From 6466f07935ae9773c2eca44ae7c975ffa75653e3 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 5 Nov 2025 08:26:37 +0100 Subject: [PATCH] Finalize report --- README.md | 28 +++++++ ...ynb => 00_breast-cancer-exploration.ipynb} | 0 ...mple-tests.ipynb => 01_simple-tests.ipynb} | 0 ...sis.ipynb => 10_regression-analysis.ipynb} | 0 ...case.ipynb => 11_regression-usecase.ipynb} | 0 ...ipynb => 20_classification-analysis.ipynb} | 0 ...on.ipynb => 21_logisitic-regression.ipynb} | 0 report/Exported Items.bib | 45 +++++++++- report/biblio.bib | 45 +++++++++- report/main.end | 3 +- report/main.pdf | Bin 591801 -> 618885 bytes report/main.tex | 77 ++++++++++-------- requirements.txt | 56 +++++++++++++ 13 files changed, 219 insertions(+), 35 deletions(-) rename notebooks/{breast-cancer-exploration.ipynb => 00_breast-cancer-exploration.ipynb} (100%) rename notebooks/{simple-tests.ipynb => 01_simple-tests.ipynb} (100%) rename notebooks/{regression-analysis.ipynb => 10_regression-analysis.ipynb} (100%) rename notebooks/{regression-usecase.ipynb => 11_regression-usecase.ipynb} (100%) rename notebooks/{classification-analysis.ipynb => 20_classification-analysis.ipynb} (100%) rename notebooks/{logisitic-regression.ipynb => 21_logisitic-regression.ipynb} (100%) create mode 100644 requirements.txt diff --git a/README.md b/README.md index e69de29..52e6339 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,28 @@ +# Neural Networks for Breast Cancer Detection on Limited Featuresets +### Lars Bogner at the University of Oslo +### Project 2 for FYS-STK4155 +## Abstract +We study an approach to classification on the Wisconsin Breast Cancer Diagnostic dataset using neural networks to expand a limited set of physical features to the full feature set. Using only the radius and area of cell nuclei, we train a feedforward neural network to predict the remaining features with a mean squared error of 3.15 σ². Using out-of-fold prediction we obtain independent predictions for the entire dataset. Using these predictions as input for a logistic regression classifier, we achieve an accuracy of 94.74 % and an AUC score of 0.99105, comparable to results using the full dataset. + +# Installation +To run the code in this repository, use the package manager `uv` for the most seamless experience. Make sure you have `uv` installed on your system. You can find installation instructions at the [project's homepage](https://docs.astral.sh/uv/). +Then, navigate to the root directory of this repository in your terminal and run the following command to install all necessary dependencies: + +```bash +uv install +``` + +Alternatively, you can manually install the required packages using `pip`. The main dependencies are listed in the `requirements.txt` file. You can install them by running: + +```bash +pip install -r requirements.txt +``` + +You will also require the `pyoptim` library, which can be found at [UIO GitHub](https://github.uio.no/larsbog/FYSSTK-Project1). + +# Usage +To create the results and figures presented run all the notebooks in the `notebooks/` directory. Make sure to run them in the correct order as some notebooks depend on the outputs of others. To compile the report, navigate to the `report/` directory and run: + +```bash +pdflatex main.tex +``` \ No newline at end of file diff --git a/notebooks/breast-cancer-exploration.ipynb b/notebooks/00_breast-cancer-exploration.ipynb similarity index 100% rename from notebooks/breast-cancer-exploration.ipynb rename to notebooks/00_breast-cancer-exploration.ipynb diff --git a/notebooks/simple-tests.ipynb b/notebooks/01_simple-tests.ipynb similarity index 100% rename from notebooks/simple-tests.ipynb rename to notebooks/01_simple-tests.ipynb diff --git a/notebooks/regression-analysis.ipynb b/notebooks/10_regression-analysis.ipynb similarity index 100% rename from notebooks/regression-analysis.ipynb rename to notebooks/10_regression-analysis.ipynb diff --git a/notebooks/regression-usecase.ipynb b/notebooks/11_regression-usecase.ipynb similarity index 100% rename from notebooks/regression-usecase.ipynb rename to notebooks/11_regression-usecase.ipynb diff --git a/notebooks/classification-analysis.ipynb b/notebooks/20_classification-analysis.ipynb similarity index 100% rename from notebooks/classification-analysis.ipynb rename to notebooks/20_classification-analysis.ipynb diff --git a/notebooks/logisitic-regression.ipynb b/notebooks/21_logisitic-regression.ipynb similarity index 100% rename from notebooks/logisitic-regression.ipynb rename to notebooks/21_logisitic-regression.ipynb diff --git a/report/Exported Items.bib b/report/Exported Items.bib index d1aaf4a..f000457 100644 --- a/report/Exported Items.bib +++ b/report/Exported Items.bib @@ -23,7 +23,9 @@ title = {Regularization and {{Optimization}} Is {{All You Need}}?}, author = {Bogner, Lars}, year = 2025, - month = oct + month = oct, + journal = {FYS-STK4155 Reports}, + volume = {2025} } @misc{elstnerLectureMachineLearning2025, @@ -85,6 +87,33 @@ abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.} } +@misc{kieslerLectureModernMethods2025, + type = {Lecture}, + title = {Lecture: {{Modern Methods}} of {{Data Analysis}}}, + author = {Kiesler, Jan}, + year = 2025, + month = may, + address = {Karlsruhe Institute for Technology, Karlsruhe} +} + +@misc{kingmaAdamMethodStochastic2017, + title = {Adam: {{A Method}} for {{Stochastic Optimization}}}, + shorttitle = {Adam}, + author = {Kingma, Diederik P. and Ba, Jimmy}, + year = 2017, + month = jan, + number = {arXiv:1412.6980}, + eprint = {1412.6980}, + primaryclass = {cs}, + publisher = {arXiv}, + doi = {10.48550/arXiv.1412.6980}, + urldate = {2025-11-04}, + abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.}, + archiveprefix = {arXiv}, + keywords = {Computer Science - Machine Learning}, + file = {/home/lars/Zotero/storage/X65FYFZX/Kingma and Ba - 2017 - Adam A Method for Stochastic Optimization.pdf;/home/lars/Zotero/storage/HPVMMQBU/1412.html} +} + @misc{mostafaBreastCancerPrediction, title = {Breast {{Cancer Prediction}} (F1 = 0.99, {{AUC}} = 0.99)}, author = {Mostafa, Omar}, @@ -114,6 +143,20 @@ pages = {2825--2830} } +@inproceedings{streetNuclearFeatureExtraction1993, + title = {Nuclear Feature Extraction for Breast Tumor Diagnosis}, + booktitle = {{{IS}}\&{{T}}/{{SPIE}}'s {{Symposium}} on {{Electronic Imaging}}: {{Science}} and {{Technology}}}, + author = {Street, W. N. and Wolberg, W. H. and Mangasarian, O. L.}, + editor = {Acharya, Raj S. and Goldgof, Dmitry B.}, + year = 1993, + month = jul, + pages = {861--870}, + address = {San Jose, CA}, + doi = {10.1117/12.148698}, + urldate = {2025-11-04}, + abstract = {Interactive image processing techniques, along with a linear-programming-based inductive classifier, have been used to create a highly accurate system for diagnosis of breast tumors. A small fraction of a fine needle aspirate slide is selected and digitized. With an interactive interface, the user initializes active contour models, known as snakes, near the boundaries of a set of cell nuclei. The customized snakes are deformed to the exact shape of the nuclei. This allows for precise, automated analysis of nuclear size, shape and texture. Ten such features are computed for each nucleus, and the mean value, largest (or 'worst') value and standard error of each feature are found over the range of isolated cells. After 569 images were analyzed in this fashion, different combinations of features were tested to find those which best separate benign from malignant samples. Ten-fold cross-validation accuracy of 97\% was achieved using a single separating plane on three of the thirty features: mean texture, worst area and worst smoothness. This represents an improvement over the best diagnostic results in the medical literature. The system is currently in use at the University of Wisconsin Hospitals. The same feature set has also been utilized in the much more difficult task of predicting distant recurrence of malignancy in patients, resulting in an accuracy of 86\%.} +} + @misc{teamPandasdevPandasPandas2025, title = {Pandas-Dev/Pandas: {{Pandas}}}, shorttitle = {Pandas-Dev/Pandas}, diff --git a/report/biblio.bib b/report/biblio.bib index d1aaf4a..f000457 100644 --- a/report/biblio.bib +++ b/report/biblio.bib @@ -23,7 +23,9 @@ title = {Regularization and {{Optimization}} Is {{All You Need}}?}, author = {Bogner, Lars}, year = 2025, - month = oct + month = oct, + journal = {FYS-STK4155 Reports}, + volume = {2025} } @misc{elstnerLectureMachineLearning2025, @@ -85,6 +87,33 @@ abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.} } +@misc{kieslerLectureModernMethods2025, + type = {Lecture}, + title = {Lecture: {{Modern Methods}} of {{Data Analysis}}}, + author = {Kiesler, Jan}, + year = 2025, + month = may, + address = {Karlsruhe Institute for Technology, Karlsruhe} +} + +@misc{kingmaAdamMethodStochastic2017, + title = {Adam: {{A Method}} for {{Stochastic Optimization}}}, + shorttitle = {Adam}, + author = {Kingma, Diederik P. and Ba, Jimmy}, + year = 2017, + month = jan, + number = {arXiv:1412.6980}, + eprint = {1412.6980}, + primaryclass = {cs}, + publisher = {arXiv}, + doi = {10.48550/arXiv.1412.6980}, + urldate = {2025-11-04}, + abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.}, + archiveprefix = {arXiv}, + keywords = {Computer Science - Machine Learning}, + file = {/home/lars/Zotero/storage/X65FYFZX/Kingma and Ba - 2017 - Adam A Method for Stochastic Optimization.pdf;/home/lars/Zotero/storage/HPVMMQBU/1412.html} +} + @misc{mostafaBreastCancerPrediction, title = {Breast {{Cancer Prediction}} (F1 = 0.99, {{AUC}} = 0.99)}, author = {Mostafa, Omar}, @@ -114,6 +143,20 @@ pages = {2825--2830} } +@inproceedings{streetNuclearFeatureExtraction1993, + title = {Nuclear Feature Extraction for Breast Tumor Diagnosis}, + booktitle = {{{IS}}\&{{T}}/{{SPIE}}'s {{Symposium}} on {{Electronic Imaging}}: {{Science}} and {{Technology}}}, + author = {Street, W. N. and Wolberg, W. H. and Mangasarian, O. L.}, + editor = {Acharya, Raj S. and Goldgof, Dmitry B.}, + year = 1993, + month = jul, + pages = {861--870}, + address = {San Jose, CA}, + doi = {10.1117/12.148698}, + urldate = {2025-11-04}, + abstract = {Interactive image processing techniques, along with a linear-programming-based inductive classifier, have been used to create a highly accurate system for diagnosis of breast tumors. A small fraction of a fine needle aspirate slide is selected and digitized. With an interactive interface, the user initializes active contour models, known as snakes, near the boundaries of a set of cell nuclei. The customized snakes are deformed to the exact shape of the nuclei. This allows for precise, automated analysis of nuclear size, shape and texture. Ten such features are computed for each nucleus, and the mean value, largest (or 'worst') value and standard error of each feature are found over the range of isolated cells. After 569 images were analyzed in this fashion, different combinations of features were tested to find those which best separate benign from malignant samples. Ten-fold cross-validation accuracy of 97\% was achieved using a single separating plane on three of the thirty features: mean texture, worst area and worst smoothness. This represents an improvement over the best diagnostic results in the medical literature. The system is currently in use at the University of Wisconsin Hospitals. The same feature set has also been utilized in the much more difficult task of predicting distant recurrence of malignancy in patients, resulting in an accuracy of 86\%.} +} + @misc{teamPandasdevPandasPandas2025, title = {Pandas-Dev/Pandas: {{Pandas}}}, shorttitle = {Pandas-Dev/Pandas}, diff --git a/report/main.end b/report/main.end index 9cf1c6f..0b84c30 100644 --- a/report/main.end +++ b/report/main.end @@ -1 +1,2 @@ -\@doendnote{endnote12}{Solely used for data splitting and performance metrics.} +\@doendnote{endnote17}{\protect \url {https://github.uio.no/larsbog/FYSSTK-Project2}} +\@doendnote{endnote18}{Solely used for data splitting and performance metrics.} diff --git a/report/main.pdf b/report/main.pdf index c4bb1b9c6fe555f683f2e5429324da076bb35a57..b08c6f85222acf7b8898ee07e42131a7d4f2f5df 100644 GIT binary patch delta 162519 zcmYiMV{qV)?*$5PZQHhut!=wo+crLRx3+EDwr#iDZf(2W`~Cf&dG6ddnM`Jq7s;Gt z=A3lIk%#`JK%r0)lVD(Beka# zTdGkFry=@@9TLqbc*hz{v=z>i3&U4XNfVm3&7O_L-=0n`t~-oiRLIS`CbiS#rk~|r zrXOABN_+xp&9R!SpU0?H79QTRs0@0VcjPJ>t8Dmv9*jz9_1sHyTI@7=L38GIXZtRY zwnomYneAd8zo&unH_7*=jqg2$d?HR=X}-PZs2%-91E$rf-64a=5C0A#PMbHJ+&x6i zpBBnXspEp<^L#=yHA3GaeT>rjSWbG{UHaI%>S6l2VF3dG7Ro!K9U#^U!B$gFs z@*Egf%i|W#t&am%wM6LD)LUzh%Ix@d6)*Nr{JZd;^8$b!^@ka#!o``sy&BHkut}?C z!)cU=sjqMEm+&T$7WeNJ%}4f!C?mkj(*X9C*;WRmj>`bXGW582@7L=h)4!K7qPSgVrn{KvIX6i?XWQUS`R)*SX^3MqRH*E5M_CZ}sZ4YbN9RSTN6u>!yzS?Ylak z{cbPQ>kClJ?Ofzg!cYhG9!<5=BwM&GXFKZ4=`i0d;+F7i6U+t@4~?(#*4DOWjHT6o z!J76ZoH)0vUxoE)PBLQomrF~1x&RaGRd0J?JI+nJ2!w8|FU!kukAUQ1O8&B`_w&C_{pvZIm{=G~46#Ii z$IxWyMd5%8*@N4%o%&?h<b(a0FqYP3?mG>c|BfP&;kek$v;lAAJbHD+jwED#g z)wK=>uH$~*(!uU74QU`YPbV6=YZRT~^3 z3{XfCkK_j+`W=fz)szuw$v~jW5;Um6)+7J%>baT=KiWl$v%9WSlE-ec=KB>9TxxYp zR3MaW-NFCk*Au%gxoUiDkn2)g3t=7lvZ0O{i4TZf&9hVZRt&CNm;QhLBEo+@z%g8xY$+ zTWjqLP`WIhqK-KWrA@*q-4n<`Eg|2qzdk81NRv(osF^m-?g$ZL5`H8*`vCv#)vv(J~mFu&r!V!1r87n7KBu*RW=y&y+3iNR9F?7|1magD}GY-CBm zvt#?09#d=73*8*#f(8`f4Pw=o0uTTt7$2~7+O%xBl)|z4$ConkEm{LD5ds$t6-t}c zjq%h@TX>iyz2q+On*(ymQ2+F3f9-YC)W&z=Y_S#5l4F&U%Ck;t``r2))-&Cvb=j9+ zQ1G|J_BOR8n46v5@@a*4bA@b_c-=f~<<-Y?Bux1E_x25wpL*AXPKby;9FX8rqTZ~o zWL9J|;d4wgT7%xHs7oV7Y1;ZEo5TvcNaEV>pufgSI>(=2ar!)GvCcYdwrc!vSih$Q z6{AYLGXBU$SE-U=FL!REc)m4bPgMf~l7z-g;Eo|8w6Sgw6HYRS|pd01reCeB|i%y&Ps>&G**UB|6e8TDbA_0Mq)XZTB-xOEVId*!E8 zRz+v&?#Bkcu3KF*)7I3F;Nj-!bMkW539ECkPmFHR7Zr-$m~#9<0TMAlY9=*9W&jVokkcoo|h>|`TXF(c-VZG7W8%mrbXpoGcwvnliPbo+Eh{Te!i z<_YU~=X&dX%J1UqfERChSzSPLQ^xA-+FPeM`~z<0EU)m^pnbeqENwXg_X2nhoBB@% zb>RIl?Uh0PT$5YJj!l~XR-npJ-j2UUclg{!A3aRZ^7H^S2$%eE%V>v*Z=+jsQ{;B5 ze2nWjl)1r|-w9i)oBJ4rJlg}JwI!v@f2|nJU8et&^BSiVIGyZ3kBB(_!+cyyj*;YT zv$D{5yEfkRM$N(-=hB;B-6&{7ywKI@*{V(+IRANeG z<0oX8GbU&Oh{4N#{yIma@A_tFurZ*ZOR6XO?y)w631$8EWYpD>n9Pn4@$#KN8LG*Q zI=?BOrE{8bX-XqX*m$(qkP@xCtnbJlCcd>&5*7Pb?RjG>C+lob4znq~}(Po#na#Jm2dRK08-!V-`UbYqKG~m1uHOqrwKjm8FSjV#% znd9Tj%z1xXfMOrFv_|vYU-0*-s}J34raRWT>5s5v_yZUzAQD7 zH#!|ahXvJ|2SX5Ay`HWiCf46i4EZz|7`pTW&v5;R)eJUxoY$bAo;>5%68?;5)gmxV z_FOAt58{ZqJy?nPep0;#m#^rOsLtXT42<3VwjUJLu3#`f5fH2Wog8s_ZS*NOKo#pK zC4;>x#77WtmKiO-$mV0;y3&YPjq2Zu{j2WGDFvo|+Fcd3@}k9HPE-;+#aa)Rx7TZ}#Y5Vmi^* zL6%K949r05u`uQ}i%ZecL49Uf?k56VP^M9z7O&=9s;1&MkLZBlZ2Kdc+&w`*kp*}u z{`9k533R{}D!gK*$L*t9(^_=s6}J@NP(dr*8|K!jhM=GU+TW43_^Z?Qe6s@w$N09a z;uJ~=n5xuK#jP=9QD=_Rjq!U{ha7%C?!>jx7o`($@8AyYgmvH5E^7E;{1zQ>bP0pw zrf!B>J(_-?m9P*IuSKvgPK-#Xr{1BRf5=P^3FlGH@Cm76sr;iP7Mzr;3$huIHS{+2 zip$#@ohR~S59(jWbO{UoLipJcJ1m}}rtv+uG)0FqB4sJ|FSS#I0a@P@VtihyZJm9H zI6QN`g@v^+M5Q3Ux(iK$4`0Dasln{Zm9<54!4mPv=Jhpm_Op$hSvBYH93n#4SduUvc-vkB5q_o)_<^_FwRVY{FTCw+L}gK zGV--5XgT?~X3BTZVok3-qH@jr1*<2#)0C_7(Yb2vf_bJQ&0{0)lom&kI@BV<63t&t z?14$7ml6fKui8WViW1UHmu?7PWDAxU)z{}FGEXB<{c^BirEW9u61k%*>9V!X<)L;% z&3eHI7#)Q85>T*K49`=5%+OS=lPF0barvQ!wN9`^X;8Bg2f}7zxwukwsuS6Aatuk5 zJ87BG%EAxxCkf>Ju;&uz3Y&z@ncYZxicB87+`ZAi4bC(x#xHDa@)cb^=c!ceg@CM{ zbQ|xfpDZctLj>)s`8O!=Mj!Nxk3&Q>AY|n_gG0&g?pd6 z3>!O+_~Ldh*G*2*DcNcDj z%7~Ia`;D0yvauUUzFQ#O`(K}=-vcv~{~L#Nf?`>rXbX zG1CTYvSE^qdIC^mop3lzs~w#>u|BKD>PDsO=J5QfH4-_-q#ep(w&LCwTAJX}{l1-f z1oy?7X--&fRS}AonTqYkS{YW=SX$$_Wm;|RDxloHl~#e9XXD60o;2|TmrjR1J(((D z%CO+LA(!Wu>_XTIyUOJ}!H$t>V<(wyu|5P$86G=no(Pof2_=`K9Fbbj`I9zsw8&5T>pky|K|@MI z$;O5b!h|q3Po00&dU{90KFKvDr=-jje0%k8C)&QT8AA;oGHLikAB?S08Bfgaw6N*2T=_S4d3&5rTck>TM1V70OBGZWOD zUbxMp1*NTw=WdcGzdGT6pVe`g%xEkMnPf)dV&*2I)RQ#|phz5)AfWPZBc3@*v(LtR zwgmKJ?(&3tz>Uhv&%nX|^4PYQ_Vnyu0YKV*X2iq|t`Kf_EzgGO#aWk&BM|iB{!AR* zRLik-KlKa0H7u8OWi&od+6Y#Sv!twY#L@_tZwJAOh#|OcJdhLI4fWU@zuvm1q2#9_ zGi*coVAquPy)iQTVmn|C%^9_Pn-qmpTrC!!(jlUKrPaGPJuQBo2pC}&B~2?3uo2Pc;_C0CPE z<9W6n9W@g-!9pET=898453HS`ChMzpV6$4%>z$#VkGr=!XL_(B)bx+y%!zbio7j1@ zPU#>`T34vXGr2KW2~%S~lOxR-pj@k-#>$XVs80PaYHdlI$(vl$$MxY4RGW`r(d+ef z0&G@WdfzkDw{iCl|5bGOU&Yp(=wP;p?eT$(z#>UjX(!iTlnvAT+0tyr1I0oS`jAun z*)Z9k2@%Hkx}}|xgtN<&YV1#x3E8Kw2zoxzQEaE1byt6*JKf9T*pj`o<5Yo+xQF(8 zUDvBL4`sYQ0GK<=0DIlwv4G?v#*h^B%i-w_eyHojwxFI?OKkVrFYW|o^ z#(U+pkF9!jdyT(!jKf7%3=TIW2r1teFE$e;WJ6h_WDah71m3gN30701BVPZcnmm-- zz_+QPl8_N|fxL-vM`-tof|);(;CcQHi3`&&YIuAnX744uVE1DRaO<$&rst<1%jb=& zR0xhh@WYwLURBoYYR`B5991}(PWXWSr!1kq;%gI@2Xcv0^N{|oc8Gjm^h$?TJ7V*0 zjw|fHy>j_`J9kk;w6rwx zGSDcq`m}u1{{Bbr$E~y4t0^aMWG(hW;L`jRN8bv`Qh4`wWX_)s7Q-} z)_trTCm^oWeGE>H027<*KUm(DyQYLWSqZwbmcsbYZ4Z*HEb!a(4I9plJJ1rZEbKhq zDVMf>Q!lwUJtjv7*pWR?(6iVQ;BEO9P0$727!gfPm;F=5;(OKe@^adx+GtmR66&Rj z0sEa;7+KfHei<_|7z6g)!|-aL19R;?6t8@D24wHl!60|Ha$}{qnYkUCuexsaYQ*4p z<=)LmwqslMWy0c09(VB;VUp}ZW&c=E%!bB5Q;+4Ft8mg@SPvC*T9#*7qKeEo@k-1y z0psU9$+sr@;}x4Mfw8Tk{q7u8+LicZ!8cd1{FiU;_}T|Q31Ry3VJwAc;{>d%clhD+ zC$LnQ;K2a}1?5CpzmSkU;ePEhyG)EBznLgyUO#=26<8F^O@>1-p?%dc$7AzRXRP={E?%sgaJVfb&`{DrW1m3XoJl~! zOq`VG|95@L_QU#PWUKYVZpuV+t5F3O?QBGu@@aTSn@yt3ie6n-C~!D|!xdG9km!IK zQg;{H>ftH%^i{(PrBBF63yNROyS`fq4o1V$RBgMuP;3kT!y7TwcIi)yqtU4{3>-Gc z6(YpY{H^vk(Y(2EkYW4^PCz41t#je(|B$I89Ez}$GcPhEiPniNTA)9u5`1$Q!LnvJ zQXYqi$5N=N!xy~|(BOdlB+Xhvk(PWQhjJ~dchslUz4u69z|zRGwxw&_9^2Hw@Uf-X z-zQAbZ@h2s6Xi zlx$)9Xe`(bb>zck0PEdlZKVGJs#1L;HpH3*{Zk z-h5l$-E#gnHfjW^7<|7i<@I0W0*b$5j{P7)>R8dSYivJRP9V9Vr$vzoHN5G`+xsH$ z_?mDxEqA_R)6m2m?C!v}Qo)B35`y$giq=?TI>cFhE0m-_lm zJfW`2Cob7_djzvSf&6{_Yu?@;iS$!3&XqbeKJd{AhDb#ibbp>a0J+RnrdYdwM*wcS z&Z=XAL@jHRla$5?63aq;Wd zS_nfjE3PXYFFwq_Eyc|ZL;ov=O!~kh@>Dnh;6jnSDH9unGKtV4mrvq;5+3r0;8Qf{jQD!@&k)NhDh#9|5fg@yj>shysQsbWx>vt_pOzx$*{`680DvYP zx(0<_nTY2zPKr=G{RkaGNaV=+`Y82c5RFCCV=;idPg$kx0LuxVG;)2^*(N7sw4e+g zFoKto&F5=IJG!+5^JiS-xum9MJecdgztw{#kPkIr`Ox3N{rP*d@`73oA2H&`BAnd< zSL~c9$nIzVRq=4AWOnsV5a}87pu{8+i={qYvu%E;5>J~Li^!3G`q~Mp&hG)VG6sdR zTLpn=&iuk}0@5)laEd@@(I4IIRrI(<@h~ z(^uzcg7Xqr2o_xa?(4SEngC;(D8 ze>xOtp;+{lSO$OO?e2oi_+6%)Yzigm>|}E68m*X)_RF8zUhn5B3u*44+(?$J0ut%H zfIG$@`MWgg=NSB;8@vw<()FzxaPFJT!ibS6Y?UE*lPN_f&bFW87e5^6) z0FO?TL!H_BIk~zq8BRq7nx6Um^>|64OlkJ;q}I$J-rZVQQzTsI<>_=?q{`@9(U0-= zHUOoXV(>9@l`v9&`0huvD1h#`R5Fb*)s1Z5X`;aV7sW>rlkv|K+Yr(M0MVAz6OL)e zgP9!n8cOlcIX;4VIx`2MO2ZT^Xn>%bQEHl1e;zV~gCPA*FK5Ps2WqRFX$+y`*DJFv zquA~=>4Z};?cnqNtUY)uY#^PPPxtyev5f-{IP0I8E_79S^n5rBK{A{b?LQC5sZ}$e zc`oD)G$|>3&XB#nZ#y2BGDxP-FvZ~HTM>x#;Dkty=qCtGI=yq#h4x>9Qq}(Wux9qA zF0RgIMt1-69E`1DS-3cfnTh}B@$tbj$(h+(xLOi(urqTc3A0cGBfs=qE;*h3`udFT zla<9Y<9|rSZ`i6|^FFVZ+P9+KxLs*@eF@Ddq54U(u}(Dje#P(QE)o4%GbHD6;$$L) z4j_hR?^MjYR&MMxps3pG``8)t`1hLPROPICOljcPt!tLCS6rcoA>DeX-TL0H>(|Ap zx##!U<`E&#+&O^?Oy$_iwbG!`<#hJ)+kL3&8th;v)m16f?hM^cqFcMYMe8TTxWw)$IN)CwrY^%iXynR8inI%TA7sA+P|7GKo* zS8i6;oy}Ii%oNn>SZxpzot-=O`F!>EKpsxvt=SP*sa#%lzeS9l_xn_C9W5w?)WDK>#ijeQ-3QJTieWVm~VVP zw}Ykmz*ctPWqnbCD>3zAwAlXdy>qWM0)xAiRRW?)Wvk!AU-i@)x?BwPoX%s%x*F%R zSmzy$55LnAB$01dO=S-cUfZ|p&mzn%52CMfcA6o{FOG@Cab|bbXx*3dNyB(_)D)bq z?-#O(t{Iu%at0he$q%OTXl1;ZxRqM|7%Mw^B^S4V(J$dDDMkJk-vfVJcP(^%*J`hz zCO!if?@4nTzU0~qW_a4~e$?nb8uuFyyo%3pwuZNN&vTMK73s3jbJNUnXsBSDA?1Gd zrwuzdY(nl~g*gVnfgUf-wz8f}!5z&8(Ig{}`<0jx;)uwPlkt13EYt0wJ&91Cq?krO zVfaOWODga~TqWS9~ZBNYngjl&GFH&vt0gc0IX4IH9{!`xG z&1aZ5w{JcmQ_5CrDI8H z;5z9X6?`x_@D=y_wyL&sVCCq{=D>l|Ou1LjPLH?!Ssse(Umt(__u#;Yw9Ap~Jrk4% zI3?nu*s9cQrZW4_p*SBAtkI7J>IHc~SFlZf4^6H>7qY`OCF(c9lq_It^lD#T-l8_t zqm`)e(d)IzrOR=urWnMFb7UT`K`*HUf=zA?xr7SkTaWqA?TCIx`h8>LUKBIm6_Z=Tk5X#@hrO3w03$ZJmmVuLyBDey}h(%nAQ`+j3mHm_AxzUx4e}BlM z80&c&-Y4uvicMBm^KfXw$t5Haw<#c=k<4x zwsOJTiqMK+g6Iki^mODC0Sq00AqG~&{6mm-Z=xk}YM9>A<}#X6wcy=%K&l6FQRV?x z$_Gq?t0*Op03&PK9PGvnpSCm9-=x~Yh-NN?0>g-g2@Y;La3)niSf_gL=0%u=f-fS& zm=t(!xV}hr=VdZua^8U3PE2a9E3ximYBw8>WBiCjpSDz0c;E$ zrZYlR@_>wW$QL~M#hob~Wh7i;Tff>soUXk@neZ@F%O(4flm)2lv@G$A0RlT6?o)y+&8M>rF;4DWZmCbn*QxDf+JJpDYRpg%?o+g_$#R3yjZ;*PVhNX+T4Sor;?iG z75^6)H9ZNr5;Cw~hPxY>&!p5~Q%{QNpH{6iLo-6{V=C=ysIA2I63Sa)3Ll^!?hW@-za{Ueb@3Q(cJ8jifiNo%tV)b3Kb3-; zT`=2P0Lt7O@$eRB1H|kZocF0gO=vR7 ztN3)^bWf&C-P$P>1ahI-jnt1ootwo1=i&@i=6hv| zzy0Oi_W|7h?IPMuXi?Ol>FQvd)2P?WYNpoM8uQruuNUr?!-@8`+SR!o7i57(!X_Up zdAna_qqwPzY%ylCQ0te2Szm*%v-AYYmXMf@_16&jmO?sjaMS{N`LB4)jv%tBs$6(P z(98$xu`YY$tR(BdxGT7l^!t$ALB&$V-$y%oK>)Gd(dm(LYNYLy>2hkcg2F#|8OW;b zQsM)EOe2LDlf`XB2xzrNrh&dXv(sU`qBqCRrL8W0t$Y43R43FETnP&t1-#C+J^5?_ za_)pe(!e!`RG_MQI>dFZjpbj|h$?PaOgEK*lMHky$Az)*A3qQmxs^d5MO92e ze*ktV+X^Pq+fKf$g?;(OfNduZmKlp1qldS%VPlC-)XD?TiL# z>0(@=BQDDtqxsOzd&tw-;9wI-i@n%WFhT13bMqQx1P}aHUd+~>oml*jWH@NlGc;&a zAtonH#LjVQ!?JXVOwiCCi-~b@)>_XpTOgyRuakBPS^Labhl{Kf=_JsTh2Y_f_0lv+ zc$~N6s6P6nK3YxfzuRBtUZpWdBrEiWzT7ejZh|A=coN0X$W zTO!YIE7L#4$lUIK_+%_2cTbN@e}B$jzVWXIi86p+o6Lhb!$m9;Rx9+_qpCyS_^)3} zss}2{^3+c^1j>wAUb}*0vR4M?i&MDK_=Ui&DcgaFLprj%?w{C^01!EJ3 zdd}>&+KV6u;LzD&Xv-n>)K5t0e4tlj@fS$7;Sz116wbsL^B}h25{6FSt9Z#FzRMZ$ z_T)rXRTgrVhnw3rMIH7Z0D(mba5ID1`uI~xWA0>z3aK@!`a1&yvtnWzghHycStl<^ zBD+;0VoX}h1=_Pa| z4l}@onO3ds{KhIkTz#mIM{CtNYFbJl+b0@cVaV3EVyJSenn7e;f&F9%SL?s{=s#_90~aJBiNMCzU3V%{d5-lqq(0ONXht4V((BJg+lLZXv~=@Z9}FDun?Hz9Go8*7}YzNZS#2(9-xXe=$4@L+Vd`UAITgYs+gG0G^*U#| zf|4&a1JH9ec?cGybKnVolMPn+(5!eO!b~O-)s~X zUKcEiw7@CUAga0Gn@kZ+GhvVU@iQ|cGbyu<0kvR?Y`0uXBTWy#4PNZ-b|@Zu=gZqb zdI0QspQnfrUY9fwehvi?{v89*M?^7amOnW!JM7qyCp6gU&W^qPHQUr7bGOyH@gad8 z1>>hkrpzVWqa=<^Kdl%ZOvO6KBb-%e|A4X*J~? zqC?vux6OuopFlzrH)!gdVNfgtZl&P5lws8L4C+k_gSKICEm3dgb|F|^g?XUrGzN#r z4N7E>xFp#9$9uyz>FDeq-_?8J#gReuN&H31oF0>JERkM8R8{aNoyE+QLaHkYKvjjG zLm!@JFP1|;tSRyDY|W+7c!0Ec`($G|Nq(DxstS5I3|u4C5T8$;Cql1805db_Lod1_ z=Th6Mdq*Lf6tN_^)!9_4`Q8rs+-5;D#dTpxWF+v8esI0!ImNPpcxfGyB0Y^?lurP6 zigwfh@l!*LNuD%;#ndOzBP(nah<8sFV-0sMwm|qu7sr|r`dzlqrm<40w3h-~sIze+ zME!1{*rLV;r%OJ-lKfi z%iJY)>iA2^;`J{)3@Hz4{{WNJx*KZS&_!9`BrX&rxOpF=O+}9Xl?Wnx5N#j}DQATAV8!}p@URB{OQ@U}4s%ge7<&XjRzD9JX;QZhh`aIl zIV&B831hz^#M@9N$Ofi>?`4f+@(?fm6cgN9Lpg1m0%D&l7!kHamb|lbaK-3 z8-Kq#mUGtKBWo*;Ihp;YZx>vE?^S6}68lQ}lGeM8&(CCpULR>UQ zbLM-3Kuq!{(s#qZf}kkxlSv~ATT&NTKsXy$Q?-((XwzK>wiLDpo^?tE;MwGVyC>H8 zvaqpoMICBX;H~{EtB$=}bCBGha3;IR)D`X-q}FJg@rU<6kvY$I0<9)v^QyLh)&}gV z`*>>)8%G$`3StFwFL{5?F^XxISEPw!``2Zw+JmMFMZQpdb_z55=oyS@h8BI3h-3Hl zVIOXul9bp09=P8=0ompzb4i6tZoRG&O9@nSJ1rcEMo3$8cjS(fsYuJ3#-a|=Smx8A z8g8c-n8R9ifjG0iC#SNc8N16)u9s)_@oU3~Od?@Ik76H2IQV!M=6HptY1#x})7KvU2BqKelvV+Ja1=UdiuOqm%(-Np~%HiSG@M2On6bMk# zIYQDU@;v3{3Dnr66wxTcH=N$C-V;e$()cv_26{;lm;tj=c1AU?#mWDMe6zKv*2 zp=1NMuil;ej*@Ekf4kcOz+T$fI+9+Qe6udlW;x!{VzWiIBXZTCit^jAvjD20Xf-mk40USE^cHH&47OK2 zmJ^PpObD8EcYq#yq}f8*${aju^+1;rqLR5>T!xFaTw_F7=FLp}aZDmI9QiMwx!$l3S zk{?W*DouA0s^;Vn4I~xtPB?-12rRhdGW-3}qFJF9ql4Jv#-ldxd_m;agyk)u#{)!7 zDoc2`h27W*a5X58knlPOxc1G6G2m;{VVg6#8&b_+foOq~0gR!06nt0gUO#%>DdTPa zD3=-H5CX2dptPSiBE$~b$DxH>hG@M5M1<^;@37st_sIQTW#i7^OXwDc!;)ZpmMT9| z6U64h)w?$8^U3@|Uxj^cp*%f?A&sfQ8-qPzQJTp(wa~#~+cKFjBiLX>m325kL6lJe zkXlxBIqCvXea|W5E0UNzzPoa=V0mn($PuXoRX~^AH1R-&npR;MuITCX^tagw{fCoN zgW(2@PvbO|dPDlZ;Hk39j~hwFT{6Vmy}s}y{dqItG#)+L&cmy*qopCtmD-WCtGcPu zAS74q1+&FZ9owp#a`Bth`$`a0g|zv*XL#ixL zY&HYuW~Pc%d{&~^do{1!Ac8iUZQ?&23;{cVPD|=s_VHu+H$mLZ2mNLr?>F%-2L}>9 z8_gbDC4qJZz2BE0P<6PXbGS%)TybSHUD`=Iob~oukVi8PBL)+7iv&`C^EMIYvOHeG zenUxi?bY0#gnfM4lePIai@YE0aH*?0PN`YAhScu%*uGp_Q=!FXjkaw&Ej9xi+B~y0=~W4j-u+t%nahm$NSSNoBEos(Btt;qx@qd*zI*z6Msa#^-I92XG)uHqcxc2 z)6IA56%Lwz;Z&YH(*~k`+f|^^_BGGScfxfc|FFNFv2vx2#)}nFx?S`&DxdwQE0grK z`ACy{V7J~x>GDwb-im@>eom8nG+-~U--c#N8p|l1Bz1e#?p^zT{krcbA>`%kQdhs3)nrT_hHLDF1j2s2K0{}tRq^= zixZ13?RtMzPfGVzrKIZGh~FbKN!F_kf~R)4>wS6(81x_f zlqdvUQOF#8w_n;R=~8@&zjpFVBi%9Uta4R+Hf^Lz;?KC8Fjb`VVck%j$d!3kYb;CT zPr0lxRiyBt{anNwFwyFNV|`8Ia>dRksf^0StV z{^y*(|3~7tU`Ive){%!cCqre{3Wfm&oPdF8lT$~xj$G5y-s~K}^!;`z-|@;-sjdj( zPPD6rRYkXjlxK;8;9J!}jPW0R&`lB%oOBE#9p$F;+CD;7>m@(#oVR zp(__GKR&EOJ0(B7U&Jt{1$Tv7>UCi_92+-mQAaQ}P4Gdns7_V8Qv+To0X~E{hjjhw zKA{x*;Xt}Qx)V2m4zm9n+!O@~SxDOUc)6e^^4H6-_#$^0xhOZGwckcRhv$D%)`F;n z3NvJg5%ro);uge<5+h9NEq?;Z${%J>Q1oEPPDm^zH+lB$bpg)DKMh9RhY8DP)T`X{ zMnUIAL-27!FtrSKBpom|zbd(SA#WN+!Sx-$e#G``h0lNh_C7^J2#8={Fk+667;_Xt zUW7g-Lr4O-*pEu$KFBBn1K=an;p%=wI7W`F^Wv61BGS_^^&KG8x6(uePL|lbvr?+y!big zI->k+;d4beI?IZ`OyWiP#p(K8{9M}y&ic)Hv&pQahpcdlSsE=XydC*yqZT*KqeK_v zu&q=hesds{8X&@epQ%YlE`AA7il;o7sULeid2?g{M}P0xi8QNN|Lx1F$J)e?_j?P) z|G>IsZS1pS zsZ$=#yG+4vlF1|oHM#ayi9e@*vT`E+=H#X9`$>?W;`!^w#-s^5m0hKvRNQdbR@c}a z!NZ0g04@0xbZYwVMMt^=B7!1-YTl1n5UWX><@?c#rE7BP?17btsS4}w8#xm|lZwXO z?a99A(}PtzY6aeo-GRZ_ipV(Hff&<*w*5V$(X@(9c*ck51}?XZho2tvs5lCl4Q}-) zC2SF5Ibvv8AA$V31Y_(*siojSz%}@1+IcH@Q;HL zD|3p1DWO8cc#|JC8Z%FhaH8|edTPE5|2hbx#exo&XC2 zl)~Agk~DYBFeLoSV<*LRxZSZF4&g$(Mdd+}IIfZt!oovgG^Zs@$=o0Y{U*bwEcS{%ut=A4nI zHk=I%UX0oiGOsWT@XxbQp>ZNXgQIZ)NaRovGhFF4+8ti`PrJBCjuyytR3|(^Fouc> z+U^NJ#q~w?3x>muJGz4dVTI>Kj7m1TaT>Z&TC!nU@_*L!|Ezghug94W6EFH-C@Ko> z;9%~Xq{j$1Xsbm5pB!gBYJV{ulSoPJ@yvz~`=P z3k&c}I;!HWh^p4w@z>(fq#RvWPw6anwRh{%?J?NO%CrCXNxBlJftRLJ+dT101t`;? z20QYYG<~oPRhx+-dztrQ<#QXSGx!%ub} zZ>QHn{>4Z6lBiJFIKQq7->UMZ%dU?i|IZ3MBJz@z90Og`vrT*B9+LqpH@(i@5~K%q zT?Z>xq%!-en=+*P?YuBym50lF^dtu_1ozUIxL#LU(Eh&b?oDxYm`ZKS!5Iow$w$qOydBkl z`@f^&QsuX91Xz_o2}s_dj7 zrwk2ey1?$0+DJz*FDrKxP++p#TY{!}GdW+p@fQN>5?B-0;NO$OFNsPpE%qj6Rlhw1 zyL&xFmQZbEvpf!WmtcdhV_#HA!x1A!a2TSV097AkVl!f)=$43?1sJYxZJQ9%QW@KG zUx)-;7pfG&jq%J9iQ>FUsm5vFiaaY~gtov8EGrdt4l&Jy!;%C*Lt4LpZGcz7Qw5g?wmByj9R1K z$4^&Lc?R>drF8H>r=xUgYE_}DwDRW4^XdHBqCV%y>Ty)Heuk&!M ze+89xPOMd^dPNn5{Tk=@z}y(91*aSP!sL1UKN?mhcnH$1IwZSa`xapjrcJ}VqA=NA za_8LyZwflUYI}#A!nBC)9(BehkDBP$J}?I>pH@z~ey>pi?jV!A!d!T&2L(G{vZnKl z70wr9)uewWP?s*7FkBr~il16AW+%z3>T2q1l0bVfN);L>^$67xxXE3W8iM@b8$^|A_x{bK+mF&#UP_ zrxt7W7Yrz3fQx%eQ&A{~f~He9`$vZfa=Va&LAQyS$_(r8=i=xc7H9}Nu>b^9k0vxf z63d#7HQUv#5>N)2`uaIp<^0bAKJ*_X7(a;BE+BaF zBCC;xm}WlfgX-u)Js$AWfq2+-If22`xGLvDmbdIiCe`xx#AAg+9h%zV>JzXd+R1-v zoCh>|$e(nlU|nLa&Tgrtehc#PTm9+xTdFjN*zCabRPup8=aV48G@&^!sU4iKP?tZA z9{qpV`oBv!ri59elH2!U z0P2#p!h1Y4YkQ^CZE|D<5gM)L{Ol$NLx={Z69s;$g5MPPcjDy1@6NHo_U60DJobO; z5$SYwwFG$tk7;FeD-}PpXe6_D!>SSzFsMI&SPF6abW|71rI=M1QVxVwN}x=CfdVVI zeEO#+u++m+Un$h*H1fie8%lEyD;Wfn12&JSCf~R8)XT}xy&wXL6|2&PL;p+*hia>R z11aCH0T)kxscycVIC)-bS}$zqDSAA{oCBY!_NemQ0PR`n`k%zg)z?Pyb z1)MNKtK`!LX-#C{skP3(44fF*A_OmgP!$fZ!&@V&7f+Yd`NJKd+ z1jkOFRn0pc0Q4qK(m-8` z`mam&D{rAwz}lW>k*@va$^4d4x`E)J%%IugYkBs`M@7`o$}M6gOUB-gM#47Qabx>R^rJ+2ltIo2>$2SCmaky58z)y6z*TAULJ1qWwe*NvVhzZ6X z*HUiH^dSvyJ?UQaCnqtN0@n!aHc1^?L0#m~EihK+n)WbC7i*T*2Xtlf&z#dn8f42j zHc5V=+&sC-7WNm`ELUMXuF;YC!H!fH{|@O5%3ct7c%z%u^xGLSC%z#jQn4V6YRTow=x)|9dgcG{(+?E@u0mj%dFtS3N;|zX@&Nvgs8Ib8KrjA6zXHgdEUs(@A z<&ZxP13NBH+F^P5$wg1(gB^YRj%J$2mV}nMVOtnTX~1hgYRji2hJB)cFX~*Fx`MB- zq)VDDJEaL7rxpJ|o3IekASv~vw_NOzHT4EL*cv#R-HR=KTHICm(&JwgOK;jJomzZz z9Yg}>t6jBQ2iVE}eD$)&2}Fh?W#w$O$IL(u&Mu?>5jMMNpAa4V+_VAM_+k6elE=Vz%UzyqR}F zVLD)}(0kHtXVBJ$eBn^4u-2tS@<$s|!-|~5#K6U7sHcOCIg5-kXrr&7 zC&r3e(Yt>FBOzI5*~`K~ndeCcyylO>WoscrX))=MOGB@z92hdWEeQPu@`uvWnQ6H$m5q<{V(-%{TPe12iv1J=d8sVdr1;G{0EPj%+yu?U5`K zW%IPuu-Z!Qz0hzwOm?;=hEvNL!gJM?1C0oR+ATl7lU+flaJ>7$V0OQpWt0j$5s*D} z3pgD!;>4^u_o#)&lL5(KN<^iMjPT(giSo8H9*1IqF4_z$&E#s1hl>l{PTI#S^GA5` z0VJwsCrlQu`i-5%%}O2t=Y^!yaeBWbe|exz+4(g`mjtBZmm7_fCsiw5x8h0R8lv9} z_CCnvN7l*DQGzNk`(58xg6Oc*gp2IW%?@2WBNK|P@jw?NNg1n71Gy2}P`J)ybG4vq z`#jR`u_R|s@_8HK^O)-`oTOUf>{b{h0)Vj`VL>IS7f4c>(N&Eq9)AeIos&y5$u*^Z zwtD$-|LSto4mFLiQ}B%1uhqo=(~)Yz?=7(Nj2+A!RAk+2ZM%k#GmUb*Tp@*<-!NdU z*j#fTgAcOBm#t7P!2-MfvO<>0fjnI3D>y}JCM&#(-E_^VEAx#FE<|FIPQqMo6Ye=>GC4PEJD0d5*XfjuVZC` zEFU?5Wtz;%jnK`O4l~ci976Vnx?&ZFq@e7KY5i5?k0X4!bwi3OEzFnE91D6|%h`wu zYnmJ~($W?vS9j)_+Tswt@=OS;1_W}^zq!8r;s^cg`|OD9nRkFhW&-vG{$qj3=Os6g zdv-l@O=Mv#Lk`{@`qug@gf*YF4Cc2%(M@r#=x(jg$qyd^4 z-Bnh2OTiU2D_A8(5MnM0o7DWPWMRSpBiEL^+xd^sJ^my#D&@Z134-@mG2jl$O)>lM zDwyDmf?Hv6abeedHN|;8ME(ET8)=vP62#BuGJr5{&P$*-8>gOSM`j3 zAjTgm8?)Bq{n1i<9)@*bn15gSyRw?X`f-MEmBshE#!k3*(mD-wZxr0^oG)AJZtu)T>KCV$uJPd`n?IG0uF90?heOCN%0;L8$ z-`E04G1T@X!3cMleA%6vO@yZt=2?wFpra8r8#^`DQO^BsG7@eAp9&b6k3gBT-dyA} z1%4Txy?~3BGkSy@rDf&Dv`A(+MY*i59t7sj&;XFdb|Mv^&3_^XEtfSQ(Qw*8_X*9l?u~O5y?5@GwQ<>k$ca zT7Sf!%g>>xIdaBa@6*exw8>@3%%)!PetH(<2?QX6t_T8Psp&fW zX4|mvh{Ac&7}33tnURL=Qy51_52B~E`2Fhk0s!?Hh7$(z!hv$B`1Eb_06)CDNPM#es zcrv@WzDkCMmuUZbA!Ckil(L5v$q9-GuVro&a+H<>tpv>*YGPS=7iSPTnag1-L>na7 zXGqCCtQQ+A1Sp0^8o9kW;Rky+DW7qo}6J@Pb{sGIdQPElGS{k@zZ#{@WxHXw82AZ8wQxO2s^Z@PL`Z*)%hD z0i@f|*bb4#DcSh<6i`XG6G_vhJlg~(wNia+i#nJYZZd{CO-xA&Bz*tdU=?13s(f^~ zshO&<03d@ALog;+e{IT-j_+|O`n$e;g6jCr@%CwGP-ELD-nl`0iUn56OHQ}Q&xTzT zg)?RwwCU5XRm*>-IP_sO@nU?yHkNshcSTUxbb3c0Dt*j)B4mV63Sy(hK;{<8dLBzsqB0DRk!HV%J6fmk03D0;4gn~?Q2vtsKZL1tOn3Of9 z`0#^zxO!(xom;3AkAZ3G3%An5fGOV%5eAB zt|b@_uD<0M?=y2o9=JMySp2OSNV?+0LG?> z!R)CYEJ`j!yuS+*G6hvwn(`67?tiBFt5>*YQrlr3TU7QZr7S}a+Mk+{4 zL^njgRVynwT{q6n=$3EN;U0zV37y$0BLMgKkChnAfx5xyci(rEBR7<5!lLM?s1%wR z7E)Y3qaUl|uth%x)k`t>i~SD2DYAQ6m`I+s7YqXOcyM+nSEmA<1OLrX-AKo8`o>(}^B~98S_a&oF}SM$+PUB7*Ligg z2A>H3GO5y&<{nTmng2i5C3YrO*8gJHxAg4d4%wZ*yL$>Bhy)QD>7tU$F>rnj-VO2p z8YSa*h)0M8wd`bLY5Xvwb@OR_d-AG`Or~V>@%FZ`_hIOz&!k4HFm9;Sq^`8yU)!S1 z^?Nza+wOYX*U6b4FQ0T}_wnFiA&*OzHW#xk+)Q8VT)Hgt@_u;e1bCrr$Tx)oB2dJ& zEm2aOh$X|qvO?*PtE>%nXy-+zUS2k5A4YI`FOIKs{a~pAT+w@2~vNp zsz_P$C~vepPA}8PKFjt`dgEbIEUTd^B!W^Ht~Nn%DYV9Fdt6PnB=dzc>Tm!U##zst z_Z+M(q)R3(aR>2UA~am+5p#xO$yRZ-oTmKq<+RBh(-yV_BUM!ZV$ecfaX&1@NLev$ ztD6WzRlz>DX#8d0?oO)2H~8@ZoW;hPs@hZ5;v*kWB_~TGO&81k$u{eG-0DtE8bNQZ13?{ z&&Nk;QEG<9MW-HMLaiks)eZbyoqmn{ z*cOE+@KTRphVs}J)h%PD>L`)3$)>LeEu(t2wx{WLH7pi$5s6A=1J(u@X5joum<>xZ&X6fG*9s>^3w~yRZ3s$ zix%NA>D=SD(p#DCH)B!hd0Jnqe!2e`%c25+)r8_a?ZdZZ&D-{YAhXHMhY!vdO^YtC z0jxN1ZIupiIU*x2Iu}_gyN)HL>#1n~o*Ur-<~d5$MPiyDMd$W|=bIHCS(tZb@%Kx9 z$h-0Do9NR!M;JGfQoZg_|8vQGYHY(Fx{N(^j!i2W>*?^qKbC`t&ufdIVLT?K--tZE z4JDS9V6JA~_ptjyfk|W%VlrsODyJ8y3Z3a0S2GL%>u@_{RriaXl%-vMfWG#H5G+n#Tue)g88SceRRq6xc*@Y`; zHf(T+O;qS2;y=t~!V((*q#_#MS`>Kl!`GH*FPz>JH804NDIfQuAUnkMV=>gY$6IY` zO;-xm`0IEG!K2p&H9Q{Wi`VcTFP#Gbf9KJ~D+ErbD`I?&MO#+(Up6Qygzl4JC6;VB zP^ZrAZV10sNK@_4Izo)v@I<0Iu6k{vJ3j`N;fIY&aIvg+<7n@Q1;Z_Mv`v$sprcb0 z{f}hZM^9`p=trn$vDg^EcFU%R5%*@I_9ws7_dcf!1XCT$ryE5PTJJj@JTBw`xuV6I zDSh&a@~RNdaP+0cPT@W(G<)GZLe?}g&TRY*6nf2;%o>kbxTrymx)pCP=rwCF=uMIf z*mJ9$wcEMSNekA;m?*eAw={U52p| z)j^F{s%h}t&OX+eX5sf650RY!>;<-py|a0|O!|UG=Yy3jdV>cr7p{!46FmIRVX&ml zc9)FoF{t%Sqh)R;b%%yEDVdZ3%{iIgv!Gs$k`B14Ji8UV=ARqFC7P6oDk1pF;eba|k2M~!>{S{$Y%=}TCl zq442{Oktgg-z#&}j~O@r2&1To--*2FL!VhbJXE;UBBJs61BrWAYkAv%Ps zD{9e1J-7GLU%?B_*GGxVp=FKEA0T%1&JMf6drm}Bt4NI>zn`CC5rsb;3E1lqh4!n^ z@buWfs3-(+(o=^0`+9?-c546Byfo^HamNk5_dC!U`kl$9%$ynnB5O#MY`3l*`WQ=k zs|D13-jP8pk_Gtxv8da%E|Ds$iI2*GKKbAN8=vmwHA&{i4z|7(#=D$z8vI(ue1c*{ zBa8|+_J-fFC}-t0%q74=t=Pvm`7SXSX$tfWux{ z*%fk56AHUa%*i7GG_NsuBJg5?b}t9DHrpJ8BiJUwMk(aU+kr5)H8?gF*31^gNv?dY zhp+S(X`oXXyUdo{mPjoP0&1zT<`>=}kJr#ihEVj?@Kx$*eJr4e0&gFW4GPWRcX7aCCj`4{5sFpi}t0X*a4BXVTa zLI2LUuZy`uEZ6oE(9bcAlg#0>S{Rc>q zZqQafgA^bJ6ME}01qGcky3Zk!BfSr9z9RfTpEpu6GE5CnfHK84Vr-f~NIO=f)88#_ zB@ZkU3DI)^HkL3nJcIK&4q$uJ>X}v_9DTIL7AxIGg2|YJBSK33p>^{$t<7a&o40)< zzW~Z=Fj4k!e=`)}q5+%DQyS^N^PL#)q(y0}Yy$vaWsB38?=~#No-C$k9RCUq&ngPW zt%zu)NAB1*!_YPvHXEj6q+5m^U=)RLN>oNWYG^iqWqRk+rQ8lY2o%oB3lT4@JI_Vg zW3Zw{o$+6&;|AhJ0bncv`7uRQ7%7>*(fcgi-Z8)8Vhs)FPJy%5&TR53Nkx883sMLj z37;@;GH^d0RsC2&uPphjDC+zwVGF^-U9uSeXASoAziS;NSBEVMYVd8uG>NnTSX@cI zVIOi(0X{W?DM21>h4nF;JZdlnmsdmb|Dz@Wi;91bKQLV*&REa-Y1Go_o*q-bqHk}0 zFlB)$nC9!*BFKxjrv&fjB8o+z40~NW;4HZ>x6?bdSz{$25ii>j@80tx_ingL@cS3c zUR190CU+*D9{?8MlPVTUFJSLZ$|hUZL7;3Ai0Mh(|^3}mN2os}tLBQXw`^Hao< zi@7^$k#!f!{DJQZkikH?fSd{ zz~m(Q#{5vmIF5AXAE?mURly{o*kf?Afi<=g^BS&ckOXcM&vUi;9T>Nn8F){znF-Dx zH?H=sJOTm8Tg#Bmak8bIzu;u=@X*F}wS`CoiJfA+h;y->4`Du8+@;3_K0UZy98@tN zg1q8U;0wq9Lf$!sZtmh0W+uMJT`XI4K@6IO`T~B8`pT+Gd8h1DhvSnGIju*R2Fv!4IHR=!=J01bHNFnhI|9P z5An~aN%_TYB<-T?#Km~P+A9RnMzQi7JZlyQY$Rp7`uqn;Zs2{{CO$KEsb|GTFfNks zvd8dMgJd1{jFkKzK2Ry(HjDJ9kq$mr*6cw}ap&AYB*LK;aj^t56bj=1;LwZuhocQ} z@()PR#sP}gfBJj2*^1de~aOYqv98~onv ztuKPeogwrONZWtm*!XW8|Mxnv8^NmWuxG3cu0HWeNkb>_4YbO=S5gXp_y&3b681BK zZz}c()Q)Hk-v59chOr8GM4Kh(Fa6gW{~HJR{o+09g6lsVKm;;H$3eN=NJ5r7Ugz46 zr)lkc8u?dfoA`O7E{b`|Gd7UI6pz1V8QgtprO#>F`S?pt^7%`0R}e$wPZ9DBE}wN% z7B#}3UXyc!0q@D){Qp1{2>jcmh5u~Qt`Yoqpu)vJ+*K!RU*9Gy zn66d?{P84Fnh10|-*NKB0i5sQGi%ogq26| zBg0K>kq7$Eg74Bp5+BusOIc&jR59l3)FOhCHC0V%Qj-}2uc3E77xMARr&-l_i6O00 z2P1;x=nbtM+ynv9BQ>xJ{03e=-${eLp@ivA!prP z2A%{vzi3IM<935Xu;}kJZ7{O{Jk!sTs_ZKU>z_MM*d4SrGP~-^7xDfHRgJ>KsErh) z!9r&AVr(KYhVqw!Hlt&TrF99?MYzu*>xR*yHa36V+oE(@rx?kek?t-3l$zoc#gm&Q z*~MeeX{ySP3A+ooShX(A28SQz`mHnQAt@=Hp8Z&}Fkp}Ko_rD?>TEg#tjS9PXVJ*4 zREQz)pE1Jbmi71LDHx?V3&npd|1^H)i;|H_6P?)Vg+U_87rGwV>0Q}bGc&8$XG9O8 zxt0!pJoTx!ikg4v8)lCP!DU6rg|d2ptSP>maS^1fu$CM>dW!wlHJ%3Nh>xo=-N|^< z(NdJBm)h^wp( zV8_mNFFu6*YXnD707zf5b;vS-*PZhOd=}T`$pb3AoDy;ZWtzLJFGCxq&lD(fP99Yx*isU5!McQ$c?@bSW|9B7R@pqN#xF3ArNiKvk}~ z{g4h1&ZHtf>gbwKJB0QR*%3bYsMpTPWr43t*LsBgUjOkcS+@ETl&rLh;zR<^;R-Nk zF@%n9?V+>@oOTukAiErEyb*i84Vl6)XX@}$h39Lqx3l}&)j)HL+)yk~bfP+hMe%mK zHaljSe(rt;-ZZtm^b>^rK}Wh1H4h!i5<#+8)SB1Ki2DPJx;J!hAzEgNN@L{4X1tQl zut#USAQ@)SO0z}XT9zonuymJJ%APPqNy%C7Xx;*f(zh%X!1lh_%oXovUy^IejQ?C+ zTOn_^&!6G_iCO)C7oCr~NmidnEU`lB3wy#0K|hd9IN=uRZ#t9aCk7ReRd#>udW_65 zx4Ss%D|vf8MB$RT{T-=eK4LwHRqidRHPudeC;hwpa`rEe^2JvlJQ(_lx>Q@A>w8KX zX-+h|xByc-AgywHz0a{s#Lp~ILdU1*dyCB2Szte`m{DiEW7}JUhlpQI&NQ8O31^tL z!e8`dw%L92b%RcOLsk8%m74ET!MX1@vL-JLy~)&AN6yv3WbDPuv#WE5dkeN^1Sg6Pjt+2N zC!1~e`s(|y(z*j<-4@WDxWcM15t^m41e2YAo+8^?YSZ|KGner)k0tR-IDe%KJkweEzG!!FdKpXRq zzMV?#Zx?2jEOxEw&7+uh@7pVOAx-)yf1I6hK&%+z2H#8DaDN9M><$zd8t&gl@`7l$ zR_~H>Xn*dB;gWPK5anmirJ}WoA;qK6+k~6xjPDYb{D3vQ2{k){`^gGBo6bC0TmmxP z0mxfet!Jk`_S8#&f=C>3VF-HoR<{<-w*DoQDH^AbBF{vmMn8rNr*RedrKhTe>5nD7ZP?3!{A%R^F6v+C-u4tJ z^)IT*ee>|}6_b_h8h>|CvF?6_9(d;lKCC|J+0@)dt^ z>?h-!fQ$hRaEXwqkP>3dW26}@rxd(R(fT{mbuk7r?2xwJ2Hv3Bo|!h zlx^7kUs56(b}0GB>@J30=clmwgIK+7o0z~oR0pAfHQQXAZ2m0tcVWaTAOWFE#(8Dc zQcS0ln{CQ}ggCfUpdwpDLD7_cB2Bu0IW}++K?uB7Tc?lY2+L-zq$5Aw^Rm@HqX4hU ziENx!2I1mQ6Zq6>T^|JDK8G1P@fxM#LHZKijm`Onh9|CC`k*sB;B&ZJLt$7XW-M_6 zBc?r~jk5ZJWE|u7@1cl3dd2VDiEOl1d;&Fk6g|XBu)`UpN|ZH@@InUDgmN`E| zl)nFz{ci#WM;lH5_yp9XA8s@dR^}unOKO0g%LZqo-%Qled5;U zxaoB*-wVIzSFdFAPzYu{wZ#DjjBS8m6Y%56nfqT)&B3Ui?$?K@ zD1vP=b0tx+RMV)e53$U3Nvf{V1J*{XKkaL@_GxEJTT_6Z@GY4rHEbLFX6gCz@D>2< z`T6mzUJbHI>J;Bs>BR9O^xmqv!TIqb)Z2wxB16`56OJ$9c|{chg0Q#cgq$9pr6xm8 zR;7*tr1_f0Mb#jE8;voXZB25dtaWIL$k#RE?)cbaa zUjOdRYl2W&MnpR5R)j+>*9RC&>3lfkI^I?)E`kBfI|v&l z(BWk@#eoDZw4D-O=QwXQFC%(vfMD>@oez}_hbnda?pumx-{yLx(B+9NJ2dTYm9@D@ z=)xj2vtLr0ylA6MWL79im;?-dWkr|J01Bk%N%x+TCKA1S5IkG`B7e?gA*R`7l&>%k zE#40Gh)Cul#`Iryl|G3S)1QEZwu?PIyj&9Fur|u_L7<)uy@oq)3 zGr@x==`kwclv;@^F5U0uyysX};7F;0R5?T2R1E3J>Yk66Iz>E_VjQ5%*Z5il>03II zNUMBC=qRa9wOL!o_WXEujfpZ8@f6%cbUi2@I+k~!f?QL`*`^>MKKLf>&%dY#pgFNE zsXpQ`-|yktQ{BOjoikEtwR<~_ot13wgn^VWTTQ&<+&9M(;fPAk8n!Z67|I`};QvJ9 zLv4q$?8G_qaz>f*dHCcw!0<+%AgTL6;V&%Q> z+f&y8zwx7>F4kW7SOtE+CLP;cgu3wRtUeE`(GV0hy^}G8J4) zC!j~=xzp08I-HQ?*JKmz2{FsyHN%@pP)A26lPfJCc$N;3qV5Muv8Jrr5X}}Ci~nf~ z)Ok5xbWfTlFOnKgf^ge7Zr%V}I&L-QApGrHAgreoVv$T~IRX6#W!!IJJUt>=_L3Lj zXeT=KC1Z7t#uy+`hvE}J#4NG?=WAh^zSG$N3QFc?0zWTRd!S4iqScI!JSjJ}CVR|n z$?g7o#qJ90NUBmFmYY2$MlL=|Ail_L4RyNs^%m=eVQ-8rSu$HUnp!t$@o z@C`C{XX(R3JZ*U&4hHW=(OgJEfM}l`T&o8Uv868dSsmc*M76`H<&om=sY;HFd0+Y9 z1)=PG*kXGubIli_5+!>Vf#uF> z`6CJ`WV5x5&t3C9eoE(P1A%(iMp=f7Tz@#3H2<=0rWb+`{qeY$5Gw;0IrfcA*q2gb zgTxc86PO(3XXY9@%@=K+27S?L|6sZG+4)5=nFqkE9a+c+3X?d2kqeoQ+@Z$}zdtP9 zFuL+kedGY~XNU={ffNkWP$=8x@seuwo>wbE*&G(!ThzYI+XhqaPCkaX?Kfp zS)>5zd*A7&jDv0(0;DR>x*WeY1kpb}R61!VyhED~nw^wGJ?>Xf{`2DQ-?Gf$*X4cg zgtBoiUwQ`WyMjmYO16c#{pjFMnEap@JucJnap2RBVn(}Q=0zM-C&{FBYJ#DaoN*`D zvLm9Wm&;lr-khD%o)Wdnf*nUv#VI)k=y!lTsZRm!p&M~1kvGf;KBQVXC(X#vF)Bx@ z?<5lo7}}KYJFMJmcymTb1ld|a>TWNsdVo{*KLFIN2~+ncyIGH7NER-`GuJYHoVmgT z#&Tc^A6o5#lVC-xC&nr_UwgF(U2*C1AA*0=sJk0xdZiLGM+t|sot!NLY&s4y1_4Y6 zoDaf$05bYrjJs11Yvm?nM)uh1_ijj>ZKXuYTD(Ze7`8%LqT z1l~y1qswXcc~p*|9qYrDk2bre2fzYX*i;RVNfKy`#7!yw1iF*R4D~cAvJ*Bm7#8}2 zJ=$iJR&@%I!S&kb^l$_st5S0>x2+_nOU4PkJ5c>N!W$C0E&I ze)EY#N2kSIVTeK}-GE_V&`@%vJUo&(L?EuETg+I!U$B2`3xHO8idO4q=>Y-EL#)b4 zkU8P>+*H4TGD;IHOF2=9m8Oe_S(?>2yQ@C_Y%cX^L<58)?V8F`$vl!F%S7Z7a(SB# zi=D3E*ej1u3n_fm0NfFV<#p~oU*O4!abhmqwz|DeG-X-}H~GJ(gYLq+rlU1{v`R7_z+ zvDT?L&=gezXp)*WPhqdi$9qc36Z`#kMOCJXS*Oj2vpC=IMV1G}sdgo>HFlRAY9ex& z-Mf!v`3H2vOB4I8MqUExniEu1hCd}=b+89AuFz#DYUmA{LoDRS9N;$^Z zFvmnBM6bVA>rd>2qMkcLklB~#3Uo(ZnpfOZM9?(Tv%fu90|MEu>WQs6r!SWIU?!XD z*1hFsArh9S@G1KIaF8$)GvxOJ5v@8=l_BqL$As>EHo<^;PXuC*=q~$_w)i?jz4Su9d^sj6ZT0*;>=|+8pQm!s&3*iyb1xZPh`muGXfn4@jO}YQu7JzbF}CBY!D;#nvES)QG%v21GA9h+GRIIhO9%lb)^mOms2;b$W_CR9}yoH za=3f27yQymJk)Tx?t@Y~3!DQz^Ij{_*~y}eQUQq#I~;UU74`HV!gYn!K`+tH%CeT% z45k1`do|Ey-79V)PA}yL*<4ZQ)ymLm!Futk>Ubid{Mk;AWxJuG;ByjK@;-`4iG}+x zdn%~V?8p}|dYT~PkH&2w@($(gr!v+;H7q+sI3dT-L!py?Z(KQ9%b9D%84BZ9*83pi zX#ns)wRH4+GPX+#l#pde$dYUN3V%c7^c$~kdEoCPdrnYMFN((4aMIMeG4Apsg%yXA zJ|a4Q5hdwZUN8RgOv7A!5Zp!dP;T5&Q|*p1gnOOwnbALYSuzkdBw)`IAnzE$Q zh|MEi!fNmkYUne)prOPNB<8%ECJU5C&(wZgRn(v`xxo3&?ZFUjFig?)w-+#~MP|dd z;ZjMRpFXj-10P-5f314wcfx8@@?6+5RD+}!{{)(B8b4!hjgO`gTD#~weGvvA0Ra%; zfF_8$-*)~Kz*2^NG;rd~7jDE4(>ut9z)gzq7=tAdk!@)cdnSyk_+i=;BKFD3sTNPO zbb}P5(g;Ji1?Kk<$49soLRjSebr`WrrWJf5Sz=&_iVM%>v*(sh&;Nj-9?9qT;G|^i zU%0?RiT!0-^LkHY1?6TWg{5&nDh&YIL7h1GV|B+jZO^zWq*?(!HjPn6-^Qud)3+sB z>w7{@|1S$5+A>KNYtGp4RYU4#RB{iKh?+?_IJ?W`9@r?p@6j|j$NIJq*c~%&kH1S0 zn`(ZOJZf?9Qrv$8t-qx&@GwJMLkt@6oMDOP%E1X#^GXf30tSeRZ2rDEeHgGP=)vc% zp0pEE9&$jC9z+CN%w#I!+k$XjeE&%M#M8!*G0qTT3)Yj)RDrA71L0Oe1r)(r@HyrI zyvAu!{b)r^t%k=U^5jRisjt{y8g7+pVHCt{;(~rm?Ww$)lH+5R7KfIMYB#`wOEZ~V z0zBk9#%k%SSeh3W3L+Sv(*!`|T@S<){>Q}N&GdrTQ_E{jt~$D*)g{8TQHiZ`^qZsR zz_uV^i@(qgT@Ri}gYcL<%G!G^2vZR=P@*k*(b*MwwvVcWexlB=|XP zzuBzGN`i1p&J=oyN&(QZ;z#bEoYnFOOAbjl@nD@&e*QTU$wc&!d6)46R2)G{*I##h zqi9jsDV*y2I62%aj{F+jKd?kXuv^8n6969_`BG=r%07~&{e%n| zFCqb!I8h5 z8xtqV3ck^ufB|jd%58}^#zVgppPzi*%u*er-HEc{)lBN4rIB@pwe~sS*+j`&!z$2L z=y|MV!++V4wULF&io_l{1xTHC*!n01wCrwJYjwIb1ZUBlMi=RGM>qOr(vs~-(m~AL zb&_a!w9p_RP~(!oXiCEmypwGm4^h+DPd+`0~}SLKRKk zQ%Zpn=l}&#iB-pe%5A+pezsLWs|Y#hw762fp}y4plMeA!fOwk6Pm9$-*`) zmvGYaJp-7ccDs2q1z8tRU}dO~zY0w%RRO^p*9UWk6-hCwv*yJy_9YJzIn>p77dq*`1h+H!(;zsrTq}p?Wx}NI0G79711`-i9K*(zQgFde5cZ}zEoB# z(rC#yEK^Mk{kXnN7hbKcD1a}nw!;lhdh56{6Zih5*qOgaFWeJdl~J+SXFpH)8mFpe zeO$2Z32f{C-0wk`UL3Dm^~X6B-yMHF8TfZ}`~N(otCF|ll*?a}ysh}7U$Uq1MLln3 zF9S|e2RucBz3^VqXX_|&C4n-hA1^GIC2ZIf$md;F^4R0%acv#<>~0q);bsYxW@;P~ z$iNynFDTXDoNEz|PJ7T>N#rPW$VOy3!7_ zu5*HiIguN&IQ5t;a$?6w&)Rn*S}&T9v#4$lJIPJLsv2}q*=QDWv7>R+CwI`i22>y` z*!}YhNTvJ!KSMrX)-)$CV5KA*UjbmYG&~((HXLTQ|LBO72^qwh2$|Wq{`;AQkeQu1 z%}56rwF$v*$sL1}`TxBqft88vUt|IeK&x)E70v&pET0vssp*nk-6j6ZzvmJ`P1_v7WohlRmk);ao@ zhe6WLj}blGGJni(LOeatIe&8Y1p00CkKM!N}Lc=_`D9QM-DuxmsmU#xoZ6 zn;%k>0HWDLptDE*Dv#?GAn@iW~C_sNbm# zrKixs`(D746Ez^2mFL}J^Xdq~{f;nSSS3N0{z2d)J1FPi+XhCwA#}|Pci9o0Od9H-N>(#rw0LrtAuF+ApWbPxEY59WxaAIf?8SrZ!1DlY=Ve--3yAvsdYj;)pznVK@QSA;B!E z8eGB_j&5Wff$%abz(9e&PTWT+^}G84j?})y8f$w6!8^zxg!Ap2rVom^zu~~snLZ+u z==MySFr#K%`xX=%RJ+Nr+D~om_Jn7_pkxHb06OIr(Yfnlkt@AwFIbGdRYuEXYyO#* z`35BlE6AUGzhNRl(kRPO1tCm4tun+#WV^$_c?h7J^V*6HfXhNm7V>LE+-I&pk#{zA zoP3%wOZhW2{e{uxMOf5)F+3@J@p~|e(;_VHe#0ywkoXU4t@VXT=U7~gHK6d!M2Az) z(hlsKB(_vO!7;3~68u0EI~7CPSugyiSN3>7WQ#LmOp#r`nniz9a<__*|E%~#u+zb@ zKWK)je#);_in@F#f_vX zn*)BrW3{ekJ>?#3C zFh`^2sWvrQ$V=N$DJsWR46^gwj0vCx+RPIB_*tvcZ&ok8mq9;qFV;pwa(KU&^uAlRIuM6cS}#lL^C1~ za_#U+06}JuEAqG=Uyj>SReZ)Qaxfu<1fBMr^gpEQ;`Hlw6zbju)vu+K6UurIxim=^ zO*@{BLJ7MJ73($D)Tq@~5yB8q^aw0go9(D`E5qpujHqP}2T)&Qg+R@@7of-jc0LOx zVCESb{oCWYI+lsZO!3WLN5drUm6im9byNzv0NwLh(&6cbP%<8|1-x#83>{#qkiI3<_3Q|$aV)aaJmgu1aZUm;{KqAjLGoK269^@vh@0jJXW7kCugx#{_~kg(oBZXN(W0wf86~ z0NDw661cdrpTncC;e^7}w*yWOCUm90P3ASp-s0NHg@1=;{Fw{rY{~{sD0dKIpj=Qj ztP|(F{ZKUenxhB3t$fcZ!6s`heai{1n#E)HIy}Sy8GA zb*Ym0H5Zr(3g}7|@Mx*DtrQrIF-2jC-pM{w_HHQmQ6MqoAc?!hnGC`G*a{LP4AWQZB0)C#?V?`r;YsV5s8` zRIgu&r2$JMi+MgmG^maF45`nOT{~=CXLVA?u#d?yHTKl>2^*|H+0heH$81%w};oWpmDg^PZ?=di__{ zW;_%dq^e*#`5iFl;y!adsQMxRRJ8&sT`3k@$)EsMgWPM&t3Xd;NL zafcJrB3JAWQW^?CD9+4=w!83zY=i|xneHzTx_3G^Buv_>2&P(EhXRGmokn@6S;NCy zUEFGZTj`O?jd>liGypDAt&5Q)jw63Hc&nR@Mt%Wp2n>{9x%(d#cc7~dRT(u+B-KFb zl@#}{OqWOD1JDE&1zrjNUeh4y>ARM69WfD(XURY2E@hb2a)z?oKgrFXQUP@cT#b8W zP%A^Fz@-(52U=}Vtejm83I;N1_^PS53WKwz*eoy7Xm^p~Y290V;C^@KW*)n4b6?>F7aQH| zFEQe8Wr1xqLS+Hmf!kQ2Hf-D;;a6z>(#FMV*yuoMI36R}+$|OsmY2t<&k$$GyuW8_ zqj-wQ44Wy8+Men8v0w3`uImgT6iGuHXfQ`1BOV>0tpI5!B=DvuD5Is%SvhhHNe;*H zKO9-_YSeK59F;3ZLn*{FJ?Ptllkhuy*5x#?K?x%Y2loZ-^jjcp`^s9f9dBdrW0 z@(t5g2(TT6oC3A1MD4=GOpqhBY7)w$6-&X2ukxuQPN&gKm?gNhI!*70%Zw=WPVj(YLLpMy`u88clAT zd+JCvD@6yJ2Pb$MD24>88-ZVv+nw9gh0Fo`8*qj_RL-K+V^0*4>KJGc<22DZBTWt5 zRTr`~g$id#L0Rc<5Z4v`i7KW3!KINSF}E`l#yf4}^Jv&ufl~G_k*s=l??J2NKvYbq zO0)><(W1+*6u#7m-cn%iVY6UL%~AX~bigCUQ36ojUD*L2IHG^*B6P}UPsSMrkv8M4 zRk>1uut*{klkXRpGe?AgWYMH;%Wn3?ByRe*q(lH#XcS+6QG}=4_HE`VlZ@RkIoTdh zq)g%kJd$1WX28XTNyWr6&*vxuNf4s=?y7<@aX@rlImZfAQ=UFQ!6hHc84Vhj1_1ol zlhw>7H^*!(xcs-cjC^r2bD5;jAGC#RJ4ilJu%0velhZK1ijMZ}Se5d99={6VD({pb z)gU7%P0VKwUv7q9a;D@y4xobY^*ghCB!uIT?VS*ZEZ0 z5ZZ#XueLvB@^ECBTs2rh^l zryCt2IS{+Q0=F#Jpa4P19Hv@7&XD!*z%QO?fl#*;Oqxy_C!Qx^QbJ*YhQIapn{f`| zg3&7~knXe3L_W=ahituJn@Z;QJaD93R>KKQMsX9QS#tC+shlqIv&^_=XdF8-aSMez zIKiXp-l~8oIC#AtN+fq-Ag!ng$m=J0BznSAFdILH?kDzEHXF^Zh3stxM{OPCGXfVk zJ)>-QiN9_Tk6Mfio<0z(x2}#Q4eznO$X1t&2V{*WNL7*~j1-HO<>CL1i~&oB;~VnL&&MOqLM<2F#Gbfe$PL%F4v_ z-&atx5wSA;hk5)5jIjP(v;W^$urhJ}&ov7XD>Ku7AxR3Ne?-Q&53qLoA_4FoG$=DS zdj=ygG+stxIS4Ew6W9NZKWF>j`131W`ya_F`RgbC+>zJSQyhsAY@)8JPUb+`;W;^u zabjoMfrgMODxIrbDy;M$OaE6sB$)X`d|hhE%zH=;*nXH%$--X;wk~07dnE8h3MmX~ z_*N5cSUC#NEU}=tEJ|UVBL2AGKTMp$(Wf%tS@Ftk!s!x{Eb^e()FI(;-~gZ`>_FKl z43QLOT{R+gGmxfzV*;p{X)`z;LXE^(5$4CPk$q|4alHgI2QmNGN#X*CACEfwaZ3VVGf9VBDhP zWn|Fc@>_^aL3d`^y7#W&v;bD9nNXpsEE2@Zl3rG&WLj)euz%2CvLA$MqJ-HMhO`u< zf;XrwDum{^ROrNn=~y3?d4voVc%V&E$R%aUgz;>DnNb);nUxbdPC(tv} zOp*dKjS3@wqcn-FC=9go6a$r2@x7l_klyXvsDmA7>vMi4dG+T%)_--?&czQul;@#e zjYWcdNdIep74H!ehgkSoTA%qmD49lK4A&Gw7n*7qsg8xq0x(3gND}V198?qpKkz3q zU?0$V2uG&3X64%B=qW&1_!tu`A`L+UYeIVW`JvL+=a6xsY4COw zNoV~2aWGZtWeDW0W1s%!C=@u|BB|jxAYP7^HA8SAfT^`mweOebFcBD3Hwl=cj2AbJ z!cJ5=*umrvqMCUV)}Us@s=GcImS+yB(Dfy5=0K~?hH&Yw2rQlwxiYo1c-ddPkO$(b z>{lP^0`wsKJ%{dUh2iIO8J{&T{yUgv0&I9u8S>k_Up~^J!>>gKi`9cneIlDA59rdq z67h3xg&c@F?E{Z))0d{ z6S{#=+{JO)z>4Eh;mLOa)@Lr4lWZoN#0%mM9OgP>n-l zz;7YPuaC?`lMeeEv_RtSUyE`%zkn5z;kCitaQZToE4lUQ~<#ny@6 zVgcTr(Q_BMr7-GL$Hba&l;Boi4wq)v0IX+mv4^Bc@m=&ZczGlJig1e%cy%5lj0pfe zMK_+=BASQ5Z0Y83cC@({1sP4)3!dQ zeWN`)XAz)2<1KCS#vA8$ik3oh#wFTg(H@4DmwHxAdrNFGFK!T$3uz^Bqfw@afLU#f zd#if36`h-$f_S1|k0e^{1v{Hr>Gl#e;uA_W{8YBHF*{^bGh~~q*qfbdkN*2`R#z`g zS)MaT_MkqiayuM_XI0D|3BaF9tlI0k7R@ZX78FaIoSE%abG_WPJ?k|++_jIyGx{CT zg_cJ7#vc%*^CO}5eB%5g>*E3-09W{kme@X#MwYo)wlEjj1zUhLQ`{Tgn zkG?F1`+CNoV)PWyjcypQ5Xo7gJ;(|poJDmj6PmgYG)^}6g+AJo3mV5M!L9OpkL3(m z!vGES2#=UOAo!Q2FjryGJR3XiL~vZmEpGzk;w|-NJEr-(Iqu+9kczQPK=tPr`?rGu zkDXT)_CL197bg+7oOm$$tKMe={-MYb22ho;>V^lZtH+ea?b=c)BhD2IMN6pI(g=Lr zj--kF*;|28rU{d2$s8|$WjKwMw1tnNVH$`zW-=6Za%Vg1)&;Tt{?=lO6{1<*!Pb5- z*4oG)fpP5Rrgl@ie|da@fX&UN?e4W%i}s7l^_@kwAy85)h~Lu?*~EnL=$VN=U`EJi z^SdWRI1=mYZ*uo>yamtyd))En-yskkdWsz43+7Mb#u~PY?{M1R4(0S1s>>UurR?Dm zs9`C5qtm?yEPrZ89-nBhwLTEyqgz~9H+^i_c;Lw0pJ4-z+`;Zj2!U{JGG)Jq?h2~Z z$cp1pln11~EK9%8!~yTl8G%Q!^zcIx&;it&^?;`FO(GtY23NyT0~fLYddtsZS7Js< zN)RrFE^FO-E^avXR>8UM-%WYVeaELh8`F(foj_Q+2%%DbkR%iIVwdz{i=rgSe%Q#k z+ahGWQIUW&QW8RAnPH_yC%Yki<9*JS0p6ftG9^O0%~@pZV2oJ(175jk2ZS}b$mK1W zP*WrwS98RO<5=N#DTEN3g4aNxF+acXymfL&GrI>+fc1QL@&-{6`g zVwfhjqa^kwPJuOqBYz*676pHjmSRM1kPFJ_U?d=7LUy!xEZV$sk+e6@ks>$)zs;|& zh(@%WG;Mi)7n!CnVAeyvUDyJ`p7aH}Do1IR5PwZhb5=w%V?#^j#^?0pL zDswpO$TS1#GMWS3wi$kL^UZ?f??vVD1H8KOuG0FCk4se_3Yhg6mPl$2(&~#FXY!o; zl#wz(SSyC>G$Om~5ZQdww}zbc$G`Dg{{(D1U>h5>+wmBzO39Zqy16U_Xy7rDn(qP1 zjYSDiHYNM;yi=l>RDaJ(*@L8lk`K0U*$ans(&A}7kL!<1TeUgeNYfjF30+Q616d#k zEfmD(Lb1XiF_()da?EAJ(fGi%FLAXf%g0%hO;I6jf((3-3MO%)6dZE>3gVr0!UdG) zTwO2vZETc`#(|cbdi&@MxD-`AFr!R08+e+ix0C;o!1lh#Mce7+- z!7$Ot&u9VB?z213vei+)a0uM{(neou&eIstaQ?eDMS4_pF8nUD^s(5gt>4ybr6JBA zM6Ubz+tNuR{nXpDZCD~IrJ+GHHx6JGvsB%2xsrl_KL$fFYNPIkkDr&8xpXv0rf(fD&4oZ&lP!CG_G0ms}rxAGn6xyN#mD{$WhrCkJ& zX}F^LXuP7GG!-$HOc?mYljg*e@2#PZ&oXFQnvL-T7vi+Ny}xO`BzrI%hj)i{ok_kldr)siAn!cy z00)|PIj<3~lnd52)77WZU73^fM_wLcv<;S~O@n%?_VAc;>RiQ+HlWLjHRaaZpqe;1 z72?`yrITmne0-TkCZJfF_pq9C+Pt*y#_F8v-v}o$q3AspSr2*Y{Fs2&cgSkPL{>~% zPrks=BUS|wp%p*bJjLp*8Nkj#P>%15cLY%-!sV|2v&q35K=maw=lFr!%XmC;m&VaG zaAZ-g=E+Qr1jG+c_(p@fbwGj(3&;XD^xe0LQsS;a?N5EQOO4CntKX@>J&hrQtFxY( zKZ5`r%)kEGj1+B#09wF^H1$q~lR@;*NEV%a2R0W#pG9>r_&=0{6?Vb_G z%0Q}2?^WRkxf(xefrkB&jlWYqy}5xF$9S=D&4Hst#OsPjvM7KQnSowrH^T+gJ*g8J z12=xh=%8hsWJ~8Z9kez}OJ<{c$pwz$sv-tPoCU^>;sF(*!8{R8lPE@Fl~b<@EF@7d zxJIcVgjn9xaB8kF0j9xIb~cXEdGGW7}dzlft;ENwar3g+AE#TLX<)6NM{F9#(~T~;=)Dg0+X}mk*@sH zD3QLa~m7-Jd^2n+s5o3-m);d;XXu zV}a^ELSk$h)8ojR?qPdXh`9oE2inQoc3( z!AFR0l>mjSOn8;rJfHT}igLad~)=C=~yK{O&4$5hnu7bhlVQuOvsr z(M=5kW7^1j+J(!4IZXxyM@bW?!tsr|(_r%8g0ydp7VWQ^e}J@O&IfW)>$9PAti3&? ze}!Ul$3{M{Hs>6aYOZt))H;T{k3ntsHC}rN9sqvN_1#9n@N2(3Z-+`bo6c1F=dscu z;<-r?^FO?JhObPb^|ONFP^>RuX9R4ox^pr-5VE9qq4)H7-&pcacA;YSIxnBLx2Nn* zK!ANsp){LLbApRTSY0bHvBmW@~g%z|27_!mcZZU?#2t~>}!UvqvT{}!YsQaZ&QYjB5 zvtyA-jEcS7v4PQMX!D^40cZ*N&7_kAYCDGeiil?f@vLTVF6FCiyK-=pc7DDLnpIcf(4E9)#bK z7zY{u=Cyc#uZQyF05L&}k`NUWsb-LtLs3CjMKy*X3dWuJC`hp8ns~AMVZj=~oe-vY zK!qpP$F||T$k&xij77}Ag<@d>;@<8EUlz??8E^_(JCRK%ZsAwq_wfw)EwNGuQ31u- zf0Gyr@(;EZ@2)GnUQ~NFN4kNpCMcheP<`*C__-?z!0x1xeonklCI)pFTGV~4o=O%7 zGR$u2M(QhxK9KFhA~CGPm9AUsPNH)HC7g+>mhTdy+n z_7fi!ksO1=)QWPWKMLcNB6Ac-YRbB-9J=kInkO}At=N{ z-rM^_pT!qd#ZJ2*?RFMM5s=XD)C@a&wh#e{ds@4Mw%TDuv zM5*2MoEVVH?I-(q?!2M>ksMBQ;pbNgV~a2RdUh!-HB^N_yzZe)n3 z-is>!$jJC-xRJM{%{h^o$VuOjE$e;g8OE`|s2R2Pz-VBs931RFlYGd^fBFIj!L z*G2u_Tdz6&J?|&i%bJcuUM;&675p>h&?qeec(3j$d1S@4R1n%NRrU zn1>TIq_xmBa;DnkCl5<`H;`Qe?aPO2X`GwmbG?rjIrey0y_{EPdT#2w9FvZOkGy=^ zL;atRbttDf_8l*iv*$6_O+C;(!ZCl*z34E9*hYFiupDl`59KofzUJ$QBoeNCWcZ74@S4;HwzYE))#;UkXIR?k= z1?D>msoS%!>6~B#wlgHq;&3nLjWLgp=Y3R&Mp+$65A8eP!MnL8+}!!RwvNV^pqJFH z+2mNS6h^giZfUfWYw%j39X?h%>RdnyRQI@w<-0bgp!w{A$=2SGnyoS*JYs{`r!Xqg zuM0^x#Y+r7sdTUrShDHHVCrV>Ax!kvzD$0+u$N<}j=dp(NV0i*UG5BrREz$oJ6(cQ zJPCqRvb--rwdxb2!A~UqPF30vclr}OJDH2T=}nWJOp@X=ZdWrm&D*@kh7nFGPm2rV zrz$PWwyTj8qhOY1JH=lgQnT&DlZ$F_hGS_DvlSw8K=76QxlQ@Bm7+ncm|7We`iih^6UDt9VAMrK9Qi+oJH3ja($3%xn- z>~wKrWvp@lPpVo%-zv*-+OC;+DTvk<5b67Oi_n#)F`6M%s&2v#X=DoBRNdz607K!` z5ron|rxY>+Wk>lfk<4_ij;(jC=bkCP+rFzSFhq5L@@h{TR~xi3IfJ4mEwf+rzN8Lw zI&3E@(&t(p@s0X}mu<$M27KSmc`LIZdf(av9Xi1*cy72!r3;YX>*+EtjI>cKhH@Ym zgc}>u#p&J8rSvf7kIQFz4bcXgD@ z_<>kCE?(+S`nKl@dYt8_j;`dod$EH>=3AcKWdQMzb6?DTuaxsa==O_d7(X0kSY)47 zc*<@C`$R@7g%<}_JHQ>)m@MGy#h%1HC$a%xNAe1|GTE1_ML&j(f9jUtWZiuKQ)T&n zU{Z9k&j1$R@#pD8$wL&_6oeR?2AJ>`y7-qelFuh}G$Tz|Fj3Zkx5rPbrc|~*wtJ{j z5q94SNA{7w`W9`{%US}3_ZWt1Y~>Evzao4+RQ3sh)+o||Oh4pmm<2Ai85`0|)Y~w? zrPK1?NTuQZYAy(Fx*M`a!v;OYj;X~hb|{T)p+;Hfi|=(=Jbi^)u%|{+nPfKscg=(b%I>nolE))9XSv~8(}^$m7FS5(Hk zVuBnVmd^x6{e2~yg7c<;T&$6`Tq&?(x*;oG-v0cZ5K$If|f)m2X;BJW3qxI3-?d!@H7Xk{nW$?&;1T6DG3TK?1q?s zLug8{8At()9#w_?s?1mf36wQ+f^)oO3P~W!pK*DPq}oV%mO9E3jdK$~qrq50mR@6V z?IB+b-zbTprI5r;>H{)7Is)~c7vdbu0y|31AN(52OEac*bS{;cDUPDU|QkE`j8em`;I24T3mZ1=+CmLC3QFL>0 zeitQ+MW#o3AvF;vBbES&a7p$?-G8!zuVAFzrR5s*grLl|7qCy=vUa^cYgHCv0J3ms zy4{SaqN^+VP5ev&55M*UPGZ{~0k6VUz7FRti(vs^D^?MxC@|`xV(8!yoCBYWZvxd# ztSJ%C-XewkE1;_dxGJ;wXrv{)ELkSJkV98^M?J1+(GQ)rF`xiSfQmv}wAP8$d6{ff z>qNJBI(1fy+yu*1Dxa(hc`(AOdJ~cQmMj8Ps+ig_Q*^BZH16kZ zsrG1QAk<%8I~?9WFy1tP+naZ$Cwmb5eXdU<;;Y=AKmwKXBtN>SMj$Dk-Xya)hCF@| zXjnxsN!B2#C2=>+tS(p>rZ~0No`e-iV zUNy(m8+K$mwk5*EitN(`YTM>8X1~w8gY`eyvdOX$jW}Zy*{fUJol8MG=jrMNTBe;Q z01mWCLuLTV%umiQunhwP3niWWx~J@(Wed@fA9A8xaSv2rt3l_;pp~D^hcNwt*;>6_ zfmUNu7IwCAh9K@RgyD5q=n0cB-uCckQO)&&;B9RkYe6Yy#qWbIMXGZH6$7m&@ZiLhu z%BzGrnIuRd&RhtVgyERwTqa7@{IsE*u%AYN5QP!U66kFtbBLhGlMrFqaoqa}2&VE1 z#Zc4^b?S&wg=>t0fF$h}1GeKsqSJ)l+H&X*k4rFO@rj)x0W9cJvZfg_Ha+1Tx7xxh zEn$HCShUJD1Ij;1uk)+4OE$%(Ke91-6y~!&!B0Q`pZ4(ES~H1Rd5`Xs&G=TXdV!Qr zsGR5jdL?h2NM9J|*1zjW^wd$jGSi~SHLyLz9@qbIK+H|)Rg(_d=nVG{ zS9ZAU4HropS#8_)HCA4#vLW(g<%3})z2^OyBp6PflZ>{VH5rzjrh_c9b{8vpcZi&n zDyX_&Gp9U|Hq^GZ$e8g?&CVGhk4=ExLlY296WYd5!<}_nXC2)gxzox5lll32xw_7n zT5r?oE_5SpVf!ws<^q;oaHO0k1OT{s;Xe=BNH{xDyBry zlVB@UiYayOgYF#z+{S&fi;pZ_0YiUz21a*!ravxG<+HC%Z^p`KCEoIkQreVosK@>! zFp8cP=*eMLWSf_GXxD~R`urUY=>-6S8nS$1>L--E(Kt)0A$E?J_63|KB5d!ewIh+9 zi{=w1VX3?GnPm0yx(e!5q;St=r4?*Z6dbYgo{{)XKr?F3(*S)|PihOw1XJo`Eq*)c zUZlz;i7Kx+zWez~;^zh~QZrr>Acp1&wlnx&e``yO*x(oy565;}as%G5*iQlC6{nQn zAjgZkWRZ$wX{F2A|0et}k*}5s=oiabG`>xyX*;3wr0#pJ^wo~%yuZEtk(Ew<7be7!t6*gR~B+p_h@t~GvohFliV=x6l_ zoGa8DC_}V!y1z~O=yflJrqThk;4Xagws-DBRB8n`9By{qdmWR(* zLybPt6(HqQa3yC9P~&%~=@~e)e+_9t zXZX=D-`!&-{~JD!6kFwZiXcDPijYQ6`Q&M9;Ux!ldA9z(>Nu_V3l{)|ZOpu+&P%R% zo+!k-=yH5-GcZVeK@|n>3?a?HLCq=&?`$*4Pphv&RePFVa#rIwV(x1Y3K;WX%J@sh z%q68HFjyTIwL7%s+D>mvzZ8*sqfkZ0+OP^|gD-qK?1%mIziD#E)8dobg6HayFCuCA z7}hs@blBl^f|lKnNL_%t;C$3+VhdqC&i9C%8UvSHQ@j)t_u9EE9Qhr$)n{IW77tqi zAtF{7QP#h^Dzu=^XzC%OyI&600PzRn)ndV8lfklg#$t=AzpWm*@OTy?;>Z{Kg13biY{Qq#I^{Pw@_LA6m_UnS1Un>*G=e5Oe z&ml12rD}|e-1)1VlJzfoUB{0v5k@$bN^55k@8_*K^45oDVjwT$WQEH``&E}q37gQz z3|-ceg);m~Uf33)D33&tMx$6ZZ29+qD_hG7#LE=naoisD4H4Qpl>Fb_CkQk5|Isz1 zIDz>P+x@nco6{;#;fOvK9ezv9M^VZruaapOPZCUC&*Al$&L2%xMi z%>UpqM>j6iA{papy)a8G) z4;+z0XW=XaHK0yMJAt$vIbhe2nGx70Flh_&gC#Ly zW&_s_sUgn`PjD?@uT>{Ip8TmJVH^MZ-Os%dF$&Z%*eH-#n=f0k>AWTZ#u0&Yf5y=8 z^Z08S)6vvb)nSTm*5Uev(>mL+gsPMJw_8rd+05tlNkw7D&l<-Dk*lM{h8sii@lehM zfV_>vJizwgv7D#({R+LyV$|uFWa?+X4AbZ3IqA@6WB3UQsJ98(=ZLxUN{2oP)wT$? zjJgS1=YS*4+S@!~@_4$Upf-`?fa&YmE|tdVv7Y;Aa@=V7<7XOC|0ecEW%WXWOJIwP zw<)@WC{KP+FW}~W(GP>4fg_|0aO{ZMKbi?17g*YV6z1JPOtz z&arl_gqZy8lyefqAI}yyKi)%xKyE$TUEVCZEqiB{Fk{@39q9LMH`nfrJ)cPd%_XQY z*42qRI5u`7Mablg6jgf0o`z^NmyOu&jQoe>#Jccl=n6t~`SipIL)cMKaja_>P|tQC z62a6qZ1+%_*m<@i{$QZ_t07|y2|O$5Q7=K?_quHU@5w5>&pDPxavwKgyaOC3-^}-+ zeJ*4j6ag-sEvKVqE4wv=>&7zHCK;E9Y;S&*=C-jmt{m@cPyktV8Zk-IK|x%F7>1O=3Nl!+(!sosdD4Yn@LVdrr>&9*O$q;famB;zajtTuyTGUER8G}M(o4unQ`CWz?w*t1F^%-ZdL0h z3n9)*AvxxR2tBSu&N7FUkS6WDshVjhhU0;F9!jTst!kFwJWonDEqSrSqYt|+By4dw zOOHxsNomocv~yIOZi+(Lo)+g$c{o!IZT#77ZZS|pTwO3C3E5Y2G1*QQ5Bw#8X5cG9 z@^bNp(Gm3|Hm|nP5EMHekedL6V!*z9E4Ih8K+OcKvR%$C4;e5Uz z*}T_e`(PQ1Z7zJ|*4;Dli(D6;ilG&bKE)oR%wc$*T@C}j#U4-5aHgjK&UCHBn`p7y zfwHSnE1UH)hf48RXXm_A`*G{4(tg9J0t6Fwft861zj88MLflC;;8~3Fbw0Pwffpy* ze#U9u%sc<+#9i@M3*}AFGAJEtp7X@1XqA7UIq8QaxDGJvaUWY-_Zl6aZP$wFY6%Xn zF28KliTQkCEdMF)*c}IT^c)8Vw+H#;{hnfTm2<8G=|I%-?@R2}39~FY7Tql&lxA!+ z?m0M;26Qll0q}o*0NPAUWQC?c&M)O2k~lg^32sjUv$3mA7a8RAO)9pFw+HGBYbOWD z2`zNI;ed(n?Jk9Bcot1Uld4ReS%x~BYK`R@A`_d5H>qqr-#V|eHWZGDKnN^iD~PJo zCsK2J5!5Frl3KwtyPBdM>#_X>AO9RWrgZR=3viLHnwt7ifSeC?fS(yI>eOV>G9-1| z>0!&!_~94@7e|GK(FZIx{qTczgwAstzQkS80E^!caoG(*bg)_S2}pgB$X@o5wqVsl zA3wneF^`~En}w|Z)+}Wdbalx{qZubn-$!hw7B%X9v96kw;jhh6wERyJ>H^rs&o%gj zHNt0D{E2xC00J}lt(CW>RO!1YzZf)8NFHYIddD1N`85y=J0G>s$xXGL2M5^pO zE&i1R@1KK0oO>5F`;XJ5s!^gew*afxQhwf1gM?M{1b#$>i(jsXGn*2V>7ugjWtr=XbELX3da?BT)I)|@S zhb}N0fEe7t&A19~e|n+fsk=M8^FU8=1mLO(kXtJ4uFig;s~ZL$>rmzL5ct=ZpJQtk z)QZn*T?vdQhN!fj=Ln&x`tQIVhaq^8Yxg&zJ<4#IIi3Fa(tv#`L$Opd$?J!`3>H^h z$VAW3$Y%-|yE;D4EKg1-5Af)CPnJDQ8?u};;OYj*_!q^n8j^^l_{lWfPlv;sh&dXi zye0%^W7@|C;^Fn%Z=cv+4pT!n2C9FW8*brU5%=#1)A3W?$A_J~ zc03Q|OkmK-ih&I&a@vWW^H-AKLk7$WH_3y=C-UX;z#&@&JuiK!Qn3{v<0gXu_%&?m zB%}DR37^K7I4yOmv&~PZ=7QE{5>%;KfTFW|>7eW|O!tFBh?o>PocHSUr-Z!5s=yr{ z=UDocDYZr1V$w1b1FQf>@>)yj)e;cjeRn_}*}?O*->&|KAQ31uJvL1)xu=L-^ulmN zX5gQkCmRs4DCYqZXoH(eX%`-7aWG{kcxIHWfs4<9ZbDu^5Wccqw~q)Ka2=u?00i=* zVyQOW5x1yY5SJ9$O4Pe1V+kClWhq5!NSU0brl0ShEccuhV-po6o%itbG>8EptuPyE zcfl2n-&{16>z$A1mIwIJ;)SB*MaY>hB_!HNGO~x{+R60OQ>SD6qa*zH#yT}-RL4k^ zr$`YnI0t{ptiF;am~`Ay%8ohW0LyM4zX@9CW4^=Rqs(-S+>t+yqBgqy%xCs7G@cnK zzv&D_Z6=->iZRj@l3WAuISn^o*bp!^&_qj>r4s`$F;8E9nKnSPqyizgIkswVcQb)S zrW}A)i#@8xAfNs7ix^%v1DSX+k1BQtExys)j2~r=ha@S=z8H=fM{NTC0}#G+R*@6N z(0(H4?}kI4gG(AR$HRb0qcRyyPt7`HK;waYW&cGv^lKuJQtxjx2i^cNKYN6vIG5Vx z#ru&sGXm`agS3SmUM^X#?h!-wLdkJ#@j)&%Jko$!_=B5&qMEHfYjl)9=6#U%rjmCC z6FC?k_Sn2X^JDl}C5F$IIADR;|CY&Q-V(Wn*gL(e?+%qlQ4;Z#P~Z70ZgIX_IS26y zJfSy2x|p9fPk~vNK4o5bZSxJ=i2O`16%^?Jgby@?h*zQ0dJJ9;!uKovNyQ^ir}j-R zS*tEk;g7*s>^yvCuL%}|FaU)dwLv;ZD#Ihu#;){IJJsq(&+}1P1+1sRM&NZK;qsST zZP^xj7T|M^ZLv>9hjA)8e`46lD%iZO-H2jV#}E3W+70?X-MIE z2NlQy3XN)GgaP3|`7S{h+~K6ay;mqTE#vK6rF?z|;n9;)2+z0JQs6oE*GTI^5wr+` zCXf|}aP3+JykWhvt!T1B@=gfBOT7oRGP8=zp#juTxsJ@t7BJc1&_^^N=@5Ai>)tBl zTO2tV(Ynt?WjI zbc~zE4BhH|M-e$Wgo&BDw};?mkBoLEVyCu9WGQ0Tn~KiWl|>}lG^;^l7!wLbh75Q0 z*kjq9x0AxSb0tC=v2mVdI^;%Da#7BhITo<=L2_t9P@!p8EHw_vt>81( zW!y9%wmX`R(XK9ZE3Mm@BF_Mab<~j={}PGW1GyvH4CvV7+~uM_KD_x7$6^3E@p-uV z-~@c{e|YZpy)dI=zgu8uM!V@^k;_9pWsxkml~3^>Zs!8&B~1pD8Q=^;693fY-NaN+ zKXM)-xsQPH%w{_uRf7_dsop{^fUKJ%%$3RVd75*2Od0$8air`$QkWh`S&$4$Q~Vhs z0{8Br2COIUqdb}nR*q^jQSkgev(MQL?Eq$l%Wt5CO-X&*$X0E<$mjSYk0+f=@8$A~ zf5ZFWLni8Y!Rf8F3Fi~hs-2hE^H`Zyzt_;b3;Yb$SufXasP@(Hfa?oIP<>?m@$9XN zCcmKC4CP$`Ax?pTpy#V02#;TvMoPYfc7{X-8=&j^{TIjU_OaM^WL9{=8%LflpR!cd zmS0>l-d{`LAyPo4i|fow9>axJ;lW$3Q$E$TeWC>U8WLPcLLp~pQ9nHrs!B0AE02?X zjrhR%iwDMoWXn^mT=@_kS3G!G{oeT{kPz#$^jDST;ZdaO+Fyi6Fv;0RQ^*xUC}bjS zWB{}?w3lHEaF7`U5}z7FLvkO`$va(&*+H7d{JHp_+xT5Nbg%X+G45o(I`djU) z*Vzm7(Vhv-_c}zlewnRZzwueqTju=>QnIDZNT*#KmdY#!6@IcWCi#ez3xzM z(F3lPRhAvv^nzT2nr~ef5@VBxmq&vcL=G~U#YV6DdZ#9 z%W;MkVMi$_x zzNO546z&IXL+++j_S?SH)T@Tj#Hd6}>=sd3iL?($$PzKD*klDxi=oMx zh1c}tNum4RzHE`X4>gw?^-UV?%We#^`_$=-7RQ+x`YbWa=>K9ZhRH0T^JtDaYud&I zuUmZAI=-mVc|fVmeopc)y1-oN+>7^IY}A2Ak~mi(~QyH+OgefT>tHaRBX( z&AWH?M_uvr-<@sG{o4ISWQiw-Y}7D_T#kyKjgdDq+9wTZBFYF0b~kOyXr|BZ@rBSP`s z%t*}fNXx1SczBl9@SqF2RUXrUS;b5qZ*nQf`_ajX z8W3z)3op6iK8DqgmDv-Sta-kInZvcvjJlbVkFLh6e>S^c{HWG{sk?fxBVoI9W{_1e zwex(9RF^g!`3m!)QLo#_4`7qp^IgO*%IRP(9M!Q^1iM}U8`qRT6h6Z=?zOR|=J}1) zxGy5cygWA&YGV;9FG|sf_zT+lg-ZjeMpES_3MWOYC2V$AK8EJ@Z;mS&3bKLj{t4jlC0GOY~E*MI;@qJA$jmyo!@`TUMy`9d&Ks}Xy*?9(kGHTj$fPm7x%UB{>-le z?Q)^ccJ9f!?$b+eTs3oSZtO6p;_a+RXR7;d28l!=84wTTR+5yU^8C*K3tAD-aRd?p zvU~yR8DRwePnwD-rP)9e^n};NyJ=@8u#~Y~vLAE-K!82}+&uoP)t8DAbe}GNUKcee zQKWI4`2Qp9o5CyU+HGUoR>xMywmP@uNOt-Ec zKft6J4!9k{9>M_dUaxF4mQGRO!nvstbG4wiO($)w&d#N_>G-u-(qw%wj8@96Q@?pH zN#~aW$(SDw9FxZEw*1dH7bOUqw4k_(o2vc$M35M5`gO*Li1L3FJgmk$H4(;FSS<>h zTtc0~fyozdXUyO2+D#_NO5lY_|Da-zbZ7)iS|Zv!`{n^prrAa6|8`A^>`HNGxKz@O z;pxmGr-r)-RGjx)QGx^Ujr>#|k3S0cmPQfD*|kwiY`tSrbwH54CBH3L6iM=FBKsKEt+Yr=BW}B3fdlk+xwG71^A@9e7_Y1FMM0JhWt1A^W9=Ry8ox z(w$9$+J;kN0TZM=GV+AGUO+&&O;TkYm6;$@~mWaUMX^qHALS!vH=yj_9T(BkM+(n6jA%F7T3K@G&a z75mv;s!2zCqCmr;0uGrCK$Jg7%r%e_kcVBtngHdXQ`kW}3>U~V*vmOuL}M2xB>k)marn9Q~%i@;<#D((^9q1 zIb||t^xB3D?8qIqDW?d%OftgU^Xm@$t zYSexXPRwb#+2x4zX@?{M2FD6^XQiJkbo~!|BJ64?{@09HIdxLO@@S0XI#aI>v)uqb z#y$qDOoq~J<4Vg+Y~s-pU+j=yl!1Y~Jj4CF=<9rv@S3Gt9ORX1=s|wZmdla>ioQYe3sXD$_Xf5I_Own(!TK zacw2)$Xd)Aw2C49zC#gvZ9D0Kpx4a`<_sBOEamZ`qnrWn{B(M^vd*>` zSaI0e-RM~iBL*Y|b@MRYpvTSSoo}My^AQhhDy3c{O(NbGCQ%~k3XOHX;689B3E1!e zbe6Ssxj=vyzgQ*i%o+jK0Hc5!tY2JkeC;jR`)!;pG*^-aZ+P7$;0aS+px%jSym2Ph zzao(#V4mnsc{*wt3mz_%?RTNgW=mLvk0qB)PRce{Hc+WK#~ppfvQt zd0sajM(29(NZztNWzY;%}(;M$*L;6CaD* ze}f!nTja^n*?D#rxNh|i%(&-@4D1z~a=z#t^XrTzQT@9$_+OINZ{9>2go8EtJsS8Y zFUNoRe0`HG9320bK4Bwb`ET{?&u`4~|7aD?|B}3ZlQx{mg5bc|02a=F`@t|seoJ3D z|0Qz1F$)nZmvU|ur&*<0uxb!uremwxqoYZzvaN<%~K#Ca{pXp zZ~pF1Mg;mj`Ty?9*IY&lVh0Su%9Jd)3XIy!Kn}u5{vE;m-x0#e-^nSOpJhPs{|zGo z13_=5l?A~eN``6&K}hy;2gdqmobOCF-+L9!`wE!Ps1z$dZ%nGLXZ!H6Wj_*8mhRrBbUkp%WXDu*;d%XuC8e&b<^`68;R zvy;Sr^0k$$sZ`!UBC#5JX+g~Nj#VPUfM}6Ys5!H$I(ntT$&L!rPm`|cXEUU|Z0cBf z@Ye(k=wXz32ad|I2`x3+2C+mnt~r5)WDy}X4JtAznb^djUYUqVDTLz4Q7Pg1$iHJ@ zC7_jK>PgQPWjxZ#suDCCLiv`tFlOAiUz?rSpI#JhFw_s4Jh#nw2nA; z{T1P}+=hdzVUCO7N+~rIF=g{PNNL~Ba?`%hdO5}7Wt7vKwQ;0#2>8^}>$M1?2aK2( zEB(o|{a$4-0u9ZnaS#df>XP7rfCV8?a-|Y*HW&$eI9kjoLQDwk_(l5H4KKAtzRcZ>vvr`ne`n2IH#RO}c-#hv$H( z-xl*{f+0T%im+nrk(0DtyCRjEis+gk(5ed|cujLcq>TuA3ncq~(sD=9&BuaUV=spg zQzC{k+bv!QaG-_6WNkCT#|Ds4?dJT3sUYg%%sLy1fdd}VW`hU3=L%lIo|@D4F~#Ty zGjhseCBRAuZQ`Y*DqfbFR>CQoQFuw#l%@O`@WM7i#Go($fy1THlQ=-cRzrg+NbWd zP9`t~-nLfQh42yL9@=9;K_c!T^}To?ZQ_c^_V_~&T0j4^*$jv&4Rpe8& zr2fGifqmErq=E>tPw4!jHlSl{EgY-&7$;T-`3`^gJ z4;4a!Ig5%{M8kvH151yF1O!S0WfyhC#V3X(^rAe(JGg#i)X#$;ED-p{gl!NEt@Ig6 zYA(#=prQF1c2qfwA3j-nE#?^y&-PoW+_(CXNG?m}vX zj`XSA>CEi!ZYN`zIExeRKg$> zwnWu-;U2zdIB_Ac*thUTYR#X@4?RZoODV?t8}8CS+rzTW(iQk4Q_0FlUFooN?eFSU zm4K4X`weO|>!mlHK%`F=nxu;wZFcd|Wqqls7$X-DPLo%ADZ00dHv_bfrjyCXGiR1C zs^6REYvmqKJ^`6?%k;(XY4ym$j$JmJ7msEDZq^Kz6s-C)(`Ml{4?6nbqC-+)Q*PFA z(*+roqYsab?o8%(m$|Al!TGg|>F)IOr$9{c6c+IxR*&Q3?!GZuJ5xELyl@|D&W?OT z^V7Q8vZEhtL*<{=m`Y~6PEqc^8vwRrp)s5N9xp$j6&-hIp%O?dt3yB5OLKRjnl} z?#)%M$%aa{u0{-xI-cOC!1{Mp;Kj~H;rDIb=E)CjpzN&bM^6Qp;di;ZQE!4?+!Tus zP!Q%rKGKKa`4y`Ne6(0@SBhR#T_|#Z0i~NpH}WpzFZwR@FXB%nFN!YAFESsbsX;(E z=5FD$!n?+M^mF*L5Czk4X`{yhuG7j zvo1m*QHjAmlesD_#e?^lD0H(Fbud)LxjtviiI3R`dlkf$QpCe>-+u&m<+y-ykD%8B zhS*cv(bQ!1kp&-U=u)+eK;MdAWIEnWRsHV_5XynASDWiZP<62%uD0Y%sn1_A*-*`8 z4D>y)Oe8*Q9wopS()HR`Q>J7jJ;O-%8%@C&98i!G&~YY6r(06Iy*9b=VQr;(ePtb8 zFSnjoj{E5z^`(Pzro4;0!{Y#mukLbovvSS%`LLr(w-w!-*Qqs~b_iFVZjqfTbbhNG zjDeR{4qM_M5=269)Tm1?vhQ#aXEcl>4*Z5>r@%y$!4PEW9-|XL5k$YrUEm=g`mKoy z%`7Jhk$FE!-T*I+&%gjC&^-Oac6`<;2OZmibZ>q(Nbi*kcC#D}Phfx;goc1oOw2jO zDSW6T8Is4q;r2xlEGcZMq!Yy^TI_;H+|ljFW8De43kGt0H@h&yE5hlmHN((&u0;)+ zyxq}sr26jn-vQ0N1&JXY4!!5B9pq4CDMwJw^bgdI6=5S`7~z==yc@Q}X|{MJ@YrEXXTIO3zUoQ7;etzo>)Cec5+U5ZfSq74s{PJhGxw6#|Aj(- z+C51UDV~U2P{uz6j0xHhWdIRsHyK|SI~tE=t8s(DuI#?Q3>%<3^IN$C0rq`gMexV$ zS&*Awa^gAqdmyK_DPv?C9)w8Cjl(lr<}K$t!2H4FaS$`rEj85kmGDi5=Pw_(EyJU5 z{Mi%>ksS_R#KehqfF(omc+;%A9g${yKkAbpX%TxRVDtidW}5t|hOb{QlN;x?b8?5d zupXnp9uc`8&j@hC>RBk|?-LuE{rSqCLV1!mf`6`vzZs{ywS>?=jc*8>F1>6{Xa!3l zA{)-BJ>44M{LwpfU2=djq*9D`Y{_56#FNv563_Sn;PYv)sHppt&LG~QZ-Q*<^bFp{oBwladHGV~9$hZ=*eaWuRRr+rFgcyb_XMqbYmsBttKb79 z(A}AC$-41E4B9a$$Di>r0ZxvvWz)0geQU^?-uPT?_+q%+$Td(Et z*wJNxrW}d+EMyAfAd(c$_-nsKZoI=b^Nkj zIpDBy1)xm}IO=R7VrGYE^aIVVpEv1bnl)q!oJdV)az@q`? z$M~XNAS`(AKcaBJ!gY*3T3x@a9Std*>pwH#o^-y2|7hWc85PT>WH zyD>Yem-wzy-%agK6_*Ui8wf(QI7)Dl{cdy(;{Lr~-O^ZE630)Z+4zn&cv?!R7+#JW zO``t33RBq0xglT;8#t~f46r!1?){WhJmjGbRP$SRySsMZ-wphuo+-WF6~t2H-pd^~ zmhonhM+W<~mb;@o`Ih6x)qcz4;H$LS1~*(UZ9<@l=E_A1hhXp*F}m3$aVtpYSO{W1adu{ll6_y-G#R09<(>?2&Wye;ex(=N5lN0rRcTS)3iU0 z-~L2Bm$*!DA^n1OA!E!r*RsOBAq+4^zi;wUHyIPKM%Y3S}ds^b#&K(3ukv~ zPtX*jrh1p`;SMZBknQ+5B0#CjYM6ZMz8z4@SKW={M*ATN-%6OZS73un3r+F9n6cHe zm)Nt-+xch7^5}g!!4%%go^LhcI#k(#ym{FQTd{Ea;h+T!h+(tEFg+RM{ZrZhU|dtz z9DxM_4h%tW#L+Vt@%~C{AXMTr5Q7lw2l|$1jj`xxB+-P!@*}r}Ln)zl*{zfM_9wtf z^W&Q9PvK5DWTo$VXxX~Q8=~+odG~hseFh=?4#Y!8nVTqY1@QXotoE5~4}Ak0=ZC#& zE$#(DjV^vO=nbphp|UQ|I+AP6(d0ERf_%B2C0uQ6hjg}-_{-J^?W27&saJLI+lQPmmGy%~r8>a$ljF1LrliA&r|c3ddUrJoCie8^umO5yj!1C? zQSXPmYBLg(s6B41)c14o@Qh19?JOm7zXuE^d+C)Gt?xHTrK!27-zG~#-}R9x4EihQ`$XsggAD*|4T}}# z+)B2N5KsVdp|5W*Z#L6L-8veon$VvXm$9cYU@X&#%XFul+%qFKN00F%HkT`I#j`-lxs<@j^E#JiT*mA2Ihd8v z{fCD_1ZNUpvEEOz*Zk~i5F<~Z5$4Dqy*fk!UZ%GO9Cn$89q}*L0C2fp^znnaQ^UdT zc)`Aq02m3myF;&56SAlTTMl)64)pGD^i#J0G<58>INc7kz=PcCFt=E2%8o$fZDQp> z&cd4{hn^RXthsY(&A&G?N{5K72>i{;;IDx$j@`2|PGR?H<9}fHkcym3a)_di<1RfV zpE?WPbmJgtH=g1y6+PXLyD;&{O3^;|nt?FMe0uE^+!-(n<6IQ5^D)77=jRF;PZU#} zsASsE$aP@i>4PMb`$)S1w{OLu9RGXK0~dVPTbPQ7Og5m0LQUSTfItN&Vq*QSTPesy z35ZnthpR%{c%x}J6<{F_gzObJZL2vS2{qy*kai|_uJm`#e7fFs&V-$;O`fS zKkXxqjpC|ho66RDuH2qTjuha=K}-T{%|aLqX_N{>KhhTkPwbNJ3S&+yJRqM{7KFKo za$6E^Okx5wSZ)-7a+t{)UEVg%c$#!+xzszml&hJ++X5DOFD8VJW{Rh3LXbUvX^$uZ zJB__aom*HGI*q2@kwjV&E&ul(0E50TR@Q;f6q(xOvvcxPFkm2M`q6Zp@`sFi+3O!{ zM5dk-L0L%R?_%n%d!$16?+#ka;z1P;gq!v(iNX!?I7V4c)!eo*V}^M0`_P=OM7)S_ zl^b~Lqwp9-$ki@&5CQ&Y93Fha428gC}zpwDie6b=whBsHbzU z+8redN3cu2eI|P&ekR4t4)~P!oKjIilWGr7d0_W@9}_N$q*d`8FT~VShSZT7&X0ra zejTdz3B+z&6xF$} zD%4U=+#iQl2XAXl9!K1+NQt0~XQnDYDtc~n<3b+bg1{-;UvO1p1(=?eMzla=qO~mc zs_fEqQP#bHRf*CFi&!&GpoIYCE=b`i&TJP`1Nu$=R^+v8fx5F^4Zl-J9K5~``d&<3 zKGbwJ2JMqJ8wiBX;Cm;y%>qO~zBmk&t!dd<`FljuFNdk^_adc7%_l{pXD>gb`}2}5 z#}jT<11%9JHiOT1!75Hta*kRh_j;t<(r&BPan^lGhL7z+H6+cA#a2w&yE1JW37l57 zP4cb78BIZN2G@n68ynBdO@^O-g>`M8xtRQJ@p=SV+OM4I$zOh`dh9oe-1bUQ0#Mfe zCY9FT-wr8siJCWU2v191P-58uhKwXibleN9MsT5`%%?Eb`v3#APfA?JU|=lK9Wf=lvLPb_Qs3yv)=M zawy+H_d@eWAz}z}topK;>UC$ybg_!Mw*N)fRT(rshzFFUDF@95fm-d$%4Riy(DuUU zQ{q3nXd=nP-6srosuIZ8>{<)kZ4v{D=TzZ5b`~;&4!%Zt1Sb(V~!7P<&()^OjzGndIJC`TeMT3kX1U){Qp_M zEeD}coAKH~rWumw#=qC|uTv0YP*ygUWTtiybTG!BENscdQ&1Fu$QE$rj1?5R2rz$P zJNU@jVIdzoxz z{aHnIda#gAxM<((3?Atjl)cH0z6mCnM5)B7qaDcdN~`+H3a5g(c|&j2W6syIg1M8R zJZ8-gk!w9TDoOy<@vBulL>nKf3LY_#D7z;RB&KhGT1Oflq!?J3$b|fOOf)O&y;tocem&rO3^*VJU|_yO{ytz71R~015GddhARTJ~ zgei17-$x)iaahKYg|lwOY1p-vHZJJ8T6Z_M8W4PKb$meXVjkQC5MoU^ya4Jxh<{rE zEpWFMLfU(AO-0cNqlC-W|a{K;1vb_vjRe4&1~y z4HfE^LgPgagk`(>FR)#W!+Yjs_3>6{PYl1m9|lGgf~>$FOe{nA8j!UBpxbD-U&|GORX_wTE-rk!z(6C|K(`Gw25$fzfo}_h`;hp(=9gATenJXy3f~1s zTq6D&^y3%M!C~-Y1dtY?4FMm%FPC?Ap`C3Yb$>c-;6oxyMzDkI^C{Dqh3w~-_gsBB zU*fm=cMhQQZan~i#YZ)z@W;UB8R09T_yl<=aei6p)VqmPZ>1LzVym z2oeC94CEUfg1`N=I5k z1k3pnKNdUZImei?#rv9a`1-N`C3nzW|E?GG*-aDYT;KfFFZV^U^R)-b<$KZjX~;vf z^!LFXMj0>L4EVNNnRhxnpXS8e_NAK-Tn(^&M=K#vaq+b&RufLUjQVZ3>C#y9qC^i^ zb@o9_e2+F#XCR$+Vjk!hiyMP>p#$l2eb%G_1|rgnGj(_C3P1^#D`!4 z1q%7;Ql3vK?(XjZKily{=aY@xA)2~OFMedU-Hoo-2NbHZK|k^<@1KjVH}eivZXivjI{c*WcY ztI_y?y#-qC`SPC%z4!#rE?@nE<->G%hIHV;^nd>czGLn~)ChioXT!FBAG;pCGCXT9 zUFco6#tai)cSrU0!NYiW#e4#=l8zi-5kL1HLH&3HF(@79qA-MVo3@0~t7*t2c+{JM zMaj*Z?iI1ry$*BeY?`qc;<2dtz^erq$l_kPYBAW2j~NDzMhU$TaLV+=Si6?Y+wMBq zf6e2q{T52aAG{)XaUfE72!KmX5Y_Tpd+J?LV;@220&e;hOFq65Nhl29g*;l_YtJxS zh~5qAy*{CfynxXqn}6H2Pg2js%FpqaOtZR+pc0Z2QNv&+6+6$)N{PozMqN(*A-5kE zDnscLQmgIG2T`73&#ts7UAUS-e<3n20ug9@jkz3P)W8?lJ{ktb!WZ;%6Sgz+qH0V& z=bT5E^8F^d8qM0VZx9sl!xA*mvq(^JFa_6W=LQ4kj5W^N^HDRkGH_W##S4575wEC~ zk{+j$8}WH_#w0#FhuCn#rAquX1G!Y^>Rc~GcRHN7=~C>ReCm1<<-q9+>6);(+~*06 zD2rp|2>zNwHuZLkYmeXnA0eSHVTv%({+3u`6{~(By~gkDAmA zmTBQ9edzyOL|!$)KyF8#2}oqtv)LaYId;x@-|Bfc zjPzwGFbUIZM-JrHpfIAVY*k+i$9*U|X^@98wrBdsE2rN&z=pngvtW0#L&-vhO2%G8 z?F5fpq9do(P}X+iAJLyq`G;H9D~OA&@u&|_X>>y26RL_Gbp$=ugOFM%kW6621+28Xi+tGUi%uNMQaa) z5z9DN>}*6XQ~gZdr%2NO><8M;+-!;Rszir5BxSPg>TKV!;Or-W9?jociA*t5;2BL( zAk@LI{8=+NL=WF>b_2@V>;Ej z`~8d*%wVWR6dP7lfYp5oxYcZS3D-1f5{QsGU&g_OEh_%h;kP7RMNVS*+a{_(L(2H_ zGsFtFbfbMUoItGzy%%0^e5Pq6$|-7%QhP)CM99i&kYg2!a%U83^)f*@x@q5u6eV`n zzi|-NSk~O0UNd5C3%0l7sOIA!M!mFzST?Jd>s@&b?MyqX05vULD~F}?1y4*3%65^C z;qFCM5!UbT;++Y}yAao+kmtgcJEK&4jjTkfl|_NF@o2O%HAZFY(EsEf_QCns4d4*4 zf+Hvx)8$o5U$*1uxk2K7m*4)8Ko6KUmJV_Na4z%qsI}6`3}yREX2(829rFU;Ob=pb zLnz}mlp}V!08WD<(^E_ThN%8|D5C%peq#(t+?uQHiGLTsuI=$u!RvGzBs)qjhL>&- zPG-x-N3r?DoMKAYG8X~Q*&ZdpDHqG_; z2{UXT-^^*I_oCL>V}ec&S=crmd`UoKIws0S|FwRFF65d#>cwg0g>OOv8b!;U#_FfA zRvu%l2oN3BRJ0#qJ4dYsd($oc#a&fYAz)JU$eJbx;*DTECU=@x6A0An=a|m6TV5RQ zmPoPon3)`uYFx`w6lv4V@IK?wNxbh40tNFT`Qp!l*lJpR&p^eG48HNsuZe^ z-!o!{lpn7R_b;EB=an%24F*FmR-%$FbQI)B0F%9`xO)17^`yOrzeFI^?8$_>43VWM z;fl(Y3IV$kDs`4h-W{sWaB@S?-7~8-LOf{J&vS}m#u)EHi^GNp4 z3OGO(Nk5ve@6?O6EY~am87bGpM#oM~t+tj?&=V5X#10-nS-uUe2HOps!?!1Ou6aZ} zd>+%NcMD^KBC}*?MF%QXaR(8Q64HUmfY)T6Qo~zZ1BWb}q;ioY8QlqCq6GqtI=o&E zJ0x@OkH2mBr9tGCpVa>BM(ZcONY&(KZ$piCst5^-2IvYh z*1b{Xj?3ySWK`SZZUUZ^^tG$VV-j$XA5f%~SS|uY%@05Scr&zQ-7jdHQb;dxB;>nj zKOIGKIzpKQ#^uwt7rKMQwA{oTTQEAVglSD0*UVqycTUMJ#cY$nDE|JtSH}b^QERi9 z!C}!uVlD-DWqJ6K^J^;+7WL6i79cQh-E0vl{d#0mX$ywkZag8dB*egYzNBXqFg*;B zcB-U7pXX^=(J|3+V7`in-n|mkr&}ds(XNYIubwYcl3sW`#*j;67XTJ&ab{0S5SCwY zqFxrX5%l}j`2Hv70PWGDH^jFCE(@In@xuoB&jnm8Q_@Y6gLYSPmq*1d*$zfrObZFZQEV{_qDBv; zy6pL7eLIyJ#m+I1S33&V_bIpxt*EfW(Owu%1Ht*1R7KF$lZY(hLH>xVFxNQj$-EY^c0FZHpH0%n4o@jW=VpdwaFs==aVAaIcSFd=h z@2Y7S$j>u^5?l9pScn z%xiq&HnGIOZQZ$mlCgS>(=hsMx!yETqg~S|sVsxazQ4A+r%lVb$>;Dihy`OZ1kP-m zNb;)@n-_X1tSVSdyI^ghe#f5;0e;9IOQpBAc5o{!<79siVR z%OxhmZU7i_6+da2_k=DbhZiYUL+NuC(J?VzzFqk-Gh>CXCs@Dq1;2S0`apU4d9l_< zmU8>KSY^O8y2Lt}fD>FF_65XAE+=>K3fV5ADvYn4ubFt8hNs@p^IIQ5=WI_ig$FMa z#vI~saEGgg?z?Es@K&&E`WH}jJGWB+Ir!-^hF=e;9W(&c?@y|nVc zpUexzS(^3=?hPrH?w(w4)^cD=eMf$g)BTPvWMI5|i#JIDC~Qp@RZU@W#VK;ffP}1k zFa&f$qgG361jSLK&Nt3ux4r5#9KOAfxBv837*H~dx{wU#>eas3$;r`sfATUjKK zWs0b62Irt9sD91IzC~IBCtFI&N$J;x%{}&2grkzGA??T}W*iCTxGG(dFK5YepTusU zTIqar?&7Ryn{rn^%CqAPLL1zyJvhS)Q~^l)$)|mLy=}5!SoR>oN#Qa4+uB8vc9f<$ z;C9-RDE0oeYf15oBNyU{q)Je42=VZ&IKQUGI)=a{{=}xJvr`Ni;R=*wxt5XjIJ3e| z1%zqI__P!FeQ&mn8ve5mA}@59aYs?YDOrxIQbr>}KPnRnh{Yj#Uk#_!cxQLs)mVml}?bZGK!_5-LF#S@Czj3HU#LH3um^+@x?$Zavg!qeb99%PX< z5#Za#=ZWlOt_P8UIG>%C1zn#@+d!%5fnelBHPUeJE!8vwwwL@@nQ zYB4%+>P)&7i9!GD0dPFvgf^YidsT_yzJNwBhn5K>W^_ZL+O>eE{B_|*-4xM_ybYX% z*y6tdL#J#iB|O<6*P04yh?vWvet~5`a4V{4cL!D}^|4S#x{emIDT#;wbr--rjzh@X z_rn>ETbb513V~Hqi7>gc><2(EYS9gfo=yQ?e;Vhij1{JkW$}hwcfC5j#3{;iV^o7p zn=&5bTG1=ty|UrFA#Eh13E2L$2T*xlP4RdCY}U4vVp!|*Z}?uJJM^Wrw+$FsYlp4! zR`kn|OG}Y@&a=9D=z0Ruy+Nq)VEuO)oq>X%4m`xFfMqv}LaJpy(P4moz`H`OHlpKH z{UPW(QrO$HEH7I~!Q^L?OG@R0-~cD1^>zu&`+f7Q1k9QgxUs`WK?`c~;77LyKjEvs zHsAE;N`?_+$L_aUYldUgj`O>g+AaP3gb3OvVS&Og`SOFywoIqjei604TsO=XQ6(Qk z?U|lF!wQ_maBgqKbl)n9ZQ~7-^PCLPH+fBOu=oeoD%OHq><9pn)BXpvEd9*yk6?4E z)jgvC$Go4ah)<)(WUI=T!+-W>2$+&G=puBg5`PwoRo)n3;K=RF-e^z=v7B=a`L3Km zG+jrhE)pA#8tZ({GVX}c}TvI7m8l|T>! z0T@IsbA_S)yqE(p$Llq8!t)j}4}m1o)JbYCXv+1v4`T)cpc?3GM#(-CT-rcOQkJI_ zyJ2WVBvE_ScTOjvtG zZ4mSwfBv8t8Nq&9dOoo_4C&Bln)v=~N+X6$EK`H&r_5LCCP6Oqk_A8}p|u12EWS*eBmM1 zd~EK+FiZ@Pg#(Al=Xc=!zv>NTVs0Wmlrr-uU4)f?Y7#=~33RasSIe=32o8$9#X0yP{<7kSg#+4*oc zp`EAmhfWU-sQl$2(c2-&q@y{^F_sv&L6=f=Q~|@!wsx0Bf6@RN4}BilEkPPjCV{ym zvpC6EjeAb0BHt(O=?S4komzh5!mO89jDyeQ^S1zi-?o3zYJfu_g(Iq0&^KHr<-%I= zx9p$emfxjkpN~q)6}d>{DZ6vk(2$)$VPi&K21bBJLb1sG;<{UH)P|W=SjSVoOfnu* z`6gg+TTiC}5^nGO9du>XKV;Rr6dDfnQucF}!%zJ`kfNcQ*DAY$m<1q;rKX$T7gJ>K zS!4(Rwc4WnDZ)cq(a9Pj%GQ~0&vH(bN|@MCvN zoN*$N1aZ}aI~HD31bJSRPaV#_l%lo`+U++dKu7Oy$h{YvOZjfg<&dk#Q>PkG)gt3w zg^q7xBnjJ?yUNrO(V2Mcdw_c{oG>k^IVMf*T+>v%NJ4-lw{DK9dVx9`q^L+!Hqm5H z%N_AfFzY-AP@y-8{vfi5YNt6Il+Ij7H_-%K?f zKxbT?(46>-AcQ*NC(@Ehm(w4vtr~xVdz-xYdQiMxi=VKFg+Km8}3S| zA{BTgZbI8lv4ooMDIB#m_}F=VKa-o%0G!o$Kvyj-Y0#!goJ?mvRGNIwGhjb2XoYDv zke+0qw+n{}+F!9+DEi+8;7)uH+u}59-Q!8)kv0zZII@uH!q3;TL3j865K%I0jeR^@ zj!B_sNHI5-3!}+MD#sCANFQ2nE<6pEJH;ijSTV!zyalNaZ7A-OK=x<;?G`OO2nglt z9xNHQXsKH!BsIeAx&;}=I3X2vIZN#hMk@-4h4*n=>3wyzQJRl3BB^1bR#p-4+&{VS!28xVVo4qqvA*ayp~ zQX=3gJi_<7OHZoLN{sa)>`XM61K^y0+C$8Poe&*}CBoV$mimcZ6*KK6rgBu~-|wB> z;p?X3YIqN6MpGE`)S}ogac1=Sr{$y;2=AsQz7MV zBSmz-XWmnmJ5BBz5wX8Ggs79n_{`DpM&u=ps17UcWc;-uuW-rKYWk2}hi<|ftqYE| zeG?t1g-0Z8miVD0K}-%V@y=GO!ab;GbsPIM!XAL%UmqUFw2@EFdw@9}&j1PEQc3AUViD)kZcsosV}9Rho9o^1n*wZi#|F3GLodj`CW|60do?;NMW zah;aHApOzmiWDS%=pM@Ch*h}^wo>r%fP)1^``-qOKSYy6hp@9mH11`zH#yDc>O6hn z>)fqi3*bg1#G-CvoPaABL@0$AIOu6!SzVHEcY^3}cj<|sk&5k&oAUmlY(=zAt!4Y1 z#yqIeBDG{_d^D2Htyz(VGQ-9x;N}}w5$k8T@$OVVlH1wKgq{Sa&D!RaOV(TE(nXqI zz(|>GM!H&l}xn{o+GK3_L$&ffLsT^So{%c5s6megX@M|A1D;W)qqYvtq{=Boq9jc-H) zVDOGZx6j@R9)Q!<`^TLl)jkQRCCGHKm|u2Jp)CjZ`W*1=thPi!t!SBp+kI=7{=HTU zSd)bWSZj+;7eEP^Ugkn;2+Uj5%!y^X0>P@DHvH!=^7r@H&L+6Y)?br6-HiMS{Pfqexz_>8EuMA`NqU}JTX_|cmZEguEQ@kE(e73Uu(W&_=SGLnsOj$ zZ8|Q+pBo;FaKh*74o7d2c!MjX+`K46`0H9xU$o|iFFa&Mf?g9DyUp{2d4*26-7UHwa>TXw7i@r$UR|( zEQMDfw|Z{l-IR7r)vF77urKbDIZi!saOC0EHecH-8EZTm2hUA%$HaD za6%lhxn|h(AK~CXTz^ENey4{6-OgFJc`d)|r>Aao*xsEJGk7zrZ!z3a`y;f=8J=*W za7B$nkuFBA+jMoY{`-f-=Gl*RP3mZWkkzV=IJxiWoZOZMcE$Rvj63Xz33wmnPFqx3 ze-(wDh0}o$NfQhxmmIUu`&v5%i_cUREFXj&tM(ryQ!Eu~3?G zZPM|!+=|C&4&&i<`w#!RZQZot2!=MH7lnQ+a)=$V{DDi~8TE>$A0Wlra%fb4X?A~Z z`cQw`c2=dNJcx<#633Cyoz12vdyk|{)o7Y|4Uei>v$;d%&O+ZN^fci+DIGbra&V58 zhgOjIB1dC9?m6yqj_|D)%TD;7lnLzU;aUzV|7Et-&qimqOlU7U^RiJZG5^x9!I21R zb0ZJCaMFq03=}oWsbMXKO4C_?t%{sJ%DL`Z^;-Mtp;w_qTjk#$F}?UO*HQSEXQ^z{ zkk>_u<3!zBzM3WguTNt};fRiTov%9iC36eXd07W#-pXV-^DgieuejkAT9auq%;;BZ z6^(kn;}CsP-fPUF4j>0!uhQ&ItA#4K&Xcm2n$o@oA;6-4gWY5s9Unx0j!Y5J#T#m7 zkSj|Bdw~|wQisPHQFSpsnszYW3lJaW1)Y)MQQuOM)UsII_o@@TVl3sUgTp0Un&4E( z!S4PMpfl7pIZEgg%l+2q^S<*9c}ZcrL?}`^)!!oLTuLxy#PT zyQs~Su){(e){;?mhNbHSYk~d?y+K5Fjqg3st7fDoox)N&7T=Ptol>5k#s{4Sq0j_W z)cTI+6IUQSA{HIR9> zSH5tp3T19&b98c-*x3jTmp#%55*0BZGBGeV3NK7$ZfA68G9WQEF*7-rF}ng41U57< zGnXOH2q%Ayy9H2P+p?&Q6WrYv4#C~s-66Ox+%-s$;BLX)-GV#8-6goYyTfCjeeTIV z_y4QjE2?0A-91{y=$>;eSdbGds?Z6U*ck!E?QEUtnCKa~0U~n3U$_8_j4bqwjLfj) zwgUG9Jv9ahOQPS06BVqw4E)`36@;M&feY8!pz+HBh3F?0;r6s0Zd$69JK#h z4iK^dI$9VT+5+SZoy~zZ9}$fWtpO@_#uh+l_y2N1#cS^DY|qWW;O6E=Z)oF0Z|7*n zPfdRdaIg;Cd2n2ixtSyXz zwoV@_Tx?B%j)0Hg02L`&fPy{H_HSd^zYS;s|K1w_6Ft*^-2Hp?Uydwn|FziA*x1g- z-q6S&nHvJcg_Hn> z9|iuqJSSsE3wviLdM69(ze;5I%gx6wi`klp*xA?sZJnK9|H@C)!VzfvvF+{*f1j+C zt(}{#=fA>1Q-EgW2cQlkGh`4GYWk(mLV0c?znj2!G70H6Z^_|<>d zoZ&C`s_yo{e<_*%5`R?i&C}k_9$@-W1n`@MDe&V5tf!NqD-hu9=mPxa`A@}v6D$)G zz{JAX8DIo7v#^Ez@ z09=1(`>{>`ld z|67y)?}Wr%tgZj0ruvuu|D!gvv9Nakx4}o#x;TFffSldO7})-AQw`wXLo0u5XKnJo zbyCiTA7db7Yi9kQO|))8O9lLxoBs|eW@~I`^4EYdv#|pV9UTqbVLy)egV+F`OdpeI0{r@~ z76vfT+uAvQECGBJ_YGib=LmoM*LkwD0T_h-68%l=00!Yd!~tLs`9qul2GKvn1z-^S z4{1E6B>xZ#fI;dHeZ-UfLm%L2*AYu5k3kHl^M zAk#U+_R;YS&Te-9P=BQB@(=u|)b$_uk*wQ4_V^=Jk3Xq? z*m?jR|919Y^JDDd==kvf`PcpJW4Qi@|Mj#20>1){VHfA^jCleqe+INYR14v`(d~}& z&5-YEBvaFQE;_cmJRw3SP*zw4B9D8p6%emiKv*1_Tbhl@wK+E zba^yrephL)id_corF5DuQY}KquV|J$$0%bCn*_?4j^NX0)Qhi3v$uCMs96*5MAES| zu-{H1SqeP0Hkf~-9_Kt(%T<}3x^T#Fw6F=lZ&635C_IG^5~Z;gJc|pXrGvr?O$qeo z8~NLj3>{c&h&vZ~OGhR)N3aj|{X|NUwx@!kjg_{@g@+EURkAv;QL}3~il$$g^z&ey zlsfibF+IYI&e-@X!U_a1Qj`4b(+;4l;9fa_8znOVstJEp~#c zPCV~AbF6>2N8ZRRw(Kr;Ph5rK!vgSZdOMN@@r5KDYeNeH!cgKF`P3wyc!r8#e<+I| zJtsh9I8<3Sr)KkN;(YL($9$-J%`;5sF5x*i_q6%eXnZ;E_`(QCFATX@&n+D!VV3fs z`QQoA8OX11;F@37ibrfFj-YDLB<;wpJ39iXMZkY$#6c2WqRwXPtYd04y)hrlcxmGj z-YCw_?m)CivGvT(&Y!}I@hiV>P_nx<2y=qQ7D{RSu0}`kp1&8R$5Sm;%;e8mlIgUQ z58wBgP+>>D;wzw{G&IO9tWJ1Woq}@$s+bgts(UQU>J>l)l@$(nT^2P!S@{_8VY*v} z{*98*ea2bRoC!0*mRD)99O7Lu(fvPL(_k0trjA+OaBj59Y(7iQ2BHZOYAWhysvr48HB)U zAvnqpRCFw(VQDo#bhcFwH<-kz)qZ@-M0;uBaUwNMPax@{xEqL*V2k5sGL%%JioJiH zrrnL!wO4)l4jWRIc?NUm=fwcAsu2uaL?KpujRt|dN3Xv18Km(IMUeuifyb}OM7Wdy z-yh|ZF&N;iu&#LBj16i5l+xg_q;?MJ=x8&c9S6Pdn&dG+vpt9>5Hm0I-=E>BQB<_- zz&W8Ead|i^GBvD;TuF)?4chb7Kns80tyX0pr}zSv_{u81)mw34ztraq8^8{zfx9B9 zynlD&;lxXP!gi~4lhmiJ(DUwbDAOU@B>YjsGOXQaO~EPlt&+#!*dJMIsf?N+r533% zUQ!F?ycKbi0nxWp3VyPOhs6!n{64aY4BC>xBcNrIL626SkTA22Xck8#C9{9id%szT zBcjJ@YuUc>EwoPA9306=1{yyyI~u!?G3zF32E|FqQ7UzhN7F|%jn!opl8!sme(__V`XTD8hR zc>TeYypdwjWTGfo554oz7-WCtH}ajiFt;~{9OBneD>*SjCU5E6zC%V59A`X~|xnJPt&mSigs10MM zsKr|}ob5d;yv|P8M%k#w&ZV_&Z~(FhlwWOT(ory}%hXvHFr@Lb zR)bMZUHD<1%)GIML)!leq1}U${<#sh>d0;o)@YwdcsiRpFxNdWZn6-uKeuuP9r@CL zs%NrBxyVyqBFK3J1`e)E@MCjj&PLcTv~IM6fM9HxM6@zCiM)Ra{iePfvasJGx&So` zg3dD%10yBh`Rix*J!e#2Azl>%I{#gwNveJQ0AtDSdfBSJp6N>FSo2txio9Tk@M^*3 zV~AV4b)2yh&frIN&Ra&%(MQH!r?p(rBz|s#2)|@4-EEmvKKi4gqLyafsU~-GsWbta1A@ zgH@m%tvV)4s)QqQU=YWtWD-uZ^)*Umrj6mZfVQZ(N-*;9VJw>ZM&~maSy`WPnJi&t zM8IF#qEd(`_&~v+T9=NIdyI-q_vE&Ys1)UgCnKr5z) zcwgUZc<_LOM@74h5Eow=Ase|3mm4gUWB%i%%r%F}#8hF12zfi|;)_ZlXM1nWtT4p8*QKp{AOnuv26< z9TPa4(@L}j`A@A0O`6zk4W5NJ25AzPw(*NJo||3d^ilBXK2b9%M7Xb9B_w!`ZbN-o z=CHwK%J>nL!(o`YjqBbr=Fxef%13&`r#-B!9b?X|i>fpMc1e+7Ew0vf$T~E)t@d5pH&`}0Q)X_9MX58{Tsa-N?fQwF zF&3E{cXTGyFuf&JHKjPXn4}_~0_8~I%(poS?Q3#kGOyG=k+`AJYe0gjb7K!@52SAg zD;R&n#e5>gPK#SbAwTxr(>t*fkBxo*iYAs;w6K0_Weqn>IOt!U!n=XU?&fZuq)pap z+I`{jvmn#K|IUwGe_q5k3{GdhZ?N>mK7E6<)b&9wqk68?1ZR4Q8EF{-tIrG<9k^RxHu(y{v`+PI-({x#JXv8RD%J0*ikDGDmprAR>#|#u+e`+ z7>$sO3=U%W7i3}T}^iMNu^i`U)=i6!UKf8?>451%%@CV zxo>_UI9K>+nZR@07 zA>A6MIiW5~VTR=*Ys4v`Ul>Yu4A_6^+Jfp$a=2QnG@KXKJy#4M`4N9ArY2C6y&Pt_ z@$0T1c(zr+Jm^bHKWKa9Ri7K4#~@1YPnL@C+B`V$J_9kICsReAHicQ|(gOST5}bN?zkNb8CD!QnW3PjjwgR`sHW7dAlSqay z>DBc2t`IAE|5>wTEHl&W4gHmP%o9(0bXp24m$tFLe$*K26P^(RRfK$K$CAWzPLB__ z%hV0A5_>8~lb}y1wbIS+2MxRWcu5}aSp#XfGjeC?MyOW(oF~;jPO7Fr8v!1%iurOD>tNFPkd6`0Y zHZvta2yfj63%tGTwDe6PyRUdW?f$*FkA*F9)XBX8St)<5Y51?IG;pdz@lnR)}S58)I7vN zA{ivJPS?ab6+P9O-CB2fsd0|CZr`$SK1KFQVxs z7ibC%+TevJ)3;UbAGOs`q8ofv`+QU6kM7cdZ{NEC#@ZdGU1%}ccm7S1FTTJ)MQsjD zk-*Tisr{6Za$2c!Pvf^kzHuqQFWI;CBn!Od z48!k2n=gJOs92Zt_V#2?w5ThNlSHXT7BKAwjIq*T_)%SpLF1-}#tEk-Yx^zio}KG$ zc)aHE6ImI`FO)JDRqaY3L#pTxU(1hIc)QyH=F1Yj5iCITJ`K}%RZLT2{-cEh7RUW3 zTYY~DE9r(gq?yRoaa$ll)pJ#ZjRv{HlmGNikzh#*>|UDf<4G+WU1P&)>oXRyel61duA(NW9{|55T_9^%76=?(ce<2C!a

A0$aC}=`W_#AK%4~2nSj+9m&!Kduv z_r6HR^uUwguP=@>i}6Fhd!}struBUZhwCoodQa-KCC9f{py^CmNnJpn*S8teOMK;X z+;vDRzCvrWH+L7Y{u*tZqs79^{Y7v1OKP4@G;546o$45BJ4DEi-Zzv;iL8H-Zo>hl z6q|M8@!rv#xQXt3(*go!$+f}s0L>BScQ$$9`!$2!e&DAiSn4Mtd(6)!lxd>+HoG;v z(&2T|)tGH}B1VzTl*`SA#WpitM{oFR205CFerIDm`3)?+0_wqe^qw~w$v;O>Im?)? z*bz+l;!G*4j_zxqa|bI|V7wBXA-3A|7u^h5bu* zZxF=ejC1H~994vP$au6FSzi#u?wEtfib9ZZ!fd(Pk)FRAI6B;R_rZVf;GEl3US#HG zkyB7>ABCtMwU2iaI0Sd@5s0LsmI(-ycuMxA43{llGBw-<+WFDnFM#?SEO2Q7YWd|; zm|amt1EJI|zSF_r&-ae&W}pV)doIOq37PcU)LokJKCtLmfzu&flV|5bxvC(V(ha|4 zCVh2LbRBE&6RUt>k#>LI(xFt+W+5NoKvH9m7A|y;MILw%93C| z`f8@omM^}~@Ij6aa@%w7*R#I)PoTM}7YF5y|*0L;S zL>fMh%To31NAoO_*3nx_4w_TNzn6gZ$^!hC;b~Bf^Lry4Hp_ozCOuXz^D7w1xELnK zqnqKV1J$*V#`2_F*Ap|%xear3O;EDL-Y!ARj6*We?6@BR>H0Sy)6~349l;oM{4*JG z{MPNO(<0s-^?}Y1Z*PbUhGY_UIfA_q6I&~pG&lX*Lgn#XQpRd;xM#%WdC%YCXO0qa zj|eL=6YZst+a!NTYLwMxMz4-Hseji?3s$bpy}Ptb1|7!k7-T_&X-P- zk5xOc;Y|6=85ozH#xD#3>ukmJqoF`>EV}_&6FeL8sRP00qR;+`NMuwdKR>5GuXZKWbcI5`it)8n_lrQiYA%1CeV8J==Ga|?kADZP`_G2+ z{p#HGm?Rz$Vh8KYKkjBQJ3l8aQ+GIKEvlU_^bEd9%3&S03gmY`WLovRoBI=@uS z82PDpU_jz*#r!rp+lg;^<5W004}g_b)5iz+S%ef%I;znSbKYF_==WOXv`Ix@pHtQ?ZEqXO(c&PNK?@$vU>+4M_7ECzGgZHEm5o)SW)*zRxYPfchFQ zJG1Mu{tK?!g4+%^eqw$R)@=bBLWW~{2g2b2#NX?o?V&PWO{knV%1p$!{Jn%veglIw zW89z=dvT}GFb$0|kA+s{Ya4A2FfZm=gT%vu{TY8E)oJy$-&u#Pcz^#2+32Y-aY)EF zd;xw*rkK*=QBm%ZbMhy;D#1TC=MU$Fu8y(XrZN1XH18^Tp(plmT7)}eNr+BTbkKWk z=ES56`e~FqE>UYZdBYg9OFVGpfMhOQ@_Y&s%}9ji`b+71e6zZA{oYC8H+9>cEilK* zSWthzL?bzd=bI-$DbZ)k+x5CW~4mf-O*+hDgtM|OA z>l-I?rOH-rZn=coIq+mk_XRG`%iqBhOt$1si{MtgSvzHt%EZqC&D4VMYI^7WU$Lto zW_ouY2O1)jGX@x>yW76UL8Y**@n+_ZWS4)8il>wKsM_EQ`*gGNDWRNnLygFl9@Qf6 z3KzDu$J%_2&CT?@TS{$J3(cXYjg?^YXnm}z=`g~?HYs(@Jw`Nq2X))pasxT?i(_C zjGvC!9v99y5K_4sn`nRRjMT+DyQpjw`9 zB2H_eqvS7jx<=0N3{}1%lIFxCBrX{K%e1zDbgg-jrwxi5ohqWKee^VIvCDxcsV{{A zuWRg~-YE>fgD<`n2rRo_lA%Hqd!^Xt3QOzP;QJ>z_6%oq4QQ3 z%(vCUSTD!d6<(Yb3eW>QkobSc%^okb8qWoJngt?F#;4XdCC%4j3i%h$<{S|8BeinnGj)KAX;lZm|@sO>k8UmlesbPECI?$$_4mp8PdYzIWt20nCVvZ6$_H^Q8 zoC~$un`#)63&z}hB2^iZ((m?w!SkTv2e;(}a@aW0oK?0{8-t7lEJ}YtQ}XME9ssY$goSsYcB4y&9;}Rw2)=y_RA!_i?%}u{DBJ+UzS%A{E z%6JIsXIj=3za8#8JCuJ_w@aMp_87H3$!CbM<0vQR3_MSQ(>j>O-;wn3ibYIRvi>fD zD+2!D zXiV%d9u?53At0g(@tV?x3)Ces4HH|OKZY>M#8KBxdR!_v`i`~nvL%Qg&!On6BXDRa zYF@97$eVX;M8zVRVm?APGu?k9KeU0M9K=7K)bxwbHamu9dYsS4dHD!P#kWb|c@C<{ z?k7saDb#7Z@|k}Xd|zz`P0@f?L;*_k8K^pQ&i7*Y%&|buD{k3A2*!E0{0&|IZsiiz zuUn;hFErmw;c-&_G8p~x^uCv%~UD)|<$PyAhQ~67kO-8V3t&*=L z78jJ>$rBWt1ia{v7qd3J4>`yHz0%)kpH^=Qeh|?sS1WjHSZ7Of?ALNQDF{hVVp4&i zsM-b|?|y$(c)d6E%zo+>T$;v=W74)C7YUOZ8o(=y`xe}?kyM>TdcvSDafV`aDUWX- zhUSi9c2mgUD47#okVyKv4P|a(K14#!rq;0Xnrq_OS9t7s(YiSL$_+FcpTexQK4?pi2`epN0bU1{>YB6}T;a6bZmI@#Jcf zG~<85xpMhqnp)q%ZHu0*#E-0JHr%Q=Vm2T{7%TXyDvKDjC~YiN&TFSYkrrBNcXz*} zP3&`oW;Ft(Eya+ZW~SJ5sedSQRgxqd$)d2dRbE7@UYLItox%;elrr;e?vrFtiXK># z{@Xl$R?1B2wsj>=aA6D3H5cP07g-~gPLPTSgke^u-3e53=9MK;sD@0s8C3Q^?nU|-uDsghN&m2SIr<)jF= zzc*^Sccf}k=w>(nLm@oOw)T6qFdD4doN0fup z?3+XV#i+X3up8#yT%wj@kK{9d395e#$@Q0=Pvt{;RamEQ-70W=1=o<2_DpV?=RvM9 z1Ox>S_m9)4d?J(!C%m+bdGDKoX0#?^E|bcJ3J9#!Gj8c8tFFfZ0!JckYt)elrJlmM z7q0T)Y3%A+qDPWRcU7nSrJ=jkQ5@_3{L0iaKMX&e)9EkWC-24-*XwRF@3wz%%8{Gh zg0`)n1V?4qzUG0zQQ)|WUAkYdrBcR_m0qP+n^Np{6B*9;_%iY((*mBe1}o_>`KrDj zYve|z(bt##Ce{A#%V2HV(`K8&oOyFy1a1!#_f3q1ulQJiV`XWi^G%k33xN2LtnoQz zy4P#Jk<5rfFJ-h4Hs1aX1r&eND`>$tJ;tY@4F1CX@#|0g?TX!>@XTq?RzKp0jwc@x zy;aomt4$-MEC`CN?!3?aYHa1bN&^{La(@=wE+F*Rlb;uLyCLO-E*B4bM5ey9HtIKu zG>@MbnEu?U_@pE;dEX5p*)$$_*r`}z;cj((&@9&&F3x!=6S{+(eiwgZSb0hmt)Y z@f1HV+veyV=<_L&(29Wi;7l2JUSomFxA8N%D(Q;1*TPb_Jq6ntr4LV+Tfqai$RKiY zAy3$34ASfib5qWkVP$_;v!+UPUEdL;1cwBqI&&d&KnN;nSlM{-msD*HjawVkrtg{) zco)YOC#|)n_`js{efDF%1=PHh3hg3V6M^cBp_*eR85eWN;9B4J{(#D3$k@l(mypP- zSil8%R~wh+>@yBhNRT*rWGY@3dz;*9#TiFHAH(NYNx4Zj)1H46L7WKGidDyg-gRe< zD8bBtKklyBm2TUkqU>))2>3^NBOZ~T!wx-=)n#iUF-6Q@)!gWn@R>eEoz?zwV%$4k zbF|y$lcj-Aq@pyUw&0uUN<^NZ>ihUCm$1PKX;e)D*DJcT!xsEEwXnjfQ2b66-@@Mk zBbmod2zyV(pJ;z|V@2t6oJHp3Jy}orMgoE;VpF}3O5WjKF}Z)?AM^Qjws$`Tx6M4yHQF!9 zH~BjA`drPjiOe^J@SAuBB-A7{h`i2HJLKo)Lll+nnjERNEtWZ1N^m)xW=UHeI)Wzc0ZIhQFXSssxsry-Jbo|?~w*^BZMFXBO#2U3!=!1s%iqh_==s! z3I6!L2)1Ew;hJ5vg^eR3yETGN)8kWlN~iIA3i}KnA^pQ54Dr{d4l;>Dt-4^vFbZVcATvBFWqX$BC!e~r$NxH z^WJ|DwO!i|!?nM3Z(pMzaz3L&NeijA{Su_qH&JMCo{xK=hKMRmYT>Bu)ov7g^b@bN z$pW2SfqX*-HI_RJpZ~6rwjKsX9Sq+H!%KxFPfgYJ2QOP6H0DnLy=?jv5eZEDSgugs z%KF=HxGV9&;AgL4OCyi5%9%b|Rqj=*@PdB<>!7^i2Zj+LvfyxmN6eJTLX_IJJ6%F2 z-fu@t`d-jy)Qhy}iOdIiS=9!4Mg?iG?X*$w3q3InjmjrRmt70&vT~K|zjW^S3TJ*< zN+sk2zQP(qXcfXgM5GO zG24UM979E@Xubo8$qqvJEtaHd*{YtWnrXw}tH7Y9rnXj;C+O-QI>SrwnOsYMjpY*rI=gwKL3w*y1k#+!?vWvxoAf@2d|KSh75bg%e{YCV&4V zJ&2O*l0f|c_4dHn&D3%9(_E7=7p!IWa-}p7(QdO*R%IL+UR$qLa)22s2#(X8mq%(> z>qoUp`&$dzlyR{Ymzggr*?gLw2ZvsML*6GubdPyCE+^Watnc1Pv9NCEM~C z#c<-WScu07+X_4#so#Intv9{loTF_%eTJ6n`yRG$mU%t~?|uHB81<}Xm|3pIhiQ$$ zjKf}S^EtFD`P5ICLG0{_x)_aAL6m)tz%D$ZkO~sUNg2RjnYvQ(si_(MPESuLGVt1o zhhZV=cvr%b7WdIqEB~-8keDYdT(KkqZ2&F{O}_v9Jt%rB*JFPN>H+&5J{NK38yEIr z9#jIF_{uQh!8wjyOsmb_NIUJVi-(#g#CmA8WZs6XgWbo67*!=}NK%dlV7Y4E}O?I7#JwmQ_JNuY5j2eO|H9 zm)J5Oq&yrNr;UFJT2Awme2aW8WV_Wq*9l>tSn0a!skiG00tuqcZYH0)#)q8Bz7UH; z^Q1&0CKfrj+_PN!(jOS_W|%_JTQ0#>9Lmi1)!)t*wE*z`^OpLIGC+jxF#JRzDP$Qu z9-T=Cu?9)C?~|oY$;wxJbDxV4{CdOcNKX)u={Su5ic!LCUZeuT>%u7E430Bu zu0k3i+~QCTDQ$`J*<8iKMP1jD%8fixMd8FEe0K;-Hwse%pn#Gc(&2%Q*~Cxop~bB* zzOI$WJIH^cOzDYWV-Z@{@399;1-o%iohs3@T&f0j;_|Fe2Q20z6?i8!9E296)%{)H zt~2;4DdK|bKV5&pBuD{Lx)$3@!7yr4)7)E%HS0ThlJ1k9LC9&DOW zv2r;0(+cYFuSXi`sLoz9HS9T*nblKG)TO{9i-Db+n;kOPeM(C`XQ<)6F&=~8mY!7ZM#e+uG=Q%c9^k3Vd2bLvsazZ z3FfK$(5OXKr4~q|*2j57sDYN*o3!P`^$rhg9}JcFIe1c<#x1K#Kd${ee1)TzoDSc` zCHPqo3ibOYuDGut7Ep84CW*x&f0Cw+VC{cw3$83xy^~O(&?L=htX5pA%za7T&r%J& zBI|iy`xPrpDzF41g`idhu?5TW2}eqUZ9eO~0!euGe7DT;hW^Mi4ca>^l!O0`ehS}O zUu$*XC!_1^XX)=uX{v>{P4W^0V8q#1UdEa@C%98(M(VrOkTfc?m^$^d9#rzu6~Vk^G{Q7bpdsF}PK*upn%3}lIP%f88Q{5b%E7e*Jy!@NY;ug)QjR85D4=4sq z#OThHs>EUDpJg5i>bh)z9PSz4zb=0&gw^KRylDG9uv*)zZy%Q@zN;*7)8HJ3uA)lV zK&tF0e6&vt#8R2Pf35Q7(U%$s^q1s|3pUOhwY;aagai`Wc4mHL1+x)4qPvWlvIU{r zE?pf6W{=NTKfPgN44!q~NmmBX*`;J(!2NssUw19IPjCQnd}GSFm1(6o-%5YPURmj= z1sbB(13ax7D5=1*TuPm^FatveCdQeJmoCCt%`kzY{Z-xsXVX7$lsm?I3(g)6Xn^c= zLBD)xhXCHifnkdVp3TAGR;r1#i%2XZ?~OUI-ic1P9Uv$}m*W0sd}lo26uS(?uRNpY zf~=#6+Pfld=EgbsMfMuocy)ifREoJ>2UHSZ%HwZulh)tX0`V~5?b-8N9lW`eUD8cB zHRa#G;HB{RU)Al0K?#a%AL`ewHM?a()KJeXGi?hYAIT3!UJFetOtL3?s16}>Z8^Oa zufnufA=BKKYMtHkc(X73<}An=FAuK%4H|DKrav)O)`h$X^#!b3y}N(0v)<#HXY@2<~S->79vA>KqPDO2O4K5TfXWqH=WBfvWj}rt!)Y6bNw-uc1dX@4d52DxeA* z2iv#-BfC-*A~M{yg}5=^^=gC;dFfr>dgp`@+?mbsor?TqRHcmY^rRCiPAyG+Rr>Dp|%uzJa8kL5iJ-{p0+dSfAhQ*$-gt3;UiyL6Fu=>qi75A1E zA(ez`x!^cqVg`RsQGQA043Z@XeIJ=23g)kNO0R8*>O2W~qV@=%!1yII1w*~y{2Rrq z98*5|3;l{zsJk{5cG`>ley~dk1jO9dMus}d&58Q{r8~a3wH8mYLXoPMv-CS`GV5%1 z8TC36YTdeeInm@UYd0c&L?<*18+kzFxiAhI|GNq+e~O6R4%(EHDXeA>C~BGQ3*gD zECz<}$R#INZdD{nhzQuX2XZSZ&DtZ+U@a1Wd@X;i80T+X_lLt2BxT9N%zZN7x6;~S zAN<1@L0pV1xl}6m5(z7P)M72H!-nJR{Ofo6y&o&<=3-8I71t@{B7ie_hnNN`%TU>> zDJ3v)2?yK@UUp&lBG6zf$9}yyEZ10g#TjHEy;r!5K~=k1B#5XINL_RFIH}-9@K0~@tJ2TQg1qQh$s=It8v+*9(WRtxE{htF&AWe_0`yi z;X{0mN(X$9*dWVti8lvEpqr0+O^-+chuKPG!_Q2MBo~8pt$ zhLZ(W%^1JFgJ5|%X8DD_&K?3^Eae8-U!8VwXBCpNKr{BA+S8YZCj;zbE)lZU@Cl+4rLR%H%sbhD-$6%H_;Z6P0GQnQ3{}?HFS3 zT=29nJ5<0r87xiHq3;+hhl1%_r}7Mnch7&jcJ58L z+E7_tHs~tVpxr#{jUPJ6b4C-U3ku?VBx8g$JYxiik0;)@XL&h8LGp-I&vKg{g@OYF z1j$y3{Ry;QGM2tXV^OL|A2qAdaY8?H#mxCCRqdm(b}g@Zm9l93qW6EYKVmI1)=Ud`3~R*}P>^DfuP#{yaqtpDT9!`rPqt?`B>W4>-UMX83Lua165bNKx6S$Wkm2xpnsu=sJR zCZOZ$0aT(IRUV*29J!Nf2~3>uxOxQR;UsnB3z?mD{CiR+3Q76J=Qy^y`$xK}@Emx< zrbcBxk}&c6;_2RQzlMLqT)BZja~;alkU9w(Jn(QjpG9qF@&qii=qKTD3J7v?vNfWX zGnbsKlm4Xj_6ojK@H&MbV7LH=L@)@GSNk8@v&Sgl@1|#=S}F3~XGPR&8WXh2JhG)< z!g6<;aAI|@g!JKUhuj$`UXb1lJPiO4IFjS9tQn<57WaGy3)+8Ak#Z1uQu)6xGZlq1 z#4r(32*NG;bjri_jf;{IJj5X-Fd_M<{3oXR+vHD_P_FW+-~NCu{8@gK<@ z%{>DnPI)QO6d9>0Vmgy`dDdf`=#dZ0en1=KFusLN(hdqS_K;P5GWBGC?G zU$f&3NOX6FYIlE;myN=<>Zw*UO-@pkqN^4!z(DfQDQ&kXh^8~DKohK23sh`e7~q5+ zLYaGhCD>Fgbm(`p=qNiP;tyftm9ve0wa6Q0fk#%lDN@Bdvzjb7Xd(T7ra(Ij1~y5#<^hBq?@#SDx^q3|>2RR4ky&1GQS`8fsrOg- zNg`b8I#Q{5nL>Nv48u97u^*#>MFiszSFUamVeYmNrT=Vy@w+}`KR>N2jNLab(a=BB z@S=zc$HKvQzfslQ_ek5xu^I6nx%!+ix9r|(f=GQ)ZxCj$BDn@yZ1(YYFv?$UvlElp z-6hg>x~C!P6cykpNsS@V-6mW7*S7wxUBKA<-%3C46+CmW8Z^$(%Rou5GQ7*G>J(oYzE?gUWJ+4+`H;yXDWV{=$N{(LS-7&t4rMP7~gGSz7ti9NSQ}>nSjh{82;2Z zH8w4B(mQE_m(i+jeTEm%xC){yAZa1HIvCepTUvXD{*p2$cLiBnS zrlR+M|1_kxBq=F|l;;#jAou1;+8E$D(Stwt0uXX0?IS9J7K<{Z@phpTnt#SXQ@N_8 zK6W6>h9^d-ip5Lne(|y#vHkp+ksp@^qcs?Z&8AA z4-T`9x);>j)7c@)EJl|i#qcI%5Cm#aBqM@o{Y63a{D^{)DPel9)n z*V@u<6gZrLo$S@?>r@?ynZt~ z>vLHFspKe9vE&YId^e3RZc#Q(z)0$xXNGVPtV0?(Wt3F^t_E@vv~;(ZnEmv_J`8UomMVd^t$eZyy3#r5zQSg=S@$l zlcUyPn~J$=kLZEDI2?(P?To!|+AimR{cVWQM`O&PgG7n1O;77nn=Nr1xkENA;r+r9 zU!yl8E2mFaNZHB-F`8zRK%9_uxx$>%)@Zzg(<=+PEID}I&~=LPUF+jhY&}<&_lI%6 zxwsjPAe2cl42VF~LQ=mYvg`={qJLo~!2^qs(Z4*%u#K^Ou%I~@_mjmlI*IasDPu{i z4_{mYEo^_vbUx|%bTPfQWD|~(OP(d#Wz?VXc@g@FimrXQ1PPELMoKHvu?2avw=N-B zo7Ukv6TD%3CVliurGpayriG(j>i+03Xu2R}d%c@5j`h$6_4fx>Uh zq!0lnRE=RyaW%muh^!P`L#OlKTvf@3Peka|CMGemyd>OK;Lg4{N~+^u%^=3kIKsN7 zj?~u=cag+~-rMl^Ak6rGiow*nj6`iwo@@AD52d2on@BATc*LI0`RJWo~D5Xfhx*Gc-1rF}ng312!}? zmvPPpDt~teP*ZEPHBCSS6p$`(Xws9=o6@UvK{{ec0vrhmCZS0$f`TYTK$>)rDxe@p z7m+HUbQD2UiZqof^~G}4d;fpty~#{+*4}Hk?_2xKM3C3iTtXd*bwcT2F$4)o2uumk zG}5=Qkc0s+m^1_ilcEt6v;YZc)Ncokpd|{22Y;~`rN1beI24>fv}wT!M5YlI1Dx|j z1Cr8!q@0qZyb=rsNWox={}8b_B|r=A1tI|>2yhOILE&ixHL)JPIMBtFKrHmnBOr_r z0VEX_<;8!51M2Q59EgBp03$fT73EH>h=8L3b1VWx5q$rZLimg;f#9J8g?f8?L*VXs z2!9smq9P&=c!LC2;5-VC!g-;P!1tj6W4Jr&pV=TZf`El9i2vy^$2t?d;W!jPG@wBQ z3WF!ccw&$!96&4&nClw?CLSovPqN`pf;jN&bO1?+I))H}4?!G&W02p8a5NrEjDLrF!9g_KiAea-I~>qaHv{0r5q}McN8msY z0v>_~(cg!JeosR@XKf5p6YK7d!VvH@-}}=7aVP}w;C-S0+^-u3>y7dI4V^&@()s%! zNKX&w1q^t_6Q!^9i$pZh{IR*92!JdM29uXp1W;E1ln=rc`n`mOuLtUfQ}Vl+IDbWe zp9j_ha3&6d3ILr^#2*?zJlqQf5OAKT0KdN-|3);Dk^mA!5CA8X3y7illbvWrIse2& z3E)5(wW~i3_2QaUqIYLQ)0-llf@}@j9Rn3TX-w5Pz;eH1gAY z;rqs+K@7?iiwD2IWQaj9*nfP)HAA=&UnF>huQ@nTX#=mgqxZ(Zu2Zu}Bid4n_S1 ziB(u@Qlmnob|ANGV=s%FSEzrMWiK_y|{spA~sK=jT z#7rJU;bM`0F^KKp{({8X_&))}W5A=~c-OxO#2kb_(-W%`y#FppjDPU_0snQ!2u~c2 z_{#gSjYI+bgMYkVQ79i2f@XXKi%<@~TovB1U7^nDE%C8Wx=rrF=AkE#3{4E0^gV>^ zsWF>d5vNS{hC*V7mT8ZxO1T}m5jkl@ET?viytkX8Mk9oZk?9&EncfYKCCI}w#!9rG zsf7o=z}h(CZn7n3E`MnHKG$32E!ngYRMC9FXdZ!O4&vIjotwX6LdnTpl3RAvWPU_M zk!2@z#Y7JsXr@Ft)YYT~yBr#seC%9v&C72>=WLJFENwBBH8Nfr9(krgCmS>rp-V%k z`s~DMQzWm{^rBg6$F#9m_0z&VU&)Qb<$C zP7$)r^oIW!ZJ7sci083=HB!($VC2}FK=BKQkb2du!c&Tzl#C6Nia{M|_4JG}yp0Mn zS@Kr2E!QO?#DDu9#urDfNKw~EmfMi)SZ5cc`9dmuRQg;zp0#e}2zsqLy}D0v-5h7% z^StOfeMWVc=vuB2pFquXDn-!pddcTfsY&GzKEutqdxd0|P(nTHYtK%VIA0ApnJC5> z&7qt>8@yusQY5G6@a}%>>|pL;MQyfBYVC_pHn+rYN`Ha^Ppx88pIEk@(oDfF9d)cU z7ri7$OINRdS>V2Y^dvMOypdN?2m=$8Hn)5MLqKL3MI=rUMHZ&#A5KK7)3+6 ziD;wTy~f&j{k#QHdx_rj6Vk;M6UL0==}IohW@Qhkr83<1pha%JDEq`4rI6eNe3G{r zDZ6Kon14~%I{CHfk2=9^>50SH7!^G5pe*f9*GJLJRz1^2&?h*ek1X5XM=qu+-?Ngb zp84`c!rqL9i;&iIK`eNh-0iDB<+ALX1;}$TK)V2@O)k#V5WGKgf^DhZVmyT*uy<~q z-#8@<-k8m7O;z5XGzpPty}747d-9ypCxlN%&3{5)(wvVg#e+O!x_t|NlSY=@ykq=s z4o`GO8bYsGvl(~2KlGHCpeDfM+?Ot1Q-Z_Q~dS0Hy4Hfm9VagQOMv91c@gMsd zR>Ro*#ft=VWg8rPvE8lql+Yy5J$FB`y7cq5bxjHOfY#nz9+sQ-;7OERn{ue2ZbsMg*89i<%}a?6?t(HJWq*3$ zDN)_2Bb$Xfn8-o0<5iQ;8|?kH$@4N}Cwx7RFl?MYN|-lrSkVbHk~K@LdztPFKfH^u zVzRQUT^cDHuqrCyOzgG!be2EF?ZZv|_^Z=~w7E7MXHEO7KXgtlktQxke^`jy%aIUc zvlmQZkx*7>oj`*!JePXU)|xz!eSZ-}v!)P>px^dU-f6ZnUeW`a>n-N)oY!lp3uzkcsoWpzd4Y?N|LZ%nn;E zB!i*W0?;ols$4l8nWUl@Lx0xj*F86`Rn0P|#6~!WJNXsDKcQRF%lOYaHlBKTv>SsL zn$wXaQ)sWtnC9o5;d1AzLwzpIj`X#NBlBhPU&l-6-Fa7lzL3u8^n6n(QSZwFqBkYc zi)C-uY;d=w()`uzwNynzvy0|$QY<-kSVLzie4X_(^X@K=-Sx@hZ-0IoHd)LTbXJvi zPU*EGMbHYt^oy>fF71OXb7So2wCy}uE#FF=_;v%wJ=T$dX|VwbFz{m$!iDjp26Z_lfHu+YY+bEQiENfg)Nu?C8QUCEWT zrn&xb-ol=1=3g$4AAc6KWKDC14FLCHf|Q?Y)LN<7KJKSDm+WIh&z`->!74hHmsw3U zEG_F7ZfMIK9Ke>fW=7{EpLqhZT zC0Ti9>m<{lT7OqM|IFs*ekpCXsAUUG{X}ZzJZE`*Q%4269mZKOYu~~5w81PQfu(bJ zh(}H|vg}!)r@Hh*DCuash(@&8s%CFGB$#%3q}mY^sRp~YIekDCG?gKx{OrDBmxp$> zAFTV`$#5q~8*gvUwTyYEU znlI-yNG*&|DN`i5v&sEwYo@F50dhutm(J2F;l-iVZ9CUoNboZO_sEQDKFt(8)zq8H zuPj&V>@_m4qH*OHzm}lbTv=AT-ezVs=8c%|fq!X@&%VA|J!0}Ct}jSwnfane6?#sRjy;Oy21@?#OqXYAo~Bwc?zTs7ge&{d(Mk?yHYU&YdZKZLdOg zA559zdrM}dLrFtXtIs<5;L*yxp2Mr$Es7s?F4<}ZC&Tp0&2J^0plJRYbpbBOq&+>C z!+-4}{orBl>Jt$rE3OzzAf!T|=p=404i<@%eJkWMF+Fy`%(cOOA+&#<>DDJ>lCD>M zcHz+<`&t+7Ut9@KQ@Zr5C~Bzn6>d7UP^rpsW!CHEj_76e+P41Sqk-I)8lP*;>7=XP z=@d;me~d(`RkS}hXtdKHAkh(h*!2LZkbfLqn0O{!15L(K&y6&xm3M)&0c)wGXg+U>?%8h8u+_C{rz@!G&G(dzx zCoM26;tH37&q}8@BuU=WPDyYZ!f>^;wfB@?t!FL6{wel!dQ+Qm7E_8g&4vxVRBZ&qkl@Drk2TrdyiMj&a=t8xg;*(c%~EvIz_~-*6${c zIOmxbrNw=#xOMeSv#fgl_>qrj<8pB@aJP?<3T`86Enr67pcIu%pRUglJ0X>`B@8Dc zvyUnr)n{YY0W}r-d6hH|Dx+W7j|D!A*e+Q;Bv6cUIo={jlaF{l#+$GfHh(;eo85Su z-R^8+LQX#g%T;nHl_d+y8VP#U8Ec&$w3msY>lUY`DWS&5G8RCd^La095xD6f&rY}` zyn%M7>@Pkjj>82?O=rD-%cmSoRvf<03oNtBW*~Sb4{mu+1MsU7#;c8_r|w_kznKt< zt4DyI#Zd`4=*xq8(5_IiNq?+Ix4FWSv=sjxCA-)ba*u~&9xK%|vP#pIosQaL+&JS- zdg{2%kll)Onyj7oH?a?P?sl1~R?HS>GvA~0jgrz5;~Zi-Z{-E8bI}h7ZVtq>>l;vy z&^I16bDLUK?F{1;?#}S)*L_1D>8~N&#-q>=I#%#ck8RUQSbn%$4}T0^K$?j+nxx5B zlt>gsqR1ECUp?9$c{Jq1g#PD*_n4qF6<>sfD$65y8;8aSI`md*WJkUw0X-tve1rz_ zRZT%g;ZyGQ=mo!Kn+vdSYA(iml@+Y~=AipHh1e}&IM8YzB&&>^bFVS?mU%{kHmpH<2f3+KXL%ZIXA#atuawNyVW=xb zDHVDP*eGd(KN<>W$CDii8uiBxPJuy73Feqn&2_O(j8 z=)F|bNATPdxkrh7qSB4eY@hq8EcWIzE7*-3VrLt#vxq$Imx|1-0fIlg(|a>~0r za>pY-?tRmx#k+TMZ(7moK7a=F(>)!sy};RF6W!U;b)r}CT~Yg09I#JMFP$ixoUElW z+IJaHoHTO`4u64`S#uPv7F{6~vFriroE%v7@mfMJvNzke`%49dY*N_7W_%vWRgmjQ zr(JAlI71!)At#k%o7Wd!hloiyqAivh3oITqW@OPJJ^GHx`n)(#d3LHekR?HXG^us= zFpzmxPDRAgHbsTbZ`RI);@d}IRF3WCIX3+Jd=2d8$bWF=?n%SGz%b#sur*PUH6E#B z2Vj{?=A+J22PZ~_*gS4aoRPa#-Z&U>Z;Xj-Y2yZ`gx*E-8K)iglgd*gy@xN`dA5Q_ zsCA4h9~~b&uxd#9YVFmAGVF5&zUWxhkh~SW#q~J<#fIwX-A;=#MP3b zr%qf9?z+ln1O_p^|8&|{`gUY^H{FVciSWb-OK|=Qo4%(FZZvxZ1t zbZ#Cc@EvVTet1isfxr<$p=9s;%I@=f-`4wpAuwUzYnbmF*Xn zDRt+a*Y5}zeGMPxSYz*q)fGFEzJxgLf6yto>EDcCcFdkv%2-io)JNDAQm$GAG&Ycf z2^A5vNR~HVvDHmCT-k4(PMgDIsDA`QN5yAu_LA1~n)0d2(KPLnlo`=$Ke;Gcul6;9 zPk*lE3TI{dL>Ud+hnJ+j&)oRUY*=%JczJ6A$L_o5*h-!gql$7}a5#GyR<%`TmTj=^ zBcy<_hu^NCH?V@W&i6PZv`VLZzD(vW?p2)EBxKB+n0)hW%QmmwDf7D$A@a@KYH_I0 zwh4Kz;0m>@n}_U`@MS1F+u+{y*dy_234bM=&c5fgQOp?hc5SUhdxT%pJqQOK=UTD& z8?Crq>xh`F+m-_7XVjRY+dYD{%v(w(%A;WrAL>Th595*i)!FAR@)%8($$wj}DYTZq z=4ZUxk~7pLryL*1)vo4ftJApk(vZfpEmFllrjzaK)Q6Ns?jw&}H%B)3ZK<=yo_`ol zI9a^13%uCyTy122KY;{$lwm^Ssz#L>`chHe3PZVF4O9i<$8u<1&zlh#6|)1DfT?9p zMP8wtm0pelF**X1lZ1S;A+NMF-ARg3m=3{h>LOp1o5q-mh2Zk;b<%)%$&c4rkC`7o zn;!p3IAP#@&xd7B=BVRk*W(+!#(#$WZcukK)NohO($ynfB{9M*MntS|xg1cnEn$_CgdJSp&Q7J<->?JW>WQ(o61zEeVZD z@m{PGm#Xg71y0C%$qXjHq88KD7Pa|A8dX}-gig{cerxH|G|h~DE0}2erGJOxEjT2b z+8?Umomm<-igZ9HeYQJOGJRYoi)M8(8RXhN2`PBYa9{5N$#FKS^R({+>Z$Ze8C2g> zOw&JiAMdLw&^+SBoD3M$*~mNHXg^o~wRBq592RZq%+dm_7o(-^er*$o+!&FMU9c!p zrtgzK1BtKY%`HF5$%wlkHh+`eV)>Ovji{r06(7n_p$uJqWc>4u!_2keSviTl7isQ~kpjy;;eIc(n^F0;nbl+O) z3IfmmutMxkQ_~reQ)SOo{o+yUWx~h2kOLVGqf<#qVcjW8<#zpI&wuz4nNDfnt})py zvTxn*k?wf>WjBE19;xn&70Or^zmFPri+g#y;SviHEm-7F!f&lkTbX4ri1mvjTk@yDa{*`SR=O)T{%$TX04L&FcD~^MxCN z2Dc>=ICc|Ozr|<>2w+JwU$fqt(I8jk<{p4lG7p|~%f3rSUw`oMX4DMxpW zgIiTToRz`D|AnikPQFtrGrFq&6`B>$2^Lw406%@4?N(oIVD)WQq+1epdXzvTaHGRF z=cOk1yf;2l>kD@&%DeVM-m7%F?f@&@7wSj*ZP*jA!VD(GmHu>_r$6vGccr2h{O996 z1LChHh3&qkqF7`cy_R~&SO~S#@AitDW_|qhdUpIB$T;ejNcB8_5{;DnnuyUO!?x!Z z`O6MvAsKFnB@YjIdx2^75o6Ri2DUWFn(E{Xc#|bE22g*$5Pu z4Cn|Fw+V9tB{BjwF_!^T0~EJJr2`Zp0yZ<30aF7Mw{FG*ntK8^G?xKW0~8T7IWh__ zOl59obZ9alHaRmfm$Bgo6$3dmHIqRxD1VJ~2UJt-vNpX*7Z8*Z5D-KlG%3=1FH%H` z5Rw3akU$bTNEf6Rr5909niOeLq&ERUdhbC%kfzehjryJQo$vhrTKBH3wX^4$XJ*g5 z^UTY`X=p5{1VP$C)sYB{pokDq2B4y+rzH&l0>y-YKv4=F9upV_4*d(J;4y=u(0?!_ zLgqgRDkvxjgTquo7#vRzi2!K3!vP{<0Fk>gB9bydAV3rdl>QeHiIM@Rf;?alfSwRQ z8;O9TDR@+nuHGn^gChp_nSZ?k_`v)C5ou}3JHNvLN-j_o3=Bd5^gtL#s0;3kU=SQ& zj0D4=81H|i;FER4U|eN{g*`nzg?~UUXdxuZL7x8(z!QdX1l)(Bp(qb11n{e2fIi3t z`lmA?3Lb!oBMkiqH%8iHJV7WZ00+QfU?>8Oi*QFkpeO+D;{anVU4Vfr6!C|w`-gA` z@YmS@M1(~Co$jybUx{Fd-@za-80q2)LU_Xv4gh-?911W{*A>EeVeSAx2!F^gA_$H~ z;^ILbAQ&8Eha>#190X8TG6H~b4gS?08jOOuV$ecp82nd@!oSksPFW2BQ9-)6KoJ-; z#jpBQVJIjVckJH6e$oX^)CtzqWBwgfMNg= zKq*NvDRBVQ4FL55I|~0wZ-3(L3jOUA`32)T@b`5^x&rKRO+fu&_E6j(g)bW90R>=C z?ofZ<|8)G*q7V@QKww}Dzz*sFLs0yk9S1}0|JbA&YIY^bcSqM{`5r{jP7l$DWQ0AE2-X@H=p1P~wsl#~ES;(uQJ|Cz=R1pBKD z;NP)Y2zw+h;!nA_bNW}w9)Br-@2~CP2mCXcJ`yKeD1h&8m0JNNfMDE1DB_x_83Q>{A&w*h)c+#VqQoB9CyXKVGK5SY8m ze|xnsAlxP>AsldO7Jn2G7Xpg^!C`21m=_dc2*ZFK|7hk9Zu)E2;4lQ#5Q&EUx?FHk zK;VD*a0>=@#$6z2oG|~mKxo{yVSbPCmkq`3(?9D_Lx7QxUyCOyaTfqWp+MdgxY6S* z34pH%ZbKnZuiw%J2n!*Q7+eSd*Rwys9*Ls(HD+l^fH3G6^ncsJ=~Wm6cXj+5k&p%m zGowPVYh+kII#9cnYo#$P(|Q9+`^@4&*%|!*r?*+bks-XJt<7 z6sb>jh^RkuYJb*Oc00mck7C#W=P+xc8u!|qRJis4K@;zkb8mD=Wm-`@D znO)`$suRzUEsC~3DH1))XcxPCdw*u>I@nsHg0ww?S9iIeVCdBD z^{}dnla`syzN9|OdE>E}PVZ}{aaC~>(pkK~pM3OAf8PV5 z@o{=?Y;U6Cr-btF30C7J;fQ$#FOh*}1?SvAuQHZ?lgQZPC;7c$%3?YqjacAmITQEh z(p2d-T7M$r4H2BTTy)>7vn!;e+1%moykqKKmxHktzxt#fZ}+1Mf=9-B{QP%bL+j8`}TcV+V}|+P4w%0cP>^|PJdDZ_4PB;QC4Vs*a_3mPzca(p>dzZ z4`LT@1yiFdAGDL0O<#|H1Y96caF#p%WJW&{V6+NLsR`jkp7p~h)>--J3~KEXqCG!F z;kP6(B_B*{59f(vrHi=z`leyTMKdyyGnyRgFXaUHQ5`QxM2%`)odpVZ#$Kp(hNse& z$A8hpFF-$%T4gH-#7A60hgZ?meAm=9J1(x=e*ai0Aw%{!$}0w&6m4ET<6Rh`Q%1lZ z`Fv5S{LdixIjTW^|M$2Iy?3W_{>7Ij%^S&TR4`>ll=sisQ<@p=w;1GKK$tCtBs z95qZIk>-b!>}0IzDXg>jK_B+rniqOp_U4)7!&AoqD^G$I)+PJ@BKO;^oMb2jIPW3>=iU-GsOF95SS)h&;;~kkC zw!GEiP7QF(;+O3_V^xvUtcEHJvC1I<10IF_CRNKv>+>!HD%a>#dWBZEMo!fSF$+xr zcRj^#mvGx7h_dYFIyJ3Da??<>* zoI)~3cj?=7Z9LXCvc1Qqw=I_%Uc^4|u^Lz^6s*KbxVJgK9RIstvi! zX!}z)S+Zp!w6Yl5a(8-;Njm2eF^a_>l<1W4bq2Imi2L5(O(W^*{7OXPJAW(6$u$k3 z1gEJ8h{`Er!;9Z5nq9BEHP{|yyR{~C1R=O)LPc@E)%o>P!tD#!l_QsU^||#AG~BI3 zG0^$2SF*g(i4QnCn9zHpp=DnP>+!wCOe!@E+0C!IW+a6Oc6Oxj1L0(jTExPlyNRYY z@2W?6pIkZjSCeeoL0n(MPk+FlO$}j}F+y(bb#vj-CYmK}p18Yz;7z5L1tl;GjU`8( zd%g4DimPe0nUCE)%`zGL`EkLUdW3~ot)5S9cM1P+8$($!sCA5%+ z{()fu-GID#X!ZbRhUQ8|;UqBBVmXZyU()QFPVC^vZYh9<{e4tlahwoV*_^>VShGN%27g&BY#=j@!X>I^ub6Q< zvi7-|#?Tn177;u(%d&n&vJ+Ug`fkl>4JI;M;c;v`nyIz)Q?slu{7bRy#+tcdzbE`{ z!ct}oU%p}0=uf2nR*;845;q9HO{v`0EwZzCemPk)(l_VRhTfi-bdX9!xiWT|HgsIE zbg->4q2XL5^nYFvC25|Zqi@bp(EDzCLdL!V@{`Aqdf~9F6`pqB;#c{=kj)3l1^Ar1 zx(qvB<~9!A+25q4{PWj^mWOkm90n2^@rBU|-Pn@mrPy=JA+J@SC!fL#3EptXx@oyg zzbezXYtOR?q8mRCNl2Vf88#!1plR^XCnUedA`r4dD!%vlo~JLxX8Ypg;Nr>nV;{Tk?#MLYvDjg$j8J>*y+RqS*T5j=uYRhb z>eF_Dwts&9Z#&}WEtPrehd$nZpIj?ZrNOooqwZJ*7nBXOBJX++7Jp?C*c6l#HaYLG z@~)C*r0A7VB%Y5rl(fn+_s;uybFW$l&*pO0#~0?JRb!&N!rW<0%7Qdk?h6_@Fdc5s zw0mrm2Zy^y+}R=i9T3=dLk`j3u?+w0HGE2S~9l4^5(UquD@;+W$L+fc-6(-e>Las^-o!pjN)DzS4)OTsHQ?ztFSgRL7<1_T!ZRa$Q?>wIpc zjq!_;2QKtTc^D7-$u>~4%gzsyZkt?>ea-TLa1_K%Q6(b(V`w&$|CItj{a=7s< zn1S*#L13x%0$LketKuH-N+UyM@$?%ne}8-36ysfiiErQQf7n3tQ%^N<&JFxKj^NDbAK=g zzn#+AdQd%DFu@_wAeCrLUmv(Kg|c69zELj(rAo)qXn5;t@EB_fB2)F#R{hc;}MKcyZz#{pFmsd)YQN zg@$A^xOM-^9OPTco?#Arvj4S&SR2onovCjF%QrrysJ;h6tE`tCxbrbTxr^KEPL zPtNjZsZHGW8V4vHZqm%gw=E2@r8h%v28YSYOy-~H_AAw&U7K->U}P(gao5u2M%_rmVb23kE27 zirN6F&UlU|-$LW7KWNw=eJj-!Q$1Y>wJ|ZdZL`R2EIcAF&2?~-R)1^454#|MXB0J7 z8-P_y-PxiYq?z3-LLIPYtuQtK3wRT&s&)P1mvm~&~N zx1d*NCv!deBqN(Lnz{2nh#-Em>B8@l`L6qjO+iLPiE6%VnWF0p^Cjo2yoTO)CHq$H zl(|Dpv`hylUi)Z=r+?oX>9mWqcf_6!5iV9Rs3;BEr;)?=G=uc6By!z5vo3x0fl;N1 zLqd>nrlaksy*pV1u>DOgI2k<89Y3jM!}(sgOpJiF`$d6rN?ssV;}N00yWBgY_Iv4- z#?BAUk`*Ky=ycUw>MiEe^2h&t5`&3ydfmHi;4x z>KV3z&%^1k(bAtRnJach$_nYfFbO{Tj^sx)r-?BTNsf$z)~W{C-pO^*b=?!?l##o8 ze!VmMERdXQ$bUi0=*#99h(J=%l0l93OO0aTBSu@{6(y{WwpQNb9)$-_7@Jtjlyp%H zKgdpepTTzG*kYc8P8=CED)M10!JhqxrHd{ghKF%39!9Z)i00Kpmn^lsaO);FbEo+S z1l~*i3q+CW6C?I^DtLng`sNR1<0fEcw>kHY{XyH3(tn>n?RNGShKUQNaE=@>)44_5 zKj|DpJ_Wc?eZJz{DOoKT*wPCm)-?V2tnfmtYoh5>+`=gXcMPoP8M>X=L|Bj&g8pzq zWSHE1{dGDrv30ICkJ8IEZRgF&_p60fTjMgKE7>X~G*kq=D6ZC*Y`G>wC+)|U#of^_ zKv7XFy?+|#L3#9zNp0uj!_rwPWt9?&TRXC09$L#OvJ|(G5Zwp{5FQJcN$SebAoAP&d4Z;j#dfV(iPkZSmrW z210{o7Rk?DOuFXQ@WAIcTz2b-@B-!%Qg#2i8UeikP z|LWE?7d-v7M1jS5B-h=Z6=2DfWc8yWUq_-6QAJHpj^(U)I3P(+oXtL_4I0ISeR`(w z)A%t=_7(Q11HZ13%TQY&QC#uJF5dKnouI(4_ib=bMqx-Y-_-bffGB*=TyLG;KrjiI zMt^$AOv4i8Y;w)&t9#(#1+{#jOlo978>0@7+A$eyPHX!q?TqGL#@i|FftSM;ho;z# zKEko17q@$%kMg2_5VbTZbh+HLZf2G4eL^=FD%WanCsd56Vn9!h z%azqA9g~d+0-;246oZ~B|6Rn97m@B{*C6rC>E}IPLu@)g?W#HLs0N93N1BE0_S8V^ z6^2tOqCKa4;_mRFuK{-k8AkzoCAf%Z1Da9v3&NB(=?QqAoW2)s9BMRa)o*4P#0k2_g(NGiJ z>bd>0a%9--#J0>+g1W+2z4C`DJg2QWU6vg#O<(T5)n-wj8n~+Y*-~BEZGY?v(t_f_ z*)jHDJhr@bjBr~@#J`C>i?Vd(Qnsd0^ztpCR$I9y)(sO$Y#sgVwHo8=Ic6TK)L=+9 zcVBZ#CXWzLuzR5~jFzRC1MLbN4qB$)*KF8;M~xz&E2kWu=OO^}ZSK zHSj8+dOMtSuE3Q?0Z^)In6Z&C*yr=Dbi%V4Rtlt_?v<^I=T8k53xC!t?hhsEi3ZYM zQkXy)R@1;X&#Et|S4wn*PUO^l9Z>Oohp6_G*QP*6&|em1(cZ4Q08ZNz&!rrF6Ci zUk_Yv*V^!Wv-udE)~c1xHzYHsVW~MPin7q zm4&7K2SB%8t9D+^O!QS|tEP==!5(u%Rs6m1?{%}&ExbP!OMel~)7&9LMP}nwr56-@ zl*8Zom#V!^Zo${z1<&1<|2UTXPC;*z{N=N{hPhzb+DU20x4;8di6rSg-AQbD;g!X_ zAK6v+RU*i4!%5^&=dtB~6HD7v^>Jy<9xOSmO29_pd+q}eCV!X@yV5VpL0MH)G@-2&Nf zsp0u@LVv>Xf#wqXZN2fQMEeUW-KjUBUwR)9Zm4?tR%NxlXwVHDV|ee0x0op{^yQth zS)EnakP1OGujo=}7CswYb-4Zua~b8*j#*e~%gKqHhZMY|YO<5V;AVYpZ*EqKmfuov zqT(oNRywVTBArQ1LsMI<+0DkQ_xBG(Z$gjk1b@=bybBm|oCInHpN*~*yIrOArO{;% ze|vvocdd4djN3PKqwRUlE$N6-<>y1wg&w7%#FLxOmPKeY-#UXef)I^LpVs1|Xzkg= zcJ)9TT1~uib6tXaq--_;;LK$g@$%2x0o=n3uF$K9TE!I}0~pL@4=+7{9%B0>J|q+c~+nBo&Z zDyjkI9jr+el`iLB|Scfn(b z!ziU1C!(+I{L`^MPejy{9N^#wUd86a4P9McHt%};Lv}Mwfer2s)r;MI4To2L&CJ3l zw5GYu9L6m*uIEKPpMnfy>X+=T*3pd@*5njVLpK6}7tWkxBpsc2#Fd#WjW%LCOMlMX z#okOHyg0ZhPJBk zi1h8M;5t9s;E%a?_{Dimk$7(v^8)$xC=x=_Por9s_bL05pr669Q;)lECwvg^XL6^# zef+(RTG5;)x}N@C*Ks2JIDZy$uu0UZNx6K`86Lx$&8ds|EE<-lJ2awi2}sBwoBiga z@1ROJ+tUeet$HqXZ`7cInxjv1eD%b^N3HH@aSKTQoXyezZA~dxfx>6{*bEvqZEbor zuTsU^Y0gnaGGI3zRqSXOh*FNIgA?lt`Sb}*FgiyfuBDwEsZZTyB7bcrC(bow%|aw9 zloBf?atO#oOi@i@Hxf?6T;JS^c<6~xbJA?x)TCfyPFB{sc(iYKEqTP?fb)YtM0VKH z>SpX=#xXHz63R*b`dj>F|MXS?m!re<|cr||l;J|>0xLWed$H+4yZBkx(f6e;)1*-w;8K|eb z{PHVHgqTaV+<7(C)u^%Jn7 z>>8ZTn~@=jog^WtcsBdA?6d14r79xL9^*5<*ky5J`*W{D+|k=7%Cend+NyLnXib-( zPs7v+<_re?*4W58c3Z+Sr5I_W?FKI*#gufDH4XT}+;3SdZrAHh^4RqEDd!Sp@5E4) z>$*&s?N~ozdVeAOt;TM?7cZs##^S}Xpc0m(wtYHkhBf>{$V=k&pN1(n$g#wrd^GW|evW*dX>eBQ?eT`njFl($qSUoAl|T7(Qv4Q&56O9* zTh~JJu0UAy&4Xv);gQb!rDO7*X}slbZN5f=ZF*kq*GdFDqVUvSO)52LS=c92+;{b_ z5*L}G-9;gWGW6Hbh-9?f-3a*9UPE{&LQ)+VAPg7UY{?C#LZ`o_@*+F*+FlUqKFhzt zHYYuL)1Gej)H(a{)saDu?f(G6F2<}dBm*5iYOx{nD@4vU|oT3Vz)!n;$we4q$f;SW@>P%u5 zj%GkIQ!{Q*?9y$hq4A*f;=e-27}j{H&}1c2-ut{}?*D@B<`GJwO%!MP`7U zqXW5YV=leY`V%GwPa=8i?+jUfy8#zj z%E3ay(cT{D;O2_-o1Y}e1!xYgx;M)|ZMAi9^mOq3*Rv(a!NT%)3Ks58ESe4=XLq2i zQwc zgDipIA4tBgrXE0mn~OWp-}i6D{~{zdHh=}l+zns`v;sLG{qYVK11(lU z!Gp&JVEz5|-zP)xY*{!u*m?gk|EIYun(w3)bR-!5N%+4iadAg4fG-mVAApISixt4e z%E5mP-~m7Q|F0JnQ_#Ozu>LtK>tN{!;QJR@aFzZ;*yG>br~UV0&;kC}my#oRVu1kK zKLc*S%Ef99{>JwIdCdPg^8atTe|!0V8SwwhN7~)a?jJ4fZ}tC=*3=$k=lyRzcwXJz zz_YLD2;KmP|7)rZ{MWiF0xdx9_WxHa>t=rn-T^TOE4%*|5#%Zj@&a0@fZWWj|1~QA zl5756FguU~P{q*|^!q{qFtM?+{vRE9tITb|mxe2N9{*7R!AtYMA*CG59W8!u7dsa> zz|_UX)Ef!hVld(Y__Bf5&;sc7&jbTlm>nG5z()Y!^!x#qjxI>Q`^n7(U=jN*`WJuV z2C#_#K|BB!i9d)Jz#{nv@c~$*{)>260W8vg5F3C+<_}^Au)O<&H~=iNe-I~tMeYv* zbCLgpz+4pmATSrjKM2f4=??;PQT{LD1#?mPgTT?%{vdF4^*;z4UE>b|N7wv=z|pn- zAaHc;KL{L8_rHh_98d2L0$ZB?7jb`pd79cgftTg?ZTDXru+;1i0$0n-#njvu2!8Qc zy8V%J{8#?34f?MF9Nzp70w-Y(axr(ex3mMd`afbWa13)tJMdWjN5c8r5PVnuVfTC9 zSS5g<{!d^wb}%LI1e&^9|Cxi|XPn)^Tl>cixa5|9AlSh2FZh333HlR+ z<2Us9E9!5(qr1yr?!YEie?f2>f5LNun`Z6pWDRuq%K|I|{RP1tvHc5z3-dQwa53%w zA`7m_A6H!9qYmIT{7Vnc#_>;d@L|XQQG?k#{ZWIRIe}lS4*%0vPPTtb|F^3g;8@^6 z0lz^1GUfh926FtXN#IW!EY5#_L2x<#Di#|!ch^7seJT_9H&4gE4uE~Q{{_JX_V^2eYvlP?`@yHZ{(@ll-hV-G z{e1qA1AFoTy8J8D|J-`!?k?a0y8ZK30uSVW@IN0gfIu&xInvU+qq%=Th)qLC$9 zZGZH8GEPvN+HFT#oW%P!@#$Esaex$&gh@km&+pmUPbME8r)Ye|4^rju7ktZ#SHCTK zwFwrJ=bKj~) zm~wGHk((`!7Lk7*TjoA~kjue$j(`w`{&XR3R5WFY_EwO+E5R&bCy_+G8qNnV2S%8s z&31jyv~%lwmr6CI%|≤wpjLhQB`6G8Jx`zSCa9t}(hXGW_X9A-3qDF8t{d&Mn3F zwnx`V%yDw}fYk*UmT+E0Ah`rPgKxsT7Xdmoebb3w@#BB$M_am_);h^wCxhQnI3%Vc zlOr`#JF!XNBt1tS&JUAV;`>sjA``2Mc%a>r)!ARKJ=)jCgHBKa!uP!0Zb#%TD2I0~ zDfDtCLPeO5S_TJ;&qi-F?3)Y4pY8VEU1#TyNNMVa=t+NQC^u2P_NFuWF@?W+Z)E`_ zl$K2^cF})i`X(3fN*v{NV|B{;QC{b)8a2+hXDP6%@h&Md1vO-k=aBlPY4;*sSyb_{ zqDaIbYXpv#SOF&jW~)3l6T#!0k4{tt;tYlU3(N1@AMbJBeyeXNQBya{FUFfu8EMWi4U@h(tb?cR3dm_7Xu3tC zLTl?T4+&Yrsap9OQn4sbJ0(j(125W(nYXt+wnOe8!L*sq@4M?Q20{JVp8p=^L&V!* z|B`>1QfwO|gQM5}FT&fd&-=jCA*+E+A6N>;)#f#pWcTw{5++E#7I*)|VqikEtT*h0 z(@cviHEFEHKIzbck}AvO3}2NXNgmp?$fFvo{SX9@jD!R)kyIer&%ClEo~x zgjj_n7tMs)2nmz0)dwca+VlOQFnQFYDbOsy#8ubLRvmE!V(tz#ATE48Kw*_B5e7Wh zXNmBiFkkEy0S=Wcz7-gz;(oo?OysJqtPnc;#6Jx)$5c+dHtR!1&W)asLjYs7iba2; zx?QIytKTfBonj)=Lfu#B7=Ue0v7<5oIevU9DHMD)9)p(V@v=Hdxwmt()!(y>*H%AT z7Q~6Gbl|S1*CLXGuAt_mdV1MWWPM)~L_BOGuzPHNZ3v`YZ5|6H+^s=;gu*aeBMdcd zM_`Z~tx$YkG7_L!c5t`S+9+Eqx`%%ja?PW!{lsn15LkEw8C;u70(WKkxSo;Pk>6wg zeFI<3z*EixaYD`Mw(eq=HsIq=fGt0AL3oTN`jHu+wXR`{|Hs&7VnLdj(}vU%!zm6c zW;LXkxrNRPWKLz40=A{G{Iz_`>Dl6QtQ<3>T}+JsS+6(cvVBcAg)!A~sh5A*^qmuz zlKu<5nzw6OGW9XP8Ov#-C_pzuY*_vm47RUj+0uGII#10E`5oc|!lrOGT~9gfBIGCv z0gmbbVnnwQH))%U_(Thx1c7DtH@k>#tItA{MTZd4&yD?oRmESB2A!y|rSe0MV_Uw& zr8>R=fnHKNHF!x7dOaB5(D;9HFH*`X(-;#I(HANXYE9`EjTX?^ImDza<7CtQEwHEx5IDYLPe z@RE?$sOm4$rtDzzAjzRgOVYv@^%v(&)-EkA{PNu`AC}J&!OH3h?(WAnyz2tunmnmJ zX&kb@m;TKl1+$)n)tXq|!%7wT=neF>AfLwsnI!F$g*D#4Foq2Qy<^N>Kz7CH%IqF!pdV1H$U@B9vdtmLb(#LrMC-1l63#Q@zmmYRC= znO`$@7^wG6ZE$j_?%oQmRPY6KdMDUL&h_m z$8YNHwK@lA4^3|%s498AH?)WZIp5J~lJ?2|O5|;^Z5n@x>mO5lyzvvb9`}J`c$YOA zSgcR1p9`P>-LZ?0^7!R5LK|nK6X!JqC9*zDF)=GfJ1lu5C@>=AV9iqvrA~~hEBc$^ z+|A*YC_N}v+{}!cR&_ou$nS5VLj^s+te0G=if%%;yox+vd8n`&ybJo#;eZ!VaK?I* z7tp}@c5i=rty|`EwZeDj49Z{un?ciCtX75=G!B)p6MyVYIoB2Np80=Z<%svNRQjd{2L{e|1+ij`}67)Tg;I zxqwN@Kr}V8P%lIiGl#%t`t$I*lt#o>8O&qSG)Hl|hCG~bG#*@vMD^gK*^=>>brVMv z5qQ3bnut%{(7tFXXGnh-N{eta z%kXv+{ez6N2o-RKlx zx02w=#fVp9`~Fi3^?EupOPN+KcJ1?ZgWGp_nUs)0Yg2PGCeYO}q-T>l-$aXHCanlnYbWe6 z)mYW}Jp|9#%13O;Icr8ns%#?2)X$q$-xp4FgjOyIM5E1jhKTdx%*JC3!la4Hx)(RIoq|neOy`HP>i`)CUJ}%E1XMBV$mHeb; zdQ7r#zwSWTGfqDzbPol!RqL^yvd;dTcrtjfaZ|G3c0@Y14}{-~Lg-l6@r!e}35o@9 zji=MM`X;4axOue>p_}2(_e|SCCDCZj7Xv5;R7(z{FMJxV@PvOU=rX+Yvx|PLanWpd zzmOVGiK3a7TE#;@QDbq9Q(MCaPYBGD+rQWJDyQ?y7}!-Sak50D*OeYgd54YN?TSXk z6pTbp|6#vtK!`D!niJONco)q!#LV`b#6u)pPD9FvbzR5UN}ckD#@bh@uGyq9%Qdp6 zrgYsjWD<3}GZTNlY>c$!#!s$L@KLBvulMGH1H)yDB9c=EP!C^?dX6ABnib$`lIQX=X{lxuc* za?12Bgr(AsZ*Jx`F4hv(wVaM$RJwbaPuO>&s88KbDJ09jENqff&)V_&7Op*Gm3`m7LZKE}Nr^I)9^a)EA zV*EOW-TAv>TrOp}9G=r1@p>ax2WT{|S7E`6@0g7i*(Su-%Sc5O;b!6I?6l6Bl{!7i z={tX&)H}0YAWdXBCQR{_wVttZ+3qJTRjS-0If4Ev!iw*Z92m~ri}E@_s+Q%$qlix< ziWp^r^}o`e4$)OUTTJ#_?_tP&B#i|RVc5r>r_0|n zyeHhXKvWY~ha|kOoHVtP{H+$sG?3lWf~tRP#!g|j1C8GV#lybQY)C98D4JluR5As# z?(Wvc5n2hD+{f5ABLhd{{kDb08Cc^K(MTdM3}T{Txy&T(^nYl7#%RyBITV)Lb-$pE zQ(Gr;F@%N7XMl&+gRU2iU5Z09z2>!-cy9wF;)gj2OY^ry48Mr_A!I>dke z(g|u|bJVqHn-g|P1;@j1C7E#so78l5(_nmbwaJQvrX^&cXZh_^hWZ%$Aw4><%~Pj*L+?R%=|fF5js> ze!lfP46(0e<@y0XvODc4ckR^wF8V^EsnyZl7x|PF>(h%0n?K$>B8Y%S1J$;tkmB4{ z57;p}>;Q^neRzMx8)|Cr9Ei4Pm)S-Uh0eawbpDb+GEl0Ug_Vi@q1_eUKdpaGDh5wM zvQ}n7ot{oqi8q3aPU71NV-|tu&3*H9CZ|y6EH|a1k6Uc6;fk0XouNWfHk?afG`*}t zYHgl`(Y`8$Y$sMJKAx&pVs_nmM&xd^Uc(cXc_jPtT)Za6$4_$t;|Su>XB3|V%qL@j4k0sG5A!F9)1+*x zHRyC9XiYz8y~-nzK@``w>m^PVjK(~QhKH|pDf8zv?6}k$#qRMdmhib&puB|UO*xB( z&`T!%(oHwU2%HgwkTri3mbd`wpT)L`PL>+J`9NAS^q!bXo+pYtXZGmV!H}9wLbfft zSeEcE4N0(3!`cC%n$MJwC1%87D&4)6jo4b!Tdy!PaTN=}+U;Va=~%`Y3iQ4LkGWm@ zMPxaOgnD|xntB%X2B@K4C?UmBK7Soj{S`Hi&0k{d3`)Tk$CQ6t9uU&ph*CF+`ahY` z3&v;kl$1V55|eKC?vq;+;6@kukz<|9?+bHHM=^8J%5VO{oe^e${}{}`+3r0jT88@r zHYavceC|wNm;7@_@(p4YDU-<)W^_tKa>B(}0!48tlVACbnwFvqH=+eKrcKOUrDqM0 zDDO*k%qi^~W)pwr+e=ip?j$4=f{UTR@Z5vjrZ@+-U26ckgU{3oP%UTUJ(a_Q{SM9# zr0kkFQK{ED$uIx_Cma! zqhAxLw_bsTY4a_!$xjzrm6LpEPeKq^4>+-ShVQtu`%5y2FVyoGjqR#jB9q^U*6PRt}iNKLpk_=49%LOT(fzm2*?1j8uGs;UY1QMYC> zXlfMM2`9vDk`c&Dm&WCEZ{mH`-tRTNTX#?wSQ?pYKFF{wQkjHAJyXd`lMESsgp`_Xg2T>kuvg-?=u9aHAp%Rd>q9zOw0$^fZ5U zyF#oBb*iCy{-8xR+;XNa8{p0ZOgBIc9?Zt8gbdMC?(pQkMVdEV9h@NVSeE&C(^5)z zj?3Ktthndsb>I_EtM?@Yl?bw!+KPQeRA)PI&^rSn&HmdjtsCMOLeL)nIJCV;rOC_z zkqq)$zfHN|=g(OlQ3Z^Wq!fA!6M6?IW)<%1-eWfcs z&@PrMK2rvL-ta8H0=5l4esXmJLs`CsC9eHe=Vq~5@D7V7-Uj{GV2A_TY{!35sX+U! z`T~aQ*IjkR<-1G#-n4vm^_}jJM)4C60e&l1FQNTF({Y_0x6Ib4cIboXxQe19Gk8Qz^Si&|y?+&LabpljdgOm^H&T!R?)bbg zjsgbs z=AaL+)Akg3bdFZ^FLFJfBMVvu{p{DDMZNy{qJsmtCYoN^R6aZt3T`d;Y$tFvd+f5L zsKLAD1qltyGK@5OP_}P2+x6UHJfpRN4xvNfArH_ds%t8D&# zo!I8dm~kJs6?j&^d+;*H*M88^IbV7$S7Ni)YoiKL_3kN$AcJ+QCK|kJ{0I@vs9*)b;u7ng#dzACB zRnS!FtmYnS1zt@H2pO~`Fg{WlRZ3+~ASk`M1Q0RM4{2}`IhFXpGF)w@a72!5lW&eX z(rbhvb-pQ!-N1if{qDslK}MXKU1i~BdChknrj?tIuwHb`zU?HLrW1uxHHq&Wf{8`{ zrWMO8rg~PG#beCS106>qnj6)U^%_G&bfaSe1un8H7*l5qs+bcZev{WBBAjE(Y0cX2 z)Pc1s=wTYc_Jc9XZuWBof+(gM#QRj^Q~ei);7=goUE6;Wyu?%!#IbX@g*OV;dryh# z@bW~`_uf!J1FY92Bz2Bao8g+%EQQT7G4G$+WqBRi1e&9)wdt8AKwQH*PD{f8SHdY$bjVF8x9vIBjysLkXZd~=hw`MDs>wK21|+@bT4m=m8G6yU+XBk2h$BbdPMiX zg|P5UO!i9=V8|*^-()-zCh;8Ai?@qOJT|aQEYX^jh6E7q=px4ckkEX| zYstrI5ksI4dcPH;SAf^t+?F;*DunE(+4jTYC#`N0@_~(bPf9Aa<_aa|cm6AE(zn;f zT+~?D6;bgyzv5rpDQXphr(YebaPGgJSD}B$2(&5?W`!NJu>6=>uD7*~lKFw^UF@H_ zirjVdTvxvu3130kim9(+uS|BkOm0O{uvZ>6rNfF?JivmK|7M1v;mB^6X*geliHpqf z!r1W`Ph4=#o=M;5omORr8ACzE5*K!aSv46y+}nv_N9(F%rRW_&Zit^h{rzehV={lc zX75UYsW!J+{?40`*k7BR?x^vCW{&cQS`*;meq^p6Ww)l&CiQkvtB1_>J?!J|FLrB+ zJy7%{{Imvt{>yO7kU6a2EEV(HM#)RBVBt!Y({L8>NgDEwgKfl09qJ+Cia4v@-xfcs z7a5*PTzJiT?8Ros3bCQS>vL}ZQnr7B$}_Vn3TtW&I@;+d>stB!}xlo3FL_nYqB1eiBfZRcfQ#Mm2zylBj1 zszc%Q^qyK^p-acUs~yhklvxSfz2Iko;IC&ph#YD!Nv_+p6~+qlm-M#ty?~UeDqDX# zKN&wVr7wdmFH*2jUJj+*WZ8eXcI~jB2!U^q)%l53OeiBfiGFIGSo~i4Z8*UMB^g&d z^0MUFBdFg5M^>~Rtye_1adN*cpaJ&AYZ8fN@8E$O2ldvBtPGckw+ zICnrs_ee6T?F?E5&^tknkvwc+(^a@rXGlSexv###<>h3y{^s{TyT7z=+dOn^(dBMB zGa?&rc9|xhCmouT<+-_2lt~Oz@={DM3A8)U8wCY<*83Q=!AY4+5Om>1r>-;Gtz~2q zQ_~V=oAbiDvrSX0@E3m)^U}6F(%AKUK$OPzM;T>o{{|W`T^K;sfXTP*^MQ%j`o_b)$$Z5zVmfTFE5lT^eOyHDXMYiap_6CbQGk`l0_Vc0!EoDjWegK2>tU_am_AFcW2gG@e+pe;;}Y#E0rMP@$xj%tel-JHf;2X&Z$>Ad=P9o zrfUaP;}oz#*4IKh_*y+Fa5BRVn$mc@U8|CCVu4Bu#pdGrnEo4(s3ins)YuKQUs0Dtx>Sa%Z^SG!uC~K-oygkac_N>F12@ zIcWXduDwIcbcP=&e++9d`r!%Je80&D1Di{9Sp~kR>yE-|gH@__1gjrgyT<>D@C*B- zmQb@OjS7Fwbv^O$Sg$D}h)^`J*Ru9o7g?>d%*#XBfD`$x(OGBhFXq@c7t;5to++oc z$mq{1q8-gJ&0`6LmqRqLKt*VpzKOX0Pu)3|&rr?Kr*Ifw8v%`z2_e?0MOL00LV4^b zQ1QmWG~(|45-UWx&V@-ONgSaMp_b4p{2dtiQ5}E5pSU7q?w?hiy5`kRz1NSalXI^8 z($U&OehJzoTyjgp&*pCApzPii^%*i%(c9MG^vsvAjFg$l#wI8SM|gJb2yQVnKH_?F zY5LGQHP>F9xZhNuGIpMCw=YO05mm_dS)gN|e+?_^4n;*clB?14@~85-7Nwp>bJyI; z$Wwp!l?U0a?bGeNKWr40<*hsT(B?ppLeo3;Js#clGnBWy|Ig%1@sug<7oID2mbH)- zmizmKO?-aRzPQh%#LBNW=Pni`3jU{wIH54(-v`o_H5`hL_)G^Ct&-Pe-wYbs7}Y_a zb{>4`8%e$ENJQZ@^#;A?{XFt9-VIN`6=>f)P%KxfDSSwiY=n%E68Mdp zdoB(;qPpFPk)8|JQpD&Y@kU}UgQ8Jl^<}{mE^h16K;-3p22|?(08D|eddE@6F5Q1K zovuILaBIi6N1339x75XM=4G8;FtJE2GJ%p(#84|Kz1ozUmd z#0}aPU5B!R`i}^veC`J%DiB;rg%J5DQTUCLY#&fo{ey$*X?pkQ(47&M% znrB}T%S6P|GCGUb4ANN=|0B}~=PsIaX=_KUb*%XJuvu5yW~_+HZP0-I4MBfEQx;FJ zgSP+@_LV^jCCym^YDYc)l}>EuU<~!EIG4$%hLVv zRIdX=%C&JHI?n5tpY!2uI?qo-z8AZyuMGwaveYB|6j(P&4U;p*6`n7r+7KXAf_FB7 zp682V?jT~98AY*rBf=mH#DqA7=^|}U)Jjs!^t6kt8Qb$>&?y)1sfB-59}|JdzFfsj zDhjBAZ;&wwl(}sOEE)R#(A>MvA*(atDqXm}46^ZA5!Lv1pT&|giwAWUUL6?b_daF6 z$ahUy29mYhzbB1b6_)f&ZbVT7rpo2cW(+R4J{ba&a@l#UH+J^)ZiiDnhXP%tCm>2- z{A;czzQl02|E5Aq2?~GdCWrGU^-d$!BElu8@_`Q0HRlL!+r`;0graiMFEhc5hwjYm zIGlrv0!$m@z;Kzlu*7{t-z%*5OA=0d#-<7K3@|z$d2QLh#71D7ave}lo>Z7KXR!Ca zz6@1-(%Js>R*McO9*+mH4eIId15fXBxA2QV9Rj}lbFje+3yBW*9&RCu#x#L_G%4@iq zXetsKg%D?xiNk-_Y(LuJ5{Paq-A#uneU)GC&2?b?DDI&(N&N(mJaE|_;D^h5N3gcW zr`>`)k2;Q>CfWGOKTKr|U^rT9%0X;YX)mK-+l4DF@s5fZsHTLz?++2?6%YA1U@E-x zdCtr!d{7mO;x3k85W9%|_;#-;nnFl}jn)L70if=6QVN(P$29~)JQ!jS2s~Yc%s6|%QiQm!YRG?Lk zsS+1Up&_*=2VAZSmg8VxTR3Cwo9bE?>0@-Kb2!)U*;EO9l)Y4JmBp7r-8$Fuo68c5 z5c!JByxM<=oX;}o={TFtN_ERNOxy$Seby3KWUmv1X#>=_M&>$n7H3$bL0I?P;ox~<(zE_Lj5Na%9VCp%FR%DPSaTJ( zZOt(znOqlA?krfqZvWo2m?5uo0}5qZUv6f^1D=1SO)L)!-m_y)MVDPKPkMa6du%`u zbC76JbmWrXHO2su5`$vcuqOrIsvVhyoP7KT_K3g7izR=?UPPs6##{EEPq}AITJ%wT z$rRJ!ujr_wFxq|GV+mZ*Yy+R8#xO?-niF|fy^gT_Af(<8wRU5Xii(KdgsS>8 z9QKz||FCQnniHKWf!T;WY`?fKYK;+wWwYP*{-!Wsoi!TDXz)wI%PKueWQBaDj-9c1 z6|uFP6_;MG{p0SWlS(*T7|Yx`V%rXVa(oe9M8;}>|Fhqr2sYpQZv;I-%9zZLduo5m zf^^)!;Q6!O_AvjjCGhk;Y z?wO}tlglCUu>!*27-oyRSJN8NDwq$8m58kgQN5F&yy!4jMhtF7RAe+62y!=BXj7o= zzt;U)f!oPqXIarn?qQ*MXV+G=_)&lP`ujKezQk;Nu@(T-yq`{19qgHW^=5(R)^Sf< zHX8rs$UWp99X`c376-H*Z*Ma$mu(U?Yef?YpIPDjd(@4)0Lq)aRr2v)dfFqEs@ta6 z_py$*w4!vUt7GD9i0pJ5T{wlZ`51y3I}rDLpV2y6j}T&c&p*(v{rVV*6m5TeVcQpu zFXLDq5Xae>@lcbAah=dh#$Sg1a7sH*>dh1Ee@_+nrXY0Plvz`D=P>si#&jBEpO&*3 zf>^+f6!Q+XT~{r{A|NUm&)B7bWJ|%H8(LG~T`tcZ>G`lGg{h>Zte`S3yWBK4&hWM6{Mr^rcPYCLR#k%~QwI*Q|?tT#_s zkyVf8QKk`)Yd=1Grr&}MF-$01b2Lh5Z|#8mrC7mzzXtR3)~FPj{ZW5Fyw8D{>&a4t zfz|wuhte3wBI#k|#i3|hL6q^>>zyxCx3NvdVbckP-?%+NY#(ld86#{RlY%95^L(}? zeLLf3Jh#C$E?LzO?ptqitR0Pv=$bClaL+8_mxb91HdQp1OC4>DX)M4M?eW}&Lg;r= z`y{#%q4Z{=dk56aspfxZ=+-3lennd6_7@s|2&+^UGfeVPzMa>XTR8Ei?Fpf+6mwtE z_gcjpiz8j)3;OXLYWDV)=8vKA+%>j6kMa(t^urpKGW2b+X`Z$&9-MyHkMs6^D!cde zIt|$ZY-Dzs%mtb6;pJfzQ=vY;GIVf!P-I$&(vI~Umii`T4U&I+DM2Q*r(WJ3rlc-n zt^bxZLJwWgKeIXS_EC$Li(lXJ>?H%UTdLXxBKJ`nJ4iJmEsyTFGZ5BVk16<^{bbh0 zRgKptx)S}1yqN774I!4Rz3a-m`ZG3q&#aO5lGjC)g%m}^AkpN);kk08Uy$n)fz5Q~ zp&I0nXkw7YGeLjx(JRDQuSq4&A5)ISGME^j5OBXhx4_(_cqS_^yy2Ofq4K4NU{J3^I+8(1r%O$QU0ic;WAp2Le z$phX(=c^C1a#rk4-X9NgsxI<7kc`U=Y^_%|xKMU{h0%X@ck1!M*+?VSx+K7M(OFQF z@KAQqcu?5-e!WD^NtLPFg^7OK$=*bYx_Ih9aA2%L5ksCLfgRy|^IrJtL(=1^qxcD~ zDqW77?-!bpschV}PxvQg!j`KW#L5~;W(*z_?ukp!4}7pNoP2596jGhCHO23=$VscU zc9>W5SBifG=#G-mW;=CdZa=`7AP(~naJ^M}n%shxQ!!%Y70&PreR{%))I6n@fO zjt#fO%i)p0psxAg0cqq}W+EC32-e+pk>p_}3sFnQ3g23xhl^b%L42R0La0BqO@t@G zrg4}Z<(6ZoS>+(=_K47&H>FcWiYMGah29czzG8p$IYX5Jm+X-oapNa5(fX6)x`ZXw zceyG%jx@C-AGkmywMdFtL31p*tZaXTJelO0kk~ivCkU4c$NKw5!{ZlX6F_o`Hs63E zefj+Wq0dCve(SuSyAs@tI7(bAI6HbgIj4Yr(}t0PIMUFZdQ}s!+zqx~JxAD$j z3>1G&i5$w>gBELFYznjM*-A&^y|XfBxm<=uJKYKG=*M*5&7E+dZx}X4D~6P#$WZ zwfjwnp!w>CVVE#XHa(eE7IRc|RkSUBmf?SKZNe`tULi~`cckCdoZP$J_F{z;OQtOu zt74}5V|0#vU~&x6uPr69C3QfR)n_+8#J+-~!j&+yoD%y@DftEOLQXgoqC^=q{)G!q zc*=Z7?3Pk$<&fmuJ+l474!zJu$Y{QMb&j8;)#|F5k8n3#paTuk4K~-h%Ic514h#X1pZ z`m#01Q`MOtQY=)AlS#N&8rQhzc5}+uZ+=z1KxFMsPN*}il_uh{gUGPe@Ev~>`rJ8w z%_|!x)D_WIGpA$B{aNUv{T>Hhl}dm2PNk>l{dh#fQwat4_E8Rg0a!*90o{=M$lzO3IjDr-3uewZS1`EsR&4R1T zS=@SnnwO&{p*zhcv)p~epf`U2n=*I$!!14Aazl@r%-&Qex58TWolM3Tx5{8LUq_FH z3CqtGUujW)+y)9E*lgsApw8zIZ_ILcSBOP&gcIL8tQzyPlN-+J8dQ^l;^;*-$CL|K zI@-|KddD{T?nY=W);ariR49Mw2w)^zYq`U$ov#Q%KHhG5XOUH>qmX|#y^%Wq2JKF> z5XA1k?~EorH*mnyCkN@RS6Ye?0Z~=N>33llF7PpIxF$%;wP?{PGGr3`JKcG2(O-$c zFT>s@>~82cNo(FRS6x!*QIq0*%HN|=#N=SfZvm$naX6%`N-ylycbp3i#P`GW7!2<{ zqb1HdS=1V`xI(i5>(qaa6$zhoy%%bTRB3&oa(hN5)!%&Hth^*`CR3q@L1|hZt0}Hd zp7pxP-uSe`hM=#qf4YzSDPP{N12JUm7Dio(Wi->1YW`(m;)CWZx+ zkh9Bt=Ya8N#3dC|be75`1!tNTJ(}_`RvH}P!Q`=V`k{N=(d$)g=%2A{9FM;-z^$z!t3GxnL zei%q_VSchU-JXA*wj`fvxD;HASQM}x9xgMTmL!kU@B^yK*`bfB(R@j2k@tuy;ljdZ3#T?__vYObi+lUfZ5GuQUVu@!VgamGYrgqyw^r zho`qz6J-cq-sR6qdd6}))M4%0@2izs6|NTKHCU=O-`suS)?wnRB%b2;>LiU24KhjYGzU~&-v3@^}W*d465i^x;U&TgL? z^l5*hF;bYFdOYLJ`B8B@{J{P`+5qA6O;2d*XmUN;;ddXE%L4zT@$f14nr?Z4w|1B= zFkReQCpx9uBzvRV=Cc)1HMFzL#c68q3Jq%`mtVu7TGxb@+TKsOv=#msb2AAxczdGj z?-{n3P2y+6>^+_sfNcIvnjII;-8eM1h(mvtEwfKLP`DS|)fyC~ueBBg!T=J5w7-=b8!~SC{pB_9RGgjLe-Q zq1N~JRvAQDS{06X5~4Qt1ZgZxKYV|+qS#Bej&)84D2K@7Pe5~@q1%a(VKiJKacF=4 zN^=!XmOR+?7oU^cx!-Kzy5aT>Sh_F2|p(+yFuR*`2h{W}+qh-p;7?R>7m)X`;OdFd|#v3JVT4U2~Sa03ZGvuqfnR$6teA8!Zq#0}s4Z7_9r%LH6d4jzA%C?eXt z_F}+ES9B|}%Z+#kITFiN8z^bB=1Y_khMl2Oy6BAss}aryX3tx6%M`;=i{@Br zP&nUpU4?Bq;j-NPcsj!RF-Ct>dzn1UPz^nuh3RI~K*Y}n1whJ1R*DgHD%4~s;+q@_ zYK3!KmTML?VJYr3hVR3`cYyIt&V_^W)$Lx*<5c{VTfI|4>#)93AUK9j@PjLt>hZ!Q zCWpFGUlU6z-?XFZ>rp5oF-`UaWN|@E7+IPVD1M_)-Q zkJCN`NQ<6V2+c;|v>bn=5X;h8=Pq~EV;=6j=+gz)36v1x0*5eYRIUJYK#RZsr>6Py zn@N4hcLfHEmE1$&yM3Cjf%vy+vrRkkkT-bj_pw9iY5C)A2SVEedKpT30myg?SM)Wo zj2NW4wdB9nKDESAD+UL*w!L}Mae!@SUj=3Y;(4pg^3Y0O6lypo5tu7~*ns?_oL_pX zp?M72t};tU)*7g6@IJKNk~}YNx%DYgaVAb1vvPFxXcq4)scn6C+=xF}mv}`*#I}MC zgGWgp{P?9wC{WM$sBZoZh~*NcAzhDB<=wYUj^8#>q3?9O1AQ@6 zs3G#HSUx$IZL$xXQxAQA%OqnsB=WD{m9%-#GI^*ypAXZJGb_YhGw7d*el?o_-?+*0 z4ruGZjaqp;Qr<%>KW~Rp{02XDOC#zm>}Bh){$i}wkbI(w#5ToBzmFZ*@q|27b)7V% ziQxY|exy0bb9AfT@-HI`;I-}%mg-}%FGky=M4SoOPhPT68UL|=4uPQ+S_C`$qWKBl z;(+fn4D(jhue@$d&oeXalmnvm*zhFp>J9$Gi&L}nU+0(BI(}nD>?FN>dYJ>~ox3v6 zYN)2e%nk+D8=m_APXP533hWO-sh(a52zrSvQsRI@5}{hD_!}uPgAT(>d$wo+y#_>6 zYV=dOU0GK~CVOmuXs%n%RErjyWW3tHPMhrYM~F?p{;ElxOH@q<5G9=k7p%jKA= z>xY5#4F5oXI21`OpKV_@bzl~`q$Q7RII**iaR(^04LMa$%{JFx_&^IH|GrLH1C&Pj z{XKA7Zcwm+M#I+~a16F#OU~z8mPSFmWv1-qe@(qY&>~jqT2pS|&Yl+NGEpjd1J^Ue zf3#J$kr7U3VJ!<&t%LeU7JVh==GCDUB#uX4)*VHE2SlIm(4!$=S(knQeG&X9ndz0V zmNYUe`s&BCF4N_25N*QpqJ(x>JHa2R*q`_@THJt+0s(L-R^o~+@3uD-fW8^#CB=-B zE}*UT;_lB(v#;}10R?p0o_!2q*^H2N_ zg0(VPpfXP2E#mW-wj1$YEN=2#)GOHnoFv_K$E5H$XDSF0Sx|0i!k4JC&tq(qa~*K? z4M*r<&w2OVZi7mi@&Z9cgy{ z&Kaz}<~7tu2#-iMxv8}RjPD*Sf=JC`9z>=Hjx$BZ@e@%m9hs02Q!wb=I2}$?cP0{l zV_8jJ2i`d;U_40lh>kSP2@l(zg95t$PrQ|E!M+{bJm7uhy910Tv?9{;MP37AAEDRSMb zv8Icjc;5Bb1fwBcATpuVP=?vjg=@%vCLP{_+UiT?rcp9j#wWOGO7EYGGdta)v&fAb z0dqcv0W6EFv;E&4xTNH0T`t3GB0hFo+WnSKz1v2?66<5F2o|VPn>>vL3h(R|SiH$D zCNV2P&Wv>a5s!+`Nx&3rle0l%8`F#pgEK>PmaJlfbq4&CPVEWxL-Q=xIi&Z0>Mf8% z33hhP@xga%-vN9V{lV}bhEErKC!xoHH($^od2td zu>jCE_8E;k^KWGjVdMslJyELaj%=&krvjH?^-B7YGgE`FK6&tlFh0z`#55g+cUUaT z!r@F5UjeJFeyE3kYlgpvWh*3q?K&|*XJ&XqdnVKwT+w18S*r}pFgXDMoeTAG>Sn9&lDZuniYi>2^7{Q+)pKEN^9k&6-wi~d~3!@BpepI*BNv`+lx^=^K3yr zOrt~anJLY0Y-Mt-;@g(7t#P<;U3_u_$=D^UE%aL|`7UUwE+;l{6+rQP(>EQ&uC|Au zdjc!gPD(zMO3H1PXr}ppD`w%q4qx9OfdulP;V$oEPw)6zNVB6Sg4U}6tDujvt)*G< zFYnB+$MX)EV>XJNnhIlK$?_P~*XJQ6f9Il6o${)HG67mH$}qjFK~F|TrooMwZoq^! z%FPzaVKUj+3Hg`J1vR@-<8U?oSBARvDEDm+@}|V)Z8Gs)qzg8GbPHu$7mn?C&62?; zu_d2z;xjwR%&M%zgL5UlvI_T{)x~OAyypfM37XUN6!e#~FbQiDbf7b#FFI!H$CBQz zc*xQTs&hl9N`Ao@m=7-MVN|3e_H=>jsM_McHdJT;Q4PgL6AiMz^+D5EzurJZs z1{@9)6`b$cnsYc);Em236>Weg{LQ;Q?@qVxqhu15nGhmB83>ri*oJoC^pb83Yl*}O z+!&N`#obbQhm4pitJ6hcyyATU8{~%D_v{+Z&-TLW%1{=6evrhXkdSLc5<2Q2_sF`` z4b`^ZpYclzn4{?Qk6qZy>!x9U?JkG+u!lL*a&oH(5qp*}pDpOgWcuNoksYTArMh_K z=ZE}QfJ?*)S17A~Kfcc{G3C4^ORfafpM3p+yv0z~WddPwd(%zIkXn38#)?sQ-9BF8 z<`Z}_6fC!YtId1)Tjtzhr~rJBT%Bj@*gWFXHqf|?PHJaI{CHC$z}WvItSFkr+ja}3 zKq6*>YmCxZ)@)twKkFsb&hrw@UzkRXyM;>g5=idFKwy3M4y)2%#W7PXiNc=S-w8aR zyC&Sc+=H(VrM*=g?fmPs>jm0+K5|JtgWp9e+Gk6DIrK)!`ta_4<-e3MHI0Ve^MFi> z7cf1P$J_SY|CpP@T^h&a@qQSW>?`5z7?rllQ??k|&{Jb zE{m8Go$pz?q7px%u_l^54mgcjpTLH%hpM)xTjgpLhe2;Lp<$SIFZ{BuMLTqT|x6Ck-BEuce(H)>^O5nk^t(S}i?o;hnLq8^#K zXkB%B=9`UG#C>P~1FnHZfNgs3E}r)L!L*$f7=vGU)n5Bk(w_`rOP>`JrsnlEz<8+> zCCK>Jp|GL;^?#dFn#10AQ@TkzNq*L@eQZg8>Qk9wBL^4fk0Dhk!MR$0^gL~^auArX z%*$g|Vhr%wa7&e$!+5AWau6~0_2*umln6-T;70lsM_Q31)IOH@7NqLW&DTe=?j&gqJrxe&VV{AZmA|EJMFLc#*3gmzN*dT(%l zh1>R$UZgCnA7(hj3~9x$3?J88BRG-HAyY?8TCeO=y{>plpJYo3hn9t!*i zX5JZIRrIMkC}8QHAF}A+_){F(=D3}qBMJ05R!mITAipI!35PGChy>3dX(||qeG&e4 zC~mbm7)8`83F}zl(}r(fzwN<9q!q}2e_uwELwvgaKrvTOd(R{cC0hq5;)=f@SWNB3A9TTt)>ZnaC->zep|DKyTo9APU;(?*;p~W0MFxZeRjbVs;q<)opl9&YxY{Y zDa+@yBHUI6`E65T8g1L0KJ2tv2A@c?m26>OYAs=r<4EID1izvb1}=^C>*2$C5U>H< z`Nsa2_N#P4F4phRF@Qhs2?S9jxi$*YE43uh$m*dVcn z_LmL{*BT65Ynm?ZR>wr&OyM(snDcrEvR&1jV(I8w*awO-8d6<}n?4GQi87k7^6jE6 zh?K%nnxx;yMn$b3E`bh_nXjkun&p@Oh5bj$ja;wm*dafqTs4)gkZp%nSQD-?)&Y zXUOz=n2S=duqcnM15KmT4kHM$g37&1@2_S-Tfu(xkerr$VnIR0sK4hyDL43LiI(@T z4yQTFOVAiINw)%>CCriTK4Y34Nrz>B_YclDL)?Vzgar+5nlc7|IEEd#qlh0bw1{Nz165zQ5R`Xqm}ew-j#EgPyj25*PiZ#7Y5?_xkIaG0lDLt&eUtYXA+ z*f}C{w~com3-ZE$qC+(ac*Z+3S5)qXht&)mCHP^!f)prl%$gfVobH(_CsYJy{~Aur zgHCOPW%BS`MMFR2C--}99=L_p<=P~7!zJt_ozx}GZpXrz2F?z5?kWFvE8_FAs{=yB zTgrD9OxSq&YAtwsdr0z~_#~FU&G`9-d!!n@CTHGfGi*tJG*O~3w^%zp7a!9Uu4LQ~ zALuZ`uFGxgc$!}wqtN>{rD3VO{<2!IIk)vI7$6l{ZR2ju10C~Hwx78tj*&d!dsu~K zH^6Uoy0EbXpeQs@9v*MPz8)d?Qbv(~Hc8OVHa$8aMT&n2h7y(a3J4cU`y>yP+Xchx z-XxbD<%l_d588|&b25%fmNjLsi59S`N8o9D_0F8jVO#%;38$`Qr+OCcpt+bd8eZ-p zT9P$#8xbT`OzOu62<}Fb{PJrq$t#y3SJj!#0?UB)T_}5)>sY!trAn1C{HT>eUPki= zwaPXOn>ym4kGDp+2T zRR#TnUxH`I@pW!r5G4iq}oImltB1g0>@*HVu zHLn_}-;~~9h_kQaxw#mbb{TT(EiqP!5dh>NNn4CnzI2_&#@#2{5N{GgGG;yG4s;ML zQqyvOt^cc`Vl;#NLEr+A#59>b95*Ic@JggEvJj+DHyl9*!P?j-&xp;e%Qek^dqj5CGrMpwp5XU6TUKumUy!4 z9|O>Db*ai9PQK7Xe!Jf&C^0zKQG~Q&8Ge@*=zmvhj+&BH@oaegQkQtg(e|&&0AuZ8 z_0kU2cO|SDROfU`$ME@N9mT3+Z0|=8{Ljv;@zXw^cd|*}QButIE|qDHJdi82ye!p! zmot$F1RyP%` za{c|_d{xD&4k1%l_uSxqJG#SWdnw`9jPZ4bTrU;N_nd+~Oc`y`$DS^&40_kHuv}M( zp*q*xDx2;f0`U5N6mA#Y&ND~6xmXO+!?D4h^|nx2Yu~-#dw7s z>Xp8cgvU#<)}3o>2C6%pe17Z zkIwX~23+aHSk}^f;{u%DbH!0KH`l=To!0FUuYU)>=voDcrs3>l907<};fhA55kUY= z#}&b~UZK8AbS(}_(Z$mqMZCd(;aDXBP^cLekoY9oQan;sd<8)g0v5m+7b6ufcRV!8 z&*wqFKfnzcaaumPF6}%5@mZO=`ZG+_77MW~ar|02;{rwP3Tp< zhlEPEOW3Z22j5SI(LO>5P&gh#L5_#jEdHer+LYUWkn;h~JYcJF z&HP{=>OaB)$kX(_w3!LPO}I9MtWD;c>@|MSwvJqAok|^-7Yl1j_>+o_>OYiMU(!j` zx^R+1r`>g1QDKpwC^Uy3P>Hnn_b$`Ju@Ac{=QK6Sc3xR`_im|f-N|)U#XoL z?Q@i8L;8>A;BJiiWy8Se5>~|s8d}R0pM0pc9jIhb8?b!utEUzP;z6a7yooT;s?ZAN zz|HqC>Li^N(aB-L*L2}t|5yPy>dPR4q(GN;@x7>ShJ%%piPOA)H9!s8@Uj%4u=?GZ zkqD3s+ELhEu2HT0s+<%?6`VEhKQ{=`f==z1>1sVrk6?z7v-E?*(CDj>PLbh4Gw_-@ z^G_@tq9z4P+iS&4e5fjIsv%+Z5R*~FHMvx7{Mf0lkO7vG;vvGZOe_jx45jbvZgQjx zBCB(~&+70?)A*5p+0fi&yL0zBgH`>*=sf#1}<-Hmvq4WPD8l z4(IaXzO|LC#zw3M|HGS_9Ei8Mr=4#oTEc??VSj7~A9G>UfMqw6{v56u$t_VorC7G6 zyF!RBfqQ07p?|J3%4ynr%X{Awu>z$EWo~41baG{3Z3<oyQv(&ZaaRWv9GCk62@RJFQv(UN zn{x;6Z2~wlmjP1)6t|$^2W1=rI5d|5Qv(#Y8~z7bFabE1;j9J}6EZS1ISMaKWo~D5 zXfhx%H!(Cdmod8n6$CgmIWdjkSO84y+)NzYjEn$gMn8y28bBAm>UCR=>bx@Wm_Y2pp*N5g`na!b8@od zW?*o2b)`43cBHp;F#SSJ3ve}mcQOMg0Ud!3E+x*Mfz{tqf+Rnhn-Q30$U}A0s1jviY&^x&~(EjvAm|5LHczXyQo-^)P_`0rS9wxFp60;v8Bxi%vkqY>yA)Bp3R z|I6k7Z^HkI@_!rh|6P!nvz67q)Kve{|9{j5*5+33|26;(t+Nwo0c34KYhd%gP1S*a zFRd)l*xcFre`_UwoeV&WAZTL>8fH2sR(eL(zvbqRV&-l@V+C_3BeTCp=5M*`U#n(i zZUa=Xbu|C$ZUK2QGX5_eXupgsLHCCvXfFSy0)p1(zw?UP7}*;CwP(z1>;MA?2LpF_ zP^UqN4dBTHT1aD{+rK6mz(8+f>jZKEfH-;sOl%$C|7t6LI~#yO@GsHdh#kNn^apVO z7=-^IP5^_*AH)S<5d9D0U<5FT{XxtC28ln21;8Nr2Z1uk{6U}$vVRaLgWMkk${_zA z#0kov@CSi1DE&d849b5HD}X`e4+8O1{ewXH)c=FHKruD{Adt@BKZpfXi-ENrXxjg} zvHoKNs>$eoKZp%vV`OUuYTf@xSXuv)SX=*z^H)z9jQ;_d01UuCkQr1usHX;wW`9oh zFAIBT&^r8a0?{@3hZ6{y|3eFkVfqgUs^L#GRuF$PcRMqn%|9$avVZ!6h+F;xf>`}C zH;A(JAIJov^CuJ=$khfkr~lA{s<-`<9pr5LUusZ)?RJ0EpkQ{O*B~1!po!BTDbv5D zf8QVf5rZZN)DLspKc~q83UBXh3tHL#vIO0800xJDKoDQYKXE`cJ6iqo#F>~tq5rsp zR*k{Q)%G81kcaa>An1Tx{sBQnxc;L#pz7WK0YQX4{^SO^dH@~%j{RSIY~<_!V(j#< z>lQSB5dVY!_2LNxx&e*gSC(vzc!DhIf;t{+1o2(zeoZm6A`M+f+`_8wiQ>8(Ci+afi{!>yY3P75G2Er~nnB}xK5>+BX5N?X z?$*?2u`xEqx@b`valI9or^cqWzxuV9c_hw%OVLvGP-%7nL)mmnj|e^$9SMWO$;pBU z9@HtA*;yr2D>%Q`llk&RNl>_j9 zin`s=mSb>$5AaUBi2j7pL4<8_nN}8aKeT}q%a^C3inNUG6>_uHi-}kb~25#Ent@stt5spqX7&2!(1@12sG4eT%Yx&!|oOtVaqWXRA_YuV{dw+7r1QsXO_b6iOME z6C1)PAye`KWBI+!_MvRtw?YJ=#VBqroIU~!7iiDzxtgOI$iY>dW*ErxlTdQjRRYOW z$@g%4x|H(H_Bnh{jZ)093)z0yyU+Cjtn1I3Q`H41@=95%QiwH^Z}K#cByU@PA*pk5 zxxFs(#{$?a$Asqt#!^*PYiC(*uU248r3>kwbcemGo{n07j0%oOqb!TB=xEQ(Rm}%% zxYGaL7ohzuxw@RiIkA|g>$Ot63?C+eqqzkc19>s$I43vF>^z0ISX913n}DmHGGBGI zbQA+#Tp!*b7y#GGa>p_~)oW*e-!v{I`C!TG3}bn*e^O3#!MVV)#}+T|e^`DUVu|@4 ztz#;?tL{B)0!hF$fNt#QAbCwN=;3xxx&kmGD->0R>v%LuWxc#-ifYwG!VHTGPz`w> zygVs=jjAAo_=qs<)4Z~6ko@!VsH_}?pY@o$G@>g9-Q8>tO=PNIlO0NbYV-GKlGX&f zJz7t-MKJ@~BTORzTlRA=W@vO8x&$7$4k5iaMg3(I{-;a0)jOGn^-hD7*i)qK;ZwLY zZHC50D!VqQH(XC-VzXndji<5MbN<^F?p-ekmn0hrCQZp>V2B-~u~R=2#zbXZ$1jYF z#ao#bI++D<5g~?{PnFt#T}ra)QXh%-c2+*)*$URD9KBn^m2cdV2^>FTtah$J^MMPK zl;|~(>9qJV0B}Ob0Z$tFEj(iRQ3MY2AM0{0e#-kTvuBzK`B%gYhAq6_AZmo?(U@f6cvr^zA1a&IP|?Il@Dl=KaKf0pS&#@?rLO{e6qT z?vbquYIo_2i&bylvS@Y49atky$NsYu<<4N}6a2_xuF$*J)u?&k?=ZCm%#i*sT}Xp^vsnkaLAbV_&kF>-3dXAbIjfea3sRaAo%6;ck< z%6xac1Z_cO>fJYg4uAYEh0Rj(hT29bh_}JM?NEg>lSxW$7#)tXGS5w5B!kShMYRHx z`psOUt)u$ZMc%R_^FU%!>zgSrTBOW(c^XIvotkByl-4%sozSrzB(`^H9BH~PT4pCH zVYt8%i9lX!M@R3P=tRQ!8CJ?&RlCVz{jC@-C6n%qB#n-LwiL63X30pc2+y?b-J8w1 zx1@eb>1wh~3Ci5#sgPAZFEL)E*tF3LXc8^*Y|J1?JH+y)YU1&POA|})D?!T99|veECVlf)wnEy@NiD)#{C?1&|&Z zFwfY}@Cto*?w4VXN0grja@LkFbQc6A8I$pdxN2KmByU;OOYCu?jc00WG+j3C2o)o9 z(Sif&o+W=qn&no7>TOs~?UIfocEb$Z$veXHs${8uk69fbeG#?jtDgBbytymR`O?YT z)LMZEl&v;LDBr5bboTx&kZQk65>UaG~8bc<~vU^{Y-Md$};=B z`?YILdyJDS?%pDOgSYmTr+u*z^}?GD+PQF`^mEfJJkx@bA?TdHBK2f%JrcnE;j7z(dR<3< z?)A1gVgSAJrUiD}U-KTGdj7<0-qZm_=mkDzM<&`nH=|M$dyc0+#z2 z!f@l-kJw7{zIvEvj%+zL2)Kb7ziIP-V*k@5sp*oug4kz4KLpq?ncqy7WTn*|0q?wm zm=bgzl(-KQ2b5NB*+ta(G4Wv2ag!VOxJ|DE)09a4BgwIWhY#Zou__WBe)F z+}m60yN4TO1>U8YyZDhYY;u>KGgpzaHMvhAhP;;F1`BlnQBWRAv;LqoVezs=$mmTzQp zO;Adcg2vvj5kY@@M` zrT&Fv@n`YFg$NIE%vpqgDve-x@rZG;@!;s#rAUd6Q}^0d^cAEw>)b)v;c<$jz4DLi z?yb4Cy#S4I$PgPnh2#NiO62#=!o@g2g5n98|-H75-dlhyD@d0_Ba zfG{?8bq!agq$2XOh)#o<^mqCW_UvQt2a&%qCj{}ssDrcI{I-(C|D>F62%1c*NHrM? zx^?cmJ3j?fCXzf4>j2t}TNDzek)PTbz|4TCXSuGwuC^KMN+?7PW^>incMD!$p%`$=5Jj;k4Nhe^r@iu_bSlrK@1B7~Zj)<0 zNL^Zq+)RC6HNk2uMS2ym0eWyT`POqZY+v9ldE1LELQ^Y>Zj?xa;GBM+`Z84{ei`U? zXpgOSq2+tYU^TJI+AsB}9o(abMU+Ex(}@V#3S^AI9tpXB8}w)NRxZU|+}TDCi|`;Z$>ww_th!S#jBnt=qoewl+HjMtzsSH+^F`YH>H1KM zZ^_B`_$MWQ^&#!`0L)J@U(>TX>%n2+FvB(QoFu281eongoAl7y8&)Wrmfx)Wm57-= zX>J?8;mzOj<8{x8P1;7IE4eQy=~+AMa7C5Q668nH!xqukLw~u#pC0e;bfKY5(+NO* zCdw80t#`nzE^_W{aUf?SxrVdO9Osk}QS7EFs*zQom{L^&dI zf!w`aa8Drfa(b?^akD%JUDSB?!=RhX#wqzv8d(&fv+EqaQVVxoeT@U;u>nu$On5Ep z)-C9B{aIOkfQPQ3l*Gzz`gLHGC+bP+*n!P$^=gjtSG%uHR9>3mA>0{j=Pwha^BW&W zg3iZ(_OEK_H&N_Vy37LX9_1}pl0 zR?>BgdFXc{t^?)$XJW7ube!4hiQkJiAGAv$VEbLc&|NphHa>&h=iUzXDlpf(T?EkM z*#J9&7u6;d3*y?D4F{>qv!#l+S!!gMv|h@8sw+rZA{}XmwrV~NUnm3#OQ6nBu+)sF ztaOnVnHJ3Dxr%rBzBH~pLKAwp$hOzF+0a%uhqrp7PV(oaAi2X$M3}K8$=C7H_(q9@ z_FD};;p*9V(v*1mZJ8*o4A^fo(dvF57s*kN!mSbu9CtIwNQNgHTNeLSo}|0VYtM{- z-GQFOF<5pVgThKzKUh*IX;;u)lAtf{-fFwHZgm?eY8Vh!#M&gqKcQv*&XOY!D1bMB0E1wDn3S#dEAz{|4VIuXB20F!DUFM1UehiA_ z_@<_rlbg4>wE3Z%K6WL6x0(fpdEG8Bv@EqpXIzOdd88>}sUNABDszwU`r3)RC5Zt% z<30-h{+a!frg4CMsVnCQ))C9v3Nvo-ef$1#>2oaWM|GHXrAQ&~AaaszHK{#+=~7OU zP7Sy-xWT&auoM;&@or_PViu_|GakJM!f!0IwyZMYN+*f>b(_Eml;Vkv{7La~>b$$x25|WZ;B!K3$n7`H!x%qZN7qY>x7ruyT8R(ARvcLb9!J;Rvp%PPeYq?Thj)Zm z3S5lrsY=qlI^7oYMK$3`2764XKj0b_dEk38EhXnD3ns_*=xYz~tk#!gid**`H6qv| zEVUMtsAsb6|7tCoMbTH^Sp7=oAQu69#e+KYdFhr`ons%rUk`JXR)QdZ-M&0W^qDd$ z1jF1kRhQZj%IB?_G z13ZwR4lSi;1O#>7E>>|v+nPccdHojINA94yGfo2}x&{>qqaD*m$-Vduph=^m)Oaso ziG}xa(#bN%J@bo9R4AE$r&F_y@7Em>QnQ+d1VZF8oO=;cRfTp{1~&K=P7%4cf|4f> z3$%03HbT}m#n2fb>a)yiFYt}6!k8pAL#c=2IhGoE)@NzH1dbB8UK$o9O1ZHQ@i`@~ zhA0EaFB)TvzCBN4Nh3|I5D?PNKdtdqcvCZ*WTr4T$?!5)#9ElM9}uXoWZvA7@@e8 z0;u^S?fDbFcp%pdJ*%gO1z1H|2io$5}p?TorIdcS^y_2se_Y$v|beg(O@CLuX>n}K>*X3 z#!(VaB#)JC&8ch?{0Jl(kh;&?R9>jQCYJMLh#nuOP9Mg4!B>x(!d#B#k7oHfd853S zJ9`0%fusqx94wDv1m~#C^W=;Kb<~wKuvrqB% z{Yt3riNOeeO+~C<7v7Y@sl`3joOp5&!>kskSE8pE%}@=G*W`l-Rd?~$=b&?qXL!mO z#-Zq+;7MOn5*X7I%OojTo0X}5E?bdK_@N|`;Aav&v+ndkcs;~v3QZ`LSjj|p%=Qa2 z<81r0#@6l)I*a=^L%Zr$5T*`y959PX?T}|iYN;)MT_>8V2()e=+K*-*r(UU@lWom0 z$d&0d7AW)VD8z`rMUV^?el*9{jH=;>{PgWpK`lcgke!qR8Y7&{f_4Y^7(OWwX`qbA z5Z1$w|0+o-+%<$B3;uZ{svwB%W&)ryc?q!o{Gpw;o7O#ZY9Q{bcs|Lb5B08kUxY%d zV!ob#rR*o@#tQ-R_I&^;jJ1TkUzmKsC9>imy#c+^UKCGDzxhTC92cyt`&6g>vtH9& z$^MY9s)6k%*B-AgPs;-_pk zBi6lRn3obL6m+z(sThX)a=}mv+7VunCP>$Rc~H*ZSG0T|pt$A9yOuek#puWzc(V5M zbCxW0w+SCkm?`s*=ELdra&i2;oQC2T?K6T>Y^TmJ%}3FTAo^nOjUP*A11ARh9Wh7{ z-wO?&9nwh>PMW9;kl3w%bRteDXHAPH*HxLru@yGuE18s%@3vL(VQx!NpGi>5Qa}@b zWzH!Frudl4NVDRh*GdV#oZ>rJ7g}(byLbU6_2+NfI>j{~G&R#I_%t-seU zwT?vda@TMD@YGWu{w=5Jj5S0PIsNT_(@deww?@-ajOy@GuE10ZsYHzxI9>l7dRtFxC#ZZK#TklH*k><<}1EZJW}?C={i--*rz4u+;n3wjWL7d5)l zU6x_N3kN1Q(pi`jtSHqmHEEQ4Lsq8b%5pa!4^Z#_T*O>g;i8osZPRYl__flaNc&u| zo;zbV&-yjrNepa~$-vjyFumKf;cH86PgHAcb#JectR}jcJiFhiWHF#2ohJaHSXe3k zEM%Ta0UrN2i>b1^M7KO?d%&lE!$y)PapZRHe5?yCiHp`@Chu8^z3{t?D$c2v?91?S zI@Y<{8dMP74&?po8^uaJvHuz2*Fk8ZoC)kS$!GtT%#smoHA*>?lm|lQNax9pD~LvD zRIpJkN2wph^y4H+(-b+7eTJeWUzvKrue~YiiYi+L&Ni!K@9z4sR~>SH7Q0T7A3C&I zh{-li;icgCn5tR{u?O9`W3!i;JCtxJX{}j*wq>qTAC)4^j9F<<)ME7U(plGr$(k)E zkU#~ES|?e2X;Q^eC^+eD*t($Y+o5iah?7ZGb20$$YogWf3vlNBM!vK|_0z7D8EJ8A zuXlv$QZ#l-uV4@fC|9 zczp7ES`-)BO8W{Iv-kpAqqhq}#q+mFG~os@y^0%S%x~_4#ma?f@VEMpsPEe7ay2KH zL4<(~(H;HwH4YDXw9{85U7JahaJkK02+Jz5m&ne2a2dYQ${B!v+t(L|4QTsbjjB*I zx`CgroJJZ{AHTbPoLaz5*2*_&wOg{!RyV4Eei|6_hrOZrE>7s+85!R)7#pOx^D%#+ z+oCy0L8Br9^`X5*vMv0&N@LR_6LiVSgdPT|Cps64h%=GsMBUUs-qFB>o=Dqu@9@^3 zzBlR*wOUPR8ea&1)lMx=SC-+wOFhLh{_-(oD{8<-!?QXn!MNGTcYb$p!0R2C5(bSC#Qp7)~VqlR8JyvFMgX~I_&;(T-0 z8u)_DAPOeY#c3>RL{I$uV-;%vUoD`m+T8wOId{er0-w?&aF|6!v@BLF}wxcoI}a z+O!XQZ@#^Mo*hHEc3Jak&JPzNQOm<#&KNidZ-j&oiwTT+AqXS8{ahZF#sQwAcq&Yg z{5h@QC{XA1F2p!L&$RUe*2^utli9lrSi~tlVrDZ#mU_}i6`T+6vbt-GpiklLzc z?BqkQ@;Q8H^LIhrZ83iqxFwem2ESMJFgL=AEl0Y4ef#<ySUoh_MM8!k*51xlov5OR$(S4>2YC^g!J`C7eAaMAO^PW%6^hle>omLcMb3t7EYHf}71u*#x`&dbCo|E@~ zuabvB1o^biY=BG^>-e|R>wbT^PFdCcf$37dTgaepn=JkbvNaJj@geOFL=?|}{L6z$ zRWCAp)m&|=pJjZ^yBCHPd%u)kiVzbL`XlL};tRTmMW;#(;}R;r%#c7Z->C(etgf`E z+N-z+pk%iO37;2k6|f*1P!xHLp~(<`4KW$$V1o$~!^8>!lsuRa<CwWc?u7ek zf5hv6(_@u5l*n6d<{Qx&&MjI8H+_eZ_#hO*{pdH_is9i^mYqh__-Tcs*_XzC*x$?` zq43oi%al-lHc`oX@4z_N4wP)6QBy6{TR&Ukj zZq{|E%1yM(7Lz0Sn*o!Oh}woE%Kmt~59YotwMIOJ*HM+tYP3l6j#+bo%7!21Q>X9h{%d<*J{n(M$2-e}dJUfxZwh zX|h&_Od+i@oZ15WIobxl>&vI5Y;dwOMaeO@u^j>PMfTmF^@qBDrIN65o9uQh(E{@% zqm3EbvrXnF45#>_66?Vz+0vI?cGounnwZil)g5gx@(^3i5oAiyN+VL@Iy919_OJ0n z!!`kw9+PtX3Bu{@-#$juP?8FQv@vl)fOXFI4ohC{1{tb`C7sOF35?p*Sx$!cVvWT8R=# z#Q6?Sgl{%qj7u7h&6rvu#D5(Xb|5s(RRGpGPpC0eR9n7(d-#3-K*5?Ry;RNOKSd&& zPc3zJ9!vm{i&HQQA!Ch;g9Ve)UNSpRK7t1mJ#CxV19K=o`oVbiOc3bg%esC}=3!TcOzs+x9OUnGiM`TMGL`MD=%T>71hc}?W47F{f_LHEe z2i$mfpMc|baQvN=hpW@^1~o!a3IWt_g#9oJr`(scBsMjJnhEqz4t!Xrjo@98T!cOV zD?cWqCY%fiFmgGxmizz+((d5Qgi5kbIOv5wA3KqM>_#^ZUP6LE*%;l-Z*V=w=WAVb z^25TX;M`1DdUQI%__vV5CkU}#3XdQ|pV z@QLz#<;H9^twPLx=`FTHN)Z|36@QL<>aNr|e<&;Wbi!<5djIgjbddq*!xyPDJ&rwp zth9|`j{>hR>dYijVs^=`&AnQ)BBySv;Yt+QBbgZ~aQUd2xy~?HUz>sS{@BD(YfJF2tdoy3g&VJ-m@3Q^K;(}9>G zb*R0FXwf8$tU6-Nl^lu8WYTZsLJh&h@y$^kXfLBY7gCZq?ip$kxrDXCzB8lcR!KUX;f|6AIyBJelXXy@J_2^Z=Y87(xu)qt{vX=2n>%ZP z$fvf=)!InYkYn%J$ji(V;3cMiQxQTpGJJ`-{fzx3(s@tjY#SdxymD9*jf35H8}?WU zXper{J$tTz(EY*E{;36#&9-)}7+i3ay&U@|=03c)EYS}&=1LB2%&DzIQW~se*Gt8l zlv>QD2`?iV%g7I7L#^d+-yjjwgv^0`XgV*Kc}jcearP|@>yc2v^^RtL6v}0I690yB^YM3{^F-M5C^oz*b8DoRl8t`!9dxuDN@U|ww9 z5C~lo(Ycu=)QJX9dC!NlgzPit!m3pOSz#@3*OcYM(Y8%(iMK|=o=(#b>PxO*q{G)Q zR{ec_=T+)t$#wQ${36t{`jf-Ku`oMjVFXF6j)3$Sr|nr#}kV#h<~ndX)$${G)eV|!+Qq?tfx#q^edvCdL^ zxlmhas)v3!_&~`5x8Avcuy!4?)U)`h|3cyS&N`lZ!r+u>LhsM2`h1@pjLehe&qUVf zzq6r|ZeO;n#t5ZWG6qyh7GCd?2g&Lwlhd0;@v9Nv7Ugluy}Z|qEBp(})CF9s17f(& zmDQSkO6BD87#e~APC&80e_d3;;2t}h?v#5cpK^0GT}R|f-eYj`KNQ*&pl$SFV#zy2 zNKue!dPcIH9QLU%aN=;XjnL+=_`GR z31FUne!7U|wKNuYq5Vxe2(eNV+ET-RMiaZaRY?%Kr60q^7x~W4e~!Wfo>v647-x7F z@ifCwDJ6<5_|2u-l0s9W=nEc=`;m}Bx8G-Uyc*ubEsIwlLFr+))!Z2ic@Zu!@GCZH zd{^h@6)wv=n*_M@_}5PT*WQ&ANj1LWuRk6}CIk5E9^UfyqRzElfiUCm%fdW^50XO{ z+rW9~nhdt-4IA2s@&- zqzT+!&ve@nNRvT`H2YBt-o-t0ldF#ehjH%HYN=#27x{dRfAh#>ZbPt-MszY2lLk19 z3!&ljFaZ;-_ZDXT`jaVF<6WF?e?U%;F00?EN6P}%OV&JzM8-t$V%`ZNNX{}pAJt7` z!~KLTd-4TN{}!TA%znYhhhmC_PBqd0!EErtqRU1(8!@5_nf;*+E6xDxgwDc{%EBpn zc^r+4Ri-^#e~ONT?Sm^lN3d|G08Lov+-pbzcIpR>kVRI<*uJc|GX{4{N zpUPSkubPhr7J5Fmg2QCNpM*oHaB#5s%Piex)}020e{Rp)h%Y3E@%G+PaMYjIE^Nax zx+R<^J=Fd@-(3or(Mox_$Mi+VAVVq|`1RY(16s7sWLMK~HpA`)J3Y3=+$^jnsX3-% z)uFu2kQr*ej)ozQG;FI2*)>+H6`QW~QI19_w5U1pnmROD-6T4ch*M4wx>pZiMMF8z zg{1QVe+!}eV`h8p7vc2&_2HU+iaNu&fI%R+#^W|;&otuv<}8P0aEhZbF^z;)OF+0@ zgsU_8Lw}8hKwA^6rpBkDdVqLfTG#EV_zYMF!4wzOjR$s<=W$Ij7>Gv^nd(X<>8QOhH ze?Pszo=E`8=Rd_31PRYY_=#=?Twr&Kj;cLnS;PBSqm)BC6I8~(yhxJ1W%hF5`2(x@ zgBuLPFbD^y-xNHHO29A(x$pJFP!KS#Rb&^h3n9Nw<*NVki!+kO7`ocV)YQe9DtV(E zJ9r>YL)p8ObB{@rxweCpRra0prP~H$f6|hB&3eFzf%Q>Yxb5*9?lR!nwK~|ln=3~& zq84DAX+t~JmvhH6fw}3utr)BUFpOnF#}+KOY#byNeMA$Ak~CgwyQ-s)S>Cn3vRnV& zF+W}G9i^BWaF@XJG3Ew6Q<<$Nt>z%bA``|3lF+^CAu;iJKOen}WzDpKG2jbwe;Ice zx^G-j=Rk3XWP+|}`ryD2njLFV>FaG49P3EJfU55(eg#Uf<}KnJ8>P)Rsy4YO10Ah; za8Cxm=8-)Tk1tejM6) z`sA1bb4f^Lz2pU1&h2_H9a}Tcf5M>VS~N6%rFp-RbH4)mw%a78n1h6)rBk(@0i=e- zCbS+TV79eh>2bi}vlE0){c#)MTfg*ryk;xq!Tq#NdjO0jAThW7c(IzUoPKtfF0;O8 zhp>^CH~djz;=VZ*HsUjWoWeGpIC*vpuH34?ade5A)V?5cR**9G3cuR^f4D){h!qc3 zwfhCLw&Rgm!hwN(jQ)d~_+m26Qp^?8o=wM!!3}4J$K{(7*Ed(fbI8YvMk;|zzgSC^ zw8fDh$mW5O?pi6RP_`WKZ++vTk}i>KZylH?69ybOKl+=k5xZ%3xLZoSe)X?7a`+4O zEbgnU;Z~yukMXu6Rx7tOEZOZ4J}9AMtU*nDkkZBcfIoMSJ zt95|vmrXx$%!TY9%Nr3;aUEgDH@j7eDTP;bE`GZd6<+d9ow(vPK}S{9;4Wlq(f!v5 zUHB#{HY0Pj%;PDR?RUnnpC}`Hwue$9aI74jvK!jA<{j7Pf7vVZrgI&lsq&KoS?eq7 z_5AJ;lr+N0o#-!*;azHek_aVO51!b--5X~20R~M3aSrYR!at3Q#TjWyDzv5_PF<5%i^>U!Xm^y{Fo{vgxEMl1^zHzw!jO3 z>fz;8Mewz=_>$AtP}M^@T}IyIcP=^)<=0hIDvh6z>QL@U^AUICRkv43l=QXa?S~5^ zQScy3!;!w!4gtz*f0O#TQ+QlPIUHX^4CXkRaN*Lyf5#uPBEtFNr=k@ zpdj91e?nw=?+4PVskUP@tmdPD))x05=zGjl-Slh0(zi2Kt-kA{E$`CI$t|O(9oWej z>R;p}jni&-*%B9iy!Jt%9u;@G68d4b;CK}cST3SL{3KoTSX_#M%fo*j)46bo3+s;7 zr=zm_=&lrh5GwSG@3=>_Z_M<*W{qA;q0CF#fWaicdYm-?H@kgQ-~9q1qFBsx7nKySQk|N zf5Z@^&^chk^=o#YYz4l_?ImRCM|xR>Vn^YrNDNUFqqbN4*ys!UfaE6&qo>9lO@ql& z>mu9w-JtwEIe+z1z7081h|@1TpV+o!yX@KpMp*UX-AXkS`V>ba(X-)s_n|k}>5+E& zBQ-3Rou@rd^OsK!{!xJhX;Sc<4oP62f9nrIxK&Uz&a~W*ZP#nI)IvCI=V@dSnepN{ zLIzVc({Ru5xeyVPNtkh}epRq8>rq`Rx~D~)b2=tY3SSlz85<&U z1HKD8Ce<<&b-bV4XbXx?9>+yKe@jGup=4Ln zB_Sz8ONlK29qNZgsZ(LtuT8Y~wXMGz+tupNSz5m&At9`RXW1tCd12;Sf3nKw3&FpG&1UJgmu{T%Z4NK40TGm2946H#KHc8?QXO%vf>wPo z4*!zBSO6&z$n~>3y7GIhe-KJb(+EXVr@PL{}nCe~EVdTjP0Vcd6A= z`uHN`WHMTg;c7o92S-DU*~b9^9oS?&km1lX`83S98`C^jtE^{84i=!uWgfs3sMTD4 zQ-2pW9cYAD)@v0LF%@Wl!#vD}kDRyyHc}Ah`6i7oig}$)OLs92RSfv5(IAj^UIP60dXTi?*m@~)kU>tVPOBMpvimwQqQdGrh?BnclY z1%dNz>bJ%xMWl~y@Si_?@0GXWftVygCYjMg_nP5|D=flq#YCCj;+PsX=$jeR!32{nE+mUR>{N<%!f5`Olg#w`Z7yB1)fj_-qmMmb_TlDUs<26f z$x_{2aW$>QHu`nYZ9wKjsBvuW@QW?1hX1*#T&=s|=Ba1^z#5LbkMYjyF1Rtyru%CL z(NDLc!*W-iSl)bYFE=o(Gu)M9Z#ok^I&;W@H8emD{JAB9BUV9ePr8??KWFIi|ehgGf9m6YAnX z<5;zi!3ND(EsaT~eP3YuO~u%ca;GRe6KXAHH`x_>*+w{b+ziXLE4Zz^nlMkSFW1Ds zORkt8e-@ry8pWL21-1ysWi&E$!NTdwGu26#tmXNexVtJb2=FC$lMz1zN^@g+vu?QC zQ_9r!a}Azh-Hk0Tu!VoQ5}M#i-BPR!hxt7LXmMGl6&dBPjHj3%kv|A-783T>V$m1& zpjh1Jevq{mgS&8J*7Xs=+WN`Z4e)T1aEy2ke>Imk9@Eoy}o8hSa`dQ!6LDMhh!#BA8X-~mlW(@{6zxZhx(x;f3zG{0xUM@a`bwibf*+`DR z?)HlyUhlXi@|CWm?v-Sc(8YLPxI}(G;w~NHkIJc`<2vL=3S6NV>(KjnTjj}#$y5Ac zf3AjH(E_#nDL06A(-VVWqZ5j6js(X}E1)lhVagbKbU#*ybg(5{)@d6p7Tt(UgVWT^ z2zQKMyp6~vcX;3LH>BFUt&L$MVLfz6C`9BqK2r$HJ$^qA;+v$stQXoTa>L&Kod9wH zTzK_SgBY>h6U9Zrf*OHAbGNzz_)MJleUD4~N@vp{@?D(Y}C^}fP>XB;u7ox>OGCi@QTawEB8oL|YDRkYL^;3%wf zdup<(`KmiTZ!EveIpZ24;hA~)^R=(=Vm!g*sWNBuXIrO4Y>kBvpb8RL5Ri&~f2L+k ziZ|6Y@te_17$tTf!Q##nE>ShXVxR!02#+T2t+PHBF9zWGmL}~^g%+EhMCaa#XyzGs zuIe3oibPgSt_F$logljC<+9|px1j_qGi=tGJ&6L(Wt*Eh*C3le$JsT*%$muGbdVyc zOE-K|Xb8G4^{fav_1Vy~-F)U7fBC$%tMV#!$~;y6dN}u3o%qamqeDyN1Es16xI8qo zvM*}6`IvrY_CPfFJ4k&(a* z8JIrjTqN(O*JUofufs;j_EauCV{9-Y<|6wM*y!w-Oa4I{0_aZv>jQjgnrzl%T{Xj-)X}4AM-E-p68);=UB!( zy6N7X<%Km{l*S0EEGtZNqbq;KbSsY9C^V@u#iB*}%1ALCQM?Zke;6>T=4d6e<_?~S zMKP57qOFy96bil{xPH%9pCRv=aJ<`PKrB+Q;S;W<=(4rV>AiJXUzZmiz1d(66?%DU zV%IENzm<{HF9n%{DHB^bv5qqD3AXy+eF@!9h~Ar~Gwa$$LzZb$(Y=ms(t@!TG1d|S zB+eSAS+Ip&tCnvQe;_i&Oh&O*qb(m@USb9t=yFY0(GX`#(op#PtG_c1=!FX|O55aDqQQ+otnMT*B~sB00SXhcV$oIGUKRO96@N+<6F0RWgsx za_`rQ36~mp{HX5cfhW8P7VdQAr+~-gx8mvFA~!K{BO>#me@IDuj0*kT8Ob-N90H2Q zObk^ucJ8p))l89-aQy?NWKGHZoPFQqOywBu;YE(Au^Z`-L2fbyk=O%a86XNz&ZBcrozRF{6FGUor|73@_WR_+FQ!Q)HY) z^dpD7KP`T-JBdu? zsK?yHf1GfM$u4nA(hF|z|M2d#(fW0(yKW~G2eOx>)@Mm!jKt4Gdlk8u#)Zif#yp6v0U0r ze=-V^R@)C&^sS*FOxq(bv7`|4hJkH-m<%RCOFp&I=7OX)sBVn4SCqP9MIZ!<7abrX zofY2TWMNktyLT^=fb?-iCSl3dwo+6YTxtpM1t#JE+a*p{m}yY9we;5z%uDvfzt^l=P*&7R7+@c-}42`np{{)-{ zWBE&wR5yva-}(D-nbMpW>x$e~@P6 z`-CrFCeA90jqrGgW?HO5DQTQkH?69mW?}5M#wPMaT~7~Xyqm9e2ZElk0gjYZZ}+e~ z8r$Je?H5#tO;O!t*9#dQtKGyKe{Ea@w^-a~tY_m~4LY!v8Y{i272^tr`UYr^cZLSD zAPm6&vGcXT{8P*E*10Szu_<5?q#TO<)#VpsH_SLXdg>_GSL7$auXSDqTNPvLZFaL* zBNg@S4nULEAH{wuOT6D`b}-P9#@JzVrWVZHmGe||>{~oMY20GZ!Ab(r)m%jQDfBjbF{<6i^V`FQ zf~-%=9o@0}kgXAk%n#K6zD#VMXF3e2L0}gmrqC_UMu1@a0Vtmg%b8_*NT@D|ZhDnn z?$wl4DdrpthJ9ir?CS5me@7bNV*HDCHKMP9xkOws>xWgISBA7ass_kPT4rnmw>{nM zq%O0LjbZ`jw)z`-mS`s*J8M_Wyg=px?ZJbN#*-0Aj$opXw$`l(xg#q0$!#REQ`#XW z9r58FT1@3(k1442s8&ZBRY~|2Va{$`HP}R(^>S=X{t%(z&_tLff5M+W6N8rUOsy1fgI$2V^(maExN6Xdb_ZIkDX&HsZw=ndOGUIqNJpm) z{!~TbD`yLj_{~fDNoHy}Of=M*NrE9Ox4ZRWK~f(rAmKNN<;KrGz>eUk9sJ=53@oq~ zc(k4<+E9f}Q)-?lf8er*vWtC_IIxi(d?Eo6meiV&C?4h6lKCSatBvjSUwm{3Vol^w zV*J!dBF|Xpm0p2OXz9GA6BMB3qC$31_m z1f4uU8u6(w{BT!rRXO^HaneUV@W4<8dn6>O?#Es{a|S4nfAd*SCc{`(`!aU@KMQqQ zH;BV~CV?1&#QsyvGnUgTn?7KOHmYAwn)bh|lB~e__KjLb>MEjrW6mY`H8D!La(TIU zMJSwi@9<4`wyR5lg%)5L*%psEPS1G9e!zEw{Bq@3NM+2y9$Jx`IJE?nbOl&IAs3JQ zxb|eYM+{~-e-=C;L(?g8)q{JzpqEmcdf$@GKXFgOSsh%NqGl-In#T6L9B>tsp`!MS zdpD&xu_qQgN1P|K=bH(mQ~%J9T5+K7X%6g#rw{P2UW>Dpk?hzjHFl9St*7XGlA0d| z3r8*}^y<6^h6B_$S0cd0|ABi-W8?4lH!!3u^(vPze`jnPT6eZ+?fwFcOKp5QI7waX z1|B;O1vQnU(O(Wcslb!#smp@LH45(lh?J@EG{|Y83bQ}EmJO&m>o@M^%?K%k70!wr zE{?=FgG&%LtHPt>mPF+pN`TqcJ&+>Tv``dcQNXKUBY2PUITnm+&{xmEufnWe!esCFe(wwU2>y$Bg3KRzQQEg-Lo_uan zqNi?{JswJhz+sRKVVD`K06;3ud~}IHci5$dHsKso4UdP@|9X@Wrf%2*;w`>Ef z=E{g|aVhA9v6rXXg{ip|f2DD&2(-8CNN|IQth z(QE%*ziW`PyL<~6HGR8xCalkRsa{7XQ)YAg>}4FZMrF*m>X#8bLEJiDXGGGMVJ-+0 z0ysFA0aF7Lx6yM5pgaOOFqZ*S0~8TAH8%<`Ol59obZ9alIW;#hmod8n6$CIgIWd$pGFM^DJGX#Wqu*rfD4?Gn(44~v{58&Yg@Cb_X z2#IoY19-W)MgBvCJBtEjL2gh>fC?8t2@Zp}U^C0W9X*|));5TTGXHr5u!7kDJR%}O z9DjrZq#Pj5P%sDvPyr!qAPx@|!618p790$PAUyvo1*^CX0^ukM1iHJsbAcROxZuuz z*3a2E0PawP4L}p(0&#YOSOR_z3{V9*K>nPJ3!53BZ3A`r)2;=#Lb!vRA%F*iJroRq zxjaO;!Ym=qfQRA$Ek$L3nj-}ECt3MV0teu)vjOmM@%)|cujt>2ps+uJL0~Z4!4U-W zgu<)=R#1BgKuunm3*mv_0DxeYzlk7!dl&daJje|MwFg-|5dP>K1dx}~0Dv9_{A)ZH zurt&V;lkwtwf{XL@OPSrQ2M@A((uL9(uh2l=bOAC>_2zf?WQpDU{Zv4pxh{I6FL0eV;iDVVi?{olt3b&-d9 zKrGdv2(Zl`!uZpy^LxeYp)iO#+y(mkodV$G;pYAy-@{&k?H=A6E)RPA!v%R*oBysT z2Lr<`fA1HsfFJEg>F%C>Q|bg253FA%KVJd;wN)XYAh> zD#Q%{%KtX~i39+^SAQda;Rm1kKZp+i)cObU1AyB9AQ1r2;BO=%1OS5mLBar_#XsnQ z3H}=iJovzH`-kEGW8ryFI1utL_)yjQU+|%<&EJs!p0}KmK^K(izT`{@2|~u z7-2058m%`zw`OsFnv*-N*b5V+pT_gPO4at!U{P~wzu5J;bMny*v_ox1?oeXRa&i^M zSI;53a_?2}sO>9R9toY>(b%mNe1&scGQ}CK6J;D&IK!N6@qU(^;Sqw92J;!=u?NA- z`NcHhhw)!@N^$JizTcwx^1Tf=cw>Ifdaage^SX3WFi{wPlGC7`6OK%>cuVgmDUr>4 z7ZpUUOU!3-vvDCUz`Amdr7cY_fcf;ioC@>oekTPMlSo3f0M#oN``7oDNG>SnDVxl2 zfyQG#)ux-#2{iR;u*i2Ej&}xMtAn^n(3NXCF7~R2*wv)EEXAAE-&;E!ehH$78fV(9 zd*q2pv)|T#>o>Ekd|>m3*Q63+-;{p@DgH1=&M%NU;u}E>rUQ89ke}H#6_>-$P621iL7~&Bdo2fkC-T4em{Vkw_7#bj5Q9xqvc%NGbrE^N&sEj?fuxS*Tj7nai% z$9tZtDwaXnFpZJ9vUAaHBr;5uK%dW$XJ__iI+O`Xb8^~(Y&K=my6+SdchEJ)qi9-J z#pc_LG83OVO3Lyf@zkPcE51NR88Jr(oMA%fY4~2tvqy(=dBNgn)rQEwrVk3itv^ae zErg?gotez#JGU{{3W3${ubnT>4%RijhXzCC1&Ds>b?Ywhre#j ziAf<{945XR$QbCL-~N(R(^aj}S*P_3%}jaBb6bDN`dK?YqqLa3C9&tV2%eifSBpnC zi{45A+4$ZRIO3Jj)WOH{YumGO?AI$l@M_k7wKF(vY@1SM&^qHWMdxb1j97sCrgJ=~ ze)>&V)7p`var>v4<{BoMj1|?fMDg)iNB!X2{o?Mxdx_Q=<0u`Kp*RwNjvpcF7wc3e zkH2vc6yYkqYD$iDtSBcj{%lfdf~(xnJ#-5E9gQ#W$(%6NH-qXqE}qA>ucTa}#hUYf zk9%8VF3ra@O=mpmWHk6OJzK+L)&}r*a2xtryeV8`k2S32u_q|<-Rx`BvyYmJt8zxV zI$JH@vb1mZPn1V zZ5_#QmqE8jM{e-LwR`#19p@P`HPVY#|3_PZ0~FGRMJQnV2BgTIf}{E6@kmF1wR+O8 zf{$YQZ888sNc&fs^iKBgwh@r|A*1?Ep&w)yoTj$%$T^>lj)<2iuqmx0VyZ;mJdWTi z$6e?N`9vmL+(jAxGduzd%#2&zw2->&S*au=QS62O<=r9KlPMzvz%4cP-B9uKAl=?T!I%UG#>-^N8Me%KR%IDus2=?X-?Sd3V=P5Ee_LJoSVYWyFD8+HX)ji_-4l?d zGO73+4v;yhXTZjC__S6V+j>@_m}n|GM55-?;mT$Dc~bSsp6e-PhP#FNw@Hx}f#)@O zS8JG-N=hNPRlUdDhi9kyZwrnGX&f&;pc#!JPFFXGXwe5<_vuWT7Ya_yX`Y^Ab8Gu& zOR|I&}2@OzY!P?2=dMw)m|`b2jru}t-#`tAgBMqrn(tJi+F&-+X3 zo+rr8oTO<_L@BfOUAK`>vnkFtjCy4Ejl}@s`sXX=?v&Hqv9u9`iZ&+GC{?N^o?A=+ zAKvB7#@kQ zhS|Qx-dW2HToHmbEt9R0TJ}zVe&8^Zf1I8R_aSoKHY)o}d@-Y;$O$H4a;Z$(K+`;m zzA1j3Fg(h2;P&;T*+l$(+J)k{zg97UE@uj-Or(u=zlS`3K6WBCg6V5eULMi-{`W*- z&F?Cil=B$eynMUqfot~BRi?Ri70TD5rR4-T%58Wphx$6?KnslK_{J1uHWeXd zOqti%kjlHQzN&B@nrW%=+G(G6PE9j3Xc^>pq5^b(Kl<9`k?UPfqOXWvnI7-^51g%~ zj!-Ft&<{(eM&dh~CJ-SLiQlGt(v;2e$)RR(thqs14IWIEa3nvrB-n44SdB#E>$jEN z`Q>&nkW_xU!h`+kE9mNxrX*}Yw(vxbDnOVLD~M;j>sRL-FLr69(w;OO!o;uHj%ZkZ z#kID79l19qAxl10t5DVs?S?)$slVAjV+sRP-oCf3>SU6!`S3RM3@AN4)Y7IRV4wz zhMlhrzvSk*T|j15mQRNz5d-_q%N_n%ya5+~%8kSuvI|FP=1$0sZs8?*ee^S^TA7Z> zm*tN>H1PxE@y-I)zi^EXcZFw_;MHF7%r5ekly`Y0+}0F9Q#bDlu&q9CCLx-9P&&RD z*0B0KJJ}7?k7r_%lS8gurScHohN>50inecFV{PF8Z7NL&xF#?Onyt&4KX5n~4;#dP zubxIae7q*gWLg0@jW_q&acI=U*>qFUJ~|Z$jw^gmFKuF4o$$WjM&jbj6~)U(J@OKG zgPra1-odZ#mSZi3m?}U?xDD$IB_ba&jeLm21Bs1W{NQ>f+bP3Yz7mdN>MzxVurY9+ z=+*}fAhqka?Y(5u%9mu^gWj!bo!L}>ermxfb4FRH(@Z7&Or$6C^m~RizO;H<)i5L+ zic4bjNNzZCZ^9Mo>DKa_9P@v(zhp{fH>u zL4yGaNjkrx4sjDgjR6#nCOmS_VI#? zuJSTsYR%KKLK9?qm3RUPfgdK7EA{U2QVhU$gEe`15fhl`G+5_7zTiTC;U#2c8K0u+ zKI_-aZ3d-uB3w~%C-su)r{rnKt&oVC3HE@XS^OULslbaPMSc!n#w<@HxrMOQ3lnDT zk>?|KuaB1qeL1?cu=42rpZRxjo#x?UC8W5eAa*8%LN8Bg$c!k|8gskpsx{HBN(~yq zh(ZS+eLGLm%FvoA5tz+?v}b>gKN{19RNZ7xAXvGhQ(@3@RQ$BO=%-nZBQqMtkwn$4 ziK(a^FQAXyq63|8s>aO~#rXbpY;3X}eOhS69_;<~Iq%4u*Do{zo}NDusTK}Q}|XAm%#rnh$Fv${LL4M0)d+==Io_r zlWn>8FC(pBgUbOPZ$4>K*PUE{PzKUHZ3YNoa%D7b{eYlfj<**EzpV@lqXJDS`0j*| z?{^b8*JNY+U9#bpnus$cq90fCy4L4WiQl=2pUkVO6Zzyw4csbF+U1SlaABw$lz*C> zrd1OwYHgR+%%Em}D=&tZ9q4|k>N(cB-xco-N)ZF~Y0&aSxq5Mjzv^YC(HwL`hFOcn zc~`id+m-fFeV^F&TZn)Sn}%RSbPB&%Yb`OjXq;_gU7)P+I9^O-MfB6L=o_4Hww4P$ zE2TQ&KJrLl8 zE-)Nvx?__Kd09>oiYS5LHjD`7G~xMm3tY}ONM=wjRAkkS_oLrzc6DQ{+l6n_git$k zu{9*x*Ii^M4tSW=kkLwpYn1Cv^6`dXq_Nr;ZGFX~iQJv^2~?Z6;FM3=n>67{qOQ<< zuP#gSPJE_+dQ0|3Z9^8lDcwZ2yP{heolXJ`xRW}N(}{oH_3HcM{`2yxJoCAYLHXC# z8fHU^*q=$zVK+|dm8~s}rDm)t)(Z+$uUH&OQoOyHpLNES2&S)V_ z9=jt;0++<0xq}Duz>8B=Ut<%W69gkQoW;Qf!`s=`y)~rYmFXKXuF3Q&^4Y$=hn&WT162c1 zKeXuRKVi@NN`W+I9=0FH5Q+X+-N^W61L<|kwv>Jgh|&j|hnDmX_a&^H&aRn*vJq!m z5+_b6+RT&B^hq~5c=XEGhZKY$!*9pBjV2X;NBu0u;}-uSd(Hca+*DcVz%T(GZW?h; z7cfokn-IGsK9za>47*UbHq8ur%3wG!=YwA^Z+wnCWG5=JQ8E2gfK997iT)y;n`&eo zgo;P&OjYLF1`~L9;ffhIf?$OB zoQbZxfI~Z5b>xQyEBt!_5tgGAzgRlgRB(57o{V4_)UFuu1(243P4(>GB@!xQL7 zo}sW3`wSdUyq*SAAB~&L>?-B)aBv3;#l%ZkJlhH;)_Bp-&jBAB2zd|rDqg4W5Bzk+ zs}_~pvV=RS7Fc}e!WF~5$M#mBO7DIJ>=jbB1zMmnP!$2yXydZX}%>DA~pZS zVJfYK0()+yJOLDcl#_(CANAdT)(SSFv#^{R)gzOc-VDk`Gy#wO>xX55@13vYw{cZ6>KY@|Hh}pcgER(cH@;h7j;@-=E z*}y*HqaP`mD6lT*d4*wrkr-q~zRO#cG0S`E(052u$n(k?n<&MGLaQ+(2Y169VOYR` z`;3BgUqgoFmCGYn`=J@7#x$SDg7YZQH74h9xjA`#YwNL(>nv_V124@KOAFX)`&X4U z$v2Q^GUrs;A_#50sIo4qZjd8<4YoX5cLw$hw`-@RDu7SPjb*ndus zj=eKG^8IGr;Kh}4WCXFd|A^aqOB{P3#+xzKX9lDR!|YB_lPk>cKvQM5E!xPsNSb&m z2hVjLN{Q3(uvcH|iDK|lEB(Bo^$71N*plSa&@kQ_OG=LV?e_GsClk)$-w|=^wS$-M zE;!rdS3=4Q2qW`<9)-)>Q{X6Lc}AdGecF|mRM`(O%L!V22I_i}6h%wbynxc%MJX`b zvOV3N*d0aRS{9LOS%}Ar|7L3}9UppK`va4U>6UOhVnri?zhse4?}gqhlA{kBHL@Isv1ZCg67@r+jQ zl|-`twnm%@NaV5jc&Dy7XSoW*!qjIiAlHd1x09jQbbMr<#hxh3=OEVQi#7Duq=rNT zT_@p_gLCI;v>kwh5fTl>lqj;ZGV65HX-F_gzdIv;tw+(mz7!I9HGLQ0{;8dG5>?W$ zLe(KiM#n$2?s@_5Hf}C9QOrJ|6O1NiDp6Q+*#<-!k_%+~x)I{Uq~$nEg+-;^iE&H@ zzy)$Sy^@j}#i895Oo}D=QC=|orN?|!LzYZxykvsr=^U&;5Di1E=uTm3i)j;6b~OHs z&Pbzwbx+tuTvb*u#agsdHL=JyRycOF-8I2C7letqZP29u6?{*horFtwf)M%H@np%h zotZ(sr^OXJ+!0+^dYfFSiYQ9wJZ7iVAuW$D;{+y^I?Hmv0oPyP2b@*0&r9V7Esq|I zPqp!{Z}coBz|!kWO+NQ*H{NNsXRowBGPw7DYVG!TSuLXLJ)uc05`RfaCBawWHH1;m zDrmUFC^}Y~kok!NMASMSI-lL6i%KuZK-nV;btMl;osSr!N_0vzXL3Tii2de3C9%$o$*UR>HYTjDOLjFV>rXR1ayZxe@ZK zStj|^oc`=5mufNHYDvRT8>TLWY1R1&IlJiEuK^+y;TO99-WARIciVuS+();G# znlvMdFM6d6dY|%VTAyhZS(1oQny2}HA?lS;=Y0zz-?Goi&(Vu^h>x*4*SEP1yrsBl zl|!Cbz&ZYeK1FEblXCYA(+aI(1z4Rcd3>CEd$yQk$D_po10Qb~ja=VYkAzGlD#>+Y zA(LoHgrRRTsdzCK&7lJBaWMR8{R><;TSnCOiLGoiiJWWhxxZ@?P8}^N^JD3M&!k1M zv6?-#sHm`?AjOJO)L}R!IUo~2d4gI7*{?9>x0vbke&uQ>Zp}8XB98(4%BWY^MQHBSmTwYf zuE+CCZS0qLAlKWUo_iMrWvtGBVL;NKx)?RqQGarJk|?GcPrzb|xX#@zJYEvk?3c*^ z@1Agd-R>(ndf{#XnzagZiJl$SX7BropIjomIQ-QQ5RtjxJ*sOZHe&*fNPTBs8kN zuK_hIa#(vR25BzrOXKY;!@}6hgN9=TPyQ?{<1pWW4xOUi?{T>|vn0EBRG4@e?-G{T zWVircQ|gLWSahsDz``4Uw{iWIHG`C$1&uLg{QwK65qRlq7^@?V06QC@WVxG+PGSaU zSR>EZ$w2x95{nJKTDH9}+vY9pr6yBnDVRiu0ccC2hSguw+OBz8e59~HQ@)d@&l@Vb z4LWacy)yno^LH?MptA+ zodqK#%49#{xMSmN(JK&^(=VLRlWCZoQltg#Rw&Y8Y}Si=*Wl-?3^N_VsMG=SdFS=+ zskp3#!H-|Xv@xjpMx}Zs>cjyFCZFG#W2K@bXHI*Pzj%#*$J9m+qgx0IxNuADy2qLa%z{WS_IC)$l#LdDw^XnU>Z{SMQYPM#REGMxZwlwUtQ3LFH zUB#Q_yW24FF?X%CPpeS)Gn`OjfK}kl&Z2u}-|=f)_Z*s!E}aWap8KKrT1zclp=1U) zDFyFlDn z*JF$V;BnB^Aj8Rgh8Lewbb80_MB?ey{OYViDvZApfVns@}Px%Wl&y0 zvnCSUU4u(r+#Q0uySoH;mxH^zLm+tY;1b;3-Q5Z9%XjbY-9KBkf6hGJGgWh{rn^p^ z?q|9gp4hcJG+RR>q|%b&?N{RipQgQz`8`^kohh$Lf0ChC5x>Qw65d z-GrKLzqRrX0LIW{&nH+5ppk2>RIFHNZnmz0)n%;`{Nq+Cb?C6vfb<}B-=t9O&!T!0 z#%|n+qwcf}WN!rw*lj-H=Xp`@dS5vIqM5Er?g> z8y4`Czs?Mz*yONKA2UInlv30?%tM$(WLhaT;x{v$>t*9{%TQ z7rC!?y!AboAS-`Jldi15NvU<~(e6&<4h#apkwWy!zT1*Z{qh8(5~_>&^~aTy--ayq zj=0v#UL=2OU?VA4=nC++zYZKoJ%w`GCfSyA;;t?aD&VwJ@-8c=SJ9buv?Ob))hRWz z6pRQA0D>N+kG^^1MalZ6CkbCg9$2Z^(BMT0C$8eIq_zdyjG786gUTMdF3p|wav*=0 z5=5vwGp1YE%<6x(9Z&&6p!hn6At!$rZsytNH8Qaf7&O+fcw^V>we2sY+AWejecw!q z%|l`ySiS0=)a$PKOVybpRT;HDAk=HF9-e*Kd_RFnFJ*V2z93W)uL z%cVcjObEFp?wPzKF6s4G{(X2VYHvzG-8(K%4069RM5(Gz?XVZKkTxXokR8oqu{5g7b^OVrC0Y5NHTJX3bbBEe$8#cT@ZGQVUFnqLpl=~5H=(> z5{4!TIB2M!TlOh)}6%+|ws=X59_X z1^3*Wx6$1LgT`)-1mu>|An3!u0{{@>d6uEMr}HsncwgB6O-=M*Jxk1Mv`}-H9Mod2 zR7w;$4DY-ZOTDqhK&pS zwtu9UaX0+A{^wZ=A|Km6B;W;M9o45M=Sp^aLOhg`UNCDZIZL%K2p^Rk0~2q7bcpCI zULTaX$C<*LqY_cF#uA%(k2DFAqU33jRI~W5FN;`%cPH=4o)`XOPI7FMVrIA%|7O_S z3&MY`xWX)cP^*8&fNB1O7%=m>HILEE9YF2pCc>{}t8LY)_-F#f!O0@y3|Vz0Pnh<*RiZM&K%202Ycf2}U7r}CxDx5iBhdsQ28 zS`%c3*nL$Fm_lLr$Tp?B2UXXVWNwA7i+=Gp%1CBD@PrK5{x`5?0ovoz(G%pgY`|N@ z6-?$KXTZ$lu~PjOktF=;VH%1>uMP(iVg7-;g5ztZGA>+{)b zkx4muwAESvGKj?F&HYh)CEFuS0}sjnMx}F@c4<-GP^Qac2L(Uv z#yy{48rG{%*+U_s^vT0UQ;N0!1Br`iM%%&pa2};SlM!`k-QP)cPg4ggZa&%D*JcJ` z5-*i-PfmCosxBp)3>?CROvMLrm5=u^I`VVgJ`%lOa_QuzpJI?V7!rg4vX$4cmLQ%g zZw<_kpKXM0Lb$`_irNt>?r}%w7i2GHWYzerFOoyl(#=n|jjyrkT-w1^+?aq5Stfj3 z?D?R^LvhfyYvOXPtf$e*6D(8WMuV7DZ#$cdcbQm{(2hnMVA;_$e-X*uO^9m-uZ~P9 z0{TVtf&aG%)I;%{6db}bCFxuG3+lIeRkf(%oxb(zCRw!K2a9Tz0o;OdT}&+)eYo?2 zsC)s@V2Mrb2o&dIvNkxLmA+?&;b0(|JbEj<{H5jWflpo*mm-I<*XR9bzhd?~*N-tL zGCFxZeCvNKfUy%GE%BD2Iz|#^FckUNJ#QEKk%glE0Jd#_7J1i3+!JGY`L%2Ebr*t)@wBU;Ia<0x?k1NlHBHMY%je1d__z5YuGe zuB7ltfw74zIzK|0$*0rwjxDB1(c^yoSx}8T)(DXjja$^gx2!yzm{FnApEBDA+wjrD zi(Xa)^w8}p5{NIM&jy7wl7G!_>$b=fTFAk}2b+5nv+*H+fG_grgrk@fdR~?)B2oJ; z8m*K-VWu0NiC(D5skme69r;e5`Z8qWM;IP4_Yp>2yl0c?&A&LoGs3cZ91jA?(S(V< zX)p#r>6fP%^^r=#5Tf3F)1eLF+`K=!VP=K{?r|tHeu-1uHmIo5gN*dp_;EU*ty8^e z(n{c*UUvpGh-2|3h7;Y)zQ#k;U|bu`5h}mwS?(b8jW4$4)C#v;oOv<*=5pr$Vs6N& zFDpyDh%ftYqU@(_w2Bp<-hUuS7zZ=Vj#G@f@-!Dqfif%x_el{U_x&4eqvKxf&~ZD9 z1fGIVvlKwZNl$#~(=MXGLT6_H`VU?#A))pe^N*=SRtsa&P z5`vrUexu-{=q z?Z14&+z=_c7p%>R7YvC%drjc7(Mhpt(2$hnM5jCL9@qR?&$;spqQaer`E&pmZib#iN-E2*_JM_;?V^Y~Ylht#9d-&Ee! z_`Qnwq|p=oL`nJ>!BeH^98aF*%Z)WYbKbwNr^P4L34P!Y(9%)m41=c_?`akQASdH))+tzUw~WFZRo?ZRfs@4FUw?xR z- zhWE|tt89oCvM%p^W}yIVg(QACPa&)b1;$G4@+{`mE_P)~?`C4(!IO<7vpuu&$UtPq z8cm5<{COu^gm*4SJ%M+U{u*dt$-`l)=wi(Q5yR~oNihE$cIxbCm7%;?4R zcWlZ;@i5*%Yplk9jHcf!z;7h4fSNiETJFiw0O`r~*G~!CbZW-_m|J>gOQ96! z(&4FSQL>emt6FJn;)l6~u(<`9xdryvMBms1#n=R{xdolskl?K6a2~Tt603>|lL~D{ zncCQ#mARFOxfQv&70%e)z}Ots*c?MfnI5x>b$VID*j(}0ocY+?!Pp%37+cC1o7x!L z{1_Wjh7A6?$Mzz9r~!`8J5!ZiVNv^SM;;H?tV3!SW*LDyfw$hO5YD@UzC0^_u72d! z_gm^{&;RWQoK33jurQxC&();&7Z&;xaHo9&g{D6}u#FH9>`ZC-L$F#+{r#}Z|B({V zFw^||Va1xBhCUUL>`eddM@>hgu*8s`^GVn87->L1ta#ez0^6p^Nm!fztYT{tn1P*v z{6y?c7z?md|3MsSpSrQ8x|PpeKSj>}h@(xR8=r6UDRTXn3pN#Q!_q)Pu&|^tg2NG} z#TP>$AhWUjU(aJ^N)luS0q43p&a2Ideh<}KyW9;}0bt;$YtlL8ToJ4jzQ*1=6e%#; z#kM&KlyMXaiBESguAHI)txI*g^=&9BSRy1#KiHm;Q6S){_=~=$g(Nu|s zXp0on?y{Wo(U`?bXJqK=sM4syPK1md=~}EDmTEQ?c8F7|c7j@0n@JW&-rU7)nYa#f(3on=xW zQUtNGu<(x%3>n$LaRg`)A+1$yYE1$vx^Qv7V(M74=ztl)3F;gnx*V3~v!h^Rb?sv{ zZ2hDw-C;@NJ=teH1mF~u%!$sHYsH4zVWMo5%sP8ie~;JC6xOI)kPdp|GbVG#2V5wk zn_Jh9c$NEKwoJgmu1(s;3en-lS}oOL7ff&bED6$dfnwG)&SBpABi07(EQBOAkqpiX z9t^Fc2$XXFY%6^U8+Y;C>$A0SGC8|tg><}@{=K{~(e%DCmqe|tbI6D&05& z@UuKzzrC^x4+x~f$WQbzNQi~Nu|ggx8jU>b+2dVNre_+=|H=JD;)dEJAo3K<%30*# z%S1+YUzL=QGtN+5sXDeSniY69Q;2!JFp8o*iB;Rp<8|z3FVuO{lc&<}0$-yi+~?}M zm$g_e#ox>`eTV0xp8wUUPiAiaRu98L8d<#CAGWNFg#k|91ivCqTt6RK@!-ZvoV-Ed zmq6ZK)NxnOFK6%&l-85dJa%6mdvMqkdl1ups2?UhPm0gKooW?6-+lQePlcq-a4Ntr zE0}P?SrW8eqkcHPS4WhSi&#=_H;4OC-0S7_GnY@}@=AfIcu_+GBVT>|;k~9)70`{d zKzf#iJ_K-B*;_mLEU9L@pI|A#4n_6Yll)jS%bw92KvOa(;D5`z!;GAW zUD?}n%iq(NoUeCJ3h>bCx*t{HC;|4QTKqVC_7@KI`Y5T{()#PWFLd3n#X*-=krA<~ zoRxBa2*PN;!%~fF}l^GMzy8s&JII$|fio#r#S9xyPp_M|1;@?u= zJLL}L>Un}Y^$wNG`RbD(9>uT|$+u#Ga+5nLU+BWq_sj9<63#>P?|nEdtM7(0e>?dI zI5wqzxaMW_Zz1YbV9aZjz&ooPo(Ln&{hhzGDrK0LjG0e)7Ll7%9iMA>R`FHYoJYA! zvjW6R`tlAE0fs7x!@Ip_CryLNIU6QE+qq+~XU?y(I!4MAinA51n`;VRODyWY{hW@- ze**IJZ?yc@pBDeV_l9bsR5Or(PMjw+Bi!5NKl&KB*uAVy%8ROtk+tWOh93z)Gd76u zVHQ!LN!K>hUFA#Q+o{N7{T{DvrF*YZn7CfLaT8&34-urp!h^lD$7h2zq%yXrHA`YS zbWOfw2>N_oiK{T0w*~U4>jJeQO$w&R=OQk+JED@?1(#fTZ0zmPnG-`|^GVD3L_n_a z*9)^F3lWcSCH}VDt`J$qlVL3JmfUQ%-fPaPd1{j%w+<_Lh*cLBGZk((l>uG~7D@Qd z&-KTX0~omOA}7~L1G2S`AL5llfOW-W9U~N(N3QSC+;zz$-oFCHqg;u5)>a~9&R(sN z2ydu=O$6Jhjoy^#^t=D`L)doBe*r%ao#i3CS~zsN$<&Va=iTx*ZsWh&!koHP_g$*j zs6QaG}^h&i`N1Xl!ACG?!YDW?z<4{ zXs3Uyg?9#zP3L5ts@j8$0*+;m$v|J<#ujw^=BRZhy^leq415*t;=JPa`_I zM`A%G)fEb`sd$h@BK}#~D&`CqzPotjHHO01-S;ZK)dEWsxx<=Mf@U54wrhSxT{w-4 z`}fK?qPPY#?C~vj<*R~x793DGXRy4@*AMw`q+0X~*LBexKbLgQup03;0DX(@$xD<6kuS&s)+&y`+ysTsa8ci?MGXaNIfnk`rq+he}kOaMgx;tT8GxNebP1Lw3l4Yrnu8#G$SZiT3tBbdYvv$1ewOB-sOVN z%I&0OdeeojXCILklG)%yh>RzD-~^ZpHoNehhU=McZ|=LBi+@{gyVmB!sz;jpFd8FE zk&M>6v{E|aL;P!ULPmi5_}ngLk}9lM_1-Xhy5Ujm_Pbs96BR3ucTMqdL;sE*Y|&%5 z9xg9xaxS?-D7oDvgL&JWK?~fo_y9W)l*u#ps+@nGNn6B>?MOH)$m} z|9i*?69t(Sm1`L1>B(v`KCi27Q=8icyzA@oE7jMwm*Uxm)lkz6SJ2v8nPEu!LqNk9 zhZ;I5{7{)PlRUtss8O!W*}v_v$+w9<-l?|JRbZol5?r6pdw~hG7>K{JZzfqw&LCoV z;5dBDbXuJZ(XGO6W28(aI`V_^4AJeJ?|Rl_PG+yQMy5BO2NnBKHrj-KgNLDGOXy@r z1PV4LM+btLo78{Vge3(R1H($@B$?Gy5bR&H33{LFh$3LKm#fyl=oNi9NdJ<~>b}F8aoeibY2i*at&?qEO**nyLk|%3Z7RfX?)id()jWU>= z_%zC$1*uGCxG+(sY$!qoM3^{JzpF(Ld3tVDCT6yy5N|3tGIPC2kKb1P(qa>emW-cSD!Y66gs7lBN)rw|eQY?64NmHK8+Qbb3uQ2Qr(GwJiPDKeJ>nGx5uZt{A%B~(P! z(l|g+$0oOtmd&nB7m?czk+P$4ox<3Z5VoZ(e;!hDi%mTCgKUpth=e=NJjFTGi7!kK zC0_ztWYe16f}?XQpkuF_a$j)A!w4VCcZ@PY4O8KF_0n%V>tXWnmVKMXl$fciU+suo z-0zA6B)0MgT8=e>Ltx9X8(t>3^@mM!=(50;ERy_+isJQ|c&af7hidqnE_y-J6Kj(x zc^^oUSmwtHUcfhYIC5Oa89JtBh34}u?|vU!aCzl9bF=zn(6bfeGo9tY5b1hd_BDW) zE)B0h_xYEGaZl&4G+YS*Q^Vvw6_CQr$R!z7zc4p(A~^z+kwPaE`pZ?YA75}Kss4m_ zWKL@Xww*~}1~h1{S2Nr?WBW$UZwv<-wwkJy{gs^B#Y5m?Xd%d2I8QE>x(Kqt!Xi)+ zYp4CT?YM^VvP6M|%)oO<3;}1ON&&ciJzaX()}%s%MjZpg)3kPc5r#@Llb=By@l`g+ zO>w@dp?v3CKxzpLv<>r;Jk3Utb-Y-`CXD>iyA7dDLRbClAQ~vbu>9>WWgM6UB%iK2 zpAyJXI#^vRzQIC=o-=h?h+qc18uVGC%E#1f=|ePk=L`IdoN?}-$!q8QFlykRqi#T4 zGDUnUX;Cy2h-jIZX&aiSiIIFFxQ7b|hwDrUMDI9k#vPNYHG4qmgK)UlXvhOP+QL+j zm7|PCgC2xqn{VLA)Ecm&vq}!*h}(J`BI{|vFqWObE23LLbeF>cC?VnAdRCKJkIx8g zW5nl_0+pM@B<^Q2Pl6AbXaRb%$|cqS99#m3>Ru^UQNLrI?aa9%Q=V@bC}#@I$)XWG zEk@%g(>GH5a>aIZTAOAYSeFo}myR@1bfQstqueamXHcF^;N~b98rfZ{*+W(rv~HAF zqz+!asf1kRJjmzrzs3n`*|6mwYFnl%6(gQ>7(hcT^0}kT_KNQ}LQ2zdF}^Y`;vLN&Uk8m9vI~+?{POleT5)DP zjz_VsgMFCuiX)@=p^_s&x90-V=E$$9du+H!-QQ=3l2PpKuzHd;k!M?d!I>IrQqXdz zAk&dGRiPan85OT@{j!!aShN2k1*I(`Xh4=(vOLYD=B6Hs{eW(xG;9?KmGiS!9T(q2 zX*hMD#50^ZD1`b5r&a=ENULAddX{Q(NQL9~0&5xqOwoAGS{q&%C@^5DsPIHTVuC2(Y|A7)SG=X_p0(3?;$`^OSL;; z#NJ^UD@YYCic(KIi@a$IZhv7on5Fck`PFPBkKpwbPCC1Tl}$IhJIJKf3BkZ%mE>u> z3vqC^`vNRsA4o}UT~(6zlKWAgMSBjsD*c=67frKa%IHyne@F0wOgpo5o!R)_Z#=KV{x>jaSYC$avXgs=YDjO z0+jd;?Hb%+C4@p{ZQLJqC>_RX1y6&hmxh{uxph1vcrBg&JMLpYtA|M`dH!7gP6&$H zjUe3d&@tE2OCa@aMSp_5r@{_r0~UYc}Z~ zLKr1)2L)c~5r^{w5u={-{MkyrS+YLygdiY?nEGuYqk*t;*=-?A0a?G(YqGwILzZW( zNUsGZ(XGXCree?qN5W+mveh|sh!TuDMt%`;Z|yu=%!ga>0$q0;OH6)yhCL9^fGK{q z8Mw|kU?7g-y}llQKXkwB8ayNzhaM~>+O|XS_04xSEz)wBIRKI01jj7htaXWJIRUj( zAe>_|h`hOt&#o~Q;1hy7YyuNP3e}-^asB&qQErMq4HvCub_5lgkU z>D#L6X&M&{?L6=2%UMm;-4$=Wy+~s{A;l6ddgI=$)%zGZv#o{Bw@WOexU*&pmNsPDL)&iKle)2R1z+9#*GOAd*{$AOK}E|$$t5)%Ox!7qpJ8vEbe#d=}E!nPVv)evId1s9L z{@?%ZU`$4SaDQJTQs{Bla;F~bc(~quuZSCKb>cs~T??8%c@)CMxkZXT9RvzL3R~P< zs19{Les6TXdpKMXs1P+~T9x#gQ};U0d%9P7NFI*FA&fdcnNz|I-?pl>Bt`lBeYroL zrIvjSRSdGuQ|gs@P{g<6gtw|%zDAEYy3m!=k^)>TtZ@QgUaybl1&aTH545Ii%^+JJ z##WR%UU=TI29BzPx#x9x%>XT13mvnm8&(*^cpX9nk^7zNc$=uZ`E-sMRgu$QJ1N6j z1r)2>__+Ss;qLcpz)I6HVQ;!UsUFUb|zfd^H}Z zP)NsY1)1@&HQ2vr145WIpl~mCB4Uv@`94J9hQg50d!OF9G)(*k&>EcSD+tz5E$+{6 zCp|4$H^xGT`Ml*^ymRE6gjC=DxqZBq82sp54avat>kxlgP&z~@Kq%V$HM5_S$|~{q ztV6(0I@unUS&YCK{sUl2TqiWNYjaM2)@!xbq)saXW)KFmdLmefTvX|nx4EV@N1Pv#@a<|@D9DS}V z_2U$ z?-o$4ZAHZXyc70&6Z57H%PuALu(g*4AJ0w?NVBbaHYYBcdHmCRN3Dh17|g?aKUB!B zC35)TU6%Q{>!-=rK?^?buY3F5XtSM`t~-Ko=QPs`=#BWq!uFhD1ZN1kiD*&6RSC0X zHhinzcc(XtvcR*OW8th($IG~7)gM6GU1x+1ho-GXG2=k^sLr#BKt!Th<;bH2c)eJJ zqSzN!}M1M3_&VXNB1O(O^k)qMgCW{_|5 zTz@gQVZNiyai=p@{+h9+kKM60@wjqN=q)k$es-Z8z0!Ox;g{X`rmOTm)@ab#U%Z*r z+|X(GVk@(02KVRpq3_|-2Vbo??CRCu-rJ%tR{W=zm?3^o?GHP>X!U+UaF0Wtgv{4! zkCqdg@c{ORMWarI5C8WS>)TICe&V{<%2m;RioM>~`|FQ`e$Mv?=tmUpaKBjg>eI)r zO=baPc?%qs_sc%qdj}u3o)2LcY0M07#Xq-bRvBdVvI&>Us+5=^YLSs`Dt~SReq@le z$)YB7kc8hBQc6~%n_db{&7`H8MZU6#iG`8r(E;CRM${tL)N^xs6 zQtw1we{Q*rBFO$0ESoMwEcx{OvPvoVcY#TNKEsIdr+n#apo{brem}B1^$$y4uM84* zgfJJfdgGM*lZ!9(=VPAG)Oxd$^h&weB-ZT%+$6xQ-C3l~($C`s)MclIc(c-ptO_*~Q7s$o4{!ELMAT%%CZOas@Nr(b7F&8>eUjbP%j;f7C&@v99Ut0vxA zNIW07qwm{Y2RXE>mtV(;kxf1#;E4;KWDHh29f{B0qga5tHkb9`K=^^e@zDAEBZTMW zyax+l^k5Uoqp%%tOO<-X7E+sEa67tBIe)n56}O(?w9!k=8ftz1TTfw17>mJvquLp) z@jFrOPil`zhOoc)8KgscGHK0i04A+Z6tnv zX-ltI5>Cn>NS(%f6@q9I1dRss4#9Mi49#7ta*!_(9Hpwsir@(4f#;ME^z0c+7}5;( zCR`Z$g$*I-8vRM=uaG<%7rMh#oG;~38UkDk`m^-Ks>h{ASJyBE@ulq(iovzRcP5|^ zeJ?F>FD-JIv4P~Ts<2;K@~bl5QxWDTAeNKuOz$6O zeC@r83;a&lIBn~$n%4PX4W)t2T!o;C8B^a>xzO;{bUWzOLA&$nPFWlPCw~q4__WoE!t_5mD`qY> z?j#}RZ$OIXYT~Ros_%owme20;WOEqGHeRr zDWW=>-dRAnmYrUhisKdU8Ehkgqm!fJ-*2bV@okoG4P zQlK;@6;uF0Gr`WIW(MVgdl23t_8sP1unR5*Q>qHX2P-}P#8#(;jZ8O1CA0&zg0X{h zpzrtKJ{H2VVmi-m$qE->b|3_UUC2SClT=Bf*BB?m!lD4EXeWZ-Na+kDslpZGn@qbh zi~tXWY%kgv`2dbC`WDf!R7vnMDMJP6d@(XAs9`B&93F5L3L)#Zc*#zw#Qs8wsQvM# z4)Re8F@45Vdt~C%1N0D=TAxI9UFXZk?p>82xR8oE zp*_{r@A_@Eg~hN((=35*oKg4X*3L>2c3|JtKC;QPk!Ar|xi04DdY2gN_91UkA@?=F z-|h!Xzz{N81ch*4Y`A|dDOu?`3i>gADrtW1Xwm`%fto_zs4w6v~n6-yPTk)x|=OMpU6(SV63pK>+QA z#w7wv!sbmxzjc5#3amu$($U?|4Rh`lcErTS-FQJl{Bf~CQH%6>9T@PHf4LZ|_;*s_ z_pL5@5iv3wOWN~U|At(YGh9dXZsZ1(DobP@?!wm&P0iAi5tFuTfML8Y?A0g6WAw(*PPT?Wgl*Um}KC zye}TbcUw@8&AzCdsR0JsqG&KXnwJg8D>Nkg&n+`GE;fH}U~cGy?U8us7a%Hri=!t( z0F}SnC)AJ#TSfLve4~EcV#(a-^dDTK=OjnevN^_=`dmXQ3*%6mn{X<5$V{#r0+`>7 za9JQYBpDXC7K<$T`g3|}*kb*S4%*TDmeD@!J zFzj*fKFnI3ujXElEo;55iUIl2XT;dH_v@Egnu_&a8TW6q=S3BNG57Uclh!*X^~TYu z1eG!cul2Oov3Q$BCiBtBns27}4IvNH)zrLO{ucmenNtAg`(n!gy5-acgyPKps)W>Rs}k$>jAx&51%$D2lTG% zd#+j^IL=d{`|k&5z43zsE4CH*V+O5izTfnso{D5f`9?X3zT7!CA|3v7)t`&C5-p*! z!`(}U=Hw8oecx+b-5FImyqDO-^i=vum5ng^n}>1qc2!NwX2hila(uM+;)Fm@s;d^b zcRiWPt@24(c(DbyC<5Znj&E4jH_a>HB_({`@d)bNH?uFX3^S+O#?)2|-MPk6pJ+8U zu}{nQXG8z23ytY{b;`TzAIs_ewll&pr!_1kSQ<^_6xs^J;BFs6U)#!24v5<4m z?M_GLb-r9ETE$d*HcwgKt_nhwSe`LQsp&#&U2l2*S0fx`!2tBL`MqhUSvEm&*#$6# ztrWFYuo;xhulU{rulB3`JQ=@x-7{L_9*)U;h8tJu)_p;Z#Ck1o$6je_v`f=kd6KJ2 z7n>sWdPH?$U9jxKn$bv-{hG0e&v-baMyfUU{G^G_$*WS*7ct&j4t>HNI)*rMdqG7B zE);`s76X=KT7Y>r60!!b+r}+7!2y>b7Qxs)SYaULuJrbM{_Yl6;1L&d)XsgtHE30E zlD#Ok7PeyGFdj0s@B-Gw8^)SWYY%sH>Ut%z20~n%>}rBLMigI+S#k*V?<1lhkqPmg zJopzDj|all-WN-e5W}Bf&w4eAP`n~x5MwON0btIzQ~*XZiD?PD@cd9#s4RrLrduzh zkce36G%6`1_`!UJY89T=cN07Ey4LWY3$P2&$fC<31w$(q?&xcnet zW@TgguWvqOs*H`A8fNH2Yg1E)hV}}?zM6grS+YccuuPi{(LU0?(n!7Hw`5!_3<+dO zwoF5|5z!neLP3({h=>BmU(8ekSQs!|j-t^6xL5_u(*Y6FeL*iT$6KQ-r`Z>S&mSEV zNTlu0HJEhlfmBQ#n8K8rnjcamJSoooWMq((4w=B3Z~;pxq{R?DNx8q5L#i&X5@e~l zN6MN{DEk#|i@DPct~_MSUTQ+tXboHqEs(uN7&@LGvL@|RkJlK8x92t7z?=B_J59MYDp+dsn!})`8p$@{K0sEiFY=9g0hsUBC{a^u#SmQjz2B zQU;uOs$&}vT@qIXW0N#eA!G456IG#$wO zZNG9$)gu3fArM4ag5?O%pMv2C@?!!c!~G&+h_D3T7;tWgdj`ha%}_&d2JYQ0P(w@z zM!tp0g&6Cv#Dp#U_)a=4xFvk+>z+4);!8 z3b2+se<$3ssp{7nZ-A^UJ`E(-P5XkvjVy!Hg;-xD^;P@S*d=YcwrSTQEzhymL22%$vZZMN@50Ule&KR<)C!F|u?S{!a4KALm@w7`j_fSETR|6{yKsv; z!JKWvwb|zeDzO)bGT}n_16~>6OnAsO;h4S4HRu>i{O792JM#&(QzOJ70!7>7M328q zBet5*;0cx0L}xI)Gvp`P*oh9Fu>>&K6Wm##x~{ zMgDpC<_McpH3&l_)O4?|V%(YbHsCJ!X?V?6v8S(?@$~YLcK&f$4Qun@`!@4Wbm?G+ zU1A4?tdE%0HHW1fJ`u2CVo&>EXjIX#s4{{$#!J$~?`3b(d<8Z#))5tB$iUh{5WBgB zpZXu{DN8c3=vA|$$%tK_ zDP(MaLb<6i$U1YU51#BfWp z*qCO}eOAXzW!jTpA1t&MP`G~f+!XNL*L*(+t-i@mV_D6@Q_^g(ORAobO8J;#4$VK% z5mQnvQ+;n`khwmt8m2x^J17Mo7(04r+1(3@rWNJ1GjIQp8_2n25ooSvf7BMsG*jhG z)^28)VJ7bY#X^o@9hW3*;8`jr_~BDmHoh6;K(11;Nsp^P8>cz}m8B!#7%Wt(azjaq zRM(ygM+sBpQYdrM;c3mp5ehmzB%kNkqbrrie3n$Buv3`f_4`&WD0S^e(O*F2&+y3C zMa(;GPpifP0a^oFHqW-?anh_1<-RNh{ho2!AWnTIjaqT%Q77 zHM1u=I4~7|TZMZ8es$oouAoExW6~n>UQ0yBq$afD?+-3xB+kqrwJf6f{Tj8lIj9-5 zirDYZ;xXEH3lfpB+IL~pSTS0aZnT<}*ZSh4x6v7GEGzmG=`YISFV=Wb8)HWvyoEP* z10!pn>=AF)*1U|cTP1?#yxRTQ=<9X7v^sJt3|V&3C&CIW-LpVw#eDSuZ73_O zQnjN&-2WQfKZobLkpEWx&uqT>&pa-E^(-&4>X)(itM4zb07F_l3>+a62Qw!N7dJd5 KrI>;^{Qm-2R55b^ delta 135752 zcmZsCQ*bU!ux+xF>^RwRc5K_WZQHi>#r|R^JGO1xwr$(F|2ennKHZn8u6~%An$@dk zrq@i!0cl$-2@|@vGY#NNFSNpTXl^mnpA7wl%?QbtVI{#>#H4GAIH%|QcfuuH&q8* z&;`uGjU8(X(~Y$wO|^lazYL*zW{2->LE6?_D*0d2)8*BNrt!o$YeVyulsM~21OCa5 zeb~~_f>4Xw_q`>+{MZ-p?bp@jS-#{`$C(OQ^YLX?2X}^|pvonY_TmV`*mG;X05(^&S`eW%|7=cO^1NwN2| z&5vxQy_{A6%@r(oJ;~O*EezqnY|?(G)8jyV&a7aTHUn$2huW2#mLHO^Df`B&&7m#LSSUws{vPC{9e{X3oL$7 zPEEG*6_4g{d2#LOc+{2cHjE{_fjCW~2KIyZj#WL8yiXcOS4NznSTECRpr~Lht7z@< zLg96CE_sls#qqpI@C`HqJgqX{`khPJ48+WjKhOXu!3?0P%r&MWs%5vo#17sz3*N1| zw#F^|1x}z`An}mE$CHLxjKRl=JsLVpVn9Rb8OXTxXup1uq(qu;#d(67a;sZNh+furKsO=(?a zt<_6uRgCDxwr7jpeK0ZY(nvHBVowilqv6ZVTVxd;lLsoVE9_4<@-G{cL&2!Qwb@Iz zRQaXOR3^q@^m;9h@RFNdgfnnRW5HFiBtfB`UtvNYX7gm$VJDpDy;chO&`c#2ZW(}z z?oWp+)u$(;hSq+irZEoJ9;0q2AS<3as~F1a`NI?1ZkR3R>He>1ERM$t!zFs2bsw=N z(1RAmm_)r=Dqrp#Gm$zKk~R18XdL znkR~Bm~z&ICBkwcO7=jm-9>z&DK&uk#22I3BJOe!S#5r13YdB`5U=2;8L(sTrGdZT z7A$~cU^{Z*8}@{7DKz0+wUA$i%CWz6(Xun|^>IA(l2c&y{k)X9f`W?%f7tVvzH^5* zmnm&^HZv|V8^+FWnLbr%D8G?k`#Fe*@Tpfe?8@y%`{ifV7TtXEzqdWg3t@qYfss;VHlxiGAVLf9e1q**PP}bokACb zhxa0FVb>u00_!Z<+fNW5QDcQMQI(J1fA?z88_i#}A5&Xg^5OiPEFc4JPtANZ^hFU} zyJcph%Taxo`gxHRVPiqElly-5wdhQ^whCbc-=xA(cAZ4*JsRX4+W`FGd@<>jCoxnU zvo#+(ePEsloV6`EgGqOtZpr4Jh)&lMK4dEYvL>vE6gLbiyGZhgx@&jd713Ys+))|T zsx6$BUh~p})-RU6sZrU}LFQF#W(VlD?yjAerd94-<@RchXHZG~jl4nER*F->sV*%T z5~Xj+{D%983mCnReg%wp$)%Khohxxf*a?hQP>w&m?<^CIdm(IG1*Q;}6Gi!h2iuD< zE*3JZ7cyAM|1yIA728fow0RhXSYE@k@-ktrJkY1(31qFJVQYoq)y^m1f)USkh~BvS zDV$l0IH~v~Nd5#Il{$UdDcvC(OM_+q%L};HtW`i`tXJ8~Ad_>CssiaX z*nXb$?L<~w0tL*o^hOW{zpSV^n+2kOgRi;XV%t?9mCXC@t5~>4U9G=Bhp00lMUt30 z9pn9L_{-IX@8;C?H>I15fpjPQy#LwGqvcUGXoLhOu>YfrU7*Z~cI9hX-Nrp?=`e0u zR6?MKgyd_uVGUSm@aWQ|85;>yvf9agSo))3}#fq?17RTn+e z#%h%s)Rd1$*{dkT8d_N)_3Ib|x{! zFY~3WsDB>r*OEJj!YzV-B=qwzXmD*c)o-6!ifOB^O3B@-GN|0A-qiBqq0)9VU6^e^ z)>B@VD7O;$c2!r#8W&?(MavG|J?Vh z$N;32ku;eZ>>WpUMThqUp^S8s5`!r!GLTD&p!EdYqywXLf)9F)OtE|iSsAD5OweL6 zXq_5YRxy4M>Q$SHg1q1C*6x9RxNGJ36xx=-DD}xuu3Gt~9rMf8^~Q7cSfG&B^HL#C zJ&m%dtD8F~fkzbyts*pfu(J>VSBe-#WC#H|tav49ka3Xw(a{#MKd;c%!O( zFOcJC4ztp4S4ZPU)#JV9NoSYZ>4;*<>aMA5dNZ3?A>Y8K|XIa{UbaNAe; zIB7@Qc@hu$=bnJ(K`60I_H zFVe}<8g=k$$5#b2lMg%?v*5v|D%nQlR@sfWE59Vt#{Ms6lFeMBzEQs@z~NQ!<7s!O zSlYvEmL!`OM=tv!K#%KPXWs$( zs#cgqq(o7yleTRFXl1ejDl#sb(eH4^6I#R2B*6uec#(-YuLC4yy~wNSJHiQ8I5rAb z;v{42@2(_eez_z?NOBTNg$re)ii}-SXz8JdJlu79s09$j&_Y%jhosto0Hv)%Eme~W zEfLN-Df9wUrWocXnT0X6tqo>=)VlpUX*EL^&8Dpz0?XRn2`Rw;pnzwFSRT6;^pe>4 z9}o#CP_9h*p$%TQR>mis=&b8G6eTufR-H8g3F972DnJNYHktm!SyR`%le#Ro+NARdLe17pk7O zt`;xR?0RyVr#kGRcIuzl>ENOsykTmi{W^@NisW#F`)`WQe8zld(yRv$2?@&*i=2EM zi)&n3MFlErwYWbUw#0N7ZIKg=kWxle2Q5|^W={}g8MKuTf=dY0&2=em&R>(7!z(zB zq1bT|@w)T30burulhAfcwXzr}+}jEja!UZx0@}(89a<>t$}z(;pybaCUj<`iD^nzq zT-ID~To!uGMl)SpF-fC)Bv@GVPFP!Invn3ckX*zU5n#{9^F zYSbI)zd86`@{9asjWF4~}zPLAqE&51ys7k^tu7SV{x|gWT*o z7d7+@05qe6;75Tqh_$Z%S8~I*iX0%p3sX(Zedmhsz|y=abz`wj3)iKHI${2AXpHJ7 z|7p?DcM}_j3?$2Xm@vo-gJ5fg_|etm+VI5ywCyFWO8bG1G?yYs(01T#iJ;UrirZm) zFH_W%&N>0Du z@GCQZPso5-ZLrqha1)g$dXpoPNla4$zF<(qrjAU6%~%vhL`-BzB=um_3`b{~MUK!A zNA^LX5D#-bn&*CFm@ScI3vDAB&i7}v_`8*52{EadxNa)fU5=zR{ngH^XEo_xxLY6mqPPS4>gPyJ+SDA7EI5oqvu*x7ys_JPu@uM&sBB#;q@$_Mq+W2=j zHGA%OH}$@-pBeTKKfXB;+JP(u8-kVbJ??=_y_ociq6>xuZGNsD|M&YZ(&W`>QP~({AbuAN?%uzF67<(gI=*^P!*N_6 zT*ML8Pup;X+b290knG>^f?N!WfPpB9i5MO-q8M4scOaynXVA{7k?mNx1(s8Bphxu_ zj}qm7owq{wk_$;%c>28bPn}lCg%3mH8s1}BaA;Ap{2C!nKfdvZqd8CqZE#731YPu0 zaK#cCj5IRjtc~Wn*bkh)qj%#fLF^ZQ6-T71%;k}254A$H$O@~J4?*k$@JIF>?XRy7 zEx`j6_=1zrCBh zCDOqDWWU~bqFTEC^@ThVU-3UZuJ(0u8v+sxCB?5?Hrp|shQvZzhN2omG<*NcN8G%p zGpLrbx47tm^bmp#K)A|%o(r@aQ^74NRI25QQPL~QHmsccQpX(n|REWInRcq4fZz)GPQ z6S^|6ti-RmGYz9y`F>|n8D()0QjxXeh-NSB))za%Kp$9S>=(;pHjF-y@QxsOVV?9_ z;r;Q9NfX1xIivCA%qwn4cqWcN`Hxuf=DshSF=GHwv+uWoX~fTm0Hqvc&+j0~Ig0iS z^)L{};abMHtUEV6=jlNLR@wOk1v6L8MfQpq(0w>lI1=-&6UGyp+%n4y1tCtx7h%v2 zG-to?v@QPQ&j=?s^GVJ|FP%xv9pC-_Kwp)Ukmf?;3}>zMqRmLZRLz!374QLj0fHw+pn}N`qheBHL40+oFS$z@pG20#5xmf z#a@^T<|J%wX?R5emjD>XgM<}`$jzq?JQF_S#N|NZN8DHL2Vp4CSKDJRm(lZy9)}Wl zBIJSkFTwKy8AQT%Bz9Z2efV%cO_FBgKp>p)M~IX&yq^C`G~X2Cm{mvTAIAz&8^fOJy)%w%FxuqD^lC?woFxBhWnj@egalYk-z> z*oQ-OQ-x}6|FC5skHf>g0~<-v7>Lgd;0V_Cz^iPV#DkcXhKSG4$1o)i4Ysml#`k46>Ny9Vrt-d-T;nJaEmPzWNiAEa2UI2LYS&5K^ZPiEhT=)Y*iVrX7<2rD&;{|h9>k2T5{Ea zZkF&PcN5fgv@^~$=2P)UCFY(0ha{%lqeqI4!Ojn5;b}F^C1?Rx5fDP+RSG}PKRe4Y zlYzgYrd8=RKGqOtBfv)wv5v^-u1no2WQX6#sclcjteWeb^V|67L7w16t`H*?QJF!h zs!;SSj!~>$T#^VFG((RG z}$0n%g zh*Vb1^f#OpISi-IOp``nAWQFcz2OW0CExGuo!w7dU1U2i~e<< zk3u~<>?t!Z5!(JiL%9Di`P;`agcLA#HD5A|l1pv%Wk;bT_wSuxLQ_VjByKDOgOv`k z|30gxyvQbmF9Pl~7LIyzq&D$67ojA>kuV;!31%R-o+Pu4dQPT@0&G%E3bsuIj$$NY za4%npCBZ233Q%TrH>jHefO4nd3kGQmbo+7%#E^WZkorlN&`wetKbgnxCK*?5;CPWF z!su4bjoj>h;gmhHYspSRk6)szWT%j3BP@?XOr3lP#B6~~GUz96$tHOQ?bKoRpzj|r zb9#=;hd=SZPw$SyxN`aur|lWyPkc!@VRVG#<+@>Z zzC8DWWhB~mTtG>o3M{;ugaIUfX{+(veRbOw^l;g|ML(jReXlGpdJ-stBUz;#5 zgv=@|e)S6_X98uMER;$P*Mh{AU|z2J$SZWHjUPWRye+$ouDo$$fm;TZlap5z-$Kf; z0|3pEkr?I^Ean;-%+#=dyZm{vB*i%8lDNoAMS7!$;*t)$?`xv&dG`_+ z1EZ)?Pqc(Gred4Ad4Q9wGQQs#O4>E}0SIpEWicuk^4}T;-a-M3TV8e&;Sdlt4sYZQ zLA>n=k)EWXb;-qpDdQI$S%{KbEKW=Dng$|{{pW6L&1}bVY=nwSmp6bv+-1sIa+-y= zYt7fKN{&frX2X)GQ(tdCX8FQ05eOX#eoP17!xk)GVVROjEh&mFScS^7Q-Sl8vt9x|~oXXJ8cPWhsnZzDumbdIZNM z^c2>6w=fG~@z@bCevb$jnWAx*>UzQ;VwT?a@N<-wo6rz@!ZE^zlrQ(r3y zmK}SMUPHc=&pI*mhusJfr7hb=01c^m+om0T*bBG)O!o^d$yNTdrv1ZHB;m9AjEOv_ z&Hiwcx#-_|uHQQE8d#gH)9j-(V^-5DbbsL}a^gdI7_i7zA;W0a)pm+NxW1*1G?0Lp z(Sy-UD1@{LshdCPyM8s*gF6oF9|tB7e!q|TbR8~sv>i5}`)p&O=t!no1v_5#!P(Qv zi&Huq+k(iXHQs*gOhg7?OzljZU7Sn}ZU4uzH?o3ZWMv~@Aow4PhX;mU#?;Q-#e#s9 znT_LrRlF%3?bt&$H@_TxMfBdPnH{M1U4_nap_>4UJqO)(qc*=+{AdyyGWl^hqPmkM+&o*{zp;?=K@r>%~&O7i_Eim4129>j}87092itn}%w z1t?svf@??Ot;D}FHo?i%_(O#YIYtyzsiR~P<9FUKF@xWz?IE(Nt9uBW$Z=A*(u3Uh z;9%QOWn_2px%u)FJ>4{vB7KlDQ+GE(B7J33RgpE?up=VIuDY+lq~UcGAvsdZB&mh{ z{ule3noToBr6`AbRov}pa?-mCE_Q}cSl3_9&{2^hd)=ZJ&9=j!=O_jRQ zyard_aUkeU$uHdEz201(NaHT4nnbZWm|PUS^8LBkGSJ(ZhOu-Xkd_QHGNJ|(ws3dm zvUnf#$#&nse~Y%(Xl!lLLI-PKv)gw}L^QbX^->=nO_b{WhJMC_JcyA3unLypGyBO_C)rjd-B-_MEk%;su< z9kTrG-~w)FjOqGOuV`|io5>!2K7b}}Bc5bSZ!9kL4?+`+5NYYHY4(HNW-E5Jm6xry z@m~ubwyo|hi6v)upG%{t38(1m%Ni=-hZU33uVfR8-)jmXOe}L9cFIucUekhr?f?}c z7}K+R77M}C^yLX-X|AZG6*(<4@a`h62`_su*bqf7g*QRJ4KrDKUbz8=6#yqExeBj) z_JSq#n@@7V`jo8L#HK`qIh&uEii>wTrf4>1kMYKUVF&MQ<4h}>605P_I&f?*N6mFU zUh+?KKYJn!0{XDj_bx*ww`fXOE-JOWw+wnzGMf5eJ zo_lGMf}fW*siy=d8X2jRw3mc$I?;2 zE6*x+b8?l^)$QX#k1dE1f! zmbfy};@yZ=>&U719lM|nmnJGrt?mT$NuPZlZ*67b@5JfJ12F!=UGdbJ)=}U*D(ccL zcd+mH=FqWOfQu$}R-?~sx{kbuT~%N0Z?ONA`K@kRtkg-RCe`A&n7J35nR8W1D4FNN zR%~_~AZHZHY@vDcayP7~S7TKTbIsN>1T&}Mrm(WT$P&W_x>ZFDQJp@qW2G@*Tu^;X zHu;6NVg74(0t_80=^*SI7#7hr#sTpoH6}?sDW)Pg}UZ2XaZCX3qM}u4@mee<& zc;X4B00#RVChDYq7!G?PQC*sz&a#Ulr@=po^$@>ZeL$tU4w(`h@ay8I7UIi(d-|Mf z=6o&2j~7OY`GpRY>9;5yJCEFa*Y5NmTKE$Ig}AJKkbP-nY_lZ9#HEhF!dLm-gZRb; zRLIoL#jRV(kGDUH=BW|yC!Tiu4fVE0HyCd70b(*cHeFSFS3Kfwc$%VgFSB4xIrgwk zc8w~0_mqA_Jwb5I<7IWfx=P7rCHiH#W!Mx+Q3)IZs{An3w6g`Wp4er^w+XzZO z7xdmr@ICNLy*`3dUFvfT8A>fmUlVZO-Q2NRG4VRak^{c^_nGR>#$qI#lg{aTKHse= z0b@(Td7dyQR!vSfk|Ky-zV4S};ZfV%n(lui>#XcdckAMFY!@0hRpY_AviNwUSZCaf zW)*$Q^{_Pj)B>QHM<3juKS-K0`1##@RB=bUPXaZsKSNT*9rB(HK>rhpO9K_>2K>5s z_rQOr46MqD!5LCjm=vRu=3j5y zLmh1m(r#v&Y3IBdm6!F|;3<$DgFED^G*yS%gPN_^)}+&kNTVunW5m0=yO9 zE1*cCoJ;in^1t%BVhv^9NZ?5Cw29LX6k~F&0nHUN0^M>I492XPhh4X9B zzk%s&&lXSJ^R5QsNwT2-vHJryk``cTz`ab2^F%61j!Z6K9YV6@WkQ=KIUc+CzWDm& zErLc&*v^pp7u+rvL%P0u=)dE>4g`p-bR3=kB4hNq`8FpJgNR4D)px2?^rt20%YmkI`@X z8SRw-wfTl9_doJ9yF5S$OT= ze}$TVY9r5=J%g`UWPXfx#B8YPGM=RVRC6P-;T#c9AK8X`t(3{I`@F^BkQ9-XK9+ie zxB{?$$kaE^bBqu`r^_=uDFB{>!hI2DuAB?fDZSF5YxOL<#^s143C+ydiiY&ZXsgRX zl<$E(2O}{%k{8zZIl%r)C71l^nm|-|txiulSt9&W7L-(;gl=H&WHXZSb)+WKx>mVH z78Sa9Xt5epD9J2x@K+g%38X3XJx2|QA_rJR)+sqdTj`CP@OVe;0YLf^$k`l-Ou6m( zS&JR2Ozj#O^f&f2Pe5HsYX4nzyI2UgpMAp4+9YXN(R08Q&1~&YDnI`yGnUmR+O|L5 z_A(G?oJ@YF`n+|!3X{TuHSE%gg<|>CHh2E*(9>mP>!k3PY)+1mLS|42Llp?s)oaq4 z{8|!j@R*j9TLz{232^X_e{4-e5I9TAYkP3bnii^_p%i4@?^-X53ImeU|Bc%GbijIq zh=F$$*Hq{3`r%?A&&`C~Nz$(rFKD}m1E^8lRJmj!h}}7QgGOQWT85Q&TRHH#+`taV zDKX+6&2BYx3aU!jPF|fOozO?~8=sv~@BQ2-c07oexG45DVw}Hb z|L*#_?D9!s$y|8%;AEkNC;L51)Ikx&MJeOysmp4{5Q0BkD(Ih;JK%%g!iUhONN^{| zQA$vg?W}ms2Y`Kmd?t7WqS;4Kk*Hm0{-xb8o9)IZ6x}i|2r)bO(!eb}k{HThpG1*4 z=tlTDkSiDyM-WW+pB>nFI=; zx2dx|;%A9~+8^ZuL3GthAq2MEWm~r79cI_=BnQXqaK`sA5w-ak9N0&+6&jQg{Sxp_ zuQbI}+sYYl5|jla_L=OV!EHM@!hRgmK5ebhmg~>pgmcZsYAUtRRWM^pfV;+v+N7;X z!Y0ryfE(fE|jGg<7rS^PFPgtyOvRDsTk|2>&gldRv;MsQvQ=o2{-#BAWh1mYwRo z_R#OPc?hjrgrHPvlihP^ESU<6KY?z!IU=-6;@s06ix%|@&A%mzA(f`7j^fY1>^qB4 zk>AtsjzO~qf0D*^XpWPTjw~uFR;nEzc@#ogla#b3k_nfoRFG%^=p7p9gZhathV{?aYJhVj;3Xyt&9m6XN*PuP)lynu$!d-$>tso0+>MvVA&x{Z@$de1+DENeZ4(6 zt}`?Wdqi?QrQlHT*U`6CM9zLWh~a7zq{zxK!I3Fz?Vv-%{)*2UJ8*C?6G0szJAy;u~z|&+S^DBxJ|^>ZA)xTnpKnK!I2a7O@QI^j(}NBSB2_~fC@Kga3?83N~qzsJs*fEmIGp^$}- zoBj8tR_Y;hW|=#3S@5E$xh@VYz4@ zsyMZ+QsGp0KA}Sy6{VCr%k5!hNY<6l+C2?y#Pju1QbPtw2t&@D{H+dcQJ~x zYym5fr#jCR@($!fTl1YmCT-S+bmP@3f!N9N*t1-TeXgT08t6A!!3jkpw#Er*Y3nmQ zv)A2r)2tk)?HpdpSX)F)z1Gyh2KB0pl|a`2x>xr*ONf{uIerS|0npn$mlk;p>&V9o zpDVqCUw8(#>A^U&@ysR%A9Maeqwhn@c;7HLA=xu9hO#_L%XUKWPenIj(n8Gfc3XId zD95$u+l94=>S;~7=CGkXA;t51RiuHY|EjX+A>A^pkiRl<>k z**WhPz0duMci!g7p*JCZlN6B5$24Hfma;Frcb73*Lo%hl3c&J51Wh}8Y?=qN{H8I* z6)hXUbH*DF6^_7I?3eD0>8bH@r7X-d$>>*fje2ge@s*}cPzEaB6>DsFnlZs1Zk*sh zLpqBC_LRQ&tUv{=?)h4=HEf)f+2E3G446mFReX#hrXoNhzqfdg`AI_7LOv7ZSW&}f zcuOJHPQn}H1aPT*7_~!3l_=rBuKv2GJj#_%%u&*cMb#mB+OcmE&YhReG2cyz%_|%L z-|CK{3l%f~OND$7xQ@9~p90|wTwQhH0|xT25i}=J3fnp@mAPG3H;OD}rD*nbVt!l` zkIKL(lZW6#bHm+pDD>EGO#Lyl`z?p7DyNm|vg5U_3^0^`kFk2CD5B0O03X8|)<>ZN zpB$PQIM{34lJL|LXB}S9vK?TVRF0{4zElEv! z{H2;UF)(8so;{A7Kl50CNBQO0GSH_|@ zXRo@R378F|Gq~M+bJ6XrO3>vF-KEBpA9$Kc{&gwT=1fOSQ=jpTEG(hi8*wy&Z;M`( z4uHm-CasiJMX??^CTyMPVw*Xg^T_)9b|eXJ|J#W;;51IGc!AXcQ^FaT9)E}UxyQUE zTx1OQTkelY$b_D{>TXTdE$~Rcn&d>^2Sj_i6VO~v#KTp$?RrHdz*u|lgY*dfN6bB* z?`7PEjZNDms^Lt`o++oT@bL(=FWK}p`D=3oGE^8p#q2fzkd=Oitn=L(KM6!sew2~E z?pMY8kF^<0Jyfq$pF8m!O~J2^av}T1Xu{ps)qx*j|C*SA35&c|1~!?O4lE*0$g4fU zL;yU9@nD#=9|nA&%EC#*;4ud3^hH9$Ad^lwvT(P6VLKDOZ@saykelOUIQSl$uDFY8oC9cYxqb_NP~hW}SQWny7Y%;Tm6bZObzW3wZFSL^r1UV#>I_>liWmDq2G z-#v_64eO{lK#Z(+3#Ler*V3K;czT)i5=~EOv}WJa=i%%6O~CT3R+}f#05(5=*o}*JR31n0~u0EqVLcrQHI6AYI`jbT8NdvHFGnQ z(A9+13JGuz7#SxwxQ37eb9v0<$6>L5y?}>{IUfrYf z;mN>rC50!zCnQ5zL(WK1S|RlT+Ectp01tMrXPM}kq1Th6A*E>fLH7$Qt(lal9?5sT z&)0*~4)v zi3_;Kyob8Od5E=Z6z{|cTc$(k|DGmzlvGlQv(ogKq)ffa`}%D0?VLaYg70=FKlA*r zjX9?bFvKC&At|oFxF+;#9*iYY3ZFf{yHt#lN!v|7l_^s^hv%q^AjRs(g-f~w5L?GL zWUk1B)68+W_kXw}tD^Gwh|)I_W>3Ux+vo#N{vOF%)-)(x=6I7j^+L-_K_z}f&oIAw zj@gB|#T(l4w~rEKo0&ubc)s~HFIDN+!SwtT-=7is5lJsx#oi9vTnL@SQ9H+i{YJ6c zr)x^u?6V6t(3fqq6_Np&U-7R}-x8QVI&z)kzKAK|JH`OmhdnEGR3L}!)KU~y?%ER;GQsnS0<#^}-<+%q(`Qxxpuafk3 zYyMoXgX414$9Aw=znQfF0gY5Vg-Uf*&O}-0+}oS)He3x9zrB&0HEFk~727rj8L62v z#2OcjBjk_P-h9`U>gxwk25#&vj9Ze6Gr_pc*hupu|8kRh+$WYR_o)S(^pnoUIoz=e zCW7bc3xT<6m80 zN&OWy@m_cCyxnEK+{D8K)+r)dE2727hpVC{?X|(Y;BM%qB^mRFtF27zAaz1im-d{M zNq9fL;BLdJYqPR=sa+C1vwCGIZ0&I9zM}OIAvJ3;Yf~GLz^Drpu!KOCU^>{kJ&77Z zMy2NjnjdEk?KCYmx7*5YtT(&bUTm;6+S+WS;59v#7KzjHTAe9M$7*>k&KLY&SBbV) zd(r=GHf~&`SL{Bt-~V&moazE$1ljj7fivtk7sp>V&95w2JsQ<~Divb6F09|CpH_g+ z`-HU$YPG&b^wenyh%pa&rd%>1GD%q1_uRr*%qN}4!@i(=keA*g(yH3~+*-qe$8DDZ zhJijPHLToOK{8aJ0w9hnFUGV}hRUU&HmHCgc6F4Lg|>|X z7#|J`ClqlO6a$b9N0HX{Ctj)q57Wy5b6PTNw-5~u#Z>pCizRPFa}3TNdwDaYy71be z)~$3zcMKtRz@n^{!(z1<7%e?<>WUcC$Ghx!d1zG`#-Y$J`t`SZ*W2K=&kzuou*~AB z7qQMxYU!0PmWhwpmc5EubR^^zC{?Ssfm+DuONJGe5(1BR7l>%bf`cIriPQwC^ zFkqej8V8|G#m8RPC!zA_jqb_XTA(4Wu|6r%7< zqs|H7f;x0~Oq)KJ>aIbLDzBC=KeK^X!Kf$~`qf;mp81y2W zD0gfbCsRt~jK$h?)enkuw30z65hNfBwLsJ^lGu98d?@DSqYp22HPfmgV{%93DfQ@l z;oa!@jNL-BLQr>B%LKsZ9{;szLJ7l^j4@W@c-UFmk{w*Gt$xgiX>f(Y@a zEGlc~WHwm>USI~aHVzKqzM?vv_*d16(?#{Krz! zgc{_a7ZDkI{WjK>{?ifg?U-1aNoNGU!xhve(H*kPi5at5sMcyini#~fw+|DtKK+v}$vhz2`n(>p-|2ci2i9ehSxPC}ZTqgGiYA+Ac9$&>l|>poT-Ux$u=RXg zbO1c#txBe5We?mJso_cR;LVfZ(ZP8)jHGEM(B3eXYpPc!YzSFSVp zyO$9X{f?Zm$Y@_3x-L)rS|zG;^px^@p{{5|SO@JUvHEZ0O$VFjcZTv6%?qgfk6jRSry8rBJh{dpH23F@d7c~}X zfU#e(o38C$f$hCbo2D*!5IZtc?80EI$=nJo>xd^|ZiTuhO*kD1OzVF7OQbD?|-w58*&K;dz5u>Qf#fFEaT-BDd z_}mvPTQTB|5j4eJ2I|9yT)4=syTUWzVik=swWZ}J^f4X@Jo@YiQsD<&I!&LmfE+VV zb~mR&-;eLYpaqw?i`|0i^Q#CoD#@M&$@^ANNuJSc`YQl*qeC1M5s0K=WmF*<*Oaq2 zSKD3w${v|yu#x_b>3qCdS@Tx+2#CS21yq@v$K(d9N`8=HcPaw6V)R5AX-S0<7u=2p zJTV(+gHyXK?W4Nx0{9a(yV{j#A4f>@wQqlfdA>bDNsS{On|SkpOUB*!vH_v9>^{7> zUooR`+9m+#CNNMuk|pb}%h1(W8eWPr?7v} z0}R!pU4bkaNsDg`WrvG$DDg>K-s9wd$Fbi$PB_IZIMa@26-%TsirUn`h5wP0(GoT~ zIuxn-eq~&DZ-0ks`8<(T1eWxwL{MU(t}5*W08#;JrF4nKx=a{T+*CbT(oQ-*PHIt zvpj&gp={fW;Kmw!xyu|Zqv9CzO;T0n)c`Ld^V0{qj%_AOb6O~LN;e8G62*3+0|6Pj z(m!vKmwCWD&(xM@!m~_k(k^c<`X^GdDt#TR@1tXr|=vkS6r$Xcpl9FA?-hhuCAVCHelpL`$5!aOBnS$L&;cWG) z;H27NqHv?dr|xx%Vb5M)DvtmZY;qCUHRwG3dy z&?P{N-75376ZAkd`FIqH-a6c_)WrWIW2B7IwT*>b>E%N2iN!=~MvajzyD&;Xbe_|; z`77F9CrHGc((y!AQ_Bj(NuDO)n8!R|5T3bnNE2r43D=ODXwK{7T^vNMs~^Yw2VQuD zlIf(HfkqxWOua0!NIgvWMs67$hy##K_ev5uYd{!-*I>$J=(SM!jL>LbAcGNi6%pO4 zd%2;x-)6Wg|pNhY@K6>0v-BsWBtnQ2Mg?I6&*ftgMBCVJL^?D&Ed&$~yHi;eJL8YYXA1V&39eb!f% za{A%5ll$DL^vmjlB1AQX4((S4;&bsETjf3usD_*@FktdOTy36mC55MQ~+w7>`I~0o@<&@7DVtP#iy)4Y0j!%fp;% zE;8~6o|hwG(1L}2o#@Bc7$-m@)JIEZXsvzyfmYtbv8+cH6seQY#hBq|wLDF|&Ez01 z%j85_V$_1fpinBsZ!x*18Nr{LH=#-<4vCQ|Az=Y@GmRmNlG=0;em<7)iC@( zs?K0*4vD&B6yD|AG>oS#FF!erp0_>!?L5oAjI-g1))!qesILAH{_nHt1-^P}IkucJ zD^kE?k6~F-{Mdw=OC;JwAz}!K4%1aE^;B~#&l!?+^4NDv6G+u6!C;3P^4x*UQUMC+9 zb26{`*G4~_Vyry3NRK-bDY@ZNJUIKO#9X4Y&RP=lmv?I_>380th~+S#>NgBI=M^eN zmqmDzinO*cl6^;qp5K}qaynskehAeC#=+M;>jECuZ6>WG`zC@g-nm(Je~%-3>%-k& zw*U$)35(VFS4*=qEO1`g7pRK2m{J*$>7Hz0?59GqWN>9@uRJrQuDS8Fm;3g7u40|$S?X{uV6omCneY(3&vM>CDJcRdnzB=V&gNwKwol-W z*1pHXhcQIJ+c1K3qb3&Bqfrb^FZk83S}h3oe)D%tO!E9RZwiqj1ZzrV`VMYq9tr?K}+ zIWb%#B0 LYr?HyHddz{DV!iVHx`?on;t)`xR;P1{Tlht3lv7%uT~+WwJ2eO`-C~Xnu9y$anlu!$uhR)I#Kj*)RD8{WsKc?ji1PqK zLaZOZOks6Af_8>{F5nleogNsqIA5J@ZtiM+2N^-H>z=2Yz{e~RzoJWZHyZ&HDTS!C zzwU^@U~*h2w%EshELO0sNqD`%HEPUs0)4$5iSaF-v6rtK4PoX9!zXb)UO8A-Ta>!U zS32DkFP0VvT&?CWZvhz!y@Y1CKY#3oWN^0WMC_OkKyfxh#Aj)3M{xXC?&X24-~;M^!ex0il)_ zm(d1CkDyK5!~nmQ%fKbP%+v41$!{x&(&BtMwPla?hhxjCY2%X*_XW6Y%m-a#*g_D4 z1u+o@-t~C$%p5xDWb~%q*3))J`iEAM`CQ~xTkMC&cJIl1dY-e%?q@yJ zVOA1=b()rxUKBz^h21N}Bz({bdM5C@Y9*z()6brMClnJhZ=k6|vH&9=?Df!2F+1r| z%hPJ8e@}!c;yatG4H+0sqcTM!XUI=-hdrWcOSD#d-vZ@k!v$qx|!d6k-KKZny zRz)e67j8q*}1$R|mG* zq(cbzbmEx%MsPZfE#6HW6Nje@V-QHA%#!MjV!V7*{D@V}7nJ;~tDE(N6>Cv97-x#W zY^(}zMz-w51`#5qYz@Sv^vQ!{Y>57u-bL*Mw3_?qgK(0cddsk`7JI>;V?)P9+{+D< zVCCgiswi;?L_#U;gZT?&lk)He>8S32SOiv2C@L+6;Hs&}-o)=d-?%4@4_qPguP|jQF)g&> z8GS+}p(XztnShRsDx#=jRfq*kL%ipz+sQi~$p!wI@yili7jZKfzD9?8bOnSQ$th*J zZtSLhkJvhshMzIa@ko&K5C(+S_$WKmpO+}f(MXy_7*qX9W=>+ZNyMG-FW1n&0*qkr zv3-Sk#3vu*{_9ngq`qUEvZ0bNuE5hF3(0<%#Lj~;e66By$gL_Ym?GB$#t%`yR~{D9 z3Cmwz3Xiu*m z(sk;^Z>xii<8H9m7E~I3Yz^{6J(^O6n3$E`swOPrnO-%-xWkxLkGc>>ACOR&b z_C8OO`eXv$qQWqC;y}@nAVqBbM`{t?7&9<8!=a4;=ni=@{rWf~G=`;t3sZmdVIG27v{B5PvRP;$0u zR1rhhIM0RS$>vf0f(s9T%*!eAIqGZes+?NjIdyjv!J5_NMU|m4P82VrEGPHb$aDD$ zWRGUa+tUoQjcD~A#bJ$Mjk*Cu%-WFCs@Omr$f{U_c=MdnIotA!wcgjZ_v?KEr0#RF z$%OIqqGwa$;EyF2{`ef;6X@Vp^fFVLm#q*VLRVl1_uqdZiT zU+I|ayb1_OTf=J*H!J-c?NiCW4t zw_7zIL2E}mR(FGZb1%qA4CPgf+|EAH z5x#v4_)A*R-dxtE7N6Qvx?+#S~~+3HX+?i0)cchvK8 zmWl~nRq)rnu7-f%Quzwf5{ByRW3`DBJjEvtEm$0hfHO<8usA#Y(B`>?qgKw4Sp@oI zhryd1;mvqgLtdz{=0aRAGl8Nrs<<$>2?cyXt)t~W8*~OmlCqt&FN%5|PvQ~8LyCTX zwSZTrN|JM68GOW=pt1H4GFXFG)|x}-gX?Y9@)R@=lU)NAjijW*+9xeh)5|2CfaYC% zrz7%@CuJ3jQFy{r|D+hEeAT|khDbl`g9(O12FpDI-cwi<`w4 z1@;XVqRdvoC9(n^vfKS~B9I*vP1u*Pm0qaNZZ|t1=qd;@>-?J-;L3$a#(2qz>tsgf zrThbvr0R?@g!KV4TE#A`8(%o-m7j$}HPdQ?CE-9=COURMvj&U61;ftF$kVk~j!n5S zS27Kg&;GCXTUAjS!mI#|2n)1iWNF`F5sD<4p_CP+N}Os$csXjC2B{9~vMcfsN6AYi z4wW|Wb+kW8=$DgBj&#`pRt4fsUNS{p6R_>aXyp$Z1fPA8CAHs!WUX7FCz`pN%Wd`+ zP5egM3QAf@n%ZOP5WGJCwhMa5y+~weW=PPseyR{^x6WYKOWGs&p^l;o^|*1Hk5aMLZlU`NF(gvm zEDE9iR2pul6+qH-vKO{0NuBd%j{L%+I#(ZAfYGNChBR*Fc7W05Sc|qCfxS2kw0vSQ zagPjn!!#t~S_n-}ch7|#LJF+Uk|L{+A$)~@f>3a6aGg$QacKYE#6C_FeY9CkWx577 z_^}=jMucn>4|d^957V?nsG&h|KW82aTw4iwPBmV_wf~p5C>HC5jD79%=hBB!Ut9BYJj@^H z8Y&J7JuxH3>65>05Pt==keuCiI>a;jv7X+*W+CKD(+SM}*4Y*X+sCgi%yVpPb>W-U zLE9Mr;PuO391ib#Jg2pliovjy`Zy!SYW;$~;zS0cz||swM5IRc=}>tH)cFg57!^f) z*4rrv8AeeP(mV8aY(5YP)gWGID#@YXQg|i%tptXcu|C|^6bN9dp$-WMK6-)c5UB9H zd`Wg(ow+l7!YwN%aQ}azew;u5chryVCnq!Ue;=?6VwN^8rcQ~o7P!EYuD0_zo0Bhp zkI`*hh2jCctRFao8>i~@<@#BbN#dp|HByYS=#&ELigWxJ4$ZoLZcPUW>b?O>yXdXj z8s@4^K(tt42Z$UHW6s{-@wizEw7uUCwQyx8E2f{=xc%b&`8_sC%1qRzU?Xkq!0K^; zr|bFV7Ae>0ZPvzcHwZ|_To*CTf?n_y*Eyzt>R5>Xfq}h8rB=*nN2|{~QNSz@yGv8R zGiCl=sm4E6`ih6B7!{zB|I$anx-VELD>AK!7`5n-0bouQG1j=NyGi9|YPwnGzyI1>G7l{! z()KyDy}$q7Km;r=xICDezHBU|@)$mxV4bo0h}5uX$=iNPJ9D(`B4BN2rdhoN{m6sh zfJN5>#w4gR=8~%Rn1q8mv^;e5A^x^0gom>%HqC09DA8pNa&XFQNU z`x0n=UQ{=|XmpBoo9Q_sK((FMXZiDWrD&%InpXBzJ%oq+y6Q-`N9}!8#|ZX)0v%Rau3G# ze0hGpbqAv556eSxFuqI4ynGMIIjF60XLb3Y6<&$OeWeopX_}PhUVmQD*ix}Qc}n~C zwng+ofIugi=qLgxSo>)OCibRG6jr;f@h%ua}4HVC@xG4S!C# z78@X{De^<;MTx_X4j<=ygL^nu`%krt%uO|p%Zldp#kckgAzzAOO=bDb!u{!vitWy%Ut0?+ zeZa5qNZ<4`@oDVW6%njR7S%g@E*vTMj*i)(cV8M|yK`vw&}F-&hO7%;kfzdMxiwww zulaMtV^Y?ePOGOWC*6r5q{ERJi-UvrObi!w+n~;x7Mh8eJ&wZ8s3fd=BKeQ`F+JXK2- zu`3fcSijjhy?GMXpZ*H{l6 zllaWe7ags7AZu1&1iy#S1|Yx}A#A{Oe2Qcdd|XtVc``Vw7Tt|rIyu>pkJCzDjI^c6 zsj~3xuBH0GMgM@X1wNYB)sIVA?s_(=GC%Z^bW83gA`>vltAH?|!`7E?k&R!Ni^HY* z<^YLv0d$MF-nT}sYvfuw73QAi7(p^feL?)WjHjr8dIYa=N18( zO^s9~V_ydenr_GQ{y$a^D`3&-$MZkD>|kmZ#SAKb<441nCgP34b@WN*d2xVh1}{HB z{kA~WHoy!dI^n4Icac0Zp={~s@-TGRSoF$Q)h1DyILL1@{25wb8FL%lt4+~Its^32 zJS#&9j$^nP0JtjO;F6s3;#6Bl5?fp}pFhoUxjcT4Vximn<=*PFQ~MWcL|zg8kR~8c zaQ(u=pcR?(<4-|$R-T$g1W(Xwg90fCCn7}XysDbpqVmA zKbYb8d*Jh2so=R0k0w}$)*Z@`=vqiRqsEUz{lmMcc5F!woXC(~&i zG{k{xg;n#J%_Tajb~kkCc|#{!L$Icu5Kq*_xT>YVsLd+(lk${b zEJ2R7dRT1`jIW0!g{uD-=Wc6rCt!H^;uMj-!f6az(FC>3)uFODKgTX;)jkvS2KHoh z(;vw0d~kf0&QT>>sIuQq;KE~@SF|X!p6=*t!BKKmNIip{ap3!7KP70VQ8Ck}X+~7^ z6AON>%%gndE~6VF_I7uk-jjWW-+$oTBiPYlMVRf|Ut2m!0U6_6cKp#P#j{g`x4^&F z9lkAn8{P*Lc`ld=9b<$Px?RPZ+y{a2jYz<6n~F!C!q1^$j$hS==2_%j`-^-oO$}Gd zlq?;~2!xo#)pcub?y~>-DrilxRU?zkK-8l}+&8bs;<>x~&b{p)=yYm|jGqO3vyvLXo=s<%7%sI95Aw}1KqxcHN(>6aqIR9uQff+a#?6=rm=Jfy(q z3%A2A0!sFwTxP^{Bx`qjjd|{?Tb#s3Yd6A8e#4n2e-VGU%Omx$p&lU(>Knn}^Mne# z=*XI7_}w7KJcOKQejypI3X5l-ncM4nD)rd4cf`*tVn^hMw5xF3e?5kJzXkqiGpvtU z>r)4U>7QH3rg4OMPCki1boZz*sO13{bnXS&osY%ib-a@i|dVpy@d;is(mYju9p4@Pdony!t>;XdJ9`V7t4_C*=&wBlgW0TCV73{CnV3 zs`NaN^M^qXa%G!lV)Cv2@N8&oKYahh)DWucSbWp^upBN_rM~wHM>ZpEGI-w@v|El0 zWlDU9L*|?)TjVt74F_fb=zw!;^v=mKhtO+G&o1^%cqHMJ$qEA|KQb}}mE~mlSRkQM_7getv-7?PaO&sQMh)_aKkT0m z0BIa3pJ{JxKPI8ZUxJFv{BxNyNxf{fj2#+PlFa9EC(tFV@5dSev*Tx~Djafvyh?MM zy}C_GKXVoPeU?|*92IZko-JJV@?)DaZ55sr0yWcckwMx<$pV|nQvxdnT4gKHerrL8 zMB;)d#|)YG&DkjvDemHdlAMfWM$t#I3!FqEdz8okaPz&(FX$#s#770yUv^f}S7T6w zR-z<>uI1;Rc~Jlqw}_W+D0symC?4j6iCswnzmK)G>ClK*NSIiL6V3{T@>CY^aQoKjI$ADHn3`_X-=$TME4z{@`d z78VQ40lD=%koqpc^*y*>F{jd}rU-(kBA?&h8WwVpFdh)(+`J|K8#3#EAm{%ZasdAp z>_3p!{{wmPqVSLL;y;Y_A^%~tocYI?)CfQ=P2xzA>Zd>ERWnD<_zc#|_%~AWra!_H z5Eb(V?#CAM3LsM(J*+oEJy=`%Uj9rm^<4PBH;_Wi=ls_r@q$IiJ9_3c%>4(VZ~?lC z&nrtBu=u%SX*JitPkHh3ZSC*B<%9pd78DQv@A8nAvA}#9+4{czKN(!B8r*wE`EZk>qMO1nfl@e5P>b?9VioJmjdG=o50>6 z5ql}~u7E}ZnKk%E0n>_^2!=0u0_uZuOrHEFwYM3UDz1?V!zZW+%8ObK-#8JQO)ggl zl@FdcSVgFsCC2$vw`ZMds-_ObqfMiU8sg$>j{tR{8OZ={6i`uY)W`YKYUAHU^MlJAo>#3lTD&xySI$kJ)Mj zB!R|v#mgCW5XqY+JwtMP+KRf5;}Marbo%OYPs&JOLiYZi7URptoJ{WN&s*@*-kYB} zxG%bI9OD3mo;887^8kb#OvK=QDrT=?mzb>486$^7C>)-O85|*Wsz8O6${`~pv$mXI zRd*TUC=Hd~v8JLvGrH2oi(PxX-<4XYDFk%;EQPwa%nBMCPlrEi`iqvm9C>_6FU46s zaFLAf*#gDKXUDvpRc!OR9dV3U9&fQ|QO7|n+pI3JKM8DLDWhWb_`8GRqfjC$k1`i} zw9M&tc>e>(!3N*_6R8xT{IDNb-vKpbNnJ8f!Tj@!*4gq56wW@O6?QfpSy^!{yPJw*`Z@5NFq8HJwhD%xPPy{Yj=O6p$xQF`=6B`RZai{2FKI=YfG z@CQY=8?E$}*Ap{Mjvl-!D8t3;>qcbHEg|Jgm^gx&ggZk!#1?@6{L*HA*WRs&%8t9D z1cTC|1Y9`HD2Cpz*>r;yeb_z3&Etksdf-G~oL2?cqNz+mX)lUK}QqD=>4<-6H9uJJ{d`~<24I0yI1(4RIL!}7B@UlF^fbzA8p>Ll;?`0w8_u=Ft|afMHaux9$|E#Ul0tj{G&f-*Vom%RVByWiYvGq?=(8}oH6;(Gj?;rJwwMUq zJjgZb&>!W2<$dKUmKD_MVwr}-4kZtH0aQ%1pdB3tUah6BJ83lD2mwkGsH8xAfdNB7 z2iBIDF4hDvgwv4k)X-jj2%idT*&D-Z8v*<$kr012V0j98JpU^F6 zJ6A;q(hHn+*+d*@;k+Y&_}Nr-CW_#z8%cIK;UPTxgVUi5W|KVsqDD5a+j;fJmVF%4 ztcjwby=w(2ez`|EPIr1Yo`fhzO_vCg>7ewHYS86%ETPTPwcKd4j7H*5^4FHF&zo2w z3tC#)J(1mtuO$WOYRbycJGpNe(;XDeAk&p-`E`Z8B;lcrR^C*=*O$k)`YAT>>ts+_ zht=;1b$FTV_)p;@rAoRDj2tSa$3S}O2t1~N8?n!&Gs3F%EO=HnQe*Wgk*$o2e z33h@Iwtx*xK?q!WP>>rgSi>Q&w~mA+g~k%eZ})x^Fu;dx4IQi9c`vX|)gQI!h6eT2 zek=@%s{@fT?Gsl4dVp3wkg7U zbbZw6!sw=N~I!VNTU>qrh$Op*|y()+j!6BVxRIDT9l|h#Xo!eDVoNG_{<*(D( zTYFZIc;g$&9j=nzzd6HUF`XclB~lFFMlV8xf0H@p-G(0kHZwp_ayxMhRKRnQo)NQY zLF1()KA*uGN9}MSSyBw;(;ve4F#~71?QEh^&0MVNnBt;Qx{=W!u_Z1NSX%WbIx#9X zt8sy@z9Cl#SAuJTpOXm}?^PfVdfx2yY;R>b=5Ehgs6p^_4x3>ps~eBnz-XyiE| ze_;d}2m)Ejlo5y&N5$n5CQdunVC*$zL2_?`@{hb~rttm2g?qi5JEBAcm%a~rABAS$ z+kaSt!Yp`0=O@rsxk^Hl7^feSS8-D}a^L;jFlCOdap)1Cl$CSF*O^TSPl`&3YtM*n z;2?VHLG@3$;9#bQZSxcA%nBoB;lz31nQ`YE;Pq+fg|*X~XIlwnEey6ZLL}@l&S61N zj|%5uJwrhi?C{*mJBu24;OSv6M}A`NJv|G06XVEY3|A(6t@%-JjIKdCma-#A|2@nO z{;z9;uzU-+S91FMVgM1HA_5D4X#Lun^xvYwIq?LYyd#0#?fpAioURnDmC&j`baTbQ zK#))+f_)MfL)(g0jMok4oLg1&rq9!HrI1ODw2eB#yBei!3M_jU9bcJsBrvpI3Nzri zcg2G^fZw)VIchn3obvXE!q~(d9h*ETFDP9imCgO5|1ve0aZnBcez$_q>RNmGBD(kCQO*{Wn74NquK2 zH}_*@s)6XQ$YTX`1dCLS-DPzGIb2kD%`GELJ$YOEn*OF;!V!_(=aFU^k*;kb>6;t)Nn9?bo68qni-he9ceY9P zZmL@oPx^=tmz-2krb-B9W@Q-&9BH!hN`eJdO#ZUW@dZ`gc^$HBdteQJQiYe`iaYTt z+nyNX&M)@-^XnfEaZT$y@ktShppr{&$Gk=tMCs$qypM?ZxT!E zuuT?`F!qkb#+CPJsGnh;lNQ+e-Fc^M3&LpNxFSq)rCsSCsN2fw~WLh-pZ zp}ii@mKobnTX`*Czxmk04nk^@jc9Ug zS<;DU8`k)(&R<`D*;lKt!+sdgi1|R7(Vtn!U9s7UVI}BNb=*^K6tmhq{|cEmvXpJHa;k zIX@$pE+0CCn?_JhH_TUJw$pMZ29?G%&AtBqL^11FfQtvH%}ZgnMlEkw zTX$U`r z&&Q0ECWwT`|NOf6cLES>lBy^y<=-mQAENr>xPSBdNp_<`T{m#J5_01}86r}IM-Y#c zZPgn%#1)J(RO`#$>nKa9v7Gf@QKEmGLDJkcRrxWkCe%h}hI~d?fD4(Lp5`_JpeUE{*XN*i&wu zZ&Uw`mc66mU3D!5)#0oWzt@($B-8oBxGB*n*p1&glM|jk3}#`vx!Woj2wC80;@9s9 z7Cejs!68!@Y7V?7`-{7uzY42gSY-i^*R`_EVXQ&*!5n_7J^-;|s9qRuZtq-$w3}Oa z^x)5-HB!_Ao29hC*Oi@@zKYOxo71fmCrJy8f9YT;9L^#U>^~VsyyY&)`$Y%5Y!!#k zMtNrypB&@;3ffgYa-!2>816SL&0YwccA}Yk4*63|G63df%xwB3qe}7&X0}1;#Xj*P z5Wr&De8OuE_7_vr<>Ey!Sa{Ow<)-X{@EGVfplevVx)AFb9IV^15_PqB6u2k30+3?^$e@e+Y$qWu;ap8Q$l z@3Drz*7-zZV@Qv*KEFfQ_bCw^Ycscs>@q#^6A(098HqsG?r5zqd*x zo5<^+7Gtpisgw6U*ep3_^rMU%50-G$%v5GHU)-%eP7keAuL&TU_4 z2ex@eDk|Mg$%-~lpk-&<=0+N~|8-9*lJBF=G6j;=#%xly^03j<`{BUI2s2wEZ#0LB zZ-K_yX|J5I1@APpZf;8DC4}4gOGF|BOGJ9#5uT%K7htXkUzU_8q8C92GID43Q&M5c z`n$2#d;f6GKdgk8n+hzm8u@0_Q5mFV4Sbc@RVwlS$b5q}k{bGjx?oO-k*LkgoH;{# zWXC~QZ5^liMFv~L)kWOazQOhg!0V4=Uj7?Ulqjdc#9}meIu6&F6g3m{Qo_&ryW3JD z?3For#UB=F$`X5(tPl}to70V%{h7>#&=6PX2C}TfW-*v4n74e?_dKms87sw?5QrH4 ziQQq|6KmnxVLjZ1MNRV!c{tINced?O*Plsne4)uRQ!)q`hG(jUsVC(|dOnkXsI{%# zszL$=!ez{-SpW5RnK@^AjTnTP2%U}lf0y@qOmYN;CeNDuu+8_D{KaE~LRF-E)J@Hw z(>b4zpZ+W3IxzB6HbP+^U)U5Z5I_hCMr)}u8o5u5^8hQ~21Q&o4!tj_(@0kB@u7g- z;xO>2>CDas9C5TL?okYw@?-tNzHo5=Jp9wpnYy9@B}RLQ52s4wOm;Be_T!ZjC4-`- z&vVBFx8Yy;IEGk~cH2wDRv!^O4*{oXBof+UwLxm70tuUJ>Zn=uPzW>w0WkFmMgcRS zrJUinCgbXkJjkq1YHt|AHr4laBT`TLgVk%k8xXqspiAh;MCr#Jk5h}}h)Js5XnBCZ zbcT8TCu!ED_k#NNlFYT!(!J2^L(%SSllSM2r-9#3(*yFqae#TIeoq1 z&uWx``@%~y@QGQL))A4i7}u&yMyAjD<4$ghpSAj>7C}T%eI@rkUj=P59GZZwW~?G( zYG#FB(%)F){k1;<|CVpyomnXLcnq|TXHi^=ftI2Fg)V{V~^5%zUFmCv$JIu}|sNnQn zUYuq58VERC?4Y_k@OBfQCkJj_IN$Ws~V$8W}!SbpJ)038$pZt&%^dx8lEj;s` z>@2Sw9Wu!wc-V+8@GW7iieJ6PsD+I(53Q!ZSEo0fiYUHxVir6o@4s$_zMkjD-O;*;o4+>IAe|4zpO4-)fpR)f+APHY&3$j3KeBk@!51A=Y zlbS0ll*a(yM4`C6EqbtDN`^UArW%zk)at_@pUMaInsK2wz>@;KgOu=_$`M00*S;9& z`SlZW-yr7S<8*jEh$ox)dyh)DU|vBMOAZ`|w9|NI7!wKW$rtdD^{_GJ3(%pM)Z*%8foZgV_j%Kua?~ z(^T*&M9us!05I$cr&u(Tg++Jk(y;7_&&0-5p>LmEyjhU>eK0C3lAr{Q&b1NW5zL)+ z;;!VoCq6($=%|^aZexPS6B1WcGJdlpbCm3LrlJ?k zHbU&5yEn_31Vv;ZR1qPi;^j6H&X$k}{W#etD&x|+Y zDs9p3Sk}!dwo#26Qcqd9R4cab@y3)93+XZ+ZJyK z0uodiviy_~APoCMOp0+}`cvPhK;1M;03_W2{s&VE_Ng`x={XAE?!0U|S6oyZ27URN z3}|i2MM9>Iaw1hk{}{n`F{<=W#$WP^) zFS&#v{^pj&BAzekD{naF?V9)U^U+d zBLxu=`V<hL-LZ9FgafOB&UF*Q}pOT|T{Q4}@Fic8d z4wucpO|2#@f%KJ?Xkjpi47*j}lc9oXO4`hGcO@gRV%0`?6sW~1mTP9d)p=J$1#HQC zBq#_mdk`)Lnt%LxL%~j(Rl|ls8C~;zx;)C^r7ppjvYme@Re?|)!nocXGV&>2$vH$L z6DLPQAw>|N06R2s(RJ7p8(S`vK})cgs@fynaTWfhcR1*cfEhtGpDOd9S9v<8VS?#9 zTqQcI3BEv87@r-IP877P%7U2v4oKujgzHzo_YcwdNgK4w4ov-1QDjz%|JGB;p%o66 zA(-PXb_#rMW!e@HbhqG#Zg37*p zIN%nWuGOq|h7|CnYMZKC-yYZuw#cijl%Uvq@Y!P}|frg5h0%0;Itfy6K z6?Ua;WLJ7PRZ>%@(7&ATbYE|;kF6dZ{hp7rEI~+nD$dEA>BFYI zfT=_AX(NuOJ-Nbip-U9D3R;#M)r~1*ts9AN+n?)=)AZ&$8=XekoJF4eQE%cuy1{r`=Ga%~ zh5y8=u2E;g4ckbC0}Wl`B_)TeDOX7fSKNEmLc;J~zwqQ(*#*<#6c0Cd&W7yUG2s2& zCfcIEtIO?Cn)XLpiCT+C&~s!9(i|8e2qVq97P7EZj;l$WhabZ{X*ueNdP4TK{Nk4o zHRtGgGo$YK^>W+8QP!Npuc+cu{+!!e^SEGk{&ZWTF-+@z1VqTNf5IGyjjp{|tjzdEHP&&3 zHLu7`Lz_~$Uc@bIHEpD+7By2nAk1P1%Roa?=r09Guc1uj!4ktYb z$Wo0@N^lC{0DCF)>8;WJlP6U#etf;rba&;v5ItScqQvG;0OM4%(lB1{>Zyrb9`7j^ zz3#_`4ON1LyHj7se#KeRO!j{Alx9pEuEvYH&S1jZz2gC7t(8n)b2ne_fN+8iB@gRE ztT=OctkS}Wh)z!XH=$Jo-zUQtOqiQ)@=gVfEToVTAOfav?=Xd!=;>S7^*m#OUjl9^ znBxfq_!?ScqS9sYcZa@y*SzuS-Q?heHapYMBR?)d72)D$E&jWq-^r6{2Z^pEt}7R! zm00G{N$SnhHZsJBv(XDzmWL19ueSssm=hZzubgH#h+FQ+JoePrHuUKUB`cmxItyB^ z-7CQkK%|ZLV;cW73+mLa^i{w$q^B?V`_*l$AMN3hyZn~8Xvim)5ijR>;OeKGZ$gG1?~g3f!TLtAPa=OE^@ofGM<*Q zog87GhztUvL;De|`&;wV1+0ojuih7EynI)4qB}QnDo-#7c1i{aeS!}^C=*+vpbsl9 z(|<`QC=oM=F%dH}|5wQGzwDp?Y}S@|X%J$NhFae-S1gwQ&!hnsrk{y@7C(R?ZRyyJ zVU+GO^>YeFtl3!`w>#5bD3}yfi2A@)yl3R2UIHg`n3*`d1TxEyv%952$!S{g1Oje< zzwFafcX!t%byXFYrjCf}&-e3#sRjo9k;@9`NEW&O1Oub%)PE%fX(s4ws;Ga#&z}LC z?+==s(bKfeq>_h#m-t!Zoc&?zB;``^{sBw%|WKH_k--y`PK7L zB^$O~19q&sW!?AR%&whMlUT)u=s23&XJ+o_XJ^qV zFgIVGJ#x_pD5mAzZF>+;Upzp%Y$7QwY>r*|D61a#-**><#I67nZqdupPpsK!&9zL5 z$4a`|`d6=>*cmYyZ5$S7@$EgO{_UUUM(cStG*uV1VWefJg4BdAvPu~#0V$11St&|+ zTs&?04ctQkzv0Ld#{*30tpkPI?A>g~CX>H{ z*gC^)@!=nY*HVF&e_3Et*~#L&$f_M@!;md^Oc$kwuD@vpfNg*iu~nWdS`SW_&V#=Gpv^0x|v?4H1j6;#lV51gL*2NiE$k2X7Yrq?BMF=CCl)RnML6{#IhXwK3Y{6#8pL{^`(!bw8?jm!S% z(z(7r(*nE|VqOS)b)KEW+LwcpzskJP z^ANrh3@h>Bh!JY`q+X-@QFh*5BVWE`x23B5Q&hE@EzP z9c89VIKIvQ>QWhB68Tq97N06H`NUH*+bK&b^^)EgPfcSd)Zajg@K#bKjp<0$eG%4@ zRRjW8N9fVaaXlF}D&wD+WMAA^CAbd(_;iso5 zZ6XjlJe-R=`3yOW%N7~%{kJf)OkG7NrMCIs&AQELiVaBpy1oiTR)shvuf%Xr{hM{C+r@7YYzudAN~_df+UR$WYJRZLYDSrOL1c$2#!I+I`0s~ z3H3bBq=V@mTZ%$SZGUcinlGIr6hO4YH+49AAXOc$=<`xc4sD`vMIyVOWN?ve40q}| z^Rx&Ev=L4g`W`trLzucWeq0t}w}OT?^eTeBz$l5?Pee(xn-(g`*7TAQG6A-yFX;#* zhlrXvTVaDW=M^I;CqAQ)ZOzi-mKa9myAFDoO8JfQ1ajqwlllzz&-!p|J2|m!+qP{xIk6_TZQDArZQCa{&x!Hn zeeeC<|E;fX)znna^xEC4_SBy0e%907?!g%43FW;#RN(9Af$(iSl_I)DrL4-0ZgHXi)Ez;Gwc{`d4y=lqs+2-6OHn(B}rS zv^xL^6xg+ZGjrdZa%a*)2JlN#@k=&MuR(V09h?^wEp8c^W^zbNw4)@+8h-nJ=-zv~ zPINA5qtB`F%}=UEH`iiHE%~-u%DGq5`i8eLBQ&E|Pm2UzF4(t*CPvK17jL`8egax~ z2og=8-+xm3R@YXUG%FIR~~J<3CA@s_Gg-{f9$jZuce|NF(cK6`%Im)s;v6Fw|n$E|37S z5%AYzKTI_R71An?IKpC)Yv%qQ9I~qTR{V|MLYM2Cm^rRCdrmWS$Jk;gGeO@sT$~P| z<4YmE&gZZEK2r=Ch9<=+4p>!cMkF~cDaNbUxq`IOFjD4O`lZjC6YpNJ5F(S37&^OD zLj5NrUIx9L_!$<>R9G5cz@MHEotzI4@uQ=C24<=dp|2e=1?ZPQ34gLUnf@Hn1}?jJ zEJon?lPRpzh7#LSWK6|vD94vL*mrYnqlsTZ9y)0P0x&PWACCmWc06n-HYh(~(`9AG zVDP|2m*LFF*duRjY2AC^^%9#?beud7EkHT~gL+q)TJ&U`-TTq4JKc&J7#D^+j7bNu673xt-pYP>yA=u?1<4v!tJ{7~p)SdCHXjFa~OIkmbTD1PtN z1*uHP`y5phvZQ5{$Lq`h`S?JB@xf&X9p!#emsU_4it*#12sMOJGz9qib zhnPLmhzeRfHkhTqjOJdx_LsR3#{VlXA3RYS8-Ap}14-V8@Nh=a)IxxXPtI~>~0 zy1z{Gd5-=Jg9J(N-?Iu8Wn^!&NRi$BL^gwa z|07?V+3q}dfheMVn=;o~9`{Y5ZC1aD9M-D*I1lIdM8z~7iaEvskdK!IO7kc5^%wLo)E;SI`$2Jc8x2|DYVeY#SXP}0bYA-`vl3gE$EQ6t?#>1>%h zS9ATDmb*E|ND4#Fv&UMwfr{r9i!&z8I7KnHNaWOKqf{i`x9ifnxBx&a`zhNr9GG)X z2Hme;|DH)65@jaEE34^}SUzy`te3~sG{JI(En>$!Pg!ZSgJtQI{IP8~=_g6*l zzv~77b;p8|NWquLO`X56m>n%L0nEGc=HuUQ7MKt4Qjb*%4-fk`U|)+K&cRe$$5ZcJ z4MJ*(C<{umftDcbSY-+|HM3W>q7P0f>tAHx4@$ryi|?$mlW~NP_|(WOgce~GTBVnq zJgHQ?#ng`@EcYYbDXVZUq5f5TJj~9e_m+UinV*rcgnjwz!Z2Z1d)0_VOA&btFv7}q z`y(d_AmD#R$`1x6A+tbzY!bks`!zw?m?r8%QI4v@gs(WV z5dIbw8RO3rY*v<3Jq=2y5zZ)hT-*KK!Ab#yUDIdqu-ADy-ESy=oXq%b7CzUD%VO5CtOt3`sKw(JQzK zio2qb*CjxR)SCU}rOMJC_r^#I7A`=8A$m&Q^(`$siWcXGpt=RgP6M-$=dWW{v5w3f zBk!kOH6k+(LX;tqXxWgyjq*E79Qq9q?_rV5y`Uf@P9E~AYYVglGbzzne^nx9#ll=n z#~}`C`nY9Uq-jGkaGy&=8y>F6_3{avEoVJCQ2sQ(Zg=MSU0tdmYdmoJfI@kUIGrD^ zoq&$o2q}n-g)#5OQ#U`pRC^4Ggu~auFQ5Q5)VAn=k&P&;Jbj3-BH$Vqk<1DRbb81q z<)OiBf|}vVg7)f9V`-|Ql!Si7(@@B>LqY>V>LjW;IGD2F3sv$sa)UkU@J$9zWu)pn zEVSe;OOO;k<`^(AJ~b6xB^Zh*1`$yv?t)^g)}=HFoK@3z1EVVU%KTDB0y~Z)z(S5; zK1bAPpbknV8_IyHP}jjj<1hPm-O9zBq%YKEI;!L_T|$L{KXE_ zU*Q+BT0hZvUc`G!@ktsQ{Sa~;cS~l+A!J1DI!P%PknbjaG$f?|>=XmgJ#7nVCl3g! z``_Qtz~0Mq&~0?`&OlOx$JYlS((I8eF+f!0=MN_ctPP<8`UyPg-(DElh4{nCa98Zc zQh^nEniW+S`UZTtMqP@HCma=E4 zS^q!SM=WUxc)-eOTHwH9AZ(1^NCh+AHz>jOPv!SN00r~6%=Z6)3pS3lc>pjLC^H*p z+MW*ZQR@u^@D4OE+doD~(|X&0;ak6ufS;j2*jZX%uz}M+L0Fm6m_mR_{sBYM4)B1b zL7CZE|M5b_O32Lq57_bFFbMnqf=4)*{-1Xa*0gVUBrDBU7g(zmKmfc4jm`Xj)upqr zv2*>~jj}{r%i)k6#qX)^G!5L;P&+|12l&y5d7QR!tRahYOe7JqBWoL@(4LEX_cc>3F13y=HR28y~p)A_%Ilw!kT;ue$%fl$V-1K5H52o7ny-M=! z`f8cWgPHe(!Rv#bj<$WLW}Lf~r9$&0?~&fZQz373p(gut4}WY<=XPc}-2JRWYehw% z`_)}rH5vRn^;2M* z9ALJ$g-u(y?QlRT`*SLQBY`aqnLGi{HlXfO?5qE3%bC1ss1Gc{I?sCW?T2g4kJQt_~k=*{C4Yz5;ms2j} zjS`yBedwz)DqKzUy6X&r{5ggg8tzCbGGGAmSawx`W*Q?o@CsZBZnMdEvoR%6AToUa?R;D$iH5Sobllo~SL=lSrJy;M|WS3WcBBy;al zDs4=lZsccyS;1w=xX;8wx>`}b3~%%Hc&LqQr`lQzNBJp46xLB!cA;1^gMBz>EOG2ckmsnhjOVS_*s1vf;TV=i#zvQ72HH0$~Ro!M=(JavI}v}>vu%+nvPl8xq!{*=OH`m z=#%b?)E|H*H(6wadgf`=2DEmi9EyRrr(Iu)U*5RqTIcGKpQh_xbHS(?zcXc5TUebO zDVnA!Uc7BRZSD#37+4a+{A?jEd#8p!6{);|3pSICo+u~+1-R5sl2iuy`Q2)^e;*(U z6U7*G?l*Xob`y$>kQ=gG2myrnQ^IbQ5}Dz*MssX%oN=2^2}zgP%S9x9=!`+Q8b47& z_6mxK2MkxRVt#`9et2y4R7OgmkmAyoUU=3A)df&7S&jM`43ak5Du!im7*|P@Es8>I zy7qAt)=U1(6pwq-A5l{Qz*&EO(;{S#FFj z4henFPtTt>Ayz1-(oZ2U2el7npe`sGsF97j-W1m z`r(dI)N+Nl!97rRy8w^`ykt_$Q>+~i+0uQ6=VExEL+56-+5Vy$QAM3{R)}{%%!{CV zyT4|7G#bc_b?8uu>MAZ)NkvXQ`L<0vx5;_$o|2}9-hpj8*UiY7*MAxcP9E~<$QKIe zM|+h&c_&D<6{kemR*!LS5_ud%+trN|`B-d0a%r@y6r4}1*a6r#ZD&F_uUh})HY`Tq z)-T*6)+@brzg=7z8YADu6#SXTK3A7YT55|WrJ7vWiv=Y;ON}p`n`HGw&+dboZ4h5Q zH>iGz98E#|z44PxH+(9qB}^L|rgro}p5(jkj$0Z}lb%#!qdKP9Ka;dk!aHYvhGkV( zS;Anevfy+>!WFG0mDyac!5K*T&nM{j;M){CW5uZA%&u)y)Z;%h@`QlH(QAV;@~-14sAvuDbu1J zp?p5B#5>yjIK^kIB5A;{LS&yZ@2dy;@biOq{NhlX0Y?CW(J$0mHR5Mze2MWl*^((Z)4lR5SnQ4?c|)QeY1LpxMG@4)60{mXGmmur#j z*zWi@qb&guIpK)N8&geu#e5S5d&QIxvV~Z#eBGDaOeWM}{r7UW%Gvbab$pSjgRB-Y zQgfG5)~|q&VUJDQ)xpA8+PSl3|3fA zfNBtbp`hwoP3cZd*@%wWT8f{?GBj-u1UN|<6-Le=8{bcih<8K(?JGmKoaWQlM>d&< zAJHuRi$Nv@TI2(;?*l_(lHMNzg3*`Y;lEekSrY+FzMkka`L|`yx@(WMd<5dIr=p)L z2zoJu#Oe~iNyv~=)%81q6`amTbw#6T#>6J?KxBmyz#N7TnHZTRb7axhE6rUN4)GMR z3n)(`!{1!6yA`R*>*6d>Fok(tdOW)I4$e88O%xDa57Zf>v9^g#LP=#bY6U!Wq*-5{ zlQjXkXF9^Cbi_5E4t)eixY*RJ{q&g}<);u~JW?S2XpT_uahAG1nLONQ>2dn6T;Ios zMIDg@kgVHD_zFjHsnL)=%=KXR7$m z+|mil9*DryfDrAtoVF7KZjF-?NkMo#q8=4)(7tCM^xE(|RTeRqa15)iuj|{;`l}vS zBzw_XIsOqWsT#H^urKlVb$G||dx6FXfMh_8`BbOtWXxDNh!_tyN%%xVgZ(J>jJ^Z- zXG!sLTBOcpCGhFT7EP&0hU@E$w3gI60 zqLDc&`p4UEA-^vg#AG-C(^%dFs^uJ&XADY~V5qEFuGSY7;Q51-g7rEgh?!wXt;ej{ zBvRmvA&(Qu(8rSnRaYR~qPwiL!u=`W-Xv(QRIE-E&Ee`SOHfvXanwITY0ChRoSj4R zn5k;fhlwNJ1Pw^yjWap1mxS01q^la~AL_68#U$E_;mgFTyG#lEmU|$4Y}B*6uP6R% z(oaS;nG^&4!1+WY?w79xH#JC_(UV!{$1FXjGz}|s>A#N05XTN&?_@MUOh=z_tc9ST zWc1~HX&S(K0vmFY3JWeKbj1otTZ^*&!1XA}Oj4vEp_;`M_Awo|rNzmq=P1megx!6)CyH`$>c=2Spfh zCUm(#0&ST*N&XP>7(h7_d8t6l1+e6X=2&XSaPo;4WJ&u+}?ZSPUxRi^FWf2YD`&0RD43{lb+p)$xPPH zE(xvfk|-AoAl6AexCXC0H;I&a$Ktm4`B4Uv6=V+96OPgWnZ%vUW`Vln8W%MEblO3cYAQ$Nz(g(+TIAm=Jsq$ zLmGRCQ&LR_PUM^j`tKsGu>TrcCcWlJfT5U(f;CNuUb+`s9|kTzGvHIy2qy0o*HlRY z-~F|)yhw2|SP8tf{<39`GGJj9d5o2h)2$P00$tXK$5R1-QjOi{z7i{#T8`E4k);r` zyqyrF6e}jidrPyDBbL>N*xhSyAGUcgT{y}d-!a;v;I@oT^Z&X2L1YPVG&4C`d$fVS?EKGQ%$^?UCUIunPY$7(%cV?q* z{@MVF52ge_qY)qxEW5`C<(10Ag=LxqLh`aBsD+>fOf-nz9qT1>WCb|MKT`V=mgEkf z$vS&>M0qA|Gks5?eEH)b7X@t_AASe4^*aKd!mkZvVWy5!ZgTYAQ0R;-k%A>&5yzLX z<;hhA5kz+}Sm6K>$T8yswbrI2J&p$JW*@ydQePJkH!#GFS;)u!Y^Lz!j_+mIendq< zU%T+9P4n>GsrHkde(h)!q=#i@CTA52P)#K>HPB&j6nrSA*5ENg`G+d#u?-yy`{P$C z$jD4@Bo(Kqm?t!D5b>w|$F#B09VJFN*?^F1e0>-(xL7S&l`N(bQeyW3t2P9UzZ@H4 z!rLn0_3bZ$j1RPpuxX|pZilfO4`x6#{O45^M( zS*PNDa-)^Y;j$^`lAIopPZ~SKMm9mp_IO#>djc%g{XNV+yJ5fWT$K!p0SqTb+#U52 zB40OIlv$401bKXn#6pggtx2o6M#7dji!iTZ3_GGLW+8c2@32IiYlBohXB-X@wT?Ij zq(X%k(67Rtge^#YL_CePJqCC6i#1_p4U>CZm-M@~0(%2{&_3h#RThMp@vtbGFaao) z+9-k}f!J?hQk}!5HKSv?qmQoP5h($0(5{QE=L6XUbY<|S%uy_;+ zNR29*j8}tw0c1-0Fyk+Am<`UDAy(m<*e0IgL@8yO%*Cn} z5!uLU5)sRccndRl2R#a^T;vZur}5KJ3d8f_GDF(uP(@DF^^oO+1Yx}h8^!X7ZX~Q@ z$07lAUXWNe!z55oY9{Frh-*E1yuLSMWVsB6lr*c8c8{TF7G-p>z$ghz6DswDCV zE}belwQjz*#XS4+YU3j+7Ip}3cQtk=4AS{wjE4pTE|j?QN_+j92P@>Wp6RGAz(u4v zdIkU6!RYl_75z=5WN0AEU9rCFJfeW;e#E(}RQY$;ka>KT%%8mGlpiK+m56gL9)VPD z&ORVOUbaCa3-DNc31B&~cF)C-{zBoHa(upOF7rPOKcA&Y^)U%LEoGut(=GxgNqBUG`WA`)js97+St52TTOdRNL6}yrcMLbx?=v*n1co??KPc z+srU~A1}03CtJ{9OMBfL@Q0p+#7q{u7c`1gUJqvF&zz;ZI)uPO%r`;qu@Yx0MTp^G z7(}ZJ)F=l;rDv}{KchPc4dQ*Dm5pbOqZ79Ad&Dm?IrsdzL8%;~+D;D$-&_;qhAI#B zi$cW;sB2f5#Z8r9cTcHc_q#B7=3h#umnSh879y+GKq@tipB2X_fGz_$&A1WWpWOAh zrw>y7*{OQS3g<7CmiibEaw!}~H$uway%(LiiV0NFS3C`hrx2KytuR1kBiwBP z&+20x*&|9;B!-h^ua-}&(m%X3MMf}KawdZ7vN~x@#6(N_(HE~=ndIc1=jdlp02*ee zt)$gWCbe!3HPvKb0Cej?GeiGF1lDGXtd9I1B8%MxlcRZp~`CUZXU~` zt@L`sap8bJFJ$y==XoG=F zX@78ks+Mtp|60ZTjpcL265Q|97hmj8%0GmADTsh002tm+on>9YaO+D0dvGR1dC(## zo0BG$^*4I!7r|Yy8v(zN?GQ63sPHGnfwYFdORZeFF0X!l4N!D@%05y&d4Io(U`r0s z5}9J00h6VKO!kza@5^VQgwsQz~D{al8G>*gYhgZ1$wA(``T;vV`2t( z|0W_I*P7}$rID~<^D9Zs4^7DKnQh?pfAQ0bCenR1`W#Lsa9~5Rkk~3%qvl^yLFc+= z;=mnYhu|^CD?SrxG(#^WMzs1S8^fmq=il@yl4zf_?G-bjW0+T>OJ~5Jn-4h(L<2~E zEH7b+H%5arFaT8x+@pGu;2G0PO0*q7JsE}0T4>} zYF)O4R$CR91q-W5ZN8;hU)6Yq3=smG`Jyx(qsiFA<$KEMhtXK`Gfw|%#MtUZ z=_jXp9dzA|(bVPN`Yl;Z6G$Sy0Ro|@E~|4yHK(1w6{Xn>p@-(%qfM`al?nhkYb4HM zb=O5Ym?^Qm;Xv0h1j)y>zta@U2)xYRe1xr6rpi~ZLdVh?1d&0t!l{G9EVLM)L=$F2 z6U(0M)SWU)hWh>7Ak$IeE^--l$7&67$yKlo-~6YNb#>3qzD(C`w_j`36R?ViKv8jK zlOdiFE^4!rR{BOt-wq$hDgbZyUE^ijrN{y?5ikbKN6n|_Y#Er*%rs?Lj7EFiAs&SI zkrMk2&q}<=3WKW`dBb1f7W)==n0LBeNMAJiksABIo~xd-p2gm&Kj}Y(T+Y$Omz~6e zuIddZ|VM>IL6E3YOhp+t9&y(;rGT(^TEiIy?tqZ+BDQUDKj9RSAuJ}*Wh zHrXxfnXS&k3k%mQybZ%HsPulkNnPBj^N_vD-m+In=VN0$endi|m*^m*_+JI4DGHUm z(SV&jAuR8=Pk+L($IIOwf+5qrFFQZNE!w=pl|7p;N3dHeG( zGzRIr%It$XgH=1N03Ll>#kJJ^7Zhuu?7XN46y`uM;-P$IC%Vxghd$0;pdeWeJab|P zAmarzO08oRa~wl3tIQO1EG)Rz;ADB~=n^D0KfX7C?|V902blGz#PZw}evqy5q1tgc zuU$KzTsG$do}S>s!Zrnt5fX098%rZ4PZ6}7*DzQlMv}x(4mw5XqViI&lFA)E@=~gX zaKqpu@5Pj(J4atS3e$FpDy3vZS9Cm?i%H*)L#lvnrt%t+R?S{1Hi8|wQu#m%6u!yW z`+>p^Ozd0*D^#^tKxI{a{hHsw!>C3>R+DN-W^AdSOMPQN2M=OAoI|hmMw;8Wv98 z=eUzsHKtN8#IU*3XDvj}Sqq1~6Y@bz+jj>}k$**-M51G_^4zgaRqF{Tq^lx4+v{zy zZB1$~!`Z9=ei&c$xwUOzZr3JpEVE4Q8XxANm2;H z!G0;D1CWi5V2g%zFKnbYmt(5id|z(%(FhE9F#r3LUr(@=*92z~#vqV)KYC!$&gco3 zX&Aq0wh-GCpWEd`UNJ7unaV((sZ7dCHdbXVI{y0hhsU)%7+`$U{Vxg}dEB@mh94(Z z9bAsvmNLYw00+!K_nE!O@db~u9mWsl{Kke5Ie;|Jk1W#kwT806-RLvOav>jj-*TER zvlGN0IOzCCno_L2$yaE3-bd12b#bj1LqLm5ZF_trb_hSHO(w5gao*T`UZrGbCtpgb zjD3gkrefmr4iNauN86M7=OD^zI=in|5UU7DCw7#uLFZZLmAxl<&W#xo!%(G7l3>t2 zuK=z&&^v&bD`o97Kt_=JY>}7i=w|Xvz?UqaYQn+^U3 zejLN?+_|5s^)1uSIx~U`hrx$PJ*^FGfaz@2-@Hc_ZO`S|)5I!2@v7LdjAV3_Q2e7|qXj*O21+)%Ub-@9OKJKvZcl%i+ zYLPl5&2%2=wV41Vx-BL0=kvb(J)g9CykyLac@@Kty(@6qXQ4JCTG;EF9l&W<$GHkL z05Z4IyKTGHE48n#I}OAuZnK$XZ!D&tevSzK591%*{_%+#YL}+|iV40`Zlsx#CBP1t zzYrD*usy=Hf8w9d;F_~yav!1~1zWgICTuwZM2y9J*Hc(SxDf{_9($>MdAw^#u<3TI zA00Va>+?C#@MRsX-bwt^gN%VGdvu`OV-p8Q< z&I6DXy!knBt`s8M3Lvxq=0Y=p?8?Tbd3ve^GLg6;0anms@DmW>Iqz7%)Qif z%R7YF2izZ0!kClrq7pu|!yRbTg%EFzP4v6r({n_ZIUp@s0oCOqWuSlXOGRbIOB)?% z)&;M(&ekd#$EqImfLkqWVL@o{RiVisT`^og>|5Kz+H(U&ec8j>6_Zf3H2_U5okM=O zlIQI35!)qH?VGq{9%%uj4lMDfdYOsc)8Np%$CPKAQ~8WuWWu38Tt@35g_`ly(AWbE zFJ|E`9T|iKM(qi04f;QS*hlrPe&Tf7W=aL%!4st@SUb8b15S5kit8q)!$2;#-Q;4K z^!jdxuLRA69-oDc5WJN>=>WFy@4djNSn5mUJdM!R0$W}JlSxey&AcTX*@2F$NK$nq z&QmH#XPDW6l1V1S&OxrsY8uGnwOSM^zTDtW%zc+X43#yX91p;%y|cB}NaiqWVzE8g zldp!m1H{7!lN@J*OW1-R1yIF3*-ZQWF1`cB2&-|Dt1e&SWH6`}2Y`_7h=-4PBY*IH zL$tjXjllYaoY7@ifFtefI!W-I(^PtuKN)StmUiC{{;o3#BM(eSSwa`llHZN+yIB?@ zCR{)T*sNC(=s;c_*Gu$C0}0H=-+`Az;7EY}ci?5yAz_goFbE}Cv1Fgu)K#Xsp<+Wx z!W=!A`ffx|dLzD00+=30b7vm@dt$_kNPd7mxYn=JPOyvSWxueVgYIC9`&pY7&FD_H z8QG~Dxyn08LEYc5Ycb-k-nL8mez(kd&dhJb(bX7gBpR&f8~T$t(RkWf8l4TBmy;aV zx?nQ0%%B`{F1_^_%gY~CA2c~sCvf0{ulN^SZ9=jx_^MQ944`cuvebp(;=%}*;N$bn zh%^=)1TOnmL-aS>B%_$TY&~{^$&aIsn9IAraMpWp~0BYj4$<>Wl6{N1P9h7DYqLraBi+bSUrl*oh%p^ z=OIvjZStob)qK>SvZ9CG9O$P*Iqc}!?C6*5=3f~TX0~e|HUZr zGx~>7!pzj_VGW!Png+rPj0X120b%@??h&IU^K+9ErRPNBv{6S@O%#>%S$Y}Y*dkGi z+^x|>L76QV*AgaC^S8+0C}-}+!<#Dyr3y^W_CkOls3q&`oh#@)#%4&X$HV3O@%p-r z0fRC}%)6yilUA_NB{e!N3WLi5qwZEUW~+STyYs_!h6QHKZk~NE6>C?BcCc5#UqJ2S zpqM`2+XHs0xc0+d@wmtViVUZ>j#!k&bQ9o!1EHqRp6&OjGDJMIvUq06ke~jcS1t;$ z0p?}_+oW3jGm<(NLuwTxR8Cas4xHZn60>v0U%R`QjR`Au;R|a`*Y64T`i@;FALge|A~*CJ+BPR8bFbCgnfe<5y1}*a ziGZQo<}vU_io8O7OwW}p`3^O8jLqc)phiH>vW$Rhco_n&J));orxu7Eu#cx^Js)KuSfF9bGVe)nm!o7D*iE9g#J7lp0bSGT@YUcT&zl zGRV)6?b@b{`n-_(bH-~PJ(L-{&&iv}<*o5FWDF$8G`~I`4Wgzxgmk3fZ~Lja8JXh5 zvJoxHp-9#d;w|hImsWSGxQ*dA3EKP7ZTZs4&|0VxM>?R9DR=Rd9uzctzzdp=L9T`V zyvF7A*f4FprGAoH8CvuQI3OQ?21%<(C9PR*R}~f3k|oy?b^i366vV{*c|bmcddvNQ z5}#rShghQF0kM-QM-Ass%pn1z)k}JfVXk|_<)c(tql31noNWivc8AT`qi{sm% z3U_NQ#%rz$+mxA|bUl_EVL@Gf(XEuvAA^=IPYwBiG6a0+W^VQatST*{ETKmZ#v2lbH)nF-iW?N9L4ux(etC<8GamAK__+v|)L4EoQ4(QI@+a_j2XP2n z_GB%ahICvO$#J#y9h@~noU#?61DsQ(1P<2Av_jX+hDpmwrVJ#qCeT}G(twN)Z9=qK~#_S}0)Cxy-yjH1X() z33F_5XVU_yz7`XFo!t;1%_c=@l~7l%33eAFub8iI1pxGh%o!J4>&%AWk?T=mAiG9$ zpL%ko4!j@7_e6Ib)K_*2>OXgbH$5Kn|;@Tc8 zS4%%O#tGML#iOMdoig*lP+xUbEN3x>M^6DP{p z8P(qD%K)gCz*yXenlg|;?t(>l_vlX6K8qC$J4?sjB;8_MC;x1=fPkQf37^Te0VgAI z8svoeWSPEoIDbczw(kjOhp%wBw?02W0z~OnO-_@WArlo}ObNSm>tOdsB9QsTCL-3X znRpZrxpbX@#hJ4d11&|FSns9AW9`{eFD)@s#R0?)8VcfpZM@9R!US0MS?IselGjpA zK!b*gOZRJ%dTNLyq~`JycT)P(gxhJDc{&}cX$F8d|I(({H}PMNg5ePQ`@Z%eZ?sr( z9V7jD@)sp>p}%gAKk1U9c`dwv=*-MPUwo_)8mAv-C=jOD<)K29Y;E!km3}@^trVS- zq5vRW)CCdj>fR34*Q*G3^~VBP>a{oZ~@-Y~TF z^Xp5h33KdN791?a9PN9)4eL}hg&(wW%KYj+x<CqdnH|4^M~iZIa_&CH-=1VzOOPp)#bQl-B&ozEPG zi0V4C>AU0OaUx;RI-UC&)W;98RmmWc);mmRU8O$k5YBG?RdhzGPY zv9I}~1$v~@ghK`%s{&7$HT0BC(vTn^>$hMO!9WO2`P$$Wi#B7;l!_j0$-&ZR+R?F@ zPG~|cxa#xDceQ|^tCAP1-c43|1qlw((mXpjHcz$e8dr5tkxn`Nh)MzYynPMA@}-pW z_nbeQM|-k7IDBZK`#B!n`<_XQzW{U%52{i2Ps{>Ul{R2KJALhQ1TxcQfFn@Xmi60b zHqFAjn&E&i;QZTs_40v?tZ6=Gq3q9#LQ#BTURdi8=EW@#R&e&$)qBz>dJ5kVwc zzO$=MEBB}xB0;0a3r)uz&Bw15m#>+Bp|hmmq0eLRKFr!C<4-oUykEjcT#_$7`n5e7 zAlVsCy<`wIUJ!;0aQ`@IudKYk3PP*jV`6()0Im7HF_wjEwhL7%pdruT(ESB=dKE?f zT}`~RS(HoiN1X57|7m=2-44Jy`6nhp{r12Pq5&DH7O z@p9gViy*v@ z@mN24Sqp6$T=0(HHSL0wS&yRU4@_+q1;-QTuQ|iv7cgcmzDEZXV)ualgt z*}Rj~nw-!GFG|A}eN6Rf>bW+5W-F$NP}Q3#_y^zbCoO*3TfbG#LH!P63S#wy!5hD< zFj(|<6uuTS>>2qeKBEL|2Dhp9{$@JzAzq!-UjLjW`q8rv&2SaWVW&=X=`HhVI-VBd zn@i1u0pn3j7blK>p&zZ7@c@2P*@d*}+ zzEr>iPO3Gc@V}h&|DuzpebdQdTWv#tub{rE?~;k^w3lQUsavB zWJWZ<6OBK}C}+v~{`UKW9UbCLk^$hvcO@yqg-nQKqlZ*%plT3hW%J;t9vq2Np){=$S^{aFZKjx;(~;r*oF0? zxbcaC!kp!yr`}*z0hZUi>V+%-IwzrqH%7F*nrQwE2c{BO^9~T#zbGm>6&z)?`Y@8Q zM6=EY=XR?zl~T&mo>O&+th1gzH8=?~CfntI%+(d3=@0gp)SR7avs4nbCpDuQV5GTT zRS1HNWf|j6`h|TSuBkon-#stSJD`=i>vsh7_}RLdCj#{ZQxGC~C_ga)OO$${9U1Rm zX9E}6C&zI%VYKt@7irWkYJgX)1a@uSrRdMW&?lSY% zQ(B?Z2v-nzQzQdZ-g>k_BBJ^}2+E$CMu_;kziX!Es>A0!dY8xul*AW0P-}3-Zd}Xs z`d~(EbUszQGWL1i;4kGmSdXgHAv0(4=ZW{#|jKWv7EW(1q7&_@ylx( zs`Z51YmZS!1g;^C*Qf`Doxi)>4_I}881z?-Zx?>HN(Bo%o3{P}2n8?1htP_8*h$P9 zH;d6&+q3K7*0IMknleNSPZ1x*Zps@%c@k~uosB53|Hiu_rldOPoSohl39oWFf!z0Q z?V!)L|D#_2Gyzuu{TfwecEfla8gicxd3ONv>z$!8`>)l?kNAQK@7`o`)|Nd>vW|KB z(K{p-$@_a};FQEgoMiTEy#?u9UKeS~SZ|YnEQY6Z@Gp2J#xTLO1ZiMY5N5XjJPDp4 ztn6vtnZRuPtepQ-`@q7+_}?-+AHcb=k2E&1M9VAx}fJ2S)g(y$0F8K3czhi2kEyO8dSR zt+h%M1c&h3{J%H+U-RD(l1 zJ3*v<)`0B%vy<<_7V3XLe5<+s|7xbRcR^@MM0S?{Apx;5v2p&3OI&?7(cNTE{H!(T zcgWh(Lk5HPKUBA_GUj ztQq1X;w7$J%$7+M7fT`RS?IEo^7eR@eT(i@Z_HQn2|7oZybL%quCOTyTLC02D6&C94T;!XlQi-2AW= zv4RB9C03eAtyH6-B~p(};AL7FC~U4I>lGu_By1#B5d#xW`tbai<#d{=YO&BOJVQ$r zW()s}BM}V{z~m#>==e7Pa9eEJ335=(EZqNMwqQ^;rgdB`Hp3LQLUm$-3YxtT-3~HD zHiGexZbp>{u+AXKo2bOOhmFXD=Es2T^SVXC+-=nREl3qzqz7_6L7UGx%~YScQPRgc zIX{RE4>7@nsF7I48MkG)kG-aJLZ(3mnLOYajeC#^U6BT=ZzaW7%SAraq3n#MacbCa zime{ZRSlt4h(<8llibt=Amc>_Z6QUS0)5W-je$*b&&?ed z1B*g)RZEzsqXGlTI`Uw#gQ}Fps!okn#=a*6)MS|8sgI_hu{x?}S{Q1<1!pT36)R%h z6ET8b*syq`AEnc*)(mN5?4=V6!BkKSDpdR^IgG*G8U)I1<)5g=fWt%U8x4r3UD3D) zPDB+02ql)~1KWTF{^bjwj7OLiB!*TNSaQNw-{n5nGs5}@4pz?+Iv(W;x&V`*ECF#& zQu&D`02{RI7$1bPAe$)^i|R?3;|c^~?$}O|3PelQeAW>E6Yml^i%C6@j2pIF*el(XFeMfs^?;MTN0&_^mGU?mv_#BdK31b5+gMnE~Uq;EMYFs$zk z{2(V>pkyK0*deD;cmW~w#$hokSe^gE0SgSj3p&6z*pQ4o1(MU2hALcP04cOD$p8(Y z9voN%0VNr?I4C-&UXH#1szx;lnvydMThNB`o9hDJ40V=P~ov0_D#Y%r>8pufKA>$$Pu` zlh46}XeEfC#KGFaNf?>N(_a4{Ro?*HS@dmPPK}w`#<=ez5MtHP}ohTLIkTU@$q?-5(aLN zm|2_%UV$bN-Xn2RN=Nr+saA$3hbXI@vYQqM=Q+#j<8!eGH#HY0<82$lmEY2iO?dPi<$$>W^wK5tW z%_WM!IxjvcT4C$NP6)3DS8Uz(K(1_icrvp43cKH;s|{@Q|dSdtWn@)Tg) zpw{FCrDUMR#{s=%G5w^qU`4(BM5tg{y;QCcMh`a#RF1u z^$zK#^r0kKsT8Rxdw>%lcF9(%0O-fGW(QRI=0 zoJYG)cP+^%2FY)h@|A*Q`HG0<0iZN<&h8%TUa(!Bue7tIv$(TVYmRS@FIcj*yUexX zC~fJ5W_uaD+_VSn5h0z?;MXp?EdKyhv5*8h&`oo^f(Hlq~jRPnR#yPaZ*15D-`p94}~5 zR$vB|BBSVEv^G*f2~5|5E-X-EyFh`q#A`V%Ef5S(eoq#q6%fil#@*{XvEzauh=it` zR^e5*i1FRkvZB`U9uPURn-D;&=8^s&F#@X+f`4-{ZM}T> zi$c2$ZM`BMFke#cTFO~3*S=C}b}!kkL^RilF3))^S4p9A!LFpxs8w0QgKkm#?Jo@I zJY`*nNz`?>cFD$RIS^w--I);tgKeL*PaYIYEa~(43+g`$XVRd^=1hy$qf(+$&sGSC zabf%Pe$L=qP*8$L=lZ*h&f2!hnxm4|eit6$n_^Kz?U0}oWcB;!+w_qJj4 z>Lm1S$K|%;!a%wwbdMNAc91I_1=ODVo@`wYzxHxbj1&*2UMuah2}m0>f$jrT)j&u2Rb3R;AGi^aP-mqr?`9 z_H?Eq*SBOGCp<<#{x-1FgK2zcaW~E$?TS-ubUta3m+@Q6UoQm+t$*i)fJgE17u)@& z=H^`3-+U15fr*E2kh(UoaIALSWM1*0LC_i~B$$XzVDxyk9@3oducmfZh192pK$*HO z6@IpTRaHC)nPJPM(Otz9VgTqFsGo(r2FZBBeH)61VhoNDbN<#P!Fb>X;b?9Hm&qG@ zSk2T(%4GGzsoB`t(Nad}z-{2EzI~YG7UA0{GIVGClX;=vKCH(~sH|Fyox<3!-e;6- zinS9XTGkpi9Zdkeg*c^*ymQ8YVPJLnQj&s$VZdF^FW17n`>%BtBn&V;-T-!gK_<&9 zvh(+j2IDaqEBa-h2n3}!W5MXSlxt;e`}{KD=&U29VV)&(VG&qA`fQ4X+>5uu6ct6M%XcP_{$yk#1ah}b+i34 zv;E9uXIgffj!1B=&>raZ&djQF`v>IxAcD818d;O}g83?+!7!CRl$6a6DDj~8u`0rM zNxt9A_E)N#M%=xp@J?rMKblcjk#ha?8#eu@`631ONF}8+TDMJa(l)~mCcV!JY<~`1*Je723+Am`T$OY8qs=%bFSrxg-$6v!PtOaGQ$74sv zD$HS=bM0sv_}f)%UrBLZs)4NN=pS+~m( z6@{hFk;1SS3*dHs`!L*oMCF1oZrmP$E|fby)IQv6ClNX0nWCQJq1WNVMp#1bWI^6s zbPXlBMDq5aC8IB{%SRd+UTnJ%vDZC+LYy7f=EDVKH6ck1v6F0U&niRGHm`KuvxVsI z80$RIKjeTE(=0#ft;a{W&cj2t0I(-I-e9m@EC{)AF_Mt9Ilp(xC4~U%G&IY{AZIv# z@N$D6y|MIDK6SC-iZmj>XVmGdrrOlO?{Xz1$5gqi^txc{b}>AEhdRE|FALhKon;`N zyGaAz0*Vfn`4bmoNt~GXLP)R{cKNlvuIsHKh8m&&1>@G-E67WsJu4?_!}e_KFtKwZn8Z2s&X1g^HSO)w9} zpKs!QIb=Jk)$F(C@Zo(Yofm|K2O9FQtS|wc_B#n4P5m$M3_7sfeld(fgbX$?X@kd6 zMp`qbe_&E-+_RXhB1k@(mCyq{{o(9y?r=|n5ai%=2>wwAebvyEN*oaXPZV;_<}-c9 zqdG3+BdjJ(;#NtmO12m~?JajQhKa6GKGU-JZoNy3isAnX;C~X%(Ts^2s=}E6Y!;hu zeg9|i1`r3$o3{4-tDcPKSAmUNVPDWzNI-S~f=+HN)1AhTWr z{c2m)u~O=RZ5;}J2uod}_yuW-M?5?uMWg<*bXkB%D^Vv{Rk3tb4;mO}2!z+^|FlB1 zn=xE&u!ajct-smt?gDDAHRfw|$4-$w=Z5#GkF2?c+1|G^)F0{!QXNotrk z?spFg#lM$qRP+EPQvIeZE}w9L?cLRgmcW8`=%wvisP}IRyh(-HTDi-R9UN+5HJ9|J z%0I}JH0rjF;+=Y*e?$Wv-`Ouss~#&ZX~vFm4e3%|Q&P^Z{d;~i6m0sJdxL_}3Wg_c zp?FQGfH;Z+r?}4GT1t>|epduZd8)mPzZ~ z4@9d{axqeN51al95Zy3$)Mc|c;^?B-yrBb+u;(zwk2jUp1fz`6^(|Fq)|FT)9Nk z!pQm&kHJ4^N90v^1fz!z=`z+5_^1Z7;AxT9KN`~~AqZkxGs6aXJ#1(0>yVL2d&K(R z)0sc}P?S7>Pf_diUemm9ukO}&T*K*TnNRiUo9BpG-_`+5t}v%E99G_hbnB!@D~o@F z^qVuI(S5=seX+{;cm9CKnHtpOzkf-QqJLhdZHI97a2JcGFy{L2ZfE00KOZIco9CJy z2Xe(wR_#~4!|kd4Txz}RseemQ?E6!8{Wi?`Sd;?jn>5!}A8@W<{D;42sh#JK1HX|t z_0Y6|AL;`ojeTS>*O=ILV%*YvXy2YWc&Z|JPh|Sp+e9w{M9tJvbs0# z!%Pbqd&-x$mlONI52z3Ru%Y#cjoEHvkX#x;NYGt(vy1MK3F~y1Q@oLk=qE4Dc-8zO zuq`G#s))9xygS<>53igMa>C*Fi$q=mZ?ht+9>@glxEyasu)mo}8C$dPRgh?>T36{I z)2qm51s-N6&h1!z^qUlU_l|*WPc87Fl=@|oVciTGdE)Y3>fyxGB5R`oy_loSVpY#mzwSv8Vw=pKtCK|vV_O@n77)21K@07|x5{!T7)PGF) zb}S8qB)Opyo@i9F&HR+!wr_Haxk=E>x#ez3WhG2x{I0W`dYx1OgEV5X6;7n#nFH&p zvK+HbmHH+NUx|~gM}mhQp@!a}X^)jh9n0|T-`$UA4ZpCxRPr1O1CVd!ygvVl2s}+X zdHsPI`tHjJ`8pESS8%qDSWM-v|&odKOr_HCy9 zaoH{N7R2%>X&7^<(y{Pb#?C@SR$hQySQlQ4q1=sgFfrWQPX`~R5X-c;4Q*|AYhz<~ ztIN!MBDj{l1sY<)x$m}S4`LC&)55H=1{jClWhh~6fuHhr0pM{wSzv^`V3AqWkx|s_ z>|TkfYI}SE6V@_b_(N#AkkY#l)Lgw?mPuhX6ZE{+LFw(UdKn;Y2<0*r@XvV~L42SI zGB3T$I*1kai!@~Os&6_Wi>hxH7=e))?9%#Sr(jjkYHMq25QMj;rlvYE6+68V(3B2E z3)6+LV~H~LrPX^oLmRK_G6DRAnCeQa+(w5P!v37Z5}>h9vsiEEw`Sau0rOY()v=7E zCuGE9#J@@+$jZ%wpRxw;{rIW)KI;W}wP+2-txcI$NQ`P=RLxDL$d$pIJdv~G%M=woYAnBVo zOuh%yt;3x&h$;_rNPMtrXuc2amu7G7ZZM}N7>=&X%@B}C5;r#(UQj((7rZ%)x6TLJ zy9{E(I~rdGAN~#!N_ML3^ z4C!hYeGAh12L1}n+4%|h4JHV&=H&&7$c6KHQ-FEo>-fj0X5=-Lxz9Us%tzt&(GQ-` ziRp)NwOKuvPLz!BrRq({Ud|HS@^p|P17y?!+P(n|-fYTzRQ?8ShM;q2P4V6R0C3-c zJ7Ox>xkH9f(F4~eK1=VE+g$b{yo$FS+3&IZ8{4TTX}XF>E_iG-1^4a4itG|(^^JAREg3kD*2HO!ACy{6vhRr zaIL;=Fl&MffWaceJZszX$p$O&gKZ7oHbt;$U-Bs$& z@xmS!e;`zdcCUHI<_OA`wvgc9xb1PPmt1vdj+S?=*9vv^h^k-(FQzu^-i)@R}D z9-i0!#BOiiBEO;uShfbuIt2N4SvQ(WaW*S4TG}kv_Yn66^yh{Sp~zyzJ(*jJNAm1E zDTJ>_qN{})<_>Blz7aSRD}A8UX&~$v^zH(i0mB_?enSb{7UCiUYG&muBZ!`XC;Cn? zU-#zVFr?P6mG|^oOdKxOqMrs7B|Cis|33U`Au_%Z$$z92$n?*w{gdN(A#z?Y0XGqQ zc<+G;m!?}G6i_Q^NYB4Sp~ z$mE<~BNt^)vazc6Wu7(mClm`#sirtUDmmzHeob(15Y~Yv!~9jr$YA%Kt*t1x%D(%T z;pg>fbuu)**0|PiHdo$Zl>Fj2u5LjXk1c8K|lPH|VC` zmkV+wn8nNXDgpC2hkB*EayJ$Yqo(J_#+CU&F!f^UoBjtc{DR>vFzyKo_sxMGCTrqH zb6#spf|h2H2lizyHef1pEJ**H9)BKC8Vx5^60RQ2uyll^cZG&y`D^Csbf~H3{9*Am zw)ojpyD8GVA^wS?G$usvxy7^tbh+BYuoS55n2}%8CQ+qTQYMOo-g++FUgfLF67izz zxnM2jqw&c&l_Y)|rw9Ha^_5GZ&CBCMvkhc!jn})HiYc}Tn7xwRLkp?4p{$SXWG%LH zo(uGg>XxREEBxc&H^i^}lG)l$LH@ zinng|Cj1)_!Rl^5>{qEA;8rqIh=i#itASfmjdGiUX9wxPLyr(?#QJdR9E3(|Tq#%t zA&@LC_y(=RMP1p!Am!j8vg^Dt!mD0T$#2>|{+L{8nAE1Vbj_0Rj)D*eDQ}E-9q|*9 zM+YOWdLDJfK6_(5l4YI3HBHStW`Z%rf{YWqnleMesVTNyW_P^~!2jx{R6GDeAE#O^ zqs4C0EIq?VQg3aXAW4#|?W0x+rOd0xsE(RiD@Ep4qNZ*yisMh-lq3^35f@q&Je0C5 z#C(7zJI=19jU~uZkLKc~7{pB6a{j7ppvmi1L?uJ;6sJuQbsr3CQliU76)nv1`ecuO zqPC%R4kw8dbzWKx{2+vS3XGc}GRmX~8$}USBp=)r@n2*O|C;WICViaVWt(CYi$lQx zt@a&lQ#}~ng|{*BvVl?m`1Ws*JSaY3fK*VXO~{mtkv5}IcStK>hLZme9uQ_hof@o4 z1`>ZJt!S%-+qCyw8DmTgDr3D@@lZH8hlne8arKj(3o7vjo7RRqF2xI)IHQ zk4|%K4Zgd#lQz2?;2RmW8Ce8ppX)w60T_bC>`DP zJ~9iZK4C%LC#vbAJ>z@w^#n=LcdYRHmR>fcVOx+;5F=2;od@Vsz3wqroqxA;v%882 zHZzoC;#68F`_Tv!uem{SABkL0Q%-?={1=fylGE`6;0%Zt3@%Ri6xX^_(Eg6W2ASvh zd}R7ah3T6%LSXe$T~;)BlY0Z;uJ#cnu88JS&J7FzF;|Lw)Z=P4%Rx>;^dqJ+@<^If z)OCqZ$?yHo_sfazX)!-SEd7KHs`U&U<*3G(aA^!~@C0hIBO~_wY-%M*NbQSGmcYcC ziwR?a2)(X~_N>IrpBeQ}xA8QSUSnUmC7_mj6z;w^U<2&O&5eBK3NK5OG%m(MG+J0x zr(G5ByjD;%oyaxLBbX+iN^{-lw&G&$`P}d>?0cMP2YHCn#gI#mdu?|*v(18iNKHWj z)F%k8-YKYmJDE7z!2^*36!e6R8&HP&g%$x0f$<<@SJtZ9?B>u9^ zV-rDdAfk}sv^G-RHdcZ#7r#9G`+yHs z^ir7?XuxLR{=sv-?d=ehIANw3;u_>uwTE}p0j;gNKK$`wF~cxC;Fqq|KXs*miou?_r9BEqjSe+ z1KMA*N^GCCf+oCR`>Lc8!&0WfChvaEpi5{7oR+Nkq%EZU3&X}@Nz^?nSm=v=yQUzk z5zkXLBw^h9$sD9wYg_+ed<9wD@0@s-@`wIztTT-0Vd)9>G9?XmLL$aio~7U{;GXR; z_3d-C$wRDMVLd12ujesMZc0#&ymN%inuvL4%+DZ25JsuU*g}Y0ZUn?HPWk$GlTt>c z9|?UUqS!9GVSH@5;JEqYDg!Es%XaKsk4UWN#N)q7RV#bK| zC%!vFBjI~PC+&_&`ksWhvWjLl1Bk=}#AT)Y}AiJf(4L&beDYLnT!v>!g+*()z0(;Y2?UPOT1bXzAl;1qQFr3x7X2 zqW4Q3UeZ`$9c$``EA2||?t`U_x(w=HiQS2OPM7ml{UofHc{GyDP*lA&rty$?avX*p zpH306$ClT&AEKWw8{GS4ep|moQ1+sY+LkkLkvD_BF|%kjD$t0yhcE_`r*pK6rip_= zKPh(SJsWGz9mmG6ZT}*c56C8%80 z(WvB{?uMJfHqX|Cl@D7}?Y@dJ;XG2$zDxq(!SZ}>I@kF-FJ4A+)FpZO5l1T>Ma)aW ztyXz9sFzApEq@Gu^^~Sd9m-RV)P@77TQb5Dt`(QX`cQF5KnBS)qRCB{Gt>LeN5=Y^8N^{(Gn$G-G%a z;9mHpFljTkvH}#%n(HdoDE8_*o)E?pizy?;$!X&b3zsSs6Pe}Kt44kQJ1V2*6#Vxq z%fa&~(hGP`Dpy%|93T)WIHO$JW~hBrqN}vd%(LZxkZ)34oIl?fjuU|JbSt?aGYqBu zwLkw)nsWh5b{OHu#)pqIhEfFo@t0)r-GGK~#O)Q*P`^bBq{N!Kf1kg{4O+H3clNN3 zGwr*W?nNr4f$3#D3@y%|YiT9nD;Sv5ctxo>aOW4?kZj_$I|2pnN0;Wzgi*Z6M8Z2P zElA~G3{yybD{}x3CcR)T)jsrfPU!0f%s-Of=owyx_slJ*4JSK96H748NBuv~Bu7XK zgiCsRng+?^!KH@u?|c@M8W(E}_%ed%jpo0?cH_9Y+0l02nx%Y7u(Nuv5ceWIp2upNDACKtRPyxfHBVkEZHVh?o`6B}-1io)q{$bK3hn=9=AayfHYqn7%>O=WkYU zmcom2yvoCudq=7Mw?8G1iL_K~{HBGAIcTj{l_|WaRe>nJF#XD<8`_jec@E0zr}Mjr z5PH3wLiV-FBpaXj)Pr(r*?@8(L-Jrg{mqCa6wQ`uG$3prx?lZK#rR78 z0a$b56F(|nns`Llr5g6B6voq#)%g`dErOD6^35E?LE^tJPc{4aFlTgHsfN8g=H!Y} zee@)BGjW*dfzy3S6Y!X^#|sgMC&{zboyOV;E{hpZH7ERGV6pF2BHn`-hD*hpW@Zu!Og#E_qhbJ_z{0^6L{0Pu+{!D!Fx3L`Qa;%qa zYBpeb=iMCI^^D}a?zCd6|IyHTHtcuXpwy7^JRt$otzoLQG@krx#u^2h&P(iz=fiET zuc&X=(QsJ%*T8hnbg$x9$bD+9vcHW(Ny!7_NmjMUdDs-R4&EKfC7u;C1+Xh}4~S*e zIUOgQBPCiMMfhWzfa`4L?{%-FDbf2dhkt+*skLA~wo1yAYAhZFFS>$CEP0eLskDB2 zaievAt4Del`G1;KBGbVV(qhEJy_vX(=dw3jr?MT_)IMuO+}yBUC4 zhdn|@a>UMJK;P<_(%+$O8){eCLE3kCl}3k4C*GJ=R_T}!lEqdyD9BIGIAQ*p`-gq)12f=^TWykrye!=XeR3e)Px$ZSP-*)NrFJbJ$zC}} zIS7s|kJETCexC@s^sa4!^=R+V(x-?jLh3P-w(l|Iae8k{B4tY0*$$0_d%damA0PX} zErQ3oTZ3RPhGc*TG56Z!nsJ!SCW|S!Ze49RoH@e3s+A=LrX>2!#nJ0yLEsz-s!4)dJpg^g8t!f06ivKJnKYt3HZDM`XoMx*`{(t{~(o zoAVpg_G6Ioj8cpM(fu!TpRW=)eIYb!#h)H#IKgv%ZhM|ktWrgWtvJuZqw}-3dhhVD zw~+BEoI$KiUGUcy0X4~@q6)xXwOq1BOkc+Xn{ z{64J91_WV7xa42tJz1~v&jS?|vaAW@)B3+_s#yc^n|$^{KMGyQ zKBzaK{y-iDPzz6$EN`zvuy7bWR-=kQSHD}g{$1dMLQ7M6OhljlX328+xT|N+F5mqP zFfy>O#~2iiMZBz;in0PApG3aON|=~g(x}*J0PLb)TA(5SCJlbr>n8=CNj*u*SXaTjvh@=BVB$rJRWjWXA3} z9>EquqPqr&Kjbn%qaCnvI(ceVO5lZEdopT~zs7q7ot7$lSR(*lMTg?y@tf<PJhGsAsk);Z+AFOkj8vPk;26BricLK`tO(rSxjzDy2>x%PI#B&MXm8&I)mLA1 ziHfyJgktf{-;{sXU9v%a8S}9inW110IUMOC`w_sea>B9la_@zo7QRJgO5>nBBQcN@ z$$-G=X8jE<9WqFy1ij8y>9?MPW;8jw$;xR~beIK$Yr~qs(Z0|Ls{iOPX5}OL7Slkq;6yDrpN=H=gQu_RacNOfOrA{gg)V za{+t=T60pkp?Xe_SKE;U#lyubG;Wza62)Au^01e)0J~ZqqCYXPeRhGzo&V&HN_8Qx z2d#=WKprhLXDTwj{82M{+Le;Wl#L7KRW<$ik+uQ2pEgEA(#+B7oI%-TPChTG4>71= zg`>>atVk{ONV{p4sWbVdluv`1S8m{5P_t8$Fce)4Zg))LQh;krJa}=*rmvSpt*mg` z>uabW>1Q|;nUkZeECxE3Hnt743P6+eiOd|5g6YMHl~nj>=I0Mixd{(Y4x>e7(v&p# z@+k+k{A;esSnzX7dF0jTmG>|9xy6$xWR~2>V0H!XNaKy!IroJq`R|&%48tV%YEzcM zvjd;R+ug*6OFpNWg;S{S<;5p#SkVERw=aISE4gSn@e)dQ^E@slh6?*|Sf@{vs2&eG z?epxs-Zl5F0|%`Em{e~M!5$svN|=sFrkZ6yOs&%Pv7J%bYqH^Q_GwFw;%W#JDb#Ca zh4L&C9Mc0z>79eE>AWD#DayZxXt;B4Qs7dhuHP9(MD#<~b8nHJz89peoMvEum`NMQyq5k%bbX;g@-=7}6!_4}G;84=c5$7C5y zoWC5g#QAn$f(@iEB4IMOBDpxkq8rZvCYz}=r*=fXJoa7~rSFgi=6L6ZQSQ59q?=G& zaSnTOZKE9P;PSxosEnxFcU2zW@9H3U{;CElCqyx5a~!;lJM`qKihmS(+GC!he8u*` z(^@>jxZ1eM?V?Ei?Il+HAwMVzd)Jo)Kc?2%L!%vLgVQz4U3f)ByR3@%WW|>OMW{rL zjqtH;PpZE%tdykP=d4R62jf>X3a;Dc;?47v5gurXeqg5&DPrWwivUx2U$AJ2981vWc0<2mc^gaqN>R z&&61F)-z985$*W?w)dUmsNh@RJdw1)ehqWK&j}h%pngdH;s)hWo!+dJUy~>KTMlf( zO9sPce;eD^Uf-z>uIC;7DtQgNKctqJMc2!9@dxtT z(3@BV%Io>i5}lFz5DC^>RWS_cF!giOQ}*T~!_2L)gt~ z=Yy$BhBKSS$4QTy{2$8=~2y>(<#>7s(lqweYTpkB^ky_KE5CE^OoURUW?Jc{j6+BdNtmJ_k>V z$R#93)G5{`N<4`IR5z4tmyH*#MZrnt6KugV+%tB!6+dz~gr^G^FL4;pEjyj%sRKxl z@jES9!xt38y!zyj1oGm0D>lJ*2wuYzzGVzBJ?g|vsbW)2p)`v>D{fd_#A{wX1@HIA z<*_J6aWsC{j6s+bCJ zd7O-hOHzsr=x@5o1k)YHkm+uPe7J`z#dFEplaSk~Dn2Y{FLy-P&9M#CBhOa@wm()edRgd7uY>Jq*PIr}d6)um=R|d){k%k!gh8zF2zc zEyL8Ke^^9SrY~Mh%%}t>U_Rd2UOvUCgz_c6AyB923lO}7Cv{&FF{Ae&mwsET&{lsO zC325i+Gx{pF0cpQUJ9k~eYPX-V(IZGo#=#TQr%?#(b_chp${w-1rc7eVXgOfh0H-* zD4(kUD-OSJ1W4}bBuXr@t7;x_KqI6fYtoMuOhgA#u=xz@xbcs|gem)7-z)TYm?C15 zMgNlhC|>o@9FMt{WL?w4C9USmgpnv=qQ^C_t|um0$|X7+==cc0;7aTF9-t8op8_kJ zJO^SyxxKXUS}pLxMz7@V=~X6ZlczVjMC|22Ky=3-%;w%;%h(Jdv#T?Kv6&d|pDjM= zm;@1=xFr+nIA5>skw4IzWb0pVojdxGnEwj;A(^=yXvojZoos$RGplcpaJjY>_sL7y zHyO=vcT(7vqV*i0^yldRCiFS&W5gg$@B0-tJ>AXnK7B6sTUDslivhubI!l>wKKG0Zge14{f;)0QS3FTT96krE;jf*^P>>$4mXL@@%`huxQeYXB@%RyV4{# zlbCO0l9ue*Y};u2N{jh10me7|^DG-2;75mKAjLka@JZ}&#vWMQzu`Z0%bxd%6wPX5 z*jj{bpI9^x&ACLf)=TT!G`{{$&R-mvD-U;^(v&MgpA4_-w-}~FH9OE;S8aa0>055S zf`3TNvtL^8jem5ZLLbuapzK2nD4SpB0eeFI0-ltAa?%&pbcl}nIKuwmA`e%?1A3T- ziMI=r0Vq_pGc<37j%~Un6{FsyjF5xmbNvFCxfiiTfb-&j*A1D%q96 za$hQ z?6Ja$!TDk)N&_ho-1uZ>RbgtYfh+Oco-?85dYkO-k|8!&8 zt_!{l-5tPCB*J~&sG{_W&_M@c2nl&^;vo{IVJ zMn;x0qx!^_yfzh;UT3!Ug9)aIpMqF2U!=?OiKlSP(TsE`6Cc}8xGBrI!Ax_`CrB}c zKp~Vrg4psMA?>$ldd7)N8-VMik@4}6KD;niZFx}Z!h9{@uKX#P?EPd!x_~{#s8`{I z;U=ppZGCi`Dp9~?Iq{?RA>K0Ku9ey+vzCf^>(C!w?&Gv9cADJj@J#w^Pi+f&r6HQ8 zLI87TJ+@=S;Wo^}f=F%~i#ccSLkuDg&oj2_3)uw}TeV*J4w0{G05DCu8aXelHU63` zTEmQegt0M?i7>E((u}G?HaA7nyZ2A*SI>)-T0ZPlimm!)d$Je!%4?m~FhU|w zHFaM>QD1Bg7=wj}0>3m~s(&ETIJrFvyVUn59~LFOo+dWtMDpI#wS1)5Wi0%1tWHVm zX~Gt~{_F)~Yh>aSR`zx(3+ z{amx{^dECDtLYLtTFKDR-6IMrv3O6rG>4T8QmEEOJ%(t4VnDF^;}hso z-*>#8T-g?;>hyHwQCouIqiydm=19fm=)LO7F$b4F^j5aqLp{zN6V~smn6&=3QApFU zFxpVIsPys9d7h(>QsnrwVyaj4PwXH-L8ru9G5-ELoXCKF)fILE+lbmvQa_y%>K;Ob zBY2H^I$;0D;S3j@YzxIY>|*}PF}Uru3y=ROQt>GpJB-ZP-56trHvwfH+#*T(W#@+B zX3RO`dPiMtPps1Mtf$tM&twC5K%@~@%1(*mzm3jv*Aaz$kvtcLAG4Nzwk072a~qLp zx;|W&5IjN6Js^e2%$YV8GxsI7kYuZ6&B6!CO+c^V>X+G2gb{>gZnq$75A(>l#}f+L$zpS6>q}D>HMfFJzI`QrWCK)Z@T0hPK6J%S3rmh@g%zaiRnZKc1464KOUZ zY~wHbe8H{F?T(vPl<TisnJ?(N(WpTW?aSXt$*Iq^j*L5;hD~Bwb-J+VRA|EgQW3< zd8C#(+no~0jWNF)8LSvhJDHTm8`_L!zJL(OOQ<>`~LcTeQcobca%1A_HxqPoKj0 zmhe{7f$5E<3+dm`1s#aA^Y-$0glbDOOld7ex^SwAfom%d;? zeU6(mCdynwQUl>H(9#;KHC_n0-9Bnp{#*!7PEK$FK6GpAtW84JpNSK~MMXCW2Na>( zrJdA6J4owZQwI9>^kSM>tiS4?=yjyh)ZS}z+=#f>ldDxS6wWHS1@!}-^6%d z_xKR3wxRys3y~0>o26G#M{V*~{s?el3q2%TFDk6+YKK~O7A6OGlv}<;Fd89KFf=SI znDxz3a6v_4Hx?leGq1Fc-0wptJUQ7N6#V@IQ;2&<51s1L*D0KwE4Gl{^z`(IFN(;B zY!8%gz??l87K^By7xNIf{v{$a#G3*mzvd?VosEvp3zmiplwf)+VyOHA>IvP`^U=szRu*?aVs=Nq@A#{$hmi-1n1`>EZzszlOc26% z@ViZfHz^n7fCs~d2xLTL!vr86_HA_hl|EDjFL+7$Q|*+1U<))NH8)16|I~4A*#$^0 z^sEf@uk7+^n;Tl_zbSnck!;{+HDlqTii$e|#5aVm0DVegQ7}9*GBOx07%+Vpux0=^ z1p;JSd18q0qBeY&_|y);fTD({=5_7%m6(A=Z|e2)C1E^_P`jo#;P&>ft@=Tf&Xkm1 z(aG6lU10jUhDe~n*H&!(HYz~lY#-(h=un0Xo9`bQGTqk|s_ z`WLIouN8piqf%0NX#itOU~&L13otU&gJNW|sRrlRU}*bj#1rMYBprVfhJWI~1F;NLS^VWFGaCbFsQ&rV$@yLM z{gDZ^hOPSTk}~C2P-?SJ82UIqk_6~D9@sMl#*^}sS^rEAbe~nG!h2-|M_+@EHeg1l zcHRfJuQsB9QvOT3+fJbn6}%PfcUJ8&^D{6HH#ZOVr$;hRDmE{e*;jI0Z*`ER4p>z8 z%D}Y;+*UgWgI9Gp&l{k1I6UzM4E~+sC2$Fxf19rl94zFGL=sH;4#fuCOBY0eECMF| zhQb8y^$iHZ0gHPR`T>?TN8}nw^%}UbJN%yg=zIel@nrM>!ME#$H$<+F#W#V<;QaG^ zcbb_ipLK;FT05UtP~Zj`_DA9eCqmYA=T$r96macP>J0+wx`})rd+bL(P}z6T zIrP%8bO01@vNXQudNRyCc!*eh)MH#ua%HW5-tO=UY`dKx13jIz1RGX*z}zJ==xoJJ z36ekqF#pK+lv+I2deSVu{VY{U1kSJBN-lt15H6e$mD}s1QO{;$*`v!d(Z?$|6hyNR z;&vjIIigEP@}j31P=wiIm%W*}mrAj@_@|)YQMuvL+Evi!Z&t;>$IZG_n{S+lmj2a z5at!V$&EP|l_Yt;`W;N7C){F-_ssltmcK{7FO4IPU*ZhT%b$8!i_V;1H~79`&r;+> z_{VVCCR6kp(TA3ohSN0#8$o$`Cfn_jWGe979p+4;JQJ;ziKWb3_PbHr?$5u$TtWdh zUWsiI_c-eC_9Gc}Y&rsTO=Iz_)~UWt?=q`FRiU-|F*Q!MdY*^lLnXdw*}e7SEMuX3 z0^9BA%mf;h^vtQ6Q;rZrPZp1HUnl7k?zixLLQVUL0j;l!#9r|=C4R2f{b=Lgc@O3T zvg5Y@GN1__)_cIh^;S~DcMeZ9T-^W!PAh7Yuu|daiejl2E1SqX{OU=39@)syl=)ef z{&4T*YKeszNzD8j!5pEDypdt;Q}`;Zfw~3)5(H0OxC+HT3m~%@ ze{4t=#TS!sEcML@2t$cyU^%=GNwk;8|$%*f0r&U;g#b2{0{63=_g&2^NXkOV*E)K)oBC= zkcv^EsJi>AtZo5RP+8%S=T%YT2MZqqJ}fu$(C>GtXZljV_y>~v2dEYcSar_?e~6%S zs}Lz=mQk&bPeY*fey}boC3AfXL}2(ha&V-#P85j5Vq@yPC*EpTk_HwH-pCW`E{(^A zy4ALf%NUP!Er!+i^7};bd12@?>H*DQYQGZVTmEiR@~A~3A*+u562wf7tsGaVcCe*Z z=0o#Y-4`Tkr@`;4+Kd+UAioEnexVb+{hf5(ISGq< z6Q$D$M$I%zA1M`49-;D?K9<-Mu8+Q^A?Hv6>xGc0-_bCz4MwHa{4m(o-Cf`kqt*xU z%@b{;$`!l-Dg*( zO|(t;y@q8}Yrv9%Q_QQ9NADy6<;zMLH9<-pa#OtI7u1V3q-`LQZtMs zp9&eXZlY#U9hB^)QV)1EeMHk(oz|i0xif8-9~RryyO>toC|)tIc}L|hq-KGbX7H;V z$Dv0Z8%U3&(XoxP4NQcbF+XR1FWqz*}I)BJ%5!6sp zIvPep?C$*h_C{upXv6^#s#a)~>jEw_&Q9CKj8ssEx2!+ce_r@2J7E`ftNK$eoplqJ zx!vb1(Ui=y=4I~ji0p|f@=EG59@UHl`S0FwkL${;DdDS zR@k~dyIxq6Z6e{>TtV#MM6+BHn%OCy@D(FWx*PkD(T=P?8%qz=LN?X`I; zVZYG&@lFDQiBS^K%Gf0GW{jJLUg*L>v*-e}EGT-9e@INslzc~58n**SG+rTI6$1Kz zeWD*!hhM)LO7=F$Rt@ycR5Hh!#0}Mx%DFalE3Ke%B1o!91qX%s1BC4?Jg63PTBr|c4O6p3f66o6}-0EgJ6xiINegl zhX0sIf58yfB_8SB52mQ2+Z4tjZliP)>ddmi9l#7xfquMhpDd{oj>LgU9H){=IKwv3 zB$b&qf!_+=s^Toc$is)ZZ0sAI4>Yi_Jm)f6!On>Icx8=7A)?^(0S?Wwbb^EiR3&Il z%s%Yp^ToX0#wnZB*Yk+_w{a;A7E$L4Q~{lse=gE}L%;sf0}dV)-7aEWd}V}eJ0nVO+_xU3K=F@_rwi7#ggc|Ab&sXvK4<+ ze-Of@Rt{zHy(h>`l*cRcu&Er-L&ewERI?X$h|H#Ef@E`8i#8*#{W7IV8@sE)v-k>> zCUI(?yiDV{*+)qqhnyJ@HIYI>aOEl?!LxT68NfD$4=z*2kEk3C!^&;i^p-J=&I?sO z)*U_TV`c4}*melzkjuhYh>NmVko|FXe>8X--j$rScZo$bh(^BbtYEjSO8d!{=>z6ZLee>UQ= zv2U*EVrfN-o3|F02&06<0o5tITS)9KZl+0EWNpU1mrk_>nRWqpe&k;lM6AOQv=;`3 zOJ8i$w@6E!ALKHs=Sz)nW>%PyR}rxXOmIKql_Z!eFMpwPO>gY~VLvI(sTjdaB_432 zx6$JbGo;aV2sAow60u$wN^(q|e^ea5!^7|H^9ii$FrK_3*~2NLw3dw!0S$kUX5*r8 z3tRmL!hX_C8&lYOZNQs*Zj)*qV${xR-o({aEN&gXEyI%8ihiAw$T#{`C&)ZnoRYop zl7s#$q9!=RvUz-50|+^4|0VE4bad97wznl=laVk26)BpH*M#I32baQpf1(sgn`xM} zprH}Q?o)D`n(XM)O0g2YxXr!A2Pj+LT`VIR*i2qIFTW7-k1TxfDc{x@MXa*IKn?5l z{4vz&NM-BC2S$0oMw6F@Bh6uDEm~L(Vg!Hdda2*gE=@C>AFfK_M&%-F#3^B3fF*l+ z?DXwH4MsU!ZB-hMi<=&6e|pgTNVSTo3DjgSKeJr;bvBPY+N7zgdW>!bWEG3v!RcMX_%3zF?_EdihX-af1-fvcxgl;pgRq`|q>4Um3$xCr z1r8h}IP~#)!J-=zYxMfD*TYC#Kw5t?5*?68{#eql8Q@(ZR`OO`f3ssKGuz?~<4Qc? zfu}V-BZZwy*EHBLZio$wX8@#%kPq!#k$BGO^Wk=yz9Cj(PvvM9^a-UF+xT^jM3)TK zuy6d$c3P;bl}O)^eBD(yhzJw?Hyn|B1`)YV=}7r_HGcBvhG^@thmB*;x{!~Gt*J+& zjr|i2KPN$AL^`Z8e_ORZRs#aAO8xHP@;?cRQ0K*XHEw)U5%hp%R?CbG7AiwcTyL|v zVVzzsslCrB27Als8V5A(#MLWT=}7{;1FP1&ncd90yJk%+CHa}SD@sYE2mBgM$SApnr-DoQZtutRX~coA0|;a1L?wlO8rZOmuan)P2|3>nKB*YgL? z_!yD}$9bGLrLSx$(DHdmv+g--f0?@ws#QPdNp;Nn?W2RK;?=mt%o2$*S3<>A>Zl;ZDDt34z=roZjEQ{I zUgRLg>R`~iX=^?JyWc7vKcvo{4nE;O)3BJ#HIa4LLSzD}MPv}Q16&9PT{gGFPh_Bw z6cSuwg1*>vXa>E;xMOpgjAm_dGBYLL1l)w6e0jLjf9yns(3BY^SJMd5Yue0zwN%{E z`|wM+6LmqL6&8;P#DwU$S#dUHsArzx_ghrwxOnFD;ffbvPI`)dqw>^k@hJEejh*88 zDX^c!a|rPfGYf%N9ZpRyo=G@2P;vVs|}NzXRfUy@ge)5r*eKYg8fTpBJ) zU3%6#!}Z&?e;Gwd_lR+r^Uz6#hr8}y&(9^v%M`k^nk@lAdFwQq;T>eBrEe42xZ?42 z1oY=V7PiJwC-;BLN@+{OcTV-lor|lI&j>_oe^?sJc`m&f_2qPjUlL`FiEi!SA9dQZ zBeKk_zPX0}eJl};ZdE?!cp8BTkM`w94f>Hx%|jeCvR*RlOr6HEETR#$+m}2aY@b;k> ze;Mzv($I4<&TAyvV{NVI;NK6_OEO$gIb}HEMYR3o0?na8Tf861zFI2}j$3Oe(GNeW z(cBaTU^q46+xBlku=a*&6`K7RxbTwXi!abqQJaTQB+&P0?l@zloKdPg&{)zf4{|K+ zJt*eK3omBix{{BB{&bh(ZaH#jSPJk(D%Rn=y*)h;E$WWr zBvGo71d5|XWb)A5gWnTh$AhRVqlM2ug}%q zy8)#^grTL#aeO|3uZ-`U)qq*ee+pRqOXMOP4Q=dN4D}W?1O<9;xqzA8At(ihFc2t`6id<4-<@`vAXW&(H#37>++Luoe|I%$~L`otE98(=~SKxziJIE)GGT zpayW#yYcPwTZLCwJFi=#5!PoxqMDICURec~9lfkM$`&MZlAMUA{O?z25 zSv|iUbuazpFq!A}!Bg!FtU#B*iQuz%ygAjU9@&FoF!yuL5!X1X2=S1~XcMx5AgKKb zJCQYoAmN0$a~R9+P37LhY>OCN=(e?GPM-z0Dd?%X1fNJY&P z5GnDLY)gSxt)4P9+yz?s(f*g9uN*80Y2VcH%crrrql$(?shxahf+3&p?Ke%p^}-LF zieD2l8Fs0=H9z{mV_*l)gmnKnzYxk*1=Eyn?2-B5tAnazUw5Ba1qzF_@k;-ok~RnZ zfB=>nd%Sq5e{&-8z=QaReUTgnW8Iyut&He2fTcZ9GmXA_=|9T{JwD8B%YE3uMnBA% z!}DVmbg5v$+-LUtt;bn{a{k+CI&-dO1uc9X%W_7f{^O)9Ro`JW&oXH}gQeuKDOLPi z2}HjvAYk<)Et+9|e}vt3+3XMZwX6IJMlvqokICp3e+244^)JX1dD3m0iJ7L{`nkDA zs99pKS70WFAsOg4+>e0tuQy;b)VxWZ!I<>?vl(&xmL2OeBHo=1fsRnGuSh_BG6|a; z!G5TzowZEbo55Y7@^~&OL$z1jbK>&6XRr9#<3!wJ!ivm9TPc)w36dIRwb}9OlWppy z25G^{e~tM!r`8`qKV$dwvY^5=r%H=qMeZ@3Af+#2)sCzF2X0Elg_QSfAQk5IyXIL+J~AINv&?ypbZjp*BplA zGS#@uR7}ID-`p|l>s2vlFx+cv(443C^-Dm#U#ez|{PY{>TjE^B!Y&5esc(7HbT|eN zfR$C##|K0sLW(CH)ufNLV5)k&^jbsP zf6B2R^=!dg(55vGMp4yjTAGAtdjrw~G_A6Lh8j;BlV4?nmt1uPx1BEh#QY+xy8>2( zz!L^L!qFk5rOnWeP#Mo=G|n4kCgNNEe!?feq2ZbdZg7f&xHA~I#wMA^LW}Z^t#&)O z7t^d^;?cmt43X-zhB|-NQ48Lso{+7+e+nbJgna!M(C1`|=`TDg%6)PU0Yuj&_$Q|P z;k+=_G3L9p`rnlnoFy-H#U9RzaA(a4F-VGz`hS@?FzJB9j&sK)ei=>PGQ{c@4_rGU znGct|n1)6-5TX6`x%4f*MP0h#;Iz<7-Fj~a!oD&V+%M72Nx}>p9M}a_kqkore?C4W z{6lg#_M~?Hd31-~#hUe2xCLm3!v~N}WDvQ2%bWgn<6x>(*~ZN+mryqknM~=n$mMah z6gE7#cEOpm*j)b>a>?rkO1 zG8wlZH89vv1RQ;LtHBv#krc;V){6-(Yor9VV567LbrTao0Wv|oI}%6Z3w9W_%`UH$ zhd`oT>vA#&A!+*9e%d?Y8-3UAH>_PRO{GIa!%1%l8Ki3e&4Ay#Gvfj^f8|e{I_c#a z>iUK+lf9VkFVQR1zw}|k((5cYw{G>tSHpq`#ka^NCDI;Bk$J)bWxOAe(ZKeC$|yJJ z6&^MSruRToz012bh#8QX7|KGUzXF05!N<98$QUqd?LWC+I^sY{2!rW;}$uPc02H5A&o7 z_QZ2v)Z>&b!!H^aDpn0`dA!tadKLSPqM=#FWx37NH1ayOJcD~Jm0P&?X8m>z7Cu>L zZh-T{`cr53b8{*;cAS`WYQp_!GCWn*i1aE|_>Fg71&k8=1YwrPe=2KX*+2lR^w6Tk z_x@ThhH-lDvz@7`iZlzoYX^Ut*5_3#H3zNaQ$K-H61O#EVwB}V$B_BsFTT&r7)XR4 z9_wSBgk$peAt<#AIQUdO!?;wnEs8TK&4RtjXZ+L1o1|zpo$8pm*_w!^k`(vA*d^HO zlq_E|sbo_;^+`ufe=&U}mTi03>}XO zkfFC~L8H$*vNEbOl(2<0k1!C5&okJBIXmg@MHc3pPn8Klf6jh85ljy313R#Wp#5n! z^d=(fi`l8XdEKW8<}bIoU^OA#oR)x{cy~^Xf_I5eEbBVnsk`#n;eu!V4=;z8Rg7(AgmbhfA|CcYl>7|Y{3K{7-@eUmL;6i z{(9O1`+|?^ge(!oxt9fcAS4P8VQH3!CC_~~d%RRsOzu33JncCdahByb>A+`3D9kRw z!m65z8yz}Kd*4I0)Rp~~;@OLHeSZ<3Os=L8V-`xY56Z*^Sp9i0?vh7xk7m*k)kB=$L()K9mm)leEaA= z?mXE0qlA~x94f&f@BJz@^m|xQM2i@Cp8STjYc~$>omA7-9^Oy%vFGsO!u{~~hytIU zkA;Ufj^Y&z)?21D+d6v0-S{$)Z83p+PE44}e<1JUJ*$@n+Xjzr8DoN0nm^4hCG-cv z+TjAT{B^2Qvi+`L-RG~xvA5;XUz4W0vo&aPA-9AmkE0-e=S_~un)I+ho0;Uom;L;+Vd&?m?Y<*CZeU`PcBWa;i8yCtd+bHT zbgD000r`Geb0wGRSK;qqJU=3`3`Xs&HSks3PSCg>xxF2FAtncddA=k!MQQVX3KE5W zdL24YDjTTpyfWA+ps;=N0A)b){{pv?e@x>rS`}E*dsZ>=f+l`3D}_8`jHbdwg*VxG zF(GAg_=Ot-R>9DfeI*kEaSY$^@g{cFaL4Nt@AoIWK7k2`vl6t^ER>(EXUixLyD%Tu z+M=~=AUkySN6NU@&{Z(0S)2o`o2w{vo{0U_BRuo0$$xQByq*|-39d6ItEmv2f8`*R zujBVww~tFz`{uI1*H|IHf(ov5^{Y(o2?;@D&bwAmg(FP+6)Fo(1McS~i4k!`)%vYI z9ex0vDGHb4J_ffqF4^F?Lx^Gz8Z2WU!WnnZ5@jA~NrPvHy)BmV z??JT-o%E@p4aOz;ro^skdxN#^sIamn*3QYhdHNMhwOe=+XP6RUZwv zNU!SU&*{B8k7gpdm>bXfT~UG3Z(?-6O5+4ZoI#svA%=cg^^jb&M+fJ@f9nFjOM38| z;&j&qTicXhn;?f=mgZGp+;aF(eN6qbbz};S!EZ3ls$k%1U%f0{34#OIhAHC96>UwB zJ|l+uWR)YO0E^*)b@!O0IGiFhC>0puyYR2sqcQEVPAot;BU!`FnU2(?K|)lM^=A&J zfyATs(C&v{bGVO7M=8|fq~urPuE)SN7B^}bBb0NBG%O&+ zYzQ)~YerNZ`iuRJI@({i zXgmnb7ViWQs(ABQ^Xsjv!9<0qVw78T!@_N5dKCyhAavKKnKzJ5CwA&Dr4ag>CL~1p zddd68szriW0_Qo{D!Xgk#b!tE7<(oK`t zZ6Z07Cf2r?~OxVxIa0%VQ7GY$}8{cQem$SHW24rLHzU>wu#oS9R*Ta{gWJuo- znUKyC^@?v$!5PeVA$0^;LE42PTl)1l{YyecI4q`RA*iC7oGd9kdl7FvjRc|x_(8r$ zTW=ILY=^R7fmu?RrL}q!Wr zZj;^5Y>J5I)5mAA%~OVq#u2?Sw~-|syV8s*xweJLIbDqn5aOEVWh>JN813aP%E6NAjP@YoGvAx&DzNFz3{nk+QD8+z-p2&Lt$S# zsO0cCe{f0%jnY+-UxS-(dz=8(u?V1>q2Zpp){+W9IyS-7*F_s+0?8_#fwZ90R6Fzs zYK)v4>Pmz=`m)?*Nx>yjUFj&8aXxQu1ur!)6K+S&WY=sNdg<1$s!xRxemn*dAFX&*LXqQf`~Ul=u`sS(cyxG@;~*UoGZB z7%ByFhw-&arVMG*8-C5ys%yC~1bX=MPUW+of`!f8MB)R@X_!ec)Gr2rhqyw4ZXjho7Op)s*rn zMZu<-au~;$FByipC^4(3;x9HBoC^e5R@n$M z8aOB^`FPZ+o!&S*G-w?jW}36E40*ZdprA%EFjkOFu0c>&&stY}#IcXJi)X@0tr#iN zeKGT$(hYw8y=~;cS9>o3OC6P7n6>c*L-Q8%oJeVP%QK&oB?jWnOJ|#7PnfAkGARoNl(EGGr6r4KEes6Ctx`9v+85_f*h0&nvI zO8^##P_kirItWkC>iQQ9)V8=xG+yctK6KfS_UBnRxqXdAT2pg5md=QX=x2xqA)#uf zD@Z1SU%ih|$9*dUqs?J3KVO5w6pD?4D=E>WC$NpIo_tCTKu2+Fasx}`e?yDIM*6Fe zgyYq|vGXN(4aq;yFFq6a1v;B&K}7t*c|=T<3C)8mpF<&f8#6>F;Hg7Q+&bHg-iNFE z9RhyYpsDyP?g|L`DdVu}=lw%1NV4oDi}8dQ{hpz=G#$R4`>6tiWgy733!9ZF#wT|y z`q8!rIA&#m|Ce$)dNo3PSqB+Wy&U_ z!G8R1mGkRDd6&p|aOe?N_W`nKG6lP0u; zx$kr%l9i@kj6*z8gVSYaVSV1by$Kw#@!d(D82WA_THY28dS;h@jU?C4p4_l+Uovf` z^FrMKPxt|TUsu%eL88f#+F?(XIuBo#e0>ZJZ1kDpw^>-{H?n=sct7MatEMX^lXiW(jNt^ z*q}qID_=UtdEQuv<4J!#G!Mx4hXx>xk{~qZ&E^F?A99KuTKdT@&XF3gzv%4-Cz=;S zc*o%nSMX6-v78A&%yxl0L8i9;EH}Ar30Kpv*`^aOD(06>7Wn8bY___QM$SVX_K|OZ!up4A9BodL||dv9U9P&@Le_ zT^o$!e6W;Kf5G@d!Xx9gd_r6uG*o625+#dgcLuL1#FI0OofDOA&boPk)6=UmjGQE^ z0a|U8m;mofPP`gpZ54##$my-Q7Rf|R&3YiYU`#_{dt;h8-n`X<56V0 z1h#NLMV`Y%5<Ue!`Wlva~q^SGz6$w;;esd zsV|71KFJHI25QpaO0ccbaK1-2s%JF482XGbGFUgTYI?*gLIu)El9J{FqR;kIq!t%+ z&xSsB%;;r;+X~agWhu8Gi`eR|gT|Q#(>`Lg$kGs|mZ*ns2BKGFEgN<=0g&tB`k_2> zWCMRcf1)KT(oj)R03Vr6m+zo(%Jba74&8iZr*&MW)pqX*+(5c3ZmB|Ki22QIg#vM8 z@h7?P6D&&q>?~oBt6xQ1t2jaLD#Ui__lq4nl!+7%z?&=`vvkT(}C<~pE+>gNAQ;|lVieDH@5iB`B z8zS!VtGu4hqJvS7@p<+?)Gk||g}I#!U~(|^`g-ga#pMYcznw4gJBNVtbsZ_^A3{%y ze+6SsLU{!02PX`(`X^U!25*K4WGXjAzaY*zS9j)~jJ$BAu(!pxTzQbp@PU_G35{_k zf71<3M%vg$y5BCQF&-6iikAepb1U5ITKj?Eq~rQnRFl0gr5f5pdLnGKKC>z=*wS%v zqsTXp)jNUgQfRPM5ASifcu;CixXm9me*=WT6iy@^SW>lQK+d|{^5L`*E)ENKYFuuZ zVGZG6&8ATK>68;9n{qWTclk>L9;T*=&3?Y%* z>R7Om3Nd=6`_-m)R!(%%vm*q%yzulhwJ5o_5MwM(rnpJTUV`3{>J8ie1blw>wIv6_ zq(c~`V2yFnLkzFlo?2OTWros_n4J8nWf;=~DwU?AyS|U=qwU~p56y?le-ILEXIHsN zpW)$URx8Pg!?KXqiV8J{Nt#M{GvRBrT5lKg>`IEktDQ)vYB{rcnSMbdlkdaWYOGAF z`Zhd~kM`xnIUf=fSEB9}3NS9tp2Iuq0SR~n2KJfE42wxw)SRJ@mr1IZrWNi^5A#}Y z*U-@)8Vp;W@fWuv_NFCXf6#g@XtH4f%It8|3lPRj8rEtT`xMFi!J&Sh={lPpt2eWp zeZ@?q_v-9~mM?P2;qTnbp00{(qk?W_;%}%eYF}PCL9!`|V#y!P} zud871-p;2aPl=U*P44cgwKDtMO`1V%o+P#dZOX82)}o8%)&d?;f1Tf>iBYHuoQ7d0 zxA^VbN3BUggT#5iyC-_%Mwovp5Jud+mY;b6PB6c^JN$WkGJ*w)oHa?Fa(OAeopK%l zoD=ODNgE32Rjj*a6(E21)5=r(V>?XGuh@Aas;TYDjexyb>g_oDv6S^iaE*;kOX0?y zT@Ko^mElx&73Elyf8(Z;59ucN$_UO^4OK1|(^%1T11~@g1mRakzLi!d!!z<1O26AOAD7=$fL)7Kxby_<{JznUSqZL z(Kv0|yk_OQ-|Kl^-~UBlb4d4xA6vn|GHd|jXSS8gm%KDte@S0l5kCqecSe#jN=o;2 z7)G}q88YgT^D!(Ocn~8oOojZ@@n%e=HrJ(C$8;;3v zx$ELS%2Rop+0Q8gF*5K^R=T!>w9j`lAAWtSjpE<7i)Nk> za?FIvM1t~#f0xF}6UQd8r@P!&bR!j67*Bp)VmKnyvdFjJG4zM_oLoXx#-QS*`=CrW4e=_w>^bD#@NEm^5$7)r573;8A z27Nzwl8x6`kEzm7?4;~6=Su|gZ+E-z)6jSgbRNRy=i?DSJsw)hIzi@f7AtuUL9^VL zA$aA@`Gmn94=PnEqIIw;)e6kFzLech;Iz1~tGtGU39&7Do5D9@awnRtr9rWS5h2C< zl@Y<;f82E*4oP@dp`}*7b|kCf`GI9+eeSu={2f7DZ1mC6CUM3-UQkm6F%eknw#Jeh zdRKVr8@gAw_k_VL%TkB^Y##&D<;)6#e3>1+MvgI2vqx=;U_7?ulJxW2Cv9CXg&aV! zNO^AmvbXR;FT!?5?{$EHfprNckg|yvOm+~Vf0Y0*(nX~(m}W$HQ&1)5mja09lKJhO zdO2XB!A=zVu@;NHu`NHL%dKnT7k#x?mKykN1G4XnJ8XL-;IJ3oVTK;ilk19*KH)bY zp)Ah1iQT;vo3%?LIk#FZ{#KKl>aD%I`#Sb6J1qlyW&_fBQI< zSWvh4^GgwdZZ}yIwr$Or@@2fWbBTaW^3_ZV7xPgOKBlld&i=CWTG@-3C?+`)D(>na zp`Li$<@~q#pt`GE{+qzd^*$D9n7&2Qggf_dqymW_#rkF2P#L~qG^h>aZQ-Z*Hv=#o zGK^Lf;E5lQ0x@$XX^e(oBb1)ae>7;M&PgJ_PZy1COVDlIQ&5WYT&>%N8zjnubBCl? zEf{o+Qy9p%Z*S$rFX4`MTqndXy1UBp+RK&?@FcZqu5oIyrgT}oWZBlrPie!Pf{3_W z+RX(9JAG!`z?d&eyW}`(GAF~Q+_8;fA5LbK--;7{jc{2Yp0HbAzdkg6e*tR>P+=k8 zF#a(Kwj=0}oA&(uV?@064sCFzsmEi?o(c9E6l+1$MQHEd4lb8Kx;Tk27Zj7uBKDjE z0U_X5g}>8daYCImEa``3`w-j*wsJArl=^3{K?}Ya*|RH5jOxljp{wz&NJ;V2&CI0R z2}(717qdK#8sZOQ*x+O5rS5kDS!1@HuWMLUNsJgP7T7y*|)30fBNHE|Bh{+;))`z zs!0bCRUAX&=8mmderf^ZV)tP%pbkUF(TP4Mt-}n9I7xUEoF&LCOVHLM#`I)nb%xhOn`4yp;vAmXz|f#L0Nguk>1t zNgmu?N1N&JQkd8>e|r0R7ASJwQKCCfk@`@+zq`v8<(U8K3>$*u8%crZz#w{fZJQGB z=~TFF4=+!C9Kua$N|-#PP}d)OY?JmH0>*{msQ1)s9xBR5_?XlgZEgogKi8x0lrGmz zZ8St0Q7ex7xK@mOUS5{%+l6>?GoaX$tU+evD@ zc(*FPvRR>yQj&y>S+S|3De_oXr?3_!^qAj+uQ!vR94fU@CdoYJsW0%;r-(sIExdjZcF zQ116QyY2>mO)19B5dG%_m!6zp;JD1hVFQ?Lui2%dD{BEPsE9Jmi9%fy`2LrSt2vTp z-^^{Xf1KWi-Hrw&MKe%n5`BwJ@2hEEdEdEx_WqvTc8>d}#QRgpv>EQFSiFI;^!u5W zkZWFG5Ojo%%_-Y!+ue_%c&2cJGH<8jkir2}ANumr?QxMpx+LS+o((YIcye0R@xWMA z337F^b&*K9rVF0=9cZA6`6mW*@^ImuyN*Joe~7w2YCl0wh$v7nLpNrs$F@ zt!zPqAclJxFZDO291USpG5iJD6uZ=c|hg>gkxc*Y`zJ|R{+ey(9w zXn+J8Hy=qIr8I;~8DYrawyG^CfwF6wv+KhiS)?!c!8!+u5WrMv2mczMC13~*n7{L{ z>U!iZeOs?208at9Y7bN(iJ}!>u`;W~e`?l{uCWHa#iMx7e)8*)&2KtV=HStDFx=<> z^AKAzWL;Cf{14Ch{EiJ^$@kJQ;(k zmvvaAtJ>*u6$U>l4j4U^-((TBXwN-Yyug(YP%38?Ph^!}sh0)E5F8I(=%R%se_9Gx zOb{#?EPEM8j;yE#uH-iQpHr8i#6}pwZJI2x$31RGWmw~yNzL)?pcmM&W?z~KYfq;6 z850L>T!cy&Z9K`mx>Tri=ZLxgykM>53G`bcQDx{M&e2WfO2Bo+!(Xg#M!e-EdZBAfv44;0bMi77%6v}CMJTCX=vJrydQg9gr@F8Y zB6CdsU4Zz6V5K$pIF9vf$Np4MeyKBn#?f9|nzKhMpX>d_FAk5Hk`;s{e~zm+V@3h% zCjZZTlimyUkqqb)^NY_A(;&Tv29VN*S!#nuV7{fI-E!I)lqJ0(8BD3`uMstAwk4#`lWy`k zr|m_KcteL3rab0?3X%cke|ADql56VWmG;^AVG2j=W#3lIEcST&763G2jUU;vhO)nG z8;KRAhX35JJLTQ9&L$1tv&2wjyxBQ=AczWi&`mWp>GpIxSy>`I{wIn2GObPC(w2Z}Ce+O`}U6F2fHy2_z z_oMD{E4JsA#0^5gw9UzcD!A#1o0}NmI+}FT+z7|5Z2a0p3M;vH&OMhBB9NF87-RG7 zX5Di3Xy%kO<;U{>HCmSOm_kpw|BOB4hY7#c)H%w7NsxR(Kok?nu1cEIVc+*#l52=L z=Wdq3i>TiE{{btS;S87Y2n7=XFqg2K0~NO|VgunH0Wp^$5e5~v#hU{nG6FF(m$91z z6u14&0~8?wF*TR5n*$WLC+`EAdjc^wm$91z6cIExGzu?FWo~D5Xfhx+I50GqFvtQF z1UEP^Gm~*pD1VJ~1yq#n);8TOpeW#w5&}bqbazOIggDF$FvJYZ3=Pssr_!l_ba!`2 zD5*#{ij+#nH|l%Nd*1W^Ykl9EwVrwQwXeOOz3+YPn}bE)kWT@Eu!gE4;AlQUexMXU zNk>OR5W^8h7lEF~b|?(WVH zazgPVkbkza-1hCk$>2uz@*30eY(1{AdsKeE@q z|7s5fM#7xYD1H>o@mGriztUh%Sp^PJLO3}=;b;`eulkf>NGKR{?4AODChGu4xWm2w zGHhUQh|RAiAg;~=#&DR6D^x@IFA4@C`5Uu^q5+~n32|Ww5dhQ$0QCUd3H(ZL#)$18gu&Kz(60P|P2RHwxqi1)!0xP+#x=bo|pI5flVKU|=-B8fpuJ zll+|>14C{8*qGrXVIBZ;AVzzF0N}6Rzuzn|@`51Xj-G$hf6rGyT~S3-O`Z2o$N%;z zDk3}p-h4um06rm6AV3f(E(#FGy!iezjekA}_E#CezhgDvHV90_pK>wh^skcL{!#$f zU)#YA_-8U*1V*+{0N39tw*ZO)!I+2O|5@w5L;imf|CQx`Q~7@vr0VME_}k6(+yDQ# zK~6A7&%X#5)w-fF8=!;0>;e40sgIz4wpIrUfw?;Uw^suV!fb*9+!mu|K0y(Fpnu38 z9EMVbc|alhFf`cik7oYh#=myW5eA3qBT%qkmkTBe2>dS}X2HM?m_9K%pK`Fv+KB1o%O)eN}MFNrl24cfR#e zY5cw96H_6+(cC9Q=w6j^tL1pZjPzNpBGpelf~rsK+jSLPPBIr}zI(4#-+!&~6glF% ztGZ=-*E)9k`Urb6!TXqzb(*pPE9X(7BD2(6WG|LJQv_89u^hy<*N}tK=U!F!#{_py z+5%N^&5#z|wvLK|bZvS@Nt2zGhv-J@0i$)%AGhvhtvJO5twvgeCF!QJYDu@9lEk~0 zQ!Vum4a`yZFm*-*Xu1Wi?|-vDM^mdj7+_QuAuKnUdrfhX_15icDvh_b+70=yklx}& z?!=QS-9x*z!>^VsJT5(%^|yM6cJj2<;-97FL=VNwkHl4ckF%IAc>!Ob^$;9wmvhMR z_b6u=HVThEeV#WIqA08-*s=yZub^lDy8NkZ7bTkZ7SEBhLg>(=w|_6Fq}|k3Y{4%1 zpwHIOf?M^Y4`=_AE1V~WI^5j%vm*k>Wrz)rGlTR+=gX!RwzY{fu?QF=V)YfikoZ}b z*bqz>-c-TTJ3L%V;wfCJn+ZI}7vY7VLca|%>ChvNk)Qn|553=QEN}k=3dQ^MR$U60 zR}j=ey?soy6y>@e_kY54)8zbgJ1AY}umhMyI>8jE>POuKCbPF=p8yweKwu$rd9QwO+k-T%`L*1a?Oped5uq=6J5inq(2X&%0R|e$i*C@XzXZ zRbR{S9U^OEJa zJPpqj;?kr~BRrzk5+Y4&KYJF2YL(+Khre1F4{_1>Q-6aZs;;MZ(rMj5Sxmv6u<^s9Z6fbTb@O36U4M2Cz^xRSDbrjo4)JLP zvYEc(`#&dx^mnz2iomGo_XFZwYxR6~QnyNkA6K8=3AJ+D*iD)!xN!*Da=USR=qByP zb!zWB(9KKtBcLtU%jZKQ(Zl+XGrRY^tSi`|H&}GTmVclk3G)qAb>HNtBMpZu57T0V zlVq%>?$?f1uDacAy`~1>l>2L0(A}5HV#-}F?o|UvEp6=P8Y&B(XEfKC30HsM)#H#m zY*RLWvbEqes&tcDX^4M)XX0FS487RqC+04~Q^IbY?p83Yr|~#Z$~HkZgnqA^(BM;A zeUs-z=YJ&?yg6J=awx21^NuN7?vEE1F>;A_`^!pGHL)1Ghz_0^^O!F;XGcHsvKU=1 z&4w>CPtq2?Xb+ z5jn{KcR+~0!%m0%Fx=fM=hYLZSk?KhR!a6xyeQ~G$Qx8!0+$L;K zVWVnweP+`e&S?oje7!wM+(1VnI}LmRq5XJctE$>b&gZ0;zAEBvd+^(v*m2l%$wACg z28f-5epY`hs(6!xuV=2Vt(?ge(xA9o{8NeHmmXEVJ27>gRtwSl=NU#*KL-~*DJB^3 zRhqa|dW)|YoL(BAwefC+q{p^*a)^3{I9?I4)%KaQ%yytPX$(X3CyZLY_;W2)?Ps#& z3dpbPyz92q?c?J|I^e=?Lu4tQ&%4uCLklTs9_fD

;%*24{|Sm>(J`Es*U~$pDGt9Qti<&OdK^L!`q#SE;Sv^ z7B7GC4Yt~hga%Mv9l9=Y^vJ~~7lS#cTb>N`-sgN1iw)cHTsfwuJ9sS5B_gwNY)K}I z@5vu0t=0(8hHr^fU;8!puhSe$ob$_Ak)Qd_`sQkBT)oY5&SK&2R0|*+fS7eMD-2HK z=dpeVx%w??t2Wp_4lrBXMx-0O5UrUbHDQ0TV|?dg97U<0T!a&5l41FVV9&pNy=v2b z6DBxU>2~^bGF@Z&r+WFj7aPUW+nc8P!|slyam(pZTzUF6lRpuVQOXTnfXmp!Z&Y=e`P%D`LRE) z0apk$|D7F4PLczcEaG}O8sbk_L4n)08Eob&H0x3=`!*a)AnNJMpt$%MrEwGdP|9XE zU0mXu47@?BcrN_@$qS;&1#7d4AzFX7@A-;wil-7Y);hbb9UT<-a_&6k1etRlqlnNT zcxYn8MCRyskq`Xf`xlegubC*n-0_@OF$K{-yjZB;IbmR?E|hO@`<|;S!Spoy?C9$G z^fNE(@2-dx;HmI&nG}C_^n*evjeKChtuH>x!K$;?d{2FROM7A$%oRDCJ`8{IG$huG z)~GQpN2%J?IPyvRTM+j(fJNSz`L_jRg?wDFU9GC7oG5x@5RT;~0wt_5&$-{QVCqqC z>)u|$`0UD5sAfuNUw}P@UXhQI^dX;tE&cKC=We%M;=mWKq4)RjN1Xd`iH_p{*Sb+t zqAPb`VMPY&dSLsw)gLE!GG~8U%YPCUkc1q|H{BGH^uP%ULW26Mlcmz`Qqvm+vJ-sO zIsf*=k2Ag1NB=0fZusN`PnHpnVf?}?AR0mJh}w7jVmaVU_Cp>$zA5=oir0#_`*v&= zq13WBT1%$rYH~HAjQAbrQ+>->X(f8)?Jq)=;jLCS*sQht%UDFo9-V(=DLK5b0)Z7d zKc(n-g=K+NE7u(KQ@x!#5_&jSyfqr7=8ax=Ql_|ti2WA_B-{+geWaTyn57rS2zQNc zN9QxN;!c9tNooXTe|(rr=YAuIda;F+aXQ|98AwZZ9ml)ec?GSHu2*u6b*7XeHw*j5 z$=%)fiB61n=G*tCpHF|avUVu^ZKSguazz&bGVxW<24zOf`GtW=g&%~z)X&EaB5oCx zCtE?4OKd6H1C#;AvYp^UzeBbOD>^>9OVklHZ=L`_zh++4c_lynW`Z|fZu&V;1fQA7 zOG}Dy^Hyu|y=;3NlYXep8hdmL_)&jjjIAlS^pn=kquR-WnY(|Y%@Xm3G)?}iA3SGY z*)z0J^x0df`vN0er$%V><0 z06wpJ!cXdNs>6RAp>}xSq1Dw|FdxM;v4?lH1sA)Q*r^?1bqpr5&`aB*uSDg>LTcb2sePbxSgbx#5m! zWT3ohbTGcX@z@KE@tnj^Yp%ok`t(@C!XeO=tpIsUN6=sWsnF zT`%HL6LnD5B5gpf1NYR0%0>N#|4-$Xmvw)6!U|?5h55yhe$`zauK-C1Mc19z(2_u@ zyLBlionXz{6nx_wW=CtOFJD8v3k=Abzv9KoHPP+_UcR7SiF4s*>rW?t37_A|)esdAm3B!k?J+gRO?a#@8tjjyRt=tqOnD zMxA`&6S}7Ys|sscni{#!2IL++r)y&@SI|b%{vbN@ehJ%)VTyVMIB&?(KUT69Ww(n z;bA#A^#$#UOTHi3?;R=(5#dW>nK*yCMeP#$@T_+V5e9G~e^2VrD_+az-!TNlS2rGf zS$HMfH`6u}vv^L+9tA6UiR#8T65wNmpjyxH^b^}}=cgj#JLl_j$vm7>_THX-zfoAT zGc6^wnyFMmNscpwWbJ&-lwPFO~Qi^{egFa+SmTInav(bwZ<$Hr^h?{KG1vU!G}PjVxF;B~ag^*OOwwlhi& zJ*S>%3Ktp}l~HIhHhk5IFH1;<)8SzQrn~7|t5Al;SrY+Ap{AB!t#!$$yDW4%e&&vR zRkrz+ns3MTa{WfJA&!>hBYT0xWNnF6|h5nJXMshMz6o`xHRL zdsyU=OmY7-Ut~IoIB>y{dKjFOEGc@T0TA*#d_DLa;)4e#6VYueKc|2Fat3){%~vX= zLjODy#z=&|| z`pkQr+BU>WH5pVtjowgyZ?3B7GDV6oBYAXjx^^@jUD1Czg}W;u=-bAeK~}bUEnQbA zbX|(8(N&?o=7J8#w~Tz5uR@nUe~ZH+IS`V`{;oYKor9kv(6!JIM#WHk_l}_o_VCY) znP8waSRE%f_KI>Hp>XMq3XVHQ8u}`dWNzIoLfgdWa;a|5m^;T$M6_@uJ8pn;)@sR7 zH^verwD5m$-((i^&042fDws*E*Y9YuT0B1QOx=<AzMfz7zzcF=_eWL0^rJv<1z5 zHZ5p!U>|PBlTPuAG!R}>1G7cCOG^|5>U`7VYUY$f4)xe;kvb|)0-)qK(Nhy4u=kfc zskj%P8A%X6+N3*`ufp_}3braAeu&o*@~65cF@k^6uBU*lUe;c}{Muc>o39X&6{L$;(dMl1E+(+6H#`4Qk#oWqU)Sn1e zXWxJIx>+3^WOEj&DK~r8@a7H7U9cs2k}vvGIjFs$TRkGWPiLsBBsPQszr|{TPT3kMS1auj)=;l;UN8Pw((Y9U7H(;u- zjD7Ipd*j?}2j@@uGI;wed(ej>lj)kWD-wS$vhnZS%e5Y7_Z+vX0_S;T2d5IN-| z0WHDViZ)eb{5|aWF*t*i4enrO0mBQ@Iqk*J>z3}%Nq1XXI@s>52K~5wYQm7l?7-hs zAkd#`!ru<6r>Xauc`y%l4dP;aK-1^1m#_7)U)f2_%ZE|)9P!wiPD%NL%Bf23qV(7i zPf2vRn0~f(c%rg)-;aA^b53{66Xkz*^fqswyMLaebq+6EY;S$DSHd{7MiO6B+i_!s zm+;DMtsCYhAps!jPm;~2*CNs33cI{*u<&+>`u*$J7|H!%3kws-yR`GPqN^LVV}*Ru zxH}>u!ESQT+r47UoczD>uT@m6N+Le&c0dk0>Nqye2=2C0Uax)IYWXSJolSqP{poG+ z#?T|&ZDn`wnvAaOW^Ml|+6H&5rF2RDjVeWxMvJ}=N;r|6Ld(G!*i6*5FLbj_r4-A0 z=3r$VXJ;~Q5{@M`AA9fWu{GrkBE`*&H5c8cJJWru2)(2Bty#-UwxP@ zbSo3W|M=Cxya;9D-Ke*T6Qow{)meNJsW}(lt?F+@rH)l$s*Ur2kjcsqoW9~DQt^J5 zH{XC582%~UrkSEn)f5UXcCZwcmcDl!bzP@m|(Jw0_Lq?>D(7GmGHyte=q7 zyxW#sZC>N!Kb23k8%rYXsxl8yitK&W(eHhQ{&9(vB zb8a8$Q{{>l3I=6WfKCMbd3TT+R|R@Hdl|v?$~m5n;i_rty13vP#QnxG8_h&#xvyR|6FpS03EBw2E*V zD&K&mMA_Ao8xtKgM4pUdZ~MM$#NLj#VbPz}NX{<_^UTE~;EYxo__>f1(T%6O#P>Pq z-nU9B$4QZbw_(?m-3c@l?81eD`sFV#-JAgT`?QnmP9Q|?Jxz-C@}cWymp(FFikH^U z@LNgV2jG87aa@t}h__r+*!^G|h45Y7+`zU$9nIVwzffa{reOs6BVA*S6gT3QRwklV zZM2yZ%%vpvHd{}Hb{=?YHqddCXVKtOEj2M2ZsMH{@7jfFh_K^|vo%-vulBos>S~DWiq$=u>06FTtuVQ4x}SeT@w(vq##D0-iRxlrcdO?Xi&e0i z$6fz~i3;Y;qrh{e{{C6)5*hC{uep7;FfTkide@wL3?oliwI7gfo~&0Esn5VAbvFh} zF9t0-OLJSaW0tRaaOf759Z~!Yv?+UzdxF*dT=$+#7l&kIo!{WoUwCm1YRXjr_Z4tT$;bJAarxqZQnb556nMTK^q?Rh~&K(#$F> zYFIw>ws+tiErNLS4{eIpKZAS!2WAr;te5c!1rq`>IF|uh2o$$iiUh2F0Wp`bn*$ZM z8VLo8Zvrwfm$91z6t}u~1w?THGM6C{1{Jpy>IDoZ0x~m~v6}-Fw>A+5@^Aq%mysz5 z6cREtI5Y|`Ol59obZ9alF*!Ljml5~^6$LUeGdMDrVG#x>fBFSbRDJ&k4AUUp9ShRk z-QC??3oP9oQc9PE(%lWxASp<9r$~26!?Rv@=>31+ndh0EVbAB=`8($@OGU1#!6ats zWDb;ea&%{6V`k+CNGPfE0a#f%m|0ob5viy&LGJdze^C*sw1KW}ASXxuzxWcaKr?qR zP14LAET-h-e+ZEGum`Yl0NA+s*?9O_Spn>lmjGG|Ish9V9}nX{>HskZpexA2%n_hu=57OY0DH7Bvj=E6S%84< zKL2BaR?x=X-I*~!&fn2r(P1#-6mf2af9fUcfEOTh1R0m^0$z<&~B zMx+90+JM~tMb>b#a`!TG1p>eXdyoat(G9HN;b;kT1%Q16G~^TkD$YR1e+eu8OMnsZ z@6rI+nA!eo?%&$K4S^j0Q8u%%aB^@qbMyf@S_7;=_CSD&v?8;+w>u-i%+d05^d zKu32s#NY8rf?R<+pK7A7|h{e@wRDWN`cee$GzL04s0`zyOdH5d4Mc z=Vs;!1h~6;00aE~X8bQgWMczZf-Kwt=0IzZBjO+HU^39^Ul`nZSCBWrfE7G=Yyj5Z zpZ{JNfoIFo$=q)BW4Z|I2{?-*}`w?Ct;I(*9=u|8UJ5K=wZW=7ZPN>1Pn zaQwfbI>3Les}j%>lb77)BN|Lao9(Zb2{_ja*!aRba;UCn$D z!7TXIH-JU_e-GjT zut@wtyZ{!-KZp;&BK2Ry!wO)L{)5;6EHZx(JAg&@58?o@$o)Z_02cW_2po&T9|Vp? z@ecyWqVxxWV^RKtz_FC!-qrsguy>6=2<%<+4+4AF`h&pUwf`Wncb)$t zKClP^d?ydrzs!L}f2{w4;E-+p zBmi!SjgPYp(D5$`Fb(t<1XthgF94R8kTUQT~0fNgmE z1;NGi{0oBX;q_Om!P?$`L9ltBzaY3)zJG!PTk-|E{>#<>oL?3muHa(1|8suS}`Iork!!28LAzfPjpOvORk+Be-EheDRecNAsep0 z#8STZqOWdC(cXwIlRf(#wKl?kX#1$v{^P!6ma) zdFEMj_!MyNOe6^Of0*aqXtOuJW-xOmUdiMW84v@GV>yca^f%e#?&o~hDmB^Nx(O%= z^zlicZ!kxvsQtwEQ|0j%{7Q>fzb$&V3Ko&&pfIMbGNCi?vuD(R)T14n`|3PLm%7+~ z=B5C`Xo$XH{rp&0){Jf9N}+;N({2)t7-5!HyNzA5&L7{qRI4a#FM5NMR`K080}L>i-{Pbh zIPWIzn4p;;!Jk~@V~HNPN0-%HEBSCd_#K%;Vmc~0N-MP!ix^JQe{1Ca{NN)?LSM>MR8nOD541i`v><78bUKUAgi#tb9?Nc1n(z8eX&)BWHJe z>?c`3B-7R>e!m?bF$l^Q2mU*lkVu;0fZ~}FEL&s4!`J>c;cd6)Jz(mP^}v=dEIH%q zf0s3uWRLS^VkStwCXaxmB4FYdIUm>w=b0uqN|HFsJ(8gXWi^(`8NNzE;vCdz)3Y8F z6VtHI4g$-Z9tma^_%PnCRpejS5ER9cCAvEjJSb2%GnJEcx_Sls&x+G*N< zjTIrt+%h76T>HGRBHY}%zWp*t8oSsOe`Xz)Trd-ED#33jUAJVK$HuMvcswIeV{j+QGuFBuC^FFSfzYj2jW72H7!x#iH; z6mlEZ2jw3^hSp>g!(CZDZhTJdf5`1|_`Zp!Zs;ZN`DQ}h`B&}54sGE3Gk_gGQeH%? z7TTdXpt-jG2mkumR#IM?x$~yf62l2LD@GNhn1!Y83uIPBh9Z`giNdu))5+=LbDTUg zq04(mWrG)bbsx91WXdCcbC#0^QGniOf3abO2N*0r ztJ0;7z)w8YGi0}K?h&?xGwFKDXcr+zk@2zB2Hr$=8*`Ji%7{-i(MjN2Wqz}d?6z(Z znk+bghXKbZYVvBlLPQQc?SHFH*=+sCo!% z*&|OEJ7xr&Wgq1H7~C!|e>j9+@Hj-+w}s7H^~pw)H8u{(&PWh>M-j^_R@e6)GA^cT zUEu}O#-y=UXLi+PcdU1nR;TO`ln&WK$e9D{7H_iNl~qAe$#VGJoN;?<6}KF2d*3%b z`)FskOFN|3y1@R+9l;8(Nh_>OsjcmVw}iB2Wq*MVMF*QFaTayje@AUR(ExGYWSx?d z{I>7zxv+dz2-en5aJTE*@NNrl))Yt_NaB$KybW#!$(i*ftk=YH?pG?wMsJ|61^GND zNF`~fEN%EE7)hg@6s9;Q--&$H1sY?|2F2r&_Dh|03r8k5!8w4`X7ERZvlU-UGc!n^ zP7ou%UzNi6(;h&lf3XX15|Pu#1_u+-5Jpk$N98F8BjcgY^AJPL<~s{xpj_&>Vtr+U z@B9vdr0l1-#LrMG-1l5`#Q@zmmYRCg!mkxK@(Z5s$mViHuw>1Bc`*>G$d(ORExVeD zBj9sN0okf1BDRJHlMy}){W+$oALExGTJ5IE_Pt!Pjbg@Sf6vCRePLwe472n4M#a~( zCltDlA3(iSHKEBRdV^QWLXtkM9HxqR9&k|{gbS@BPs{B$QF}EOi~L+6-!%cjKju!U zX?u}IY4w;{@NV?KYWqh=Gw_H_g#>Vx!#BM{eB3x_Mc%CS>1f4TndZ2srd!-4@tfK^?al$(1G5{5w-vlTo7#keoU(LUBzW9pAL{sPzIzHkh(8KXf(21Ewg0CLbRf4lf7kAE&Bw8`gBL^<`rNv!u% zOw39#j!T}2ii`+ZnDcLkQYS_=lmg7LZ|87}mG70xZ)Qf#Dmxz+6!w0gK?UE#Y!qLq ziEcqRy^7ppdaAM-$_B4@IN}E8owDBK1lDuX>`t$B%d~V`Awm@4b3TLkDk^ntoGLog z2`4yJe`o4_XZ6`8mvm3J$Ik)btncc-d({s1DaVRba4E1E0#Y~(Uqa+Hul`nsT*UDdL{vU_ ze=kC=&(7^Xg#AO5HCm~)%U+)aTS+A#kC}^0^GVYWD?8GIgN~cD;~&?Gs~x zaeBCM)%7xw4`iwNtRutV^M&u1*hWF5-*sxz`s75urG*VN3>C8zz3)vq*AwuW39)v< zy(B?X?&cRp~scRrmUse|+U8P{?KbW+I9s>u2g6yoVm|eN~HSVqI@- zI3U;T9(hWZQ5P2-!R63Tl>;Qc^=CclCWc`N;?M;7dL6@r{yc$o2^dZ-Qoh-}FL~K) zc!A6&mJv!A_x$^OnXOImH->%smB_#{hS{(mvBV&c{i z_E<2R^GLxig+m2YbyZ<&T+&Jv&W*_jPD%!$s+)&HC<)CpMxp6|Xovc$%3Pde6)PaFeLk*)gnsLIEQRt;Y! zk&^ZELah-;)!RcTMiu#DUjM+Ye^ZK>vx(Ezxxqe_dM9hz{&w&E9}*kZC&}$_oB^t& zKqNG3C;JY3)}-NEnr@V4svte#hjHz8WvAod!NusXYx{3&s0g;%LIU1rF)#g9*BQgT z9B4aqa7*MeSMK%gi&pYR1mUy@H^c0Ht8^e&wpd=4RLe}gfgwFMF>67sfBS{xQJVv; zmw@YS*On$|VUr?ZPz-~3Uf&AwOGTdXeju-^dlepZX_XE2aI0PjqTvwe9Xv@$Od82n-&J;} zbxaUOM58E2MIJ^ZR6%=fe>24tON(~G@HYQ`CWUf6ot~jWD<8Mka$WEK9bP6SY|zHc z!kh_obp+|vsKGbUWRy-T^0v7X_UP?c<@p^1&)CX)EXg?=M#i_9gpjE%Tb17zj&+4r zF7ZWUEI!7NRfmrwedH8z5&juXe!RnzK_j6)fn3l3-kH>p_R3X8f4fc&X84N@$5?1d z4H{dDN$3)O-W}qHxvw)}*F$QNZswlti0(uYQ+*x%l)^rzLPtzL{P&_NCBKrb%8FOK zw2hKBkFc=x{#@G$fwi7;n7@Y+(dfsMzE>eam+EoknHg9=*4l`R()xI4wX~MM&>@A2 zHtGFbuA zrHdkxQ^B*>e?~R;ztVT!?0m&de-$5;5y7qJrD&uGDs`Xcle*KuryIhY9uWWO&F^Hk zbr^27k5CgWNu6dwB=|y$!gKSQp`5Bn(?&I8!uI%86MCzfn-AK@SvsoavR`-;{oHVa zM#399P*J&^ZhGKMdlxo-J^7~Lj8uhnjpDI3Fa0`le}+H|V3;ycc8(S_)bEx4GIro| zat?Y)y8vGkhC`&<|P8d+5voY*@ zt|y?4%Ok2c9#g9qa^+~vs@e7VwJ@CP^7~h;^5s%8vy4mn2vlr!YzE8yZbq+^+`%8( zB@VfbfAN7GS7K8yvvenS{&GKkGK+$m(}_g!5c3mW#i(M_SsiVG2p^gR<2WRVjL^za ziK2x_6wYVM^#ka{K|61KZY^NssgLZoJmwGGf?li_bzs*Br3}c_N$|X&F$HGn3h^E^ zrS&#~$3uHqJs6Sj6eHj@)7D4gj^l1g`pE6Mf1RNxXzvo#<*7L%r%QImKln=0-tq!< zZW0bsq*_z}sc@30MOj-3Uhl=@3cxZ#(PFx)j^S*LY*GTd=TdGh)c1sJYv6(o3 zup4OjCMf>l8}+8da-xzc)=LFbDC^Ene@#4rwSehetiu;1;An#Xwy-z@YrGOFanyxj zYz!=yxum_px=ssvd#3Gyu>6k41#P_gYU$==+Q5fCrN`4uflw{!+R2lRFJ+7E+!pTr zv{&6Dq}>g0Y7PehbPhIe+OZlBu-ZC7O>C~4YmkmfP-JwOYT0K&kr<1OU%MN|f4h#n zSd#q?+ph`}pEm1oBIGy(COG4)x31k7>n3-1BJs0&KKHDgSgN@p?{?TlkQ|4bXfTBO zL_;Rk7KdL5`z;cklSvj~D{w#$FcJG=(MW_RoJ24tnv)Zkp~_n6DT+-6KD&hO&Ev72 ztT0=p$FV!!SU538S6i=f)wq6tfBU}Wm;XVSLk%m}I{e7aw3Ga`bH8lNg+ybslZPMD z2?=K5iz-_H?))1NKD8!_T~9vwxt%_+V|3UN6vcY~?us|u%)uoHbOxk;q&4ao>| zVm?SUe9$=E`bD_21QV|(f9G-RgRkpWC4z-SI?9$!(*5;nGM>Ls2DaL=X3qt(^jeux z-ntz2!BNrer=Pfs-msA^LCP1a7d`Q1*Q2U#t_we6 z2s9rb_@8KSM(Q(uz7loNAKFBGRN6G=nRgjBHQ$;($mne;QDcW)K8ylc>f!V-IT5=|Rw%g=oJjAd*59)wSy%OTn7$BID^e#u;+7zC6#l9fw6EdGf?Mdco>C7L9tSp+;60Ak_^t6(elP;^p%xEB#B72d-up}fADc)3jE11&lUEBxu&C; zxo8!(9&l!a8Q|ZCGH|y0%!!ubtixu-O^VN*8t9R=bR^%rsU%@CeZq)IiA+ws7)vBC zDq-?3yHVFxa^-$wNr_<_dt2dE4J6EItBO6LrD8T^{&k7s-u)5L6#rr* z+l~zY&Cz#ie+8(XwfXL?~)n|sF$Vp_+GtUDjxL8gLkNQ5Z)G1S)X6E3ub%x zDrI|Jcx&0e7fxVsT;z{mUM>X9WnAZnG6!p@L~ z7dbECnDw2qCRxRI*vGFqw$C6+NyZ{KI>YZvxbk1re_oMb?93jkp~DCj{Der{$Nn*z zrSpXd8kg6PG|@pqYq43NSF3UaWH4X2*AXhkL51D9fZXQ5{p1qMk{!1{SHoO^I<1#FBbLa)H zr-V)te>8t9Ww8jlakO=1BhI5<^KmdwQpKzc#9l z6{kJ`)*CLkuY(ri%{yLW{GDO4ku%tUv+X=%Q%W+RDa33l)l&2QEg)AL<@1LpA}LXx z!FDzu_6YiYiljR=diJgk!$^7vr5D__%(ALhe~USso?XGo<{Mtu+~od#o*8WCbKd>p zmtKEFC6TK-O5vO3qzp z%{SIkOokeH3HaM-QS?QBx}4KenIHRJ5f1lw)}TV21VtfDRh0tBJF#4IkBqPSlgc8?`h35T$_CVpq=77;8h3kl&|v3l(02SSZ^C&8qe%@ zg|Xg^V;sbHuAS7K`j!lr(V10@wt&FNhhj;7Zhvkv!!UX4fX>#@N0Eok1AEL)9gQV# zNv(uaVLNeWZvYFqcCFSG2MShSdnJuOe|jQ!W<+AbW&z6W6Rwih`(44vt>|P4+}T1* z<=9do5hugepE3K)eS|bnwAhYFOb`Op_;1!xf9326GnOJ;VbLG8hS{lOVanbeL^G>J zxBz_T+C0RGj>=d2SvvNW&0wUbvD*z|L#R_7#cQ1w$>@g*W$6HS4q&<-YH)uxe?c{D zh`Qn@Pc{wWyxHpD1X;(j%=??B61sC7=Kg1;T_^8--vnCywlEYz$RbK>_7zdx?Vv%Q z&k$)2-yXDYh+YUlyZqzO4k8t%Gy8-xNNfGJWr8g&8J^L3jFKeePCC1C@31lWeBB@~ zY;V|i=~o8FPK%2eHk9Xn1EFb#xjslX_XuP(m<@bCa$TD|bq0j#MOA zPL#s-p42fPTb5X~2g=S?XRdvv>r;?@99KfR4BEWWX>K`eD}2J_>IAxqLK91T`!C&_ z#VWyDOr8W=w1>ekN4D9H!xDk^Umk7cby0M`hVY;ygHg{7K#Fc5j~@g=f9+{ssIYs| z6OfcCI${VYdQ4@XTU-kV)%nz=nUfxucl(~d>66EA0^9eh4g)2L+LXuRaBi05VAnS9 z$E}JbAG(9?JiW67WcMi-Js@k>A%Dd0q8Pf&SU2k}AAP0~mnn84l{{SI(|#@&Cll4{ zcR#nQ<(XEV7d&mSzQ4;8e@_oK1YT+o(?{Z==kU&t>J$VlFzLf5u>q-s4pTdvTQiqq z+qc@Sz0KLNSjw_z4Ug40AaY&c^_nIF_x(>_6%R4pxM-MdQ0&nL(QuSRM`myd8|QZ( z5_}#CesH7XOM2#RH;|J8Zuz{?4>LI;(NR%=?`9(%jyd4{sSLDje>(cHNa?32FVAyB zK6>b8!<$r3U`8G=j`81P+xM(V*>KQD)arPNJi5dv1r)fQ&yfbNg3kI4W>K!sUUabm z*M!q6TPg>qLZQuNUhVj9=8s)gyda@rIfju2Pl|R3r&gUI;HVo{U~MM)`HYWo z7k+nUOl`o2b%-3@e{dTeuJhVLznipBWLT3bJwY{!E(eQr7J;a}dEe8FgE6UNuHHB9 zeZrU|WwR9_FF$6JYo|Gnc~vaFZxGo&nK16*Gy_lTcJ^Q9_*!uQ-_$P%PE8_xqgBkE zFb6L`hicfhacoCbOV6z-R_Cq2E#*4YHq6~P-*kF{j_I(+e}a{QbrR=JVBug#K3u(9 zNHj>Wz<*N~bqB}gZ(mG;sx!*@*eqzKd|G`6wF0lM4TKE-Auv8t5nb}u0bfvhbqOG1 zXb@KKEOH_d!ZKWCu6Rg>WSeV&GSX{|E_J>ohgHvDBm3f;C?ihEuDWouyymw7)6C6B zP$xR((0ZIqf8B}9sP>WX9D<3(;HDYVJGN?8n8kC<$P*1)B8D5qiuD>@M0B%b0vRr< zD-=U_4629|B4LZyF*1VVhx3|^|A`}OW$^tpf?bFS@=oS+B!Vc0I>ftFlM{m%hR{Ng z@Qz(EZc?i0o3V4a1u8|G-KQiCcm+b~J0Gav0oLncf8tuF=&cB?X_oviGO_QT+U0m1 zTLr(^;y9Ll{1x=+-T9qV{;Zr1wzqI*olx<<^2_`OR_P1na1_>#X9ioj{6y;SzX(0M zMR>;VRSGGc0n44K7~C#A(J6za1BcqzvmzOec>z`WR-cNl4@(Zr<|p@=#zgnWXnp;$ zK51xBe+%^n^h*EOE(vxkk`^S}>*MqZ6S*OBjJ~kX>ffyfti*9Xc0@VDsD%A084kwVrvQ|I5!r z0&~~4raJ3IuZrryv%ZU&biBvT$IyuWFvBeh3D5^cGcCfSQKWVB=>+`Vmx*!=y%{Vb z&DHSb)7QRdihZj`T111=yvDfJR~l|PfAQ&Z;^l>E|D*$@^n>!R79?KnlrnRjC#Pz= zJov&%OddH!GYd;aSV+N|>WxfTf zi!0WFc0!b@B>_v;chLT>_XnHUpK!@S9{u1m?BwS{tF#B}# zAz&ig{jxvOm+L(NQ-0?ydNH7Cfpt54_fVSH$ggBm=CPiWnfcRQ+|EwfsxCC#RJvpj z0fM(YAzpI@k+1e@De@8hyg55ZR?kiKKbS;ngU_A*|0_+MOYBW*~1U zrkLl{K3)_x1ir<2(0a5jmAb}Mn9*XK_IOBHPr)T_QH}wK-wR2cONOJOdUP)G>C||C zuG9ZesxiD8!RO=sbVEirmDmEizR-$B@WiOMa&;)(r&XT}QuGa4B{^6Sf4Rm9wUg=y z4}T-Ms5m=F4ZR1$KYubF75kzGIkH!i3I!EfmHd#Xm< zyzD;}oi1iSZ))04UJdv9guskEw-UckAoe+m*gn3Se@KKm`}+k%()b0wDpgm;BJvx$s+RbBtUbfr8&}bSHIa^| z$2v>S1riNI1lZYrA@EzG4@4JGW6N#)Q2SA9{QkRRvw>mr{mj)!NcQHjEjT)7Um@HBcoIa_>Qnpe@p(OBvB-Nr21V2 zF=q}w7d!o2!IK|DPgvVDpK5)KUp|sAy2l4}3R-0~uQVw02=wcn&^WaDWR8jY+P%P! z_MfU5(l*wl*nYKTB=pkXLrQ7-{7jZX6(gQ-s}ZJ5d)7xTi3#}{XIf7j^ogd-fUBF; zS%_^J36%}n#7%VVfAlm}`bKYgYs_!ldd+|t=XT!Y^CYoOo7I7QP4(%Z2z?F`HO$#p zIGo{LZ1&qB61`6mQWSgRJ`ajIP}#Mt>r5stZ9XOszG7{BkYvULR5*+c=u#-~;-giB z8hNu$5pFDXjYI2=FVgRIx;xufoYf$pUk!y`fKvk__w|0(e^hEBUO4$#I|lTJA%D3~-Ic7Lv#^29_YTEeokGxBvof1I8vs6oh(9+G>>mros7QpYq{jTqIRp_Ecv1(Kgtq53^5$yh5enZPKaoR!4L)}*R<5XVK#kGp_rscEBcIazPtWW1KH zPz2=Le+uEY=}?UmjrRhQzYA2s{U9$|c-Th1^-HZOGPF+&$mkF{?s#Le_9%z&I)Ijg zG7@p(v3Dgpl=hjscU(Yp>$W-)g%|qZHGfhrvW*QBdmYxgSA%wBTiyy#RuWP+LQNiX z39H^>ez8D713PLXQ?fmL?7kCx5&r($s#wetf9ekyI`xN{j8drs(_rDYvQLoBG3tHT zhU+pMY(QNz7lPHcVUS0{Vap}N3F=9vMw-V?qT%PyN!xJROwbc4pI2zjxw!*G<^hZF zf&2vJB`@)zJ-wiWya?oy%LM{w`;a>QU+ZlEhd_A0HYZn{$F{{meOHwZTXXLw+M)t~ z{AkBXNq>>?PI-sX+o{jp(*``jIy960P}itt8$TY4!BOJ&vXMj1J)J0i>s`fm|);gJcFWY(eZHJ28DDX2a&X-iyxeohS!~x_8t|A+E z)=QfwPku3;!fi5z9F&$&Um3%=1)Q4c_+)g@oPU_WC3ods%CR%4=T}$xc^d%!z|#@T zCdy4KbSIbAfM8t0fN!jetlFU9sUkxrU1@_sPpNZ0>I^F#Fxpfb#PAJumR^Sf7_(8- z8aJr)2p6$-=nHTu2RcE}IhKrY7F56(E?`E`f zlRvp!j1uJOl`9#fJoGLeiM}|^R?G-@PH7!3AUf&Ipd!By5`srv`xsK*w(?>XJFhA6 z*N#Fc=>Zv*V$U?OT5I2jkIJ8j%Y1VQEPq2ZdR1{k&9PDVhE7Gs?-_4;$B7KLuQ4`> zi8PubX5QqmxhX*v1-sL<8GT;NM1a3=opX5MLP%XPp!ll8Fu@jt`lcB7f-Shy1`#cM z&v}P@9k=@e;DAJ@0dVP!6fLnn1;0zj5Y##r&XIC zB4dkY4)V%7@FfV69D3k)AqpaS~?xJ`f)3q3fhSofw zYneeW6>^0`R_+nwAB42;zS`WO(noAgl7#O)&BO``k#+J$Co;dQAjHBO)e~o}?~Rx3 z?Dm@(quXo;y;Q-~>IrIfdV4dK()bX-uPYs^W|g;g1(wpoYU|%tPfR7ZUw^~k>L?A? z3;jF~%a1wWU-0dgN`*-+oCr|-odIgBW+VH9>N$#TX_)=v7E!)oTbJHsmLQ0w zSG-pepz=`{?2SZ6QemmPL8!^Ngw$2qy9@35sdh-xokM9_lHFgB#SXRe*)k@ufMeV9 zS(P>FHIEy&P-w?Jlh%DYXMY{)qnN+l!Yexww)ih)rLx<7`&`8v-u$tlENwz$`B-wY zNUJ53%!&=%+Lp)sZDEGM&=O?5bHJfnD;rlK6L3U(E-|(}s-!{~en+yME=wa4t*$Gu zy%b}GLdHpsS&fgq$r~g1OFrRP`$;pN=aFb9A4UWo(1#^wl+jQtV}IHX_@NXzR{&n* zv%oIT!%ZV*X(v$p5kQa>vc_Aw=%qup-!pJvcBn<^~f^mA8Bu5 z(IFLO82gy-!(?rQVA5PzPUd!Z)G|Y#iT$*Cba|uj;mbF8o%IYY!zeB#H@dePdSBv= zG6RV*h`&yeRYwHdtbggkVAL-HnNlK+Aas3|reE={AsdgBc<8H#g0+>x@9{ZhGd@3Z zZ5fqw$g8|kd8BggNsxlDRLYsL_3-u`*qXf-%*Yp7m4HXOx<+iWZz}uu&zFa2090yF z7R(5c(br@JMFN8xtM^I~F)@heyuSXES6_5|DT7rKuE&w527hI#Lmi84Lv!vj1p*}< zxJAB9T%*iAr0#BaD`gqZeLEq=&+@uOyn{A;3QfPt$B;SUKiBgpCOjGOXR*qZNK1yJx)?Y-W1|ByuMt6*QG(E3}snGjyxdNIkc zvd?9}914H~@z83@Ynz$NVleP<0Y1V?#7>O*GZ%M7aQ#o?YZ8*00c2=}k2}n!g`aVl z67JNQ{D0@ys$ia-za`UcBPVswKUxJDDxCeSsgri-!ZR6y4@KC%6X>{6h)zK=*}l@< z{hk^ZEm@_y&>W98Aur67fd*vjo3;;4tm6V5D)1RKKBP=zZdh-n$el5k~`8P!2- zp>O#X?L3^?2LwB0k#DdZM+?_nNQSvdzY6)WT7SBU-4md1Dp(ZreK?(6pod>mpdotT zU79XTm&nTVucgw=%O@Yyj+O#F*3) zaUN*CA>MXTu02x%*|$hexu-<-dpG8mD^o%0+GiKh$Vf6F6H`5o9|cb{ashals4yy= z9E{Tt>0fYq5XHS}mQVwg&DmbJv-ALA!++%=sK>lcqE|?tAi~tF@+e9ar_xvt*9@ zsac3hy|}-Ctjcz= z*VULb?FYGC#A!Qh6ZcgYRKF0lA%7GP{Zd0e9?l4)a~V?m^!imME@E*83@wb$=o;^= z%D^husWl7ADDehOCU`3Ao_v8Oo9P8!d{?E;oBV>}r?S~Salr>FdMFH`gyFnF!@Cmmu$oa-%~cA5o?6=l9|;_A%kn zkmoIER>5rZV+6-{XzFpl8V*S25Pz|sVkRZAhu5oLLNE^06Qt5+yp6mh?yU1pE#CKN z;ZYy67bV$(yU+Baw;Fp-y?@Jln-vW^M_1f9RApO$H+I^}_g;QaVQVOE-I|XSbAeRF z(0Pin^ZIamuI)tIctxtprCcamZ5&|{(UqGkOAC3T`ugh}v&X^86o!DR>X=MWpG32W zpNjxtNWdqW_4bC<@$}$Bdb&p8^_8%t+Cg6_Y+E=*{I8CVV&(TF4}a!7snpmxH^D42 z8^9*pxoxDMw&P;XX2{aM7^s=Kz9~i}Mw;B3ZhQue(3u%c3?wm*v6qg5^O?~gDF5E} zwB^rO35#p0P7;^g<>WtF;U4!AQ$;^QaYjJB=cyU+Xw$*t&Wknxy#5reARjnnX+xh= zH~LX=5sOwzBsgH0$batNYQ@u4Lb6NHEiTbQe!J_%K>lWIiVFXW@EA3W>QYG4)~BZT zcEJI2u%$kxMy5r*Pf+PzZof`v${iOIbL1(7q4#a&gGp9OE7d_f*VC(r+q6k z@V3=yR+?9`&V@~JVp2ZSpPAW)D!D&XNhEo_+NLm`XksvZHnpX4zhZr;(Psr==0mOn zVTZNUQsaQXP=xw5z7if3Fr571w#wa*e~D~%y)C?KI>_gJl|o2bX=P4G5ISn?&rn1X zeG}b5@9(P0^?z?OZ(@l&jvd17vM!$U&mDS8r=^5|rQdf-z9d$z64K7Ygg{f6?vpi> z8nYuutaID5&5_@q0O7md&l?e`}8DPNy)x-k}F4>J?qV&L);kz;g^{w5%FT zQC~=$cEEx0J5=>-tl`M;_ z6a53&v1Y#Fed55+XbjhU7fgbs54xKJP497=3x9DxFHe8?Fg5W!4wJK#Ed8SdFfD9c z;a{Nyg^Va{UIq9!|;p%k{oPt9eVDTSdtc!cExM z!a~y}MVW6mvlkbVNA*{hMf=Okt7(wp~NmYs)XW~AcD>66TlXkq-ntL@==@4Mp`y)!THayQD|V(bPH-b;WThij!2>m{h3?{ERC_JoW6FzxWQ~ z=K4O1lN(@bXpkL*AI0v4c!~P9fSj`7`Fd@!CDyyU-bSWHazLw^?B zcmkOi=1r1vq{^c$vIAb8&BTuV{mw3+#`$a#>|4UM4^^*&Fy02;Pygz!q!64>D1^~B zf`XP|6R=5A`cOn}T3Qoj~{ zt?yRY$Ev%FE{~1VGy~oIiOy|psg{#n^nyhk>ke5lDgR`a?->Z5HzWtP9#c5TIHgPn zVYobL@azTTq=xztWFniOc!6oh>(Q-#mH!(|=)GeU)nL#oSu79Xh_KE92PObpD zyv)4AHJ4bMBkas&aT$M+8GN#b}&-6-%`FH<`xoqLEwmrU5qL>U@nXb$JiBq)OjHfqRM zMao1GT^4jxzyQl%Y+BMTvZ@#{UHFVyqn-)$`j=R!&u^DMV1LZvqEvWqSVt>yX(H-u zk9eIgR){$cB?e8ry8AW~;JYyQp-U*c?DFP?r)__*(Lhy3o~Gd@yo_?QEM!we5GeY( zDB>b`7C$j%IYA7p@Mr!>A+xjKMI9*tiK24y#d2Ju?DJ?sKb>0gl0&a|7cW+3bJ-2S z^7Tq{?K7Uy;(uynd*yDA<4^l-wtJ+y%5_sgjP8@(5-+M)R@gI`c!w*`#859&>S-yD z{pnbFDA60Thbt6VDvzep((t!o4Ca`^r-BaM4-9Q8gQTr#XBekXw{)3)2wVDWJ|{F9A9ctC@Us%X4}#fqI;MzopG2e1~t<05cLf;78h~=ORMZL>(vCT|Oh4={>UkIy1d+Co! zo=p(Ct3};PJ5E2Q1|8gt0io*MNXA zEi|{S>F<O+vOnQYf2OO@Oe36h7jUsIVN3|SR%}~Y2>-5*L|oZi z_kRjv(ynn+kdUT)V%B@o+%J|@RYy&;JODxiW=MX#RGXg#PXX(fxDD42g*p-_;q>$} z1CE$H!3_2fD>(K&`|s-sDt_p8ug3XT@kHTivdOfU$&}z!5;h5xz{{yd!#kwlvkEnQ zmT%3Ov03I9$1NMeX=)PMVnnWJlzJZgS+!H+ZIkL4PAtl=?^b?UQ4 zGsM7DBc|mo!bn0!3z&?&DdDU+4hjTzioK>T#I4R|p|Be5kcU~=X~sGt2lrt6$Om>m zmw-?69=r8@I`JF2s(mn-RZ!lk9MVfqwrCzE(1e;%#uc+5uWXvYxN*iV z7eQULfb0RG5-W^v(jE@RHmwl-+LFlvv)J~xi?+N;7ojmgE4V6CwmEBi7eFlm~VnSd%moABQBT1$G4qzabqHETTMjZ zpAOCS)i#ra4%$HESQ+D7^_QC$OLLH_OiRj#`{Lc(H^BtwTam?QsZ~f#rm8o@ds`1N z4h%atXg+)nf#@9!li~O0&)+9FUxj1xmPs)$cB~RDP7o$iPVh}rWSEF{aewcpPMq7U zcKymhqc-^p=twdUAEs@y{CHvv3bB(>^^y*+h7QdYXUA|)tAmOH7ANt3ynVJGrdO56 z2*^F3C7x{9>122}rONqE)9h|*<7rYHjyh;g!UkxPd%Z$!@O(NTZ1tpQQ+mLqE+d2`^() zVY_;mA8itGe!ulb`D$i?nX7TTIuAf~zc6O^vKy=}w(k%9y}FVndJP|FSxa(-Qspe~ zLh`861xN5gKvtDW81wdU9hurfHHc<*V6{g2J()(jt~`(7P^;rh2Y=SRKB(f?od#ln zcd0NHQYa-L(wx}A4t?m^e#7I$&b1vvESL7i>8B7`*W+fB?`kN0(i!}i%1er{pBBY)nvOgo@!Mk+pF6M07g zV!OhE?Oe()=;B__<`;iBeBXOx+PJLbhIN@AgcFdn*q&hv+kF_?nU1l39iBBfi~mmm zy%9q07CCg8)ey(Wuehfk>yWBwYqc{OTykn-F~46&qBfH{p5$yMD!F`+|Ow>RmPI6aOLQ2rR48` zg>EB{;u@O3;X4+c*AO-qV%MnVYeYo#Ww_;em6?gp>OM@taz7{h;^4;6tJY!zu^x9S z&Jik7R)m)d_jfYQDee}E;p-Y3{~PWIpT!C#;b*7%V}BFl=BJ!QX{!^%r;G3$<9pwz zx!Oo%Q?vSHi%x-bZ!>=@8ivlm?5{?zIq*ofq@aoGIqo5KL8QUkT;@s4>JAgo5lrMO zl&p#wXn!Ip&5)VkeyBDUmbtuvnVt8a_qa|R#j$}4;c4&Vs3xK!*}BYiK@LXW8Lgb% zd6ogmu76m{H4i?R5Ms2L&)cGqGDx-i@g>(gJ<0@8W}w7p>bTzo^&Nz5kEVn-@xGB zyopYm>BrY-DVWZslI0HzQ6|2FP@>|*>`9X9O77#Y;voMn6i6EFNrEeU?a%d93= z5r0Ss-~jKrdK83y7|3t|PIp3ND~8PA8v+y91m5c#^{s-9UswT8dWAYCuly~nRy(a* z_W)&EEs~_ypK>xQlHjc(T=RkOFTaD)GJkRnsllz%h)n$f)GRTKduOx(l}{+cKQ2d_ zC~?njvZlL=QyN^y)>)PXs)Qe7nv^MHwDSt4=-3`Z?^E0}L9_3I#H77Zo=-{diq?72 zuW=?bIQ$taeYn@kcD695_vLF1Rmr&qe5#dxqp#Qf5(^MSSjUH{=!5HWmnUwY-G89? zhuE6ogiuv;&UEVIp7kNplVWk!vl;n;RkMYhR2Fg>>64SNmWQZhJnce&Lol@*!8{m} zi&B-@j~eE-h>KNv4l2>(th{>LVpv}uq7Fq`3`C1xHl=&|!vR(f{qzU&uo9R7PjoMn zN7^vcX$Dz=?6XoMv0NY|C7aG7^O)*Y;Gjp|ko4y@pe7VVHPpb>df*eBl(C{`H2DwT zK#XY`g#MMFcB1jwTxR_Qp(=u1qJG-l*Y!BE$yFWAmZs=KKNU5ST3`UvVSj6%E!79y zNjKS#asfN=TQd3GEMo%W-)M_1#Tt26mrzk&*HwnrO-W}X?%f;CMD6`GGJVwMRUI?1Hn1GvKB2?HW+j)u%j3r4ArQ5EC0UVYu zhc)X+?^zqDt{oa;!fF1?9)C3y3xaLdG3c)Z%eM^&j+oxGR<)ntL&BPL5QEYIRr}C* zAx&fDwC+_?oU$aVEWGWB?RYa24T%47GK9+fu2=`{st}6V2t{!kh7DWq3AK)A_N8Ue zwH*zF=``hw_RhXPknkSl2+?wrta*V@8G&(g!ha_KNJ4-gavhB@5Cp(hn8t^9;z&{Hohm#kesgSsex)Bt)=s#VpwHQq_dzL zhes~+j5TV^QWj26D44uezknd|9fC(dBgP@3bM5d)5&l%G)0A-n)%*19%e zb}x!i^@LRQxS+B1PJbry&tR7~&xsI0q%9ot*tasVv1A}l=DvNuHCFCQ&m&&HHrS|* zdZwT#ZvfvxUML{SXkiW=b9+NFY3ki4h2$JICvRh#-{!r&Ue$S;Gql4D_;Kc zpe6&htUNqt?BBc>i_b`;NoSvjoTaHZ!ez`Ts6I%PB}L2P2Wpbj-TX6rvx-s7-%UhK zSyaqG4xA6(SbuzyIIjk0j=Cmwq$ky4MRIhzakFa-7o|*L{-&aJ7I&X(9A08~;aw|fTsr1}!}e_7M%G`2VyRXg?qpHh zMTqbP-j2Z_J*U1H(jeKsM2YWyM$Ag>Lqfuimf7g$2!C7P=&+(D0k!~=NYWr#S~ynk zHZL9I&5Js%(cT|b`)+>;rJm}{l_@|2B~4E~9Ps2P^SEt%J&1<8r;cWSEN{w62;JkN zz6fbq%7*D!x!nGFq(PJy_G-DBD@*y7t zH$L9T0e@M#$_+S>d`P@8C)i zj$dCA$6XOC`NbAdz@R}5zXDdMXFt_v`Z`mvWpN>RZJARriH$`)i z#+;?Hi%L!zXNXPy@mZPzmfr!5inw~zdfHC=s>qqJ4t%1_9${oTz(?dTM{EzYsze4r zpnugO4IJDwSKZ-)dz6vce!qnk6^@C!Du??@yJUn*$?yxqo#=o5wo}->5tfdDjKzCJ zz~zsi=R`|IgVP(6%Tz>rTdQe5`abnn3BLQk0ES|5p*JU1;GL^&h?7EU8iwf=4LZC` zVIVqC7KyYdJFQG^wAQoyXEy3H%m?MeKM$RXcj+=dgU3WC~uHxHLB`~0jXBDz_@F< z`jp~vrvLb9(!pqH{H8^6l2Iu#k6{0vDmCOoBsC1JL;JILNdp}XJvwt}2L$z{hJO#E zhbvCNEg`cJxdHW|%d z15hDOksU<`SF=3$&-8E~)7NkX`A!R8q^s207Ieq^;JJ~Y)UR; zid$1-43cOLzT8E}v$rI;)r8U-%rR`vGg4n9GmjZt$^%V?tkN~8Qw_zCS!@dsfA>fI z8lmI(m|1><_h3G-Q>}Ef28Cv0yBU@d`iligneCbq?8yF1Le;!OY#-KRuz%948?q{I z@A64KsqPqxW1%A^El{NBOR~sPaC>L-#k0L)irNP#2blmP%XM1b+`O-PAg5{0exg(iQ7C7*eC`OXPhfe5oo=XITpSux+Ym zRcM^}{GmL9q78bJR1N%Pu2zw;c7q#@Ppmr7157hx%tBa}(W*=cf#d?qE{CQsJFYJ%%>1QK8wAt1dDMl}1 zpx7D`_0-kA{(8|mpc7G)6l);ELdM!dGT1$4nvwW_+88%N`p19=!%oy`H>@~(wjpZ} z2`6A#7cxU#AUA9la(}*})*9k>(k=mlp%mw`cEFiNA3B|=P&>R%T=9buL?L@bIX?#s zVy=q^hS`!sZ6>!pbsSJsOROM+BIgx|wlnB_8<||dm7YC1VSB*0a_UD|GvLTTr5$2p z?Z!OCO@EXq=Gz|t!nSsJLRYi2_3%|jITYxqxPFrU1Dc&#<$o_=FPZe{qf(vxI=@EF zg#ilHLge=6i{tIp;>M_+VR}R~3Vq)@@uK_2fYjN#Q>qJeRCt`t+=FG1-L-7V&unGk z@bl^?s@-M?-Mb2hKVTD_4k+%ww(C9uOgVxFgJcGTvZcfqAM9ats@Ou=D`S*mPdFxL zWG78tUC!Dxoqqy5;QJ6URgz{1WirP3AW_PKsM`2Qi$399>^dK>HFK}BG!a<#vpxR2 zJ4H|gy5^5g;o*oEWlp7UW91$DR@eU}K9DvYq)|)9{y7^$ULys(kodYPH=|tBcXxFY zQ|`9YZDuoVG$tjC?|fL~fW-Qy^P(!bx?G`V zKO0K@&ox-dY!GFtmkX8%_a~BdmW+jHKnd6-z=a!dD;VP;Z65OHEVCxH20$=*4vzh z5RyCqpkI-s8Q&sc1LzLg*S)i1q$7WpC7u#pgIuQ6zyiV3WK;6bAB;+4loY3r1JP&D zQpVZoT;>v&%hVmv z_cjy{sPr|>u{Bs7H>jQmw69$b#s}We2)MJ?7vCMu1 z_EX^1TDBQ4s=S>Jl#1-W#)3oaM&`;fGp$H_ME^2LCT;-<*0-}N6WK{50|?S~>8{DD zbX_wJm*a!zYS0Y0Rj7$EsH3#2AyzVMls1#GhEUIH%apA@rM{j(Y2wVP3@~wSO&A z03|mKj1OZ0G@GVSCX-k7lt_lSR5X!67Dl%Xg1Ls?dy|9txckC$rBHdC6@hqSSwvyX z&-$6Po$;MBHS0p?R)e=mySxM3*Ae2iyl?yt zSM`LF2)cRpfAarhFQuvH&lgU!*GOQ>TY$FYq15mA5arJ7X1ZN?joZ9i=zk>yDi8xe zC7lsIQ_+mATPib!8>WhjjSOWVQFZ_}UlG=*IN8MMqYI~{2 z5zA4|;_dylNfy&l&zAhWa7u9_DC(whA5-hjW}hU#_pEt1SD(V{@N7s{d8SReokTEl zAQioDZv`he;2lP&SFq65QcvArS@@xBP+!6dVFGGncWO0~EI=p9k-40W+78DF_v}D-8%`90D^om$91z6t|`+ z2w5-!GdPz4S_l*pF*h{|FHB`_XLM*XATc&GFgKSl$O07tH#V1F5e6!M`vY+0+x7(t zcSjv|$HtCr+qUiO*tR>ijgC6DI<}3DZQFP`_uPB?-2eAgy;rG9eq&;+Io6tU?b^F` zi1FnVsrij;41gjw){fNlG;|yQL1`s%b^sk6BMluL12i$Ql9{6=@ZV-=VilmhgPDyr z$3FrD?ScA^A2uO<$B#IFX&Y;Rgp(zJo)JLL!a>iR;&qek-88nW4TlKw96?6lnFa zqM^PeK+(p~4Cv_gKNMtKrjCxb9JI79E-p0sRt_{a_9i^!Q~(!$Ge=W^0?+|y?+i2o z{3RG5qi+TLTNw>BF+j=G%;9glqK&bmi@rS&@L{ktGXz>Yd}KIT8v*SBABzJN#ianU zwm|E@gQflspaT4RZvgZ(^#7&%clKXIX4d~o);Bb?v9i^-b~Cd!0T`QE0s*ojQZ$aP zj#L1BYoosc^(`HLY(Db!o%PKu^$k7({#Cg?K!jf&p#LH8-|`#`?agc*9cUcPEdP>7 z`xni}E(=>53EEg$0j(V!p#Q2*$jlyS__6J7w11zhg|&@~wa34Kv6;1z@n0g0oNQ^8 zt9RbXAbaZSCtN@@L0O)FHO8Xaoy^@jx@&Om^py%W&OGco@00dNdl}hSTe|%T|JV7_im8gKs)$nlt@yt_0RbCVfCn`r zGk}_bnGQgIPtU{#VEuUT`d=D3eY1a;LH8$D+}hX%!2Wl&AKUbwik<)60A&9j4syW% za>>|ybS)4-_NU3U=$PpYKYr2we{S`ErTqWv@V{98-8@ z+ivC{V&)1ok~4EOH2u3}{b?4kcURu*OeE&pGpzYz<7R^Sg}1<(rqL2Lk8p+ATnKr8%T#7YOC75Rf0J{s~5 z`rwrMgFZN=|DX>}nLp@*Q}(}z?SoV95BlI#_=6Y$w2FTa6M$Cf5BgA~{0Dukr}|&S z{=up72YvYT|Dcc64gZUnKPoe{vHUoe|5%uRnEtX@S^dfQ>%3@<{sHL$w7@@*;iJfp z^U`-P{j+O-Mc6rgwECZ<4`IgtNcuo#|L}frnEV5N)bNLm=|icho2@C(`X3P=wtwb- zNVNC|{807JwLc_V{eko!a{f>;f23M}^x!}IAJyCZS^Xo~=6~EDwcGx2e~{UJyuMg} zTLO(8|5)k&ZTVGe&1K8ka&{O8uv(|>UMVf>gE zT1OX~f4Dy~oc;kncEb4|@I#EtKThGJOt(LaeZ;y0?f)kGpXoDnvj5QH_^(Uy?E3^@bMYXVyzs`#;gT&Q=)8JOVuuflF1Rd<9jocHDZ1-Txt8S}yy z_&aC!!XGyfj~RZxH(IEn(pl)Nd!~kSpf1+6f*TubQ+ZAzaz7nANI5a=Np^H-=rLLw znV_CGDGa*Y@Xe5;QQ2MY&Se~ma*;Gu+?Si4e<5u+p@9V%j|~5U&c?a`>3qH^RWY)xT6i_1SDQ73=5-&Q|g!1jQ$)$=6}-~=tpMYFsjH)$eAg=GkGO;wG%0>U<5lD7>YuA)$GCwc!A$+{gtD? zj|K2bIFH0bZpB42zep{Ky6azmg^T9?p`;AAfaDo;z0rjNTQ*aOzh{7qeTJ%Yyng7N zBq#yF%ju_#yZ-Z7ooZlewk*RXkg=8Nc{4LT~&6c6J<0%(R3po+ADpibb7V z+SW3S<)K!LGJG!G19?kP>(8|Eq%mHZ|4mjQQ&|GGYV1{(@`2!WBPeA$Hn+=J_J|LS z@d)Rv*GQtGV)-=F^~LgkGkx)F8lKL8SHw4Im-B~FAVsyI zb^QKN&5XB<6XRXBb`2vE;`bI@PG2m}_l`?(&)H@fcbMa3{SHchuYxR4-Xpb5q_3flk9PGufu>0Iy?+6zGrbGq8icqZ&hAB)Jck~g>I&dfOG8bCBFjcMyff^Ve9wCDz`C5;aFz><~LE&5LLe zAHLKB(4;B4P=X_WQ<20lL9}sbyhv&>Q>tHlcP`IHV00iQnhI7=g4e- zfxlvSe8o3C(p-HUo;u^bY2w)S1a(fd7Nge?KLQ5X(iu7S&?AqQ*R<{;pU>S$EmBL( zf(QxFM&XrfwJS)cNg&|vY%Nk?+VIyVAHG{bm9E|p@f|&XAuqKpLvVu#68zMyBhqg2 zp#`7^j{qLk@|rkB@*=S9XAo*~&DUjp7g#b(1^mjQ`a))3u3^;%E4Unt1e#bQNAW6C zwO-`H_eHj&v?dwAMqYC79(;Om2XaAf84oexCb;leK|Q}Gf0}x$y=&szKD1GS?;)&k#wss_z`+=Ahzqi;9X}ym;|mSclwvI$0R+r&Y-un+sd|MMS2@i>4|EszmiSk8^yyl!#q+qx3AZxUlV&sB`b;6 z#7J|0kH&+RxIIO<;G$E9&LIdiiL+1w!E9km8!GWf;xCLXkPbpo8>X=P6V%dOBr~TI zc;RPF4wL)GR@D;=Ykd;LLd2qeRE#lYrb=1-XdW-2&w(7n?g>1Le7S&6{khOxp}aT9 zSo#etG85vcpKY(6-dd7ZIM?X-f*?agX}A&;Y(ujDtv>QMDPO3b2v&MHm0Z2Zpb@QB z7lnlu6B?Yb3s1TC)1Ww+=w2^zP_qk9Uizgeo_OiuBx~=0FmDQitUHOEu@}^E$y}rK zqh6)?TN8)s{Yhu=J_N&tPd^h?ylMJ>oJupd(K#%I%%f8tGXar&5_I>tWgn1`_au?iA z18MFqP35r`_QaeB+&Z@>^>x)KIMoD~!fue*&Hz%Qy+p1N_R##NgC~^{s>Qm2m-kUsub&>`JNrT%=UOsKxSnAL)tTa4h*H>M zn(}iM{MD-XnL;KMk*d}P=y;lc>_eeJvF>E2W0ON_r#1Mw%IjBZw!R%iPJ4$A@%~wB1WN&%Ygitl}%Tb^v-)P7*w}mn1TA`-_hk} z-L+qyShHl9L7{r9d?(C`{7w=jCVu|NkAC9!g@Fu_dZV`>Dz0qxf9K+VM-iiTC&joQ z-6yql%_=0%i;DX^5j&Rb*6v$$LCuN+c2l1}AL{o;S*VjgdAij6&A+Pu=kF(veK{g- ztF&$<=F2NbFK<@qZ_(j{%V(~x!)R79b;WP`d>7)jW1~~=qWbK=M;;^1yu38C-Ce=T zFfT;hL=TN1liF>a*ejHOg)<9=f40u!{9(Z`=FEHLA1lZ&qMr_H@vX2^dJtFm*RsXB~5U4kBuQ#DtzD7?}VAKXK-e z_XtR3tpsq!FM^mII>s>qHeFP)`_G!9Z}4i{Z-^%xJCXK=QW}+ zjQ%I!{&s(LQG~pI&D<(3>o8+eR=N)PiW773;0Ra9JICmEzZ079`3%r`2K?dcKm;HP zpDHD^k!Q6V$ z{kae)e$;80G9`a#(V$V0QQy$;h0srJ$ByNV$V)ISrs@5El7pjUaXZD|R~;MErP=)~ zbs6_aj#uN^(o|TbCpS4Yr{ZmxEXt4~#=IDgosBe~t_n1!MqP~13b&Oedkr->-6u|A zVp~>Jsa(iq@XN#SwLYKm(4n*PlY~alc>DAthmGglm=T7ENUOOg-_v@}f*KjQxP&T` zlHvNAN2Y#%8S`_@{`}-^=L?#*IxPVD+pv|b)a<65(eJpFyB~r`vrst+6{2}M+l`k5 zJOfUalc^VR(lr7O#n4yf6kw{4+qqEFQ&Zje`BFP+Kil6|MDQSg=UhQiB+^yk2Rw$i z)2l`@Zwtm7gPC&kyE&JSKokgRki5vF8k-`N<4$RRe;S#`e6~jbuIt!xCtSN`0tbD! zy2j@!3!yGKG=MHtRG!r|B{L7S1y@Uvd2mWu;k5!`Ae7_Vi4T2Q0#9#;eM@wuGZpu9 zI+L+w=3cRTb>9vRB&-agt9Dq>MgUzD+F;OapC6rO>=tC7iOjH7>}r&4znGoxT_=nb_1$Y6#~b$6~VsATe-#Pt7wr- z7MyuIK0lL8v4*`cdh3(%kjggQ5boY{_twf?m4OBuKcU|F#`Dyfla>A^pP$FsagPeB z{aP#jD0oroX_;-cAdpZfp=y|p;uDX247SC84Z4Ueb&I483$K=b3iu2jlsEM;m@_xL zm>tt1V>XdU3U0Fsx>ojE>=DIN*m>bJkcBk05ImPy6C*us&XiQC+WzoQxVb`ay88^O zLT66q`!d$z%jhc%;a+M@Rl8}SntasFk6@3b9m&z3HMwMex|sh|3=5wnc5C6^;mf#x zn3yiFUn|W)5;mIp)aUBFdP2NTDg900^eRWU*xXG=Pi_C}aIXhM2DGMC^9ID3-juW+ zz+J~cLTqt6?J6L`1O7N=c;EV_aw$hq-B#U^%u_=&h$DUZ?0J-MW))#D@N9VRvWjNy zo2^p2slV-mti@8S%4=uI4>y51dh~67U6@20zuDso=PWsL?}l!0;57CV8m5FJqemr96)BOmujDT3 z8xwVH-_HVZ+x(87@p__e%{I$_E0#Cm!Up~!g-i_+yrY_C?~FOZftF-LJo=!zVQcR; z{>tqq_bit*E;g1MB@%X%u-nHJ+WfRuliDNq8}D37r%j^`?1TW40PtYsxbPbhifs1K zhzVB`_8loKS@%kgcv;O$?=pK5e6M=}L8giUT-CukYWZ9phSAy{WRj$RxQ2$w z-4?xdQk@~Ppe*!uX5rpE+wi_u2uMiMVMDN)&s^yRUB?_#$8JC$GmQ{`qOmKJ|90sh zNwe|oSOilZ2w1ASQcaV!+!JMAj0-}OADjaoMb_LgJ*BBXUzCP_LOVb!1k8nZRwU|N zo@|Qvz#DTWeSV0q-De*Xy61j0DJEtu`A&@H-reHwQK=_RAG_i+WQe_iQ*6aAR!eWw zqi!XfNzz?gU-?30FB1lN$q7G6F@Hm)%DRWuql+>`C5D}5SDGXIL>du z{o00IW4!KL4KZPVMtp_O8tAJ~Bg4w%_^kXUZxpe6v`~pzagO(s>oWnHzdIcGfraEG zAHVkN`4UF(uZAExF5fu-hCq40mcd)_j`S0MvGzWBoJfb%A!1KneF(yc2oHz95-&r!JTjWr1y_-c???e-l`ajliU_gM6*r z)77BmH4!9Q(ArG1>T@h3%Mf~TjbQTrIM(@k&Xp-jo`4~2mkWc!1PND`es0Hvr65J% z$a#H~Va(G6s<`}bG_70O&3z0=UPg9-xjb1q2g#E3{ix+%AAyeX9Xz9xl^#)>5A zB_mJ}7AN>|o3kR>H!h8og{eV^jZaZsd6k~Buc4Dyih4BRe?r*pI(^>Ir32?s@F(XE zx8sfHasOJ?f4_p~qRik(hr!jqCrR<@4EV&_P?L|b)@!Rtlo)&YwTERw=@W4HtUu#$ zWl^)N*c7e5e?I_mdnN9EtSQb`Ug67l&_<4okf#lxK4YmyjH4_>@S3luMN}qK#=qVSYu)QBE8iZmsB0rG%dK92Nb^)-W_|!txLFJY)-W#hfnny6^O}?$#9VXW-pQmdf zjR#SG&PUv`2O#`nB_`_|BAb8lRlbi#pT=+}e}c2A$80qUiXBqgZM@CyNw;D8=U%^$ zvc3(TbQj!1#(VVEFz6M(>)31*9U6UizNEP5fiV~r#LD_4TsUi4$2_C8=n3=npjFo} z%7xfB5^5^Q6l8-vneX6oT4A2y#&B0Zz@4%eHGS^EIb@037g!@jsEO-1Gxzdx=FN3B ze{t@Q8A$UEXF_RobJ2Z09sA?v?9v01ZN^Vg%!ZH(LA#^xj2?=o0!Dj#9FPfM-wO<$ z?9&M1j~mGJ;aIGGx517oW=;qv)l`^4F&8xCDHshZiS}<`hwFW`Q7mg>s_cXN>HH% z1Cr{g&CReE6{_eP)Jna;%9C@Ye>obDddc_J=TKIZ*r~*aereUK?JhRSQ$3Zf$2F;Mk zL1P_d(wBGq)G1Be?DcN77UxVDyqP{5ZbwXHr?Q{?@ua{~kS(Q*exfP;e>|{|hI;0@ z3?4|m1$OuHO0rms?{|u$-UlI&Gm4ff{^Zw`@pBMOg;d5k`5uQM+-a=!60{xy{_~Kg zgT(J5nh}D;36dPJZUbQgb^0!lD=(6o!t!Rm)3wUz+uI(rCHtJY_LHyoty+xuM5`yz z5>VXq70ozkeQq4lSqluUe+uZNR8~yuzcQA{4~t}49Q8rE8>sZU{hhdCi08M+)@_R!;N~`Vx(4Yl zgrmoG^ZVd{qQyJSs)+(f1SO=OeSZ=xapMZ-N=(}OqDtsZ(=i{re^D%6E44NbjErTc zMzA9;wk)zUh|V(CdpX0DJ;j703f758OjtA?TlI^rs-t|*iS|R2z1FI&)0E#YN`WfA zqlpjKzpLa_ z4kC`}^LrdgdRIBVI?vuIg@I>0TvSuBZ=Kw{kg8)8+^|oR?r|)+XV`!aa`iitP_{UZ z8=iKcUvIzXEsIg?R11_>v=@rRyvefsXh;)&=~9{Ng|^!ke>=him!EobSIL0__4v45 zR_~cv(!oJ{m3`3iP}9uA7nO(_5_q=CxD4=4gDE~kKRr?*5-mSiog{aqlb*N}7CkuIDvNwZWV1QId!xSe$CpCi*7C^JQAXwYGWtEBzuhQRBXbf83QkI{(~{fo0Nae-Zi$Nv}?= zTQcA84R}+8m^s`=QzM<8>7S74XMSM@ciML2t^HPDXvwnfd_3NZA@lZBw%a|-a`_7p>lOR55ttU3 zL7_Q}f8U1VvVJkO+k75z5X)#jlR(d$_!Weyp2-D|OYc?qlld1obsWY)$#-u)!D%Cw zsW7m3YSY}tVTWS;MRLm8xK74&&wJ+KVR=`4f%1OWD4xm@jMF~qsR3nra)Am&Le&nH zXd{19g|Gv4sS@NkE*U4j%JI020JOk`as(?6e_;8hyKjXkYDG%%c07~&))Fco=h%~@Gpbr-}ankUV+biaX=2x>ylT*mP7b1 zw=I0QUL=wlV|mxnD`Vc~nKYD`n8gwCQ^ZWj?|t`sMO@{=h~5re!;MZdOM5=^gdqG) ze+o?vB{lgum!utX-{}r~6)=kAMlo&yg@i-C^yv zjF9v)H_M*vI|i0QXlTwmVX;_ed0kG$lv&-jg1ebRxNSBomazTjiKV58K zTaESjRo>a#OYZJx$w&4}M1wmlweU(rGkCc+%%d6+rZr;O{2_~)XdmrHo>it|e+}Hj z`?hi|LQw)Zw{q0Z0L}xR6OR~1Tw0qTMPrbk)~rdH7DG=qL*^#~+sDbe6hICH{6%$9 z@94SW9u-?@zBu}v=>?2N5N3a-$BvPJ=IiwLSWAJ}-ARPv6+3-(oLLH>5?hVuol2&K z3!6UZ{_32t%IN91Va%y8L8uz{e@swyWe>rZI(qbg1NQlPsu>nD#iA(| zz2_5ro(YdL;I%M47STRNY=U3l4Gu{onvTsWZFHr+^$Y9patZ!n?e>y(fH-c04 z7tt2g;IHj-@|5wED=Q{JSU3Y@uJoQ_JYRhwei{f+$*4w)5YdPtMt)Dce@g;`b?ziCXG0q0IU=*LC0w$Q3q6jZ)$k-3a&v2R6Yffl zHm|XRg{#e8yBwQDQ0s80xHy2jgJc1BOr(~ranuo++8+baSi0lQ=MsuWzIcN~77bn7 z%-?{nua^p5oO%D)nJW%vOK`Quc3p_dNfF~B*J7KVSD`-@LC6%0e<6_pwd6>0q@MKB zmq?T|8MQ=zQkA&DubhYbStey85h7hK8`F7e3>TG#_*k6OQs7?B2j|sEI22(sF{eN{ zwB4=H+LBx$l;VrKRJ42X=blIhK^N4~Z0)fh%;^vhRYGXGFt@9@CqSE%nA0&nDURoa z;ApPd>YFIBN3^p;f1>~wPX!zDs9{iV$dd!NMCu1;ps@Qc>Y3sVWMH{i-B*Ps&OJP|aibbzWeVYX+3Iyz5Wl9NOWDCTq&;3$VwYKnb9U zRch* zA7C;~1(|4Fvg{bQyUBpVlE&JgutQKmQVjbist#G3Scf@Dv|sR=m49HTb7{j+z;d%{ zqx&L#?JTxMe|JfbA|yz9JfLUaU|~(uZO?2y7g2b!5Grfw=-X>dmRO zR*7UUJqt|ro-2XSX=w7p61F$XtV?MAt0^LA^)2E(eZ6@)pBU8zhyI_oK2heeAc3a^}+ey@$j6u z$T64ne%6!D-(q?SYT*<)49-P10bt3WO_tAECh<>1UsI>RID z+b-t>BrH+qjDp4_y%G-Fx;9}w*libJ2rz8$e}Lsz55|g`T?)7pcXb}spR9l*u7)&f_lEWbI=iVah?GKa&=hC0&lITCWui-#w8l$nl;09cLt&3pfT>`HZ ze|F$tI9+9ax1Qa9FF7Q#)k0bNW)Oc0*A1f#tD-q{NV9M!V(YopC5;om4ASBJ`XZpe zRwjUGh5Y?XH5A!xmU|y-<4l-5t`McCVX74L;GGxYRfG4T1jVqflU-bjKdHAB_)_4u zyII#{^{2{pucQad#s>-Y7!flY%I4DUe;jeUPn(vi8juQPHI7`sY%>DYD=u|pA6iG$ zIRO*wb9y4}O?c>O)?Sh$%1BuR{l#k7U`D;!0Hn%KI}WV;4}k(lgN95FNewn-HET<| zL%2?ev&K(d?imZh7r!qY(ky*q&d@7+t^$yJfQG&f%z+6cbIce=U!y;qNiH zkm2J}Z9HwRo>=2ki_NB?3dmqvzxv~I**)E8M_z!mWq5?M4I(RSmAE%*$eBH%9lNw z2ykZP!#<1T@BKPa{RRmxp1BpG$+zTrL!*LQ=`?6j!;)Kq0F zE4sB4Cj$uLm-$hUntAoGt^n~}_VvJ*t+T{ibwDQ(t_GSgRPW($zh!&gBoX#-AP4q) zY~K>yf0uoX0yrpXJI;86VE9YVAjnDPW_-N)G>q91?W;=ktKiAqR0-)c9m7sq_CAnr zw6(Xvq3pbl@f4B3oBFTnT#2P@^nkBh8c(ZPV`>hpSP!K#F+E}EuqevgHp>Kq%?yf6 zC0gb!%|sEQP9+0%pU2yMFStgBG|$^iTH}>`f68xGyXy)h8WphuWJk{4m(bYJ5Xe40 zDZH1&jhl*qv7 z$HY;o>tbwqN_0F6*zJxBAl!)VQZhfY@TS!gyuX?Lk_jx45b$rO9#)QR<>-Z^iQRB` zf3*0uunu(__ltl-3ZC_xB?j)U0{eO|(z7lW)TPO1zCkm?yv+R^JBIpAkQmo9Q@a+S zv_pz#T1w5m=RB6-#C@*b^eno!QXbcEfep>qnwm~jcIzGLmWRe+`6ZBU7$&{)G>;QTqagJ^=r~;5ebQFvWVx z%4EYr?vtRm+;-!hdn%6oTX5KJvhfi;L;EBV4xAiGMj`8JsR5VH2Uii{-@nhsaGvDE zUR-M8O||MUlKW?DzvS71^F21;*1sDo?L7*ArS6BUvG+|OIZ1o2Euig5ofA~Be`<-) zPaH&~Yy?lf{Y4!)N%+*&#zKj7l2&5Oh^N9VyZ!Aq_2}I*tz+{^&1eXKxvX7gHoz7e z^kGAdv({52G?4Yy-!B8J6-+i|ERCM=&83|`keD1wq?5|@z*E;z4T>OJC z)pe!HI1A@0=Ydx@gFzQ}26_v^e|kNQ@S^IA4EhX|Rdn-bLEbSh=%#n%&kcm1jpTMD zF;_+{g>x>^PU!3<`!!@&{dX~)==>xJWZE8s*N?4a!iwE z3tWd0LG-uZY1qO{LsYZ{`53`#A=sHf<0E$`vKF8u#qpPi-&Xufhc(nMe@()okU=U* zM|x$#V1s^mCs4T0jALd_Zkr7!Tmu2?zNV^>Ajs67^7I6bLOW~ zi4|{|>_^gZoyK_|0OV~k2)j4q$4f=KQHH`9%}u3PW-=`=HhCnd+sUvVw#Yoa-8xup z4%Mz9l8Ttcn#4x3`P;4+e_{9`q~lK}L=!&6&h8Qsh+V3Oq*PF-=TcH#Aec{4K&&4X zzuXa*W27XIq1v96aK>7O_FIYAaQGRX{Xs$j17wEbl908}?@6=}=OI~4VeGw>;Gi6o=(7MON)f01m5)ZJ9qpEo_4 zkHqE8oS3_*p?`{k4e~wmDXbgfYTr8iUKA8oOZjHV^v0OGQe}jxKpWfEmbEbVq(tvy5{b!k>6lN68wK67UInWw^=k^VC7o2{M zsfSA^pKNfryE;ikai$cDn`XIZ{CPWQ|Bf-)RUV9H%eCKu;6 zZJq&{9PigqVcW`rPt%aLZzrZb{Y*=E=Fc1J{If5HUF!$B!+v*^i~5Tn0m|6wAmnrn z9M)8Yb$Nf&e+3L#JG0DMq<`0O9K<)oMCdcLu%zvpvX76uBn!8KXUxD_sW$=jS{FK? zdIj0BZXc{p2#|!Gj5rZT|NVwcJHs~plUj~2lH4a6m6h}Q(~AVH&x|YKq0_GxZ`Vzg z@2+_w$&dbaRlg&JN)&%Y1-ZGy@Nzb?xk4@%Lda%Se;*O#OIEYsFAViojH1FgetUpa ze+WN!0&KEcE^6GSe&V>R!e4K&ZUC%+y_n3(mAo7^>|$X_mgq7%@A%<*3zynuLRxmD zvLjgEW$%1x6-F~S#-;T!D*JKc*HjtT-ta!z6ostK1a0ZbauMtgV6Bs`r7z6%@M=}1 zA_S=ff0ODyvt>Dr z`QB|{;U$xx_N5$kU>_^tY#+3+xdL;~q2kL|ya2@>il9vToW#~kjv1V{q636gBmTSt z|8Iu&HM+eo#W>_(*T;uFH3+C~QME%v=$hD!f3i-^sQZz4J0p^D9X5xTuxCk8w^JzD z6CC+Q(9(2K@!*)Q)j{1`XJ=90M!8=0O?DG3Uec*4%?uOxke$Kh9&$O}#br&_sXxVF zNF1mQ?mFyv&F@3ubxnureqv~4i_OrBv_JZZ!+m0gKKmaO>ACHY(nIQ`n0?Fh_71FMAag&OkxAun4;16prg3HmCWjc*^E zs5%a9)648OFdbcVd>|aJ()<-tG!0J!TU$ZQ;A@Xiv9D4qb5+?pXi z7IAS(ko3`fu`2}2nB;@ytd74d6cK^4e-*Pn_Y+b$Wo~N<$2)XH>q=FF6A53hwm~NS zmvY!N#^K`5eiz3q_HCg#&x6>;WtfHaL3*d`|Ki^$GwNm_vA)pM zy+6CQ5Q_}7^v>mVC)#U@E@x$2rHV@1qvl&vP-@^=Rt>#KR`I7g{X8V5KQ^=~f9l}? zw4m$eU2q_@=xN=}c?gdbbu_;lM(veT5Rd1yN)bp6J@N~m$^TlfJ)BJ#8E%_VeHT>x z6*bkWNav?QzoHO9{I6k1KTESiY30>#R_;V7^}v)*uyg74^>ci;?3Vx-DT9mM-=5Kf z%M)1_fj5=Ah*q?d@Cafki`$&Tf88?(ARuwR8#*xQZ8$`}RY=~g8G5B1*`GR!X~2h> za_=)~R-*xwx-5iHnj@GRC>^Kq>;HL!n9p!h5h2e$2+cIoxwQifF?7N>uq88GtH|*! zxPZPMJFB%R|B4K0}{&kge)QalZgo&zi$&rT5+fpPt(9x z-IXqeDFkZczIaAYV$-v2YCye%z)@K%ku$28V@E5Y}9VFhYEa&3GY z_A*GjVuyv_o6j&pf4`~w7|ldD$OuK`_MGE_t{%zMB!#M-icg-vH=DPaK6mbS%un_R zgVYBvR;}zKX>bnV!g5Qs|OzzO{bc&Ul^t zsT3sS_(YC;$rK-L)>4e=@NP?DS(hfF3gsST4pU>3kC$d}r&>jFD7SyO8`qkWe8mty zX|U0i5eI+!e_WK8Xu_6Pfy?e!G<)3$h^n^rQkm^hYiT=;8z7YE$2$(ggj;p&8OrS) z{ONm?C0i6*o)PP~xR*E-qwmNTXBw}LdX$*^FF9G=X)D8dFdzI@e$QO=zD^8J2TA2- z;}|0h`p?(Q!HlZz!&36a8OV%QW@n$Bd2dir%nwz(f6|_9Tu2AhV_#CbSUMe|Z|M44 zM=MW;3_WQs_4pU}L(JPy@QZsx!BFT0uI@S@;61;O?}`|fGS#k;&HCg=dB7}hXM0lW zkcL5VCkE7^dga!55DsUP4N|)B+nwdjSxggna?a zOTIvWf9s4t`<{??pyp$@Mjc&48DjK55R zXBKoy<2}!iIkGg%baiimbY&4`B66#>1v_IIc$H?{F7Wc}Zhqk%_v#%XFiWbu>zfJ% zbT@4dG*+dKf1kx_+p1bFrCsZ<5bw`OTgY3Le-nL5omxEl{7E&wIUj;NA|&Y$4=RV@ z0WA1%HE*+n{tF{Ut_S)L6XB|)%T$+V^o*H`r**`ONV_jicHnrfJb0z#i%Gah)2RLl z_vdK1yB{8#%WPwHBy|JRp`CQWQwrsh|ctjjcHjS_iq6ZMXl;TMeGtu~TpLEJ?`T}9MW)Sm0=>Dhs56i?&|OfZEx zZ|t%yQ62=}@Xnw?m;&{4e`l)9mKvmxe-r_Sj~a<0jf>yM~!^>G1K2>8D45p(>gafQ8RzT+n4Hh>m_*Ge=W6uoR01_ zRSYgMqasqiWxU^4rZCckbcoz-oMf&Iu><8K!rBXopT_ygoi_jk4Z97qdgt_!kcW1N zVPlgpu8c74@oh9q;zeCQ*IledtG`tVfW6N7+qiBFqi}>zNgraqLBq)%!&|!7wLbw-II?EZ9Wg>D%$ER$Hdx zikc0fxPKi-CnZ{fVb$o9qa>xqIHgd2-t+Y*6D~vAn-QoXHA~LVf6;WIm87GauCvd$ zS~Q|P&l!@f*)b~Y`FY7?Jic4KYi(_t0h;3}pRwggF+Ujuno6XOEUsALz0e4=kMwH+ z(*`r9+0%f+ongrm@$+nL%hN6x z?PB-Ua>}Ce>qX(cf0>hpa1tIq9S7RPDN7qatwC(h%N1Et9fTp zzMf#n?s>m$`d(w7WeRHf`1~pRD?_k>bZta9H)c*%)-wz$XsE%2vkr!-M^;2Oy8OLJ zm|_`@qh6Kgev|f2`I=fJLY3GLYz6h1*_7ZNix`q76xn4}e;ZP?^}`d@%B{Ed>$NUW zO~{6(A9+>D)#AndO;l7WFkAtg2l$A%bZl3!Ja?jaKpG=rQ zB8yY~PF(3{E6BUZ*8H7bduv6!(}QCKr%hAYe=(i)@~ma8$*}@7%BCR%vnDYX4*#j4 zweKm~;MXxxf9qglbMu98Ysu*V`J2zjfBTtQJkOiDIVY99Ai=_Z+v20|D6fMsk`3Iz z`c7x_BX%eZPvrN2FUsXy2AM5yq#kpiOIh-po!DT$0=T77f2@|pmY%I1r-Jyoi zFEOQrFzZ8;Nz<fromzAe}lBMa^PI1{rf1Q`54`(i2kDe9!IMvsfbbVyq zFYnwaf737aH@LZX&or!E%`CVmx0+`#uQ#M-&w89`0`|BSaw{mMLrj*RU&jgOhr z8s}*Krr$&ieg4w#LpdC0bLkAg;rT9j_bxgUf6^q)sj_5ecIVFfAgm#`Tt^t^ecou) zF=+M1wc`XGP)NXy%!0>z`qQf7fK#XsDM`sN$=09h*(2W_0dgSFoQUS)h%;_zbfGlJv(333GLNWu$WYzfP#xk7duck@89D&A0 zBQ$lWoj4a$AtQZbSI4~foVuqz>kfuNe`EeTI_zGKoF)r-b4=mSa70a1w<+$}n$7X&(m)D*@t1|k;Et*(r^=}nogHV3b2wcvBToQe5dtJA|fBSxk zorhfD2@-SKA{Y&c6^5>t+PLqQwo(NN7J+QDx|F^A%ex0V4Z0y^47245D`z&`1pIg7 zb5r%nRm6!g_#De#OvJhi7wI8I9GBZO8nWE<+r#1;q{ETY$QkwMWZ3?JFA(zrGI%MQ zJ}wM(&~R#@tolu~yo%$o7Qn33fBqK`7$>&8+R7q^^-+_Q~$l0Y=H!W=a-=C%Wm7<-K7_sj}dAJpR#g-rfN`7Gb&+ z!>qizysxFI`OW?FGH*;6GY95^X3+$Z$e!QHscJlmL2$zlc69%p`c2K(ZEkjw4Y*j&`yDIPtNLzZ`MT z0Kp;nRt};5Zbj`b>xNX@Jl}VWo1O=tscu{TibhKijIRu!7%_W(x?347!07X9__nVN zJx>2UBh|E+B5PQbcA14>e>~(%#&r5o#EweYY{bIF>RZFz$6x;K0loD~}u*=H*T-z7(PlR-&w&e;7pHKQ7zo)M{UL*OU(6M>NDC5wI=6~Gn#37z?oZkfE;K6@LU`7+D*W*s@Bq4>UiL<` zZ8Hr_pd>n)phBuI7^_dfqx(?G6*$~&oC%w|#(_%K3Lj&ze>Q~5CfhnK2>VAfwGw&i zJm%}4ANM(M=iLWcqNr=~#a1JXuOSP+0)!M#AGX^lnQ>wZurWlgR(y1%`JsWwU;w94 z$~A9lN#P;Vm}?=150sEj7p{8^{%p~+j{!dPQm4;Fpntdv?oAUvDKydRS*S2^4O=|q zx6SV9ZVsH2f6Om+8T}##p(31mKqzUzXAUhOdAwz9+ZvhT*0SpToBG0QJARcE_m#%86~ow7y@jlJn0= zlfeX|5t@|*#60x$HL?Q>SUep`SpDz6ybl|atJwtvR_bx}fKn`3$Vk-Aga6Ol1)-cn z(P%~x%;we<2z~OZ8i%{hB|D#bW*ENN4sh?2dZO!J2#bU-WBp6q(@9raaO&7Z97z+P`Bb93AO^42gm{xm*6A`N4N7K3E2VzG&L}n5fBLzm#!fRMVF8( z32nEOB?)W-m+JQd6t@&82_XXmG&M4p5fBLzm#!fRMYoVE3E2XdiXj6Ow|y=N!2<#` zHJ1@?0TZ`VG6}u|mlk3J6qkK23A?x8HVFm;12i=@muxNx%eS;T2?qn04T1v{x6eHZ zECT{HIF}I+2^6zRh)azPMy00JN-SCuL&GF6()YK>2uIYrMd6S|;|&xAvx$}*GM z>WoK4VbKwpaz>(qf691t@Y-6rr|ESm1V(&jgmIY`MSo_r!?2K$jq*pGpn3hS2j?*raG~%q-Q%+@0N|`ATJE?3Yoy(lYh_uvECZh*u9XQJ}bEO82 zEYL9}UYX1bO*Ag^78+>Kc<;+S>5Hr^N@6H8$X-AvbR=|_N@S%04=EKoN|U--iRIC8 zSxHN1Rexrp5Wr1qft~?Wg2zg&_5|jDrN^@Zph9B4fL;QR1$y+>KywmJ2nz{-K6)N{ z3!)XJr7>`pqQDr?7E&6}Kmf2S^`3x!LK@<4sb{@Hk7jr%3kK+w4l^vq6;?q@|0HoH zGb>G;HT8^9ndC7`2DZG8^n054DMwHlQiZgq5q}Tvor!-d^w;!pIjMoJl|!o@QVT zFX2DGTUG7@5o`7>aB=(OW_q7?quDRX3-_T!X{og(#+6YRH_q;3yLq**u-D{1?MfY~ z4MYSde2V=r21;Fk(v)W3XI7eh-iCN>6U&?O<` z3yffEfYKL=_ynCa;z&vFoMAel7)`?6dZGx(mTU<^1gJ7EdTTS~J>*E+4Jz0TWPeZ0 zu@r*#DS|RbSuoOMXYEN+a)Mw-rAcNjM5ME{MQLTYiY*G_;YojZKQz(Wl3igxa#oKy z$_lcXV_Pz%6NbG+u=kMdX=Q|Uwzu-oQ;|;r>p%8>*PiLPwSst+%ItF}2KS&*=!ds= zXu;LOa-<<)K=uSD!#*v7`~`cwVt?&up0bMcFD22JCCkEd3rn*Cy(ilnNjt%K-YrH_ z-&nSzAkWscmUV(R5U-KT~U4>`z8+s?HbRf@2+BR-l622yQw&G2bKk4Ws$9Q$#IT-TpQkFmXOkpcLUoqL^I8DI`}&I4((m? zCJFCUf^l}{&i}%U*dj9dGJj}Cdor|P#B-eXg5s!Ggsq2vQ#7N!kJ^G_QbAF*q<8_@ zNLrd%795jH!ASB4k$B6F;fXNxy_M|T6nZ>S@TLz7qqgATAO*Lspa>Ksw@JUNl|iZw zOikFyp)YCoD9#r&BaGHKu*AGW!$^r;uj-i%b)Z0cq_}6e)s&a`Eq})fM|`*~S<%oc zNil@tn`Mi-(bltf8%aK45n*UIXhK?&-7JFzRB+%R?JRg6>{Z&Wu_YmiIL6RmV{T^)B9)ydx=tP_!(V z1JwkM`zT)pL&k0#+YkicG2<{Dkml z*3p8YkFqeeeHcxB!b*xQVI){9&P?b7jrO!3snYS$e(BqQTIh$C<$z~~OoU9z|GcqG zZJXGwg#JOK5AjMeZ?G z7QERxyQM>xnWr%H`z?^bINFVS6tNO}JCXW;mJQs#5o<#k-t&nXc}&-)YTg6|LO;^d z%H)BLBPD7-&3`!%8&CKk2}N?v*-mnOIMPyGK+%^gEABf*{vO&uLlJ}Od=C5$=N6nE z_f=fPuzPw%TOsD5*msdaBly>CV7PgwtV=j;6Z9@^ZV#vh6>dmqCZ{Rq%XqY?Lo4$Ezc80MYYx9`lz+&ZXy|lVNOt}BXeCuqDS#;i zsRCgn*e6;k8HoTV>bu-{P2v}pD?-Z9p(Zn_G06-0L}a*z z39VO(*Q7R_bn!8g34kVVO-YrAiNCn`2~r zmxy=Y1Al6IOihgxM);6gP|*|B;tDMsJ#ojv{>femx_D4a^u%Et+u6qVam?2Rx#!YV zM8G3|6X1+BcKsO6UK>Tby#xYg7Oj!=6YKG4%cgR}BbvgrsiTL7+#c$nV36c(P$2X} z%PYgXD4c_{pbhKUm+6#Gl`Hu&mA7=3;Zrj%eSeHc0P^9KXZZAOsYDF~o{t#LdOomF zpSFcyr0Lj0MX_c&+AO#&ld&6G>m5gQJ|$wE*yDmb^y7%8ik6rX2K*@q7sb_9pr_F| zCybVgcQ4nUd{@Ks8rtxlPBe60;{Jw;-!Vde?EU9Id->`2n<~qnFXro?tJV2(db3_E z_kZ~Lcyf(52WRgOkIw%0>Fa|JqQHyUzG}oB7&*}&d-;>q zc{N`n+=24x+y}ZJsw|hA)L+>67dU?2>%%V`$Zx9Uba9a{^HsjiZyyCg zpHIPF7j%>TZtuVUy_4dfo*W*(eG(LxBZ@0hT+l6T`7nwX<*4EZtorl(IDelgehJ0@ zjH*YS^KWgf%`RG>y?Ooqu(D{_ci~P|I>!g z?E>HF>%&iP-T=O5qq_x{+mB^QH|E+LSIjNYKOe-3WR*1y6|-ywA(Yc{QuP ztPiG_@RRwvx?E0X7t__vZ1O$7nSU&+dGnT^mc)f0my?U>`DBKEzPi1>o~);fc|EZi zSXY0q+gO#mV0G~R>GvF&A*J|0Cy`Tz|fpwhVM{N+pS zW#nO(nEy5ZTAc#}xY*zM_l}$48;2dBdwzCy`09+IbHm7qdXv(j=0Sk-VheER*l7gE zJYUTwtE;^27orKg`SH=&$;odlxxbUDtqJ2Biggu5TUR`!y7FVAt2D*9llVlvwkkVC z(-#NNUjFieg&&Eg_;xXRxPLPb<>7kNP_<_VhmO7*-Sm4~PrEVMr?dC34xz+5Y|}fL zES)f@HNLxyS{LQNF@ixgDXE|?i1j#?EH3h=D&tZ_Z{d9SLd;Mir{SCmd z=j%Ao)%x+i6 zRe#+s)>R8ETrYplTv8*mzmZir#Sk|b~t=MFn{L|BqA3hzBSoUtUT|z&+EsLheQ4ivvK2;;S z3Hbv%xzh>d?C{;o4^(Wv z8%ZvzNiNDhjAh4DOF!nm%wLVf-A|N=t`XfMpXJ~Za?}U!UMA3CW5kxvVX4V-x%d_^`8-)(e4Y`~ z)31|RH9yDMYFSN?i~{)A(>W5<_3dmuy_tQF5Rw2OxTud3ZG7x5_&odY_VDy2<6{Ql z!R~)t(r=_R9e)!_@c`%;JBrTDa(Z30)!Ag!w@1IeJb2Br-YpfiCJYx#q#XU!O7ge2 ziE?znPQp*?yp1X2%E_?+q;^UzpFTZ#dGelRA3KH^NJ2LxbWMV5vxo58@$7=b?lsj- z!6Ey0QkyLC^Yd4~K09L3@0KQ76NaO0LAugiApLO6rGKT5YnMJQEqyR$T-VV|p?1m? zFAqPw`SpZ_-*OHq=|_Y3AsyWrKZK#wk8dVIkHbt`y`2K+o2MUtdwvY{PRDN&OjG3O z2Rdh2KdRpmr@Qxjz^HHPkew(IUAJ#3*OWrqs_zsVU!47N_S-M4dif*GAMOsNxKkPM zVj%s8xYt0X;=Y@qt> z=QTglAap*MtSWlb|2m>&DgJ>uvCN-Om#Z})#eX66Xc8azs`+esvA$aIPuQ9Ebn$*Z zJzrdqL~?hDy5AFYC9++KY+ItTk%+$4?@6R~m#A}x-xbJr1tPIG0+A;+5c$49dUt`k zx8fa%MA4NO1}Hjzm6C9=Cq)Vb2_3e@v!+Y(5m z0)Gv7k=tFM&fR5KAol}=L?V#w3*>hfsB^8@7D)b3Aerp50VgW&vGZHENF8}>PaZmO zHSz(V3t#k@)Td2P9CMG**uLoL*h|W-;OJDZFCvZyD|Jpy(2 znBA4AXRsDPA`!?B2y~A)-8t8G^NB$8(8zz&-(cn*VY+ji>&Tbo@#2<>Y}~+5VY=qON#p6SrWdR1_fW~u z-*A70PZysGdt26JDtgccT(W4>WE~~Fu*^cgEs;sBh(Qa_6jeL*sTYy@Y z7N0s8fRAbM@oIorH^ibvTrb0Gy3~m>pQ3+JKLj)$iw^suX75~!y-QVc16sHSNT&v8 zvi`0dnAzZH%W;+JYJu5AWNKrZM8CK~t)FbKWgD!m8C)w!Ibdzu0B^+>YsYWDqqSRR zuq`vFmYAxuSYrm$Hbc#x*D?cUFpU|E8lS0CZOwp9Ok)$>@$J+mouZj%>IPwL24{bU zH3tyZ4Z>QCi(W_Uz$Q&`wKxIbxWb4Z=muc51z?pLQxEJ$)u64mpe<9^RRd_N25s3! zTMhs$8-S(RvSx~Qs}jiK9dCe1iEZIPHYSl7SqG)f#_Zr618BXeWeclhvP#YrGMUfTv5|e6Hr0 zgHq#sy23BhPI3G6bTMD8m$&EiOErN%T6{!*|D8@*T+XZIyXx|GHd#*p;a`8Wy}4OW zuVeqoY=)H#rj_ZmYPMQq-VuJvb-S!yPtLE9nUAW;5?7O#aYBgsxcWN+w{=JNyR!MC zi(i;GznJ4^AVLxXI*|+I({j3cvRqESzg;fyL%(Z$3vzjUd;Ru1*cC)BQ}SE-dhYdP zeKT92xcDr8{$)A2xtgA@WKn-OqP7Y3^Gmy6ZprOKZ87^?EiY3w&!($$IMS3{NRdAlL1el*CFS?==MLmqwm%Hah1Y-maiaI8KTaO% z2l&vkp|0sEe`mQn40|Y~uW8$r1M-m(L|-2g+xAf*scoN{nA^URv}>&I>e!L0Jz<*; zmNsEuCP%}fH|z=E{%}dd(XqcwFR@L7sby=3)Gyr@$sx;zwkLFaqX=8mVhn|4JQdVK zWPv({#UXH)WqTs+-qL@yhM=JexiA%Sv-XB=Ou&+MdDQ#{M;L}er2_lOChX>It4J>^n?7HK1GxQx(oT^(vo{vX|h zPZK|F{B+yuoPI1!soDEDJ%lnH-9yLFtX*8*Z3_)s#CK6#XSRtq!~j=Spwq6o8~lG< zzd`et@dyPI0yr?2XkrP02QxA-H83zam+)c<90xNpFf}kRIhPY-35^3ZH#wL6KnWZb z3NJ=!a&vSbHVQ9HWo~D5XdpQ^AeVtI2`OJQF}F}K;Igsd(hsRDNLA2xPAo~x$xr9f z4@gW;EmklwGf*&42;$QBFD=Q*%ma%ULPY!$b3t5la|jot-ZwubRX?;i6{Z-dtsphe zF}Wl&KTp9DqR`Hc3jj%LAg`D42n7?D5T6GPmse{E6@N51GF=KUQ)zl-AT&5K3NKe6 zTQMLrATeDEFH&!BbRaZ0I3O?}QVK6gZf0*FG&nFIFd$M2FG)loTRb^1K{G)(K|)1B zMKD4`LNhZ&IXN^%FgG?eH8wFtGc-ORJUK8yGeI~(LPbJFFhWB@Gc!avIW$EuH#Rjj zHZesrG!H&q3NK7$ZfA68ATcsAHIs2rD1RN6OKg=@5XaBld)s^4($bceM_VYREfi>h zf`!xaeziObh4N@AR0=L6CK9zP6XPRs;}?yIiCZ@c8ysUa5+fKNiN>hWgoVLGQxOvs z7sjX?jMVe{ck#>2eBV9aIWzy6(-6Ybv$u8E)$FL0Kz<(&Q zQLH>DfaAoDVwHh%aDvzotRg6ZNn#^d6`(S~1F^%nRbURWDq@Flt3eI18e+q^b3rY! zT4D!r>p(rRMZ^x^&I1j^nuzVkZ3Od)H51#1y8tXCwwTxu?jq1cY$LHj+-A^1Y!k7) zxQoFOVv*P$+*Uv){S>j?xNTq=v406-yKvh<2Wi)ycj9(}<;1k-9k?q%7qJ#%+i_Qd zRm9d1+lIRubQ4=kY%6XLSOeCAb)Xll2OGcxpbu;Wn?MBpU;u0eTYwhT*R&1oPaD^X zbX4U)$x+snPUTre84e+QIE$rvsK~1Rn1wJ`s*^yDa0CvW>43_3FIWjyfPWsKV@aY1 zaaV(G+>=;jDC;LB>i`Qxo%|G-pdM23xP|6V^zqaDf|oPBcLqxtdJsz)It!-2IdC34 z#8cAgbc*Nr&<3-iJ{EPl!f6Ai4IE*5!NS0QdP*<2dV$7qbpX0|gzhNYrR>HKY>?Es z1T57xSUCKLRzbzSkZn*)mw!~yWMucg3Lp?CIgTtGSvdLgbrzmlpjFU@b8@Hp-fH1O z)B~MOOKJI~EU$B*L~p}YyL5qNxKcaXb%gE+J<~Zlv1l+y=o!C_eSlVrn*rsH+I_!S zVO%?)$bQ_cJ`R~PnvbTBXIqr4Jyp0?&xVHU{c39fLRv@z$&wsPY3>VyKDh%W{+Oc z&orQD-AJ!Sq!Q`r*bX%2I?SGVPcI{tsBuWgM`mZ-YWCu^o{ocUU<^>aNZZF9Btog< z7K%FocU0U3j)C2PyDaW8d#fp!-FzijE_XhmV=!U(Lc&F?SAW|QUO$%b#$>{!hxPHj zU=TKFnQ^`~<73w_Dd*ax z3Y9TrvOFk&GEfevTUN5%wUS=0u-x6>WmQ_f;Y(RnmLI(@YmVig_(xW?+)8ghmJa>y<%Fw0Bz#bxaQ$e) zM-vI3rfQWwdnDnD*Al+Ek??IX;l}2K?`jjizn$>o`$h@3UP}1o>x5q`wY(++>df^2 zi~eEJt$~^{#Fgm+*Nv@vLAQbaiP3F9p)v}UQK*bUWfW>AScP?`56%9Gbuau6m!CU# Wmt1iP91S%&Ff%hS3MC~)PeuxrG?MB7 diff --git a/report/main.tex b/report/main.tex index 30d68ce..840e208 100644 --- a/report/main.tex +++ b/report/main.tex @@ -48,15 +48,15 @@ \begin{document} -\title{Using Feedforward Neural Networks for Breast Cancer Detection on Few Features} +\title{Neural Networks for Breast Cancer Detection on Limited Featuresets} \date{\today} \author{Lars Bogner} -\affiliation{University of Oslo} +\affiliation{University of Oslo\\ \url{https://github.uio.no/larsbog/FYSSTK-Project2}} \newpage \begin{abstract} -TODO +We study an approach to classification on the Wisconsin Breast Cancer Diagnostic dataset using neural networks to expand a limited set of physical features to the full feature set. Using only the radius and area of cell nuclei, we train a feedforward neural network to predict the remaining features with a mean squared error of \qty{3.15}{\square \std}. Using out-of-fold prediction we obtain independent predictions for the entire dataset. Using these predictions as input for a logistic regression classifier, we achieve an accuracy of \qty{94.74}{\percent} and an AUC score of \num{0.99105}, comparable to results using the full dataset. \end{abstract} \maketitle @@ -64,11 +64,11 @@ TODO \tableofcontents \section{Introduction} -Sampling the cell nuclei of tissue extracted using a fine needle aspirate from the breast allows for a very easy separation between benign and malignant tissue. While very good performance is already shown with straightforward approaches, like decision trees \cite{bennettDecisionTreeConstruction1992}, we try to improve the efficiency of the detection process in this paper. +Sampling the cell nuclei of tissue extracted using a fine needle aspirate from the breast allows for a very easy separation between benign and malignant tissue\cite{streetNuclearFeatureExtraction1993}. While very good performance is already shown with straightforward approaches, like decision trees \cite{bennettDecisionTreeConstruction1992}, we try to improve the efficiency of the detection process in this paper. Using a very small subset of features which can be obtained using basic image recognition, we enhance them to a larger set of nuclei features using a regression model, based on which the straightforward classification can be continued using the large set of features. -Starting from the features of radius and area from the Wisconsin Breast Cancer Diagnostic dataset \cite{williamwolbergBreastCancerWisconsin1993} we obtain the full set of features as presented in the dataset using an optimized regression model. Using this regression model it will be possible in the future to use standard classifiers while only needing to measure the radius and area of the cell nuclei in diagnosis. In \cref{section:methods} we present the methods necessary to implement the corresponding neural networks and how to optimize them. The results and a discussion thereof is presented in \cref{section:results}. Finally, our findings are concluded in \cref{section:conclusion}. +Starting from the features of radius and area from the Wisconsin Breast Cancer Diagnostic dataset \cite{williamwolbergBreastCancerWisconsin1993,streetNuclearFeatureExtraction1993} we obtain the full set of features as presented in the dataset using an optimized regression model. Using this regression model it will be possible in the future to use standard classifiers while only needing to measure the radius and area of the cell nuclei in diagnosis. In \cref{section:methods} we present the methods necessary to implement the corresponding neural networks and how to optimize them. The results and a discussion thereof is presented in \cref{section:results}. Finally, our findings are concluded in \cref{section:conclusion}. \section{Methods}\label{section:methods} @@ -124,22 +124,22 @@ Starting from the features of radius and area from the Wisconsin Breast Cancer D \label{fig:neural_network} \end{figure} -Neural networks are the continuation of a linear combination by introducing nonlinearity. While linear regression uses a linear combination of input features to create an output, neural networks add a nonlinear function to the output of the linear combination. This is the entire secret behind the powerful possibilities neural networks have shown over the last years. +Neural networks are the continuation of a linear combination by introducing nonlinearity. While linear regression uses a linear combination of input features to create an output, neural networks add a nonlinear function to the output of the linear combination. This is the entire secret behind the powerful possibilities neural networks have shown over the last years \cite{elstnerLectureMachineLearning2025,goodfellowDeepLearning2016}. A feedforward neural network (FFNN) uses a layered structure as shown in \cref{fig:neural_network}, where each node represents a separate linear combination of it's inputs. A general node uses the inputs $x_i$ to create a weighted sum $z$, which is then activated using a so called activation function $g(z)$. More detail on the activation function is provided in \cref{subsec:activation}. The output of the general node can then be calculated as \begin{equation} y = g(z) = g\left(\sum_i (w_i x_i) + b\right), \end{equation} -where $b$ is the so-called bias of the node which in turn leads to a shift of the weighted sum, irrespective of the inputs. The linear combination $z = \sum_i (w_i x_i) + b$, can also be rewritten as a vector-vector multiplication of vectors $\vec a_{j}^{(k)} = (b, \vec{w})$ and $\vec X = (1, \vec x)$. This leads to a simple expression for the output of a node of $y = g(\vec a \cdot \vec X)$. All parameters of a single node in layer $k$ and position $j$ in the layer are contained within $\vec a_{j}^{(k)}$. +where $b$ is the so-called bias of the node which in turn leads to a shift of the weighted sum, irrespective of the inputs. The linear combination $z = \sum_i (w_i x_i) + b$, can also be rewritten as a vector-vector multiplication of vectors $\vec a_{j}^{(k)} = (b, \vec{w})$ and $\vec X = (1, \vec x)$. This leads to a simple expression for the output of a node of $y = g(\vec a \cdot \vec X)$. All parameters of a single node in layer $k$ and position $j$ in the layer are contained within $\vec a_{j}^{(k)}$ \cite{elstnerLectureMachineLearning2025,bishopPatternRecognitionMachine2006}. -We use the term layer, as the output of each layer will be used as the input of the following layer, thus presenting a clear hierarchy. This hierachy is also useful for calculating the gradient of the final output of the neural network, with respect to all the weights in the layers, as we can use chained differentiation. Calculating the gradient with respect to all the weights is necessary as the optimization techniques presented in \cref{subsec:optimization} rely on the knowledge of the gradients to find the optimal set of weights. The gradient of a single node given the input $\vec{x}$ is given by +We use the term layer, as the output of each layer will be used as the input of the following layer, thus presenting a clear hierarchy. This hierarchy is also useful for calculating the gradient of the final output of the neural network, with respect to all the weights in the layers, as we can use chained differentiation. Calculating the gradient with respect to all the weights is necessary as the optimization techniques presented in \cref{subsec:optimization} rely on the knowledge of the gradients to find the optimal set of weights. The gradient of a single node given the input $\vec{x}$ is given by \begin{equation} \frac{\partial y}{\partial \vec a} = \vec{X} g'(\vec a \vec X), \end{equation} -where $g'$ is the derivative of the activation function with respect to it's input. Then given the input of a node in the previous layer we can calculate the gradient with respect to it's weights using the chain rule of derivatives. +where $g'$ is the derivative of the activation function with respect to its input. Then given the input of a node in the previous layer we can calculate the gradient with respect to its weights using the chain rule of derivatives. \subsection{Activation Functions} \label{subsec:activation} -Activation functions provied a source of nonlinearity in the otherwise linear combination of inputs of a neural network. This nonlinearity is key, as it provides the basis for the famous Universal Approximation theorem, which states, that FFNN can approximate any arbitrary real function given a large enough number of nodes \cite{elstnerLectureMachineLearning2025}. While one might come to the conclusion, that if this nonlinearity is of uttermost importance, the right choice of activation is complicated, it is proven by many practical applications, that already very simple activation functions may provide superb performance. +Activation functions provide a source of nonlinearity in the otherwise linear combination of inputs of a neural network. This nonlinearity is key, as it provides the basis for the famous Universal Approximation theorem, which states, that FFNN can approximate any arbitrary real function given a large enough number of nodes \cite{elstnerLectureMachineLearning2025,bishopPatternRecognitionMachine2006}. While one might come to the conclusion, that if this nonlinearity is of uttermost importance, the right choice of activation is complicated, it is proven by many practical applications, that already very simple activation functions may provide superb performance. \paragraph{Rectified Linear Unit} For simplicity this paper will therefore focus on a set of three activation functions. A very simple non-linear function is provided by the Rectified Linear Unit (ReLU), which shall be defined as @@ -148,7 +148,7 @@ For simplicity this paper will therefore focus on a set of three activation func 0 & z < 0\\ z & z \geq 0 \end{cases}. \end{equation} -While this function provides all necessary properties of an activation function, we will provide an explanation, why it is not well suited for the optimization procedure of FFNN. The issue with the ReLU is that for $z < 0$ the gradient of the function vanishes. This poses a problem, as mentioned above, the optimization algorithm relies on the gradient of the output to adjust and optimize the weights of the neural network. A vanishing gradient disables a possible optimization. This problem is further amplified by the fact, that the gradient of weights in the first layers of the network depends on the product of the gradients with the previous layers, thus a vanishing gradient in one of the last layers has adverse effects on the optimization of weights in the previous layers. +While this function provides all necessary properties of an activation function, we will provide an explanation, why it is not well suited for the optimization procedure of FFNN. The issue with the ReLU is that for $z < 0$ the gradient of the function vanishes. This poses a problem, as mentioned above, the optimization algorithm relies on the gradient of the output to adjust and optimize the weights of the neural network. A vanishing gradient disables a possible optimization. This problem is further amplified by the fact, that the gradient of weights in the first layers of the network depends on the product of the gradients with the previous layers, thus a vanishing gradient in one of the last layers has adverse effects on the optimization of weights in the previous layers\cite{kieslerLectureModernMethods2025}. \paragraph{Leaky Rectified Linear Unit} As the ReLU is otherwise quite optimal due to its simplicity, we will mailny use a slightly modified version of the ReLU for the majority of nodes in the studied neural networks, namely the Leaky ReLU (LReLU). The leaky ReLU modifies the standard ReLU by introducing a very small but non-vanishing gradient for $z < 0$. It is defined as @@ -157,18 +157,18 @@ As the ReLU is otherwise quite optimal due to its simplicity, we will mailny use \epsilon z & z < 0\\ z & z \geq 0 \end{cases}, \end{equation} -with $\epsilon$ being the leak coefficient, a small number, set to $\epsilon = \num{e-5}$ for all studies. Due to the step in the derivative at $z = 0$ our function is still nonlinear, but providing a positive derivative over the whole domain. +with $\epsilon$ being the leak coefficient, a small number, set to $\epsilon = \num{e-5}$ for all studies. Due to the step in the derivative at $z = 0$ our function is still nonlinear, but providing a positive derivative over the whole domain\cite{elstnerLectureMachineLearning2025}. \paragraph{Softmax} -The last activation to introduce for our studies will be the Softmax function. It shall be defined as +The last activation to introduce for our studies will be the Softmax function. It shall be defined as \cite{kieslerLectureModernMethods2025} \begin{equation} \mathrm{Softmax}(z_{j}^{(k)}) = \frac{\exp z_{j}^{(k)}}{\sum_{j'} \exp z_{j'}^{(k)}}. \end{equation} -Additionally, to providing a source of nonlinearity, the softmax function has the special property, that the total output of a layer, i.e. the sum of its outputs is regularized to a value of 1. This property is especially useful in the case, where it is used as a classifier, where each of the layers outputs corresponds to a probability of being memeber in a certain class. While there is no gurantee, that the output of such a layer corresponds to a frequency in the frequentist interpretation, it is very useful in combination with the Cross Entropy introduced in the next section, as we have a nice behaving gradient, useful for optimization. +Additionally, to providing a source of nonlinearity, the softmax function has the special property, that the total output of a layer, i.e. the sum of its outputs is regularized to a value of 1. This property is especially useful in the case, where it is used as a classifier, where each of the layers outputs corresponds to a probability of being member in a certain class. While there is no guarantee, that the output of such a layer corresponds to a frequency in the frequentist interpretation, it is very useful in combination with the Cross Entropy introduced in the next section, as we have a nice behaving gradient, useful for optimization \cite{kieslerLectureModernMethods2025}. \subsection{Loss Functions} -The function of the loss function is to quantize the discrepancy between the prediction of a numerical model and the true values. It is also sometimes called cost function. It must be a differentiable function which is minimal when, the prediction aligns with the true values. The choice of loss function needs to be specific to the task for which the model should be optimized for. For the regressional part of this paper we will use the mean squared error (MSE) as a loss function. The MSE is defined using the prediction $\pred$ and the true values $\true$ as +The function of the loss function is to quantize the discrepancy between the prediction of a numerical model and the true values\cite{elstnerLectureMachineLearning2025,kieslerLectureModernMethods2025,goodfellowDeepLearning2016}. It is also sometimes called cost function. It must be a differentiable function which is minimal when, the prediction aligns with the true values. The choice of loss function needs to be specific to the task for which the model should be optimized for. For the regressional part of this paper we will use the mean squared error (MSE) as a loss function. The MSE is defined using the prediction $\pred$ and the true values $\true$ as \begin{equation} \mathrm{MSE} = \frac{\sum_i (\pred_i - \true_i)^2}{N}. \end{equation} @@ -176,9 +176,9 @@ Its derivative is therefore defined as \begin{equation} \frac{\partial \mathrm{MSE}}{\partial \pred_i} = \frac{2 (\pred_i - \true_i)}{N}. \end{equation} -The advantage of the MSE as a loss function is, that the loss value can be directly interpreted in the context of the metric on which we optimize. Furthermore, it gives the advantage, that compared to the mean absolute error, which uses the absolute deviation between the target and the prediction, that it is continuous differentiable and that large deviations are penalized more than small deviations. Especially in the context of small measurement uncertainties this is important. +The advantage of the MSE as a loss function is, that the loss value can be directly interpreted in the context of the metric on which we optimize\cite{elstnerLectureMachineLearning2025}. Furthermore, it gives the advantage, that compared to the mean absolute error, which uses the absolute deviation between the target and the prediction, that it is continuous differentiable and that large deviations are penalized more than small deviations. Especially in the context of small measurement uncertainties this is important. -The second loss function used is the cross entropy loss. It is used for the case of classification tasks and is defined as +The second loss function used is the cross entropy loss. It is used for the case of classification tasks and is defined as \cite{elstnerLectureMachineLearning2025} \begin{equation} \mathrm{CE} = \sum_i \true_i \log \pred_i. \end{equation} @@ -197,24 +197,24 @@ where $\mathcal T$ is the true-positive-rate and $\mathcal F$ is the false-posit \subsection{Regularization} -As the number of parameters in a neural network grows very quickly, special attention needs to be payed to avoid overfitting the training dataset. Overfitting means, that the available degress of freedom of the model will be used to represent all the datapoints in the training dataset perfectly. This will lead to very poor generalization ability of the model. This phenomenon is part of the bias-variance trade-off, which is ubiquitous in machine learning. With increasing numbers of parameters in the model, i.e. due to an increase in layer size and/or increase in the number of layers, the number of degrees of freedom also increases. With a larger number of degrees of freedom the variance of the model increases. If there are too few parameters, the bias of the model will increase. The goal is therefore to find the middle point, where neither the bias nor variance are increased. +As the number of parameters in a neural network grows very quickly, special attention needs to be paid to avoid overfitting the training dataset. Overfitting means, that the available degrees of freedom of the model will be used to represent all the datapoints in the training dataset perfectly. This will lead to very poor generalization ability of the model. This phenomenon is part of the bias-variance trade-off, which is ubiquitous in machine learning. With increasing numbers of parameters in the model, i.e. due to an increase in layer size and/or increase in the number of layers, the number of degrees of freedom also increases. With a larger number of degrees of freedom the variance of the model increases. If there are too few parameters, the bias of the model will increase. The goal is therefore to find the middle point, where neither the bias nor variance are increased. -A useful tool to control the bias variance trade-off is regularization in neural networks. Using regularization we can decrease the effective number of degrees of freedom, therefore decreasing the tendency to overfit the data. The decrease is thereby controlled by the regularization coefficient $\lambda$. In the limit of $\lambda \to 0$, the solution approaches the unregularized model, while there is a steady decrease of the number of effective degrees of freedom with an increase of $\lambda$. Regularization works by adding a term to the cost function of the form +A useful tool to control the bias variance trade-off is regularization in neural networks. Using regularization we can decrease the effective number of degrees of freedom, therefore decreasing the tendency to overfit the data \cite{elstnerLectureMachineLearning2025}. The decrease is thereby controlled by the regularization coefficient $\lambda$. In the limit of $\lambda \to 0$, the solution approaches the unregularized model, while there is a steady decrease of the number of effective degrees of freedom with an increase of $\lambda$. Regularization works by adding a term to the cost function of the form \begin{equation} \label{eq:regularization} C_\text{regularized} = C + \lambda \sum_k \sum_j \lVert a_j^{(k)}\rVert_n^n. \end{equation} -\Cref{eq:regularization} is the generalized expression for a L$n$-regularization, where $n$ is the degree of the norm in \cref{eq:regularization}. The most common types of regularization are L1- and L2-regularization. Using a 1-norm for regularization leads to the benefit that it has the tendency to force an ever-increasing number of parameters of the model towards zero. It thus effecitvely reduces the number of weights that influence the final result. Especially in highly optimized architectures this may be advantageous, as it reduces the number of computations needed for each evaluation. On the other hand it is known to produce less stable results, as the L2-regularization. +\Cref{eq:regularization} is the generalized expression for a L$n$-regularization, where $n$ is the degree of the norm in \cref{eq:regularization}. The most common types of regularization are L1- and L2-regularization. Using a 1-norm for regularization leads to the benefit that it has the tendency to force an ever-increasing number of parameters of the model towards zero. It thus effecitvely reduces the number of weights that influence the final result \cite{bishopPatternRecognitionMachine2006}. Especially in highly optimized architectures this may be advantageous, as it reduces the number of computations needed for each evaluation. On the other hand it is known to produce less stable results, as the L2-regularization \cite{kieslerLectureModernMethods2025}. \subsection{Optimization Algorithms} \label{subsec:optimization} The goal of an optimization algorithm is to tune the weights of a FFNN in a way, so that the cost function will be minimized, i.e. the predictions and the targets align. Most algorithms use a way of gradient descent to update the weights and find the optimum via iterative optimization. Gradient descent uses the derivatives of the cost function \begin{equation} \delta_j^{(k)} \equiv \frac{\partial C}{\partial a_j^{(k)}} \end{equation} -and update the weights given a learning rate $\alpha$ using +and update the weights given a learning rate $\alpha$ using \cite{kieslerLectureModernMethods2025} \begin{equation} a_j^{(k)} \to a_j^{(k)} - \alpha \cdot \delta_j^{(k)}. \end{equation} -To decrease the computational resources needed we will use a more advanced version of this algorithm in this paper. The Adam optimizer updates the weights according to +To decrease the computational resources needed we will use a more advanced version of this algorithm in this paper. The Adam optimizer updates the weights according to \cite{kingmaAdamMethodStochastic2017} \begin{equation} \begin{aligned} m_j^{(k)} &\to \beta_1 m_j^{(k)} + (1 - \beta_1) \delta_j^{(k)}, \\ @@ -226,11 +226,11 @@ To decrease the computational resources needed we will use a more advanced versi \end{equation} with the optimization parameters $\beta_{1/2}$. As shown in our previous article \cite{bognerRegularizationOptimizationAll2025}, Adam tends to converge faster towards an optimal solution. It is also thus widely used in machine learning optimizations and will be used for all training procedures throughout the studies. -The most important constant to tune in the optimization procedure is the learning rate $\alpha$. It controls the step size of each update of the weights. If it is chosen too large, the optimization may overshoot the optimal solution and diverge. If it is chosen too small, the optimization will take unnecessarily long to converge. A good choice of learning rate is therefore crucial for an efficient optimization procedure. Chosing the learning rate in this study is done by judging the convergence behavior at different learning rates graphically. We then choose a rate which provides fast convergence without divergence and an additional margin of safety. +The most important constant to tune in the optimization procedure is the learning rate $\alpha$. It controls the step size of each update of the weights. If it is chosen too large, the optimization may overshoot the optimal solution and diverge. If it is chosen too small, the optimization will take unnecessarily long to converge. A good choice of learning rate is therefore crucial for an efficient optimization procedure. Choosing the learning rate in this study is done by judging the convergence behavior at different learning rates graphically. We then choose a rate which provides fast convergence without divergence and an additional margin of safety. \subsection{Data Splitting Techniques} \subsubsection{Train-Test Splitting} -As we mentioned before, there is the possibility for our model to overadjust for the datapoints present during the optimization process. To still get an accurate measure for the models performance we will only use a subset of the dataset for the process of training. A random subset of \qty{20}{\percent} is reserved for testing the model only. This means, that all performance metrics are evaluated in the same way as new unseen data would perform. Due to the random nature of the subset, the performance between runs may differ stochastically. This may be counteracted by redoing the tests with different choices of training and testing datasets and averaging the metrics. +As we mentioned before, there is the possibility for our model to overadjust for the datapoints present during the optimization process. To still get an accurate measure for the models performance we will only use a subset of the dataset for the process of training. A random subset of \qty{20}{\percent} is reserved for testing the model only. This means, that all performance metrics are evaluated in the same way as new unseen data would perform \cite{kieslerLectureModernMethods2025}. Due to the random nature of the subset, the performance between runs may differ stochastically. This may be counteracted by redoing the tests with different choices of training and testing datasets and averaging the metrics. \subsubsection{Out-of-Fold prediction} \label{subsec:outoffold} \begin{figure} @@ -278,10 +278,10 @@ As we mentioned before, there is the possibility for our model to overadjust for \caption{Illustration of predicting the full dataset using out-of-fold prediction to avoid contamination of training data. Modified from \cite{bognerRegularizationOptimizationAll2025}. Red represents the training dataset, while the features from the blue part are used to make the prediction. Finally, all predictions are combined into one dataset of the same size as the input dataset.} \label{fig:outoffold} \end{figure} -A further hindrance in our approach with using the output of a regression model to make classifications is, that for an efficient workflow, we need a full but independent prediction of the dataset. To achieve this, we use a workflow inspired by cross-validation. In cross-validation you use multiple folds of the dataset, whereby for every fold, a different part of the dataset is used as testing data, and the rest of the dataset is used for training purposes. This provides independance of training and testing data while being able to test the models general performance for the entire dataset. For out-of-fold prediction, we use the same approach of changing the training data to leave out a specific part each time, but furthermore we use the model trained on each training set, to make predictions for the remaining set. Combining all the predicted sets into one set, allows for having independent predictions for the entire dataset. Having the predictions be independent is important, as otherwise no statement can be made on the generalization ability of the workflow. Using this approach we can expect the same performance on new and yet unseen data. +A further hindrance in our approach with using the output of a regression model to make classifications is, that for an efficient workflow, we need a full but independent prediction of the dataset. To achieve this, we use a workflow inspired by cross-validation. In cross-validation you use multiple folds of the dataset, whereby for every fold, a different part of the dataset is used as testing data, and the rest of the dataset is used for training purposes. This provides independence of training and testing data while being able to test the models general performance for the entire dataset. For out-of-fold prediction, we use the same approach of changing the training data to leave out a specific part each time, but furthermore we use the model trained on each training set, to make predictions for the remaining set. Combining all the predicted sets into one set, allows for having independent predictions for the entire dataset. Having the predictions be independent is important, as otherwise no statement can be made on the generalization ability of the workflow. Using this approach we can expect the same performance on new and yet unseen data. \subsection{Implementation} -All the code necessary to use a FFNN is implemented from scratch in \texttt{Python3} as the \texttt{easynn} library. The source code therefore is located in the \texttt{/src/easynn/} folder of the accompanying git repostiory. It is separated into two main modules, called \texttt{schedulers} and \texttt{feedforward}. The scheduler modules use modified versions of our previous work on using gradient descent techniques for ordinary least squares techniques. It is also structured in a modular way, so that scheduling tasks like early stopping and dynamic learning rate adjustment are easy to implement in further versions. The base scheduler implements an update method of signature \texttt{def update(self, gradients: gradient\_\-type) -> gradient\_\-type:}, with \texttt{gradient\_\-type = List[\-Tuple[\-np.ndarray, np.ndarray]] +All the code necessary to use a FFNN is implemented from scratch in \texttt{Python3} as the \texttt{easynn} library. The source code therefore is located in the \texttt{/src/easynn/} folder of the accompanying git repository \footnote{\url{https://github.uio.no/larsbog/FYSSTK-Project2}}. It is separated into two main modules, called \texttt{schedulers} and \texttt{feedforward}. The scheduler modules use modified versions of our previous work on using gradient descent techniques for ordinary least squares techniques. It is also structured in a modular way, so that scheduling tasks like early stopping and dynamic learning rate adjustment are easy to implement in further versions. The base scheduler implements an update method of signature \texttt{def update(self, gradients: gradient\_\-type) -> gradient\_\-type:}, with \texttt{gradient\_\-type = List[\-Tuple[\-np.ndarray, np.ndarray]] }. This can be overwritten to implement any optimization algorithm. The method takes the gradients of the cost functions with respect to the weights and biases of each layer. Each layer is assigned a tuple of two arrays of the gradients. It should then return the updates for each of the weights and biases in the same format. The training process which fetches new updates after each evaluation of the loss and gradients will continue will the \texttt{cont} property of the base class is true. The main structures of the neural network are implemented as classes in the \texttt{feedforward} module. Again it is structured in a way, where it is easy to be extended in the future. The FFNN can be constructed using the arguments \texttt{class FFNN: def \_\_init\_\_(self, layers: List[Layer], scheduler: Scheduler, loss\_fn: LossFunction)}. The layers are constructed using \texttt{class Layer: def \_\_init\_\_(self, input\_dim: int, num\_nodes: int, activation\_function: ActivationFunction, regularization: Optional[Regularization] = None)}. Using this structure it is easy to control the hyperparameters of each hidden layer carefully and for example choose the activation and regularization of each layer precisely. The weights and biases of each layer are initialized from a uniform random distribution in the bounds $[-s, s]$ with @@ -298,7 +298,7 @@ For the comparison against ordinary least squares regression, we use the framewo \subsection{Use of AI tools} -During the writing of this paper artifical intelligence in the form of large-language models (LLMs) has been used. Two different LLMs have been used for a faster writing of the code used. Mainly for short rewrites of code structure and brain storming the model GPT-5 by OpenAI and the model DeepSeek by Hangzhou DeepSeek Artificial Intelligence have been used. At no point was artifical intelligence used for writing the report. All output by LLMs has be checked by the author. +During the writing of this paper artificial intelligence in the form of large-language models (LLMs) has been used. Two different LLMs have been used for a faster writing of the code used. Mainly for short rewrites of code structure and brain-storming the model GPT-5 by OpenAI and the model DeepSeek by Hangzhou DeepSeek Artificial Intelligence have been used. At no point was artificial intelligence used for writing the report. All output by LLMs has be checked by the author. \section{Results and Discussion}\label{section:results} \subsection{Introduction to the Dataset} @@ -325,10 +325,10 @@ For all studies we use train-test-splitting with \qty{20}{\percent} of the data \caption{MSE for different numbers of hidden layers in the regression network and nodes per hidden layer.} \label{fig:regression_hyperparameter} \end{figure} -As mentioned in \cref{subsec:activation}, the Leaky ReLU combines the disadvantages of the ReLU while conserving non-vanishing gradients on the entire domain. Thus it is not surprising, that they perform similar in our studies with the LReLU providing a more stable training process. Due to this finding we will use the LReLU for all of the hidden layers. +As mentioned in \cref{subsec:activation}, the Leaky ReLU combines the disadvantages of the ReLU while conserving non-vanishing gradients on the entire domain. Thus, it is not surprising, that they perform similar in our studies with the LReLU providing a more stable training process. Due to this finding we will use the LReLU for all the hidden layers. \Cref{fig:regression_hyperparameter} shows the resulting MSE for different numbers of hidden layers and numbers of nodes in the layers. We can observe a general trend of decrease in MSE with a decrease in the number of hidden layers and an increase in the number of neurons per hidden layer. This general trend is to be expected as with an increase in the number of hidden layers the gradients in the last layers can converge towards zero with deep networks due to the multiplicic nature of the gradients. Additionally, can an increase number of nodes per layer better capture even complicated relationships in the data. Due to the adverse effects of an increase in node number, we chose a conservative set of parameters with 32 neurons per hidden layer and two hidden layers. -In a second substudy we observe the influence of the regularization constant with and L2-regularization on all layers. Further studies on variable regularization per layer were not carried out. The studies show a steady MSE for a range of \numrange{0}{e-3} with a sharp increase by a factor of \num{10} with the step to $\lambda = \num{e-2}$. Thus, we choose to use $\lambda = \num{e-3}$ for the regressional model. This provides a stable insurance against overfitting of the training data while at the same time not negatively effecting the models' generalization ability. +In a second sub-study we observe the influence of the regularization constant with and L2-regularization on all layers. Further studies on variable regularization per layer were not carried out. The studies show a steady MSE for a range of \numrange{0}{e-3} with a sharp increase by a factor of \num{10} with the step to $\lambda = \num{e-2}$. Thus, we choose to use $\lambda = \num{e-3}$ for the regressional model. This provides a stable insurance against overfitting of the training data while at the same time not negatively effecting the models' generalization ability. \begin{figure} \centering @@ -337,7 +337,7 @@ In a second substudy we observe the influence of the regularization constant wit \label{fig:regression_errors} \end{figure} -The final set of hyperparameters is then again tested for MSE on all of the features of the dataset it is optimized to predict. The results of the per target MSE are displayed in \cref{fig:regression_errors}, while we see an increase in the upper bound of the per target MSE, to \qty{e1}{\square \std}, in comparison to the unoptimized model, the general trend for the MSE shows a lower baseline. The lowest accuracy show the predictions for the texture and perimeter. Both show an MSE in excess of \qty{5}{\square \std} in the mean and worst values. Especially the perimeter values are surprising, as one would expect the perimeter to follow from a simple relation with the nuclei's radius. All the other physical features behave similar with respect to the prediction error of \qtyrange{e-3}{e-4}{\square \std}. In general the prediction uncertainties are smaller on the error values of the physical features. +The final set of hyperparameters is then again tested for MSE on all the features of the dataset it is optimized to predict. The results of the per target MSE are displayed in \cref{fig:regression_errors}, while we see an increase in the upper bound of the per target MSE, to \qty{e1}{\square \std}, in comparison to the unoptimized model, the general trend for the MSE shows a lower baseline. The lowest accuracy show the predictions for the texture and perimeter. Both show an MSE in excess of \qty{5}{\square \std} in the mean and worst values. Especially the perimeter values are surprising, as one would expect the perimeter to follow from a simple relation with the nuclei's radius. All the other physical features behave similar with respect to the prediction error of \qtyrange{e-3}{e-4}{\square \std}. In general the prediction uncertainties are smaller on the error values of the physical features. The total MSE over all features amounts to \qty{3.15}{\square \std}. While the performance is not perfect, it is sufficient to create a classification model based on the regression output, as not all features are equally important for the classification task. Based on these hyperparameters we trained models using 5-fold out-of-fold prediction as introduced in \cref{subsec:outoffold}, to create a full version of the dataset using only radii and areas as inputs. For the classification task we will use this dataset. @@ -352,6 +352,8 @@ Based on these hyperparameters we trained models using 5-fold out-of-fold predic \Cref{fig:regression_comparison} shows the performance comparison in the MSE between an ordinary least squares (OLS) regression and the optimized neural network. Both show very similar performance with only slightest differences in the MSE. In general ordinary least squares underperform, compared to the FFNN by a small margin. The very good results of the ordinary least squares approach is surprising. +Ordinary least squares regression is only able to capture linear relationships between the input features and the target features. Thus, one would expect that the more complex FFNN would be able to outperform the OLS regression by a larger margin. The small difference in performance indicates, that the relations between the input features and the target features are mostly linear in nature. Using OLS for describing linear relations is also advantageous, as it provides a closed form solution for the optimal weights. Thus, no iterative optimization procedure is necessary, which saves computational resources and time. The closed form solution also guarantees that the found solution is the global optimum of the cost function, while iterative optimization may get stuck in local minima. + The excellent performance of the linear regression suggests that the additional challenges posed by using the FFNN are not worth the extra development costs. We thus recommend using this approach only for datasets with complex relations. \subsection{Classification on Regression Output} @@ -363,9 +365,9 @@ The excellent performance of the linear regression suggests that the additional \end{figure} Similar to the structure of the regression model, we use a FFNN using a variable number of nodes per hidden layer in configurations with different numbers of hidden layers to create a classifier. To use the model as a classifier, the loss function of the model is changed to the cross-entropy loss introduced in \cref{section:methods}. Additionally, we change the activation of the output layer to the softmax function. The leaky rectified linear unit is again used for the purpose in all other neurons, following the results from the previous study. The parameters are optimized using loss minimization using the Adam optimization algorithm. -As above, a parameter scan is used to find the best parameter combination for accurate prediction, whether the nuclei are part of benign or malignant tissue. The results of the parameter scan are displayed in \cref{fig:classification_hyperparameters}. For the number of hidden layers and neurons, there is a clear maximum in the area-under-curve (AUC) score for the case of 64 neurons in either a single or two hidden layers. Using the full dataset in the form without regression AUC scores of \num{0.9962} have been shown using logisitic regression \cite{mostafaBreastCancerPrediction}. To account for stochastic variations in the training success the AUC is averaged over a total of \num{50} runs for each entry in \cref{fig:classification_hyperparameters}. The regularization constant is chosen to be $\lambda = \num{e-6}$ according to the figure. During \num{50} runs this combination of hyperparameters is able to achieve an average accuracy of \qty{92.70}{\percent} and an AUC score of \num{0.9781}. While this performance is not as good as the performance using logistic regression on the full dataset \cite{mostafaBreastCancerPrediction}, it is to be expected, that the performance decreases when using only a limited set of features to create the regression output and following that the classification. +As above, a parameter scan is used to find the best parameter combination for accurate prediction, whether the nuclei are part of benign or malignant tissue. The results of the parameter scan are displayed in \cref{fig:classification_hyperparameters}. For the number of hidden layers and neurons, there is a clear maximum in the area-under-curve (AUC) score for the case of 64 neurons in either a single or two hidden layers. Using the full dataset in the form without regression AUC scores of \num{0.9962} have been shown using logistic regression \cite{mostafaBreastCancerPrediction}. To account for stochastic variations in the training success the AUC is averaged over a total of \num{50} runs for each entry in \cref{fig:classification_hyperparameters}. The regularization constant is chosen to be $\lambda = \num{e-6}$ according to the figure. During \num{50} runs this combination of hyperparameters is able to achieve an average accuracy of \qty{92.70}{\percent} and an AUC score of \num{0.9781}. While this performance is not as good as the performance using logistic regression on the full dataset \cite{mostafaBreastCancerPrediction}, it is to be expected, that the performance decreases when using only a limited set of features to create the regression output and following that the classification. -The performance is further evaluated using the confusion matrix in \cref{fig:confusion_matrix}. Potential asymmetric biases for false identification, i.e. wether type 1 or 2 errors are more pronounced, can be detected using a confusion matrix. It shows which relative amount of each class (malignant or benign) is identified as which by the classifier. No signifcant asymmetry is present in our FFNN based classifier. +The performance is further evaluated using the confusion matrix in \cref{fig:confusion_matrix}. Potential asymmetric biases for false identification, i.e. whether type 1 or 2 errors are more pronounced, can be detected using a confusion matrix. It shows which relative amount of each class (malignant or benign) is identified as which by the classifier. No significant asymmetry is present in our FFNN based classifier. \subsubsection{Using Logistic Regression for Classification} @@ -386,6 +388,17 @@ Additionally, we compare the confusion matrix in \cref{fig:confusion_logistic} a \section{Conclusion}\label{section:conclusion} +It is evident, that a classification performance close to the one using the full dataset can be achieved using only a limited set of physical features. Using a FFNN based regression model followed by a logistic regression classifier, an accuracy of \qty{94.74}{\percent} and an AUC score of \num{0.99105} is achieved. This performance is comparable to the performance of logistic regression on the full dataset \cite{mostafaBreastCancerPrediction}. Using only six features derived from two physical measurements, the radius and area of cell nuclei, this performance is possible. + +It is therefore possible to reduce the data acquisition for future diagnostic purposes to a small set of physical features, while at the same time conserving a high classification accuracy. This is especially important in the context of fast and cheap diagnostic procedures. + +Additionally, we have shown, that for the regression task on the limited feature set, the performance of ordinary least squares regression is comparable to the one of a carefully optimized FFNN. Thus, for similar tasks in the future, it may be possible to use ordinary least squares regression due to its simplicity and analytical solution. + +\subsection{Potential Future Work} +While subpar performance of the FFNN regression with a high MSE on certain features is evident, future work may include modifying the optimization metric, to better judge the importance to certain features for the classification task. This may lead to better performance of the regression model on the important features, while at the same time allowing for a higher error on unimportant features. + +Furthermore, the use of a large set of features in training of a FFNN to make predictions on fewer features during application, is a promising field of study. Using such an approach, it may be possible to create models, which can make accurate predictions using only a limited set of easily acquirable features, while at the same time leveraging the information contained in a larger set of features during training. + \onecolumngrid \appendix diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..edc203e --- /dev/null +++ b/requirements.txt @@ -0,0 +1,56 @@ +asttokens==3.0.0 +comm==0.2.3 +contourpy==1.3.3 +cycler==0.12.1 +debugpy==1.8.17 +decorator==5.2.1 +-e file:///home/lars/Documents/Studium/UiO/data_analysis/project2 +executing==2.2.1 +fonttools==4.60.1 +iniconfig==2.3.0 +ipykernel==7.0.1 +ipython==9.6.0 +ipython-pygments-lexers==1.1.1 +jedi==0.19.2 +joblib==1.5.2 +jupyter-client==8.6.3 +jupyter-core==5.9.1 +kiwisolver==1.4.9 +matplotlib==3.10.7 +matplotlib-inline==0.2.1 +mypy==1.18.2 +mypy-extensions==1.1.0 +nest-asyncio==1.6.0 +numpy==2.3.4 +packaging==25.0 +pandas==2.3.3 +parso==0.8.5 +pathspec==0.12.1 +pexpect==4.9.0 +pillow==12.0.0 +platformdirs==4.5.0 +pluggy==1.6.0 +project1 @ file:///home/lars/Documents/Studium/UiO/data_analysis/project1 +prompt-toolkit==3.0.52 +psutil==7.1.2 +ptyprocess==0.7.0 +pure-eval==0.2.3 +pygments==2.19.2 +pyparsing==3.2.5 +pytest==8.4.2 +python-dateutil==2.9.0.post0 +pytz==2025.2 +pyzmq==27.1.0 +ruff==0.14.1 +scikit-learn==1.7.2 +scipy==1.16.2 +seaborn==0.13.2 +six==1.17.0 +stack-data==0.6.3 +threadpoolctl==3.6.0 +tornado==6.5.2 +tqdm==4.67.1 +traitlets==5.14.3 +typing-extensions==4.15.0 +tzdata==2025.2 +wcwidth==0.2.14