diff --git a/my_submit_prediction.csv b/my_submit_prediction.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0fdced4a6e3adb7e3e306253820a5d49e6e26448
--- /dev/null
+++ b/my_submit_prediction.csv
@@ -0,0 +1,80001 @@
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diff --git a/name.txt b/name.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9507a8f55aa1d568f671a8e1360ea9f08e3d3d43
--- /dev/null
+++ b/name.txt
@@ -0,0 +1,5 @@
+卢程军
+2020262927
+
+智能图像处理135
+机器学习与人工智能246
diff --git a/report_01_accident_claims/LCHJ_report.pdf b/report_01_accident_claims/LCHJ_report.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..3cf350e282153961a3190b9a6c0a5a8adb4370d6
Binary files /dev/null and b/report_01_accident_claims/LCHJ_report.pdf differ
diff --git a/report_01_accident_claims/LCHJ_report.py b/report_01_accident_claims/LCHJ_report.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5106de7659442145f2a85e3819214bb7a241c69
--- /dev/null
+++ b/report_01_accident_claims/LCHJ_report.py
@@ -0,0 +1,163 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Report - 报告题目
+# 
+# * 姓名 卢程军
+# * 学号 2020262927
+# 
+# 
+# ## 任务简介
+# 
+# 一个典型的二元分类任务模型；
+# 训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。
+# 数据的格式为：
+# CaseId,Q1,...,Q36,Evaluation，
+# ![image-2.png](attachment:image-2.png)
+# 
+# 包含了什么数据:
+# 
+# 数据id, 数据特征（0/1二值）,评审值
+# 其中，数据id是不必要信息，可以剔除；
+# 数据特征属性共有36个，属性值是≥0的数值；
+# 数据介绍：训练集中共有200000条样本，预测集中有80000条样本。
+# ![image-3.png](attachment:image-3.png)
+# Evaluation是0/1二元评审值，0——予以理赔；Evaluation是test中需要预测的值；
+# ## 解决途径
+# 
+# 主要包括：
+# 1. 数据预处理
+#     
+#     1.1 首先我们可以检查有无缺失值——结果是没有缺失值。
+#         print(train.isnull().any())# 缺失值检查
+#     1.2 查看各变量与Evaluation的相关性
+#         print(train.corr()['Evaluation'])# 查看特征相关性
+#        可以发现，在36个问题中绝大多数是接近0的负相关，只有少部分为正相关。
+#     于是我们查看其中为-0.006511的Q1和为0.410700的Q28，当然还有作为估计值的Evaluation的分布情况
+#         for i in ['Q1', 'Q28', 'Evaluation']:
+#           sns.countplot(x=i, data=train)# 查看数据集各变量和类别的**分布**
+#         plt.show()
+#         
+# ![image-4.png](attachment:image-4.png)
+# 
+# ![image-5.png](attachment:image-5.png)
+# 
+# ![image-6.png](attachment:image-6.png)
+#         
+#        Evaluation——审核能否通即是0和1的分布，绝大多数都为可以通过并且占据17/20。
+#        Q1——取值几乎都为0，和它相同分布的特征还有许多，这些特征难以体现区分度。
+#        Q28——有不同的取值，虽然大多为0，但分布还不算太过悬殊。
+# 
+# 2. 选用的方法
+#     
+#     2.1 RF（随机森林分类）——先使用表现最好的标杆模型，使用其默认参数，在三折交叉验证下查看PR-AUC的值
+#         RP-AUC数值为:0.8429819733147138
+#         耗时：51.35982632637024秒
+#     2.2 类不平衡处理和移除方差较低的特征后模型表现都不大好。于是我们直接对RF模型进行调参，会发现多次调参仍提升不多.
+#     于是考虑选择其他模型，在这之前我们先分析标杆模型和其类似的模型。
+#     
+#      2.2.1 LASSO逻辑回归模型
+#         LASSO 的特点：趋向于使得一部分θ 值变为 0，也就是说 LASSO 认为与θ= 0 对应的特征是完全没有用的，而剩下与θ不为 0 所对应的特征是有用的，所以 LASSO 可作为特征选择使用。
+# 
+#         机器学习：模型泛化（LASSO 回归）https://www.cnblogs.com/volcao/p/9305682.html
+# 
+#      2.2.2 随机森林分类模型
+#         随机森林的特点：是在以决策树为基学习器构建Bagging集成的基础上，进一步在决策树的训练过程中引入了随机属性选择，即每棵树随机选择样本并随机选择特征，使得具有很好的抗噪能力，性能稳定。
+# 
+#         随机森林（原理/样例实现/参数调优）https://blog.csdn.net/y0367/article/details/51501780
+# 
+#      2.2.3 XGBoost分类模型
+#         XGBoost的特点：
+# 
+#         ①使用许多策略去防止过拟合，如：正则化项、Shrinkage and Column Subsampling等。
+#         ②目标函数优化利用了损失函数关于待求函数的二阶导数。
+#         ③支持并行化，这是XGBoost的闪光点，虽然树与树之间是串行关系，但是同层级节点可并行。具体的对于某个节点，节点内选择最佳分裂点，候选分裂点计算增益用多线程并行。训练速度快。
+#         ④添加了对稀疏数据的处理。
+#         ⑤交叉验证，early stop，当预测结果已经很好的时候可以提前停止建树，加快训练速度。
+#         ⑥支持设置样本权重，该权重体现在一阶导数g和二阶导数h，通过调整权重可以去更加关注一些样本。
+#         
+#     其实发现标杆模型都有一个特点，即可以特征选择，对稀疏特征有较好的处理能力。
+#     所以最终我选择XGBoost分类模型，因为他即结合Lasso回归中正则化处理，又结合了随机森林中集成思想和特征选择，所以对处理Q1这类特征有较好的反响。还有一点是根据上一篇文章对xgboost的处理，发现仔细调参后可较大幅度提升性能。
+# 
+# 3. 实现过程遇到的问题，以及如何解决的
+#     
+#     3.1 数据特征不平衡处理
+#         类不平衡（class-imbalance）是指在训练分类器中所使用的训练集的类别分布不均。
+#     于是我尝试使用过采样SMOTE算法：
+#     
+#     //
+#     
+#     from collectionsimport Counter
+# 
+#     from imblearn.over_samplingimport SMOTE
+# 
+#     #print(Counter(train_y))
+# 
+#     smo = SMOTE(random_state=42)
+# 
+#     train_x, train_y = smo.fit_sample(train_x, train_y)
+# 
+#     #print(Counter(train_y))
+#     
+#     //
+#     
+#     可以发现在SMOTE处理后，两个类别样本数量达到相同.
+#     RP-AUC数值为:0.8399920675137803
+#     耗时：107.54732489585876 秒
+#     以上实验使用过采样的样本训练后，分数下降了，时间还提升了。这是由于过采样它不可避免的带来更大的运算开销，其次当数据中噪音过大时，结果反而可能会更差，因为噪音也被重复使用。
+#     
+#     3.2 噪声避免和特征选择
+#     由于在Q1特征中，表现出绝大特征值为0，这是方差极为低的特征，难以带来有效的信息。
+#     
+#     3.3.1 移除方差较低特征
+#     方差为0的特征会被自动移除。剩下的特征按设定的方差的阈值进行选择,排除方差较小即特征差异不明显的特征属性。
+#     
+#     //
+#     
+#     from sklearn.feature_selectionimport VarianceThreshold
+# 
+#     sel = VarianceThreshold(threshold=0.1) # 选择方差大于0.1的特征
+# 
+#     train = sel.fit_transform(train)
+# 
+#     test = sel.fit_transform(test)
+#     
+#     //
+#     
+#     经过多次实验，选取方差阈值为0.1.
+#     RP-AUC数值为:0.8300324079765956
+#     耗时：39.80928683280945 秒
+#     移除方差较低的特征后，分数降低很大，时间却降低很多。因为许多特征都表现出方差低的情况，移除后剩下为数不多的特征，此时耗时自然下降，但信息不足以支撑模型训练。（使用scikit-learn进行特征选择）
+#     
+#     
+# 4. 最终的结果，实验分析
+# 
+#     使用XGBoost分类模型，使用其默认参数，在三折交叉验证下查看PR-AUC的值
+# 
+#     RP-AUC数值为:0.8238557416902802
+#     耗时：30.34992218017578 秒
+# 
+#     发现分数降低很大，但耗时降低。但不要紧张，我们参考网上同样使用xgboost的（竞赛文章）和上一篇调参方法（有时间可以三个参数一起调），在经历几个晚上运行终于调出较高的数值。
+#     ![image-8.png](attachment:image-8.png)
+#     
+#     ![image-7.png](attachment:image-7.png)
+#     
+#     RP-AUC数值为:0.9653026782164909
+#     耗时：139.36438059806824 秒
+#     由于提交截至日期结束，使得测试集预测结果无法查看，以上是训练集的训练结果。估计是模型有些过拟合了！
+#     ![image-11.png](attachment:image-11.png)
+#     ![image-10.png](attachment:image-10.png)
+# 
+# ## 总结
+#     ①本次是分类问题，数据表现出部分特征区分度低和类不平衡问题。
+#     ②处理以上问题，我们进行特征选择和数据处理和。
+#     ③如果未有太好效应，则直接进行调参。
+#     ④调参提高不多，则再一次审视模型选择。
+#     ⑤其中②③④需要耐心的交接进行。
+# 
+
+# In[ ]:
+
+
+
+
diff --git "a/report_01_accident_claims/Report1_ \344\272\244\351\200\232\344\272\213\346\225\205\347\220\206\350\265\224\345\256\241\346\240\270\351\242\204\346\265\213" "b/report_01_accident_claims/Report1_ \344\272\244\351\200\232\344\272\213\346\225\205\347\220\206\350\265\224\345\256\241\346\240\270\351\242\204\346\265\213"
new file mode 100644
index 0000000000000000000000000000000000000000..db51691fb86de5ecad4dd098f973ebb7674ae83d
--- /dev/null
+++ "b/report_01_accident_claims/Report1_ \344\272\244\351\200\232\344\272\213\346\225\205\347\220\206\350\265\224\345\256\241\346\240\270\351\242\204\346\265\213"	
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "image-10.png": {
+     "image/png": 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d9p2Ct13lytbylmJtwQ+RPf0cesBDAc8HQbUb0UHk03QAvlA5eFI4EwTx0ubbbksCoAXCSy6RefP6Aq17odd4khcRPcCYXTBle82hw27fbV1HZXP/gJvuBTTCBtzHhwc8kOfgcPK1QxYEbZl520sBglO7otfAVPRrIusvkaldN8n87LRMbb8+2s55OG+h+4vlNQ6AsK+WR19VAoKAQEQBsWAbMIcHRmwE0G7jWOhVMsiPJ4gbBgTdAyRD+910L341ZOvpbrn22I7EK2fs62d0G788csPx3YIIJKaPAZRL7fgWBLeemkxE72wkD9v6qheAnAVBPEW86/SU7Dg9JRtOTMgTD44KHjhB1BD7/OWqQrQQTynfemwiPn56ovjQyWLr4p4Oblsf3yc4dfeN7n4+vBQa4Da14TqZOzccPQhy4L5o3103uung+Ph4NO2Pl1DjHkIf+BBRnNnflogIxuXf/iyZvm998dgyjgjiBdN4vx8WPAWM18go4JULgphCRn6UBSDUMs8O98XA2de1XWamzXs2Bwdl/glPyAZBQOEdd9T9/XSAOH11C0AN7/XDK13wOhkFREQGW3uiF0WvO9wlUzOzsrG330HgFfsOyTnvW/PF6Wk3zXvj4S6XBu8HBEDaXxy559hxB5aAyc39x10EcjHPE/cS6c1PL94fWEFEEL9Cou8exPZi2s26lgdcsB8apx8qAUHtVwVB+xlTv/pZAdHuwzF936DC4pKBIB7sWNu7JX6/H+AOUb6siCCMH5+Zcg9/4Onf0BPHefvGZiblxMXiL5SgvNnZWTl37pycOHFC0AmLtdzV3is/trnTLbcf6JX23mOZy97ufrm0rculvaytS7r7i3YePHpMrt7VIz9xT1TWT23pkit3dsszd/aklit2dMvjt0Tl/PjmTrlqZ48g/2K1OVRP/9Y7pfc5T5Xea58qvc9/lhzb0ya9N17h9vW13iADvT2xfccO7ZPe69dGaZH+2qdK34bnxcf779sUlfWcp0Vprlstvc+N0ve1PFcG+vvitCjb1XnLs2Wg92i8P2TjYu/bs/NOadvyfNl533rp6zsq+hlrtaXz8H65b+stbsG2fka+9oO74nTYxj6kRRrNHypT9+1sa5Xe3u447cBdd8nAqlX5yzOeIQO9vcU8C3wu9fYfk6u33Sc/t/FuWb15i1tftbVNevr6pau3V9bes9Xtu3HHLtnRcVguuXOT/Pydm+T2+/fJpXdvdsdu2rk7tm97x2FZt3NXvDx9S5Qf5T+7bXu8H+U9dVNU38/debdcY47dvGuPdBytX5u1r/q23iK9Nz5Rem/8mcqWmy+VgSMHpH/PZuktbGuZXBfHUGpBLerhA2AKsAUYQ6Gs1Bp2lErjH/dBEMexD4veA+hDoJahkIiI4pKBYNbUbh4IagN0GjmrjNB+/KqJ5scaHQQBXve618mDDz4o/KMCVIAKUAEqQAWoQK0KgCnAFmCMcmFwoUAQ4KeRviwIBCgiDewDDy0ZCOIBDkzV6hSurq/o3ereERh6fYyC3EKAIGj9DW94Q639zfxUgApQASpABagAFUgpAMYAayi75K0rAUGN9uH+Pmwr2AHuLPxZKETaM2fOuHsJkR626BTxkoGg/7CIL9Atg/vcVHEonYKgRg/9vPrZvrPQjwgidPvwww+nOo47qAAVoAJUgApQASpQqwIPPfSQu/VMmSRvXQkI5pVTzbFFB8GQkXgtjL2/b//IcdF7BvNA8Gc673CvhMFPzIUWRBV/pP0WB5Q+CEJ0/lEBKkAFqAAVoAJUoF4KlAt45aYLMVSt+5YFCB4cOREDm39/3xM773C/I2wbqhFBP23eZ4Jgvdyc5VIBKkAFqAAVoAIhBcoFvHLTWRZaqO1lAYLnpsYFwOeD3I+03yr3nelJza8rCOIew6v62+Inj5/Vf19iG08l4+lklEsQDLko91EBKkAFqAAVoAL1UqBcwCs33ULBny2n7iA4PjPppn3PTI1lvsh5em5WMB2s07v49ZCBi2cFU8bWWN2+e/iwA76WoQOZaZB2cOK8PGdgp0vbPRY9HaNlQHT+UQEqQAWoABWgAlSgXgqUC3jlplOGWch13UFwIY1dyLIIgvVye5ZLBagAFaACVIAKQIFyAa/cdAvJQVoWQZC+SgWoABWgAlSAClCBOihQLuCVm07hbSHXBME6dDyLpAJUgApQASpABZa/Ao8++qj88Ic/TBn6/e9/X775zW+m9le6o1zA05c7LyTglVsWQbDSXmV6KkAFqAAVoAJUoKEV+MEPfiBf+MIX5P3vf7988YtfTMAgIPDTn/60PPDAA/KlL30pcazSRpcLgnjZ89jYWPC5iHKBrtp0BMFKe5XpqQAVoAJUgApQgYZW4Bvf+IZ8+MMflve9731uURhUCNT9//Iv/yLf+c53qm5ruSB44cIFGR4eXhIYJAhW3b3MSAWoABWgAlSACjSqAoDBj3zkIzEMfu5zn5NPfepT8eePfexjNUEgdCkXBBHNAwwiMgggRL7FWgiCjerBtJsKUAEqQAWoABWoSQEfBjUS+IlPfKJmCIRhlYBgtVO7teYjCNbkQsxMBagAFaACVIAKNLICjzzyiHzgAx9IRAK//e1vL0iTCILz80ty42M5dIzO4R8VoAJUgApQASrQvAr853/+Z2I6WCOCes9grcoQBFcoCOJR83d+5p1y6PWH5IqXXyFrfm1NxctVr7hKRv7niHzpm1+q1c+YnwpQASpABagAFahQAR8C8fDIhz70oTgyuBAwSBBcgSD4/R98X1749hfK417wOFk1v6qm5dKXXirv+8L7nOt+/pHPy/4/2y8v+vsXyXe//90K3ZnJqQAVoAJUgApQgXIV8CFQ7wnENLF9gKRWGCQIrkAQ/JMP/4n86At/1IFg5xs75YEvPiCfe+RzFS/v+sy75JNf+aTz2U9/7dOy7pXrHFQ+9gWPlVNvPlU2DH71a1+T3v4BufX29W65a/MW+chH/rncc0EefPBB6ew+GuefnXuBLNS9EWUbwYRUgApQASpABRZRAbxIGk8FYyoYEGive/YBEgXEak1rCBAcHb8ozbh0dfdU3K+Pfu9Ruec19zhgm3vbXE0vmdTKLQRqhBEwOPbWsbLK90EQQPgHr36NFl9y/c53/UMMgchLECwpGRNQASpABajAClDge9/7nnz2s59NQKA2C78qgtfJIHJYyx9YY7kz1qrlbmC97KsGBBH5W/uytfKzL/7ZeEq3FgcJQaDCYOvvtcrXv/P1ksWHQHD4zDl55BvfKJkXL878lV97GUGwpFJMQAWoABWgAlSgcgUIgss42lgLCAIGAYW1/OVB4E++6CcFU9Dl/FkQ3H+wXbbet0PKnR7+/Oe/IAc7Drv0bdt3OiBkRLAc1ZmGClABKkAFqEBpBQiCBMGglywUBKJwC4Iv+qUXuxB0udPDf/e/3ubgb3J6VsYuThAEg73FnVSAClABKkAFqlOAINhEIPjuz75bXvGeV8gj33kk11sWEgJRkQXBX//N35I/fe2fOaArNT2MeyP+y4tf4tL+1Zve7O4N5D2CuV3Hg1SAClABKkAFKlKAINgkIIhpXEzn4v6+Ta/eJF/99leDjrLQEIhKfBD8+Mc/Udb0sE4L44nhT3/mM2WB4Be+8AX5zd/6bdmzd78DyNYNd0r30T75wz/6Y2dHsNEi8oMf/EAe+MAH5Bf/yy+JTkEjb//ACXntn70uNy/ahzRIizyAVZSBslAmys76w5Nfb3rzW+TMufOy+d5tLi/yw/5feskvy79+8pO5D+Qg/x//6WvlcFe3y3vHnXfL6bPn5R/f8x5XL8Ab5WEd+tN2I+Kq9WMNe97xjnfKd7/3vVA27qMCVIAKUIEVogBBsAlA0EKgPugRgsF6QCDOEx8Ev/Wtb5U1PazTwnhYBHlwb2BWRBAPlQCIAEJIE1oAZ4AbvGzb/iHv7/zuq4J5tJxd9+9zUGfzYfuT//ZvgvseNV1o/asve7ngNQD2Dza8573vjaEzlA/7AJav+v0/ENjo//3zRz8q7Yc7g3Vrvpf/11e44yEQ/MpXviKT0zPB/GrPyeHTAiDnHxWgAlSACqxMBQiCKxwE8VQvnu5VALRrC4N4X+A1v35NMF0lD4aEThMfBJGm1PQwIlGIqAFI8PoYvD8pCwQBVYj4aTTu/IVRB1kAnYceflgAlPoeQ0S73vt/ohdkq61/+3f/y+VF/pe/4v+V//t//9XB6xf/4z/kda9/QwxrJ4ZOysMPf1mzyde//nUXfYONiFqiHtSH9n7wQx+O72nE8b/4y7+K82EDkT7AJY7tPXBQ/vp//o2gPuTF6wDe+N//3O3H8ft27nY22QKQRiEQbXr1a/7Q5Uf9f/PWv3V50Z6Nd28OgiBsHxkdc8cAz7/+G78Ztxtl4/U+Ghk9cfKUa5etn9tUgApQASqwMhQgCK5wEISbvvidL878lRHA4D994Z/qBoGoPwSCpaaHASOItPX09stDDz2UC4If+9jHHSwBml75O78bnM7E+5bmXvBCBz6YOgUI4c/eh/jLv/Krwcjbu//xPe6pZZQPKNU/vBQbTz/jKWhE5/w/RAGnZ+dcnXjFEKKa+EN0D8CJ8tBGRBVDf//+75+KYdGCpM0fAluUBSAEwKEOLDYiCHB+7ete7/YjP9rnR0lRBoBYo50AzVCakN3cRwWoABWgAo2jAEGwCUAQF3D8LFzWT87hV0hspFC3bSTwW9/7VvwrI5W6dwgE7fQwooP+H6JaABgAE8AnKyKItv3Bf3u1S4tpTAU8vzx8VvhEpOzd7/5Hl8SWmwWCeN8homeA0r9685vjot///gdcvVkgiISIEh5oPyy4Bw9Ai79HH/22vOSlvyIdnV0u8pYFWFY3q5HeOwl90Pas/IhKAlR9EIRG0KpUftiqkVsFctcA/qMCVIAKUIEVowBBMAsER/pl85Hb5fIOf9kozx8YLu8t3KfbZeMNO6UvUMepQxvlho3tcipwTF9QfWDLTRU7mr5Q2n+PYCkYVPjTtYVAPFiy8092uinmcl4g7RttgcZGphQybLQMeXVaOAvY7HsELVC+9Fd/Tb7y1a+6CCTq9Bc8cIIHRxwAFX7ZxIIk6pudf6GbVi7nZdcatUR5mN7FPYrYF7qfz9ck9BkPbsDmD3/kI64stdUHOUxtYx8AFHCb9We1sborEKOMt/3vt6d0srrhnkqkK/e9j1m2cD8VoAJUgAosTwUIghkgNtR/l6w6dHPG0iaHM/IpxGHtYG/HsSI0OjC8QW64IbxsPHS6mHb8oiwkCML9yoVBC4HI9/qPvt5FDMv9JRHf1QEWeo9eCEh8oFHAslEoG7mzIIj7AJEOsFLJYu3wp1G1HEyL/sZvvdKBWQjuoOdf/tWb4nsTNR/uuUP/42lge0+hrwvKRFQR90LqU85ahr+29mKaGMfxou28BzlQvv4qi82vIOnXUeozbOUfFaACVIAKrCwFCIIG6M4PHZQ7e7a7peXIbRkQCDi8Q9YV0t159IgMXAj9FvJpObTRAt9GOdTfLhszooCARgXBvh1Rvquvvrpib8uKCGpBpWDQh0DkqxcI4h668YkpBzV26tOfFoYNWSBo76MrBTL2ON5PiPsD9e/LX/6y/NZv/078ChWbFtt4cAIPjvivU0EUD69qOT54MhNE8fDKpz71Ka3Krf/jS1+KHzSxdQEiAc0A0L9/xzviCKYFOQVBpANk5/0hH8oP5bf1lrONaW7+UQEqQAWowMpSgCBoQPBw981yfW+PHDzRI7u7b88BwU2y+QTStUtLx81yx4kACCL650NfhRHBXRsWHgThvlkwGIJApK8XCLqy3/BGByo6PazAh2laG4HS/QAWGxF88MEH3RO72G9hstrTFHCIh0B+9/d+30EY7LCQlPUqF2iKewABS4BbfeJW8yK6qDBo22Kf2NWHSdT2rEiqgmC1EUE88AK7/Eis1ss1FaACVIAKNI8CBEEDgt09N8KUFPEAACAASURBVMv1/eej6dkTbTkgqFPDw7L98O2y9VQaBBHV0whfPF0cgsNC/TYiGKU/JhvqEBFU1/ZhMAsCkb6eIIgnU/F6FIUSjfDh5cy430//LDxZEMS9fPiFEoCNH+XTvLWsAYaYSj3af8zVUQq+tC4FQ7yfUGESr2TBnz5tDJvx9C7Shv7sQyE2oqdTu6Xu28u6R9DWzyhfSHnuowJUgAo0jwIEQQOCo0O75KkdW2T38JiMnjkoV2fdI9h5RE6Pj8lA313y+MMHZciW4bZPS9+OnbLRTg0jOlgBCAIMa5kafsqvPEU+8eXsBwng4gCQl737ZfLsX3+2/PnH/jzT6+sJgv70sEa78OsgFpCyQBD3wemrWPBePoBk1h+gEw912Kd/8UskeMULnuy1r4bxywAwAdy0DkwJ/4+/+EvpP37CPRHsR/M0vwVVhbk3v+WvXVkoz0Y9NY+uYY9CpObFMQuIeEDF6qR5sbav1bH5EbnU+yotVNu8uv36N7zR3b+Ip4z/7d//XXdzTQWoABWgAitEAYKgB3GnB9rk8kO3yNVdW+Xq9vDDIpcc3iTrOm6Rxx/eJd3n09HAOAI4fkx22KeGy54axv2FO2VLFU8N6wukH/uCx8qr3v+qmt0UkHHhby64h0WOvPFIVeVlTXFqYYANQNHQqdMuuodIF159Yv+yQBBp8JQt3oeHMvCuQLwz0P8DcL7gF37RpbEvaLavUgEQIp3/B9gEmKJ8vDga09H403sZYS/exRf6sy+O1qlrjeihPMBkCOTsO/yQzoKchV9MOSOt/2ffm+jnR314LyD2AzRhA8DW/7O/moLp7pA2fh5+pgJUgApQgcZSgCDogaCDuNFh6T62V+7svkue6kUFH9+xSVp69sruoTOJJ3yL8GfB0APBRF04tlEOnbbpo2192riap4bhfi//x5cLQPCSl1wir/nga+TR76Xhphw3xWtj5t42Jz/+Cz/uljd94k3lZEulKQWCOj0MMMESeh9gHggCjOxPxCHqB0jD08BY8NCFfZjDv89Po5CoG+mQHvlgN6ZRX/LLL40jczYvngjGr40gH+71++3ffZV7OTTy4VdC8GshsAXH7T2CNiKn+fCkNPJBC/zKB/Yjny4WBCEw0usvi+B+xNf8f3/k6sRT1G//+3fET2ln5feflB67OOHgGzagbECr3ucIyM6LXKY6nDuoABWgAlSgYRQgCCbgLA1lp0+0FWEQEcCRdJoQBDqYS7wmZqccwrsDE/vsU8XYjt45iGllvHuwWhDU9/7pOwFrXQMqX/j2FwYjV+V4OuAi9PoYzWunhwEuoZck54EgyvnOd77jonY6laoAZNc4hsge0tq/cvKinNBvBuf93q/WHfqd4ne8811xFFPT2TUAElFGQCH2h6ZwAakATJtPt9FWgKk+le2DJNqPKWZ9sbTm89eAwdDvM1v9uE0FqAAVoAKNqwBBsAQIjo7jgZBoivj5A2NlRAEtKCYjgskHQvyIYDIt4LJaEIQ74pdAXvqul8rTfvVpwV8NKQcOAYD4/eE3/ssbq4ZA2FIKBJFGp4dD08I4XgoEkQZTnp/5zGfl5f/1Fe6eP4UavKPvl17yy+73fUPTsMiLqVFAnf9OP+TFPhwLTZ8i7ze+8Q33ahlEEzWShzU+45UzOB76wz13sEsjb8iDn7/D+wfx6yP403sTAXyI1Pl/0Ba/s3y4q9sBIcq4ODnl7g/EsWMnBt3+EAiiLERT3/ve/5N40hkQiZdZo1yUwT8qQAWoABVYuQoQBEuB4PkeaSncK3hl74CcH7OgV2o7CXeLCYIr12XZsnIVsAD+5//jL8rNxnRUgApQASrQRAoQBLNA8Ey7XJ/xsMjl3T1yOitfYr+C4DHZsbFd+sxLo0fdgyT2HkFNW4TLWiKCTeTDTddUAN6p02fcwzXv+6d/ymy/vfcy74nozAJ4gApQASpABVa8AgTBBLgVIWyof2MZ7xEsps+/TzC65y8YEcSvjeh9g/bn6GqcGl7xntvEDbTvB8S0tf9rJ5DGPiWdNa3cxBKy6VSAClABKlBQgCCYAYIhsKt832k5FXgqOCoHEUAbEUxDJTqHf1QgpIB90hlP/OKpXjwxjOU9732v4Gft9B5JvCom697IUNncRwWoABWgAs2jAEGwriCYhrtKYJIg2DwnYqUtRcQPD8WUekoaP5UXihhWWh/TUwEqQAWowMpUgCBIEFyZnt0Ercp60hm/kgJIxMusGQlsAkdgE6kAFaACNShAECQI1uA+zEoFqAAVoAJUgAo0sgIEQYJgI/svbacCVIAKUAEqQAVqUIAgSBCswX2YlQpQASpABagAFWhkBQiCBMFG9l/aTgWoABWgAlSACtSgAEGQIFiD+zArFaACVIAKUAEq0MgKEAQJgo3sv7SdClABKkAFqAAVqEEBgiBBsAb3YVYqQAWoABWgAlSgkRUgCK5QEMT74775rUfly1/9mjz8la9yoQb0AfoAfYA+QB9YQT6A6zuu87W+L5YguAJBEE7x1a8/whN+BZ3whHl+maEP0AfoA/SBkA/gel8LDBIEVyAI4htCyFm4j4MIfYA+QB+gD9AHVp4P4Lpf7R9BcAWCIKeDV95JzoGbfUofoA/QB+gDWT6A6361fw0BggO7NsjuwRmZn5+vy3L2wM1y1c33St9ouHwcX71mrdzSPlJb/ec7peWqtbK6pUPGymjLwMBAVf2a5Sjcz0GEPkAfoA/QB+gDK9MHqgIGEQFr1IuvFqrcVS1XrpXVV94su08VYXCse7PcsG5dZcvmngCAjUt7y1pZfe0eGQ7B2WinuPrXIE2bDEyFYbG8xs5I35YrZPWaK2Rj92RJ4QmCK/Nk5SDMfqUP0AfoA/SBhfaBFQ2C8yMdEYxd2SoHzkQwmAWC1yHituaZcl0IEkMgONohLWvWyjV7htNgNjsi7a1XyOor75KeMz2y8cq1cvmWPpkJAWO5+wr1Xf68g3K2RB6CIAeKhR4oWB59ij5AH6APrEwfWNkgOD8vM4Ntch2icldulr7J7KjcYBtAsE0GS0CWRvBGDmLad50cOJsu8+w+HCtG72b675XL11whLcEp4hHp2blDtpexbLzlVtm0vXTaO3YcqapfeZKvzJOc/cp+pQ/QB+gD9IEsH6gKGBplajiGtiN74ojg/OSwtLftk0FvqjYMgjMysHePDKTuARyRA89bK6ufd1BGPHAc6WiVy9eslevahkwEcEYG2653cJiGwSHZBlBdwOXS1v1V9WuWk3A/BxD6AH2APkAfoA+sTB+oChgaDQQVCN16eI+LECZBbV6CIFhIe/mdPTJhge/UHrkG4NY2lJgWHunYIFeuWSuXt3bIyKwXKZwdl757Ixi8YVufjPnHbfnYLkwF+3Uk2uLnKXzm1PDKPFk5CLNf6QP0AfoAfWChfaD5QHB+RgZ3Asiul+1DxYdIUiA4ezaK+l3ZKkcSEcEZGbgXD24YEATkbVvnIoFXbuhMQ6ACm0l3+Y2bpX0458EPgiDfacgXW9MH6AP0AfoAfaDOPrCiQbD4dPC+5L1/U32yBU8UP3ePDBcicz4IRq9+uUJaOrxXv5w/KDfpNC4igmN9suXG6InesiJ98/My0rlZbkD9a66X3QZGE9G+4SjqWM2rZxgR5DfGhf7GyPLoU/QB+gB9YGX6wIoGQTyAsRH38gUeAhnraHVRvfWd4256NwGCIx1yS3CKt/AalyuvcNG/aNoW9//dKps6I2CMnkreLD1j3tTwWI9sWrdONnVH9c2PHpf2Xg8yNXKI9WCbs++a1nvLepBk+8GhePqaILgyT1YOwuxX+gB9gD5AH1hoH1jRIIgIWwLwLGjNnpWBYwUoC6Qb6z8oPYkp4XmZ1/sL9+yJHvDw7hFEfRFgtkq7n7cw1dvSUawzEQG0thkQLPshEvOyaYIgB4qFHihYHn2KPkAfoA+sTB9oXhD0wCsTGON043KkVV8OXXjSt44gqBHLbYNeZDG2p7B/9ngEpQRB3kdS5/tIeBFYmRcB9iv7lT7Q3D7QVCAYwV61r2ppk+6Dt8qWfjxgUn8QHGzDfYe3SvtICRAMPFTCiGBzn9Qc1Nn/9AH6AH2APlCuDzQVCI70ZL+MObqX8GbZmPli5x7zNHC9QXBYdl+Ll2C3yWAVr5khCHIAKHcAYDr6Cn2APkAfaG4faCoQzLsnr/TUsI3MZYPg2QPrZPWaVjniPyxSyT2ChfcUXn7vcfNSalu/2S48XXzDgbPxOw0Jgs19UnNQZ//TB+gD9AH6QLk+QBAs3HO3MCA4KT13IpK3I/m6GtRRNggWnkxec4VsOVZ8z2EmxBaeLrYPoSwVCP7xa/9MsMD5Pv/F/5DxiSm59fb1qaWrp08++alPZ97T98AHPyydPb2CdbmObNOh7hf84otcfthTqj7Na/PpvtC6WvusPlruS3/t5U4z6HHy9NncNmta1L9j9/0pXaE1NEc7tHzUiXz6WdfY97/f/o7Ufj2ONdL45dnj/jbSh/o7b1/INi0XmqDvStmp6fPWvg5adp5t/jHfVpSZp4+eA3lp8mwudQx+AJ/JO5eyylAfwnkC+/77X/yl62/rO1l5S+1Hf0GbUDocy9JDbfJ19z9n5Q/VZ/dpf1h/KrdOtaFU3dX2CfoQZef1pdqvtpRa+/4KLbAvtN/qxO3mA8jmAsHZcRnYeTANaYGnhjPBy4FjRkRw5nj0fkL/l0gqAMH4t5Gfd1DOlpoWnp+Xie673GtmNnYXX069XEBQYcwOLOUMeEhf6iJry/S3dcDUAR+DM2zB/qyBH2lxPGSzXz7KqAZU0Sb/AolBGfugSx4IVqsH8oUG/rwLMtqL46UuNHo8C7RRhq0btmDx9cz6DE1KgSDKUzt0jXr9MrN08NOV8k/1La3LrrUO3QdYh6/4dVTzGTr67UJ9WPLKs/0ILX/jlb/j9FLbtD1Ip/bnlVfOMS0T5dn6VRd/HfIf5LO+o/WGbNT6bLmhMjUdytby0D/lwjTy1QqCaoO1tdR2qC1qP8orZ8zS9NA0pKse57r5IBB9Xu0fWCOflczsZSHottjpV6FCjfQNjPbJNvfi53tlYCZtnKYbLMvYMAjOHLvXvV9wfWcRyuJGlxMRnBqSbc+NXjRtf/UkLiNg20j7rQ4E7dPFiw2CIbj66795qxs0Q4OcHdgwKIXS5O3TgVxBIS+tPWbrDV3wyx1UKwVBXx9cTDB4q224uPltsbZCo1IXoKwB3F44S2mNtCgH+sI21Tmr7FL7kd9edFC+1lEqL46rJnl22PbpRTaU3qbTujW99kPe2rYD/alfLFAWPitMIF0lbVRbSq3VVi1btcmyWe3VPrA2Wj/XclUzgCLSlrKn1PHQ+YU8qKccXw71F8oMfVny22Dbau300+FYVlqbT7d922Fjlv6h/aqxlqdrlAPdoQvW2sd63K7Rr6GyQ/tCOiO/+oYtl9vNCYDa7yseBAe2AayukMvxSx5XbZADGT/rVjMIxj9Jt1l6JtOgWXJqeGpItjtQXSv+7yBng+C4tLegfcmnixcbBNWZMIDpIGYvNnoc66wLhB7DIIW8mgeDZ9bAlVcW8mQNvFoXBkqUgQXgFRpMsc+vHxePxYoIWk3ztFC9dI20fnuwz7cd5dsLhj2Odvtl+J+txnqh9dNkfbb1qt2oU/XWflG7EcWy9SEP7Nf0Wv/v/7fXZE6dwxab3o+mhMDA1qF2wg7Y/9GPfyIBJygb6TXdQq7RPpQPXbDWdvh1WBtgJz7bdqEc224c93X1y6z2s/ZJlg/Y/rB1QEP7ZQjHsC/kM1qHtkHbqtHPrLqRXtPmjQNaJ9LrttqK/KjHftYvBdiH4/ZLg6ZTm2Eb2oX6UTbW+Jyli+bHGmlDYGzT+Nvoayz+fn4mCFYDg40RERzrlPXup9zWynWbOuTsVADQClG2WkFwpD36pZLr9gyHQ6V5EUH8TJ2LBK6Vy1s7zNPJBXtnhmTvXW3uF0YOdB6XgWNYOmXvHfjN5LWy+nkHZcREC5cSBDEoYnB693ve6wa20CDsD/AYhDBg4kKPe5UwSNqBCQOXPwDjuB08bXps24tbaMAM5Q2lQzkYmG35sHWxQFDr1TpxMcqDVnsB0YuNDvxYh/pD92k61Kl59eIauggivR4P5YFutkx8Vi1DF0hff3xGW7UO5PV9x9bh26zaYW3T6X5Nr+3PW2s7YEtWOvgoLvqh49oGrbuWte+7tm2+hqgXtkNvnJewMWSf3YfyqrUP9eA8RnmhcmBP6Fz264PNtgy/r6wf6DHVWNsKLWy5fjocs2l9XXHc2mu3bbmwVf3DLw922jHN6mM1z9vWaXzomZcudMzX2tpq28BtguDKBcHZEWlvXRf//Ft2ZK04hVzN1PDEsXvlOgDZc3fIYGDa2dWbAYIzp/bJTTGs2lfUWGg9KwfWZbz/8KoNcsR71+BSgKAOcHaALndwwQCLwQ6RHB1QNa8OrABMv2wM3NhnB0AdNFGOXoiw9gfE0KAf2mfL8W1Cet1Xzhp2ABRgCy7Kajf2+22xOugxvdD5dUGjUNRB+0TrwVrLQJ22Duy3n1V3beNHP/bxBEyFLqr+PtSBRe1F+Vp/yGbfJr/dWr4tE9u2fdpG1OWns+2DTSgPusEWtbEcu2xa9KNqhP22XruteWpd4zyw9mqd2jZfQ+1X5FFb/XYjr/aL3a7FVtiB8xm+7vdP6LPWr3XCDgAU8lt9cRxp7fmsfmHLxbjwgQ9FbVa9NJ2ty+qCevz6bF12W+3E2uaz5UEDW5fNg3Shc1bThGzVY1jn9RPsQV9ru/189rywx7jdvDBYDQQiT2NEBE2ULA8CcWxw7zq5Yd2+4IMk6bzePYKTQ7L7tg35L3/2f2s4tg2/VXyzrD94NvdVMTOTkzJxcUSGXTQwigoOnh2XmcADJYsNgjr44SKFQQYDkQ9odpDGtgIbBkoFvNDghwFPBy6s8VkHLDsAYx/s0Eidps0aFP28ml8vllqHrV/3aXtRhu4rtUYetNlewJBHy8+yU/NBs0ovKtBgaPi0q0O19fvBfoZ9b/nrt8YRHXus1DZs0zrstvYz2od+1v5Du+yFMNR+zWPbjW0tE/ppP2Pb1u+XZ9NpXyF9pSCoNoX00DrUXu1ba1soX94+6y/QTP3btgFp9BzS/bqGLbADedW3VSfUi7zQALYjra1Py6hmjfKwIK/q4pfj95Eet/v9vKq/aow82h7dZ9tq2+SnQ16bFmWj/VirLTa/3UY++GFe3/nHbB+Vm1/bpPaUm8+eI5oXa+uTdj+3mxcC0ffV/q04EEzDno3INdb2YoOgDiJ28Nd9WIcuuPa43bYDMwZBO3jadNhGWgsT+KwXSh20cVzhw+YPDfr+RQfpQwNnKK8t299WOxEhQR32uJaPMv1v8VY3pPMvClqOlo/0/j4/ymrL1LShta+F/zlUDvbhQgo7VX/cIoA+UDvQDqTR/Wqz6mBtgSbof7/dWgbyWrts/SjH6mLTaR2a3r9ghz6jTs2na5SvcKX7kE7tDbVJ01WztmVrftUZ+mq9egxr7EM+tRXao30WFKANzhs9d2z+ardRJhbkxzqkqW+H1gWb9bzXPtK2oS1YNC3WfhptK/xH64cNfjocs2lD5zXqhWbIa7dt/bqNshQOfRs1ja6R1o5dul/XIVtRJmxRO1UTzaNrHPfHEj1m+0X3cd3cEIj+r/aPIBhH9JYfJC4HENTBCmsd1OzFIG+gxACXdZGwg5Y/MPuDOi4mOoirDTp4wi4cV5v0KWc9rvXATn+fHqt0rYOwf2HEZ98e2GXrzbMjdFFBXiwo22odqgd12TRol18fpoaxT9NBT1zIULfqoBpjSs9eiGCHXtiRFjYhr14IQ/YjndpqdcB+pFdose3T+v30Wqfajs+62DZhH/L66WwdWhbag7RoJ3wM274mKAd5ta5a1iGNoI/qjG2FBFuPtgf5kRbpfDuRxvc3W0Y122i3tt3XT8uz9us+rH3dNB363PqR5vH73bZV02Dtp8M+mxb12DFBxwYdQ6CTbofKxTF8wYHOOAcsbNv0Wq9Co9YTWqNOP6+2I5Re9+XV7ZfHz8XxoFm1IAguY6CrNkq5HEAQA5gORv6FJ+9k8wfj0ECo+f0LBtLqhQLbGBT1oq4XEzv1qcdQHrbtZ+zTAde3Qcv296tdWWt7cdQ0qBP71T5cmPSYXSNdVn3Io1Bl82Dbvwjjs38xQ7m27dpuvaiUWmt5Np/amtWuPJu1DeoLWpbut2u0x7cvlN7XIVSGD7A2jW6jbPUxCxHQD1E52w/at5q32rXqatul2qBdWi6Oa1/YfaqP2o3yYCfsV/30mN8vWretR8vOWyM9lt9/9R/G7y5UO+xaxwgty+qr+2CTQlPIDrXRlqvt0TKw1nRWR9uH0BT6Ya35rKZ2W4+jj20bbHlanz2u+Xyddb+uNa+1tZxjSAP79QuC5sFay/R9xKbhdnNCIUGQIBj7QK2DgA7+epHChRUDsl4gMfhl1YEBTwdyO/hhoMV+/wKAsjQqpGk0HdJi8EU5qB/rrIE3b3DUdlh7YD8+oy5/f1bbdL/qo5+xVljIGrw1LdJl1ZfVNuRFnciL7Sy7Q3ZpvVlr6KYwoWlUyyw7NR3WeTZruiz99TjWtn159dt0Nr9uWx8KXbg1nV2jDTbKhgusbbv2rc1TzbavVVY/omwcs/bjs/a/1q19h/5TKFAf8NOX8kst01+rnlqubwPS+2X7/a2fbXtQnn/u+f1u+8Xa5afDsay0Np9uQxvVC9u+HVnlab0hONWyK1lreag/a7GaadmaT9ug+7luTviz/R5DQIUbnBpexgC52BFBDKYYeDAo6RQrBmx1NL0o2EFLByo9FrpQaH6s9QKAMjAI6wXGptF0dsDViwnyWZuQVgdGW7cO8GrrQgyaVh+Ub21G3bDLvyjaNNhGOpvXlhlqm+ZH2ZrX6oL92ka7X/OF1n6dVjekVz2tnbYc2xeo28+PtH6aWvS3bUR91i5sa/v9Y74Nmk51sse1DdDGRgPRFhyDDVaDWrdRZilNYIt+SfLrQ360R9uix7XvfC1C7dI8WWuUZb8kqA9qetsvti1WL2zrGKH57FrbgbXd72/bdoXa7fu09nXW2trr14XPKE+/HGQd17Eyqw5/v6+Dtsn6s60L/hmKCNo03Cb8WR+okP/i5ARBgmDuAGydjNscdOgDjekDgLaFhtnl5AulwM3aCvAqBYI2Pbcb0+ebsd9isqtwgyBIECQImocOmnHwYJt5oaMP0AfoA43vAxXyX5ycIEgQJAgSBOkD9AH6AH2APtDgPhCTXYUbBEGCIE/+Bj/5+U2+8b/Jsw/Zh/QB+kCtPlAh/8XJCYIEQYIgQZA+QB+gD9AH6AMN7gMx2VW4QRAkCPLkb/CTv9ZvkczPSAR9gD5AH2h8H6iQ/+LkBEGCIEGQIEgfoA/QB+gD9IEG94GY7CrcIAgSBHnyN/jJz2/yjf9Nnn3IPqQP0Adq9YEK+S9OThAkCBIECYL0AfoAfYA+QB9ocB+Iya7CDYIgQZAnf4Of/LV+i2R+RiLoA/QB+kDj+0CF/BcnJwgSBAmCBEH6AH2APkAfoA80uA/EZFfhBkGQIMiTv8FPfn6Tb/xv8uxD9iF9gD5Qqw9UyH9xcoIgQZAgSBCkD9AH6AP0AfpAg/tATHYVbhAECYI8+Rv85K/1WyTzMxJBH6AP0Aca3wcq5L84OUGQIEgQJAjSB+gD9AH6AH2gwX0gJrsKNwiCKxAEv/zVr/GEbvATmt/OG//bOfuQfUgfoA8slg/gul/tH0FwBYLgN7/1KEGQIEgfoA/QB+gD9IEm8QFc96v9IwiuQBD84Q9/KF/9+iMcAJpkAFisb5ysh9EN+gB9gD6w/HwA13tc96v9IwiuQBCEM8Ap8A2B08TL76TlQMo+oQ/QB+gD9IFafQDXd1zna4FA8AJBcIWCYLXfDJiPClABKkAFqAAVaB4FCIIEwebxdraUClABKkAFqAAVSChAECQIJhyCH6gAFaACVIAKUIHmUYAgSBBsHm9nS6kAFaACVIAKUIGEAgRBgmDCIfiBClABKkAFqAAVaB4FCIIEwebxdraUClABKkAFqAAVSChAECQIJhyCH6gAFaACVIAKUIHmUYAgSBBsHm9nS6kAFaACVIAKUIGEAgRBgmDCIfiBClABKkAFqAAVaB4FGgcEB9tkdUuHjDlwG5Jta9bKtsF5mc8FOaRrlfZRL91oh7SsaZPBzLzIZ46j7jVrg0tLx3iuDWMdrcZuz45E/ePS3rJWbHnoHP5RASpABagAFaACVKBeCpQNgo6dwiwUYiTLM46FMjiqVF5w3ir8G2wzkJSAwhy4ykgHg6yBaZgMgGDbUAr4SpcD29KAl65vXhJlFcDz0idfVq9+Z7lUgApQASpABagAFRCwhoWxfD4yzAVWCbCRMpsN1jkQzEjrM1GC9woBs1XziQheFA20Rie3o0geCkruV5DMyF8wMJUPUcgCmPnl4XNRsIxycwm4EHU0YlqxGBHkGUoFqAAVoAJUgArUU4GBrq3SEs+4GtBLzFqm94eALYK6KAC2MCAYsdUqB2cFULOg5FPkvAEq5PGNALQFDTf5ojJRsTc1HCDZRBTPF2ywzUBiJKBvk6srFWot1ksQrKfrs2wqQAWoABWgAlTAjwhadkpxVsw64KTArXfuePpYgt0ygmsaWEtyWlTWKheJcyA2JNsstaYihUWjfOhy0NbSKqst4GmDMkCwHff3od4Mo5MRwSItuwavaZP2Uf/+QTTIRhGLeebnrXARTXNqmCcoFaACVIAKUAEqUE8FKo8IRiyTNS0cMVMxqAWYTIGgF1yz8Ge3lY1WbWsrAJmCm64TIGihKrqn0FKtA0H3YAcgqwiMKQML9yNmQV42HRejfniopd2LSBbr6XAPhfgCJhselcWIYD1dn2VTASpABagAFaACLiJog2zK4Cl3rwAAIABJREFUWIE1WKUYnCtwV2pmMzkjW+SfwrMWqeBb8jmQJA8VIoIJkrQPX8QgGEXQLFzFxpp79DTsGBFm0dAiJKoxHfHUsKvblOEE8D8XyNbVqZQb26aAmo74Kahq+2Kb24YcPTMiyBOUClABKkAFqAAVqKcC5T41HDFKMpBWKjimx5Vz3OeKQXCtrLIFuG0l1wRsRaFKhT0YrKClNKrHIsOQPgpdosysY9qIeB1oQHzMo2drdwISE+kKEOu9Dgd5r9/aXc++Z9lUgApQASpABahAkysQ3yOobAVG8aN8GuRyt7GlH8ZNB8mSwGh5yD7PofwERlIOs9vx1HBcgLtXz8w7J0BQDY8qLw2CGqnzXt3iIK0IiTAS9auB6fsFk43VRunaASAiiFbgBAgW7dA8WidBsMnPTjafClABKkAFqECdFXD3CHrBKMsjyW07uxnmF4U3+w7nmOPAP47l0jCpnJUPgj5A+SBojlcCgslK0TALgt60cwURwWRjDcAaO4sCo86CMC0dMsiIYJ1dn8VTASpABagAFaAClz65NecHNnzYWyAQjCOMUfmWw+y2QmViargIThoBDAMWCvJDlUqbiTIK9xzaaWQLgo5iC2DmnjguAwSLdVvbFPT8CKLut2mjKCTvEeQJSgWoABWgAlSACtRTAcw+JrnIhz/7eQFAMBgMK9aRBkHvHkEY6+BMo2ceVWpjUJCFu9zpXTNtG0Mc9gWnotOAGT2kokCXrFftKa6L9wQGX2VTEAj2cmq4nq7PsqkAFaACVIAKUIGurTckAmfhoJmCWpF1/GBb8nMy6JXgNuW3nHXRhgg83U/MFUFKjVn5a74+hicoFaACVIAKUAEqUE8Fyn1qOOKwxY4IRqxHEKynB7BsKkAFqAAVoAJUoGkVqAwElyYIRxBsWvdkw6kAFaACVIAKUIF6KkAQLHHT4lJOSXNquJ6uz7KpABWgAlSAClABgiBBkGcBFaACVIAKUAEq0KQKEAQJgk3q+mw2FaACVIAKUAEqQBAkCPIsoAJUgApQASpABZpUAYIgQbBJXZ/NpgJUgApQASpABQiCBEGeBVSAClABKkAFqECTKkAQJAg2qeuz2VSAClABKkAFqEDDgGDxt+fsT7Qlf+6t+JMkC//CQ/fzKOan6LJfKxP9/Ir9ebvstPl2onP4RwWoABWgAlSAClCBeinQGCCI3/yNf1M4AsE0aEUAVk8YrBboqs1HEKyX27NcKkAFqAAVoAJUAAo0BAi2t9gfL84CwXlxUbsCMGK7paNDthV+1DgGR0Cl+aHjeH9hijj5w8jFev2IICKUxXLaZDCeYk5HBJNl2rTzEkU6i3aiTLWJIMiTlApQASpABagAFainAg0BgqsTU7Llg+DqNUWQc1E5B4EGxEY7pMWm8Y+bzxYE7TbKTX5OgmAEgcU6/c8RUBbttMcJgvV0fZZNBagAFaACVIAKNAYIxtPCuKcuAwQd1BWjaUk4y87n0rnyo3KzppZteXY7Pe1rQdBu6/2AyXocCNr2GTglCPIEpQJUgApQASpABeqpQEOAYBLOIpAqTssWp2htujSsRVAWyhfdfxiCNoW3cNQvKqsYzYug0JRjoM4CYzQdPC7YRxCsp3uzbCpABagAFaACVCBPgYYFQb2PzgKW3c4Cwex8BuDi+/2yQFD3WyjV6V9TTq0g2LU1r+94jApQASpABagAFaACNSnQRCCYnJK10Bht5x9Pg6XCINYG/jK3NX2yHkYEa/JfZqYCVIAKUAEqQAVqUKAhQLD46hjAVARS2ZG9CLiC4OYe/rBTuUkomzcPhzg4NBE9W57ddukS+SwUFqaU12i0MP2ZIFiD9zIrFaACVIAKUAEqUJMCjQGCZT41bKN8KVgLvh5mrdj7CpHf5Qu8XiZZXgSQxfsNLVwmQTBdZhEKcYwgWJP/MjMVoAJUgApQASpQgwINAYLtLcWngS3srfRtdA7/qAAVoAJUgApQASpQLwUaAgTdlK19xUrgYY6VCIUEwXq5PculAlSAClABKkAFoEBjgGBhCtWfxl2J8GfbhM75/Oc/z4Ua0AfoA/QB+gB9gD5QFx9oGBC0gNQs24wI8tsaFaACVIAKUAEqUE8FCILLeJqZIFhP12fZVIAKUAEqQAWoAEGQIMizgApQASpABagAFWhSBQiCBMEmdX02mwpQASpABagAFSAIEgR5FlABKkAFqAAVoAJNqgBBkCDYpK7PZlMBKkAFqAAVoAIEQYIgzwIqQAWoABWgAlSgSRUgCBIEm9T12WwqQAWoABWgAlSAIEgQ5FlABagAFaACVIAKNKkCBEGCYJO6PptNBagAFaACVIAKEAQJgjwLqAAVoAJUgApQgSZVgCBIEGxS12ezqQAVoAJUgApQAYIgQZBnARWgAlSAClABKtCkChAECYJN6vpsNhWgAlSACjS7Am954LPy1g9+Th797vcbTgrYDNvRhlr+CIIEwVr8h3mpABWgAlSACjSkAgAowNQDDzwgHR0dctlll8kTn/jEhlhgK2yG7WhDLTBIECQINuQJTKOpABWgAlSACtSiAKJpAKknXfrkhoC/EKTCdrQBban2jyBIEKzWd5iPClABKkAFqEDDKoBIGqJqAKzHX7dNHrfvlfLYjj9qiAW2wmbYjjagLdX+EQQJgtX6DvNRASpABagAFWhoBXQ6uJEgUGEVNgME0YZa/giCBMFa/Id5qQAVoAJUgAo0rAI63apw1Whrtb+WDiAIEgRr8R/mpQJUgApQASrQsAooSDUaAKq9an8tHUAQJAjW4j/MSwWoABWgAlSgYRVQkFKwarS12l9LBxAECYK1+A/zUgEqQAWoABVoWAUUpBoNANVetb+WDiAIEgRr8R/mpQJUgApQASrQsAooSClYNdpa7a+lAwiCBMFa/Id5qQAVoAJUgAo0rAIKUo0GgGqv2l9LBxAECYK1+A/zUgEqQAWoABVoWAUUpBSsGm2t9tfSAQRBgmAt/sO8VIAKUAEqQAUaVgEFqVoAsOs3/0Ee+fb3Ehr83Ue+mHgx9aYX/a187ivfkt97278m9mte5Md2pXao/YnKK/xAECQIVugyTE4FqAAVoAJUYGUooCBVKYBpeoAd/iz4KfQB/LCNtLrPgqBC4D9/7msVA6DWr/bX0hsEQYJgLf7DvFSAClABKkAFGlYBBSkFq0rWCnIW7jS/gp8Con7WtJrXwqLmrWSt9tfSAQTBJQfBIdm2plXaR+dl3toy2iHXP7m1lr5lXipABagAFaACVCBHAQWpSuBL0wLq8qZ0cVxBz4KgbusxLa+atdqf08SShxoCBFevWSuppaVDxmJwAkwF0mBf21ASsOI8Hnhh/2Cbq6elYzydB8cSdWr+cWlvWSvpPNF+a3c6TaHOQLljHa1y/dbukh3IBFSAClABKkAFqEB1CihIVQNhiPblwdwvvPHD8tAj33H3/in8/ek/fMrlgbUaHaymbs2j9lfX+ihXY4BgAOYG29YaMMuIqs1HgBgEsBQQRuC2rQ0w2CaD/vFKQHC0Q1rW+HBYgFXTFtcGD2AjW6O0lz65th+SrsUxmJcKUAEqQAWowEpXQEFKwaqSdTUgCD0Bj+/6+EO50cRy7VD7a+mnhgXBeQdbCmxZIDgviKyFI3ka0Sus4/IiCNs26B0vGwSzIoTzEtlcnAYGCNp6YCtAEPuxRufwjwpQASpABagAFaiPAgpS5YKXTVfN1LBGEDVCqJ9tuZVsq/21qEMQLET+HDAWonUuUmcid+7evXJB0AFlEfYS9/15UcYgCLa0xhFJgmAtrs28VIAKUAEqQAXyFVCQqgS+NK0+8BGa4lXQy3pYBGVg6vi7//mDxBPHWna5a7U/v5X5RxsUBAv338WwlhURjKJ7paeGvShgCObKBUGkC00texAIQAyCoLs/Ee1rlXu7GBHMd18epQJUgApQASpQvQIKUuWCl58OEIg/BT4cVwi00T7d50Oj5vf3+/VkfVb7q1dA3OxjXtBqORxbZR+4iLcTD1hEIBcfM/fdlYbA0AMbgendOoGgb3PR3iG5nfcI1uLbzEsFqAAVoAJUIFcBBaks0Cpnv0YGbUUWDFFGFgjiGN4jiL9qYFDtt3VXut2gEUHv/j33UEhyOtZN7/qRORfpM08Xu4hiIbpo4LEIZ3oPYggW1QYPGkPRxEA0MD8iGJU9MLC/0v5keipABagAFaACVKBMBRSkygG+5ZhG7S+zucFkKxYE5+cLgJeIHCq8mXUWuBWgMX6Yw302YBjDnTetXKi3GNkzdRWeYtYys6eGFQQ5NRz0Wu6kAlSAClABKrAACihILUfIK8cmtb8WKVYwCOpTuvnvEsx7qthFFeP7EAtgGX+OYM3lz4g8JmGwMH1twJQgWIvrMi8VoAJUgApQgdoUUJAqB7qWYxq1vxYVVjYIImrnHt7w3+mnUboIzpLApsc0b2jK2UwvG7BL3lCZvm/RryeavjZlee8eROfwjwpQASpABagAFaiPAgpSyxHyyrFJ7a9FnYYAwSRgGVCLp2cbc19uRHCwTfhC6Vpcm3mpABWgAlSACuQroCBVDnQtxzRqf34r848SBJcxTDIimO+8PEoFqAAVoAJUoBYFFKSWI+SVY5PaX4sGBEGCYC3+w7xUgApQASpABRpWAQWpcqBrOaZR+2vpAIIgQbAW/2FeKkAFqAAVoAINq8Bll10mgKnH7XulLEfQy7MJNsN2tKGWP4IgQbAW/2FeKkAFqAAVoAINqcCj3/2+dHR0OJh6/HXbGgoGAYGwGSCINqAt1f4RBAmC1foO81EBKkAFqAAVaFgF3vrBz8kDDzwgT7r0yQ6odJq1kdawHW1AW6r9IwgSBKv1HeajAlSAClABKtCwCrzlgc+6SBpAClE1nSZuBBCErbAZtiMaiLZU+0cQJAhW6zvMRwWoABWgAlSgoRUAQCGaVsvU6lIJAJthey0QCNsJggTBpfJh1ksFqAAVoAJUgAossQIEQYLgErsgq6cCVIAKUAEqQAWWSgGCIEFwqXyP9VIBKkAFqAAVoAJLrABBkCC4xC7I6qkAFaACVIAKUIGlUoAgSBBcKt9jvVSAClABKkAFqMASK0AQJAgusQuyeipABagAFaACVGCpFCAIEgSXyvdYLxWgAlSAClABKrDEChAECYJL7IKsngpQASpABagAFVgqBQiCBMGl8j3WSwWoABWgAlSACiyxAgRBguASuyCrpwJUgApQASpABZZKAYIgQXCpfI/1UgEqQAWoABWgAkusAEFwGYPgiRMn5OGHH15iF2H1VIAKUAEqQAWowEpU4KGHHhKwxvwyZiHYtmq5G1gv+86dOydveMMbVqLvsU1UgApQASpABajAEivw+te/XsAa9eKYhSq3aUFwdnZWTp06Ja973evkwQcfXGJ3YfVUgApQASpABajASlAATAG2AGOANRYK2OpVTtOCIARFB4HWEbrFPD4XakAfoA/QB+gD9AH6QC0+AKYAWzQCBIKFmhoE60XXLHd+2X8DYh+xj+gD9AH6AH2APkAQJLAs85tYeZJyoKYP0AfoA/QB+kD9fIARQYIQYZg+QB+gD9AH6AP0gSb1AYJgk3Y8v13V79sVtaW29AH6AH2APtAoPkAQJAjyWyB9gD5AH6AP0AfoA03qAwTBJu34RvmmQjv5rZo+QB+gD9AH6AP18wGCIEGQ3wLpA/QB+gB9gD5AH2hSH1g1P9gmq1s6ZCwgwGDbWtk2WD8KJeFTW/oAfYA+QB+gD9AH6ANL5wNFEAQQtrXJtjVrZXXm0iaDAWCMOnBc2lvWZkJlqpNRn6unVdpHSwsAKM22yz9m7BztkJbM9vj5Ava7/KY8bX8OQKfaijxO36GKv3GNdbTK6jWB+tWO1LrQD2hzWxn1oX056aB7S8d4tt0ZOji7M75gZOpTUTs9n0E7XH2F9ts2VeoDJfzS+WKhfLQzVx/bPxX4QN/9p+Qx949Guo+el2tbT8ljMpZruyYK/TMhe7ek023qysmvdVg7q9qO6t40lOwXtKNoX/JY0A/Q1i3n5UIpG0poEmk1LHvt2FLIozY6jTM0RX5r94Wu4cTnea3fszUq86z0GfvTGozKppL1+n2ZbEu6TKOt2hbXkbQnqLux1z/uzmV7PmWmHZJtawLjedYYinIqPTf9MSWrbJxrtYwnmW00OjNN9nWB2jSUNqsGCxfy9kD0LzsiaGCjLMgqDA7u5FT4snCTVV5gUHEOhvQZxzAw2MEia6AIOWpBCxsdzRoEs7XJGigwSJaAqrhtyUisBQ83SKfsTOqXAJOE5qp9EhJT5RttcCxRnjnm4DbU/5lfKDL6rFBmxfBobYn7fVzGRucl126bz26X8BVn35pk34yNDrkvQJka2fLRF6ELasZ+BxUlQC0JKD6M+Z/nBWUqCPkX/Jo+D52Vx3hQhPJygcVq47YjQCrLPgc7eYCDtifhSdunuuZpkdR1Xi6Mno/gze8PD7rStqNNvh2hff64Yfou1dYS+YfOJvrYtTfQN6pHqXXWGJjKlxqXojYhf+b5UeKcS9SRKj857sWBgpY22YagRGpsyh9/EnWlfNPvH36mXivHB1Zta8PUcKu0FL49OShInUDRSVU8mZMgFhooKjv5k+VFDhbaVxA+b/DwB4u8tP7JnsgbHmTcVLkrMz3QqD5OjwwN04NTGrTSeia/aedpmwIgtMmHj9S+bK1T5almKEO//Ts9ioMs8ugtBc6fLJjPR0Ac0iF7H8oO90d2nkL/+G1X+0PrTF8pz2bt/+IAmZ8P6dP6zIsPIcXy0gNPMm0ED37kEHDiYCCOEGnU0AeUdPl5dRePGWjxdEW9NrJWzOPXFZWRSAsAygIYD8D8NgcjggHbwvkifRK2uLywEZpZnZMaqs6aF/3j13FtF6AymS+tS1FT7eNQWYmyc7UqVR/6o8JzzJzXzo+9MS86HzLOAT0vM8853z8Ksyp+nSjHH89QpkuHuovjUlrjQB2ejzAPNWoGH4imhjNC6PaCnhQjAodtbZi2TANRep+N/ul0gN0XApHQvsgp06BUdNbUscRAkzEo6eCCAUW33aCYHEQiPaLBUkFHdbFwZrf1eNY6mDZhc7FtURnZuuA4bEwAiT9IYqAL7csYAFPleemc3mX4gK9XrEcFtsR5PBui/ehb61O+bpE2ad8sQms0TZVfhl4sExrDntw+K9hi2+rSe31VaJde+NGurIu/BQ3dnp8vwkOkSfEzACUZrcIxHw6i9KHIXq72iAZmgE15IFioNxFtK9oerDsVJfP7O90+B2kGltKaFMuwfTA/jwhcVvQRx3xttZxiG1w/xu2L8iQgTiE9TqN5/Xb4n7WunLXrnyz7bb7ssS31ZRL+G4+V0bllz3Ed14LjR+o80HOugvHZljE/L6gP9aM+nOPx+VnOeRkcT6wu3A6eg9StoaZ/8/pwlb0wbusodT+dglESRhwM6De8gnPoQFCsPBpkbH263dKB6TUtW0+6ZB1ajqsr81teVEc8CMCWxEAQgAUMKDqg2e0MEGxpaS2mNydCur3ajmrWgXZoXQkbC2VjXxDG2sRN/Xt9UwRBO/BC/+w+SpRvyiun3Ric7UVC+xJr15+mPHussu1A36pmhXXajqi9sW0JXymlbfYXIFeeKys7TUJP7buCH1oISQJEZJOFK5tWQdAHDI0IOhCMp3BDQBHBR2UgWMjjQFC3NeKYsY5hB+0p5EnsK0QwvX0Jf6g2IujyRQDswFABLLCOARsRQNyzqbDrwCqjbaYcC97JfsyY2kW5cZsjXdz9nR68xnYl0hf81fP5CGLLjcp650OhrOA5Cv/WcbPwBTQ+jwrntRsrQ1/OMF7pOZ845wLnsB3v7LbfTj3fXLmFcc3Yl/AdPy8/rxigYT9njQP5+1e5b1AVnzAYMIrg5gYKvZiZdQLIzMkWpddvgTAwWV7Umd4+PdE9CNRvgPGF1W9L1QONV39isEsPWLAjam9hEDI6xLZl7rNaFDrMwZ2/HzZlQ9V8Ydo1obsddLUPUvtgc7E/7clUbFfYkbL63m+zvUjY8kvlT+RzmoThattgur9sPdhGWxLlFabC4n0JX0m319mq/qVpYZPuU30z11H/lbqJ3cJdEiAim7JBMG2zahBHv2J4AGj4EcHs/FqOv3Ywdf/ZzKlOa6ufN37gIoafQv0OtMqJYFVub8qGzL5Kl53qi6HRxEMhUdkFgHMPzWRE/rL0ivsGdWs5usaX2pyHfgCgvo6a3t+f2ebw+OL8XsFN88L/jd/75xbyRONQ+rxMlKfnkSs3Pa66L61aT+hci8eE9Pjl6sGYq/nVdq4JfvSBhA+sck8JmxMFJ7R/EdfP8QUzCG7pgTM86A7JNndPoj1B04NFAg7dYJF8wCFcdsCGqgcavWgn9Yg1cOUWBx9/IEzbFx5k0+mKbXB9YQZgN7CZvvLzjnW0SUvhJunYTgyUpgyXJ7WvNhBMgGfgBMvTBsfC+bNtQhuKF5qkXnG7y7LD65OErxTLjZ9shI7qiy7SgQdTxmVsMIqkh9sRleP6MvOLAHys6EvVgqCDMhORiiODW867iFYcEXRgsBAgiDIAbBkRrtyHRSJIslEz55smYuf7d/Q5A65C7Y73WeAt2mp1dmUDxOLoW54+EZw9JjhdHB3z25UE4qINiTYGQXBe5hP7jV9m7Xe+X7CxbAhEud75UDiHEuCm5xXOAzMWhfy7eD7gXC5+CUucu4lzrgIQjAEQX5aj8vU6lVgnztniOZbQXdvEdQIOqJE511a4bxSnhgsnddaFGfuLF9nCRbpwAUyceN7FrjgYFC+I0RR0m7Tj1SiuXgxA/kma3ucGJK/8YN0WfKodaAKwm9SgMO1sdCvqY9oav4cxPMjmn2xmAHUDn6+RcdTCwIynv1s6OooDbwr6QvcIFvrTvmqj4PhZ/qB2l9snvjZRftO+1ImWbRPyJi4mhbyhfWon1qn+8y98CV+JtI3a1yaD7pj/5UW/JESRW9XC+jzqdD7aNpS+fzPV5qhOCyipKJQHVzatbau/HyCyacjCAbYtIBlfyrDLlp/czgAbz9ZknlB9EeQVISq73HRZYQBLpQNoFmDPaRvD4inZdH8BBONI2nm5YM4Jp6F7FQ90i2yNYduUg33FNqCdBd3j/Tl5Y3Cz7UH6QIS0DBCMp5HL6tNojAqOqaFx1wNBe46nz0Wcz8XzJD5HEudcMU3cbxi/tB67XVZ7Qj7GfbG21JDgW/CBVe0d0YkWAUThKcbQSW++0SXvuwsDTnwBtc6mUGJO/ujinIa+RESwUIYr00KeLTsrjalLp05TA11woEnblAaJ4qCSPuaDTNYgmwN3rk0oJ4IJO9AmT+airbDDDbJoN9qlmlutUvt8W5PtigdtW4a3nR74/fszi2Wq7a4/VXuvvKSPhfOm7PIvFN5naJPq+0y/TtcJuxNlxLZHfZvoH9Rtb1ovFbUw7bcQ58OKgode4G1a2Nd3fwR3iXxxBPBUFPECQOj9bqZe7ZfK19nAloyEhTWN6ovAR9sV22DALd5XsDlZdhG2VKNonYRdp0sBtnztXOTNAV0AulJRzww4i6d0bVsLaaG7g9AMvRJgZ0DQgelZ6XP9lndvYrKtvl6lPwf8OOs+Xh1fCn3hj3/B8aCQNnGs6vFZ9S2Oj6Fz2+2Lz1PNw3VpX6BGzaRR8YXSiI60dYgCoS+CPdHdBdwDMneBdPsKJ6Z3HOUNthXueUuc/HC4IsgU603vC9VbTB85bipNoi7YVrBBL4AWFux2wCarQbLe9ADq7LCQ4UeftP7ctTfIZQ1oox2yrfDSZ9iYACS0ye+L1D7UUwBS6GXSp8pTe1FGxheG7P0Gel2/2CizN/Ak+s07lhERVNBXIPN9Ibv/CuXn1Im86YtK1O92WjfpF2m7/eOwMdFfhSeFFYgsuPh58TkJM0V4sPtdGXE0Sm0KwUgBpuLpUU1bah0qK8qThLWscgr1xtEwL10CkArHClG7YuSt2PaQTtG+ZBqrkTseg1pUh9NNbfKO6UMYSegsQlrRrsKT34VyXFRxKFuvou0FTQqRRvWH4vGcKWN3jqKOch8S8fTWc7yw9s+jhA2FNP65FfJrzZcYUxLnXCXjs9psxi7PbldfYkxX3yk+FOmfe2oj16ov183gCwYEow6PL3iBi3x0gU1DD4RK5jMX/NDJmTj5Ua9eUP2ITbKccgakVJpUXTmOnRg0wjYpZCSdwxuMHCS1SrtOnTuwCuuWLKdgWwxZyfa7G6ddv3gwazRODLLYH5flaWtgTwEqArhknanyTF1F29H+ANRlae/2e8Dqlev6MQt8NUphfVTTFtobPTwSsMmrp9iG0hHMKK31i6RWibLy6omPhX0iBShx+rTvJkDLwcpw/CskQXjIKSuewlxUEIyAxT7ogDb5gHXtFvPLHgUIvPb+s1FbHWQlIS/YFxpZK2jgdNYyECHt0ohdpLPV1tmkUOjyw+5Q5NCzA31i08EGtKVkRNYrJ9RvKDthk/WPxQdB/wtgGLI8n88aI0Ltxbmt53l8PBp7/LoTn7088ZhWSd1xfVZjbgfPM2rVcFPOQRAMncCDbYWLHk4enFiFi7mecElAshdLAIh3wUydgEjvpQlE5Bwc2It/1raFHM9OtTdzHQ8aaZswgGg7U7Zona6+ZFuQL7M+14YofZxOy8o8oczg56WNBznNi8HTS1N8fUxhIHPwlLRZT/BUeVquW6sd4bzB6d1Cf6iOWk8aWNNlxvokplwDg3EBBks9nevqjtNGfRTy/TxQju1P6BKwCcdDvhj7WzFPKRB0kar4nrTidCBgJY5EOQBJA5UPWNkgUbSndBsBHUU7bHoLU3a/bjvAitsS2Ru3IaHpqGwCoDqYS9YVKiPZzii90y0Gp0LELQG9BSiN7VHQi/Yn7cI+Pe6/71H3o46krVG7/Xq8fioXbHNBsJL+y0+b+nKd6JcoL85Ne04jT3wueec+MTL1AAAAgUlEQVRYAuZC50TWuI79qfMFY1B6rFD/cuOKn8fUGdsYaFNcBo81HNiw7/LPaV+fXBC0sGNPcr+QxfpczoCUSuNO+uwIWsJ2DFj+oMFBgIMAfYA+QB+ojw9wfK6PrvRX6lqBD6xKgFAFGZmvMuKmXtSLPkAfoA/QB+gD9IHl5gP/Px11UOu17E+WAAAAAElFTkSuQmCC"
+    },
+    "image-11.png": {
+     "image/png": 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"
+    },
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"
+    },
+    "image-3.png": {
+     "image/png": 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"
+    },
+    "image-4.png": {
+     "image/png": 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yr2FEfjhRNurMn6u6FD5KNxxJ/fHtBW+t2klXPlgm9bzUa0v9PHhu8L3OQ2jU7wThKMIIGwjXBhAawScz7yEAAQjEj0DJCA05+V9++aU9/PDD1qlTJzvllFPsvPPOs1deecXmzJmz1p2bNWuWjRkzxqqrq+3YY4+1448/3jp27GhPPvmkKy+n3zv+H3zwgd1222121lln2Yknnmhdu3a1F198sWYsmnf4JRY++ugju/HGG619+/bu1b17d3v++efXimLI0X/33XetR48edsYZZ9jpp59uV199tU2aNGmt0JNEy/jx4+3KK6+0k08+2U466STX73feeccJBl2cRIbv71oXm3IAoRGu84RzCl9sIDMbQGikPJz5EwIQgEDMCJSM0JAz/tprr9lVV11lZ599thsvtskmm9jAgQPtl19+qbltS5YssdmzZ1uvXr3ssssus4svvtgJjMsvv9wJiCeeeMIkQiQaJF7kzMvBl3CQgOncubN7L1Hy1FNPuTIqp+311193OQ/HHXecnXnmmXbuuec6EaP9xIkTayIZEhlvv/22E0ISORdddJFdcMEFdsIJJziB9MYbb9j8+fNdnbqBqlcC49RTT7ULL7zQ1XvMMce4verxURcxkIhIfdVc/Oo3Xmi0bdvWjalTYri/htSyef+bHA3G55OjgQ2Qo5H3RysVQgACECgGAfmrytHQD/feHw27H0XJ0dCv+Z988ok999xzNnbsWLvzzjtto402sv79+9uvv/5ac80aEqVohCIYctqfffZZ++KLL+z777+3Tz/91L777juT4y1YeklktG7d2rp06WKfffaZffPNN/bggw9au3bt7LTTTnN1q9zvv/9u1157re233352ww032Pvvv+/6o0iIboDExm+//ebq/Prrr+2OO+6wbbbZxvVTZSVoFAnZfffdnVj66quvXJ+1l3jaddddXVlFTD788EO7/fbbbbvttrO77rrLfvjhB1dWQkORjUyEhgSXhIYMQ9eb6bCrGpANfYPQwMlEaGADCI2GPkE5DwIQgECkCPz555/WqlUr509mOrIm1wsoitBQp+Vg+03DlRo1amSDBw+2P/74wx92kQIJDA2DGjlypM2cOdNFPCRGUpWYIh+amUlDm+Tc+03HNXxqn332cUOa5Kgr6iDhcdRRR7k6fVmJF035tdNOO5mGYClCMXr0aDdUSgJGAkWbbo7auPTSS+3AAw900RkdV5SmefPmTqjofH+NirpI7EjASDhJKCgqkSoyfHnfH+11LFVoENHIbKgHQ2LghA3kzwYYOhV8MvMeAhCAQPwIlJTQCN4eCY311lvP+vXrVzN0Sg62fv3feeednXi44oor3HClfffd1/2636dPn5rogBz/yZMn2/7772/dunWzuXPn1vzqr3ruueceO/jgg91ekIcOHVqT56HIgt8kQoYPH24bbrihaVjWjBkzTO0cccQRTv3pXB+F0GfKMdl8882dGJHzL1Gy2Wab2X333efO9fVK7CjSoSFdgwYNqqnDf17XXu1JaDC9bf4cJpxPWGID2dsAQqOuJzWfQQACEIg+gZIXGkOGDHE5F7pVilhMnTrVRTp22GEHJzI0zEmOv4ZGHXnkkW5I0o8//ujKahhWs2bNnEOvc/2v/nLU+/bt68TJNddc43I+NJxKuRmaRzi4SZRoeJYfxvXTTz85MaFoxN13321aw8ILDUVeHnvsMVNuicSJoh3a62+JFJ+3ofp1YzVsSuEqiZP6NokfH9JSe2rr8MMPNyWrqy71syAbQ6cYNsPQKWyAoVMFedzSCAQgAIGwCZS00Fh//fWdo65f/7XpV3wNZdJx5TcoIqCch59//tmUgK2Zn5RILoGhsqNGjbImTZq43As54n5okhz1AQMGuGFSynHQMKZ7773XCQ3NHJW6KSqx8cYbO0EQFBoPPPBATX6E6lc/lWAuYaEohcpqv+mmm7qZqxQd0aayEig9e/a0Fi1amOqpbZPAeOutt5yoUV/Vv+uuu87Ky8ttyy23dNEa5a0UbENo4GQiNLABhEbBHrk0BAEIQCBMAiUtNDR0KjWiMWXKFDeMSQ76M888swZ7DYfStLi33nqr+5VfguM///mPc84lMiQw/KYhWYpKKAFc0+dGNaIhcaIpezXVr6IfeimPRH3fYIMNnOCQaCnYhtDAyURoYAMIjYI9cmkIAhCAQJgESlZoKIogoaEhTopY+E2zS+24445uqlitTREUDxpCpdmolOitnAytc6HEbP36r7+Dm6IJhx56qMud0HS1GuLUoUMHN1Vuao7GiBEjnFMvh199UZ80TEszWima4IctaRpeDYNSTob673M0lLOhqEXwOjT0yU+763M0gtfi+6q61YbEhIxBL0VrtPc5GsE++PNC2yM0cDIRGtgAQiO0RywVQwACECgkgZIVGopGSGhIPAQddOU9SDzoV30f0ZBDL7Fw0003OaFxyy23OCdfDrjWudBsUhpa5TfVoURyJZG/9NJL7rBEidbOCM46JSdfU+5KEGy//fYuP0ROvmaJ0sxXyunQDfKbprLVcU2Rq1mstGnBwYMOOsit4fHxxx+73BH1V7Nladrcc845x8aNG1eTP6I2/cvX6//WXpv2ul4lpCunRCLEf+bPCW2P0MDJRGhgAwiN0B6xVAwBCECgkARKSmhomJBEhda66N27t/3v//6vS5iWCNCMTkqmllOt6IASobX43quvvmpa10IRBP3CL7HgxYMiE8pn0DS0mnb2888/t2+//dYeeugh56SrrF91XPkVWgdDIkbDqZT7oZfW0dAwJQkCraOhTTNfaZiWEtI1m5TW0dDUtTrWuHFj5/xPnz7dldVq58qr0BAufa6y7733nt1///0uMqPIipLX/ZYqKvzx1L0Ej5/5yud+pJYJ5W+EBk4mQgMbQGiE8nilUghAAAKFJlAyQkO/8isioIX6LrnkEicO/t//+38uWVr5CYpWaKiUysmJ79Gjh5144oku+VsiQkngWi9DSd5K7tamWZqUSK1f/eWUn3/++aYVxLWCt0SKZpPym+pV9KGiosINi1J9Kq/hVIpSTJo0yUURVE6OvepVvxTV0Krgeq/+aH0P1eMjHVp3Q9EU9U35I1pFXKuZ671WJ9fK4KpTm/bZCA0N3xIHRTcKtiE0cDIRGtgAQqNgj1waggAEIBAmgZISGtOmTXOL4x1zzDF29NFHO3Gg93L2NX2tZnTym6ICihBIEEg4dOrUyZ5++uk1hlnJaVfeg9bTuP76690wKjn4EhMTJ05ca4E/DbVSvYpAaMjVSSed5PqjNT38pkiCBIHKSiRoJiiJCIkM5YK8/PLLToj48tpL8EyYMMGV9TNjqQ1FN7wgUTnVrf56sRGsI/W9yiI0sp/3n7USYIYN5M8GWEcj9cnM3xCAAATiRaBkhIa/LXLK/S/8/lhwLye8vq22pOp09abW5/9OXWXctxmsQ86+NrWn98Ekcl8+073qUN1eZPh+1HY+QiN/zhKOJyyxgYbZAEKjtic0xyEAAQjEg0DJCY143Ja/eunFQKbiIJNr83XWVxah0TDHCIcSbthA/mwAoVHfk5rPIQABCESbAEIj2vcn4whEvi8DoZE/ZwnHE5bYQMNsAKGR7yc79UEAAhAoLAGERmF5Z92aj2ZkfWKOJ/jpbUkGb5iDhGMJN2wgdxtAaOT4IOd0CEAAAkUmoBldtdyCco5rSxvIdxfXyXeF1Jd/Al5oyDCY3jZ3hwmnE4bYQPY2gNDI/7OdGiEAAQgUkoBmR5XQ0AytCI1Cko94WwiN7J0iHEmYYQP5tQGERsS/KOgeBCAAgXoIIDTqAVSqHyM08usw4YDCExvI3gYQGqX6DcR1QwACSSGA0EjKnczzdSA0sneKcCRhhg3k1wYQGnl+sFMdBCAAgQITQGgUGHhcmlNeRrt27VzyzsKFCwvXbVYGZ1VoVgbHBlgZvHDPXFqCAAQgEBIBTWgkoVFWVuYWqM5lPbhsukgyeDa0ilRWQqNt27YueUdCI9P1N3LuLkIDJxOhgQ0gNHJ+lFIBBCAAgWITkO84d+5ca9GihRMaWjqhEBtCoxCUc2wDoZHfISAMqYEnNpC9DTB0KscHOadDAAIQKCIBhEYR4Ue9aYRG9k4RjiTMsIH82gBCI+rfFPQPAhCAQO0EEBq1syn5TxAa+XWYcEDhiQ1kbwMIjZL/KgIABCAQYwIIjRjfvLC7jtDI3inCkYQZNpBfG0BohP2kp34IQAAC4RFAaITHNvY1IzTy6zDhgMITG8jeBhAasf8q4QIgAIESJoDQKOGbX9+lIzSyd4pwJGGGDeTXBhAa9T2p+RwCEIBAdAkgNKJ7b4reM4RGfh0mHFB4YgPZ2wBCo+hfBXQAAhCAQIMJIDQajC75JyI0sneKcCRhhg3k1wYQGsn/ruEKIQCB5BJAaCT33uZ8ZQiN/DpMOKDwxAaytwGERs6PciqAAAQgUDQCCI2ioY9+wwiN7J0iHEmYYQP5tQGERvS/K+ghBCAAgdoIIDRqI8NxQ2jk12HCAYUnNpC9DSA0+DKCAAQgEF8CCI343rvQe47QyN4pwpGEGTaQXxtAaIT+qKcBCEAAAqERQGiEhjb+FSM08usw4YDCExvI3gYQGvH/LuEKIACB0iWA0Cjde1/vlSM0sneKcCRhhg3k1wYQGvU+qikAAQhAILIEJDTmzZtnZWVlVllZaUuXLi1IX9cpSCs0khMBhEZ+HSYcUHhiA9nbAEIjp8c4J0MAAhAoOgEJjZYtW1pVVZUtW7asIP1BaBQEc26NIDSyd4pwJGGGDeTXBhAauT3HORsCEIBAsQkgNIp9ByLa/sKFC61NmzYu1LVgwQJbuXJlYXq6cpXZSrPlZjZ56gLrUN7LmnfrY3tWDLIm5YMNRy6/jhw84RllG0BoFOaxSysQgAAEwiKA0AiLbMzrRWjggEbZAaVvpWGfCI2Yf5HQfQhAoOQJIDRK3gTSA0BolIYjh8POfY6yDSA00j+fOQoBCEAgLgTmz5/vcjSqq6vJ0YjLTStEPxEaOKBRdkDpW2nYJ0KjEE972oAABCAQHoE///zTWrVqZRIay5drYHz4G8ng4TPOuQWERmk4cjjs3Oco2wBCI+dHORVAAAIQKCoBhEZR8Ue3cYQGDmiUHVD6Vhr2idCI7ncEPYMABCCQCQGERiaUSrAMQqM0HDkcdu5zlG0AoVGCXz5cMgQgkCgCCI1E3c78XQxCAwc0yg4ofSsN+0Ro5O+ZTk0QgAAEikEAoVEM6jFoE6FRGo4cDjv3Oco2gNCIwZcFXYQABCBQBwGERh1wSvkjhAYOaJQdUPpWGvaJ0CjlbyGuHQIQSAIBhEYS7mII14DQKA1HDoed+xxlG0BohPBwp0oIQAACBSSA0Cgg7Dg1hdDAAY2yA0rfSsM+ERpx+tagrxCAAATWJoDQWJsJR8wMoVEajhwOO/c5yjaA0ODrCAIQgEC8CSA04n3/Qus9QgMHNMoOKH0rDftEaIT2iKdiCEAAAgUhgNAoCOb4NYLQKA1HDoed+xxlG0BoxO+7gx5DAAIQCBIoKaGxZMmS4LVn/H7p0qW2aNEiW7lyZc05wfc1B1e/Sf1sxYoVaxTR56ozuK1atcodS3fcl9N5y5cv93/W7HVusE39rTa1b+iG0MABjbIDSt9Kwz4RGg19gnMeBCAAgWgQKCmhkQvyZcuW1eu4Bx17LwokDHSu3/zxoDDwnwXL6Vi6MsE2/Hmpe5VRu7mIjQULFlibNm2ssrLS9D5dX1LbzcvfK1eZrTSTnJo8dYF1KO9lzbv1sT0rBlmT8sGGg1kaDib3mfssG0Bo5OWpSiUQgAAEikYAoZEGfSbOfJrTag7pfP/SQTnpegWP1RTO4xtfv/a+Td9uts3IMBAaOHs4/NhAMW0AoZHtk5vyEIAABKJFYP78+dayZUurrq5e44f3MHu5TpiV11a3nO/Zs2fb1KlT7e2337aJEyfa2LFj7dtvv+wfBwYAACAASURBVLXUIVVyzv02d+5c++yzz2z8+PFuP2fOHCcYVMZHOX799Vd79913bcKECa7O1157zWbMmOGrcHuV1fArlX3rrbds3Lhxrs733nvPfvrpp5qIgaIQqlsRiZ9//rmm7Isvvmgq+9tvv60VWVHZL774wiZNmmSjR4+2F154wT744AN3vRqKFbyeNTpVxx8yjNatW7uIhoZRiV9BNiIaRG2649wX07mPUtsIjYI8dWkEAhCAQGgE5s2b54RGVVVVsoWGHG4JAA0FatGihW211Va20UYb2YABA9YSBT4yIHEwefJkO+200+zvf/+7XXDBBfbmm2+uMSRp5syZ9sgjj1i7du1s++23ty233NIOO+ww69+/vwmuNjn6Ehlff/21DRw40A4//HDX/o477mjt27d3ZfW5NrWp13fffWf333+/tWrVyrbZZhtT2eOOO86Vlfjxjr/qliC55ppr7OCDD3Zld9llFzvppJPs+eefd8LED6NyDWTwj+pW34NCI4PT8lMEoYHQQGhgA6ttAKGRn8cqtUAAAhAoFoGSEhr6xb9nz5529913W+fOne1f//qXc9wVJUi3SRjcc8891rhxY9t8883tsssuc9GQoJN/0003OWEhx/7pp5+2xx57zE488UQnPCQUtEkMaCiS2t1///2dYFFE5amnnrIzzjjDDjroICdAvID4/vvvXdmtt97aunfv7urs16+fEzw77LCDvfTSS6ay2n744Qe7+uqrbdttt7Xy8nJXp8SMRMlee+1lY8aMcW2nJpm7kwP/eHGlvforw5DIqaiocCIpUDTctwgNnEyEBjaA0Aj3OUvtEIAABApEoGSEhhxoDXtSpEDDpRTJWG+99WoVGhIGcu4vvPBCe+ihh0xRgssvv9zeeeedmlvzyy+/OIf+1FNPNQ1tUtK0RMszzzxjxx9/vB1zzDEu2qATNFzrrLPOclECDWvS0CTBlzA5+eST7dhjj3X909Cp5557zk4//XQXeVFEZdasWa6eESNGOFHSpUsXmzJlihMEr7zyih1wwAGuDokXDQ9THzR8SlGQa6+91g35UpTEC6SaC6jjjTcMhAbDeKI0lIa+lJY9EtGo4yHNRxCAAARiQMD7k4kfOuXvhZxt/br/+OOP2z/+8Y+0QkPO/rPPPmuCcsstt9iHH37oohqpQuONN95wTn63bt1qhkmpHQmarl272t57722vvvqqa3rkyJFODCgqEowuSDBceeWVbtjV9OnTXb6IhI2GV2mYl+rym3JFLrnkEvfZ66+/7oTNo48+6oZh9e3b13788Udf1AkODRE7++yzXS5IzQcZvvGGoT4sXrw4w7PyUIyIBr9mE9HABoho5OFhShUQgAAEiktAPrf8ybKyMufTBv3fMHtWlGRwf0HKV1Dy96hRo1zehaIWqUOnvvrqK7viiitc3oOSvCUAmjZtukZEQ/UowrDPPvvYjTfeuFa04Pbbb3efDRo0yH2mYVSKaGj4lIYmaVMdiopIWChfRKJEURGdqyFWGgLlczdUXpGYW2+91UVXFEH5/fffrXfv3rbZZpuZRI+GU/m6lcCtaEyHDh2cqPLXn26vfgRfin6oLs0SIKGRmiyfro68HUNo4GQiNLABhEbeHqlUBAEIQKBYBCQ05E/qh+9C+pNFFRreEVeitIZOKZE7KDSkvOTon3vuuc5BFyTNINWsWbM1hIZU2cMPP2yHHnqoy/tQvXLI9dJnyu045JBD7K677nLDpCRGNETK523opuscDaGSYNl0001djociGDfffLMTGsqv0KYIi+qV4Lnzzjttk002cYneSgJXOxtvvLG9//77a4gS3VgNe1Ky+ZAhQ5zYUT2pm/qgmbBUt2aumjZtmntpdi4llxfSMFzfEBo4mQgNbAChkfqo5m8IQAACsSNQkkLD3yVFBOTca0iTHG3BUF6GEsb3228/UyRCuRHa5NDrmE8G1zFFHm677TY78MAD1xAPvv4HHnjAhYokNFSvhIaSxO+77z5fxAkN1aPhT+qLptuV8FCC+b777utyPRRd8JuGRvXq1ctFP1RW/VZye6NGjdzsWMGQlASLksOPPvpody2+jtS9+vbggw+661A9EjF6aVaudddd19WhPhZsQ2jgZCI0sAGERsEeuTQEAQhAICwCJSk09Au+LlzRAg1XkqCQw65N61loaJOcfg2VOuKII1yStkI+//znP53zrZmYJBYkCHSuBIjyOIKbhiEpKqIxaRIR/m/NBCUR4Tf1Q9EUiQdNi6thWnLqNTxKCd5Dhw6tiVKorKION9xwg22xxRYu70JiQkOnJAyUFC7R4De979ixoylRXbNh1bapb4pkKMIj0aXkdL30fo899nCzXmkYVsE2hAZOJkIDG0BoFOyRS0MQgAAEwiJQskJDQCU01l9/fTd0SrkO2rRXErhmdbr44otd4rWSr5VQveGGG1qTJk3c++HDhzsBoITs5s2bm5LB//jjD1eHhIwEi5LBldCt2aZ0bPDgwU60qF4lV2sYk45/8sknLhlcTv0333zjhkgpsfvII490M0b5HA2V12xVZ555psv90HoeimAo10RT20qsKPLiN/VHQkd5GhIh2tINnfLlFTlRv3xUxOdoKCGeZPDSmumHmZ2431GxAWad8k9o9hCAAATiR6DkhIYcbf06L+f9ySefdLNOKc9C61YoB0KfKUdDU+BqmJLWqFACthx8raVx/vnnu2lsFS2QY67cDeVAKPdCK31ralkld8v5VxK2prhVGQkKTYurZHBFRJRPoYX+VFYCRGtwqLyEjiIMWmFc0Yg2bdrYxx9/7IZwqS8qq5mstAaIohDalASupG0Ny3r55ZfdLFUSHOqPFhC8/vrr3Wrounb1WTe9tk399JsiNm3btrUePXrURFX8Z6HuiWjwazYRDWyAiEaoj1kqhwAEIFAIAiUnNDQs6b333nN5GBrupByE6upqe+KJJ5yYkPOvLZgXob81tEo5E3L+NbzJlxFADXPSCtqKNGj4kSIicvoVTVB+ht/kuKusZqnq1KmTW1hPSeBaL0NJ11rXQyJHzr6EjpK8NURKeSEafqUhUqpXC/ZJiKisNkVPlCuiqIauRWt4qC4JIA3/Ui6HhJUiFRJT6YRGqgjR3xJNEjCK1ui9BFBBNoQGTiZCAxtAaBTkcUsjEIAABMIkUFJCQ462hjq1a9fOTQerGafWWWcd22CDDdywKAkDTSebbpMA2XXXXZ1A0JAlCRE54xIFcsKHDRvm8jmUTL3TTjvZUUcd5WZ6CuZMqF4JCA2LkjDZeeed3YJ6Stbu06ePm/5LzrxuiupWPsa9997rZp9Svdttt51bHFAiQrkZPvqgsoqMSDgpl0RCZLfddrMTTjjBiQ6f0O6vS/Wne6k+f1x1SsgooqKEcr3XZwXZEBo4mQgNbAChUZDHLY1AAAIQCJOAfMeSmd5WFyvB8Nprr7mVt7X6tiIQGmI0evRol8egHIl0m0SK8hy0YJ6ERXBTvYoqaNiShmMp90PrYcj595t30uXAa1iTBI/KqW1FWHyOiM+P0Hly/DV8S+2qn5olS0OuFF1RPcFNAkVDvNSuVgTXzFkqq776tn15/V3fKyg0NEWuojGp9fj68r5HaOBkIjSwAYRG3h+tVAgBCECg0ATkO5aM0BDcoIPtf8HPB3TVK+dcL71PHXql4749fa73/liwfR1vyJauzdR6fLvBfuhYupf65iMaCA0Sg6OSGEw/Ss8WSQZPfZLzNwQgAIH4EJCPWXJCw98eXbwcar3C3rywUOQhKDB0vC5xoT5msqmc8i9q2/S52lH7vk0dq+2F0Cg9hw4nnnseRRtAaNT2VOc4BCAAgegTkJ9ZskLD355shIbKylnPdBPgdFuwjmAZP3RKYiA1KuJFQbr6gnWk+9yLjKDQ8fWl2yM0cDqj6HTSp9KzS4RGuic6xyAAAQjEg4B8zJISGvG4LX/1sj7xkOm1qB4JDYkHvYholJ6zhoPOPY+rDSA0Mn3SUw4CEIBA9AggNKJ3T/LeI91kLzYyERkqS0QDxzSujin9TpbtIjTy/pVAhRCAAAQKRgChUTDUxW3Ii41M9xIkfsE+nwxesCtg1ilmHGLWKWyAWacK9silIQhAAAJhEZDfqcmFtIREZWWlW9MtrLaC9a4T/IP30SMgw/BCQ4aRuh5IqD1GaOBkIjSwAYRGqI9ZKocABCBQKAISGloAuqqqaq3847D6gNAIi2ye6kVoJGv4CcOJuJ9xtQGGTuXpoU41EIAABIpEAKFRJPBRbhahgWMaV8eUfifLdhEaUf6moG8QgAAE6ieA0KifUcmVQGgky1nD+eZ+xtUGEBol9/XDBUMAAgkjgNBI2A3Nx+VIaCgvo127di55hxwNHNW4Oqr0O962i9DIxxOdOiAAAQgUj4ByfpWjUV1dTY5G8W5D9FpesGCBExpK3tH7gm0kg5MITDI4NkAyeMEeuTQEAQhAIEwC+rG6VatWTmgEF6wOs02SwcOkm6e6ERrx/iWYX/K5f0mwASIaeXqgUw0EIACBIhFAaBQJfNSbRWjgqCbBUeUa4m3HCI2of1PQPwhAAAJ1E0Bo1M2nZD9FaMTbQcPB5v4lwQYQGiX7FcSFQwACCSGA0EjIjcz3ZSA0cFST4KhyDfG2Y4RGvp/s1AcBCECgsAQQGoXlHZvWEBrxdtBwsLl/SbABhEZsvjLoKAQgAIG0BBAaabFwEKGBo5oER5VriLcdIzT4LoIABCAQbwIIjXjfv9B6j9CIt4OGg839S4INIDRCe8RTMQQgAIGCEEBoFARz/BpBaOCoJsFR5RribccIjfh9d9BjCEAAAkECCI0gDd7XEEBoxNtBw8Hm/iXBBhAaNY9k3kAAAhCIJQGERixvW/idRmjgqCbBUeUa4m3HCI3wn/W0AAEIQCBMAgiNMOnGuG6ERrwdNBxs7l8SbAChEeMvEboOAQhAwMwQGphBWgIIDRzVJDiqXEO87RihkfbxzEEIQAACsSGA0IjNrSpsRxEa8XbQcLC5f0mwAYRGYZ/7tAYBCEAg3wQQGvkmmpD6EBo4qklwVLmGeNsxQiMhXyhcBgQgULIE5s+fby1btrTq6mpbtmxZQTisU5BWaCQnAgiNeDtoONjcvyTYAEIjp8c4J0MAAhAoOoF58+Y5oVFVVYXQKPrdiFAHEBo4qklwVLmGeNsxQiNCXwp0BQIQgEADCCA0GgCtFE5BaMTbQcPB5v4lwQYQGqXwbcM1QgACSSaA0Ejy3c3h2hAaOKpJcFS5hnjbMUIjh4c4p0IAAhCIAAGERgRuQhS7gNCIt4OGg839S4INIDSi+O1AnyAAAQhkRmDVqlUmoVFWVmaVlZW2dOnSzE7MsRTJ4DkCLMTpCA0c1SQ4qlxDvO0YoVGIpz1tQAACEAiHgITG3LlzrUWLFk5oLFmyJJyGUmpFaKQAieKfCI14O2g42Ny/JNgAQiOK3w70CQIQgEBmBBAamXEqyVIIDRzVJDiqXEO87RihUZJfP1w0BCCQEAIIjYTcyDAuA6ERbwcNB5v7lwQbQGiE8XSnTghAAAKFIYDQKAznWLaC0MBRTYKjyjXE244RGrH8+qDTEIAABBwBhAaGUCsBhEa8HTQcbO5fEmwAoVHrI5oPIAABCESeAEIj8reoeB1EaOCoJsFR5RribccIjeJ9B9AyBCAAgVwJIDRyJZjg8xEa8XbQcLC5f0mwAYRGgr9kuDQIQCDxBBAaib/FDb9AhAaOahIcVa4h3naM0Gj4M5wzIQABCBSbAEKj2Hcgwu0jNOLtoOFgc/+SYAMIjQh/SdA1CEAAAvUQQGjUA6iUP0Zo4KgmwVHlGuJtxwiNUv4W4tohAIG4E0BoxP0Ohth/hEa8HTQcbO5fEmwAoRHiQ56qIQABCIRMAKGRIWCB8tvSpUttyZIl/s+19iq7cuVKW7Fihfnzli9fvlY51aNyflP51HL6e/Hixa4evdc5dW3BdtX2smXL6ipe52cIDRzVJDiqXEO87RihUedjmg8hAAEIRJqAfNG5c+daixYtrLKysk7/OZ8Xsk4+K8u0LjnyX375pT388MPWqVMnO+WUU+zcc8+1iRMn2h9//FFTjZzzRx991K6++mo777zz7IwzznDlb7/9dvv444+d419T2MwWLVpkL7/8sl111VXWoUMHO/HEE+3aa6+11157bS1hoHbGjx9vl19+uR177LF2+umn26233mpvvvlmsEr3ft68eTZhwgTr3LmznXDCCXbyySfb9ddfbx999JETJBIVXsioz88++6x169bNTj31VDvnnHNc2WnTptWU8WXXaqiWAwiNeDtoONjcvyTYAEKjlgc0hyEAAQjEgEBJCQ1FA+T8SxCcffbZ1rJlS9tkk01s4MCB9ssvv9Tcrt9++80JgQsvvNAuvfRS57x37drVjjnmGKuurrZ33nmnpqyc/XHjxtlll11mRx99tBMkV1xxhR1//PHuvEmTJq1RduzYsXb++edb27ZtrWPHjnbxxRc7EXHJJZfYhx9+WFN24cKFTgBJXKgu9UXi4aSTTnJtSEBI4OgGzp4924kXCQyJItWlNtSf7t27m8o2ZENo4KgmwVHlGuJtxwiNhjy9OQcCEIBANAjIT9UP52VlZS6iUd/InHz1uigRDQ09+uSTT+y5554zOfx33nmnbbTRRjZo0CD7/fff3bUp6qH3+mzUqFHO+f/222/tgw8+MAmI/fff3+66666aSMX8+fOdGGnVqpWLYnzzzTf23Xff2Q033GBt2rRx58hhlyD5+eefrUePHnbAAQfY3Xff7QSAohMKJemYPlN4SduUKVNcRGXHHXe0Xr162aeffuoEjiIlO+20k/Xp08d++uknJzQUZVFk5pBDDrH+/fu7soqQSFBtu+227tisWbOyvncIjXg7aDjY3L8k2ABCI+tHNydAAAIQiBQBCQ39uF9VVZXTkP5sLqooQkMdDA4fev75523TTTe1AQMG1AiN2i5C573//vvWvHlzJx4U9VCehgSBxp1pKJYEid+++uorF4U4+OCDneMvofHMM8+4oVUaLiWBIlGj7Y033rCLLrrI9thjD5s+fbobFqWhWxpaddZZZ9nMmTN9tTZ58mQnYI466ih76623nIBRRGXLLbd04kbt+k1Rmt13391FTVRWW/D6fbna9hIairzIMPS+YNvKVWYrzZTVMnnqAutQ3suad+tje1YMsiblgy0JzhPXgAjABjKzAYRGwZ68NAQBCEAgFAIlJTSCBCU01ltvPevXr59JOAQ3LwKCx1555RUnNJS7oQTtP//80+VFSEzcdNNNayR2a1iTyu277772+OOPuwjI/fff74ZBKXKhnArfxvfff2+33HKLG8YlQaCw0oMPPugEjPJJfJRDffn6669dvTvvvLMbsjVnzhw39GvjjTd2fQnmmvz6668uB+S0006zp59+OiuRoeiPrk9RmYqKCieMJJYKsiE0EFPdM3NCcdaTzwmhUZCnLo1AAAIQCI0AQiON0JCjHdw0nMrnaQwZMsR9JLEwdOhQ54zfd999weJORNxzzz0uVKShWYpgaDiV8i3uuOOONcrqBvTu3dv++c9/ulwLCYubb77ZDafS8C2d67cff/zR7r33Xttiiy1szJgxbjhWz5493RCw9957ryYkpciFcjeUSK7IyIgRI3wVte4lniRydK5EkCIpitYowVx9LNiG0EBoIDSwgdU2gNAo2JOXhiAAAQiEQgChkUZoeNISHBqO9NBDDznRcNttt7nhUvpcQ6cUDdGv/hIaKisnXS+JEAmC1q1bu3wKOf2KcEho6Hhwk4Ov4Vt///vfnXhQVOK6666zffbZx0UiFFnw24wZM1xfGjVqZIrIKE9DwkW5Jp9//nlN1EJ9kGBRXomSwiWIatvUb+V0qB7li1x55ZV2zTXXuHO33nprhAZOL04vNlA0G0Bo1Pbk5jgEIACBeBDQD+bK0dCESvKPC7EVLUcjeHF1DZ3y5ZQzoWRsCYYLLrjAJWT7aIfyFh555BE7/PDDXYK44PlogMSDRIkStCVGpObCiGgoDyOTiMbw4cP9Ja21l2DS8C7NaqVZAWQM7dq1c+832GADJzSCw7fWqiDfB4hoFM2pYyhS8ocixe0eIzTy/YClPghAAAKFJaAfzDVpkoSG96HD7kEkhMbo0aNrcjSUzxDcJBQ0XKpv377O6W7fvr2LGEiVSUxok9BQgrfP0fDH9ZlyNBQV2G+//eyJJ55wQ5IeeOABF9Hwiq62HA05/iorAaNIStDJV46G6lWOxgsvvGDBHA3NpqXIid80y5USz/VSP+vadF0yBO31Uv9VlwxDU+SqD8Hrq6uunD9DaCA0iCBgAwydyvlRSgUQgAAEokCgZIWGHPP111/fRRyU+6D8BL0kAARFC/QpkqGZnzSFrISFPguW07SzEhpaD0NJ3drkkGuaW61noQiBxIHOk7DR0Ckt6Cdn3g+1ev31190MVZp1SsO0VL8iEBrypHUxFLWQ8FF55WFoUUCt6fH222+7tiQ4ttpqK5fX8cMPP9TYlBYnbNy4sVsTRDNmZbvpejUsTLNOSXgUbENo4GQiNLABhEbBHrk0BAEIQCBMAiUlNOQw65d+CQElYK+77ro1K3Nr/Qv9iq9f77VatxKhtTK4BImiG8qd0OcC5qMReq+Ea00De+ONN9rUqVPddLZawfvII490w478bE2qXwnlinJoBiqV1ToamtVJ62go0qH1LlRewkYOvl8HQ+t/KI9C0YxddtnFRVp0Hdr0mfqpqXc1lEv1avYqnb/DDju4WanU/2w3LRrohYaETsE2hAZOJkIDG0BoFOyRS0MQgAAEwiRQMkJD4kARAy3Gp2iDohX/8z//4xbh0/AiTVGr6IDKaGE+rRp+4IEHurKaeUmrf2sYkda48CuJKy9DQkQriGt2J9WrXA4Ntbr88stdff7mKSKhhQIlTLTCt8oqEqIIhd5rUUDVJ6GhIVFaH0OfnXLKKe5ziQmdpzU3FK3QECttEkYTJkwwvzK41vTQSuLqg8SGksR17V7w+P7Ut5fQECPVgdBg7H7cxvbT32TYLDka9T2p+RwCEIBAtAmUlNCYNm2am1lJQ480NElJz8qFOOKII9wsS8qn0FAnDVkKllGugpKktVCeZpjS4nwSBXLAFemQo69og5z9E044wc3cNHHiRBf9CN5+wfaRCQ2j0hoXmspWi/b5zedCSGyMHz/eRUFUTn1SQrmGQaWKBgkJTXer6Ij6oMRurc2hfvroS+o5vr3a9giNZDhqONzcxzjbAEKjtic0xyEAAQjEg0DJCA1/OxRZCDrfigzoWHDT33L4Vc7PJqXPvQjQex990Hs58cHPfP2+zuBn/phyMYLOf+o5vlxwX1cZtaGX6gzW689P1wf/Wbo9QgMHNc4OKn1Phv0iNNI9nTkGAQhAID4ESk5opN4a76AHj/tj3nFP56SnOxasI/g+XdlUcRIsH4X3CI1kOGo43NzHONsAQiMK3wb0AQIQgEDDCZS80EiHTsIg9ZWuXC7HgvXnUk9Y5yI0cFDj7KDS92TYL0IjrCc89UIAAhAoDAGERhrOQRHg36cpltMhX6/2UdwQGslw1HC4uY9xtgGERhS/HegTBCAAgcwJIDTSsAqKAP8+TbGcDvl6ERopGJnelqlNmd4WG2B625QHI39CAAIQiCcBhEaa+xYUAf59mmI5HfL1IjRSMCI0cDIRGtgAQiPlwcifEIAABOJJAKERz/sWeq8ZOsWQmzgPuaHvybBfhk6F/qinAQhAAAKhEkBohIo3vpUjNJLhqOFwcx/jbAMIjfh+h9BzCEAAAiKA0MAO0hJAaOCgxtlBpe/JsF+ERtrHMwchAAEIxIbA/Pnz3aLX1dXVbg26QnR8nUI0Qhu5EUBoJMNRw+HmPsbZBhAauT3HORsCEIBAsQnMmzfPCY2qqiqERrFvRpTaR2jgoMbZQaXvybBfhEaUvhXoCwQgAIHsCSA0smdWEmcsWrTI2rRpYwp1LVmypHDXzKxTzDjErFPYALNOFe6ZS0sQgAAEQiSA0AgRbpyrltBo27YtQgOnF6cXGyiaDRDRiPO3CH2HAAQgYIbQwArSEkBoJGPoCUOIuI9xtgGERtrHMwchAAEIxIKA1oqT0CgrK7PKykpbunRpQfpNMnhBMOfWCEIDBzXODip9T4b9IjRye45zNgQgAIFiEpDQmDt3rrVo0cIJjUINxUdoFPOuZ9g2QiMZjhoON/cxzjaA0MjwgU0xCEAAAhEkgNCI4E2JSpcQGjiocXZQ6Xsy7BehEZVvBPoBAQhAIHsCCI3smZXMGQiNZDhqONzcxzjbAEKjZL5yuFAIQCCBBBAaCbyp+bokhAYOapwdVPqeDPtFaOTriU49EIAABApPAKFReOaxaRGhkQxHDYeb+xhnG0BoxOYrg45CAAIQWIsAQmMtJBzwBBAaOKhxdlDpezLsF6Hhn8jsIQABCMSPAEIjfvesYD1GaCTDUcPh5j7G2QYQGgV75NMQBCAAgbwTQGjkHWlyKkRo4KDG2UGl78mwX4RGcr5TuBIIQKD0CCA0Su+eZ3zFCI1kOGo43NzHONsAQiPjRzYFIQABCESOAEIjcrckOh1CaOCgxtlBpe/JsF+ERnS+E+gJBCAAgWwJIDSyJVZC5REayXDUcLi5j3G2AYRGCX3pcKkQgEDiCCA0EndL83dBCA0c1Dg7qPQ9GfaL0MjfM52aIAABCBSaAEKj0MRj1B5CIxmOGg439zHONoDQiNGXBl2FAAQgkEIAoZEChD//S2DhwoXWpk0bq66utsWLF//3g7DfrVxlttJsuZlNnrrAOpT3subd+tieFYOsSflgi7PTRN9x+rGB7GwAoRH2A5f6IQABCIRHQEJj3rx5VlZWZpWVlbZ06dLwGgvUvE7gPW8jSkBCo3Xr1lZVVYXQ6J6dc4QzCS9sID82gNCIXaRkJQAAIABJREFU6BcE3YIABCCQIQEJjZYtWzp/ctmyZRmelVsxhEZu/ApyNkIjP44SDiccsYGG2wBCoyCPexqBAAQgEBoBhEZoaONdMUKj4c4RjiXssIH82ABCI97fI/QeAhCAAEIDG0hLAKGRH0cJhxOO2EDDbQChkfbxzEEIQAACsSGA0IjNrSpsRxcsWOByNJS8oxmoCraRDE7COzkx2MBqG0BoFOzJS0MQgAAEQiEwf/58l6OhyYXI0QgFcTwrldBo1aqVmyVAQkMzBxRkQ2jgZCI0sAGERkEetzQCAQhAIGwCf/75p/MnJTSWL9ecouFvJIOHzzjnFhAaDR/uwVAZ2GED+bEBIho5P8qpAAIQgEBRCSA0ioo/uo0jNPLjKOFwwhEbaLgNIDSi+x1BzyAAAQhkQgChkQmlEiyD0Gi4c4RjCTtsID82gNAowS8fLhkCEEgUAYRGom5n/i4GoZEfRwmHE47YQMNtAKGRv2c6NUEAAhAoBgGERjGox6BNhEbDnSMcS9hhA/mxAYRGDL4s6CIEIACBOgggNOqAk6+PCjZjU746bGYIjfw4SjiccMQGGm4DCI08PtSpCgIQgEARCJSU0FiyZElGiFOFweLFi9eY+3flypWWemzFihVu2i4/dZcvowb1PnVbunTpGofUpuYXTp1jWOfqM71Ud7pr0GfBNvS3+qN9QzeERsOdIxxL2GED+bEBhEZDn+CcBwEIQCAaBEpKaDQUuRz2dK/66vOiI+jwe+Ggz4LiQHWligzfpm9H5XVesD7/WXCvz1UuF7GB0MiPo4TDCUdsoOE2gNAIPtl5DwEIQCB+BBAaId+zukSBFxJ+n2tXfD3aS5T4l/7OdkNoNNw5wrGEHTaQHxtAaGT75KY8BCAAgWgRKBmhIWd79uzZNnXqVHv77bdt4sSJNnbsWPv222/XGo6ksjNmzLD333/fJkyYYOPGjbO33nrLfv75Zxcp8LdQEQNtv/zyi6tz/Pjxrs433njDfvzxRxeh8FENlZPjP3PmTFf2hRdeMJV/77337KeffloruqHyvqzaf/HFF11/fvvtN9dm8B+18cUXX9ikSZNs9OjRpro/+OADd70aoqV2s90QGvlxlHA44YgNNNwGEBrZPrkpDwEIQCBaBEpGaMjhfu2116yystJatGhhW221lW200UY2cOBAJyqCt2XhwoXWv39/a9++vW2//fa2xRZbuOXTdUzCxG/K05DjP2DAADvyyCNt1113tW233dbatWtnvXv3tu+//94NX/LlfdlDDz3UttlmG9t5553t+OOPt0GDBtUMm5LI0Uvi4+GHH7bWrVu7OnfccUc79thjrV+/fjZ37tya4VMSOxI611xzjR188MGu3l122cVOOukke/75513//DAq349M9giNhjtHOJawwwbyYwMIjUye1pSBAAQgEF0CJSU09It/z5497e6777bOnTvbeuut55z833//3d0h/fIv8SCRcNhhh9nZZ59tjz76qD377LN28skn2z777OPOlRMuMaDtvvvucyLjjDPOcOXk3J966qnWsmVLu/322100QSJn/vz59sADD5hExjnnnOMiKqpb50n4SPD4yIOEg/ogcVFdXW1PP/20Ez7qw3bbbeciF3PmzHHRla+//tpuvvlmJ4gqKipcWQkXCZi99trLxowZY7rJqcnnqSbpBY726oc3DAmzRYsW1Vxv6nl5/3vlKrOVZsvNbPLUBdahvJc179bH9qwYZE3KBxsOXH4cODjCMQ42gNDI+xOWCiEAAQgUlID3J+XPBkf5hNmJdcKsvLa65UDLOf/uu+9cVEJRiPXXX9+GDBlis2bNcqf56IAiEqeddpqNGjXKfaYhVxripKjFBRdcYO+++66LVGh4lKIeJ554oj3zzDMusqCohZx7CQhFFRSZkKOuoVfnn3++q0NDsnRMZQcPHuzOP+qoo9xQJ3VEQ6rUzhFHHOGGQEkI/fDDD66sxM4VV1xh06ZNc32QeDrkkEPslFNOqREgGnKloVYSKtdee6199tlnLmLixVFtjILHiWjgiMbBEaWPybZThEbwqcx7CEAAAvEjUDJCw98aOdv6df/xxx+3f/zjH27Yk3Iv9Cu+nP9PPvnEtt56a7vqqqtcPoc/TyKkY8eO1qFDBxsxYoSLfMjJ33fffZ3jL9GgOjRzlIRJjx49XFREw7V0TIJGUYZOnTrVDKfS8Y8//tiV1RAt5Y+ofxqipYjIlVdeWTNMSsdV9rzzznNDpJRnoujLyJEjXX81pEqixm8SVYqUKHoi4ZLthtBItgOHg879jYMNIDSyfXJTHgIQgEC0CGhEj3xaRTRSZ1cNq6dFiWj4i1HYRmtRKFrx97//3Tn1ihbouGC8+uqrtvHGG9uDDz7okrH9eRIREh/HHHOM3XHHHTZv3jwnHvbff3+75ZZbaoYWqR6JgjvvvNNFGoYNG+bq1jCq4447zm666Sb3ucrpJYHSq1cv23TTTV3bOnbXXXe5YVp9+vRxffJ9UM7HbbfdZjvssINLUlckRnkcm2yyiSkBXX1S29okmi666CI74YQTnKjydaTb+774vQzBK1CGTuGQxsEhpY/JtFOERronNscgAAEIxIeAfFMJjaqqqtIQGhIM2pRLoaFTyo1QREObksA1y9Rmm23mIgWpi+Mpt+Poo492CeWKWtx///0uaqG8Dznpcu4VZdB7CRUlcisvQ8nbUnJK5r733nvd565BMxddGT58uG2wwQZutihFEiRGmjVr5vqgPvlNgkjCQqJEeSMSHhIwSmrXLFNq22+6sRIJGtqlaIoEiJ8ly5fRXjx+/fVXmz59upu5SkOyNIPVl19+6SInyvtQH7yACZ4byntyNMhD6Z5MpxkxlP19RWiE8pSlUghAAAIFI1ByQsOTVQ6DIhd9+/atERpy8p966iknQJQ7EQzxyNFWpEFRCeU9yPmWwCgrK3OCQ058sPxDDz3kPlMZRR7k9Cu6IKEQdNoVOVDUQ+JBuR2Kqtx44432n//8x5544ok1pt5VkriiHIpgKCdEwkPiR9ehYVU+4Vv1K69DIkHCSMnhtW1qX6LowAMPtEaNGrm6Vb9m2vrb3/5m3bp1c9GNYJ9rqysvxxEaCA2EBjaw2gYQGnl5qlIJBCAAgaIRKDmhoV/w5TTLqVckQLkNPqIhR/2ll15ys1F5gRC8M5rdSVEJDZ3SpkiEppTVcCYfKfHlNXTqoIMOcgncEho6V8nhOu43H01Q1ENiQXkXEjsaiqXcDwkE/e03RTAkQrbccksXkZGYkPCQQJg8ebITBL4fytFQTolmwNKsVbVtir4ogqEIj/I9HnvssZpX48aNnUAKCqja6snbcYQGTiZCAxtAaOTtkUpFEIAABIpJoCSFhoBLaGjo1COPPOJmi/I3QQnZmkJWEYgpU6a4w3LGJUIuvfRSlwyuCIQ25XNITJSXl9fMXKXjcvK7d+9uyt/QUCwNadLwJQmNLl26OKHjIwRqT8OqlAyu4UoqK/HQpk0blxMSHDqlRPWzzjrL1askc/VJERj1V6JEEQ/Vq5eGQ2lM3IUXXmivvPKK62+6oVPuAzMXjVHbPiqioWFKJhcHhEb2Qz4YJgMzbCB3GyCi4Z/Q7CEAAQjEj4D8UQkNjf6RP+l9zLCvpGjJ4HK05bgrl+LJJ590s05p2NBXX33ljisnQw66Er41Za1ml/rjjz/cMCRFDDS9raao1XttmuVJM0lpalmtyK1pZfVSdECiQlPWfvPNN67sO++842aMUh2aKlfgNWxJEQQNqVIuhdqWqPHT20oofPjhhy7HQyJCAkfT21522WVueltVrKl2JQjOPPNMJygkENSv5557ziWNX3/99W42Kz+0ywsc16mUf3w0RId13apXIioodlJOyf+fRDT4NZuIBjZARCP/z1ZqhAAEIFBgAvI5lafsf7hOzX0OqztFExoahiQnX9PSanjSuuuu66amVS6Ehi0poiChoMXytDaFogcSAop+aF2L5s2bu5wIiQTvlCtHQknfEhZagE/T5moNDkUk/BArD1pDrPbee2+3xoYiHcqzkEBQvVrXw6/4rbU+dK7yNrSwoOpU5EWCRjNOSYgol0Ob+qvkca0GrsiIhklpOJjW4GjatKmNHTvWCSupSN3gdEIjVYTobwkWCR0JDQmidOeFYiAIDZxMhAY2gNAI5fFKpRCAAAQKScD7vyUhNORov/7666bF+DSrlFYFX2edddzwqQ033NCpLTnz2uTEa/iSIhLbbrutK3/44Ye7GaqUJxF0uiU6FGlQ0rXKSggoIqK6fO6HohTadK5EgEJIu+22mxMHimQoIV0iw4sXCYLPP//c7rnnHidCNt98c1ev1vDQLFkamuXLShSoHeVuqI/bbLONG4alPkh0+MUIXQfMXN/V/9SX6vPHVKeuSwJKoS5FgAq2ITRwMhEa2ABCo2CPXBqCAAQgEBYB+ZUlE9HQxerXf+U2aFiRXhriNG7cODfs6eWXX3ZTvMrhljDQjE4aIqXZqRQVkEhR8rV38IM3RUOV9LnKqT5FR7zICJaTA691M7Qy+AsvvODKauiThkWlbr4P6peGZakfOk/Dq1RPcFMOhYZ/KRdD16UIjOpVVELXHdy8mKhr74WGojKa9xihkftYc8brwxAbyN4GyNEIPr15DwEIQCBeBORrlozQ0K0JOtf+F/xi3TIJCQmEVNHQkP4Er8u/T61Hx3XN/rp9uXR7hEb2DhFOJMywgfzbAEIj9UnO3xCAAATiQ0A+ZskJDX97dPFyqOtz9OWYpyuj8+vLnte56TbVJ5GhIVKqw9fvy/u+pZ6r47Vt/ty6Ppe4UVsqq7pqe6mMhk4R0ci/44QzClNsIHMbQGjU9kTnOAQgAIHoE5CfGXuhUZfznXoL0pX1Tn6wbPCYnHIlkcvx9sd1TK90szHJmdemvc7z5/j6dZ5Ehvrioxq+X2pDn6duOiZBopcvGyyjY+qLrzf4md7rc/XDixr9XddL7SE0MneGcBxhhQ2EYwMIjdSnOX9DAAIQiA8B+ZqRFRrpHG6hVaeDn6U68mHgD7YZfF9XW76c9qlb8FjwffC6gsf1Xp/pFTwerLeuz3WOP8/vg+emvlddSohv27atm8lK62sUbCMZnERgksGxAZLBC/bIpSEIQAACYRGQzxkZoaHOSDRMnDjRraYtRzfVKdbfcno185LKySHWOTqe+goLWib1NrQvOk9b6vmpf6frQ7BMus+zOaa6EBrh/ELLL99wxQYytwEiGtk8uSkLAQhAIFoE5E9GSmgIz/3332/nnHOO65hEhP+lXnsNNdJCclp74t5773Wf6VjQyfbvi4na98HvM+2LL1/fPl19wXPSfZ7NMdWF0MjcGcJxhBU2EI4NIDSyeXJTFgIQgEC0CMifjITQ8B1RTsO1117rhuxoylhNz6oO+peml9W0rVqHQgvuaUtaRCMoGGp7n86MgmXTfZ7NMdWF0AjHccIhhSs2kLkNIDSyeXJTFgIQgEC0CMifjITQ0OxLWldCK1537NjRDj30UHvyySdNq2e/9NJL7qXVvLVGxXXXXWetWrVyi9x5nEEn27/3nxVj7/vg95n2wZevb5+uvuA56T7P5pjq0mrg5Ghk7hDhPMIKG8i/DSA0snlyUxYCEIBAtAjIn4yE0NAidFpR+//+7/9s3XXXdS+t3P2Pf/zD/vWvf5lW7m7UqJFtscUWbpXuiy++2N55550amkEn27+v+ZA3DSIgoaFV1Hv06OGm4W1QJQ05iWRwEoFJBscGSAZvyNOTcyAAAQhEikBkhIZWnh4+fLj16dPH2rdvb02bNrWePXtar169XOTikUcesUGDBtmwYcPcKtmffvqpzZkzxw2bUjRE+RteYPh9pEjHsDMaxobQyP8vtPzqDVNsIHMbIKIRwy8PugwBCEBgNYHICA11RMJBLw2fGjp0qM2aNcut5aBcAf+S86stNUHci4vgnrucGwEiGpk7QziOsMIGwrEBhEZuz3HOhgAEIFBMAvLLtS6bcqsrKyvdem6F6M866RpRUrff1DFFKmbMmGHfffedTZ8+3b788kv74osvbNq0aTZlyhQnRHx59vknQI5GOI4TDilcsYHMbQChkf9nOzVCAAIQKCQBCY2WLVtaVVWVW1i6EG2nFRq+YQmO3377zSWA33HHHXb99dfb1Vdf7RaOq6iosM6dO1t5eblbR0OCRNPbsuWfAEIjc2cIxxFW2EA4NoDQyP+znRohAAEIFJJAZITG0qVLnWj45JNPXALypptuarvssovtscce1qRJE9trr71s7733tmbNmrlX7969HSeJDbb8E0BohOM44ZDCFRvI3AYQGvl/tlMjBCAAgUISiIzQ8BetVb+POOIIN83t6NGjXWTj5ZdftldeecW9Xn31VRfN+P777/0p7EMggNDI3BnCcYQVNhCODSA0Qni4UyUEIACBAhKInNC46667rHXr1m7NDOVppNuUDM4WLoHggn2aFaxgG9PbMrUp09tiA0xvW7BHLg1BAAIQCJOA/EnlaFRXVxc3R8OLh759+9oZZ5xhb7/9dq3Z6X7WqTDBlHrdUqBt2rRxyTsIjXB+reVXcLhiA3XbABGNUv8m4vohAIG4E9AIGS20LaFRqLzqOpPBtRq4psC6++677eeffzZNaStH178U5dAqgz7aQY5GOCaI0KjbAcJBhA82EL4NIDTCeb5TKwQgAIFCEYiM0JCQkGhQLsYFF1xgjRs3tksvvdRuvfVWt5Df4MGDTfkbSgLXQn5aGVwJ5MuWLSsUq5JqB6ERvhOFowpjbKBuG0BolNTXDhcLAQgkkEBkhIZnq8X69tlnH9tmm21st912s3333dcOOeQQO/zww+3QQw917/X3gAEDalYD9+eyzx8BhEbdDhAOInywgfBtAKGRv2c6NUEAAhAoBoHICY13333X7rvvPrv//vvtnnvuWevVs2dPd0w5HGzhEUBohO9E4ajCGBuo2wYQGuE946kZAhCAQCEIREZo+GTwTC6avIxMKOVWBqFRtwOEgwgfbCB8G0Bo5PYc52wIQAACxSYQGaGxePFix0KCQ52aNWuWSwSfM2eOzZ492xYuXOiGSs2cOdOtHO6TwYsNMKntIzTCd6JwVGGMDdRtAwiNpH7DcF0QgECpEIiM0JDA0LRXWqSvc+fOdvHFF7uXEsK7du3qplnV1FgdO3a0bt26mRbx05ZNJKRUbmo+rhOhUbcDhIMIH2wgfBtAaOTjaU4dEIAABIpHIDJCQwg0JGrUqFF20kkn2fHHH2/t27d370888US3Wvh+++1nO+ywg7Vt29aeeOKJ4lErgZYRGuE7UTiqMMYG6rYBhEYJfNlwiRCAQKIJREpoiPSUKVOc2Hj88cfd/sknn3R7TW+rVcMPOuggO+200+z5559P9I0p9sUhNOp2gHAQ4YMNhG8DCI1ifxPQPgQgAIHcCEROaOhyakv2Vp6GZqK6/PLL7bnnnmPYVG73vs6zERrhO1E4qjDGBuq2AYRGnY9pPoQABCAQeQKRFBrK1ZDY0IJ8wU0J4F999ZVpKNW9997rPqpNlATP4332BBAadTtAOIjwwQbCtwGERvbPbs6AAAQgECUCkRAatYkFJXoHk731fty4cdayZUu78847o8QxcX1BaITvROGowhgbqNsGEBqJ+2rhgiAAgRIjEAmhEWT+0UcfWd++fW3gwIHWu3dvtwK43vfp08duvfVWa926tR133HGmHA5FPlKjHsG6eN9wAgiNuh0gHET4YAPh2wBCo+HPcM6EAAQgEAUCkRMaTz/9tJ1yyil26qmnuiFSJ598snuvv/W+Q4cOToh8+eWXtmzZMvPrb0QBZpL6gNAI34nCUYUxNlC3DSA0kvStwrVAAAKlSGD+/PluJJKWqJDfXohtnboa0foYV111lVs3o7Ky0ioqKtz7a665xm6//XaXBD5jxgzXWSIadZHM7TOERt0OEA4ifLCB8G0AoZHbc5yzIQABCBSbgPxJpTxUVVUVX2goV2PFihWuI0r81kt/66X3mnVKm/6WyFDORqHUUbFvVKHbR2iE70ThqMIYG6jbBhAahX7y0x4EIACB/BKIlNCQgPCb3s+cOdPNMvXhhx/ap59+anPnzvUfO6ERLF/zAW/yQgChUbcDhIMIH2wgfBtAaOTlcU4lEIAABIpGIDJCQ9EMRSkWLVrkFu3TrFJK/D744IPtgAMOsAMPPNCtFH733Xeb8jP8bFR+XzSCCW0YoRG+E4WjCmNsoG4bQGgk9AuGy4IABEqGQGSEhohLaHzyyScmMdGmTRs7/vjjrVOnTi5P44orrrDTTz/d2rVr59bQ+OKLL9xNYuhUOLaK0KjbAcJBhA82EL4NIDTCeb5TKwQgAIFCEFAQQf5kWVmZKe+6UDPF1pkMPmLECDez1Pnnn2+TJk2yn376yTQ11qxZs+z11183Hdf0to8++qhjREQjHFNBaITvROGowhgbqNsGEBrhPN+pFQIQgEAhCEhoKO2hRYsWTmgo37oQW51CQ9EMRS0U2ZCI8Anieq/ohfI19DkL9oV7qxAadTtAOIjwwQbCtwGERrjPeWqHAAQgECaBSAqNG2+80Smf6dOnrxViUf7Gjz/+6HI17rrrLseGiEY4JoLQCN+JwlGFMTZQtw0gNMJ5vlMrBCAAgUIQiKTQ6NWrlxvLJSERnGVKnf3jjz9c/kbbtm3t4YcfZuapEK0EoVG3A4SDCB9sIHwbQGiE+JCnaghAAAIhE4iU0FBntL311lvWvXt3t7jHdddd51YBHzx4sPXu3dst5HfYYYfZ5Zdfbq+++qobWsUUt+FYCUIjfCcKRxXG2EDdNoDQCOf5Tq0QgAAECkEgkkJDid9KAj/zzDPtkEMOsSOOOMIlfysv46CDDrKTTz7ZxowZ46IbXpwUAlaptYHQqNsBwkGEDzYQvg0gNErtm4frhQAEkkQgUkLDg9UUt3PmzLEffvjBHn/8cdMQquuvv97t9bdyNPT5/PnzbfHixf409nkmIL6aYlhLxis3pmDbylVmK82Wm9nkqQusQ3kva96tj+1ZMcialA82nLvwnTsYwzgqNoDQKNiTl4YgAAEI5J1AJIXGO++840TFoEGD7Pfff3fz7ypXQ7+w629Nf6thVJMnTzYNm9KcvIWObATbkzAK/h18X9sd8wnsfl9bOR1XGb3UTn3DxDQrl8qoD5pCLBchpsiScmGqq6tzqqeua0v7GUIDMdUdRz8qjn6x+4HQSPuU5CAEIACBWBCQPxqZ6W3VGTnTffr0sbPPPtsNn5KjrOP+pb81rKpjx46mvA054Pos35vqVDs33XSTnXvuuW64lhYLvP/+++3zzz93zfl2tdeUu/fcc4/rt9b46NKliz377LNueFewb3L+Vfa2226zk046ya0X0rVrV3vyySedMPBlVaccfS1KqDyVc845x0455RRX7zPPPOM4BctKXHz00Ucu8qOhZSeccIJdeumlNnHiRCfQfNls9ggNnN1iO5m0jw0iNLJ5alMWAhCAQLQIyJ+NjNAQGjnMt9xyix1zzDH21VdfuXUz1En/UvTi448/duto3H777aHQVFTg/fffdyuSn3rqqSYhUFFR4QSH+nXrrbc6AeAbnzJlit1www121FFH2QUXXOAS1SUKtLDgwIED15ii97333rNrr73W5Z3o8wsvvNCJDQmrp556qkZASJC8++67dtlll9mRRx5p5513nhNXp512mqlP48ePd8PH1AeJLwkSraAu8SJRonq1qvoZZ5zhxIaGQWW7ITRw8nD0sYFi2wBCI9snN+UhAAEIRIdA5ISG0OjXfiWAT506tWZYlBcacsDffvttO/TQQ+3mm28OhaQcbPVBbVx11VXOiZ8xY4a9+eabThTIgR85cmRN2w899JAdffTRzsGXOPj+++9t6NChTnjo+Ndff+2EkgTBAw88YIcffrgTBR988IFJpGg6X0VB2rdv7/JPFNX5+eef3fS9//73v+3KK690K6JrAcN+/fq5BHkJDwkubSrbt29f23nnnV30Q0PKFHVRZGjXXXd11+CjMDWdzuANQgMns9hOJu1jgwiNDB7WFIEABCAQUQKREhqKJGgolBx3/YqvoVHKyZC40Gfaz5w50znQcuDloIexzZ4924mGE0880UaNGuWiLGpfLy0mqCiB1vDw21lnneWGNY0bN84dklBQ8rQS2Pfdd193HYrUfPfdd3bJJZe4xQjl+KucNl3TfffdZ5tssom99NJLLsn9jTfesIsuusiaNm1qv/zyi2/KiR7Vu9VWW5naEy9NB6xoisSZIjF+03Wob4rCPPbYY/5wxnsvNHr06LHGsK6MK2hoQXI0yNEgRwMbWG0DCI2GPkg5DwIQgEDxCURKaKgz2l555RU39EdOun7B1y/006ZNc3v9cr/HHnu4XIiXX345FIJysJVvUVZW5nI0JHa0aSiXhiYpZ0OCQMJHM2C1atXKiQJ9HtyGDx/uohTK11CdyvnQkCrlUKRuEg2bbbaZ3XHHHU6QaBiV2tIwKSXB+03i4bnnnrONN97YhgwZ4j7TuTvuuKMb3qUhVH5bsGCBE0SKzEjIZLNJGKnPftYpRWMKtiE0cDIRGtgAQqNgj1waggAEIBAWgUgJDf3Crw7JsVcitSIHBxxwgB144IFuuJD2+lvra7zwwgsu0Vq/6Od7k5Ot4U4amqXhTBISGi4l4aH8Bw2bUhRC+SIazqQFBK+44gr79ddf1+jKE0884cSCrmPWrFku4VtREgkVRUeCm3Iutt9+e5e/8eWXX9qwYcNcm8oPkWDwmyIlGp614YYbumFYv/32m40ePdoaNWpk9957r/3000++qGtDfZVg01Cw+jYfOVI5cdU1tmjRwgkYprdlCAvDmLCBYtgAEY36ntx8DgEIQCC6BCIlNHxEQ7g0XEiL8mnWJyVEa5Yp7eX8K5rwxx9/1CSK9ItJAAAgAElEQVSI5xuvhIYiKN26dXNDkhRV0Erk2ktw3H333SYxIGdceRbKuSgvL19jhil9pqiEzlEUQ+JJw5ckNJQwnhohkNDYZZdd7JprrnHDozRsTHkbGra0cOHCmkuUuPn000+d0FA/lDsioaEIh4aSBYdZqQ9ad2SvvfaqM59FAk/Dr7ReidbMUF6KEtYVidl6663dtQX7UNOZsN4Q0eDXbCIa2AARjbCesNQLAQhAoGAE5NtrZI5+rK+srFxjgqQwO7FOJpX7vAhFCpTfICfa5zWo40Fhkkl9mZbxQ6fk6Mvx10xRihQoGVw5D5o6Vmt8aPvss8+c0FDkQdGF4KYpazVMShEYCSMJDx/R8Nfhy0+YMMF22GEHl8ydTURDbHKNaCiSoeiLIi0tW7Z0ERzle8goNthgAxfRQGjwa3Yxfs2mTeyOiIb/lmAPAQhAIJ4EJDTkX+rH7NQRPWFdUVqhEZZwyPYiJBj23ntvF8UIJldLHGixQAmNiy++2FWrGabkkCtKEcyP0Ica/qSkdUViBFlrWijvQmJD0YbgpuiNhj/5HI2nn37aRULqytFQ1EP1KkdDIkVT8CoS47dscjQkrlRee70UcVE+iIaNSYEiNHD4cPqxgWLYAELDP9HZQwACEIgngcgIjajgU5RAU8VqwTutUh4UQF5oaPYobRrKpBwMiQdFFvymc7Texn777Wf9+/d3M1dJlGh9i4MOOshNa6shWtoUqenZs6dLBpcY0ZoXGpLVvXt3a9as2RrDoZRwrlmntthiCxs7dqyrV5EWRSC0irfvr9rXAimKUijPxM865dv0/axrL8HRunVrp0AV9SjYxtAphs0wdAobYOhUwR65NAQBCEAgTAIIjRS6+iVfC+MpzKMkaq11odmlNIRKi+xp6l3NSuU3zYyl3A0liivXQYnkmhFK08rq5dfRkCjR7E8SGhIqSupWBEIzaXXo0MHlg2iImESChlo98sgjtt1227mcCU13G1xHQwv8aSVwRUbUN+VnKKohEaI+aPVx9UvraChUpbyObDcJDUU0dL76XrANoYGTidDABhAaBXvk0hAEIACBMAkgNFLoatiQ8is0HOr00093Q5+UDC5xIEFx3XXXufUq/PAniQVFLyQqNNRJieuKcEh4SCwEowiaqlcJ3hpuJdGitTI0FEtl1WZwiJIEjpLMtUaGFujTcC1FT9RGcNYtnaPFDVWXX5FcfVB+iASMhmXpJme7SWj4MXUIDYbNFGPYDG1idwydyvbJTXkIQAAC0SKgkTryJ6urq4uboxEVLH7YkXIfbrjhhpoEcAkPzfSkYU0SDz6hRXtFFxTlkBCR4OjcubOb8SmYIK56JQp0vsSKBIMSzpXDoWRs5YCojN/UxvTp012UQkJEIkJJ588880xNhEFiR+UkBBQh0axcSj5X3Rp69fzzz7updX2d2ewRGjh5OPrYQLFtAKGRzVObshCAAASiR8CPkJHQSJ0MKazepk0GD6uxfNYrISDHPigIvLOvvV5+PQrBVFm9fJnUvgTr8Z+pvMSL/0x7ne/r8+X8cf938Ob5Nv1nfq96Mt0QGjiZxXYyaR8bRGhk+sSmHAQgAIFoEkBoRPO+1NsriQYvRlRY74N/11tBPQUQGjh5OPrYQLFtAKFRz4OajyEAAQhEnABCI+I3KNPueaGRL7GB0MDJLLaTSfvYIEIj028AykEAAhCIJgGERjTvS9a9QmjglOGYYwNJswGERtZfBZwAAQhAIFIEEBqRuh0N7wxCAyczaU4m14NNIzQa/p3AmRCAAASiQAChEYW7kIc+IDRwynDMsYGk2QBCIw9fDlQBAQhAoIgEEBpFhB/lpmUYrKOB45o0x5XriZdNIzSi/C1B3yAAAQjUTwChUT+jkiyB0IiXQ4YDzf1Kog0gNEry64eLhgAEEkQAoZGgm5nPS0Fo4Lgm0XHlmuJl1wiNfD7VqQsCEIBA4QkgNArPPBYtIjTi5ZDhQHO/kmgDCI1YfF3QSQhAAAK1EkBo1IqmtD9AaOC4JtFx5ZriZdcIjdL+HuLqIQCB+BNAaMT/HoZyBQiNeDlkONDcryTaAEIjlMc7lUIAAhAoGAGERsFQx6shhAaOaxIdV64pXnaN0IjX9wa9hQAEIJBKAKGRSoS/HQGERrwcMhxo7lcSbQChwRcSBCAAgXgTmD9/vlsuobq62pYtW1aQi1mnIK3QSE4EEBo4rkl0XLmmeNk1QiOnxzgnQwACECg6gXnz5tWsy4bQKPrtiE4HEBrxcshwoLlfSbQBhEZ0vhPoCQQgAIGGEEBoNIRaCZyD0MBxTaLjyjXFy64RGiXwZcMlQgACiSaA0Ej07W34xSE04uWQ4UBzv5JoAwiNhj/DORMCEIBAFAggNKJwFyLYB4QGjmsSHVeuKV52jdCI4JcDXYIABCCQIYFVq1aZhEZZWZlVVlba0qVLMzwzt2Ikg+fGryBnIzTi5ZDhQHO/kmgDCI2CPO5pBAIQgEAoBCQ05s6day1atHBCY8mSJaG0k1opQiOVSAT/RmjguCbRceWa4mXXCI0IfjnQJQhAAAIZEkBoZAiqFIshNOLlkOFAc7+SaAMIjVL89uGaIQCBpBBAaCTlToZwHQgNHNckOq5cU7zsGqERwsOdKiEAAQgUiABCo0Cg49gMQiNeDhkONPcriTaA0Ijjtwd9hgAEIPAXAYQGllArAYQGjmsSHVeuKV52jdCo9RHNBxCAAAQiTwChEflbVLwOIjTi5ZDhQHO/kmgDCI3ifQfQMgQgAIFcCSA0ciWY4PMRGjiuSXRcuaZ42TVCI8FfMlwaBCCQeAIIjcTf4oZfIEIjXg4ZDjT3K4k2gNBo+DOcMyEAAQgUmwBCo9h3IMLtIzRwXJPouHJN8bJrhEaEvyToGgQgAIF6CCA06gFUyh8jNOLlkOFAc7+SaAMIjVL+FuLaIQCBuBNAaMT9DobYf4QGjmsSHVeuKV52jdAI8SFP1RCAAARCJoDQCBlwnKtHaMTLIcOB5n4l0QYQGnH+FqHvEIBAqRNAaJS6BdRx/QgNHNckOq5cU7zsGqFRx0OajyAAAQhEnABCI+I3qJjdQ2jEyyHDgeZ+JdEGEBrF/BagbQhAAAK5EZDQmDdvnpWVlVllZaUtXbo0twozPHudDMtRrIgEEBo4rkl0XLmmeNk1QqOIXwI0DQEIQCAPBCQ0WrZsaVVVVbZs2bI81Fh/FQiN+hkVvQRCI14OGQ409yuJNoDQKPpXAR2AAAQgkBMBhEZO+JJ7MkIDxzWJjivXFC+7Rmgk9zuGK4MABEqDAEKjNO5z1leJ0IiXQ4YDzf1Kog0gNLJ+dHMCBCAAgUgRQGhE6nZEpzMIDRzXJDquXFO87BqhEZ3vBHoCAQhAoCEE5s+f73I0qqurydFoCMCknoPQiJdDhgPN/UqiDSA0kvoNw3VBAAKlQkD+ZKtWrUxCY/ny5QW57JJKBl+5cmXWUDUdmF7F3BAaOK5JdFy5pnjZNUKjmN8CtA0BCEAgdwIIjXoYpnP6dczPBRxUZ/5YsMoVK1ZYtmKjoecE2831/YIFC2oUaKGmI3N9XrnKbKWZNO/kqQusQ3kva96tj+1ZMcialA82HMV4OYrcL+5XLjaA0Mj1Sc75EIAABIpLAKFRB38JBDn9QTGhY3K8vagIRh5UNt0WLJP6eepnvs3UcoX+G6GBg5iLg8i52E8+bAChUegnP+1BAAIQyC8BhEYannL+5fDX90pzas6Hgm2nipDUv+trTIJIL11HthtCA0cxH44idWBHudgAQiPbJzflIQABCESLAEIjzf1YsmSJi2LMmDHD3n77bXvhhRfsueeec/tp06bZokWL1nDef/vtN/vwww/t5ZdftnHjxtlLL71kX3/99RrZ9V60zJ071959912bMGGCK/vWW2/ZDz/8sEYvJCjUxq+//urqnThxor344ov25ptv2rfffuuiLMETVF5lJ0+e7OocM2aMvfHGGzZ9+nRTew0Z+oTQwEHMxUHkXOwnHzaA0Ag+6XkPAQhAIH4EEBpp7pmGSklk9O/f34488kjbbrvtbOutt7Z99tnHbrnlFvvyyy9t8eLF7kw55KNHj7bzzjvPmjRpYrvssovtt99+dv3119vnn3/uBIvKyNnXXMISDR06dLCdd97ZGjVqZPvvv7+r8/fff6/piYTDzz//bGPHjrVjjjnGtt9+e9cHLeF+66232i+//FIjdFRWYqJv376urPr673//2w4//HDr1auXTZ061YmWmsozfIPQwFHMh6NIHdhRLjaA0MjwgU0xCEAAAhElgNBIuTFy3CUIrrjiCue4X3PNNU4cKFrxxBNP2CuvvGKKYPhNkYYzzzzTWrdubSNHjnSfd+vWzQ477DDr1KmTaf5g1ant6aeftvbt29shhxxiQ4cOdfWpTPPmze3SSy91ZVVO+R+KjDRu3NiOP/54GzhwoI0aNcq6dOliO+64o1133XX2008/uTpVv/ok4XL66adbv379XD86d+5sm222mfXs2dO+//57V+fChQtr+uJOXv2P+hd8Kfri5z2uqqqqyUcJnhPae5LBSXjvjnOei3OepHMRGqE9aakYAhCAQEEIIDRSMGvI0rBhw5yDf+ONN7rhRzqmqIEcdQ2r8kORFPmQqDjuuOPsnnvucZEGOejvvfeeXXzxxVZWVuaGXEk4zJo1y80hrAjGoEGDXMTkjz/+sNdee82Jmp122smdp7Ia8qTIicSDBIeiKzp/0qRJTkwcdNBB7jx1XREL9UH1qt8SIDNnzrR33nnHzRrVsWNHN5RL4qG2ZPUUBO5PGYYiKAgNnN4kOa5cS7zsGaGR7unMMQhAAALxIYDQSLlXs2fPtnPOOccJDUUzBgwYYLfddps98MADzrmXw69NTrveH3300S6ioVwOn3StvYYtaYGSa6+91kVIlMNxxhlnOOddQ5+8WJEwURubbLKJjRgxwiQ+FD05//zzrUWLFk40qD69/HCubbfd1kU4JICUi6FyZ599tn388cc1V6PrUP/btWtnjzzySM3xTN8EDcPPsPX/2Xvv+KiOs2047x/f+6Q/eRPX5HHiBAwqiOYKGDCmN4OxcXdi7MROHDeQAFFVVmAMju34ibtNxwbXuCVxixvuJa70JtToRQV1ru93jTTiaM/RaiXtrna11+E3nLNz5ky55tac+zr33DPBPtumdLJoyKIhi4ZkoF4GRDTaNJrqYSEgBIRAuyPg1Cedq7iGs2JRvWEfp0XRF4PTmy677DIT+GWfvhL0w1izZg2oxHOq0bp160w6Tn/yd+h+/vnncemll+L666831pDXXnvN/L788ssNtpZo8AedzemHcc899xiLxJNPPmmmWF133XWGpDANiQatKmvXrsUpp5xifDJISuhUnpycDJ/Phx07djT0G9MuX74c/fv3N0Sp4YbHBTveGVg3p2A46+rxeGijRDSkZIpoSAZENEI7rio3ISAEhEA7IeDUJ0U0ADP9qUuXLvj+979vrAErVqzAF198YSwIAwcONMSDq0rxoDXhjDPOwJ///Gez6pOzD0k06DNxzTXXmOlWL7/8siEatDzwIHGwU5m4mtSZZ55ppkvl5eUZgjBq1Cikp6c3EA0+w/S0jJx88smGlNDCwXzpVH7fffcZi4etAy0lzz77rCEanALWVOcyT65YxelaGzduBFfVYuA128upU5wuFrFDRENKpoiGZEBEI2JDrgoSAkJACIQTARENP3Q5rYmrNtE/4v777wdXX2Lg9CH6YdA5m87YPD7//HOcc845xpGbfhH2oAWASj6nSpFs0Lmc06LoCE6riCUYNv0777xjrBLMl8vX0lF8woQJmD17Ng4ePGiTGbLAlaxINBYtWmSsKC+88AKOO+44PPDAA42IBlfFovM5Hc3nzZvXMK2rIbP6CwoASQotHyQsnMLFwOv/+q//wtSpUxuc1P2fDctvEQ0pmSIakgERjbAMr8pUCAgBIRBpBEQ0/BDn1ClaKbg87NNPP93oLpeb5QpTtGDwoBVg0KBB5jdXdnISCD5LUsLpT5zGRMsDp2JZi4YzY97jClEkD8yHK0xdcskluOGGG8Blb631g/nQeZwkgD4jtESwTqeeeqohE86pUyRHdA7v169fwKlTtHTQesE6cNUsTttiePzxx9G7d285g0vpldIrGWg3GZCPhvNNoWshIASEQOwhIKLh12f0e6CSz6lLVLx5WB8FWgiuuuoqs6IUfTS4ChVXnCJ54CpPTqJBawidtOk7QWsILRF08Oa+HCQPfJ4HycPDDz+M448/3lhBWD6nZjEtV63ifhrMl2SDfiDMl1O76CtCMkGfDVotaClxOoOzY7mXB9sRjDM420grCOvKurGOdGafOXOmlreVotluiqZWiYqtVaJC3V8iGuY1of+EgBAQAjGLgN0ugfqk1afD3Ziodgangk7LAh3A58+fbywMtHLQd2Lu3Llmsz2uQmWP6dOnG7JBR276TJA4UOHn8rZchpbL0/IgKZgzZ46ZokSSwClanFJFC8Wtt95qpk7RuZzkhUvWcroTl7zl83bJWjqU05l85MiRxj+E+dIawT0/WBatECQmdFbntC6SDFpFrE8JyUqgw3mfU8GIAf1EWKeIHZo6JaVexE4yUC8DIhoRG3lVkBAQAkIgLAhQ17XbJYhoAMYP4uuvvzbkgStN0YJARZ3L1dKPgatIffLJJw2d8eKLLxoLCB2nuVkepyClpqaafS2sf4ZNTIJxwQUXGEsHncyfe+45swcGp2lxgz1aM8j8SFZIME4//XSTN3f9JokgoeAKU3fddVeDPwbTc3+N7t27m2ldtF5wY0Fu7kdfExIgWkJoFeFyuNaSYuvEM++x8+09/ubSvbRoTJs2TT4aUnyl+EoG2kUGRDScI7WuhYAQEAKxh4CIhkef0W+By8ZS+acCz2lN3KWbjtEffPCBUciplNMCQEvFSy+9ZKYukRjQX4IrSHEPC06Xch5U3um8TQLDHb5JBEhQOMWJaVku82SgEzj306BfBy0bzJdEh1YVWk6YlnUgKWBabgI4ceJEQ0S4VC53Jqcfx6ZNmxqZqviMV2CZNp55UjBoOaGpS6tOxff0lVBPh1F+kqdgZUBEw/kG0bUQEAJCIPYQENFoos9oKeBUJlozuM8FV4batm2b8WPwf4RTlb766itjWaBzNp/ZvHmzKy3JAack0SJCKwTzpY/Fli1bGinzVPgtieFytkxHywnz5dKz1o/CEgPmSydybhr46quvmkCfETqH0+/CedhnAp0t0RgxYoR8NPQlu12+ZAeriCpdxyYtIhrO0VvXQkAICIHYQ0BEI4J9RvJgLRHOYqn0U7n3PxjPZ/yDF0lgGubBsz2Yzh7OvLyed8aJaHRs5U3Kufo3VmRARMOO4DoLASEgBGIPAeqWJBpc3Ig+v/xQHokjqp3BCYBVui0YJAdW2ee9thxeRIP5Md7/YJn0nXCSB5vG1tGebTxJgn8d+Zt5+LfDPut/FtGQIhoriqjq2bFlVUTDjuw6CwEhIARiDwHql/Q75iqsJBqRmoof9UTD3zJABZ2O1Ix3HtZ73iryznv22osk8F5T8fY5e7bkwJIBSyTsb3u26Xnf1svGWZLBvGzb7HNeZ6YhA9XUqY6txElJV/9GuwyIaNhRXGchIASEQOwhQB1TRMOj3wiM/+Efx99U4Hn2v+f/bKh/N1eevW/rxnqSPFiS4ay3TeM8i2hIAY12BVT1iw8ZFdEI9dtD+QkBISAEIocAdUsRjTbg7VTO25BN2B511o/kwgZnvNe1iEZ8KHFS1tXP0S4DIhphez0oYyEgBIRA2BGgjimi0QaYnUp6G7IJ26PO+rXkWkRDCmi0K6CqX3zIqIhG2F4PylgICAEhEHYERDTaCLFTeW9jVmF53Fm/llyLaMSHEidlXf0c7TIgohGWV4MyFQJCQAhEBAERjTbC7FTe25hVWB531q8l15xiJWdwKaHRroSqfh1fRkU0wvJqUKZCQAgIgYggIKIREZhjrxASDW5YyJ3BZ82a5VrFKqwtqj0K1AJc7Pfj9aWYOO1BnJv2MHpMX4Ze05Zr8zhtICgZiCMZENEI62irzIWAEBACYUVARCOs8MZu5hQMEY2O/7VYFgH1cbTLgIhG7L5HVHMhIASEgIiGZMATARENKaDRroCqfvEhoyIankO0IoWAEBACMYGAiEZMdFPkKymiER9KnJR19XO0y4CIRuTHf5UoBISAEAgVAiIaoUKyg+VDwZAzuJTQaFdCVb+OL6MiGh3s5aLmCAEhEFcIiGjEVXcH31gRjY6vwElJVx/HggyIaAQ/biulEBACQiDaEBDRiLYeiZL6WKIxfPhwzJgxAxUVFZGrmVad0qpKcbSqUiwo++1ZRxGNyA29KkkICAEhEGoERDRCjWgHyc8KhoiGvnq3p5KpsiV/Ihod5KWiZggBIRCXCNgP10OHDkV6ejoqKysjgsN3IlKKCmk1AiIaUvCk5EsGokEGRDRaPYzrQSEgBIRAVCBAn99hw4aZGTJVVVURqZOIRkRgbn0hIhpSMqNByVQdJIciGq0fx/WkEBACQiAaEBDRiIZeiLI6iGhIwZOSLxmIBhkQ0Yiyl4OqIwSEgBBoIQIiGi0ELB6Si2hIyYwGJVN1kByKaMTDG0dtFAJCoCMjIKLRkXu3lW0T0ZCCJyVfMhANMiCi0cpBXI8JASEgBKIEgeLiYuOjMXPmTMhHI0o6pb2rIaIhJTMalEzVQXIootHebwOVLwSEgBBoGwIlJSXgKqYkGtXV1W3LLMin5QweJFDtlUxEQwqelHzJQDTIgIhGe70FVK4QEAJCIDQIiGiEBscOlYuIhpTMaFAyVQfJoYhGh3q1qDFCQAjEIQIiGnHY6c01WURDCp6UfMlANMiAiEZzo7XuCwEhIASiGwERjejun3apnYiGlMxoUDJVB8mhiEa7vAJUqBAQAkIgZAiIaIQMyo6TkYiGFDwp+ZKBaJABEY2O815RS4SAEIhPBEQ04rPfA7ZaRENKZjQomaqD5FBEI+BQrZtCQAgIgahHQEQj6rso8hUU0ZCCJyVfMhANMiCiEfnxXyUKASEgBEKJgIhGKNHsIHmJaEjJjAYlU3WQHIpodJCXipohBIRA3CIgohG3Xd90w0U0pOBJyZcMRIMMiGg0PU7rjhAQAkIgFhAQ0YiFXopwHUU0pGRGg5KpOkgORTQiPPirOCEgBIRAiBEQ0QgxoB0hOxENKXhS8iUD0SADIhod4Y2iNggBIRDPCIhoxHPvN9F2EQ0pmdGgZKoOkkMRjSYGaUULASEgBGIEARGNGOmoSFZTREMKnpR8yUA0yICIRiRHfpUlBISAEAg9AiIaocc05nMU0ZCSGQ1KpuogORTRiPnXiRogBIRAnCNQXFyMYcOGYebMmaiqqooIGt+JSCkqpNUIiGhIwZOSLxmIBhkQ0Wj1MK4HhYAQEAJRgcDhw4cN0ZgxY4aIRlT0SBRUQkRDSmY0KJmqg+RQRCMKXgiqghAQAkKgDQiIaLQBvI76qIiGFDwp+ZKBaJABEY2O+pZRu4SAEIgXBEQ04qWnW9BOEQ0pmdGgZKoOkkMRjRYM3EoqBISAEIhCBEQ0orBT2rtKIhpS8KTkSwaiQQZENNr7baDyhYAQEAKtR4D6JInG0KFDkZ6ejsrKytZn1oIn5QzeArDaI6mIhpTMaFAyVQfJoYhGe7wBVKYQEAJCIDQIWH1yyJAhhmhUVFSEJuNmchHRaAagYG+zA8N1kIEOHz4cXCUgUoJh2lJ7FKgFqgF8vL4UE6c9iHPTHkaP6cvQa9pySPmU8ikZiB8ZENEI1wivfIWAEBAC4UdARCNIjAlUTU1No9T8zeC/JjDTMlRXV5vgfMgrH3uf9/wPxgUyM9XW1vo/0ui3f90a3WzmB4nGiBEjzLrHgerQTDYtvy2iITI1NX4UaZGmwH0totHyIVRPCAEhIASiBQHqsYcOHYIsGk30iCUT5eXleP7553H99ddj8uTJ+Mc//mFIBL/0k1B89NFHmDdvHq666ipMnDgRU6dOxUsvvYSDBw82ypmK/3/+8x/Mnz8fV1xxhUmfkZGB1157rYGUWMLBtJ999pmxKPz2t78FA9O+9957YH1s3Xg+cuQIXn/9dcydO9fkadO+//77KC0tbVSHYH9wgxURjcBKkJRE4SMZCK8MiGgEO2IrnRAQAkIg+hAQ0QiiT/g1f/369Zg0aRK6du2KQYMG4aGHHgKtCSQZH3zwgSEDF1xwAW688UbcdNNNuOiii3DDDTfgqaeeMiVYywNJAqciXXrppbj55ptNGl7/6U9/wquvvmoIBNOSSHz44Yf4wx/+gHHjxuG6664zJIfk5He/+x3eeecdHDhwwJTP89tvv22Iy2WXXWaeISFivnyO9WM9W3qIaIRXgZKCKnwlA83LgIhGS0dupRcCQkAIRA8CIhpB9EVBQQHuvfdeUIkfOHAgLrnkEjz22GNGeafVYfbs2caXgZaOb775Bhs3bjTpR40aZawQe/bsMdOr9u3bB5/Ph3PPPRe33347Nm3ahK+++goLFy7EyJEjjSWCZdFCsX37dtx55534zW9+g5ycHGMxoXWDcd26dTPTmTZs2GDqwPOsWbPQo0cPLFiwwFhBvvjii4bn77rrLjDflh4iGs0rQVIUhZFkILwyIKLR0pFb6YWAEBAC0YOAiEYzfVFWVmamTJFgcHoTrRWclrR48WKUlJSAJIIO01deeSU+/fRTkxutB4yfPn26ISa0NpCQ0EJBa8SFF14Ikg47RYqEg0t+/exnP8OXX35ppjq9/PLLxoIyZsyYRiSB92kp6dOnD958801DSnju168fpkyZgq+//rqhRUVFRWbqE60iTNPSg0SDbZs5c6bLD6WlebUovXw05KMhHw3JQL0MiGi0aPRUYiEgBIRAVCEgotFMd/zrX/8yU5xIGjhFiY0S9ekAACAASURBVD4QJBq0aNAv4pNPPjFkYtq0aea+JQ/M9r777jPOL7RCcCrU6tWrjVWEZMU5lYn5rFixAscdd5whNSQpzJ8kg/nSKZsH8965cyfuv/9+nHjiiXjxxRfN9C2e+ZvxvG8PkhnWm1Ovli9fbqODOrN+LHfYsGGGBLH+ETtENKRkimhIBkQ0IjbkqiAhIASEQLgQENEIgCynJHFaFP0c1q1bZ1LS8sApVI8++qhRxP/5z3+ib9++yMrKMtYFJrL+GMuWLcPYsWONRYBK+4MPPmj8Jpin82D6Z599FieddBKWLl2K3bt34+GHHzbTqe64444GZ26m27VrF9asWWOsH6tWrTKWEZ5pDWEeJBdMx8AyOUWLm6Q88MADziI9r0km6I9CoSDRYFn0R6FjO8lQxA4RDSmZIhqSARGNiA25KkgICAEhEC4ERDSaQJZTpv7yl78Ya4bTGkCl++KLLzakgRaOv//972YaE/0o7EElncCuXLnSTJPiM1x96n//938N0SApcR5My3x+/vOf45FHHmlENOhfYVeNInmgteO5554zxIKkhL4XPJNocCUslkMfD9aBU7sWLVpkyMLf/vY3Z5GNrpmWq1MxLYkUiRBXt7r11lvxq1/9ylhVRDTCOw9d8/yFr2TAWwY0darRcK0fQkAICIGYQkBEw6O7qHjTqZvO3PSnWLJkiVk6lsvbcjWp/v37489//jOeeeYZo/QHY9GgvwNXquJKUNFm0eASvU8//TSuvfZaY/3gdKnRo0dj8ODBZjoXp1+ReEXskEVDX7Nl0ZAMyKIRsSFXBQkBISAEwoWAiIYHslS8OSXq/PPPx/HHH2/8H04++WT85Cc/wQ9+8AP8+Mc/Ngp4YmKi2SuDjuLWR8OZHX0mqKzTUsApSZzyxGlX/j4aVOLpo0E/ixdeeAF79+41zub00aCST5LCg5aK3NzcJn006BOSl5fXUAVOo2K96KPBaVyBDpZBCwinW/GaFgw+T9LBOlirSqA8QnZPRENKpoiGZEBEI2RDqjISAkJACLQXAiIaHshzilJ+fj64RCz3oHj33Xfx1ltv4d///rfZq4IEhNOh1q5daxR/rszE/S0+/vjjhtxIVjj9iNYObtxHoD///HPj70Eryf79+xvS0npCJ3Pu0cGlcS3RoW8ILShU/Pk8V65iHrQ8DBgwwGzcx0y4Nwd/cy8O60vCeK46xbpec801Zo8O5hHswbQkF7TqcDoVSUjEDhENKZkiGpIBEY2IDbkqSAgIASEQLgSoT2pncA90aT2gYk8HaVocqGhT8ebXfe7+bZ3BeZ8kgWSDPg3cF2Pr1q3GmXvChAlmOVsq/ASaPh10zj7vvPPM3hhMy8BVqeg0/vvf/974YLA627ZtM/trdOnSBX/961/N6lYkMtxzIykpyUy/2rx5s6k5z5yORQsL82c6BubbuXNns6dHa/bRYLs5hYobDLL9LSEqHpAGHyWiISVTREMyIKIR/JiplEJACAiBKEVARKOFHTNnzhyzvC39NkhGeNidwUkWuCM3/TfoMP7HP/7RTJey6Th9itYRbq43fvx4Y91gGrszOPfbIHFheloxmJbkg/d5poWDU6+4vC6tKZzmxA7kmb9JgHif6biLOYkO99xwWlpa0lwSDU7fEtHwdlKV865wkQyEXwbkDN6SUVtphYAQEALRhYDVU7kCKmfIUBeOxPGdSBTSljLoEM4pVM6DFg6u8MQlZ+nDYb/wkxhwM7758+fj6quvNgp+amqqmTJFK4ZNx+dpFeGULK48RTLCjf5IPLhfhz04dYodQVPTRx99hMzMTJMvSQSVfk7jYhrmyzryzPScQsV8OY2LaZkvdxMnYbAH6xDsIaIRfiVKiqowlgwElgERjWBHbKUTAkJACEQnAvwgTp9f6rAt0UPb0pqoJxp2Twkn2SBJoEJPYmHJgwXBmc55zfv8zWd4dj7HOK+D8Ta9P/NjB9lOYn689irPK1/GOctvKo2NF9EIrABJQRQ+koHwy4CIhh2RdRYCQkAIxCYCIhqx2W9hr7WIRviVKCmqwlgyEFgGRDTCPtSrACEgBIRAWBEQ0QgrvLGbuYhGYAVICqLwkQyEXwZENGL3HaKaCwEhIASIgIiG5MATARGN8CtRUlSFsWQgsAyIaHgOz4oUAkJACMQMAlzgiD4aM2fObJj+H+7KR72PRrgBiIX8RTQCK0BSEIWPZCD8MiCiEQtvC9VRCAgBIdA0AtwigdtAkGhwsaVIHCIakUC5jWWIaIRfiZKiKowlA4FlQESjjQO5HhcCQkAItDMCIhrt3AHRWryIRmAFSAqi8JEMhF8GRDSi9Q2hegkBISAEgkNARCM4nOIulYhG+JUoKarCWDIQWAZENOLu1aMGCwEh0MEQENHoYB0aquaIaARWgKQgCh/JQPhlQEQjVCO68hECQkAItA8CIhrtg3vUlyqiEX4lSoqqMJYMBJYBEY2of1WogkJACAiBgAiIaASEJ35vimgEVoCkIAofyUD4ZUBEo+l30FHwXw2Oorr+DBw1yWsBVAHg6i51MU3nojtCQAh0LASi729eRKNjSVjIWiOiEX4lSoqqMJYMBJYBEQ3vIZ2qRC1qUYMKVKME1SivpxuMJ8koBlBWTzi881CsEBACHQuBuo8P/MDAjw31R91g0a7fHEQ0bGfo3AgBEY3ACpAUROEjGQi/DIhoNBqWzQ/qDZZoVOMIqnAQVShFFWqNDaMWFTiKg/Vkg6Sj7rDP6VyHn3AQDh1PBkg1RDQ44mkfDTvyR/FZRCP8SpQUVWEsGQgsAyIazb0kqFTQclFhEtYpTlQ2Kgzt4HfNmvpJVEypIAwkAx1LBvj3zb/7aD5k0Yjm3mnHuoloBFaApCAKH8lA+GVARCOYlwCtFlQ3jh1UPBjDO5X1NIRUpFxBGEgGYl4G+LfMwL9tEseGiVL2D58RDvZRN6WqLp0j+tiAEeYrEY0wAxyr2YtohF+JkqIqjCUDgWVARKOZN0jtUaCmAqg5AlSX1QX+rq0CjlYrCAPJQEeXgdpKoKYcqD4CVNTUGTjLjwJVHBdKgNoy4OiRelpS6+QfzQwuobstohE6LDtUTiIagRUgKYjCRzIQfhkQ0WjmtVJTA1QWA1X7gapdQFU+UL0HqD5YF6r2AVV7FISBZKCjykDlbqCiEDhSCJTsBw6VAMUHgJq9QE0+UMX7+4Gyg3Wks5khJRy3RTTCgWoHyFNEI/xKlBRVYSwZCCwDIhrNvEz41XLbN9jy9P3Ie2ohip7KNucdT92LnU/eg8I1i7B79QIFYSAZ6KAysGf17dj3xDwcfHw+SpbfiYol96J82Z04snoe9q/JwM6n7sK+d14CSvcDtH60wyGi0Q6gx0KRIhqBFSApiMJHMhB+GRDRaOZtUXkQ+PZtfLzoenyz8CJsXjAMGxdcgPULLsGGBRdj04ILsHnBKAVhIBnowDKwacFobF4wBjtyxqMw6yLk+cZiS84grF84GJ/c+TtsfeohoPwgUNM+RKO4uBjDhg3DzJkzUVV1bCW8Zka3Nt3WqlNtgi8yD4tohF+JkqIqjCUDgWVARCPAeF9bAVQWAd/+E+vvmIiC2/vh4LxEHMjphf2+PtiXcxb2zeuJffOTFYSBZKCDysCe21NQtKAHChf0wr55vVGcdToO+lJQmPNr5C5KwNd3jkP+U/cB1cV1/lwBhpRw3Tp8+LAhGjNmzBDRCBfIsZiviEZgBUgKovCRDIRfBkQ03G8PriBTcqQEqCkDKoqAL1/CtzkXIi/7TBzI/DX2Z3TF/owU7MtIxp6sLtid3amZ0Bm7s4MNzeVl74c6P5tvc+dQlxvq/Jqrv70f6nJDnZ+tZ3PnUJcb6vyaq7+9H+pyQ5dfka8TCnI6Iz/nNOzKSjB/9/uyuuDAwk7InXcqvpk/Enufvg+oKRXRcA+nimlPBEQ0wq9ESVEVxpKBwDIgouH9Fqg+Wo2jVBwqdgPr/o31d1yJndn9sC+zC/ZmJmJvRgp2Z3VDUXYSCn0JfiERhb7GoSA7EY1CVhIKTEiuO2cn1d9PQEE2Q1dHsHHOc1vyc+Yd6NpZnr32K9e/XQ2/bfrmzm3JL1Ddnfe86uBXrvqjXu78cGnoT/94L0ydmAe69nrWL/8o6o/87NOwM7uTCXlZCcjP6I6CzETsyumEPN9p2Dh/LA6uuQ84cgCorttvx3tUCV+sLBrhwzamcxbRCKwASUEUPpKB8MuAiIb3a6QG1ag1RGMPsO4drLvjGuzIHoi9mUnYk5mM3ZkpKMpKQYGvG/Jykh2Bv1MaB1937Mz2Dz2wM7tnfeC1vZ+CndndsNOXjJ2+pLozfzcKTGPT23Ow+THPYEKyX5msg1e5tnz/c307GtU7mHb452N/e+TXgFGg9gTbjmDxC3V+geruvBdsuRYv/7MHfq6+aWP/dtj+SGggGjuykrA9qxdys1JQlN0V+VkJ2DJvHIpX31fnDF7FnXQif4hoRB7zmChRRCP8SpQUVWEsGQgsAyIa3q+LahENEQ1D5uoV9AYlOliFP1ji4iQTga6DLdefYNjfIhrBEXuvPohuonH06FGQaAwdOhTp6emorIyMQ7qcwb3fHVEVK6IRWAGSgih8JAPhlwERDe/XAolGDS0albJoHLOmtPGLt76g11uFnBYrL8XWK05Eo86K1x7EL/qJxqFDhzBkyBBDNCoqIjN9S0TD+90RVbEiGuFXoqSoCmPJQGAZENFwvxaOAjBEo7YdiUY2p05RubQKJhUsZ7Bfqu25rV/Q68szX+79y7VTnmKBaARqRyjws/1hMWlqSpn6o2Hqn5WpsMhzJPpDRMM9SgIiGl6oRFmciEZgBUgKovCRDIRfBkQ03C+G6CAazSmylmDYc1sUW0tqnGVaBdoq5/a3La+5M5/zz8//dxuJi1FgnV//A7WjufoGi18kFNtA7VB/RN6HSUTDPUqKaHhhEnVxIhrhV6KkqApjyUBgGRDRcL8aAhGNPSF1BrfKLc9WEfZX0INVxpmHf34OpdSllPsp6C5S4CzX1skZZ+vrdXaU68rXSTZsOq88nHE2nfNZr+lHwZIAZ972Olj8mirDH5tg83P2g71uqgxbV5ZFLPzLtPf9z03g5+obm87/ef/fNl289IeIhnuUFNHwwiTq4kQ0AitAUhCFj2Qg/DIgouF+NZBoVHHylJ06tb5u1antvoHYk5WE3VnJ2G1WnUlBflCrTlnFzHmm8maVUZ6tMsc0TgWOv+295s42P6azZdXnFRaiYcvwOjvbEOja61mvOI88XG1qTkEPBX5eZbBu/nVmWeqPxrJscWquH+z9YPCLRH+IaLhHSRENL0yiLk5EI/xKlBRVYSwZCCwDIhruV4MX0fh24TUg0dhdTzR2ZaWgMLslRMNfUaZiahUq55nxzrRNpXM+439tlV5HPi6l3H4959lLWXOWa+vkH2fjHeU0qnuY411taq4d/jg19Zvt8mtbQ1leZXi1sxmsGvJz9oO99irDPz+W6R/nV+dI9gXLcrWpuXY0hb9/PNvl17aGsrzKCHV/iGi4R0kRDS9Moi5ORCOwAiQFUfhIBsIvAyIa7lcDiUYlqlF1tBSo2gOsfwffLLwG2+qJxq6sZLOHRtNEw28vDZ+fkmQUQMY1pVA50/PaK63/s/a3Te9UtqyDtEORNYqajfdS1pxl2jz9z84yInztX3/726Vc2zpbfJo72/TO9lic6kkZy3KV40zPa5uPPTvvO/Nz5Gnb4Jk/87F1t3n6n51lRPjaWXfntQsnW2fblubONr2zPU786q9d5TjT89rmY8/O+8787N+I8yyi4R4lRTS8MIm6OBGN8CtRUlSFsWQgsAyIaLhfDZZoVFqisaGeaOQMxK7sJBRlJ6MoOwUFnhYNbt5HotE47PQ5FRur+Fglyyo//mf7jH98c7/tc/YrMxUpqzi1RDGz9bNnZ7mOMryUPKeyGeja61mvuKby8ErrinPWO5hrR9sayrX48WwVU8fZVSbzsGUFyk/9cQwni5f/ORB+oe4PZz/baxEN9ygpouGFSdTFiWgEVoCkIAofyUD4ZUBEw/1qqAVQgWo4icbXi67B1pyBKKonGrRmFPi8pk45dwm311T4HcqSUUqtMkUlntdWMXUq9f7PtPS3VYStwlSvlHkqxf55s062Ls6zrat/eudvr3KddbDXtAwEYx3wyi+Y55x1as21V7m27s5zJNqh/jCyYkieE3uv67b0h1d+IhruUVJEwwuTqIsT0Qi/EiVFVRhLBgLLgIiG+9VgiUYFSoHqPcCGd/DVomuwpZ5oFGYn11szUpDncga35KLx2fUVvIFYUIG0yrtTmbRxLVGQrWLsPPsrTsEq6M66NEE0vL7sN8T5l9vUb2ddA137Px9sO0KNn3897O9AdXfes+ntOdh2qD/cPiAWQ6+zE/NA117P+seJaLhHSRENL0yiLk5EI7ACJAVR+EgGwi8DIhruVwOJRjmqUW6JxsZ6ojFvIAp9SSjwJddbM1Lqp0g1JhV5Oe7fx6YuWSXGS8F0KpOtIBpBf+0NRvl21sWDaARVlm1rOM5e+AXTrgBp2qVNwbZD/eH+GwqHXHnlKaLhHiVFNLwwibo4EY3wK1FSVIWxZCCwDIhouF8NLouGk2hkJ6F1Fg1/BcZLwXQqk7wOFDwU5qAUZa9ymZdXWSQYzqV3+ZvpmD7QV2Le829vqH+3pB1ebWstfu3ZDifhs9f1/RFU34e67s78Okp/ONtkrxOQm93JhB3ZSdie1Qu5XHXO1xX5WQnYPG8cDq++DyjdD1SVuweUMMccPXoUhw4dwpAhQ5Ceno6Kioowl1iXvXYGjwjMbStERCOwAiQFUfhIBsIvAyIa7nG8gWjQGbx+6pTTR4NEo2U+Gl6Kt5diRqXRKpCBzlZx9lOWg1I2myrXq2y7j4GTbNQrtoH8PEw9vNpsFbdQnFvSDn8s24JfKOruzKMt7WC76vsjqL53lhvq67a0I5r6wwuXBOT6OpmwPTsJ27J6YUcWfbS6Ii8rAZss0SgT0XCPpoppVwRENMKvRElRFcaSgcAyIKLhfg1w1amgnMGbXHUqFFOn/JVj/98eCn9QymZTCqF//iQXPetDC4kGSUhQdfFS6oKNC7Yd/u2yv1uLX7D1CzZdiNoRdryba0+I2uEksO3epro25/rqiMYOXye4iEZ2HdE4RIsGiUa1LBruEVUx7YaAiEZgBUgKovCRDIRfBkQ03K+AJomGbyCKshJRmJWMwqxuyM/u5rEzuJtk0GfDPZWovRSzYMsV0XD3WXPKdmvuB9sfliD5n6PdouFf36Z+Rwvxc/dhLBCNw4cPY+jQoWbqVGVlpXtQC0OMpk6FAdRQZymiEX4lSoqqMJYMBJYBEQ33yO5JNBZegy2+ASjMSkBhVhIKSDQYgl51yl+BCZGC2eIvwMGWK6IhouEvs4F+BytXIhruEaftMSQaw4YNw4wZM1BVVdX2DIPIoUMQjdpazpTtuIeIRmAFSAqi8JEMhF8GRDTc75jGG/btBTa8a3YG3+obgKLMBBRmJqEgMxDRaLxZHzfvcy9vaxUzfsl1hqYUMWe8Td9ePhoeX5+dhKdh6hQtOWEM/mU24OjEyus6EH5hrG9TWISkHe1Qb//2hKQdDpn2z7+dftOiscPXGZw6tS07CVuze2G7mTbZFTuzE7Bx/jgcWtN+U6c4golouMfxRjHV1dWGgfHsPOzvI0eOeHrR19TUIBgyQnZHL3ybn7MMXjOf8vJysJzmmCDTOvPh79YeLG/s2LGYOXMmSktLW5tNy5+rPQrUAkT74/WlmDjtQZyb9jB6TF+GXtOWQ8pd+JU7YSyMo0UGRDTcQyiJRhVqUI0yoLqOaHy7aBK2ZtcRjaLMJBRmdjNWjTyz+pLzS69V+Oo36eNGfa7N+qwyRYXXSxH2j7OKsfNs83CcjSLmrIvXtSU4jueMgujM216zHk7/DFsve98/j2j4bevW3Dka6hqoDl7198efaQLlEQ33vNrhFRcNdfWuQ64v0RCN7TmdsM2XhC2WaOQcIxoHRTTcA2k0xFilvSmywCW7mjqae9b5HPNh+qbys/e90nCuW1P1c5bRmmsRDSmb0aJsqh7xK4siGu7Rm2+eatSgxhCNfcCGtVi3aBK2ZQ/ArswEkGgUBSIaQSl/VLas4tjcOUiFsk1Ew1/Jaq5+QdYpKCz8y9ZvN4Fw9oewd+MTPpkh0dju6wwSja31RGNbNvfQ6QpaOzbMHwcRDfc4qpggEKDlgpaOQFYOpmktEeHUKVo0Zs2aBV5H7JBFQ1abqfGrWItUNe57EQ33yNsU0TAWjYwEFGUkoTAjGfmZyfC0aASlXDsVRxGNSCqOsVmWU15ENCLZh7FANIqLi42PBmfIBNJZ3aNd62Niwkdj//79+Oabb/Dmm2/i5Zdfxosvvoi1a9di27ZtZhqTf/M5vYjp3377bbz66qt49913sXHjRs+0JAA27RtvvIEPPvgAW7dudVk1aM3Yu3cv/vOf/+D111/HP/7xD5P/hg0bcODAgUbTpFifPXv2NKT95z//aeqxfft2tMbLn+0ZM2aMiIaUXhEfyUC7yYCIhv+bBmiSaGT1R1FGVxRmJKLAEI0kEY2gSFX4vjZHUuFs37JENNoL/1ggGiUlJRg+fLiZiu+c3u8e3UIXExNE4+9//zt+//vf44wzzsBvfvMb/PKXv8TIkSNx1113GZJg4eCUpoMHD+Ktt97Ctddei7POOgunnnqqeW7q1Kn48ssvGyn6VPo3b95s8u7Rowd+/etfm2cmT56MvLy8BgsESQaV/aeffhq//e1vkZKSgs6dO5vdFbOysky+zulWtFw89dRTmDRpEk4//XScdtppOPfcc/HXv/7VEB5b32DPLHv06NFGMDiNKmKHLBrtptTpa3rjr+nCo/3xENFwj7wNRONoGVBVP3Vq4SRsrScaRRmJdRaNDGvRsH4Z9WdP5ZuKon9ozpJh7wf5Bdtz6pRf3ZgmJPXzb4v/b5ELb5xbi4vwDS2ewfeDiIZ7jGRMTBCNnJwcTJkyBatWrcJ7772HV155BbfeeivOO+883HTTTQ3TiUg0aO247LLLjIK/ZMkSkKRwGa+BAwdiwoQJ2LFjh3H4JjF4//330bdvX/Tr1w9/+9vf8Pzzz2Pu3Lno0qULUlNTsXPnToMap0WtW7fOkJArr7wSDz30kCnn9ttvxymnnAKSjfXr1xv/Dk5teueddwwJGT9+PFiHf/3rX0hLSzOEh/nTMtKSg6auUaNGiWjoa7aIj2Sg3WRARMM9aluiUesgGusXTsK2rP7YNbcriuYmomhusrFq5GUFo7BYJdESh5ae+XwQ5biIRiDi48yvrfXzb4/Nz1mGroPqw4D9LFzbjmHL5VBEwz1GMiYmiAanPVHR37Vrl1mai2cSDloXBg0ahC+++MK0jmagRYsWYcCAAcbaUVRUZCwcn376KWbPnm0IBKc8UdHnNCZaRGgdWblyJQoLC3Ho0CGTL0nBmWeeaaZIMc8tW7ZgwYIFSExMNMTBpmW5F154oSE2zz33nLGA0KJy4403Yty4cYa8FBQUgESBaS+++GJcfvnleOmll7x7o4lYLkdmiQZJT8QOWTTaTanTF/z2/4KvPmjcByIa3iMvXcFdRCOzLUTDXxkP9newyqW1XDhXmmrKeuGvbNkygq1TMOmYp385+i1MYk8GRDS8x8iYIBqsOi0QNnAJWpINWjVojaASz3skFrfccoshHyQn9qCV4ZlnnjFTnu69917k5ubio48+Ms/36tXL+GTYtPS3oE8Fp1wtXrwY9A/55JNPDJm44IILzLVNSwLA6VC0lvzlL38xFo3du3ejZ8+e+MMf/mAsG5xGZetNywfJhk1r83GemZbP2ECiQ/IyYsQIY5kR0Wis/EgZFB6SgcjIgIiGc6Suu7YWjRqHRWNdvUWjqN6iUTg3GfmcOhW0RSMY5ZxprNLvPHspZ5ZYOM9OksHrYIkG83eWF+i6Je3wqrfiRDZiSwZENNxjJGNigmhwShQVbx5Waf/4449x3XXXmelQtFAwno7aV199tXGcdj7D5+gQfv7552PatGnGT4JO4vTjoJM1yYE9mM+mTZsM0bjjjjuM5YPP0nIyffp0c8+mJYFhPvQFoQc/f5PE/PznPzek4NtvvzX1tg43K1aswBVXXGHqwPr5HyybqwDQd4SBhIrEguSHzjvp6emeDu3++YTstywasmhoqpJkoF4GRDTcI2sjolF9bHlb66NBZ/CGVafCQjSCUMRc06T8SUZLiUYQZRoyIqIhohCMrHScNCIa7jGSMVFPNKh8U1HnmQcVcDpEz5kzx0wn8vl8Jp73OZ2KU5Poi8HDWhN4zdWkhg4dahy/SUg41YlpL730UmMxMA/U/0drCS0aJA+csvXvf/8btHzMmzfPOI/btJxqxXzp6E0Cs2/fPkNi+CyncNEfxEmQXnjhBdDH4/rrr29oj82LZ7aBFhSSFa58xcBpW6wDiY6IRmS+3OoLuXCWDLhlQETDOVrXXfOtdGzDvn3AxrXghn1bsvqjsNGqU9aiEcgCYO8Fo6DbtM0oaYZk0JLhTy6c1o3665BOX7L1C7YtzbQjpHVTWSJA4ZEBEg3uDB7NG/Zp1Sn3OO5SyDldaeHChejfv7+xGlAZt4SChIC+EZzi5DwsCRk2bJiZ0sSpVs8++6yxfvzpT39qcCa3z+Tn5+Pkk0825IFWCebbvXt3kNTQ2mEPEo3PPvvMTN/iqlYkGkx/wgknGJ8O+mfwoPWC1o4nnnjC1I+rUXkdTPPII48YH5Nf/OIXxjJy0kknGT+SH/7wh4ZoUEgidsiioa/ZsmhIBmTRaHLItUSjym7Yt3EtviHRyK4jGgUZiXXTpjK7IS+LyncwinegNFaB59kqSx6koYFgBCAZDc/bfEJ9dtbVXnu1zdmWUNdB+R2TE2ERbiws0djh64RtjOSDXgAAIABJREFU2UnYancG9x3bGfxQO+8MLqLR5HBed4MWgjvvvBODBw8GpzVxuVpOLeIUI0454nQqWgw4HcpOV7JZ0tpBfw4uXWutFHTapjM3CQMPEhZaTEgmjjvuOGM14TX37KAfhp12ZfMkMeAKU1x2l9OquAwtLRAkB1xdikvn8rCbotDng9YW1sHrICEhcaKPCJfS5YpZDHRW55K6XAmLdfWaduWVX5vjRDSkZIpoSAZENJocSkk0KlEDQzRq6iwaJBqbs/ujILMr8jMT6zbry+yGnSEjGn4KY1BTo5wWjZb4Y/iV1SZyQkIhohFuZVf5h1JmW5YXiUaurzNINLZnJ2FbVi/syEpBga8r8rITsGneOBxafR9Qth+ojuDCPo4RTETDAYb/JRVwOnLTV4ErSNEqQYJBckBFnlYLWiKoxA8ZMsRYFmweTMMlZrt27Wqct7niFC0RXF2KK0lx0z2rvNMfghvy0c/i/vvvN/4bTEsCw925SWbsQesKl7rlKlecKsV6sA50Bueyu3Q4dx5cpveiiy4yFhl/IuRMx3xsfdgu+pDQv4REg2VG7BDRkJIpoiEZENFocsil5yCJRiUtGiQam9bi6zsnYVN2f+TXE428zGTsJNFokd+Cl0LOOOfXf2vJIIlIDBCcJIPXbSUattwA52yvMkQ0RAJaprjHGl4iGt5DZdT7aLDaJAYkGdyXgs7enJ5EQkBfDafCTgX9nnvuMUSDS9fa5W258zeXpyXR4DQoPss9Mu6++25jfXjsscfMbzI9kopZs2bh7LPPNpYFkhnuQM7pWgkJCWYlKi5vy5WgSHbo3E0/D1ogSGg4fYo+GLRcPProo8bnglYXWjeY9qqrrgJ9NZz19u6aulibJwkWLSpcKjdih4iGlEwRDcmAiEaTQ64lGhUOovEViYavjmjkZSZiJ4mGcQRvStFuilR4xTuIRoMlgyQjoYnAeyEmGg3l+ufr/C2iEWtKsurbdhIkouE9VMYE0eBysNxYjz4WL7/8spkyxb0x6NRtN+CzFoDXXnvN7K/BHbm5WR6nIdFxnM7UdP7es2dPAxLWQZy7jNM3gmnp8E0rB5V6EhQq+iQbLItTpEgWHnzwQUMWSF64uV9mZqYhP0xLssKVqDjVitaLNWvWGCd1khfuzcG60Nk82INTuThdijuD0xlcFg23k6ocd4WJZCD8MiBncPeobYmGy6LhIBrHLBokDj2aCV7kwsaRZNQTDaPsW/+LQNaMlhANR/4NU6Q84hqV7SQXzmt/omHzsW1xnuvb1FBm2xU+Kc3CsF1kgFOnsjsj12vqVFbd1KnDdupUlaZOuUfUdoyZOHEivvvd7+IHP/iBcbTmtKYTTzzRTG/il36uIEWFnFYCWhrefPNN0OGaiv2vfvUrQxA4TYoWCOdBUvDVV1+ZPTLo7M3Vonr37o2bb74ZeXl5jawO9L/gXhy/+93v0KNHD0MwuLcFLSfMg9YVHiQ89N14/PHHDbHp1q0bOnfubPIlYaLPB6dDBWvRYFrmZzfss+U42xG2a1k09DVbFg3JgCwaTQ6xLqKxsW7qlNNHo4FoZFG57tlMIBFhOquY+5+71U198rQqeBEOp/Jvr/1JAJVSj3I846wCa6dN2Tz9z84ybN5OcuG85n2br87CIoZlwBKN7E7YkZ2E7Vm9kJuVgkJfV+RnJWDzvHEwRKN0PyCi0eS42i436OvA6Ua0ZjDQ8kCrAf0u6KhtLQRUymlV4PQiOoq/8cYb4E7gnC7F6VYkFs6Dyj6VeE6XeuWVV0zeJClMy2lYzoN5c1oU82V+TM/9NbgxIPPwP+ir8eGHH5p60sry1ltvmSlYJAqWPPAczMFnLNHQhn3h/3Krr+PCWDLglgFZNNyjdVOrTjmJRn5mMvKMMzhJRDBEI4Di7Ukw/JX85n47SYBV6limVf5t+V5xNr0lPE2V5SzDmY8tw3m25TnyFvEQ+YpFGaCvVHZn7CTRyDpGNIqy64jGlnnjUEyLhoiGezCNhphglXL/uvK5poIzrTN/57Uzjdc1iY3zsM+SxNjpXPY+7zG9DTa+ubOIhlvpkSIoTCQDkZUBEQ33SC2iIaIhC4QIYoMMxADR4Id4uiFwnzj/D+ruES40MTHho+HfVCrsVqG392wclXj/ezYNz1T+GQKlCXQvUP6811zH2Xo669TctYhGZBUqKbDCWzLglgERDfdILaIhotGgZMbiF3jVObSWoxggGvTzJdGYMWNGs/qqe8RrXUzMEQ36YtA52+vgFCY7NcnrPuM49agpMkArhPX18HqeJMGW72Wt4PP+dQtETLzK8IoT0XArPVIEhYlkILIyIKLhHp2bIhreG/YFmjplncQ5paipqUTN+UUEUPobHLjr83ApmCzTGfiV2vnbq062Pk2cG8qw+TinS9lre09fxUVYOoAMiGi4B0kAMUc0PFvRwshAFosWZtUoebjyFdGIrEIlBVZ4SwbcMiCi0Wi4Nz8s0ah27Az+LXcGz6rbGbwwIxEFGcnINzuDW6JhSYXzbAmGU6H3UuCbIhNNxTdFLLyUOi+l3yvO69nm4mw+XufmntV9kZAYkQERDfcgGa9EwxOJKI4U0XArPVIEhYlkILIyIKLhfkmQaFSjBjWWaGxYi3WLJmFrVn8UZXRFA9HI6Ia8LCfRsF/07ZkKuJ8yFTbHb79y/MvVb3dfCBNhEowMiGi4B0kRDU9Moi5SRCOyCpUUWOEtGXDLgIiG+9UgoiHS4iKIwSikStMxiUuUEw3OuqGPxtChQ82+bHQFiMQRl1OnIgFsKMsQ0XArPVIEhYlkILIyIKLhHtVFNEQ0RDQkAw0yEANEgxtADxkyxBANf59i9wgXmhgRjdDgGNZcRDQiq1BJgRXekgG3DIhouIf5BqJxtAyo2gdw6tTCY1OnijISUUgfjRZPnbL+GU35XvjH2/QeZ30975hfz9Wv0devIhruQVJTpzwxibpIEQ230iNFUJhIBiIrAyIa7leDJRq1DqKxfuEkbMvqj11zu6JobiKK5iajoCVEo2GFKH8y0dRvbo7n3CBPX5gbvjBLGY8+Zbwj94mIhnuQFNHwxCTqIkU0IqtQSYEV3pIBtwyIaHi/GugK7iIama0lGtYi0RSp8IoXyRCxELmMChlogmgU1u8Mvrmddwanj4amTnmP43EfK6LhVnqkCAoTyUBkZUBEw/0qshaNGodFg1OnaNEoqrdoFM61U6e4whRXnnIuZWuXe6Wi2BqSQeIhohEVSmZH/lKvtgVnGfIlIje7M3J9nbAjOwnbsnohNysFhb6uyMtKAInG4dX3AaX7gapy94AS5hgRjTADHMvZi2hEVqGSAiu8JQNuGRDRcL9FGhGN6nofDb/lbY/5aDiXsvVfzra1JENEQyRD1oyokQESDV9n7PB1wvZ6orEjKwUFJBrZCdg0bxwOkWiU7QeqRTTcI6pi2g0BEQ230iNFUJhIBiIrAyIa7lcAiUYVahDchn3WeuFPMpzWDEs4ePaaJuWMq08r/4zgvjbrq7xwCrMM5PoSsaOeaGzLTsLW7F7Ynp2CfF9X7MxOwMb543BojYiGeyRVTLsjIKIRWYVKCqzwlgy4ZUBEw/0qsESjym7Yt3EtvuHO4Nl1O4MXZCQiPyMZeZnJyDNKjhfJ8PoibQmHk1g4ry3J8HpWcVHzhTvMiq3aGV2ybonG9pxO2OZLwhZLNHKOEY2DIhrugVQx7Y+AiIZb6ZEiKEwkA5GVAREN97uARKMSNTBEo2YfUE80Nmf3R0FmV+RnJiKfJCOzFX4UAXcGb0V+Unr1RV8yEFYZINHY7uuMbTmdsLWeaGzLTkFeTlfk+hKwYf44iGi4x1HFRAECIhqRVaikwApvyYBbBkQ03C+D2nqiUUmLBonGprX4+s5J2JTdH/n1RMOQDEM0rJUiwNk5DUpEI6xKoawB0WUN6Aj9YYhGTh3R2OJLwmZfL2zz1RGNHSQat4/DgSfvA47IR8M9miqmXREQ0XArPVIEhYlkILIyIKLhfg1YolHhIBpfkWj46ohGXmYidpJkZNECQYLhnP7kde2wVIhoiGjIAhFTMkCisS2nM7bmdMKWnDqisdWXgp3zuoJEY/3t47BfRMM9kCqm/REQ0YisQiUFVnhLBtwyIKLhfhdYouGyaDiIhrFoZCW1nGg0LHfbhAVESmhMKaEd4Yu92hDYCmQtGoGIhiwa7nFUMVGAgIiGW+mRIihMJAORlQERDffLwEU0NtZNnXL6aNRNnWoN0Qis1EjpEz6SgeiSgWZ9NDh1Ss7g7oFUMe2PgIhGZBUqKbDCWzLglgERDfe7oKlVp5xEo84ZXERDSnF0KcXqj9D3hyUaXHVKzuDHxsvvHLvUVbQiIKLhVnqkCAoTyUBkZUBEw/2GENEIvbImBViYxqoMxALROHz4MIYOHYr09HRUVla6B7UwxIhohAHUUGcpohFZhUoKrPCWDLhlQETDPbKLaEgpjlWlWPUOvexGO9HgCEaiMWzYMMyYMQNVVVXuQS0MMSIaYQA11FmKaLiVHimCwkQyEFkZENFwj+xNEQ33hn1JyGvpqlNy9pazt2QgpmSARIM7g0frhn0cwUQ03OO4YgCIaERWoZICK7wlA24ZENFwv44s0ah27Az+LXcGz6rbGbwwIxEFGcnIzxTR0Bf00H9BF6bRhaklGjt8nbAtOwlb7c7gvmM7gx9qR2dwEQ33GK6YegRENNxKjxRBYSIZiKwMiGi4X0kkGtWoQY0lGhvWYt2iSdia1R9FGV0hohFdiqAUc/VHOGWARCPX1xkkGtuzk7Atqxd2ZKWgwNcVedkJ2DRvHA6tvg8oa58N+ziCyaLhHscVI4sGpFBGVqEU3sLbSwZENNyvIxENKa7hVFyVd2zJVywQjeLiYuOjMXPmTPlouIf0+I2RRUOKn5fipzjJRSRlQETD/Q4S0YgtRVCKu/ornDIQC0SjpKQEw4cPB4lGdXW1e1ALQ4ycwcMAaqizFNGQQhlJhVJlSd68ZEBEwz2yNxCNo2VA1T6AU6cWHps6VZSRiEL6aGQk1zuDN7HLt88RLwfgmHIADqfiqrxjjBhx6lR2Z+R6TZ3Kqps6ddhOnaoqdw8oEYgR0YgAyLFYhIiGFD8vxU9xkotIyoCIhvvtYYlGrYNorF84Cduy+mPX3K4ompuIornJxiE8LyvGlCYRHhEeyUDLZMASjexO2JGdhO1ZvZCblYJCX1fkZyVg87xxMESjdD8gouEeUBXTfgiIaEihjKRCqbIkb14yIKLh/Q6gK7iLaGSKaOhrvIhl3MmALxE7sztjJ4lGloNoZB8jGsW0aIhoeA+mim0/BEQ0pPh5KX6Kk1xEUgZENNzvAGvRqHFYNDh1ihaNonqLRuHc+qlTsmi07OuwvqYLr1iTAWvR8NVZNLjqlLVo5Mmi4R5AFRM9CIhoSKGMpEKpsiRvXjIgouF+JzQiGtX1Php+y9saH43MZGjqlL7wx90X/lgjCm2tbwwsbysfDfc4rhgtb6vlbadK8fVSfBUXWbkQ0XC/jkg0qlCD5jfss0QjBTuzmwtSyKWQSwZiUQZiYcM+EQ33OK4YEQ0RDRENyUAUyICIhvt1ZIlGld2wb+NafMOdwbPrdgYvyEisW3EqsxvyskgwujcTLAmRohmLiqbqHN9ya4nG9pxO2OZLwha7M3jOsZ3BD7bzzuAiGu5xXDEiGlIyo0DJlPUgstaDaMRbRMP9OiLRqEQNDNGo2QfUE43N2f1RkNkV+ZmJyOe0qcxu2BkU0SARIdmIb4VN7Vf/x6IMkGhs93XGtpxO2FpPNLZlpyAvpytyfQnYMH8cRDTc46hiogAB+WhIyYtGxVN1ii+5FNFwvwxq64lGJS0aJBqb1uLrOydhU3Z/5NcTjbzMZOwk0TAEojmLhohGLCqYqrOIEWXAEI2cOqKxxZeEzb5e2OarIxo7SDRuH4cDT94HHNkPVGsfDfeIqph2Q0BEI74UOinw6u9olAERDfcrwBKNCgfR+IpEw1dHNPIyE7GTRMOsOBXM1CkRDSntUtpjVQZINLbldMbWnE7YklNHNLb6UrBzXleQaKy/fRz2i2i4B1LFtD8CIhpSPKNR8VSd4ksuRTTc7wISjQrUwIto5GV2RWOiQRLRwy94WTisn4bzLOUzVpVP1Tt+ZLeBaMyrIxqbfL1gicZ2EQ33AKqY6EFARCO+FDop8OrvaJQBEQ33O6HlRKMndmbbYEmHF9lwxlnCET8Km5Rz9XUsyoCIhnuMZMx3vKMVG00IiGhI8YxGxVN1ii+5FNFwvxWCJhrGGZzEwpIMe2ack1Q0dU2yIeVTGEgGolkGRDTcYyRjRDS8cYmqWBGN+FLopMCrv6NRBkQ03K+FponGAORnOaZOiWiIKIkodngZENFwj5GMEdHwxiWqYkU0pHhGo+KpOsWXXIpouF8LTRGNzTkkGgnHfDRENDq8khnNX9pVt8hYgkQ03GMkY0Q0vHGJqtj2IxrA0aN0dgQ+2FCOCdMfQd+pjyElfSV6TF+JntNWoffUlTh96nKckbYsdGHqMpwRyhDKujEvr7qlEYNQhTZi6VW/UMeFrK31mIW6fqHOL97a64ffmWnLURdIrpah5/Ql6D59OZKnPY5e09dgaNpjmP/YG2asqImq0TO8lSHRqDShGqg+DGz8EF/d+XtsyjkPO7MSsSM7Adt8DInY4ePylyl1ITsFuQwmrhtyG752W3+MunMe1+BvCN2Ql82QYjb/4waA0R26Iy8rmBDt7VD9olvOoqd/dmYnm7/57b6u2OZLxhZfD2zP7oZ8XwJ2Zidh0/xxOLTmf4GyfUD1kfAOTk3krg37mgAmVqOPHj2K6urqNle/vYhGzVGgCuDCjVi7sRrj0pfgrGlLkTjjCSSnr0bK9NXoOe1x9E5bhjNTF4cupC3BWSEKZ6YtCV29bBs967YUZ6U5wzKclcawPGA402C3FGemsp7O0AY8PesXOkzr+sbZVl4H195j6fyfD3X9Qp2ff307ensb43d26lL0mbIcfaesMH3de9oS9KgnGj3T12BI2mPIeewNcGX4to94bR4yI5ZBA9EwFxXA+s/x1aI/YWPOYOzITsKWnK7YcHsXbJzfBVvndcX2nATsMCERO3LqQm5Ooll/P9eXhJ0M2cnIM6Eb8rNS6gOv634XZKagILN7kCEF+RkM3cIYbBndkZ/hDD2QnxFMcD7Da5tfOOscbN7EuiV4B9svoUoXTVgFi2nHTmc258zshtysboZk8G95d3YSijK7Y/u8C1G2+l6gbC9QI6IRsYG6IxZUU1ODTz/9FPfccw+uu+46XHrppbjtttvwxhtv4NChQ6bJTBPs0V5Eo7remlEC4O1NtRibvgxnTluOhBmrkZS+Bt2mr6knGlSUHw1ZOCvtMYQyhLJuzMtdt8UexKhe+U5dhrOaCXUEw59YPNZqPN31Cy2eZ6W1ob2GkBCbxopsXZ6hrmeo8ou39rpx65O6BOdOXo7+t63AOanLcPrUpeg5bQWSpz0BEo3BUx+Db/G/zUeJeCIa3Bmc7TXfkypqgHVf4qtFf8aGnKHmi+bG+V3xzR2nYd2Czth8+2nYPo+hC3bkMHRtCLk5CYZs7PQlmi+fVE7ysxgsweiGgqw6pbcwsztaEgoyUhDWMLc7Cub28As9UTC3V5CBaf2f7x7eOrcAk5Zg3R5pw9q3LcBJ9UhBgflb6In8uT3NJp252YkoyE7E3qwk7MnoiVzfBJQ/8Tc/osFRpKkQrJYYfDpZNILHKmpT0oKxbt06TJ8+HVdeeSVuuOEG3HTTTbj44otx/fXX44UXXkB5ect2hGwvouFv0Rg/fTHOmboY3dJXoMf0FeCcbTNtynyJ91eUW//brYT6K6Ut+x1Sa0uql9LJ+tQTC/8v+6krcFbAsBxnppKoOUPbrDChxs87v1a2t8HCYy0CPHsRj5b1sXcdQ5lHvLW3MXZnpy5D38kr0G/yKpydusJMH+w1dSVSpq5Gr+lPYfDUJcha8iZKUWcFjdoBOhwVo47A+VPlR4D1n+KrRX/E+nlDsDUnGRvnJeLb27lRVwK2zE/A9nkJ2J6T2BB25CRhh48hGbkNwc4nd0yfqp+ClJ/VAy0KmT2Ql9E9zIFlMPR0hF7Iy2Do3Uyw6ZzP2vzCXe/g8s/PbCHmLe2jtqSPSP8Gh1P45SwW6lEnuzszu9dNocrphLycztjtS8CujF7Y7rsMZY8vBkpLgWrOF4n8IaIRecxDXmJpaSnuv/9+DB8+HFOmTMH777+PjRs3YunSpRg0aBBuueUWfPvtty0qt72IRu1RgFYN0qL3N1ZiwvSH0S/tYfScvhhnTHsMZ6cx1BGKM1KXIFThzLSlCGUIVb1sPu66LWuYv35m2gocCytxZuqqZsJKnJG6HGekLvMLS1uNp7t+ocXTTPcyc/adbeV1EO1lmkYY8TnO/yeGoa5nqPJrQ//GZHvduJ2VuhznTFmFc6Y8jrMp02n80PA4uqc9iV7Tnsb5U5chY8nbKK7XuVs0wMV6YuMRXgmUHwTWf2B8NNbPH4wtOd2wcV4y1s/nuRu2zOuGbTnJ2N4QumGHL8URumOHzx1ys7sjlxv9ZfVsVcjN7IHwh57IzezlF3ojN+P0wCGzt98zzIN5RaLOwZXRWtwj9Vw0YaW61MnUjqwUbPedhq3zT0Hu/F+hKOc0FGZy876rUbpqNVBSU6dctcPYJ6LRDqCHush9+/Zh/PjxuPbaa/Haa6+Z7O00qVtvvRVjx47FQw89FHSx9PNoL6IBvkCP1n2s+2DDEVw07SFDNLpPX4pe05bijDSGZcZHg34aoQnLcXracmMpobWkzSFtOXqbEMb6pa3A6Wkr/cIqnJ72eFChd+pK9E5d4RdY79bUOcT4efUBlUwT2OZVOH0q22rb+wROTwsUiIlNX382CwpE+wpO9W1tVHe2JVBbeS9W2+vXH2mrcEbaEzgjdTXOTH3CkOfeaavRPfVp9Jr2LAZOXY6ZS9/FQbNTdtDDW8dIaP0zKg4CG9/HF3+5FutvPx/b5iVhmy8J2310EE3GTl8y8nxJjUI+p0g5Qp1vhvXRSEJedhLyzBQqO5Wq5ee8zCSENyQjLzMZeRndHCEFeRkMzX2FtukczzIvE8Jd7+Dyr5vC1nLc+VwBHYGz2J66frS/25Kn/7Ph7dvgMFIdLE51srszk4s//Abb5/8CefNOwS7fr1GU2Q3bfZegbNUSoLgMqA5++nwoB8ri4mIMGzYMM2fORFVVZKwqWnUqlD0IoKioCAkJCaYTOYXKHiQMtHRccsklmD17to1u9kxB4FSroUOHYtq0aS2edtVsAYES0KQBgH8O7319CBemPYizpjyMhGnLkJi2AsmpK9EtdSW6pzGsCE1IXYGU1OUhDd1TQ1Q3ttGrflNWIMWElUiZYsMqpExheLzZ0D11FRqHlehucG1Fvb3qF2I8U6YsR+ItS5B06zL0oCKdvgY9p65Gz6k8BxOY9onGYdrjZhUzrmQWfeHxxnU1de/I7fXqA/YX+/Yp9E5bYwhH79QnkXzrU+g59e/oO2U5bnvsLewF0D4ujoEGsgjcO0q2UQps/QgfL/wtNi4ajLwF3VA0Lxn7spOxnyGncdiXkwSGvTmJ2ONLwJ7srtidVRd2ZXVBUeZpKMzojMKMTihoQ8if+xuEPczphPw5nf3Cacif06WZwDT+z3UKf31bgElrsS/M7IR98xKRP+fX2Dr9F9gx85fYf3sydmWfhqKs0IXCzM5QiBYM+DfbBYWZpyE/+1TszPkFCnw/R/6sE1A0uxO2Z41EybJFwP58oJrreUb+OHjwIAYPHoypU6eC1g37ITycNRHRCCG69M/45ptvcPLJJ2PBggXYuXOnyZ0kg8fKlSuNYzgtG7W1fDG5D8bTgkFyQQFgsAx08uTJ2L17txEOTtEKZygvKQEqylF5uASHDgNvfVyAsTffhbNvvQ+JaY8hOXUxuk1Ziu6Tl6DnlMXoNeWxkIWekx9FKEMo68a8XHW77TH0NGExet5mwxL0vI1haTNhCXpNXopek3muD1OWoFcbMHXVL8R49rjtUaTc/DC63fQwUm5+BD1uZd8vRa8py9BryvJmAtMwrTPY9lKOojWwjs46d/T2Nu6HnqlL0SN1OXqkrsDpk5fj7FuX4sxbVyDlxuU4ffIaDEhdjNTFr6MAwMEaoLKyMi5CVUUlqsrKgYoKoHw/Kr58Ay/NnYiP5w/Ct74UbOFX/Vk9kD+rO/LmdMfOuSkNIXduN+RmJGNHRjK2z03CtjmJjpCArbMTsGV2V2ye3QWbZ5+GTXNs6IxNc4IPG2d3QljDrM7YOOs0v9AFG2d1DTIwrf/zncNb5xZg0hKsnWk3zznN+N9syeiCTbOJUSdsmPmb+naxfaEInbBh1m8UogWDmZ2xYWYXbJjZFetmd8Y3c36NDXN/jdyMztg+JwHrs0di36r5wN4tQPF+lJdXovxIoFBhdEHqg6EIZWVlKCwsxPnnn28+XPO31U/d2mjoYkQ02oils5P4cv3ss89w/PHH48477zTWDWZvScWaNWsM0bjxxhs9l72tqKjA2rVrsXDhQuNMPmvWLMydO9cwz65du6Jv374g2aCjeXp6eljDzPR0ZM+ajTlTZ2B6WhZ+O2kaftVrBI7rMw4/6D8BP+k3Dsf3GYPj+4zF8X3H4bi+F4Ys/KzPeIQyhLJuzMu7boyf4Bcuws/OuThw6HMRjuvrHya0CUvv+oUW05+eMx4/PefChsC2//QchouaCTbdsWdNXn3G46d9xkVpYFsbt7eurR21ve5++EmfC/Hjvhfhx30n4rizL8LPz7gQJ51xMX7a+xIcd9YV+OU5F6DfuEuRlpWD9DkZ4NjVkcPsWbPAMGvGTMycno7MmbMwLz0V6b+/HJf374QbBp+Amwd9F5P7/zem9TkB0/scj+n9fopp5/4E0879bxOmnvt+UOexAAAZbUlEQVRjTO1fF9L6/wgmDPghUhvCD5A68AeYMvD7DWHywO9h8nktC7cN/C5uG/hfYQzfxW0DvucXvo/bBvwAtw34YTOBaZjW7/mw1zlYPL7bYryd/ZM2+IeYMuj7uKX//4c/nfN/cMNZ38Et/f8vbh3wXyELzE8hSjA497u45dwf4OZ+P8ZN5/4EN/b/KW4Z8N+YOvBHmH7ef+Omob/BrZcNxoKZtyIjfSpmps/CjIBhJmakzwiprpeWloZOnToZokGLRiQOEY0Qouxv0cjLyzO5WzLSnEWD7PLJJ5/EpEmTMGTIEDNdatSoUeaac+oY6GQ+evRojBkzJqxh7OgxuGDEaIwbPhYXDJ+AEYMvQt8Bo9Fn9AScdcF4DBgzEsNGno9hI4dg8OiROG/M2JCFgaPHIJQhlHVjXu66jcXAUQwXOMI4DBw1Pqhw3ujxOG/MOL9wQavxdNcvtHgOHD0WA0aNxY9OPgWnJHQz9R44ahwGjLwAA0byPD5AGIcBoy7wC8xvDAaMGh2lgXVjHf3q3WxbiUMsttfdD+eOHos+Y8ajz5gLcd7I8Rg+9AIMG3ohBg25GAOGXITBg4dg9LBzMH7MeRgzaig4bnXoMHIURo0YiZHDR2LE8JEYPmwEhg0diqFDBmPE8HMxdvQZuGBkd4wf2hcXDRqOiwcNw8TBgzBxSH9MHHIuLh7ar3EY1g8Xj2Doi4tGMvTBRaP64KIxjcOEsedgwtizWxQuHHMWxoc9nI3xY9j/9WH0ORg/ug/Gj+7bTGAax3PmeeYViTo3XwaxayneNv2FY8/C2JG98T8n/h90PvV7OH9AAkYM6W7aduHYsxGawDacifGjFaIDgz4YP6o/xo88HxeMHIExI8dh/MhRmDB8AC4Z0x8jLxyMfhePwIAJozF0zGiMHjEGo0fw7B1GjRyNUSNHYeTIkSEJHJOpO/L86KOPgjpnJA4RjRCj3JyPBvfUmDNnjmepJCSHDx82U6W43wYDBYHnbdu2Yfv27di/n+a24M1otJK0KpRXoPRwCY4cLkNlaTXKDlWjoHA/9pSUYfeREhws2Y+yw7tQVsypXAdwqPRwyMLBkkMIZQhl3ZiXd90Y7wzFOFjMUBI4lJTgUKl/KMahUobWYepdvxBiWnwIew/sw5Dhw5CWPh0Hig+j+EgZissYjjQTyurSHilFsSuUoPhINAavunbk9rr74HB5KQ6UH8GB8nKUlB1BZXEZKorLcfjAEezedRD7duWjdP92VJftwZGSg2Z6J7+WdcRQWlKCY6EUZaVl2L//IPbuO4A9+/ejcHcBDhQX4sDhPBw8sB+H9pXj8L4jKN5fguIDB+vDARw+UBeKDx5AyaGDKCkmbvWh9CBKyg6i9MhBlJYfOhYqDqKkReEAissjEQ6iuNw/HEJxeTDB/zn+jkSdgyuDeJe2IhQf2Y+8om0YMmIgbkv7M/J3b8ehsr0orTyI0spDIQp1dWuZTLRUhpQ+eHwPo6S8DCVHKlB8pBaHjwAl5UB5eQXKjhRjX1Up8o6Wo7DqCA5TNzvCUNl0KK9ARXnopk9xWj59NLhoEfVIHqHYVNpToXVEimg4wAjFJTuQjJGrTr366qsmSzt1ipv2kWjQstHUYa0fPPM5e6YwMNBng/HWf6O5s82jqfICxjv3kKnlFLA6x3BuUFXLJamOcsUC/qrzQQmYVwtuHkVo/7Wg6KCSNl27prfdcULpfx1UoS1I1HT9QnOn9mgtyo6UYfSYMZibMRdVNdWopby2KdSC+UZvaGv7/J+P5ra661aDo6g2AaafwYUiOARwTKgBjtZUA7XlwNFK/jDjFseuDh1qj7WvtvYoajgum8C/h0rUHq1AbW0N4TDBrKrBMd0z1OAo0xrsjp1rUY22hqNg70U61OIoWhIiXb/gyzOCbpZgpF9l8IGYH6koxeixI5A+cxrKyuumqfAvqa196nw+8n0bPHbxVzfKfJ0eYCXFakf8e6jCURzBUZTjKKo4VlTXBAxW7wvl2eqQLVAp2pxURKPNEDbOgA7aDzzwgHG2IbH44IMPsH79eqxYsQLnnXce6J/xn//8xxCFxk+2/ZfXS73tuSoHIeBGgAsUcBof5+FbIu1OpRghIASEQPwiwBkInPLMWQwaJ+NXDtrSci+9ri1xbalLa58V0Wgtck08R+bJDfm4FO3ll19udgP/85//bHYGp5XjqaeeMlOjKCg6hECsImBXQuPCBJyap0MICAEhIAQaI8AZDlxKlEva29kHjVPolxDo+AiIaIShj+3qU/fccw+uueYaM12KO4K//PLLZm5cGIpUlkIgoghw7v11112HRYsWmeWYI1q4ChMCQkAIxAACHCepA9x9993GosEPkTqEQLwhIKIRxh6n1YJfMewhK4ZFQudYR4AWjaeffhrvvfdeRNbhjnW8VH8hIATiDwFuuPvss8/iww8/jL/Gq8VCoB4BEQ2JghAQAq1CgFOm+CLVIQSEgBAQAt4IcJyUJcMbG8XGBwIiGvHRz2qlEBACQkAICAEhIASEgBCIKAIiGhGFW4UJASEgBISAEBACQkAICIH4QEBEIz76Wa0UAkJACAgBISAEhIAQEAIRRUBEI6JwqzAh0DEQ4Lxj7jC6Z88eEw4cOGCWudWCBx2jf9UKISAEWo8Ad13m+Lh3717s2rULhw4darQwTOtz1pNCIPYQENGIvT5TjYVARBHw32iKL1DuB3PFFVegR48eJnDPmFdeecW8WEk2GLjMs4hHRLtKhQkBIdDOCOzevduMj1z+u1+/fujUqRP+9Kc/4fPPP2/nmql4IdA+CIhotA/uKlUIxAwCTrJAkvHSSy+hf//+uPrqq3H77bebvTS4VvyQIUPw+uuvm301uMoKV6RyPhszDVZFhYAQEAKtQIDj3ebNm7Fq1SpMnjzZbNTXpUsXXHbZZfj4449bkaMeEQKxj4CIRuz3oVogBCKGwPr16zFlyhTzpe7hhx/Gli1bkJubizVr1iAhIcEQD8bpEAJCQAjEGwL8wMLpUt9++y3effddfPnllxg0aJAhGtpLI96kQe21CIhoWCR0FgJCoFkE3nzzTZx99tmYOnWqeZnaB4qKijBu3DizC+5rr71mo3UWAkJACMQFAk1Zb8ePH2+IBjc31SEE4hEBEY147HW1WQi0EgHucvuTn/wES5cuNU6ONhs6hWdkZIAv1SVLlsDfr8Om01kICAEhEA8IkHjQwsExkT5s77zzjvFbi4e2q41CwImAiIYTDV0LASHQJAJ8aT7zzDP41a9+hdWrVzciGvv27UNOTg7OPfdcLFy4UC/UJlHUDSEgBOIJgTFjxuCSSy7BBx98EE/NVluFQAMCIhoNUOhCCAiBQAiUlZVh5cqVOPHEE/HCCy+Y5Rv51Y6B9/7617/i/PPPx913362lHAMBqXtCQAjEDQKjR482ROOjjz6KmzaroULAiYCIhhMNXQsBIdAkAkeOHMHjjz+Ok046yVg0uHeGPWjtWLBgAfj17pFHHrHROgsBISAE4hoBEo1LL70UIhpxLQZx3XgRjbjufjVeCDSPgPW3oOWCS9uedtppuPPOO5GXl9fwMJey5VrxF198MZ588smGeF0IASEgBOIZARGNeO59tZ0IiGhIDoSAEAiIAK0V9uBa8FyucdKkSWZdeO6AS5JRWFho9tbgfhpvvPGGSU5iokMICAEhEI8I1NTUmLFx1KhRmDhxItauXat9heJRENRmEQ3JgBAQAsEjsGPHDuOLceqpp2LWrFl4++23wSVv09LScMopp2Dx4sWgYzhfsk6CEnwJSikEhIAQiF0EaAHev38/vvrqK3z99dfmw8zQoUPNanyM4xhKnzbr36YPMrHb16p5cAjIohEcTkolBIQAYF6Q69atQ3p6uvlKN2zYMAwePBicHjBnzhzwnn1xkmzoEAJCQAjEEwLcsI87g3O/Ia7Cx8UzTjjhBKSkpGDIkCHIzs7Gxo0bzccYkhKNk/EkHfHZVhGN+Ox3tVoItBoBvhxJKLgbOFeY4nK2dADnruF0GNchBISAEIhXBEpLS81StvzwkpmZiaysLLPHEH/7fD7jw1ZQUGAIBkmGiEa8Skr8tFtEI376Wi0VAmFDQNOkwgatMhYCQiCGEbBWC2vpZVMYx+CMi+EmqupCICACIhoB4dFNISAEAiFAgkFncB76MhcIKd0TAkKgIyNA4mCP5sZCEgxnevuczkKgIyIgotERe1VtEgJCQAgIASEgBISAEBAC7YyAiEY7d4CKFwJCQAgIASEgBISAEBACHREBEY2O2KtqkxAQAkJACAgBISAEhIAQaGcERDTauQNUvBAQAkJACAgBISAEhIAQ6IgIiGh0xF5Vm4SAEBACQkAICAEhIASEQDsjIKLRzh2g4oWAEBACQqAxAlr2szEe+iUEhIAQiFUERDRitedUbyEgBIRAB0SgsrIS2pelA3asmiQEhEBcIiCiEZfdrkYLASEgBKIXAVk0ordvVDMhIASEQEsQENFoCVpKKwSEgBAQAmFHgBaNbdu24dVXX0VRURFo5WiP4+DBg/j888/x2WeftUfxKlMICAEhEPMIiGjEfBeqAUJACAiB8CBQUVGB/fv3G2WfCn9hYWFD4G/eC/U0J1ozdu/ejfvvvx8nnHACnnnmGezbty88DQTA8pj/4cOHG3a5t4W9/fbbuO666/CHP/zBRuksBISAEBACLUBARKMFYCmpEBACQiCeEPjXv/6Fyy67DKeeeipOOeWURqFz587m3pdffumChMp7TU2NK94ZwftVVVWGqPDaSVhoSVi8eDFOOukkvPDCCzhw4IDz0Sava2trwdCSg2WNGjUKc+fONZYLPm/rXlpaih07dphg45g32+esr7M83rNpnPFNxfmn0W8hIASEQEdCQESjI/Wm2iIEhIAQCAECVLapSP/973/HpZdeioEDB+LBBx/EihUrGsLjjz9upjbt2bPHVSIJhPOgkm4VcGc8r5mWlhOeLUk4dOgQlixZgpNPPtmUUVJS4v+Y6zfryylWTkLARJb0OOOd9aFlplevXpg8eTL+85//NMrX1o1523waJaj/wfyc+TOav217/O955aE4ISAEhEBHREBEoyP2qtokBISAEGgjAlSOSTSuvvpqE/Lz81FWVuYKb775Jp5++ml8+OGHDSVaUrFz50488MAD5h6tEiQlH3/8MVatWoW//OUvuPPOOw2BoS+GnYbFZ51E4/XXX0dxcbGZTsVr5sdpTvY4cuQIvvjiCyxatAjffvutUfBJEFhf1uvee+/FggULsHDhQmMlWbdunSmLpCQvLw/33XefsdScddZZ+OMf/4h58+aZvGjNoJ/Ik08+aQLLIyasH9vBaVXM+4477jDteP75502ZTGcJBtOwvu+//77BktPB7rrrLqxevbohrW2HzkJACAiBjoiAiEZH7FW1SQgIASEQAgRINK666irjp9DU9KVHH33UTKGaMWNGI6sFCcBTTz2F5ORk42dRUFCAr7/+Gg899BB+97vfYezYsSZMmDABt9xyi1HEqdzzINGwU6deeeUVWKfstLQ0JCUlYfv27Q1l0SLx8MMP4xe/+IWZZkUyQGJC4nPTTTfhiiuuAMsYPXo0xowZg9mzZxsHb5Imko7U1FQzRYvPn3HGGRgyZIhJT7+Nd955x7T/8ssvb0CTdSGpuPHGGzF8+HCT74gRI0ybHnvsMdD6YolGTk4OTjvtNFMG63LJJZdg8ODBJn8SHGKkQwgIASHQkREQ0ejIvau2CQEhIARagQCVZWvRoJJNPw2SBFoobCBx4JSnjz76yCjzVLap4Nvjm2++MUo9fTlIDKjYM+6ll14CyQN9O7766itDKCZOnIi+ffuavPk886HSfvzxx+Of//ynIR6c1jR16lRDXFgH1o/H3r17QbJz3HHH4bnnnjPxtHh88sknWLlypSmDfha0vJCoMM9HHnnE+H3QOsF7Z599trFmvPzyy8bKkZuba6Zh0SJx7bXXGqLFspj+3XffNXFDhw41BIpto+WEbWDca6+9ZurF/7Kzs/HDH/7QTM3iVLBPP/3UkK8rr7wSxIVEh3nqEAJCQAh0VARENDpqz6pdQkAICIE2IkCLxrBhw/Czn/0MnTp1Ml/n+YWeoWfPnuAXe5IFn89nlGySCJIUTl2iE/ekSZPw29/+Frt27WqoCQmC80s+LSVU/E888US88cYbhryQlJBo/PSnP20gGh988IGxUPTu3dtMO7JEgxaGpUuX4nvf+54hMSyI06JINniPdSkvLzdkZevWrejTp48hHFT6efBev379jFXlvffeM0SFeTOQNNAiwkBCQF+NzMxMY5ng1C/Wk/EkXCQ1F110kSEhjOfBtL/85S/NdCxOt7L1euKJJ8x0rWXLlplpXCax/hMCQkAIdEAERDQ6YKeqSUJACAiBUCBAojF+/Hicc845uPvuu80UJU5TYuAXeirm9K2gzwGdxqdMmWIsBVS06YvA6VF0GqeFggo5p0SRMMyfP99MPSIRobWE1gxaJEhU+CyJCKdO/b//9/8M0bAWittuu81YB5yWE+ZJxZ1kiPUlQSB5oMWFZc+ZM8dYJFjOxRdfbAjNNddcY+pOjJg3y7/11ltN3SxunP5EosHnaNXhb7aB076Yz7PPPmuIB8kHy6TF5IYbbsCgQYOMPwnzIdHg1DESCks+SHxYz//5n/8xOHo509s66CwEhIAQiHUERDRivQdVfyEgBIRAmBCgQsyv+Zzqs3nzZrPfBH0XbKDCTwWcG9rR14HWho0bN5opUjfffDPGjRtnHKpJHKhg05mbZIE+E/Rx4DPp6enGQsApTZyCROJAXw0n0WAcHb6nTZuGlJQUc982mVOnSHpIVP7xj3+YaPptLF++3JAfkhmWSR8STp2iNYZWlrfeesukJVEKhmiQTPDgFCkSDS79yzbZFbNIukhWOA2L5ZOUkGjQ7+PFF1805IfPi2gYGPWfEBACcYKAiEacdLSaKQSEgBBoKQL0eSDJuP766800JP/nrdMziQdXV+IGe1Sq6fBNKwDJBKcLkWiQPHD1pzPPPNMo/WvXrsWmTZvA6UxMT6LA5XO5WR+JBf0uOHWK5IG/6c9A60SXLl0aLAasD/0puKLUj370I6P8M47TuVg+fSY4LYu/qfyzPE6TooM7SQ8PWhQ4nao5iwYtF2xvUxaNf//73w0WDU7Z4pGRkWGIB8siISH5ENEw0Pz/7d09SjRBFIVhQTDU3ERxHSJiZiLMOlyAgZGRqWZuwcD1uAO3MR/PhQtF46jBNzCWp6Adu7u6u+rt5J6+P5U/IRACf4RAhMYfedGZZgiEQAj8lACjWiM0hETxABATDOVxYzTbZ4AztIVYSYDmsVBhiYAQxmTjORBapY8St4QHASLJXCUoYVJEgbK0+grPckyCtvAmpWaVnj05OamkavckYnhThCzt7+9XOJNxS9g+Pz8vbwkPi/F51vv7+/rs7KxCuiSkaxLL9VUVynX69hw7dEr4lLl2jgavhtK87ql/52iYt5XEHdN4NAgr9+m+S6Ex5q/URfkTAiEQAhMRiNCY6GVmKiEQAiHwPwgwtDXeBPkMvBoMfYnb4+bLPYNff0a8cCmL+0mAZnATFM4JO2KMPz4+VhK5ECYCQ76GFbmVrD08PKxwKYY3YUFoHBwcVD5DP+f19bVK0ao+pdqVjbCxavne3l4loBu30rbK2cqXkL9hbEKdhIFJGpc7Mno0rBVCPAi3EoplIxb0IbScIxA0IVL6X1xcVJlbQubt7W29Wq3qmGu678PDw9r6HJ5NWOHgnPK4xmxdDZ4WzbnmXgfyJwRCIAQmIBChMcFLzBRCIARCYBsEJGff3NyUcX91dbVWwrY3hrzSr3InNMY5Q91q3sfHx+V96BAi5xnSwqXkZfjKL6xJlaa7u7vySBwdHVX1KKFTXd6W+OB5EDrFm6A87v39/fry8rLWsCCAbm9vqzSt0ClhWz0WC+1JZLcuhl9eD3kaPCo8NBYJ1AgASeM8MO57fX1dgoSg4qUhMogI3hPNnNybAFORy/0JGmMhjpw3V0KlQ6fch0ejmzmdnp6W0Pj4+CiB0aFZ3Se/IRACITADgQiNGd5i5hACIRACWyAgAVzZViFAy40n4fn5udbIYFQz2H3dV+pW6VfejGVjvPNiqEil39PTUxn81quwwrZcCtWZfPW3boZQKetU8IYwxBnxxMbLy0uFLskLkbDOyyD/w/M14+EpsGCgECcbz4YwKxWylN6VG6IRBVYIV0XKuJTsNS9ihxdHgjphYUw2/VW0IlR6dXMVuYgc9+lmDASGcDDPcl03a3d4hhK7uKWFQAiEwKwEIjRmfbOZVwiEQAhsmYBQHxsD3Bf/DpP6LgTIeYY44/urL/ntRdB3U7824IVw6dfNcfvjsT43/m4a6/Ja92vBM17/k/9dt2ybnrvsl/0QCIEQ+M0EIjR+89vL2EMgBEJgCwRGw5iBT0hsap8JAMdsXxnTzn0mNBzr6/zaiAj3GxshMPblgSBM+viy//La3m/xsBQky/0eLxY9vr7H+GtMy9Z5LON1I+Nl/+yHQAiEwCwEIjRmeZOZRwiEQAiEQAiEQAiEQAjsEIEIjR16GRlKCIRACIRACIRACIRACMxCIEJjljeZeYRACIRACIRACIRACITADhGI0Nihl5GhhEAIhEAIhEAIhEAIhMAsBP4BPKPJQ40w9CsAAAAASUVORK5CYII="
+    },
+    "image-5.png": {
+     "image/png": 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"
+    },
+    "image-6.png": {
+     "image/png": 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"
+    },
+    "image-7.png": {
+     "image/png": 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"
+    },
+    "image-8.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report - 报告题目\n",
+    "\n",
+    "* 姓名 卢程军\n",
+    "* 学号 2020262927\n",
+    "\n",
+    "\n",
+    "## 任务简介\n",
+    "\n",
+    "一个典型的二元分类任务模型；\n",
+    "训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。\n",
+    "数据的格式为：\n",
+    "CaseId,Q1,...,Q36,Evaluation，\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "包含了什么数据:\n",
+    "\n",
+    "数据id, 数据特征（0/1二值）,评审值\n",
+    "其中，数据id是不必要信息，可以剔除；\n",
+    "数据特征属性共有36个，属性值是≥0的数值；\n",
+    "数据介绍：训练集中共有200000条样本，预测集中有80000条样本。\n",
+    "![image-3.png](attachment:image-3.png)\n",
+    "Evaluation是0/1二元评审值，0——予以理赔；Evaluation是test中需要预测的值；\n",
+    "## 解决途径\n",
+    "\n",
+    "主要包括：\n",
+    "1. 数据预处理\n",
+    "    \n",
+    "    1.1 首先我们可以检查有无缺失值——结果是没有缺失值。\n",
+    "        print(train.isnull().any())# 缺失值检查\n",
+    "    1.2 查看各变量与Evaluation的相关性\n",
+    "        print(train.corr()['Evaluation'])# 查看特征相关性\n",
+    "       可以发现，在36个问题中绝大多数是接近0的负相关，只有少部分为正相关。\n",
+    "    于是我们查看其中为-0.006511的Q1和为0.410700的Q28，当然还有作为估计值的Evaluation的分布情况\n",
+    "        for i in ['Q1', 'Q28', 'Evaluation']:\n",
+    "          sns.countplot(x=i, data=train)# 查看数据集各变量和类别的**分布**\n",
+    "        plt.show()\n",
+    "        \n",
+    "![image-4.png](attachment:image-4.png)\n",
+    "\n",
+    "![image-5.png](attachment:image-5.png)\n",
+    "\n",
+    "![image-6.png](attachment:image-6.png)\n",
+    "        \n",
+    "       Evaluation——审核能否通即是0和1的分布，绝大多数都为可以通过并且占据17/20。\n",
+    "       Q1——取值几乎都为0，和它相同分布的特征还有许多，这些特征难以体现区分度。\n",
+    "       Q28——有不同的取值，虽然大多为0，但分布还不算太过悬殊。\n",
+    "\n",
+    "2. 选用的方法\n",
+    "    \n",
+    "    2.1 RF（随机森林分类）——先使用表现最好的标杆模型，使用其默认参数，在三折交叉验证下查看PR-AUC的值\n",
+    "        RP-AUC数值为:0.8429819733147138\n",
+    "        耗时：51.35982632637024秒\n",
+    "    2.2 类不平衡处理和移除方差较低的特征后模型表现都不大好。于是我们直接对RF模型进行调参，会发现多次调参仍提升不多.\n",
+    "    于是考虑选择其他模型，在这之前我们先分析标杆模型和其类似的模型。\n",
+    "    \n",
+    "     2.2.1 LASSO逻辑回归模型\n",
+    "        LASSO 的特点：趋向于使得一部分θ 值变为 0，也就是说 LASSO 认为与θ= 0 对应的特征是完全没有用的，而剩下与θ不为 0 所对应的特征是有用的，所以 LASSO 可作为特征选择使用。\n",
+    "\n",
+    "        机器学习：模型泛化（LASSO 回归）https://www.cnblogs.com/volcao/p/9305682.html\n",
+    "\n",
+    "     2.2.2 随机森林分类模型\n",
+    "        随机森林的特点：是在以决策树为基学习器构建Bagging集成的基础上，进一步在决策树的训练过程中引入了随机属性选择，即每棵树随机选择样本并随机选择特征，使得具有很好的抗噪能力，性能稳定。\n",
+    "\n",
+    "        随机森林（原理/样例实现/参数调优）https://blog.csdn.net/y0367/article/details/51501780\n",
+    "\n",
+    "     2.2.3 XGBoost分类模型\n",
+    "        XGBoost的特点：\n",
+    "\n",
+    "        ①使用许多策略去防止过拟合，如：正则化项、Shrinkage and Column Subsampling等。\n",
+    "        ②目标函数优化利用了损失函数关于待求函数的二阶导数。\n",
+    "        ③支持并行化，这是XGBoost的闪光点，虽然树与树之间是串行关系，但是同层级节点可并行。具体的对于某个节点，节点内选择最佳分裂点，候选分裂点计算增益用多线程并行。训练速度快。\n",
+    "        ④添加了对稀疏数据的处理。\n",
+    "        ⑤交叉验证，early stop，当预测结果已经很好的时候可以提前停止建树，加快训练速度。\n",
+    "        ⑥支持设置样本权重，该权重体现在一阶导数g和二阶导数h，通过调整权重可以去更加关注一些样本。\n",
+    "        \n",
+    "    其实发现标杆模型都有一个特点，即可以特征选择，对稀疏特征有较好的处理能力。\n",
+    "    所以最终我选择XGBoost分类模型，因为他即结合Lasso回归中正则化处理，又结合了随机森林中集成思想和特征选择，所以对处理Q1这类特征有较好的反响。还有一点是根据上一篇文章对xgboost的处理，发现仔细调参后可较大幅度提升性能。\n",
+    "\n",
+    "3. 实现过程遇到的问题，以及如何解决的\n",
+    "    \n",
+    "    3.1 数据特征不平衡处理\n",
+    "        类不平衡（class-imbalance）是指在训练分类器中所使用的训练集的类别分布不均。\n",
+    "    于是我尝试使用过采样SMOTE算法：\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    from collectionsimport Counter\n",
+    "\n",
+    "    from imblearn.over_samplingimport SMOTE\n",
+    "\n",
+    "    #print(Counter(train_y))\n",
+    "\n",
+    "    smo = SMOTE(random_state=42)\n",
+    "\n",
+    "    train_x, train_y = smo.fit_sample(train_x, train_y)\n",
+    "\n",
+    "    #print(Counter(train_y))\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    可以发现在SMOTE处理后，两个类别样本数量达到相同.\n",
+    "    RP-AUC数值为:0.8399920675137803\n",
+    "    耗时：107.54732489585876 秒\n",
+    "    以上实验使用过采样的样本训练后，分数下降了，时间还提升了。这是由于过采样它不可避免的带来更大的运算开销，其次当数据中噪音过大时，结果反而可能会更差，因为噪音也被重复使用。\n",
+    "    \n",
+    "    3.2 噪声避免和特征选择\n",
+    "    由于在Q1特征中，表现出绝大特征值为0，这是方差极为低的特征，难以带来有效的信息。\n",
+    "    \n",
+    "    3.3.1 移除方差较低特征\n",
+    "    方差为0的特征会被自动移除。剩下的特征按设定的方差的阈值进行选择,排除方差较小即特征差异不明显的特征属性。\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    from sklearn.feature_selectionimport VarianceThreshold\n",
+    "\n",
+    "    sel = VarianceThreshold(threshold=0.1) # 选择方差大于0.1的特征\n",
+    "\n",
+    "    train = sel.fit_transform(train)\n",
+    "\n",
+    "    test = sel.fit_transform(test)\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    经过多次实验，选取方差阈值为0.1.\n",
+    "    RP-AUC数值为:0.8300324079765956\n",
+    "    耗时：39.80928683280945 秒\n",
+    "    移除方差较低的特征后，分数降低很大，时间却降低很多。因为许多特征都表现出方差低的情况，移除后剩下为数不多的特征，此时耗时自然下降，但信息不足以支撑模型训练。（使用scikit-learn进行特征选择）\n",
+    "    \n",
+    "    \n",
+    "4. 最终的结果，实验分析\n",
+    "\n",
+    "    使用XGBoost分类模型，使用其默认参数，在三折交叉验证下查看PR-AUC的值\n",
+    "\n",
+    "    RP-AUC数值为:0.8238557416902802\n",
+    "    耗时：30.34992218017578 秒\n",
+    "\n",
+    "    发现分数降低很大，但耗时降低。但不要紧张，我们参考网上同样使用xgboost的（竞赛文章）和上一篇调参方法（有时间可以三个参数一起调），在经历几个晚上运行终于调出较高的数值。\n",
+    "    ![image-8.png](attachment:image-8.png)\n",
+    "    \n",
+    "    ![image-7.png](attachment:image-7.png)\n",
+    "    \n",
+    "    RP-AUC数值为:0.9653026782164909\n",
+    "    耗时：139.36438059806824 秒\n",
+    "    由于提交截至日期结束，使得测试集预测结果无法查看，以上是训练集的训练结果。估计是模型有些过拟合了！\n",
+    "    ![image-11.png](attachment:image-11.png)\n",
+    "    ![image-10.png](attachment:image-10.png)\n",
+    "\n",
+    "## 总结\n",
+    "    ①本次是分类问题，数据表现出部分特征区分度低和类不平衡问题。\n",
+    "    ②处理以上问题，我们进行特征选择和数据处理和。\n",
+    "    ③如果未有太好效应，则直接进行调参。\n",
+    "    ④调参提高不多，则再一次审视模型选择。\n",
+    "    ⑤其中②③④需要耐心的交接进行。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "编辑元数据",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "main_language": "python",
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/report_01_accident_claims/data.zip b/report_01_accident_claims/data.zip
deleted file mode 100644
index 33d0f49eeb6992ae025c3d99fc52f95bd61071e3..0000000000000000000000000000000000000000
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diff --git a/report_01_accident_claims/data_tutorial.ipynb b/report_01_accident_claims/data_tutorial.ipynb
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index ca68092123db1cf81600af9209639ced5cbaf4f8..0000000000000000000000000000000000000000
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+++ /dev/null
@@ -1,931 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exercise - 交通事故理赔审核预测\n",
-    "\n",
-    "\n",
-    "这个比赛的链接：http://sofasofa.io/competition.php?id=2\n",
-    "\n",
-    "\n",
-    "* 任务类型：二元分类\n",
-    "\n",
-    "* 背景介绍：在交通摩擦（事故）发生后，理赔员会前往现场勘察、采集信息，这些信息往往影响着车主是否能够得到保险公司的理赔。训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。\n",
-    "\n",
-    "* 数据介绍：训练集中共有200000条样本，预测集中有80000条样本。 \n",
-    "![data_description](images/data_description.png)\n",
-    "\n",
-    "* 评价方法：Precision-Recall AUC\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Demo code\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# read data\n",
-    "homePath = \"data\"\n",
-    "trainPath = os.path.join(homePath, \"train.csv\")\n",
-    "testPath = os.path.join(homePath, \"test.csv\")\n",
-    "submitPath = os.path.join(homePath, \"sample_submit.csv\")\n",
-    "trainData = pd.read_csv(trainPath)\n",
-    "testData = pd.read_csv(testPath)\n",
-    "submitData = pd.read_csv(submitPath)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "参照数据说明，CaseID这列是没有意义的编号，因此这里将他丢弃。\n",
-    "\n",
-    "~drop()函数：axis指沿着哪个轴，0为行，1为列；inplace指是否在原数据上直接操作\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 去掉没有意义的一列\n",
-    "trainData.drop(\"CaseId\", axis=1, inplace=True)\n",
-    "testData.drop(\"CaseId\", axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 快速了解数据\n",
-    "\n",
-    "~head()：默认显示前5行数据，可指定显示多行，例如.head(15)显示前15行\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Q1</th>\n",
-       "      <th>Q2</th>\n",
-       "      <th>Q3</th>\n",
-       "      <th>Q4</th>\n",
-       "      <th>Q5</th>\n",
-       "      <th>Q6</th>\n",
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-       "      <th>Q9</th>\n",
-       "      <th>Q10</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Q28</th>\n",
-       "      <th>Q29</th>\n",
-       "      <th>Q30</th>\n",
-       "      <th>Q31</th>\n",
-       "      <th>Q32</th>\n",
-       "      <th>Q33</th>\n",
-       "      <th>Q34</th>\n",
-       "      <th>Q35</th>\n",
-       "      <th>Q36</th>\n",
-       "      <th>Evaluation</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>15 rows × 37 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Q1  Q2  Q3  Q4  Q5  Q6  Q7  Q8  Q9  Q10     ...      Q28  Q29  Q30  Q31  \\\n",
-       "0    0   0   0   0   0   0   0   0   0    0     ...        0    0    0    0   \n",
-       "1    0   0   0   0   0   0   0   0   0    0     ...        0    1    1    1   \n",
-       "2    0   0   0   0   0   0   0   1   0    0     ...        1    2    2    2   \n",
-       "3    0   0   0   0   0   0   0   0   0    0     ...        1    3    2    3   \n",
-       "4    0   0   0   0   0   0   0   0   0    0     ...        1    4    2    4   \n",
-       "5    0   0   0   0   0   0   0   0   0    0     ...        1    2    3    5   \n",
-       "6    0   0   0   0   0   0   0   0   0    1     ...        0    3    1    6   \n",
-       "7    0   0   0   0   0   0   0   0   0    0     ...        1    3    1    3   \n",
-       "8    0   0   0   0   0   0   0   2   0    0     ...        0    2    1    2   \n",
-       "9    0   0   0   0   0   0   0   0   0    0     ...        0    2    1    7   \n",
-       "10   0   0   0   0   0   0   0   0   0    0     ...        2    5    0    8   \n",
-       "11   0   0   0   0   0   0   0   0   0    0     ...        0    2    1    1   \n",
-       "12   1   0   0   0   0   0   0   0   0    0     ...        3    3    3    9   \n",
-       "13   0   0   0   0   0   0   0   0   0    0     ...        0    1    1   10   \n",
-       "14   0   0   0   0   0   0   0   3   0    0     ...        1    6    1    2   \n",
-       "\n",
-       "    Q32  Q33  Q34  Q35  Q36  Evaluation  \n",
-       "0     0    0    0    0    0           0  \n",
-       "1     1    0    0    0    0           0  \n",
-       "2     1    0    0    0    0           0  \n",
-       "3     1    0    0    1    1           0  \n",
-       "4     1    0    0    1    1           0  \n",
-       "5     1    0    0    0    0           0  \n",
-       "6     1    0    0    1    1           1  \n",
-       "7     1    0    0    1    1           1  \n",
-       "8     1    0    0    0    0           0  \n",
-       "9     1    0    0    0    0           0  \n",
-       "10    1    0    0    1    1           0  \n",
-       "11    1    0    0    0    0           0  \n",
-       "12    1    0    0    1    1           0  \n",
-       "13    1    0    0    0    0           0  \n",
-       "14    1    0    0    1    1           0  \n",
-       "\n",
-       "[15 rows x 37 columns]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainData.head(15)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "显示数据简略信息，可以每列有多少非空的值，以及每列数据对应的数据类型。\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 200000 entries, 0 to 199999\n",
-      "Data columns (total 37 columns):\n",
-      "Q1            200000 non-null int64\n",
-      "Q2            200000 non-null int64\n",
-      "Q3            200000 non-null int64\n",
-      "Q4            200000 non-null int64\n",
-      "Q5            200000 non-null int64\n",
-      "Q6            200000 non-null int64\n",
-      "Q7            200000 non-null int64\n",
-      "Q8            200000 non-null int64\n",
-      "Q9            200000 non-null int64\n",
-      "Q10           200000 non-null int64\n",
-      "Q11           200000 non-null int64\n",
-      "Q12           200000 non-null int64\n",
-      "Q13           200000 non-null int64\n",
-      "Q14           200000 non-null int64\n",
-      "Q15           200000 non-null int64\n",
-      "Q16           200000 non-null int64\n",
-      "Q17           200000 non-null int64\n",
-      "Q18           200000 non-null int64\n",
-      "Q19           200000 non-null int64\n",
-      "Q20           200000 non-null int64\n",
-      "Q21           200000 non-null int64\n",
-      "Q22           200000 non-null int64\n",
-      "Q23           200000 non-null int64\n",
-      "Q24           200000 non-null int64\n",
-      "Q25           200000 non-null int64\n",
-      "Q26           200000 non-null int64\n",
-      "Q27           200000 non-null int64\n",
-      "Q28           200000 non-null int64\n",
-      "Q29           200000 non-null int64\n",
-      "Q30           200000 non-null int64\n",
-      "Q31           200000 non-null int64\n",
-      "Q32           200000 non-null int64\n",
-      "Q33           200000 non-null int64\n",
-      "Q34           200000 non-null int64\n",
-      "Q35           200000 non-null int64\n",
-      "Q36           200000 non-null int64\n",
-      "Evaluation    200000 non-null int64\n",
-      "dtypes: int64(37)\n",
-      "memory usage: 56.5 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "trainData.info()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "~hist():绘制直方图，参数figsize可指定输出图片的尺寸。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
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-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9247518>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce925f860>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9276ef0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9215588>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce922ba90>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcce91c8160>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce91e17f0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce91f8e80>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9194550>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce91acbe0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce91492b0>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9160940>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9178fd0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9115630>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce912fcc0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce90cf390>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce90e6a20>],\n",
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-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9099780>,\n",
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-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce904d4e0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9065b70>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9004240>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcce901c8d0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce9033f60>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8fd0630>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8fe8cc0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8f86390>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8f9ca20>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8fba0f0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8f53780>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8f6ae10>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8f064e0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8f1eb70>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8f3b240>],\n",
-       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8ed28d0>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8eebf60>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8e89630>,\n",
-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8ea1cc0>,\n",
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-       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fcce8e56a20>]],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 42 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trainData.hist(figsize=(20, 20))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "想要了解特征之间的相关性，可计算相关系数矩阵。然后可对某个特征来排序。\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Evaluation    1.000000\n",
-       "Q28           0.410700\n",
-       "Q30           0.324421\n",
-       "Q36           0.302709\n",
-       "Q35           0.224996\n",
-       "Q34           0.152743\n",
-       "Q32           0.049397\n",
-       "Q21           0.034897\n",
-       "Q33           0.032248\n",
-       "Q13           0.023603\n",
-       "Q8            0.021922\n",
-       "Q19           0.019694\n",
-       "Q20           0.013903\n",
-       "Q4            0.011626\n",
-       "Q27           0.004262\n",
-       "Q23           0.002898\n",
-       "Q7            0.001143\n",
-       "Q31          -0.000036\n",
-       "Q14          -0.000669\n",
-       "Q29          -0.002014\n",
-       "Q10          -0.002711\n",
-       "Q12          -0.005287\n",
-       "Q1           -0.006511\n",
-       "Q16          -0.007184\n",
-       "Q18          -0.007643\n",
-       "Q26          -0.008188\n",
-       "Q11          -0.009252\n",
-       "Q24          -0.010891\n",
-       "Q22          -0.011821\n",
-       "Q25          -0.012660\n",
-       "Q6           -0.016072\n",
-       "Q2           -0.018307\n",
-       "Q15          -0.019570\n",
-       "Q9           -0.021261\n",
-       "Q5           -0.023893\n",
-       "Q3           -0.026349\n",
-       "Q17          -0.028461\n",
-       "Name: Evaluation, dtype: float64"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corr_matrix = trainData.corr()\n",
-    "corr_matrix[\"Evaluation\"].sort_values(ascending=False) # ascending=False 降序排列"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从训练集中分离标签"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = trainData['Evaluation']\n",
-    "trainData.drop(\"Evaluation\", axis=1, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "使用K-Means训练模型\n",
-    "\n",
-    "KMeans()：\n",
-    "* `n_clusters`指要预测的有几个类；\n",
-    "* `init`指初始化中心的方法，默认使用的是`k-means++`方法，而非经典的K-means方法的随机采样初始化，当然你可以设置为random使用随机初始化；\n",
-    "* `n_jobs`指定使用CPU核心数，-1为使用全部CPU。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# do k-means\n",
-    "from sklearn.cluster import KMeans\n",
-    "est = KMeans(n_clusters=2, init=\"k-means++\", n_jobs=-1)\n",
-    "est.fit(trainData, y)\n",
-    "\n",
-    "y_train = est.predict(trainData)\n",
-    "y_pred = est.predict(testData)\n",
-    "\n",
-    "# 保存预测的结果\n",
-    "submitData['Evaluation'] = y_pred\n",
-    "submitData.to_csv(\"submit_data.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "acc_train = 0.682140\n"
-     ]
-    }
-   ],
-   "source": [
-    "# calculate accuracy\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
-    "acc_train = accuracy_score(y, y_train)\n",
-    "print(\"acc_train = %f\" % (acc_train))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 随机森林\n",
-    "\n",
-    "使用K-means可能得到的结果没那么理想。在官网上，举办方给出了两个标杆模型，效果最好的是随机森林。以下是代码，读者可以自己测试。\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "\n",
-    "# 读取数据\n",
-    "train = pd.read_csv(\"data/train.csv\")\n",
-    "test = pd.read_csv(\"data/test.csv\")\n",
-    "submit = pd.read_csv(\"data/sample_submit.csv\")\n",
-    "\n",
-    "# 删除id\n",
-    "train.drop('CaseId', axis=1, inplace=True)\n",
-    "test.drop('CaseId', axis=1, inplace=True)\n",
-    "\n",
-    "# 取出训练集的y\n",
-    "y_train = train.pop('Evaluation')\n",
-    "\n",
-    "# 建立随机森林模型\n",
-    "clf = RandomForestClassifier(n_estimators=100, random_state=0)\n",
-    "clf.fit(train, y_train)\n",
-    "y_pred = clf.predict_proba(test)[:, 1]\n",
-    "\n",
-    "# 输出预测结果至my_RF_prediction.csv\n",
-    "submit['Evaluation'] = y_pred\n",
-    "submit.to_csv('my_RF_prediction.csv', index=False)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.00177294 0.00207449 0.00187096 0.00471492 0.00443815 0.0029538\n",
-      " 0.00364967 0.00652341 0.00235713 0.00739511 0.00245106 0.00106103\n",
-      " 0.0007513  0.00090631 0.00150727 0.0037793  0.00183821 0.00196833\n",
-      " 0.00209665 0.00726069 0.00816243 0.00107563 0.00559247 0.00766561\n",
-      " 0.00760666 0.00028462 0.00025573 0.18472067 0.25559838 0.21436631\n",
-      " 0.0425301  0.00662325 0.00297955 0.03148822 0.03907383 0.13060584]\n",
-      "[0 0 0 ... 0 0 0]\n",
-      "acc_train = 0.931525\n"
-     ]
-    }
-   ],
-   "source": [
-    "# freature importances\n",
-    "print(clf.feature_importances_)\n",
-    "\n",
-    "# Train accuracy\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "y_train_pred = clf.predict(train)\n",
-    "print(y_train_pred)\n",
-    "\n",
-    "acc_train = accuracy_score(y_train, y_train_pred)\n",
-    "print(\"acc_train = %f\" % (acc_train))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  },
-  "main_language": "python"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/report_01_accident_claims/data_tutorial.py b/report_01_accident_claims/data_tutorial.py
index 2933e1a028e7e2c6ede97fd057f7324964b626eb..aa233a76a0402ccf449876755a33c8c70b5e677b 100644
--- a/report_01_accident_claims/data_tutorial.py
+++ b/report_01_accident_claims/data_tutorial.py
@@ -1,169 +1,169 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext_format_version: '1.2'
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-#   language_info:
-#     codemirror_mode:
-#       name: ipython
-#       version: 3
-#     file_extension: .py
-#     mimetype: text/x-python
-#     name: python
-#     nbconvert_exporter: python
-#     pygments_lexer: ipython3
-#     version: 3.5.2
-# ---
-
-# # Exercise - 交通事故理赔审核预测
-#
-#
-# 这个比赛的链接：http://sofasofa.io/competition.php?id=2
-#
-#
-# * 任务类型：二元分类
-#
-# * 背景介绍：在交通摩擦（事故）发生后，理赔员会前往现场勘察、采集信息，这些信息往往影响着车主是否能够得到保险公司的理赔。训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。
-#
-# * 数据介绍：训练集中共有200000条样本，预测集中有80000条样本。 
-# ![data_description](images/data_description.png)
-#
-# * 评价方法：Precision-Recall AUC
-#
-
-# ## Demo code
-#
-
-import pandas as pd
-import numpy as np
-import os
-import matplotlib.pyplot as plt
-# %matplotlib inline
-
-# read data
-homePath = "data"
-trainPath = os.path.join(homePath, "train.csv")
-testPath = os.path.join(homePath, "test.csv")
-submitPath = os.path.join(homePath, "sample_submit.csv")
-trainData = pd.read_csv(trainPath)
-testData = pd.read_csv(testPath)
-submitData = pd.read_csv(submitPath)
-
-# 参照数据说明，CaseID这列是没有意义的编号，因此这里将他丢弃。
-#
-# ~drop()函数：axis指沿着哪个轴，0为行，1为列；inplace指是否在原数据上直接操作
-#
-
-# 去掉没有意义的一列
-trainData.drop("CaseId", axis=1, inplace=True)
-testData.drop("CaseId", axis=1, inplace=True)
-
-# # 快速了解数据
-#
-# ~head()：默认显示前5行数据，可指定显示多行，例如.head(15)显示前15行
-#
-
-trainData.head(15)
-
-
-# 显示数据简略信息，可以每列有多少非空的值，以及每列数据对应的数据类型。
-#
-#
-
-trainData.info()
-
-
-# ~hist():绘制直方图，参数figsize可指定输出图片的尺寸。
-#
-
-trainData.hist(figsize=(20, 20))
-
-
-# 想要了解特征之间的相关性，可计算相关系数矩阵。然后可对某个特征来排序。
-#
-#
-
-corr_matrix = trainData.corr()
-corr_matrix["Evaluation"].sort_values(ascending=False) # ascending=False 降序排列
-
-# 从训练集中分离标签
-
-y = trainData['Evaluation']
-trainData.drop("Evaluation", axis=1, inplace=True)
-
-# 使用K-Means训练模型
-#
-# KMeans()：
-# * `n_clusters`指要预测的有几个类；
-# * `init`指初始化中心的方法，默认使用的是`k-means++`方法，而非经典的K-means方法的随机采样初始化，当然你可以设置为random使用随机初始化；
-# * `n_jobs`指定使用CPU核心数，-1为使用全部CPU。
-
-# +
-# do k-means
-from sklearn.cluster import KMeans
-est = KMeans(n_clusters=2, init="k-means++", n_jobs=-1)
-est.fit(trainData, y)
-
-y_train = est.predict(trainData)
-y_pred = est.predict(testData)
-
-# 保存预测的结果
-submitData['Evaluation'] = y_pred
-submitData.to_csv("submit_data.csv", index=False)
-
-# +
-# calculate accuracy
-from sklearn.metrics import accuracy_score
-
-acc_train = accuracy_score(y, y_train)
-print("acc_train = %f" % (acc_train))
-# -
-
-# ## 随机森林
-#
-# 使用K-means可能得到的结果没那么理想。在官网上，举办方给出了两个标杆模型，效果最好的是随机森林。以下是代码，读者可以自己测试。
-#
-#
-
-# +
-import pandas as pd
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
-
-# 读取数据
-train = pd.read_csv("data/train.csv")
-test = pd.read_csv("data/test.csv")
-submit = pd.read_csv("data/sample_submit.csv")
-
-# 删除id
-train.drop('CaseId', axis=1, inplace=True)
-test.drop('CaseId', axis=1, inplace=True)
-
-# 取出训练集的y
-y_train = train.pop('Evaluation')
-
-# 建立随机森林模型
-clf = RandomForestClassifier(n_estimators=100, random_state=0)
-clf.fit(train, y_train)
-y_pred = clf.predict_proba(test)[:, 1]
-
-# 输出预测结果至my_RF_prediction.csv
-submit['Evaluation'] = y_pred
-submit.to_csv('my_RF_prediction.csv', index=False)
-
-
-
-# +
-# freature importances
-print(clf.feature_importances_)
-
-# Train accuracy
-from sklearn.metrics import accuracy_score
-y_train_pred = clf.predict(train)
-print(y_train_pred)
-
-acc_train = accuracy_score(y_train, y_train_pred)
-print("acc_train = %f" % (acc_train))
+# -*- coding: utf-8 -*-
+# ---
+# jupyter:
+#   jupytext_format_version: '1.2'
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+#   language_info:
+#     codemirror_mode:
+#       name: ipython
+#       version: 3
+#     file_extension: .py
+#     mimetype: text/x-python
+#     name: python
+#     nbconvert_exporter: python
+#     pygments_lexer: ipython3
+#     version: 3.5.2
+# ---
+
+# # Exercise - 交通事故理赔审核预测
+#
+#
+# 这个比赛的链接：http://sofasofa.io/competition.php?id=2
+#
+#
+# * 任务类型：二元分类
+#
+# * 背景介绍：在交通摩擦（事故）发生后，理赔员会前往现场勘察、采集信息，这些信息往往影响着车主是否能够得到保险公司的理赔。训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。
+#
+# * 数据介绍：训练集中共有200000条样本，预测集中有80000条样本。 
+# ![data_description](images/data_description.png)
+#
+# * 评价方法：Precision-Recall AUC
+#
+
+# ## Demo code
+#
+
+import pandas as pd
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+# %matplotlib inline
+
+# read data
+homePath = "data"
+trainPath = os.path.join(homePath, "train.csv")
+testPath = os.path.join(homePath, "test.csv")
+submitPath = os.path.join(homePath, "sample_submit.csv")
+trainData = pd.read_csv(trainPath)
+testData = pd.read_csv(testPath)
+submitData = pd.read_csv(submitPath)
+
+# 参照数据说明，CaseID这列是没有意义的编号，因此这里将他丢弃。
+#
+# ~drop()函数：axis指沿着哪个轴，0为行，1为列；inplace指是否在原数据上直接操作
+#
+
+# 去掉没有意义的一列
+trainData.drop("CaseId", axis=1, inplace=True)
+testData.drop("CaseId", axis=1, inplace=True)
+
+# # 快速了解数据
+#
+# ~head()：默认显示前5行数据，可指定显示多行，例如.head(15)显示前15行
+#
+
+trainData.head(15)
+
+
+# 显示数据简略信息，可以每列有多少非空的值，以及每列数据对应的数据类型。
+#
+#
+
+trainData.info()
+
+
+# ~hist():绘制直方图，参数figsize可指定输出图片的尺寸。
+#
+
+trainData.hist(figsize=(20, 20))
+
+
+# 想要了解特征之间的相关性，可计算相关系数矩阵。然后可对某个特征来排序。
+#
+#
+
+corr_matrix = trainData.corr()
+corr_matrix["Evaluation"].sort_values(ascending=False) # ascending=False 降序排列
+
+# 从训练集中分离标签
+
+y = trainData['Evaluation']
+trainData.drop("Evaluation", axis=1, inplace=True)
+
+# 使用K-Means训练模型
+#
+# KMeans()：
+# * `n_clusters`指要预测的有几个类；
+# * `init`指初始化中心的方法，默认使用的是`k-means++`方法，而非经典的K-means方法的随机采样初始化，当然你可以设置为random使用随机初始化；
+# * `n_jobs`指定使用CPU核心数，-1为使用全部CPU。
+
+# +
+# do k-means
+from sklearn.cluster import KMeans
+est = KMeans(n_clusters=2, init="k-means++", n_jobs=-1)
+est.fit(trainData, y)
+
+y_train = est.predict(trainData)
+y_pred = est.predict(testData)
+
+# 保存预测的结果
+submitData['Evaluation'] = y_pred
+submitData.to_csv("submit_data.csv", index=False)
+
+# +
+# calculate accuracy
+from sklearn.metrics import accuracy_score
+
+acc_train = accuracy_score(y, y_train)
+print("acc_train = %f" % (acc_train))
+# -
+
+# ## 随机森林
+#
+# 使用K-means可能得到的结果没那么理想。在官网上，举办方给出了两个标杆模型，效果最好的是随机森林。以下是代码，读者可以自己测试。
+#
+#
+
+# +
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+# 读取数据
+train = pd.read_csv("data/train.csv")
+test = pd.read_csv("data/test.csv")
+submit = pd.read_csv("data/sample_submit.csv")
+
+# 删除id
+train.drop('CaseId', axis=1, inplace=True)
+test.drop('CaseId', axis=1, inplace=True)
+
+# 取出训练集的y
+y_train = train.pop('Evaluation')
+
+# 建立随机森林模型
+clf = RandomForestClassifier(n_estimators=100, random_state=0)
+clf.fit(train, y_train)
+y_pred = clf.predict_proba(test)[:, 1]
+
+# 输出预测结果至my_RF_prediction.csv
+submit['Evaluation'] = y_pred
+submit.to_csv('my_RF_prediction.csv', index=False)
+
+
+
+# +
+# freature importances
+print(clf.feature_importances_)
+
+# Train accuracy
+from sklearn.metrics import accuracy_score
+y_train_pred = clf.predict(train)
+print(y_train_pred)
+
+acc_train = accuracy_score(y_train, y_train_pred)
+print("acc_train = %f" % (acc_train))
diff --git a/report_01_accident_claims/my_RF_prediction.csv b/report_01_accident_claims/my_RF_prediction.csv
index c97766e88fd7be335b249cd339689f5c1bf0c6a6..24e0ecb27abc79482b08c62f824434b55b356b6b 100644
--- a/report_01_accident_claims/my_RF_prediction.csv
+++ b/report_01_accident_claims/my_RF_prediction.csv
@@ -1,80001 +1,80001 @@
-CaseId,Evaluation
-200001,0.0
-200002,0.0
-200003,0.13409567952468873
-200004,0.2920866854659094
-200005,0.0
-200006,0.0
-200007,0.11644606782106774
-200008,0.0
-200009,0.0
-200010,0.12409811496532085
-200011,0.0
-200012,0.8710944185045817
-200013,0.0
-200014,0.0
-200015,0.2920866854659094
-200016,0.0
-200017,0.7467303657898114
-200018,0.0
-200019,0.8416960224915283
-200020,0.0
-200021,0.01598840472566602
-200022,0.0
-200023,0.40505485865544394
-200024,0.0011280482521665964
-200025,0.0011280482521665964
-200026,0.0
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-200030,0.0
-200031,0.0
-200032,0.0
-200033,1.0
-200034,0.0
-200035,0.0
-200036,0.0
-200037,0.5745647766994021
-200038,0.0
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-200047,0.0
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-200050,0.0012888536468091333
-200051,0.08456493506493507
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-200056,0.00448897659980248
-200057,0.0
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-200060,0.4196714152313304
-200061,0.37189902788012885
-200062,0.7683554555992768
-200063,1.0
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-200065,0.02066325111440682
-200066,0.6212379976180553
-200067,0.0
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-200070,1.0
-200071,0.15100370437698576
-200072,0.025046578592826604
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-200075,0.0
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-200077,0.010926187513493016
-200078,0.0
-200079,0.13409567952468873
-200080,0.14820449505323693
-200081,1.0
-200082,0.37630560630655757
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-200086,0.15100370437698576
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-200088,0.2659828390563683
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-200090,0.0
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-200092,0.09921990186317892
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-200095,0.0
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-200098,0.7467303657898114
-200099,0.8914001880366618
-200100,0.0
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-200103,0.12093397679305999
-200104,0.0
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-200108,0.6154323403425359
-200109,0.0
-200110,0.06588260451985269
-200111,0.0
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-200115,0.0
-200116,0.13409567952468873
-200117,0.0
-200118,0.0
-200119,0.9710822227232463
-200120,0.8039679952992631
-200121,0.03
-200122,0.13409567952468873
-200123,0.009611543404480671
-200124,0.054071994611011816
-200125,0.0
-200126,0.46460900619745404
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-200129,0.011657543339923415
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-200131,0.7508095238095237
-200132,0.0
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-200137,0.13409567952468873
-200138,0.0
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diff --git a/report_01_accident_claims/my_submit_prediction.csv b/report_01_accident_claims/my_submit_prediction.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0fdced4a6e3adb7e3e306253820a5d49e6e26448
--- /dev/null
+++ b/report_01_accident_claims/my_submit_prediction.csv
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diff --git a/report_01_accident_claims/report_template.ipynb b/report_01_accident_claims/report_template.ipynb
deleted file mode 100644
index 21eb1ce711b96fd98ffe44be0b1697fee849508a..0000000000000000000000000000000000000000
--- a/report_01_accident_claims/report_template.ipynb
+++ /dev/null
@@ -1,57 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report - 报告题目\n",
-    "\n",
-    "* 姓名\n",
-    "* 学号\n",
-    "\n",
-    "\n",
-    "## 任务简介\n",
-    "\n",
-    "这里简述一下任务是什么；数据的格式，包含了什么数据；最终的目标是什么\n",
-    "\n",
-    "## 解决途径\n",
-    "\n",
-    "主要包括：\n",
-    "1. 问题的思考，整体的思路\n",
-    "2. 选用的方法，以及为何选用这些方法\n",
-    "3. 实现过程遇到的问题，以及如何解决的\n",
-    "4. 最终的结果，实验分析\n",
-    "\n",
-    "要求：\n",
-    "1. 数据的可视化\n",
-    "2. 程序，以及各个部分的解释、说明\n",
-    "3. 结果的可视化，精度等的分析\n",
-    "\n",
-    "## 总结\n",
-    "总结任务实现过程所取得的心得等。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  },
-  "main_language": "python"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/report_01_accident_claims/report_template.py b/report_01_accident_claims/report_template.py
index 8bb238545007c8fbb881d108dc1b5aba24cc81e8..0022f2f77943e10102b6deabdc33ba99d76c4a89 100644
--- a/report_01_accident_claims/report_template.py
+++ b/report_01_accident_claims/report_template.py
@@ -1,45 +1,45 @@
-# -*- coding: utf-8 -*-
-# ---
-# jupyter:
-#   jupytext_format_version: '1.2'
-#   kernelspec:
-#     display_name: Python 3
-#     language: python
-#     name: python3
-#   language_info:
-#     codemirror_mode:
-#       name: ipython
-#       version: 3
-#     file_extension: .py
-#     mimetype: text/x-python
-#     name: python
-#     nbconvert_exporter: python
-#     pygments_lexer: ipython3
-#     version: 3.5.2
-# ---
-
-# # Report - 报告题目
-#
-# * 姓名
-# * 学号
-#
-#
-# ## 任务简介
-#
-# 这里简述一下任务是什么；数据的格式，包含了什么数据；最终的目标是什么
-#
-# ## 解决途径
-#
-# 主要包括：
-# 1. 问题的思考，整体的思路
-# 2. 选用的方法，以及为何选用这些方法
-# 3. 实现过程遇到的问题，以及如何解决的
-# 4. 最终的结果，实验分析
-#
-# 要求：
-# 1. 数据的可视化
-# 2. 程序，以及各个部分的解释、说明
-# 3. 结果的可视化，精度等的分析
-#
-# ## 总结
-# 总结任务实现过程所取得的心得等。
+# -*- coding: utf-8 -*-
+# ---
+# jupyter:
+#   jupytext_format_version: '1.2'
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+#   language_info:
+#     codemirror_mode:
+#       name: ipython
+#       version: 3
+#     file_extension: .py
+#     mimetype: text/x-python
+#     name: python
+#     nbconvert_exporter: python
+#     pygments_lexer: ipython3
+#     version: 3.5.2
+# ---
+
+# # Report - 报告题目
+#
+# * 姓名
+# * 学号
+#
+#
+# ## 任务简介
+#
+# 这里简述一下任务是什么；数据的格式，包含了什么数据；最终的目标是什么
+#
+# ## 解决途径
+#
+# 主要包括：
+# 1. 问题的思考，整体的思路
+# 2. 选用的方法，以及为何选用这些方法
+# 3. 实现过程遇到的问题，以及如何解决的
+# 4. 最终的结果，实验分析
+#
+# 要求：
+# 1. 数据的可视化
+# 2. 程序，以及各个部分的解释、说明
+# 3. 结果的可视化，精度等的分析
+#
+# ## 总结
+# 总结任务实现过程所取得的心得等。
diff --git a/report_01_accident_claims/submit_data.csv b/report_01_accident_claims/submit_data.csv
index 04f70342e8c5aacabc01be033ba17ea8ce0f4788..919a2c21671e0e33240a11b157478f86e6fa02fb 100644
--- a/report_01_accident_claims/submit_data.csv
+++ b/report_01_accident_claims/submit_data.csv
@@ -1,80001 +1,80001 @@
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diff --git a/report_01_accident_claims/xg_binary_prediction.py b/report_01_accident_claims/xg_binary_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..b97b5e7e0736d656f22589b450c2056ced23ca2f
--- /dev/null
+++ b/report_01_accident_claims/xg_binary_prediction.py
@@ -0,0 +1,120 @@
+# -*- coding:utf-8 -*-
+# @Time : 2021/2/28 21:27
+# @Author: LCHJ
+# @File : xg_binary_prediction.py
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from xgboost import XGBClassifier
+from sklearn.model_selection import train_test_split
+import time
+from sklearn.feature_selection import VarianceThreshold
+import seaborn as sns
+import matplotlib.pyplot as plt
+from collections import Counter
+from imblearn.over_sampling import SMOTE
+from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
+
+
+def loadSimpData(showDataSet, Feature_selection, oversampling):
+    """
+    读取数据集
+    Parameters:
+        showDataSet - 是否可视化
+        Feature_selection - 是否特征选择
+        SMOTE - 是否合成少数类过采样
+    Returns:
+        训练集的变量和类别
+        测试集的变量
+    """
+    train = pd.read_csv("data/train.csv")
+    test = pd.read_csv("data/test.csv")
+
+    train.drop('CaseId', axis=1, inplace=True)
+    test.drop('CaseId', axis=1, inplace=True)
+
+
+    if showDataSet == True:
+        print('缺失值检查：')
+        print(train.isnull().any())  # 缺失值检查
+        print('-----------------------------------------')
+        print('查看特征相关性')
+        print(train.corr()['Evaluation'])  # 查看特征相关性
+        for i in ['Q1', 'Q28', 'Evaluation']:
+            sns.countplot(x=i, data=train)  # 查看数据集各变量和类别的分布
+            plt.show()
+    y_train = train.pop('Evaluation')
+
+    if Feature_selection == True:  # http://bluewhale.cc/2016-11-25/use-scikit-learn-for-feature-selection.html
+        sel = VarianceThreshold(threshold=0.1)  # 选择方差大于0.9的特征
+        train = sel.fit_transform(train)
+        test = sel.fit_transform(test)
+
+    train_x, test_x, train_y, test_y = train_test_split(train, y_train, test_size=0.00001)
+    if oversampling == True:  #  https://www.zhihu.com/tardis/landing/360/ans/352279936?query=%E8%BF%87%E9%87%87%E6%A0%B7%E7%9A%84%E7%BC%BA%E7%82%B9&mid=331063386fac4c24426fdd7a29ab153b&guid=BB7FF503CAF63F483C6A99DB4962E5AD.1577001064504
+        # print(Counter(train_y))
+        smo = SMOTE(random_state=42)
+        train_x, train_y = smo.fit_sample(train_x, train_y)
+        # print(Counter(train_y))
+
+    return train_x, train_y, test
+
+
+def XGB(train_x, train_y):
+    """
+    XGBClassifier的调参及建立
+    Parameters:
+        train_x - 训练集变量
+        train_y - 训练集类别
+    Returns:
+        clf - 模型
+        best_params - 最优参数
+        best_score - 最佳分数
+    """
+    cv_params = {}
+    other_params = {'base_score': 0.5, 'booster': 'gbtree', 'colsample_bylevel': 0.9,
+    'colsample_bytree': 0.4, 'gamma': 0.4, 'learning_rate': 0.13,
+    'max_delta_step': 0, 'max_depth': 5, 'min_child_weight': 1, 'missing': None,
+    'n_estimators': 800, 'n_jobs': -1, 'nthread': 4, 'objective': 'binary:logistic',
+    'random_state': 0, 'reg_alpha': 1, 'reg_lambda': 1, 'scale_pos_weight': 1,
+    'seed': 27, 'silent': True, 'subsample': 0.9}
+
+
+    model = XGBClassifier(**other_params)
+    clf = GridSearchCV(estimator=model, param_grid=cv_params, scoring='average_precision', verbose=1, n_jobs=-1, cv=3)
+    clf.fit(train_x, train_y)
+
+    evalute_result = clf.cv_results_
+    # print('每轮迭代运行结果:{0}'.format(evalute_result))
+    best_params = clf.best_params_
+    best_score = clf.best_score_
+
+    return clf, best_params, best_score
+
+
+def predict(test, clf):
+    """
+    输出预测结果
+    Parameters:
+        test - 训练集变量
+        clf - 模型
+    Returns:
+
+    """
+    y_pred = clf.predict_proba(test)[:, 1]
+    submit = pd.read_csv("data/sample_submit.csv")
+    submit['Evaluation'] = y_pred
+    submit.to_csv('my_submit_prediction.csv', index=False)
+
+
+if __name__ == '__main__':
+    t1 = time.time()
+    train_x, train_y, test = loadSimpData(showDataSet=False, Feature_selection=False, oversampling=True)
+    clf, best_params, best_score = XGB(train_x, train_y)
+    print('参数的最佳取值:{0}'.format(best_params))
+    print('RP-AUC数值为:{0}'.format(best_score))
+    predict(test, clf)
+    t2 = time.time()
+    print('耗时：{} 秒'.format(t2 - t1))
diff --git a/report_02_Titanic/Report 2_Titanic.ipynb b/report_02_Titanic/Report 2_Titanic.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..03d826ddc0ae26f471234ae6081b843249f584b6
--- /dev/null
+++ b/report_02_Titanic/Report 2_Titanic.ipynb	
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report 2 - Titanic\n",
+    "\n",
+    "* 姓名 卢程军\n",
+    "* 学号 2020262927\n",
+    "\n",
+    "\n",
+    "## 任务简介\n",
+    "“\n",
+    "\n",
+    "    泰坦尼克号的沉没是历史上最臭名昭著的海难之一。1912年4月15日，泰坦尼克号在处女航中撞上冰山后沉没，2224名乘客和船员中的1502人遇难。这一耸人听闻的悲剧震惊了国际社会，并导致了更好的船舶安全规定。\n",
+    "    海难导致这么多人丧生的原因之一是没有足够的救生艇容纳乘客和船员。虽然在沉船事件中幸存下来有一些运气因素，但有些人比其他人更有可能活下来。\n",
+    "\n",
+    "”\n",
+    "    \n",
+    "    实践技能：\n",
+    "        \n",
+    "        ·Binary classification\n",
+    "        \n",
+    "        ·Python & SKLearn\n",
+    "        \n",
+    "一个典型的二元分类任务模型；\n",
+    "训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。\n",
+    "数据的格式为：\n",
+    "CaseId,Q1,...,Q36,Evaluation，\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "\n",
+    "包含了什么数据:\n",
+    "\n",
+    "数据id, 数据特征（0/1二值）,评审值\n",
+    "其中，数据id是不必要信息，可以剔除；\n",
+    "数据特征属性共有36个，属性值是≥0的数值；\n",
+    "数据介绍：训练集中共有200000条样本，预测集中有80000条样本。\n",
+    "![image-3.png](attachment:image-3.png)\n",
+    "Evaluation是0/1二元评审值，0——予以理赔；Evaluation是test中需要预测的值；\n",
+    "## 解决途径\n",
+    "\n",
+    "主要包括：\n",
+    "1. 数据预处理\n",
+    "    \n",
+    "    1.1 首先我们可以检查有无缺失值——结果是没有缺失值。\n",
+    "        print(train.isnull().any())# 缺失值检查\n",
+    "    1.2 查看各变量与Evaluation的相关性\n",
+    "        print(train.corr()['Evaluation'])# 查看特征相关性\n",
+    "       可以发现，在36个问题中绝大多数是接近0的负相关，只有少部分为正相关。\n",
+    "    于是我们查看其中为-0.006511的Q1和为0.410700的Q28，当然还有作为估计值的Evaluation的分布情况\n",
+    "        for i in ['Q1', 'Q28', 'Evaluation']:\n",
+    "          sns.countplot(x=i, data=train)# 查看数据集各变量和类别的**分布**\n",
+    "        plt.show()\n",
+    "        \n",
+    "![image-4.png](attachment:image-4.png)\n",
+    "\n",
+    "![image-5.png](attachment:image-5.png)\n",
+    "\n",
+    "![image-6.png](attachment:image-6.png)\n",
+    "        \n",
+    "       Evaluation——审核能否通即是0和1的分布，绝大多数都为可以通过并且占据17/20。\n",
+    "       Q1——取值几乎都为0，和它相同分布的特征还有许多，这些特征难以体现区分度。\n",
+    "       Q28——有不同的取值，虽然大多为0，但分布还不算太过悬殊。\n",
+    "\n",
+    "2. 选用的方法\n",
+    "    \n",
+    "    2.1 RF（随机森林分类）——先使用表现最好的标杆模型，使用其默认参数，在三折交叉验证下查看PR-AUC的值\n",
+    "        RP-AUC数值为:0.8429819733147138\n",
+    "        耗时：51.35982632637024秒\n",
+    "    2.2 类不平衡处理和移除方差较低的特征后模型表现都不大好。于是我们直接对RF模型进行调参，会发现多次调参仍提升不多.\n",
+    "    于是考虑选择其他模型，在这之前我们先分析标杆模型和其类似的模型。\n",
+    "    \n",
+    "     2.2.1 LASSO逻辑回归模型\n",
+    "        LASSO 的特点：趋向于使得一部分θ 值变为 0，也就是说 LASSO 认为与θ= 0 对应的特征是完全没有用的，而剩下与θ不为 0 所对应的特征是有用的，所以 LASSO 可作为特征选择使用。\n",
+    "\n",
+    "        机器学习：模型泛化（LASSO 回归）https://www.cnblogs.com/volcao/p/9305682.html\n",
+    "\n",
+    "     2.2.2 随机森林分类模型\n",
+    "        随机森林的特点：是在以决策树为基学习器构建Bagging集成的基础上，进一步在决策树的训练过程中引入了随机属性选择，即每棵树随机选择样本并随机选择特征，使得具有很好的抗噪能力，性能稳定。\n",
+    "\n",
+    "        随机森林（原理/样例实现/参数调优）https://blog.csdn.net/y0367/article/details/51501780\n",
+    "\n",
+    "     2.2.3 XGBoost分类模型\n",
+    "        XGBoost的特点：\n",
+    "\n",
+    "        ①使用许多策略去防止过拟合，如：正则化项、Shrinkage and Column Subsampling等。\n",
+    "        ②目标函数优化利用了损失函数关于待求函数的二阶导数。\n",
+    "        ③支持并行化，这是XGBoost的闪光点，虽然树与树之间是串行关系，但是同层级节点可并行。具体的对于某个节点，节点内选择最佳分裂点，候选分裂点计算增益用多线程并行。训练速度快。\n",
+    "        ④添加了对稀疏数据的处理。\n",
+    "        ⑤交叉验证，early stop，当预测结果已经很好的时候可以提前停止建树，加快训练速度。\n",
+    "        ⑥支持设置样本权重，该权重体现在一阶导数g和二阶导数h，通过调整权重可以去更加关注一些样本。\n",
+    "        \n",
+    "    其实发现标杆模型都有一个特点，即可以特征选择，对稀疏特征有较好的处理能力。\n",
+    "    所以最终我选择XGBoost分类模型，因为他即结合Lasso回归中正则化处理，又结合了随机森林中集成思想和特征选择，所以对处理Q1这类特征有较好的反响。还有一点是根据上一篇文章对xgboost的处理，发现仔细调参后可较大幅度提升性能。\n",
+    "\n",
+    "3. 实现过程遇到的问题，以及如何解决的\n",
+    "    \n",
+    "    3.1 数据特征不平衡处理\n",
+    "        类不平衡（class-imbalance）是指在训练分类器中所使用的训练集的类别分布不均。\n",
+    "    于是我尝试使用过采样SMOTE算法：\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    from collectionsimport Counter\n",
+    "\n",
+    "    from imblearn.over_samplingimport SMOTE\n",
+    "\n",
+    "    #print(Counter(train_y))\n",
+    "\n",
+    "    smo = SMOTE(random_state=42)\n",
+    "\n",
+    "    train_x, train_y = smo.fit_sample(train_x, train_y)\n",
+    "\n",
+    "    #print(Counter(train_y))\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    可以发现在SMOTE处理后，两个类别样本数量达到相同.\n",
+    "    RP-AUC数值为:0.8399920675137803\n",
+    "    耗时：107.54732489585876 秒\n",
+    "    以上实验使用过采样的样本训练后，分数下降了，时间还提升了。这是由于过采样它不可避免的带来更大的运算开销，其次当数据中噪音过大时，结果反而可能会更差，因为噪音也被重复使用。\n",
+    "    \n",
+    "    3.2 噪声避免和特征选择\n",
+    "    由于在Q1特征中，表现出绝大特征值为0，这是方差极为低的特征，难以带来有效的信息。\n",
+    "    \n",
+    "    3.3.1 移除方差较低特征\n",
+    "    方差为0的特征会被自动移除。剩下的特征按设定的方差的阈值进行选择,排除方差较小即特征差异不明显的特征属性。\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    from sklearn.feature_selectionimport VarianceThreshold\n",
+    "\n",
+    "    sel = VarianceThreshold(threshold=0.1) # 选择方差大于0.1的特征\n",
+    "\n",
+    "    train = sel.fit_transform(train)\n",
+    "\n",
+    "    test = sel.fit_transform(test)\n",
+    "    \n",
+    "    //\n",
+    "    \n",
+    "    经过多次实验，选取方差阈值为0.1.\n",
+    "    RP-AUC数值为:0.8300324079765956\n",
+    "    耗时：39.80928683280945 秒\n",
+    "    移除方差较低的特征后，分数降低很大，时间却降低很多。因为许多特征都表现出方差低的情况，移除后剩下为数不多的特征，此时耗时自然下降，但信息不足以支撑模型训练。（使用scikit-learn进行特征选择）\n",
+    "    \n",
+    "    \n",
+    "4. 最终的结果，实验分析\n",
+    "\n",
+    "    使用XGBoost分类模型，使用其默认参数，在三折交叉验证下查看PR-AUC的值\n",
+    "\n",
+    "    RP-AUC数值为:0.8238557416902802\n",
+    "    耗时：30.34992218017578 秒\n",
+    "\n",
+    "    发现分数降低很大，但耗时降低。但不要紧张，我们参考网上同样使用xgboost的（竞赛文章）和上一篇调参方法（有时间可以三个参数一起调），在经历几个晚上运行终于调出较高的数值。\n",
+    "    ![image-8.png](attachment:image-8.png)\n",
+    "    \n",
+    "    ![image-7.png](attachment:image-7.png)\n",
+    "    \n",
+    "    RP-AUC数值为:0.9653026782164909\n",
+    "    耗时：139.36438059806824 秒\n",
+    "    由于提交截至日期结束，使得测试集预测结果无法查看，以上是训练集的训练结果。估计是模型有些过拟合了！\n",
+    "    ![image-11.png](attachment:image-11.png)\n",
+    "    ![image-10.png](attachment:image-10.png)\n",
+    "\n",
+    "## 总结\n",
+    "    ①本次是分类问题，数据表现出部分特征区分度低和类不平衡问题。\n",
+    "    ②处理以上问题，我们进行特征选择和数据处理和。\n",
+    "    ③如果未有太好效应，则直接进行调参。\n",
+    "    ④调参提高不多，则再一次审视模型选择。\n",
+    "    ⑤其中②③④需要耐心的交接进行。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "main_language": "python"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/report_02_Titanic/Report 2_Titanic.pdf b/report_02_Titanic/Report 2_Titanic.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..f1515c0f28edf6c9c63bfe62d68284b769386ed4
Binary files /dev/null and b/report_02_Titanic/Report 2_Titanic.pdf differ
diff --git a/report_02_Titanic/report_template.ipynb b/report_02_Titanic/report_template.ipynb
deleted file mode 100644
index 21eb1ce711b96fd98ffe44be0b1697fee849508a..0000000000000000000000000000000000000000
--- a/report_02_Titanic/report_template.ipynb
+++ /dev/null
@@ -1,57 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report - 报告题目\n",
-    "\n",
-    "* 姓名\n",
-    "* 学号\n",
-    "\n",
-    "\n",
-    "## 任务简介\n",
-    "\n",
-    "这里简述一下任务是什么；数据的格式，包含了什么数据；最终的目标是什么\n",
-    "\n",
-    "## 解决途径\n",
-    "\n",
-    "主要包括：\n",
-    "1. 问题的思考，整体的思路\n",
-    "2. 选用的方法，以及为何选用这些方法\n",
-    "3. 实现过程遇到的问题，以及如何解决的\n",
-    "4. 最终的结果，实验分析\n",
-    "\n",
-    "要求：\n",
-    "1. 数据的可视化\n",
-    "2. 程序，以及各个部分的解释、说明\n",
-    "3. 结果的可视化，精度等的分析\n",
-    "\n",
-    "## 总结\n",
-    "总结任务实现过程所取得的心得等。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  },
-  "main_language": "python"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/report_02_Titanic/titanic.py b/report_02_Titanic/titanic.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a970a3ad9d6120e0c91d4afc8c2137e026ac511
--- /dev/null
+++ b/report_02_Titanic/titanic.py
@@ -0,0 +1,244 @@
+# -*- coding:utf-8 -*-
+# @Time : 2021/1/20 11:40
+# @Author: LCHJ
+# @File : titanic.py
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+%matplotlib inline
+
+train_data = pd.read_csv('data/train.csv')
+test_data = pd.read_csv('data/test.csv')
+train_data.info()
+test_data.info()
+
+train_data['Survived'].value_counts().plot.pie(autopct='%1.1f%%')
+train_data.groupby(['Sex','Survived'])['Survived'].count()
+train_data[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()
+train_data[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar(color=['r','g','b'])
+train_data[['Sex','Pclass','Survived']].groupby(['Pclass','Sex']).mean().plot.bar()
+train_data.groupby(['Sex','Pclass','Survived'])['Survived'].count()
+
+#年龄与存活的关系
+f,ax=plt.subplots(1,2,figsize=(18,8))
+sns.violinplot("Pclass","Age", hue="Survived", data=train_data,split=True,ax=ax[0])
+ax[0].set_title('Pclass and Age vs Survived')
+ax[0].set_yticks(range(0,110,10))
+sns.violinplot("Sex","Age", hue="Survived", data=train_data,split=True,ax=ax[1])
+ax[1].set_title('Sex and Age vs Survived')
+ax[1].set_yticks(range(0,110,10))
+plt.show()
+
+sns.countplot('Embarked',hue='Survived',data=train_data)
+plt.title('Embarked and Survived')
+
+#船上亲友人数与存活关系
+f,ax=plt.subplots(1,2,figsize=(18,8))
+train_data[['Parch','Survived']].groupby(['Parch']).mean().plot.bar(ax=ax[0])
+ax[0].set_title('Parch and Survived')
+train_data[['SibSp','Survived']].groupby(['SibSp']).mean().plot.bar(ax=ax[1])
+ax[1].set_title('SibSp and Survived')
+
+train_data_org = pd.read_csv('train.csv')
+test_data_org = pd.read_csv('test.csv')
+test_data_org['Survived'] = 0
+combined_train_test = train_data_org.append(test_data_org)
+
+if combined_train_test['Embarked'].isnull().sum() != 0:
+        combined_train_test['Embarked'].fillna(combined_train_test['Embarked'].mode().iloc[0], inplace=True)
+
+emb_dummies_df = pd.get_dummies(combined_train_test['Embarked'],prefix=combined_train_test[['Embarked']].columns[0])
+combined_train_test = pd.concat([combined_train_test, emb_dummies_df], axis=1)
+
+sex_dummies_df = pd.get_dummies(combined_train_test['Sex'], prefix=combined_train_test[['Sex']].columns[0])
+combined_train_test = pd.concat([combined_train_test, sex_dummies_df], axis=1)
+
+combined_train_test['Title'] = combined_train_test['Name'].str.extract('.+,(.+)').str.extract( '^(.+?)\.').str.strip()
+
+
+title_Dict = {}
+title_Dict.update(dict.fromkeys(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer'))
+title_Dict.update(dict.fromkeys(['Jonkheer', 'Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty'))
+title_Dict.update(dict.fromkeys(['Mme', 'Ms', 'Mrs'], 'Mrs'))
+title_Dict.update(dict.fromkeys(['Mlle', 'Miss'], 'Miss'))
+title_Dict.update(dict.fromkeys(['Mr'], 'Mr'))
+title_Dict.update(dict.fromkeys(['Master'], 'Master'))
+
+combined_train_test['Title'] = combined_train_test['Title'].map(title_Dict)
+
+title_dummies_df = pd.get_dummies(combined_train_test['Title'], prefix=combined_train_test[['Title']].columns[0])
+combined_train_test = pd.concat([combined_train_test, title_dummies_df], axis=1)
+
+if combined_train_test['Fare'].isnull().sum() != 0:
+        combined_train_test['Fare'] = combined_train_test[['Fare']].fillna(combined_train_test.groupby('Pclass').transform('mean'))
+
+combined_train_test['Group_Ticket'] = combined_train_test['Fare'].groupby(by=combined_train_test['Ticket']).transform('count')
+combined_train_test['Fare'] = combined_train_test['Fare'] / combined_train_test['Group_Ticket']
+combined_train_test.drop(['Group_Ticket'], axis=1, inplace=True)
+
+
+def fare_category(fare):
+    if fare <= 4:
+        return 0
+    elif fare <= 10:
+        return 1
+    elif fare <= 30:
+        return 2
+    elif fare <= 45:
+        return 3
+    else:
+        return 4
+combined_train_test['Fare_Category'] = combined_train_test['Fare'].map(fare_category)
+
+fare_cat_dummies_df = pd.get_dummies(combined_train_test['Fare_Category'],prefix=combined_train_test[['Fare_Category']].columns[0])
+combined_train_test = pd.concat([combined_train_test, fare_cat_dummies_df], axis=1)
+
+
+Pclass_1_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get([1]).values[0]
+Pclass_2_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get([2]).values[0]
+Pclass_3_mean_fare = combined_train_test['Fare'].groupby(by=combined_train_test['Pclass']).mean().get([3]).values[0]
+# 建立Pclass_Fare Category
+combined_train_test['Pclass_Fare_Category'] = combined_train_test.apply(pclass_fare_category, args=(Pclass_1_mean_fare, Pclass_2_mean_fare, Pclass_3_mean_fare), axis=1)
+p_fare = LabelEncoder()
+p_fare.fit(np.array(['Pclass_1_Low_Fare', 'Pclass_1_High_Fare', 'Pclass_2_Low_Fare', 'Pclass_2_High_Fare', 'Pclass_3_Low_Fare','Pclass_3_High_Fare']))#给每一项添加标签
+combined_train_test['Pclass_Fare_Category'] = p_fare.transform(combined_train_test['Pclass_Fare_Category'])#转换成数值
+
+
+combined_train_test['Family_Size'] = combined_train_test['Parch'] + combined_train_test['SibSp'] + 1
+combined_train_test['Family_Size_Category'] = combined_train_test['Family_Size'].map(family_size_category)
+le_family = LabelEncoder()
+le_family.fit(np.array(['Single', 'Small_Family', 'Large_Family']))
+combined_train_test['Family_Size_Category'] = le_family.transform(combined_train_test['Family_Size_Category'])
+fam_size_cat_dummies_df = pd.get_dummies(combined_train_test['Family_Size_Category'],prefix=combined_train_test[['Family_Size_Category']].columns[0])
+combined_train_test = pd.concat([combined_train_test, fam_size_cat_dummies_df], axis=1)
+
+
+missing_age_df = pd.DataFrame(combined_train_test[['Age', 'Parch', 'Sex', 'SibSp', 'Family_Size', 'Family_Size_Category',
+                             'Title', 'Fare', 'Fare_Category', 'Pclass', 'Embarked']])
+missing_age_df = pd.get_dummies(missing_age_df,columns=['Title', 'Family_Size_Category', 'Fare_Category', 'Sex', 'Pclass' ,'Embarked'])
+missing_age_train = missing_age_df[missing_age_df['Age'].notnull()]
+missing_age_test = missing_age_df[missing_age_df['Age'].isnull()]
+
+
+def fill_missing_age(missing_age_train, missing_age_test):
+
+    missing_age_X_train = missing_age_train.drop(['Age'], axis=1)
+    missing_age_Y_train = missing_age_train['Age']
+    missing_age_X_test = missing_age_test.drop(['Age'], axis=1)
+    #模型1
+    gbm_reg = ensemble.GradientBoostingRegressor(random_state=42)
+    gbm_reg_param_grid = {'n_estimators': [2000], 'max_depth': [3],'learning_rate': [0.01], 'max_features': [3]}
+    gbm_reg_grid = model_selection.GridSearchCV(gbm_reg, gbm_reg_param_grid, cv=10, n_jobs=25, verbose=1,  scoring='neg_mean_squared_error')
+    gbm_reg_grid.fit(missing_age_X_train, missing_age_Y_train)
+    print('Age feature Best GB Params:' + str(gbm_reg_grid.best_params_))
+    print('Age feature Best GB Score:' + str(gbm_reg_grid.best_score_))
+    print('GB Train Error for "Age" Feature Regressor:'+ str(gbm_reg_grid.score(missing_age_X_train, missing_age_Y_train)))
+    missing_age_test['Age_GB'] = gbm_reg_grid.predict(missing_age_X_test)
+    print(missing_age_test['Age_GB'][:4])
+    #模型2
+    lrf_reg = LinearRegression()
+    lrf_reg_param_grid = {'fit_intercept': [True], 'normalize': [True]}
+    lrf_reg_grid = model_selection.GridSearchCV(lrf_reg, lrf_reg_param_grid, cv=10, n_jobs=25, verbose=1, scoring='neg_mean_squared_error')
+    lrf_reg_grid.fit(missing_age_X_train, missing_age_Y_train)
+    print('Age feature Best LR Params:' + str(lrf_reg_grid.best_params_))
+    print('Age feature Best LR Score:' + str(lrf_reg_grid.best_score_))
+    print('LR Train Error for "Age" Feature Regressor' + str(lrf_reg_grid.score(missing_age_X_train, missing_age_Y_train)))
+    missing_age_test['Age_LRF'] = lrf_reg_grid.predict(missing_age_X_test)
+    print(missing_age_test['Age_LRF'][:4])
+   #将两个模型预测后的均值作为最终预测结果
+    print('shape1',missing_age_test['Age'].shape,missing_age_test[['Age_GB','Age_LRF']].mode(axis=1).shape)
+    #missing_age_test['Age'] = missing_age_test[['Age_GB','Age_LRF']].mode(axis=1)
+    missing_age_test['Age'] = np.mean([missing_age_test['Age_GB'],missing_age_test['Age_LRF']])
+    print(missing_age_test['Age'][:4])
+    drop_col_not_req(missing_age_test, ['Age_GB', 'Age_LRF'])
+
+    return missing_age_test
+combined_train_test.loc[(combined_train_test.Age.isnull()), 'Age'] = fill_missing_age(missing_age_train,missing_age_test)
+
+
+combined_train_test['Ticket_Letter'] = combined_train_test['Ticket'].str.split().str[0]
+combined_train_test['Ticket_Letter'] = combined_train_test['Ticket_Letter'].apply(lambda x:np.nan if x.isnumeric() else x)
+combined_train_test['Ticket_Number'] = combined_train_test['Ticket'].apply(lambda x: pd.to_numeric(x,errors='coerce'))
+combined_train_test['Ticket_Number'].fillna(0,inplace=True)
+combined_train_test = pd.get_dummies(combined_train_test,columns=['Ticket','Ticket_Letter'])
+
+combined_train_test['Cabin_Letter'] = combined_train_test['Cabin'].apply(lambda x:str(x)[0] if pd.notnull(x) else x)
+combined_train_test = pd.get_dummies(combined_train_test,columns=['Cabin','Cabin_Letter'])
+
+
+train_data = combined_train_test[:891]
+test_data = combined_train_test[891:]
+titanic_train_data_X = train_data.drop(['Survived'],axis=1)
+titanic_train_data_Y = train_data['Survived']
+titanic_test_data_X = test_data.drop(['Survived'],axis=1)
+
+
+
+def get_top_n_features(titanic_train_data_X, titanic_train_data_Y, top_n_features):
+    # 随机森林
+    rf_est = RandomForestClassifier(random_state=42)
+    rf_param_grid = {'n_estimators': [500], 'min_samples_split': [2, 3], 'max_depth': [20]}
+    rf_grid = model_selection.GridSearchCV(rf_est, rf_param_grid, n_jobs=25, cv=10, verbose=1)
+    rf_grid.fit(titanic_train_data_X,titanic_train_data_Y)
+    #将feature按Importance排序
+    feature_imp_sorted_rf = pd.DataFrame({'feature': list(titanic_train_data_X), 'importance': rf_grid.best_estimator_.feature_importances_}).sort_values('importance', ascending=False)
+    features_top_n_rf = feature_imp_sorted_rf.head(top_n_features)['feature']
+    print('Sample 25 Features from RF Classifier')
+    print(str(features_top_n_rf[:25]))
+
+    # AdaBoost
+    ada_est = ensemble.AdaBoostClassifier(random_state=42)
+    ada_param_grid = {'n_estimators': [500], 'learning_rate': [0.5, 0.6]}
+    ada_grid = model_selection.GridSearchCV(ada_est, ada_param_grid, n_jobs=25, cv=10, verbose=1)
+    ada_grid.fit(titanic_train_data_X, titanic_train_data_Y)
+    #排序
+    feature_imp_sorted_ada = pd.DataFrame({'feature': list(titanic_train_data_X),'importance': ada_grid.best_estimator_.feature_importances_}).sort_values( 'importance', ascending=False)
+    features_top_n_ada = feature_imp_sorted_ada.head(top_n_features)['feature']
+
+    # ExtraTree
+    et_est = ensemble.ExtraTreesClassifier(random_state=42)
+    et_param_grid = {'n_estimators': [500], 'min_samples_split': [3, 4], 'max_depth': [15]}
+    et_grid = model_selection.GridSearchCV(et_est, et_param_grid, n_jobs=25, cv=10, verbose=1)
+    et_grid.fit(titanic_train_data_X, titanic_train_data_Y)
+    #排序
+    feature_imp_sorted_et = pd.DataFrame({'feature': list(titanic_train_data_X), 'importance': et_grid.best_estimator_.feature_importances_}).sort_values('importance', ascending=False)
+    features_top_n_et = feature_imp_sorted_et.head(top_n_features)['feature']
+    print('Sample 25 Features from ET Classifier:')
+    print(str(features_top_n_et[:25]))
+
+    # 将三个模型挑选出来的前features_top_n_et合并
+    features_top_n = pd.concat([features_top_n_rf, features_top_n_ada, features_top_n_et], ignore_index=True).drop_duplicates()
+
+    return features_top_n
+
+
+
+feature_to_pick = 250
+feature_top_n = get_top_n_features(titanic_train_data_X,titanic_train_data_Y,feature_to_pick)
+titanic_train_data_X = titanic_train_data_X[feature_top_n]
+del titanic_train_data_X['Ticket_Number']#后来发现删除Ticket_Number后效果更好了
+titanic_test_data_X = titanic_test_data_X[feature_top_n]
+del titanic_test_data_X['Ticket_Number']
+
+
+
+rf_est = ensemble.RandomForestClassifier(n_estimators = 750, criterion = 'gini', max_features = 'sqrt',
+                                         max_depth = 3, min_samples_split = 4, min_samples_leaf = 2,
+                                         n_jobs = 50, random_state = 42, verbose = 1)
+gbm_est = ensemble.GradientBoostingClassifier(n_estimators=900, learning_rate=0.0008, loss='exponential',
+                                              min_samples_split=3, min_samples_leaf=2, max_features='sqrt',
+                                              max_depth=3, random_state=42, verbose=1)
+et_est = ensemble.ExtraTreesClassifier(n_estimators=750, max_features='sqrt', max_depth=35, n_jobs=50,
+                                       criterion='entropy', random_state=42, verbose=1)
+voting_est = ensemble.VotingClassifier(estimators = [('rf', rf_est),('gbm', gbm_est),('et', et_est)],
+                                   voting = 'soft', weights = [3,5,2],
+                                   n_jobs = 50)
+voting_est.fit(titanic_train_data_X,titanic_train_data_Y)
+
+
+titanic_test_data_X['Survived'] = voting_est.predict(titanic_test_data_X)
+submission = pd.DataFrame({'PassengerId':test_data_org.loc[:,'PassengerId'],
+                           'Survived':titanic_test_data_X.loc[:,'Survived']})
+submission.to_csv('submission_result.csv',index=False,sep=',')
+
diff --git a/report_03_Fashion/config.py b/report_03_Fashion/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..42e74c6f829ab0b27ef5a2928989ae907d4c3647
--- /dev/null
+++ b/report_03_Fashion/config.py
@@ -0,0 +1,25 @@
+class DefaultConfigs(object):
+    #1.string parameters
+    train_data = "./data/train/"
+    test_data = "./data/test/"
+    img_example = "./data/test/"
+    val_data = "./data/train_validation/"
+    model_name = "resnet50"
+    weights = "./checkpoints/"
+    best_models = weights + "best_model/"
+    submit = "./submit/"
+    logs = "./logs/"
+    gpus = "1"  # 训练时使用的GPU数
+    accuracy_loss="./logs/accuracy_loss_exp.txt"
+    # 2.numeric parameters
+    epochs = 60  # 训练轮数
+    batch_size = 200
+    img_height = 28
+    img_weight = 28
+    num_classes = 10
+    seed = 888
+    lr = 1e-4  #0.0001 0.00005 0.001 0.01 0.0005
+    lr_decay = 1e-5 #衰减系数
+    weight_decay = 1e-5 #权重衰减（L2正则化）可以避免模型过拟合问题
+
+config = DefaultConfigs()
diff --git a/report_03_Fashion/data_aug.py b/report_03_Fashion/data_aug.py
new file mode 100644
index 0000000000000000000000000000000000000000..93562979e1c80f92254b95d17800786a12ac2b09
--- /dev/null
+++ b/report_03_Fashion/data_aug.py
@@ -0,0 +1,126 @@
+import os
+import shutil
+import numpy as np
+import cv2
+import random
+from PIL import Image, ImageEnhance
+from skimage import exposure
+from skimage.util import random_noise
+
+image_number = 0
+
+raw_path = "./data/train_validation32/"
+new_path = "D:/Graduation_project/plants_disease_detection-master/data/train_validation/"
+
+# 加高斯噪声
+def addNoise(img):
+    '''
+    注意：输出的像素是[0,1]之间,所以乘以5得到[0,255]之间
+    '''
+    if img is not None:
+        return random_noise(img, mode='gaussian', seed=13, clip=True) * 255
+    else:
+        return img
+
+def changeLight(img):
+    rate = random.uniform(0.9, 1.2)
+    # print(rate)
+    try:
+        if img is not None:
+            img = exposure.adjust_gamma(img, rate)  # 大于1为调暗，小于1为调亮;1.05
+        return img
+    except:
+        pass
+
+try:
+    for i in range(0, 56):
+        os.makedirs(new_path + os.sep + str(i))
+except:
+    pass
+
+for raw_dir_name in range(0, 56):
+    raw_dir_name = str(raw_dir_name)
+    saved_image_path = new_path + raw_dir_name + "/"
+    raw_image_path = raw_path + raw_dir_name + "/"
+    if not os.path.exists(saved_image_path):
+        os.mkdir(saved_image_path)
+    raw_image_file_name = os.listdir(raw_image_path)
+    raw_image_file_path = []
+    for i in raw_image_file_name:
+        raw_image_file_path.append(raw_image_path + i)
+
+    for x in raw_image_file_path:
+        try:
+            img = Image.open(x)
+            cv_image = cv2.imread(x)
+            if img is not None:
+                # 拉普拉斯增强
+                # kernel = np.array([[0, -1, 0], [0, 5, 0], [0, -1, 0]])  # 定义卷积核
+                # Lap_img = cv2.filter2D(img, -1, kernel)  # 进行卷积运算
+
+                # 高斯噪声
+                gau_image = addNoise(cv_image)
+                # 随机改变
+                light = changeLight(cv_image)
+                light_and_gau = addNoise(light)
+                # cv2.imwrite(saved_image_path + "Lap_" + os.path.basename(x), Lap_img)
+                cv2.imwrite(saved_image_path + "gau_" + os.path.basename(x), gau_image)
+                cv2.imwrite(saved_image_path + "light_" + os.path.basename(x), light)
+                cv2.imwrite(saved_image_path + "gau_light" + os.path.basename(x), light_and_gau)
+                img = img.resize((512, 512))
+                # 1.翻转
+                img_flip_left_right = img.transpose(Image.FLIP_LEFT_RIGHT)
+                img_flip_top_bottom = img.transpose(Image.FLIP_TOP_BOTTOM)
+                # 2.旋转
+                img_rotate_90 = img.transpose(Image.ROTATE_90)
+                img_rotate_180 = img.transpose(Image.ROTATE_180)
+                # img_rotate_270 = img.transpose(Image.ROTATE_270)
+                # img_rotate_90_left = img_flip_left_right.transpose(Image.ROTATE_90)
+                # img_rotate_270_left = img_flip_left_right.transpose(Image.ROTATE_270)
+                # 3.亮度
+                enh_bri = ImageEnhance.Brightness(img)
+                brightness = 1.2
+                image_brightened = enh_bri.enhance(brightness)
+                # 4.色彩
+                enh_col = ImageEnhance.Color(image_brightened)
+                color = 1.2
+                image_colored = enh_col.enhance(color)
+                # 5.对比度
+                enh_con = ImageEnhance.Contrast(image_colored)
+                contrast = 1.2
+                image_contrasted = enh_con.enhance(contrast)
+                # 6.锐度
+                enh_sha = ImageEnhance.Sharpness(image_contrasted)
+                sharpness = 1.2
+                image_sharped = enh_sha.enhance(sharpness)
+
+                # 保存
+                img.save(saved_image_path + os.path.basename(x))
+                image_sharped.save(saved_image_path + "sharped_" + os.path.basename(x))
+
+                # img_flip_left_right.save(saved_image_path + "left_right_" + os.path.basename(x))
+                #
+                # img_flip_top_bottom.save(saved_image_path + "top_bottom_" + os.path.basename(x))
+
+                # img_rotate_90.save(saved_image_path + "rotate_90_" + os.path.basename(x))
+
+                # img_rotate_180.save(saved_image_path + "rotate_180_" + os.path.basename(x))
+                #
+                # img_rotate_270.save(saved_image_path + "rotate_270_" + os.path.basename(x))
+
+                # img_rotate_90_left.save(saved_image_path + "rotate_90_left_" + os.path.basename(x))
+
+                # img_rotate_270_left.save(saved_image_path + "rotate_270_left_" + os.path.basename(x))
+
+                # image_brightened.save(saved_image_path + "brighted_" + os.path.basename(x))
+
+                # image_colored.save(saved_image_path + "colored_" + os.path.basename(x))
+
+                # image_contrasted.save(saved_image_path + "contrasted_" + os.path.basename(x))
+                image_number += 1
+        except:
+            continue
+
+    print("convert pictur" "es :%s size:%s mode:%s" % (image_number, img.size, img.mode))
+    # else:
+    # continue
diff --git a/report_03_Fashion/images/fashion-mnist.jpg b/report_03_Fashion/images/fashion-mnist.jpg
deleted file mode 100644
index b65ac689305d77ed26bc945726ac7b005781d02c..0000000000000000000000000000000000000000
Binary files a/report_03_Fashion/images/fashion-mnist.jpg and /dev/null differ
diff --git a/report_03_Fashion/main.py b/report_03_Fashion/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..1aad58717c4a877624d5615a1e1589dc5bb2df7a
--- /dev/null
+++ b/report_03_Fashion/main.py
@@ -0,0 +1,269 @@
+import warnings
+from collections import OrderedDict
+from datetime import datetime
+from timeit import default_timer as timer
+
+import matplotlib.pyplot as plt
+from torch import optim
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+
+from dataset.dataloader import *
+from models.model import *
+from utils import *
+
+# 1. set random.seed and cudnn performance
+random.seed(config.seed)
+np.random.seed(config.seed)
+torch.manual_seed(config.seed)
+torch.cuda.manual_seed_all(config.seed)
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+torch.backends.cudnn.benchmark = True
+warnings.filterwarnings('ignore')
+
+
+# 2. evaluate func
+def evaluate(val_loader, model, criterion):
+    # 2.1 define meters
+    losses = AverageMeter()
+    top1 = AverageMeter()
+    top2 = AverageMeter()
+    # 2.2 switch to evaluate mode and confirm model has been transfered to cuda
+    # model.cuda()
+    model.eval()
+    with torch.no_grad():
+        for i, (input, target) in enumerate(val_loader):
+            input = Variable(input).cuda()
+            target = Variable(torch.from_numpy(np.array(target)).long()).cuda()
+            # target = Variable(target).cuda()
+            # 2.2.1 compute output
+            output = model(input)
+            loss = criterion(output, target)
+
+            # 2.2.2 measure accuracy and record loss
+            precision1, precision2 = accuracy(output, target, topk=(1, 2))
+            losses.update(loss.item(), input.size(0))
+            top1.update(precision1[0], input.size(0))
+            top2.update(precision2[0], input.size(0))
+
+    return [losses.avg, top1.avg, top2.avg]
+# 4. more details to build main function
+def main():
+    fold = 0
+    # 4.1 mkdirs
+    if not os.path.exists(config.submit):
+        os.mkdir(config.submit)
+    if not os.path.exists(config.weights):
+        os.mkdir(config.weights)
+    if not os.path.exists(config.best_models):
+        os.mkdir(config.best_models)
+    if not os.path.exists(config.logs):
+        os.mkdir(config.logs)
+    if not os.path.exists(config.weights + config.model_name + os.sep + str(fold) + os.sep):
+        os.makedirs(config.weights + config.model_name + os.sep + str(fold) + os.sep)
+    if not os.path.exists(config.best_models + config.model_name + os.sep + str(fold) + os.sep):
+        os.makedirs(config.best_models + config.model_name + os.sep + str(fold) + os.sep)
+
+    # 第一个参数表示打开的文件名称，第二个参数a+表示可读可写，r表示可读，w表示可写
+    with open(config.accuracy_loss, "w") as f:
+        f.writelines("epoch" + ',' + "valid_loss" + ',' + "best_precision1")
+        f.writelines("\n")
+
+    # 4.2 get model and optimizer
+    model = get_net()
+    # model = torch.nn.DataParallel(model)
+    model.cuda()
+    #SGD 虽然能达到极小值，但是比其它算法用的时间长，而且可能会被困在鞍点
+    #SGD 因为更新比较频繁，会造成 cost function 有严重的震荡。
+    # optimizer = optim.SGD(model.parameters(),lr = config.lr,momentum=0.9,weight_decay=config.weight_decay)
+    # 交叉熵损失函数,通常用于多分类
+    criterion = nn.CrossEntropyLoss().cuda()
+    optimizer = optim.Adam(model.parameters(), lr=config.lr, amsgrad=True, weight_decay=config.weight_decay)
+    # criterion = FocalLoss().cuda()
+
+    # 4.3 some parameters for  K-fold and restart model
+    start_epoch = 0
+    best_precision1 = 0
+    best_precision_save = 0
+
+    resume = False
+    # 4.4 restart the training process
+    if resume:
+        checkpoint = torch.load(config.best_models + "resnet50/" + str(fold) + "/model_best.pth.tar")
+        start_epoch = checkpoint["epoch"]
+        fold = checkpoint["fold"]
+        best_precision1 = checkpoint["best_precision1"]
+        model.load_state_dict(checkpoint["state_dict"])
+        optimizer.load_state_dict(checkpoint["optimizer"])
+
+    # 4.5 get files and split for K-fold dataset
+    # 4.5.1 read files
+    train_data_list = get_files(config.train_data, "train")
+    val_data_list = get_files(config.val_data, "val")
+
+    """ 
+    #4.5.2 split
+    split_fold = StratifiedKFold(n_splits=3)
+    folds_indexes = split_fold.split(X=origin_files["filename"],y=origin_files["label"])
+    folds_indexes = np.array(list(folds_indexes))
+    fold_index = folds_indexes[fold]
+
+    #4.5.3 using fold index to split for train data and val data
+    train_data_list = pd.concat([origin_files["filename"][fold_index[0]],origin_files["label"][fold_index[0]]],axis=1)
+    val_data_list = pd.concat([origin_files["filename"][fold_index[1]],origin_files["label"][fold_index[1]]],axis=1)
+    """
+    # train_data_list,val_data_list = train_test_split(train_,test_size = 0.15,stratify=train_["label"])
+    # 4.5.4 load dataset
+    train_dataloader = DataLoader(LCJDataset(train_data_list), batch_size=config.batch_size, shuffle=True,
+                                  collate_fn=collate_fn, pin_memory=True)
+    val_dataloader = DataLoader(LCJDataset(val_data_list, train=False), batch_size=config.batch_size, shuffle=True,
+                                collate_fn=collate_fn, pin_memory=False)
+    # scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,"max",verbose=1,patience=3)
+    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=4, gamma=1.2)
+    # 4.5.5.1 define metrics
+    model.train()
+
+    train_losses = AverageMeter()
+    train_top1 = AverageMeter()
+    train_top2 = AverageMeter()
+    valid_loss = [np.inf, 0, 0]
+
+    # logs
+    log = Logger()
+    log.open(config.logs + "log_train.txt", mode="a")
+    log.write("\n----------------------------------------------- [START %s] %s\n\n" % (
+        datetime.now().strftime('%Y-%m-%d %H:%M:%S'), '-' * 51))
+
+    log.write('** start training here! **\n')
+    log.write(
+        '                           |------------ VALID -----------|----------- TRAIN --------|------Accuracy------|------------|\n')
+    log.write(
+        'lr       iter     epoch    | loss   top-1  top-2          | loss   top-1  top-2      |    Current Best    | time       |\n')
+    log.write(
+        '--------------------------------------------------------------------------------------------------------------------------\n')
+
+    # 定义两个数组,用于绘图
+    Loss_list = []
+    Accuracy_list = []
+
+    # 4.5.5 train
+    start = timer()
+    for epoch in range(start_epoch, config.epochs):
+        scheduler.step(epoch)
+        # train
+        # global iter
+        flag = 1
+        # 4.5.5 switch to continue train process
+        model.train()
+        for iter, (input, target) in enumerate(train_dataloader):
+            input = Variable(input).cuda()
+            target = Variable(torch.from_numpy(np.array(target)).long()).cuda()
+            # target = Variable(target).cuda()
+            '''
+            在使用pytorch时，并不是所有的操作都需要进行计算图的生成（计算过程的构建，以便梯度反向传播等操作）。
+            不禁用时，结果有grad_fn=<AddmmBackward>属性，表示，计算的结果在一计算图当中，可以进行梯度反传等操作。
+            而对于tensor的计算操作，默认是要进行计算图的构建的.    
+            在这种情况下，可以使用 with torch.no_grad():，
+            强制之后的内容不进行计算图构建。
+            '''
+            # 这里梯度是累加的，用于计算图，不能清零
+            # with torch.no_grad():                      #禁用自动梯度中的梯度跟踪
+            optimizer.zero_grad()  # 将所有参数的梯度都置零
+            output = model(input)
+            # print("====4.5.5 output====\n"+str(output))
+            loss = criterion(output, target)  # 计算训练误差
+            # print("====4.5.5 loss1====\n" + str(loss))
+            # loss = Variable(loss, requires_grad=True)  # 生成变量
+            # print("====4.5.5 loss2====\n" + str(loss))
+            # backward
+            loss.backward()  # 误差反向传播计算参数梯度
+            optimizer.step()  # 通过梯度做一步参数更新
+
+            precision1_train, precision2_train = accuracy(output, target, topk=(1, 2))
+            train_losses.update(loss.item(), input.size(0))
+            train_top1.update(precision1_train[0], input.size(0))
+            train_top2.update(precision2_train[0], input.size(0))
+            lr = get_learning_rate(optimizer)
+
+            print('\r', end='', flush=True)
+            print(
+                '%0.6f %5.1f %6.1f      | %0.3f  %0.3f  %0.3f        | %0.3f  %0.3f  %0.3f       |     %s     | %s' % ( \
+                    lr, iter / len(train_dataloader) + epoch, epoch,
+                    valid_loss[0], valid_loss[1], valid_loss[2],
+                    train_losses.avg, train_top1.avg, train_top2.avg, str(best_precision_save)[:8],
+                    time_to_str((timer() - start), 'min'))
+                , end='', flush=True)
+
+        # evaluate
+        lr = get_learning_rate(optimizer)
+        # evaluate every half epoch
+        valid_loss = evaluate(val_dataloader, model, criterion)
+        is_best = valid_loss[1] > best_precision1
+        best_precision1 = max(valid_loss[1], best_precision1)
+        try:
+            best_precision_save = best_precision1.cpu().data.numpy()
+        except:
+            pass
+
+        save_checkpoint({
+            "epoch": epoch + 1,
+            "model_name": config.model_name,
+            "state_dict": model.state_dict(),
+            "best_precision1": best_precision1,
+            "optimizer": optimizer.state_dict(),
+            "fold": fold,
+            "valid_loss": valid_loss,
+        }, is_best, fold)
+
+        # scheduler.step(valid_loss[1])
+        print("\r", end="", flush=True)
+        log.write(
+            '%0.6f %5.1f %6.1f      | %0.3f  %0.3f  %0.3f       | %0.3f  %0.3f  %0.3f       |       %s       | %s' % ( \
+                lr, 0 + epoch, epoch,
+                valid_loss[0], valid_loss[1], valid_loss[2],
+                train_losses.avg, train_top1.avg, train_top2.avg, str(best_precision_save)[:8],
+                time_to_str((timer() - start), 'min'))
+        )
+        log.write('\n')
+        # time.sleep(0.001)
+        # adjust learning rate
+        if (int(epoch) == int(config.epochs / 2)) and (flag == 1):
+            flag -= 1
+            scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=4, gamma=0.8)
+
+        # show plot
+        Loss_list.append(valid_loss[0])
+        Accuracy_list.append(best_precision1)
+        # write accuracy_loss data
+        try:
+            with open(config.accuracy_loss, "a+") as f:
+                f.writelines(str(epoch) + ',' + str(valid_loss[0]) + ',' + str(best_precision_save) + '\n')
+        except:
+            pass
+
+    best_model = torch.load(
+        config.best_models + os.sep + config.model_name + os.sep + str(fold) + os.sep + 'model_best.pth.tar')
+    model.load_state_dict(best_model["state_dict"])
+    try:
+        # 将每次相对应的准确率以及损失率附在x上
+        x1 = range(0, config.epochs)
+        x2 = range(0, config.epochs)
+        y1 = Accuracy_list
+        y2 = Loss_list
+        plt.subplot(2, 1, 1)
+        plt.plot(x1, y1, 'o-')
+        plt.title('Test accuracy vs. epoches')
+        plt.ylabel('Test accuracy')
+
+        plt.subplot(2, 1, 2)
+        plt.plot(x2, y2, '.-')
+        plt.xlabel('Test loss vs. epoches')
+        plt.ylabel('Test loss')
+
+        plt.savefig(config.logs + "accuracy_loss.jpg")
+        plt.show()
+    except:
+        print("非初始训练，loss&accuracy数据不全")
+        pass
+if __name__ == "__main__":
+    main()
diff --git a/report_03_Fashion/recognition.py b/report_03_Fashion/recognition.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4161123b39171651a3dc656b530a32602605cbf
--- /dev/null
+++ b/report_03_Fashion/recognition.py
@@ -0,0 +1,174 @@
+from collections import OrderedDict
+
+from PyQt5 import QtCore, QtWidgets
+from PyQt5.QtWidgets import *
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+
+from dataset.dataloader import *
+from models.model import *
+from show import *
+from utils import *
+
+global test_dir
+global test_file
+
+
+def test(test_loader, model, folds):
+    # 3.1 confirm the model converted to cuda
+    csv_map = OrderedDict({"filename": [], "probability": []})
+    model.cuda()
+    model.eval()
+
+    with open("./submit/baseline.json", "w", encoding="utf-8") as f:
+        submit_results = []
+        for i, (input, filepath) in enumerate(tqdm(test_loader)):
+            # 3.2 change everything to cuda and get only basename
+            try:
+                filepath = [os.path.basename(x) for x in filepath]
+                with torch.no_grad():
+                    image_var = Variable(input).cuda()
+                    # 3.3.output
+                    # print(filepath)
+                    # print(input,input.shape)
+                    y_pred = model(image_var)
+                    # print(y_pred.shape)
+                    smax = nn.Softmax(1)
+                    smax_out = smax(y_pred)
+                # 3.4 save probability to csv files
+                csv_map["filename"].extend(filepath)
+                for output in smax_out:
+                    prob = ";".join([str(i) for i in output.data.tolist()])
+                    csv_map["probability"].append(prob)
+                result = pd.DataFrame(csv_map)
+                result["probability"] = result["probability"].map(lambda x: [float(i) for i in x.split(";")])
+            except:
+                continue
+        for index, row in result.iterrows():
+            pred_label = np.argmax(row['probability'])
+            submit_results.append({"image_id": row['filename'], "disease_class": pred_label})
+            # 输出单个预测
+            print("\n====Result===\nfilename:" + str(row['filename']) + "\tdisease_class:" + str(pred_label))
+        json.dump(submit_results, f, ensure_ascii=False, cls=MyEncoder)
+
+
+def main(test_dataloader):
+    fold = 0
+    # 4.1 mkdirs
+    if not os.path.exists(config.submit):
+        os.mkdir(config.submit)
+    model = get_net()
+    # model = torch.nn.DataParallel(model)
+    model.cuda()
+    # # time.sleep(0.01)
+    # best_model = torch.load(
+    #     config.best_models + os.sep + config.model_name + os.sep + str(fold) + os.sep + 'model_best.pth.tar',map_location=lambda storage, loc: storage)
+    best_model = torch.load(
+        config.best_models + os.sep + config.model_name + os.sep + str(fold) + os.sep + 'model_best.pth.tar')
+    model.load_state_dict(best_model["state_dict"])
+    test(test_dataloader, model, fold)
+
+class Ui_MainWindow(QMainWindow):
+
+    def __init__(self):
+        super(QtWidgets.QMainWindow, self).__init__()
+        self.setupUi(self)
+        self.retranslateUi(self)
+        global t
+        t = 2
+        global filepath
+        filepath ="./data/test/"
+
+    def setupUi(self, MainWindow):
+        MainWindow.setObjectName("MainWindow")
+        MainWindow.resize(512, 368)
+        self.centralWidget = QtWidgets.QWidget(MainWindow)
+        self.centralWidget.setObjectName("centralWidget")
+        self.retranslateUi(MainWindow)
+
+        self.tableWidget = QtWidgets.QTableWidget(self.centralWidget)
+        self.tableWidget.setGeometry(QtCore.QRect(0, 60, 813, 371))
+        self.tableWidget.setObjectName("tableWidget")
+        self.tableWidget.setColumnCount(0)
+        self.tableWidget.setRowCount(0)
+        self.tableWidget.setStyleSheet("selection-background-color:pink")
+        self.tableWidget.setEditTriggers(QAbstractItemView.NoEditTriggers)
+        self.tableWidget.raise_()
+
+        self.pushButton1 = QtWidgets.QPushButton(self.centralWidget)
+        self.pushButton1.setGeometry(QtCore.QRect(100, 100, 80, 23))
+        self.pushButton1.setObjectName("pushButton")
+        self.pushButton1.setText("打开文件")
+
+        self.pushButton2 = QtWidgets.QPushButton(self.centralWidget)
+        self.pushButton2.setGeometry(QtCore.QRect(200, 100, 80, 23))
+        self.pushButton2.setObjectName("pushButton")
+        self.pushButton2.setText("打开文件夹")
+
+        self.pushButton3 = QtWidgets.QPushButton(self.centralWidget)
+        self.pushButton3.setGeometry(QtCore.QRect(300, 100, 80, 23))
+        self.pushButton3.setObjectName("pushButton")
+        self.pushButton3.setText("展示")
+
+        MainWindow.setCentralWidget(self.centralWidget)
+        QtCore.QMetaObject.connectSlotsByName(MainWindow)
+
+        self.pushButton1.clicked.connect(self.openfile)
+        self.pushButton2.clicked.connect(self.opendir)
+        self.pushButton3.clicked.connect(self.imgshow)
+
+    def retranslateUi(self, MainWindow):
+        _translate = QtCore.QCoreApplication.translate
+        MainWindow.setWindowTitle(_translate("MainWindow", "基于CNN的Mnist识别"))
+
+    def openfile(self):
+        global t
+        t = 3
+        ###获取路径===================================================================
+        # test_file = "./data/test/"+'3-27.JPG'
+
+        openfile_name = QFileDialog.getOpenFileName(self, '选择文件', '', 'picture files(*.jpg ;*.jpeg;*')
+        test_file=openfile_name[0]
+        print(test_file)
+        # get filepath
+        length=len(test_file)-len(test_file.split('/',-1)[-1])
+        global filepath
+        filepath=test_file[:length]
+        # print(filepath)
+        test_dataloader = DataLoader(LCJDataset(test_file, test=True, test1=True), batch_size=1, shuffle=False,
+                                     pin_memory=False)
+        main(test_dataloader)
+        self.imgshow()
+        # return test_file
+
+    def opendir(self):
+        global t
+        t = 0.5
+        ###获取路径===================================================================
+        test_dir = QFileDialog.getExistingDirectory(self, '选择文件夹')
+        global filepath
+        filepath=test_dir + "/"
+        print(filepath)
+
+        test_files = get_files(filepath, "test")
+        test_dataloader = DataLoader(LCJDataset(test_files, test=True), batch_size=1, shuffle=False, pin_memory=False)
+        main(test_dataloader)
+        self.imgshow()
+        # return test_dir
+
+    def imgshow(self):
+        try:
+            # print(t)
+            global filepath
+            show(filepath,t)
+        except:
+            print("===展示中断===!")
+            pass
+
+if __name__ == "__main__":
+    app = QtWidgets.QApplication(sys.argv)
+    MainWindow = QtWidgets.QMainWindow()
+    ui = Ui_MainWindow()
+    ui.setupUi(MainWindow)
+    MainWindow.show()
+    sys.exit(app.exec_())
diff --git a/report_03_Fashion/report3_LCHJ_Fashion - Jupyter Notebook.pdf b/report_03_Fashion/report3_LCHJ_Fashion - Jupyter Notebook.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..34bf54540c749b56e241d1306ec40c1b72f4d26c
Binary files /dev/null and b/report_03_Fashion/report3_LCHJ_Fashion - Jupyter Notebook.pdf differ
diff --git a/report_03_Fashion/report_03LCHJ_Fashion.ipynb b/report_03_Fashion/report_03LCHJ_Fashion.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..50442634b274c56e630a9606db4314e8c9b50384
--- /dev/null
+++ b/report_03_Fashion/report_03LCHJ_Fashion.ipynb
@@ -0,0 +1,806 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Report3-fashion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* 卢程军\n",
+    "* 2020262927"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 任务简介：\n",
+    "  >背景介绍：FashionMNIST 是一个替代 [MNIST 手写数字集](https://link.zhihu.com/?target=http%3A//yann.lecun.com/exdb/mnist/)的图像数据集。 它是由 Zalando（一家德国的时尚科技公司）旗下的研究部门提供。其涵盖了来自 10 种类别的共 7 万个不同商品的正面图片。\n",
+    "\n",
+    "  >FashionMNIST 的大小、格式和训练集/测试集划分与原始的 MNIST 完全一致。60000/10000 的训练测试数据划分，28x28 的灰度图片。你可以直接用它来测试你的机器学习和深度学习算法性能，**且不需要改动任何的代码**。\n",
+    "  ![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 解决思路："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用神经网络对图片像素数据集进行训练以及分类预测。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.载入数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    " \n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    " \n",
+    "fashion_mnist = keras.datasets.fashion_mnist\n",
+    " \n",
+    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fashion MNIST数据集被下载在地址:/datasets/fashion-mnist,其下存在四个gz文件,分别代表的训练集特征和标签,测试集特征和标签.每个图像是一个28×28的numpy数组,每个像素值在0到255之间.标签在0-9之间,代表着衣服的类型.Fashion MNIST数据集,它是7万张灰度图像组成,可以分成10个类别.每个灰度图像都是28*28像素的图像.我们将使用其中的6万张进行训练网络,另外的1万张来评估准确率"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在代码上补充一下每个类别的名称,组成一个list,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat',\n",
+    "             'Sandal','Shirt','Sneaker','Bag','Ankle boot']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.处理数据集"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "训练集由6万张28*28像素的图像组成,所以可以得到训练集的特征的大小是(60000,28,28),同样的训练集的标签的大小是(60000,),每一个标签是0-9之间的一个整数.测试集是由1万张28*28像素的图像组成的,所以它的大小是(10000,28,28),测试集的标签的大小是10000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The shape of train_images is  (60000, 28, 28)\n",
+      "The shape of train_labels is  (60000,)\n",
+      "The shape of test_images is  (10000, 28, 28)\n",
+      "The length of test_labels is  10000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The shape of train_images is \",train_images.shape)\n",
+    "print(\"The shape of train_labels is \",train_labels.shape)\n",
+    "print(\"The shape of test_images is \",test_images.shape)\n",
+    "print(\"The length of test_labels is \",len(test_labels))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将数据归一化："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将原始数据可视化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dfVHyqDqBiUgDGcUVwGst80zM3Z82s/mj3xURaQg5GxOr5rOT15vZy2a2wsyqO/cWkcaS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\nH8L9mI+KyBmYAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe14e780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.imshow(train_images[0])\n",
+    "plt.colorbar()\n",
+    "plt.gca().grid(False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "train_images=train_images/255.0\n",
+    "test_images=test_images/255.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可视化归一化之后为："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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53SPzOtI5d4tzboxzbqpzLj0bolgDzWfjJ0mSJQDOAXCeq+QM59zDzrlhqPwR\nLHrPdc41dc49WdAgTXXOHV94/Vrn3FuFvn+qty/WiNG+zECSJPX6D8ApAP5RkMcA6FOQBwL4GEAn\nVD6ovQpgQKFtLoCuAJ4HcBqNtbLw/wEA/g7AFd77BIA9i3x2AuDkgnwFgNsK8mQAexXkqwDcspbX\nRwLoW9/Xspz+Afg9gF+ltA0DsHtBbobKjOSx+fbXtzBnx9FYcwG0puOjAFxVkO8BcAy1xeZvUEHe\nE8DU+r5+5fZP85m/f1X3U/PaMgDtUKn1ng+gZeH1ovdcAEdXzUWhXwsALQG8g9VRyJvW93dtqP+0\nL9ftX71rgFBp/nqoID9UOK7i9SRJ5idJ8i2ASah86KniMQD/TJLkX0XGPKDw7w0AEwFsh8onVcu3\nAAYX5PsADHDOtUDlhnyp8Pq9APZMez3ztxTMKwBucs6dj8pr+nXh9dh8V/ENgEcjYx8E4Gn7Yob5\nexAAkiQZBaC5c25TiKxoPvMDa+GfS5KkqsZJ2j13CoD9nHPXOef2SJLkYwArAHwO4C7n3FEAPquz\ns88X2pdroV4fgJxzrQDsg8qNMBfAxQCOr1LJAfiCun+DsHbZKwAOpr7B0ACuSVbbRbdKkuQfGU5J\nSZFKgHPuXFLFdkiS5FoAZwFoAmCsW+38HpvvKj5PkuSbyMftDOD1GpymnXuthRQ0n/nEOdcNlfNY\nVQjqU25GkXtukiQzAOyEygeha5xzVxR+iHdG5Q/sEQCeqbtv0XjRvqw+9a0BOgbAv5Ik2SJJkq5J\nknQGMAfAgAzvvQLARwBuL9L2LIAzXaV/EZxzHZ1zbYv0W69wDgBwEoCXC3+hLHPO7VF4/VQAL6W9\nXpA/AVBlXxWGJEn+SjfGBc657kmSTEmS5DoA41H512JN8dfeOdcDwNu0cX3bWuYPAKp8EwYA+LjQ\nXxRB85k/XKV/5J2odBMo9qNV9J7rKqNjP0uS5D4AfwLQp9CnRZIkT6HSubZX3XyLxo32ZfWpb+/9\nEwFca157FJUPI4PX7L4GFwK42zl3fZIkl1S9mCTJcOfc9gBeLSiIVqLS12iJef+nAHo45yag0i5a\nVdb2dAB3usqwv9kAfrSW1+8pvL4KwK5JkqzKcO555kLn3N6o/OvjLVSqUnet4Vh/B/C0c24hgCcR\n/jX5EIBBBRXwMUifP6By044B0BzAmTU8l7yi+WycNHHOTQKwAYCvAfwbwE3FOkbuuVuh0kH2WwBf\nAfgpKn8sH3PObYRKzdEvSv1Fcor25VpQKQzRaHDOPYdKp/iF1XzfSFQ6Eo4vyYmJGqH5FKL8aEz7\nsr41QELUGkmS7F/f5yBqD82Wyz2nAAAgAElEQVSnEOVHY9qX0gAJIYQQInfUtxO0EEIIIUSdowcg\nIYQQQuQOPQAJIYQQInfoAUgIIYQQuaNaUWCtW7dOunbtWqJTSefrr78OjlesWOHliooKL6+//vpB\nv4022sjL6623+lnPjvfpp6sTmjZt2tTLHTt2DPrxGHXF3LlzUVFRUSzb9TpRX3OZdyZMmFCRJEmb\n2h63HOfzk08+8fJ3v/vdoG3DDTfMNMYXX6xOWvvZZ6srJmy22WbreHbrjvZm46IUe1NzWT9knctq\nPQB17doV48dXL4TfRpkVr1wRZ8mSMH/hiy++6OVBgwZ5edNNw7Ii22+/vZf5Brxs2bKg36uvvurl\nXXbZxctXX3110K9Jk2yF3vk71+T7Mn379l2n96dRk7kU645zbl4pxq2N+UyLCK3pGn7ppdUJYLt3\n7x60derUKdMYc+bM8TJ/v2OPPbZG51SbaG82LkqxNzWX9UPWuSxJHqCsDwCsvfnzn/8ctD3//PNe\n/vzzz4M21tJ8+eWXXh43blzQb8iQIUU/d4MNNgiOWdPz2muveXm33XYL+rVs2dLLe+21l5d//vOf\nB/3K4a9TIaoL79uYtnP+/Plevvvuu4O2G2+80cusqa0N+JxOPfXUoO26667z8gUXXJBpvG+//TZ1\nfCFE40c7XgghhBC5Qw9AQgghhMgdegASQgghRO6o81pgs2bN8vKhhx7q5c033zzoxw7N1meHo73Y\nudk6Ja5cuXKt7wFCP6IPP/zQyzZajCNSnnvuOS+/8sorQb8f//jHXj7qqKMgRDmS1Qemd+/ewfG7\n777rZd4TALDxxht7mfe09eNjPzne6wsXhvUVV61a5WUOQrDj/epXv/IyBy/su+++Qb8HHnjAy/b7\n8vWQP1A61lk+7brF/D9jJZhq4nQ/ZsyY4Jj9N9955x0vb7PNNuv8WY2Z2g6EyMopp5zi5Ysuuiho\n69Onj5f5fmN/x2uCdrkQQgghcocegIQQQgiRO0piAoupy37zm994uX379l62oeNsfrLjfec7q0+b\nVXZs8gJCFRnLbPICwkSIbG7jzwHCxIqs9rXj/fWvf/XyAQccELQ1a9YMQtQXWUPdd911Vy9PnTo1\naGvXrp2X7drnvcptdi8tWrTIy2z2srm2OGEim714L9pjvnc8+OCDQT9Opvjf//43aOPrUZu5vPJE\n1mtVk2s6cuTI4HjKlCleZrMsAFx22WVe5rkcPnx40K82zCjlQtY1G+vHx9wvaz6/r776Kjjm31Oe\nr2OOOSboN2PGDC/b33Hep7W9F6UBEkIIIUTu0AOQEEIIIXJHyaPAbFQHq76bN2/uZas6Y5U5q62B\n0GT1zTffeNnWAuNjVm/bCBIen/vFos/YlGXV8Xx+jz/+eNB20kknQYj6IqZCHjp0qJfHjh3r5c6d\nOwf92Pxr9y2PnyYD4d5n9bqNTEsz2dk9zOPzvu3SpUvQ79lnn/Xy008/HbQdfPDBqeebB7KaOezr\n9r6bxr/+9S8vc8mh0aNHB/1uvfVWL3fo0MHLb775ZtCPI7o4UggAbrnlFi/36tUr0/k1dNLMV7F+\n/Ptp4b1oI6LZVM397G/mqFGjvHzkkUd62dYC3G677bzMLiQWO/66Ig2QEEIIIXKHHoCEEEIIkTv0\nACSEEEKI3FFyH6Bly5YFx+wDxLZjm1GW/XKsjZnDa9NCV4HQNsl2T2vPZGJ2VPZL4ozRrVu3Tj0/\nrmoPyAdI1D0xPzmGs5bzmv7kk0+CfrEs7ewTFNtz3JY163KsX9p9wIbp87kfcsghQRv7K3IWa3vu\nNqRfrGb69OletteNw9jHjx/v5aVLlwb9Tj/9dC/vtddeXrZ+PjwGy0DoYzJz5kwvb7XVVtHzbyxk\n9WGL3Q+4LeZ7w3vv/fffD9p4j22yySZetr5HN954o5c7duwYtJUyJYU0QEIIIYTIHXoAEkIIIUTu\nKLkud/LkycExq0XZHGbDX/nYhplzaGT37t293LVr16AfF2bksL2mTZsG/Vi9x6Y4zlwJAMOGDSs6\n3vLly4N+nMmSQ+KFqA/S1NyHH354cMzmIU7zMHfu3NR+1iyVpiqPhdvWBPu5rBrn72vvK3xPsPcV\nNtGccMIJRcdrzGQ1L9i0JFyIlE2HLVq0CPqdeeaZXr755pu9bE0eXAxzyZIlqefHodMTJ04M2rhY\nNc9zXkxgWQsdWxYvXuxlNk1+9NFHQb8JEyYUfY81e7Zs2dLLvDY+/vjjoJ8tZF5XSAMkhBBCiNyh\nByAhhBBC5I6Sm8BYlQwAe+yxh5fvv/9+L9uCi1zMjlWdMaxqdtWqVUVla5birLJsHrMRW9dcc42X\n+/Xr52U25QGhmn327NmZzl2IuubVV19NbbNRmUxMnR7L/szEMtVmIWsRR3uuHKVms0mPGzfOy3zf\nyktWaGum5GvH1yBWdJrv47Z46d/+9jcvP/PMM14+8MADU8+pbdu2qW1sHmNTCwB88MEHXr777ru9\nvPvuuwf9dthhh9TxGzKxuZw1a5aXL7zwwqAfu3Nw1Na0adOCfuyG8tZbb3l54MCBQT82b/I9xRah\njUVmZ6UmZnZpgIQQQgiRO/QAJIQQQojcoQcgIYQQQuSOkvsAXXLJJcEx2yL33ntvL/fu3Tvot2LF\nCi9bHyC28XNV6VatWgX90jLWWps+j8fhedYviUMo2X+JQ4bteVhbZ96paZXiNH+Emmbp5TDRrCGi\nFvYn4c9tKD4jnMoBCLMmx64jz2EsEzSPEbPPx8LW09ZLLDSd14QNdWc/BJsO44EHHvAyZ6bNC7HU\nAoxdNzxHL774opdPOeWUoN+dd965rqcYwKHZ/HsBADvttJOXOSu09W2z4d2NhVjmZk4dc8899wRt\n9je0urRp0yY4Zj879rc6/vjjg37sUxS793NbrFJDVqQBEkIIIUTu0AOQEEIIIXJHyU1gNsTxhRde\n8PKjjz7q5eHDhwf9uCDe7bffHrSxmYoL3dnwzDRTCavpgVBFyuo2q8LlsMBrr73Wy9bMtdlmm3l5\nyJAhQRtnTbWhm3kgq3nIqjfT3pdV7WnX0B/+8AcvL1iwINMYlpiauVx58803vcwFfYEwcy+rrnl/\n2DZrYkorvGpNW9wWC51PK4QYK3zMa8L24+LMdt/mvchp1r3J90EA2HPPPYvKFk5Fwusma7oE24+L\n1/I9FwhdIw4++OCi7wGAefPmpX52HrAmL95HvJez3uvYrQUIf+N5jl566aWg369//WsvZy3QaqmJ\nOVMaICGEEELkDj0ACSGEECJ36AFICCGEELmj5EbvSy+9NPxAsrNz6Nv2228f9Hv88ce9fNVVV6WO\nz7ZJa9NP8zOwtv40/yBbMoPD6vv37+9lrnILhHZQW304j34/MdJs/Fn9MTh0GQAmTZrk5YcfftjL\n1leFwzVPPPFELz/44IOZPhcIw8avv/56L//ud7/LPEZdw2vd+uUw7E9nw6N5zmwaAm7j8a0vDvsX\n8PixMPiY/T+tnw2p5fuF/V7z589PHV+kk3UuGW6LzWsM9mGzqUjS1qH1E82731fM1zLm98P7nq/h\naaedFvTjezB/FvvuAqF/mE2zwHDZjXPPPTdo47IbWZEGSAghhBC5Qw9AQgghhMgdJdf/HXnkkcEx\nh8FPmDDByxyqCAA//OEPvcxVfwGgS5cuXmb1qw1vZ7VaLBMtq/C4krtVAX7yySde5vDJm2++OejH\nbbYiMme8ttmvGyuxUNa0ENh33303OGZVKlcxt+kTunXr5uVOnTp52Ybuzp0718tPPfVU2qlHeeih\nh7z82muv1WiMumbixIleZhMekB5mbsPgWUVtzcRpanM7z2mZva1ZivdtLAN42v62r/M9wWatZTMK\nzyebu8WapJmw7Ou8bmL349j9guG1d++99wZthx56qJdPOukkL1tTWczckgdqmrU+LXs+X3cgDH3n\nSvOcpgAInws6d+4ctNlniCo4pQUQukNwpYYY0gAJIYQQInfoAUgIIYQQuaPkJrDp06cHx2xi4uip\nXXbZJej3yiuveHnKlClBG6vtYpEGaRlmYwU50yIa7PmyWrVXr15Bvy233NLLVp237bbbpn52ORIr\nGsomFGsmYWJqVlaLXnbZZV4ePHhw0I8LV7Zv397LO++8c9CPzaCfffaZl21B3Q8++MDLl19+eer5\nsfnVntNFF13k5bffftvLbNoFwsKM9Q2vfbsP2GSRNfOrHYPfxxmjrTkkzbQV25uMXVNc5JIzWtuo\nHzad2e/IY9xyyy1erk5kYLmTNcN6qYlF6qX1s3AWY+tOMH78eC//+Mc/9vKsWbOCfrvtttvaT7aR\nkdXEGLtXZF03/PvHLiRLly4N+h122GGpY7Rr187LvGdt1mn+XciKNEBCCCGEyB16ABJCCCFE7tAD\nkBBCCCFyR8l9gKzNle2977//vpdtNuVYODqHMrJt0mb1TPPniVWcZr8R+7nsD8LnZ/0M2L+EfVwA\nYNGiRV7mkO1yImb7ZWJ+PwyHOHJ1YCAMXeQs2T169Aj68dx+/PHHXl6xYkXQj8Na2W+IfQKAcL1x\nyOQNN9yQOt6OO+4YtLHPCPu72JD7csKGATNp1Z/tPPOaiPlvMDFfvazEQvN5n/H+tqH+nM3dnhOP\nyfPZmKgvn58YWTNBc5Z3APj+97/vZc7mDgBPPPGEl5999lkv2/VgfTTzQE3WQFrY+9p48803vdyz\nZ08vL1y4MOjHKUXsPf2KK67wMv/W7r///jU6J0YaICGEEELkDj0ACSGEECJ3lNwEZk0oXJSSzRrW\nbMCmKKt+Y9U1q+DtZ6WFcNt+aQX8rLqU21q3bo00OMTPZqxdsGCBl8vVBMYq0qzq6VtvvdXLd9xx\nR9C2ePFiL1uV8w477OBlXg/8ntj5xcyZPK82669Vs1Zhw2KHDh2aeh5/+MMfvPzXv/7Vy1tssUXQ\n77777ksdo665+uqrvWxNvHzM5j0bssrhx1nD1msD3uvWBMbrlM/dZodnEyDfY4DQrP3f//7Xy+US\nOt6Y4LmM3WOuu+46L9t1+JOf/MTL//73v4M2XqOHHHKIlzkDPJDdjJ8X0kLk7e9YWqFxu1e4QDn/\nxlfnvvHHP/7Ry/wbfOyxx2YeIw1pgIQQQgiRO/QAJIQQQojcUXITmI20SDNRcNE0ICxaGDOBxdTR\nWTNBp6n+rdqPP5ezU7JZDwjVg3YMzoZZLnCBTAB47rnnvPzOO+942UbGsDmPvxdH2gBhUVKO4ALC\n623bGDZP8DWNmTPZ/GHXEEd38fzZoqacXdQW/uzYsaOXt9lmGy9b08qgQYNQLsyePdvLrJ4Gwrlg\n86816fH3q0sTGBPbw7wWrQkslkWezTJdu3Yt+h5RO/A90pqlfv/733uZ93rbtm2DfhxRuvXWWwdt\nPO98n2qIJi9e67xmY3vP3u9qGsWV9v60PdG3b9/gmLM1czReDOt6wvuS70UxN5SsSAMkhBBCiNyh\nByAhhBBC5A49AAkhhBAid5TcB8jCNl22I9pM0NaPIo00nyL7WWw7tbZ/Ps5apZj9J2Lh97Hs1PXJ\nkiVLcNtttwEAhgwZErSx/1Us+y7b2Tnrsr0enL3TzhH79rDvkPWd4rXCvkj2s9iPheeBv5Mdg23O\nXEkcCNeD9VNjvxMev9z8vDgzOZ+ntaGnZUG3c5aWYR1ID6O1oc7Wzp8Gj89jxMJt2ZfMrln297Lz\nxHv1vffey3R+5YK9r2RNX1Hbn83zYueY9/r06dO9fPHFFwf92J+OqwXceOONQb+YbxZnjWa/t113\n3TX1PaUmlk4hVqG9JmlJapuYD9FRRx3lZc72DAD//Oc/i77H/gbz+Pbez76XvXv3XvvJVgNpgIQQ\nQgiRO/QAJIQQQojcUXITWNYQUmtesGowJi2rszU3pYXLx86Jx7BqZf4sNiXYsG82w1jKpchiq1at\ncOqppwIA+vXrF7S98sorXp46daqX582bF/RjE8KyZcu8bEOP+Zpa1ScXmK2oqPByzOzCqnX7WWmh\nobYIKJvs2ExiVcy8Vmy6Az4PVu/b8PIf/OAHXr7++uuLnl8pGT16dNHXY2YpNoHZ780Zea2JKU1d\nnzVdRU3ha85za9cRm2PtPYa/Z20Ub61LYqaRWLh0bVz7NLcB3hNAaIq96aabvLzPPvsE/TgVxcMP\nP1yjc+LvFTunuiSWtb4m8/D2228Hx3fffbeXrVnRZsKvImaK4t8qew/43e9+5+UPP/zQy9adIo2Y\nSS2W9qZ79+6p76tJSg5pgIQQQgiRO/QAJIQQQojcUedRYFlh9ZtV76ZlxoyprWMqxrRiqNaUsXz5\nci+zCcxmIeUIBGsiqK/MucWoOhcuSAoA/fv3L9rfmvbmzJnj5ZkzZ3rZZnblTKzWBJg2l1YNysUN\nuagevw6E5kiO6LJmSlaFx9TibBaKzR1HVLEJBqj/TMK26GkVdn2nZZnldQ+EJoWY2TltX9ljPr/Y\nNebPtdc0zWRnvzubaq2J236XxkJtr79YNFPMFMcZnjt06ODlyZMnB/0GDx68jmcYrj02rdd1Jugk\nSbyZPpa1ntcem5cA4K677vKyjZZm+H782GOPBW2c0T/tHOw58j7iaDwgNE0+9dRTqefEv5OcfT9m\neuM9CoTra8CAAamfJROYEEIIIUQG9AAkhBBCiNyhByAhhBBC5I6SG73ZXwMIw1BjPjtsO7R2fLYz\nx8Lp0jJtWlthWsh9zH+Hz71Lly5Bv/Hjx3vZ+lmUSybo9ddf3/vF2CrnCxcu9HLMrtqyZUsvDxw4\n0MvWzyfNBwVI9+uwa4PHTAuJB8KweH4PrzsgDN2MVQ/nc7frhDMn8zq3viS2mnpds9deexV93fqG\npPkk2LngaxLzI+Lx7bXjY/YNsNc/LcTajsfnFMtUzePXV1bdUhDzy2EfrsWLFwf9eK/zHo6R1afo\nf//3f4NjXlPs9zN06NBM48VSo8Qy7rMPUF3jnIve/4oxceLE4JjnLHaPbNu2rZc5vQgADBs2zMuH\nHXZY9HyLceKJJwbHBx10kJdjoem8t7OyaNGi4Jh9KnfbbbdqjxdDGiAhhBBC5A49AAkhhBAid5TE\nBMZmiVj2y+bNm6eOwarqWHgqjx9Tn2cNr42Z19JU+l27dg368XnEVPDlgg3btsdpsJkyZlpg85MN\npU+7HtZUmFawNvY+ni9riu3YsaOXeW1YNXvse6WtG3v9OOS3PnjyySeLvm5NvHzMJsJ27dql9rP7\nKm3t22vHprM0sxkQXuNYP563WEbntDkrdtyQiJml3nrrLS/bcGa+B9sC1DXJmszZnseMGRO0sUk6\nLTt5jJjJNta3Pgvbrly5EqNGjSp6Hsccc4yXec2yWdLCqT1s9QQ2N9l70AUXXODlmAmMOfzww708\nbdq0oM2G2dcmXMwYyL4OFQYvhBBCCJEBPQAJIYQQIneUxAQWKzzKKnI2Q1hiWV/TVJ9WBZYW+WXf\nn5ax1n4um+I4cshmgo6ZwMopE/S6wirXmLe/VdWKuuWZZ54p+ro1LbNZitf3HXfcEfQ7+eSTvWxN\nmFx0lte+NbdxW2yvp73HRhryMavQbQQcF/S12cHTsJFT1iRYCqruE1kjrmJRYLUdORPj7LPP9vKM\nGTOCtieeeGKdxo5VBLDwWrFFQ+uSL774ArNnzwYA/PjHPw7aLr/8ci/zvmEzom3jiDJrzuT3xQqK\nXnLJJV4+66yzgn6//vWvvTxixAgv77fffkE/m4G/NrEmQOu+kEZNMp5LAySEEEKI3KEHICGEEELk\nDj0ACSGEECJ3lDwTtLXLsS0yFh6cNZtrWphssfdVkbWacczGzH4GPXr0CNpiFeobkw+QaBhw6gG2\np9uw57T9cuSRRwbH559/vpcfeOCBoI19h5YuXerl9u3bp54TY/08eG+y/4PN7M3v69+/v5c5/BcA\nXnrppaJjF/vsKh5//PHgmP1cSkV1/Rli/fmec8ghhwRt7Ddy6aWXBm0nnXRSps++6qqrvMz+Zhde\neGHQb8cdd8w0Xm3Avwu2unhd0qpVK5xxxhkAgL///e9BG6cn4HO0+5ArwPO65wzfANC6dWsvWx85\nXgM33HBDURkA2rRp42X267zyyiuRBv/GxVITZMV+r6y+ejX5bGmAhBBCCJE79AAkhBBCiNxR5yYw\nVsXFikRySC6r5YBQjR/L3ppW0DFWhJXPz6rp04prxsL57fnFCvoJUQp4D7KJKqtq2XLttdcWlWNY\nlTyfB+85e7/gYw6lj2WRz0osizVn5uVCkkDpTWCffPIJRo4cCWDN9AF87+NixDbzL98/+buwDAAz\nZ8708o033hi0cegzF9ocPnx40O/Pf/6zl7mgata1UVNiZj++x9uCvfWFrRgwduxYL3NBbVvgmdMw\n8Pfi8Hgg/L2KXRtOSxK7Nmx6i5kvaxJ+bn9b2dxmM0GnpZ2w9xS7trMgDZAQQgghcocegIQQQgiR\nO/QAJIQQQojcURIfoLQSFJZYimu2EVpbH4fDfvTRR162qf2zhrQzbGO1fgaffvqplzldt7U98rlb\nnx9r3xWi1PzjH//w8pAhQ7zM6xmo/XBWxu6RmtjrawP2w+CK90DoE8X3nN13373k58V8+eWXmDt3\nLgD4/6tYsmSJl9mPiu+JQOjnwffBzp07B/1OOeUUL/fs2TNoe/75573Mld2nTJkS9BswYICX2Y/I\n+i/xfbHUfjnsU3LggQeW9LOy8pvf/CY4fvDBB73MZS3sbxX/TvJvkr2G7Itjf3fYv43Ht/6wvKZs\nigtmXe8Vsd9j+3uf5gMU8+XNijRAQgghhMgdegASQgghRO4oiQmMs3BaNWhWs9Qxxxzj5RUrVgRt\nHBbPnxULied+sarxrM6zJrUWLVp4uW/fvqmfxepoe058HkLUBWza4Wrotko477OsWYBjxFJP8HEs\njDatzard+TgWVn/QQQd5+a677graOLXFD37wAy9zhey6gLMHZ4VdAQBg/vz5XuaM3Pw6EF4rXhtA\naPbitWGzSfNasSY2pi7D0dkEdtNNN3mZK7DXNTaUnK89Z9C+4oorgn7jxo3zsv0trG322GMPL++9\n994l+5yY2YzXHZBeMaIm4fdrnMc6jyCEEEII0cDQA5AQQgghckdJTGCrVq3yckz1bYueMdZjviHB\nqjn7/WPfWYhSE8s4yxEg1lTCcPSYzUDMsJq7tqPKYrCZ2Zqxe/XqldrGJrDzzjuvRGdXGlq1ahU9\nzhsc7dcQ5pJNsyxbZsyY4eUJEyYEbZMnT/YyF7kFQjMo/z7ZKgZ33nln0c+1biPrup9j5tBLLrkk\nON52222L9rPuNTVBGiAhhBBC5A49AAkhhBAid+gBSAghhBC5oyQ+QFyleJtttgnaOEyyf//+qWPE\nQuRrI/ytlHBY6Jw5c4K2nXbaqa5PRwgP76sbbrghaON92759+9QxyqW6dhqx+wOn0OBQaSD8XnXp\nsyRKy//93//V9ynUGvx7an9bTzzxxJJ9bm3/5sbG22+//TKNEUt7kxXtciGEEELkDj0ACSGEECJ3\nuKxFQgHAOfchgHlr7Shqky2SJGmz9m7VQ3NZb2g+Gw+ay8ZFrc+n5rLeyDSX1XoAEkIIIYRoDMgE\nJoQQQojcoQcgIYQQQuQOPQAJIYQQIneU7QOQc+4b59wk59xU59zDzrmN19L/HufcMQV5pHOub92c\nqciCc+63zrlpzrnJhXlNTwJV/bEHOueeqK3xRBztzcZLKfZpljnXuigNms84ZfsABGBVkiS9kiTZ\nAcCXAH5S3ydUhXNu3TMw5Qjn3K4ADgXQJ0mSngD2A/B+/Z5VJc65kiQDbeRobzZCynmfiuqj+Vw7\n5fwAxIwGsJVzrqtzbmrVi865Xznnfh97o3PuROfclMJfq9cVXvupc+566nOGc+4vBfkU59zrhafl\nv1XdUJ1zK51zVznnXgOwawm+Y2OmPYCKJEm+AIAkSSqSJFngnJvrnLvSOTexMEfbAYBzrqlz7m7n\n3Djn3BvOucMLr3d1zo0u9J/onNvNfpBzrl/hPd0i45xR0FwMAzC87i5Do0R7s/GQtk+vKOyhqc65\nv7tCGt/CX/nXFeZkhnNuj8LrTZxzDxW0DoMB+JTbzrk7nHPjC1qJK+vjS+YIzedaKPsHoMJf6AcD\nmFKD93YAcB2AfQD0AtDPOXcEgEcAHEVdjwcw2Dm3fUHePUmSXgC+AXByoU9TAFOTJOmfJMnLNf0+\nOWU4gM6FTXW7c24vaqtIkqQPgDsA/Krw2m8BvJgkST8AewO4wTnXFMASAPsX+h8P4Fb+kMID0Z0A\nDk+SZHZkHKDyh/L0JEn2KcUXzgPam42OtH16W5Ik/Qoavyao1CpU8Z0kSXYGcCGA/y289lMAnxW0\nDn8EwPV/fpskSV8APQHs5ZzrWcovlHM0n2uhnB+AmjjnJgEYD+A9AP+owRj9AIxMkuTDJEm+BnA/\ngD2TJPkQwGzn3C7OuVYAtgXwCoB9UTm54wqfvS+AboWxvgHw6Dp9o5ySJMlKVF7XcwB8iMoftDMK\nzUMK/08A0LUgHwDg0sIcjASwEYAuADYAMMg5NwXAwwC+Rx+zPYC/AzgsSZL31jIOADyXJMnSWvuS\n+UJ7sxES2ad7O+deK+y7fQD0oLcV2797ArivMOZkAJOp/3HOuYkA3iiMw3tY1CKaz7VTzv4Pqwp/\n6Xmcc18jfGjbaC1jxCq4DQZwHIC3AQxNkiQpqALvTZLkN0X6f54kyTcZzlsUoXDtRgIYWdh4pxea\nvij8/w1Wr0cH4OgkSd7hMQomlcUAvo/KdfA5NS9E5XroDWDBWsbpD+DTdf5S+UV7s5FSZJ/+GJV/\n3fdNkuT9wh7kuS22fwFgjQy7zrktUanl7ZckyTLn3D1Y+zoR64DmM045a4CKsRhAW+dcK+fcdxGq\n7orxGirVcq0L/gInAnip0DYEwBGF1wYXXnsBwDHOubYA4Jxr6Zzbora/RN5wzm3rnNuaXuqFeHr4\nZwH8nGzTvQuvtwCwMIVi3/4AACAASURBVEmSbwGcCoAdXpcD+AGAq51zA9cyjqh9tDcbOCn7tOqP\nhwrnXDMAx2QYahQK5knn3A6o/MEFgOao/MPjY+dcO1SaT0WJ0HyunXLWAK1BkiRfOeeuQuXNcw4q\n/0KM9V/onPsNgBGo/IvzqSRJHiu0LXPOvQXge0mSvF547S3n3O8ADHfOrQfgKwDnQrVc1pVmAP7i\nnNsUwNcAZqJSLZv2I/l/AG4BMLnw8DK30Pd2AI86545F5ZwGWpwkSRY75w4D8LRz7szIOKKW0d5s\nFKTt0+Wo9POaC2BchnHuAPBP59xkAJMAVM3hm865NwBMAzAblaZNUTo0n2tBtcCEEEIIkTsamglM\nCCGEEGKd0QOQEEIIIXKHHoCEEEIIkTv0ACSEEEKI3KEHICGEEELkjmqFwbdu3Trp2rVrSU7k22+/\nDY4/+OADL3/6aZizrlWrVl5u06ZNSc4HAJYtWxYcV1RUeLl58+ZebteuXcnOYe7cuaioqIgljasR\npZzLUvP556vzH65YsSJoW3/91amB1ltv9fN9s2bNgn4bbLBBic4uzoQJEyqSJKn1RduQ57Ohor3Z\nuCjF3tRc1g9Z57JaD0Bdu3bF+PHja35WEexDzuWXX+7lMWPGBG2nnXaal3/2s5+V5HwA4OGHHw6O\n77rrLi8ffPDqnE8XXnhhyc6hb9++JRm3lHNZat55Z3Vi52eeeSZoa9mypZc32mh1UtLddgvrpnbs\n2HGdz4NTSBRyLa4V51xJ8tY05PlsqGhvNi5KsTc1l/VD1rmUCUwIIYQQuaNeM0H/5Cc/8fJLL70U\ntLFJzJqYWDt0662rC4J37tw56Lf11quzgLdo0cLLS5eGNTBZw/Tll1962ZpX2rdv7+U77rjDy8OG\nDQv6DRo0yMvdunWDyEZWjcpPf/pTL7/++utB29dff+3lL774AmmcddZZXn7zzTe9/NlnnwX99txz\nTy/feOONQVuTJk28/M03q0tRsRlOCCFEeSINkBBCCCFyhx6AhBBCCJE79AAkhBBCiNxR5z5AL774\nopfnzJnj5d69ewf92P/Ghsh///vf9/KHH37o5VmzZgX9OLKMIzYmT54c9PvOd1ZfhtatW6ee05Il\nS7y85ZZbenn58uVBv1/+8pdeHjp0KEQ2svoALVq0yMubbbZZ0MY+XBtuuKGX7Rzdd999Xuawehse\nP23aNC/zOgFC/zP+XPYNEkIIUZ5IAySEEEKI3KEHICGEEELkjjo3gT333HNe5gyZNmSZTRFfffVV\n0MZmKjZLsAkFCEOT2ZRhTRScJXiTTTbxMmejBoCNN9646Gd16tQp6Mfmu5dffjloGzBgAERx2NTJ\nWZyB0MT03nvveblp06ZBPw6DZxOozQTNpjM2xbLZDAjn+Re/+EXqudvzFUIIUd7ori2EEEKI3KEH\nICGEEELkjjo3gS1YsMDLXFA0ZgJjU5btyyYLa+ZgswljM/WyyYozAbPJy47PJg97fhzBJBNYHDYx\n2Wg/hqMH2bTFJsvYGHYt8Bi8nqy5tWfPnkXfA4TRaJtvvnnqOcg8JoQQ5YfuzEIIIYTIHXoAEkII\nIUTu0AOQEEIIIXJHyX2ArD8E+9twhXaWgTA7r4X9NNj/ZuXKlUE/DolmXyHr58HnyO+x587v22ij\njVLPj32AZsyYkdpPhNfKhqAz48aN8zL722y66aZBv3feeafo2NafizOIM+yXBgCHH364l4cPHx60\n7bTTTkXPyaZjEEIIUX5IAySEEEKI3KEHICGEEELkjpKbwDjLLhCalVatWuVla3rgTL3WZPXJJ594\nmTNB21BnNkWwSc2aKDjknk1gth+bVDi02ZpXGJtNWoRkLYA6YsSIoq9bE9j+++/v5dmzZ6eOzSaw\nXr16eXnSpElBP15TRx99dNC2xRZbFD0nm2ZBZGfu3LnB8fz5872sFBJCiNpEGiAhhBBC5A49AAkh\nhBAid5TcBLZw4cLg+Lvf/a6X2YxkzU1sXrCZljn7L7/PRoGxaYs/i18HQhMbF0q1pgyOUmrfvr2X\nbYZgPo9WrVoFbWx6adOmDfIOzy2bMy1szuJs3WPHjg36tWzZ0su8NmyU4cCBA73MZpYTTzwx6Hf1\n1VennlNW852I8/DDD3v58ssvD9oOOuggL7O5c4cddijpOd13331e3mabbYK2nXfeuaSfLYSoG6QB\nEkIIIUTu0AOQEEIIIXKHHoCEEEIIkTtK7gP00UcfBcfsO/Pxxx97edSoUUG/k08+2csdOnQI2tiv\niCt5s/8OkJ5Z2PqacD8Og7f92rZt62X2PbHVvrfffnsvc+ZrAHj77be9LB+g9JDx0aNHB8dLlizx\nMvt/2PW1bNkyL3MqBZv5mTM3z5w508s8d6L6cJoL3hc2HcT5559ftK1bt25Bv8mTJ3v5nHPO8fKY\nMWMynY/1C7z77ru9XFFREbRxWo5mzZp52d5/GiuxtB8xbr31Vi/36dPHy3y/BMJ7Jt/7evbsGfTr\n2LFjps/NyjXXXOPlHj16BG0//OEPa/WzRMNCGiAhhBBC5A49AAkhhBAid5TcBGZND5zFmbP72n4T\nJkzw8p577hm0sVqcQ2OtyYvV8Rz6bjNGs9mLM0bb8HYOzefsz6+99lrQj8fo1KlT0Pbmm296eY89\n9kDeSVOzcxgyEKrneb5smgE2g6Zl+Lb9mGOPPTY4vuiii7x80003pZ67QuIrSSsEu3Tp0uCYi9Z2\n7drVyzGzCd8j7PrYe++9vfzEE094eejQoUE/NnPZ/Xf66ad7udRh9uWITTeSlpbi+eefD45POOEE\nL7Npy157zrLO98/bb7896Mdm0H79+nmZiw8DobnaZhB/4YUXvDxv3jwv8/wDMoFlxe5rXgM8X927\nd099XzneF6UBEkIIIUTu0AOQEEIIIXKHHoCEEEIIkTtK7gN01llnBcdcrXv58uVe5lBKIAxX5dBx\nANhoo428zH4/1reHw3C53IW1Z/IYbJtmfyUAeP31173M6futbwiH9d55551BG5cCySPWzyAtDH74\n8OHBMfv68PXlshhAOM9paRCANcPnqzj11FNTz+/www8P2h577DEvl6N9e11g/zn73WLfNW0+d9xx\nx+CYS5ZMmzbNy5y6AAj9PnjOfv7znwf92Nfu+9//vpd/+ctfBv3Yt4dTcljSfM6ANUvpNCR4XoHw\nHml9fqZPn+5lvt9x6RgAeOqpp7zM82evU5cuXYp+li1Tw8fvv/++l8eNGxf0Y38je+7HHXeclzlt\nyowZM9BYqQ1/Gy45dNVVV3mZ/fQA4KWXXvLyYYcd5mX2mVyX80jjtttu83KvXr2CtgEDBlR7PGmA\nhBBCCJE79AAkhBBCiNxRchOYhUPJhwwZktqPVdU2KzCru9PCbi2s+rVqYDbLNG/e3MvWTML9WIX/\nhz/8IdM5iLhKlNMb2LDWLbfc0suc/ZvNoQDQuXNnL7M612aXtdm7q+D1CQCvvPKKlzk7eWMgZg5J\nuz61xQ033ODlfffd18tsVgTCjMxsQmnXrl3Qj1Xje+211zqfH6/ThmDysvdBPmY5zUQJAM8880xw\nfPPNN3v5vPPO87LN1p1mVlq8eHFwzNeUTddNmzYN+vG65HQVdr3y2rDpK3j9shmNM8UDa5rzypG0\n37jqmKbZNYBNzo8//njQj82FzJQpU4JjTh/A19T+Vtck1QunwAGAn/3sZ0XP44gjjgj6yQQmhBBC\nCJEBPQAJIYQQIneU3ARm1XdppiirZuaoEVZ1AqGqj8ew0RocGRBT6fP7eGyOCANCVWoMG+nExFTQ\neSA2Dxz5ZdcDR8+xOtfOORe/ZFOZLWjJWYX5s957772g3+WXX556vmeccYaX77nnntR+dUXVXoup\nwnk/xuZi0aJFXv73v/8dtD399NNefvHFF6t9ngDQv39/L3PEDo8NhHs4zTQChFFKMRMY700uxgyE\na4czBi9YsCDoVxXpZCMQ6xN7n+W55evGGbgBYNttt/XylVdeGbRxJC5nxWdzNACccsop1T5fjgB+\n9tlngzbOGM1mbGsq46zDtpIAm994nux9pS5MYFVzEys2G9uzNYmksvexyy67zMu8HtisDITRXuzm\nsckmmwT92HTG1Rhs9m+uksCRvHYeONLbnvvuu+/uZXaNmDp1KtYVaYCEEEIIkTv0ACSEEEKI3KEH\nICGEEELkjpL7AFn7JfvAxHwQrN8Pwxl+ufK6zQbK9v40vyF7HjyetTnHMgunjdfYMgTXBJ4H6wPF\nfjqcDdxm+WTfBc74befE2qqraN26dXA8a9asoufHaRCA0LfHhsiPHDnSy1yB/NBDDy16DnWFXd9Z\n1+CFF17oZc56bq8Jh71yiCqwZmXvLPztb3/z8oMPPhi08TVm+7/N0n7vvfd6mX31OPM8EPp8rFix\nImhjfzK+l1h/ha233hpA6DNUV6Rl+7X3Up4/ni9OFwAA++yzj5effPLJoI2vN/v5sL+VJe0aWthv\n5Pjjjw/a+Jj9PP76178G/Z577jkvs18gEPpt8f3CZhqvC6rmKes+tPuX11lFRYWXra/M0qVLvfzu\nu+8GbZwehDOls78VEN4LeS/b67bffvsVPXd7P+b9xvvSVm1gH0/O8A2EPlyHHHKIl22aBfZTy4o0\nQEIIIYTIHXoAEkIIIUTuqPNM0Ayr26y6lFWato1V0qwetKGxbM7i91gVI4/P4a9WnbfNNtsU+RZr\nUhtF6RoTsdB/zqLNKlJWkQOhCjfNHAasabbMck68HqwpgdcUm+uAMAs1F4S0ppWTTjop0zmtK9VV\ntVt69Ojh5fvvv9/LVSafKrbaaisv27DXSy+91Ms2xDYN3pusngdCNTxffw6NBYDevXt7mVNo2CKO\nO++8c9HxLHxPsBnh27ZtCyD7WqsJVWsya7bfO+64Izhm8xXP68CBA4N+bEaybS+//LKX2fQQuw/y\n+cXCvrPeI9ksbtMR8O+HNYnyHuR7iXWtsOkxSon93UkL/WZTFhCma2BzkDX3s/nRXvvvfe97Xh41\napSXOTQdCDOsV61zYM17GldjYKwZivczpz6we4d/x216CU67wIVy2cwLhObBrEgDJIQQQojcoQcg\nIYQQQuSOejWBxfjggw+8bKMw2LTFWPVbWhFDa+ZIM7fFosXYu92qA7MWaG2sxK6bhaOsWFVts25z\nJBKbOGbOnBn044gXNn/YiJ2sBS7ZJGpVzhxBU5Pop9okSRJvDrQqZFYbx8wNZ599tpc5GsuaRq64\n4gov77LLLkEbZ/Xl8ex8jh071suc7dfu7Z49e3q5X79+XrYqdDZncbTe+PHjg358HqySB0IzK69h\nmy24yhxUSvN2dYvR2nsQmwTZNGLNmVx02n7PPn36FG3jiB1L1kz3sWvHa2jQoEFePuigg4J+XITV\nRnlyFn9e//b8Sm0CW7p0Ke677z4AoXkYAM4880wvc+STjbpkMxV/T2vO42zYNpKKzWocYWvXA9/v\nuACu/U1Ly7hvqyDY4rNVLFmyJDhm85W9N/NnTZw40cu2YHZNkAZICCGEELlDD0BCCCGEyB16ABJC\nCCFE7qhXH6CYHfjVV1/1srUJcugz2+qtbZrtmdxm7cDcj30LbKVx7sc2TGt/53NqzNXfs2alZYYN\nGxYcs28B+wDxtQbCMEwOebVh07w25s2b52Vrm+bP4vONZa/t1q1bcPyPf/wjtW9d88UXX/js1ra6\nNs9TrKI6+xSwL44Nded+NlXEOeec42X2O7CZevl92223XfA9GPb7GDdunJc7duyINDhseI899gja\nJk+e7OV99903aOO1yHufK6YDq9dLOaW4sCHBab4XNnsup3Kwmc457Jwzp8fg67Zw4cKgjeeFfTyt\n7yZ/7qOPPuplm1aBsxNbnzD+zeC1Zv3jYvu9NmjevDkOPvjgop/Fc5a1sjn7Idp75Jw5c7xsP4v3\nFb/PjsH3SZ5Lnjv7Pr5/2t9q3vfs22Tni+8psX3Fv+N2LU+YMCH1fWlIAySEEEKI3KEHICGEEELk\njno1gcVMJRzeHDNZscnDmsDSwttjZilW/XMopR2PsxFzuChQXqrxUlKT78kh1EAYqs4hmTZsmueF\nwx85Wy0QZqnl9TVixIigH68HNgVZU03aOcSIZcAtFeutt55XI7NJCQivCWefteG2rFLmEF0bKsuq\n9gsuuCBoO+KII7zM+yJW/JALN1ozzJQpU7zMZktrKuPxeQ5tUUgeY/To0UEbm1PZVGgzEFdlyC2V\n+WTlypV+XQ8ZMiRoa9++vZf5u9h7FZuVeN1asyeHGE+fPj1o43XMKQKeeeaZoF9aAVRr2kozNVtz\nCK9ffo+9J7z11ltetvuWj9ksY8Ov/+d//gelxDnnP/+EE04I2uzxusLf2f628n7h62HvVWn3OPub\nyWOwXJ+/fTYbeBakARJCCCFE7tADkBBCCCFyR52bwNIKT9qIK85qaU1bsYJ7TJp5zKqueYy0IplA\nqOpjE5ilullcGwOxgqIcvTNp0qSgjTOWcj9bDJUL4nExTqv25EyhHFkwYMCAoB9nIuZ1YqOaeK1x\nRtkY9aEGXm+99bx5gyNsgDAai6PpWrZsGfTjyCGeF2t64EyyXMQRCM1ebL7iiB0gjGbhbLzW3MQq\neY5YsiYwPua1aDPicpSLnc9FixZ5OVZYssrcVKp93qRJE5+h2c4lH3ORVi5iCYSmMr6GtqglZ+C1\n15TNY3wNuIAxEJqxOcrK3tMZHs9eX143PEd2vnifxUzXXAjUXs/TTjst9X21wfrrr+9Nzfba8zGv\nS2tu4t+rWD/G3oN4bnkf2THsb14Vdo7Sfnft6zwey3at8VqJfS8ew5rVuXhrVvL3Sy2EEEKI3KMH\nICGEEELkDj0ACSGEECJ31LkPUJrt0NpHuQKuDV3k8F32AbFZKG323yqsbZrPid9j7aj8PluFnGHf\ngPoIia5N0my4QPg9Y/4Qv/71r73M9mcgvB7cZm31HPrO/WyWXrb3c1g3Z4UGwirYHBpu7c/sE2T9\nWMoJ9jWwc8H7JZY5nf1yeP+xLwgQhh/bNcF7lcPn7Z5L89mxvl8cEs2+TOzjAoRzyN/L+hqwH4n1\ngWJfGc46zGMDq33LSpXlff311/fX4fjjj8/0Hnuv4+/C4eh2Lvna23swr332sbH3sOXLlxcdz1Za\n533L68FmZ+bxuF+sSridC17znCLAZu23a6CU2LQT9ljUPdIACSGEECJ36AFICCGEELmjbExgNtSW\n1bGxkD4OhbP9WG2bFk5r38dZptkkAIThiGnqYSBU1VoTQTkWR7Vzwt+Hv2fWsN8bbrghOOaQ8732\n2itoGzNmjJf52tiQV1aF8/nZgovWXFrFXXfdlXpOHJpv1dL8WTakupxwzvm5steOUzbwfNqCmVzw\nkFMIxEJbLXy92GTF4dZAuIfZjG3H5vFioc48b7xO7frg+4zNnsymM74ncNi/Hb9csPcVzq7Mck1C\nhYVorJTfThZCCCGEKDF6ABJCCCFE7qjXYqiMjbTImrE2Zopis0nMBMZjcASCjTrg9/F4bDoAgNat\nW3s5lqm6XLCmQ5sNuQobacJZgP/yl794+eabbw767brrrl7mbLsAsNtuu3mZszjbDM9p5omYOeLx\nxx/38mGHHRa0PfXUU0XfY8fj+YtlguZ+9R3pd9RRRwXHbFbi4qB2Lth8OHv2bC/bYpW89m1Wdb5G\nvP84kzcQRtSxqdmacjjai9+T1Qxl1yx/R7u/2SwXM8cKIRoH0gAJIYQQInfoAUgIIYQQuUMPQEII\nIYTIHWXjA8Qhs0Boj7d+Buxzwxlrrb2ffTHYD8JmpeWQX/YBsmHwPAZ/lvWlYB+ghsgjjzzi5R/9\n6EdetteNfUEY6zMxbdo0L++0005B2+TJk73cvXt3L0+dOjXol5YR1l77oUOHetn6/TBpWcItvIZs\nZluG10a5pTpgfxnOnG2zaDdGYj5FQoh8Iw2QEEIIIXKHHoCEEEIIkTvKJhP0nDlzgmMbospwEbxu\n3bp52RY+ZNhsZotactg3j81ZoYEwFJtNHjZkm2kIYfA2W+7FF1/sZTY/sqkwhjUv8by8+uqrQdsu\nu+ziZQ69tp/F4ctc3PHII48M+h1xxBGZzjEt1N+aTNh8ZAt1Mg1hnoUQQqxGGiAhhBBC5A49AAkh\nhBAid+gBSAghhBC5o2zC4K3vBZediPnisK8QV4YHQl8RDrO3afnt+6qwvix8jlx2I1b6IFY5u1zg\nkhFAeK0233xzL/P1BMLrwyHx9juzH431lRk3bpyXO3Xq5OW+ffsG/bhMxty5c708ZMgQpMG+R7xm\ngDXLO1SRthYAoF27dqltQgghGhbSAAkhhBAid+gBSAghhBC5o2xMYDYsmc1N1izRtm1bL7N5xZo5\n+H08nq0u/9lnn3mZTSPWXJNm6rLV5ZmsVavrk9NOOy04/s9//uPl6dOne5lTBADpmbZjoeRNmjQJ\n2vh9s2bN8jKHvQNhhu4RI0YU+RZrYjOIM2lpFux7OAN1LA0AmwNjnyuEEKI8KP9fZyGEEEKIWkYP\nQEIIIYTIHWWjq58xY0ZwzCYPa65YtmxZUdmayj766CMvr1ixwsszZ84M+i1evNjLkyZN8vKuu+4a\n9GMTEJvH0rIKNxSsWeqFF17w8vz58718zz33BP2efPJJL3OUViySKiu20OpTTz3l5YEDB67z+Ftv\nvXXR13ndAWGm8R49eqSOV24FUIUQQsSRBkgIIYQQuUMPQEIIIYTIHXoAEkIIIUTuqHMfoLSwcJv5\nt6Kiwssc9g6E4e5t2rTxsvXDWLBgQVF5p512CvpxxuB58+Z52Ya9b7zxxl5mXyHOlmxpCGHwMTg7\n8+9+97ugzR5XYf25uMo7+2wBYUoC9rdJ89GpLbjifb9+/bxs1xqfX6tWrVLHU+i7EEI0LBr2r7MQ\nQgghRA3QA5AQQgghcoez2Y6jnZ37EMC8tXYUtckWSZK0WXu36qG5rDc0n40HzWXjotbnU3NZb2Sa\ny2o9AAkhhBBCNAZkAhNCCCFE7tADkBBCCCFyR70/ADnnWjnnJhX+LXLOfUDH0RoTzrmBzrknUtru\ncs59L6XtQufcxua13zjnTnbOHZH2PrF2nHNHOucS59x2GfvPdc61LvL6ymL9I+NUq39knDOccx1q\nY6y84Jz7rXNumnNucmHf9q+FMUc65/quax9RPTSXDZ9SzCGNnfqb2xCp9+QlSZJ8BKAXADjnfg9g\nZZIkf6qFcc8q9rpzbn0AFwK4D8Bn1HQAgOMA3ADgCQBvres55JQTAbwM4AQAv6/fU6kRZwCYCmDB\nWvoJAM65XQEcCqBPkiRfFB5mG3ZxvJyiuWz4lPMcOue+kyTJ1/V9Hky9a4Cy4pzbizRDbzjnNik0\nNXPOPeKce9s5d78rZC/kvyiccyudc1c5514D8FsAHQCMcM6NKLQ3R+Ui2RrADwHcUPic7s65Xs65\nsYWn6aHOuc1o/Fucc2Occ1OdczvX7RUpP5xzzQDsDuB/UPkAVPX6wML1WmOeqE8T59wzzrmzi4x7\nsXNuXGEOrox8/o3OuYnOuRecc20Kr6XN3xqvO+eOAdAXwP2F+W+S9lnC0x5ARZIkXwBAkiQVSZIs\ncM5dUZizqc65v5t9eZ1z7nXn3Azn3B6F15s45x4qzMdgAP7aO+fucM6NL/xVmzr/Yp3RXDZ80uZw\nrnPuysL9cYoraOidc02dc3cX5vcN59zhhde7OudGF/pPdM7tZj/IOdev8J5ukXHOcM497JwbBmB4\n3V2GjCRJUjb/UKkx+FVK2zAAuxfkZqjUXg0E8DGATqh8mHsVwIBCn5EA+hbkBMBxNNZcAK3p+CgA\nVxXkewAcQ22TAexVkK8CcAuNP6gg7wlgan1fv/r+B+AUAP8oyGNQ+VcI1jJPcwF0BfA8gNNorJWF\n/w8A8HcArvDeJwDsWeSzEwAnF+QrANy2lvmLzWvf+r6WDeVfYS9OAjADwO10TVtSn38DOIyu740F\n+RAAzxfkiwDcXZB7Avia9m/Lwv/rF97fU3OludS/as3hXAA/L8g/A3BXQb4awCkFedPC+5oC2BjA\nRoXXtwYwviAPLNyDdwMwAUCXtYxzBoD5vIbK6V+D0QABeAXATc658wFsmqxWpb2eJMn8JEm+ReXE\ndy3y3m8APBoZ+yAAT9sXnXMtCp/1UuGle1H5sFPFgwCQJMkoAM2dc5tW4/s0Rk4E8FBBfqhwXEVs\nnh4D8M8kSf5VZMwDCv/eADARwHao3JCWbwEMLsj3ARiQNn8Z5lVkJEmSlQB2AnAOgA8BDHbOnQFg\nb+fca865KQD2AdCD3jak8P8ErF4He6Jy3pAkyWRUPqBWcZxzbiIq10APAPLRKwGay4ZPZA6B4nN1\nAIBLnXOTUPkQuhGALgA2ADCoMOcPI5yn7VH5R+lhSZK8t5ZxAOC5JEmW1tqXrEXq3QcoDefcuQCq\nzCGHJElyrXPuSVT+pTHWObdfoe0Letv/t3fm8VJVV77/LYc4REUZVAQZHUAQMCDGIc4haqI+h25j\n0kZjdwbzYtR0m2jSpt9TE/WZl2jSsU1iXmxMiDGx7eCMAzggqKhMoqgoKKIiiESMJAj7/VF1N7+9\nuGdT93KHqnt+38+HD6vq7Dp16uyz9zl3/dZaey2a/02rQwhrM183FsA5rThMX0SptEWVzKwHKpPj\ncDMLqPyFF8zsW9UmuX6aCuBYM5sQqn9C8K4BXBFC+HkLD6m0fdHRVMfWFABTqhPmV1D5y39MCOE1\nq8T2bU0faboW/HWwQZ+Z2UAA/wJg/xDCCjO70e1LtCHqy8anmT48s7qpub4yAKeEEObzPqr9/BaA\nkah43lfT5jdQ6bf9sD5Wsmg/BwB4f5N/VDtRtx6gEMLPQgijqv+WmNngEMKcEMJVAGag4gloLe8B\n2B4AzGwYgOfpASluCyGsBLCiSdsGcAaAh2g/p1X3cQiAldX2ZeVUAONDCP1DCANCCLsDeAXAITV8\n9nsAlqPisvXcmiHHuQAAIABJREFUC+Bsq8QXwcz6mNnOzbTbrHoMAPA5AI8W9d9G+jX2v9g4Zra3\nmbFHbhSApklwWbXfTt3wkxvwMIDPV/c5HJWbLgDsgMoEutLMdgFwbJscuNgA9WXjU9CHuUrU9wI4\nl+K69qu+3w3AG1WP/Rmo/EHbxLsAPg3gB2Z2+Eb2U9fUrQeoGc43syNQeXqdh4pkdWAr9/ULAHeb\n2RsA7gRwD227GRXX3zdQGexnArjeKmnzLwP4IrVdYWaPoTKwz27lsXQVTgdwpXvvVlQeRn6/YfMN\nOB/A/zOz/xNCaPIaIYQwycyGAphWHVurUIk1Wuo+/z6AYWb2FCrxRqdV3y/qv6L3b6y+/wGAA0MI\nH9Rw7GVmOwA/rcq/HwJ4CRX3+7sA5qASe/BkDfv5DwC/NrPZqEikTwBACGGWmT0D4FlU+mlqW/8A\nEVFfNj5FffiZgvaXAbgGwOzqw8vCatvrANxqZn8HYDKcFyeE8JaZHY/KffTszH7qmtIvhWFm96ES\nfPtGCz83BZWA7RntcmBCCCGEaDcayQPULoQQPtnZxyCEEEKIjqX0HiAhhBBClI+6DYIWQgghhGgv\n9AAkhBBCiNKhByAhhBBClA49AAkhhBCidLQoC6xnz55hwIAB7XQoojkWLlyIZcuW2cZbtozO6sv3\n30+Lgi5fvjzaW2yx/nLcfPPNk3ZGa6d++GHxgsIf+cj6hY//8pe/FH5mzZo10d577703dthtxlNP\nPbUshNCrrfdbj2OTz3muPxuVrjA2OQnmb3/7W7Ltgw/Wl8D66Ec/Gu0tt9xyk7+Xv4u/BwC6deu2\nyftvDe0xNutlXK5bty7afL79ud92222jzWOU50sgvQa22ab+1oyutS9b9AA0YMAAzJihsjcdyZgx\nY9plv53Vl08+mdZRGz9+/fJfPXr0iPb226fFmPnhaNmyZdH2N9J+/fpFe+bMmdFeujStm/j2229H\ne/LkyTUde1tgZrmqrK2mHscmP9z6mxr3Z3vis1z59WabbZoDvLPHJt/U/G/JbWP4QeTVV19Ntj37\n7LPRPuCAA6K96667bvTYNsaiReuHwbx585JtxxxzTLRrfVDm3wu0rm/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b55ydzPvz45Kl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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1488de10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "for i in range(25):\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.grid('off')\n",
+    "    plt.imshow(train_images[i],cmap=plt.cm.binary)\n",
+    "    plt.xlabel(class_names[train_labels[i]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.构建模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "本模型使用的就是相当于两个全连接层，对 $ 28 * 28 = 784 $ 个像素分别作为输入进模型当中。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model=keras.Sequential([\n",
+    "    keras.layers.Flatten(input_shape=(28,28)),\n",
+    "    keras.layers.Dense(128,activation=tf.nn.relu),\n",
+    "    keras.layers.Dense(10,activation=tf.nn.softmax)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "上面的神经网络存在三层,其中:\n",
+    "\n",
+    "    第一层是tf.keras.layers.Flatten(),主要的功能就是将(28,28)像素的图像即对应的2维的数组转成28*28=784的一维的数组.\n",
+    "    第二层是Dense,如果你上过吴恩达的课程,或者有一点机器学习的基础的话,这个可以理解成全连接层,这个层存在128的神经元.\n",
+    "    最后一层是softmax层,它返回的是由10个概率值(加起来等于1)组成的1维数组,每一个代表了这个图像属于某个类别的概率."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.编译模型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=tf.train.AdamOptimizer(),\n",
+    "              loss='sparse_categorical_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "优化器是AdamOptimizer(表示采用何种方式寻找最佳答案,有什么梯度下降啊等等),损失函数是sparse_categorical_crossentropy(就是损失函数怎么定义的,最佳值就是使得损失函数最小).计量标准是准确率,也就是正确归类的图像的概率."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.训练模型："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "为了开始训练,我们调用函数model.fit,训练为5次.当模型训练的时候,损失和准确率展示如下,在训练集上达到88.99%的准确率."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(train_images,train_labels,epochs=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### 6.评估模型：\n",
+    "\n",
+    "    Iteration: 500. Loss: 0.3705102205276489. Accuracy: 85.23\n",
+    "    Iteration: 1000. Loss: 0.3478332757949829. Accuracy: 85.12\n",
+    "    Iteration: 1500. Loss: 0.31125935912132263. Accuracy: 87.27\n",
+    "    Iteration: 2000. Loss: 0.27672433853149414. Accuracy: 87.19\n",
+    "    Iteration: 2500. Loss: 0.2388908416032791. Accuracy: 87.35\n",
+    "    Iteration: 3000. Loss: 0.26195991039276123. Accuracy: 87.63\n",
+    "    Iteration: 3500. Loss: 0.42269882559776306. Accuracy: 87.58\n",
+    "    Iteration: 4000. Loss: 0.31439778208732605. Accuracy: 87.95\n",
+    "    Iteration: 4500. Loss: 0.3554019629955292. Accuracy: 87.89\n",
+    "    Iteration: 5000. Loss: 0.22052042186260223. Accuracy: 87.93"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在测试集上完成准确率的评估,调用的是model.evaluate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000/10000 [==============================] - 0s 32us/step\n",
+      "Test Acc: 0.8634\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss,test_acc=model.evaluate(test_images,test_labels)\n",
+    "print('Test Acc:',test_acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这儿的准确率是86.34%,比之前的训练集的准确率略低。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.进行预测："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对所有的测试集的数据进行预测,model.predict()返回的是一个list,我们每一个元素是对一个图像的预测结果,这个预测结果是一个一维的包含10个概率的数组,概率最大的元素的下标就是结果."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The first picture's prediction is:[7.3955198e-07 2.7160230e-08 2.8339787e-08 1.9584274e-09 9.2936625e-07\n",
+      " 1.8886285e-02 9.8643750e-07 3.0995591e-02 1.1765458e-04 9.4999772e-01],so the result is:9\n",
+      "The first picture is  9\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions=model.predict(test_images)\n",
+    "print(\"The first picture's prediction is:{},so the result is:{}\".format(predictions[0],np.argmax(predictions[0])))\n",
+    "print(\"The first picture is \",test_labels[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "绘制前25个样本的预测结果和真是结果展示图,正确为绿色,不正确为红色："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PaZ98Ysu2FLUmyTwawGQARwE4b5W1ZtVxAoBZSBdEEa7iNgOwi0/86fTnrwOY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\nZswI8vHHHx+d23XX+p1tuD7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/lM3OzD4z7EvzjW98IyrHqV44tUBjOeWUU4LM/pPWD6ho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YI5OzTr6GV68uLCBx98EGyrV/P9KcvEzTEoNuZi/fXXDzbHH9il/6y38zls3A7XsSjGwtap6PtA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\nAR88OY7jOI7jtIAPnhzHcRzHcVrg/wE8rnxE5REBwQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c41710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 10))\n",
+    "for i in range(25):\n",
+    "    plt.subplot(5, 5, i + 1)\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.grid('off')\n",
+    "    plt.imshow(test_images[i], cmap=plt.cm.binary)\n",
+    "    predicted_label = np.argmax(predictions[i])\n",
+    "    true_label = test_labels[i]\n",
+    "    if predicted_label == true_label:\n",
+    "        color = 'green'\n",
+    "    else:\n",
+    "        color = 'red'\n",
+    "    plt.xlabel(\"{} ({})\".format(class_names[predicted_label],\n",
+    "                                class_names[true_label]),\n",
+    "               color=color)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为tf.keras模型对一批数据进行预测结果的优化,所以,对于单独的一张图片,需要将其加入list中,其实就是增加一个维度.也可以只预测其中一个样本,拿第一个测试集样本举例,预测结果仍然是9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 总结："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  本次实验采用两个全连接层的神经网络进行预测，达到了接近82%的预测准确率，由于训练样本过于单一，导致应用在检测上的效果并不很理想，可以考虑增大样本容量进行实验；对于不属于训练数据集中的商品类别，可以赋予一个新属性，继续进行训练检测。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 爬取淘宝图片\n",
+    "\n",
+    "### 首先利用requests模块获取HTML页面"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import requests  \n",
+    "import time\n",
+    "import os\n",
+    " \n",
+    "def get_one_html(url):  # 获取一个页面的html页面并返回\n",
+    "    try:\n",
+    "        # headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'}\n",
+    "        headers = {\n",
+    "            'authority': 's.taobao.com',\n",
+    "            'upgrade-insecure-requests': '1',\n",
+    "            'user-agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36',\n",
+    "            'sec-fetch-dest': 'document',\n",
+    "            'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n",
+    "            'sec-fetch-site': 'same-origin',\n",
+    "            'sec-fetch-mode': 'navigate',\n",
+    "            'sec-fetch-user': '?1',\n",
+    "            'referer': 'https://s.taobao.com/search?q=%E4%B9%A6%E5%8C%85&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200312&ie=utf8',\n",
+    "            'accept-language': 'zh-CN,zh;q=0.9',\n",
+    "            'cookie': 'miid=253146491399089190; thw=cn; cna=o391FTBhsgUCAdrD5AnR+QjC; hng=CN%7Czh-CN%7CCNY%7C156; tracknick=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; tg=0; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1%26_ato%3D0; t=a7362f9a33953472d0663cbb296a53fd; enc=jXmwR%2BzLAdHsQMP0d%2F9pId1Dn%2BW%2FtphblF4%2FsFXBDkrBKBLkGD4tksHXhn5%2BPTVPEfnLekpkIzYNZENT3sCchA%3D%3D; _samesite_flag_=true; cookie2=1bb1c10d29aca83180c0390f0d1fb1fc; _tb_token_=33b66b43e3335; sgcookie=EBnq%2FRUJ6VqRYs7GbgSt0; unb=2269301708; uc3=id2=UUpngTzc2Y13gg%3D%3D&lg2=VT5L2FSpMGV7TQ%3D%3D&vt3=F8dBxd7CT335hOeshsM%3D&nk2=o688bJ2t2lOAtK8MXRkc9rS1xdZODw%3D%3D; csg=bb101fa2; lgc=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; cookie17=UUpngTzc2Y13gg%3D%3D; dnk=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; skt=d1b053176dd80fc6; existShop=MTU4Mzk4NzY5Ng%3D%3D; uc4=id4=0%40U2gtEEjyK2oSMoVT0y3FwsMQmuba&nk4=0%40ofkQgMIYV1w9DtinNnKgQnwEY%2B9mIA3bhXzykl%2Bh56ME; _cc_=UIHiLt3xSw%3D%3D; _l_g_=Ug%3D%3D; sg=%E9%A2%968e; _nk_=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; cookie1=UNcJvhnwxmsnc%2BQXwkd900lS4%2BSdsCS20k5pE5xYJ2Q%3D; alitrackid=login.taobao.com; lastalitrackid=login.taobao.com; tfstk=cFMhBQjc8XPBkECPG6wIVOcoz-9AZZIURSFZQvf4ybSgh8kNiq5Nge_NRPune_1..; mt=ci=20_1; v=0; Hm_lvt_eaa57ca47dacb4ad4f5a257001a3457c=1583980126,1583987707; uc1=cookie16=Vq8l%2BKCLySLZMFWHxqs8fwqnEw%3D%3D&cookie21=VFC%2FuZ9aiKCaj7AzMHh1&cookie15=VFC%2FuZ9ayeYq2g%3D%3D&existShop=false&pas=0&cookie14=UoTUOafM2gaxgg%3D%3D&tag=8&lng=zh_CN; JSESSIONID=AF10B48836FA6F017CE4F6D93629F9C4; Hm_lpvt_eaa57ca47dacb4ad4f5a257001a3457c=1583989886; isg=BM3NGh5pr8U80wicTAKS00EO3OlHqgF8Ot6V2g9TpGTZBuy41_jeTHTUcJpgnhk0; l=dBEklFpHqPrWkOUQBOfNqASSiU_ONIdb8SFy7q0UFICPO7CHlfwOWZq1YKLMCnGVH6kWR3Rp9jjQBqLO1yCrnxv9-3k_J_DmndC..',\n",
+    "        }\n",
+    "        r = requests.get(url, headers=headers)\n",
+    "        if r.status_code == 200:\n",
+    "            r.encoding = r.apparent_encoding\n",
+    "            return r.text\n",
+    "    except Exception as er:\n",
+    "        print(er)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 利用正则表达式获取每页图片的链接"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_pic_url(html):  # 用正则提取每一页的关键信息返回\n",
+    "    pic_urls = re.findall('\"pic_url\":\"(.*?)\"', html, re.S)       #\"pic_url\":\"(.*?)\"\n",
+    "    img_url = []     # 创建空列表，装每一页的所有图片的链接\n",
+    "    for one_pic_url in pic_urls:\n",
+    "        img_url.append('http:'+one_pic_url)\n",
+    "    return img_url   # 返回图片的链接的列表"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&emsp;&emsp;下载图片保存在本地"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def write_to_file(page, img_urls):  # 写入文件（下载）\n",
+    "    i = page   # 利用页码，防止后面的写入会覆盖之前的\n",
+    "    n = 0\n",
+    "    for pic_url in img_urls:\n",
+    "        pic = requests.get(pic_url)\n",
+    "        with open('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/'+str(i)+str(n)+'.jpg', 'wb') as f:\n",
+    "            f.write(pic.content)\n",
+    "        print('---第{}页第{}张图下载成功---'.format(str(i), str(n)))\n",
+    "        n += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 主函数：程序的入口\n",
+    "\n",
+    "#### 可通过输入关键词以及爬取页数来获取所需图片"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def main(keyword, page_num, url):\n",
+    "    html = get_one_html(url)   # 调用函数得到该页的html\n",
+    "    # print(html)\n",
+    "    img_urls = get_pic_url(html)  # 调用函数得到该页的所有图片的链接\n",
+    "    print(img_urls)\n",
+    "    write_to_file(page, img_urls)   # 调用函数，写入即下载图片\n",
+    "        \n",
+    "if __name__ == '__main__':\n",
+    "    keyword = input('请输入关键词：')\n",
+    "    page_num = eval(input('请输入要爬取的页数：'))\n",
+    "    try:\n",
+    "        if os.path.exists('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/') == False:\n",
+    "            os.mkdir('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/')\n",
+    "        for page in range(0, page_num):\n",
+    "            # 裤子\n",
+    "            # url = \"https://s.taobao.com/search?q=%E8%A3%A4%E5%AD%90&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=\" + str(-3*page) + \"&ntoffset=\"+ str(-3*page)+ \"&p4ppushleft=1%2C48&s=\" + str(88+44*page)\n",
+    "            # 运动鞋\n",
+    "            # url = \"https://s.taobao.com/search?q=%E8%BF%90%E5%8A%A8%E9%9E%8B&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=\" + str(-3*page) + \"&ntoffset=\" + str(-3*page) + \"&p4ppushleft=1%2C48&s=\" + str(88+44*page)\n",
+    "            url = \"https://s.taobao.com/search?q=\" + keyword + \"&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=\"+ str(-3*page) + \"&ntoffset=\" + str(-3*page) + \"&p4ppushleft=1%2C48&s=\" + str(88+44*page)\n",
+    "            main(keyword, page_num, url)\n",
+    "            if page % 2 == 0:\n",
+    "                time.sleep(10)   # 每爬取2页停留10秒\n",
+    "    except Exception as err:\n",
+    "        print(err)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 所爬取图片如下\n",
+    "\n",
+    "### 外套:\n",
+    "\n",
+    "![coat](images/coat.png)\n",
+    "\n",
+    "### 裤子:\n",
+    "\n",
+    "![pants](images/pants.png)\n",
+    "\n",
+    "### 凉鞋:\n",
+    "\n",
+    "![sandal](images/sandal.png)\n",
+    "\n",
+    "### 运动鞋:\n",
+    "\n",
+    "![sneaker](images/sneaker.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&emsp;&emsp;四组图片，每组有440张图片\n",
+    "\n",
+    "## 数据的预处理\n",
+    "\n",
+    "&emsp;&emsp;将图片转换为28 * 28 的灰度图，并构建标签"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "path = 'data/coat'\n",
+    "# for file in os.listdir(path):\n",
+    "#     newname = 'a'+'%s'%i + '.jpg'\n",
+    "#     os.rename(os.path.join(path, file), os.path.join(path, newname))\n",
+    "#     i = i+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "def rgb2gray(img_path):\n",
+    "    img_path = 'data/coat/'+img_path\n",
+    "    img = cv2.imread(img_path)\n",
+    "    #     #获取图片的宽和高\n",
+    "    #     width,height = img.shape[:2][::-1]\n",
+    "    #     #将图片缩小便于显示观看\n",
+    "    #     img_resize = cv2.resize(img,\n",
+    "    #     (int(width*0.5),int(height*0.5)),interpolation=cv2.INTER_CUBIC)\n",
+    "    #     cv2.imshow(\"img\",img_resize)\n",
+    "    #     print(\"img_reisze shape:{}\".format(np.shape(img_resize)))\n",
+    "\n",
+    "    #将图片转为灰度图\n",
+    "    img_gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
+    "\n",
+    "#     print(\"img_gray shape:{}\".format(np.shape(img_gray)))\n",
+    "    cv2.waitKey()\n",
+    "    img_resize = cv2.resize(img_gray,(28,28),interpolation=cv2.INTER_CUBIC)\n",
+    "    img_resize = img_resize.astype('float32')\n",
+    "    img_resize /= 255\n",
+    "    # print(img_resize)\n",
+    "\n",
+    "    # print(\"img shape:{}\".format(np.shape(img_resize)))\n",
+    "    # img = img_resize.reshape((28,28))\n",
+    "    img_resize = np.array(img_resize)\n",
+    "    return img_resize\n",
+    "# print(\"img shape:{}\".format(np.shape(img)))\n",
+    "# cv2.imshow(\"img\",img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1760, 28, 28, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = []\n",
+    "for file in os.listdir(path):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "# a = np.array(a)\n",
+    "# print(a.shape)\n",
+    "path1 = 'data/sneaker'\n",
+    "for file in os.listdir(path1):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "# a = np.array(a)\n",
+    "# print(a.shape)\n",
+    "path2 = 'data/sandal'\n",
+    "for file in os.listdir(path2):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "# a = np.array(a)\n",
+    "# print(a.shape)\n",
+    "path3 = 'data/pants'\n",
+    "for file in os.listdir(path3):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "a = np.array(a)\n",
+    "taobao_train = a.reshape(a.shape[0], 28, 28, 1)\n",
+    "print(taobao_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1760, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.utils import to_categorical\n",
+    "y1 = np.ones(440)*4\n",
+    "y2 = np.ones(440)*7\n",
+    "y3 = np.ones(440)*5\n",
+    "y4 = np.ones(440)*1\n",
+    "taobao_label = to_categorical(np.concatenate((y1,y2,y3,y4),axis=0),10)\n",
+    "print(taobao_label.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&emsp;&emsp;使用所训练的模型进行预测并得到分类精度如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_taobao = cnn4.evaluate(taobao_train, taobao_label, verbose=0)\n",
+    "print('Test loss:', score_taobao[0])\n",
+    "print('Test accuracy:', score_taobao[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test loss:0.23454068487286567\n",
+    "\n",
+    "Test accuracy:0.917900025844574"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看出，实验效果较好，但仍有待提升。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 总结\n",
+    "\n",
+    "&emsp;&emsp;本次实验让我充分了解并掌握了神经网络的构建与学习，并且学会了如何进行简单的爬虫程序编写。在编写爬虫程序时遇到了许多问题，例如爬取淘宝网站时总是遇到需要登陆的情况，最终通过查阅资料解决了该问题。本次实验任务提升了我自主解决问题的能力，使我受益匪浅。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/report_03_Fashion/report_template.ipynb b/report_03_Fashion/report_template.ipynb
deleted file mode 100644
index 21eb1ce711b96fd98ffe44be0b1697fee849508a..0000000000000000000000000000000000000000
--- a/report_03_Fashion/report_template.ipynb
+++ /dev/null
@@ -1,57 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report - 报告题目\n",
-    "\n",
-    "* 姓名\n",
-    "* 学号\n",
-    "\n",
-    "\n",
-    "## 任务简介\n",
-    "\n",
-    "这里简述一下任务是什么；数据的格式，包含了什么数据；最终的目标是什么\n",
-    "\n",
-    "## 解决途径\n",
-    "\n",
-    "主要包括：\n",
-    "1. 问题的思考，整体的思路\n",
-    "2. 选用的方法，以及为何选用这些方法\n",
-    "3. 实现过程遇到的问题，以及如何解决的\n",
-    "4. 最终的结果，实验分析\n",
-    "\n",
-    "要求：\n",
-    "1. 数据的可视化\n",
-    "2. 程序，以及各个部分的解释、说明\n",
-    "3. 结果的可视化，精度等的分析\n",
-    "\n",
-    "## 总结\n",
-    "总结任务实现过程所取得的心得等。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  },
-  "main_language": "python"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/report_03_Fashion/show.py b/report_03_Fashion/show.py
new file mode 100644
index 0000000000000000000000000000000000000000..26ffa8a13561530c53af163359a797eb16eafbd6
--- /dev/null
+++ b/report_03_Fashion/show.py
@@ -0,0 +1,88 @@
+import json
+import os
+import xlrd
+import xlwt
+
+from PIL import Image
+from pylab import *
+from tqdm import tqdm
+
+from config import config
+
+def show(filepath,speed):
+    plt.rcParams['font.sans-serif'] = ['SimHei']  # 步骤一（替换sans-serif字体）
+    file_submit = json.load(open("./submit/baseline.json", "r", encoding="utf-8"))
+
+    file_list = file_submit
+
+    count = 0
+    rate = 1
+    sum = 0
+    for file in tqdm(file_list):
+        filename = file["image_id"]
+        test_path = filepath + filename
+
+        # print("real_ids" + str(real_ids))
+        if (os.path.exists(test_path)):
+            ids = file["disease_class"]
+
+            # 打开图片，并转换为数字矩阵
+            img = Image.open(test_path)
+            # 寻找对比度图片
+            example_path = config.img_example
+            pathDir = os.listdir(example_path)
+            for name in pathDir:
+                if str(ids) == name.split(".", 1)[0][-1:]:
+                    # print(name)
+                    example_ = example_path + name
+                    example_img = Image.open(example_)
+                    break
+
+            # # 错误分析：”‘P’, ’RGBA’, ’RGB’这是PIL
+            # # Image读图可能出现的三种mode，每种mode的图片数据都有不同的组织形式
+            # #
+            # # 修改部分功能为：将读图的mode全部转换为“RGB“。
+            try:
+                img = img.resize((config.img_weight, config.img_height), Image.BILINEAR)
+                if img.mode == 'P':
+                    img = img.convert("RGB")
+                if img.mode == 'RGBA':
+                    img = img.convert("RGB")
+
+                example_img = example_img.resize((config.img_weight, config.img_height), Image.BILINEAR)
+                if example_img.mode == 'P':
+                    example_img = example_img.convert("RGB")
+                if example_img.mode == 'RGBA':
+                    example_img = example_img.convert("RGB")
+                sum += 1
+                try:
+                    filename = str(filename.split(".", 1)[0][-1:])
+                    if str(ids) == str(filename):
+                        count += 1
+                except:
+                    count += 1
+                    pass
+            except:
+                pass
+
+            # 转换为数字矩阵
+            img = np.array(img)
+            example_img = np.array(example_img)
+
+            # 计算识别率
+            rate = float(count) / float(sum) * 100
+            rate = ('%.4f' % rate)
+            # 3x2显示结果
+            plt.ion()
+            subplot(121)
+            title('Real_ID= ' + str(filename) + '  <-> ' + 'Rate=' + str(rate))
+            imshow(img)
+
+            subplot(122)
+            imshow(example_img)
+            plt.show()
+            plt.pause(speed)
+            plt.savefig(config.submit + "show.jpg")
+            # plt.close()#不使用每次清空缓冲区，为了成帧播放
+        else:
+            continue
diff --git a/report_03_Fashion/statistics.py b/report_03_Fashion/statistics.py
new file mode 100644
index 0000000000000000000000000000000000000000..18b0fd82a1dd9c35e15ffa80dbe046289614e969
--- /dev/null
+++ b/report_03_Fashion/statistics.py
@@ -0,0 +1,97 @@
+import os
+import pandas as pd
+import xlwt
+from pylab import *
+
+from config import config
+
+# 定义函数来显示柱状上的数值
+def autolabel(rects):
+    for rect in rects:
+        height = rect.get_height()
+        plt.text(rect.get_x() + rect.get_width() / 2. - 0.2, 1.03 * height, '%s' % int(height))
+#绘制柱形图
+def draw(file_name):
+    # 使用pandas第三方库读取txt文件
+    train_log = pd.read_csv(file_name)
+
+    plt.rcParams['font.sans-serif'] = ['SimHei']  # 步骤一（替换sans-serif字体）
+    plt.rcParams['axes.unicode_minus'] = False  # 步骤二（解决坐标轴负数的负号显示问题）
+
+    # 设置对应的坐标轴和具体的内容
+    _, ax1 = plt.subplots()
+    ax1.set_title("Statistical_val_data&Count sum=" + str(sum))
+    # 使用的折线图的方式
+    # ax1.plot(train_log["category"], train_log["count"], alpha=0.5)
+    # bar方法使用的是柱状图的形式
+    a = plt.bar(train_log["category"], train_log["count"], color='rgby', alpha=0.7)
+    autolabel(a)
+    ax1.set_xlabel('category')
+    ax1.set_ylabel('count')
+    # 设置坐标轴刻度
+    my_x_ticks = np.arange(0, 9, 1)
+    plt.xticks(my_x_ticks)
+
+    # plt.legend(loc='upper left')
+    plt.legend(loc='best')
+    plt.savefig(config.submit + "Statistical_val_data.png")
+    plt.show()
+
+# 生成excel
+def classexcel(file_name):
+    input_txt = file_name
+    output_excel = config.submit + 'Statistical_val_data.xls'
+    sheetName = 'Sheet1'
+    start_row = 0
+    start_col = 0
+
+    wb = xlwt.Workbook(encoding='utf-8')
+    ws = wb.add_sheet(sheetName)
+
+    f = open(input_txt, 'r', encoding='utf-8')
+    row_excel = start_row
+    for line in f:
+        line = line.strip('\n')
+        line = line.split(',', 1)
+        # print(line)
+        col_excel = start_col
+        len_line = len(line)
+        for j in range(len_line):
+            # print(line[j])
+            ws.write(row_excel, col_excel, line[j])
+            col_excel += 1
+            wb.save(output_excel)
+
+        row_excel += 1
+    f.close
+
+if __name__ == "__main__":
+    # 获取总的目录
+    path = config.val_data
+    # path=config.val_data
+    # 获取总目录下的文件夹
+    fileNames = os.listdir(path)
+    # 最后结果保存到txt文件中
+    file_name = config.submit + "Statistical_val_data.txt"
+    # 用于统计样本总量
+    sum = 0
+    # 打开txt文档，首先将存在的类型写入：这里我们有两项分别为 类别-数量
+    # 第一个参数表示打开的文件名称，第二个参数a+表示可读可写，r表示可读，w表示可写
+    with open(file_name, "w") as f:
+        f.writelines("category" + ',' + "count")
+        f.writelines("\n")
+    # 遍历刚刚得到的总目录下的文件夹
+    for fileName in fileNames:
+        # patha为每个文件夹
+        patha = path + '/' + fileName
+        # 统计每个文件夹内的数量
+        count = len(os.listdir(patha))
+        sum = count + sum
+        # 再次打开，这次写入每个文件夹对应的结果：类别-数量
+        with open(file_name, "a+") as f:
+            f.writelines(str(fileName) + ',' + str(count) + '\n')
+            # f.writelines("\n")
+    print("------sum=" + str(sum))
+    # 将绘制柱状图抽取为单独的方法，参数为txt所在的地址。
+    draw(file_name)
+    classexcel(file_name)
diff --git a/report_03_Fashion/submit/.ipynb_checkpoints/report_03LCHJ_Fashion-checkpoint.ipynb b/report_03_Fashion/submit/.ipynb_checkpoints/report_03LCHJ_Fashion-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1270314955cdd24f0b9dcfb49cb760c78e34c713
--- /dev/null
+++ b/report_03_Fashion/submit/.ipynb_checkpoints/report_03LCHJ_Fashion-checkpoint.ipynb
@@ -0,0 +1,1198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Report3-fashion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* 卢程军\n",
+    "* 2020262927"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 任务简介：\n",
+    "  >背景介绍：FashionMNIST 是一个替代 [MNIST 手写数字集](https://link.zhihu.com/?target=http%3A//yann.lecun.com/exdb/mnist/)的图像数据集。 它是由 Zalando（一家德国的时尚科技公司）旗下的研究部门提供。其涵盖了来自 10 种类别的共 7 万个不同商品的正面图片。\n",
+    "\n",
+    "  >FashionMNIST 的大小、格式和训练集/测试集划分与原始的 MNIST 完全一致。60000/10000 的训练测试数据划分，28x28 的灰度图片。你可以直接用它来测试你的CNN和深度学习算法性能，**且不需要改动任何的代码**。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 解决思路："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用卷积神经网络对fashion-mnist图片像素数据集进行训练以及分类预测。\n",
+    "## 数据分析：\n",
+    "fashion-mnist 数据集 包含60000张训练数据和10000张测试数据，每张图片是一通道的28X28像素的黑白图像，共包含10类；\n",
+    "### 数据预处理\n",
+    "下载训练集和验证集；[https://github.com/zalandoresearch/fashion-mnist/tree/master/data/fashion]\n",
+    "\n",
+    "提取数据集；\n",
+    "将数据集按标签分别放入对应的文件夹中\n",
+    "#### fashion-mnist数据预处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding:utf-8 -*-\n",
+    "#@Time : 2020/12/30 11:07\n",
+    "#@Author: LCHJ\n",
+    "#@File : savemnist.py.py\n",
+    "# coding=utf-8\n",
+    "'''将二进制格式的MNIST数据集转成.jpg图片格式并保存，图片标签包含在图片名中'''\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import os\n",
+    "\n",
+    "def save_mnist_to_jpg(mnist_image_file, mnist_label_file, save_dir):\n",
+    "    if 'train' in os.path.basename(mnist_image_file):\n",
+    "        num_file = 60000\n",
+    "        prefix = 'train'\n",
+    "    else:\n",
+    "        num_file = 10000\n",
+    "        prefix = 'test'\n",
+    "    with open(mnist_image_file, 'rb') as f1:\n",
+    "        image_file = f1.read()\n",
+    "    with open(mnist_label_file, 'rb') as f2:\n",
+    "        label_file = f2.read()\n",
+    "    image_file = image_file[16:]\n",
+    "    label_file = label_file[8:]\n",
+    "    for i in range(num_file):\n",
+    "        label = int(label_file[i])\n",
+    "        image_list = [int(item) for item in image_file[i*784:i*784+784]]\n",
+    "        image_np = np.array(image_list, dtype=np.uint8).reshape(28,28,1)\n",
+    "        save_name = os.path.join(save_dir, '{}_{}_{}.jpg'.format(prefix, i, label))\n",
+    "        cv2.imwrite(save_name, image_np)\n",
+    "        print ('{} ==> {}_{}_{}.jpg'.format(i, prefix, i, label))\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    train_image_file = './train-images-idx3-ubyte/train-images-idx3-ubyte'\n",
+    "    train_label_file = './train-labels-idx1-ubyte/train-labels-idx1-ubyte'\n",
+    "    test_image_file = './t10k-images-idx3-ubyte/t10k-images-idx3-ubyte'\n",
+    "    test_label_file = './t10k-labels-idx1-ubyte/t10k-labels-idx1-ubyte'\n",
+    "\n",
+    "    save_train_dir = './train_images/'\n",
+    "    save_test_dir ='./test_images/'\n",
+    "\n",
+    "    if not os.path.exists(save_train_dir):\n",
+    "        os.makedirs(save_train_dir)\n",
+    "    if not os.path.exists(save_test_dir):\n",
+    "        os.makedirs(save_test_dir)\n",
+    "\n",
+    "    save_mnist_to_jpg(train_image_file, train_label_file, save_train_dir)\n",
+    "    save_mnist_to_jpg(test_image_file, test_label_file, save_test_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "##### 原始训练集：\n",
+    "\n",
+    "![1.png](1.png)\n",
+    "<center>未分类数据</center>\n",
+    "\n",
+    "##### 预计分类后的数据：\n",
+    "\n",
+    "![2.png](2.png)\n",
+    "<center>分类后数据</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 数据分析和均衡：\n",
+    "\n",
+    "![Statistical_train_data.png](Statistical_train_data.png)\n",
+    "<center>训练集分析</center>\n",
+    "可以看出，训练集60000张数据中共包含10类，每类6000张。\n",
+    "\n",
+    "![Statistical_val_data.png](Statistical_val_data.png)\n",
+    "<center>测试集分析</center>\n",
+    "可以看出，训练集10000张数据中共包含10类，每类1000张。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "配置超参"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DefaultConfigs(object):\n",
+    "    #1.string parameters\n",
+    "    train_data = \"./data/train/\"\n",
+    "    test_data = \"./data/test/\"\n",
+    "    img_example = \"./data/test/\"\n",
+    "    val_data = \"./data/train_validation/\"\n",
+    "    model_name = \"resnet50\"\n",
+    "    weights = \"./checkpoints/\"\n",
+    "    best_models = weights + \"best_model/\"\n",
+    "    submit = \"./submit/\"\n",
+    "    logs = \"./logs/\"\n",
+    "    gpus = \"1\"  # 训练时使用的GPU数\n",
+    "    accuracy_loss=\"./logs/accuracy_loss_exp.txt\"\n",
+    "    # 2.numeric parameters\n",
+    "    epochs = 60  # 训练轮数\n",
+    "    batch_size = 200\n",
+    "    img_height = 28\n",
+    "    img_weight = 28\n",
+    "    num_classes = 10\n",
+    "    seed = 888\n",
+    "    lr = 1e-4  #0.0001 0.00005 0.001 0.01 0.0005\n",
+    "    lr_decay = 1e-5 #衰减系数\n",
+    "    weight_decay = 1e-5 #权重衰减（L2正则化）可以避免模型过拟合问题\n",
+    "\n",
+    "config = DefaultConfigs()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[WinError 3] 系统找不到指定的路径。: './data/train_validation/'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-457b983482ae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     71\u001b[0m     \u001b[1;31m# path=config.val_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m     \u001b[1;31m# 获取总目录下的文件夹\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m     \u001b[0mfileNames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;31m# 最后结果保存到txt文件中\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[0mfile_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubmit\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"Statistical_val_data.txt\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] 系统找不到指定的路径。: './data/train_validation/'"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import xlwt\n",
+    "from pylab import *\n",
+    "\n",
+    "# from config import config\n",
+    "\n",
+    "# 定义函数来显示柱状上的数值\n",
+    "def autolabel(rects):\n",
+    "    for rect in rects:\n",
+    "        height = rect.get_height()\n",
+    "        plt.text(rect.get_x() + rect.get_width() / 2. - 0.2, 1.03 * height, '%s' % int(height))\n",
+    "#绘制柱形图\n",
+    "def draw(file_name):\n",
+    "    # 使用pandas第三方库读取txt文件\n",
+    "    train_log = pd.read_csv(file_name)\n",
+    "\n",
+    "    plt.rcParams['font.sans-serif'] = ['SimHei']  # 步骤一（替换sans-serif字体）\n",
+    "    plt.rcParams['axes.unicode_minus'] = False  # 步骤二（解决坐标轴负数的负号显示问题）\n",
+    "\n",
+    "    # 设置对应的坐标轴和具体的内容\n",
+    "    _, ax1 = plt.subplots()\n",
+    "    ax1.set_title(\"Statistical_val_data&Count sum=\" + str(sum))\n",
+    "    # 使用的折线图的方式\n",
+    "    # ax1.plot(train_log[\"category\"], train_log[\"count\"], alpha=0.5)\n",
+    "    # bar方法使用的是柱状图的形式\n",
+    "    a = plt.bar(train_log[\"category\"], train_log[\"count\"], color='rgby', alpha=0.7)\n",
+    "    autolabel(a)\n",
+    "    ax1.set_xlabel('category')\n",
+    "    ax1.set_ylabel('count')\n",
+    "    # 设置坐标轴刻度\n",
+    "    my_x_ticks = np.arange(0, 9, 1)\n",
+    "    plt.xticks(my_x_ticks)\n",
+    "\n",
+    "    # plt.legend(loc='upper left')\n",
+    "    plt.legend(loc='best')\n",
+    "    plt.savefig(config.submit + \"Statistical_val_data.png\")\n",
+    "    plt.show()\n",
+    "\n",
+    "# 生成excel\n",
+    "def classexcel(file_name):\n",
+    "    input_txt = file_name\n",
+    "    output_excel = config.submit + 'Statistical_val_data.xls'\n",
+    "    sheetName = 'Sheet1'\n",
+    "    start_row = 0\n",
+    "    start_col = 0\n",
+    "\n",
+    "    wb = xlwt.Workbook(encoding='utf-8')\n",
+    "    ws = wb.add_sheet(sheetName)\n",
+    "\n",
+    "    f = open(input_txt, 'r', encoding='utf-8')\n",
+    "    row_excel = start_row\n",
+    "    for line in f:\n",
+    "        line = line.strip('\\n')\n",
+    "        line = line.split(',', 1)\n",
+    "        # print(line)\n",
+    "        col_excel = start_col\n",
+    "        len_line = len(line)\n",
+    "        for j in range(len_line):\n",
+    "            # print(line[j])\n",
+    "            ws.write(row_excel, col_excel, line[j])\n",
+    "            col_excel += 1\n",
+    "            wb.save(output_excel)\n",
+    "\n",
+    "        row_excel += 1\n",
+    "    f.close\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    # 获取总的目录\n",
+    "    path = config.val_data\n",
+    "    # path=config.val_data\n",
+    "    # 获取总目录下的文件夹\n",
+    "    fileNames = os.listdir(path)\n",
+    "    # 最后结果保存到txt文件中\n",
+    "    file_name = config.submit + \"Statistical_val_data.txt\"\n",
+    "    # 用于统计样本总量\n",
+    "    sum = 0\n",
+    "    # 打开txt文档，首先将存在的类型写入：这里我们有两项分别为 类别-数量\n",
+    "    # 第一个参数表示打开的文件名称，第二个参数a+表示可读可写，r表示可读，w表示可写\n",
+    "    with open(file_name, \"w\") as f:\n",
+    "        f.writelines(\"category\" + ',' + \"count\")\n",
+    "        f.writelines(\"\\n\")\n",
+    "    # 遍历刚刚得到的总目录下的文件夹\n",
+    "    for fileName in fileNames:\n",
+    "        # patha为每个文件夹\n",
+    "        patha = path + '/' + fileName\n",
+    "        # 统计每个文件夹内的数量\n",
+    "        count = len(os.listdir(patha))\n",
+    "        sum = count + sum\n",
+    "        # 再次打开，这次写入每个文件夹对应的结果：类别-数量\n",
+    "        with open(file_name, \"a+\") as f:\n",
+    "            f.writelines(str(fileName) + ',' + str(count) + '\\n')\n",
+    "            # f.writelines(\"\\n\")\n",
+    "    print(\"------sum=\" + str(sum))\n",
+    "    # 将绘制柱状图抽取为单独的方法，参数为txt所在的地址。\n",
+    "    draw(file_name)\n",
+    "    classexcel(file_name)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.载入数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import Dataset\n",
+    "from torchvision import transforms as T \n",
+    "from config import config\n",
+    "from PIL import Image \n",
+    "from itertools import chain \n",
+    "from glob import glob\n",
+    "from tqdm import tqdm\n",
+    "import random \n",
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import os \n",
+    "import cv2\n",
+    "import torch \n",
+    "\n",
+    "#1.set random seed\n",
+    "random.seed(config.seed)\n",
+    "np.random.seed(config.seed)\n",
+    "torch.manual_seed(config.seed)\n",
+    "torch.cuda.manual_seed_all(config.seed)\n",
+    "\n",
+    "#2.define dataset\n",
+    "class LCJDataset(Dataset):\n",
+    "    def __init__(self,label_list,transforms=None,train=True,test=False,test1=False):\n",
+    "        self.test = test\n",
+    "        self.test1 = test1\n",
+    "        self.train = train\n",
+    "        imgs = []\n",
+    "        #only img test\n",
+    "        if self.test1:\n",
+    "            imgs.append(label_list)\n",
+    "            self.imgs = imgs\n",
+    "        else:\n",
+    "            if self.test:\n",
+    "                for index,row in label_list.iterrows():\n",
+    "                    imgs.append((row[\"filename\"]))\n",
+    "                self.imgs = imgs\n",
+    "            else:\n",
+    "                for index,row in label_list.iterrows():\n",
+    "                    imgs.append((row[\"filename\"],row[\"label\"]))\n",
+    "                self.imgs = imgs\n",
+    "        #归一化[0-1]->[-1-1]\n",
+    "        if transforms is None:\n",
+    "            if self.test or not train:\n",
+    "                self.transforms = T.Compose([\n",
+    "                    T.Resize((config.img_weight,config.img_height)),\n",
+    "                    # [0, 255] -> [0.0, 1.0]\n",
+    "                    T.ToTensor(),\n",
+    "                    T.Normalize(mean = [0.485,0.456,0.406],\n",
+    "                                std = [0.229,0.224,0.225])])\n",
+    "            else:\n",
+    "                self.transforms  = T.Compose([\n",
+    "                    T.Resize((config.img_weight,config.img_height)),\n",
+    "                    T.RandomRotation(10),#随机旋转角度\n",
+    "                    T.RandomHorizontalFlip(),#随机水平旋转\n",
+    "                    T.RandomVerticalFlip(),\n",
+    "                    T.RandomAffine(20),\n",
+    "                    T.ToTensor(),\n",
+    "                    #自然图像的一个统计特征\n",
+    "                    T.Normalize(mean = [0.485,0.456,0.406],\n",
+    "                                std = [0.229,0.224,0.225])])\n",
+    "        else:\n",
+    "            self.transforms = transforms\n",
+    "    def __getitem__(self,index):\n",
+    "        if self.test:\n",
+    "            filename = self.imgs[index]\n",
+    "            img = Image.open(filename)\n",
+    "            img = img.convert(\"RGB\")\n",
+    "            img = self.transforms(img)\n",
+    "            return img,filename\n",
+    "        else:\n",
+    "            filename,label = self.imgs[index] \n",
+    "            img = Image.open(filename)\n",
+    "            img = img.convert(\"RGB\")\n",
+    "            img = self.transforms(img)\n",
+    "            return img,label\n",
+    "    def __len__(self):\n",
+    "        return len(self.imgs)\n",
+    "\n",
+    "def collate_fn(batch):\n",
+    "    imgs = []\n",
+    "    label = []\n",
+    "    for sample in batch:\n",
+    "        imgs.append(sample[0])\n",
+    "        label.append(sample[1])\n",
+    "\n",
+    "    return torch.stack(imgs, 0),label\n",
+    "\n",
+    "def get_files(root, mode):\n",
+    "    # for test\n",
+    "    if mode == \"test\":\n",
+    "        files = []\n",
+    "        for img in os.listdir(root):\n",
+    "            files.append(root + img)\n",
+    "        files = pd.DataFrame({\"filename\": files})\n",
+    "        return files\n",
+    "    elif mode != \"test\":\n",
+    "        # for train and val\n",
+    "        all_data_path, labels = [], []\n",
+    "        image_folders = list(map(lambda x: root + x, os.listdir(root)))\n",
+    "        jpg_image_1 = list(map(lambda x: glob(x + \"/*.jpg\"), image_folders))\n",
+    "        jpg_image_2 = list(map(lambda x: glob(x + \"/*.JPG\"), image_folders))\n",
+    "        all_images = list(chain.from_iterable(jpg_image_1 + jpg_image_2))\n",
+    "        print(\"loading train dataset\\n\")\n",
+    "\n",
+    "        for file in tqdm(all_images):\n",
+    "            all_data_path.append(file)\n",
+    "            #aiaiaiai~bug啊\n",
+    "\n",
+    "            label_dir=str(file.split(\"/\",-1)[-1])\n",
+    "            label=int(label_dir.split(\"\\\\\",1)[0])\n",
+    "            # print(label_dir)\n",
+    "            # print(\"==========\")\n",
+    "            # print(file)\n",
+    "            labels.append(label)\n",
+    "        all_files = pd.DataFrame({\"filename\": all_data_path, \"label\": labels})\n",
+    "        return all_files\n",
+    "    else:\n",
+    "        print(\"check the mode please!\")\n",
+    "\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fashion MNIST数据集被下载在地址:/datasets/fashion-mnist,其下存在四个gz文件,分别代表的训练集特征和标签,测试集特征和标签.每个图像是一个28×28的numpy数组,每个像素值在0到255之间.标签在0-9之间,代表着衣服的类型.Fashion MNIST数据集,它是7万张灰度图像组成,可以分成10个类别.每个灰度图像都是28*28像素的图像.我们将使用其中的6万张进行训练网络,另外的1万张来评估准确率"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在代码上补充一下每个类别的名称,组成一个list,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat',\n",
+    "             'Sandal','Shirt','Sneaker','Bag','Ankle boot']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.处理数据集"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "训练集由6万张28*28像素的图像组成,所以可以得到训练集的特征的大小是(60000,28,28),同样的训练集的标签的大小是(60000,),每一个标签是0-9之间的一个整数.测试集是由1万张28*28像素的图像组成的,所以它的大小是(10000,28,28),测试集的标签的大小是10000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The shape of train_images is  (60000, 28, 28)\n",
+      "The shape of train_labels is  (60000,)\n",
+      "The shape of test_images is  (10000, 28, 28)\n",
+      "The length of test_labels is  10000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The shape of train_images is \",train_images.shape)\n",
+    "print(\"The shape of train_labels is \",train_labels.shape)\n",
+    "print(\"The shape of test_images is \",test_images.shape)\n",
+    "print(\"The length of test_labels is \",len(test_labels))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将数据归一化："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将原始数据可视化："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dfVHyqDqBiUgDGcUVwGst80zM3Z82s/mj3xURaQg5GxOr5rOT15vZy2a2wsyqO/cWkcaS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\nH8L9mI+KyBmYAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe14e780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.imshow(train_images[0])\n",
+    "plt.colorbar()\n",
+    "plt.gca().grid(False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "train_images=train_images/255.0\n",
+    "test_images=test_images/255.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可视化归一化之后为："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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53SPzOtI5d4tzboxzbqpzLj0bolgDzWfjJ0mSJQDOAXCeq+QM59zDzrlhqPwR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\n7LPRPuCAA6K96667bvTYNsaiReuHwbx585JtxxxzTLRrfVDm3wu0rm/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b55ydzPvz45Kl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\ni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2fi5z4eH1xN2y/syufHS6d878rdd3PHwfDcyvO7/15f8bm54/G0haxeK7oLCCGEEKJ0\n6AFICCGEEKVDD0BCCCGEKB11Wwk6V0W2KFU9FyvE5CpB57RSjkHg1WtFHq7I7PuEU235fHN8A1Bc\nsTQXg8JxAP57c/p2WeHYjtdeey3aPj2aq+nedtttyTaO6eJxmos74HY+NoA/x6nevvQEHxNfOz4m\ngeMVao0Z7Gg222yz+Bs4DgdIYyM5ldyv5H7AAQc0u43HG5Cmi/vSAlxFm2PtfPkAhs+9T2/neddX\nbmY49Z1Xq/cp1v369Yu2j0viNHBOv/Yp/H4V+XrAlw9g+Bz4PudtufmN51J/L+Qxwe1yqywwfrwV\n7S8XC5q7vtoaeYCEEEIIUTr0ACSEEEKI0lG3OgC7xLw7j93Atab0MbV+Juci92mXtX6u7AwcODB5\nzenpXFqgqPKzx1dD5ZRa7md/DUnC3BBOg2fJgyUJIO0n7/LOVZBmcmmwDLvN+TNnnXVW0u4zn/lM\ntD/5yU9Gm2UST63V4TuadevWRWnKp/HzeLn//vujvd9++yXtxo4dG21OkX/kkUeSdlyqwMtjnMbO\nC6r6BWZfffXVaHOYAKfsA6k8xhKrl3L4N/J16FOqWb7yJRd4sc2jjjoq2pxGDqQSW73gSzywNMnb\nuPQDUHsl81orrxeVqsjtw8uofA3xWPZ9zpIl39/bG3mAhBBCCFE69AAkhBBCiNKhByAhhBBClI66\njQFivF7Iq8W2ZkkDr3uyNsmphD7tkr/Ll55nWhOX1JXhcvs+XZVXc+c054MOOqimffsYD+4z1pJ9\n/EA9av+dDcdR8Hn1mjz3kz+vtS5xsfPOO0d7yZIl0c4tbcJj7sc//nHS7rvf/W60R44cGe099tgj\nacdxMx256nRL2HrrrbHPPvsA2DAehGPZ/u7v/i7afq7iZT64VIQvG8Hn6o477ki2cfwRx4H5+Mfh\nw4dHm5eu8MvP8HXEsXv+mPi7eG721wbHEfH1BABDhw6NNi/x4VeUP+2001Bv+PsTx05xvJXvc44B\n8suT8PgrKikCpHF2RSvIN/e6Cd8PXGaB+6TWFe/bG3mAhBBCCFE69AAkhBBCiNLREBIYu8g9uSrD\nRdSa+ufd9ux+5u9tyf7LCKer+jT4XXfdNdovv/xytEeNGlXTvkeMGJG83mmnnaLNko53F3/qU5+q\naf9lgtPb2XXtV/Vm6chLkOyiZ6nMn39OR37nnXei7SVS/m4ef96FXpQS7Vey53T5WtOGO5ptttkm\nrusltnEAAAd5SURBVNruV29vT77whS902HeJ2mEJjCUqXw190qRJ0fbyLoeRcPkHPy6ZWkM5chWe\neU4/7LDDou3LkvDnfKmC9kQeICGEEEKUDj0ACSGEEKJ0dKoEVquLjTMLgA0rYDbhF1Hj1xxZ7qPM\nixaO81Vuc+5CRllgKSw7sN0WsFsVAKZMmRLtXLaD2BB2k3O1X87UA4C+fftGe8KECYX7mzVrVrS9\njM1SFy+aefzxxyfteMzlFtrkbC/+zMknn5y04+MYPXp04bEL0Vn4asqLFi2KNktgPpyAZX1f8Zvv\nZbwPX5G9aPHSXLY1b/PSG2fz8oLFPrOUZfBly5YVfldbIw+QEEIIIUqHHoCEEEIIUTr0ACSEEEKI\n0tEQMUB+xW+uPsvp6D5WgVNluaKq11hZ92Q9k9N4gVS3zK0GL1I4rdGnL9cKn3uO2fLxW0VxPz5+\ni9MufaXxssLxVNdcc020/Xi5+uqra9ofVxlmO4df1bw18DXg5w6eI3jVeCHqBR8nydXLOWbHV10+\n55xzmrXrkRNOOCF5zfPzKaec0mHHIQ+QEEIIIUqHHoCEEEIIUTqsJVWLzextAIs22lC0Jf1DCL02\n3qxlqC87DfVn10F92bVo8/5UX3YaNfVlix6AhBBCCCG6ApLAhBBCCFE69AAkhBBCiNLRcA9AZrbW\nzGaa2bNmNsvMvmlmDfc7yoaZ9aj220wze9PMXqfXrcuNF3WNme1qZjeb2QIzm2dmd5nZXi3cx45m\n9rX2OkZROzT3zjKzp83soI1/StQbGpfrabgYIDNbFULYrmrvDGACgKkhhH9z7bYIIXzY3D5E52Jm\n/wvAqhDCD937hso1ua7ZD7b9cegaaSeqffkYgP8MIVxffW8UgO1DCI9kP5zuZwCAO0IIw9vjOEXt\nuLn3UwC+E0I4bCMfE3WExmVKQ3tOQghLAXwZwNetwllm9gczux3AJAAwswvN7Ekzm21m/7v63kfN\n7M7qXzJzzey06vtXVp+IZ5vZDwu/WLQZZrZHtQ+uB/A0gN5m9g9mNqf6/g+q7bYws3fpc581sxvI\nnlvtz8nU/kdm9kS1P/+p+v7RZna/md0M4JkO/8Hl4QgAa5omWQAIIcwE8KiZXV3trzk09rYzsweq\nnoU5ZnZi9WNXAhhc9TzUVoFRdAQ7AFgBZPsOZnaJmT1vZveZ2e/M7F867YgFoHGZ0KmVoNuCEMLL\nVQmsqSzmgQBGhBDeMbNxAPYEMBaAAZhoZocC6AVgSQjh0wBgZt3MrDuAkwAMCSEEM9uxw39MedkH\nwBdDCF81s74ALgcwBsBKAPeb2WcA3JP5/L8BODyE8Bb125cBLA0hjDWzrQBMN7NJ1W0fB7BPCOHV\ndvk1AgCGA3iqmfdPBjAKwEgAPQE8aWYPA3gbwEkhhD+bWU9U+msigIsADA8hjOqg4xbFbGNmMwFs\nDaA3gCOr769G8303GsApAPZD5V7zNJq/JkTHoXFJNLQHiOA1Ne4LIbxTtcdV/z2DyuAbgsoD0RwA\nR5vZVWb2iRDCSgB/RmUg32BmJwP4S4cdvVgQQniyah8A4MEQwrIQwhpUJM5DN/L5qQDGV708Tdf0\nOABfrE7YjwPYEZW+B4BpevjpNA4B8LsQwtoQwlsAHgKwPypj+AdmNhvA/QD6ANil8w5TNMMHIYRR\nIYQhAI5BZcwZivvuEAB/CiF8EEJ4D8DtnXXgYqOUclw2vAfIzAYBWAtgafWt93kzgCtCCD9v5nOj\nARwH4AozmxRCuNTMxgI4CsBnAXwd6//CEe2L77PmWOe2bU32l1B5cPoMgFlmNqLa9mshhAd4J2Z2\ntPs+0T48C+DUZt4v6t/Po+KZHR1CWGNmC5H2sagjQgjTqh6BXqjMo831XW2LPYqOROOSaGgPkJn1\nAnA9gH8PzUdz3wvgbDNrCtzrY2Y7m9luAP4SQvgNgB8C+Fi1TbcQwl0AzkfFHSg6nukAjrBK1tgW\nqDyMPlQNjF5hZntWJc+T6DODQgjTAVyCSlxCH1T6/mvVfcDM9jazbTr0l5SbBwFsZWZfanrDzPZH\npX9OM7PNq+P3UABPAOiGimS5xsyOANC/+rH3AGzfsYcuNoaZDQGwOYDlKO67RwEcb2ZbV+fXT3fO\n0QpC45JoRA9Qkw69JYAPAdwE4EfNNQwhTDKzoQCmVTy1WAXgHwDsAeBqM1sHYA2Ac1DpzD+ZWdNf\nLhe09w8RGxJCWGxm3wMwBZV+uD2EcGd187dRiQV6FcA8AE3LuP/YzAZW208KIcw1s+cA9AMws9r3\nSwHE4EzRvlTj6E4CcI2ZXYSKvLwQlT8utgMwC0AA8K0Qwptm9lsAt5vZDAAzATxf3c9yM5tqZnMB\n3B1CuLATfo6o0DT3ApWxdmYIYW2m756sxovMQmU5iBmoxPWJTkLjMqXh0uCFEEI0Bma2XQhhlZlt\nC+BhAF8OITzd2cclBNCYHiAhhBCNwS/MbB9U4kb+Uw8/op6QB0gIIYQQpaOhg6CFEEIIIVqDHoCE\nEEIIUTr0ACSEEEKI0qEHICGEEEKUDj0ACSGEEKJ06AFICCGEEKXj/wOY7eP4wcBAPwAAAABJRU5E\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1488de10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "for i in range(25):\n",
+    "    plt.subplot(5,5,i+1)\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.grid('off')\n",
+    "    plt.imshow(train_images[i],cmap=plt.cm.binary)\n",
+    "    plt.xlabel(class_names[train_labels[i]])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.构建模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "本模型使用的就是相当于两个全连接层，对 $ 28 * 28 = 784 $ 个像素分别作为输入进模型当中。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ResNet(\n",
+    "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+    "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "  (relu): ReLU(inplace=True)\n",
+    "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+    "  (layer1): Sequential(\n",
+    "    (0): Bottleneck(\n",
+    "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+    "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+    "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+    "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (relu): ReLU(inplace=True)\n",
+    "      (downsample): Sequential(\n",
+    "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+    "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      )\n",
+    "    )\n",
+    "    (1): Bottleneck(\n",
+    "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+    "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+    "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+    "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (relu): ReLU(inplace=True)\n",
+    "    )\n",
+    "    (2): Bottleneck(\n",
+    "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+    "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+    "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+    "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "      (relu): ReLU(inplace=True)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "上面的神经网络存在三层,其中:\n",
+    "\n",
+    "    第一层是tf.keras.layers.Flatten(),主要的功能就是将(28,28)像素的图像即对应的2维的数组转成28*28=784的一维的数组.\n",
+    "    第二层是Dense,如果你上过吴恩达的课程,或者有一点CNN的基础的话,这个可以理解成全连接层,这个层存在128的神经元.\n",
+    "    最后一层是softmax层,它返回的是由10个概率值(加起来等于1)组成的1维数组,每一个代表了这个图像属于某个类别的概率."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.编译模型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=tf.train.AdamOptimizer(),\n",
+    "              loss='sparse_categorical_crossentropy',\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "优化器是AdamOptimizer(表示采用何种方式寻找最佳答案,有什么梯度下降啊等等),损失函数是sparse_categorical_crossentropy(就是损失函数怎么定义的,最佳值就是使得损失函数最小).计量标准是准确率,也就是正确归类的图像的概率."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.训练模型："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "为了开始训练,我们调用函数model.fit,训练为5次.当模型训练的时候,损失和准确率展示如下,在训练集上达到88.99%的准确率."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(train_images,train_labels,epochs=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### 6.评估模型：\n",
+    "\n",
+    "    Iteration: 500. Loss: 0.3705102205276489. Accuracy: 85.23\n",
+    "    Iteration: 1000. Loss: 0.3478332757949829. Accuracy: 85.12\n",
+    "    Iteration: 1500. Loss: 0.31125935912132263. Accuracy: 87.27\n",
+    "    Iteration: 2000. Loss: 0.27672433853149414. Accuracy: 87.19\n",
+    "    Iteration: 2500. Loss: 0.2388908416032791. Accuracy: 87.35\n",
+    "    Iteration: 3000. Loss: 0.26195991039276123. Accuracy: 87.63\n",
+    "    Iteration: 3500. Loss: 0.42269882559776306. Accuracy: 87.58\n",
+    "    Iteration: 4000. Loss: 0.31439778208732605. Accuracy: 87.95\n",
+    "    Iteration: 4500. Loss: 0.3554019629955292. Accuracy: 87.89\n",
+    "    Iteration: 5000. Loss: 0.22052042186260223. Accuracy: 87.93"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在测试集上完成准确率的评估,调用的是model.evaluate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000/10000 [==============================] - 0s 32us/step\n",
+      "Test Acc: 0.8634\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss,test_acc=model.evaluate(test_images,test_labels)\n",
+    "print('Test Acc:',test_acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "这儿的准确率是86.34%,比之前的训练集的准确率略低。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7.进行预测："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "对所有的测试集的数据进行预测,model.predict()返回的是一个list,我们每一个元素是对一个图像的预测结果,这个预测结果是一个一维的包含10个概率的数组,概率最大的元素的下标就是结果."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The first picture's prediction is:[7.3955198e-07 2.7160230e-08 2.8339787e-08 1.9584274e-09 9.2936625e-07\n",
+      " 1.8886285e-02 9.8643750e-07 3.0995591e-02 1.1765458e-04 9.4999772e-01],so the result is:9\n",
+      "The first picture is  9\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions=model.predict(test_images)\n",
+    "print(\"The first picture's prediction is:{},so the result is:{}\".format(predictions[0],np.argmax(predictions[0])))\n",
+    "print(\"The first picture is \",test_labels[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "绘制前25个样本的预测结果和真是结果展示图,正确为绿色,不正确为红色："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PaZ98Ysu2FLUmyTwawGQARwE4b5W1ZtVxAoBZSBdEEa7iNgOwi0/86fTnrwOY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\nZswI8vHHHx+d23XX+p1tuD7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/lM3OzD4z7EvzjW98IyrHqV44tUBjOeWUU4LM/pPWD6ho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YI5OzTr6GV68uLCBx98EGyrV/P9KcvEzTEoNuZi/fXXDzbHH9il/6y38zls3A7XsSjGwtap6PtA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\nAR88OY7jOI7jtIAPnhzHcRzHcVrg/wE8rnxE5REBwQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c41710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 10))\n",
+    "for i in range(25):\n",
+    "    plt.subplot(5, 5, i + 1)\n",
+    "    plt.xticks([])\n",
+    "    plt.yticks([])\n",
+    "    plt.grid('off')\n",
+    "    plt.imshow(test_images[i], cmap=plt.cm.binary)\n",
+    "    predicted_label = np.argmax(predictions[i])\n",
+    "    true_label = test_labels[i]\n",
+    "    if predicted_label == true_label:\n",
+    "        color = 'green'\n",
+    "    else:\n",
+    "        color = 'red'\n",
+    "    plt.xlabel(\"{} ({})\".format(class_names[predicted_label],\n",
+    "                                class_names[true_label]),\n",
+    "               color=color)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "因为tf.keras模型对一批数据进行预测结果的优化,所以,对于单独的一张图片,需要将其加入list中,其实就是增加一个维度.也可以只预测其中一个样本,拿第一个测试集样本举例,预测结果仍然是9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 总结："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  本次实验采用两个全连接层的神经网络进行预测，达到了接近82%的预测准确率，由于训练样本过于单一，导致应用在检测上的效果并不很理想，可以考虑增大样本容量进行实验；对于不属于训练数据集中的商品类别，可以赋予一个新属性，继续进行训练检测。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 爬取淘宝图片\n",
+    "\n",
+    "### 首先利用requests模块获取HTML页面"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import requests  \n",
+    "import time\n",
+    "import os\n",
+    " \n",
+    "def get_one_html(url):  # 获取一个页面的html页面并返回\n",
+    "    try:\n",
+    "        # headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'}\n",
+    "        headers = {\n",
+    "            'authority': 's.taobao.com',\n",
+    "            'upgrade-insecure-requests': '1',\n",
+    "            'user-agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36',\n",
+    "            'sec-fetch-dest': 'document',\n",
+    "            'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n",
+    "            'sec-fetch-site': 'same-origin',\n",
+    "            'sec-fetch-mode': 'navigate',\n",
+    "            'sec-fetch-user': '?1',\n",
+    "            'referer': 'https://s.taobao.com/search?q=%E4%B9%A6%E5%8C%85&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200312&ie=utf8',\n",
+    "            'accept-language': 'zh-CN,zh;q=0.9',\n",
+    "            'cookie': 'miid=253146491399089190; thw=cn; cna=o391FTBhsgUCAdrD5AnR+QjC; hng=CN%7Czh-CN%7CCNY%7C156; tracknick=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; tg=0; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1%26_ato%3D0; t=a7362f9a33953472d0663cbb296a53fd; enc=jXmwR%2BzLAdHsQMP0d%2F9pId1Dn%2BW%2FtphblF4%2FsFXBDkrBKBLkGD4tksHXhn5%2BPTVPEfnLekpkIzYNZENT3sCchA%3D%3D; _samesite_flag_=true; cookie2=1bb1c10d29aca83180c0390f0d1fb1fc; _tb_token_=33b66b43e3335; sgcookie=EBnq%2FRUJ6VqRYs7GbgSt0; unb=2269301708; uc3=id2=UUpngTzc2Y13gg%3D%3D&lg2=VT5L2FSpMGV7TQ%3D%3D&vt3=F8dBxd7CT335hOeshsM%3D&nk2=o688bJ2t2lOAtK8MXRkc9rS1xdZODw%3D%3D; csg=bb101fa2; lgc=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; cookie17=UUpngTzc2Y13gg%3D%3D; dnk=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; skt=d1b053176dd80fc6; existShop=MTU4Mzk4NzY5Ng%3D%3D; uc4=id4=0%40U2gtEEjyK2oSMoVT0y3FwsMQmuba&nk4=0%40ofkQgMIYV1w9DtinNnKgQnwEY%2B9mIA3bhXzykl%2Bh56ME; _cc_=UIHiLt3xSw%3D%3D; _l_g_=Ug%3D%3D; sg=%E9%A2%968e; _nk_=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; cookie1=UNcJvhnwxmsnc%2BQXwkd900lS4%2BSdsCS20k5pE5xYJ2Q%3D; alitrackid=login.taobao.com; lastalitrackid=login.taobao.com; tfstk=cFMhBQjc8XPBkECPG6wIVOcoz-9AZZIURSFZQvf4ybSgh8kNiq5Nge_NRPune_1..; mt=ci=20_1; v=0; Hm_lvt_eaa57ca47dacb4ad4f5a257001a3457c=1583980126,1583987707; uc1=cookie16=Vq8l%2BKCLySLZMFWHxqs8fwqnEw%3D%3D&cookie21=VFC%2FuZ9aiKCaj7AzMHh1&cookie15=VFC%2FuZ9ayeYq2g%3D%3D&existShop=false&pas=0&cookie14=UoTUOafM2gaxgg%3D%3D&tag=8&lng=zh_CN; JSESSIONID=AF10B48836FA6F017CE4F6D93629F9C4; Hm_lpvt_eaa57ca47dacb4ad4f5a257001a3457c=1583989886; isg=BM3NGh5pr8U80wicTAKS00EO3OlHqgF8Ot6V2g9TpGTZBuy41_jeTHTUcJpgnhk0; l=dBEklFpHqPrWkOUQBOfNqASSiU_ONIdb8SFy7q0UFICPO7CHlfwOWZq1YKLMCnGVH6kWR3Rp9jjQBqLO1yCrnxv9-3k_J_DmndC..',\n",
+    "        }\n",
+    "        r = requests.get(url, headers=headers)\n",
+    "        if r.status_code == 200:\n",
+    "            r.encoding = r.apparent_encoding\n",
+    "            return r.text\n",
+    "    except Exception as er:\n",
+    "        print(er)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 利用正则表达式获取每页图片的链接"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_pic_url(html):  # 用正则提取每一页的关键信息返回\n",
+    "    pic_urls = re.findall('\"pic_url\":\"(.*?)\"', html, re.S)       #\"pic_url\":\"(.*?)\"\n",
+    "    img_url = []     # 创建空列表，装每一页的所有图片的链接\n",
+    "    for one_pic_url in pic_urls:\n",
+    "        img_url.append('http:'+one_pic_url)\n",
+    "    return img_url   # 返回图片的链接的列表"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&emsp;&emsp;下载图片保存在本地"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def write_to_file(page, img_urls):  # 写入文件（下载）\n",
+    "    i = page   # 利用页码，防止后面的写入会覆盖之前的\n",
+    "    n = 0\n",
+    "    for pic_url in img_urls:\n",
+    "        pic = requests.get(pic_url)\n",
+    "        with open('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/'+str(i)+str(n)+'.jpg', 'wb') as f:\n",
+    "            f.write(pic.content)\n",
+    "        print('---第{}页第{}张图下载成功---'.format(str(i), str(n)))\n",
+    "        n += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 主函数：程序的入口\n",
+    "\n",
+    "#### 可通过输入关键词以及爬取页数来获取所需图片"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def main(keyword, page_num, url):\n",
+    "    html = get_one_html(url)   # 调用函数得到该页的html\n",
+    "    # print(html)\n",
+    "    img_urls = get_pic_url(html)  # 调用函数得到该页的所有图片的链接\n",
+    "    print(img_urls)\n",
+    "    write_to_file(page, img_urls)   # 调用函数，写入即下载图片\n",
+    "        \n",
+    "if __name__ == '__main__':\n",
+    "    keyword = input('请输入关键词：')\n",
+    "    page_num = eval(input('请输入要爬取的页数：'))\n",
+    "    try:\n",
+    "        if os.path.exists('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/') == False:\n",
+    "            os.mkdir('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/')\n",
+    "        for page in range(0, page_num):\n",
+    "            # 裤子\n",
+    "            # url = \"https://s.taobao.com/search?q=%E8%A3%A4%E5%AD%90&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=\" + str(-3*page) + \"&ntoffset=\"+ str(-3*page)+ \"&p4ppushleft=1%2C48&s=\" + str(88+44*page)\n",
+    "            # 运动鞋\n",
+    "            # url = \"https://s.taobao.com/search?q=%E8%BF%90%E5%8A%A8%E9%9E%8B&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=\" + str(-3*page) + \"&ntoffset=\" + str(-3*page) + \"&p4ppushleft=1%2C48&s=\" + str(88+44*page)\n",
+    "            url = \"https://s.taobao.com/search?q=\" + keyword + \"&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=\"+ str(-3*page) + \"&ntoffset=\" + str(-3*page) + \"&p4ppushleft=1%2C48&s=\" + str(88+44*page)\n",
+    "            main(keyword, page_num, url)\n",
+    "            if page % 2 == 0:\n",
+    "                time.sleep(10)   # 每爬取2页停留10秒\n",
+    "    except Exception as err:\n",
+    "        print(err)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 所爬取图片如下\n",
+    "\n",
+    "### 外套:\n",
+    "\n",
+    "![coat](images/coat.png)\n",
+    "\n",
+    "### 裤子:\n",
+    "\n",
+    "![pants](images/pants.png)\n",
+    "\n",
+    "### 凉鞋:\n",
+    "\n",
+    "![sandal](images/sandal.png)\n",
+    "\n",
+    "### 运动鞋:\n",
+    "\n",
+    "![sneaker](images/sneaker.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&emsp;&emsp;四组图片，每组有440张图片\n",
+    "\n",
+    "## 数据的预处理\n",
+    "\n",
+    "&emsp;&emsp;将图片转换为28 * 28 的灰度图，并构建标签"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "path = 'data/coat'\n",
+    "# for file in os.listdir(path):\n",
+    "#     newname = 'a'+'%s'%i + '.jpg'\n",
+    "#     os.rename(os.path.join(path, file), os.path.join(path, newname))\n",
+    "#     i = i+1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "def rgb2gray(img_path):\n",
+    "    img_path = 'data/coat/'+img_path\n",
+    "    img = cv2.imread(img_path)\n",
+    "    #     #获取图片的宽和高\n",
+    "    #     width,height = img.shape[:2][::-1]\n",
+    "    #     #将图片缩小便于显示观看\n",
+    "    #     img_resize = cv2.resize(img,\n",
+    "    #     (int(width*0.5),int(height*0.5)),interpolation=cv2.INTER_CUBIC)\n",
+    "    #     cv2.imshow(\"img\",img_resize)\n",
+    "    #     print(\"img_reisze shape:{}\".format(np.shape(img_resize)))\n",
+    "\n",
+    "    #将图片转为灰度图\n",
+    "    img_gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
+    "\n",
+    "#     print(\"img_gray shape:{}\".format(np.shape(img_gray)))\n",
+    "    cv2.waitKey()\n",
+    "    img_resize = cv2.resize(img_gray,(28,28),interpolation=cv2.INTER_CUBIC)\n",
+    "    img_resize = img_resize.astype('float32')\n",
+    "    img_resize /= 255\n",
+    "    # print(img_resize)\n",
+    "\n",
+    "    # print(\"img shape:{}\".format(np.shape(img_resize)))\n",
+    "    # img = img_resize.reshape((28,28))\n",
+    "    img_resize = np.array(img_resize)\n",
+    "    return img_resize\n",
+    "# print(\"img shape:{}\".format(np.shape(img)))\n",
+    "# cv2.imshow(\"img\",img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1760, 28, 28, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = []\n",
+    "for file in os.listdir(path):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "# a = np.array(a)\n",
+    "# print(a.shape)\n",
+    "path1 = 'data/sneaker'\n",
+    "for file in os.listdir(path1):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "# a = np.array(a)\n",
+    "# print(a.shape)\n",
+    "path2 = 'data/sandal'\n",
+    "for file in os.listdir(path2):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "# a = np.array(a)\n",
+    "# print(a.shape)\n",
+    "path3 = 'data/pants'\n",
+    "for file in os.listdir(path3):\n",
+    "#     print(file)\n",
+    "    b = rgb2gray(file)\n",
+    "    a.append(b)\n",
+    "a = np.array(a)\n",
+    "taobao_train = a.reshape(a.shape[0], 28, 28, 1)\n",
+    "print(taobao_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1760, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.utils import to_categorical\n",
+    "y1 = np.ones(440)*4\n",
+    "y2 = np.ones(440)*7\n",
+    "y3 = np.ones(440)*5\n",
+    "y4 = np.ones(440)*1\n",
+    "taobao_label = to_categorical(np.concatenate((y1,y2,y3,y4),axis=0),10)\n",
+    "print(taobao_label.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&emsp;&emsp;使用所训练的模型进行预测并得到分类精度如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_taobao = cnn4.evaluate(taobao_train, taobao_label, verbose=0)\n",
+    "print('Test loss:', score_taobao[0])\n",
+    "print('Test accuracy:', score_taobao[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test loss:0.23454068487286567\n",
+    "\n",
+    "Test accuracy:0.917900025844574"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可以看出，实验效果较好，但仍有待提升。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 总结\n",
+    "\n",
+    "&emsp;&emsp;本次实验让我充分了解并掌握了神经网络的构建与学习，并且学会了如何进行简单的爬虫程序编写。在编写爬虫程序时遇到了许多问题，例如爬取淘宝网站时总是遇到需要登陆的情况，最终通过查阅资料解决了该问题。本次实验任务提升了我自主解决问题的能力，使我受益匪浅。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyCharm (report_05_Jigsaw-Puzzle)",
+   "language": "python",
+   "name": "pycharm-8b0e993c"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/report_03_Fashion/submit/Statistical_val_data.png b/report_03_Fashion/submit/Statistical_val_data.png
new file mode 100644
index 0000000000000000000000000000000000000000..0955401e4c4d79e9f2d9b79d2364209977bb33fc
Binary files /dev/null and b/report_03_Fashion/submit/Statistical_val_data.png differ
diff --git a/report_03_Fashion/submit/Statistical_val_data.txt b/report_03_Fashion/submit/Statistical_val_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bf59ff5c131fc0ad64eab6b3f72057a4ff68f25c
--- /dev/null
+++ b/report_03_Fashion/submit/Statistical_val_data.txt
@@ -0,0 +1,11 @@
+category,count
+0,1000
+1,1000
+2,1000
+3,1000
+4,1000
+5,1000
+6,1000
+7,1000
+8,1000
+9,1000
diff --git a/report_03_Fashion/submit/Statistical_val_data.xls b/report_03_Fashion/submit/Statistical_val_data.xls
new file mode 100644
index 0000000000000000000000000000000000000000..8090bd12bbcbc841d307590b50f806325efa7f34
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diff --git a/report_03_Fashion/submit/baseline.json b/report_03_Fashion/submit/baseline.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf804a8539b9821b6be3b8de010b1564b96eaed4
--- /dev/null
+++ b/report_03_Fashion/submit/baseline.json
@@ -0,0 +1 @@
+[{"image_id": "test_0_9.jpg", "disease_class": 9}, {"image_id": "test_100_3.jpg", "disease_class": 3}, {"image_id": "test_101_6.jpg", "disease_class": 6}, {"image_id": "test_102_7.jpg", "disease_class": 7}, {"image_id": "test_103_2.jpg", "disease_class": 2}, {"image_id": "test_104_7.jpg", "disease_class": 7}, {"image_id": "test_105_8.jpg", "disease_class": 8}, {"image_id": "test_106_5.jpg", "disease_class": 5}, {"image_id": "test_107_9.jpg", "disease_class": 9}, {"image_id": "test_108_9.jpg", "disease_class": 9}, {"image_id": "test_109_4.jpg", "disease_class": 4}, {"image_id": "test_10_4.jpg", "disease_class": 4}, {"image_id": "test_110_2.jpg", "disease_class": 2}, {"image_id": "test_111_5.jpg", "disease_class": 5}, {"image_id": "test_112_7.jpg", "disease_class": 7}, {"image_id": "test_113_0.jpg", "disease_class": 0}, {"image_id": "test_114_5.jpg", "disease_class": 5}, {"image_id": "test_115_2.jpg", "disease_class": 2}, {"image_id": "test_116_8.jpg", "disease_class": 8}, {"image_id": "test_117_6.jpg", "disease_class": 6}, {"image_id": "test_118_7.jpg", "disease_class": 7}, {"image_id": "test_119_8.jpg", "disease_class": 8}, {"image_id": "test_11_5.jpg", "disease_class": 5}, {"image_id": "test_120_0.jpg", "disease_class": 0}, {"image_id": "test_121_0.jpg", "disease_class": 0}, {"image_id": "test_122_9.jpg", "disease_class": 9}, {"image_id": "test_123_9.jpg", "disease_class": 9}, {"image_id": "test_124_3.jpg", "disease_class": 3}, {"image_id": "test_125_0.jpg", "disease_class": 0}, {"image_id": "test_126_8.jpg", "disease_class": 8}, {"image_id": "test_127_4.jpg", "disease_class": 4}, {"image_id": "test_128_1.jpg", "disease_class": 1}, {"image_id": "test_129_5.jpg", "disease_class": 5}, {"image_id": "test_12_7.jpg", "disease_class": 7}, {"image_id": "test_130_4.jpg", "disease_class": 4}, {"image_id": "test_131_1.jpg", "disease_class": 1}, {"image_id": "test_132_9.jpg", "disease_class": 9}, {"image_id": "test_133_1.jpg", "disease_class": 1}, {"image_id": "test_134_8.jpg", "disease_class": 8}, {"image_id": "test_135_6.jpg", "disease_class": 6}, {"image_id": "test_136_2.jpg", "disease_class": 2}, {"image_id": "test_137_1.jpg", "disease_class": 1}, {"image_id": "test_138_2.jpg", "disease_class": 2}, {"image_id": "test_139_5.jpg", "disease_class": 5}, {"image_id": "test_13_3.jpg", "disease_class": 3}, {"image_id": "test_140_1.jpg", "disease_class": 1}, {"image_id": "test_141_0.jpg", "disease_class": 0}, {"image_id": "test_142_0.jpg", "disease_class": 0}, {"image_id": "test_143_0.jpg", "disease_class": 0}, {"image_id": "test_144_1.jpg", "disease_class": 1}, {"image_id": "test_145_6.jpg", "disease_class": 6}, {"image_id": "test_146_1.jpg", "disease_class": 1}, {"image_id": "test_147_6.jpg", "disease_class": 3}, {"image_id": "test_148_2.jpg", "disease_class": 2}, {"image_id": "test_149_2.jpg", "disease_class": 2}, {"image_id": "test_14_4.jpg", "disease_class": 4}, {"image_id": "test_150_4.jpg", "disease_class": 3}, {"image_id": "test_151_4.jpg", "disease_class": 4}, {"image_id": "test_152_1.jpg", "disease_class": 1}, {"image_id": "test_153_4.jpg", "disease_class": 3}, {"image_id": "test_154_5.jpg", "disease_class": 5}, {"image_id": "test_155_0.jpg", "disease_class": 0}, {"image_id": "test_156_4.jpg", "disease_class": 4}, {"image_id": "test_157_7.jpg", "disease_class": 7}, {"image_id": "test_158_9.jpg", "disease_class": 9}, {"image_id": "test_159_3.jpg", "disease_class": 3}, {"image_id": "test_15_1.jpg", "disease_class": 1}, {"image_id": "test_160_7.jpg", "disease_class": 7}, {"image_id": "test_161_2.jpg", "disease_class": 2}, {"image_id": "test_162_3.jpg", "disease_class": 3}, {"image_id": "test_163_9.jpg", "disease_class": 9}, {"image_id": "test_164_0.jpg", "disease_class": 0}, {"image_id": "test_165_9.jpg", "disease_class": 9}, {"image_id": "test_166_4.jpg", "disease_class": 2}, {"image_id": "test_167_7.jpg", "disease_class": 7}, {"image_id": "test_168_4.jpg", "disease_class": 4}, {"image_id": "test_169_2.jpg", "disease_class": 2}, {"image_id": "test_16_2.jpg", "disease_class": 2}, {"image_id": "test_170_0.jpg", "disease_class": 0}, {"image_id": "test_171_5.jpg", "disease_class": 5}, {"image_id": "test_172_2.jpg", "disease_class": 2}, {"image_id": "test_173_1.jpg", "disease_class": 1}, {"image_id": "test_174_2.jpg", "disease_class": 2}, {"image_id": "test_175_1.jpg", "disease_class": 1}, {"image_id": "test_176_3.jpg", "disease_class": 3}, {"image_id": "test_177_0.jpg", "disease_class": 0}, {"image_id": "test_178_9.jpg", "disease_class": 9}, {"image_id": "test_179_1.jpg", "disease_class": 1}, {"image_id": "test_17_4.jpg", "disease_class": 6}, {"image_id": "test_180_0.jpg", "disease_class": 0}, {"image_id": "test_181_9.jpg", "disease_class": 9}, {"image_id": "test_182_3.jpg", "disease_class": 3}, {"image_id": "test_183_6.jpg", "disease_class": 6}, {"image_id": "test_184_7.jpg", "disease_class": 7}, {"image_id": "test_185_9.jpg", "disease_class": 9}, {"image_id": "test_186_9.jpg", "disease_class": 9}, {"image_id": "test_187_4.jpg", "disease_class": 4}, {"image_id": "test_188_4.jpg", "disease_class": 4}, {"image_id": "test_189_7.jpg", "disease_class": 7}, {"image_id": "test_18_8.jpg", "disease_class": 8}, {"image_id": "test_190_1.jpg", "disease_class": 1}, {"image_id": "test_191_2.jpg", "disease_class": 2}, {"image_id": "test_192_1.jpg", "disease_class": 1}, {"image_id": "test_193_6.jpg", "disease_class": 6}, {"image_id": "test_194_3.jpg", "disease_class": 3}, {"image_id": "test_195_2.jpg", "disease_class": 2}, {"image_id": "test_196_8.jpg", "disease_class": 8}, {"image_id": "test_197_3.jpg", "disease_class": 3}, {"image_id": "test_198_6.jpg", "disease_class": 6}, {"image_id": "test_199_1.jpg", "disease_class": 1}, {"image_id": "test_19_0.jpg", "disease_class": 0}, {"image_id": "test_1_2.jpg", "disease_class": 2}, {"image_id": "test_200_1.jpg", "disease_class": 1}, {"image_id": "test_201_0.jpg", "disease_class": 0}, {"image_id": 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"disease_class": 5}, {"image_id": "test_38_7.jpg", "disease_class": 7}, {"image_id": "test_39_9.jpg", "disease_class": 9}, {"image_id": "test_3_1.jpg", "disease_class": 1}, {"image_id": "test_40_6.jpg", "disease_class": 6}, {"image_id": "test_41_1.jpg", "disease_class": 1}, {"image_id": "test_42_3.jpg", "disease_class": 6}, {"image_id": "test_43_7.jpg", "disease_class": 7}, {"image_id": "test_44_6.jpg", "disease_class": 6}, {"image_id": "test_45_7.jpg", "disease_class": 7}, {"image_id": "test_46_2.jpg", "disease_class": 2}, {"image_id": "test_47_1.jpg", "disease_class": 1}, {"image_id": "test_48_2.jpg", "disease_class": 2}, {"image_id": "test_49_2.jpg", "disease_class": 6}, {"image_id": "test_4_6.jpg", "disease_class": 6}, {"image_id": "test_50_4.jpg", "disease_class": 4}, {"image_id": "test_51_4.jpg", "disease_class": 4}, {"image_id": "test_52_5.jpg", "disease_class": 5}, {"image_id": "test_53_8.jpg", "disease_class": 8}, {"image_id": "test_54_2.jpg", "disease_class": 2}, {"image_id": 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2}, {"image_id": "test_73_6.jpg", "disease_class": 6}, {"image_id": "test_74_2.jpg", "disease_class": 2}, {"image_id": "test_75_3.jpg", "disease_class": 3}, {"image_id": "test_76_1.jpg", "disease_class": 1}, {"image_id": "test_77_2.jpg", "disease_class": 2}, {"image_id": "test_78_8.jpg", "disease_class": 8}, {"image_id": "test_79_4.jpg", "disease_class": 4}, {"image_id": "test_7_6.jpg", "disease_class": 6}, {"image_id": "test_80_1.jpg", "disease_class": 1}, {"image_id": "test_81_8.jpg", "disease_class": 8}, {"image_id": "test_82_5.jpg", "disease_class": 5}, {"image_id": "test_83_9.jpg", "disease_class": 9}, {"image_id": "test_84_5.jpg", "disease_class": 5}, {"image_id": "test_85_0.jpg", "disease_class": 0}, {"image_id": "test_86_3.jpg", "disease_class": 3}, {"image_id": "test_87_2.jpg", "disease_class": 2}, {"image_id": "test_88_0.jpg", "disease_class": 0}, {"image_id": "test_89_6.jpg", "disease_class": 2}, {"image_id": "test_8_5.jpg", "disease_class": 5}, {"image_id": "test_90_5.jpg", "disease_class": 5}, {"image_id": "test_91_3.jpg", "disease_class": 3}, {"image_id": "test_92_6.jpg", "disease_class": 6}, {"image_id": "test_93_7.jpg", "disease_class": 7}, {"image_id": "test_94_1.jpg", "disease_class": 1}, {"image_id": "test_95_8.jpg", "disease_class": 8}, {"image_id": "test_96_0.jpg", "disease_class": 6}, {"image_id": "test_97_1.jpg", "disease_class": 1}, {"image_id": "test_98_4.jpg", "disease_class": 2}, {"image_id": "test_99_2.jpg", "disease_class": 2}, {"image_id": "test_9_7.jpg", "disease_class": 7}]
\ No newline at end of file
diff --git a/report_03_Fashion/taobao-master/README.md b/report_03_Fashion/taobao-master/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b9e71e1276b9e5a4c0b6240084f19a5d9e95341f
--- /dev/null
+++ b/report_03_Fashion/taobao-master/README.md
@@ -0,0 +1,13 @@
+# 功能
+根据输入的关键字到淘宝中搜索高清图片
+# 环境
+ scrapy, Python3
+# 用法
+运行mian文件，当提示输入时输入像想找的图片的关键字
+# 图片保存位置
+工程目录中会新建一个文件夹——images，图片保存在这个文件中，
+# 不足
+* 一、只是把图年保存到本地，未保存到数据库中
+* 二、目前只是测试了服装类，其他类未测试
+* 三、目前只是能保存图片，如果在浏览图片时看到某个图片所展示的衣服很好看，不能根据该图片追踪到淘宝店铺，不能筛选同类型的图片
+
diff --git a/report_03_Fashion/taobao-master/__init__.py b/report_03_Fashion/taobao-master/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/report_03_Fashion/taobao-master/getHtml.py b/report_03_Fashion/taobao-master/getHtml.py
new file mode 100644
index 0000000000000000000000000000000000000000..582a8a52f0866a54106df585d6a9e8a5dc5a3e4e
--- /dev/null
+++ b/report_03_Fashion/taobao-master/getHtml.py
@@ -0,0 +1,6 @@
+#coding = utf-8
+# import urllib
+from urllib import request
+page = request.urlopen("https://s.taobao.com/search?q=%E8%A3%A4%E5%AD%90&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=-21&ntoffset=-21&p4ppushleft=1%2C48&s=396")
+# print(page.read().decode("utf-8"))
+print(page.read())
\ No newline at end of file
diff --git a/report_03_Fashion/taobao-master/items.py b/report_03_Fashion/taobao-master/items.py
new file mode 100644
index 0000000000000000000000000000000000000000..25181b3469c6b5e49240ae48f15f91f68bfd9d76
--- /dev/null
+++ b/report_03_Fashion/taobao-master/items.py
@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+
+# Define here the models for your scraped items
+#
+# See documentation in:
+# http://doc.scrapy.org/en/latest/topics/items.html
+
+import scrapy
+
+
+class TaobaoItem(scrapy.Item):
+    # define the fields for your item here like:
+    # name = scrapy.Field()
+    pass
+
+class TbItem(scrapy.Item):
+    img=scrapy.Field()
diff --git a/report_03_Fashion/taobao-master/main.py b/report_03_Fashion/taobao-master/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a6e84a9237b60695dd5bf146a1453a58675a7d6
--- /dev/null
+++ b/report_03_Fashion/taobao-master/main.py
@@ -0,0 +1,8 @@
+import os ,sys
+from scrapy.cmdline import execute
+
+base_dir=os.path.abspath(os.path.dirname(__file__))
+print(base_dir)
+sys.path.insert(0,base_dir)
+
+execute("scrapy crawl tb".split())
diff --git a/report_03_Fashion/taobao-master/middlewares.py b/report_03_Fashion/taobao-master/middlewares.py
new file mode 100644
index 0000000000000000000000000000000000000000..264a1b87883dc4120a3c5c9bd1c1b4168142f8fe
--- /dev/null
+++ b/report_03_Fashion/taobao-master/middlewares.py
@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+
+# Define here the models for your spider middleware
+#
+# See documentation in:
+# http://doc.scrapy.org/en/latest/topics/spider-middleware.html
+
+from scrapy import signals
+from fake_useragent import UserAgent
+
+
+class TaobaoSpiderMiddleware(object):
+    # Not all methods need to be defined. If a method is not defined,
+    # scrapy acts as if the spider middleware does not modify the
+    # passed objects.
+
+    @classmethod
+    def from_crawler(cls, crawler):
+        # This method is used by Scrapy to create your spiders.
+        s = cls()
+        crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
+        return s
+
+    def process_spider_input(self, response, spider):
+        # Called for each response that goes through the spider
+        # middleware and into the spider.
+
+        # Should return None or raise an exception.
+        return None
+
+    def process_spider_output(self, response, result, spider):
+        # Called with the results returned from the Spider, after
+        # it has processed the response.
+
+        # Must return an iterable of Request, dict or Item objects.
+        for i in result:
+            yield i
+
+    def process_spider_exception(self, response, exception, spider):
+        # Called when a spider or process_spider_input() method
+        # (from other spider middleware) raises an exception.
+
+        # Should return either None or an iterable of Response, dict
+        # or Item objects.
+        pass
+
+    def process_start_requests(self, start_requests, spider):
+        # Called with the start requests of the spider, and works
+        # similarly to the process_spider_output() method, except
+        # that it doesn’t have a response associated.
+
+        # Must return only requests (not items).
+        for r in start_requests:
+            yield r
+
+    def spider_opened(self, spider):
+        spider.logger.info('Spider opened: %s' % spider.name)
+
+
+class RandomIpAndUserAgentMiddleware(object):
+    """使用爬虫使用随机的Ip 和 UA"""
+
+    def __init__(self, crawler):
+        self.ua = UserAgent()
+        self.ua_type = crawler.settings.get("USER_AGENT_TYPE", "random")
+        super(RandomIpAndUserAgentMiddleware, self).__init__()
+
+    @classmethod
+    def from_crawler(cls, crawler):
+        return cls(crawler)
+
+    def process_request(self, request, spider):
+        def get_ua():
+            return getattr(self.ua, self.ua_type)
+
+        request.headers.setdefault('User-Agent', get_ua())
+
+
+class RandomUserAgentMiddlware(object):
+    # 随机更换user-agent
+    def __init__(self, crawler):
+        super(RandomUserAgentMiddlware, self).__init__()
+        self.ua = UserAgent()
+        self.ua_type = crawler.settings.get("RANDOM_UA_TYPE", "random")
+
+    @classmethod
+    def from_crawler(cls, crawler):
+        return cls(crawler)
+
+    def process_request(self, request, spider):
+        def get_ua():
+            return getattr(self.ua, self.ua_type)
+        # ua= "Mozilla/5.0 (Windows NT 10.0; …) Gecko/20100101 Firefox/59.0"
+
+        request.headers.setdefault('User-Agent', get_ua())
diff --git a/report_03_Fashion/taobao-master/pipelines.py b/report_03_Fashion/taobao-master/pipelines.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec6b09b74d3b646298c4b4c12130e4b55af0e870
--- /dev/null
+++ b/report_03_Fashion/taobao-master/pipelines.py
@@ -0,0 +1,11 @@
+# -*- coding: utf-8 -*-
+
+# Define your item pipelines here
+#
+# Don't forget to add your pipeline to the ITEM_PIPELINES setting
+# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html
+
+
+class TaobaoPipeline(object):
+    def process_item(self, item, spider):
+        return item
diff --git a/report_03_Fashion/taobao-master/scrapy.cfg b/report_03_Fashion/taobao-master/scrapy.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..398bc2577fa81a0c24a5c7f7889e7cb38b059d09
--- /dev/null
+++ b/report_03_Fashion/taobao-master/scrapy.cfg
@@ -0,0 +1,11 @@
+# Automatically created by: scrapy startproject
+#
+# For more information about the [deploy] section see:
+# https://scrapyd.readthedocs.org/en/latest/deploy.html
+
+[settings]
+default = taobao.settings
+
+[deploy]
+#url = http://localhost:6800/
+project = taobao
diff --git a/report_03_Fashion/taobao-master/settings.py b/report_03_Fashion/taobao-master/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d179409d5aff9d96a850d076d5bdf1725097815
--- /dev/null
+++ b/report_03_Fashion/taobao-master/settings.py
@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+
+# Scrapy settings for taobao project
+#
+# For simplicity, this file contains only settings considered important or
+# commonly used. You can find more settings consulting the documentation:
+#
+#     http://doc.scrapy.org/en/latest/topics/settings.html
+#     http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html
+#     http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html
+
+BOT_NAME = 'taobao'
+
+SPIDER_MODULES = ['taobao.spiders']
+NEWSPIDER_MODULE = 'taobao.spiders'
+
+
+# Crawl responsibly by identifying yourself (and your website) on the user-agent
+#USER_AGENT = 'taobao (+http://www.yourdomain.com)'
+
+# Obey robots.txt rules
+ROBOTSTXT_OBEY = False
+
+# Configure maximum concurrent requests performed by Scrapy (default: 16)
+#CONCURRENT_REQUESTS = 32
+
+# Configure a delay for requests for the same website (default: 0)
+# See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay
+# See also autothrottle settings and docs
+#DOWNLOAD_DELAY = 3
+# The download delay setting will honor only one of:
+#CONCURRENT_REQUESTS_PER_DOMAIN = 16
+#CONCURRENT_REQUESTS_PER_IP = 16
+
+# Disable cookies (enabled by default)
+#COOKIES_ENABLED = False
+
+# Disable Telnet Console (enabled by default)
+#TELNETCONSOLE_ENABLED = False
+
+# Override the default request headers:
+#DEFAULT_REQUEST_HEADERS = {
+#   'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
+#   'Accept-Language': 'en',
+#}
+
+# Enable or disable spider middlewares
+# See http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html
+#SPIDER_MIDDLEWARES = {
+#    'taobao.middlewares.TaobaoSpiderMiddleware': 543,
+#}
+
+# Enable or disable downloader middlewares
+# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html
+DOWNLOADER_MIDDLEWARES = {
+   'taobao.middlewares.RandomIpAndUserAgentMiddleware': 543,
+   'taobao.middlewares.TaobaoSpiderMiddleware': None,
+}
+
+# Enable or disable extensions
+# See http://scrapy.readthedocs.org/en/latest/topics/extensions.html
+#EXTENSIONS = {
+#    'scrapy.extensions.telnet.TelnetConsole': None,
+#}
+
+# Configure item pipelines
+# See http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html
+ITEM_PIPELINES = {
+   # 'taobao.pipelines.TaobaoPipeline': 300,
+    'scrapy.pipelines.images.ImagesPipeline':1
+}
+
+# Enable and configure the AutoThrottle extension (disabled by default)
+# See http://doc.scrapy.org/en/latest/topics/autothrottle.html
+#AUTOTHROTTLE_ENABLED = True
+# The initial download delay
+#AUTOTHROTTLE_START_DELAY = 5
+# The maximum download delay to be set in case of high latencies
+#AUTOTHROTTLE_MAX_DELAY = 60
+# The average number of requests Scrapy should be sending in parallel to
+# each remote server
+#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
+# Enable showing throttling stats for every response received:
+#AUTOTHROTTLE_DEBUG = False
+
+# Enable and configure HTTP caching (disabled by default)
+# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
+#HTTPCACHE_ENABLED = True
+#HTTPCACHE_EXPIRATION_SECS = 0
+#HTTPCACHE_DIR = 'httpcache'
+#HTTPCACHE_IGNORE_HTTP_CODES = []
+#HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'
+
+import os
+img_dir=os.path.join(os.path.abspath(os.path.dirname(__file__)),"images")
+print(img_dir)
+IMAGES_URLS_FIELD='img'
+IMAGES_STORE=img_dir
diff --git a/report_03_Fashion/taobao-master/spiders/__init__.py b/report_03_Fashion/taobao-master/spiders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebd689ac51d69c5e1dbbe80083c2b20a39f8bb79
--- /dev/null
+++ b/report_03_Fashion/taobao-master/spiders/__init__.py
@@ -0,0 +1,4 @@
+# This package will contain the spiders of your Scrapy project
+#
+# Please refer to the documentation for information on how to create and manage
+# your spiders.
diff --git a/report_03_Fashion/taobao-master/spiders/tb.py b/report_03_Fashion/taobao-master/spiders/tb.py
new file mode 100644
index 0000000000000000000000000000000000000000..95b44b1acd7d156d96182fd956fc2e577e59af96
--- /dev/null
+++ b/report_03_Fashion/taobao-master/spiders/tb.py
@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+import scrapy
+import re
+import time
+import urllib.request
+from taobao.items import TbItem
+
+
+class TbSpider(scrapy.Spider):
+    name = 'tb'
+    allowed_domains = ['tabao.com', "detail.tmall.com", "s.taobao.com", "item.taobao.com", "dsc.taobaocdn.com",
+                       "img.alicdn.com"]
+    start_urls = ['https://www.taobao.com/']
+
+
+    def start_requests(self):
+        return [scrapy.Request(url="https://www.taobao.com/",  callback=self.start_search)]
+
+
+    def start_search(self,response):
+        keyword = input("please input what do you what to seach ?").strip()
+        keyword = urllib.request.quote(keyword)
+        for i in range(1,2): # 这里可以优化，可以写一个自动判断是否还有下一页的函数
+            url = "https://s.taobao.com/search?q=" + keyword + "&s=" + str(i * 44)
+            yield scrapy.Request(url=url,callback=self.parse_search_page)
+            time.sleep(20)
+
+    def parse_search_page(self, response):
+        """处理搜索页面"""
+        html = response.body.decode("utf8", "ignore")
+        try:
+            # 查找uid 和是否属于天猫，因为淘宝和天猫的详情页面不一样，得到是一个tupe
+            uids_and_isTmail = re.compile(r'"nid":"(.*?)".*?"isTmall":(.*?),').findall(html)
+            for uid_and_isTmail in uids_and_isTmail:
+                if uid_and_isTmail[1] == "true":
+                    detailUrl = "https://detail.tmall.com/item.htm?id=" + str(uid_and_isTmail[0])
+                else:
+                    detailUrl = "https://item.taobao.com/item.htm?id=" + str(uid_and_isTmail[0])
+                yield scrapy.Request(detailUrl, callback=self.parsePictureUrl)
+                time.sleep(10)
+        except Exception as e:
+            print(e)
+
+    def parsePictureUrl(self, response):
+        """通过详情页面得到存放高清图片的网址"""
+        html = response.body.decode("utf8", "ignore")
+        try:
+            pictureUrl = re.compile('descUrl.*?:.*?//(.*?)\'').findall(html)[0]
+            #必须加http才能访问
+            pictureUrl = "http://" + pictureUrl
+            yield scrapy.Request(pictureUrl, callback=self.parsePicture)
+        except Exception as e:
+            print(e)
+
+    def parsePicture(self, response):
+        """打开存放高清图片的网址后得到是一个json文件，里面有各个高清图片的详细网址，得到这些详细网址，然后交由scrapy下载"""
+        item = TbItem()
+        html = response.body.decode("utf8","ignore")
+        try:
+            downPictureUrlList = re.compile('src=.*?\"(.*?)\"').findall(html)
+            for downPictureUrl in downPictureUrlList:
+                item["img"] = [downPictureUrl]
+                yield item
+        except:
+            print("can not find down page")
+
+    def parse(self, response):
+        pass
diff --git a/report_03_Fashion/taobao-master/test.py b/report_03_Fashion/taobao-master/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b1204e65b5ecaefef005bd43f5a3d1ca8f4d9cf
--- /dev/null
+++ b/report_03_Fashion/taobao-master/test.py
@@ -0,0 +1,36 @@
+#-*- coding: UTF-8 -*- 
+# 导入请求、报错模块&正则表达式类库
+from urllib import request,error
+import re
+# 定义搜索词并将搜索词转码，防止报错
+# key_name=request.quote("笔记本电脑")
+# 定义函数，将爬到的每一页的商品url写入到文件
+def savefile(data):
+	path="C:\\Users\\czn\\Desktop\\report_03_Fashion\\taobao-master\\taobao-master\\taobao\\taobao_img\\taobao_url.txt"
+	file=open(path,"a")
+	file.write(data+"\n")
+	file.close()
+# 外层for循环控制爬取的页数 将每页的url写入到本地
+for p in range(1,8):
+	# 拿到每页url
+	url="https://s.taobao.com/search?q=%E8%A3%A4%E5%AD%90&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=" + str(-3*p) + "&ntoffset="+ str(-3*p)+ "&p4ppushleft=1%2C48&s=" + str(88+44*p)
+	# 拿到每页源码
+	data1=request.urlopen(url).read().decode("utf-8")
+	print(data1)
+	# 调用函数savefile,将每页url存入到指定path
+	savefile(url)
+	#定义匹配规则
+	pat='src="//(.*?)"'
+	#匹配到的所有图片url
+	img_url=re.compile(pat).findall(data1)
+	print(img_url)
+	#内层for循环将所有图片写到本地
+	for a_i in range(0,len(img_url)):
+		this_img=img_url[a_i]
+		this_img_url="http://"+this_img
+		#每张图片的url
+		print(this_img_url)
+		#将每张图片写到本地
+		#定义存取本地图片路径【retrieve()不会再本地建立文件夹因此需要手建】
+		img_path="C:\\Users\\czn\\Desktop\\report_03_Fashion\\taobao-master\\taobao-master\\taobao\\taobao_img\\" + str(p)+ str(a_i)+".jpg"
+		request.urlretrieve(this_img_url,img_path)
diff --git a/report_03_Fashion/taobao-master/test2.py b/report_03_Fashion/taobao-master/test2.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a8ee814d0777baac0ac75f8ab66466ce7749ed6
--- /dev/null
+++ b/report_03_Fashion/taobao-master/test2.py
@@ -0,0 +1,69 @@
+import re
+import requests  
+import time
+import os
+ 
+def get_one_html(url):  # 获取一个页面的html页面并返回
+    try:
+        # headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'}
+        headers = {
+            'authority': 's.taobao.com',
+            'upgrade-insecure-requests': '1',
+            'user-agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36',
+            'sec-fetch-dest': 'document',
+            'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',
+            'sec-fetch-site': 'same-origin',
+            'sec-fetch-mode': 'navigate',
+            'sec-fetch-user': '?1',
+            'referer': 'https://s.taobao.com/search?q=%E4%B9%A6%E5%8C%85&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200312&ie=utf8',
+            'accept-language': 'zh-CN,zh;q=0.9',
+            'cookie': 'miid=253146491399089190; thw=cn; cna=o391FTBhsgUCAdrD5AnR+QjC; hng=CN%7Czh-CN%7CCNY%7C156; tracknick=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; tg=0; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1%26_ato%3D0; t=a7362f9a33953472d0663cbb296a53fd; enc=jXmwR%2BzLAdHsQMP0d%2F9pId1Dn%2BW%2FtphblF4%2FsFXBDkrBKBLkGD4tksHXhn5%2BPTVPEfnLekpkIzYNZENT3sCchA%3D%3D; _samesite_flag_=true; cookie2=1bb1c10d29aca83180c0390f0d1fb1fc; _tb_token_=33b66b43e3335; sgcookie=EBnq%2FRUJ6VqRYs7GbgSt0; unb=2269301708; uc3=id2=UUpngTzc2Y13gg%3D%3D&lg2=VT5L2FSpMGV7TQ%3D%3D&vt3=F8dBxd7CT335hOeshsM%3D&nk2=o688bJ2t2lOAtK8MXRkc9rS1xdZODw%3D%3D; csg=bb101fa2; lgc=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; cookie17=UUpngTzc2Y13gg%3D%3D; dnk=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; skt=d1b053176dd80fc6; existShop=MTU4Mzk4NzY5Ng%3D%3D; uc4=id4=0%40U2gtEEjyK2oSMoVT0y3FwsMQmuba&nk4=0%40ofkQgMIYV1w9DtinNnKgQnwEY%2B9mIA3bhXzykl%2Bh56ME; _cc_=UIHiLt3xSw%3D%3D; _l_g_=Ug%3D%3D; sg=%E9%A2%968e; _nk_=%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu5566%5Cu9896; cookie1=UNcJvhnwxmsnc%2BQXwkd900lS4%2BSdsCS20k5pE5xYJ2Q%3D; alitrackid=login.taobao.com; lastalitrackid=login.taobao.com; tfstk=cFMhBQjc8XPBkECPG6wIVOcoz-9AZZIURSFZQvf4ybSgh8kNiq5Nge_NRPune_1..; mt=ci=20_1; v=0; Hm_lvt_eaa57ca47dacb4ad4f5a257001a3457c=1583980126,1583987707; uc1=cookie16=Vq8l%2BKCLySLZMFWHxqs8fwqnEw%3D%3D&cookie21=VFC%2FuZ9aiKCaj7AzMHh1&cookie15=VFC%2FuZ9ayeYq2g%3D%3D&existShop=false&pas=0&cookie14=UoTUOafM2gaxgg%3D%3D&tag=8&lng=zh_CN; JSESSIONID=AF10B48836FA6F017CE4F6D93629F9C4; Hm_lpvt_eaa57ca47dacb4ad4f5a257001a3457c=1583989886; isg=BM3NGh5pr8U80wicTAKS00EO3OlHqgF8Ot6V2g9TpGTZBuy41_jeTHTUcJpgnhk0; l=dBEklFpHqPrWkOUQBOfNqASSiU_ONIdb8SFy7q0UFICPO7CHlfwOWZq1YKLMCnGVH6kWR3Rp9jjQBqLO1yCrnxv9-3k_J_DmndC..',
+        }
+        r = requests.get(url, headers=headers)
+        if r.status_code == 200:
+            r.encoding = r.apparent_encoding
+            return r.text
+    except Exception as er:
+        print(er)
+    
+def get_pic_url(html):  # 用正则提取每一页的关键信息返回
+    pic_urls = re.findall('"pic_url":"(.*?)"', html, re.S)       #"pic_url":"(.*?)"
+    img_url = []     # 创建空列表，装每一页的所有图片的链接
+    for one_pic_url in pic_urls:
+        img_url.append('http:'+one_pic_url)
+    return img_url   # 返回图片的链接的列表
+ 
+def write_to_file(page, img_urls):  # 写入文件（下载）
+    i = page   # 利用页码，防止后面的写入会覆盖之前的
+    n = 0
+    for pic_url in img_urls:
+        pic = requests.get(pic_url)
+        with open('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/'+str(i)+str(n)+'.jpg', 'wb') as f:
+            f.write(pic.content)
+        print('---第{}页第{}张图下载成功---'.format(str(i), str(n)))
+        n += 1
+ 
+def main(keyword, page_num, url):
+    html = get_one_html(url)   # 调用函数得到该页的html
+    # print(html)
+    img_urls = get_pic_url(html)  # 调用函数得到该页的所有图片的链接
+    print(img_urls)
+    write_to_file(page, img_urls)   # 调用函数，写入即下载图片
+        
+if __name__ == '__main__':
+    keyword = input('请输入关键词：')
+    page_num = eval(input('请输入要爬取的页数：'))
+    try:
+        if os.path.exists('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/') == False:
+            os.mkdir('C:/Users/czn/Desktop/report_03_Fashion/taobao-master/taobao-master/taobao/taobao_img/picture/')
+        for page in range(0, page_num):
+            # 裤子
+            # url = "https://s.taobao.com/search?q=%E8%A3%A4%E5%AD%90&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=" + str(-3*page) + "&ntoffset="+ str(-3*page)+ "&p4ppushleft=1%2C48&s=" + str(88+44*page)
+            # 运动鞋
+            # url = "https://s.taobao.com/search?q=%E8%BF%90%E5%8A%A8%E9%9E%8B&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset=" + str(-3*page) + "&ntoffset=" + str(-3*page) + "&p4ppushleft=1%2C48&s=" + str(88+44*page)
+            url = "https://s.taobao.com/search?q=" + keyword + "&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20200627&ie=utf8&bcoffset="+ str(-3*page) + "&ntoffset=" + str(-3*page) + "&p4ppushleft=1%2C48&s=" + str(88+44*page)
+            main(keyword, page_num, url)
+            if page % 2 == 0:
+                time.sleep(10)   # 每爬取2页停留10秒
+    except Exception as err:
+        print(err)
diff --git a/report_03_Fashion/taobao-master/tools/crawl_ip_from_xichi.py b/report_03_Fashion/taobao-master/tools/crawl_ip_from_xichi.py
new file mode 100644
index 0000000000000000000000000000000000000000..76b3aa00f0df7b6267227723561e933fcc737f23
--- /dev/null
+++ b/report_03_Fashion/taobao-master/tools/crawl_ip_from_xichi.py
@@ -0,0 +1,53 @@
+import requests
+from fake_useragent import UserAgent
+from scrapy.selector import Selector
+import time
+import pymysql
+
+
+class GetIPFromXichi(object):
+    """通过西刺得到可用的IP，存入数据库"""
+    def crawl_ip(self):
+        """爬取西刺的免费IP"""
+        ip_list = []
+        for i in range(1, 20):
+            headers = UserAgent()
+            ua = getattr(headers, "random")
+            ua = {"User-Agent": ua}
+            url = "http://www.xicidaili.com/nn/" + str(i)
+            response = requests.get("http://www.xicidaili.com/nn/", headers=ua)
+            # time.sleep(3)
+            selector = Selector(text=response.text)
+            alltr = selector.css("#ip_list tr")
+            for tr in alltr[1:]:
+                speed_str = tr.css(".bar::attr(title)").extract_first()
+                if speed_str:
+                    speed = float(speed_str.split("秒")[0])
+                else:
+                    speed = 0
+                all_text = tr.css("td ::text").extract()
+                ip = all_text[0]
+                port = all_text[1]
+                type = all_text[6]
+                if not 'HTTP' in type.upper():
+                    type = "HTTP"
+                ip_list.append((ip, port, type, speed))
+
+        conn = pymysql.connect(host="127.0.0.1", user="root", password="root", db="outback")
+        cursor = conn.cursor()
+        insert_sql = """insert into ip_proxy(ip,port,type,speed) VALUES (%s,%s,%s,%s) """
+        for i in ip_list:
+            try:
+                cursor.execute(insert_sql, (i[0], i[1], i[2], i[3]))
+                conn.commit()
+            except Exception as e:
+                print(e)
+                conn.rollback()
+
+        cursor.close()
+        conn.close()
+
+if __name__ == "__main__":
+    crawl_ip_from_xichi=GetIPFromXichi()
+    crawl_ip_from_xichi.crawl_ip()
+
diff --git a/report_03_Fashion/taobao-master/tools/get_ip.py b/report_03_Fashion/taobao-master/tools/get_ip.py
new file mode 100644
index 0000000000000000000000000000000000000000..f20f7abf6fb0b34d30a4d67c70a96f0d65dd408c
--- /dev/null
+++ b/report_03_Fashion/taobao-master/tools/get_ip.py
@@ -0,0 +1,61 @@
+import requests
+import pymysql
+
+
+class GetIp(object):
+    """从数据库中取出可用的IP给爬虫使用"""
+    conn = pymysql.connect(host="127.0.0.1", user="root", password="root", db="outback")
+    cursor = conn.cursor()
+
+    def get_random_ip(self):
+        select_sql = "select ip,port,type from ip_proxy ORDER  by rand() limit 1"
+
+        result = self.cursor.execute(select_sql)
+        for ip_info in self.cursor.fetchall():
+            ip = ip_info[0]
+            port = ip_info[1]
+            type = ip_info[2].lower()
+            judge_result = self.judge_ip(type, ip, port, )
+            if judge_result:
+                # 这里不能关闭连接，因为每一个请求都会云获取一个IP，如果关了，就只能获取一个
+                # self.cursor.close()
+                # self.conn.close()
+
+                return "{0}://{1}:{2}".format(type, ip, port)
+            else:
+                self.get_random_ip()
+
+    def judge_ip(self, type, ip, port):
+        baidu = "https://www.baidu.com"
+        proxy_url = "{0}://{1}:{2}".format(type, ip, port)
+        try:
+            proxy_dict = {type:proxy_url,}
+            response = requests.get(baidu, proxies=proxy_dict)
+        except Exception as e:
+            print("invalid in or port ")
+            self.delete_ip(ip)
+            return False
+        else:
+            code = response.status_code
+            if code >= 200 and code < 300:
+                print("effective ip,the ip is",proxy_url)
+                return True
+            else:
+                print("invalid iP ")
+                self.delete_ip(ip)
+                return False
+
+    def delete_ip(self, ip):
+        delete_sql = """delete FROM ip_proxy where ip='{0}'""".format(ip)
+        try:
+            self.cursor.execute(delete_sql)
+            self.conn.commit()
+        except Exception as e:
+            print(e)
+
+
+
+if __name__ == "__main__":
+    get_ip = GetIp()
+    ip = get_ip.get_random_ip()
+    print(ip)
diff --git a/report_03_Fashion/utils.py b/report_03_Fashion/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..38a1e8e296841211ae5a3b3a32cdb32ef40e5798
--- /dev/null
+++ b/report_03_Fashion/utils.py
@@ -0,0 +1,156 @@
+import shutil
+import torch
+import sys
+import os
+import json
+import numpy as np
+from config import config
+from torch import nn
+import torch.nn.functional as F
+def save_checkpoint(state, is_best,fold):
+    filename = config.weights + config.model_name + os.sep +str(fold) + os.sep + "_checkpoint.pth.tar"
+    torch.save(state, filename)
+    if is_best:
+        shutil.copyfile(filename, config.best_models + config.model_name+ os.sep +str(fold)  + os.sep + 'model_best.pth.tar')
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+def adjust_learning_rate(optimizer, epoch):
+    """Sets the learning rate to the initial LR decayed by 10 every 3 epochs"""
+    lr = config.lr * (0.1 ** (epoch // 3))
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+
+def schedule(current_epoch, current_lrs, **logs):
+        lrs = [1e-3, 1e-4, 0.5e-4, 1e-5, 0.5e-5]
+        epochs = [0, 1, 6, 8, 12]
+        for lr, epoch in zip(lrs, epochs):
+            if current_epoch >= epoch:
+                current_lrs[5] = lr
+                if current_epoch >= 2:
+                    current_lrs[4] = lr * 1
+                    current_lrs[3] = lr * 1
+                    current_lrs[2] = lr * 1
+                    current_lrs[1] = lr * 1
+                    current_lrs[0] = lr * 0.1
+        return current_lrs
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size))
+        return res
+
+class Logger(object):
+    def __init__(self):
+        self.terminal = sys.stdout  #stdout
+        self.file = None
+
+    def open(self, file, mode=None):
+        if mode is None: mode ='w'
+        self.file = open(file, mode)
+
+    def write(self, message, is_terminal=1, is_file=1 ):
+        if '\r' in message: is_file=0
+
+        if is_terminal == 1:
+            self.terminal.write(message)
+            self.terminal.flush()
+            #time.sleep(1)
+
+        if is_file == 1:
+            self.file.write(message)
+            self.file.flush()
+
+    def flush(self):
+        # this flush method is needed for python 3 compatibility.
+        # this handles the flush command by doing nothing.
+        # you might want to specify some extra behavior here.
+        pass
+
+def get_learning_rate(optimizer):
+    lr=[]
+    for param_group in optimizer.param_groups:
+       lr +=[ param_group['lr'] ]
+
+    #assert(len(lr)==1) #we support only one param_group
+    lr = lr[0]
+
+    return lr
+
+
+def time_to_str(t, mode='min'):
+    if mode=='min':
+        t  = int(t)/60
+        hr = t//60
+        min = t%60
+        return '%2d hr %02d min'%(hr,min)
+
+    elif mode=='sec':
+        t   = int(t)
+        min = t//60
+        sec = t%60
+        return '%2d min %02d sec'%(min,sec)
+
+
+    else:
+        raise NotImplementedError
+
+
+class FocalLoss(nn.Module):
+
+    def __init__(self, focusing_param=2, balance_param=0.25):
+        super(FocalLoss, self).__init__()
+
+        self.focusing_param = focusing_param
+        self.balance_param = balance_param
+
+    def forward(self, output, target):
+
+        cross_entropy = F.cross_entropy(output, target)
+        cross_entropy_log = torch.log(cross_entropy)
+        logpt = - F.cross_entropy(output, target)
+        pt    = torch.exp(logpt)
+
+        focal_loss = -((1 - pt) ** self.focusing_param) * logpt
+
+        balanced_focal_loss = self.balance_param * focal_loss
+
+        return balanced_focal_loss
+
+class MyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            return super(MyEncoder, self).default(obj)
diff --git "a/report_04_Tetris/Report4LCHJ_\344\272\224\345\255\220\346\243\213.ipynb" "b/report_04_Tetris/Report4LCHJ_\344\272\224\345\255\220\346\243\213.ipynb"
new file mode 100644
index 0000000000000000000000000000000000000000..1b8ee739ce1774e7cce835a1ea5c1a52632e62f4
--- /dev/null
+++ "b/report_04_Tetris/Report4LCHJ_\344\272\224\345\255\220\346\243\213.ipynb"
@@ -0,0 +1,555 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report4LCHJ-五子棋\n",
+    "\n",
+    "* 姓名 卢程军\n",
+    "* 学号 2020262927\n",
+    "\n",
+    "\n",
+    "## 任务简介\n",
+    "\n",
+    "### 1. 任务类型：强化学习\n",
+    "    \n",
+    "#### 背景介绍：\n",
+    "    俄罗斯方块、五子棋是两个经典的休闲游戏。\n",
+    "    《俄罗斯方块》的基本规则是移动、旋转和摆放游戏自动输出的各种方块，使之排列成完整的一行或多行并且消除得分。\n",
+    "    《五子棋》是一种两人对弈的纯策略型棋类游戏。通常双方分别使用黑白两色的棋子，下在棋盘直线与横线的交叉点上，先形成5子连线者获胜。\n",
+    "    通过构建游戏仿真环境，并研究强化学习方法，让计算机自动计算最优的策略，从而实现让计算机自动玩。\n",
+    "    \n",
+    "    五子棋游戏，通过自己动手从零训练一个AI，去体会AlphaZero自我对弈学习成功背后的关键思想和一些重要技术细节。这边选择了五子棋作为实践对象，因为五子棋相对比较简单，大家也都比较熟悉，这样我们能更专注于AlphaZero的训练过程，同时也能通过亲自对阵，来感受自己训练出来的AI慢慢变强的过程。经过实践发现，对于在6*6的棋盘上下4子棋这种情况，大约通过500~1000局的self-play训练（2小时），就能训练出比较靠谱的AI；对于在8*8的棋盘上下5子棋这种情况，通过大约2000~3000局自我对弈训练（2天），也能得到比较靠谱的AI。\n",
+    "\n",
+    "\n",
+    "### 2. 要求：\n",
+    "    研究OpenAI Gym，编写游戏的仿真环境\n",
+    "    研究深度强化学习模型，构建游戏的模型\n",
+    "    研究并编写基于PyTorch的代码\n",
+    "    测试所研究方法的效果\n",
+    "    自己手动编写一个不用机器学习，通过规则实现的方法；然后让机器学习方法和自己编写的规则的方法对弈\n",
+    "    按照report_template.ipynb撰写自己的报告。\n",
+    "\n",
+    "## 解决途径\n",
+    "    结合AlphaZero算法本身，从自我对局和策略价值网络训练两个方面来展开整个网络的训练；\n",
+    "\n",
+    "\n",
+    "![Example.gif](Example.gif)\n",
+    "\n",
+    "### 1. 自我对局（self-play）\n",
+    "![self-play过程示意图.png](self-play过程示意图.png)\n",
+    "                self-play过程示意图\n",
+    "           \n",
+    "    完全基于self-play来学习进化是AlphaZero的最大卖点，也是整个训练过程中最关键也是最耗时的环节。\n",
+    "#### 1）. 使用哪个模型来生成self-play数据？\n",
+    "    在AlphaGo Zero版本中，我们需要同时保存当前最新的模型和通过评估得到的历史最优的模型，self-play数据始终由最优模型生成，用于不断训练更新当前最新的模型，然后每隔一段时间评估当前最新模型和最优模型的优劣，决定是否更新历史最优模型。而到了AlphaZero版本中，这一过程得到简化，我们只保存当前最新模型，self-play数据直接由当前最新模型生成，并用于训练更新自身。直观上我们可能会感觉使用当前最优模型生成的self-play数据可能质量更高，收敛更好，但是在尝试过两种方案之后，我们发现，在6*6棋盘上下4子棋这种情况下，直接使用最新模型生成self-play数据训练的话大约500局之后就能得到比较好的模型了，而不断维护最优模型并由最优模型生成self-play数据的话大约需要1500局之后才能达到类似的效果，这和AlphaZero论文中训练34小时的AlphaZero胜过训练72小时的AlphaGo\n",
+    "    Zero的结果也是吻合的。个人猜测，不断使用最新模型来生成self-play数据可能也是一个比较有效的exploration手段，首先当前最新模型相比于历史最优模型一般不会差很多，所以对局数据的质量其实也是比较有保证的，同时模型的不断变化使得我们能覆盖到更多典型的数据，从而加快收敛。\n",
+    "#### 2）. 如何保证self-play生成的数据具有多样性？\n",
+    "    一个有效的策略价值模型，需要在各种局面下都能比较准确的评估当前局面的优劣以及当前局面下各个action的相对优劣，要训练出这样的策略价值模型，就需要在self-play的过程中尽可能的覆盖到各种各样的局面。前面提到，不断使用最新的模型来生成self-play数据可能在一定程度上有助于覆盖到更多的局面，但仅靠这么一点模型的差异是不够的，所以在强化学习算法中，一般都会有特意设计的exploration的手段，这是至关重要的。在AlphaGo Zero论文中，每一个self-play对局的前30步，action是根据正比于MCTS根节点处每个分支的访问次数的概率采样得到的.，而之后的exploration则是通过直接加上Dirichlet noise的方式实现的.在我们的实现中，self-play的每一步都同时使用了这两种exploration方式，Dirichlet noise的参数取的是0.3，即:\n",
+    "$η--Dir(0.3)$ , 同时\n",
+    "$ε=0.25 $.\n",
+    "#### 3）. 始终从当前player的视角去保存self-play数据\n",
+    "    在self-play过程中，我们会收集一系列的  \n",
+    "$(s,Π，z)$   数据，  $s$ 表示局面，  $Π$ 是根据MCTS根节点处每个分支的访问次数计算的概率， $z$  是self-play对局的结果，其中  $s$  和  $z$  需要特别注意从每一步的当前player的视角去表示。比如  $s$  中用两个二值矩阵分别表示两个player的棋子的位置，那么可以是第一个矩阵表示当前player的棋子位置，第二个矩阵表示另一个player的棋子位置，也就是说第一个矩阵会交替表示先手和后手player的棋子位置，就看  $s$ 局面下谁是当前player。 $z$ 也类似，不过需要在一个完整的对局结束后才能确定这一局中每一个 $(s,Π，z)$ 中的 $z$ ，如果最后的胜者是  $s$ 局面下的当前player，则  $z=1$,如果最后的败者是  局面下的当前player，则  ,如果最后打平，则 $z=0$ .\n",
+    "#### 4）. self-play数据的扩充\n",
+    "    围棋具有旋转和镜像翻转等价的性质，其实五子棋也具有同样的性质。在AlphaGo Zero中，这一性质被充分的利用来扩充self-play数据，以及在MCTS评估叶子节点的时候提高局面评估的可靠性。但是在AlphaZero中，因为要同时考虑国际象棋和将棋这两种不满足旋转等价性质的棋类，所以对于围棋也没有利用这个性质。而在我们的实现中，因为生成self-play数据本身就是计算的瓶颈，为了能够在算力非常弱的情况下尽快的收集数据训练模型，每一局self-play结束后，我们会把这一局的数据进行旋转和镜像翻转，将8种等价情况的数据全部存入self-play的data\n",
+    "    buffer中。这种旋转和翻转的数据扩充在一定程度上也能提高self-play数据的多样性和均衡性。\n",
+    "### 2. 策略价值网络训练\n",
+    "![策略价值网络训练.png](策略价值网络训练.png)\n",
+    "                策略价值网络训练.png\n",
+    "                \n",
+    "    所谓的策略价值网络，就是在给定当前局面 \n",
+    "$s$ 的情况下，返回当前局面下每一个可行action的概率以及当前局面评分的模型。前面self-play收集到的数据就是用来训练策略价值网络的，而训练更新的策略价值网络也会马上被应用到MCTS中进行后面的self-play，以生成更优质的self-play数据。两者相互嵌套，相互促进，就构成了整个训练的循环。\n",
+    "\n",
+    "#### 1）. 局面描述方式\n",
+    "    在AlphaGo Zero中，一共使用了17个 \n",
+    "$19×19$  的二值特征平面来描述当前局面，其中前16个平面描述了最近8步对应的双方player的棋子位置，最后一个平面描述当前player对应的棋子颜色，其实也就是先后手。在我们的实现中，对局面的描述进行了极大的简化，以$8×8$的棋盘为例，我们只使用了4个$8×8$的二值特征平面，其中前两个平面分别表示当前player的棋子位置和对手player的棋子位置，有棋子的位置是1，没棋子的位置是0. 然后第三个平面表示对手player最近一步的落子位置，也就是整个平面只有一个位置是1，其余全部是0. 第四个平面，也就是最后一个平面表示的是当前player是不是先手player，如果是先手player则整个平面全部为1，否则全部为0.\n",
+    "    其实在最开始尝试的时候，只用了前两个平面，也就是双方的棋子的位置，因为直观感觉这两个平面已经足够表达整个完整的局面了。但是后来在增加了后两个特征平面之后，训练的效果有了比较明显的改善。个人猜想，因为在五子棋中，我方下一步的落子位置往往会在对手前一步落子位置的附近，所以加入的第三个平面对于策略网络确定哪些位置应该具有更高的落子概率具有比较大的指示意义，可能有助有训练。同时，因为先手在对弈中其实是很占优势的，所以在局面上棋子位置相似的情况下，当前局面的优劣和当前player到底是先手还是后手十分相关，所以第四个指示先后手的平面可能对于价值网络具有比较大的意义。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "\"\"\"\n",
+    "An implementation of the training pipeline of AlphaZero for Gomoku\n",
+    "# @Time : 2021/1/21 11:40\n",
+    "# @Author: LCHJ\n",
+    "\"\"\"\n",
+    "from __future__ import print_function\n",
+    "import random\n",
+    "import numpy as np\n",
+    "from collections import defaultdict, deque\n",
+    "from game import Board, Game\n",
+    "from mcts_pure import MCTSPlayer as MCTS_Pure\n",
+    "from mcts_alphaZero import MCTSPlayer\n",
+    "from policy_value_net_pytorch import PolicyValueNet  # Pytorch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2）. 网络结构\n",
+    "在AlphaGo Zero中，输入局面首先通过了20或40个基于卷积的残差网络模块，然后再分别接上2层或3层网络得到策略和价值输出，整个网络的层数有40多或80多层，训练和预测的时候都十分缓慢。所以在我们的实现中，对这个网络结构进行了极大的简化，最开始是公共的3层全卷积网络，分别使用32、64和128个 \n",
+    "$3×3$ 的filter，使用ReLu激活函数。然后再分成policy和value两个输出，在policy这一端，先使用4个 $1×1$的filter进行降维，再接一个全连接层，使用softmax非线性函数直接输出棋盘上每个位置的落子概率；在value这一端，先使用2个 $1×1$ 的filter进行降维，再接一个64个神经元的全连接层，最后再接一个全连接层，使用tanh非线性函数直接输出 $[-1,1]$之间的局面评分。整个策略价值网络的深度只有5~6层，训练和预测都相对比较快。\n",
+    "#### 3）. 训练目标\n",
+    "前面提到，策略价值网络的输入是当前的局面描述 \n",
+    "$s$ ，输出是当前局面下每一个可行action的概率$P$ 以及当前局面的评分 $v$ ，而用来训练策略价值网络的是我们在self-play过程中收集的一系列的  $(s,Π，z)$ 数据。根据上面的策略价值网络训练示意图，我们训练的目标是让策略价值网络输出的action概率$P$  更加接近MCTS输出的概率$\\pi$  ,让策略价值网络输出的局面评分$v$ 能更准确的预测真实的对局结果$z$。从优化的角度来说，我们是在self-play数据集上不断的最小化损失函数：\n",
+    "\n",
+    "$$loss = (z - v)^2 - \\pi^T * log(p) + c||\\theta||^2$$\n",
+    "其中第三项是用于防止过拟合的正则项。既然是在最小化损失函数，那么在训练的过程中，如果正常的话我们就会观察到损失函数在慢慢减小。下图展示的是一次在$8×8$棋盘上进行五子棋训练的过程中损失函数随着self-play局数变化的情况，这次实验一共进行了3050局对局，损失函数从最开始的4点几慢慢减小到了2.2左右。\n",
+    "![self-play局数.png](self-play局数.png)\n",
+    "self-play局数.png"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding: utf-8 -*-\n",
+    "\"\"\"\n",
+    "An implementation of the policyValueNet in PyTorch\n",
+    "Tested in PyTorch 0.2.0 and 0.3.0\n",
+    "# @Time : 2021/1/20 11:40\n",
+    "# @Author: LCHJ\n",
+    "\"\"\"\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torch.nn.functional as F\n",
+    "from torch.autograd import Variable\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def set_learning_rate(optimizer, lr):\n",
+    "    \"\"\"Sets the learning rate to the given value\"\"\"\n",
+    "    for param_group in optimizer.param_groups:\n",
+    "        param_group['lr'] = lr\n",
+    "\n",
+    "\n",
+    "class Net(nn.Module):\n",
+    "    \"\"\"policy-value network module\"\"\"\n",
+    "    def __init__(self, board_width, board_height):\n",
+    "        super(Net, self).__init__()\n",
+    "\n",
+    "        self.board_width = board_width\n",
+    "        self.board_height = board_height\n",
+    "        # common layers\n",
+    "        self.conv1 = nn.Conv2d(4, 32, kernel_size=3, padding=1)\n",
+    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
+    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n",
+    "        # action policy layers\n",
+    "        self.act_conv1 = nn.Conv2d(128, 4, kernel_size=1)\n",
+    "        self.act_fc1 = nn.Linear(4*board_width*board_height,\n",
+    "                                 board_width*board_height)\n",
+    "        # state value layers\n",
+    "        self.val_conv1 = nn.Conv2d(128, 2, kernel_size=1)\n",
+    "        self.val_fc1 = nn.Linear(2*board_width*board_height, 64)\n",
+    "        self.val_fc2 = nn.Linear(64, 1)\n",
+    "\n",
+    "    def forward(self, state_input):\n",
+    "        # common layers\n",
+    "        x = F.relu(self.conv1(state_input))\n",
+    "        x = F.relu(self.conv2(x))\n",
+    "        x = F.relu(self.conv3(x))\n",
+    "        # action policy layers\n",
+    "        x_act = F.relu(self.act_conv1(x))\n",
+    "        x_act = x_act.view(-1, 4*self.board_width*self.board_height)\n",
+    "        x_act = F.log_softmax(self.act_fc1(x_act))\n",
+    "        # state value layers\n",
+    "        x_val = F.relu(self.val_conv1(x))\n",
+    "        x_val = x_val.view(-1, 2*self.board_width*self.board_height)\n",
+    "        x_val = F.relu(self.val_fc1(x_val))\n",
+    "        x_val = F.tanh(self.val_fc2(x_val))\n",
+    "        return x_act, x_val\n",
+    "\n",
+    "\n",
+    "class PolicyValueNet():\n",
+    "    \"\"\"policy-value network \"\"\"\n",
+    "    def __init__(self, board_width, board_height,\n",
+    "                 model_file=None, use_gpu=True):\n",
+    "        self.use_gpu = use_gpu\n",
+    "        self.board_width = board_width\n",
+    "        self.board_height = board_height\n",
+    "        self.l2_const = 1e-4  # coef of l2 penalty\n",
+    "        # the policy value net module\n",
+    "        if self.use_gpu:\n",
+    "            self.policy_value_net = Net(board_width, board_height).cuda()\n",
+    "        else:\n",
+    "            self.policy_value_net = Net(board_width, board_height)\n",
+    "        self.optimizer = optim.Adam(self.policy_value_net.parameters(),\n",
+    "                                    weight_decay=self.l2_const)\n",
+    "\n",
+    "        if model_file:\n",
+    "            net_params = torch.load(model_file)\n",
+    "            self.policy_value_net.load_state_dict(net_params)\n",
+    "\n",
+    "    def policy_value(self, state_batch):\n",
+    "        \"\"\"\n",
+    "        input: a batch of states\n",
+    "        output: a batch of action probabilities and state values\n",
+    "        \"\"\"\n",
+    "        if self.use_gpu:\n",
+    "            state_batch = Variable(torch.FloatTensor(state_batch).cuda())\n",
+    "            log_act_probs, value = self.policy_value_net(state_batch)\n",
+    "            act_probs = np.exp(log_act_probs.data.cpu().numpy())\n",
+    "            return act_probs, value.data.cpu().numpy()\n",
+    "        else:\n",
+    "            state_batch = Variable(torch.FloatTensor(state_batch))\n",
+    "            log_act_probs, value = self.policy_value_net(state_batch)\n",
+    "            act_probs = np.exp(log_act_probs.data.numpy())\n",
+    "            return act_probs, value.data.numpy()\n",
+    "\n",
+    "    def policy_value_fn(self, board):\n",
+    "        \"\"\"\n",
+    "        input: board\n",
+    "        output: a list of (action, probability) tuples for each available\n",
+    "        action and the score of the board state\n",
+    "        \"\"\"\n",
+    "        legal_positions = board.availables\n",
+    "        current_state = np.ascontiguousarray(board.current_state().reshape(\n",
+    "                -1, 4, self.board_width, self.board_height))\n",
+    "        if self.use_gpu:\n",
+    "            log_act_probs, value = self.policy_value_net(\n",
+    "                    Variable(torch.from_numpy(current_state)).cuda().float())\n",
+    "            act_probs = np.exp(log_act_probs.data.cpu().numpy().flatten())\n",
+    "        else:\n",
+    "            log_act_probs, value = self.policy_value_net(\n",
+    "                    Variable(torch.from_numpy(current_state)).float())\n",
+    "            act_probs = np.exp(log_act_probs.data.numpy().flatten())\n",
+    "        act_probs = zip(legal_positions, act_probs[legal_positions])\n",
+    "        value = value.data[0][0]\n",
+    "        return act_probs, value\n",
+    "\n",
+    "    def train_step(self, state_batch, mcts_probs, winner_batch, lr):\n",
+    "        \"\"\"perform a training step\"\"\"\n",
+    "        # wrap in Variable\n",
+    "        if self.use_gpu:\n",
+    "            state_batch = Variable(torch.FloatTensor(state_batch).cuda())\n",
+    "            mcts_probs = Variable(torch.FloatTensor(mcts_probs).cuda())\n",
+    "            winner_batch = Variable(torch.FloatTensor(winner_batch).cuda())\n",
+    "        else:\n",
+    "            state_batch = Variable(torch.FloatTensor(state_batch))\n",
+    "            mcts_probs = Variable(torch.FloatTensor(mcts_probs))\n",
+    "            winner_batch = Variable(torch.FloatTensor(winner_batch))\n",
+    "\n",
+    "        # zero the parameter gradients\n",
+    "        self.optimizer.zero_grad()\n",
+    "        # set learning rate\n",
+    "        set_learning_rate(self.optimizer, lr)\n",
+    "\n",
+    "        # forward\n",
+    "        log_act_probs, value = self.policy_value_net(state_batch)\n",
+    "        # define the loss = (z - v)^2 - pi^T * log(p) + c||theta||^2\n",
+    "        # Note: the L2 penalty is incorporated in optimizer\n",
+    "        value_loss = F.mse_loss(value.view(-1), winner_batch)\n",
+    "        policy_loss = -torch.mean(torch.sum(mcts_probs*log_act_probs, 1))\n",
+    "        loss = value_loss + policy_loss\n",
+    "        # backward and optimize\n",
+    "        loss.backward()\n",
+    "        self.optimizer.step()\n",
+    "        # calc policy entropy, for monitoring only\n",
+    "        entropy = -torch.mean(\n",
+    "                torch.sum(torch.exp(log_act_probs) * log_act_probs, 1)\n",
+    "                )\n",
+    "        # return loss.data[0], entropy.data[0]\n",
+    "        #for pytorch version >= 0.5 please use the following line instead.\n",
+    "        return loss.item(), entropy.item()\n",
+    "\n",
+    "    def get_policy_param(self):\n",
+    "        net_params = self.policy_value_net.state_dict()\n",
+    "        return net_params\n",
+    "\n",
+    "    def save_model(self, model_file):\n",
+    "        \"\"\" save model params to file \"\"\"\n",
+    "        net_params = self.get_policy_param()  # get model params\n",
+    "        torch.save(net_params, model_file)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在训练过程中，除了观察到损失函数在慢慢减小，我们一般还会关注策略价值网络输出的策略（输出的落子概率分布）的entropy的变化情况。正常来讲，最开始的时候，我们的策略网络基本上是均匀的随机输出落子的概率，所以entropy会比较大。随着训练过程的慢慢推进，策略网络会慢慢学会在不同的局面下哪些位置应该有更大的落子概率，也就是说落子概率的分布不再均匀，会有比较强的偏向，这样entropy就会变小。也正是由于策略网络输出概率的偏向，才能帮助MCTS在搜索过程中能够在更有潜力的位置进行更多的模拟，从而在比较少的模拟次数下达到比较好的性能。下图展示的是同一次训练过程中观察到的策略网络输出策略的entropy的变化情况。\n",
+    "\n",
+    "![self-play局数2.png](self-play局数2.png)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TrainPipeline():\n",
+    "    def __init__(self, init_model=None):\n",
+    "        # params of the board and the game\n",
+    "        self.board_width = 6\n",
+    "        self.board_height = 6\n",
+    "        self.n_in_row = 4\n",
+    "        self.board = Board(width=self.board_width,\n",
+    "                           height=self.board_height,\n",
+    "                           n_in_row=self.n_in_row)\n",
+    "        self.game = Game(self.board)\n",
+    "        # training params\n",
+    "        self.learn_rate = 2e-3\n",
+    "        self.lr_multiplier = 1.0  # adaptively adjust the learning rate based on KL\n",
+    "        self.temp = 1.0  # the temperature param\n",
+    "        self.n_playout = 400  # num of simulations for each move\n",
+    "        self.c_puct = 5\n",
+    "        self.buffer_size = 10000\n",
+    "        self.batch_size = 512  # mini-batch size for training\n",
+    "        self.data_buffer = deque(maxlen=self.buffer_size)\n",
+    "        self.play_batch_size = 1\n",
+    "        self.epochs = 5  # num of train_steps for each update\n",
+    "        self.kl_targ = 0.02\n",
+    "        self.check_freq = 50\n",
+    "        self.game_batch_num = 1500\n",
+    "        self.best_win_ratio = 0.0\n",
+    "        # num of simulations used for the pure mcts, which is used as\n",
+    "        # the opponent to evaluate the trained policy\n",
+    "        self.pure_mcts_playout_num = 1000\n",
+    "        if init_model:\n",
+    "            # start training from an initial policy-value net\n",
+    "            self.policy_value_net = PolicyValueNet(self.board_width,\n",
+    "                                                   self.board_height,\n",
+    "                                                   model_file=init_model)\n",
+    "        else:\n",
+    "            # start training from a new policy-value net\n",
+    "            self.policy_value_net = PolicyValueNet(self.board_width,\n",
+    "                                                   self.board_height)\n",
+    "        self.mcts_player = MCTSPlayer(self.policy_value_net.policy_value_fn,\n",
+    "                                      c_puct=self.c_puct,\n",
+    "                                      n_playout=self.n_playout,\n",
+    "                                      is_selfplay=1)\n",
+    "\n",
+    "    def get_equi_data(self, play_data):\n",
+    "        \"\"\"augment the data set by rotation and flipping\n",
+    "        play_data: [(state, mcts_prob, winner_z), ..., ...]\n",
+    "        \"\"\"\n",
+    "        extend_data = []\n",
+    "        for state, mcts_porb, winner in play_data:\n",
+    "            for i in [1, 2, 3, 4]:\n",
+    "                # rotate counterclockwise\n",
+    "                equi_state = np.array([np.rot90(s, i) for s in state])\n",
+    "                equi_mcts_prob = np.rot90(np.flipud(\n",
+    "                    mcts_porb.reshape(self.board_height, self.board_width)), i)\n",
+    "                extend_data.append((equi_state,\n",
+    "                                    np.flipud(equi_mcts_prob).flatten(),\n",
+    "                                    winner))\n",
+    "                # flip horizontally\n",
+    "                equi_state = np.array([np.fliplr(s) for s in equi_state])\n",
+    "                equi_mcts_prob = np.fliplr(equi_mcts_prob)\n",
+    "                extend_data.append((equi_state,\n",
+    "                                    np.flipud(equi_mcts_prob).flatten(),\n",
+    "                                    winner))\n",
+    "        return extend_data\n",
+    "\n",
+    "    def collect_selfplay_data(self, n_games=1):\n",
+    "        \"\"\"collect self-play data for training\"\"\"\n",
+    "        for i in range(n_games):\n",
+    "            winner, play_data = self.game.start_self_play(self.mcts_player,\n",
+    "                                                          temp=self.temp)\n",
+    "            play_data = list(play_data)[:]\n",
+    "            self.episode_len = len(play_data)\n",
+    "            # augment the data\n",
+    "            play_data = self.get_equi_data(play_data)\n",
+    "            self.data_buffer.extend(play_data)\n",
+    "\n",
+    "    def policy_update(self):\n",
+    "        \"\"\"update the policy-value net\"\"\"\n",
+    "        mini_batch = random.sample(self.data_buffer, self.batch_size)\n",
+    "        state_batch = [data[0] for data in mini_batch]\n",
+    "        mcts_probs_batch = [data[1] for data in mini_batch]\n",
+    "        winner_batch = [data[2] for data in mini_batch]\n",
+    "        old_probs, old_v = self.policy_value_net.policy_value(state_batch)\n",
+    "        for i in range(self.epochs):\n",
+    "            loss, entropy = self.policy_value_net.train_step(\n",
+    "                    state_batch,\n",
+    "                    mcts_probs_batch,\n",
+    "                    winner_batch,\n",
+    "                    self.learn_rate*self.lr_multiplier)\n",
+    "            new_probs, new_v = self.policy_value_net.policy_value(state_batch)\n",
+    "            kl = np.mean(np.sum(old_probs * (\n",
+    "                    np.log(old_probs + 1e-10) - np.log(new_probs + 1e-10)),\n",
+    "                    axis=1)\n",
+    "            )\n",
+    "            if kl > self.kl_targ * 4:  # early stopping if D_KL diverges badly\n",
+    "                break\n",
+    "        # adaptively adjust the learning rate\n",
+    "        if kl > self.kl_targ * 2 and self.lr_multiplier > 0.1:\n",
+    "            self.lr_multiplier /= 1.5\n",
+    "        elif kl < self.kl_targ / 2 and self.lr_multiplier < 10:\n",
+    "            self.lr_multiplier *= 1.5\n",
+    "\n",
+    "        explained_var_old = (1 -\n",
+    "                             np.var(np.array(winner_batch) - old_v.flatten()) /\n",
+    "                             np.var(np.array(winner_batch)))\n",
+    "        explained_var_new = (1 -\n",
+    "                             np.var(np.array(winner_batch) - new_v.flatten()) /\n",
+    "                             np.var(np.array(winner_batch)))\n",
+    "        print((\"kl:{:.5f},\"\n",
+    "               \"lr_multiplier:{:.3f},\"\n",
+    "               \"loss:{},\"\n",
+    "               \"entropy:{},\"\n",
+    "               \"explained_var_old:{:.3f},\"\n",
+    "               \"explained_var_new:{:.3f}\"\n",
+    "               ).format(kl,\n",
+    "                        self.lr_multiplier,\n",
+    "                        loss,\n",
+    "                        entropy,\n",
+    "                        explained_var_old,\n",
+    "                        explained_var_new))\n",
+    "        return loss, entropy\n",
+    "\n",
+    "    def policy_evaluate(self, n_games=10):\n",
+    "        \"\"\"\n",
+    "        Evaluate the trained policy by playing against the pure MCTS player\n",
+    "        Note: this is only for monitoring the progress of training\n",
+    "        \"\"\"\n",
+    "        current_mcts_player = MCTSPlayer(self.policy_value_net.policy_value_fn,\n",
+    "                                         c_puct=self.c_puct,\n",
+    "                                         n_playout=self.n_playout)\n",
+    "        pure_mcts_player = MCTS_Pure(c_puct=5,\n",
+    "                                     n_playout=self.pure_mcts_playout_num)\n",
+    "        win_cnt = defaultdict(int)\n",
+    "        for i in range(n_games):\n",
+    "            winner = self.game.start_play(current_mcts_player,\n",
+    "                                          pure_mcts_player,\n",
+    "                                          start_player=i % 2,\n",
+    "                                          is_shown=0)\n",
+    "            win_cnt[winner] += 1\n",
+    "        win_ratio = 1.0*(win_cnt[1] + 0.5*win_cnt[-1]) / n_games\n",
+    "        print(\"num_playouts:{}, win: {}, lose: {}, tie:{}\".format(\n",
+    "                self.pure_mcts_playout_num,\n",
+    "                win_cnt[1], win_cnt[2], win_cnt[-1]))\n",
+    "        return win_ratio\n",
+    "\n",
+    "    def run(self):\n",
+    "        \"\"\"run the training pipeline\"\"\"\n",
+    "        try:\n",
+    "            for i in range(self.game_batch_num):\n",
+    "                self.collect_selfplay_data(self.play_batch_size)\n",
+    "                print(\"batch i:{}, episode_len:{}\".format(\n",
+    "                        i+1, self.episode_len))\n",
+    "                if len(self.data_buffer) > self.batch_size:\n",
+    "                    loss, entropy = self.policy_update()\n",
+    "                # check the performance of the current model,\n",
+    "                # and save the model params\n",
+    "                if (i+1) % self.check_freq == 0:\n",
+    "                    print(\"current self-play batch: {}\".format(i+1))\n",
+    "                    win_ratio = self.policy_evaluate()\n",
+    "                    self.policy_value_net.save_model('./current_policy.model')\n",
+    "                    if win_ratio > self.best_win_ratio:\n",
+    "                        print(\"New best policy!!!!!!!!\")\n",
+    "                        self.best_win_ratio = win_ratio\n",
+    "                        # update the best_policy\n",
+    "                        self.policy_value_net.save_model('./best_policy.model')\n",
+    "                        if (self.best_win_ratio == 1.0 and\n",
+    "                                self.pure_mcts_playout_num < 5000):\n",
+    "                            self.pure_mcts_playout_num += 1000\n",
+    "                            self.best_win_ratio = 0.0\n",
+    "        except KeyboardInterrupt:\n",
+    "            print('\\n\\rquit')\n",
+    "            \n",
+    "if __name__ == '__main__':\n",
+    "    training_pipeline = TrainPipeline()\n",
+    "    training_pipeline.run()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4）. 运行结果展示\n",
+    "#####  从头开始训\n",
+    "从下图可以看出，在程序运行到第 $batch=50 $ 时, 训练的模型第一次战胜纯的MCTS AI，虽然是$1:9$，惨败！\n",
+    "\n",
+    "    kl:0.02429,lr_multiplier:1.000,loss:3.5649590492248535,entropy:3.1650099754333496,explained_var_old:0.449,explained_var_new:0.576\n",
+    "    current self-play batch: 50\n",
+    "    num_playouts:1000, win: 1, lose: 9, tie:0\n",
+    "    New best policy!!!!!!!!\n",
+    "![batch50.png](batch50.png)\n",
+    "##### 加载第一次训练局得到的模型上继续训50局得到的结果如下：\n",
+    "AlphaZero VS pure_MCTS 1000 战况：7：3\n",
+    "![batch250.png](batch250.png)\n",
+    "\n",
+    "##### 加载第一次训练局得到的模型上继续训200局得到的结果如下：\n",
+    "AlphaZero VS pure_MCTS 1000 战况：10：0\n",
+    "![batch200.png](batch200.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在漫长的训练过程中，我们最希望看到的当然是我们训练的AI正在慢慢变强。所以虽然在AlphaZero的算法流程中已经不再需要通过定期评估来更新最优策略，在我们的实现中还是每隔50次self-play对局就对当前的AI模型进行一次评估，评估的方式是使用当前最新的AI模型和纯的MCTS AI（基于随机rollout）对战10局。pure MCTS AI最开始每一步使用1000次模拟，当被我们训练的AI模型10:0打败时，pure MCTS AI就升级到每一步使用2000次模拟，以此类推，不断增强，而我们训练的AlphaZero\n",
+    "    AI模型每一步始终只使用400次模拟。在上面那次3050局自我对局的训练实验中，我们观察到：\n",
+    "    \n",
+    "    ·经过550局，AlphaZero VS pure_MCTS 1000 首次达到10:0\n",
+    "    ·经过1300局，AlphaZero VS pure_MCTS 2000 首次达到10:0\n",
+    "![batch300.png](batch300.png)\n",
+    "    ·经过1750局，AlphaZero VS pure_MCTS 3000 首次达到10:0\n",
+    "    ·经过2450局，AlphaZero VS pure_MCTS 4000 取得8胜1平1负\n",
+    "    ·经过2850局，AlphaZero VS pure_MCTS 4000 取得9胜1负。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 总结\n",
+    "·以$6×6$的棋盘和连续4个棋盘开始是不错的选择。在这种情况下，我们可以在大约2小时的时间内，在$500-1000$个自学习对抗中得到一个比较好的模型。\n",
+    "\n",
+    "·对于$8 ×8 $棋盘5连的情况，可能需要$2000-3000$次自学习对抗才能得到一个好的模型，在一台PC上可能需要1天左右的时间。\n",
+    "\n",
+    "通过本次五子棋AI游戏项目，能够了解和熟悉应用强化学习的专业知识，在项目中遇到了很多很杂的问题但同时也真正学习到了很多有用的知识和技能。包括如何科学合理高效地分析问题、找出问题关键，进行精确的数学建模，并用合适的框架组件去搭建相应的网络结构，以及如何有效地组织训练网络模型和调整参数。\n",
+    "在本次项目中，由于个人能力欠缺，难免不借鉴前辈们的优秀经验和知识结晶。但贵在研究透彻推陈出新。总之收益良多！"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "main_language": "python"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/report_04_Tetris/Report4_\344\272\224\345\255\220\346\243\213 - Jupyter Notebook.pdf" "b/report_04_Tetris/Report4_\344\272\224\345\255\220\346\243\213 - Jupyter Notebook.pdf"
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diff --git a/report_04_Tetris/game.py b/report_04_Tetris/game.py
new file mode 100644
index 0000000000000000000000000000000000000000..cac0d2fac709a18c5cd12e1b70ac54a2267f9ed5
--- /dev/null
+++ b/report_04_Tetris/game.py
@@ -0,0 +1,223 @@
+# -*- coding: utf-8 -*-
+"""
+@author: LCHJ
+"""
+
+from __future__ import print_function
+import numpy as np
+
+
+class Board(object):
+    """board for the game"""
+
+    def __init__(self, **kwargs):
+        self.width = int(kwargs.get('width', 8))
+        self.height = int(kwargs.get('height', 8))
+        # board states stored as a dict,
+        # key: move as location on the board,
+        # value: player as pieces type
+        self.states = {}
+        # need how many pieces in a row to win
+        self.n_in_row = int(kwargs.get('n_in_row', 5))
+        self.players = [1, 2]  # player1 and player2
+
+    def init_board(self, start_player=0):
+        if self.width < self.n_in_row or self.height < self.n_in_row:
+            raise Exception('board width and height can not be '
+                            'less than {}'.format(self.n_in_row))
+        self.current_player = self.players[start_player]  # start player
+        # keep available moves in a list
+        self.availables = list(range(self.width * self.height))
+        self.states = {}
+        self.last_move = -1
+
+    def move_to_location(self, move):
+        """
+        3*3 board's moves like:
+        6 7 8
+        3 4 5
+        0 1 2
+        and move 5's location is (1,2)
+        """
+        h = move // self.width
+        w = move % self.width
+        return [h, w]
+
+    def location_to_move(self, location):
+        if len(location) != 2:
+            return -1
+        h = location[0]
+        w = location[1]
+        move = h * self.width + w
+        if move not in range(self.width * self.height):
+            return -1
+        return move
+
+    def current_state(self):
+        """return the board state from the perspective of the current player.
+        state shape: 4*width*height
+        """
+
+        square_state = np.zeros((4, self.width, self.height))
+        if self.states:
+            moves, players = np.array(list(zip(*self.states.items())))
+            move_curr = moves[players == self.current_player]
+            move_oppo = moves[players != self.current_player]
+            square_state[0][move_curr // self.width,
+                            move_curr % self.height] = 1.0
+            square_state[1][move_oppo // self.width,
+                            move_oppo % self.height] = 1.0
+            # indicate the last move location
+            square_state[2][self.last_move // self.width,
+                            self.last_move % self.height] = 1.0
+        if len(self.states) % 2 == 0:
+            square_state[3][:, :] = 1.0  # indicate the colour to play
+        return square_state[:, ::-1, :]
+
+    def do_move(self, move):
+        self.states[move] = self.current_player
+        self.availables.remove(move)
+        self.current_player = (
+            self.players[0] if self.current_player == self.players[1]
+            else self.players[1]
+        )
+        self.last_move = move
+
+    def has_a_winner(self):
+        width = self.width
+        height = self.height
+        states = self.states
+        n = self.n_in_row
+
+        moved = list(set(range(width * height)) - set(self.availables))
+        if len(moved) < self.n_in_row * 2 - 1:
+            return False, -1
+
+        for m in moved:
+            h = m // width
+            w = m % width
+            player = states[m]
+
+            if (w in range(width - n + 1) and
+                    len(set(states.get(i, -1) for i in range(m, m + n))) == 1):
+                return True, player
+
+            if (h in range(height - n + 1) and
+                    len(set(states.get(i, -1) for i in range(m, m + n * width, width))) == 1):
+                return True, player
+
+            if (w in range(width - n + 1) and h in range(height - n + 1) and
+                    len(set(states.get(i, -1) for i in range(m, m + n * (width + 1), width + 1))) == 1):
+                return True, player
+
+            if (w in range(n - 1, width) and h in range(height - n + 1) and
+                    len(set(states.get(i, -1) for i in range(m, m + n * (width - 1), width - 1))) == 1):
+                return True, player
+
+        return False, -1
+
+    def game_end(self):
+        """Check whether the game is ended or not"""
+        win, winner = self.has_a_winner()
+        if win:
+            return True, winner
+        elif not len(self.availables):
+            return True, -1
+        return False, -1
+
+    def get_current_player(self):
+        return self.current_player
+
+
+class Game(object):
+    """game server"""
+
+    def __init__(self, board, **kwargs):
+        self.board = board
+
+    def graphic(self, board, player1, player2):
+        """Draw the board and show game info"""
+        width = board.width
+        height = board.height
+
+        print("Player", player1, "with X".rjust(3))
+        print("Player", player2, "with O".rjust(3))
+        print()
+        for x in range(width):
+            print("{0:8}".format(x), end='')
+        print('\r\n')
+        for i in range(height - 1, -1, -1):
+            print("{0:4d}".format(i), end='')
+            for j in range(width):
+                loc = i * width + j
+                p = board.states.get(loc, -1)
+                if p == player1:
+                    print('X'.center(8), end='')
+                elif p == player2:
+                    print('O'.center(8), end='')
+                else:
+                    print('_'.center(8), end='')
+            print('\r\n\r\n')
+
+    def start_play(self, player1, player2, start_player=0, is_shown=1):
+        """start a game between two players"""
+        if start_player not in (0, 1):
+            raise Exception('start_player should be either 0 (player1 first) '
+                            'or 1 (player2 first)')
+        self.board.init_board(start_player)
+        p1, p2 = self.board.players
+        player1.set_player_ind(p1)
+        player2.set_player_ind(p2)
+        players = {p1: player1, p2: player2}
+        if is_shown:
+            self.graphic(self.board, player1.player, player2.player)
+        while True:
+            current_player = self.board.get_current_player()
+            player_in_turn = players[current_player]
+            move = player_in_turn.get_action(self.board)
+            self.board.do_move(move)
+            if is_shown:
+                self.graphic(self.board, player1.player, player2.player)
+            end, winner = self.board.game_end()
+            if end:
+                if is_shown:
+                    if winner != -1:
+                        print("Game end. Winner is", players[winner])
+                    else:
+                        print("Game end. Tie")
+                return winner
+
+    def start_self_play(self, player, is_shown=0, temp=1e-3):
+        """ start a self-play game using a MCTS player, reuse the search tree,
+        and store the self-play data: (state, mcts_probs, z) for training
+        """
+        self.board.init_board()
+        p1, p2 = self.board.players
+        states, mcts_probs, current_players = [], [], []
+        while True:
+            move, move_probs = player.get_action(self.board,
+                                                 temp=temp,
+                                                 return_prob=1)
+            # store the data
+            states.append(self.board.current_state())
+            mcts_probs.append(move_probs)
+            current_players.append(self.board.current_player)
+            # perform a move
+            self.board.do_move(move)
+            if is_shown:
+                self.graphic(self.board, p1, p2)
+            end, winner = self.board.game_end()
+            if end:
+                # winner from the perspective of the current player of each state
+                winners_z = np.zeros(len(current_players))
+                if winner != -1:
+                    winners_z[np.array(current_players) == winner] = 1.0
+                    winners_z[np.array(current_players) != winner] = -1.0
+                # reset MCTS root node
+                player.reset_player()
+                if is_shown:
+                    if winner != -1:
+                        print("Game end. Winner is player:", winner)
+                    else:
+                        print("Game end. Tie")
+                return winner, zip(states, mcts_probs, winners_z)
diff --git a/report_04_Tetris/policy_value_net.py b/report_04_Tetris/policy_value_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b5f1c29a377fee590dc8869f4cdcea842f0007
--- /dev/null
+++ b/report_04_Tetris/policy_value_net.py
@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+"""
+An implementation of the policyValueNet in Theano and Lasagne
+
+@author: LCHJ
+"""
+
+from __future__ import print_function
+import theano
+import theano.tensor as T
+import lasagne
+import pickle
+
+
+class PolicyValueNet():
+    """policy-value network """
+    def __init__(self, board_width, board_height, model_file=None):
+        self.board_width = board_width
+        self.board_height = board_height
+        self.learning_rate = T.scalar('learning_rate')
+        self.l2_const = 1e-4  # coef of l2 penalty
+        self.create_policy_value_net()
+        self._loss_train_op()
+        if model_file:
+            try:
+                net_params = pickle.load(open(model_file, 'rb'))
+            except:
+                # To support loading pretrained model in python3
+                net_params = pickle.load(open(model_file, 'rb'),
+                                         encoding='bytes')
+            lasagne.layers.set_all_param_values(
+                    [self.policy_net, self.value_net], net_params
+                    )
+
+    def create_policy_value_net(self):
+        """create the policy value network """
+        self.state_input = T.tensor4('state')
+        self.winner = T.vector('winner')
+        self.mcts_probs = T.matrix('mcts_probs')
+        network = lasagne.layers.InputLayer(
+                shape=(None, 4, self.board_width, self.board_height),
+                input_var=self.state_input
+                )
+        # conv layers
+        network = lasagne.layers.Conv2DLayer(
+                network, num_filters=32, filter_size=(3, 3), pad='same')
+        network = lasagne.layers.Conv2DLayer(
+                network, num_filters=64, filter_size=(3, 3), pad='same')
+        network = lasagne.layers.Conv2DLayer(
+                network, num_filters=128, filter_size=(3, 3), pad='same')
+        # action policy layers
+        policy_net = lasagne.layers.Conv2DLayer(
+                network, num_filters=4, filter_size=(1, 1))
+        self.policy_net = lasagne.layers.DenseLayer(
+                policy_net, num_units=self.board_width*self.board_height,
+                nonlinearity=lasagne.nonlinearities.softmax)
+        # state value layers
+        value_net = lasagne.layers.Conv2DLayer(
+                network, num_filters=2, filter_size=(1, 1))
+        value_net = lasagne.layers.DenseLayer(value_net, num_units=64)
+        self.value_net = lasagne.layers.DenseLayer(
+                value_net, num_units=1,
+                nonlinearity=lasagne.nonlinearities.tanh)
+        # get action probs and state score value
+        self.action_probs, self.value = lasagne.layers.get_output(
+                [self.policy_net, self.value_net])
+        self.policy_value = theano.function([self.state_input],
+                                            [self.action_probs, self.value],
+                                            allow_input_downcast=True)
+
+    def policy_value_fn(self, board):
+        """
+        input: board
+        output: a list of (action, probability) tuples for each available
+            action and the score of the board state
+        """
+        legal_positions = board.availables
+        current_state = board.current_state()
+        act_probs, value = self.policy_value(
+            current_state.reshape(-1, 4, self.board_width, self.board_height)
+            )
+        act_probs = zip(legal_positions, act_probs.flatten()[legal_positions])
+        return act_probs, value[0][0]
+
+    def _loss_train_op(self):
+        """
+        Three loss terms：
+        loss = (z - v)^2 - pi^T * log(p) + c||theta||^2
+        """
+        params = lasagne.layers.get_all_params(
+                [self.policy_net, self.value_net], trainable=True)
+        value_loss = lasagne.objectives.squared_error(
+                self.winner, self.value.flatten())
+        policy_loss = lasagne.objectives.categorical_crossentropy(
+                self.action_probs, self.mcts_probs)
+        l2_penalty = lasagne.regularization.apply_penalty(
+                params, lasagne.regularization.l2)
+        self.loss = self.l2_const*l2_penalty + lasagne.objectives.aggregate(
+                value_loss + policy_loss, mode='mean')
+        # policy entropy，for monitoring only
+        self.entropy = -T.mean(T.sum(
+                self.action_probs * T.log(self.action_probs + 1e-10), axis=1))
+        # get the train op
+        updates = lasagne.updates.adam(self.loss, params,
+                                       learning_rate=self.learning_rate)
+        self.train_step = theano.function(
+            [self.state_input, self.mcts_probs, self.winner, self.learning_rate],
+            [self.loss, self.entropy],
+            updates=updates,
+            allow_input_downcast=True
+            )
+
+    def get_policy_param(self):
+        net_params = lasagne.layers.get_all_param_values(
+                [self.policy_net, self.value_net])
+        return net_params
+
+    def save_model(self, model_file):
+        """ save model params to file """
+        net_params = self.get_policy_param()  # get model params
+        pickle.dump(net_params, open(model_file, 'wb'), protocol=2)
diff --git a/report_04_Tetris/policy_value_net_pytorch.py b/report_04_Tetris/policy_value_net_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..570cc07e985b51feed5d4edc485a6adb7f67a1d7
--- /dev/null
+++ b/report_04_Tetris/policy_value_net_pytorch.py
@@ -0,0 +1,158 @@
+# -*- coding: utf-8 -*-
+"""
+An implementation of the policyValueNet in PyTorch
+Tested in PyTorch 0.2.0 and 0.3.0
+# @Time : 2021/1/20 11:40
+# @Author: LCHJ
+"""
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+
+
+def set_learning_rate(optimizer, lr):
+    """Sets the learning rate to the given value"""
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+
+class Net(nn.Module):
+    """policy-value network module"""
+    def __init__(self, board_width, board_height):
+        super(Net, self).__init__()
+
+        self.board_width = board_width
+        self.board_height = board_height
+        # common layers
+        self.conv1 = nn.Conv2d(4, 32, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        # action policy layers
+        self.act_conv1 = nn.Conv2d(128, 4, kernel_size=1)
+        self.act_fc1 = nn.Linear(4*board_width*board_height,
+                                 board_width*board_height)
+        # state value layers
+        self.val_conv1 = nn.Conv2d(128, 2, kernel_size=1)
+        self.val_fc1 = nn.Linear(2*board_width*board_height, 64)
+        self.val_fc2 = nn.Linear(64, 1)
+
+    def forward(self, state_input):
+        # common layers
+        x = F.relu(self.conv1(state_input))
+        x = F.relu(self.conv2(x))
+        x = F.relu(self.conv3(x))
+        # action policy layers
+        x_act = F.relu(self.act_conv1(x))
+        x_act = x_act.view(-1, 4*self.board_width*self.board_height)
+        x_act = F.log_softmax(self.act_fc1(x_act))
+        # state value layers
+        x_val = F.relu(self.val_conv1(x))
+        x_val = x_val.view(-1, 2*self.board_width*self.board_height)
+        x_val = F.relu(self.val_fc1(x_val))
+        x_val = F.tanh(self.val_fc2(x_val))
+        return x_act, x_val
+
+
+class PolicyValueNet():
+    """policy-value network """
+    def __init__(self, board_width, board_height,
+                 model_file=True, use_gpu=True):
+        self.use_gpu = use_gpu
+        self.board_width = board_width
+        self.board_height = board_height
+        self.l2_const = 1e-4  # coef of l2 penalty
+        # the policy value net module
+        if self.use_gpu:
+            self.policy_value_net = Net(board_width, board_height).cuda()
+        else:
+            self.policy_value_net = Net(board_width, board_height)
+        self.optimizer = optim.Adam(self.policy_value_net.parameters(),
+                                    weight_decay=self.l2_const)
+
+        if model_file:
+            net_params = torch.load('./best_policy.model')
+            self.policy_value_net.load_state_dict(net_params)
+
+    def policy_value(self, state_batch):
+        """
+        input: a batch of states
+        output: a batch of action probabilities and state values
+        """
+        if self.use_gpu:
+            state_batch = Variable(torch.FloatTensor(state_batch).cuda())
+            log_act_probs, value = self.policy_value_net(state_batch)
+            act_probs = np.exp(log_act_probs.data.cpu().numpy())
+            return act_probs, value.data.cpu().numpy()
+        else:
+            state_batch = Variable(torch.FloatTensor(state_batch))
+            log_act_probs, value = self.policy_value_net(state_batch)
+            act_probs = np.exp(log_act_probs.data.numpy())
+            return act_probs, value.data.numpy()
+
+    def policy_value_fn(self, board):
+        """
+        input: board
+        output: a list of (action, probability) tuples for each available
+        action and the score of the board state
+        """
+        legal_positions = board.availables
+        current_state = np.ascontiguousarray(board.current_state().reshape(
+                -1, 4, self.board_width, self.board_height))
+        if self.use_gpu:
+            log_act_probs, value = self.policy_value_net(
+                    Variable(torch.from_numpy(current_state)).cuda().float())
+            act_probs = np.exp(log_act_probs.data.cpu().numpy().flatten())
+        else:
+            log_act_probs, value = self.policy_value_net(
+                    Variable(torch.from_numpy(current_state)).float())
+            act_probs = np.exp(log_act_probs.data.numpy().flatten())
+        act_probs = zip(legal_positions, act_probs[legal_positions])
+        value = value.data[0][0]
+        return act_probs, value
+
+    def train_step(self, state_batch, mcts_probs, winner_batch, lr):
+        """perform a training step"""
+        # wrap in Variable
+        if self.use_gpu:
+            state_batch = Variable(torch.FloatTensor(state_batch).cuda())
+            mcts_probs = Variable(torch.FloatTensor(mcts_probs).cuda())
+            winner_batch = Variable(torch.FloatTensor(winner_batch).cuda())
+        else:
+            state_batch = Variable(torch.FloatTensor(state_batch))
+            mcts_probs = Variable(torch.FloatTensor(mcts_probs))
+            winner_batch = Variable(torch.FloatTensor(winner_batch))
+
+        # zero the parameter gradients
+        self.optimizer.zero_grad()
+        # set learning rate
+        set_learning_rate(self.optimizer, lr)
+
+        # forward
+        log_act_probs, value = self.policy_value_net(state_batch)
+        # define the loss = (z - v)^2 - pi^T * log(p) + c||theta||^2
+        # Note: the L2 penalty is incorporated in optimizer
+        value_loss = F.mse_loss(value.view(-1), winner_batch)
+        policy_loss = -torch.mean(torch.sum(mcts_probs*log_act_probs, 1))
+        loss = value_loss + policy_loss
+        # backward and optimize
+        loss.backward()
+        self.optimizer.step()
+        # calc policy entropy, for monitoring only
+        entropy = -torch.mean(
+                torch.sum(torch.exp(log_act_probs) * log_act_probs, 1)
+                )
+        # return loss.data[0], entropy.data[0]
+        #for pytorch version >= 0.5 please use the following line instead.
+        return loss.item(), entropy.item()
+
+    def get_policy_param(self):
+        net_params = self.policy_value_net.state_dict()
+        return net_params
+
+    def save_model(self, model_file):
+        """ save model params to file """
+        net_params = self.get_policy_param()  # get model params
+        torch.save(net_params, model_file)
diff --git a/report_04_Tetris/report_template.ipynb b/report_04_Tetris/report_template.ipynb
deleted file mode 100644
index 6517f7a66c67f9f62b5fe6a6d361728d5fdf51f7..0000000000000000000000000000000000000000
--- a/report_04_Tetris/report_template.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report - 报告题目\n",
-    "\n",
-    "* 姓名\n",
-    "* 学号\n",
-    "\n",
-    "\n",
-    "## 任务简介\n",
-    "\n",
-    "这里简述一下任务是什么；最终的目标是什么\n",
-    "\n",
-    "## 解决途径\n",
-    "\n",
-    "主要包括：\n",
-    "1. 问题的思考，整体的思路\n",
-    "2. 选用的方法，以及为何选用这些方法\n",
-    "3. 所用方法的详细解释，包括理论上的背景、模型、实现的细节\n",
-    "3. 实现过程遇到的问题，以及如何解决的\n",
-    "4. 最终的结果，实验分析\n",
-    "\n",
-    "要求：\n",
-    "1. 游戏仿真环境的实现\n",
-    "2. 程序，以及各个部分的解释、说明\n",
-    "3. 结果的可视化，精度等的分析\n",
-    "\n",
-    "## 总结\n",
-    "总结任务实现过程所取得的心得等。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  },
-  "main_language": "python"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/report_04_Tetris/train.py b/report_04_Tetris/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..f733c4a30c66589593b7346be58192881e42d7b0
--- /dev/null
+++ b/report_04_Tetris/train.py
@@ -0,0 +1,194 @@
+# -*- coding: utf-8 -*-
+"""
+An implementation of the training pipeline of AlphaZero for Gomoku
+# @Time : 2021/1/20 11:40
+# @Author: LCHJ
+"""
+from __future__ import print_function
+import random
+import numpy as np
+from collections import defaultdict, deque
+from game import Board, Game
+from mcts_pure import MCTSPlayer as MCTS_Pure
+from mcts_alphaZero import MCTSPlayer
+from policy_value_net_pytorch import PolicyValueNet  # Pytorch
+# from policy_value_net import PolicyValueNet  # Theano and Lasagne
+# from policy_value_net_tensorflow import PolicyValueNet # Tensorflow
+# from policy_value_net_keras import PolicyValueNet # Keras
+
+
+class TrainPipeline():
+    def __init__(self, init_model=True):
+        # params of the board and the game
+        self.board_width = 6
+        self.board_height = 6
+        self.n_in_row = 4
+        self.board = Board(width=self.board_width,
+                           height=self.board_height,
+                           n_in_row=self.n_in_row)
+        self.game = Game(self.board)
+        # training params
+        self.learn_rate = 2e-3
+        self.lr_multiplier = 1.0  # adaptively adjust the learning rate based on KL
+        self.temp = 1.0  # the temperature param
+        self.n_playout = 400  # num of simulations for each move
+        self.c_puct = 5
+        self.buffer_size = 10000
+        self.batch_size = 512  # mini-batch size for training
+        self.data_buffer = deque(maxlen=self.buffer_size)
+        self.play_batch_size = 1
+        self.epochs = 5  # num of train_steps for each update
+        self.kl_targ = 0.02
+        self.check_freq = 50
+        self.game_batch_num = 1500
+        self.best_win_ratio = 0.0
+        # num of simulations used for the pure mcts, which is used as
+        # the opponent to evaluate the trained policy
+        self.pure_mcts_playout_num = 1000
+        if init_model:
+            # start training from an initial policy-value net
+            self.policy_value_net = PolicyValueNet(self.board_width,
+                                                   self.board_height,
+                                                   model_file=('./best_policy.model'))
+        else:
+            # start training from a new policy-value net
+            self.policy_value_net = PolicyValueNet(self.board_width,
+                                                   self.board_height)
+        self.mcts_player = MCTSPlayer(self.policy_value_net.policy_value_fn,
+                                      c_puct=self.c_puct,
+                                      n_playout=self.n_playout,
+                                      is_selfplay=1)
+
+    def get_equi_data(self, play_data):
+        """augment the data set by rotation and flipping
+        play_data: [(state, mcts_prob, winner_z), ..., ...]
+        """
+        extend_data = []
+        for state, mcts_porb, winner in play_data:
+            for i in [1, 2, 3, 4]:
+                # rotate counterclockwise
+                equi_state = np.array([np.rot90(s, i) for s in state])
+                equi_mcts_prob = np.rot90(np.flipud(
+                    mcts_porb.reshape(self.board_height, self.board_width)), i)
+                extend_data.append((equi_state,
+                                    np.flipud(equi_mcts_prob).flatten(),
+                                    winner))
+                # flip horizontally
+                equi_state = np.array([np.fliplr(s) for s in equi_state])
+                equi_mcts_prob = np.fliplr(equi_mcts_prob)
+                extend_data.append((equi_state,
+                                    np.flipud(equi_mcts_prob).flatten(),
+                                    winner))
+        return extend_data
+
+    def collect_selfplay_data(self, n_games=1):
+        """collect self-play data for training"""
+        for i in range(n_games):
+            winner, play_data = self.game.start_self_play(self.mcts_player,
+                                                          temp=self.temp)
+            play_data = list(play_data)[:]
+            self.episode_len = len(play_data)
+            # augment the data
+            play_data = self.get_equi_data(play_data)
+            self.data_buffer.extend(play_data)
+
+    def policy_update(self):
+        """update the policy-value net"""
+        mini_batch = random.sample(self.data_buffer, self.batch_size)
+        state_batch = [data[0] for data in mini_batch]
+        mcts_probs_batch = [data[1] for data in mini_batch]
+        winner_batch = [data[2] for data in mini_batch]
+        old_probs, old_v = self.policy_value_net.policy_value(state_batch)
+        for i in range(self.epochs):
+            loss, entropy = self.policy_value_net.train_step(
+                    state_batch,
+                    mcts_probs_batch,
+                    winner_batch,
+                    self.learn_rate*self.lr_multiplier)
+            new_probs, new_v = self.policy_value_net.policy_value(state_batch)
+            kl = np.mean(np.sum(old_probs * (
+                    np.log(old_probs + 1e-10) - np.log(new_probs + 1e-10)),
+                    axis=1)
+            )
+            if kl > self.kl_targ * 4:  # early stopping if D_KL diverges badly
+                break
+        # adaptively adjust the learning rate
+        if kl > self.kl_targ * 2 and self.lr_multiplier > 0.1:
+            self.lr_multiplier /= 1.5
+        elif kl < self.kl_targ / 2 and self.lr_multiplier < 10:
+            self.lr_multiplier *= 1.5
+
+        explained_var_old = (1 -
+                             np.var(np.array(winner_batch) - old_v.flatten()) /
+                             np.var(np.array(winner_batch)))
+        explained_var_new = (1 -
+                             np.var(np.array(winner_batch) - new_v.flatten()) /
+                             np.var(np.array(winner_batch)))
+        print(("kl:{:.5f},"
+               "lr_multiplier:{:.3f},"
+               "loss:{},"
+               "entropy:{},"
+               "explained_var_old:{:.3f},"
+               "explained_var_new:{:.3f}"
+               ).format(kl,
+                        self.lr_multiplier,
+                        loss,
+                        entropy,
+                        explained_var_old,
+                        explained_var_new))
+        return loss, entropy
+
+    def policy_evaluate(self, n_games=10):
+        """
+        Evaluate the trained policy by playing against the pure MCTS player
+        Note: this is only for monitoring the progress of training
+        """
+        current_mcts_player = MCTSPlayer(self.policy_value_net.policy_value_fn,
+                                         c_puct=self.c_puct,
+                                         n_playout=self.n_playout)
+        pure_mcts_player = MCTS_Pure(c_puct=5,
+                                     n_playout=self.pure_mcts_playout_num)
+        win_cnt = defaultdict(int)
+        for i in range(n_games):
+            winner = self.game.start_play(current_mcts_player,
+                                          pure_mcts_player,
+                                          start_player=i % 2,
+                                          is_shown=0)
+            win_cnt[winner] += 1
+        win_ratio = 1.0*(win_cnt[1] + 0.5*win_cnt[-1]) / n_games
+        print("num_playouts:{}, win: {}, lose: {}, tie:{}".format(
+                self.pure_mcts_playout_num,
+                win_cnt[1], win_cnt[2], win_cnt[-1]))
+        return win_ratio
+
+    def run(self):
+        """run the training pipeline"""
+        try:
+            for i in range(self.game_batch_num):
+                self.collect_selfplay_data(self.play_batch_size)
+                print("batch i:{}, episode_len:{}".format(
+                        i+1, self.episode_len))
+                if len(self.data_buffer) > self.batch_size:
+                    loss, entropy = self.policy_update()
+                # check the performance of the current model,
+                # and save the model params
+                if (i+1) % self.check_freq == 0:
+                    print("current self-play batch: {}".format(i+1))
+                    win_ratio = self.policy_evaluate()
+                    self.policy_value_net.save_model('./current_policy.model')
+                    if win_ratio > self.best_win_ratio:
+                        print("New best policy!!!!!!!!")
+                        self.best_win_ratio = win_ratio
+                        # update the best_policy
+                        self.policy_value_net.save_model('./best_policy.model')
+                        if (self.best_win_ratio == 1.0 and
+                                self.pure_mcts_playout_num < 5000):
+                            self.pure_mcts_playout_num += 1000
+                            self.best_win_ratio = 0.0
+        except KeyboardInterrupt:
+            print('\n\rquit')
+
+
+if __name__ == '__main__':
+    training_pipeline = TrainPipeline()
+    training_pipeline.run()
diff --git a/report_05_Jigsaw-Puzzle/1.png b/report_05_Jigsaw-Puzzle/1.png
new file mode 100644
index 0000000000000000000000000000000000000000..2a9bb477be3bd2b56bc2c7af5c1115e163b03d47
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diff --git "a/report_05_Jigsaw-Puzzle/Report5LCHJ - \346\213\274\345\233\276\346\270\270\346\210\217 - Jupyter Notebook.pdf" "b/report_05_Jigsaw-Puzzle/Report5LCHJ - \346\213\274\345\233\276\346\270\270\346\210\217 - Jupyter Notebook.pdf"
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index 0000000000000000000000000000000000000000..c16b2e3c279c5422e56357fce583b7a469da27cd
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diff --git "a/report_05_Jigsaw-Puzzle/Report5LCHJ - \346\213\274\345\233\276\346\270\270\346\210\217.ipynb" "b/report_05_Jigsaw-Puzzle/Report5LCHJ - \346\213\274\345\233\276\346\270\270\346\210\217.ipynb"
new file mode 100644
index 0000000000000000000000000000000000000000..7e20327b76111d9f119db830868a524c4d1890a9
--- /dev/null
+++ "b/report_05_Jigsaw-Puzzle/Report5LCHJ - \346\213\274\345\233\276\346\270\270\346\210\217.ipynb"	
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "%E6%8B%BC%E5%9B%BE%E6%98%AF%E7%BD%AE%E6%8D%A2%E4%B8%8D%E5%8F%98%E6%80%A7.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report5 - 拼图游戏\n",
+    "\n",
+    "* 姓名 卢程军\n",
+    "* 学号 2020262927\n",
+    "\n",
+    "\n",
+    "## 任务简介\n",
+    "\n",
+    "拼图游戏是广受欢迎的一种智力游戏，它的变化多端，难度不一。拼图游戏也有置换不变性，不管拼图的顺序是什么，输出总是固定的。下面是一个2x2的网格难题的例子，我们将在这个项目中尝试解决它。\n",
+    "![%E6%8B%BC%E5%9B%BE%E6%98%AF%E7%BD%AE%E6%8D%A2%E4%B8%8D%E5%8F%98%E6%80%A7.png](attachment:%E6%8B%BC%E5%9B%BE%E6%98%AF%E7%BD%AE%E6%8D%A2%E4%B8%8D%E5%8F%98%E6%80%A7.png)\n",
+    "<center>拼图是置换不变性</center>\n",
+    "解决一个3x3网格的难题是极其困难的。下面是这些谜题的可能组合。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$2x2 puzzle = 4! = 24 combinations $$\n",
+    "\n",
+    "$$3x3 puzzle = 9! = 362880 comb'ns $$\n",
+    "\n",
+    "为了解决3x3难题，网络必须预测362880一种正确组合。 这是为什么3x3拼图很难解决的另一个原因。"
+   ]
+  },
+  {
+   "attachments": {
+    "%E6%95%B0%E6%8D%AE%E6%A0%B7%E6%9C%AC.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 解决途径\n",
+    "\n",
+    "为了解决一个3x3的难题，网络必须从362880中预测出一个正确的组合。这也是为什么3x3拼图是一个难题的另一个原因。\n",
+    "\n",
+    "为了让大家学习如何使用卷积神经网络，首先尝试解决一个2x2的拼图游戏。\n",
+    "数据\n",
+    "这个数据集包含2x2和3x3的puzzle。你可以在这里找到它。\n",
+    "\n",
+    "https://www.kaggle.com/shivajbd/jigsawpuzzle\n",
+    "\n",
+    "下面是一个2x2网格拼图的数据示例。输入是一个200x200像素的图像和标签是一个4个整数的数组，其中每个整数告诉每个片段的正确位置\n",
+    "![%E6%95%B0%E6%8D%AE%E6%A0%B7%E6%9C%AC.png](attachment:%E6%95%B0%E6%8D%AE%E6%A0%B7%E6%9C%AC.png)\n",
+    "<center>数据样本</center>\n",
+    "\n",
+    "###  要求\n",
+    "\n",
+    "    ·分析拼图游戏的特点，设计实现的方法\n",
+    "    ·在Kaggle网站上，下载数据\n",
+    "    ·编写神经网络代码，测试自己算法的效果如何\n",
+    "    ·测试所研究方法的效果\n",
+    "    ·分析自己实现的方法的问题，以及如何改进\n",
+    "    ·深入思考，如何解决3x3的拼图？或者更大的拼图？\n",
+    "    ·按照report_template.ipynb撰写自己的报告。\n",
+    "  \n",
+    "## 2. 问题分析\n",
+    "在排列不变数据上训练神经网络非常困难。 拼图游戏就是其中之一。\n",
+    "\n",
+    "目标检测网络一般都使用如ImageNet的大型分类数据集进行主干网络的预训练，借助大规模数据集上学习到的特征表达，能帮助检测算法更快地收敛，但这种方法带来的额外数据集需求和计算消耗是不可忽视的。尽管有一些方法通过改善权值初始化来优化直接训练检测网络的效果，但这种方法通常收敛都比较慢，需要更多的训练时间，主要由于主干网络在直接训练时会面对大量的无效信息，过多的背景会带来冗余的计算消耗，造成收敛过慢且效果不好。\n",
+    "### 1）. 置换不变性\n",
+    "如果函数的输出不通过更改其输入的顺序而改变，则该函数是置换不变式。 例如：\n",
+    "$$\n",
+    "\\begin{eqnarray*}\n",
+    "&f(x,y,z)& = ax + by +cz \\tag{1}\\\\\n",
+    "&f(x,y,z)& = xyz\\tag{2}\n",
+    "\\end{eqnarray*}\n",
+    "$$\n",
+    "\n",
+    "如果我们更改输入顺序，则第一个函数的输出将更改，但是第二个函数的输出将保持不变。 \n",
+    "所以第二个函数是置换不变的。\n",
+    "神经网络的权重会映射到特定的输入单位。 当输入改变时，输出也将改变。 因此，为了学习这种对称性，权重应确保即使更改输入后最终输出也不会更改。 \n",
+    "所以前馈网络不容易学习。\n",
+    "##### 拼图游戏也是排列不变性。 无论拼图块的顺序如何，输出始终是固定的。\n",
+    "### 2）.数据分析和数据预处理\n",
+    "#### （1）数据来源\n",
+    "#####  ·https://www.kaggle.com/shivajbd/jigsawpuzzle\n",
+    "#### （2）数据简介\n",
+    "    ·这个数据集有100K张拼图图形。\n",
+    "    ·有两种类型的益智数据，2x2和3x3。\n",
+    "    ·每个图像有4/9块随机定位。任务是使用神经网络找出所有部件的正确位置。\n",
+    "    ·标签是一个4/9的整数数组，代表拼图中的正确位置。\n",
+    "    \n",
+    "##### 参考\n",
+    "    ·该数据来自于Animal-10数据集。\n",
+    "    ·是科拉多·阿莱西奥创造\n",
+    "    ·联系:alessiocorrado99@gmail.com\n",
+    "#### （3）数据分析\n",
+    "![数据分析.png](数据分析.png)\n",
+    "##### <center>数据分析</center>\n",
+    "·图像分为train ， test和validation集。每个图像切成4张并随机重新排列。\n",
+    "·$2×2$ train 训练集共有93136个拼图图片，像素大小已经归一化到$200×200$大小\n",
+    "![train.png](train.png)\n",
+    "\n",
+    "·test文件夹包含2176个拼图数据\n",
+    "·每个拼图图片都对应有.csv格式文件正确的拼图信息label，标签是一个整数数组，表示每个拼图的正确位置。如图：\n",
+    "![lable.png](lable.png)\n",
+    "\n",
+    "### 3）.解决思路\n",
+    "我们的目标是将该图像输入到神经网络中，并得到一个输出，该输出是4个整数的向量，指示每个片段的正确位置。\n",
+    "#### （1）网络结构设计\n",
+    "    ·首先，从图像中提取每块拼图(共4块)\n",
+    "    ·然后将每片穿过CNN。 CNN提取有用的特征并输出特征向量\n",
+    "    ·用Flatten层将所有4个特征向量连接为一个\n",
+    "    ·将此组合向量传递到前馈网络。 该网络的最后一层为我们提供了16个单位长的向量\n",
+    "    ·将此16个单位向量重塑为4x4的矩阵\n",
+    "#### （2）重塑的必要性\n",
+    "在正常的分类任务中，神经网络为每个类别输出分数。 \n",
+    "由于拼图样本可能包含多个类别的目标，因此需要特殊的训练方法，\n",
+    "\n",
+    "有两种对比策略如下：\n",
+    "![对比策略.png](对比策略.png)\n",
+    "\n",
+    "\n",
+    "\n",
+    "我们通过应用softmax图层将得分转换为概率。 概率值最高的类别是我们的预测类别。 这就是我们的分类方法。\n",
+    "这里的情况有所不同。 我们想将每个片段分类到正确的位置(0, 1, 2, 3) 。 并且有4个这样的片断。 因此，我们需要4个向量(每个片段)，每个向量具有4个得分(每个位置)，这不过是一个4x4矩阵。 行对应于要评分的片段和列。 最后，我们在此输出矩阵上逐行应用softmax。\n",
+    "\n",
+    "![网络结构.png](网络结构.png)\n",
+    "##### <center>网络结构</center>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. 实现方法\n",
+    "#### 使用的网络框架：Keras框架·"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "输入数据形状为(4,100,100,3)，即输入4个形状(100,100,3)图像(拼图(100,100,3)作为网络的输入；\n",
+    "#### CNN网络框架结构定义如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -*- coding:utf-8 -*-\n",
+    "# @Time : 2021/3/3 16:50\n",
+    "# @Author: LCHJ\n",
+    "# @File : model.py.py\n",
+    "import keras\n",
+    "from keras.layers import TimeDistributed as td\n",
+    "from keras.layers import Conv2D, Flatten, Dense, ZeroPadding2D, Activation\n",
+    "from keras.layers import MaxPooling2D, Dropout, BatchNormalization, Reshape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_model():\n",
+    "    model = keras.models.Sequential()\n",
+    "\n",
+    "    model.add(td(ZeroPadding2D(2), input_shape=(4, 100, 100, 3)))\n",
+    "\n",
+    "    model.add(td(Conv2D(50, kernel_size=(5,5), padding='same', activation='relu', strides=2)))\n",
+    "    model.add(td(BatchNormalization()))\n",
+    "    model.add(td(MaxPooling2D()))\n",
+    "\n",
+    "    model.add(td(Conv2D(100, kernel_size=(5,5), padding='same', activation='relu', strides=2)))\n",
+    "    model.add(td(BatchNormalization()))\n",
+    "    model.add(td(Dropout(0.3)))\n",
+    "\n",
+    "    model.add(td(Conv2D(100, kernel_size=(3,3), padding='same', activation='relu', strides=2)))\n",
+    "    model.add(td(BatchNormalization()))\n",
+    "    model.add(td(Dropout(0.3)))\n",
+    "\n",
+    "    model.add(td(Conv2D(200, kernel_size=(3,3), padding='same', activation='relu', strides=1)))\n",
+    "    model.add(td(BatchNormalization()))\n",
+    "    model.add(td(Dropout(0.3)))\n",
+    "\n",
+    "    model.add(Flatten())\n",
+    "\n",
+    "    model.add(Dense(600, activation='relu'))\n",
+    "    model.add(BatchNormalization())\n",
+    "    model.add(Dense(400, activation='relu'))\n",
+    "    model.add(BatchNormalization())\n",
+    "    model.add(Dropout(0.3))\n",
+    "    model.add(Dense(16))\n",
+    "    model.add(Reshape((4, 4)))\n",
+    "    model.add(Activation('softmax'))\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用时间分布Time-Distributed (TD)层。 TD层在输入上多次应用给定层。 在此，TD层将在4个输入图像(行： 5、9、13、17)上应用相同的卷积层。\n",
+    "为了使用TD层，我们必须在输入中提供一个额外的尺寸，在该尺寸上TD层会多次应用给定的层。 在这里，我们给出一个额外的维度，即图像的数量。 结果，我们获得了所有4个图像片段的4个特征向量。\n",
+    "CNN特征提取完成后，我们使用Flatten层(第21行)将所有特征连接在一起。 然后将向量通过前馈网络。 将最终输出调整为4x4矩阵并应用softmax (第29、30行) 。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CNN架构详情\n",
+    "此任务与常规分类任务完全不同。 在正常分类任务网络中，重点更多地放在图像的中心区域。 但是对于拼图来说，边缘信息比中心信息重要得多。\n",
+    "#### 填充(Padding)\n",
+    "在将图像通过CNN之前，我在图像周围使用了一些额外的填充 (第3行) 。 还要在每次卷积操作之前填充特征图( padding = same )，以保护尽可能多的边缘信息。\n",
+    "#### 最大池化(MaxPooling)\n",
+    "避免使用池化图层，而只使用一个MaxPool图层来减小要素地图的大小(第7行) 。 池化使网络翻译不变，这 意味着即使您旋转图像中的对象，该网络仍会检测到它。 这对任何对象分类任务都是有好处的。\n",
+    "但是这里我们不希望网络成为翻译不变性 。 我们的网络应该对变化敏感。 由于我们的边缘信息非常敏感。\n",
+    "#### 浅层网络 (Shallow Network)\n",
+    "CNN的顶层会提取诸如边缘，拐角等特征。随着深入，层倾向于提取诸如形状，颜色分布等特征。这与我们的情况无关，因此创建浅层网络会在这里帮助。\n",
+    "网络的其余部分非常简单，具有3个前馈层，一个重塑层和最后一个softmax层。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 训练过程\n",
+    "使用sparse_categorical_crossentropy损失和adam优化器编译模型。 我们的目标将是一个4单位的向量，告诉每个工件正确的位置。\n",
+    "\n",
+    "Target Vector: [[3],[0],[1],[2]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将网络训练了5个时期。 我从学习率0.001和批处理大小64 。 在每个时期之后，我都会降低学习率并增加批次大小。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import copy\n",
+    "import keras\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from PIL import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "\n",
+    "from model import get_model\n",
+    "from utills import rearrange, load_data, extract_piece, swap_piece"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "base_path = 'data/puzzle_2x2/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining essential variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cuts = 2    \n",
+    "channel = 3\n",
+    "im_size = 200"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load pretrained model, you can download the model from [here](https://www.floydhub.com/shivaverma/workspaces/run_LEzD4uEtzqggKf5p2risz7NE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model = keras.models.load_model('model/jigsaw.model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Or train from scratch model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# model = get_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "adam = keras.optimizers.Adam(lr=.001)\n",
+    "model.compile(loss='sparse_categorical_crossentropy', optimizer=adam)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_val,   y_val   = load_data(base_path, 'valid')\n",
+    "x_train, y_train = load_data(base_path, 'train')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "32/32 [==============================] - 31s 955ms/step - loss: 0.3124 - val_loss: 0.5629\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1e06c899688>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x_train, y_train, batch_size=64, epochs=1, validation_data=(x_val, y_val))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_test, y_test = load_data(base_path, 'test')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import clear_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing indivisual Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1JBicq4kJTVtai5S8HkyLtmzFumvMMGMakoFVcvpKPzwTl5yz6OnELO8qtDHHvDOZShWFVFbjrpx6TDFisZouCwF8IiSv/rHM6dQqzYqyAIckPaazViEEH1QhkTGMLHjW2ikTg7VTKBJ9yeEnfL7eKoeKCabYPkb1KmLOOlVi9fmOY37GcfJwxGQw1wjWOoyz05qoKkfIz9hHP+EgMUaCn7kiKSXsP4YU4e9oOx6P/PzP/1F+7ud+gf/g//rv89f/P3+d3/red9nv91xd13z0rQ948OSCY9ph15GHjx/Ry4pw6Tn6A90wIEGQDLZV1k7EGnECXtFlcYqoYsDgMMZOVtYHDwirVTuRekrcuES8fdC8umYIVEtbZ9UyxsgwDNndL655BgMt2EoBurqpSII+uDERAwuCkaYkvffU1lDXNdYKVWXJkV7mPcjCIyjxdySEEidqajUMAWJg7DuGlFmJIZGSuq8xBQ29BKwRohglogASE4RERGNWgxCCLITQIKm48UxmaGb05ZRezpLcWYYTrF9y3oX4UuJsUenLoBf5CpPKe8YFCuBY9p0yUEYGO4HAdJzZi4jqISzOKaECVM6blBWlSUhSAFCBOL1QxTciBBZhieTnp4IcoqL2ZSvchOJ9THhP5o34MObwIu8tA83GWapMGLPW5tSyU+NQwo9g55DVB8SMiFH+gZT79I7tvVACDx8+5PPPn+LE8F/9r/zTfPLxp/zSL/0d/vbf+Vv84Ivf4jDu+PjbT5A2cVqv2HdHUh2pVyvAEJJnGJRFVlfVhK4bZ/ICLA/c5n8FU9kpBTf6ER80zl9vWuq61lRf0LiwAJIherwfGIYBYywx+UxcqkEMMXlCHBnDAFkTW+NICcZRF4a1imuE4Ol7FSbvcyiQV2kMGdmmmtKnxfoUASxpyfK6us2zlSyvOefwo2ccuyklqN9VHMU5l7EJs7Awel+MyUSUEPHDgCQhRj2HEsNL1Fg3GiYAFe6BUVleNSuDps2yIIlRoFNyuCJZURWXXUOWtPjJt4mkue9pccf8fFVJihgFSZkBR2zBBiCke0ogC2gqLnRW8AXUVIXHdG+Bid6tHgYUTkWKcdrxeN8CJ7JQziFUyB7mnedn55CzqpQFWdU1dV1Pz8gYwxA89AaTqinMEOOz3hNCQjGOr5G/90IJxJD45MNP+Qe/+g9oVzV/+A//HJ9++inb0zV//W8Ekgk8+uADnnz7MY8+PcdYuNpfIq2lcg2HXccweKw4onOgBkxTOokM/swuqxFDVdW4uiamRBhGYoy0TTPd3KIElum25U8IHu/NGyGCiOjDSJ7gI8aELLyBkjN3xjD6geQTqSx0svssmdcuJV63GQiEqqom4hLMlOayhYwyl4UUQqA1FR4Nj47HIz7n++u61nx3IqvGBBicK97HrCRTGBjjmJVizoFbi0kOxCJJ03oxzQpqqj8AFa7sEUTy8XI2JyOj+d5BytmBhFrQInCFBKSofJoAtSKZE9U4x90pBPULJCJBNHUyZSYo0XzWJ2m20kVAYQLeBF1DSIEEdUvl8xm5T0axiJiUOZpKIoOJEcFCh+nanF4AH4v3JDjAWIurXE6FO2xdYeuKKisCsYZq1HQ0aPgjMmpIsEhHxhx6vGt7L5TA8dgxDoFPPvmUqqp59eolMST+qX/qT/LJZ5/wGz/4NR48OaU9r9k8bHm1f67pHkmEmBhDIuRilhhhCBEzDJqeK4IbPePo8TEhxirX3DpS8HkhWjBu8lJ1SxNgVdxn5ywx2uyGe4ahZxjqvIBjtlQGosm57XjPhatwlePYHSflohrfEpOZikUEQ5Ia7z3DYKb4X2P7WeHALCglRFnWDtTOEqOSUYahV7e+LO7Mk9DvjYBkfoKma6tKU5ShDwyZv6HM46ycpCJ6g2KgqsRCWWxZKUhWZpR0Xswps4zmi8z1GG+GrSk/A/2+GIPJQloAxEhUjV9SiGSrrg9EMyAJjIlgZaq0CZlLcsdCCvML5d6ChpLMxWlF3RDnMED/jjNZJ0X8ApWfqFEF9J1ik4TP90jKcY2y/GwOA1QJ2KwUKv2pq4khGVLEkpRNmBISAsYHYlacEe7gNfe390IJjOOIcw3juKd2hidPPuT16xccrw98/MnHfPKdj3h5+4Lf/OLXuepeMVQ97cmKAeW7u6qGFEhBCEFj/DGng4ykKQ88jD0pZYAlJ/MVAQYWyHuxSjMBZHa/l/+mpNTe4/E4LX7vvYJnObYzaExrbLZ0JhEIYMDnVJ1JBpMXcYxZODCY6IDEOPYTcaoQhRQg9FMNwawYtJ4hBHWLx8x+m+LdqJmQUZTrT0rUrqLvR7wdaZoVdV1Tu4rKVYzjqDURIWR02yrD0RqccYQkxDBbtBKWpEkYMmMwW02TlCZLTtOJgMmKRd32OdU6KVWjqL4xRgWthD8pqtewUB6z6KZsbed9FtNclF9aCnpJr2VOb2Ebqx7XY5hJkErMjpZHkKb/EPK9jhiUmQLZ6pPVRTJvVK9O4EhRhtlFM06wzmBzTYytDaYSTJWZqxJxyanXY0xmGEaoNN1LiKRc9/Gu7b1QAofDgV/+e7/C7//Zn6GuK3b7W/phoG1XuGZNdIGTR1uevvohRzmQItTNiq4f6Mdxdr1SmtzdYLJrKipY3nv6btBF6fSmeR9ynBzwKRCHgdHWAJNVW8ZqE9JqZxCmoPUFJFSKsIJ1Ux5dUk4FzoU/xhrSkIuKkmDTVNqS74rQ9/30eWACg8raKZWH5XzrHDOq5xEIQRiHkRB8Rt4NIeTrCh7v7XSdfX8E1JOy1k6W0PugBUZhBKeCZM2MkqeoUpyQyfPRK51DgomBWO5RihnR11RcwUNSBvZiYlJaKldFMAxYgyl1IsXLUBOa3ezCDGWRL1v8ns9ugSYyfTv/nbO52cvXjIJEsoc3W1STQ7cJsIQMZDLtP8XlMcgQQinM1uOWdHV5rkpwnPEUsZK9mKw4SlrVGkzSFG5CKcvGe33dWDCRcM9rfNv2XiiB/X7P3/pbf4uzkxO22w3tpuXs9Jzd4YbBH3GVEIPXNImxXB+O2KPBp0zLRIuCYlQ3UKvw5jr6kEpJZsC4nEdPSbkBRQnk9wfboy63ydRanwt6YkZ9ydwAmxHmgtZn0CumzAGYq/lmAI/pgagbrPs0KRfCWM3nG2MwOMY+TPsQ0Uq1+ySRopyK11IQ5JljEKbUW4zVDGah4UuMSmIahoEQEiKaVo0hEMQwjiPjMDKmcbJSVQYstUIPUrZAd9bZwvtUZZBz90mImdADTKJA5gFYUU+ixObFghasRHKqr3hqChwySaFk/oC+LrPk5X2YItRZU0xZynKyS8WRYY2iVKZj5bfECCbpsWLGDtSbN9O9mog6S/0+HWsKLO5kT0qQkabf5vsYUsJEpbuZNL8rJeUq0671XO49i7dt74US8KPnu7/26/z8P/lzuOZjKmqiS4Qq0o+ePnrGcSBV6sqnQwIfcQIb2zB4YQxH/DgoTpOgbloqLMEnfJ+Io0AULGrFJEVSGCAcIR6ROBBTovdClJEKRfV9HBl9mPjh1llsaWaStHdAzLFtDFqdmIIhYTP4ZRBRAM0skXATENHcdohBtbyatsyOi0QJJIx6CmJBmizgNrvXQhiTIsICtamxjfLabRJsACuOuq0RoLMdh2jpo1YnWmNVAY2Jyig+EjJ2cDjuEGPp+o4xdZjG46oKm7QgN0UIPhHHqGnIYulTQNk5gRSFhJZIizekNCIJbFIIRrK7PlleY0gl9gYUy2d2s0kkGQE/WU9smoS6GPypkkFMBuQyFTmql11IW0tXYJb9LD1Jj6+KUll+BbeQbGASmb+QCklajYuhoP9QicueTg4hCiYgJmdIRIuAUEZgyniHEfWygo94GzEmEQKEkKgqZU1qyAeIJY0RCVo0ZxB8VLwsKcI5FXm9bXsvlEDwgc9/8EOePn3K9vwUt3bgIsEpyjrEEakTyQpRNM1mEkhMVNZgxGFF6FNgHBUVxge1Gj6RAkg0OFPhCigjSt01eCwjkYGUAoNviHgwKnBJIiEp4OKs0xitslNIwOgzI02BwEhiHMOUchOBkPPtSEa+kxYfxRz7QkJSJCbBLOrtMWGyQApbFBJJXu5RFU8Yg7rRLpJ8LlYJGhvWbsVmtcEZS21rbDLUZsjBbk73RaG21RQ7ez/SDz0YYfQj2IitE64SHBbjLZCpt0Fz3RLzWaaAECjMx5AMKRmIFkk9ZEBXhSlloc00axYWcbK4KdOail0MQO7XIIAoNVsWllam/zS3bzFTZWJJ8wVZEBvKrrL5LJ/R54PG1TErOil1HkIS9WqKpzKlRJNgMiYi2NmeT16HPmft9jTzJKSwJEkU7kXwCWMiwSZsiKQs+AU7UtAbrS4MMYPhCgQqjpQV5PuuBGIMvHz9is+/+JyLDx6wOmuoXTW5c4mYK/M8Q+wzWp8LakKcgBNl/yk1WHnYwx1UH3FoMxhdVqZY9mQV4ErkGv40dfAxmbEVZNmqTM+7INwlP1xWoR9Hffx2ziQYY7BeiJXDWsNQGkiUEt2Upn2CKhVN/+UOOMUSTgCUmTvIiCqIMQZtlmIVmY9EvT5rpy43RoSmUaQ/+siQ+ywko+BXWPAQJs8Ema8u3QuvUaumlnXGMwyS2fpMPrQ245iid/1kcbkXAjorgDt/Lv6aHWcV2kJGKnteJgzl/g7y2f0IH7ls6e6XEywyKwvfe+mal9Dh3oHnZ7t8XabQ506l35TVykVOPhBMbjAyqhIUYxYMwTCVJb8rtf2u7b1QAsYqSeV2t+Pm5opj9whpUIuc7aAK9ZEhdFO1YIgjIYxUySIxThYmpcgw9tg+F9qgfQecKKEzxpAh6aSus5lbY3k/KpMueFJ0+eYpt1959zbXECjxRmmeAFqSSsqvMfcADAUFzyBcVVn6bpgBRXtXSxehd1VhLSaMaIpIQwxd4sYajLUYq3ngEANjHLHJ5qYSMllREaGqK6w1NF5jfu9HXD8Aggmadx5GpRmH4CGZyVNJIRBNIKSAxFJ+rQtvUoB5nSlnoGTjRDHAqeS4lMEyg2/FUC7WqTDLxhLXluw7zCpCxc3kL5V9TiphGexLCRbK729TMEXZpQlsLnubAL/yjIrfshSwst+FBntbcdVUOp3jdimFYEsYI/cSiCJT8xBjSoq3KOvMfxnVW40+EMdcOBZC9mLi+68ErHU8eHSBGOiGjsPxluRGogtQKX3zenfFELQPXpKB1EWiePr+SNdHKgGTlCqrcWik64+aEnSWum6QXETk04gQcmHPXJpprSUGBa2CD4wyZC3rMxHDEMaBHiYFEGMp051z9EsMKKU49c8zUsAxq0Cc17g0izQs01koXTRGtIuMuAm1LwCVrRxV05CAcdA4N6RIRKmkxll17cdem7O6ClNZmspBVI/FWuWfuxSxoyUdNQTw3kOpq8hdg6L3jMkjmUEp3hJC1OZJkC16dvEXwJtB+zdaMVgpbb5ynM7CG7jj0jMBc3OJVt5y/F8c7iKHxTaXdKOU48h0xOn1coz7VrnE9YpxLN8qJCYWApUWz3khZHe8gntuyFteK70Tpu/JjIgsA8CUXX1JYEc/dWtKMZFyClB7E2o5dPSlVDlCeHcl4XuhBBA4OzuhXjl8Gun6I8GN2Baa1iEkun6HMQHve47DDUM8cnq2YfQdXXekdZZV5lcbWylpaBi0wCbWOGcnD6L0DRQBMeoUWKvvl8osH7QSMAbtwAPazCWmNJV8DsOAoO2lCjMQSdrSrIBHeUFN7l2IBEQfTEoYVMhNcWtjzL5PzIs7g2TWEEW5Bal0+RXJjUcUt/BBPYGGBusEk5TROIQB50tJq5vy0NYZqlQx9p2GDCTcaBnD3GhTrNF0ZvawJsnIDUwIaQLihJJBS7nwJmULqqFLJXZqhrF49OTTmZQBLAQ+v1e8gsnpyJpwyr/Lcn9Lgc/0ZhYkHVkoi8n0TnHV5AFMbnQCSUZVTgEtMv7zVgt757U3lcD8sZwqXLjrAqr1lOFDCsqoHYuByRkhW3oZljR0zL0XQsD349wTIUQti37flYBzlu/8zLd58tFjticttoEoAykmbAiM6UAyHutgjB1dv2ff3bLeVMSYw4KYQLSKzlZuIg3FmHIu12vdfHbtM5ozLS4RbWzinMMPYSLyzNyAKufp7Z1FgkS1H5JmMNA0hKhYx0x/NZSiuhiKC220860pdQGZ6CEeYwJQykgrbNL0Xud7UlKXsLJOUXarOfrB95gBmnYAU+XCFvUMIgGPNmwpjTJK9upw3FM3bWaqqVDEpK2rqqoGZxjsoJmBAM7kCrocrRlrlKZbvAD0fkaylhX1ckpHHEG0dFkfAYYMjqVJxidPIKU7bF6KW7/QB5Mi0U33b2UW84LCFy8godX3Jbgo7UcTae4zmwQpnO7si8hSEYEKaXq3kAMTfnRXec1uS1Esyn5M+X5IbkqTY/oQEC8kp8Jc+YC3M4ktol6woC3TwqC9CUuPxJ+YJyAinwH/W+BDVG/9lZTS/0pE/mfAvwK8yB/9S0lbjb1zW61b/tA/+QcwjcXHDh86omiaaTgm9sM1x37Hob9h9AfqVUU0Ne2qIoSOtq2pzcyYE1GrXdVWOw9XFq2FybX0ySsLzEOMA/3Q6TwDa1mt2syqm3ncmn+3NE09UWm9H/E+1/HbkvvX0ICoHkNxxcRkrn1uRJJQNN0m0Yq9JLl4JUzlwmIMSWIOVZwy7IBxAoaEtm2x1jGEkW440h2PeD9QNVZFPwTCsSNGbblurCGIdtDRxpQqGF3XY6zF1vUyKqY0ETGVU4EOMr2uFjg3DY0mL+S7IJSZfAEBY6jE3BG9AqcWT8BMgl6USBbyhRJIWSgLNVefahaqTECyk2jPrr9+K2MCsnTkZ2Uxnw05bOMNYHB+f4kLvHubEgbTfUl3FIcqKc0SGYpSUcMlQatQJ9QlxgkrwE7otIaaeV8paVOYMKoXYBLKyfgJ0YY98K+llH5JRE6AvyMi/3F+799KKf0bv90ducpx8nDN1fUVLy+/oucG2xpsK6TRc7O/ZAgHbneXhNDz6PFD+sGy3a4Zhk4plahbrdccsqus6D65yeI4jloNGAIxgYTI6Hu6rickT107tuuTslT05CTz6V1FncuTU0oMfc8gQ45t84WkOVW1bPphsTgnOa0YMwJffL5shWLSnPDU895oRx8KFTgX+BYgKGllYowKmg7jyKE/UEVHO6zAKqPwuL/FR4+rnYKIxmjNQ+5O68QR8USBKnf5UYu4qGorKF9KExU1Jg9eiB6Szf35UzFyud89urCLeLmCAeR7m9naCFpUNKXnYW40mu7/LFz2vOiVznx3bIwUHnN29bXC8O3x+bJF19145N7nlz0C87+F2fcuRl55/S5CvxDI7AEtQ6l8q/X/QkmSoinJ3OdQ4uwJhOzZ2Ewi0+eW7oQ2d0OUu9t/biWQUvoS+DL/fisiv4pOHvodbyF4fvN7v0437BEHXzx7zfZ8xSpWHG/27PobkgvYKlHXFeB5+OABYoT1esV+t6NyFZV1hMxzLze9bVeICPv9jqZpsNbgM9NP03AVrgrZK5CcsnOIaCvuGGNOF1ZKP87lminCerXGGEPXdYzDoE03oqbKVs2Kpqo5HA4cDgf8YDg/P2e93Wpn3+B58fI5daqQ7PZbY8HV+HHQeFeEtm5oqmZajqXKUWsVYlYEgbqtWMtaMQDf44IwjANjHDj0kaavWK1XNE1DjIGuP9B3A5WrleMQPfuD5+b2lrPzC3aHPd3QIdZwfrLhdHvB7c2Rfufpw5Gh69jYU6paGI/97DWUWossmDGZqdWAZJfVAGIMLrPcVE41Ji/XGUS5+ilxZwLPpJgySKZOwCz+BntHsCXn45edl0SEMRQvJtv/ibxTUrAyezrFlY/3PJ2cf19SyLM8TOdTBtO8AQYu/k5TuDJb80KbLgZJPYGkgF9OObPYxxgjYxb2FFK2/lEVS0xf67D8rmACIvJTwC8AfxP4k8BfEJF/CfhF1Fu4/LrvH457/pO/+zdZn6z48OPHjAy8vrkh3A4c/Q5xkZOLLe2qRiodFNKNPaTM0MsPraoqXII+D2+YpgT1HV3XsVqtSKl0xpFc0aexvs/8ehHLOB45HI7a2KNuqKoGY6yi+VOjyrIgYRz1dZsfTNusca7OXWMzSGYNdVVhRQHIQ99rQwqbiD4BUfEMDFEL3zFYzQwYQyVqybxV76EsohA8mjYu1YiCtYJ1go2i03eMJZrEEAfoE33f0+c242IFI9qcggxyAtpTwVqqSouF2rblcNspPTippY1o8ZPG+uosqEVXlD0kxQNKEOCYq+cmFl+21kXYpnWdBS8uLH6chB6UP5Et7LQOTVmPc0Agb+PNa8aAt1jnydu4Ey68KUQFR1KUac5gKH14/rDcs8CzwkvlTGZ3qFQtxbn4auYWMAMgS+s+eTH5s0sAJSsu0n0C8t3tx1YCIrIF/n3gf5RSuhGRfxv4y/mU/zLwbwL/8lu+N40hW60b1icV65OKeqUVZ2MaSTJQNQpW7btrTs5OaNuGYezojz3W6kOumxYrFiPaIMMfO7quZ7VaMY6DWrWuo21bdf8H5fE7pyO+Eo0i4iEQxh4/ePyQGz1IJLkITkuKrbH6I7mIKLO4rMl9+Iym4spSdc6xXq/v9ChIKbf9Qht3lBy/npObrKk1mlKzGVWPOe42ohwBtVZxWoyGhDXoBB5rFQdYNZquszD43GJtHEmScHVuIJIMKQWMEU5OTggpTSWrpY6/yj0NYogYq9TksuCcVeG2ot2JpnZaCCYyVeu5AgoWtH4B6d2RRWEKsTL5kTjZezOtcfXmSg7eTGHGEuxbbnfj88K6S9N+yufLFKgpRfiGN71UEvf+uif0E7GoeAn3z2oKefSdqWEJZYZA+YIsd7q4XxkvSAthXxIu8snLu3XAj6cERKRCFcD/PqX0H+g5pWeL9/8d4K++7btpMYbsyccP07d/+mOSCSQzEMIRWyfaVUs0juOw43A8sGUFUqmbO4607Vo74yStCbDWaiGQn12mYRg5HA5TNxZr3GQdjKmoXKOpQe/xMrLb70lRefUxRPpugCQ4W7FZbWialhAilashGcbodbEYwRrtURDzMIkYI85aXNNgRDsYDZ0yHsd+0OYermIcI6MPBBGcsdg8PLQyDou2yU4h5iInj4jNFltjdJPjwWAMISP/pIgzFu8SVpSqO/rcbzBGqlp7GzSu0UzK6LHOcX5+zuurKxLk1Kc2U1W+gyfEoBiMNeB1gRkjCnKKKoLiCSCSR2Fpnl7rDu7+t1zc82KG2XYpcLYsxY0YlKWZ8QvIocQcAhSBmoVbFsI2W9q5E29WXsW7KKHIgiww4xKzQC+9iTRrmftrfSkzC+u+8HAgF2HJrBgmqFJmLyEfVxbv309TFgXJdAfT140d+LGyAwL8r4FfTSn9Lxevf5TxAoA/C/zyj9qXddpEdPCB/XFPNAOrVU27qRhi4LYfSARiGhm9zY05M1gmBu8DtVPhDmEkxjRZ+d1uR4yR7XbLMAwMw6jWLLvc2skmg2+ZdqaTehykPHVGNDdfVS1V1TKOB4xxVJXJlYQGkrLURQx+DASvudxVu2K9XhFT4ObmhuA9Yh3BB6xY6qoiRc/A7LqZAkZWlVrcBNEHxr6nGwascTRtq5m3ELACOEtKVhXK0BO8lhSHFNBOW0qzThGcWNqqoml07qPxii24yrE92XJ1c83oAy7zEKwVuuNO73vU2g1nDckrKGqkKIC3KAGUI6BKoDjy9xRAmv8pQ0YKQ6/k0suSno1iaUZCdolngRdMxgTvKoDZ5c9KYGonls9KlhwBpuExUPgeaSFbCR2Q+PY1nd72xxtRxpvCW3ZdSEKKb6paSyX1Jff2v9j35DksmFdpycJ6y/bjeAJ/EvgXgb8nIv9pfu0vAX9ORH4+H/V7wL/6o3aUSOyPV/g4Euho1466tRgXYfSk5EniGX2vBUVDT1W1mqtOgvcRL0rqGceAYGhb7YxT8uzb7ZbD4aBdh0PEYhgGT0r9pH2NtbTNmuD3xDgABuca6rrBWu1dqA0+1f1UynHCZQ6BjoMCIjhbsV6vODs7Zb1Z0XVHdrsdILkzskaRZeSXc05JQ0Zdf2cdTemXKLlCcRjpjx3Oav2BdXa2xMYBge4IPntKItpyHHFEDH4YiT5S52tCcgVjfgrO2TtotrWWpmkQ8dzsdpkc5TK3Ic/Wy667lvrn2gBRUBApcwYWRB2466gvw+2Upti6eNCpWHBmdmDu46GbaJp12mEqZOUZe8gNwnKjkjyOTHKzz1Bc67DwBMrpLLTTwspnWHnx3lu8gOV3y6m+sfKXAEg5zFxItQT+pt8nJVu+r97DnXKojDFosRN8rRvAj5cd+P/ytuv6bY4eW24icOxuwCRMrSxBTGD0PQmPcTDsOo6dw1rts1fXAkkXm2AZhpE0xuyuVjR1Q+m4s1qtsNax2+3xPlAm8nbdwPHYkYC6blivN7TtiuOxA4SmaedOO3VL8JEURz1u1IVgjKVtV9R1Q13XDKM2Lqkrx2a94fTkhLquGftBh4dEtYRNXdN1HWH0GLGsmnZqGhKjDpysbKUeh6uIooVGKac3UwiIKyPTtZuQGKjrimFUiq8nEaLHoR7BMA74QWsd1nGtk5fGMVdBKmnrZnfNMI6IUQVQ1zXHvmO/32vdg22yEVMuQ1lkRZHKJI8JkxOEkht95FL/OynVIr/3Y+XCp5nX78wOVDJNbsG2sKZlcnF5aVkVWCx8yS5ok6K0wAWW311y/eWOy69XJvc8grlyrygMmb/9tZmBItBpoQ1TuRlSrP/sEUzXNF1zDoHKvcyhaYosns3X+QHvCWNQpwIF2rYi4BlChx9HovHgwIchTx2OOFd62UWORy1NDSEydCNehHbVsFqtNZ9PYL3e0qL5+sPhQEo6TxCEcdD24WIMdb1i1a7pj1cY41itNqxWK9q2zcpGswDOaRaiNCMxxrDdnnJycgLA7e2txtS5sWmMia7ruLm5ZXd7yzAOGANN07Lb7ei6jqbRc16tWpS805FSorI6T0BnJyS6fuB4OGqrK5Gpp70PPoOBwnrdYnq1KMPgMZU2q0yZpVjm69k8q4DoORwOnGxPqSrHV89fcDz2nJ6d67UDh8Oeoe8wYpWlmCCMniopSJonkN/txiuQCywp5l5g8nmlLOx0d4EuPN4F6j4LShEq5fGn7G3oZkqqcJKx+8CgWn5lkc4FXncn9CxxiiWgNx9l+vW+5nrLNqUq7wn+na10TS4g3/LsJ9eeKQwoQ2CilCItFoNggGDU0ylu1dQG7e3be6EERj/iKsODRw/YHW7wokM+jbMMQVt8r9drNtstTd2w3/X0/UDfXeWGDwkTEq5ds1pvOD07JaVEP/RsNhvEGq6vL7m9vcUYS92uKDMHRAyVazg9Pefho0f8xj/8EmssJw9OWK/WxEgeXKKU29K9p+s65RC4ms16zdnZGTFG+r7n+uaaw+FI0zRakRgDr1695Pr6alJkJyen2tar66mqiqap2aw3JPIchNFjMLiqmoRxtT+yq6qJ119ZlxVabojqhFW7BqAfOsbR0663NHVL8B5re6wNuEo9pdWqZbSe66tbmrbBWser168QLGfnF7iqYuh79vsDKWX6stHKSh8CLiujpTeOyMy5kSI82g5c7zeTnJVYfyliUv5aWruF628yjjNZ5vvh7vR3YdQt3lq4+iy8gztWf/EltaBvl/J07507KbjFbqTsiLcI//R6/rikO15AsfxvnEHOOCwnTcXJ+yieg9Yk3OGkvWN7L5SAN5H+seFldWBnO47HI+v1mvVqSzgOePHUVc1m8wHOWH74g19nyOSc8tnaCtXGcXK+RSwMnbLimrblwcVjbm+OWLNiHDzOCN3Qs92ecHa2VsDuGNlfHtlWD2iahp/5mZ/m1etnvHr1grPtCSEM3NxccbjZU9WOm8tLjDgenp1wc/WKDx48ICB0t3u64y2xqbm+sYzhVglK4cCDizXPnn9JXW3Y3b7g7KTRmQKmYlOvOVufEXygT5qRwDm2JydcXDxgt9/TjQMYyRWRwjB6GlvRNBuGYSB54eLsAbU78vz5c0z0XNTnbFYbbm6uqcRjG8UZbg+3iNNpS27jeLF7zc3QcQwjVWXoY+A4ePohMqYVq813WDUtcYwcdwPeCa6tkSjE3hPEkbCUYaJRIJmCwgeS6ESoqbBnQu7fEoPrX/Ovy9A4gUmlyIrJ3S19F6YSbhTwi1MYUNxshSWHXqs5UypIhUpsabdWOiGbnIUp4+R1IrLgg3ZQ0jHlqpyCV45+qVmcwxLURReZcIni5htjptJvSQEj8Z5KzPogaZGQYp6C5GnLxXOY28brF1IpSk3MHsE7tvdCCYgYbm9v1TUXjZcFrQJUEo9we3vLyfqEly9f0nUdp6en2W1fQYJPP/mQh+dn06jppmkyOKaLYrfbcX19zenJGVVV0fcKnp2envLZZ5/R9z273Y4PP/yQFy9e8Ou//uu54lDTfYVLfjgcGa+Vcrs5bbm6uiYl4cWLF4hYnj9/PvX/r+uGhw8eKSh4e8uTx4948OABn3/xfVarNSWVFXM6yhiLOPU0Ykq0bYsxBj8OHPZ79vs9fd9P7cvFgA8jldTTZ0sNQ9NUGKOU7NVqxTB07PeazRDRJqvH4xHntL9iDJ7Xr684Pz9l6APWWg6HAyFEmrpi6HqaShuZVivFIvq+p8JqdSDyllz0XTNdCnjknoWblMEin/7uxcIEFEzuct7SMsYor3FfubDoC/C1qN07D1/O+Q72sHhdit9ejlcyHMv8/vJ8fnuHnqKP6XCyqE4qWZV8CyXzrt9a5Xhvey+UgOaglRPt8jAN52pITDP4Hjx4wOnpKS9evODs7Iyf/unfNwnbzc0NLjfzBO1eHONxyg68fv2Sw+FAXdes12tWqxXeK8YwDANVVXF5ecnNzQ373Q3Pnz+n7zs+/vhDTs829KOO6U5GCCSOQ6+L2elwjs1qy6urS9brLafn5yQj3NzccDx2hBBZrzecnz9kHANNU/Ho4ZOplbdeY9DUZYw501ATYsoFQnZqLWaMpuw2mw3b7VYHinQdIQRWq5bKrrQ3gFPB7/seK5bNeoUfe/a7W4wU6nUucJLI2cmGVy+v6Q5HLk4f4mxDfzxy3B+o65b1ZkO7aqirGpMEU1tlFI5CCtqwRXL8LxlYK72GVOj19zKam3uegK6BdMe9ffdamZgBd91dZhBOEzRv4chJ+f78grr8ix4QkyLJnyiuepqBx9lCz0AdFCxEn2kqoZAG8RQyEMzW+42rLVq04CTy5meW6df5zbuhiSoCvSKZtcLbbyjviRLQRbzi4cOHDMNA3w3az88Y5d2Pns1qg/c+g3VrLi4uiDHy1Vdf4b3n2bOvSGHgwYMHAHRdx6NHW6xxfPXVVwA8fvyY4CN1XXN2dkbfD+x2O7773e/S9z0PHjzgZLvmwYMHXF6+5sGDc8Qkrq8v0cKfgIhlvd6y3W45OTnh9nbPOHodFVXXnJ2fUWoS6rpmvz/Qtg0PHjzk5fOv6Puezz77jGfPnhFGj/eRGPvc/jxiJPtvyeRWZ7OlKS3PNus1JycnHI9Hur7PTUAcTRORFGlqB6zY728xFqrKTe3IjRGapoGcVKoq9SK+8s/ZbDb0w5Hzs4fc3OxIEVarFXXtQERbjx8GKiosgjNuik9ZCnVe4cJs2LWGK71hqctWxOMtEfCbHzRmOm6MOj+glGar6KU7gjyB+RMeUGL+RcedBQAhebbgxFOQBQov6oqIZFUk8/MpdPLpHjArgncVGKmw51gl35qZvpTm/xem9uZFB5RPvv2OLu/Z13tY74USAHBiNE0mljBq8YMxhnW7Ip1qk8/Ly0sePHhA2yr6L6K9+Y0x1FVDCOritm2tCxcIcZweXFU13Fy/Zrvd0jQtdd1wPB558eIlq5Xy/V+9esVqteJ4PPLVVx1ilGRzerplszmlcjXDMPDkgw9JwM31jpcvX/Lxxx8D2j4dDOdnD1ivWyBp05N2xWZ9on0SB896vWXoekQGxjFixGoDk+zGGWMZ+54wam34/nCg6zpd+CHkRaA2pbI2W3fouuM0g+Di/IwqpxyVFmwmWnLhAVhrubm5IaXEatVqv3q0MUpV1azWLSlFHU7itVGFER2gaaKZ+P93hDe725LjbF3fki3coo34/e3rDda8+4V1FhEF7Bfot94VVXIJ5ji8SBDqLt/JAuZviiwYfNPeZHHgokDmwicxmq3A5B6NQh7kqh6J4hhp2s20+5JFSYmUjzu/tzi6ZNBQSpERLN2fdP9L07X8/1E44KxlHEcO+yNt29LUNX3fMY6watdsNyeklLi6uuKjjz5hvV7neDVwfX1DVVU8evKYFAaurq548uQJJycnfPn0S8ToWK3SV3/0Qy4uyiPBJiFJPH36lI8/esx2u83knltSQBtroMxEJdC0XF/fEmPk/Pw8DxtVhdQ0DbEyHA8d3kdOTrZYa9ndHqidoWlabq5vOT09JfpIDNA2hqpqSEnwY0DZiTUic2lxdzxOSqDve/ygswX7XBhlEPq+4+bmZjqvzWZDVati0LRjnDIYMUZWqxbvPfu9gqtNrbyIm5sb6qqhbWtS8oxjoKpbNusNZiuIF5xzpF6HciSZLbiK/oxUF6R/bsYqM/o9bbM0/ghey1v9hNnBj4Q0pwwLBfnOJ9M9Z3yS79lVL1mBiQ68cGlkCsoFicu8/T2ws/y3wAOW6czlse+Labl/d/yiO/7+rFDu38eipqR4MQamWZDv2N4PJeAc0UcOuz11VVO5Cj9qay/J7x8OB16+fKULtK756qtn3N7e8urVK07PznTEWBiUUxA8+/2Oq+urHFdXOf9+VCCRuXgkJaapPV988Tned5yfneFD4Nj1tG1Du1pT1bU2FakdfdfzxdMvsMby4OEjHj5S1HdVr2lXLbdXR26udhylZ7vdUtctu9u9tt3OC0hnBwB5OKrNLdFi0OtuqpoYe/2saBchZ22eTaijz/pjhx89vvYcDwcGrx2WYwgcDnuqytGamuN+z3G/z22pA32vtOu2bpTY5LRUumlajFguX73i9PSMzXoDSTsQbVcralcRh1K/oK3XBeV5TFRgIdfyZ6GZl+VswpfAYL4fb7jlX7dNMpyteSqJyCKrM+vuDQGbixDmg00svcU5pHv+RHbHS28KEbkXIshE6CHdFbpJ+eWswBt4QLbmRXFMnokwCbzk9uQz4+pNr2GpDvWXfAeWcdlbtq/JHv6j24xox18/akcUayxNrQy6YRi4vb2l63qurq558eIFwzDy8uVLvv+DH+CDWucvv/qSy8tXbDZrYgx88fTzaUTXbnfLdrshBD9Z5qoqM/0iq9WazWZDjJEXL17w4uVL7SE46shya1VAHjx4xJMnH9G2a7SVuWO332OdY7PZ8NHHH2VATjMDTd3kAqaOGBNN0zKOgbZdEUJkHINyHIxVF9ur2x1CRMQy9NqM1FrHarVmvVrjrI4aV/AzqrvvA7e3txwPe9qmZr1e4f3I8XjAIBwPR46HTokyIWmn42HMQJ3Jwg5t03J7e8v11TXXV1cIibbV4qcwenY3tzz/6hlfffklx8OBuqqp62oad363ImBe5pPRkns/Rt587Uf8wBT155+iAMow0DIHkTseR5pFbAbtlk0NFy2/ylbGjU+CdOeCyudnj+IO2CkFh7gLUN7xLhafm34v3yvnLYvzJc3v37nFS3WXFdLi1R/RAe39UAIAQ9drS6SYOOwPpBgxommo/W5P3yvFt++HLPTPePHytfIAHj6k645cXr5mGDp2+1uuLi/ZbNe5LDdRN9oVaLVa6zizzItfrVZsNhs2mw0PHjxktVpNXYGm9SGWqmo52Z6ybrfECOvVlkePHnNzc8swjNR1zaNHj6jrmr7vads12+2JCl0OE05OTmkabQlWMgI66iwyDKO6+WPui4jkIibhZHvCdqOkn6ZuiTFmCrS2RAtBx4ilpOXHq7aFlDgejkqOyspIGZZC06xwrmYcAoJlvdpSVzXBR25vdlPY4ZylrpTKPHptrPp3/+7f4wc/+IE26pAc56cMbGX3Wz2s0vgjC0GhtebuRZKr4koLwvn15d/MgpnDiZhCzuOn6ThxGsqSKAM5loDd0sKWYjzJwi2iJeJL7oLkCchqoUvT9hk3IJXvmTwtqgjl3YwHkCdQvV0K53LhOLcQEzSvXxo0yL0f7r92X3llRViasy0U7ru29yIcCCFwe3NLU7UYMVy+ek3dNFR1jR8D1jiur68BuLi4wHvP5188Zbs9papqzs7OSf4xX/zgu3z/+9/n4cOHNG3No0cPefXyNev1mhASp6enVFXFOGjacbs9YbVaZ0Zc4vHjx3TdjuNRi32KNZ+b7Bq8j/T9gDGWs7NzhmHAiKXrOvq+z2h6nZHrhKscZ+0ZdeWUXbjZ5Phcrz2lNM37qysV8rrWa6/aU47HjvOzB+x2O0QMm82WcRw49h1dp265MVqC3DYtx2M3DRlJSasDV6t1TrVGTk/POD/X8OTm5sDjx2u9znbFyxevppLrx48fY61lv9/jfUJsRQJev77i4U89YLvdKl4xajVkAaHmuvbZCpUMVbq3qO+k195w3POHFqCeKgFtdya54jPGmJ1/LS+OKeVR3SrIAtqlB6Y+DTHvr9CbiwsvJGIe44XRtHWpVNToQcG70qWIHBJMzTzyaZcrMWK0cSuzxwKzMtH9Fn+GSZDNnfvDpJgowOCiAquEJPcbkSzrISR/713be+EJjKPnZH3C48ePiT5gxXDcH3j96jXBB9brtfIDTk755JNP+e53v8tms+JP/+l/BmMs+/2B7WbLerMihIG2bTg9PUGEaaTWo0cPefz4EW3b4pzNk3j7qe9ACJG2WXN6esqHH37IMPjM599wfX3Dq1eXhAgnp2dsN6eMY+Dy8pqLi4c0TYP3iS+//IrD4cDpqVKCb29vqVzFkydP2J6cZJdWplTfer2mcNeHccRaw8XFBY8fP2az3jD02jug60ZSsjipODu74OTkjFWzYuhGrq9vGIYxewkNzmqTkM1my2q14sunX/H97/2Aw6Hn7PScRw+f0DYbxlGHpxixjEPM39Fn8NFHH03K6quvvuKHP/ycH3z/+/wnv/RL/Df/G/8s/+1//s/y+vVrxnGkyh6VzQKmU4bkjrGy/7lWRXrLz/wOaDfeOFnHIqmK8N9xleWuoJXU3vSZHP9PtQTZu6B8jpRnH5IR/7kJ6p0zFjKCL3eE+v4V3Qk5lq8Vj+e+9TfFS0i5g/RiJ9Pn5K137M279+b2XngCAnz00Ud869PP+K3f+i2aRt3X7tCpVXSO6+sbjscj4zjw8sVrXr+65dGjR3zve9/jN37jNzjdqIv36PEjzs/P+OEPP+fq6grnatpmTd93/Mzv+1l2t0dIwpdfPuP16ytF/hMMgxKSPvrsIz777DOePnuOqRymqnBNi2sadocjxt2C1e67u8OBB48e0Q8ddS3c7vda3JObeXo/4oNnHEe6rmPd1sRk2e1uaNuWttUiomFQT4CTM7bbDZvNlsvLK/pex029fPGK8/NzVqstjx8+4eXLF1yO12zWGpKst2vatuV43OOMy3UNOmLdiuP69oYwRJypSCFxe7Xj5nJP27acnz7UhT9GwhixUnF2cs6qWbHb3XL1+pKb2wNNe8q6bfnw8ROapmHdtjRNQ3d94OL0nDiE3Psv426ThVfhyH2JeNO/nSRR/z8tYv8FyjXN3aMIwmxX7wrFYrlLgeQkC1A5N7KgTu5YFv5EyJThqRin7LoI+HSuaTLJU8luuCdqEx4xe0TTlS7O5Y6ISv6/yQsgH6MUT5k7DUaWd+9dYcfdT765vRdKwBjJk2ng9etLraG3jnW7orKOoR/5+OOP8d5zdXXNxx9/zNXtDbvdjs1mw+BHbm6v+NYnj/hDf+gPIWL53ve+RwiJtl1zfn5OXTd0XQfA9mSLff5iCgnOzs45Hjo++ugjLq9e8MknnwFwdnbBxcUFpycac//K3/8HDMPAB08+wIhFxPL69WsSkUePHhBj5Hg8sj05I8XEfr8DIsfjgRgCXRe5ubma3EnnlBXYdT1N27DerGhaHSby7NmX3N4e2WxWVLbmwcVjhsFTOa01OD09Z7VqlZxk4PmL5zx79kIVy63W/p+fn/Ptb3+bYRi1H4JU3N4cef36RisFTy9yk5QRP/aMg/ZZbJu1egijZiLGceTkrOGP/cIf4Qc//AGXL694/Pgx/uDx1uKHAeKb8TCJRd+Nr7NF+d2lkEyvLwt8yrvlR7gL2s2gW64iUOs9CVyaUTJh4iuU92OKMwawELIS76tQpgUAKIsPMHkAMQvvHYDvbde7VDJlmzIOKP9BZvBUcYLi3icdCV++lpgGk9w9yo8OB94LJRBj4nu/+VsYcTx7+hViLZvthvVmgzWO29tbzk/PePj4ESHC+cU5f+yP/THtjyfw2affZndlpl5+Xz59yvZkzfnZAz7//CknuQjnF3/xF9luTmjbLX3f45zj0ePHPLh4wG9+97d4/vwZP/9Hf47LqytevXrFer1lHDzr9YbHjz+gbVd8+eWX3FzfYoxjvd5wfX2Fq4RXr1IeQOq52D7SkWr9kWHsMEZo25pf+ZVf5ssvv+SP/4k/hs5AiJyfn9L3HQ8fPubTTz7l9PSE3e1O3fw+cnpyxmZ1ykcffsJud8z05xX/xB/4lJgCw9DT9x0vnr1kvzvy2aff5vT0lGfPnvHpp98ihITgODt9wHZ7CgibzQlGKpp6w8sXVwDUteP87CExea6vL7m8vObly9cMw8g6V0k2jRZsPb74QCnL+yMPzi/YXV1jccogLFt607rFtzkB91zj+1ax0HXf7ta+ySooFrFY+zKfUv0B1DsoFN7JG1iY6SKEJUSQInjT3vU4RTALgWgGAu4OT1ye20JhfO2mhNFFLYi+Nikfqx+a06wgTs9BotY0xAXjSb6uwSDviRJwTicGHQ8HTrZbdvu9jlAqk3J8YH848PTv/X3++J/4ExyOB66vr3n06AOsrfNeEuPY8/TpF/zKr/wqp6dnfPrJt6bY/+XLFzx8+JAHDx5hcNpuu2r4zk99h816y5dPv+L6+oqnT58yDJ4/8kf+CNZWpAjb7ak2Bhl9TvE1OGfYbk+ARNcfeP78JZWzXDw458tnT9nd7DJqPzIMRz771qfaNyF6/DjSieDHke3mBGO0D8B2uyGlxPXNNa6yfPzRTyOifQQO+57a1VwerzRzEUX7GcaO/f5A3/U8evCYs7MLRM65vrqlcg0vX75iGALrdUtK2kfh7PQBZ6eCiJl6F2hPhC2Xl6+4fH3F8xfP2O1u2GzXfPrZt3n46COefvklt7e3WldwPGKdZbfTVu7EefHrwixWeummf527+vXbfeLPnVTZG5vWzr7xfgYA76TlyCW3ZXBMnk5dQED1ZErAIbkasJRQy/y6aMyuh8ndlOb5IPOZ3VN8i5MjlS7iMgt8AQELEClWQ4Ki5ABVUiFNsdQ0D7NQun/E9l4ogVW74smTJwB861vf4vr6Gp+0Zr3vOs4uLrBVxX/6n/0yf+rP/GlCCLx6dcnHHx/ZbDbsdjuqqmKz2WSr29I2DaPvp8Kj66tbjseeum6JAXa7HW0buby8ZL9TSq5zFZ8//QIwfPzxx/zd/+yXiREuLh6watfahjwEjHW0q5aqbnn4uObzz5WvsDvccvHoguS1A/DZ2Snej1xevqJpGj788AkXF+c0bT1NJN4fdhy7Iy9fvmS7OSX4wHe/+xukJPp3HNnv91xeXjOOgaura0Lw2szkZMOx63j27Dnj6Hn86DHPnj1jvV6z3+/5wQ9+wLpZU9sKi+G4OxB8YrPZTj0KgvHa2gyTcYGR2lW0VYNsTnj86CGffvQxq805X+6Vefj61WtEhPV6zeVXr1ifP0DIBCyYffsFMSb9NhYjvB0TmBt7zgDfm3uLvFH7vwALp/3fM9z51SlTodWQWjE69xi85yW861oWry+t/texIOeQYT6jMmeyhCCmpCzNnM5cDtWBXDWxCAWW2ZofpQjeCyVQyl0vLy/59re/TdO2dH3P85cv6YeB7XbLarPhwYMLhn7gcDiw3W65vr7m2HWM48gHD3Vh933P+fkZp6dnWnzkh9yEA168eMnpyQVto0CaVhi+5rA/8uzZM05PzxgGtepnpxd03cDZ2Rm3t7c8ffolq3Y9KZ3tiabdNs1qUkI3twPejzw+/4D97V6tpZWJqnt1dcVq3ebx5BXJKidi6AdevHjJerWh73q+//3v8/jRE54+fcoHH3zAwwearjsee168eEnbNlxcnGvrr+7A8dCzXm/45JNP+Hu/8stst1suLh4SY6RtddaCc46+PzL0nqpqENFuysPgaRu9lhBj9gg2PHz4kJubK9qVFh7FGNmsNzx69IgPPvhApyv5wMXFBUM3YLGYZN6oC1gChL89X/iu5XyjDPj+h5dx/vJdszzuvfeWcbye5L2dzgX4y3fuKx+5/0t625vveH+5yZsfSLJ04ws9eRESLLgNkGdBLDCCgg38xD0BEfkecIs6gj6l9EdF5AHwfwJ+Cm00+t9NP2L4yH635+rlax48esjN1VUuxAg0lWBNxX53xeADv/ALP8fN9TVts8GkKyw15ydnvL58zRigSxWjgLcNz6927Pd7Hj/6gMv9La9fXfLxtz6m2bRcXV3x7MVznK3xUecGbNYbDocDUhnGLnB9dcuDiwuapuXF/jl9d8A6aIIQU+D1Fbid5aQ7AZM4OTvli2dfgHW8fPWaV6+vuLg4p12vqRrHGAaabYOPnuevvtT2Xc0KL5Folbn3+nYPybI9/4CjT+Ai4qBqFCP51V/9VdbNBieWlduwqU7xLvLw5DGb7Zq22rJtzvjgwRO6/YCrLJeHjjEZ/Bhx7ZrN2lJXuRNxN3DoD8TrwOPHj+hyr4Lt9pTBCzeHgeOY6L3h/GQNSdjvO47dgNgaH8GammhAx3zNza6YhqHqf5AYo88urskot5lc8wQ5lo15DFfpT1CsvwpENIYyyy8liDpqegIQEwmMPiOyNTfl+wtS0+hmIHNC4Mv3U1Y+Nnc0jsXNjyp8llw0ZKbvlbRikkgwcUohhhAm4SzXqPfHzG3CjBCjZoKUnCWKAZiU51hGrEtgdKitYgV3QVBGmbsn5xLxlNMP2n353crgd8MT+KdTSi8Xf/9F4P+dUvrXReQv5r//x1+3gxQj680aUuRw3PPw0UPWmzW7w4rLq2uO3YEQEh9/9i0uX99gLKyahp/+zs9wPA4c9x03Ny84jjc0TU2SikN/ZLc/4qprtpsNq80aYy0JraffbLf4UTsXO3GEGJQp5y0nJ2eMw8jF+QWHw0FbbRNzK7hASoGUH+TucAskxBrEGPqh53h5ZBjm8VPGGna7W4w17A+31LVDjHDsu6n7UeVarKtI0bA5OWEcRpq2wlU6sajrevq+VwEWo5OTh4AzNafbM07OtuxvD1Supu+GnK6zbE7PuLm9ZfAB0zgqp6PH+77PnAyZUmImaU1DTOBDQofZJg7dgN3dEgZtQNJHZSeGFEkWrKm0XmBaZzpM9a79T3PaLafrZJG2U4Fm0WKrgHV3I/uENiubCUTlGDMPYOoVlNIEnOnfKpoCufPO3M48B9HT9wpwqOwgmc69MA/LdSx9/ZmrlwOX3MxU0DZ206fSjDOUECRQiEA6qVrrFCJiE8aCsbNSMCZNMj1VSZjpIpkGQy5bEH8NOPiTIAv9c8C/m3//d4F//kd9oWlbPvrkYw7HPVVd8eTDJ3zw5ANF+61Q55r34D1d32EEzs/PePTwIWPf01RaY/D61Wv2u4PGS0mBr8vLK2pX8/jxY/q+53g8UNUN5+cX2h14GOn7QbGBvU4qKrH8arUiRJ0S7Cptta3DTGMm+6wYcn3COI4Yox2QtKtQRSJN3Y289xMvwPtAXboNxzg1OtHefdqb4PRMOyBJRoUvr16DMHUVKjMOrTWcnZ2x3Wy5ur7COcfVteIGfd+zXq0m4pJeX89+v+fm+oZxHDk7O8tNVvxEXNrd7thnWjJA33VcXl5yyIAt5DZcQa3NRJ0tMXx5sBPIVf6d3doiDClb3dmSl0KgBftt2nmaPr/clum8ef8oJrEAAd/sbHS3y9H9vgh3qMSydL/zcct5FwW3OM6Ux1+m+O6dB4vXzfJ4mUJtjLa1N3nG49tmKkpWesWrMKaMXput/5tp1rvbj6sEEvD/EpG/IzpWDOBJGT6S//3gbV8UkT8vIr8oIr/YH0a2Zyfc7He4psJUlsNxz/XNFcM40K4amqbh8tUrDIntds12u+G43/PyxXOc1ZZkJJOnseozqKoaQXCu1nJbYznmyrsYQp7o69nt9rx8+WoC67SbcJkOrECjdgLOk419yOCcDkLReLtDBK6vr0CY0pWlwKmq6glPuL3dTfehbRrWG20wutvt2O12GGM4Oz3NlOKBBDpQtW60g5JzdH3P4bDHWMPZ+Rmr9YruqGXFZSCrD4HRj+x2t9o1ab+n6zu6vqPvO5xzPH78iPV6zTiO9H3H4XDg1csXXF6+ZhyGbJDzIMykbM6yCNUF1vu4LOl563ZPiNT6F+7/PBPgjkJ4y2uTX/FGCnLx7/RnXgiZiz9hbfJuATd33uONf8uxJ0IRafIwlufxdceY4/vFTxZk/ckzJY3JY+/MVKdglvtZ/mdKmLVUBLI433crgR83HPiTKaWnIvIB8B+LyD/47X4xLcaQfeuf+DAd+iOd7+nGnu//8PscDwd2+x3GWm3E0XtevnzJH/j9fwhnDYfDwPXVK4gea2pWTYuPG5xx+MEj6A08OzkjpcTt7Z5Vu2a/P9IPA7fXO8YxaNPNbCUfP36MqexkyY/HA1XlOD05oRs6hqEjBI8YydmFNoNvDZc3V6SU2O/3nNSn1HWVB3ckYhyV04DGiKVlWtO0bNYnONewuz1we3tLSobTkzPqpuH26loBuc1JnqK0YXdzi7Nu8opC8Jy0W3zQvodt2zKGgd3+hiS5fXkIWCM6JzC7w65ybLcbzs/PCSGw290yjoriX15dsj8clGJtrCqePCRlNH4afCoIKUaCTzixU+yr2QCyW5pj+4JoM6uJqMG3Wv/sgpcFa3J3zFKLYCYvYy7nWfbsS8I8zpxsBNMCA8wnNbdFX3gmd9bl9BsgeT7BrABk4cUI5fgpA5TaUGQZGr3J3Z+Pd1cxzBbdTJ6AwiZmoVTMIr4pQ1jmCs7EXaU190Hka5TAj+UJpJSe5n+fA/8h8MeBZyLyUb7Ij4DnP2o/wzBweXtJs17Rh4EffP4DXl6+olm1nJ+f473n9etXHI+ab3/98gVXr55jJPFT3/4UkyLHwyH3CNDGHJWtIQpnZ2d0XcfL5y8nV9paS98P6DDThv3xSFVVfPzxx2w2a0Y/8ujxQ23iWVW0q0YnBlubOxppefJud0NVOdabFaMfs8UKhDDmCj915fpe25MfDz0nJ2dcXFxwPPasVxtOTrY6UchYmqambWrtTjyO3NzccnV7Q4yeqtYUqK0UJ6gbncm4Pxz0/l1fTzFr0zZ0w0BKESPC6ekpHzx+zMX5BevVilXTst1sWK+1t0JdVZOlcGZ2OZ3THgbOGKzR+DiEkRBGJMZFIcvMpNeUV5wlUphorsWDKOdZqLplPoN2+o2TIlh2BIqLzy8Zffe38rIK/uzeT4i6MdOQ1dn9NpMVlSyEU15+sqzy1nO/X7CjCmEy7zkUKr+Xfch8DpP1zsJ/z71/U6hLjYDcOfb9kuVZuZUA693bjzOLcAOYlNJt/v2/BvzPgf8b8N8H/vX873/0o/aVSLy6ek3dVtRtQzd0kKBqalbbNWIt9c0t4+tLoh+5unyFiMUPPecnZ3xx2HH9+pLkoDKOqqmpm5qh72mqNtccjByPHYKhqbRXAcg0EfjB40c8+uBDuqdHAB48fJDj5AheNXLbanXg4XjgcNxzOBw4O1eyTwg6O8G6lvV6zdD3XF1dEqPndneT3e72Thuyhw8eAzo8ZL1ZU1UNIepYM+89McXJFbRGp/HqgmTq9ns47Lm5rXj+4hn90LPb34BoaHF6tqUlsV6v1fobCyFhG9HOwa6aqNSVddjKslqtCRnzkHwsbeg6IBiGvqdGZx/YZHWcmrmfPCtrZPmLgmOJhEStBExJQTRtySVTiCCAJ2lLrrJCUilLXtQbLuNsPULG6+5ZvRzzlw8mQKyZXrhjpWdyQrb0GTqcBH2COadGsVNWAab6/wJGKO9hDv41l68gSZqUDHqcDPhNGGK50pzlmEx20BdjitP0ZFPan7wtXZreGaQBP1448AT4D7PGccD/IaX0/xCRvw38n0Xkfwj8APjv/KgdJeD56xdUVcUQRhLQDz3PX7wgxsTjR4+pP/uUr756xm/91m8hKbJZbXj14hm7q0uG4UDb1HiSdr8VXZzbzQnea/vxGLWrr4FpctDx2EMaOD+/4LPPPsM6vYHr9Zqb6yvatmYcBqwRmkaZiSEG7CB5ArI2Ar29VZCtqiqqynF+ccbLZy91nkEcSUkrIZ88+YC+H3j65efcXO9In+ootN3tXicNrVqCV4FabRo+ePKYi4uL3ND0wO3NtRYlIRx2+4m1tj8etfHK2CEWjt2erj9wylZpxcNACnlCsrFUzk0tybturx5SpS5+VVk22zWjH+iHIc8ytJAClbHTqLRyL2Luzy93zdBsrYtfXt5irnabXHoy6p5mVGH61oQe3vE19Lc7YJz+G1NacmjyMRcLjaWVvAvYlfeWZbiT6z/hAfNrd1MD83VK8QZKfp9IKaGaQcc0CT0szzmSknZvTgRi3o8xGnqVBmKaTmXySKYmq294JuXevtsb+HFmEf4m8F98y+uvgD/1O9lXjIF2rSO/vvryS+1Y4ypMdt832y0ilpOTE169fkFMCZcn5XYHYdU2PNk84eagffnHfiSMnu12TXfsODs9RVq1qOOosXQh8IhYPv74I376p7+jQhtGjBW+973v8Z3vfBvrDE1b4yoFAceuJ5FwlSGlwDj23N5oGlFbiak775yhaWqcW5FQQXG5RfnQ+2m2n/eR290tlatxbiBFWK+3XFw8QC4Mjx89oq6V23B9fQ0pEXzk9dUl5+dnNKuG0jtw6HtcZXnx6rVaaudwznA4jgx9jzWWVd1QO4sx2oTVjzEzLcH7gX446mCLFAh+ALShSl1VrJsWh2XlWh2FPkSSjBBtHkEeJqBKFcGi6DYv5snSanSRF3ARqlkiU5xj/wlKTLlM+C0AZFr8//2BO2/6KSo0E9GmKKtUBH4+Rvl99gIWx16mJSYtJYvvmjtCXzyAO+c+FSSV0a151ymoR5N1YExM8wbVY9L7m5IavihxcQ1fDwTe394LxiAI29NTbm9vsbbieOwYncdVNf0YePb8BdGHyQXerrc8f/6culZA8Ic/fMZNd6TZKNAlwDD07Pc6e+/V65f8wT/4B6nrlr/xN/4GDx8+YrVqc+3/GX/kj/4CJMP3f/B9VquG4AfOz085Ho80bU3TNBy7Az6MhOCxVjg52WCt5fb2htvbW5pVy2azwlpVFpvNhnEc8GFExHBzo97C3/ybf4uf/dmfzd2EPN/61rcyO7GHJIyj53DoePrFD1mtdL7hqm25vLychmleXV3i/YBzFmMEHxKudrQ0hDiCQN00HI57qCqctUTrIAT8MDKIoTcWk7S/Yn/s2d/usJXTKUMpcsj1AfvdnsN+z6qp2Jtb2nqFS5Zh6GirlYYDCwkrST7FBphq40Vg6IYJA0jZHUYk4wHhbtFLtpwmze6BKSFDdn0Lqg7ZK8hCPcXTZGWSIiGHGSan3PIM9RmnyFtpOpsyTjFlAsq1CSrcFE+lnG++TnQce9ms0UY0KSsbESU7FQtuMqYSQiRGT4gB5wfEgrVgncVaRQeVm6Q3NJIWzW7QsDXN9+J3ogjeCyWQ0Aq8tl3R9wMJ7ZRjTUVKkqcBKynF5YKhYexJKbBaNZycbaCy9D5y3O958OCCb332KW1T8+VXXyAiPHz4kKurK2L0xOhZrRoePnrAg4uHtG3N69evub295uOPP8jnpNY7xoiP2hsgxoCIjvBWLayfsU7daGvtxObabFek1GYPwBFCCReq3CbdZR7+JV3XM44eY5yOIPcDqalxlcF77Sd47Ht8HCEy9Sc45g7ECeUX9P1A73uOxx1tHm56OGhHIiMoAy56hiFylERdWYxUhDgw+p7R9xyP2lS1bWvWY0uIPjcvjbjasVmtWFXruUszOn2YHK8jRQUUwZyfsRjRISUli5DjZWNUcBLK6DPZ3KmBngWaEl9PbjZ3Xp8VweQ7cFdaJ5BiAuumLVvRpaG+s894152+u+/FridrPu88j0lYKJsyE03nM9rFRNepVPnukcg3dmo7NE9x1PfmUelvKoAlJvG27f1QAglcVbESQxgDvRmoXMVmvaVtVloL74TN6Rnb1Rpn4PXrl4yjptrqpmIrFjn2kLSF+clmnSuuBOcMY3/k6voSkYSxGuOvVis22zXdoO8ldLqvpqX0pnk/EpPOnFutGqrKMo4D/dhrNR8xt+SyGVNQYtHJyclkVarK8eWXX2GMy9OID4DOLAg+5ZblpRlJJMWEdY5zZxl9T+cT/dBp//psvXR2gKLprnKsVyt8GBnCACJYZ4kCfhxZtS2rVtuSj33P0OuQV++Vxtv3fZ7FoHyMomBSTn86Z0ne67j1zYaVW1NZrfyMhNkbmEA1FXqN9+fMgbrHMctbQcJTjrMFsxg1LnEeuCkpswALqFe88GVsX6CDBXiXz4JkkjJ1J1c+IpO1Tot/l2GAuvCaIiwhfxa1pbK5jzje2SQ/J2UOmizAMUruVKQuvIioY3LvmjSU1/MvYKek/N0pLigKcxEeLMFNKanZnwAm8Lu7JaypqNqaOCac0d5+282WVdPqwkc4Oz3nZKNTd5vVhn685JCBLWNq1k2jVibpjIIQPZIi6/WWy8vXvHz2FUiirh11bUlJGIYDL18+Z7ffcXp2wjiO0zmpAciTh4y6zlXtSIdIP3YK+qGeQVVXuemnhgvGwDCMCuR5xzGnIT/88CMOhwP73YGePhOSlNxRGpxqiXBg9J4+RMLotcpRFNSzzrDarHC1U0tsTG6J3nDoD5rGtFaHtlYVq9WKi7MzrDF0hyOH21224urN9H2XPbEWIdH3HcOgTMimqnDWEAzZk7E4IxgrGocmpq43k3XNNfup1O6X3nzFqk9GudjMYtnTXaHKLr9ZAo/ZIN61llkm31jnCwyCRBKZm/JMQrcwkiJT3A2qlPX5zDRd7lvY+ymKe8pgnkoU73gx95mKIirglqL8CkdCFUGkALBpUZEpJR+SD6lTWIoinFOeZuJovG17L5SAINiM6DtXI8nS1CuauqVyDSIGK0LbhsyvD9Rtg+tqQoIwjhnBX1FVjuBHbq+vwCh55uL8lGPfcX1zhUjK+XuNRYexpx8HhvGo9fxTsYje6Bi1PZjmxDVvXteOQyeTlyFiaeoK5ywQSTFyyBODxnGcEHVjDD/1Uz/Fs2cv6I5f6ACPNLu8QC7g0VmDfa/eRswdfrzogjLG5HFsLSnphKRh7Bn8kMMOR93UdN2Bpmmoa1UEbV3TuBqHjhRzxpJiUpwg9ne6F7dZocYYYUzUTYuzjhQigYAqbtEmFjEixi6eZ/agpTD9yAUtpca/aAF9f54XsNhDXrN3KLb5O5Mnfy8rISZbyjTP4EvlGooQ3OnOIzMTB1QHmWJh1TcpgYUSo3LIcwcI+LotZdB+AYwglI4kBRMpiqikB80UUuV7lw2FAoJJ7/kEON6tSUAkezlxoQAkeyFv394PJSB6g2OMSFJiilpG8D5hbZ7DV9VKhR0Gqqbh9PwcSdpMJPkMsqTI0I/UVRaUlfbGPxx3jGOvpBeKG28JUfn9KXkSIQM3ukjVA0yE4CGCtYJULjcKtYRg8iQjbbZZVW6ygsfjga7rp5BAacuatryqb6iqhqapc6vw2YVrmoazszNOTk+4vjlMSslYO8V7rqrYbrc5G1Ex+pHRjxqiJC3GOru44PpaMy8heJhwl5roV7mlm8nYRyL4kZSHlUoOJ4w1aKmApXG1ZgR8wOd6CmN05IjmrbPrS5q6Chd+4pRDL5Z+Ibi6sKd3mS3lNM5kUgRq7GVKp02WdPJ+Uw6tp6A+YwyahRCYyUJWyUBmoYD1+6bEFQhRj5tmgc2xznS+9xmH+RK4kzmYJLz8k/+TQhbSEKlwQJjuWppCnJg7LKuCA5NJQ5GiwwRTPLCk+ZFZCbzjPPP2figB8vitCORKNpJhHAJBhsxcqxDRCTz9MFA3LZvtBiFyOOwZ9h0mGUL0dN2Bs7MtDy7OiSjR5XjY41ymraZAQpuDiBVtM21gGDoqq/X+5bmpEGdXzBhFagWMNRoGVBXOGVzuz68orU5TVsyASWC899zc3OY+AzoPoO/H/NB0s9ZolaGohQehMkpBJiUq4xC0ocfp6amOEM8hkWRB3Gw2XFxckFLg5cuX2b3vaKo8QLQ0p0AdyapyGCM0rZKounHQcxeUnVg5KuMgah++mK9LAUdDyl15poWW4+nZ7V1wBko8nZG1EnvPFjG7sBhtG55DwUngye1DJkuYvYE0g5EpLlJtMRKNwcSlZVyyA9+RHVjm2XO24I11u4QE3nAL5O7nFiGAMWXV5+OnePd6iq5ZYCss0oWl9dkyS6GwTOE4GIrHVI7P+64EQBiHUcdvNS2VqyGJFurkAhVrDVaMlrlGnSy8PdkQwsjgB+LgcRiO3Uh3PEzThrwf6X3Pbab4qjBr+WwIo7aQSlGBtKijzsc8ilyBOmUUWmNnq5EFHcihxdx3v8Ry5ac8ePU24NmzZ3Rdj4i2HrdWU0bWWkohSt/3OgbtqENATGuoKqUWO1FEuao102CdpQra+KOqHN0gE1BJStP1DMNA7wb1nAalNTujY8RK0dF6vcZWFWm/45iZhKqAHSambDlNHjOGCruZF/O88ItQp0kAyFTcGBf8enS5xjwfQFKchNJgFlx6k+dwzHN671s2M4GCOo2oeHFkBVCachYlsOzQs/QESgz/RlMOkcUzXq7cEtrcE7KlN0C6owjKfstalCAL/bjcT77alIHBtJiPmA+RHX8Kpfnudxeg59ekC98LJZBIBK9NIHTunoUkjFGn8QTvOewONE2tE3lRN9wYy9D3dMeRGCJVZRFRJtsw9IgVTtZb/E3Qvv65bsBYQ0iBYVBrLdipkWPygZg8PowYEUJUJYBJ+DjivcEHT3ZbdA5fJJ9XmsC9mJQOK6I962IIuKri2PU0dcvo5ynBIFSVgoFA7ogUOB4VvSdC7Soq12jtecjkoKHHBKNc/nIvY6Qfe3Y3N9zcXBN80IrJoOeVQlQm4DCQXEVdVVhXMXrPMHraqqJuWlKm+Bpr51hWEsY6TLJYYzV0E0sKaRIS3QzGJKKUDAAKypkc9KacESixb86XR5F5fgEmd6BeegOzo3w/7TUpAYGgGkA/mz2BYsm1Th/s1LhzFrzZCc/EGyOEmPtISBG8EuKUPoOlKCpxXx+Uwp5UaNULAMPkikBrNW1qF41CyNbcZMFOaIbCmewlF42Bpl0taAahXMtCqUUFFb5W/t4PJRATtdFYuxJBogcMziQl6AyehHCzv8Fg2G63bJoTGCEcO4xvsQ6G2NP5nvXZCVSOy90tj9pHHMaRY4iYpoaqok+Jbjhmt17Zc5Gk9fOxJ0jAo41ORQTXOKraInZkCFqG3K4cpg/0/RE/GlYpYZzDpEQQYUiGMRmiV55BXTcYtBaiso7BR65vD/g+UFhpdV3lOoRchLTXJqAjhgdPPprap5ESXbdHJFJVDu9Hgu9JfmDTWOrkuX31HBk6jA+YEJEQsUnwSRh84NgPjDFhmpYQR6p2hWnXjM4yJmE0OVORDJXoQJOu6zjbNkQjrM7OCOPIqlmzu9khkjRVmBE9I9pGfkmc8WY/CTVkWZlSX9kSM3sI5fW7Vq0QfkQVFBm3yTiONgAxxBTxIeCDNkYpsXWSABJZCYhYBJfRBjdNLwqFrWgTXjyBQJBEFIOPluCF4JUfYmstqfbJE5IHazCVgEmMwTP6QUNG45R8FJRXUbuK2jm9zkvPpmmJKbHfH/A+UdUtVVWTgH4cCDFQNRvqtiaEnuNwRCTRNBVV4whR17MSyCwhKvXeD1r1WTXNO+XvvVACgmrGGCMjnpDd7RQj3qsVc8Zm9DQX1FiZAKyqsoSkAJV1Lg96ULe66zoE0aGbVjBOXfeQAiHHt5OLlZR0o9VsKRM5ipXIvPT8byEBTddgJM+Wi6So56zpxQhJlVop7rDisLaiqnRcFmi4YzLmIAKVszQXihk4q8cR0bChHLtMKI5xZBiH6VxiLlJRCvNIimmaZBxSypiHTFmLkDkDfd8jwU7MxLcxz2IBzRbIs3XaHq106y0ivOyLr/fQTgqgTCkqYF5RBlaKJ5Dv61u82HJsa+3U9celNGUIIynjMdnlzp7Z8vtOIojN8x4tKZcu58WYFYn2T6A4Q9bhgsVbQ3CGEHsdGkNpL6ZQncnPyyTYNGtVMikRo4ZBRgwmCIKu8+5wwIpgq5q6qnFG17ERQ4iRylhq57AINoIxFaZKJHKvClMx9HtN+YakQGMyOCxV7TDZA37X9l4oAWMNVVNrb7TsYhdENOZ+dQHt72atYJxgKwNiqJuaJlvIELWVl81MrOOxZ3/Yk1Jis92qtTCKmEevblJMc/yoSsdPaRlYlLBGtSyCft9Zh1iDM1UOgWUeZAqUBWiyK73ECJxz078JIQRPynMIrNVR5ZWraLcN19fXDENQ0LKqNEa2Ns84iBn915JhLZQqiznk9KDyHrzXVKexjrpWDGG1WrFarzNxKOD9EXF5VtACCDPJaBNRtOoyLpSAiOIGcxrPMJfBZXc2A1iGmoQq1jnHnuY5foDlrhKAe4pAFnF9rgQsIQWoSx9TIrg44TkuKzVYhACEfBSDMvc0XRoT2GTVIwgy9xJMBqiBihQsMRg6nwgxh0kpYJMgVmtEUvYcSgm2ILjK4oxTtCNlAlRK1NsTNpsNVVXjR51WTW6ZHkIkURFJDMeBw6HH1VZT08bQ0rCpVpyt2+l+jMPIMHhSjLiqAmMzyPz27b1QAgVBHuOo8XSMytLMVkJTUVHpqdaBhWSUZeWcoWkrxmAhZPApJUJ27/vBU0A+7z2Sx83M6ReZrL73IzEvotJ9rmjW5AM2MimMmCIu525J2m+vWE9SUDZdJFv/OFlXIy6DgBoXq+LQkuWUSrbB0bYtjW2whz2h0wGk1tUZTLQkZq9nDCMhRcQajHEMY0cYx3xNARg4dh3GVayqisY1OKfKwDmHqypiGuh9gCHcAadCCBNeEoiMwRODHqsAdJWzk7egX00ZNIQZzzdayswia5A/Oq0Dcmx877XlZ6buO8bknpFMSjot4nufIjFa/LSe7rbjjmlEyTWWvKAUf8r6xKbAaAwpudytymijv1SRjCU5oaq3lAIp9Sy9cv6dZpC89xP20FgtinNiVe+FQAoZQhDNfsWk7NHYa9equqoxdUWK6tUmr5jM6WpLVTn64Ug4BPrxwP64o2pqqrqaZm0YY3BrDRdMnJ/p/e29UAIxRfbHPcfuOCmBFPShWeOmSblWXL7ZgdH3RGOVtWaZp79OKLFk8Ezz8H030uVpQIioh1Fiv6RgYggZEMr51pAXjh8jLuTaftE2TzFGQnZ3QwgQZkvjhyPjmF3SJIrqOzDGTddXkGafOw0Bk6tvs6Wf7gVBKw7DkAlJdmKAxTyWXPsCaFPKcRg5HveM48ih07kCSQTrKuq6maYmh6SVlHVdMwQPfk5pzu4tWRHq+Y7ek0LEOlXLmmqdweeCSwkwjdaGnAa0d1N+SyQ878AYM3URKpu8SwlkZH/GBKYPUeWwJ9ioDVEnJZWzNV6rHjQUsKRoJuFX+1PhIgoSp0iKBpKD6IhiiREqU2NLpil6fAyISTkVHQk4uuOeyjiMjYhXQNmIobEO5yqssZw/fMAwBG5vbgj0GGto6hWbzQnr1QoS9P2ICLlBzCOsc7x8+VxTwF3PxYNzTk5PaVct4zBwfXPF4XjEYKlSBVTvlL/3QgmklOjGI11/JMQwtU4GwVkVcpP51iYI/dApMCTqEocUQIQklpBGdVdjZPSBrusJUTWjWlObiSI5hiwLMTdnIKPiIUII6qqKCNGANUnjNWvVtmUCiveR4D2S0f4QZ8s/c9Q1Zk55cRYh11OwWDtPK4bMM8gDSUv6zzpt4eVsjvGyl6RgkMkehSobYxwxjrOyyS3NdL5A0Fg6ZBudv1/SY4pGp6lXoSLhESuCl4BJGgJEAhIT4owa/xQnBaDTdOYyXRFtT67Xe5c2u9zsG6/cUwIwN9y0sxJwU3qQifUXbcTHokizPioZCxsUB0hCDJI9whKZJBJOC6GjwcVATBZSQwyWGBwxGCwGm/SoRgRnXCa2aTemEEaqEVZ1Q2WdVm3aipPNCeen51rPYYRHj7fsd0cuLy/Z7fakKKyajdbOtC1tTpuLWNarFednZ1TOcTjs2B8OhBBYbdacnJ3Rto3O7HjxghcvnrHb77UPgUn8VX7prfL33iiBwQ8MfpiIM7EgxWJBND1nJGE89GOOw4xTlz1b9MI0SxhCTPS9kl6SqFs8ek9MCRvtNPWldHXNTZ1y48xsRUKYMwQUN3LZ8YapPVRRPJprtrNbDIjklGbO8fsx4GzFKOOU7qxrR9PoPIBx9NrF2KuOattmwglKOl5EQc6VW1PVGkYcj3t88Blz0PO2o6ZZRSSHRz3GWaqYuwdZh8kwhjGGJBkPCLObLaLW1DinoZQogzOIkEavXY8y23Ni4WZ8sOSvRcAZd1cBLDwB4e0gIPNt1HME3KQErGIO98DLosxCjJggBMmhwCIlaKyHJIRkc3cepZFPnYskEpMqhxAdMaoSSLEiBkuIBhkTRjvPKxvZCG3tWK1qKusgBeQssV2vlaI9Bpqq4cHZBRdnF5CE3e4GfzyQxpG1q9lerGmbFet2Q1U3WOMIY2DoB1IM7A8jh5c3E4jsXMWqrpHBQKfNddtmRf2wZuvW7HY7TWO797x2IKFVcv2oBBWDmaqubFISTUhCMl7TWqNKgrV+qjRLgBiLWKtocIz0/cDoZ4sB6nWGEPKD1oaWhflmjc1dhosl1Jba1pRVPa9wBcbUbdf+kj6j3DmPPBFOBJfd0RhzxWDQ7kVTtsAWLKAiJe2FMPQ9ViSDeA1VduEVf1YvxllDVTUgLbaqNFQ6HjAGqqbVSdlGiUIhRrqhh4Mizm3bUjUNVbUolLnHL19mTZKkCe8TBFs5JCVCDFOHI0mFmpupvYVFmBmBlZm74E5KoIQL6e4x37UZZOp4XPoAptxmC0rmQPLfmm9fRh0za0/L1G2U/LzJ2JDcmSQcE5gIxIqUquwNOGI0SErYpDiGdYa6smzXLdv1mrapMTHSHQ6crrcYEYajFoE10RH3I5eXl3z/e9/j6dX3AWG9WvP40WM+ePQEqVYweKVoR6hxE67VHTr6QXkstavorOVyt6dpV1xcXCiTVtCeEIcjKQZc9W5Rfy+UADk1N4yDAk1GCTTzgikfi/iQppiyeA0pJSXDlP5tmZASo8a2JqVMznCzhZ4EFoIPWcASMUDMoF4sVbAipKg5aEELhjQetQgWa9D+gCHk78jC+qu2NrlKMEbNO2+3WxUmmxuM5v6FpWuwdY7Kltk5aWIljkMgJiU+GWsVHEsRax3takVMCe8HDNpYpIBnse+nNKCxBltVSPAUKQwxaMpskdYrFZmlcMdYZfFJUg/KOYskxfNTTHl6iNbKJ5Ei+3M6MWcJikxmdZqPNRNcgMnNXyoiDR8mVj2lnMeIoQxELt9T/ZCZnMa8oVwSxSMwSozKXBFtdCpTUZMVg0sQoyPFClJFwBGSNpZ5fP6A05Ntrk6MnG42HPc7DImb168wQ0Jc1LB0GGlWW8Zdz9Nnn/Nrv/brvHz5gt7tiSFwfvGA0/UpNgr4yDj0BB9IUahshTUV/aEjjoHQjVzvdvy+7/wMx2PH9csbHjysuIk3HG+PNG3Dze0NfX9k9J71un2n+P04jUb/ADpurGw/DfxPgXPgXwFe5Nf/Ukrpr33dvjQG1eo7xJBMxOQHuNTiMTeoHKO63IUnrmkypiKk4osmIpKym5c0FVli3OJjxpggztztVLCBVFJDSXtAiGYKkkG70iTJqUQlf1RF0INWrFXO4JyGLJWtNdQonWrKtQB13bBqV1S1y51lPJVTOrG1MI5a/CMyu+cQJgHRFmWaVXE5/acNUJLyC0Rw3jP6MXs2edouOe2ZfGZFRoyZ88lFZEqIRdLaCSNCyjwDsYIJmiUgW9CiMFJMk2DP/fTvxfvpbhFOThjeCRXe6JAjxUJroYzJWMuyO69iMYohFWOhCP+8L5ONhYgFmbMMKQODBUyMKakytDV+MIQobNdbtptzPnv8KZ88/pBV0zB0R8ahp7aG33ylU7OONwcIkcMA/bGjdjWusex3B37z136TX/27/4CYAqvHjQKGUWhsQ2UrCAk/ePzoCT7SxSNGLP2hxxrH0A34zuOkpraR2jW09SpjMwaJht3VnmHsGL2/E1Ld336cHoP/EPj5vFAs8AXadvx/APxbKaV/43ewM1UAMYEoEqtNF9RipwzWxBBJIah1kkHR9EQm9+R2zIkFwIO6qfm/sq/yVrrzW7Y0SdFrmxdJypVk5OOEoHUEkjMI0aBED5TFpjwER7KSQ4lMrS1kFLRrUtdpCqepG+pGR52X/vYpxVzkoy66wU78BXJTFISpLiDEQAx+yjR4Hwg6X3vCNKy1uuhzHF3AySSySKPN9+ZOSg0ohfjTPZMFwJdSjrezQshKq7TULgM17gKBUv43H4NMIprCr1lBsFAi03oWoWS+yjcSs5KfyU7TY56O46xFyUvqzU2pxqTmw2JJGPphQBCqqiYMCSuWJ08+4g/8/j/MTz36lBqrqH7niclze3nD6+evuH19yfnJluO+Zww9tatYr9bEMfHq+Suef/lS50XWhtfPD1xctKybLY1rCGOkDz3j4PFjUNbpqM+VaDDiGY4D52cXfPXVM07PzthsTui6gbZtOTk5Q0jakep4nOYkvmv73QoH/hTw3ZTS9+8Xdvx2tpSSpgSzhUqB0pw1u+3oQotFEQChJ1g3Ww/jVGgX7mRJpRWQjPyAY4qZYDKXuRZLKykj1KLgXso4gEEwSZRnkAUsh73Z9Z9xhxA1haRYQ8kL63Gs1fzvOOqYs1XbZkBQSElToRNvQEYdvuKV5hyjpkytqzQDkav9ymKP0TN6teoxZS+I3HSkqhTJLkBmZmMayyIVOVvmyWIaRb1MBvpmF2GZ789WdVGZqNhAwVryHIM7wUDZT97l0isoUH6BYBafLWtiOc5biTlMnkjKbYbSHUUwhw+AkrxQXkXZccxKYGqPRiJVQNJn6GzFenPOJx9+xk9966dZB8ftq2v2t3uGrmfsBi5fXRGHyOXL17Si/Rf6/ZHmtMZguH59zeff/4JXL19jjKN2NTeHI+2TDeenD6mrlnEMSlwbR1XoPjJ0A13XU9ctRB0w+2S75fPPv+Dk4oLtyZbbWx2Ic35+we3ulsPxyP5w0AEx/wiUwL8A/HuLv/+CiPxLwC8C/1p6y1Ri0bFlfx6gPasgFvcbXWBZGFX4VBhNEkLSop4UtM9AWYi2lgmpJ81u/wI2RDvp6EIxyTCX8AqTuciUZSOiaDg5dmTOT1uMtu/OFXba2WiBJjGPgjIocBizYItIHmAiuQdBlUEumxVBwI9GeygWkhNBKb8hsWrXVFWcXP5CAdatdCnSeymixClxFmdNvsTZzY4pQsy1CjmWL/F7yhY5TeOtmLyfJHGq7NPCCy2PLQj/pBzN3Xy+W+ANxfKWc9H9Mp/j5AEU1ZSYinWKB5EVRZTsrU3qR73ImEr3pEUp8KS4yurKCiyjDSIJks0rJrBqWkYv9F2gcRseP3zCyeaUvhu5fXXJ4fUNox8Yup6b1695/fwVm2aNk4rjvuNss0GqROVqdjd7vv+9H/DDH37OOHrOTk+xznLRRB49esLJyQWC43jQNvUppzf9GOi9Vy5H8Aydp23XHIaeMQWSEYx1NOsV5w8f0Kxbnr98jk8Bn6KmxtufYO2AiNTAfwv4n+SX/m3gL+fn/JeBfxP4l+9/Ly3GkJ1/sk4WXSQpkYtI1YXUjkPZLRANx3MXCwhRATPIJB4FycpDL+4dAhLmXnfTHLkM+hU/MSUFt6bhjqKZBWfstJDL61UmMGlxiMmW1U9lxxgt5ChKoPTRK99p23biBKRI5qeXhZwyUDfH/eM44n3E2Yq69irgEwbC5PJbmzJIWWiiMrnmCpgy5c2V4ZjxgTSP9UiLzjtlBp7CXvpZKcrCGsQqrdskylNDUEBtri/Q3+09R6AwLBWES0jMFp2EJAVFSUykJZMK0siE4Jf9ZYhYHUhhmlQUi7JDAeWyzxRzOJLIY8N0HWjwpZWEcVHHYMQBDj9GDvuOm8sdG1Ox3Z5w3O/pdweuL294/eIVHzx4wHa9pTKWttmwbrYAfPn0Kd/7/g95dXnDarVifaLt7D776Ds8uHiMEcfuVt13a0V7VwA+JaIkXF3hY+AwHGi3a54+e8p6vaUPI0MIbE+3nD48oxt7vnz5TGcWVJZuN3K66Px0f/vd8AT+68AvpZSeoQ/zWXlDRP4d4K/+dnZSVbU2rciL2hmHNcqoslP9p7rjlnnSirYey273NBcr5bSXz3hDWVjlaMroIo+mLriBAklel3IuUCkEnsq6SeGICLVzi7ReJPqQax8iWIdYPXdjrB5KLNbO32nbhjJ+y6RSt6AWfhgG/Dgi1hOCTMNRi0LQ4SkKgN6vSdCZhzIzFu9tafGj2ZSYZ9plRZhBthLzT5soczFl0K14RNHVSEyYkrXJHpMtfQHETfsxEiYLnvJj0BHqC/d78lYKHnQP0Spuf05Llm/q3RACM+iTKOnUef+SlWL5mCqDckIzSFG8mmHwaP/KDd0x8vz5a042T/jskzVNMNxe79nf7OmOfVbQLbe3e9p6ReUckqBuaq6ubnj2/CXXN7eAUr59SCQxPPngY9qmpTt6YhpIkjRblFuhhXx9pnL0x44+eA5Dz9XVDT91ds7z69eEFDl5eIbUllevnvPF86/oo3oCgcgQ/Ttl73dDCfw5FqGAiHyU8lRi4M8Cv/yjd6H5TknKvktJR3FVVjnV1rjM3DMEq+BdieXvjGtelJbqj1W3KpWmDDNsNJ9vmhYsME0XdpkDUFf1bPHt3FjEGENlXY6fBW/SXBNQYuMMZunIK5OxA3VDScooMwnEqfs59D193+H9SCIQvVKex1H/TolcOThOrEBg6hqsSkDdvhDcNGW5AHXk4p4ZQ1AFUDoXC3byAKZwARWIaOxCYDMQaC025TbZZOJM5lvYjLxPyL9o+yuZ/PhZKS9ByPLZ5e/qpmd2YFYCxXhP0IBMquPeGkiz17fYt35v9gLvbwVktlmRgfIJrAhtveL09JzjiytubnYcjh1E4XRzRnww8uLLp9r9uesYJTCOB7744gtevHiJiKFdbzTD5UeePPmIi4uH+Tw9QosYzTAF5vVjsOS8EOIsr26uMNZx9AOHmyvadUM/dhz6I1e7W673t6SU6Poe19TcHg/vlL4fSwmIyBr4M8C/unj5fyEiP5+fx/fuvfeO/SjV1TkhxZEUY46/NRQw2RUVsdqj3cypwNKpVzKD7M3nmVl8MAlE2Qq77U53maAaszT8sFY7/Na57LYAYcsBFwo4KpNNjMVHLbaBMIU199NexaLbjCkk4jQePMaEdcIw9DkFGLLrL7lx6YBzFVVVuAczX2KK6VMihCIEhTsfc8//mcx0h3NfYI1FKq2k2wr7uaDt03GMTgwgxgkIFFlwKZQ2mJ+zXwB9s09f7mNKzOBibmOeUrHeeryQW5UtMMI7CmDSMRSgb5FifANlVGygnMPy9YJZtO2KRMV+NwANDx8+4uHDR1jj2Gy2fPD4A/arFcfdLfvrG+qqmro0ffXqkurUsdvt+OqrZ9zc3FA3LbVz7I5HEMPHn3zCyfYMHwZC8rnjUSIwZh8mTUogeCW5VU3Nq2cv+Oyzb3PsD1zvd1Trmm4cOHQHxjBqYZn39OPAZrPhZvcTUgIppQPw8N5r/+LvdD+CwZkGkgencb0RzXWWTooK+MZpoTeunuJVyeCh80EJNM7m7r5qmUfvCcFTNdkFzyCTzbF+jJExBPw40sZ2OicbDJWpqWJNTYOTSoGwbAmN0RY1fhw1HwtgHTZV2Fx+a6Lk/m+J2gkxBXw8kLy+HmTgMOxJBDwjVCPJe4YQcgehnM/PQKA4h0cYUyRGbYQxMmLEMBKRODB6T5eOBBfYD6NmCKyi9IICq9FrSbATmwHCRBKL8yFnZmbMJMbIkAaM1XLV467DVYZxGKnQQhixMy6gaT4QiZp8E/U4arvK5b25OMxqcs77AONAHP0s9KHQufO6UtgeJ3buvWCsVjOKZkF8yCi4EYIE7UQdE6PEPO1Hx7NjBZFdVj45q7To5qsTPrVJzFXXc7rdgAtsmjP+f9T9Waxu27bfB/16Mcb4ilmtelenuIWPnXNtx/ci5QURIhJQLAVFSBjBSyKIEJGC8sJDYkACEVkKAsILAgkCAiSSKEoEJFGkkCABKWyi2HG4ru7x9T33FHvvs/cqZvUVo+i9Nx5a732Mb6619r45dqLlsTTXnPOb3zfK3qp/a+3fXjz/hE235Xx9QcRy8+YrhjTgNyvojxyS8OijT9n1A93FY+76nr/01/46b968plk3BBk4Dneszjo++c4FrtszjMonYIiYmPDOsG5spsHTKsZgLPtJwDaEZHlx+YLD9UDC0votyTzh7rihvXEcp3OMf8rd9UuQM1brZxwOr94rfx9ExWBxZa21JJcttzE0OYa21irVdYzEXCjjsqUpgy8KWm+dw7cadyvxp+CmiWmyOX7XjjMlDfU1XSYo+YZxudvKUC04zM0lplgXDVixIkwhqhKJicK1V9xXRf0txhY3WkOUTH1BTMpyrAVPygycJJJkTv3Nac9szDLybUpzjGgbrU4LIlOoj7m9VnLthGYCQOpxgAo+kb2TYBc4QgUAZ7tbKiuttRWVL95Rsa0mx+zLkmGLHj4ZU6fxVtfcAdGBTbXmQL8Ws/zyGWit1px6NLlwv3iPCVNrQQomUH6fwU9DSEG9ABsw4hdYSVKOwiTY3JNyOB4ATd1uNhsuLi+UnDV4VquOQ+OJU9LwcdWB84QpcDj2HPc7xkmb2kKKWAubzYpHjy+5vDxXqrtpwrZNxhCCwpfVa83rDKGxhuSarDAc++NEFL3vMSjL9vHQM/ajUr4JGpo5R+s/8C5CoLbIFqfOGVcFuwhqzBOHUhJN8CwQ+9J+67Lb3rZNFXpNC81koAbBZNCuAEwuZRZdXyrmpAJPlS8wtwaXPLwaSsMUA2Pu3xcRrJSqRadFPy63CVtFn7XBKWQLr1Y+RPVWUgbrlumz4u7Xenk57fArW0yJNKoCmIeoaN5NCVoy2p5ZjwprkjWWKMq3GEyoQF3FNIqwsXDfc5bELF+DDLTO7zEVaJRKdTf/LaPvIvO15W35bJcKw6KNOrUQyVoetsrXDNGDGoFyPxEtD9dS0FLglDMmRQmQSKKj4Y7HHa27IMUDQuDsbI1zgmkcm+0Zx0PPbtrhvOdsuyUMmjK8vbvluNtRQtAQAptNx+PHVzx78ZTz83PtOg2B1qjRMiLEkNmek+Itpe8EdBCvMWqU2sYyRV2rcRoZ+wOHneN42BPDhLdC0xg8Qtd+6L0DhurilTJVa22eR+iqEij8czFmC1TKRZlzTyV2LEpBRFTAF5mH5bRYQAHDgo7XuFi/J5R3ziSt4SapsMUUKZUIISsBY7TbUCRox6NkgGl5oTl+1bZitVoxE4vMSqCYMlNj/hIGQU6thTADoqUNOGn/hFYXzgVSFTfQ9a+utYh2EGZvSl3nSMxKo9xHrFWm4XL+D/CAqgRYWPbybmMWAgvTmEG46iXk99vMlrRsBMqAja6FhWdliiKwpRgBkNrnACanZBcKcokYlocruZM0LTMMUseDKeOU3rOYItEMEOHm9hXGJ6yPmOTYbLcc9gf2uz2uaVhvNvSiXZaH44Hd/l7XSq4R2Ww2PHnyhMePHtOtlEhEFZKGISl7d4hyT4pZDGsVk+nVLSTBF16LGEhTzzQ0HE2iPx4hDTQWOm+xkli1Hzi9mKFMX3WZbXZuOFlam1QWfMF5rK2WB5aIcH7OD9JLta/AzEqjpOn0ixqvQpk6g9YcxKgAmDFZaGNFz0NN21nEuQJjZkRfS3ejKSSaam8wRbAXLLHkpZh7HUyy2WrPOa8ixBIjzqMejSmWmtw2vBg4EbQdOsV5rgMyt1A752r+PCWdLlSEvJKfILndVuocPGMs1qEFXmlhsSlyrkp66REIyvug4J1ZaotMC+eRUFJZy1ydmc+3eicLJZKxC6wWlIUCiOZ9V4+mHNMY5qU/swdLDrX0MxnATROuccQ40PiGX3z9M47DHdY/gWTpVivWGx3iKlGQ1YowjBhbWrc13asgow6Wuby8Yr1eU7I2xumY+zBFHQJDwJfx6CKlxVGfcRJSLiU2okC5hoYjhJ7xGJj6IzYFGiu0FogDXQ3p3t4+DCVgMv+a9+o65sIak6H3ms7KRR9lIRlj6oPOe8r3LC0q6cwCJX+br17fPyuOkOLJeVkEU8g3lCmDJDOtOMxlt2SBKYtIDWfSgqVUYnvNXJus4UGqtVRU3xJRSy7J5LkIGTk3YNJcC2+TWmCbQ5tyDhrm5BK+KUJSpibFNMDILBjKBVDusYJpS1e8NNGkrEhUONC6jDyGzFTBKkJf8JRstAo0YE09d3nwv2IPDitZCZbnwyzMGAPOVuVvTMaN804t6nlhQhV+zRzJrHhsDmOsquTEvB6MSVlJZK8wKY/FpmsYjxPtynJz95Lbu1ekzz7B2bX28282bLZb/VgSeu+YMqefSGIKE9ZaLi7OefToEZvNBjCkFPOzU57JME7EMOGsYFuHzykTZ5RQF2OYJNIPozZx2RZnhCATJk0QR2IciWOPQ1mVHZE4Htms/hPoIvxbuZVS2koXlTsK1SjPzDhKBFkskTkR6GX6bSb8VIVS0mwi88JQgTOUMuGaEquxIxmQKYtNMGlGbEtMDlRPAqccdd5pEsHaXIaay5XVOqUZGMy/q+dT+uALKp7I2F1260tcGJU3YWllF9eOzIKYUpw1iAhEraAsAlTOqYQLIUSMU3yksgyVv0dVduX6jVHaNJWZksazD3CAfD+Ld2XnUWqq5GZPTTIbkHEOMam+LyeH9O+2gIV2DieyoBROA5FTwbdAGdqhdQZZ4WYwsNScmKy89RiSsaAINmJswrpE00IYEi/ffM5u/x0u2hbrG9rVivOLi0oeut81OtWZiEgkpcB6veLx4ydcXl7i84wJ5aEM6OUmwjgSw0BjjfY2tKogvPOVT6LvlTU6pCmHgILEHhM8RK+A7zTiiRiTcDIRhohff+jAoCG7r5rTjilV8IocV09h0kGMItpmbB/E9cyCUBRAyuw4IUVCmKfblNZXFkJQPQXrs3DrQogZkDGpTKdVISvehVrLXLaaZsFScKp4GqYCPEUJpFzBVSYDS1ZuNpd3GpyyIFnl/I+Z8UgwGQNwKLOSA3Ruoy6qDGiKZNZa6n0pglfsbwgTYfKaIp0mHbyCQXJWZQYlDcEIPsY68FNPMqcCk62/k62sqPRl5YUy7nmvTU3Zi1piHcuOQ3J4BSDF2yv7twZxi+PlsK30NzhsJUG15SmW7shlWJBK45DkSb/FC9AUoyqOSNM29NMRZxuiTFw9ueLLL3/Gz158xK99esXarWi6lvPLCyQqPfjh/p773T1jDJU/8vzinMePH7Feb0ogijWWYRyQFLFl7UvCBCFM4J3BNY6m8Ww3a8p8xtYaRhISJ9I0MR332hUZPClMSAw5yztBjIRxxHH2XvH7MJQARRi0265w4aWYKqIclSZHEe1FLGmwlV5M0C5DEkiKhNxjEJLW4jdeG3dM8pVKvNBSVwJQZ6vllFxjX4FDOHGTZyUg1RU0SRATlQkpKwNryznPxTuVmdicKgGtKoRkBGNzX3xVViFbOrISsNXFfqjMimAbYyhd2SdWWLQS0RU2jjTzIj5MTaYkmADRpWxZZ6taxoxXANQUQE/7+GsApKWJ+b7O5byq1vLH8jQebe8taTJU8GuUWLCCEscXbwCUPViyq69TjWaVxxwiYLQuoIQmRj8rBQ8o52ygaxuud/dsVxcM44GPX3yXX3zxNT/72e/zyZM/wub8Mc56Vqs140YH3nbrFcfhQJhGpjhhnWV7tmF7tsmDbLU0HWuVLyDqeHubsaGQEiF7vcY4Gt/SZZbpFCaajDulODCNPcfDLeumQVYtEiISc9GRBAWzw5gZE9+9fSBKQBiDMt/EoA+/AGIppoyeFkS6oTTsWbuo4ivBsyHH0QVH0NmFOo5KKb9nDyMpw7CYXLlHLkpidkcXgkGOayugZu3MRShSMxKNdzUkSYnaYVhy/8YWNiMyecjMSTgLsWBXVvkCwghYnGuVzcc7yM6uSC6oQstboygSDYZutQbRVKAVmCQPdjE53YghDDq0xFmHX7dzSLZQAvXayX0MU6I1XoeaDDqgo3G5NsNpW3XBTXQ2hD4nrBbkuNS8rXByRkRDAXLjVcYksudvrcE4h3U5r18/Sw0LjAHfdNi8/8I2XJqlqv3IWSfnW2yr+EBII8O0Y5h6Ugq4xnF9f8vloyvOtxfcvxn48e//DS7PnvP5Lz7nZ5//jO2vPkVi4ubmlrP1mquzM+LQ8+TJEz7vOqw1XDx5zHe/+10E7fEviiClxHq94ebunsPuQNu0PL66pHMgMdC1DZcXF6xXLdYabT1frzV8fuX40Y9+hG9bGm/Y3d+wWnVsNhsOceT+9g5EWK9WvHj6hGfPnr1X+j4IJaAx/6isvTGq0BcwqiDlzFwBpYFkHi6Zi3FyoxBSCDJVY1qrTMSSDGJ1n8YmCtPPzC+gQFFBxIvFs9Zmi6X1CKWc2BiDxKRTi6Ii6d56kiw8i2xlIAOQZAbiGsurN2OKOTPzFGRrHNao26gLvYQx+jnNXEiOpwtgqDURmEzQYTL0JZKZjxZCUzMkc2wO1E4+TZPNiL8qz6xlXc7Xe48zHp8VoHVaxWcl4oBI1MF/VkE9vac5rWdmrysvBJQTVKXV5HUAiqCTW5vFmaqUqtdW3P78zMjTo00BdPNWPAHnOwX+RIjjpCxJzrBab1htW2AgpYGzsw1ds8K5lqsn53jWEBvu9wd+/6c/52LzCZ998gmffPaJ1mgcD+z7A2MY8a1jvV5xcXHG+cU5jc8YCoah7+n7nn4c8auO88tLLNr2m7zj6eNHPL26VAUggqRIDIEphw+PHl3y6aefAMLr168Jh8DxuGcYjjrQdpw422559vQxz58/5+J8+175+2CUgLbKhmxVAbHzIhcNBYw0FS2WiqhrRZ7LC2IeBOpUAJIKmQ6XSDXt9TAHDwvXsViMBebgrKYvC4VXRfNjquGLfkR5ApfzBQqQWY5li8tqLIWZuOIbIhofIuByfYRzp4Do4r6luOzCy/9Zavuzo0zCsRnHmC1oSY8OmdtxWSRRLK2GE0KyJntNQBSkdEk2Bu/a2jTkM3NRLZcyZMUwF2stPYyHoUd93WZ3P8mJwldFMLMUGWYcSEi5tNpha02IUSFahHMAHk9ICRsjqVCn+TlVnYBhCDTOazHQeOTx5Uds15cMvYYar19d8+Pf/wld1/Hxi2f040DXdpydnYMR7nd3dKsmM0EfOGSco2s7fO5QTSIMkjiME8f9PYe7W87Ptmy3W/phYhonhv7ANA40TustUpjYnq05O9MybOeVK+Pu7q6usbZpadqWs4srNpsN+/+kGoj+Vm2CMMbMkhOFMrUnIzUqlAVpzgJa6vaNM1r0Yiy25PlNpHSYa5ov92Vniq5ikWtNQSrklRkoesDObI0WLnnvaXxD65t5URqdJ2iFTFJJPufS7pyZb82Mb4gUzoTFV9U3GYhEtFDHCs5l6q9i5aoFL4Ivb52vs1qB5kwgJZsLpzJQKXMDUwgRG8sFG+bdzYJqyPcoZmub1DPzXtlyWtfkmo5lFaCpMXzp8CweiWS3vRQtITPjT/25vpZmQFBjgtovUBVnmhVJrSzMqURZKPllNiUFMi9ELvO2CpwkCblPQ+dHWusRRg1jfEtIMI4R51YMY+DV6ze8ev2Gs/Wa/f0NL5484eLynPV2TQiBs7MtbatKIExTJmoFt14DlmkKvN7dMY0TEiY2647N9owpJl6+uSaOA4fdHWHqubq85OJ8iys5fyP4xtG2DSEd2fdHRISubfGNjsmLKXF7v+frL3/xXvn7MJSAiOZJC8+/OmyUKjQVyvzQa2ost70um4iK9ZD5/VIJIwpOoCPKSkHOUjApqqC0qZXKupxbLorAZRS7eAsuFwmFqDiDM1673QgZBGQGEI0FHKX7Mde/UVrjDAoclhMrqbBlJmT2nlNGtjlZ6IWZ2OUqPGeNjs7Kl7ScgGRMwhmbu2rL90WeXmbcTfsPoKQhtdDI1fboEtLMSLxFbC6dtjNJiOrnHMKp25cPlt3+fGBrNLMCqPCXZ1+YCzJaLlYVtxGDkcgcZaQKABqWSgBKykDrKXLGh6DFOFG9rm6l1OGNX0H0rFYXHO4ih0Oga7Z06y2r9TrP+7OMkxYHNW3Do0ePuXp8iTeG9WZdOSn0PCzTpGxR1ze3DCax3my4OD/jYr3WOQIp0Q9HhmPPNIXcK+NyWJw5N7zHNxpyCPcox63VDJexhCQc+4G+H/jq1QfeQIRoIUpKyvlvKB1vqGAYtZYzb8DSEuQvmQUBCmagAFp51TkPxFzGyawIckypHYapFtNoldxMNGqN1cEX2Y4Xr0SRZV3sYpSaa55CVDAN9TRstmYq/Ob0++IaBAPFs5iD+Py3bI0NtVW2fOmgUj0nY2xVaPppLbuNlBZh3crQEUmmRgQVQCuAWz3w/BkFF7V9mlLDUZ6BJs1nobNmTk+a+Z4vLqvuV5fEaXgw1wVkjyDH9uXzikMsgMwSFyHzNSw8AdcYTO7UFEqRl1K7t67FGE/TllSpKu79YWTooVud8/TxC55ffZ9H5085v7jQo1lNXW9WW5480aKgqT/qQJkQdILTGEjxoB6GCN45Xnz0MU8fP6JrGu6vr3n55hobIyYFUhjxxrJddyQMu8OBMB1JSSdSG9MwTcVT8WAgijBOkWEMHPoBSYkpfODZAYHcQBOz6y9VEMuDtrk89qT3X2Yh1trqukYpRUFAjadt4SEQyfTkOa2G1Ly1jidLFfSr/fReKqhTxmcrUQlVYUkm7Eg2p3lCyHXscz68EJ9oHtrW7zXSz3gIpV5iAY5WD2Bx7eX6ZqQdXFMeuKkpQikatQBqi62U05oCgJ54HVKVYBW44uKbWfnWmgKBUnFnjMEbUz210nRVFEDBd+bzyP9l18MUWFLqqZ+8X5jvQakVcAW7SPOilxxPLkMIY0U7U1NUh8/midfWYF2DMVqT348jaUKxJQmcnT3hxdPv8eLZJzy9/JRNd0HjLVOY8E2DMdo3sF6vqyKzTrMwSQy7+x2H/YG27bi6uuLxk6ecf/opm9VKWYOGid3hiAkBEyfC0NN5i7cQo2aKjod7+v7AMPZst2tub2+VMr5pSDEyjEpF57znbrcjTlpl+L7tA1ECpUmipNTAkhCb5rHWTvkGH1YKlsVnMohY4sfqJSwsytLKJJl79UWkppG0qE0/M88TNDl+zVh+bVbRzaC175qmFkICa3Wwh4Y6c3u0fja72cvv9XqoYUoZqPI+8Ezv3VwmvTzvGh5Q3N8sSab6TovzP1Wey61UK5qchiyUbPU5nICM5biSQ4BsSVkq44KdlPOfz/PE4VlY/nr2GbuU/LlCalouonAhmrIOFvfprZ9jmNecoIrYGHJcgRDr8I/Or+m6LZ2/4jsf/Srf+fQPsd1c4mXDyq+wBsY40bQNx8NRh5qm4rI3bNYrgrMYsRx2e8UHQuTx48dcXV3hV2vu9zuOuwOrzZZP12uG/Z7rly/Z7fbchZFx6Lk83+CssD8cmcYjh+Oeq6sLDoc9IbQY15Ik0o8jFq3CFYHD/sDh8IEDgxTXPAtySdlZU4pnlGbMoDlipdVyeTY8eXVo15c1LnedZVhpGXKmlHv1c7sueSJQStWtNhlXUDe6DPyCWgIqgZgyh57NxTKotbOiBTI2ggkJrE4LipKHPxhbeQmUzHKBCdTlnE/YCMkJU5iYZNKFWRmBbW6eK+xAyhsQRb2axgkjAe8SrszaRhc2aPVekkAiICYgNk8gdko/Vm2oUbIOQiHp1MYnpT854KwKW0hGey4kx+tGy2cVs1Bvx0jGYkroVTThg61gO+rBJ60bQHKVJIgJTLLgOVxkX8gen8SgqeByrCWxbD5kHEtNttOR6ZkaXiatfYgB0uTZrK948fgTHl++4GL7hI+efY/Hl89J0TIdJpygLMpWi332N/ekYc92rePI+v2OzWpFs97Q+pYGw7g/sLvfM+33tBje3Oy5u7vVjIFrtImuTbj1Gam94/7Y09/uGcVwfrZhHyyHkLi/nwhuIknHkLshQ7IEUbHej4nDeGS32xNqa/nb2wehBEQEEtnN1tbKJALW4F1H166wVpWAcw3WdiQxxKBlvQ0GIwlroqZRvLafJhGImf4KINM2GRNwnizQvdbM55r4RKNgm7XKLoPogiKqMEaIBFarFZ1vMkKvzD0GiEHABmwM4EeS60lJmX+sd+AiMY00pkORAu1hOAkHFIBA2obDcE9iwntHGnokCa31OGNIRpA0aT7fe6ZpBGtxvmOSI1ivrL0xVyWm3J4qkWM4EFIAl0gmMU4T1nmMb5iiVKrxoK2VeGkIaQACiZG9HKHRUWrDEAgCznS0vsMZxQhsBmC1ACww+k0NAsoEoaV/b8hzGXNZuBBJRIwI1nlcY5lizxgHpVdzjYZXMtOBgWUwYw01xM6ekVYuquK0Q5eLuzzONFi8ThyeVBGYYOn8mo/PP+NXXvw6n378PS7PLjE0pEmva9sGvBXCNELUtRecxUrg13/1e3z87Ak/vnlDa8iAX8dF0+COPT+6+R3uf/ELDldP+Ly9AEmcn6/57d/+a0zDwHrdcXF+jrl8DnTc7nfc3k488Y77o2fVrbiLkftdw3Z7xX08MPYTxnhc22GM4S7o2HoxK9x6C9y+U/6+VQkYY/4PwD8AfC0ifzS/9hgdQfZ9lEfwvyZ5toAx5k8D/wiKP/3jIvJvfOsxAGuKtk4VyXU24W1Si8OU5wxYUupBMo+dKRWGuYe+9FwXhNyULjuLiCeFyGQnpikQ0oTJmtwYq7wAfaBtWmyjdQGSZyBIssQJGufoug0eD+K0y9AqkBmCptwMHisNTjot1zQOZYlzOqkIQxzmfv1KdvgA/DNiaZJHxOKSyTwKggtGR6Ebg6dBUgQsZjTESQhuAhsYTcdZuyVNQgxKW668DYZpr/a+aVvCMGCixZoGkoMQKHUKRiyMBgkejBYCOWNwpsPTYYxVKi8i4BFxJLGYJESJObOiFaCVsKVkP/KzKqGFxei0nUxNlkLufxCdMZGCCrV1ndYmOA+i74lBCUCcM4SphHlm7pCU4mUqjrBdrxmGkfvbHX0/YsSxWZ3x6PIJz58+5enj53z28ffYrM44217SeW39jWnMjpVhTGPmlpi0BVtE2ZacwUrD+aNLLp5c8vyTj/HG0O8OBITzJ4/5+Luf8Ysvf8F/+Jd/G/uH/zg/+p0fcdzvEBH+1J/6UzTe8u/9u/8u+909beOJcWK1WnF2tuWLL36OszCGic3ZmkN/0GYxFuFxrktZyPF75e8P4gn8H4H/FfB/Xrz2TwL/TxH5p40x/2T+/Z8wxvwQHUTyG8AnwL9ljPmBaNvc+zeDVvBFARNzzJm0dt5pB5dzLV2rM/2GMSAp0XYOayFFtWI+s7OUgSAVBKpRcMLZjgCkGJnGxDikvAiFYYg03RZnWrxpURFXMEs5Az1pNBzigHeRNlOHr9oNXdeSkjD6gcMwgOkQ14FbMUUduW6iQvMJtNZAlOXeScl25JshuljH/UgzrbEkzCikydC1nsY5pjgSQmCKAaLBNg0EC96xMRcIhi1nHF/2dG3Dtl0TzKRU5ghbYOiP3Ly64+L8AhsjzjaY1GBDVDBWGuWuR2htR4NFazBGTHDEQWPOaRSmPtWWV+t8vafeZSVgAuPU5G7FXKWYwV+fMz5YmycXl5xLRPAISadHR0ft07AN5PFsw1GVr3OWpnWs/HlNgYpkjLDgKhnf+fqLl7n4q+Hi/JKz7TnPn7zg04+/y8cffcbjq6dsui0StVksTsr+JDnkwTl8q2lSE8FMCYiEXj0c6+Hi2WOeHD+iudjgjaXZrAnjxD5OHB3IumN90fLlzSteXX/Nuu2IMjGlnvGYeH39ks8+/ZQXL57ze3/jd2mbls35WjskHRyGe9ZDowC0607Gjwui2RmnqVrn/yZIRUTk/2OM+f6Dl/9B4O/JP/+fgP8X8E/k1/8FERmAHxtjfhf4u4A/+03H0KqADmtFe6jRWv7GrbA0kFt4rdEqwhhi/lyLQWsMbLK53VSF1S347iVnA4Z+IkwJksXi8bZl1eowUWMNjRvx5kJzsuJIebafDqizGO9oVh3rdoNSjjc4GpgaxmDUuwgQpxYmi508bWqxhBkAy+6vjZpuNMlUnsTs0yqICFyaDcmrl3M83nHYjdiVZ7Ve0UkHRhiZOAxHQp8wI5jGITk/PL0MbLsz9tPI6BMXF2eYyXF7c8PZ+Zaz9pKzs0eMw0AaBuzgsU1LJ+Qafa1oi0FAWpIH47QMOAEhOSX4nBwmeazrsHQYUfp4ZwWJkAgksbTtuYYglJHbGXS1Vr0nHG2ezgSQbMT63G+RGYZKN5SNFiKY6LBR+xEsFjO13N4VNp9l9kJXTAUHxXC2veLp06c8f/acJ4+f8fTxU548esrlxRXe6sSgFCBFLe7yja9dnikGxjTqvIk8SJYU6WXAEok4Ns8uORuecDAJb+D8couJCbm9QbYdLx59n+987/v85N/+//Lp917wG3/kh/w7/86/wz/7v/1nMS7x+MkVv/V3/QM8eXTFzz//MX2/5/b+mtXZCrxyX9webjnfbFWSTQGWS+GUkpZqqfuDCrjF9stiAi8kzxYQkS+NMc/z658Cf27xvp/n175xc6bhav0ZZpECtNbhbGEMhvE4kqTVh9knrHX4doM3HkPH9Zt7+uOOtm01Xs8jvkDR+dKaWgZxertm23XQaUrHOsfQDNy+DviuY9Ws8YWXTbTuvtKPm4b1as3l5SVXV1ecbbe1uaNpGhp3hrb3nrbKWpsF3xiOx2MNe/J9rI1TWg4ciLnK7LC758uf/YwvP/8ZDuHibEvbNAxDz5vr13y9f8n17R39bU+g597u+MXXLzn2PT/52dcM95Hv/+qn/Mk/+fdxefmIn/z857yc3nD75popDHjvub3fE1LEWG308U2D8w1TiBzHgEiDby2uFXwL7cbSrr0SZaJ9Dt53ONsgoiQZKQ6kpD3y0zRwc6/l1WFQDsSS0bBWy42XbEbzoBjd9B6qp+H8XAJe2aLKPRRT6dWK2/6uisHLi422OJfMSpDct6JhiBFL27SZkclof4T3OFydCGU6j/Fzx6IxMPZHvHOsu4avf/ElZ5sNl9tzhoPm61uvDETjMOKso/lLfxE2F7x6/ZK/9NcCZ5crNuef8Ojykt1ux//1X/mXas/BcOw5HG8xxrA79Fw9Pmcce/zaEdKcOq4EPLlIy3pb2lfeuf2tBgbfFXi8DQEDZjGL8PzxGY+3f/i0IajGMEZHM8ceTwMGmi7SdR3b7XllFD5rjsQQ8pSfPInHaO/6MIyM48g0jQxhwESDw2FE222PvZJ/xODo7JqrzRUvXrzg0eNHbNYbWt/gGw0z1qsV52dnuYegwTmvoUXhGPQek1YavGatXLIfc6bO0OEpDFJIoe9KuWhKY//L80cMfc8+bhjWR3bmhrHXVI9phTUrzjnnPu45TD2rqSNZRR+2aYOJho8vPsVcWi5WZ9x8cc/Ln7zii9/7is8+/ZTnF2udbDxOJOvppxGTdBqvlw4vDV1KeElMpsVEwYSANRE5RkIyyhRsLclYxGtGA7T1W50oUSbcUWhYaXsrhigWkaDtsslA7hpMMWghVqlFyHUZBqPTp0TLw4kBiYqJWKc1JDHz86UabkjOwcxEJKVm4bAvZJ46+dnbhrZZ4ToFeAtWkUL2BkWHt1piDlFhNCNjTHkMOyCJkAJeIJkGt+mYjDCYxEE0JRmNZ7VdsT7fIDGx2++x4z3PP3nC3d2djrAjcX3/UjEcDFPUKkHXKQ4hMTGMA9/99DNubxUbiJlsBgPJaNEbBu24tDGHde/eflkl8JXJk4aMMR8DX+fXfw58Z/G+z4Av3rUDWcwi/MEf+6H8yb/nH6qkEoUwRHLSvFbF5fShMYa2benySG9jDP2wYxyPtdilfD7GyJgtz5vrN9ze3BJjxDeeGCPHg3ZdYXT02aPtMy4uLnj69ClXV5d03UqVQO4clJQYjorSy+Qw4vECSAMCPji8WWPxucBF6nmURZdEh1rUUdsGUiYXSSaSTAbOJsFMlpVb8/zyOfFZz+7mGpNBqGkaSH3ATUKXGjZ0YBva1Zp0LoSNQLrlybNntN5zuBs5Hg6s7RnxCMdDjxVhtV4TbGLV6oj3IELrVqxXW3zbMkbhbogIgcjIlPbEvmccBsQK4qyWrDqdgGS9Ryc/RSwQk5Zvh0E9gTglUsgYkEidKlyEfjldxGeeSb1RZJBT5vmOubIzZSr6lFDOQ3LtQa2XyFYxK5fVajWHCklL1qdxZIhBqbuMoWtb5XCIuYhMZrp0rKFtFdcpjVUpJS1IE50i7RpVbPuxZyKx3qxxbcthnBjHvTaibTrGceSi3eJa2GxaQsa8QkwY55WJWxTkNsZwPBywDfTpSLIh17k4LD6XaheiVLQRy8yMWe/aflkl8K8A/zDwT+fv//fF6/+cMeafQYHBPwT8+9+2M29brta/MsfNxlTOgFJvniTliTxBZwvgMKMlBYO1sHIt3VoJHUv1mPczU3GMke9/6rm+uSaEyKrrwBj6vmcclURytVqxtmvapsE3Slc+TRPhqOi3tULbtmxXj5iGEUmCl4bGOSKZbBRL7JU7wLoylNTg8SowOmIZsxgN59CGm5TU2pmkSiDJSJgSDs/V+RXT4wM2BA77e6axJw4jMk4wRWQKjIeeKR457nt29/eA4eryEY1rOez3HEQ4325ZPe74xRdfctzv+I2/44e8fvNaG2o02QpJ8KZl3W44257TT8L+qFVphWw0SmRiRJzk9GTEuEAKWnpbGJTU2o+EOGKlVWFEx3kVkhFDLqdGLS+l2MdByvUQmjnVOg7NUGjzmGThE23Q0KGnbqZGk6xQ6kTp3Fg09mNlslbgT+csWgyubXLloebWvS9KSWstoiiTNFH5KJQbYkFnbzQ93TYN4xDqOg5JaevLKDGTeSe6teNud02QyJQct/fXXJyf024apnFkTAGH4XAc2G42iE2suo6bmzdYY9lstwxjxFjB2tkD0j4SsK5Q071H/r5NQI0x/zwKAj41xvwc+B+hwv8vGmP+EeCnwJ/SZyR/2RjzLwJ/BQjAP/atmQE0FeZlM+fKKQ8+k39oNh3rWhUiSuGHaNchkGwudtGThmQIcfYIQBHlTWdJTco19oloG4zXeP7y8pJwf8CKQyYFlbyoZi2Vd4pQG2xqcoeaIVgAp2CkmJzSzDGozKh/vT6thKnXZlDDbzLybPMiGmPAeI83YLzh/OKC4XDPNB4JozBNI+OgijFMA/2w582bO4zzrDZntF1DyIU0TdPovPsYaL1js1njrfIvnp9dcnt3x7rbMIwTr+9vWK23PH/+gqurR7x+c8eXL2/Z7/a8+OQxr24O7G5vWV91xJTo9zut7BQFErUyU5REhYCIkl4qU1QZapoHHVU3VTIhaqwufGEggkX3pCQkJKIplt1iJGUSVG2emj9Drlws9GValyhEnSaV15BF6xPU8CQKFZwWnYHJRVJlTRq0aYsYNDTJcZ0INQ9ljSOFPIA1QeNU1GLmnXBZUam3GEgkrDeEONJ0vqavC8lKxOAanQ9hnTI3G1dqOUImu0EzB0n5LXXN631w708O/IGyA/+N9/zp733P+/8M8Ge+bb+nm8EvK+ZywYxkISmwgisoOqm27UoNFyySyt/VTXyo+6ZgMKnDinaKTdNEHJ3WHMQOQoNEJeuYrYer61TjUrIbq805ZbBP4f5XBt+YGXAXCosZMDHMJbSGXIdALp4pZS4mFyyZxJQCzgirbcf5owvGac807bFecA00rcE3Wvse00gMI75rMJOwj5ExjhnXcKQUOB4HMEK7ajkcD6w3W5qmYbPd4LvIGBOPnz7h+YuPuLy6Atty9fKO/eGGxjlWqxa/s4Sx5zAdWW/XTCKkOGlIY21GqmNWAFqkRJ4zmVuYasu2PtJ83VJcpGLJU36uWaGXNZLQZ17p4AIlxWJJmRo9KwBXiiHLPqGMqCtpQ2MMySr9eEYUMyNUYn6ApfOwnGJuIzcamtSVKtnfyVkFLW+2tYZFl6geR9sv8r0w6kHqc5LsJZUbJFlxKIaRhEw4a3UtmXx+xuq9LlT2yKJb893bB1ExqOW+46LGHX34RSAory9+llz2S9KKwNSS8LPAvgMIkQjetSREx38PAsnqxGFaQp/Hm5EteAajjFkcGijc/TUnY2YLAKDmYVGowcPPA4XsOiu8soTsQgm4riGMkSGzx65WDZdPLpimHcf+BtdbmpWjHRvWQ8fZ+Ybz45Z+Clr3MA5EEofbA5v1hvOzDTEJx8MeSYnNes0UJ5oUWG/XbM63bJ3n7OqKZ89e8OjJI7ZnFyQcn971jGHH8+dPuZQVN/dfcghHYhgIIY//KtSekunGRUetKZV3aYLSe2MW8L+GbxnMM2Xhl8YuFcTaUHbiLObFLQkpSkDUCuang7HgipXMwpGSlqTPatqQjI5KE6F2UlK8tOWZFlLFfN56HqY4dshimeZAJ39Oz60oAbNcz9U61A/CwzWXGyeEDPTnnpqyfxZXUxVARjjzod+7fRBKAJNw9lgfsMpFpqKsGqwoB334zub0UF4sIhYjftGMs1TZeptiTtFYSfQhEsYJZx2db/HGE4aJlc8PqWJTspTnRZ6Zql1NOd8ESpk1k5rMZz4rg3J+ZvGOIvgzjwDY1kO0OmM+jqy6jnWzYXuxYXXX0ew9vnU0nWe17biI5wxhYn9ULGHqe6xrGfojSKD12vMQU8BZnfWgYGvk8vKCi0eXdN0a17ScX1zRbVb41nNxdcn3f8VxfrXi6UeXvLr9nH//LwxYZ2haT98fMN5jbJPvXUap8/PRhbhY8KY8HnNyX/S6M6i3TJ3mPxuZF7csrLWGHeq+SaaTK/fW5nZm/U72ApZLQ/K5LDoSZTY+hbjcZCu7WAk5fMiENEUfCVCYrCpfhKkDUmYFMMt+FdBqXJbbrHAkzx0sLel16rMYtIhr3lu5TzWkeciUs9g+CCVgELwbTuL36go+MKE5+qoWpSoBWpCYFcj8KTO7BpCiuookiBPECecMXeNwVjiOg/JhLRcIhW8w/y5FYc+LRjLvn1gU/HugBFjurvwop1e2YDidr9N4TKNDVsOUmEKg6SyrdcfZxTnHwz3jeGScGkJcEWLicNwSojCNR47HIwcS0zhgJDJ1Hd12y9lmTYqRGCecbbDWcHl5waNHj+jWG5pujfctUYR+mmibFR9/8gnf+7XPENtz/6OX9MORdqNWerNdMwbteCxtoPOlGC2LRpQHIpUuf3nrXghzp+fSjyviWL3exRpJmaNgfhby4NNlD6evG5lnMsxlzInSpPW25SzTrlJVVPNiMFWx6dJQ4ZfMGlWPmbEgqT/Py6r+ncyktbiOueLV1E5ZJdXxs2GRoNgFZZ5UyZItDdq7tw9DCZgse6YowzkQOFWMpr6+nKaDkG/AtHiwi50b0A63hKQht/aOWJvwHrzL7moaESl89AsfqugRkUJIM7uioK6ksdV9NRJRKId3+mHlgWjF//weQZH3co3DFPHG49s1YRoYpkhjE75tefz4MamM847adDX2E85YUkgc9wfub++57m84u7hk261ZNS2rtsNZw93dLYfjkcePn/LRs4/4+ONPePbsI3zbIcZzHEaGcSKmEWsaQuh5c7/jV//wp/y9/8W/j3/pX/vneXP3Fa5FJ+JgwXi9ojRfpTGZSK1aJaN3xqRKDDq3dqecJ1/elNnzmikQVYIKhVtRwlVQpBx/aaGXniTqrRUFULpLCyt1CfFMMSLzV3W/FSVGTKq6TglaDYnCBs0s7YuHXxRIVTU1/liEmWjnZfEsjJi51BoDeAyWEu5nzz+P6DPFf8Fh8N/gBcCHogTQoo+lMJQuM9WUplp4yG5mIbCofACCMCHkh7i0HGRgycIUDlr8YSbaDpoWMJO6iU5Zh5YNLSWWyyeW97t4oWjzpNpfP5uK/7C4RhbXt1QE+tPDxyRAjNB4R9euIKxhOpBSorUN3dkFMk7EYVLwKVmmEbbrgX4TGfvIcAx0G8/Hn3zKer1WHvzjQD8FpmHibHvGr373V/nN3/otnj1/zvnVUzCO+2PP69c3xLRTIsxxwnnH3/mbv4VrJ3725Y+4u7vj6vEj+vFAlFhrASjYgNhq2awoB6MqBEGMZgBSns4Eseb9xbjZTV6GW8UfEMn94ZArlUrwmz/kKrA7e2uZwIVFPwGB4iGkrAjISqB4JnVKcjanyp9Y2JeU1kx7EdCOTmtI0c5zDETynAztQdGPpYpN5L3g6tPPTEhlUVUgMWMOkeIugcmMUEWRMadaK5mJ0fBvBtTfvX0QSkAx1DIrLWvy3AM+a78ibFnbiaaTyrwBY3qsGeo0WSnIKvMCscYx9EPupnN0XUfbJiQdsxVKxGRyCWhWHO8EpDJiXNYis8OpPklaHLX+YbEDqkuY2Quq8azOh1ha20HSrsRVs1b6tXBkTAEvkc1qw7Mnz/A4PC3WNJjo2KwvefLoOS+e3iPNmm61ZhxHXr98ye5mR0qJjz76iD/2x/4YP/yjv8Gn3/ku7WrNenMBzuObIzEYrG0ZQ6BdbdhePOKnP/spzz++4r/w9/2X+PR//V1+9OO/TEgTZ+dbsL5a5Br5ShlHpgoy5gxA0puoHIKStJrPWozEKt/Fos328h0BglF7d/Jn47JSyDG8yYi/yVOIS5hWMjGiikgzGSxcBTI35OwDSIm/8yAEMTn8IxuupF5hwRGEfG3FcyD3SuRPzPZl4fYXXEAWyyYvLhFBYlL77qwOFgEkmYxFZeVbF1n2BRaK9V3bB6MEDNqqWWLJgp6a7PbNd8VSKW/rl4AZ0EGUGScoDKKgQBUGjDBOB6ZpYuVWON9ibKzVfM45wqiLy5mUU3QLV9AoUcYcv9cjQFE8C9dLFn9+WAsPs+f3dgRrwFnadkUYj0jUuXPetwzDQSsWw8DV2Zonjx7r1KNkCBOMx0Tj11xsE2fbS24PAzc3t/SHA8OhhyRcnJ3xG3/HD/m7/7P/OZ599IKXL99wf39gfTaxObtERFivtzjfKSOxWHzT8sf/+J9g37/hL/yF/5Af/ehHfPSdT0lm4vX1G0gxu/Jzms2S5wRkg1/ZkVGuBqRY21jd+pSp2/MkSR7enUrikkM8xFGccFOVwkwqM/9cYmWbzzDMsXndTnGc2eOzsydqXFVyKVPRacWj0XDAnC6Nssca5CyPZ05/mJMD1RdanEsJnZa07Xlt5jkQD7cKvFYM493bB6IERNNqiojk8V/ZBpT87qLiyQA2JcRGJGpsZ8SQQsM0TVmDuwrEpOzKyQgyOpw4Oja0aY2MQhoTzrSs/ZrRjBqfhqSjwAy1ykxNvqMSgFYXVbW4Qecf2hgplWPlK3d7V+WkDL0yc/+Vb9XyBOz0igYIBKZxhCS47gzTrAlj4E3ILcrnn7Jdv0DOb5i2XzO+fEl/f0+zciQjtP6C4x34zvD9H3yPP/ZH/yi/+Vu/xeOnz7g/HLCP1nRimGLArhIrwDUWYzpub2959eY1tj0jiuHJky2uPefq4il3Nz1YofFnOrzTgJipClgk1zpYB87h0rKLT+bFnJLOYkDLZoCFEEu91yKiMa7N8bFikGDU+yqPpISRxhoQndJUXPzKIJWFKSWVoYRRjki7tMI5niRWFxwzVRozS77WAhGQwwL0uMo8m2nmqjEpnojJ528rZyVpYf5zAZLJpCiRAHnIqpjAkEaCcVr12BqsEaKtdyELicE5IZp3gNSL7QNRAnl7gKHoVh6HvP3e8g6ZpwQ77+tFlaEg1loa63jz5jXOOVarFXWUNzpYpIwUK4dcYhBLoobFy6cnkV9/R3nCw0tZ/Frigrf3VmLfWYvrSrNGi1w0JnSUtJUDtmdnPAe2mw39MBBj5PHdkd/78Y8ZxpHnz5/zR/7IH+GHP/whT58+ywM74PHjx9zc3CEieO/p+57j8ch2e8Ynn3zC937l+2yutvziqy/5j/6jv8jt7gYRoes6Dv2eaRyx3mK8Wbi773pWi7ToMn27rOo0p38ut2cGet91X7NwLN/0LtdrsZVioSKUs3XVz59cRXUGZ8U+X9GDSz1ZI3LyGkXh51Fw874euA8PsBCziP+X+ywNUS7zIi57Zk6u5+QGvr19WErgG7Zl3rh8X16odRab3m7fFREd99T3HA4HLi4ucuORr0qg7Lt0AhbkXzhdoGV7+Hs+w5q/nlMzpwtG88TL65DTtChLeZlDDu1OdMrykyvYonPqseSuw3Itm82GruvqfTh/oiy4n332GU+fPuV73/sejx490u7BKdT2Z+cM3isu8+rVK37605/ifcPTJ884uzwnfJ74//32X+Q/+At/Ad9ahmFg3ayQJHRtN3euLe7RfC+zYcpuTlGwpca+NI2VdurT5039TB2A+q1rpYTVsvA4HrjHD34uIGBhn6aWey/es2h113NX0DORqVRNwSw1NVhEuV47y2da9QoshPvk/i1+t9ZqVWUuBy7dtq6c06L24eH6NPnevW/720YJnGzvEErvPWLmcWZlYCjA8ag583W3om1b5Q+wNufK44nCKPt738/LY5ZtKeha2nr6AJeurWY6pCqBIixvX2MWHGsXrwllJqMqsVDHr8cYtIc/E66UxXrWdPz6H/o1vHesVxvatmUctbW6UGOHacot2YYvvviCP//n/zx/9a/+VWKMXF5esTnbMkrgZ5//jJ/87KecX20xxjCN2o673q4Y41gFuF58EqhzEqXG0vUOFdxAL1ez3EU4q3s0g4MqyPJQXr51q0pgEZAXOq5SvWhMsbgn+vc0156/7AKLrPyQtUy3uA3zZpYYRVGCLIzFOy7J5Od98vtynZUjVQVl8tAVqUoQ5rX7twEmAKVBQ3+Wig/Mt2f5vaQPCxtwqgQRS6uY0jwnMMbI+cUFTdOo25/d7Tof0FqdnHviBi7cvhN38duu5d0vL13E09fnB6WG7+24yJQVSKqt1sbYOm1IRAhhYgrahuq9ciq0bcv5apXboCX/XV35ruswBvaHA957xnHi937v9/jt3/5tvvzySzabDQD3u3t2w5Hd4R5nLeMwsmo7xqREGWHS1CzL66uPa7EIly7yO+6ENaYk0t4So9Od/sG2gvYXoa4NO6KkLadhiMkKoJQ+L726PI/ALr0DauZId6FXVNx7yWGcvCO7VD4/v38RzpTXFz+f3MeaWix5UJUTY2eFoOc9exzvKn1abh+OEoD5oosCOHGT5u+yUATl93GamKK68zp3T8OAoe+x1nJ2dsZmrfPhhmHA5SGdAtrVtriBei6zpn2fB3By6iXee7C9L5QwBQF8zyb5vaURqZJGGm2eKg1I6gpqTj7GidGOpJiZEa12D2q7bJmULHjn6+tKtjKpRxACX3/9FV988QXjOHJ5eYn3Xsk0QyBO6mncHO4wKJbSNi3DoCXEuS2wnHy9hwspnK/9Xdcs774lM/nKN1u0d93F+bCnDWVp8cyX4cG7PMB3vS//AWqfROl5MFSksFztIvQoXmLV6Q+WQVWOtsQ0s7o8SZbWz5ebMx+neI/vmub0ru3DUgJ/0O0d7noMgZhi7hl3TNPEMAyM40jbtpydnQGzd5CMAedPmIwKEUN9fA8Wx1uAC8vYdfGwRGYhrpr5dJEZe7qcTQ4S6wPOz7+EDjHN2ZNKslKR4HlkesE6hDycFU4Gk7g8slsxAeVfKAQtItqevNvdczzqcMubmxsELcZ6ff2K+/2eXb/j/NEZvnOsVyvuD8rAa0slX7mHxZJWf3/h6eWwIAfvVXGYGjYsH/dsEP7jKIHZJTazQj0R4uwBlGGpZg5dlgrg4bPWEEWvwBqbZ2Ro+UApbFsWmVWhr78tFAGiAK+Ieg/LUW313Qsf6IFissbMIdSJEjhVWkvm4Yfb355KYLGVB1YmFMWUiHmBl8VfbsYwDLPbn6morbV5UoswjRNt21ZQr2wnaPBSCZTgK7t05WURFdQTC/AOXOEhFrB0JtTV1/doIw5qCcu55bkAUqmmqQrQWjtnRpoVbdcuphLrtCeRCKKde+v1WoulcgFVjIGXL7/myy++QEBJXJqGu90dq82ahBJ/TONIt1aqtUQpZ8vdePWGMSvWnB6dQx5OvISiOCQj4g+e9OJe/8fdpOqa4t25TDhTuCMX78zp6KrK6usn55KfmKFWrlQQfi7XKXciE90s6kty0KBrk0Wln53R/UXD4gzuLTzACgwandEgKBdmuUUlfCnZrfdtH5ASOLW0s7DNeeJyR5az98oFt22LiYFht+N4PBJCwEAV8OPxiLeOzWaj1jJkJpksODEzC7dtq2ezUB71DB9aCciIteTYj8Vn55/futKFQniYbqq/GXW3WWh3MYI1xaJrHYQkHS4S84TS4tUUMkzXHZWEQlIdh9Y0LauVZhD2uwN937Pb72mbhq7TGXm73Y4pA4bTFPjq65cY63j29Cm2NbjW8fWbr+jW7fIRnpy/lry+7UG9M4341r1aCvs73NmTez1b6JO3mAefzPiAtVaHxZhCALtMrSmRyVv6ZqHgH75Yo9bav7A8nTlTpK/N7kF5zZmly6/3Z54fWWyN5hvK4Z3RvoCCVShzY6qHn5dOrhz4hlqBD0QJJEIY60JZzrWrWtGU2WqJ43GvNfRti3M2I+Pq4hbkW0Qn9LqFu6/gl8bABRNIKTGOI9ZaBcKy613pqh4Iaj3jHFPajD9M0zQLrk05LHxXLKlPUUuX56yEXm8mvfRKlz5MOkTSyKz5RRLWpszMG5VViEmpvSVhnKXpWoyzdDHqQJVxZL1eZ5dfyVXv70e9p63HWc9ut2N3f8/19RtSSjx9+hQR4f7+nt1+n7EB4e7mltVZx7Y9g6S8DN5ZpqRMvWVxZ2dfa2IkkKaA9Y3egXcoxuKOF4GcS21PlW8Fco1ZSGlOCRu00Cz/qUxbZmEJrX0/xc4yln6X9Zzdc6mOSRmfV6K1E3dOTLX21lrmNEBpr15gA/UgqklNxldM3o9et55E7mCoLdHG5HpIkVrrUtdL2ec3BFIfhBIQmWOWEzAGan7TWlvj1DLxdbfbMY6jWkXvquAX649o3jlkgW3qUAxzookfbsbM6O43nfNSe6eUUzPfmMg+/VsMkWRnb8TYnJ0QqbTZ5SMJwYmgmZDc7ZbZdQuQVCjTilWLKWHMnD0QmbMnIUzEqDsfx5Gb6zuePn3KD37wA372s5/xF//if8Tnn3/Ofn/Ee8/lxQUXF2f87PPPeXN9y3dbx6effsqbm9cailFQ8gWusbhsC6QU6v19634/uEdilthAzga9Q3mcPJB3PU9ZvkXmF3NQfqqki7XO55XzhUWGlGdgcd7lIq0KYSokROUGiFlghMVSn7bHl7VYBH72CVjURCxDy1lhLnEBrKtsyzDL0xzqvP/e/bJjyP5nwH8ZGIG/Afw3ReTG6JCSvwr8Tv74nxORf/TbjgGnLvLi4BVAObWYpxeUUiIMgTGG3AikGlAR8ZDXh7rIy5HaJ/sooNQijj9V0PP48uU5LJWXHkfLjatFPFlkJxCP5qoTlclY4/ZU8YylsNhkCUbLkecCoaicfaI9C9Z72gfWcwyBYTgiEvG+AeZQoZRYe+/Ynq3YbDY8evSI73znM25vb2nbhru7O+7vdtxe35Ak8uLFc77zvU/56Zc/4+7mjsPhQLtqyTwi+bnN980uZDOWMKmGS3MytN6dLAmm/Lx4Xd7ePSVcfGhNH0SQJ89RxFQquHcpgXIUZ+ZCIwViS8kvGeU35L5IEksWIpO7SkEyK5IV6jHKZ/W3lMMBYIEdaa889XW1lEJSUkNtzc7erLUW4+aboGtDKGhFAQrft/2yY8j+TeBPi0gwxvxPgT+NTiAC+Bsi8if+APs92QqAUb4XiwhqZUMIrFYdIQQOhz3GGNbrFU3jORwOHI9HhjBV12tGRaV6CCcouZkZaecqsaw5s3Yt+d26CN7CK6g9DaUnIOW20ncrAShlqqezFbIyCYsJumQL+Q3Gb562O7+f7AmVY4y7ndKuj2MuoXYUDn4NKQLH48SrV694+fI1iOH58xf85m+2fP/73+f29pZffPkLfvr7P+Xq6pJf/8M/4Hu/9l3+1X/9X+Xr65d0TVtpynS6kFRdV+Qqe7jEE8q1ZVUlpwJUNH9+MVF69fOfatHPQywmv6Gew9v5+RpeUo49Fwot3nWyT10j5ZkXc69CtgwJ6qOoIMG8zbiAyVcvmQTVnhx7xjfyZSzONSHYOr/R1nDAWu1NebjelpmBb/KifqkxZCLy/1j8+ueA/+q37efbtoeW/uH3EEIubjkt8QxBY94SCy2VwGx963nXn1NKbw1kKNmCtxWJKo9aEafvPg0JFp5MQfO/SQk8PIa6//o5Ba5UGZmTBWv082Iyq7E2CKUoeXJRdvellFBRQ4QQlq64yZ7RXGL99ddfM44jP/jBD/i1J7/G119/XUHCs+0ZBPj404+YUuTP/nt/luPxyGbVMcTxdL0XT6remtmiFUV74m0tv6CeTz1Xo+y5c4ZlkbZdAnP1gObkOZ6sgfoM3sZ5TgFg3rkVg1Dfl2BBUJTb3st78nO2b6c8axxvixJaXC/UrEBV8Pmg1tpFWfXba6i8Z1llWnCC2hfzju1vBSbw30InFJftV4wx/yFwB/wPReTf/rYdlAuqQvoABCou9/Go8elms2GaJu7u7tjv99oG3Hi8a/P+Tieylq2kz0ocVrT8civhRFlAS+EuMfq7FszJ+5LkDsS3NXPxGJYu2hxipEwdra6qmDlWfmspFSWDAk5idNJPjCFPR54qrlAyH9OUS4vrfdVjX1/fcHt7y83NTeVa+PKLX/D69RuGYeD+7p5f/PwXvHzzkm69YpiO7G7vsCuLaxzDMGTO+4L6L85UFt9KbFDuTW60K0Khl7UQiLL+M828egHkiUVZ6GbYflZA9fvS+qbFab3tGp8oH2bewIdbWRPGKOPv7BlS8YNlzchSgc+ZgfzzA0NRSGyWnuby/Or9qc/+9F7Bgp5t4QXETOP+vu1vSgkYY/4H6HyB/0t+6UvguyLy2hjznwH+b8aY3xCRu3d8to4h+/Szz05d7bffe2LRjDG1IaiksZq2hdwJOFfHpZMHWarkjMnTXB4cAxY3u7rLDxHiZWT68Exlfs87VlAJc4Bavbe0WCbTm9eFwIxFlIe+BPeUjru4eoqsK9NvmcaT31MVgRZQTdPINIWqBA6HPU+ePGGaJq6vrzkej1y/ueZ47JmmwNAPeO8YjkeePXvKR5/9Oq/+3y8RhLZrmeKYyT7ftnrltphyywy17LnUOpQGoof3vyrdZDA2F+GkU+X89jPQA+k9XU571tidhTIuzMFLBfCW0l88t/rkzelnlqfxQAVmxSXzdzv/JXNRVQVQ1sJscNT7LCQsJ1eYj22h4g12wWVRFFlZx07+JqYSv28zxvzDKGD490q+W6LTiIf88583xvwN4AfAf/Dw87IYQ/bH/84/IQ/jlqULXTWltUzTxM3NDcfjEaB2weEcU4oV9CrWL+VagFIktHS5ipZeHmO5CB+c74PzA5gxA/2+XEjv07yzEhBJOGdrtV+p5qsCvFgQS0+iavcUF56FAdxJqKRYR8IDIjoWXKsEdQhGCLHuv2l8Hap6dnam93rMvfBRh4Fst2uSCN22JYTA5myjg1XnjuZ3bsWCFwE0WeiX1Zrla9myXX6X7PEY847y7vccrzyjWQnkW//Ai/smBbA0AA+Pd3KOC8kv1CWn51LWlL5SnJOCDtis4E/DRurCKlmJd/ZOLTwAyeXkdf0VRWG/4eHwSyoBY8zfjwKB/3kROSxefwa8EZFojPlVdAzZ7337DkFE+f2Q7MIU9zhrSOsMiGEYA+PYkySwXmnu21jDtCCtLFZPB0kWth9hCmMFYrTZs/igxWpkoEdSReeLNX3ndhKy6I2v3PlmgQCfgHdzs0gdziGSKaTnElAFnOYuR+cbrDGECClqN6HJDS914VjNHBgLzglNYxHrsV6wPuRBFYkYJ6ZRkBiJIeKMJ4yB8/MLVuuO43jEdEJIo5JaSMD6yPn5Gfv9yDhGUpsIJjHFhMXiSuhiSlPX7BAbstU3C9e/auG5m44aJ2eXlxwyZI+lLGopw0XMsnpTUC5+wbgyxrycxULA8rGK3GYb/cDQFgG3FWsBReMNmvar52OyAqzDUxIncpz9IyU2MfWYtnAjGps9vVlhCFJ7Q5brU8yiQM5AThFk0FTm2pSyp7Lm7KmX8HD7ZceQ/WmgA/7NvABLKvDvBv4nxpiAMkT9oyLy5luPQS4oSZV/h5TC3OYrFhbFQKt1y9porXtMSrRZrHBZQDGpUrH1gQrj2OdUnKkPwFRRdXlB6tDRIDO3QNGsxnDyu6BC99BLEJuRXyN1oEV5KMYqO47uGyRJnnOYhblUshmTgSftGvTZakZitRrWzo8vpTL2SxUqxuvYLesxSffRkFilQIoTcZpIU0QmrSUYp8D6fItfWY5yoL3wOAJBBsZ4z2Bu6NNr2rMLbOywZ5YhBvxqlS0WGobkQhjJ7AKCxRgHFvy87rMyyFa4xse6YE9wlCJr+UuMzkMkpcw4tShFNoVRqBBrLiX7oaW1NVEnGAot3MJP0C/Rwakakto5RBQVOpzR1B2CSUnHhs2yk42Ers9CxFqOqdUVhigpl/tK7pvL5KfzjsCq8qgN2bnSUbKyS6jyqSEF5LHqWmPxriitbL/sGLL//Xve+y8D//K37fOtzzEL0dKdLS59SXGVZqDLy0tSStze3jKOo1bC2WZ24d7huj0E85ZRnt60gkcsPitF1AteUaoYefD7AyUgiuCXuN5wGucVU/EQFX5wL2tfQ7kfkmO/0iX5sMDqXa9Zo+mj4rE471mvt1gs3jb0PrA/aKflMI4EazDe4fCMkzDGiSgR26q7GQkYGnCCMxZvfBbAHF+TqbqkKPTi+5q3XP93fZ2i+m+7soKmRh/yP7z9nGdg7+TZU8LAOVQwtnge2TuYH9ED76VcSglpVJGXWoFs378RNarb4pHPYVF+fZkNMfN6WGITD5EzkxVFZsmtSmr2y96/fRAVgwUAmX+VGr+W9F8BBNu2zfXsU324IQRw9gFam4tI6lDJUxfpROdnrZBtO/NyqO9QF/TBvySnr5UtiVQEu7ioJ5mPxTIplq+cx1KZlEpCEcnXP+MbBfw8VT7ytiIQtQhhCqSo4ZZzlrZtSUFIaWKcEjjHcRqZxohbG5IVjtPAvj9gmpFVYwhTIsYelxzJ6ABNTAG1skCiQFXKJbOpzOXDYG1aCF/BBOafofRELJ9Nyp8XZsLPWQmrK1+U6SzYp8trifxnNVAzEyVNlwVJWDwvqSEKUEMQY7OHl5lFlU7cYnLJUD18FugyFmExGiHvT7+sLV/6h7KqTK1BkewtFZE2Op+iULZnSnFTNVZeWzX8eT9+Ah+IEqgW08zVbH3fMwxDTXM1TcN2u8V7z36vvQNnZ2dsNhvGcaSfZtR8LuxYiOY7FoY8+B3zTmxbP54X0bJi8GF/w2Jv9YEWC7M4EElSLec0i32XfT60hEUJQm6UMvO49aW1KHjKw+sslk/DLCVnTbnG3npHt1nTWsO0v2dMPeIghMRx6jkMBxqfVAmEiRASXjzRBAwhL36rsw/q4tbVrcFWDgcwWDsurmte+CYLgno5px5bea8uavUyyA5VSuSMyNuewPx9GXoslESutJlfM9UjzEeb762ZR6dL3qmxet0pPfBAlub/m2Vvvk5XbkY+50JWknXfrFSKJimWfxkO6AFLkINB/4Z9q2394fZBKAGY3b4QQuUELAUOblEFN46jkoLk14plnFLPuEC7l5b34cOo6bfFaynl8s2HNyy7Vjx477cpgfroFkrAGBRAMjZnLeCEolwSKZ26xkXYUy5ML/dkfu1tL+dEKPLwTe+8jiKLOuU4JcHgcN7TOsH4hrUVTLREP5EmiBZS6zCdITWB0AeiiRgTiDZiZMLisCYRq7XWlmJri++cee+NRUyoQrfMDJyEAKcOWL3/s/IwIPatLMJ8r7853CiaqgRqsnh9aRaWz1RZi+cTMzm8mecYLL7eGp2X6qfm78uvJX6Rzy1PRzYFSCyeci4Sm69jvq4oKU/jkgoulxDs27YPRAnM6aGl61+EvMktrofDgTAF7ZIzhtvb2/recELovggL8iNJGRzTB/swNFhYYYk1Z53/cCLw7/IEKF5FEUJSdVEfKgFrqaBPiftK6m8Zwz5UAMWiFyWgIGNxcRcLtFqlHCpNUz6nOUYW0Tr+ZMA4q6PWEZr1iiRCLxETPd3FBoJgu55gjgQ7Iq7BOLA6+hEQrHFUPz3DXqoINHdYvkdjskutFkxy/Fq+YxTpLueo55zqMyxW+USoKcelCrgtVn5xP+bKOgNGu+/LFqUiF/OzpGR83l5XJ7jAO76+KX35rk1EiMULNWgmJTcf1duKxYrUdGQBBotXorMwF/c138P0BziVD0IJCFLj/2HQwaSFEdgsLLG1FutzC22I9H1fsQHbdOT+yxOLOIN6D7Q8Dzy3Ctxp2q64VAZTa/RV8PXv3wwMLrgI7EwyofFrccs1jehs6WEo8e9peKBt0qku4mU9hXvQGFLOwywEQJmGIinObLn6moJ3Y1DewX6YMK3DdI4wJsRCs2lJYWKUA/14YJgC1kDbCDiDpERE5zmabKUKIKcezoO8d74H37gWiqJ64GXN91eqoIB5ax5FsdCG0xhc2X8Wx36QdqjVmVlyRBKpZGJgwda7sMRW08omAXEGRpdeQyo4Qv1+GqJgEkGSZsBwKvzkoqICUGZNcCLPRjShZfVng9OMVGZJEtDK1ZQq3vW+7YNQAoicYADF1W8aLXAZhoG7+/taxHJzc0MMkc12w3a71Y64BKHQ8JmHu5+t4MnrPPA+c7xetfpCATx0/SuY+FYo8MAtf6BwSsRZjn6isKRY0NndjVE9j4IFFM+gAGhLhaHXvnCrBbbbjXpX00gIqgBijHg/sT+MkFuKd/sjngbrYYgjIQ8TSSSG6cgge8Yp0WYWIRUQV91q43LdhWSrJMVDeejfL39+qIqZF/zivi+vbwbu0oP91TuqfzPFjBa3WbMbD5VSEfzZp1BhlXT6XOvuKCTKpkaOxmhvf37c9Su/u+xh8X3+kjyeDWPq6PV6LXmwilney3xNxplaJ4BVVi1O6kyyB7z0WN+zfRBKQITq1hcPoJQJF/Cr6zqSCGNWFEs3zzlHKGOlgaUFKvswRpAUF5V5p8MbS5vxu1JPD29goe/CKIlIaWIqg06WTRySZgtU8rcmdzDGCNIIzaKScXmsQnZSPKJSN+G9nzkIsnegwKGc/L0oC1czFJGUeRibdsUGj7ET7fk5drPiEA8kO7HdnmFSJB574hgYpoH9tEeCIwx7CDqfoOtajGSGo1ytZmuDVPZsskIQSdBlT8EU9p6U+TRztYaYk/z4iXeVdYVIgvg24Yw+l4dhAm/d1/q90IaLzBzi5Vj5AuzCSytSfQI+Wpvz+vPzLet2KXgPvZk5BFRaPO9UgFVotUCu9K+I5BoEazBW280d2eLbQjJrqLM7F9e8DE8+eCUA1H76ZTxcLMFywS/d4qnEu/K21n6YGy7of31vDjOkfua0ZLV+9h2KYPmQHyL1S3Rec8k5XVYX45znhmXt/xwOlM+Wa1ke/zS+ZXHM4oZDUYLWWmVbNooQa+9EUYwQLFgreN/Qdp16YmkgMDHFkZgmMOqZxJCwSXkKYypZiWz+MiJdB7lWy6ro+RzX8lY0sLyuEro9/NvJc3jrX4UKqvtfHQCKg55xisU5FLFf+iJmcazlvpcZg/IM9TgFk9cCqcqoWn6WlMOOXPZo9E5R96YKMuaReZIrIAtps7KX5sjjpC+AXBBVcIP5Hs4neCoX72qoK9sHowSWdf1LITvV9DORZiHFmK3u7FaegEFyuu5qiujBdhJzzq/WvxVXPeU4q3TjzS56IXp0J5pfreO8pSQ1Nq8uWypsQKeYgF7XqWJZIumVYUiWCmCmwFZvI2Kcjqqy+R6Hwq9oBJfJKLz1tE1DlIYxCTFOhBiyEjA4sTjXYE2Ldw2GfG/zyG9tBJIKChrUQlozV8udNGKfKO25TfhdSvf05zmQWHj7s/ADp2OKliI+C7NYla5czaChH8WPL19lst9sLIRUFW11/Uv4UlWHfi95AlNfXSh1JOMMWumphiIzGVlbRxkqrGSrh1Huk3GzlU8yL/IadsrbYPb7tg9CCRQX1jDTZD+Mv5cuVL34qO9tmoaIpr/eQmvh5Pd8RD1G/u3UVTpdfCcCneZahGVLbrEc1SIt3b+FB1KOrB6orUIcF8dbejHLoiDFodyJF5DSsmFErfBD97ftOrU5tuAis9QYY3BW5wo4Y+iaFmwkhAY7GSQGUhghJrxpaP0Kz4rGdjisMubgsMZl1tuSKivPlUIwnM/rbUU74zVlUT/AAN75fN4mAXko5Mu/LY9dfq5ZiqKw8oOp/xZCXb0IozF8UQlJCuhW3pvP3cji96ydS69DPlP9T4U3kjAkXHaZJFt4TU1m7yqXuhduhVJkJlohN2MB5OvIa0/Sqbf5ru2DUQJF05Xfi5DMQqeisnwPhhozuwTWRKQgtkuhf6AElgvoXUrm4d+W55FSIsX5nGpYsMAB3vJ537NJeUgLQS7FLw+Vj96T0zDnYexX7tvyGtbrdU67jrNFeGBKY0jEEMEKzlu8MZiUiOPI0B8JYcCbhpVf09gNTjocLpcIWUoHxsPrFpgr5Vhaynz+OUwqcbAagfhgHzLvDMmsOuU5vR0C6ncWr799v1h8spyjWSjz4qHNCj5jB4vXdIf5DPO4+iLcKqhzaGCsLM5roYyyIkpS2sVnY1IsijFz5qJGM8YssACp78snWL9O1tHfDp5AAduKMC4XcxH8InRFaZTQQL+cMv8yC0Xd/4NjPdxO3f3Tvy+V0GnhDtXdBmaeOKjXoD9zsvDVm5vZXt5mK7InvQHVBVx4GEvhX3LmP7xvIlK9iYJ21+p2ScQoxCmRQmQYRiYOmhIMPcPhyHDYM/R7oky0TcPKd7TNBqYWkxw69nuOoc1JxvK0l3DpXC9fq6pDJLvmpxbroTtus+ws4PwsXabuzNisUgo+Mt+gt/Z8+tN8LUt8QmTZ+bnwvBafNmZ+5S1vtH69+z3lCkvnYC03yopARIe/QK4NsctrzZ2T5SpFZv6Bqqvelaqdtw9CCcznPy/yU8t9iroaY/IkXRWAki3QoZqn/eEzmcu7b8Kp1eUtgk+l9U41Xz+fw6wg4CE92nzeNUZY/J4MtSiqjEBbbkvPp96PHC8XoHQJED70HMrnAMZpJExBmYfzOUiKTOPINE5Mk7qSKSTGMBKmnuO053g4MA4DhICzBm88q2ZN12wI0Ws+O2VlksAVcCubwrKIS+HUSRKuSt07cIGlRTtZIEUdZDCsCH95wLboA1PcgsXHT8PAGSiaI/ZZ2SzuZQ4DRIQIyqjMUhGfKgC9/qx2CqBnTWaLMrNyYqEEasszlawU1DtJWd7LOVibiUWNnZWGEYxz2ERWAGbGJ/OlKj3730Qr8X8q2wMLt7S0ZaE752odAZBnDmjGIMSAcW0dwXWaFjGnbtRJLPBQu2e3bOE7JEnEFAgxnLr/2Y2NSTMUVoDqbjskM7mIVQevWAJdqNnNj4WNZ+naz0qgpC3LtJyiBOAUJF1mJx5ez5JHUJLOWDjue/a7nhAS2IZ2vWYbV1iJRGuZji2Ns7S+wfk1xkacTazbNatuyxgVSDQx9wQUK1fub7mW/J8UC39i5POzeRCrzkKgq78QadTIjtm2l+dbP/vAGyuvPRTaElqcHnX2AIqLr+c8vy8i+ELnRkIKcrc0NNkCl1Jj5X2wlUXpxEO1Nsf2UquHReYm7OoBGRXkpvHV8MWgDWFqDE693ofhIQK8Xwd8GEqguJTLjrklcWh5vQihNbbO3asIuDG5J3vWrN8EhsCpm051uUsnmC6MhwhrEbZq+XP8tXxf2V09hwXNlYFKApEyiUp5iA9dznIcpKy1GR+ARVUi87GqJSssy6VmIE4M08jhcODu9p7DfsC7jtVZnk7cOC7aM4I7YncTvdwg7kDAA4EYRrpuzbrtMKOhdS0JhxFX77upYNt8rSIsZ2o++P7A8j8I4YTlB9/nzpZjLhSIyOKXurP372J5/2T58E5P2ZiHnkDetclqybwNRC+F8V0YTllnRk70ycnaNUbrApazJmMeQa+Xr9OJkFzINMcS9aLNN2iBD0IJJAHfbnIZ8IjLZBlj3wPCerVCRGjwSiphDYYGyb3syUTGMHEce6W/ulhzZOT69hXnl2f4tWU3jgru9Nrq0jpPYy02avGJAbxzhHbU+X7aCwKiLphrPOIcIUZCVELQJNqM0w89h3DEtpa2a7nwW0wUxmlkHALk8KVtW1rfYq0jpcgUDRSl5jzWOiQ64uiwydN1OvMPiUiTueaMwRpBnOcYD0iEtu0QZ+iPI+qWWvqxp21bptCwu7ujaQxh1Jbhzfmaw3Dg0+8+5+rqEffHAxddh996vnjzc/pp4LPvfZ8fPvk7uD9e8+Of/C4vX37F9f6eUaBdrdnzmnFKnK0vWLcbRCwhlUXtleBEYp6Qp1a1TMmZGXMLnFhChLkfAt4jsw5wZfjKHI6VuLcYjfrZB6FYeS8lJVd+r665aIouJSKRZQkwBmTR+DRb8qXSE0XyxeLFMY1jNnIhG6qFhc7n1xmIYcIKNE7ZCawkrAhd29F4j7MOLxYbBEngsaQCziZLbyMxn19KM/V9kEhID0hKHmwfhBIQ0Yo3IeriCQkJpSBFdM4eS0uXO/lizD9HxAghRY59Tz8e2R32Oqp7HOtNjyFAzMUbFrQXXOMrl5fqOOikXq3tsLWFlKTWNUX1PGzpbUddP5sBPWcdkHnxrFKHV4ttNHwonouxJYK0VbtLKhkPyzTFOiTUO4/zHuPmOgDQxa1cimoJnMvtrQUMSpG2dXRtw2QScQLvLOfnZ5ydb2m6Bj86fv8nP+F7P/geIQSub97wxZs7nh6vuHi0YbPZsNvvaHxLmBKbLnH16IowQL8bOByOtM0Ka32Nrd/xlN/z+oPw/1sXiz638lVj7CzY5X6We1M2826VMm+m2GSp1rVY/ned9lvWXg+y+K5+jLd5MhbZoi8+U3AJm1t+rXBSFPfwvSKRhE4wNhiMs2qscpl0WRcPMaZvAgXhA1ECKSX64agXkRa1ztbW8U7l9Zj9yxiVm6+AbOJ0nuF+f698BOMAwDj2DEPP5eWFkmymhDfakSXWK91FTCQBlyC6LFzJ1PNBEpK5/WNKuTNLSCZlLa8FJylquDAy5S4w0TtsIJnElGBKUckiRNuKvLGINaQoCuBlN6/xDlnlKj9vsMmqESwKzGhjUpLEMA5MU1DW5aZhHCesK1kTwTmD95b16hxnhf3untubNwzDAecdvmvYHXZc315jveH+/p6X918xcsCvP6Vbr3DOI0m4P9wz9ZFpnEhRFeGqa3NRECdud50g9FYM/vY2u7/fIqwn712+/93YwPsUwcMsRHHnLVZxnHeArWV/S/ymfJ3k6Q21Ddh7HS1nK8C7wH9QwhTjbPY6FuSrZT+GqhAKW5XN3o4VSyJlK3+KgTw8329qKf5lx5D9j4H/NvAyv+2/LyL/ev7bnwb+EbSx/B8XkX/j244hoq5z430WiFAv1hpbFYA2Q2iFVcyxYKIUzUTG0NOPe459T0wxD+DUGYbb9Vor4HKHmExCMKESjjpj8Fawvp0XbcpVfRFiSHVAiGBIRhCT6vy3kAJGnOa8W+2hV42tu7LG1Mk5Rgxx7HXB+QZnImFKDP2grb8CbeNIxrN1W7rCp5hJRL3V/YsIMQlhHOn7nrPzC5qmURr2xmKs0LYOxGEdnJ2vWbcN03hkd7jDvDJsjgceP3/GR5++4PrumtVlS0Sv9e7+nuubazbbjrOzDfvdgfV6zeXZlfI+HCZat6ZbrQhTQmLK8fmyImABcC2A2AdrbLEWymtvrcR3/r58X7XeJ1OJ3v3zfKBTwV5uc+/AadZqeQpvZbIExEglXvVOPcOM/TELK5gCHi+E1Lq563R5KGud+vj5D8XDCAQkcyBaTutoHqaM37f9smPIAP6XIvI/X75gjPkh8F8HfgP4BPi3jDE/kEI4957NGDQMMNojHSktuypsZNQ+JmUNUoDQZC9d/yYEhJALM0TjeiM6lpvEOPVa0mu0dHcMI0G0JruxHpzXmyteY9aYlBpLHQFiNKRkiFHP1dgFxVN2HxUghDEFjE0zNJNHRzmr8+SNMVpxKEZDmph0jsL+yND3pCR4b9kcGp49f8bmbF2vQ0/NZAJTIYU8WGQcMQjeOwqLzjSNtH7N+fkGIWKNEJLWASSCDhF5c6C72PCd733G7V//K7x8/YqEsDvumKxle7cimS2rVcfNzS1PHz3iO599h/V6zdhH2sYzhQlJZiH081ayFDBPRUoZqKuW9IHCyKtitvFLJbE4xPvc3grw5bX1zp/RUNOa2e0G3qJ5LxjGMvyqAsYDBZAPsnTJrQUjtl5ned9cx6D343Sf870TeUAgKoJzihGQFgAyc/PQQwDy4ZSih9svNYbsG7Z/EPgXROcP/NgY87vA3wX82W/6kHXKOiNicN4xTRCSaji76EsvVncqRTQUJZAwNiIm4huLmwz740A6JC4unA7OGHpWq02Nm2JKxCR4C85CKvxuow4FIWplncaa2YqgA0PHKSgNllM339jsRuaHOTGBKAi05DOWZMFo33dCSFOgnyaGoeew23N/d8/xcNSJSs6yOvNYb7i4uqBdtTgzZ0mWKUHLHB6BThxOKbLb7TjbCJeXTxnGnv3hnmk8sj/csT7rWK06rn/xFfvjjqftM84uN/z4d36XJJG7+zs6PJfHc9rO0XYdMSamGHICK+Eaj/WOoe9xtsneTwbXSpaCOc5e+AQ5xp2lUx6okFLQm/XFYhGXdMM7lABFCcyvvG8zxUWrv8+CLOWc8v3VFPx7vIET10Wv0JJZh6pVXp4ddT2UTdnTbBV23W1+T1YEJX3gyLyMmX3YOIsVhynz0XlbEXybN/A3gwn8d40x/xA6WOS/JyLXwKfobMKy/Ty/9o2bNYYpDIhE1t0aY3NTRUpIpqbS9IeinVFCjs1T9QRsE0jZExACd/fXTFOg7TStctwfNI4qgiQ5PYclaQs8kUQaU53tp5NjbI33qMpj0VhiE2KixmYkiDCSEGfwxfKLxSQFGz1aZz+OI+Oxp98fOB4O7Pc7DrsdfT+QYsQ6yzE6Lu7Oubu/xTeOVbemazuMKU1MUSfTugIKKS4yjj1jmHj15hUiwqMnFxyP99zf3zCOB479Ad9amrVnjD3RBL5+8xXdusV3Tv/Waiw7ThNjmFh3nu35lhAmfvHVlxyOB6xta3WbCsgMCIhq0LyIT0HBpVV9e1uACg82FbO53r8Yh2/ezHt+fsc7i7CafBZicnFWHjt+ojQenPOcE6a6j7posxIT5XTMZ2GKm0gW2gwmzC3oS6Wmz9tZN1ONF+WQMypLjpRsvRa/fzM4+Msqgf8N8E/lu/JPAf8LdCbh+57qW5tZjCH7+NNP6Ic9zja0baOCXG6a016AEEKmT9aR3CGGWQkgmDASzEBII+N05M2br7m9u+fRoyuurq44HHc66cc1GLTazWBIWFJmd9ECntnFJpUW2VlzqweSMxYZMAxmUtRfIkkSvY+YxtK6Bm81jSNBICRsBCuG6Thy3O/Z3+/oDweGvmcaB6JEnejjDeISQxy43d+qR3Ce8I3D2xYwhKBkoc7ZOiRkCiP9eKQfe17fvKRpHK+vV9zcvGIcj1gnjKlnCD0uOqKZ6DYNP/n893n60TO+873vsJtu+OTTj9kPd0ouOk1MVvjoo4/Y3+558+a1Tonu1oQxTzqe71CuDSi4QKpW/1sR+v8Y21JVvA0fvDtMOP1Z5u9VcOysXOqONQxcKrj56EX4zcKFmN34jBqfKC0W+y3KwFiDMk1ZnFVMQ0zKrdmlSCxqFsHqDPiCg6VSX2GobERFAZD3/S2Y7C+nBETkq/KzMeZ/B/xr+defA99ZvPUz4Iv37KOOIfuNP/5DGWOPjD2bzQowWK8dZcYbWt9wv7vH+iZnCCKCDhcp3VZTGEh2JMnE/f6WV29ecnd7x83Na7quYQojh6OmuQzaa9A4TzKORCQawziOECxGVNkUl9vVOC1nLohar4CmJsVGxjgyjApIjquEwZGkpW1abLKkKSBBMEFIfWR/vyNNE31/ZAw9OKFdN3hRgfKNw595Jhn5+s3XhKxkrLe07SN849nvdex4t14hREIaialhigOHYcf1zSvOthsOwxlTGkk2ECVyGHcch57N5QZcIjBwffOa5szz9OOndJ93PHnyBLcTkpkwRpimgWfPnuFNw/Ew0nYt1sAYJpqm9C9IDdHqYrQWkXCqBIr8zIshf7rIlH54IVcnn1usveUv73797XVXBcvmBqYCQJuTeFzPqMxJVO9+DmhOzi5bcT11DckUKtJQtUwlMIK2CGfhdsVtX7SHl3Rhie9tKUpcMi3lwa+xpsxTvjdvh0fC2ynDh9svO4bsYxH5Mv/6XwH+Uv75XwH+OWPMP4MCg38I+Pe/bX8xRVwDN/c3rFctq26DdTAOERsnTNfx1cuv+Oijj5niSEgDznnCFPCto20bjv2E8QnjhK9e/YKbuzdITNzdXyNENqstu90Nzmnsul5vcG5NxDBpYTiHYYdMhvV6jXeWMQQtz7S6QGIMTNNASgEciIk0nWe17hhSop+O2muwskgKkMAmq4VDw4DHY8Vyv7/j5s1rri4u8Z3DuJbVekXTOMZxoO97rLN05ysO/Z5f/PQr+vFjpjAQU+D8fItrLC/fvOR+d8/z588YpiN2gNV5i9jE7nDL65uXPH7ymPPLNc8/ueTLL3/OT376Y+52N1xcXPLZr3zKz77+nN/53b+K8Q1vrl8xiJZmv7l+RbfxPHr6BGMTw7jn7u4OkuXRk8e4xjEdpwf+rV1Yv9z56BTVnkLQsM4ajCyanGaUQNdCyYGXv8rcUGZz3PyWNTfm5PVSBPSuMuri7rtM7OIw+EXX6RxPa8l1KQY67RqtklDPW0omaNFKrTDT/H7Jo+l08Mui7BsFMo3VcfN6R+zy8vI5lIEuOjXKOksMuaGtFD7Va6A6KN+2/bJjyP4eY8yfyHf194H/Tr4Rf9kY8y8CfwWdVvyPfVtmACCmwJs3X7Pb7dlu1woO9oFjPzBaz+F4YAo97aphd7hnHAeunmzYj3vud/ecXZzTrBx9PPLyzUt2uxu6VYPDcHl1xtdffcVxf8QYz4sXLzg/v8CYlin19MMRBLbmjO7ME/YBsRPHKXAYdjqqO9/gGNTdTqLCb73R8WnjxH7YcRz3iBW8XeGaBkxkCEfiEJiGESeeVjzeOz797qeEYeL6ekdKgZXv8OsG2xpMC1hhkAN+42mCpQ9HxAuXj/Va31y/5m5/S98feXNrMM4Qj4HttOIw3XO3vyaYga9vvuTP/YUjXef54sufsT/c8dEnLzh/vOV+uMWvHa+/eoVvNzhpiPcTKYxcnJ2xvVixalv6cY8IhBQ1fRszAu4MNs1xaxkrVibzlMwJoBywGWitCuA9YNUS5S/fqyCbRRl8jeGXrbrFdYc52yAPXiuf1zbfZUBtTJnxSM40zdWAc9/Ru3IZhVuATPEbF+FB9hOMWSi4cq2lMQkWZU7qaS6zFnnQShL1VCnjzgp+YbVceH4OZN7Db1cDf0vHkOX3/xngz3zrkZcn4R3r85YkU7aMid3xjsO+x4jlcOhJJHxjiDIRCTSdJ6WR3eGObtPgO2F/f8eXX37O/f6O8/M11jg++eQ5v/uj3+H3fu/3uLi4Yr1pMC5hXGLTnGGcI0yRMfV023PCNBGI9PHIMd7rwE6XPYGkbj9GBR1viRKYhsh9f0sfj7SuZXu+YnW2JYXE4X7PcdwTx4gMwjFC5zqa1mG9YXO+wRihXXeIEyIJu3ZYD7v+nvXZijO/ZTwO/OL1l6zWLYnI8TCQrBZJ3R/uuLg8I6YR1xratcF0wuMXj/j6668RF/k7/8QfZX3p+fyLn4KP3O6vufv9WzbbNWMc2N0dWW03tOsruq4hygpvDSkEUlDmpHGMTEOCkKnYbOkcLKmsPFQVq8VVEqqlt9bqVCaZ41w4dffVoThNu9W/1deLAMwWr7419/TXiUFSvuay5BqLL+L3ByuY4i3Y7AGCppKTSQuhhLm2nFkBiGB0REr9nZwlIc0DWoyZO4JLZmHJF1EyDKX5r/SMGBOwtDN50oKvoACLS+X5t8QT+E9ri9KT7Mg47UAS97trhmPA+45Dv+fq6ooxDJoGbC3DuKcPR1wD1mte++s3X3N7f433lrPzDcMw8PzFY9pO3++bK0I8cn09MaWB7qzl6uKKKQSiBPqwZ2JHjMKUBsRPWJeBN8BEkKCrrt2o6j2ORw7DgbvjLUKibT2+M3Qrx9gnyuRh5y0hk3Y6LLe317x48YKPPn2BkNgdd9ztbujDIeMRia9vv+K+v2flO0yE/XHPFAaub67xxnN+dgY2cb+/Z7X1JBGOcY9bOdpNw/PNU45jz5u7V/z45z8GF9mNO7xYHr244tgPYByrbcP1V69pNy1n2w3GRu7vX9Efe9brFudRurEpMRwDJilHQWN9Bp60fkHnC8yg2nJCc53CW7ZF8L0Uw8qiTFYW5e2FVLOy9TCPmMuub94htRsnLWz10hMoL6fcpZiHvqoG0c+ZB1a7DBp7kOhY7HJuW19eouTRb1KJEDS/UfVWVm5p+ZmSPckehKBVquo1LN5XfjCGdzlVD6sH37d9EErg2B/4az/6S1jjOVwdONtcsB8OSLI4sbSd4+LijPv7axKB7dma290Nx37H5myF9cKrX3zF69cvsd5w9eSSrum4v78jpcj2bMXZ+ZonTy9oV47DYc+xF8bpkigrpjRyHHr2A0gcIGYyTZsXts2FS0SMSThvSH5iDCO3xxtu72+4293i2ga/cdzcXtNPg3oChyMxRVbNCtc1yBQgk3smEmMYGOPIcTqAF9y2ZTjuub59zcvbr0hRuFifs+7OuFpd0HQNu37H4X7Pc3lO4zxvbl6RfCSYCbsSurOW28MbmtYRvfBqd83hd/c8f/GYUQZ2+56P5AX7Ycfv/eT3uTh/gnOGtvVM08B+d8d07LFW2LQdtrO86a8Zp8AwTVoZScrdkGR/dy5dLaa21seb2R3Wt5924dXNnBbazL7C8l1FsaDKZTFMdN6N1PedOu3l9GYat4cVgfr5TN1tZvjPpkRa5t8lj0Q7yQxkATdK3WIRQrbyD0HMt4p6WGAhy/dmoNS6HKoUb8jqe3UitxapPcRA/iAKAD4QJQCirmxjSUwIkfWqZegj/djTuI62a3hzcwNA1zW8uR3oxwOr1DBMPW/evGa333N5ecn5ZkucAjFOfP3yK4zRz1xenrPZtuAiTWsZpj0vrwPHodeCI+cgHvVG50GaNgKiKZzCaehjg4uO49Rzv79ld7jj/nCPD552pWO7pjhCIpfTCuMkMOmiG8YB2xr++l//EXe7e5INnF1uePLRU7YXW5w44n3k8YvHeNewaVbcvLzl9iA8e/6MVdOCJKyHICMTE/vjHXj4xasvedE9ZXe8Z7jv2R8nVtuO86stj5494u4eXv3sK37y+U8Y+4lXr9/gmw7fqMfz6uVXXL95RdM4Hj++4MnTK/rUk15nejCJWOMrWCc17pzBuLJpnZcy7SbhxPqV7w8bXubYX4WnuPFVUK26zTYTdTykGZ8PL4tw4IHbn+Pl5Zcxp7F0GZRaTk1Z2bTWvyD1Jk/+K78rkYitqL8twCbKPOVyX0D97pwWmmn12HxPZXbvjTW5CU2ZR61TQNCezGy0pJRL2qXWZWLMww6Jd28fhBJo2obHjy8Yx0gMIzGObLbnSOq5PRzouhX92IPJPfhGaDtPmAZubl6zCRt2+3tinOjahvV6RWrUY3j58ivGsUeYODtf0a06bGPxbQM2sTvcceyP+LbF4Yhhnx+gLuoUMjFHBJIuDBsdjTSEFLANtJuGVfJE0bJcHRKrY7hsaxnDxOF4xCXHqumQCFMKfPXqa97cvKFZeUYZsSun3kDrePTsMaurJ0iCzq+Yxsi4GxnCQOsbmnWDWCUm6VYNWKHbdAxjj28bgkx8/eorLi8+YYgj13c33O6vGcc9OvVmApu4eHTOFAaGcWKclFYshsiji3Muz85ZNS3jMGR2HCU48WZexFpEo52LmIUQo1baOENMBiHOsNdC6EtxTAkBQiZSmRXE/D49JjhnFr+7k+aYd1nDd1lFx/zZJVNTea18L+e6jMklDy2Ikmp5u+TpocWLcHkYS2lrLkkUZy3OKTjsnBb+OIzOp8z3tcyLAA2D6jXmkW7WuwWfhTI3lfMv92AeYPPt8vdBKAFjYJh6bm/uIXrCVWL90RbrtcS2W7Uc+z3eNxyHkRgn1usVMUXurm+JBKZpBLKFcJZVs+Hi4oL9/o4QBmKMbDZrrHfgDG3XgTNMMuG8VsgliSQmKI8tGUKKysCTUCAMkzsRtbJu02xoNg146Kce11qsA4xUHkQZEsd4pHGei4sLLI5hGLm8vGRztqbbdgQm9scDciM8ef6Yx0+fENs9r756hawTjx5fYi4sq1XHbrcjTpGua7Xpyihyf954+uMe2xhCmnh9/Zrt+XN8q23Id/dvCCnw9OkVIOwOO87WFxhx7A9v2Ky3tM5zfr5lvelAEmM/EEIe9eYyqSuuDjhRRpy51FVK7prZsiWZE4HFc65AX14A5XdvTmnTyla5JF3EucKsNFtVyBl8MVrReeIFvL3mvFnwU+a5DEX4l4pgqQTK+VTimDwYUJIqALEFmLQZIJ0nAlny1KLsCehxMhW8mYHWeu3MwlzORZiVXxFyclhk6oyCmaR39iq+WRN8EEogSeLz68/xpmGKEXpDu2+xeEZz5PVu4vGjp4TUM8QD3dkTXGpJjEypx7hzRbnTyDAOJJN49skzXu1eIjJx99Utv/abv8LO3WGdxa08tEG7Ee2EX4NpR9I0cfz6nqbxiG2IUQgxYpylWXU0jSdGoR8GRKAxlmiEKfZYn9g0XuPwYOiajnV7hhXDUQbGsWe1btictbS+IQaPaz7BWKFZNRyGA7e7WyIRR2DlLdgNcXMFIthVi8GQ0shkD4xmZJgMNqqlaZuGPnR03Ybrl/ecd0/49e/+kKvHZxz6vRYamQv6sWMcrXo3ZsP9ISJyZEyJaBKr844wqKcQxGOiMPV7VnLENx2Rhhg9Dp2nIJk6C6O168ULcAas9epRiSrUaEJpLcgY3NxfUWaS2kaXpADJzNTbJvvt3miRTSnwURr2LKg5hehynC5C7TeB7IFkgbPytjfx0CtYIu3GzLyWhfmqyVmRWe0VT0DqtTmrbMFLMhGbyUjE6ABWl4lryMCjkdx/UPAOqz01xaynTNViXMEsBImlyjY3tRWw9CGk8o7tg1ACMQVe3b7i8eVjhjTBYFntO9bdGaMM3F5fk0zk4vyC1aZBbOTu9ppDv8M4jRPX6zX7/oAAm+2G49Rzd7jj13/wK3z55nOunl5yGA9IiHinvPo6MET5Co0XRCaSTBnesdrB6JLGZN4gTmfz4UFsJJQafglYX6yTth1PYyI0icao1VyvN7RdA0YLmkyKnJ13WG/xrcN3gtiRMYy0Prc103DWbZXwM2v7GANupUKTYiQStR/CecYw0bUrrl/fQrI8Pn+G80daDyEa7GaD8y39MDEFQKwCl1GZmbQQa6KfemIaaBqLsS1hGpFxj+0MxuhMRJsJPWxxW51KXZKo2EkUkIQRhzda8x7R0W1WNFFXhNlXISkWT0vBk6YIEDcrAQu4hYWswooKj6BCUDMLiyoVW8rvAKfYmrrj1mTXfC4actbgna3DXcqk6pQ0G4KkfA/sW+GGtba67EBtFa4dfRnHIJ+jieX3rMhKrYOZeYTrnAHIiidhczMaJRRjFv4yzagqg2/YPhAlkDj2R/rVwDQG4iSs2w3etGCEYez54svP2WzWXD26ou+PfP7l5xz6PevNhiTKHaBzDBVs+dGPfodXr17yJ37rj/Hs2TPu9jdK7DH0SA+l0803DRjDyjl18xuPdaW3u1CZewQl7xAMbdcoUJM1v/c+F8hk0pEYkDZlizEhKXJ5ecHV2UUm/RiYhgEh4sQiqLVqGo9tlKJMH7gCPSl3kXnr8c4hNHqsysOocWKUxHHoCVPmYrQemw65GStH5VYJK+M46T3ve5SWPDAMPbudoT/slWi08TirIVE/jAijgmXo9Vtrc/dkdnnVNWCaYAwattlGm7YgI9zIgsNBa+XVJba5Yi4n0HLOX2zecWbrdWKyFTc5HFAFo6FALlsuZLNAyQ4YqArLIriUFUn2LFwWfBVWi3WWxmXClyQ1JRdF1EJbHbxi7DvIUq2tpC4ljTcrgEJNNmdHSqtxNdrWUEhnqsLI+ymXNNdIzKmRZQj1rjqL920fhBIAjWlDCoQUOPYjzlzTNSs2qzPaVcvt9R37446r9Ih+7Lm+fUMk0nQOLHTrjrNwxjgNfP7zn/PX/tpf4exyy5vXrxBJHI9HbAPH/qj970aHQa7Wa1arlWr8mHDe5np3tPAjW48kQojaLON8HjUG1RqKSYSQiUBTpGtb2sazO9xz2O95/vgJH3/8MSkEvvz857pgRTn/gwhYBUhbb3He5r4IIaTAlEYFlRqlHRNxxJiVRwHSrZAIpCgM06Qlrzg8A84ZxikwhoDgEKN9EofDgRgjZ+szSNqQ1Q8D/TiQrKXve1VaITDFiIsJZ0WFLxNfStJhmjEGnLV422BECGask3VEIiFOuKa461Th8zm2LyXBTtvkNXcukpUAOoY7exE2KUpeALYl+UcR2DK7zzLPhLAud6Mag0+xCryzFp8V/jI08E47KZcZCiOi1+xyNqHiILoVAa9exYJeroQ1J8VQUohB8yaiHad5X4WlmMVn7IPfy3Exs3J4GAV8EyzwQSgBay1t65U1xzuOsefm7prtZsvl5QVPnj5imkZu7m9oXjUIQj8daNctzarRB2Jhe7Zhuhn4yU9+n1evXvH9X/seP/nJT3ANbLdbdsc7rQgMASwzSKP1lbmPX9MxIWlnoUFjTGudeg3k3v3sqiZi7u8PhBiJEnFpIqVAiPq9aRxn52d0XcPdcU/f91ycbwkhMU4jKSZ855RGvdEc/DiNiBMmmZiSgp4eS2NbbSRzECftdjTWIpnXwHiHSXlQSxxpsqUcwsR+d0CMxTctIUyIJLqu4/LykqNT5QaCz6mrKQSOxyNDGMFojbsi3zo23vtWW16NhRTxVr0Ha4QQPHFK2XMBO+YwyOhAc2sEZ0WLsawhD4rWjEItqBH1MmzK3kDCFvDLaZOZdlAaCvWWMTrNyJY8urGUgTIuW+YUwfsZRKtehVPPxGbA0fvTrIPSyVsQp15PBFOpyal8EhXcK15iFub3kXsUJZAQnUOB0t+VseNvhQ+YOvGqsk/b3FacvR6zzMnyt4ESEMnU2M7RNJ7joef+7p43N6949uwZF+eXrM86xrHn9e1L9ocju+M95xcXdJsWg6glcpZHj64Yxhdc37xSemYRXrx4itjE7/6NnTIJtQ1N42i6llUehQ5aOUZeVDZYRJT41DqPazydb5nGwO6wryhyJObhqENGeIEp8ObNG1Ztx/l6w7OPPuLp1RNubt/w5c9+xjgOtO0lKU2EqGXKbtXhMz4wTlHnGVijDEVWc9SJRLSR5ITklFqqjwMETVe5pqFtGrxp6MeRYRxZscqhQ2CKk6aZGodvHJvNhu12w2azhhgIYcRaw2rVZeITOPS9dmhiiEl0XRqL9w2NUzo4bx2NNThvcA4QS+ssMUHbWKxt8MYyuQOQhd0omYuzYJ3M1X82IzKiee7CsFyYOm121UtfPZo50/1mhaTpWZtLb7UOX2sDnHopIrRZwG3O07uKDcyZAR0PDgrY6QASdfUzXuHMqbllznqUun9bQpkshcViL8oZcmWjKhRk8fniOSzSqNZoXUDxBMSe7rcQ3BS5qjf8G7YPQgmEkK1ojDSupe0aUgrc3d9yfftG4/QGVm0HIry5fUlKibOLF6w3LWMMxD5iMHz08Uf84A//Osdxz5dffs5v/tYf5+nzJ3z19RcYDE3bKCFn2+AbR+MbBXiSEkeYbJKMpbK56uLUHPkwDuz2e3xWJKA0XmUqsbbPR5CId4b1esW6a5GUhczpYgsZiBOTVBAyXZnSrCWwgjhRxWi88imYQEQBtsDIlEbGOGghkxW8tFjjiCYxmYkhDdzuNJSYptLxpwvCe6sU6F1DkpD5CCJimsxipM9lHEfGGAgJJAApIp3BWo+3DRZL6z00DSKBEAbSOGJS0ipBiTjTsFmvOKTjSUqweACK+ak3Zyy5Gk8xPT3dRHYBsKbJ046yMCnKmGPpbAUXoCNWpyIblNRVp0QLTYmjyYJTFVMuRjLaA2CzUimzE8Tq/sXaXDGYt2ypTwqhDKfWv6RAinCWGuT8dxEV6kwcTR05VvafFaUCh/PxTLlOUh5blK/sD6YDPgwlME2ah56miRg0ndO0DrGJY7+jH7a0XVNZhEUC55cXrLYrpjRRRjnFOBHCiPeWzz77hN/50a0qjetXfPHFF2Cg8y1N1+C8xpHTOAGa/7bMxSYxa+dSDz+FiWGcOOz3TGNPSBMh6EQY47PbWSrJUqLxhu12RbdqGKcBCZH1esWLF8/5+qsv2O3vEJNoWofvFBAcp4E0RU0B5YftncG0jiQjMSXGkBmNw8gQlBxEDYjgBp/ZlxNTnBArHIehhjnOe0QghBFjGrquUW8nRbyHlBzeW5quVYsZowqe8zRmhcGDdVUBNNbhsXSuwRphDHr9nfM0K5v5INXCWSt0VmsaVPBKVd5p5Z+2wpEx8TS3yOfaC28tja0V9BRaU63yzI03gLdaoWdSKRRQADKlhFNkZLECS7VgKTFWQhB1tZ3Wh8j8Hpu7fEXsac3DwmXXs5vjehVc6sRhxRGyUJffjWYHKkszM1BY74Mp2QIFLc1idubDUuQ/SI0AfCBKICWh61aEMdAf96yaVq1U2+Eb5R9sVyu+fvUlu90B4+HqySViI2+uX7PeanydRvj55z/nxz/9PZ49f8wPfvADXr1+yaHfczje07QaTPuM/k9TIMSkcW0HZPBPckwNGh8mSUz9yPHQcxwGgiSYVBi7rqFrNdUXcqtxTCP3dze03vH06oonT65IY6DfHRnHHmsNh8OBpvNsNmuadUOQwHHs6cMAxtB2XusDTJO5C/Tc4gghBoapr19giRYYjwQEEUM0EdtYrO+QYcA36gFp6DLRWI9rSkbEQNPifAbEWq9pPmexjacxqAAEi5EG71oa29Dk4RdeLI0rWMEKt97QtA0pCXe7A4fjgIlCmynLStqqoNsVNDNAvt+FnVfRdakC5q3BF2A2VwLl3eXcvO7T50q8krrUKlC14JE0T43IJcXWCJaUMxdZCQkLxVL+Po9VdY0qRer5nwqcmEyHL+Tas4V05x8KsFvUjyz+rNdeHLjiCbxHqA2zZ1FrFqBksL5p+yCUAMD+/sju/p6zzZZxnLg8P2e7Pcukl7eklFit1H011hKjxuGr7Yrbu2v2dwO72wMxBB49vmS77RBRF33VtTiv5bFN02CMcueJKAhmMYz9wDAMjNOR1WatQKBr8d4xDpHD4cDQj1hjWXerzFqcR5OFgGtViASHnSyPr6548eIpm41iGdOx53DY0x8OxDix2bZ06xXdukEcTEFzv0hSnEDA09A0KjjDcKQfBqwlA3YH+qEniPLVp0nm/LTxTHFk6Cds7ogjaojTrVu2ZxucU8yjutuIzjeAPMacaoGmEAkS6PyalV3x6uVrHp8/YtiPtLbBBAUAnXisREyCFs8kifE4EofAoyfP6ON1TqdmslSJNfWXUmSMkXbl5xgCJgAAM+FJREFUc8gwz2QoygKo1XXAwq2mWtOEFnGVrQJ15X2AbxqMhJP3nFjz/FoBicvxReaReAAxW/WqzKydMYJybtaykPpFFiMXVgmVryLBHA7ksCVJSXEWz9RmtuY5FajEJGmGU4X6/Q+yfRBKIITA7u6e3W4HU+Lq8oKu62i8YxonxpjwF5esN6usACKHfmCYevppZL/fM/WZppzIOA4cjoc6dCMEBa9iBXqy5UAfgC9FHb7F2MS6W2WASScKGZNqVZu1Rkt1yekrdMCEDkg1xNjivGOz7nDW6PCTeGA89EzHgRgD1pGHS5JxgKTZhDQRZNRBFFGQMXGwQJ4DEKZRMwfjwLE/MoaQMSUVipC0VdmYwDgFgii7UeM8OLA+z7NzSkueUiLFyDTNazZah03z1NtkMuqNpYzuOl+fYT6y+MnRmAamiak/avVjoxmTw9BzfXtLS4vxykK02azAoGPcouS43Wqar9HsUMq06DFmcDQVhiKb0f1FFV+x2tbU7mFH6QwkU8AVm1/agk0FDoETl/mhItDXyn+n7ja1ECe7+xXB1z+nus7UzC9TdoIKe+YJISqXmNK25/stUBVM+UydUgy1NmO5VVASqd/nA7/fG/gglAACnpbOd0iCi/NLHl89wjrLXu6ZMsV316zAGHb7Hcf+oIUxSXvvrWtYrztidIhEDocd2+26FsIIUYtSyM9PtA1TU0GN5ohtZGUMq269wAUySYQRrVN3Fu9UhaRcA9s4z6rx2o8AmF7o2iZX9U3EUXsbkkScdzhj6LoG6xXMCUGbd6ZJkfiI8si5INBLzjurAhqHkWHomYIKimAqg3KIgTio76lFS0KKujgavGY+nME48iy/kPdR0ksGYzRLgykWBU1TJS01Tgidb3h69ZiN2dLiGPdHNs8bus5jTOI4HHn15g0Ey9nmMWIdfT8S5G5RCSiIyRzNIlUgfK4L0FkNWjClrnwWX9G4uWAJZY6DGsd8wjHl+Bowml60DwThIQ33w5z7Wy70+8zq4m3JUnsFDIsionJ+kKnIs3Kw2XspXowRnblRPJ+cIqzFUhlveNeZLBWZzeS5yuHw7tNebh+EErDG8ujyMWebM8Zh4PGjx1xeXBLDxNj3pBC0yssZQtQc+jiODH2PYPBtS9eu8LYhxsCxPzIMA6t1AySmaUSIeTBHAUysFr5Yh3fq+otLiNHYNU4JSUEXnEjt9CrvB80mRFFqcmsMTc61h2nEWqGMALONg9SAc7nN1GZ8IjGkyBgGhrHXrzjmQialJg/TWNNhLiu0ME2azlxaI/TeSFBpUjRblUHEZWpwrT4MKUGubVDeutnVNITcGGOrE2qcxdsWGXMaFcGJpbMenyySLGaCaRyUTcgIF6tz7KOWCRimyJvxjvb/396Z/NiWZWf9t/be55x7I168Nju7yi6qcNkWnpQwYkIjQIhuYkAC7JGRkMCS/QcYCQnEiAliBEggkD2hkxDNANGICUiIViD6ssuFweXKzJcvXxPNbc7Zey8Ga+1zTrzKl1Vk2ryXmbFToYi878aN0+3VfOtb3wqFXApVjPdeZzKUKRCBVzHEzj0mB+kAT94palFJkDA3AYlYUxdafWzc/BvmQd3ALI66RYQu0knb4zKH/bB8tz/dXrP34dfGs6ZZTnyOGtrP3uTT1iw2qotAidZvxQOQVh5c8IbZ0z/PUBRhuYft39ux6ur8Pnh91DFkfxv4AX/LXeCpqn5FbEjJ/wC+6v/2r1X1J76Dv8Gds7uoVq4uL9j0GwLClOs8BKTd5DyN5Gky7LhUcrG2yzQEhn6g5MBx3FOKDeAQaTLhRqNduOEW3lqNO3qIbJ2EXYxQIDuyLFUtikhC6jq61APGxydPNkXIS2JRhCoyG3GbCRCN6dZZ+mE94qY5ULINH9kfduzHneX4AaLJklKqKQynzuclTNnHobXautfLxa5RKd5aGqxLLcRo4FgQn+Jkw0vtmP2455zbP+faf3bMQ4rUqoRsysnTXhjDESGhYyVo4PzZOY/ef8h+PBC6hMaEdAPbW3f4/JvfwyE/4XDYsz8eGeuEloyAgZR9JHaB88tz37A6t2Q3SjAO4FVaS+7Czy9ukO0+NI3DRqt3iTNdmIStaNDyb3sOLX0IwYy6+vtaIlHnXNveb+ZQl4xdVvE7fjnXOAE6R6FoQNw6SauI1PXveuXk2kBb+5IVHtCmJFcHSNWNi8zzFNvffbEV+EhjyFT1jyzHKn8eeLZ6/y+o6le+g89dLWHTbem6aMSnrOwvdxyPB8qUjfapatWjanyAoeu5RDgcrAFm05+yGVblIreYM1e+Gnc8IF6Td2nvlEix5cmCesNLETfPxcLkFEzMoRt6UtcjuBFQA7hqti46UWyUdIpugApSnAATA10yqmulktVAuMPRBoIcpwNFlJB8jgCdg2CmalMdabZQsnHRjRFY1ctl/lCLe7WuT6QuzQIWVavV+t0IiKc9sdEQ3QhUp8KqYsM0WTr+8pgZL0d7PW2RyTCL/fmOJ+8+4cn5U2qAIoGskdff+hxf/oEf5PW3fg3H44GL3SUXu0t2xz1jHe1aR/Nmp9vb3tJtsmxt1zTU38a8FGf6OXe/VpsL2SYVt3zfn63Zm4t17XlEPnvyqss7540ze3zPw7VJdazQe3Xgbt6A/nNo7CWW2r5/XhD/neqSZmrS57aJzbBrk2KbS6jh2ndtZUJZuhKvg4/XZyh+O5WhjzWGTMx9/GHgd3y7z/mwJQhd6tj2A+GWUqbCbjxSinn8vu/nzRolkGJkszFRhvE4kvPSLqpqTUCpa2OdhM12IOdAF23j2wV03ni0uvc8j85DUfE4Uv3mGMc9MXS9ocsihGzU2joV31gV8b/fJRtImqcJnQpUpe86pOvMQKhFKNM4cvRGnmM5QoQUO6syiNDocLXYwM8YIl1njVUiwcQoYG7PNWex9NmnzkhRIVmPRW0NTnnyASzVWDmpt5FWK4TZNghQlLFkJAc0C3mvHJ8dUC3EXulKMHxml9mEgU4Tzy6uuDwcefRsxzvvPEFr5Hf+7t9Md/d1cp243O84319yub/k8nDF+dU5V/sdZ7dvU9SIVblMVsFA5/sTpVi+61GQiFhnp7F5qavKwCJm6p68XSf3qG247Erxb/bqFubL9X+XMEdH+LVZtynPVQgamOrYgzTDDKilDVVkfm/oIlKrzaUIwTEoP7/nuiXXWEXDH2KMbVTDtXNvVYi1FsIHrY+LCfwW4F1V/fnVa18Ukf8InAN/SlX/5bf7kBgim37L0Pdsh45nz54wjTZSrOs7TrdbNn3PMAzsjweCBPoumsJO15G6jtPTU7bbLSI2iVcClGKtq33fo1pI0cpPBvAlIwK5WVigGwBFfQx5mayxSNUAtSDW9CIi1BiJCFNVavEZBaUjBXMltdrmryVDsQjBPt48a63FSUgj4/FgjUJ9IKh7CM8LK1DyZD0GIULXX/NklgeXGekWxy5E1l1sdl6lGoW4jKMdVwttewPQkivcWOeh6eipKmM2IxCzMh0Ku6sdNWdiVGIFHSe2m4Fb21u8/fa7/PIvvcN+nLg4wJOnl2xPbvODP/BF3nzrLe4+uMutW3d4oJnddODp5TO++fBtyqN3fdpPoO8GutT5MFAjNNVaDJPxKKtFfFXMeImTgUxUpC5VgtXG9dtraUFYNvGyZH4mWkQ1YwzrxN2NSPUS7OxxW4LgkYeBgqvW5vaUrby5ONcgqBKa4xFZ8ICw0iEAZ0EuFY51JDD/nZns9ME4wnp9XCPwY8DfXP3/28D3qur7IvLDwN8XkR9S1fPnf1FWY8hu379FP/T0fUfXDTw7f0oueVaZaRs9uLqK+k3s+4HbZ7c5Pb3Fgwf3GYYtKQWsF1u5vLwwQ+A3MkhrFjGlnejMM5vlaA9DrQUtJnNVcqGUauO+wGvEdX6kGhtNtRoy72Bb0WBkHQzhRsyA0PJMlmGquRSmnJlyJmsl1qUrLZeMYFLnh+noSL7a51RHvRFH2BsQFIihm6OIWi2CqNFSlVoKecpM40SZRtDi6LoQU0ZCMn6A42vqk28rBpCGYmXFcZyQUbjSHeRKOR5J6T6x71GJTKPpNrx2f8P+mLl4esG/+Vf/li993/fxvV/6NTx4/QG37p7xxmv3eO2NN7n/4DW+8c59vvq1ryJRSV1gGAaCYvJnxyMUw3jMwNmmB+frEAgSncg1mQekOhIHq1oZiDczq3PxpQ1zdXcggWW4alh+b5UiNONC69v350DVc/aZ7rv8zuxmlGtGYS4v1m8tUc4fMqOa9inirzVjEWZqVesv8OjHZyl+2PyBj2wERCQBfxD44faa2jTio//8H0TkF4Dvx4aWXlu6GkP2xhfua91W0u2B/WGP3IqMuXJ+PLAJGc0D9TBSrg7kDKm/Ta3K/buf4/UH1s121vekKKSNcDjs2O/26GgXViucDHfYHfeEBH3wxpKIc+aVnK13oRK4vLhgmibS0BMGYLQy36FOSB3p00AuI2M9UDhAGLGSYaRoYRi2bIcBVDgcR3bjweCFPiFJOQQL0S5Q9hLYi7CvQqkwlIDUnhAG2PTsDweu9lfkXKzVuD9BS2HKR4JYr34uI6UURBInrqsgYqSaJC5ymdXC/6zoFKB0BA2zwEcoCY7WTdfFDomBXDJjmTjWzFWshBxIWdnEgd0+sx3ucZgCV9OBfRX+97sPTffw3m3ufv+XeP/th0gR3rx9j7rLPPzFibNuoudA3o1EGbh39y3OHpzxxhuFz33+gs99/vv42i98la9//efIac/dW7fQSdAMm80puoVDGtszOG+YoNbdKQWIgeNxQlQdn7Fx88fjEVXTp5Sp0qVAR2KalHEcnd0X6ELnHa0J79ql6fmZ2KRt+hQ9tieDFmoNIIkUEpCsHCte4vMybhMpmSMQgeCThaxNPBhPBKhS56nX7Wvt8Q0YtgpNPh5mC9OqBQ2aaNWRF62PEwn8TuB/quo32gsi8jrwWFWLiHwJG0P29e/kw2o1EkzO41zCm5HcxswrxfL/Yp4gxuSkG+OkG/KdqdU8szHfXPd9pQhbazZ01nNKqqHKJWemabImE0efwVR+JGE3TKzsNuXM6INLa6lUb+tcjjODmBc3Lnpw7kGFbA9dLhMIdKljs9lQapz744FrY6+UxjKzEM+os86e8/Cx9fi3UmitgeDjtoJB7czt+RKopbr2nQNPrhsY1l5qdX/s2nmYXA2obc94CoEaElnzPBpLRExYJRVOt1t2NXN5fs5hdwX6GnkqXJxfUKPSn3YMw4YvffH7TTuxizx9/C5oJkwBmqhLhCL1GljWjq8ZhZTSfC9aCK0sKUCM0fAhNyBVDPzUVopLEUmRubvJQ/cgWB2/lKWcpzp76QVY1HkGgbC6ls1ozfyB9RVeQMDGSlwLqPr+Wr3b1xz2e+fV6t8/LAVYr480hkxV/xrwo1xPBQB+K/BnRSRjcNNPqOrj7+Bv2MbNkxF7xLr9YFFdzTkb13+yzdzFzpD2YABaKROZyYVJJtMPLEaooVoYjFQKgVgDVEWqOA1VqVMmH0cOk+XKs4HwagKrFtNaKnmqxsMfJ0qtSLBIIk+VMR4934tesrM+91qLCaKGwNXVJcdpREQ52faEeMKUJwt3Y0RLIcvSBBLmvNOB4xDIZfEolisbc7HrmjIRMJUZJAxgjTixUlOZjcf82QJBI40KaYxJiKJIzQSqdQsoXmWBGKzFGgkEKRwzTEfrFuxS4piPjIeJB6/d47AvvP/oHW7fO+WNz71FPo5cPL1kLJU73OXua3e49+A2JycnlFL4uenAxfkja9DqAqWMlKKOG/imkaUE1x761sNv0ZHMP9ek8/XddBvbZLoo8zYv2trE16KjsBCMZtov3urcMAAH/wpqG8uvqQGROqOFwRuK5gMP3o+ogSgtHfN7HVYswvZZLbNpaZuYXHwzLrPgjSzX6eNWBz5oDBmq+kc/4LW/C/zdb/eZz692oFPOFK/79/0wI8I2mrwsXk9xrxcRXBpr2qE6+gcWQrRSW6kZz97sslt3kNX+1cNIxYDAKc9luICgpVCLIfMigZR6YkztXM0wOWAVqnVBhhA4hgOqDUNofz1Q6sQ0Gvf7cHB0PUQ2m601ILnRaiCYFntIui7RnMCMXWTT8mvdjjEEupg8EkjzAxtCdCzEjECb4qUNgHJDkHOey13WUONeEKia6GUiJtN76LtEjEaO6rWDDmIXKUEJk5CnA0M3ULenjE937Pd7VGHoA8/On/Do3bf53Oe/h/H+a6S+EntFcyKy5fJipOtP2G5vkauwP06kKHSbjlAUJc+eVloN02HdFCI4XhBE5jkRYOVKUWcbqvUPNAdT1PQscjHD2Lx88M7L9r72mjgv5PltpSyGQKUCkbCWDZKl1Xnt1VvJD8Ql1paOVNT5nK5rmFYNS8TonIHGmlzhE3MO4H/xQ4KCV4IxaJGAgXClTHNYNEs04WXE2EMUhMTQG3pci4Xmh2lP5WAVgGCqM10Xqdo2A+0uGVCWC9pVaxQikjQS1TZK9Y0w1cnyKQ10XfMMaUamYYnsFKVotVHdcgTF32vMuyCWhkzZe+X9PCUatz9GmHIwNuRkmoBaq2kfdNZGnadsdOBqdfk2096svl8vR67bde26fq5+mAaHmJJNC1u9Y9IoztlprQY6xRAJ0TbFQCFJpMsd22lgSIEuBFM7cq2/4poEx65nu9kQM+z7c47HPfvLHad3z3iSR549fp8n7z3k9Te/i5OzOyTpSfRI7Th/cs7J7Y6aA9Pk04yT0ncRqcmQ/yYkqnrN2yffsKUYl6SGeu0Za+8H/BnxtDJHo0oHjwodCJ17AqKnHlqREuz/1YeQtDTAn9Em8mF/pH1X5kEmiiXp0V07TnJaHSfi3AE31FqNCVndIFiAuvAhLLgz5uo6DfpO1ythBEA9FbAQ3h5mC4dqLZSY6F3NJsUONJBiR4qByctj03Rg0oPLXjUUv3roLCZzpVBzdj3BglaTze5DJMfEFCJHDQuqbrI2pBTpum7mK8y5phiQ1t4jEg1krBmyEGsxxaSQAMcMyCxNcJ47Bgs1jZIcqDV6KBlM9CRafbqUgk5lxgqaBxQvAbaHPOaMtpqyD1NdPJBPyLEDMFVaJ0Q1ph26oO1GToEimV6tq7FPkU4Mb0Az5EwaOloXZJLA0PfEAc5u3YZD5fJyx8ntDds+Mh0OvPfwIW+8/4Q7994k1g6pHaI9wkAtAa2JmDpC10GcqFLR2PoIZGWEzZi10L3rujn0bfp/DS+YPXpVMkoKxsiLfWcNTJOTjAJz3781UJlRVP9qskiydAnZv4VFWrw54cZJaOrBwora66B/c1TNo7Q0VITFoxuo4VGKRRRRMB6BQMjMzVbt2qwJRh+2XgkjYMaxkLPp/8XoHtl54FqswSQGIwyVmfNZvb5n8mJTtYnBVaN3xxni23VpLitNXjIz4r/rz4up40ypI9bJbxgQhBQ7+mGgHwZvQxbL68U3SuwIEZJ3weH8/FImOzOXk1YCBWnc0/nMtZ2Lz9WLMdGpUV+LYq3OMZKn7F7BDEdoijn+kKHMoOQ0TvPgjBJX0LB4I017OKpltRqjGSips+Z9G5xhBB3jDzRBzihWBqt5QnNlPE50EaY6MR721imJeeaz27ep+8rF42cc9uecngxcHQpPHz/m0bvv8eD1z3N2J1MmIdCz7W+5gEogdQN9P1BkYiKDFGpQtDZk3MK7uXLn9yxEwwmKi8dW9aKsYAY1erQXhNglkipp6IjZZkVaqC1zc64JplqvgwYMQJxHr6027upr1kdoDnkFHi5oQNNJCGgtTWWMMEcTwd/jGzm0sW9WaA4O9DaDtf77yvL/H24CXhEjAO1WLrVWy4N09mElm+fWqhbKa4GanMhTnLhRKMVSizxNTMfRSzn28McYKRIoWsz7eSTQ8uk+mTewPM668/p+w2a79VbhaOPJR+YacoiR1IUZyKxa0WpUYq14Ld8m0Iu9MOeEtVaq9/TbM2IbjtghBNRHcgVHlGups1frQjTG2pyPOMEpFP9fB7y801DAkPHWuyBOn0bQqLMBMTygUYRtLUi3NVIJaudYJ2TKTMcjIQljGTnu9+S69GmcnJySb2UeP3zC7uqcN976bqY6cnV5yePHjzl/dsHd3UgeC6KJFIX9NJJzJcae1PfkrGQthNAqJbaaNLw6Wjrn5D5FmpY6lYVItVBvsQ2SEhGcWdnNHYqNpCW6UIMblRoxzj5YSiD+2rzRm4xYcDESWV1ENyzBjZJFA8GbiHR+h19wWkWhAZQzMNnKjKF9qn9vkYunMzJ/0IvXK2EExC/SfBFaibCBWmKhdskFiiP9gDqZp0zT3BqsxQdgZCsNiql02u8oK09nP1tYZVTkznsIanEZKS9BphhnBp40yxwsN4xiYFlMCahQMvi8gEYmaTdSvVQ5012rMk3WQRejqdvOaLdASouxKHOTEss1cm9t1t7/RnWdwhn5PxKClcRSTNcQbyM7VevTjxF1RmMbeol/niMe5KyUIoRcGboOPRR0UjRbBDDViVqy56XQh45hsyGfTGy2G6b8lKvdBQ/uv8nTi4knT57w6NEjvvsL3wcamI4FemUcMyEmRKKlNSmSx0qMdcZAYFX2Ww0bsdFccs0oNKCvbe723safkSbGkiKhusy4GGDIDJ5CrpWpFC/7Oq4wDwtfcvUGKBLE5zUuxru9b9EFaL97PYxfI/rPh/XPp0IL8WgVFXH9sz4sJXg1jADmvW3Ihr0WxHTfozeKpNRRc6V4A1EQA1SsTl+IEo1zr0pVF7H08F1wee46zRek8a5b91/jhUWxFl+kiUwGzzxsA7b8swl0lKqLlUZRD/8XRtcCcFp+2pp7TBzleJwAIwLF0JuSroCI0iUzgk07oHmExjewIRmJNtWmcRGMkGIP3aEe6LreDJnPEkwpERxvWXvIrjOmYfQSW63V8A3HV/JUKDkQinJ6siWPGUmR7dCzd55/EBv9VUq1c0qJYXvCg9dfZ7+/4tHjR3zhSz9I3BS++e45Dx++R8mFlDp2ux1RI1Oe6IcNRWGqBVKkjH6fgiXqKjKDZM2oqlh5zliUXp6LcY6k7GGTGfCznN+dTxCbERDLYjhc/TdonTUkczFwNkbDjpo24kwhDoGYEt1gnaYxBo7jSPX7Z12BzK67PVNrzkP7fq2CsCrxrQVR1u9rxq6d5/ysfxtc4JUwAo0IAzqXs2JyzxVMx069PGje3YJqxHNmoMpAkERDtqo39BjAZ6lEyXXuy7c/7GFtMdEPwxBawwVzGWrOqtzM2sW1xqPKyvOoX+xVyKosXVwz8Ucbwq80tZ5IJIp9BbzDLDhhpFZTM6p1yRvbMcq6k2whLLVKCNHzfm+zLZop1Y/RRi2b8fMgNDQv1UQ3ZeWNXJs/CeQgbLcDm9MtEHi2v4L9jsPeqhhTViKJXrb0w4YHr73B42ePOD675HJ3RUgnxC6yP1xxcXVOTELRzHF/ICczYmOZOE6j6foFy+VBXBzFBF60ecxWKvSTEIlzviytnLcK6VPfeVNVQ/k9RQgtqrLScEXJWkmO/EsKNq4+ODmnhf4aTPsjRWLXukjN+MyMP8y4L+H5XBO47tVX3+2wr/+/0DCH5fca5nGtEthSlHaML1ivhhFQpSnISLQIoEsdXewsFVA8vLeb02a9i9iN0WDDQWrEEW6Fan33JkBydNwAlj0r3rapxt13Ac4yZZrKrK5rvNpCLte+b8ZK0gLEwJJ/N+hHvRNP1bDIbO+afFSY4QAm3pni4LP4ClILNZgO3gzOhUj2BzoGn5Iky5RbJM7lQ6sg2AkrBdVM0YlSZBYjFQdX7VrobACCb/xKi5J8g8VgHZK9ICcD227D7c1tYuwITzv2NVOvKuN0pFRx2XADdStCGgYKF7zz/nt813d/ic3plsO442p3zunZCTnAxeUzui5QyaaTOI3EkL3JxkeeSUBSQqJVY1rZj/V99WvTQLbZ0Xjvg1UEOkiBqsbdqA30cxCukt1wOEQYA/gwFUtFWrS3pBldZ01tXW9RaCmFUJonbm1f66/rXv/5tKDtjzUjcj5P/3nuUJyfwflyIKtjfNF6JYxAWyKQopX/YjTJL2uRwYwEzQDYJFrTyogexyfzFrrU0bUeiFQClmp0oTMjEg2Fb+OcSi1MxdmG2TbeDMiBs8HwPB8aOaNZV/P2S46mYrJhcyuqgqoN76jV31shaHACTmezFPveN7fp7B2cAl1K8YpDMfUgP4oY0iwlbuWvQAFqCIafoNSa0SqUmiklMKFoySRMjdfsXJhLV0EM+LPHviLOldewUFq7TcfmVsdpPeE0bU2rIUCeRna7K/b7PbHfGGdfhP3xwNXlge50YNTCo6dP+NwXE9uzwLPLI892z4gD3hJcGLYdclQqrkRUKqGPVLHaaooRSR4NVNdoVJ09+Qz6aVj6fxSkVhs4W7G+kN5Ea6VWjy7irOxrpcjGDHQEHjU1ZE+jVIXovf9mm7z111Ov5uHtMVsEPhy19Ke+Emy+MaaF0dKbFsy5QV51O645NK1iVBuwPn+OG4j2mIZPgBFoo6RoB4/Onsxk4IU2r6618zbWWgiChIFKpJTMmCcDb7IgNdKFjphkzrVDNEpriJGC1fVzKyNVRcQn7bbNjngovVzqRb/AN3WxUlLRSofn5y5SbSErNDS5AZYhmqLRMAycbLb0vRmpUvzYy5VTkSdKtklHORcvTERiNLpw9Bl60ObRsxxbrZQKuQpjBi2Z4u3CXXAZL/eywbUMwTYMakNU8Cit1c1FxEqik3J5dcXTZ+c8vrxgf9gxFWNRjscj4eKcsYNYezbbLdoPTEDQQn8yoB08udzx7OJ9njx7D/rIsI0MJ4n6NCMO+JUKQZIZNB9SEoLMikmEiFSlBjHCkixtvYj3RgRD9GsVE2AJlk7ElKjoDAxWlvw8BWOB+sNAVcOh1sPIG1AcQnu+lggQmgFonnjBvGQG72xQClXm423l3XYPC8Vk1fxzzAmGOdJoikrNCc176rmI4kXr1TAC0ozAEvrUpv3mLZ/M5IrlhALmgQyk6ZCajFdeK1Iiooko6lLgHakz6m7FpK6rqnH08+SNSz5sQlxJyFt1UaWotf02OufknIamMVjFXi+lkJygM99xb1ayJ8NZkGI012EY2A4bhn4ztyoX1B4KNTmxafRUxac0xRg9Ympov6Unc1DYGD9qkUCuSsiCaKWKFSwrAVIHMZkcmj/MEUsgrFTrX6IujNF4CEf0oIxXBy7eP+ftdx6StluG7cCD1+4j/cCTZ5e8/+QZfRh57c4bnNy6xzsX/5vzXebu/TO6kw1JI6f7PZqUpxfvc3LnzKdMj1zsnpFLds9b0SpWrddKiA6MVseOwiIa2tD2dZpgWo9xBubCarPM0Y2zMls5EJiBWFTnTbwW6HgedVcs/SlYOThowwAwsHmFTbbnV0SMLGRU1aWMu4oyQ5RZlnwuF0YHgZ06npvmZNtP7Tnw6kb4EEPwahgBvLhRPewv1SifItiMezfDFrSiTSHHL2BSEDWmXdBKpKOTiibQmHwWXSEkZZyWdk4bIjpZR1+eUGWuby+lNIsKqncG5pJnlDiX4ii9GwGc8z9b8qVKoBIRKRaeVttqcxnUuxXNiJiQyZSLjQAbRw6Hg2EW2bySSDL2pMuGtS7AJSVZUpNaMzJPAipUFQqgITrqrS691hHcU4kqosWEVmumkJmmAiUyTUd2R0EvR+IlXFxccHF+zkBlc/cO9+7dZzi7w3BywdOne+pkSP7usOfyas9+gi/cvc/ZnTNC7CkhcHb3hON0xVm6RS4HjleZ9957yP6wt2uilUpyVWLbYCaNFl0t2h7j4uPsQrDUa/KwPWIdgdHLjUFgKplO+xnNb1/B2ZhzHf5a2ue8DI8U4nPoe9vwBs4KRZV+5jLYM748FcvvUuaMZQGhwTfzwhNYVxFa6bFI44Ws1IP0ehTwfITw/Hp1jIAj/+YEzRPhirdmYg2mchlJVE1RRkt1eeXq0ZYDdjExBPHebKPRqlSE0XOoQlUb0jk1wY4AIfSzhZ/rtawiFCef5LKw0cCOAWnov5/UHPKFFZhjeWPJjegRZvdlRCgrxU1jng3A8Xg0A1QLIoGus+hnefiWKGq+4X4MVZXg4Jb1x3guG6E6kKgtcviW+6FO3y5MeURyT9EAUyUfDnSTPWAxJc4vLhgFzl7rOTs7Y3t6hzfeEKZ95dmjHU8ePSYNPZsBXnvjAWd37yCho0hgsx3YHXdMZYQJDoc9z86fcDju5tJbrZjEe7CQHDW9QRWfBISH6iUbCUosoqn+3ojPOUhtlLpVjNab6vn6ewPjPqgkl1KagTfxduW1s13fCxtlurq+vrmDYwiGP3ouz1IdaPd03V4MzA6qHQvgWFO99traKLzyRkBUSdUAOateZaYaKE4UCiFAMghlQojVwvSORJGOAoR0hOTdgbHSV6MG2/y+ybQAA2RVRhWmLJQSyNmaVaiWO9ZJDKwLHX3tCFnINaPTAZ2O1GzzA6Ci1RSNixqyLMkajDKWp6YYjYQjVv0gQhQrXcaUkQRZd+zyyCQ2Jqwma1w65oljHRjrZDTlovQE+hDY1sj2qPRqMwRDZ9OB99NILaMZ0R7IsHWwNKk1VEUHDY/5COKc+hTRKNQos92qfaWIMmomT5kwFsq452p34ES2nI9HbsXbnE8jD59MXO1Gtpcj4zRydq/Sn/RsU2I4LeTjJbvjyC88fJfujY4HX36D826HxJ6T773L3Xv3kTuBMR7Y9Ilf+trP8+TxLyPxQAgjXR+QE2W3v0JK5hYDc9MF1UudGH5RC+MxQ1U6Mc8vElzANtv5ItYarRkpk3FFciap0gG42pMU9YG1UHIlZUhiToICORqYWdQ+N6hQ80TWia6LbPoBDUpIQiiV4/FAnSxSSSmRiAbayhmlZlSFRPQKkT3jMbUmuiXSMPxBLbKUYiSqFIxFPxsq7wvRYhLzayXj59YrYQQagAUN3zR8mgbQNczA/7XR7YPaQIyJQojZBTKc+BM9m9DqOWBhmpqm32SjtYoN6NCss0ZdlORDSiJUQ/lrKUa/LRmtBXA5Kg/B6jXvL6svQ4hrq016SU6rCaBQTAJcSiSXidR3IMKYR8YyUdz7NbmrJD5oU8XaoIs1MkU/huDtpo6tNo1SGulFPOC04KpSXClZDcc0dJ4l6ik1M5XMOJnOgxQhaEJCYDeOlPGS9x+f8/7jc7Qm7t4/48G9tzi9fYt9PnA4HOmHyOntLcfpFpf/Z+L177rLye0tWQowse2E7e0Tbt+/zeZk4Pzp+zx+/AhVm9SkQbGesYJGRz1El8uLazn6eYqYfQgxEHBF4ha2r1y16ELHti69xkPxmq6TumZmqVquHuMiYlK0eMep/d02ylwM1yWHiZh6aw/2Nu9SWvThN0ldYbhwTeHZCEyBRjbzE5xxGnzSso2Ti8S6Gt7Kgld43DxHCR+0XgkjAKs+eTw3amWdJrssYS4XWveW5YdFKkKm5gnjtkPjEwQXqMw1+xDPI/tp5DAdmXKhFAujpEKSOJfrYmghoHpvfxMbXcX50ph/fi9XddsGCs7hYLVUBgcitSyqQZZuWGkyZmvlzcXwgHZNmhZAFPtaM8DW5cl5rX6c21FZhafzw7b0I1xLJfzfipcnSzFjEEsiidFpr66uuHi248nTSw6Hkbv37nB25y6b7QmKyarlMjFsOlN+UiWPcOvWKV2fgMJxzOwPV9SaOb11wnYY+F9ff8TTp09Mlj0YJhRDZCwjMQbrnGshfGgKSovxMoPpxlEM07mmAuzn2IRH1kB0S4EUvZZXz/cgBO9Gtd/JiqdL/ryFhZcgDmPZsSaks+eklOQVADMYpVaixLlvo/XOcI2O3EL+1pvSnr02LxFi1WvzCJ7fW698OgArI7DOo3muLMLqZvqmrKVQqlJkhGr17+rlrKCVmieO05FjObI/HjhkY6FNpbhWgFi9vgtIFJswHLCBECxyYaU28o2VKOsK3JHVcTaQrh1n9Y5Ac/+LEbi28VhKhoTGWyiUYv8evZTYoY7irzALuxo46GAPdl24BC9a6wcrT/naMa/Zaf4CKgZ0lVI5jhPn5xdMT5XDsZCGDSdnt5lK5RvffIfDdGA3XtH1kd3+hMPxinffeZsQlTt3zuj7RMZmLlxenXN5dQ5SCVG4uDzn/OIpZ3c2TIoZgdQZwScIXUh2v7y7cqmVL/lw635shiJ4Q9HC2lyqBXZ/fXLRfB9NrDa4J8Xvc0qJzlvJc84EVWLVmVNi0nBGIZaAa0AmQhSSE8xKzVZWDNaHEqqiRFc38pFwGlelRJmPvVV81vewSaYlL3e3ZxIWx7DGEz5ovRJGoN0ccAqFWCdf814zNbYZAW1AyEI3HjHPnkSsUxAhaqVMI4dpzz4fOeQjY7GxV7ltPoxvEJOQ+uginQbm5DwtZJ1mTT01EQ/TA6a1R1hxtD0iaB64eh0Xnx2gDtC1z61tPvWK3llmD+MPnwgJJdQyP5wzsOSP6sJjX0Cm9lDPxqmh2TCb1ClPK1rrAoS1vxGCTVCqWplypY4TV/sD5SDEtOXWnS395oSr/YGnFxc8vbxgLAf6ITAMiXHcc3nxjNsPEnfuntEPiaDCfoTjuOf8/Am7/QWbIRlYmw+EMFiXZzACmWQb6ZZCIrkRiC72oStP1whg0cHh1n8CfEvEoNUrD+5QglcKkreuq1cCrGU8uBHoPAUAKRUJ0TtUjdHZIoqAOJmrsylU0bGJEjH+gDu3qpTa6v5l3vQNSF6TgpozNLKbq2qT0aKre9b2VMtKl2v1ovVKGAFYbtBKwtE2XtCZM9DWgto3RpcwaSYHtRnwYky9UJU8HTlOe45lZKyZSSeKqFFA2+aJgW7oGLYdQz9gEUBGtTBOLKEi69JLcDbdtx7XOhhTXYyA1jpHAsBc918VRhYdOe8FCCGQwOYcVhvE0R6S5unU88ulVLiQWdqQjhYWt69aq3URrq7/epPM19+NZVW8aUfAyUfEjhgG+nhiJdCYGE5O2dTKdFU4Hg+EKGxPTjg52VDunXN6a7DBMBrYbHvK/sDV/oInTx+x3Xb0GxtHplKJCZJGUhC6GKjBRsknibOEWvDIr92XgCk6WfTUkVI3szBrrTPlW6vNRAQzxIuKVXCOBpRcCBLnaCIlU7autVKjIt4xOk9DkuD3GVPAcjXo6NwEm7JkaWHw6o6ijKOxWEsJpm8R/CsuQ2Ts2BIxCKlrJcLMNNp9iiF6atKqQ3YPU4r0fU/f9y/ce6+EEWhe0dhRbqW1qd0Y8SaTrcEmhG8ph0k1Xf2CEzIqWLigJg2eR8Y6mYYf1ixi4VorDyXiJpE21q8AzrIrrQmEOc+ec3DWeXTDlPwGBMBhuLbD56hFLf5om64Zsbke3h4QkncIGjsyBWtcsTFsq/QjBObgfx3i12w18Njed73sGRqCtlrtHsypgnMjSik2E6F1LaZE2myJpUfGnsNYePe9X0Zih3SRq8MV5xfP6IfA/dfucffOLY7jHm57m3W1YWKpE/qamPKBdx5+k64XUh/YbDsKma6LFBIiStclaqj0MdKH3jyiU3ZnL3gtpG8UaFvtJ9+G83Vqa454nNTTlKWagrN97+Y0ous6eqlQ4ypSFToxWbOYTNpN3bKLRKKTrgw3cmMklSCFKJYyGIhovTAprMROPfQ3YlMiRKjTaKXzXOy4VxjH+ryaAO2L1ithBFBlmqbZCIA4mISBJAqiYjrswTruajVtgTadN4dC9iahuZU2G+B2LIa2jzWTRdEYCU45DcEGjYYuErvomvHVS41uhYMg1ZFWtRZca651vEBBCeAMNRODZN7w18gaXlBekzma0dNGlprD8GBqv80bqN1o0W/N/dZchmmamGo2WnJqIeWSDkho8m0L5tHuQxtsuUi8eV4Zls0AgWHY0ssJxwu4ujzw9PyCw+SsjDISYuXOvQfcvXuXLgnPzp9w1iVqnSAUxjFTQ2Q46RinzDuP3kbJ3Npu2Gwikx45GU5AM6NObIYeDdCFSC/9tcgsuPJRwXAbEZnVf+d6v4cz19IGmI1e9IlUooUSTL8iRJ29qHipum3EEAInXSTk0Uhcqn69O2Chbps4ipKSEGNvqE5jw6rJwkO2bDC28D2Zw2uSaWlRxjJjZADp5MQ2cBq0X491VCfrNPUF65UwAsoSjha1bNakxTw8cyNAXDkvVxlSY5GQQ2YSVyuulVgKuCc7TkdGzYw1UwI+FCI671iX77HtUeP9N8BnQf2ZvURFrURUvURIRUPxi7142BbRPL9CtLKO5bBOey3WKWnWu/H58Q41tysfcPVU1am+OqcYpRa/rtZj0I4F/DzWiLMToOz8dMYhYouUEKueeHRTfZbBZnuKTtb0c3b3PmF/YHc4EIfE2Z1T7j+4y1SVw9UVVZVhMH3CzdCzm0ZCDGxPB+JRTGBECv1bb0KEOhaGoaOUxDgd6OMAaWm5Xj/o0UFRo25XOm8OC8H1KFaU27ZJNC8dmiAzH0XDUkZtUUDXDct9m41pIKkgJfvgmowmNU2GEKh1YsrW9JZSBw4axuZEaiNBuddeAcutFNmevzD3d/hbi/FS2ng8g6rEtSieEzJhuc8vWvJhqOH/ryUi7wFXwKOXfSy/Cus1Pp3nBZ/ec/u0ntcXVPX15198JYwAgIj8e1X9DS/7OH6l16f1vODTe26f1vN60Xpx3eBm3ayb9ZlYN0bgZt2sz/h6lYzAX3nZB/CrtD6t5wWf3nP7tJ7XB65XBhO4WTfrZr2c9SpFAjfrZt2sl7BeuhEQkd8jIl8Vka+JyE+/7OP5uEtEflFE/ouI/CcR+ff+2n0R+Wci8vP+/d7LPs5vt0Tkr4vIQxH5r6vXXngeIvIn/R5+VUR+98s56u9sveDc/oyI/LLft/8kIr9v9W+fmHP7KOulGgExRc+/CPxe4NcBPyYiv+5lHtOv0PrtqvqVVZnpp4F/rqpfBv65//+rvn4G+D3PvfaB5+H37EeBH/Lf+UvS1FpfzfUzfOu5AfwFv29fUdV/BJ/Ic/t/Xi87EviNwNdU9euqOgJ/C/iRl3xMvxrrR4Cf9Z9/Fvj9L+9QvrOlqv8CePzcyy86jx8B/paqHlX1fwFfw+7tK7lecG4vWp+oc/so62Ubgc8Bv7T6/2/4a5/kpcA/FZH/ICJ/3F97U1XfBvDvb7y0o/t460Xn8Wm5jz8lIv/Z04WW6nxazu2F62UbgQ/qaviklyt+k6r+eizF+UkR+a0v+4D+P6xPw338y8CvBb4CvA38eX/903BuH7pethH4BvA9q///PPDNl3QsvyJLVb/p3x8Cfw8LHd8Vke8C8O8PX94Rfqz1ovP4xN9HVX1XVYtai99fZQn5P/Hn9u3WyzYC/w74soh8UUR6DID5hy/5mD7yEpFTETlrPwO/C/iv2Dn9uL/tx4F/8HKO8GOvF53HPwR+VEQGEfki8GXg376E4/vIqxk3X38Au2/wKTi3b7deaiuxqmYR+Sngn2DKin9dVf/byzymj7neBP6e924n4G+o6j8WkX8H/B0R+WPA/wH+0Es8xu9oicjfBH4b8JqIfAP408Cf4wPOQ1X/m4j8HeC/Axn4SdXnZJdeofWCc/ttIvIVLNT/ReBPwCfv3D7KumEM3qyb9RlfLzsduFk362a95HVjBG7WzfqMrxsjcLNu1md83RiBm3WzPuPrxgjcrJv1GV83RuBm3azP+LoxAjfrZn3G140RuFk36zO+/i847sTbv/I66AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(1,30,7):\n",
+    "\n",
+    "    im = i\n",
+    "\n",
+    "    plt.imshow(x_test[im].astype('float64') + .5)\n",
+    "    plt.show()\n",
+    "\n",
+    "    inp = np.expand_dims(extract_piece(x_test[im]), axis=0)\n",
+    "    out = model.predict(inp)[0]\n",
+    "    out = np.argmax(out, axis=1)\n",
+    "    rearrange(np.array(x_test[im]) + .5, out)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing whole test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on 2000 samples: 62.83%\n"
+     ]
+    }
+   ],
+   "source": [
+    "solved = 0\n",
+    "x_test_proc = []\n",
+    "\n",
+    "for i in x_test:\n",
+    "    x_test_proc.append(extract_piece(i))\n",
+    "    \n",
+    "x_test_proc = np.array(x_test_proc) \n",
+    "out = model.predict(x_test_proc)\n",
+    "\n",
+    "for i in range(len(out)):\n",
+    "    pos = np.argmax(out[i], axis=1)\n",
+    "    if list(pos) == list(y_test[i].reshape(cuts*cuts)):\n",
+    "        solved += 1\n",
+    "\n",
+    "print(\"Accuracy on {} samples: {}%\".format(len(out), round((solved/i)*100, 2)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 训练结果展示\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Correct prediction percentage for each position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[80.4, 81.95, 80.1, 79.9]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_val = []\n",
+    "\n",
+    "for i in range(len(out)):\n",
+    "\n",
+    "    pos = np.argmax(out[i], axis=1)\n",
+    "    val = (pos==y_test[i].reshape(cuts*cuts)).astype('int')    \n",
+    "    all_val.append(val)\n",
+    "\n",
+    "all_val = np.array(all_val)\n",
+    "[round(i, 2) for i in (np.sum(all_val, axis=0)/len(out))*100]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  结果\n",
+    "在进行预测时，我们的网络会输出4x4向量，然后我们选择在每一行中具有最大值的索引，该索引不过是预测位置。\n",
+    "![11.png](11.png)\n",
+    "<center>训练前</center>\n",
+    "\n",
+    "![1.png](1.png)\n",
+    "<center>训练后</center>\n",
+    "因此，我们得到一个长度为4的向量。使用该向量，我们还可以重新排列拼图并将它们可视化。\n",
+    "\n",
+    "#### 训练结果展示\n",
+    "\n",
+    "训练后，该模型能够正确解决80％的谜题。性能达到预期\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 心得体会\n",
+    "\n",
+    "本任务是一个图像拼图问题，为了简化难度，选择2x2的拼图模式。也就是将一个图像分为四份再编号，利用卷积神经网络学习四个子图的深层信息并提取出有用的潜在边缘联系，使得训练出的网络模型具有识别和按正确顺序组合四个子图为一个大图的能力。\n",
+    "本次任务是一个单纯的CNN深度学习任务，与典型的网络不同之处在于输入为四个顺序随机的子图数据，然后要求输出唯一正确的组合序列。\n",
+    "通过本次任务，我深刻地理解和掌握了CNN卷积神经网络的设计、搭建和优化，也学习到了如何高效地分析问题、有效的设计解决方案。\n",
+    "总之，通过本次任务，使得我的专业技能水平有了很大的进步，受益匪浅！\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "main_language": "python"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/report_05_Jigsaw-Puzzle/accuracy_loss.jpg b/report_05_Jigsaw-Puzzle/accuracy_loss.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..549ceaa651b4f65568e8cfe4b4b4d72e29421ad8
Binary files /dev/null and b/report_05_Jigsaw-Puzzle/accuracy_loss.jpg differ
diff --git a/report_05_Jigsaw-Puzzle/main.py b/report_05_Jigsaw-Puzzle/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9453628f7d115c13f2a9aaa55be30dd789fced6
--- /dev/null
+++ b/report_05_Jigsaw-Puzzle/main.py
@@ -0,0 +1,88 @@
+# -*- coding:utf-8 -*-
+# @Time : 2021/3/3 22:22
+# @Author: LCHJ
+# @File : main.py
+import os
+import sys
+import copy
+import keras
+import numpy as np
+import pandas as pd
+from PIL import Image
+import matplotlib.pyplot as plt
+from model import get_model
+from utills import rearrange, load_data, extract_piece, swap_piece
+
+base_path = 'data/puzzle_2x2/'
+
+## Defining essential variables
+
+cuts = 2
+channel = 3
+im_size = 200
+
+## Load pretrained model, you can download the model from [here](https://www.floydhub.com/shivaverma/workspaces/run_LEzD4uEtzqggKf5p2risz7NE)
+
+model = keras.models.load_model('model/jigsaw.model')
+
+## Or train from scratch model
+
+# model = get_model()
+
+adam = keras.optimizers.Adam(lr=.001)
+model.compile(loss='sparse_categorical_crossentropy', optimizer=adam)
+
+x_val, y_val = load_data(base_path, 'valid')
+x_train, y_train = load_data(base_path, 'train')
+
+model.fit(x_train, y_train, batch_size=64, epochs=1, validation_data=(x_val, y_val))
+
+## Loading test data
+
+x_test, y_test = load_data(base_path, 'test')
+
+
+# from IPython.display import clear_output
+
+## Testing indivisual Image
+for i in range(1,30,3):
+
+    im = i
+
+    plt.imshow(x_test[im].astype('float64') + .5)
+    plt.show()
+
+    inp = np.expand_dims(extract_piece(x_test[im]), axis=0)
+    out = model.predict(inp)[0]
+    out = np.argmax(out, axis=1)
+    rearrange(np.array(x_test[im]) + .5, out)
+
+## Testing whole test set
+
+solved = 0
+x_test_proc = []
+
+for i in x_test:
+    x_test_proc.append(extract_piece(i))
+
+x_test_proc = np.array(x_test_proc)
+out = model.predict(x_test_proc)
+
+for i in range(len(out)):
+    pos = np.argmax(out[i], axis=1)
+    if list(pos) == list(y_test[i].reshape(cuts * cuts)):
+        solved += 1
+
+    print("Accuracy on {} samples: {}%".format(len(out), round((solved / (i+1)) * 100, 2)))
+
+## Correct prediction percentage for each position
+
+all_val = []
+
+for i in range(len(out)):
+    pos = np.argmax(out[i], axis=1)
+    val = (pos == y_test[i].reshape(cuts * cuts)).astype('int')
+    all_val.append(val)
+
+all_val = np.array(all_val)
+[round(i, 2) for i in (np.sum(all_val, axis=0) / len(out)) * 100]
\ No newline at end of file
diff --git a/report_05_Jigsaw-Puzzle/model.py b/report_05_Jigsaw-Puzzle/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..aac7016bcaebf2e54d808733b16601e3f572f0d4
--- /dev/null
+++ b/report_05_Jigsaw-Puzzle/model.py
@@ -0,0 +1,44 @@
+# -*- coding:utf-8 -*-
+# @Time : 2021/3/3 16:50
+# @Author: LCHJ
+# @File : model.py.py
+import keras
+from keras.layers import TimeDistributed as td
+from keras.layers import Conv2D, Flatten, Dense, ZeroPadding2D, Activation
+from keras.layers import MaxPooling2D, Dropout, BatchNormalization, Reshape
+
+
+def get_model():
+
+    model = keras.models.Sequential()
+
+    model.add(td(ZeroPadding2D(2), input_shape=(4, 100, 100, 3)))
+
+    model.add(td(Conv2D(50, kernel_size=(5,5), padding='same', activation='relu', strides=2)))
+    model.add(td(BatchNormalization()))
+    model.add(td(MaxPooling2D()))
+
+    model.add(td(Conv2D(100, kernel_size=(5,5), padding='same', activation='relu', strides=2)))
+    model.add(td(BatchNormalization()))
+    model.add(td(Dropout(0.3)))
+
+    model.add(td(Conv2D(100, kernel_size=(3,3), padding='same', activation='relu', strides=2)))
+    model.add(td(BatchNormalization()))
+    model.add(td(Dropout(0.3)))
+
+    model.add(td(Conv2D(200, kernel_size=(3,3), padding='same', activation='relu', strides=1)))
+    model.add(td(BatchNormalization()))
+    model.add(td(Dropout(0.3)))
+
+    model.add(Flatten())
+
+    model.add(Dense(600, activation='relu'))
+    model.add(BatchNormalization())
+    model.add(Dense(400, activation='relu'))
+    model.add(BatchNormalization())
+    model.add(Dropout(0.3))
+    model.add(Dense(16))
+    model.add(Reshape((4, 4)))
+    model.add(Activation('softmax'))
+
+    return model
\ No newline at end of file
diff --git a/report_05_Jigsaw-Puzzle/utills.py b/report_05_Jigsaw-Puzzle/utills.py
new file mode 100644
index 0000000000000000000000000000000000000000..d170bcb6896fd7f539c62a9bc1becd7196366c21
--- /dev/null
+++ b/report_05_Jigsaw-Puzzle/utills.py
@@ -0,0 +1,109 @@
+# -*- coding:utf-8 -*-
+# @Time : 2021/3/3 19:34
+# @Author: LCHJ
+# @File : utills.py.py
+import copy
+import numpy as np
+import pandas as pd
+from PIL import Image
+import matplotlib.pyplot as plt
+
+
+def swap_piece(im, pos, true_pos, cuts=2, dim=200):
+
+    """
+        swap 2 pieces and returns image
+    """
+
+    im = im
+
+    cut_len = dim // cuts
+
+    x = pos // cuts
+    y = pos % cuts
+
+    x_true = true_pos // cuts
+    y_true = true_pos % cuts
+
+    piece = copy.copy(im[x * cut_len:(x + 1) * cut_len, y * cut_len:(y + 1) * cut_len, ])
+    swap_piece = copy.copy(im[x_true * cut_len:(x_true + 1) * cut_len, y_true * cut_len:(y_true + 1) * cut_len, ])
+
+    im[x * cut_len:(x + 1) * cut_len, y * cut_len:(y + 1) * cut_len, ] = swap_piece
+    im[x_true * cut_len:(x_true + 1) * cut_len, y_true * cut_len:(y_true + 1) * cut_len, ] = piece
+
+    return im
+
+
+def rearrange(im, label, cuts=2, dim=200, channel=3):
+
+    """
+        rearranges the image according to the label
+    """
+
+    cut_len = dim // cuts
+
+    new_im = np.zeros((dim, dim, channel))
+    for i in range(cuts):
+
+        hor_cut = im[i * cut_len:(i + 1) * cut_len]
+
+        for j in range(cuts):
+            piece = hor_cut[:, j * cut_len:(j + 1) * cut_len]
+
+            pos = label[i * cuts + j]
+            x = pos // cuts
+            y = pos % cuts
+
+            new_im[x * cut_len:(x + 1) * cut_len, y * cut_len:(y + 1) * cut_len] = piece
+
+    plt.imshow(new_im)
+    plt.show()
+
+
+def extract_piece(a, size=200, cuts=2):
+
+    """
+       extracts each piece of the puzzle and returns
+    """
+
+    cut_len = size // cuts
+
+    if cuts == 3:
+        a = np.array([a[:, 0:cut_len, :], a[:, cut_len:cut_len * 2, :], a[:, cut_len * 2:cut_len * 3, :]])
+        a = np.concatenate(
+            (a[:, 0:cut_len, :, :], a[:, cut_len:cut_len * 2, :, :], a[:, cut_len * 2:cut_len * 3, :, :]))
+    if cuts == 2:
+        a = np.array([a[:, 0:cut_len, :], a[:, cut_len:, :]])
+        a = np.concatenate((a[:, 0:cut_len, :, :], a[:, cut_len:, :, :]))
+
+    return a
+
+
+def load_data(base_path, path, cuts=2):
+
+    """
+        loads and returns data
+    """
+
+    data = pd.read_csv(base_path + '{}.csv'.format(path))
+    path = base_path + path + '/'
+
+    x = []
+    y = []
+    total = len(data)
+    for i in range(min(2000,total)):
+
+        im = Image.open(path + data.iloc[i]['image'])
+        im = np.array(im).astype('float16')
+        im = im / 255 - 0.5
+
+        if path.split('/')[-2] == 'test':
+            x.append(im)
+        else:
+            x.append(extract_piece(im))
+
+        label = data.iloc[i]['label']
+        label = [int(i) for i in label.split()]
+        y.append(label)
+
+    return (np.array(x), np.expand_dims(np.array(y), axis=-1))
diff --git "a/report_05_Jigsaw-Puzzle/\345\257\271\346\257\224\347\255\226\347\225\245.png" "b/report_05_Jigsaw-Puzzle/\345\257\271\346\257\224\347\255\226\347\225\245.png"
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diff --git a/report_06_Sudoku/Accuracy_vs_step.png b/report_06_Sudoku/Accuracy_vs_step.png
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diff --git a/report_06_Sudoku/RNN.png b/report_06_Sudoku/RNN.png
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diff --git "a/report_06_Sudoku/Report6LCHJ - \346\225\260\347\213\254 - Jupyter Notebook.pdf" "b/report_06_Sudoku/Report6LCHJ - \346\225\260\347\213\254 - Jupyter Notebook.pdf"
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diff --git "a/report_06_Sudoku/Report6LCHJ - \346\225\260\347\213\254.ipynb" "b/report_06_Sudoku/Report6LCHJ - \346\225\260\347\213\254.ipynb"
new file mode 100644
index 0000000000000000000000000000000000000000..ebc9a03379c7ff102d8b65ebdfce009d8bb25e92
--- /dev/null
+++ "b/report_06_Sudoku/Report6LCHJ - \346\225\260\347\213\254.ipynb"	
@@ -0,0 +1,440 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "c9d0a2c8-372c-4d81-bb7a-919eee6391f9.png": {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Report6LCHJ - 数独\n",
+    "\n",
+    "* 姓名 卢程军\n",
+    "* 学号 2020262927\n",
+    "\n",
+    "\n",
+    "## 任务简介\n",
+    "### 内容\n",
+    "* 数独（shù dú, Sudoku）是源自18世纪瑞士的一种数学游戏。是一种运用纸、笔进行演算的逻辑游戏。玩家需要根据9×9盘面上的已知数字，推理出所有剩余空格的数字，并满足每一行、每一列、每一个粗线宫（3*3）内的数字均含1-9，不重复。\n",
+    "\n",
+    "* 数独盘面是个九宫，每一宫又分为九个小格。在这八十一格中给出一定的已知数字和解题条件，利用逻辑和推理，在其他的空格上填入1-9的数字。使1-9每个数字在每一行、每一列和每一宫中都只出现一次，所以又称“九宫格”。\n",
+    "![数独.jpg](数独.jpg)\n",
+    "* 是否可以用卷积神经网络(CNN)解决数独问题。数独有空间特征，因为它有特殊的数字排列，而CNN擅长提取空间特征，尝试使用CNN来求解数独。\n",
+    "\n",
+    "### 数据\n",
+    "这个[数据集](https://www.kaggle.com/bryanpark/sudoku)包含了1百万数独的数据.\n",
+    "\n",
+    "* 示例的数据如下所示\n",
+    "![data.png](./images/data.png)\n",
+    "![image.png](attachment:c9d0a2c8-372c-4d81-bb7a-919eee6391f9.png)\n",
+    "数据集包含2列。quizzes栏目是未解的游戏，solutions栏目各自的已解游戏，共7805条数据。\n",
+    "每场比赛都由81个数字组成的字符串表示。以下是从字符串转换而来的9x9数独。数字0代表未解游戏中的空白位置。\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "[[0 0 4 3 0 0 2 0 9]\n",
+    " [0 0 5 0 0 9 0 0 1]\n",
+    " [0 7 0 0 6 0 0 4 3]\n",
+    " [0 0 6 0 0 2 0 8 7]\n",
+    " [1 9 0 0 0 7 4 0 0]\n",
+    " [0 5 0 0 8 3 0 0 0]\n",
+    " [6 0 0 0 0 0 1 0 5]\n",
+    " [0 0 3 5 0 8 6 9 0]\n",
+    " [0 4 2 9 1 0 3 0 0]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 要求\n",
+    "   * 分析数独游戏的特点，设计实现的方法\n",
+    "   * 在Kaggle网站上，下载数据\n",
+    "   * 编写神经网络代码，测试自己算法的效果如何\n",
+    "   * 测试所研究方法的效果\n",
+    "   * 分析自己实现的方法的问题，以及如何改进\n",
+    "   * 深入思考，如何使用强化学习的方法实现求解数独问题？\n",
+    "   * 按照report_template.ipynb撰写自己的报告。"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## 解决途径\n",
+    "### 1. 设计模型——序列问题\n",
+    "假设一个数独可以在约束递归神经网络的框架下解决，这似乎是合理的:\\\n",
+    "    输入序列是可用的线索，输出序列是在空单元格中输入的数字序列来完成这个谜题。它应该是循环的，因为还要填充数字以解决剩余的空单元格。这个问题进一步受到了限制，因为行、列和3x3单元格中不应该包含重复的整数。\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    "### 2.数据处理\n",
+    "为了在网络中处理这些数据，我们将每个测验变换为大小（81,9）的矩阵，其中数字是one-hot编码，空单元由空向量给出。因此整个数据集成为维度（bts，81,9 ）的张量， 其中bts是数据集大小或批量大小。我们进行这种转换，以便能够在数字上运行softmax，将每个单元格中的分布视为分类分布。\n",
+    "#### 约束规则\n",
+    "    任何行、列和3x3单元格都不能包含重复的数字。\n",
+    "    为了以网络可理解的形式描述这个约束，建立了一个“constraint mask tensor”。\n",
+    "    这个张量的维数为(81、3、81)，其中第一个索引枚举拼图的81个单元格，第二个索引枚举约束(行、列和单元格)。\n",
+    "    最后一个索引枚举约束所讨论的单元格的单元格。\n",
+    "    因此，第一个单元格的行约束由前9个单元格中的行约束描述。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 数据加载"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.utils.data as data\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def create_sudoku_tensors(df, train_split=0.5):\n",
+    "    s = df.shape[0]\n",
+    "\n",
+    "    def one_hot_encode(s):\n",
+    "        zeros = torch.zeros((1, 81, 9), dtype=torch.float)\n",
+    "        for a in range(81):\n",
+    "            zeros[0, a, int(s[a]) - 1] = 1 if int(s[a]) > 0 else 0\n",
+    "        return zeros\n",
+    "\n",
+    "    quizzes_t = df.quizzes.apply(one_hot_encode)\n",
+    "    solutions_t = df.solutions.apply(one_hot_encode)\n",
+    "    quizzes_t = torch.cat(quizzes_t.values.tolist())\n",
+    "    solutions_t = torch.cat(solutions_t.values.tolist())\n",
+    "    randperm = torch.randperm(s)\n",
+    "    train = randperm[:int(train_split * s)]\n",
+    "    test = randperm[int(train_split * s):]\n",
+    "\n",
+    "    return data.TensorDataset(quizzes_t[train], solutions_t[train]),\\\n",
+    "        data.TensorDataset(quizzes_t[test], solutions_t[test])\n",
+    "\n",
+    "\n",
+    "def create_constraint_mask():\n",
+    "    constraint_mask = torch.zeros((81, 3, 81), dtype=torch.float)\n",
+    "    # row constraints\n",
+    "    for a in range(81):\n",
+    "        r = 9 * (a // 9)\n",
+    "        for b in range(9):\n",
+    "            constraint_mask[a, 0, r + b] = 1\n",
+    "\n",
+    "    # column constraints\n",
+    "    for a in range(81):\n",
+    "        c = a % 9\n",
+    "        for b in range(9):\n",
+    "            constraint_mask[a, 1, c + 9 * b] = 1\n",
+    "\n",
+    "    # box constraints\n",
+    "    for a in range(81):\n",
+    "        r = a // 9\n",
+    "        c = a % 9\n",
+    "        br = 3 * 9 * (r // 3)\n",
+    "        bc = 3 * (c // 3)\n",
+    "        for b in range(9):\n",
+    "            r = b % 3\n",
+    "            c = 9 * (b // 3)\n",
+    "            constraint_mask[a, 2, br + bc + r + c] = 1\n",
+    "\n",
+    "    return constraint_mask\n",
+    "\n",
+    "\n",
+    "def load_dataset(subsample=10000):\n",
+    "    dataset = pd.read_csv(\"sudoku.csv\", sep=',')\n",
+    "    my_sample = dataset.sample(subsample)\n",
+    "    train_set, test_set = create_sudoku_tensors(my_sample)\n",
+    "    return train_set, test_set\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.设计网络框架\n",
+    "为了在未完成的拼图中填充空单元格，我们为每个单元格评分该单元格是特定数字的概率。这是通过九个可能数字上的每个单元的softmax激活函数来完成的。在循环网络的输出序列中的每个步骤，具有最高估计概率的单元格将用softmax中的最大值填充。当我们在空单元上循环时，拼图将一次解决一个单元格。\n",
+    "\n",
+    "![RNN.png.png](RNN.png)\n",
+    "<center>循环关系网络模型RNN</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SudokuSolver(\n",
+    "\n",
+    "      (l1): Linear(in_features=27, out_features=100, bias=False)\n",
+    "\n",
+    "      (a1): ReLU()\n",
+    "\n",
+    "      (l2): Linear(in_features=100, out_features=9, bias=False)\n",
+    "\n",
+    "      (softmax): Softmax(dim=1)\n",
+    "  \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "\n",
+    "class SudokuSolver(nn.Module):\n",
+    "    def __init__(self, constraint_mask, n=9, hidden1=100):\n",
+    "        super(SudokuSolver, self).__init__()\n",
+    "        self.constraint_mask = constraint_mask.view(1, n * n, 3, n * n, 1)\n",
+    "        self.n = n\n",
+    "        self.hidden1 = hidden1\n",
+    "\n",
+    "        # Feature vector is the 3 constraints\n",
+    "        self.input_size = 3 * n\n",
+    "\n",
+    "        self.l1 = nn.Linear(self.input_size,\n",
+    "                            self.hidden1, bias=False)\n",
+    "        self.a1 = nn.ReLU()\n",
+    "        self.l2 = nn.Linear(self.hidden1,\n",
+    "                            n, bias=False)\n",
+    "        self.softmax = nn.Softmax(dim=1)\n",
+    "\n",
+    "    # x is a (batch, n^2, n) tensor\n",
+    "    def forward(self, x):\n",
+    "        n = self.n\n",
+    "        bts = x.shape[0]\n",
+    "        c = self.constraint_mask\n",
+    "        min_empty = (x.sum(dim=2) == 0).sum(dim=1).max()\n",
+    "        x_pred = x.clone()\n",
+    "        for a in range(min_empty):\n",
+    "            # score empty numbers\n",
+    "            constraints = (x.view(bts, 1, 1, n * n, n) * c).sum(dim=3)\n",
+    "            # empty cells\n",
+    "            empty_mask = (x.sum(dim=2) == 0)\n",
+    "\n",
+    "            f = constraints.reshape(bts, n * n, 3 * n)\n",
+    "            y_ = self.l2(self.a1(self.l1(f[empty_mask])))\n",
+    "\n",
+    "            s_ = self.softmax(y_)\n",
+    "\n",
+    "            # Score the rows\n",
+    "            x_pred[empty_mask] = s_\n",
+    "\n",
+    "            s = torch.zeros_like(x_pred)\n",
+    "            s[empty_mask] = s_\n",
+    "            # find most probable guess\n",
+    "            score, score_pos = s.max(dim=2)\n",
+    "            mmax = score.max(dim=1)[1]\n",
+    "            # fill it in\n",
+    "            nz = empty_mask.sum(dim=1).nonzero().view(-1)\n",
+    "            mmax_ = mmax[nz]\n",
+    "            ones = torch.ones(nz.shape[0])\n",
+    "            x.index_put_((nz, mmax_, score_pos[nz, mmax_]), ones)\n",
+    "        return x_pred, x\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 训练模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 batch 0: 0.058021027594804764\n",
+      "Cells in error: 3858\n",
+      "Epoch 0 batch 1: 0.05709225684404373\n",
+      "Cells in error: 3376\n",
+      "Epoch 0 batch 2: 0.056353434920310974\n",
+      "Cells in error: 2601\n",
+      "Epoch 0 batch 3: 0.05516035482287407\n",
+      "Cells in error: 2093\n",
+      "Epoch 0 batch 4: 0.05350525677204132\n",
+      "Cells in error: 1769\n",
+      "Epoch 0 batch 5: 0.05152469500899315\n",
+      "Cells in error: 1555\n",
+      "Epoch 0 batch 6: 0.049802351742982864\n",
+      "Cells in error: 1574\n",
+      "Epoch 0 batch 7: 0.04713556542992592\n",
+      "Cells in error: 1466\n",
+      "Epoch 0 batch 8: 0.044824860990047455\n",
+      "Cells in error: 1308\n",
+      "Epoch 0 batch 9: 0.041409000754356384\n",
+      "Cells in error: 1323\n",
+      "Epoch 0 batch 10: 0.037488117814064026\n",
+      "Cells in error: 1260\n",
+      "Epoch 0 batch 11: 0.03383788466453552\n",
+      "Cells in error: 962\n",
+      "Epoch 0 batch 12: 0.029419725760817528\n",
+      "Cells in error: 892\n"
+     ]
+    }
+   ],
+   "source": [
+    "import dataset as d\n",
+    "import model as m\n",
+    "import torch\n",
+    "import torch.utils.data as data\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "batch_size = 100\n",
+    "\n",
+    "train_set, test_set = d.load_dataset()\n",
+    "\n",
+    "constraint_mask = d.create_constraint_mask()\n",
+    "\n",
+    "\n",
+    "dataloader_ = data.DataLoader(train_set,\n",
+    "                              batch_size=batch_size,\n",
+    "                              shuffle=True)\n",
+    "\n",
+    "dataloader_val_ = data.DataLoader(test_set,\n",
+    "                                  batch_size=batch_size,\n",
+    "                                  shuffle=True)\n",
+    "\n",
+    "loss = nn.MSELoss()\n",
+    "\n",
+    "sudoku_solver = m.SudokuSolver(constraint_mask)\n",
+    "\n",
+    "optimizer = optim.Adam(sudoku_solver.parameters(),\n",
+    "                       lr=0.01,\n",
+    "                       weight_decay=0.000)\n",
+    "\n",
+    "\n",
+    "epochs = 20\n",
+    "loss_train = []\n",
+    "loss_val = []\n",
+    "for e in range(epochs):\n",
+    "    for i_batch, ts_ in enumerate(dataloader_):\n",
+    "        sudoku_solver.train()\n",
+    "        optimizer.zero_grad()\n",
+    "        pred, mat = sudoku_solver(ts_[0])\n",
+    "        ls = loss(pred, ts_[1])\n",
+    "        ls.backward()\n",
+    "        optimizer.step()\n",
+    "        print(\"Epoch \" + str(e) + \" batch \" + str(i_batch)\n",
+    "              + \": \" + str(ls.item()))\n",
+    "        sudoku_solver.eval()\n",
+    "        with torch.no_grad():\n",
+    "            n = 100\n",
+    "            rows = torch.randperm(test_set.tensors[0].shape[0])[:n]\n",
+    "            test_pred, test_fill = sudoku_solver(test_set.tensors[0][rows])\n",
+    "            errors = test_fill.max(dim=2)[1]\\\n",
+    "                != test_set.tensors[1][rows].max(dim=2)[1]\n",
+    "            loss_val.append(errors.sum().item())\n",
+    "            print(\"Cells in error: \" + str(errors.sum().item()))\n"
+   ]
+  },
+  {
+   "attachments": {
+    "13542e57-f20c-4a0b-bce0-f903e1a938d3.png": {
+     "image/png": 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"
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"
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x7LMg5J2avacQme5TWr0F+tm7FzpYVq+0yxvHHiL1Y/Xk02GkomRzqP6cbNKoQOOOC7Bnx9Ezxd+6cni+4+02nIdNFLmDo6TctHEqKlfUbrS8tSZaLwWf/9lvw33cgnFWPE6Zroal1L6XcucDyftGPtPCHdDt6UVBdoz3cpi8kaQy7KHB+3HOfTCP/P+P54Ny0tbS1hIDf7wYkKQiv1Ic6SlLZ/DH6wyk7aXt08CAJBVJKpJUJAYkBiQGUsNAKqSSBrvJMmQvSWJAYkBi4NTHgCQV2UNJrYciHcKp7xCkDaUNp8WAJBVJKpJUJAYkBiQGUsOAJBUJptTANG0PRz4ve8kSA6c+BiSpSFKRpCIxIDEgMZAaBiSpSDClBibZyzz1e5nShtKG02JAkookFUkqEgMSAxIDqWFAkooEU2pgmraHI5+XvWSJgVMfA5JUJKlIUpEYkBiQGEgNA5JUJJhSA5PsZZ76vUxpQ2nDaTEgSSWGVMhXQvNXm6jcoSMf80n0aZUun5cvrsSAxMDpioFEUslcoKM+H3yC/p023EcWfkpaRDnqjfVoqNxjtdggU6L5RGROmkdRNDQ/JJ+U99D+9GSfm55UtnwudDb0k92XmGg+28bsBy46u4NPdg98Sl/qLNSZ1IXUxWJjYCSp0GBZ73vo7KjSYFmlL1ho73bRedGMDaY0qjGKk4ceBCYyH2jDH0IqovlGyZr2NxrP4BId5ZuKyHFBwaYtVz4/3guvnG3CO+xhbmsQzOu2ClwScvZJIxJcSOp1PL1KfUl9nUgMjCSV3B0t+IfqMLmoguqlFczCRv2SaFCocSpJyx1CKnw5ovn4Z+T16fPCiIRBlfY+fewtbXl62HIkqegPOPBfLSOvhARCYiXnlueRq3L3SMS/K80w4tqeNo3kNxjxj4FGlCyS8pERReGvq3Bet9E97KH7gQ1vvgLzvMmmrJSGQYLN9qfpfLRROSdsJ6s/DUq23URhnQX7TQedD2zMPFmB3plQrqD+lJUkjnsblUsKqJJIkSS64q9baN5WiMRdF86nKMiu4SLCkUh5D5ZQGIjEKVoe04+6woS7j4SNNmJju7N8o1JiW/MJD/4zRrRtZ5jwSEz5dQvtMqo8+dvp4bCkHZe+HUeTyiMu/J0mchypxBlVXWlh5qiHMDa0RacpuvPx0xRJZMFkJOXLrKuhAxfefSWUrtagbyyhscOF/14NekxoUlbusFTJZ1EIpuj0r9fRHUUqv2jCfjqYFrytAo/EYt+RrKs42aL6Y87d3dZA82sGjOt0VH9kowsHzXUhoYnmU5ebcA+7od02WXDe89B5zoyQgWh5rG1pxJSnpDLvwZ83oCp5VHfacDZloXRImGUPzQ2SVJi+Zbr0He0fk41SIRXtPgf+O1Xo3PpD5gbilFuonLnw5U8iC2aApHyFf2jDP1iFzpGeUsmiuDKP3JS7tpS11dGkcqQBk4t9nb21AR8tWN7C9rL2DEtF9ddz7h68O7L9bcBKvoD6Hg/dh7XwHh3RJOcjMbP9vVVohbDO6nU12u7queE9UbmsfcoyA83nm3Du0SZe+4iSio42PHTuL0pSidmtyPQuU0kuSwEDU5MKffm3ePC3mciVVKjs74Ly0GmKJLJgiknKp66vogMH7n0mzL8oonh2BipHMKycSdJEUvmVhQInS7msgg5s1FaEzlhE7jj66zl3B43LQxnkef2eFrxva/1pIpF8fblbB6aXlgXTS+s5GZSkkuWKtFc0D61ff6QiSUVUbzKfJJbFxkA6pDIfrkMswwy3JhE/TZFEFkwpSfkUJYPCJrKm4qAbrIV0322heWNuanJJJJU3BkhltYU5tFFbGTpj1o5RKXOevN7YNdnSzE/zMLKorx4tQyQfk9ubXgrLi5teEilvVBsn+S1avzyqr9hwNsvpr0l0KZ+RRHMyMTCaVOIW6lUV2U4W2VLPEdGXn4xUdljQVxdQHPjLczvHWMOSyGLcfCS/amaRv0iD9aM2/KNNlCeYhmJySXpSSUVQf6LOXSRf325jjFSSyIzX37TXtH5yob4/pTmtPuXzklhOFgZGkgp1/ocaMLnDZurFZAeSg/qasHdL1wR2VVDMhPfILrEMmQrLhvdYo+j6w/E6SjG/sTwkHZWPHIzLrSpCXxMdlSgXkxHDdFueieyTRSpElqj+RMiC1j2Yrkoigd6aSgVaPrRRf03lI+E9UblENpXv62g+14R9d3HiNRVSjtxSLB0hw5RMTx0sjCQVxTFgv+9hbnuFHn40Pl+G/ZaLzg4T+VzodNRVFmaOuZj5QRnmdRpKt5iob7Ph76tCL4f5GDB6TsqF+69G/0Ck/vFsf01ANF/+jha6f7Bhf8uAsV6DfkMJjZdc+AdqMKyFclm5w1LVL4T1CXZ/NT+v9e5x9aNbilOa/iJ1EdWfqHMXzaeeb8I7Qrb+WjCv11Biu7+ejdv95SCJpJhe09j9RcpSzinBPexhdmsF5Y0aSuzw4xZ9KrJi9ZTpqeOopK1OHVuNJBViyMyFBhrPtDH7vovuuzbc75rQ2lGHTaYqcmtNtF6yMfehi85+G+7jFoyzwm2uPChI/uLmGty33f5aiP/ywu24SfkUNQPtq1W4uxx0j3ro/s7GzLYqyhdlJpo2yN/Z6q8HsXUNlvL1S5tURPUnShbC+cg5lStMNF9oY458BmVvG85DJRRjz6mIk4q6yoS738XcfCmyNZnHgMg1/UzLpdw5mj3BZ1om6DCIyJN5Th3HJW21dG2VSCrSeEvXeNI20jYSAxIDSw0DklTkvv+JRnVLDciyPtK5SgwsDQxIUpGkIklFYkBiQGIgNQxIUpFgSg1Msqe4NHqK0g7SDouJAUkqklQkqUgMSAxIDKSGAUkqEkypgWkxe0dStuydSwwsDQxIUpGkIklFYkBiQGIgNQxIUpFgSg1Msqe4NHqK0g7SDouJAUkqklQkqUgMSAxIDKSGAUkqEkypgWkxe0dStuydSwwsDQxIUpGkIklFYkBiQGIgNQxIUpFgSg1Msqe4NHqK0g7SDouJAUkqklQkqUgMSAxIDKSGAUkqEkypgWkxe0dStuydSwwsDQyMJJVhn4IfDHO7WMaM+wR9mnXJfbmBzpEWKudFP/UvKuNE1Y/YxT9Yha5MVi9W/7TrR4J+sVABfOpzsWcUJYvKy/Hhp8kz9pcmC1vA2jROOq196af5LzfRfD4IHfB2G/Z3SihwoQPGba+SyUL/ZhOd4x78w/E2JqES8tdaaO8IQk281UL7/xnIB9FYiQ7GkSvSDqbXpPqNLfdjJTS32+gcJiEz2rD/zUDBjOKa1u8SE81n25glIRp2ByEQuOCBpH65rzbj8bfXQpF7V9LWn6hcahcB+45THrNLGqmonnlZmWtr6MCFsynb75wmkor/+wYqa4NAVf20iEIranhe0Mm6Ttsp8vVWPAP2By68u/ITx7s/UfVbqqSSOacYBjkjWLmmhObrLro7SsgFLzVzOt2flqN5r7fgHvFgfzY+Bg9vmzSu07Cv+mdleCSI2LyFchDkzN3noftcGBtonPYq9QKqW234R224L9tDSSW7sYY5uPAeMWlQPPOrNXgfeph7TO8HuhtHrkg7iM5F6je23KMuZp+yUL5Bh7nZgvOuh+62aBwe5WyT6nluqxUN1vakEQnWlvvbJvwP6wv91eW5vl5IO9LWn6hcEf2R+omWl8Z7wJchqmf2jKIVUP+1h+7/llHgovgmk0oKPWJWibTTE+W0FSUD88cOuq9bKBYmJ09av9ct5LleUho6WKqkwrdNyWVh/sRBd28dph8SRd/pPKr1ezbkudzmJrpHGjBPQmclLfsW/sWG/0ENuhZiJHNzHV20YHV798Zpr3pjHd3XaiivyCD3N81YUqHlveSh+7QRcZSkd+ujATMIYDaOXJF2EBsJ1+9lD90E+5LRgvljF/5rFgp8FNlLK5iFi+ZVIWZEw0rnv96Cv78KbcT7diL0JyJXVH8kn2h5JG+af6J6JjLJqKZwZwtd2GhcHtqK/JYKqVDnud1EYa0F+w0HnQ8dzGytwFgWFUaHV2u4SH5vt+E8GJ0uYEpSP2qg/lQLswdcdPfZcL9nQp+NlkflvlqGdpUF+00HnQ9seFss6J1oPlamaJq5vobOcRu1i6crh9bvF2VoiXpRkd9owf65jc6hIALjIyVoTuislJIOG+LTRoupP+K0jUdsdN+twzw7qkOCAbWVRa4a3leUHKqveuj+tx47KlRXmHD3kbDHxlSRJJn907Ivme6jTkwN7aRurFFSqZzJSEW8vepcDrmAoGhvNWb6i+g2/wkN2jmh/ki7lP9L5LZR/cj4ckXaQWSI1U+svYqSR+2XHjoPFCOOUcloaB/z4N3Rm06h5POEB/+ZKIkqZ5jwCPmsC3Wf/0YL/r5KAqmkrz8RuaL6I/lEyyO6yV1povViMP26pw37fgOFgWlBUmbS3zh6JmXRkf5BD53/CkfHTIYQqRglFSr/p6uUqVghPefZRHtLBRaJJf7lYBj7Shn5TGh0dbkJ97Dbmy4g+VhMdDJdwL2YSltH+7ceOjuqsD6twbiljPabbm/koIflUbm/bMJ+ugrrL3sxzL0DHro7wukHVkfRVKkU0XzHw+y9xUhPUPR5Pl+vfq1EvWSuqWLuuAP3XhOlDRrMr1Tg7u+1g41ylGwWxQ0ajA0ayo/aINOS1eB/cq/IEemi6o/0YP6uhc6+BsrLo46P1w1/ra62MAsHzbXx+dOKeU9kpmlf9aMm3EMuvLs1FNwMcit0NF9z0Zk3+tN9fDvZdVJ7Sb5hpMLKGEzp6PVIHSUjfD8G8wyTO0k7xqlfnFxCjuWfefC3lSJTWMqZhCw82J/rYYE6u3kP/rwBVcmjutOm8/dKxyQTgGhuCNtbuKsN/50KXT+hnRduBDSoi8H/p9HfJHJH6U+0PHWlhZmjwfQr8aebrZ7fmI9OCw62Ne7/cfRM8hoP2/APNVCeU6ByU1+k7ERS4Rdc2bW/3YwAgTrPw3WYtdDA6rVBz2l5eI/2iPZWoXFTSup1Qb5zw3y5r5Chfx1mPbynnEXinrfR+GToeKjcgSmT7K0N+GT6wQ2fjVNi3D2iLP0hG/6eKvRgsZDWBXUYI4bUcWWRe6J6yX3CQHljIUJimb8K2jGzsB1J01+LpT/SZgrOLR7IZo7ub9qw/91AsbqwDUxnFKCPOvD3WChyHRD2Oy1zmYHm800492gR3PF5RK7Tti+RmVltwt7bG0H6cDH7wxIKIxy7SHtJuaOczmBblVIRzT0eOo9qEQzx+ZLkjtsO0fqNkpu7tYEuHDh/X0TByyD35zrqO1z4aKLS6WGGPG/2SUVHGx469xcRSyrfbMPfW0X5SzV4B1z4x1zM7azBWj1688e0+itMIHeU/kTL0+5z4L9TjU6/3tCbfmUjZYIBJTcwKOAHCP21TnE9K10T7jEPs9vqcH/jwicDheeqMM/t+eZEUvEP1VFZXUCR/zs/G5mmoM5zl4UC53iVZcHwdD0HDuJstg4MYwfyESVo9zrwB8rjXxB2TeX+akDuZRV0YKO2YrgjY88PpuoVFcwet9FcHxLX1KQy0I5BvfB1IC+QagQAuKraa8eFC9uRRCqLpT/SFjrFeW4BxTVFGJuCEdfPoguvkTbXDNiHPczclU8covPPTXKdtn3V5SU4+13MfN+EuU6D/tky2rtdzI2YplME2zvK6fBtV9QsSt+z4b9fhzm7ECss7yi5k7RDuH4j2ktG36UHyLx8j5S7e1vw3vXoSC8zgbMrfotscnDgPWCitK4I/TMm2r924f+2jhLXQWU6IWka+ptE7ij9iZRHyZb4020mcjxJXFBeMC2Y+VIjflcc11keh7yL/9gG6UDN/CDA/ecs2Htc+Lur0AwleaQisnWVOndu2yg11sDwNFrpEPxxPY648nggsOu4fMpqC3Noo7YylMHyJ6XGf/QWDosVFZngL/+1FnxO+Ull8L/H1m9ALyS/Mg832eYAACAASURBVKuh9sMW5g66EeP7hBwnIJU4uXy92HVcvmn0x8rl096WQwf1y+Ltkbu9Bf8PLVhBz5R/Nu3rNO1L8fy4C//VgSneNcFC87qwY8K3Q7S9o5wOK49s7zUebqP7+zbqV47ujQ+TO3E7yE6rmDUfVjeWDpPLfqfTVPUsChfmUNxMetk26heFuov6jTyqr9hwNmdjRyq0R16MTs2rl1boTrnmNQvxl4b+SDvGlUueGWVfkfKYXtjsEZ8OHvlQ7Xx0UNAfIIS74lh5/WnGIXpmGx38/4kuWairAtx/Sk2RVBJ65LTSJ2KkMkhm05DKo1Gnzgw1Fakk6iWL8jMu/LfqqNxQhMYMvrl+ckYqKemP2DdjZ5G1QodAX7iARO2bovfpb8Fibfep6OiV/HYi/owU7Us3F7zuofuf0V1simXQDRVsoZlvB1ucFmnvKKdDdafnUP6hje7BFuqfHE0oo+RO0g4iP6l+tI5j2Fep62jv99DhtkX3ylBhCi7Uk/yDf4pdomsv9hejOlJS0t+gPPb/MLnsdxH9sbwkHSyv7093WNCZz+DSPLcUwZcz7JqWJ6BnhqUFGyyyOt1g4d6eS5FUBtdUrh+2plKBlg97Df01lWDXCml0f02AU0xvTcVeuKaSklMkcjNnDUzzrS7A+PaUI5UEvShKEc3fLZz+ydxE5pvjRypk4ZqMIIet8yyG/ujC60/JGY3wTArVKd0NFT9SUT/R60W2rl9IODz4FV9H87km7LuLU62pjGtf9TwDrReacL6jRzaSkLr1X+pdForcgrC6tkp7xnFtEm0vKX+U01FqBVS3O/D3N1BZNVp3pKxRcidpR1L9yO9JclkekpI66A8GC78xU3giW11JGflrTFifyUfWlciUJznT0/xUqKe09Scql2/zMPuO0w66prKrElmLVDIqMmQ6bGDxnJc97FpUz3Qr+M6BEfrFZLONi+YGkZGKwOFHOo3yVgvO08Hur2BXV/eVMgrc4qt6vgnvCNkaasEMDos573noPBsdSim2AfuAh86LFbqry7jZRPuNYPcXdybgZEzfTL2m8mYL9gi9kOFkeasLf28DtS/o0DfoKN9Vg/2TJjlfHjv9pV5dpYRj32nQ3WBk91eBJ+BF0l/mqipmj7uYecyEea0G8/YqXLIbb+AwG3Mkpe+7dEuuzm3ciAN8mru/BssfZd/iP/e+EEA3fgTnTvjn1Sur9EWihx/p7psK6OHHnWUUuI6TaHv5w6Pmw234RxqoBgeO2RZiOv3wogv/wzaam5IPJRMnlaRn0XaI1I/pR0Quy6t+zMLscQ/eN/KRXaXsd+WcElxyyHRrJXr4cYse6WTkbiajewfe/SbMDRpKX7Rg7w78RrB54kToT0QuaYuo/kTLU1dZmDlG1jbK9BBs6RYT9W02/H1V6OWw4870mJSK6pkeliWHVp8IDqPeasHZS/x1CfmswJoKmwLi08E5O+rcXy1Dv8aCs8tB52BwTuWMsHdAGkQXca8w0Xwh2Fe9tw3noRKK3GctWMMzF+poPB2cU9lvw3usDH2gF3NKkMp2E8UkvcxpqD3Rwhz5BMUBG+4jJRTPJ1sm40mFvBjGP9Uxs5/slCE7rRzUV0dBtBj6I44kt75MPx/SOeiiQz4fQj67EbNvXmn1Fuhn7453JAwHJFVXkZ1/Lubmhy/48/nHuR5FKuoaC95vXcw9a0YO6LHyaXvXlcNzAuTc1cMmijEHOEXaO+wzN+Tdm72nQHv/ilJA7a3480qD7yWpp4hc0XaI1K+vG0H7Ktkcqj8ni7wVaNxxAVYObYOiIHspd75tT/CZluCgJ8tL2lG42UL7lfC8l/tdE/pM6IdOlP6S5JI6iupPpB09vajIrTXRein4XM9+G+7jFoyzwvYy3Yik1D8L6VlB9uPc53WIPe4Pd3mO3P0lUhGSJ865iz4r8y2cB5Y6kTqRGJAYOFUxIEklZnHvVDWmrLd0RBIDEgOLjQFJKpJUFuyYWWxQSvnSMUoMnLoYSIVUJABOXQBI20nbSQxIDKSJAUkqcqQiRyoSAxIDEgOpYUCSigRTamBKs7cjy5K9Z4mBUxMDklQkqUhSkRiQGJAYSA0DklQkmFIDk+xZnpo9S2k3abc0MSBJRZKKJBWJAYkBiYHUMCBJRYIpNTCl2duRZcnes8TAqYkBSSqSVCSpSAxIDEgMpIYBSSoSTKmBSfYsT82epbSbtFuaGJCkIklFkorEgMSAxEBqGJCkEgMm8pXQ/NUmKnfoyI+IN54mu8uyZG9RYkBi4HTAQCKpZC7QUZ8PPkH/ThvuI/Gf9k5ShnpjPRoq91gtNsiUaL4kedP8rigamh+ST8p7aH96ss9ITyNfPttzLvRT3JeYaD7bxiwJC7A7+OR5zKf0pc6kQ5YYWBoYGEkqNFjW+x46O6o0WFbpCxbau110XjRpMJZxjKg4eehBwCHzgTb8IaQimm8c2ePmpfElLtFRvqmIHBcUbNxyZP7pQK6cbcI77GFuaxAM6LYKXBJy9kkjEpxJ6nk6PUv9Sf2liYGRpJK7owX/UB0mF1VQvZQEuLdRvyQaFGqcStFyh5AKX45oPv4ZeX36vCAi4U2lvU8fe0tbnh62HEkq+gMO/FfLyCshgZAYyLnleeSq3D0S8e9KM4yAt6dNI4HFRfwjwBEli6R8NELaX1fhvG6je9hD9wMb3nwF5nmTTVkpDYMEI+1P0/loo3JO2E4GehqUbLuJwjoL9psOOh/YmHmyAr0zoVxB/SkrSRzoNiqXFFAlkSJJdMVft9C8rRCJyy2cj0TUW8NF1CORCx8soTAQiVO0PKYfdYUJdx8JG20siO3O8iSlxLbmEx78Z4xo284w4ZFY2OsW2iWpTPn76eG0pB2Xth1Hk8ojLvydJnIcqcQZVF1pYeaohzBWt0WnKbrz8dMUSWTBZCTly6yroQMX3n0llK7WoG8sobHDhf9eDfqQ0KSs7LhUyWdRCKbo9K/X0R1FKr9own46mBa8rQKPxGLfkayrOLmi+mPO3d3WQPNrBozrdFR/ZKMLB811IaGJ5lOXm3APu6HdNllw3vPQec6MkIFoeaxtacSUp6Qy78GfN6AqeVR32nA2ZaF0SJhlD80NklSYvmW6tJ3sH5t9UiEV7T4H/jtV6Nz6Q+YG4pRbqJy58OVPIgtmhKR8hX9owz9Yhc6RnlLJorgyj9yUu7aUtdXRpHKkAZOLRZ69tQEfLVjewvay9gxLRfXXc+4evDuy/e1/Sr6A+h4P3Ye18B4d0STnIzGz/b1VaIWwzup1Ndru6rnhPVG5rH3KMgPN55tw7tEmXvuIkoqONjx07i9KUonZrcj0LlNJLksBA1OTCn35t3jwt5nIlVSo7O+C8tBpiiSyYIpJyqeur6IDB+59Jsy/KKJ4dgYqRzCsnEnSRFL5lYUCJ0u5rIIObNRWhM5YRO44+us5dweNy0MZ5Hn9nha8b2v9aSKRfH25Wweml5YF00vrORmUpJLlirRXNA+tX3+kIklFVG8ynySWxcZAOqQyH65DLMMMtyYRP02RRBZMKUn5FCWDwiaypuKgG6yFdN9toXljbmpySSSVNwZIZbWFObRRWxk6Y9aOUSlznrze2DXZ0sxP8zCyqK8eLUMkH5Pbm14Ky4ubXhIpb1QbJ/ktWr88qq/YcDbL6a9JdCmfkURzMjEwmlTiFupVFdlOFtlSzxHRl5+MVHZY0FcXUBz4y3M7x1jDkshi3Hwkv2pmkb9Ig/WjNvyjTZQnmIZickl6UklFUH+izl0kX99uY4xUksiM19+017R+cqG+P6U5rT7l85JYThYGRpIKdf6HGjC5w2bqxWQHkoP6mrB3S9cEdlVQzIT3yC6xDJkKy4b3WKPo+sPxOkoxv7E8JB2VjxyMy60qQl8THZUoF5MRw3Rbnonsk0UqRJao/kTIgtY9mK5KIoHemkoFWj60UX9N5SPhPVG5RDaV7+toPteEfXdx4jUVUo7cUiwdIcOUTE8dLIwkFcUxYL/vYW57hR5+ND5fhv2Wi84OE/lc6HTUVRZmjrmY+UEZ5nUaSreYqG+z4e+rQi+H+Rgwek7KhfuvRv9ApP7xbH9NQDRf/o4Wun+wYX/LgLFeg35DCY2XXPgHajCshXJZucNS1S+E9Ql2fzU/r/XucfWjW4pTmv4idRHVn6hzF82nnm/CO0K2/lowr9dQYru/no3b/eUgiaSYXtPY/UXKUs4pwT3sYXZrBeWNGkrs8OMWfSqyYvWU6anjqKStTh1bjSQVYsjMhQYaz7Qx+76L7rs23O+a0NpRh02mKnJrTbResjH3oYvOfhvu4xaMs8JtrjwoSP7i5hrct93+Woj/8sLtuEn5FDUD7atVuLscdI966P7Oxsy2KsoXZSaaNsjf2eqvB7F1DZby9UubVET1J0oWwvnIOZUrTDRfaGOOfAZlbxvOQyUUY8+piJOKusqEu9/F3HwpsjWZx4DINf1My6XcOZo9wWdaJugwiMiTeU4dxyVttXRtlUgq0nhL13jSNtI2EgMSA0sNA5JU5L7/iUZ1Sw3Isj7SuUoMLA0MSFKRpCJJRWJAYkBiIDUMSFKRYEoNTLKnuDR6itIO0g6LiQFJKpJUJKlIDEgMSAykhgFJKhJMqYFpMXtHUrbsnUsMLA0MSFKRpCJJRWJAYkBiIDUMSFKRYEoNTLKnuDR6itIO0g6LiQFJKpJUJKlIDEgMSAykhgFJKhJMqYFpMXtHUrbsnUsMLA0MSFKRpCJJRWJAYkBiIDUMSFKRYEoNTLKnuDR6itIO0g6LiQFJKpJUJKlIDEgMSAykhgFJKhJMqYFpMXtHUrbsnUsMLA0MjCSVYZ+CHwxzu1jGjPsEfZp1yX25gc6RFirnRT/1LyrjRNWP2MU/WIWuTFYvVv+060eCfrFQAXzqD8aeIZ/cv9xE8/ngk/tvt2F/p4TCwCf3WT1PVDqtfemn+SdoxzC5tLxLTDSfbWOWhCLYHXzqnwuSN6iLzLU1dODC2ZRd0DkYp35KJgv9m010jnvwD8dja5zyWD2H1U9Rsqi8HB+GnGDH/lIvfIVoPiKP1u9jJTS32+gcJiE42rD/zUDBjL4nk+iZlD+t3SbR3yi5TMdppZPoJc6+iaTi/76BytogUFU/LaLQihoqrYaNU07aTpGXrXgG7A9ceHflJ453f6Lqt1RJJXNOMQxyRrByTQnN1110d5SQ4whQ/bMyPBJ8a95COQgO5u7z0H1uYUwd3iZpXqdh30naMUqucrZJ9TK31YoGJXvSiA1KpmgF1H/tofu/ZRRioqiK1k+pF1DdasM/asN92R5KKqLlMTuNqh8ji+5Py1HMXG/BPeLB/mwvFpNoPiKT1u+oi9mnLJRv0GFutuC866G7LRrXZ1w9k7LTsNu4+kuSy/ScVjquXobZN5lUUugRp9XowXJOlNNWlAzMHzvovm6hWJicPGn9XreQ5xzqYBsm+X+pkgrfFiWXhfkTB929dZh+NFhb4V9s+B/UoGuhbjM319FFC1Y3vMeXl+Z1WvYdtx1JcscJn0x6lYU7W+jCRuPyqH6ZrkTrp95YR/e1GsorMsj9TXMoqYiWR+Qn1a9PFo9qkRFWbnMT3SMNmEGnVTyfCvPHLvzXLBT4qLSXVjALF82rQh2No+deW0b7A9HyxtGfiFxm57RS0Xb06jYcf6mQCnWe200U1lqw33DQ+dDBzNYKjGWhIVlFsmu4SH5vt+E8GD/toX7UQP2pFmYPuOjus+F+z4Q+Gy2Pyn21DO0qC/abDjof2PC2WNA70XzjKj1zfQ2d4zZqF09XDq3fL8rQEvWiIr/Rgv1zG51DQQTGR0rQnNDBKiUdNpKnC1hbF1N/xHkaj9jovluHefZCHZJpMn9/FZoatk/dWKOkUjkzvNdvywoT7j4S9tiYKpIkKy8t+47bjlFySfRP8wkP/jNGJKy2coYJjzjFdVG90J7zQQ+d/9Ij+VkbSSpaP3Uuh1xA8Lm/HU4qouUR2Un1I6SjtrLIVUN8KEoO1Vc9dP9b788OiOfLo/ZLD50HihGSUjIa2sc8eHf0pgfH1TNpS1p2G0d/SXKpjknE3StNtF4MppH3tGHfb6AwYrqUPBf3N65eRtlXiFSMkgqV/9NV2hNhles5zybaWyqwSCzxLwfDzlfKyGfCl0FdbsI97PamPUg+FhOdTHtwDkZp62j/1kNnRxXWpzUYt5TRftPtjRz0sDwq95dN2E9XYf1lL4a5d8BDd8fk0yhKpYjmOx5m7y0OfVlZu5PSXv1aiXrJXFPF3HEH7r0mShs0mF+pwN3fawcb5SjZLIobNBgbNJQftUGmJavB/+RekSPSRdUf6UH/XQudfQ2Ul4cOg9eV+lET7iEX3t0aCm4GuRU6mq+56MwbkWky9kxaMe9JeWnad5x2JMmlL/W8B3/egKrkUd1p03USpWPChYfmhhD3JK/xsA3/UAPlOQVqzNQXaes49evregSpiJYnWj8mk6XqaguzcNBcG4+bUflIR6b8Mw/+tlJkqlA5k5CyB/tzvTLH0TORl6bdRPUnIpfkUVdamDkaTCMTf7rZ6vmN+fjpUqa/uHQcvSTZN5FU+AVXdu1vNyOGo87zcB1mLQS+ei3pebZRXR7eo0y9twqNm1JSrwvynRvmy32F9JbqMOvhPeUsEve8jcYnQ8BRudxQmSgre2sDPplGccNn45QYd48oS3/Ihr+nCj1Y3KN1QR3GBFNYonrJfcJAeWMhQmKZvwraMbOwHUnTX4ulP6JTCs4tHshmju5v2rD/3UCxurANmdUm7L29kZcPF7M/LKFgLMxHy1xmoPl8E849WgR3cTYcdS9t+xJZIu0QkUv11icVHW146NxfRCypdE24xzzMbqvD/Y0Ln3TUnqvCPDd8N5geROrH8pJ01EhFuL1j1I/JJu03HnXg77FQ5Dqi7HeWjsqXu7WBLhw4f19Ewcsg9+c66jtc+Gii0ulhayw9C/iDccoT1p+AXFKWdp8D/51qdBr5ht40Mj/iV3IDgwJ+gBD4tXHaoSTYN5FU/EN1VFYXUOT/zs/2h6ekcdR57rJQ4ByvsiwYtq/njEmczdaB4f1APqqsex34A+WR+4N/VO6vBuReVkEHNmor4h3UYBn8/+oVFcwet9FcH76cU5PKQDsG9cLLJ4ZVjQAAV1V77bhwYTuSSEVbJP2RtpDpiuy5BRTXFGFsCkZcP4sulKrLS3D2u5j5vglznQb9s2W0d7uYS2l6i9cpf522fUXbISJ3nJe6+I9tECKe+UGgv89ZsPe48HdXoXHELFo/XkejSEW0PNH68XKVmgH7sIeZu/IL3nPRfGQ0X3qArDP1OivdvS1473p0BJyZwHmmbTdR/QnLJf50m4kcTxIXlBdMl2a+1Ijflcl1ltPEXzKpCCzUU+c+uG10YNgerXToKON6YnHl8cBi13H5lNUW5tBGbWUog+VPSo3/6C30FSsqMsFf/mst+Jzyk8rgf4+t34BeSH5lVkPthy3MHXQjxvcJOU5AKnFy+Xqx67h80+iPlcunvS2HDuqX9exBcfC4C//VganRNcGC6rqQ0Ply0rhO077jtENEbvT9yKP6ig1nc3bBSIUuXL/kwf+f6JSxuirQ36d6+hunfrxuh5GKaHmi9eNlkuvc7S34f2jBCkYUg7+z/5Py0TWYehaFC3Mobia9dhv1i0JMieqZyEvXbipMQdyPI5fNHvHp4JEP1c5HBwX9AUKuPzsiqhcR+6ZHKgk9clrpEzFSGSSzaUjl0ahTZ4aailQS9ZJF+RkX/lt1VG4oQmMG31w/OSOVlPRH7Juxs8ha4QtMXkzWabBvYs4uh+rrHrr/Gd31o1gG3YjAFlSZE0kzNVK0L11UFmyHiFz6fggs1CvKkAXprE4XpN3bc7SnP079eB0PJxUxu4nWj5fJnuk+FZ3F4POQa9F8NG9dR3u/h85j0Y0MonomZaRrNzH9jSWX+NMdFnTmM7g0zy1FkDKT/kT1wmywYEMEh7/0SGVwTeX6YWsqFWj5cBTRX1P5SHivvybAKaa3pmIvXFNJySkSpWfOGpjmW12A8e0pRyoJelGUIpq/Wzjsz9xE5ofjRypk4Zocfhy2zrMY+qMLpT8lZ02iZ1IydFfXwEiFvAy7LBT5rZ9rq5iDi9b1UVIidlF8Hc3nmrDvLk61pjKufdXzDLReaML5jh7ZSELrRHZrCbZDVK7Ilk768pOtszsHRnoXk0VuF80NjLzF68c7nOGkIlaeaP14meonKkNtP0k+Ugf9wWAjw2zoV1hZInomeVO3W8p4oWsquyqRNSgloyJDpsOGbN5gOohLRfQiYt9kUhE4/EinUd5qwXk62P0V7OrqvlJGgVt0U8834R0hW0MtmMGhN+c9D51no0N5xTZgH/DQebFCd3UZN5tovxHs/uLONpyM6Zup11TebMEeoRcynCxvdeHvbaD2BR36Bh3lu2qwf9Ik54pjp7/Uq6uUcOw7DbobjOz+KvAEvEj6y1xVxexxFzOPmTCv1WDeXoVLduMNHD5Tr6xSB0gPP9JdKxXQw487yyhwHQ4G/DR3f7EyWTrKvsV/7n0hgG78iDk/M247mEySxslVzinBJYdCt1aihx+36BEy7R/yeyI4JHmrBWcveV9KyHPORLR+/KFV8+E2/CMNVIODzto5IcmLlidaP6IH4qRK33fpFnOd28DD62qcfCSv+jELs8c9eN/IR3apsjJF9czy8+lUdhsT90ly1VUWZo6RtbUyzOs0lG4xUd9mw99XhV5eSKZ8eXHXonpJsm8iqbApID4dnLOjzv3VMvRrLDi7HHQOBudUzggBSRpBF3GvMNF8IdhXvbcN56ESijGf58hcqKPxdHBOZb8N77Ey9IFexylBKttNFJP0Mqeh9kQLc+TTHAdsuI+UUDyfbCWNJxVCRMY/1TGzn+xsITutHNRXR0G0GPojDiK3voz2Dhudgy46b7XQJp/JGNg3T/OtK4f768l5pYdNFId8pUFdRXb+uZibjy74x70Y496LcxKsDHWNBe+3LuaeNSMH6tjv47aDPUfSOLn0/biUO8e1J/hMixW1Lc33ce5zJCTf/Qt32YnWj+zK5N9v/nr2nkJ/6kS0PNH6ET0ord4C/ezd8QTAdCacL5tD9edk00IFGnf8gJVDZZLNJAJ65p9h19PZTUVuDNwzmSSNl6sit9ZE6yUbcx+Sz9LYcB+3YJwV9bt8OaOu08LfSFIZVQH+tzjnzv8ur5PnNKWOpI4kBiQGTgcMSFIRWMQ6HQwt2yAdlsSAxMDJwIAkFUkq/emNkwE4KUM6NomB0xsDqZCKBMnpDRJpX2lfiQGJAVEMSFKRIxU5UpEYkBiQGEgNA5JUJJhSA5NoT0bmk71eiYHTFwOSVCSpSFKRGJAYkBhIDQOSVCSYUgOT7H2evr1PaVtpW1EMSFKRpCJJRWJAYkBiIDUMSFKRYEoNTKI9GZlP9nolBk5fDEhSkaQiSUViQGJAYiA1DEhSkWBKDUyy93n69j6lbaVtRTEgSUWSiiQViQGJAYmB1DAgSSUGTORrrPmrTVTu0JHnwrOKMrXMJ3t1EgMSA3+sGEgklcwFOurzwSfo32nDfWT4J8pHKVG9sR75vLZ/rBYbZEo03yhZ0/6mKBqaH5JPyntof3qyz0hPWwf5fBAq4RITzWfbmCVhAXYHn4If+JS+1JV04BIDSwcDI0mFBst630NnR5UGyyp9wUJ7t4vOi2YkGJCIQRUnDz0I/GM+0MYwUhHNJyJz0jw0bsQlOso3FZHjgoJNWp58bjLAK2eb8A57mNsaBKO6rQKXhIh90ogErZL6nUy/Um9SbycCAyNJJXdHC/6hOkwuqqB6aQWzsFG/JBo4aJzK0XKHjFT4ckTz8c/I69PnRREJbyrtffrYW9ry9LDlSFLRH3Dgv1pGXgkJhMRAzi3PI1fl7pGIf1eaYSS/PW0aiW4w4h8DjShZJOUjI4rCX1fhvG6je9hD9wMb3nwF5nmTTVkpDQMOvP40nY82KueE7WT1p0HJtpsorLNgv+mg84GNmScr0DsTyhXUn7KSxCFvo3JJAVUSKZJEV/x1C83bCsjwNhLNRyLgreEiDZIIjA+WUBiIxCkql+lHXWHC3UfCRhsLYruzPEkpsa35hAf/GSPatjNMeCQW+7qFdkkqU/5+ejgtacelbcfRpPKIC3+niRznsOIMqq60MHPUQxhz3KLTFN35+GmKJLJgMpLyZdbV0IEL774SSldr0DeW0Njhwn+vBn1IKFFWdlyq5LMoBFN0+tfr6I4ilV80YT8dTAveVoFHYrHvSNZVnFxR/THn7m5roPk1A8Z1Oqo/stGFg+a6kNBE86nLTbiH3dBumyw473noPGdGyEC0PNa2NGLKU1KZ9+DPG1CVPKo7bTibslA6JMyyh+YGSSpM3zJd2k72j80+qZCKdp8D/50qdG79IXMDccotVM5c+PInkQUzQlK+wj+04R+sQudIT6lkUVyZR27KXVvK2upoUjnSgMnFVM/e2oCPFixvYXtZe4alovrrOXcP3h3Z/vY/JV9AfY+H7sNaeI+OVJLzkdjk/t4qtEJYZ/W6Gm139dzwnqhc1j5lmYHm800492gTr31ESUVHGx469xclqcTsVmR6l6kkl6WAgalJhb78Wzz420zkSipU9ndBeeg0RRJZMMUk5VPXV9GBA/c+E+ZfFFE8OwOVIxhWziRpIqn8ykKBk6VcVkEHNmorQmcsIncc/fWcu4PG5aEM8rx+Twvet7X+NJFIvr7crQPTS8uC6aX1nAxKUslyRdormofWrz9SkaQiqjeZTxLLYmMgHVKZD9chlmGGW5OIn6ZIIgumlKR8ipJBYRNZU3HQDdZCuu+20LwxNzW5JJLKGwOkstrCHNqorQydMWvHqJQ5T15v7JpsaeaneRhZ1FePliGSj8ntTS+F5cVNL4mUN6qNk/wWrV8e1VdsOJvl9NckiCQFmgAAIABJREFUupTPSKI5mRgYTSpxC/Wqimwni2yp54joy09GKjss6KsLKA785bmdY6xhSWQxbj6SXzWzyF+kwfpRG/7RJsoTTEMxuSQ9qaQiqD9R5y6Sr2+3MUYqSWTG62/aa1o/uVDfn9KcVp/yeUksJwsDI0mFOv9DDZjcYTP1YrIDyUF9Tdi7pWsCuyooZsJ7ZJdYhkyFZcN7rFF0/eF4HaWY31geko7KpygKcquK0NdERyXKxWTEMN2WZyL7ZJEKkSWqPxGyoHUPpquSSKC3plKBlg9t1F9T+Uh4T1QukU3l+zqazzVh312ceE2FlCO3FEtHyDAl01MHCyNJRXEM2O97mNteoYcfjc+XYb/lorPDRD4XOh11lYWZYy5mflCGeZ2G0i0m6tts+Puq0MthPgaMnpNy4f6r0T8QqX88218TEM2Xv6OF7h9s2N8yYKzXoN9QQuMlF/6BGgxroVxW7rBU9QthfYLdX83Pa717XP3oluKUpr9IXUT1J+rcRfOp55vwjpCtvxbM6zWU2O6vZ+N2fzlIIimm1zR2f5GylHNKcA97mN1aQXmjhhI7/LhFn4qsWD1leuo4KmmrU8dWI0mFGDJzoYHGM23Mvu+i+64N97smtHbUYZOpitxaE62XbMx96KKz34b7uAXjrHCbKw8Kkr+4uQb3bbe/FuK/vHA7blI+Rc1A+2oV7i4H3aMeur+zMbOtivJFmYmmDfJ3tvrrQWxdg6V8/dImFVH9iZKFcD5yTuUKE80X2pgjn0HZ24bzUAnF2HMq4qSirjLh7ncxN1+KbE3mMSByTUaj2Uu5czR7gs+0TNBhEJEn85w6jkvaaunaKpFUpPGWrvGkbaRtJAYkBpYaBiSpyH3/E43qlhqQZX2kc5UYWBoYkKQiSUWSisSAxIDEQGoYkKQiwZQamGRPcWn0FKUdpB0WEwOSVCSpSFKRGJAYkBhIDQOSVCSYUgPTYvaOpGzZO5cYWBoYkKQiSUWSisSAxIDEQGoYkKQiwZQamGRPcWn0FKUdpB0WEwOSVCSpSFKRGJAYkBhIDQOSVCSYUgPTYvaOpGzZO5cYWBoYkKQiSUWSisSAxIDEQGoYkKQiwZQamGRPcWn0FKUdpB0WEwOSVCSpSFKRGJAYkBhIDQOSVCSYUgPTYvaOpGzZO5cYWBoYGEkqwz4FPxjmdrGMGfcJ+jTrkvtyA50jLVTOi37qX1TGiaofsYt/sApdmaxerP5p148E/WKhAvjUH4w9Qz5p/7ESmtttdA6TUAlt2P9moGBO1x7WLtF0WvvST/MLtGOsfJebaD4fhCJ4uw37OyUUBkIRDLYvrh2KkkXl5fgw38Q29pfiw0Nkrq2hAxfOpuyCzgZtxyUmms+2MUtCJewOQhFwQfxY3ZRMFvo3m+gc9+Afjsdq7qvNeLzstVDksC2KKyJbRC6rI0lHtVe0fmnn4+uXxvU4dmPyRumF5RmWJpKK//sGKmuDQFX9tIhC6+Q6gLgGpO0UeRmKZ8D+wIV3V37iePcnqn5LlVQy5xTDIGcEK9eU0HzdRXdHCTnOSah/VoZ31MXsUxbKN+gwN1tw3vXQ3TZd/BXefknXadhXtB1j5SNByeYtlIOgae4+D93nFsYaYu0b1g5GKt2flqM2ud6Ce8SD/dmFsY4UrYD6rz10/7eMQkxUVuVsE95hD3NbrWjQtCeNSNA0pV5AdasN/6gN92V7OKn8bRP+h/WF/uXyXCRgnyiuROX2dZfQ3pxg/dLOx+qXVipqNyYvCQcs37A0mVRS6BEPEz7t/RPltBUlA/PHDrqvWygWJidPWr/XLeQ5hzptm8nzS5VU+LYpuSzMnzjo7q3D9EMHRgKSmT924b9mocBHD720glm4aF4V5uXLS/M6DfuKtkM0H2lf4V9s+B/UoGsh5jI319FFC1Y3vMd0MaodfVJ5VIuMOHKbm+geacAc6BSS3mzhzha6sNG4PN4GouGd1Rvr6L5WQ3lFBrm/aQ4llfzXW/D3V6GN8X4MwxXRiahcklekvaL1Szsfs29aqajdRPWSVK9USIU6z+0mCmst2G846HzoYGZrBcayKDjpMGwNF8nv7TacB+OH9+pHDdSfamH2gIvuPhvu90zos9HyqNxXy9CusmC/6aDzgQ1viwW9E82XpITB3zPX19A5bqN28XTl0Pr9ogwtUS8q8hst2D+30TkURGB8pATNCR2JUtJhQ3w6YzH1R5yd8YiN7rt1mGdHdagoedR+6aHzQDHi7JSMhvYxD94dC6dd1BUm3H0k7LExVSRJZuc07CvaDtF8pG5kmoc6WTW0u7qxRkmlcmZ4T6Qd5F1TW1nkqqH+FSWH6qseuv+tLxh90xHPQQ+d/9IjowQmi5LjEx78Z4zI78oZJjzSGVgX1k+dyyEXECPtxQ+Z/sp/owV/X0WYVEbhitRTVC7Jm9Rekke0fmnnI7rOXWmi9WIwDbqnDft+A4WYaUZmn2HpOHYT1cswWey+EKkYJRUq/6erlOlZIT3n2UR7SwUWiSX+5WA645Uy8hkObMtNuIfd3vCe5GMx0cnwnnuRlLaO9m89dHZUYX1ag3FLGe033d7IQQ/Lo3J/2YT9dBXWX/ZimHsHPHR3DJ8uYHUeliqVIprveJi9txh5eYblH3W/V79Wol4y11Qxd9yBe6+J0gYN5lcqcPf32sFGOUo2i+IGDcYGDeVHbZBpyWrwP7lX5Ih0UfVHerx/10JnXwPl5aFDY3oijqH8Mw/+tlJ0yuRM4pw82J9b+ExaMe9JHdKyr2g7RPORuqkfNeEecuHdraHgZpBboaP5movOvBGZPpy0HepqC7Nw0Fwb1TFxPMbDNvxDDZTnFKhxU19khDnvwZ83oCp5VHfadN1F6Zhw4aG5IXwvma1JOopUCne14b9ToesnlAS5kStfBm1vAq4G84+SK9JeUp5o/dLOp660MHM0mAYlfnKz1fMH89FpxsE2x/1PSUXQbqJ6iZPD30skFX7BlV37282IQ6DO83AdZi0Elnot6WG1UV0e3qM9sb1VaNyUknpdkO/cMF/uK2TIXIdZD+8pZ5G45200Phm+EFTuwFA+e2sDPpkucMNn+QaPuiZK1R+y4e+pQg8WjWldUIcxxhCdyRDVS+4TBsobCxESy/xV0I6Zhe1Imv5aLP2RdlMQb/FANnN0f9OG/e8GitVoG3K3NtCFA+fviyh4GeT+XEd9hwsfTVQ60by0zGUGms834dyjRXDH9Cyapm1f0XaI5iPtyKw2Ye/tjUh9uJj9YQkFI6qTSdpBnjEedeDvsVDkOnpUv10T7jEPs9vqcH/jwicdv+eqMM8N3zVq175z0tGGh879RUxFKt9sw99bRflLNXgHXPjHXMztrMFavXATAZWfgCseByNJRaC9pKyCYP3Szqfd58B/pxqdBr2hNw3Kj1iV3EBnn+/4B/5qHLspgnrh9Rx3nUgq/qE6KqsLKPJ/52cjw2fqPHdZKHCOV1kWDIvX916IPii2DgyfB/KRSmr3OvAHyourPJX7qwG5l1XQgY3aiuiLGPf84D31igpmj9torg9fpqlJZaAdg3rh60B0pBoBUK6q9tpx4cJ2JJHKYumPtIVOcZ5bQHFNEcamYMT1s+gCPBl1lR4g8/c959nd24L3rkd75BkOQ7xu0rhO276i7RDNpy4vwdnvYub7Jsx1GvTPltHe7WJuYNpvknYoNQP2YQ8zd+Uj045Er8V/bIMQ2MwPArmfs2DvceHvrkILCG0c58TbapRzL36LLOY78B4wUVpXhP4ZE+1fu/B/W0eJ61CS8kRwJSxXoL1UL4L1E22HSL6+n9xmIseTxAXlBdOMmS814nfPcZ3gcewmggNex8Ouk0lFYKGeOvfBbaMDw+Jo40JHGdfTiSsvrgFx+ZTVFubQRm1lKCPu2bh7xn/0FpCLFRWZ4C//tRZ8zkhxzw27F1u/Ab2QZ5VZDbUftjB30I2AxCfkOAGpxMmNq2Ncvmn0FyejtzXRQf2yqD3odEc9i8KFORQ3k16YjfpFIZnHlTXtvbTtS+oj2o6kfPT9eNyF/+rAlPGaYAPDulA3k7Qjd3sL/h9asAZGgnRB/yUP/v9Ep6DVVYHcT/XkRt/fPKqv2HA2Z6caqdCedjE6la5eWsEcWaO5JoqXQdsPwxXLN4zMRNtLbUtGAgL1E22HSD6mZzYrxKeDRzlUOx/t7Pc7/uHuOVZef9pyiN3G0QvT8bA0PVJJ6JHTxpHha9ojlUEym4ZUHo06dWbQqUglUS9ZlJ9x4b9VR+WGIjQGjM31kzNSSUl/xL4ZO4usFTo/+mIGJGrfFL3PAKnUdbT3e+g8Fr9AzPKlkRop25evk2g7huWji+ive+j+Z3S3lmIZdIMGv4Fh3HawzQLdp6KzBKT+7LcFGyeyOt044d6eoyMb+v4KLtTzehnm3Pk8/LVil+gajf3F3hTYpLgaJle0vXyd+OvB+vG/8deT5uv7yR0WdOYLuDTPLTHw8oZdi9ptWr3w8tMjlcE1leuHralUoOXDXkh/TeUj4b3+mgCnwN6air1wTSUlp0iUkjlrYJpvdQHGt6ccqSToRVGKaP5u4bRE5iay7hA/UiEL1+Tw47B1nsXQH12Q/ik5UxE9k5Khu5cWjlSIvgng9QeDBeLZ0P48QGk+X0fzuSbsu4tTramMa1/1PAOtF5pwvqNHNpIsqJ9oO0bk6zuTXRaK3IK1urZKe+6t60NSHrsdn+j1/vkyWBuoXLLFe+fACOlisqjvorkhlDvO1lRW/nDnriJ/jQnrM/nIWiKZ2qMjlf4IKYPymLgiskfJpVvaE9pL9CJWv3TzkbrTNZVdlcjal5JRkSHTYTGbKJiuh6UidhsHB8PksPvJpCJw+JFOo7zVgvN0sPsr2NXVfaWMArcoqJ5vwjtCtoZaMIPDXc57HjrPRofeim3APuCh82KF7uoybjbRfiPY/cXt4T8Z0zdTr6m82YI9Qi9k2Fne6sLf20DtCzr0DTrKd9Vg/6RJzj3HTn+pV1cp4dh3GnQ3GNn9VeAJeJH0l7mqitnjLmYeM2Feq8G8vQqX7MYbcqhR/ZiF2eMevG/kI7sJGThZmubuL1YmS0fZt/jPvS8E0I0fMedEWBmi7UjKp15ZpY6cHn6ku34qoIcfd5ZR4DpiTC6fDmsHcRal77t0q7LObZDhn+0fznwiONR4qwVnL3n/SshzTkw5pwSXHM7cWokeftyiR8ieP6xoPtyGf6SBanBwWjsnJKnczWQ07sC734S5QUPpixbs3cF7zm1OEMWVqFzR9orWL+186ioLM8fIGlcZ5nUaSreYqG+z4e+rQi8P73zxNuWvRe0mqhe+7LjrRFJhU0B8Oji3R537q2Xo11hwdjnoHAzOqZwRAogIp4ttV5hovhDsv97bhvNQCcWYz1BkLtTReDo4p7LfhvdYGfpAb/aUIJXtJopJepnTUHuihTny6YsDNtxHSiieT7ZqxpMKISLjn+qY2U92TJGdVg7qq6NgWwz9EQeWW19Ge4eNzkEXnbdaaJPPr8Tsr1eyOVR/ThaDK9C4beJxIFVXkZ1/Lubmowv+cXnHvTfMGZNy1DUWvN+6mHvWjBzU5GWItkMkH9XfunJ4PoGc43rYRHHgoCIvn10Pa4fS6i3Qz949nLjpe/lx7rM5e1r0XMTgrj2a71LunBnJd6+BghXF3rDPqhAfMntPob9RgLS3cLOF9ivh+Sz3uyb0mUG/IYYrcbkKskLtFa+faDtE8+XWmmi9ZGPuQ/IZIxvu4xaMs6J6YbZPSkXtJoqDJHkjSSXpYfZ7nHNnv8l0aXzkTdpB2kFiQGLgZGBAkor8SnG/53gyACdlSMcmMXB6Y0CSiiQVSSoSAxIDEgOpYSAVUpE9j9O75yHtK+0rMSAxIIoBSSqyh5JaD0UUdDKfdFASA6cvBiSpSFKRpCIxIDEgMZAaBiSpSDClBibZ+zx9e5/SttK2ohiQpCJJRZKKxIDEgMRAahiQpCLBlBqYRHsyMp/s9UoMnL4YkKQiSUWSisSAxIDEQGoYkKQiwZQamGTv8/TtfUrbStuKYkCSiiQVSSoSAxIDEgOpYUCSigRTamAS7cnIfLLXKzFw+mJAkkoMqZCvp+avNlG5Q0ee+wS3fBFO3xdB2lbaVmIgHQwkkkrmAh31+eAT9O+04T4i9inuQQOpN9ajoXKP1WKDTInmGyw/zf8VRUPzQ/JJeQ/tT0/2uek06/PHWhb9FPclJprPtjFLwgLsDj61HvMp/T9WHcl2p+MIpR7T0+NIUqHBst730NlRpcGySl+w0N7tovOiGQneI2IQxclDDwL1mA+04Q8hFdF8IjInzUPjWlyio3xTETkuKNik5cnnJgOscrYJ77CHua1B8KjbKnBJ6OEnjUhQKKnfyfQr9Sb1diIwMJJUcne04B+qw+SiCqqXVjALG/VLooF5xqkcLXcIqfDliObjn5HXp8+LIhIGVdr79LG3tOXpYcuRpKI/4MB/tYy8EhIIiZWcW55HrsrdIxH/rjTDiHV72jRyXFzEPwIcUbJIykcjx/11Fc7rNrqHPXQ/sOHNV2CeN9mUldIwSHDT/jSdjzYq54TtZKCnQcm2myiss2C/6aDzgY2ZJyvQOxPKFdSfspLEDW+jckkBVRIpkkRX/HULzdsKkTjfwvkUBdk1XCQ/EmnwwRIKA5E4Rctj+lFXmHD3kbDRxsjY7ix/XEpsaz7hwX/GiLbtDBMeiZ2+bqFd4sqR904PRyXteOrYcTSpPOLC32kix5FKnHHVlRZmjnoIY2tbdJqiOx8/TZFEFkxGUr7Muho6cOHdV0Lpag36xhIaO1z479WgJ4SoZTL4VMlnUQim6PSv19EdRSq/aMJ+OpgWvK0Cj8Ri35GsK14euxbVH3Pu7rYGml8zYFyno/ojG104aK4LCU00n7rchHvYDe22yYLznofOc2aEDETLY+1JI6Y8JZV5D/68AVXJo7rThrMpC6VDwix7aG6QpML0LdNTx+H+MdgqFVLR7nPgv1OFzq0/ZG4gTrmFypkLX/4ksmCKT8pX+Ic2/INV6BzpKZUsiivzyE25a0tZWx1NKkcaMLnY4dlbG/DRguUtbC9rz7BUVH895+7BuyPb3was5Auo7/HQfVgL79ERTXI+EtPb31uFVgjrrF5Xo+2unhveE5XL2qcsM9B8vgnnHm3itY8oqehow0Pn/qIklZjdikzvMpXkshQwMDWp0Jd/iwd/m4lcSYXK/i4oD52mSCILppikfOr6Kjpw4N5nwvyLIopnZ6ByBMPKmSRNJJVfWShwspTLKujARm1F6IxF5I6jv55zd9C4PJRBntfvacH7ttafJhLJ15e7dWB6aVkwvbSek0FJKlmuSHtF89D69UcqklRE9SbzSWJZbAykQyrz4TrEMsxwaxLx0xRJZMGUkpRPUTIobCJrKg66wVpI990WmjfmpiaXRFJ5Y4BUVluYQxu1laEzZu0YlTLnyeuNXZMtzfw0DyOL+urRMkTyMbm96aWwvLjpJZHyRrVxkt+i9cuj+ooNZ7Oc/ppEl/IZSTQnEwOjSSVuoV5Vke1kkS31HBF9+clIZYcFfXUBxYG/PLdzjDUsiSzGzUfyq2YW+Ys0WD9qwz/aRHmCaSgml6QnlVQE9Sfq3EXy9e02xkglicx4/U17TesnF+r7U5rT6lM+L4nlZGFgJKlQ53+oAZM7bKZeTHYgOaivCXu3dE1gVwXFTHiP7BLLkKmwbHiPNYquPxyvoxTzG8tD0lH5yMG43Koi9DXRUYlyMRkxTLflmcg+WaRCZInqT4QsaN2D6aokEuitqVSg5UMb9ddUPhLeE5VLZFP5vo7mc03YdxcnXlMh5cgtxdIRMkzJ9NTBwkhSURwD9vse5rZX6OFH4/Nl2G+56Owwkc+FTkddZWHmmIuZH5RhXqehdIuJ+jYb/r4q9HKYjwGj56RcuP9q9A9E6h/P9tcERPPl72ih+wcb9rcMGOs16DeU0HjJhX+gBsNaKJeVOyxV/UJYn2D3V/PzWu8eVz+6pTil6S9SF1H9iTp30Xzq+Sa8I2TrrwXzeg0ltvvr2bjdXw6SSIrpNY3dX6Qs5ZwS3MMeZrdWUN6oocQOP27RpyIrVk+ZnjqOStrq1LHVSFIhhsxcaKDxTBuz77vovmvD/a4JrR112GSqIrfWROslG3Mfuujst+E+bsE4K9zmyoOC5C9ursF92+2vhfgvL9yOm5RPUTPQvlqFu8tB96iH7u9szGyronxRZqJpg/ydrf56EFvXYClfv7RJRVR/omQhnI+cU7nCRPOFNubIZ1D2tuE8VEIx9pyKOKmoq0y4+13MzZf+f3vf/ixHcd3/R0zN1j5qd2p2tmYftY+Zmrn37k7JBgwqDAmSYxAC/ANlIRGUACIuB4PthBiJchkSl0m+oVJxHgSnIIUxqBxiHAdsLF8jbAECVI4hxmBkg5ABycJIsh7W51s9O73zuLM7vbtzdVfi/HCr58729Dl9zmf60885oa3JQQyIXLufabk8cI5mn/eZlgk6DCLyKM+Z03CRr2bXV4mkQs6bXeeRb8g3hAHCwKxhgEiF9v1PNKqbNSCTPtS4EgZmAwNEKkQqRCqEAcIAYSA1DBCpEJhSAxP1FGejp0h+ID+sJAaIVIhUiFQIA4QBwkBqGCBSITClBqaV7B2RbOqdEwZmAwNEKkQqRCqEAcIAYSA1DBCpEJhSAxP1FGejp0h+ID+sJAaIVIhUiFQIA4QBwkBqGCBSITClBqaV7B2RbOqdEwZmAwNEKkQqRCqEAcIAYSA1DBCpEJhSAxP1FGejp0h+ID+sJAaIVIhUiFQIA4QBwkBqGCBSITClBqaV7B2RbOqdEwZmAwMjSWXYp+CjYW5Xyplxn6BPU5fcF5roHm+jekH4U/+iMpZLP+YX50gNijSZXlz/5dKPlz/Mfu4n7S/T0Hqmg3n2yf03vU/aB4LB8TKWMx2mn6hMtx5Xamg954UOeKsD4+tlFCKhA3J3tWJDKjj7dRRH+FBEv8z1dXRhwdyaXdI5ENWPBWvjIR6CqROJGSRaD9HyRPSTpCyqP4kPV850NW7zw1ywEBL563V09nghON5oo/NvKvJelNphfh1mZ9HyRPEsapdhek56X1S/YPmjcBXMF3edSCrO75qobvACVQ3SIgrt6Rq0OGXGvbecjaJkqzDet2Dfk5843v1y6XcmkMoo+0nnarCP2VjYqYeDb/1APW3Bt0bpJ4pD+Q8qbj3mF3VUvCBn1gEbvWfDsYFyf9OCc7ix9D26MrckMB2XLaKfVCqg8Ssbvf+roBATRVVUv8x5RT84HXvHryuj9aqF3p4ycgHSE62HaHki+nFS6f2oEtbxRh3WcRvGZ/yYTdktdSzAgr1dc4MFanfVYR+2sfCYMpGdRcsTxbOoXTgG0kpF9ePyknDF8w1Lk0klhR7xMOHT3l+uRluSMtC+Z6L3qo5iYXLydPV7VUc+8GJOW2f2/KyTSpL9VjpMcJJ+oj4q/IsB5/06lJKPkcwtDfTQht7z7+W/0oZzsIaSIA5E9GO9z8LdbfRgoHml37AGdRfVL/iMlMtC+76J3v4GNCdc7rj1YOWOKk9EvwGpPFoKjcRy21roHW9C8zq3br4XbfSeUkMEwkZXDprQYgK7jbLzOOVNgudRdgn6I43rcfQTwVWSTqmQitt47tZQ2KDDeM1E97CJuZ1VqKvCoHSHYesDkfze6sD8xtLpAqa0/FEVjR+2MX/IQu+AAetbGpT5cHmu3FcqKF2jw3jdRPd9A/YOHUo3nC/JCNHfMzfW0T1loH7pdOW4+v2sglKiXWTkt+gwfmqge9SLwLi9jJLpN0xSWYEBsWmAWbYfm1LQnrThPB1++aUPabBhobXRrzP3i3yJBusAC3usThVJkpeXln/ZdIZLFrKvs7yl7pJK9cP+vfzfteEcqAqTioh+7kjmiI3ufw/vhYvqx+3CGll1u4HeOw1o5y7F/rj1SCpPRD/WZsjtLHI1Xx9JyqH2io3e/yiDWQQmK/+JEkrn+flYvaQ/Zf7ooPYR3x+8vqPsLFreJHhOsgsrM3e1hvYL3rTqvg6MB1UUJpgeHlc/EVxx+w1LhUhFLcuQg3+KDOZsXmi/8Wyhs6MKncUS/4IO8x0bvZcryGf8fPJqDdYxC+50AcvHY6Kz6YLAiyl1FHR+Y6O7pwb9UyWot1bQed3qjxwUvzxX7i9aMJ6qQf+zfgxz+5CN3p7w9APXUySVqkW03rYxf38x1OMReTaap69fO9EumetqWDhlwrpfQ3lzCdoXq7AO9uvBRzlSNovi5hLUzSVUHjXApiVr3v/sXjFApLNsPxfkizacRRWylEftJcNdD5C6Gpu4QGuz719uz7Ri3rPy0vSv/FEN1lEL9r0lFKwMcpcoaP3cQndRDU0bFe7pwHm76q6fuI1kbmkdeV1F9GM2VB8x4BxtorIgQY6Z+mLliern2oWNfP62je6BJiqrww0z122segiUN45+XAe3Xut0zMNEa0O8nsG87qj+eANlNWxzETsHy+HX0fLGxTPzf5Kd5TU65k7Yfju5Te+3B4vjTw+Po58orrgthqWJpBJcuOPXzm4tNPftNp7HGtDqvuPk670ewmr/ntsz2V9DKTClJN/g5Tvfz5f7YgsOK6/h35POYXHPO2h+0geSKzcwBGaVzN7ehMOmHyz/2WGVj95nRlUeNuDsq0HR+s+7uqABNUCi0eeG/S9ql9wnVFS2FEIklvkLrx5zS+uRNP01y/YLg1xBBza6DxYxilSkVSpaz7Vg3lcK4W6Y3YfdT9u/TE5mnQZjf38E6cDC/BNlFCINWOEfOnD211C5rQ77kAXnpIWFl+rQ1/mLzKwsUf2kngbrpI35XQ1Yv7bgsI7aszVo5/vvBreBiH5ctrbDBtuE0/t1B8Z/qCjWwtgTrYdoeSyfqH68PsxG6qMmnH06ioEOK/89mErlIlr7bHRJRKeWAAAgAElEQVQfLYXeLVE7B8ti13HlsbK0QScpGc9u/gQ7lx4w4bxdC0+r3tSfVg2OgKVcpLMf7Ph77dU4+o2Dq6htgv8nkopztIHqugKKwb8Ls4NhJyvMbTz36igEGl5plTedsakPzIExd0amPSL5WHml+004kfKCSvNrV+4vI3KvqKILA/VLwi8Ef2ZUKl9VxfwpA61N/ss5NalE6hG1S1AfZiNZ9YByTa1fj4uX1iOJVGbZfuOAPGibNK7T9q+8ugzzoIW5b2vQNpagfKaCzpsWFiLTdMWvGnBOmLAf0lDeWITy5xo6v7Lg/KaBcqDjJKpf8R87YAQ29x1P7md1GPssOG/WUAoQmqh+zLasB509v4Di+iLUrd5I+cfl0AyCaD1EyxtHP+5/qa7COGZj7p78YKaE/xZMJTmL8rcMOO81oM2H3yFRO4uUNy6ek+w8aCd3acgFSeKiypLp4cxtzfhde4FO8Dj6ieIqaJe462RSEViodxv3yPbDaM8zXDnfydF8TMm48uKUj8snrdOxgA7qa3wZcc/G3VP/04Lzcx3FqoyM95f/chtOwElxzw27F6tfzDSPNF9C/Yk2Fo5YIZA4jBwnIJU4uXE6xuVbbvuFcZBH7WUD5rbsyJFKnO6T3EvTv249HrfgvBKZ4l1fxby7NuR3TNweZTE8ZSxfXnV3KrWu83Eqoh9fQHb+NzxlLK/15P5JX+44+sXZsr+l1ETjCl8/0XqIlDepfrkvteH8vg296+sVlSdlslAf6aD3uw4aV4dHgyyviJ2DZY4qb1o8R+3My+OzQsE0epRDNvLhzv6g4+/vKuTlDaabh7xvorgK2mXYdXqkktAjdyvHhn1pj1SiZDYNqTwabtS5Q6cilUS7ZFF52oLzRgPVm4oocWBsa5yekcpptp+LgzEX6oeBd9z7aor+dReLX7XR+6/wriRJV90NFfadS8+NBPWVjLK7hmT8pd/oiegnSXnUf2Gj+1Ax1FOXsgo6J21YX8q590X1Y/7IGFlkdZ8EmZ68s2fcHL4frIObL1IP0fJE9QvK43Xv/TA82xHKo+RQecJA70gbjU/6tg3mEbEzzy8llCeKZ3G7yHCnIffoUHhbEEjzgSUGruOoVFw/MVyNksV/S49UomsqNw5bU6milPd7GYM1lcDujMGaQMCA/TUVY+maSoqNYuacyDTfugLUr005UkmwiyQV0frt0uF85uamu100bqTCFq7Z4cdh6zyzbr9xtjgyoEqOgtazLRj3FqdaUxnXv/IFKtrPt2B+XQlNA7k6sbl01knaq6MYWHiXN9TcEUj7Rn/EkL9Og/7n+dC8PpuCYWcqWt7IgpUpop/bSHzPgvNSZIR0KVu8ttDa7MsV0y+Dyo/Y2ZrwmZSMu4vNH6kwuSL1YDubRMsT0Y83VCyVP9G3Gbdt8Dd2LdULqO024Rxsorp2OBmK2Hmc8kTwLGoXJtddU9lbDa0ZSRkZGTYdNmRTRtQWwf/F9JOhCeAqWO6w62RSETj86E6jvNGG+ZS3+8vb1cV2fxUCi2nyhRrs42xrqA7NOyxmvmuj+0x4KC8ZKoxDNrovVN1dXeotGjqvebu/AmcC0p6+iTPS1Gsqr7dhjLALG3ZWdlpw9jdR/7wCZbOCyj11GN9vsfPCsdNf8rU1l3CMu1V3Nxjb/VUIEvCM2086rwzrmI35ndXw4ccdSixppLn7K+rjUf4t/nP/pLm78SNw7oSXIV9dcxvywW7GbVW4hx9fqqAQ6DjlbmGjThP2gxq0zSWU/1KH8aaH58AaCC83mMbp5x4aPGFh/knv8OjtOsz97H0pIx9odET1y1xTw/wpC3OPadCuL0H7Ug0W20W5K7ymIloP0fJE9WP2YKRW/rblbuFWAht9uK3c6ZsXLDiHO2htHf+wdtTO45Qnimdhu6zVMXeSrZlV3EOc5Vs1NHYZcA7UoFT8Djmve1Iqqp8orpLkJZIKnwIKptG5Pbdxf6UC5Tod5l4T3SPeOZUPhXsL7iLVVRpaz3v7r/d3YD5cRjHyWQumdOZiBc2nvHMqBw3Yj1WgRBbczghS2a2hmGSXhRLqT7axwD5ZcsiAtb2M4oVsi208qTDAq//UwNxBy92t48BEY10YbLNsPxcHlwfOK+3zPtMSc0CNYUFey3b+WVhYDDdySeAW+T3amASfkdfrsH9jYeEZDYXAaITnYQ1dbmPFP0/Azl09oqEY+doEy1e4RUfnZf8ckvVNDcpc+P3g5QbTOP1c+328jNZuA91jFrrMfg8u3a01jn65TRX38ybdIxa67PMm/770XIRoPVy5guWJ2I/ZQ2r3F+jn782HjjNwW0lSAfU34s9xRdsr/kwwjdp5nPJE8TyWXTZoaL/ofW7moAHrcR3qOcl4CdaJX4vrJyErgCte7rB0JKkMeyh6P65xj+ah/2fjY2/kB/IDYYAwsJwYIFKhrxSHFnyXE2xUNjVmhIGzHwNEKkQqRCqEAcIAYSA1DKRCKtT7OPt7H+Rj8jFhgDAgggEiFeqhpNZDEQEc5aGGiTBwdmOASIVIhUiFMEAYIAykhgEiFQJTamCiHujZ3QMl/5J/RTBApEKkQqRCGCAMEAZSwwCRCoEpNTCJ9GIoD/V2CQNnNwaIVIhUiFQIA4QBwkBqGCBSITClBibqgZ7dPVDyL/lXBANEKkQqRCqEAcIAYSA1DBCpEJhSA5NIL4byUG+XMHB2Y4BIJYZU2NdE89dqqN6pIJ/waXJ6Qc7uF4T8S/4lDIyHgURSyVykoLHofYL+7Q6s7Us/7S1idPnTjXCo3JP12CBTovlEZE6aR5JKaB223c/Kdz412eemJ5VNz/UB7Maz+En8p8xZGAbjtviofmS/8RoAshfZK20MjCQVN1jWeza6e2pusKzy53V03rTQfUELBQMSUUoy81A29IPnaA914AwhFdF8IjInzePGPbhMQeXmInKBoGCTlkfPjf/iclLp/agywI2Lnxt1WMdtGJ8hsidcjY8rstny22wkqeTubMM52oAWiCooX17FPAw0LgsHhRrHWW65Q0glWI5ovuAzdL38oDkdNh6QyqPhGPC5bS30jjehRQJhnQ6dSMbZgS3y4/L6cSSpKA+ZcF6pIC/5BMJiJedW55GrBe6xCHhXa34EvH0dNxJdoernCTpSlCyS8rERReGvajBfNdA7ZqP3vgF7sQrtgsl6sVJTZUFfB9N0Djqonre0Dm5Qst0aCht1GK+b6L5vYO4HVSjdCeUK2k9aw+KQd1C9rIAaixTJovT9qo3WHYVQ/HPhfJKE7PpABEYWufAbZRQikThFy+M+li/RYB1gYaPVJbHdeZ6klEWrk9tZ5Gq+TSUph9orNnr/o0AOYDKpLPp9eRsRsi/ZN4iB0aSy3YLzkoZcwgssr9Exd8KGH6tbh3XQRm9RjY85zkZAKYxUMhvr6MKC/UAZ5WtLULaU0dxjwXm3DkVZSgbBisddS/ksCt4UnfKVBnqjSOVnLRhPedOCd1Rhs5jee5JtFSdX1H68cbd2NdH6sgr1BgW17xrowURrY6Dx9cgnKZ+8WoN1zPL9tlWH+a6N7rNaiAxE5fK6LVdMeXkdI1UTrQ1+XblMSqlhIwzMBgZSIZXSAyact2tQAusPmZtYo9xG9cNLG/ekEQgHR1K+wt934BypQQmQnlTNorgmj9yUu7akDbXRpBKZgsne3oSDNnR7aX15fYalovbrN+427Duzg23AUr6Axj4bvUf8aSLRfMX7DDj7aygVfJ3lG+puvWvn+/dEy+P1k1apaD3XgnlfKbZTwfONk7JRqfqoCWefjmLG122cMijvbDQ65Iez2w9Tkwp72bUdNpxdGnJlGTL/u6gCGxZaG5c2AElkwUGXlE/eVEMXJqwHNGh/XETx3Exq0yKJpPJLHYUgmV1RRRcG6pcsrS+vT1w6jv36jbuJ5pW+DPa8cl8b9tdKgykwkXwDuTvVwXNMP2mV1vfbpoAMd+STLDeufmndk+oqjGM25u7JDwg1rbKpnLO7kSP/nl7/pkMqi/46BNvuyf8c2Ght9hsn7twkshDNJ0kZFLayNRUTPW8tpPdOG61P56Yml0RSeS1CKut0LKCD+pql9eX1iUvdxl3QfpwsGutGyxDJx+U6i2rIVlJXYxOKIb+JlBdXtzTv5b7UhvP7NvTu6LqnKZPKOr2NEdn77LD3aFKJW6iXZWS7WWTL/Zd70OPdo0NZV0Ax8pcP7BzjoEmLVHh5LJW1LPIfK0H/bgfOiRYqE0xDBcs7raTCRnoC9hNt3EXyDfw2xkglicyC9kvzWpLyqP/CRu+H4VFVmjKorLOjQSM/rrwfR5KK2/gfbUIL7OKSL+0vljbW+z1Gd01gbzU01812iWXYVFjWz8cd7q4/nGqgHPMbz8PSUfnY7qDc2iKU9eFRiXQpGzFMt+WZyT5dpMJkidpPhCxc3b3pqiQS6K+pVFHK+z4arKl8xL8nKpfJduU7ClrPtmDcW0xlTUX+RBULsNC+kRbouY0pXfnGk3wQ74ORpCKZKoz3bCzsrrqHH9XPVWC8YaG7R0M+5zc68lodcyctzH2nAu2GEsq3amjsMuAcqEGp+Pm4E/qNlAXrX1X/YNvHs6G5fZY3KV/+zjZ6vzdgfFWFuqkE5aYymi9acA7VoepL5XL5w1LZKfj6eLu/Wp/rH9hUAvq5W4pTmv5iuojaT7RxF80nX6jBPs62/urQbiyhzHd/PRO3+8tEEklxu6a5+4uNqMrftuAcrEEJbCjgsiiNf7HJLmSXlcLASFJhSmUuVtF8uoP59yz03jFgfVNDqRNusNmLn9ugof2igYXDFroHDViP61DPie9ZsvzFbXVYb1mDtRDnJ0u34yblk+QMSnfVYO010Ttho/dbA3O7aqh8bLJPeOTvbg/Wg/i6EE+D+qVNKqL2EyUL4XzsnMpVGlrPd7DwvoXu/g7Mh8soxp5TEScVea0G66CFhcVyaGvyJCCX2v0F+vl782Cj00nKoGeogSUMnD4MJJIKOeP0OYNsTbYmDBAGznQMEKnEfKX4THcq6U8NE2GAMLBSGCBSIVKhKSXCAGGAMJAaBohUCEypgWmlekYkl3rlhIHZwQCRCpEKkQphgDBAGEgNA0QqBKbUwES9xdnpLZIvyBcrhQEiFSIVIhXCAGGAMJAaBohUCEypgWmlekYkl3rlhIHZwQCRCpEKkQphgDBAGEgNA0QqBKbUwES9xdnpLZIvyBcrhQEiFSIVIhXCAGGAMJAaBohUCEypgWmlekYkl3rlhIHZwQCRCpEKkQphgDBAGEgNA0QqBKbUwES9xdnpLZIvyBcrhYGRpDLsU/DDwgSf7krEfYI+TR1yX2iie7yN6gWTfXJ9ufRjfnGO1KBM+Sn45dKP+2CU/aRMFso/tNA9ZcM5Nn1duMxx0lH6jVMOy5u5vo4uLJhbs0tImn2yP/tHZbR2G+geY6EhOjD+XUVBG42rYfrl7mrFhmhw9usoBjAhKle0PFZPEb+JynXzXaah9UwH8yz0wpttGPerKASCAroymf2u1NB6zgvR8FYHxtfLKERCNIjrJyN/vY7OHi9UxxttdP5NRd6LZsvKGUcuC3bHQ2QEUycSc4mXy9Nh/uW/p5WK2jkobxSeg/nirhNJxfldE9UNXqCqQVpEoT36hYgTlva95WwUJVuF8b4F+558KIb7OHVYLv3OBFIZZT+pUUBtpwHnhAHrJ8aKkMoo/cbxMcsrlQpo/MpG7/8qKMREM5X/oAL7hIX5H+qo3KRA26bDfMdGb9fweDOj9Mv9TQvO4cbS9/LKXCjQnahc0fJE/SYqVzpXg33MxsJOHZUtJZTvqMI6aKP7AzUUMdQt75iN+UUdFS+YnHXARu/ZcAwmUf2yW+puJFF7u+YGFdTuqsM+bGPhMWWp/QTkZs4r+sH9WBt5XRmtVy309pSRC5B8EFej/BvMl8a1qJ25rCQ883zD0mRSSaFHPEz4tPeXq9GWpAy075novaqjOEW0QVe/V3XkhwBr0vrPOqkk2U/+dAO9n9dRuSSD3F+3TjupJOk3jl9YL7Bwdxs9GGheuTQoHQvApn3PgvNzHYVgtNTLq5iHhdY1cc+Mxl/+K203EmZpBK7GkStSHrOJiN/GkVv81w6cd2pQSn4HNfOpBnpoo7rKv1f4FwPO+/Vwvlv6+fSen09MvyyqL9roPaWGCISN1hw0oQUixorKDeJFymWhfd9Eb38DmrPUtyxvmvgLyh52LWrnvm6j8TxMRvB+KqTiNp67NRQ26DBeM9E9bGJuZxXqqrBR3WHYeg2t3f4w1vxG/DBW/qiKxg/bmD9koXfAgPUtDcp8uDxX7isVlK7RYbxuovu+AXuHDqUbzhessMh15sY6uqcM1C+drhxXv59VUEq0i4z8Fh3GTw10j3oRGLeXUTL9F0YqKzBgxw6z2ZDbuC0c7XKW7Scv5JDzGhK3l5ww/SVfosE6wMIeq1NHkmT+T8u/rCy3x3nERve/w71cjjNJyqP+Cxvdh4qhaTEpU0LnpA37zqXTZUn65f+uDedAFaNJRVyuSHmsPiJ+E62vSz5P2nCeDjfu0oc02IxsN/rYZ9NLLJx0SfbvyVvqffL5cOCeAK5Yg57/RAml88LvtvSnrLwOah/xyxOV6/s6A3W7gd47DWjnhsvneVia5F9mm9zVGtoveO3kvg6MB5dOCwbLHHY9jp1ZGUl4HiYneF+IVNSyDDn4p8ih0K79xrOFzo4qdDaM/YI3vH+5gnzGd5K8WoN1zOoPY1k+HhOdDWMDgJE6Cjq/sdHdU4P+qRLUWyvovG71Rw6KX54r9xctGE/VoP9Zf/hsH7LR2xMeFgcrnHQtVYtovW1j/v5iqCeT9Fzc73392ol2yVxXw8IpE9b9GsqbS9C+2J8GYPXgoxwpm0Vxcwnq5hIqjxpg05I17392rxgg0jPJfiKkkmrM+xT9y15Y9REDztEmKgsS5JipL9aIVX5sw9lVDk3pSB9mjacN47PhxkcEf4V7OnDerrrrJ6yjJgdGQByH48gVKY+Xy9NhfhOV6zZ2izacRRWylEftJcNdj5K6GizYaG3233P5oxqsoxbse0soWBnkLlHQ+rmF7qI6dHppmH5c/2jqjv6PN1BWJ5Prjlj/to3ugSYqq8M+DcoS8a+8RsfcCW+6j7WT23R3WrC3GJ4WDJY77HocO4vgeZic4P1EUgkuPPFrZ7cWekHcxvNYA1o94JDrPeZf7d9zmX9/DaXAlJJ8g5fvfD9f7otsSqQBreHfk85hcc87aH7Sd5gr93gTWmB9J3t7Ew7a0C3/2WCFR10zoyoPG3D21aB4i6iuLmhAHTHVMKxMUbvkPqGisqUQIrHMX3j1mFtaj6TprzPJfiIvv7RKReu5Fsz7SiHcDbP7sPtp+1fqabBO2pjf1YD1awsO6zA9W4N2vo9Rpkvu9iZ6MGH+vyIKdga5P1TQ2GPBQQvVru9fUf0K/9CBs7+Gym112IcsOCctLLxUh74uPFoVlStaXtCuo/wmIpfVVRuQioIObHQfLCKOVJjczDoNxv7+SN2BhfknyigECCCom2tztu6UMALmz0jlIlr7bHQfLYXewXHkuvXZYYNtYur9ugPjP1QUa75vWVmi/i09YMJ5OzIteJM3LRgYmUm5SGc/2PH32qtx7CyKZ263YWkiqThHG6iuK6AY/LswG1q8dhvPvToKgYZXWuUNYzf1DTsw+s7IcDeSjylaut+EEykvrgKu3F9G5F5RRRcG6peEHRr3fPSefFUV86cMtDb5jcLUpBKpR9QuQR2YjWTVA8o1tX49Ll5ajyRSOZPsN6pxCtomjeu0/Vv8xw5YAzf3HQ3axhKUz+ow9llw3qyhFGjw2Ciz/BBbd+k3ir39bdjv2G5POxN4Z0T1K36VbXIwYT+kobyxCOXPNXR+ZcH5TQPlYEdMUK5oeUEfjPKbSH3Haezk1WWYBy3Mfduz82cq6LxpYWHEdOgo/YL1kOQsyt8y4LzXgDYfftfGketO7Z9fQHF9EepWb6bhx+GNGCL+HbSTuzTkgiRxUWXJtGDmtmbsdLgT6ASPY2dRPAftF3edTCoCC/Vu4x7ZPhftcYQr5zsvmo8pGVdenPJx+aR1OhbQQX2NLyPu2bh76n/2F1SLVRkZ7y//5TaCTop7bti9WP1ihvfSfAn1J9pYOGKFQOIwcpyAVOLkxukYl+9020/05Y/Tf9x7afpXkvoLvs7/hqdu5bXeAvyf+B0Tpqc7TdXIonBxDsVtrNdpoPGxcB5R/dweajE8BS1fXnV3NLWuC+NeRO445XGbJ/ktSW64Pcij9rIBc1t2yUjFzfe4BeeVyFT6es/OG8M2FNXP9UkmC/WRDnq/66BxdXiUN6lcLr+/JddE4wrfHyL+5Xbhs0LBNHqUQzby4c7+oOPv7wLk5Q2mGYfaeTw883rGpemRSkKP3K0cGx6mPVKJktk0pPJouFHnDp2KVBLtkkXlaQvOGw1UbyqixIGxrXF6RiorbL+kxikOtJPeU1P079AF6aziLsBbX8qFFua5zlJDQYdtm41sX2W/T6OfZJTdtQjjL8ONo4hcnieYJpUn6rdh9XXbA4GFeknKofaqjd5/lUL2lHTV3bgSt9GB1SNJP0nJofKEgd6RNhqfXGozUbmsHhkji6weJjfeWTZu9u+L+HfQTu7RofC2IJDmA0sMQX8Nuxa385CNHQl4jpObHqlE11RuHLamUkUp77P3YE0lsOtisCYQMGB/TcVYuqaSYqOYOScyzbeuAPVrU45UEuwiSUW0fmtj7p586KXJ3Mzm4eNHKmzhmh1+HLbOcybZL+nlZ6CVHAWtZ1sw7i1OtaYyrn/lC1S0n2/B/LoS2kji6sS3Cr8U6UFfqve3Cm/2GxP+4rEXXPmGt7AfmWpheUT0Y2Xkr9Og/3k+NP/PplYW2K6pyAiJ6zpM7iTlsTKF/JZQX5Gtrm6jyDqje3UUAxsS5A01t77tG5faOUk/qV5AbbcJ52AT1bXxz4vKdTcm/IidmQmfScm4u9PCIxUR/zLd3TWVvVUUA5ucpIyMDJsOi9kMwp4Z9SdsZ7b1fQw8D5OZTCoChx/daZQ32jCf8nZ/ebu6ei9XUAgYRr5Qg32cbQ3VoXmHmMx3bXSfCU8hSIYK45CN7gtVd1eXeouGzmve7q/Anva0p2/ijDT1msrrbRgj7MKmUSo7LTj7m6h/XoGyWUHlnjqM77fYRuHY6S/52ppLOMbdqrsbjO3+KgQJeMbtFzwspj3SgXO8iZp3sDa61ZP5JM3dX1Efj/Jv8Z/7J6XdjR+B8xC8jMEhvye9w3u36zD3M9yWkY95+eU/0jF/yob9d/nQ7kleXlwap1/uFjaKNWE/qEHbXEL5L3UYb3rvR2Ath5eXJFe0vHH9liRXOq8Mix0u3FkNH37coYQ6D/LVNZeo3cOP7m6oKtzDjy9VUAh0UEX0c6ctX7DgHO6gtXX0oW5hudfUMH/KwtxjGrTrS9C+VIPFdqGOONzKfRPnX3mtjrmTbK2u4h7OLN+qobHLgHOgBqXid8h5GUmpsJ35IV1BPA+Tm0gqfAoomEbn9tzG/ZUKlOt0mHtNdI9451Q+FO4FuItZV2loPe/tv97fgflwGcWYzy1kLlbQfMo7p3LQgP1YBUqkd3dGkMpuDcUkuyyUUH+yjQX2qYpDBqztZRQvZFsr40mFvRjqPzUwd5DtIGI7Tkw01oXBNsv2G/ZZC4ax+fsKS3pd8lq288/CwmJ44XMYqMe5H/dS8+fl9Trs31hYeEYLHVzkv7t4/njg8yv72u55guiuH5ZfyuZQ+ylbxK+iFNgWz8salsbpx3rRhVt0dF72zzVZ39SgzIXfN1G5ouWN4zeR+rr2uzxwbo3Zj32mJXAA0a0DO7exseKf23irA/MRDcXArk+WT0Q/SSqg/kb8ea9ou8bsIiLXzbep4n72pXvEQpd99oV9hifyuZk4Hw/zb26DhvaL3mdkDhqwHtehnrPUv3FlRu+J21lCVhDPURnB/0eSSjDjqOu4xn1Ufvpt9HCV7EP2IQwQBs5UDBCpJMxHnqmOJb2pUSIMEAZWAgNEKkQqS6abVgKIJJMaQMLA2YGBVEiFwHB2gIH8SH4kDBAGpsUAkQqNVGikQhggDBAGUsMAkQqBKTUwTdvDoeepl0wYOPMxQKRCpEKkQhggDBAGUsMAkQqBKTUwUS/zzO9lkg/Jh9NigEiFSIVIhTBAGCAMpIYBIhUCU2pgmraHQ89TL5kwcOZjgEiFSIVIhTBAGCAMpIYBIhUCU2pgol7mmd/LJB+SD6fFAJEKkQqRCmGAMEAYSA0DRCoxYGJfHc1fq6F6p4J8zKfEp2Vyep56g4QBwsDZioFEUslcpKCx6H2C/u0OrO1LPzktYhz5041wqNyT9dggU6L5RGROmkeSSmgdZp+Ut9H51GSfm55UNj3nNzbuJ7sv09B6poN5FhbgTe/T6AKfFCc7+nYkW5AtTicGRpKKGyzrPRvdPTU3WFb58zo6b1rovqDFBiEapbhk5qF4gZi0hzpwhpCKaL5Rsqb9zY2PcJmCys1F5AJBwaYtl54f7+WWztVgH7OxsNMLgnVHFRYLxfsDNRTEiew6nl3JXmSv5cTASFLJ3dmGc7QBLRBVUL68inkYaFwWDgo1jpJuuUNIJViOaL7gM3R99rwwImFQyd9nj7/Jl2eHL0eSivKQCeeVCvKSTyAsVnJudR65WuAei5B2teZHZtvXcSPgDYt8JkoWSfnYiKLwVzWYrxroHbPRe9+AvViFdsFkU1ZSU2VBWgfTdA46qJ7n15OD3g1KtltDYaMO43UT3fcNzP2gCqU7oVxB+0lrWPzzDqqXFVBjkSJZlLlftdG6oxCKVy6cT5KQXR+IvMci6n2jjEIkEqdoedw+8iUarAMsbLS6JLY7z5OUMt9qT9pwnlbDdfuQBpvFYt+41C9JZdLvZ0ejRX6cbT+OJmMYjUwAABrgSURBVJXtFpyXNOQCpBLnUHmNjrkTNvwY0ro7TdFbjJ+mSCILLiMpX2ZjHV1YsB8oo3xtCcqWMpp7LDjv1qGMEbKVy5PyWRS8KTrlKw30RpHKz1ownvKmBe+owmYxqfck24rLCqai9uONu7WridaXVag3KKh910APJlobfUITzSev1mAds3y/bdVhvmuj+6wWIgPR8nid0ogp75LKog1nUYUs5VF7yYC5NQupy8Is22htJlLh9qZ0thvZD5p/UiGV0gMmnLdrUALrD5mbWKPcRvXDS1/+JLLgTkjKV/j7DpwjNSgB0pOqWRTX5JGbcteWtKE2mlSON6EFYmRnb2/CQRu6vbS+vD7DUlH79Rt3G/ad2cH2PylfQGOfjd4jJf+eO6JJzsdiejv7aygVfJ3lG+puvWvn+/dE5fL6SatUtJ5rwbyvNPHaR5hUFHRgo/tgkUglZrcitzulRC6zgIGpScV9+XfYcHZpyJVlyPzvosrQaYoksuCGSconb6qhCxPWAxq0Py6ieG4GcoBgeDmTpImk8ksdhYAs6YoqujBQv8RvjEXkjmO/fuNuonmlL4M9r9zXhv210mCaSCTfQO7OyPTSKm96aVNAhktSyXJF6iuax9VvMFIhUhG1G+UjYllpDKRDKov+OsQqzAXWJOKnKZLIghslKZ8kZVDYytZUTPS8tZDeO220Pp2bmlwSSeW1CKms07GADupr/MaY12NUyhvPoN34NdvSHJzm4WTRWDdahkg+Lrc/veSXFze9JFLeqDpO8ltYvzxqLxswt9H01yS2pGeIaE4nBkaTStxCvSwj280iW+43RO7Lz0Yqe3Qo6wooRv7ygZ1jvGJJZDFuPpZf1rLIf6wE/bsdOCdaqEwwDcXlsvS0koqg/UQbd5F8A7+NMVJJIrOg/aa9dvWjhfrBlOa09qTniVhOFwZGkorb+B9tQgscNpMvZTuQTDTW+71bd01gbxXFjH+P7RLLsKmwrH+PV8pdfzjVQDnmN56HpaPysYNxubVFKOvDoxLpUjZimG7LM5N9ukiFyRK1nwhZuLp701VJJNBfU6milPd9NFhT+Yh/T1Quk+3KdxS0nm3BuLc48ZoKK4e2FFNDyDFF6ZmDhZGkIpkqjPdsLOyuuocf1c9VYLxhobtHQz7nNzryWh1zJy3MfacC7YYSyrdqaOwy4ByoQan4+Tgw+o2UBetf1cGBSOXj2cGagGi+/J1t9H5vwPiqCnVTCcpNZTRftOAcqkPVl8rl5Q5LZafg6+Pt/mp9rtS/F9DP3VKc0vQX00XUfqKNu2g++UIN9nG29VeHdmMJZb7765m43V8mkkiK2zWN3V+sLOm8MqxjNuZ3VlHZUkKZH37coUxFVlxPSs+chop8deb4aiSpMEdmLlbRfLqD+fcs9N4xYH1TQ6kTbrDZVEVug4b2iwYWDlvoHjRgPa5DPcff5hoEBctf3FaH9ZY1WAtxfrJ0O25SPknOoHRXDdZeE70TNnq/NTC3q4bKxzITTRvk724P1oP4ugZPg/qlTSqi9hMlC+F87JzKVRpaz3ewwD6Dsr8D8+EyirHnVMRJRV6rwTpoYWGxHNqaHMSAyLX7mZbLA+do9nmfaZmgwyAij/KcOQ0X+Wp2fZVIKuS82XUe+YZ8QxggDMwaBohUaN//RKO6WQMy6UONK2FgNjBApEKkQqRCGCAMEAZSwwCRCoEpNTBRT3E2eorkB/LDSmKASIVIhUiFMEAYIAykhgEiFQJTamBayd4RyabeOWFgNjBApEKkQqRCGCAMEAZSwwCRCoEpNTBRT3E2eorkB/LDSmKASIVIhUiFMEAYIAykhgEiFQJTamBayd4RyabeOWFgNjBApEKkQqRCGCAMEAZSwwCRCoEpNTBRT3E2eorkB/LDSmKASIVIhUiFMEAYIAykhgEiFQJTamBayd4RyabeOWFgNjAwklSGfQo+GuZ2pZwZ9wn6NHXJfaGJ7vE2qheEP/UvKmO59GN+cY7UoEiT6cX1Xy79ePnD7Mc+9Z+/Xkdnjxcq4Y02Ov+mIu9FE+XPL3c6TL9J5Q4rT7S+7qf+L9PQeqaDeRaK4E3vU/+BIHlMt7Hy/VEZrd0GusdYSIoOjH9XUdDCuBHVL2qX4fWVkBWth4B+onJd22SyUP6hhe4pG86x+HfEtd+VGlrPeSEf3urA+HoZhUjIB1G5LNgdD5ERTJ1IzKVoedP+L4qDoJzM9XV0YcHcml22zmQiqTi/a6K6wQtUNUiLKLTDwAwqfrqul7NRlGwVxvsW7HvyE8e7Xy79zgRSGWW/7JY6FmDB3q65Qd20u+qwD9tYeExZEqhtubA0Sr9JZI4qT7S+0rka7GM2Fnbq4aBkP1BDQclE88l/UIF9wsL8D3VUblKgbdNhvmOjtysc50ZUv6BdRtU3bf2E5TYKqO004JwwYP3EGEoqrl1Y8LdFHRUvOJ11wEbv2aUxnbjsUfXNnFf0g/uxNvK6MlqvWujtKSM3ZcePy49LRe3Mn5VKBTR+ZaP3fxUUEqLu8mcmSZNJJYUe8SSKiTyzXI22JGWgfc9E71UdxcLk5Onq96qOfMrAmnVSGWU/Scqi+qKN3lNqiEByd7XgoAntNATgGqWfCO6ieUaVN059RcMni+Rjow/texacn+soBKO0Xl7FPCy0rukH0BtHP17vUfVledLUj8tkaZJc+dMN9H5eR+WSDHJ/3RpKKoV/MeC8X4dS8t/tzC0N9NCG3vPvcdlJcnk+V8dcFtr3TfT2N6A58UEKg/mnuRaxMy+fjWoKd7fRg4HmlcurVyqk4jaeuzUUNugwXjPRPWxibmcV6qqw8u5wbX0gkt9bHZjfiB92yh9V0fhhG/OHLPQOGLC+pUGZD5fnyn2lgtI1OozXTXTfN2Dv0KF0w/m4YUXTzI11dE8ZqF86XTmufj+roJRoFxn5LTqMnxroHvUiMG4vo2T6AJfKCgzYscNsNuQ2bgtHu5xV+7EXNP+JEkrnhW0r/WkdPXRQ+4hfZ+4v+RIN1gEW9lidKpIkLy8t/4qUJ1pflwSetOE8HSZb6UMabEYCG/t2Ec+XR/0XNroPFUPTHFKmhM5JG/ad/ekPUf14XVk6yn5p6ycql+WTF3LIeUSR+5vhpMKmq5yDNZRkH2vyFoa/Nqof9u9x2aPqy/OwlNlS3W6g904D2rlhfPd/l5G7WkP7BW/abV8HxoMqCpHpzWCZw65F7cyfd0daR2x0/3v5ZwOESEUty5CDf4rszutyhfuNZwudHVXoLJb4F7xh9ssV5DO+k+TVGqxjVn/YyfLxmOhs2BlwsNRR0PmNje6eGvRPlaDeWkHndas/clD88ly5v2jBeKoG/c/6McztQzZ6e4YPY7nOw1KpWkTrbRvz9xdDPelh+Ufd7+vXTrRL5roaFk6ZsO7XUN5cgvbFKqyD/XrwUY6UzaK4uQR1cwmVRw2wacma9z+7VwwQ6ZloP3f0dbyBsur7l9s2rZj3rLw0/TtNedH6uo3Eog1nUYUs5VF7yXDnvaWuxiYK0drct4t4vgwqP7bh7CqHp84+zEjKhvHZpY0etzdLo/rx35Lst1z6Jcnl+vF0FKnIH9VgHbVg31tCwcogd4mC1s8tdBfVJdNVonLdkcDfttE90ERldbxt5TU65k54026s/dum99/zxfD0Jq/DqFTUzqwMlld9xIBztInKggR5Gae+mLxEUgkuPPFrZ7cWAqrbeB5rQKv7DYJ8vdfzXO3fc3sI+2soBaaU5Bu8fOf7+XJfZL2MBrSGf086h8U976D5Sd9hrtzjTWiB9Z3s7U04bBhr+c+Ock7wN2Z85WEDzr4aFG8x09UFDagTTGGJ2iX3CRWVLYUQiWX+wqvH3NJ6JE1/nWn2k8pFtPbZ6D5aCtmA+0ZapaL1XAvmfaUQ7vjvomna/p20vLj6srK0Aako6MBG98EiRpPK8HzMJrnbm+jBhPn/iijYGeT+UEFjjwUHLVS7S3HF7RinH/tNpL6i9RhHPxG5XHeejiIVliezToOxvz/yd2Bh/okyCpEOzThy3XrvsME2MfV+3YHxHyqKtbCNSw+YcN6uhafdbupPuwVHSFIu0okPdui9dmgcO0s9DdZJG/O7GrB+bcFhHftna9DO99tSbrc00kRScY42UF1XQDH4d2E2tHjtNp57dRQCDa+0yhu2b+obdmD0nZHhfSQfq1TpfhNOpLy4yrpyfxmRe0UVXRioXxJ2aNzz0XvyVVXMnzLQ2uQbe2pSidQjapegDsxGsuoB6ppavx4XL61HEqmcSfaT5CzK3zLgvNeANr+0rkH7THudtn8nKW9YfUUbCdF8zFZsdFt+iM2j9xvP3v427Hdst0eeCbyrQbsO04/lEanvcugnIjdYB3Y9ilTk1WWYBy3MfVuDtrEE5TMVdN60sBCZXh1Hrju1f34BxfVFqFu9mYYf+xsiXLsw0tmlIRckiYsqoelNpnvmtmbsNLcT6NyOY+fiP3bAiHPuO159P6vD2GfBebOGUoRIo3ac5P9kUhFYqHcb98j2udE9LL/xiOZjlYgrL65ycfmkdToW0EF9jS8j7tm4e+p/9hc2i1UZGe8v/+U2gs6Me27YvVj9ItMZ7FlpvoT6E20sHLFCYHIYOU5AKnFy43SMy3c67SdlslAf6aD3uw4aV4fXhOL0nfZe2v4dt7xR9Q03EnnUXjZgbssmjFSG5+O2Yo2d3MiicHEOxW2sV2yg8TG/08TzsXSUfux3kfqK1oPLFdFPRC4vj6fDSMXV73ELziuRqfn13gaGjb5tJpHL5fe37ppoXNFvh7hd+GxPMI0e0ZCNfLgTP+jQ5wYjeV7eYLp0KF76G2Oc/w0vMchrvfr+iV9frvu0aXqkktAjd43AmDrtkUqUzKYhlUfDjTp3/FSkkmiXLCpPW3DeaKB6UxElDqBtjdMzUlkh+0lKDpUnDPSOtNH45PITCntR1JT9O055SfV1348UF+qjDYPUUNA5aKM7ZNt2kn6i9hOtxzj6jWNnXu5wUsmh9qqN3n+VwhsYdNXdCMM3MIxT34yRRVYPN868s2zc3L8/aP/26FD4Ox5I84GlA16HUamonSVpyIaNrOJu2LC+lAvZYZRM0d/SI5XomsqNw9ZUqijl/VHEYE0lsOtnsCYQMHR/TcVYuqaSYqOYOScyzbeuAPVrU45UEuwiSUW0fmtj7p58yLmZm9l8ePxIhS1cs8OPw9Z5Zt1+Ur2A2m4TzsEmqmvDL2MccCVHQevZFox7i1OtqYzrX/kCFe3nWzC/roQ2knAdRcsTra/oFlHRfFxP1gAp3/AWamOmGEX1E61v2vqJyuX1ZelwUpGhsc7tXh3F4FbrDTX37FT7Rh+PInLZjq/Kj9gZl/CZlIy7m8wfqTCd3DWVvVUUA5uXpIyMDJsOm2DxXMTOLvmwreUvRUZml+r9reWb/foG7TfNdTKpCBx+dKdR3mjDfMrb/eXt6uq9XEEhYED5Qg32cbY1VIfmHToy37XRfSY8NJMMFcYhG90Xqu6uLvUWDZ3XvN1fgb3laU/fxBly6jWV19swRtiFnROo7LTg7G+i/nkFymYFlXvqML7fYhuFY6e/5GtrLuEYd6vubjC2+6sQJOAZtp97LuIFC87hDlpbxQ7Vprn7K+rjUf4t/nP/pLS78SPm/EK0LPZ/tLxx6iudV4bFDuXtrIYPP+5QQmQqmo/rJ/+RjvlTNuy/y4d2bbLfx9GPlxdMo/V1yxSsBy9nlH48TzSNkxs8hKg90oFzvImad2A7uIVdvrrmNqju4Ud3F1YV7uHHlyooBDq8UZns/1i519Qwf8rC3GMatOtL0L5Ug8V2oUYOmcprdcydZGsbFffQb/lWDY1dBpwDNSgVv6MdJzfunigOBodgn/QO1d6uw9zP2tcy8hOQWZwuwXuJpMKngIJpdA7QbdxfqUC5Toe510T3iHdO5UNhFnQXs67S0Hre26e9vwPz4TKKMZ9HyFysoPmUd07loAH7sQqUSC/rjCCV3RqKSXZZKKH+ZBsL7NMchwxY28soXsi2ksaTCmsI1H9qYO4g28nDdpyYaKwLg3JW7SdJBdTfiD9vE8UVB6q8lu38s7Cw6C988t+mTeMaCV6mvF6H/RsLC89ooQOE/Pe4NFreOPV134/LA+e49nmfaYkcCBXNx/STsjnUfsoWZasoBbbjc93H0Y8/E0yj9XVlShKyAvVw8yboF5QVvI6TO+xzKaztmr+vMJgJYL333MaKf16EnZd7REMxsIs0KCt4HSfXLW9Txf3sUPeIhS777BD7HE7k/Imbb4OG9ove54kOGrAe16GeE24ng/JGXYviwM338cDnehiuHly6O22UrHF+G0kqogXFNe6iz1K+2fgIHPmB/EAYIAykgQEiFfpK8aAHlwagqAxqmAgDH2wMEKkQqRCpEAYIA4SB1DCQCqlQz+SD3TMh/5P/CQOEAY4BIhXqoaTWQ+GgopQaGMLABxcDRCpEKkQqhAHCAGEgNQwQqRCYUgMT9U4/uL1T8j35nmOASIVIhUiFMEAYIAykhgEiFQJTamDiPRVKqddKGPjgYoBIhUiFSIUwQBggDKSGASIVAlNqYKLe6Qe3d0q+J99zDBCpEKkQqRAGCAOEgdQwQKRCYEoNTLynQin1WgkDH1wMEKnEkAr7mmj+Wg3VOxXklyHcJr1wH9wXjnxPvj/bMZBIKpmLFDQWvU/Qv92BtV3sE9FRw8mfboRD5Z6sxwaZEs0XLT/N/yWphNZh9kl5G51PTfZZ6jT1+aCW5X6y+zINrWc6mGdhAd70PgUf+aT4B9U+VG8iqFnEwEhScYNlvWeju6fmBssqf15H500L3Re0sYO7SGYeihcwR3uoA2cIqYjmW05junEPLlNQubmIXCAo2HLKpLKXNhDSuRrsYzYWdnrBhe6owmIhcX+ghoJWke2W2o5sQjZZKQyMJJXcnW04RxvQAlEF5curmIeBxmXhoFDjVMAtdwipBMsRzRd8hq7PnpdJJFwq+fvs8Tf58uzw5UhSUR4y4bxSQV7yCYTFVM6tziNXC9xjkdSu1vxIavs6bmSxaOQzDhpRskjKx0YUhb+qwXzVQO+Yjd77BuzFKrQLJpuykpoqTPhRCR10UD3PryfX3w1KtltDYaMO43UT3fcNzP2gCqU7oVxB+0lrWFzpDqqXFVBjkSJZlLlftdG6o4BM0Eei+ViEvvWBSIMsAt43yihEInGKyuX2kS/RYB1gYaPV2NjuPN+olPlWe9KG87QartuHNNiw0Nq41C+jyqPfzo4Gi/w4+34cTSrbLTgvacgFGqw4p8prdMydsOHHfNbdaYreYvw0RRJZcBlJ+TIb6+jCgv1AGeVrS1C2lNHcY8F5tw4lJnQqL3dYKuWzKHhTdMpXGuiNIpWftWA85U0L3lGFzWJS70m2VZxsUfvxxt3a1UTryyrUGxTUvmugBxOtjT6hieaTV2uwjlm+37bqMN+10X1WC5GBaHm8bmnElHdJZdGGs6hClvKovWTA3JqF1GVhlm20NhOpcHtTOvsN7QfJR6mQSukBE87bNSiB9YfMTaxRbqP64aUvfxJZcAck5Sv8fQfOkRqUAOlJ1SyKa/LITblrS9pQG00qx5vQAjGts7c34aAN3V5aX16fYamo/fqNuw37zuxgG7CUL6Cxz0bvkZJ/zx2pJOdjMb2d/TWUCr7O8g11t9618/17onJ5/aRVKlrPtWDeV5p47SNMKgo6sNF9sEikErNbkdudUiKXWcDA1KTivvw7bDi7NOTKMmT+d1Fl6DRFEllwwyTlkzfV0IUJ6wEN2h8XUTw3AzlAMLycSdJEUvmljkJAlnRFFV0YqF/iN8YicsexX79xN9G80pfBnlfua8P+WmkwTSSSbyB3Z2R6aZU3vbQpIMMlqWS5IvUVzePqNxipEKmI2o3yEbGsNAbSIZVFfx1iFeYGW4fZlty4aYoksuBGSconSRkUtrI1FRM9by2k904brU/npiaXRFJ5LUIq63QsoIP6Gr8x5vUYlfLGM2g3fh21HyeLxrrRMkTycbn96SW/vLjpJZHyRtVxkt/C+uVRe9mAuY2mvyaxJT1DRHM6MTCaVOIW6mUZ2W4W2XK/IXJffjZS2aNDWVdAMfKXD+wc4xVLIotx87H8spZF/mMl6N/twDnRQmWCaSgul6WnlVQE7SfauIvkG/htjJFKEpkF7TfttasfLdQPpjSntSc9T8RyujAwklTcxv9oE1rgsJl8KduBZKKx3u/dumsCe6soZvx7bJdYhk2FZf17vFLu+sOpBsoxv/E8LB2Vjx2My60tQlkfHpVIl7IRw3Rbnpns00UqTJao/UTIwtXdm65KIoH+mkoVpbzvo8Gaykf8e6JymWxXvqOg9WwLxr3FiddUWDm0pZgaQo4pSs8cLIwkFclUYbxnY2F31T38qH6uAuMNC909GvI5v9GR1+qYO2lh7jsVaDeUUL5VQ2OXAedADUrFz8eB0W+kLFj/qg4ORCofzw7WBETz5e9so/d7A8ZXVaibSlBuKqP5ogXnUB2qvlQuL3dYKjsFXx9v91frc6X+vYB+7pbilKa/mC6i9hNt3EXzyRdqsI+zrb86tBtLKPPdX8/E7f4ykURS3K5p7P5iZUnnlWEdszG/s4rKlhLK/PDjDmUqsuJ6UnrmNFTkqzPHVyNJhTkyc7GK5tMdzL9nofeOAeubGkqdcIPNpipyGzS0XzSwcNhC96AB63Ed6jn+NtcgKFj+4rY6rLeswVqI85Ol23GT8klyBqW7arD2muidsNH7rYG5XTVUPpaZaNogf3d7sB7E1zV4GtQvbVIRtZ8oWQjnY+dUrtLQer6DBfYZlP0dmA+XUYw9pyJOKvJaDdZBCwuL5dDW5CAGRK7dz7RcHjhHs8/7TMsEHQYReZTnzGm4yFez66tEUiHnza7zyDfkG8IAYWDWMECkQvv+JxrVzRqQSR9qXAkDs4EBIhUiFSIVwgBhgDCQGgaIVAhMqYGJeoqz0VMkP5AfVhIDRCpEKkQqhAHCAGEgNQwQqRCYUgPTSvaOSDb1zgkDs4EBIhUiFSIVwgBhgDCQGgaIVAhMqYGJeoqz0VMkP5AfVhIDRCpEKkQqhAHCAGEgNQwQqRCYUgPTSvaOSDb1zgkDs4EBIhUiFSIVwgBhgDCQGgaIVAhMqYGJeoqz0VMkP5AfVhIDRCpEKkQqhAHCAGEgNQz8f45wXH66uwNqAAAAAElFTkSuQmCC"
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"
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Y7p6Dfu9S5E50gWZCR7k2qTOMj72k6UHaG9OiEfzgKnZtXInFc6dhztTxWLl4Dm5dOIqchBCpz6X0e8Hc/6PrZ8V5bF69BC8kn4NiwPtNc8T87ISkQVOQuX4PsvccR8bqHUjsMRIx37ZETLUWUiy2Znto7l6X6teMGH+sWeqJGRNHv/oM+vXziD5P6X1s9o2t90oArz7yHkKuHcWB9YuxaOoYzJkwHBsXz8CTiwegjbgLY/R9uRbvV2HQKIA35iFM0Q/kWAW+dgVRgruWpAADryQhS+tGCeBNjnyCGZPGolWLZhg91B3jRgxGh7atMHPyWKRF+0n7oLb1g76s4wlKOjm1Q7s2LTFtwihMnzha2JJ56gBeAbvBDzBkYB+0bN4Eo4a6Y/QwD7Rq0RQD+/ZE1JPb0nygBPDmjusA44V9KEhPRsHzeBSajDB5X4RmUme5sEvwrADw6pMjsG3tUgzo3QPjRw3FpLHD0bdnN7Rs0RTb1y+H7nm4NP3LGsMy99OX1xGDBuCrL79EFxcn6e9hRYD3m+aIreWMjPV7YAgKR+6lG3hx5Bw0XreRuWYnYqq3lQK7BM1KAG86Ae+S+cWfPfQZNHnsCNSrWxvNmjRC5ONb0saQEsCb5n8VIwb0RKf2rTHaozcmDR+Irk5t4dymBbz2b4AhylvVwGuIfgBD1H0pZox7UmGwXhoD8L7KpQADr4L+kA28+RmxOL53C2rXqom9W9Yg+2kwcp+F4uyhnWjSqAGO7N4o7YNa5qResi8C3q4uTiI6rXkWBn1KpDBDSqQqItQ0gR3euQE/1/hJRHhzEkNEVO7Evq34qfqP2L1pFfLT5fyMqgjwjneCdtUE6DZMh3bleAG9pnvnVQO8xrQopEY+RnKEL3ISQ6FNCkdSuA8mjBqKVs2bIS7wXqV/D5jfDzTmd25Yjh6uneDm2gmunTpWfuD9pjmiq7VAyvSlMMbEI2WCJ2J/dkL0960RV7sjYht0Qcy3cqK7SgEvvYcNKVHFnz30GRT55DZat2iGqRNGQZMUJu2zSAngzQq6gbO71iD0xnHQdm7YbQReOYwuHdqgb/dOSPO7ql7g1WbBSMAeeU+OxfriZZ5RwVmeu1aTAlUaeOl61HRN7NTUVHG97Js3b+LatWu4e/cuwsLCoNFoUNp1sC11sGzgpZ/L6edziu7GBXoXT+ypUU/EpDl8UH8QRJon1Mp4Xwy8c6YKWKeIHZ0XTQyV8fW++ZrodW5atRiNG9ZH4L3Lxa853OeG2EeRI1nnogTwirQFSmsgm90HBc9ioCbgNfuDNC629Bis+mWeiNCF+dwo9om5bWW8p9cedP8KunXuiMO7NopotRqAN5qiu3VdoHvoJ6K6MT+1Q0yN9oip3k6kMhAMy0pnUAp43xwP5AsKJNT6uSbOHdkFCiy82aa8/1PfxjjfIoiTmGpgKtGXOaL7y/RxaNKwHiJvn1Q38BLsUmqGDGPgtRRXqkS7Kgu8BLtJSUnw9PRE7dq18Y9//AN//vOf8ac//Ql//etf8fnnn6NDhw44fvw49Hp9uQaDdOBNicLc6RPRumXzYuClD9S0KD/07NYZzh3aIjGkcufBEvB2cekgUho8+vXGUPd+WL98oYiw0LmUd2KpqOPoNd6/chotmjbG+hULRT415U/v2rQSTRs1wPWzh6VNmIoBL6UbqBx4KbL++NYF3Dx/FFvXLkWLZk0wfNAAZMQEVPoxRGP1xdNgTBozXOSQJkc+xviRQ1QR4SXgTXAZhLyUNGRu3CvydnUP/KC764OM5VsQ17ALqI0s6FUipeHNz4rcxFCMGDwAnZzbSV9HoBTwvpmnmx18E2MH94Fzm5ZIeHhB3cAbcQfGcEkW84gjvOWiF8c8qMoCb1paGnr16oUvvvgCQ4YMwZYtWwTcnjp1CocPH8ayZcvg5OSETz/9VDxWUFBg9QiQDbwUeTi0YwNq/VwDB7avE4t0tElhuHB0j4hONG/aWGoO6ZsTg4z/KQWDFhrRgiPKxVw0ZyratW6Bbl06iohXZYdeU3qM+Bmd9G/XuqXIu3Tt3FH8HErQSyAmQyfqg4G37NSQuIB7GDKwL5zatUb9enVEHq//PS9p0XVZPiytH0NqFI7v2wrXTs4I9L4CAq5xKgLepH7jUaDVwZSQhJzTXkidsRSZ63fD9DQJLw6dESkOMZKgtyKAN+DeZTSsXxcrF84BpZmU5rPy7qsI4NVHeeP87jVoULc2Vs2bDJ3shWsVuPCrQJv5K+zehjFcgkU/ZOC1mlwc94AqC7ynT5/G119/jTNnzkCn073lYQLc9PR0jB07Fk2aNEFKSspbbcw7KFocHh4OHx+f1ywsyE/6BPws9JFYqNayaROMHTEIk8YOE5N9m5bNxYILmYumyvsh/1vH0QSQGRcIqtZAqQx0T1E6ShGYO20CKMXht46392P0+iMf38aYYR5C91WL52LNknno16u7iDBSBQFZr5GBt2zgpcVpT4PuI+LxLVw+uR/DPPqLSh9Z8YHS9Jflxzf7oUVRlLdL+d6G5EiRi6wq4B04EYUmE/S+gYhv2RPRXzdHzA+tkbFqG/KSU5HYewyiv24mJcqrNPDSZxD9QkAL1ryvnJL264zZ50oDL1VseHh2L1zatsSQvt3x9EFRdNec5vBmJLhc/1c08IbdhDH0hhyLvs/AawYVvkeVBd4NGzagUaNGyMrKEsOAoLW02+XLl/H9998jJiamtIfFPsoDHjx4ML777jvRltrT9hD3flIjBvThSRFGgt79W9eKSOnCWVNw5fRB8ZMoRbsqe0qDeSIoef8iIVgAi2sXFwHDJR+rbNsUnaPodMtmTeB78zzy0imXNAZB3ldFlHfe9IkiAizjdTPwlg689D4oqW9BZqwo09eofj3cvHD0tcdKtqsM2/rkSKxd6om2rZrjwrE9eHDtDG5dPC6+MLVt3RJXzxzC83AfaV+UZVdpoHSFRLdRKNAZkLX7GGK+ayXAtmj/SORnZiNl2hJEf9VUFcCbHu2HPm5d0b93D1C5MtljREngpchu4OXD6NnFCb1dOyLo6hGRyiAVdilXuCKBNzejCHRDrsMow6K88TLPUObczQ9ULQWqLPAeOnRIwOmDBw+Ql5eHksBL22QU+V28eDHq1q0r8n3LGhrUNjQ0FNRXSQsJkF8b1zzZU3oDbVNFAMoBpJQAKpFV2RetlTahUO3R0UM90LWTM6hkUGltKss+ikgP6OOGvm6uAs7N/qCIo3vfnmLyzIiVk0fKwFs68L45Fui9cM/rBOrXrSNSfd58vDL9T+/PaeNHoU6tn9GiWWNhlIr04w/fo1q1b9CsSUOcOrQDeWmWnfu7zk0J4I1v1w+mhOfI3nUU0a8B7yjkZ6gLeL29TqFO7Z9FXWclFs8qBbwEteE3T6B/j87o2rGtqMFL0VvpsFvRwKsh4CXYvSbHIu8x8JYFLlVwf5UF3oSEBDRv3hw1a9bEqlWrcP36dfj6+opqDQStZ8+exZgxY/Dxxx9j/vz5AoqtHR+FJp10eDOnAVABfvowpYjR0T2bRA7aif3bpD/fuyZUax/XPAsVOYvmyYXu/e96iZq2tIinstdRpRy/8aOGoG2rFqJeJ/mALD7wPpzbtcHoYe6giLW1upTWXhHgpcVq5gtMzOn7a5WGC9BM7vJqv/lxW+8VqsObGRcAirSbtac0mI0rFolyfdfPHpKifWn+kLGPxjtd2MDnxrliu+t1UkQYKSf8xrkjouQanZuM51MCeGNqtEPupVuiUkN80+5FkdzvWyN9ySbkPU/FM5WkNNBn54qFc0Q6VfD9K1L0ftNn5EfpVRqivBF77ywG9+0BpzYt8ODsXgG6BLtmK1fqQonKD68dX5ERXgLe4KswBl2RY+F3GXitBRcHbl9lgZeisgEBAejXr59YmPbee+/h/fffx0cffYS//e1vwqpXr46lS5ciMzOzXENA9qI1+jClCChdDYiK7BPgLp47XUSJ6IpfWfFB4kNb1mT55oe3rf8XZMSKK2KNGT5I1B89dWAH6OpSLnQhitYt8fD62WKIsfW5lDqetL16+qCYJAf0dsPB7evFQsJB/XuLxVPnj+xGniRYUQp4tUtHwHBsAwzndqMwOx350UEwnNwC/YGV0EzrJg98FQDe5EhfTBw9DMs8Z4pSUsf2bsHsqePRsF5dTBg9tNL/QlDauKRFa8VVGiT/wiEbeGkxGqUvJPWfAGN0PHIvXEfKlMXIWLNLLGLL3nscMTU6SKvUoGQOL6V/uTi1FxeOIR+U5htb9ykBvJlB1zFucD9U//47zJ80Emd3rsa5XWuEee3fiHT/a+qt0qBJhzFIJvDeYeAtF7045kFVFnjJnQS9VGvX398fJ0+exM6dO0VFhn379sHLywtxcXHFkV1qa+1NCeCln//XLfOEe79e6OnaWVyl6eCO9cUF6ysr7NLEQT89hz26LnKP3fv1FMX2abHXgtlT8PjWeVEM3tYJRunjTenRoMoYV88cFFdn6tmtiygJRxc+uHRiHyiCLes1KAO8TtDtWCggtyA+HAUJUSh4Ggnazgu6L2rzFkeAK2GEl8Y/VcOgFJg+bq5w69YZIwcPEAvAnoU9+jWnWk50VJYf39UPpTksXzhblCmjBZ3vam/N49KB11xn9/tWeDZgAnKOX4Du/hNor3sjffEGxDVQR1ky+ix6eO0MevdwhdeJ/Yp90VYCeFOeXMHgvt3h3LYluji1RVfnVzawZ1d11+EVwEvRXS85Fn6bgddacHHg9lUeeJX0rRLASx+gBFyUJ0qXEqaoLv1MSpNgZYZd8yRtTI0WUGh+/Rmx/q8t8qrs52B+fXRPNYXTYvxBC18IxEo+Zj5fW+6VAV5naKZ0hWZWL2hm9X7dZvZE7kSXSh3hpS8c9FM0XWWQfu0wvwcoxUEt74E3xwSNGzofStUwv5ffbFPe/xUD3l8jvbE12iOurgtiazuLBWwya/AqfeEJuqIajR9dckTxe7e8Opd1HPlWdkoDVWagq6klP778lqU+uQKtmsuS5aTDGOgFY8AlORZ6Ey9NvGhNSc5RU99VGniVdpQSwFvWByvvl7PIpzLpqAjw2hq1teZ4BVIaKpN/1PBaFANec6RX4XslUxoqwn9KAO9r+bVl5d3K3F+RObw5aTAEXILB/6IUMzLwKo05quqfgVdBdzHwOh6EVsQkaX4OBl4eP+axUN57Bl77jiEGXusm2AICXv8LMPifk2JU2uylqXxXSrXulXNrNSjAwKuglxh47TvZlBcSKstxDLw8fmwdiwy89h1DDLzWTbACeP3Ow/DkrBSj8mac0mCdDxy5NQOvgt5l4LXvZGMrLNj7eAZeHj+2jkEGXvuOIQZe6ybYghepv4LuGRie2G7G4Csc4bXOBQ7dmoFXQfcy8Np3srEVFux9PAMvjx9bxyADr33HEAOvdROsAN7HZ2DwPSXFjEGXGXitc4FDt2bgVdC9DLz2nWxshQV7H8/Ay+PH1jHIwGvfMcTAa90EK4CXYNfnpBQzBngx8FrnAoduzcCroHsZeO072dgKC/Y+noGXx4+tY5CB175jiIHXugm24EUKDL4EuyekGJU340Vr1vnAkVsz8CroXQZe+042tsKCvY9n4OXxY+sYZOC17xhi4LVugi3IToHh0XEYHh6VYkb/8wy81rnAoVsz8CroXgZe+042tsKCvY9n4OXxY+sYZOC17xhi4LVugi3ITobh4TEYHhyRYkY/Al6ddS+CWzusAgy8CrqWgde+k42tsGDv4xl4efzYOgYZeO07hhh4rZtgi4CXoruHpZjR7xwDr3UucOjWDLwKupeB176Tja2wYO/jGXh5/Ng6Bhl47TuGGHitm2AF8D44DMP9Q1LM+OQMA691LnDo1gy8CrqXgde+k42tsGDv4xl4efzYOgYZeO07hhh4rZtgC7KfF4Gu90EYJJjx8Rm8NHJKg3VecNzWDLwK+paB176Tja2wYO/jGXh5/Ng6Bhl47e4c6lwAACAASURBVDuGGHitm2ALspJ+Bd39MHjbbsbHpxl4rXOBQ7dm4FXQvQy89p1sbIUFex/PwMvjx9YxyMBr3zHEwGvdBCuA994BGO7uk2JGn5MMvNa5wKFbM/Aq6F4GXvtONrbCgr2PZ+Dl8WPrGGTgte8YYuC1boIVwHt3vxTYJWhm4LVOf0dvzcCroIcZeO072dgKC/Y+noGXx4+tY5CB175jiIHXugm2IPMZDHf2wnB7jxQzPjyOl0atdS+CWzusAgy8Crq2QJMFY4Q3jOHqtJyQewjzvomQezdUaZGP7kD/PAK2QoO9jjclBkO7fjI0ngPUaYs8YAy4plr9ye+m1EiYUiJUa/pwXzzrNwpP2/dVp3VyR9jxY6r8/KHPzdB7N5AZcAOGsJuqNWP0Q7zMNyk4U77qmoBXf3sP9Ld2SzHDg+MoZOB9JXAV32LgVXAA5McGQbtiKLTLB6vS/Kb0Q6vvW6Hh1y1Uaa7NuuN5qK9qgYtAyxDgBYPvWdWaMSFQtfoT8BoTA2CK91WtGeN8oY94DH34E1Vapt99DOjQT5WfP/S52bhaS9zatARGkZdKP9Wrz4y+VNpLr+BM+arrfALeWzuhv7lDitEFLBh4X+lb1bcYeBUcAflR/tB69oJ2fk9Vms+ITqjxYS18/defVWnNf2yLZ8E+qgUuii4aIm7DGHpdlWYIvQ5TUohq9S8CXn+YYh/BFOejTiNYT41UrQ+y44LRubErvnqvpio/g6r9vRaurJ4P4x05P9HL+qnfmn6Mj05WIPAmQn9jB/TXt0kxw/3DDLwKMo7aumbgVdBjDLz2BWUGXvuCMgNvJYBkBl67gjIDr3UTbH5GIvTXt0N/basUM3gfQqGBc3it84LjtmbgVdC3DLwMvLbk/3KE174LjjjCa3/9OcJr/8hwhUZ4CXhvUHR3ixSji1cw8CoIOSrrmoFXQYcx8DLwMvBySoNd0yE4wssRXhsrHlQs8CZAf20L9Fc3SzHKmWbgVRByVNY1A6+CDmPgZeBl4GXgZeAtf6SYI7xVLcKbUAS6VzZBL8EMdxh4FUQc1XXNwKugyxh4GXgZeBl4GXgZeHnRmmUTbX4GAS/B7gYpZrizjyO8lklfJVox8CroZgZeBl4GXgZeBl4GXgZeyyba/PSn0F/eCL3XeilG1SgKDRrLnpxbObwCDLwKupiBl4GXgZeBl4GXgZeB17KJtgh4N0B/aZ0UM9xi4LVM+arRioFXQT8z8DLwMvAy8DLwMvAy8Fo20Qrg9VoH/cW1UsxwcxdHeC2Tvkq0YuBV0M0MvAy8DLwMvAy8DLwMvJZNtPlp8dBfIthdI8UMN3Yy8FomfZVoxcCroJsZeBl4GXgZeBl4GXgZeC2baAXwEuyeXyXFDDd2oFDPObyWqe/4rRh4FfQxAy8DLwMvAy8DLwMvA69lE60A3vOroT+3UooZrm3nCK9l0leJVgy8CrqZgZeBl4GXgZeBl4GXgdeyiVYA77lV0J9dIcUMVxl4LVO+arRi4FXQz4oCr2cvaD17Q+vZC5r5PaFVwHxGdEKND2tJu1LRV+/VFH1V+3ttVHu/FPt7bfG4ud3Xf7UNmJv/2BbPgn1gC3T+1rGmtGgYU6NgSIkURtu077eOseYxpS4trA++Cl3QlTfsKgwh12AMvS7NDKHXYUqSD7xK617SR8ZEf5hiH0E2tBpjH4FMH/0Q+ugH0Mc8FP/TPqnPJflKa6T9m/rT+Jc99s0+UPrCE+bPGvGZJPnzhz6/qv29Fq6sng/ZwKu/tRtkupu7oP3V6H8qwyXbKvRKa2lx0J9bAf2ZZVLMcHUrCg25Cs7y3LWaFGDgVdBb0oF3nhu068bCcGEHTL5XkRfpB8OVA9As7KMK4P3yLzXQtUUfXD1/E3eue79md6/fx+5NB1HjX41gnoQqM/DSJP/k9kUsmT8DQwb2xYjBA7B2qScSQh5WauDNC7+BXctnoVcXJ/R1dS429x6dcPvgBuSF3ajUwKtLjsD9K6exctEcjBjcHx79e2He9Im453USuucR0rQ3A5cSwEtQmxpwA4c2LsXYIf3g3ssVU0Z64Pze9XgRdkeArzToVQB4o/1uY92yBRg/cgg8+vXCqKHu2L5+ORKCH0j9wkc+qAjgbVu7M04fuYCzxy6hZU0XaZ8/SgJv7rUduLl5HmYOdoN7l3aYPKArLq6ZgZyr29UNvKlxRaB7ein0EsxweTMK9Qy8CmKOqrpm4FXQXbKBVzO3OwwXdqIg7RnyY4NRqMuFKfAutIv6qQJ4CWSbVnfCnImLsGDasiKbugzb1u7Gi+wcHNt3Gt99UFfahKNUhJeiWeeP7oZLh3YY7tEf65cvwJql8zFtwij43bmE/IxYKeClRIQ3P/wmZo72QNMGdbB27kRsWzxd2I6lMxF2+QBMYZU7wpsc8RjufXvBvW9PrFg4W2hP282bNsapA9ukA5cSwPsi7C7mTx6FhvXqYMqoQVi3cDqGD+yF+nVrYefqBdBFP5AX5VUAeG+cO4IJo4ZgmecsbFy5CPNnTELb1s0x1L0fEkPlfeFTGnjp86j6Pxti79ZDSE/LRMrzVHRq2gtf/aXolyhbv3ArBby6m7txYukkNK9fC8O6O2Hd1MGYMtAVrRrVwZ55o0XEV2aUt0IjvKmx0J9eBv2pJVLM4MXAqyDiqK5rBl4FXSYdeCnCu3oUtBsnQbtqJPITomAKUA/w0gRQFL19NaFQ1Hfi0JlIT83A8F7jpaVP0HMpAbz0c2603x107tgBc6ZNQGrUk+KUhuynQdA+D5cCuzTZKwm8vTq3R8qDMyKNgVIZzFbZUxpyEkPw6Po5pEQ+hj45AoaUKMQG3kPPbp3Rx80VadF+0vQnHygBvGG3TqNZo/qYPnYo0oNuinSGp48uoberC7p1ao/0wBuVGng1z0KR/TQYFG03pEZBkxSGY3s2o8ZP1eF1cj8KMuV84VMaeOkzYmTfifC5/wQnDpxBYvwzuDTpWemB9/n5jejRvjn6urTC0zPrRFpDhtdWzB3WCx2a1UfksVUwSExvqHjgJdhdLMUMXhs5wqsg46itawZeBT0mG3hFni5BL9kv7sh/GqE64KVJpqRV/7gBTh0+jyC/EDT6to206K6SwLt3y2o0bdwQvjfPIznCFzH+d38FsEgBWwTFNFnbakoCb3fnNoi4chBJ3qeQ7nMeuuCr0lIZzNCsVA6vOYJuziele/ry0bpFM8QHeduse0m/yQbevDgfBN84gSYN6mLD4pkih5fSF3LC72L80P7o6twWqQHXKy3wltSGtkl7+pJ39tBO/FT9R1w4tgcFmXHSfKBUSgN98W5e3Qk3vO5gwbSlWDJ7NZ49TVIF8AYfWo4mdWpi5QR3kcdL0VzTnb04u3IqalX/HmdWTC3eLyPSW6HAmxIL/clfoD+xSIoZLm1g4FWQcdTWNQOvBR4rKCiAVqsF3VtzUwR4aXGagwAvTTodGnRHfMxTkdZAkFoUAX4diksCsjXbSkR4KZ1hwuihaNu6BTav/kVEFZ3atka/Xt1xcv925CaGSpvslQLe2WMHo3aN6ujRsS1cndpgrEcvXNixAi/8LkmFXqWA903ooqjugN490LtHV1VEeDNDbmHi8IEiout9Zi9ivM/j4v6NcGrTAqs9p0IXVXlTGsza656H467XCRzZtUnkrjt3aIOe3bogxu+utPFPz6UE8NJnDKVOrVq0Edcu3kK9L1ti0YwVqgHesCMr0LTez1g8uh8ovcEMvMeWTMKP31XDqkkeKgfexdAfXyjFGHitIRbHb8vAa4GP/fz80LVrV4SFhZXa+uXLl3jLADDw/ja4fvnnGlgwfRlSk9PQx3mwVNhVKsJL0awBvd1Qrdo3Iq3hwPb1uHrmIMaOGIx6dWvj0vG90iZ8JYCXFq3tXTkXS6aNwomNi7Fv5VwM7N4J9WrXxOG1C0AVHMwRWlvvKwJ49cmR2Ld1DerWqYV9W9ZU+hxeY5yPWJQWfvu0WKzWsG4dtG3RFPXr1MKUUYOR9PgKjLE+lT7C++JpMBbOngIXp3Yif9qpXWvcPH9EpPeYoVjGvRLAS2lUvZ0Gwd83EO6uI8UvTotnqgd40y5tgUeXdnBq3gCP9/yCF9e2I/r4Gox064j//Oc/mDesF3RqTWlIiYH+BMGupxQzXFyHQn1OqfM276x6CjDwWuDzW7du4dNPP4WPj0+prQsLC/Hw4UOcP38eFy5cEHb+wgXcP7gVmvm95C8oc5AI78+fNcWtq/dEtYZanzVVD/D2KQLeY3u2wJReVKYpIeQBOnZoizHDB+FFQrAU6FUCeAlitYGXoQsqKkOmD7mGJO+T6N+9Izq3a4nEeydVA7xUKePSiX1o1rghZk8dj8y4QCm6lwQ12SkNlL5Aebu0aK2zUxtsWToHF/ZuwLLZk0SEd+uyedBEeFd64KVUBoLetBg/RD6+haXzZ6JvT1eEPLwq1QeygZeiu3W/bAGvM1exbe0eEemlL8dqAV5zObK72+ajS+vGaN2oDno5tYRr26Zw79IWtar/gPnDe6sbeI8vgP6YpxQzXFjLKQ2lUkvV3FmlgTc0NBSLFi36TVu8eDEGDx6M999/v0zgzc/Px7Bhw/DTTz+hRo0awn6qUQPD2zZEDsGp7Bq5DgC8NPH06uCB58+Sxc+JFHWxJl3BkrZKpDTQIp3RQ91Rr04tBHhfRv6vubqU6kDlmTp3bI/UKDkLp5QA3rJq7a6fPwkN6taC/7k9qgBeWqx24+xhtG3VHJPHDsezsEcCtAjESgKrrduygZdyeG8c24HaP/+Ew5uXl8jhvYelsyeieaMGCLlxAtROSmkyyVUaStMzPyNGQG/LZk2xesl8qVFe6cD7l5oYP2gGEuISscJzPcZ6TMU4j2k4dfgcMtKysHz+OnRr1Q9fv/fbv05Z8vlDbZSqw0u1d8OPrsLxJROxY/ZIeK2dAa91M9Hg55+wecYwFac0xBSB7pF50Esww/k1DLxVk21LPesqDbxHjx7Ff/3Xf+Hjjz/G559/XqrRT0QfffQR/vd//7dM4KV0huTkZMTFxb1mz+5egoYuEMHA+xrMmvN0N6/aKfJ3nRv1kB7dpclGNvASTJGtWTIP9evWgc/Nc8UlyKgGLNXj7dHVBekxlRd4S0tTIAheOWssGtWrjaCLlR946csF5Y86t2+L0UM9iheqyYZdgjvZwEsQe2zrStSs/iMofzcv3re47u7xbStFpYN7p/eI/WoBXtKJFgu2bdUCc6aNhzZJXqUS2cBLX6ynjZqHyLBohAZFIDQwXFhKUipMJhPiYxKwYdk2fPM3ORfcUQp4zYvRzBe0oEVre+ePRr2a1XF7yzz1Au/zaOiPzIf+8FwpZji3GoU661IazPP53r17MXPmTFDQ6969ezAajaVClHknBb4CAgKwevVqTJs2DcuXL8eTJ0+Ql5dnbiLuaT3Q9evXsWDBAkyfPh3bt29HfHy8SIs0N8zIyICXlxeWLVuGqVOniraXL19Gbi7XFDZrVJ77Kg28lH7w3nvvYevWrQgMDIS/v/9bRvt37NiBf/7zn2UCb1nCF0T7iyuhSQNeiuwSQC/sC+2yIchPiIQp6B60SzyK9kkGa9lXWjNHRQh4G37TGv6+QaLgO1VqMD8m81428NLETlD14OoZNG5YH+uWLxDpCzTBP751Hi2aNYHnzMnSSpMpEeGlhWnxt48h+8lFaAIvI8ffCyFe++Hq1Bp9ujgh5cHpSh3hJf19bpxDl05OGNDHDRGPb0KfEgnK5aUUB9nQKxt4KXJ78/gO1PzpR2xZNgfZoXegjbqPFL9rmDF2KJo3ro+QmycrbYSX9I0N8BapDDTuqTQZleOjsmQ/16yB3ZtWwZQaJS3KLht46bOn5r+biAoNLX5yhrAaztiwfDuSk1IwxG0M6nzRQtoXcKWAN/PyNqRc3Izc6zuQc20HAg8shVuH5hjarQNSL26G4ba8q65VaJWG5GjoD8+D/tAcKWY4u8pq4E1JScGgQYPw9ddfw8XFBY0bN8b3338PCpBR+mJpN9p/7do1/Pzzz6hduzZcXV3RqFEj1KpVC1euXCk+zmAwYM2aNfjyyy/RokULsTaIfhnu0qULoqOji7tet24dvv32WzRv3hzdunUTbSko5+npCY1GU9yON6xToEoDL8Es5eYS0P7W7fbt22IxgK+v7281e+sx6YvW5rlBt2kyjDeOwnTvHApfZKAgNQGme2dgvHYI2mWDpUaTlQJegtrBPcaI2ruThs3CF3+Wn85Az6EE8BL0ap6F4Zd508ViHbrK17plnujexQWdnDvA/+4laZO9EsAbde0QBvXqjDljBmPzwqlYMXOcgN2WjRuISg1lpTyUFhl+1z4lFq1RRYa+Pbvhxx++x+wp47Fzw4piO7RzI9Jj/KXpr1SEN8X/GsYM7ocmDephzoQR2Lx0DsYPHYB6dX7GklkTkBt+T046A6VFSE5poC8VNOZHDRkoqjNsWbMEMyaNQbMmjTBoQG/EBcotCycbeMv6Qq2WHF5zRYaLq2dgVK+OWDdlMFZMcEe3ts3g2qYJHu1aKLUGLz1fxQPvXCmwS9BsOLPSauClANgHH3yAffv2Cbh8+vQpOnfuLOA1MTHxrXmedmRlZaFNmzaoV6+eCJ7p9XrExsaK4whsU1NTxXG0DoiYY+jQoeJXYZ1OB+KLatWqYcqUKcWVoCiaSxFegm+CZPoFeezYsfjwww9x48aNUl8D73y3AlUaeLOzszFkyBBs27YN9HNEWTf65kU/PyQkJJTVpNT90oF3bg/ods9HXuQTcaU1utpasYU+gnbVCFUAL/1cOGvcQpE31+SH9tKiKW9OZkoArzmCmBkbKEoyjRwyEAP79hQAHPzgKkypRWkPBEu2mhLAm+5zDruWzcKEwX0woLsL3Ht2weIpI+Fzaju0QVekRXcJhpUA3pSIxxg9zB29e3RBr+6v28ghAxAfdN9m3Uv6TXaEly4rTPbM9zJ2rPTEiIG90bd7Z4wb0l+kOmQE35IHuwoArzEtWqSTLJg1FcM8+olyfONGDBYVMiiPmt4f5vdISR3Lu11RwDt6wGScOXoRLX+u/JcWpjSGoIPLMGdoT3FZ4UGu7bF6kgeoXBlVZ6CFbeaUBxn3FQq8SVFFsHtgFvQSzHB6hVXASykDnTp1EpHVnJxXqRAnTpwQ63hOnjxZ6lxPwTNKjfzll18EtFJaBN0OHz6MP/3pT7h69ar4n9IX6P87d+4UV3aiVBrikB9++AHPnj17q39zX97e3uLYTZs2vdWGd1imQJUGXhpIlF9D9XXNg6o02eixd7Up7TjpwCsWwPWCdkGfUk2jkpQGAtNqf6+Nb9+vI1IZzDm9bwKrrf8rAbw0cZsndGNqtLjal7jilAI/pysBvBTBpdJjBLeawCvQBl4RF50wR3bN9++K3lryuBLAS9rTFdYoZ/pNo/1m35QXsN48TjbwlszLNcQ8FDV3KaWBLidsiH0EE5msBWsKAC/pS0aRXuGH5AiRTkJ51W9qJ+P/igLeb/5eC99+IP/zSKmUBoJaWrimubFTmO7mLumga4blCgfeg3Og3z9Tjp1aDkN2msi/pRxcs5UV4CLgpFSGESNGvDalh4eH41//+hfmzp372n7zP5SrS2t9KG+3ZNrDsWPH8Pvf/x4bN24UTTdv3ow///nPIHg1MwcxyKhRo8Q6ocePH5u7fO2e2h45cgR/+ctfBES/9iD/Y7ECVRp4LVapnA2lA69koH1XbrGSKQ22wqwlxysFvDImckv6UAJ4LQFVWW2UAF5LdJPZRknglQq2ZUGy5JQGmdpa0ldFAa8lnyflaaMU8JphtCLuKxx498+Cft8MKfZ0y2QM7uMmcmopr5aM0hPOnj1b6qweEREh1uvMmTNHPG6G0qSkJJF3+yYImzuh1ANKZ2jVqpXIxSWgfv78OQYMGIDf/e53mDdvngDhmzdvCjCm9AVKgyDYJVimPF5aQE+L2czPae6b7imVomXLlmjYsKFY4FbyMd62XAEGXsu1srolA6+c0j7lmWjoGAbe61JTFKwFYQZeSaXFyoJZS/Yz8CqyINbSzyQGXuumzfykiCLQ3TMNegmWtH0q5k+fjEmTJhXbhAkTQLX1S7vRxaUoNWH+/PmvPUzwSpFfyr0t7Ua/AFPO7xdffCGgl/Jte/ToIRaw/c///A9mz54t0iYpZWLixIkij9fNzU3k5Xbo0EEsivvv//5vkfrwJvBmZmZizJgx4pjTp0+/FkEu7bXwvrIVYOAtWxubH2HgZeC1JIpVVhuO8NqeB12Wtpbu5wivfX3AEd49UvNxyxMRrtgILwHvdOj3TJVihhNLUKh78dZc/iZUmhtQeTBaVEaAXPJGJUc/++yzt/aXbEO5uLTQbPTo0ejdu7eoqHDw4EH89a9/xdq1a4sjtxTZ3bVrFzw8PNC/f3/s3r1bpEr83//9Hx49elSyS1AeMUWHKZ1iy5Yt7yyN9trB/M9bCjDwviWJvB0MvAy8loJVae0YeO0LW+QTBl77+oCBt4oB77OIosjursnQSzDDscUo1L4NvKXN8gTB6enpaNCggSgFVjLP9+7duyIVoawFY28CtDmP99ChQyJn99KlS+IpS7ajbWpHaQ0UOaYyZFQRwnyjCg5Uh5dKolJuMFVr4JttCjDw2qbfbx7NwMvAWxrIWrqPgde+sMXAa3/9GXirIPDunioFdgmYDUctB16azAk+J0+eLPJ1g4ODRVSWoJSirJ988onIt6V2aWlpIAg2lxsjeKW0BjPoUhvK6+3YsSOaNGkitmkftSsJ0vT/w4cPRSoEpTpQH3SjaDFFhek5qUIUlTnjm+0KMPDarmGZPTDwMvBaCreltWPgtT9wcYTXvj5g4GXgtSXSay3w0mTu5+eHGjVqiLq6lG5AsEtpDrTQjKo8EKSay5RR2TG6EeieO3cOM2bMAKUxUPkxZ2dnfPfdd69deILAla7EtmTJElF1gSK3NWvWBOXxUt1e843ygSkVgmCZ+jtz5oywU6dOgRbW8a18CjDwlk83i45i4GXgLQ1kLd3HwGtf2CI/MfDa1wcMvFUMeBPDod85GfodE6WY4chCi1MazJM6RVnpYhD9+vUT6Q1UHYHq61LElm4EvHRhiPr164Ou1mreR8fQVdGokgJdnY1q69LiuJIRXYrcUt3/du3aieOpb1rQFhkZWdwPbSxcuBDffPMNqlevLi54QVdsI6NqDjt37hRt+Y/1CjDwWq+ZxUcw8DLwWgq3pbVj4LUvbDHw2l9/Bt6qCLyToN8+QYoZDi+wGnjNEzxdwpcgl/J6KdWBbgS7ZBSppcfMqQa0j8D2xYsXYj+lOlAOrvlGj5uPJ+jNyMgQV0+jvul/82NiAxD9UGWIN43Ko/Glhc0qWX/PwGu9ZhYfwcDLwFsayFq6j4HX/sDFEV77+oCBtwoC7/aJ0G8dJ8UMhzzLDbwWT/TcUDUKMPAq6CoGXgZeS+G2tHYMvPaFLfIJA699fcDAW9WANwz6bROg3zJWihkOzkehNlvBWZ67VpMCDLwKeouBl4G3NJC1dB8Dr31hi4HX/voz8FZF4B0P/dYxUsxwcB4Dr4KMo7auGXgV9BgDLwOvpXBbWjsGXvsDF0d47esDBt4qBrwJYdBtGQft5tFSTLd/LgOvgoyjtq4ZeBX0GAMvA29pIGvpPgZe+8IW+YmB174+YOCtgsC7eSx0m0bLMQZeBQlHfV0z8CroMwZeBl5L4ba0dgy89oUtBl7768/AW9WANxS6TWOg2zBSju2bwxFeBRlHbV0z8CroMQZeBt7SQNbSfQy89gcujvDa1wcMvFUMeJ+GQrdxFHTrR8ixvbNRqOFFawpijqq6ZuBV0F0MvAy8lsJtae0YeO0LW+QTBl77+oCBtwoCL0V31w2TY3tmMvAqyDhq65qBV0GP5SeEQ7dtGrRbp6jSQjxHYED1RnD7tr4qbWTjdnh+/jD09y6o07wvwhB6G8bIu6o1g/9VGB9fVq0ZIu/DlOAH09MnqjRjnC+MQddhDLimSst+dAnTBo5Brza9VWl92vbBowObYPA9BYPPSVWaMfAKXpoMCs6Ur7rOpwjv+pHQrR0ux3Yz8L5Sl7cYeBUcAwWGXJieBanW9E+8kDGtG9InuKjTxjgjs3tzZHZtpkrL6tsBBv9bMKVFqdOSw6FdOwGaSZ2gmaxCm9QJhltHYUqPgSktWp0W/wTataOhXdhXlaZZ6oFc/+vITQxTrRmSI0C/1qjV8jLi8LIwX8GZ8lXX+fEhRaC7Zih0MmznDBRqsl49AW9VaQUYeBV0f4FRg7yUcNWaMeAqNDO7QzOlkyotZ5wzMrs0RmbHhuo0t9YCeEtLd1DDPlNyBLSrxyJ3XAdoxjupzuh1G24eQV5GLNSgd2mv0RTvB+2q4dDO76lOW9wfxtDbqtW/NJ+obl/m0woG3mHQrR4ix3ZO55QGBRlHbV0z8CroMQZe+4IyA6998y8ZeO2rP4EVA6/9faA6wE1/Q7OKBt7VQ6FbOUiObZ/GwKsg46itawZeBT3GwMvAa1N0mSO8do0Kc4S3EkSFOcJr/+h2hQJvMHSrhkC3wkOObZ/KKQ0KMo7aumbgVdBjDLwMvAy8nNJgzwgfR3jfiFa+Gb3k/98N1Ay8ClICd12RCjDwKqg2Ay8DLwMvAy8Drw2RYo7wvhtIlYb2igTeuGDoVgyCbrm7HNs6GYW5vGhNQcxRVdcMvAq6i4GXgZeBl4GXgZeB155jwObnrmjgXe4B3bKBcmwLA6+CiKO6rhl4FXQZAy8DLwMvA6/NwGFDBI9TGjilwebxV9HAu8wDuiUD5djmSRzhVZBx1NY1A6+CHmPgZeBl4GXgtRk4GHjt/7O+DT6wp/+lPHdFAm9sEHRL3aH7pb8c2zSRgVdBxlFb1wy8CnqMgZeBl4GXgVcKdJQTuDjCyxFem8dfhQPvQOh+TQFTvAAAIABJREFU6SfHNk1g4FWQcdTWNQOvgh5j4GXgZeBl4LUZOMoJu/S8DLwMvDaPv4oG3l8GQLuorxTTbRjPwKsg46itawZeBT3GwMvAy8DLwGszcDDwckqDDWPA5vFXkcAbEwjton7QLOwjxbTrx6EwJ1PBWZ67VpMCDLwKeouBl4GXgZeB12bgsAF2OMLLEV6bx1+FA29faBf0lmPrxjLwKsg4auuagVdBjzHwMvAy8DLw2gwcDLwc4bVhDNg8/ioaeBf0hXZ+bzm2diwKcznCqyDmqKprBl4F3cXAy8DLwMvAazNw2AA7HOHlCK/N46+igdezD7TzesqxNWMYeBVkHLV1zcCroMdkA68pOQxpkY/w+PoZXDy6C+cP7cCDyyeQHuUDeiwvJVyqGQOuQjOzOzRTlAHX3Mku0CweDP3epdBtmoHcaV2goX2Sni9nnDMyuzSGTdDZseGr450biO0s9y54MW0EchZMRe6i6WI7s1f7ona/tpHynG6tYfC/pUh0y5QWjZyEYPjfvYSLx/fi4rG98LlxDlnxQdKez5QcAe1qBYB3XAdoJrpAu3gYdNvmQ7/DE9plo6CZ1Akaemy8kxTLHdcBhptHkJcRK00TQ0ok7l89jdMHdrxlZw7uQKz/XZBvbIaUXyFZGeB1g2Z+T2hXjYB+3yLoj66Efv9iaFePhtazF7Tz3KClx2WYAlday8+IRdD9Kzi1f9trPjhzaCdi/O8g34YvGLL89q5+NM/CEO13B3cvnQC9bu/Lp6B7Hi5t3Lz2/BUOvL2LxhCNI1ttzWhOaVCQcdTWNQOvgh6TDbyp4Q8xeugAtG7eDM7t26Bjh7Zo0bQxxg3zQMzj61Jhl+BZUeCd7ILcGd1henAJhYUvkR/hh9xZbpUfeLu3guHKOeRFhsEUEYy8qHDkJ8bD6H0TWcN7IkMlwJsc4YuFs6eiXeuWYhx1cm6PLi4dcOXUARAQvDbhlRMAFAHecR2QO9EF+iNrUfAsFvmJ0ch/GomCpFgYzuxArkToVQJ4Nc9CsXDWFJDexebUDvXq1ka1at/g8sn9lRt457lBM88Nut3zkB8Xgvz0JOQnRaOA7hMiodu7sAiGZcAu9aEA8BZmxWPJvOn4ptrXcG7XptgPrp2cxZe/gsw4KeNfxnuotD7o9dEX1JbNmqBu3Vr4+uuvMKCPGzLiApR53RUJvNEB0M7rBe0cNzm2ahQDr4KMo7auGXgV9Jhs4E0Oe4BNKxfi9vnDSAi4g6Rgb5w9uA2NG9bH7MljoUkIkgq9ygGvC3Ind4L+4Erkx4chPykW+ZH+6gDeTk2QPXYgsob3QmZfZ2T164ic2eOR/zQW+kunkNml2auIcMnocHm2FYrw5iaGYsGsyQJ0vU4eQGLoQySF+SDgnhcSQx5ImzSVAN7cse2h2zANBRkpMF0/AY2nOzRz+sJwbCPy055Bt3OhtCivEsBL0dvsp8FIi/EvtoSQBxjm0Q8d2rXG02B5+hMsSY/wEvAucUde5BPkP4uBbudcaJcPhW77TAG8ebEh0C4dJC/KqxDw/jJ3Olyc2iHy8e1iP6TH+EOTFCZt/JcGqzL20RdS31vnsX3dMvEFlUB9QO8eyIgLVOa1Vzjw9oR2Tg85tpKBV0HEUV3XDLwKukw28Bqfh0H3LPg1qNUlBmPsMA+4dGiL5yHerz1ma4qDUsCbO6UTtEuGIS/SH4bT25AXeE8dwNuxoYjgZjjX//W+QVFEt0tTGO/fginID5ndW1V64H10/SyaN22EA9vWw5gaBX1yhNSoonlSVwR4x7WH4fweFGSmQPPLMBCUijSGmT2RHxkA05NbyJ3kIgV6lQBeszbme4KXQO8raNa4kYj8UsqD+TEZ97KBl6K72tUjUZCaANODC9CUSGEw3j2N/Izn0K4dowrg7drJCSmRT97SW2ZKiQwfltYHvW/JtM/D4d6vJ/o7EvDOdYN2Vjc5tnIER3gVZBy1dV2lgbewsBDPnj3D8ePHsXnzZly+fBkZGRl4+fJlsR+pjZeXF86dO1e8z9IN2cBbGsDmxvtjuHtfdOvsjJSwh5UfeClHd0Y3GG+ehMn3OjQLPVQFvCI3l9IWurVAlkdXZI/ojdxVnsh/lgjd6SOVPsJbkBGLzasWo1GDerh0fB+2rF6CudMmYP3yBfC7cwmGZHnApQjwjneC8coRFKQkiOiuAF7K2Z3WDXlBD0SKg2ZOvyIQtjGXtyKAl2CGInV1a9cS+ZgEWzKBSzbwUk6lZvEAmEIfIi/cF9rVo6BZ2BfaFcORR/sin0C7xKPyA++86WjUsD6Wes7EqsVzcXjnBkQ9uQ1DatRbAFwacFaWfZTLO7CvAwFvVAC0s3tAO9NVji1n4LWUV6pCuyoNvHFxcejWrRs++ugjfPDBB/jkk0/g7u6O0NBQEOjSraCgACNGjMCAAQOsHg9KA68xOQy3zh5C44b1sGz+dBiSQio98Iro7rY5yH8aDu2GqdDM7KFK4H0xfRTygv2RHxeNgow0GO/fRtawyp/DSwtyZk0Zh+o//ogerp3g3q8XJoweBuf2bdHJuR3uXzklbcJXBHjHtivK383Jgm7bPGgmdkTueGdol40UEFyQ/hyaRUNUA7z0M/rAPm7o2a0L0qL9kJcub8EaQZl04KW8Wsrh3TEHedH+yAvzgfHeGeSFPEB+bDB0ezyhoRX2lTyHd/3yhSJ3d+SQgfDo3xutWjQTPnh47azULxxKg7HjAa8/tLN6QDvDVY4tY+C1Glwc+IAqC7z5+flYsGABPv30U2zatAm3b9/GokWL8OWXX6JZs2Z48uSJgF5LgJciwrm5ucjKynrNXqQnwyS5coI5yktVGaJ8r6Onayf06OKCaN/Kv2hNVGWY3x95IQ9huHwQmmldVQu8WX2ckDNjNHIWTYf+9GEY793Ei0lDkFGeXN2yjlEgh9eYFo0p40bi088+xYRRQwVkGdOi8OTORbRp2RxjRwxGTmKIFOhVAnhF+sICD+RHByI/IQqGE5uhP7QaJv+7KMxOR2FmKrS/DFcN8D64dgYN6tUREXYl4Eg68FKEd35PAbyUd5//LAr54Y9F/m5+TFAR8JZIc7AZfBXI4SWdE0Meinzp3MQQUZnkzqXjYvwPHtgHGbEKLf4q5+LP3xoXjgm83aCd0UWOLRuGwpwMB0Y4PjVrFKiywJuTk4NWrVph7ty5yMvLE5oR3N67dw8NGzZE3bp18eDBAxAYvyvCS21GjhyJWrVqoXbt2sJ+/rkWRg3zgCEpVGrUlYCXYDch4C6GDuwDp7at8PDKSVWUJcud0hmGU1uRnxgF7ZoJ0M7rC+3iwQKAabLULB6C3Omu0io1SC9LVhqYUnqDawsYvM7A5OeDzL5OyJRVqUEB4KUSW7OnjMO31aqJFenmyZR+Rp81eRxat2yG+KD7lRd4xzshd7wTtEtGwHT7DPJjgkX+N+WCm7wvoiDtGTQLPNQBvGnRWLZglkgvofJwZl/IvJcNvCKHd9UI8R42BdwWpcm083tBu2IoTI+vIz8pBrp14yt1SkNZ+lJqQ/16dRHkfUURX5T1vLbsdzjgjfAvSmWY3gVaGbZ0KApfMPBaA4WO3LbKAm92djbq1KmDvXv3ipxdc94u3VNKQ4cOHVCjRg2Rvzts2LDfTGmg9AfK/929e/dr5nX+FCjtwByVlXX/NOAORg8ZKGD37sWjMD6XD9X0WqUvWpvZHXlPbqNQm4OChEgUPI1AQWIUCrW5KNRrxT7dtrkQkWAJtXgrBHgFBDeCZuMyFKQmI3ucOzKc6stZuKYA8FJJo40rF6Fmjeq4ffF48cRO+xfPmYZmjRsiNuBu8X5bJmNFIryUl0ulyeh+YkdoprpCM6UrNNO7F6XGhPshd1o3VSxaSwp9hM4d22P0UHe8SJATVX/TX0oAr27XfPEe1h9bA83c7gJuRamyvQuL9h9ZCc3cHnLSGhSK8JbUib7s0eLB3ZtWi/JwtKiz5OOVedshgZfSGaZ2kWNLGHgdGWCtPbcqC7wajQbOzs6YM2eOiOKWFI6gNyoqCq6urvjxxx/xww8/iNzekm0s2S40aqTDbmLQPUwcPQTtWrfAjdMHFYkgm8FcNvDShSV0W2bBcHzDKzu1FQXPokX+peH0VmiWDK28wEuR206Nkdm5yasoLu3r2hz6c8eQn/hUbh6vAsBLObx3vU6gYf26YrEUrfSmCT0zLhCDB/SGm2tnpEa9vXK9PJO+csBLF5ZwLoJaAb8dikqVpSXBcHo7csf/Wrmhki9au3RiH+rU/hlHdm2SVvv4TT8pArw7ZguwNVzYIWryUtoCpTkYTm5AoeYF9AeWVGrgNVc3MC8OpPuchBBMHjscLZs3RbT/HQbekukXFVmWLMIPmmldQGs9ZJj2lyEc4bUEVqpImyoLvJTGQLDbtGlTpKWllepuWtTWq1cv/PGPf4SHh0epbX5rp+xFay9in4h6u7Vq/oSVC2fB79Y5BNy5gIDbFxB6/zK0lb0OL11sYlJH5E50LjbN9G7IC7iL/Eg/5M7ohtzJHSvtldboohK0ME27dwty5k/Ci/HueDF1uPg/P+U59FSloWvzVzBcWgqENfsUAF4Coqy4QEwaMxxO7Vrj1IHtoIjW6l/moVGD+ti5YYUod/QmOJXnf8WAd3IX6A+ugm7zLGhXjYNuzxLkRQWIKg2auf2kRHfpam1KVmnQPY/A1PGj0K5NS3HFrPLoa8kxSgAv1d3Njw4Q+buG42uh2zYD+qOri/J440JE2TJpV1tTIML7PMIHm1YtxoVje+B78zy8L5/EMs9ZIpd6iedMaJPCKv3CNVrgeNfrJK6ePiQW31FNYbpi4t1Lx8WXV0vGhsVtKhJ4w/2gmdIZmkkdpZh20SAG3t+ClCr2WJUFXvJzQEAAPD09ER8fX6bbExMTMXv2bJH6UGajMh6QDbzx/rfQpmUz1P65Btq1ao72bVr+ai0wsHd3JATelRpRlh3hLfWSwVSlweca8oLuI3dWD2n5u/RcslMaioDXTVRkyAsPQl5UGPKiI5AXGgjd0T3I6u+imiutRT6+JRavObVrI8CXrvq1btkCqQt2FAPeqa4wel9EfnwEChKixT3l72oWD5MGu0oD79Og++ji4gTPWZOhTQpXDLBkA28RyFKVhlnIC76PguQ4FDyPQ35yHPLDfKDbNa/SX2mNLrIydvggdHJqL65Y2aFta3Tu2AErF83G83DfSh/dpfQLWuzo0qEdmjdtjDq1fhZG205tWyPY+6rcc6ho4J3cuShdiVKWbDQG3jLgpIrurrLAa87ZNfv9zf9p/5v73vzffGxZ97KBV5MQiKC7F+F389xbFux9qfJHeEvJy82d2hn0s5N26XDQtobq9JbSrjz7ZAMvLUYTVRh6tkP2UDdkj+6P7FF9kTWwMzJcGsuFXYoEKxThNf+USz/jhvveQOC9y4gLvCcuQGFx1KfkT55lbCsFvJS/mzvDDdrFQ6FdRnVgB0EzqbNU2FUaeClnly44QZd4NvtDpvbmvqQDr7nc2Lwe0P4yELoNE6DbMg26DROh+cW9OMXB5uoM5udRIMJLKQ3p0X6gL32B3pcR5H0ZT4Pvw3zRDyX9YfaLrfdZ8YEI8L4samdT/Wyz0dUSXyQEqxt46fLg4ztKMe0CD47wlgUoVXB/lQXeivC1bOA159ZW1H2FRHgpzeFXkwm7SkR4zRedoEjvmyatMkPJlAeFgJcmW1NaTKmToszJXingFYvWKHf3DSNIlWlKpTTI1Phd4KQY8FLeLpUoe8OkpTIoCLy/pVlF+ua3XsdvPWbJazSV8SX0t/ot87EKjfA+gWaiS1GOPuXp22haT3cG3oqAHZU8BwOvgo5i4O0kLVpbKSK8JWG0IrYVBN4yJzeJE6ViwCsZbMuCZKWAtyK0Nz+HksArLYprhtvS7hWI8Jq14fvSv/S+pUuFAu/jojQGWngqwRh4FQQcFXbNwKug0xh4GXhFVLi8cMzAKzViWxbYlrWfgVfiFdNKg1lL9jHwlvpLzFtQKvGL6lt9VyTwhj0uiuqObQ+NBNPOH4jCbK7DqyDmqKprBl4F3cXAy8DLwDtWykUgyoJSJfcz8DLwvgV/SoJlZe27goE3d5wzcse0l2LaeQMYeBVkHLV1zcCroMcYeBl4GXgZeO0JTZzSYOHP9pUVNivD66pA4M0Le4zcsU7IHdVeimnmMvAqiDiq65qBV0GXMfAy8DLwMvAy8NoQKeaUhiqV0pAX+hi5Yzogd2RbKaaZw8CrIOKormsGXgVdxsDLwMvAy8DLwMvAa88xYPNzV2SEl4B3dAfkjmgnxTSzGXgVRBzVdc3Aq6DLGHgZeBl4GXhtBg4bftbmlAZOabB5/FUo8Poid2R75A5vI8U0M/uhMDtdwVmeu1aTAgy8CnqLgZeBl4GXgddm4GDgtf/P+jb4wJ7+l/LcFQ28FN0d1lqKaWb2ZeBVkHHU1jUDr4IeY+Bl4GXgZeCVAh3lBC6O8HKE1+bxV5HAG+KL3OFtkTuktRTTTO/DwKsg46itawZeBT3GwMvAy8DLwGszcJQTdul5GXgZeG0efxUNvMPaIHdwSymmmcbAqyDiqK5rBl4FXcbAy8DLwMvAazNwMPBySoMNY8Dm8VeRwBvsg5whrfHCo4UUy53aG4VZnMOrIOaoqmsGXgXdxcDLwMvAy8BrM3DYADsc4eUIr83jzw7AmzOoJWQYA6+CgKPCrhl4FXQaAy8DLwMvA6/NwMHAyxFeG8aAzeOvooF3UCvkuDeXYrmTe3GEV0HGUVvXDLwKeoyBl4GXgZeB12bgsAF2OMLLEV6bx19FAm+QD3I8WiFnYHMpljupJwOvgoyjtq4ZeBX0GAMvAy8DLwOvzcDBwMsRXhvGgM3jr6KB170lcvo3l2K5Exl4FUQc1XXNwKugy4qANwJ5Keo0Y8B1aGZ2h2aKfcG1vM+fO80VOQvGIWfhJFXai8VTkbJ1N57vPKJSO4yc7Uug3T5HvXZ8I3QX9qvXzu+B7vAy6A4tVqcdXQ5jjI9qgdeYFo3LJ49g18YN2L1JnXbiwH7odFoFZ8pXXecF+eDFwBbI7tdUiuVMYOB9pS5vMfAqOAYKTFrVflBTVMAYdAva2T2hndZFnbbYA6Y4P+Slx6rS9NHBeFx3CO7+1UWVdu/DLki/cBl5GaS/Cn9azohF7rLJyOrWEFk9GqvSsod0hDH8oTr1LzFmTGnRqjwHQ0okWjV3xu/+37/h//n931Vp//miDlJSUhWcKV91LYB3QAu86NtUiuWMZ+B9pS5vMfAqOAYYeO0MygS8sU9UOVESIOqig/C43lDc+3snVZr3P7oig4C3BLiobVsAb/dGyHZrok4b6gJjmPqBV23jxvx6CXhbt+goQPf3/98HUKN98WXdigPewEd40a85XvRpIsVyxrmhMDNNwVmeu1aTAgy8CnqLgZeB1zzxleeegdf+UWEGXvv7oDzvncpyDAOvdRNsHgFv3+Z40bupFMsZy8BrnQccuzUDr4L+ZeBl4LVl4mXgtT9sMfDa3we2vIfsfSwDr3UTrADePs3xomdTKZYzhoHXOg84dmsGXgX9y8DLwGvLhMvAa3/YYuC1vw9seQ/Z+1gGXusm2LyAR8ju3QxZbo2lWM7o7pzSYJ0LHLo1A6+C7mXgZeC1ZcJl4LU/bDHw2t8HtryH7H0sA691E6wA3l7NkN2jsRTLGcXAa50HHLs1A6+C/mXgZeC1ZcJl4LU/bDHw2t8HtryH7H0sA691E6wAXremyO7WWIrljOhWrghvXl4eIiIicOvWLTx48AAZGRl4+fLlb54MPZ6bm4snT57gxo0b8PX1RU5OzlvHUbu0tDTR7/Xr1/Ho0aPf7L+wsFC8litXriAmJuat/n7zRfGDrynAwPuaHHL/YeBl4LVlwmXgtT9sMfDa3we2vIfsfSwDr3Vzap7/I2T3kAi8w7uhMMO6Kg1arRYrV67E999/jx9//BHVqlVD+/bt4ePjU+bJEMRGRkbCzc1NtK9Ro4Y4vlu3bggLCyuGVIJXgtwmTZrg66+/RvXq1fHll1+iTZs2uH//fnG7kk/09OlTNGrUCH/4wx8wb948FBQUlHyYt61QgIHXCrGsbcrAy8Bry4TLwGt/2GLgtb8PbHkP2ftYBl7rZk2T/yNkdWuKrK6NpNiLYd1QYCXwnj59Gv/85z8xa9YsAasEqPXq1YOzszMyMzNLPSGC5AEDBuCrr77CsWPHRCT28uXL4ri+ffuKSC8dmJ6ejmbNmgmQpsejoqJw8uRJcVznzp1FhLjkExgMBsyZMwcE0O+99554TQy8JRWybpuB1zq9rGrNwMvAa8uEy8Brf9hi4LW/D2x5D9n7WAZeq6ZMCOB1bYqszo2k2Iuh1gGvXq9H//79UatWLSQnJxe/+G3btuHDDz/E1atXi/eV3AgPD8enn36KadOmIT8/v/ihLVu24P333xfpC7ST0iT+/e9/Y+LEicXRXEqfGDx4ML799ltQNLfkjZ6PYJv6+eyzzxh4S4pTjm0G3nKIZukhDLwMvLZMuAy89octBl77+8CW95C9j2XgtXS2LGpn8nuErK5NkdWpkRTLHtwNeWkpIg2AIqNmo9SC0m4pKSkizYCitSVzdv39/UXUd+nSpaUdJvJ1P/jgA6xdu/a14yha/Mc//hG7du0Sx1H/DRo0QK9evUS02GQy4fnz5yJ63K5dO2RnZxf3n5qaCkqJmDx5MkJCQvD5558z8BarU74NBt7y6WbRUQy8DLy2TLgMvPaHLQZe+/vAlveQvY9l4LVoqixuJIC3SxNkujSUYok922L/+rXYvn17sVG0NigoqPg5S27QorBPPvkEM2fOFLvN0JuYmIj//Oc/GDNmTMnmxduxsbEid9fd3b14oRqlI3h6euJ3v/sdFi5cWAzbJ06cQJ06dUBtlyxZgp49e6Jhw4a4dOlSMSwTkK9Zs0bk+tJrotQHjvAWy13uDQbeckv37gMZeBl4bZlwGXjtD1sMvPb3gS3vIXsfy8D77nmyZAuT30NkdmqCzI4NpVhI+/poW7c2atd+ZZQPe+DAgZJPW7wdGhqKjz/+/9l7D6gskm3v+973nntuWO/33XvuPWcc8+jMOU50dBTFhKJgVgREshkEA+acc84556yYsxJUJOecc0ZBMqjzf9du52HQEeXhqQZa9rPWtpvu6uq2qrrr17v/tasFVq5cWbGNVkje8MMPP8DOzu6d7ao/yFO7aNEifPnll5g/fz5u3bqF9evXSwPTyMNLAE1SBwLZZ8+eoXfv3tI+kiu0adNGGhTn7+9fAbzkUSYoPn78uHQKBl5VSWu2ZODVrPw+ejQDLwOvJh0uA2/dwxYDb93XgSb3UF0fy8D70S7yDzvL/Dzx3FAXzw10hFjOuOHIjY9FXl7eO1ZaWvqHc9MGlYeX4LXyT+XhnT59euXN0jp5gcloQNuWLVukQWmdOnWCubm59HejRo1AUghKQ1pf0gePHDlSOldBQYEkVzAyMpIiMaSkpIAGwI0ePRrDhw+X0lBINE9PT7Rq1QqzZs2SQppV1gn/4YJ4Q5UlwMBbZdFovoOBl4FXkw6XgbfuYYuBt+7rQJN7qK6PZeBVrx8t8/XE82G6eD5ER4jljh9RZVgylVyh8hWSxpY8wASclXW+vr6+kueXgPZjPwLRFy9eSB5hgtnr16/jL3/5C0jGQL/z58/jf//3fyX5QuV8jh49iv/5n//B48ePQfII8iaT57dv376Sde/eHf/xH/8hDXgzMTFBTExM5cN5vZolwMBbzYKqSTK5gbc0MxpP7lzGljVLcfviCRSnR0HkA7402BWFSy1RuEAQuM43RuFyaxTtX4SSy3tQ+uAcii/sQMESc3HnqHyt62xQFucntEwKUyMQ6HYfF4/vw65Nq7B78xrcvHAMqRHeKMuKEXouWYC3kSHcmg2Hf58ZiJm7HwlrTyFq2k54d7KDW2MjuH0pzp41N0HO7ftCy4TaN5Xzi4QgON+6gP3b12P7hpU4c2gXwrycQIAh8h6QFXjHDkT+qukoOrkLRecOonD/BuRNt8YLC13kijJ7Q5SGewotk4Rgd+zYsEJ67tCzR2XXzx0V/gwSWZeqvOi5SW3l0on92Ld1HbavX4ET+7ch4Old0P2tSidiKQfw/uf//Qpjxjpg5cotWLFy8zu2eMl6fP9jD/zLvzYRZm2+7YqMjMyadIFqH0PAm2Ogi+zBOkLs+dgRaoUloygNtra2EvQmJSVJ109gvGfPHjRr1gwuLi7StqKiItB+8sbSj9IQIFeGaJp0YuLEiWjXrl0FoF68eBF//etfQcvKaXft2iWB8NOnTyVg3r17tzRAjUKjkU2ePFkCZ9L6rlu37p0IEtIF8D/VKgEG3moVU80SyQm81OknBrvDYoQxvv76G8yYbIfC1HChD2vRwFswzwjFJ9bidU4aXmen401ZKV7FBKNwxSjFAG+w+wMYDxsK42GDYTPGCmNHmqOfXm9MnzwBiSHuQstfFuBtbISw0WuR7x+NPM8w5Nxyx0ufSOQ89IVfr2l4SkAsCHrlAt6XyWHYtXElevfSgfkII9iMtsSAfvoYYWQAz0c3hL54yAG8L2ja1PGDUex4AuWxESjzd0eZhyvKgrxRdHwXci371Gvg9Xh4HW3b/gSz4YaYZDsGE23HSMuDOzaiKD1S6D0gAjrfz4OekwTp5iaGsB1jDdsxI2FkMAhDBvbD9fPHhL40yQG8/99/fY2lyzbg4uUbuHjpumSXL99EfHwSnj9/ga7dBguDXQLnWgfeobrIGaQjxNQFXurpSX9L8gHS4gYFBUneWNIAk8SAoigQqNIAMy0tLdy8eVOCA9pGutvbt2+DdMCkx129erU00IzglUKP0Y8kDTSZhb6+vjQBBYUpo/N17dpVCj9GkoYP/eLj46UoDRSTV/WrDMyqbbz8eAkw8H68fDTaKyetbb8qAAAgAElEQVTw0kN769plsDQbjr56upjpYC/cOyEceOcbo2iLA4rPbEbRztl4FemH8uggRQFvjP8TXDyxHzF+j5GXGIzn8UG4fOogOnfSwv5t64V2lsKBt5EhvH4eh5e+Eci56yV5dZ+1NINfr6l46RWO9JP38OwrM7gJgl45gPdVThwCntxFzx7dMXfGZKSEeyEvMQRP7l6WAHj21IlC60A48BLsWvZG4dFteJUcj4Iti5E7bjBejOqHXDsj5Nob13sPr+ej6+jerQucb1yQPLrkFSWjL0yiv3K8D6si/i7JjEZSiDsSQzzwIiEYeUmhiPZ7jDHW5jAxNEB6lK8waJcDeP/lXxvjz//eHP/+ny0qrHHTtnjw8DHu3nXGX/73H/g/f2osDHprFXh9PJEzWBc5A3SE2PMxI/A6W72Z1sjLS+HFaKY1io1Lk0kMHTpUglgCAgJNkihQfF2SKNCPvLu0TjOnkUeXjiMgJgkESRtUx1FYtKtXr4IkCpQvnYNmWqNBbDR1cGUZhXTQb/+QhIFmZlu+fDnPtFa5YNRcZ+D9QIFRo6MZUVJTU0GhRWr6JiUX8JZlxcLt/hUYGQyWPqePMDbAzCn1H3gL5xujYMHwt7bIFOXB7ooDXurQVabqfHNiA2E8bAgc7MYjPzlMWGcpGnjJextksAClGc8R5bATT/9mIHl0aXvCmtMojk2Dn46DMC+vHMD7OicOzjcvoIt2Z1w9fagCsJ7HB2KstYUELS+TQ4XVgWjgJe9u3iQTybNbfPkEcq364IW1Pl5Y9gbtk7y/BMX1WNJAwNujWxc8vnMZ9AKiug+Usnz//qXrLsuOxcrFc6SXpvigZ8L+T3IAL8FsZfvnf/kSvfsYISU5DTNmLgH9rWTgzR6ki+wBOkLs+Sj1gFfV15NHlqYKdnV1lQaM0cAx1T5aUoxc0tuS5pd+tI3AlsKd0XaSJpBXljS9quNUqEHQS7F3aSphmsXNy8tLGohWFezScSShoOgONKju/fxU+fLy0yXQoIGXYJZE5TY2NpLWxtnZWYqhR3oaegMj8ToFiKaG+bHGWFUxywW8aRHesB8/GlvXLkdSqCcUA7wqfS2Br0KB90OdelygG/rr98GSuTOEetnlAN5g48UozXyOSPutePq3YZJ8gYA3fsVxlD9/ifCx6/H0i7fbNZU2yAG8VP5U3iRfmDdjMpJCPECw+/jOJQzoqyfpMUsFaqmFA69ZT7xcNR2v83JReGw7ii4eQZmvG0rdHqJg71rkjh2IXAUAb8eOv8BmtBWmTbTF0vkzcfvSCeQmBgsDxQ/dZ6K35SWFINDtHp7dv4qje7ZAr3cvzJxiJ3l9RZ1LDuD9kD5346bdiI6Ow49tewqF3VqXNPh4InugLrL76Qix5yPVA17qy9UFyk+lr7y/8npV3PD+9poc834e/PfbEmiwwEuN6M6dO5Iuhj5D0KhICiVCuhuKjUfx9uzt7aXPCLq6ukhISFC7zcgBvMXpkTi4cxOszU0QH+SOjCg/Bl4VSL+/lGHQ2vsdYUFKOHZvXo1uXbXx8NrZCo/j++lq8rdo4CWpgk9nexRGJiH99AN4fDcKbo2N4dV+PHLue+F1SRmiZ+2RPL+awi4dLxfw0ifph9fPSdBLGt4JY0di8IB+WDjbAcmhbwdoifq0LgfwFmxfjjdlZXiVGINS17soOrkHJY9u4FVGKorO7Eeutb446JVh0Frg03tSma9ZOh8bVy6G3bhR0O3ZA5vXLEVuYogioJc80+FeThhjZS61nW5dOmPsSAsEPXsg9B6WG3jJk9viq1/g4xOI48fPgwa0fQiINdlWq5IGb09kD9BFdl8dIfbcWn3gVbuj5wMUUwINFnhJp0PTBw4ZMkSaxcTHx0cKTk3aGxKi037yAN+/f1+C3iNHjlRZqQTPNAd2eHj4O5YQEyH04UnQ5ON6GyZGBrh65hBKM2OQEeXLwPs+6Kr+lhl46eWDRqbT4Km1y+ZLWsCagG1VxwgHXhqM1swEccuPoTguDVlXHiNpywVp+dI7HK/yixA9e1+9B17STe/dshbDhw3B3OmTsHrJXAlWCFie3nMEjcKvqkzV3S4ceM17oXD3asmLVObvgdyJw/HCrCdyxw1CyYPrkq43b5rVW2mDCFmDDMBLL3mZ0f4oSouQ9NLpkT5YvWQeunbpDLd7joqROZD8KMLbGX6P7+Dskd0YP9oKR/duRUGKOFlSbQDvcJNxSEvLgKWlPf7p/zRSPPBm9ddFlr6OEGPgrRJbGuSOBgu8NNqS5rTeu3ev1PmQZIFCf/Tr1w/5+flSYyCQJe2MmZkZPhRwWtViSKczYcIEtG3bVhKhS2L3tm1hN2602AE0KWFYOGeqFJnBy/kGQjwewu3+VQwZ1B82Y6wR4HYPuQkhwiBb9KC1ivBmn4GkgToyp+vnMbCfPmY52IM6fYIpUZ5FyksO4CX5gnsrc4TbbET6qfvIcnyM+GXHED1zD0ozXiDcdlOF1EFTL68cHl7yzN11PCV51E8f3FkRmYRCZY0baYGRFqbIivGvv8Br1hMFmxfjzetXKDq9VxrARnpdgt78LYvwpiAP+atmvIXgegi8ldt3ZS2s3+Pb0O7cCUf2bEZ5Vv2P3Vv5/0H32qvsWBzatQl6ur3g9+SOsPYjJ/CSd/ff/qM5jh47Cz+/IHzVuqNwOUNtSxpKvT2R1VcXmX10hFiOJXt4VZzCS6BBAy/pdE+cOFGh2VmwYAFMTU1B0wSqfiReJ2kDAW1VP4JlCkxNoywrm4/HU6EAlBHtJ0kZOml1gL6eLvT79EIf3Z746ccf0L7dzxg0oC/cH10Tdk4G3j923KpOXnrRGNgPk23HSjpq0bArF/BKENvIEE+/MMTTLw0lSQNpeeNXHkdxQgb89WbU60Frr3JisWvTauh064IIL+eKlwzavmfzGmkwFX2qpvITYcI9vKThXToFr1/moujYzoqIDBLwbpyP1/l5yF8zq94Cb1VlGuzxED26d8W+7esgUkNd1fnk2P7gyhnJS3393BFhXmq5gbfNd10RERmDzZv34N/+vYVw726tA6+XJzL1dJGpqyPEsi1G4HWWelEaqurnebvyS6DBAi+NqKTp/Gi+axo1ST8aXUmD2FR/0zZKZ2FhgXnz5lVZ2+QJ/qMBojW89AnR/8lduN66WGE3LhyXBuuMtBgBp5sXJO/W+96LmnYODLx/hCb6XO7ldAMGQwZKAwfjg9yEgNWH6kgOD68UcqyJccVgtadfGsG74wS89AxD5iUXuLc2r9dhyShKw/F9W6HVsQNcb1+qeLmjz9MLZjmgn34faVDbh8qzJtuEAy8NSJswDOURQSh98gC5Ywe91eta66PI8TheZaQgb/boeitpoGcLRcGoLBshac/pQ7vQscMv0uC1+h65ga73ZVJoRduhdkHbKMxj1y7aeHb/irB7Wk7g/dOfm8LGdgbSUjPQt7+pLHKGugDeDD1dZOjqCLEscwbeKsGlAe5osMBLntsNGzZgzpw5FUGhyVNLRvCq+lGgaAo6ffLkSdWmai9FA6+qwyZvlsoyo98OWqPP6gTEqjQilsKBl2ZaWz0OxWe3oOTSbrxOjsbrjCSUXD2AkvPbUbh6LAoojUqDq+lSBg1vjP9jKRxcL53uOLJnCx5dPy8NoHp0/Ry8XW6hWOBMX3IAL3lzIyduRfzqkwgbtVaaZe35Qx/keYYjoO8syfOrqZRBdbwskobsWIR4PsSwoYNgYjgUJGugWQZJx0sDB9ctW1AhcxBxD4gGXorAQCHICnatxqv0ZBTfuihJHIouHJZgt8jxBHJH9q23g9YI4HZuXIXNq5eCPKFU9ptWL0HvXj3gYD++QtojouzlyiMp1AMLZ02VZumj6BI3zh/FqsVzodO9G+bPcsDzuCBhz1G5gJfkDP/1l29w/cY9PHr0GH9r9L0scoY6Ad7eusjoqSPEskwZeKsNLA0gYYMFXoJairVL0RcIcqv60WA0CihNcfPU/ckFvJU7AtIrTp1og3XLFqKw3gOvEQp3zkJ5TBBep8TidXqiBLzSekIECrfPqPfA6+18E4P662Ngf30JfGmGJpUtmjNV8n5Vrh9N1uUC3vDxG5Bzzwt57qHIfRKI1IM3EaA/E08VMrUwQYT7w2vSxBPmJkYS+I62MseeLWulKZ6pzEV95RAOvKTXJS/vqH4o2LMGZd5PJW9vWYCnFK0h19ZAnHeXNMCCB62RZ5cGeFFkBqnsjYZijJUZtq1fLnnWRZW7JvfNp47Njg2QZlqjQWo0WxyFdaQQazRxjOgoH3IC73ffd4eT0xPYT5yDf/23ZrLIGeoMeHV0kCHAGHjVpZbPO32DBd73q7WyV1e17/1t7/+tSlfVsjaAtyQjWvKqEPiK7mxk8fAuNkPhWhsUrrP9gxXQvnru4aUR6hT79UNGMzRV/tT7qY73U/tlAV6aRa2ZCTy+HSnNuub14xg8a2n61rPbyEjYtMLk5ZXDw1u5zCiOamqEtwQp9KVD5V0XeR/IAbzSpBLk6SUgtRmKvInDkTdhGF7QJBQiY/DKALxUtkVpkSBopFnuCBApNCLNskZ1I7LsK9e1yHW6RpoZjiJNpIR5ITnME1mx/qBwd6L/D3ICL4Uga97yF/zf/7+1bN7d2gdeL6Tr9ka6Tk8hlmVqyhreqgClAW5n4JWx0msDeEV2BO/nJRx4NZUoqHu8DJKG98tIzr/lAF6V3KA2lnIDr5xlr8pbNuAVEYGhOnkI9vCqyoWXf9T3f6hM5AJeAtHastqMw1vq6YX0nr2R3r2nEMsyYeCVEXEUlzUDr4xVxsA7XJweV13YpfQMvEI9tupCMgOvrrgpgqsDtx9Kw8ArTI/7IaD91DYGXvU62BJPL6Tp9EZqt55CLHO4KV5xlAb1KuEzTs3AK2PlMvAy8H6qQ/zYfvbwVs+L9rEy1HQfe3jrvg40rcO6PJ6BV70OVgW8aV17QoQx8KpX/p97agZeGWuYgZeBV5POloG37mGLgbfu60CTe6iuj2XgVa+DLfHwQmr33kjp0kuIpRuZ4nUmx+FVrxY+39QMvDLWLQMvA68mHS4Db93DFgNv3deBJvdQXR/LwKteB0vAm9KtN1K0ewmxdEPW8KpXA593agZeGeuXgZeBV5MOl4G37mGLgbfu60CTe6iuj2XgVa+DfQu8fYTALkFz2jAzjtKgXhV81qkZeGWsXgZeBl5NOlwG3rqHLQbeuq8DTe6huj6WgVe9DrbE3QvJXfogqZOuEEszMGNJg3pV8FmnZuCVsXoZeBl4NelwGXjrHrYYeOu+DjS5h+r6WAZe9TrYCuDV0kWSAGPgVa/8P/fUDLwy1jADLwOvJh0uA2/dwxYDb93XgSb3UF0fy8CrXgdb7O6FpM59kNhRV4ilDjHDKx60pl4lfMapGXhlrFwGXgZeTTpcBt66hy0G3rqvA03uobo+loFXvQ6WgDexkz4SfuktxFKGWDDwqlcFn3VqBl4Zq5eBl4FXkw6XgbfuYYuBt+7rQJN7qK6PZeBVr4MtfuaFBC09IbBL0Jw8mIFXvRr4vFMz8MpYvwy8DLyadLgMvHUPWwy8dV8HmtxDdX0sA696HWzxM28kdNRHQrveQix5IAOvejXweadm4JWxfhl4GXg16XAZeOsethh4674ONLmH6vpYBl71OlgC3vgO+oj7uY8QSx5giVeZ2epdBKf+bEuAgVfGqmXgZeDVpMNl4K172GLgrfs60OQequtjGXjV62Al4P2lL+La6gkxBl71yv9zT83AK2MNM/Ay8GrS4TLw1j1sMfDWfR1ocg/V9bEMvOp1sEVu3ohr3xexP+kJsaT+lniVwR5e9Wrh803NwCtj3TLwMvBq0uEy8NY9bDHw1n0daHIP1fWxDLzqdbAEvLHtxAFvYj8GXvVq4PNOzcArY/0qHnjj/FB88wCKr+1Vpt09itKUUNR1p1fT85ckhyNl90kkrN6rTFuzDy9drqMk5CFKQh4o0B6i6NYRFJ7cqly7sBeliUGKvQdqeu/Ul+NKM6Nx+uB+rFy0FCsXK9N2b92Ogvx8GXvK37OWgPfnfoj9QU+IJfZl4P29dHmNgVfGNqB04C3LjEJZehjK0hRq6eEoy4pWbmefFYPy7Ci8UqiVZ0WgxOsSilwOK9ZKo5+iPDsa5VlRCjZqR+yprasyePMiHr/mJijXXqbg1zevZewpf8+agDembT9Ef68vxOL7WrGk4ffibfBrDLwyNoHPAnhTQ1CmWAtVPvCmR6A8Q5lGL0olXpdR5HIExa5HFWd03aWRTxRb/lK7ofaj5Jc+BvW6f1l5nohf37ySsaf8Peu3wNsf0d/3FWLxfa0ZeH8v3ga/xsArYxNg4K1rWGbgrUtYZuCtBy8qDLx1D4xKh/baBN6n3oj+sT+iv+srxOL1GHhlRBzFZc3AK2OVMfAy8Gr0GZUkDezhrTPPMHt4WQah0f2rdNBVXX8tAm/hU29E/dAfUd/2FWJxfaxRzlEaZKQcZWXNwCtjfTHwMvBq1GEy8NYZ7JIEg4GXgVej+1cFjEpf1irw+iDyhwGI+LavEIvtPZKBV0bGUVrWDLwy1hgDLwOvRh0mAy8Dr6b6bZY0sKRBU+CuZeCN+H4Awv/RT4jFMPDKSDjKy5qBV8Y6Y+Bl4GXg5UFrdamjliQxPGiNoVcT6K1N4H3ig/DvBiLs7/2FWIzuSJSn88QTMmKOorJm4JWxuhh4GXgZeBl4GXhZGqHRc0ATWBVxbC0CbwEB77eDEPbNACEW02s0A6+MjKO0rBl4ZawxBl4GXo06OpY0sKSBJQ3snRUBrZrkUcvAG9ZmEEK/HiDEonuOYuCVkXGUljUDr4w1xsDLwMvAyx5e9vCyh1ej54AmsCri2NoE3sc+CP2HOOCN0mHglRFxFJc1A6+MVcbAy8CrUUfHHl728LKHlz28IqBVkzxqG3j/PhihrQYIsSgdljTIiDiKy5qBV8YqY+Bl4GXgZQ8ve3jZw6vRc0ATWBVxbG0Cr6sPQr4ZjOCvBgqxiB5jWNIgI+MoLWsGXhlrjIGXgVejjo49vOzhZQ8ve3hFQKsmedQi8Oa7+iH4myEI+mqQEIvoPhbl6Tky9vKctZJKgIFXxtpi4GXgZeBlDy97eNnDq9FzQBNYFXFsLQLvS1dfBH0tDnjDu49FGQOvjJSjrKwZeGWsLwZeBl6NOjr28LKHlz287OEVAa2a5FHLwBvYeigCWw4WYmHdGHhlRBzFZc3AK2OViQbe0sxoeDnfxPb1KzB/5hRMm2SLlYvm4MHV08hLDBHeMZRlRqEsVSy0lqYEIzPcHZeP7sLCmZMxzX4cVi+aBZdrp1AQ7wfaL+6coSgTHHQ/LuAJ5s90wOQJYzHlPVu+YDbSIn3E1YMMwFuQFAy3uxexc8MKzHaww8zJE7Bz/XIEP72DktRQiPRGlqWFocRLPg8vTf0bd2U7ltlZYNZoYwSf3SgUkOWYWvhVZiS8H13FFNsx79qEMTh/eBfK0sOF1oFcE0/kJobgzuWTWDZ/JmZMnoCta5ch6NkD0DNKo5c8TcBMjWNj/J/g/NE9WLV4LhzsxuPE/u0oSotUxLWryrcsKwbJoZ44c2gXFsx0wEwHe2xevQSBbvdA+1TpNF4y8MpICZx1bZYAA6+MpS0aeIvSIrB/+3pMmTBOAt01S+fDfvwo6PXuiQPbN4D2a/xwq9RpyAG8uTHeWLt4Dnr16IqpdmOxauFMjDI3QZ9eOnA8vgclyUH1GnhTwrywd+tabFq9BJtXL8XmNUuxbMEsaHXsgJEWI5AdGyCuDmQA3lg/F1iMMMIYS1OsWDATKxfOhonhEBgNHQT3e5eFwpbcwJv38CC2zhqLTu3bomP7tnh8YHm9B97XmZG4efYQ2v3cFgtmTsbmVYuxedUibF69GA8cTyoCeAtSw7Fn81ro99HF1Ik2WLtsPsZYm8HEyAA+LrfEtf9KzyKRz7U3L+JxYPt66PXuBUODQVJdzHKwB/2/RJ5H1ryyYhDt/1h68R5hNAxL58/AuuULMHOKHS4c26tc4HXxRUCrofBvPliIhXYdi7I01vDKiDmKypqB9wPVVVZWhvj4eOTn539gb/U3iQZe8p7kxAUgJz4QhWkRKE6PQmaMv+RhGdhPH4khHkIf2KKBtzwtBMFPbkFXpzsWzZwseXqLEgMR4X4PRkMHYrTlCOTF+NRr4CXPSWlWNEozYySjv5/eu4IunTvhyJ4tkodLmHdFBuDNjPSCy42zSA19hqLkYMl8na+jb59emDd9EvITA4VBr5zAS97XpweXY0R/XUy1MkC3Tr/gyUGFAO+5w+jZo5vkVSePbmlamGS0rgQPb6jHI+j27CG96NELXnFGFKL9HmOE8TDMnT4JL5NChT6HRIPjq5w4hHs5wcf1FsK9nGEweADmTJ2oKOB9mRyKFQtnY7jhUPg9viNdO/UHLxKC8CI+SNHA69/KAH7NhwixkK7jWMNbfWT57FM2WOB98+YNXr169Qd7/fq1BLv9+vXDrVu3oEr366+/qt0YRAOv6sFPQKWyovRIrFw8V+qAYgOeCu1oxANvKHweXUGPbto4vntjhTc3N8YHUyaMgZmxAXIiPes18KrqQLWk8qfPiAQwQc/uCy3/chmAlyQL9Fmdliq4KkoOgd1Ya1iYGCIryksRwJt6czemWBpg/bRROLJ0suKAV6dHN/g4XUNevD9eJgSiJDVMWLm/I0tJj0C5QFkPPXdIytBJqyPuXTmN1zlxUpunl/H1KxZCT7cnIrydxd4Hgj29qhdSAt/smAAYGQxWFPDSdQc/e4A+ujo4vm8bchODkR7pKzlDitMjxcIulX0tShryXHzh99UwIbBL0BzShYFXbXD5jA9osMAbFBSE3bt3Y+/evX+wlStXonHjxrC1tcW+fftw7Ngx5Obmqt0M5AJeeot/etcRty+dkLRzPXW6Y96MKSBdnQrERCxFAy9pczPCnmHCGEvYjrJAyJNbSAt5CqcrJzGonx62rlqM4qRARQFvSpin1GGSppe8LiLKvSIPmYD3HSDKiJC8vcYGg+BgNxZ5cQHCwEsuD2+B82GcXOkA04F9EHVpC06vmqoo4L117jDatfsZliOMYWVmjOkTbXD11H7kxvlXvIS8X0c1/lsG4L17+ZQEvHcuncTr578DL0kbOnbsgEfXz1WAcEVbFgytovJVIvBSmV89c0iSUR3ZvRlzpk2C4dBBsDYzwdE9W/A8LlAs9NYy8Pq2HAafpkOFWLD2ePbwqk0un+8BDRZ4d+zYgX/+539GkyZN0KpVq3esefPm+POf/4wvvvhC2q6lpYW4uDi1W4FcwBvt64rxo60wdFB/dO+qDbvxoxDp4yL2IZcdCzmAlwalBbpexyiL4ZI3aNjg/uhFEodZUyT4pf3iBq6JH7RWuaMlT8utiyekzv/q6UPCy18uD29leCpMDsa+LavRtUtn3Dh7SBjs0jnkAl7/k+swvH8vXFo/EyRtOKUg4CXv+rO7lzB/xiSc2LsFJ/dvlYC3WxdtbFyxUKikRKpnwcBL7T/c2xm9e+lIoJUa7g3yKga53YfBkAH48ccfJBh7/Txe7MufTMCsROCl5w6Bbps2baRn6LrlCyXnB71wUH+wde1yqU4qP6s0Wq9l4PVpIQ54gxh41eaWz/mABgu89+7dww8//IDevXvj5MmT8PLygq+vr2Q3btxA69atsWHDBvj7+yMwMBAlJSVVtgOSPQQEBMDZ2fkd8/d2Fw9B2bGgz+gZkb6SZvfJ3cuwGz8aq5fMk/RbGj3Y3utU5ADe7AgPrF0yGybDBmP72iU4d3AblsyZCuOhA3Hu4HYUJQYoxsObnxKG+bMcMLC/PuID3cR38DJ7eItTQnD11AFJU71h+QLJw1gZhjVdlwN4s+7uw7xxIzBzpBGy7u5FietRRQEvlWlRSggKEoN+0+6GIyfGB6sXzUb3Ll3g73JD6EuHHFEaKJrBsb1b0be3LsxNDDHRZozkXRxpYYr27X7G1bOHwcArY+zhnDgc2rUJ33zzDebNmIwXicHSs4ekDQtnT0N//T6gKBTC+oJaBV4/eLcwhFfToUIsUNuGPbxVkkvD29FggZcGpnl4eMDGxgYDBgzA4cOHkZPzdjQnDVgjGL506VK1WgRpge3s7NCuXTu0b99eMlqfaDMWJRlR4h485HUlCKoEpqShu3RiP3p2747Hdy6/s69yupqsiwbe8rRQPLh8HF06a+H84Z2gAWu07XmUp+ThJVlDrPdDxQAvaRX76ffG0nkzpQGENSnjjx4jI/AWp4bi7sVjGKDfB4tmOSA93EMCLZWuV1PYpeNFA2/J46N4sGsRdLtqYduscbi/a6FkS+3ModW+LXbPs4X38dUocDosJFqDHGHJKpcvrZNR5Abn62ekLwXXTh8UK2sQ7OFVPX/oZY9kVXu3rpPCJJLMgQZtdtXuDJebF0FeyI+27UrPsLpMp0QPL0kazh3Zg++++xZnjuyW+gSqF+oLzh/bK0kdKHylsHKtReDNdfaHV3NDeDY2EGIBnW1QylEaqsUxDSFRgwVeVeW+ePECBw8elDy9Y8eOxdOnTxEWFiYB78WLF1XJPrqkAW0EySEhIe9YbBR9Un8XUIU9hH7rMKhjeXjtrPRJ2vHUQbwS2JEIB97UEJzctwWdtTrA99EVCWxJvlCeGoLT+7dIHb7PQ0fpbzGxeOWVNJw6uANdtDvB6cZ5qXMRXtcyAW9JWpgEWEMG9sOMSbZICX4mHHblAN7Sx8dwacMs9O6qhX462ujfs4tkXbXa47tv20CncwcstDEFhSsrdj2qsckBvB96kSCZw6Nrp98C75lD9Rp4Kz+/6NmjAlt6sV8yfwYGD+iLuECxg2crn1P0uiKBNycOLrcuopNWB5w8uKMi9jHVxZlDO6V25OsqMDxcrQOvETwbDxNiAZ1sOCzZR+mlYe1s0MCrirxAy3l3lnUAACAASURBVNDQUNjb20NbWxsODg5o0aIFqgu8VTWZN2VF4t6yScqQFoG0CB8UpkZInmPqZOgzlqTd6tYFHg+vCT2fcOBNC5VikHb4pT0uHN6JggR/FCcHSZEZyMtI8YQj3O8qwsObmxAsfco1NR6GdJGTTVR+YZEBeCkElvv9yxg2ZAAmjLVGvP/jirBYtK+yB/JDcKbONtEeXoLY9Nt7EHxmA4LOrJcs+OwGbJk5Fp07tMPZNdMR67gNhc5HNIZdOpccwEtlnBHuKUkaaKIPis6QE+WNlQtnons3bfi73Kz3kgaCzrykEBSmhkvPIZI4eD66jn56vbFhxSIpZKJoMBWdH0WVoHBqFN2ABnxRHF6avIdCewl/ca18TwtaTw7zhInRUEybaIPMaD+UZERLyxlTJkhh1hJD3MX1BbUMvB7NjODeeJgQ8+tky8BbFaA0wO0NGnhV9a0C36KiIjg6OkJXV1cCXgpLpslP9KC15FAPaXavNUvm4fShXdLbPc2u0027sxSTUQlRGhIDXDHGyhT6uj2xZdVCnDmwFfOnT5Ti2NKEFAXx/ooAXv/Hd0ChpbauW47ybJm8+DIAb3LQE5gMGyLJSratXYrLJ/bh8vG9kt13PIH8BGXE4VV5cJWm4c2LD8DCWQ5YNn+6NGDt1P6tmGY/XpICbFixQGj5Sy8ngiUNBJ70FWnP1rVYs3Qezhzehe0bVmLooAEYP9oScXJo2QVBogqaX+XEgmIJH9y5URrg1aNbVwwbMhC7Nq2WwnxRrHNV2vq4JCAnYL986gB69Xgboef0oZ2YO2MyenTvKs0aJ1RKV8vA697MGM8aGwoxBl5NCObzO5aB97c6VUEvDUDLyMiQBqvVJBRZ5SYiGnjzkkKlDmb21IkYa22OUZam0kjpq2cOV8zwJdI7IdrDSzIFkjDEeD3EzvVLYTvaElamxnCYMEYavJYV4S7tr+9RGujT4fVzR2EzxlqaxlO2TlEG4I3xcZJmWTM1MoD58GHv2CyHCaCJKdTx4n4srRweXhXoVl7e2bEAtiMGwe/kOiGeXVXecnh4acCa44l9mDvNHmOtzTDSwgRzptrjxplDeBHrJ9TDLhfwUnunWLwzpthJz6CJ40fj0M6NSAr1UIR3lO5fkiHRzIhmww1hamwA+lJjZmIIu3Ejpel6ZbunBcJ7fko4Hlw5I3mn6f9C3t3bF08gPzlMAnZhfUEdAK97Y0OIMAbeykTC6wy8n2gDKhD+RLIP7hYNvGXZMdKnq4KUcGk2IwJgmg6zlMDoAwPaNH1oywG8Km1uUVIg8mJ9QZNOvIzzq5iEQrVfzFI+DS/NdEczSpGnRdNyrvJ4GYCXPqmTlzEvzv8PRhMg1HdJgwpGKy/znQ7h+f39oPi8lbdrui4H8FL5koa6ICkIL6ke4gOkdZHl/s5LiAweXmqv9AmdnkP0DCLAEupRFAiFVd1bFEqNZBkkY6hsst/Tgv5vKpilpaoeaFl5e1X/d7W31yLwvnD2x7OmxnBrZCjEfDra8qC1D9JJw9zIwCtjvYsGXrUfVBo+XOUEXjFAG/IJ+YN8wFsrdSED8L4DQxkRwry5H8q3tjy8moJtVcfLAbwfKidZt8kEvLXS/jV8fvE1CgqNVsvA69Z0OJ42MhJi3h3tGHhlZBylZc3AK2ONMfB+Ckjl3s/AKytMfQKYGXjlfaGoVt0y8Mr3BaahAHktA+/TJiZCYJegmYFXRsBRYNYMvDJWGgOv3ED7qfwZeKsFRZ8A15rmwcDLwMteVkFe1rqE61oE3ufOAXjSxASPGxkLMc+O9uzhlZFxlJY1A6+MNcbA+ykglXs/A29NYVXEcQy8DLwMvAy86nSxBLyPm5jAtZGxEGPgVaf0P/+0DLwy1jEDr9xA+6n8GXhFgGtN82DgZeBl4GXgVaeLJeB1bWICl0bGQsyDPbzqFP9nn5aBV8YqZuD9FJDKvZ+Bt6awKuI4Bl4GXgZeBl51ulgJeBuPgMsXw4WYeweWNKhT/p97WgZeGWuYgVduoP1U/gy8IsC1pnkw8DLwMvAy8KrTxT53CoBz4xFw+mK4EHvGwKtO8X/2aRl4ZaxiBt5PAanc+xl4awqrIo5j4GXgZeBl4FWni81xCsCjxiPw8IvhQsytgz1K0nLUuQRQ7P28vDw8ePAABw4cwMmTJxEWFoZXr159NJ/Xr18jISEBFy9exL59+3Du3DnExsaCtqt+aWlpOHToEHbs2PEHO336NF6+fKlKKl1HdnY27t+/L13HwYMHpXVNJ8SqOEEDXGHglbHSGXjlBtpP5c/AKwJca5oHAy8DLwMvA686XSwB78PGpnjwhYkQe9JhotrAS0A5b948/OMf/0C3bt3Qrl07aGtrSwBc1URUtN3Hxwd9+vTBjz/+iJ49e0rH9e7dG15eXhK8UjkEBwfDyMgIurq6Fda1a1f8+c9/Bi0zMzMriismJgZjxoxB27ZtoaOjI+VJy0ePHlWk4RX1SoCBV73yUis1A++ngFTu/Qy8NYVVEccx8DLwMvAy8KrTab718Jrh4Rfk5dXcnnaYpDbwkqe1SZMm2L17N7KyshAaGor+/fujV69eyMjI+OB/hzyzxsbGaN++Pdzc3EDQHBQUhH79+sHAwAAvXryQjiNvb2Fh4Tvm7OwsnW/x4sUVYJyfn4/Ro0eje/fuePz4MXJyciQjTzN5fflXsxJg4K1ZuVXrKAZeuYH2U/kz8IoA15rmwcDLwMvAy8Bbrc7yt0TZTgF40NgM978YIcQeqwm8BQUFGDFihAS3z58/r7h0kicQBN+4caNiW+UV8tx+9dVXWLVq1TsShlOnTuGvf/0rXFxcKid/Z3358uVo1qwZPDw8KrbfuXNHyu/y5csSBJeVleHNmzcV+3mlZiXAwFuzcqvWUQy8nwJSufcz8NYUVkUcx8DLwMvAy8Bbrc7yt0QEvPcbm+HeFyOEmOsvE5GfmIHy8vJ3rLKutvL1paam4qeffoK9vX3lzZKXVwW07+z47Q9fX180bdoUW7dufQdMHR0d8ac//UnS4H7oOPIYkxeXvMCV9bskqfjmm29w6dIlzJkzBxYWFtLy2bNnn9QSf+g8vO1tCTDwytgSGHjlBtpP5c/AKwJca5oHAy8DLwMvA686Xexb4DUXArsEzVdaWWD+BAfMmDGjwqZOnQqSEXzoFx0djZYtW2Lp0qXSbpVml0C4TZs2mDJlyocOQ0pKCrS0tCR9Lq0TUJOHmM71T//0T1i5cuUHQfX27dv48ssvsWfPnop86ZwEuP/93/8t6XpHjhwpXTtpfEky4erqWpGWV9QrAQZe9cpLrdQMvJ8CUrn3M/DWFFZFHMfAy8DLwMvAq06nme0UiLuNzXHnC1MhdrGFKcYZmsHc3LzCSLJw8+bND15WeHg4WrRoIQFq5QQUXeGHH36AnZ1d5c0V6xTBYefOnRIsW1paYsuWLZg8eTJ+/vln/Od//ieWLFnyB+ClYygNgTRpc1U/gmUTExMJlGfOnCnpfQmCyYv87bffYty4cSguLlYl56UaJcDAq0ZhqZuUgVduoP1U/gy8IsC1pnkw8DLwMvAy8KrTb6qA9/YXphBhzh0mozA5S/K4EkiqrCo9bHx8PFq3bo358+e/c9mJiYmSxIAAtKpfUVERzpw5I2mAKVoDweyRI0fQqFGjP0gdKA8KYUbyCRXAqrzJtCSv7n/913+9E5GBrtna2lry8qanp1d1Gbz9IyXAwPuRwtF0FwPvp4BU7v0MvDWFVRHHMfAy8DLwMvCq049mOQXidmNz3PrCVIg5dZiM4rTfB59VvhYVYFbeRlEZKAQZeWkJjlU/T09PNG/eXPLiqra9v6T8yMhzW1JSIml5adDZX/7ylz8MdqN0NBDub3/7Gy5cuPB+Vli0aJEEyqTZrfyzsbGRwp6RxIJ/6pcAA6/6ZVbtI4pDwpA6cS5S7WYr0qJs5uKW1TLcsFquSHvosAn5EX4oTw9XrmVEoTxTmVaWGYXSeF+Uxngo02I9UBJ0FyXejijxvqJc87mCEqWa7zWUpQRDqeBclhWD0pRglCYFKNboxfXX1+XV7vc0SUjAe6uxOW5+YSrEHnWYjJIqgPdD10nREEjvS/KFiIgICWDJs7pu3TpJrkDgSz/S55LEgMKF0Y8AlgC5sueY4NnU1FQalEa63so/8gbTPtL9JicnV94lrd+9e1caBEfaXhVIU4xeisM7dOjQdwa4/eFg3lBlCTDwVlk0mu8ofOyJmB/6IvpbPUWaz9/7Y3cTM2xvbK5IO9LBHrkBHihPDVWmpYWhPDNasZ29UiHl9+uOQ4nfDRQ5HUSR0yGF2kEUPTqgWCt2PoyyRH/F3gMS8Cb5oyzOS7mWHFTLwGshBHYJmtUFXur1yatKUgMaOHb9+nUpHu/333+PSZMmgUCVAPTatWuS9IE8uPQj0HVycsL27dtBIcWuXLmCsWPHgo6jSA2VvcWUPjAwUAo7Rp5ciiDx/o/i+FIcXpr0gmZYozzp/BQpgjzD/KtZCTDw1qzcqnWUBLw/9Zegl8BXWaYPnzYDsKeZOXY2s1SkHdWaiNwAT5QTOCrVGHjrFHbeAu8hEHgp0pwOodjpAIqdDirTXI58PsAb740yJVotAm+mUyBuNLbA9S/MhNiDj0gaqurECUCvXr2KQYMGoWPHjpLEgby+pO+lHwHvrVu3JCgm8KUfAS95Zfv27YtOnTpJRjOqEeyS17jyj44/e/asNIMaTSpR1Y8iRjg4OEjnp+vQ09PD4cOHpUFsVR3D2z9eAgy8Hy8fjfYy8NYtKDPwfgb6wey6/T8w8NYxKDPw1j0k1ybwOgfiWhMLXG1kJsTud6xaw/uxzp2glGY0i4qKkgaXqaIi0HYymgmN9qli59I2AlsaTEagStMCq2ZXo/PQftWP1knuQGlKS0tVm99ZqtLTeQm06VwUs7eyZOKdA/iPapUAA2+1iqlmiRh4GXg19iyzh5c9vJp4l9nDW6ft5x1JgxK9u3TNtQ68lrjSyFyI3es4pcpBazXr1fkoJZcAA6+MtcfAy8DLwFu3Htrf9bg1uw728LKHV5M2xMCrXgeb6RyIq00s4djIXIgx8KpX/p97agZeGWuYgZeBl4G3ZqCpCWSIPJaBl4FXk/bEwKteB5vhHIgrAoH3Lnt41auAzzw1A6+MFczAy8DLwMvAW6eD3VjSwJIGTaUUtShpIOB1bGKJS43MhdgdBl4ZCUd5WTPwylhnDLwMvAy8DLwMvBp4iXnQWoMatEbAe6mJJS42Mhditxh4ZSQc5WXNwCtjnTHwMvAy8DLwMvAy8EpxeDX1tNbV8bXo4U0n4G1qhQtfWgixW1pTedCajIyjtKwZeGWsMQZeBl4GXgZeBl4GXgbe6nW0BLwXBQLvTS0HFKkx01r1rpJTKbUEGHhlrDkGXgZeBl4GXgZeBl4G3up1tAS8F5pa4fyXFkLsBgNv9Qq+gaRi4JWxohl4GXgZeBl4GXgZeBl4q9fREvCeb2qFs19aCLHrDLzVK/gGkoqBV8aKZuBl4GXgZeBl4GXgZeCtXkeb6hyIs02tcOZLCyF2jYG3egXfQFIx8MpY0Qy8DLwMvAy8DLwMvAy81etoJeBtZoXTjS2E2NVOrOGtXsk3jFQMvDLWs2jgjf5eHzE/D0Bsx6F/sJi2AyDt/6EvYoSYPnzaDMCeZubY2UwwuDa1wI5mltj1lTX2fjMGe/8+Bntaj8LOFlZCz3VUayJyAzyhMXSmhb2TR1lqKIqTg5Ef54+8WD8UJASAtok+j5Sf4KmFSzOj8TI5FHmJIX+wl0mhKMmIrtO4pdUJ8k/B/IvTo1CQEoa8pBAUpISDtlXnWHXTiJ54osjpEAofHULeg/14cW8fcu7ulZb5Dw+A9gmH41qIw1vwcD+e39mD53f3oPDRARQ7aQC47x8rU1gyug8KUyPe3gtJoShMDUdppvg2JOfEE6VxXiiK9kBe+FPkhj1BfoQbSmI9xYcxq8UoDQS8p5tZ4WRjCyF2pdNUHrQmI+MoLWsGXhlrTDjwftsHGQs2oiQwDMW+QSj2eWtFnv5IdViG6G/1BMEuQbM8wLujqYUEtce0p+LpqjOIveuNBOdABJ96hIuGy+o98BLYZkV44tTeLZg4biSszYdj6Zxp8H5wBSXJweKhVzDwpoR7YtokW5gaG8B0+LAKG2FsAJvRVoj0cZEFHNUFzarSv8qJQ5SvKw7sWI+Zk+1gNtwQG1cuxsvkMFmuWw7g9T22CqsmWWLC8IGwHKwHuxGDcGiRPRKvbBMPvbID7wG47V+CUQb6cLAcikTHrfUeeOkF6cHVs1i5aDbGWpvD2mIElsydDrd7V1CUFim0HckFvAS7yd73cWDjMtiONIOliQFm2I/BzRO78TLiKWh/magwZrUKvEE41cwaJxpbCjFHBl4ZCUd5WTPwylhnwoG3TW/k7DqOspR0ZK3ehYz56yVLn7sOCQPHKMLDS8B7Snc2El2DJHuy8jQezT4In13XcG/KbuxsLs6bLIeH90W0DxbPdkDvXj2wbulcnNizGVNsR2NgP324372Isve8wRp7fQUDb25CMG5fOokLx/bhwvG3dmzfNuj31sUIIwOkRXgL7fCrAteabn/zIh7njuzGgL56GGNtDu3OnWA/fjRyE0NkuW7RwFv46CCubZqNBeNNsG++LY4vnyzBr173znCwNED6jZ1ivbwyA2/Sla2wGzEQndq3RV8dbURf2FjvgTch+BmszEzgYD8eh3ZuxNG9W2AzyhK6vXRw68IxlGWLk+HIBbxZQS6YbjcaPbt1wdpFM3B02ypMGW+NrtpaOLlzrVhPby0Cb4pzEE4KBN7LnaaikMOSyUg5ysqagVfG+pILeEuCIhCnY4roH/r+biR3ECJlUEkixHt4CXb3/H0MQs44IcElEMe7TnsrY2huid1fjZRMpHxCDuD1uHcZXTprYd/mVShMCERpSiiSA5/AytQY0+3HIy/WV6yXVzDwUgf8vvm63kH3rl2wY8NK0Kde2l9TIK2N49IifBAX+BTpkb6wMDWG/fhRigFeki2QnCHv/n4UPDqIQqdDePnwAA4utEOn9j/D4/AKxQBvwYP92DfPBiMN9DHNahj6KQR4SQYT4vEIuYnBKMmMltp8fJAbTAyHYsLYkXgeHySs/csBvOUJPvC+cxbdu3TGluVz8fI3KUOC5x2YGQ7GaHNj5IU9UaSHl4D3RHNrHGtsKcQuMfDKSDjKy5qB9wN19ubNG5SWlqK8vBy//vrrB1JUb5NswBseg6ThE5HQ1xrxPUYgpm1/xCgBeJtY4Ezf+chLyMDDWQdwqP1EnO4zF8e7T8feNuOgkjuIgl7RwEtyBsfje6HdSQtPbp7Dq/RwCW5JyrB60Wzo6fZEhPu9eg287wMpaXZ3bVqFHt26wvPRDZBk4P009envyjBOmmPy1CkJeN/X6JKe9/ndvdgz1wadfyHgXakY4PU4tBymA3VxZcMMrJ5kpRjgpfasaueq9lSSEYVpE21hbDAE6ZE+wu4BuYDX5+5Z9OymjWPbV1d4c7ODXTDeagTGWAxHXrhygfdYc2scbWIpxC52Zg9v9WilYaRqsMBLUPvy5UuUlZVVQO3r168RHR2NgwcPYuHChVi5ciVu376NvLy8GrUGWYB3xzG8epGHItLvBoSiwPkZMpduRay2Yb2XNGxvbI5btttQklsAz22OiL7picygOGT4x8B3/y0c6exQrzW8BLy3zh2GVscOuHvhaAXwkqd3/vSJ0Or4C1yvn8Hr30BYYzkDySMEe3jfh9fMKD+YmRjCdowVnscHCuvo3z+PHH/nKRh4n9/Zi8vrZ2DPPFsssjGFfo/OmDHSEGk3lSFpyLy5E7NGGmLheBNk3dyJNQoD3srtkaCUZA5GQwdhKn2lSRInj5EDeEmbmxXsgjmTx0veXOdLhxH08BJO7VqHIf31cHr3OpTEKlPDSx5eAt4jTSyF2AUG3hqxy+d6UIMF3oKCAkybNg1ubm4VwBsQEAA9PT387W9/Q5s2bdCiRQs0adJEAt+ioqIq2wB5gckIolVGfuECVw/E/NRfmNQg+ts+SJu0BNlbDyF14mKkTV2Ol5fvojwzB9mbDiK6bT9h55Jj0Nr2JuZ4OOcg3rx+jdz4DDxZfgrnBy+Gy6JjeJmYiYCj97Dn69HCPL2iPbwEsOHP7kG/dy9MsxuHKM8HyIn0gtOVU9K2tm1/wp0LR/E6I0Kcl1dm4H185zI6aXXEqQM7KqQOlWGgPq8rGXhJq7vExhSWQ/QwoFcXmA/qA/dDK0Aa3/e9wBr9LYOGt/DhfpxfMxUm/XrC/8Qq5D/Yp2jgzU8Ow7Z1y9CjWxfcuXRS6EufXMBLg9ICH16Elckw9OiqjX59eqGbdiesmOOAzEAnxQ5aS3YOwlEG3ipZg3doVgINFnhfvHiBjh074tKlSxKslpSUwNbWFt9//720LSoqCkFBQZg+fTpatWoFJyenKkuaIPfJkye4fPkyHB0dJbvs6Ij767Yi+keREPqbZvd7fcmbK4Uh6zAEL6/cQ2lMAuL1rQVKG8RreAl4aYAa/QKO3pdCkZGMYVdLa3huu4K8xAyc7DWrXgNvcVIQzh/agX59dDGgbx+YDBss2YQxVvilfTvcu3QMrzPeSh3qu4e3KD0Sa5fPRx/dngjzfIRXOeIG69QGKCsZeEnLm3t/H7Lv7EHk+U1YOsEMo4f1Q9TFTfUaeIseHUDEuQ0YMUAXhxfZgeD35f19WD3J8h1JA6UTEp5MprBkqvZZlBaB88f2SrC7fcMK5KeIjfYhF/Cm+j7ATPsxMDMagrN7N8L54mFsXjYHg/v1wZGtq1AY7aFIDS8B75Hm1jjUxFKInWcPb5Xc0hB3MPD+BryJiYno0KEDdu3aVeHxpQaRnp4OfX19LF68uMr28erVK0yePBlaWlro1KmTZFqdO8Hml66I+kHwYLL3tLoUiixj8Wa8ysxGstW0+g28jc1xZ8J2vCotx8OZByrkCyR1uD1hO4pzC3DJeAV2NHkbukxTLa9oD68q1i5Bb/izu7h59hCundyPQJeb2L9ljTTwy+uBY4XUob4Db3zQMwwd1A9zpk2SYtmqIEApS6UC7/vxdktcDsPryEr01O6I48smS3F6NfLqOh/+HZoFe3gLHx7A7rnjodtFC7vmjMPpFZNxYtkkjB7WF107tsfO2WPhfXQFlAC89MLneOogevfsgfUrFiIn7q2khyBV1D0gB/DSoLUrh7dDW6sDHpw/UOHNfRnphlXzpqJf756IcbuhTOB1CcLhFtY42MRSiJ3TnorC9OdV9t28o2GVAAPvb8AbExODdu3awdXVtaIFkEyBBq6R53fChAkV299foXTkMc7IyPjdMjORcu0uYn4S6+F9PxJD9Hd6yFq7B+UZ2UgaMaleA++OJuaShKEg/TkeLz+Jnc3fTjRBgPtgxn4UZuXi/JDF9RZ4CWBV0FsZZl/G+WHKhDEwMzZAeoibODmDzBrea2cOS3IG+oxLHbPIjl4UMHwsH6UC7/swS8Dre2w1emp3wK4546XoDe+nqfHfMgDvvvk2MOjTHUb6OjDqqwNDfR106dgOP/7wHQb07IJjSyfWe+AtzojE9XNH0FevN1YsnIOsGH8JckXfA/IArzcOb1mJLp06IuDBRQlsVXF3aTsNqlVtFxKLtxbDkiW5BOFgC2scaGopxM5oTwX1N/zjEqASaNDA+8svv2DWrFm4ePEi9u3bhx9//BE3btx4p2WQ1GH06NGS3vedHdX4o+ixp1ANb0z7QVK83ThtQ8S0G4TYDkOQaGADmniiyDMAcT1M6jfwNrXA/u/HI+aONxIpLFm36ZKs4WhnB0Tf8kKqZzgOtbev15IGAt6UoCfIjvBEQXwAcqK84Hh8D3R1uuPE7k3iJ5+QScNLEQ5mT50Ig8EDkBzqKcyj9TFAFbWPZlmjuLsUnszcxAg2Y6yQGuEtzRxHYdVEnYfyER2Hl2ZUCzy1DslXd0gzrOXe24+UazuwecZoKUrD3e3zxU4+IRh4yXObfXs3kq9uq7D4y1uw2GYE9Lp3gteRFci5sxtFpEV+f9a0mvwtg6SBIjLcdTyFAf30MGvqRCSFekoTThSmRYC8viKhVx7g9cHtU3ulQbKHtqwARWfIj3RD7LNbsB9tAYMB+qAQZUJglyavqAPg3d/UEiKMgJc9vNWAlQaSpMECb25uLnR0dKSBaV9//TVat26Npk2bYuvWre9UfUpKCnR1dbFt27Z3tlfnD9FRGuJ6mSH//hO8vHYPz/edxosjF1DsG4ySiFikTl6KaKHyCfEaXpVEwXHEaik6Q+x9X3jvuIqY217IiUzGbbsdb72+v83Gpkpf06VoSYPKw7t+2XzMnGSLDcsXYN60iRjYTw9L505DVriHWO+ujB7eUI9H0OvdC6uXzgMBgEhIlDMvCicV/OwBVi6ag3kzpqBbF23o9uwhra9aPBdJoR5C/y+igZemEp450hD2IwZjzWQrrHcYCXuzwdDtqoUlE8yQcXP373KEytKEmq4LBt4PQazSBq0lh3pIL3odfmmPBbMcsG3dcmwlW7sMR/dsERqtRA7gJZBN9XuIGXZjoEdtf4oNNiyeJYUk09XpKk1CURyjTA0veXj3t7DG3qaWQuwUA291UKXBpGmwwEu62/DwcPj5+b1jpOWt/IuMjMTcuXNBERzU/YkG3phfBiNt+kq8OHYRL28+xMvrD5G94ygSje0QI3hwnBxRGghcpVi7LaxwdsAiaXa1yKtu8Nt/E5eMlkuD10TG4pULeF2un8GK+TPhMGEsFs2cguunD4BmYFMBcWW5g8brgj2WBKMEje4PrkkeXpp0QqRHS07YVV27r+ttKWYqxd+daDNaMlqfMWUC4gPd6jXwFjw8CNe9S7Bh2kjMsDbEJLMhWG5njptb5iLz9lvYfV/jW2M5A0FyLQBvwcP9uLBm+13BzgAAIABJREFUKpbZmUle3w9BcY23yeDhpS8ac6ZNrGg3duNGQmWL505DRpSvsDYkB/CSfIEs1e8BTu1ejzmTbTBpnBWWz5mChxcOgrS8ZfHKDEtGwLuvhTX2NLUUYgy86lLL552+wQJvbVSrcOAVOpOaaka1qpbyeXhr6rFV9zg5gFdjgFV36mEZgFduKP2c8hft4dUIXmvi5a0F4K0xzFZH4iAD8NZm+5QDeIVJFUiuUB2rZUnDvpbW2NvMUogx8NYG6SjnHAy8MtYVA69lRSQGdWFVRHoGXmWFGatNEKnuuRh4BWlxqwO3H0rDwFs9KK0OuNY0TS0Cb6JLEPa0tMbuZpZC7AQPWpORcJSXNQOvjHXGwMvAq7FHmD28wj4vVxdyK6dj4GXgrdwe1F1nD696HSwB7+6W1tjVzFKIHWfgVa8CPvPUDLwyVjADLwMvA6+yvcwMvAy86kJu5fQMvOp1sAS8NBHRDhrvIcCOMfCqVwGfeWoGXhkrmIGXgZeBl4G31nW7lbW+rOGt0y8EDLzqdbAJLkHYKRB4jzLwqlcBn3lqBl4ZK5iBl4GXgZeBl4FXAy8xa3gblIaXgHdHS2tsa2YpxI4w8MpIOMrLmoFXxjpj4GXgZeBl4GXgZeAtixMYJqymg89qelwtDloj4N3+lTW2NrMUYoe78ExrMiKO4rJm4JWxyhh4GXgZeBl4GXgZeBl4q9fRxrsEYetX1tjS3FKIHeoyFfk8tXD1Cr8BpGLglbGSGXgZeBl4GXgZeBl4GXir19GqgHdzc0uIsIMMvNUr+AaSioFXxopm4GXgZeBl4GXgZeBl4K1eR0vAu/kra2xsbinEDjDwVq/gG0gqBl4ZK5qBl4GXgZeBl4GXgZeBt3odLQHvpq+ssaG5pRDbz8BbvYJvIKkYeGWsaAZeBl4GXgZeBl4GXgbe6nW0DLzVKydOVbMSYOCtWblV6ygGXgZeBl4GXgZeBl4G3mp1mYhzCcKGr6yxvrmlENvLHt7qFXwDScXAK2NFM/Ay8DLwMvAy8DLwMvBWr6OVgLeVNda1sBRie7pORX7a8+qdnFN99iXAwCtjFTPwMvAy8DLwMvAy8DLwVq+jJeBd18oaa1pYCjEG3uqVe0NJxcArY00z8DLwMvAy8DLwMvAy8Favo411CcLaVtZY3cJSiO3uOhUv2cNbvcJvAKkYeGWsZAZeBl4GXgZeBl4GXgbe6nW0vwOvBVa30NwYeKtX7g0lFQOvjDWd7+KJ0DYDEfr3/oo037ZGuGa8EFdGLFGk3bVbj5fhfihPD1euZUajPFvZ0Kjk6y8JdUKJ1yWUeF1WpBU/O4fCyxtQeH61Iq3o0nqUxngp9h4oy4pBSYw7SsKcURKuTCuN9cSvr0pl7Cl/zzrWJRCrW1lhZQsLIbazqwN7eH8v3ga/xsArYxPIfeQD96ZGcG88TJEWoDsRxdH+KE0NU6iFoywrGuVZMco2Bt46A56yjAiUpYcr16I9UbBmHPLnGSnSCpaYozTEtc7qX9OXNQl4A26h+NkZxVqp3w38WlYsY0/5e9YxLoFY1coKK1pYCLHtDLy/Fy6vgYFXxkaQ+1AFvIZwb6wse/blMAToTkJJbADKMyKUaekRb0GXgVGxwKApcGh8fEYkyjPCldn+CdZjCHjHomCekTJtsRlKg5UOvDdR4nYaJc/OKNJK/a7XKvCubGWF5S0shBgDr4yAo8CsGXhlrDQG3joGZQZeBl1NX3YYeOsWlBl46xySaxt4VwgE3m3s4ZWRcJSXNQOvjHXGwMvAq7GHUVNg4+M1g34GXgZeDe6ht5IG9vBWt5slScPyVlZY2sJCiG1l4K1u0TeIdAy8MlYzAy8DLwOvwgfcMfAy8DLw1pqkIdolEMtaWWFJCwshxsArI+AoMGsGXhkrjYGXgZeBl4G3LjXwrOGt2/bHHl71OlgC3qWtrLCohYUQ20we3nSeaU29Wvh8UzPwyli3DLwMvAy8dQscGpc/e3jZw8se3trz8LoGYklrSyxqaS7ENnWbwsArI+MoLWsGXhlrjIGXgVdj4NKgs+VzC4BtBl4GXg3uQfbwqtfBRrsGYnFrSyxoaS7ENjLwqlcBn3lqBl4ZK5iBl4GXoVMAdGoAHBqXPwMvA68G7Y+BV70ONtI1EItaW2J+S3MhtqHbFOSxpEG9SviMUzPwyli5DLwMvBoDlwadLZ9bAGwz8DLwanAPMvCq18ES8C5k4FWv0Dh1tUuAgbfaRaV+QgZeBl6GTgHQqQFwaFz+DLwMvBq0PwZe9fpNAt4FrS0xt6W5EFvHHl71KuAzT83AK2MFM/Ay8GoMXBp0tnxuAbDNwMvAq8E9yMCrXgcb4RqI+a0tMKelqRBb220ySxrUq4LPOjUDr4zVy8DLwMvQKQA6NQAOjcufgZeBV4P2x8CrXgdLwDuvtQVmtzQTYgy86pX/556agVfGGmbgZeDVGLg06Gz53AJgm4GXgVeDe5CBV70OloB3bmsLzGppJsTWsIdXvQr4zFMz8MpYwaKB99mXw+D5jTn8tGzh12nCH8z7p9F41sQQ7l8Og3tjQ42MzhWgOwklsQEQGTi/ND0cefH+SAt1R0LAY8kyIjxRnBIq9DzSNadHoDwrRrOpZT/R2eWnhCEh+BmSQz1RnB4l67lEAmxpZjRy4gKRGOwuXX9GlC+K0yMVc/1UFvR/eB4fiMQQ+j+4IyPSF4VpEWL/DzIAb0lqKHKifZAU9BTx/q7SvVCYFCy+/WdEQJaJJ+YaIn+hKQpWjkLhOlsUrBmP/AXDUTDXUDwcLzZDabCr2DrNjgWBaEFqOFLDvRAf9AxpET4oShPf/uUC3uxHRxF9bfc7FnNtNzIfHEbJszNCrdTveq3F4SXgnd3aAjNbmgmx1Qy8MhKO8rJm4JWxzoQD7xcGCB+9Bvk+ESgIiK6wwuA4FMenI+3gDbg3Hw6C1foKvFlR3ti6dilsRllihNFQGA8bDPtxI3Hp+B7kxfmjLD1cXMcvM/AScF07exgD+uph/CgrpEX6CO+YRUKuKq+SjCg8u38Fc6ZOhInRUBgZDIbtGGtcOL4PeYkhivg/FKVH4uk9RyyY5QDz4YYYYWyASbZj4Hr7otjrFwy8xSkhuHH2IGY72MHCxBDGBoMwxsoMB7atQ3qYh9j2LwPw5hPUrh6H0kcXUB7iidfpCSiP9EPBGhtI++YZiYVeOYA3KxapEd7Yv209RluawnjYEIwfZYHTh3biRXyQ0PYjB/CWeZyF49YF6N2tM4bo6cBAv6dkhn174viq6Sh1VzbwzmptgRktTYXYKgZeGQlHeVkz8MpYZ8KB98th8Gk3FqEmSxBquvStjViKxLWnUJ6bj/jlR/Gs0bB67eFNC3PHtrVLcHT3Jty+cBRXTx3ALIcJ6NalMy4f3ye2w5cZeGP8H8PKzATdumhjyMD+SAnzEtpZqgBV9DLS1xXDhgyAwZCBOHN4pwTtkyeMRRftzrh+9ghefcKrLfp61M2PIOLxnUsYMrAfptrbwPHUQdy5fBJ7t67Fg6tnxNaBYOClrxszp9hi1pQJOH94J+5ePI41S+ZI7X/9snkoEOzpFe3hJagt3D4Dr2KDUe7vilcJEXidmYyC9faKAV7y7K5bvhA9unfD5jVLcffySWxcuQi9e+ngxP5t0pcDddtkVenlAN5yj7M4sWYGumn9gnMbZuPRgRUVFum4U6h3l7zFtenhDXcNxMzW5pjW0lSIreg2Gbkch1dGylFW1gy8MtaXaOD9kNeWvLnJ2y6iJDEDAb2nCfHu0nlkkzSkhf0BagmCjQ0GY/KE0aDPvcIkFDICb2FaONYumw/78aMw08FOMcD7KidOAsTOnbRw/exR6dMuddZRfq7oq9cbC2dPBXmAq+rA68P2jGg/jLU2x9SJtsiJC6iQrRBckNdd6DUKBt6S1DAkBj5BUXJwxX2QnxiIpfOmQ693L0T7OIlr/zJ4eCXZwsIRKFgxCvnzjSVP7+usFEUBb4SPiwS3qxbPRTHVb3Ys6IvB4rnTMWzIQCSFeAhrQ7IB7+oZ0O+hjagru4QD7vuSiNoG3hkCgXc5A6+MhKO8rBss8JaVlSElJQXFxcX49ddfpZpLS0vDsWPHMGXKFNja2mLTpk0IDQ3F69eva1SztQG83j+PRb5fFLIuucDjK1ONpQwqqJYDeCvLFWidjLSL3k7XJNiaP2OSYoD33pXT0md0+qy+bMEsRQHvXcdT6N61C26eP4by7Lca5xj/J5I0Y/XSeSj7bZtQcBTkNX6dEwenG+fQvas2bl86gTDPR3C5dRE+LjeRHRMgATxBhrBrFwy877/Mvb0nwrFvyxp069oZIW536zfwkmSBZA2k451rqEjgdbt/BV21tXH+6B7QCyC1FVqePLAdXTpr4dH1s8K+csgJvD21O+Lu7qXwObURYZe344XzMVngtyEC76tXr5CQkABvb28EBgYiLy+vghM+BgOFhYUICwuDl5cXQkJCUFBQ8MHj3rx5g6ysLPj7+0tp6Rg6R+UfcUdSUhJ8fHykNDExMSgtLa2chNfVLIEGC7zUIC0sLBAVFSUVGTUsMzMzNG/eHO3atUOHDh3w9ddfQ1tbGy4uLmoW69vkcgMvQWnEmLUozXiOqElb8OwLg3oNvKrOnjp5tzsXsXDWFEyzH4+hg/rBxHAIPB9cqfB6qdJqtJTBw0sdGA1QGzfSArs2r8bLpFBFAS917ulRvpg9bSLGjbKE46lDIABeNGcaLE2N4eV8U1hnLww6K8Hy6+fxOLpnC35p3x4LZjrAYoQRDIcOlmB92iQbhHs5iYNdOq/MwEvtm7S7NqMsYDZ8GNLDPeo/8P6m01Uq8Aa63UP3bl2wY8NKlGW9jeRB9/Wm1Uvw008/SrIGFQhr2oblAt6Ta2ZCq/3PMO6vC4O+Ohim3xNL7C0R4bgTxW6nhYJvbQJvmGsgprU2h0NLUyG2rAYeXnKCHTlyBD169JA4gFhg5MiREsBWBQLkNCNAdnBwgJaWFjp27IhOnTph4sSJiIuLewd6ydl269YtDBs2DJ07d5bSd+3aFVu2bKnIvqSkBAcOHJCuoX379iCjfFetWoUXL15UpOMV9UqgwQIveXKpQaenp0sltnXrVrRp0waHDh2SGi55e11dXaGnpyeB8MuXL6ssWWrs9DZGb4UV9uY1cu57wb2pkTAIVXlfVZIDGqCWcfo+CoJj4atlK0zOoMpfjigN1MET8Lrfd8Ty+TMxc8oEDB82GGuWzJU6+7ceL0HhzGQA3qK0COzctEoC3sRgD9DfSvLwUgdOkoW7l09JGlj6jD6gnx50uneT/l/PBQ/a0RQY3j/+VU6sBCrf/P3v0vXfuXwKsYFP4Xj6EPro9sS8GVOQnxwmDnplBt6CpCDs37oGPbp1wYUju1CaFsbAW3ngmwyD1rLjAmA/fjQGD+iLO5dOIsrXFTcvHJfkDN999x32bVuH18/fen7fb3/q/i0H8Ja6n4Xv6U24sHEuvE9tgNfJDdi7aBJ0u3bCzDEmyH50RMHAG4Cprc0xpeUIIba0BsD78OFDfPPNNxKsurm54fz585ITzMrKClVxAEHytGnT0Lp1a+zduxe+vr44ffq0BKoEweT5pR+xgpOTk5QfwfCjR4/g5+eHq1ev4s6dOxWM8eTJE7Rs2RLGxsZwdnbG06dPJZj+61//ipMnT1ak4xX1SqDBAu/u3bvRp08f6TMCfV6wtLTEhAkTJGCtXISXL1+WGm18fHzlze+s0/F0U6xfvx4bNmx4axs34KD9/LdhwjQMEVYZdFXr5N3162KPougkpO67JkVnUO0TsZRD0qDy1hLUlqaHSfKFgsQgeD+8ClNjA2xZs0TSNqrSabwUDLzUeXm73MLQQf1x7ewRKQRWbmIIlsybgUED+knhjUhDSunU7RhrM72Pyy3Joz53+iR4PLyGILf72L15DQb208epgztQkiFYB1vJQ6vp/5M8b3Stf//73yXwLflNs0sQv2bpfPTppSMBjKbnqTheRuAtSgnB5RP7pGte+//aOw+oqK7t/7+1fv+X119eyouJNfoSU+yVYqGp9GbvBbvYe++osXdj773FGgsq9i6gogIqNjqodAYw3//axwwBg5FhzsG5sO9amxlm7j33zPecO+cz++6zz/gReBkWAB312Sh5JnvSWlIOGNWqh5fa9vrpw+jRuQPsG9vCzdkRbVs0FXcM6tSuhdVL5oBCZ7L7gBH9VwXwpp6nLAybRTaGdJpUdmELUs5twtxh3WFRuwaubpol1ctbuB5eAt7WUmCXoHlCPW+8iIzLNV7/0T8UMtC7d28BpI8fP87edeHChShdujQIRPPaQkNDhcNs0KBBIA8ubQS3dFzZsmUFANNrBMwtW7aEs7Mz4uPjs/cTT349hp4Tn3z++ecCiPXvBQcHC6AeMmQIiDl4M1yBYgu8BKh0y4E6KnXMrl27YsCAAb/rSHTrgUIcHjx48FZ1ybs7fPhwAdAE0cIa2aGXmT3Of2F8irC3AWxI/wXQRT/HnXaTpYYz0PlUAm/OAZ3glwZ+GvCdHRrjSeBZeQO+ZOBNjQrBzMljYWNVHz9MGYtFs6Zi/g+T0dzTFRbmdcUktssnDpg08FIGhhmTRqORtRWCr/llD+qJz+6ibw8vcVs9OuR69usyBn2ZZVBIw6aVi1C5ciXs3/bbpDt6nWIwzerUhv/ZI/Lqrwh4U8ODcGTnOjS2tcLYYf0RE3JF9HupdzhUTForAsArfpDG3EfMfX9cO30Y547swYOAc/hpy2pYWpjh0I712bG9xvZdFcD75qQy+l93cSsOLRoH81rVcXjJeE0Db/8KreFdrqUUG1/PG88j4sQYT+N8TstrQI+OjhZcQCEMOaGS4mgJXOfPn5/XYSLOtmTJkgJU6Rz67cCBA/jrX/+KTZs2iZfIm0te4Dlz5oi4XPLW7ty5EwSzdHdYv+3atUsANjncqB7EGFSH8uXLC8eafj9+NEyBYgu85LG1srIStyGePn0K6lj0P3XQuLg48UuMgs7ploKnpydevHjxVmWpg9MvQ7qtobe09DREHb6gJKSBYPRS+VaIO3QBLy/extVKHaWGMxQm8BL8Jj+9hUmjh8C+kS0e3PAzWeAlL+LqxXNErCvFjpK1aeEpJlBVrVpF5LOlFFkm7eGNuY/RwwYIb65+NjrVVxcditFD+onbvBSjbOxAr+p48vCePbJbgMn6HymF1K/e9JgHWDx7qggNoIls0s6vAHjTIu7g5L7NaGJng5GDvEUML10HsmFXlHn/MpKmecnNjavxGN6cfUP0/Zj7SIsKxqQxw+Bgb4dQ/zPS+k9hAS95eTdPHyriek+v8tE08Par0Bp9yrWUYkNqdMGODVuxf//+bKPwAZoAltdG8bYElWPGjMn1Ns3xobtKgwcPzvW6/h+9h5cmvBMD0EYAS2D7pz/9CTNmzBCvEWd8/PHHIn6XYnItLCxAYTTkVNu3b58ActqRvL/kabaxsRGxvRQm4erqiubNmwsnnSiM/xisQLEFXvrVdOzYMRE0TgHm1JHol9dnn30mYnttbW3x7bffiuBzirnJ+astvyq/OHFdDfCW8ECg/RCkP4tB2JR1as6hYKW1zKh7eHjDT0xOo4k6Lx76Izr4Co7u3oBGtlYY2KcbEsIkruwm2cNLgxetRkZ5PJOevbbnj26JdEbODk3wMPC8iI+l/XIOqqb0nGJgCRQpLdmG5QsQHeoPitulbBP2je0w0LsHXj4x7cUnyDPXs2tH8YPj9iVfUBtQmAYtoNGnexfxvzTNJQMvxej6HdgKxyZ26N+rK+5fO4mER4HCEh8Fmn4Mr1hNrRkSJ3YQqcnST+9DVlwEkhcOfZ2qjFKWyVxxTUEML93loNXVHvifxfNHNxEVcgN7Nq2AnU1DLJ4zLTtVmYw+pAJ4X/qtx4X1M/Bg/1LQimsxvqtxcf0MtHRqhBZOdnh88EfNxvAGnQ6Ad4VW6F2uuRTzKu+BerUshNeW7uiS1axZU8TX5jWO3717V3hyp0yZkv02jf0016dSpUro1atX9us5n9AkMwo1oLCHadOmiYnuFJZQrVo1/PnPf8b48eMFQ6xfvx4ffPCBCH84evSoCKm8desWHBwcRN3I+UYblbdy5Up8//33ogxiFOKTSZMmZYdC5Dw/P8+fAsUWeEkeuk0QEBCAcePGiclp1KErVqwoOlnjxo3F63QLIuethvzJ+novZVkaSjXFo8nrkPY0BoEOQ18vNiE5TlhFSENWdDBO7d+Kpm5OYqW1/r280L1zOxHKQLPUA88ckuvlkgy8eQ2AtJSt1iatPb1zWWRpoPjF7p3bC0h0c3JAmxZNcdF3n8nCOulPAEF2+cR+Ud9WzT3h3dNLrJbVvnVzXD11UK6HXTLw0mqCndu2xPfffydWWhvQuxsG9O4qbPjA3ggm77Qpx/BSOrKpXtBdPobMezeQFReJV7o0ZD64jcxgf6SsGI/E4R7yPMoKgPdVfJj40Uf9nRZc6d6lvUgrOG3CSERKXi1RBfBGH1uNvm090M69CbzbeaB3Gze4NWqIVs52OL5skojnzSvsoaCvFWYMLwFvnwqt0KtcCyk20rwH7voHITw8PNsoHWliYmKeQzl5fr/88ksx9ufcgY4hNqCwxzc3AmIyOsfo0aNFZifK7ODm5iZAl5xoNL+Htq1bt+Lvf/+7SHuakyvWrl2LTz75JDtml8IcyOFGnmHK/kDnp4n2NLGe0qUSu/BmuALFGnj1cmVkZCA2NlZ0LLo1QbcvKKwhZ4fU72vIozLgLd0M97rPxINhy0RoA8Hp2+J8C/q6CuClgTw29Bp8927EqoU/YJ7PeCyeNRkHt67Gs9vn5cIuQUMhAC+FORz/aasYQMnTmBcUm9JrNABnxNxHVOh1HNq5XsQhz5s+EVvXLEHIjTNi4QaxjxETdQrj89LkwOBrp0UKqXnTJ2HTqkUIvXEmG4il1UEy8FJWhvVL52DO1LGYNXk0Zk4ajVmTXj/OnzEBYXQ73dSBd1JHpO1fDd3JndCd2AGd73boTuyE7uQuJM8fjMQRpg281Dco7/SW1YtFDP6yudNx6uAOvHj8+vqV2f9VAG/S2Y24vPEHrPcZhHkjemDu8B7Y+sNQkZIs5dzrlGQyU5NpGXhH1+uDF1GvJ4e9OXbnddeWPLkUXtCtW7dcd3UpFy9lTdCD65tl6f+n0EbiB4rJJaY4fPgwPvroIzGpnfahDBAlSpQQIEx3mfV1OHjwoLi7rI/Z7dSpk5gsTxCt36hsgmhra2vBJ/rX+TH/CjDw5l8rg/dUBbwEoxc+c8OF/7pJj93VA7IK4NXHKGZGB+NVTAhexYbiVWwIyPMr4g0j70od7AsDeGnwpBRG5DWi5zIHS2nQ9ga86utIM9FfPX8kjCZ9qTqfynKp3vQZ9PXXfzZp55QMvNTPf+v71P9zm+zrQEmWBvLyDnNH4jC335tM2KVYYQUeXn0f0fedV8/Dsiep6d+T1X+ovLSAg0iTnRuXJqpd2oqMS9uE0aQ18uDKBF29R7gwgff26QD0rtASPco1k2Kj6vXGcwOyNFD6sA4dOoDy4tIENv1G3tVSpUqBwhBoIycZLRSRMyOD3tOrP4ZieUeMGCE8tbSwBG00d6hq1aro0qVL9rH0+vLly/Hf//5XpEIlEKasURR6QZ5d/UblUe5eSqdKMM2b4Qow8BquWb6PUAW8eihV+agCeGV6rvJVViF4eGUNjFzO6wUATE4HBcCbr74rycurBHhzZGrImaZMyXMFwFuYfUwV8OphtDAeCxt4e1ZoiW7lmkuxkQYCLw3uFE5AcEuhAxRTS9kRaE6Pvb29AE0CW8qfSzn69QBMr9HdYdqXcvhTSjOKwaW4W4rp1YMxgTKFUNLrNHmOPMq0CBZ5bWmCmh6yKZ0ZhULQpDfyGJOnl/L6Ur1o4py+vHzDCO8oFGDgVdgRGHjl5RMtECQw8GrSa1uYQPLOczHwyovHLQgoM/BKnYBWEEAubODtUaElupZrLsVGFAB4Kb6XVjSjGFqKxaW5PQSjtPgDbQS3lDaMYm4pJpc28spu2bJFhCHQ6ml0HHlyJ0yYkL0yGh1HG3ltKWSCsjPQJDoqn4Cacvzq96FlhynjA8UN61dZo8UwWrVqlb06rCiM/xikAAOvQXIZtjMDLwPvO4HqjXAD3t/EPL0MvAy8Rlyj7OE1bMykkIbuFVrAq1xTKTbcQODVAyeFD1y9elWkK6UV0MjLSlBLG+1Dnt+9e/cKT67+NVryl6B1z549wntLcb+UbSHnRseSUTgE7UvgTF5iguCc5dMxSUlJog5UHu1H+1O6Mn0dc5bLz/OnAANv/nQq0F4MvAy8DLAmBrCGwgsDLwOvoX0mx/4MvIYNnbdOB6CbAN5m8CpnvBkKvFRbQ4HyXfu/6/28FCrIMXmVw6/lVoCBN7ceUv9j4GXgZeBl4C1QOA7H8L4GbQ5pKFYhDQS8XSu0QOdyzaTYUAM9vFIBgAszOQUYeBU2CQMvAy8DLwMvA2/TgnuJGXiLGfD6w6tCc3Qq5ynFhtTraVCWBoU4wEWbgAIMvAobgYGXgZeBl4GXgZeBV3ZasoJMPivoMYU5ae3WaT3wNkWncsbbkHq9GHgVMo7WimbgVdhiDLwMvAy8DLwMvAy8DLz5G2hvnvZH5wrN0bFcUyk2uF4vxBuQhzd/teS9tKoAA6/ClmPgZeBl4GXgZeBl4GXgzd9AS8DbqXwzdCjrKcUGWfZk4M2f9MViLwZehc3MwMvAy8DLwMvAy8DLwJu/gfamnz86lm+GdmU9pdhAyx6Ij4jL38l5ryKvAAOvwiZm4GXgZeBl4GXgZeBl4M3fQEvA26F8M7Qt6ynFBjDw5k/4YrIXA6/ChmbgZeBl4GXgZeBl4GXgzd9A+xvweqBtWeONgTd/uheXvRh4FbY0Ay8DLwMvAy8DLwMvA2/+BtpAP38ALkqAAAAgAElEQVS0K98Urct6SLF+lt05pCF/0heLvRh4FTYzAy8DLwMvAy8DLwMvA2/+BloC3rblPdG6rLsU68vAmz/hi8leDLwKG5qBl4GXgZeBl4GXgZeBN38DbYCfP9qU90Srsu5SjIA3jiet5U/8YrAXA6/CRmbgZeBl4GXgZeBl4GXgzd9AS8DburwnWpR1l2LeDLz5E76Y7MXAq7Chky5eRbC1O4IbuGrSwjr1RnrYTWREBWvUQpARcx8Mne8HOnVRoYjduguRC1Zp0+avQuKl08iIvIuMiDuaNN2jG0jZtwwp2+dq03bMQ+qRNUg9vlGb5rsRaRd3Iu3qXs1a+s1j+CUjTeFI+VvRAX7X0aq8B5qXdZVivS27IS4i9rcT8LNirQADr8Lmzwi6isSBrkjs76RJS57WE7pnQQyMse8HGLUO6unhwQhx74+AUo7atJIOiFmzCRlR2gVeAerUf+PCNGm6yHtIXjwYSRNaadMmtkHq8bVIv30C6bd9tWnB5/FLZrrCkfK3ogl4W5b3QLOyrlLsNfByHt7fFC7ezxh4FbZ/xu2rSOzvisR+zpqzhL5OSPJh4NU6dL7P+hPwhjYdiICyzggs56I5CyjjjJi1RQB4o0M1+6NVF34HyUuGIGliGyRPaqtBa4dUXwJejcIu1buQgbdFeXcpsEvQ3NuyK8fwKmQcrRXNwKuwxRh42TP6PoHzfZ+bgddEwiAYeN8jKDPwGjLE+vtdR/Py7mha1lWK9WLgNUT+Ir8vA6/CJmbgZeB939D5Ps/PwMvAa2z/Yw+vCXiGC9HDS8DbrLw7PMq6SrEeDLwKCUd7RTPwKmwzBl4GXmMHfC0fz8DLwGts/2XgLX7A2/RLN7iXdpZi3S28EBfJMbwKMUdTRTPwKmwuBl4GXmMHfC0fz8DLwGts/2XgLV7Ae8PvOgTwlnGGuwRj4FUIOBosmoFXYaMx8DLwGjvga/l4Bl4GXmP7LwNv8QLe637X4fGlK1zLOEuxbuzhVUg42iuagVdhmzHwMvAaO+Br+XgGXgZeY/svA2/xA153icDblYFXIeFor2gGXoVtxsDLwGvsgK/l4xl4GXiN7b8MvMUPeN2+dIVzGWcp5sXAq5BwtFc0A6/CNmPgZeA1dsDX8vEMvAy8xvZfBt7iBrzX4PqlC5zKOEoxL4vOiIvkldYUYo6mimbgVdhcDLwMvMYO+Fo+noGXgdfY/svAy8BrDPx2seiMWAZehZSjraIZeBW2FwMvA6+xA76Wj2fgZeA1tv8y8BYv4L3mdw3OXzrDobSDFOtk0YmBVyHjaK1oBl6FLcbAy8Br7ICv5eMZeBl4je2/DLzFD3idyjlJgV2C5o7mHRl4FTKO1opm4FXYYgy8DLzGDvhaPp6Bl4HX2P7LwFu8gPeq3zU4lnOCfWkHKdaBgVch4WivaAZehW2mDHj7OiGhvwsSx7RH0vS+SJrRD0kTvJAwyAOJfZ2Q2M/ZaEvo64Qkn57QPQuCsYPWm8enR4ciKvQ6bl48Dv+zR/Ho1kWkRYVIP8+b55Xxvy7mPkJunMY1v0O4nsP8zx7B87CbmvgMOXVIeBqEmxeOIfD8MSQ+vSO1/kqAt4Ib7tn1QojboN9ZsMsA3KrSEoFlnRFYzsVoCyjjjJi1m5ARdRcZEfLgNe3pbcQFX0HoZV/cOLkPdy8cRcqTm1LPkau+0aFS25Wu1Yjgq7h9yRc3zv6Mu1dO4vmjm6BrI2ffkvFcGfBObIMkn85IWTwEKcvHIGXpcCTP7IHkSW0lWzuk+q5F+m154Jp26zheXD+Ehye2I/DAOlz7aTVuH16P6Ev7Qe/JPJcoqxCXFibgdSjnhMalHaQYA69CwNFg0Qy8ChtNCfAS0A5uitSNc6G7dQWZD+8gg+zONSQvHW/ywJsaFYLTh3ehl1dHeLo5wdPNGe3bNMeW1UuQ9EwucMkYcN8sgwb1gd7dUd/SHG7OjnB3eW1tWzaD/9mfkRn3UPqg/2YdZP1PPzz2b12DhvXrwdmhCe5d9ZNadxXAS0Abt+0oUm6GIiUw5LXdDIHuWTTSH0cixH0wAkwYeDMj7+LGyf3o0bkd3JyawLxuHXi1b4WoOxc1AbzUZ7atWYouHdqgmYcLPFyd0NTdGUP798bN88ekQ68S4J3YBsnzB0B36Qgyn91HZvgDYRl3riBl3RQkSYVe+cCbevMYdi7xQYdmrmjhYg8PBzt4ODRC/y5tcHHncqTelAy9hQy89uUc0ah0EynWnj28CglHe0Uz8CpsMxXAS57dlA1zkPnsIdIObkTy3KFImjkQKaunI2nmAJMH3nvX/ATkdu7QBr77tuLqqYOYMWk0GtvZ4MT+rdIHTFlwqC+HgLdHlw7o1rk9rp/+Gbcv+gq7c+UkXj6+LRUY9edU9Xjf/ww6tW0FxyaNYGPVEHcun5RafxXAG1jeFXfqd8W9xn2yLcRjMBIv3UTS1SDcrtXepD28BLyXjuzCEO/uWDh9Apq6OaOlpxsigy5oAnjJuzt32kTMnDIWJ/Zvg/+Zn7Fzw3I42TcSP2KjQq5L7UMqgDdpcnvozu7Dq4R4pB3ZgJSV45C6fS6yCHzDgpA8qzeSCYqlgK8a4D2zdQl2LJqK89uX4dreVdi7bDqaOTcREBx+bo9cL6+GgbcdA69CwtFe0Qy8CttMOvCSd3d8Z2Q8vIv04zuR0N8VFHpAYQwJ3o7CZIQzUBmqQhq2r1uGOrVr4czPu4Q3lDyi0aE30L5Vc/Tv3Q2JJu7lfQ287TG4X68866ritq4K4E16dhdzpo1H726dMHX8CNhaW2kDeMs6Cw8ueXH1ds+xH9KeRCJywVZp3l0KiVAV0pDyOBCJYf5IDw8S4NvCw1UzwEv9O+5hINKjQl5fv7EPkB5zH2uXzoW5WV1c8t0n9S6HEuD9oTsyH9xC5r1rSPLpJOA2aUJrpB/bjKyEeKSsGG3SwEthC7rbvsi4cwK6oNehEpl3TmLF9FEwq1UTAQfkhlCkFyrwXkXjcg6wLd1EirUz78CT1hQyjtaKLrbA++rVK7x48QI6nQ6//PKLMNmNJxt4CWpT1kxD1vNYJC8eK7y7KWt/QPKSsUgc3Q4JEmJ39cCsAnjpdujSudNh3aA+yCNKIEcDKEHusAG9hacx7OZ5qR4i2bD4Gng7oHP71ji2dzNOHdyBoEu+IIDUCuySJqcP7RS3pM8d3YNFs6fC1lojHt4csbkCeMs4I3zaGqSHxyCk6TBNAK8+vjbt6S0M7tMNWgLevK4n6ve7N65A3Tq1cfbIHtMHXp/O0N08j8z7gUj6oXt2CEO6325kxTxD8qJBJg28+hhdAt/kwKN4eeMwHpzchqE9O6Bxw/oIPr5Fsx7eK35X0aisPWxKNpJibc3bI4bz8MpGG82WV2yBNyEhAe3bt0fv3r1x+PBhREZGgiBY5iYdePvYI3X/WrxKToDuxhlkhN1Fxv0gZEU9Q8btK0ia1sekQxoyYx9g+9rXHl4CRf3gGRl8HW1aNIWlhTn8zx0B7ad/z9QeaXD37tEFjWys0dzDFc6OTdCkkS18JoyC7Nu5qj57xL1r6NW1I2b7jEdy+D0smu2jWeC9VaUVEs4F4OXxS7hVqYW0cAaVHt6iBryxDwLQr6cXPFyd8ejWBanXrhIP78TWIlY38+Ft6K4eR+repUg/uROZj+8h7ef1SJrSQVI4A02Akx/SoAfep2d2YXivjmjb1BlOdtZobF0fG+dMQFLAUc0Dr22pxpBhDLwyiUb7ZRVb4H3+/DmqVq2KkiVL4osvvoClpSXGjBmD06dPIy4uziCPL3mIMzIyhLeYPMbCMjKQEnARif1djc6Y8JvX1RFpR7bhFwAZd28gabo3Eoe1QPLCUcgKD4Pukq+0TA0qPLwEcMG/xvAS4B7csQ5nj+zG5DHDYWFhJjxEV04dRGacaQPvlZMHRVaDp0GXxef5cf4MWJqbCU9pamSw1AFfNvSmRoYIL3vndq3w6OYFkNddq8BLUHq/1SjoouLwZMwSBJRxMjozQ87sDqpCGooS8CY+u4sVC2aKSZw71v2ItCi5GSFUAK+Iz53XDxk3TiErPgJZT0OQFfMUmWF3kLJucrbH11RjePXAG3d5P3YtnYYV00ZhRJ/O6NzCDUfXzkOKxoHXrmwTWJeyk2Ktzduxh1f7nCrtExRr4K1VqxZmz56NNWvWoE2bNqhYsSLKly8PNzc3zJ8/HwEBASBPMAHtH21ZWVminC5dusDLy0tYFy8vzOzWFi/7Gp8iLBt4vR2QdmgzfnmVhZSti36L3+3nJGJ6M6OeIZHSk0lITaYKeGnSy7kje+DdvQvcXZ3QzN0FY4cNxMjB/VDPwhwB546aNDDqAVSfjYG80S8e30Lvbp3RzMPV5L28104fFjPrt65eIlJJxYUFYs60CbC2aoBrfoeRFC4vNEPJpDV9SANlYijvhugVe5AW+gR3bXtJDWdgD++7f3QmR9wTGRvsbKxEH6LrQH99yHpUArzTu0B3/SQyH91FyvqpSJ7VCylLhkF34xQyHwcjZclQzYQ0UFgDZWV4fu0QFk8aCjd7OxHDKzU9WSHG8FJIg23ZJrAqZSfFWjHw/hG6FLv3ijXw1q5dG7t37xaNTmB75coVTJ8+HY0aNUK5cuVQqVIldOvWDVu3bkV4ePhbOwcB74IFC0R4BIVIkPXq3Rvz+nSWC7x9HJC6fQlepaYg5ceJ2TG7CRTqsHslsl7EIcmnl0kDLw2EFBYQ9zBA5LMNvXFGTFobP3IwXByb4PGti9IHTVmDb17l0GchiJ8+cTSaNLbFw0DTjkEmb7S5WR3069UVIwZ5Y/jAPiK1WvXqVdHTqwN2bVyB9Gg5+VRVAi/F7waZdULKrfuI23oEN7/2lOrdZeD9Y+BNiQzGvi1rYN/IFpPGDAOFNeiv7byuk4K+Jh14J7YRuXezXsQg3Xcbkia0eg23FOawajxeJT5H6v5VoElspu7h1Xt66VEXdAKXd61AA4u62Dh3otx8vIUOvI2lwC5BMwPvW7GlWL5R7IF3165dv/PgxsTE4NixYxgwYACqV6+OEiVKYNy4cW/tIPpJb7keAZEnV2pIg7cjkheMwKv4GAG+wvP76yIU6Sd2IyvyscjiYMoeXgJEspwD4H3/s8LrOGpIPySH3831Xs79TOE51Z2AMOdnIM9Wv55dRbq1qGC5aZlkf+ajezZj+EBvDO7XE4P7vjZXJ3tUq1YVXTu1BWXRoDAHGedVCrzlXPCw2xRkxL5EWC8f6eEMDLxvB176gXds7ybYN26EUUP6Z9/VyHlNyOg/VIYK4E1eMgyvXsYh/fhWJE38FWwJhFeOw6uE50g7tNakgZe8tyk3j+WCWsrWcGzdPFjUrYUt8yblei8nGBfoeSEC7+VTV2BdphEafGEjxVqatUFMRMxbx25+o3gpwMCbB/DquwB5bh8/fozNmzdj27Zt+pfz/Sh70ppYRW1YS+hunEXG/dtImtkfiSNbI2XJOGRGPkb6mUNIGOAmZeKaqpAGWnjiqt9BEORSOrI7V05hzPABwlN09dQhAZIqBk5ZA/DToEs4uX8bHgScQ/R9fzFJZ92yebAwq4O50ycgJSI4FwzLOq+McvS6UjiG3qjcxXN8YGdthXtXTyFL4sIZqoCXvLuBX3mIBSiSb4YKT694TR/uIOlRVQxv0qNAPAk8g4fXTqKXVwe4O9sj0O8gHvufRlKYv/x8vJJ+wFBfoT509ufdsG9si55eHUF5tePCbopUZfEPA0X/l9FX9WVIB17KrTuzx+u0ZBS+sHIskmd2R/KCgci46gvy/KasmWTSIQ0UvnB49WzcPLgO4ef2IuL8Xlzduwo92jWDXQNLsfqaVkMaCHityjRC/ZK2UqyFWVsG3nwTS9HfsdgCL6Uks7Kywr59+37n4X2z2clzW5AMDtKBV58fd7o3dLcuI/PZA2Q+CEJmxCOkXzmJpIldfw1nMH55YVXAS+m7aFUmT1dntGjqDjdnB7Rp2VSk+CIY1g90pvoYeO6oSOelr7+Hi9Pr27qjhyL87lWTr/+bumpx0hqB6F2bXkh78AxRS3YgsIK71OwM+olrKoA3M/IObp05jLYtPOHYxA61alRH1cqVRR9yc7LHNd+9Jg28dI1S7uZvvqmIxrY2aNnMXVzHdC23buEpJqHq49vf7GsF+V8F8CZNaiOyNGSEUYabJ8h8EozMiDBkRT557d2lLA0mvPBEzOX9YlU11ya2aOnqgOYuTeBoZyWeH1gxS6QqK5An923LHxeyh5eAt15JWynWnIH3TZwp1v8XW+ClrAoXL15EVFTUO4G3oD1EBfAKLy/l2x3VTuTiTVk9TYQ5JA5vJSV2N3uCXF8nJPn0hO5ZkFSIo3AAitulVdZoWduT+7eLuF39bXS9F7Igg2NhHEOz0m9dOI7jv9b/8M718D97BAlPSKfcoRqFUR9jz0F6B10+CQp1iH94U25bhwcjtOlA+ZPJyjqLFdXCevrgjqWX9PJVAi9laIi+cwmHtq7G3g3LsHfDj7/aMvy0cTnCb583aeCl65Qyq+zdvAp7N63Cnk0rs23vllWaSEtGMCuWD57rjdSN05G2cwFSt85GyuJhr1OSSYNdNWnJaGnhR6d2gFZbI8D9afkMnNq0CGF+O0SoA8GuVj28l05dQYMydrAsaS3Fmpm1RjSHNBQUYYrcccUWeAujJZUA76+LS5AH9vXqag6vV1iTkJlBD7v0qMrDS4BGmQ2y4h8iKz5MPBoLbYV1vB7GyYP1W/3DshPt698vrPrIOs/rzxMmYFfmZ1AV0iDia2mltdKOymBXVQyvLjxIAG1W1D3kZQTE+n306cuMfpQY0kB9jsJeXl+7dP3mNnpfZh9S4eHVT0ZLIvCd0FpMXBOPBLpSYVcN8Oq9tzRRjVZbExZ0Qm7u3Zze3kL08BLw1i9jB4uS1lKsKQNvYaCOZs7BwKuwqVQCb044VfFcJfDKAjUu5+0Ti0xBG5XAq/fCqnxUEdJgNLxG3DHcAywZeAuzb6kEXj34qn1Ut/CEHnyVPxYy8NYrYwuzktZSzMOsFcfwKmQcrRXNwKuwxRh4TRvICnPgLo7nYuAtAJwWBGjfdQwDr6QUY+SxNdQYeA0ZYsnDa1naBnU/byjFPOoy8Bqif1Hfl4FXYQsz8DLwFkfQ1X9mBl4GXn1fKOgje3h91YUq5Axb+KPnhezhtSxti7pfNJRiDLwKAUeDRTPwKmw0Bl4G3oIO9EXhOAZeBl5j+zEDb/EC3ounLsO8tA3qfNFAirnVbcmT1hQyjtaKZuBV2GIMvAy8xg74Wj6egZeB19j+y8Bb/IDXrLQ1an/RQIq51m3BwKuQcbRWNAOvwhZj4GXgNXbA1/LxDLwMvMb2XwZeBl5j4JeBVyHgaLBoBl6FjcbAy8Br7ICv5eMZeBl4je2/DLzFD3jrlmqImiXqSTHX2s3Zw6uQcbRWNAOvwhZj4GXgNXbA1/LxDLwMvMb2XwbeYgq8n9dDTQnmUoeBVyHiaK5oBl6FTcbAy8Br7ICv5eMZeBl4je2/DLzFC3gvnLqM2qUaovrn9aSYMwOvQsLRXtEMvArbjIGXgdfYAV/LxzPwMvAa238ZeIsb8F5CrZINUL2EpRRzqt0M0RHRCkd5LlpLCjDwKmwtBl4GXmMHfC0fz8DLwGts/2XgLV7Ae/7UJdQsWR/VSlhIMcfaTRHFwKuQcrRVNAOvwvZi4GXgNXbA1/LxDLwMvMb2Xwbe4ge8NUrWR5USFlLMgYFXIeFor2gGXoVtxsDLwGvsgK/l4xl4GXiN7b8MvMUPeKuXrIfKJcykmH1tD0RFxCgc5bloLSnAwKuwtRh4GXiNHfC1fDwDLwOvsf2Xgbc4Aq8lKn9mJsUYeBUCjgaLZuBV2GgMvAy8xg74Wj6egZeB19j+y8BbzID35CVU/cISlT4zk2JNarGHVyHiaK5oBl6FTcbAy8Br7ICv5eMZeBl4je2/DLzFC3jPnbyIKl9Y4PvP6kqxxrXcedKaQsbRWtEMvApbTHfjMuJbN0J8S1tN2oshnaF7ehvGDlp8fMHAXxcVjLSr+5B2fpsmLfXsFsSMH4iIHp00ah3xcsMspJ3bjLSzm7RpF3ZAFx6k2WtYFxWCtMv7kea3TbOWvNEHyctGIHnZSE1ayuZZeJWSqHCk/K1oBt7ftOBn8hVg4JWvaXaJuisXEetojRj7Bpq0+J7toXvCwPu+gF0XcQepPy9Gym4fbdquqUhZPRgpy/pq1zaNRgp9jl1TNGmp++dA9yRQs8Arrr24h8iIC9OmxT5A8sJhSOzvhMQBzpq05Cnd8Crhefa4pvIJAW+lz83x7ae1pZhdTTf28KpsMI2VzcCrsMEE8DpZI9ahoeaMIJ2Bt2CeWVmALID3yBKk7pmuSUvZPQ0pqwYjZYk3Upb21Z4t8UbypjFI3e2jXTswV/vAG/t+r0NjrmdddCiSFw1H4kAXJA1y1aQl+/QoZOA1wzf/rS3FbBl4FRKO9opm4FXYZgy82h2ojBnkZB3LwPueIZmBV9ueYRMAZQZewwZY8vB+/7lM4HUtkIc3LS0NAQEBOHToEHx9fREREYFXr1794Yf55ZdfEBcXh7Nnz+LAgQPw8/NDbGws6PU3t8zMTDx8+BDHjh0T+9IxkZGRuXaj8z1+/BhHjx7F4cOHERQUBJ1Ol2sf/scwBRh4DdPLoL0ZeBl4jYFfBl4GXqO9y+zhfa/QzsBr0JCJsycv4rsSdVHx01pSzKaGKyINXGktKSkJM2bMwHfffYfq1avj+++/R6NGjXDx4sW3fhiC2rt376Jp06b49ttvUatWLVSqVAnu7u64fft2LuglmF6/fj3MzMxQo0YN1KlTR5xnwoQJ2eUT7BIM16tXT5RD9ahcuTKWLVvG0JutkuFPGHgN1yzfRzDwMvAy8HJIg9HQakxIBQMvA6+RoRSFGdJAwPttibr46tNaUsy6AMC7a9culClTRkBvWFgYLl26BBsbGzg6OgqPbV4AQJDcrl07VKlSBT///DOePXuG8+fPi+Nat26Nly9fisMIjKl8guJZs2YhODhYeI/Jm0ym3x49egRLS0sBzIGBgbh//z5GjBiBChUq4MSJE/rd+NFABRh4DRTMkN0ZeBl4GXgZeBl4i+/3AHt4DRkxgbMnL+CbEnXwv09rSDGrGi4GeXiTk5PRoUMHAZvR0dHZlSePbMmSJXHkyJHs13I+uXPnjoDR8ePHg8IV9NuaNWtQokQJAb/0GoU42Nvbo3379qBz0b5ZWVn63bM9wRs2bECpUqUEPOvffPDggfD2Dho0KNc59O/z47sVYOB9t0YF3oOBt/gOdMaArv5YDmngkAajYZk9vOzh1ZCH98zJC6hYojb+90kNKdawujOePY0QgEhwqbe3xeNSHC2FD3Tv3j0bPgkAbt68iXLlyuGHH37IkweuX78uAHXhwoW5Yn1/+ukn/PnPfwaBL20XLlxA2bJlMXv2bGzduhUjR47EpEmTRPgCAbB+GzhwoPACUwyvfqNQCE9PTxFeER8fr3+ZHw1QgIHXALEM3ZWBl4FXD68FeWTgZeBl4NX2dwh7eA0bNU+fvICvP6uF8p9Ul2LVKtTHlElTMXfu3Gwj2Lx27VqeFaPQgS+//BLkqaVNP+GMQhQqVqyIAQMG5HkchSBUq1YNHTt2BMEoATUB7Lhx4/CnP/0JPj4+4rjt27fjww8/hLW1NRo0aIBWrVqhYcOG+Oqrr7BixQoB5HQshUFYWFggJiYmuw5UAIF41apVRRhEnhXhF/9QAQbeP5THuDcZeLU9WBUEUmUew8DLwMvAq+3vEAZew8ZQAt6vJALvV5/XRIN6ViKWluJwyQg0d+zYkWfFKDSBPLBTpkzJ9T55fmkSWq9evXK9rv8nIyNDHPPFF1+gf//+ILClSWg04e2vf/2rAGiCZwqN+OCDD0SsL3mFKZwhPDwcLVu2FPtS2AKV1axZM1hZWYmsD/pz0KO3t7eYTEcAzpvhCjDwGq5Zvo9g4NX2YCUTXgtSFgMvAy8Dr7a/Qxh48z1cih1PnziP//23Jr78uJoUq1fNAQ8fPEJKSkouI6jMa6NUYeXLl8eYMWNyvf306VN8/fXXoPjZNze9F5gmpi1dulTE6FIGBvL2Ll68WMTwkleZNgLhf/7znwKKc8bubtmyBZ988omIEaby2rZtK7I4kIc359a1a1cRckFp0ngzXAEGXsM1y/cRDLzaHqwKAqkyj2HgZeBl4NX2dwgDb76HS7Hja+CtgS8/riLF6ld3QGR4VJ6V0INqzjdpolrdunUFrOaM87169arw/M6fPz/n7r97ThBLGRsorIFibimG96OPPhKPtPOZM2dAXmAKdaDy9XWgvL2fffYZ9uzZI8qk2F4CbPL46jcKkXB2doaTkxNevHihf5kfDVCAgdcAsQzdlYFX24OVTHgtSFkMvAy8DLza/g5h4DVs1PQ7cR7l/1sdZT+uLMUsq9sj4i3Am1fNCFL79Okj8uOSt5c2gtIFCxaIVGXnzp0TryUmJiIkJAQJCQnZ++QEWHqRoNTLy0vk2aX0ZrSRZ5Zic5s3by5ifOk1Kp88wJ9//rlIgUav7d27F6VLlwZ5fvX7UNoyivWlUImcMC524D/5UoCBN18yFWwnBl5tD1YFgVSZxzDwMvAy8Gr7O4SB17Cx8zXwVkOZjypLMctq9m/18L6tZrSyGnlX+/XrJ7IqUN5cWkiCUokR4BKg0gpslHN33759ohgC0MuXL4uQBfIG08pp5KWliW6UoUGfqow8wIsWLRJhEzSRjibPbdu2TQA2TWDTe27J0+zi4iLijelcVB6lS6NzUvm8FUwBBt6C6Zavoxh4tT1YyYTXgpTFwMvAy8Cr7e8QBt58DZXZOxHwfvmpPOC1qNbEYOAlLy9BKsXhUkYEgkyKqaWlfWkj4CXQpTRlBMO0EfBSOAIdQ2nNKGMDZSjf4sgAACAASURBVF+gzAupqaliH/2f58+fCy9tzZo1xX50Dlq0Ql8+7UfnuHLlCjw8PMT5aR+abLdz585seNaXx4/5V4CBN/9aGbynCuCNsW+AWEdrxHVqiRfD+uPFyEGI9+6KWE97xDg0RKwko/PE92wP3ZPb0vJY6mIfIPHpHQRf98O5Y3tw6tBO3DjzM+LDAqGLuS/tPAWBy/weQ/VMeBqEB/5ncdF3H04e2I7QG2eU1F0F8KbsnoaIjRNwY/FQ+M3sD98Z3rg8fxAiNk0EvZe6Z7o0o/JSVsldeCJ1aV+ETPfCkWGtcHT4b3ZsWCsET/NCylKJkLzEG8mbxsBo6MxjpbTknVMQtno0zszuhxMzvHFzyVC83D4JKbumyj2fgjy8Lx7dwtkje3Bi37bfGb1O7+f3enqf+9G1HB16A1dOHoTfoZ24fGI/nt65jPToUGn1Vw68A12QONQTyfOHInXpWCSNaYOkgS5IMjL3bs7jC3OlNQLecp9WRamPvpdi5gUAXhroyRNLOXAJOikHb87QBYLRuLg44WmlhSRoo9doYlxoaKh4nTIwUPYFfegBva/fjx51Op0IiaDyb926BQqRyPm+fn+CY39/f+EJpswM+vLEzvzHYAUYeA2WLP8HqADeWGdrJMyehozQYGQ8DEVGyD1kPnmM1GM/I65jC8QSEEuAXhXAm/AkCJPHDoOLYxO4OTvAw9UJDo3t0LenF4Iun5A2yKgcRGmQnDllLJo0soGlhRmqVKmMBT9MVlJ3FcD7YvsUzOjWFO42FvCwsYCbtTns69VBV1cbXFkwWCr0qgDe9GX9sLybCyp/+w2cLWvCo0FtYU0b1MY2bw+kLjN94E3cOQUHJ/VAi0aWcG5YF+7W5uJxds9miNk83uSB977/WXTr3E5cv55uziCj67lG9WpwdmyCR7cuKrkeZF7XBLVnDu9Cl/atRd2burvA3cUR40cMwvOwm9LqXxjAm7J8ArKex+BVciJSlo7RPPCW/bQqSn5USYoVBHj1sJnfkd7Q/fNb7tv2K+zzva0eWnydgVdhq8kGXoLQ5727IOPJI6T5ncDzXp0R36klEufMQFZsDJK3bIDwAEuAXhXAS56fJbN9sH/bWty7egqPbl3A4V0b4NjEDgP7dNeEZ4iAd+uaJVizZA42rVyIehbmmD9jkrQBMuegrgJ4E3dOFR7dS/MHIWTlKISuGg3f6d4Cunp5NkLMlkkm7eEl4F3W1QWN61bDtfEd8HhWt2yLnd9LEx7em0uHwqF+HfRr3gQ3Fg7G/ZUjsWlEJ9iZ18TmkZ2RvFOil1eBhzc1MhiRIdcRfvfqa7t3DReP74NVg/qYMGoIksLvKrkecl4bxj6/ceYwCHLHDh8E/7NH8OT2Jdy+5Iurpw5Krb9K4E0kT+74jsi4dQmZ92/j1ct4pJCXV8Me3lMnzqHMJ1XwxYffSTGzqo0NmrSmEAe4aBNQoFgDL/1SolsE6enp2bcK6DVK/0G3IyhwnG49FHSTDrxN6uPF+BHIio9DwpRxAm4F4LZwge6mP1LPnEKMs40UL68K4E2PuY+kZ68Hw8zY17F55GmZPnE0bK0bKgsNMHZwzHk8AW9KRLAIwaBQBhvrhpoCXvK6pu2ZjrS9OUIXdk/H6PYusK9XG4/WjtUA8DrD0bwGHs7o9jvATV7i/bvXChzmoCSkYSq2j+mCOtUr49QP3kjb4yM8urFbJqCXpx06OlkhapNEL69k4KX+n/N6oOf02s71P8KsTm0c/2nL795/c//3/T9dvxNHDUGLpu54fPuSCGGg76HMuAfIjHsoNbxKGfAS1A7xQNqBddDdOIu0vSvx6kVsEQHeylJgl6C5bpVGDLwFBZgieFyxBV4CW1oOkBJDjxo1Chs3bhRxOZT3bvjw4bC1tYWbm5t4v6DrVksHXvLw9vFC5qMwJK1Zjlj3Joh1sUV87y7Q3Q9B8raNJu3hzWugS4sKwbTxI9HI1hr3A86a/GCZ8zMEXz8NGyttAa8+Rjdhx1Q83TAe91ePxnHy8NpYoKe7HaI2T9QA8LqgXvXKWNTZESu6uWDPgGYImeaFpMUSYZdigRUAb/KuqVg9tD3q1aqGawsGIW23j4jbfbl9Mka1c4aNWU3cWjpUvC4ldlgy8Obs//rnLx7fgnePLmju6Ybwu1dM/hp+GnRZhDGMHtof547uwZI5Plg0awqO7d2M+IeBUuuvCnjJu5s8bwgyH94RkJu6YSZevSwawFvq48oo8eF3Uqw2A28RxNaCf6RiC7wUdE5pPmh1E0odQutnT5w4UeTg+/bbb8XSfrTeNSWDnjdvnghiN1Rm2cBLsbkxzrZIXDIXuqBbSN6zA0nrViL98gWknT6JOK+2Ury74jwKJq3pB8icj0GXfOHqZI9B3j2R8FTeBLmc51D1XKvASx5e/8VD0MnJGi5WZqhXqzpaNK6Hc7P7ayKGd01PVzQxq47Ojc3RztZMhDe0tTXDuTFtYfoeXh8cnNwDtatWxo4xXkjaOUUAb9ia0WjVuD7qVKuCc3P6awZ4ySMacPYIrOrXw7zpE0HhDqquN1nl3rlyApbmZvBwdUbrFk3R06sD2rVqDpuGDcTdGpqUKutcSoCXvLujW0N35QTST+1F0lBPpG2YVWSAt+THlfDZh99IsdpVbNnDayi4FOH9iy3wUm67ChUq4McffxQzK2lt7Ro1aqB27do4f/68WM+aPLuUhJrWtI6Kynu1Fuob5C2mVCa5li9MTUXC2VMio4KMSWSiDPsGImQhwWcCMkLuQnc3CLqAG8h88ggpB/cirn0zTXl4o0JuYHDfnrBvbCti52iQyeuWqazBR3Y5WgVe8vJSpoajPn2wa1xXjO/ohm5uNvCb2Q/Ju3xM2sNL4QkPf+iKIJ/OiJ7XE5Fze+LkiDZwsayJjnbmeDanh4mHNPjg0ZrR6OhoBcf6dbBmaHvsn9Qdw9s4oX7t6qhVtRJOz+ybHepgtJdXsYeXQgFWLZ4NC7O6IsuB7GtMRXn+Z35G7Vo1YWFeF4d3bURy+F3EP7wJn/EjUb+eBc4f3Wu6wEtZGQa5InXLfGQ+uIMknx5IHOBc9ID339/gMwnGwFuE6bUAH63YAi+teV2/fn3o04oQrLZp00Ykl86Z+uPYsWMiF9/9+/ffKi8llR4yZIgAY/IKk1lZW2OgrRWi7SWmCqMY3lGDkfnsCZJWLUVcCxfEujXC8wE9hcc35ecDiHFrJMXLqyKGVz94EdTGPgjA+BGD0djOGod2bUCaxFRA+vOoftQy8IoMCpQ2bPc0UOYGn26e8LS1xL0VI00aeJN/DTV4My6XMjeYV6+EC+PamzzwUuqx28uGYVIXd7RsVE94dmd0b4pp3TxhXrMqLs0doBkPL6X1at+mBbp0aIN4idkNVF67QZdOoG7tmujauX2uOgeeP4p6luZYNm86suIfSoFe6R5eCmWY1hOZYXeRtm81ksa2E6nI0rYvej1pbc00JI5sqdm0ZCd9z+GLj77Hf/9VUYrVqswe3reCSzF8o9gC7/Lly2Fubi6W+qN2pzx4np6eoNVOck5UozWuKYl0zjWt3+wnBMi0ZjatoJJtixdj57jRiJaQIkzvIY5pXA9JG9cgM+IZ4r3aZHtzY5rUR8q2Tch48likJhMT2Yw8r0rgJdj1GTcSje1ssGfzKk3cBs1rANYy8OpjeQl46flPE7vDslY1HJ3WJ/eENiPy8gqolpyH903Qpf/TlvXFrn5NYV6tEo6PaG3ywPvaaztV5N2N2DAOZLGbJ2BsB1c4NzTDg1Wj5KUmU+jhpXAGyl9bz8IM639coJm7M49vXRR3lfr16porI0Oo/xlYNaiHudMmICs+zCSBl7y5qWum4VVSAjIfBSMzJBCZwQHIiniMXzJ0yHr2EOkn9ogwB1nZGgozD+9J37Mo8dF3+PTfX0uxGpVtEBEe+eaQzf8XUwWKLfCeOnVKLB9I61LTsn3z588XAFy3bl2xYgqteX3v3j2xwoqDg4OY0GZoH5EdwyuAd+0KZMZE4Xlvr+yFJigzQ8r+PcgIe4C4Ds1fg7CJAi95geZOnwA7G2tsW7MEyRH3pAwseQGp6te0CLxJu3yQsHNqrlhdem1Rv9ZiItWZ2f1FFgc9EBvzqAJ4k5Z44+WiPrliden/6W2boH7NKrgxsaPJAy95eGnhiZyLTNDCE5STl8JLXm6brAngTY0MwfSJo2Bn3RB3r5zSzHVMMbqDvHuISXZPgi6JetNdpxP7t4rYXko7SDAv4/tDhYc3aVovpO1dgfT9a5F+YK14zLh+Gq9Sk5Fx8RhS1/+ApCHu0tKTFT7wfotP/vW1FGPgNZRaivb+xRZ4aeWUgQMHomTJkiKWlyauLVy4EKNHjxYgbGNjI5YILFu2rFhmMGeYQ367hHTgpZCGEQORGRGO1J8P4vnAXnjeoxMS5v6AzGdPkXrwJ8S62pks8FI6oBULZsKsbm1MGj1M5L0MuX4GIddPg5LZ61OWyRhoVJVBAyOlMrrmdwj7tq2BpYU5Rg7uJ2KQb1/wBWWdkHVuFXl4H60bi5WD2+PEjL64uXQY/BcNxeaRXcTiEz3c7RC+YbxJhzREzeuJ9b3ccGxYawRO6oRrEztiZXdX2NauimEe1oiZ3zMXDOflEc73awqyNJB3lyaq0cITfjOpDYbi5Axv9G3aRCwE4r9oiADhnDBsVByvQg8veUpp8RiKw08O19YP1593bxKgTovIBJw7ivPH9sKrY1sBwTJXTpQOvINckTjQGYn9HX+zfg5IXffD67Rki0chsb+TNNilFdcKG3g/+49E4K1kzR7e/AJLMdivWAIvTTKjjZbto7Ww586dC19fX7HmNcX0zpo1S2Rp6Nixo1i7muJ7C7LJBl6xipqLHRLnz0TG3TvIfHAfGfdDkBH2EKnHDyPexLM0xD7wF8neq1evCvvGdnBxtBerrtHKa61beIJmT8uCRVXlEPCuXjxbrLRm3bA+qlerJibsNLKxQo8u7aUunqECeB+uGQMCW1phjYxuoTs1qIvBrR0QsGSY8PzqwxyM8e7SsSo8vOFzeqC/S0MxSc3FshacLGrC0aImRjezxb1pXUQqsXwD7buWIVYEvIk7pmBcR1e4WpnDpaGZ8Ox2c7PFmVn9hOfXKMB9cxljRcBLHtCfd28EXQM/bV2d7SVVdd3JLpd+XG9YPl9kaqDvISf7xujYtiVOH94JyhdO17mMc6oA3pzL/tJzEeawjhYfCkfKktFSYbewgfeE71n898Nv8NE//ifFqn1vhXAOaSgIvhTJY4ol8FJL6qH3ba2akZGRvRjF2/Z51+vSgZfCFChTg4MV4ts1w/P+3fF8YG8879FR5OSVEbubHS+sIC1ZSmQw/M/+jIvHf/qdXTl1AJTPU8Ygo7IMGggf3bwgVpZ683MEnDuCVBP38Cbvmia8uIFLhuHivEHCgpaNQPy2KUj9dRKbsaCrP14F8FJIw7PZ3eE/qRMujGuHC2Pb4fbULni+sLcIZdBCWjLy3oavHwf/RYNxYe4A3Fg0GJEbx+cKcZAGvYqAl64xWp2MViajFRRVXnOqyqa7MQ8Dz+PqyYO4fuYwokKuC9CVBbtU78IAXhGrO7YdUuYNRtKoVpoH3k8//Ab/+cf/pBgD77sopXi9X2yBtzCaWQnw6qGXwPcN08OqjEeVk9b+aACTOdj80Xm08J4KD68eRgvjUQXw/pH3VirsKlp4QhrIvunJfdv/ioD3zev0zf9N/foqrPoWCvCKMAcX0GIUYrnhQa6azdJAHt5P/l0RH/69ghSryh7ewkAdzZyDgVdhUykDXiMnpOUHiN8X8Jr6QFmY9WPg7StvAtq7whfyel9RSEOhQq8i4C3M60DL5yos4H0zzEHm/4UZw+vrewaf/PtrfPiPClKsyvcNOaRBIeNorWgGXoUtxsD7QJO3Ok1lgGXgZeA1Go4ZeN/rdxADr2EDLAHvR//+Gv/6RwUpVpmB17AGKOJ7M/AqbGAGXgZeY+CZgZeBl4FX298hDLyGDbC+x0/jP//6Cv/825dSrNJ39RH+jPPwGtYKRXdvBl6FbcvAq+3ByhhYlXEsAy8DLwOvtr9DGHgNG2AZeA3Ti/c2TAEGXsP0MmhvBl5tD1YyoNWYMhh4GXgZeLX9HcLAa9CQCQLef//zf/jH376UYt9/yx5ew1qgaO/NwKuwfRl4tT1YGQOrMo5l4GXgZeDV9ncIA69hA+xxAbwV8Pe/lZNi331bD884pMGwRijCezPwKmxcBl5tD1YyoNWYMhh4GXgZeLX9HcLAa9gAS8D7r39WkAK7BM0MvIbpX9T3ZuBV2MIMvNoerIyBVRnHMvAy8DLwavs7hIHXsAH2+HE//PMf5fHXv5SRYt98Y4nw8AjDKsF7F1kFGHgVNi0Dr7YHKxnQakwZDLwMvAy82v4OYeA1bIAl4P3737/EBx+UlmIVK1pwHl7DmqBI783Aq7B5GXi1PVgZA6syjmXgZeBl4NX2dwgDr2EDbDbw/qU0PpBgBLzPwjktmWGtUHT3ZuBV2LYMvNoerGRAqzFlMPAy8DLwavs7hIHXsAH22HE//O3v5fDnv5SSYl9XNGfgNawJivTeDLwKm5eBV9uDlTGwKuNYBl4GXgZebX+HMPAaNsAeO0bAWxZ//qCUFPvqa3PO0mBYExTpvRl4FTYvA6+2BysZ0GpMGQy8DLwMvNr+DmHgNWyAJeD9698YeA1TjffOrwIMvPlVqgD7MfBqe7AyBlZlHMvAy8DLwKvt7xAGXsMGzmPHTuEvfy2D//t/X0ix/31lhmfPOEuDYa1QdPdm4FXYtgy82h6sZECrMWUw8DLwMvBq+zuEgdewATYbeP9cEv8nwRh4DdO/qO/NwKuwhXVXLyHW1Q6xzjZKLNK+AaIdrZSUTXWO79MJuqdBMAba3nasLvYBUqNCkBYVqqT8t51X5utpUSHiM8gsM2dZuoi7SD26DKl7f1BiKXtnIHGnD5J3T1dSfuqeGUhZPRQpy/orseRl/ZGw2BtJS/oqKV/Ue8s4pO6ZrsySdk4FmbJzHJwP3dObyq4xun7pOqbrOWff1crz9OhQpEYGK6u/AN4lI5E02B1JQzyU2IuBLng5yFVJ2VTn5Om98SrxhcKR8reiKUvDP/5VHn/5W1kp9s23lIeXszT8pnDxfsbAq7D9s16+gO7GVehuXJFsV/Hi3Gn0amCJvVMmKTrHFeju3EJWWhKydCnSLSMlEWNHDsPKZYvxSpcqvXwVdc5Z5itdChbOnYWpE8chMy1ZTf3Tk5AV+wSZUWHSLSsqDHHBN9C9qT1O7lwP+l/+eR4i89k9ZD65q8RSQgMwsKULts+fiqynis4TEYrMqIdKLCPyPqYM7I4F44ciS9E5smIeISstUUn/pOuWrl+6jul6znl9aOE51f/A3p3o26s7kl7Egq5pFfXOfBKCzHs3kHnPX7ql3r6CYc2csHbcUGQFyy9f1PlhEH7JzFA4Uv5WdFxcPHxPnAGtuCbDzp67jLS09N9OwM+KtQIMvIqa/5dfflFU8utiExITYW5hgeUrVig9j6rCMzIz4ebmjlGjRqk6hfJy+/XrhzZt2uDVq1fKz6XiBNExMaheowZ27NyponjlZaakpsLK2hpz585Vfi4VJ6B+06ZtW1A/0upG1y9dx3Q9a3Fbvnw5zM0tkJCQoMXqQ6fTwcHREZMmT9Zk/XNWWvWYmfNc/Lx4KsDAq9F2py9oCwsLrNAq8GZkwN3dHaNHj9ZoCwD9+/dH27ZttQu80dGoUaMGdmoVeFNSYK1x4KX+Q/1Iqxtdv3QdZ2QUjgdQtk70/Unfo1oGXkdHR0wuAsAru225PFbgTQUYeN9URCP/M/C+/4Zi4H2/bZDCwPt+GwAQP1gZeN9fM5CHl4H3/enPZ9aWAgy82mqv7Noy8GZL8d6eMPC+N+nFiRl436/+dHb28L7fNmDgfb/689m1pQADr7baK7u2ycnJGDRoEA4fPpz9mpaeZGZmYtKkSVi7dq2Wqp1dV4o3W7p0KWbMmIGsrKzs17X05MWLF/D29saZM2egxfi5tLQ0DB8+HHv27NGS7Nl1pX5D/Yf6kRb1pw9C1y9dx3Q9a3Gj70/6HqXvUy1uFEoyduxYbN68WYvV5zqzAoWqAANvocot72Q0QNKAqdUJU0Wh/qQ9mVZhpSi0gdavAa33Iao/tYFWrwGt11/r17C8EZFLYgXerQAD77s14j1YAVaAFWAFWAFWgBVgBTSsAAOvhhuPq84KsAKsACvACrACrAAr8G4FGHjfrRHvwQqwAqwAK8AKsAKsACugYQUYeDXceFx1VoAVYAVYAVaAFWAFWIF3K8DA+26NTG4PmqgQFxeHCxcu4OTJk7hz545YccfkKppHhWhWcWRkJK5fv45jx47h2rVrmkpaT9pTdoOAgACh/alTp3Dv3j2hvxYm7uj7DtXfz88Pvr6+uHr1Kp4/f67JiUepqaniOjhx4oT4DHl0OZN7KSoqCkePHv2d3b17V1NtQBO+6LOcP38epP/ly5cRExNj8p+BsntQnY8cOfK7NqDPER8fb3J9Jq8K0WTBp0+f4uzZs9nXMX030TWuhe+ivD4Tv8YKqFSAgVelugrKpi8yf39/uLi4oHLlymKlrCpVqmDatGmaSK1Dgwyt7lWuXDn87W9/g729vWYGGGpOglsnJydUq1YNtWrVytZ//PjxmgAuGuxpOdjatWujTp06qFmzJr7//nu4uroK8FXQZZUVScBFKck+//xzfPbZZyK9mrKTSSx49+7dou/T9UvtoG+LWbNmaQZUKA3Zrl27YGNjI/oQ9SW6HsaNGwfqY6a80Q/u1q1bi/rq9ae6f/LJJyhbtqz4MWvK9ae6kf60QmLdunVRqVIl8T30zTffoFmzZsIBwsBr6i3I9XsfCjDwvg/VjTgneeJatGghvujIuxgSEoKpU6cKgNy+fbsRJRfOoRcvXsSECROwadMmsaSnra2tpoD39u3bIu8leehCQ0MFAE+ZMgUlSpTAqlWrTD5NHHnY6c7AuXPnRN958OCByOVcvXp1MVhqaYnVsLAw8YOJlob94osvcPr06cLpxEaehUClVKlSwisXERGB8PBwYS9fvtQE8BJM0d0Z+tE3YsQI8QP80aNH4m4NeXlNfZlhgsXo6Ohs3Un/GzduiM/j6empiWWG6buHrtkmTZqI6/n+/fviO5WAvUePHibfBkZeQnw4K1AgBRh4CyTb+zuIQhjKlCkDWgNe/yuebitaWVkJEKZbvKa80WBDRrfjaHAhD5FWbiGSrjSYp6en55KY9CevdYcOHWDq+uv7TK4PAIhB8ttvvxUQ8OZ7pvg/eRHph1PLli3F3Q0CSC0BL93huHnzpilK+846JSUliR9Hbm5uIrSKPO1k+u1tfUz/vik+0gIUdJfgxx9/NMXq/a5O1NfpzsayZcuyx4HExERxp6Zhw4aagPbffSh+gRVQrAADr2KBZRe/aNEi8UVHHgkaWMgIHmm1ILpF+vjxY9mnVFIegaOHh4fmgDcvMegWKQFvp06dTB549fWnJUkp3vLJkyfCW0e3dimsgWIAtbDR3Q26nUsxl+RZL1mypKaA99NPPxUrlC1evBhbt25FcHCw+CGoBVikOQME7BMnThQxsBROReEY9GNciyuW0XcRrThIP/jos2lho9CqqlWripUG6c4AXc/UhywtLdGlSxfNzOnQgtZcx6KjAAOvxtpyzJgxqFChAugWVs5t+vTp4jZpUFBQzpdN9nlRAV76sUHLepJ3aPXq1bk8XSYrPiDiFCl+mkC9dOnSIryEJrFpAbgI1MmzO2zYMDGwaw149+7di6+++gqNGjUSP/gItMzNzXHo0CHx49WU+w3VjbyL//73v1G/fn1hNJ+Awkr+97//CY8jXdta2ig0hpwF3bp108wPVtKYvm/MzMzQp08fcZeDHAh0TdNEYL0zREvtwHVlBVQrwMCrWmHJ5dMgT5MTHj58mKvkuXPnCs+vVm6TFgXgpUHlypUr2bF/BGJa2WJjY7F//35s2LABvXv3FpMgyWua89a0KX4Wqh95RQm2KI6RNq0BL8WPUiw4xeOTR51iqikkibzsBF+mvFGfp4mnf/nLXwTgkoedvIsUi9y+fXvhJb1165Ypf4RcdaPPQ3MfaMIaxVZrZaOwMPqBRHc5KJa3QYMGqFixItq2bSti8+lz8cYKsAK5FWDgza2Hyf9Hs6DLly+fPdjrK0y3FclTp5VbckUBeCm1F8XL2dnZQWsppWhA1BvFHQ8dOlR4i968c6DvX6bySJoTGM6bN0+EZFB6PnpO8YwE8DTpzpQH+7fVbdu2bfjoo49w4MABU5H6rfWgH0YEvN27d8/OyECfiyZyfvzxx2Ly1FsPNrE3UlJSRMYGyjLx7NkzE6vd26tDk3/pzsDIkSNF3D2FklCqR5oT0bRpU01kjHn7p+N3WAE1CjDwqtFVWalLly4VgzvlTtUPnvRrv1+/fsLTSHkZtbBpHXjJi0UzpMkzp4+n1oLub9ZR34d27NghMh0cPHjwzV1M6v9169bhP//5jwgBoB8aZDTwf/DBByI8Y/To0ZqMX6T4VwqL2bhxY/Z1bVLC56hMYGCgyEpCP5Lou0e/UYYG8pTShFqtbPQD6ssvvxSZV+g7SSvbkiVLRH+hcSDnRrHUFM9On4s3VoAVyK0AA29uPUz+P0oyTl/QCxYsyI73I8ilyQp0S9HUc2DqBdYq8BIgUio4mqFOcYt0O1pLG8Uc0y1oPehS3ek1igGngZLieE15o4F8xowZmDx5sjBKCefu7o5//etfYrIOeUpzQpipfRbSPS/9KUyDYJEWAjH1jcJhrK2the4UlkEbhZqsX79eQJgWvNT6Oi9cuFDA+5kzZ0xd9lz1W758ufCm57xeqd9TPnDK4qOlsJJcH4z/YQUUKsDAq1BcFUXTLduOHTsKbxYNLAQAda6OHQAABWpJREFUtJAATWTbt2+filNKLZPqT7feaKUj8o5Swvfjx4+LiRaUVsfUN0pBRhOmaFBZuXKlmDxIYQBkdEvU1GNgKecowRXFXlL4C8V8k9f0u+++E3G8phyHnBPSc/aTtWvXigmb1Kf029v21b//vh7pBynVl+IvCUoolpf0p8VjaEET6l+mvpG2lL6LQqvmzJkjPgfl5a1Xrx4cHBxEPK+pfwaqH6VDpPrSXQItpUakul+6dElMfGzVqpVYcIWuZcr2QYtQUEiDVrKtaKGfcB2LjgIMvBpsS8rE0Lx5c/HlRgMlTVqgOEYteHfptifFYNKMbro1TbO9Cdbpi5pmF5v6RjPUP/zwQ+GNI+0pNZDevLy8TD4tE6Wto/Rj1GcoQwPVnWao02QX+vFEMGOqsPi2vkEASfBFdz9MfaOY0Z49e4rwI2oD0p9WuqOVv2gFRa1oT6mwKC2Zvu/T9evs7CxATCt9iH4g0XcPfXfSD1WtaE99nO4SkEedsjRQ/6F2oNAeytTAWRpM/VuA6/e+FGDgfV/KG3Fe+mKmX/AUv0WDPN1i10r8GdWb6kwxizT5RW8EkjSImvpGt3DpNqK+3jkfCVhM+XY6aUsDO2UJoLpSO5CRl5EWE9DSgJ+zn1CGAIIXLXi1SGOaaEeedVrtjm6l03Mt3N3IqTk9pwVYaLIm9SHKVkJeUi31IfKmk/7UHlqqt74dKBSJwtloAht9J9FcAvp+0uJn0X8mfmQFVCrAwKtS3UIu29S/6PJTv/zsU8iyZp/OlOuWXcli9uTNNnnzfy3JoaW6a6muefWBol5/rX++vNqMX2MFjFWAgddYBfl4VoAVYAVYAVaAFWAFWAGTVoCB16SbhyvHCrACrAArwAqwAqwAK2CsAgy8xirIx7MCrAArwAqwAqwAK8AKmLQCDLwm3TxcOVaAFWAFWAFWgBVgBVgBYxVg4DVWQT6eFWAFWAFWgBVgBVgBVsCkFWDgNenm4cqxAqwAK8AKsAKsACvAChirAAOvsQry8awAK8AKsAKsACvACrACJq0AA69JNw9XjhVgBVgBVoAVYAVYAVbAWAUYeI1VkI9nBVgBVoAVYAVYAVaAFTBpBRh4Tbp5uHKsACvACrACrAArwAqwAsYqwMBrrIJ8PCvACrACrAArwAqwAqyASSvAwGvSzcOVYwVYAVaAFWAFWAFWgBUwVgEGXmMV5ONZAVaAFWAFWAFWgBVgBUxaAQZek24erhwrwAqwAqwAK8AKsAKsgLEKMPAaqyAfzwqwAqwAK8AKsAKsACtg0gow8Jp083DlWAFWgBVgBVgBVoAVYAWMVYCB11gF+XhWgBVgBVgBVoAVYAVYAZNWgIHXpJuHK8cKsAKsACvACrACrAArYKwCDLzGKsjHswKsACvACrACrAArwAqYtAIMvCbdPFw5VoAVYAVYAVaAFWAFWAFjFWDgNVZBPp4VYAVYAVaAFWAFWAFWwKQVYOA16ebhyrECrAArwAqwAqwAK8AKGKsAA6+xCvLxrAArwAqwAqwAK8AKsAImrQADr0k3D1eOFWAFWAFWgBVgBVgBVsBYBRh4jVWQj2cFWAFWgBVgBVgBVoAVMGkFGHhNunm4cqwAK8AKsAKsACvACrACxirAwGusgnw8K8AKsAKsACvACrACrIBJK8DAa9LNw5VjBVgBVoAVYAVYAVaAFTBWAQZeYxXk41kBVoAVYAVYAVaAFWAFTFoBBl6Tbh6uHCvACrACrAArwAqwAqyAsQow8BqrIB/PCrACrAArwAqwAqwAK2DSCjDwmnTzcOVYAVaAFWAFWAFWgBVgBYxVgIHXWAX5eFaAFWAFWAFWgBVgBVgBk1bg/wM9y8pEVUUQZQAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 训练过程展示\n",
+    "![image.png](attachment:3d735115-47b4-4a5d-95f6-4a12f34ac157.png)\n",
+    "<center>开始训练：Epoch 0 batch 0</center>\n",
+    "\n",
+    "![image.png](attachment:5854c1de-bbd0-46d5-8081-28656a1a08da.png)\n",
+    "<center>训练中：Epoch 4 batch 42:</center>\n",
+    "### 以下是经过10 batches （a batch size of 100 puzzles），20 batches 后和50 batches 后量网络给出的三种方案。\n",
+    "\n",
+    "![image.png](Guessesafter10batches.png)\n",
+    "10 batches 后猜测。该机器学习模型非常不确定，并且会产生很多错误。\n",
+    "\n",
+    "![image.png](attachment:66694d30-e1bc-498e-910c-6563534cddd5.png)\n",
+    "20 batches 后。我们看到网络对中间的8非常不确定。这个数字也是错误的。\n",
+    "\n",
+    "![image.png](attachment:dd673045-773e-4d6e-bed7-0c42fa780420.png)\n",
+    "在50 batches 之后，我们看到网络对其猜测更加自信，并设法解决了这个难题\n",
+    "\n",
+    "## 验证误差\n",
+    "\n",
+    "![image.png](attachment:13542e57-f20c-4a0b-bce0-f903e1a938d3.png)\n",
+    "<center>Validation error (in terms of cells in error);经过70批次后，错误率降至零</center>\n",
+    "\n",
+    "![Accuracy_vs_step.png](Accuracy_vs_step.png)\n",
+    "数独训练网络的精度随步数的变化而变化。即使是33个给定的简单数独游戏，也需要10个关系推理步骤才能解决。\n",
+    "\n",
+    "在大约两个epochs（100 batches with a dataset of 10k puzzles）后，模型可以自信地解决验证集中的所有谜题。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 总结\n",
+    "\n",
+    "机器学习数据集中的Sudoku谜题包含很多线索，这使得它们非常简单。网络不需要很多涉及的推理，这可能就是为什么这种简单的方法运作良好的原因。当面对大约20条线索的谜题时，模型可能需要扩展更多信息。这可以通过向特征向量添加信息或通过使用循环网络结构更好地推理前面几步来完成，例如重新校正错误的猜测。\n",
+    "强化学习（英語：Reinforcement learning，RL）是机器学习中的一个领域，强调如何基于环境而行动，以取得最大化的预期利益。 其灵感来源于心理学中的行为主义理论，即有机体如何在环境给予的奖励或惩罚的刺激下，逐步形成对刺激的预期，产生能获得最大利益的习惯性行为。\n",
+    "通过本次任务联系，我学会和掌握了如何有效地分析问题、建立问题模型，如何利用有效的手段和方式去描述和解决问题。\n",
+    "数独问题可以转换为有规则约束的序列问题，可以借助之前的神经网络模型进行很好的建模和计算，但更有挑战性的应该是如何利用强化学习的奖惩机制去训练数独模型！\n",
+    "总之，通过本次实验，让我受益匪浅！"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "main_language": "python"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/report_06_Sudoku/Validation error.png b/report_06_Sudoku/Validation error.png
new file mode 100644
index 0000000000000000000000000000000000000000..443a8ee3c3a6e4a8057250bef090ab8fd6dd8759
Binary files /dev/null and b/report_06_Sudoku/Validation error.png differ
diff --git a/report_06_Sudoku/dataset.py b/report_06_Sudoku/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4f8128d80da61f0f637ac0f12d8bee5532fc2ac
--- /dev/null
+++ b/report_06_Sudoku/dataset.py
@@ -0,0 +1,59 @@
+import torch.utils.data as data
+import torch
+import pandas as pd
+
+
+def create_sudoku_tensors(df, train_split=0.5):
+    s = df.shape[0]
+
+    def one_hot_encode(s):
+        zeros = torch.zeros((1, 81, 9), dtype=torch.float)
+        for a in range(81):
+            zeros[0, a, int(s[a]) - 1] = 1 if int(s[a]) > 0 else 0
+        return zeros
+
+    quizzes_t = df.quizzes.apply(one_hot_encode)
+    solutions_t = df.solutions.apply(one_hot_encode)
+    quizzes_t = torch.cat(quizzes_t.values.tolist())
+    solutions_t = torch.cat(solutions_t.values.tolist())
+    randperm = torch.randperm(s)
+    train = randperm[:int(train_split * s)]
+    test = randperm[int(train_split * s):]
+
+    return data.TensorDataset(quizzes_t[train], solutions_t[train]),\
+        data.TensorDataset(quizzes_t[test], solutions_t[test])
+
+
+def create_constraint_mask():
+    constraint_mask = torch.zeros((81, 3, 81), dtype=torch.float)
+    # row constraints
+    for a in range(81):
+        r = 9 * (a // 9)
+        for b in range(9):
+            constraint_mask[a, 0, r + b] = 1
+
+    # column constraints
+    for a in range(81):
+        c = a % 9
+        for b in range(9):
+            constraint_mask[a, 1, c + 9 * b] = 1
+
+    # box constraints
+    for a in range(81):
+        r = a // 9
+        c = a % 9
+        br = 3 * 9 * (r // 3)
+        bc = 3 * (c // 3)
+        for b in range(9):
+            r = b % 3
+            c = 9 * (b // 3)
+            constraint_mask[a, 2, br + bc + r + c] = 1
+
+    return constraint_mask
+
+
+def load_dataset(subsample=10000):
+    dataset = pd.read_csv("sudoku.csv", sep=',')
+    my_sample = dataset.sample(subsample)
+    train_set, test_set = create_sudoku_tensors(my_sample)
+    return train_set, test_set
diff --git a/report_06_Sudoku/model.py b/report_06_Sudoku/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d24e1ec27635a31ae09044e6ff3963921b86f8c
--- /dev/null
+++ b/report_06_Sudoku/model.py
@@ -0,0 +1,53 @@
+import torch
+import torch.nn as nn
+
+
+class SudokuSolver(nn.Module):
+    def __init__(self, constraint_mask, n=9, hidden1=100):
+        super(SudokuSolver, self).__init__()
+        self.constraint_mask = constraint_mask.view(1, n * n, 3, n * n, 1)
+        self.n = n
+        self.hidden1 = hidden1
+
+        # Feature vector is the 3 constraints
+        self.input_size = 3 * n
+
+        self.l1 = nn.Linear(self.input_size,
+                            self.hidden1, bias=False)
+        self.a1 = nn.ReLU()
+        self.l2 = nn.Linear(self.hidden1,
+                            n, bias=False)
+        self.softmax = nn.Softmax(dim=1)
+
+    # x is a (batch, n^2, n) tensor
+    def forward(self, x):
+        n = self.n
+        bts = x.shape[0]
+        c = self.constraint_mask
+        min_empty = (x.sum(dim=2) == 0).sum(dim=1).max()
+        x_pred = x.clone()
+        for a in range(min_empty):
+            # score empty numbers
+            constraints = (x.view(bts, 1, 1, n * n, n) * c).sum(dim=3)
+            # empty cells
+            empty_mask = (x.sum(dim=2) == 0)
+
+            f = constraints.reshape(bts, n * n, 3 * n)
+            y_ = self.l2(self.a1(self.l1(f[empty_mask])))
+
+            s_ = self.softmax(y_)
+
+            # Score the rows
+            x_pred[empty_mask] = s_
+
+            s = torch.zeros_like(x_pred)
+            s[empty_mask] = s_
+            # find most probable guess
+            score, score_pos = s.max(dim=2)
+            mmax = score.max(dim=1)[1]
+            # fill it in
+            nz = empty_mask.sum(dim=1).nonzero().view(-1)
+            mmax_ = mmax[nz]
+            ones = torch.ones(nz.shape[0])
+            x.index_put_((nz, mmax_, score_pos[nz, mmax_]), ones)
+        return x_pred, x
diff --git a/report_06_Sudoku/report_template.ipynb b/report_06_Sudoku/report_template.ipynb
deleted file mode 100644
index 1db93a237a306712d7e535c5211c86db247a264e..0000000000000000000000000000000000000000
--- a/report_06_Sudoku/report_template.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Report - 报告题目\n",
-    "\n",
-    "* 姓名\n",
-    "* 学号\n",
-    "\n",
-    "\n",
-    "## 任务简介\n",
-    "\n",
-    "这里简述一下任务是什么；最终的目标是什么\n",
-    "\n",
-    "## 解决途径\n",
-    "\n",
-    "主要包括：\n",
-    "1. 问题的思考，整体的思路\n",
-    "2. 选用的方法，以及为何选用这些方法\n",
-    "3. 所用方法的详细解释，包括理论上的背景、模型、实现的细节\n",
-    "3. 实现过程遇到的问题，以及如何解决的\n",
-    "4. 最终的结果，实验分析\n",
-    "\n",
-    "要求：\n",
-    "1. 数据的基本情况介绍\n",
-    "2. 程序，以及各个部分的解释、说明\n",
-    "3. 结果的可视化，精度等的分析\n",
-    "\n",
-    "## 总结\n",
-    "总结任务实现过程所取得的心得等。"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  },
-  "main_language": "python"
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/report_06_Sudoku/train.py b/report_06_Sudoku/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c7544a843dac11ab7cabea43e5ce064bd7f176b
--- /dev/null
+++ b/report_06_Sudoku/train.py
@@ -0,0 +1,53 @@
+import dataset as d
+import model as m
+import torch
+import torch.utils.data as data
+import torch.nn as nn
+import torch.optim as optim
+
+batch_size = 100
+
+train_set, test_set = d.load_dataset()
+
+constraint_mask = d.create_constraint_mask()
+
+
+dataloader_ = data.DataLoader(train_set,
+                              batch_size=batch_size,
+                              shuffle=True)
+
+dataloader_val_ = data.DataLoader(test_set,
+                                  batch_size=batch_size,
+                                  shuffle=True)
+
+loss = nn.MSELoss()
+
+sudoku_solver = m.SudokuSolver(constraint_mask)
+
+optimizer = optim.Adam(sudoku_solver.parameters(),
+                       lr=0.01,
+                       weight_decay=0.000)
+
+
+epochs = 20
+loss_train = []
+loss_val = []
+for e in range(epochs):
+    for i_batch, ts_ in enumerate(dataloader_):
+        sudoku_solver.train()
+        optimizer.zero_grad()
+        pred, mat = sudoku_solver(ts_[0])
+        ls = loss(pred, ts_[1])
+        ls.backward()
+        optimizer.step()
+        print("Epoch " + str(e) + " batch " + str(i_batch)
+              + ": " + str(ls.item()))
+        sudoku_solver.eval()
+        with torch.no_grad():
+            n = 100
+            rows = torch.randperm(test_set.tensors[0].shape[0])[:n]
+            test_pred, test_fill = sudoku_solver(test_set.tensors[0][rows])
+            errors = test_fill.max(dim=2)[1]\
+                != test_set.tensors[1][rows].max(dim=2)[1]
+            loss_val.append(errors.sum().item())
+            print("Cells in error: " + str(errors.sum().item()))
diff --git "a/report_06_Sudoku/\346\225\260\347\213\254.jpg" "b/report_06_Sudoku/\346\225\260\347\213\254.jpg"
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index 0000000000000000000000000000000000000000..73a056a555a5d83814d68347b07da6a01261dc40
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