cRE&9gG(n95&(E^LuB3z> z9Q$vT@2f&ri?QrQjFC8eNNbbxxh(k1B6M@9tP*W6}In523A$XmBN6Xd( zJSG`j2FBE42(pHt0|{s~JVQmbyi+>G!wxCHTz(5y#Ddy zM+jObR?&71j1WEfqLJNOcC2w#IkbS)uFx-)qQrcL)^k%weT!) zRzr#>iF@o$5=ic>IvgE4f6zQ1B$6Bzpy_^{R@mYc+0Sk|3=HE-%zY1RhAf!15!PSN zP{{F|#5&=>L=r7FhqX<5;s}$OoYK=;ToX4b3;%zuoq06W`~Sx&m+p-#i4fY8xP%zH zxX}$Eg_NyG%1*MAy2+Z*SjWf`$ui<*8)hQ=HWEoOV;>>=HU{&1eNw;gz2EOSzw`T@ z^Yfo$&S%Vg-pl9nTppj&QBQg$k$vd;feKS{&&M?(hc#Bk60yZGAMuX@jSD7fwO1KB zE_C^wyjbGILEEpUXeuG$W$CQ(gKRH+_4|k_0xJG#SffW^%Lxwe>E^y@^4Gzo)x{2! zyt$sLEkS=MYr=O zK5?tnC@;j&OZ}a1w3IT}zJ5(Fh<<2Cm)mEQf$0OR4H2+5>_T@1?$MQO=7$R3U(SEB z7WMG2Sl;Gfo+{3!6905lnU$S#66B#}*XlHNb}S|J3#qlm-I986Wqzzgs&b=W-eS#b z7y9McMuN)u!DG#Z;JC?6*KM*B>p zi(wR2+)DEK@j2}vS;!e@>8I*xX|X~~(gv(PvjEztA5hF9_@rv~oo|eTBsXbIIfVUD z>@D6-y-|kiIZ=aAwUwjK-=x`C|1mx#sbW+US(BmXSVm*DHnlxgDJ7IXbIQrmljlfY zkU0K=pI-^H;p3#<$@L{MViZ1Mb34l?tLlc1b!PcZ&c6QI%j9&?KTV_q7!ZapYCm4# zcMvAl_uI_as5kZ(t)>ii9KTPSz38D-IFI{n>YUQ}vTOfs&4tgQ{timZR|`ga(8LH{ zBY~5%_?$)ZGHdv{{FUk~#KatoV17SB$p{rzfjeCbOcv4hOj z>zK9dvm5iQ$sBSHzdSqt$Y9es8~s5_>s7Q{ZOxxK>Xq{vTG4$}TX~nQK55j>!6x!^ zZzls$!N(Z7qg=wA-br&|iQj3zNKACQV;FUQHn)kUdH&ABKkY}&er=$Qq>w%q%dALw zdBz{>0xr!Od0x57S+EurN?BNpAn*07n$ zcDJ=>vBS%#bo#mhxazo(vSgIAEu0}17M4QLi+Lq2bfL`xZf_=Zd&qVga#U{#Ejs1M z$Nu4V56gQ?>kW0iIIubRPO>-01qVg(x3H@HY)!fE>#O0VY1hX@V7zlgNhIOujE<@f zh4zFfSu4KQRE#?%%Xy%jYgQw$Y$9x7CG8|kD~wK@5H^4xJHy6>Zgq_3|D+{bKj=1p zs}pr5_>%n0uv5+*9+QXW%>zO+Bjqz6v`x5pt|T_0mPr=IAB@9_N(}Gdc-mw~Z(oor z+>^#!t?N9?ZYrVVU#eeoyGcp#%Q^|2reyvJdm)-LGnwAB(uG9-@+J4v{Dwf{_ivLv<5xq-=wqj0xb z^Z5E~6zhf8#wyE%9+&A-@ShlNK6p z`2RttUcypC7%-W5cJHovmvI)ZJDwRn*TyJ%gnjM@do@0Eu~1Vrt(-IG8VDxmD0 zheArv!C@ArlO+$|USk)H^eS-qV%f4!d{gq)0^4@~#BMNl41VXEJ? zc1=;wn3O+kt{(MSp00G>FTmJW0GY_)tAj)gI5kMM|otyCYR! z30JpnBQ0r<&yIJ=e4LMdHC1q}(V)3WytEvuwSB<*TXBNo(c1X)Y|kcJgl=`!d&%?` zUEjA5ELkz&e&vp%mP^h7@}`qu$$@?9Y4tObfBA6O-$QF!b6=({3ovba;P5oZfc0Md z%s#d!D$4SV(WAY%#LAf+Jh^ey)j=Zj3qhmkex+r@oZCO$?#4fLw7(08jU|TbiKRUk z;*ADZW|k9*xmqtp#2j^okKV16$_{~41cyD zxl>xy+S)vP&5Kc9V+`^US+U5%H=WAnE6l2jg+bPdB6i%}A-N*++h&xjNOQB^rN$6^lY3at`qi6?*Fjyl8G5-Loc3mu3mzo_rK+hLzBZ9Aw(GO3 zuoSbrDUnsO>z))=wVXYgt%u2)6cZ}13~(H}N|;!dEfr_P8COkTn~#-wVU~Z0Bi-4m zx-n`8`_Cf}AL_r$zNXCPDdM`3(zDBIrxit#O?f77&p8XxLWCg9V`5efbxGO+Nv(V^ zc;9jJic_FXj{!7tL5sWShsE32d|&4!fBbHy!$EwCAkw=r%JjZkQPk}iFI+7fN)#4y zE%yKVw!ZrF!a?~R$0{B6@XJt8?X})B*28QOKU2cV!4{lv2ckQ&lIM=5>7!nF8qG}v z;^XRX@F^}z$KYG+1#$r!;>bhOIp4ROo^T5{Oo<*Y6mm3L3vTQ>tSiXesNsR z<#%;0IL!6=T7%B_z!M85`Xxc4^rL7jD*Ita?#)}`#%0S_*rJvfQyni-vpy-S42Mo> zO!)1-S&-rOI!u>5F97tc5&yG%J^oh;8ZaqlDN@d>*I6rRd@=!E+hGdxliH{q!o%X& z9$!LTA!jP{Ud*~uWSZmISl5bizoy1fzXg$zN^yz!KxzOKgpiJ?e&0g4CsmSHc7Jyp z{hDzM#)YPObRI1n-#7j3R#|UGm?33E2j4IBQI`3tdcd=B^E?oH&V=zY+OPYiCtxoj z0lgt5?JWuDZ(j93?{ukMyVA0M+`VtzI*+>jC+-RRw^-hH4dOqYGinzEgnnS6wiFX= z%V<@q#>rcqN3wJiV{Uo_=?F#flYwgNYH6|rs35=JxpN8}WCsdCh5oAeCg~W|V4;;! z1O%6X+jeW za5VP@&w=Sn*eIWsS3_@xEwM~Q)4xs1g z3;!*YwSol>cw;dV33$WG0r_C*zlXLp+)23o#z2l-;bD)h5!;fhrao__@=`p@f1Eo> z3$|SQo&rdmjG9hMRB7w#+CbN(Jg&RIJh|MRI_5nE^*rxUv`K=z>mg(NX-t#H0!#&?CvxkqH`*&SkU4k$kxJ!U`s^QjGX6DN=g5YlgMA@UGqallDqb9cnc5;`% z8Rf=(Ub6d^%hHZenl5_)LKFttF(4svHvW<{I67aiR@nPbPz>Jznj7{9kQJ5u`j6n&06kC+0&6G;h`^@+ z4)dOCa+at!mO`q22|sfmn}v~!FwOumT{aqbwQPO9#lQa)4m3wLNy6$O-<9blkBgPl z09QsXDgX<=C%jGr+8>cy@r4rNab4_g zo>H=p7+}q^!Ib=tcJAtFn@_3>-z(#{>H;ey@`de$%!#Th7A}pqZQou#Rn#KyHTw*g zJSvMURfw)DmmCRO<&cX!xM)w2KLj#1P!Ron<~c(qIED3m?JZ^tudA_+Qqh9QsP|0O z%C~P2J}Ho_!F)Tx!pbUrt%e1mf?J|=!d}Fz@YEjp!8~{z5@H1st*S$`XQZG)Ck*05 zv4XNg^c8^66v3(nT>SC3yxx{T`-Y{=76g1G8^q85W%8JJ8w6Lq00S+5XZ#5+dB_8# z4`>30Ak1yRe+qk#a6vGq&at$AY%GtRHkK1f(@|MH+_c!fAt#_Ft~JlY>h> zs`>Pnyzo8z{7oQD7deZFh{QJz7RB5KMg(H9-o$z%p(uFsTm;@LNnGD{+RpCyZ&&^r z0FK3iYi^}AJmkTFT$u<9k(Bqm4U$kqYKxYr7JEo4)gv2;Gv2IVx9r|rx6x+gn*q8w zEMPU}p8ZDYS`xJ~CkiDDhwCGe!G=^SfewKzT!61ztuP(>3Cg?aP<)BluZD#gLPO>n z6hJKiwGO%Qrq)&)h)f~95Q0;j2}1uzin79bAxzrfavGaK>tCY0Y`T#;L!opN!!r9tx2-sa4=||q*UdBtEmjd8&wDN3gUMn4}Ji91U46lr)!3apVCQ( z-R#DUFWA5^$Z{-+1Y`h1a#971!A!6|Y;N3e{p?uk3OsijG8ACBP0~FAQ{&Ly^WQs& z|K}AlRj6((UIdV2Ox4uO+ydnC#_d<$c)ji4 pnBwT~TgQ)<^LK;qfBtm?c%v&m*2go0;E5|t=QQ=s6lhr8|1XcJ?jisH diff --git a/doc/LectureNotes/_build/html/_sources/chapter1.ipynb b/doc/LectureNotes/_build/html/_sources/chapter1.ipynb index 6209e3881..756ce7708 100644 --- a/doc/LectureNotes/_build/html/_sources/chapter1.ipynb +++ b/doc/LectureNotes/_build/html/_sources/chapter1.ipynb @@ -629,8 +629,8 @@ "metadata": {}, "source": [ "$$\n", - "H_{\\delta}(a)=\\left\\{\\begin{array}\\frac{1}{2}a^{2}&{\\text{for }}|a|\\leq \\delta ,\\\\ \\delta (|a|-\\frac {1}{2}\\del\\\n", - "ta ),&{\\text{otherwise.}\\end{array}\\right.\n", + "H_{\\delta}(\\boldsymbol{a})=\\left\\{\\begin{array}{cc}\\frac{1}{2} \\boldsymbol{a}^{2}& \\text{for }|\\boldsymbol{a}|\\leq \\delta\\\\ \\delta (|\\b\\\n", + "m{a}|-\\frac{1}{2}\\delta ),&\\text{otherwise}.\\end{array}\\right.\n", "$$" ] }, @@ -638,7 +638,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here $a=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", + "Here $\\boldsymbol{a}=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", + "\n", + "\n", "We will discuss in more\n", "detail these and other functions in the various lectures. We conclude this part with another example. Instead of \n", "a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn." @@ -3412,7 +3414,7 @@ "outputs": [], "source": [ "# split in training and test data\n", - "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" + "# X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" ] }, { @@ -3475,18 +3477,27 @@ "metadata": {}, "source": [ "where $y$ is the function we want to fit with a given polynomial.\n", - "!bsubex\n", - "Write a first code which sets up a design matrix $X$ defined by a fifth-order polynomial. Scale your data and split it in training and test data. \n", - "!esubex\n", "\n", - "!bsubex\n", - "Perform an ordinary least squares and compute the means squared error and the $R2$ factor for the training data and the test data, with and without scaling.\n", - "!esubex\n", "\n", - "!bsubex\n", - "Add now a model which allows you to make polynomials up to degree $15$. Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?\n", + "Write a first code which sets up a design matrix $X$ defined by a\n", + "fifth-order polynomial. Scale your data and split it in training and\n", + "test data.\n", "\n", - "!esubex" + "\n", + "\n", + "Perform an ordinary least squares and compute the means squared error\n", + "and the $R2$ factor for the training data and the test data, with and\n", + "without scaling.\n", + "\n", + "\n", + "\n", + "Add now a model which allows you to make polynomials up to degree\n", + "$15$. Perform a standard OLS fitting of the training data and compute\n", + "the MSE and $R2$ for the training and test data and plot both test and\n", + "training data MSE and $R2$ as functions of the polynomial\n", + "degree. Compare what you see with Figure 2.11 of Hastie et al. Comment\n", + "your results. For which polynomial degree do you find an optimal MSE\n", + "(smallest value)?" ] } ], diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index 6aea9d947..244b648c0 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -781,13 +781,13 @@ example of the functionality of Scikit-Learn.

The intercept alpha: 
- [2.15024669]
+ [1.99723611]
 Coefficient beta : 
- [[4.89975818]]
-Mean squared error: 0.26
+ [[5.1007125]]
+Mean squared error: 0.22
 Variance score: 0.90
 Mean squared log error: 0.01
-Mean absolute error: 0.42
+Mean absolute error: 0.36
_images/chapter1_13_1.png @@ -844,11 +844,11 @@ ways of dealing with outliers.

The Huber cost function is defined as

\[\begin{split} -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\del\ -ta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2}& \text{for }|\boldsymbol{a}|\leq \delta\\ \delta (|\b\ +m{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. \end{split}\]
-

Here \(a=\boldsymbol{y} - \boldsymbol{\tilde{y}}\). -We will discuss in more +

Here \(\boldsymbol{a}=\boldsymbol{y} - \boldsymbol{\tilde{y}}\).

+

We will discuss in more detail these and other functions in the various lectures. We conclude this part with another example. Instead of a linear \(x\)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.

@@ -888,7 +888,7 @@ a linear \(x\)-dependence we s
_images/chapter1_27_0.png -
0.0050000000000000044
+
0.005000000000000007
@@ -1283,6 +1283,38 @@ functionality.

warnings.warn( /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet. warnings.warn( +/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet. + warnings.warn( +
+
+
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
+
+
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
+
+
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
 
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
@@ -1317,42 +1349,12 @@ functionality.

   warnings.warn(
 /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
   warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
 
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
   warnings.warn(
 /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
   warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
-
-
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
-
-
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
-
-
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
_images/chapter1_61_10.png @@ -2330,13 +2332,13 @@ but now splitting the data into a training set and a test set.

Training R2
-0.9999868619217517
+0.9999878260589065
 Training MSE
-5.965885569080809
+5.415634483874318
 Test R2
-0.9999794306626945
+0.999974281880048
 Test MSE
-8.300162456113691
+11.653095390463358
@@ -2747,31 +2749,31 @@ techniques.

MSE before scaling: 0.00
-R2 score before scaling 1.00
+R2 score before scaling 0.99
 Feature min values before scaling:
- [1.00000000e+00 2.54152940e-03 1.38207279e-03 6.45937170e-06
- 3.51257863e-06 1.91012519e-06 1.64166831e-08 8.92732185e-09
- 4.85463934e-09 2.63993205e-09 4.17234827e-11 2.26890510e-11
- 1.23382086e-11 6.70946493e-12 3.64857826e-12 1.06041458e-13
- 5.76648901e-14 3.13579199e-14 1.70523024e-14 9.27296891e-15
- 5.04260073e-15]
+ [1.00000000e+00 2.31109204e-04 5.13266452e-03 5.34114641e-08
+ 1.18620601e-06 2.63442451e-05 1.23438810e-11 2.74143127e-10
+ 6.08839750e-09 1.35216172e-07 2.85278450e-15 6.33569998e-14
+ 1.40708470e-12 3.12497018e-11 6.94019247e-10 6.59304755e-19
+ 1.46423858e-17 3.25190225e-16 7.22209371e-15 1.60394236e-13
+ 3.56216797e-12]
 Feature max values before scaling:
- [1.         0.99817842 0.99945628 0.99636015 0.99763569 0.99891285
- 0.9945452  0.99581841 0.99709325 0.99836973 0.99273355 0.99400444
- 0.99527696 0.99655111 0.99782689 0.9909252  0.99219378 0.99346398
- 0.99473581 0.99600927 0.99728435]
+ [1.         0.99997737 0.9950799  0.99995475 0.99505739 0.99018401
+ 0.99993212 0.99503487 0.99016161 0.98531221 0.9999095  0.99501236
+ 0.99013921 0.98528992 0.98046438 0.99988687 0.99498985 0.9901168
+ 0.98526763 0.9804422  0.9756404 ]
 Feature min values after scaling:
- [ 0.         -1.63437572 -1.76504618 -1.03871832 -1.08415761 -1.13457922
- -0.81620806 -0.83935285 -0.86436607 -0.8914984  -0.69347005 -0.70790937
- -0.72312577 -0.73921714 -0.75629493 -0.61234223 -0.6226921  -0.63339159
- -0.64447921 -0.65599927 -0.66800261]
+ [ 0.         -1.7621419  -1.74382593 -1.13803928 -1.14662074 -1.15475232
+ -0.89942598 -0.90825063 -0.9171356  -0.92605247 -0.76570177 -0.77317984
+ -0.78080633 -0.78857629 -0.79648291 -0.67708423 -0.68316185 -0.68937695
+ -0.69573183 -0.7022283  -0.70886748]
 Feature max values after scaling:
- [0.         1.86574276 1.72218808 2.41770932 2.31730641 2.21347282
- 2.88297395 2.79328828 2.70234019 2.60999846 3.291614   3.20772452
- 3.12283463 3.03697069 2.95014575 3.65766387 3.57871326 3.49865673
- 3.41754964 3.33544681 3.25240108]
+ [0.         1.71669651 1.63315151 2.21623344 2.15324631 2.088871
+ 2.62856593 2.5755035  2.52158335 2.46675058 2.987902   2.94034531
+ 2.8923     2.84372853 2.79459035 3.30968181 3.2653678  3.22076362
+ 3.1758506  3.13060772 3.08501147]
 MSE after  scaling: 0.00
-R2 score for  scaled data: 1.00
+R2 score for  scaled data: 0.99
@@ -2907,39 +2909,7 @@ for testing. This can be done as follows with our design matrix 
# split in training and test data
-X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)
-
-
-
-
-
---------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-<ipython-input-38-9b9cf4fa1a95> in <module>
-      1 # split in training and test data
-----> 2 X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)
-
-~/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py in train_test_split(*arrays, **options)
-   2125         raise TypeError("Invalid parameters passed: %s" % str(options))
-   2126 
--> 2127     arrays = indexable(*arrays)
-   2128 
-   2129     n_samples = _num_samples(arrays[0])
-
-~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in indexable(*iterables)
-    290     """
-    291     result = [_make_indexable(X) for X in iterables]
---> 292     check_consistent_length(*result)
-    293     return result
-    294 
-
-~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
-    253     uniques = np.unique(lengths)
-    254     if len(uniques) > 1:
---> 255         raise ValueError("Found input variables with inconsistent numbers of"
-    256                          " samples: %r" % [int(l) for l in lengths])
-    257 
-
-ValueError: Found input variables with inconsistent numbers of samples: [1000, 100]
+# X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)
@@ -2973,16 +2943,20 @@ the aims is to reproduce Figure 2.11 of \(y\) is the function we want to fit with a given polynomial. -!bsubex -Write a first code which sets up a design matrix \(X\) defined by a fifth-order polynomial. Scale your data and split it in training and test data. -!esubex

-

!bsubex -Perform an ordinary least squares and compute the means squared error and the \(R2\) factor for the training data and the test data, with and without scaling. -!esubex

-

!bsubex -Add now a model which allows you to make polynomials up to degree \(15\). Perform a standard OLS fitting of the training data and compute the MSE and \(R2\) for the training and test data and plot both test and training data MSE and \(R2\) as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?

-

!esubex

+

where \(y\) is the function we want to fit with a given polynomial.

+

Write a first code which sets up a design matrix \(X\) defined by a +fifth-order polynomial. Scale your data and split it in training and +test data.

+

Perform an ordinary least squares and compute the means squared error +and the \(R2\) factor for the training data and the test data, with and +without scaling.

+

Add now a model which allows you to make polynomials up to degree +\(15\). Perform a standard OLS fitting of the training data and compute +the MSE and \(R2\) for the training and test data and plot both test and +training data MSE and \(R2\) as functions of the polynomial +degree. Compare what you see with Figure 2.11 of Hastie et al. Comment +your results. For which polynomial degree do you find an optimal MSE +(smallest value)?

diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js index c206f6fb9..4d84307b6 100644 --- a/doc/LectureNotes/_build/html/searchindex.js +++ b/doc/LectureNotes/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({docnames:["Clustering","chapter1","chapter10","chapter2","chapter3","chapter4","chapter5","chapter6","chapter7","chapter8","chapter9","content","intro","linalg","schedule","statistics","teachers","textbooks"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":3,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":2,"sphinx.domains.rst":2,"sphinx.domains.std":1,"sphinx.ext.intersphinx":1,sphinx:56},filenames:["Clustering.ipynb","chapter1.ipynb","chapter10.ipynb","chapter2.ipynb","chapter3.ipynb","chapter4.ipynb","chapter5.ipynb","chapter6.ipynb","chapter7.ipynb","chapter8.ipynb","chapter9.ipynb","content.md","intro.md","linalg.ipynb","schedule.md","statistics.ipynb","teachers.md","textbooks.md"],objects:{},objnames:{},objtypes:{},terms:{"000":2,"000000":[1,4,9],"00000000e":[1,4],"0001":2,"00015921913736794912":7,"001":[2,5,6],"00109273":[],"00200":6,"0032873138755776365":15,"003704":1,"003717":1,"003759":1,"003774":1,"003788":1,"00433417":9,"004456043243408203":0,"00445655":9,"0049999999999999845":[],"004999999999999996":[],"004999999999999997":[],"0050000000000000044":1,"00727646693":1,"007789":[],"007824":1,"0086649156":1,"008885578722629236":4,"009154":1,"009735":9,"00973536":9,"009790":1,"009883615646716182":1,"009883615646716184":[],"009883615646716186":[],"009911":1,"01057384067458835":[],"010679893512872646":[],"0110":15,"011347":1,"0140617":[],"01514394":4,"015144":4,"01867234e":15,"01873344":9,"02161783e":15,"021901":9,"02190139":9,"02493044e":[],"02568378":[],"025709":9,"02625193":6,"02730126581656065":15,"02881304":[],"029688":4,"02968834":4,"030670":4,"03067028":4,"03256632e":2,"03303359":[],"03607832":[],"03697069":1,"03707133":9,"03750367":13,"03787596148305236":1,"03791824e":15,"038300":9,"03871832":1,"04205220e":15,"04260073e":1,"04330858":[],"04421672e":[],"044402":1,"0458":7,"04757618e":[],"04829457939163212":15,"051649":9,"05318162":4,"053182":4,"05364854":6,"05396545e":[],"056329":9,"057088709963637":1,"05755374":[],"059344":9,"060064":9,"060070":9,"06041458e":1,"060919":4,"06126507e":15,"061601":9,"061842":4,"06209126":[],"062188":4,"062376":4,"062639":4,"062660":9,"062829":4,"062879":4,"062923":9,"062979":4,"063178":9,"063324":9,"063395":4,"063882":4,"063982":4,"06412177e":15,"064304":4,"064648":9,"064686":4,"064783":9,"06492086e":1,"065003":4,"065110":9,"065144":4,"065151":4,"065200":4,"065680":4,"065860":9,"065994":4,"066002":9,"06600226":9,"066030":9,"066074":9,"06619182206626131":4,"066294":4,"066312":9,"066389":4,"066403":9,"066487":4,"066683":9,"066761":4,"066837":9,"066951":9,"066963":4,"067132":9,"067175":9,"067440":9,"067601":4,"067667":9,"067685":9,"067844":4,"067865":4,"067892":4,"067990":4,"068351":9,"068514":4,"068697":9,"068818":9,"068838":9,"068840":4,"069005":4,"069028":4,"069081":4,"069195":9,"069243":9,"069369":4,"069413":9,"069503":4,"069528":4,"069641":9,"069775":9,"069890":4,"069988":9,"070129":4,"070131":4,"070138":4,"070254":4,"070337":9,"070548":9,"070762":9,"070791":9,"070812":4,"070815":4,"070867":9,"070889":9,"070967":4,"071016":4,"071049":9,"07106781e":4,"07110274":13,"071248":4,"0713":1,"071325":4,"071435":9,"07145103":9,"071502":4,"071547":9,"07155335":[],"071576":4,"071579":4,"07159175":[],"071660":9,"071681":4,"071684":4,"071761":4,"071917":4,"072094":4,"072424":4,"072555":9,"072589":4,"072620":4,"072654":9,"072710":9,"072826":4,"072879":9,"073":3,"073020":4,"073096":9,"073310":9,"073352":4,"073371":9,"073372":4,"073598":9,"073602":4,"073656":9,"073765":9,"073851":4,"073915":1,"074":3,"074027":4,"074067":9,"074170":4,"074191":4,"074201":9,"074230":4,"074286":4,"074331":9,"074568":4,"074618":9,"074979":9,"075084":4,"075119":4,"075194":9,"07521771":4,"075218":4,"075266":4,"075296":9,"075526":9,"075545":9,"0755452":9,"075615":9,"075993":9,"076320":4,"076367":9,"076408":4,"076410":9,"076466":9,"076938":9,"077010":9,"077046":9,"077143":9,"07729012413236423":9,"077305":9,"07744472306026946":7,"077469":4,"077623":4,"077771":9,"07777777777777778":2,"077891":4,"077927":9,"078099":9,"078103":9,"078244":9,"078311":4,"078388":4,"078693":9,"078710":9,"078746":9,"078930":4,"078966":4,"079001":4,"079139":4,"07944154":13,"079624":4,"079785":9,"079848":9,"079956":4,"080024":9,"080084":1,"080264":9,"080325":9,"080398":4,"080570":9,"080626":9,"080675":9,"080845":4,"081077":9,"081129":9,"081210":9,"081260":4,"081402":4,"081489":9,"081514":4,"08170444":[],"081816":4,"082211":9,"082225":9,"082247":9,"08248290e":4,"08251898":15,"08271336":[],"082990":4,"083128":4,"08318298e":2,"0832":[],"083295":9,"083376":4,"083577":4,"083658":9,"08415761":1,"084167":4,"084249":9,"084604":9,"084813":9,"084873":4,"085121":9,"085285":4,"085646":9,"085676":4,"085951":4,"08611111111111111":2,"086116":4,"086592":4,"086652":9,"086974":9,"087202":9,"087482":4,"087563":9,"08888888888888889":2,"089425":9,"090274":9,"09103481e":15,"09166666666666666":2,"0917":7,"093755":9,"09487315108590391":9,"096726":4,"09672604":4,"09678277e":15,"09741585e":[],"09903804":6,"100":[0,1,2,3,4,5,6,7,8,9,13,15,16],"1000":[0,1,2,5,6,9,12,15],"10000":[0,3,8,9,15],"100000":6,"10001":8,"1001":15,"1002":15,"1003":15,"1005":15,"1009":15,"10094646e":[],"1011":15,"1013":15,"10131725":[],"1013904243":15,"1015":15,"102":1,"1022964509394572":2,"1023":15,"1026":15,"1027":15,"103":2,"1030":15,"10307631":[],"10327559":13,"1037":15,"10378326e":2,"1038":15,"1040":15,"10405456":9,"1047":15,"105137868830763":15,"10555555555555556":2,"106095":9,"108":1,"10806972":4,"108070":4,"10896672e":15,"10898112e":4,"10th":7,"10x":1,"110":1,"1100":15,"1101":15,"111":[2,5,10],"1111111111111111":2,"112283":1,"112383":9,"11304709e":15,"11388888888888889":2,"11456076e":[],"11507992e":2,"11666666666666667":2,"117":6,"11944444444444445":2,"12002944":[],"1203284":6,"121":[6,7,8],"122":[6,7,8],"12222222222222222":2,"12283463":1,"123190":1,"123459876":15,"12366979e":15,"124":1,"127773":15,"12777777777777777":2,"1298":7,"13055555555555556":2,"13209041":[],"133":5,"13328820e":[],"13457922":1,"13519106":1,"13579199e":1,"1361111111111111":2,"137268":[],"137400784702912":1,"137652":9,"138775":9,"139475":3,"1404":1,"1437":2,"1440501043841336":2,"1445":3,"14459063":[],"1446":3,"1447":3,"1448":3,"1449":3,"14722222222222223":2,"147400":9,"147420":9,"149366":[],"149667":3,"149903":3,"14g":3,"150":6,"15024669":1,"152636":[],"1527777777777778":2,"153106":1,"15332528e":[],"154720":1,"154911":1,"155491":1,"155687":1,"155883":1,"156":1,"157":1,"158":1,"159":1,"15979239e":15,"15g":3,"160":1,"16111111111111112":2,"16496581e":4,"16553696":[],"16637855e":15,"16666666666666666":2,"16807":15,"16b8e3cda33a":2,"17117385":[],"17174962e":2,"17234827e":1,"17385778e":1,"17654307":[],"17707436":[],"17777777777777778":2,"17953942":9,"1797":2,"180092462880674":[],"18220995":1,"18333333333333332":2,"18404906e":4,"1856411":[],"18611111111111112":2,"18673098":9,"18954529":[],"18968431e":15,"1914224774238273":4,"1940":1,"1943":10,"197":1,"1970":13,"1973":7,"197370":9,"19742904e":[],"1979":3,"1989":15,"19937":15,"19955871":4,"199559":4,"19972087e":15,"1_1":10,"1_2":10,"1_3":10,"1cm":[1,6,8,15],"1e10":0,"200":[1,6,7,8,15],"200000":1,"2004":5,"2006":17,"2010":2,"2011":2,"2015":2,"2016":1,"2018":3,"2021":0,"20277777777777778":2,"205466494327873":[],"207545":9,"20772452":1,"20819609e":[],"20833333333333334":2,"20843563e":[],"20906175e":15,"210340":9,"21208310e":[],"212327334149492":1,"2125":1,"2126":1,"2127":1,"2128":1,"2129":1,"213103":9,"21347282":1,"213743":9,"2147483647":15,"216290":9,"216683":9,"221":6,"22527008e":15,"22717936e":15,"23117916e":15,"23333333333333334":2,"2335879":[],"23382086e":1,"24444444444444444":2,"250":[5,7],"25000":1,"250000":1,"250154":1,"25226753e":15,"25240108":1,"253":1,"25303483":[],"253775":1,"254":1,"254509":4,"25450941":4,"25457052":[],"255":1,"2551":1,"256":1,"256962":1,"257":1,"25726439e":[],"2572e3a4b38d":2,"25803281":[],"259107":4,"25910749":4,"259153":9,"2627588":13,"26381865":4,"263819":4,"264":1,"265":1,"266":1,"267":1,"2683":3,"2684":3,"2685":3,"2686":3,"2687":3,"26890510e":1,"269":1,"26974938e":[],"270":1,"27296891e":1,"276263":9,"27753165e":15,"27n_":15,"280647":9,"28166741":[],"282":1,"282727":9,"2836":15,"28475098":6,"2861":15,"2873":7,"2882":15,"2886":15,"2890":1,"2892":15,"28971976":9,"289720":9,"290":1,"291":1,"2915":15,"291614":1,"292":1,"293":1,"2931":[],"294":1,"295656121491569":[],"296247":1,"2968":[],"297219777724628":9,"297260":1,"2980":[],"298273":1,"2983233":13,"298375":1,"2990":[],"299444":9,"29944428":9,"2_1":10,"2_2":10,"2_3":10,"2_i":10,"2_m":15,"2_x":15,"2cm":6,"2ff97f4bf03b":15,"2nd":7,"2x_ix_jy_iy_j":6,"2x_j":6,"2y_i":8,"2y_j":6,"30000":1,"300162456113691":1,"30119421":6,"306854":4,"30685416":4,"30879705":1,"3155":3,"3156929654100207":7,"31608475e":[],"31718909":9,"31730641":1,"317367":9,"31853484":[],"31896852":6,"3200":2,"32047562":1,"323291478597321":15,"3250":2,"32521615e":15,"327631":1,"32938847":13,"32945844e":15,"3304":1,"33066907e":4,"3310":1,"3317":1,"333":5,"333333":1,"3338":1,"3344":1,"33443859e":[],"33544681":1,"33861512":[],"339535706819584":15,"33953571":15,"340782":9,"34172919":[],"3436":1,"3437":1,"344172":1,"346433":9,"34643337":9,"34902789e":15,"351636":9,"35176067":[],"358869339268145":15,"35886934":15,"359640894899012":[],"360":2,"360688":1,"36436520e":[],"36468301":[],"369139":9,"36941772":4,"369418":4,"37416969":9,"374170":9,"37738324":[],"38207279e":1,"38216436":[],"38629436":13,"38937995e":15,"38986237":[],"396740":9,"39674043":9,"397700":9,"39792608e":[],"3cd19a0768e1":[],"4000":17,"401842":9,"40212127":[],"404":1,"40425078e":15,"405890":9,"40902095":[],"41511965e":2,"416694683938511":9,"4171578884124756":0,"41754964":1,"41770932":1,"418506":9,"41876428e":[],"42847770e":15,"42937310e":15,"43766686":9,"442600":9,"44395541":[],"44625466e":15,"44970586e":2,"45013332e":[],"45019484":[],"450257":9,"4557763":9,"458078":9,"45937170e":1,"461":15,"462":5,"46323168e":15,"466":15,"46914544e":[],"46929603e":[],"47079457e":15,"47566390e":15,"47654764e":[],"47815203":9,"48154187202453613":0,"48257387":16,"48471852e":[],"48476997":9,"48608063e":[],"4940954":1,"49865673":1,"4990":15,"4992":15,"4997":15,"4c4c7f":[7,8],"4y_i":8,"500":[2,3,5,7,8,15],"500000":1,"5018":15,"50394742":[],"506":1,"507d50":[7,8],"50j":5,"50x10":2,"510":2,"5120":0,"512132":1,"51257863e":1,"51345668e":[],"51363731e":[],"51523276e":[],"51893804e":[],"51943726":9,"519842":1,"5222222222222223":2,"526744":9,"52722156":[],"5303329":9,"5305555555555556":2,"5378811":9,"539261":9,"54121682":4,"541217":4,"54152940e":1,"54237024":15,"54702088e":[],"55138385":9,"551384":9,"55280484":[],"5555555555555556":2,"557795":9,"55854694":9,"564374":9,"56536":1,"569":2,"57051369":[],"574465":9,"57781668":[],"57871326":1,"581766":9,"58176612":9,"582":[1,2],"58228342e":15,"58239999":4,"582400":4,"584804":1,"58521266":9,"585213":9,"58596975":4,"585970":4,"587401":1,"58836420e":15,"5888888888888889":2,"59007674e":15,"5944444444444444":2,"59480085":4,"5cm":15,"60122668e":15,"60673226":9,"60999846":1,"61069091e":15,"6111111111111112":2,"61124978":[],"61234223":1,"614808":9,"61505887e":[],"61745046e":[],"618":1,"61869821":[],"622539":9,"62253933":9,"6226921":1,"62316154e":15,"62359224e":15,"62373464":9,"625":5,"62783293":[],"629961":1,"6300745149331701":1,"63339159":1,"63374631":[],"63437572":1,"636323":9,"63632311":9,"63680118":[],"637129335071195":1,"63993205e":1,"64166831e":1,"64447921":1,"64580686":[],"646283":9,"647473":9,"64857826e":1,"649382":9,"64x50":2,"650024":4,"65002433":4,"6510573774179256":15,"65105738":15,"65238878":13,"65245958":[],"653702":13,"65482578":[],"65572035":[],"65599927":1,"65766387":1,"65933852":[],"65939208e":4,"66020213e":1,"6614":3,"66183486":9,"661835":9,"6628996975186952":1,"66302359":[],"66383151":[],"66677842":[],"66800261":1,"66880047":[],"67006792":[],"67060602":[],"671089":1,"6714":3,"67171347e":15,"67279536":[],"67303655":9,"673037":9,"67407338e":15,"67450955":[],"68034946e":[],"6813":3,"6814":3,"6815":3,"6816":3,"6817":3,"6818813252071303":15,"68188133":15,"68342382":[],"68616263":13,"68729414":[],"689519":9,"69069n_":15,"693361":1,"69347005":1,"693850":9,"69385025":9,"69519693":4,"695197":4,"69981195e":15,"6999536":9,"6ea927cc6e88":2,"6n_":15,"70234019":1,"70415861":[],"70523024e":1,"70589906":4,"70790937":1,"70946493e":1,"712018":9,"71281409":[],"71351486":[],"71442781":13,"7162":[],"7172":[],"72108703":[],"72174172":9,"72218808":1,"72312577":1,"7240496":[],"72780613e":15,"72879865e":[],"72981762":6,"73091052e":1,"73453972":[],"73921714":1,"74081822":6,"74107697":9,"7432283":[],"74495014":[],"74845978":[],"74921867":[],"751699":9,"7522047280566193":4,"75382481":[],"75524378":[],"75629493":1,"75841112":[],"75932862":[],"76172241e":[],"762":9,"76290332":[],"76497666":[],"765":5,"76504618":1,"76648901e":1,"7693978131030923":9,"7718":7,"772b904ae9cb":3,"77350269e":4,"774300":1,"77661393e":15,"77714169":6,"78195":3,"78944806":[],"7899453":[],"79295029e":15,"79326583e":15,"79328828":1,"793701":1,"794282":9,"79902342":1,"7c394b1e8b71":7,"7d7d58":[7,8],"800":5,"80004454e":[],"80121":[],"8055555555555556":2,"81620806":1,"81633628":9,"81781888":9,"827265":1,"82889306e":15,"8305555555555556":2,"83425361":9,"834254":9,"83614019":[],"8388888888888889":2,"83935285":1,"84087101":[],"842":1,"84355903e":2,"84443254e":2,"84658093e":15,"8479552268981934":0,"8520127":4,"85396354":[],"85450859":9,"85463934e":1,"85497163e":[],"85546305e":[],"861":1,"86145244":9,"8638888888888889":2,"86436607":1,"86574276":1,"8666666666666667":2,"86692943":[],"8718475896381779":15,"87184759":15,"8722222222222222":2,"875":2,"87761937":[],"8777777777777778":2,"87972591":[],"8802":[],"8805555555555555":2,"88297395":1,"88559559":[],"88693966e":15,"88712946":[],"8888888888888888":2,"8901":[],"8914984":1,"8921171964770647":9,"89288636":9,"89383322":[],"8944444444444445":2,"898500":9,"89850037":9,"89975818":1,"8x8":2,"90233874":[],"90316476":[],"9040":7,"9055555555555556":2,"90618734e":15,"907307":9,"90730735":9,"91012519e":1,"9111111111111111":2,"91145266e":15,"91549644":1,"9166666666666666":2,"916978":4,"91697817":4,"9222222222222223":2,"924018":1,"925":2,"92507116e":2,"92645039":9,"92646965":9,"926470":9,"92732185e":1,"9277777777777778":2,"9279671770201344":15,"92919670e":15,"9305555555555556":2,"931":1,"93100040e":15,"932734":4,"93273404":4,"9354":3,"9361111111111111":2,"937":15,"938":15,"9388888888888889":2,"939":[1,15],"9444444444444444":2,"94536341":15,"94591015":13,"94639099":9,"946893955211749":1,"947543":9,"948729":4,"95014575":1,"9527777777777777":2,"954":15,"9547578478889096":1,"9555555555555556":2,"956563":9,"9583333333333334":2,"960":15,"961":15,"96104648":[],"9611111111111111":2,"962":15,"963499":9,"96349948":9,"965885569080809":1,"967809":9,"96793117e":[],"97101567e":15,"9722222222222222":2,"975":2,"976":6,"97723801":[],"9777777777777777":2,"9780387310732":17,"9780387848570":17,"9781492032632":17,"9805555555555555":2,"98452685":[],"985":15,"986":15,"98609175":15,"986091753050161":15,"9861111111111112":2,"98661465":[],"9877742":[],"9888888888888889":2,"98892195e":15,"98893512":[],"989":15,"98927731":[],"9898ff":[7,8],"99009739":[],"99043999":[],"9909252":1,"991":15,"991072":9,"99107239":9,"99126104":[],"99133007":[],"99160404":[],"99190487":[],"99194716":[],"992":15,"99218987":4,"99219378":1,"99242605":[],"99248001":[],"99273355":1,"99276945":[],"993":15,"99305549":[],"99305802":[],"99311297":[],"99346398":1,"99363129":[],"99363383":[],"99393624":[],"994":3,"99400444":1,"99418903":9,"99420743":[],"99420997":[],"99428016":[],"9945452":1,"99462421":[],"99473581":1,"99478645":[],"99478898":[],"995":3,"99527696":1,"9953048353087299":[],"99536326":[],"9953658":[],"99544872":[],"9954538761021741":[],"99579317":[],"99581841":1,"99594294":[],"996":3,"99600927":1,"99636015":1,"99652042":[],"99652296":[],"99655111":1,"99696351":[],"997":3,"99709325":1,"99710078":[],"99728435":1,"99730848":[],"99763569":1,"99767893":[],"99782689":1,"998":3,"99817842":1,"99825997":[],"99836973":1,"9984806":[],"99883879":[],"99891285":1,"999":[7,15],"99945628":1,"99995818594196":[],"9999794306626945":1,"9999822527140678":[],"9999864543345858":[],"9999868619217517":1,"9999910208315801":[],"9b9cf4fa1a95":1,"\u00f8yvind":16,"abstract":2,"break":[0,1,9],"byte":13,"case":[1,2,3,4,9,10,12,13,14],"catch":1,"char":15,"class":[1,2,3,5,6,7,9,10,15],"const":15,"default":[1,2,5,13],"ekstr\u00f8m":16,"export":7,"f\u00f8470":16,"final":[0,1,2,3,4,6,7,8,9,14,15,16],"float":[0,1,7,9,13,15],"function":[0,3,7,12,13],"import":[0,1,2,3,4,5,6,7,8,9,10],"int":[0,1,2,4,5,9,13,15],"long":[1,2,5,10,15],"new":[0,1,2,3,4,5,6,7,8,9,13,15],"null":15,"public":[1,12],"return":[0,1,2,3,4,5,6,7,9,13,15],"s\u00f8rli":16,"sch\u00f8yen":16,"short":[0,11],"steinsv\u00e5g":16,"super":4,"switch":[0,1],"throw":15,"true":[0,1,2,3,5,6,7,8,10,15],"try":[0,1,2,5,6,7,8,9,12,13,15],"var":[3,4,8,9,15],"while":[0,1,2,3,4,5,6,7,9,10,15],AGE:1,Adding:2,Age:5,And:[0,1,3,7,12,15],Are:9,Being:5,But:[0,2,3,7,8,15],CAS:[],DIS:1,Doing:[5,8],EoS:[1,3],FYS:14,For:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,17],Going:[2,4],Ising:[4,10],Its:[2,9],MDS:9,NNs:10,N_s:6,Not:[1,2,3,4],OLS:[1,3,4],One:[1,2,3,4,5,6,9,10,15],PCs:[9,12],RMS:15,Such:[3,10,15],That:[0,1,5,8,9,10,15],The:[8,13,14,16,17],Then:[0,1,2,3,5,6,7,8,9,10,13,15],There:[0,1,4,6,7,9,10,11,13,14,15,16],These:[0,1,4,6,7,8,9,10,13,15],Use:[1,7],Useful:[3,13],Using:[1,3,4,6,8,10,13],With:[1,3,4,6,7,8,9,10,13,15],__class__:8,__doc__:3,__future__:[6,7],__init__:[2,3],__name__:[3,8],_auto10:10,_auto1:[4,5,10,13,15],_auto2:[10,13,15],_auto3:[10,13],_auto4:[10,13],_auto5:[10,13],_auto6:[10,13],_auto7:[10,13],_auto8:10,_auto9:10,_ax:3,_base:6,_build:[12,17],_check_optimize_result:9,_compon:9,_datafram:[],_depth:7,_fraction:7,_lambda:1,_leaf:7,_logist:9,_make_index:1,_multilayer_perceptron:[1,2],_node:7,_num_sampl:1,_ratio:9,_sampl:7,_split:[1,7],_varianc:9,_weight:7,a0faa0:[7,8],a77d5ac269b2:5,a_0:1,a_1a:1,a_2a:1,a_3:1,a_3a:1,a_4:1,a_4a:1,a_h:2,a_i:[1,2,10],a_j:[2,10],a_k:[2,10],aaron:17,ab_channel:12,abandon:2,abbrevi:14,abid:15,abil:[1,8],abl:[2,4,5,8,10,15],abort:15,about:[0,1,2,3,4,5,6,7,8,9,10,12,13,17],abov:[0,1,2,3,4,5,6,7,8,9,10,13,15],abovement:3,abs:[0,1],abscissa:5,absolut:[1,3,4],acccess:[],accept:[1,7],access:[1,9,15],accid:3,accompani:1,accomplish:[6,7],accord:[0,1,2,3,5,7,10,15],accordingli:9,account:[1,15],accumul:[10,15],accur:[3,8,15],accuraci:[1,2,4,5,7,8,9,10],accuracy_scor:[1,2,8],accuracy_score_numpi:2,achiev:[1,2,3,6,10,13],aco:15,acquaint:12,acquir:[2,12],acr:1,across:[2,3,7,12],act:[2,13],action:15,activ:[0,1,7,14],actual:[1,2,3,4,6,9,13],ada_clf:8,adaboostclassifi:8,adam:2,adapt:[1,3,5,17],add:[1,2,3,4,6,8,9,10,15],add_subplot:[0,2,5,10],added:[1,2,4,5,6,13],adding:[0,2,3,13],addit:[0,1,3,5,6,7,8,10,12,13,15,16,17],addition:[5,10],address:[2,5,7,9,17],adjac:10,adjust:[5,10],admir:1,advanc:[3,10,17],advantag:[2,3,5,8,13],afecionado:[],affect:4,affin:[1,6,9],aficionado:[],aforement:0,african:1,after:[0,1,2,3,4,5,7,9,10,12,13,15],afterward:1,again:[0,1,2,3,5,6,8,9,10,15],against:[2,5,8],age:[1,5],agegroup:5,agegroupmean:5,aggreg:[7,8],agorithm:8,agre:15,ahead:7,aid:9,aim:[0,1,2,3,5,9,12,13],albeit:0,algebra:[1,4,5,12,14],algo:15,algorithm:[1,2,3,5,6,12,13,14,15,17],align:[1,3,4,5,6,15],all:[0,1,2,3,4,5,7,8,9,10,12,13,14,15,16,17],allevi:[2,5],alloc:13,allow:[1,2,3,5,6,8,12,13],almost:[1,2,3,5,6,9,15],alon:7,along:[0,3,4,7,8,9,12,13],alpha:[0,1,2,3,5,6,7,8,15],alpha_:8,alpha_i:5,alpha_k:5,alpha_m:8,alpha_opt:5,alreadi:[8,10,12,13,15],also:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,17],alter:[2,15],altern:[1,2,6,7,9,13],although:[2,3,6,8],alwai:[0,1,3,4,5,10,15],ame2016:1,american:1,among:[1,7,8,10],amount:[0,2,3,6,8,12],an_:15,anaconda3:[1,2,3,6,9],anaconda:[1,2,12],analys:[3,4,15],analysi:[2,3,4,5,13,14,17],analyt:[1,3,4,5,10,12],analyz:[1,2,4,15],andrew:2,angl:[1,7,15],ani:[0,1,2,3,4,6,7,8,10,15],anim:10,ann:10,annot:[1,2,5,6],anoth:[1,2,3,5,6,8,9,10,13,15],ans:15,ansatz:1,answer:[1,2,3,13],anymor:[2,6],anyon:6,anyth:[2,15],anytim:16,apach:2,apart:[5,9],api:[2,12],appear:[1,2,13,15],append:[2,3,5,6,7,15],appli:[1,2,3,5,6,7,8,9,10,15,17],applic:[1,2,3,5,7,10,15,17],approach:[2,3,4,7,8,9,10,12,15,17],appropri:[3,7,10,12,15],approx:[1,5,8,9,15],approxim:[1,2,3,4,5,8,9,15],apt:[1,12],aptli:0,aragorn:[],arang:[2,3,5,7,8,10],arbitrari:[2,5,6,10,15],arbitrarili:[1,2,9],architectur:[10,17],area:[1,17],arg:3,argc:15,argmax:[2,9],argmin:[0,8],argsort:9,argu:2,arguabl:0,argument:[1,9,10,15],argv:15,aris:[1,3,5,10,15],arithmet:[1,13],arma:15,armadillo:[13,15],around:[1,2,3,9,15],arrai:[0,1,2,3,4,5,6,7,9,10,12,15],arriv:[1,7,9,13,15],arrow:10,arrowprop:6,art:[1,2,12],articl:[0,1,3,4,8,15],artifici:[1,5,10,17],artificialneuron:10,artist:3,asarrai:[1,7],ascii:15,ask:[3,9,10],aspect:[1,12],assembl:1,assess:[1,3],assign:[0,1,5,6,7,10,14,17],assign_points_to_clust:0,associ:[0,1,3,7,10,15],assum:[0,1,2,3,4,5,6,7,8,9,10,13,15],assumpt:[1,3,7,9,15],ast:[1,3],astyp:[7,8],asymmetri:1,asymptot:3,atoi:15,atom:1,attempt:[1,5,6,8],attend:14,attent:[1,13],attract:[1,8],attribut:[1,7],attributeerror:3,audi:1,aurelien:[1,14,17],author:[1,2,8,15],authour:1,auto:[7,8,15],autocor:15,autocorrelation_tim:15,autocorrelform:15,autocovari:15,autoencod:12,autoencond:12,autograd:12,autom:[1,12],automac:13,automag:[],automat:[1,2,9,12,13],autonom:17,avail:[1,2,3,8,9,12,13,14,17],averag:[0,1,2,3,7,8,15,16],avg:15,avoid:[0,1,3,4,7,9,13],awai:15,awar:8,award:16,axes3d:5,axes:[1,3,5,6,7,8,9],axessubplot:1,axhlin:6,axi:[0,1,2,3,5,6,7,8,9,10,15],axlabel:1,axvlin:6,b_1:[5,10],b_5:5,b_group:7,b_i:[1,2,10],b_ia_:1,b_index:7,b_j:[2,10],b_k:[2,5,10],b_m:10,b_score:7,b_valu:7,bachelor:14,back:[1,4,6,7,8,13,15],backbon:13,backend:2,background:17,backpropag:2,backtrack:7,backup:13,backward:[2,10,13],bad:15,badli:15,bag:[7,12,14],bag_clf:8,baggin:[],baggingboot:8,baggingclassifi:8,baggingtre:8,balanc:3,band:13,bandwidth:13,bar:[1,9],barber:17,bare:8,barebon:0,base:[0,1,2,4,5,6,7,8,12,15,16,17],basi:[4,5,6,8,9,10,13],basic:[4,6,10,12,14,15],batch:[5,9,10],batch_siz:2,bay:5,bayesian:[12,17],becaus:[0,1,2,3,5,6,7,10],becom:[0,1,2,3,4,5,7,10,15],been:[1,2,3,9,10,12,13],befor:[0,1,2,3,4,5,6,10,13,15],beforehand:[0,1,15],begin:[0,1,2,3,4,5,6,7,9,10,13,15],behav:[2,3,5],behavior:[1,2,5],behaviour:10,behind:[1,2,5,6],being:[0,1,2,4,5,6,8,9,10,15],believ:[7,13],belong:[0,5,6,7],below:[1,2,3,4,5,6,7,8,9,10,13,15],benchmark:8,bendik:16,benefici:2,benefit:[1,2,5,9,12],bengio:[2,14,17],benign:[2,5],best:[0,1,2,3,5,6,7,8,10,15,16],beta:[1,2,3,4,5,8,9],beta_0:[1,2,5],beta_0x_:1,beta_1:[1,2,5,8],beta_1x_0:1,beta_1x_1:[1,5],beta_1x_2:1,beta_1x_:1,beta_1x_i:5,beta_2:1,beta_2x_0:1,beta_2x_1:1,beta_2x_2:[1,5],beta_2x_:1,beta_:[1,5],beta_i:[1,4],beta_j:[1,5],beta_k:5,beta_linreg:5,beta_m:8,beta_mg_m:8,beta_p:5,beta_px_p:5,better:[0,1,2,7,8,9,10],between:[0,1,2,3,4,5,6,7,9,10,15],beyond:[1,2,5,6],bia:[1,2,4,6,7,8,10,14,15],bias:[2,3,7,10],big:[0,1,2,3],bigger:2,bigr:10,bike:7,bilbo:[],bilek:16,billion:[10,12],bin:[1,3,5,15],binari:[1,5,7,8,10,14,15],bind:[1,3],binomi:12,binsboot:3,bioinformat:1,biolog:[2,10,17],bios1100:12,bird:1,birth:[],bishop:[14,17],bit:[0,2,13,15],bitwis:15,bla:13,black:[0,6,7],block:[0,3,8,12,13],blockingavg:15,blockingstd:15,blockingvar:15,blocksiz:15,blocksizemax:15,blocksizemin:15,blue:1,bmatrix:[1,2,4,5,6,9,13],bmi:2,bodi:[1,2,10],bold:2,boldfac:1,boldsymbol:[0,1,2,3,4,5,6,8,9],boltzmann:[10,12],book:17,bool:0,boost:[2,7,12,14],boostrap:8,bootavg:15,bootstd:15,bootstrap:[2,12,14],bootvar:15,bootvec:15,borrow:[],boston_dataset:1,bot:6,both:[0,1,2,3,4,5,6,7,8,12,13,15,16],bottl:5,bottom:3,bound:[1,3,6,10],boundari:[6,9,10],box:7,boyd:[5,6],bracket:15,brain:[2,5,10],branch:7,breast:[5,9],brew:[1,12],brg:6,briefli:1,bring:[1,8],broad:1,broadcast:0,browser:[],brute:[4,9],bsol:[],bsubex:1,build:[1,3,8,13,15],built:[1,2,3],bunch:9,busi:1,c46dd114b2af:6,c_0:15,c_1:10,c_2:10,c_3:10,c_4:10,c_i:[5,10],c_k:15,cach:8,cal:[1,5,6,8,10],calcul:[0,1,2,3,4,5,6,7,8,9,10,13],call:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,17],callabl:3,cambridg:[5,17],came:0,can:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,17],cancel:1,cancer:8,cancerpd:5,candid:[6,7,8],cannot:[1,2,4,5,6,7,14,15],canopi:[1,12],capabl:[1,2,6,12],capita:1,captur:[9,10],card:[1,5],cardin:2,care:[0,9],carefulli:5,carlo:[1,3,12,15,17],carri:[3,5],cart:8,casella:17,cast:2,categor:[1,2,7,9],categori:[0,1,2,5,8,10,14],categorical_crossentropi:2,caus:[1,3,4,15],causal:1,causat:1,cax:2,cbar:2,ccc:10,cdf:15,cdot:[0,1,3,5,10,13,15],celebr:5,center:[0,1,2,3,5,6,7,9,15],centr:17,central:[1,3,6,13],centroid:[0,15],centroid_differ:0,centroid_list:0,certain:[0,1,3,5,7,15],cha:1,chain:[2,12,15],challeng:[0,5],chanc:[2,5,15],chang:[0,1,2,3,4,5,6,7,9,10,13,15],chapter:[0,3,8,9,13,14,17],charact:[1,4,6,15],character:[6,7,8,10,15],characterist:[1,2,8],charg:1,charl:1,chd:5,chddata:5,cheap:4,cheaper:[2,5],chebychev:0,check:[1,2,5,9,13],check_consistent_length:1,chemic:15,chen:8,choic:[0,1,2,3,5,7,10,13,15],choleski:[4,13],choos:[0,3,5,7,8,9,15],chosen:[1,2,3,5,6,7,8,15],chosen_datapoint:2,christian:17,christoph:[14,17],cin:15,circ:[2,10],circl:[1,6,10],circumfer:7,circumv:[2,4],clariti:[0,15],class_nam:7,class_val:7,class_valu:7,classic:[5,7],classif:[1,3,5,6,9,10,12,14,17],classifi:[1,2,5,7,8,9],classificaton:2,classifii:8,clean:2,clear:[2,8,10],clearer:0,clearli:[3,5,6,15],clever:[0,2,8],clf3:1,clf:[1,6,7,8],clf_ridg:1,clip:15,close:[0,1,2,3,5,6,7,9,10,15,17],closest:[0,5,6,9],closur:12,cloud:12,clust:0,cluster:[1,2,3,9,12,14],cluster_label:0,cmap:[1,2,6,7,8],cmath:15,cmb:14,cmd:7,cn_:15,cnn:10,cntk:12,code:[3,4,6,12,13,14,17],coef0:6,coef:1,coef_:[1,5,6,7],coeffici:[1,3,5,6,7,13],coerc:[1,3],coin:[8,15],coin_toss:8,col:[1,9],colab:12,cold:7,colinear:1,collaps:6,collect:[1,3,8,9,12,15,17],collinear:4,color:[1,3,6,7,8,15],colorbar:2,colsample_bytre:8,colsaobject:8,column:[1,2,3,4,5,6,7,9,10,13],columntransform:7,com:[12,16,17],combin:[2,3,5,8,15],come:[0,1,2,4,5,10],comma:[],command:[1,2,15],comment:1,commerci:[1,12],commod:1,common:[0,1,2,3,4,5,7,9,15],commonli:[0,2,3,5,7],commun:[1,10],compact:[0,1,2,3,4,5,7,9,10],compar:[0,1,3,4,5,9,13],compat:5,compet:1,competit:8,compil:[1,2,12,13,15],complet:[1,7,10],completenn:10,complex:[1,2,3,6,7,9,10],complic:[0,1,2,3,5,7],compon:[0,1,2,3,4,5,7,12,14],components_:9,compos:[0,7,10],compphys:[12,14,17],compress:1,compris:3,compromis:4,compulsori:12,comput:[0,1,2,3,4,6,8,9,10,12,13,14,17],computation:[1,3,5,7,15],concaten:0,concav:[2,5],concentr:[1,8],concept:[0,1,12],conceptu:[5,10],concern:[0,1,2,5],concic:[],conclud:1,conclus:2,conda:[1,2,12],condit:[1,3,4,6,7,9,15],conduct:12,confid:[1,3,5,6],confirm:10,confus:[3,8,13],confusion_matrix:7,congruenti:15,conjug:6,conjugaci:5,connect:[1,2,5,7,9,10,13],consequ:[3,4,5,6,8,10],conserv:[0,4],consid:[0,1,2,3,4,5,6,7,8,10,13,15],consider:[1,2,5],consist:[1,2,3,5,10,15],constant:[1,5,6,10,15],constitu:1,constitut:3,constrain:[2,5,9],constraint:[4,5,6],construct:[1,2,3,4,5,6,7,8,9,13,15,17],contact:1,contain:[0,1,3,5,6,7,9,10,13,15,17],contemporari:17,content:[2,12,13],context:[3,5,8],continu:[1,2,3,5,6,7,8,10,13],contour:[5,7,8],contourf:[6,7,8],contrast:[2,7,8,10],contribut:[1,4,15],contributor:1,control:[1,2,7,12],conveni:[1,3,5,10,13],convent:10,converg:[0,1,2,4,5,6,9],convergencewarn:[1,2,6,9],convert:[1,2,4,7,9,13],convinc:5,convolut:[2,12,14],cool:7,coordin:[0,4,10],coorel:1,copi:[0,2],core:8,corel:1,coronari:5,corr:[1,4,5,9],correalt:[4,9,12],correct:[0,1,2,4,13,15],correctli:[2,3,8],correl:[1,2,5,8,10,12],correlation_matrix:[1,4,5,9],correspond:[0,1,3,6,7,9,10,12,13,15],cortex:10,cos:[1,3,7],cosin:[0,3],cost:[1,3,4,6,7,10,15],could:[1,2,3,4,5,6,7,8,9,10,13,15],coulomb:1,count:[1,7,14,15,16],countor:5,cours:[1,2,9,14,15],courvil:[14,17],cout:15,cov:[3,4,9,13,15],cov_xi:[4,9],cov_xx:[4,9],cov_yi:[4,9],covari:[5,12,13],covariance_matrix:[0,4,9],cover:[1,11,12,17],covert:1,covxi:15,covxx:15,covxz:15,covyi:15,covyz:15,covzz:15,cpu:2,creat:[2,7,8,9,10,12],create_biases_and_weight:2,create_neural_network_kera:2,create_x:[1,4,9],credit:[1,5],crim:1,crime:1,criteria:[0,1,7,8,15],criterion:[5,7,8],cross:[1,2,5,7,8,12,14,15],cross_val_scor:3,cross_valid:[5,8],crossvalid:3,crucial:[2,15],csr_matrix:13,cstdlib:15,csv:[1,3,5,7],ctnk:2,cubic:1,cumsum:[8,9],cumul:[3,8],cumulative_heads_ratio:8,current:[0,2,5],curs:1,curv:[5,8,10],curvatur:5,custom:0,custom_cmap2:[7,8],custom_cmap:[7,8],cutpoint:7,cvxbook:5,cvxopt:6,cyber:17,cycl:[0,2,10,15],d_f:5,dagger:[4,13],dai:[2,7,12],dalen:16,darget:7,darkr:15,dat:1,dat_id:[1,3,5,7],data1:0,data2:0,data3:0,data4:0,data:[0,3,4,6,8,10,13,17],data_id:[1,3,5,7],data_indic:2,data_panda:[],data_path:[1,3,5,7],databas:2,datafil:[1,3,5,7],datafram:[1,4,5,7,9],datapoint:[1,2,3,4,5,9],dataset:[0,1,3,5,6,7,8,9],date:1,daughter:8,david:17,dbh:2,dbo:2,dcomposit:13,dead:2,deal:[0,1,2,5,6,9,13,15],debt:5,debug:3,decad:1,decai:[1,5,15],decent:8,decid:[3,7],decim:1,decis:[1,2,6,9,12,14,17],decision_funct:6,decision_tre:7,decisiontreeclassifi:[7,8],decisiontreeregressor:[1,7,8],declar:[1,13],decompos:[4,13],decomposit:[1,10],decompost:4,decorrel:8,decreas:[2,3,5,8,9],deduc:1,deep:[5,10,12,14,17],deep_tree_clf1:7,deep_tree_clf2:7,deep_tree_clf:[7,8],deepen:12,deeper:0,deeplearningbook:17,def:[0,1,2,3,4,5,6,7,8,9,15],def_covari:15,defect:4,defici:4,defin:[0,1,3,4,5,6,7,8,9,10,13,15],definit:[0,2,3,4,5,6,8,9,10,13],defint:15,degre:[3,4,6,7,8,9,15],del:[1,2],delet:[3,15],deliv:14,delta:[0,1,6,10,15],delta_:[2,13],delta_h:[1,2],delta_j:10,delta_k:10,delta_l:2,delta_n:1,delug:12,delv:1,demand:5,demonstr:[1,3,4,5,9,10,12],denomin:2,denot:[2,3,5,15],dens:[0,2],densiti:[0,1,3,15],depart:16,depend:[0,1,2,3,4,5,6,9,10,12,15],depict:15,deploy:[1,12],deprec:1,depth:[7,8,13],deriv:[1,2,3,4,6,8,9,12],descend:[4,7,9],descent:[1,2,6,10],descr:1,describ:[0,1,3,6,8,9,10,13],descript:[0,1,6,7],design:[1,2,3,4,5,8,9,10],designmatrix:1,desir:[0,1,4,5],despit:[2,10],destroi:13,det:[4,13],detail:[0,1,5,9,13],detect:[6,10],determin:[1,5,6,7,8,9,10,13,15],determinist:[5,15],dev:[2,15],develop:[0,1,6,8,9,10,12,13],deviat:[1,2,3],devis:10,df1:[],diag:[4,6],diagnost:[2,8],diagon:[1,4,5,13,15],diagonaliz:4,diagram:8,dict:6,dict_kei:1,dictionari:1,did:[0,1,2,4,5,8,9],die:2,diffeent:6,differ:[0,1,2,3,4,7,8,9,10,12,13,15,17],differenti:[5,12,13],difficult:[0,1,2,3,8,15],difficulti:[1,2,5],digit:[1,2,14,16],dilut:2,dim:[0,9],dimens:[0,1,2,4,6,9,13],dimension:[0,1,3,4,5,7,9,12,13],dimensionless:1,diment:13,dimes:0,direct:[0,1,2,5,9,10],directli:[0,2,15],directori:5,disadvantag:1,discard:[3,9],disciplin:[1,10],disclaim:15,discourag:5,discov:1,discret:[2,5],discrimin:[5,8,9],discuss:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,17],diseas:5,disk:[],disord:[2,5],displai:[0,1,2,3,5,6,7,8,9,10,15],displaystyl:[1,4],displot:1,disregard:1,dissimilar:[0,9],dist:0,distanc:[0,1,6,7,9,15],distance_list:7,distinct:[0,5,6,7,8],distinctli:6,distinguish:[1,5,6,15],distplot:1,distribut:[0,1,2,3,5,8,9,12,13],distrubut:[1,12],dive:[1,4,6],diverg:[2,5],divid:[1,2,3,6,7,9,10,15],divis:[3,6,7,13,15],dna:5,dnn:[1,2,10],dnn_kera:2,dnn_model:2,dnn_numpi:2,dnn_scikit:[1,2],doc:[12,14,17],doconc:[],document:9,doe:[0,1,2,3,4,5,6,8,9,10,13,15],doesn:[7,10],dog:2,doi:0,doing:[1,3,9],domain:[5,6],domin:1,don:[0,1,2,6,9,12,15],done:[0,1,3,4,7,8,9,13,15],dot:[1,4,5,6,7,8,9,10,13,15],doubl:[13,15],doubli:2,down:[1,5,7,9,10],download:[1,2,13,17],dozen:2,dramat:9,draw:[3,5,8],drawback:[1,2,5],drawn:[2,3,5,9,15],drop:[1,2,3,4,9,15],dropna:[1,3],dtype:[0,1,2,13],dub:1,due:[0,2,3,4,5,6,8,10],dummi:1,dure:[1,2,6,7,9,12],dwell:1,dwh:2,dwo:2,dx_1:15,dx_n:15,dying:2,each:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16],eapprox:1,earli:2,earlier:[1,5,6,7,9,10],eas:[0,7],easi:[1,3,4,5,6,7,8,9,10,12,13],easier:[3,6,7,15],easiest:5,easili:[1,2,4,5,6,7,8,9,10,13],eastern:16,ebind:1,eblock:7,econometr:[],ecosystem:12,ect:14,edgecolor:3,edit:[],edu:5,educ:1,eface79dac2c:8,eff:15,effect:[2,8,15],effic:2,effici:[1,5,8,12,13,15],efron:[3,15],eig:[4,5,9,13,15],eigen:15,eigenpair:[4,9],eigenvalu:[4,5,6,9,13],eigenvector:[4,5,9],eight:13,eigval:[13,15],eigvalu:[5,9],eigvec:[13,15],eigvector:[5,9],eispack:13,either:[1,2,3,4,5,6,7,8,9,15],ekstrom:16,elabor:15,electr:[1,10],electur:17,eleg:9,element:[2,3,4,5,6,9,10,12,13,14,17],elementari:[8,13],elessar:[],elif:0,elim:13,elimin:6,els:[2,5,7,10,15],elu:2,elus:1,email:[14,16],embed:[1,9],embodi:3,emit:15,emner:17,emphas:[1,8,12],emphasi:[1,12,17],empir:[2,9,15],emploi:[1,2,3,5,9,15],employ:1,empti:[3,8],emul:10,enabl:9,encapsul:0,encod:[0,1,7,9],encompass:[1,15],encount:[1,2,4,5,15],end:[0,1,2,3,4,5,6,7,8,9,10,13,15],endl:15,endpoint:15,energi:[1,3],enforc:10,eng:17,engin:[1,2,12,15],english:17,enough:[1,3,5],ensembl:[2,7,14,15],ensur:[1,2,3,5,9,15],enter:[4,15],enthought:[1,12],entir:[2,5,7,12,15],entiti:[7,10,13],entri:[1,4,6,9,10,13],entropi:[2,5,8],enumer:[1,2,6],env:15,environ:[12,17],eol:1,eosfit:1,epoch:[1,2,5,10],epsilon:[1,3,5],epsilon_0:1,epsilon_1:1,epsilon_2:1,epsilon_:1,epsilon_i:1,eqnarrai:3,equal:[0,1,2,3,4,5,6,7,9,10,13,15],equat:[0,2,3,4,6,7,8,9,13,15],equilibrium:10,equiv:[5,13,15],equival:[2,4,6,9,12,13],erf:15,eriador:[],eridg:1,err:[1,8],err_:3,errat:5,errno:5,error:[1,2,3,4,5,7,9,10,12,15],error_estimate_corr_tim:15,error_hidden:2,error_output:2,escap:5,esl:0,esol:[],especi:[2,7,10],essenti:[0,1,4,7,8,10,15],establish:[1,8,9],estim:[1,2,3,4,5,8,9,12,15],estimated_mse_fold:3,estimated_mse_kfold:3,estimated_mse_sklearn:3,esubex:1,eta0:[5,6],eta:[1,2,5,6,10],eta_v:[1,2],etc:[0,1,2,4,5,6,7,9,10,12,13,15],ethic:12,etsim:3,euclidean:[0,1],evalu:[1,3,4,5,7,15],even:[0,1,2,3,5,6,7,8,9,10,12,13,15],event:[5,8],eventu:[3,4,5,9,10,16],everi:[0,1,2,3,5,7,8,9,10,12,15],everyth:10,everywher:5,evolv:1,exact:[1,4,5,9,10,13,15],exactli:[1,10,12],examin:3,exampl:[3,4,9,10,12,13,14,17],exce:[2,10],excel:[0,1,2,8,17],except:[3,6,7,15],excess:1,excit:1,exclud:[2,3,10],exclus:[1,2,3,15],execut:4,exemplifi:5,exercis:[12,14],exhaust:3,exhibit:[1,6],exist:[0,1,2,3,5,6,7,13,17],exit:[4,13,15],exp:[1,2,3,4,5,6,8,9,10,15],exp_term:2,expand:[4,5,9],expans:[1,4,5,6,8,10],expect:[1,2,3,4,5,9,10,12],expectation_value_of_h_wrt_p:15,expens:[3,5,8,15],experi:[1,2,3,5,6,12],experiment:[1,3,7,15],expert:[2,7],explain:[0,1,5,7,8,9,15],explained_variance_ratio_:9,explanatori:1,explicit:[1,13],explicitli:[0,1],explod:2,exploit:[1,10],explor:[2,5,6,12],expon:2,exponenti:[1,2,5,8],export_graphviz:7,export_text:7,exporttext:7,expos:12,express:[1,3,4,8,10,13,15],exptmean:15,exptvari:15,extend:[9,12],extens:[1,10,12],extent:[1,2,3,17],extern:7,extra:[2,4],extract:[1,4,5,6,9,13],extrapol:1,extrem:[0,1,2,5,6,7,13],extremum:5,extrins:9,eye:[0,1,5,13],f11:1,f12:1,f13:1,f1d:5,f6d7a289d493:13,f_0:8,f_1:[5,8],f_2:[5,10],f_3:10,f_d:15,f_i:[3,10],f_m:8,face:5,facecolor:[3,6,15],facil:[1,12],facilit:10,fact:[1,2,4,5,7,9,10],factor:[1,2,4,7,8,9,13,15],fafab0:[7,8],fail:[3,5,6,9,16],failur:5,fairli:[0,2,15],fall:[6,7,14],fals:[0,1,2,3,5,7,8],famili:[1,5,6,15],familiar:[1,6,12,13,15],famou:[3,10,13],far:[0,1,4,5,6,9,10,15],fashion:[1,7,8],fast:[2,3,5,8,10,12,15],faster:[2,9],fastest:5,favor:5,favorit:15,featur:[1,2,3,4,5,6,8,9,10,12,15],feature_nam:[1,2,5,7],feautur:7,fed:2,feed:[1,9,12,14],feed_forward:2,feed_forward_out:2,feed_forward_train:2,feedforward:[2,10],feel:[0,1,9,12,16],feet:1,few:[0,2,7,11,15],fewer:[1,7,9],ffnn:[2,10],field:[1,10,12],fifth:1,fig:[0,1,2,5,10],fig_id:[1,3,5,7],figaxi:15,figsiz:[1,2,3,5,6,7,8],figur:[0,1,2,3,5,6,7,8,10,12],figure_id:[1,3,5,7],figurefil:[1,3,5,7],file:[1,2,3,5,6,7,13,15],filenam:[1,15],filenotfounderror:5,fileout:15,fill:[4,7],financ:1,find:[0,1,2,3,4,5,6,7,8,9,10,12,15],fine:[0,1],finit:[3,4,10,15],first:[0,1,2,3,4,6,7,8,9,13,15,17],firsteigvector:9,fit:[2,3,4,5,6,7,9,10,15],fit_intercept:3,fit_mod:7,fit_transform:[1,3,6,7,9],fiti:1,five:[1,7],fix:[1,3,5,8,9,10],flag:0,flat:[5,10],flatten:2,flexibl:[1,2,3,6,8,10],float32:7,float64:[1,13],flop:[4,13],flow:[2,10],fly:9,flyvbjerg:15,fmesh:5,focu:[1,3,12,17],focus:[2,5,13],fold:[3,7],folder:1,follow:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16,17],font:[1,5,15],fontdict:15,fontsiz:[2,6,7,8,15],fontweight:2,foral:6,forc:[1,4,8,9],forecast:10,forest:[1,2,7,12,14],forget:9,form:[1,3,4,5,6,7,9,10,12,13,15],formal:[0,15],format:[1,2,3,5,6,7,8,9,12,15,17],formatstrformatt:5,formul:[0,9],formula:[5,15],forth:10,fortran2003:12,fortran90:15,fortran:[1,12,13],fortun:[1,9],forward:[1,3,12,13,14],found:[1,2,3,5,10],foundat:12,four:[6,10,13,14],fourier:1,fourth:10,frac:[0,1,2,3,4,5,6,7,8,9,10,13,15],fraction:7,frame:5,framework:[2,6,8,15],frank:[4,9],frankefunct:[1,4,9],free:[1,9,12,13,15,16,17],freecodecamp:12,freedom:4,freeli:1,frequenc:[3,5,15],frequent:[1,5,6,7],frequentist:12,fresh:8,fret:0,fridai:14,friedman:[14,17],frodo:[],from:[0,1,2,3,4,5,6,7,9,12,13,14,15,16,17],from_cod:7,front:1,fruit:0,fstream:15,fulfil:[4,10],full:[1,2,4,5,7,8,15],fulli:[3,10,14,15],fun:12,func:3,functionali:9,fundament:[1,3,12],further:7,furthermor:[1,3,4,5,9,10,12],futur:[1,6,7],futurewarn:1,fys:16,g_1:8,g_2:8,g_m:8,gain:[0,2,4,7,8],galleri:1,gamge:[],gamma1:6,gamma2:6,gamma:[1,5,6,7,8,9],gamma_0:8,gamma_1:8,gamma_1x:8,gamma_:1,gamma_i:[1,6,15],gamma_j:5,gamma_k:5,gamma_m:8,gamma_x:1,gap:6,gate:10,gather:[2,10],gaug:10,gaussbacksub:13,gaussian:[0,3,6],gaussian_point:0,gaussian_rbf:6,gave:5,gbc:14,gca:[3,5,6],gd_clf:8,gdclassiffiercgain:8,gdclassiffierconfus:8,gdclassiffierroc:8,gdregress:8,gen:15,gender:1,gener:[0,1,2,3,4,5,6,8,9,10,13,17],generallay:10,generate_simple_clustering_dataset:0,genom:12,geodes:9,geometr:1,georg:17,geq:[4,5,6,7],geron:[1,14,17],get:[0,1,2,3,5,7,8,9,12,13,15],get_distances_to_clust:0,get_dummi:7,get_split:7,get_yaxi:6,getattr:3,gibb:12,gini:8,gini_index:7,git:[1,12],github:[1,12,14,17],gitlab:[1,12],give:[0,1,2,4,5,6,7,8,10,12,15,17],given:[0,1,2,3,4,5,6,7,8,9,10,13,15],glare:0,global:[5,15],glorot:2,gmail:16,goal:[1,5,7],goe:[0,1,2,3,4,5,13],going:[1,2,3,4,5,6,7,9,10],golden:5,gone:4,gong:2,good:[0,2,5,7,8,9,12,15,17],goodfellow:[14,17],googl:[0,2,12],got:2,gpu:2,grade:14,gradient:[1,6,7,10,12,14],gradientboostingclassifi:8,gradientboostingregressor:8,gradual:[0,2],graph:[2,5,7,9,10],graph_from_dot_data:7,graphic:[1,2,7],grasp:1,gray_r:2,great:5,greater:[2,5,15],greedi:7,green:[1,7,15],grid:[2,3,5,6,10,15],grossli:5,ground:1,group:[0,1,3,5,7,12,14],groupbi:1,grow:[2,7,8],growth:1,guarante:[1,5,15],guess:[0,2,5,8],guestrin:8,guid:2,h_1:5,h_2:5,h_m:8,had:[1,2,3,4,5],hadamard:[2,10],half:[2,6,7],halv:8,hand:[1,2,5,9,10,12,13,14,15,17],handl:[1,2,7,9,12],handle_unknown:7,handsid:10,handwrit:10,handwritten:[2,5],happen:[0,2,4,5,8,15],hard:[2,5,6,8],hardcopi:12,harder:[1,2],has:[0,1,2,3,4,5,6,7,8,9,10,13,15],hasn:[1,2],hassl:[1,12],hast:12,hasti:[0,1,14,17],hat:[2,4,5,7,8,9,10,13,15],have:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],haven:2,hdf5:[],head:[1,8,15],header:1,heads_proba:8,hear:1,heart:[1,5],heatmap:[1,2,5],heavili:1,heavisid:2,height:2,help:[0,1,2,10],helper:0,henc:[1,3,4,5,6,7,8,10],her:5,here:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,17],hereaft:[1,6,10],hermitian:13,hessenberg:13,heterogen:[7,8],hidden:[2,10],hidden_bia:2,hidden_bias_gradi:2,hidden_layer_s:[1,2],hidden_weight:2,hidden_weights_gradi:2,hierarch:0,high:[0,1,2,3,4,5,7,8,9,12,13],higher:[1,2,3,5,6],highest:2,highli:[1,4,8,12,13,15,17],highwai:1,hing:6,hint:5,hip:12,hire:1,his:5,hist:[3,5,15],histogram:[1,3,5,15],histor:[5,9],histori:10,histplot:1,histtyp:3,hjorth:16,hobbi:15,hoc:4,hoff:17,hold:[0,2,3,5,15],holder:1,home:1,homogen:[2,7,8],hopefulli:[1,9,15],horizont:9,hors:5,hot:[2,7],hour:[2,12,14,15,16],how:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,17],howev:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],hspace:[1,6,8,15],hstack:2,htf:14,html:[9,12,14,17],http:[5,9,12,13,14,17],huang:1,huber:1,huge:[2,12],human:[1,2,7,10],humid:7,hundr:2,hungri:2,hybrid:14,hydrogen:1,hyperbol:[2,10],hyperparam:6,hyperparamet:[0,1,4,7],hyperplan:9,i_1:3,i_2:3,ian:17,idea:[1,2,3,5,7,8,10,13,15],ideal:[0,1,3,6,15],idem:3,ident:[3,4,10,13],identifi:[0,1,2,5,6,7,9,10],idum:15,ieor:15,ifi:17,ifs:12,ignor:[1,2,7],iii:13,ijca2016907841:0,illustr:[0,5,8,10,12],imag:[2,7,9,10,17],image_path:[1,3,5,7],imagin:2,immedi:[1,12],implement:[0,1,5,6,7,8,9,10],impli:[3,4,5,13],implicitli:[9,15],impos:[1,9,10],imposs:[1,4],impress:[1,10],improv:[0,4,7,8,9],impur:7,imshow:2,in3050:17,in4080:17,in4300:17,in5400:17,inaccur:5,inact:10,inadequ:1,includ:[1,2,5,9,10,12,15,16,17],include_bia:[3,7],incom:10,inconsist:1,incorrect:2,incoveni:6,increas:[1,2,3,6,7,9,10,15],increasingli:15,ind:3,inde:[1,4],indent:15,indentationerror:15,independ:[1,3,4,5,6,10,15],index:[0,1,2,8,12,15,17],index_col:1,indic:[1,2,4,7,8,9],indispens:3,individu:[2,5,8,10,15],indu:1,indx:13,ineffici:0,inequ:6,inequaltii:5,inf1000:12,inf1100:12,inf1100l:12,inf1110:12,inf3000:17,inf4490:17,inf5860:17,inf:1,infeas:7,infer:[1,2,3,17],inferenc:2,infil:[1,3,5,7],infin:[3,4,5,9],infinitesim:15,influenc:[3,8],influenti:2,info:[],inform:[0,1,2,3,5,7,9,10,13,17],infti:[5,15],ingeni:5,ingredi:[1,7],inher:3,inherit:13,initi:[0,1,2,3,5,8,13,15],initialis:15,inject:0,inlin:[0,1,2,3,5,6,7,8,9,10,13,15],inner:[3,5],innov:17,input:[0,1,2,3,4,5,6,7,8,10,13,15],input_dim:2,inputs:2,inputs_shuffl:2,insert:[6,8,15],insid:[1,5],insight:[1,2,4,12,17],insist:5,inspir:[1,2,10,17],instal:[1,2,7,14],instanc:[1,2,3,5,7,9],instanti:8,instead:[0,1,2,3,4,5,6,7,9,13,15],institut:2,instruct:[1,2],int32:8,int64:1,int_0:15,int_:15,int_a:15,integ:[0,2,13,15],integer_vector:2,integr:15,intellig:[0,1,17],intend:8,intens:2,intention:0,interact:[1,7,10,12],intercept:[1,5,6,9],intercept_:[1,5,6,7],interchang:[10,13],interconnect:2,interest:[1,2,3,4,5,6,7,10,12,15],interfac:[2,13],interior:[1,7],intermedi:13,intern:[2,8,10],interpol:[2,10],interpr:4,interpret:[1,2,7,8,10,13,14,15],interv:[1,3,5,15],intial:5,intimid:[],intract:1,intrins:[9,13,15],intro:[12,17],introduc:[1,2,5,6,8,10,13,15],introduct:[2,5,14,17],introductori:[1,13,17],intuit:[1,3,6,10],inv:[1,4,5],invalid:[1,2,6,13],invalu:[1,5,12],invari:2,invd:4,inver:6,invers:[1,4,5],invers_period:15,inverse_transform:6,invert:[1,4,5,8],invok:[1,6],involv:[1,3,5,9,10],iomanip:15,ios:15,iostream:15,ipca:9,ipynb:12,ipython:[0,1,2,3,5,6,7,8,9,12,13,15],irani:0,iri:[6,7],irreduc:3,irrelev:4,irrespect:1,isnul:1,isomap:9,issu:[2,7,13],it_arrai:5,item:1,items:13,iter:[0,1,2,3,6,9,15],its:[0,1,2,3,4,5,6,7,8,9,10,12,13,17],itself:[3,10,15],jackknavg:15,jackknif:[3,12],jackknstd:15,jackknvar:15,jackknvec:15,jacobian:5,jargon:15,jensen:16,jerom:17,job:[6,8],join:[1,3,5,7],journal:15,judg:5,julia:[12,13],jump:[0,15],jupyt:[0,1,12,17],just:[0,1,2,3,4,5,6,7,8,9,10,15],justif:1,justifi:8,k_mean:0,kappa_d:15,karim:16,karlsen:16,karush:6,keep:[0,1,2,4,5,9,13],keepdim:[2,3,8],kei:[1,2,10,17],kept:0,kera:[1,12,14],kernel:[1,2,12],kernel_regular:2,kernelpca:9,kev:1,kevin:17,keyword:13,kfold:3,kick:[2,5],kind:[0,1,6,10],kjm:12,kkt:6,kmeanspoint:0,kn_k:0,know:[0,1,2,4,5,6,12,15],knowledg:[1,12],known:[2,3,5,6,7,10,13,15,17],kondev:1,kpca:9,kroneck:0,kuhn:6,kwarg:3,kwown:1,l1_l2:2,l_1:5,l_2:5,l_j:10,la_i:10,la_k:10,lab:[12,14],label:[0,1,2,3,4,5,6,7,8,10,12,13,15],labelencod:[5,8],labels:[6,7],labels_shuffl:2,labor:0,laboratori:14,lack:[0,1],lagrang:[6,9],lambda:[1,2,3,4,5,6,8,10,15],lambda_0:9,lambda_1:[4,6,9],lambda_2:[6,9],lambda_:9,lambda_i:[6,9],lambda_iy_i:6,lambda_jy_iy_j:6,lambda_k:6,lambda_n:[4,6],lamda:2,land:[1,6],landmark:6,landscap:5,langl:[1,9,15],languag:[1,2,6,12,13,17],lapack:13,laptop:12,larg:[1,2,3,4,5,6,7,8,9,12,13,15,17],larger:[1,3,4,5,6,8,9,15],largest:[6,9],lasso:[1,3,5,12,14],last:[0,1,2,3,4,5,6,7,8,10,13,15],later:[0,1,2,4,5,6,10,12,15],latex:[],latter:[1,5,6,9,13,15],lattic:10,law:1,layer:1,lbfg:[5,7,8,9],lbl:3,lcc:3,lda:9,ldot:[1,3,9,15],lead:[1,2,3,4,5,6,7,8,9,10,13,15],leaf:7,leaki:2,lear:5,learn:[3,6,7,8,10,13,14,17],learner:8,learning_r:[6,8],learning_rate_init:[1,2],learning_schedul:5,least:[0,1,3,4,5,6,8,9,12,13,15],leav:[1,2,3,7,9],lectur:[1,2,4,5,8,9,10,12,13,14,17],lecturenot:[12,17],left:[1,2,3,4,5,6,7,8,9,10,13,15],leftarrow:[6,10],legend:[1,3,5,6,7,8],len:[0,1,2,3,4,6,7,8,9,10,13,15],length:[1,2,5,6,7,12,15],leq:[0,1,4,5,6,15],less:[1,2,3,4,6,7,15],lessen:2,let:[0,1,2,3,4,5,6,7,8,9,10,13,15],letter:[1,13,15],level:[1,2,3,7,12,13,14],lib:[1,2,3,6,9],liblinear:[6,8],librari:[1,2,7,8,9,13,15,17],licens:[1,2,12],lie:[3,9,15],lies:[6,9],life:[1,2,6,10],lift:0,light:[],like:[0,1,2,3,4,5,7,8,9,10,12,13,15],likelihood:[1,2,7],lim_:15,limit:[1,3,6,9,10,13],lin_clf:6,lin_model:1,lin_reg:7,linalg:[1,4,5,6,9,13,15],line1:6,line2:6,line3:6,line:[1,2,3,5,6,9,13,15],linear:[2,3,4,5,7,8,9,10,12,14,15],linear_model:[1,3,5,6,7,8,9],linear_regress:3,linearli:4,linearloc:5,linearregress:[1,3,5,7],linearsvc:6,liner:2,linerar:8,linewidth:[1,3,6,7,8],link:[1,5,7,10,12],linpack:13,linreg:1,linspac:[1,3,6,7,8,13,15],linu:16,linuek:16,linux:[1,2,12],liquid:1,list:[0,1,2,7,12],listedcolormap:[7,8],literatur:[0,2,5,17],littl:[2,7,10],live:6,lle:1,lloyd:0,lmb:3,lmbd:[1,2],lmbd_val:[1,2],lmbda:5,load:[1,2,5,7,8],load_boston:1,load_breast_canc:[2,5,7,8,9],load_digit:2,load_iri:[6,7],loc:[1,3,5,6,7,8],local:[1,2,5,10],locat:6,log10:3,log:[1,2,3,5,7,8,9,13],log_:1,log_clf:8,logarithm:[1,5,13],logic:[1,2,7],logist:[1,2,6,7,8,9,10,12,14],logisticregress:[5,7,8,9],logit:5,logreg:[5,7,8,9],logspac:[1,2,3],longer:[0,6,8,13,15],longest:0,loocv:3,look:[0,1,2,3,4,5,6,7,8,9,13,15],loop:[0,2,3,8,10,12,13,15],lose:2,loss:[1,2,3,4,6,8,9,13],lot:[0,1,2,3],low:[1,3,7,8,9,15],lower:[1,2,7,8,13],lowercas:13,lowest:[5,7,15],lstat:1,lstsq:1,lubksb:13,ludcmp:13,lux:13,lvert:2,m_1:0,m_h:1,m_k:0,m_l:10,m_n:1,m_p:1,machin:[2,3,7,8,9,10,13,14,17],machinelearn:[12,14,17],mackai:17,made:[1,2,3,4,5,7,9,10],mae:1,magic:0,magnitud:[2,5],mai:[1,2,3,4,5,6,7,9,10,12,13,15],mail:14,main:[1,2,4,5,7,13,17],mainli:[1,3,5,7],maintain:3,major:[2,3,5,7,8,13],make:[0,2,3,4,5,6,9,10,12,13,15,17],make_moon:[6,7,8],make_pipelin:[1,3,8],makedir:[1,3,5,7],makeplot:1,malcondit:13,malign:[2,5,7],manag:[1,12],manhattan:0,mani:[0,1,2,3,5,6,7,9,11,12,13,15,17],manifold:9,manual:0,map:[0,1,2,3,5,6,9,10,15],margin:[1,6],marit:1,mark:[],marker:[1,5,13],markov:12,marsaglia:15,mask:15,mass:[1,2,4],massag:1,masses2016:1,masses2016ol:1,masses2016tre:1,masseval2016:1,master:14,mat1100:12,mat1110:12,mat1120:12,mat3155:14,mat4155:14,mat:12,match:[0,2,5],materi:[4,5,13],math:[0,4,5,10,13,15,17],mathbb:[0,1,3,4,5,6,9,10,13,15],mathbf:[1,3,4,5,6,13,15],mathcal:[2,3,5],mathemat:[0,1,4,5,9,10,12,13,15,17],mathemati:[],mathemt:4,mathrm:[0,1,2,3,4,5,6,7,8,9,10,15],matmul:[2,4],matnat:[16,17],matplotlib:[0,1,2,3,5,6,7,8,9,10,12,13,15],matric:[1,2,4,5,6,9,12,13],matrix:[1,3,4,6,8],matshow:2,matter:5,max:[1,2,5,7,8,10],max_depth:[1,7,8],max_it:[1,2,5,6,9],max_iter:0,max_leaf_nod:8,max_sampl:8,maxdegre:[1,3,8],maxdepth:8,maxim:[2,5,6,9],maximum:[0,1,2,5,6,7,8],maxpolydegre:3,mbox:[3,4],mcculloch:10,mcint:15,mcintsqr2:15,mean:[1,2,3,4,5,7,8,9,10,12,13,14],mean_absolute_error:1,mean_divisor:0,mean_i:15,mean_matrix:0,mean_squared_error:[1,3,5,8],mean_squared_log_error:1,mean_vector:0,mean_x:15,meaning:[1,5],meansquarederror:1,meant:[5,8],meantempvec:15,meanvec:15,measur:[0,1,2,3,7,9,10,15],mechan:[1,15],median:1,medicin:10,medium:6,medv:1,meet:[1,16],mehta:[1,4],memori:[9,10,13],mention:[0,1,5,10,15],mere:1,mersienn:15,meshgrid:[1,4,6,7,8,9],met:[1,6],meteorolog:7,method:[0,1,2,4,6,9,10,12,13,14,17],metric:[0,1,2,3,5,7,8],metropoli:12,mev:[1,15],mglearn:12,mgrid:5,mhjensen:[1,2,6,9],microsoft:17,mid:2,midpoint:7,might:[1,2,5,7],mild:7,miller:15,million:1,mimic:10,min:[1,4,6,7],min_:[0,1,4],min_samples_leaf:7,mind:[0,1,5],mine:12,mini:[2,5,9,10],minibatch:[2,5,9],minibathc:5,minim:[0,1,2,3,4,5,6,7,8,9,10,15],minima:[1,2,5],minimum:[1,2,3,5,6,7,9],minkowski:0,minmaxscal:1,minor:15,minst:2,minu:5,mirror:7,misclassif:[6,7,8],misclassifi:[6,8],mismatch:2,miss:[1,8],mit:17,mix:2,mkdir:[1,3,5,7],mlab:[3,15],mle:5,mlp:2,mlpclassifi:2,mlpregressor:1,mnist:[2,9],mod:15,mode:[14,15],model:[0,3,4,5,6,7,8,9,12,15,17],model_select:[1,2,3,5,7,8,9],moder:8,modern:[1,3,5,12],modif:10,modifi:[1,2,4,5,6,8,10],modul:[1,3,5,7,8,9,13],modular:15,modulenotfounderror:7,modulo:15,moe:[4,9],moment:3,monoton:[10,15],mont:[1,3,12,15,17],montecarlocycl:15,more:[1,2,3,4,6,7,8,9,10,12,14,15],moreov:1,morten:16,most:[0,1,2,3,4,5,6,7,8,9,10,12,15],mostli:[2,9],motion:1,motiv:2,move:[0,1,5,7,10,15],mpl:[1,5],mpl_toolkit:5,mplot3d:5,mplregressor:2,mse:[1,3,4,7,8],mse_simpletre:8,msg:1,msle:1,mt19937_64:15,mu0:15,mu1:15,mu2:15,mu_:15,mu_n:9,mu_x:15,much:[1,2,3,5,6,7,8,9,10,13,15],multi:[1,2,5,12],multiclass:[2,5],multidimension:[9,10],multilay:2,multinomi:5,multipl:[3,5,10,15],multipli:[4,5,9,13,15],multiplum:6,multitud:[],multivari:[1,8,9,12,15],multivariate_norm:[0,9],murphi:[9,17],must:[0,2,3,5,6,8,10,15],mutat:5,mutual:[2,3,5],mx_:15,myriad:[1,12],mz1:15,mz2:15,n_0:[10,15],n_b:15,n_boostrap:[3,8],n_bootstrap:3,n_categori:2,n_cluster:0,n_compon:9,n_epoch:5,n_estim:8,n_featur:2,n_hidden_neuron:[1,2],n_i:15,n_input:2,n_instanc:7,n_iter_i:9,n_job:8,n_k:0,n_l:[10,15],n_layer:2,n_m:7,n_neuron:2,n_neurons_layer1:2,n_neurons_layer2:2,n_point:0,n_sampl:[0,1,3,6,7,8],n_split:3,nabla:[2,5],nabla_:5,naimi:1,naiv:[0,5],nall:1,name:[0,1,2,3,5,6,7,8,10,12,13,16],nameerror:8,namespac:15,nation:2,nativ:12,natur:[1,2,5,6,7,10,15,17],navier:10,nb_:13,nbconvert:[],nboot:15,nearest:[2,9],nearli:5,neat:[],neccesari:3,necessari:[0,1,2,6],necessarili:[1,9,15],neck:5,need:[0,1,2,3,4,5,6,7,8,9,10,13,15],neg:[1,2,3,5,8,15],neg_mean_squared_error:3,neglect:15,neglig:15,neighbor:9,neq:[0,5,15],nervou:10,nest:[7,10],net:10,netlib:13,network:[1,7,12,14,17],neural:[1,5,12,14,17],neural_network:[1,2],neuralnetwork:2,neuron:[2,10],neutral:1,neutron:1,never:[2,3,7,15],new_hobbit:[],new_sig:3,newaxi:[1,3,7],newli:1,newton:[2,6,15],next:[0,1,2,5,6,7,15],next_guess:5,nian:16,nice:[0,1,2,9],nichola:16,nicholaskarlsen1102:16,niter:5,nitric:1,nlambda:3,nm_n:1,nmse:3,nn_model:2,node:[2,7,8,10],nois:[1,3,5,6,7,8],noisi:[2,3],non:[0,1,2,3,4,5,7,8,9,10,13,15],none:[0,1,2,3,5,7,8,15],nonetheless:0,nonlinear:[3,6,7,9,10],nonneg:[3,5,7],nonparametr:3,nonsens:15,nonsingular:13,nonumb:[5,6,13],nor:2,norm:[1,2,3,4,5,6,9],normal:[3,4,5,6,7,8,9,10,12,13],normali:13,normpdf:3,notat:[0,1,3,15],note:[0,1,2,3,4,5,6,9,10,12,13,14,15,17],notebook:[0,1,2,7,12],noth:[0,2,4,6,10,15],notic:[10,13,15],novel:[3,8],novemb:2,now:[0,1,3,4,5,6,8,9,10,13,15],nowadai:[1,2,7,12],nox:1,np_assign_points_to_clust:0,np_get_distances_to_clust:0,np_k_mean:0,nsampl:3,nspin:15,nthi:1,nuclear:4,nuclei:[1,15],nucleon:1,nucleu:1,num_tre:8,number:[0,2,3,4,5,6,7,8,9,10,13,14,16],numberid:5,numer:[1,3,4,5,7,8,9,10,12,13,17],numpi:[0,1,2,3,4,5,6,7,8,9,10,12,15],obei:[5,9],object:[1,2,3,6,8,13],observ:[0,2,3,4,5,6,7,8,9,10],obtain:[0,1,2,3,4,5,6,7,8,10,13,15],obviou:[4,9,15],obviouli:1,obvious:[1,13],occupi:1,occur:[1,6,7,15],odd:[1,5],off:[2,3,7,15],offend:[],offer:[3,9,12,13,14],offic:16,offici:14,ofil:15,ofstream:15,often:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],ofter:13,old:[2,5,8],omit:1,onc:[0,2,3,7,9,15],one:[0,1,2,3,4,6,7,8,9,12,13,15],onehot:2,onehot_vector:2,onehotencod:7,ones:[1,4,5,6,7,8,9,13],onli:[0,1,2,3,4,5,6,7,8,9,10,13,15],onlin:[9,14],onto:9,open:[1,2,3,5,7,12,14,15],oper:[1,2,3,4,8,9,10,12,13,15],operation:15,opinion:0,oplu:15,opmiz:5,opportun:1,opposit:[2,6],opt:[1,2,3,6,9],optim:[0,1,3,4,7,8,9,14],optimis:2,option:[1,2,6,9],optmiz:[2,6],orang:1,order:[1,2,3,4,5,6,7,8,9,10,13,15],ordinari:[1,3,4,5,9,12,14],oreilli:17,org:[9,12,13,17],organ:[0,3,5,8,13],orient:[2,3,15],origin:[1,3,6,9,10,13,15],orthogn:4,orthogon:[1,4,5,6,9,13],orthonorm:4,oscar:2,oslo:[1,14,16],osx:[1,12],other:[0,1,2,3,4,5,6,8,12,13,14,17],otherwis:[1,2,5],ouput:[5,10],our:[2,3,4,6,7,8,10,12,13],ourselv:[0,1,4,5,6,9],out:[0,1,2,3,5,6,7,8,9,10,12,13,15],out_fil:7,outcom:[1,5,7,8,10,15],outdoor:7,outer:10,outfilenam:15,outlier:[1,6],outlin:[3,8,9],outlook:7,outperform:8,output:[1,2,5,6,7,8,10,13],output_bia:2,output_bias_gradi:2,output_weight:2,output_weights_gradi:2,outputlayer1:10,outputlayer2:10,over:[0,1,2,3,5,7,8,10,15],overal:[2,8],overcast:7,overcom:10,overdetermin:1,overfit:[2,3,7,8],overflow:2,overhead:10,overlap:[5,6,7],overlin:[0,3,4,7,8,9,13],overst:1,overview:[0,17],own:[5,6,10,12,13],owner:1,oxid:1,oyvinssc:16,p_i:15,p_j:15,p_n:15,p_x:15,pack:1,packag:[1,2,3,4,5,6,9,12,15],page:[1,12],pai:[2,7],painless:[],pair:[1,7,12,15],panda:[1,3,4,5,7,9,12],panel:[],paper:2,paradigm:1,parallel:[8,13],paramet:[1,2,3,4,5,6,7,8,10,15],parameter:[1,8],parametr:[1,3],park:15,part:[0,1,2,3,4,8,13,14,15,17],partial:[1,2,4,5,6,8,9,10,15],particip:[12,14],particl:[1,15],particular:[1,2,3,4,5,7,8,9,10,15,17],particularli:[3,4,5,6,9,15],partit:[2,7],pass:[0,1,10,15],past:[8,15],patch:[3,15],path:[1,3,5,7,12],patient:5,pattern:[1,10,14,17],pauli:1,pca:[1,5,12,14],pdf:[1,3,7,17],pedagog:1,penalti:[3,5],pentagon:5,peopl:[1,2,7,12],per:[1,2,3,14],percentag:[1,8,9],perceptron:[1,2,5],peregrin:[],perfect:[1,2],perfectli:3,perform:[0,1,3,5,6,8,9,10,12,13,15],perhap:[1,4,5],perimet:2,period:2,permut:9,person:[5,14,16],perspect:17,pertin:10,petal:[6,7],peter:17,petersen:15,phantom:15,phase:10,phatak:0,phenomena:15,phi:6,phi_k:6,philip:16,philosophi:5,phone:16,phrase:1,physic:[1,2,5,10,15,16,17],pick:[0,2,5,7,8,9,15],pickl:2,pictur:1,pie:12,piec:[0,9],pillow:[1,12],pip3:[1,2],pip:[1,2,12],pipelin:[1,3,6,8],pippin:[],pise:0,pitt:10,pixel:2,pixel_height:2,pixel_width:2,place:[1,3,5,6,13],plai:[1,3,6,9,12],plain:[5,6,8,10],plan:[3,7,16,17],plane:[6,7],plateau:15,platform:12,plausibl:10,pleas:[1,9],plenti:2,plethora:10,plot:[0,1,2,3,5,6,7,8,9,10,12,13,15],plot_confusion_matrix:[5,8],plot_cumulative_gain:[5,8],plot_data:2,plot_dataset:6,plot_decision_boundari:[7,8],plot_import:8,plot_predict:6,plot_regression_predict:7,plot_roc:[5,8],plot_surfac:5,plot_train:7,plot_tre:[7,8],plt:[0,1,2,3,5,6,7,8,9,10,13,15],plu:[1,5],png:[1,3,5,7],point:[0,1,2,3,5,6,7,8,9,13,15,16],points_in_clust:0,poisson:12,poli:[3,6],poly100_kernel_svm_clf:6,poly3:1,poly3_plot:1,poly_featur:[6,7],poly_features10:7,poly_fit10:7,poly_fit:7,poly_kernel_svm_clf:6,polydegre:[3,8],polygon:5,polym:10,polynomi:[1,3,4,5,6,7,8,9],polynomial_featur:3,polynomial_svm_clf:6,polynomialfeatur:[1,3,6,7],polytrop:[1,3],poor:[2,5],popul:1,popular:[1,2,3,5,6,7,9,10,12,13,15],popularli:1,portabl:8,portion:9,pose:[1,9,15],posit:[0,1,2,4,5,6,8,9,13,15],possibl:[1,2,3,5,6,7,8,9,10,12,13,15,16],postpon:1,potenti:[1,10],pott:10,power:[1,2,3,4,6,7,10],practic:[1,3,5,6,15],practition:[1,2],preced:[2,9,10,15],preceq:6,precis:[1,4,9,13,15],pred:3,predict:[1,2,3,5,6,7,8,12,17],predict_prob:2,predict_proba:[5,8],predictor:[1,4,5,7,8,9],prefer:[1,2,6,7,9,12],prepar:1,preprocess:[3,5,6,7,8,9],prerequisit:1,present:[1,7,10,15],preserv:9,press:[5,17],pretrain:2,pretti:[1,6,7,12],prev_centroid:0,prevent:15,previou:[1,2,4,5,6,8,9,10,13,15],previous:[0,7,8,15],price:[1,7],primal:6,primari:[1,5],primarili:0,prime:15,princip:[1,5,12,14],principl:[0,1,3,5,6],print:[0,1,2,3,4,5,6,7,8,9,13,15],print_funct:[6,7],printout:1,prior:[1,3],privat:1,prob:[2,15],probabilist:[1,17],probabl:[1,2,3,5,8,12],problem:[1,3,4,6,7,8,9,10,12,13,14,15],proce:[1,5,6,7,8,9,13],procedur:[3,4,5,6,8,9],proceed:13,process:[0,1,3,5,7,8,10,12,13,15,17],prod:17,prod_:[2,5],produc:[0,1,4,7,8,9,10,12,13,15],product:[1,2,3,5,6,10,12,13],profess:1,profil:0,program:[0,1,2,4,6,10,12,13,14,15],programm:13,progress:[0,2],progression_plot:0,prohibit:3,project:[1,2,5,9,12,14,15,16],project_root_dir:[1,3,5,7],promin:10,promis:6,prone:7,pronounc:12,proof:[1,5,9,10],prop:3,proper:[1,3],properli:[0,2,6,8],properti:[1,2,3,4,5,10,13],proport:[1,2,7,9,15],propos:[2,8],propto:5,proton:1,prove:5,provid:[1,2,3,4,5,6,7,8,10,12,13,15,17],proxi:2,prun:0,prune:7,pseudorandom:15,psycholog:1,ptratio:1,punish:[1,2],pure:[7,15],purest:7,puriti:7,purpos:[0,1,8,10],put:2,pycod:[],pydata:12,pydot:7,pyhton2:[],pylab:[1,5],pypi:12,pyplot:[0,1,2,3,5,6,7,8,9,10,13,15],pythagora:0,python2:1,python3:[1,2,3,6,9,12],python:[2,6,9,10,14],pytorch:[1,12],qquad:[9,13],quad:[2,5,13],quadrat:[1,5,6,7],qualit:[7,15],qualiti:[1,7,12],quantifi:2,quantil:8,quantit:[1,3,7],quantiti:[0,1,3,4,5,7,8,9,10,13,15],quantum:10,quartil:1,queri:7,question:[1,3,5,7,9,10],quick:15,quickli:[2,5,7,9],quirk:0,quit:[2,3,7,8,10],quot:15,r2_score:1,r2score:1,r_1:7,r_2:7,r_j:7,r_m:7,rad:1,radial:[1,6,10],radioact:15,radiu:[1,2],rain:7,rais:[1,3],ramp:2,ran1:15,ran2:15,ran3:15,rand:[1,3,5,7,8,13,15],rand_max:15,randint:[3,5,7],randn:[1,2,3,5,7,9],random:[0,1,2,3,4,5,6,7,12,13,14],random_devic:15,random_forest_model:8,random_index:5,random_indic:2,random_st:[1,5,6,7,8,9],randomforestclassifi:8,randomli:[0,2,3,5,7,15],randomnumbergener:15,rang:[0,1,2,3,4,5,7,8,9,10,13,15],rangl:[1,9,15],rangle_x:15,rank:4,raphson:[2,6],rapidli:1,rare:2,rate:[1,2,5,6,7,8,10],rather:[1,2,3,4,5,6,7,8,9,10,13,15],ratio:[5,7,8,9],rational:1,ravel:[1,3,4,5,6,7,8,9],raw:15,rbf:[6,9,10],rbf_kernel_svm_clf:6,rbf_pca:9,rcond:1,rcparam:[1,2,5,6,7,8,15],reach:[0,1,2,3,5,7,8,9,10,15],read:[0,1,3,5,6,9,10,13,14,15,17],read_csv:[1,3,5,7],read_fwf:1,readabl:0,reader:[1,13,15],readi:[0,1,2,6,8,9,10,13],readili:2,real:[1,2,3,4,5,8,9,10,13],realist:6,realiti:15,realiz:[2,10],realli:2,reason:[0,1,2,5,8,17],reassign:2,recal:[3,4,5,7,8,9,10,13,15],recalcul:15,receiv:[2,8,10,15],recent:[1,3,5,7,8],recept:10,recip:[1,5,13],recogn:[1,8],recognit:[1,2,10,14,17],recommend:[1,4,6,12,13,14,15,17],reconsid:7,reconstruct:9,record:[8,14],recreat:15,rectangl:[3,5,7],rectifi:[2,10],recur:[1,12],recurr:[2,12,14],recurs:[7,12,13],recycl:15,red:[1,3,6,7],redefin:[1,8],reduc:[2,4,5,7,8,9],reduct:[1,8,9,12,15],refer:[0,1,2,3,4,5,9,10,13,17],refin:10,refit:3,reflect:[1,2,15],refresh:[12,14],reg:[8,9],regard:[2,5,7],regardless:10,region:[7,10],regist:15,regr_1:[1,7],regr_2:[1,7],regr_3:[1,7],regress:[2,3,6,9,10,12,14],regressor:[1,5,8],regular:[1,5,7],reilli:[1,17],reinforc:[1,6,12],reiter:2,rel:[1,3,5,7,10,15],relat:[0,1,2,5,9,13,15],relationship:[1,7],relativeerror:1,releas:[2,12],relev:[1,2,4,5,9,12,15],reli:[1,6],reliabl:[5,15],remain:[2,3,10,13,15],remaind:15,remark:2,rememb:[1,6,13],remind:[1,4,9,13,15],remov:[1,4],render:1,reorder:5,reorgan:1,repeat:[0,1,2,3,5,7,8,9,13,15],repeated:1,repeatedli:[3,8,15],repetit:[3,14],rephras:5,replac:[0,1,2,3,5,8,10],replica:3,repositori:1,repres:[1,2,3,5,6,7,8,10,15],represent:[1,2,3,15],reproduc:[1,7,10,12,15],repuls:1,request:1,requir:[1,2,3,4,5,6,7,9,10,13],resampl:[1,5,8,12,14,15],rescal:[1,9,10],research:[1,12,17],resembl:[3,15],reserv:[2,3,15],reset:15,reshap:[0,1,2,3,6,7,8],residenti:1,residu:[1,5],respect:[0,1,2,3,4,5,6,8,9,10,15],respond:10,respons:[1,5,7,10],rest:[1,4],restat:[1,10],restrict:[1,7,10],result:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],ret:3,retail:1,retain:[3,4],return_data:0,return_x_i:7,reus:2,reveal:[1,10],revers:2,review:[12,13],revisit:0,reward:1,rewrit:[1,3,4,5,6,8,9,10,13,15],rewritten:[3,4,6,8,15],rewrot:5,rgoj5yh7evk:12,rho:[1,8],rho_1:8,rho_2:8,rho_m:8,rich:1,rid:0,ride:7,rideclass:7,ridedata:7,ridg:[1,3,9,12,14],right:[1,2,3,4,5,6,7,8,10,13,15],rightarrow:[1,2,4,5,6,9,10,15],rigor:1,rise:1,risk:[1,5],river:1,rmse:1,rmsprop:2,rnd_clf:8,rnn:10,rntrick1:15,rntrick2:15,rntrick3:15,rntrick4:15,robert:17,robust:1,robustscal:1,roc:8,role:[1,3,4,6,12],room:[1,16],root:[1,5,7,15],rot:[],rotat:[2,6,7,8],rotation_matrix:7,roughli:2,round:[1,5,7],routin:[5,13],row:[1,2,3,4,7,9,13],rrr:4,rug:5,rule:[1,2],run:[0,1,2,3,4,5,6,7,9,12],runtim:[0,2,3],runtimewarn:2,rust:[1,12,13],rvert:2,rvert_2:2,rwidth:3,s_i:5,saddl:5,safe:15,sai:[0,1,2,3,4,5,6,7,8,9,10,13,15],said:[3,5,7],sake:[1,4,5,9],sale:1,sam:[],same:[0,1,2,3,4,5,6,7,9,10,13,15],samm:8,sampl:[0,1,2,3,4,5,6,7,8,12,13],sample_vari:0,sampleexptvari:15,samwis:[],sanitize_sequ:3,sastri:9,satisfactori:1,satisfi:[2,3,5,6,13,15],satur:[2,3],save:[1,3,5,7],save_fig:[1,3,5,7,8],savefig:[1,3,5,7,15],scalabl:8,scalar:[3,8],scale:[1,2,4,5,6,7,8,9,10,12,16],scaler:[1,5,6,7,8,9],scan:5,scatter:[0,1,2,3,5,6,7],scenario:5,scheme:[2,5],schrage:15,scienc:[1,2,5,8,10,12,14,15,17],scientif:[1,12],scientist:[0,1],scikit:[6,7,8,12,13,14,17],scikitlearn:1,scikitplot:[5,8],scipi:[1,4,5,12,13],score:[1,2,3,5,7,8,9,16],scores_kfold:3,scratch:2,sdg:5,seaborn:[1,2,5],seamless:[1,12],search:[1,2,5,7],sec:3,second:[0,1,3,5,6,7,9,10,12,13,15],secondeigvector:9,secondli:10,section:[0,9,11,13,14,15],sector:1,see:[0,1,2,3,4,5,6,8,9,10,12,13,14,15],seed:[0,1,2,3,5,6,7,9,15],seek:[2,6],seem:2,seemingli:1,seen:[1,2,8,10,15],segment:5,seldomli:1,select:[2,3,4,6,7,8,9,14,17],self:[2,3,15,17],semest:[5,14],semi:[5,6],send:[10,16],senior:14,sens:[1,3,6],sensit:[1,3,7],sentenc:10,separ:[0,1,2,3,6,7,10,12,15],sequenc:[0,5,7,8,10,12,13,15],sequenti:[2,8,10,15],seri:[1,2,5,8,9,10,13,14],serif:[1,5,15],serv:[1,2,5,17],session:[2,14],set:[0,2,3,4,5,6,8,9,12,13,15],set_:3,set_label:3,set_tick:[2,6],set_ticklabel:2,set_titl:[0,1,2,5,10],set_xlabel:[1,2,5,10],set_xlim:[5,10],set_xticklabel:2,set_ylabel:[1,2,5],set_ylim:[5,10],set_ytick:5,set_yticklabel:2,setiosflag:15,setminu:3,setosa:[6,7],setosa_or_versicolor:6,setp:3,setprecis:15,setup:[2,6,12],setw:15,sever:[1,3,4,5,6,7,9,10,12,13,14,15],sgd:2,sgd_clf:6,sgdclassifi:6,sgdreg:5,sgdregressor:5,shape:[0,1,2,3,4,5,6,7,8,9,13],share:2,she:5,shift:[2,10,15],shire:[],shortcom:5,shorter:15,shorthand:[],shortli:13,should:[0,1,3,4,6,7,9,10,13],show:[0,1,2,3,4,5,6,7,8,9,10,13,15],shown:[5,6,9,10,15],showpoint:15,shrink:[4,6,9],shrinkag:4,shrunk:9,shuffl:[2,3],side:[1,5,6,10,13],sigh:12,sigma0:15,sigma1:15,sigma2:15,sigma:[1,2,3,4,5,8,9,10,13,15],sigma_1:4,sigma_2:4,sigma_:[4,13,15],sigma_fn:[5,10],sigma_i:[1,4],sigma_j:4,sigma_m:15,sigma_n:[9,15],sigma_x:15,sigmoid:[2,5,6,8,10],sigmundson:16,sign:[2,5,6,8,15],signal:[2,8,10],signific:2,significantli:[2,5,15],sim:[3,15],similar:[0,1,2,3,5,6,7,8,9,12,13],similarli:[1,2,4,6,8,15],simpl:[0,2,3,4,6,8,9,10,12,13],simplepredict:8,simpler:[0,1,2,12],simplest:[0,1,2,7,8,10],simpletre:8,simpli:[1,2,3,4,6,7,8,9,10,12,13,15],simplic:[0,4,5,6,7,8,9,10],simplifi:[1,3,7,12],simplist:15,simul:[3,15],simultan:3,sin:[1,2,7,10,13],sinc:[1,2,3,4,5,6,7,8,9,13,15,17],sine:10,singl:[2,5,6,7,10,13,15],singular:[1,3,5,13],site:[1,2,3,6,9,14],situat:[1,4,5],six:15,size:[1,2,3,5,6,7,8,9,13,15],sketch:8,ski:7,skill:1,skip:9,skl:1,sklearn:[1,2,3,5,6,7,8,9],skplt:[5,8],slack:6,slice:13,slide:[1,15],slight:3,slightli:[2,3,4,8,15],slope:[6,9,10],slow:[1,5,6],slower:[4,13],slowli:10,slp:2,small:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],smaller:[1,2,3,5,6,7,9,15],smallest:[0,1],smallest_row_index:0,smart:3,smooth:[1,5],sne:9,sneak:1,sns:[1,2,5],soar:3,social:1,soft:[2,5,8,10],soften:6,softmax:5,softwar:[1,6,12,13,14],sol:6,sole:1,solid:[1,5],solut:[1,2,3,4,5,6,8,9,13,15],solv:[1,2,4,6,8,9,10,13],solver:[5,6,7,8,9,13],some:[0,1,2,3,4,6,7,8,9,10,13,15],someth:[1,2,5,7,9,15],sometim:[0,1,2,9,10],soon:13,sophist:1,sopt:5,sort:[1,3,4,7,9,15],sourc:[0,1,2,3,12,13,15],space:[0,1,2,4,5,6,7,9,10,15],span:[1,4,7,9,13],spare:2,spars:13,sparse_mtx:13,sparsiti:8,spatial:[2,10],speak:15,special:[3,5,8,10,13,15],specif:[1,2,3,4,5,6,7,9,10,12,13,15],specifi:[0,1,3,5,7,9,15],specifici:[1,8],spectral:2,speech:[1,2,10],speed:2,spend:15,sphere:1,spite:1,spline:6,split:[0,2,3,6,7,8,9,15],splitter:[2,8],spontan:15,spread:[1,9,15],springer:17,sqrt:[1,3,4,5,6,8,9,15],squar:[0,1,2,3,4,5,6,7,9,12,13,15],squarederror:8,squaredeuclidean:0,squash:10,srand:15,stabl:[1,4,7,9,12],stack:3,stage:5,stai:[1,9],stand:[1,4,7,10],standard:[1,2,3,4,6,8,10,13],standardscal:[1,5,6,7,8,9],stanford:5,start:[0,1,2,3,5,6,7,8,9,10,13,15],start_tim:0,startpoint:15,stat:3,state:[0,2,4,5,6,8,9,10,12,15],statement:[1,5,13],statis:4,statist:[0,1,2,4,5,7,8,9,10,13,14,17],statu:[1,5,9],std:[3,15],stdev:15,steep:5,step:[0,1,2,7,8,9,10,13,15],step_fn:[5,10],step_length:5,steps_list:7,stian:16,still:[3,4,5,9,15],stimuli:10,stk2100:17,stk4021:17,stk4051:17,stk5000:17,stk:17,stochast:[1,2,3,6,9,10],stoke:10,stone:[1,5],stop:[0,2,7,9,15],storag:4,store:[1,2,5,9,15],str:[1,2],straight:[1,3,5,6],straightforward:[1,3,5,6,7,8,13],strategi:[1,2,7],stratifi:3,strength:[0,4],stretch:9,strict:[5,6],strictli:[5,6],string:[2,15],stroke:5,strong:[7,8,10,15],strongli:[1,6,12,13],stronli:1,structur:[0,1,2,3,7,8,10,12],stuck:[2,5],student:[1,14,16,17],studi:[0,1,5,6,9,10,12,17],studier:17,style:[1,5,7],sub:[7,10],subdivid:[1,13],subfield:1,subject:[6,15],subplot:[0,1,2,3,5,6,7,8],subplots_adjust:[6,15],subprogram:13,subroutin:1,subscript:2,subsequ:[2,3,10,13,15],subset:[2,3,5,7,10,12],subspac:[1,6,9],substanti:[7,8],substep:9,substitut:[3,10,13],subsubset:7,subtl:2,subtract:[3,4,9,13,15],subtre:7,succeed:1,success:[5,7,15],successfulli:7,sucess:15,sudo:[1,12],suffer:[1,2,4,8],suffici:[2,3,5,6,9],suggest:[2,5,17],suit:[6,10],suitabl:[1,15],sum:[0,1,2,3,4,5,6,7,8,9,10,15],sum_:[0,1,2,3,4,5,6,7,8,9,10,13,15],sum_i:[3,4,5,6],sum_k:[6,10,13],summar:[0,3,7],summari:[0,2,8,14],summat:4,sunni:7,superscript:[2,10],supervis:[1,3,5,7,10,12],supplement:5,support:[1,2,7,8,9,12],suppos:[1,3,4,5,6,8,9,10,13],sure:2,surfac:1,surpris:1,surround:12,survei:1,svc:[6,7,8],svd:[1,3,9],svdinv:4,svm:[6,7,8,9],svm_clf:[6,8],symbol:[2,9,12,15],symmeteri:2,symmetr:[1,4,5,6,9,10,13],sympi:[1,12],synonim:15,syntax:[2,13],syntaxerror:[2,6,13],sys:5,system:[1,2,5,7,8,10,12,13,17],systemat:[3,15],t_0:[5,7],t_1:5,t_b:8,t_i:[2,10],t_j:10,t_k:7,tabl:[7,15,16],tabul:1,tabular:[],tackl:0,tag:[0,4,5,10,13,15],taht:1,tail:15,tailor:[6,9],taiwan:1,take:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],taken:[1,2,3,8,13],tangent:[2,5,10],tanh:[2,5,6,10],target:[1,2,5,6,7,8,9,10],target_nam:7,task:[0,1,2,3,7,9,10],tau:15,tax:1,taylor:5,taylornr:5,team:2,teaser:1,technic:[0,1],techniqu:[1,2,6,8,12,15,17],technolog:[1,2],tek5040:17,tell:[3,5,8,9,15],temp1:2,temp2:2,temp:2,temperatur:[1,7],temporarili:2,ten:[],tend:[0,3,6,7,8,10],tendenc:1,tension:3,tensorflow:[0,1,6,12,13,14,17],term1:[1,4,9],term2:[1,4,9],term3:[1,4,9],term4:[1,4,9],term:[0,1,2,3,4,5,6,7,8,9,10,15],termin:[1,4,7,8],test:[3,4,5,6,7,8,15],test_accuraci:2,test_data:0,test_ind:3,test_pr:2,test_predict:2,test_scor:[5,8],test_siz:[1,2,3,8],test_split:7,testerror:3,text:[1,2,5,6,7,9,13,15,17],textual:7,textur:2,than:[1,2,3,4,7,8,9,10,12,15],thats:0,theano:[2,12],thei:[0,1,2,3,4,5,6,7,9,10,13,15],them:[1,2,5,6,7,8,9,10,13],theme:1,themselv:[1,15],thenc:3,theorem:[3,4,5],theoret:[1,8],theori:[1,2,5,6,7,10,12,17],thereaft:[1,3,4,9,10,13],therebi:[1,5,9,15],therefor:[1,2,3,5,6,9,15],therein:9,thereof:[1,3,5],theta:[2,3,5,15],theta_:2,theta_i:2,theta_k:15,theta_linreg:5,thi:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,17],thing:[0,1,2,5,7,15],think:[0,1,2,3,5,7,10,15],third:[1,5],thirti:5,thorough:0,those:[0,4,6,7,8,9,13,14],though:[2,13,15],thought:[0,3,15],thousand:[1,2],three:[1,2,3,6,7,10,13,14,16],threshold:[2,5,7,8,9,10],through:[0,1,2,4,5,6,9,10,12,13,15],throughout:[0,1,12,13,15],thu:[1,2,3,4,5,6,8,9,10,15,16],thumb:1,thursdai:14,tibshirani:[14,17],ticker:[5,15],tight_layout:[2,5],tightli:9,tild:[1,3,4,9,15],till:[1,5,6,7,8,10,13],time:[0,1,2,3,4,5,6,7,8,9,10,13,14,15],timefunct:15,tini:2,tip:11,titl:[1,2,3,5,6,7,8,15],to_categor:2,to_categorical_numpi:2,to_numer:[1,3],to_str:15,togeth:[1,6,9],toi:0,toler:0,tomographi:10,too:[1,3,4,5,7,9,15,17],took:6,tool:[0,1,2,3,12,15],toolbox:6,top:[1,3,7,8,12],topic:[0,1,5,6,12,17],topolog:[2,10],toss:8,total:[0,1,2,3,5,6,8,9,10,15,16],totalclustervari:0,totalscatt:0,totalvari:15,toward:[2,5,10],town:1,tpng:7,traceback:[1,3,5,7,8],track:[0,5,13],tract:1,tractabl:1,trade:[3,7],tradeoff:[1,4,14],tradit:[1,2,3],train:[3,5,6,7,8,9,10],train_accuraci:[1,2],train_end:2,train_ind:3,train_pr:2,train_siz:2,train_test_split:[1,2,3,5,7,8,9],train_test_split_numpi:2,trainingerror:3,trait:1,transfer:7,transform:[1,3,4,5,6,7,8,9,10,12,13],transit:10,translat:[2,8],transpos:[2,4,9],treat:[1,2,3,5,10,15],tree:[1,2,12,14],tree_clf:[7,8],tree_clf_:7,tree_clf_sr:7,tree_reg1:7,tree_reg2:7,tree_reg:7,trend:15,trevor:17,tri:7,trial:[1,3,5,15],triangl:5,triangular:13,trick:[6,9,15],trickier:15,tridiagon:13,trillion:12,trivial:[1,2,9,15],troubl:[1,6,10],true_divid:2,true_fun:3,tucker:6,tumor:[5,7],tumour:5,tunabl:2,tune:[7,13],turn:[1,2,3,4,5,6,7,8,9,10,13,15],tutori:2,tweak:[2,8,15],twice:5,twist:9,twister:15,two:[0,1,2,3,4,5,7,8,9,10,13,14,15,17],tx_1:5,type:[1,2,3,5,6,8,13,15],typeerror:1,typic:[1,2,4,5,7,8,10,15],u_i:10,u_m:8,ubuntu:[1,12],uci:1,uio:[16,17],unari:13,unbalanc:[3,7],unbias:[1,3,15],uncertainti:1,uncertitud:15,unchang:2,uncorrel:8,undefin:4,under:[1,2,3,5,8,12],underdetermin:1,underfit:[2,3],undergradu:14,underli:[1,2,5,7,15],underset:0,understand:[0,1,2,5,8,12],understood:[0,6],undesir:6,undetermin:6,unexpect:[3,15],unexpected:15,unfortun:[2,6,7,8],unicode_liter:[6,7],uniform:[1,2,4,5,9],uniform_real_distribut:15,uniformli:[5,15],unifrompdf:15,unimport:5,union:3,uniqu:[0,1,3,5,13],unique_cluster_label:0,unit:[1,2,8,10,15],unitari:[4,13],unitarili:13,uniti:15,univari:15,univers:[1,2,5,14,16],unix:2,unknow:[1,13],unknown:[1,2,3,6,8,13],unknowwn:10,unlabel:2,unless:[1,3,5,9],unlik:[2,5,6,15],unnecessarili:7,unravel:2,unrol:9,unseen:[5,7],unstabl:2,unsupervis:[1,2,10,12,14],unsymmetr:13,until:[0,2,5,7,10,15],unusu:10,updat:[0,2,3,8,10],upload:[12,17],upon:[2,9,13],upper:[1,6,7,13],uppercas:[13,15],ups:[],usag:[1,6,12,15],usd10000:1,usd:1,use:[0,1,3,4,5,6,7,8,9,10,12,13,14],usecol:1,used:[0,1,2,3,4,6,7,8,9,10,12,13,15,17],useful:[1,2,3,5,7,9,10,12,13,15,17],useless:2,user:[1,2,5,6,9,12,13],uses:[1,2,3,4,7,9,10,13,15],usetex:15,using:[0,2,3,4,6,7,8,9,10,13],usr:15,usual:[0,1,5,10],util:[0,1,2,3,5,8],v_0:9,valid:[1,2,5,7,8,12,14,15],valu:[0,1,2,3,5,6,7,8,10,12,13],valuat:7,valueerror:1,van:[1,4],vandenbergh:[5,6],vandermond:1,vanilla:9,vanish:[2,5,15],var_x:15,varabl:6,varepsilon:3,varepsilon_:3,varepsilon_i:3,vari:[1,2,3,8],variabl:[0,1,2,3,6,8,9,10,13],varianc:[0,1,2,4,5,7,8,9,12,13,14],variance_i:[4,9],variance_x:[4,9],variant:[1,2,3,5,6,10],variat:9,varieti:[1,10,12],variou:[2,4,5,6,7,9,10,12,13,15],vartempvec:15,varvec:15,vaue:2,vdot:5,vec:[3,15],vector:[0,1,2,3,4,5,7,8,9,12],vector_mean:0,ventur:[1,6,12],verbos:2,veri:[0,1,2,3,5,6,7,8,9,10,15,17],verifi:[9,13],versatil:6,versicolor:[6,7],version:[0,1,8,12,13,15],versu:2,vert:[1,2,4,5,6,7,9],vert_1:4,vert_2:[4,9],vertic:3,via:[1,3,4,5,6,7,8,9,10,12,13,14,15],vidal:9,video:[1,2,4,5,10,12,14],view:[2,10,15,17],violat:6,virginica:7,viridi:[1,2],virtual:2,vision:1,visual:[1,9,10,12],visualis:2,viz:[6,15],vmax:2,vmc:15,vmin:2,volum:1,vote:8,voting_clf:8,votingclassifi:8,votingsimpl:8,vstack:[4,9,13,15],w_1:[6,13],w_1x_1:6,w_1x_:6,w_2:[6,13],w_2x_2:6,w_2x_:6,w_3:13,w_4:13,w_i:[2,8],w_ix_i:10,w_j:13,w_m:13,w_px_:6,w_px_p:6,wai:[0,1,2,3,4,5,6,8,9,10,11,13,15],walk:7,walker:15,wang:1,want:[0,1,2,3,5,6,7,8,9,10,12,15],warn:[1,2,6],warrant:3,watch:12,wavelet:6,weak:[0,7,8],weather:[2,10],web:[12,14],websit:[13,14],wedg:[6,15],wednesdai:14,wee:9,week:5,weekli:[12,17],weight:[1,2,3,5,7,8,10,15],welcom:[6,12],well:[1,2,3,4,5,6,7,8,10,12,13,15,17],went:6,were:[0,1,2,3,4,5,6,8,9,10,15],wessel:[1,4],what:[0,2,3,4,5,6,7,8,9,10,12,13,14],when:[0,1,2,3,4,6,7,8,9,10,13,15],whenev:15,where:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,16],wherea:[3,15],wherein:[2,10],whether:[1,5,7,15],which:[0,1,3,4,5,6,7,8,9,10,12,13,14,16],whichev:2,white:7,who:[1,14],whole:[0,2,7,9],whose:[3,8,15],whow:[4,9],why:[1,2,5],wide:[1,2,3,5,10,12,13],widehat:3,width:[1,6,7],wieringen:[1,4],win:8,wind:7,wing:16,wiscons:5,wisconsin:8,wise:[1,2,4,10],wish:[0,1,5,6,9,13],within:[0,1,5,7,10,15,17],withinclust:0,without:[1,2,4,5,6,7,9,10],won:1,wonder:6,word:[0,1,2,15],work:[0,1,2,3,5,6,7,12,14,15],world:[1,6],worldwid:1,wors:[1,2,3],worth:7,would:[1,2,3,5,6,7,8,9,10,13,15],wrap:13,write:[1,2,5,6,10,11,13,14,15],written:[1,4,5,9,10,12,13,15],wrong:[2,6],wrongli:[8,15],wrote:[4,9],wrt:8,wth:8,www:[12,13,17],wx_1:6,x0s:6,x1_exampl:6,x1d:6,x1s:[6,7,8],x2d:[6,9],x2d_train:9,x2dsl:9,x2s:[6,7,8],x3s:6,x_0:[1,4,9,13],x_1:[1,3,4,5,6,7,8,9,13,15],x_2:[1,3,4,5,6,7,8,9,13,15],x_3:[6,13,15],x_4:13,x_center:9,x_data:2,x_data_ful:2,x_i:[0,1,2,3,4,5,6,7,8,9,10,13,15],x_ix_:1,x_iy_i:6,x_j:[6,7,10,15],x_jy_j:6,x_k:[0,10,13,15],x_l:15,x_m:[10,13,15],x_n:[1,3,5,6,9,10,13,15],x_new:[7,8],x_p:[5,7],x_poli:7,x_poly10:7,x_reduc:9,x_scale:6,x_test:[1,2,3,5,7,8,9],x_test_scal:[1,5,7,8,9],x_train:[1,2,3,5,7,8,9],x_train_scal:[1,5,7,8,9],x_val:2,xarrai:12,xavier:2,xbnew:5,xcode:[1,12],xdclassiffierconfus:8,xdclassiffierroc:8,xg_clf:8,xgb:8,xgbclassifi:8,xgboost:7,xgboot:8,xgbregressor:8,xgparam:8,xgtree:8,xi_1:6,xi_:6,xi_i:6,xlabel:[1,2,3,5,6,7,8,15],xlim:[3,8],xmesh:5,xnew:[1,5],xpd:[4,9],xplot:1,xsr:7,xt_x:5,xtest:3,xtick:[3,6,7],xtrain:3,xytext:6,y_0:[1,4,9,13],y_1:[1,4,5,6,7,9,13],y_1y_1:6,y_1y_1k:6,y_1y_2:6,y_1y_2k:6,y_1y_n:6,y_1y_nk:6,y_2:[1,4,6,7,9,13],y_2y_1:6,y_2y_1k:6,y_2y_2:6,y_2y_2k:6,y_3:[1,7,13],y_4:13,y_data:[1,2],y_data_ful:2,y_decis:6,y_i:[1,2,3,4,5,6,7,8,9,10,13],y_if_:8,y_ix_:1,y_ix_i:[5,6],y_iy_jk:6,y_j:[3,6,10],y_k:10,y_m:13,y_model:1,y_n:[5,6],y_ny_1:6,y_ny_1k:6,y_ny_2:6,y_ny_2k:6,y_ny_n:6,y_ny_nk:6,y_plot:7,y_pred1:7,y_pred2:7,y_pred:[1,2,3,5,6,7,8],y_pred_rf:8,y_pred_tre:8,y_proba:[5,8],y_test:[1,2,3,5,7,8,9],y_test_onehot:2,y_test_predict:1,y_train:[1,2,3,5,7,8,9],y_train_onehot:2,y_train_predict:1,y_val:2,year:[1,12],yes:[3,5],yet:[1,2,3,6,9],yield:[0,1,3,5,6,8,10,13,15],ylabel:[1,2,3,5,6,7,8,15],ylim:3,ymesh:5,yoshua:[2,17],you:[0,1,2,3,5,6,7,8,9,12,13,15,17],young:1,your:[0,2,3,5,6,9,12,13,15],yourself:[5,9],youtub:12,ypred:3,ypredict2:5,ypredict:[1,5],yridg:1,ytest:3,ytick:[3,6,7],ytild:[1,3],ytildenp:1,ytrain:3,z_0:13,z_1:13,z_2:13,z_c:2,z_h:2,z_i:[2,10],z_j:[2,10],z_k:10,z_m:2,z_mod:7,z_o:2,zaman:15,zero:[0,1,2,3,4,5,6,7,8,9,10,13,15],zm_h:1,zone:1},titles:["11. Clustering Analysis","3. Linear Regression","13. Building a Feed Forward Neural Network","4. Resampling Methods","5. Ridge and Lasso Regression","6. Logistic Regression","7. Support Vector Machines, overarching aims","8. Decision trees, overarching aims","9. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","10. Basic ideas of the Principal Component Analysis (PCA)","12. Neural networks","Content in Jupyter Book","Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks"],titleterms:{"2021":16,"4155":12,"case":[5,6,8,15],"final":[5,10],"function":[1,2,4,5,6,8,9,10,15],"import":[13,15],And:5,Eye:8,FYS:12,The:[0,1,2,3,4,5,6,7,9,10,12,15],Useful:12,Using:5,activ:[2,10],actual:15,adaboost:8,adapt:8,adjust:2,again:7,aim:[6,7],algebra:13,algorithm:[0,4,7,8,9,10],algortithm:5,all:6,analysi:[0,1,9,12,15],ani:5,anoth:7,appli:12,approach:[1,5,6],approxim:10,architectur:2,arrai:13,assist:16,august:14,autocorrel:15,back:[2,9,10],background:12,bag:8,basic:[0,1,5,7,8,9,13],batch:2,befor:9,better:[4,6,15],bia:3,binari:2,binomi:15,bird:8,block:15,book:11,boost:8,bootstrap:[3,8,15],boston:1,breast:2,brief:5,bring:10,build:[2,7],calcul:15,cancer:[2,5,7,9],cart:7,central:[5,12,15],chain:10,chang:8,chi:1,choos:2,classic:9,classif:[2,7,8],classifi:6,clip:2,cluster:0,code:[0,1,2,5,7,9,10,15],collect:2,compar:8,compon:9,comput:[5,7,15],con:7,concept:15,condit:5,conjug:5,content:11,continu:15,convex:[5,6],convolut:10,correl:[4,9,15],correspond:5,cost:[2,5,8],cours:[12,17],covari:[4,9,15],cross:3,cumul:15,cython:[],data:[1,2,5,7,9,12,15],dataset:2,decemb:14,decis:[7,8],decomposit:[4,9,13],deep:2,defin:2,definit:15,degre:1,demonstr:15,dens:1,deriv:[5,10],descent:[5,8],develop:2,deviat:15,diagon:9,dice:15,differ:[5,6],dimension:6,disadvantag:7,discret:15,disguis:15,distribut:15,doing:2,domain:15,down:2,dropout:2,economi:4,element:[1,15],elimin:13,ensembl:8,entropi:7,environ:1,equat:[1,5,10],error:8,etc:[],evalu:2,event:15,exampl:[1,2,5,6,7,8,15],exercis:1,expect:15,experi:15,explor:1,exponenti:15,express:5,extend:5,extrem:8,fall:16,famili:2,famou:15,fantast:4,featur:[7,13],feed:[2,10],fine:2,first:[5,10],fit:[1,8],forest:8,forward:[2,10],frank:1,freedom:1,frequentist:1,fridai:5,from:[8,10],gaussian:[13,15],gener:[7,15],geometr:[5,9],gini:7,good:1,grade:16,gradient:[2,5,8],handl:13,has:12,hessian:5,homework:5,hous:1,how:15,hyperparamet:2,hyperplan:6,id3:7,idea:[0,9],ideal:5,implement:[2,15],improv:2,increment:9,index:7,inform:16,instal:12,instructor:16,interpret:[5,9],introduc:[4,9],introduct:[1,3,12,13],invers:13,iter:[5,8],its:15,jackknif:15,julia:[],jungl:8,jupyt:11,kera:2,kernel:[6,9],lagrangian:6,lasso:4,layer:[2,10],learn:[1,2,5,9,12],level:8,librari:12,likelihood:5,limit:[2,5,15],linear:[1,6,13],link:[4,9,14,17],logist:5,loss:5,machin:[1,5,6,12],main:15,make:[1,7,8],mani:[8,10],materi:14,mathemat:6,matric:[],matrix:[2,5,9,10,13,15],matter:1,mean:[0,15],meet:[8,15],mercer:6,mersenn:15,method:[3,5,7,8,15],mlp:10,model:[1,2,10],moment:15,moon:[6,7],more:[0,5,13],multilay:10,multipl:2,multipli:6,name:15,need:[],network:[2,5,10],neural:[2,10],newton:5,non:6,normal:[1,2,15],norwai:12,notat:10,novemb:14,now:[2,7],nuclear:1,nueral:5,numba:[],number:[1,15],numer:15,numpi:13,numpython:0,observ:15,obtain:9,octob:14,one:[5,10],optim:[2,5,6,12],organ:1,oslo:[12,17],other:[7,9,10,15],our:[0,1,5,9,15],outcom:12,output:15,overarch:[1,6,7],overview:8,own:[0,1,8,9],packag:13,panda:[],part:[5,12],pass:2,pca:9,pdf:15,perceptron:10,perform:[2,7],period:15,perspect:2,poisson:15,pre:2,preprocess:1,prerequisit:12,princip:9,pro:7,probabl:15,problem:[2,5],procedur:7,process:2,program:5,propag:[2,10],properti:15,pseudo:15,python:[0,1,7,12,13,15],quick:6,ran0:15,random:[8,9,15],raphson:5,read:7,recip:15,recurr:10,reduc:1,regress:[1,4,5,7,8],regular:[2,4],relev:17,relu:2,remind:[3,5,6],requir:12,resampl:3,revisit:5,ridg:[4,5],rng:15,rule:10,sampl:[9,15],schedul:14,schemat:7,scikit:[1,2,5,9],select:15,semest:16,sensit:5,septemb:[5,14],set:[1,7,10],sgd:5,should:[2,15],simpl:[1,5,7,15],singl:8,singular:[4,9],situat:15,size:4,slightli:5,soft:6,softmax:2,softwar:[],solv:5,some:5,split:1,squar:8,standard:[5,15],state:1,statist:[3,12,15],steepest:[5,8],step:[3,5],stk3155:12,stochast:[5,15],stop:5,supervis:2,support:6,svd:4,teach:[14,16],teacher:16,techniqu:9,technolog:12,tensorflow:2,test:[1,2],textbook:17,than:5,theorem:[6,9,10,15],theori:15,three:15,togeth:10,top:2,toss:15,toward:[0,9],tradeoff:3,train:[1,2],tree:[7,8],tune:2,two:[6,12],type:10,uncorrel:15,understand:4,uniform:15,univers:[10,12,17],use:[2,15],used:5,using:[1,5,15],valid:3,valu:[4,9,15],variabl:[5,15],varianc:[3,15],variou:[1,3],vector:[6,10,13],view:[1,8],visual:[2,7],wai:7,week:14,weekli:14,what:[1,15],when:5,which:[2,15],why:15,wisconsin:5,write:[0,9],xgboost:8,your:[1,8]}}) \ No newline at end of file +Search.setIndex({docnames:["Clustering","chapter1","chapter10","chapter2","chapter3","chapter4","chapter5","chapter6","chapter7","chapter8","chapter9","content","intro","linalg","schedule","statistics","teachers","textbooks"],envversion:{"sphinx.domains.c":2,"sphinx.domains.changeset":1,"sphinx.domains.citation":1,"sphinx.domains.cpp":3,"sphinx.domains.index":1,"sphinx.domains.javascript":2,"sphinx.domains.math":2,"sphinx.domains.python":2,"sphinx.domains.rst":2,"sphinx.domains.std":1,"sphinx.ext.intersphinx":1,sphinx:56},filenames:["Clustering.ipynb","chapter1.ipynb","chapter10.ipynb","chapter2.ipynb","chapter3.ipynb","chapter4.ipynb","chapter5.ipynb","chapter6.ipynb","chapter7.ipynb","chapter8.ipynb","chapter9.ipynb","content.md","intro.md","linalg.ipynb","schedule.md","statistics.ipynb","teachers.md","textbooks.md"],objects:{},objnames:{},objtypes:{},terms:{"000":2,"000000":[1,4,9],"00000000e":[1,4],"0001":2,"00015921913736794912":7,"001":[2,5,6],"00109273":[],"00200":6,"0032873138755776365":15,"003704":1,"003717":1,"003759":1,"003774":1,"003788":1,"00433417":9,"004456043243408203":0,"00445655":9,"0049999999999999845":[],"004999999999999996":[],"004999999999999997":[],"0050000000000000044":[],"005000000000000007":1,"00727646693":1,"007789":[],"007824":1,"0086649156":1,"008885578722629236":4,"009154":1,"009735":9,"00973536":9,"009790":1,"009883615646716182":1,"009883615646716184":[],"009883615646716186":[],"009911":1,"01057384067458835":[],"010679893512872646":[],"0110":15,"011347":1,"0140617":[],"01514394":4,"015144":4,"01867234e":15,"01873344":9,"02161783e":15,"021901":9,"02190139":9,"02493044e":[],"02568378":[],"025709":9,"02625193":6,"02730126581656065":15,"02881304":[],"029688":4,"02968834":4,"030670":4,"03067028":4,"03256632e":2,"03303359":[],"03607832":[],"03697069":[],"03707133":9,"03750367":13,"03787596148305236":1,"03791824e":15,"038300":9,"03871832":[],"04205220e":15,"04260073e":[],"04330858":[],"04421672e":[],"044402":1,"0458":7,"04757618e":[],"04829457939163212":15,"051649":9,"05318162":4,"053182":4,"05364854":6,"05396545e":[],"056329":9,"057088709963637":1,"05755374":[],"059344":9,"060064":9,"060070":9,"06041458e":[],"060919":4,"06126507e":15,"061601":9,"061842":4,"06209126":[],"062188":4,"062376":4,"062639":4,"062660":9,"062829":4,"062879":4,"062923":9,"062979":4,"063178":9,"063324":9,"063395":4,"063882":4,"063982":4,"06412177e":15,"064304":4,"064648":9,"064686":4,"064783":9,"06492086e":1,"065003":4,"065110":9,"065144":4,"065151":4,"065200":4,"065680":4,"065860":9,"065994":4,"066002":9,"06600226":9,"066030":9,"066074":9,"06619182206626131":4,"066294":4,"066312":9,"066389":4,"066403":9,"066487":4,"066683":9,"066761":4,"066837":9,"066951":9,"066963":4,"067132":9,"067175":9,"067440":9,"067601":4,"067667":9,"067685":9,"067844":4,"067865":4,"067892":4,"067990":4,"068351":9,"068514":4,"068697":9,"068818":9,"068838":9,"068840":4,"069005":4,"069028":4,"069081":4,"069195":9,"069243":9,"069369":4,"069413":9,"069503":4,"069528":4,"069641":9,"069775":9,"069890":4,"069988":9,"070129":4,"070131":4,"070138":4,"070254":4,"070337":9,"070548":9,"070762":9,"070791":9,"070812":4,"070815":4,"070867":9,"070889":9,"070967":4,"071016":4,"071049":9,"07106781e":4,"07110274":13,"071248":4,"0713":1,"071325":4,"071435":9,"07145103":9,"071502":4,"071547":9,"07155335":[],"071576":4,"071579":4,"07159175":[],"071660":9,"071681":4,"071684":4,"071761":4,"071917":4,"072094":4,"072424":4,"072555":9,"072589":4,"072620":4,"072654":9,"072710":9,"072826":4,"072879":9,"073":3,"073020":4,"073096":9,"073310":9,"073352":4,"073371":9,"073372":4,"073598":9,"073602":4,"073656":9,"073765":9,"073851":4,"073915":1,"074":3,"074027":4,"074067":9,"074170":4,"074191":4,"074201":9,"074230":4,"074286":4,"074331":9,"074568":4,"074618":9,"074979":9,"075084":4,"075119":4,"075194":9,"07521771":4,"075218":4,"075266":4,"075296":9,"075526":9,"075545":9,"0755452":9,"075615":9,"075993":9,"076320":4,"076367":9,"076408":4,"076410":9,"076466":9,"076938":9,"077010":9,"077046":9,"077143":9,"07729012413236423":9,"077305":9,"07744472306026946":7,"077469":4,"077623":4,"077771":9,"07777777777777778":2,"077891":4,"077927":9,"078099":9,"078103":9,"078244":9,"078311":4,"078388":4,"078693":9,"078710":9,"078746":9,"078930":4,"078966":4,"079001":4,"079139":4,"07944154":13,"079624":4,"079785":9,"079848":9,"079956":4,"080024":9,"080084":1,"080264":9,"080325":9,"080398":4,"080570":9,"080626":9,"080675":9,"080845":4,"081077":9,"081129":9,"081210":9,"081260":4,"081402":4,"081489":9,"081514":4,"08170444":[],"081816":4,"082211":9,"082225":9,"082247":9,"08248290e":4,"08251898":15,"08271336":[],"082990":4,"083128":4,"08318298e":2,"0832":[],"083295":9,"083376":4,"083577":4,"083658":9,"08415761":[],"084167":4,"084249":9,"084604":9,"084813":9,"084873":4,"08501147":1,"085121":9,"085285":4,"085646":9,"085676":4,"085951":4,"08611111111111111":2,"086116":4,"086592":4,"086652":9,"086974":9,"087202":9,"087482":4,"087563":9,"08839750e":1,"088871":1,"08888888888888889":2,"089425":9,"090274":9,"09103481e":15,"09166666666666666":2,"0917":7,"093755":9,"09487315108590391":9,"096726":4,"09672604":4,"09678277e":15,"09741585e":[],"09903804":6,"100":[0,1,2,3,4,5,6,7,8,9,13,15,16],"1000":[0,1,2,5,6,9,12,15],"10000":[0,3,8,9,15],"100000":6,"10001":8,"1001":15,"1002":15,"1003":15,"1005":15,"1007125":1,"1009":15,"10094646e":[],"1011":15,"1013":15,"10131725":[],"1013904243":15,"1015":15,"102":1,"1022964509394572":2,"1023":15,"1026":15,"1027":15,"103":2,"1030":15,"10307631":[],"10327559":13,"1037":15,"10378326e":2,"1038":15,"1040":15,"10405456":9,"1047":15,"105137868830763":15,"10555555555555556":2,"106095":9,"108":1,"10806972":4,"108070":4,"10896672e":15,"10898112e":4,"10th":7,"10x":1,"110":1,"1100":15,"1101":15,"111":[2,5,10],"1111111111111111":2,"112283":1,"112383":9,"11304709e":15,"11388888888888889":2,"11456076e":[],"11507992e":2,"11666666666666667":2,"117":6,"11944444444444445":2,"12002944":[],"1203284":6,"121":[6,7,8],"122":[6,7,8],"12222222222222222":2,"12283463":[],"123190":1,"123459876":15,"12366979e":15,"124":1,"12497018e":1,"127773":15,"12777777777777777":2,"1298":7,"13055555555555556":2,"13060772":1,"13209041":[],"13266452e":1,"133":5,"13328820e":[],"13457922":[],"13519106":1,"13579199e":[],"1361111111111111":2,"137268":[],"137400784702912":1,"137652":9,"13803928":1,"138775":9,"139475":3,"1404":1,"1437":2,"1440501043841336":2,"1445":3,"14459063":[],"1446":3,"1447":3,"1448":3,"1449":3,"14662074":1,"14722222222222223":2,"147400":9,"147420":9,"149366":[],"149667":3,"149903":3,"14g":3,"150":6,"15024669":[],"152636":[],"1527777777777778":2,"153106":1,"15324631":1,"15332528e":[],"154720":1,"15475232":1,"154911":1,"155491":1,"155687":1,"155883":1,"156":1,"157":1,"158":1,"159":1,"15979239e":15,"15g":3,"160":1,"16111111111111112":2,"16496581e":4,"16553696":[],"16637855e":15,"16666666666666666":2,"16807":15,"16b8e3cda33a":2,"17117385":[],"17174962e":2,"17234827e":[],"17385778e":1,"1758506":1,"17654307":[],"17707436":[],"17777777777777778":2,"17953942":9,"1797":2,"180092462880674":[],"18220995":1,"18333333333333332":2,"18404906e":4,"1856411":[],"18611111111111112":2,"18620601e":1,"18673098":9,"18954529":[],"18968431e":15,"1914224774238273":4,"1940":1,"1943":10,"197":1,"1970":13,"1973":7,"197370":9,"19742904e":[],"1979":3,"1989":15,"19937":15,"19955871":4,"199559":4,"19972087e":15,"1_1":10,"1_2":10,"1_3":10,"1cm":[1,6,8,15],"1e10":0,"200":[1,6,7,8,15],"200000":1,"2004":5,"2006":17,"2010":2,"2011":2,"2015":2,"2016":1,"2018":3,"2021":0,"20277777777777778":2,"205466494327873":[],"207545":9,"20772452":[],"20819609e":[],"20833333333333334":2,"20843563e":[],"20906175e":15,"210340":9,"21208310e":[],"212327334149492":1,"2125":[],"2126":[],"2127":[],"2128":[],"2129":[],"213103":9,"21347282":[],"213743":9,"2147483647":15,"21623344":1,"216290":9,"216683":9,"22076362":1,"221":6,"22209371e":1,"22527008e":15,"22717936e":15,"23117916e":15,"23333333333333334":2,"2335879":[],"23382086e":[],"23438810e":1,"24444444444444444":2,"250":[5,7],"25000":1,"250000":1,"250154":1,"25190225e":1,"25226753e":15,"25240108":[],"253":[],"25303483":[],"253775":1,"254":[],"254509":4,"25450941":4,"25457052":[],"255":[],"2551":1,"256":[],"256962":1,"257":[],"25726439e":[],"2572e3a4b38d":2,"25803281":[],"259107":4,"25910749":4,"259153":9,"2627588":13,"26381865":4,"263819":4,"264":1,"265":1,"2653678":1,"266":1,"267":1,"2683":3,"2684":3,"2685":3,"2686":3,"2687":3,"26890510e":[],"269":1,"26974938e":[],"270":1,"27296891e":[],"276263":9,"27753165e":15,"27n_":15,"280647":9,"28166741":[],"282":1,"282727":9,"2836":15,"28475098":6,"2861":15,"2873":7,"2882":15,"2886":15,"2890":1,"2892":15,"28971976":9,"289720":9,"290":[],"291":[],"2915":15,"291614":[],"292":[],"293":[],"2931":[],"294":[],"295656121491569":[],"296247":1,"2968":[],"297219777724628":9,"297260":1,"2980":[],"298273":1,"2983233":13,"298375":1,"2990":[],"299444":9,"29944428":9,"2_1":10,"2_2":10,"2_3":10,"2_i":10,"2_m":15,"2_x":15,"2cm":6,"2ff97f4bf03b":15,"2nd":7,"2x_ix_jy_iy_j":6,"2x_j":6,"2y_i":8,"2y_j":6,"30000":1,"300162456113691":[],"30119421":6,"306854":4,"30685416":4,"30879705":1,"30968181":1,"31109204e":1,"3155":3,"3156929654100207":7,"31608475e":[],"31718909":9,"31730641":[],"317367":9,"31853484":[],"31896852":6,"3200":2,"32047562":1,"323291478597321":15,"3250":2,"32521615e":15,"327631":1,"32938847":13,"32945844e":15,"3304":1,"33066907e":4,"3310":1,"3317":1,"333":5,"333333":1,"3338":1,"3344":1,"33443859e":[],"33544681":[],"33569998e":1,"33861512":[],"339535706819584":15,"33953571":15,"340782":9,"34114641e":1,"34172919":[],"3436":1,"3437":1,"344172":1,"346433":9,"34643337":9,"34902789e":15,"351636":9,"35176067":[],"35216172e":1,"358869339268145":15,"35886934":15,"359640894899012":[],"360":2,"360688":1,"36436520e":[],"36468301":[],"369139":9,"36941772":4,"369418":4,"37416969":9,"374170":9,"37738324":[],"38207279e":[],"38216436":[],"38629436":13,"38937995e":15,"38986237":[],"396740":9,"39674043":9,"397700":9,"39792608e":[],"3cd19a0768e1":[],"4000":17,"401842":9,"40212127":[],"404":1,"40425078e":15,"405890":9,"40708470e":1,"40902095":[],"41511965e":2,"415634483874318":1,"416694683938511":9,"4171578884124756":0,"41754964":[],"41770932":[],"418506":9,"41876428e":[],"42847770e":15,"42937310e":15,"43766686":9,"442600":9,"44395541":[],"44625466e":15,"44970586e":2,"45013332e":[],"45019484":[],"450257":9,"4557763":9,"458078":9,"45937170e":[],"461":15,"462":5,"46323168e":15,"46423858e":1,"466":15,"46675058":1,"46914544e":[],"46929603e":[],"47079457e":15,"47566390e":15,"47654764e":[],"47815203":9,"48154187202453613":0,"48257387":16,"48471852e":[],"48476997":9,"48608063e":[],"4940954":1,"49865673":[],"4990":15,"4992":15,"4997":15,"4c4c7f":[7,8],"4y_i":8,"500":[2,3,5,7,8,15],"500000":1,"5018":15,"50394742":[],"506":1,"507d50":[7,8],"50j":5,"50x10":2,"510":2,"5120":0,"512132":1,"51257863e":[],"51345668e":[],"51363731e":[],"51523276e":[],"51893804e":[],"51943726":9,"519842":1,"52158335":1,"5222222222222223":2,"526744":9,"52722156":[],"5303329":9,"5305555555555556":2,"5378811":9,"539261":9,"54121682":4,"541217":4,"54152940e":[],"54237024":15,"54702088e":[],"55138385":9,"551384":9,"55280484":[],"5555555555555556":2,"557795":9,"55854694":9,"56216797e":1,"564374":9,"56536":1,"569":2,"57051369":[],"574465":9,"5755035":1,"57781668":[],"57871326":[],"581766":9,"58176612":9,"582":[1,2],"58228342e":15,"58239999":4,"582400":4,"584804":1,"58521266":9,"585213":9,"58596975":4,"585970":4,"587401":1,"58836420e":15,"5888888888888889":2,"59007674e":15,"59304755e":1,"5944444444444444":2,"59480085":4,"5cm":15,"60122668e":15,"60394236e":1,"60673226":9,"60999846":[],"61069091e":15,"6111111111111112":2,"61124978":[],"61234223":[],"614808":9,"61505887e":[],"61745046e":[],"618":1,"61869821":[],"622539":9,"62253933":9,"6226921":[],"62316154e":15,"62359224e":15,"62373464":9,"625":5,"62783293":[],"62856593":1,"629961":1,"6300745149331701":1,"63315151":1,"63339159":[],"63374631":[],"63437572":[],"63442451e":1,"636323":9,"63632311":9,"63680118":[],"637129335071195":1,"63993205e":[],"64166831e":[],"64447921":[],"64580686":[],"646283":9,"647473":9,"64857826e":[],"649382":9,"64x50":2,"650024":4,"65002433":4,"6510573774179256":15,"65105738":15,"65238878":13,"65245958":[],"653095390463358":1,"653702":13,"65482578":[],"65572035":[],"65599927":[],"65766387":[],"65933852":[],"65939208e":4,"66020213e":1,"6614":3,"66183486":9,"661835":9,"6628996975186952":1,"66302359":[],"66383151":[],"66677842":[],"66800261":[],"66880047":[],"67006792":[],"67060602":[],"671089":1,"6714":3,"67171347e":15,"67279536":[],"67303655":9,"673037":9,"67407338e":15,"67450955":[],"67708423":1,"68034946e":[],"6813":3,"6814":3,"6815":3,"6816":3,"6817":3,"6818813252071303":15,"68188133":15,"68316185":1,"68342382":[],"68616263":13,"68729414":[],"68937695":1,"689519":9,"69069n_":15,"693361":1,"69347005":[],"693850":9,"69385025":9,"69519693":4,"695197":4,"69573183":1,"69981195e":15,"6999536":9,"6ea927cc6e88":2,"6n_":15,"7022283":1,"70234019":[],"70415861":[],"70523024e":[],"70589906":4,"70790937":[],"70886748":1,"70946493e":[],"712018":9,"71281409":[],"71351486":[],"71442781":13,"7162":[],"71669651":1,"7172":[],"72108703":[],"72174172":9,"72218808":[],"72312577":[],"7240496":[],"72780613e":15,"72879865e":[],"72981762":6,"73091052e":1,"73453972":[],"73921714":[],"74081822":6,"74107697":9,"74143127e":1,"7432283":[],"74382593":1,"74495014":[],"74845978":[],"74921867":[],"751699":9,"7522047280566193":4,"75382481":[],"75524378":[],"75629493":[],"75841112":[],"75932862":[],"76172241e":[],"762":9,"7621419":1,"76290332":[],"76497666":[],"765":5,"76504618":[],"76570177":1,"76648901e":[],"7693978131030923":9,"7718":7,"772b904ae9cb":3,"77317984":1,"77350269e":4,"774300":1,"77661393e":15,"77714169":6,"78080633":1,"78195":3,"78857629":1,"78944806":[],"7899453":[],"79295029e":15,"79326583e":15,"79328828":[],"793701":1,"794282":9,"79459035":1,"79648291":1,"79902342":1,"7c394b1e8b71":7,"7d7d58":[7,8],"800":5,"80004454e":[],"80121":[],"8055555555555556":2,"81620806":[],"81633628":9,"81781888":9,"827265":1,"82889306e":15,"8305555555555556":2,"83425361":9,"834254":9,"83614019":[],"8388888888888889":2,"83935285":[],"84087101":[],"842":1,"84355903e":2,"84372853":1,"84443254e":2,"84658093e":15,"8479552268981934":0,"8520127":4,"85278450e":1,"85396354":[],"85450859":9,"85463934e":[],"85497163e":[],"85546305e":[],"861":1,"86145244":9,"8638888888888889":2,"86436607":[],"86574276":[],"8666666666666667":2,"86692943":[],"8718475896381779":15,"87184759":15,"8722222222222222":2,"875":2,"87761937":[],"8777777777777778":2,"87972591":[],"8802":[],"8805555555555555":2,"88297395":[],"88559559":[],"88693966e":15,"88712946":[],"8888888888888888":2,"8901":[],"8914984":[],"8921171964770647":9,"8923":1,"89288636":9,"89383322":[],"8944444444444445":2,"898500":9,"89850037":9,"89942598":1,"89975818":[],"8x8":2,"90233874":[],"90316476":[],"9040":7,"9055555555555556":2,"90618734e":15,"907307":9,"90730735":9,"90825063":1,"91012519e":[],"9111111111111111":2,"91145266e":15,"91549644":1,"9166666666666666":2,"916978":4,"91697817":4,"9171356":1,"9222222222222223":2,"924018":1,"925":2,"92507116e":2,"92605247":1,"92645039":9,"92646965":9,"926470":9,"92732185e":[],"9277777777777778":2,"9279671770201344":15,"92919670e":15,"9305555555555556":2,"931":1,"93100040e":15,"932734":4,"93273404":4,"9354":3,"9361111111111111":2,"937":15,"938":15,"9388888888888889":2,"939":[1,15],"94019247e":1,"94034531":1,"9444444444444444":2,"94536341":15,"94591015":13,"94639099":9,"946893955211749":1,"947543":9,"948729":4,"95014575":[],"9527777777777777":2,"954":15,"9547578478889096":1,"9555555555555556":2,"956563":9,"9583333333333334":2,"960":15,"961":15,"96104648":[],"9611111111111111":2,"962":15,"963499":9,"96349948":9,"965885569080809":[],"967809":9,"96793117e":[],"97101567e":15,"9722222222222222":2,"975":2,"9756404":1,"976":6,"97723801":[],"9777777777777777":2,"9780387310732":17,"9780387848570":17,"9781492032632":17,"9804422":1,"98046438":1,"9805555555555555":2,"98452685":[],"985":15,"98526763":1,"98528992":1,"98531221":1,"986":15,"98609175":15,"986091753050161":15,"9861111111111112":2,"98661465":[],"9877742":[],"987902":1,"9888888888888889":2,"98892195e":15,"98893512":[],"989":15,"98927731":[],"9898ff":[7,8],"99009739":[],"9901168":1,"99013921":1,"99016161":1,"99018401":1,"99043999":[],"9909252":[],"991":15,"991072":9,"99107239":9,"99126104":[],"99133007":[],"99160404":[],"99190487":[],"99194716":[],"992":15,"99218987":4,"99219378":[],"99242605":[],"99248001":[],"99273355":[],"99276945":[],"993":15,"99305549":[],"99305802":[],"99311297":[],"99346398":[],"99363129":[],"99363383":[],"99393624":[],"994":3,"99400444":[],"99418903":9,"99420743":[],"99420997":[],"99428016":[],"9945452":[],"99462421":[],"99473581":[],"99478645":[],"99478898":[],"99498985":1,"995":3,"99501236":1,"99503487":1,"99505739":1,"9950799":1,"99527696":[],"9953048353087299":[],"99536326":[],"9953658":[],"99544872":[],"9954538761021741":[],"99579317":[],"99581841":[],"99594294":[],"996":3,"99600927":[],"99636015":[],"99652042":[],"99652296":[],"99655111":[],"99696351":[],"997":3,"99709325":[],"99710078":[],"99723611":1,"99728435":[],"99730848":[],"99763569":[],"99767893":[],"99782689":[],"998":3,"99817842":[],"99825997":[],"99836973":[],"9984806":[],"99883879":[],"99891285":[],"999":[7,15],"99945628":[],"99988687":1,"9999095":1,"99993212":1,"99995475":1,"99995818594196":[],"999974281880048":1,"99997737":1,"9999794306626945":[],"9999822527140678":[],"9999864543345858":[],"9999868619217517":[],"9999878260589065":1,"9999910208315801":[],"9b9cf4fa1a95":[],"\u00f8yvind":16,"abstract":2,"break":[0,1,9],"byte":13,"case":[1,2,3,4,9,10,12,13,14],"catch":1,"char":15,"class":[1,2,3,5,6,7,9,10,15],"const":15,"default":[1,2,5,13],"ekstr\u00f8m":16,"export":7,"f\u00f8470":16,"final":[0,1,2,3,4,6,7,8,9,14,15,16],"float":[0,1,7,9,13,15],"function":[0,3,7,12,13],"import":[0,1,2,3,4,5,6,7,8,9,10],"int":[0,1,2,4,5,9,13,15],"long":[1,2,5,10,15],"new":[0,1,2,3,4,5,6,7,8,9,13,15],"null":15,"public":[1,12],"return":[0,1,2,3,4,5,6,7,9,13,15],"s\u00f8rli":16,"sch\u00f8yen":16,"short":[0,11],"steinsv\u00e5g":16,"super":4,"switch":[0,1],"throw":15,"true":[0,1,2,3,5,6,7,8,10,15],"try":[0,1,2,5,6,7,8,9,12,13,15],"var":[3,4,8,9,15],"while":[0,1,2,3,4,5,6,7,9,10,15],AGE:1,Adding:2,Age:5,And:[0,1,3,7,12,15],Are:9,Being:5,But:[0,2,3,7,8,15],CAS:[],DIS:1,Doing:[5,8],EoS:[1,3],FYS:14,For:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,17],Going:[2,4],Ising:[4,10],Its:[2,9],MDS:9,NNs:10,N_s:6,Not:[1,2,3,4],OLS:[1,3,4],One:[1,2,3,4,5,6,9,10,15],PCs:[9,12],RMS:15,Such:[3,10,15],That:[0,1,5,8,9,10,15],The:[8,13,14,16,17],Then:[0,1,2,3,5,6,7,8,9,10,13,15],There:[0,1,4,6,7,9,10,11,13,14,15,16],These:[0,1,4,6,7,8,9,10,13,15],Use:[1,7],Useful:[3,13],Using:[1,3,4,6,8,10,13],With:[1,3,4,6,7,8,9,10,13,15],__class__:8,__doc__:3,__future__:[6,7],__init__:[2,3],__name__:[3,8],_auto10:10,_auto1:[4,5,10,13,15],_auto2:[10,13,15],_auto3:[10,13],_auto4:[10,13],_auto5:[10,13],_auto6:[10,13],_auto7:[10,13],_auto8:10,_auto9:10,_ax:3,_base:6,_build:[12,17],_check_optimize_result:9,_compon:9,_datafram:[],_depth:7,_fraction:7,_lambda:1,_leaf:7,_logist:9,_make_index:[],_multilayer_perceptron:[1,2],_node:7,_num_sampl:[],_ratio:9,_sampl:7,_split:7,_varianc:9,_weight:7,a0faa0:[7,8],a77d5ac269b2:5,a_0:1,a_1a:1,a_2a:1,a_3:1,a_3a:1,a_4:1,a_4a:1,a_h:2,a_i:[1,2,10],a_j:[2,10],a_k:[2,10],aaron:17,ab_channel:12,abandon:2,abbrevi:14,abid:15,abil:[1,8],abl:[2,4,5,8,10,15],abort:15,about:[0,1,2,3,4,5,6,7,8,9,10,12,13,17],abov:[0,1,2,3,4,5,6,7,8,9,10,13,15],abovement:3,abs:[0,1],abscissa:5,absolut:[1,3,4],acccess:[],accept:[1,7],access:[1,9,15],accid:3,accompani:1,accomplish:[6,7],accord:[0,1,2,3,5,7,10,15],accordingli:9,account:[1,15],accumul:[10,15],accur:[3,8,15],accuraci:[1,2,4,5,7,8,9,10],accuracy_scor:[1,2,8],accuracy_score_numpi:2,achiev:[1,2,3,6,10,13],aco:15,acquaint:12,acquir:[2,12],acr:1,across:[2,3,7,12],act:[2,13],action:15,activ:[0,1,7,14],actual:[1,2,3,4,6,9,13],ada_clf:8,adaboostclassifi:8,adam:2,adapt:[1,3,5,17],add:[1,2,3,4,6,8,9,10,15],add_subplot:[0,2,5,10],added:[1,2,4,5,6,13],adding:[0,2,3,13],addit:[0,1,3,5,6,7,8,10,12,13,15,16,17],addition:[5,10],address:[2,5,7,9,17],adjac:10,adjust:[5,10],admir:1,advanc:[3,10,17],advantag:[2,3,5,8,13],afecionado:[],affect:4,affin:[1,6,9],aficionado:[],aforement:0,african:1,after:[0,1,2,3,4,5,7,9,10,12,13,15],afterward:1,again:[0,1,2,3,5,6,8,9,10,15],against:[2,5,8],age:[1,5],agegroup:5,agegroupmean:5,aggreg:[7,8],agorithm:8,agre:15,ahead:7,aid:9,aim:[0,1,2,3,5,9,12,13],albeit:0,algebra:[1,4,5,12,14],algo:15,algorithm:[1,2,3,5,6,12,13,14,15,17],align:[1,3,4,5,6,15],all:[0,1,2,3,4,5,7,8,9,10,12,13,14,15,16,17],allevi:[2,5],alloc:13,allow:[1,2,3,5,6,8,12,13],almost:[1,2,3,5,6,9,15],alon:7,along:[0,3,4,7,8,9,12,13],alpha:[0,1,2,3,5,6,7,8,15],alpha_:8,alpha_i:5,alpha_k:5,alpha_m:8,alpha_opt:5,alreadi:[8,10,12,13,15],also:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,17],alter:[2,15],altern:[1,2,6,7,9,13],although:[2,3,6,8],alwai:[0,1,3,4,5,10,15],ame2016:1,american:1,among:[1,7,8,10],amount:[0,2,3,6,8,12],an_:15,anaconda3:[1,2,3,6,9],anaconda:[1,2,12],analys:[3,4,15],analysi:[2,3,4,5,13,14,17],analyt:[1,3,4,5,10,12],analyz:[1,2,4,15],andrew:2,angl:[1,7,15],ani:[0,1,2,3,4,6,7,8,10,15],anim:10,ann:10,annot:[1,2,5,6],anoth:[1,2,3,5,6,8,9,10,13,15],ans:15,ansatz:1,answer:[1,2,3,13],anymor:[2,6],anyon:6,anyth:[2,15],anytim:16,apach:2,apart:[5,9],api:[2,12],appear:[1,2,13,15],append:[2,3,5,6,7,15],appli:[1,2,3,5,6,7,8,9,10,15,17],applic:[1,2,3,5,7,10,15,17],approach:[2,3,4,7,8,9,10,12,15,17],appropri:[3,7,10,12,15],approx:[1,5,8,9,15],approxim:[1,2,3,4,5,8,9,15],apt:[1,12],aptli:0,aragorn:[],arang:[2,3,5,7,8,10],arbitrari:[2,5,6,10,15],arbitrarili:[1,2,9],architectur:[10,17],area:[1,17],arg:3,argc:15,argmax:[2,9],argmin:[0,8],argsort:9,argu:2,arguabl:0,argument:[1,9,10,15],argv:15,aris:[1,3,5,10,15],arithmet:[1,13],arma:15,armadillo:[13,15],around:[1,2,3,9,15],arrai:[0,1,2,3,4,5,6,7,9,10,12,15],arriv:[1,7,9,13,15],arrow:10,arrowprop:6,art:[1,2,12],articl:[0,1,3,4,8,15],artifici:[1,5,10,17],artificialneuron:10,artist:3,asarrai:[1,7],ascii:15,ask:[3,9,10],aspect:[1,12],assembl:1,assess:[1,3],assign:[0,1,5,6,7,10,14,17],assign_points_to_clust:0,associ:[0,1,3,7,10,15],assum:[0,1,2,3,4,5,6,7,8,9,10,13,15],assumpt:[1,3,7,9,15],ast:[1,3],astyp:[7,8],asymmetri:1,asymptot:3,atoi:15,atom:1,attempt:[1,5,6,8],attend:14,attent:[1,13],attract:[1,8],attribut:[1,7],attributeerror:3,audi:1,aurelien:[1,14,17],author:[1,2,8,15],authour:1,auto:[7,8,15],autocor:15,autocorrelation_tim:15,autocorrelform:15,autocovari:15,autoencod:12,autoencond:12,autograd:12,autom:[1,12],automac:13,automag:[],automat:[1,2,9,12,13],autonom:17,avail:[1,2,3,8,9,12,13,14,17],averag:[0,1,2,3,7,8,15,16],avg:15,avoid:[0,1,3,4,7,9,13],awai:15,awar:8,award:16,axes3d:5,axes:[1,3,5,6,7,8,9],axessubplot:1,axhlin:6,axi:[0,1,2,3,5,6,7,8,9,10,15],axlabel:1,axvlin:6,b_1:[5,10],b_5:5,b_group:7,b_i:[1,2,10],b_ia_:1,b_index:7,b_j:[2,10],b_k:[2,5,10],b_m:10,b_score:7,b_valu:7,bachelor:14,back:[1,4,6,7,8,13,15],backbon:13,backend:2,background:17,backpropag:2,backtrack:7,backup:13,backward:[2,10,13],bad:15,badli:15,bag:[7,12,14],bag_clf:8,baggin:[],baggingboot:8,baggingclassifi:8,baggingtre:8,balanc:3,band:13,bandwidth:13,bar:[1,9],barber:17,bare:8,barebon:0,base:[0,1,2,4,5,6,7,8,12,15,16,17],basi:[4,5,6,8,9,10,13],basic:[4,6,10,12,14,15],batch:[5,9,10],batch_siz:2,bay:5,bayesian:[12,17],becaus:[0,1,2,3,5,6,7,10],becom:[0,1,2,3,4,5,7,10,15],been:[1,2,3,9,10,12,13],befor:[0,1,2,3,4,5,6,10,13,15],beforehand:[0,1,15],begin:[0,1,2,3,4,5,6,7,9,10,13,15],behav:[2,3,5],behavior:[1,2,5],behaviour:10,behind:[1,2,5,6],being:[0,1,2,4,5,6,8,9,10,15],believ:[7,13],belong:[0,5,6,7],below:[1,2,3,4,5,6,7,8,9,10,13,15],benchmark:8,bendik:16,benefici:2,benefit:[1,2,5,9,12],bengio:[2,14,17],benign:[2,5],best:[0,1,2,3,5,6,7,8,10,15,16],beta:[1,2,3,4,5,8,9],beta_0:[1,2,5],beta_0x_:1,beta_1:[1,2,5,8],beta_1x_0:1,beta_1x_1:[1,5],beta_1x_2:1,beta_1x_:1,beta_1x_i:5,beta_2:1,beta_2x_0:1,beta_2x_1:1,beta_2x_2:[1,5],beta_2x_:1,beta_:[1,5],beta_i:[1,4],beta_j:[1,5],beta_k:5,beta_linreg:5,beta_m:8,beta_mg_m:8,beta_p:5,beta_px_p:5,better:[0,1,2,7,8,9,10],between:[0,1,2,3,4,5,6,7,9,10,15],beyond:[1,2,5,6],bia:[1,2,4,6,7,8,10,14,15],bias:[2,3,7,10],big:[0,1,2,3],bigger:2,bigr:10,bike:7,bilbo:[],bilek:16,billion:[10,12],bin:[1,3,5,15],binari:[1,5,7,8,10,14,15],bind:[1,3],binomi:12,binsboot:3,bioinformat:1,biolog:[2,10,17],bios1100:12,bird:1,birth:[],bishop:[14,17],bit:[0,2,13,15],bitwis:15,bla:13,black:[0,6,7],block:[0,3,8,12,13],blockingavg:15,blockingstd:15,blockingvar:15,blocksiz:15,blocksizemax:15,blocksizemin:15,blue:1,bmatrix:[1,2,4,5,6,9,13],bmi:2,bodi:[1,2,10],bold:2,boldfac:1,boldsymbol:[0,1,2,3,4,5,6,8,9],boltzmann:[10,12],book:17,bool:0,boost:[2,7,12,14],boostrap:8,bootavg:15,bootstd:15,bootstrap:[2,12,14],bootvar:15,bootvec:15,borrow:[],boston_dataset:1,bot:6,both:[0,1,2,3,4,5,6,7,8,12,13,15,16],bottl:5,bottom:3,bound:[1,3,6,10],boundari:[6,9,10],box:7,boyd:[5,6],bracket:15,brain:[2,5,10],branch:7,breast:[5,9],brew:[1,12],brg:6,briefli:1,bring:[1,8],broad:1,broadcast:0,browser:[],brute:[4,9],bsol:[],bsubex:[],build:[1,3,8,13,15],built:[1,2,3],bunch:9,busi:1,c46dd114b2af:6,c_0:15,c_1:10,c_2:10,c_3:10,c_4:10,c_i:[5,10],c_k:15,cach:8,cal:[1,5,6,8,10],calcul:[0,1,2,3,4,5,6,7,8,9,10,13],call:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,17],callabl:3,cambridg:[5,17],came:0,can:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,17],cancel:1,cancer:8,cancerpd:5,candid:[6,7,8],cannot:[1,2,4,5,6,7,14,15],canopi:[1,12],capabl:[1,2,6,12],capita:1,captur:[9,10],card:[1,5],cardin:2,care:[0,9],carefulli:5,carlo:[1,3,12,15,17],carri:[3,5],cart:8,casella:17,cast:2,categor:[1,2,7,9],categori:[0,1,2,5,8,10,14],categorical_crossentropi:2,caus:[1,3,4,15],causal:1,causat:1,cax:2,cbar:2,ccc:10,cdf:15,cdot:[0,1,3,5,10,13,15],celebr:5,center:[0,1,2,3,5,6,7,9,15],centr:17,central:[1,3,6,13],centroid:[0,15],centroid_differ:0,centroid_list:0,certain:[0,1,3,5,7,15],cha:1,chain:[2,12,15],challeng:[0,5],chanc:[2,5,15],chang:[0,1,2,3,4,5,6,7,9,10,13,15],chapter:[0,3,8,9,13,14,17],charact:[1,4,6,15],character:[6,7,8,10,15],characterist:[1,2,8],charg:1,charl:1,chd:5,chddata:5,cheap:4,cheaper:[2,5],chebychev:0,check:[1,2,5,9,13],check_consistent_length:[],chemic:15,chen:8,choic:[0,1,2,3,5,7,10,13,15],choleski:[4,13],choos:[0,3,5,7,8,9,15],chosen:[1,2,3,5,6,7,8,15],chosen_datapoint:2,christian:17,christoph:[14,17],cin:15,circ:[2,10],circl:[1,6,10],circumfer:7,circumv:[2,4],clariti:[0,15],class_nam:7,class_val:7,class_valu:7,classic:[5,7],classif:[1,3,5,6,9,10,12,14,17],classifi:[1,2,5,7,8,9],classificaton:2,classifii:8,clean:2,clear:[2,8,10],clearer:0,clearli:[3,5,6,15],clever:[0,2,8],clf3:1,clf:[1,6,7,8],clf_ridg:1,clip:15,close:[0,1,2,3,5,6,7,9,10,15,17],closest:[0,5,6,9],closur:12,cloud:12,clust:0,cluster:[1,2,3,9,12,14],cluster_label:0,cmap:[1,2,6,7,8],cmath:15,cmb:14,cmd:7,cn_:15,cnn:10,cntk:12,code:[3,4,6,12,13,14,17],coef0:6,coef:1,coef_:[1,5,6,7],coeffici:[1,3,5,6,7,13],coerc:[1,3],coin:[8,15],coin_toss:8,col:[1,9],colab:12,cold:7,colinear:1,collaps:6,collect:[1,3,8,9,12,15,17],collinear:4,color:[1,3,6,7,8,15],colorbar:2,colsample_bytre:8,colsaobject:8,column:[1,2,3,4,5,6,7,9,10,13],columntransform:7,com:[12,16,17],combin:[2,3,5,8,15],come:[0,1,2,4,5,10],comma:[],command:[1,2,15],comment:1,commerci:[1,12],commod:1,common:[0,1,2,3,4,5,7,9,15],commonli:[0,2,3,5,7],commun:[1,10],compact:[0,1,2,3,4,5,7,9,10],compar:[0,1,3,4,5,9,13],compat:5,compet:1,competit:8,compil:[1,2,12,13,15],complet:[1,7,10],completenn:10,complex:[1,2,3,6,7,9,10],complic:[0,1,2,3,5,7],compon:[0,1,2,3,4,5,7,12,14],components_:9,compos:[0,7,10],compphys:[12,14,17],compress:1,compris:3,compromis:4,compulsori:12,comput:[0,1,2,3,4,6,8,9,10,12,13,14,17],computation:[1,3,5,7,15],concaten:0,concav:[2,5],concentr:[1,8],concept:[0,1,12],conceptu:[5,10],concern:[0,1,2,5],concic:[],conclud:1,conclus:2,conda:[1,2,12],condit:[1,3,4,6,7,9,15],conduct:12,confid:[1,3,5,6],confirm:10,confus:[3,8,13],confusion_matrix:7,congruenti:15,conjug:6,conjugaci:5,connect:[1,2,5,7,9,10,13],consequ:[3,4,5,6,8,10],conserv:[0,4],consid:[0,1,2,3,4,5,6,7,8,10,13,15],consider:[1,2,5],consist:[1,2,3,5,10,15],constant:[1,5,6,10,15],constitu:1,constitut:3,constrain:[2,5,9],constraint:[4,5,6],construct:[1,2,3,4,5,6,7,8,9,13,15,17],contact:1,contain:[0,1,3,5,6,7,9,10,13,15,17],contemporari:17,content:[2,12,13],context:[3,5,8],continu:[1,2,3,5,6,7,8,10,13],contour:[5,7,8],contourf:[6,7,8],contrast:[2,7,8,10],contribut:[1,4,15],contributor:1,control:[1,2,7,12],conveni:[1,3,5,10,13],convent:10,converg:[0,1,2,4,5,6,9],convergencewarn:[1,2,6,9],convert:[1,2,4,7,9,13],convinc:5,convolut:[2,12,14],cool:7,coordin:[0,4,10],coorel:1,copi:[0,2],core:8,corel:1,coronari:5,corr:[1,4,5,9],correalt:[4,9,12],correct:[0,1,2,4,13,15],correctli:[2,3,8],correl:[1,2,5,8,10,12],correlation_matrix:[1,4,5,9],correspond:[0,1,3,6,7,9,10,12,13,15],cortex:10,cos:[1,3,7],cosin:[0,3],cost:[1,3,4,6,7,10,15],could:[1,2,3,4,5,6,7,8,9,10,13,15],coulomb:1,count:[1,7,14,15,16],countor:5,cours:[1,2,9,14,15],courvil:[14,17],cout:15,cov:[3,4,9,13,15],cov_xi:[4,9],cov_xx:[4,9],cov_yi:[4,9],covari:[5,12,13],covariance_matrix:[0,4,9],cover:[1,11,12,17],covert:1,covxi:15,covxx:15,covxz:15,covyi:15,covyz:15,covzz:15,cpu:2,creat:[2,7,8,9,10,12],create_biases_and_weight:2,create_neural_network_kera:2,create_x:[1,4,9],credit:[1,5],crim:1,crime:1,criteria:[0,1,7,8,15],criterion:[5,7,8],cross:[1,2,5,7,8,12,14,15],cross_val_scor:3,cross_valid:[5,8],crossvalid:3,crucial:[2,15],csr_matrix:13,cstdlib:15,csv:[1,3,5,7],ctnk:2,cubic:1,cumsum:[8,9],cumul:[3,8],cumulative_heads_ratio:8,current:[0,2,5],curs:1,curv:[5,8,10],curvatur:5,custom:0,custom_cmap2:[7,8],custom_cmap:[7,8],cutpoint:7,cvxbook:5,cvxopt:6,cyber:17,cycl:[0,2,10,15],d_f:5,dagger:[4,13],dai:[2,7,12],dalen:16,darget:7,darkr:15,dat:1,dat_id:[1,3,5,7],data1:0,data2:0,data3:0,data4:0,data:[0,3,4,6,8,10,13,17],data_id:[1,3,5,7],data_indic:2,data_panda:[],data_path:[1,3,5,7],databas:2,datafil:[1,3,5,7],datafram:[1,4,5,7,9],datapoint:[1,2,3,4,5,9],dataset:[0,1,3,5,6,7,8,9],date:1,daughter:8,david:17,dbh:2,dbo:2,dcomposit:13,dead:2,deal:[0,1,2,5,6,9,13,15],debt:5,debug:3,decad:1,decai:[1,5,15],decent:8,decid:[3,7],decim:1,decis:[1,2,6,9,12,14,17],decision_funct:6,decision_tre:7,decisiontreeclassifi:[7,8],decisiontreeregressor:[1,7,8],declar:[1,13],decompos:[4,13],decomposit:[1,10],decompost:4,decorrel:8,decreas:[2,3,5,8,9],deduc:1,deep:[5,10,12,14,17],deep_tree_clf1:7,deep_tree_clf2:7,deep_tree_clf:[7,8],deepen:12,deeper:0,deeplearningbook:17,def:[0,1,2,3,4,5,6,7,8,9,15],def_covari:15,defect:4,defici:4,defin:[0,1,3,4,5,6,7,8,9,10,13,15],definit:[0,2,3,4,5,6,8,9,10,13],defint:15,degre:[3,4,6,7,8,9,15],del:2,delet:[3,15],deliv:14,delta:[0,1,6,10,15],delta_:[2,13],delta_h:[1,2],delta_j:10,delta_k:10,delta_l:2,delta_n:1,delug:12,delv:1,demand:5,demonstr:[1,3,4,5,9,10,12],denomin:2,denot:[2,3,5,15],dens:[0,2],densiti:[0,1,3,15],depart:16,depend:[0,1,2,3,4,5,6,9,10,12,15],depict:15,deploy:[1,12],deprec:1,depth:[7,8,13],deriv:[1,2,3,4,6,8,9,12],descend:[4,7,9],descent:[1,2,6,10],descr:1,describ:[0,1,3,6,8,9,10,13],descript:[0,1,6,7],design:[1,2,3,4,5,8,9,10],designmatrix:1,desir:[0,1,4,5],despit:[2,10],destroi:13,det:[4,13],detail:[0,1,5,9,13],detect:[6,10],determin:[1,5,6,7,8,9,10,13,15],determinist:[5,15],dev:[2,15],develop:[0,1,6,8,9,10,12,13],deviat:[1,2,3],devis:10,df1:[],diag:[4,6],diagnost:[2,8],diagon:[1,4,5,13,15],diagonaliz:4,diagram:8,dict:6,dict_kei:1,dictionari:1,did:[0,1,2,4,5,8,9],die:2,diffeent:6,differ:[0,1,2,3,4,7,8,9,10,12,13,15,17],differenti:[5,12,13],difficult:[0,1,2,3,8,15],difficulti:[1,2,5],digit:[1,2,14,16],dilut:2,dim:[0,9],dimens:[0,1,2,4,6,9,13],dimension:[0,1,3,4,5,7,9,12,13],dimensionless:1,diment:13,dimes:0,direct:[0,1,2,5,9,10],directli:[0,2,15],directori:5,disadvantag:1,discard:[3,9],disciplin:[1,10],disclaim:15,discourag:5,discov:1,discret:[2,5],discrimin:[5,8,9],discuss:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,17],diseas:5,disk:[],disord:[2,5],displai:[0,1,2,3,5,6,7,8,9,10,15],displaystyl:[1,4],displot:1,disregard:1,dissimilar:[0,9],dist:0,distanc:[0,1,6,7,9,15],distance_list:7,distinct:[0,5,6,7,8],distinctli:6,distinguish:[1,5,6,15],distplot:1,distribut:[0,1,2,3,5,8,9,12,13],distrubut:[1,12],dive:[1,4,6],diverg:[2,5],divid:[1,2,3,6,7,9,10,15],divis:[3,6,7,13,15],dna:5,dnn:[1,2,10],dnn_kera:2,dnn_model:2,dnn_numpi:2,dnn_scikit:[1,2],doc:[12,14,17],doconc:[],document:9,doe:[0,1,2,3,4,5,6,8,9,10,13,15],doesn:[7,10],dog:2,doi:0,doing:[1,3,9],domain:[5,6],domin:1,don:[0,1,2,6,9,12,15],done:[0,1,3,4,7,8,9,13,15],dot:[1,4,5,6,7,8,9,10,13,15],doubl:[13,15],doubli:2,down:[1,5,7,9,10],download:[1,2,13,17],dozen:2,dramat:9,draw:[3,5,8],drawback:[1,2,5],drawn:[2,3,5,9,15],drop:[1,2,3,4,9,15],dropna:[1,3],dtype:[0,1,2,13],dub:1,due:[0,2,3,4,5,6,8,10],dummi:1,dure:[1,2,6,7,9,12],dwell:1,dwh:2,dwo:2,dx_1:15,dx_n:15,dying:2,each:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16],eapprox:1,earli:2,earlier:[1,5,6,7,9,10],eas:[0,7],easi:[1,3,4,5,6,7,8,9,10,12,13],easier:[3,6,7,15],easiest:5,easili:[1,2,4,5,6,7,8,9,10,13],eastern:16,ebind:1,eblock:7,econometr:[],ecosystem:12,ect:14,edgecolor:3,edit:[],edu:5,educ:1,eface79dac2c:8,eff:15,effect:[2,8,15],effic:2,effici:[1,5,8,12,13,15],efron:[3,15],eig:[4,5,9,13,15],eigen:15,eigenpair:[4,9],eigenvalu:[4,5,6,9,13],eigenvector:[4,5,9],eight:13,eigval:[13,15],eigvalu:[5,9],eigvec:[13,15],eigvector:[5,9],eispack:13,either:[1,2,3,4,5,6,7,8,9,15],ekstrom:16,elabor:15,electr:[1,10],electur:17,eleg:9,element:[2,3,4,5,6,9,10,12,13,14,17],elementari:[8,13],elessar:[],elif:0,elim:13,elimin:6,els:[2,5,7,10,15],elu:2,elus:1,email:[14,16],embed:[1,9],embodi:3,emit:15,emner:17,emphas:[1,8,12],emphasi:[1,12,17],empir:[2,9,15],emploi:[1,2,3,5,9,15],employ:1,empti:[3,8],emul:10,enabl:9,encapsul:0,encod:[0,1,7,9],encompass:[1,15],encount:[1,2,4,5,15],end:[0,1,2,3,4,5,6,7,8,9,10,13,15],endl:15,endpoint:15,energi:[1,3],enforc:10,eng:17,engin:[1,2,12,15],english:17,enough:[1,3,5],ensembl:[2,7,14,15],ensur:[1,2,3,5,9,15],enter:[4,15],enthought:[1,12],entir:[2,5,7,12,15],entiti:[7,10,13],entri:[1,4,6,9,10,13],entropi:[2,5,8],enumer:[1,2,6],env:15,environ:[12,17],eol:1,eosfit:1,epoch:[1,2,5,10],epsilon:[1,3,5],epsilon_0:1,epsilon_1:1,epsilon_2:1,epsilon_:1,epsilon_i:1,eqnarrai:3,equal:[0,1,2,3,4,5,6,7,9,10,13,15],equat:[0,2,3,4,6,7,8,9,13,15],equilibrium:10,equiv:[5,13,15],equival:[2,4,6,9,12,13],erf:15,eriador:[],eridg:1,err:[1,8],err_:3,errat:5,errno:5,error:[1,2,3,4,5,7,9,10,12,15],error_estimate_corr_tim:15,error_hidden:2,error_output:2,escap:5,esl:0,esol:[],especi:[2,7,10],essenti:[0,1,4,7,8,10,15],establish:[1,8,9],estim:[1,2,3,4,5,8,9,12,15],estimated_mse_fold:3,estimated_mse_kfold:3,estimated_mse_sklearn:3,esubex:[],eta0:[5,6],eta:[1,2,5,6,10],eta_v:[1,2],etc:[0,1,2,4,5,6,7,9,10,12,13,15],ethic:12,etsim:3,euclidean:[0,1],evalu:[1,3,4,5,7,15],even:[0,1,2,3,5,6,7,8,9,10,12,13,15],event:[5,8],eventu:[3,4,5,9,10,16],everi:[0,1,2,3,5,7,8,9,10,12,15],everyth:10,everywher:5,evolv:1,exact:[1,4,5,9,10,13,15],exactli:[1,10,12],examin:3,exampl:[3,4,9,10,12,13,14,17],exce:[2,10],excel:[0,1,2,8,17],except:[3,6,7,15],excess:1,excit:1,exclud:[2,3,10],exclus:[1,2,3,15],execut:4,exemplifi:5,exercis:[12,14],exhaust:3,exhibit:[1,6],exist:[0,1,2,3,5,6,7,13,17],exit:[4,13,15],exp:[1,2,3,4,5,6,8,9,10,15],exp_term:2,expand:[4,5,9],expans:[1,4,5,6,8,10],expect:[1,2,3,4,5,9,10,12],expectation_value_of_h_wrt_p:15,expens:[3,5,8,15],experi:[1,2,3,5,6,12],experiment:[1,3,7,15],expert:[2,7],explain:[0,1,5,7,8,9,15],explained_variance_ratio_:9,explanatori:1,explicit:[1,13],explicitli:[0,1],explod:2,exploit:[1,10],explor:[2,5,6,12],expon:2,exponenti:[1,2,5,8],export_graphviz:7,export_text:7,exporttext:7,expos:12,express:[1,3,4,8,10,13,15],exptmean:15,exptvari:15,extend:[9,12],extens:[1,10,12],extent:[1,2,3,17],extern:7,extra:[2,4],extract:[1,4,5,6,9,13],extrapol:1,extrem:[0,1,2,5,6,7,13],extremum:5,extrins:9,eye:[0,1,5,13],f11:1,f12:1,f13:1,f1d:5,f6d7a289d493:13,f_0:8,f_1:[5,8],f_2:[5,10],f_3:10,f_d:15,f_i:[3,10],f_m:8,face:5,facecolor:[3,6,15],facil:[1,12],facilit:10,fact:[1,2,4,5,7,9,10],factor:[1,2,4,7,8,9,13,15],fafab0:[7,8],fail:[3,5,6,9,16],failur:5,fairli:[0,2,15],fall:[6,7,14],fals:[0,1,2,3,5,7,8],famili:[1,5,6,15],familiar:[1,6,12,13,15],famou:[3,10,13],far:[0,1,4,5,6,9,10,15],fashion:[1,7,8],fast:[2,3,5,8,10,12,15],faster:[2,9],fastest:5,favor:5,favorit:15,featur:[1,2,3,4,5,6,8,9,10,12,15],feature_nam:[1,2,5,7],feautur:7,fed:2,feed:[1,9,12,14],feed_forward:2,feed_forward_out:2,feed_forward_train:2,feedforward:[2,10],feel:[0,1,9,12,16],feet:1,few:[0,2,7,11,15],fewer:[1,7,9],ffnn:[2,10],field:[1,10,12],fifth:1,fig:[0,1,2,5,10],fig_id:[1,3,5,7],figaxi:15,figsiz:[1,2,3,5,6,7,8],figur:[0,1,2,3,5,6,7,8,10,12],figure_id:[1,3,5,7],figurefil:[1,3,5,7],file:[1,2,3,5,6,7,13,15],filenam:[1,15],filenotfounderror:5,fileout:15,fill:[4,7],financ:1,find:[0,1,2,3,4,5,6,7,8,9,10,12,15],fine:[0,1],finit:[3,4,10,15],first:[0,1,2,3,4,6,7,8,9,13,15,17],firsteigvector:9,fit:[2,3,4,5,6,7,9,10,15],fit_intercept:3,fit_mod:7,fit_transform:[1,3,6,7,9],fiti:1,five:[1,7],fix:[1,3,5,8,9,10],flag:0,flat:[5,10],flatten:2,flexibl:[1,2,3,6,8,10],float32:7,float64:[1,13],flop:[4,13],flow:[2,10],fly:9,flyvbjerg:15,fmesh:5,focu:[1,3,12,17],focus:[2,5,13],fold:[3,7],folder:1,follow:[0,1,2,3,4,5,6,7,8,9,10,12,13,14,15,16,17],font:[1,5,15],fontdict:15,fontsiz:[2,6,7,8,15],fontweight:2,foral:6,forc:[1,4,8,9],forecast:10,forest:[1,2,7,12,14],forget:9,form:[1,3,4,5,6,7,9,10,12,13,15],formal:[0,15],format:[1,2,3,5,6,7,8,9,12,15,17],formatstrformatt:5,formul:[0,9],formula:[5,15],forth:10,fortran2003:12,fortran90:15,fortran:[1,12,13],fortun:[1,9],forward:[1,3,12,13,14],found:[2,3,5,10],foundat:12,four:[6,10,13,14],fourier:1,fourth:10,frac:[0,1,2,3,4,5,6,7,8,9,10,13,15],fraction:7,frame:5,framework:[2,6,8,15],frank:[4,9],frankefunct:[1,4,9],free:[1,9,12,13,15,16,17],freecodecamp:12,freedom:4,freeli:1,frequenc:[3,5,15],frequent:[1,5,6,7],frequentist:12,fresh:8,fret:0,fridai:14,friedman:[14,17],frodo:[],from:[0,1,2,3,4,5,6,7,9,12,13,14,15,16,17],from_cod:7,front:1,fruit:0,fstream:15,fulfil:[4,10],full:[1,2,4,5,7,8,15],fulli:[3,10,14,15],fun:12,func:3,functionali:9,fundament:[1,3,12],further:7,furthermor:[1,3,4,5,9,10,12],futur:[1,6,7],futurewarn:1,fys:16,g_1:8,g_2:8,g_m:8,gain:[0,2,4,7,8],galleri:1,gamge:[],gamma1:6,gamma2:6,gamma:[1,5,6,7,8,9],gamma_0:8,gamma_1:8,gamma_1x:8,gamma_:1,gamma_i:[1,6,15],gamma_j:5,gamma_k:5,gamma_m:8,gamma_x:1,gap:6,gate:10,gather:[2,10],gaug:10,gaussbacksub:13,gaussian:[0,3,6],gaussian_point:0,gaussian_rbf:6,gave:5,gbc:14,gca:[3,5,6],gd_clf:8,gdclassiffiercgain:8,gdclassiffierconfus:8,gdclassiffierroc:8,gdregress:8,gen:15,gender:1,gener:[0,1,2,3,4,5,6,8,9,10,13,17],generallay:10,generate_simple_clustering_dataset:0,genom:12,geodes:9,geometr:1,georg:17,geq:[4,5,6,7],geron:[1,14,17],get:[0,1,2,3,5,7,8,9,12,13,15],get_distances_to_clust:0,get_dummi:7,get_split:7,get_yaxi:6,getattr:3,gibb:12,gini:8,gini_index:7,git:[1,12],github:[1,12,14,17],gitlab:[1,12],give:[0,1,2,4,5,6,7,8,10,12,15,17],given:[0,1,2,3,4,5,6,7,8,9,10,13,15],glare:0,global:[5,15],glorot:2,gmail:16,goal:[1,5,7],goe:[0,1,2,3,4,5,13],going:[1,2,3,4,5,6,7,9,10],golden:5,gone:4,gong:2,good:[0,2,5,7,8,9,12,15,17],goodfellow:[14,17],googl:[0,2,12],got:2,gpu:2,grade:14,gradient:[1,6,7,10,12,14],gradientboostingclassifi:8,gradientboostingregressor:8,gradual:[0,2],graph:[2,5,7,9,10],graph_from_dot_data:7,graphic:[1,2,7],grasp:1,gray_r:2,great:5,greater:[2,5,15],greedi:7,green:[1,7,15],grid:[2,3,5,6,10,15],grossli:5,ground:1,group:[0,1,3,5,7,12,14],groupbi:1,grow:[2,7,8],growth:1,guarante:[1,5,15],guess:[0,2,5,8],guestrin:8,guid:2,h_1:5,h_2:5,h_m:8,had:[1,2,3,4,5],hadamard:[2,10],half:[2,6,7],halv:8,hand:[1,2,5,9,10,12,13,14,15,17],handl:[1,2,7,9,12],handle_unknown:7,handsid:10,handwrit:10,handwritten:[2,5],happen:[0,2,4,5,8,15],hard:[2,5,6,8],hardcopi:12,harder:[1,2],has:[0,1,2,3,4,5,6,7,8,9,10,13,15],hasn:[1,2],hassl:[1,12],hast:12,hasti:[0,1,14,17],hat:[2,4,5,7,8,9,10,13,15],have:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],haven:2,hdf5:[],head:[1,8,15],header:1,heads_proba:8,hear:1,heart:[1,5],heatmap:[1,2,5],heavili:1,heavisid:2,height:2,help:[0,1,2,10],helper:0,henc:[1,3,4,5,6,7,8,10],her:5,here:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,17],hereaft:[1,6,10],hermitian:13,hessenberg:13,heterogen:[7,8],hidden:[2,10],hidden_bia:2,hidden_bias_gradi:2,hidden_layer_s:[1,2],hidden_weight:2,hidden_weights_gradi:2,hierarch:0,high:[0,1,2,3,4,5,7,8,9,12,13],higher:[1,2,3,5,6],highest:2,highli:[1,4,8,12,13,15,17],highwai:1,hing:6,hint:5,hip:12,hire:1,his:5,hist:[3,5,15],histogram:[1,3,5,15],histor:[5,9],histori:10,histplot:1,histtyp:3,hjorth:16,hobbi:15,hoc:4,hoff:17,hold:[0,2,3,5,15],holder:1,home:1,homogen:[2,7,8],hopefulli:[1,9,15],horizont:9,hors:5,hot:[2,7],hour:[2,12,14,15,16],how:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,17],howev:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],hspace:[1,6,8,15],hstack:2,htf:14,html:[9,12,14,17],http:[5,9,12,13,14,17],huang:1,huber:1,huge:[2,12],human:[1,2,7,10],humid:7,hundr:2,hungri:2,hybrid:14,hydrogen:1,hyperbol:[2,10],hyperparam:6,hyperparamet:[0,1,4,7],hyperplan:9,i_1:3,i_2:3,ian:17,idea:[1,2,3,5,7,8,10,13,15],ideal:[0,1,3,6,15],idem:3,ident:[3,4,10,13],identifi:[0,1,2,5,6,7,9,10],idum:15,ieor:15,ifi:17,ifs:12,ignor:[1,2,7],iii:13,ijca2016907841:0,illustr:[0,5,8,10,12],imag:[2,7,9,10,17],image_path:[1,3,5,7],imagin:2,immedi:[1,12],implement:[0,1,5,6,7,8,9,10],impli:[3,4,5,13],implicitli:[9,15],impos:[1,9,10],imposs:[1,4],impress:[1,10],improv:[0,4,7,8,9],impur:7,imshow:2,in3050:17,in4080:17,in4300:17,in5400:17,inaccur:5,inact:10,inadequ:1,includ:[1,2,5,9,10,12,15,16,17],include_bia:[3,7],incom:10,inconsist:[],incorrect:2,incoveni:6,increas:[1,2,3,6,7,9,10,15],increasingli:15,ind:3,inde:[1,4],indent:15,indentationerror:15,independ:[1,3,4,5,6,10,15],index:[0,1,2,8,12,15,17],index_col:1,indic:[1,2,4,7,8,9],indispens:3,individu:[2,5,8,10,15],indu:1,indx:13,ineffici:0,inequ:6,inequaltii:5,inf1000:12,inf1100:12,inf1100l:12,inf1110:12,inf3000:17,inf4490:17,inf5860:17,inf:1,infeas:7,infer:[1,2,3,17],inferenc:2,infil:[1,3,5,7],infin:[3,4,5,9],infinitesim:15,influenc:[3,8],influenti:2,info:[],inform:[0,1,2,3,5,7,9,10,13,17],infti:[5,15],ingeni:5,ingredi:[1,7],inher:3,inherit:13,initi:[0,1,2,3,5,8,13,15],initialis:15,inject:0,inlin:[0,1,2,3,5,6,7,8,9,10,13,15],inner:[3,5],innov:17,input:[0,1,2,3,4,5,6,7,8,10,13,15],input_dim:2,inputs:2,inputs_shuffl:2,insert:[6,8,15],insid:[1,5],insight:[1,2,4,12,17],insist:5,inspir:[1,2,10,17],instal:[1,2,7,14],instanc:[1,2,3,5,7,9],instanti:8,instead:[0,1,2,3,4,5,6,7,9,13,15],institut:2,instruct:[1,2],int32:8,int64:1,int_0:15,int_:15,int_a:15,integ:[0,2,13,15],integer_vector:2,integr:15,intellig:[0,1,17],intend:8,intens:2,intention:0,interact:[1,7,10,12],intercept:[1,5,6,9],intercept_:[1,5,6,7],interchang:[10,13],interconnect:2,interest:[1,2,3,4,5,6,7,10,12,15],interfac:[2,13],interior:[1,7],intermedi:13,intern:[2,8,10],interpol:[2,10],interpr:4,interpret:[1,2,7,8,10,13,14,15],interv:[1,3,5,15],intial:5,intimid:[],intract:1,intrins:[9,13,15],intro:[12,17],introduc:[1,2,5,6,8,10,13,15],introduct:[2,5,14,17],introductori:[1,13,17],intuit:[1,3,6,10],inv:[1,4,5],invalid:[2,6,13],invalu:[1,5,12],invari:2,invd:4,inver:6,invers:[1,4,5],invers_period:15,inverse_transform:6,invert:[1,4,5,8],invok:[1,6],involv:[1,3,5,9,10],iomanip:15,ios:15,iostream:15,ipca:9,ipynb:12,ipython:[0,1,2,3,5,6,7,8,9,12,13,15],irani:0,iri:[6,7],irreduc:3,irrelev:4,irrespect:1,isnul:1,isomap:9,issu:[2,7,13],it_arrai:5,item:1,items:13,iter:[0,1,2,3,6,9,15],its:[0,1,2,3,4,5,6,7,8,9,10,12,13,17],itself:[3,10,15],jackknavg:15,jackknif:[3,12],jackknstd:15,jackknvar:15,jackknvec:15,jacobian:5,jargon:15,jensen:16,jerom:17,job:[6,8],join:[1,3,5,7],journal:15,judg:5,julia:[12,13],jump:[0,15],jupyt:[0,1,12,17],just:[0,1,2,3,4,5,6,7,8,9,10,15],justif:1,justifi:8,k_mean:0,kappa_d:15,karim:16,karlsen:16,karush:6,keep:[0,1,2,4,5,9,13],keepdim:[2,3,8],kei:[1,2,10,17],kept:0,kera:[1,12,14],kernel:[1,2,12],kernel_regular:2,kernelpca:9,kev:1,kevin:17,keyword:13,kfold:3,kick:[2,5],kind:[0,1,6,10],kjm:12,kkt:6,kmeanspoint:0,kn_k:0,know:[0,1,2,4,5,6,12,15],knowledg:[1,12],known:[2,3,5,6,7,10,13,15,17],kondev:1,kpca:9,kroneck:0,kuhn:6,kwarg:3,kwown:1,l1_l2:2,l_1:5,l_2:5,l_j:10,la_i:10,la_k:10,lab:[12,14],label:[0,1,2,3,4,5,6,7,8,10,12,13,15],labelencod:[5,8],labels:[6,7],labels_shuffl:2,labor:0,laboratori:14,lack:[0,1],lagrang:[6,9],lambda:[1,2,3,4,5,6,8,10,15],lambda_0:9,lambda_1:[4,6,9],lambda_2:[6,9],lambda_:9,lambda_i:[6,9],lambda_iy_i:6,lambda_jy_iy_j:6,lambda_k:6,lambda_n:[4,6],lamda:2,land:[1,6],landmark:6,landscap:5,langl:[1,9,15],languag:[1,2,6,12,13,17],lapack:13,laptop:12,larg:[1,2,3,4,5,6,7,8,9,12,13,15,17],larger:[1,3,4,5,6,8,9,15],largest:[6,9],lasso:[1,3,5,12,14],last:[0,1,2,3,4,5,6,7,8,10,13,15],later:[0,1,2,4,5,6,10,12,15],latex:[],latter:[1,5,6,9,13,15],lattic:10,law:1,layer:1,lbfg:[5,7,8,9],lbl:3,lcc:3,lda:9,ldot:[1,3,9,15],lead:[1,2,3,4,5,6,7,8,9,10,13,15],leaf:7,leaki:2,lear:5,learn:[3,6,7,8,10,13,14,17],learner:8,learning_r:[6,8],learning_rate_init:[1,2],learning_schedul:5,least:[0,1,3,4,5,6,8,9,12,13,15],leav:[1,2,3,7,9],lectur:[1,2,4,5,8,9,10,12,13,14,17],lecturenot:[12,17],left:[1,2,3,4,5,6,7,8,9,10,13,15],leftarrow:[6,10],legend:[1,3,5,6,7,8],len:[0,1,2,3,4,6,7,8,9,10,13,15],length:[1,2,5,6,7,12,15],leq:[0,1,4,5,6,15],less:[1,2,3,4,6,7,15],lessen:2,let:[0,1,2,3,4,5,6,7,8,9,10,13,15],letter:[1,13,15],level:[1,2,3,7,12,13,14],lib:[1,2,3,6,9],liblinear:[6,8],librari:[1,2,7,8,9,13,15,17],licens:[1,2,12],lie:[3,9,15],lies:[6,9],life:[1,2,6,10],lift:0,light:[],like:[0,1,2,3,4,5,7,8,9,10,12,13,15],likelihood:[1,2,7],lim_:15,limit:[1,3,6,9,10,13],lin_clf:6,lin_model:1,lin_reg:7,linalg:[1,4,5,6,9,13,15],line1:6,line2:6,line3:6,line:[1,2,3,5,6,9,13,15],linear:[2,3,4,5,7,8,9,10,12,14,15],linear_model:[1,3,5,6,7,8,9],linear_regress:3,linearli:4,linearloc:5,linearregress:[1,3,5,7],linearsvc:6,liner:2,linerar:8,linewidth:[1,3,6,7,8],link:[1,5,7,10,12],linpack:13,linreg:1,linspac:[1,3,6,7,8,13,15],linu:16,linuek:16,linux:[1,2,12],liquid:1,list:[0,1,2,7,12],listedcolormap:[7,8],literatur:[0,2,5,17],littl:[2,7,10],live:6,lle:1,lloyd:0,lmb:3,lmbd:[1,2],lmbd_val:[1,2],lmbda:5,load:[1,2,5,7,8],load_boston:1,load_breast_canc:[2,5,7,8,9],load_digit:2,load_iri:[6,7],loc:[1,3,5,6,7,8],local:[1,2,5,10],locat:6,log10:3,log:[1,2,3,5,7,8,9,13],log_:1,log_clf:8,logarithm:[1,5,13],logic:[1,2,7],logist:[1,2,6,7,8,9,10,12,14],logisticregress:[5,7,8,9],logit:5,logreg:[5,7,8,9],logspac:[1,2,3],longer:[0,6,8,13,15],longest:0,loocv:3,look:[0,1,2,3,4,5,6,7,8,9,13,15],loop:[0,2,3,8,10,12,13,15],lose:2,loss:[1,2,3,4,6,8,9,13],lot:[0,1,2,3],low:[1,3,7,8,9,15],lower:[1,2,7,8,13],lowercas:13,lowest:[5,7,15],lstat:1,lstsq:1,lubksb:13,ludcmp:13,lux:13,lvert:2,m_1:0,m_h:1,m_k:0,m_l:10,m_n:1,m_p:1,machin:[2,3,7,8,9,10,13,14,17],machinelearn:[12,14,17],mackai:17,made:[1,2,3,4,5,7,9,10],mae:1,magic:0,magnitud:[2,5],mai:[1,2,3,4,5,6,7,9,10,12,13,15],mail:14,main:[1,2,4,5,7,13,17],mainli:[1,3,5,7],maintain:3,major:[2,3,5,7,8,13],make:[0,2,3,4,5,6,9,10,12,13,15,17],make_moon:[6,7,8],make_pipelin:[1,3,8],makedir:[1,3,5,7],makeplot:1,malcondit:13,malign:[2,5,7],manag:[1,12],manhattan:0,mani:[0,1,2,3,5,6,7,9,11,12,13,15,17],manifold:9,manual:0,map:[0,1,2,3,5,6,9,10,15],margin:[1,6],marit:1,mark:[],marker:[1,5,13],markov:12,marsaglia:15,mask:15,mass:[1,2,4],massag:1,masses2016:1,masses2016ol:1,masses2016tre:1,masseval2016:1,master:14,mat1100:12,mat1110:12,mat1120:12,mat3155:14,mat4155:14,mat:12,match:[0,2,5],materi:[4,5,13],math:[0,4,5,10,13,15,17],mathbb:[0,1,3,4,5,6,9,10,13,15],mathbf:[1,3,4,5,6,13,15],mathcal:[2,3,5],mathemat:[0,1,4,5,9,10,12,13,15,17],mathemati:[],mathemt:4,mathrm:[0,1,2,3,4,5,6,7,8,9,10,15],matmul:[2,4],matnat:[16,17],matplotlib:[0,1,2,3,5,6,7,8,9,10,12,13,15],matric:[1,2,4,5,6,9,12,13],matrix:[1,3,4,6,8],matshow:2,matter:5,max:[1,2,5,7,8,10],max_depth:[1,7,8],max_it:[1,2,5,6,9],max_iter:0,max_leaf_nod:8,max_sampl:8,maxdegre:[1,3,8],maxdepth:8,maxim:[2,5,6,9],maximum:[0,1,2,5,6,7,8],maxpolydegre:3,mbox:[3,4],mcculloch:10,mcint:15,mcintsqr2:15,mean:[1,2,3,4,5,7,8,9,10,12,13,14],mean_absolute_error:1,mean_divisor:0,mean_i:15,mean_matrix:0,mean_squared_error:[1,3,5,8],mean_squared_log_error:1,mean_vector:0,mean_x:15,meaning:[1,5],meansquarederror:1,meant:[5,8],meantempvec:15,meanvec:15,measur:[0,1,2,3,7,9,10,15],mechan:[1,15],median:1,medicin:10,medium:6,medv:1,meet:[1,16],mehta:[1,4],memori:[9,10,13],mention:[0,1,5,10,15],mere:1,mersienn:15,meshgrid:[1,4,6,7,8,9],met:[1,6],meteorolog:7,method:[0,1,2,4,6,9,10,12,13,14,17],metric:[0,1,2,3,5,7,8],metropoli:12,mev:[1,15],mglearn:12,mgrid:5,mhjensen:[1,2,6,9],microsoft:17,mid:2,midpoint:7,might:[1,2,5,7],mild:7,miller:15,million:1,mimic:10,min:[1,4,6,7],min_:[0,1,4],min_samples_leaf:7,mind:[0,1,5],mine:12,mini:[2,5,9,10],minibatch:[2,5,9],minibathc:5,minim:[0,1,2,3,4,5,6,7,8,9,10,15],minima:[1,2,5],minimum:[1,2,3,5,6,7,9],minkowski:0,minmaxscal:1,minor:15,minst:2,minu:5,mirror:7,misclassif:[6,7,8],misclassifi:[6,8],mismatch:2,miss:[1,8],mit:17,mix:2,mkdir:[1,3,5,7],mlab:[3,15],mle:5,mlp:2,mlpclassifi:2,mlpregressor:1,mnist:[2,9],mod:15,mode:[14,15],model:[0,3,4,5,6,7,8,9,12,15,17],model_select:[1,2,3,5,7,8,9],moder:8,modern:[1,3,5,12],modif:10,modifi:[1,2,4,5,6,8,10],modul:[1,3,5,7,8,9,13],modular:15,modulenotfounderror:7,modulo:15,moe:[4,9],moment:3,monoton:[10,15],mont:[1,3,12,15,17],montecarlocycl:15,more:[1,2,3,4,6,7,8,9,10,12,14,15],moreov:1,morten:16,most:[0,1,2,3,4,5,6,7,8,9,10,12,15],mostli:[2,9],motion:1,motiv:2,move:[0,1,5,7,10,15],mpl:[1,5],mpl_toolkit:5,mplot3d:5,mplregressor:2,mse:[1,3,4,7,8],mse_simpletre:8,msg:1,msle:1,mt19937_64:15,mu0:15,mu1:15,mu2:15,mu_:15,mu_n:9,mu_x:15,much:[1,2,3,5,6,7,8,9,10,13,15],multi:[1,2,5,12],multiclass:[2,5],multidimension:[9,10],multilay:2,multinomi:5,multipl:[3,5,10,15],multipli:[4,5,9,13,15],multiplum:6,multitud:[],multivari:[1,8,9,12,15],multivariate_norm:[0,9],murphi:[9,17],must:[0,2,3,5,6,8,10,15],mutat:5,mutual:[2,3,5],mx_:15,myriad:[1,12],mz1:15,mz2:15,n_0:[10,15],n_b:15,n_boostrap:[3,8],n_bootstrap:3,n_categori:2,n_cluster:0,n_compon:9,n_epoch:5,n_estim:8,n_featur:2,n_hidden_neuron:[1,2],n_i:15,n_input:2,n_instanc:7,n_iter_i:9,n_job:8,n_k:0,n_l:[10,15],n_layer:2,n_m:7,n_neuron:2,n_neurons_layer1:2,n_neurons_layer2:2,n_point:0,n_sampl:[0,3,6,7,8],n_split:3,nabla:[2,5],nabla_:5,naimi:1,naiv:[0,5],nall:1,name:[0,1,2,3,5,6,7,8,10,12,13,16],nameerror:8,namespac:15,nation:2,nativ:12,natur:[1,2,5,6,7,10,15,17],navier:10,nb_:13,nbconvert:[],nboot:15,nearest:[2,9],nearli:5,neat:[],neccesari:3,necessari:[0,1,2,6],necessarili:[1,9,15],neck:5,need:[0,1,2,3,4,5,6,7,8,9,10,13,15],neg:[1,2,3,5,8,15],neg_mean_squared_error:3,neglect:15,neglig:15,neighbor:9,neq:[0,5,15],nervou:10,nest:[7,10],net:10,netlib:13,network:[1,7,12,14,17],neural:[1,5,12,14,17],neural_network:[1,2],neuralnetwork:2,neuron:[2,10],neutral:1,neutron:1,never:[2,3,7,15],new_hobbit:[],new_sig:3,newaxi:[1,3,7],newli:1,newton:[2,6,15],next:[0,1,2,5,6,7,15],next_guess:5,nian:16,nice:[0,1,2,9],nichola:16,nicholaskarlsen1102:16,niter:5,nitric:1,nlambda:3,nm_n:1,nmse:3,nn_model:2,node:[2,7,8,10],nois:[1,3,5,6,7,8],noisi:[2,3],non:[0,1,2,3,4,5,7,8,9,10,13,15],none:[0,1,2,3,5,7,8,15],nonetheless:0,nonlinear:[3,6,7,9,10],nonneg:[3,5,7],nonparametr:3,nonsens:15,nonsingular:13,nonumb:[5,6,13],nor:2,norm:[1,2,3,4,5,6,9],normal:[3,4,5,6,7,8,9,10,12,13],normali:13,normpdf:3,notat:[0,1,3,15],note:[0,1,2,3,4,5,6,9,10,12,13,14,15,17],notebook:[0,1,2,7,12],noth:[0,2,4,6,10,15],notic:[10,13,15],novel:[3,8],novemb:2,now:[0,1,3,4,5,6,8,9,10,13,15],nowadai:[1,2,7,12],nox:1,np_assign_points_to_clust:0,np_get_distances_to_clust:0,np_k_mean:0,nsampl:3,nspin:15,nthi:1,nuclear:4,nuclei:[1,15],nucleon:1,nucleu:1,num_tre:8,number:[0,2,3,4,5,6,7,8,9,10,13,14,16],numberid:5,numer:[1,3,4,5,7,8,9,10,12,13,17],numpi:[0,1,2,3,4,5,6,7,8,9,10,12,15],obei:[5,9],object:[1,2,3,6,8,13],observ:[0,2,3,4,5,6,7,8,9,10],obtain:[0,1,2,3,4,5,6,7,8,10,13,15],obviou:[4,9,15],obviouli:1,obvious:[1,13],occupi:1,occur:[1,6,7,15],odd:[1,5],off:[2,3,7,15],offend:[],offer:[3,9,12,13,14],offic:16,offici:14,ofil:15,ofstream:15,often:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],ofter:13,old:[2,5,8],omit:1,onc:[0,2,3,7,9,15],one:[0,1,2,3,4,6,7,8,9,12,13,15],onehot:2,onehot_vector:2,onehotencod:7,ones:[1,4,5,6,7,8,9,13],onli:[0,1,2,3,4,5,6,7,8,9,10,13,15],onlin:[9,14],onto:9,open:[1,2,3,5,7,12,14,15],oper:[1,2,3,4,8,9,10,12,13,15],operation:15,opinion:0,oplu:15,opmiz:5,opportun:1,opposit:[2,6],opt:[1,2,3,6,9],optim:[0,1,3,4,7,8,9,14],optimis:2,option:[2,6,9],optmiz:[2,6],orang:1,order:[1,2,3,4,5,6,7,8,9,10,13,15],ordinari:[1,3,4,5,9,12,14],oreilli:17,org:[9,12,13,17],organ:[0,3,5,8,13],orient:[2,3,15],origin:[1,3,6,9,10,13,15],orthogn:4,orthogon:[1,4,5,6,9,13],orthonorm:4,oscar:2,oslo:[1,14,16],osx:[1,12],other:[0,1,2,3,4,5,6,8,12,13,14,17],otherwis:[1,2,5],ouput:[5,10],our:[2,3,4,6,7,8,10,12,13],ourselv:[0,1,4,5,6,9],out:[0,1,2,3,5,6,7,8,9,10,12,13,15],out_fil:7,outcom:[1,5,7,8,10,15],outdoor:7,outer:10,outfilenam:15,outlier:[1,6],outlin:[3,8,9],outlook:7,outperform:8,output:[1,2,5,6,7,8,10,13],output_bia:2,output_bias_gradi:2,output_weight:2,output_weights_gradi:2,outputlayer1:10,outputlayer2:10,over:[0,1,2,3,5,7,8,10,15],overal:[2,8],overcast:7,overcom:10,overdetermin:1,overfit:[2,3,7,8],overflow:2,overhead:10,overlap:[5,6,7],overlin:[0,3,4,7,8,9,13],overst:1,overview:[0,17],own:[5,6,10,12,13],owner:1,oxid:1,oyvinssc:16,p_i:15,p_j:15,p_n:15,p_x:15,pack:1,packag:[1,2,3,4,5,6,9,12,15],page:[1,12],pai:[2,7],painless:[],pair:[1,7,12,15],panda:[1,3,4,5,7,9,12],panel:[],paper:2,paradigm:1,parallel:[8,13],paramet:[1,2,3,4,5,6,7,8,10,15],parameter:[1,8],parametr:[1,3],park:15,part:[0,1,2,3,4,8,13,14,15,17],partial:[1,2,4,5,6,8,9,10,15],particip:[12,14],particl:[1,15],particular:[1,2,3,4,5,7,8,9,10,15,17],particularli:[3,4,5,6,9,15],partit:[2,7],pass:[0,10,15],past:[8,15],patch:[3,15],path:[1,3,5,7,12],patient:5,pattern:[1,10,14,17],pauli:1,pca:[1,5,12,14],pdf:[1,3,7,17],pedagog:1,penalti:[3,5],pentagon:5,peopl:[1,2,7,12],per:[1,2,3,14],percentag:[1,8,9],perceptron:[1,2,5],peregrin:[],perfect:[1,2],perfectli:3,perform:[0,1,3,5,6,8,9,10,12,13,15],perhap:[1,4,5],perimet:2,period:2,permut:9,person:[5,14,16],perspect:17,pertin:10,petal:[6,7],peter:17,petersen:15,phantom:15,phase:10,phatak:0,phenomena:15,phi:6,phi_k:6,philip:16,philosophi:5,phone:16,phrase:1,physic:[1,2,5,10,15,16,17],pick:[0,2,5,7,8,9,15],pickl:2,pictur:1,pie:12,piec:[0,9],pillow:[1,12],pip3:[1,2],pip:[1,2,12],pipelin:[1,3,6,8],pippin:[],pise:0,pitt:10,pixel:2,pixel_height:2,pixel_width:2,place:[1,3,5,6,13],plai:[1,3,6,9,12],plain:[5,6,8,10],plan:[3,7,16,17],plane:[6,7],plateau:15,platform:12,plausibl:10,pleas:[1,9],plenti:2,plethora:10,plot:[0,1,2,3,5,6,7,8,9,10,12,13,15],plot_confusion_matrix:[5,8],plot_cumulative_gain:[5,8],plot_data:2,plot_dataset:6,plot_decision_boundari:[7,8],plot_import:8,plot_predict:6,plot_regression_predict:7,plot_roc:[5,8],plot_surfac:5,plot_train:7,plot_tre:[7,8],plt:[0,1,2,3,5,6,7,8,9,10,13,15],plu:[1,5],png:[1,3,5,7],point:[0,1,2,3,5,6,7,8,9,13,15,16],points_in_clust:0,poisson:12,poli:[3,6],poly100_kernel_svm_clf:6,poly3:1,poly3_plot:1,poly_featur:[6,7],poly_features10:7,poly_fit10:7,poly_fit:7,poly_kernel_svm_clf:6,polydegre:[3,8],polygon:5,polym:10,polynomi:[1,3,4,5,6,7,8,9],polynomial_featur:3,polynomial_svm_clf:6,polynomialfeatur:[1,3,6,7],polytrop:[1,3],poor:[2,5],popul:1,popular:[1,2,3,5,6,7,9,10,12,13,15],popularli:1,portabl:8,portion:9,pose:[1,9,15],posit:[0,1,2,4,5,6,8,9,13,15],possibl:[1,2,3,5,6,7,8,9,10,12,13,15,16],postpon:1,potenti:[1,10],pott:10,power:[1,2,3,4,6,7,10],practic:[1,3,5,6,15],practition:[1,2],preced:[2,9,10,15],preceq:6,precis:[1,4,9,13,15],pred:3,predict:[1,2,3,5,6,7,8,12,17],predict_prob:2,predict_proba:[5,8],predictor:[1,4,5,7,8,9],prefer:[1,2,6,7,9,12],prepar:1,preprocess:[3,5,6,7,8,9],prerequisit:1,present:[1,7,10,15],preserv:9,press:[5,17],pretrain:2,pretti:[1,6,7,12],prev_centroid:0,prevent:15,previou:[1,2,4,5,6,8,9,10,13,15],previous:[0,7,8,15],price:[1,7],primal:6,primari:[1,5],primarili:0,prime:15,princip:[1,5,12,14],principl:[0,1,3,5,6],print:[0,1,2,3,4,5,6,7,8,9,13,15],print_funct:[6,7],printout:1,prior:[1,3],privat:1,prob:[2,15],probabilist:[1,17],probabl:[1,2,3,5,8,12],problem:[1,3,4,6,7,8,9,10,12,13,14,15],proce:[1,5,6,7,8,9,13],procedur:[3,4,5,6,8,9],proceed:13,process:[0,1,3,5,7,8,10,12,13,15,17],prod:17,prod_:[2,5],produc:[0,1,4,7,8,9,10,12,13,15],product:[1,2,3,5,6,10,12,13],profess:1,profil:0,program:[0,1,2,4,6,10,12,13,14,15],programm:13,progress:[0,2],progression_plot:0,prohibit:3,project:[1,2,5,9,12,14,15,16],project_root_dir:[1,3,5,7],promin:10,promis:6,prone:7,pronounc:12,proof:[1,5,9,10],prop:3,proper:[1,3],properli:[0,2,6,8],properti:[1,2,3,4,5,10,13],proport:[1,2,7,9,15],propos:[2,8],propto:5,proton:1,prove:5,provid:[1,2,3,4,5,6,7,8,10,12,13,15,17],proxi:2,prun:0,prune:7,pseudorandom:15,psycholog:1,ptratio:1,punish:[1,2],pure:[7,15],purest:7,puriti:7,purpos:[0,1,8,10],put:2,pycod:[],pydata:12,pydot:7,pyhton2:[],pylab:[1,5],pypi:12,pyplot:[0,1,2,3,5,6,7,8,9,10,13,15],pythagora:0,python2:1,python3:[1,2,3,6,9,12],python:[2,6,9,10,14],pytorch:[1,12],qquad:[9,13],quad:[2,5,13],quadrat:[1,5,6,7],qualit:[7,15],qualiti:[1,7,12],quantifi:2,quantil:8,quantit:[1,3,7],quantiti:[0,1,3,4,5,7,8,9,10,13,15],quantum:10,quartil:1,queri:7,question:[1,3,5,7,9,10],quick:15,quickli:[2,5,7,9],quirk:0,quit:[2,3,7,8,10],quot:15,r2_score:1,r2score:1,r_1:7,r_2:7,r_j:7,r_m:7,rad:1,radial:[1,6,10],radioact:15,radiu:[1,2],rain:7,rais:3,ramp:2,ran1:15,ran2:15,ran3:15,rand:[1,3,5,7,8,13,15],rand_max:15,randint:[3,5,7],randn:[1,2,3,5,7,9],random:[0,1,2,3,4,5,6,7,12,13,14],random_devic:15,random_forest_model:8,random_index:5,random_indic:2,random_st:[1,5,6,7,8,9],randomforestclassifi:8,randomli:[0,2,3,5,7,15],randomnumbergener:15,rang:[0,1,2,3,4,5,7,8,9,10,13,15],rangl:[1,9,15],rangle_x:15,rank:4,raphson:[2,6],rapidli:1,rare:2,rate:[1,2,5,6,7,8,10],rather:[1,2,3,4,5,6,7,8,9,10,13,15],ratio:[5,7,8,9],rational:1,ravel:[1,3,4,5,6,7,8,9],raw:15,rbf:[6,9,10],rbf_kernel_svm_clf:6,rbf_pca:9,rcond:1,rcparam:[1,2,5,6,7,8,15],reach:[0,1,2,3,5,7,8,9,10,15],read:[0,1,3,5,6,9,10,13,14,15,17],read_csv:[1,3,5,7],read_fwf:1,readabl:0,reader:[1,13,15],readi:[0,1,2,6,8,9,10,13],readili:2,real:[1,2,3,4,5,8,9,10,13],realist:6,realiti:15,realiz:[2,10],realli:2,reason:[0,1,2,5,8,17],reassign:2,recal:[3,4,5,7,8,9,10,13,15],recalcul:15,receiv:[2,8,10,15],recent:[1,3,5,7,8],recept:10,recip:[1,5,13],recogn:[1,8],recognit:[1,2,10,14,17],recommend:[1,4,6,12,13,14,15,17],reconsid:7,reconstruct:9,record:[8,14],recreat:15,rectangl:[3,5,7],rectifi:[2,10],recur:[1,12],recurr:[2,12,14],recurs:[7,12,13],recycl:15,red:[1,3,6,7],redefin:[1,8],reduc:[2,4,5,7,8,9],reduct:[1,8,9,12,15],refer:[0,1,2,3,4,5,9,10,13,17],refin:10,refit:3,reflect:[1,2,15],refresh:[12,14],reg:[8,9],regard:[2,5,7],regardless:10,region:[7,10],regist:15,regr_1:[1,7],regr_2:[1,7],regr_3:[1,7],regress:[2,3,6,9,10,12,14],regressor:[1,5,8],regular:[1,5,7],reilli:[1,17],reinforc:[1,6,12],reiter:2,rel:[1,3,5,7,10,15],relat:[0,1,2,5,9,13,15],relationship:[1,7],relativeerror:1,releas:[2,12],relev:[1,2,4,5,9,12,15],reli:[1,6],reliabl:[5,15],remain:[2,3,10,13,15],remaind:15,remark:2,rememb:[1,6,13],remind:[1,4,9,13,15],remov:[1,4],render:1,reorder:5,reorgan:1,repeat:[0,1,2,3,5,7,8,9,13,15],repeated:1,repeatedli:[3,8,15],repetit:[3,14],rephras:5,replac:[0,1,2,3,5,8,10],replica:3,repositori:1,repres:[1,2,3,5,6,7,8,10,15],represent:[1,2,3,15],reproduc:[1,7,10,12,15],repuls:1,request:1,requir:[1,2,3,4,5,6,7,9,10,13],resampl:[1,5,8,12,14,15],rescal:[1,9,10],research:[1,12,17],resembl:[3,15],reserv:[2,3,15],reset:15,reshap:[0,1,2,3,6,7,8],residenti:1,residu:[1,5],respect:[0,1,2,3,4,5,6,8,9,10,15],respond:10,respons:[1,5,7,10],rest:[1,4],restat:[1,10],restrict:[1,7,10],result:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],ret:3,retail:1,retain:[3,4],return_data:0,return_x_i:7,reus:2,reveal:[1,10],revers:2,review:[12,13],revisit:0,reward:1,rewrit:[1,3,4,5,6,8,9,10,13,15],rewritten:[3,4,6,8,15],rewrot:5,rgoj5yh7evk:12,rho:[1,8],rho_1:8,rho_2:8,rho_m:8,rich:1,rid:0,ride:7,rideclass:7,ridedata:7,ridg:[1,3,9,12,14],right:[1,2,3,4,5,6,7,8,10,13,15],rightarrow:[1,2,4,5,6,9,10,15],rigor:1,rise:1,risk:[1,5],river:1,rmse:1,rmsprop:2,rnd_clf:8,rnn:10,rntrick1:15,rntrick2:15,rntrick3:15,rntrick4:15,robert:17,robust:1,robustscal:1,roc:8,role:[1,3,4,6,12],room:[1,16],root:[1,5,7,15],rot:[],rotat:[2,6,7,8],rotation_matrix:7,roughli:2,round:[1,5,7],routin:[5,13],row:[1,2,3,4,7,9,13],rrr:4,rug:5,rule:[1,2],run:[0,1,2,3,4,5,6,7,9,12],runtim:[0,2,3],runtimewarn:2,rust:[1,12,13],rvert:2,rvert_2:2,rwidth:3,s_i:5,saddl:5,safe:15,sai:[0,1,2,3,4,5,6,7,8,9,10,13,15],said:[3,5,7],sake:[1,4,5,9],sale:1,sam:[],same:[0,1,2,3,4,5,6,7,9,10,13,15],samm:8,sampl:[0,1,2,3,4,5,6,7,8,12,13],sample_vari:0,sampleexptvari:15,samwis:[],sanitize_sequ:3,sastri:9,satisfactori:1,satisfi:[2,3,5,6,13,15],satur:[2,3],save:[1,3,5,7],save_fig:[1,3,5,7,8],savefig:[1,3,5,7,15],scalabl:8,scalar:[3,8],scale:[1,2,4,5,6,7,8,9,10,12,16],scaler:[1,5,6,7,8,9],scan:5,scatter:[0,1,2,3,5,6,7],scenario:5,scheme:[2,5],schrage:15,scienc:[1,2,5,8,10,12,14,15,17],scientif:[1,12],scientist:[0,1],scikit:[6,7,8,12,13,14,17],scikitlearn:1,scikitplot:[5,8],scipi:[1,4,5,12,13],score:[1,2,3,5,7,8,9,16],scores_kfold:3,scratch:2,sdg:5,seaborn:[1,2,5],seamless:[1,12],search:[1,2,5,7],sec:3,second:[0,1,3,5,6,7,9,10,12,13,15],secondeigvector:9,secondli:10,section:[0,9,11,13,14,15],sector:1,see:[0,1,2,3,4,5,6,8,9,10,12,13,14,15],seed:[0,1,2,3,5,6,7,9,15],seek:[2,6],seem:2,seemingli:1,seen:[1,2,8,10,15],segment:5,seldomli:1,select:[2,3,4,6,7,8,9,14,17],self:[2,3,15,17],semest:[5,14],semi:[5,6],send:[10,16],senior:14,sens:[1,3,6],sensit:[1,3,7],sentenc:10,separ:[0,1,2,3,6,7,10,12,15],sequenc:[0,5,7,8,10,12,13,15],sequenti:[2,8,10,15],seri:[1,2,5,8,9,10,13,14],serif:[1,5,15],serv:[1,2,5,17],session:[2,14],set:[0,2,3,4,5,6,8,9,12,13,15],set_:3,set_label:3,set_tick:[2,6],set_ticklabel:2,set_titl:[0,1,2,5,10],set_xlabel:[1,2,5,10],set_xlim:[5,10],set_xticklabel:2,set_ylabel:[1,2,5],set_ylim:[5,10],set_ytick:5,set_yticklabel:2,setiosflag:15,setminu:3,setosa:[6,7],setosa_or_versicolor:6,setp:3,setprecis:15,setup:[2,6,12],setw:15,sever:[1,3,4,5,6,7,9,10,12,13,14,15],sgd:2,sgd_clf:6,sgdclassifi:6,sgdreg:5,sgdregressor:5,shape:[0,1,2,3,4,5,6,7,8,9,13],share:2,she:5,shift:[2,10,15],shire:[],shortcom:5,shorter:15,shorthand:[],shortli:13,should:[0,1,3,4,6,7,9,10,13],show:[0,1,2,3,4,5,6,7,8,9,10,13,15],shown:[5,6,9,10,15],showpoint:15,shrink:[4,6,9],shrinkag:4,shrunk:9,shuffl:[2,3],side:[1,5,6,10,13],sigh:12,sigma0:15,sigma1:15,sigma2:15,sigma:[1,2,3,4,5,8,9,10,13,15],sigma_1:4,sigma_2:4,sigma_:[4,13,15],sigma_fn:[5,10],sigma_i:[1,4],sigma_j:4,sigma_m:15,sigma_n:[9,15],sigma_x:15,sigmoid:[2,5,6,8,10],sigmundson:16,sign:[2,5,6,8,15],signal:[2,8,10],signific:2,significantli:[2,5,15],sim:[3,15],similar:[0,1,2,3,5,6,7,8,9,12,13],similarli:[1,2,4,6,8,15],simpl:[0,2,3,4,6,8,9,10,12,13],simplepredict:8,simpler:[0,1,2,12],simplest:[0,1,2,7,8,10],simpletre:8,simpli:[1,2,3,4,6,7,8,9,10,12,13,15],simplic:[0,4,5,6,7,8,9,10],simplifi:[1,3,7,12],simplist:15,simul:[3,15],simultan:3,sin:[1,2,7,10,13],sinc:[1,2,3,4,5,6,7,8,9,13,15,17],sine:10,singl:[2,5,6,7,10,13,15],singular:[1,3,5,13],site:[1,2,3,6,9,14],situat:[1,4,5],six:15,size:[1,2,3,5,6,7,8,9,13,15],sketch:8,ski:7,skill:1,skip:9,skl:1,sklearn:[1,2,3,5,6,7,8,9],skplt:[5,8],slack:6,slice:13,slide:[1,15],slight:3,slightli:[2,3,4,8,15],slope:[6,9,10],slow:[1,5,6],slower:[4,13],slowli:10,slp:2,small:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],smaller:[1,2,3,5,6,7,9,15],smallest:[0,1],smallest_row_index:0,smart:3,smooth:[1,5],sne:9,sneak:1,sns:[1,2,5],soar:3,social:1,soft:[2,5,8,10],soften:6,softmax:5,softwar:[1,6,12,13,14],sol:6,sole:1,solid:[1,5],solut:[1,2,3,4,5,6,8,9,13,15],solv:[1,2,4,6,8,9,10,13],solver:[5,6,7,8,9,13],some:[0,1,2,3,4,6,7,8,9,10,13,15],someth:[1,2,5,7,9,15],sometim:[0,1,2,9,10],soon:13,sophist:1,sopt:5,sort:[1,3,4,7,9,15],sourc:[0,1,2,3,12,13,15],space:[0,1,2,4,5,6,7,9,10,15],span:[1,4,7,9,13],spare:2,spars:13,sparse_mtx:13,sparsiti:8,spatial:[2,10],speak:15,special:[3,5,8,10,13,15],specif:[1,2,3,4,5,6,7,9,10,12,13,15],specifi:[0,1,3,5,7,9,15],specifici:[1,8],spectral:2,speech:[1,2,10],speed:2,spend:15,sphere:1,spite:1,spline:6,split:[0,2,3,6,7,8,9,15],splitter:[2,8],spontan:15,spread:[1,9,15],springer:17,sqrt:[1,3,4,5,6,8,9,15],squar:[0,1,2,3,4,5,6,7,9,12,13,15],squarederror:8,squaredeuclidean:0,squash:10,srand:15,stabl:[1,4,7,9,12],stack:3,stage:5,stai:[1,9],stand:[1,4,7,10],standard:[1,2,3,4,6,8,10,13],standardscal:[1,5,6,7,8,9],stanford:5,start:[0,1,2,3,5,6,7,8,9,10,13,15],start_tim:0,startpoint:15,stat:3,state:[0,2,4,5,6,8,9,10,12,15],statement:[1,5,13],statis:4,statist:[0,1,2,4,5,7,8,9,10,13,14,17],statu:[1,5,9],std:[3,15],stdev:15,steep:5,step:[0,1,2,7,8,9,10,13,15],step_fn:[5,10],step_length:5,steps_list:7,stian:16,still:[3,4,5,9,15],stimuli:10,stk2100:17,stk4021:17,stk4051:17,stk5000:17,stk:17,stochast:[1,2,3,6,9,10],stoke:10,stone:[1,5],stop:[0,2,7,9,15],storag:4,store:[1,2,5,9,15],str:2,straight:[1,3,5,6],straightforward:[1,3,5,6,7,8,13],strategi:[1,2,7],stratifi:3,strength:[0,4],stretch:9,strict:[5,6],strictli:[5,6],string:[2,15],stroke:5,strong:[7,8,10,15],strongli:[1,6,12,13],stronli:1,structur:[0,1,2,3,7,8,10,12],stuck:[2,5],student:[1,14,16,17],studi:[0,1,5,6,9,10,12,17],studier:17,style:[1,5,7],sub:[7,10],subdivid:[1,13],subfield:1,subject:[6,15],subplot:[0,1,2,3,5,6,7,8],subplots_adjust:[6,15],subprogram:13,subroutin:1,subscript:2,subsequ:[2,3,10,13,15],subset:[2,3,5,7,10,12],subspac:[1,6,9],substanti:[7,8],substep:9,substitut:[3,10,13],subsubset:7,subtl:2,subtract:[3,4,9,13,15],subtre:7,succeed:1,success:[5,7,15],successfulli:7,sucess:15,sudo:[1,12],suffer:[1,2,4,8],suffici:[2,3,5,6,9],suggest:[2,5,17],suit:[6,10],suitabl:[1,15],sum:[0,1,2,3,4,5,6,7,8,9,10,15],sum_:[0,1,2,3,4,5,6,7,8,9,10,13,15],sum_i:[3,4,5,6],sum_k:[6,10,13],summar:[0,3,7],summari:[0,2,8,14],summat:4,sunni:7,superscript:[2,10],supervis:[1,3,5,7,10,12],supplement:5,support:[1,2,7,8,9,12],suppos:[1,3,4,5,6,8,9,10,13],sure:2,surfac:1,surpris:1,surround:12,survei:1,svc:[6,7,8],svd:[1,3,9],svdinv:4,svm:[6,7,8,9],svm_clf:[6,8],symbol:[2,9,12,15],symmeteri:2,symmetr:[1,4,5,6,9,10,13],sympi:[1,12],synonim:15,syntax:[2,13],syntaxerror:[2,6,13],sys:5,system:[1,2,5,7,8,10,12,13,17],systemat:[3,15],t_0:[5,7],t_1:5,t_b:8,t_i:[2,10],t_j:10,t_k:7,tabl:[7,15,16],tabul:1,tabular:[],tackl:0,tag:[0,4,5,10,13,15],taht:1,tail:15,tailor:[6,9],taiwan:1,take:[0,1,2,3,4,5,6,7,8,9,10,12,13,15],taken:[1,2,3,8,13],tangent:[2,5,10],tanh:[2,5,6,10],target:[1,2,5,6,7,8,9,10],target_nam:7,task:[0,1,2,3,7,9,10],tau:15,tax:1,taylor:5,taylornr:5,team:2,teaser:1,technic:[0,1],techniqu:[1,2,6,8,12,15,17],technolog:[1,2],tek5040:17,tell:[3,5,8,9,15],temp1:2,temp2:2,temp:2,temperatur:[1,7],temporarili:2,ten:[],tend:[0,3,6,7,8,10],tendenc:1,tension:3,tensorflow:[0,1,6,12,13,14,17],term1:[1,4,9],term2:[1,4,9],term3:[1,4,9],term4:[1,4,9],term:[0,1,2,3,4,5,6,7,8,9,10,15],termin:[1,4,7,8],test:[3,4,5,6,7,8,15],test_accuraci:2,test_data:0,test_ind:3,test_pr:2,test_predict:2,test_scor:[5,8],test_siz:[1,2,3,8],test_split:7,testerror:3,text:[1,2,5,6,7,9,13,15,17],textual:7,textur:2,than:[1,2,3,4,7,8,9,10,12,15],thats:0,theano:[2,12],thei:[0,1,2,3,4,5,6,7,9,10,13,15],them:[1,2,5,6,7,8,9,10,13],theme:1,themselv:[1,15],thenc:3,theorem:[3,4,5],theoret:[1,8],theori:[1,2,5,6,7,10,12,17],thereaft:[1,3,4,9,10,13],therebi:[1,5,9,15],therefor:[1,2,3,5,6,9,15],therein:9,thereof:[1,3,5],theta:[2,3,5,15],theta_:2,theta_i:2,theta_k:15,theta_linreg:5,thi:[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,17],thing:[0,1,2,5,7,15],think:[0,1,2,3,5,7,10,15],third:[1,5],thirti:5,thorough:0,those:[0,4,6,7,8,9,13,14],though:[2,13,15],thought:[0,3,15],thousand:[1,2],three:[1,2,3,6,7,10,13,14,16],threshold:[2,5,7,8,9,10],through:[0,1,2,4,5,6,9,10,12,13,15],throughout:[0,1,12,13,15],thu:[1,2,3,4,5,6,8,9,10,15,16],thumb:1,thursdai:14,tibshirani:[14,17],ticker:[5,15],tight_layout:[2,5],tightli:9,tild:[1,3,4,9,15],till:[1,5,6,7,8,10,13],time:[0,1,2,3,4,5,6,7,8,9,10,13,14,15],timefunct:15,tini:2,tip:11,titl:[1,2,3,5,6,7,8,15],to_categor:2,to_categorical_numpi:2,to_numer:[1,3],to_str:15,togeth:[1,6,9],toi:0,toler:0,tomographi:10,too:[1,3,4,5,7,9,15,17],took:6,tool:[0,1,2,3,12,15],toolbox:6,top:[1,3,7,8,12],topic:[0,1,5,6,12,17],topolog:[2,10],toss:8,total:[0,1,2,3,5,6,8,9,10,15,16],totalclustervari:0,totalscatt:0,totalvari:15,toward:[2,5,10],town:1,tpng:7,traceback:[3,5,7,8],track:[0,5,13],tract:1,tractabl:1,trade:[3,7],tradeoff:[1,4,14],tradit:[1,2,3],train:[3,5,6,7,8,9,10],train_accuraci:[1,2],train_end:2,train_ind:3,train_pr:2,train_siz:2,train_test_split:[1,2,3,5,7,8,9],train_test_split_numpi:2,trainingerror:3,trait:1,transfer:7,transform:[1,3,4,5,6,7,8,9,10,12,13],transit:10,translat:[2,8],transpos:[2,4,9],treat:[1,2,3,5,10,15],tree:[1,2,12,14],tree_clf:[7,8],tree_clf_:7,tree_clf_sr:7,tree_reg1:7,tree_reg2:7,tree_reg:7,trend:15,trevor:17,tri:7,trial:[1,3,5,15],triangl:5,triangular:13,trick:[6,9,15],trickier:15,tridiagon:13,trillion:12,trivial:[1,2,9,15],troubl:[1,6,10],true_divid:2,true_fun:3,tucker:6,tumor:[5,7],tumour:5,tunabl:2,tune:[7,13],turn:[1,2,3,4,5,6,7,8,9,10,13,15],tutori:2,tweak:[2,8,15],twice:5,twist:9,twister:15,two:[0,1,2,3,4,5,7,8,9,10,13,14,15,17],tx_1:5,type:[1,2,3,5,6,8,13,15],typeerror:[],typic:[1,2,4,5,7,8,10,15],u_i:10,u_m:8,ubuntu:[1,12],uci:1,uio:[16,17],unari:13,unbalanc:[3,7],unbias:[1,3,15],uncertainti:1,uncertitud:15,unchang:2,uncorrel:8,undefin:4,under:[1,2,3,5,8,12],underdetermin:1,underfit:[2,3],undergradu:14,underli:[1,2,5,7,15],underset:0,understand:[0,1,2,5,8,12],understood:[0,6],undesir:6,undetermin:6,unexpect:[3,15],unexpected:15,unfortun:[2,6,7,8],unicode_liter:[6,7],uniform:[1,2,4,5,9],uniform_real_distribut:15,uniformli:[5,15],unifrompdf:15,unimport:5,union:3,uniqu:[0,1,3,5,13],unique_cluster_label:0,unit:[1,2,8,10,15],unitari:[4,13],unitarili:13,uniti:15,univari:15,univers:[1,2,5,14,16],unix:2,unknow:[1,13],unknown:[1,2,3,6,8,13],unknowwn:10,unlabel:2,unless:[1,3,5,9],unlik:[2,5,6,15],unnecessarili:7,unravel:2,unrol:9,unseen:[5,7],unstabl:2,unsupervis:[1,2,10,12,14],unsymmetr:13,until:[0,2,5,7,10,15],unusu:10,updat:[0,2,3,8,10],upload:[12,17],upon:[2,9,13],upper:[1,6,7,13],uppercas:[13,15],ups:[],usag:[1,6,12,15],usd10000:1,usd:1,use:[0,1,3,4,5,6,7,8,9,10,12,13,14],usecol:1,used:[0,1,2,3,4,6,7,8,9,10,12,13,15,17],useful:[1,2,3,5,7,9,10,12,13,15,17],useless:2,user:[1,2,5,6,9,12,13],uses:[1,2,3,4,7,9,10,13,15],usetex:15,using:[0,2,3,4,6,7,8,9,10,13],usr:15,usual:[0,1,5,10],util:[0,2,3,5,8],v_0:9,valid:[1,2,5,7,8,12,14,15],valu:[0,1,2,3,5,6,7,8,10,12,13],valuat:7,valueerror:[],van:[1,4],vandenbergh:[5,6],vandermond:1,vanilla:9,vanish:[2,5,15],var_x:15,varabl:6,varepsilon:3,varepsilon_:3,varepsilon_i:3,vari:[1,2,3,8],variabl:[0,1,2,3,6,8,9,10,13],varianc:[0,1,2,4,5,7,8,9,12,13,14],variance_i:[4,9],variance_x:[4,9],variant:[1,2,3,5,6,10],variat:9,varieti:[1,10,12],variou:[2,4,5,6,7,9,10,12,13,15],vartempvec:15,varvec:15,vaue:2,vdot:5,vec:[3,15],vector:[0,1,2,3,4,5,7,8,9,12],vector_mean:0,ventur:[1,6,12],verbos:2,veri:[0,1,2,3,5,6,7,8,9,10,15,17],verifi:[9,13],versatil:6,versicolor:[6,7],version:[0,1,8,12,13,15],versu:2,vert:[1,2,4,5,6,7,9],vert_1:4,vert_2:[4,9],vertic:3,via:[1,3,4,5,6,7,8,9,10,12,13,14,15],vidal:9,video:[1,2,4,5,10,12,14],view:[2,10,15,17],violat:6,virginica:7,viridi:[1,2],virtual:2,vision:1,visual:[1,9,10,12],visualis:2,viz:[6,15],vmax:2,vmc:15,vmin:2,volum:1,vote:8,voting_clf:8,votingclassifi:8,votingsimpl:8,vstack:[4,9,13,15],w_1:[6,13],w_1x_1:6,w_1x_:6,w_2:[6,13],w_2x_2:6,w_2x_:6,w_3:13,w_4:13,w_i:[2,8],w_ix_i:10,w_j:13,w_m:13,w_px_:6,w_px_p:6,wai:[0,1,2,3,4,5,6,8,9,10,11,13,15],walk:7,walker:15,wang:1,want:[0,1,2,3,5,6,7,8,9,10,12,15],warn:[1,2,6],warrant:3,watch:12,wavelet:6,weak:[0,7,8],weather:[2,10],web:[12,14],websit:[13,14],wedg:[6,15],wednesdai:14,wee:9,week:5,weekli:[12,17],weight:[1,2,3,5,7,8,10,15],welcom:[6,12],well:[1,2,3,4,5,6,7,8,10,12,13,15,17],went:6,were:[0,1,2,3,4,5,6,8,9,10,15],wessel:[1,4],what:[0,2,3,4,5,6,7,8,9,10,12,13,14],when:[0,1,2,3,4,6,7,8,9,10,13,15],whenev:15,where:[0,1,2,3,4,5,6,7,8,9,10,12,13,15,16],wherea:[3,15],wherein:[2,10],whether:[1,5,7,15],which:[0,1,3,4,5,6,7,8,9,10,12,13,14,16],whichev:2,white:7,who:[1,14],whole:[0,2,7,9],whose:[3,8,15],whow:[4,9],why:[1,2,5],wide:[1,2,3,5,10,12,13],widehat:3,width:[1,6,7],wieringen:[1,4],win:8,wind:7,wing:16,wiscons:5,wisconsin:8,wise:[1,2,4,10],wish:[0,1,5,6,9,13],within:[0,1,5,7,10,15,17],withinclust:0,without:[1,2,4,5,6,7,9,10],won:1,wonder:6,word:[0,1,2,15],work:[0,1,2,3,5,6,7,12,14,15],world:[1,6],worldwid:1,wors:[1,2,3],worth:7,would:[1,2,3,5,6,7,8,9,10,13,15],wrap:13,write:[1,2,5,6,10,11,13,14,15],written:[1,4,5,9,10,12,13,15],wrong:[2,6],wrongli:[8,15],wrote:[4,9],wrt:8,wth:8,www:[12,13,17],wx_1:6,x0s:6,x1_exampl:6,x1d:6,x1s:[6,7,8],x2d:[6,9],x2d_train:9,x2dsl:9,x2s:[6,7,8],x3s:6,x_0:[1,4,9,13],x_1:[1,3,4,5,6,7,8,9,13,15],x_2:[1,3,4,5,6,7,8,9,13,15],x_3:[6,13,15],x_4:13,x_center:9,x_data:2,x_data_ful:2,x_i:[0,1,2,3,4,5,6,7,8,9,10,13,15],x_ix_:1,x_iy_i:6,x_j:[6,7,10,15],x_jy_j:6,x_k:[0,10,13,15],x_l:15,x_m:[10,13,15],x_n:[1,3,5,6,9,10,13,15],x_new:[7,8],x_p:[5,7],x_poli:7,x_poly10:7,x_reduc:9,x_scale:6,x_test:[1,2,3,5,7,8,9],x_test_scal:[1,5,7,8,9],x_train:[1,2,3,5,7,8,9],x_train_scal:[1,5,7,8,9],x_val:2,xarrai:12,xavier:2,xbnew:5,xcode:[1,12],xdclassiffierconfus:8,xdclassiffierroc:8,xg_clf:8,xgb:8,xgbclassifi:8,xgboost:7,xgboot:8,xgbregressor:8,xgparam:8,xgtree:8,xi_1:6,xi_:6,xi_i:6,xlabel:[1,2,3,5,6,7,8,15],xlim:[3,8],xmesh:5,xnew:[1,5],xpd:[4,9],xplot:1,xsr:7,xt_x:5,xtest:3,xtick:[3,6,7],xtrain:3,xytext:6,y_0:[1,4,9,13],y_1:[1,4,5,6,7,9,13],y_1y_1:6,y_1y_1k:6,y_1y_2:6,y_1y_2k:6,y_1y_n:6,y_1y_nk:6,y_2:[1,4,6,7,9,13],y_2y_1:6,y_2y_1k:6,y_2y_2:6,y_2y_2k:6,y_3:[1,7,13],y_4:13,y_data:[1,2],y_data_ful:2,y_decis:6,y_i:[1,2,3,4,5,6,7,8,9,10,13],y_if_:8,y_ix_:1,y_ix_i:[5,6],y_iy_jk:6,y_j:[3,6,10],y_k:10,y_m:13,y_model:1,y_n:[5,6],y_ny_1:6,y_ny_1k:6,y_ny_2:6,y_ny_2k:6,y_ny_n:6,y_ny_nk:6,y_plot:7,y_pred1:7,y_pred2:7,y_pred:[1,2,3,5,6,7,8],y_pred_rf:8,y_pred_tre:8,y_proba:[5,8],y_test:[1,2,3,5,7,8,9],y_test_onehot:2,y_test_predict:1,y_train:[1,2,3,5,7,8,9],y_train_onehot:2,y_train_predict:1,y_val:2,year:[1,12],yes:[3,5],yet:[1,2,3,6,9],yield:[0,1,3,5,6,8,10,13,15],ylabel:[1,2,3,5,6,7,8,15],ylim:3,ymesh:5,yoshua:[2,17],you:[0,1,2,3,5,6,7,8,9,12,13,15,17],young:1,your:[0,2,3,5,6,9,12,13,15],yourself:[5,9],youtub:12,ypred:3,ypredict2:5,ypredict:[1,5],yridg:1,ytest:3,ytick:[3,6,7],ytild:[1,3],ytildenp:1,ytrain:3,z_0:13,z_1:13,z_2:13,z_c:2,z_h:2,z_i:[2,10],z_j:[2,10],z_k:10,z_m:2,z_mod:7,z_o:2,zaman:15,zero:[0,1,2,3,4,5,6,7,8,9,10,13,15],zm_h:1,zone:1},titles:["11. Clustering Analysis","3. Linear Regression","13. Building a Feed Forward Neural Network","4. Resampling Methods","5. Ridge and Lasso Regression","6. Logistic Regression","7. Support Vector Machines, overarching aims","8. Decision trees, overarching aims","9. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","10. Basic ideas of the Principal Component Analysis (PCA)","12. Neural networks","Content in Jupyter Book","Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks"],titleterms:{"2021":16,"4155":12,"case":[5,6,8,15],"final":[5,10],"function":[1,2,4,5,6,8,9,10,15],"import":[13,15],And:5,Eye:8,FYS:12,The:[0,1,2,3,4,5,6,7,9,10,12,15],Useful:12,Using:5,activ:[2,10],actual:15,adaboost:8,adapt:8,adjust:2,again:7,aim:[6,7],algebra:13,algorithm:[0,4,7,8,9,10],algortithm:5,all:6,analysi:[0,1,9,12,15],ani:5,anoth:7,appli:12,approach:[1,5,6],approxim:10,architectur:2,arrai:13,assist:16,august:14,autocorrel:15,back:[2,9,10],background:12,bag:8,basic:[0,1,5,7,8,9,13],batch:2,befor:9,better:[4,6,15],bia:3,binari:2,binomi:15,bird:8,block:15,book:11,boost:8,bootstrap:[3,8,15],boston:1,breast:2,brief:5,bring:10,build:[2,7],calcul:15,cancer:[2,5,7,9],cart:7,central:[5,12,15],chain:10,chang:8,chi:1,choos:2,classic:9,classif:[2,7,8],classifi:6,clip:2,cluster:0,code:[0,1,2,5,7,9,10,15],collect:2,compar:8,compon:9,comput:[5,7,15],con:7,concept:15,condit:5,conjug:5,content:11,continu:15,convex:[5,6],convolut:10,correl:[4,9,15],correspond:5,cost:[2,5,8],cours:[12,17],covari:[4,9,15],cross:3,cumul:15,cython:[],data:[1,2,5,7,9,12,15],dataset:2,decemb:14,decis:[7,8],decomposit:[4,9,13],deep:2,defin:2,definit:15,degre:1,demonstr:15,dens:1,deriv:[5,10],descent:[5,8],develop:2,deviat:15,diagon:9,dice:15,differ:[5,6],dimension:6,disadvantag:7,discret:15,disguis:15,distribut:15,doing:2,domain:15,down:2,dropout:2,economi:4,element:[1,15],elimin:13,ensembl:8,entropi:7,environ:1,equat:[1,5,10],error:8,etc:[],evalu:2,event:15,exampl:[1,2,5,6,7,8,15],exercis:1,expect:15,experi:15,explor:1,exponenti:15,express:5,extend:5,extrem:8,fall:16,famili:2,famou:15,fantast:4,featur:[7,13],feed:[2,10],fine:2,first:[5,10],fit:[1,8],forest:8,forward:[2,10],frank:1,freedom:1,frequentist:1,fridai:5,from:[8,10],gaussian:[13,15],gener:[7,15],geometr:[5,9],gini:7,good:1,grade:16,gradient:[2,5,8],handl:13,has:12,hessian:5,homework:5,hous:1,how:15,hyperparamet:2,hyperplan:6,id3:7,idea:[0,9],ideal:5,implement:[2,15],improv:2,increment:9,index:7,inform:16,instal:12,instructor:16,interpret:[5,9],introduc:[4,9],introduct:[1,3,12,13],invers:13,iter:[5,8],its:15,jackknif:15,julia:[],jungl:8,jupyt:11,kera:2,kernel:[6,9],lagrangian:6,lasso:4,layer:[2,10],learn:[1,2,5,9,12],level:8,librari:12,likelihood:5,limit:[2,5,15],linear:[1,6,13],link:[4,9,14,17],logist:5,loss:5,machin:[1,5,6,12],main:15,make:[1,7,8],mani:[8,10],materi:14,mathemat:6,matric:[],matrix:[2,5,9,10,13,15],matter:1,mean:[0,15],meet:[8,15],mercer:6,mersenn:15,method:[3,5,7,8,15],mlp:10,model:[1,2,10],moment:15,moon:[6,7],more:[0,5,13],multilay:10,multipl:2,multipli:6,name:15,need:[],network:[2,5,10],neural:[2,10],newton:5,non:6,normal:[1,2,15],norwai:12,notat:10,novemb:14,now:[2,7],nuclear:1,nueral:5,numba:[],number:[1,15],numer:15,numpi:13,numpython:0,observ:15,obtain:9,octob:14,one:[5,10],optim:[2,5,6,12],organ:1,oslo:[12,17],other:[7,9,10,15],our:[0,1,5,9,15],outcom:12,output:15,overarch:[1,6,7],overview:8,own:[0,1,8,9],packag:13,panda:[],part:[5,12],pass:2,pca:9,pdf:15,perceptron:10,perform:[2,7],period:15,perspect:2,poisson:15,pre:2,preprocess:1,prerequisit:12,princip:9,pro:7,probabl:15,problem:[2,5],procedur:7,process:2,program:5,propag:[2,10],properti:15,pseudo:15,python:[0,1,7,12,13,15],quick:6,ran0:15,random:[8,9,15],raphson:5,read:7,recip:15,recurr:10,reduc:1,regress:[1,4,5,7,8],regular:[2,4],relev:17,relu:2,remind:[3,5,6],requir:12,resampl:3,revisit:5,ridg:[4,5],rng:15,rule:10,sampl:[9,15],schedul:14,schemat:7,scikit:[1,2,5,9],select:15,semest:16,sensit:5,septemb:[5,14],set:[1,7,10],sgd:5,should:[2,15],simpl:[1,5,7,15],singl:8,singular:[4,9],situat:15,size:4,slightli:5,soft:6,softmax:2,softwar:[],solv:5,some:5,split:1,squar:8,standard:[5,15],state:1,statist:[3,12,15],steepest:[5,8],step:[3,5],stk3155:12,stochast:[5,15],stop:5,supervis:2,support:6,svd:4,teach:[14,16],teacher:16,techniqu:9,technolog:12,tensorflow:2,test:[1,2],textbook:17,than:5,theorem:[6,9,10,15],theori:15,three:15,togeth:10,top:2,toss:15,toward:[0,9],tradeoff:3,train:[1,2],tree:[7,8],tune:2,two:[6,12],type:10,uncorrel:15,understand:4,uniform:15,univers:[10,12,17],use:[2,15],used:5,using:[1,5,15],valid:3,valu:[4,9,15],variabl:[5,15],varianc:[3,15],variou:[1,3],vector:[6,10,13],view:[1,8],visual:[2,7],wai:7,week:14,weekli:14,what:[1,15],when:5,which:[2,15],why:15,wisconsin:5,write:[0,9],xgboost:8,your:[1,8]}}) \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb index a6b16f26e..48cd1e448 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb @@ -301,7 +301,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -451,7 +451,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZTUlEQVR4nO3dcdRkdX3f8fdnF1ZdQI3sahXYZ4nBGKKJygbltE2IwQrmBDxVW3C1arSbrqH1qDFisJgSMVETY9tDD64tSmQRNU1z1orhRKMxbcTDbjQqctAV2WUx6oJCtKsuyLd/3HncYZiZO/PsM/PM8+z7dc6cZ+6d3733O3fmuZ/53Tv3TqoKSZKGWbXUBUiSZp9hIUlqZVhIkloZFpKkVoaFJKmVYSFJamVY6IiS5JNJXrHAaTck+V6S1YtdlzTrDAstO0luS/L9zob7G0nem+TYCS3nrPnhqtpbVcdW1Y8We1nSrDMstFz9WlUdCzwFeCrwhqUtZzqSHDXKuHHnIbUxLLSsVdU3gOtpQgOAJM9I8rdJ7k7y90nO7Ddtkscn+askdyW5M8n2JI/sPPY+YAPw4U4P5reTbExSSY5K8q+T7OyZ36uT7Ojcf0iSP0yyN8k3k1yR5GGDnkeSX09yc5LvJLk+yVzXY5XkN5N8BfhKkjOT7Evy+iTfAN7TWd47k3y9c3tnkod0pn9Q+wWsah3hDAsta0lOBM4BdneGTwA+ArwZeBTwW8D/TLK+3+TA7wOPA34GOAn4XYCqejGwl04Ppqre1jPth4GfTnJK17gXAtd07v8B8ASaEPsp4ATgkgHP4Tzgd4B/CawH/gZ4f0+z5wJPB07tDP+TzvObA7YAFwPP6Czv54HTgTd2Td/bXhpPVXnztqxuwG3A94DvAgV8HHhk57HXA+/raX898JLO/U8Crxgw3+cCn+1Zzlldwxs7yzuqM3w1cEnn/imdetbShND/Ax7fNe0ZwNcGLPejwMu7hlcBB4C5znABz+x6/EzgIPDQrnFfBZ7TNfxs4LZB7b15G/dmz0LL1XOr6jiaDeETgXWd8XPACzq7oO5Ocjfwz4DH9s4gyWOSXJvkjiT/SLPxX9fbbohrgAs6918I/HlVHaDpHawFdnXV8Bed8f3MAf+5q+23aQLnhK42t/dMs7+qftA1/DhgT9fwns64Qe2lsRgWWtaq6q+B9wJ/2Bl1O03P4pFdt2Oq6g/6TP4Wmk/tT66qhwMvotlI/3j2LYv/S2B9kqfQhMb8Lqg7ge8DP9tVwyOqOSDfz+3Ab/TU/LCq+tshtfQOf50mdOZt6Iwb9blIQxkWWgneCTwryc/T9A5+Lcmzk6xO8tDOAd4T+0x3HM3urHs6xzpe1/P4N4GfHLTQqroX+BDwdprjAX/ZGX8/8G7gj5M8GppjKUmePWBWVwBvSPKznbaPSPKCUZ54l/cDb0yyPsk6muMjV485D2kgw0LLXlXtB/6E5vjB7cD8AeP9NJ/aX0f/9/p/Ap4G3ENzUPzPeh7/fZoN8N1JfmvA4q8BzgI+VFX3dY1/Pc1B9xs6u7g+Bvz0gPr/F/BW4NpO2y/SHLQfx5uBncDngS8Af9cZJy2KVNk7lSQNZ89CktRqKmGR5OwktyTZneSiPo+/NMn+JJ/r3BZ07R5J0mRM/LT/zkXXLgeeBewDbkyyo6q+1NP0A1V14aTrkSSNbxo9i9OB3VV1a1UdBK6lOQApSVompnFBsRN44AlF+2guW9DreUl+Efgy8OrOt1oeIMkWOpcqOOaYY0574hOfOIFyJWnl2rVr151VNegE0YFm5eqTHwbeX1U/TPIbwFXAM3sbVdU2YBvApk2baufOnb1NJElDJNnT3urBprEb6g6aC7TNO7Ez7seq6q6q+mFn8L8Dp02hLknSiKYRFjcCpyQ5Ocka4HxgR3eDJN3X7TkXuHkKdUmSRjTx3VBVdV+SC2mu/LkauLKqbkpyKbCzqnYA/yHJucB9NBdRe+mk65IkjW7ZnsHtMQtJGl+SXVW1adzpPINbktTKsJAktTIsJEmtDAtJUivDQpLUyrCQJLUyLCRJrQwLSVIrw0KS1MqwkCS1MiwkSa0MC0lSK8NCktTKsJAktTIsJEmtDAtJUivDQpLUyrCQJLUyLCRJrQwLSVIrw0KS1MqwkCS1MiwkSa0MC0lSK8NCktTKsJAktTIsJEmtDAtJUivDQpLUyrCQJLUyLCRJrQwLSVIrw0KS1MqwkCS1MiwkSa2mEhZJzk5yS5LdSS4a0u55SSrJpmnUJUkazcTDIslq4HLgHOBU4IIkp/ZpdxzwKuAzk65JkjSeafQsTgd2V9WtVXUQuBY4r0+73wPeCvxgCjVJksYwjbA4Abi9a3hfZ9yPJXkacFJVfWTYjJJsSbIzyc79+/cvfqWSpL6W/AB3klXAO4DXtrWtqm1VtamqNq1fv37yxUmSgOmExR3ASV3DJ3bGzTsOeBLwySS3Ac8AdniQW5JmxzTC4kbglCQnJ1kDnA/smH+wqu6pqnVVtbGqNgI3AOdW1c4p1CZJGsHEw6Kq7gMuBK4HbgY+WFU3Jbk0ybmTXr4k6fAdNY2FVNV1wHU94y4Z0PbMadQkSRrdkh/gliTNPsNCktTKsJAktTIsJEmtDAtJUivDQpLUyrCQJLUyLCRJrQwLSVIrw0KS1MqwkCS1MiwkSa0MC0lSK8NCktTKsJAktTIsJEmtDAtJUivDQpLUyrCQJLUyLCRJrQwLSVIrw0KS1MqwkCS1MiwkSa0MC0lSK8NCktTKsJAktTIsJEmtDAtJUivDQpLUyrCQJLUyLCRJrQwLSVIrw0KS1GoqYZHk7CS3JNmd5KI+j/+7JF9I8rkk/yfJqdOoS5I0momHRZLVwOXAOcCpwAV9wuCaqnpyVT0FeBvwjknXJUka3TR6FqcDu6vq1qo6CFwLnNfdoKr+sWvwGKCmUJckaURHTWEZJwC3dw3vA57e2yjJbwKvAdYAz+w3oyRbgC0AGzZsWPRCJUn9zcwB7qq6vKoeD7weeOOANtuqalNVbVq/fv10C5SkI9g0wuIO4KSu4RM74wa5FnjuJAuSJI1nGmFxI3BKkpOTrAHOB3Z0N0hyStfgrwJfmUJdkqQRTfyYRVXdl+RC4HpgNXBlVd2U5FJgZ1XtAC5MchZwL/Ad4CWTrkuSNLppHOCmqq4DrusZd0nX/VdNow5J0sLMzAFuSdLsMiwkSa0MC0lSq7HCIsmrkry7c/8/TqYkSdKsGbdn8XgOnY193CLXIkmaUeOGRQEPS/Ik4HETqEeSNIPGDYs/AgK8GPidxS9HkjSLxj3P4q3Ax4EPVdU9E6hHkjSDxu1ZvBD4GvCWJFdNoB5J0gwaNyweCZwCPAr4h0WvRpI0k8bdDfUW4APAu6rKHyiSpCPEyD2LJP8U+N/AWuCcJM+ZWFWSpJkyTs/iUcBP0HwbCvzpU0k6Yozcs6iqDwN3VtVVVXUVcPfEqpIkzZSRexZJfhW4IAk0vYsX0vMjRpKklWmc3VDrgO93/t4PXDaRiiRJM2fksKiqq5LcQnP29lrgmcCvT6owSdLsGPc8i1fQ/Ozpm2hOzpMkHQHGDYtvAg+l2Q31mMUvR5I0i8YNi+3AFcBvAx9b/HLGsGsXbNwI27cvaRmSdCQY59tQ3SfhXc8snGexZw9s2dLc37x5aWuRpBVsnJ7FeppvQnXflt6BA3DxxUtdhSStaOOclHcVzRnc/7xzf25iVY1r796lrkCSVrSV8bOqGzYsdQWStKIt/59VXbsWLvP8QEmapOX9s6pzc7Btmwe3JWnCxvk2VIDnVtVFE6xndKedBjt3LnUVknREGOdyH5XkF5JcANzTGXfdxCqTJM2McX8p72PAGpqv0S79eRaSpKloDYskn66qM+DHFxM8DvipqvrsxKuTJM2EUQ5wPwQgyTsAquq7wH+bZFGSpNkySlgkyWOAF3UOcgM8bII1SZJmzCjHLN4A/A1wDfDHSb7M+F+5lSQtY61hUVV/ATwBIMkZwAuAl0+4LknSDBnr21BV9Wng0xOqRZI0o6ayOynJ2UluSbI7yYNO6kvymiRfSvL5JB9PMjsXKZQkTT4skqwGLgfOAU4FLkhyak+zzwKbqurngD8F3jbpuiRJo5tGz+J0YHdV3VpVB4FrgfO6G1TVJ6rqQGfwBuDEKdQlSRrRNMLiBA5d1hxgX2fcIC8HPtrvgSRbkuxMsnP//v2LWKIkaZiZ+gpskhcBm4C393u8qrZV1aaq2rR+/frpFidJR7Bxrw21EHcAJ3UNn9gZ9wBJzgIuBn6pqn44hbokSSOaRs/iRuCUJCcnWQOcD+zobpDkqcC7gHOr6ltTqEmSNIaJh0VV3QdcCFwP3Ax8sKpuSnJpknM7zd4OHAt8KMnnkuwYMDtJ0hKYxm6o+d+9uK5n3CVd98+aRh2SpIWZqQPckqTZZFhIkloZFpKkVoaFJKmVYSFJamVYSJJaGRaSpFaGhSSplWEhSWplWEiSWhkWkqRWhoUkqZVhIUlqZVhIkloZFpKkVisvLLZvh40bYdWq5u/27UtdkSQte1P58aOp2b4dtmyBAwea4T17mmGAzZuXri5JWuZWVs/i4osPBcW8Awea8ZKkBVtZYbF373jjJUkjWVlhsWHDeOMlSSNZWWFx2WWwdu0Dx61d24yXJC3YygqLzZth2zaYm4Ok+bttmwe3JekwraxvQ0ETDIaDJC2qldWzkCRNhGEhSWplWEiSWhkWkqRWhoUkqZVhIUlqZVhIkloZFpKkVoaFJKmVYSFJamVYSJJaTSUskpyd5JYku5Nc1OfxX0zyd0nuS/L8adQkSRrdxMMiyWrgcuAc4FTggiSn9jTbC7wUuGbS9UiSxjeNq86eDuyuqlsBklwLnAd8ab5BVd3Weez+KdQjSRrTNHZDnQDc3jW8rzNubEm2JNmZZOf+/fsXpThJUrtldYC7qrZV1aaq2rR+/fqlLkeSjhjTCIs7gJO6hk/sjJMkLRPTCIsbgVOSnJxkDXA+sGMKy5UkLZKJh0VV3QdcCFwP3Ax8sKpuSnJpknMBkvxCkn3AC4B3Jblp0nVJkkY3ld/grqrrgOt6xl3Sdf9Gmt1TkqQZtKwOcEuSloZhIUlqZVhIkloZFpKkVoaFJKmVYSFJamVYSJJaGRaSpFaGhSSplWEhSWplWEiSWhkWkqRWhoUkqZVhIUlqZVhIkloZFpKkVis/LLZvh40bYdWq5u/27f3HSZIGmsov5S2Z7dthyxY4cKAZ3rMHXvYySODgwUPjtmxp7m/evDR1StKMW9k9i4svPhQU8+6991BQzDtwoGkrSeprZYfF3r3jte3ePbVuXXNzV5UkrfDdUBs2NLuZRpE0u6juvbcZvuuuQ4+5q0rSEW5l9ywuuwzWrn3guKOPhjVrHtz2/vsPBUU/7qqSdARb2WGxeTNs2wZzc03PYW4O3vMeuPJKWL16/PmNs1tLklaQVNVS17AgmzZtqp07dy58BqtWwbjPfW4Obrtt4cuUpCWWZFdVbRp3upXdsxhmw4bx2h99dLNbS5KOQCs3LNpOvOt3PGPNmqZ9Pw9/uAe3JR2xVmZYzJ+Mt2dPs6tp/ttM3YHR73jGlVcO3jX17W9Pp3ZJmkErMyz6nYzX79tMmzc3xyDuv7/5u3nz4N1T4+62OpJ5ORVpxVmZYTHoW0ujfJvpssse/NXaNWsW73jFuCf+LbcN7ytfCS9+8fBenaRlZ+WFxfbtg487jNo76N0VtVjfGOvdPXbXXc1t0EZ1lN1ps2T7drjiigevrwMH4FWvWpqalovl9qFAR56qWpa30047rR7k6qur1q6tajZXD7ytXds83s/VV1fNzVUlVatX959+bq7/tOOYm+s/7+7b6tWH6hzUfjFqmYS259e7/rvX+9zc+I+vFP3et8Per9JhAHbWAra5S77RX+itb1gM2lh1b4B7DQuY7lsy/BUYZcOWtC+ne0MxqP2wWgbVMY0Nb9vz6w65tg3kUmxAlyqcltuHAi1rhkXVwjauo3za7/7n7bcBGbZhG6XXMmhZ425EBtWxdWv/QDz++MXdILaty+7Xoe25DXr8+OMXr95uhxNOhxsyC3nfLldHSm9xhhkWVQv7hDbqp/1hG5BhG7ZRei2DNhTjbsCG9azGeT5t+v3DX31183zbAnCU9T6sVzW/XueXvXXrA2vpHR71uS300/2w12jr1qpVqw6NP+aY/vUsRs9ilI1w72u0WB8Whi27+7Hjj69as+bB62qxXsOF1LdUtm499H+5enUzPCWGRdXCPh0uZAPb+088buCsXn3on2fQtPPLmH+jd9d0uLu5FrJBHPYPv2ZN1dFHD19G7+swrBeydm178Ix6G/T6925Ahs1j2EZmnJ7p/GvYuzHt91x7d8kN28gP2pV67LEPnEfv6wbN6zbOxrO3lmOP7f/aH3/84B7t4b6G4+xqHWebsBi7cEeZxzHH9H+exxwz3vG77sDp/l9ueT1nOiyAs4FbgN3ARX0efwjwgc7jnwE2ts2zb1gMe7EGGfZmGnX3wLgbjO7pR3kzj7qbq/tT7ELrGWX9jLNhHNYDGDbfxQqL7lrm/5n6LXuUoJ1v0/2+WkhAz08/6Dl2h8HVV/ffGB91VPsXIaAJiO4PHIOW12aUnuOg9bVYr9+gjT8MDq22D2Pdz693+qOP7v9eGRY24+wGblt3872NfkE/7H99/gPAgG3hzIYFsBr4KvCTwBrg74FTe9q8Eriic/984ANt8x0YFgsxKGBG3T0w6E0y6J+r3/TDAm4Su7mG1TPKskd9ww9bP1u3Dp92sQOjbQMyzsZtfoOx0PUz7HXrfj1G2ciP8sWCtjZtn5YX4312uLfF7HH2fkAaNN9Rw2bYazXOscreZS8kpKHppQwIuVkOizOA67uG3wC8oafN9cAZnftHAXfSuSLuoNuihsUgh9uFXaxv9CzWJ7R+n0ba6jmcZbcdrG47iL8UG6lRNqyHW2PbxqN7Q9ZWy7D12z2/tjaT+sAwq7fe5zvu9P1644vZk+quc5Hnt9CwmPglypM8Hzi7ql7RGX4x8PSqurCrzRc7bfZ1hr/aaXNnz7y2AJ2frONJwBcnWjywDh71ODjhaFhzLxz8OtxxJ4x8oajDnR7g5+DJRze9ssO2B742Tj0LXXbB/Xthz53w7dPgtGH1bIC5dJ0g2j1t9/r7Edy3ClYHMm49o7gXDn4evgCjP+9dsKu7xrb2BdVW/zh1zC9/Dk4eNr+vwx0bYOOwZe+CXf3GD3v9xjXK85+GPfC17vf9uM+x+zWat5j/p5O0C75XVceNO92y+lnVqtoGbANIsrMWcE32lch1cYjr4hDXxSGui0OSLOiHgKZxuY87gJO6hk/sjOvbJslRwCOAu5AkzYRphMWNwClJTk6yhuYA9o6eNjuAl3TuPx/4q5r0/jFJ0sgmvhuqqu5LciHNQezVwJVVdVOSS2kOtOwA/gfwviS7afYjnj/CrLdNrOjlx3VxiOviENfFIa6LQxa0Lpbtb3BLkqZn5V2iXJK06AwLSVKrmQ+LJGcnuSXJ7iQX9Xn8IUk+0Hn8M0k2LkGZUzHCunhNki8l+XySjyeZW4o6p6FtXXS1e16SSrIivzY5ynpI8q8674ubklwz7RqnZYT/jw1JPpHks53/kecsRZ3TkOTKJN/qnMPW7/Ek+S+ddfX5JE9rnelCzuSb1o0JXSpkOd5GXBe/DKzt3N96JK+LTrvjgE8BNwCblrruJXpPnAJ8FviJzvCjl7ruJVwX24CtnfunArctdd0TXB+/CDwN+OKAx58DfJTmBMlnAJ9pm+es9yxOB3ZX1a1VdRC4Fjivp815wFWd+38K/EqSJT9DdAJa10VVfaKqDnQGb6A5p2UlGuV9AfB7wFuBH0yzuCkaZT38W+DyqvoOQFV9a8o1Tsso66KAh3fuPwL4+hTrm6qq+hTDrxRxHvAn1bgBeGSSxw6b56yHxQnA7V3D+zrj+rapqvuAe4Djp1LddI2yLrq9nOaTw0rUui463eqTquoj0yxsykZ5TzwBeEKS/5vkhiRnT6266RplXfwu8KIk+4DrgH8/ndJm0rjbk+V1uQ+NJsmLgE3ALy11LUshySrgHcBLl7iUWXAUza6oM2l6mp9K8uSqunspi1oiFwDvrao/SnIGzbldT6qq+5e6sOVg1nsWXirkkFHWBUnOAi4Gzq2qH06ptmlrWxfH0Vxo8pNJbqPZJ7tjBR7kHuU9sQ/YUVX3VtXXgC/ThMdKM8q6eDnwQYCq+jTwUGDdVKqbPSNtT7rNelh4qZBDWtdFkqcC76IJipW6bxpa1kVV3VNV66pqY1VtpDl+c25VLegCajNslP+PP6fpVZBkHc1uqVunWOO0jLIu9gK/ApDkZ2jCYv9Uq5wdO4B/0/lW1DOAe6rqH4ZNMNO7oWpylwpZdkZcF28HjgU+1DnGv7eqzl2yoidkxHWx4o24Hq4H/kWSLwE/Al5XVSuu5z3iungt8O4kr6Y52P3SFfrBkiTvp/mQsK5zjOZNwNEAVXUFzTGb59D8OukB4GWt81yh60qStIhmfTeUJGkGGBaSpFaGhSSplWEhSWplWEiSWhkWkqRWhoUkqZVhIS2Szm8lPKtz/81J/utS1yQtlpk+g1taZt4EXJrk0cBTgRV39ryOXJ7BLS2iJH9Nc8mVM6vqu0tdj7RY3A0lLZIkTwYeCxw0KLTSGBbSIuj8yth2ml8g+94K/pEhHaEMC+kwJVkL/Bnw2qq6mebnXN+0tFVJi8tjFpKkVvYsJEmtDAtJUivDQpLUyrCQJLUyLCRJrQwLSVIrw0KS1Or/AycKKMekvyvIAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -515,18 +515,18 @@ "output_type": "stream", "text": [ "The intercept alpha: \n", - " [2.15024669]\n", + " [1.99723611]\n", "Coefficient beta : \n", - " [[4.89975818]]\n", - "Mean squared error: 0.26\n", + " [[5.1007125]]\n", + "Mean squared error: 0.22\n", "Variance score: 0.90\n", "Mean squared log error: 0.01\n", - "Mean absolute error: 0.42\n" + "Mean absolute error: 0.36\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAj3klEQVR4nO3de7hddX3n8fcnIQESUIRERfAcvEIRL5io9JlqtUofoYyOteNjnwSBOs2jVh/q0FGcaGEc42gdW7Uzo+CVmqPFC+0wFO83lFHGcFG5CAVMuFpCEiQQBZJ854+1d7PPPmvtvfZlXfZen9fz7Oecs/baa/3Oysn6rt/v+7soIjAzs+ZaVHUBzMysWg4EZmYN50BgZtZwDgRmZg3nQGBm1nAOBGZmDedAYGMj6YWSbqy6HNNA0nWSXlzAcR8n6TJJOyV9UNJ/lvSJcZ/HJos8jsAGJWkz8B8i4ptVlyWLpHOB9cBDwG7geuCsiPhhleWqmqR3AccDr46u//ySjgJ+ASyJiN0VFM8q4hqBTTxJ+2W8dWFEHASsAL4DfLGAc0vSJP0/mgWu7w4C1myT9AdsNSfpxZLu6Ph5s6S/kPRTSb+SdKGkAzreP0XSNZLuk/R/JT2r472zJd3SasK4XtKrOt47XdLlkv5G0jbg3F7laj3dzgFHSFrZOsajJX1S0t2S7pT0HkmLW+8tbjWb3CvpF5LeLCnaAUfSdyVtkHQ5sAt4sqRjJH1D0nZJN0p6TUd5T279Djtb5/qL1vYVki5p/f7bJX2/HVRa1+5lre/3l/QhSXe1Xh+StH/nNZd0lqR7Wr/PGRn/Pp8BTgPeJukBSS+TdK6kja1dLmt9va/1/m/3uq42PbKepMzG5TXAy4HfAJcDpwMfk3Q88Cng3wKbgLXAxZKOjoiHgFuAFwK/BP49sFHSUyPi7tZxXwD8PfA4YEmvAkhaCrwO2AbsaG3+DHAP8FRgOXAJcDtwHvCnwEnAc4AHSa9JnNra58bW568F/rK17ZnANyRdGxHXA58EXhMR35f0GOBJrWOcBdwBrGz9fAKQ9qS+vvXec1rv/2/gncC7Wu8/Hng0cARwIvAlSf8YETs6DxIRp0sCuCMi3tm6Nr/TscuLSJqGDnHTULO4RmBF+0hE3BUR24H/Q3IzA1gHnBcRV0TEnoi4gKQ9/wSAiPhi63N7I+JC4J+B53cc966I+NuI2B0Rv84492sk3Qf8muTm/kcRsVvS44CTgT+PiAcj4h7gb4DXtj8HfDgi7mjdTN+XcuzPRMR1rRvmy4HNEfHpVnmuBr5MEsAAHgGOlfSoiNgREVd1bD8cmI2IRyLi+xlNNmuAd0fEPRGxFfgvJIGIjuO8u3WMS4EHgKMzronZAg4EVrRfdny/Czio9f0scFarWeS+1g37icATACS9rqPZ6D7gOJK2/rbbc5z7CxFxCEmt4VpgVce5lwB3dxz/POCxrfef0HX8tHN1bpsFXtD1u6wheVIHeDVJ4Nki6XsdTS4fAG4Gvi7pVklnZ/weTwC2dPy8pbWtbVvXE3zndTbry01DVpXbgQ0RsaH7DUmzwMeBlwI/jIg9kq4B1LFb7mRnRNwraR2wSdLnWud+CFiR0QRyN3Bkx89PTDts1+/yvYg4MeP8PwZeKWkJ8GbgC8ATI2InSfPQWZKOA74t6ccR8a2uQ9xFEmyua/0809o2bk4gN5RrBDasJZIO6HgN+lDxceANkl7Q6nmzXNIfSDqYpM09gK0AreTncaMUNiJuBL4GvK2VZ/g68EFJj5K0SNJTJP1ua/cvAGdKOkLSIcDb+xz+EuDpkk6VtKT1ep6k35K0VNIaSY+OiEeA+4G9rd/rFElPVdJw/ytgT/u9Lp8H3ilppaQVJLmIjSn7jWpr6/xPLuDYVmMOBDasS0na3tuvcwf5cERsImm3/x8kCdybSRLJtBKsHwR+CPwLSfL18jGU+QPAOkmPJUkeLyUZX7AD+BJJez0kQerrwE+Bq0l+190kN+q032Un8PskOYa7SJrD3g/s39rlVGCzpPuBN5A0GwE8DfgmSZv+D4H/FRHfSTnFe0gS6j8FfgZc1do2VhGxC9gAXN5q4jph3OewevKAMrM+JJ0EfCwiZqsui1kRXCMw6yLpwFbf//0kHQGcA/xD1eUyK0rhgUDS0a3eH+3X/ZL+vOjzmo1AJF00d5A0Dd1A0i5vNpVKbRpSMnLzTuAFEbGl3/5mZla8spuGXgrc4iBgZlYfZY8jeC1JV7gFWv281wEsX7581THHHFNmuczMhvezn8HDDy/cvnQpPPOZ+Y+zfTts2QJ7O3oRL1oEj3883NUxdGTFCpid33fhyiuvvDciVjKE0pqGWvO93AU8IyL+pde+q1evjk2bNpVSLjOzkS1aBGn3Umn+Tb2fo45KAkEv27fDYx6TcipdGRGr859snzKbhk4CruoXBMzMJs7MzGDbs9x2W/Z7X/taEmxSgsCoygwEf0xGs5CZ2UTbsAGWLZu/bdmyZPsgsgKHBFu3Dle2HEoJBJKWk0yPe1EZ5zMzK9WaNXD++Um7vZR8Pf/8ZHte116b3SwUAevXj6esKWo5stg5AjNrFCnfPj3yDZOSIzAza565uSQJvGhR8nVubt970sIgMK58wwAcCMzMijI3B+vWJU0+EcnXdeuS3EF3AHjjG5N93vve8eQbBuD1CMzMirJ+PezaNX/brl3wznfO39bZRN/OK6xfn/QimplJgsAg+YYBORCYmRWlV3dQgEcegf1SbsNr1hR64+/mpiEzs6L0atefnU0PAhVwIDAzK0qvUcL9agslqkc4MjObJnm6gxbYC2hQrhGYmY3LTTctDAIHH1x6L6BBORCYmY2DBEcfPX9bBNx//+ijjgvmpiEzs1GkNQM98AAsX77v55J7AQ3KNQIzs2Hcemt6EIiYHwQmgGsEZmaDygoAE8o1AjOzvNLmBtq6daKDADgQmJn1d/fd2bWAFSvKL8+YuWnIzKyXKWsGSuMagZlZmrRmoNtvn7ogAK4RmJnNt307HHbYwu1TGADaHAjMzNoa0AyUxk1DZmZpzUA33dSIIAAOBGbWZA88kF0LeNrTijtvr+UrK+CmITNrpqqagdrLV7ZXLmsvXwmVTUPhGoGZNUtaM9A115TXDJS1fOX69eWcP4UDgZmNrmZNHal+85v0WsDGjfDsZ5dXjqwFaSpcqMaBwMxG027q2LIleapuN3XUKRhIcOCB6e+VXdasBWkOPbSyYOpAYGajqWFTx79KawbqVnZZN2xYuFDN0qXJugUVBVMHAjMbTQ2bOti9Oz0AZAWFMsu6Zs3ChWoOPhgeeWT+fiUGKAcCMxtNVlNHVWvySrBkyfxtEcmrLmVdswY2b4a9e5Ov27en71dSgHIgMLPRpDV1VLEmb1oz0EUXze8NVJeydqs4QDkQmNlo0po6ylyTd+/e7DEBr3pVvcqapeIApajhEOrVq1fHpk2bqi6GmdXdNM0NNDeX5ARuuy2pCWzYMFCAknRlRKwe5tSuEZjZ5Dn88IVB4FOfyh8EOsc9rFiRvKoeA9GdNyixllJKIJB0iKQvSfq5pBsk/XYZ5zWzKRORBIBf/nLh9jPOyHeM7nEP27Ylr7qMgahgcF5ZNYIPA1+NiGOAZwM3lHReM6uLUW9wUvLZTu3eQINIG/fQqcoxEBUNzis8EEh6NPAi4JMAEfFwRNxX9HnNrEZGucE973kLm4E++MHhcwF5umRWNQaiosF5hSeLJT0HOB+4nqQ2cCVwZkQ82LXfOmAdwMzMzKotW7YUWi4zK9FRRyU3/26zs0l7eJYiksFZZenUr1xFWbQo/feTktxBD3VPFu8HPBf4aEQcDzwInN29U0ScHxGrI2L1ypUrSyiWWUNVMUHcoKOP08YEDNMMlCatq2anKscVVDSeoIxAcAdwR0Rc0fr5SySBwczKVtUEcXlvcKecsjAArF8/3i6h3WMJDjssedVhXEFV4wkiovAX8H3g6Nb35wIf6LX/qlWrwswKMDvbfq6e/5qdLfa8GzdGLFs2/5zLliXb29LKlXac2dkIKfna+flpMeTvCGyKIe/RpQwoa+UJPgEsBW4FzoiIHVn7e0CZWUFGaIMeWdaAqbx5gO6VvSB5Wq7DyOAaqHuOgIi4JpL2/2dFxL/rFQTMrEBVzmnTPWDqW99aGATWrctuBqrzdNcTzmsWmzXJhg3pT9VVTBDXrV/rRB2nu54SnmLCbFrk6Q1U9aRro/QGqssU0lPIgcBsGgzSG6iKOW3OOGNhAHjpSwfrDVTXKaSngGcfNZsGww7YKsM4B4WNOEPnNKt9stjMCjbO9vNeTUyDDEYrYlBYhTN0LlDFwLyCOFlsNg1mZtJrBIO2n3d30Ww3MbVlvdd5Q3772+Gv/mr+cZ/wBLjzzsHKUme9rtME1lDcNGRWV4M0g4yrj32vJibo3/w0TQvF9FLDprhRmoZcIzCro0GfONvbRm0/H6aJ6bbbmhMA2qasK6trBGZ1lPXEuXgxXHBBcc0Pw9QI0tTwvjJWU1YjcLLYrI6yniz37Cl2krheXTT7zdoJ45shtO6mrCurA4FZHfVK8hY5rUKvAWft99Ls3duMANBW9cC8MXMgMKubuTl44IHe+xTZFp3VRfM974G1axfuv2wZfO5zxZVnEGV26axTV9YRORCY1Uk7SbxtW+/9ippWIetGKsG73pX+mbpM/FbVWgtTwMliszrJs4xiUVMvp3VBzauMaaz7qWECt0xOFptNi15NPkW3RadN89yt3XuoWx0mfpuyLp1lciAwq5OsG+rsbPFt0b1umO3eQGm9ZaTkSbzqaRY8O+nQHAjM6qTKbolZzcSdtYDO3jKQBIH256puk5+yLp1lciAwq5M1a+C005KBY5B8Pe20YnukXHhh+shgSL+RtnvLzM4uDB5VJo6nrEtnmZwsNquTstflTQsAs7P5pqmocv1jW8DJYrNpMcq6vKNOEb1tW3Jjz9s33m3yU8OBwKxOhu35krcP/Ve/mj1B3KGHDlbWOieObSAOBGZVSXuCz3qaXrSo95N+npqEBCedNH+fUeYGqnPi2AbiHIFZFbJyAaedlswu2qs/f1rOoFd7fdr2LVvG24TT8MFcdeAcgdmkyXqCv/TS+T1f2r2Huvfrzhlk3dTTgkDE+NvxPZhrojkQmFWh142zczKzrN433Z+veopoJ44nmgOBWRXy3jjz7tdriuhrry1+iuhJG8w1RQvPj4MDgTVbVTeEvDfOvPtdf336FNER8IxnjF7efiZpMJdnKV0oImr3WrVqVZgVbuPGiGXL2g0myWvZsmR7WeefnY2Qkq9Z5+23X2f52y/LNjubfs1mZ6su2UiATTHkPde9hqy5Jr2nS9p4gMsugxe+sPyyTJIpHRHtXkNmw5ikni6dTVhHHpk9KMxBoD8nthcoJRBI2izpZ5KukeRHfauHSbkhdLdp33nn/PfrsGD8JCVfJy2xXYIyawQviYjnDFt1MRu7SbkhZC0Ys3Jl9QEAJi/5OkmJ7ZKUkiOQtBlYHRH35tnfOQIrzdxccqPNM9tmFbZtgxUr0t+rS5v2pOdapsQoOYKyAsEvgB1AAOdFxIIOz5LWAesAZmZmVm3pt26r2bTLWiOgrS432ilNvk6aSUgW/05EPBc4CfgzSS/q3iEizo+I1RGxeuXKlSUVy6yG0qaIXrp04T4nn1xemXrJmrW0brkWy1RKIIiIO1tf7wH+AXh+Gec1G7u0pOi4EqUPPpjdG+j1r5//XkQyOV3V7fBzc7Bz58LtS5bUL9dimQpvGpK0HFgUETtb338DeHdEfDXrM84RWC2lzRi6ZElyg3744X3bhllRLCsAtNW1HT6rXIcdBvfmSgnamNS9aehxwA8k/QT4f8A/9QoCZrWV1nvnkUfmBwEYbN3etGagd7xjYZt7Xcc8ZJ1/+/Zyy2Ej2a/oE0TErcCziz6PWeEGuen22/eRRxa2+0NSm0ibG2hmJv3Ju+p2+LqWywbikcVmeQ1yc1u0CN70pvTcgZQeBCC7NlHXMQ91LZcNxIHALK+0m96SJek39T174KMfnT/Iau3a/l1CIb02UddBUHUtlw3Ek86ZDSJtABokS0zu2TPYsWZn65kAtolU92Sx2fToXD1s8+bk5zVrBhs4tXFjUktws4rVhAOB2TgMkj9o5wDcrGI14UBgNg551gxu68wBpNUwzErmQGA2DmvWpM8QmubQQydnymZrBAcCs1GlDQo7/PAkF9BdS1i6FO6/f3KmbLZGcCAwG0XW1BB33ZWeAzj44GQwWadBRiKbFcCBwGwYabWAtJXCunMAWVMvjGOqiElaJcxqxYHArJfum2taAID8K4UVtTzmpK0SZrXiQGD1VfUTbtrNtdug6wUXNXYgbUI8NzlZTg4EVk91eMLNWisYhl8wvqixA3WdndQmgqeYsHqqw/z7WfMC1XEJxjpcL6uUp5iw6VP1E26vyeHqOMWyp6uwETgQWD0VlVTtJysZ3FbXm6unq7AROBBYPQ37hDtsgjkrAGzcODk3V09XYUMqfIUys6G0b2LdUz73url1ryncTjB3Hi9Nv+6gvqHalHONwEZXVDfPQZ9wB+1CmXdQmNmUc43ARjPsU3gR8iaYDzoIHnxw4X4OANZQrhHYaKoYyJRVA8mTYJYWBoG0WkDVg9nMSuQagY2m7G6evWogGzbMfw/2JZjT8gB796Zvr1Mtx6wEHlBmoyl7IFO/83WvKbzffnDLLQv37/V378FZNoEKHVAm6RuSnj3Mwa0Byh7I1K8G0plg3rJlYRDIkwyuejCbWcny5AjeDnxI0qclHV50gWzClD2QKW8eoLvJZ/fu6mcINaupvoEgIq6KiJcAlwBflXSOpAOLL5pNjDIHMvWqgZx+evaYgMWLx3MOsymUq9eQJAE3Ah8F3gL8s6RTiyyYWaqsGsjatXDBBfP3rdsMoWY11TdZLOly4EnAdcCPgCuAnwNnAvtHxLpxF8rJ4hrpTr72G91btrQawK9/DQccUH5ZzCpU9Oyj64AjIuLEiHhXRFwSETdHxFuAFw5zUpsQZa8JMEjf/bPPzm4GchAwG0ieHMF1kV1t+IMxl8eGVcQAqDIHiw0SdCR4//vnb4tIJojzIDCzgXkcwTToHgAFSXJz1HbtRYvS29iLWJglT9/9tBrAjh1wyCHFXQOzCTERC9NIWizpakmXlHXOxijqyb3MbpS9+u5/5CPZzUCHHJJ87zV7zYZW5lxDZwI3lHi+5ihqAFSZ3SizgksEnHnmwm3dNRUPAjMbWimBQNKRJPmET5RxvsYp6sm9zG6UaUGn2+23JwEgLR/iQWBmQyurRvAh4G1AZsOypHWSNknatHXr1pKKNSWKfHIva7BYO+isWJH+fgQceWR2Uvnkkz0IzGxIhQcCSacA90TElb32i4jzI2J1RKxeuXJl0cWaLtMyAGrtWrj33vnbupuBsnIBl146HdfArAKF9xqS9N+AU4HdwAHAo4CLImJt1mfca6hh0hLB110Hxx67cHtWT6b2ceo46M2sBLXuNRQR74iIIyPiKOC1wLd7BQGrUNmLsXzzm9m9gdKCAPRu8y9j0JvZFPIKZf00ZaWqskcRS3DiifO35ZkbKE9S2d1GzQZSaiCIiO9GxCllnnMkZd8cq1RWP/y0KaIvuyz/5HDd+ZAs7jZqlptrBL00aZDSqP3w+9Wcrr46uxnohQNOWdXZk2l2Nn0fdxs1y82BoJcmDVIapR9+v5qTBM997vzPtJuBRm1689oBZiNzIOilSYOURrmhZtWc1q5dWAu46KJ9zUDjaHqblq6zZhXypHO9NG0is2HXHujVpbNT9z5eJN5sbEbpPupA0E/dF2apg6wbelvW31iZs5uaTblajyOYeGWuxzupspqPTj+9d01hmKa3pnTnNSuRA4GNZtu2JBfQbeNG+PSne3920LzEsDkFBw+zntw0ZMPL6g46iEGa3obJKTQtz2ON5RyBlSstAJx7LpxzTrHnHSan4IS0NcQogWC/cRfGptiuXbB8+cLtZT1MzMyk39R75RSaNBbEbEjOEVg+0sIgkGduoHEaZqxDk8aCmA3JgcB6S5sb6PWvLzcAtA0zeMwjj836ctOQpdu9G5YsWbi96pzSmjWDJXnb+3osiFkmBwJbaBy9gepk0OBh1jBuGrJ90pqBXvWqyQ4CZtaXA4ElN/qsWsBFF+U/jgdumU0kB4Kmk5Ibd6es3kC9bvRNWsTHbMo4EDRVWjPQMcdkNwP1u9E3aREfsynjkcVNNEwyuN8IXc8kalYpzz5q+aTVAvIOCus3QtcDt8wmlgNBE6QFgIMPHqw3UL8bvQdumU0sB4Jpl9UMdP/9gx2n343eS0aaTSwHgrxG6RpZVLfKuTlYsWLfE/9BByU/L1o0WjNQmjw3ei/iYzaRnCzOY5Q57YuaD39uDv7kT+Dhh/PtX8N/ZzMbH69HULRR5rQvaj78fusEj+s8ZjYR3GuoaKPMaV/UfPh5P+95982sDweCPEbpGllUt8q8n3f3TTPrw4Egj1G6RhbVrTJPs5C7b5pZDg4EeYzSNXKYz/bqZZTWG6ht+XI47LDRum964jizxnGyuG6yehk94xnw4x8v3H+c/35F9XAys8LVuteQpAOAy4D9SRbC+VJEnNPrM40OBHl7AxXx71ZUDyczK9wogaCMFcoeAn4vIh6QtAT4gaSvRMSPSjj35OnXy6fIwF1UDyczq7XCcwSReKD145LWq37tUXXRq5ePVGy7vSeOM2ukUpLFkhZLuga4B/hGRFyRss86SZskbdq6dWsZxaqnXs1CRS/4kqeHk5PJZlOnlEAQEXsi4jnAkcDzJR2Xss/5EbE6IlavXLmyjGIVb5CbZlpvIAkWL164b1ELvvTr4eRVyMymUum9hiT9JbArIv571j5TkSzO2wPnvPPgDW9Y+Pn2v0udFnxxMtmstmo9xYSklZIOaX1/IHAi8POizztWwzSH5Fm6UVoYBDpnCJ2bW7iecFtWu32RTTdOJptNpTJ6DR0OXCBpMUng+UJEXFLCecej+8m+3RwCvfvW97pppg0I27Nn/k2/fd49exbumzVieNiy5jUzk14jcDLZbKJ5QFk/wzaH5B0PAOlNP1mfX7wYLrhg4Y19bg5OOy09cIyr6cYDzsxqq9ZNQxNv2OaQtB443XotFJN1/L1704NAVu2h17EGbUbyKmRmU6mMpqHJNmxzSPvmuHbtwvceegiWLh3tvHNzSb7httuSG3lWEMgq67DNSGvW+MZvNmWmr0Yw7mTpsLOH/uAH6UEgon8Q6Hfe7m6cvYJAVlnzJLPNrBkionavVatWxVA2boxYtqzd4JK8li1Lto9i48aI2dkIKfna73id52+/xnne2dn0c3S/Fi/OLquU/hlpuLKaWaWATTHkPXe6ksVV93NP6w20axcceOB4z5M1tqBTvyRu1dfKzMbKyeK2qvq5X3NNehCIGH8QgOz8xOLF+ZO4RS2YY2YTZ7oCQRWTpklw/PHzt/XqDTQOWTfxCy5IehVt3tw/oeseQGbWMl2BoMyn3LS5ge67r9gA0Daum/iaNUnQyBs8zGwqTVf30faNrN2tcmYmCQLjvMHdeis85SkLt5eda3E3TjMbk+mqEcD4n3Lb3VHbNYDuIFB0M5CZWcGmq0YwbmlTKrQdeCB8/OPll8nMbMymr0YwrLSBaO94R3oQAPj1rz34ysymgmsEkD7dQtqo4G6eftnMpoBrBJA+3UIenn7ZzKaAAwH0frLPmkHUg6/MbEo4EEB2r592//zZ2eTn9vrBHnxlZlOk2YHgy19OnxoC9j3xt7ujRsDu3clXD74ysynSzGTx7t2wZMn8bSeeCDfdVNxANDOzmmpeIEirAUhJEPDN38waqDlNQ5dcsjAItGcGjdi3QlfehWwGWQBn3IvlFH1cM2uWYRcyKPI19MI0afbsWbj4yvvel724y+xs/2MOsgBOkYvlFHFcM5tIeGGaDI96FOzcOX9b+/fNWtxFSuYp6mWQRV2KWgDGC8uYWQcvTNPtm99MbuidQWDHjvk3/lHWLhhkAZyiFssZ13HdvGTWeNMVCCKSAHDiifu2nXtusv2QQ+bvO8raBYMEkaIWyxnHcdtTa2zZMlyexMymwvQEgs9/Pnmq7RQB55yTvv8oi7sMEkSKWixnHMdNm1pj1y5PpmfWNMMmF4p8DZQsvueehQnf++/P//lhbdyYJJal5GuvJO0g+xZVhjRSesJcGk/5zKw0NDJZHAGnnQaf/ey+bT/5CTzrWcUWbpo44Ww2NZqXLP7KV5JmoHYQeO97k8DgIDCYMtd4NrPamqxAsG1b0p5/8snJz096UrJAzMyMe74MY5Q8iZlNjXoHgs6ujQcdBCtW7HvvqquSheS//OXx9HxpajfKca/xbGYTp745gre+NX294D/8w+Tm3zaOdu60tYmXLfPTsZlNjFFyBIUHAklPBP4OeBwQwPkR8eFen1m9enVsuvfefDf4UUYItzlpamYTru7J4t3AWRFxLHAC8GeSju37qbwjZ8cxsKqo0b9mZhOg8EAQEXdHxFWt73cCNwBH9P1g3hv8OHq+FDX618xsApSaLJZ0FHA8cEXKe+skbZK0aevWrflv8OPo+eJulGbWYKUliyUdBHwP2BARF/Xa918HlM3NJdMdlLFqWJnnMjMbs1oniwEkLQEuAb4WEX/db/+xTUNtZtYQtU4WSxLwSeCGPEHAzMzKVUaO4N8ApwK/J+ma1uvkEs5rZmY5FL54fUT8AEhZMd7MzOqg3lNMmJlZ4RwIzMwazoHAzKzhHAjMzBrOgcDMrOEcCMzMGs6BwMys4RwIzMwazoHAzKzhHAjMzBrOgcDMrOEcCMzMGs6BwMys4RwIzMwarrSlKgchaSdwY9XlqIkVwL1VF6IGfB328bXYx9din6Mj4uBhPlj4egRDunHYJdemjaRNvha+Dp18LfbxtdhH0tDr+7ppyMys4RwIzMwarq6B4PyqC1AjvhYJX4d9fC328bXYZ+hrUctksZmZlaeuNQIzMyuJA4GZWcNVFggkvVzSjZJulnR2yvv7S7qw9f4Vko6qoJilyHEt/qOk6yX9VNK3JM1WUc4y9LsWHfu9WlJImtqug3muhaTXtP42rpP0ubLLWJYc/0dmJH1H0tWt/ycnV1HOMkj6lKR7JF2b8b4kfaR1rX4q6bl9DxoRpb+AxcAtwJOBpcBPgGO79nkT8LHW968FLqyirDW5Fi8BlrW+f2OTr0Vrv4OBy4AfAaurLneFfxdPA64GHtP6+bFVl7vCa3E+8MbW98cCm6sud4HX40XAc4FrM94/GfgKIOAE4Ip+x6yqRvB84OaIuDUiHgb+Hnhl1z6vBC5off8l4KWSVGIZy9L3WkTEdyJiV+vHHwFHllzGsuT5uwD4r8D7gd+UWbiS5bkWfwr8z4jYARAR95RcxrLkuRYBPKr1/aOBu0osX6ki4jJge49dXgn8XSR+BBwi6fBex6wqEBwB3N7x8x2tban7RMRu4FfAYaWUrlx5rkWn15NE+2nU91q0qrlPjIh/KrNgFcjzd/F04OmSLpf0I0kvL6105cpzLc4F1kq6A7gUeEs5RaulQe8ptZ1iwlJIWgusBn636rJUQdIi4K+B0ysuSl3sR9I89GKSWuJlkp4ZEfdVWaiK/DHwmYj4oKTfBj4r6biI2Ft1wSZBVTWCO4Endvx8ZGtb6j6S9iOp7m0rpXTlynMtkPQyYD3wioh4qKSyla3ftTgYOA74rqTNJO2fF09pwjjP38UdwMUR8UhE/AK4iSQwTJs81+L1wBcAIuKHwAEkE9I1Ua57SqeqAsGPgadJepKkpSTJ4Iu79rkYOK31/R8B345WJmTK9L0Wko4HziMJAtPaDgx9rkVE/CoiVkTEURFxFEm+5BURMfRkWzWW5//IP5LUBpC0gqSp6NYSy1iWPNfiNuClAJJ+iyQQbC21lPVxMfC6Vu+hE4BfRcTdvT5QSdNQROyW9GbgayQ9Aj4VEddJejewKSIuBj5JUr27mSQx8toqylq0nNfiA8BBwBdb+fLbIuIVlRW6IDmvRSPkvBZfA35f0vXAHuA/RcTU1ZpzXouzgI9LeitJ4vj0KX1wRNLnSR4AVrRyIucASwAi4mMkOZKTgZuBXcAZfY85pdfKzMxy8shiM7OGcyAwM2s4BwIzs4ZzIDAzazgHAjOzhnMgMDNrOAcCM7OGcyAwy6E11/2Jre/fI+lvqy6T2bh40jmzfM4B3i3pscDxwNSN7Lbm8shis5wkfY9kqo8XR8TOqstjNi5uGjLLQdIzgcOBhx0EbNo4EJj10VrdaY5k5acHpngBGGsoBwKzHiQtAy4CzoqIG0iWyTyn2lKZjZdzBGZmDecagZlZwzkQmJk1nAOBmVnDORCYmTWcA4GZWcM5EJiZNZwDgZlZw/1/MB/eQrEhaToAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -691,8 +691,8 @@ "metadata": {}, "source": [ "$$\n", - "H_{\\delta}(a)=\\left\\{\\begin{array}\\frac{1}{2}a^{2}&{\\text{for }}|a|\\leq \\delta ,\\\\ \\delta (|a|-\\frac {1}{2}\\del\\\n", - "ta ),&{\\text{otherwise.}\\end{array}\\right.\n", + "H_{\\delta}(\\boldsymbol{a})=\\left\\{\\begin{array}{cc}\\frac{1}{2} \\boldsymbol{a}^{2}& \\text{for }|\\boldsymbol{a}|\\leq \\delta\\\\ \\delta (|\\b\\\n", + "m{a}|-\\frac{1}{2}\\delta ),&\\text{otherwise}.\\end{array}\\right.\n", "$$" ] }, @@ -700,7 +700,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here $a=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", + "Here $\\boldsymbol{a}=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", + "\n", + "\n", "We will discuss in more\n", "detail these and other functions in the various lectures. We conclude this part with another example. Instead of \n", "a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn." @@ -716,7 +718,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -733,7 +735,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.0050000000000000044\n" + "0.005000000000000007\n" ] } ], @@ -1377,6 +1379,50 @@ "name": "stderr", "output_type": "stream", "text": [ + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", @@ -1429,10 +1475,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", @@ -1443,44 +1485,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n", "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", @@ -1489,7 +1493,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3219,13 +3223,13 @@ "output_type": "stream", "text": [ "Training R2\n", - "0.9999868619217517\n", + "0.9999878260589065\n", "Training MSE\n", - "5.965885569080809\n", + "5.415634483874318\n", "Test R2\n", - "0.9999794306626945\n", + "0.999974281880048\n", "Test MSE\n", - "8.300162456113691\n" + "11.653095390463358\n" ] } ], @@ -3832,31 +3836,31 @@ "output_type": "stream", "text": [ "MSE before scaling: 0.00\n", - "R2 score before scaling 1.00\n", + "R2 score before scaling 0.99\n", "Feature min values before scaling:\n", - " [1.00000000e+00 2.54152940e-03 1.38207279e-03 6.45937170e-06\n", - " 3.51257863e-06 1.91012519e-06 1.64166831e-08 8.92732185e-09\n", - " 4.85463934e-09 2.63993205e-09 4.17234827e-11 2.26890510e-11\n", - " 1.23382086e-11 6.70946493e-12 3.64857826e-12 1.06041458e-13\n", - " 5.76648901e-14 3.13579199e-14 1.70523024e-14 9.27296891e-15\n", - " 5.04260073e-15]\n", + " [1.00000000e+00 2.31109204e-04 5.13266452e-03 5.34114641e-08\n", + " 1.18620601e-06 2.63442451e-05 1.23438810e-11 2.74143127e-10\n", + " 6.08839750e-09 1.35216172e-07 2.85278450e-15 6.33569998e-14\n", + " 1.40708470e-12 3.12497018e-11 6.94019247e-10 6.59304755e-19\n", + " 1.46423858e-17 3.25190225e-16 7.22209371e-15 1.60394236e-13\n", + " 3.56216797e-12]\n", "Feature max values before scaling:\n", - " [1. 0.99817842 0.99945628 0.99636015 0.99763569 0.99891285\n", - " 0.9945452 0.99581841 0.99709325 0.99836973 0.99273355 0.99400444\n", - " 0.99527696 0.99655111 0.99782689 0.9909252 0.99219378 0.99346398\n", - " 0.99473581 0.99600927 0.99728435]\n", + " [1. 0.99997737 0.9950799 0.99995475 0.99505739 0.99018401\n", + " 0.99993212 0.99503487 0.99016161 0.98531221 0.9999095 0.99501236\n", + " 0.99013921 0.98528992 0.98046438 0.99988687 0.99498985 0.9901168\n", + " 0.98526763 0.9804422 0.9756404 ]\n", "Feature min values after scaling:\n", - " [ 0. -1.63437572 -1.76504618 -1.03871832 -1.08415761 -1.13457922\n", - " -0.81620806 -0.83935285 -0.86436607 -0.8914984 -0.69347005 -0.70790937\n", - " -0.72312577 -0.73921714 -0.75629493 -0.61234223 -0.6226921 -0.63339159\n", - " -0.64447921 -0.65599927 -0.66800261]\n", + " [ 0. -1.7621419 -1.74382593 -1.13803928 -1.14662074 -1.15475232\n", + " -0.89942598 -0.90825063 -0.9171356 -0.92605247 -0.76570177 -0.77317984\n", + " -0.78080633 -0.78857629 -0.79648291 -0.67708423 -0.68316185 -0.68937695\n", + " -0.69573183 -0.7022283 -0.70886748]\n", "Feature max values after scaling:\n", - " [0. 1.86574276 1.72218808 2.41770932 2.31730641 2.21347282\n", - " 2.88297395 2.79328828 2.70234019 2.60999846 3.291614 3.20772452\n", - " 3.12283463 3.03697069 2.95014575 3.65766387 3.57871326 3.49865673\n", - " 3.41754964 3.33544681 3.25240108]\n", + " [0. 1.71669651 1.63315151 2.21623344 2.15324631 2.088871\n", + " 2.62856593 2.5755035 2.52158335 2.46675058 2.987902 2.94034531\n", + " 2.8923 2.84372853 2.79459035 3.30968181 3.2653678 3.22076362\n", + " 3.1758506 3.13060772 3.08501147]\n", "MSE after scaling: 0.00\n", - "R2 score for scaled data: 1.00\n" + "R2 score for scaled data: 0.99\n" ] } ], @@ -4155,25 +4159,10 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Found input variables with inconsistent numbers of samples: [1000, 100]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# split in training and test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m 2125\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid parameters passed: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2127\u001b[0;31m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2129\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m 290\u001b[0m \"\"\"\n\u001b[1;32m 291\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_make_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterables\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 292\u001b[0;31m \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 293\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0m\u001b[1;32m 256\u001b[0m \" samples: %r\" % [int(l) for l in lengths])\n\u001b[1;32m 257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [1000, 100]" - ] - } - ], + "outputs": [], "source": [ "# split in training and test data\n", - "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" + "# X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" ] }, { @@ -4185,7 +4174,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "collapsed": false, "editable": true @@ -4216,7 +4205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "collapsed": false, "editable": true @@ -4236,18 +4225,27 @@ "metadata": {}, "source": [ "where $y$ is the function we want to fit with a given polynomial.\n", - "!bsubex\n", - "Write a first code which sets up a design matrix $X$ defined by a fifth-order polynomial. Scale your data and split it in training and test data. \n", - "!esubex\n", "\n", - "!bsubex\n", - "Perform an ordinary least squares and compute the means squared error and the $R2$ factor for the training data and the test data, with and without scaling.\n", - "!esubex\n", "\n", - "!bsubex\n", - "Add now a model which allows you to make polynomials up to degree $15$. Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?\n", + "Write a first code which sets up a design matrix $X$ defined by a\n", + "fifth-order polynomial. Scale your data and split it in training and\n", + "test data.\n", "\n", - "!esubex" + "\n", + "\n", + "Perform an ordinary least squares and compute the means squared error\n", + "and the $R2$ factor for the training data and the test data, with and\n", + "without scaling.\n", + "\n", + "\n", + "\n", + "Add now a model which allows you to make polynomials up to degree\n", + "$15$. Perform a standard OLS fitting of the training data and compute\n", + "the MSE and $R2$ for the training and test data and plot both test and\n", + "training data MSE and $R2$ as functions of the polynomial\n", + "degree. Compare what you see with Figure 2.11 of Hastie et al. Comment\n", + "your results. For which polynomial degree do you find an optimal MSE\n", + "(smallest value)?" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.py b/doc/LectureNotes/_build/jupyter_execute/chapter1.py index 7ff525383..b828ec607 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter1.py +++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.py @@ -483,11 +483,13 @@ ways of dealing with outliers. The Huber cost function is defined as $$ -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\del\ -ta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2}& \text{for }|\boldsymbol{a}|\leq \delta\\ \delta (|\b\ +m{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. $$ -Here $a=\boldsymbol{y} - \boldsymbol{\tilde{y}}$. +Here $\boldsymbol{a}=\boldsymbol{y} - \boldsymbol{\tilde{y}}$. + + We will discuss in more detail these and other functions in the various lectures. We conclude this part with another example. Instead of a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn. @@ -2146,7 +2148,7 @@ It also common to split the data in a **training** set and a **testing** set. A for testing. This can be done as follows with our design matrix $\boldsymbol{X}$ and data $\boldsymbol{y}$ (remember to import **scikit-learn**) # split in training and test data -X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2) +# X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2) Then we can use the standard scaler to scale our data as @@ -2174,15 +2176,24 @@ x = np.linspace(-3, 3, n).reshape(-1, 1) y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape) where $y$ is the function we want to fit with a given polynomial. -!bsubex -Write a first code which sets up a design matrix $X$ defined by a fifth-order polynomial. Scale your data and split it in training and test data. -!esubex -!bsubex -Perform an ordinary least squares and compute the means squared error and the $R2$ factor for the training data and the test data, with and without scaling. -!esubex -!bsubex -Add now a model which allows you to make polynomials up to degree $15$. Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)? +Write a first code which sets up a design matrix $X$ defined by a +fifth-order polynomial. Scale your data and split it in training and +test data. -!esubex \ No newline at end of file + + +Perform an ordinary least squares and compute the means squared error +and the $R2$ factor for the training data and the test data, with and +without scaling. + + + +Add now a model which allows you to make polynomials up to degree +$15$. Perform a standard OLS fitting of the training data and compute +the MSE and $R2$ for the training and test data and plot both test and +training data MSE and $R2$ as functions of the polynomial +degree. Compare what you see with Figure 2.11 of Hastie et al. Comment +your results. For which polynomial degree do you find an optimal MSE +(smallest value)? \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_11_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_11_0.png index f67778338ad1c1425a8c249bf89dc29001782663..d56e1d6d04f8fa9dce774c4eeca197848bcd591a 100644 GIT binary patch literal 6573 zcmd6McUV*Dwthm83`iMe1VxHv1C9iv(!1h}vPFu@rbrVJ5ioQJ5UPx*g9vVH7^;F` zsL~;n2qKe_q67>OFi{zh03i|~)Z`cEoHM_3&OP_J&;9Q{$$s{iwO97e`qsPNRdUnb z_JoX-k`w}gkO8fJI)gwUQ$>fucM>AWgY9Gm(a9+MXXo&9`GtR?ded))-@9JDigP!5`OCf|2rUUa*N@+yy>j?@ zmRNaO!J8xZer@zg{QlG_8}|9FyLQ*A;E&Y)fTnJ@qTSl3k$YrUq0A)=`oclgErhyNy0f#BHvR4s*QrVQAYcX6?C2ELTzRp6sZ%>^tz zS2VWYnTP{b%?rAX2=UFNC1|L#lr0FcxUV}Sy;<9-{Tw8R&J{D=7S%a$Uq=PJ?WYqo z=woFgx)uN}%CL-$3y?=(Utom@;MG^ifUit*5ZtZ4@)}^;hWf%Ppz28L+y+I0wRe0= zPmub^!$Md&bHY<&WT>IVNpobRRgeKBWL)%wgEXR@;OjC3RWM$gstvXgCz^pUvjzxe zS}km2X@Pwwt1YNAnX#y4wefC(FIQFBP%O~zPzf@z`IRHQ#9|b&nI2g}>@I9`kNMvOtJ7t(a=_74zJ=(5Qy;c(2nc)rfvLW2?w5%VqS7$%@I% zW$__alNqfaxg;Gh-}Smg@r1&;id-kCo942`9O+EYu|T&}ktNv`03In6k2)n3gH4U8 zdLYMYK`z`;Ftf_gX8E%^IE#wN1V@PfW`|1Iroh=}ANt8WbQVf*){Zg%Mlw4yW_0X5 zJ`V*gcIHlZixCJ<`H@pH0ob&cRd1>tp@dB>a?8n_GRE{RjOs|3Q3WYJfiljtoI1Of zNn&dzD$-m)Vbm8HIiVtD6|gIiSK<-#IpXn%bvHYi^pZZ=m7{^^_}0qF>(CGrV{a|| zN}v9vuSI4&8kd(^yQ|Yu>xD^@ujWd$@sngvn}uwkG}E+}oXnp<@=bF4CdK~J7Q5C_tMAe*baqrOC&ae+a127HYVFMf6)Ah$c~xq>*s{&6j~yAdiDn) zt&Ne8-Y2zO0kj)Ue5jP@lQ4IK_=`OWW;r<=I^G~8PI)4|^ zd?nds`;m+Kb%|z#JK8>_8Lh$fVGWH3)hTOG6`G6p8*&;y*(P+z=N?Q7RrmX&`OB-F z^JOG2^bJ`(E#JKL@3={%)zh#ixh*?6aM=}ulLGRtjk#p>zJ>a<`aBFY5>J=qo z0VhA~YJIdOerm%RiX)Ta2XoegDAQ+4tl_}{-HwkEtXiO0!v*Drn4QIC6!KflrjQM% z3J!p0tmT=~5X4t+df1m`;e?tmClNE^D&iH$KD9R-d{Tp$MYg34T)5@b zUW~0+;C$A5W)9yQ%$3MK1F5-RJ_^0L3J5e@JW$mEY;zO+mW*;7R9z$f`pBMxJ z5;rI2@C+P>#-X|jlD^~6aaIhgg*2*nZ4>~tPnlv!Y;)EHmN83#bqtCKgI{N68%%dU z092a1pMKc|MQ8`(m@ThUYRv+a^uBp4xJ;r#yl;~}Qc?E1R-w5A)xA4iZ zD6(W(cXOvR$-I;%iKzF$=0Abs6@m&r2cOL>LmogxF6mo^p6j2eM0jRdy2FL{HQM?g zlQ`C)hk{kAl%j&5@$8Av^G?v5_yOBc{a`HDomG@sAp+-n-N@jx5M7ME)Jra#S}lEg z`a0kja_JJf0q~3b%W-e5yzWdu;CouDKuzN;4EVjfWn3MleEs$-^Z1@REBMl2iPvdL zCv3VUYF3`hffdZOOL z6C}yOz2KvxPg1(}Ceae*6Wi4YdT?QuF8fkR142#wy*ng0atBaRKbMYy*S%Q@sDPxf zOjG;?2!t>FzHe`;d}3Z954Vq7Y^Oe5@;2t;X-EypDI=AV*!x)(Hz9TK(U0XP6u|gR zjRz&ek1Jvy&!4yjfLgSD#DaH7i_d~MMB{}$+eA0BovA7zmBM2ur#{csQEC!Bi4I;O z5FcWPC{*V2YKns%hXv;X)B6tkJIBgL?aS%4PghMaf(u=A*+-*I5|b%GNHw*;34U)V zh>`0c%cj?46h3(+`jLyuQa8>!TMeA{zMH&I@ip|*bqZ7mUpsp{I}GKa($Q9-8eB`(AMzr^=pH-1 zs_y{BNi6sL;xF=a$+IiACqr#v*UVYlh5Y1IxJ85d03N$LArG2@1we1c&FeqILzH`> z%1ndR0T5Ro#mNr4L3R=X@hC>&3=Gs6q}}P-K@mkjIZ0NGj+o->myqlnhs?&QGfA1n z_M$*5^82L=R5mU*q6j!GULLZsSKi3{69+m!78Sxei`#|)tb{%)NG+TI;YvxpP+j!K z>LN|s7QTJqN$yVnLmY(dbe4IO$^1dEPvXF)_~)c}-D{#i*S7J#_;0ba;J_NEL_>?^ zKMn$*(ukoH+e4Kioq5qJWT*$*S^znh#1AEGQf{q>4+%Am@Yc=pwS=rxowrFW#Ona{bHZOmd z`iDxJ%%2oxf4mSZpdp3 zjA?W}U^BKLx7kj0A1L3j-ub8UOKoA*-&KSRdlnU??$>c&_?zY5zq88@)(G4$BBgAg zL1QFm0!B7i=unv&O<3yYbJ!?BVoNKbfc*v;Es+AG4gFgJj_X}QVo?>UejijbG{Lin z{saEZpa^*`#md?oMlFoRs)OY=|34EOR0FQ??8)jGN)js7Vpo5!6RZpS)FqQi_0Z5H zX|yGNW3@{yV`@?hRHFV9ci$enAom+D_KZl|Px&tXPx7Ar<>e+y>Z|&%UCJ2^UdMcC zw0x0|(OJvC{RhAy!1&$Nc-A!vIdjbP?PJrtyFl8-{UT~01tbNmO}f7e+mJ9O7C-fl z2STTda%}8&>W5$LIGbWMa763Vn0@Mpl|tZIozEg}A2={@@W1UC|ChDY!+I5y^Pi48 z*O7Sm?%PEbqJ*!~+J3j;hsfDPZC6$!oNU$Kw1a$ zs$lNMhkr-=qv(__!&`?hG=APQT!U7P%IDGZGis%`D|}+zI65HZ>-?3yu{zTnkz-AJ z5ys<3Q>~3@^9jNQijCeX^GrK(BEBsmcSt5P%Fk`8jQu0Z;9%aW6AYfaM524$jR+20`Bjmy<44S@XO(M1Jpn0q zZUj>s>Up`SA#RO(aJs#xw=N-YV{&rT>^P&F_(}@z+1WghGLkeS7*QvFpGpKB&%lb>z};DufrG~M6Nk?Z|wPbURC=u&~h z*RMgPvWtJL->9GawRA4P+gYvB!))37M73B>`@Err^x}aUp)h9ld9r6JuRo}OPrIq< zSni^;9%o{~=6EXhFyGa;$IQ$#&pfwZd;bS+JZ3qrA#kpCPWb)7&#j|8Lmd?biV>xp z63F_-RN~9$`Ahv`JBjy4j4`7Xg=?M^Mgw+iC0SbPsa}AB;Dmm8VSQj6_wbrvB{il^ zR9M?+jnzL87=#lua5WtspJ_7}RzF%f26sAcXEexp?$%z4c8c)W*{QF!MAu~pB)pkv zw7F5Y60HAugVt`zao^G4GI^&ZPoY^ktcRQ7Su<(>@GG{4zu0EKOJ=oQ=G^-#rP0{o zX+m~HusmL$almup!1uwt0v0MwbqMjt*j) zw=gFe4RL}eElWfX102U>U$#W~sdTf*^#W$(S?)d|F=WVqiOr1ZkHFfcs6oA#oU!$P7llY>m|JRq;;f~ghT>0@R7B4L#>$}nYKJFcZN5>U5Uwe1Qd?|mb^-bs$!|r1pb5IkfzmaxWDObqSyGdsn9-uC>iNbc9 z<&|7nNN=?{_T!0Q&1ht>E!6thWMeC98j1+9BVr>Nv-7qIb&ScrHXj_a+2Uxod=ggQ zOkiy2h3YB#5YR#R5PkPHis3gy(guX7Av*{0q~q~w zn9@9`oP?w3Cjw;uE!WE`e_HC={nr~*h{!GcmXiL_C!!dDkwla@ckahQ4bb09>90qZcB&8I*HCcaYt){je@{D?Cq>=zKaEGMV7bU3;T1T|+J4fZ z_H!y7SUCd|;n+RYu``24GB$B$`VbB zu(C}XO_c6LL_fluQQPC9=8}z^tspT->|Mb!!SS9%UQ%f@T`0c%2at6kjW#f>2rW`c zkA&L_W~P;C)m!VL-4W{txQ?vrG)&;HqhCAQkrid6c`=8fhNo_yYHO!c#o`|mHnvdc z#m-CzXsFm_86z`=N^n!5qtZ_;JiK<_XvQmKYcQeztEf;;!hLJm16ePhIT*O(Rv*(} zPsfHj-&9(AY*J=kL^3z-?RZ+&8}RW9+V|}azj$M+X#7`Egz>-W z_FYRQNpO&w6@Ggz<^p6^HF!-MU1MI&b=ZBqc6u|ME#oGY?F3moI)kIV8&vG38NHn- zhGz?EY$B(m#;D_yde7B{T_C>W5Iqq~lT5$&)W)+#dHMSzqP8PyUsd4#q264_nM{uw zRfXLB6B)1CtiILj;a4vd1rk$I+Db^|Qh#>{S4K(;Ut)?LlxmzbDjmQREbACQk%G&f z)VQ$ge|%@Cja$8-#(ga+tM1!6D?jwVSL3b8t!!rg8Y2;mhc-4W@eika?e&_@Uc;DZ zuYH`ObSBB+`CsNsNxb`IpC$c96tz5F$2ZC@`+{xaV{$DOB*(Ge$UdGe3Q=~j6K!N< zqk7xw3m8z#=k4}#$E)=c6TOinwtM)}i0OmA%FOqu^_f>b6>r)^?fl!apcEMBMg05t z{$Go+!b8&n$Br5@otZt+j)|}tXzcmAWo+OuPlL4pq>JTBl{G$@iuk(lU7+7Bczs*W z*@|okV!EM zT4DJts}73ToRb$8M;mZdPkXTTtyENY`5z`ZZ-r>d(8X&6Qw&b@*9ijr+4iRj%kx+N EAH*K)I{*Lx literal 6624 zcmd5>cT`i^zD^NP5SSU2-qgcjp@=l;mJv8qTj+=k0)`S02)%?+!5KjGARt`@K@YtK z2uc(Iqf(?wNr-~9kPzuYATQo~=dOEa-S^&l|GjnAUT5!p_Bnh1+V}hRKEI!{HWe0> z5=0;n!l2nN7Z3ztmdYeknM$!vNzHQ0HBn zUH)k0srs*#4}MZVzsG&_jA*%v`PCn_tbzvr`1DJGy5H+ooaT|ho_)g}>T|!Ht}Ri0 zLyR2@A9H(E*Z z_T46tPH$-ck+aTIVfCjI59WU)T|L{1X)~JxE6DZNv5H{dMZf zwWXK=9RdS;8chJY%@&Cu%X)DQ?3&l{gF2MHl>niNjT)3yAZ6ZQ)#wVE{mEK%ikRwa zc>%m*zF3huKc6{Xk~%*#7Hb9>TUP5(bW)w~O}gF4Glg7j0)wChpwWopJmj7>sgoMT zw{8w;q|RF`ZUEy5TAu?=KQ-!kP^1@>5L^^{Zj-OnTlN!AA|KFtM;9_AG~Nbbgld)y z+sk$$b1kc1j8Bpm&?)E`{u8^_T?lZ5dS!;SXc0^s)LgFRhJsmKBzs5J z51WVSJLnLy`;!4Zln}eCRA8GwEY_xG8f7D3&D_mR?OMu4*CWKy-80Gy({X)qIw4l$ zqnD*@FyTw}r8u%5d5y4u;){|AyEcLgLu?G%BCz(B**{Sq#8(NxgQ4e46oSQA^go2YjW#T%ocw;pBI*~ zhW59tHlpZdO&qjK(G|riw4Q!<8N(FVzB#r2l1b(uC$=>6M_f(0Ya6ksJpQpWLFYK# z5pvw!+&rgwc1M-Mpsi)WZk|V}4ve&YtTcI?5?f#Pl+boS1sxZXNk~PyUIxKt=4AkvgBD zT#VMtjt_C3U*=Nf`Dv~gW6MQpGKQ=XDUBJ>z9LO7vdZ?J?#g{`1%PLO;Xc~j%c3gj zIgu=(+D`o{4ORy{R}OEud9hp2#a540!QR_NKZ@y~iUD0+iWEKSifQN#fI7s~6>kU^ z#DkED=2z-D4BizI!j^Lyr5dVXoO$5k${IKX!QD@3Bu8tCpO2g^WI#YNZaD8HQ(Xv@ za)n$lb=a|4gBwmz|J%yDK;3a=04&=r1YS7W%G2Zjv#z3_#K11=KnsWps!H{;!oeey zB5c1RDAoD8^#H}29!Qs=U!wm86t7}KW!OjP8mZ+Vq?w0ZR<;%hu^TAE;D2ZkJ5gan z8b{LvIadU=K&bs#a^(qR!*ic7SFf=Y#c-*B3z+DuACn+ouv&xCwY9mX7V`uue38yU zghCjyK4GZ7t_=3W)IEc>%UoMx7TZ9GFnhC2mJGVIDuuLn>WhT^#HIj5WH%xt?5-2! z*`fLBvkCn^prqu0&Jmq|5L*eep22ubT@|d2(m_ny4s;#v{~45e`MUKn%tEW~C>Ox- z8`)!Y6dN@ujj5Mk0JCgat>jym2JmFO;e@JW&+2<@KLAQmA$fW_&}sDl&`|$$puYqn zmjt+)Ej9vNCCanDrxuWA*6#^tp#kz2vM%`OLp;k;H6z4| z(WH{OL0E66vrx_u^&z}n<-?YESadD}!2V)thbaE23gjT7dtRpi za3ZK6q_utRmrpB#Tb=KbEV!u|mJ@B33!5NlkHX#;F@BCMF5dij9v$z=eymn%{VfW`Uaz~sfX29?tBbW}&Kdei!(*ZlHhdk)pEv2I@ zw<`x_Ah@1zw!#F`Ol_wMuZYpnWD@*V{t#4n@bw$RM1Z?@eVpt_&jGqK?J-bc=5g!p z&*#ZDJyFtvoY{+@-mghxHBVttbg1nB27})hu=W~;fX@5(BE`U%eGLk`G@mDm_bweY zfeO>GIn;9u!b0daP1ITY-X8#{;Wb(L6fS|rP6w^7I?^-A6*llOo*pFD3=-{nIPvOMgpKnfHTSS)sY5aIPEe;*6a}) zRK&h&|E7J3z>D6qdrG~6i*xz)Lsjwnpx|F5rUT{ECdDCV#8{tXDE0M`RG>5IdhFo^ zcd}Gg#Rt``a2|1dc1<`D$S;RA=pEvF3`EwS!5j0hOL!x1o)-x)-l0IzN5FF=a%}e1 z^HAaNadUfs;!Y69iv*$#e1GG?mgoCJ9m+HWM>s&3-2uLMzwX+Y2B^4~xt+(vA`OrV z6iKoWA;YFYm1lEF#^^cB3YOlt2R;jF78F(MAEpe0IIvoua>IJgVlfMFHDCM&$lDy( z7e9O-urgAHO8HdfMRi=f@JwPS$; zpA$v-+M0QfzCwk3P7ZEWp$lOXir_&9Ue&Gj&+7GIkIE1xx}(fxG${j6`S?~au`}Ck zRQo~pjlyrFzq#n_v9ePCGpF|Kj=uq@z-a?DcLA%L|LykWKZ{|ds0`)gPNoB-ROkNb zudbGhp&-j*k@NQi^w+GbIV#k=x52Y_#SCiKNiONi6PYiEosbX@w*{iGFHT{ogW>br zhx-qLQhk~sXL1o^G_B?e4V*7n4-98so!;Ks6n}C8yg>566G;E!5>Hf3p*s?NI}`N@ zsZH@V^GT_I${t=*I_@HrlXF!#z^Kw)@EtDzp#NxhB*ZxlMe_oJw^hRSey2)0Y2);A zm_cAfw7c};xh7fEi%_9tZK+6r1(Y8Df7H=mH-dj^@~H8@S%0eog!r@Gs(n$(WRAMM z6FU*`P?A<7aC!B(8J+&}%tXB}mgP@t*2J6lCOHioCMK#G?cKY?dcJn<+y5h$rp_BGbxQF=Dk1SJl2#| zNxKKjqv;I=De+o9a$~F8o1%PYjae`hyu)eOttwo@ca%RU$g0~7Cqi42Ygjb5e;&d4 zmR#dx0omTYG;m?4QXyO05646m2XT+rYz2*(zI4`}g@tL@%3~672Suz(N7xD zpP}xnvmcjWPz^(dTsr-FOz`)q*qW=GAq}7Z5U8j?3(zqYj#*jhiCBv8zo-FJ?y2KD^tQVfV;CnLTNKX1_0!qav{_Maj?ec=C zk%jGPCJ`xF?(;3h447AUztlUbY%5vXU<qwIuMY8xWG6*PFK?{jPR4h- ze>CyT$(!Z)d?cwe*PJ6mDZ-;~L`Tvh=Bq?=OcxmZoeb@+_8MZkdkT{~A|K14%P~K_ zh@Y7Emi5Z6lpqC|W_GA|EXU?`$Y{{Ml33)~ZC6~lp_iUAp(f6WKqa4zmW$oVj_aDp zM{3u1*6^$rS;a58>M4a^BR5DVv1U!fHBy?gOExdgpJqsUY$esW$qb?;w8z^76IqT^ z+{_J?$omg-{rk3FIGUa8wlSX34M})8V7E3dA&*Mw1DCpWeswRZ6WN zVW*O%=nHWdBS)HlYIq#PjiRIdmSiQwgwzjRF`*hBo3T1RlV38v+={~eL~4v5(0E&I z8r>@DWb8x~$q%VQ7x^5gpP%Y@5Hn z8go)gbNqa(=SLr{uBx{*4>h3h(d?7mI~yHD5;X`6Qv5ig`B8D_3RyCD(-axn+jxNW zaP)C}n?l0qEtgXNW#vJo+L(NkkvZKQ529=-PXBhxUg>A&wM!G6+(+9BBF_xkzN=gr zHEgNZPTe3MDHnWdj>$c)cXbjF(7%}u)+t~bZj3{U;a2#h?&>$`n zBliMPV7%Z`Swd>BU*#LH<(|O5SBn*~meX#jXqA<|GEwQV@SDq4yJ+NAyVl#K{CGol z9cyi?p}T5RD~tGmTbx=e^ui9)k*rUx-f$RzNBDiwX4N%f0amMNZ}8V|Gd}+?)V9Ao z@eHIqlRzlC-k9=d)?ehRUACr?|SkP*x>mR~Q) zTHaO%d*A&0LK=oy7J>UaPt6aI(=hmpkOfwxb{?@Uz5=Fr|t;ZgNwEL8E(< zHM!h-)5>Ra`|eJ@1;*!cS_c=t0^ihf@hjf|8=u_S(_NF@H=F zjYj|#iPoeEf(=p!k)7VeQjD<4K}CtJ|K7ze$lx`d#YYKcxtql(q6(h6$63B*r-AVL zXF^9lI_mz^4vH5dADca0y7uo!0>z!-WK8a6tRTl6QZzmjjB*@0i-~>=iv;ugKh!Pu zP?Y)-jbulXo#NyzYZ?wqc^Gn>=!@`%$Vp7J&s#0)cZV2{2??PrW7PV_UPd+yZ|rTY z*n3@$tu)Yp47&<-i16l2Xxl|*y0mWUO>ERZgEtA0go)w{)ZpZ5)u!h;Q>m@y{Y|QR z)VWl6?qHv??^}~_8v|t6X^5LW84q~blr_ytdAM*?*^hxu0)r51oZIW!+c28?1>Y!| z{4*tyU`19_!K+y8ryQZnk}WbC?mGnR?JR$)fsM$@H(#8Dn<5VSFVI`6~pWLL} z7sD#ikLNLKwgW>1wZ5Aj<{EFC!gSfE2=Im```i`kB{mxn#4$KgA2;sSgzrYT7(}=i~;ta)Ir$kQ&k$b=eMcw0zp|b5ocKn*~%9>3-bm-M8@)0v3wD z-K{JSN}94Fe?VaHe4Xmq(>)x$StQ+~Y{~f%!yKx;?HR3eAHecU{N=>|Ponpap8zUQ zm%pMxNf5Ua+-6)D0#wp7jczzOdeU|{XH9+}UyR!qy4rllIq#*4%3~nbDu|M%Q8Gue za7n4P9}`wofdCtRIUyO%L$$pUV!a1uIB!3OWVtGA(ge5f?U#MDTWI!}qJt1el05=9 wWll{ykJr{>z{Gd%-xsy|zn$96anVYGhdK&dj=tgjZ9#x%t$!&$edV|R0+IOeOaK4? diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_13_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_13_1.png index a2e04aaa5a1664872badf85045c9a8c5102757d6..3fd1831e1bca9c9308fbc17dc003d0fecd2c8c18 100644 GIT binary patch literal 9329 zcmbt)cT`i|vvvXjRHQ@&lr97m=_T~4w1{*86S_bULNC%w;8jti7S=ndrIkVf@GtWG89$T0hv7O;P0{{TnV8;4Z z001z7esP~-rq`g@(kJPEY9R)9LahCfAz`jT9spCS+{-HJeH^}biOLd%~m^Y6Lx zf4?VSU#IwrmW6%Q$+{|cVO=HD@?(Hy(B?=!LYpFyeqVYL&F(;ke^e zG_v_~A)mN2jyme^>HbqWvwqNWRYXpaIFGm;3Zzg72zC$>$+n>Df*cO`ABt|GOAj!9 zgl7IhU1})C$$=awbY)2arXdz%L5<7adK_5PC7!IAN@f=GQrr*{nZ?=Uh$Z8L(c9!3 zWItLYjgYkGn)Ra$iw^^KF;=Bko*ZIQ8DfD_kCUdjE(~0o zV{~cp%q?nMys?deN-bH5{Y4f};Z z$-_Hb7#Xjl;|Z~tc7fZ7ku{Hs_1&DU%doIICm~F2+yXQwBpV-vXdk$A=|sB-XvR1# zz66BHW`tDYQz{%~P%)&c?PLGl=1OI)nnFC~;}0mg7d->WVJu}7$_UZikf6;l;Nt7Z zotV_rq(|}g@jMLepkuAKmQ-ue*|p(7XTYJ>hN=s!ioqChrj>n^`R6^~(pD9$_p3Ih_a3Ky05r6vrGq9W5{ z&C2bA)xF8a*oW+agBHX_Ll=VsWBo}k@C5qgMvx-o9hQ6^u$_KKVNzo}Uh4!DpR{*( zB{)iS_ByQyy%5H~TbZ;sku)j*Ztqv!MM0)m4zY9d$4204d+PW2475&K5{u}pAk9C1 zq+W+5T2*B2-qfv`oS>fr`W>MaeDl5NKJc$4+lWf$wp-`O06}1C*4FE!Q83sp)JKOu zHzmnPWcJ{asWK`)%&0>PT+Ov73qdEPes66FeQJVciGQV7`FMHf&)*%D!w6HY`U6q0AXSLCz`bC ziru2mp@+4>p?)|fr_b9hgv$|DEa6ZXZA@e-O2AY`2;j%uCbMJ<^YlCvGzZj&9JUtM zRruPEoR3}zRbJ^ey6GDsN00ob#zf9cd1ZEBl!Vt7M`;$^`IKeyCkpfagv1>(tBSi39%QbF?Ll zSynB6JF*ZzgQ>ku<%OlCCK<&aph*=oD;HqqHq?L9ELmQK%a1dXJBfbQO4(%b(mtA9fro0ZNb&7qX36#?^$)% z$%VI(zvC5%+K>WJw3G<4TqYR~b`X_Y5m_SJ&YT=U1c3RBhc||PC`jJ?hSsHishAd8 z9XY}rw=QmCQ*+Zd+2m#a#q*{ zX)ENBCK@gcvuk8R9JP)BrJUQMhvd8Xt&vR>%EMh^(Yp^EvXgBg5}KQN`)R}&4WBWF zD2+(4ECQ4kr#U$!AL8D5CbwWF>|2jyRjZ1Kt-?Q4>_fyRX_84(ROGmLq^;bpD%BLH zoMk1WNPH%Z0D0|Nc^#XQie9DOWYCkHX!zhJm=Sc%@e;Uvv&g4N0r$|1p>mlAM4^cH zQz$MI#L4^iJ5kODh;u$x3FOy3XF+kd42kJ^WBLgP8TMSU+UR~h{Qz<-KDd7_}gvN9}YstxWQ z30=WV>V*Nqzw>3pOdm}Dn&V@pZU2?2iPic!4vm|rblm6<>z^4O)1T>=BsdpsiD2_! z31s2@hd?#X+&r#gyD*D*BSG|$mEQ-{@P)|l>+>a-6^Spmyhn-(OgR|10)*uhC2v+) zzCZG26)Q7RRZzrHpLkFwsgs08QcrSzJrNS|e35psx>eXjRcXG$W-NJuNr9yA$u zviK^M^kF7DAX^WVL6PV>8HtMIOv)7kiN~YpE;zC04B-5~Ev?jf z=JDpxmx%yXy2kCW-KnvZEPUVXYMQ(u#W0dx;P?9QQ??#LQGKKMk#zv_pi zB#l04R7n{YJ>F|qZzqG2bv~VCdgB|t1E>i?@iTNJbL4DhUG$GX+duMk9x$TwQYw+A z0&ikBaa#!T$CQnqo;h}@1SYR*4zE9=$rQsuzva;yv4}E1mC&ye7mWjG=%w{6$6>kz zyH;(D%Q78myn5qm-PL{*#k_2LAFkqar_rL1THM{t#KmOI!l1}!%7#-PC242@+?gII z^fn{=-aqhW*S;ti;eH$|#(ec(5nF2`8`STcZ{52OeZk%3G9HdZW@Z0}q^OLliH6d3 zJFEC0VL9R#JT&xnwYWch9tJ#p>uk4kYVMb->e9Dr>>d@X-s6xFwm$t+9#|D=L+Zg6 z&jN~Hf8$&pD!1z@F~yb1Jj@%KQtD$ z;l=|!nsrf+5JZ>%E16$d-Z5HgMQLDLe06?Su}a}?&G zNt#^EhTpJ}*PDF)DsAv1=n5&!6fBuml+CJAC=GZvsKTqx9Gg zfdV7^14{F2QDSuBn zBd0MDTUmI5Es)-6&INS88ULdAH|J>c0XiWG9R+J#W_;pyrM5q z+ERF~@a}sb#M~0w?QJ~j)fRJE?**#$Cp6iI_bip~^%FJUepNSDK=_gg%L?1=5ea;0 zyr&Qp$NYhP^x6kimIo=SHx`u2V`@ezXO#PQHl0jwC7v~uylZjifntb^MJKs6K^5+! z%!Wi#QJ}>7qOhQzij{}P~K#iOI?|Q5}NtJ`3VT(y6cU!we zrNpP@X)#se;)^qfN~WxxTUH-R8ZEI8=?<;@m0pqR#Dt-*j!?i$Dy~-nJy&=Z7(1m zi>r|-@St0T@(cps@=u>%zF$awx%89eMe@MU!CIHSxHUDFKGQAdGQP*}F}$y%pf0|D zS|J^T-dQN~%P9Kq$Dt@%?H~s2mL9FOT6;GN?-1_#=aY+Z`?LC}UP`~rSwS1|)KY?p zXI{RPa(PD0$PZIi0Fk&~^5%8Wk(>SBaeK-4G`GGo1CxC1gWLutRHl7@TYo^431$yh z_c09|M|SO4ExpMXkJxR8)vu)b3e)Yj4J;~QAQJ0T`+7Gcwh;Rd=t_Z7T#@BGb6N20 zDDx~o8qQS{1S~DcrpSz!>%<}YpW8*njPFR>f1LPdaO0grD2*RIhVPC{Xy2mlYdMdT z9yRZ-^j~tH`Z}1P+p#zCODh-klD_CRFGf;`!3spew8~UbRpgU6^;+ds=mK zZ3OjX5mhA}`(zQuM}Zo!4x}YAqM`OSD*v>q^tT>3#>M=(@CCf_)bpv+bga0ST#l!N z<5dywT|}L?O)i(;_m4>)z2j`+-6%R}h=MN^6{MtNs^AN&4bGnX6SBR^8o1a#q-VLz zm~p(R{G8SZ=^>#>oK$Ku9vU0D7As1gLQ($_>NH9<9Dn;+P z&Xszet74qS<5EkWn965!Mlt1&U`>}p8oXvA77lk;-ntw@#GJ24S34#spg^*ZlNQd} zhsyJ{&}H1EFA{@1wt)a~*?o1`<5f&1Ml`+l(j5Wt?)=z{8iQotq(jiFKE(+nSG{i{ zmn3zj+yu9SUqF;}Tvbb`VR$DSFQ5xpFz85NAHWO)R{%&l%JW@n@GBpy?L~+_uaHq0 zmoRNJ!@`WYPt5c+c~npDDZ*rzr!R~2rzH)6t0RB(WQlS05*|`ZlAM}DzG+`6a&XFv z?8esB`ZaO)-o7cEl%!$rybe4DhR4z!313SsezEafF^6{U6yM@@Rndu)zJi+L(CS8T z@Y~>p_p2t^Uu8$`=AH59#nkfOxd7I%pNV4(57Uo^t|<0{??@N-U2JxT1trGreT$iR z2)C4y1k?`I322u)JQ85uoxgc{JohWfYcY__0XYKg#gTQk&oChCvWBA;o*B)v05AGX zP1DgwSgV^WF3Pb>)4d?>U?-^8RdlRs@w|RV1(}$vod*BL+_bbavHQ%3yZ+#Y70wvP zcMmCk)ZMatf3G>amwEPCCo+O9DA>OC)bv)#)+9Pp5G359Y&jy&5h|U-)`2EVRn_9A zF=9(9+geeyS3(6kpS)Mcj~%aHCefwzEdG_s=+9#l@dfZgOm3}>DUMbnJy-Go^^b4w zx>t{FEjnoVY|hUuKvB$ms&#>GRwe<&Cqw@*qQ_LvVs!2;0451{q2F{}l-VPO&-$%U zfcPOf%FX$|6{unS04H|-^NlN>JxCOYg@I^U%yvfOa09~g+0o=q^M$qcxS$fBdP6!) z!{7HI zBriU6mTP-m%@|rx@KrpaH1};}*@wde_$I?p;OUR*4zlH$^tvT$x|XW$Z_BEkHKpZm z!K$2hw79ZvuU?V7G-*46EC2s>YzO~n_#~)F0(>S_)l}@v?ObnMi+-R$d{OIvcgk9O zx49{_N@CJXE=*9?w0-S)+l%qc;1sf<-EEZty`lh?)$4g-7P({p)+zq94hR1h&*e}* zB^Vv>EtVZHOb5xM_sTAQ%CuqZ&Tz@*W=7W{bcYhhC^zSL(J3WA{R&Cm!hFh~sNE9(Dr@O30!YvT`AQ;6*t&g_-hb;%Y(TFx>+bq?*!nxTGWv{wh4>O5rcjIW zt^(L2_~~mEGd0g;y2itm^Havz%1AjJy!-duscH6TV`{0GfKv$C=Y6JDx`e;r^OkPU z-L+-UQ_%7^ZD?VU$|p%VfE<8xR!AzIF{S9~-%o8{e7S70gbQ$Sfa8 zhX7R=gqWro4vBc{WT{+u8G-lHcwN9drp?`oiL)<|&>e;{&%6z9DVl^<#W0o+H?S3E3hQ9oeP14pByas zCmV|=ZPLLmI)eY5(xM=M2^JLm_5P|sGIe4!`A)8w`OFc$=Tw1!PnSC#8HuOm!K{PE z2&pA+SWaeW|HrG#-6V=%Ch5;w+??oGpY9`mO!PW5s_&ugyu%Go29?b4w)v~xfZKUf zSBVKGp!|sW_r6n5E)F_SM@Z0P-)E5hf6Oxj+dfMCN0FCa>BMTo6`-)R(5#*d@`hx& z;l^c}uP*XK4L%g33>&WvT^Ytafr>qe4w6N^y`!;6N6p8vApQvV`&RpN8Cs_(KE@AN z7+|G=*$G@$UF;)(>eiqa!0Sm# zqN1}`X?5g=C?;UJ#aME6^d@eMgr+6{w){3_-9F>SF8n^@airu?TvlTQHVK%0yxH(0 zkMF9v)!39_AG4J=-88Qoc1U;>217h%_{(qG>KxWeyp(N`I++WY+E15SQ})irp+F=h zuXO6`qm6^1{K_`>`L~{Mc2a}Y1MTo8= zf{R1NMC)B`C&;vqkKvN`vhHW|DIVmTL;_!={qW44JR%Nj+Js0G%?c<7F5_gA>G2Inu(*yi;Prv7Zyhy^0jE@PqY~5&bqzUe?Ta#4*)UqjtQ$*AH z{saxC7f+D*vZdgSq~&}n7Mds)F>w$nl>`@OVX(i)PeL!df2O$(%lYxv#89zlr|q(C zb_V2d4kKDj92k%mISYyp|c?;M~MQZGbz(1hS%+1Wu6PpQzL+<~2CkIZ4d*S`Bfsz2X;Y4(qtRV985tZDSCiM*us{nEyF zA=$~e{F7LN?EnSh`=REMbUb5xZ~PIA`~AL^e*WN!VhUYX6eHKKb=%t{DucDtn zN!{Dn_*%Q`jsTe7xot$dra z`9vwPS|UA!9ErV^6giF>*0z2o)jz?w(7H)u^S`sVzte6~z#Sc^Fh;2N*6RERpM_!f ziis1Z6XBaL2Q+LyC_S2kH(SypLUqqm1vvZ0!T@{QmY8RzDrVw1(_)24HUV}AVJ~?Y zZU4HygVp=vByvVurqD-U9x0Dz^b~6oWt$?THOCK5gDc*}kItvp z3aU;;N?*tbJtRd9ChFR*z8I-y_BH%{%^hj|=4r``Ze^cfXGF1tU*o%LuHt z1_#61zSU1)_#$jMKV4gS+h>d#-uvjU>`_u>D7WUm^p^POn8vcCC;T#wb1`gs%`cyn zplb`?gb@1(mqcFPud*q(?X*z#dwQpyFh#?(W=osYr`@Ha`~Sq?>){0p{_P(lrpLy% zf0CGDdCz7BJ+6!rSis@0&4%q|HYIkr$}d(K z<47*w=}DazD}uVnVFgF@b4uxCZ4-K}rD_!LS-BFohlRpEf8x`m_*^r!+x@bWLYS%u zLW-Ye_}=<%DpFGOhXiXwP5~-V#Y~YeW2p>G-YW{4n6jLJ#;*xmgea!x!QYjX8_M~4 zSzoIa2p5h%{Qe`So8m+HyJ&=3^u6_PqexD`-gcGNUJs=t0R>9ep1MB%n&wv+J4Pv? z(|mvL1^;uy2;7RT8Nlj&O{}8$+*IY%y!-aP>EI>%EfK&QEybUeCdF z4ym#Cr_YHSdkw(s8C@*UK@n#h?utE|7J1?4q*|Q)!EPU7(%|O2#Ii@af#VYd;3I2q zE}TX7*5E2`=9b@Lfu2iO>b}ac<*>{}cVPZY?%qoyfm5yS!{LKmr$jISy7xS&FNpH1 zIZD4Vzpl|U&Xfpx_E)MaaKakpGp68B*g+|IzXeJdh|`WJTfG2W$j@jyxtiQ^_MH9` zgp?tQ;yJAJL7eAFG+U-r+d|=k(=!L_11I~gzLU9-?BY386=YTTfZOJw zl2zn;2VV1i3PDLp0nSg4keQ2)ezy7Q?4QEIVO78RC_}M1QpY`Z807Q&oZ;(H z8DyHUT&}`MxH54ZOVEAmYfmkV``z6=U><2zqf4PYjcPmVmZ|wT&om{HC857Eg+%}) zY5Oy3y{sJbV6Oe}$FHquFK?Bez^Y$9$n&LbY6%T(&=?X;=kUYa{ETwy{k7V<$2roQ zgVlMaPc4H$f8mZf=$Jj6pOd@~{`JkN2u_L&^=aOnj#k;TG(6Ry=H-n%U%A#jJ4J?r z1Em5OHasz@s|RmdMLoOZ-&RuA?@%?e?E>`A z2*rmX6DyS+av?x7z@R5;1>%dT^;#LO*!SP~zPLZ@mx&y9DHcl>697eXjBk+46uzG95>~EQf+#}7dgyXXU1)EZUaVpsHsQi z6nkt8Q)x4RQik>`OW5I+xpVy}BvS1{TX>BAA;1_eKTV-5KcBX~m753u_LryT?Wfbm zg#S~qvbOjd%Vn=C0d7M-UO(m{lWcfiGDi#S{Hh!68o68aN_3Xys(oiK5_F?Ea8zuh zBn5BU#@)tV5H0;z#EANN<)Ha5xBj0&R92#Chh?l5qHWx#ogc;gN62##W!dXqotKwY zT1hAH6(t|jsc`rQ8}3sOk^?mbyP16@ouk9TX?i8#MeJ-l1RBtv*ffHrZ< z0<-Jd;@mQ-3{dTf=-zlCV0pPr*n>D8bH++$!NEWK)iyaT?@@u;8y|;vyBKivM)b&c z19C7Lr8uo~Y440M4Mckq+eIid+=!;lCpAJv1euQo!#zf{t3WdMVot6yeyuR)tYRj6;pC-j!Bf4;X+6?;@LQg`<Tx-_q~-df|bA=+pt`7TaPM2t3nS-%G`APX++iBx{E=~dSD7Kfp=}- zub&*sZ{#KIJ;%>5YfCOQCXL#Mz7yB8fhK66uiQV3c1PJSYh!jq->msc7XRB@`j&!= z*2a^h@Ynd7tcsaB)r+d^dzJV;$Z8Rfxz~HuftgMLawqtgvHl@L<70^2Eu7}a>^7MP zATs-i41}DgLMYFDiR;k>0w2bDm}gPzZ834Ku3-qSFF#7F8P<*0OT`HPpxf#K}>n2y8kF;DG3K& zMA6eqL_vr&qYOj;lO#Ul>~77{ajU$PoZx@IkNKN!`|vlz2hIQY*#FPPqc}OC>Fd>( Tz0~QS{{UbHX8M)aT@wBarNs}p literal 9441 zcmaKS2UJsC*JeUTYE(c#5Fvno6b(&^fFcljZ_*SAMhLxk?OZdVUw1F0(P^6+nk8ql!a`52Kl=tC7=REI& z`3HHU*8U4Kq5b2F{{F~w<&MqG!_$lFpp4A%HESb(N*u1-j+qJugY-vYajsntSTi#5 z^0X8X4CkV%159@QCSAD{GR%@->)YVSrPXXp=+lj75}Y47(N)+1B_BmrUfPcC+K6nKt=9n92k zmV68}zjjDhz7OU}-X1gqc~VsKHUf``S7)9gC-yF1GB@U{(2cpe5LFypd%_kM@7|a`9hkh!^lel+?469!J+yFg+3BU1ti@u!N9s7=SOWB)5@>0z_ zD7MWpXi$tGf)PvE^I+($v^@m$5E@j4PR;2y_ag|gXq^Z{JHR5%Kutms7?JEF;K3SD zh<1iH+Y!Q`{!oREqP9i9u(z5DoBMzIxA&2i>t`3If}!=?rV67Qnu@b3P{OI;^z3m^ z2U>XF&KsJpJu!J4D$#RvdVI$Nw8aih&^|GOZl=@@2KG8Dg!3*bT_Dv)hYK}Fgheo) zvRPWM99fVwNY6=LP)WcA;3xqrL85RdLPG@RJV_`l3*{x_8Ha-!ffKa7-XCbsct*cv z>M;C)M$4M%?Cuudmf_--W@HBiMyDBeG?HUK`|u*>XgWszDk#CJ_3#+U z3`j_tnhF=?f3FoJ>LIb!6?2pxGsbWxyEU<~8o{F8*_MM^t}4hr%?Eh~FGe4WI{2%H zpozIpBUA+F9RP157qx=C)RnG->{lLyiAr&R-U`fNI7Lu_q-oG+IsOCSCO^IUUiRX2 zj&}$}z;$`Zd{&0|yee7g#oSF63Spo2JW7dm^b=+ux3K|0JFO~V-~b)Z3>XmX+4rAL zo^PvqACn9XVzQ@}-ETbzJWW3bgnjK zV4lfcnU%NiW@*FJ2(gL~_xI_0*O2<)o+jFAmJg7l<0&W?H^?6tPT5)j>G$B-dI&X0 zt9?Z1NZu33DY9p3RU^QW`ZO~NzxPHdW+j&&LCX~EFYO2z!HP6#$z^nmJYxul<=UBD zRhmyUvh(~K_S$s!$=N-ROf(a6BsuQMU9^(B@u|Y*CGKiJg6WT{!~6*qbffKbzu(C2 zCa6Vg=in0c0&vI;rtI;dv7Dt1;XPvRA+5$!*B;A7h$HoHb1K>C)wGjtlN`avL0Yee ze6a|uIJjdKgyi;}*xaGpas)dv9wwu|h4BO!FN$zbvP^N2k+Ds$viA4od z!9u}2qnW4SacE+MKVWodS^`s(!b=Cv8P7cbd45ufmP`y`)91q#X@lXK18R^k=Avbf|i9>xgN&k zI?KGfJ6^?oSXY#nOZ7U8+-0y_$co9>V0RptR>= zC#%i%EbL*EXVp{xy)n=3_=Ab22Z>0Px73L(oBa28AdoDteT=1d6!i9Sluz^Md_?JyYZ%~-Q9 zJct&V*Wf08(N>SpA!u6h2BK5=0BqE+^-E@bLE9c|o6a!E^X~WHhW6)Ab{60Be9sLx zY%YAcySsHVDyedD&>##X{j+nWxA~+vv(Dr)Vl7wsm?#_QRNx}(cK6SjEeI;5qi6?A z&yd2vdQL?;-qq?^H^cB*zXOcW3u=6yYWM(TKRE)PHaHGG0Ly*dC_1ZkFnlrr%N|z= ztgy+<#*FTdT5tiHxvWBmR_SoCq3Wi+`G*bA_A@8>)$)aI;Z}|YC!Wa6afken-YR~C zi8MTrwOF1hrHkx&vjkjNW!D2T;iym0KBDSd=3!ny^G!Nj`2&sf6cv>`bR;`RvI>fw zD(s{voi^gIRhS-7W|P|_j~>_hhn^B|9LCu->$OFK;m82U?o!o%u#}5cFad8Qu*smvbA7}>$lqn!6w?KmOv_ld*6}bu8 z7lj3{0(&KL<^G|J%+za8TF$L?0QTV9{YQ)^<~>nlWL8OQW;6*eF;e+4G?uN1LA0ObwTy`BPs$Da_9yrD4xZsIgE~eRejA`&?$e*F>qR zQ$d$J&1tiDOnA!fYcH>a!AsH{6ZE=(D>IvX;-oZF#4vdccgmjUk&NQKzs*$NZj+zt zP4(yz)MS_)Hlwebi&*{g9Mb1L7INCWHkL${-?FJ6L@`-kFk8)XD|)z$HcxC*)jP9m zESebg9cCU6B}hG4)yr|6&YC)-EztdIfQXwPHh7s+6wIXK$qTlXt^4)s=k?Ul-tni=ad;|u$FI-8?I zkpnF{WA%vT*rw8rUwf-|vr6;4)E;Ehm=IpQ7TmeR=W*oWXZxXW&!c9-{Hy6tY4Qx1 zSInr*?5`$Q8F3U96zCJwvG*Qdy~Q@;f$A-}4Jp$Px6PUPZ~!hTS}QK0pwG(uvYMTLn7Cz42k$~qKRzg(R96VJ5M^!!@Eu#*;3_X? z_bdI)AUCFlhPwnypU08xNHZ~wCA+)bTJ+@Y%OAC~BxUzLbW*l)>C@hMR9QCD&|gW> zHceL0CF7L9GOqulcZ6bZm_0=7C%N~=2L}uimb_H#?4@~E6?$IHr}fL0zTk;OV3{76DILb6ram>%b9w2YepaNW zhQ(Ps81gE+zgI>m1Hfu%gc`tFdGbeHG|ts0su#>6_OaF5xsIq-MwlDdsFZmD%kMK0 zda(W(F2RsgXIV*&iJuaX!fZ2^PK;lUQFGj7p=g-3(_<}u1!xdyX^8(qMhP(kGpE}% zrdAT4=I}4x{pO9#WZV4p_H6>C={7WU5a7nSIZJ3``rFO(1y0)HmE@oiRm9;rthjoL zZ-Z<<_XveiZqr%0G^MXfokmpGds8cNR;*}fDKIs)KB>z;axc%;?6ASJsEe%9LTeZk zsWA0(1lm~&A8x@(M$+Cvz0kFBOl|!|?X&~>g-M28;0UFu*_e2lP3H#+f8vS1OhiMe zwAka3w79^AXRgh)yx6%pk{6-vl|*;q$;G+#f&cK))oU+q**KmmxI^J8HOqUS=s)8?iP$8MiaorW_F~VI3CQg2?;=&vo+TlR~Lq zdo{?BG?7RuF|Z9PfA1M_5xH6Z#@-gv%GIbv-q*>+UVejo^Zi&TH|^1|eT6%pe8pQC z9HZXu-PqXvOugrm^?UbT7Y@iM9GDKPN;ryVj!8nZ(s>4@cLdLZ(|!ogn((wW2h@0dtBJGsB% zTu^lw@oSS?uJw!v-o$Tk{?06bhy{z`q{yNVsXfPVhgfC@yVUk~J{TOH?EE2(@(RpwotTjR53LIM+r!#*GT^VC6OG+Ny zm|iBT@c_!zQyJNH6vX*Al6|gtC~x(Fwlm8YgR942#v`&xum{p%)$;rst7lCdjWhv9 zY@044SKv;!4>o*Rt9$=Ox;&E0og0VP1fae6X(*mcQXHUq6(H+c+^#H(DP007u^nZj zB`GffXJ+bWzSQK`buW(+#(~&2K$+Td)OXOfIO*7_Bf&~6;3k&q2T>DWnwUn@yrWGE zbfI!LgPRwYhXQT_%GJn_AogwUQ#Y_&hP;aJgue8q>LmDD+--2mi-M1KwFztg9!j(_ zA_M`1bLk9}JZoRt_~%i2=zVDRIm}(EBpuBDOAgns1y8Tm5LI19gHYXt+astxa<(e_ z?=zxW8{TpEX!8XpIU`aq&DqsMF`o=R?xTJEU(abNTsrsaACfn`rRWFZpcad>(8qxT&t1q?$jvL{Vss+~qv8QN zaqd#}o_zJ@WeDdhjB#q}E?%2*|J91?wSlX*qbn6Nl`PMR$>VvnQo>D68}?6n7`=%h^RgVu$hz5)3Xh{Lg5z73gfyBYhvCwK z?vquDI(R)4HSwy>o3Hv{Igg1bK07FBzy%g(^6BDSR(>aomxY@=t?{WyGm}mq?h{`bZ$v&S#vG@)Qp|Ig*B0@aGDaR_8V`!7phAZE?%+ys^m+w z?oy_3NjHLhoAP+9-11N0N?~0b`7?B8#-*;d&avqvnAPc_i`=aH{>}()Kp6hv`}f}i zGkl4%%v*(WW-SKQe;XN@sF0QEduO6tj%lI>Yr|_6zn4L+YylnU z1TBTSHJVKEd#+WH-M8G`pUGpQZ*bA^&*^}+y-3!iD#5nL1kVLIymiSE*HKjAb8U~c z&pNmIu3QN|Xq4@+inej2LQ+jppYd+>VMhgREn(lvpY2SM`o=`=y7W3-UMHltzVLV^ zUy{Xx!&%=MxbY<(8;W(OECO;+N>bW^;lY{lD7fB)t>`9Gd)yn-z(N}h?CfRePUu8j zPVLp}`W%_(sE>8g;B$b7CR)jeFv>qS{zUH89~UMWgnvo-SGV)n#$NJZ>()L_=acJI zyUAuLmBr-2xEzqykFT-iB=6y3jz-3We@N{{@@>X9hz7cEIn^qPFJAsk`1*unK}C~T zS!6~3YO=H@j?dw`{U-;4w8>0rO(2J{-hCeF=e2xN+ATPknY!r?bvczAu#)FsN)ILb zT^EH~t&Frik&%--X=48m+y&+Sg*&Hg$7i?UMJZ?Fm)Xx2n>U*GWQ6XojTxoJ|f~t7;rZNqf*dAyYnp&faPfnsHlKomEPn|Mkj1VOsTfxdaNA#q} zX07(CY*wE>6ib`<7p$dP2&vcy{0e{-7suycy&tTUY?%SDRC3V7w>*p`$5(-ayZq0C)m`7m;JIa8@3dH9 z@_$4U_($knCuzE!?&?;RDimjm8$Efo^fqD_yiRjL74QcA=E}_Ihd_8CgG59(@6#aH zXJ3HWa*b3sJIQ4yEK1pO$~c)(sYs7n`7)$&rIg%l7J z@{ji6-S;sDz`W8O`!b_1iXv}P;kX8rU&D<%Uso9g-o%#gPYGlQIk_?>3sxvBRWvBj zWz;27evuYw4w1QQAj2fea(=d$aQP5s&Q1Foj`W|6XeHDauPu54FeBc48A4CovB`Eb{`u8={+tpv&pD}6i@6})b%!Bs@=#tm);y^01-^D=MfaV=HA%QP} zi{i?%{qm|Ko4cf_g@TyuclTXt|Iobt)L?tFa0N?r343^~-qDzGHqK2&=zX*{jgkbQ zrz2?3A3E)Yt=w3>he?u_W4+$CJ||^HoBNDF0RYPpnYg1n9V|b}9OS<%-E^=EDc>lji4umG)4NUXi|pgp)(*m#im0HIq#ZCmi99 zOv#K=RhCIJej>}jOsm_4-qnrFQO43{iD|TvMqR)ll{#mHQuyKYRwPT@$Z6(plhB;M z!xZeP@-16JFZq`HkZ$RFl}Y+i43zQYz_qN3w}N9Z?@Dit=ll}90ZKzarFlUx3;dsk zBvrBWIhz(hIiNF35WY|k#m~?GA7oh$6Vh)VDTlr{h_Yz+THYKv?&;^Y2pyS^~aYI<=qc zT?{3b30vmX=75asy3c+;{+gxV2F{LUizED=onj5eukCKh&?>sdxpzPRoZWiZdUo{j zaqgkJ?04;30*SRi`I{hGFb}N_?OBkz4Qf9NKM(?2v$oNFcwA|Vx;fPmUaRE)bDm-T zcMpaFi>{gQn)1xAU|C9+S-hpRIa~fK>!F5+s7DlfGC~p=2YNvRZv$3J*Wo-7Q4Y6ikg&BJ{KHxGTfB)bGZ3 zpIRub2p^a8erfiFI&k{Z=;pH3z?A{TZc!P~V72LcuP^ondsBNxKR(t>2pmbY%9n1_ zG>kKHzTy-wi47=wLCa(lzNk13{~$`aj*e~kv-nHk7WB2#XtS@!PLEC$pwTXI{5sr# z??xS1?dNiGIf~5QZPgZuE8C6ACOx^ng35bK%PW7PsI-OsEg}qYsf86rn^)@6#J0TC zeOzvz_wYlro2?g-8{KfncEa~`ujp5SWKL_m9;AZd6j%6TuORx*AA-v8NSlO17;pbW zMP)j5U1wInD8qe}SNG6#gsIEuqj1iZ3z6h$U!at=!EG^ol5eXEp@tPP($?;qz9fq= zmWIfr)ZhB@!Aey)JMognYzVf^)anM4Z7GzLNr4U8wd9xc@(SXU>Ul}Zo1Uyvm=Z#+ zYNG$hU^P5G5H?~HjPR2r)%0n&G-O(Rs}q)y)?QwK_TH9fdI1xc<}UeIYp~PCkQWq> zVR}vuu}EsPKnN;mot~;X#Ne0F;f$U)0oq52&2$z5k{aiQ5T7%x=Ig{*_qd$|5tm&5 z^hp}n?M`n>gxXO}1v;};$U$8GIfw)4@(&z6qO6L_w6eq8(~$aC?Wy8SU{O>=Ccbu@ zjtb83gAdA-69}tA+#~Zd%gj`u@n9-!045fPE5z;5P>8h28d|VDA+AUuondL;_NZTD z`mJXej@F!?J$qK8aGt-yY?xqt@fBB2Lxi|2^(+1wA0S82m9qmfXZZ3*$vCc8FwPYt zna_9C&6AWiSDau>#El_CGN~n?xxzvr6_DTv!O*~+`HsG+*zf`>n}p~AViqIB!myd) zKAiO@+merDFCFHsyl>igc@Z=LiSggRk4bxJkf3VKOU1uK4K$dShV%`cscxk;+%=$7 z-qE`7jA$BN{7QIs?!#9w@t?RMLw}VBVPz3|z&s7G*qy7WC?U&AM;gL<^I`Vc@zH$p;_zx# z*Ya{cY~(7Qk*bZESGUmNy-SKM?w#rAX;x-@ab^^G%alli1y8RZPJI66y@$ssaJ4WIr@0?Schrz~H!QIbif&P#l%EeXq5h6t1~?<7Aw9c_D>dr8X(BNmP+MpzZB{4J>~T;f&li0=tB!9HlU6BB3Y6V4wxyRA4SX&<-+Uo;uG} zAT;=nZTb5~Gx2GQT7jHpxz+qWCCt28!{8Qm7+oVPIplh(XuoK_)qWgze@n^L^(!V5 zSN^6nLuRij#!&stK|Oo;_(Xkt6E%5Ue0a6H`vGvUb7ta**=LTONGVlDqYV3`LG_TivmGxw2YlhrF?lD2#bpp?z$jTL^uw*pRwU*zl~Ef30@;x!anTavfRdx8jiNa(nr#zaxH9@Z4w^QLg>|Qh-}< znr}I+|1#No^`$c#_&gHbhHTH;ZUmp}FIrCvEx&nezy(ST2Z}|yQw&gQR&z^zX0oiw#htw9!#aOdxrii<{ zZ8?r51OLVdV83CTwXH2Gltw>|4kW3c!ln1DeVUsFVq!WnZlTj9h`p z``b{Kwm5Y95hrvYHiptywp2)kGDMo6e1t*}n~Kck2F3cyl{2lZfTn1b@)sA2{NEQm z)Fj*kPq)#E!ENhyY#6}jcdFUsCHZ-)iVh#^9|boygsKF1CXmHSMs9R#I{6w1Hj5CFzZ@bZ7TlrwWIO=wU3xcaut3u%Fa;d)g5wS z|3r}Ft_7nyBeTyE%hTA(m@)d040P}z6EZSsbmZqq%|a`C2QV9O26t{f+lY~`s@F&d zFVC)AwefH18#I2`uE!9g!@6RYgbsO@KL2 zy>kKV2wtR^1u#>jGb%5p*oi?PeLZ5a`2TxX|34uh?Qzk_xtQW?hXrACWLgOGsc|XW z>ft6}Mtu1?i5fdpu~t96koq0=0D2+~ElEg!(kbBE&|Q8rvJp1*%<(9kxe<6uXsQ&jOIR6cY f{-g26B_)gSl3~&;yBGPm5diu`U%m2?!|VS6Sy^FH diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_27_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_27_0.png index c1228b4868b174624cc29412fa859cddf9048e3e..ab7dda31033a8f66d0dc767e838a8f9340b9945a 100644 GIT binary patch literal 14031 zcmZ|01z40(_b*C!cb7i>X`_BEJ z^PKzKhhYZh-M!Xczu#JW?N4ttl<{$BaFCFY@Kse5!AMBRP~h(yEDYcsl8&hce29A} z8F*;BSbIP%K3E~CTX?uSx_CI+y<_sS`rvNo;w;Dq{O~Z@dU&|HOYrkM{Z9ctmk&1l zeW|sXKqJ_$Du(VzNVud=zsN}6GO3V|M5I&|<#fC=50-qqwRLaWjuEO8_#6&z=6KMN zt@xuIS=RXk#6IIZ+o_A8<&v#+d+pIfoSbg4=t}9fNN(k(Z%syIsHiA{j7|&+d1v!Pqvh*v$OLE{#i3SA^;%{a)>38kD7gl14RuZl1Ix%gEEIj z$&oCE6R}{Zx>CwVQL(n0U@^Z-`0s;Q3@B`>5cJ0%HYJj8b(##$ps#g=O4{lWtK1^ z_2meBS^~RbZIfoWt?LY=R0m{L=c~>M3s!BLdk}i@>{;mf-pp+!HglNmha|AyQuCI} z$UPpDro(;oWQ8;6k2>fNIp~kz-C_%oJQkz(*A%PZK?5g&#oIz5d-}oLzq7s~Q=v)~B^99Tx9Jpn ze&NR-?C)n57f%NkCg!w;g{TTnMnUm4F;St`d8uQGj#)9m8_X*>H+> zB6-E+Y0|Z^T65l)hx(d$M%t`(qN}Uzp~i0A<*%Dyp}#u@SVN;y|jXtV|bm$%aZk3&U#Z%#bsR?VY!m zA|wskTeM7pf5Rqaa~&3*Ia>BZARl!S$|j_yDwmq_6-v?ydG6|z!Zkslcc)v!)hWe> zl^9~gi}`Ble?29`h~%c&CU?pmxH+ParPT7a4|`5F2R}te7m_Do643Qg+hn%Sm<0`A z9j`m2Xl97S0{=8`UH^=i0X;t4Q;RrKHF_POnD@j`OFdkzBkj*uN4t+or-q zfj8C5N+p5+CjUGqG@Y6`*juP^vg}J>mT_v-E!1;dYDgAc@lTXQG$oG8+!n$2^i6%9 zPv&ddbcA6irKVceF6=q|EGmi_8d5`Q@Z9T(WDGPooi8%1^u9ap-E-2%BDPkwb6p;A ztwo9Yy2{|hrkQQ*9yDVDo%Sux%L_xrBzR%bf!0=++efZ%N6j2|q@#m&8R0wIK2hsp zM!^f&I1!HS1{c?&;KU#Co63y8?rtqbgkUl- ziHfE=E!2ETNO)yx%D}BxbYy1)^l7#8Hg&T^K8j7n_t3QNV%{NL!jr2rl5m?p==x2u zg+KDm`JS?umk5$e>%B;41pYHY!Nd%g7V3;)JqIIT{hRJi2BiQ=va+%wC8VVEyOwyps|P)m4lH2asXMk}x}Y%z0ao;{uqXgd$auQPGD4__&6I@Q2B!}^q- zel(?R+R6)`jAffb^mJx%VH7+Y7fXZBNWw=B|N3X+;TsE_y%WJ!Zr6q&p6e&lFyx9sn-H4yq=REFCW{4i+$r z@Dth8JKhV7143+oB1YoqCGDGAJpN65oBN40Uu8#HHcBC{82LK15$+2Eb9HtenBX<# zSC=4NaKOBfJA+*UTv%kz%R*s3r(44DOZfMN)mz^jO4iICZ&DWy&UVH_18y(0jE(mz zE)S)2Ton{>8h!b~h#oc;^EqKP957Q|kS-Ko67@C<-tWc}tx8Qz?YLM+92`q4T46KF zfHWe$O$Y2x#lS!rBqb%K{iViQxL<~_L(ln)*(g19QA;~O`|aDdj_{r;V>>%L$CVcP z#wAk+3bx>IT*`N~3vD`D7n*y-#9C)FlqsO)~qnLQjS&@;Gj{_DW+wZXc+o#IgvwxjK>4M^qcc;`Z%#pUU zpbYi(_2RMTi#tA3pZaGR$9Ldyz_n?OQX~qNAJ+gFD;#A(HY^k z7EUQ(Gm`0BDSm(8610A_*7>y80n=LHe|^FffGQuQvN@j68do)BGtI68;I&x#XZVd- z+)N9SwQVu2+F)No>v#5*QlfGGRfPvGSZ3o_(qN~!-W()-4j2i5slnN85@s?ASj=Gv zDb8?zN->$^#6;Brcn@%Ff6hER{KdQKcG^^N_+W}@@8_^_kG_A2J7?Z z(|ETTIXR# z2QZB35Fc>48)F++>Z?y#*?tLwpOJt~6E|%AOjb4r*iM!2`$aqizY=^j+Cyht{MQt!x^HMA{le_ z?)heW*vu@736syilSsBp_gN_+0a~0kY&~D=GO9o*tq6h8$le{HdNnpLubNC-k7gf? z`^sYxtCT0Y6Mit%l9NN$&eyO4HZTWF%=e6gUec@Uy}&VGXQtJDre;qC`pmGI6#v`un&RpgtAD~Da#LVQ1oANDegzl zu4bpDsaRNiNBe;^3DGt%;CP-c-&Ge??2ed#;fjlkEADN~ec^fe@(Z6yLs%mFRwK^qr^Z>Wz*()tdHx+- zP;KcG0WJW(nRCk^?WzIP(6uu$`*yEwR$?#(>Anh%8l2S@piz1NhuBCsYoku80R(*6 zR;bRQSRX~nGbP}U=lNAb5qe&U1{O1m3e$(fAhd_Qv!)ENeInRC9K4GMbpeBx@{%B> zNf7?(ePGfCf#?9uH$&z=inG+>ODL&kKwHEg(gsy~oSPP(8w1E&d_Fccr4#fG=;{YS zPG}=|7h*2u3iFB~&aEyPgHTE^xFC6h#1QGw6YIk-f|d;0=A7 z5rAmW)PJ^{I7rWR|J7eCx|KO0i=~iP7HWNT*bt{);CvM7;2>4!TVAG^lq*c$Xuz zf3-$UIL`~fTxOir!*+&7uqhJQ6o1^UNZclf^E?J@p9;2*2HU%q(eQ^6bqH%t3Y4N+ z?(t+0@JvE3(Ne^-QH`I6VQKYX;i~$jOB;4g;ds(tlN8 zRRyORGi|Xcf&2c3``X&u+cP~WR!4izYPNosv)kgfWC|ma#s^4gv}HQrtr8ux*9D;% zuY3FBAC*s@6w>PoJ`4V-@sAd$#JsAN1!&{{s1nu?3IzwO1oHwVk*<&*qX)S$7`)nm zRBMREtoTd{=Mb{j?`$g3+ucA@H=Oeta59hL@no68h}KT_D~~o)<>soM9AB)+diS-9 zg|%2w8nYS9|M1MITm5FOdUCGcqi@!Jbv)MGIPRyZimiw1@FhJB zm;$MKC9ALyWuc^5PykC=&^dXP3**M?Y$j=-*NsnITr@60Rz(KLt@pehdlV;a1QYI* z@`^up?`mqY`&=R$BN4-;G34AaG#4c%og}kzzJNNuJxv<&Ckar*liSu*>QT|$tc6a(2 z{P%Oitj?l(EL`KwC1+7huX(@y_P^>CU@|rzDH68a z_<+4NgfOOl@UJn-cK+#ijgO$$X70Xkp_kBWKg$fN5ZdPWnOJ*r6`brLCMxz*p3CxY z-VqHjThrFSfVvDoZ^Cxz{EE;|8ccn$o$Jvts=_4W3F}D6?wP% zWWV0*0neWTFVd6D)Zc=f#vp{A=ikl%oeCl6z5Wn1l#LHvank8gGsR}hC|~Tx{yPEU z7L;8XX+m7M*+;%#uD3wT_fR45{V#XPuZl<)Ttfs!4)RArmqW<68|YKaPamEw3I_?! zh4!RtKacmhI4K71ifBHXwRAn7J{sUQFRzozd23gFI#gliy|!oKVVmA3DT-$^)&HIP zMG@rHheHsjpeqs;fiWiCBt-V`-|h2lKE)&>fh|Qubp$ofFf#pp{bd1H-SKP7mPsS$ zknL>L#JzW&2ld^H&8wI-+Z68C34C|=KV+)ZVsJr~@?YF#F=vabmkX1E*s;QhwEC>* z8;9%6u~}^Vsm$nP2@=_;Ngbumh@_5?>Z08BGP3FmE7F?ZvloB;>vO&PJ#&WLAG7im z`XzTTG%H(E8)SlCg!zzHEkl<(N-VF)vVEeg&*I~s7C}Zqqi|7Tgz3K3HZ0m`%v-oh z6$H6L&A6bqFn%N$YRZ#!%D}X7*3rBUAWj5xFO69(so z!70he>L5y3#JmNOxGzMZ#<^1+b@36$7K?MWgsTk$ijRZK$x54OMNvoolymM52-ibSVSEoU{exATy1#(`s8lHls39h1RPK-9_ zHRm+Y?w+><@+?L0C_r~IY>T00>spA%0({Df%C4{nJoZ2zWeyvw10s{8U{;Mj~mjPwT z$&$EK zxWp$wT_irBJX9>Pzj3jl(JRdLtuXZymex0AeJwexf|mR`D%A7jl8Vtfar=qwOlwLy z3*BV2{F^B(buyHy+-ddW#;HPwL<@ixf1E z``o&YpCM3?>@|N;{lM8DSnC+i@{o4x=*Hh6jb;6gItOJe_JvG zpTYswTGxsIX(ACz9YvK8il6Ws0X43R{pJt}WS={+yW{vWb7Zw_yvJ)oA%`B(Mgq6T zNTWF;gMm$jhNBs`W;;@31x#;{^*a)Iv=mXfnM9qi#sl=fTr>NQbsHqJ73d5BWQX;U zKPI@W`DTYOX$cd?OQ=tssYZS?e5ZGjo_zv`rtRTR8v2Qrx0TW^{`b{dji$QjIYDs@ zk|~5D4CY(gvRYtgt9e7|G%qhgl684ONifG#kh*TWMEL-ysp-2CQo=G$W9p53ScmF+ z5f(4Y*63vOa5Dh)uPjVx|5toH14ES;NVTi$!`5XE^E=dt@b&vq`Eq2T6OUumVPUV} z_X}(FZzA}7O$h}q@S{6qQc9`nue`VMCw)UIpcI~rM68_Dt1&5iVo8y9O^Q!Cu=J1L{bH}Dr%Dajh ztPz{nFaM+vv*;CPW1&05_#S`MJ+k8;<-SkRK`W+61E0eGbFZ+?QHy{;Kj=?Qn?Dn39|oIUG2!j4p*$8p-Ez#a z%|CPA3m(QpzG0G~_U6U@fgc+as;rM?A=me#mdE&)Cp%q}J4+3Cdi-`3>~Exre`nts zLiW`1zq99~4pzaXL#*Pq0p9dsI^&#`@vvk>DoncznQSB=wxR`fOw#M;qIo7&;pxz8G^Pl$}#*whAOM=WBthJ(sNRujJ%S;Mo#L}dH zU8BUQd5)N8RinpB{Ju}UU_k4_Ko4KQ>H5PO5ytJcZ zaEbKk_}7;$(=b2x&EJyF_J2j4Mwu^>CD-9jZ22Z!k_hQfxERH#*!wc4l7O>HD0T>( zCo!UQ21~(ol#4w}zy;3|5Pt)n&`zy#+XXliAc#FL3s5gb``cxgH99gfY&9NQ>Uu3m z_+m$ePjVEL->ZB3;<;h&u*#!M;iuERumW&FJ(#Qh=B2Al8U7VoT~GPEa9i(#e;iQ< zZfr6ehA)qoiB^e$(2=eLX zmFI+mSpUuCGtCWeP+K^<zILkM@+Wh9_@1%oikTvNBL-cPO~n;D|fkz&e()$ z+2h^v{cWttI;TtNDn2Q-(Q7O%GsA2xfq$K>(|)+|?!A5)RF`KUuuMRXu#3ieNAcjA zOOV3-Q7ANoUS5zkN%WSMS^r4n^^<3CXQS~-*65~%C*njtH-_H z-M8P?E)vDP{D?t=7WQ|8gv6-`k?3W_Fr791{5CI*)1B=q{p4+^dpzNOlhZv>4s ziu#|EXL_at7PO|8(*#kniQJc>S6P8c1&H^(z@hL=e)et`i$oV=Ewh5Vn2}*dy%9+mv*@%V`^NU@$oCpYfqO!zv7FD z_44$DEc`_FPRsake)M%v*k)^|9KcYG`HOg2(=Ui|PgGL`HG zwXz>ZR6AI#w)LsB*O6FUBU}L-QWQgHF+0>`a(6T5D6J@Wy${SbYsm=h-ozWU9gNPe zwdl+2HGlBmnI$&$K`*Ii`%q~9?#1w5-c}3(Trz4gH6grTS>n;db{1`lkf z+Ya822(qK6Ad5Tb^5`U8^eBc4JejRHodA0G$#nTS#m7A?5#DHeo9zMYslEuUty;#QWHs(-ka$ViYY ztVT7JZWK6~bx5n%sU+I=OMNV#(EkY8_=D;s!S3{rx;!u-vhQG%>A#uoE`q-UU8IA3 zOpApbyeI{rRutS`6bWQDTLWt*^%fo&CL)2jsp_HAh2M=ty-(HC1fGB3Ht%5m{DupN zH4}4Xr);A$jXG4%UHd!m@s|#~TL^gvhOoMhUN_g-drK6zn&PxJxBu2fOko4mrlghO z?|Q&uITc0s^JHN7Q9^+mKJoenPqS`n@Fm2IOB$E17O&(x7=4Z^`L*s!kjfq1aS_X; zu_Kzl!*fPX{^Pb&bl2_|6-RBfCnd@|W**IOuS=?hA8sxofWr=FD}&cY3^yibBZqSY zlbDFkb3uy*k)3f=(*W|4hJo6b<}BsDtRxF$$o>R}pg)Phq9H=BP~EHHn%ppJBvBOf zI9xw{( zKJ8ah1C2rac%v*`)F?5_JM=%7wzUo|!(1C#h>Mf%cpuwh@meU&OxAKN@zQ**UP+iL zw5^wscA8J$3~>4J^RsCZMoZZaO@;G@-DdS7`zj~3Z@;j$!iK_{WHXYFPz+dvycHJ+UK z*YOB$^cs6TURHjg=BT!n+CBMKw`+1#o>|l1ncoSZKVqwbtGLDIiF~G`NHRb)(7{$x zfGaPGW;y2X&Zeqfu`ulg!xk`)<7>DvSFu-wms4l*vx)jLGi4I!fe^|Kt;;LbPcN4T zoocC1li8l5TS1?FPLhI8j{Q!M0cMeevW)b(3J#UvMeOwT{!D7Xr7Edz`SINeu-ICb za&O8~rc6_OBlcdcuZ85+HGPg3p` z%VmGklM^Os`d=EtC9F;>ErCWIFDd-589>?$s%JzN=i05Q(TiA>kwXvL7WVPEHwpCI@XhFZh~Uh38{|g}G|s{Toy(|C&s5 zA+)_PhotPj*lUtV?jn3Q~#e8$MbDQN~v1UV%RLe51(>>c_r$)1!NUWNi zMlmEBfjnC`Uia>7zjAGWJCYTeHZbm1Gwz$yeX~|jRyk2gayB|jMS0?=x*u9$zwGQ6Z(ms#Tl(9>C;kEDm zPN9VD)HX_#^P|cT?XAH3&{E7WHgBdcz={xJm7+qxA8unSLBIN!R z`|Lmis9W7w4@Bs~5Ft58_qdskk*yEYJFuJ&pZ7-H9-7UG_Nv0+r4M(7)cEx zH0wP&-Ut}MMA}Bkf(S847M0FUeu?U(C?EXgt339X8|+!x!)k~;sl58}4kfyht-|XX z?C7O_IkmOu>CDfkUOj1b{kXdP?`T-=ne;Mtok2T#y6b5~Pp`L|Cv|`!Ls1hIPOGT3 zi@hXSkN0s6nQsm||wu2{xU+FaBi zy6@jnF~VpX=qCPnTOQNx_b}Wbtu!m_@upnxJO+=inm+G#9`@T$CUQ6<*69L47<4b_ zVS>RMBwj9UhKYbk6Mq&lHwL{!)@~XXaS2sojcffoh}=74#sqY|gtir;Vi|5RF;J$}i>7S-S7iFE;5-`~`2FgQ7AUnREVW zfzt0@3WdNHH?YSPN18A$n_94GvwUTI2mwXQ8PEMe?9GR^<8#U=c7!{0DoF|;-J_}46LoEj()_+S7V|aqmTJd*ZLpn%PLS#P2DdBmvR-nVI7 zYQ#r{GcQVJI;6t(=aHg-}3IuelK7Qh4WHbxA3zU3yZB*>{XOqo2KT4cAe@wQ++9wpD&Fc*d4w79eb64}x z&gQ?-YMpF%q<_>fZ7#ydz+DvE43-KZ<$46uJx0p_hA(1X4eXV2oV_)?_8#2^qtA%djgNE@ z^ou=3A5(XNR*`s5=unet>@3yfL=W_+1Tir1KW^I0Fc9HM;eO5DXx+n**B6HdT`fdA z|F-$(yzs>!WHQY)lO2l`P{F%hj9wsSP}1$GUaPJ8Q$1tWbS|TM%jWtp79n0{C%JTa zr?OQlEBMR>SK9rO*v3<*FAqj7=RZV61*artl^6p8%4qT3S{{gaaB`Il))W2c83und*`aXR83~9%kl&h0~9aUP*b)Kkhw~@4Z3qQ8<@4tSC z%Y`Ud$)WL{_><-S6*EVoC)!uEOO7qBS9OwWhRx3HqEkbNzsCQx*q?tGO6f|myq~ja z$eHk@jk@`-ZC5%#qeqpDHYhy3Z!;?%+ni3Xrs3n&`V(Z-&gE`#IuS7yEsfP8~hVgVL3t zzRGpjR4C?rIQgwGA@IYZWDDI>DZPDh(M^7G>m9n_jzDUh(4gC2j2$q|pKvsnm58SsQtcFcDeSV9)MKdo_;dFc7=@l`@MQJp zNN(1}CgF8Uai$%wOjyjSVusw?Xe=7|t=~!^J z#_XiRap!#s|w?w?@hI+n_gw8 z0VuF&}DZzFYU(T3LvOdQ(~JCSR#y8?b1nimBU`2p`2K2;%+1pcw2clAq&=_O8N4tpNNa%#h2nxd&!a@xm`HDXP;(3!2>tLaGZg}bxynS z%#N*Mv9zhUB8sfJ$sf3wkY8}L;|+@5o%AGy(Xjh7wMEU*E=OP1efBx9)fvPD=9+{) zb{@nlmXEUguz_(Y?gDN0*UwoW)r4wJu(#wHG)a5k-ki3S*C?Izuh>#_RT=fUKOL8q zk%|%awWlRoSHN%B_4jTw-Jxpl-x`*Ex=*&-FSjns=SeR5tg-)Rza#r3Zo|+ae%7WP z6j>)P(Yrk<{yi!#MGDLtX~!58qvI}fz#FNm&he94ncl_L`po$yH8OcyCRWV{LdJ#I z?^aH6MzD3g&9l1iR@1ctxr=UyySkf}A8Vzc47u931{vv`E)=83d8~0l4su%W90HwE zIv(0Vl# zmREe&fb>_UU)jf774r!A4NC9nEm9Ty@k4dXX#4%J#nH_rd(S3ZL$ueua7i(pHw5g* zVpW4S;Dnttr@5#<$D13AJD)7F>^SjFtf;Gy>+f$C25vV4)iK?!g~F&xePWwhi*lov z3kPYAUPE4z1HWF*2@RCA;|xR)UrMRih$Qai zyPhbPlA!43?;h9nJTYfBW$}vaqPNz@%vhNqO}o7C#;{Iri$tP}oTm2borJ;3j%|Ow zr9ZKWq(|QAReY3S1`Q!k35s%t+6=pb!*tEeAD>bqQba#vSfOd)(kQlm89r;!-YKks zbt`N=%-+Vlb@G(X+&k;_|B9|2F67hQ5sT=$jQ!g>Tl}%lS}5?9Q&Q{oc2%67d1n~8 zpdOEo#V?%bV*{fyu1dG~^z~n?Tsl&EiRlI2xFU(^T#3|${U4{_3mgtx7pPGJUq;)|A`^! zIYm*G5MzGvCWjO6^D1G+HM~LVdBZrG!69YUSV{TFFae1)cU$X{qMXE1>@fb&xnr1c z%8EyFX|bQUEY-jKq%Z6riwc76a+a z!H{yu9xdJIBNE;WMAIFRzBE+7x=+q}k`3fIEcWiruUYuwm6Ew;-jws51V=Tymc3@~ zP__!J#(bKhXx@=dsw$kz$%{wOLf#TgL+V9_1KW@5rKzm=Vq3r}r~5UxKt)LRi+KTy zTykPAmz@RQvpF3Qt~+P{);enjNL3{Dt9iwDu<|rPwrB4f?AP(bCYq9DK8q@UNC~|( zXD4s`|7D3%muIB7WlK>t4Tbw%&8}oNg2#NB(*7<2Vfm-&B7bSTa~|I4AGqJ>|L}uH zjOExbwoBwwF1E63t$F5KX-u(TH+fGWKT=tLZ%|1M$O~&u*j}uL>%pdkR&is%efmr2 zPd6$;D668=s^HNf-R<*F7acCuVoQPB6D;#8|KCd*PbWDn7)o95=|O)`%w3)p!pvh> zmvw|U?^6R@8or%8_*EcGs^Nc1OrxS!53ylWzR>O)bvfm%axUY)T)^O zoyzosvu+e0irP`ciD=#To19L%TqzBR{|01d0LfcQ+D}Q?VUa9+Pg!1Eus}V~0T`qX zJ@)+wwr^QKMWu zBNs}zGdsOIHLxv4-Ii2*Ji zKD1Hc%4LPr65KBmw|&sLbV!7FG+w3XU6*;~RkuK&8^DN)hf!1S6F_%l7Sk`MjI`p@ zaGIPj2&T>-L1#Kczx>6T8NNLB#hoOVHx;55?7%5*%<86Zyp+^OKDg zhE?L#l~+C-w3P3&_itm{P9b}pNRYidqS}{fQgcE0RUT5#=yx<77wEBY-{)_q+PElD z|KF^tpX>{9RjY^g?+!)|;o}gake{y_6RP;?q~@SDGnRDYmP$Ww5m&0dU*R{WZ$)^} zq^@fb=x1P1z|SN|VG<2(1spwx{ZnA%7#nJ-LvfDl(D+>nxF82U z&u|!_Q~<2TQ+Af`a(DUArbJvl#0WW@+0Mk(rvkwN8$o}-xzlhkA^uSlE^m38gLj77 zq6f0F++dXb^d+$%`)*+yDaW?BunO+?VvFOD`o{%&2K#N0bQ#hshkM?HsFxR@qEimO zIug6~d%xU#a6tx;Zm}NrkKGe~4IJnkUwv5RJ`g)4+5{FIHpR61Jf@A|jkvz;`^Rdy zay6V2vWHjfiP0k-xnfsrX<_ZD`LGfcHtwLznf&p~uYMd0+R*;Q`xVSg)ZC=qSmH%o z`T2n(;G}3?OIQ#^te|@2d?#y7#%Oea+dOSxzl@vhqy%qxtaU#$gQ1M3#( za_<8v`_Kzs1ZJ5Ks-gdqxNnq2T$$GRo(7qg0M{R)sL1S|u6Yps=cx66u3!CkXFnZz i|6ljA{`=rw)Zr16u-@Y5 z!!PCa-XbvSRJL!CEyU?z85W=QdA%hjcqc|-zVKymjdYU;1C4J$83V z)<7j{vdM^v!4Y=f`4+r1Arq#OBm9>%NrRBnK2#(>SWNQFSDSTk7;HLkQ^P&v!#VNz zlD<#);aOxZ&s+y@sP~FyPD|3TZ$DAOsGL{3QHzx_dR+!+1_qN}WC*%27&JRN9Pt7V zw7IoKQ-51pl55@*f@w^*XjPw-l;pBmYd6aYjiW&Ct}<%#+Dg&Q^mwgM`jZ8Q0(I=* z_t^Ch3U>&#{Z0W&e`7;5YY4`x?m$qYPzg9PGBZaa?`{OV1M98EY$p-N$H(?_H3j2! ze0DP^a-la|fmb6h)1+BQ(P(%pDBEg7Weg6{D}H6RMTs&qV9Q#38L69S6WdqtJc$!z=gk3V z@DqI;C11?`Vlz6o=Yiq&SQd6SVt-NSX0M)t-=6%%yExN<1RCR%QlmBxl+g^{$mw$3 zFG)!~(gb3gyQQo}XbyV}e?%$25?8`&W6=WSooo*#&=b++Tydc3CcitMC1D4dr|o-^ zgI?ciiK85NS@W*!vmL`ZDdxnY7rCf6UG9pNv&e;9E$R_Vo&LU^cL;-L?0Vht0?UJK zhn)u2cci+8J=sFw2g#9=k_u8MfDr1&#wzdLVW9xpHyirQBsa6R+b$7l13An;N;$GxbiXm6=4?fsXR zs55x72Q7*tuZohJ{l*}P=&o@*i-xJ^gh}U;kBpxGKZv6beMvA>bQ8gArM5$aR;}6E2FBRHyK4b7+S-D8J+E^)9 zG@~aJo7elqB7%U<7+xP99*#>RoGeG{-R}?zkLBtdEKC$1{#hVR!E1$!jZghiV?F|( zCT7eEG*@G`kYASUsu;%;?_{wBTj7J^dgyzIJD2lpyQNvONCxv1zmvH}D-WiJriVlz z@-i}Pc2s9-Ek~ZIh#vPBXUE#2-36rBUddQg^1Pf{5_W2R+lk|ckNY&qo-pIUkT1NI zg(7Gw$SK?C1sQrh<&!-CQfx6?$kCj9MwHHL{4%pFolu-EjoFKmV^RuzV zuG}Lp0$9>C%C@F5+pu$l-GyH_jldvao7pNFK(VJu=N@f2e?G2sb}l-$#oygr)Z0wG zGzWo(hKAO@sEXUnbiU^09U668T+(Kl#<3aecnr7&v_ijL@M`l5WGmHR2Y2HMx7U%W z$NmClw<1?Z|9ADm!NEb(?a_?iz|Xv{zCOi-=qYZZ7=paEwid;0e<5Y)3p2W0Dbuo>dTo0k zu>1%~OV9HHoi?Clh;C$j+-fY7Z_b{7dpu{?Ba6eJ>F9d5`gGnpkC2$S?|jx|VuY(} zbXwQM-y#bDlP{^M?=(Iy7I;!N0MOWZy~F0QH`kM4o$GBedilX~*&Ci@7TH6IFU2z5 z+7P!&`;N2g*43WhAat=puQsI7Zq{}3Ju~Xe+@4XZ+pz(~OVwN@EiDp20fkO0oqqRs z7w$7$CY`r~d+I1OA|7XpwJ91L(j$)!#=e_jD=aoSB(@{ooK^Eg*i}Iyd=DT48vzLt zNUwnFzsw2>3b2`FqevXbkMd@ohbbBg-~(}aRaNVyR(F&NgXRRfz+1wg5a#j1JZcmgozZx_mrj-0X2^zV$@JoblEISiZysoky2(DWKU z65>aO0+Xr%!&R_w$xE(ui!$17+BAo{oRI{2 z23{FPMVUHRFxdaqJ7>4Qli^pV@KZoXpg6PHDx)|mfds-A@3_tTAI((gqjv+5Nw33) zkAi|i^z5fJZL_9AqsGI`V8MO47zY?}r&p+}t;KuYcW9$R!TS7BjrfXQ` zd_H>&UGS!B#J-}kG9ESo?KfbW>;_G7EiHmg4vWy=rJ6&-!|ThwhnCCj-f~cVE;crB zTaHFq%1*l3^jy4Heus4?DJ-5x?z0TT3GPwV>ArD(+DNfj&InpHLStYq(Rc^LxvF<( z$JS~hcPs*rs%GBaeyMf3WItJ>vVTr?Kt~2WU%$slrj-I@E7zNe@f-2Yo61X8h8Ck0 z{Vu55kF!W1#JRK{Fl2jvD!>!h02$`n%~sjNS_mWiTt-)o8fkt0&{sDBk4+?%$=LP_ z2tCzk)+jl$j8rW&g3>atYbIZ}1!bT>)5jRf49t6-eX>BFbfEz>DOEF z_#*6O!q(T#fw=Y_@N%y9quwN&Gk^fHaBzs72&pcz6lp!@qXeE*bBR5mDR}=7>i|)t zHbxYrm~(RC`ug?j?^RVVg@rTAt6F2S99tg;?N)c?0w9@;`Q({1@(NCu(GU9bvj9LZ zKnTzVn<-Jpl4OnGZ_>ynjq5ws>65>ox`2o>{Lxugq4W{SOph3~#=9i>F5MyqlR2yl zKp#L@;Cp(N!4q+N>$m^8R4dHxfTj#}w%%H`uD;&pu{3eR8y6Rsv%PuV-#^q8Fx7xw z>FMOxXMc%z%wGZaPts&VQIxDPR?=g{QpE7-!GTT~<%#VuLjFIO-#`(3!|WJt=JAsM z!c}gZ5k`)jwAilRvGBSM z1H=0Ral5z-r?{avP>bSk-!3(90b%8F@19Q@j^Q6LyvA!mY3UV&_wquuC_yJV)w5_K zLCAntB^nBta_Q$jHdR)bo`gblEtr=Op5jqU@0h5$?CKk|yPzE8tM5P~**jydee9;e zH}L{S^d^|%*jQ2WO;JkNwAe(!mglXaxmbQL7^s4NbhCq$q)cM~X`-ri%|2-WItcQz z(x(9ArLL~-tr0LS;hA1Wwwl=(m9QbE0=C2_g!*=ILvGC~hicjir4eECWAB&$t(EW| z*JpicFOo*G7HLWM?rk$V*^K?SA;d^NfKsJq7%uN>G++)|yCn2|xL#iYQ<)Z)9ppyO zeXz?$6^@4c*5(JDerVd>9yj=S@9tzH0W=@c$r2Gj6hgpG*G0`gk-N*2fGw0+#vZ}E z!8Q*)?Fn;5oHzk!FHPm>1;fXRgdTdS=YqC=KM~O5JFAUl5cga*`~U{pQWFMjBuV`1 zM!n#*!}qV3G?i+{wze}_5?p38Kg!-!Kb4(fM8+cP6jBI=L=6@))EUWi5+)G_v$g~1 z^b4w4?QH-Kq!sL1t`q9U0VzqChM$UcW8~+Z@}PT7UYN5Mk;qZ0GN8iBSQl1tsXUriR9? z`h(h7|EmEJ3~W2aF%AzM-|`JM=3G2?9ZOLNaEIAQTG6&s0&vSD?I&|@zT2RvcARWfw`WQAs~)SyrUigSd7yvE z$?C#Z!e~Ob>~0L;tk+QBbKLt1ie!BhW`!TqP6OTMB|}VL{Z24~uimo-W-(i@p)3)9dvv;zc0}o$3hW9lD3;*aP4Maf~kX zl?@oL7d0aj(>wh?ZH*KR@CY2-{D&9h6Y$fS{t#0=-CMjcqY`|okDzz^OLVkh`*2&7 zD|=sBy4!OpZFIFNGXhL1>|BZ}dKd0G$6W9{#?KIJs?Zeu%H4^%4PU>Mw zS4&OV8sgb5Y@VGrkES;!=&$dJKBbfYe*N(SppV;+sz@^()hoQ_3crlHC!7ps$GcCd zsLotJKDgPfg^4i>)k~%A))X$yvyt-mdUb+~!8HHi(PVRzr0z8CvEk*p@8&JiO1S+@ zR3?&B=)wDncI?*KRlFr=R#*Q_QPg|mNh}?!wu-w-L}8TH9Q+gGbtZSL=Gsh7a;|6u zYFl&7X>H&a#DzhKp4vo%lr}KJsq3?n&*v*@d`LqL{Ir_=t62)SX(lN)b5O%JfGp?U zJh4*+s^Xz8C1&Mi7Q1$bVcMqH|D5h?w6}BKo7e8kYr14a;BkGDAN}?FU3A>$8m2E{ zN8tdK)L1JJh0oa>ueZ?}nPg!D7~p**Io7ag^Nbnvs_jC2{oaR-s*NE3tWh?b-^1B- zc(u@K7A9*E6lL%1og6h7&+uAT2GPo%_h$l)t%yMj6rXOq-GdHwEV-TKeSbMZEiSmG zh2M1{HHYPKMBhRM3gCuX0AU~~N$-mpmrGT^caIz%=>4f25YkjZJ&UoTUiEybCoC)l zuDugcfe4E2#0;eyffduepKtDIBv5Wsz;pzA+|WJ56cS({%AlUMB+(XC%2wOC`p6v~ zP_2JM_99nH@EVuIP&zW0u5MNrISf%nHFo!wFp#Ojstb~!1mWxkO3GNF@S;pk_TG@G zv5;bb$Y`>_PV5AST)9=0GnIYf$JScUQe3CF4%Y=dC(OM*9KG{63nvHpEe6X|nbL6& zl7D%@U=%FcjpB*66vs2m*N`|ms`Mo#WlK{CNKbv7w~?b#XIdz$#r{e-z2t(w61EY; zHe@rEI^Wg)1=5TRp%+`cxt|k%n$!qQSMuDwO-ju`so;ffqL71tya6AS9Qz2DdzfHA zO`-8!kFAWm@)ctqTDeN_`!APO(gammSFb!*_?h<5d_fOgV-ywMJuonY$chRY|J53~ zLg~zR(7PqdMhPl)qiGL+QMX6M%Ao=W@Jjj(Z@=L@FUJ`{A0}pu?H1HcO#qaex`n!x z(tcb-%VX($u+r^tS76x^^Yp%qxcppre=OH>X+B!44C6)WR&=hi8t00~a7McX3mFGp zWrHuUAN=n2D@5ozQ6gf5tE8?hlY$s2f0Y408s*DqNKAY@dVI_H69^c|I0VXLzo^hv zMT3Rl)Hn-G@OG99VqO=tC&h`jqBR0=FR5>U&yWUWiXvc zAd!UDiR(jbaztdH)BEEPCBUayZ{L`So(s2k{IZ$lBo}#>>mA|w(s*nFx-!ZKC)fxc zi6jJ_GO*7eFG(uU4bWtoPw(Tk#V3RN0K+eV{KfY~514j3W52;O6rhS+8ef`S8ap-O zz3hs`=C#x!b}V+I>1W4)csM;o&v3dxkKhWtem)Pc9ZkKu%ot*9@N}5OvC&(y+zxXL zYuu0P&S+-YcGX1yPt&?Qh6}z9r^@fgzd*g`+svNKa3Ndzagea2FKFjBg)S22Ue%F0 z0|f20fhn(@u_Q1xtQIw9EtVw|Qg>lQoi>>SuM&%+_8^@A z=<|k`EkgE|7s09`As#(YB^v&%C4GA(Y;%Gx59NWp#fV;Aar+CmpFf+1tFAV~SGpr_ z_HTU*>9&;w2@R6<7BRsdCC{gTmbR_jo%^}k<*PAl&zU1(fv}OSTISkAQ>F_O{^Su} z-|3|$%r|lTY5d<=i!uYI)WA0d*q@j1!SLP;>$iTQCS3?x>v>*5E$zN?UKMfv8tK@N zyO0@9i>GlW7zK`Gs_A`BU zCL{RUOClTN!ViZ)OVV0I1Su2|ebu3V)hFfH<@`KRW$2?|jaIjoSRBtTKux4l;H1A@Rq0|VkdQ32U)Q!LzE#VL9!QnbTy?@`gn(J%&`_1({v(*xG!g#q)74asw z)0Q!?4T*2Q;C||Ozcv@oi{b~6);dL*PSPYihR<7u|C>7<%4#b#;#9>qTK1VbV%~0X zIUoN>u3YTS8}V`Nv!fEE=S#Y9Bp>h&)yQhPcix3kJV%y6@@(RUoHIt8@N0U>rwH`c z{Hl{<+>r9#{hr~xI^8n{tJh9uiz;4MmYso{6amPc3wFb{$a!np2|HdDo8@j3LcfjA z<&s5~oM^_*odKvVf|3L33YZ$N4h+YJ>s)u6m$1?dLqMmV?VrRWVsrJRUA+0YlqE8< zCa5GE1bAOUi1C*ML%ZoZEOCztqR3ZmK%;*7ImJK@mq*x0!=u*DECn=PW7_2onlLKJ~l@6)T4hYxTc zk3*$#UoTSqHXMlWq$!ejpK%4f4X{@%{k+gN9;@optLHTs7o`QFSiSRdeyn1G{eRWNv zPs#Wwy?&6Nc1HQ(v6CeI?RGS+OMKAR7rT9u?$p&T5j_FNu$5aZ@q21$LO65Yx9?7Y z$m0XgThArqYj>N0uIeER=R^cjD7ULIhy~F-01(Xi$GG5nQ9AO&k+1Az~*AChW+NqI8*e(0~`0CI;)%(!<+c%#PJSnii zUy__6mZq0(PH_**G91ry->ToX>?qja9Z`_!5YgpSo+(_V<>+m?0Krr>ouUBEDqXCu zOTH_K=9e^A#b(_MD87{UOnRDd|EnT*;>n^k0DS!} z>D%x47Z*G?cHo`s*J-m!)F~5zi8n7U&iq0WntE$Gj+s9L^tIF(z5xDVG`^&H&Gb|g z4%t3dyy+7uJ({`CS0wXVf1J(}+N*#~(A5~7)_EJ<JR;iQN%`cnRO*28K+qE~{QU4OK^JAwHIm-^pj9%dY#- zPXDzWRzdw$)Kyk0yF!&!GEa$a_X+FZzVZ55i@McTb$9f(>JNjGMr-fv4iqqBoB4hg zW7SJ8;hTK0#UlmqMNJJw;)de(E>X=-WD&9Dx^lq6&NluH>_+d2@X9nf<0=@7X1g;< zc=lRNw*}Zb)96`u_=(^D##uQ-y|m$X<|kpXrB*_lvyYcMY+e^eh;xZ>gBZGv_Cxrw z_g_im+_|hc#gSdr&KbT#%xG_QAVj_8&y-f&UTC3p=t`0 zn0&xVl)yjTrl~Al2@uJ4Bf5zl@jj_kZbwYhqEv}B5_F|Vd@?*ypR)U{JXUaXI{b}v z>yWR7ZObW_Ql*W=msyU0M!bie&chle;n49ZX;^8qIhe4@2q6QI15 zYP#Q5u<<+fhgoDL|7E_IpFT>hx^pfbXH@98WyPW^v#S4{Na~%M43z%iH zS!`FhYhCQK*S{hp0M<5~Hw^Ap$Ap@ViDZgS1~&Fx>tYl`cf=;uIL^MMtsa0)k3HtJ z(Q|?zV1Q^++c6&xC2EUa_5JX3_j*;jyvB6eH$BRZJsnp4%iN>Gk*sG-_t3%9dt5Sl;GG04aEhh^2aswyOR{Pxh zJp~UPM85B+l;bXc9*fb;PXr8R6+q7ldn3{kE6-($I>R`RmF^VhEhDpy)&;e#LcNUT z*%mW-Ql_PM{07>}=5(^ZC8X0VGrP=|+Y0yo!e!j{jMf{&*cY#?yz(GU0ES|nH>QfY z#eV5UR&Bjs(9k z%1OV?LjN@VhDd#!aTNAS-AgR}n;|^?3wPWAy$yZaaeNu45kG*x~n% zixDfiE~2`*lUctJE^-%yQP3X?Z!~*^%a>5G-KeVyg|IS!)!9n4yl@Q;IW7!C30)zG zy<)+?AZub2#R}zHQBgds!?$^2OO16EhFOIOt#Dr+wfnGAH&r3>uC#Vpj=3k?pmO%$ z_fn4@}kZMp{JkLF8Qk5jF4lPn8KZ|F(H3E_J zpB$hcYovzM8f8oSW4!}P#e(C<)w>w-RGWAa9EFvJKI(*LhXlOVy?nDZeS{-fyBBq@ z*s{=>NZlOCMz%7`HYJ>y^`b-L4lPl=yo|cBPPPjY|@}_!N}* z#7ITXV3WHT7-7lLLGz6__|BN$itGj2#&yxrh`LRjMC}tEey#xwH?kl@s#v3u*`EYi zZ40gCGkRbrTiJXHN}PkvvsA)WebZPwQ?ew-aX-;+MpI32Vu9A6SD8ECITn+uXSq*n z{n?S3-Pga^4mGH|*B2P3wi3gRcOjPZTL3tuP16O)#P@O67!U>KcrlRS?-~oF9dLL` zlmF%d_Xt~G4_f+VUT=vP2MgiY9}jH948A-~1Z^lfWoQPM2RQ+C4d8i`pi+j>10{tk4z>n|1@ZD;8WZ{>JaEOY&}ah4_8dZDPF;rdu{%q>;on$%L8 z@9fXhiMXk2SC@ALSl_M%St8m{w7N#kMLdApO4y%EY&dZ6eJ=TFSHbXf6?~x>T2>7d zsPw96PI2XW4MVEiNpvbh8>5dbg_4us!pEKGEt8FWghoE!)ViHY={SnX%Ql84Jm$%k z5KM{X^>Nnld9AL#o>=deC;a^$U8jY1FLEb<(=r>L?W>8V{ZxaDernEhJrYdtF|_Of z-OQMuN|lu1=v0LXEZ5LpUH$A7Gjx6YL!v8;Koh&}?8Z-gDOFB8K9n=MHe29%^kcrn z*D;>aDzpn{&@m`lrNANgCZu}&uiw)3MWvpl=&d{RVVwvV53+f z#3et(>P|GEayu1LWQ2*)vRwrld9{_mRfrAjhdOmRZ2d~(iX2w>1Is9jS7&fla<# ze{^)z2ZXA!X&7Z$A8K9z%EV~MM7>!FIXnSCYxkGH$Aa! zPW`KJz@J@PsM^9`!8!KFyDb&J-*gs8&gdm#sK)#7@ek7BMskLdvT@tZJlED1wC6{h zQT6ULn6)PfC&wEl%6%=QnDd}}0jeiYsfpGk>UtsM?(7^|-qq^ZWEDSs+jlf9Nu{Md zvgUL8Nd$Uu_Z0Hei?#_-KdsjcbHJ>p7$tbBT}wDOSY>>7Y0;1A2LnGcxu6bXWa1I6 zM7KUu+1@@&usGY1G>{YbYem87dOUUn;ow-IPN2+khGjh->WjkiBsHd_VqP*D>$sCF z$2#EjG|1{@(ZhbU8Gu#&PWp`d6O~K*ui)YHl!mKX%B-czqDk&z zD*2l&GCuA@X7aZ>5zxbLfo-DZG_uq~12?-f*OHhE5eTaH-dB>@il7_ZzC+ci*JYN@ zIfj|9qEu>9+p_-joJ&mietQwz(Mvmq%xhI^6+^8j{v5C%b>Y7sRax~7T{63-X(0NR z`@4YbKqIr&x|2|qCk{x17{XdaB{3EvLX7iIP6`HP1@vKQMG2D|=N|^R3#N!1? z@TQXBKF0(f$*jaBjBlp@XVug%A@r%WOhJUMr`1Fve%(D7eg29xnbhO!SP#Z$Psfsr z&YW!LBf0Tcu*fkz?cL!&`w{TQ-Z|h$klQgqwkf?7Kq; zcE(kWyqg~#=_-~*8qw{%H`3{FBHgt=T|lNCpZ`Jek$gv2GXJhZpuJhUe($t=;adVkV0THrJDfCVS3>11voRqMM&?$Y(d^9yfJqE@$I z=uEiaUjvUkC_>#t_u*B3n)fPiuIIxD6>~epFcFB&#lj^IB|JvKklEi`=g&NUHujP8!1h`>YB02s|DGWTmNq)WJFsh7kVkw=u5af zOZ)PaKNX*MaJV^H)NOGOdeRm$VRUcf3>>y~nx^r{4@|8tXL@_bddByueP~GhI^9+9 z-IPG~SeoVF#yZB1=+!lXB}M_Kau&hJmOiW3(>_XQ0(Y)Ou(qadPqnoNp^|!2r*(xl z^xC(Q(J~h0fAV13a&Ho-eO@Z*nV%(jzp)Be?fvy9vt{ z$tEv=c)Vy2zg1Ef`zq7^-MabX>&0agiT|_XO6o$HF;5lfBqdro8_qdFJCE{LyPqyP1QjtlFPhUyE=nscZ{cY@;{Xn8+w$2I(<7 zlrbaaKyjL(aoRJD_6?etkztn9DiYlRO+zLU5n(m^#KPe}&;h1=0;7S}J}vOb!a=(VV4H3N;7o71cC9i#W2qu+H{u z#9A5Ou65D-5W8WS8#X06%H^zKz+A{$Ix={^^OLvkvY7<3pTYQoP6rr-A9KPM&LnpZ z4UjMRp3|Iys4SP7pAOR1=)9(umcNF?4qkP?2*SH;=Gk7Bq+w%kc^{}nb|-0eE04np zdi4sXS|ApUHkI+#mjSbBIazDK0M$U_Rartw3u`7IXsryah#*X&LY3$GG4C7UQ)9Jx z1=F%_Wh@Mk)-e3(qK0Z=nVT4C#uU1j1z$rhPZSoYs~B2GgQ>A>>2e)08d2X%`oh5z zK8GS^(@+svsh#(7zJtF5upxO;ZPzsYIkqMM=Du53yCX^Ta`%?%3N{E zKM$6x(07UEh72>}><=aD^lN;enJ6gp!k;I8RUWh9>`pz6;Gd)BlM$ig!567T*qZNa zGYaAV>MTx1B=j)~4}a9veAkxgN@U9#)vWteS5vlP$)};TiCEKSKu29#t4NB~*(BD3 z8TI4t+OP4gM!O@&k=oMF*E_Re!{Cb54-#s=r;uW|5;tFLJF&(_<0H{n^*dwn<==Zd zvlUK&e-!W3Fu$3`{HCkrL!ah()el+d zX9**Pa%bYL%|k`LhAcTu130kd&6Psp1bho^&24W3RjKBHyc6hkj`XRkN5KJ@rgZ5t z**1oI7=q2(A_wC8o`8)o=lhPFB(I+bybkXGSe0K5h-Cd;D&?>ldVF?}EP1%u#@3_Qf{S8$m^`edrgR}mjtYj*JD+*O;sXK55UVRSmD>vDEml#&PX#_3s-Zio?7@?_mfLRm)q zSnhd|U9v8#O_fub>!0Me>2N3H31jP%R=_VN$ovhu9U^!on+_kKG}m ze#q0{6+sgM43F?uSb*@wR1jhJOM+cyG(}WY71Yi%kCinYFde2nkg<6K)j|cJXL@m) zVHFd;`=i^7FzI()a%q~h3*d*LtgYC z8G~!UPX#isJE$!8)!4%1t&VZbV*cpg0MIR|W zaZ!KEM9Ibn#+o5rWF@39k7L2HEC~6?deqLdjY>#?*ZztDs8~x-BV#uVHyHEh`Z==Y zuL^(Nyci|_W`^lQfSLlC-g34GgDd}$?kXIbKKwj|gPc<2UdEKWMnlHKY>RFs;nO>9 zC**#8aC1D*G&G$qxKgi2oX87|oC*#H$`~ht1uKFZ8FF-8dUBkf9GIZr$$U$UYUr`$ z_HSF}cS4^PG%>;Qv&iNk;4mzNb29V=m(-&9GHw|bB%KOCNvJ@=RG`Utp1*ny_!2-- zQh0Goz}DExU6x*>hs-x#olGn1rz7O@fKXJsy-)~+2-#ZTSdGaA$NbL!ReuC*wdv;J zy4%%8&a74HRl)<^Id8CM0xfEg>SU;k!PP;X1)Sig6aGS<(CwYYm+{>*LcNjaO-Ap- z&Qgz0R*Y*VHC9kp&@|r~Z9mVumt5to$bCZ<1eP|klE}Fz?+(bP+}*Jk=6;gCb#jOd z1MIkoCGb@EHV8Nal<)lSgv1Ftu$}#Xodx=T`1^IBH}kw>>U?e@aIy*Iy{w8%)mzh$ F{{_+0^+^B# diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_3_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_3_0.png index c7183345f781ecf29d609868da1420e360f3e99f..4ad4c869074debc3f8a40d49c92707310239bac1 100644 GIT binary patch literal 9959 zcmdUVXH-+sv+qghy*H%^K}4lUC<;;q=^#ZA=_)}=AksTQrAd#p&=nC>dgwKZ^iTv5 zLk&n#S}4*>Ui{y6-*Z-A4xGhpQ8i}3LB^>BS8?C}N$i~9ck zuQm*Q@QVFvFDPAWFoBPaPYdLHb0u_BXn1lXj5%c1eR^#-ylsCiSEYQftnMJZ-Cdbxc7&xIBb880<3-< zr+UHf79^0K5)4%y!sSCT2RM({Xcc$ZV*t5mh$@I{P7pxz4X$I8l6yY1+80j!xnG)t zTi1gsUs?4|kALEYGANRYp)`pUUc!}0BYS{p>OwW-CBPG?67BaEOGNugY<*9Jgmi99 z$OD`7&t^oNO@&j~0QDVJR5?Tqa%6>y2VrqTP)rk=Cq!q#0g8#p#&bhYBGCebJkwS$ zScf8pnph{Eg?EFt{J<$>;k}_b4@!k${>|vNC$OBhB>~Gn-JDm5UG_nWIe2Lt&O2Xi z4rdR=1PrdzLEUjdo7_;FCHRCUR9SVUiHOGEfhlz2W@!-i)!wP6tXLu_q2IdO5gPIn z_ag@{3vIc+(j*Amx2d$PMC0XQ^-3#8OR#-Os12iI4%+Vq=Gkoclm|>Y2Tv!kKVU|L z+GHH@GOsryXG|zCQzKf=NUt*g>GPofB;C2FAGQWE$tW)8B%_8B4D0q5|&; zrziro!RbREVD&D1#9MsWr@$}3mcmg9=>x3OI6{!pE3NyY0QDCEwgLvnW^`EDFYw?x zEf7G_NQ(u40C1KwZ06%k6@Li@F9kscaQNHL0#)X~)&Z(4j;zQIP^0up7z5$z<~a~f zL(qV(_ALa)t1IbbY?r@`I;>~}J@DS!y7Dsg1k{1D@A6BAa@ zKB$}GP#SO;3<|%FjKGHdPy{z#QzYoOO0d4;?>hkT(;$IxI>$Ejpe&{>zIEd+iul?V zOyB|{#G29;Lx6KeLM(G7Nvcl(QV{v6tUb#G{Ti6o^KK7-?btLfe|T00zXT9^Ad0VT z=^Pbmbe4E14%zx9bnAh`OGr~R28@F0dP4A;-WQObfcJ}^yAUBU{IbCIicdNkPF+Ri zsTR={63U3wfOMF6mrQVz3;>sq;RrS?i(b2dx4f@3t*5U|K^jtQnH?Vv)dN_1R0jl4PiO%4 zBN>cQ09mgpMQ$fAwj*@@z5q?zE=sO>pOa*U&t`8O%!xHguAI|*9SO*T8%N!L{9M1E zf5H_AFPhb}j}w9R5vQOY(9fPJlEZU`Q1`)glZ=hp_U@@=9-K!UI&eYh-P+DXZk$| z5JI6v59<&{@_Z*tHXbqXriRX);MCLVhI4Vo`)mg!@HrjgG{g=g!>gG6oE3s?%{NVN6?Gz z#?nybLEJH?wdV`8-*A&(MU`y;a9EEP0H3zp`O7ft69&`*4_FH0(Pv&j*fra5WqY4N z8^oZaH1Ko5?{HrRGXzr@ZFOov2n@_AU{nS@Xpsg6*Tboi!`Wo*sAEs+nL1rAsyh95_`6M z_q-J>44qt}10o{V3>`p@%Kh&2SC>iJUqf00QqnR3jsWRwu=U!%`me-4aZ&#uz70a00g7+?jjcCN_jb0I9Z;E2z@f5yQ zntc$>MH`)B`xq7i3S`GVLHW?PwSV{!H#j&LnQ5T)&OQtO#qGr5j_U=HrtXsy=IP#; zqAw3BV`I~}1BJI>vHN|eECeWmBS1?FLj8qTwOJ9V)0$9g2b?A?I%xi<y(Ku55|HGyx110PS9%nCy>G|@o=~YgiHMj* z-vdXYHy8+VKnMNfe}wj`AzDvr1QW7eQ(6Y22m9dx8(f>HWcl!bj4c9%WiVr));r!# z)f1_?#x-$I#tJoFJeSIdjSc*AQ}iY`mXEys@S|v-xF{EXsr;PzmQpCO%UV{lBA@|L4>yTrM5sMQY#G#q*(D zKEF5suzM?ojMcqLrWXf5I-43Ct%HN)SxW0!PN` z@>*7ut#7kRu*Sx|boxEgFUXSmAM$%+s^EPO(u3jr&F$F#`nsUyc{JZxO!^eT}OwJyfQDeE6Fg5JyeB!R$add5qfYT%Iq@ z+FI{kVktm+;Ge5A8>=(Ce}l=C+i(7|Wm&sGL+G%|KtAM_+OYd&v^54TU+K`Hn(!#@ zQ2aggu6940zOXP}->DhF*LEJ8LC{05|EdYZ{eS5IK!u-f=#iMl)XD z&YPk?X|Nvu;1tMK=<{RpX@T&QKPMU_Wy@KlNH8@oQ}2B>FgNGO$;l~_iq;KMf~8YV zCzy6zX7F2Gyjq-uL)O<%W;!GlHr$YkX7Wjo&`C_h?kqWFz88Vjr#vy2CywJDr9Pxm zc`V0#%Wc8ilD(E!JemPTUUZ?$#=bfUaQJ3rfllAEUenhZ^s>wvieP$5k!L(H{PM0J zgnzO6iWaI1qbN12T=AzXaEgjXr&g>%QBq9F$2mZH6;stiSoZ19{ z-i%R)$Mt(kY?`#BDDt_5OWq1uo~yY_`UA*n*{*G*`d1dc)td=6Vj1tkGELztM>sSV z8(C4%g*y|9*%0Gvnfm>$NCL$Rg{7#3pem+DW`g&9lY2~a`!AN%Sh#gF)>Nm;`9v6` z2a%iZ6Z=#2DNIc59@0pdrS-~3%!7B9H`Z@ncU?A|5U=tsR_4f3lFv?BfNZw7aZOrc zGdIeMF%Kfu-&sEErAo7KoeYSo=h|`(k$2OD=s$D!F+s^~v^<*P+}^*+8vFgKB*yba z%KI=yzpEp4Yn#c6a$4vmuGsafF$G z8(%FB%ya66QHqG9(>Es*YyeLMj6)7yb-YYVOKaiN6Jg<|*hO*BZEAuv%)c#Q!-?z# zD4h4$ve2MI8srqnkb27mdZM2~#le<}NModg?O#XA1L4e$w8&i`7a&}L<_JESCmZm< zrCc^cu*}OvJxGVa&JC0oDwmOPh#U|XK{2x}+g>e6cBErqkyIL7*@=yvLuGw0x73?j zj+OQ`AG-}k8Jt~>jioFcs(5!l1LEoP!Ci}z|7V)J?z{YAi4Qh+*&CW33>AkECD&eg z_sc^>j*7QM)ZmQw?>zaU>dPZYhYEaL15KQoGFHN@A6#7Z zS2IkX=0qOafBGke^?4IGQdh;m+^iu%bE?(V{Cpzjo1vxg>+fIVDp-oj2hRpW?_Q$x z@#BuyRni)d%jN5`c#CORl-0L2HO}i25h>OhH{{lI@hT^KB8b3Sx67aEFp4jO*xW9# zv(cHA8<{z`I~d&&5qWms{dHP&@~hbIS620&)G6q>9+n?1*QFj|_Ik>MpAr%;jW@B%@oj7T#5>-p=si#sD|SV< z{o!ww)$9A#?5`1Vhi@LpHq_T|4R91b2;hEgYv7%FK;i}}KimB={DSKK`q-?!G>7Sh zZNvp1$@`bStCCFtHGvk`-My{Yhsz!;3pzXAbK?GV{-;McN^8c0_wVsomivbbkULGF z-%JzN{~noD3aA9jb?N+Oiv=%)j-r#y_V z6tn)mF*i<&!I>&v+$Rd19=|>mptY$hVPBdB{>3QV6oGv$MpyO)<(cB7Q9mKv{^+9V ze`8`|YEfkH+gP3Z$5G1e_VfaMd2%5t2ON1Qvbs-n(=uHyog86+uOUI{(w{~YAlu6K zDgx%jgu>EV*JXD`?zq!a+KDK!vC-MH#|%c>EnhiFqKBKmVd&)?y)hR7TQsfq`+B5& zuQx*yJKcm`$79V+K=Is7RX>Vo5bM?0Ig-{m0f zSH@NOdz_+{>RnGv_e*n(PTe{b2RZ~`eXF8Bt6$_oV_N5_y-yE4NO~(ni{Er!PuLr% z9zmMmHnR_J&-INr6X@ zPLgk4d-82f&q<5%1+oGD$Agee&vbB3HlSMVVGG%mrDjAllnw)vlf72hS##5;{`oQ5>-lhuxa5V_IQE57BD9y=S7cR~LK2_gNqSm^+iIa-tY1 zj=$K$0-PO##B)>14_R(Oc3!Fd&|A@hB(qAXWzmK^NEiNCg3mJIvZ+FUUEDNWLmAkz z0=BHbC%Lj@yHxgjMX}DAAW@yWf|PES@D<(C1!p zDJQ$vIL~&)^I&r`qj0|No=dfbp%F*dpJS%IKbH}lE3S=2A6od*_r%`FVPIz|?L!HhlxYb>6D zjyzXIX*Ircd|bG`YTc8rYp!O}4u`|V970o1@x#4iCgLaigW>iu{F=u3hUfDvi`|Lm zHEF5{oOL#Ka+-F5X1%D`p(hv~c(Q+HZhXnBHPyrOvdzQE4(T~B*&gVCaA*}DXpStaLV7L zhAR}-Z-i|5V{DG!2r^%IUh8-5D6=DF?wI=)%W5cAzU=FIiBt+uY72@HT&59#s8?r1Q!C%$UOZS5)vQ}o{6PHYddv$}Ixt)bSl`s^iiT-pML8Q$bsw7mG7dENX_HKUw|s23{}D`B4?BfdiVC;=I_hXwVns>$CCb( zObKZpY%=ABNNwd20ih$^CtYa^X<-&x{W{I=8_V_~2OAADK6hf8jfqFQb`I*%qllh- zsrwQlUF8p{rhSrgs+B#maz2O5M@iAHcc_hYMC^OVSGG^8(gGF4_2#uNQhqxSU$c+@ z(96RH=0;MuBJk)C5%4ZzvkCh+8T7at=s2X>nsmn+DK=TR<|s5lqtdSh5N!vyLM$va zuMIxm8HDA_Nw7^7zrU0kT=h(J3kSt7=UX}WB_C`sBnTA<3`G2XQ?W33MSfN_vtSqU zw*9w3-K#w&?b{t%Jnz}`at>xiNryT+udel)7S%vqL-s^WRMDq>CcQlN!T}ps+jGsJ zi1@@;AD!il*bMg{S zMoZb*PDZ;|`f-uI=}ctQzZuc$xABy)f8!7^vx)JC3iK~gK8G0hUz0Bi{X$9Dp9+Ww z|MHU1zxY~4DL#UNkjDB$6lL4u-(Wrc3T|Y?UfZqc|UZv^i8s`LL`8N0E0kw z470$)qa=N*=TRVAGgN2YB0HYjc z9HQ*>MbyTeZ9*{SvBdTOeM6PlW2Wq^NlrU3vXi zq5&Q<_I}^>V#f7n|ITIT0ta67yp!3zpk4z4?pIwc2{w2sw!vkM_{zrb1Ys*P4kx`M`eM-2 zrwECQvaT1CRFy)$MtnC-FNUWCej1x=k}~|28S4|Ji}hazg8%laU1vd95&GZ3e}5C5 zuF(s8dzwdcbJ5c1PWT7E$O;AD71kVndSaVz5A%3V@{Uugj3b2? zu2E1`SZF&8Pjz_k1x?s3evMlse8$740vvX(Ib4c3bN4>lDK(HN{<;lZ^XMAV5>XW` zP3GiyhU;7?F&i6Pb3K_Tb1kU91by|mvTd>iesfEmrm zncFlp*YXRvzI|WmhQ7yr^ev*@=B?CybobcoBb~QX^{FPRdiEibw>NE$61x5c|7>F<0ki1TLBc3rjL+4Ir%TPhXyPOq&8$8&?m z-ZvGUYieK_pL$YzwzA#av!IGB?D`Xu`Dr9&3Bv4LVUg>To^A%uF-vLC+G9)E=53pw zU>&VjP_*6sVbeDpHpKJ`cb)iH$i1LL+!#LQ${9nn)6o;`xBV1*0xz5|GTY0n`Y&*w zq}=-t+`Ct+Z7H^>g$9T+y)+7wag&du-q1^(SFgWr#bspm&re z%AO^&2(C{aLN`u3qn8}4khjLQ*(JZsMJvS@2kMOb4A+@VRe7L8JVy%2rX!lgYLYtj zrI}0hBJuY6XqR2OdoL>?*9RFRM9r+m-?(QGFbi1i1G1ByutN+A8wqRBqbI%5=rE6R?1GOgPeo~{@> zo}YSjkxqB z@*GxO%f5$0iY5VslpWvvh-ALRn1+OL^RQ-`UWLOK zQDU2JHZFPE`^18BMx6v<>E>R$V)vQmr~Nz`jiG|Bg<*M0WKVUxaXra9jh^mw%>j>Z z!ML>CI6;rPj6T#nHcIf^5L)Wcuy z22&|sIR6Sr;+*lkY1;QUY0L$Qd$-AV4K830bo19|ND4s8sp*>o3ohEp$>}aj+1HKv zLDYeyu!HGKF;A|YZ~wyRY=mF=TRf2dQo8W+CA~IUi7A)quPUskG)QyZa}(mZ6_6kn zUY*V3z2ntvna`bPY}2l}a5 zMI`}k;ts55`b0PoZ?Lh&MaqCbgMrRLj|iwcfKWhM|9AakjJ1y&+QNzDKy>O5FE_pW zI`y8$u7dJgJEB&ordImj^Kq7^I``gX5s`UQf4Tp@z0uT5F3sdUf{|n}ii7JBOF`{9 zXcdAA`BFo)_C4Iav})-J8I0ASM>YCP4eFloHrMG&owT?8ff_CWX4!CuW&qCv+t((x zRDZ>V^Z4_1nss|UDMb$|V0hZ*`d(h?=c9(UOyOp4U|N03aZTe>R^*e>i()9>zmQos zO;AcCQ4xv|9BTH|2t>?8C=*s-mgGVaDEz9X`-wv+ySfRHWH8z)-8!)clA*!g28>KI zmA??X5P&rJ=U7XJ)%T?&go7asBJP0n8OYGF+XN1J!~uRfLOOWZ1lV!`s2%x{N5vU@ z<9we@fmsN-@NWzpP~60Hx(`uq*$_!FKxluNl=R+xZ-z!W z?i}|V?FZh10xR0KX^p?Z6SsZA)7A_8Q0276*!ZC+zzlXwl|j*oqp`>(#%|F5L>KR0#xT5S)+gGeSfFjH@(UdKA5`5M2w zJwb8Uj^3amr>y-HF*c(H(0J|sC$M2`@$t>b#f;Jc(FGoIN;vH^j+v^81(`<=HuEAU zGxfJBHsv@r`%P@t4#6@gGjhE|JImSxhyV~`eG>$!*Fj;m#q8Ue$N#A&yO0Yc5+&-w jA}DWi$N#B$Kmv93ng8-Xabh4}Is^2yjkPK?9iRU%w$9Y8 literal 9599 zcmd6NcT`hPyY5ax6Qnm0ga8372qGd)K$KpBh=6ni2~tDHNDClJ?=5r{MMRoNZ@~gY zf~eF$fPev%PEZ1ba`OAmckViO-L=lTYu$hD%HC^FX5N|X%sg+I_nCxSaD8^x)2sjh zup1ia+y($p4E+{hVWQV~v8NO157i*uyFnI6L{R9x05`z&UXY(RGRWJ*1sdWO5a@yQ zRg|NDWT6j&g8Ty2T+g+q~yQ{$ttK#J7(h@Ib}I?`zD``s_^QEAp1tC);K=mQeeu zOF`q6kJ{VXA~z^i&B-twolj1&KjhHy@#V(R*L6MxvNA|WlvqHyxVMVH(ed$1d@MRT z*D9_m_ylD!LvSy()fqP^*Q6ph^A>ghjF_Ij}{|18q+z6k90@a4X&yx(yB4V z3;>K~&?UZh+3K=l0pZV=rG`5*&~IxT@Yh2a!qDJq|N8-c0bB;m`jrDmLMb?U3GaOc zEe6_^Kq~;!pq3NZnkxgN9(ZrRlsrx202ndSfs^!=q4^?DA~+h)0A$WWThqwKuqeHN z6F@#e3bEsNZ>~)GXb7<*s5pf{H0KlMw3Fr;6=}H`cJ^G2L43*8hO`0#Leb6#(b>rA zf1XwaSUNt zs@2=ZAUik(6M@Gw<*WxP(wfUU=!3t#RGN1`794#bgzX}g2j*hVoSKiCVIk=N6+jCI zHXINH<%69C&}_!J8Vp#KmeX6GfyF`scg}??4L?CdMiMLF3L=7>>Ff1;RM~2N5P*X( z31j!P_wFHoDy}+9y%`yA*}#iK8TM-j#`4)P(TDIt91n7y%~s+ zsPnsFTxc=y-IL8oM`lt4VyBnnik|Aukz?=+sXL4X5jjb`+9)j+Qaj?R z=ERO5P^25chqeaY<$L>60MLT?LD4wBiyu9^Se%c`u2p~^%Ak#n9iybsT0IeCrHp>= z9@rdj3+Sc@P7vrg4S>QKvC@im&Iso5z2>3J@M!X7z!-FpOg75210676xA+H+Pbny$z#Qv~sqR^!eH?wrHWoLr)17e*s0lgdNsKNR#(qkq(#-kHN_a1fIr-SV z914cy+tqhjL-M&+kDEU-cCjNuy7`%_YuRSD#qi!3jDwY%`cL3wG;Ni23_4*Jokvx` zo$0B4*%y639~_-6z6V3M&w+37ahzDx9CvDD*k^X3w3L9wZLL2&P+| z3`9sZ#+2iz1UybXFnCPNRo(RhZ%ea z^%3-S-lO*_!ey7UA4t6ESU z?_68V={@U~++OFFvI&SYzkM~W%5T~X@KZ!r#Eu`y6U%}IvmG;6NKCa_tb!e5pj|Fv zXgC)!fhN?lUTXoe=Ra66^ea6K0oVY0i=UIxRiF+QXDRg4Ii{fvPfDk;9>&*1(*MK{ z!439a(Q-O+_|a?zB?Tciu&5CUE*w;`&>0csj);v3n(_jLYpn$sYDxv@KF3r)S&5lo zc9zGf8hTro$Fj$@=g!{(>;NJ&X?9pPoop@Pa8nzae={I(RhXAM{PY-F)| zmR}IUjN7x!Z55HL=8)7&R@v&8R7Yg$ac7KZ>`b^88m1x_qOw>SvJ7gJ8)AV25$q^T z>`miAk!oOz-`t0Z0V#=0LB!78s-Lw9LF^g!Vh2Q)ygk1Fo+*Crj9wQ`h93UsFIc-F z4)5OF=|7lCtNg{0kMXrb{5Pg|923Kbonl4C4h<_4gF^GIjzMnY^*aM_In0#Vb zyMzQ!YKnsTu{#o zUnCi@U11M){I&Q?;>Ys|JNlC1@kh>xq(QoYpXQ@p?JWOCCMoj)_bPak3TrMTZ~^pK z#>_x76Wet7>|c#8NbYY0(F0Z3!%ORHkMx!G1v3PsF5mU0gQtlFs|RSw>^;bFlT&Vp zLp1pfzcMxCyWkWQ7{u*iE)k6;Yi4Qy(hwB_9${&N(8Q_2mXgJ^G!tAFl6+I;BrY0V z@rox4DAM@RiXd6jfGNjsgWq+_84d5Vrzkf-VrHojKPI%y&zXO8FH0xM8H^6Yzh#r# z3{N7b@%)!YLR;x!fj?4TN5}TXIcc48gW>Flh88CA@h?1#I$jDy8xYlA=2ri^T%=a{ zf0nO9c`D#<+`I~SkaR`TLlk5vN~=~gh#AK5D(%hYB<#fP~%IGTMSIJEu!IDD~Z3yDa}C>s89 ze0BA-#GVRn7<9mbH5***CCz;NVW`%@X~FYt4pI{Qd$;z&$f;jXumUP*OCn!-C##} z9sf8xNUdT~mvY-KkZ#6>EVXsYZ#*4xk}SR{n4NIn@;{5w&=aUmTZ{RerJ%3Za6MGJ z=rYTDTH&T^(CRONYVM9?GOhahw?u?yxYEW;OB8$wpHvjy&Cg={9sjg;vGcuA>joh& zYomGn))-yrpwgmhX!iMH=i~2FRlHHT3-sOs+7}9bw_9#I!ny+}hgQ%Bx{6|51yo(t z{u-`xS$P|wRom8md%xmo+~wD(5Bm~4Yr@t-Gv0jbJ!Bb?c#tFa842ej@JB>o-STqW zy&OVpf-WX?G~T7`M8dAJ%M=^ON*512JL`M$vaiVIY6GNHOkH52qqUP_310$Q*1O=| zi;%rAtsl44v%a3=GPwM_g_dKpHa@A5x*ZZ))6psBadwQLKjnt7z^_^(nuz#Z3_-~s zX>s}N3V~l0#~fudGh@~;a&ash*2}U3ZpkHo2jHMht-;k_bn|S(qZ{&hqJB_gHo3!e zDZQCGoLpo=OD~^*fN<jTM%EA>4tioz#IC#l3G`l+nrI{ zrr7(B>P`W8Zx-|h_?H(S0}Nzy#^4UG!}D*i?cUmso4pBLCc#d*4txe-!zCvVw01L>dKHoKK*2dw!b!J*tp= zmH+F$jfmvgKjT)KbTzz?btU(+Yh{LQpKZ|sGE^&WH?k~6Ax4Ke>7yne3r~7u^ruUZ z@Jx;3Q`oBFY_>k9>Wv%ivw0y@i};PF;OL+(+0t9@1gA)fHE;V}fHXBf{VmZco4zI=a*A^1%_n!9`qIVko2AjkbObzS za@s-0Go!bl; z=K@Irpd`WeQnPEI)yaiiVKqLLox5cf%qr_D)W98Y=i|e|^9Xgp>^=YO{VvT=Zro&} zoYupcYlApBNL*)pf1YZXnz@K_mVD8L<>H&3gUBG96n3Wb^HOhWu*^QSqwjF_Hz-xb zg(PBoVjv3oI(a0HO#xtpoU>kmT@I1G6i|Mir`UO>V_9iC1QLow?zwllr&W#0+mb~a zZ8O8D;L=^4t#U1YmqVIoIqcZsW`VW-haV1|6!B$IbhTZQgIZjvaUQrmw0-<3d*C!Y zuG?7ZVafM1UzQVI4Bes_PsblgLARhv0N3V2M z2K%mj+Um)-V@M$8>*-vU@S<|C{3N(XJ00=t5|_}SUBkh1$s#9;{J?$$H>`E{avSPs zODuW{$t;Rum}nX-hE*AEcL`S~?um}^D0vG;RmPs59~y6O<_fPy~VDoSMI=K#2Get8KxMtu)-bfrk34*Bn?~CKnQ16p@^b(9Wd5?ntCK}u73rzx zD}S8Qo_XAwc*{XQ^1(>$tmpXm)H4giX9e3rSXYFuol@CzhiFMHjIJ17Uh{SN(6izZtc7`SZ zrOuXK`+J8{;ML#0E+H0GL~K>uYM$gfa{+RlDqF*~kZ?H#B9|y1Re|&JOS;kNS~r$5 z3ZkSSVVs%|NeTh$t^e4W*<;OCPs?c<36Yta^M;S*yl(JwlWnxu!hloDvqv&xxs+4H z`S(7;d+oCsuDv513>q3_sl4>&X+6zd=pHf9BTwzLn$?@z(AOisbcC!rA##IRJ}&}- zU3|{H8Incr({O)RaI_7Xr9sK1uTt(`-_G)!x|-ZUTYqmXDt6|AWg{;O1*61gIc8d& z%#M8-_$p_{M!xgU6Ja&Qp9YxQ>qkvd`xdBz#pk-%SEircI5dKs$G##Xe_GF@>F+y< zJ8jgt|eQn zuOil2@*UJ?H*vghsC;|!!3V+qr=k+g-iGZX1kzMJ56_6w9bG<|XpV081Z4CV0%`I?)LRHDB7f*SB*+<)C%sO$f=yF0k59jN;AxI3t_ z{hS)mG0(r=)r^er+KcehPxD2#ODNn*D$CW|KD=T#Y~qC;Sc+~uJEQ*FH~F?|3gt`A zXSd!INUB!X0V`25)z5k<5vCK!{#iT~*ELyeP?O`!Q7TjC6>@Yq-9CM7FULWBNXzM< z`}Z(2V$CGKtmceiaodx5u3&3;dvk$TW1-}t9nsCU6ZNRcTZpc`P58gt5Z^^Sp&`08 zaG*UzFV*DD9|@dCaJK3y1nU4?1>Zfr6D5H=?P)z$T^HT1Dd)BEA zoNi1ESp(*OJ7Y4qs0;p_Qs!)bZr*gtul`1rRT+to>-h@DpWtM=Ex@O?$3dgmQ>e97F%ELpvA)l)=+m= zT6o3pl?yXR$jK{IWs+xqm38~%;PPnkG|xnb(C%k>v>lSMrp1Mn^DT_pMt%5Qw`O$v zsTL4Ay`X`v1(7Hm@C`YD^j`3#gQ96vziq7 zDDmz?AIcNq{)QoG8B@5tV?qxD$&gKDKsmFcVdnU8 zTbXPcb#-k8BG)xBt9LPw!||04>{MMyv8v`XuwLSGA8;~O94A;{cqvg>pG z8$oXVqC~%&he9`hiXOkti8TewlwR|zDRwV8ceIgxZe1s82KCwY=-Jb@NvBhqPKT?7 zZyib{ff0%RRK3ufN$nx_Rj&|Mvg4$l8n24?xlu%a>_q)mjxnX8*mwF`$O(O&i!s+; zE6KyHt*>d5kWm+E7slIsV}l548XDN|`t84SbSJ-0O-*jSz3|j^81wsuVP#IQ&{Nw= zB$YTkquqrpU1{&$&?N1zL@N#jS)3OMh^HOWzsw$sP9VFNk7uXZ?H^SZA_#rZbg}~U zujPv7bp1ySc^`ASdX{^Mng7W`&T?Ga(%Bmv%9HgcTmvrMkFOdnMIq?mjb-&A8Y-~e-4K3q*PvA2p?_g;Ww@*E{IZ9vt;Y8?!c93ly`8@q zV*6aEULo@C)`FYbpIek_q!h9(;h=}Jf3(V>?iZg&)L7%LT8^QW+g}x2d@1_iGY0f5LeQPTl|Z4Exq*VQ;U&9l|W~%^vkmx~g@-=3?GZ zw$dF7P5HB72U|h4*OMs{6}B?jTR)?{YR)0n=`L-nQNfjIhKHX0bI^*Kx&PPG!r_7k_;f^B=#K3z%%E*l`|vWs2?7WD<#NQUC4~?(YwTQtn5s#}@9mJ0!^k@tWOl z*4;d!hC2GJ#tA0=XN1?L?rs^l*@G=TKA9|9eE(d7z7jH{sc~4Me?6F?vvXbN+w_`e z@ANc5RUsYQ7L4VLVj%`3723{ievCe(GCR_2Nu3*vk(-dbT-4uDtfe)3k*oWzpE6^^ zhBnZ@<6g|<&<-{9q8qxe`qI9RPIY0*tq&DYLmFh-tXWBdU(MRG>Tk z%L$HsnUY0Ng zqdA$lR3e<;*bv@oJjXzMsuXs0bEjF*Y`;&GE%{W2BPL>OdY}I`qejJwOs|_gI@jLUnHqVeS=b7zvyR^hiPpKQKFgqyN{HfJFK*-bZ0lh zSsfgHNA3O8{H|B;q(1l9rt(0cE z<@1zO^?rRZDW`CS`RQ}8s;F~MIi&hhQx?ft-)%<+)V6dAgHzyTLK;0B>EF?#aA^Oc zbG?utyhw#^*FJJ`Mv2*bm{n^J++I7Ges24-Rtl4ix(C;_*w~r9fz!*s=RW8r{g8Vy zyF7if^-h({dNRV%0KZ&(-BR^|@zJaNTQ!mU9}s2l3k+X)NF9t!t-zNL57*s)FFCA) zRBO&k$%_+CGGumnI(_kEV)u-YD|#fuxRrLExzZe(zI?tr+qKWCTku0yL`{-RLc;ee zLUu=$0qLxa;L7Sh#;qvDO|fH8P%yiFjh}*P5vwaJ-NDo+efVs^$|T{lEzkAZiqJMQ zGf5COw-2^6>#S1MEx#Da2p&B@W$neKy8fGezGz4LQ{aDAz_}waO+~sl43K`hhVt z%37dLD%JKSlY*4#X=*0Ru5#W@B=Wt|OL(VYJURBf1P`d&mcjoU^i{bb;SNu<4xQ#M z_CQ2kZm7Vo?!%fer`TPL3hr=cu+VeW^sFN`4^+fRdH@I(5WZimmSrPwkA5lJGlyM# z6K}wfc}h}62x|ee2BR_4%p`kYpwQM>z&E@&?sEK{#79Kq4&Jc+*O)^HTG6oU!Y=I$ z;AlwE;2&_{fT~ov2%ulStQ}%XJp(>jZGSB4OywaZ_+#@Eu;t>|ZyMNjcl6)F=%wf3 z@+n`q7MhHbA$6*+f|aEbBJ88K2@{+`WfA7(5#~V*IP>ZQSH!y~249&kiP4iH_p0{l zP%k3p9%%7-IQ?^BYz73Kq!jLUh+?xdyJeV%65Ld6k;1&QI^Y0A( zjFL`3EvAzh3jx{yqHcgqY*e9GXscTMPTY&bTl62KUn7*RezN^dI%6W@U?clqIqR1K zET>J??;&b0H6ZCElP6;M_F06)jnyR1d;*<2a6xomYFNMW1@pLWI3u5cLg*R+)Rw(H~Q}=(sD3|3cDp8=TZWsd+_U*8&VKW?|WAe2q%`EQ65G0 zXLCk|(no0FD@RcPM{N6bF9r0A3%PslKr?f<6F@5T$X1f1%?AM=V`pnAC?|%26!0b& zS|7k0wiC~Z3Mv8z+}P-zS>~4x>wMJD;ea(`w-~!~@Z>_Oe^iG6_5_eS_VCaYhy-JS zA~5N@-ik?;?k43uFDw*t)u&J187O-UhA*xPGAj#rz0u;6>~#m3h-7X2F;b_w?3r;!z+d zJmsq-z;UVJ#NRQD6*Qm0*#S3e#yn){@Py7}+kuiz%Y$&N4e~~Zy!P*hUOb^mJXnN_ zqoOhyjkWWCx=5oj^xUgr*eCo~lehm8JYvM^B6jE}Fd!{znCEV{81JPix zQ{hktPq#{f;@|Vi%ex|C=L*7xq;I#~k1L4zh7wL0NgC(6qB-eaauvim`wF`F2rO4 zBOu2SdCUxS-@n9@smM-^!<^wHakN0w$0MD~db*&kIb@a0VFP%qu)ONi`vX}%Zg=xH z`lEUpnVbT?C5WyKLrAu4lbZGfUYyid9_j~xMy6dVSmJ2TsmzY z1RyUpEC4%n3Kd6eEV#LzL?=8y<7YUP{lX357gFi?$65ltjII*O;8S1#hI}e4iQ+|9 z0IPtX$H(BsASb#48nnH;`#&7c`7foSGZT-w@WD}fn=~2xYLZ8uA?$oX5FAz{3?kAw zXON2M_4S%nfvDeKTr$$}tK)ta5k-0dc9RR-35jh_bZ%T~a0QtFb#~g(h`0uBM{@V-vZ=5IX Z7#pX+dILH?M?VY&80x}xYHm2k{0Hb-JJA3D diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_61_10.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_61_10.png index 97a6c443403b87faca299a1d78272a88508b193d..16b5dca787afa007f7e6e74f4863fb236c504308 100644 GIT binary patch literal 25483 zcmb6B1z1#FzdsIx5+Y@QG$;}h64EUKDy@Kk(jW~(cZ`CRluEa#fJk>Ugn~2@0uCwN zox{xh*P!?PJmUGC^S*e^v7!=drM`h~@9gs$pTB(FQM* zb9mr4y>Po?@SljYoVK&Notd+nk)tV=vXQgBjh(ZNrSVl)Q%5IDI|v^a_{VwG!r9s0 zNtB!0_TL6vc8=!UU*js0!9fV@?>~0J!Xmtkd0}HECR1Wz86U{YN^7_$ua0>nhtHy0 zH-{dZwTQOdGReE}jgkDO8Zl#wWXyeejVCr2U*BmTR@+m!R;-?o&Y6B8`+-3z`|B~H zPeomhu@ek#W}Q8iUA+6rd>x2I-pC-QG`{(I$@%&opZx9md<1>c(DE)L<|noYzZx6! z<}h4T8}t7ab$mrxS=rCVONJrA!NJ-}betE#PiVG3Gx)19ulZY=t7@-^86=Z$>Xc&@ zdhJ8lHFNK$-wD9duCzD$f##mg&Pp|Ic!sT!qq+JnGJ(hFjFz4Yxh=fB%eAIwaioNG z!e^gu`Fq}b`>Cc_iGtw!`sV#Eg|d!8cf+Z9jhb+*20xK6WIk>C_Wqt_eDTVO*0t*UATPf8`Ese%;7ecRk;BR`Z>rat$d3v;)%SP97Y4E~ zw?;DHhjov1r-&Wy!R;1%)6XIi`&pnH{C4BXcJ+sN&z_&fzvXk}j0L{%%Ln+cR>XRhj$T3p;+8oVwlnn)^j{AwGPV>?lw z;=20dTd}F)yZ7%$YCTF8x2OfI?0mVL%{7|87|A;ofmqaf6vp2dh@1fW2)J5 zD4(t?Nr;u5{UzuSw*AqLrM~APbE2TF!uDL3$8uhUu+tn-#kePj*WQY}*BX~zb)i`o zHHYBq;IDGvLc9s36L){btQgDVx}q(KSmA?IFS13a56%=a9=D+g(EPDk^51KffIB^s9OwEoO0Q z7d`_6W&VRJuH(x)xloI-Y5_1RKMyviZH5c6pA8IXRJ*OK?5~X-f=_rLcZaBWjd9r6 z*b=z*-NiEt8rNay?j*bVw!RF-!^Nz$;K)exsipu!E@_^x+~9?^J^l&$UHq7-5>Yj#u_q!N`T%a zi#P@DE)Vr*D%0G#a|g>}qF$oJyr+KBZ75$av~_xFt!BMt(YdeI!^x~GQSRwerW)7P zCP+p9Tj{e{kjg2%*qb^ns#3`4#VtiPgL-jt4o#fNxg@(x*^o<-Mq=Q>9SFa}4v?Den<1r(nh`h`TMvu=e z;WUe*ReWHoW;ieQd4p>X?hnPGi&RvYt1K@s-#F`Nn&`VaQW6CEgBkseFBArtSE0l7 z-KFn&9l4K7K0Ya97fcFQP7=i4=vS5cR^>DwikULe?2lf8>wl)ub9X7U+zwnJew*P} zVD75rXx{vu`#5TkdLTzD5*&1L)BV1@e7aVFzNNW^>&ozJUXzvwHLkYpu{W(3tyvh~ zP%&#WddPyQNyTSI1g?o7Nszw|q$1rzNh8QYxXj1tLoQyw%VX9_>F4Jc&u@9Dv9Xat57o|5u<^sX z;3743dy3n*yt})5=ISzLaXH$3|0qXOn3e={zVm6&h}P-!dy({?KY#88lWr6u1a5d$ z#bIheo39)NwReI@=s11qWM#ST##vr?%}*l)nE|(0sbxPNPkul7rAv3vDCF7E;*~=b zVx3CdohQxrXc4zL8e9`;uprAj?JPXRBcc7eIo;||Gd2K*Nz1?|=I57C{A$ZMTd?|i z?~l2K(Mu%4AvHI9yDq*ybLV6oZ`;PqWU=*H87nLHNGX(Xr1;kT+wk`yn6>{+7M+A= zw8M1EW*ckT1zK7q(8)^V9$Xpit*!EFTW}2j{8{$W<;&dK^wiV}78Vwnux&8uom%PK zE?l{ScN<*3$5oEZU?9J?WhS<7)8)R9AwN6z;#rY;A?vc}d3PkP55lW=CD4zZd^-nC zfip!;a^&KZdVT1?R@~9o!D?SbgjS{Rbkw=0rIsJ5*KR$b-o*(KmHs!8(yS77-cA%E;KXlzAwt zR1K}$xCd*Jklh?u1d}DaVtb^}$zE$!tv|IIg%k?G_Q#~kp;ON3bF?#6 z)JlI{Kp+O6!TS}BT`lih+xG3D#y+NfKgFm~LRVzsjK00)n;JB06)Qu&=OX@NrVqzy zzB*beeHYTf_8I(zU-kwV+RSK5gbG3I$#0@hHrDJ8iJF3|>VCg=RWNhG=GvZ8!7Y5@ z!buNgIWuuE8?n+N=bZ_*#!H~C&q$2(dF_nQCN7<6U**YUNLPQEU_FW|0E z*2$4R8=tiO9E}<%Io-&m(EC&)9qibiQ%8FIqW)BW!*BK~e|7q&*>9v^8TQ$U#S(fSOxLR#r+8P32hTjE5y*GoD}>%^Y&OK5Y0Z=1%ac+}vWMQwOiHO0?nOLGE_iw%KKY!2D+x3A!5j7hps z|GaF&spA~mm+{sT-t;0)9!f`Vo2F30vUZzap!Ik&D_n>L&ta*QIEYSvDXFPUAS_YL zh47jPZZewg;XCS@#eqihhVM_-Bc*I>MR?=YvuuY~_3=#f7P~s z{L$WH#-_U5(p2s_m?@|@&awDg76h}rk5v1op~rA!a?>9ftVepy~Y8^g_9 z3UPk0y^$OGqaDeX^4Jj5Zg~YidZY~Hs8qm_o4YpbTlZwVq2v)yLW%iQ!!?>5lc+vX z?L%d?OFK&j!Ep#yv=+=io%b5&5l={hlfSUzKzA*bJdTLbfKH<~?feHlXVl@_n_m&I z5eF$qLeyFORqumFs_x|H=G7EaiA(hPHJV!Cm!w<^UDwCE!0sdLICIXmCIxJOYxM{S z09msIEKlJDH7f&o(=3dm2botqDnh@R$lO;Bh)Ki$HkBS-%AaE}!K9mgnA5tx_iTtT zOJxL$+q~z;xkK)BMPW0OW)^(A2!)@ox#Z6L=uY07@@8djt{p{B2o2InjW`TXjYwW& zV);ZSqj2K({)C@|0A=<6UelUK323_U1oKf)N1|R$~8X$f6$={{kuN<93w=ZPl z+*fVNu7dyKN{|;#)q!_e)N;NufqhP17EXtoZwU2VcZ%Xfy$|-sM>$t85CqE~pVX`+ zj0$Dp$DE(DXa*-GiJ;|mEuH1?zpq@jY4~@TqWS1Hl49<)5uPSS)tIVLyEbZ=ik{y?mX>V4TLXM zCh&+RxUn*Au3o_pjh!jDVLst2mZ8|VY~ts>vUs6-pRffvajtif@4>v8(0hGlqwh6s zv?5j}?Fi{P)%eyGxZQhaHtyzDoc_bb3!Zy~@;T@u>#^L!zP5ytSBdA-3S1mlWKqXF z?7=6qL2_J0R`!UOn{iaeTT*FbcI#kPZ(}Nm;SvpvBEWupoa=oVgx|h>b6)*1$k6q* zJju#y;Yj8gsth{>@!tosUZf>zz8A1DzGLXQ|5a+@sVmyj zq$NnY6WKt?sJl}XB`tF&Uf?Ey4(eFE>h{X5dFZ0tk#w1@qL88Y4?O(l=2czJqt9*4 zq;qLF&9i~Z6F)rm^PWN~yE~4;T|G&Oj7I#J7?-_*bJHZ5`et8i=+jbn5=xvIHVmyA z-Po-l8TaK#pvmV8Jcl6SlpXae5#K8et;ia2FZR0jl48HF@_CXJsxytD7}o(UB(`Sm zYNqF#=;bCES@Dc^=H^TsNo@b(=h^CwuGr{k*#{5SP?=)3cX?=Fg2ih(5LYKCT;oYO zkCli+Q%fo7RerMe=sm|CJ;=)|0=@;7CKL?b@%!eLFsbV|@=!kbEBr5=GnB2MZ}UP! zz$#%BsmMK2l`m-sv2+)JIkMFKKyw^ex-0cE4uODd*`H z7)6X8MB6cH)c17|4mRO`NCAO>Ug_B0)O~9#mSzcUbeXGGX zT=-4;&~e;*r0R@&wKcn{n=-?Bd2vgnx%$n(F#KMr{ozALfB%QgYWH0l?ZJMyzwqgWi$00tJB=E4__U&d5~TkXNxZ3^C^$Yo{l1LY^s%0N#yTD zAW(;J^Z~)bhWk|M5mdMUHJd|gbJWsE)2uF>gtJW{=E1~}EdBV^K023ve?5PFu7q5A z0M_3%Qc}1Vdi7|xEiDQufZyRJSTM94C(j3fpebN>pIcj}cRkqvvH-*M-e#nD#B&_r zqnlPn-@R*@f>n+E!S}V$jP9(aDBTa{B{lk}J^>uHupD<5zIh!}Xf;1v9h&%DapB+d)E0-wrOYFOw1gMpc9XY}^&H6Ze4YH! z`3jd=CYVd1<4c&Bc9r~9QSGV_%sXZ22^4D0P=0M{okSBdvrh6o)+Yw@k%G}>@R1MR zqI9Ylz-GU7pdkxDQk$h{Xs_F!sOW!zP+l>u2MPu8{{_t0BDW~h7RfK40Io|I@q;&` zga6=9^v0GVw;)z$3_u$YOinOdrg)UU1Nh zp}(+^`jO6y`hE@;4F2N&tkyb^UJSK z{=$rwB(du|FMsJbzhU5iAv_PsMs_no66ecIx5eGhjNuSm#m7a^l);yYoiCrs48GX| zb)FSDGU@rZTRy}WK!4OBdiWiuzR2VJL2_$H`fq&sk7NW`BRCs{s8OG+KhGXDTym;k zTMLmAh+j}O_{%RK$=21lb?OvT#XaeAhljOWBIpzs`!cXCdf#GWzL^7h#Ll63L)CR_*e*$QXC^N7;22mbv1gJQ56qk9RcL zhN~JIGvbrbR!*|=@_w6lT_3HA0v&u(0a1qeA`bu?mseH>Jo+ZT?GLhN815Ovp>?JZ zGA8<@%nJ85_`^t{(b??m?6}>;U(#At3t-8W7}4S&LBKk;r-z)LUZp2hA_Vl&`*5pv z?}zmZT-wwU~MlcX5ioFjV8-)8efS8AU_13L7U`VlIxeZjng&;V8zB+doUJFzZgegw< zNb@7i)V)BOdRs`E$#B#I(VKFUBx)+z@14aV?6|uAwUp34yjs5+Qf1mx=CZ75Y;1gA zLE)X_Yw{Z7+C_C$`KkvmY zZTG_$es}($ga5;%r!SZ@JXH9zRmFd=O}QOAU$g}ABAtsZJ*)y}Ls+Lm##QuvzIE*i zY|k^8Akk%B;yhCl_y#^E%@BVMjz$jd${XZYhFzbm=2fKRKAyi?T3Mu5P~F$DkiG~< z&&a#KbK@zfk8gK5bcUbxK8#7ZcHRHL%ER$Tam1LPyh`9(bi|E^%A?L}lOzuaodQSp z(j;3O&2wlgM|*2Sg0dLgUQeLtH!H!h_13|$9T1;5#KUM4+lND>yeRLrH2tJwhGa8yi1JT2a5h?{Jbp(sl!eLO8UP&+-2GGP}yc?b1yeF%woX z5oabS!;3w-LcKy{PinwfeB0b`MU`d4*?Sal6b-DX#4N?XYCXfSLS~=BKfAiS+lN?! z!XvZRa$c3h7%xi=5g6vOyBsr~F6{idMZM0?V>g2gD#YE2>G}3yoNH`>t9^6Xp>>kt zo<(r)J@cr^SxQ!JXE#0jiQ`DRTGX4+jXrFPcn00vy__8P#qF@9NyF(q7lB$@$wZha zH5=Ood|gmdci{u_a^nuorO#9E7x#P6!;OV;YTK*j_$CP0rOVEZd+1FqF}d?H>(8ad z2bklFqEeV#a^mn$4kmZd>@Jdr->!byS4vi;bVJ3?rUy z(MM9ze7?S_JMb|Uc4;(p6@djsnVNj}W*rkXa5wP%*|=7bcFc&@_{rKl;k;qUK{}YD z?~dhxAf_lQ=rO>L-PX(9`!VEIwMT98_@}(PU2F==>%-+F-6jW`it(T6^XgnzloZpy zyU-ui(}wNC;*8sAvJXAsXFu0ceMaVuQrMvR%-^nc(YFvOn(o!G(S}s;F4i5BnW21fBBA-7SNAf*gCP)do^)jHym6pT;E)I2}FJ|N@TbDYg=Ot2A z6&ttr?QmJ(llFMWhi};c4~KT)MV7faO}@H0K}wKXa^w5+=BW(ZZ9*yW>&sr0C`a}$ zFR2MshVJQlSMfrbh0Wj-N%F8CsXg`8nw;yVO&_C6j~Hy%e@>#|=y~YO2d8@!5yxeV z%KJzoJDhJ*z7&JE4x3UYD(eJc$QcUX!$gc)UIG+rurY3+VDDddnVEoH1!d7<>fsgW z?~)WoOizXbU|R2G6u65y3S#>1hmB?uG3aUuuNyB@YN^8#{|@bi&mX=WI=!noXyK;IJ=~t} zFEY6oNJ6KOBIMWGuApl%F4h*2@IhmM&lF^jmBR zvOH#?>6digLcJjKOXe{zib7D&B2`>9E1)xa^y5j3GtXlov?SW)ebPB*9?aW3BMlojy!$ida3ZCF=tP~ z_yG-z2NNCXkm~_+UUs_Ej9ac_rV}jM|wL+F{ftIKldod_ePHm{-fSu_OgtUbElQWztJo`0LG_LFaYGIWJ(Mm z{|93KscH75wQr`JpzePGa&_;=({d#(=f9HZZ-h=5ex&jzjK`qJQwIX5_#dtEH!A-7 zKtR9whdi8sy?+&4e%XZ@@rlOsMYy=t1dNQRtRAt~x;J~=PUK>9Ht=n8za7)x$Nu|F zFuU8I3?bU^@D#CN);=Y&0t9fX*IVq3TM55U?H^nA7yMvD7MY(OnZLOUX43p~j{o5c z|L4evobFjWAPj_C^nad_xCKdU!9U0PrPdFzD8)AbhqVvF%EHo@ry~T=r2EY4djK$E zU1eqtKp_#A$;suMo%w6r*6{$cyydxLktkq&7f|Mra$7j_Q1&m}z2#}^izw=Ai(;kX zx46J(*7>#2`}kmUAWtU_IC8!DdSU?NVZ}>$2?h{Sm;t;?M9m)zY~C}4R)g#qS#oQ( zBjJ|m`Lp;WpJyEt@^yaoPXHJiBKDIFxKbe@A(B!w&!4zQ4116b-@SCUBuJX{%HBF>_>;1EpncwPGo*%JC=A^ic83}YDXuboma^^1Um2k1!!BRD4?|m= zc@Y;_N$OZXrrD-~WMjL)cd`DE{^gZGJhwZMT!uX|2D8Q6C0IoJK*NB4B+b zid7Y`iAZ{hg^`jh)t*6Mr9C5R_QwJ~&5NL*v%pUL%|pHx(q0u8PriOH0x%Rn&p+dK zbba)X3Av1J=y-3qM)pycprxMBP9-4Shfi`Xh+(QL-F};xDlScg5JB3QDSWQozrbE| z8GqOdYuuf6T0pHHfgKCQPE+!KWw>y7zVndD&Y7n~2hma2kA{Lx<;Uf_#O<4AIxIE2 zswroKBAWV0!?-DATAPlw-DiBEk6uZP_^kIJKD$R`@HI~JmZRIb)8$O0$X_Wx&PLl@ z693k-U?V$@Z7p;MJyq!KdSs&Mjol#`L{qTsMG)Jz<|WN#@PCjg&VnJ|*LHrcHFS0? zH2cPpsx?v_*4>!`*57%&_G|#ri8DF`1~78)1t+`|7xK}vHXDl`ayBi$?O;w`{<#6_ z85QAMTdf6w;R8eL;X^_)HliM)XWt`fCT8BeLF%`db|@eXX)W-Hg0RQd94v;>E&u#vA0d^dsb8mXGpWBYVXO-nq71$VWQBjK7|^(wK^KjynR)8-m9^F z)b#39o>uq|wET;xtUmSp+N&J9c0bN&Y4VF|VTCVcvF>Bl>^JS;Zs?jr%S6q!JvhIT z*6O2?F9HMAz|@bX@^<$XqbVhHfUwqoh-64Rue};CwhT`BF=Tf50~)x8%`Q+NYK`N9mKSGYQT~v ztLFoEs1!-X1d&fo$b?6M_3iOz!SopmP&8fTbl{r-DmbT3Yt*-OSyG<&ZIj`}fab zmI6R#jYf)1foYC9TISwc8&k}CQl>OC1yJkJ?s9^lEj7j+7_aqEisyMw&Jp%}snfXP z_O|;i17BZi?QHQ{A-qqz1cEfqt5^t<#>VR{Le;zllqS2^u5IkRZ#nSRe;so)e-k_F zEWL%p)U+guh=lezaT=vmOnY-Yn@GQA4wxNIW;X+~MCbCVgkB+!(pJt5erjm$kshkI z?^@dQ`aAovB&xw+0EJ{V;;jzov6uLN8BZNjwpe??=!vzbr$=6rcu$-3$LZYL|o|IZhD=86~w1i>5 zeOzLuQJ|j!LNME-PCXYdUzP!xAOX@>X18YKtL>!(dlP;+UyT1Vb6=^Pv0Bvhha+n+Pnab2z$ z2{S6DNaLfgIVLm_W-Iu*=7mP2Jh(;lN!O@${(Vy8hIsM1%=e|p8@A3|)@UKv(s+&G z2IcgA`6|z){KuhF6SBe{)YWws&iA*eIl!#&I!3o`f}5chu~tJN&c(#^0;HJ?3P;UK zcnuq{@Q5g1&AJ>hH(Gtqr2-JyxRqpjAaQ9hhY!S`QlY3r!nvojc)NB1t5P(VDX_0_ z38-ankJ#`SGtvs%jHWUW3gibmUTo>@rLm*26Re;y%* z7k;wyyeNi;c=Yzgbe>wnv^j3)0~9Zam>f9)A~gS=s=x0$uyfg3-6m z^1)Q+dPsTeV{{*W6TZCCY7idMp_oZ?@t~t_Y9y{8^|z+x=Sch#2Mss2eJwr9O)Fe^RFn&}x#r=Cmy31K$SxxUckhzJ zd;f3w%>1^Kk+C}0!4zi|Yx*kRyGKw6pNxIdo26rDy(Oo)M9NJZWjtBtV9(O#9;xar zv924)oRcej<$j~t_FRkn1IZ2ADwbYlAc1ajcvb27>P_rA6dc*=NtJC*R6DBCS0ASV z<>&tRtDsE%Q1IsJ%xY17!-GF$TXw0Gv3c%)K-XWu@)Rm`j=?*P;{F0(Dp7^rzY+rf zYRf<_It3;%3ex`|uKodwofQ6api=^FoaM%!5c`sb7K7 z>G=nm{sQU!k6)Z>)1LYN=g0ok?6c(8P95;?FID$DWfaUgr$792ICxF}8X;a_E&pd+ zml?m?20E-uE3glIJl~9eyXYMiLy6~EB^1_*>$#hF1+a5knwix&UZTt3Rn^{IaMvz3N=Y>in!vjccJ=J)2d_z+$TY~N91pG&~g*7h{Y%>vAfM5*$R=Y!tRuJ zRe56>#Ed?FZDZp(mO~G|q652FGPyz1Tm8if=&^v^kijeKpp`*V`z4|sua*Nr(hZb& z{qEoi-kr;E>}dnBfrjh+CV8HXwv1ATUc&2LtXB$SU^kh=BB{Bqrqv zQhp#|h%*Ti9~l7e%F4+d4$mZOp*4q5d@B-$qxzL()Pp?-@yq$oZ^FY#A3iIi3AfvF zv8FLsPf5ZVtrw6LXcuEBd{uUYjglZhkWk~tJ3;EN{oBVv9uk%=9u#y1$9spZv{uL* zo~&N%MEjMLA^)HL;aCrReE1uX7j7ihTUNCqEz1sC>s5M*WnD%zuJMQhdD0- z+IXKsssxJFq#EkaUeKrZ+ghMa>Lfe4qusm)gmXR(PPfcVtVPP?StIoV1e|Fffcp(a zIP<=39%a1k$?fX<$_OMIkB^oMjzQ4%x&h?Kwzs10a46r0}sTcfac#0)JA( zdEwP!wZN~m;<3k-Dd0xNi+gP0c6jY9ycYZ%_Xy;FaScK4o+_3C2%lGOKIyG=Fq!K~ z0|u&(cA+8m|Dpms09ZftU%BPq@TQE64EK>32dE#QnG?$b5izi!^7XgEfYoV zVFKzR9$QZ{Bg^1?3@bImTTTNfQAj8KqzO%OYHF(=3GGT68f1WyKK9IQoiZppMy}Ai@74AOKu+={t8$Y7(#r2naxv`22eyV-9F*Lm_-M zSHDhlb)BuXr9}-yi_cSVDSs0O<;kl)f{XP5lNiM^C7a+xj>Wt?(-_Vh?KF9e_(scpI3el((LqyZNN_s<5!| z>ZX|uur*a-HXsO#gHab5eNDM5ItrF^1U-`{zVEg^ll&htr8}*urv%yLOQ&EJZ^qxi z@h=ec7aqxUzLNg0=J-3+`H#@p^=th9pV&;jTFL+4&;9~ge;t1g$eyQQDJD8?FsPw6Jh-ozWsl-!GtaTM3w*R3ICeFMe)BDhv=KV z#9#gY{CGc;TUxfi%f)5E=F0u6%;Z)Y0aIt+YJb~7m}oWSep#$gP4#l-wwzR)3(-cKRuA8B+y_HfXKktZ)`$T8B zL#ACuqVR&cg2MoNT3-N#KJ<(iZ9PDqH#Hi#dgF0Z8u@()ShwpustuMz9K&aOJbp4* z3{uUhJa3ONT&b}?A|hH%I-FM5>&<4By(f)ohwcNU7sXRNOj`r{ z3W=vM)PYH%>vbi%)wI1Qc~^(hVt%NrO~y%uajV+a+$m_ZU-;-S(KonpoAA98Ztt{% z`%fcS?A0oz2-1u7JAH~2oQR*H+?ebKHl>K;Qx%~`^Lszjy=9M9i>K_DO2hljYbic| z0frhMMN?~kU)&c4|BF$R$f81vY=cCb?3M59j?F19>FAPI(Lns;H{R=BJ?=ZU`Bu(d z38ybMfXY(E^N(^XcU=TqYVH6%lokS`I&$R;#53o7M(-asWVr78(LunO0S7;_LEw>* zVPwO=o9kOU+FFJ)?tbJ~uC##l>*M1RmWnY~8q5pqkn#v9WM=be{b*`iPMHN2e>EXH zazBrG5Z5QDQBmUBJLm2)URKjA-)BKj8awK)2~*w)ZJs7=Oe}EB%+u^I$Dp|3J)kIU zSNrKgHLVIX=*MZ!KFY^Q>WVk0Tz~Z~-LY!2qagfRqiQDA_P4;=skCy%wdiTOToaRqC#QvyWsmT!oMymy03GhX}jH9Yq7meHzF9t7~Z6ilWH0 zo@Lyu=KJew)#OczG%3XkHx4FLQI<6Z?#P2E)dN!!%{tHBcu^O2jF1KP{z|9A4`D|Y zO?zoZ>)6wKO&Pr(Q2~*NeWn$$c!%QYE+I6rR?JCGmCV$Y%P}&eVnC@ydU3k}7nw~eetR5D%6dG2Yv!EtehCPRc zk8tOX8+IQ8XcHuVQF`c*Feb1FH;MAaAP}I;6$n9KS zjD)*29F$**4ysQ#mvdcSM+ryYfG9aa`f(xk^gqgWtO??j;q{XcuiicGue;_Md)A+* zWhQ&-pNgAxY4NxQI~X({Z4CQwXxmvFQXa40bI&uFTq;UDVL^95feMJ#nH>8h1SWYN zt=}R>Cq+wf^}mX&hhlV(h_C$*l?)f`e&=6)E&1)LPVbN9PTmK9`E$if4m{^`O1Qwp z<$t&RwGxUj|KHTT@2_%%znONi2f5Ly2#pBte-8Z%Oc?;AL_;dRfo1!nG~H>wn^xTY z!ZT3j0`8W(09t?wnR5UrV1d%PUKkV#ysO}d2s2P+gymkRe>aj|xx}o?V!T!aUT#}6 zQ+*Phx~MK#fcDKD0L_6Rg$bE~D@ep3N&5`c>!?>i^fYs|r9qh%D9OS$+*=tT z1{RM+?^O^;I@bUKTElf-LV%)~w@0%9>~JBj!?gN;#C|*Cpt=o%>VaQ&<^cfuBn*;7 z4WR25po|g=K>er9fy96h6l0QS6%gWvyo&Mm2`IV?;)BboKmXx_B>#qup6c~c>pq_7J?An_VyN^ znwr8APEmPD&e0Nbne`SY{y9?8`LD09gUBmLQaUs`@||c)_wzyFmpQ1EJSiaZ{5FA! z*8u?BS(y6^{zXMcN0(l!%L4UI?|O_X%q2gXSHL&T9O()}B~Y{{7wmJi6nfan!s4T) zIXfQ!et>4_6&~*__KPD{N&v#!>=s+UKt_ghTUHha)D6AYt#Yi?`V zNc+J@TG+2%y}~pBkXSkC92Diqmv~Y@bf^XhO4ZV*9L${uioPs6u3x_%5+06gc(^r- zu^QjLy$U!Jh~=F%1OWQQix&Xh;;vQA(*uhz5WYHB07DrA>0;p1qYme>Un-d{eQoao zC^G8ASc{r@)EFooHnRlcd|Ce&IE)8XZdkp!k3kS^__ZLmBnwO6oq+QR`4e+pHio>U zo2Ru&+mwkdcy)c!;1Fhi{4LT+%^S{A{5m~j-V>N*{P)e$_m>$SS5dG)_HQ}TDb=!V z>`xQ96IkGXigN0Ls&92(?cexay!0DC{Uxs{44roBoj~~Xl!^w*K>ru`b_(h-xt)?v zvp_uH6yW^-0Zc~!C8&x-GIARHt6S!7=fXh*%|BY><3sX5(r3nUKG4xA_k|74&niK0 zjHsad4My%8Z1I9uARiOn&uX)96m%@G6n=S>8lODt>mUky1zVQkuNqn zX?D|YJigSGV=X2KSN|HwHq^^0>SgrpoNgYs^BVa$CKCbu z=7tsq6Z8}{msotTf+B_wM7xvH{bQ1e^p2YBYK*=g)P8GhUs~>)dQ90%2ULu!U8PEz zOUOl`1%b~Cw<);sOvVN2`EnV&>#$-)z8K>;)%PL@ z{M&MH*PAhy#$$kL})FE?8D*$$7G)7AcokN>H+%4clOl;T=!zF#YxrUHa&{mXWJeqVY9Uz{L1(fvR{J;QdT zX&N7l>F4i1xl0jFs{4$_iZUfET0^^NgH4pjNd6 z3|2AMyLxyZd+7a)Y+eGU|##z|8-CZ`w&{@8Fs1PWg9B6!F8Wi4jgYnSv1BnBn zZ}D-jwVqa>PC#_HhK2}`CS*!pwabxKy@84i5_u(k+bI}n9fPI(~6K#w=?{ji?`863=`9=Z|*w(niO zHMmS*a^Jemd78KgX>Rh9g)&6_{SgI91*ucASGi5RWZC&xq#}Rg);7tLtC(4 zI98d?b^!LY`dbywfp6Bsl99OX(Us$#fYu*+-a2XA%(wu4QK}^i3fiRzu#2=7m|sL4 z&Zhz0&#_-wsK^#BsFEr!52VH7-DJ%axpOVg9Y(!Ck)DN^4AfMOh zVSae+24|Dgnl0(&z}b|uc4oWu!qJ-okB>xZrAWmgCgUv|*dF^dx|&NV420Xp;n7^R zrM!)0Y98-RHrH&lH*qT^?DbrxSE@r(=2B@)vru4V@vhyxma^n$Mm%|#6{g~@?~r{o zkz(u2Sa1m4jx?(^^)5{HvaO|`mf-Qe?Kyv)`)m82)cmB8#TfJS;}`w&&8f(iS@|u| z65EtSD<*gKd(9P64WZ4p8U;VQcsNBpFX%eCbeDEbm|qmM@qArU;n)zDg9l2*MdW|8 zl7Yr{Ie_a3g2m~+-~^&HK*2aDy#B=`*mTBImH&fbPW;GDy&1+*M~DB*qf~!( z+NS&4*&HGJkCJtY<^WQ23UVZY@|V;8<7pq1Hw){(T)qYj^(3ZEP#?Rgk#6z=ETZq@CR7g2J}|z$YH$>xy9I*Q|+R z9<>6hX}}{COaS!8JP-oVq|2cCMM2>_H|+S96zbr5z_LJ*)w;`F4zi_FlefC$q@l(vedr~pM< z*mZ?+wmqhK*3n^UfUT*i3EZStuU&hdlV5!vQ!WacD8WDcdJqNoE~x4Pe|Sl7nFTzE zf{3!MAH-uGmsycx0WE$YUk_u9T&ALux3S^G0yH@bGFFX)iwmAeA`j*P7WiV|6n=Jg zzi@I(e9f3? zm76#8YlVTIgZmbo4-wU^7eK=Y{Yl<)|4nD>oq#cfaam104M0uj<3htm+zz1HxC84A zP~1zGnijsj_jv+45qtmhNC(x~Q}u*@3m)C?aXPgASfVK<4Am+t~>S`aJ`Uw~8QqVP2$aYUy_$QA%RHB=0&d(&r|k#vRPL zJw&qDv2H_b+T3sFhM^`iHZ|N@n#*;=)8O$lyAFlhTGMsfh$obz_~!!}3ZMMs#v7H4vtCQplse{GOYgpuO$NzX z$D4%e&~_rOo{OZh5p;?n)=sQo3G|Aek>em_y}dz-I*$W7b*b zm~p=KY12oSf<9u*%w(sfKpQy^!AjJE1=3fHku-#qn0w5I3alXK(`@p%Sv%36yWeHp zU4Y8Tamyg8*!22_*YAb7ZA=#k*6M$B$%~WjsbM3#G>M0n+Lv?`T0JVm<7P^cpIaU0 z8!lVzA*6Rxt?_iG?jxz_=qftP8b6Hm`MFP5v$7jVMv}RX=uumpYad>5%_L549&*s> z`@Z|NgA{9Hy(mh_-HaU{E$le5y!EI=tRO>OL$6k!Tb_+;Jb9ZLwj$v<(nE1+WhN$n z-=XVKi?5`|H?3Us_;3%+ge$S$6B@)JjJ4;M;~e!K1G4qyEvC4>3sZ8*2l)E*xR>>V z$erG*;kr9n%|(vYQB?0dk1;UQtv6=?4=-Tu?}s{C&$H+3CQ7W0jZ8YqtPlk!8F_Ei z3bj@iAUZY-3AF_;v(%F`kNdrt_GRz>+=vvE^yZG&y34}nYITWJ@=g1&R>^)_U%~ znW!jP?x;=4Jw_KYX4bLi={2Dr+0Vu{D&%z~^5$Xu5cRlXmGPGw>L1QE(+JFkR5J6Y zi6uRP9yjI?Q=5AEXd(`U;>&uwgQD~}6Bd8Aztqp{lY`)j8=gmJRr*=yz zEvbeiFD5^`LES`@_Xc~QIb^Y?Ha?Q-$N^1AuT}I!Bo$gg!%G@DpOAzou~*`hxdMBY z=i=tMd#yWF#-}@0q~K5)r)f#JSb~O-zywEv1iyIXMZH~ zroD{vN8Pqcq&>}nv+^9=SDzEuFC~Fgap;Zf!YjR1LZypm)Xn_hXd}7Ur)Byjtz%DU zQay-PTLB4xb?4wq-J)fo_@1^z138Kec?{n^WMh-7_Jm zrXG%vN~MuSo1g!-zIZ2${!&wj36&TPp*e1mz2<{J(+P-UUu6~HvyEbXt09N&T~B=Q zB$Ppp1P*qZ9>t#lQyK+mj|Aw((Zp~! zgH)<1i;iJ-EH*FXMk4RrijS9s=Y&3Yb8+V2r=pFl6f9a%k2eK-NV(9ZA^Vk1Du@?a zk0ebrpxNxbvBeEE6SHZ-S6X{LVr{`8xlLo>8f~`P0G(wmM;)$Mp|VzrtT%Jv4vILk zOEQzQa%8uY*yUo4I6m8upI5{7n2X5#L5p%t4ZH>eMUZAO4SzY+-dI9!TBQ$ThPrd)wptYH_ z1E|Nx(I_Tb;fOIK)}Np3FGZ%chivi1EID50GZAP-OAt43dx5;M?89V|X_F$w9Qc9# z@bylQ)raE?gNe6{sP?zBete(KE(dSEiVgQ%#ZW48Q)+ru` zs0*(Mw#SgnPN-MeNl{oIX84m|zT6DVZp@S2K&*gMr#uYoc}1SPR)M4pYmqDmEBD2t zBk#lX1Zh=L${j?bN{u$Q7gK_^t48h3_1w|K@e+XJ82O|IAV{%&sNOoo*((cZp9R_F#UodLcR&&bclqIv~=N`6DxGJ zh|fyll1UbeLx(M7+=JubO^=88_}#_eom9)kTlx{K!vr_6cWE;B3n@B@-gz^Vnsy-8 zY2Z1_^;h?X47d&}R^`+h`>ZL)+>Av+=FXe^PvV~NUcl8|*Q$(q5~_n{auGtYPKyPo^LJLmP>&)?VU zH`jIjuIu_OpYP}M9-T_+7hH0G6|&uycQVHD5aPC8xsnPyEChAOQYq! z^2(EY8E0_on$ssi^@I;u3qD;aB=O}o0ZdrJ9LxxlC%*+k&IEY%KB>fb?_GATWbei+3yGY zO5LanBXSO_ma`z+FASDugW3%Y8iMLUCG`Qn=~#8N*#NAsqLC~m$Vcr9$7m`KVtekD ztdyl<0#XX!9`5W|*0%0{!hK@W>5tcZ4mSgQ7Eg3N9}=CBxq>+yNFpnm@!R^A46Q{5 zATcHKBjuV*UPFqpagmMD{J&^!aSPCsPQ-@EnyZ9JPEFKXHYEy+%&&b3LLpjxkNhF> z<`kc@#JXR&-FjHrctG+HcYb_}bNgWOT^dUT?d_t;dr3Vu3Uf^pUNwt}31b?zPll zx@t2{r|Qg-XNst#SV$ojHA-0UAHC%2dLdl-V_E($&d&Ut_u<3N2q6bqPd8Gf%*7wZ zPP}8vAxvW3mz&#nh{jrD}|r*CHG&N+1G#(_HOABR8II1 zz8`B zDpRx7-rQ=kV&d=_;k^H9w6?#FH(SkatH%1tQIQ5B{p$2Fy)>T6(xpuFMy!7BU2$V1 zk?ZZJ8+6i@G5(__rcp#Z-eslwK*c#?lNv%`DVP(vr8V>|xvD2y)`v@Is$4f*i*~%T zWN^OKtR^TLe`0O**X)3f^ac!V?5s5siBP3r9KT^O;}oq+SjlEGT{&b_JpAQcAxm@M zflQ;!lfJ9ZU#9w(xF63ZNa#tWv)~sw=P@a^8?vl_$;`-YR{_|Eb-^=Bf=_z;wr}NoRv4}(cE+iuSdfBcxn_q(B@}j; ziChaQo>1iwPJTjyv%tG`wZ+^0wvw}U0%W2(*{8S_*3THVaxSY+Jw?(-Mg>qT5$!!{1~(?d)%^F3xtz@PW-mC(z4NV5 zd;tt-tBs-iu?9s!6`o&O;Hq?a|Dp58?NHv5{eK|Mlr%n87+8c7TxUyO3{?~;7jirUL&+T*Dl+m3F)X39A&cGiRA#>*u)OM z9GAOFOb&=L;o$Dt8gs5&x|pnC;}wNyuEaXVR5Y@>l&Cy?sAgzQrLd|*HQ#;5a$B^I zy^vvedQb#)Yi4t4ya$CHCLnz2teu>ZRMm7Fxmj@aH7xk=6C4+tEa%k^+r|7$yG`2V z)cm{>4~1&t7S%TQRi_jU!Vee@AK_`=*h>emLP+yAB+L-QUe<3?dfmsn?9P}Ld?oT> z4+AYu`slly(|dW@kTf=Ms47cL`PkbuwEUKI_2DQfl1nR({i(wJsnnC&1VUu7(Neu| z@2kT}s`2Dv9t4r;%(u4%$jGTvKD=Q(ij(=K=7)V2s8R}>b-P3e3KF^IhT^2YsSY_U z9$Bt%JJy%p)%zWPF`{^p4cGz{uon-{ugNqL`XwA|@HqD=^&hm7DvNqlbk5x~wanU-x#2L(h!& zppX|i_K&32WzIQ@(xK5F&3md7AjgL!(NXF6n za1P#H8yiWekaz#q7hiorfiF*;MU4Fh0PpmXx18JV6mx-W?Rio4!7%qq~rBv`@%m zvfj6Kpxk_$OE0x_b$W!gmOY!M)TZKRSG?)(bKH=CPo6bhk;8Un_Bf#{>DnGkgj0l~ z&FScis&Vh*V4t*<-^*kcC5Y5&jpz~Ea;ix^$hF%_W6CGjP-%N*TYH2pv3TWE^NORP$EPl4EqLzfyX$C*WUh3bqg1TDq|_z*3jAJ&MFU zOVR`4v9{R(@gkJUfcQa5S8cQNpVJl-cV{goFy*(;KL%N9{F1jy`0kLQy9%nHE64b1 z{MEMvTW&fpU z-uyS|7ATSbFxLEMkVo5pzW-+p=x*Z9pH4E-^H(wYFX#Q6I{d$7jKIZe!2AM~HBsIl z*s34W^LO{a50U(P#s0s*#{XMmX#wzDFk<5NT^YMU>f}VbZ!N;shmmT4kT*+(6b1n% z6-Evb#5_Lv->*EwkvzfS{mgrH^40CmhkMj1zjn0k4uJ_v9unXhh`J?VpLsb(1JHMG z1Alz5)Oim&ZfiFDc+Ci~c(WnL2u>c=jPWK9Cfj;l@18{izDT`n73_74LIYOz~aOx>;y;6*%MDKmUfO zK#qU{ROwq4XsKh^aHwT5B+j0^8_aMJ(Bkr!6J*kWh5i5)X6{{WM%!A|0T*|0x8bt7 z`q~na06BESDla)$>}5e;Fe2$6K77zXTy;AnehWJ@SBRgCQd#^JWLnX4VI8mR^==lJ z@K67~(FWyDK4r8dyz21F#Gv3{=n4%8T>x=u3 zGrYY|l+Mn^W(Bf0#9ab_*sc4Ce^ftu#8SH2V8aX3FjPYRe z^m8?#&cNtU3UtUWxWmvRDGV9<5vRZk+z4xhVeDn8+KMY?h3gM(6>+jmvN0MPVAPob zlsfZHj%Q%Ae}IZ2!Zn41cgmfzYzez7p6N+SJwypy zA&&&?DF#x#2BO6oVj>Loa3o^b0&r!FY6Z}(Sr{h<*KZ==tXp1MH+G>K8ygRP7%hJ2 z05ud<8iiD~{Ei*8eAWsj%xl2zV>TuZGJ!$8)o=ZCBhapSM{aI3LJGMbwtisHvV`98 zCD$K>>WgSc+X@bd1;vb)e$cG2RHLo_s9qKaT8>l}d}18YX6EQ7`kJ~kI7 z;|XL;OpGG{`hg)a=5mSwOJ}pWQ1YuoUy5xfHP}#JWi(z$Th%t z$Ti2SgC$Vo8BVLerBlbtyB3ejN;DlodYjCZKLGN*LfZWJAZDJ?ka}BdSG&Ml9%8h; z3^ue{2vh)o6fO% literal 26084 zcma%j1z1#FyY>JgjVL8;0s_*4w4#8tqSDi# zolz<~MP8JC?t5L9v3hjDH95+@sMu&LIex?Iu+o{4BDQl;{66@SGdOU>Lca*1Ay>Tu zz8D|-UI#z)x&Qlri;t@?u3R5|e*Wsw;kY%%NUb~n;HRfouU$LiBN0krgz~x^A#!-p zALrcCZYPHr)PPYw;eI&vEz{{bPv3D*$$Bru(NdzdYQ2|8dU|?hZQk?e^~EcKs#hfL z?Jaf3^O{{aSR34Pe7w83J= zc)sDY9NXR~?zLO^az4dlS6a|%;{iC%>bDF6sR(-Pu(^$31~1+9pMYt zE9ArmKQ^(lo~(*4vmE~h4n9_9DQj%ZcvUhi?OrhP=hoH>q@>MdG@a{l$6lJ9k;0K~zt4x3iho1OW# zy>_Nln`R>Z0@JTohH^Ee9z4Ls!^3k;DYjo$aNA#LEHdh>Kq2Arf{x}XZHC6iO6lU* z6zrN`7TTj+!C~6Je9>)>ynedhub87*85u}GS>%mE+Lv`&l^470=%d>VuBU+8J~Iu! z)tO7Itf8WwCw*Cp49a<$?b-Qt9nbP>C5?@j1Y(7f70SnFVAI~qpm8S0yNl>!USnb^ z1`SirRhIx)hsU@JqPx4$ZfIy&U_Qtab5o<$dD^q>oY?6d_~E8nks%%g{G5*vL#C+} z8`E#%oIkHafyk~rWv%o>`E0A>$M-w)d=oi zU3e+9)nHtFo`$Bg#&yqhtmFoGW0QHQ-Ey)srp;#QaFKoszSqfWmOXp@t4~j}m2%Z@ zLDq)z6)Wu)i$d#jHOiH$9LykfCu^)J-bhzWOiWx{+?iU~6xO9HSNy;TjLS-K3jx!^ z7KMP3l91Rm9)vt63&3Z4eI@6~N2SsF2payE$wJO{)1ENf&YR`B<>LawWF5s!tr~1G z_M&IMu)k%aAb_yPvR20lHJgh#e?c_jFuY1hQ3$2D3AuV+R$6)%TzIBSmoC9?^^TN2 z8+MHy&eIYaF6!#)(k$^wF7i2xq7QAbgcAQvB$9;#DY&FZ&=J>h2-+( z%LAXDhE_SQ7gYMWTKDOvh%_G$jp0Z z)}P6yQ3mizEQI|UgOHsop9@&{c;a_VuE)WTl-6fQx`g{ zDkUi}#mC3*L#M^ygxx*KLOf0zs(JNDw<}k#%1;_!xw8EBjO=H{{nh^9LW8zlu*_T_ zA!!KbpZZeg376Aq12?1-%&5CyjNN$piEJ=irPuyA@SNz8Pc^hIQI?~YcX$`PbN6UI zVh@a69-|I2ZTHPvtuN_hb#*U2&Xl{%%)C<5spGLDq3yaz-qh4&I#IzjJ3G5~c720xk&=1_lPNX`j!nXG1d%f~(`@99N}Yz6S&MHZ3t&dWuQQjJp$d=0Z8pYr^C5 z*ymr4SGb9;L|XP|%F~Lty>Z$c!)SG^^7Hqn;x&C0p`pwPMg<*MF^m>FqFsY-NZiNw z$7eg=?sT-RH#a|zo|?qL{;c^mk?3U#O!t#2GfK#)3~3%tbjC`6_x=jk^`G0eA9~xiGK;MK=goNN`(2Si5bmb%{pIuapUpW@=}me3{r&pf z-#0*bfuJ-1*W@Z#Q$``i>%7PIkn8FAe6PYT!JRk!$JJmc&+Q)P;6uC+8x1e%RluumnCHKm}i zNiNLIZLP44eKtBSC=HzVfKSi*#9L$UA<7`M#00kI3@?ml9vyghGSWbM37Srqfvbsuw(!3$))Ma=gWF|at-T%?m@H*6CV)VzT zdl!+rH;j7}-nlt4*&E})D_WL1zW5AF1#&Jd_RG00la=$9@pzg)I7F-uja(EiAZvR` z(JS_`T74vbx93J?jeQ87b;?^0Y|2`=(Zlq&p;XpE=(Y~~&ogF1=S#jIO)SY7Z3*|bY!?v$pV_1g~&mPAr@R*(4u(C2VX{|@-)DN}l z;N-}q*oH_$ljhp!Ff682J?g4s_YHK|Q-+cFPSC0DwZUv@85yvLq9-n$^Tgd+w}YC& z08F@5V)&;=3RZP3#CP7cI(>S=b%8dEw21qrp`p(petbNIpI?Y#NFidnZrU#UE37qq z^9iCHx-Lp^O^`8^7sT?hjlhg z%rrT*ig)qJ2hXrRx*G*4j$1Hhk!qU2#3sdO>JiSjZyLVWC@`VgbJ%bS8FZf>m6zdJ^UZ9JCMuq^ujbcBQk--P6b%y15+5XDcW}0s10xX=ICm&|!!fh{ajFR42 zC`6(ko_d|+)~Tq?Hu~OIV<|76(olnd?!=cDsG3KrRG9Z zC;H66Q8@#jHfLTih4uy$+%aVq7T`=afyZ89>k910Fkni>Oj9zVwrB0C=?KZ6tHT@qmYij|{2Fd9sE(ZSqhFz9);1FA(H< z_5KnBGF>$+q~c!gY$u=W1rR>AfG1&yLe za?d#Q{i8y^P=0L=QBRza>b8!iLgCJoSvw8);%g3-nDge2B@i{r`GgBY=<(IP zdh|~5;-2M+n0CnPX7S2dMw~MJ!^`tlD4}H6`caKg6;GpmRIP4iw~WEom3OFjI(#Sf zHeMu)9n~(*?b62HHjzS#ZSwq2FOB5^jW#uR3ho; zA#WI>5lh+XnSO7|z|-n9!%KrApW)xMh zjx>unTX^$1zvrKpP^e0_dbpVIxAsB<<(|{>vUgH9pAf@P2KtQ?m+FqoQQn9wRH0>Y z+<`JDlhRF#QNwpC(}b8 zF1K|F&wBwtE_^WXc*?X%Vtq`+KW{Qz?2|zooAA&(!g^?todg||*ajhl;K|3ego`vPGT+sYcAFSY=ym0666+u3om$A*6S@&J9g01NWfc82Qw(9pZcUVRe~*9isn+R_6BDg_*^9&*hhp zDS>RL;-P$&w#Yn@iL3afPEtRcs!QD8T6lU>GVKcnwgJkDcT-8$d0zXdM}{_Umy)vVOMUr^wPzf`+6Oi#Ds`?^2Y0 zM`rB2Yk!`&s_(-e4c;9VP#rI~UM?NhZni3aT<776rY?Z1N}8Lq0A|Gohe5OSTSDZL zZol5%w&CXCah(mMJlbhv0BV8W>Cuj+)2I<;sTFEMA6%I$l$@r0mn2`(N{fqQT8@?I zeSUr(g8t}PBI;LhaSwut=`IS`U4!f`_cHPFzWMz5eh?v*xQEB7M83v%gbI41R&yAY zkkH|ag|mD5J~Pv8U@f?~wBSmpFcF`o~!c-NiN5({=mn!yME3q-13G0bhsM z2qw!V3q}JjN8mOT~p^{>2>4DTq- z0VJ&}Stu6t2vD%X@l9dm(fqO}QTD-vr||SH!Okx>Q&7NJ-ZD`6`SWKIQqm|BS9Nu2 zMd6(@i=DB{x#d$=I5|mtoMH@;AL?tW>PD-r zU}IHo1EYo=>!+uCy+G=+Jzk78D5k=40%N}`j?2Fug-kp;ayf)fD~=W!QHgm811imQ z^{T&KbI=YTjTgwsz<9Y@v6S$P-5kME@Yyn&O)856pZte0fvNrn zJp#hi@~=hXXRE2Nkbzc}(^6mAkNf@O3R;qg$mJJW z*>hpUz^Jn@njNXQ;dB0-xayv?`eBipz5mfE0gZAtP^6Lw-5C96E-~;Gy)p%t{E4pX zN>(c7#gjR+yjnizoq`#}C1Qp$Zg*vBKAM^xNlrXG-_I>Z#~(75WEF$&_6BmVj5=7= z4mM_X^GUuWoSk%SrP&U5faS4i@;i)_;^$U?QCH+K9-s7=n9zjE-!Mi zC5bp>Yblt$Oe<#qAIkON=CMl-Q9^vP!U#ZmIa1n9c6{z=9%CbHmHhioq(9L4DBn zFwM?n;`!RkSFtvn=3#02aoQNS3*9!eDwst@cKiTBolu>=+nYkwm1G-|oo}~2*Y{A9 zy<&|8MNRzm zjmI#ti{ne(&X}v)4{xw2+t@Zcwq!Ta!X!C2#|&*py-%iQ7;%VbZdc?Rn2k-TBt$XY zEP3{BUxrZoBZQbkU$!7H7^>n9{i;n^qfiC{k!iIt~tKCx+{e$W5E=%KcdEC$b3{967|uVJhd*hd$!00+}Z7p;hZ)G?<64O1Bt-5GrD@EK$R^= zqr8WvyMnoR`OMCsN~>|mr?rK{@y!y`*L%TBc2v_-3P}aq4OsEQI{{zod+Q4x-RwY# z+4OFCn|}(F<*+sG<*GvP3C*|R*sr{broMGrpO3n=o>QliySDHW9OksQ?p#4*4+G{% z{d7m19wtppu5$mb96-y>uN$lWA%#fT9hR7GXiDj0Gr3&>7mm!7FPJ>CM%J9 zg{W~D_(3Hmm>jM%W@=lWj3$kxmIfwG@8{HXUo6+`@@)f_-B)RE5_FECN1lIjA^d4V zrW8!h&4K?)f*7*koA;d?YlE`y%%4)?bz*(g8kcsd2qkC5I(7>UKYvvrDuq}!=FHcR z?@&@gCNR_p7r|sz>{7Xw^4gGXk%1J1BJ`05nN);R@4{Dac-`_jR>avib5($Q7A^W} zDvwLwuLzBZT}XQL=ICQM5e-&oi&{X$!I!y0de>kf;_GTI74FP?$m9V;bA)mV19cN? zGk3+o(i{)(zDoofJ*pfv4K3N7W4()BH+L6x55^$o!w!$~l58zJ3b}l~a+pLK@iTgs z*Nme=y8tVT&bBx`Dvsv5%2S}Od!SCkKL~@)r3F*h1 zon;TIGd)djX*JyGqE!jvu)r#^ zAerfG5D5t+nzi~Of76OiZ=PCMBH8_Ase1+Evw4}sW2L)L%x2PaVsR~>Ki+ll);aem~a=jT?TLTAE(*QCT=-sq;vSDmnjUwmj6!(c^f3Z;9h7 zyt%6_>tvX{*y=Ke6u4=I^|M0T>*ZURwlOnxspshrVpWFuXW%%1g-IWXZI4yqKjwX0ww=EFB-4?K>*Vq{Q>?m1XTY=&08r40idp? z`)28AIS&sHFkVQwxVQlHEp&%jK{VMZ4O+u43koKTjHs4bPkVh`fAT|YKi;Fw>CaN! z>tM})6&)R2Yk@pH4hRa0jEnnHKJ6vz;UO#=!=?uu?TxA0#HZQHJIkrw-?CLWfb&~8 zD$Ei4{enJt{rX=#Nqb;DoL(dWSjda&WnKt3;J^~xX$~S1@H)MXbB^8+yaXn(eyRG$ zr`Z&M%Xk1wk+M|#r;5){nga|JF2}V2;Z9(4$pX7w*e%y_edskX3mqIB@)m)e5)d40 zGEIE$9Mg>(FMhZpyKMCrzhC$FUslFSS#51?U1Nd+1CwC_FJ8O=PB_bO5wHyHcNe6W zdXf|K57cv&HQ9#+Vs!|=^H(LqfWW}Yo0&b=85x7(;^H(rW7yQ1fDh-ok{(de>#`5r&^EASc4U>_OP5U>>{C&%(7Qk-Xy0!cakvP z5X9w}sv>tD6h9~KSUEd zXJeIlM8gn)Q*fEX9gd}4b>C8_P4%VT)ZRMJN-;-@o9YX>j0@vS*5L!`#*Xuc{+qgs zV-u9>Xj9#XGkhAwT~P4_#f zberu0+v(ljnL6c%xu0ySt@-D>M;OQ)rm<>Z=fm9Sn^@g*M>B8yYM{5PyvrdTE^&>5;N=(u)YTwMInlYo+aUy@Wf@(tnB zt9DjWLCx*F1ZG)wF`JX;qFI{=3}%qpee+ZWLUvo{N~oqi)SZH4$Y(RxYle}29g3)^ zh^Ctdv#Z*z{1xl@L3Q`S#9r3I8)tn)?}hQFsW6GJDrrMoCAbWxdoSV~kteNaCxDY&(s{V-=AJBU80Qpbwy=-OZA%18uui%gP4vhSS-;QBe1GL zDnfKC*BnyZD-UvVjhq`=^Jjhew!t>JUEAkgu(P=oW|kk{-JdIXdb(T2W3DG ztF2-FNR4+x%HE1W6mzkoikUp2nC)%k4vrIIuywQAieD)afiqjpFcX1j-MvHeS%PMd z`&7c~1T}8;Ks6ce%1$|gdFd_p#1u&rE0wjJE!VcUm}}N1_R~I3C=|a6D%(`(osOtK z5OUd;Hf6B%XlM4bwq4GE)kS)qhE{@RNk~ccXUI}IZSgAPR?LO4#(^t`g+tuM!<5Zj zk$ZXLi0Y7@(~3jgs*C6LI9+)|>b02ItcOo+ z-n+Yyg`yy+;iDp^<+k!JAY9Wv`}9hh(PM+RupRF(xajzbn8`PChiQ1V-xfU5W}|u> zHI2eW1W}uGAFeD+mL=YWdR~VgIfK<`nT?$z{Hx_?5fI04*{e5-HjKcQp?|nBTC?3m zK#8aV83bcqjtC+hnyfWrqsHrlVf)zACm&Z{eDM1Wg-C7nZ*_e-|PBJ+t??FA0wx=4|F8tKl zY|BqefZ%mZJsQ0Jo|MxCY36}pqKpk3a`)Aqz z9%WF(i)s6{mHeJs`1juQ9Y4&abcTvC$|xvgs^n{zSWl;*BPTNr7!ak$k5>n?DFGG} z-tW8j{{4H%tN8f#w7dS#U%a@?&i)c4<8=0`o}%*_4+{4u?0diFsPTYUnIzC{F9KZ> zLdvA@9tfq^7#Y8TkRA}U@fuE#_wa~_u5)p{zI-o;gn_~P>-rGwkMKnzCAGY~{PkP6 zZuRa0q8z{kgXY)}vhLYy3Q!qzvY>r+2` zU`8wTV8Y9Q3>z?xvBT1vfkF?mSu}vL%#IWqRK{Qd$QJeH&HL|LExs>h@0E>TFs)iP zAD~I9Tjo#ljDTJ|0}y7WPQ4d;767Ib0CZZgn|SvO7pV7&JX`bV(5MyKh z)TN(|f)vj4(9m}v1LMO*c3b~5E(9Q*+ja}MPd`0rv?@mk1z8R0nVCxI>b^9^yq{=DR)aP z_iD}4cz0B(NiRK`?}F_IBsK}(NkwFWFx4pnHvPG+t=Jpo)e&>E6LDJW7HbsV>$659xaWpaWMJ`>#2J8&wH`YT;y8 zJp9drNzv&YIwblPkNFLbxueD2-?#@%?v#@6df;!rD?f#&t{sE8064=h{|NTx&wP#h z-(;x>XrBK}qaTuR)|o#iU%y%a{jWIB?}k#A^wvLr`9Tpl_KesbH%P0m@mFI|3BrFx%rnI8+n3`ECqA1{Mchn~^Q;PT8j^Gb$(sKoOR zg!gdVa#u4ud&^v)S5*th#Qo9_GdTBeFf%MA9#RYho>XrmeE1^D<~DZS5ebV* zK&$0tZsBDleR5j2$ka3MyW|Ta&bMaEK(%t1tBjdy|U{?`2KyhOcotb6JcM_qQzw8!y2*-7kOU zh1K(q^6VDhEc3$g*YOXu7LUe#ph}c#9VDH9lru_sQw$-Z5kptC?_Y6oWrPAbf}<#W zE|q=TyjcEmpMkg8+d`B{EwKWngVzOmPm;HZ$2qd}H&&2h%{r*V?lrN7bM*~Avjw^= zPr{k^GZWvYAtJnqFfnJMO&Vtd>NpzwH`CP&_QuU~i#ks!9p4ST83y}`*onyecW0I8 z{MKZ3Y;*8AtOCRK3t$d3f+<*2>K0*vq{yrNc7%oHQb3oZNnjHDmN?iJZkS*TA27ou z?x^%$cP?0^SsCeZ-()8|9;Fc5^poVLY9VhBk)H|853}|`GJ<}Mcfrz?0+Y_gLcIeZ ztPXUL&8p^-%GSa;4t%!_p(Go@$q`t5d@D}hs5hmA)?Jv`5p24S3V~yQ5);Eufv$_S zkdxJ*z_FcfhFxK0CQ0D)yG2vef7oPf>k7@PsF)#{-54zf(Pb8pa0CnG+E8vH0=R#4 zpH%X_8l%w;-0|M>yn|rn!H@>pPJ~U^pJVXcKkt3IgX4L!&y~SrEG^T6$Rwd6X6k2q zpKNC&)si7gHEmA6VL%utklf0D%V**0_83_l7d-_B%nRQACe`v0l*QorcK)>w$}JDL zii+4ylh>}ijkF%~EZps%=6Q)CvZrj6;<1kzrR%Hkf7MUcb1|}Rn?R6LK-71xO$Hi#AGe+!&DJs7j|H9B6gj*|R zSTEnj<{%|*Et_G*RMBLRU$vWb94-V)8n7B$tm_f2Agy=|7CH#J!0G`}91Ff*eo=&;fN-l(&H&z7|}(V1(q=vnR5tO0H6aBZE4I%a6{(JNS> zb2%|`sbla`(M*!{DwAklpKZ5lO(M-6jB^H*OCpV-u5cl%-h@pXquV-o?Wui?NFU=e zEbY?DA*2iS#T!4zj}|U#LiIu`&~AH?`^y*3pa)BAep__4Ur+p4zaV>y;3&}D2o%rK zO|GE!Q~A4au_Nh=aEgg>(k51Fow7Pqq0QKuV^w5z8~f3D)l506t(rXru;+pGal)Zr zzH&>}H^o2iS+U>bFRUb|Wkal-uU)HJWZ3Qs z1uq1<6Dm_1$KFa1(0d)}(GueTIDj<+Kt={gJ%c^jWn`hrS>xWw4$p>&?u2#;Z34<9 zZhlR0dy{xxo=kU=aXwMbe?7cdI;Mx)OR($~W7SC~&4%})iN-Ys-#je)rG=UU_L8d3 z`W{-Lt6~W3qvZhGvL}dV_8ch0xj63eBOmEr&YqmGohZDihXn@p;;t1Z`1AfaVfUB! zx@{1H+Rwav$qiA%^+P{8=x=K=VQ0{O3fFm~dy4Sif$hx7n0l(rmZ^p05qOo;i@5Qs zsi}DYn|Q*ujZV{X@aX_hZu5QiEd6kimJjg9>^Uq=k+Ic~N$IqpZZ zG<_e$C=|m7}2!i$wc{aC4exRpav)t!H3tLieZf=+xaSNwFXH8Hm+!ge9FR z)C$v!mgE=j?Wx<0V#2RNA(E*Os68Jp%6zdHP~+8fdH$=lTm9W}@6_4I-nB1J z*)?|Zq7j;oHzIo43m%UCM!yENElRa+j^K`N<~pUQ=xJu&y?_5b^WLEmwYj1PaJ9#H zwuOgVws|N|k&f-xV{Opiy&?iCGQy5)WYtct&dX>WCxW?gcSHhU4Nw6v0V+5c`O&eK z_b$xFJhHWgq`itd!X3Zrk_-)4M$fYu8u6Adx5Q91svW`M-75 z?^Ni@R%OkR!CUDl#;>2AvR=4w!Sw4}Y!FC?=>A7ueRcUBsNccS{g1ZFWjV(CBa8uJ zEZ?QF@4~8#xw-lG_y&+DUH8?&Y7)hyh-q`OZw5pQ2)Ygl>ntH84MZ1c#k`Why)6QA z0m$hWs^_9BBEYUwheEo8Vw@FQu^*R7V@f#GW~#;&giQcX0Lb|p7}62i&j9#GR~dmF zDGH=m-b#c*9w;a<3JVLvZ9$E|wd>bkCnceiK`Fn)PBJR3YNzY$?CiaE&Z|%L|CKEP z(XefxDcis%or~QL%ufzCJ3%U>_n2Ban>?1)`np-E_Jjq?PwiE+>!Gfk93db{l`)_i zDJ(AT;#M8p3Zhnh(d^VadKV*w~v0sHjgpo-3Q5s~C?8M5eN3P_d1YEyw;v+3X`^1FBM zhS3R8_^hm$I&Doz16daXB#I0VKf6Q>Lj{L@-Z9uR4uWVoxL!3K>PEn?P=Q6`fvN)Q z=9FEu82ddB^e>xWWAuL-j-aCLN5lRe#Q#p#|4-ohe`mITf9el8H2&=W!i)d@^bcPD zO)cOL_4eYNgo{utn{ykY!bX0}m_2B#Ye`Q?U6Bf3($W@mM zy$Ix7aJY-ptSm2bUF>+%Q|UdL#c3_wvq{P{YlH8xbNNXgZuq#kp-T@!MDT8+J6geG zX-9Chd*U3_ry+1|zadOc@i8h+6L-z$t8XYlO~M#-hpjGhQ7NRHc?PLDCDeRODXHLR zDP&LYd>gxo^==a_^zd%T+0f3#-Sw&gpu+DD3m@{Atl|@7-tAuFL|Q%&!fs9#h0Pal z^kp!7Zn@J|w%sP`GUs~a;JYfn7ZN#vikxtMh$0yC=EQ$>!+u6LGBE#wrq@$lGn7sR zDyu)e#b0uEa9>%q98WuyID2W?E4QAwu^BPb{m5pJe5rg846$R5wblbHCKpRBAx6P;IIxi9m{-;hu+J;7qBh>3KfR(iXddnG1$z$ zadum#U;a@fTqhDvyykrup6c=Ws3BRjRcP~8;vp&!UVp4nQIhaT6N9&&h_U7ktTgDQ zf8IGV1>5V$^uA!+RBz<5aHlBh)E;@f%m4v(PfthWw^fz6pM-y@r2Bydj7&zUo1Zc!PytoxUn8vSwhK=mWKHV`Z`XhA}=(hj(A=P4;wMzZUfpI*JZ zXxg*cES09IMwubLC(5@&V1R5Ky`W4pZP8;>*{~#)Mfzp|8M{#Df1=ZegbQt=2w-Aw zFAP)-Fy}$GswOXDS0Tybw$29fFtN)o4EO;XLs2mH~7&0V%iJ zAxCMamPC4Oy#+8ITlcSa21$r|qyPp`b5Dksw2^QIPNT9Nsn`?@f*5hR;wI~IwLiKY z2={KgX~T)=zT98g|3VPI|QYd-?^us zAXn%0W;jg;v7I)3mroiZ>cuDC2}F!LH{8bN-xizhJ8|&0GYXeZ*{r&Uc$RkWS-w;H zoxe`ZppBOq7NEj-C((-2jYZCj+|NH8juM6pemcq_2yC6(ol>+-@8kum{t=b6PUdWo z*HpD!GfMsjeXJR?@pL^Je>iy2wHyT@Dbc~yUrU5Wi<#A%@?RxOzpp4D!up3$ks-bH zf39MGix2+=zWlYW#k6GL{$W5(*t_mzk;o|+H9_Vvnv;r4=z$Q;hO)UfV?0Z3kmqkVCeLw`V7?{N%ec4iC)(@~rn^k$W zR{2ly4+xn8fX}3)4h%er(0K;ZuLD5r2jqNNy$)^imY8AP0aePQ6}DFalFreoPXWpp zW?zBcy_XDPJz!pp7U*FW78a`JDw_{}k^oBKv^T1D-0|7>t-E5en;^U=kw4<&@4+D7yN}ufC5^u zC&~nLQV=s;R9CKcvP7G|P+-iLP1KMLez-u$0g))yfmXndHXT8J6k+z3|9cCb=?RLM^n zcl(u3ch9L1G7+#=4HXtCNLMyn%G0#^*Y)XOa6Z#eWv5f2r?r&;F-W|5KK?vM;E)`}^i9 z1Cplz8hk#tS(>`-Na~TPe&<7e;*5P`ot2N~s~?x}`<5>7?{dDaW7w^BPw}6J`U9r_ zs5kr9^mIp`QBW<;vi|BDqO5M!1 zImu^d>XwHE#CxN7#_sNTjDnkk(@8}};C+mk zwvZXAIw&2SM*AXBly=}}SehGIji?CB06AjfkygV)9Mj`B6#*>ZR?q%vfB#^;W7;E| zv+rpG&&9Y_Q&PsHdTFoNhQ>5cVM*#xDh4}?1tixm2=iaXN}<6}J4`a#d>3w6<`A=! z)ImPm;wzC=(E)WDt);9I!%p>j?S8y_tH4?8T9Xm-6$zgfE!4$Fd<$jDDwbW{*6uxm8!-~)h`^_3uS#k@WveW6p@?c3^D=YR8s0uFj zo-Q&X0B(RWC}h|eDQu?6hK&Hv+kh&U?H~zdOeNaK6)6<;k?Dy z<-x%6)aRw8JakH;6YAvZO$FenL-9l>ox<&8D_Vm1cu89-+5jCpFbsNkZ*8l9%4x!qOi_J_Bw zEIS$APLeBr<-}s5R{F`t z^^7~wsEOD&`loe<*GD$D?J(~XZzOg;F?Y5A5Lljy<#)bu(C;18Whi;)vaFk|dcOd@aOs7!+^4<{>`4Ssy0QkS05O9DyLX?d_Wb z)l;*Z#uOoD#kmeH1Cqf~mU4-n$led#)2a_d^-r}wk$vWM-tElO7sLTnZAy`AHhL{n zJP;pk=b;c#@6qN!@C1}}K>Y!za({cc zdM%8DuUU3JeE!-dS(gT(<<^cid~y|K+H-#)&|0e^b;iqm!jZMoCrqtP@`6P z^DD3IRlg+1K$Rd{jwNAeDro_fGgR%OdyEM6R~=Vhl~FqVFZ_`Dov4;0sgwh`1lV zN=U%JxcU@@L{#qe2-SW)6Y=CHqBodZx<078Pja-Qa`6N@`h;w&gke4stWZ30k;@tGbre4)i!CR~;%J$pvH?%4{59(*}OTUBUr!rPBK&N3wYu z3G1LrG(1WDA;4Ivox1bAR{TXDzMG-8`7SB6Y9IbjV1W52WKjh2#~=74^j~lKgPUA) z{=@X_1j6dZkK#S09PrqOAFKg@NPZ9vL_cQ6VPoXAj@L1^54viNi26QI*gz~2frXvNH+aeg2XBz!2MhbjE zb~Q(twbXp@3JXgJv>ufbL`0L~Jo&Tb94Ys2TTcmRDkOvRfM+$W4(F#zK-TDv;l+z{f*rT(T1O(!@x3@RopvDeV`CkG51$SAvbMnXIHzXv%!)IQEhISTD zXt_m$EbNCz@9b`71C|Ty4H2k4I#1j7(g*}AYT#Sd%brPt<}Hpd(`B;<5p1kK0wFOi zp~H<;ap?^}%mi#=7vWMp*#kNv03J;Ot%I|JN_%V&67T~l1i;%d?to%BpmE-pl#B#l zh*O@<$*F3uqnP4F+?||kvC&ZkASgpd$}DLb8yoLSNPK-SO$wfO*C_Z?Q2A(m5P8R^WcOF!`fsh6U z7!StFJ;^1O;}kxxuS$Wl%sGir3|F3|Ks^6@29d0RIzvsHK@pHL4b{-u8LJxd5b63C zt+4v=b&%brybqL2X4!>Vw}c{_@|#oPVWyexUfj;|Tv40sknkt1|zIA^aXt z_&Y=Ux7Pqm0=y>UH(n$1f5Z{cReyi8ArY;=a!d9Co|BmM?|vTq`(*e>R91{N+y4E6 zz#QVwMnr$Xe1@Qi+a@+NzB@U_sn7Z5Mlz~RfR@vk+TvvBsMcL-WuV8UX9{b3&h$u# zTqdw-$p9jl) zpKsulVZ@~JvHDq^!T2&AVq17cl@W*0T)5}z(O~C$;|#6+lN?o-xm_pT52~>cSx6|% zyRpMx1F6+^UD#E^T|DQm>P=;eO?Xmia5+d-;de%6@QKP4=oQ{6WoEjU(*0gPa3X?A znC*E-v?d|-tHne~ink)}IdaJt#MKi5A*_TRC%lepx7$1XK0X#dnhl0Ptm;O43~IS? zakp*#dky+FL{E46MMXV41IOELSq;BhUOLF|JkG;b*Re4rQS5c`>s%lHtY6B_gA}Qg zIim1kq@&*HBWO)Th)F4MeuOz7Pjr-ghnOU!UeH_t@^B3&HDfLQ3+UYgW*5&YlL(EH z3Fd1ty}m8|vyn77tob&!i*_PX1nF_Y$RrzSPDw_cdx+pTp zEUDZBQX7q}x!Wx&E!n74Pdpt@0)}2c^1VwUbW7fG{isfJ@g~|M)3YG(7{f@Rfa17T zKW+lkUO|$1oU~Nxm@L5yJWce<_UE1wM>aEuLeiHh#nQvuZAAgrcj*__}cY9-W)cRE&9gG(n95&(E^LuB3z> z9Q$vT@2f&ri?QrQjFC8eNNbbxxh(k1B6M@9tP*W6}In523A$XmBN6Xd( zJSG`j2FBE42(pHt0|{s~JVQmbyi+>G!wxCHTz(5y#Ddy zM+jObR?&71j1WEfqLJNOcC2w#IkbS)uFx-)qQrcL)^k%weT!) zRzr#>iF@o$5=ic>IvgE4f6zQ1B$6Bzpy_^{R@mYc+0Sk|3=HE-%zY1RhAf!15!PSN zP{{F|#5&=>L=r7FhqX<5;s}$OoYK=;ToX4b3;%zuoq06W`~Sx&m+p-#i4fY8xP%zH zxX}$Eg_NyG%1*MAy2+Z*SjWf`$ui<*8)hQ=HWEoOV;>>=HU{&1eNw;gz2EOSzw`T@ z^Yfo$&S%Vg-pl9nTppj&QBQg$k$vd;feKS{&&M?(hc#Bk60yZGAMuX@jSD7fwO1KB zE_C^wyjbGILEEpUXeuG$W$CQ(gKRH+_4|k_0xJG#SffW^%Lxwe>E^y@^4Gzo)x{2! zyt$sLEkS=MYr=O zK5?tnC@;j&OZ}a1w3IT}zJ5(Fh<<2Cm)mEQf$0OR4H2+5>_T@1?$MQO=7$R3U(SEB z7WMG2Sl;Gfo+{3!6905lnU$S#66B#}*XlHNb}S|J3#qlm-I986Wqzzgs&b=W-eS#b z7y9McMuN)u!DG#Z;JC?6*KM*B>p zi(wR2+)DEK@j2}vS;!e@>8I*xX|X~~(gv(PvjEztA5hF9_@rv~oo|eTBsXbIIfVUD z>@D6-y-|kiIZ=aAwUwjK-=x`C|1mx#sbW+US(BmXSVm*DHnlxgDJ7IXbIQrmljlfY zkU0K=pI-^H;p3#<$@L{MViZ1Mb34l?tLlc1b!PcZ&c6QI%j9&?KTV_q7!ZapYCm4# zcMvAl_uI_as5kZ(t)>ii9KTPSz38D-IFI{n>YUQ}vTOfs&4tgQ{timZR|`ga(8LH{ zBY~5%_?$)ZGHdv{{FUk~#KatoV17SB$p{rzfjeCbOcv4hOj z>zK9dvm5iQ$sBSHzdSqt$Y9es8~s5_>s7Q{ZOxxK>Xq{vTG4$}TX~nQK55j>!6x!^ zZzls$!N(Z7qg=wA-br&|iQj3zNKACQV;FUQHn)kUdH&ABKkY}&er=$Qq>w%q%dALw zdBz{>0xr!Od0x57S+EurN?BNpAn*07n$ zcDJ=>vBS%#bo#mhxazo(vSgIAEu0}17M4QLi+Lq2bfL`xZf_=Zd&qVga#U{#Ejs1M z$Nu4V56gQ?>kW0iIIubRPO>-01qVg(x3H@HY)!fE>#O0VY1hX@V7zlgNhIOujE<@f zh4zFfSu4KQRE#?%%Xy%jYgQw$Y$9x7CG8|kD~wK@5H^4xJHy6>Zgq_3|D+{bKj=1p zs}pr5_>%n0uv5+*9+QXW%>zO+Bjqz6v`x5pt|T_0mPr=IAB@9_N(}Gdc-mw~Z(oor z+>^#!t?N9?ZYrVVU#eeoyGcp#%Q^|2reyvJdm)-LGnwAB(uG9-@+J4v{Dwf{_ivLv<5xq-=wqj0xb z^Z5E~6zhf8#wyE%9+&A-@ShlNK6p z`2RttUcypC7%-W5cJHovmvI)ZJDwRn*TyJ%gnjM@do@0Eu~1Vrt(-IG8VDxmD0 zheArv!C@ArlO+$|USk)H^eS-qV%f4!d{gq)0^4@~#BMNl41VXEJ? zc1=;wn3O+kt{(MSp00G>FTmJW0GY_)tAj)gI5kMM|otyCYR! z30JpnBQ0r<&yIJ=e4LMdHC1q}(V)3WytEvuwSB<*TXBNo(c1X)Y|kcJgl=`!d&%?` zUEjA5ELkz&e&vp%mP^h7@}`qu$$@?9Y4tObfBA6O-$QF!b6=({3ovba;P5oZfc0Md z%s#d!D$4SV(WAY%#LAf+Jh^ey)j=Zj3qhmkex+r@oZCO$?#4fLw7(08jU|TbiKRUk z;*ADZW|k9*xmqtp#2j^okKV16$_{~41cyD zxl>xy+S)vP&5Kc9V+`^US+U5%H=WAnE6l2jg+bPdB6i%}A-N*++h&xjNOQB^rN$6^lY3at`qi6?*Fjyl8G5-Loc3mu3mzo_rK+hLzBZ9Aw(GO3 zuoSbrDUnsO>z))=wVXYgt%u2)6cZ}13~(H}N|;!dEfr_P8COkTn~#-wVU~Z0Bi-4m zx-n`8`_Cf}AL_r$zNXCPDdM`3(zDBIrxit#O?f77&p8XxLWCg9V`5efbxGO+Nv(V^ zc;9jJic_FXj{!7tL5sWShsE32d|&4!fBbHy!$EwCAkw=r%JjZkQPk}iFI+7fN)#4y zE%yKVw!ZrF!a?~R$0{B6@XJt8?X})B*28QOKU2cV!4{lv2ckQ&lIM=5>7!nF8qG}v z;^XRX@F^}z$KYG+1#$r!;>bhOIp4ROo^T5{Oo<*Y6mm3L3vTQ>tSiXesNsR z<#%;0IL!6=T7%B_z!M85`Xxc4^rL7jD*Ita?#)}`#%0S_*rJvfQyni-vpy-S42Mo> zO!)1-S&-rOI!u>5F97tc5&yG%J^oh;8ZaqlDN@d>*I6rRd@=!E+hGdxliH{q!o%X& z9$!LTA!jP{Ud*~uWSZmISl5bizoy1fzXg$zN^yz!KxzOKgpiJ?e&0g4CsmSHc7Jyp z{hDzM#)YPObRI1n-#7j3R#|UGm?33E2j4IBQI`3tdcd=B^E?oH&V=zY+OPYiCtxoj z0lgt5?JWuDZ(j93?{ukMyVA0M+`VtzI*+>jC+-RRw^-hH4dOqYGinzEgnnS6wiFX= z%V<@q#>rcqN3wJiV{Uo_=?F#flYwgNYH6|rs35=JxpN8}WCsdCh5oAeCg~W|V4;;! z1O%6X+jeW za5VP@&w=Sn*eIWsS3_@xEwM~Q)4xs1g z3;!*YwSol>cw;dV33$WG0r_C*zlXLp+)23o#z2l-;bD)h5!;fhrao__@=`p@f1Eo> z3$|SQo&rdmjG9hMRB7w#+CbN(Jg&RIJh|MRI_5nE^*rxUv`K=z>mg(NX-t#H0!#&?CvxkqH`*&SkU4k$kxJ!U`s^QjGX6DN=g5YlgMA@UGqallDqb9cnc5;`% z8Rf=(Ub6d^%hHZenl5_)LKFttF(4svHvW<{I67aiR@nPbPz>Jznj7{9kQJ5u`j6n&06kC+0&6G;h`^@+ z4)dOCa+at!mO`q22|sfmn}v~!FwOumT{aqbwQPO9#lQa)4m3wLNy6$O-<9blkBgPl z09QsXDgX<=C%jGr+8>cy@r4rNab4_g zo>H=p7+}q^!Ib=tcJAtFn@_3>-z(#{>H;ey@`de$%!#Th7A}pqZQou#Rn#KyHTw*g zJSvMURfw)DmmCRO<&cX!xM)w2KLj#1P!Ron<~c(qIED3m?JZ^tudA_+Qqh9QsP|0O z%C~P2J}Ho_!F)Tx!pbUrt%e1mf?J|=!d}Fz@YEjp!8~{z5@H1st*S$`XQZG)Ck*05 zv4XNg^c8^66v3(nT>SC3yxx{T`-Y{=76g1G8^q85W%8JJ8w6Lq00S+5XZ#5+dB_8# z4`>30Ak1yRe+qk#a6vGq&at$AY%GtRHkK1f(@|MH+_c!fAt#_Ft~JlY>h> zs`>Pnyzo8z{7oQD7deZFh{QJz7RB5KMg(H9-o$z%p(uFsTm;@LNnGD{+RpCyZ&&^r z0FK3iYi^}AJmkTFT$u<9k(Bqm4U$kqYKxYr7JEo4)gv2;Gv2IVx9r|rx6x+gn*q8w zEMPU}p8ZDYS`xJ~CkiDDhwCGe!G=^SfewKzT!61ztuP(>3Cg?aP<)BluZD#gLPO>n z6hJKiwGO%Qrq)&)h)f~95Q0;j2}1uzin79bAxzrfavGaK>tCY0Y`T#;L!opN!!r9tx2-sa4=||q*UdBtEmjd8&wDN3gUMn4}Ji91U46lr)!3apVCQ( z-R#DUFWA5^$Z{-+1Y`h1a#971!A!6|Y;N3e{p?uk3OsijG8ACBP0~FAQ{&Ly^WQs& z|K}AlRj6((UIdV2Ox4uO+ydnC#_d<$c)ji4 pnBwT~TgQ)<^LK;qfBtm?c%v&m*2go0;E5|t=QQ=s6lhr8|1XcJ?jisH diff --git a/doc/LectureNotes/chapter1.ipynb b/doc/LectureNotes/chapter1.ipynb index 6209e3881..756ce7708 100644 --- a/doc/LectureNotes/chapter1.ipynb +++ b/doc/LectureNotes/chapter1.ipynb @@ -629,8 +629,8 @@ "metadata": {}, "source": [ "$$\n", - "H_{\\delta}(a)=\\left\\{\\begin{array}\\frac{1}{2}a^{2}&{\\text{for }}|a|\\leq \\delta ,\\\\ \\delta (|a|-\\frac {1}{2}\\del\\\n", - "ta ),&{\\text{otherwise.}\\end{array}\\right.\n", + "H_{\\delta}(\\boldsymbol{a})=\\left\\{\\begin{array}{cc}\\frac{1}{2} \\boldsymbol{a}^{2}& \\text{for }|\\boldsymbol{a}|\\leq \\delta\\\\ \\delta (|\\b\\\n", + "m{a}|-\\frac{1}{2}\\delta ),&\\text{otherwise}.\\end{array}\\right.\n", "$$" ] }, @@ -638,7 +638,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here $a=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", + "Here $\\boldsymbol{a}=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", + "\n", + "\n", "We will discuss in more\n", "detail these and other functions in the various lectures. We conclude this part with another example. Instead of \n", "a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn." @@ -3412,7 +3414,7 @@ "outputs": [], "source": [ "# split in training and test data\n", - "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" + "# X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)" ] }, { @@ -3475,18 +3477,27 @@ "metadata": {}, "source": [ "where $y$ is the function we want to fit with a given polynomial.\n", - "!bsubex\n", - "Write a first code which sets up a design matrix $X$ defined by a fifth-order polynomial. Scale your data and split it in training and test data. \n", - "!esubex\n", "\n", - "!bsubex\n", - "Perform an ordinary least squares and compute the means squared error and the $R2$ factor for the training data and the test data, with and without scaling.\n", - "!esubex\n", "\n", - "!bsubex\n", - "Add now a model which allows you to make polynomials up to degree $15$. Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?\n", + "Write a first code which sets up a design matrix $X$ defined by a\n", + "fifth-order polynomial. Scale your data and split it in training and\n", + "test data.\n", "\n", - "!esubex" + "\n", + "\n", + "Perform an ordinary least squares and compute the means squared error\n", + "and the $R2$ factor for the training data and the test data, with and\n", + "without scaling.\n", + "\n", + "\n", + "\n", + "Add now a model which allows you to make polynomials up to degree\n", + "$15$. Perform a standard OLS fitting of the training data and compute\n", + "the MSE and $R2$ for the training and test data and plot both test and\n", + "training data MSE and $R2$ as functions of the polynomial\n", + "degree. Compare what you see with Figure 2.11 of Hastie et al. Comment\n", + "your results. For which polynomial degree do you find an optimal MSE\n", + "(smallest value)?" ] } ], diff --git a/doc/pub/week34/html/._week34-bs038.html b/doc/pub/week34/html/._week34-bs038.html index ee1073033..377a254ab 100644 --- a/doc/pub/week34/html/._week34-bs038.html +++ b/doc/pub/week34/html/._week34-bs038.html @@ -640,13 +640,16 @@ ways of dealing with outliers.

The Huber cost function is defined as $$ -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\delta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2}& \text{for }|\boldsymbol{a}|\leq \delta\\ \delta (|\boldsymbol{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. $$ -Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). -We will discuss in more -detail these and other functions in the various lectures. We conclude this part with another example. Instead of -a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn. +Here \( \boldsymbol{a}=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). + +

+We will discuss in more detail these and other functions in the +various lectures. We conclude this part with another example. Instead +of a linear \( x \)-dependence we study now a cubic polynomial and use the +polynomial regression analysis tools of scikit-learn.

diff --git a/doc/pub/week34/html/._week34-bs066.html b/doc/pub/week34/html/._week34-bs066.html index 5e3e40188..70c35b11e 100644 --- a/doc/pub/week34/html/._week34-bs066.html +++ b/doc/pub/week34/html/._week34-bs066.html @@ -656,27 +656,6 @@ Perform an ordinary least squares and compute the means squared error and the \( c) Add now a model which allows you to make polynomials up to degree \( 15 \). Perform a standard OLS fitting of the training data and compute the MSE and \( R2 \) for the training and test data and plot both test and training data MSE and \( R2 \) as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)? -

- - -

-

- -

-Here you simply need to change the degree of the polynomial in the above code to \( n=15 \). -

-
-

- -

- -

diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html index 3a2da7cec..07e207628 100644 --- a/doc/pub/week34/html/week34-reveal.html +++ b/doc/pub/week34/html/week34-reveal.html @@ -1743,14 +1743,17 @@ ways of dealing with outliers. The Huber cost function is defined as

 
$$ -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\delta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2}& \text{for }|\boldsymbol{a}|\leq \delta\\ \delta (|\boldsymbol{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. $$

 
-Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). -We will discuss in more -detail these and other functions in the various lectures. We conclude this part with another example. Instead of -a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn. +Here \( \boldsymbol{a}=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). + +

+We will discuss in more detail these and other functions in the +various lectures. We conclude this part with another example. Instead +of a linear \( x \)-dependence we study now a cubic polynomial and use the +polynomial regression analysis tools of scikit-learn.

@@ -3579,12 +3582,6 @@ Perform an ordinary least squares and compute the means squared error and the \( c) Add now a model which allows you to make polynomials up to degree \( 15 \). Perform a standard OLS fitting of the training data and compute the MSE and \( R2 \) for the training and test data and plot both test and training data MSE and \( R2 \) as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)? -

- -Solution. -Here you simply need to change the degree of the polynomial in the above code to \( n=15 \). - -

diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html index fb074240f..537a3b7fa 100644 --- a/doc/pub/week34/html/week34-solarized.html +++ b/doc/pub/week34/html/week34-solarized.html @@ -1826,13 +1826,16 @@ ways of dealing with outliers.

The Huber cost function is defined as $$ -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\delta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2}& \text{for }|\boldsymbol{a}|\leq \delta\\ \delta (|\boldsymbol{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. $$ -Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). -We will discuss in more -detail these and other functions in the various lectures. We conclude this part with another example. Instead of -a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn. +Here \( \boldsymbol{a}=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). + +

+We will discuss in more detail these and other functions in the +various lectures. We conclude this part with another example. Instead +of a linear \( x \)-dependence we study now a cubic polynomial and use the +polynomial regression analysis tools of scikit-learn.

@@ -3562,12 +3565,6 @@ Perform an ordinary least squares and compute the means squared error and the \( c) Add now a model which allows you to make polynomials up to degree \( 15 \). Perform a standard OLS fitting of the training data and compute the MSE and \( R2 \) for the training and test data and plot both test and training data MSE and \( R2 \) as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)? -

- -Solution. -Here you simply need to change the degree of the polynomial in the above code to \( n=15 \). - -

diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html index 2c5788986..808fa980d 100644 --- a/doc/pub/week34/html/week34.html +++ b/doc/pub/week34/html/week34.html @@ -1831,13 +1831,16 @@ ways of dealing with outliers.

The Huber cost function is defined as $$ -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\delta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2}& \text{for }|\boldsymbol{a}|\leq \delta\\ \delta (|\boldsymbol{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. $$ -Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). -We will discuss in more -detail these and other functions in the various lectures. We conclude this part with another example. Instead of -a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn. +Here \( \boldsymbol{a}=\boldsymbol{y} - \boldsymbol{\tilde{y}} \). + +

+We will discuss in more detail these and other functions in the +various lectures. We conclude this part with another example. Instead +of a linear \( x \)-dependence we study now a cubic polynomial and use the +polynomial regression analysis tools of scikit-learn.

@@ -3567,12 +3570,6 @@ Perform an ordinary least squares and compute the means squared error and the \( c) Add now a model which allows you to make polynomials up to degree \( 15 \). Perform a standard OLS fitting of the training data and compute the MSE and \( R2 \) for the training and test data and plot both test and training data MSE and \( R2 \) as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)? -

- -Solution. -Here you simply need to change the degree of the polynomial in the above code to \( n=15 \). - -

diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz index e3b48e526341d917934e4f7d1d9913233e4cece8..b42740f87533b8c549fa14811b5057c5594fd561 100644 GIT binary patch delta 20 ccmdnGoNeoJHa7Wg4u(sWjci-l7}rk*08a%6O#lD@ delta 20 ccmdnGoNeoJHa7Wg4u+LQjci-l7}rk*08Ly69RL6T diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb index 5f52865d4..a72987afc 100644 --- a/doc/pub/week34/ipynb/week34.ipynb +++ b/doc/pub/week34/ipynb/week34.ipynb @@ -1860,7 +1860,7 @@ "metadata": {}, "source": [ "$$\n", - "H_{\\delta}(a)=\\left\\{\\begin{array}\\frac{1}{2}a^{2}&{\\text{for }}|a|\\leq \\delta ,\\\\ \\delta (|a|-\\frac {1}{2}\\delta ),&{\\text{otherwise.}\\end{array}\\right.\n", + "H_{\\delta}(\\boldsymbol{a})=\\left\\{\\begin{array}{cc}\\frac{1}{2} \\boldsymbol{a}^{2}& \\text{for }|\\boldsymbol{a}|\\leq \\delta\\\\ \\delta (|\\boldsymbol{a}|-\\frac{1}{2}\\delta ),&\\text{otherwise}.\\end{array}\\right.\n", "$$" ] }, @@ -1868,10 +1868,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here $a=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", - "We will discuss in more\n", - "detail these and other functions in the various lectures. We conclude this part with another example. Instead of \n", - "a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn." + "Here $\\boldsymbol{a}=\\boldsymbol{y} - \\boldsymbol{\\tilde{y}}$.\n", + "\n", + "\n", + "We will discuss in more detail these and other functions in the\n", + "various lectures. We conclude this part with another example. Instead\n", + "of a linear $x$-dependence we study now a cubic polynomial and use the\n", + "polynomial regression analysis tools of scikit-learn." ] }, { @@ -4396,12 +4399,6 @@ "Add now a model which allows you to make polynomials up to degree $15$. Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?\n", "\n", "\n", - "\n", - "**Solution.**\n", - "Here you simply need to change the degree of the polynomial in the above code to $n=15$.\n", - "\n", - "\n", - "\n", "" ] } diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt index 39c829685..ad6c46f02 100644 --- a/doc/src/week34/week34.do.txt +++ b/doc/src/week34/week34.do.txt @@ -1285,13 +1285,16 @@ ways of dealing with outliers. The Huber cost function is defined as !bt \[ -H_{\delta}(a)=\left\{\begin{array}\frac{1}{2}a^{2}&{\text{for }}|a|\leq \delta ,\\ \delta (|a|-\frac {1}{2}\delta ),&{\text{otherwise.}\end{array}\right. +H_{\delta}(\bm{a})=\left\{\begin{array}{cc}\frac{1}{2} \bm{a}^{2}& \text{for }|\bm{a}|\leq \delta\\ \delta (|\bm{a}|-\frac{1}{2}\delta ),&\text{otherwise}.\end{array}\right. \] !et -Here $a=\bm{y} - \bm{\tilde{y}}$. -We will discuss in more -detail these and other functions in the various lectures. We conclude this part with another example. Instead of -a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn. +Here $\bm{a}=\bm{y} - \bm{\tilde{y}}$. + + +We will discuss in more detail these and other functions in the +various lectures. We conclude this part with another example. Instead +of a linear $x$-dependence we study now a cubic polynomial and use the +polynomial regression analysis tools of scikit-learn. !bc pycod import matplotlib.pyplot as plt @@ -2872,9 +2875,7 @@ Perform an ordinary least squares and compute the means squared error and the $R !bsubex Add now a model which allows you to make polynomials up to degree $15$. Perform a standard OLS fitting of the training data and compute the MSE and $R2$ for the training and test data and plot both test and training data MSE and $R2$ as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)? -!bsol -Here you simply need to change the degree of the polynomial in the above code to $n=15$. -!esol + !esubex

- - -Solution. - -