From 1a47c5459afbebc5c7064fe26aa113195d0f46a6 Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Tue, 18 May 2021 20:15:56 -0400 Subject: [PATCH] clustering --- Clustering/1-Visualize/README.md | 130 +++++++++- Clustering/1-Visualize/images/popular.png | Bin 0 -> 14881 bytes Clustering/1-Visualize/notebook.ipynb | 95 +------ .../1-Visualize/solution/notebook.ipynb | 231 ++++++++++++++++++ Clustering/2-K-Means/solution/notebook.ipynb | 135 ++++++++++ Clustering/images/turntable.jpg | Bin 146971 -> 63515 bytes Regression/1-Tools/README.md | 10 +- Regression/4-Logistic/README.md | 4 +- TimeSeries/1-Introduction/README.md | 2 +- TimeSeries/2-ARIMA/README.md | 12 +- 10 files changed, 500 insertions(+), 119 deletions(-) create mode 100644 Clustering/1-Visualize/images/popular.png diff --git a/Clustering/1-Visualize/README.md b/Clustering/1-Visualize/README.md index e9f273d87..5fc1600db 100644 --- a/Clustering/1-Visualize/README.md +++ b/Clustering/1-Visualize/README.md @@ -4,11 +4,16 @@ > While you're studying Machine Learning with Clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare. ## [Pre-lecture quiz](link-to-quiz-app) +### Introduction Clustering is a type of unsupervised learning that presumes that a dataset is unlabelled. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. Clustering is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music. โœ… Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes ๐Ÿงฆ๐Ÿ‘•๐Ÿ‘–๐Ÿฉฒ. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos. -### Introduction + +In real life, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans. Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed. + +> โœ… Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it? +## Getting started with clustering [Scikit-Learn offers a large array](https://scikit-learn.org/stable/modules/clustering.html) of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-Learn and their appropriate use cases: @@ -19,35 +24,134 @@ Clustering is a type of unsupervised learning that presumes that a dataset is un | Mean-shift | many, uneven clusters, inductive | | Spectral clustering | few, even clusters, transductive | | Ward hierarchical clustering | many, constrained clusters, transductive | -| Agglomerative clustering | many, constrained, non Euclidan distances, transductive | +| Agglomerative clustering | many, constrained, non Euclidean distances, transductive | | DBSCAN | non-flat geometry, uneven clusters, transductive | | OPTICS | non-flat geometry, uneven clusters with variable density, transductive | | Gaussian mixtures | flat geometry, inductive | | BIRCH | large dataset with outliers, inductive | -> ๐ŸŽ“ Let's unpack some vocabulary: +> ๐ŸŽ“ How we create clusters has a lot to do with how we gather up the data points into groups. Let's unpack some vocabulary: > -> - 'transductive' vs. 'inductive' -> - 'non-flat' vs. 'flat' geometry -> - 'distances' -> - 'constrained' -> - 'density' +> ๐ŸŽ“ ['Transductive' vs. 'inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning)) +> Transductive inference is derived from observed training cases that map to specific test cases. Inductive inference is derived from training cases that map to general rules which are only then applied to test cases. +> +> ๐ŸŽ“ ['Non-flat' vs. 'flat' geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering) +> Derived from mathematical terminology, non-flat vs. flat geometry refers to the measure of distances between points by either 'flat' (non-[Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) or 'non-flat' (Euclidean) geometrical methods. +> +> ๐ŸŽ“ ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf) +> Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured a few ways. Euclidean clusters are defined by the average of the point values, and contain a 'centroid' or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to 'clustroids', the point closest to other points. Clustroids in turn can be defined in various ways. +> +> ๐ŸŽ“ ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering) +> Constrained Clustering introduces 'semi-supervised' learning into this unsupervised method. The relationships between points are flagged as 'cannot link' or 'must-link' so some rules are forced on the dataset. +> +> ๐ŸŽ“ 'Density' +> Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density. ### Preparation -Open the notebook.ipynb file in this folder and append the song data +Clustering is heavily dependent on visualization, so let's get started. + +Open the notebook.ipynb file in this folder and append the song data .csv file. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data: + +```python +import matplotlib.pyplot as plt +import pandas as pd + +df = pd.read_csv("../data/nigerian-songs.csv") +df.head() +``` +Check the first few lines of data: + +Get some information about the dataframe: + +```python +df.info() +``` + + +RangeIndex: 530 entries, 0 to 529 +Data columns (total 16 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 name 530 non-null object + 1 album 530 non-null object + 2 artist 530 non-null object + 3 artist_top_genre 530 non-null object + 4 release_date 530 non-null int64 + 5 length 530 non-null int64 + 6 popularity 530 non-null int64 + 7 danceability 530 non-null float64 + 8 acousticness 530 non-null float64 + 9 energy 530 non-null float64 + 10 instrumentalness 530 non-null float64 + 11 liveness 530 non-null float64 + 12 loudness 530 non-null float64 + 13 speechiness 530 non-null float64 + 14 tempo 530 non-null float64 + 15 time_signature 530 non-null int64 +dtypes: float64(8), int64(4), object(4) +memory usage: 66.4+ KB + +It's useful that this data is mostly numeric, so it's almost ready for clustering. + +Check for null values: + +```python +df.isnull().sum() +``` + +Looking good: + +name 0 +album 0 +artist 0 +artist_top_genre 0 +release_date 0 +length 0 +popularity 0 +danceability 0 +acousticness 0 +energy 0 +instrumentalness 0 +liveness 0 +loudness 0 +speechiness 0 +tempo 0 +time_signature 0 +dtype: int64 + +Describe the data: + +```python +df.describe() +``` +## Visualize the data + +Now, find out the most popular genre using a barplot: + +```python +top = df['artist_top_genre'].value_counts() +plt.figure(figsize=(10,7)) +sns.barplot(x=top[:5].index,y=top[:5].values) +plt.xticks(rotation=45) +plt.title('Top genres',color = 'blue') +``` +![most popular](images/popular.png) + +โœ… If you'd like to see more top values, change this `[:5]` to a bigger value, or remove it to see all. It's interesting that one of the top genres is called 'Missing'! + +Explore the data by checking the most popular genre: ---- -## ๐Ÿš€Challenge -Add a challenge for students to work on collaboratively in class to enhance the project +## ๐Ÿš€Challenge -Optional: add a screenshot of the completed lesson's UI if appropriate ## [Post-lecture quiz](link-to-quiz-app) ## Review & Self Study +Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped? + **Assignment**: [Assignment Name](assignment.md) diff --git a/Clustering/1-Visualize/images/popular.png b/Clustering/1-Visualize/images/popular.png new file mode 100644 index 0000000000000000000000000000000000000000..b80f30332ef348eba5ec1443eab6b7bd84321806 GIT binary patch literal 14881 zcmeHucTm*bwq;`$vl0|ZN|yAgAW4!6*yN04B7|gM==pRY@pwABclHXb4 zp0kRbsk7UCM-zLd_T1B8vJiS|4)DL)9(A# zCPUFow_UZNXQpus!+P`xY#GMy{f9f3Ft#TBWikP$rgisf_adgg6zDJYsm*nBE|(_J zvt286pWlypdDFI)3xf$%j$mFF4RD%X;Tm=b=qxb$*>eEH+B;)Ng3+9QYRk@lB*HyNrMbj!>(yp1LN=K$H4W{9?SMtkzaC*<#)$i*2a+X*!d3ec0 zvda*+Wh^W_=JhLImhFn}ULtH|tJYK#(lzuNwe9Livm}l+Hp^VmkgweHu^6mmr|aRa zy({c_w<{Y;n7R*#Fli0_cKz1jM7xFWIr)6<_Cuc0dtG>| zA{yb>WlLjq9%aI-itqb_>3oTO%F5QILQCb2{qMwL7xStXqIGqDb+h1Mt&QWiv9jaRH>$xDjHP`lSXVYWDW#Vaz zdd*H(&tm)JR@(^-8H-jJ$;dT29AnFCliG!4Zn*>w!des=xru(MT_QoEjDC-pzdG`$ zU1<3(H-WKYXHTK`=(*HH-WU=&cH@ z-p=DOdO}ze;FR-R67V`e7-))IuP^kj;PG}JT4@t0Dl^buYB=M?7dhFS_F#WnJ#9p( zbwQbqjIw)qvH1(4z*hh8Li3?hQ735%^?kN&X1`q6o+R17fNwoU*{4mQS^HG4zgFlLDKkPG?Zq` z()USElpOYObK73~?QStH>Gev)xxuG!Y;wq}ZOE+u=SPFa@aiAL$>?=ks_@Pl0wGq) zG`-k5hQ~DcWkQ0RXsC$g9?AWJyBmXM!)^r6^?D43=^$qV#(>{fB%ilPDW%$;1Dltp zygcO5!)H_JxGf*Q@}a=^RQ5^HUH3I2GmDH=Xdk!Kor%Xr*vf5NuVGyk=|JZZKjKP) z@H$R#>v-(;&h9q*hD)2U$LPIq%bwOM@1J9l^M?=iek`!)FRgA?%br-QGOVi}S@`Y- zcQq{^-d9|Z|LfDacHdniU!RM7r5lNd{K6ml9mgD!(!gLV7fadg_@bFu^gX-`40h+q zuT1FP&{pO)8#1*WS1d5xoi}UKBi6`bu^L>zHrjj#k7pcoX=Kfm*bH45E?9ng#7m~y zi04H|Mw)`i3q!+&(Q2!nhmQm>{Ks@TFz*ZwDM#ntTgq9(!pEy@GU!ze{TO?aB$epd?vD)8eie7+FPi><+L!|BBxc7J+k%vN6@?T3i>Kd{u>vV zqH&$1^+$b|mezkh%TLqb-p1NQbMt&_!|2p1kUUr2f*9#D$r$ z%XqzbH{XLL&inm}P9q!;_Ua3`s$JYL)YBbz{&x6WPjnO)r+dHT^92T}Pj!>}A|8&r z=axyY8TbkNFAv*^lH@DUZLiFAF^P(9zw9-&T6%hB{mk{T@kJHOjk(CZUDKArL@^CA zt+LX+uvbDFf)2N+t}*T{4pvRKDD&TcNc8{w>5i9GxskbG04e!`=y6O^YEsZa44J{` zEia7+0d%Eh!BK2Rbb;0b{M$3fqZ4_izgCqA-yHU-!hJ43bYg)Me>*_WH*vBFJM3+g z=)gt)PQ+^PoWl2yu3~;T8t(k+HA3~meWxAwZAFWOg!Md1ytYiDuWcepJ}cqp6XCDJ zF42VAg3wTc=xUt6`eexc>4y&&LhpY#u>GoWvZ*&Q_N(G{sGt9VEYp@Zcyf%bf1N4& zOq>bk9bJj>v(>Sn^=7B3@7n5ipX=%R_wdSfw7E1a6`J_<-OpI;dLu7}*BLlEyt z{`X2{wTeFbY$kXu;B(Y-g!zRe>sie|oW_WaUZ&qKcJ!)w7;Yt4COUPG*tTo7veqKX z%Y#WW|9bcWM*3+teo;k4bhjMG&*SFir9U{=wa6f9-EX?we;mWAwJMI($Z?3RzaeMV z?)yV$&z=o*a)S%r@k-peqf?Oky9{N3i?8jiv|AVpr^*;>6 z>As^obJaSgNao#%yRUY0i)7&A%Ps!?R{pu@fEWFZ9Qki}rfeP`77>y1Ek$a+Y}hBF z&hXpspW|xjvKI(DE6s)XII>kTLOzGkuv1b}w!{l*T;#KT859H7VQK7Gpc_%#*y{CU@t6Bo&-sdIn-cE@X#NY{tZ zboi0%FZNuUyX*_^fDyZ2X)ES4^5C%ZGGRkH9KI%|r!(woiRvw{c_SDlBqS_;O|_7o zIH6{2oXSBF^zhN6NLcm6gz>>cho-7(g}v6vc2mTjeXxQB?Kb6I3}b3+X^~@)4F0O2 zk4xhla=#tRXBSmx_=3^H%Zr_pQ*L&4c4cFuZSZr!&Way31%>N56I;SFn-LDa4JwFC zvV4Dk|9#PWqY$wL8j2pM#Z5O-+|pf`gB5Zf-W#*GpZvaA7lQduwa=SvY7< zzcIH#znx{@J?&$ZTrU|TIknX(f+S8*@t`eX#KzvRQSL~W>q^C*tE;PPv@E&8#)ehT zSqtnof*nXot+J&I3ZV78n_s;WnBcQzpa}!8rfy3W^HZl(0K~pgX~IBi)j&gPb;Jk*_ksAANwVaYpWmVO3GxE_3iFu zf=hIIx(eCOJ%&gBwJ?U~|L=*>|4PCCw=LAZs{46wZ!eXAV`>PkaCUAk$J%1QhFZ3& zQlbb>HAV6SR(WG-uq#hj=%3_GCLOGsRki1MgH=ynWNmG2x7h2HCKgioLD)YaEpb!Dpwx-Psrbo_i$SB?gy$4_{@*!?O~G28x9JPHYHVXw8L zi%XY+C^2=|XIt*#4Qs`_cb}p}_4hv@At~8pSis89e@{R_Kw4g2-N3-0;N~AZeXDbw z-pk8NCcM3)gV%mU43d_Won0ng$W8sxp%bf9v38b2)gCKrYp_h;n#K0^cGBYwUh4~N z69InVPkPQ{FguaD*Xd>23ykVJ^YulA*E&@!hieGRDk_uH(=1reAMX$II*i>J^xY#i zH8eB~GBYsTuBoXxIHZ5?-gmi|6>H3;4~hl`Q06LDL*$a`__Qc-$R~>66NJ4izrVdd z*O?W;n5B`gw>26-$A}%qZN$SKfLVDa{ zSk!A>S5kyF?arNnAFgLUMv;s2vR*mbk?7)j6fWs;DY0&X>hRxBK>seH`}cm%`$a82 zQ!_KGiG~+~o~xOKg@v7EcKVPw+JL%+0QPYHDG*#ub#)SuB3+-KU!0tpN`Jv1b>-&G z>T+6iY>MW27sB8y*?oO|mTZaT)3CPAy1TEv6AUIXP#~o$JcgypB~?RegEGZYcNbSH*vlOe5qQ6=rP- zm)*~^V%?vUKp6J&?k_AWD`R967S`Q=_@t_=Y%mZBb5LElmzS2-Ye=@q<>f@Xs^a3} zefu5%HWLOTRzqln(#;Za>NJARiredplVjm!SV_qzJ3BiQ>z^LpzH-#gh@+b>;6;Jac_(~I@Q$H#=Wuf>Df7# zJ>mvC0|SFwJW^y^TNLHEzs;kMDfd?Ay4W`utCwrG<=5tWG$NSg$S5gUa=NlpQyDe! z-TSmdk%kLjdZHax|NLSGB8kC4U2$>o*ZdA|yuH0q=C~~OsR7{>6ck7(AvaZVg5ySP zi@J`G%-y?+ckX;TVMlq@TJwzRqc`Dy5jD-C;SzgYjplS&vVHLI-&U#odjb4kUwQp6 z_p_@gHdJC24Z(g1E9g8ER_V5!myzHp4blnJaOB?wJ`@;w)$Rq7p|tofUu3cEFWFSc z&Yery81~h_5&K6jK_-likC#|>T|r<+CFK@m_ZC@Y8Os#`t4gQJHL!@3P}QAA(7YUuwt_(DpLo|hc2 zRkteCDHC&Z+30m3PJtw#e&Y<(LdkL23~ct&u|CaPw;o=;eA(8$>goOFz$7ZNNdMV3 z!Kwpr$$X{O4U;#g;qBjviT$bc4;cU54}3RIUQ=wRPWbw@mucjXYAEp=s!{Ca{+}+W z{}siGy^|{mMp1Ps!Kn;+4}CWciokpDi#^p43O);96C+kFM;(dTwKm^fmAt$>2uIbu zUE(qPUD*7j@&Q$`)-PXPwwQLTOg1}3yUctQcbfX51hKxlQM;#G<+iL2B7qdIh_2=z zP^Ft7cx}vmczkRQFgO9Xu4yw^sivu^nXN~tgrL35z_7MhHca7)hwH^&RAo^ZD<~)c zH=wei`~9QCYhyv&e{kB>&d!dwyS;qSwhHu7Yz~?4g*qaY7v=oW53`>a{H#WaBuR|r zS$@=1RM}x+r!_e!9zS`~)YF3qqM0BJ=&o;Yh}@^ z-x5pd`w9aB0!&-uJnB{=*)?`&AHuIOW`@kd@a=ye?|+{O|MF)>rWW89?4RsO4300H zwco$gyDDlH?WgQd@weIeFI>}~rWsfsF+vk6u!@UceNDG-5k#T81N*q2C{@Ua zusJkTVaWx27v##=+S+Px!X+41ENMy5nt*1*ZP{ArPK-Jot%%qz^{Z(%HjBHo0D$V!gyE*c*o>b4_-MCh3=OFNlsPxrk z#1g0{PIsSlX8Uh}TVlJ%wtH%K(?ILw*bY`AAFtG=U-E8EohDwrqWt?_{}f2H>(^Dz zieGRASHRK1;RWCW(BjKiuU>i4lvCt%U4a{htHFblLCzi+g!2o9iu?FQG$3oz_I5XF zD~5&I@x;M#LEC|HhgPnAd>OYMlLFUtK-#I6+3HqQRLs`X`Kp?lrdxLB&MvGdHc*d# zw=Bulul4Dx_9z+~8&ge_CRfW;eBV3yzR~)I_R{wFbH1Ukr$l#>z%4{&AUV4_AQxDZ z4z4k*52%-EVq4N3#y`(OiIS~TQK;ujbUAqVFgp)VXD`!-XF57M&SRgBo}d%aSz21U zqLLnbMLqYGS3f?`Z-;W?H}5nUi$8wY+kAg34zyc29wLMyXt{Pz56n7V%T9aOEY>^l z5JZ7Y6o-<5K@yVHU?a>A@9ma>Sj{UmX%?)y!~cR|=Oi`1A_)me;In5hz!Z(*(#-}( zlZ}=2X;|<+fuVCnW^L-y(t+u+F>kik=5t_Yq(LSErbMv&U#AvuyaN;t{E7syWoKsc zNg6@();NK_THjhOkSa2J?v*Yndz?)U=NG_}^<3-HSgYM5Du9OW1aVsIG%XKq6V`3W zBZKOO^~{2@(%mn&?;&kZsqdY9nATkkOd}Pj7{T&jgo9*JKVnu{ZD;V9lzeryLxCO4 zAjBRIA3o$U`h1**-`>wY*qaObL$kA4GSOV9s0dHJX3_*QeltoWIOJhkdb(wAp^4|l zpxd8?Og2TTxVn~_etDHFg4<#h64D0Hc5;o8Ks!A?Huk0?qo{Ha66Jyungvj>0xt*p zbp1SD>(+!{twL17p2a?!la$1jW^UvXsnvLUm_-NP0zW$9q9yVHQlw4+_Cu4}rPBT9 zo0D`Rxxg=11O$2;BR^cA9<+u0L2rOv-}o|C7sS-7Fh+2m4F_|0jkqx^RHXXyFVnwFrx1mZBDtDHswI|cs1`_(3hl0RneQyI(r}sYmP<%TSd}73yuyVT z85%NThlYmcy77PFVs+Ko!_zZcyUez7KCg=3TTdGDNslZu)pE;;xQBFlw`M-|>m4Fq7R z-EiLb@AnqJ7u=?$rS;t1oc@Im)gFdU0{DK>LYGvCTWjDnFcP+xs-$IQp?vBfBPUM_ zBxi>c)XdSyS0ApyZF>7LPVT-MDz=DNUmDCgdX72Mq&Z5^e#8&mfAZ~sIXIQRGP^_w zQ`G`P2@!p1l6s}b=UC*;L9=GxD|iEkm;@Yc-Wbk=wu2nH40;65$%Irl_LLxrqozf@ z&Z{#@!09s+jS*BL-sMBSdpHQ?xdg8zB|x?{xJOIi5|oADsl@4CBYn`D!2=d3xVwRY zf#HnL&tJU-7NelkRIuc8sNjWAO6@-G3x8B8=-?U+kY;TH?Z@(DK=u zWr5>u2G0txlL85udQi+zG#>$#{By+4bm_<|0#(jr5ddXZ=;_7wHrsrWnu?2~#_Lw8+w_;J zRk8Zu>aDrKs(kLArsyPvI-gPo-@e-tKW@6v1f7Z4hQQ6I4HUGREtboiamar!X@*7 zAgWL+Qt_CFZGR+9`#33@RA$qkzSv)8wLC-ssgXU^5}ScccnB2@F)_b$?3_NU;#?1? z$EjtM;f-EKMyA2x^ICsbgpCTz&(BAm$C)#i^!4>wG=e^inG3iE^9jJc07#^zre?y9 zS@z_`FV$=>4RF8J_V9C^Fbm=T3tV-?0N`%+fK~3izMu)+uqzxKvX?Ghs$iKt8{$v_ z2{i*&36+p*F0xVm{4g{^uCfX@fBJv;_))FG(ZnqJsiE=7i`%V2%jQr&07=Kdzbw3V zS+;d=;nvQtHeSbxyJ}g=b-j}mLN)hg&evaD|6X8(ZWAGYL&IHAQPU?|b&E{pTxQ!J zClA=30o2{wuHEZ+BN3#Uqt1Eoz=4^is^#vO>t-l;l9H0NJr!Mb;>YU|q+QX-%e@_N zk`1KD=;$bTk7{Uh`D}CFzWod6;A}1c9m+khml(0rZHYZd8oYc-)@sE1nEK;qXufE< z%yoYIIbP2T2x?+#n(e*0q6I@aU=(Xu*1DOCbKQrUr8QnCquhbrRX20Z;dhyP35=QvE*9s#w_*`*RNEbthqUboX~gY~ zc?jcgPHn;&{dTqS49aQ2`*x}D- zKkWRqy*Zr-$z}rqDg%wt(j~%%9uhHNE_e18nH{Y!J6(VO*UXGL^d@11#^$xCZ%w81 z>>INL4@ra^YbHESQ?hF_-)@VDi2UK3 z-h70Cfb0-Sz%i2WNb9Y^1Hj@oo_1I`;YEBrCs=tX0)f`Lz9!%1wH@G(7xq%O>dyTJ zmZQ#`sK${7Ccp6$v9Die!-+u@NwBf8d9L@G@!AdDM2QNzs>di)Sba{gaX6b=Up*Wb zHx7HFi=eL?ublz+gIoP31;`yUSp|9H1w&|%Eqv@HRjt(KDw0Ej{oBuEKv+m#5_au=Y9z^1-)fjx}HFhgHF zp5NgX+C)c3&@6>yptjuH+-PpPD+0%LH*?qKLLiw%2i-C(xMvJ(^m9xCJqgsK0%wVi z6cH~tIG(>p10r9(Ooe#Qf`mx3bdPUefE%ee=Lzya5`tVQLq5LPu#OZ|kUWSBB#KbH zlvww+H+H>Aby(F%gtWA+TD$>O^c+;^>fivYLM1T}rUU5-Dq9>_-Sjw7c?5hfjZj)) z6a=WbjT}*V`Hw zUZs7dHaVaOX4m`edTh!DrzWl_od=u}f<-AqSpha$HHe@b1jFrVGIQSxQXrXlTo>-i zz2?C}!iTSXCe-2WHdiL4u~;SWESL}xwxcxK+nE+czBUR3kOEURe3-+(j&vNR^GsX66UTaB?{C*SuDiAPgXUv27$X>tZ%bqW85jZOYN|Rr}!J$e5Tc z7^u7rdb|r(jxY!;616-X&45#M>QGdfbGL+27_-=~iV6cn0^O%UrC|l34V@hrb}T`) z4UAw|e0w87N=oYJTEWUl=etvYx~q^mP@P%q?rcK+@{J+%<{LO(q>~nUi_#23ymV&a zPCCLa-Bzvj!GUCv0RsnGVMU$b+oQ7!I7!nEore7F<=Wxiv^NJFWX~IehA?=CL3s|8 zr3NiC3U0mJ2o{AoDCvNu?e|G_^Fwl>qStYzJqhsU={6=^j5!Nep1uk46-8_Gf)o#?&t4A>$Wf&)oUD8ycX zj6OO(<8%|~1R(7mT&<#`6W8>TEd?arJSt}+uc(<@vpoh{36F^2b)Hci^japM*oLMw zBUoYR&=Ya%G2k}qTGk8H&{p0fyAI1>VqvjbU+6^$xCSbwxw%<}MIk}8E|454Cb@*0 zG5StrN9sp8p~OXt`wu@BojYv33Q7oG!0CAC@SP11k%1cIhVN9AnzeCH*iq6g{OiMhWr3e5JSaY8@8}9763(BpdpdCg<$&#P{NN? zJ@|d$sc?8#7#Z&jR=H__zqi({ZD#?ERnntJzk!oTe)jCxgryAEKF6z9&18feC!W^) z9y^98AiNW=&1Yh4j5I|PIFHcRDT1cC6POI(LPM69n6=R&z4h_Q2|?f8YW3WE$pAbc zDxCJ!A=Iu?&Sob#L$LTeok!!03TRdny)qAqrs{6#H5CKRyYz@I3bV zx>?8-wd_=+Fy1}b|EDnX-P;|w^y<~6eCKS{tngd6ZoPzT24F{%Nz2v0LZGqBjQykQ z%EwP?xrc?OG)IAr06hqRE>2MAkVA5aOS4omilGL8v2foW2;12ON2MXjeG~LpNk!R~=)0?l!r&iO+`BW)^YrwT`=KcZCz%aPLZpG53h<_ot_%zbL319e z8S*qRu+$BSe8k<|y#YF3PzrtpYEFmZ3e8#IwpNuO5#w}e<34qDse!~q<0eRCpE;AP zj@O#a$**Y#q$VdPuM1_-dH-#?+-|rg@afa9uphiWJMNKeDw4<^hTC54R8hFPDaAJa zDoTWfMG6caRa6rJ7qy9O%iex+TnPY_oR&6Eiq_)}WQM(Kh40Z|L!~=V@XN>XCpSg1 zv%@h#>~w(Fn+-{44)zo9$2aJCGh*SzDUmZ2QLx>Q4xh4w%g{1_eY%Rw6ku?wvo|gI zK)LR8~ z9U5H&6A3XB>ffUAAxPv|y1m~4P0`%TaJ>FdE*TfxEA}CJ;#L}2JVzf>nh%O|fv)Jv zRJsjgXDDC6h2NAF6A7^;KMAS2vbx#{TZ^hksJt>kokC?~u$rRWa8;{rb+!Y}B@NUO zGE)$z0_0tS^}@gztJ@R#ovX$4l*yoy=B5*UyFuWf2|(yLp*R4cp;cu1{Lo42h5$`A zN02X&*O{QPzZgGe*MZ;zNmoqGKv!?}xXTijmol-uod52fSR}W;9-t4J=>Q!XF0^tc zkuyyJLXAFqyvhe*Xq@ww!r)K~>RKaBG&ecqT zG?=TS)MuoQz6M511?MC02cwSg$os`8X-He(t5@eyMa8LA{1udcWPChc&}FWnr-yHN z`Y3$zBO~M7g$3)1%F1l7jU}+%Jc(;POjsBZRD{a)*Yl*u7jGYPJk!wBqztJ8!Xcd{ z!TlEULm>GLjf|ik z4^*l7-i{MLHa|?@i58o7X)ZEuATN*$gE%}%{Lztmj9(6jq>6LLVf*eP%t2F}fWC-n zwl|i0kmdp{4zhg!;P!kBixn-@>L5pz0n5oxpH2cTZVAp6;y_yW5fTtpUk9fY45=bT z8yzw~zIPA+&<;M`21^~UGqobpDa_H>L zqa7Ch4?iJjRzXY7ffE5xJ4ikc1>&_q7R*St$j*=3YWaG)&?!ZYF`yfmk>;Mdk?XuX zq>BU`=%C5j*+z5nw5JSKaRN?~sMJEvD`X|SNEY(E1IZ>drV5$WLep} zHdeq@?5g%~T3a60r}O^t0F3{1Kp3oYO&UPJJoITJ2?7H)h5pk4{sDiwH5qw8Ic+|>u2BY{vU@KA!ua4I&l0-rba2Y8mctT)rIQJpXbDmh+AT`Z8wjBj%>!S+3Pw0#y4l6C1SU|v zd<$?N{qUId`0-9KS|VS(NQP==9<~XU^y|yR+#n6WqLX$TbkV4JcH4elMnOTt-MxH= zNGt)N2*tiE%*m<2jM$LpJm&|8Ge%@VqUQ76c;x1|fZ2$2A(RNxx z%rF#ny#W^h4S=K03k>3PW-2jIX@|8{ zd;CHsEsRxIf;y&7<6=HAVP)?>4z4*Wt|4&xDqV7rK@1g67W96ot{Fdla*8hRk3Fda z<93Pwb10!vWxW5;@v!J<$`txbJzz#7OCLsN75>NwfwV&;n^(9*A9X^D8P&W1oI_sA z!Z2zi1&pZ%QXyXV3TA|x3&0U*mRj(z^bz^?s=%ChaV_vvk=1~FNyx!CU1rP(#c6O3 zf$PkoNk;Y<==E=ln}vZuQPR`XL(E>(3{5?;GxYET0E$bGuG?gS_}tP`9-!+SSWd34 z7SQx#y$nwL{x8?zZ}^BVf{pN!fe#SN7_%^F*}kOxM<4c`SP)$LfBGj6qkAMheKBPj Tzlp~1DhyUa{!ZR4gNOeE*j!aZ literal 0 HcmV?d00001 diff --git a/Clustering/1-Visualize/notebook.ipynb b/Clustering/1-Visualize/notebook.ipynb index c55a5fe81..46c72bc1c 100644 --- a/Clustering/1-Visualize/notebook.ipynb +++ b/Clustering/1-Visualize/notebook.ipynb @@ -22,100 +22,11 @@ "nbformat_minor": 2, "cells": [ { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " popularity loudness danceability\n", - "0 48 -6.699 0.666\n", - "1 30 -5.640 0.710\n", - "2 40 -7.127 0.836\n", - "3 14 -4.961 0.894\n", - "4 25 -6.044 0.702" - ], - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
popularityloudnessdanceability
048-6.6990.666
130-5.6400.710
240-7.1270.836
314-4.9610.894
425-6.0440.702
\n
" - }, - "metadata": {}, - "execution_count": 37 - } - ], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "from sklearn.cluster import KMeans\n", - "\n", - "plt.style.use(\"seaborn-whitegrid\")\n", - "plt.rc(\"figure\", autolayout=True)\n", - "plt.rc(\n", - " \"axes\",\n", - " labelweight=\"bold\",\n", - " labelsize=\"large\",\n", - " titleweight=\"bold\",\n", - " titlesize=14,\n", - " titlepad=10,\n", - ")\n", - "\n", - "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n", - "X = df.loc[:, [\"popularity\", \"loudness\", \"danceability\"]]\n", - "X.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " popularity loudness danceability Cluster\n", - "0 48 -6.699 0.666 5\n", - "1 30 -5.640 0.710 3\n", - "2 40 -7.127 0.836 3\n", - "3 14 -4.961 0.894 0\n", - "4 25 -6.044 0.702 1" - ], - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
popularityloudnessdanceabilityCluster
048-6.6990.6665
130-5.6400.7103
240-7.1270.8363
314-4.9610.8940
425-6.0440.7021
\n
" - }, - "metadata": {}, - "execution_count": 38 - } - ], "source": [ - "kmeans = KMeans(n_clusters=6)\n", - "X[\"Cluster\"] = kmeans.fit_predict(X)\n", - "X[\"Cluster\"] = X[\"Cluster\"].astype(\"category\")\n", - "\n", - "X.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "
", - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "image/png": "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\n" - }, - "metadata": {} - } + "# Nigerian Music scraped from Spotify - an analysis" ], - "source": [ - "sns.relplot(\n", - " x=\"popularity\", y=\"danceability\", hue=\"Cluster\", data=X, height=6,\n", - ");" - ] + "cell_type": "markdown", + "metadata": {} }, { "cell_type": "code", diff --git a/Clustering/1-Visualize/solution/notebook.ipynb b/Clustering/1-Visualize/solution/notebook.ipynb index e69de29bb..6399e11bd 100644 --- a/Clustering/1-Visualize/solution/notebook.ipynb +++ b/Clustering/1-Visualize/solution/notebook.ipynb @@ -0,0 +1,231 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", + "display_name": "Python 3.7.0 64-bit ('3.7')" + }, + "metadata": { + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Nigerian Music scraped from Spotify - an analysis" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name album \\\n", + "0 Sparky Mandy & The Jungle \n", + "1 shuga rush EVERYTHING YOU HEARD IS TRUE \n", + "2 LITT! LITT! \n", + "3 Confident / Feeling Cool Enjoy Your Life \n", + "4 wanted you rare. \n", + "\n", + " artist artist_top_genre release_date length popularity \\\n", + "0 Cruel Santino alternative r&b 2019 144000 48 \n", + "1 Odunsi (The Engine) afropop 2020 89488 30 \n", + "2 AYLร˜ indie r&b 2018 207758 40 \n", + "3 Lady Donli nigerian pop 2019 175135 14 \n", + "4 Odunsi (The Engine) afropop 2018 152049 25 \n", + "\n", + " danceability acousticness energy instrumentalness liveness loudness \\\n", + "0 0.666 0.8510 0.420 0.534000 0.1100 -6.699 \n", + "1 0.710 0.0822 0.683 0.000169 0.1010 -5.640 \n", + "2 0.836 0.2720 0.564 0.000537 0.1100 -7.127 \n", + "3 0.894 0.7980 0.611 0.000187 0.0964 -4.961 \n", + "4 0.702 0.1160 0.833 0.910000 0.3480 -6.044 \n", + "\n", + " speechiness tempo time_signature \n", + "0 0.0829 133.015 5 \n", + "1 0.3600 129.993 3 \n", + "2 0.0424 130.005 4 \n", + "3 0.1130 111.087 4 \n", + "4 0.0447 105.115 4 " + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
namealbumartistartist_top_genrerelease_datelengthpopularitydanceabilityacousticnessenergyinstrumentalnesslivenessloudnessspeechinesstempotime_signature
0SparkyMandy & The JungleCruel Santinoalternative r&b2019144000480.6660.85100.4200.5340000.1100-6.6990.0829133.0155
1shuga rushEVERYTHING YOU HEARD IS TRUEOdunsi (The Engine)afropop202089488300.7100.08220.6830.0001690.1010-5.6400.3600129.9933
2LITT!LITT!AYLร˜indie r&b2018207758400.8360.27200.5640.0005370.1100-7.1270.0424130.0054
3Confident / Feeling CoolEnjoy Your LifeLady Donlinigerian pop2019175135140.8940.79800.6110.0001870.0964-4.9610.1130111.0874
4wanted yourare.Odunsi (The Engine)afropop2018152049250.7020.11600.8330.9100000.3480-6.0440.0447105.1154
\n
" + }, + "metadata": {}, + "execution_count": 33 + } + ], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\nRangeIndex: 530 entries, 0 to 529\nData columns (total 16 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 name 530 non-null object \n 1 album 530 non-null object \n 2 artist 530 non-null object \n 3 artist_top_genre 530 non-null object \n 4 release_date 530 non-null int64 \n 5 length 530 non-null int64 \n 6 popularity 530 non-null int64 \n 7 danceability 530 non-null float64\n 8 acousticness 530 non-null float64\n 9 energy 530 non-null float64\n 10 instrumentalness 530 non-null float64\n 11 liveness 530 non-null float64\n 12 loudness 530 non-null float64\n 13 speechiness 530 non-null float64\n 14 tempo 530 non-null float64\n 15 time_signature 530 non-null int64 \ndtypes: float64(8), int64(4), object(4)\nmemory usage: 66.4+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "name 0\n", + "album 0\n", + "artist 0\n", + "artist_top_genre 0\n", + "release_date 0\n", + "length 0\n", + "popularity 0\n", + "danceability 0\n", + "acousticness 0\n", + "energy 0\n", + "instrumentalness 0\n", + "liveness 0\n", + "loudness 0\n", + "speechiness 0\n", + "tempo 0\n", + "time_signature 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " release_date length popularity danceability acousticness \\\n", + "count 530.000000 530.000000 530.000000 530.000000 530.000000 \n", + "mean 2015.390566 222298.169811 17.507547 0.741619 0.265412 \n", + "std 3.131688 39696.822259 18.992212 0.117522 0.208342 \n", + "min 1998.000000 89488.000000 0.000000 0.255000 0.000665 \n", + "25% 2014.000000 199305.000000 0.000000 0.681000 0.089525 \n", + "50% 2016.000000 218509.000000 13.000000 0.761000 0.220500 \n", + "75% 2017.000000 242098.500000 31.000000 0.829500 0.403000 \n", + "max 2020.000000 511738.000000 73.000000 0.966000 0.954000 \n", + "\n", + " energy instrumentalness liveness loudness speechiness \\\n", + "count 530.000000 530.000000 530.000000 530.000000 530.000000 \n", + "mean 0.760623 0.016305 0.147308 -4.953011 0.130748 \n", + "std 0.148533 0.090321 0.123588 2.464186 0.092939 \n", + "min 0.111000 0.000000 0.028300 -19.362000 0.027800 \n", + "25% 0.669000 0.000000 0.075650 -6.298750 0.059100 \n", + "50% 0.784500 0.000004 0.103500 -4.558500 0.097950 \n", + "75% 0.875750 0.000234 0.164000 -3.331000 0.177000 \n", + "max 0.995000 0.910000 0.811000 0.582000 0.514000 \n", + "\n", + " tempo time_signature \n", + "count 530.000000 530.000000 \n", + "mean 116.487864 3.986792 \n", + "std 23.518601 0.333701 \n", + "min 61.695000 3.000000 \n", + "25% 102.961250 4.000000 \n", + "50% 112.714500 4.000000 \n", + "75% 125.039250 4.000000 \n", + "max 206.007000 5.000000 " + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
release_datelengthpopularitydanceabilityacousticnessenergyinstrumentalnesslivenessloudnessspeechinesstempotime_signature
count530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000530.000000
mean2015.390566222298.16981117.5075470.7416190.2654120.7606230.0163050.147308-4.9530110.130748116.4878643.986792
std3.13168839696.82225918.9922120.1175220.2083420.1485330.0903210.1235882.4641860.09293923.5186010.333701
min1998.00000089488.0000000.0000000.2550000.0006650.1110000.0000000.028300-19.3620000.02780061.6950003.000000
25%2014.000000199305.0000000.0000000.6810000.0895250.6690000.0000000.075650-6.2987500.059100102.9612504.000000
50%2016.000000218509.00000013.0000000.7610000.2205000.7845000.0000040.103500-4.5585000.097950112.7145004.000000
75%2017.000000242098.50000031.0000000.8295000.4030000.8757500.0002340.164000-3.3310000.177000125.0392504.000000
max2020.000000511738.00000073.0000000.9660000.9540000.9950000.9100000.8110000.5820000.514000206.0070005.000000
\n
" + }, + "metadata": {}, + "execution_count": 35 + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Top genres')" + ] + }, + "metadata": {}, + "execution_count": 43 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "top = df['artist_top_genre'].value_counts()\n", + "plt.figure(figsize=(10,7))\n", + "sns.barplot(x=top[:5].index,y=top[:5].values)\n", + "plt.xticks(rotation=45)\n", + "plt.title('Top genres',color = 'blue')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/Clustering/2-K-Means/solution/notebook.ipynb b/Clustering/2-K-Means/solution/notebook.ipynb index e69de29bb..edce473cc 100644 --- a/Clustering/2-K-Means/solution/notebook.ipynb +++ b/Clustering/2-K-Means/solution/notebook.ipynb @@ -0,0 +1,135 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", + "display_name": "Python 3.7.0 64-bit ('3.7')" + }, + "metadata": { + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "# Nigerian Music scraped from Spotify - an analysis" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "output_type": "error", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'seaborn'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'" + ] + } + ], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from sklearn.cluster import KMeans\n", + "\n", + "plt.style.use(\"seaborn-whitegrid\")\n", + "plt.rc(\"figure\", autolayout=True)\n", + "plt.rc(\n", + " \"axes\",\n", + " labelweight=\"bold\",\n", + " labelsize=\"large\",\n", + " titleweight=\"bold\",\n", + " titlesize=14,\n", + " titlepad=10,\n", + ")\n", + "\n", + "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n", + "X = df.loc[:, [\"popularity\", \"loudness\", \"danceability\"]]\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " popularity loudness danceability Cluster\n", + "0 48 -6.699 0.666 5\n", + "1 30 -5.640 0.710 3\n", + "2 40 -7.127 0.836 3\n", + "3 14 -4.961 0.894 0\n", + "4 25 -6.044 0.702 1" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
popularityloudnessdanceabilityCluster
048-6.6990.6665
130-5.6400.7103
240-7.1270.8363
314-4.9610.8940
425-6.0440.7021
\n
" + }, + "metadata": {}, + "execution_count": 38 + } + ], + "source": [ + "kmeans = KMeans(n_clusters=6)\n", + "X[\"Cluster\"] = kmeans.fit_predict(X)\n", + "X[\"Cluster\"] = X[\"Cluster\"].astype(\"category\")\n", + "\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.relplot(\n", + " x=\"popularity\", y=\"danceability\", hue=\"Cluster\", data=X, height=6,\n", + ");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/Clustering/images/turntable.jpg b/Clustering/images/turntable.jpg index df676a0ef3275003f980a353eea6ada0aae4c4b5..df77b3e0c9c89681805680b21329e3cc2906dccf 100644 GIT binary patch literal 63515 zcmbq(gzsG7w4F7F|3;=|J0I2_M14Qiq2}1L~tN*)4 z`GxfVH2y;V-_|HWzfk`77+L4vWzSsz+!x3?C_yMl?*YiTNGP~S&piMtgf29s|H!{a z2th$cMM49hW4w5YiG^s;{RV)9f;fkQijInbj`{-K1qm5|f{ThrjmC-p{tddADi?u? z6Ai|XcX8rxbJVzNKA3V4b~;Z=#PbBa_^bYz){KbvEWssLMY1;VoKAz!Jkhnwcq%B5 z*l~ki>Z3)SrdxhecksouwEOo>87)i5OH2|{21ce&`~tFa^4dDOKr3sIjfba~cSvYh zctmV+N@`mAuY$s&;*!$pdT2vqQ}geh-oE~U!J&zn*}3_J#ic)6+dI2^`v=T0GvNBkcg z9}z&LWe`v_wf=!Ix3K(&$Hmpn{U4_9fkDClgDW91>3^W*<^LC4Z5`yl_`16P7vc2( zLfqK=FUX7ki*gwt0YF9|K-?)bG*t9|^5O*s#)}uPUScAaSNPbku@NgiE&(nM;zvpR z_AN0b4Fx#`4HGRb6E7F9l$8E6LJ89Uw~{3QHp+j@hl_Z)Zt3U*rs>QC=;fE_g#{J# zq;1S(^FRcO*8AbA&>w71NvD%1+v(ZfCp+`$&3BYjiK+*#RI!&@zRU2|W?MM>n4(f& zSSs+n8L62?p{dSZs#C5hiJbZ{$Cs5JjxPt+E)Ak3C9+&@HuVL1JcTZ`CXP9{oDt#{ zLSB;ATXIo}))W1bMQW+3>i8r9g>Ory@ypmH_cR&xUFBWkY4d_;F{#oq@F_i)xRw=p z3TO>NESOI4Aj}h3iDfy^#7N2Cq-H9mZKA0=-3g(`{(5i5R=O4C1iylo@yTRK`3O_H z68C}5F*#UqI3?vq6Q_DgsSn|P!rlt6?T1$n+gwyFE}!Z>i1vj#hT$t?V0d;^e6g;8 z87AEapu3H!$(Ve+HWNXoG_Fr(0dAWCwsH3KOc05{weFjWG(A&OTDl9jS6ONWb1U{F#V*Pa}C%fqAn5{=NinFE# zKH9L#Wu4Vi`|vFpBot7)M;VqV_bB5H4k?9pEI0Yc3^p;7q7LYM&j(ivsdJX&nRSfQIW-qmDW>=F`C>T9a2~nG9 zztc#vw*pP(@&WmJ{Vb!eGnR^#!nVd!a}VI(Vt;#^b?YumyDI!l5}(XfOWf<}IYQm* zOI4Fgi&D#%V}8dMviJ-Tb+`f(mPqzZAo9JRX=j???VY>dt z75Gv}ua!1krq~s!H2mhT+>R=>HB3W-p*2lV2}L^44%17$0i<}m5TvHLDj#1Ft)cWY z6|4#kt*1^+(ItK%<{dcRHY?B$qc)9 zeN%+=##1$e>_-eQPxhy1gHsl+w6qt;e+2XSckgnAFDF@=>Oz+lB=alPw5EQ(zu|w4 z?>%M~v{u>ko@l=5Mexxygj4Zr4kzOX9YL1wG(P!akJ6|*W+*@RTU-uSs0))kgdYSZ zQO}1Qe-C=2FV9&)X-!HH!ywtSMf24TKT9k1m^;Uy7~w3n7ut1p&=9`#s2a*FJp4I% ztvQq5ycF@*{+0 z4x}kuHSKb;>L+qUK9AKsIPJpz5>g@*U}{plj9b8LTpQMQqK>*&m-**oSrfw3Po$!N zNs>F}JvmycoasICPCD{=s7h+8CW2Z@@+^D5M+$(ta$3P^Nub=sB+g~?H@q=Fq{C%W z-vrYP8*no4d{tCbs-5Osw8-%rY6?G2%rCQCw&H}A4EA`N-3>cfYc+WHVr{Vs>s6OR zO1qQlu)3XhJ2mfcxM2#iNkuzc-9WD)XbQu0ovz}DDLoeGyCu(?ifRFRGQ01o3sYA? zSG;ZhE(a|ye5Z$XnO%4J#T81(2~>h}KC^VO`y~GPSdlYA^D;_HoTNj3!NVgZP*lU7WT?0gzn-cwe!yj}_ z9#Wi{?{r?kEUO{ehn4G8n*#(3S`TShA;$YNS9KtqbaEFdDB!%yJ({|ZgOrvoiO#!V zZ!M@G$3LJ%PFq!L*>bYpuHKHTZ?|~kF8aalfaO4#E%`8Mm$5Cj!Fz^Z=A*z`L*3p? z2zM5k!S{Z?SZKr-thIAnX#>cl5{(XD36<^wlNR#XSO_GMGPznn`go4fH6-VjB|x)z ziq8FzNR)W(xS;&v^w05c3#UNVSPV$6Hpf$j!bR%MHD9lI#smb|+62>J8P9;iE7=T7 z1M*`U84NDhuRTQ!6OG9^ssdbvv_DHekq6URwpG>VP-4f93F0kD3PpNFyIupR#6WrIrcqKG0yq#oFqy%M4 zYdTp81lnfpwzSJc`QEX z>>%!`A*tg^;pO?8I&03*4$YatBClce^3-+K{z8H;9WFaK?G00nJNF#dppmNw8(%}i zrS$!qwY!yaMNdO5@EfhgpCim#!{PKGd0k8@ymz)#$>`zA@pOD$sY|(ZFNWmrSvjgc zGlUuCDDLlZ7xD_w5r&8J>`mCtTu^uriOL_hjW2o3=@6_^DC)SrE!_EiOZhRig>a4n zh&!RM4B09asO=%HikrI<>fe6yEc_VM@|6`zxGZgIS|k}M`Ny@-78GWe4~7zey22O0 zP`)J)cMT~$87NHh2$VQHC#;NVlz-5%+w4Kv_W%U*-ewC}^3gh!SrxPZhbW7Vgek4+ zWSEL3;ACW{MVFq7?}e1fETj-wgHjW3hwo>AN{(YLdJg=Z6Ylm-iDCit_uhnjV%kX( zr|_=``8E=#A{RkBY^7jmcmQ+P?X)uZ+z|P$6h11v#Xmxnd2a&&>S9IBLxNrQoq2vib|sbW(tyBlhB~O)M+>JDycut*YlarKZ@QC zY+tmvR*m&Z)Z)KR^J_MR8eW%Rh8e66#td4 zG{GFASKj{hX?KrrD|e}G7pB+)d*3`J<_}#R&8C=tN;{q6xXNbR9pO&u+&QkXCQTSk z{jCCD{Dp=Q8=lyY%qlyi&5$}$#3S_%d{pouE3rVlB^oRMUFICj4B+cqaw@JWwr@8| zsNQR;Ot`eK(Kh6r2kt6i-$7HHAU*jSffC`g`HhL6n^Vd=E8D&n{l*h}V% z^TCi&ZhVm?GZH@;300#zUW`5Al~N6nqwj-=epVI8q5^+G<|BVg!`s-s&~d-T>RJ&lLAB6_EP8dAL%Y-_ZeFA z(KWnMw_`pvAGYoy;^dXr%w^^?pmZRZJ`(&w2k!Dl9*3J65ftMDKE5wi{n;ehtCqw` zjcz16h}R*P*qF5gE?AZ<#3y4VlY?eTy#s@k@@3DE}%p!&((pN~RK{$-O^m zSC7AZa+&#tmvi|H5b4J(bkOk>8u{(`P>AQ$v`=4SzPaz={=!!6ci@FHW0(Ck|L<=` zzw${0-I?MmuG9$Q_)NZLbMpVbjB2+PV5kQD`IX}m@L|s|#Y+d`4?a4dIHO910|I)# zuh$xeTg^GL`A1geSnhS`O# z-!8yN<$_Al@{IYGpo^s+i4@=uH2HaTckZp^%nSR(TYv~N^Vsj>BK8JzO*4zSa~&sB zv#UOY{@xy&HQSx;>X&lo%Aq7?$=;)d8ad~RHlS2=yC_=S-h!u zrL?e?JYHFKPxWiN-*Y{11Z$>M$>to`cdz>TJeO4fJjT!MEY4=(y1J{?@42{4lkx>j z^v5@FzbfmA1~WYTR>_W&Gbu;0=b*`Od0+6JGA+1&{$OLYrGohS(mkz8{yKSceAjCD z`U>18qWSwi6nAmYGs)xE#X8NXq7+WvQ1Zok%qIc|s`tdS2ehAX4i8+uKaFhzi58m( zgKZAve&<(-#X(GD(6bzr^dwhXo^Ph{&(YRnvw%NTv@HP#)>CCL|OOZ<7I{@ z7V(CqYDVQqe%(Of4X!Gf9%HW5mR@naTF8dVkF+PvgCWYfYb*o9jLf4aOxyf*8Lgm5cfm-C4!mVQwVY^CYz*~Rq#vO#O%DQIj5WjTkySqiH zzn4YLOhV^#;BR!$QTRx0q}jN2UTLJN>xwljuMkys4{VU|m}O`5e#B(MDEu?Fa{6(i z4zA1>r0wR4L1>)P_S2YbezJ2};M#*xxrU(Y+h}#zHUX+y~3!1_cGwFJn_~SHv#PT5PX&ygY7r z9^V;$%Q`){IfgN<%T&Q?U=tp8O--Nb-MV*%lWAAoiC+ZLgfF>TFEYzpBJZUlyZ0P_ zTDI_=c-d1Xs32gA<7P))cr^JN6;+}wSDSjDT%|*=uyu#MO=&n1(1~Lz7wc-Z($^t)mvDP_kCtNR=Egs%(BG^U8Lq?T^BN ztM`X9g-dUrkd=~#sbiiY^Zl=$H;=-?8EcF;Mme*1@!ffi5&oBl(nqU&^DZ&JF@DOqqyU{g zoWLeMJD3kU^^DZBM_SAY*6BbFg3gt=Of6#X_-p9%3Jl%J3FJK~-pSAW;L^OTwivp* z{F+-NFUzwx7S`$AcDQEZA8O<`bqgD-(q5yw?Wi!j`T20a;%ink0xN82Jst9QJS^K# zm|6?$<8grY%W7}Jdz^cgs1!O%jMkn)cUjBHd~f=>`?BLM^k}EVgXQH(B(Z)DNM~Vh zaPg}S7vgXcHn^p%T_+n*XwJ`g7zq_aE`5ld+j`k8*Hm{Yw>PM;He!_El)4NNRbK07 z;Pt}}Ir0WQg(Z?ko8@uXDNB=;;70@UMFHOGw|=>Afz$6?5q8V>E&WEe_f)&xiWW-c zVIeWjd+BcjkNUJc)(WX`Yiv;;HdGojo5{ZO*rL+gKEJd|hG*Muw>ubg`E?{LJ(`{E zcHC#lwL9OJ?2dVhW}jchBzbpadEt2j*~VW8DrL$I#~W9-Pt_s`zdlZzlCuahg}z#n z`Q*l${2ffsq;y=h_a2~D$T`gPE}qC$&zpRs&1Ka=d-K%ke!d9)^Fe*^@smi(67P9O z>79chtZG0YdrEmM#wRT?^WL6__`BniFZgv$e_n>7i=daQM@t zAuSYF0sxy+18-X~42;n%&4XMGh-hpty>Iv)L?y1)V#M#huccUQJ2JXYWW73h;y-Wy zA`L!k_y43wm3Mrb6?nCtgQBF8g^|4Xs$tZHze}yOmNvBRj*5Qmv0?RApybQ3?_-Ah zvW#PLm6e6ju^;{TE=*Z{5x$?pr0H z%w}>Jopt{C5pY{I3cGq$ybI?v(g+0mn^8@%_=&`zR9_Ds?_FfHxH6DQllU$xN^{wTQ;t`eVU-_B#NNJ|`6- z`EdE0P@C3;^<0lG{WmSfs66>6E+sh_%()Ne8SlG6LRCC-4@2lR^JdI)&F%xg@{PjGTxu)0Gj~ zpl6KU_K|x3N%SaN@A?^lGd?LI)_F0~Qb|2OcTc7Ch#a{-`fySZ!xw`ye3qR^PIDs%Rn< zetP_z`JPnn&PWS3cE8i^RZ&YNckB-1)8e_0$?>fW>ECwRdE^c#f%zIKx1MP|6+Q!G zy&M=e*RSsE#|MH|vnF?>tfr?X+|3WS3L+O{M541ASPqhNFV6J_Vcv#hq@t(~J7zXZ z9Qd4ov7e}*Su&e6rQA43+T@{TNz)hUQy=*SKI+zOuN^I3Zt*<(ni{tkDXQ&1_8GnH z*Ujp#+0pO2kJ_`kr{FdnqwQD|--P{#;bg0q4=VPgPf?3ER7JNP&?jf~6sB|EZ{wKX zWMNeE!m!8Av-u?ELY;kbr{C7z`H zHXA8VrsXo9D^yo6CzfjV>Z`nk59MZAY1jRO0Tqo;uiI56Q|IPw12vkv?!u2S-)N%dPT_zdR?)DQv8WS&I9ezRlhJ zAEC2sF;5X}y5#bYe~ZPZ&(>Lctk=O_usj(%027VIaWJ$GLC55)7BbtF*%c3}++h#V zC(VV0;M_4$N!_0+q*bUf21QDZCMS3vzowE0=sW=CGD%<5jQ z{za^HWDLOwWdTTc83` z+IpvfDaw7DC2Dup>VsfoSfLg8!O%cOCBCTlQ383C!}TSjbC=n2)BUPG>6yp6X%gT{&n*;)n@0u_AQ3&b-Q=Bcgi~m54&Tpy-Pf%^zGkP@`bg) zvewprEU~66IN!@u`5s=0YES!~Jp&Hb4u^Igo?u4R{$H*+)=eK;u04;h@-H(Rw${;h zJBS`FE*cKM6~i+Gy&W+A%yEpzje$XA^@fgAoUTb+;4ixfSZ*$@b@hRFOJ*H* z+t_I~t<;iab_~s#Xfexm1h09ZnD|$EJ8XfQ+s#Tqp(>fWtC%bScAx$B&5Z~4iKpR( zqOGU&5QoSyr{iYvknzHjpRt_OBlcyHRj^v6dZw2;OUIx3Ja}hF8)tL$kH5 ztZmlk_nmp^e_B;gd7A{=_K6TN^jjO6gWTz}m6wC5_x6y3zOiFKu`7t^B#QG6*NZVe zI(3iF@Iq~`4W9vI6r}#zJ%0*6ZnsN^r~fQyBxw4mYHk&AWO?$o4WBEjNvPB3QjECo zrDb!;%t~^kL%}*vp+B3chMsR>i0=wHmBHIpBL0KhXycbx4=%q`(sV=o1YRwk-#h~h z4j*y$MOhdBZS`PA!kn;AUXYrPM&Xw!Q|mb1H!(+k%-Y)uaH?e3I9T{*P*nLo%VAz< zx8~C8adaL%CjQdkwjZ=>q#c7bzNY>lx_R7EN!guwwuYD+7!n1`Yzl9zr#EZ!IS20TJne|QfBo6(OG@NrR-Vhw{8RSq9jm5;*x|j9 z74wmy!_k9shSp`GC>5O5`vrUS-|(oB7HPGTU~U7~Ob5;RYxgAg*7=(K8Q!k*c0fF_ z{udOsU!mi|qp>ph$0t*-7r8?uZ8uzomQ#lHut?{h0Y(c?m7V@YMgnA|MhUR!i=3f= z8c$yA0qxe`_UlB4f@LHNT4ydfFy_}lSjQi4qx|dPAy)#CbvM6zW__o(QuU7hKIRYK z#(k7QTD#$2}79QN^(ptT#G8j4QVFhyPuQ=1eztR#*a!JrnLE=Si z0T{n`uoIgu-f~Fd1-$Ya=h8m0ehD6F)~Z<0l}n7oyBmtlVd0VY4Q5=Oq!4!U@@{ZhdfXnx*S}ng9 z4k2vcJLhiwk-d_uT=?-S^Xk!I)Kk&$^TUZckAetG1hn!N_b}h^F|Sh!0)ez&<8A@RcF4?je#vOvmC-xQCptd@FK9r{k)(TV7QhOWgURbwhZNaCKIqbDh zFHhU4k>sAq_3B-L+(lwANi!tP12nns5`HPtJ%lJ3XfHctkDEpA5}=N-^~*HX^$R~A zXWvD#$BrcX-q{cjr0)BVIFlD5(sS357vF|e;+5kfwCVOuI!GQ3+DDE=^e(>*w;%XB z9D6#<`bvt?@A<|`1Ouih2UDfu7t6pMK6`E&qk+O{!%Ia5er&oWtz-{idXsvS0 zuqxkl&(E)zn@juEjppCV@3ddoQ@C+AFLPMrC68SBJ_BUN9#Y0wjtw4)k~#S8mVZ%m zM)*;!`;HzOr9J~xy=ghtF1&d(+U{a+i)b$0l%7OKX4j>s+&wxZDZ_M$Af%ZKQt0Le-&t+g)DUElQ zgN{90-Jb7S!x&T3Y54{Ae%o%@?x)jCcO&9By4zp%dcm@!Q@eD4t7S<}&J0qC6EsAad*us7*SZ+qd-e^h4ou;zYLzHm=An;(Ae zB{t(M53iD0X~|BxtHQr#FIe}iGT8CEaUV+q&ADT~7S0RC<>{qK9<0qyYC6z!eILqd zv`|Cl^wUQG7V{qQCTVkb-h+urAJ|M$Z(m_RIpnNga+&{SVX){+HOm5P--4Bx$o65O zy?4_2VuHVny8fuvo_>XL)9`9N%x1hEKCYPm2j$eaLs?g+!)oW4r8wK$+x5|iXb!$^ zbfEn}6B7o*{p&ZL@M!OAOK;b1b>XGYP&uSkDWpGP3^%sIay0`dOtW8vIginr5cGY5lg%?maR;(jm(O7Vw_uo2(Ebc;Dh*<9Z4^krN{x~vXd}+xz>2!hc$R9vTE{| zy~#gtt=yMzI$^JEaz1hq=KWMd`Sq)hAT-laSw^iJZ13FeAkDYarY%(BZKa=flGQd& zYBs%SFU;zWyh*x0y2ST)VSoE32iDhe`;(`XLhBfojWPfWOi8Am8kF9Zh^eAv~8*1FY_L+2k**)#jv+VrPK>}pS4!yl% z#LKZE*y~ewJL*%>EaP`v9M7Bh^PBw}C>FtkHZ^2)nerO)H+E``H3n-<6`60Bgn`5E zZ6GYbOWJnOuG1oE#Yx5|dG*OsruC`Y%oon9^Fxj%0x~W&_V8>bwv&iONUNaLdhR10 zH~jeMf<)=b@0NGV@`Fl#d^x7GHKl1QSTb%D*JNl+W1g*Yrk}!S)IBP?GQB2!QQd+! zp7~RGZ@F4O`5J9`=9fU$v}(;?JR#nu42-msBuc#}cI+h7(FBfAugED5(T4iAVBVOe zyJY)Rfg3-oG6!M5VGwo`97XLaW&D~fthmlQfCgl!9VzP2mQ zc^g^HlVj*-j#}ncd{o|BF~gslKlB9CkliK+0I&7u#>gJVX#?*}>y`^ED4HqCV}^f^ zAAW7>4r99;oBMj4Uiq~$!{G7~_fGlBNV){rEbw6XwgW86r>F#^x6m)1Wg@R*mJY}_ zSLYW`*`)W4e|!mR!VB58RQ|6Rvn+E{?q_Ag7|#CI{VL zF=J>tC#5BCBBOK7L8YX|t4Qy`<@{N0Ud9-0NH@k)0T?1AP!0M4;{YUzX7ubJ!ARc= zEF!;i#;h&(i?Y$$xnEb4mU89Jkal>C(H~FOzcXB}7tp-QeqI8+Kzx0NgogYKQ12V2 z?J2DlM4qJ(-=ukx{(gnctWWB#r1?@)cOx3_*Bi*hGhpCXVPRNnjb2VKx$5+7jz{R@TQL0w_ZHy z)txr7Y_y3PL5M+Q0@IEeo&lQ3o9fdwmcbZLlIyg*?m9LKMR)mxgk3F~CD<`nfM>wB zEP2~w>3pt`oTt(<@igps3N>IH;@cu1Fzt18bd*>HOUxDUgJsYg3B{NK#iA$4#Ck3Y z3JTKrndE$~>?@$g*sthLi#-F;Eg1=x=$WHJT?Gw0YedHd%Hjj3+C>jx1HwrQ?&`wx z6#59grCpH&glhY11v3KQ212ROD}P_Bvv4j+^5Z_0mv{-F%Xl}3s3W?^{>sbM>1Jaw zI~%y9!AqBYFu4%Zq@C~!G2rv$iAL3T z7-59)2;W%|(<~dWl3b0%(zoB*_l6bJg-eI2ryE^ur1U2}s1BN2!4x8o2H!H|!}wCH zU!Nd76^b5s4vSf2H!1!7{@b&O#zjhe|EaPHmLpc&W5{R&O`^fu^P$ygv?PdsCAzL} zkpq9TzZ9hy-5DaNI{X%xms=21^mwj0ySK-H(!1e*tboVxX-i^HFP`?pZ0^3vR&YJF z#N!oppNyCryV;{>KzmTi02Y>*+NzOce%38;^slh}6+JD%Q@xZ=d#@&P@j*@BgL)8IV@UQGtrvCjL(I)sD49auOILGV zbOU0uQ`z)zDWa!4RYnE80#gNdDUl0WMibS6EvSldVuo#i{=}B-Gr)Ji3Bo}^{5r}c ztNS#?J*(o?bxs%9($^;^KFKj*SA1K*OMznMn)zwuZQFNnF1qr z^o%PGV8mh?4&g{F8Ke-oU^9Qkog>>2(WjG~srhu9=LD(eUINFg4P-2K2! zBIi8OwK_r1+RkqKtQ!^gFu=m}lZAopqjHNjc`2Hg(m>JuMpWO_a2`U6*0C{U8185u z9@{eG6PF5khRK8@Sss7)n~uY1-__wWuw{>E=1MoFQF+g46!QM1d7ph6{F*P}#^wMa zhYRjSpl|M;&j3L)%Mz_raXbjWnag`{B+Cu92dmK-0lJyzC%k7C1!Gc!x!%2xFqx0NGbH`yX`&e3Rcyb@$g$ z3>TSKcVm0B#k_n5q#|9|q}t(1e&B&XuGRG8n#^3Jw-iKABYws-ldF|*W?wllCJGsl z8c?zmMfPr-dsY);b97TQd}4~(VMWsydEJG z&XC5XJj;)GK(#Yl35_qR-1ytAlx*1t98*ArvccIDEx~-Af_$Es7T-+!_9I`reJLN4 zfGHr5)JJn*$C)E==Mn$#2b4=F^zk zkO-hlGgRG1_+UrXBR5ym73 z)faOWX=@wp7VqutuT{P88ph`tnRX<1`2a~wk>a<^OV_F^d(HEUdno@q$|@U{cN5E0 zKHFPbBk#(}@@uq!P7|q_#ubCaln?egOSgUtnW&+zr4h~5I_g+nwGA1g{WJl_f07Em zmJHM=z$MhFW6w=kXoCa>cm0iGLD*=*XH{V_nQ?UPrLcm`--j&Hpo3eV5EB`$Z z=oL~AEB!>TeteMph29~Bs7Byn#f@9!z6C`L$$14&}Nb}aoQfv(gl}$ zGJf2GKp-4bIxO{}T-klwvbnXU9gZ{ue;f7a;1u1;(wJBDxY$$4{wf zjLCLhQSt|6RW{dWTPE5w!=S1)9?L4c>qBeW%;m_8Ls8U$<5pqFG9)z*k4orD|JZO)XCzn{GgG{?g?$vwwN&Z`F+F|F%@m z))x9Aq-lz$!5>l1XHFy3e&Kgh;t z4gc{4WWw^;)s;wUPxneNY6RN8 zqiM<`&GOZV89>#*M-~+xb2*w|xcwr=rzze7q*E!Wrf=`8FJek*?S-LoxKvr$R(f#w z@Y}dGU2B>KkEbH3HC3x2r0xbM?Q;SC!+4OoUIkR>&p>Nr!)M@$T?JD0nePi#C-v`b2Tu6{5(qqG zf%Xx|4g4rI=fbO{RjprmyVRXIBAx;GcOa7-uC*L%S)oKxkEr`vs^)_GvKxzb$($gi z$GMB_an0Fa4(JEOFud9=YfH2a1&+CZt88zDNf%|GG&dEmT(< zW@53KqGV4dc}Ql=9lq4I%?}4;x$!_j(!7ZiKFomJE*kpXA zLxF(DS$Voqll#`HB*%Sc;+qsrB5CNiSLjbSlR$@pTtvJoV>*;}Z28#**KkrV{x}F^?$$(tD0hoa zWk{2yig8dw{!Nlf{t}(?X}WrxOMa7~LcKR6QAe8m{$NES`EaGmol<`rVT)>D-_pKu zEX3)+|9wrkM8V-1DxX+ipc(K{8luN0^#NOq>0-BvRctEH_!va)tN!@KiwD# z1H_6kYA;kJO@0gD5Gs^ymwZt*g<)TZhFeU=cK+ffEtGEz;2ecjTP-=)Jrz-T<4bK6 z@MEGd>AHG`q-(N@wl2)B#q>DMP4RuYwNx&8B`+>D9>#a*(cvKD{z)e%v+#UV!%WGyQO?Pt2F~ZdUSzI_0sQzW*{)K$!p4s8qVt>W|JUQ zvSr}ol|NaGNVaJGNAeGMU!OS!%7Je3_9PQ{Z9Xkoon(Kqk}54$xPq+XN3DgQPo*r%tw zW&XQ-=!tu$HVEz-Ji%WsWnPgV@-ZG09YHOLJqHmmHL9XXo%^dgJ=D({bPK&*e8+_= zdp67|Mfo?!&doD~5~&ASS2!X~9j*TuBvsB$ZG%b2Gx@G5vH$MphvZZ&#TVA{5>p8g za?88|&5bjI7Fm<$QBQU`2B70~P?M3KPiuI2W<$fGOF_fJJ6UM0q8%gBZR~7ZMYELq z3$tFQE`6x`uupJs^_Lk5+Fx7;I<5-262iyFmZ{w}T|aoBoKhd_*P=*y39)t=gHw`x z2uc35cPeuoY@n5$(9X_qzVn?RXvthKGF#vgy3AomPc0oveXY{@3{WxE2@j$TAvSK_ zcPV{3@JW_X3dTbxWNsKsEYX9!P<*0xK1osIbs^*_&ReF&)E9VHM~&g9DgV8qgCcd3 zq#n#ZJMD2SI}RiCkmgbF&7Bp9q(3pgaB~6xCx~BzBNc>*mhsL_*o2YCS6)da3ANDX z1quhs*qd>Mp3-GbP36gnX7vVf#N7mzF|nyvt-kJXF9^_Qim&P9iGGzMwQPrrdW>am88763V5j)-ZmfKmO#pugA3CR&kNS|g+LrXzP(u2Iv_Xz=WdcaJ@6KZkVG(dKJ`>8Wm}(K$)YHx$X|v=>H<1vsGy%W(1N z(L+Eb$Uo#fadxcFJ)8Xwnwwj*zE)IKqe6f~MJLR*4ROMVB(n4q9q(9;*(RShcb_2o z{Po>A9rF*(9SW{``*8eqg&ML-^>Jm5qx~1b zSeq3zc3Q<~SvA=PHthIGzKFD8=FBD%iUexxAw*wO*6xozsPry4ml;hfon*oNoZlSX zsT_sUV|;ZaaBBrauvk(OM(3+fu%hxcxpES1r9;b9z1&~2h!+j0ZDjSZ_z}~FR?mhuEl{sS40s@P2cc~1ij2p}1@1E$5-D=k3FvPdn z8e2L^VujneTRe)ST9ai4&01`%i6Ic)cg^28g77xKbH$Q(c>kpRfv7iA3_A;=7kFv2 zZkyc|C=!SBkoxoFml9_-DI&G4_cZ`>DGYCQZQ?4WH6OMq1Xbg1xn1oUr)Y^18=48u~mrt&K(>MKh=}RP-$29LuXHL|$-i+8; zJNL|_`^{9qfce7Zd~Qk!)k~Wm4NUmT9f)cQ1t~-@es-NJfR(dVA}_CZbJ4zn z$&0H{+1v(hzax^7rX-1=_X4Z#BBBL#367!tXz^;(#MmJX{3d&Ip&yj2(}P8>$U%oS zFFC@BZzPM}I-UOGYI--iC2^bGSv}l=!6aMzySy-iJ>UD6&WT!_JijEQI*lAMDNfLpnSKS*Mf7ZU{f7>^LBX z9f=^q+|lN1j{o(7@FN~aH#f5;)-U=9G~PY!0YQWsSpz#PsZs- zU2(XA>5_PF#IVxRB($KgmpW3T22)1@sh}WWs5@TUNpM3V{6pi$l!jo?KOyNvJ^{7G>@{jV>|Og_CjARM>_(wC=DW6LS-U*geY+UaIsXi zk(ZW$5u{X!tM+FLA)G`i79o_*jlJK-9Q`&PWrI5;sl56jEzq(g zLap3RW5}mWv?P#ogKH5zk@C*oznMs7Wo3u2ds)>Qt6%Abs1pm(Fx!|`kV9rJI8eFn~j&KHqOLrQ#W+|3)b69Ti8ibTV?O$4o8yFbqu(G1{ z6_G`pu8_SItuP$n1_z+E@LO$gXMcXx>UsiP)zXiq4Kz6`z&-2Rt~ zD+5e&pr-=u0&ti182MQwWLm_gUq}ob%nZqk6uWxWK?!8n)PDw#_uKz9O061JbVdKS zTmp(TVD{|zXgKSO+8!~{w|Z1LJHL;zBJRvOpD;j2?5I5rKBK0`lYVvCcZxJYRaU~I z^u;S!w@CeLE0}k@tG2WAPGO;#;peIgCa3(2BNk?xtDSqPI|lDc76>KMdFz|U!wyjl zQ4h4wG9F?3a5u0+n9B{N8El$bChYqjM(UB~31tSBgSgki+?^`|E0UA%Byl%(wl`Xn zKct|_IGRknKR?(P(xo4|`sC5I0gD1>d%^I;->qS|A=Ekf27QeWmlI8HEnb;^%~HF4 z&q?f*n-k$Nau!Jy1(H5GTpdQK%g0YJuM&rTVKhSZoKRK2L~f|wGFNh~Rh5{> zDUn@7SUU%%o0%Ej_=>5`VDv|ueL|gU8-sHprS5kI4JC^+4dyrY-5&m+BX;&a=lx&l z0x#Z-?irmuxaTR)z^R4>31aP*p^`cW6wca+<^5Xb z`c4r~WT9rbOa3kvZ50(ao=T5#z&$Bh^u|xc8GfGk*ncQbSA5JnQq|9Ye-r(4)o zDwEA*&Ysp6dl}J-6_wSa=(Z42w-bK#Hfl>mrU+!7wVJ_2lm=P{kYPd9&;9VzIH}by znhBZv{HEqZP4ZK64a8XcxWL_})ws7<>oxP#lKvMT0sCl^bEcMx5B5FG9v0@cvX)6z zbK&E^9`qta3F%Y&)T<3M(jszB)gwjLXKK5I_O>Ni-|aCAe=s7zw@u2L@v}|(R<%-< zVSA{`7dUgEA2PZsi8a@lObzDQU^{&)DpkPf=S5w$+wIhEVbo^Zz^bd3*6_^fe&v&e zP3!?|QRkzP({T?rGA2J*A=?=jDmV8TH?3y#aSa+-oN+($Ao15V9xt04!<=jqV~6 z_jX;^3K1;t$@0{P)W;7v@1Za`@0RA!pvu4oVgZ80Z%oU6aVF`ZSa7Y3-|}&gRIQ?H`N#mDvYdN^xl&W*c4=qEIFmx|@f! znNLfcV}Q|MaYM*fFKIdr6uqK_zmoyhg&}=&0?-Gq9YVra_ujsuI83@hO_wbQ%WtZ0@pIMoMw^IqAJ3e za3t*cfg~??U`CR`>X_h3;~`gAPME|{l`KwN!Oy(-gem`AY*ROoYG`1Yt7*8%ZfX<*`$Paf7*c*G^mYvEY81%|`uhs{V@gHEZ*;2fB5t|D zYwoKA!Np|f?O&+BGC}Asfgqbos&Zc)+iyqo*+sk}{FVR7{2ep-i@2zr>PGGBGPY!$ zU0&Hm+?>#IKh)dK$5j0yNm?6KY=4WcWr1gvgD#z;w5p+!rWgF~+hDq*ThQOinQ?1+ z?2+huLffo#d}m4#P3^Rn+`i8~os-7r`^LXMI#K6LMqB6vxo5QSHq%;Pq$6{K6S8+O zMf1Ce3b6moNWED2#nx>4u=9`89oG^XIr=x47wV4p;SP&{O{1OtJz~t>6I1Ma4f;Dd zeOq~3xz?#f`LdohtL_w04^h?lXgWA5R56V+=R1`hAG3)j-Ea8ncXlNvu!g12V8>Kw zLL6U04XRg2^QILfLY}IXo1;AQvj=5beR!Y6YCw8AL0@>Mu5c1r23=VsRPZz6S8K<9RFpDk!R`Q9f2;Eb`GeaaXjsc*&G1gp|jAw##DtAyh2-+LO|(V>a%2 zbPtm5IApNQZL~hBYE{i;E!g>1tV%E4MLfmZ;q(rT&^K60dZ+0)M7>+nO;I|f0_aTig_%8Xc1!F1#4S%l3$VEv+A2kH$cDOLNjWuWF5 zsP>$=qHHzSu%zcWyXnA@7j_psq3_>2105csy0>L%HKxzCRaZrSlDHhJN&Ut*uons6 zotK3Dd4F-OdsmyUD=S*KhM`g<6Gv-hBd@B^LuQ0_5e0E21?_CP;swWJ_sp-3x2uZg zh!VsGShiB&Psn&)%pFtQdcCo+(kV=q;+0HPXNuS2l>xW-risqX{9DX|PKx_d^8O7y zt6hZOFLTP;=QSL)HxY;-Wose?EG?emC$_erwo^FujLvMGCM?c%O(!&cO5#2wh^ke< z-fwK(-<1@kMpI7Eb#L{A)EjpZDx7{E-4u7l+;fX0{@MP1;N6MPO_kq5wH9$J;(Jy%ZJ*@xoZo9LDXNM}Nzw9IYCBI2jA6qr z^Yfi7fLW|Rmen`kSl#IFK`%?I@Sw&6v-`61k1t@tLtY37vEOrzmtyOmq^Y!-7EG>r znTDXLem?FE1Qo`(V;TpgW?lTP=#xY2_h43;>N^!}c=6^4{vDk6#pq;q%%Yy1ZNEWS zK=h_u0qi3F@o>EMz6XCpPZ{926oTw7>Na-lL!5i0j9Q5?anhyiBErJ}?5dEW=sA@c zW`fK&9EsN6r!a5QD|=+Vy0OdmYQY?u14=2}oVh0jMW^>junP?PcyM=c3Ihr3M~-c%MmtGLrt52J_Gu1<9^9RuIV_5WS%G(6ot>D#KQ1?s;zTlY71v$s(ZoR z`rTyqy>z>9MzJnS!F*ndLX_Zz4dzL6Y3I_owHjuPOZ1cx;!1?@LWsJi5VB@Rnp{_k zU3sQeNq6RR9_psXS)$?>*5ZZ@)|D+h`Be3B<^(y;eBZTB`j0Ytq;4{5oD9#=%!vI{ zyNDQDUl|@lwLsm(1lP|#o3y|H3vd2a)yS1Q_Yq5|td&Fgk{d2vS;4iirGlNTTE7H6 ze&tu589UP<>g+%$BI`5CP})vW><_~y2HOK=@x!iX7}AT_c>{W zoSbVBQz)a21X4ko&`gX{9r0NYTMS`O0y3&4Wt6X47t@Zn@*?4<8jfP5g|MP6VHYNu z-xnj#H2(;?6L9}$o`{?z^&3H7m`96;>2Uv4i6^#A$bJ4pl=dkQiVCgWDfeT1a~G>3 zr*;*2G%3U<5e246-%ZY5)!0Sg-`y@jV%JDY7BlAz=$%TuG>a0kKxe8AsdMH`cFh@j z_FGnL%_p{pHE~jVAgk=ce9!b8M})az;45Cfz(>n%!rvlRqKcUVi+&->eDne#QZfLI zPge|Ep&7r8kkLr%J>(DY(_42ozBy;Abp_Tm7`!A+cr??Shm3pBSb9r1go|RmGB8b% zfYUutfAP}|C(EC(qwYfmS)xsppQr?yas$tA_RPQ@{9=8bwt2Frjm(F&Q0H?4ikgjS z*6QNIUt4$;-*LFdD$PgXixY-fX!Ym0H-6?z*VHyjo1BiWw={7lZDZuuACa}$jszyW zm2>bL3)uPjcA9i7J{o$c(@=2ISUOF{E`ruDji1t27g~@t2;amY57yn=SZg`j|A+9B zn=7Pn%9rubTI-h+&rcLJ4s6Gexy zQ79`-TfAQ6p8}ZzWmLwz&YO?<@VPQ4;?M$&o>DQPr+dKs5gQfew;TF+$yE5`3GXIp z*sGA)OH&$4sKPC55I;E!_YD^7fvQ-}T#jqHT&z>#IqiY*`2iggGN_a}DYx_5(jU&< zLQ3Ln6RcT%kPex=iN(JjH2lVucpQ48{WeRcv0>Qt*_VATd`Vx=OlKf%a|iMk>+UAb zPb#$Cd2?1;=|%^VH(D8rq~?@UT%s}Qfw+;JG{?ydF6WUqg(0G$)qon+@x7hf&eJ5i zJTnd*O1n;*pX!`wIIABR078VExH8nzOXyWj7KZ*2y-67G+C@B=hO2e-fvc!4f~)mU zy~ZrXfID%~G(LD|eHYIsW_vo5mC-orh;u}7=q^H?kBu72S{_`O2wd!9w4YAZkFD{EcH+4PxY?rdGp+<m(4k>D8>I%<;LZKLdB5KaTwj zo)WY(;pAk7&(!q1M>C-yd;O|j$eF0ZpTe;8YmH^2lfTGi?iyW%e=4VP8zv53o7UC5 zqUbs;w4~pByC$%qfQPPT0=;OW_pE{Fb{G!}3jQV7U__5M)8|8Bg+wxQp4ye;XN!DJ z9m!EQX5RM~lcc0!7KtzCqv$GXi#|{S-w&5vgs5~y6RhR_o&4|eRWq!Gbr{%1BnSuL z%`n4Z?dvZ%q2auCdQUHI%N_FvPJQG>pTWFkmH&&ghd58)gDH$O?AC24to{3@Fmg26 zFu|=^OX*PMbFILXeVb#q*7Pjt)ABEl*Gsjian7vVCX;g;{gOK@&hF1hP>+R^D0GUDMVLhYEpVSfbU@KEz;BbyKV2Yj#ahoMe2I^w}2&@>(Zd z657?+Z;L#Uc2M_x;nEa!*N&1?vHYg(oC$4D{8!}fW4`{izU!hvHL%;>2R=zK zYw_ZasR-Zev8=e`q;BE~%A}0mPh?F%Z^@Zb-p>4l){HrEEq|vyyTOIt4hbhmY%I%S+t5-ya!q_R$VcQcNI} z+|JS?*_`e{s#Cs`b-*-H_JecMz47+gi;}tDl}{eochja#{>&eTTH!^0m9wYV$iF)z zW*cP%Phu5JR{I@jJ6BKI>ZNO3Y)0-+ z+b0nuvQCMx9 zQG6A$2{I!#=9H9TmfE2=a7ig~!kqbVGx*Fd!V_GKb;G*EA0a1S1Go9Rle!o|?g_Uq zF=qUFu~$P0^!-@1%5}8ga-w~YDp~T$<-k7)!jvE7sDzxZ>dgrJ;@+!0zoHv=3V2SQ z^NCmY;7nTM8SL~N5F)HbY6o7e9(^)aJZwkrZjLJGIK6Fw++3ZWQ{Nc(XgS`uix3Lb zJK4Q4TtyGIxcS+%Jv6LN{ywCV)0F2q<@exB1piHqIoYkKHg|ripINp|z2Aj%xEbwx z$e#wc@sm5R_B{?Ls1ZjcQbE+?iT(uE+FdJ1L0>7s8HXkV$DYmag!*k%vko@R)xiY? z?KW>w_GyoN{e|jBWKdoE7BrMb;(y&TQE_`_L=aXMO5oaif}Aavntgz=fej9OuFe@-D)RYI*CJq$PIGL(aYIF~3M6Z86*OjfJYNy;)@d`wISHYx%x*z|d0H;vF$NUpX zEmT_$_k;g3qKP#oA+Bvljfq^G8Q1n1^fA0u+~0QfJC!u~!E*XeIr@CcqW$F7CYAW7 z+?y!AgrMfAlg0#|8AVq6>1-1z!4B|!mF%F;g)dGXahD>U-c0|dUmyc1 zinoQIKY$TVZpowrTe~{HbKMQc!dPmN@;)C`PTKnAW-%i4J9oOHac#mkPlUf#^{iX8 z7bU$X!a`qDRo}X?1Y;V9E<$zp3|#vfK%p(J3QMDW>@Zo`&swvK2sv$9&0NeXM#fjw z=f)?o?W7j*J`%-bl6eg@B+kC2U?_{iW^A9uqS*mD;Lt7fa#NBK@H}r!Sh{fPB|t7 z&Z*z&_1a4Q3*@&iJ5e3R&ZE)HT8_bk3)Q;_VoPLdbCj~_?Oeyz7jH*%eQQQc*m{d} zT3t+A5~a=@F7EB*H%}>iTG?z-`4ijv#W=BtTm_p_!*$i>)h0LG|Jc)fEm;3};G;Gr z1Om?0`Yac#ANqj8hl+t&=4&zNL5r)dp5v`ooiNx?DSG`tMvZUSf@%<(}gCx3zzHzCzebCFzSbcNUrt#J29b#!~u>G~L zO+!_7OkbLno^bRx&PiLz{6zn)>f807uVXiQ&ucq)XwaX_UByP~1sl%SMac1f)L+#K zG0C_V182g2DN)U9j{enfu8M5*gLj)e8sBvBw+38KS%-;fLFTA-QlyHq>knY%`{Cc!DklgvZ<5O;yFf7>mUBrWTbk0AiiA~ zr{xWL3~%e5G*y;yDweX25l56k);oUQj^=R{^L~$>Lr;01d^QmzwDVw6_HffP_Dp!U zDuSi9Rd7(Pv~9moWbh@Mpncamf}QX$+v9XS16cYN0%K-6JT5RdVo==)Ry{9C38zX_ z1?B(B9aCuux7FXHtB0}jy}vH^P&+WhLf@;bN%iyHs8Uz`te2z}a@CQ1WS)EY2y|FG zZuHE2$f=UA+Vr?3*NQK27K;9XCk_2ca3n5E;h~xm7JWYJww79q;nOSXAJE;WJ2~ZP zsI9s{;^AbWI=%kqR@lzJZKh;`wW6itPQw&Y!=oKm@6Vi{z?OV~ek4KG$ZlcZG)js= zG9&8Z(u7(mZZ%V$(L!~k&^XoGJ;PK(oKIftZDyU{I%jbt?a%EVeCfBlngKlw+0IpM zHU9%{Pdp5%yNKBN9q)W6GmpJm=QLYK!sXi_gV>LcAoG3XZe|P*{E)f8gK6DGII0lr z&(K$h`8qkJu=Aq+wjDh6Feof0@v96*Q+Vg4$T0WPqU;5!=1JJnY6MZ}-mR3Sw}8P% z(eYCr@HYhXv}HBp?%y`jB*myev5$chgjq2pBlY@^Ob%5Fi83ivL>u^i-!@Wj6$Uc} zKfkG#HF=z;HL;sYjE_V;dm=ky)JYisb~mvpar1c`imoIw&3gD^lqb4P|2w9AOJYTN z?XRNF6O;Or4sWAaukH;hkVsh@cHc2$Oy`c*26hdfbHlP=FX`J)WMG~&^jM4IC4W2L zISOppBl1d-16gNH`k3Urdx8py@5spJ=-tjvO^cA#79Odid($^6$Nc1s*L8`XPy#w! zadO9qGi|$wD(DgA&fLyDkg)k-#67z>iMxhTfUy>8*90r8N07++lVj;A5xRc9%0lNo7?j`AvIt+`wVgtRb8keG{Y@4E}X#izYIRqk`wOm26gfnL$_8n+lr2jD(e;} z$bVeUXk7o;g9mwou0AR8B;5gJ0?Jw}W$$UQ*SK)rcZ2$2GIUR8f{Y{OT)jpZFXL3O zG)=#ig_%w)1XIh=%AUgc#(Lt?<)*qYkhwes()Cdw7jCAWip6i%J{VfqLR5)_$iIOryme zhy2KVv5WW{ny72^kha50OFqme*6_X&w4GhR%gT#NedI2g82aD~X53Q>ISiJvx~8-x zk0wdfsCslyGG{s`e_AY zC)iD8Uq%KLCZDo#qov=gePVypsvmyx1r$cZBs4`7#pfLE+8zxgQM?Ys%Vs_^@)JV_;`OzthP+wjQN zs(8VSYID3j6-B^pMr_Z{ug);!3=&bvk-89{YVwpU&1ZtSN0+1oeM3b?qWiks)lS0d z-`%&S+t_o|w^~uN=(!T-1dXZC__F2Z2sww_q179pCX`~5A7%zzoEmZ&&a_@P?(Fv$ zuKZ#hR(`rYMgGru?Y1f>oZ(=rwszo4lANWe$`nQh@pXJbQaJd_Fe44+C#OZ?QcBKY zIl9)dJRkogN~w!|I(6q!>q$wI1(l=^YW$s&0@+V`@P&){>~V_M2skcLDPDE`ii_Iu zyty3#C&f>N%0^+6of7mzq3bnu-l}kCLb~8|Nw6a4ySeEPF4di`3r8*0m#0P;rkL^< z;pbWe=^rP3)>T3FTMM?$O|VYjStsq`N$+wu=v;^U+S#(78#KoW@ItYo+}v}%NDW4@ z@CH==Q06Px5Or&AD-WzRDz+l!ie)u~zfS;c1l= zQ!$PzPjlCa)e*0Dx;^hB>U$(3hlA^y{W&4z?OB2j1YkD#YuJEtcTlNLCNj zb^4&4r=ED19M_Y(!a2=mHn8Tyxij3; zEZVk;u%|cxfh2;ZebFW{6Gl>Yfnsn!>4dySPkAlx z`GLeEH;Or{4ss1%iBgO)a<{Z7{#+uD#{QUzo-n0}z8}}#8Cl2ci)U3t^V|FX)Z-AZ z5HPVCuYVx=WlntrdHjW9(`s#CftzTEM<@MLTuJ2ecjFDy=-^*&er>Dc^D{wJ^W!X% z3256iW%X?z@wCO}6wzv_{n$E)I!f@RTr^TjjyJ{FUzUEn)z zoZlHr% z-Lyx3UnAhA`DzOjEm`)->F(b98ByfGT}1qrEa^!jB79$SUr?lwnV8V1XWqO`3{pG}`(%aAu3H%3A=Yi5N!=uw>NZup2IjRDK=ZSHB= z8Q!NdO!o^X3|=#`3r%)Fi&7UF{LM&c|qKg7oo7P2<}H{Q{o- zixg3Z_^WA?U(LA^xi>L;zLQGRe*ToDhC~KqEWiZv9m)k$%r?(DH8D3yQzN~9GTh$5 z?&s1jPj9$1KL0=~G=8t2ie@(rp&;;W22pJBwW=*V=jy9_9#JtWGqjRby&?S}8{yC;0wJ3QapyoBf=@nV+cJTj?%F+&VBx{qL6ehZbe(Yb3HJp z_zu?x-?$wHA<=I-^;z=9%$hBm&-53zK>f^G*Mk9AkFDRgxA#dZ>O_^u-|J7YU^i7h z>zHX6-EX>{w>EHILgJ7txy^A!Lt9r5O^JwWzC?~js6G_@|AiM0U_jx8v~5y7H04*q z_;6JW$w;^Wj#~8X)xfOj`38l|3bqt!41w@a>JM?@D$3@j(~a>U;?#{ny*;PyE4T+oXGk+QuNy+?imKsa##~IpAqkThq5QRmTdw}K8zo>zLdYg4TChjo^5uQZn%3rfS-**j4iI64l!5;MX;e-G z*g9`7RBW;$w>z!3H@RMd&tqv2rrG!mOuG(c{J_!I?K)|ztn`_R^~)fMAl0)@U_Apf zQvFz&v}3fwD(CGvz>p9J9C{{-5kD1JO_*V2JX3UD2YbCiDTo>(eqA0m3KxmW`!|1S z8<6jzMKJ!dHp7N>J2+x9?GG|yoC>wQi-1eU7qK(}>^eKxVk!WolgF^Nxq>|SS^)p}4kux3j?cQN|7$8%F1`Qyi^DxUV( z#lQk}%Kfv5oAPlJ^I017`eSHoyCJ(})+q*|S{!FcXqlJZiW+ASD#7yIE&}Wuxv-1y zDWN~bj3~m>*lk029$uzP()2EECe~ko1WS?OPN5Gbjr;RAZ6X#Db9Nrsux7~?pP=iS zw+K+CEj8#P&Zy0^Fw|2)-@n2#QBiO9j>EsqBjipjqGK>TQ+?5@Ip&6jqLtFEKS~%K zC~VUN%9_}4fIf!|jBlC-77|^EuB-HHmT?sR4l-g3wK7&o)w>_~23PF0%sclQCH^bQ zzHHM}^7}<=r-Yaj{_9?qHiyrm`c3UxK>f?1SU3c`q}Pt=u=BMoFmrb@?t1s~nvCGk z(E9ynh#*4KM*rupRllSc!u|}Knrc`hx6K%0Y$am@w5}L>DjNOC@N-HljvijopQ*skyhF!L0MrP%+r0?Ww3v;JL5~aH4jW8v^F!D7!hia4hPw#1 zrL(j9ovU;5e~7B|D%h0$AR;%lmz%3-!A;x#@Ga-sHwmHYFjXV4d^J#>_Ac$Qi9X72 z|4uO>>JFDxdjGu=l)S|dc36b<`>T-Ff>bZ;u+kvLV}Yt8hAN*lUSJR^)XB_e$vF$Oyg5I~eRtkDH$H&6LwUPF}ql_xC;VS;?=Pn@ew8sz}j$ zA9#HeZTvEji*c%V3lUIR{;4nmPD?0)ayxDxA3)K$H_?wqo4!AuW* zuDpakFVdhK6L>MZeB_qVekcR%4nIj^ZrU<8Z~V3^y{X|<8RYzqihoI;{d@D~20c>- z-$c_H2k@A5GT1Wc`v>WG!4}Hj77G0{G{`Mt>3XceGWq#{gDz!^6)H&&O+$#BsS9gE%m z-TaY@fkJLRWNjuQY5ZFH$f!T#RQ-SO_rEt(*J==E>Iy)^hPN=R1;BEeL_}@3v;cM{ z*~U%6Ws{)G9zawOls-XiK*xc_Z^-a_r$jNUEN(cJ&QQ z%ByNx?c*j>BvejYx%h-8=2kZLj{jd_A&lfMV#BQoEtmU;&;8Rbg5~XJXh+h_hOk<~ z!0jRodUg@VcM;DZ2J0^3Wp2OlQH^&mbLZ@9LB^MB41QvhT7+R_gR~(Aj^9OmC$1^( zBCxv%jjIiz?|_}7Rr0|guG zBIdxoPhUe!?(>4>6^CCE;2XOLxsF`~)pQpTR%BGQ`sYH{8fv{7f%^4JNbg3aQc7u7h#y{71SRv26o*=OaU=iO()h}gmx@Yt2vntz9&EA z0XhNY{QnWM3^7QAU4)Vsy!Y}masV{T0jM)j`aB%}rXR0WwTqYr1Y@ZX6?A5L3pi~m zxLeNy1#{gaG3_zTqql^yB2`wicZVDy#$6_$AQdP|IGYD{`l3ctw4w(RLLcy`t zwi$*KrC>07t1p5P9JKi-I0m?D{``MK0uO3iw}9|<81x--(boHEOaF43 zhp{_nJG&!{u+)EDld(Iyh@C57&kkV!E`stFjod}-@p<#QNN5)k@y_)Xks)RW@Oh$K zV;8~Ld*qFISkaRrMKh=w2K_{0m|_)xcEBVas8geL_4BP?8f9^(39Dha9qoTzN}e&7 zNd@DKyc{t9#}+(-434bdA4H&N@~&TbJ-UUN89mW?{|wmk%7B>rL0g6FxSiw2o|zT^ zoo5_uEQLrg3ZxQw&In}*MuM=7(g~|Ixd8?xSmYHk9Q&9hQGoS;09L?PQRh{%^NC*q z{|Zs;BI>N9pUYHTI}BPaKG;4n9d`)aN;)aQ4kemv7=1RJl%CnSB4~H(<*_!0x$jv6 zh2ZNh4C6Qiz+;L4v+N=|Q4Eb?t6vrcFb|eFk)Js6eq4$OCt+X<&`|699){A#Y5Njz zilF3Ze=aGu$g}>8k?V#F-jJ5Bghd@U(4$wWc>!}pGa*JZXbz(EfkNCsj5jLah+V|K z=UIMbpbY8YThGmbn1hvAkd-Ox-80j9cYSi^eZY~gr0VTK7{l0l4~9@cr0Ezk^HjWvQSR?WaGF z9>l=+9)nhp0PU8Yw~XOK)8}KadtjE9?;dE@0qwrL=yKpG=Pe>EDQc^1R%_g1nE9#l zw?JZ!c)&$T45OGx`QOv(FF>Jh4*vX1%p4d$haIeu15|bah-uC6l?26E_hzQ=1&At_ zJu>A+OcOPyeKz9#bm3i?rs%O;fPZViSV{kh=i9>yhO6-|qFEl8iX3q@hRb3HWd>2?!lD8+p{;8=|>BwCtQHgQyoYyZDy69D$pg4uuy zQ;Hv#EkV@%B7fCAJI6V~Q+(sdFUcYh_40Qh(KVn3y5(jy0HO^vMC_qPD`4v;c@|gE zgNwU}*_PP-J#m-~V2v#B{$m#(15~9{?UdPr2hUyFZ*~J<0$KP1n{2$ni-ZN>p(rO_ zLyQ!_D~RR+>b_+igc?g=B$cqHkjMWLJ_ulSJv=O0+}lKR-3oZK)At5Wc>6p6vRQ1%L5%7K}@9kzrOVkKUPb25m{*dQtwhXGzV%m*4IOW#5iKi*eF*TF)N-2 zoN7C!!CiY<N@Qng3a7&)F}H6~I&gw6*9U+e-k|2 z_`d*SPW^^kKk%WiUM-X@lxaGzJ@X|o&VdoIo7uuB*xha23|K}0kZ5SzzrdHFz(IoeqG5Z?sRWwQD1@5++;AxcUo`c3F(m9{x7Z-6dGo!gYL3xu==*)3 z8>|Ay4$Zu*y87TX0E&OU`9Dr$o<}iwKCn04kEGV_5x2)Qz2fk!U9B}d{{xK0DO|K=jars0FrQN|?-z$dGJ=5PdC ze*%O)_U}>v8{o8y>&{&|1Hf(Z0gkP!rUhoa&$H-78cSRH@nJF(se z>M2_RrT8Kk`MGu%G2TOc3Eu5sW4BMJwa9ntk17ssd^jkM2cs@AA5acChGd)s)$#hr zs3H|tm01}P@FzwI^Fy=7x`LIRU6^UHuV>j?C`|`&K-eAhUjVISfe_dFUnCMQwVVWm z3$DOuQ^S)6e_%M+*%x%76ls{QwO7|L6tXW;KcM~NGH#8rXwMz-EX zltJO57z(&a;di-amSv`O1(jY7@G)GYl6U-igoYzxy5Yp7D%YNew|oI;JKAmwv~* z7Mge+aiXn!G!FIeSZXMa#-^Y|1tco#UEx5|?wlXJQ4NA$U`Qb3@$6xp%U??qV+`%# zo}k)xcgEJwQY@3@T5#0J7DY3y!bflKxdB;z{r>9Tgo_+tdtMN=o&|sH{Azzt3pDz4 z{G!D5TdIPSx%YD&$anZozUtjHIuK<$^9^bj1ezAp*$Yiv?wF0}a`vLQ-7e~6?`NlS(c? zGC((Dzkq^cmF<`6pLx3o`}bgh^_{Y_u_ZSK=qwXL>%+eXWmz$)K!MAO_x}dFj%n z{{V5^nSz>{?tT7sUErP4eMG95}7 z`A3qskTj;hK5)@g-Ya1c3;FX?-uz^BDidz=ksV-S@$E%!1_` zEP)^#@6OVMrhrn)rP_eG!q(L>&n!*zX<&0G09vc5m3?KrWQ?W^NqQy$mkzwCO?UJa z&Fi>{b$Ny&`r^EV^!#TOngY3f3u2+9J-GDl^q2U+g38bR#fb;4wDU;})`t!CWpicq zW13Ho{cYF*+QO^aefmw^^wksP{MVJ&XR1%d3F{D%S$mw_ntwZvk^AQ?HeF)Yf!!Rj zd=q}ykAK)dc`kZungXVr=7DyALfqw!Xy z`_TmOICzDVJ|XYY>Os`7Vf_Pmt?|>YZijaZ)U?67rY=~+JO=f>O72>svulmKW4a}@A>JPVOG zbN2$C19PJA`}IHjll5OgUziE-)2ZqpdHC54#4LkAKND$7`=LRWH_mSY$BO1KpZ1cr z(LZ1n!hJ}_L!8hnXsJQ4*|0XZS9{Sot~%%M#PDof+ZDS%1yL@;OEQP7bnh5^L#MnFM+k+ z8ud>cGX-v1$2QvhLhyk9Y<`v75jP4N4d!L!U4$3>Nx_0{HHA9xXJ3VXW$Tz)_M82! zdQe-DX@dySZ2Pnqr+p+;Dx={*6=|;`y}ohjZPXxZj@2-+wT1$5O3x!PE4u= z3wZL8yA%9uAH|6?JvNRgqX=Dlh+JND^@v7gY!5?C`0@8tK@UY??5$P3 zk6vp~*$rqCII@DAuft|%llfYr2sF#oW1B|;<-cWK$6mGKKKw6G#fxBW{XeqaGA^q3 zi5mt1Nu^6-MMXdskWN_vDFIQCF3Ck{kdBouVd+i*k&^C`ZV(XZT1x3{b&G$PbZH8aqr3w-w3y$PjwUn1Z73jz*P;6b|=2(5c=mfmC z*v5W8==V4o=z&2Zrm=LBpNP$Bw{}{2nIRs6Rj@4I_}~p_3sejKuCBGXJn>~c{;qfx zPy%?^5VK~|%lzZZ7~y{e$^Q{BIzKT2+*!1agi2kNNhGdL(b-p*)zIUkUd5}=l99k9 z7v;a@zN0Yj&*7#*Yq0IPm{dleqPMfUcy>NFGdI6)FBu*`iW0PrHquJk6!-YLH{*d< z7-9!fZ0UCvKMFnsn)T!K5%OiHh1ro$a#v;5fY{Tz$9+UO*?&J#yun2J;6-DMHrCAI zRBDE|u6q-Rnf>hw-_~VC0O>@Wba^TMHP~Y!Q5eKPSG8q<(X%+CXWaQ&!^j0>kPs}| zettq(;XeXOJWEL_5Cnb)FV?`(G8EOhR`!vP1`tVd-3~&(?FC2B$GHmP&j^gw5h}D!%K3&@YQ;CzN4Z z8hv;N+uboJa6x%DafDgoyk1g`;!`j=w!<~F?MXl*q}!r?bf?^UrH;iF{^Z+oMkk~ZVX&W105>@Jv+T(L)C=F@(j z+-p5vqr@5{(}%sb*Q??0wBo~zRB9bm@hT0p;nEsh6RCzpRv`nyUss1tr>Xzeex@C; z!obiVbi@;Leqc&V6RpY$kfJPUW2v#vI$@LCpJ6|4Py8##I0 zLIJQ1O<{Q%fqW3&n~&~qr0NZ+i_uF19Gm?t{&h>vT;W>_^p9+@O7iL_6{!i?>oEr0 zP}YFhqxx5ic7K7zReHW#-S)Nn;@-_yYD$V^hQVS8mR8;H+ml~FJT1f~p2c8ss#ZH! zZ`Rw5ZswN&vKx9;ylMAw?X)`x9f`N9DF0qvUZey5{vW|ez1;*|1)c|RI>7S`nlpi6 z{{k4tlR6C3TZ4{*ooaVuCm;@2zkQanTK}fvLp5+hF(5fo@3nZx5su3it9vVEffr+9 zVsxJbu2TY?=`BI7fg;bm<~&_k_i5TE-SpdIDv<_xUkZ)Eh{=LVBjAoen8;lW>_>n$ zrXYY)Tan#_aW~-jLKb8@ORUq1+Tsr{^#;9)FOB?)_A3SePjS!J|K)8a5+GSy0Gz)S z^AkYo4|SE48JO`8T8k7MG#y_XEjd;0bw`{Bm5r$kkIz#gGq*N1UmG_;{_+;# z@4f%J1K3nVp7;Q>1)8i%ITEX%G~K)zBo^ND0*1a5iEY#$P6X7xkpZv(Lkpl_55(wz z)I|Y_LUyWwX;}ZC-sIzGr&R^6Xs9W$%M z+if%yJHpWS;5h9YB6THvd?^-oNCc!7(ulIM5~lFi`U|iVPvCh-whWDw6oG9Z=bub1 zFcKdYi}XJHFL?V`1)s^k_~PJe68|?68)P7ct~MqELjAt9;s42fsEHwaW6NjS4*VBj;r^W% zBuW64heaT6(#rn7Ni_BKJf4BMVnB9I*}L)6x5`d#vRYHE`}N@lPtH%9Zv&SA;-_G~ zV|Ax%>#e$S{tcjRKY?^=3QLbC5zqs4D{P0c&j4@iuTqVp^wh=QYu94X8P+M}Gt<`^ z0@ABBD@K0MzXP-L2cfF_v3)=UzE>ePg}qzzbw9ftYH=l!k4?BEaC?jg%Wt*$ioG~{ zq*e(Z45at}{M8_#G6e(+Ts;UpNrFi@P&f*QoT=ymPf&!{Us3#Zdxm}H%ZK;R3_OkI zEDZX~z824-s@pK0<$6z?Vc6)VWI}u7%urUIpKm@IZ7^5_T!?(%_H{oph7yDhr!dqJ zKYs0Pz@05&a=&K0YFOnEX!m#mHUxqz;1NzqvVhW;7=ac^EQ3xMP%v)$U%FTznMzfr zHS=Zcw!mYFOGahVtnM>j!v&omwa7rdQ`k>9Cpx{iI^^2r>-GE1(uh3D&Down*g2cx zU?iSPnSlZ}#Kg!b_=i`vYkAOIp$aH6Y7wX3<0w;V*kyh>0NlOSY9N*gtOFR)?R&af zV*i5QiEQm_Z6LY@Ry(+;iV^rwfup-G<0Wo6JV;(;ZY!arWTQs&DGt1Z0Qu8BAo2vt zjIik^K)P0>geOw4-orn*(pW_aHC@fu0s0V%2dbJP$@V0pTN9}c>o*cWCE+Ew9#2-q zCgEUl1Nv8jgW?&p0TYN91Egkv=N|wH8vira$FY%k!f294-*1IV z{|?AVz~B^KonQqf@wz8+KT*26mb-luSBA3RVEz6dq&hG1)AG+4CG26nHHQ9O2i&T7 zA=vifKmUThr<2lYfM=QjRP3KQ_nSkI6~=>c`PUyyVp0J;`%GSMHwA!N#W?@8mlhWR z89+sz^5xu3byC+$OvQHB!EtF8Fx7)^yEN+t_)+<+Ix*k`UG<^!a`D3}VCM#6@R9nL zTtE%Uej0codi;!CH~z)HDk|Wfg3AEoSFZ;&uCI5(0u4dl$!b&v97M2HDUMJT3lzqa zfxiuiIWGEk4d$r=T10r%b#n;Zv}^eUP!#oN`z8M1O2D#0q4-jKG7$4p1C{DIJvMPq zpxD(5L&MRkSUxD=nP6B^;IEKV3D{;SUVpQoYZTMH4Le{g#m9m$aA3m$miy~Owj|(@ z&^&l;Y(56C)c?tSBO7w|zqhcP0v!&G7s?Udi{z272A6x z9Ov|4|K1u{w^NdsRN!kEJ&+Is0*0LZ6FOjGKyC#%pa1V;>i^!W9*U2x3fuW- z-=BDa*dQ+EpY(vx8;$`Sz;Nw9UlLql0NAB?1zrQP1vodZmsJ4d0yw*-A|fQXMN33W zKtQ7_kf6DHuRgJ$r~6}-e5+serB!)l45{b|HnGFvS-6)`-+{D+<18{REP@q=+F*f| zT2GN^_vg6VL^z5BN)>^(sUItS za#w|CGPaW^H=CujUf+4K2ayr2g%AGx@^^Zo2Xl*rbu!F~@Ck2i8}Q1qx5yPTPwQ4Y z{nDjo4e7{33MKc~&)g#IvRWln_IF-NPKJ;jO*}Z|Jpd%BX4=TSz3TAYa_;*0#qpZ-4>HnL zRimP}jSF)`AN)$wJmY4UcuIiNOcrZ{5|GOlkopG=lRao~LA>G?L3BveUvS7lAv_vP(B4_3lO5&RkuNHPs9*%gX z8uoC-(`Ba3TLLaO^=FYOrIi6yPtNkQc<)h2X}$cBnIM~5Np<|tK~4DSE`PKMN84?+ za^nw}P!i`y4yZR&^x#MDBz`7OHY~lTl>PhO`aUQsC@cF9#Zw4@pl|D24+m)yO zlfLBEQHp?2bDg&@+JxCyF?*j!D`}o0KA8kHD=c$Jv89E)u_(NZHDbT=dV*DYZ^YPx zK+f}?oe8)VeZBpPzU0=edWmVZCo=In4$3A@td?gyCu_ErO0SL%xYtCWOfyLmEYCov zss`d2mH^Kmt{kp28|>Ft5yCYP0F~$zxVgRdwIUA_(BIf}=^wR6xX(>sQO(`Xk)Wb0 zLRzqW=FPy?OWe!-Lnxpx)z6AJvFEt~&q=GEphceDJV^L!jS^GXyu`2NIX8C?23#~j zam*)`)TKdEA`MXGQ2H2~(nNe*Deslmrt2Ng3JXC>dCdU!^07rT&BvEJpP006A<^L&BUm;w5}k_g#};m~ww$#J#0{jG?(a=Nrs)cfvfh%%cslMKrk&H+<~f?0 zSCjYs)gCFAFmi!Toc}PSC5@T?4yNG>>I|;cNvkrfW9unW-D7u>CbS$IgD;qPzb_A# z4x6b)J(Y?06Gwocqa=p$wJ4=n$jNRd3ZWtG$4~v;I8rrbR`2S4lXHg0;NQY7$;$bm zh&PG_Zh<;+FQbU(Rri$9ZnZz%o*art8((KiLYUr#+Or(0i5@cbtA@@vM&VUE&)PHj*r^>75-OXH%1vj1mN8C1SFTOf>v~+EHsN3UT z-=R1!-BQFSK9XLJSylFD%{QTZTAVUQbFH-Flk%8sdxxKY9 zRsY)>;)$U0=>b}Xs$cL@MM4PlH<3Vyk`9D>Ng4n+j_)dF>)m&`qf)2*EvR9mUg&OQ z@8YvQ1Am~%w?LV)|7r;Lpxm6=@A4j%E5DGkFr;9!F2wllevX^z<+(2^{YdYVYNSv7 z^DQ?rQ)nOmS%DL& z1yEGmA|a|VUm`k7U8u9YS>us7Q-J%VMe({AL0tvZ^pv}R`@HONewzL|U3d<~hVV;z zN&UabMLO|CDH_&qzgfd{zJ>9s^41p`pRc}`T7~@UuA(IbnyQEh2yc;){2RtbNKQyV z%Oy+$v{&7CJ53BOtex)JME?J}tN!tj(qI~EyVhJJ(p~c1nzIvp!5R@$&N80_I(|qY zTOWkD`y+R8+_|UWZH7ax(ClodxG@(vB-1H8V(Q^z{ z)2hK$t+MCjiV!#eJtVtYNHi!GM6I|L)x{&dOy@@2+OIumYQ&*%5rP)0TbXB`V>`?_^^3hU@A7c#K!IOi^=Mzl$2|;mH?bw*mS8 z9Y{BO5u~p&yq!Bl<1F9hcF`p$!D-#zeq>v=66ChC>Fx4)RYDObH=UepM0Q7juP6=A zHB~%O%Sm>Q);@r~S=rl=K}lujo3J)r+nBs}5uyv+Hc~)~MlH*&%UkU^#@o&Cc|`sh zwlaHx*9{n0?&&aA7bQ%%w#ejgd${RF`UaAV>99VruU$Qxa#Z!gB(V1LAUHkU0uP%^ ze43uE$*n{Vi1{TV>7=5#$EugMUH1B>U)VTxv9aYd&CEz^1h_+6kFjgiB57_L%L5nP zQTx0&M~qY}=R=*_sDQXccK-#0SzRKh`dw zKWdDs>oLN|ZcnN?+C^V(B^PbS87S9xd)Uux&B{{RPHR%RDbGzSc3o5525 zX;F)K&hEcmC6}rb5m;SwUb|*cs^5uHU0g8Y3`=%7SbJ?XC znL3MEWVo|WhNPA!N?bY2U`0oEi%)*>NAY-^3aZ(wqujs0%|ddh1SmxlSYf&xv9|D; zsm8c$gyBw(Z&_6U|JQ~0H=t2{^0?Za2Z4edhz+383u3~!?V59|PivWA4L#q=#3Rq4 zvW?_x!j3tl)ifWuxkw66%y*0>N4JXZ+7%&$els~g2PmV5*i8fN#so>@6QfQCX7-@d za*KCgWmF!1_5vkOmE?~TjX=`NB>Q(pf>oxDAMPC|lDi2)TXmK#n=^dctwhHWzh?~d zb6yn9m61vanl3Hj9V!D4+f){9TzBadtpzotN1tDV18M6EA~1M;oLf=5ZiEpsNaN2- za1G*5bbX<;97oNDyms;G@?C0*zR8}d*+~T$^LA>RFOpe!EG3T)xrEaIoK36jzQN3P z^ixsYpjVlg^%sAe<9EPajsi6UnKrE+vK|-h%E{cfclDofjmwab8>C%xk5DKAO{0&4<_7@jr%Z=|35bxZXXA zT<^05pRY-A=Ur#!EF#1fZ{TTtX--I?l$wlvuF`?%)!MByzb1g|*8PpSV>*I;*r1ga z>esXmY7-_44rFhM-*v(UD4%N|`}mGXwWObFJb!RwS_3cF_GaT4Tky#rh0a&7_U>NW zb(W0e(JLhp(UZpPkU>qrQgl(qjI`1^YSHSz79 zs|QEF)Jt8hEQ|5JErP{%E7`)gw}sA>p1(h@>udAot{P(|3P9pY*{Kd^6zr1_kVLL! zi0r{OywcZ$^|a}k(EV4#9*JR?1}JtLJgj$mMS6>V3-QC~5z=nazW%`J>r46P*G7U= z)1>r;D7qz$H){ch0neYfU8LJQ1yIS+=bsJxM1+s8Yl-C(hd{zb*&~u3<7l!1m&1D_ z2#?jh9>W6b+w*`X7a1!lyu^;s!=z+oXRyZt-s4gRY_* zZb2A94!IZD-#6WOn_QhjuVGm(cq(!FF!-HTCH0baB{GxbE?+>?sc#1#v z_6;VKNj;D1uaZ@JCq8X`yM8BOYLd9I-2x8#Hu%R+8fP@x_imLfF7U8V{u3wY%LR$< z{ip0(H+=)YEZk|7gS?`I7F_D^NlQ+}%tHoTL-4Q~dGOs!Grs!npuD@z93eWLbx#Zq zsORoXmo=k!0L}tK^iP84Id??x_f1mND+M46v*d81tH8%pL{;yw@zE!rh%gfDZQWAZ zfvdCEN|`v8U*8HZDDd$xjmM0rOk6@Ml6mhsv+D9{6|(rAAMzNV)kTi&UcI#!WN{My zi@GdKWYO}^ydRB)LVv}PTq5Y(oxCkj4 zNX^JrYG0ehC?dJg3V%h=tG$jZ>ZJ|JIazxXx!~vx!h}FUBVF?+ChwhEOk8Lw#d^d; zniQx%p8O#6ky~b^)@%-)ZvA$3voO$|GMn%=(^#fe1*_nG*Nq4$$Enqcrpz^Vk#;91 z^CN*0PsZB_uE)-ITeY)wb7~TwdJHEQk$433o8_|Ns@%F#9kJHeV^XS^Jlto~0qsgJ zF?npWX*it^BCP%TX$-IB}XR7(;a>HF?S!@<^L6*BVAS>OWm zMz6kP7r-Rv7&(;H?$QumBT+mALz*sEEz_ToJ}k~d5Kg7Lycca#od zBH`Yt0;^nt&_H!b%*VYX4_7SD^zRja`F7B-B~BnkA-0BMn+4_z9FjJ$+?Ksts&uWy zxeULk1Xzw2pwoqzdeR`9~(%TM9WM`LPuzk5%UfJCBvdKo*u(n@C;+ z7Fi>$bg4egJ$sk?6#wGTy+8R)7|mS?y-k_zd&sWavIKp9$wuGe~7pP4?r{IbQ!Ondjg(n7z|6<|Yy*GqY>7PU3!pP6>FX6*c(AuT0!t4D z%Q^zwafI=)AUyyR_OCQ}Wq3i{SAROATw`x31|rm~Ml!}T zc=#vI<6U+*sy6S*gj5-p6rEVuW$-^$!LCBX^Ge3cI+*=(2@FJ z>yqSe_g_4`a6Y&CnHKgl~27iZs1lMi9Y0%pvDB6eoS!YxE;=Ig55p zPkl75n&CtYpHIMBWQ@XjBI$eJ2FxzlMyi9=b;-^WM_UDDAxvJh^IfO+tw%; z^GU?@A+uCOEZwud_C)tOtje{3ynvY^tB5zFzIGOQ)16$^%sMLj0~__#wsxnVQtu~M zKbn*7X|;nTfF?zKxIZROGDuzHysmqFHj~9T zhL7J$uD+$tM3IzzE6M6dTxuS3v7;7 z80W3mq;wH3nprp*FD65fF;?us%(Oq?!0DJKBocv;9 zsgc~sZO1R;RLpDFSvP{dpk5h%Y%kC`*^NtsU(x{;Wg%=$_oNvH=+WH8A2kOzVT7CY z7x6mt46yJT>`r7Bnjq4uKQS)SCH5fJcA}EkwaF(j&0gZMbzBTD5%ce<-4~DZY$zwj zz7@F&5~c;EzwR}mD*e4hG}G3DHw+9q100(bSA>F6Q%szx%+rY2|4=5ur`CS4Xd zmXK3Oo2_6I7PHNGY_uZy+v64(aT37wONxsmDX?&SWh}cH4mX4s#j?s{@&%Bj+%o}a z);slrL5bNn9>ZPthXoLRQJ9L$Fc$f$aJ|cooW2W>J_mG0h3}`D_k9_-2XZXevO?E1 z0Z9+xg*6BW$tj#Pv7hR~vvK{?-cY|cN3Wc6+L^X!9+tjpB_;k*O`Wle=%L7gRKz6M z-6x&$yS|>g1cS=++cTjxai#i~&iM^;ZQ3W?vYTCRb$CQoM+ug3VxrHEtqNH9xMCyC z%5)h>-t}p_H7@e&p^p^P@T24?B=s{NxAV|^!dt^`f~>f`)p0WeMM-V$oU6T2(-o(8 zPLoh3?~kW&cK@bvH%?2UIJQZ5C!D=u9Rn^O{^A-T=Oa@)#o@#im(!~mk03jk6xmGt z+!`I)th28Q=l+fROS#A?pc zBRR;vgplLRr{5S?o!K|;qvbH{<^<96RBv?2%k<)%<=CXmZ>{%3@7~05M)tc!a)+%; z>Nnx3PK~AfTE;N!l5*4M?xr-P)1PlZs?}?-5TkV$cs*ahGqkg&@f5oX&UWSaAZs>0 z;3&ZQV{U2q@&w$-e(?qSYg(oU+yZ~b6EJ= z#0r2V?K$XK5gL)LWY0}fmu?APo^)4U^nU5o1@MzpZTB5V3U4T|Ki?fmbY}fxJ$uol z-Qb2itVbq9s_POCTV4^*@cV*vlPA9fY1+d`;OD`AAfi@2s*A}vc!V!|>FNXTtlO;< z>w?gePsI1`iUt{#xn5qUsrPe)(8LxWkG1)qp7iWLQ(#T%oB9-hHS^lzf`Vy8%kD~@ zua7BQaP`k8N%IMo*Z13#aYW`G@Le_OzN=bK`7G7v_%Gev?$TnnKiw3K zE?6&V!eni}_~{U65U`_onSDp{b9>d~*cGnY>W%|M_Es#1-t3~A?e_!RoX+KF{lfM7 z`!eiCLQ`%UT0xTr6Nm+%I7O%XmkHC9xjVf0rgkDC;1)7}{7LSFbkLxW`^v{$#)$ir zKyzr4X!-~S7E4cvew|zJOaLt;n1l-Mjd`E;A$Vw{;G~f}*`;fH2WjcTi4^O*3q2P7$;tE9iEXF3sR(Xb)C*>o2 z_-|OH^Evh2Eb$xDWeCrn2=S{y!93|qdF;LI|N82AQXHT#1=q0x=5=+I6dY{##jB! z`J5*H)DXLfJ4s6_UEaJ|$TO*z-&^fOX|^jxU3dzv2|Gngtc;ndyq=NMHkk4j5vs*o zP7G-8L0k96$u%w;eQ^=1Jqhe?pjS}ZCbFQT)jv0q&p{n=(CtMrYjj)0vPD(I<3P!% z!EYTPI!~T3c~io0x|o<~)7!Ao($yr3FvU!hYF|D<1i#>8bwM zp5MaO;2&i88c=7G2$FHbZE_4B5wVaESKDQIsgA zt^PTajanTC#LJB1uH6gaXL4+BM!sSc~Hdsm{{Bigg3r>~FYWP1Ajy5#(tGscm9 zC8G$zZ0m0$pb&}&=IZLx3D~W#8oScba&bc2NE!k%fXMJi131z7m0lQ3^z75a?d7xf z=J?V$KfdM6smdz^lD{_imC0HAJNb5M|HJFmnNvj{bFRB!-%xy~|ad)0e#($iUc+)o$86Y8OJJ7TcNdO-Uw9Gf> zBJIKb{WvA)LK*7yG6k?Lt*` zl=$mVYoYkMj~tvY9{_s4`B}Q{Dp0R#{PnnPyE-p^ zYDv{4`*~;lvCW?9c}s#cmz2Ok*yHU;=JELwDZl30i@H70je2hC7kJ~{PMN*#(FG=$ z?``|V*;Et0?LQv%eytKawn0hzn`|eB!ZSalCFM7-jRIaJ(R_$ziade9>Sx_Q%BsP9 z;kE#0!M0w|V@~25y8Dz_InKa1fsm_CPh6GJSGbWRH>U8n7hgNgL!o$zF9WxTZ_*(q z868(Ta;UgQX^2E#aV_;~@Qdlctb=liM~L-DiV=#^9{o__t}WdCy+2ByPf`&i&(-2T z2D`;45?N2ON^@rE*^9X01H!SqWfH1vuE1DA zh0E|z8zNK(=|I2i%0u{so$@@wkw)GZ=J1y2XZO=~-U82Rq~q_LoM`LLkSa*1rZeoT%?dVBq2UzoJGeqV%q^g0>eDEQ>_37gzai`E z1i}@}8?ZZ2CBYx+bjHIEo&rOE@ipunqCwriSEzrn+4lwfbjPdR!`pYynEWW4&4Nsc zl0i(V$Nzq!&yTeIOqO*CcRu+;8#g$p()=6BDP3ezG>iJee!W}&dlqcRAt8f9kgzS_$5-~rTx$LkFKD9&s z&UQI?^uiPG?8b2OgA9;CLMeyj{A_1K{#K;)S8qOKz<9;JVm9}v&Csd9;RL}~a6#rIpl4_<@$=R*;aekZf_L(=KPdHq z-PZTKe&Z>Pw97CL$U?491Ymy}7M+@1_NLQM*wspf4x3`mM;)mY>V3KsmSgt=C*Y4c z-@Fea054FVZ{VsHJ>t!20kKD)e_CAY4zi{h0s78anDL?TE1|jrM=YX#lD~3G9AgM{ z9k0VjbrslLjBb{iuUU*vqgNAyiOw7#m%yN6STvtQq;T@V!_W$bN%q~|2nZjfbEGab zc&?sxD|{G&&36~$gugb-?)&~RZg|yBcrx<&Dp$Y#&y^!-B@aWx^gnO{*H_l*?KMCj z939ME>D>a`7QO*3e@u1G;>Cy6r^rg3B~Fv_3(1c zM*Ev%eHh{J^W zCouFU<90&d9)g0hyPquH6x%G@&`f}fk~dwNcJ+uHClT<%j+kqsTZDJ~lbQx-?jd|; ziM(EzOihH+?_#4MsZt>2i?-JIPP$VKYk2?}=dSceJW=tN1^I_TtfldHh_!$Xm8Q%X#JpT&Vor(TBqh*H7yKr%9!cfPWv4A?j`eysFO72#aj-WU2a@la)<1| zxe7Pqdf9TqT{aZ?RrHjM)%DX%_^D+Ybf9ysu^#nY8x}@^;FYWlX*W_^}umZclwr#bMw>@DRq>zY{BiS9HzFd>seEq%P zAglCQs*}uT-P!C*vL&m1Wn53F@>rX))!fq@?@0maM@KGY=?;j>MLq@ zVpZ>_^-)sEtP4K;p4(Rw)(dt#SYhK?Nj>wq+WJDI&-4uFrJOcniP6HON3WOl36=z5 zO1^p4#84^j6fH{|N)MBw8eau0DsdygSY|a1vnHAdsic@K!XGqY^p^HpR>rj#(VVw# zp~rGWTcyEtzqw`kS5h?KfJYC4UrUR9EcAObo>rCp9U z-R@x%14ypDT_dMPucX~NhGo1x*!u5NrvGq4%=3ZQ9+i%IyO{imxbgzVm1;Eo?xtH1 zH01637V_3zq1W@6-g)KK1G28#lEj<=Omchtjv zb&x8Oe=C4`E$!e+pcCf*2L=Fv6s+;;Po}nRvq~EhF*b(?#lZNej|E38cD?%-`zbY| zECxnpcWUC<(=duDuud7Gwg;u$W<@-|Mfu|Fnng_!a3wLjpTZ zKJ6g)b6PJyo^`5j$&_a2*h7FL_z3nM7KW2N3hcaGZ%psI3=5z&qMKLQWcj{l&LWUL zuAA+;`&5luq*_*x`mZR0L&W{UzCKCb<|#bmg-;i8!7*#Uezwh1+d+1F6W@S=PlAPX zn>6)*qAj1no;J0lQtCB^hbLs4I8ZtWG1X=FXNo!s4kg}o)DVw(j^fEUeq^CR6)k1@ z6>J#7(|SDb|K&O_h-1HTCKZvl%;91$7#MzE1`K3HZbn{KwmJDB=w1 zS5xj&J)niIp)?_$IJv3A)Sp~<$nOInKe$D=_jm9fW4v9hoP~H)>CW@w*%%A48s(4= z6L!636;y6MwnyeCbEti++#(Nnf*6R7UOEBY+0~4l=3hR4{N-R&Ay%8p`;V|87eJK( zv_z{&#fJVu!5(Avs)Pa3htN&Cc3?Kb^VH+=$!`rd{}D`l`?XYxRT((ID+4X2YkvWB zr4Th{%S@V=_?eEiZ1IX;wVa0)m@!pz@VmYwp0)DV!-4oGE^|_yZ@znfjBVE-1)YlY zdRykyGe|h@WXeC;oAcRiU;W$6n8%wR%j+n?x|Q8q=V_Nx)ZpU1{Vh2k*==A`V)+DM zkI@=+`A5faIDqN2)fo*-dz@)$K|c1B&M`WEH~!Xa&WK5$_FE;FaGj42UNqJn;I(4b z+ca15F~FIb?^ac3<1TIuz_P=BI5vZ2i@HzmM8hRBJ3Zbleo)i{wMq0&n28q9w2ASG(tO(vg`@dyPCgj}KT#L~X z5C4Meho_%%I+KCPAdIvT=`m@Lt?tw8d0aeX7ltP6sHlsP7@N2a^aAgg^Tb{N0hN&K zZs%H4z3=`cGT`HE_V}+;gRL$6iv{0W#;-EEiIoF_i#`wu)qBMr@AO+C5)#CXq679$ zI(Dsd*Tq^QjwfR88S18wFD91n#I@U&3}+9$uNckovF8Dl^sWmW4fHTGO*}z63-QlI z&)Lh=s<0L^qvg)Cy_N^ z$gdzNo!gm^ZWlFov#<4hkFw=FNCvpDy}<*EK82EB9qR?dXHkl{nf6r*EjR<)>e^Y) z`hI;qq8e>hEndpNjjup@VMc-JJG)8DT`o_|VxAeUxx@}+Gl-N)bx|34u)mfbpU3+E zJ=EU5^-5qjeUb3vD#1m&ao++B_83OJxN;AFMt$mXX@k+dk?r>bcQBO(5P%A?4B#t5 z3kh9AFk80mz>%X9-_-B7(PO9!+o-ZG?Azp+Nvxh#{o?Bj|5l5bM7uslh!=Uapqo+m zTkqK8bcVfotaRlMgZ9n0@rM%icYIrZO`xoOyW4vPB1XaP<7Oz8WX7XuG%lg3QWW0dyMZnj>c58kf#MB*f|1Xid zljara6@7Cdz2;@7KgR$Cxplac=Zts2uITN4gS4S^_^qW|kxbH^I9>i8-8PR`;MXOY zB|&@u{v2DTwCXdOBd20^*Fdo(uEf6-H&PL_e}GTjO%nvrcQ7=K%US=5lCmfJgg7E zs76gO?#A)NFeg8>52tUZbU6!@;MJdq{k8Ad%9SVdsZzjYl#Kb;2iEv2t4-j0wuxIgfQJMCd#1Hyi!@D9 zUNx%lk3Lq86-5(oL`~}_?k%ii0F)XGZIF9e82IhQ8`u=Xt6lzF1HaZQRq@R!^q#&% zEki`(r0;igk*S#9k4N)S-3+zLS)3;W+tKap`i^ifkikr;|F@kjlz-d6Z@V!*fN_X0 zMHKH8n+%A#D78Kw|NUlbV7qcAv-ij%+-&U8&I>q>z3MZ7XzIRnh7Gr?YyrEIP%P;D zKY}l8b&pQhMN}b3hacg{4_$HKM>9uo3=ftBg5{HOpG?`$mkj8h$k?ryh0gizb|bYR zXIhw!D=1&sYS|Su`#9%kArowKaE9;Qn>+h0D6^~*wT3-z3oow=qxw8Yu`wa6T@GLb zkuk9G{|GWcNSdIRRX(+KK|(zHjxxF)yb@{i2*G3hdqwz0#1&u&ga&w9w$cpS0Cq+VZ%0`i~!shB1q z3Vq5e{cdp-ux}-Y9~ND=s)DbJ?O@Z>c1Goxj)$@pRiBJ(ja=5Tx&%Jyjzo;G$3c2;et2vuizD7q|&}#JG6!0%wuEI zh$;YqQ%dj-KsJtiE8hTS1}>&4uQcP8UXpLZR(lGqV@F{_LTG*AHz`>f)`73{$~e~O zMsZqJt-6ju)gE&@0ng0Q^|=!6bxo$-%EyNQ_kiJhg&2>Z-y&AsjK1HhEIeZoQ=W$1 z%={`A;{OpW$undHc)Tj}0xM@!0`4$V60FdjMP3SNBgsm#^-v#nuXp*fgS71H0#`C_ z=e|{2$of?nJEy_iPO=VM{&`~Q-;(F(X2qGdGxB&3G?3A(k{H``2rgs&;=w)9m6E347v%uFqkawSxVTl8{*xG*?d(wBKS5? zwk4c^0+@II<(|17(3cOm%vAv1XOUe6c!fj-z+G=RLUC6<6c6r5IOLTnl=Z{{wCqnl zaVmyD&ch`bUKaw6kJeqJm!E#%BnCU7R(NsiPr6$e+Q)Ny?9-< z#;AVZud5Xo60fioU!I7o>0fJYcMshK8kDERB*H2AO5k3i_@3QzPT96SX&bFKBKa|) z^di$i5^v-T8GNfOcN!ri&sEzZ$oFC@_;iK5vp{Fk?Ury1@Cc8A*L)0gpGU&592Qx; zS$*oviQbN8^@Q#3@a4ng+lMaiv@tLg-xi+uTU?CNYEsdJ&bau8D{*r^+JX7_YVrMc zQ%56!@B^UZ^GZ41k>`7cRPp%|ur6)>HC8FS)-n z1z^=yNU6|vlG6v&f6a$r1s9WJvrPd%c{`t>fJ9!p zTVJA8Z$0K~lU9h{_caN?+4DFE1(?S?UU*ucvYQ6mgz}q+yUe{uT0b7!1KS{1L+Ymn z9B4MaY5Enb-W0@Igd$Q=P;VR1{y0~O+F(kD{c&TJBLq(p8u*9C z5-9`}r28s%H6J~f79Pvly}O8rZN*gF1ItoNI(D_~S)4ioA@&zRF3!dxd+)Fg;?f%2 z><;b2JI_oZAN7pk6$CSTtSH3(I)XEfC6Qcm71*D{q0eH7&j}eUMU&)JAL`yx4em3d z4f_(MJ|~c#Il}^y2&`BA+8Cg3zX;pbg={w;ha~Uj0l3ZRxHHC(DLqspREXJx^JU52 zhk<;wtp6UKQmh!$ZX0`oXL1-}U!e#tg{=N6isXOgSYhxTTwH+j4!rC{Y){w}^(3OS ztkrwDI`Xf9jTm56RRRnJ*!h4=XT;f{8w1dk=m35e?KS%O>p{1dBh8F&E#1q8fW>72 zjxPmKNKk7#16}a5sVoh0%&qHNVV0%80(9t--uGbYw#~o5_FDmMk(7kmtx!|(_FTIEP%fotJzIPdU>UX+PmV8>9Pu$& z#oU4&@&gdg13U;-RtxBTy@og-cMAqy*yPDa0F;l5hMicPzp3y63#d-cOc;KF_43(# z6A?D9gRZJ;?oltbOv|0gD~{86DzfEm)iP#1s18=vaN zI_UVEen9P^6=ZuJFTwX@>ql3RU3s>{^nV1)TQux%-5PFl?0;qn;1Z(Kx2K=DE@hUR z1>)@6#q&bVSble9$3@2@XP%zh8%q%>6d& zBDp3*2@`S|nR35w?w86Xw=AJ0axb@Gl_Hm9n0 zCv9paMG1kKV&SWb4nmPtupK9(`XvD|^wZGc*mIxde2~@VPPBHYL))H50yH=$P(Vff zj#K3D%eRECX%||Ny12TVx?7dAdlfm2*#Ox4RhQg@!1lf~`%uBdH_kgeL9scAX?1~N zla`v747wY{uNoaTnk{!bdb+)%J#zG|qMS0%tnWKd8fAPNfxLpulAu6}r=REp?hj^7 z0b1gN`Hz9f)N#N7qtsQ$b2AzASIT2u1o?|-4YgBcPYN;TaLyw+l{^)NIg!@@>d92C z-w#SVugc_oD6*;W%vX+SP3`N)LQqn3ot$teqdK{P8O)XGJ*EO@FTCrnNre2XJ=p_ z+#WdO;yaSNm08yP+|q7> zg=Uiu;8Bhr$6hY7GD+;3N8_@>O?JuYj9-K5WZtI7KnY^lZ59D}r= zP8xax4ZC+|hHjW~*n+-6JtgJlxuSjb%qONqEX6bE$>lBuux-xK14rL7+f;QUK$hVs z$CP_#R$>rjka))>fl_pU-|$ZyVH7leYibM4AZ&&wEf^!HiJB?LPNH2=6uR0GGMFr} z`LP|l13!h3yRtY41R(L$+#rA8{Zg|HBx?Bb?xWVf^M@8~g(F)wf71STM;n~!x=mTa zF1~!JSrufhjLVk6Tg+f?rHXG$vQsJSgljoNEz5G&Y;CX~+l~^oLl>Y%nF2n#CG&PC zEf6FZ!nTu}yRn;+z<>1{MVt<{RKWJjHu+{8Yy-Em>~2nZEe>H{MEpUKNhaEWKZY5$ zHZ?7-r(@J_Smp<)I3Z}M8#75h0ziKBO~(*}>{@puu{-GtVzU)pNQ7Nm1dsh|&N*`R!dbo2*w zu|@4qz%B+K51s(O^uU>}Nsl)7UAVOGw$laV-m(SkkUu4NsAtPRFY#CEJ6v>0&T1+s zxyR}2q$CF3ZWCy(7x=H)=(UMIylYZ@ME?VINOIzP!3BvG%IfI+hGv9ufqx1!s9>=p z%RXZ>ZgH8P>GEHqf8kyd?Pqbj;%qZ{-D{fHG5+O^THJyyT|mi- zL*tqQ!pt|<0b|PboaDx14>Vul`ngkobE?`1St*S(jSC<{U-fGiyH=ly=oWa@!PHz+ zf~dd;{j=ipXvj;sa{=MiT`%iu2t*(lSnB<>rQvSLJAmsrV`d3OJBpNj@9jzP)Bz>& ze=d*iu00(UYn1v)dh#A2A&IoF?o69-t z4!8W|)sT>6oZHXMMi%_o8{ z&XVe<6!KokR=-CjWNq67BCTb8RKQ$q913^^0rh_+!bk~-xew*L<8t>Tc8qNB{tFW@ z+?qcN98&;*SmN97_3U6*igwW^cep1nQwwb*k5pg1Bh+X6R^H*#wZWU{j1AqUI>H+F{4ZnFdydQ+|t!f0NqtR9!qbX{nq6fBa3# z2}vu}EW3ap!T~Qr*KM{a%s}&-P|8y}x&Yk3ya*JfJaixfo*~GGL}L345^tL_t^+Qu zLGC^964P__pIiq{Px$!(c`PhgC?AeacnBZKZ$6wAB%B##h}+vl`?rGjLhWSj;s;W^ zUZ_m`kL6nxv6xi~6^uv-u@4No&eA)wYD+$dRSv?;n3A78*hQ*)+LCxrbgU(Y zrmi=EdR#C8j59&N2>TK`^K~ABn@Zp(y{y?hP>gQjrx&md9d@VYS!xs=Xwiz>9Uua# z$(6jGpD(A>uaMLp5I^F)tt{N6i_WKX5wqF8!|NRR*=acy+P!K6EkARjC)?FY3 zYHjwii|n9eJd^&%awoJ~{+=z7=RRFQQ1YQ;9I`yrTvn~sZa30N{9h#sJk3x!h^;BH z&~-fM5Q38u|HE>~F><_ho}bUz-P?hFvCe6^#EmleS|R^02r+!fV~5-WeuPx z-^vG)Im%;80J5ghbGVoa{5fm|tt;t&Fpgn-j_pdUhs~M+QTP3x;aFOFKt6xa&_eBl z=AWBep&e4c$TUT6>fohjpMC0^yn5x8G0G)hUOtN66~BX<&Ozp#CYAXX?^dL|?N-(y z@wU&4SqX@Ny_-8r%4U2{)#8&BYVMh?&ohrP38z9Wj9*Y2VZVTsOpkxZG_Y{QS;mYq zESdnXi^)suZqpiMENy|SW$;)b9lTs+QCaT<1nPuS`yO;_*%8~rGyj>HXf_%z(pMsw zz|--BKO9f$8gAMjB=L>Nm@g7GUF68;Go1c=p>UCzG_W9_!EQQ z+e2Ah%D4%p5cs_F@WOX+J0J&SmQT>KMw0qE4Vb@$AVZYfj~eHx)v|W@^v&FKL?&QP z>OKq&eyig825_<0h&Kqj9;wwpteXGWfZd*J^owkAoo)O(F5t8mqkuyeDg#>MUyeYU zeg{;#{SCBI*UD_bPspy?09g?QV{q8gVGN17UrgTrv1s;0sPAt6O16Wv-c&1>D|EZM z=gUnj>n8D}lw4FZq$X?aBd%Rix#qhSGjH)8i;JSTHbolQ$&a1cxfRvoKVlBV2gx|^ z$it=>yiaG24inuCS&$^MOg-a%(0I=5x=TILik$zh;EHHe$m@2CojJS;YBfbtb-w;ltzvM=XXbY| zAHzCY_^qs;@&`1Kng@>;3m@Yielsu0fX5&*V8oo$75Q3>YXUm2)VIb-keI2H(gPVj zuF5&t2Aa-d=<4psg6on0N`NqRLS99(nn`?&F}r9%2fNN%Ocl{jH!wJ1RFAFA+Inu4 z{uKY}z)weoizkdhTnTH2-;cIC*UuvA_>^S&_ELmqbZu>q2cBIc3&l|A2o~#DjTCTwR z=|q;X_>^@}#ph@Uk4MPnHE|5)G;?w}OPnkWj=wR58T0oS{pjV7eqIZK>s z@t{MoD=vwIZ-Q_se`2hPDM<~NL1Z}T%m#E0@-W<+?1>rV6Sr8u?}pIbJ6}cHEm)Va z0;bsUC`D>8_x9woD!|anHDwiCBmhn4EC`YYah|XaU=#T<;_Y<|2>f zgSdsyn0Fv_CV^E<*FCEO@~Y1vQf*tN=g6^E1bg|?ECwDgJd0KVgpE4frCJ+ z>@OYiZD2S}irF~s8_sims&gI0F6&&V5t$t7Li!c;s7)6JS#E@LH(YJ~{LdGqK;iV3 z-8M&I`t^b?zj{88#P%y3)s>1bhh6pku~tN%2GG$d2l7a7>RCAEjIN`08Noh}oRq(9 z8lFnY=oIF2yWV~#{5e>nTj7G(55n4aT=7w{UNz!L7Sd%0#sG8@pH}5k>IVMu@<>6-m6%qPFt9iH^a6~yIW344HZNj|yC+Av!I%u8Z|jGAp%?qdtx#Y~dF+S9XuaC0 z28OBRCTC?IZD>9JEMEHI1plsxTFXHl^*N2ARcrCJ&80w*!n^s~R&+s$1SAUKjNjFS zslGR<&}^<3$dnmJ>HISffHn?#*L*HMzLH`&H~gK9g=z4r$;$R`@7kPe^xkmGF1(|F zRWr0mUvo-0t(csRvpSwI6M1rX~3#R#AoR`}2K{+TAA`u{L& zHw=oXemGIf(t;|BdN`KA1Ko`nqTG#pvDtX?>VmP2`n!c*}Xi+qzzEO<%41@T{WeT7^ztbXrXmz2t&6 zg0)Np?|@TVgWuqzh?Pn<)xXmFZ3Cv3YOg4KrI)cDg9PHoMtonPM%RZ{HzY@_<+9~d z!7DiZr3+%r+m_s{tiGp9AUTbA9tZm#SWSc6G zx#hf7SgjQJWUB-*BWQzx_Yllx#H?#rmL45D3b3Zk`~P_U3H>a61Y-ER+KjCE9Z}cj zXt4q;lWXX3<&FP7FGdJ~F8gUd#^ZjMlc2O`PWp=aFB<80(tF{_-609`#)ya^M>vsk7|VoJk-7d zP}YML%87(_wq3hj81%95V`;tht~Ej=IC9TZJX}I0l|`sjQ#{1rSvZf{N#BExQeE$L zz1Fr;&9moT_{x&5UE2lKhR41kZlCQQWzVkoOh!`BT=SA^23bMorF}zoyw@UfEp#Ry zQ{!$Sb0MPQyU3CeL4M^FN!fvtJ%4roYWX6%WmBD(DG1GXNn&RUDZr{4AStAO^?UPY z(X$_9pE+e#sQl2rTap6;PkMzTBb!3*^>v-#Q3B^XI!gQ{i}@X(KQKXNkb=bQ?~`7& zs>WNJj6az!_eSIyv$#d62&2Rx80cPfWr?Rg?s6_!$KDxyd7N*cZm*OjCp=0x)`I;$8Z+=oo;`E)-RD zWvL&!{V4P5&9X10yYwKdHYGdauVs{#(dr){yIrMYZtZ4Rew}* zSh=j{5PL~jdP8tCj^peSBd=Lp^AAO0U#x*8gRHiC2&t{0-m7#t7GG`oWUIt5esNUH zyre76Z>iNY$V3Y-reNWxj_;0d0h9_r5dQ%B!7H=tkG5bd^QJ&KyKiC!UQ@{Up0ww7 z#kPns&3a~s*n2F~P;`zt7a|!ISDkSO5?g!k=Ufr(pYN7-Rby|(T(_DA?Zdl~=(1dp6<0|QUCN%=EU-p+TrCeZ= z-rg8lEg(5vZ!Cg`o&5Gjh~dC5rFp~N3G%U39|1ktX1sP2bW4QYl$`5TQlq*VV^Bo4 zJQ=2tsi*m}q({$^g-uK-R4=pGOZ5@6~*$i;rtbyoj?@|+VRL^ zJxy9~vW$m(&wkrM`F_^WL)A7`k)KVdcs+9br^v;hN&I=S=uB75*K0yPmr4Od^5k_-r-oo{CB?eoYsS0{ zR&JH;km!A%ZO_CSLtcvH=m^0WB@Kbrmcx`TuH$I4rt6@gJ{BoGaE>H+K{tCG5`@)B$9@Y^8W=W_36)Yz zp)fnOEo&$yH~bN#3Oz~|Ze=*Q$n$i>aLM(@0p7_K$$y|C3$>yCHgc)bNQB{){n)t= zW->aCr*&cjJGuHRn;nGXujcLDY<#XH2Z|}cLuOBN|CAUBDa$XD4gwQGSa$9z7D~C> zl63cS&;3#1ybh1PB3nf9N;)&{l*>x>ige2q?zttCtsSPkGkBYHFTAlG7Gb4bLw2CT zlsqMlg3t5BLDzRq?L`A!`pq^{D(Np!ZE*RgU4~!pKIHzDu8TTXw^Ona)+jpV9}ymf zFEJ-L@jKy&Yg+$(psvR1?HJ`FcQDc`>29!hfb_3WjgH&z?OA)&W{kXMMMYKvov(7J z&DE3jPg2r;8ahQ0pFb~uH>@Y$7|(ZsJ#E02;|P(N4=ZBSNVWLhrVPH3*Qwe?%>0Dy z+VBh1Tez~{nz=5HFqC2x54y>7!Z@xtPiIZ0TAuN3uNRxK}z{oqG6zkxb5R>6WL znNSj8n7X!NwDQ3|{iKeco0p29%p*#aX)i)o$KeN-{dt%C~==GG~MWX;_&EgJDse@^|4RX(~v1uu=!r} zxpJZ2kH}^fG4Nn-6BG4QV#7*;z%kAohY`N`iHvur^D*6>C1ZB=drt*WNi{9=Up%+( zmYGf(^g(a`c*LJllodzqkyvR8TLq8<4~R8Ru0RVSmfEOL-(71HYND36`_Qpz=jLdtbm(TbRQB<>kobGp zI9^``zY!U~#OyPYV?nRRVyY($IG#@&sfWLcI_)D!8}7$`+LV0u+qe2}RK`9+_tU4( zxbx>M#cVXdMSEo;n5r?I@!K|X1@9CsOh?RBx2~2phOXBBs9rCqO1+9ySZ>yzTjJ)NV&kKIXg!m6zI44gA+1x4-nXmi1w? zUc%rF?bA{}iilkzX`B7ufBx?6EEBtTNLwI6|NP=;^G__8$UEXBaRzL<` z|CJzrL22}IlzIs6`H2SB76BVMli*T2nP_c=_7|tkbI4-E)QMaZ1G=#Ku zJ}dDN+-nEJ&gYRH8N)F$P*H|M2}jJ-=pSS1l`pv7=JV84yL4NU5F?f{16je%On49fiOhj4Q@nL}o}I~uo`~e-&hTpiPHoZ)0(VDo8)cAn4|x3f z7sdvbSIMZGwkdDeaP$n~DSdeS9nUGVg4AG5=@wW=rxs!GYMzuhS?LP3+>q8B$euC^ z8iB5?m7mQv!$~~crq-;8yk5_#aA-jM_sMIL@&Zl!0? zQ^};N;3R1FE=A{?1sr?H_!H>(i&VB9{ff*NaytGz=73{AV71Zq$LJ(ZP`Z{WoBH@d zN)Df7i`=5XIhE7gI*%NO%$bwQwT|yyeXdu_V0T7PPcaL<6)Q?6&-N)7gx}UZMt9#H zKZbAnM|N;#oCMYiP+MxB>SNFUhj<0E&5(59EmgbY`dGBUV7? zsaj5*<(oQm`58YK-HtrWl#EcQ6}pm-#4&&UMV`yXJ={)tz$ylOzCi$fsxi)b3vGdJ zfZjv!YdLA_;qjo6NT&R!@3_ZbBwa7psy8c=0n~m{h~VRDrlhE= zb&iTg8s?R3qx|5yC(h)-+!u133KVz$6dr-wYiTVgfXdCb0QfT^wok=w!rN&mrLq@S z5|0Z>^yhK9HYEE8K3>KQ0ls`HG1MLT#$7fjVSKOr<}FyU)M)ByeG$Z2Aqua6p1cpl z2LIw;xgN&*zU5WM)NK~NYrZTroT{*hz9D{)q9-;yR!B`6+K)C~EF_HVTQtxW-Wk91 zQ0oOYu#D-zuk*wAWID^IhR2Wme5mg{*QW9k-{TZCg}=i`jMa5f7E-KcG;k$~fIrWr z6@{RXB{V)Yyz~oS z)+L>Bd4v(R+03c_B;vGsM?k2YNUc6y7|D~DP{+t*Hi!U_i=1{dR2dJtCyhKH#X4OB z*3g~KEyK%?MBLK0e#vm4*ik~@;mVgARbS!&(()zF?pi22hEN5+#tzVP2?_OM(AY71 zyP3n5`D%w?|JSS4b~gYv!jPa2WUdct-jV$VsDl>Jn|154XCG zWP9tkT~p)d^_Ld-g#wPi;%Y;9UvB&pDHYv>zN*o%6mSl*{!DH8*a$^O)m!qzNn7@p z=we|;8xKW7M8O=tS#i0mv6@W}H2jj4lA)6Hq-zQTz2>Km&h+Dl5JGAY79YCW%ec|0 zGS6l#aJ&^4zStc)2&^fPGSu%qZT0(nxg=I`#i@Z-JIVFYNwN`)9RzmOjL4=_tSP+I zBIsp0@xnJKGu&1y%FEAX+0XRBWK<1pI9T8mWvws)TiSH(sQQO*OLeF((mDqa$6Rc( zzwqXkKF>lS?CoXP%K$-SbFlTshki+AXp`vVNj;ru!!LftlGn&)xM=J={@H%`r)#rD zVq(9xQ8Ux1i4mK~#w;BBy{ZT6lw8W+D_8;T)7Os?Gmxma;h5T+4@v<0T__j3HTiBm zukkPD8BrG_XCje6LgEoXL3n~U!mE7t%XJd1+f8-IKlJ8&5IY*NSuf}UdF97S9k}7Z ze=v1RI9Hf|^RH%&yG*F;!tL5qB>tw!F%_uG2T~N~_gaB!F1jJuc|&>3J0+^3#oDd{ zkFEfPKBU+t9+1}H4{Ab@)%F(h%*n?V__-Hkn0m--8P*4?Ii8FN{e+Bkd6Jq;1PMLs zPaycV6X`sA5*5vl;2NrYvtQ-`)s~GrDDA5a2uCDKM5^CXc^T)#?+>o2NDylG*VHo zJJO|FoX%5+M_Qj5J3YxAL3p<~f9=nSV2&8IRk^}^!82DNkSA< z+Bh+l+Ui2Ey8@|l8j-OHCwey;7_ z%85fc4eYq#*2-<83K|L7jC_VWuf%FE2^LOGypji|{T6V}{D!$a=XEW>9f`?IgP=H8 zD5g*zL6g&D^XqPGloihLL>TQ0)A%eAw&_-MAinh(+i#to==<4`dBKrc0q_@93rf%?g556vI221Tp@$v4&G|F>Ck35fq*9?u?D{1uIlcEKaZZiqhR*gRZ%RffQkwA z>Tg5od``E5woqO1&SCiZ+hX9K0qgHxPa2UAxaCzo|3tTlvSvyB2$Z+x0K{@EczW<86I2b zTLvasUzU6lWQBo(QreT8&jwXkS6Gr&&>E`uf(+#;Exs4;B0efFiVIq`rZjo|kL51n zb95CorN3~7kobZs;QQW0D+v?@n{gi}*rIhujr-K`EfevOt)_dWnpfrd_GvBb=- z`K#8U(eTr=K@12^{HlASI!e8XxGH~G{X!iDh`YRL*a3Kx>txG=CQFd_hz+Fs_E?b4 z_|*LcUZ%>Pe?g`eQ-oMWXe5PP}^70mM$gOKlP!3)D`F(ZY z0m^eXR}DwQqv;isSY_4%bN+`gYP3TM#=fk39i|Rk2iY3q6E)3wPfN^jxL4_brY#(o z$2_qY3(a#;5#Nf2`!9Pn?puM%3i6;5UnMDi9y$}|9t^Z`wmLu3pcv_Rzn9bY-u?~l z%QXq$p=ArBTQ4X%OnhkWhk$KYjWyDJu4ybMw>UNHJ_Gq%{NP=9@rGlRvyIb^O1n|i zEd4&530xL0r>XiYfZ}NT0craIbcbYa^2W;E1P024Rp2O>AJLm_cw^jnoUho4oh=cN zyRUmM<6h1XE$e?Q(TB}WAd)@%-=V62|FJ+JBK8@JAl7Z)*UU#udXoqJETV#|XNwKC z^`RPfK|Yc4wE;Q_IhRDR6Yecve@8avlJE@mLL!XqT0$BfPa&$`@>{Dy`eSbX+Av91 zxX?0#+O9a$SskEgp_>Fy2U7bMu0Mxc$VH^(Scsu~0}t6F(;;w$>`5iXIoXP?7ls4? zj8P%^{qp-l(K~J{LH)({hr8snEA*zn9&r0Gea))4#;3(v{vo#HrC)$4gek6gPq5E$ zKjubVGG%)0?TjoeIzg7CFoqfT{-Af5Qk=b6?2}xK%GKkn5j03*zFbVBPkklKqMTzD z)MJT~)aL*$=Hwk^_!Iu5LIX>2AL>to?P}67sb-vQS;e6)VRSH$@vfq958>6vcBI@< zOX9nyEh2CfWDJM2NDdOE@CoGFYWoiEnyI6YNPoKLWUd4ha3q%Rnw$I@BtCq*n3TFT zWrK2-m5(fO9B@q3k~hyWAm5n(J>>z7!hz(tikz(qE19-}t=qPvsjK@&d(2H+#WQmX z?^3k{xLctbk;Ju^1^z{UNjWE-x7p>N>o6R&M4UZx7NYKB`?yZvH8%UaE61JPD)t=djEE zRFb?Np}mUM%X(No(3~Ey>kSl{sz1CAs?&%o6}EosCH|ws{ay!+QKi3es(&bxrj`pB zZin0c2k>vLTLl-T-=Kc39yYhrirQN|xUMJe-g)lwq?p#%@yh*flHel!mOFvj#Q ze6wbLXzuRD^Em*kWZE8DvzJ0DU%jRnKLf^hH#)~h4^AZpIwz#~Rx#koUD;8v@46Yx zE41f+)xW>P%_;po$8BYwChA~2K~-E3yHu#9O2NLVg@TLj7?{G~cFyiI^`3hVmHUXJ7H|{Evlz+OyraSDSttiyo_US8$^Jb@5oQwLkB0 z>sdy)P^9zstLk$1179>}qf>e`o>0X1bYDYX&fJ{(H1WE5kbz5M?_3aEHe?D}ESt2U zTiu}8rTeHLpIuwJ)i~69?>|{PZt7saGTBdi5B|j{rx2K$o%T~0au!T@9%7|=JoEqm z(m>9=)PV5OW8r0rP-w^^^x@)<3JaKulkXEj`dwv JiIM-!{vXhTghl`W literal 146971 zcmbq)i9cIi*LIX@(JEr5gP9m(te9#j6=_rit)ZqOhERl-8rq_jR%j8TgqmWeh^Q)V zL#bKBoT?58inhiY`{jP#=l#Av;5+B+v-dB*y?1i0v(MUVt!w4))ZZ@v9;mscIe>)) z0Jz3H0e`;&lCIwO^#%a0Uk88y0KjR$Nftf;E3?JIJOM1CfRq1e0{~DKvH#mfu*m=XVd5I`!z%OA~>7ghs8jhniq}2-P%I^YQ7gwpV04 zg|vR9g%;c}tBdy%PX5(+dI2|_V)ip$&~K@fMnkPgY#byY$yL8N?yQ;6HJ_} zCsSyRui2sd5FQOyLRt2Zs(5h`9w5iko&SrF_P-Fneg7}WjsJ_14lo6rU?N~Xsgoz!*#A9F zpFVZ!^y#x4oXqnq@C^4E<^|;C=RL)7e6lrl92&vscT)m>im~E3CsWY zBr||Btp5=oFLQDIk=0(gModW4mB-Zs!4OAT4jP}?8ey%79?)sF00vi}AJS3FBMR6@ zux>}WD?qiXTib(Yf6})pyjMr6K9Mit^BkcEZ6gL0o+d?Z$2H-x^LIBVhfdKp+cFOS zEXsYn4#t5Se3Rs<3T7bn0#6q5KO|4VEbJ(*mbbvY5QVNyFRDNkza|DW$V3MeoFOLJ zy(Yd>ij%YI<%Ot^1M`s`#6lRDT0n;Lg^a|>hAW8LJ~l%Sy^^}4oy6At(2h5e&>K!5 zg!0xUVyTG)37dK-PGtr`BCl0|nD?#;M{f-X3sqszozwa<6tlrEV>y4?gxW3X>4}=* zq@FC1Gw~K`&$v{LnOw zyyPJO@SRR3WEA>ViSbhUQA9^iAQ^O$FH27q;GkO~;591a+(cRjkDB6icBB7V~GO6^0 z=F&10NA4WOo7w=?5(!n5$Hj^hHaxA3%rara4yW8Qhfhkdw-Wh{(;V>mfj(YMINdBr zm>G;EO~FYfIN29g!j=vTmG({IlMJyHGxziXlf;*DzAEGtLp%y$Vlai((qR}(59ASs z)~JA|nG|>w4e?vanMFt37ZkOc6lTu%z)HXSS5hsVKRebnX4t$C;wVTjGUQo1k;LTgrwHXm-UF zcoc-Q!n>BHZ1j*zj}#z$w4wyk^+NUl<4+%N0*M}ezU%%KD&~=Ml@1_RQ+Px1^^*FL z-{V&qwNqT2jYmgpcSndWVNoUdp|RlkjV8~-=59I^N8y(Z$C#wOAdHe>mU;mlK0XlH zvkE!puoY~AsRC%MG||;o8RhCPAE)a766Naj($~Q$715%J&vz6k<6Cg@qk_)^*?S3t zwm^}jENkt%kL9v8i*0b^J1%6P24{F8!7IIsOuVcvt659ppr!YqhzdmR(Ndz98A$Cd z77y{0^D2-8^YS)b`R17}1F{y6SGnBbc?Vbm6h(N+dKB9ju!qlT&xE-YM0ViCid46mE!lu_qLvxL9nS8GyU^-!FKmRutvi6!G`z&r6v6C`;XN_=);9 z6mOVuH2U_ShP16hSNIF4U%rcMjq5rN+6>t0>Hf9$rp}vZiSQ9MO2KydPFI0dLhQ=5 zyL*Xe3)~f<4IoFMiGF73Q^S7xA>TW*aC4GYFsAOeVW!ZRjtA2fvVgT5)C7g}g08M_ zr3q4JHoU~kUX*r0eOM^}EMr5m=ulY%3Vws5H+9CtosyX!tkR2wKk=3svVYu1ct!W{ zmXnl2G4eUB<)L2sRi_b`J3KsJdUzmkoPH8Ej+)sDic7j0@01mYwhEvXJlKH0Tsyst zb7Y8Ch?+Vp#>!#O_2cYq&3%`fby`NUS6{yS;NSGQIr9s@0wU&AHY%Om)u3mFS?xq8 zVn?`fsWY}`>r!!QQ0nQ9EbJ3PnlJ1X#?SF71dN<@;RQ;EbI7u-4L+!ewPFQ_Zn=QC zdZHt5xD><<4UaU9VA`x!Bi=!72W_gd=mp8~cjPOJd6`c+Vi0vm->~39@?1oP$u-zUMtTJ#^^&P9Sfj~(7-LmelY!%}3Z<(3nPeZAxP)sOE zbXWbXG+eO|N+n5=ixHpkl*wxv#hXCO?}O$nn0sn)%6fw}Anv?+YJ~L7 zc#`d)49J2PYM-n|p!U^joR|qNkP|b1)g=PCv-Xag3Lzov#`ud=NkqyMWY)+)cc=o= zTnd^<7%&KXz7z9fY{GHkx5waj#A^#ME%ea$%Hx!;!B`xgd>uYT1S_NK&jLA6(`cY1 zY(b$j-9rw}RC}V@Wpr~{X&ksv&lp54^AijS)u3m4j^B({sm_s&Nh|wf5@Z_rfI1P; z6y|K?WL;wc!OLwLi9kHT;6f?N3n`GIf;wz1XHBFEB9R8+RV(j@tubFtTct$TP-gAP z|5!Ef`K8|Ra*~{bV`|Hp3U6PWl7}oHspy$D^bEdGsm?yhOv0C8wqSmms76UDEwImo z$TAh%D5fJ38;6Dzgh%?A){(7flyG}O($K3J4|Tk`9|57;fvDX$4J&zaZCpGAQQGdM zK_U=OGe^|&6;%|cXW^D;V$x;IWd?~v*%OtD%IK&}4Iq0W!5FL-5Se$4u zWr`l4OsN7(&d7KP1DkK(+|<5^9uE-g*=bOh5!Kk3$g7EQhSOtQ=L-bH{lx-MGyy%- zGS<(-ePE59kYj@RM^w%PiC;yMYpOEHinSk&g&Hz!(waUR*OYkm2~{XxyNv@S=+Z=z zu@5C>Pm11ntUX<1o+K!7t;YZ;q5|t9jJ`w{CE~~^+QY;gn2hBK?@N6+6?Yp|`1C7p z89GRogh|8mD3}yVa1a()6Q1KScYdWySbpaP3S)6vV$xWe0I>L3G1m%2Ge8bqw_x(F zt6Y20PGmW~!jvSF)IH!CP-xb9&WYvJ%3=p)FjuzxRPEJz{Q~MFChSbO5@QgCu?pCKH(__PuGK|3Qx0sGS%5NF8k$-K zO;itiH--<#r{7ug;4xnvNyTrk6r24hm`GT%QzM~>$|lH{=CBS4OJm*=we)gz=L9+a z^yOX;?W;*eA3)PZLzd}T&!}%WvYydRMF<%XkDEg|3}sG_(oxu&^guwI*{TU2Ii#GY zXd_+5_Zw2?+Sg(|?`9Yf{JKB!rHQvAve?B!HoV=FIPxrg7}*5-38kv^C)z(n=PVHf zM@r4p8&;$PzE2E37ZW5rYd7MS$mgzC54w}7IS@Dn&B?2eFhN z$`+PR`JP@^FX|14I^JvfX(_nf3S!6Xl>s^ZJiXDBuo>xT4FI}mv}mYSP(4hSKVet> zqk4EPr{q19iG+)uvgPgSVu?s7A%k3^HvmPN6Ko1isANz#T??jgxf+}FSRmgV+wP&Q z3G)H-rE|jjp_m3>aT3#@NK+9|BaO;gWu^COK&}vy3emzhv@Ae4P_nL+<7M}?CLzgi zZ%->F_%j;sc`zA-w6=uP?FjT@A+SM62|5{RB|;#=iDx{*tHij_N(SeT9{hA;!DquZ zJ>)zG6LJ(CI4_i#mw91&Lki26Dh_iZUe9XmVQS)@58b586u6xilkaqlSf)zSJ<)1t zimbO91^3NL+ewqhpX|Ex8j3&?BdDe+w^-E1p?#2ZsbDKL9iwRV3m&pqc0Z_DzG z^T-k|uPo#K(d<6q>ERh**4=?k>MJ%|>A%@KW=~danlMhQeP%4wY@)!nG7iO#kCFTF zC?_)9<|Ua+7E$2o#gSBm>?$^cPlG%{&fq)Z*B*D7v4Y>^gPw3`(#CghU7MV8 zTNN5u$xgvWgK2T3y+|+9UrFzaFaZ{(#w$g!fEDJ&0t7u_7^r0yybOzz>GLV=>K@nZ z+JYj4#$jDQ;iKK&a-N(}9B%{q4H*1vUkm5eNY=1oFSsLw-c(;w+1KH|xqUWE-z`fk zAi5_Z)M|FabZd2{mn0PWgdU5NdM5#@wbIvkPq zu3!WB^65aU5K*I1i^JJR=9*(xLP>;L=|5&2Wgwg_5U|$ZJ95T%w43JSn@q()G_{E~ zdQ5FbWv;-o^j^6zm*(rGj=EdKx2v`T!ln=Ro+TA6R@|ip4x7oHhn)@woYZ)8iwMD2 zv8{$FE3SD}v8{RVo2`vKH+k3Km|Atq4@&k$dgFmvX+TaO`BHDrGol)sFLWA|G{K(} zhkgjPz^_9a>_XHpL zw<)X#05jV>ogfP=5!D_<7u=#k-hH>W#Nyu2IG3G^YD(dBKReD)@>({EutZ=*SD*&( z365o67(0{|92l%8H3@5|8&YmcFNcp0SKM7ZarJF2hO#i8b3XsxJ%J3Vd-JFVw?pT3 z72M+f9D2(s=~9O;NVwd&OJ3wFouZ|I>3(!~bOA}5zw0{;Z7ar0()8ATLKJ{#!ABJ6 z=9=xmG%bjhNJ`ojHQ=I??2?Gp zCW$RWc4vrA(9sr0`vg=!Q~v;!dX0I?nftQ>fqB`bD}dDP)5$PN^C|JHhd>~wX26N? zX)LY+)lIC2rdL?O2R4d^m{wC6(!-9d(2Kky!D$UDR?pQ1r*-gTB@4h%3{D&`vO6Aq zCY&IppwWaRMZ(h+F@3PD1dv>1xhn$QZalVqw%2f6QdPQF95Ho6Vc z&Xgu*%_+BzclFYhnx;v>&Merhqehyx%7-zEY?UUF)wx=-v69g$^s;FAHxEj_=@BR2 z7D0sP5kxMRFhlMZoMDB*Own4vS_;jQmPpmGp_E~1<}?+m1Z9S|#6+`Qt*AHSibCH2 zFr2SaX6w`S3k{nuB0UGMQv_$usyGf+dAw`bSJ`)bwiH3i{HJYc$Hno~8 zM*I$`fwtOQ=6)Yl30}eX<|IE24GByTpAV_I-c&hxTheI%F92GgBpl)?g@_172FT+s zNpQhrfFd}cNF_XRtriGY@wSBspfUnVZO^L}xZ6lC$e?)B_06F#`(DVD;8NAk#Y=#! zIlHm{K)uguh!%K&Z0k7)xsS`RD`2o*HNtKjI$B);Q?!C9nv%PpcwU9Ulu6(ca~;!r zL!Tr4!_AWollUjjU+`;xE)z0AXLHqZCI^34ho%nR=5RZhRwTy;kJ8HPrCoH32 zNxsc!QFtIlP5P@ITTv0QV721MjDa;saStKEd3YYt^^#t)cFddu=svf+=V<8WMrAOqglP}}?vcMlUb zFB7UB5``zjs|wsTm~oZQ`nf~LLv;L>>qN|MUm7jcNNdA4>r>817EBX%d4=Pw%!)J5 zF;A}`+0)BNnp-Ley9oic>TDQ1l_Wz)=d2ZwWKU9FK`DNtKXTqLGg|N6{0o@*@$7)} zwaJMTHp|zzg2#*6;g;dzzfW-Hg!YI`^^==d=J=n?(q)b_@!yY(Uj5!X+*ej?(_i3q z#^zs&TR1^`Yfe`*>wZG#@*M|?JfWLo{D`%b88Nds;A>kkdm=lr0nLXdvCxfO^jOJE z&2R1zW|Ee8%ecX_=w^Q+VkGri0`3*J+OnuS+nRs|ADWy7GWXct7Co|Ptmc?{a$(y* z@9u9DT;ET2`pubz@3(I-%D&AV5DhDa?ZW;7N}?TjlG`%&{sQ|iX)!r+^x7|S`2F_fOZz`_qDW!kTG@=K&vieB)(nS7-g%CcEJg0Eh99fu$s@g9 zVLE06c!{;Z0>{y;qya1PfVX;4B~{@D7+A}Lm&-!n&*3H2??2-yx_c(4+J?7o##_<+ z*^C=i6xuObT}YB6W#Ptz)T-Yhvz9c#s_?#%F1Tlb+gY-*9@FOfrJ@k1|JkYE(aE)9 z1I9l%U`C+e6JFRB{AOW=U(TOs*X+2(z$uHG`Y zO&ZJR-&Wu7y13AKKi_h568&P0tW7wDmc@~^iNb`J<};KLv$aRqnS$^qG`6)`k{qXI zJGpDjx0{Yazd-}@3&PC0B{A2~@I(>wO(_$zPt4^I>lf`{Ab$J^U^|$R*hELBh`_xNLL-- z*nJ=B-hNPNjB7hpFHM-1I3rEe1CrZG9$^{~OB#9FCS4Al+F@^K2emh}VRgI=<;(x= z=ovzyvz8b`G@tRkRTSl)KnP6uz%VrOR(U!;jJff=UehfjLdYhs6KQ~@(E>Sd3J$im zRILwps)&40Q}_9w$40SM5-m3O6;rn`>cE|?UOe%hks?o%1->hj^)5?pW9V|2KxgPS z$_r< zPTLzS`MjTTm=|t1d;hGz%ii1z1zg87s`NmyP3m(L8p5sYdX@G$q-43Zr0t=#fB#_$ z;NXJraOAN1g}JT2fXYqzC3!=K#c!S=q07@r-L^JhY33Gch(LCNWbfzTT{z(zz zkM)fMd+|(q_qSWPT2W6?j2nh@Rn*N<&Hx14f#UQMk&30z)Yft&KG0h`qztwULkkl+ zsLW^ogbuU~wFujEcG%gPGzz2otySffO$py4kC~?~zcqDJAKT8f(?6`_`C{mLMwg3} z^p*YRte3HlSDyz&UD6%OFU@|z>Yt6=gny#scy1UUl7cDS*qMLd&HREp!gOGXyZMMo z;Wa&T{9lwT4&!HVZPi6+5E<7J>Xge5ocyh{p`0!$yb6E?@Ec;*gIdnMju)P zdU&aID1Xa*PxT^g!(?y$oa-Vztc`T!klW+h7sYt2i|g#ws0Hh3mL;uM6;~FO*16w0 z5f0~Ib=1Ynrw5-lW^Fg|zHE!inZS7aViSPC!ZflEx~Qu0mOb{9mqfG#6#8yp0RGjc zcn&^{%0bbUDIHkcS(0cmrHs6H0sdq+xAoxoc{8Nh)w429krvgYl8s6lzU2^XmuYZc zoU*C^=loPoNYZ?!{Sqx~ z3oO?rtN|V6#L($$kOGYT%=P>mFnE_Q;jE~48lKB{iVT0@fx+ro7Q%}`g4Wtgs1@E6K?I>RC(289d*NQKJxLP_egJ}`-WV+8{?&t6JYaWjqwq3;NKdgLamWxa448jgZkrNSv-C&0|kbdto^ck3$Wv@Z4Wg zE{~Ppq>=Qgl#Us<^Q4m`O-+)OFVl1pB(jj#&XTOUbI@eu8V*b%!|j>6(#KqL00Tc| zp2}A`Uf`XkAqyKy@@0A|yj<6r+YojvE62>FmV=gMOSS@o9X(BuSQG`hvgK59RUl zbn#b~`k1x}x zMNka_s(>TqgxrD#iD*P2n#tvyM3YfOEiscUc)Lfq6wD1(shAh#BeVTe_rY%VmY{%O z48!yZ$!%!yW=Cdfr*C2YSz|%Tj``^aKB*s5gDT57{n*~bv2l)qUQk5tHYMG7=l}St zL1=Hxxpb$XWCqbraUe;r6f8YniMJbHC|R_eR3J=AI7OVvX-m)AP=#<>Get7g3K-&uCSW-BS79HqG%b;pe24s#x8Ut&J zHrL~}Qaq*uD_d)(3m685Z3Dx@<6?~CNbgVz(h^*(B~NH~=d^%Gn_ZNY;I!bmHlcEo zDu(yAxm0fZ2uX5J$0l|E1G-VwwLopiJfIF$-!x?JGe zQWtXYufux|IY$h-e>B?KXUl;5$EpZw;EDa=@7IC1Mmz$aB=qG_`a2bnRnsE(|FIl> z!++~Wf`WOMP1cuQ-HIrmPlwhEx<9_%V?@2Au%gqKvHUPA5o$T+3l(BXB-7!{jhiZb zp8SX{J8;&~(csjlz{@J@kENbwU!vTlf9&$iIOe=;t1z$nHK{g=WU-<$MEwh(bb~n7 z)y?jL94A}ygXWZG zNCaEbPP%=yFBl($v_wAuy{o9HTxGC~+8(wk3Oh#WW8jW*C@)FJH*NEFKer-XW4{Qs z`Kj2NXz^g2CA3;OvOKwEpq`0YOntbnQ!Nx0G<$`4pW>6M@fMC4ciV>p^3&Syf<-0oZjQq!5aGc0g@GH^FP_tB!!wuVoJm zec;}rIx4;#rv}r)hn)UQ>9E9Y?%{0oJp?#MCAwp62DZ4PTat=uTJmeNH@yX`MfR3C zE!8rN{|zl?w%NV7vpVi)vJ+Ot6~cLt^;Q3Emp9t+#IUrlshS5%=-v4Zc z)*D(#OlOz~CWS%;RqU=M8fspdFfw2oo(Z;O4_RATq9~|04USD(dWCq=`nVxKx`dbW z2guQG)D9Av4O4__6S`JmGBLX zE;Yj+iSiA(oB-K8L?!dFRR@N8@ht=-TE152FJ?)a{~ofYi1@&T$6ioeI~M!uJ}B$C z@%cQ(2@UcHXAh^+IJJ?m&GZ*kP$p}Fn~zt<#KWD7K@To04o44L8`w@vU>eW$N9w5m zVBmt5{zSw}retfcebWA|9tb?u-Ya@f{)6#5OKyz=$9I>{t*l&svZZ7Ht+!cnJtNKnav0#uOH)3W1TRiJJbbBqKFNiw=qn-%yD$>f@A38C6U8#b}~!)VQ0$K z&6fLRITFRIq2H5UC1;j@i%!`&SMvK6_d&v+pi8>J8}C{BvAmog5LMF7CzHZmRIf9_ zW1W^=?J)Si0J}*WE(;u^7<3UNp?FcnQ{+^4sVJ2p36%ftLivaW+XAsPVZ51NA2bm$ z+DbxBv zoBt%9`{-=4-z;gz&FjoM9ZQM8pOq7zwc>*^G@#FACeKfQd&S#1X*aC(fR!9psC%>~|xe?3c^>;`uk;D?qrG-wmW5!0Wgl zh=1dW7>g6t|3p3Q3VLbj>RDQ#8o?y#))I}=YU~sgrzeWarV$CKmEQ3}6C9s@SH7%fwli^!Oz%}j>gJz16T#$5;L6*PG8*E(z&a;pGyx34 zVQHe|ju}MizhZ}X6z4}*HqpkWgB!h2sh(&KYGL6Ou>fTz0%3lC(OG6u(0gU1RYR_Y`(7mx(>xhK3>ZH1 z>C5FCi!#64?rgT{Mv-KVq&IiyQY8K}P$ZK)87RJ*UNq2`AX0o{)o@~bqQ%F3#lYyb zR>YRx%+2xXfQc0UfCvVfxE|$Ow(qwll(^W!n)_pNsMLTfo8!YI1{rJA?<~Z!_x?2@ z33*elreIQR6a+U-WJ~FHzHFUSXQt48T5DImPyL=QCsUD|oFP6avm|Eu!NVnAlxUEo z($osiS{jSW*h`tH;5e?FYqEQY1}=&=)mbZQ+`YA&9zR@`WA^O@9xhY(;L_pT)kyKD z-Gd@GG++aDUCW?Gv{m`OL*ac3|NY(RVAy%BUom#45&G|Wv{DifGAWOMqPjxpbTl)` zrN^vGfFh@$=+ThxlHdnD5V2wwrsX?r%Q?Qal!PvxakC^A7J7-HZ&aN{*PfSM`LGpJ zqbl^pxorE*UK9IX;RK>HYiYFZ!qNoih`eI{s-!yFTl{6dxIAoZ!HV#?c<86fjoU={ zLIqBaTTEE-@yT|@8gg3A;Ms#f#SK+$?av4p+t)B7LGwu|x6Di>|1fH9jYV{xJ zkN2mIALiP1gTjaT1eOy5h^r^LaBfhcjF+jcUWjgZYD2NhDpTA;CP~Btr9wIHX9$6c zD$7hSWzfA*f+i^h$bzG$urG0Fx&F9lGzdreV&YPG_p>q^0?Tz z7tNv}!cebt6yk0UjYUmoE*ZG(o6SGxFIYR|Puy z4BIR6gam6kC2R&mpx^DG){WC|FSctHzB!@D(#(B}1B!UP5Md9O(VTy6IMZ}9rN|?G z`DKod1~?;uayS_ut@S+?{FH-ar1h1?SAx1h8G&wD36y$e^}PPo@)xjly9cpXIBa}T zmvuE_V&3P+=hI4#)Xr0%2-CM{H$%O=-<-Fy7p!WAM63jo>PNTU4*54O(rXeHn?L(A zz>FCu_09Jr4KQu-BT^=ikbTtc)Ju{wIPAQN*LJUKfpDH z-#;ntr*Rxx_7)0sVLobqDo;^xIXn4oIlI|iNzHCa?LN(J&8~KhVrfMg^DdJeMTEn| zh8d7thLUsY8?7cGyT`XcCkx$VWhAMuIq0S+rg1BWQvT_#W;ne33LM$1JHzSp>-U9Q zz?5OKo?NrlR7;DA<-=9MA<_AV0p{lIa@Patr`(*w4xMoX-d*$<)aba^$fOrOaMWU%k(D|9doOaluhbrNy*C>C;stXdLb* z`NgCBdO@gwW&K6Nu);UzRA5hji)S@F1%9luXIkqZL}4kN`|^3TkAda0i(r8QW4bAn zD&05IQe4Hwq-?m>y~-3j^BOEVe;?i4Cj_i59!q*#|NdwkW+ zuaBnHeZTC5!*u}=P#%r&JSw;5}o(Kzs(@&W@(BM%^vViql1+^Q`{ zH7j5HuO7w@IqFs4Re$Ren!J`<)wJsq_+##C_JLzF@`2D<`y+1A#aWl@D;LK04U1z9 zJco^J4TEQe1~yZ2oUG3=-u&9rU)8k`T_OyIV_6VcGq>2`8Mk;^cbWB$1pDo^YsNK= z9tt43rL&@Wf|qWh{YlXf`Fzx%hk!`@vPpGWk@0A+#53!koq@ordY5Euy0vJD=vVhaIf_`rE%%oqwqR+1oVag180w zPA%CKjYk&M*r%S2zVpoFfKbGtHC;_>8Q4Y(&gdz6 z67d=*<7Y>&R8+oqBiMjjd{Yc~XT{(OG#F z>NTLVB9^0@kMWpaV0=5)op`L$GlW+RKcfyEX^|=8TpIP3d8PS}m>CP?(pfSm#!a&W zSBP3;vU)W+NaIs4(zF#9f3JnM*(l9ULbhjmw38ddGkQx{PuFu@OB*%}yZL(u6Tez0 z9(4JQR-u!TRRi4ZIJq06v95mY)W^OW2|*DoYtKL)?C@yw)8P~PW{fhK!%+3ng< z+VSkW&;4nuZD9wI-9K+0x(*mC%-EPyVjFw2uWv^Gu zqn&w6pz37D!ndk&`HNR}^!@_msJ|qm#pimqj)U{}9kS=Gb8hXI#^RyPj91Ix(IT zoA;L>)~-w4;?q zn=4zppIf*ySLQt{FOShSmRw z5=#&N)7kzKFH#8#f1-Ya#Z9f$=ATVY6Wb4|-b@_`cU?^CPWOP0=d`ia*#}^mHnM zuj%GL&fbiWv}7IZPXBQx!9V#kZRp^(#L;%_xi=H<@n^qPy~t*4bYF4XTbC(+7c5Ux zUxwQ#Bn~K=pYYNs#;Y&zRGtWy>AUo1WHE%M>%{*}6~*^0%uRlJ3hLOPfUNNTz^CO? z?%D7_Zi{P)UaA9vueW~d876M;?qBc8#GGBfSFKwem$&rHp<(*FOK)1{&%KPp=KYlH zy_+wsQ#ve^)*gg4y(76!9VxCucQS{*SH=gW{u1EL2Vp&&Ud7PH*JFc2{kL5U{!A*a z=Q?~lEHNYxGhAf~Bm!hQI1oq=`8z(c{61yBJaye(Bz*y0R1tpY3Tf6*Z~yUZSJyg; z`qNUYS?t1Q#OS6&tSL1x|NZuZtO-}GpG)5(*4I-m1fI;Zvi`bvXnXH$>&0Bpz)+Gj zq7?7xH1_F1^!anP17Uf!N1-*kYj0yOKI{r;%id#HpaN%I^~>%}7I&6s8k`GwH@-3~ z)(FTe$KUsV;Mv!F`={9t{kYOi!>7~x!}6;!4_5Vh^!uxCJ3Tq$pJ^9C9L@?`8??LZ zzjcv&wt`ag&xi_d4G(9Do)RQm8S-#>%*e@-`qU(0G;?S_*8;Q zKO}1}s{dX~%IKktYv_vBZPn!%8{_xCq8hENt|hk(T+lZ2{o3bq0W&n(jU*Eg$RRAh zZ>q-5@Da`;+12!Q0NQuM=EZ{^MyRY#Z3}8PElpc}P4iO%EaXKM=Ym+cY;Eg~-k$+V zu4>K(;Ia4>Wa|9oQ|jlNbzi`TytJt?j>~hmpO$TUsWxvt_1cu17H5)Gdk(45(Tl`S zb9y0pPFmKn|iq5kWJ1HA^+}gRKva)zE zXdS(x5`FyqxG{67?e>1ZD`Y*wxwgg$f4^1!46ojMgNQ9{RnTnUPC~WH3L*a&PjIzC*jHzqAgS{l#pOOFF9xk>k0f|r2p0rZo7T5$CrgJstWondANz>sY`Mw+ zSeI@mPU|t=_hKazq4}$&PQB@+c#+yjk-JX^D=gQKJgr~v^|u{Vj33{Rvfc_J2gm5t ze*EL$Y*cY@yRFp1YNIXjSL^v5z4MVqif`veYjB3N`kqVF&hN5ks;)_V4UgG<%^TnizPqAAP>Tc1JjP z@01=jbdGR8n6jZk6Bd#GSh=|P>gRIgJC!HNJ=<*+VORWAjfR_0?BDEr;W6nE7qYM-NQX?_-c?G)+f{SA}(Gr>gEUbI2j)0&m61(U9a}u z0^q`z)(nG5c#0~+`5?I^&v1X2mfNzZ^TDubo68#i`}ltCcI00`Ppe9CKet%_`pWys z=bNezmYx1Iy7t9P|4_%DYyODmh;TVq@+Y^oW;N*A;o%Scnm0!*LOsNqq1^*H4(yp){utIBpd&Q2HjWH`nROiJ#<8I9m zteMonAOe?O3gfip7_t$|RNLq%`)Sh{4gEPu%a0zMdLW9Ka6b48$lp>aj>r7G3r*X> zG*v4j3UQ7<{4969tGDh`{|7j-pP&s4G5hejJmY|XmF%|KmgG0&>B()RB4JL`Bp z2U!?G1AoJyfwD^ovZ zw$1%{Hzs5bwr}xIRLKWq8!~Eo^RrrZv;Tq7#6jNDy#=3TRmSnXD<7eM&Jp^MnP1;K zH6MPV#TMwNJ-$FYxQ+VUyS3++L(+rq70 z_nul){eGQ#8XKmX2+^Um{dz#@pHBH^clhae|Kh3XbN)ilS*PjKPru>bxz1XC(aEjO z9s2%H)7glahTz}!zuyNn&K}@E|16J!8qzt>1oXgshbDhWMo!XmwC-(7W=Lf6b zs*Qgv*8T#jH}7Xp@6921Qc`-q>})sSq5D;p3#sqUZ;_=|=Lg0P^fNx7lg;<(o%2q6 z{TqB=zYCo+BOr{W)miR-mv7>Ii7b|MbiV{MQ&tpzcu&cytS;ET_0_k|ynAD_S5rW{ zE0BHD&}0LZQ)-yxY6IQnY2E-{v*{V@tHgdcMhfR!r5jqx`v6&<8ugq|3j~bTrwZoWmftg;@Jq zP+py0tl6-o-P-Z)NQ8sYN|a6=>f7vX)imhxAB;Apy7@1_b!^i<*5c2-)Ch*M&%$xo z%8c0T^mu}e0X*+Iy5pXa>Gt7SqvnXrzCZ7Wtp>wBeRf(=Jg_G3U%0T?tGlVaZ_D7y zh1cBq^%vmMw%KBxrWEa&xpAz{q$4+1(pDlnjpQrWjcy*CT3mi_{c8Vd&!2?o#;Ne( z!`Y{<4x<^dS}DUnc3Oo$mr+;#h;i9GxTt&0@L5DtQ~LI;Q&Q*S%YQ5U@cmNKnh=|3v@(toKXU`OEiF8~cp&aT{QxfsoGSUocG>AY{WB59+x#r_yZNyARR`PIX;`Q*!{sPGNh*dge84gN^+-E?+$tSLQc$Dupc zVNdKJ=4FiK?>?-}lkTDV=PRz5&!&BwN16RGmMex4J9$dfCc4+}@0L$Lq`EtaU1s}E z4G!wHR&Ar}%mW*(VHaYeQ^X2_2ZZJrD|SO^lb;8a!*4a)I$NgVuvERQx-(Z58UiL` zo({%uZRuzltaWjV8?5DoAJWnq3;r0DY#jHDM))1HY_@6i%{T@5RM)w3MYLX;RCExZ z`Yqp3`fk3RVLa@5q%%hGs-S0`Dp$>#$4CeKT+nw{8$T9$<(-vR_U>VNQ$go8X9~ z-`}4<&3Bd6-~B?;`T7?iROks~{_}@jR#PrOA@Lb9K$-KAIYADcPZ_6-HC>RdQvf_; z*Ql$_eSD-;=&^EZ^_xt;8xYv!1d`Tdrk%jLAE6|5u3;alkG%2r(I~$0wt1~7FlE`W zWQ5wjpSkoG;I$P?9(9dT*t3AE@Rjub)|;um9(|m7{@2ANx%RyLqv8h>DKVMJjpK)T z)O*{8ixDn9fBpgniv6cHIb>a;jOOReqL%lcHr^duYZ>?Thco8#M?j$SNIg46_8$OkjrL9{}%-y|<$+7V1I%r)0 zv>Qonc(>c0(3^{>Oo9;nMe< z?K4N(Wj0PJ>jyt$tIXL~ z`QI2{ER04B!OI1BTSM|`xZ6!CVw;Pl{EJUI2KqnBUgxQF_h`4VgwlKv*S8z^o~}d+ z3T|gw{BRTw-PYe>0S5V;$}c;GU)U9&65HLXtFNscHiT4^G4zeV&7_3iy0#u2?D>IT zZwH=U-&1M+(UR5>KDlJjpkPxcQ=3%+Tr9nytDY^jfb%2;C)S^L3ls8TXodAuH0~TN zDy@o!ei^z@lT9Qyozu~|8r{?Qe`xv&sJ5D}T`E9vN}&{r6n8CdDK4S726ro3T-)OA z!JQzbXmKs>?oM%cNCSl4)A#%DtgIw!<(xS)Tc5pW&t!|gWOv0+xz1&$G8a^-Ia!UD zbGV9IUjGJBmX|S&u)&IZKT+#CzahpSKH0%@xhF%|H(T4o7apZ(o0VU`pr^z)O+;#1 zzf>$0DKlFi0n{kh*>kBlHLetbC6-K`EF_GFlzMr~)wAcX=QbOW#RnxUr-HFG;}Hwm zy@+xGq`!By8|uf3Q|6PLvjsO1V9ls{{)V#V52%$Nb2j-Z=S_kYMxt_@e=j2P&_89@ zPNxz)7Y-yc6mtcsNz{%o0{l%n$+Jb>Z`mh%0m_Tw z(jIzSf25=ezOtV&o#~w|+VAjTOkO^yxfh*n9ena?pp-vE&vDbedh{Zj zt?b@$DLQgz6?bR%cCftKM~03h4No4q)7w}5_NRZ|AAhpPKXqp?=gLp$9~!{PfFt&7 zheA@rwo+q~&0TBu?D6XO>C8r->WADHlZvf>QMl*Ys1Z4D96P6wu@l8Ld#r67uxdxu z*SB@g^2unOMwiahT0-vZNBLNc#vU2sqd^&7-h8it9nk;d-YZpMHekPoHB?xt+j_Rx zL~6gBE4Y5Ph;YjJhYtj{t~-O|w@N~<+tPa=@jsh8-FCchhDSd3)rZi^lP|$q^DIXdWVHTuQwre zuI)pYNtHU$v)~DLI;xs~eQ{O|qig&680uMP4PQU5a)WF&{7l~v6&@9%9Cj2Edo`XM z%cU<9v4d%P`4=Ul{vJ2u0}iF+#20c3M`8NPbl+Pl*TwnR25B#GuOs9+YnjpSqkE32 zbrvMkEqb}AZ9C1CF!%Y2heyFNh1E8N$xS;vM+-JGj-_ScVxZG6WdDhS*`sO zn;d#f=Jm*cbHTsx7lrCE4el#E0Lr@(m7Op|KWhW!AYrs^V`(Jg^FF4!iR)8Kov1>RmgvI_vsxqI&)`vz0K(9l!I%Fzz*7O$A{;KD zvR4b(@Ca|YZYqKN}w~0{Bbfn50oKuSjHM~ex9E+^{%!n z@pQ;Z`*SAvgNgEGKdC#9BBQR2VNYdh3%suC!`}YwM9}4rdI8_-rzl{`l9IrBPdsxbL?T|l#`b@1T54~{sU}IJ!WfK^r=Gxb%4;5p(HpHR( z5+n1sMnu+m#Klt|6mh%)Nqt26hf;5a4!?81&0BRPXtxZ_Sv#$Ys7ovl`P`JngfxW2LaZD?&kG=Kpa^ zQ9+uE(h2R?!2#v8W=_<6EMa{IqNK$}? z+tvEdEbL(N&s*3qGnQ>^%$7rP@s4wNqUrC*{?vPsp|i4P&QHIaL}NuK-?Geo#&(Lw z&l)30W`Sa79+gQ7em<~1>L|EgpY}hTo;tYez&b9!YZp7)ek6un`B#oy2c!u)%%>r- zk`3mW);;biR!=C@k3or^6(NWK4#W*Av77wz!5_+CB+Uk>;<0l#x5jGU7bo^}pNHtW z(E+)Y@Z1AO?Vpj}?T00i`5znYk%tfW(I)~xIP|C8E3kQdT12V#Sl>vg`-Aftl7pF6m*2Q46;2i5Hk zhljSKKaW<>Sf7iiv~TJ3lw76Ed)Y6mE9G(Z1mS}AFD6CBroZwjJus-NyI!u8D2e4v z!pd5{BVq-sO+|dMEio6CL)yq4ZdeD$(;`-uTJlj1kZP$TF%(tRw+-YLWr}mUqv8jxUJsSfr8Xj{$I`JvNmtelyRcq8?PJ-$4gb&Dr$ zx!hOg?RUuB`Et|DU8gs=;3`q_rmlbUb?y08KmB|N7B(Y&__Ubr&@x!)E^MykfV0s^ z`qj%)jXV~jkAsC9VMe9R;%r{;KeH7ilb0q{d9hx-@8upF>zfhT_)+MO>s-H~r!*bP zpK`mJ3t$1&A=X{W zE`RL^o}Wo=KJFd2cC}29+FX+DnRfLrtxw!l3brH83XXJDxpoJEMZv8c_(w{bi7Qc1 z#?>*K?xWYm52}Gy>`xpBZX~fbq(56$X71B6L(jI#bgOT2VhwF|8K3C7p$~sQ#m`F+ zP^&CQ**U)Z=W)kiE15wlc6V*KAK5(?7IxRMx34iSVyS|?>x!XF zX&j>CKuf4F&%&U|0BDZFPmcLN!OCQ!Y&(nuhpZvR$gKu^Tnkfm+(fe8W)6Pdt3QfnyTBBO#Je%Zv@Jh zA`8T%wMUwL=SZex-VMJsgT1WBQYrjCm89wf{Wateldyxc_fC#%<9m`^GSd>#_OLqX zi!VR))d&jD>B9$Tlwy`XVkuUuB`Afc>G%8a^(u2pl}Ql4RclgOi2Gus>g`T{>*m2$ z+#t(6`>yFlXdi4eeiw^4Nx{B_B}Wi6ahF|fv31%8%Dr{@b6*Qbiif^A8ooUXSG*s- zX8XisfaFkyY^4Y6+csz9USD3_Xz2qwdYRmMb3&PXp3J5+z3K1#L`?+S#T1zFHfGp> zC6z?#G+8-n`CSajI`N!0ORQ*B_woH}jS)W!UJTu9fBz&Vw#^+Hh1RfH?VzZ@k%i?Z z%)~o;T3u1GFLZMUIPVKlu}gGI^pYs(F;ds8ne=j6+uR4|r`&~IM%Ma`?N}jRnLlJM zI>ueXCA0}d+#p7J+9%)0anY0m$1zj|P2P-)%36Eu-QlL!LHHL_zjiCr%KhG4_WOcm{PhEwt*g#$+iujSG#8gCz5s*EN1T&Nu+N{4 zvxmUPgQ5GCS7L{u;M9+SxH-Vy{|v1QuX^I7RS?PoCcu5}_+m$aIDtP8Xe2I_+ddaPVR9MN zGe*q~E8GE2yyzxM!aBkwr1NOb=i(Nmdh>Bw`H}C{fIqm_V$Kd z;OHaDI>f2C#8$j|(Fdst424<@XDoNkOjn?2c|U)P`0Oz+OS+es0reHl&xelPLSYtwm24+S=bZ` z5}!*XnlGIj)p|cY3pVgm-}buRbXwmOnT3+ObMEZkxSNKSH+?3k-<5SNBfmG$vbAc< zZom#>_f4y>k&PYtH71`1ZVj-jS(?fk4=DBiMSXqW3pvT*$j&&lD_WFzcQ33m*==YD zIV@xDNI7qdxosNX1kH*9Wq4bYz@n%oR|L(~>NB(rM_h|n+e735jr__q9m(B8*0r1GG2RHadYS|Ph3=c<{T!q(kxOc>j*0OJxw;u znXF-LvF<4I5AnjrL^aop$zwsf@>oUm&_a%ith^B_x!q?H@w*smbf$4q*BCBVKSt~c z>Ew1bPRNpe*rxaY_|1BIM`?fNJ3NaBC}53o@%D`pq?ju>aJ?uJ?Eu}bXHAE~C^CKB zKmmG#n}{;pv-CfX@kI&6y872CeMxIu?`|d9Zv80ib#{Y6q#8Bt43ye$Kt+^ zb@cpiN z&C8SoBI=VT$K-d9St6?p1>W|iQ5RP1$@02Hl{(+tb#|eh#EHA0rrmOHA$zo0@-|vq zD4B1bb*=AS^7ROG{$l*-d!L^qcec;)?HV{|G&1Yc@P5GaceSn$GcYxEnLzt|%q96? zsk%HBd(w}>re;bsOw5*4&nxm3MQf!S3=3iPVXQNi?} z`@Q$wOZ<=`nIBJQ!dJtovtZQ5tW4Z_K%KGT{-^|@m=~j(@9b1Et8}#Ah`i!0$yB-m zVYWP}_VBxUzY3uEalj+%%=y^Ad=#$5$3Me|llZb%SJPhp$a|%nXcjd0FrB8iOe$1S z^Rpl$YOm!`7QrE4vhX?J$$6AI4Z@#Msvk4P$`~dr?Jis$<6-^nrPW^)ZBNiF=-KU; z&!6_TkdBn+vERkKk;>y&KQ#OkFAsoPF@nZ8CmoHa`hB*OcP?E)D@Df7*vHUVj)>IA~atIl}1roDZ7U zaQ3Ed1)H!sitnhIgj?DqbY@iuXeeCB&F4!{oCvj|K9P8}+rKlYLU3SA4{ae3Vj3$yE?GLB#k7f__IY;-i^^av%zj~6s!DDk#@ZSpax)FXoX0x$&2Qg7EMKMT z2eoyzTt}|+AB)VFou2-Agl8OQuXueu@MQ ziuoBHrIb!#m<`X4se0(c9no=y;45hl@DO_oBLrm+jUr+ov$d1E?r@NuqfkxPD6d%l z+G;3EIr~rik+oKg^&myr@q>HUyzF1{}xNBi9$Pb8x^__jFx@sHe1eGs( zfn>K|ivcnGzHia;bS7oM_;Cw0sB&1OJqU`%W{Nx)a9O(y% zv(XJ{JU=h8T4Xx8=A0pOj+gW|G;1&L$#TQqo4Uq+SwLn{Tf4DsI%$faB#V91esw95 zAj*@_ zpXQKE|8b;SDN8$Dh4tC_>nV+r*mJ{hp)0RkQ7zZ)OZ@xS^Razz@6~v>F7WTo`~*Z) z3`gc2h5P%D*w)7`YrU8t&3$(c80HyZW(N)dvj9kLm%Q_E1-y07>oVH9pnv3npTEarE#Mkt_RH%pip?)%=LPBf@!~6IB};RoixyX=g&bQk+`=3M&l#vTuVUWfo0}! zTIzq;SS}B3eTVS(o7v1dneX0lSR9H_mI6s;!`mZex&z;{wrgUt;AkVzVa^F;AP-FiDXS4%wR% zlyHb5BWdE<;||73gO&WW3NxKAKarLjUR8?kVC~*-U295tYjo;+W}!XIf8B102=;En z%W*0?I^1#J`P%GVPC2dPc(}Uuc|Kcv@){ZYYUZKwFkWN0A2GUj`@lbgq zDy9oRgG}F7x6vfHdTSz&2W3NY@T@ulW#;c%K&QM5QqvtP#2?=W@Z zQa#i-ujCxG9#%$=KI!=WMaf+F#XovUstWsBj_EX$rOTN$R*|Cx+Ie04>FB8C8-wI+ z%bSov8ZmlxP?Be45|}5BnSKf$tw z$yQvvaWmh|Vn@Es_S&$^pA{fkeED?@kE;Er5I!$A5z1-EHp&5(TEmy8mZRy<#TTWD zY@}YYVLA~WZl4Uex~LFk)?^Mz6@5&65tR2NDIZ_PaujXgktJktxgvp={7Dn+1~;=c z`&6mTSEq&R^jTNUdStuV-c9qLETk1$f5A>m`lFa%NAiBJqGRF3@lJT#htm&AS@P6l zy((d+9&F>YO9wZfW5#7p_fi5Ic0HVYHx;w$ z(~niHst-Cua~Jwj{$_#MMW4yDDwqIP*dj*HptWdcB&$UGs=)O{7v^(9k=6l4yu{2h z%!Pzk>@HUC$OwqgQIo0rA}HuNx2s>WsZA$hXCbH3z#@5E*|r~gLzD+bnvb_IAV*yI zL{ywrgLQtvR>55>fgB^w#1EQ-xy!gZsFNw~wD*5gD z`5v*|BxRb;axsXp%)iYyBBsr|@r0sH#3_TKN5qq7=Xlw9wC?Jj)7if6^4`fA=jPHU z-f3ff^S^v0I#%yF7Lvg1k={IkEj-WO7ZR%HVHl%t!?>5PNuz7ydlFyT) zu17sfId@dU=2NL%J(-d6vuejdDJF&9(*DknB%xWToJ`}#?ee$QciDMv`fINgLKVei zchFx^@Ka=Qg@^M2Cmt;h!I(c6S;-7b)Y3EJELIN?++GuY<&c%wmUn0DWBsY4!p^QM zR}hPi`Mlbx=&FoDoc$w}s;L|-oZDik)^vE1)jiF{EiB_kfmVT23)j#!>(F^hcJR?gXSywgOmag&nr2AE)ZaPef(u zv0?+JsS1&8k07S?@f?)KaC%oTITxW~u%LdljD9ge$%IWbVfjmNlA?B8Nx68k6_y?2 zX;?ST5BhrC`e}94lieK5O=e5JrF^Z1h%};_(#wqS;?$KxdHO@9+rFojRanzG^IIur6~=1L)B;4{VGJ0IqG3M!7rp$cKQsA*m} zbf{F89XlfPqqHbJ%JT6$Q=C8|D->6w6?5p1h^%;3u^kn;yD+%j<-PHAYzJ zVjiApoz~OCjzf^Y-_1NYpUY(uL8n6|rBvAK;7}e(y}U1lLz3V7Y-bo#wLij=kEB#? zWH<>-v~5YN0vjY`?U`cT9>Q)RG1iLO!gQY~v`%e#t7=;DWkJkQ@B{51-_!LO!DX<; z?0EI0b^6N4O_*e7P>lN4MlsHcb6H~4+T37ZfBD3MLw~L@de_FT^#R9w_D^V6hsZSd z6pG3c*vahTR)*sqXpBaYO^8>qSX?a_I;6zCL$WDhUQ@%F5>Kbe7$;91N1RBgBQ-N* zYSCo^+csmNekU)5;UdTi!$VONd`YV&@%v5kwsj)jhpH%;izt(^i^8^NPxm;fv`~V% zeYmkMY`a(>fh{5J!_2N8Sm_-J4IPGDqtj%SW($xrfE5(8t{^ET_Y@8M(CxZ;K?Q5u z<%9Eco06oTKF|t&-bHi}Kj+i&J$)Hga)szm7G&n$Dcl^cnvu5cVxFJ4h-{d_*RTwe zf0a;djhX+%JOh`1K%e%abB*YoKWNM~wyZ>t#$=#YH*js{;J; zJevWHAvHp_T8)7jI=P*Q{C7i>&y^No%c>-UN2`q7Zu5`~$UT&ZJ0yH--B-&2(oT7zFEWsWsPU7uu*M0*nvHQg=8S{;ch5cN#tYh}Cq<0P{w|@s0FM5Dg8K9?itnKD z`=xR$lj+l>y1s$GC{TS;joIF~?xm&4XKc=WuUHKdv{uLo^dZ)0nw zc4Qx_Tyo$egh$#ki86!(p0&I<_->ch>}}9cYS2y~GoK33nQzkZ3vZD?EHIB*JsYz* z*HT-dG-O&CVGLu z1?X_7xjARIWPcxGPIUz_$1P}{)@BCI)@CKFLn~|+F=UfOp2xNet8VN|Dn{VE5=nK* zD+OJ4VzH)t7}5pD1+)txH#&`Pdy_N=%B8~d0LnbowW_kU!~ToXo_lE)YWuSchKZY4 z{HXKNB75ftZ$Nu9t_3)yQ_Z-qE?A93`TnHRl!Vf| zq9W%~kS?Y-yEA4dr&&@>R}unRcaRRgWit0WY|SDEZ97VmM~ViL53|C_XGo{!a^Ndi zqx-tx{Tn#?vC*W3k`1I5y@19oSh0B+6@Ln6B~w&Em^(L?cBzfoo3j z8X*b5-P^+W2k@>eN#Ir{n`7Pkz-o(}X5qNGkXq#L$2%Li@)eURc&Os*G@`k=HLz?z z=(f@p_$+%t`w}RUw1t4Nf5M-fR_-Q|PR!)M*R_kU5X1?FS>F#Y@i-BAS4Nzz!voDJ z@osKjrMrd}^L0MrVi*cR(W*I!R|Hf2W^lVj#aqe|M&7)5W%!nM;zZrM*gacAqeC6~ z4^DlhI6LV89jPG@on!yIEOK(OeOFgkTgiCJvuTDrPe79Je>bZ=CqSh?B+~@0Qvi$G z9Q#l<8;Bvv$;nB;5VEkpDE@VB-a_cgH~368(Wu`sMX>T7O`f9@j<|&z_diM{)k2{p zRP54IxSu=Pbw%-n{fD1duOtVTeAmShv z>cMv3zo=v{6r9p&a4DduB~+CF(jSil44HFSw4}S(Y~>c4>!FvlJMmFUr6?H61+fSU zRo*Q~h2?wKmXv(_G;m62&@`Qz+>{Ggi+BuHo>~A&@9!Th)QlTd~@^o7VipuaPMOa%~y%rPYPE*sqVG^^2 z2!Ex`2^fZWLdIG}NvS%!aAw;u;Q8ODSqNmd8&JPY~0l)`o?wCIl!INZUSFb(#Ivz-Rt6rnC0S1 zdsxiXk&nP_P^j3xMJd!LMC`;Tw&~qLoT||!?bB%*@iH|@eTSS;w8&s|eAQQaR3_F; zjD`&9FlN4z_wt0GvtyoGV9M}Fb?p^O{|?WVqzQmlN#P1cVwLY^z6=?F(opZGR@CSt zg9&*1)b!N^qCc)yD&OA&EW%+W*1{?S8XeRp=H}61hm;cWig~J@5t45K8kMEP{w%ed zK=L%7L*=B{z^qwf(&vR#&e=XsEe{V=SaTL=iGX<-#cQmcQA}rfl$t1_b3=`PSW>D^ zRVOP^Na7Zc(56O&Q3H}nmsbezr2=RjqdB{dG=KnV{6$gM{I>sZgu6^Vx#y^t)y7S# zs*jHe6H`-D!*sLrbp`=5l66zwpxGJM6It2Ff`VdOp?^)pkOPV^**N!Xb}uL>=-s#g z%#Lcvn~#uu^8oJP%}G}XyV{7f($dO68M0Z=BA_GbAB`4%K9;1qvc%q1Q3augd4_)2 z7be!$P8<;F9uUb^`r$V=lt?rZnKXVB z<(zE*Y^q)el?9q|lky(p!Adl{>=bbO2d8BMggBLr72>>qMKtybhTV7Z7e(dUR704$ z8Yn{|t6Bb)q>40Xz0tuc!CBTioP_+Q&H{WM ziz7s$lXFpEaWl|bLHrrvoZbF4(4mNAXOd8W5E-*2W|7Xp!xUZaxX!jQ%vA25$s4q> zVda7!9v&VowH?7_P2gB^gGBsOCvpQrWd?06 zFd*HPw#~?2{p;-a@byDZQWbD*ZCNzcJ3w-n{x?RdQr3`woXWc@SW>g!2C1&Lw@`op z8S|W60TbwKfO86>b7It_ZMb))eTvu7SdO1_T6VqIj6{hac)#i1K2|i5;y-= z|D!W6T~cen`Xp|S7nAWOq21$y_5OebdrVA}-;jGj@mlfKeB9nkLeLo9QdPh48%ifm zhz_ee6(Ck>bI{r*{FxM4W%!M3Wbfubo%K&TmMj3dXO%8R0mo#3(l_BRxU{drwrqJ# zLd5p{Eg8hux_2rxZOuA9{VzK&e{!J8woG zR@iAH5FuFTr^Qt^W=(9;C3SUmf~4txQPz_I{~Fl&k9UTEqXmZT*$RA-RhltjrMg^{ zWVDS2K*b;+XmklfSvstoou@vp{eRzghCKTx1jV+ZqlAfw+)dKH@j9(HtdxzyXi#A& zSk;cr9(V{;`Tp|Bm+mK%|GK5XBM$tpe_b$5_3<+Wh)Nfre%v9oyfny*D zs!7bO+dl}^CLGfl@&-31$0gQ1YU^RbS>O-L5;nyzK-9aj-C3bvwe*>}dRW~6X|fS~ z`;AG6uzDRi`Tr&}M1c(haH?d1oDEpcSVR{`yaZsYh#`AS&f%%qZoAp&e3eOImCx)f z)PLWq(PR?v|MwBx4Ug6Wo-)r8r9)L$IJ>bq`(0*cAamP#kR|9Kz)Q0#lTf375dgqz z0H<~q@}NDFbG5~hx4@ADpy~fnu=uW`!IXdAVP?b~3Z%RHkT;aF@U%s?aw1j-jA%}SmE+5r{&&Gzx5 zy}Yd?XS+mBGt+&BW`;o%jvNgVuKBGi+mY|}m5C!Ctyk+h*Y1^e?_k2rfNv4olD(vKD#uD7pS{+Yp0@L+ZEUY%ymS%ylL42i2~ z9U}!%7X4lRTUDZgsNVoUx?aQB7`cDR*WiPHSO@8M-_iQyF2dxJK zhBJdeLXj9fLu^c3f&Te-B{+emGD_fDR zu*!eKWU`$7!#0kQ50Mb%?gvx)lGrP^`{WZqDH$5wj`7I-OcjDmgmB}ZzV9t748$Hx76m)a3e05EP+iXV8 zOl4A^WJou0rPR+tNo^;w;NFpuE{TtMl}-Rp4m-U#ixMUv20o0uXt^_oGp?W!w9vP~ zwi(w z!oab!RcAPT7dnEMj;N4Eo}VOYeSu=?|9MG`W;8mFNWaDXN3W2O>=|=js&Y1Kb9bXU zc(E=fJ0fj>Vw;tHtNw1yp7O_BCImHgl+Yv_~G6Zr5RUaA_OfE^ar zPE?$6)(?IDRgpUXa0I`_V%8mO7jAQ(>?H(5e)qqZMk6Awxdkn46OC>JOuqcG0r0Kp zKM?ZIEqBc=A3qJ=0UKsvqfhca zVu_VIv@yAvgH(MD4E&J>KL5~b`AAs8oX9k9-Sa9~{yYQwvbNa7UKN1m$6gHosvUlZ zYHN$d0S%b>9rDJQ^9>FV9B?UMZH^W=JMq|i+E|`SN*JPE4)4?nek9#?Q6Pyw2{IvP644~Sch-e2 z>HI~RNp^F(2ZOIL0dw}Lbo&xeMmIP28r4C@{ABfqJ2luFzQ|H?=tz<UG9dA!C+1u zOyeiZ2@OOwV7^J7cLK)AN-RY0ZBf!tgp)T;9e&w0t6E4^83eDhF0U2_4J2U8t0y{O zdc4K97_CuCp@Vgi1Gsx4nfu^jV=|etNqLuL5r3!{ciMxd_I%8;yW2@B^>$dJ+7Z*> z_)in!JKs53fhOtegsC|Fdgp;NzUQnC_a+V6D+Mpg6gi+%v8@JpS3sQEjm-lf3Sz@v zXi>4Jf67@15aAURl*MZh{YN`5;`AI|<@90;J2&^XGJEMkD4jINmF%hJY$uvxq+C}q zo}k9<;0VX{N96IGAt6M6nMH8av?dqte`jVubg?yJ!kvMt=1GYb#uzTPA%h;JRfj=e>rMs8qPgX1H`#jax z%D0q#-G}}<$1<#1x*=tHObdR3-3JH%p^IHB`@zzH4TQJ2vU2X- z4<4RM{IidrQp$R2C|0e`_}|7%*DfqAEi!+$`E3VlQO1-=!Cf@N8se!_+&0kAg0_6w zZI$zQTWB)cNT&oTPkITTW}ezyImO>x2gmw`)jABY8s&C)MYR@iycvdjM#5i23QH;2 zF1%CM4%xIlpRhYW?AGdbB3Z2H7B*P0>^r#6>gJnnSFB8?*(u*>h{$I4EZr#nw$A35 zOtU?xBvrY(>whHt+Ji0{He0$nGt$NTGwF2uk z{OM+yzO7gA0j&4SP)#h6;vnS|tZfP867gwVhs)RdD7y&!*ccBkB?Z z&SORjLC|pZgh3kO=fI{N}&A4Y6#nYNbOEmiWjGKjV!k5l7!56OL= zYi78YK(qIN4eZplko1#kAjr##t4UGGRkIsXr>O$D2x2M%<&$>aP1H11HWf_SQZU`u z5y1pxnhcf( zegxp?nlkEo?$GiXUa_&of(<*iL3 z8K(^P0FH;^Q2ybZ63dzj+4nKWA-~2yCj{D#WrNAw`UhilCsn(AsWUt0$N`CoLY7m_ z?JE-pUbJr{a%?J8sR=`pRGXBnV(Ltwk$G9ly60gpnjm2Hf`T3qhClWQ{f<$ z+e+`+iWsb4^A6!sx8KE`?|BZnB9btZYWKI|0<9X2>9SW%HyM+9Yd`EJ1+sA0)N4#N z3nc(q=|SemK}Go@hh8}aIPZr-me?yoZy_{iKU@~iVa;+N=iz1oK=Z-vt~I4fW|U&5 za?s4R4e9Z6kkf)5*r4A(agB9zu}H6VvEtoFURk2XKY#pptdquSEo!O(18~M{hfy5T~%luESo(;~fm7u(+C^zM$fk}&y8Vn%OXvW28+JK$4wm{ZY0#;z;l@N5 zkfnIRCKHSfX*aP{8-XQ5!3ivRJj6wGz+ig6(;{Bpa7E%tSC(N*XIx2jB93@3BgdMdS&t{#Ag#%IDg$o zYyiM~m+h+1|AGdd+~Li!meYoQ#^`_Xc8{v$+x~^C8;}n4B?1B*;FtuC8)nHbB8frx zSF}|NW=x^>_2FOQW~ra?%iA`quWG=$UBuFGWm4*y6W?7UyB@&g9D$FwmG&InEJwxO zB1U1fTq_wh^R7Fp3`lLYPBzx6ZZ`6S=|!=gVHJ8CNz~Ap6NafLQTh1#S6t%i98e%U`d3(wUq>X~1=&TCyzuGbgf}C4W&O zIsKz$ONgu3Gs3{l?LX_J_o8woTQ2yvgt^C=^toY^$@}N5TMJKh36Hti+rIv{$_WjSz0+mgt0c)5jah+6~UmQm|sk8&(=}{oe(@%#8P+XsBfmyBhNxgN` zu5tvpI88K!;{K_gw?8A*e=hk39|gl-F-6jX10y6x)6Ypi&C zDHbnczCp%d5U*M9dKw4+0g{yy+VAqA=KZmFoH+F9fjjQqvT+nWVMyzTb^VQb^eGC9 zASPw#m!X7xgvBIzkS%K;ib4`pt6-J0q)z4w;!}j*wM3n0M;Ck^Z}pa~Z4JLP)TEa@ z)^x^ygPnV>sE*sfP?DT7Cv_qwpX9&D{fEF8^8+e&2?%%)01=zV{%2W`7FwQ=#OXw4 z3pW>20^zc+TaSoPyj}G^=A_G{nMSqUM4GRjq1rI!H&l5c6?|u()Rk8XIC@@~MIe(J zV_Kf)$OseBVnrVg5qoXITFQqu-90Bab-+t(myHpNR03Z{Nebd398Mh=YBZj4Gu{$< z3Dp)=?ss#SotXpJh8nY9J8^LPHadMuPBWlRw`Fv9cjLQv-9QQiD$w7Xy?2V1FjJI2 zc;D05cnie!M+*XBb~yGX|1uv!#F(ximmj4->J6F{8i_eENbgF_yuXG97tMH-XH zyNd1~EE7yS;5((buet2aik-4EM2@&zx(4`#L1~P?Y z{Lf{Yl5|zQC$DvU=f zP;eP~t)z-DV{3Y$9VGIj?biifXa{`YiOpAwe(TBS#s(3jK;o~KmRK(EJ$H=(k(y**y(lp@^Jno2lZ5)u-$=~ZMGL)i$a6_Pd24xi;pks^wj>M*fuEg z<40X!)Af?Lzg)3!$XW?zaRuX3!LB@=KrRUhagngI z)o;p*)@*{Dhz&P{xOLd+&{Ol14iCpy!EB0fg;aHBJce;~#bh_DGqe|0jURQvWs0^% z8gBQ&+1)jSJ?YFcu;$x>53afFva^87iJnvD}J>B%CGzqX_#ocRb&08zHRWrc$! zag-QL-9i^AoqnJty17{ScQro!m0%tjfs|gUSzbIfn zk*OL+N9u)}^3h<^#dO#CC@X6ABfd=eA6V5uAdpOlQOHu+{-y6sDDSb2iz5cOaqV@n z=uD*T%;e^lOpEu3<)XCQWQum;%3l-@7on0VGLBE0*jNW;Wons+Q#Yf%_KL`rY-RTT zIs=17RQIXAph~AHa1CV7h3(%m!-=en4@!?>&xNs&$NyvMJ>c1T`}c85i&n*YtRnXK z)C#TIq0}x_eOgp)MXUBqk=6<|V$~*UR+m{dY6d~n*4iuf7Be#XJ3i0%^?&_2`bv|N zbDwjc_j!-&y6*c{M@t)%_i+HTk8rR7z(RSI%Gct%{J6>;lX0i|-$~`qFE5Wgv&x=@ zkKYQajAq^-wfXzx{RcQ(%&a>4s)Js?t3|*lmHiBBno(mz;Jxp2(#D_u3*n6~j;xsK zjZ$)Z)&Qx0M@6g7lljj#-1cB+ZN1IRlFFl#rbI;{MFl@tP`GHmO)+$hfKvB4USk18?`{awYYUIOeT0NdMz z9su9Q`*2jOA`Wl+``h1VNi`~5ed&y9(v5js#utuFAC`P^Ut?XS zwg1Upacxh3e0ey=cSe3}(0(p4?^7=kUG>7HPL?i$!47^aAM2a~z(F`Y2Y%TM%D)7} z;fFs}J_@7>%Jal{I(C1r>GAgGi#0~Z4Bp+6F|Xe%2WpoWsQNXx2n_SsN+>Wtr)`iP z*|TR_yl!B3OX;S?qfY|q*FGnkEHq?KW$p3?Y?(*@qAp>HE^&h}nLvFK=LQU7CfC&m zId@k|$I|mk{^3-;`%okv5FW@~tFXI(d;RUBt+MtWU+aAjq(!Yk0aA@}(X0mmbNjhs zjxVJRaD30u@h_lbettoXDsbq9=fuC1BDbYjy(*ENDS1Bfpx+2QU^e__k!dbb^v$q> z@nYeEf)C$u_g!2959`jzHqUg#xsCAb=Ezoy_{ZmKzjdN+Vej5`UgxN2 zm+QW_`^LgH{Z;+zS@d2$aM$LbF6_KLC4S>0+x?Dzz^ioLbKjaHOr0-GVe6D`maoUp z9spib@XjV*IBn}yMT;Weez~opBFC#u*1n){k1zj^im)+8HSqlNAWeiDeHtz1RNV0J zQfn@gJT4_cp~{E65m)CiGdkY)TCk(+r8@7E`*%S3U?pV{roAU#QIB;#SM&Z;6y+{` z$erBtLnt?@XROC>J3=?hRyix{=|BF&D*h%0UFHD^aicZS!;zH+_QnjO<0pi7a;j#? zpFBpN&7rUh3UF%%G65i(K>n<93ib=o*0ul`zh``0*BLp1bnAkW0We=nX#anKiaTuS ziEeq?Go#Pf1P{~tK9g1~q;EPr83;KY4BIC!ehP33mixh*s5}#R|4zIO=bm#;naFpa zf4=-=wFPEp&fpd-@yzU-O8;%0;0`?KCawJYW!ga-FN~HNX)xX$v6zoT^>&XZlwf}mirMuAa2CpzEU~H4bW<@Ijn$TIvjxe*3e!? z^nsFiJ_G^0^yU_Y{10AA-y*Y0*&#qYpb5N0Won zOX2xtFu0;B6W}CJ4Nq*Fby+)}@o~HCUg5njI{xvy!X(44<;(4yfE+I%ww?)57ML)9y zsKxO=WIPh!BaBN*pT}Bp-~bjZE}cqe$ZV;PJ1Wys%a8Jkx+Jmd%Xc_L&$Jn^k_$s= z(9_eUDe78UU&orOe{Az3E;Kft0{=hovqwBh{VY`*+JUuwG2C6s$CI0I|K9uC`eSvE zwg8FCLnlG>&8Yj=1&2Q>FZt+c$F*lp6~2i2q@n_s;{mv?)Q3}Jy~J98WF~&U@Cg3@ zzV@?|;{ya_PAUMs)7W^z_i-H{L7(wy>;P9F8oo7=@wjNB=+Pw}qnkd4V*r!+^^@-7 zi`LxzzkapW*5u=U$zzBB*r+~0V-{Ttm*bL>T<%@`&4jrTxr%J&o>985pO($A6nMpe-(@3i|8+*yiC; z0IzSfbZG{xIc$OP3XP!!IE2n*CH*x(kI07sd1O~P)1r+3P&ptcOIA!^mNvP;dA-29 zjmN=WCcgUja+qV)b%2(%;=#SR56vx_X8S`|gAGzmE?EFr7?pp20IoI<&9~X>LdXs% zvqS6e0g{PD=L*8ddJ&b|q5}yscIA%@!FW-#(*JP8-Ce5jo>p4C&Js;i=JR{m+;UrW z_=0BuNIcsCD}#4CGEpolR%`4{^-QZ)sMUXqxHdmO%7t^e_{mE>{X7vmg0%&$I!H^& zFB_%ve4qY4b>;Ja6s;d!==q&2XGOP zsW0VUJuG?toU-DyFWP3(T=4+{i|w(b3!(3S{TB$RAir=vd1ABj1<*&HhCks=xCz96 zKuXl_-pNyN{qYX)yFIPo{;MJ6y@pl(<%>qu`9&9OC5<&prQM6^ zcEO!r^i%{RfUCd%F5Rn;Vn}Y|HOt`ja=B?sJo;ZazU}WO^R4+)?+bOb>8$#P z>_o|IQAX)Md&~f34u}adv!d`QQ9QV*H85=Z*T=ARa3j?3`B1CB>}(q0YRLPRhbXC^ z*E)Y16%~E(^OI#=w-H?cNS`sni;xlxLzJNB>==2{bT$rv&c=zKPoL}#&Sw5_paOcQ z(-_!vJGg*v0=fhsR|#%$RJd)zn{oYN>dY0OtD*%)-i&1k_$tS}|BNIq0+0+epmmcc zbX@+m2#~ZlR$Hp0!Co~}-y}cc@(Q2Fa+zay(=DCk|4&o^0O1iUbiJS?a1&y8d%DJh z07?Qjuppjh8Su3K|BbFE1kro3W%P!WVMBgk0oEQ6S1!(%8TaDq;*ONxgVf`%m3_NQ8i1RK$vVKA$WI{v&Gy1 z$65vy>3^wiU&yWg6`MGNt~~7H*8U%z3+Oj4lpOZ|C+KMW*CyZ}FS5YHU?4NAQGK-C z-v@YEfc>e*CWQCpe)cWzFB67tzEFYRwq3&! z{vJF}Nz5y!l$)Nv`3_e~*I41xIJRt9@5pYZjeGRLxc(sy{d8|4c)>oFq41; zwroO0E@3d2u$NER%eB1)Xny}vDf}C_E@~bi`Kh*w0|ZkfcUu$+cn2&NyD35FbAY_|_>flc`k_3t*BcQ~(p zh`N1f)PJEc1}^jC3qaaIdH$_J>``BK+8Ce#4|z}V3q7y2AZ|hjqSIBcK*#-MN|tS2 zgp6yvKqH%U77%$D#jy%2%onLSyyy4NNMmCrd*j|z*90|be!98iHRVq8kDU}_( z=bI{M^`DElYT-{D1Kzu2rCs#z-~$>KYAE#x<@;7*%H-uP^SFCg;!?|IZ!j)61e25C z1&DEQRf6z}Qai6w|JyR}_?ZZFwDvUUzX-3&WW5u?2~`sW`MShyBGVs4;E}E+-5lp4 zH>9^y=Pd_LBi@t+?&PIoa?_CUYbSQH7qS#SR3iHICGeie^hvrI z^pWrec*=9j_v@_@`18xrTAKqJAJ_zmh7NjT^yhk3FV{hZaL3ZTMx&FM-XtCd&hS4Y z^uSC6wqw_?i%c7UEBW^y@{IbTZFNFvj&W$jR;mLx8mSwun zdvx;@WHT8Y6lVGpDn3g=Hj%k7?6|8LGojo%op_Cy^OnZ6Q2i}esqF1iGxA4{h-ZI} zs+`l6cR?mFnF_iegY|l-o`!u>4sDv{P{*T7wmPn1QLfCt-wM>GL|)1E(f(sdu4}@{ z_FEY<9GIv<@&GOqQgKipaN`5E&NfvCrsR`p?5p29>i?jeN0?|5mLG)FPzQ}eYoaBG z+(gp&WG(gZ#<%WaZPxb8TGz5=(g?XlIfH$R(4Z30n+jpqk7)Nk z_M!Z_L#ReG=y2)Vo|V%A=|LD6iyvhQc6g6)Qi)+O^jBEKCI+oK3J(4`(sL%;MN%b* zr6=PmtM^e8pHHrN`3vy}GkN5*5vzdp3Nn+|e((pk&9?Rqy2PMg6N%CjWlgF@muPY1|4EB(418E!q?gn zUoMFld$j)Q9qxOe4C&k<GbbZoplH z1hFM%_p9X8j@#6ZL^siY^MLR(%(b+L(Bw!cRgb(qbye8CK+jG@T`|P&(Zo_@)Z+WU zRC=G*eYUxELI~i6<6UU&Jz6!_+uwF?{aOgL9g3GOU@!(gHGtYsU(>3wYNpFvCTDL}qUfqc| z(4>+-EUs0Fu2qkydEt|Tb}>R0ZX5@RRoszOg))_Q;dy`tP0wp=DE`3X|#FwU5ssGZ@y62D9=Tng>DVLjcDH&r2cv(C4pyeqbfd2 zqFE>4Rl=-bV#*kun{%{nidV*~7v@teAI}d^AB>@5*#UKeiOq&^SE&fOq=*vvq1rbQ zWuN+JQxQ$8!7k_W8>4Sp+CvL>gg5h)b<>}|imh|ScT3+&E|c+aoKkD1 zx&%%AgCRdj8H!F~j+S!t&`2~tvfpnayJ7Y%^zyCeX<{%tpl1h%8-quC^qS`-)$u8z z1n5VJ0q89k`o8#z`m9o`hr-xPZ&``diC^6G3_qTg(I{eA>>Z|-$Ceg%WCvtS zLsJCcB9}VBy-L`+oL4iuj=U)ctVCs~xP1U3x-<4fPaP5qe43);Od;_6>(n78djFt)UCKS5&;j?ssZFTg;AD>=2LNjNW~ zj51+__@dL2*n|)FwcS=<)^E+4r zD@A5T3OJb-z&vMjJ8j%{C=polpP>ct8{5@j*q6+rMQpCP@XunhNPlg=#B=8KA1lGn<5O`G+-2K=84wf0xt)EP38ena4#@%(J<9rmYxN^jt)j+z-2 zt!ATYCi)V(=H-D)j@#sYrL*iT6 zrw7l(ln&^?y0H9@_6Wk@GB|NV16oS4QS#lAeMp+`ci5;g;v=Jn)b+5dJwfbPMT21V zed_5OJ`eWKo3xlZc3xUPlQGWhM?_A@tWLkG8%{zL6T*t9!Be1n_%oHn_~lFS_w_lU z+9Uo$GfEj>Va>K}N0GlZiria;j-hP77ihO@ zGfC|`#q+NAjH$P|vcNspyhFr$4DU#V&A`4*Ua`%B4xD@JiR}lWrrcFVEF_Fz7gOT%eScumlPjChZR;S4 zrfw{^Fr_I2*T{(KIy-|$3wsjvWOw(_Ms2^4q$_`^rW&9DD&XzoZYVHO`uvyH0B3Uk z59+^E*O8~$@PIIFqC6UUdMZvF$O_vHX#<9|0+a`BA$(q{g>9v1tqA*^ZSko5zp_4v;sVq;b;W4%@;%6l%@_(sfWsj3bj_Jtnp~uO?$1WJt zREns?*?f|QigoLOpyNJDTK07G;*g`@*}+Wc-dEejG(F-9xCtsqF1JUVxlr)6VY`2D zTb0Otpe_oAoXIADS6xQJs{J*}H=zMA8)F4*;EFNKz;8J1Uc{z#nG$8ZW{`mRsIC@u4rDQ>uW36~ zzF_u=&!KBl1zKK5$!T^@A!pQljP@e9+vx38()`Qv0DplX_&9WTF5~Nuk2~=R1%>ap zXT9QN&3I6RCJ06Rm?c^kx*9m?SsUyPX9AD9P{NN1&~dtRVC+b=a@5P@$ zC#xgQXin;ae?gaafpu$Z?@ig#i(6X+0!!PkdS!jAJb>tVc=v8rAej|36+e2x9e}|k;o8u=iq4~ z%i3}ku=LAcs@Hyw6n+|V94K`|o(K8ZnbL$>K9{mZ%GjR0&ln&nQ|3I6QG1}+%mpXO z!GkIeFC)@Q`*?^w*fj<^zS)ROj-hB`D6Z4UQ^ZG%dY-X9VwqHKKRtX2Rc77p)4}v!a&lj#hum2&o z%nge7sR)jQxI;kuJxyN7&ZW&p*FPF+;-5O;b@BDB82VoDLAV(7mI3Q|U3hm0q^HA8 z5`S$R{1YBg%{JAiv#zR*6eZ$FW+3wAO_!gzj5TTseiT<_pSCBv=&Oy>@pYM&eaXxr zya^u|JVki&-7!Lfk24NMDR^HOf=0#JWU1bYFy_3yYxvT=oUof^y+{o+Srgs};2F*$ z(0i;qlEv5(66KD-EaRl;tL=3MJ+s*)+kHCd?vH+lgj_S|LZ&mrU0r!vmSsNaw;V^1 zjWvPm%J4v#``Ip?n{U|eF&7fY!5_Fl(>8dnd@% z?KFLXdRXwWImpk#@tyOB^MzyCOTAK&$Yjo{KMg=}&gS*Fw+M z(APKvah-0xaV)pLlffqNYDz>I3AIZCk5jf0#aQy}Aa~q(Nb)U}Xg-kDz4#(ByuxEu zS(00rP$r!r_06R=?@Co{asjqzQ%I_<8QSHa1!t1S%A)-&kj>dNB5!7>XM+B-zCvh4 zL~3@87zrXdg=@uv&J)wsb-Z3bQDBw9@PFurriw4U$@O*2faKB_J4U+Gzlny%z@xA{ zTfTswvpwInMS~C3HCrOYkBTT=KYaH`3EWP(Dwe~aCC*TJgXwfK=9(1GJ0c^(#?KZeW@?;teaD%I>7)h?&o4>K)2Ux_Nkz~jxEcE(TO&BfJM53swQHC&LqSQr zkpy#}_{$N0HcY0vHOMUPz??j#?gOuM96mZYs$Fb@RJ9ud~!0+;_%1v$2MnBkS6 z!jx(iN)E7=rd?u9DRi2CQ|>QSQv^A{DT_yDa`y+Z+YEQY{M56yOsfDUlY2FFBy$DJ zm3bES zl#-cUU+}6f9CsJGBiRGb03Rw}*^L8cmG%Lj@V0Ep6w-{7XEx9Cb3UKev0RU|T$(F7 zMXD8Q-a1xLz)FH`rajj$d16@?kW-eJbZ;UThVXXNfGg;|IDm)EcI;)Gt-OgZlJ~j6 zPg5__wGQQWk)(~CUYJhRP__pz_+a*=^osa%WLJzKG3UNz^)x8yUJN>-aHAxKjegR{ z@0rG48+gbMLxgTykZc(8seM8;)PmxF++dQ}?^ENHJpk=M+LO+ooSDafo{mglcVXV7 zq@(9Gmq=r@u#JPYbbS_n#fqtX_l8CTi5l24ct2@&S;Rx$lPh}X-ExALT3yDIcpwsMg@4$H2=oXJ&0TJz3yOG5IL&% zEUV|0F=b8Gtcg#l(fzel+z$=)>ctp|c&%kCov-Ucx_6;%%U~b3p1TH9i745nsqVYt z7^%kGA1iz|p6iRr>8esoM3blSMk68dMePz>fvVBgV&nIrV8mi;5&V>uRYRNW-y(kjX2M3B|O511-DSFr~Xc`ZOE zI7K{BLy+Rr?VDL7Y@1OekX8@p@`lsH-)`Ve=}x27?4Y}k(oNe3$ZiP&#vk!K2r=wSzjXV!J&Qn?i9v~!h?a5eI|r_|Ka1XWtQy?L zuUPnFrOMFCQ`bgTVxr_Xg@gt_rtb6wUrw*nq?56&!pb&n4ePL6p%UC|%$$OMKTy8d`Wj)!KK68JCWcx7N4Mgu~vIeP{V4jPL zEyR4wgu&m-;W4XnPSG<-RRpE;+!|nM?u|(JSNV;`RK8g~vr45#n|Du(kjiN>$G&tcz7)9v4wL4ZZZDQHbv}CGdJ5h{;q~T z0O*!;myn89x`tL~d%{4}Q3va!3S25wrJFUmKpF}jr}vPn*e||y5n}c;#LZH|jIdTu z8$>r&X}elKwF46a{8Gw|&UM`JDZ`S}!yfQe)O8eLB`5D%G+ecki1lt46_qW7vbC5o zHpc$R2+7YieCTpXXc;XOmJm4osws;wBA14wMs$+myApH6&j&n_TXGaNHt_0B@!jE= zR?v`R@{Oowi$VHVof2@pFYUj3e4Wq25aoQ2oU{r^(U)LZ$%IyxQGmR%fCsJQ9E<zzSdOtg(b#@oR&6$qe&C1F}oc;;mS zDrVj1h;>tLi8(Fg4^^?5Nisej@p52QnLa`-eO}6RYMFP#t>;-+td>ziF9$Qnn4}P> zo=R6Ij}F1^;fw`+*LcAHj_U4657Xw$dL4Q7^eLlFQlRbdQD%RgHJLl?@c3zF7(P}r zJ{gwP5nLwmj<#;vHAzn1jO=dE@rE{5H zDg6z|8QhggooQuy#InjyZ@P>x1j9xdk7{3yho;+(C$dNaZ@mvD2XsFJZonPpUe-sI z%V>)DwBG4!6nUhH1eqNikaEe&Z?}Ii8N&aoh0X1abWwi5VY|Egsr8x~+uLO!nc1s0 zeqV*Sx}tAsiT3a4F-WF|PiUs|LEy%cC$_Ixq}9nPB%wa0bE?id_Sn*4eBJ@Jks3zp7Txkgel6-Lz| z!{{KmDCtOTocpF`p{XZyQqj=n!;DV;=c2AZeobP>f2kVzj2V;~z^N#lO27DSm+WyW zD-XFPH&iAV^@Cdm^ae;w&ipBy#pD63kb1AW`0g6!Xw2BXx|h-Wqi*h`h>`o{bf1%b zE0;$12h>aw*s=#*WQ|3g?|~Z966;LW~Q}97TGQ^^Kn) z>xixqF`?~CdF=|`Int3m%GWXuUa&6pG8NJ4Ys4QgTRU}eXEC9GS_Fog{Y#aw37vG9 z`+O=37UzxerEDH2srf9Q+0=ozYmc58hu|d9NpPnoBiqX108}qmHJC*T#{bC%k^ycf zMVp>HMVy*ept703Gg)9vGMbn(qq?AO2ImO-C#U7FJQVDL^lnE~Xhh(No!D71_+~H!*mk zEsNY3Jh>DGE{1MbVi#1{C|JhUfH0ses76C)HGtIC8MPZ|u;C8mdXRJd8uDP$fe`l8 zRPNYxx7Cc$x>GZD@mF+eTdwg1?stgVfshA#;I)`jd7Bz3xDSp3(c85F_ZOY2mg?D1PMD6wwW^N|`zT#Fp|oPcJI9hPI#2CsITl)btkA##VC2)D znr~tXL?tDpH}2mW)n^QGb4%s+noIE!|B-Q(=Eqad`(26d$p>O!;W2b47P;2g1I%6B zMI6oz_M)8X5w*IA<0D}CGVmVgp6rR8BYu{*wGO<;ltZ$kG?f8M&x=>IQ{-VJC{DZr zO_78Dguvs$dyKX?+K_13gK!lR@XCLpwHy8{?&2GuGhj{52$C0erO}&QCO+VYCffHB zyGB7f%Fy6S3SgDNm?GIZ_xwcDIhleQ!nbd>Q_grkt{ za}wOO=+huYS?}2x;1{DP`#Pa~V+kf5^7g{&(ync;aE{8RI%4ty0N5A@| z;SlY;ua|BZr-~83)(t%kOv;VTHsT)Lqnl+cgLU4@ER_<%MbL*{`+Y?Bbh~xpYwgLU z&PpjG)?H^sUnldDTO&e$?4*m!Hd#~hJgXTS4(-*5k9<;m~oRsq#zP*KGgkqbwiZ4^OT7=q|>Vo zdDFlXsKd|YU(N5ZLQCQd#n*?tXFX!u1W69zK4BB<1sGKg%iuQXJhcH#V4^z@n0E)L^#W^;vAYG ztz}(`K3g#58IyI&JvQ_ozxh30^*{p+l*}l_^CMT4Ll?cCo1ysx%bcpArM#2*G#?GS ztE$2Ct}W#V$e`WO=EJb4j9~jQL)rUdx{j&FQdF;Ixx0(I**2>8FlVMoq!yN(k~uoz zJm3_8+h!LjTwkJI5cuyYJ{ukqk?zvjDX2jqSy&cd`!aqv{)!)ylUbU2ej;)1^Na#k? zM<;XI`8cp2TeuZ(8MoSDQXaeyKe83%PH5n7Dw*Xw8T zAu#WPo&Ozioqgl}?GL+rQK@3aSpkuP?^p_c*D_oexRu$kKjCxK$?-r~yEvIhE9Q6; zW>RT*Tj;o1M8Cew&j|Ugm*pW5aqPmY3+UT)nck$XrWLHWr(^600R zrDM|ezt4)RDl&NN#lOcAuanSb7vvjuD^<}K5uEmXGOTMRWBi;|U_(q)lKh4hw|S^d z)ZdhILe8(C^c)smrAuOZMwCT46uO{l96`miutsoYiiCn_tnD1{Z zk6F}{dX@CKvL&{Hh=mNKnczw+@hQ9mLk^Hft`h72Qgt&n4&+0pP5bPRXj|vdWMHXE z2UG@s=3lCbDuO$F3ZN$y$Xo9KpfRLk1xb!0Z%V*rQTz2DMpwI#P87nmGBL@HM|y7C zJWRc!z%51kJ&2YV3ba!blrE^=-&P!99?hZ31uEuzi&8Dd2qao|Qm2^fScA5Z-kH%n zW8&pGheb6o{#K`F-xj8at%rK9<|*&2tB~>(pAFNTPczmx{FsMpNcO!{hV4^*)08Tw=(%vcK ztz9z{2R)(xO)|>_uezju$OX_gDaZ300I76D2Hi&m^BLRGospm)a;puMR(RhN}jy62~n@)%j;nt z-7_=uN|0YK56D>L8j%6V)C1SYyLfSHwq+^6z?Ue?l{`JrqVLaxdjiULopU7`pQe*9X9L z)Vd8DbVn!g_$WAV1#pT4>L5OL+o#)SY~Lv%y9Ybd*tLQWx!PEIJuk)|WD+xjpsA>m zU+;7b)su1y-Fak<@Pq@fYHPVv)e%^bO+ZJK(CpIX&!>FgcnHFTq@BBwJ1|6DaM@NR zLHryL__2S|u6^+k@aR@^AKo!X23}WD4jo|YP!;R(>5$U1H^NVBsZ;Fp%TD&)*27F? zB;yppzNK50$<`Flrw3V(8@$M00*U>>E)18h0d?2yn8nGQEbw3sY(DZ-cz1ypyxn}K z$6p;3~4LVPq-! zQGvCeAKe8{K8FtF-LXt%SlkFRD#5-h4%_S9f-wfmSbQci?mHpM?2<2^Rpi8Lajywe zZsNhB6qwhQT=+NeENvB`guM4{vEr69; zu_F|A#c4Muu7XpiZt+Aj5_$|Lms=QuJi30uzcQl;8heOv zO#$J{>@EE%aSA7M7rx_RCbQfz$cS+k^dJPwa?H;*x`ciA)O2Z$v|Xgz1VsC+vPKDq zF68jW;LK6kn=aA0Jx{GUaBhciwmf(WY!g>YF+melLCd4}O0%WpoKZ0u#MX}$9oXW2 zXf?oyF9XQ>*~bk9)OPhoHKyMRs>!;DmF`*pkbOaS^46OQ*YJk`zK1a;?9Pgcd5oS^ z1E30^UlQ8y!yLi3SpdSXjRRDvF5$ z+5Fhc?+`0T1@nmD7woAfazSSSU-xB@A9|p7K48)PQ<>@yFSQpAarVXa%DtW-hxWKy zu#8CrBj%kiDM0^hB~3(iQMz;nE||hft8~82XCs}uWcp-ifHrwiAOXNC(l!CXpqjdP zpPtlWi;zx`#)rYtK6#Nau=WHHDlG%M;+VT=*3k#*-23fiFTYom970-*_@y_wmOLJM^xvoi$)EQk5ne+Io#_5Ji6E zw|L4V{&=1zIG2vG(BZX;`v@k9nNmjggKNM>8&w8TRC5K4U-8*R`eb&bmr*1I0t$#% zMK!?M;yun3X6!)53@f5&LsXf@8X^$H&XU~3uu$X6`46vS2D=4qZ|-CtmP5+o0}n@# zH@3%z6ebzO^i9f78EF6Pp}%GVeSeL0Qg3BO8~Hfx{0n@n8FMwDvJ8 z55L@{_B>{Houa4sPr^Q$7naMv4FBzORVu5-)C&ldbR%RL+g@~>h$(_v3uknHLs1m zqf}+E*sJ*P-;7am38>PKm}O6Wk%jSQZKGlm#%3V1AwxAx(lF0x{8&4T@c zyn9T`n^I~#DSpux$Puy>TCPA@O=J$gi(~DD`!ZqRitz}9oS?Kcv3KKAa z^7_Fsw=;Ld;wD6o8FQ`q1O14b_Lcmy8>!r^-d!aKXG;RRTdoVZXAU9M^ za1n^@lj4mR$^WQSp9_{X;i{3x+H3AR6Pif^XR_vT(}PN3I*Id779jBQz4K<{!Odzw zo8J|gnU?ON$%Wu@+k!?y(Z!_$?j^-Qe%tj9P|5$)NwZ5)35J%*R@&ON^qNS@cZpdP zW7)GDLQ~8}fOOwdNG64T`(KNWN2^u&9z%}2m#f?3+}}B4-G?%53s*2*=pkq3TVMBY6QsqP0n&o~v5`;}I3EO63r1o|&=|nWby|BjAb@SEU2b zwhBMd`+j87>v9M~=QWL7jd2b+hHmWjqGHZPbw*H;Y$9?QmB6r0Zz6dQImB1xUBA2+ zSn8I;eQ8Cr8kYdsA!K^2?~yE-<}%KP0djk&yArWn2!9GagwSvE+;B;vS{dXB{wg1` z$yKH0olgG_2zhAF>+6;oa}Y;7D`VJwWUuCVk=5w?!jLzZc4R!Z%yfYJ6>RzouA( zh209lPR|mat4(_)|5?db1|Lqzm89$JG_D$@;nh+;4TlC3$Wb$L*$Xt2;f& za6%Sp@g={N-0+E+n`IiSjWn1eBI}p4yn9bInbZnNRhv}&W@D4yPIdA|se+6(*80xN zCw|~A&^?>cLv2DP@(3u&Bk5fA-BL5qkUlHTl}L>tqHAY7c=+TJ%+5MgT`mFY`x7LZ z9u(%*2m0h}^W_}bds^Z)sJLunrosBuV2F)yTk)O1_H|lX$69+`5iJ&-Vld2DC5zVI z5wB-{DXa6mxS%aRDCiBWzoE;HSxKCJx z6~ts*NG{oUP)1l1F%@8bU)5^p($U1h{7k$>{M;8^Xrw^0O4>`3l($azyn|2b_CtY7 z*HvfKMeq*u>jO{4?)vJYyiYHQ@hzT$UnAhhzAkC@F66+Xtq)3L4NO5t%A#lSqLe0$ zI0?>6@}SdyoCV@*NTVq(f&+JP%Nt9{ekkQ+}!$RA9MQC5a&2q?q$MTFu#@tBWV7>b;uZ8nwD zB?qEg=kkW5tZpd#9-2RiVhril$nF$q3zjkYM1vk9&8gWPQfhvOEdJ^YykaXEO|y%2 z?!wns8@<8BVxL;`$;```wU$k(^Cl%u*^fE3y^P=l+m52dUoz-A?g$jKPdl&hTa0Rc z7u;MBdHz--ion(3=YY|8@DFNyg`eA>+h})1;*{GHkpAFPx<=w(2Q%UgeMG8QC2JKe zjGxFHk*gZ?b>L6#?9pKs-(Q~u9qlg@`e*&f_1dm5B~0lC;eE!(!JOdK;gIF?kk&wn zO%?t|`Fu9>Ly49th?R<^(HqQ?h@N~|@!3oeK`rerG{q6zn@LGH>CN)gE|rzJ{!OLy ztxmA!V(C4?HL{KE{z-`K0nM9EO?8`~UFu)?5VT~NmbyJ~2FFokH%i4;>C0USg%h8# z7ESg_i8RZ^M|T<#jWQvAk_#%P+ZF!ns=TwyLiZ)Q^RjA$SQvsSVbAM;8W~OYbHPxQ07Q>ZmyKMRUr`30YCRPG+N~~1sgY@cF2TQP>OB$0d7f|k zIAxq52oYSU&4&u~+3eWD-f_c{vs|3>$e zO_x-nkZ5nTgJ~aSpD9&MZn1rL`FOSbEH~Ka=>M^FC4f+MZ+}XqeNwU|6J?)hQRu}@ zlCn*9B8r4kLJPvJgshV#ifTf#me68tLSs!rLMn_cDvWhz+_}DI`hV|Rmge4bp5^zf z=iGBYHzmpJxjQND5;wVEhpL~wTKtxV+58Clyihf-Hy-A%r`g@sW`D}o^r0r_^o|XT zhAY*pWH}G*15y_W65HkiZE<&>+50a~?TMS2d|WL3AWp~Ok>elpl?mmx!E&UD3WN;8 zb7y|ke&{sOhnfkE)nyBI%x8=_eOY>Lx9rEgt2}cTy!@Z1ag0m)NpJJRjA=~4dEWw> z&DWs_$s?qR#1PoTDzMPmI-nWgNq=Ze;l!;pc;Bx6AvQ}&GBxibg+uRCAx4gRqs}eE zu*qRkY{XSsURY-Pnc~4BvVQ4yA=3JK{Me1GnFac5yee5Yw)8fIle1OOlsHNM8s|MT zS+>;acWJ-{TKADHOj?^W;D$Kr-N}UIgaLL<;#bWzFn{X6foNEEz-$LbV-^XUaI*Jh zy*8&mLM(4kc9(|En(wS_!)&|Lv19bfT|Aw!ZDFK!?XIzZ=Y$qYTh||38@xU1=7RGM zBts=+eQWXGmqhFwcl8FMnPv7 zVTF_-DJwg@uO<6mOZGlCzVd;$YdUjGckNz#>NZz7r|#fOAzCh94D_<}JJ)W|vQN!z zNO5?K>?FnCLEt$QO(Rz?Hh7V#F9a9H`_56_J1rfbC*s@Bm zU{7RJsWzg2En;HjG*44AM%vK71`DjM*4sHy=X?kYCi&=!!wb;Kb<+mNy^=-EmJz+T zuFzvX)}1`4`c^nHu1A0@pk-Si*Q6iGb8bR$@CFZa=A%}@qzniVkAJhQ;4vvZ#7aQ+JCt6 zhsc z7+dg%>Mm?=pFnw&RP|DYPPRsXmKZnix{HKcf@p}yNs@sI>dI{UAh zME3HMXM!~G_o!|=8VTp}TwWw}@~$nsB|RNPKH(vc_a5Wv9P}GoZE7o_x#}xynzQ%Z zyP+m`dq>sR-DBQX59s;PBXu{hBro6W9m0IU{`L zu)%CVY!)-X>C4gKEmM(X)Q_u@N_-oYHLvja%na|~?*IzgHCO+*O)!M#U!=TYr+)ee|g7vVl`OBu^gB;3bRJfAEiRnbOvjOu&nLFK=8Vi1OTi zjQ;+++m-A#bu@f%Q@CWMq<;|z;RZdwMbpQ@-$%rLPFu(0dMbrV9IdL+5U>XI1`Z>? zHQuRb)FR6vSLMl@;D7b!0=)4iD)(a%oLZLkG&<-{{5wjrD0R9h(eI!XuM({rX-F~` z2%kwM?(zO`*t=;>!FsXCp{2c^_9>gaOzzh3w^90^rq}EUOg7;eaN;^==mU#{mj*WN zq`6Z^JmnW$U>oIlU%6Y^Uu64{yt+TT>JJ4(1bdaMHgAto{JgLGZ_CuxWM)}OUsF@m zp!aZ?lUF^l{)&?#O^fMc_&mI>@yL*zwv-qbky^qYy~%qJE{5v{c>5I*Kgl+HXkpu5 z_|@E3qxwmJ7rwUT8`76N6z>SVQt#Uq>% z-nBsbk8bn@Q|RGYQx9*Rohj)7GUBS`l)wr@J{C0WP1M6`@}$@J`G{ zAWVBdr6E>>YNCE)7!nfQ{B3m{@aT5hswS#)wx})?q<5O0j2^`3pJ>jSE@DePIj1y^ z_K*3ssCErB_&lx`P$Wn?ZTfm0S^ZN6#y21IHUrK}AIob}Bnesb8`^Dbi^J(1~p;ldrKQRy8C*%TkwE!fOPQA8dmFn znS0Id6ba7w;**BzQZ%s-NGI~Uc&vD6N8@37`TN)+S&p$-t+`3^wf}TCNWIz^aMbT+ zJ*o+Uw7ch;p?O2_hJaA?qnEX-bc#@u_8RsBRbCTmK2`I*v7~pmQ{4VWi@0JL$Hynt zOa>#65uf4Fr)Y>CS5%Zjp588r79<^B8nM(tds+0{f^P$2CUg)D)30HrYBneBA~9V6 zK21#>1MdSeq~lu#hc9Vw|7v{a-C%^v-I4>;q3{=Do=IB|hr%-{_nW@n-9DV%)7r#2 zL)%gGi17HS;H=0@@d{IqO-l#oUA~Cx%z3e#uFJDuY!TCoiy&GCSNGLe1O#5ae<}L3 z+%74Pi2mgMU_Ey4%U1812SveR8)xXN?oaQ1{hMp-w^ywDDiiyztzN!cjDwa>y5K6n z|1GTyMDI4Lx-t$MYHA;T?1^5cvbDuO*sMQ3qV<$c`uKfg&y))f5B5Pmy($Wpx*qVy zso$;hx_QI8cLgR>yOv!FtfVmdejlLu4fb_@6C?+yBxjP&oF>j|m8zg|zxt7K@)VjH zX18nhq}9v`SQQW%#zy+voOEhVL}=?EM7`Pa1C9KO95Lx%Do4x)1uhO4+)I zz#}DR=3KT+DmHbTk!Oi!F=VcZ{uk?@y*A`)-0M3-(GHguq)IIB8}6UUcqUW-HalsF zbl;w_1HNSMx<1X!xgyyJX-52^gXIlTf2%nnb)+7)-|1T$bv8-Ad5}2xWT@HoCebM6 zKhkAFlN$AQ|HG-JXHN8N7_47Ly8BVPC11v9eKNUH(n*BER3TDF`+2FS#i)l7?IrIv z;)!=_kLav++dg*7DdwY|Rwzx?Yz3t*Sk9+sN)#UnKC3S2gGbz^%Zt{|+MV}Y(;6tu z`lPwXG%p3x=Y|xY0@<3+27djV*o>UfT#Zj%i-g0o58O5q_cwV5uP2R(hl!0jk`n2y z64=9+I-_r;`uo`)gPSCib8j1ysRsEyeJidGY%@yIwib~1){;-X6Hd;hl#pWBT|+hF zO>u#THMr!C(MY7dg*)yy({;eVR{nv%im{$|dfh6Q=(Yg+S^a@J(_=m5$6iJ z*M@(eHkB;he&Ev9)}fx&&bx1VmF;jST%qa|eVt~Mcu1d0ks0_Eo=459)fOX&CCM)T zj#a$1*y%h|loiBC{9aKU6&Bp>>eE!ttwct-FCH(wcAC*T^I6iF)URTiw?b@sH||Dk zm}!698dzFcfA%h3U{KNoOUGg@sw|5(s`1UbHVI6Z{n3DAtb4e2e(<#7Ot07AFf1`6 zr%$(^dCO$|y&pjBr%q}g7qRm{>V3J}MV3aHWat~_c9Ohdw{Kw5 zF>a}kX>g93S>o+kS)TDdRml?n=X;ep+4rU$X;apU-fuJE}Ks>)ub?gx)p zNAWYp?M-6#;e_u}RN>v17j6vw-aEUeOvP_RnvIF0W}6%fEDg#ecdXqb&GBgYsZ~D0 zDe-mvF~kmxPdY&EQ`gxgn!DY5;pCe@?e$91V@Q}(@wR2pt~y1tKWY+^^w3tZ?Muv- zZs%SaY@&+nZTfm4>DY$IS3}Xx!@oz^u_lGCQ@gga`w{8(34x!;@TJ*J;>(QlxB5Q~ z(}-aQoZj}pr*7x2=B8B+j&Df{TWqiV7i4#3IPS|QgRzbG(coKO=&*`|Q={)da+!mMtXJKD(1#?e?pGP&E}>!}Vqt1Ml@YSH zods1qYS}@ZKY~Vj-8w92Dol7h_#@cNWpDJDqolRWR>t?^hGOBwehc72wM&s!1D7_llok z4{1H&F4K=Ti|t~H)JZOLdbw=sWVAE0!7QNS&4Z1$15hpcCWNP{7;h0jnjsxvSA6%G z|9jW3ow)%V(eH^5uRl+W(HNSSadBxg|tdiV~=b z5&bn&nljY*o6{mEgGnq&DEk7w|4D!Cop|*&DT@k6N0$&fFYSYk&1iGk_QsODS{1K1n67(D>pk(6RE4XwD;( zpW_E*>)P$3vexdt(Y3ZHEa;%m73s$oa$+O$L{kUbsI~fYdS95r4(aynOUwsBH;q|` zw-FpRM(z>Z@w}nEQ9s;ho2~QHfuV>eLkaC2!bxu7dB{f-p(m0hRb=mU(&1feJ+e;u zx~%U!XU?ehJKKIVQeQgaLaI!d;Gz1bPu{iCZY=oTerPT|>SMNkySVfNx^CAaQQsn2@X?YiMkt>QLilC}f8PxVL&SF6=p zF62)q%lYqVlKHTaSuJUs!O>u?5Nj2#G*GNo-$rQKw%)UUeuvQw*dC`JZj3+FY5H5f zX3s&l2cy%mhkOO)E(ye6y$~&~TfH{ZRO{f1ccE%wvOnv#S2pQvxsVut+`*zh>0pG+ z(F7UBsI;EYH}bZaich)eNV2 zWU1H%y!#S0J+dwF^sidl&4+igdanLw^fGG=_u5RxluNq?*E!m9z_~|LbB)Ykqh12J z>5Y$yq>7|&O*COLF}ooe2=>s~nN1oEnGzh(e`8zB6F zM+xS|P2T#L7ABPbj7YyYCo5rGq$e0?kf)O+HtTaGq;=lsSPgM(fGKg%XQrg#>5=q$ zIeQ23#|0ab_RL=Dx%=v`i=fo58nIv_X4;oJZpOSXQpyJ3SJhre+qoj-<$nT&bJe>F ztHqeEf>kc^zH+CN_E@y{aGid|CUbYa7tDy2ye;Z;_CxoDI3H~f+(nh_T&rR**_f~+ z{QTj7!j>eR`~#x_g}+akeR0vrZV(s?A+?mfLJrNmX_ebZN*#Y6P&8IZw2?mRT24Nka=EYS58JYZ8$bhemM56>ToPwM(2n-2dZj6q=1yb?V{&LaqAKI z(#k=e{VuC(EW@ab#zreE(e>Ik(fYok@9X!3)7 zqlmhQr1y+b{N>rwGvD88Z;qFwdzlW0ek`nwzF6mCqShvH*l^pOm(KRa-OYOr)4YF9 zIA4f4r}z4eInm;7nE05Z*_T@SCY@Y?1yjc-E!f&9f%%e<;_&@j+T~U@H?S_dK7n)djCUtO0!ua(|#tM=HSX2=(Ym`SB5r9Pm+ z(&O&X9gk;^UUa$?Zg7c+l|OgnMtOpqLAhA}Ti?>oAa40H^}Wjq zTl=rDBHT(lq`gK3`d%U1W=u8zGdj_8f%o4yA-yY1tYxrnZCv{?o{&!T0i{caguG3c z#2&o$ZI$zf(%n4?S*M}8HIUl9veKbl#Z3Ns<)*_wnxDuhtXy>_{?^aLzgNpozWA!6 z_Px>5t;U1=IRA?wMd9!XpT~~Zw8csoH$0ZV>CwH=uhAE@sog~;qY?yA~QBej2b z_~2yxgAC=hg8?PnERy+}Rks+hf4BdMWH*6m5#Ms@ZQ^dS;TG5Z{+rZHeon0KgVh@~ zsf~@M#y1&BpY2`c^BT%5wMo{}8%$LsyB_+zpcqNqaTa5|`SA&kwb%%2?w{Muut#?> z+C@^r-(*jMM5ard#khr0$Z z4XQ|H-tJ1;S^nT->DfUz7U{6qz|~h_vfm#RrOLpL;PGoR?s117wUincmmWw~KR&}d zF6H!ffRxJpe(_>{{c%_MYv220(xtlwKlIl$g_G89PurTr-$~5>^EUHMPk?{@}?5#kLgK|GMJU8*^8AwTIzJ z%a_0DPT~YORl`~hqblO|@jG}OdT?yRZFh^_J5znKAN-t;+moZ@j9;xhq(*+QU2jHl z)mQyEA5F3ChX&g-uq9zP1jSZ5e@y`sL#MI;EB-`0_?yCU zyBuVi5snp&6QkzBryr^XpiR3wI32&Xp6?A0CfbaR31Y>js!>x{R;4XeIMSz@*<-(g z=V()n8jA!?#)4_2#VqMMv>TGl3Z}oPWK0}y$L)?3P)0Q9Hly?BoYrmifcL0pP)2eL z%-A|x*>f=QMU}{fhmKE@Ef)z*8rc(vejVp7M}|_(z7}L2svM(W#g80m*P!!3?sY2J z=l5K3-^}M?AXMVr;~8XXXD5DS-G7#7{VR0v)aL`gq*S2?DAVA^n}w&@gDV`Vo6RgQ zBU5A%bkI3lWnqy}fJKM%%*>@|Q}r#} z$C;xr>0NjOU@kgnnT<^C{S8BBiN;f~NGmu&HTNETIa-9!@>J+kjlc0Hu%ns$5}43# z!tLPaq<{jB8(fp*N5qO5=zn!MgePP8{J)(jGS8^NZClxkgagQz3X)*WTO!A~0r^Co zwIOn^Y=U{@ZjLz-Ih&w2G4T+tLVJ_fa|M6n_zXS%9y+5UFLNq8t1*g;U2v6sFp6gIe5&SG;xKkE$Cu@bWq1`&%2X*rX&QdUc}zZ4kQsv zGjbNnMDrwgy=SzSOGO4MEfTIe<{%Tvhea2P*k)snW%SdNM4Lh0+rQyyIrtggr~8S) zbzg4jpcAmIeqVJ#JBMT0Irlm+oO@GfcvBqHPp3R&N2K6dMat_L%fePl>hFC4N2DHw zUS|cjw{sq?Va^i21-B2(Yv>TWiSwryRHqS7`uSNz{tx~1!gBl%{S^HS{R~d*Mnd{f z!S^`vCPpek1^>{h7UU;`8{ikGm;d{pmt6B!^snf1ry`)4R74_HG8RtGs#1IN$n#2$ zY4Ra+1`E@xaj%mo&8azW_!&}+{j4mxy1g}XY(S~+GIG9sJkv?^uQo|E2frRQ7d7{8 z zUb%uy%u_}>rrm}e4EoTNM)$B{OCe4b&xdQ zUBej*>R6>Mt{|a9LdE?8D%WVtGiR-?9|t2-przU@ye#SXMK5m~Gm<^@DqzReR@V57 zESXA6Ba+S}_HL|wOneN9YCY$_t#i0u<~FKX!#fOzvs>g|rcscckXn z8XkqgngAXB#{#Pp-pGsnemy5|T$WntWY+U{Vo$KZggi5u0Urj*k9!9?i4L>hA(zly z!8on?(gcGQ=p^-BDhEy~4w=ZIl_b%Ue?~a1@AdN0Hk%KQH=CMv9|~z7!MQC#I!t}7 zcVM-F5(kn`TtY6x-kleu%Ry5u-`63kl@jletE#b&^2krnH8Ns(EbC~VqCo2bL_IqI zpqcf3pw|)8StO8V{jCm?Ca5-9F4Lrm-qzoFpV9p~9JleT3DrJ>?|rS?C`IRf-8+TE z&zd3UrvsX(U1Ja8<}zn<_f8SJ12TUp_UiUT4J5RieoKDx_tzrfJO?L@1*}lS+4WPv z{{}RBWEl=Gk`a*DWjSN?IjGn1JIM@_idguT^NT&-!NT1RPMQ0A@G{-m*T2wi>6xv{ zq!4GfD$awh-tD{|K$~Y-WDGbZw5lys=uG05V!6q=W5H|hBp7r`BUiPxy3!4%wRDCb zmAu=$fCcwk9}H^U$EZIPq}9tZx@5I*KFDX&BoKlIJpKZ{z3kWQKF)z1W@7~-2GgOl zWF%v42ORIId!^uq5$SR#&xXR0@Y9B~qt%21(JxD=70-f)!>BE<-MO@v;?iVZ)h*f^ zEb8m7*_$|%+8u~|Ns;{watFs$|59fv^u)XIg2P2b-+7Q6dv6vu>fqROsj`aW;>biJ zZHm19yOY#6(Yo<4<$1~vQXrhrx-alQaJWfZ0Wme>;b3jyoD4hz7GlT-Asv%$lwpl` z$VV1_Jsc~Hh2xCBqq(zz$b;6&JYGAmcML7#Q7pBQ!OMME`6U2?JkQ}V_Fob@6bk1G zJ?_Wh7*=Kr#7fnu0niZ)YC~NZU;^AY{?hpKQKJqD6)V0gHV)89+FdoqM&E6Ri`>E& z`ru-ja3W`;23kM*FEUiL6*8+V_)eE+%c|5J`7i1Z?17FIw$iQYSZY`>yCjco=VwNVt_51NU<%oNypib5OZ zfg>`Af`+Hwq2o4-gqlts6_z*`34;b0GGa4E$3^J;tp<}M9HP!|b>aA2v_Bm8(H}Cr zn6xt)--X~22!6vnZjrzwE)rBwh}r|Rg-&1?{@y})OCZiK5^ll>BRm9)rSsg)@2Mri z;$Rgzn35^-u=0nf0>T1n%n}y~;TWH^J{ky731zpi5&mvL%xsY`n!iY3AA>=zEE3@O zsn4+5KC95Bg#2Dre8p8Q!SL=ok73ySsjQX?(R|DthGNaan6MPT{RfH#!Oavn@~IB~ zBqLaM3x__BV8!?~7z`)j(b{^8gf@6ctUii}Ex&2FBR{vnQRQkD5p!%yDWOdoE)r&J zsf&bFz?vdRj*Yg_@O3~89Y5v*#B7HwBNF|&NEkuEIRUrPzzHx5Z5Md#{L?2c{f{18 z*KTO3usP>svHhthP_Fi*hKuC=!l3;p+IvKeYhc;lFVhQXNNFsU5&8yO&MF&!8NtXgJTh=Nznf zbG7X9K)*`G;P$xt%(sZpXEsQ=+b=5;kpqYk67H#^u-$+w z3M9ezPxKKR1miphcHj|SmaJ7?30;7 zpmzh1qyHkIAHg2KcZbwmfkv9$<(hmJ?u=mHEt@9&rUB^d%wGYN2@iUnVMppl6G z>1-Ja*;pJM*P&zJ^VrUH@CotWz zxiqeF!6G3GE^j8WHm2F{+Zm7n=Hj$S5MLy$Hm6Mjry*l7E)yL==a3OCxDzg@0yfiW zEMRd9xDY<8Qpg_-x_AIJm)PgG@!RQTD(hp#jAlkx@`C4L9T4jvIy!0EICq-&=vW!CyvC z&6>6bZBBc5m*azw})s5VWiizJ4YX(-sLtIDiqb3=WAo-U5OI z8{#g3(ZOn2AoalsP@o}kGqc>K1L*@}Wv%*cu}J8Mvn~c6ZpbRcqGP~2-xS}2--J_^ zkv}MV1Xu*3t67PqV7ULh;Q)#~g6rge?9a_YCQZ^AN{2z{0<&lK+7BLPCSr4OvCdeC}kIDfQINoujdqaH4$Ks z_YVw{uRzRRAVN0`fNBFFFt!Yt%5%a%Iw2mUnJxS{Nn9js<3|-s6=YHm=9PJDP23C@ z+(p@|Q8tAGW3Uk12IWg1#>xQp`(QKxRS?QOk$}r9p93#Y03L?$nfHgtzpCHx5yl?3He6-F7 z85wcHO!OtpqZbJ@?Fg6!eBP`EyvpIt8WO%yw8`uo48S6Rvch2wugYu z_fbgvFs=?U$j>pcm#f9Hsq)FryXVU8Ev;=QP_anPFw^XfLnpKyJ|UU2-6a7d3N*2 zn8J%KjOF2&_Um+RUT0I#+wPzwM^6l}^EY?ib-4MSh=bhyZ~|1b^5G ze%}~$;PS0864+ZMVq5HnAIP1`wDt%D!u20d+x6lCa8aJL?vM3b8fdr>Z9OA5k#E zV~gQfQsu+7ekBjB(TPq#M>B%${0-JPeGT^lCj@Lgod#tedZ)~=R|}}r-<*M)`GI&_ zgGrOqO^=3iNGuTZ7%M%CVe|D1c%f&_sgU@hoQL_>7=hk_ttNqVo?;vX;od~$P^`$dPkxsASG+*KG^yIC zlP!rn0L7R;aJs7470wN{RlVE=qRjt;a`t2ir^kK!?7hAFXy|uIYiVTg=Za$b*PL}~ z-@JjEIt0JnUGP7zJbSuAMJT?4)w`dw|&rT2i@5_OC_INtAb8Rn0Sh)X>k4 z_3S)2+n9NeKD*p2(T#VM7YWPBe^ZXhaLa19tmmhYTsa8tU}86cr20ys8#5Rq> zxrkO3;Gr5BqBKqmw#@co1zQr2hqNM5SIY5%I!%5beF9rN;vW~=77{MS+ep0pJq95OP zLi}4;W)4QabaStj0r-sD`pd^FZt0Ns<}g8{x(TP(kglo^>mM;ec*Hz}KX}XlZZZNx zb)Y=ta)q*yi6Hh`R7408uc*niB&V4ub@UH|(X{kA$_t6)*eGp|$);m3_PjFbH#t9$ z>oxe2##---pNhaE=i-1)FK|*Q8|2;ELF3gj++W)Evh_DB9sehxJgIH#F^^2?|K^|^)E8ty0tHWHsqt` zA^5;FPb-XIu&mI{HSmghWMH)mIMM1z^6^Y$K-1ClZF8^}aeC8fX>{KC}HCB_tQ<*!F1XD9T$0%1EZ+ z0+2%Xu7(P08vrdCKLU~aP>Vu$qlHiNL-2lN3mQj_$p+uvmE#U}K^D3f22!|dyNY>Z zv(KQ8yQAXHPe-(E&s-&sBFiT+G%W6R}k5sQ(a>Wbf_Its-z0?V60_5)#g!!z1ypRlwXigV`KDLK*2yN zfBhl^tRGG5*P!-K}O1sRk1-ER+lT2 z@Rg8VQFtbyIq4#Bk^HT&Yk~Kl>7`(FOcgut1d$NX-{A&A`pZvA2vqLRD^LrwWKYK=5jy}Eh{bUz!Ly%fiqRA|UBsccBJGTWXc(X}ur8 z?gO)o&!t8>Ss+o%VnwU}3f)rMhK|s2Q5d5Ua0GCA6uK?ho1vGm z(tr_vo;J1lIXY;bn29hu_$p*>GDO*W;CYh^7D)JdcP+Fk8!)g88GfT|!p~!bK#~RG z6(1~!{Gvjl6KLl{1|*=5upxfMHGb(Xw!e;6Gn<__YU7D&>6`5Rx|8Var(Yk8y-`^Cjr2Pnc|ho20z<>;P2q_|^s`2$c&$+ly0pRCXJk zpAN*IgMZl04-A8$tJ0r3=!I^8p-BI;bhwAeMY%iR!(trVLP(Tos9&KO-6qn+suQCu zC}!ZA=6e!=(PPlHxFKTSaiV(9>@^}JA(qf7DM1@lZ*gI`_pQ!}NS_qvCrZr%HFXha!jl3361rvVm{!Vs&-gqFTNBSf*27) zF}Ht>K_(Rh4CbeTV3VMMW#E-VPdHfdvpgs8=J)ASBf-e@+YsFxXs5n`w*i2BY>z*2 zRp9fvMmTNvp(4j|HDIEix`52z+m+p-bZUvQnSOtud8WTjnQbZh$&1tPHdfOCaHpUQ^<(++vVj=t*@e#^B0BPssc|gWe^)>tWbnpQWv>D4|DMQX-?IeyWH){3D0bF*w z$LXjyK(Ky%;%{7{J;g5jXz2h5O>Ov>Tm@}TIMcn;P^BP?C!cHi7Pt^-Ih_U$4L7mh z2g475?Qy98YA~)eK!-ld@7P(^nEXBA)0_f4Ez$civ`E-sYgxeW?)H&?u2J*J4K$Bx zCig}PunxhzL!>Fape^Yylyf7_6rZh3BTahxT0yYiva+z3y%p{ATHW!S1`d-W3v;#6 zrt{SJ4jX4|eFVqQH#RobID@N;_}A@RiVnRdqa3h+QHUSDuo&^kD!&t%1fR{`Y(JKr z_vT!8#v(!PR*aQ-#Gx?Sl&WQ;Z8#mCMasPW_sS`}MAr@@7QAjLy}k#UrJ*upY{iUE|sa!6Z1v{f|rtFZ!{#4+HlZ^s?jIZPNi z5y9r6)QPrjONJ{{#u--}R3LDD(T6O&dfE@1Zpc*+#v?UFpf&^-I^Kb(LWBPTY6jh!mI1ZI&aCL*dla@Ej_iwb{d$t*Diz5i-U={`v>_hlXBv6?yk-H*>hy|jU zC>6f`oh;xjg-@UaN$h2L58(c0FTqx)s9@)Sk}v*r1WZgT4|2K0%uD5)(Z+k{g&nhv zyDm`K3TAE;-m-k#msSjpC|EJtj=QQFp7BmvI!0%&Q?hM1LYx1H)_&}$X;qTR{3Q4!qE z_qHHlK+4^PuPJA`&2p6?`;aA}Q@M1eXIrxk4HLixh11*hBhnx$y9cgF28zMtDBH-aA@;6Ji=t1ZH>QbgR0NQ6jPw$%yLCTk^eyu<4H$*bowENz-i?kP8K0blUV zgWrCyh2HJt|C%Lz$b{284H`ixr^aeaI8UGl?@RN{zy)5F=1!{G#QcTKCo>%DcNLMx zSMB>%w|TYzE5K-MaE-4~u4P*y5S|{?WrIs}q<0uqK^>0mCO@eVJ`Tfgfv%k}1;nJ# zGoB@MVhL$d$AiL7#zIN6IuKMp=j3-E$gZ%C>M1`G4|3NAsEYvnt8y zRL&H?-E-4zYap zT*Q$USgb)NWmAy3N2O@>s^aE6cvb`2hW8F(^r0062(Fp=__(czc_X+Aa01x6O~(8K z$6F3G=7J=f|FS>p{{wTVu%|k3k`3DPcTFD6%A{U7dEA0^Na4R61XDI~;(Su)1w8g0 zej;WCkRZr4Llp>F1FMd|M!6QEKZsI>HtAu2MIvl48m-bN^ZNM}lg%_GTX07YE&v;S zaTG?lwH=Bd1GFu10UPQB5dA><^~>FG<9p|Q&*qxxW?^(y_SXLAXupaTX6H%e#MgkM zQ}903+R=$^hSk0$Ehszk+zR@vVkf4N4ZI9SYC51T60CF^Y_p`nV8DS48CZCV)&r;pL>2(z((C3N z)}_y1H#`{YzC4oz&b-|X-+0^UQP3a0{@8N4A~I3<;YS((N0UcTp_@P!7*XuTM`VU! zO3LwHKv?R5kI&k&N}NoFUsyc>n~hh$!B3gTq%l8z$wy!yK*3Um5kv+Qjs|nv@6%F;Ckh)Y@K6K#-WdK z0rxIEHQJ9d9X*EMH36W(l&lnNw$yL=lfJ%tGmFBeL(`Kj_sOmp0PGHBLjgOL#*NM$ zd-w&GuWMQ_d%c)OIdW~+ocB%^ML42;ZTHMFk5I9*c{@3f&Dg@*?wUo*j$gR#d*shI z`!`pL3>zGk3)FLk-@Z>Xwj#3AEbILahDvg)idb9TZ$3KBeErc1YH-{W zt=(_5C!L(=2uKMVv#!}J$V2}?ZMP}H7-H>bzUjB{}7M>Px z77569!NHUwJ-eUMCk21p@jclwhzy93_mla4c9VOiKr=}#u=hf+QDoc1K0CXA zmi3`2N|g~+{>1FyK3@+naqk8*Nwepv2kODr49T~xy3LJy_k%dW464!hg{Ox^8G+T*6Ph%ytrYKz6!h;O{j}hSO-W-~?qY}YDn{Zhv zuVumVY&vsf{BP!2j%X{ux|zb>;NC+-W;_gFO|IB{Nd_h(D-s;P?4TjYv`<65+vaTB*Hs45}>@3bbRBAe_1T*U9!~A9x{`Rmy!u zO-68B_QT7`PlUl6umKdI0bq!A*gkt?_GEHgo~GZbqBjfaFVDvt9ICmdX|W6yv|dJ9 zS3Gee*;Lp#4FZkE_Y;R`bMCoF1<4R7YgJn?ZXDgJIDj#(wJ?W}voTi(C8_NzjG znamB1$YP;^)E37Z$%*65 z(|R2byGfJIbtt3sdvgLoj@MK94A_nP zY!(Sh8O_1JOf)Oe_OgW^s{8JRK;0;MZ%F?}fVJP1i7l`d_SPV2S_xsvrY52N%_{S^ z=C{Jj)#CmlO!tIE!WJ59_>B^2@=SkVOnAB6!19~ZW+UxOuRdrJVM{aOcKI{HgLfyk zdwjROuNoE^sF-v8>Dzs3xyMtcHYdO`Y?Qi*(YlZ65e3m$mjnHBz|@-I&A&H~=Cz}r zkguZ8TEY_U@H&bolW!nbhDkQr$Ym$)^L_{ksVMszoC{unM=lcP;rCZ`UM4hjRpGEf zbm2GThGs1G1U!e1YrN^-YJ-nHG#Wre@AA(3zdg>+x8?@t4RowdHUBoW z`E!m!5^n-Od}7iX1|qyNJ{c-_ZL`|E1-@ka5(mK*uIqmOSp)GkEK(SmOvdEk+z!v~ zBD>GN@JY$pb6j7fY$8MMH6MQPeT_q?~?DpW9(8d1zLocxYfXUbknjBPE9&2YVL?RDAl zlnk%+TgtHx$9-Gvb4=0^*2>Q*C>v(D)qW|e7IM6$!>MskhUfYNO(<7v)M%TFpD1H? zT%~u)Or=EH^V!k9R!6mD+%e_x>p=bRgWuNQo1DmS`K{M_>Q`2wUbikQP*&?a$zYgT z&0M~$`SOr%Q}Fgm4R@d=98xpga7q_UH0e8FJiix<@ z_@9YhJ;2{|B-gBH0Tn9bFMy8zRR#PWWr`VP)Xi%Vgd`Mt079MjKGo1(D(AA~};LudoPCv0S&f)6QEQrKIIj zt1nASy=cprI$mIRLg&x%^f2?JPk4rf>pp(ky)0fs+9LYQ2@c@&v=+|ND0e9t+vm_Q|>iGiMF`vBUvmUWg9a_{=Nzr8NS z*DA_gwbZs9ni@7=T8@tx#` zMS?~kpm;-Ob0F`QAwZG8hYCtehd;C-CUsi!?Lhp!|CI9VyV=FIIc;W3b+SVQoa4?i z*{VxK@6B18+6ui#%4l;tNi=3=gN36N(y|(4{vNF&VoPaL?sAU|U0tHqt$``lLYM(p zHtAEac{o(cMY-*Puj}b->)$UOMpuAWPwdXDw!K{Ata#FgW$qB8|Fz|bq4gfon|>v` z{Y>_)W;r;=jhBA>nWEjQSs+}o{CW%X;q0>|wijZIznJUJUv;a#UpG|osKC`}rk46T zIrZ;5P;J)f>0m>h8C8CwiC}8o{4OnYFTg)dh9Q#!$9iGmZ#?lwz5;lvfz5j6p0J)W zT>x7++|ZHbkj7~`tY)s?G<_Nza2+&w$}C${mfzUaS2=2Z{?YSxc}Aqq4m!^Qiq7u| zPC~_*V1Sz}UZ#s2^B7>Svk>glvwpkH;{?F5)j#tQ5Fg6)Ybq03`s31mI!5?CDF^T~B}&VGpv zd)1@JUqnuYez+O*x&p}TtIQ3y->2l$d*$ppTIz?vF73Wt&}2AIBQt7sR<7bM>i~1F z)$I4m`yll5C2)(Mvw$?FR>VlCRVcc>CNC1+(%IS_pMjTl{z?GEFvFvEiN-nm4zLa< z7CBl4+1{*7R`Uk`P5!WCIIgkb$N95=D$R}HdYr_5p*(!bE~D8lOE_|>_0m_rrzym- zr{!`MYKQKU(mlB{xNr5|0|ZIKc+W}VpS!c#1Wkc7jdhLqA-QL`l z+X~n2m%pH*Q&gQs>H91`WSR)?9G{MyFYqDVDZz z%O|gJjztCB+k)VZKVDv=$YaFiY*^r&@4XV9DjOd|Vvp$IS*nAP4BDu-cVAC@a6- z8oE3LM`tD3gm-CZoEh)1t<(vlT!O$l_i3@Dhc~dV$ua^GE)JJ-nFN-=0BT1#$Uk?y z`HVBb0#U_}ZH!p2Fk=m16wKIa#$!aVSl`4O*mog00e5{xV%al(ixAcmUTs-A+-+qP z%7GU%|B!LumZt~jLq;K2kvMA;e}gbqE@DIGru$6veWxj6CIYU61dnVwD#|jPsm(#b zR{j0_y&_Cm_Vp}o@C&FxQ44#2HVGw)hV><~!)OWPLk+gbm>ygHk{UMHE_KZN9RO~h z;_K|qX_COq>3%`wa0ItxXhZ*Gnfft-#GpLIlMCkQStb3b zrI^6_I19Rt;5p|DVv7$l>vFsCaS*S;Z;$4{i7gh&ljB>L1&<_IPQ>%8BgdJJ)iI`Y zgOI793g^iOke{058|I%3*A}CPEal$_(T-P&Q(kJ|(A;w09IJm6ST7+7#jTG0`Zgob z+Mg5hGH+lYsv;Gr8v`O>o?N~`&{T^%bnE1Eb8eS#(c`SoQ3DApcSevaiRjPikG<#y zR&59I0_Goqb}$WV^1%KjHRxgqd}X>UUUZtg2*n zuyZ!+_>$wnoj^9vEU>S#OnU#<5qD9~g#|T{ z9zL?gcWrUxO=SW@aS)lS$X#kqiZH{_>{_J@&gFd?8}n-Z!JNT@aO(I=fmAXuJW#iP zUZwCypfp zNbYNmPW4LO|kY*k71#vSxMW9++h|bbf=+Mgu&?HR}!tAv6a_RBeN&9{vN{ zVg5{xA=Uj2N8u|6!%1Sxpcsj4nqD$yBAWtTY=@YbWHOX7NH+^HsCX;4A_Q351o=IG zGPPu`TUv;P4fTRpwc`y!h67d4=H~N|mlf`awY+?$WBZL-^Wf@ROktMtiQm9eBPaGN z>IsEtpMQ7|`A}6Y?m2ZEuOb|8&^A6LIoLH0@2NG#(@agLr)|O6M7a{;L3L>|;agxm zvpLd)uksgs*(p(e$o6GISsV2Wr#E_j1zQOS0!i1YadzIdwB4p!*rp7V!v@sAu|Hdflo}}#AQ=7@42;NMV0Y}0T`*R2tUO1Cy@tr&$+;Clumr_Ym)lk zgMV8aW%!)3YOD=zgupHQ)nOW22zJzzJg$Gz{mB;q0vY}9N;&eybk%PkZrtUz&NhZd zJP&=pOH+e+wK*n)WaZ*uar-`TO(a_b8Y1k!;qYSKk~stzssIR708r4{B6=4CdfI=R z$E)*3f6Qv)o0bruc9KG*jz={D^aAoyWf?Tc$<>xWO=u)Gh^fVFcfPoco#M9^8qLQ{IRkccVozkg+e2{5Z`;$YU4TZT2k0X z)@XvY8h17 z=HFR{ z@AFJKY__7Y%0+vAkU(V_MQqc7l668#<)LiH?RyvvVIw{8Ri5lZ92A{a@znSuSgMT$ zl&l3=JB(Z5A!zSM^|@JYwvJ*yf;ym^Zp<3Y+pQ5YY4#foY6;3iL53JL80xW`AhVwt zx7blaG+F)@sNL-}!&bSjHi?KJR@sCNmp9)a55<(OhOt~%Q|c|b0>-1(BxVKd-2FFn zrL-FV7*zTjI;!bFdqSq%WQ#zS1xOgeHrUJVa%!)CYF1@rn3NK4WCl~3Q$AmfyP09z z(+N3F#b1-*15a57Z2kmuPHpn1%*$9|s~pXT2s}vH`ilvzON|a2);UZLSRpF7Ms6Lu zo0hqYsV?*nU&5*ZHEBY~55H&frZ6-oY)D%a41A!X*bSr#U?E}zLZ~>63G9z$iRhIl zyY!NG4QE~9WI%V9Q?Ua!M>?{RHDHMXa$H^LRt$`A3seJItYHL3@QF)qvYd0eoSB~rYeKF-l~`t^mweN^dNd3 zS#dJuUwzc2RyA=Tt~Sicdf%Z$f;Ho1Idv45u;b_?0yi1N%qWh0(7LEGmSH&rib4qV z40z8KrFzkAV4b;B)oWW z7}^!KZlJD5?D=91(M4cb0Gs`Fc{Bu|>maBRsX7sc-(tbPp!Qk>$757NB#1mlO} z7eHexPsTb$^&d2u!j3RY_n&|8Q$wt}e{8*pnY=&$+`mZCdN?W9q|_}rS;T%OtO8@0 zKbzv7Ei;1z&u{{EEKrqjl%wts<_yi^y7ym4yaz182v?CYEdU;9V5nn)9v)=6Ws+FY z*5^f^g|eZT!ZYBqnirQ7hO?{jy&(0UMxaZ!`QwG&G!s1ZE^hMI79M#(2vMxU4motV zW+IUTs3}oLzM_2J(HTJUM6wBy3V@b_;z_S}k!46{&8ax>-_u8|rh}x{q9^BeF0EvY z!BpG^{S52=tNMC6Y|ZX;B$wEdF4zeH3<2|0T8z)!@$mBVcDdxIz;38at$7*+vg=_b zLXwY{Omq@?V8o*SpS%OuKbI0~SkFfy@f(wNl}N;|Lzf{}oWF zL3&pjWD6e3pN@<`CL_t_S!R%PS#bTU3x(*IXcLW9gX|4G+)AzO>hq$PUUv^|E`Y5^ znsTaJ(28Bmw%0+Ap(~Rln*-!FNAIT4%wdKKLZm{W355i0F(YlnNg+-H1clOZUeB%qT)jsWoV09$h+#HbMq_Udw5 zmH)e>qjS@_Mfl#NC
    0z>FEOxFY9TmN5J*57hKOjT~uB#3gFJ5EM`;rSt^2{ z#Z1JUXxM0L5crY_vJk5)23-UBFtJ|E+5ij?cy#(e=(OKg*vbeDA7B448M^xB{11L| z?)|?FQ|?*idKu{7o5@`}Kaoem>tFL1A1`JlTEBQw@hb7V?%(NF*w)B1*iklgW!4zh z5448?_q;XSETabmUxsDcBK!f>|~3tgzmvGDK>>pKBG^$pyADm*L?u zu$871p~AB_({e&a+Sy+KfX$f%0O1}H6$=&&a_&I8sBwY-u~l`W3Wl$X0h+!E1ET}; zm$Y}-)}`Gpi4{>k=-TOWGsM*$JJkOsklK~7aOA$8i)w;lW&MnS;#R@euh%)Zmun;^ z9hAAr1Kg1T3IdZ0o;G-K6Mzu#1EC@f01D_~C>plm0wywDLnrrQ4uN*<_`LE#gQ6XZ zmJ{25Gk76ATU@czUQzuNp>b-G;BDwe9O(DLGhOhO+klFp{j}B&{31_=xB#gFk95o; zgTOF2nAAG{pDNEtfuN(rrp(bWAd%LCX(9p2fs3&Dd9{HmO37v}z95_81G{Jn z3@Z;rI*uaHf7Uw}X#%F>M9>iWm_P>rWU5ML0t5gs2Okp*W5J6w@w$0Ngy>qmGX_AQ zv_a2&OdJ~k@OmHCowsU(1EAB(tby4D-;LO?!KJG;05%lB0C4DjB8w;y{6VvYlyLJu z7Wldz*yY9aze^9}K6LGhv+h9mUZUEDtA@{h26t8bDli9HA@=zrU7onRf?XTgF#LUO zAtAt!f}!R;5JxsR=8xkzDxr^aGLyE2N6Y|Z!8owIuAr8wK?A-Q$I=Y$L@T$!R#Zb* z`SW%#pAr(r&-G#704883!D5bC40PdaOLQOvEp1cK<@rA)0ojGTyfqSK8JWGbZ{Js_ zD~s)We3Dw8DTV)sPvVO38`3f;^ffC`~njip)K?=^_(#ZkasD>J_9HR z%`7Simdd~vjN}Ip9AHzF-WeN#<}nB%+n^y#O=5`*wubFKo&t{{DvC@P_-(@U0C+w% zP#3XYvj)&j2Z%-qB)%!6@f>80!u1B$Hb-C-H}4yPx%o|y1cf#zw%s0Bvdfp6@r276 z9kup`#UN{G1oSquBDEOiB2E0|*b9Jn8)C2_V1jwXzSpH^U9J$E2N3TOVpRgVCX<{G z{@xN6Su;xFS8MGE8pJRGB#-~! zC67-4#5m>LryHw)xsQo`D02bj0D>+^l89Qv3mFz-s%0yEBrU4BWHz!kwlZ2#@WdsN zMB=W$|2DG0<%nAwf!ct3!?0i&SSlJD-2mglV+>+l#MS8nOh!^Fb~p<*9Dt`|c7ey@ zZ+MAsmH~_d?M^&!Lm(!_oPT_d8?hXb35Xp4=jpIFOSB8o&a?HV1X(CP9aT*51!m0z zQY7(0ESIe76p-5ynLQ#4><3TLVe=3Dsev=)GwgeLHHT(!MXZSl^x`t~LI8&6hvIKL zIPAnf=`6)EGsDoGY5#G!4`a>Di6%-o2R9D7LDd$7iTwYkigmC-2&@n z8A!$Iu7dc1ypD_D{WGxS{uEsW zn~5Cc$iRV_1sFVIt+w#N4?#D80x@D>&cw$}b7Eo+g8-m|mg*63p#KvP?8M3f>@S`F zG!q46hW-U@V>^)OuLF#o2Gj%)c@ZZR#vtEC?26i8gIeP|l%D~j~spzf|s%WQgKt~H>JQZrp0+Ot$%77k=PFeJ_@u@Mk9usB~E?IE~-Pq}ZAHsen zcFKcsF?O9<;cQ~a6CljILf9@AI_6M)3|JtPs1I;*wb2UQA>16SWCkbVKI|PQNEs*t zP@4x~%h|;9*B@%mh@d_Gjwzr^A)@wKp%+v{z9W&e(5EZXNQ{Z`#9_)f>jM>(;lYJY z7R7`+L_(;pJPm*lw9=1q!vqlnmJq{`t{u>*xescK%h)UW25r;J0HufJR3$&2Nmg_z zO;rHt63%{46c2>5@QSW{#)Pw~im8}kYccAmH&FxR34uZ?`hi85aJIDkn5D=?u%nXkc!E3DLWRx!hbgdFJh!{oe)|2%z!2*X6j09pTqBCIzP9G?q%ppMzF zIZ;Cdfk6*Tx<$ButWo3#D5j$OrtUd9zwrWDC5fz(vc!pkbwAb6{spewjG%mi;azqpV5~j56w%$ zFgAo>=$=`v9+pf*1b7sh2CVJ>f%M=HWj&xQ&DIkU7fnVaR|7TnR8(Vu(vC=S8_N4M z2{(RJjlk*Tz(}H*i(fseDbWK~#yJ3RiH@b%^=UO|tm~Hmi!CPHha5u>{Uu;SpjoX> z(77@*Gc!SW$~IiQxr^MK3x{o%`Rn=n!|-<;K}rm0jAQ*=HdsYh1jZP=T{n-IM1!yg z%zEX&SOng41;AwCIY1BwplE>fs6wm>Fm~dWYV74)Vjs=|u>q_;9n@Tb*JIN(NZc&0YN{Hq0hBWYzQ7Ip^>J8O+c25aK%)>1!}GEM=YtY%W|e>h z(6Pz_Km}N@fQ>}nKs+~7f`}1FG-fL1G=%L44z8vI7T8FM5HOS@LGV1dhz0GRupgi~ zN0%oB#p!OqzN+%H`Dak$^#qRp>^Mq%ILI4t&?8$U z1PejaaLNFDiH6~Cr-B6M|1tpx`o9lZOicX;OM;*V0etO0Y5yZv_5px7;du&vV*w|~ z=Uz~=0ueQ+go+8#|4BFmJ`K!t3&{Ep6ideO#McpFI}?cC13hvDDa##z!(a~U9d`=B zIwE|JXkttclWk6fK!fnemAD6Zl)*NJn#kX!a7@9iP6Y6s6rwDGpnjbPHhANWh&c71EZ_$y7-$qB z25iFzPpvdT`~@zkPPiOHjOH2u&UOKIfSD%RP9fM31U?-zh~FhNihv?NGZYUh);REB zK+ytrasihg1TA!e;(xQZk_VdwltAKw4tO^h=zsV~{2*Z10X!$ZIYwK?i*Hz~CZbhcF_GG4sDH z`oCEMP025y9zc8z*yzUr^h&H2M}h_i_|11voF8~tA>;hB-Vy0iPF!2AK`(fL&*HrQ zI}yTtwhFj6DRG`KfU3ax=L>=$BTOWj4iWECzD7!N=`sT;!=+2~6<-GQB(-)}S8m~d zN6MK&7H>%x#clk-KKOk=VqFQeLM*5yEayE7{c9Eq_S!ox?Zgm!QT?kKvIFucJ`V~iaPE3IX6*yBDyv3 z+kY!QzmGAuWC5ao>%LsZt*38!`>*>~xitLx5%+Syp=O`DPg66?;g?xG);c5YjZ(MS zQhUJzG(~?!2}a9`e=NB4L&D!}l?>${EnkuubLb-ZzOLjT{yadH$(EYrKU%q|=uc{x z`MPLvM2WYjn6mfg)AHVKS*EOL>i}~j2lBPpElC6)orL57R$YvY-4B%=(%=niQ#i=+`S83 zQf`*zzuF2s=4_DfV6dOY>XfzvaZj$ob-&r0*| zSzOE3c~%OEY8jaQl1^1*!P2v3YqfzmoPG$R%&g$3Q!HX?y6F?rpt;stY&@<52wzzTb3XA#>p4vetqb+y)&990RKn+H^FPI^{%CP)Q+rX zq)w65e6RgPnXRUqR}EYnar52RMo-~@*tc=+o<>IM$Y|4!b54xwY4bn-ta^;efg_Wp z!5;hpjc!7GL`Ks^wZxc`MICkTQNq~_xC&5iX-CvTv>6z%seejU42#>f1YsNUf`f9U zeQ)JVZ!u-KQS{?te~A%Hj?E&B;(65br-6g9Pb&?sT0*eaSaBb*WNJ|>{W9N(3iD7` zrnX7J)TMEomsWx$-&}LQP?>)B*%50hS*YYW5#a9q+V|;^v7~9cH=BOMx{KQz3Hzur z?t6{77a=#4)av-+83}eZ;o13z4ybpZsjnFKBsV_`Krm>!Mm$yG;^5hkEzPva)crIW z-t^Ja>SwSA?{~!uDUJlT{Oietnv!)YvJO= zNb-JEzwF67x$yswK$qPV99AEc<2h}yLQprXR00$~@X!fHv&qBvB6Pny(#)tA~3wza%$VB=e1$q!TJMGGW;LmW2U_vE^~&VmYnG}f32obqCmX{djP0mw zWh%WM8XC?AB+i;bSsRR#xJi$LCs_5Xb;(aY|9&YhdSt|*z1$jdPk!=^!d+@ITp6XE zZu2OYQcK2O8`-X*rn24WqZg`tT zq&r+ZPA!j5+a{Rk*ku%Nlgh|{;q*&$hLPMB(x)N6S>XK1;|#0$F&A;#`07vJ;FAS` zh97i2`e#Ya*DTPH8+(Hf#}<`hWd5oo*Y!mQtK>YBS^ZWS@}S@MW-6u=-L4pAQ82hR z>9zW{f4iu64DQLj&QN)USI1kCOIlpDFsZ0Nq43Wi)WvP;=xW2uw>+H)l~M2I`#xG8 zjPt#(l|@eWXEuvD8sw+CUZ$$i-d$;Y9c30@@WCW+w7F)b^XrR2`|b}PPUC|u?=UEu z1P_#^Xud9UYM6f~s`T3`fIa@fQ+=;*O>Xd}PLokq(rXo2t(u=#hEqQHXU;0;MUFWC zxH_BGPeONFa>X6*;7ge_nm8>x=QZ>GGgX;o&!=16Dz-)~_nbGx3Iy~E+DZ=UWNKao_nXjmq$sDkL)ldwN~t36$IHO++00!7hjN=m=wnK z@;-edaowc)d$6V(TkQ|wv!1np$>v#i=m-gWj}l9mk)7~v{r6AHAA<5IV#$9uP`-)Z zX1HYiSZP)HXgZ-U=?=@go0xALOZcor0rIgEMHzqZICIQc0I9{qf~Gfal}cDm{(4njw+xQiZZsSeA>8yfEnDMg{@}9J}6H@m~ry>+Y) z;M%!Sl#fY#pzxX|Nbh$WF*bPc3H`}qNz9Ly+f=tr{q5*V;h_s{hFft1J!u4u*RG*v z^i}QF_T?GR%GuW_XiwP!;mMad+U*~Zg{)b`IxfexklCh2??&n??YycjvWR75*cw|^ zmOSnay<=vr{uKM8(F$@q#PUbV>Uqc~gS6clHqq9PDnEXk66#L3d@p&oF_H%NvQ%?P zFg)6=fFcU%#1Bwtv*59JrD1j^dKMj83(8!zO%|Z&saVthY@)4s;I`F)lzLiR>Rl_@ zt5yys3Xg;{KHg*+CArgKU0)!-cF&WWH<2p-0S312DE_>7lOzEW#p5>VIOM)|(HtGv zHF*1Bq%Gb(S4iP-ZieJntl6hyimpq_^i5pDCw`%S*K{7;9Ypbt;F#xOgg*W<6eN?eO#6yn0@>b|>mnq9zH^r5He7H1Drp0txqWeeIa7bQB`(X1oT zr4nRy&xg)FDp5HKb(nJg{%2s@n~cgLuHhESnZKHW)5X`u9v^fp6-rS0I4Qrm`)OGE zxe053phLewcM>~1?S@|{I|2=C@?T&-?T=;1 z7;_t`SuuRyjR@(|`F}XeAniG|A0KJR%}+cv{4SGsKPs&CVph*J)fhmcPrTW^f?6j! z>QDyT0|R(&w@RbR4Q7OEO|i6fh45Ck{hoQw6e_XnxFCPgd58rF~6^8 zy``W*CcjJO`iyoDpYU>|XV}w<%3l5;)U1jysZ6Dlx>f7J?teSnqQBJ&E#7)NyqAEF zi#3$~SJ-|RyE}EI^B5)KJMrrhQuqPQKL6+$SR7sNRKA~@?@|t{MKNN!PF>~f8r5I> zsCDqx9j)R9*hs7`_}C_$gl0?y8J!BQ+!EKLwTOC~j2WnMK&~>cy%|X}Q9f!X6|2~t zw2_;ZZ#w^h_+-&pvg_;`!Aqv!xt{~}!S~4ApOdEw1{u6LdoRcPq^?mnZ-)1Gn4iS2(|i!lDJy-s=1uVJ1G{o)Yq_*XH1B>#-Qcqn=If{!pGW#hXQItXeYr$m z%hMQtz2+iR{iVm`R_@yL)wIILLGmBjH_q2Tu7#XcZYmsMq~Hn{{U5arEx22>?n=2eSk zm6{E?@>`sE#n&PE@1iIt1?Qg^GIt*~o^^j5JLyvG*? zbISCj=E33k!mAao);-lcP+M?Vd2z4&QsfQ1vS00Isb|i-rHs)t;g@(Ux)-Hcn)9g* zXBQsCUUt34WpG_jTuYCF&F+&T$-O1Bj+-H6(H8*(qF1_ z!;PU-_diLvsMS2p%3skQ+0_aeCfoKqaYrvpxLvT6+UmKBh6F%kvr*ALmar>IUJ(0_ zxc&fM)IN!V_D3nHWBY#3X3-hW9=o5l$w?+G=(Y!@^>yPFuU@=}&30n!cf=ZFvdAa< zZnQ1#Z7aRlF5>OehV!6h?z*pE%yK>%6ielB`T~`CBY*$9;@Jt=^;vq6Z7H$cT!)o+ zFz=B5ZkO3jTp9+eba%9{TJ=a!_G+qXIOS{4j-GEOJjx%E+*t2^qZ7@!%N$}i#u{M# zxWCnihNSWIn<&afkoX!_f6y1geq-Ux)n)qQGzMFmx;rv5%Y zK*~{mmz^>&e7K%>`s{&XV*ol~DzZ84P75{p@q>uhOQL!kwH(xR8@`nk&$arBMl*Ai z_S~}s0ql;XCfCpjr>bXbK+P3{$` zrZD<`-dS1gPVDPraCx99 z#st}ujXEoAE@xqj1-nB0t^6Z~><_FB)?^nR1z@-wu9s(jpnRHUSi)bCdpF(T@~GLQ zJ<@~@v-E|1?*>hI*--p~6zl!YCw}^7W?OVuuQ`0&+$MSB<*sH%*q2dw>00}_9UaBs zo|nwB^U&`~zP~fCgO*6e(!*w!&6#JS2IK2^lxFFWc$J*KI3vP3mgRix)xR4nh&yaZ4^)a>i0D zB@2Qyll3Zsv?Fil$KJy?Y;uIq?>@)(tfl2WBwMC5AltYUTqp3cw5Rg{KL<6#^{C7d z|BR&{ymS&NH&?r*{}xFS&+KI&0Y~+cULv_nMh1Q$A)zF>#Bk#_Jvg-Yx;HvKqNw&~ z&no8scV;gs0|}Jgc3XVZ$_lIU{UO#q+|YtrjxDLR_F%7@w$H8CLb&tEgP;s>0}%Yh zd#9xxju6R2k8z4OO6|`c+H>CzRUS_qs8?C!wsgTzS-Rw`Z+tx+i0OC4Y>mRldP24A zkJRnUCdg8PFfYfWEkZR7l@(vUt6TfFCLulE?YpH&t%Ch}Cl+38EiRJcMlBS?<)E>y zlpOVLA@nTx)M3;kdI68DWgv??=aD8M6$C!BTwUN?pf!t10i0-ck4|`Me?P6C zQ}ynPPf~sDUnT4YQ_UtE?Q(E&<5vQB2L0U@{O1QS@+68hVr#!O+R3>G*~VYX`SPxE z9r$Mcs*Ls<)%2yazEK@>%t0}NoSWZvMAE#sN~2>UZyPfU>=;kgXMpB^l~xzL@tJm& z)Y_56NODN}!JQbCzW_GbvSpcft>k~>#lM)*x;gi4 zL|jwK{ykqv#F?DMEMfrjLF`Mj<$}^I^{#nvM7Fc!a3$ zZjPFTRUe(tt<4$QG5y)xv@><4^o4J^{bq-7dDHR2M`$y<&&|lzE&)fK($&yWh|Bf% zmef!8kz)&c`-{K!J08FkY~V>rt>Nw%ow9|k&yrhXTG`ElVm=bHAwz7)_kLLUr25pW zc3;12jwB{GOW>l-FP%D-UFo>G|961a{Wz2m8X9_&Jzz|lTIu-nJiJaXei7q=apyYr z(Q;zL9QZm~EsxT#EHsnBDXf|xHa1)+-|atx0i$c7R_i!%>YzRWE3BTva!=1z059w2 zyw@D|IH|c>>5aW8w`YK4Pm)Ai>zP(PIh#gDU_GzjA6GUhj7ReE-Fd~;%OSb%1trcR zW|!=2=&sqdw9G3U^oh}as=*#7J9QP^lIIo22;NyuAD35jVaMyv`>6hv9F_BVq<;N9 z9jlhZw`X(f&uruwNWNT%Arywb1>EqzfAElp4=Ets_wySzkuNcDXYw1ii?6R6b%3JQ zV;ZwR?_ELl_S?<|r5w2X99Yu(-W|j14aSfA4q*HnMxs#aUhvJ}(R%)ATw6*w-<|6t zhXL*fzJTOLf5emIJ|5@YJZKm+n7X>d6{qgCX~T_85%hQ3rnP&y=81Y7Z8yFZM!Utj z5LA|@8P3c2&BF5Pn8SE-hDYW=?j5=LfiERXHe@}&yb_y7|0(1`wa#?BRP~F_56$$R}JB0X~mrHOlQnRYo9%lw!3};WtOBxdvG;yxtYWN(=fg%=WloT$wHM z(Yw98wgR|xJBKdYC5c4U77IH&i*hOWdiiU9ojO z`0e;7sTh%sKMb;(}QEPjE+UT8)H`cz4X7r0fgAs}tdysuCQJAQa&O zukKldl5dc!_%iS^q_e9Htrlb^;@VYAprW0}AzdmHo8uksI*`Ir&a~CA&;#Y;N8_yH zv}*_jPij5LBRXWu?4r}3^d{eS_lU-4Gc-|*_V$I&LwuFhmr`&oEpwHID>_ej{ z=_`(yYnC1$8LDVujP)npzH3q z%Q^77kHa4tg`&Kx+SkmU?8nBAYML^w<&5HsA;*sLG!Zuj^;6aZ_BoEP=3`D$EIL z0_HDG;>&Z0bN2e->Ur+gT`R5nEk&m{nnpQ@D~UEGaA?p<26EG6>gyNBA#r(Cdig6I z$;Yq!U@01 zmp3ZZlfrY5jm>@`m=ss)WW$5n(o;&Nr?*lZdi_;w&&ExkslUIa!>G{asj_S>2q*ca z;!Ve8`!heJ%lp`ku5xQUc6DnoXI`PpsTF<8Q|H}cMnJbCwz0vg{Z`FLwPDxusFb^M zyiQAy&Q!8O|7YlD)J8bJFgmv({Ce1Z)kke&=%t|ZVbI#i#mGc4p~A*gHhINSG#S|U z5-*&d*L(U5QQz5=A81Z2ygVYf|H2o-#`C>v!bv|$;FxwU2CrM2iI$LrOYGGR!WFtC zq(>9Ecmc06Ve}f!Xc3*|kK(=~u9w-I zUqLS?Q}V5*Nl>Q3Yl0W6)WEl~FuUsD)*ZUe(K_4|#ccw}*mWqFyyW0TtR>EuA zX+7<_JXAR>{YTzzNl+@JzO0oRlg!&9pYIzD*`%GZFKbWqQ9Ll(eHpd&&I^%$%+5Bu zD%hH%e8bMsEbv+#U1On}c>kEwUOX^NHTjT^m!wPWFR|90guM{#L!e5cvBv zo?u@wr*8N=gmT!ok=19a+{**`=4(lqz!vSMb!bQwyH?Nv+d!(cSaY1}->z%!zT|S( zc1Xj{KYio)6}fb+VeG)>t<(+F6LH9toA0K?I>?05Vvd_6;ny?!y6b4eMq0$`RUX}{ z?=xEN81;5&?W#S_pIt)^xu|x--HnR#xsx`@MQC;I?Fm}>je|PCH%sbo*8Byz{9Y>? zgYX=T$eg8b=8CezVp?5p$a7p9df2jI>xDvML2YBsk)Bd!QwS|Gv24`oD1%N}>&o|@ znh(C8Ek%Q4Xcsn|b!g+Mm0!h9Yx78!-ctRmycxKo7k+Dg^`HO7`;!z9f``4kl_L6Z zY5JG{c>SV1-hI(zInbd!JZ)l`*uJ)83*o6BPpnQ1O67Wcu)lDaN|hhe+@g%SFP6&m z?iZumV)7{arc^}rxZL7={=;K;pQM`9M8&3OwVB-af~R#*ff&)Fq0YgnuG88D$&s+X zM!oC~+&T94D9T&Q@f)kEKSgeBUc*Y`CEtIlqVKM zzsWmKoi1>-*FaOcN6p~vB7Yk?IW}Ipycb8RCCN##6Jt*KnWU7o_DP|O1>FqRbcNEs zp6)H^*mw3tG2fJNu>!MtNpYHbEWe{EHow!A40Ek&tBS4l-N~W1KOm=PSI9BW*OKDk z7FI2UTDo(#g&Lal509QNxOl;N;Q^RiJ^g+IIppI7Z}`9V-W(a|_~&fJVe{p&+-Pkc zOvv`T6nay@Y|s7LxNyJ7iyjPxLcNV5G`{_@&jBCY{LVt)8V6&ds)V#z_z(Dls9#7+ z9&8<7N|WkaX{25QR)O038+}`sYDP>>K&a@QO@dwZqqv#4{Ihw%fWg=;r#C8l%h^4l z6BI{3j@2`V&kCiyJ;M%uD$;R}jQOi9tJ+J4G7OHk{j;6V-{fVVx%mfU+$E~a(=+N# zXSrGrmoiLs)ElkEy+*x4sjbxAln)R0uw$P{B}plH9SE02+#DL&3_)+fH|N6*7@Dla zk8Mgia2@mg;nw%Ii~_{V3=r9)#$51JR+BDd2MN*cfCC2 z;7b4N?Rnkzxk9p3*vj`?Gd-S+by&@EHq6xpH(D#*V`aZ%_b|$y{%T|fXI(!N7kPQ< zr-)g0eHCdf+Qt@i2XZtJZ^JmUH7PC8v3;N#+7r=w_UzjO@h5?tc(ssd?E3{BZl~N^ z1c_@c7V7MSxC1A!9~=3eJDp-a6U7w49m`5T>)~anY}?Gnnr}b&imkV)Rjd`4RdiGO z?9q3tg=Yf31IL>qsj8E{T^mObo02k>#OVBJyiUnkq08f;(S-%VWj<*NzIadjW`#O_ z&liPU%63h(L#6lDCFFa(^p$2ckwc@)O!cFR^m_1nAuY$dM` zBBL@I?v34OiCn+){9B-dB( zz)w>|Iv%~d>xNp}8dvO@DyjFD{3JfZ9;-L}Gk3|eXeLx*;Pb&{ebjdT^yJ{f9bDSk z!h&wO4I8(OyzY?$E(g=f2%|6SmjAkOaU=F8I;kb3{#TdF+9*wG%i>VT%u9{1cZxA2 z*9RrGIviS-)qXBEZ5>5jxbYpgkA*V4^wEl3D!-|9nj4Zkan7n*JF4Y*zhGnRC2i^R zK<&AL*5pQddC^t+I}8X#u`bC(_^z*#FVEcU=yzIger#&dC2ZXx8$XJ!OrFlr$%M8@ zJnn_ELe!`5{Mdn{YYY^E-KdA(zWMx8vDpwls{2Pi5$di9aalQ*XiqVDC#8_@c4Dz%ynm2fbV6-T|lc_=xr=z_3WphjgDF9PE zANiMmHiGGru$Y%93LPBb^qFsr!*$_tBYw{sXsc5A%{Bt=z@wPFUbmcODaGEoelmi{ z>*wi5vIWRbE7yCR2m0}-Ih2~!zm?MLRLk+DV{Kg-MWzo*B`#JcMjqx+?_X3kpjVsY zHAlja-rXVZ%;)xVVyr{0|C6xQn-f!QK3X|U`qP8oKdim5V+b^73X{+DtZr-g+ZAcz z6aQ1RAtLj(K6ffgvm-oU+P&S0ZKf;X;i-$TT(3lW$!9JDTaM^?M-n--}b_ zdj7!nz~pdmZ|W8EwY5R&N7#6tL%Mh4Z^z@+%h_w~LK?)*&DeE^)D+E1ByUS`3(Cg} zT3K>5>*-rrp0vsRAor+0+X!#z8=_X*i#Ym5$@sj#G@D{>^UAzu-O8Qh%p_7RmYr|x~_juJ}j*-?X^n&IzSXg@hltmnTHm%)>yqh zKAm-_BQ3bMr5lfV!8XEmeGiurRmzPWFC9v09p~P1s&vLMc&oKtqXD(MKZz8SVL?nd zy0M}3YiIShV?|!^d^wOsU!)yAD~R1X$$So}tl!zI_~*OUJSgYwyvgoqZ+6A|`=gmg zUx~XtDf_B5F}-f8N$9#+G+lN67tcJpH&&XI)A@GC_BHRVNN!LRw27?_{AJ?@gv*T= zoBdDzl7RIHcOlo`rdiq#ZAw`!&0HOD7mw0$?~zLp@5d!o>|Ukm^?9e=>krbR@I7u{ z7D#&}VR+@wb-hRAO9q?~@KE*-;tB&6;Q>Y}d56I#4*#y#3lWP*%a| zTfPsS*+@;LUK}z|w9WiS&Ki0XqBVW-0pIu0N8aIyxL&I>UglkD7u|mIJXVxYmne3u zcud*%5?5!ca4hb0BVu!Vv0~Z}SNo)uP{$wQj#g~ld?0-MNpdEZY4bWXws6}$-xtqs zg_w{rJgiG4%FRw;p+-i7KXAy?0N)WZ2T5n zKbRrt63SX(W!tX`za!e1vS0@FiFkn@;G#Tm9yPD(8QrOfLiiBfo?uM*Bw_U}`B)N6 zi)r1j_DWwsD?U~{zFIg@t^z;$ys+Tbx6p9?pRXa|9M_7-4F3Whwo`O-C#rEnSVlodA+l6DPyPSeoQv+oX%ln}N_(noW zvT6k3=ScUN+j;YMoBsb@3b+M9!&*M7PeSeNL3`PYX^n2jf1W&8s=1f889z4yNA*&u zT8|H9hCM!}2HEzRs}+sY7EZrC4*>V-n=)@xS*S zp8b!km-oufGYD)KVh)LYhi@*>j@|waP71%THY;yOf;|^-NCes@=RS|pf+`6*;@ z!jQd1K7FNDhtxLb022SwNq_Up>`T=fMQ0`ZKD&*t`zk(L7x!l$**vI)U!Y8U zVt1z3kZ-ne5^b-nuue)AB~AHY0zd~#vE=&!%RqW;Ye2JmQZFRI29UY0FCmH-II0zZZXL2d2@3^#0y`afs z{O9*(pT3_6xn?2!-%YM)duL49X^Rgea_Cq^7kgU@r@8T+@X!1Fpltq(^rBmfoJl{l zIFIh4_S|)Q=Z5_2@r=at+{yZGD*AhxlkNrs`DTBMOP1%1ex7T76Aw@S%Qk%H==~hr zwaCG4$4gb_VqI|IVx@4eS8;x)?&mn{p@G^S`r^~}2J$GUxKsM{UyGU^mn7LGLFwh9 zq}v@B5l*K$gV(M3YA?cjlA{p6<9VWSU+}+j$#59KG6LiD+DY2f?pvct)xYhV&w>z9 zsXc?AEk|rDkCW#4pd@pfoY9vHG()JO#t>J|AM#x~XWhMfs0j0yZG)IP#gpcTr^fk& zn~p_L-HknUtTp6qE|%XO?&N?x|D5zO0aC-4qd%SmI}hm2%L_bD*StH_c4|bh5j9FAyj{F zWFcX2gtiq+-YiMxGk6~5CNOf~b794IcL1GsdJ?^HJV`r?b5P?B`A8`?hxNSsyJ4@> zRyNt!(CR~xm{JU0I+fw?OCJVIk=3i(UmXv=KN5$gsNVDVy%{yEQBPA~T8m(|jF-Xi zJ2J~nPw`oTepmJ+3*-;{Lkz($lA8q)jCvMKFz(^|P)n9jzr;Q?9MC$$xMS$L*3PyC zaAoI(;~#djKk22R5NR2?3uEXpc3&gXN`n1x{C00W{QVT|(&1BL-%tf?t!_5r^NDYI7{P?1li)jA6uvsRrk~>k|i7#!|692KU+SUGOO3nxG z;S%+2h1tI+ul+P?__RR-Po)cy`(f{Y)C)4r(aYa(*2_FSd$_q)F^EZL!;j?Wcs8kS zoN1PiQz5=N_tO;d;j# z-Zfw5?p6zQiFqR6d0WT>(XcQ#6fJq++)u#t^yOuFPVbGC)|E7=oswPJzbkVr@$F?% zWfimCN(=X?eXa{@x(Zg%uN3f?nVn!`k7;pegww8iLXEY7&a%Rufl~C*q$z(KGr4#z z+>W3Av=Q+^FYMw6+&-+yijU#ymm|<(KtofVDIFUr<;M>_rG!@1?t(s3QI$Jbws&#h zv0F4EZnJEKnu7+NYz>M>RRX9Q+l(W($>rH$yPsnTrpHxJWa0}UGdxHM&7nj(BEa1V zmRKEf^0qsKez4`AQ`eb0ojd6b4yW zse2S_JX}L;$ckjT2mn1+hZ_i7HjghQYezCJHFisnQzW}O3-|hKwyPO}yse}P7F@>J zgqPMhy=U4%`aEjhB2_~EsoXuRot>uTQ+ z_FYGu&6fqXtO*y}p1+6cW>EY1!7u04JZ*4C#8=SJ$7z{I zFTDM+=9fj(j)C-3YJ#E-pQ6I4x)8MrcBr)~PB6=*HK39yPr zTRrI{CjJ2QvGImycrL@}FH_)@PjjA-i}br|DN+m+pDgd*`GqCbPTnaqYFA3?*BWAS zxtqQO&lQH{%UZ?gLn?KF!-VMve?1bCV==ATe{rmfg4=hO`^S!b81W*v!BHs` z@W*xeFe9O)xSXweUi?uISC8zLNk2nkM4wLP&ND0>bf5c6xxn2KT0HKQ9jtsU_-z{M zV2)oksAnjF`7vJ8=|2av_1HE{s^`bcA1=+e*YX$!4221=N9U`~W`-tOy|m+1jncR@ zujCtSYut|BhnIJqP17~fHA1tAX z=N(XwOzVIvk-wjl?`v_)8oC59nj5iV1pVX9<`WYtTl9sxZA{m;B*ut|PHwaFRcvQg z$cC1BDS_2kaka*6@2QYHpbgQ(tkBxiv1fb3oH%I5nX_zFsp$jttMANHz_6Q7C(}2@ zJRZe&XBP~&@(DZ}q}vh=e^>J=@~rOwhZrgp1H$q8%^f@iwDm!hyB-lf5qGPk4EogP zvE7sHICl`MCXIf8$u^C4=43tOws*CO0Wgg{#>OSEX!S(Wr##G_@SxM zlbpKL*S+=pn=AoyUh%jszChHy>2PdiD)##Ub%W2&HTXvOMw_Lz;c^G)=Ef!;jaHjN zeoMv2TPVwhEuZ$0uJGkFLihZoQywH*&fc0Q4@oSPn|@`lQ$&=QFpL%_@HeC>re-t6PG)!zJr_a_~qAj>Ra}kOvPS(RdlxU=}sQb$p*A=t{3ph6;mhp;>&s z70jxKQ#7|bFxa7#1W?qgN{@CwFVC36TD4!Vq4l(072Ama+3ov^$TMd&w9BoAPUD<4 z3_2iL$M?z%E*c5HsMIz0i(&Q^M5|$SFF#~mX^v85Ut=qPgEF5#&#%|qm^DnJ$X*C& zk0*9T7NV%rMGh;s{23<>g@^lk1d#yBq^=6FvWh>BN+k)cxu`=b9Jc;7WTz(`j^S_B zsCpSbv42>ijk8IhYX(kh-an_Fd^R{PXBdJ-J|#(xwY6NK92sL)u2s4w&U8QZhvo3% zo~@PTx~l*IyNX+}SLx{#Z<3uhtjW$Bs_qGiW}Jm_c>GD<2KSf=J?G7yWQFGsZ^Gik zf?EE{9|p%{pdHOhNYTrf^jkQJr=u5smv=*Py@xxcnHXhx}3t7^Wrju`DH5Mu-iL1_px~k*kp3m@V->xhO zkI(YNmas?e-(EKw(hFIsjeZ@zie{y`i>wFew`H*AE%+j&5>#cNDS9U{iop`K3$t?`pQ#wIthR&9z<$MIwf@exSv=x7 z%Po?T8k)WXK@+D*dsoJq?4by=M-;EqhZ6XiGfF*;{0k1NL3F zFFO9e{$sKAsN&)lW%OLo$;56ikz$C0HM3my0@?RbGQr8y$!v$COM*+yOX6o4L|(zs z{GFb``|Z2=;EYSHAAl@wmyIEx0f ziw!q}pKtIQQW)@wen4O8ER9=9{mIZxk&wWdBT3? z1UsFFP0rn3LF3itNLv*3}VMI3BGXXcoIzyCge#9nYNm z%cMM9^5{b97m*&I?(<~I`oPt!S6$u{Uz1*==1vK%o^qlA}2`y`?{RCkBz*^Uy&_Q5yPT>1qlpI_ZC6VSG z196-@KXqf9{9#j$69jjS?%sCpwRu>j(buH1;zwBcC9%7>xH{SCX&lYr!y&-I)t0km z(wCCO;E%$drp*ij6^g19B*aHd>0LvEfc*#U3E zL8^g}b-b#=t?%ESkJmbBaF))KjHlQW^9}q8$!y`-3OHlu&=VoOvdH6k0if8hY|1YK zjPTn5xYBCuP>X1cWshwLqf%p%aYpkQ(dADKb}!|9lQg%Ah8G~lYEaqOzE><*vc{Sx z7Hy~*|4*BPxgyWml&h$3z->SX+r)qqOd%bdcLW|mBIm1d_J=6UJ&}ZI2%)kMtIT+R z=v{w%A1EL0zd!gqd>bHKGo>m`sNT;(LOr6+>eg(5Fh=2zg&sIW2d$fmsi3y4?8e~S z*T93BI_ER&_i%_ajfnWT+ns@{Q+FHPh0^G>w_zjRei1cic>z-U0`g*%^tgIh_cxeaTGc0^I7TjQ(>X5gG9oggT{n8S&fyL z6ad})To{0t5qEZsg||5Pu7{#D$I_dV@qO0;a#t5}#Dw(5&Qg2wH@20pweazADsS;R z=Rmo(C>lyrzke*6@>;Lb0Uc#MNQDh3K-XA@FO+UTv9g-hKE`R;BU7ST3a_72ZOe)3 zM2Hbqtm)439OA{*Vh`f8yw`>AX_-jB+q02_IP*%dWd;$=q?L%@Q)cMjgtFd02W;Ih ztkA&J&O?n%--`$fTa4%A$;SAR0Z8@ldEX~_TItm@?&*a*F|lWY%i@ON@5JA6SFL&# zI-yF27Tkw5)LS^tw*BL8xv97*+nAXLwtHVp!-hwMM%sA1xPWpZrtO(}9JY#Xz8y9Q z$|Ks3w-Z8}_%JyL$R+O}0m`#@H^yyQp13tM9<@HYA_hd^jinExb`+hYXGiGUS@T7d z-id$J?D4@__Ngr3&P?Njhn=c-`H_cCvb-VUAuc^?|XfRuv0(zzQjsCB-?J8OXQ8euv1~cPr!(sw5TwTufpdRdWjefsX*Osy(IhIE%KPe zK;4;P=2^m#Z-5w1SSNR0UShSQOTYj*n-5X7pnLk!+abZ=uDph3bk1@hMbH_pfV&sV zcO4)o%4;A5Qh61unacsWDE>LsE`3NyBKd;7+I{!L?!-Ii5YCG5Zfgmq<~)2c$7y;Y z{Vz>(eAyGT-d2PaV&D8~9MLp!Chrltrky+`Vr;{NFmOUlR@e7y4`El{ti3DErGoYM;vxEBz1|5sIU$>rvxIFc zlC?WSAa2Xy@$vDFHpcGunpw>5eUfKGMUwUdB?X@X>#@}I+8joC1ZqFMw(3H{_sw5Q z-PhL}I&#{7b$G#Xo*(RKAG&l8MfT`nrw?(55@ku%Zr#3v9z>j@hD+VQzN`RA`E|i# z)Wz%yF(9m@un@2dMV*Z_#w|gXvU#llIUEqSIN%QlUykX(xeY}Eo+2J^8+tHwkp7Q& zlTjg?XMr6Iw=FyvX2(r~UQ+|Q8geQsAf#A4l zE%G?C8Y1Kr>hhn-yi2{idO)jmisyl~uhS8+y!&N~cn^ZOb5o!pU6ft}Hi)nTU@SBg z;uVrR7shR8kXR8$&*I?Xj=XEky3$Y z#27yDR^yq}bd2wwsdA?E^1e5IJq0hAa-ek2hN@kaU2a$)%M0*D3@VftQKYYQGI7O21yocSfN2>b{!?T~MG9f$c0mdu zV1?WmOUbSYv=SC-r4n7Zki3E~(Jgk5C_sr}c09jbK9!4O)UXNlCgUH%mM~BWwM@vn znJeFk8fJML&4M!{&0W8fA8)iHJ18Q$2gNHIWT};#LEY7lIUSwSk;y|-KZ}I}TU5^1fYvGxf)5tU(M$~-Ykx6Gn zoe$%)z|O#nV0kG2n=5DhH$qGTf}QchC@x=G1|)SUEM=NgqOrufI}(i<;q&p5Z}!UH zK_cPusmg0Zb)%X~wckCEO*A#%!-J;|c049urp`Efjh@Su z6!T9c9nBm@Hp=pUaA?cD?D!jezd<2&T_gMyhZjhNG3P+v{IY5Z?;XaG zi?}#Y$yPKp(`XPcxBN&_CP)cIYJn4QJ&!#I`~0NaS$PN^+C2b0U7Wn-@_Ft&z`6IS zpih-*-ND^_wM?QP?pkN&)5qZz%Xo{y2C?KRo6O1eot>rcgom}2jq&P($%o+-=@{;+ z7Ei~{?SHF71mdqVqm4oTeNJ#U03CW|bY>5dxb#Q~sZl+Ra~;?-Il6Y253ME|FP}r%ju62Gba? zU2Uk%%nw-#dicV$H^lB$c1!|E!2cwqN^h28XI00r{f}av?bIP?1%QIMFx-2a8oQan zdC<6be(4PKGAlzkkUu|$Bp-R4mQ=3zghS5LbJHzOM>ho`VKD@ zl-&N3?(804P@eqfd>bc5c~S?`TOoha==Is)RNesc6QFAgNCzjkRu+0u^Wo)xk1)oQ)T7Onwl5xU46{TJs?LECk;xIJdAJK|5NW86;I$&($IJd$ zT6DOA>)?^!4hEoLVB{%{H4-n%Ghh89R~%MBvejn1V8>5;;{dIPHlkA>&-cd~#Nk=K z7%rtu-8c%#laFA zl5_B@b#;I$x6_I+Xpr>Fe~c8#DJS^xkVk3UxuIcUJ-}pbNjX@)#1hAO7IF8`e>sKN z6h15sQ{Bi;Y2-Q&&BlFR6Lme|{E^C)UXKdRw57E`(T-#|- zhZ3dMB?2Zh7ol+snB8T1)p-)5QlY%ix5Z#2lFe{2!fjgWRl?N}>lRrzr-tn;Zl@E3 zaimwVntXJQ@EcUAx7mY>bpcFUy+z6}gH_dU^6{$(6}*L86X<(duzUbk?NHdNt58|D z&2qY{i#BqY@t8j_HZn5Yn>&AoVdp``IJ!d0(8Hk;rQYJ{puho5e!napg^`?-DwS=> zVm*t_cK5OK6qHomy7a8?-m(=hV;D?+nmpe4<3{Zh!|HAl6&fyTkV?C<8j{HdoY!aQ zUSf2D>OTr)gqgp%{>y+H+0*}EL&gKQROG)*odP&8)oHH!WZLK|xBKMJG57;UnGL=V z#_r0aR?GM|HX9GT`Zg@M=FhLV?$h@l^DqcqIWPj|4-Hgz*SlLn>@8WNrX>l<7Eh)U zw0n}m>=zk>b<@;78U9#_$%SxkIh>__pdZ#o&Hbkiypj|}5aC7MA&J1*hEjrlE!|ja zL=jxFEC@{bx`M)bFnU5TYI`_O0E=(x@KdATGudhLMb4^*v*5-;Knw2P`Q2GOo|11RB34ae^XzYovXKd~ZK%dTNfhavm~ zhCTng`UvUKLD2}U#uu(Ob4hOte^l`crq_%Q9b>W=jVscSjiL^1{y3kanlraPneI{L zb=2Qv3+h~l$yDfCUq51FB<2)@b>h=ocDd*&z9*u_^_*XZo`6X6Cb62RDv50E?y1$# zk9Cp<#KR4@8l~`#L8+A={$bd{^Hq|!H`Gk5r`5e&jv|iW{|F0{@_C8!T)qJvKh8*o z@hdxe(eXL1L51IOeOtl3N1dDJz2>Ub0fOu~{cFB2M;ZqXlsAU|CQFA4NPo`+6YyF3 z(byb_LjYh0<;MDR;jLJ!jqQqO$@UEWK2Eor_5zbEAjT&}_ z3e~7R&WVHAP%Cd{uf<7GVRo@?5@J}+33b)CVOGu!?>41^^K4rC*LO$OdoJH(V4tnL z^{uKJ#jN7k2QBPFTmG7jQF~`>{346>&On4tWAFF7Vb1;sR=za~z7+wS^8+d^V6*md zf9}dYi40-_M)-r}QP@#r%fbnc`v~)qee}XSiuYQUfFZj%IV`UO_Ns)J*SVsyXLq@4 zb*!*neJPI$>%GWWpJT0KDnjurDd!U1m9eQd=`0YxL}F0gz;SD%OS_LVFHu|#*t{MG zjXss)Oe)8Fx_@~$ww~6kyCC|b966HLS{N}XZ(gL#gpmm}sLzu}7|XK?`qJlku)5?^ zn?TZ%LORxJ3LkJ=E+4UIwzRsO=Swlqtpe7B#fECSvBx8E8;Djb^?8!4vYK5XZciiu zujR8QB@!W>qkijBn-qwF)pqUezlf& zjQ;*U5R{#+GLW}TvtAeq1@N?snRF*c>GI+WgS!g41D2fB)@z}8Gy-mT0A9-3E@zwq zDV$TogC+9yM4XvNv-ljy39D@`Wuk{BOypulS(~u80d<<~EwABd33jZwbIOBgC`$}? z9_>8ZGJe0eVQX%vZ%=bRqXduJyMD@m`4S-z`D+R_%PZ3IRPXKTl|Y9v`(|eU73hg+ zaF?v`Y8D4vtVwi&4o2 zV?TgK>C}@iYv1H z{_Rjtjoy)v2Zl*)dj>v@Lusq7{#(9(L)pKCj|*U9$59Axq+lp}CXzO!g25zu2co(J za%Y;aAY86LUG_t9W~hZ&jx}9ql1%?-<(6S$P0jIue~K;C($jt92d2@yKg!Fr_4ijB zuk&W>Px{d8%06a~UYrl%qt9b*x{kC@SXC5!xZ`v&T|qnYv1L|%y=Tn?yr6`zFRS;g8d{|247p=UjLN*v=it4xSf=HM z&;AAiXJLzAM|PEy!g4%ya|N_I`FX-#xVuz^E|>6YdboWm_XgA&ewAfn@$TLLmAQn3Fe$xjyXD4+ws^G0Zoclt5Yn{yj0!*vz zUvi+;o3G3muKenO)}1sqdhe0YrmDLpRH3`j4*k{wSlH$j$Pn&F+ zMef)qjTGA`ol+;QfU-6rGm8tRDmelv0Hm z_CHg)rh^f8G>Qs!AF@}2chHK2!eBwlm>B|S`TKUTY%=A~951#6Yi9s5kZpAQAs6z8 z4*$eK=m#HQ!a!sy@gfHlm{FBD~ew~oAW_W2?$ zhK93#Cv_5&cJ5B@^G<64{f_Vv)BNBS;F$~^cmb=6>Zv{JIVyg)x`bqvUTHzUZF5+GPVp+FJ@Cu?4CP0}pUO$RaZt%p_s5- zP)^S|0giXz92y$Q*5A;XJK1Qmc)q$5EOEYxcW(?3|4eN8Ocv-Drb6?2&VTyGdfoy2 z6_*Z)=A;?;3YDnA4^<*ISDUDXEd-_Me{t%-dwjnVt+sAHQ<%*Y@S0h{VDK*RJI?-ZK z=KOr)S*OF;CbtjUKyD8-H*r8<$pDgf=IY)X@xtMR5mgB!I0gmWJid6)W}0fLa;8mL zs&1P}P$mW1hnYmfa1D<02=-l`!dD{bn-@*_YYx+|24{;d<4y8?NDrL8s_Ol)%MTCF zi%rrp=c8vELo4>!EuMcGHEvn!nQvLUd^I}f;qqcxI+ja2K)f6#=FF;TXLs!7X+m`w zQLBJ|6-`=BtSSiy8apw8#1!-m8npRs$su;UnZ^!dC~nLS75bsC=-ScJml^`2xHtXb z$_3|(1@BfXQeXZ^0Xjbaf+~CL66)EZZkCqJ|MSXHT>RR~M4md&PG%x^(jkHGS8^IK zk9q%8{uT^hYt7Sd_37g+uf>`Esg?KZZN(_mVugXN1NSsbMA<_m9OQ-#mU_bn7>5Dl z-h@`ku=3!&gTku^lt;iDBZFuTUB%V?>XSC#lawjI7+-e0v#HXKhMG{ayq zSsTn#`1{f|Te6qT{=z(bA_b=xvjs5kOsfi*8$pNx`J9=c8f$>OT|*$}{fkmI(Et&B zW}%6H!>3hehq^kfyyEjjqD4%+-v4?v_GA7(wMLhGn$xq6)Ma{-#y(2GalatNmt&!u zv;9I{RXfjL z80o#(M73;aF=tf6OfL_az^kW_U#}it=A{a3h_KO(apOE+^Tp^--wn~eRZslXlDzJ> zG`v0vTa&75l~d=gYL+DIEvXL3w{kD^p~yAj1YM{$jBff-B~aRPFahihV|}J!JK>u= z*~V2lFQ;`eoNqUh-94lce013J@AJ7f(Dl~Ot_v+MDmn6?mK+mgec6c~=Uk851N@+m z5Mw+oCHOL8UNl7~w&wkF+YdHvJl~%U*hL)bsy)LpW~D6c}IImoO=Krw*-o ztMsQ+a8<+{C6@gvuPEyV=K54fYB3sE$Nu2pHQ)YW8g~D91{KW0gIbmUjd;R2c0JKM zxu~a1!f5-z#+K&tJm$6xiqH6CLpuJy(j|nV@f{x~1%*F-rX`0~S}`z_orTJ=?z6_J zJyn~xgQg8Zm~f9eHD0SDDOHZ7NQ=;Qr_lO-$}p7^S@HIt(nXV^H|$<(6XNvL_i`0G zfWL_v>I$r7^pn8ymW==pq0j@0GcAq)I>!j!j;TJPM_Skz`Zh;0W%>d!EfYZK#CA_iSH%-@;NqZ zJHUp!(5JA-#95I7^wbb7Wd1OD&dZ@qm{YJ06NXaxs0?tO;K0N{?mhG$y8?mu6U9aZ zdw4cENnDYZPZ_9tJ;b=R+3stWa@OW($ibH%dOIB>YmztEtH*T&_urF$gt3b-V}KGW zt^HtKXx#I5rF!nMS7%g|_U0zdhkxTNJ?r3H=7BGz-K}5BRnFky$AHWGw*|6dDH6Pf zk6Kb4qL=00(yzS?9$tBpKW+5-@jDYz27Dx#o^=Qs>f=o(*^;Qvyq#3zhYbM{lR$C0Q`-#c7PzAw#eSv4Q(F{5=UdHZ}M z&!g^~8*!GUZ<38?Fzvr@3;mu;1C31c4)LQ%@K`@;y5a?~_j%!LOJKp$cv4dxBFy?{ zAZlRC10cSoci{_sA&~{}B_F*)^B^@+>!)(5Dyr>H zTdWJ;XRk^LE{9wX7G(mt(|LRF{yy-k=MHW4C&_&vw{o7^%#v8$GEH?*s+L?pvlNt| zR-qHl@uWJoA!<1gq8z2b<@P=on()Fz|ELKRpKtALhf|f3sRO$)oXr^lp9u5ju$hDS z)s4w}+|DJl0%WpFd|AlYisLW3VcfFHGV+0rpAL3;PcbBomWbMw7q7qNOl19<2^x6c zQ5g*wK^+mwvb@~Vr|z8x2aZ{z7Sd|&QBKy;|zWqt0J=j?Koa<|CHDjXEB zq#n!odQJ&18IEKJ3y!5JPWCxn(+$RO-<7!fnXLsXH5qK5Jj)(B{octLx6+T@Zw{P& zB|u`BLs{jUl457B-oGpIB|7OPuOZwP+pr0A7AXIWSxj9+t9&}|4XKjMY07~W*bl@W zgq;+4){Qqrjy0>ED;jRNPXGQWjBe(+v8Xn(6{%k`Z)fY8>`@?@wiCTTm#CL7+5>2=Gff8M9ll51CSDkEH{t(5Jx4mXcn@@Zz|TUB_Z% z?YouLsOjwGdZy$EOQ`*&Q&vE74gld*i5uQC@=h&d`W2blc|-JFi#=Q7u*)1oVCtmd ztJ&8Kn99@r*xLFK57tiyTlfNix+ExTl1@*0P(NzEhldyss@3^+k13>qiJ|SBJbONK zSdEi9Q4zK$aQ;2%HA(sRj!t^CmkB{DtJxs39M%4>mTdW1a0l^9$~H0q=P-E7xim|$ zUTdI(HqX%>>fP8$%`(mi>CGzVD+0JUG~w57RqH`yQJOPu(lG;$x{4WGArBhx{q`zA*my%dk_X zXJpZu`65fITR)*L{-#Z7cuu1K%O{si;)0gmee~m8Oc@&Y9UMIlo#}Dfs~wK;>vj32 z(j0>H;EbGj6gG}ITCChZ;`;qANs z0VO~WWJ+_bD(SaEjBNGf^K-o%y3Jri$o%yt8^iFPAIfk~{c64^HfJ`bvF=cp@rI0x zcY$G`I$gDr$@N=yquNQ*I??E4rM>~hw!m~CPefhl>n*W-1TLyOvm18HOJm=*nEdAyhuTss@7s-n8eO0RZVa?Fp{ z?;+Y#5h;Z<1JbNch(p(_Ha2pdVefx;h)32B6%@V@7<|ZeBE48i{5hWIxI@w#6i7j< z*1H2((4GdLiuoPpf6OwQ@Ng1UK>b(P@>Kk){zvn+wctgwo#tzr6#+WqeH=s0;e+6^ z9lg4?_O(fiRljZ!wV_t` z+;5=EP!cdP`nl7npn~+*JKx6jxbbNHC{~dYsPHfCFckmud;jD4JngfHVPr@5iq-RMn_OAXG~RRA@Zniy8lQ!se#-+R+cNK=+@IvpE8Ih$;I zcy8$~Eo6{V@<`EE=AH~%2BvoJdkMudh-s!*S7{{8qlhA9h0$u&ff--eRVXxc6nqS^ z7;NeRZupLTrQsKLgovvpCyALD)OKh&?lGv)x!qV%guj%l%X|c7FMXwg2Nl^FawaF= z+vB9=WUZ!t5~v9m;@x3ACPg{H`#kBXNP4^`ms4~U_lxw-{Oj$Oc2@n;k~D9-ORd z8;(9SCVZQYJEx!nZ3SdfLUHc#smh1g)#;o`hDe-Sp$vfK4g)`I4r>MjwRSTp^-PJA z!e(cM6<7s-8)}VsF?P3gNl_YKVFv=2-A& zRSZ6KTr(&@+Z&T!F<|kLo+F)5m8!^KPD%^k+_Z|wy@x-tBxXEBaB=B?!mIIvwk?@z8`vI>zC-#=yHg2lH8 zYubXb`oWwb-osN0?z=(k9~J~Fe*Gl5ZPw4!ki6>gP-@8I>AAYJYQb6M&*c0E|7`Ep z)GWe7P`bf&df;LGId-UkpnZycFm)EgFY zN()KMX-D*Mu4NfFjxRAc;M#B@yx3lCOqk%O{p}Dm0ONDge)yTq&l)AZ&Uu8K)+bB< zGx|w7K#lFdTU60ZfDR5)SU^-;z33p+0f>t;SYORH80s#NcH!)bZ~TkL*SkM{I4}{J zz2Qw{v@%whoSGDr@m}c*ep+S0m%SkOVo#w6l|19AQ9_Uyht{K&K6?k&aDOiIz^xGM z)qdG`7#0w1Ypr8&tJGR-H#xrh>8@;a;A#ZPFJH#NwCwc7%hAv+S`vM_pZ$ySxKp#<0K6MA>7%gK3YH4*t)` z7&~U*-|_^{i#m?2-0ICInc?T&=hyLY@$l$*i@-HrNaf`;&tq=IG;Aa--CeGaHE>l+ z<`i}S|NoF4l{5$Aph8D!-pBQj#SExgrE4XZu&w#KRgMJL4RSPvR_hDtQ_s-myP+Jl zDhGH*>ILiSFNTY9((1PgCEKksps!iNDYcV{{0fOv43b0KEok@+yFE2@fBcz4^ZP5M zt%7#_?fC94mCI>K+bWmdN`p92qYpvn6&k*dA^S+zbITZfOu712pBcWW8x{OiysN+q zcE_nCx0%z?vhXWc9aKS*s!e$L0PR0T`Fq@DR=rc{6T*8|(|X7Si_=LTO12}w4({2Z z50UYm;H@SDT=7^EnoRw{uA`~4hpHFoH7Y!iYG+uMb@8Nn`pQX_U41p=>A9|4?ylq^ zKU+5bF8;?`(P^L3W_Gw59%TTZ0!M*nlCt9%Vw^b~O%ec0)xMmRwWWTEk$G+M*)gK_ zi&{X&mm4}(iT~n2*+p(~O&I|O$+-lWsP^7G^`5p6dS>h zg1!dMVjyoky4~Cr)c0|{!m^EWnULx7RrP&}S{{C|BUa-M<+Y~EP-e@Vzz7dtbnu^t z*VOi!cNe88lHpxB&G?!0z|U?sn4aQNyM*B1o%bIyJX%#A?0#W8F@q^B)A$q1^ES77 z9||9V;5t$ln1jlB0^DxN%)&#qdY@o_9 z){jw>kec^#mKs#1(Of0fE;mv^dQ?G9_-LQE_1Td`HD$O_sB-On3;Fq^m)z8UyTIbX z&)IjehM{FiGC6T91(iOZTt4S%AvLwGaT+1IWGpjEK!>ta^VSV*FZOG*mcJ*e|Kd)m z{w=B>kD@z?UU_Kt?RE(nee*?D;Z{^c76a0pff$gGWhiYCTgF~-n=cRWKN`4JR~znw z-ri?ZUVu!w169O?SWhV|yB=7IwK*?Efb#m`=grro?#6TIIWi zTnP#<FR5_ioGMQv;StCDbil%z@X0S`9+> zyryNR4~aOYs^w}74#wXc|T zzUNTfX`J*mllQz=8}y6$t^u!J|E9lmQ?~8*7)LSPr_fKgp8Y9h2OYwx`i0zG{l7numzA`;zA=;p8NQ>@Be5V;p3{uuGCK9e?AWxo@Cs3Z$y=vFs9=Q!c>_+6I>5t zxe17(m`X@*<-NbC?~j?bZV7_ArXE>dZ!7g3dPDv*+qb~oWg7e#x4mw`Ts>uF{re7Q z)!>ZeYi*75Z@jNLxx20hHUyDA%b6}DecCb0`)AhKm177a7x8|3Fq47b?;<2RhQL8T z4=CSCrND7i0s_ALruR=(ix=MriE(}JSNDdSC;4S)@4xg#?J7rSr#( zG>fK$X}ADolK$yD9>27~42~V|6>FS-#vE%$cjd1Z7Pk*x&9aavXl#717FWGu)IG&3 zHT=0tE9f)eS^nNI!Kq@%j5H2Z;&Ausp`g~S@A^cPttR-FaZWMnksyxv#=VPY zXJ!lD3f>ydCY?G_GPwU+eJ>qXaqa)aS6|TE0Yf7s>J5?#SB(b|+-qemXP?%5ysG@X zYTn%YQ&#@cTgTq#6DD47AEwz38IemRUOSF)KIZ@a%+L`X7sWoR2B*BNzd~O> z1j1FE5CcD@y9x_sACyqc&3BuDWs~T2PJw&xQJF4sBJ7Ec&C}A!3+uk$6mM(PNw^VS zxO1{B_zVcNTb`Hh6N73pL_Y-9^xTepTm9hltqjQ+<-1Q|4Q%9lWO_T*?ORxfp)AL^ zq+HVHyfQ(HsGzN*(tOKscX7-!6Vb*Yogxn&iK;rCBFT4e8v*gL%`9@Ozm9QTQmH)f zU2)vNgtO|%x2d>4HLi&`NG^xzGao8h%(1g0(X;G&)5vB2|7b>?fiW3^a(=cp8%PvIyQr-n?@Gjp?;sAg^Mq#a8hprxiEPEGLT@jaYKdZs9rSs zS&(}CF2AMPxo$pga#TQSJd-pYv$Vq%QtwgKf8KMJ8-H=aY1I?kMQHk<0l3|=5X1% za(eJVofZ3p(;e8pPA)O&-c;98tStR9&_+m=SI81tFC{) z#}KiMW3zb~ZdbgWG5(=oj5$AUyLI9n;?-Rb^Sezd*CbuPDU3`69T8pDaTUq9V+F8C zwTzFs_&26Kuob>i4{6w#;}HBb>3X+c^Asz7u-hDEdAX3cLD5y$rbxbf`3yCxbNTd> zYsCL({1JaYHFvR!wv5<3oyVO7Pnf8?WDw6QcuZb#oK?X52l%^YW;>={I7aY4yQu0! zFJ#OpG2_~0+N3Rta zc6Z;Qo+5WX%p@u3Xg&9KiR+aqNlELKeCG24hW|fr6$;(CjTA6c3w;lV$|1#S(v*0Z;IymvyP-eUS|EDb+?u^QPL zWS!PV!EzPw$t~zIr7kpYgw2y~!kw*BRrBD#q90X9csz~cGy0!E|J9S=TwPm4wCZDg z(^c8sTt&#FH9b+oqK#Zbo+xSm%9dFrsa7Ssl$Xwa8U!OQkzI4#DC}f3`SJTNTM-2B z*8@~xDD|-C+2y5N@&2;%d7|(JGj-Z!8|mZw*-}U6O4|S9=-T6%{{Fx2B`Fz_TPgQN z?tI8T$tCyOu$9O?mlATzWhI3zdcDi&XZdXk1v1r!G}jNZ0O5GaW6YO_0_uzgp&&P5i>)95yGJ-TjQfDIvC&mz$kUImbosdPDv|HYzHT#i;}B{(S-uX0JXu#s>?_A!<(3N1d=?z$ zX!&hPpaZv#u*zx{DI4t@gsTZ{S4H`Z#g{f zV&b^1ersFvMdM?bH9hnkG>g>JYRqTdV>0qjoQCm+T$R{>x)J zBZS9+SyooyJ z)L!5UxwW`Iv{&cu&(sH`?q$~##>;D_8P5%KYg|YLHD<7Ip^ww5{cWxQg))*gMr>w; z`YTO|CnYwoBBKYT=AB=GR&8+ERLcsxk^3h88SCo#*splz^Tuy0*EXG^(J43n#fVZ9 zxi-X^X91L?<6G9qnj9%%N%_ko(Kvid+lI{vS)J$2;?C0xY?1v}koPP2s#<#yXCJd^ zTJdaaofdYE9GV&{|ES?q*z75 zD9$mc!X=D*Mo*aM9qT}?X2s4l)yU+VIVurKgA;qGd5gFXj%CzR7wT*Ks4th-K1GBp zlcks%D1wHZH`1UziH{GNl_D$)c;~gK7Cu^lGBXPxD=JFf29i&s?A%~>1>()wr zGx1{9Z~P7w_)7@5A0Yxl3xM9IXCmXIGd)ud>vTXX-^o4eXjGj`TULg#ozNoopw%)i z?Fu3%W?b6;o~Vbz7RneI2UTZX^;LmM21y$%vqF^RG<2~CK-oh-S;}z^>-FtTnvAN^ zFx<;8Wg)pZw;3H{2QbQ@(x>wT+h=}UZW)X_oqq?W*=^q$&16$;e~BKlu6&y*@4diP ztlYa5b_PfiKqX59E|^cG?FxYpxvv^)1KJ%z);QW+ZPYw+c;^Y_d|+d(5X=18Bk7-o zcX7f(D=o=YLxBfo0&YMuI@m=Wuua;R^fm65_g|h?(4~vE&$QV-tBlo3rx=}HO{2fm z61;lpQ%(h&xOVt8AN-OjXp-1~zMZ$J_Gc9Y*Uwnc( zK~%l$C>xj)$V3eNkNhs^2sK0{q7}k>&vk)Wz528KJ|gIf{mP{5uMR@RHDR~iYrC}_ zL|s~v6#DUkm)UMbh}5pJ>p!HEczA*TLo!9gtmOlgoa>>K5KK>4?T5BE{CHh}IrbU( zU&Q?wYR=0F=S8-xW|(hhlIy^|gI*@D|Rjn{>p>^nS zSF1*#l46yR+XiaoVKaV1q75P2DfPd|;hp~<{;6OBc{#`B55cdCw+gA5AQrHy^w!m- z%_tQ*)kTe*;bgPr`R!|)Y{}Ha%2p!}p=jsmRs&BAV?Yf?W3@~fll8N_>4cG!+K6e> zV8qf!LDj#oh_V;?4p<|$nK+{ylGF@gqz1FRR{*$q znli|~p~-mHG3Vg1JiZCx(;v4eie@ytY&H7K&kLhb-G^r~YZNVFc%P#2n+g;>IHyxO zZHQVJ8#X@et0js`-`?!*abc-H8mRB;+#{Ta5PTH8(b8KseBL2xAowa*Y-pSHP&@qjP?&r4 zbRx@iS@z==B{jFQSII4|1#&nC`%2_PW&& zRA)>ER?~K}wia+E*hbnP>a>tFXiwVyXF$&rPYcdT~Xf}tU7@5Q2M{xaul=}K_VE~eOnyxIClD7nBD4&v;$;KSC7_X%Ga^`!ThP}xl`%Q47dZJ&TEmqePIZFl^A zC84?}bR+LvNOL@=Ao66XXXeOG2~^RjF@FCuZ{n3t$%#Nt6Nj8ks9rjL8Z6D7mOx2A zRxCvVuq82(kmTm=dQX-^=hb7&;prXuwZ{*_S`3J_aRm2_VmsKBo#60p$8nwXT z2gkt~)Bl5Vq@(pKbIEL0MB*<`$`RgZYU267JYRm1KGXu?|ET#Uy%#mnv2FC1hnBMy z0w{`fK|3ouNlDdZ~yrET#B`LN=Lb2Z0_e=uqBmTk0u>lc#MLX*(K4zYI<3IbUO^rI=(IERwQANWcBVj~;ZND*1?~}zpu62%tM~>+dNez%^eD&4 zq?0mo_Ia6(*WRbHu%-4BQS~N&dH(n0RdflNUz_F=l1I`)e!FAJHt<(Dbm4aOe*=R7 zl@)10O@QgT5Ei{(D-c2IV0Y|hWw2;16Re)LZ0orCUh*buZl!tZFWrrZSX zXsEq${XM%ELsfXqGun){&`9)a-OYzr+_G=IBtfYOM|N_~(HA@-Fg;uB5G!KMl$4Wm zefz2lv{YM#*WMcXwq?`39r34r@{Y!-1->HNt=p-0P6*HWd@N*^w9yW*W_+Q&ANB1m zgifl2?`!|09W$oY>m6KpGg;X1`4+NFk29XsH0hJVb5|=B>1V8!7dtiZZn<=~no{V_ zpnh10#zBdT;qH; zot6gp2{;&wz_P>d2>5LJSO6^3?%35<3~ea(VTFU&pAfZkcAGW)*g`|jk8bp;TG3lb z=2sa{B;?aHb>Pu;0BP5SEplP=BPE*lo9_5^V~9@qgQF!M?KX{`MU~}$(yhGsxd+Ox z2s~X4xKQ{UqYb4Q0zD9oW4q+ZMGV>Z@18ljZ2k8Nr5P!&PmB9;fruKd7$Z)mWU9ReTg< zellpOQ$Wb7Lu$jl-x3}NZS|5j_x=c%N%3!uF_vccag=vT!l;0!k+Y6#w;)M3Y}Y6; zr{Dj`c&rv?E|z2cmH*Sz#Ar;?lPlvF`mgFzehK>^2j!^fXs4RsE`o z*mY*7p!qL)W_qj}L^24@$RC>v{DoL*3DXijE7riA4~w*^)o`Q4X_0zD-2^CcH5pMg zzYq`WlgPGLfatOob7 zeQA>K4GmKROrIwnd=|UDqI97l_y8t9RaUKdMb^H!E$-(5nR#4k&A8(b5 z0if*mV1-rd)M`ruESAHFrMOECG~Jct8JARgIw-Xs6n0(uP`tS!-jOVH57`=$r9g#! zU&``^Eh;+9NM9e^NB2{>z0AyT@~Udnbw%%nHIYR2QOTL2h^nl(cBj+6n_(Yi>$U%B ztC6YEee1nUi?1r`hOgm8fNahm)xU)j!G@w}j|}{1Fa}(Ka-x~>tX9_)$HSJC7j1@{ zx7~w3vt6=K@E;h4IeY39iqXp)eSZYaG5q3H5-90mFKdA?gx)N*dSobW<3GTUIEB)o%HlQ#rPbqes&^c7ZA7TD%@w=JnY@60HC7gx?5i$O1RNaS|Za|3RL*BR&iC{ z>G^G&yDi!_*_jesu90)x^DK&+h+GgY6k9*YH*uOez4-tjjm!YZhtyg#hXCX=yZM% zi~vY>kQ@j4jp>4;DdIPLF$JjQhvghkj6ugw+84Y~u_xC?)Piis*r~{;WoQWINHXu) zuZ&2OQtZyw$poPlvr1v#OL<}INbxBN2OQs+yjTD0LFxgu{^oW&=dqcBmB3AhY^yYy zO;}1^eN-#`XXs3fZC@Tif#pTO?Y3pqQcsEF!;V%N&Ez=Sc}Zb8Q<;lg{R#ZiD@tU^ zn^24{g^@LVWN#tWe~pqY{c$KP%Hv}vEmtCnV2XwYJ@BNt)TB8b`#~IT$@|NrS06KR zMy)f)i7kZ=ewwG#oZ-;c9JR(6JZmS{qARi8_BC*dyfn-KV)Ajv`p~k(Igm`cK{d&D zMr#LL-a$R!w?_#{M3PS+$&dTOkGyKr{mYYcu+Im5DxCxWH~dv0Gr%C z+ZMkKt+16&yt|LtcXZM=V?D<0To4~C>LiPt#VD)aijs-0%aEVD^l*rI!tCt9iA+UZ zP(7MsLv_-l6Tu3sCj<86Y+g5MnQZvtfdN!Fz`_?w=aQk`N9^&cD>(rluR6CNcRv0&s zm8G*&cUOlk#CB(XAC(8%Aw5gkYQGbSHr)%?M|I)Z`gZzGJ8|-cM`A*~PO|AA_#>Ee z$-D){RB11zQz>jz+t{|Q*-c8QjuPM3r2;^8SD0K1LyU^-UP?*bN`pImWchq&ILS7x z?V;HgXa4e}5&^#fDPy08z`H375N%`^Pz;VU`XZ7XS^ur_I&KEn2wuL?knvpO3xwYI zqh8}M?-S>M=+`+G^3>2lM)jvk1T?ZD4N}e#wb@Qsb{e7QsN-}SlH1IqNR+v>mYx}% zoho%!n2J<5f`yQq$xqs@-)c;T;nEn7wB>dQg3!!`!d|Cy3~E$upU@m;GdsLegxCkx3Syfcz zGBtI{SNKesqi2PaUq{-aaE!W@tSMth8&x^Q~ zrB7NFH#4m*cdORJI8&m>jwuReAKtXSGrKde?!0xwX_GR%gL~zu;K?=J!(@X8`LNCv zP^}N;unk_+&>F?355KLEH*oovHM%ItN-Uik0#6~uej-uRc6HF1B#~V7#{TqA?@cMpjCkyAI{f8Pc>3rTz~C9TomtJZ!E~h zj2i6GzM;OKqC<_mQzoKH+M7uEpOtiP7=n|_I~X&n$C-E@vZigXSD&4R*%&#Y-jawD zm_h8eRche3Ma^)c4@iQ&D{P!Q>uzd7=SF2N|BKB+xP(MrsRkWP2Pz|)w ztuVVFm2GG48L*leQ-D>5{pC?roFd~l^qPg{xQiU&71y20_yq70&O*yS|5kWHXbk;* zo~wAP_tYS;@D|~?w|-`9lL%Lu%>zSwHtl;ZlRSWFiK>Xv;?L4oe~6hnWMPx(5qoDATsPTh;JSQ0(YMu}n%NoyE!yrz(eelP_&Vgg?bK z;?!md$XW<-sq0u#luw?a+2f_q_iJtjQsx+6bs5LB8=U!9Wyoj z=UY>WjLjd>zdY_Zt)Lm4)c=6&+m@!{!@-5!g*DnvPfuKbu|*p#Z`p1Z8l*zCTYy2d zyVzp!g4in4pOJfJ{A?O0XYDUfVh)9w!`tJS8+*LfCS*9TeDO-YO$%b5MDU+Fy^ss{ zGPBj1GSQUGkP@dhn%vI2zdX**T1MwWdP7b2W-tKhdSFo zV;D$x=sK?i;PknKAJhjExb8F!2d7sH`6VLWRWJe`t)N}X?huwzkIrk`m{oQjhWlid z{N1GF@ z{RlvhPVUZxa)qw9J99OvnX4H3Cio5TE9jxYw}jC>`%2S6gB%Ol)2`-LU6m6Y93?{` ze9ohmIIMBA19wSC!eWAR6(leWyX`lDudGh-dlP_$>8QKC`Ya;;SyenDr;L$)Xk zIos%|($)6t^tkpS+c~P@*Go3)V}<5-9LbZJywi^OTug~KfDTs`GPkTm^uO)YQKnH{ zTNYWI&vF>>argtn437hp+l+FXdDJcY{9=ABxG8u7Cn{Oh&95jpsPTi!I8R$vW@{Op zjY9V}dOS=fiSAV8{b_%F#XcT^77f;Nh^sUeqejbKv5hL(n1zh2vg}M@LLg2*eoLeX zm3jb9=R;jncq6p~+fb%NLQ&h~zdYUPe|hM-^{AnyRu2knerkF>2(GWe{z7nPAfH9Ho92FLRF@ zl?$D!`bTqzXThI0ZHjg^P&HQ93!5m_+IgMjKoq`#qvPK#YpAi!(prx>^Nw+kh%g@2 zUV?>Q{}wo~=^pF05m-c4#tS`UdDru+bQL$+hGQjH?(Tl+7qphB6c6)$PAJERU$ch( zsn<2AYRW6&)P%cMd4YM~_4a8TWp4$*wm_{&ZIrb_$A;o=#V7t%AsFr`gjf}Pq4+eU}QZX`ytZF+*}$t$1leY@l2lX?3e#&b)* zhYIO_vf<-7(p7yeU4}zwjCSLV+h5yJ{ev&S>D5|W2Bhke7>xCoC*KJqvnMz~Zd|Dj zP;-JH8A)jqg;h`+feSe4G^(;_j&dCIEBp@uHm@Cn>Y@&)=BmU@C0HFf@$-w4+C5vi z-H3GklmCsrZg=jQlPyGlG-Xln{wp10tm*CTT4y^MxdeN z0YLHv+jv*qY7fN~x?X#CvZNAo&&3Iee$%tc;{}f`&MA@8#hk~!LP&(t5L}wiL#(;) zHru$ro!%2KW}H4p*~e4WIZ&U&lijW&?9p6hwF^`! z(DV^Nvfy__Dcc}A`2uSMd?XaZ1{}!n)yZP`R4r#oajI*@4!qeO2i{sg6;)ppr$Uk9iW6i)>Tds z=(d!$$-P6$n|~>V5i3`f*d<>z$iCLd=tQ!RQ|%*+rorkH?UO2yK{YC-VaqeQuHsf? zGlS`?9k(#=rx5!2a|Zv){OL zr@s9RmZKa|t`NjOIQ(SKIqmYNBUe884m+8K!eu)&igbHN5}#IU1Yh}-j`Eg{;9#V1 z@@B(v@`-!5Ds+8=550JXb3pw7B@wf`QrshWDXt)}Q75N|_x_M!o8s?aYfj28EDwqe zihEcIzn-%)nm-wsCM+g`T4@L;^%OHIs22udm%cqWiybu$B~+Ls0Hu=2g>3|W7QZ+0 z?1f~28tH0%yunMtyy>Qq!!hjl=Llr2cltgpW5=5hN{tUgZ6egQp3OVcmS-?xH}$8s zEcil!_Fl!a3TK?>QswmSd(1UN-<9q z)3>;*oWQ+v>Q7mdrS>ZKXQ-}-d%;v&@XIf!oyRx4j~d0;4c7!H%o2P0(0vTW&=Kkr z36y5=K?E#rWm7HUrg2gL;z;yvjqWR~>_s-SdA`%xs&%dZU!IR&U~KvlIC%r8 z?OZNM{>SV8<(aJo2C45J3IM}J5UU|=vm&*-cowPERot7`-ydW zk>EdpK^*M)D%AF2d86hClEt<1mu zc{3F^%ZcZ(BFQwH zM*oEKADZJ|uZh@nRoM=HZ{_Tgmn8tfNfk z?V+WVSU4BJ#W}6@qBy_8Gm`QY_#@6Y8+F9(P@4Df09Ln;SuhkZ@usUZZ5u%L@v;f7 zEc-8yRjUh@Ix9frw?FE0EW88HM7|n z-kUDmE#g%^Idk1FxY1s4!AGrwd$3fiF2W=}vA^}v)_l>|8R;g3`2V2Elu=w%k-o!& zGlhOn`i$Y-|AU(SBh_s1UJ#Ja&L-z^5|vV1Z~z8yFqhf5Ekj8ZTi|AYOo|1bD|2Swt~lqk@d zR*(Fz)yKsELFh-K=3wivU(Q?`*t$oR{Dac#LJC*lX5w(0U754mEx-z_sD~V(-H(2- z>T}#WQVLtOKkPN_MHb^x*0z;SUr~|wQoxq;opp+l#}-Q4r&u5CcTrv(edP!TE#~V_ zO+$&8)ypsp6hlQ4vHfgEM)C87mrRn!EsqLj+f+AtRFCneExM4;MeJ1O)k`Z%oxuuM zvp3Dqp%Dr*|CB?Z_W7}6J)fac8!(Ov$MS2XQ@>^Jn)3xEg{OFt5!1KL9A_9&z?S#T zLxsc5x;LX^#;j3Y3BfF*=R=`4-=rk;6zXMC9_!8?N~at?<-0=-PHdWPt{LxylI;@P|&qsBYF6FlT z<@xrPhXayMGC?A(M>w!WjiA#Fi72gu0zU!+W$T9nKBGop^Z1Q>#s&fV^{!v|qW!`k zQ{A2LZI{wlsaV6EhIUY0EXSoV8|ocPqwfoLd$hF~A3qz+85L=U30bz1zuxJJ+%rBP z8Dkeqlv1T?c2#hfAoP^uDQfy@PJ!;v{hT?x zug8Wwla{&~>dax9&oxUZe70ND%hyXWTmL>=P#CgZ?T8?50t8Ds0(0BYVLJ^QC_RbE z&CHIV4M#ge-`xwPLephl?bOE3(il=iNH^79r-V(_D>JeC^2HiDhW`V6f&KwoFx+AM zo=6TT`b@FwnzMUMucS){=&;S#Gyl}c%1znxeQe7|jujJ5u^CrrZEXVgPX?RB_}C80 z#!RM`wiU;Fmy^HRlQQ&;k8Vu$m3CexQ4Tz7V<9Dyu%zn}nA}>lni%(CRY}^W2VaW3 zx7b^VIYMBLD;bFTylIG701vCH;Xk*dc5jVqRR9_pdgomL3<44)4ytztvZ|LTfv69| zVDJi9V@ja?n=7b(w;idC@HQHc<=C+){Z9~hUMY+t2+(SjY{FkLmlk1Z$=26Ulscb9 zG)f?t(PE9eWpj*@b*zM>R?L29pA+bClFtES=U6rv{3a@bGBdn=0t@(6cB;2sKFjq- z<#8IOtI5ifD@g4mghFB`ikQgWD)EUV_HA|qbi0kZU$Y!{@zb|EBCUtivf5`_ow+fe z`+x{37Y=s0Cau_if7G4n(;bJ>xU!Bss@h9^Ip2^~^_DUCL&)Lud$p`oEkXgx*;3s$Ikjrot{~t99v`NjDqoi-$eEQdFt4|cM5n2<8Lubws*dRIlrN48 zT?zn9w{7ZO&fB+ zSjr+Ydo{1`5brgL-XSA#DwyN?Q?5a?doaqu_E80)^Sx{LrUoFWMQz@J`q>r{Kh?iH z=gBIZj38&m!l1COmX}<<61+g=&;;s%+<7ixz*iwnQ#VZ_sz%xbmr|S-y5;qs3nZXG z85oXjFn^Z7MvCt_&)yo5+Z%iTm*>joQ>$+IZz3UG(1O@dE4^ylIT!7;*2Rk!C0Wuh z)z4QyJs4eIt4*Bc=mt4vX;HDpXdn@>lNV5nPA7K`p<^Zi+eVN)dQl+sre8v*TcLiy zSWwqW4}XL_9F6J$8DYbQZIXxoH4h4kqwCYL?(zPrjP&VP+Vn!EPemAfsSJpvF9ubU z^}(k3q;KL*S_jn7_QU9r{km5^69oy`dBO3!Z5MyjzAG$T@IUvPgtgxuLdlo=R^QcR4>g2|zas^8U_CmVWOF?nAOx?I#>fbXcA z`7V_OTjgyDKalv`z6eS_auZ^>cf@4~CN|9e(;vM>Y8{pH9R?S%Ks(o3wA&grgoH6a z{PGHQf}tNg>kGrZfF`?uL;A#k&`8I`5q+Q|8rSs4Cf{g^Vj1!k~ZiGe^>t+R# zjncqHi7%_VtnSy#3Av?(K~bGv{;$w;xW{(+)cMtI*V=o|#LHuI0q4w8cF6Q(@w*v! z<{K<72EVQSg96)!GNDYt!_lFL))`N3tW%5we)kgJ*ZPE$^fDWWPO8#-lE&2we|g>< zAe$cRqvh_FnTif?8Yi(`z08>T;=vvnk!jANNqaLA^bfu%CB;v0hdQ1OJaK4|&#=XGpV3nWnpbSf`ml$Vq z4}opCF14Xm&S5sx*>4%+{~%bN+%*mZP#=@Uz2O}xcAz(?HQLrIw5OPlaW%o)1L0?@ znojI8%H;+VVvGt|3k51I2_H;n8fzv^8M0m&x-y1X_V zzZ(mH@Z|x*3S0c==_{H2E_Kk+<2NycVXq*>?@@|rfJ(6nLzIn={^e;KZ3slR*>~RNg_GWB z^`a(8A$=SR%JaG@L?EgQ{o`i#t0b@+rOP=OnuP!uhxum=qZK}gv#syclr~><@B+ub;A``F=rPJ!I7RzQP`y)eO9EzyGS!bK8P1{pC{B zeVaMH4_l?EM?Lya!B;x%Ca}Wl7npBMpZ%_g=l)W@9)T7Zj{YA_Z8@GjLC2``4RqDQ zxCc_9Ww|1?+mj@bmN)9$LnjK?xEeyB;Dz%nk&~U}cl#Vn_bU(_=Lt1@|YJ9r5& zkzB}_qA_gKSeCZ~t>TXpwB4!OeqC^9yaYwhY}vb8Fi=DZ(3GXvIKq~o#!4YirKu8| zUT6eVQL9LUh{kWIJq2Gd&t#zMPTzsL(*5uJj(S+0!J|VLDA2HZhs`mFT>ph7 z5J2zAX}+v!oGMZa5Oi;Zw=(Jv;R^f!Wk0E113Jv&qpH3F+13qeB)yVKc^%AM>OKw9 z6Vv{TajD>cJew?CLQ|3|rEhBR6W@nQ3HuehdXr=ttD`k zwtt-EsRK6X=|zskA)6ghuvOb8YvipPgr_X9#|%LgD~#oDby$bwQ}Vi)lKQ^*tWI*O zqomRNvArqyQF91JZFuI3cC9vNyYi$K3f-f5FRzmPkv7axp1_eI+u$$jR0~VvS&Lj@ zc+r$k&>VMvt~7q@I4yd>nS0G0?8h}@#mDe8^_%K*w5y`j{i5j)c3J;gQm&YU(o<8J z&)0@Eb3ucf_NzN;c~N`Vtb{m)te%DMLpr36c{}~O&w2*Tqgs;CcoivA3!Dyir^tD@ zFRYw$#vdk<$QMb^NPgMGfD7Z+xGSjMT=^bT4)deyvV~|Um*vZ4PjJ+JxaC6j?T@O5 zT=1pEzy+5&4&^z$(HvMA!{lNeg3M(KKTIFGf0p#E9ZpNJwZm^KSJL=Eiq{)!2uQe= z;W2qxBIP(p@ffG=i0mOqp4`iLOwp?X;N1J zqanUjw`?JKl3a^Xbu~|(Q&df)EwW<$Yxy0W36vnMz(3@yMcm9H3lxg5!KU1nRFS4= zb46N05L*_2clGC@=r4{e4s(2*27w|yxjk+ZM)a2LWA>yBz{z4~l>}v0m28+raUJN> zd9TKryd~9$w>0=+kOTW*J4W4KZr6Ez(|4Xa(BgmVbBNq7b}6D@T2j@h>Fa9&Zmrs} zB19qvDjwaQi#xpKw(}onRk?)zbee(&`-9(4OGOSGpoCIYiuK8Xy0hsf=S@_7=9``T zJn}q+_1-?6+e2;d$7;PXPEUsL4R{{x!|WH;N2orxN@$xpYwv?!4>dQ=xM!>OLU$%D zL8CDZYt3smQ1AcWI(hVQo>y9G=+BDb=G(mQe>Q*h_y@ASgJYZq+Tp0oj}GaKg!4R& zWeOkedJCrQo~H%!t=?7~QXEK~;EMP+R)(jX3BstF42|BIvxG4&4cI?PVP-?nsBYrL zg=LIjblo^$V7&#pRcpXng3Xz#9Q667;vFou#$ZWD5RQO5^U^<44zl1p;ohT`u7;?M zmu?zIedqiV>ANyVQ-%n27d{JL9x|v@Zjck}Fw4qfaYAwB({6jXsJu z;*6?al@o9j$6mH>M<7{titMl%NzuBrZ6qjjGvx|`GH|CS_g*J&TK~b+YKToXM!*|N z)$r@C@Hs35ztie{X_uq|(|*ERD4R<_(G3aRU@c`Vq}T~9^&@xqm!Ewys}0jd4`PI& z|M7D1)s8uq`+u|7v7^&^ZVx7d-~(7kWBz8uK6bKc3i z#?`o%5yNrVxZ)}!*KKIeFCT-#KPX2*p4f)|Mg%+K|A1o=-R>a0iaY(4ZD~WbagwOO zL;kD$=0kmd^|)x%-b|jck6v|jxh3X^1y)5)OMn{q&4wB|{l+=vJ9QCM4MIv!k6BJW z(pZq7sHjsAXWF7_&&D{N3LG@VuUkUE5WWtU>PMh?AbFVc>DTz%MvZsssFCk?RL(d= zn*0`$$?-c`rSX|faX#EnK!Vh46a$1mV@l*8A!PcwkS&I?fy!BOIsr!eaF z;5fWnb7L(kPZls;G<8KTE?m_zWF3h3E~i#?B+-Tq4=(yHTd35xdEIhY3a3jM-LGaB z@TZ09%_P#_@rA;chC5k?dm4@}7|Cn$G$ssvVTVmGI8*2Om{BaIJHsd1K{)M88xiQ+ zx_VB3jNBA(bp|AiKu0FQ*caSyAoEYf;3-FGwmbWV#29{Coso7#YyY`OzlRjX=EKb*m$gYna4Onc+OZAi0`XlCM5ZH$D1lTeLQaERJ|$xn54l#*SKZd;gx2`Lv8N@ zB3*_2DcELfC}|kR1ukf&#T@r&q2bfxBOLoI*gStlW7+4on&EMfwec->+S7l-qD`g;a!K4+6Qe>{~hb ztffGH=m@bZDX2Qq5Qm&B>1>&jdH(EK+g8N84oKdal$?*tZUDdbg>p}Ll`9^!jrS9I zr_={dQ^KPFsvs@3^1f`(ZT%}>TaKW+6K9P0d}rb>t0dLu8`&@i<}S`*uUwqObIKmW z9PN%MK|(dWbD6_IHrv;XHL_f`tO;S3dJfN0f3cBIaW)(X5d(z9WAhW9wcB#XKf`|n zyS7b9(s2gOq{}jhw$?WxvW14q-w(Gxw@>EKERrP_^M!^E5#FpD;QqGBAJk@*GM4m)7+0Oq{WF66R9Tkf( z6Qmt|P`|2_^gJ+uPPj(zxUBD#Wyfqj>)^}$@R*kUSZ1z|crKFd+*WTAS@+PF;4esL z*f0|$EN%~t=WFg1dg6CZxrq>Sv~d!s&e2Rk-xgk5v~Y)G3H+4s*?;{^_T}7;Rr*=d zs7A{XZGUlhVyf<$_0Q+S3zrw{L&_34*K$TW{4*(bL`7rS zJhQ6O%QyOkecnXJqSpPO@N&q9N|_~Qw*$iGL%^x5*G&{{$$u_dy-?8<12}d(+7Z7# z!OcPa@Z}n-E$~83IfA$UvEp^Wh=@^TquQic^lo9OBQUFa+CJZ-2HwR8i!I+aTn`ey z&ChnZ`lF~g<@l<*BHMgsA%7Ek=KGh^1z_ZGxV6 zq=&w)%v! zi_)+x{V?C)zFHxpr~7qBul1>3=aE0<6Gb=!)hrR*-f}QNA~TiTA7M9S6|SbSh;p++ z3mNS@W!6C*4{o?=`+~1&c$ZhF)&A)t5GzFdPxy(K(sW9*Mt_&Pu|^kU#aNo&IJvYI zsIk@OiWES42lJ<@R4&JU6Ss#NeF~=-YKW=78Vz&yrK2M|ie7$tY`0yOl}>I{=t1=g z7dvpTzD50lNa~U7CqJu`JxjbjbCRxneOPFW*ahfHNxkT1OJ`5HgJm63`s1w)o+z-=k@s9{Wk$e==TQ znwdbs zDs2=iO2H>+@!GiKp^uGj?ZFNgPfJla(^KJf9gr|lrxYQ zEQ~DQa+7|XHykE}AO7=tnHjG#lY3}nLCx9X(ZYU`t);4?_81T|IceLDin;V^M`X!p z`ZaFaHb3lpSH4}j6hu5`Y>;XlUPJ7|_6UZZmhHDxl5s8isE4TCvh4~dYU#F$AsC#R zsB$J=BsV~)%LAAy$B$?|7}UEeM}1b)qa}~(OxqbmzwU)R8OCpicvRD(fpe%C1p5u1 z#?W6LhB0j3KFf>_BCCSM%T+OcQ%JA`EbPWy$bf z6K|~W!5>?0;(cj*t-d#Gqk--JK+VU_^9o9c5G158Vt8PGr&OuwW8mog<)IGm<^ak> z82soUPA=y=)$@BQGAV0EYANk#Wa&}gz`%Bj@sOJ8ktlzTHHvKaUc+6!g(q_LX?fGX*uJx2s1Fh7;BcSerMgQ}5pifl zhtNkf^%mn5y!@TAfJtB6&r8Ie3TsFEdvI*6mHD+3{)$^SaesNP1Il~M4a8{&*m5{P z1v4NKa_On|S$QOnA!oh93->nd{+|w0|GN!9?=MdU>Us13K<66f$MLK_Z6n-p6!GHK z@C<`M>u}|%)0D`t0+rf5f)q|7VkZ0v^&e9AjB(O8lka)ew`0T(3si5z4;@>o`Z27r z9L0RBGxX=RJ4}nGk9N*_F`_JX;&8ztb^jSzzl&F&epo0t9e?=LpYm~~AU8I_#tF3o z;SbCN5O%u+_gMe_I$oozgiEw~5}(Doloj&cl>Yj~ZV9&FPSz^;lnwiy#Q&=9|Mu}y z)|8BX)N9igV2?>DNlAs49QR10r3erX~QC&+}?Oz|(<&UB5 zlimIkJE^0?EL)Njb+-lu(nv$T9gbU){vT@(T|;RcRIwd+{xWgtx4fquMfUUx_q_MyC-C{FcZ^P`b9{a8wRpyg%7|o42xXzqd}DA`POr8 z^Qdl`h#Bp(NPAWI1SL|qbm!$_%&j!%ocSOaM9+Z~JTK}8RpEL$2Uv)OAI7l2+&t#-+{Bg&$eTmp zrnhG9JAVP@$GcdTVNWBHOtt&qx$%jLEK+|s=YM}zLrfxST~QFmv(YRCv+})RA<%ONV-uyeI(CHv z$E9!Vf01Wf7`aQf6qF=p+VP8>C{Wm~2dkqWGiH?6Uv#()dCi{TKAm8r5i7m0q}4~w zx6wS%v|EfhYq8y;1l#@kphQQ?IobT;$(cjXt9z{sa(#k!UmxhuNOX0ht?3o2 z!{ENjO-R>qLM74RVIjmE)mbWCyS+dKJL^C$_4{mqc!3{YscWET7}N8ojbbP!e6GM^ zA&n?L<;@LLQ$>=boP3$(@jca`R*LGuaoMeUCXy)Tj^$(d+Cq>6W)$btN}u|KDFCat%Z7kvoz5ZOr|CzY}u5uHzBD!)tgqr+doE~_ zDkGJ#I-+x5@{yRsJnCW4NRqSz_-OC3mu4Zs^E3R*(SnY7QP8Ca<+(lHKKU-2r4% z4hXNcj&tJWO@gx#?Uxg#cBV2Hk~I>9#0 zj!$f*s&;X`wx}XvPn7Y`0}M4$z29Q%eR}I`Y~M}=jTeYj)JaukVyC7=q_!tH`p!d(1CkST4ugaMaVCt ztW~x5f02t)bpn{ffdIk^Qwe-{A+Lq^%A|&(+m+pOk0q>2F7(NLv;=(1%H`7NrlSmB zr&n;p4pnQlqMgt7ZhBNO+0!O6eHP8%ly#V}ME?Yw(=s>kS$zD@*Lg&_*^>xPJ#3yY zaWk7feXoJHvDvEF=1(svzQaPx1|Iv~Dcm5l`ncKQGT1&xo8km2u^uM}208v#McW<6 zSVoyqnY>Nd&sc%%0o$z(|GgXz?0Iw5$;0$N=@J5Kc)D1G*dBMGMiB8|j7I^L_r>cL z%6pcrdC=$s>Mb$hT_`vS-Q8cLIh24`PHO4?>M){D2g{Q+Upb4+9#k-xS65&=sG6|9 zo|)Ab#ns?c%C=)6D%h)U7!f$QJjzafSoenjW|M zT_mO|cv`X(OSP+A9$l9%80%x6q&gceC~SJzCuJ+I9~x9Rlv>uGHJd*8O!CyszziNK z{YO)L#+8=&{8RyR+y#!zX1I=DWjOYES~p9zPHdKvJ;_$&iI>~18F0+y1MEw`qf4@Y zTzv#`a2L2Z`$mc1k>zhU1Ghq1qsLzNQJXKsAL0$}9uK(FIo}rPTgOQzevz4@qF<2Q zIbmc+!;0oYA)GS%s>~*?d@jz2h3u=`vE5^z?8KY_p-w(k2!iK*L7 zdT$17lF#2iFC<68vAmQR*C5*#gVoLXWBbprY7ViN7gdDYqyx9Z?Na2Tqfa88`?|OL zVaL!*FIH6%V=&q58|5#%t!UxvKsE#<;Xv>nX$EAR#RRl)BuAM!$Mrx~h2LRn^;wj! z#QC9P5(C9;w<{a}J`=4G;bO(&jdsrd-DPa1mi!_8(DMpO)fUiesL=%w|0WMO!cwsn zMvu0T8zSHRxkQjTqu*B*?!T}PNPE_l9OH+|l`6(2-) z5)J^%J;_nk1|GuwnshxzDGd&dTn1LtrWwk{kMYbb#I;Pis71a>D zMzc_TC@@i6?9EB#wuHq#?r-I<9~B;exlar-Tm;4vUb?VseF$(-vD@ir4?=~lxF{birNk|9Nb|8%VQNCnhnxAz;1 zyP9$AdCgosRoP2|i-l16DpbiFQ&YCCi9gsiShkLe;=(~1^MS)b;xWG^^jk}}l_keM7I>q5MF z;dB%ivk~L#p|2bAQc&hmwF*ZDkng^9gOPy&a%~psn4K4;F&sD!mx5}!dRrkIIt}zU zE8D9c4UtA9+1(Px4Gn^D;v$#_Pxuk@ciGANsBobo38 z))rgWytKIk^ft<0I1xv$_dUH-Hqy>rqMUHPK(~dp=UOY5ib;-nhR@nK@<4sPvSlJV z1?v{qClfQt?@Xj)_rg=(+#!$p$D_F>5m)_Snc>zGT-=b`0`|{O?bO_0dTrF$~mfJ^P?` z?GDqAAzVvtQ3{G17<&cr$lK0*yJhnAYkkENY;PLG|9w%N^j6mn85xO=wz@cpySe#G zzNyzg5lDV+shd%4vmA!qRMyCfk^(rmqqAicvAx%=@rrvb)(S znzan(E~3U^PR7%MPg3S2mZYVNBqiA<}%n@2wxf22>eW6zu28Cf}qVUSDs?dwvW#c*ACE=F^^_ zQ)(clczHX*s0)Iwc4nQySbSRyX-)|f>?t}1Kr!!myqT1YxOHXNmP^iUoe-`U-S<|z z>JyfZT&!1M$dq4MdH%O(M7az!L!pza-lI%AYFh1ouH!Y7C4bKFf{wE}e*k64H~^8> zf7x(Iy89JSVSpF&6tg^*C}tNlN*tyG*^)o_<}pW>V!??6QsCRa9ynw)>tXuO&#L!dbQT8WyG*v6);^ z#vD2Fy>3|D_<-xzJq$Vc#Ew58^N7SmIhEW^{#G7s02ohNRpQ(uT~*EcA)C)`!k?^v z#3QxO+9$}X5SEpz4*iV8OhsGgOZMlWX$rc!a$xa@!}|IpWxJHis@rUpmj`AUyLpc> zvWhG%CaeR~lb>SK(hN8bhixGbIt^X~#aA&O&Gbxz6^L-sdeAY#E{$rFsFA>%L1@B#jYO__K%DBwH zK`LCf8vbI*pt15bjf%HH>fGlGaU@3*2jh@*doAvg1|B{l)jMeHCjJ#f4!c9gg)vYV<2XM*-} zCD`T>_ptv+#~6K@M8y&d@eiMy+ZM_H(L^``)Sy};;?b?kz5o*qP&g3ywdHhX#!^7K zK62dII##OHALfq@Q!S-wtN-W7#l(Q*WqPdsq-*WS3tl4TZMC{%8)dB42+C#nFsL~f zb=)-FpRewRhwi*`S#cK2=Mi=!e$mKWvQ!}lsB8N~vKBtvenuT?fuW}RqZu*)n3Hp$G_Ys6qVa8U!J}QOryno^jw(F{;SJ`*GkOSL zx*B27b=+tcr|(3gTL{a)6)W#87s+OuQVS)M>C!l@7ULD%QjV5{D zp?hz<=vKNilr{8ZbBxBxrC;h9X`xxkw$zILi&({r+^;rwC_ z@ON{&1MS7_1yzvPd{H4#g$7;6KcJkV?&%pxpp6cSG`bVA5`hM1IeWNo%PFmjchiZ;FhVZ-<=sJ zI0mVmzU?8KT_l}}={;n?qfABByGI0M5ZyyzIl8y9RaUowk|fN$0m=A8PaYI<*rVS8 zJE;F5`W?|g!7Nt_jF4hx51oWD!k*-TNtiJ_1v>t>1eA}^diUv7B;EaXG}%PhCv-fE z+7b{D(d`F`2h~rifF2$@rv7XCk@%%x6O^yY@TE1C&F3dH{(jq3ZWXeSQ@PnkNI?YU zZjF7$YSoO=3}bUZMs$K=hhJ1TW?Nmbbi zOLvVS#_U90nNWeotuuxFWbpEk*VA2AE*x&IWp@d4X@QWqJN>cQ+>(u|0Q}8m40Z&n zNHJE_Sd8H@a}9mou#>G!oA)GD3?v3syumr#`Q-+Jox79|&%-RsNF3UiqFOjn#hKCS zQ!PPTRQu?hsq%6zJon6%vo9Lv1X8-rZhq{ZwDt3Rkb`VUT2HD`Gloa~D)5YBR{PCx zn}DVcd6o8+ytQaoiVS5!F1*T>ZHGZ2*w#9tDLyKw!{^4eKbdnaN*d3~({f5C5trwA zt)`v0R=tVDKUeAqj|7~HBeQR+`FC@@Qiztf%-;>Wzv0A2S>QtErSlP#Np8z!t@ zJ5NH*qQ~|zHczk!A4S<3Im7Guh%`gNZIQprTg6G=f!x9s8h;r~-F_^zSv1m0Su^myrPTE9B;h0gJA#Mn!xC((t7))E;N)5gLNkvcc{>Y1e|5}w-G!*Zuh zvqEHwTivAb*mmtc1E%_G(dlVZL-1w6Z;qU}GiMVqNNlZ?inK20+n~TJpAbtF+t;!u zpm>wKX{ZA8>)ZQ(96=0-d!My(!rotH9;p01Rk;^P;o&6trg5z@3K0=6k8TMay`GsB zT;}M?R=a&65FsJByBIWUA)=w5S$DasP_9lw-j6U$>t0a8bN^N_oMfo%Vf@#I0`6LsOp!^d{^!N|!;?>_xvF3hFMb@zjaATO?$OWMRBb*rh#J{_;rR_`grfhRJi5L+&n1*r zOUZNnP)^Ik!)2jsBTbLjRl4Gd1Zn}(aQg|!|_HQA|CFj>-4MjpA2u2|ahV^Prp95gUD{yIpWX_l- zOIH)EB(`(musF5?<9co6YKo#P4`d?`-A?~dant9jLyk|^FhcFnBc#PA?1%kvmuO}< ztyX_l*J`sS?5%Zv+uk2PoBwG3+=~-*QNkPALhG=~{EcUIxFy+`1XR%keg{7>e~hm& zYFEqaZ0bFGlW`dJwvQc5b}&^r$4~g-WU>(Ntc)^NS0rORN8m*s7DrSQ$98(?x8ENT z9wONEVn%uPLd!t*YPD>6*ka1uuP~JZ_{R1W=AE7bYql6Ny0udG^Kr}dV-IaBve!Rp z7H!Rp%AcVw0cAmg;hk5jhey4)`(E5koY{tT!sMDKLz+-bI?jG-Pgg~743_Rvh|VX= zbRmSMf_Vfk9cDi|kFt*=n>)i4=j9oY%fwnq(u8O+W%;E3JKwoICqwmA%O!z-R|F|u zZ^{NZSUL{%ed8pD@a&S}1ybG9&KxRWE91!*J$Z(um=LwQ*CR&htDehe^3-*j=O-`+ zopt-?;WW!DJ@+v>jn)4x&phW?b5;PXF$$DFfAG0KFzl?q|BU7uoRtxSR)6TdVX{B&@^aUTf_@~5 zZosE?^$zwW%7~~+@Un)^ONbH)vOnIP9yhVJfrVu|v8qhioo5lS5W<_2@Fi5Mi_q7c zTHwGe4lS5tt5OGr%)T~Ou5$Y;<8cRkbSE*4@IBR@##wV|@ej=gC_v)2I z73-x0QKOlD-p(LW5MxV@{Gq)>(?U?g?Z>g_$8cWQ`72Bom>KT;#9z1SqX*njR&kGg zyItTIXf%m-KXCRKTcG|H{G2aw)C?~;txlk@yHNs8>xYOba#R=vhzjH99u$&0qQQEYm_GsU{{Vp{8d3FR%*@Jw>IHXk1G1W(Ud4S;Ir##GS9PeD z;-P%}4_-c8vaOZw_I4;^C_0*X$dJj~BeY zOxd9rzpq}{Vs4vdq44@bpW0N<;5NTVa-7!IORS4*@6jg02zO1DOFTp^cqa@}^Ua;o z3pmwYFpn=w*dMxZio}(eEZk(?h*7TGX_5F%m;?wZZ-47R3zfh5y8g&huo{i>XXFTk zAx9=rX{KXk0r-ue>yD#a$brsZmYEaafzO5JaW=$r{Kai&trwp(KYZZI^EBX>Pxu+O zU-Y0@h&|cE+%8U?pAnnbb(@=S;51Cw7J3A5+{c0dvJ9`oafB$>H*O9JsB+{mr3p_r z_)TW0)$DgNH@lTCk1?sdgmE5FlPhBBPu|E(L#p30u%j<<9>GdWS1WhzQ(QJSS49~Z zA_*PI1N|2>a;ft4PXkX7>Fx()qeoJ7;||kLT9O8TrYCT?+c&ke(HIsE(b*T8Es@ zwA@?~Wm>A;c<6XXNJbGYp?T+7xuV8j^!7fKAUVP}(AB$zdr7A{7*fSiVp4!ao%@Ib<1^0T9PpWfCAxjb<*}kqJnmu8X}hy!>8)wPVqr8cjb^ zR2c+MNAzmGnD#s-glfg1r4br7?T}K^DOt9`kG;8jnevgwYW-8*5logxsV4+2V_GDx zk`kfhkv3kb{5%G4y!3D6)b;UBjlfuy_43I|5m#WotfG!Zf`(8C_U0Y0$=&BtwSlw3 zoCP!c&u8nJHoG)Mk5R9DAPYtpp2~vv5Lb2%u%aUCv_plB*NHz#D*KCjB1B8Zz|P}v zu@t$?PR?YvD>tPy?Y3XvjsFx7{_>4_byk?E=br{W`Z&shlJn~-X#yay)o_+J-g|lJ zbO>~e8Mm?((2wevzCPMAym8PZQura$HLP!4`K(V^7|Nh`zfnC@%<0IKb5P;3NMC~B z*NA||`+47dgSf?-5*{A+XV{f%zy}95DGQx?JHcP0BKw=~HN|#4610ogHzRI|o^&n8 z=!IT2CYhyIm4Blg7DURl(G`&0Ipy<=@!Ufp637V{l~f|D&YY;$it z8Bwd1Z0YhhitRV}|AwA~#8`9+MWZ;4;~){d}ciNSk7(@W*A) zpkSQRcGvU#zfL;u3rr!84wze?^+`SWET#P6k)mPv$Fst7{FMD^7drV`wN4-Ge}gXW zC@YN?h*Cswvg(Jep&Hk8)lD;$(|A}9ng7a;LQ0z{vK1odDCTu?j9790>Q&m~`eqlS z8Oe^x#zDxB9tG?ZWr}6fUGGQ6N-bNJVQmVr-W*=DX+Ng@6APdFVa>*Zbvji*J`vZS zcjxk|+4YYQ6*^0%TG+|vizKzHte%!B%Qk_*)!PSwYbM|P-x=x1FJ`w0OgXzJ2;a?- z7q@){5)Kb|zq~R{cw%^Ur7x8VeN$WiDn)MW#tW8Kg#+sES=RE;Iw8hFZQ6n@*MFq* z&BqZhf;zetU4t)FhMr&kQ&!74%-Qx7vKk6Jr+=FFpZ)4(Om=@8HahO~j(roO=@-a%N+a9NdDXSEK;BvHb9_*~d)QMy|4zIZdl;|Ucm;1&&?a~?Q?1bnVrzIrpWqRFo z6^=XZ%Sbod9orPp*u8p3n-Z~NmW3)s?;;PFAUt6U(*&}>nN6KX2m2;kSmxzs%twyk zxyEn?P)5>B=eyje<}n-B0Gd8L3I_8-^ zVwNYqxyLw~d+p==01@Bhf|NW`!}*WN^a85RQ)cB^J8Ixlg}lWq-=UFKZK*xU=C+cZ zhAfl*pWV93J4Owb!-TrD(aS^X^m5?BzWVEGHB;4`CAA`|;Dvlu0Hx8-%@UHVAI<`k z+=Hul?)6-8H;n$Q$fD%JxY=ljP@q${eSC?)m9|GfJ0;H(8?%#n(`pC9F&7APBWK>- zP7P!p4%&6d|vw5Ni!j5MiI|iZMr|Se4EyUiY?3lAcFZCp=CXVHWcF zJPAmQQSc$fV@n#mitrxbEh+!Gnen^^?3|f95YaWp6hD|f*>f$S)PlXgc;g?#ne(87 zJ%!|)W~eUroOQcJ?K9mqY|&8RzhFFv0)7a4#Y6{;!t2leb{+BkkZ`c}6)kl+w{aWIADzSWa7qI9?i7L=9>ymH7v@VT>KTc<$-!&bJl>f;Y{l zG2et7bELuqZ<;>A8PKJN^j}7WTpFuN^>1XlSEBZV&(`DaeF(C1mNx0a%vauyw&BZ! zftLhusYeFw&wSm_A%KC?S7<6SbCR7^wdw`01XV6doO+k`kdUP(GY;Ogn2qLnBva2Q zYhV4_BUf77{UZIGbpflhxI8M7K`BtjKLeB0VjpwM7BedOkevL!wTocdu=~_g-JY^o zT_xnk?y`yS>Fw-v1i|D(Kn<_~I5rx03-=tI1eip{A13Z*0g=`NwD^I}>D*;pa&JES z_uC1F^QM$=@TwNm4_gL`KYX2R16fb}j#g6jK+4H~^8f&A)vYB8vT5k}eywANkn?Ps z%zGQ-lH+QGdEijCYSl6oZ3O#EZLzpG50bw2(|LkWUo&P*dy}$?E<6MnvlnSCml$jm zK(Oe=59Ww~uo8h{grTWk!r1^bzmFact8*#|Hh7sPaR`_+b3PzhNNrd|iiRfAOD)GM zdBvL1Ykpuh_CH>kZYhyh7da6C*oEc9KjFzMQ6+LSX>mqVox4P$CG5)$^r!g`mHub) zLQ4X4&hpUW?&4tFOlge&R6bTz-SEiMBC!p;b>#K5+ws1hJeaofS-Pv->t&Aw>*>Cz zz3|&)V;da1LHCk=qX{`EDaO@`v7v5w@ZL=C6~tr!|MP>2;9X*L=h-$Zu z1<80`IQYyDOJ=(df`8coPZ>8m09NU#IY-*G3H7Gx>&v2Vu-N6R^rR(7rR23Vg z7Bm0RnT6}!XEuhZZ)H?PlN9;19N`uVGgLn38Ttv8KMGXHAyqhDY;Qk1Uz3~0*$*mz zmerXuw55lEkF>02duEp%jG>H+Dq~+Kb-wZ-M)edq7W#hIlY8Xbe*93 ze|atY2X)5}-XE9NaN)G&h_c<*70&ozIEe0QQs?FECs963v*i#)WQ_t7$zKBMHpG^W!UxcQ=>2TS=wB>)YcjZIx5II~f5x3l# ztNXRQ06^7&$LSS>=rt4<^FM~_XLu@$cc3no^A86gMn;4_US%gf>uXD+TlzsO@^P1!clCHY{7D6O>NfDkzedR zv~Ig{Vi4lak-g-Bh1=$Cnd>*Y; z{@0}i(?m!uc`KAQC>>+6sfBuCe*U%1j?1Ls>b`26%Ju~LZ%&7(+Lk89S46`)XpO$q z&|o*?LY1nrvs$WLVr^{jXq@Tz`x@;kt(ePjf_>8%RS=YXCqpeQsPP1)71ziYA9*+9 z$hsQ&L(uv8D=*xyAO(im5bFn)+O)kl3-EIfn*UysFw`q1jD-5X_~pP*h34yJ$69$@ zW6M{+^9S!Amw}pyI;ov3eDU4XAOTNj8eAd2`T?)&fRozqxLE9@0A*-W2~v^iP}x7N zk=F{l*O=y|-&A6fqb@H_r9uZfuf_+HFx#1(97cw54bJM&7qn4- zLyK0VL&RfqfzcQ43BS8(|f2I0cVw`@l7kfcVcgk6CU9^Yc#(t1i#@rq65QlvAr+<#yfk=ocs6i zc&JK>e$i;@Kyic?&}_U_?2BMx8>-wo_tx+0Tt8mJvz|b{1O9a5j2MnoIVUQUOHWwb zs=YS0gwrDwMl8@@bJmpFzvD<_){HFT&~?Ks+yAcfG6^7*uR6V{@BPCmIZ$k)o_UeM z#c^P0^^SzY$I=iDV-IEeQt?8I1XGN#;r2IOvnBM^_tp(T#=FGTA>!At$JZj#^#gH& z>j}Pr2x8;6p9`VQReqCq{A0hhvWmqbpgXe;NNr$f$o{Ox4pm{PbgP_q=2BU&r?psmGwjO&(hQ{GOP;C^BQkCcDB5@)%!gfT&{B%F~M(7h@i_N31&sGqx; zvn0QX8IZ(f+G?;e1sIH8*ty*Rm&A{jW=JSCR~0*&*o$vazw}M5bCE%x8IcFHKfz1y zl`<5(E+k~n`W;cx13q(3EMQuuzkRSw_C6^0Nfpi$o-W?Ff7&dOF@Ne7_$>J1cIv3! z)z-1M`zaBNdRv>V&u=S1a5f(ITZpwZD@Xrq$Zm_D(1V4Ke4mGzuzukdFx_{`6rrb) zWyJnq8=CBEO8aFvZ+3t*&lYriv@l{LPFd4LN|zf17jWx;+HTlI*dx#gUv|~YV65$nN02VyD-b9}zqWup9wXE5{*{D+x_959ltUtQ$*D^M<62SA<}&(9DF zA7DHvh1;^iJy)Iqwzt!OY;HLG?&4s}vMh*#igrG!7G67rRozuzPuW&F%<8;XK5KGr$WrA0MFW_=p?~Tv~zU1%j|< zm!#I4<%x&-YVHWl0-fcLg7&BZ_?4jW8?GFBdF*I;NfC`F?vo6C4q7$iftRk$s)43B z84*m;ozKKbF#?I}Mp)e){tbs$fv8Vq8_$pIUEbO`AA78iw)T_Xx_#;U*Z3n1)|tq5 z?D$=nMRwpdjFB!2p?|7B}h=b2HhEgCIMJLt>jYnonPkNdn=Budci+&!;he zQfCX@bGF`oE?I)kkCoCDQAL3SPipV8vaO6%uDVHg&&Gb*09eK2s_DSte+M+vu)8=v zIQr-oUv&F<)p0WX9kM^E7pSc#ff1fTfXnUop%YeaetIJhN0^Itb>46AHxUwqZq21w z=nJnoJ4U3Ov#OqIg9F_Y6JcUU_V%T3X?5MAcTfA)+qDJ!@wvkgN2< z+1fUOF!xJ5-m_jNSC%&Y6}BmXrk|)dae3yaUIG=duY5;@QI6dIChU3BO#6bQU`w55 zsQKgPuAN%7ng+taWH>|Nol*9_?P>pE;ae))K4O{EJX}k$vs}JKTtR-0l23UAniR&( zaHW!wAQPvc1{E|{bWh;RKKH{!&W|oFPS-;W4QGvxyAK!ntNNBd-QWp5X=2J>?2QYb`Xe-yo`im)t_8|xz+ z3jRnsYjGErT>=?Z9b7i;hHq5vy~66m&Ma^PB+l@IBI9dk`_7sjDzVHT$sK4TM?g^BWw8FGm(*Gi`OzIOp(*D=&maowyc=0tpt17NwQ= zreg1gh4mL{)ZL$AUHC`l`wY16&(Y8?I_qzuQQ9>IYsZic2||@cB5m1qu^?^=m&?AA zSan>7jaUYUacT>AdFPete7sVE%XA0ts=ljyubQ2P);+!i_tCM@u>9v487*= zT)Kj_MdoPAmO8%s#9u;e`y`cu&cP`v?;>ijK~eVF9dY$aAt7w1ZG{~r`6{uk0X}{G z#Q=TSRPKjjk5L~(JNNR+gh@*jP@Rm)BzpFU6@`DYhsP`2G0xT)IuZE`IGC7G@=sQO zbWT#(6Qrmn->Ei{tOyNMqhz;2@`p5Fb{VUp2=kL5F|f^Mmp1YI)KU#FDB$sbn2M;w z6|I(5;-9>+8cE&}UGBD{Tx%RtC3Hvw<|w7r?9p~u&>Sts?=-ZMn_Wa*H9hlw`;z$t z;*IQd$+4^cZKYlxX`75p*`)6|d=2C4NsSkgmwJ7@MEfSxFwHX7e1TWT4T1xoYxa`P z-#aiK`t<9%X_>W!7pRRv2>kVLS*4gSA{R<zVKdg#I8mbD9Pk0{B2#M|A3V6pA%Btq2kekt>jttEkZp+8z~pBzPt z&3&Y5S`tiLb}NX}SksrA*wSqv>1Xw*M9lrQe@?QpEa7B3X^WOJf`7f`X>7mwq=CKa z_(`ZCQ@TS@00L?xv@~4(!}QRe;W4aMXCPZyJqY}AGgi*!@S5?5qV?RyMXpX~_Xsn{ zc~_J?HPmmjy$V?#=b7#ET6#_PrRo^J-Kke@+{R04Am>533;ubLI&&}NT84H*zd_$F0On8 zhT5c&zd8#;OPG;=9wP9cfYmYzb6=uf91JUpe!8EM*g=*aec@^lJeIXN0TdR`M5ybZ>T0@;hLgK}60d82{6dd0@T6J$vzn;S}h zo%m9;l911G+`VyviL&dT;IDB(!LB2d<3DqA#HrIzqCbcl!-l>n5}V2nJ=bAO=I+R? z(o14L^}^|UWhk5KhAJdb*S`Iwt8(eLoy8)t>!_OXGJ&%j{Ac~rb?{$YfYmC=j8JR; zy5w%$;@?k9-&?qm%PZ)+%k|9kK&W8=x$uNggF!=SQ}!i~rCU|-krbisv>8Q7GF0wA znwwn|M+F%|bECZz0~8n)-#FhVws||8%M<#$mD{h4>EI(11{Xs4{Shl)?w4`szeDKx z7oLI4Tdch_MSQU%hpe$vb6+xH&f22h0ft$3=IY5hk`i0jPDGB7QdzaZUCMiPs!nCb zlA>Ly#eSr~6J*16^+s!)%+@x-@_B?m)}qtutfe&AQx**f6(=i00f>}gHJcz^0^vm5`ns#q@^Vmj4gJ*w=L%1&oU5H;#suj~3e{W01 z7EL`^xYEByx<8ob+BI2P4{V2~sgLMn>_3l_Q&h~el+c2O;6iGR!y9+9m-O@SBC)q_ zt!_5ITJ@yx8%!(-Z))=FOjMbK`MOQ^sK#G=7$EzWW2$CBejBp7uE`ECR5nBn+8uMZvM{lXQMAg2^e{9JDNe5~+cooQ3l2DxxxAm4eAvlcz@pLLeeo38kmKB&{Cv0FvYhxpwNOrZjTu?*|kL-?j{@dHGwEWk9?Qfuuw%7ZjRrX`aXZa`k z)?ffQOLGtJfViAMbOsE^E~^^Y{N->|-jBNtBa!{O*0=%s#^@b|Pi)}>%Q!1~NjabS zWASH_tIu42;}{ogw}k+p4mXJ$QnQP<3IB};q+<)k+cV3mFx4#{g*HLw7mgqVvuPdG zExhqjSzr>ZTNjSHGEBnVjFxZw!)>7`*s+gVkhswgf5#-ntpNY9X;yXY-m%Gr_W_D7 zJ;mJPPG1%G%&ck~DcmyQ-9S_fNgrv*MKTN+M5*C;-%$4y!WKN8aR1y#8bV_|b2okD z?Zcn8k0b^Qf9_D{*1VMbVRLL=0$B7k-p6(l6el2IJN9wrqZ9QIvi`R z>iB=s6YHEL#hPuPIbjX@u>Q~tmjTu0|BVG-1UR_A0!w3@iB^0!(d3A9H(FsY*FNQZVc1h-Pf!x~2GP znk&)PA+zZLK8!smLGLH!eBrdwpVHif?3#hB<_56G4XoxKdDO@|=-2Ddp$q7|MYHF* zZe_aHUBvUNwYl1<)XP4ED(l(+?e?pUIDSJi z^Hq>V4jm6f#@WRwPlRIwqGMc$KZsp1L4cjB?BA&WS*Ea+2Mg@2a*cvNU*$z^%>HUR zr_Wc5a9GJA{*jcqkwLw*1a1Dn%3D}at&N#i8~^C_2f{=b80A-VkJzz2RVNi?S+CXX zm=}iX&Q|IwpPQSX1>BFXx*PAjv|%%F786Y1x*e;1PKZ~qDobYL>|QBz@F z1G&VwqO~Y)35{+p2Lq;vb4S6?ABpIQ{NtrK|6Gx{GU5r~IH-79MgsWy^OP42NI4_G zrLWWf@osS~d>H>G=06%^237Lse)l-yT2R@l9}%Tq&7HcEB?5r|Dq;enb^q7VwfHmr zfB$Z}kR%(0@=fkmK4m6_mE0v8Hgk#0{Zc-XBC<-QHkaH+bD3)xa+ymZ3L}@fWx3SC z%1DKgY?*z2uYQk*KVZ)L^*ZNyp09JBPh#rSl+eB&M>{$|<8-8r?JCKt%YS|Ku4PDP z6#Wv*J*rXoyh<;)-h=O|PqQT_kY_4(4<&3&dS!5bxnq9UW@y5DfOOvhrYoEj+EiOx zoA0qPYdK6^-cz|12zwQC(lxcV&0y;*DNBmk4kw)0?u6-2b+)&J#EXEeR$9qvCbU}; z%eJudKa6b#9?tzH#!7h}Bn>ei-vzB6sXOlHF(!O7a#phGe6i_jp@OB>ofkvZ`nJ_h zJjcSQ4Z4(G;Os18n$QvQI_r2m9Un9Z`G~k#Nex$Ft^K#fMOH^^yyg-TG}CGsLb$Ak zgD4Hur94VK2IB%xuXm>A-SrT`clT+bR%e0<1f|;mn1sukJg7ceXVdR45>71 zZB+^?jjB}Iyq6SSdSYtDPC!4wZN>)cr{62y(5i=Jogws{^1vuTj==VnLDRqchA4EU zCgcB=^4K`PaY>!@`*T@Phji5NKPP6+S>{z&+woJ}k({!hC^ZYx1D$p$(zBGFQORJv z_Tdwn_D3|N_+gcrm8WV+Fy}&GORDnvkY$ZW)<3IYc@fW$#8syofgAR~zB`Gph@;5m zq|e&7t;pK~-Ox~9TAHgKc=vc>$y3DC2M(7OLI~3nxQ*BC3EWrP&lzf*Bo-B6qUaCQ zh91Aml3Yz@U5q_LFR;`1CK=Y$krKR!2{uZioqi-)P556s&qLON9YXyZoRja4*p>-3#MD-jPgMvCUWT)d z?SdyK)xPz-UeYMTcaHzvHGmXivcUN=qq=?2wwBky{N3$2gsB`+YgA_G zHK*eA7i*89<{vPFuRXg`D=lOVpxqzxZcXt$FPLS*UeKUL)k6g};OHqVHwA4=yW4vX zMMS;dd7{!4+lN``19*&DC`u9&(-j$kL`0hSZ_D{~^9~OAJFs_)s}e9-I*=ENgl_VS zk?^)@X_LFkxrDDU!s-dkRVTuq5Yo+1K?sL~r@W^cH^%dtNqA9zS3@q=D z?=4uKOqhJ6NaLhQ6lxX`7t$4gWQ6vh&`ciW)uU;Roc{_H~G%B0^y70Dghtbs^@w8y)S)%1^c0Mj7 z9Jqm3u!BRr5~@oFLKH_Yva|G4mI@xFD2iuW8AUAeKj7azs0p{ZOM+_qfN+cX!1Z9I zZC~EvK@eIzc;ZKN3Z6p2k9s?uMfCLa2=rK=EeNcM_>O~B4~cI}O?%YfMJgf55xa7J zH0ezteB#NKlCANsgC6H!)?NB8Yx3g&6>A}2Cv@8P+-B3`#}6T!h#e^Ix|2v=UybvL zo{?MHEzE8PjRYc0WVT4QqP#56DQC(Nrh}I@6=6S9Ma;{u&-x}{MwH9AWxh{8&x6wp z0K%*RFjhHQ0x$FsQbv_quuLl7OQKfSXUD2^hWn0CuXACnM6|;f9n;o@Lu_KBkpVZ+ zhsDiW+h>!+}b|dx%Y1prcBu0-Wu4MJX?O$xF9a> zkeKfAW$feq8t0)}ZEWg%kd%59#I%1FkA!~eXrtYGZzgMevx|zni7{pPUSH_bCC<@rLP8Ib(7bwq6sz0d&70sGo_DgP7D{`#3D>5X4oJ7-QvIAD&1dg!U3 zg>pMKhzRB0a{XsLhp+CCqkXB&dt;#49_d?iK=ql5VlkSt2F7-%O=9>!RGx!KIaOBP z-f#$pxhmEHQX_EFtW@IZ5$1ulx3Bf89dmE7W1~pS7W~95e-)MfYFcZ307-OD{sobo zf7M=i=qW}m|8>rv)? zv}2Fqr!U&)#*I#IR1`#q;}5E(Er;5<+^((Hu_cL-WNv_eo$$^Nltr0D6^V}}h{ zw``gERM$J(b<0e}kEM5TEK5oCYW&2rzPFK{wJ=8(6P%Rhr5#P?DwLTruOyq zc=d@t1uZFET{1H4rCS}Cx2*vk>bbOP>Cw9OL@hD%aqwOCkK9iH=OZ5_O`O-t_OjBs zbfJFypFmn|L+~(&(ojt#&QG{&bJu1&{!iO%eFkXl)J0-h+Nw^(j|2rNfut1i*aZ{b zNV4lSfN;Ruu+Uo4Q#qT!GkcJu1UcqW z()4b?H}~IYcAT`iN-F2>o5bF-yK9U6#*-gZDjZHa;maGD%71jkpPla=r$9=C_ZG>T zUeU8%#bCfdMx#QrngDzayV-+q8F6+raFvfZE_Axbb1#PO8}= z&F2=zh}1pP{+7oGw0LoGCP|ldFS90Y9WRryj#fLGT_k&|*eZ4m>*>Rkwt)B_D8o~i zVIY=;B3b|7K5gYBk)1%;_O;+%B;l=wT5j6xFT#jPs^}lk7bOx$Khz_I`VvwQN&fp0 zAzsDL4?eb0p+4Lm%R@bGDyS#8thP5?5jviJc$VyyE^RV9 zx)B45PB%9*kG&8~=$2m+625!zGpfKn({|N*1}KXx zUK3?@oVMDyw;@;kSIzEC&3!w<%u}G8DBL4>;x4nZ9a+=+jOTaaLq2@Qp7~R~XIL6z zJ1}4Trf;_l| zFtCgHrp}R9$;Z43hvf0>1Q~MaV23D4fB;$63hj)x5FP@Z_T6o~e7Lieo=@1n++;*x z+p=NXH&7~yO;YlTe&mWHp=RlxKvodmZYHZ$Z;O?vzZFfZpE`S|{_3N$RbSuAm1~!? z*3OAp@f^@;OFTPaKSstx^Wbs~00$qDo=fai!E2_1>ro{)W-^Yn`+fz7-}Z1pXFz^` zdKZO!=ViHD?I~mB^|WWLHT-qe`TetE=+Q=TyD^oi@-+%v1i8Sv}v)y$3Y>bxC zydZp~d=~w?BD(4ozbfZWZ)|U?8WG>hgbu#-#yd@v&Dira+zSfZSW1nYURF%`Z}cMl zl>^vNpN-|$5=J(NxLAH6QOS;>$gJ2aD9Jos!nxS6C6vA2^UBM-i+7xr+6x~%69uZj z8D*R^9O9ymjq+=#N7{(njuW=`Nk zxzu@j-D2i_KS2RU>QA+OSc9}#EMEiG#y!wB{i`^QFbqKouZ#yfh<0s0N|%%rjA1c$ z{OwF7femok0^ejBMHtFs!+u?=Aas_QJRfngKIjNT{!nJjGDDxV267WjGP7W;7Y#=| z`Nau$MzQL1<;vYgA096h+mz$7_MO@HZP$9iy>qK|V;@KD7{R$Z=rwFeuK$5B<7k`^ zDZ8h0gLG2m&TN0b?YZF@C6>~zb&I&(6l+D{n4;2+KB8lf z1v_}S1ix-frjgVC>JA@8w+6{Q<}W^#VyCMga#L2g_|ID*6Gf1GVK?KWM?B`zbu;fV z0q);Jx2~CBlMN5Q#>py%p|*XIwv|=(3f#BvZ1rsR&#lxjZ%6Bviqd>9(J_!|Rb!wP zxO};0nydzTBnePDJ9a^$XyqX571oF7A^ewwwot_nN&IrPQ89T!H>C)>l8>*)zaDs{ z{jli8GW^t=$h^Z7r?uqk11BzhyK8^S2HC-bj^cx3vtE7BR|f0ulpbxkhW5Yz@!mjn6r4jz=e%scNFd~kNX@g~yi z4)Rg?)8lsP45+@X^-rmoD0-Rwb)??bkFg5e!F|zlkS9vU{tAY-yy;ZR>F>Tza(r_W zJ=}FQ_mBy+IOQv=Hk{r~`}_L-B?_kBli)Y~SKhDpu~rYURj$LNFo|myq91eHBUEUCS0swp8tous)U9`SWhgUs^_{>PP+iWwA?F1hE@L1h55pR>s$#{3$s0G;Q~QTTwLB`&h?yqf3bM zgXCKM#8dNE_=^EOPtFQYur$sMQ1kbu)<4Mb(1XG{ zHt5Mxg4A1naR1^g_!zu_FblP$n=_pE)|`J5yk*QQUFPPOT?EEKnL&i7b7>_dh|D~| z+~JX)LMc{SR#1IM$3IlH9U75y*VtWr6e7X6U;pu>wSr!d3k(oi@wjTxG(FD0;LiY^ zM0tI;&Ozup`b*Nil(4bI!h5Ov>^g(Z@EQ#L5{!wh4 zK{GaS!scz|UG-gAz3^OCSJwTPe95$*zM?uMYvaf~l_kMKrwBg~S~Up2e730C zIKfHMO}3$S4#gR-WIc(6S(V(g;-aBtQG)dtuZ8?BbxMQhIrZdjrsUKVN!PHs!tGC4 z%0@9aNT!N-_IQNdz8{1?JkMVo#;HwPLkThFhr27yZJ+PWtlFK@Y>o-~C&ZiKlx|b$ z3&&esfJxUOdB6X%ReLTuISFxA^hBwwd#N=+uatzf=Pm_i8m{BRFj z02;v_MR;Sfkbs=m$hS5H)Q;!?Qvnfx>4U9{i;t0}l>ku}y0m9EmVEwZqVZ*iNt9BN`8x%XN z|2%q<*Gp=MB?lNdO*;uZ4#&C!bq2O23D5h;Kp#>4sa4{)=DuqN=ZlZ;Uk+idUI~7X z%)2b_ML253iC^_#m2x81(cCZ0Z=5ba)^FDB;vuJ1`J|14uY0CI}xxK%)@e3jY&KCRzM`vw?c2PS;Cq@($)mvCGZaCZ~ zYt@xUD6b9?Ijf8jPo2eTJf74+L=~reZ`(MbHO1dY7&s%qMsBi{2Z31KDyxt3&(Gg% zS|bVhi^Bx){mn$daIq@}qqGdC=rJrdV?6k9LMOQT!{vZCqY8EjNn%9E(|l&&!%o(v zGO*Tu^0G@EKoRy57~A*354hA7%{QB)-s-`ch1pFO@EV7JvXg_DirX&?Ep3ENy^-Enp8GNNa&-p{u{(3DUR3iorI_wOb+M#9>x%NF_! z+55B96OHT*2dg0O0$v2Bo+&wSFEz>qPUU}Nn#t38+ony@?$0qhr~*sYao0MS6IGxj z+LHHPByAxSH)Wahhyna92Zo0+I>je}l*CrKlni-s2?;pi8y201or^rNo*JC~_e+YT z9sR|U#qj*jAuH~zIZONnZ*5x-&*M2D8c@k;w!d&1$_a=kzGpzIm$K;m@qg5& zC?Bfe)uq4=Mi#S!)c!-)vHT&W)*$~R-WT5&K6oQ3gDHMg6j}1rOyc3i@M^4~Xg>9L z%ve)C(=g-R=gz4xIeBSM(ln>l<=OcefTd0>^$2P|%Jr@m7gXW3T1 zK9aufsxJa6Q-7>!$Ei2ZV=*;OlksCnp6rR?8bGy@wcd-w%rr5_L_3kQC8E>`?{Yxb zJOO_J4t4^FTE>}csV%RHp>Q9gdB^5rZX89|S>L0{les*ipdX%)=-aTf0&K-owcnB6 z2+8MoCQ5oojA2Pi%O8pLZC|V1Y>M~fiJOM`DZp>GGs&`0oG+1!CN3j;d$)tcXbTl}+!@qs#0J{K+u zu@*-B0!ZXS&dY$DS~mu{qubF#9UfJ+V`K8Y1}BO7P?wo;BaAr)cj$D#MPq42j{?8v z&EZLK?KE2&bHNyamgHVQU=wBj+QXa(L~?+atB#oBe_IM@Rl+1KkRQRT$$wk?f&p<_ z=$kSOwqoBFnpe6sHL(cX^ev4~V@eB~Qu}<~(;@}61NNFO{j7X>KiVOwCpDf)Y_UT~ zHel}}dQ+ka9nmG&YoFZO=3h%}ixX~?vL9^@vw{BNM^#?Sdy4X^Ut}M5u<3aAhaS#b z+YojSqK9GMDse31zAO7QH-LHhBWzSqRrl#uges4&>VNcOJnl5bc;#J#_w)5o^ueiS zPT4a0szV8BChI`*AT!f{qXp{oe7Y{QCHh1E?97eO7zfNcB6tS(5xMxZNE83$gcM1o ztCFhc!1IeNjdihhUi?9n*iKFoIT`)54G;Bn5^B|wK(yb4QdhD>_6$u8TyZ>LlEhs} zNqn*n;g=Uhd;>EV$RY>*?HZJR13pf=TSx*-w+p5I&gfNpw{o_7kTPWtw`U>#9q-P- z=Y~>fhOcV7k_LI6;aiZT4QYyU{)vI-Q5^(=|j z^c^G_e>(yb!BUyoEfe<2U3}8SAoD49{l@a>r5h+yGwdDoiwQP;@pm;xx#&8Ven%5* z^VIllW6DY)BIry^&U&PCVZ}t?^NQ$+g&v?tpB*q6V2poqlkcn?*y^eqdu6T}DJ<#j zb8#Tt79b4%b>u(Z9FB$9Xj^Tf8BjTv9M7yact`_qk$~7&$Y27`HgGI*2(x`ukeV;p z0>;gxTL?iJx=TP$)r~p^0n(BwPj}ZE3pAUQ{eJ`7H_94>clmgtBY+}tLf?A5K;_aG zg4dOi3?RdNXf4uk+;A;M^kYuI#>+3iD6Kug0{9zpk(;T3t0r2PpK%vAxP;1^aKngA z)ZiOw7B^w=ijhUzjap{%;ydi)3PBlItEw=nNYa zwyvpX=>=fG+Yh$inUiYAm*UH*CC7Y+Rt7nill>?9)`90}BZbO?*zWVL#^s}kPz_la z%03fXHMnX2FrbL=hDN?vC_(pBL_K@1+dpg&C= ๐ŸŽ“ A machine learning **model** is a mathematical model that generates predictions given data to which it has not been exposed. It builds these predictions based on its analysis of data and extrapolating patterns. -> ๐ŸŽ“ **[Supervised Learning](https://en.wikipedia.org/wiki/Supervised_learning)** works by mapping an input to an output based on example pairs. It uses **labeled** training data to build a function to make predictions. [Download a printable Zine about Supervised Learning](https://zines.jenlooper.com/zines/supervisedlearning.html). Regression, which is covered in this group of lessons, is a type of supervised learning. +> ๐ŸŽ“ **[Supervised Learning](https://wikipedia.org/wiki/Supervised_learning)** works by mapping an input to an output based on example pairs. It uses **labeled** training data to build a function to make predictions. [Download a printable Zine about Supervised Learning](https://zines.jenlooper.com/zines/supervisedlearning.html). Regression, which is covered in this group of lessons, is a type of supervised learning. -> ๐ŸŽ“ **[Unsupervised Learning](https://en.wikipedia.org/wiki/Unsupervised_learning)** works similarly but it maps pairs using **unlabeled data**. [Download a printable Zine about Unsupervised Learning](https://zines.jenlooper.com/zines/unsupervisedlearning.html) +> ๐ŸŽ“ **[Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning)** works similarly but it maps pairs using **unlabeled data**. [Download a printable Zine about Unsupervised Learning](https://zines.jenlooper.com/zines/unsupervisedlearning.html) > ๐ŸŽ“ **[Model Fitting](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)** in the context of machine learning refers to the accuracy of the model's underlying function as it attempts to analyze data with which it is not familiar. **Underfitting** and **overfitting** are common problems that degrade the quality of the model as the model fits either not well enough or too well. This causes the model to make predictions either too closely aligned or too loosely aligned with its training data. An overfit model predicts training data too well because it has learned the data's details and noise too well. An underfit model is not accurate as it can neither accurately analyze its training data nor data it has not yet 'seen'. @@ -72,9 +72,9 @@ TODO: Infographic to show underfitting/overfitting like this https://miro.medium > ๐ŸŽ“ **Feature Variable** A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. -> ๐ŸŽ“ **[Training and Testing](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets) datasets** Throughout this curriculum, you will divide up a dataset into at least two parts, one large group of data for 'training' and a smaller part for 'testing'. Sometimes you'll also find a 'validation' set. A training set is the group of examples you use to train a model. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. A test dataset is another independent group of data, often gathered from the original data, that you use to confirm the performance of the built model. +> ๐ŸŽ“ **[Training and Testing](https://wikipedia.org/wiki/Training,_validation,_and_test_sets) datasets** Throughout this curriculum, you will divide up a dataset into at least two parts, one large group of data for 'training' and a smaller part for 'testing'. Sometimes you'll also find a 'validation' set. A training set is the group of examples you use to train a model. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. A test dataset is another independent group of data, often gathered from the original data, that you use to confirm the performance of the built model. -> ๐ŸŽ“ **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." [source](https://en.wikipedia.org/wiki/Feature_selection) +> ๐ŸŽ“ **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." [source](https://wikipedia.org/wiki/Feature_selection) In this course, you will use Scikit-Learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. @@ -114,7 +114,7 @@ Now, load up the X and y data. 3. In a new cell, load the diabetes dataset as data and target (X and y, loaded as a tuple). X will be a data matrix, and y will be the regression target. Add some print commands to show the shape of the data matrix and its first element: -> ๐ŸŽ“ A **tuple** is an [ordered list of elements](https://en.wikipedia.org/wiki/Tuple). +> ๐ŸŽ“ A **tuple** is an [ordered list of elements](https://wikipedia.org/wiki/Tuple). โœ… Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target? diff --git a/Regression/4-Logistic/README.md b/Regression/4-Logistic/README.md index dd29e24a0..135503534 100644 --- a/Regression/4-Logistic/README.md +++ b/Regression/4-Logistic/README.md @@ -105,11 +105,11 @@ sns.catplot(x="Color", y="Item Size", Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore Logistic Regression to determine a given pumpkin's likely color. -> infographic here (an image of logistic regression's sigmoid flow, like this: https://en.wikipedia.org/wiki/Logistic_regression#/media/File:Exam_pass_logistic_curve.jpeg) +> infographic here (an image of logistic regression's sigmoid flow, like this: https://wikipedia.org/wiki/Logistic_regression#/media/File:Exam_pass_logistic_curve.jpeg) > **๐Ÿงฎ Show Me The Math** > -> Remember how Linear Regression often used ordinary least squares to arrive at a value? Logistic Regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://en.wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus: +> Remember how Linear Regression often used ordinary least squares to arrive at a value? Logistic Regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus: > > ![logistic function](images/sigmoid.png) > diff --git a/TimeSeries/1-Introduction/README.md b/TimeSeries/1-Introduction/README.md index 4e92e5fe7..9380dabbb 100644 --- a/TimeSeries/1-Introduction/README.md +++ b/TimeSeries/1-Introduction/README.md @@ -21,7 +21,7 @@ Before starting, however, it's useful to understand what's going on behind the s When encountering the term 'time series' you need to understand its use in several different contexts. ### Time Series -In mathematics, "a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time." An example of a time series is the daily closing value of the [Dow Jones Industrial Average](https://en.wikipedia.org/wiki/Time_series). The use of time series plots and statistical modeling is frequently encountered in signal processing, weather forecasting, earthquake prediction, and other fields where events occur and data points can be plotted over time. +In mathematics, "a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time." An example of a time series is the daily closing value of the [Dow Jones Industrial Average](https://wikipedia.org/wiki/Time_series). The use of time series plots and statistical modeling is frequently encountered in signal processing, weather forecasting, earthquake prediction, and other fields where events occur and data points can be plotted over time. ### Time Series Analysis Time Series Analysis is the analysis of the above mentioned time series data. Time series data can take distinct forms, including 'interrupted time series' which detects patterns in a time series' evolution before and after an interrupting event. The type of analysis needed for the time series depends on the nature of the data. Time series data itself can take the form of series of numbers or characters. diff --git a/TimeSeries/2-ARIMA/README.md b/TimeSeries/2-ARIMA/README.md index e7c1c5a68..aebdaaedc 100644 --- a/TimeSeries/2-ARIMA/README.md +++ b/TimeSeries/2-ARIMA/README.md @@ -5,22 +5,22 @@ > A brief introduction to ARIMA models. The example is done in R, but the concepts are universal. ## [Pre-lecture quiz](link-to-quiz-app) -In the previous lesson, you learned a bit about Time Series Forecasting and loaded a dataset showing the fluctuations of electrical load over a time period. In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://en.wikipedia.org/wiki/Stationary_process). +In the previous lesson, you learned a bit about Time Series Forecasting and loaded a dataset showing the fluctuations of electrical load over a time period. In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://wikipedia.org/wiki/Stationary_process). > ๐ŸŽ“ Stationarity, from a statistical context, refers to data whose distribution does not change when shifted in time. Non-stationary data, then, shows fluctuations due to trends that must be transformed to be analyzed. Seasonality, for example, can introduce fluctuations in data and can be eliminated by a process of 'seasonal-differencing'. -> ๐ŸŽ“ [Differencing](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing) data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series."[Paper by Shixiong et al](https://arxiv.org/abs/1904.07632) +> ๐ŸŽ“ [Differencing](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing) data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series."[Paper by Shixiong et al](https://arxiv.org/abs/1904.07632) Let's unpack the parts of ARIMA to better understand how it helps us model Time Series and help us make predictions against it. ## AR - for AutoRegressive -Autoregressive models, as the name implies, look 'back' in time to analyze previous values in your data and make assumptions about them. These previous values are called 'lags'. An example would be data that shows monthly sales of pencils. Each month's sales total would be considered an 'evolving variable' in the dataset. This model is built as the "evolving variable of interest is regressed on its own lagged (i.e., prior) values." [wikipedia](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average) +Autoregressive models, as the name implies, look 'back' in time to analyze previous values in your data and make assumptions about them. These previous values are called 'lags'. An example would be data that shows monthly sales of pencils. Each month's sales total would be considered an 'evolving variable' in the dataset. This model is built as the "evolving variable of interest is regressed on its own lagged (i.e., prior) values." [wikipedia](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average) ## I - for Integrated -As opposed to the similar 'ARMA' models, the 'I' in ARIMA refers to its *[integrated](https://en.wikipedia.org/wiki/Order_of_integration)* aspect. The data is 'integrated' when differencing steps are applied so as to eliminate non-stationarity. +As opposed to the similar 'ARMA' models, the 'I' in ARIMA refers to its *[integrated](https://wikipedia.org/wiki/Order_of_integration)* aspect. The data is 'integrated' when differencing steps are applied so as to eliminate non-stationarity. ## MA - for Moving Average -The [moving-average](https://en.wikipedia.org/wiki/Moving-average_model) aspect of this model refers to the output variable that is determined by observing the current and past values of lags. +The [moving-average](https://wikipedia.org/wiki/Moving-average_model) aspect of this model refers to the output variable that is determined by observing the current and past values of lags. Bottom line: ARIMA is used to make a model fit the special form of time series data as closely as possible. ### Preparation @@ -290,7 +290,7 @@ Check the accuracy of your model by testing its mean absolute percentage error ( > > ![MAPE](images/mape.png) > -> [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) is used to show prediction accuracy as a ratio defined by the above formula. The difference between actualt and predictedt is divided by the actualt. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://en.wikipedia.org/wiki/Mean_absolute_percentage_error) +> [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) is used to show prediction accuracy as a ratio defined by the above formula. The difference between actualt and predictedt is divided by the actualt. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error) If this equation is expressed in code: