{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Midterm Exam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Instructions\n", "\n", "- The midterm consists of **THREE** main questions. Please choose **TWO** out of the three main questions to work on.\n", "\n", "- For each question, you need to accomplish one ultimate goal using the datasets from [`demo_data/midterm`](https://www.dropbox.com/scl/fo/bvbqmm07keanvx9vn4qz0/AFGZ151dXH2YBqk8TGidFto?rlkey=30y2xpt4sqg42lamaqdk8xx0u&dl=0). Each question is further divided into two sub-questions to help you accomplish the goal step by step.\n", "\n", "- If you fail to produce the output from the previous question, and its output is the input for the subsequent question, you can use the sample output files provided in `demo_data/midterm` to start the sub-questions. For example, you can use the sample output of Question 1-1 for the task of Question 1-2.\n", "\n", "- As with previous assignments, please submit both your **notebook** (`*.ipynb`) and the **HTML** output (`*.html`).\n", "\n", "- Deadline for Midterm Submission: **12:10 (noon), Friday, April 19**, via Moodle.\n", "\n", ":::{contents}\n", ":::\n", "\n", "\n", ":::{tip}\n", "If you have any questions regarding the descriptions of the tasks, please send me an email message. I will response ASAP.\n", ":::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question One (50%)\n", "\n", "### Question 1-1\n", "\n", "Please download the dataset from `demo_data/midterm/jay/`, which is a directory including lyric text files of songs by Jay Chou (周杰倫). Please load the entire corpus into a data frame, preprocess the raw lyrics, and save them in another column of the data frame. A sample data frame is provided below.\n", "\n", "When preprocesssing the raw lyrics, please pay attention to the following issues:\n", " - Remove symbols and punctuations in the lyrics\n", " - Remove (English) alphabetic characters (including full-width alphabets, e.g. `ｔ`)\n", " - Remove digits (e.g., `01234`)\n", "\n", "A complete output csv file is also available in `demo_data/midterm/question1-1-output-jay.csv`. You can compare your result with this sample csv.\n", "\n", "------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- A data frame including both the title (filename), raw lyrics, and preprocessed lyrics of each song:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
titlelyriclyric_pre
0我是如此相信鳥群離開了森林 整座天空很灰心\\n蝴蝶不再被吸引 玫瑰盛開的很安靜\\n遠方的風雨不停 城市蒼白而孤寂\\n徘徊無助的人群 焦慮著何時放晴\\n故事裡能毀壞的只有風景\\n誰也摧毀不了我們的夢境\\n弦月旁的流星劃過了天際\\n我許下的願望該向誰去說明\\n隕石在浩瀚的宇宙間旅行\\n璀璨的夜空裡漫天的水晶\\n我的禱告終於有了回音\\n我是如此相信 在背後支撐的是你\\n一直與我並肩而行 仰望等太陽升起\\n聽...鳥群離開了森林 整座天空很灰心\\n蝴蝶不再被吸引 玫瑰盛開的很安靜\\n遠方的風雨不停 城市蒼白而孤寂\\n徘徊無助的人群 焦慮著何時放晴\\n故事裡能毀壞的只有風景\\n誰也摧毀不了我們的夢境\\n弦月旁的流星劃過了天際\\n我許下的願望該向誰去說明\\n隕石在浩瀚的宇宙間旅行\\n璀璨的夜空裡漫天的水晶\\n我的禱告終於有了回音\\n我是如此相信 在背後支撐的是你\\n一直與我並肩而行 仰望等太陽升起\\n聽...
1英雄人生不是ㄧ個人的遊戲\\nㄧ起奮鬥ㄧ起超越ㄧ起殺吧sup兄弟\\n好戰好勝戰勝逆命\\n管他天賦夠不夠我們都還需要再努力\\n你的劍就是我的劍\\n艾希的箭可不可以準ㄧ點 嘿\\n你打野我來控兵線\\n不要隨便慌張就交閃現\\n旋轉跳躍你閉著眼\\n卡特轉完會讓你閉上眼\\n悟空蓋倫也轉圈圈\\n盲僧李先生ㄧ腳把你 踢回老家\\n擊殺 雙殺 三殺 Penta kill\\n扛塔 偷拆 插眼讓我傳送\\n...人生不是ㄧ個人的遊戲\\nㄧ起奮鬥ㄧ起超越ㄧ起殺吧 兄弟\\n好戰好勝戰勝逆命\\n管他天賦夠不夠我們都還需要再努力\\n你的劍就是我的劍\\n艾希的箭可不可以準ㄧ點 嘿\\n你打野我來控兵線\\n不要隨便慌張就交閃現\\n旋轉跳躍你閉著眼\\n卡特轉完會讓你閉上眼\\n悟空蓋倫也轉圈圈\\n盲僧李先生ㄧ腳把你 踢回老家\\n擊殺 雙殺 三殺\\n扛塔 偷拆 插眼讓我傳送\\n擊殺 雙殺 三殺\\n迎接 勝利 最後讓我...
2雙截棍岩燒店的煙味瀰漫 隔壁是國術館\\n店裡面的媽媽桑 茶道 有三段\\n教拳腳武術的老板 練鐵沙掌 耍楊家槍\\n硬底子功夫最擅長 還會金鐘罩鐵布衫\\n他們兒子我習慣 從小就耳濡目染\\n什麼刀槍跟棍棒 我都耍的有模有樣\\n什麼兵器最喜歡 雙截棍柔中帶剛\\n想要去河南嵩山 學少林跟武當\\n幹什麼(客) 幹什麼(客) 呼吸吐納心自在\\n幹什麼(客) 幹什麼(客) 氣沉丹田手...岩燒店的煙味瀰漫 隔壁是國術館\\n店裡面的媽媽桑 茶道 有三段\\n教拳腳武術的老板 練鐵沙掌 耍楊家槍\\n硬底子功夫最擅長 還會金鐘罩鐵布衫\\n他們兒子我習慣 從小就耳濡目染\\n什麼刀槍跟棍棒 我都耍的有模有樣\\n什麼兵器最喜歡 雙截棍柔中帶剛\\n想要去河南嵩山 學少林跟武當\\n幹什麼 客 幹什麼 客 呼吸吐納心自在\\n幹什麼 客 幹什麼 客 氣沉丹田手心開\\n幹什麼 客 幹什麼 客 日行...
3開不了口才離開沒多久就開始 擔心今天的妳過得好不好\\n整個畫面是妳 想妳想的睡不著\\n嘴嘟嘟那可愛的模樣 還有在妳身上香香的味道\\n我的快樂是妳 想妳想的都會笑\\n沒有妳在我有多難熬(沒有妳在我有多難熬多煩惱)\\n沒有妳煩我有多煩惱(沒有妳煩我有多煩惱多難熬)\\n穿過雲層 我試著努力向妳奔跑\\n愛才送到 妳卻已在別人懷抱\\n就是開不了口 讓她知道\\n我一定會呵護著妳 也逗妳笑\\n妳...才離開沒多久就開始 擔心今天的妳過得好不好\\n整個畫面是妳 想妳想的睡不著\\n嘴嘟嘟那可愛的模樣 還有在妳身上香香的味道\\n我的快樂是妳 想妳想的都會笑\\n沒有妳在我有多難熬 沒有妳在我有多難熬多煩惱\\n沒有妳煩我有多煩惱 沒有妳煩我有多煩惱多難熬\\n穿過雲層 我試著努力向妳奔跑\\n愛才送到 妳卻已在別人懷抱\\n就是開不了口 讓她知道\\n我一定會呵護著妳 也逗妳笑\\n妳對我有多重要 我後悔...
4床邊故事從前從前有隻貓頭鷹 牠站在屋頂\\n屋頂後面一遍森林 森林很安靜\\n安靜的鋼琴在大廳 閣樓裡 仔細聽\\n仔細聽 叮叮叮 什麼聲音\\n乖乖睡 不要怕 聽我說\\n乖乖睡 醒來就 吃蘋果\\n不睡覺 的時候 有傳說\\n會有人 咬你的 小指頭\\n這故事 繼續翻頁 再翻頁\\n你繼續 不想睡 我卻想睡\\n然後我準備 去打開衣櫃\\n去看看 躲著誰 去看看 躲著誰\\...從前從前有隻貓頭鷹 牠站在屋頂\\n屋頂後面一遍森林 森林很安靜\\n安靜的鋼琴在大廳 閣樓裡 仔細聽\\n仔細聽 叮叮叮 什麼聲音\\n乖乖睡 不要怕 聽我說\\n乖乖睡 醒來就 吃蘋果\\n不睡覺 的時候 有傳說\\n會有人 咬你的 小指頭\\n這故事 繼續翻頁 再翻頁\\n你繼續 不想睡 我卻想睡\\n然後我準備 去打開衣櫃\\n去看看 躲著誰 去看看 躲著誰\\n紙上的 城堡卡片 發光的 立體呈現\\n奇幻...
\n", "
" ], "text/plain": [ " title \\\n", "0 我是如此相信 \n", "1 英雄 \n", "2 雙截棍 \n", "3 開不了口 \n", "4 床邊故事 \n", "\n", " lyric \\\n", "0 鳥群離開了森林 整座天空很灰心\\n蝴蝶不再被吸引 玫瑰盛開的很安靜\\n遠方的風雨不停 城市蒼白而孤寂\\n徘徊無助的人群 焦慮著何時放晴\\n故事裡能毀壞的只有風景\\n誰也摧毀不了我們的夢境\\n弦月旁的流星劃過了天際\\n我許下的願望該向誰去說明\\n隕石在浩瀚的宇宙間旅行\\n璀璨的夜空裡漫天的水晶\\n我的禱告終於有了回音\\n我是如此相信 在背後支撐的是你\\n一直與我並肩而行 仰望等太陽升起\\n聽... \n", "1 人生不是ㄧ個人的遊戲\\nㄧ起奮鬥ㄧ起超越ㄧ起殺吧sup兄弟\\n好戰好勝戰勝逆命\\n管他天賦夠不夠我們都還需要再努力\\n你的劍就是我的劍\\n艾希的箭可不可以準ㄧ點 嘿\\n你打野我來控兵線\\n不要隨便慌張就交閃現\\n旋轉跳躍你閉著眼\\n卡特轉完會讓你閉上眼\\n悟空蓋倫也轉圈圈\\n盲僧李先生ㄧ腳把你 踢回老家\\n擊殺 雙殺 三殺 Penta kill\\n扛塔 偷拆 插眼讓我傳送\\n... \n", "2 岩燒店的煙味瀰漫 隔壁是國術館\\n店裡面的媽媽桑 茶道 有三段\\n教拳腳武術的老板 練鐵沙掌 耍楊家槍\\n硬底子功夫最擅長 還會金鐘罩鐵布衫\\n他們兒子我習慣 從小就耳濡目染\\n什麼刀槍跟棍棒 我都耍的有模有樣\\n什麼兵器最喜歡 雙截棍柔中帶剛\\n想要去河南嵩山 學少林跟武當\\n幹什麼(客) 幹什麼(客) 呼吸吐納心自在\\n幹什麼(客) 幹什麼(客) 氣沉丹田手... \n", "3 才離開沒多久就開始 擔心今天的妳過得好不好\\n整個畫面是妳 想妳想的睡不著\\n嘴嘟嘟那可愛的模樣 還有在妳身上香香的味道\\n我的快樂是妳 想妳想的都會笑\\n沒有妳在我有多難熬(沒有妳在我有多難熬多煩惱)\\n沒有妳煩我有多煩惱(沒有妳煩我有多煩惱多難熬)\\n穿過雲層 我試著努力向妳奔跑\\n愛才送到 妳卻已在別人懷抱\\n就是開不了口 讓她知道\\n我一定會呵護著妳 也逗妳笑\\n妳... \n", "4 從前從前有隻貓頭鷹 牠站在屋頂\\n屋頂後面一遍森林 森林很安靜\\n安靜的鋼琴在大廳 閣樓裡 仔細聽\\n仔細聽 叮叮叮 什麼聲音\\n乖乖睡 不要怕 聽我說\\n乖乖睡 醒來就 吃蘋果\\n不睡覺 的時候 有傳說\\n會有人 咬你的 小指頭\\n這故事 繼續翻頁 再翻頁\\n你繼續 不想睡 我卻想睡\\n然後我準備 去打開衣櫃\\n去看看 躲著誰 去看看 躲著誰\\... \n", "\n", " lyric_pre \n", "0 鳥群離開了森林 整座天空很灰心\\n蝴蝶不再被吸引 玫瑰盛開的很安靜\\n遠方的風雨不停 城市蒼白而孤寂\\n徘徊無助的人群 焦慮著何時放晴\\n故事裡能毀壞的只有風景\\n誰也摧毀不了我們的夢境\\n弦月旁的流星劃過了天際\\n我許下的願望該向誰去說明\\n隕石在浩瀚的宇宙間旅行\\n璀璨的夜空裡漫天的水晶\\n我的禱告終於有了回音\\n我是如此相信 在背後支撐的是你\\n一直與我並肩而行 仰望等太陽升起\\n聽... \n", "1 人生不是ㄧ個人的遊戲\\nㄧ起奮鬥ㄧ起超越ㄧ起殺吧 兄弟\\n好戰好勝戰勝逆命\\n管他天賦夠不夠我們都還需要再努力\\n你的劍就是我的劍\\n艾希的箭可不可以準ㄧ點 嘿\\n你打野我來控兵線\\n不要隨便慌張就交閃現\\n旋轉跳躍你閉著眼\\n卡特轉完會讓你閉上眼\\n悟空蓋倫也轉圈圈\\n盲僧李先生ㄧ腳把你 踢回老家\\n擊殺 雙殺 三殺\\n扛塔 偷拆 插眼讓我傳送\\n擊殺 雙殺 三殺\\n迎接 勝利 最後讓我... \n", "2 岩燒店的煙味瀰漫 隔壁是國術館\\n店裡面的媽媽桑 茶道 有三段\\n教拳腳武術的老板 練鐵沙掌 耍楊家槍\\n硬底子功夫最擅長 還會金鐘罩鐵布衫\\n他們兒子我習慣 從小就耳濡目染\\n什麼刀槍跟棍棒 我都耍的有模有樣\\n什麼兵器最喜歡 雙截棍柔中帶剛\\n想要去河南嵩山 學少林跟武當\\n幹什麼 客 幹什麼 客 呼吸吐納心自在\\n幹什麼 客 幹什麼 客 氣沉丹田手心開\\n幹什麼 客 幹什麼 客 日行... \n", "3 才離開沒多久就開始 擔心今天的妳過得好不好\\n整個畫面是妳 想妳想的睡不著\\n嘴嘟嘟那可愛的模樣 還有在妳身上香香的味道\\n我的快樂是妳 想妳想的都會笑\\n沒有妳在我有多難熬 沒有妳在我有多難熬多煩惱\\n沒有妳煩我有多煩惱 沒有妳煩我有多煩惱多難熬\\n穿過雲層 我試著努力向妳奔跑\\n愛才送到 妳卻已在別人懷抱\\n就是開不了口 讓她知道\\n我一定會呵護著妳 也逗妳笑\\n妳對我有多重要 我後悔... \n", "4 從前從前有隻貓頭鷹 牠站在屋頂\\n屋頂後面一遍森林 森林很安靜\\n安靜的鋼琴在大廳 閣樓裡 仔細聽\\n仔細聽 叮叮叮 什麼聲音\\n乖乖睡 不要怕 聽我說\\n乖乖睡 醒來就 吃蘋果\\n不睡覺 的時候 有傳說\\n會有人 咬你的 小指頭\\n這故事 繼續翻頁 再翻頁\\n你繼續 不想睡 我卻想睡\\n然後我準備 去打開衣櫃\\n去看看 躲著誰 去看看 躲著誰\\n紙上的 城堡卡片 發光的 立體呈現\\n奇幻... " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jay.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- When removing symbols, please make sure that the characters before and after the symbol are still properly separated (as shown below):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Song Title: 斷了的弦\n", "[Raw Lyrics]:\n", "斷了的弦再怎麼練　我的感覺你已聽不見\n", "你的轉變像斷掉的弦　再怎麼接音都不對　你的改變我能夠分辨\n", "＊我沉默　你的話也不多　我們之間少了什麼　不說\n", "哎唷～微笑後表情終於有點難過(握著你的手)　問你決定了再走\n", "我突然釋懷的笑　笑聲盤旋半山腰\n", "隨風在飄搖啊搖　來到你的面前繞\n", "你淚水往下的掉　說會記住我的好　我也彎起了嘴角笑\n", "你的美已經給了誰　追了又追我要不回\n", "我瞭解離開樹的葉　屬於地上的世界凋謝\n", "斷了的弦再彈一遍　我的世界你不在裏面\n", "我的指尖已經彈出繭　還是無法留你在我身邊\n", "△斷了的弦再怎麼練　我的感覺你已聽不見\n", "你的轉變像斷掉的弦　再怎麼接音都不對　你的改變我能夠分辨\n", "Repeat ＊～△\n", "==================================================\n", "[Preprocessed Version]:\n", "斷了的弦再怎麼練 我的感覺你已聽不見\n", "你的轉變像斷掉的弦 再怎麼接音都不對 你的改變我能夠分辨\n", "我沉默 你的話也不多 我們之間少了什麼 不說\n", "哎唷 微笑後表情終於有點難過 握著你的手 問你決定了再走\n", "我突然釋懷的笑 笑聲盤旋半山腰\n", "隨風在飄搖啊搖 來到你的面前繞\n", "你淚水往下的掉 說會記住我的好 我也彎起了嘴角笑\n", "你的美已經給了誰 追了又追我要不回\n", "我瞭解離開樹的葉 屬於地上的世界凋謝\n", "斷了的弦再彈一遍 我的世界你不在裏面\n", "我的指尖已經彈出繭 還是無法留你在我身邊\n", "斷了的弦再怎麼練 我的感覺你已聽不見\n", "你的轉變像斷掉的弦 再怎麼接音都不對 你的改變我能夠分辨\n", "\n" ] } ], "source": [ "print(\"Song Title:\", jay.title[100])\n", "print(\"[Raw Lyrics]:\")\n", "print(jay.lyric[100])\n", "print(\"=\"*50)\n", "print(\"[Preprocessed Version]:\")\n", "print(jay.lyric_pre[100])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Also, when removing the alphabets, make sure that the alphabets in full-width forms are removed as well, as shown below (e.g., `ｔｏｎｅ`):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Song Title: 你怎麼連話都說不清楚\n", "[Raw Lyrics]:\n", "這首歌沒有唱過．但是是我寫的．然後．寫給一個好朋友的歌\n", "那．我自己重新來唱．我覺得應該．版本真的也不錯\n", "想別的 可是在你眼中察覺什麼一閃而過\n", "怎是像是寂寞 於是我會更沉默\n", "沒說的 可是在你眼中察覺什麼一閃而過\n", "而我看她笑著走開 於是我裝做不懂 怎麼能拆穿你的不同\n", "偏偏 這地球 這麼擠 這麼小 這麼瘦 太陽刻意曬得那麼兇\n", "記得離別在拆散一點以後\n", "你怎麼連話都說不清楚 那溫柔的ｔｏｎｅ我聽得清楚\n", "他站在我的身邊 你站在我的面前 怎麼這樣心裡會難過\n", "你怎麼連話都說不清楚 那溫柔的痛我聽得清楚\n", "你站在我的身邊 他經過我的面前 怎麼這樣心裡又難過 為什麼\n", "想別的 可是我忽然察覺什麼一閃而過\n", "於是像是寂寞 於是我會更沉默\n", "沒說的 可是在你眼中察覺什麼一閃而過\n", "於是像是寂寞 怎麼能拆穿不同 怎麼能拆穿你的不同\n", "偏偏 這地球 這麼擠 這麼小 這麼瘦 太陽刻意曬得那麼兇\n", "為什麼你出現在他出現以後\n", "你怎麼連話都說不清楚 那溫柔的ｔｏｎｅ我聽得清楚\n", "我站在他的身邊 你站在我的面前 怎麼這樣心裡會難過\n", "你怎麼連話都說不清楚 那溫柔的痛我記得清楚\n", "他站在我的面前 你經過我的身邊 忽然之間心裡又難過 為什麼\n", "經由他處經過 為甚麼你卻又聽的清楚\n", "你站在我的面前 他站在我的身邊 忽然之間心裡又難過\n", "為什麼\n", "感謝\n", "DJGhost\n", "修正歌詞\n", "==================================================\n", "[Preprocessed Version]:\n", "這首歌沒有唱過 但是是我寫的 然後 寫給一個好朋友的歌\n", "那 我自己重新來唱 我覺得應該 版本真的也不錯\n", "想別的 可是在你眼中察覺什麼一閃而過\n", "怎是像是寂寞 於是我會更沉默\n", "沒說的 可是在你眼中察覺什麼一閃而過\n", "而我看她笑著走開 於是我裝做不懂 怎麼能拆穿你的不同\n", "偏偏 這地球 這麼擠 這麼小 這麼瘦 太陽刻意曬得那麼兇\n", "記得離別在拆散一點以後\n", "你怎麼連話都說不清楚 那溫柔的 我聽得清楚\n", "他站在我的身邊 你站在我的面前 怎麼這樣心裡會難過\n", "你怎麼連話都說不清楚 那溫柔的痛我聽得清楚\n", "你站在我的身邊 他經過我的面前 怎麼這樣心裡又難過 為什麼\n", "想別的 可是我忽然察覺什麼一閃而過\n", "於是像是寂寞 於是我會更沉默\n", "沒說的 可是在你眼中察覺什麼一閃而過\n", "於是像是寂寞 怎麼能拆穿不同 怎麼能拆穿你的不同\n", "偏偏 這地球 這麼擠 這麼小 這麼瘦 太陽刻意曬得那麼兇\n", "為什麼你出現在他出現以後\n", "你怎麼連話都說不清楚 那溫柔的 我聽得清楚\n", "我站在他的身邊 你站在我的面前 怎麼這樣心裡會難過\n", "你怎麼連話都說不清楚 那溫柔的痛我記得清楚\n", "他站在我的面前 你經過我的身邊 忽然之間心裡又難過 為什麼\n", "經由他處經過 為甚麼你卻又聽的清楚\n", "你站在我的面前 他站在我的身邊 忽然之間心裡又難過\n", "為什麼\n", "感謝\n", "\n", "修正歌詞\n" ] } ], "source": [ "print(\"Song Title:\", jay.title[200])\n", "print(\"[Raw Lyrics]:\")\n", "print(jay.lyric[200])\n", "print(\"=\"*50)\n", "print(\"[Preprocessed Version]:\")\n", "print(jay.lyric_pre[200])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1-2\n", "\n", "Following the previous question, create a cluster analysis on all Jay's songs and find out the similarities in-between Jay's songs. Please pay attention to the following issues:\n", "\n", "- Use `ckip-transformer` to word-seg the lyrics into word tokens. \n", "- Please use TF-IDF weighted version of the bag-of-words representations for clustering.\n", "- Please include in the bag-of-word vectorization:\n", " - (a) words whose minimum document frequency = 2; \n", " - (b) words which have at least two characters (i.e., removing all one-character word tokens);\n", " - (c) words whose parts-of-speech tags indicate they are either NOUNS or VERBS. However, for nouns, please EXCLUDE words that are pronouns （e.g., 你 我 她） or numerals (e.g., 一 二 三). Specifically, include words whose POS tags start with `N` or `V`, but exclude words tagged as `Nh` (i.e., pronouns) or `Neu` (i.e., numerals).\n", "\n", "Your output should be a dendrogram as shown below. A complete `jpeg` file of the dendrogram is also available in `demo_data/midterm/question1-2-output-dendrogram.jpeg`.\n", "\n", "------\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The Shape of the `CountVectorizer` Matrix After Filtering: ( `Number_of_Songs`, `Number_of_Features`)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "(212, 2031)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jay_bow_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Sample of the `CountVectorizer` Matrix After Filtering:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
一下一些一切一半一幕幕一樣一次次一生一統一行行...默劇默契默片點亮點心點頭鼓勵鼓掌鼻子龍捲風
我是如此相信0000000000...0000000000
英雄0000000000...0010000000
雙截棍0000000000...0000000000
開不了口0000000000...0000000000
床邊故事0000000000...0000000000
..................................................................
大頭貼0000000000...0000000000
不知不覺愛上你0000000000...0000000000
熊貓人0000000000...0000000000
哇靠0000000000...0000000000
默0000000000...0000000000
\n", "

212 rows × 2031 columns

\n", "
" ], "text/plain": [ " 一下 一些 一切 一半 一幕幕 一樣 一次次 一生 一統 一行行 ... 默劇 默契 默片 點亮 點心 \\\n", "我是如此相信 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "英雄 0 0 0 0 0 0 0 0 0 0 ... 0 0 1 0 0 \n", "雙截棍 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "開不了口 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "床邊故事 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "... .. .. .. .. ... .. ... .. .. ... ... .. .. .. .. .. \n", "大頭貼 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "不知不覺愛上你 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "熊貓人 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "哇靠 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "默 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", "\n", " 點頭 鼓勵 鼓掌 鼻子 龍捲風 \n", "我是如此相信 0 0 0 0 0 \n", "英雄 0 0 0 0 0 \n", "雙截棍 0 0 0 0 0 \n", "開不了口 0 0 0 0 0 \n", "床邊故事 0 0 0 0 0 \n", "... .. .. .. .. ... \n", "大頭貼 0 0 0 0 0 \n", "不知不覺愛上你 0 0 0 0 0 \n", "熊貓人 0 0 0 0 0 \n", "哇靠 0 0 0 0 0 \n", "默 0 0 0 0 0 \n", "\n", "[212 rows x 2031 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jay_bow_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The Shape of the `TfidfVectorizer` Matrix After Filtering: ( `Number_of_Songs`, `Number_of_Features`)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "(212, 2031)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tv_matrix.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Sample of the `TfidfVectorizer` Matrix After Filtering (Please use this weighted TF-IDF matrix for clustering):" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
一下一些一切一半一幕幕一樣一次次一生一統一行行...默劇默契默片點亮點心點頭鼓勵鼓掌鼻子龍捲風
我是如此相信0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
英雄0.00.00.00.00.00.00.00.00.00.0...0.00.00.130.00.00.00.00.00.00.0
雙截棍0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
開不了口0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
床邊故事0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
..................................................................
大頭貼0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
不知不覺愛上你0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
熊貓人0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
哇靠0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
默0.00.00.00.00.00.00.00.00.00.0...0.00.00.000.00.00.00.00.00.00.0
\n", "

212 rows × 2031 columns

\n", "
" ], "text/plain": [ " 一下 一些 一切 一半 一幕幕 一樣 一次次 一生 一統 一行行 ... 默劇 默契 \\\n", "我是如此相信 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "英雄 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "雙截棍 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "開不了口 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "床邊故事 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "... ... ... ... ... ... ... ... ... ... ... ... ... ... \n", "大頭貼 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "不知不覺愛上你 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "熊貓人 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "哇靠 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "默 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", "\n", " 默片 點亮 點心 點頭 鼓勵 鼓掌 鼻子 龍捲風 \n", "我是如此相信 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "英雄 0.13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "雙截棍 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "開不了口 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "床邊故事 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... ... ... ... ... \n", "大頭貼 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "不知不覺愛上你 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "熊貓人 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "哇靠 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "默 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "[212 rows x 2031 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jay_tv_df.round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The Pairwise Similarity Matrix of All Songs" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
我是如此相信英雄雙截棍開不了口床邊故事夜曲 + 竊愛以父之名美人魚我要夏天我的時代...對不起伊斯坦堡反方向的鐘情畫兩個寂寞大頭貼不知不覺愛上你熊貓人哇靠默
我是如此相信1.000.000.000.010.100.090.080.010.020.02...0.000.030.020.070.000.010.000.010.030.06
英雄0.001.000.000.020.070.000.010.000.000.00...0.040.000.010.000.000.030.000.010.030.00
雙截棍0.000.001.000.000.010.010.000.010.020.01...0.000.030.000.010.020.010.030.120.010.00
開不了口0.010.020.001.000.020.020.070.000.020.05...0.030.050.030.020.020.030.000.050.040.00
床邊故事0.100.070.010.021.000.040.030.010.090.01...0.020.040.020.000.050.040.000.020.030.01
..................................................................
大頭貼0.010.030.010.030.040.000.010.010.000.02...0.030.030.000.000.031.000.000.000.020.00
不知不覺愛上你0.000.000.030.000.000.020.000.040.000.06...0.000.000.020.030.040.001.000.000.010.00
熊貓人0.010.010.120.050.020.000.050.030.020.03...0.030.030.020.000.020.000.001.000.060.00
哇靠0.030.030.010.040.030.010.060.090.020.02...0.020.030.050.010.020.020.010.061.000.02
默0.060.000.000.000.010.030.000.000.000.03...0.020.000.000.000.000.000.000.000.021.00
\n", "

212 rows × 212 columns

\n", "
" ], "text/plain": [ " 我是如此相信 英雄 雙截棍 開不了口 床邊故事 夜曲 + 竊愛 以父之名 美人魚 我要夏天 我的時代 ... \\\n", "我是如此相信 1.00 0.00 0.00 0.01 0.10 0.09 0.08 0.01 0.02 0.02 ... \n", "英雄 0.00 1.00 0.00 0.02 0.07 0.00 0.01 0.00 0.00 0.00 ... \n", "雙截棍 0.00 0.00 1.00 0.00 0.01 0.01 0.00 0.01 0.02 0.01 ... \n", "開不了口 0.01 0.02 0.00 1.00 0.02 0.02 0.07 0.00 0.02 0.05 ... \n", "床邊故事 0.10 0.07 0.01 0.02 1.00 0.04 0.03 0.01 0.09 0.01 ... \n", "... ... ... ... ... ... ... ... ... ... ... ... \n", "大頭貼 0.01 0.03 0.01 0.03 0.04 0.00 0.01 0.01 0.00 0.02 ... \n", "不知不覺愛上你 0.00 0.00 0.03 0.00 0.00 0.02 0.00 0.04 0.00 0.06 ... \n", "熊貓人 0.01 0.01 0.12 0.05 0.02 0.00 0.05 0.03 0.02 0.03 ... \n", "哇靠 0.03 0.03 0.01 0.04 0.03 0.01 0.06 0.09 0.02 0.02 ... \n", "默 0.06 0.00 0.00 0.00 0.01 0.03 0.00 0.00 0.00 0.03 ... \n", "\n", " 對不起 伊斯坦堡 反方向的鐘 情畫 兩個寂寞 大頭貼 不知不覺愛上你 熊貓人 哇靠 默 \n", "我是如此相信 0.00 0.03 0.02 0.07 0.00 0.01 0.00 0.01 0.03 0.06 \n", "英雄 0.04 0.00 0.01 0.00 0.00 0.03 0.00 0.01 0.03 0.00 \n", "雙截棍 0.00 0.03 0.00 0.01 0.02 0.01 0.03 0.12 0.01 0.00 \n", "開不了口 0.03 0.05 0.03 0.02 0.02 0.03 0.00 0.05 0.04 0.00 \n", "床邊故事 0.02 0.04 0.02 0.00 0.05 0.04 0.00 0.02 0.03 0.01 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "大頭貼 0.03 0.03 0.00 0.00 0.03 1.00 0.00 0.00 0.02 0.00 \n", "不知不覺愛上你 0.00 0.00 0.02 0.03 0.04 0.00 1.00 0.00 0.01 0.00 \n", "熊貓人 0.03 0.03 0.02 0.00 0.02 0.00 0.00 1.00 0.06 0.00 \n", "哇靠 0.02 0.03 0.05 0.01 0.02 0.02 0.01 0.06 1.00 0.02 \n", "默 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 1.00 \n", "\n", "[212 rows x 212 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "similarity_doc_df.round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The Dendrogram of Songs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../exercise-ans/midterm/question1-2-output-dendrogram.jpeg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question Two (50%)\n", "\n", "### Question 2-1\n", "\n", "Use the datasets, `demo_data/midterm/chinese_name_gender_train.txt` (training set) and `demo_data/midterm/chinese_name_gender_test.txt` (testing set), to build a classifier to determine the gender of a Chinese name based on the bag-of-words model. The training set text file includes around 480,000 Chinese names and their gender labels (around 240,000 for each gender). All names have exactly three characters and they have been randomized.\n", "\n", "The first step to the building of the classifier is text/name vectorization. Please create a NAME-by-FEATURE matrix using bag-of-words model. However, do not include all characters. Please include in the bag-of-words model only the following features:\n", " - Any Chinese characters that appear in the second position of the name (e.g., the `英` in 蔡英文)\n", " - Any Chinese characters that appear in the third position of the name (e.g., the `文` in 蔡英文)\n", " - Any Chinese character bigrams that appear in the second and the third characters of the name (i.e., the given name, e.g., `英文` in 蔡英文)\n", "\n", "For all the above features, they will be included as classifying features only when they appear in at least **100 different names** (i.e., the minimum document frequency threshold).\n", "\n", "The expected output of Question 2-1 is the bag-of-word representation of all the names in the training set following the above filtering guidelines. A sample has been provided below.\n", "\n", "A complete sample output of the name-by-feature matrix for the training set is also available in `demo_data/midterm/question2-1-output-tv-matrix.csv`. (It is stored as a data frame with the Chinese names as the index and feature names as the columns.)\n", "\n", "-----\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- For training data, the shape of the NOUN-by-FEATURE matrix is as follows: ( `Number_of_Names_in_the_Training_Set`, `Number_of_Features`)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "(480000, 975)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_bow.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- A Sample of the NOUN-by-FEATURE matrix (Training Set):" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
一丁三世丙中丹丹丹乃久...麗萍麗霞麗麗麟黎黎黎鼎齊齡龍
孫遠光0000000000...0000000000
吳昌財0000000000...0000000000
張俊達0000000000...0000000000
馬豔蘭0000000000...0000000000
宋燕敏0000000000...0000000000
\n", "

5 rows × 975 columns

\n", "
" ], "text/plain": [ " 一 丁 三 世 丙 中 丹 丹丹 乃 久 ... 麗萍 麗霞 麗麗 麟 黎 黎黎 鼎 齊 齡 龍\n", "孫遠光 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n", "吳昌財 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n", "張俊達 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n", "馬豔蘭 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n", "宋燕敏 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0\n", "\n", "[5 rows x 975 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_bow_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- In particular, bigrams that passed the minimum document frequency include (there are 287 bigrams):" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
丹丹云云亞娟亞楠亞男亞萍亞麗亭亭亮亮佩佩...麗珍麗琴麗紅麗芳麗英麗華麗萍麗霞麗麗黎黎
孫遠光0000000000...0000000000
吳昌財0000000000...0000000000
張俊達0000000000...0000000000
馬豔蘭0000000000...0000000000
宋燕敏0000000000...0000000000
\n", "

5 rows × 287 columns

\n", "
" ], "text/plain": [ " 丹丹 云云 亞娟 亞楠 亞男 亞萍 亞麗 亭亭 亮亮 佩佩 ... 麗珍 麗琴 麗紅 麗芳 麗英 麗華 麗萍 \\\n", "孫遠光 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "吳昌財 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "張俊達 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "馬豔蘭 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "宋燕敏 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 \n", "\n", " 麗霞 麗麗 黎黎 \n", "孫遠光 0 0 0 \n", "吳昌財 0 0 0 \n", "張俊達 0 0 0 \n", "馬豔蘭 0 0 0 \n", "宋燕敏 0 0 0 \n", "\n", "[5 rows x 287 columns]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_bow_df[[col for col in X_train_bow_df.columns if len(col)>1]].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- For testing data, the shape of the NOUN-by-FEATURE matrix is as follows : ( `Number_of_Names_in_the_Testing_Set`, `Number_of_Features`)\n", "- Please note that the feature number should be exactly the same as the number of the vectorized matrix of the training set." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "tags": [ "hide_input" ] }, "outputs": [ { "data": { "text/plain": [ "(120000, 975)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test_bow.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2-2\n", "\n", "Following the previous question, please use the NAME-by-FEATURE matrix for classifier training (I used the Count-based version, i.e., `CountVectorizer()`). In order to find the best-performing classifier, please work on the following steps:\n", "\n", "- Try two ML algorithms, `sklearn.naive_bayes.GaussianNB` and `sklearn.linear_model.LogisticRegression` and determine which algorithm performs better using *k*-fold cross validation (k = 10). Report the average accuracies of cross-validation for each ML method.\n", "- After cross-validation, you would see that Logistic Regression performs a lot better. In Logistic Regression, there is one hyperparameter `C` and different initial values of C may yield different performances as well. Use Grid Search to fine-tune this parameter from these values: C = [1, 5, 10]. (You may refer to [sklearn's Logistic Regression Documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) for more detail on `C`.)\n", "- After determining the ML algorithm and hyperparameter tuning, evaluate your final model with the testing set, i.e., `demo_data/midterm/chinese_name_gender_test.txt`. Report the confusion matrix plot of the results as shown below.\n", "- Present LIME explanations of your model on the gender prediction of the following four names: `'王貴瑜','林育恩','張純映','陳英雲'`.\n", "- Finally, perform a post-hoc analysis of the feature importances by looking at the top 10 features of the largest coefficient values for each gender prediction (see below).\n", "\n", "\n", "------------------\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Cross Validation Results" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Accuracy of Naive Bayes Model: 0.8543645833333334\n", "Mean Accuracy of Logistic Regression Model: 0.9804437500000001\n" ] } ], "source": [ "print(\"Mean Accuracy of Naive Bayes Model: \", model_gnb_acc.mean())\n", "print(\"Mean Accuracy of Logistic Regression Model:\", model_lg_acc.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Best Hyperparameter for Logistic Regression from Grid Search:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "{'C': 10}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.best_params_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Confusion Matrix of the Final Model on Testing Set (Normalized):" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Confusion Matrix (Normalized %)')" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ConfusionMatrixDisplay.from_estimator(clf, X_test_bow, y_test, normalize='all')\n", "#plot_confusion_matrix(clf, X_test_bow, y_test, normalize='all')\n", "plt.title(\"Confusion Matrix (Normalized %)\")" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Confusion Matrix (Frequencies)')" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAB64AAAamCAYAAAAJtJQ6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAC4jAAAuIwF4pT92AAEAAElEQVR4nOzdd5RV1fk/4HcKI713BARBbCgSFbuiogZj1BhN7CaafO01xtiTWLDFLhoLtijGEiPWiIq9YmwIgopYkM7Q+9z7+8MfE+/cC0w/w/A8a81ann3vOfvlds/n7L3zRo0alQ4AAAAAAAAASEh+0gUAAAAAAAAAsG4TXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQqMKkCwAAAICa8uyzz8a0adNKt7fZZpvYbLPNEqwIoH6aOXNmPPXUU6XbBQUFceihh0ZhodOPAACUj1+OAAAA1Evjx4+Pq6++OtLpdERENGzYMH7+858nXBVA/dS6det488034/PPPy9ta9y4cRx00EEJVgUAwNrEVOEAAADUO+l0Om666abS0Doi4uCDD47WrVsnWBVA/ZWfnx/HHXdcRtu9994bc+fOTagiAADWNoJrAAAA6p0XX3wxPv3009Lt5s2bxyGHHJJgRQD13zbbbBNbbLFF6fb8+fPj3nvvTbAiAADWJoJrAAAA6pXFixfH3//+94y2I444Ipo2bZpQRQDrhry8vKxR10888URMmjQpmYIAAFirCK4BAACoVx588MGYOXNm6Xb79u1j//33T7AigHVH3759Y7vttivdTqVSMXTo0IylGwAAIBfBNQAAAPXGjBkz4p///GdG21FHHRVFRUUJVQSw7vntb3+bsf3ee+/F6NGjE6oGAIC1heAaAACAeuPJJ5+M5cuXl243adIk9thjjwQrAlj39O7dOzbbbLOMtkcffTShagAAWFsUJl0AAKxtpk+fHm+99VZ89tlnUVxcHLNnz47i4uKYM2dOFBUVRbNmzaJZs2bRoUOH2GyzzWKTTTaJjTfeOBo2bJh06eukMWPGxMiRI+OTTz6JmTNnxvz583Pe75xzzol99tmnlqur2xYvXhx33313vPrqqzF//vzYaKON4v/+7/9i4403Tro0ImLgwIE526+88srYdttta7maH8yZMycOOeSQjNBwpeuuuy769etX+0XVc1OnTo2hQ4fGRx99FAUFBdG/f/844YQTok2bNkmXlogVK1bE008/ndE2cODACn0H33PPPXHvvfdWd2mrdccdd0SvXr1qtU9g9ebOnRu33XZbvPfee7Fs2bLYfPPN44QTToiuXbsmXdpa46c//Wl8+umnpdvvvvtuTJ48Obp06ZJgVQAA1GWCawAoh+nTp8czzzwTb775Znz++eervN+KFSti0aJFMW3atPjiiy/ijTfeiIgfRnvttddesd9++0WPHj1qq+x12qJFi+KKK66I1157LelS1kqpVCouuuiijCkdP/zwwzj11FPjxhtvFF7XYY899lhiwXXZka71TTqdjlQqFQUFBUmXEhE/XChw8sknx6xZs0rbXnzxxRg7dmzcfvvt0bRp0wSrS8brr78es2fPzmhzURJQUUuXLo0zzzwzJk6cWNr21ltvxZgxY+L222+Pjh07Jljd2mPgwIFx8803x5IlS0rbnnjiiTjxxBMTrAoAgLrMVOEAsBrLli2Lf/zjH3HUUUfFvffeu9rQenUWLlwYjz/+ePz2t7+Niy66KGbMmFHNlfJjy5cvj7PPPltoXQXjx4/PuQ7h8uXL4/7770+gIsrr3Xffja+//rrW+12+fHk88cQTtd5vbVi4cGH8+9//jt/85jd16vP72WefzQitV5oyZUo8++yzCVSUvBEjRmRsd+3aNTbddNOEqgHWVm+++WZGaL3S/Pnz45FHHkmgorVT48aNY7fddstoe+655zKCbAAA+DHBNQCswttvvx2/+c1v4q677oqlS5dW23Ffe+21OOaYY+KJJ56IdDpdbcflfx588MEYO3Zs0mWs1aZOnbrK277//vtarITK+Ne//lXrfb7yyis5Q9S12TfffBM33HBDHHzwwXHDDTckckHA6nifZvrmm2/igw8+yGjbZ599Ii8vL6GKgLWVz9fqM3jw4Izt+fPnx4svvphQNQAA1HWmCgeAMtLpdDz00ENx++23r/I+HTp0iJ122in69u0brVu3jtatW0eLFi1i+fLlsWDBgpg6dWpMmDAhPv744xg9enSkUqmM/RctWhTXX399fPfdd3HCCSdEfr5ryarL0qVLc4Z2G2ywQfz+97+Pnj17RsuWLbOCjLoy9W9dsbq1B7t3716LlVAZzz//fBx77LHRvHnzWukvnU7HY489Vit91aYHH3ww/vOf/yRdxiqt7n3arVu3Wqykbig72jo/Pz8GDRpULcc+++yzY88996yWY+VSWOh/zaEu8Tuo+my++ebRtWvX+Pbbb0vbnnjiiRg8eLALiwAAyOL/jgGgjLvvvnuVUyHvvPPOcfjhh8dGG220yhMtrVq1iq5du8Y222wThx9+eMyaNSuefPLJ+Oc//5k1Ld6jjz4a8+fPjz/+8Y/C62oyZsyYmDdvXkZb69atY+jQodGoUaOEqlr79O7dO7bffvt46623MtobNGgQRx55ZEJVUV5LliyJp59+Og499NBa6W/s2LHx2Wef1Upf/M/gwYPjkUceiZkzZ2a0d+nSZZ1b1zmVSsVLL72U0bb55ptHu3btquX4DRo0iKKiomo5FlD37bDDDtG7d++sZYKaN28ev/zlLxOqau2Ul5cXu+22W8b/X33++efx7bffrpMXWQEAsHrOkAPAj4wYMSJnaN2xY8e4/PLL469//Wv06dOnQqMD2rRpE8ccc0zcd999sfXWW2fd/p///KdejlRMyvjx47PafvaznwmtKygvLy8uvPDC+NWvfhUdO3aMJk2axFZbbRU33XRTbLjhhkmXRzn8+9//jpKSklrpy2dYMpo2bRo33XRT7LrrrtGiRYto1apVDBo0KG644YZ17jNv/PjxUVxcnNG2/fbbJ1QNsLYrLCyMq6++Ovbdd99o27ZtNG/ePHbccce45ZZbom3btkmXt9YZMGBAVtvbb7+dQCUAANR1RlwDwP/39ddfx80335zV3qtXr7j22mujWbNmVTp+u3bt4oorrojrr78+nnrqqYzb7rjjjujXr1/07t27Sn0QWcFFRETXrl0TqGTt16hRozj++OPj+OOPT7oUKmH69Onx2muvxW677Vaj/cyYMSNeeeWVGu2DVevYsWP8+c9/TrqMxJWdHSJCcA1UTYsWLeIPf/hD0mXUCxtvvHE0b948Y1akt99+Ow455JAEqwIAoC4y4hoAIqKkpCSuuuqqWL58eUZ7jx494pprrqlyaL1SQUFBnHHGGbHjjjtmtC9fvjxuvPHGauljXbd48eKstlatWiVQCdSuBg0aZC058Oijj9Z4v//+978jlUpltFmzktpWNrju1KmTKWgB6oiCgoLYZpttMto++eSTWLhwYUIVAQBQVwmuASB+CHfGjh2b0da8efO45pprokWLFtXaV35+fpx77rnRoUOHjPYxY8bEuHHjqrWvdVE6nc5qKygoSKASqF3t2rWLnXfeOaPt008/rdG1p5csWZI1g0TLli1j4403rrE+oawFCxbEF198kdHWr18/F1AA1CFbbrllxvaKFSv8vw8AAFkE1wCs8xYvXpxzXesTTjghWrduXSN9NmnSJI444ois9toYHQnUXwcddFBWW02uP/3CCy9kTPsZEbHffvtFUVFRjfUJZY0fPz6rzcUTAHXLJptsktVWkxfXAQCwdrLGNQDrvBdeeCFrmrp+/frF3nvvXaP97r333jFs2LCMNZnfe++9SKVSWdP9ltfSpUvjgw8+iDfffDMmTZoUc+bMidmzZ8eyZcuiVatW0bp162jfvn1su+22sd1220WbNm2q65+zSosXL44XX3wx3n777Zg2bVpMnTo1li1bFi1atCgdmdmvX78YMGBANGnSZI3HKykpiZKSktXeXtby5ctj2bJlqz3u2hS0ffvtt/H666/H559/HhMnTox58+bFggULIuKHiyKaNm0a3bp1i169ekX//v2jb9++lX5N1YRUKhWfffZZvPXWW/Hpp59GcXFxFBcXx8KFC6N58+bRqlWraNOmTfTr1y+233776N69e42PnCwpKYl33303Xnrppfj+++9j6tSpMW/evGjevHk0b948evToEf369Yvtttsu2rdvX6O1VMXmm28eG220UUyYMKG0bdSoUfF///d/0bZt22rtK51OZ4XihYWFsf/++8fHH39crX0tWrSo9PXy1VdfxZQpU2LRokWxcOHCWG+99aJ58+bRrFmz6NGjR2y55Zax1VZbRefOnct9/FV9PpSdAn3lfVd1/7y8vGjQoEG5+y173BdeeCHeeOONmDBhQsyZMycaNWoUrVu3jl69esVuu+0WO+20U6WOXd+t68H12LFj49lnn42PP/44ZsyYESUlJdG6devo2LFj7LjjjjFo0KByzx5TUlISY8aMiVdffTW+/PLLmD17dsyePTtWrFgRrVu3jlatWkW7du1i6623jh122KHGLvCbOnVqvPrqq/Hf//43Zs2aFbNmzYoFCxaU/o7p2LFj7LrrrrHddttlzaqybNmymDFjRkZbq1atonHjxjVSa21Ip9MxYcKEePXVV2PcuHGlz8uSJUtKn5fWrVvHVlttFTvttFN07NixRutJ+jtz5syZ8cYbb8R7770XM2fOjNmzZ0dxcXEUFRVF06ZNo2XLltGnT5/YeOONY4cddoiWLVtWew2VleRzmU6n45NPPonRo0fHhAkT4ttvv40FCxbEggULoqioKJo0aRLNmzePnj17Rq9evWLHHXeMrl27Vlv/G2ywQay33nqxdOnS0rZcn98AAKzb8kaNGpU9nyYArCPS6XQcd9xxMXHixIz2a6+9Nrbaaqsa7//KK6+M5557LqNt2LBh0aNHjwodZ/78+XHPPffEM888E0uWLCn3fltttVUcf/zxsdFGG1Wov4iIe+65J+69996Mtssvvzy23377iPghsB42bFg888wzsWjRojUer3nz5vGrX/0qDjrooFhvvfVWeb/nnnsurrzyygrXuyajRo3K2L7iiiviP//5T0bbjTfeGH379q10HyNHjozLL788o+3oo4+OY445plz7f/DBB3HXXXfFp59+WqF+O3XqFAcffHD8/Oc/r9C06Z988kmceuqpGW177713/OlPf6pQ/yuVlJTEiBEj4h//+EfMnj273Pt17do1jj322Nhll10qHGB/+OGHccYZZ2S0HXLIIXHCCSdExA+fAU888UQMHz48pk+fvsbjFRQUxODBg+PII4+Mdu3aVaiW6jZw4MCM7c6dO8cDDzwQzz//fAwZMiTjtiOOOCKOPfbYau3//fffjz/84Q8ZbYMGDYrzzjsvTj/99Pjoo48ybrvuuuuiX79+Ferj+++/j3vuuSdeeeWVNV58Utauu+4aRxxxRPTq1WuN9/31r38d06ZNq9Dxc9lyyy3j+uuvX+PxO3ToEA899FDp9vvvvx9XXXXVGl+DZT+nIiLuv//+GDZsWEbbOeecE/vss0/OY+T6DM3Ly4trr722ws/PSg888EDceeedGW0NGjSIW265JXr37l2pY1bExRdfHK+++mpG388880wUFlbuOu1c32/nnXdeDBo0qEp1VkcdP34fFRcXx9/+9rd44403Vnuc1b0eViopKYl//etf8eCDD8acOXPKVV9eXl5sueWW8X//93/VdqHA+PHj46abbir391zbtm3jl7/8ZRx88MGlF2iNHz8+jj/++Iz7re75mzp1ahx66KEZbf3794+//e1vlfgX/M/hhx8e33//fUZbrvfw6qTT6Rg5cmQMGzasQp9RG220URx33HFZawqvSV3/zvziiy9i6NCh8cEHH5R7n8LCwth5553jqKOOig022KBC/VX1d9uP1fZz+WMlJSXxzDPPxPDhw2PKlCkV2neTTTaJ3/72t7H11ltXuv8fO+WUU2LMmDGl223bto1HHnmkWo4NAED9YMQ1AOu0sWPHZoXWnTt3zlqDrabsueeeMX/+/Iy24uLiCgXXzz77bPz973+PuXPnVrj/Dz74II4//vgYPHhwnHDCCeUa8Vwe48ePj0svvTS+++67cu8zb968uOOOO+KNN96ISy+9NFq1alUttdQHS5YsiWuvvTZGjhxZqf2nTJkSN954Yzz77LNxySWXZK2vXhvGjBkT1157bXz11VcV3vfbb7+NP//5z9GvX78444wzolu3btVS0+zZs+Oqq66Kd955p9z7lJSUxJNPPhmvv/56XH755XVyVOduu+0Wt912W8ZsDiNGjIgjjjhitReFVFSuKchzTVVeGel0Oh544IG49957Y8WKFZU6xiuvvBKvvvpqnHjiiXHQQQfV2fWOR44cGUOGDIl0unauJ95nn33iww8/zLgwJ51Ox2WXXRZ33nlnuUfmrjR27Ni4++67s9pPPvnkWgmtI374jPix9ddfv9Kh9dpixowZceqpp8bUqVOrfKzx48fH1VdfHV9++WWF9kun0/Hhhx/GiSeeGAcccEAce+yxlf4dsWzZsrjrrrvi0UcfzTnTwarMnDkzbrvttvjoo4/ivPPOi6ZNm1aq/7rou+++i2uuuSbrIqDymDBhQvzxj3+M3XffPU466aRqGRmf5Hfm4sWL46677orHH3+8Qq+PiB/WUR41alS89tprcfjhh8eRRx5ZoYv4qkOSz+WUKVPiL3/5S6VHNo8bNy7OPvvs2HPPPePss8+u8gxF3bt3zwiuZ86cGYsXL45GjRpV6bgAANQfdWfOSABIQK4RGz/96U9rbVrln/zkJ3HppZdm/PXv379c+6bT6Rg2bFhcddVVlQqtf3ycp59+Os4666wqHWelMWPGxCmnnFKh0PrHxo4dG6eddlosXry4yrXUB3Pnzo3TTjut0qH1j33++edx2mmnVUvQURFvvfVWnHnmmZUKrX/sww8/jFNOOSVjGuzKmjt3bpx00kkVOgH/Y8XFxXH66adnXfhSFxQVFcX++++f0TZv3rx44YUXqq2PyZMnx9tvv53R1rdv3+jTp0+Vj71ixYr4y1/+EnfddVelQ+uV0ul03HLLLXHrrbdWua6a8Oqrr8YVV1xRa6H1Sqeddlp07949o23mzJlx1VVXVaiWhQsXxmWXXZa1RMPuu+8e++23X7XUuibpdDpr9GJNT5OctOLi4jjrrLOq5bP8ww8/jNNPP73CofWPpdPpePzxx+Pss88u1wwrZa18zz/88MMVDiVXeuutt+LEE0+MefPmVWr/umbSpElx6qmnViro/LGXXnopTjnllJg1a1aVjpPkd+aSJUvi/PPPj8cee6zSr4+IH15n9957bwwZMmS1S85UtySfy/Hjx8cJJ5xQLdNxv/DCC3H++edXePaTsnJ9Ptf271IAAOq2+n0ZOgCswY+v+F9pu+22S6CSikmn0zF06NB49NFHs27Lz8+PrbbaKgYOHBi9evWKNm3aRIMGDWLWrFkxefLkePXVV+ONN97ICobHjx8fZ5xxRlxzzTWVHpkzY8aMuPrqq2P58uWlbbvuumsMHDgwevfuHS1atIhFixbFzJkz45NPPomXXnop58m0b7/9NoYOHRpnnXVWpeqoL9LpdFx11VU5g9qmTZvG4MGD4yc/+Um0bds22rRpE8uXL48ZM2bE9OnT48svv4wXX3wxa5rSadOmxaWXXho33HBDrYw4evnll+PSSy/NeZK4Z8+esccee0Tfvn2jbdu20axZsyguLo7p06fH22+/HS+//HLMnDkzY5958+bFmWeeGVdccUVsvvnmlappZUjy4xOlm266aey9996xxRZbRJs2bWLFihUxZ86cGDduXLz55ps5p+JdunRpXHbZZXHrrbfWuTXS99tvv3jggQcy3ouPPfZYDB48uFpGHv/rX//KCjira7T10KFD45VXXsl522abbRY//elPo3PnztGuXbto1apVLF68OGbNmhWffvppvPTSSzmnGH7kkUeib9++sfPOO1dLjdVh9uzZceWVV1YpiKmsRo0axZ///Oc44YQTMpaXePPNN+Pxxx+PX/ziF+U6zvXXX5/1GdO1a9c466yzam2E+4IFC7LC0voeXN9www1Zo8wr48MPP4xzzz035xIj/fv3j5122in69u0brVu3jqKiopg3b15MnTo1Pvroo3j55Zfjm2++ydhn3Lhxcf7558cVV1xR7tkdUqlUXHXVVfHmm29m3dawYcPYbrvtYpdddomuXbtG69atIz8/P2bOnBlfffVVjBo1Kt59993S75dvv/02rrnmmjjssMMq8WjUHV999VWcddZZGbNmrLTxxhvHbrvtFltuuWW0adMmGjVqFAsWLIgZM2bExx9/HK+99lrW76rvv/8+zjrrrLjhhhsqPKNCRLLfmUuXLo3zzjsv54WmzZs3j1133TV22mmn6NSpU7Rq1SrmzJkTU6ZMialTp8b7778fb7zxRtYFUC+++GI0adIka0r0mpDkczlv3ry4+OKLc16U2rlz59h///1jo402itatW0fr1q1j/vz5pb8hP/nkkxg1alTWrFCjR4+Oe++9N373u99V4tH4QadOnbLapk6dWuFlkgAAqL8E1wCss1KpVIwbNy6jraioqMLr3yXhmWeeyRla9+7dO/70pz9Fz549s25r0aJF9OzZM3beeeeYO3du3HzzzVkjML/66qu47LLL4uqrr67UqPPbbrutNBDv3LlzXHTRRVkjMJs0aRLt2rWLTTbZJA4++OAYNWpUXH311Vknzp966qk46KCDsp6PffbZZ7VrdV599dXxzDPPZLRVZm3duuDtt9/OeTL/iCOOiMMOOyzntIpt27aNTTbZJHbdddc4+uij47HHHou///3vGeHYp59+Gk8//XT8/Oc/r9H6v/rqq7j88suzQutWrVrF6aefHrvsskvWPk2bNo2uXbvGT37ykzj++ONj+PDhcd9992UEsAsXLoyLLroohg0bFi1btqxwXc8++2zp67Rhw4Zx1llnxR577JEVtLVq1Sp69OgRgwcPjs8//zz+/Oc/Z4V0EydOjGeeeSYOOOCACtdRk1q3bh277757xnTQX331Vfz3v/+Nn/zkJ1U69oIFC+LZZ5/NaOvQoUPstNNOVTpuxP+C07I23XTTOOmkk2LTTTfNuq1JkybRtm3b6NOnTxx44IHxzjvvxJAhQ7JGXl533XUxYMCAnIHJj9eb/rFca90PHz68WoLRO+64IyNwLSgoiD322CP23HPP2GCDDaJly5axaNGimDt3bnz55ZdZ31dVtcEGG8Tpp58eV1xxRUb7bbfdFltsscUa1wZ//vnns75DioqK4uKLL47GjRtXa62rk2ut2CSWQ6gtH330UdaFHX369Il99903+vXrF+3atYuSkpKYP39+TJkyJcaNGxddunTJOs7MmTPj4osvzvruXX/99eOss87K+Z3ZtGnT6Ny5c/Tv3z+OOuqoGDVqVFx//fWxcOHC0vt8+OGHcdNNN8Uf/vCHcv17HnvssZwzigwYMCD+8Ic/RNu2bbNua9myZfTq1SsGDRoUX375ZQwZMqR0xPhrr722Vk8Xvnjx4rjwwguzgs5WrVrFmWeeGTvuuGPWd1XTpk2jY8eO0bdv3zjssMPivffei6uvvjrjwq+vv/46Lr300rjqqqsqfFFJkt+Zt956a87QeuUSN2Wf66ZNm8b6668fET9cwDVlypT429/+Fu+//37G/UaMGBG77LJLlb8PVyfp5/LBBx/M+nxcGdgPHDgw63d+06ZNS0PlPffcM4477ri45ZZbsr4DH3roodhzzz0rHTTnCq4rsuY3AAD1n6nCAVhnTZ48OSvY6N27d51fF3Py5Mlx8803Z7XvuuuuMXTo0JyhdVktWrSI888/P+eI5v/+97/xr3/9q1K1rTyx2bNnzxg6dOgapw3Oy8uL3XffPa655pqco39HjBhRqTrqi7LhYETEkUceGccee2y51gIsKCiIQw45JM4+++ys2/7973/X6PTEy5Yti8suuywjcI74YTTmnXfemTO0LqugoCCOOOKIuOmmm7LWTS0uLo6//e1vlfo3rHydNmrUKG644YbYc88913giv3fv3jF06NCcgdiIESNqfarn8sg1Arqy7+0fe+6557JmbDjggAOqPII/lUrFsGHDstp32WWXuP7663OG1mXl5eXFdtttF9ddd13WiM/i4uJ46aWXqlRjdZk9e3Y899xzpdtdu3aNW265Jc4999zYZpttol27dtGgQYNo0aJFdOvWLQYOHBgnnnhitdex9957x09/+tOMtuXLl8df//rX1S7XMHny5Lj++uuz2k899dTYcMMNq7vM1VqwYEFWW/PmzWu1hto0fPjw0v8uKCiIk046KYYOHRr77bdfdO3aNRo2bBhNmjSJjh07xlZbbRWHHXZY9O3bN+MYK2fzKPsbaNNNN42bb765XBd6FRQUxJ577hm33XZbtG/fPuO2Z555Jj777LM1HmPq1Kk53/O/+MUvYsiQITlD67I23HDDGDp0aMYyK7m+O9cWQ4cOjcmTJ2e0rb/++jF06NDYaaed1vhdlZeXF9tuu238/e9/z7r4ZPTo0fHqq69WuKakvjM//PDDeOKJJ7LaTzrppDj77LPLdYFCp06d4sorr4xdd90167abbrqpRr+7k3wuS0pKsgLniIi//vWvsccee5Tr4tRmzZrFOeecE3vvvXdGeyqViieffHKN+6/uuGWVHdkNAMC6TXANwDqr7BTEEbFWTFN3/fXXZ42Q2nLLLeO8886rcOj+s5/9LE466aSs9ttvvz2mT59eqfqaNm0al1xySYWmo9xss83i8MMPz2p/8cUX62QgWFs+//zzjO2CgoL41a9+VeHj7L333rHZZptltH311VfxxRdfVKm+1Xn44Yez1kxt1apVXHXVVRWeir5Pnz4xZMiQrCDy9ddfr9RJ+JXOO++82Gijjcp9/xYtWsQ555yT1f7VV1/VybWue/fuHVtssUVG21tvvVXp9ecjfjgZXnZEdMOGDWPfffet9DFXGj16dNZrpkuXLnHuuedGgwYNKnSsnj17xtFHH53V/vzzz1epxury4ws6OnXqFNddd121rA9eGaeeemrWd9+3334bN954Y877r1ixIi699NKsYHvQoEExePDgGqtzVZYuXZrVVt5pqiti+fLlsWzZshr5q4iV/968vLw499xz45e//GWFZ0h5/vnn47333stoa9u2bVx66aUVnkp6/fXXj7/85S8Z79F0Oh033njjGr+/hw4dmvV7ZsCAAXHiiSdWaFRwUVFR/OUvf1krZsxZnQ8//DCeeuqpjLZGjRrFZZddVuFZHlq3bh1//etfs0LCoUOHVnqN4tr8ziwpKYlrr702q/3QQw+NX/7yl+WuIeKH307nnHNOdO7cOaP966+/jo8//rhCxyqvpJ/LGTNmxJw5czLaNtlkk4wLPMojLy8vTj755KzP1JEjR1Z6nfBcF17mWq4AAIB1l+AagHVWrqv76/oorS+++CJGjx6d0dawYcM477zzKr3G7kEHHRRbb711Rtvy5ctzTtdbHocffnjWycHy+MUvfpEVTs2bNy9rmsl1yaxZszK2W7VqlTXyuDzy8vKyRsxE5F7jvTosW7Ys58jeM844o9JTLPft2zeOPPLIrPaHHnqoUhc3DBgwoFJTW2+11VY5A8byjC5MQtm1itPpdJVGXb/99ttZ78m99tor5wiqyhy7rKOPPjoaNmxYqePtt99+WSfbx40bl7XeaZKKiopiyJAh0aZNm8RqaNiwYVx88cVZj/Nzzz0XL774Ytb9hw0blvV67969e5xxxhm1tq71j+UKPCr7mlmdq6++Ovbee+9q/zvqqKMqVc/RRx8de+yxR4X3S6fT8c9//jOr/cwzz4xWrVpVqpaNN944a+mJcePGZV189WPTp0/PWgd5vfXWi7POOqtSszc0bdq0VtYsrkm5npff/e530a1bt0odr1OnTlmvr+nTp+f8rF2T2v7OfO+997LWce/cuXMcc8wxFa4h4oew9Le//W1W+49nvqhOST+XZX8/RkSlfptH/PDe2nHHHTPaFixYkLXGfXnl+nxe3QwfAACsewTXAKyzcgXXdX1dxEceeSSr7fDDD8+aprMiVo6mKBs4PPnkkxnrr5ZHw4YNY//9969UHS1atMi51mB1r+u6Nin7epw9e3bWCJry2m233aJ///4Zf5UdLbMmL774Ytaajj/5yU+qvAbywQcfnBV8f/bZZ5UK4H/9619Xuo6BAwdmtdXV1+lOO+2UNVXrc889l3N65fJ47LHHstpyTUleGQsWLIiuXbtG8+bNIz8/P9Zbb73YeeedK328pk2bxiabbJLRtmTJkvj666+rWmq1Oe6446J79+5JlxHdu3ePM888M6v9uuuuy7hQ4f33389aD3y99daLiy++uFzLF9SE2hpxXZf07t075ywl5fH+++/HV199ldG2ySabxHbbbVelmg499NCswHl1Mxw8++yzkUqlMtp+8YtfRLt27SpdwxZbbBHbb799pfdP0jfffJMVQrZv377Ks1nst99+WaPoKzPzRG1/Z+aaIvy3v/1tpS/SjPhh2YmyF6h+9NFHlT7eqtSF5zLX/8989dVXlZ7FaL/99sv6DVnZ36NGXAMAsCaCawDWWbmC68qMZq0ty5Yti5dffjmjrbCwMPbbb78qH7t79+5ZofHChQsrPCqnX79+VQovNt5446y2GTNmVPp4a7suXbpkbKdSqbjpppsqFTg3a9Ys/va3v2X8VXS6zfIaOXJkVttBBx1U5dGYRUVF8bOf/axc/a1OkyZNstZ8rYhcr9NcSw/UBQUFBXHggQdmtC1evLhSa8BOnDgxPvjgg4y2AQMGVHoEWVnnnXde3HffffHEE0/EyJEj47HHHqvyyNnevXtntVX2ZHt1a9WqVaUv9KkJgwYNygpWFi5cGJdcckmsWLEi5s6dG0OGDMkKPk4//fREl9nINU12TV2UU1ccddRRFV4aZKVcQde+++5b5c/nNm3aZC1N8NJLL2WF0yuNGjUqqy3XzCAVVXbN9rXFCy+8kNW29957VymojfjhIo4ddtgho+2dd96p0JrCtf2duWDBgnjnnXcy2po2bVqlC5kiIho0aJC1fvuUKVNi9uzZVTpuWXXhuezQoUPWZ8TEiRNjxIgRleq7X79+Wb8ht9pqq0odK9esJ5X9PAMAoH4SXAOwzsp14qSi66jWpvHjx2etZbfddttVeD3KVdlnn32y2iq69l+uk5MVkWsaw8qODK0PBg0alNX20ksvxXnnnRdTpkxJoKI1W758eYwdOzajrWXLlrHNNttUy/H32muvrLaKvk579+5dqaloV1rbXqeDBw/OCoAff/zxCod7uUZbl52KvLrk5+dXy4VEuZZ/mDdvXpWPWx0GDx5c5SCjup1yyinRs2fPjLbPPvss7rzzzrjqqquypp/dZ599cn531KZcFzfU59F77dq1q9Ko4k8++SRjOy8vLysMq6yyo7aLi4tj8uTJWffLNc1w9+7dq2X2gQEDBtTIVPE1rezzEhFZ0zNXVtnnZcWKFRVa3qK2vzM/++yzrAtkdtppp2r5vDz++OPjuuuuy/ir7s/huvBcNmzYMGfQf/3118ett95a4RmVqlOuacHXxvcsAAA1x2WNAKyzcoUiCxcuTKCS8skVzm255ZbVdvxcx6poIFjVED3X1IZ1ORCsaYMGDYp//etfWVMbv/vuu3H44YdH3759Y+edd46ddtqp0mtHV7cJEyZkTd27+eabV9tomnbt2kXXrl0z1r78+uuvY+7cueV+/dXE67Qio9dqW7NmzWKvvfbKGGk1ZcqUeOutt8o9ffucOXOyRpF169Yttt5662qttbo1btw4qy3X1NJJqIuP3cppv48//viMcCHXeq0bbLBBnHrqqbVZXk65Ao+aeI7PO++8nBcT1bb+/ftXOkScPn16TJ06NaOtU6dOlV7buqwNN9wwq+2LL76Irl27ZrSNHz8+K5is6oVvKxUVFcWGG24Yn376abUcrzbkuuCrsLAwevXqVS3HX9XzUt4Lymr7OzPXc1d22YfK6tSpU3Tq1KlajpVLXXoujzzyyHjzzTezPg8ffvjheOKJJ2LbbbeNnXfeObbbbrto1qxZtdRXHrkuLBJcAwDwY0ZcA7DOynUirS4H119++WVWW58+fart+G3bto22bdtmtE2aNKlCozJzhUQVkevEVa6R8euKRo0axV//+tecoUI6nY6PP/44brnlljj00EPj97//fdx///1VWsOwOuR6nW600UbV2keu4+Xqd1XWxddprnWoH3300XLv/9RTT2XN+HDQQQflnKa5Lqnq9Mc1qTo/v6tTt27d4qyzzlrtfRo2bBh//vOfE1vX+sdyrWeda0RffVGVgHfixIlZbdU11f+qjvXFF19ktU2aNCmrrexI/6qorpCwtkyePDnr83X99dev0ijnH+vYsWPWjEK5npdVqe3vzLJrsEesPc9pXXoue/ToEeecc07OCweXLl0ar732Wlx++eVx4IEHxtlnnx0jRoyo9mnTc8n1+VwXvksAAKg7jLgGYJ2Va3RBXQ6uc01vW92jRjp37pyx7mA6nY758+dHy5Yty7V/VUOiuhwyJaVbt25x5513xhVXXBHvvffeKu/3+eefx+effx7Dhg2Lzp07xw477BDbbbddbLHFFrU6BX5tvU7L0++qrIuv027dusW2224b7777bmnbRx99FJ9//nnOdaB/bMWKFfHEE09ktDVr1qzWRp+m0+mYMGFCfPLJJzFp0qSYNGlSFBcXx6JFi2LRokVZIcHaoFGjRnX6RP0ee+wRH330UTz55JM5bz/zzDOrZVrn6tCmTZustrq65nx1qMro6Fyfk2+//XYMHDiwKiWt1qqmCi+r7IVzVZHrNVGX5XpeJk2aVOvPy6rU9ndmrsejXbt2VaqhttS153LgwIHRsWPHuOyyy1Z5v5KSkhg9enSMHj06rr/++th4441j++23j+233z423HDDav/NM3369Ky26nz/AwCw9hNcA7DOyrX2adkpNOuSXCfDqmMN2B/LNQp93rx55Q6uqRmtW7eOq666Kj7++ON48MEH45133lnt/b///vt49NFH49FHH41GjRrFtttuGwceeGBsscUWNR66zp07N6utul+nuS46qSvrFtdlBx10UEZwHfHDutV/+tOfVrvfK6+8khUE7rvvvjUevBYXF8cjjzwSL730UkybNq1G+6ptuT5r65oTTzwxXnrppawLurbaaqs6MWX2Su3atYu8vLyMmSbq8nd5VVXltZPE52SuCwJzBddVHdX7Y9X9nVPT6srzUlfken2sDZ+ZEXXzudxkk03i3nvvjZdeeimGDx+ec0T7Sul0OsaNGxfjxo2LYcOGRdu2bWPnnXeOgw46KLp06VIt9U6ZMiWrra4sdwMAQN0guAZgnbXBBhtEfn5+pFKp0rYJEyYkWNHq5ToxVd1rwuUKotblNabrmi222CK22GKL+Oqrr+L111+Pt956Kz777LPVTg2+ePHieOWVV+KVV16JTTfdNA499NDYcccdayzAXrRoUVZbdQecuY5Xl0/C1xVbb7111vrgL730Uvz+97+P1q1br3K/xx57LGM7Pz8/DjjggJoqM0pKSuLBBx+M4cOH19spn+v6FOsRPzzvud5XH330UXz44YfRr1+/2i8qhwYNGkTbtm1jxowZpW31ObiuypTDSbyfcv2GqOnfM3V5NoNcknhe6vJ3Zm383q0pdfW5LCgoiEGDBsUee+wRH374Ybz11lvx1ltvrXHk/cyZM+Pxxx+PJ554InbZZZc47LDD1jhLy5rk+nwWXAMA8GN1/4wJANSQxo0bR48ePTLapk2bFsXFxbVax9dffx1jx47N+PtxmL5SrtFIS5curdZacp1wW9tGLq0LevToEUceeWQMHTo0HnvssTjnnHNi1113XeOItbFjx8aFF14Yl156aSxZsqRGastVQ3X3let1Wp2j9eqr/Pz8+MUvfpHRtnz58lVOBx3xw2tm3LhxGW277LJLdOjQoUZqnDt3bpxzzjkxbNiwehtarw0+/vjjGDZsWM7bUqlUXHrppbX+Xbk6ZUOP77//PqFK6rYkAt3yhpDVOeV/TX2/1ZQknpe6fFFirnXrly9fnkAlFVfXn8v8/Pzo379/nHTSSfGPf/wj7rvvvjjhhBNiq622Wu0FValUKl5++eU4/vjj4+GHH17tBZNrUvbzuUGDBqu9eA4AgHWPEdcArNM23XTT+PLLLzPaxowZEzvvvHOt9J9KpeLMM8+M2bNnl7Z17tw5Hnjggaz75prafOHChdU6CiXXya9c/VJ3tGrVKvbZZ5/YZ599YtmyZfHRRx/FW2+9FW+++eYqp1d+6aWXYubMmfG3v/0tCgur9+fgql6n1Wn+/Pnl6pdse++9d9x1110Z7/UnnngiDj300CgqKsq6f9nR1hE/TDleE5YtWxZnn312fP755zlv33DDDWObbbaJ7t27R8uWLaNVq1bRsmXLaN68+SpHoT7xxBMxdOjQGqm3vpozZ05ccsklOS+gWmnWrFlx+eWXx5VXXlknRo/36tUrPvnkk9LtWbNmxezZs4UhZeT6nNx1113jvPPOq9U6ck37XJ3fE3U5lM0l1/Oy8cYbxw033JBANcnLtRzIwoULc35H1TVr23PZtWvX6Nq1axxyyCExf/78ePfdd+PNN9+Md955J+d7MpVKxa233hpz586N3/3ud5Xqs+zsVj179qzSTBIAANQ/gmsA1mmbbbZZ1mjD559/vtaC6wkTJmSE1hE/TOebS66TYd9//320adOm2urJNUpNILj2KCoqim222Sa22WabOOWUU2LixInx5ptvxqOPPpq17uLHH38cd955Zxx//PHVWkOu10uu9Qyrwuu08ho1ahT77rtv/POf/yxtKy4ujlGjRsXee++dcd8ZM2bEK6+8ktHWp0+f2GyzzWqktjvuuCNnaL3NNtvEscceG3369KnwMav7woz6LpVKxZAhQ7LWNN99993j+++/j88++6y0bfTo0fHggw/GEUccUdtlZsn12vjss89ihx12SKCauquuBIK56ij7W6gq6tJsAOVRV56XuiLXhQ1z5syJVq1aJVBNxazNz2WzZs1ijz32iD322CNWrFgRn3zySbz88svx9NNPR0lJScZ9H3zwwdh8881j++23r1AfxcXFWRdVbrzxxlWuHQCA+iX5y+MBIEE77rhj1ojlN998s1pPoK7Oyy+/nNW27bbb5rzvhhtumNVWnWtyz5w5Myus6N69+zo7CiLXKMKqTlVZ9vGtSXl5ebHhhhvGkUceGffdd1/O9Wj//e9/5xy9XBU9e/bMaqvutePHjx9frn7J7YADDsh6fT/22GNZU38+8cQTWSerDzrooBpZH724uDgef/zxrPZDDz00rrjiikqF1hFrz/SydcVDDz0U7777bkZbp06d4swzz4wLLrggaxrcu+++Oz788MNarDC3XMHHj0N2ftC5c+estiRC3q5du2a1TZw4sdqOv6pZG1Yl1/f9ihUrqlRDSUlJuR/bDh06ZNUwe/bsKk3HvDbL9TqtrtfHggUL4ptvvsn4q87fQfXluSwsLIytttoqzjjjjLjtttuibdu2Wff5xz/+UeHj5vpcFlwDAFCW4BqAdVrTpk1jr732ymhLpVLxxBNP1Hjf8+fPzxrt3bRp0+jfv3/O+2+55ZZZbR999FG11fPBBx+Uq891Ra61vRctWlSlY+YKXGtDixYt4vLLL49OnTpltC9dujRee+21au1r4403zhpZ9Mknn1Q5BFhpxowZ8d1332W0devWba0YiVVXdOzYMXbccceMts8//zw+/vjj0u2lS5dmfT61adMmdttttxqp6cUXX8wKybfaaqs47rjjqjQV9aqmyyfbJ598EnfddVdGW35+flxwwQXRpEmT6NKlS5x22mkZt9eV9a67du2a9Zk9duzYhKqpu7p165Y1O8VXX31V6+vJb7LJJllt1XWB0/LlyyscXDdu3Dirrarf99988025H9fGjRtHr169MtoWLlyY9V23rth0002z2qrr9fHwww/H0UcfnfFXnRfX1cfnslevXnHVVVdlfRePHTs25ww4q5PrcznX5wEAAOs2c+cBsM77+c9/HiNGjMhoGz58eOyxxx7RrVu3Guv3kUceyToxeuCBB2aNaFupT58+UVRUFMuWLStte+utt2Lu3LnRokWLKtfz/PPPZ7X17du3ysddW+U6kT1r1qxKH2/ZsmXx/vvvl/v+1113XUaQ2LBhw7j55psrPQK+UaNG8Ytf/CJuueWWjPbJkydX6nir0qBBg9hss80yLoSYM2dOvPfeexWeUjIXr9Pq8ctf/jLrooXHHnus9GKVF154IWt6+f333z8aNGhQI/X8eH3ilQ4++OAqhdbpdDrnBTlkmzt3bvz1r3/NWtf6N7/5TUaItNdee8W7774bL730UmlbXVjvOj8/P7beeuuMqe0//vjjWLx48Sq/U9dFeXl50bdv33jjjTdK21KpVHz66aerXKakIu6+++547733Mtr+8Ic/ZM2I0aJFi+jcuXNG6PXFF1/E999/n3O0bUW8+eabsXTp0grtk+s1UpXv+4iIt99+u0L379u3b1aA+tFHH+UcnV5RI0aMiOeeey6j7Te/+U1ss802VT52TcgVXL/++utx/PHHV3nGj7IXEBYUFFT78hdJP5fpdDqOP/74jP9f6Nu3b5x55pmV7rdHjx6x/fbbZ3x2RPzwG7Ii79mynw/t27ev0f/XAgBg7WTENQDrvA033DAGDBiQ0bZ8+fK45pprsk7iV5fx48fH8OHDM9qKioriwAMPXOU+RUVFWaMdV6xYkTUqsjK+/vrrrFC1SZMm6/T6oLnWDv/iiy8qfbzXX389FixYUO77FxUVxaRJk0r/PvvssyqPIOzevXtWW9lwsjoMGjQoq+1f//pXlafKXLZsWTz11FPl6o/V69u3b/Tu3Tuj7Y033ogpU6ZEOp2Oxx57LOO2Bg0axH777Vdj9eSaRj9XeFERo0ePjq+++qpKx1gXrGpd6379+sWhhx6a0ZaXlxdnnHFGdOjQIaN95XrXSSr7fbV8+fIKXSy0rij7eyci4plnnqnycWfMmBHDhw+PcePGlf599913Ob93IiJ23nnnrLaRI0dWuY5c3xFrUlBQEK1bt85oKy4urnR4nUql4j//+U+F9qmp52XJkiVx9913Zzwv48ePz/r8r0vat2+fdbHD999/H59++mmVjrtgwYKsi5k22WSTrCWDqirp5zIvLy/S6XTGb8iRI0dmBNmVUdXfkMXFxVkXDgwYMKBGlh8BAGDtJrgGgIg488wzs6YZ/eSTT+KGG26o9vB65ci2slMnH3LIIWuc7vjggw/OanvggQdi+vTpla4nnU7HzTffnBUq7rvvvjlHHa8rcq2p+95771Xq9bB8+fK45557KrRPrjXNKzqCq6xcwXnLli2rdMxc9thjj6zX8ujRo+P111+v0nEfeeSRmDp1akZb7969Y4sttqjScddFeXl58Ytf/CKjLZVKxeOPPx4ffPBBVuA7aNCgGnmtrJTr5Pd6661X6ePNnTs3rr766qqUFBGR84T62rZW6Zr885//jHfeeSejrXnz5nHeeeflnOGhadOmcf7552eNrr777rurdfmKihowYEBWTVX9zKyPBg0alDVd+CuvvFLl2TcefvjhrDXld91111XOEjJ48OCstkceeSTmzJlT6Rref//9GD16dKX23WijjbLaKvv6ef755+Prr7+u0D5bb711VjA4bty4Kq8h/9RTT2U9pv3796/Rz/OqysvLi/333z+r/c4776zS5+9zzz2X9Rotu1xQdagLz2XZ4H/JkiVV7r+qvyHffffdrLbtttuuKiUBAFBPCa4BIH4Y3XHCCSdktY8YMaJaw+uZM2fG6aefnrUm3MYbbxxHH330Gvfv1atX1nSeS5Ysicsvv7zSIykee+yxrBO9DRo0WO3o73VBjx49sqZFnjZtWqVOZN9zzz3x7bffVmif7bbbLuuE/8svv1ylETO5Rh+2a9eu0sdblaKiojjooIOy2q+//vqs4Lm8xowZE/fff39W+69//WujdSpp9913z7rA4Jlnnon77rsv675lQ+7qVjZIi6j8mvBz5syJc845J2bMmFHVsnKelK+JWQqS8sknn8Sdd96Z1f6HP/xhtZ8Nffv2jSOOOCKjbeV611UJHquiRYsWWcsGvPXWW1lrp6/rGjZsmBUKplKpuPzyyyv9WH344YfxxBNPZLTl5+fHr3/961Xu061bt9hqq60y2hYuXJjzQrryWLhwYVx33XUV3m+ljTfeOKvtySefrPDvv2nTpsXf//73Cvefl5eX8+LEq666KhYuXFjh40VETJo0Ke69996s9sMOO6xSx6tNe+65Z9YFpR999FGlRy7PmTMn67utYcOGsccee1S6xlWpC8/lTjvtlNX2wgsvVKrviB8u2Krqb8i33norY7thw4ZZnwEAABAhuAaAUoMHD44dd9wxq33EiBFxwQUXxJQpU6p0/A8++CBOPvnkmDRpUkZ7o0aN4oILLojCwsJyHefMM8/MGgn90UcfxRVXXJE1intNnn766aw1jyMijjvuuOjYsWOFjlXfFBUV5Zwq/cYbb4z58+eX+zhPPfVUpabQbdmyZdZ0k99//33OULE8JkyYkHPq0upY1zSXQw45JGv6ytmzZ8c555xT4WBrwoQJ8ac//Slr3dLtt98+Bg4cWNVS11lFRUVZ038vXLgwa9TsVlttlXMGgOqUa43Lxx57rMJB2tixY+Pkk08uDb2relFD+/bts9pyrce9Npo7d25ccsklWcHcz3/+85zTOJd11FFHZa0NO3PmzLj88strbJmNNfnZz36WsT179mzThefwy1/+MitwGjt2bAwZMiRrROqafPXVV3HBBRdk7Td48ODo0qXLavc96aSTskbJv/jii3HbbbdVKLxevHhx/OlPfyodNd62bdty77vSLrvsktU2fvz4rGUTVmfu3Llx/vnnV/rijUGDBmV9b06ZMiUuvPDCCgees2bNij/96U9Zo2S33nrr6NevX6Xqq02NGzeOY489Nqv9+uuvzzlyd3WWLFkSF1xwQdZvt1/84hc1NrNQ0s/lgAEDsi4IGzlyZNYa0+X173//O2tWhg4dOpR73e65c+fGm2++mdG211575VxfHgAABNcA8P/l5eXFBRdckHUiPuKHUQLHHHNM3HPPPVFcXFyh406cODEuv/zyOPPMM2PatGkZtzVp0iSuuOKKNZ7c/bFOnTrF6aefntU+atSoOOmkk8q1puu8efPi8ssvj2uuuSbrtn79+sUvf/nLctdTn5UNQSJ+GE112mmnrXEE9ZIlS+K2226Lv/3tb6Vt5T3Bt9JRRx2VFbw98MADMWzYsHKPvF6+fHm8+OKLcdZZZ2Vd2LDxxhtH586dK1RTeTVo0CAuuOCCrLUjv/nmm/jtb39brmnDS0pK4sEHH4yTTz4560Rvy5Yt4w9/+IPR1lW0//77r/GimVyj56tbrtDotddeixtuuCEWL168xv2nTp0aN954Y5xyyimlJ9fz8/PjxBNPrFJduS7gueeee+Lpp5+O6dOnx9KlS2PZsmWlf2uLletalx2V3r1795yzj+RSUFAQ559/flbw895778Xw4cOrrdaK2GWXXaJFixYZbc8++2witdRlzZs3jwsvvDBnaHz22WeX63dEKpWKkSNHxumnn571+dypU6dyvY423HDDOOSQQ7LaH3744Tj33HPLdcHgZ599FieffHKMGTMmIiK22GKL+OlPf7rG/crq0aNHzt9/Q4cOjeHDh6/xIpqxY8fGCSecEF9++WVE/PAYN2vWrEI1FBUVxUUXXZQV5n3wwQdx6qmnlmuN53Q6He+8806cdNJJWb85mzZtGn/84x/Xmu/N/fffP2tE7ooVK+KCCy6If/zjH+W6yGLixIlx0kknZT12HTt2jCOPPLJa6/2xpJ/LBg0a5Pz3XXTRRfHKK6+U+8KQ+fPnx7333hs33XRT1m0DBw4s92tp5MiRWc/XAQccUK59AQBY95RvaBcArCMaNmwYV155ZfzlL3/JGpWwbNmyuPfee+O+++6LzTbbLHbYYYfYaKONomXLltGqVato3rx5LF68OObPnx/fffddjB07Nt57770YO3Zszr5atmwZV111VdaIjPIYNGhQfPPNN/GPf/wjo33ChAlx3HHHRf/+/WP33XePDTfcMNq0aRMNGjSIWbNmxeTJk+PVV1+N119/PWcY1LNnz5wns9dV/fv3j5/85CdZI/a++uqrOOaYY2LgwIGx/fbbR+/evaNFixaRl5cX06dPj3feeSdGjBiRsfb4dtttF7vuumtceeWV5e6/T58+ceCBB8a//vWvjPb7778//vOf/8Tuu+8effr0iTZt2kS7du2iadOmMW/evCguLo7Zs2fHmDFj4vnnn1/l6K+TTz65/A9GJXTr1i0uuuiiuPjiizNOWBYXF8eFF14YvXr1it133z369u0bbdu2jaZNm8acOXNi2rRp8e6778aoUaNyTvfcpEmTuOSSS6J169Y1Wv+6oHXr1jFw4MAYOXJkzts7d+5cK2tQbrvtttGnT5+s6cGffPLJeOWVV2L//fePvn37RqdOnaJVq1axcOHCmDVrVnz66afx3//+N955552sUb6nnXZabLvttjlnlSivfv36RcOGDWPJkiWlbQsXLsx50U+DBg3i+eefr3RftSnXutYNGjSICy+8MOtik9VZeSHV5ZdfntE+bNiw6Nu3b62vP19UVBT77rtvxiwXb7zxRsybNy/ndPTrsr59+8b//d//xa233prR/tFHH8Vxxx0XO+64Y2y//fbRp0+faNmyZay33nqxYMGC+O6770q/W8ouexIR0axZs7j00kvLPZL1N7/5TXz++edZ37PvvPNOHH300TFgwIDYaaedomfPntG6desoKiqKWbNmxZdffhkvvfRSvP3226Xv/caNG8e5554bzz33XKUek9/97ndxxhlnZIV6t99+ezz99NOxzz77xJZbbhkdOnSIJk2axIIFC+Lzzz+PF154IV555ZXS+xcWFsYll1wSV155ZYVmaImIWH/99ePss8+OSy+9NOMzbeLEiXHyySfH1ltvHTvuuGNsttlm0apVq2jcuHEsXLgwpk6dGmPGjIkXX3yxNDz/sQYNGsRf/vKXGlkepKbk5+fHn/70pzj11FMzgtvly5fHXXfdFU899VTsvvvuse2220b79u2jRYsWsXTp0iguLo5x48bF66+/nvU5F/HD58R5551Xoc+6ykj6uTzwwAPj+eefj88//7y0bcmSJfHnP/85Ntpoo9htt92ia9eu0a5du2jbtm0UFRWV/n6cNWtWvP322/HKK6/kvECgdevWcfjhh5frcUin01kXEG255ZbRo0ePcu0PAMC6R3ANAGU0adIkhgwZEvfee2889NBDWSds0ul0jBkzpnR0T2X069cvzj777CqNdj322GOjYcOGWeuTplKpGD16dNa61WuyySabxBVXXOHk/o/k5+fHOeecE8cdd1zWurapVCpefPHFePHFF9d4nJ49e8YFF1yQNU1ieZxwwgkxY8aMeO211zLap0+fHg899FCFj7fS73//+5yjy6rb9ttvH0OGDIkLLrggI/yLiPjiiy/iiy++qNDxWrZsGVdffXX06tWrOstcpx100EGrDK4PPPDArLXWa0JBQUH84Q9/iNNOOy0WLVqUcdu8efNyrm++Kg0bNoxTTjklBg8eXOk11Vdq1KhRHH744XHXXXdV6Th1yZgxY3Kua3388cdXakr4QYMGxXvvvZfxGkqlUnHJJZfEHXfckXOd8Jq03377xfDhw0vDx5WzThx44IG1Wsfa4JBDDomGDRvGDTfckBGspVKpeO2117K+d9akdevWcdlll0XPnj3LvU9RUVFccskl8ac//Sk+/vjjjNuWL18er7/+erlm6CgqKorzzz+/SsucbLnllvGrX/0q53fr5MmTy/05cOaZZ1bpoo2BAwdGgwYN4pJLLsmayaEyv+8aN24cF154YfTv37/SNSWlffv2cf3118dZZ52VdaHEtGnTYvjw4RWa4aGgoCD+8pe/RN++fau71JySfC4LCgpiyJAhcfrpp8d3332XcduECRNiwoQJFep7pYYNG8b5558fTZs2Ldf9x48fHxMnTsxo83kMAMDqGE4FADkUFBTEb3/727jvvvti1113rbbjNmvWLM4666y49tprq2WK5sMPPzwuvPDCKo08zc/PjwMPPDD+9re/Ca1zaNeuXVx//fWVHqU0cODAuPHGG6NJkyaV2r+wsDAuvPDCOOigg6ples+GDRvGmWeeGYceemiVj1VeP/nJT+L666+PjTbaqErH2XrrrePmm28WWlezPn36xOabb57V3rhx40pNuVtZvXr1iiuuuKLC0+v+2MYbbxx33HFHDB48uNrqOvTQQ3MuG7A2mjt3bvz1r3/NGp2+3XbbVSlIOO2006JTp04ZbTNnzowhQ4bU+nrXHTt2zJolwHThq/bzn/88Lr300kqtC/1jAwYMiDvvvDM23njjCu/bqFGjGDJkSKXfZy1btozrrrsudthhh0rt/2O/+93v4uCDD67Uvs2bN48hQ4ZUy+fmTjvtFNdee22Flxgpa+VnYm3MnFFTOnbsGNdff30MGDCgSsfp0qVL3HjjjbX+WCT5XLZp0yauu+662HrrravU90odOnSIq6++ukIXQTzzzDMZ223bto0dd9yxWuoBAKB+MuIaAFajY8eO8ec//zk+/vjjePbZZ+Odd96p8BrXET+snXjAAQfEoEGDsta7q6rdd989BgwYEPfff388+eSTWaMVVyU/Pz9+8pOfxPHHH1+h0VHroh49esStt94ad999dzz33HNrXOsy4oeTe0ceeWQMHjy4yoFzgwYN4uSTT47dd9897rvvvnjvvfcqHAY1adIkdttttzjmmGOqHFBURp8+fWLo0KHx7LPPxv33358xjfqa9OjRI4499tjYYYcd1pq1Odc2v/zlL7NmkfjpT39a6QsuKqtv374xbNiwuOmmm+K1114r9zqc3bp1i0MPPTT23HPPNa7ZXVEFBQVx1llnxV577RXPPPNMjBkzJmbPnl3uz9q6IpVKxRVXXJE1/X6rVq2qvO5tkyZN4vzzz49TTz0147Pp3XffjYceeigOO+ywSh+7Mvbff/946623Src///zz+OKLL8p90Uvfvn2zLu7ZYIMNqrPEOmX77beP+++/Px555JH45z//mbVm9epsscUW8etf/zq22267Kr2GGjduHGeddVbsscceMXTo0IzpjVeloKAgdttttzj22GOzLpyorPz8/DjxxBNj0003jWHDhsW3335brn123HHHOOWUU6p1Ku7NNtsshg0bFk899VTcf//9MXv27HLvu+GGG8avf/3rGDhwYK3MmlHT2rVrF0OGDIm33347/v73v8fXX39d7n3btGkTBxxwQBx00EHV/hu8vJJ8Ltu2bRtXXXVVvPDCC/HII4+U671VVvv27WO//faLgw8+ONZbb71y77d48eKsmYn222+/av+eBgCgfskbNWpU+c4GAQCRSqXis88+i7fffjsmTZoUxcXFUVxcHHPmzImFCxdGUVFRNG/ePJo3bx49evSILbbYIvr27RsbbLBBrQRuy5cvjw8++CDeeOON0vpmz54dy5Yti9atW0erVq2iQ4cOMWDAgNh+++1rfQrX2rBixYqsULewsLDa1u2eOnVqvPXWW/Hf//43vv7665g7d24sXLgwWrZsGR06dIguXbrEHnvsEVtvvXWNnSyeMWNGvPXWWzF+/Pj44osvori4OBYuXBhLliyJoqKiaNSoUTRt2jS6du0aPXr0iE033TS23nrrKCoqqpF6KiqdTseECRPijTfeiLFjx8bs2bNj9uzZsWDBgmjRokW0atUq2rZtG/369Ysdd9yxyqOUWDtNmTIlXnzxxRgzZkxMmjQp5s+fH0uWLIlGjRpF8+bNo3v37tGnT5/YYYcdonfv3jk/Y0tKSrICgqZNmyYWXlA7UqlUnHzyyTFu3LjStt122y0uvvjiBKtaOyxfvjw+/vjjePvtt2P8+PGlv3OWLFkSzZs3jxYtWsT6668f/fr1i/79+9fYOrXffPNNjBo1Kj755JOYOXNm6W+Zjh07RseOHaN3797x85//PGdQfM8998S9996b0XbeeefFoEGDKlRDSUlJvPfee/H++++XXrCyctmQ9u3bR4cOHaJv377x05/+tMYvCCspKYlx48bF22+/HZ9++mnMnj279Ldns2bNokWLFtGxY8fYcssto3///rHRRhvV2wu90ul0fPPNN/Hmm2/GBx98ELNmzYri4uKYN29eNGzYMJo3bx5t27aNzTbbLLbYYovYeuuto0GDBkmXXSrp5/KLL76I0aNHx4QJE2LixIkxf/78WLhwYSxfvjwaNWpU+h27wQYbRI8ePWKrrbaKTTbZpFK/o4cPHx6333576Xbz5s3jgQceKPc04wAArJsE1wBQTUpKSurFqBYAWNuNHTs2TjrppIy22267Lfr06ZNQRdSW6gqugcpbsGBBHHbYYTF//vzSttNOOy0OOOCA5IoCAGCtYI1rAKgmQmsAqBs23XTT2GuvvTLa7rzzzoSqAVi3PPLIIxmhdffu3WO//fZLsCIAANYWgmsAAADqnd/97nfRsGHD0u3Ro0fHhx9+mFxBAOuAuXPnxiOPPJLRduKJJ7rIFwCAchFcAwAAUO+0bds2Dj/88Iy222+/PdJpq2UB1JQHH3wwFi9eXLo9YMCA2HbbbROsCACAtYngGgAAgHrpkEMOiY4dO5Zujxs3Lt54440EKwKov2bMmBH//ve/S7fz8/Pj+OOPT64gAADWOoVJFwAAAAA1oaioKIYPH550GQDrhHbt2sV//vOfpMsAAGAtZsQ1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAInKGzVqVDrpIgAAAAAAAABYdxlxDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiCpMuAFasWBGTJ0/OaGvWrFnk57uuAgAAAACAqkmlUjF//vyMti5dukRhoYikpqRSqZg3b17SZaxVmjdvLhdhnedTmcRNnjw5jjnmmKTLAAAAAABgHXHPPfdE9+7dky6j3po3b14ceOCBSZexVnn88cejZcuWSZcBiXLpBgAAAAAAAACJElwDAAAAAAAAkCjBNQAAAAAAAACJssY1iWvWrFlW25hXukXb1gUJVANAWQsXpWLDAV9HRMSX73SPJo1d9wZQVxzSd/OkSwCgjJJYEW/EsxERsWP8NAqcfgNI3LJYGm/H8xltuc5LU7PGvNI92rZ2XikiYubsVGy+69dJlwF1jl/OJC4/P/uLqm3rgmjXVnANUBc0XpRX+t/t2hYIrgHqkKK89ZIuAYAyStL/O59RFOtFQZ7TbwCJS2c35TovTc1q2zo/2rX1vfiDFUkXAHWST2YAAAAAAAAAEuXSFgAAAAAAAGpUKtKRilTSZdQJqVzTAABGXAMAAAAAAACQLME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQqMKkCwAAAAAAAKB+K4lUlKTzki6jTiiJVNIlQJ1kxDUAAAAAAAAAiRJcAwAAAAAAAJAowTUAAAAAAAAAiRJcAwAAAAAAAJCowqQLAAAAAAAAoH5LRTpSkU66jDrB4wC5Ca4BgNVq0jg/Sqb0SroMAABYKxTkFcae8cukywAAgLWOqcIBAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBEFSZdAAAAAAAAAPVbKlKRiryky6gTUpFKugSok4y4BgAAAAAAACBRgmsAAAAAAAAAEiW4BgAAAAAAACBRgmsAAAAAAAAAEiW4BgAAAAAAACBRhUkXAAAAAAAAQP2WSqejJJ1Ouow6IeVxgJyMuAYAAAAAAAAgUYJrAAAAAAAAABIluAYAAAAAAAAgUYJrAAAAAAAAABJVmHQBAAAAAAAA1G+pSEcq0kmXUSd4HCA3I64BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASFRh0gUAAAAAAABQv5VEOkoinXQZdYLHAXIz4hoAAAAAAACARAmuAQAAAAAAAEiU4BoAAAAAAACARAmuAQAAAAAAAEhUYdIFAAAAAAAAUL+lIh2pSCddRp3gcYDcjLgGAAAAAAAAIFGCawAAAAAAAAASJbgGAAAAAAAAIFGCawAAAAAAAAASJbgGAAAAAAAAIFGFSRcAAAAAAABA/VaSTkdJOp10GXWCxwFyM+IaAAAAAAAAgEQJrgEAAAAAAABIlOAaAAAAAAAAgEQJrgEAAAAAAABIlOAaAAAAAAAAgEQVJl0AAAAAAAAA9Vs6IlJJF1FHpJMuAOooI64BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBEFSZdAAAAAAAAAPVbSUSURDrpMuqEkqQLgDrKiGsAAAAAAAAAEiW4BgAAAAAAACBRgmsAAAAAAAAAEiW4BgAAAAAAACBRgmsAAAAAAAAAElWYdAEAAAAAAADUbyXpH/7wOMCqGHENAAAAAAAAQKIE1wAAAAAAAAAkSnANAAAAAAAAQKIE1wAAAAAAAAAkSnANAAAAAAAAQKIKky4AAAAAAACA+i31///wOMCqGHENAAAAAAAAQKIE1wAAAAAAAAAkSnANAAAAAAAAQKIE1wAAAAAAAAAkqjDpAgAAAAAAAKjfUhFREnlJl1EnpJIuAOooI64BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASFRh0gUAAAAAAABQv6XSP/zhcYBVMeIaAAAAAAAAgEQJrgEAAAAAAABIlOAaAAAAAAAAgEQJrgEAAAAAAABIVGHSBQAAAAAAAFC/lURelERe0mXUCR4HyM2IawAAAAAAAAASJbgGAAAAAAAAIFGCawAAAAAAAAASJbgGAAAAAAAAIFGCawAAAAAAAAASVZh0AQAAAAAAANRvJZEXJZGXdBl1gscBcjPiGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBEFSZdAAAAAAAAAPVbKp0XqXRe0mXUCR4HyM2IawAAAAAAAAASJbgGAAAAAAAAIFGCawAAAAAAAAASJbgGAAAAAAAAIFGFSRcAAAAAAABA/ZaKvCiJvKTLqBNSHgfIyYhrAAAAAAAAABIluAYAAAAAAAAgUYJrAAAAAAAAABIluAYAAAAAAAAgUYJrAAAAAAAAABJVmHQBAAAAAAAA1G8lkR8lxlNGRERJpJMuAeoknxAAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJKow6QIAAAAAAACo31LpvEil85Iuo07wOEBuRlwDAAAAAAAAkCjBNQAAAAAAAACJElwDAAAAAAAAkCjBNQAAAAAAAACJKky6AAAAAAAAAOq3ksiLkshLuow6weMAuRlxDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiCpMuAAAAAAAAgPotlc6PkrTxlBERqXQ66RKgTvIJAQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJKow6QIAAAAAAACo31KRFynjKSMiIhWppEuAOsknBAAAAAAAAACJElwDAAAAAAAAkCjBNQAAAAAAAACJElwDAAAAAAAAkCjBNQAAAAAAAACJKky6AAAAAAAAAOq3ksiLkshLuow6weMAuRlxDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiCpMuAAAAAAAAgPqtJJ0fJWnjKSPC4wCr4J0BAAAAAAAAQKIE1wAAAAAAAAAkSnANAAAAAAAAQKIE1wAAAAAAAAAkqjDpAgAAAAAAAKjfUpEXqchLuow6weMAuRlxDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiCpMuAAAAAAAAgPotFflRYjxlRPzwWADZvDMAAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASFRh0gUAAAAAAABQv5Wk86MkbTxlRHgcYBW8MwAAAAAAAABIlOAaAAAAAAAAgEQJrgEAAAAAAABIlOAaAAAAAAAAgEQVJl0AAAAAAAAA9Vsq8iNlPGVEhMcBVsE7AwAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBEFSZdAAAAAAAAAPVbSTovStJ5SZdRJ3gcIDcjrgEAAAAAAABIlOAaAAAAAAAAgEQJrgEAAAAAAABIlOAaAAAAAAAAgEQVJl0AAAAAAAAA9Vsq8qLEeMqI+OGxALL5hAAAAAAAAAAgUYJrAAAAAAAAABIluAYAAAAAAAAgUYJrAAAAAAAAABIluAYAAAAAAAAgUYVJFwAAAAAAAED9lkrnRyptPGVEeBxgFbwzAAAAAAAAAEiU4BoAAAAAAACARAmuAQAAAAAAAEiU4BoAAAAAAACARAmuAQAAAAAAAEhUYdIFAAAAAAAAUL+VRH6UGE8ZEeFxgFXwzgAAAAAAAAAgUYJrAAAAAAAAABIluAYAAAAAAAAgUYJrAAAAAAAAABJVmHQBAAAAAAAA1G8lkRcl6byky6gTSsLjALkYcQ0AAAAAAABAogTXAAAAAAAAACRKcA0AAAAAAABAogTXAAAAAAAAACRKcA0AAAAAAABAogqTLgAAAAAAAID6LRX5kTKeMiLC4wCr4J0BAAAAAAAAQKKMuAYAAAAAAABqxLJly2LixIkxbdq0mD59ekREdOjQIdq3bx89e/aMoqIi/a7F/VYnwTUAAAAAAADUI8ccc0x8/fXX1Xa8/Pz8ePHFFyu0zzfffBNPPvlk/Oc//4n58+fnvE+LFi1in332if322y+6dOlSHaXqt5b6rQmmCgcAAAAAAIB6ZPbs2Yn1nU6n4/HHH4/f/va38eijj64yTI2ImDt3bvzzn/+M3/zmN/H000/rdy3otyYZcQ0AAAAAAAD1xLJly1YbYtakVCoVV199dTz33HM5b2/QoEGk0+lYsWJFRvvy5cvjmmuuiQkTJsTpp58eeXl5+q2D/dY0wTUAAAAAAAA1KpXOj5K0iYAjfngsalLZ0db9+/ePXXfdtUb7XGn48OFZYWrXrl3jiCOOiC222CLat28f6XQ6pk2bFh988EE88MADMWXKlNL7jhgxIrp06RKHHHKIfutgvzVNcA0AAAAAAAD1xKxZszK2t9566/j5z39e4/1++umnMWzYsIy2Qw45JH7/+99HQUFBRnvnzp2jc+fOsffee8eNN94YTz75ZOltt99+e2y++eax6aab6rcO9VsbXNoCAAAAAAAA9UTZ4LpNmzY13mcqlYprrrkmUqlUadv+++8fxx9/fFaY+mOFhYVx+umnx1577VXaVlJSEldffXXGsfSbbL+1RXANAAAAAAAA9UQSwfWbb74ZkyZNKt3u0KFDnHjiieVaQzk/Pz9OO+20aNmyZWnbpEmT4s0339RvHem3tgiuAQAAAAAAoJ4ou8Z127Zta7zPBx54IGP7mGOOiaKionLv37hx4zjqqKMy2oYPH67fOtJvbRFcAwAAAAAAUKNSkefvR381qWxwXdMjrqdMmRKfffZZ6XarVq1i0KBBFT7OvvvuG02aNCndHjt2bEydOlW/CfdbmwTXAAAAAAAAUE/8eKrwhg0bZoSUNeHtt9/O2N5ll11Wu97yqhQVFcUOO+yw2mPrt/b7rU2CawAAAAAAAKgnfjzium3btuVa/7gqyoae22yzTaWPte2226722Pqt/X5rU2HSBQAAAAAAAADV48cjrnNNE55Op+Pbb7+NqVOnRnFxceTn50eLFi2iQ4cO0a1btwoH3Z9//nnGdp8+fSpXeERssskmqz22fmu/39okuAYAAAAAAIB6oKSkJObMmVO6/ePgetasWfHPf/4zXnvttVWuady2bdsYMGBAHHroodGlS5c19jdv3rwoLi4u3W7evHm0bdu20vV37tw5GjVqFIsXL46IH0aPL1iwIJo2barfBPqtbaYKBwAAAAAAgHpgzpw5kUqlSrfbtm0bJSUlce+998YRRxwRjzzyyCpD64iImTNnxtNPPx1HH3103HLLLbFixYrV9vftt99mbLdo0aJK9efl5WUdo2wf+q29fmub4BoAAAAAAIAaVZLOi5J0vr90fpSka27N6R9PEx7xQ8B5+eWXxz333BNLliwp93FKSkri0UcfjT/+8Y8xf/78Vd5v2rRpGdvVMWK3WbNmq+1Dv7XXb20zVTgAAAAAAADUA2WD6wcffDAWLlwYERH5+fkxYMCA2HvvvaNbt27Rvn37yM/Pjzlz5sRnn30Wr7/+erz88ssZI7Y/+OCDuOyyy+Lyyy+P/Pzs8bArp5peqWwYWhllRwKX7UO/tddvbRNcAwAAAAAAQD0we/bsjO2VoXWfPn3iwgsvzLludaNGjaJTp04xcODAOOKII+Liiy/OmDb6nXfeiUcffTQOOeSQrH3Lhp01MRK4PEGufmum39pmqnAAAAAAAACoB8oG1xERgwYNiptuuilnaF1Wjx494qabboru3btntN9///2xaNGirPuXnX68OkYClz2GfpPrt7YJrgEAAAAAAKAeKDtVeNeuXePMM8+MBg0alPsYLVq0iIsvvjhjnwULFsRTTz1VbXVCLqYKBwAAAAAAoEaVRH6UGE8ZEVGjj8OAAQOiU6dOpdvbbrttNGzYsMLH6dGjR+y9994ZYfWrr76aNV142WPPnz+/wn2VVfYYjRs3zrqPfmun39omuAYAAAAAAIAKWrqopFL7LVtcuf3KY/vtt4/tt9++Wo61//77ZwTX48ePjyVLlmSEqI0aNcrYZ8GCBVXud968eRnbZfvQb+31W9sE1wAAAAAAAFBBZ/Z/LekSalTPnj2jadOmpSHpihUrYuLEibHpppuW3qds2FkdI4ErE+Tqt2b6rW3mZAAAAAAAAAAy5OfnR8+ePTPa5syZk7HdoUOHjO3qGAlcNpQt24d+a6/f2mbENQAAAAAAAFTQtf/duVL7LSheFhft8U41V1MzmjdvnrFddpRu165dM7bnzp1bpf7S6XRWOF62D/3WXr+1zYhrAAAAAAAAqKD1GhdU6q+oUUHSpZdbUVFRxnZJSeb63M2bN49WrVqVbs+bNy9mzpxZ6f6+//77WLJkSel2mzZtomnTpln302/t9FvbBNcAAAAAAADUqFQ6z9+P/tYWZUfltm7dOus+vXv3ztj+7LPPKt3fuHHjMrZ79eq1yvvqt3b6rU2CawAAAAAAAFjLPffcc/Gzn/2s9O/KK6+s0vHS6XR8/fXXGW1t2rTJut92222XsT169OhK9/nuu++u9tj6rf1+a5PgGgAAAAAAANZyXbp0iYULF5b+lR1VW1ETJ06MWbNmlW43bdo0evbsmXW/sqHnq6++mjWleHksW7Ys3nzzzdUeW7+1329tElwDAAAAAADAWq5Hjx7RoEGD0u2vv/46vvnmm0of75FHHsnYHjBgQBQWFmbdr1OnTrHJJpuUbhcXF8eLL75Y4f6efvrpWLhwYen2ZpttFh07dlzl/fVbO/3WJsE1AAAAAAAArOWaNm0aO+64Y0bbAw88UKljffrppzFy5MiMtp/97GervP9hhx2WsT1s2LBYtmxZuftbtGhR3H///as9pn6T67e2CK4BAAAAAACgHjjwwAMztp9//vl46aWXKnSMKVOmxMUXXxypVKq0bccdd4x+/fqtcp8ddtghevToUbo9bdq0uOWWWyKdTq+xv1QqFddff30UFxeXtvXs2bNc01frt3b6rS2CawAAAAAAAGpUKvKjxF+URH6kajCe22KLLWLffffNaLvsssviwQcfjCVLlqxx/9GjR8cJJ5yQsbZ1ixYt4qSTTlrtfvn5+XH22WdHQUFBaduIESNi6NChq12Hefny5XHttddmjO4uLCyMP/7xj5Gfv+bHSb+1029tqTuVAAAAAAAAAFVywgknRO/evUu3U6lU3HHHHXHYYYfFww8/HGPHjo25c+dGOp2OdDodc+bMiZdeeinOPvvsOPvss2Pu3Lml+6633npx+eWXR6dOndbY7yabbBK/+93vMtoeffTROOaYY+L555+PadOmRSqVipKSkpgyZUo8/fTTcdRRR8XTTz+dVX+fPn3K/e/Vb+30WxuyV1AHAAAAAAAA1kpNmjSJa6+9Ns4999wYM2ZMaXtxcXHceuutpduNGjWKFStWxPLly3Mep3Xr1nHRRRfFpptuWu6+Dz744Jg8eXI8+eSTpW3fffddDBkyJCIiGjRoEBGxyj4POuigrOnO9Vt3+q1pgmsAAAAAAACoR5o2bRrXXnttPPjgg/HAAw/kDDAXL168yv132GGHOOuss6J169YV6jc/Pz/OPPPM6N27d9x4442xYsWKjNtXFaQWFRXFWWedFXvttVeF+tNv7fZb0wTXAAAAAAAAUM80aNAgjj766Nhnn31ixIgR8fzzz8fMmTNXef+mTZvGtttuG7/61a9io402qlLf++23X/Tv3z9GjBgRzz33XMybNy/n/Vq2bBk//elPY7/99ivXdOT6rRv91pS8UaNGpZMugnXbnDlzsqYjmPpJj2jXtmAVewAAABERe3ful3QJAABQ5y1LL41X48mMtscffzxatmyZTEHrgFzn/f/6+q7RtHVRQhXVLQtmL4uLdnolo602XpPpdDqmTJkS48ePjzlz5sTChQujYcOG0aJFi1h//fVjo402ioKC6s9mli1bFl9++WVMnz49pk2bFnl5edG+ffvo0KFD9OzZM4qKauZ1od/a6bc6GXENAAAAAAAA9VxeXl507tw5OnfuXKv9FhUVxSabbBKbbLKJfuthv9UpP+kCAAAAAAAAAFi3Ca4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASFRh0gUAAAAAAABQv5VEXpREXtJl1AkeB8jNiGsAAAAAAAAAEiW4BgAAAAAAACBRgmsAAAAAAAAAEiW4BgAAAAAAACBRgmsAAAAAAAAAElWYdAEAAAAAAADUb6l0fqTSxlNGhMcBVsE7AwAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASFRh0gUAAAAAAABQv5VEREnkJV1GnVCSdAFQRxlxDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiCpMuAAAAAAAAgPotnc6PVNp4yogfHgsgm3cGAAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQqMKkCwAAAAAAAKB+K0nnR0naeMqI8DjAKnhnAAAAAAAAAJAowTUAAAAAAAAAiRJcAwAAAAAAAJAowTUAAAAAAAAAiRJcAwAAAAAAAJCowqQLAAAAAAAAoH5LRV6kIi/pMuoEjwPkZsQ1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkqTLoAAAAAAAAA6reSdH6UpI2njAiPA6yCdwYAAAAAAAAAiRJcAwAAAAAAAJAowTUAAAAAAAAAiRJcAwAAAAAAAJCowqQLAAAAAAAAoH5LpfMilc5Luow6weMAuRlxDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiCpMuAAAAAAAAgPotFflRYjxlRPzwWADZvDMAAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASJTgGgAAAAAAAIBECa4BAAAAAAAASFRh0gUAAAAAAABQv6XSeZFK5yVdRp3gcYDcjLgGAAAAAAAAIFGCawAAAAAAAAASJbgGAAAAAAAAIFGCawAAAAAAAAASVZh0AQAAAAAAANRvqciLlPGUEfHDYwFk8wkBAAAAAAAAQKIE1wAAAAAAAAAkSnANAAAAAAAAQKIE1wAAAAAAAAAkSnANAAAAAAAAQKIKky4AAAAAAACA+q0knRcl6byky6gTPA6QmxHXAAAAAAAAACRKcA0AAAAAAABAogTXAAAAAAAAACRKcA0AAAAAAABAogTXAAAAAAAAACSqMOkCAAAAAAAAqN9SkRepdF7SZdQJqfA4QC5GXAMAAAAAAACQKME1AAAAAAAAAIkSXAMAAAAAAACQKME1AAAAAAAAAIkqTLoAAAAAAAAA6rd0Oj9SaeMpI354LIBs3hkAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJEpwDQAAAAAAAECiBNcAAAAAAAAAJKow6QIAAAAAAACo30oiL0oiL+ky6gSPA+RmxDUAAAAAAAAAiRJcAwAAAAAAAJAowTUAAAAAAAAAiRJcAwAAAAAAAJCowqQLAAAAAAAAoH5LpfMilc5Luow6weMAuRlxXY+VlJREOp1OugwAAAAAAACA1RJc12NPPvlkHHLIIXHdddfF22+/HUuXLk26JAAAAAAAAIAsgut6Kp1Ox1NPPRUzZ86MESNGxLnnnhuffPJJ0mUBAAAAAAAAZBFc11Pjxo2LL7/8snS7c+fO0b9//wQrAgAAAAAAAMitMOkCqBlPPvlkxva+++4b+fmuUwBY20z5uii+GNMo5s4qjPlzCqJZy5Jo3X55tOu8PDbcfHHU5Ef75K+KYtJnjaJ4RmHMKy6MJs1KonmrFdFzs8XRrffSyMur/j6XLMqLCR81jmnfFsX8OYWxZHF+NGlWEs1arogNNl4S3TdaEgU18Otl0YL8mPRZw5g+uShmfN8giorS0a7LsmjfZXn02HRxFBRUf58AANSEdHTstix6bb44mrcuiaYtV0Sk82L+nIKYN7sgvhjTKKZ9WxQRNfBjNgFtOi6PjbZcFC3arIimLUqisDAd8+cUxPw5hfHVuIbx3ZfrRTpdP/6tAADUf4LremjBggUxatSo0u2CgoLYZ599EqwIYO3yu936xDcTGlXb8fIL0vHstx+V+/4L5+fHv/7ePl7+d8v4bmLDVd6vbadlsfPP5sTgI2ZFt95Lq6PUWDgvPx77e/t4ZUTL+O7LVffdos3yGHjAnDjo/6ZH+/WXV6nPVCri3Rebx+N3tIsx7zSJFctXncav16gktt9rXgw+clZssf2CKofnEz5qFE/f3zZe/nfLWLIodzrdrvOy2OewWfHTw2ZFm44rqtYhAAA1okvPpbH/b2fELvvNjVbtVv+bbfb0wnhlRMsYMaxtfD9pvRqvrfMGS+Pm/0yIJs1SpW1HbbtJTPuuqFLHa9Nxeex39MzY/aDi6LCG3+Lziwvireebx+N3touJn1bf/+MAQGWk0vmRShtgFxEeB1gFwXU9NHLkyFi69H8Bxk477RStW7dOsCKAtcvs6Q0S63vkI63i9j93iXnFa/6KnjmlKB6/o308dW/bOPKsqfHLE6ZXaTTyyEdaxR1/6RJzZ6/5IHNnNYh/39UuRtzTNg4/Y2ocetq0So1KnvJ1UVx5cvcY936Tct1/6eKCePmJVvHyE61iwJ5z45Qrvot2nSsenJeURNx3dcd46MaOa7zvjO+L4v5rOsW/bm8ff7zp69hu0LwK9wcAQM1osF4qjj1vShz4u5nl3qd1+xVx4HEzY//fzox/39k2hl3eKZYvq5mTxw3WS8V5f/86I7SurPz8dBx84vQ46uypUVjO/2Vp1qok9vpVcez1q+J46V8t46Zz149F800nBABA3eSSjnomnU7HU089ldH2s5/9rMLH+f777+Pdd9+NcePGxeTJk2PFCiPMgHXDsiV5sWBO7V/XlUpF3HVZp7jmtO6rDK2btlgR+QXprPbly/Jj2JDOcf7hG8aypRUfgpxOR9x7Vce45rTuOUPr/IJ0NG5akrvukry4/5pOcf5hG8ayJRXr+5N3msSJg/qsMrTOy1t1vxER77zQIk7eZ6P45vOKjZJZsigvLjqq5ypD6/UaleR8nBfOK4iLj+4ZD93UvkL9AQBQM5q1XBHXPfHFakPrhfPyY+G83Ke/8vMjfvH7mXHdiC+iaYuaOe/x+4u/j959F1f5OA3WS8Ul938Vvz1v1aH1ogX5MX9OQaRWkZHv/os5cevICdG+y7Iq1wMAADXBiOt6Zty4cTFx4sTS7c6dO0f//v0rfJyRI0fGPffcU7r96KOPRps2baqjRIA6bfaMzLNA/XaaHzv/bE6Vjlme6azvHtIpHr6lQ0Zb81Yr4sDfzYjt9ppburZzKhUx4/sG8d5LzeOJYW0zpjT/4LVm8bczusU5N39dobWvH7i2Qzx4fWaI22mDpXHQ/02PbfeYF+06L4+8vIh5swviu4nrxcRPG8VT97WNSZ9l9n3lKd3j/L9PKlffEz5qFBcd2TMWLfjfaI+CwnTstn9x7LTvnOjSc2l06rYsihqmY8HcgpjydVF8Na5RPPtA6xg7umnpPnNmNog//WrDuO2F8dG89apD7h/7+5+7xOhRzTPaNtpyUfz61GnRp9+iaNNxeZSsiJj6zXrxzgvN4+Fb2secmf97Xdw9pHOsv+HS2Gnw3HL1BwBA9WuwXiou/cdX0XuLzFD42y/Xi6fvaxMfvdE0Jo1vGKmSH36MFxSmY4ONF8dWOy+IfY+YFZ17/C+87b3F4vjLvV/FOQdvuNplaypql/3mxM+PmVUNR0rHebd+HVsPnJ/ROuP7BvHMP9rE+y83i4ljG5aOGs/PT8f6Gy6NLXZYEIOPmBUbbrakdJ+O3ZbF5Q9NjNN/1isWzHVaEACAusUv1HrmySefzNjed999I78i6cX/N3ny5NL/btCgQbRq1arKtQGsDWZPy/xq7L/L/PjZUdVxsmnVRj3eMiu03mnfOXHm376JJs0zh0vk50d0WH95/OyoH9Zc/uctHeLeKzuV3v7yv1tFr76L4uATZpSr7w9eaxr/uDYztN7n0Flx0mXfRVHDzFHHLdqURIs2i2KzbRbF4CNnxT9vzuz79adbxguPtI69fjV7tX0uW5IXV5zUPSO07thtaVxw+6SsE48REU1blETvLRZH7y0Wx6BDZsezD7SJm/60fqRSP5yEnDW1KO6+olOcdtV3a/z3vvZUi3jmH20z2o7/63dxwLEzMy4wKGwQsf6GS2P9DWfEvkfOjMuP3yDeeaFF6e3Xntktem0+Pjp2M1oFACAJB58wIzbuv6h0O5X6YSmYf97cvjSs/rGSFXnx5ZjG8eWYxvH4He3i16dMi6POnlZ6++bbLooDjpsZj95aPbPrdN5gaZx+zbfVcqw9DiqOHfbJXK5mxN1t4o5LOseyJdnnfFKpvPjm84bxzecN46l728S+R86K4//6fRSt98Pv+64bLo2jz5kat5y3frXUBwAA1cVU4fXIggULYtSoUaXbBQUFsc8++1TqWN9//33pf3fo0KFS4TfA2qjs+tZtOlZ8/eSKWLwwP269qEtG224HFMd5t03KCq3LKiiMOOy0aXHaVZknxB68vmPMmbXmdetKVkTccE7XSKf/d2Jv5/2K47Srv80KrbP6Lvih7yP/MCWj/Z4rO65xyvCHh7aPyRMblm63bLs8rnz4y5yhdVl5eRGDj5gVv//z5Iz254a3iXnFq/83L1mUHzf9qWtG23EXTI4Dj5u52lHxDRun44LbJ0W/nf43wmXhvIK45fwuq94JAIAas16jkjjkpOkZbUMv6BLDb+iQM7Quq2RFXjxwXcd45NZ2Ge2HnTYtGqxX9bWoG6yXivNvn1S6rvWCuauernzN0nHkH6ZltDxya7u45fz1c4bW2fLi6fvbxu1/6ZzRuu+Rs6JtJxdhAgBQt0gj65GRI0fG0qVLS7d33HHHaN26daWOVTa4BlhXzJ5WJrjuULPB9b/vahtzZ/2vz07dl8bpV38bBWvOnUsNPmJW7PHL/41yXjS/IB64Nvf6zT/22tMtY8qk/60P3abjsjjtyu8qNM34r0+ZFp17/O+7Z9bUovjwjaarvH/Jioin78sc8fz7i7+v8Mjlnx8zMzp1/1+/qf/H3n2H2VWVfQN+zsxk0mbSSIVUCITeQQJIU0QioShFioCIUvykqggCgg1BQEFRqiAvvUhvQkCQXqSHQICQhIT0nsnMZGb290fISc5kJpm+T5L7vq59vbP27L3Wc3Z4zcn5nbVWdSZeebLLSu6IePTWdXL28N5ku4VxcANnphd3SOLMyydEu+JlH2S+OqprfPJex5XcBQBAa9hhz/nRsfOy92UfvtkxHrqp8dub3XhRv/h09LIvVHbuUhPb7T5/JXc0zAm/mhxDN1+2PPdlpw+M+XMb8QZ/OUO3WBT9Bi17rzx9crv458Wrfq9f20M3rROvPV2abRcWRuxi6xsA2lhNZBzLHcCKLBWehyorK2P06NGNvu+BBx7Iaa+//vrx1ltvNbqfxYsXx+zZs7PtgoKCVfaz+eabR1GR/5yA1d+sabn/W9baM65H3ZP7BaPDT5ma8yFcQx171hfx7APdsnvyPXVPj/jheZNXOnO6doB80A+nR2m3hu0TvVRRu4g9D5wdt/5p2Ydnrz7VJXb8Wt0f+P3vv6U5s9pLulbFHgfOrvPalSksitjpG3PjvuuWLeU4/sMO9V5ftTji3qtzZ9Qcd87kBu0/vlTv/otj/+/PiHuvWTbmXVf1jrP/Pr7hnQAA0Gzrb5a7Us+b/y2NaMKHv9VVmXj9P6Wx/qbLQuYNNl8UL/+760ruWrnd958dI5fb1/q+63rGi493jRMunLSSu+q3wea5r/W9Vzpn97JunEy8/GSX2GGvZe/Th26+6hWPAACgLUka89CsWbPi9NNPb3Y/N910U/OLiYjXXnstXnvttZVec//990fXrk3/hx1Avphde6nwVpxxPe3zdjHx42Vha7v2NU0KcSMieq+3OL7y9XnxwmPdImLJrOs3ni2N4fvMq/P6ikWZ+OCNTtl2JpPE3oeufG/q+myy3cKc9sRP6g+QP3i9c057q50XNGp2+fL61ZqlXXuZ9+W992pJzPiiONvecMuy2HL4wnqvr8+3fzQ9/nVtr+zy6i890TUqFmWifceVL60OAEDL6dazKqc99t2mr4Iz9u1OOe11elfVc+WqrTukIk794+fZ9pj/dYrrf9uvyf1FRHRbJ7eej1vwtfZoxmsFAIDWYKlwAFjO8jOuO3Sqjk6lzd/jrj4fvZP7wdGwrcuaFYButcuCnPZ/H+lW77Vj3sydqdF/g4rotk7jZlsv1bXWh2mzp9f/vbjPas2K3nrXBfVc2QC1JtV0Kqm//lefyl1GfPf9m/YFgZ79FsdmOy4LvCvKC+LtF+tfGh0AgJY3f3bu+812xU1/D13ULvfe+XOa9q3Kdu1r4tzl9rWeP6cwfn/ioOyKSE01ryVfa3Huv22a+loBAKC1mHENAMtZfo/rdfoubtRS0o01Z0buX8PL713XFMvv+RwR8eGbneq5MuKLz4pz2hts1vRlAhctbPgHXr+4anxEsmxp7cJ2Tf/gbcqE3NfQb3D9z+/VUbnB9XZ7NH3vwu33nBfvvbIsrH7lqa71Lo0OAEDL+6LW+8AhmyyKZ+7r3qS+hmyS+z64dt8NdeIFk2ODzZbf13pATP28aX0tr/Z73sGblNdz5aoN2Tj33i/GN78+AABoSYLrPNSjR4+4+uqrG3z9zTffHC+++GK2vf/++8eIESOaNHZ1dXWceeaZUV6+5B8zG2ywQfzsZz9b5X2dO3de5TUAq4Pll5tep8+KS+clScTEj9vHtEnFMXt6URQULJlx3Hu9yhgwtKJRQXft2ROdS5s24zl7f5fc+yePax+V5Zk697muPXaXHk1fJrD2h2krW169uH3LLKldXR3x8pO5W1TU/tBxqfKygpwl2dt3qIlBGzX9A7+NtynLaX/yXtOXawQAoPHG/C/3C5pf+fr8uPmPNY2e3VzUrmaFLyDW7rshdj9gdux3zLJ9re+9ule89ETLbKf26fsdc97Tb7XzgijpWhUL5jb2I70khn9zbs6ZMf/zWQ4AbasmyUR10oqzRFYjNZ4D1ElwnYeKi4tj2LBhDbp21qxZ8frrr+fce8wxx0SPHj2aNPa7776bDa0jIoYPH97gWgBWd9XVubOgeywXwM6cWhT3XN07Xni0a0yd2L7O+9fpWxk7fm1eHHLytFhvyKpnT3fomLtUX/mi5i0juHBe7sznmppMTPykfc7Mj6W69ayK7fZYtv/1hls2fcZ17dnM6w2pqOfKlvPwP3vG5HHL/hw22Kwstvlq3cuOf/5J7p/XgA3Lo7AZ74DW3zT3WU38pH0kSbTq7HwAAJb5bEzHeOXJ0vjK3ktC58Ebl8dhP5kWt17et1H9fPeUaTF4uVnIrzxVGp+NadyXEtdbvyJOW25f6w/e6BT/uKhxdazM3FlF8fjtPWL/7y8Jxrv3qooTLpgcl50+sFH97H3o7Nh+j2Xvlz8b0yFeHVXaYnUCAEBLEFyv5u66666orFwWjnzrW99qcmgdEfHCCy/ktHfYYYcm9wWwupkzoyhqapalj+v0WRzV1RG3/7lP3PW33lGxaOVLYs+cUhyP3doz/n3nOrH/96fH8edOjqJ29V9fe5bzzCkrubgBvhi/YqA+ZULdwfU3DpsV3zhsVrPGi4iY8UW7FYLrr+w9r56rm6+yPBMP/1/PuO7X6+acP/rnU+oNjid+nPtcujZjdnlERGn36shkkki+/GbsgjlFMXdmUXTr2bx+AQBouJsu7hdbDF8YnUqWfBn0yNOmRkVZQdx7Ta/s+7T6FBQk8Z0Tp8cRp07Nnlu0sCD+eUnjAufiDjXxy2s+y9Ywf3bL7Gtd251/7R27jJibXRHqG4fNjnmzi+LGi/o2YKwk9j50dvy/30/KnqmuirjuN/1W+ZwAAKCtCa5XY3Pnzo0HHngg2y4sLIzDDjusyf0lSZITXHfq1Ck23XTTZtUIsDpZfn/riCUB5yX/b1D854HG7ZdXXZWJ+67rHeM+6BjnXvtZlHarewnw2jOTR7/eOaqrIwobvmV0jreeL1nhXNn8lv3QrLabLu4XleXLxujSvSq22rnumc9NUVmeiWmTimPqxOL4dHSHuP+GXjHji2VLkxcUJnHGZRNip5WE5dMm5S5lXtK1eUuyFxRElHSrjvnLLbc+9fN2gmsAgDb06eiOcd5RQ+K3t46Ljp1rorAo4ofnfxEjjpoZ/76zR7z3SueYNK59LPhyVaLSrtWx3voVscVOC2Of786KvgOXTQIoW1AQ5x41JD55r3HLhNfe1/qPpw1Y4b1nS5jxRXGcdcgGcck9n0SP3kvecx584vTY88DZ8cQdPeKdl0piwtgOMX9OYSQ1EZ1Kq2PdwZWx6fYLY+9DZ8eQ5fbFrq6K+P1Jg+L1Z7rUNxwAAKRGcL0au/fee3OW9f7GN74Rffr0aXJ/n332WXz++bLlrbbbbrsoKvKfCLD2WH5/64iIO/7aJ8rmL/mgq6AgiR32mhd7HzorBmxYEb3WrYyCgoi5M4viw7c6xYuPd43nHuyWM2P7redL45KfDIwL/zkuCurIjzfcsiw6dq6ORQuXjLFwXmG88u+usfO+c1e8eBWmfb7izOeIiLIFTUzBG2DUvd3jybtyV/n47ilT69xTuzHu/Evv+MdF667yuvU3XRQ/OHdybL/H/JVeV16W+/BL6vkiQWN06V6VE1wv/TMEAKDtvPdqSZy+/9A45qwpMfwbS77IuN76lfH9s6c0uI9XniqNGy/qF+M+aNwS4XscODu+dfSyfa3v/nuveOXJltnXui4TP+4Qp40cGkf/dGrs+e3ZUVgYsU7fqjjitGlxxGnTGtTH+691in/8rl+89+qKX3gFAIB80LrTsGg1CxYsiH/961/ZdiaTicMPP7xZfS4/ezsi4qtf/Wqz+gNY3cyelvtlnaWh9UZblcX1//0gfn3zuPjqfnNj8LDy6FxaEx0710TfgZWx+/5z4uy/jY+/P/Vh9N8gd1nuV0d1jX9d26vO8YraReywV+5M4Zsv6xuVFY1bsi9JIm74/bp1LhO4aGHr/FX/2tOl8eefDcg5129QRYw8ZkarjLe8goIkTr1kYvztyQ9XGVpHrPgMmjvjOiJWmEVf3krPGQCAlRv3Qce44NjBcd1v+jX63msv7BfnHz2k0aH1eutXxKmXLPvi/+jXO8WNFzV+/MaaOrF9/PHUgXHJTwZGdSMX+7n/hp5x5oFDhdYAAOQ1n7Kupu6///5YuHBhtr3HHnvEgAEDVnLHyi1YsCCeeOKJbLtLly6x++67N6tGgNVN7RnXERFf+86suPyBsbHekMo67sg1eOPyuPz+sTFww9zw+vYr+kTZgrr/yj30x7mzI8aN7hhXnjUgamoaXvd91/eK/9y/ZDnz/uvnjp00b/JznR6/rUecf8z6OUuEF3eoifOu+6zZs60boqYmE389p3/88dSB8dmHHVZ5fe3Z0KVdm7+kd+3gur4/XwAAWtfu+8+Ovz/5UfzwvC8afe+PfvVF/PXxsbHrt+Y0+J7iDjVx7nL7Ws+bVRi/P2lQVFe1/n7R2+42Py677+M4+28TorCRC+Qd+IMZcd1zH8a+R86MiNZ/zw4AdalJMlGTFDiSgqhJWv+9A6yOrAO9Glq0aFHcfffdOeeOOOKIZvX56KOPrrDseHFxy+/LBJDPZk3N/Wux/wblccrFE6NdccM/2Om6TnX88prP4v/tu1EsrlgSZi6YWxSP3rJOHHzi9BWu33DLRfG178yKUfcuW3L7ybt6xMJ5BXHaHydG13Xqnx1cXlYQN/6hX9x//ZIZ3ft8d2b06LM4br+ib/aapR+otYTFlZm4/rfrZsdbauk+0xtsvqhFxtluj/nRuevEiIioqszE1EnF8cVn7WPSuOKY8NGS2TDVVZkYdU+PeO6hbvHLqz+L4fvUv8c1AABroiSOPGNqHP3TqSv8ZtqkdvH+a51j8rjiWDC3KJJkyRcP1x1cEZvusDD69F+cvXbDLRfFedeNj5svLY9bL+8TESv/EPnECyfF+svva33qwJjeCvta1/aN786M0/74eRTW2qFmzoyieP+1TjHhow6xYG5hVC3OREm36ujTvzI22a4sBgytyF47YIOKOO2Pn8ewbcriyrP6R021D8wBAMgvguvV0EMPPRTz5i37gH748OExdOjQJvdXWVmZs+x4RMR+++3X5P4AVlfb7zU/+gxcNrN6hz3nR4dOjZ+NMHjj8tj7kFnx6C09s+deeLRrncF1RMQpF38eH7/XMcZ/uGyJwhcf7xZvvVAa+x4xM3b8+rwYuGF5lHarjkULCmLy+Pbx+tOl8fDNPWP29CWzxHutWxknXDApbr+yT07fnUqbvyx2RMSUCcXxuxMHxUdvdc4536HTkqB+x6+tesnuhhq6xaIYukXdIfi4DzrEP37fL14dtWT/wMUVBfHr44fEz66YEHt9e3ad93TsnPsM5s9t/tuf+XNyPzFsyS8IAACwaj88/4sV3l+/81LnuOXyPvH2CyVRfwCdxOY7LozDT5sa2++xIHv26J9Oja49quJv565X7717HDg7vvW9Wdn2XVf1ildHdWnmK1m1/Y+bHj/+7eScc5++3yFuvqxvvPLvLlFTU38Avf5mi+LQH0+LPQ+ckz237xGzokv3qvjtjwYLrwEAyCuC69VMRUVF3HHHHTnnDjjggGb1ec8998TUqcu+obzNNtvEoEGDmtUnwOpop73nxU57t0xf+x09Mye4/ujtTlFelqkzCO/QqSZ+f9uncd731o9PRy8Lr8vmF8a91/SOe6/pvdKx1h1SEb/+56fRuUtNlC3IDVQ7t0Cg+vyjXePyMwbGwnm5fXfvtTh+83+fxoZbtsxM64YYskl5/PrmcXHjRf3izr8uCelrqjPxp58OiKFblMXADStWuKdj59xnsGBu4QrXNNa82blvoTp0FlwDALSVXUfMWSG0vvVPveOWy/quNMRdIhPvvVoSvzyicxz0wxlx4oXLAuEDjpsZH7zROZ65r/sKd/XfoDxnX+v3X+sUN13c+vtaD9tmYU6NERFP3NE9/nJ2/+wKTyvz6fsd4w8nD4qXHu8SP7ty2WpSu+w7Lw778bQVvvgKAABpsiHjaubRRx+N2bNzZ5Sdd9558Zvf/CbeeOONqGnMpqgRMX369Pi///u/nHM//OEPm11ncy0sq2nyAZAPhmy6KEqW20u5anFBjPugY73X9+y3OC67f2x87eBZ9V5Tly13nh9XPPRRdgnAsvm5f7X36LO4rtsapLIiE387d734zfFDVgitt951flz1xIdtGlovlclEfP/sL2K73ZetPlJZXhC3/bnuD906dKoVXM9pfnBde8Z17VndAAC0jsKiJE6oFeTee03PuPmP/RoQWi8vE/dd1ytu/VPue8gf/WpytGuf+/6xuENN/PKa8dlVdubOKoyL2mhf6xMvnJyzPPjzj3SNP505oEGh9fKefbB7XPmL9XLOHXHa1OjWs+n/XgBIQ3VS1bQjqlbdOQCpM+N6NVNcXBzrrbdeTJo0KXtu8eLF8fTTT8fTTz8dffr0iX333Tf22Wef6Nu370p6WuLaa6/N2dt6zz33jE022aRVam+MDb4yvsn3Vn/R9GXTAVpKQcGSJcPfe6Uke27uzJX/tduppCZ+fuWEOOj46XHvNb3itWe6xII5dd+z49fmxrd/ND223nVBZJb7vGzKhPY51w0aVh5NMfmz4vjdCYPj43c75ZwvKEzimJ9/EYf+eFoUpPj1t0wm4uifT4k3nl22NOPzj3aL6uoJK+z713u9ypx2c2dc19SsGH4vv08iAACtZ/g35kbv9Za995o+uV3885Kmz3y+/Yresde3Z0e/QUveM/boXRW77TcnRt3bI3vNSb+eFOtvuux99SU/GRjTJ7f+vtZDtyiLTbcvy7bLywriql+uF0nStMD833f0iH2+Oys233FJn8Udkhhx1Kx6vwAKkI+eifvTLgGAViS4Xs1861vfihEjRsTbb78djz32WDz77LNRUbFsWdSpU6fGTTfdFP/85z9ju+22i+985zux4447RkEd6cIzzzwTTz31VLZdVFQUxx9/fJu8DoC1QZfuud/mrb28dH023HJR/OKqCVFdFTHug44xc2q7mDOjKDp3qY5+Ayuj76CK6Fy64goTSRIx/qMO2Xa/QRUrLJPdEM8+2C3+/NMBKyw73mdARZz9t/GxyXZl9dzZtoZtXRY9ei+OWdOW7PO9uKIgpn1enP3Qcamls9GXmjureW9/5s0uzPmwsKRbVXRdxze3AQDawnZ7zM9pj7q3e1Qsavo3KhdXFsSTd3ePo3+6bAu1bXdfkA2u9zxodow4atmqSHdc2Ttef6b197WOiNi+1mt96d9dsu99myYTj926Tja4jojYbvf5gmsA2kxNkomaJn4Ba03jOUDdBNeroUwmE1tvvXVsvfXW8ZOf/CSefvrpeOyxx2LMmDHZa5Ikiddffz1ef/31GDRoUBxyyCGx9957R3Hxkm8Ef/bZZ3HJJZfk9HvYYYfFuuuu26avpT6fvDIoevVs/lKuAGkqbp+7n3VNI1eTLiyKGLrFohi6RcOW4/5ifHGUzV/2v52DN27cbOskibj+N+vGPVevuKf2biNnx2l/nBiduzR9S4Yx/+sUo9/onG33HVARO39z3kruWLlMJqL/BhU5H959/mn7FYLr/hvkBtcTxnaI6qolz7cpPn0/d8n3ARtU5Mx6BwCg9dR+j/veK53rubLhavex/ibL3n/ve+TM7M8L5xfEzKnt4lvfm9HoMTrV+kLpXt+eHfNm537usXhxJv59xzrZ9uBNWv+1Dtmk7bf+AWiOPePAJt1XGRXxQjzWssUA0OIE16u5kpKS2H///WP//fePcePGxQMPPBCPP/54zizs8ePHx6WXXho33HBDfPvb346vf/3rcf755+csEb7ZZpvFsccem8IrqFvnTgXRuZMt2IHV25xaS4N37926s3JfeSp35sf2ezY8FK6ujrjy5wPi8dvXyTlf3KEmTvrN57HvEbOaHc6O/6hDXPOrZfvqbbbDgmYF1xEr7i1dey/uiCV7XA8YWh4TP14yG72yvCA++7BDbLBZ05ZR//Ct3OXTh26eHzPQAQDWBrVXNZozo/kfbc2amjuLuUuPut+3dy6tiR//blKdv2usY38xZYVzC+YW5ATXrfJaa83Y7tylJora1UTVYp/BAKuHwkzT/rewMGnkbAIAUuFd6RpkyJAhcdppp8Wdd94Zxx57bHTr1i3n97Nnz44bbrghDj/88Jg4cWL2fGlpaZx33nlRVOR7DMDa68m7use3N94ie1x2+oBm9ZckS2b2Lq9H7xX3QV44ryAWzC3MHtXN+HfUi493zf6cySSx8zfnNui+xZWZuOjkQSuE1v0GV8RfHv0oRhzZ/NA6IqL/+rkzn2dObc4yh0vUXn69rmccEbHj13ID8v89W9rkMWsvDbnj15sXvgMA0HCV5bkfZbUrTuq5suGKO+TOhq49Rlpa5bW2z32tVYsjqqssHwQAQH7Ij3fitKiuXbvGMcccE3fccUecfvrpMWDAysOXn/zkJ9Gnj/2MgLVbv0GVsXBeYfYY82anVd+0EuM+6JAzc6Oka1Wdy/CdffgG8Z1Ntsge/324W5PHe+fFZWHspjssjB4NmOGdJBFX/HxA/Peh7jnnN9isLP70wNhGLze+MgM3Ko+CgmUftk2Z0D6mft708LqyIhPjPsj9ckCvdesOrr+yd264/Fyt19tQM6cUxfuvLltesX2Hmthq5wVN6gsAgMabXWvWcd+BlfVc2XC1t5qZNT0/vtg/e3rrv9bZ09tFYo9NAADyRH68E6dVtG/fPvbff//Yb7/94l//+ldcddVVdV531VVXxcKFC2O//fYz6xpYaw3ZZFG0K66JxZVLvtM14aOOMWFs+xi4YcUq7qzbv67N3Sd6h73mRVEdGe0m25bFh28uC0Kff7Rr7HHAnEaPd+dfc8fb7+iZ9VyZ6/HbesSTd/XIObfZDgviN//3abP2s65Labfq2HrX+fG/55bNWP7P/d3jsP83rUn9vf5MaZSXLVsavN+gino/zNtshwXRs19lzPiiOCIiPnq7U7z7cufYYqeFjRrzX9f2zvlgb/g350b7js2f+QIAQMN8OrpjbLf7si8O7vj1efH0v5r2pcSldqr1Jcdxoztmf/75wUOb1fdSl9zzcWy187L3nkfvuElM/bx4pfd8ulwdEUtWEbr9iuZNPNjpG7Vea60vggJAa6qJTNSEL0xFhOcA9TDjei3w3HPPxU033VTv7+fOnRtXXHFFHHfccfHWW2+1WV0A+aRzl5oYvk/u0tp3/rVpHwqNfr1TjLon98OzfY+cVee12+2R+8HRq091iXmzV9yneWVee6Y0nrlvWfg8YGh57H7A7FXeN3NKUfztvP4553qtWxnn/2Nci4fWS9UO5e/6W+9Gv96IJUsa3nxpv5xzw/eZW++S5kXtIg4+KTcgv+F360bSiMx52uft4oEbe+acO/THUxveAQAAzfbKk7nbtuy235wYMLTpqwStt35F7HlQ7nvnl2uNkZbar3XT7cti293mN7m/0u5VccD3Z6x0DAAASJPgeg1WXl4el112WVx44YWxcOGyb/UWFhbGAQccEF27ds25fuLEiXH66afHn/70pygrK2vrcgFSt/9xuR/iPHV3j/jP/d0a1ceUCcXxmx8OiZqa5Wbl7jO33uWkt9t9fvTst2yWcMWiwvjDjwc1eK/raZPaxR9PGZhz7sjTp0RhA7Lg26/ok7NvXkFhEuffMC66rdOMjbZXYY8DZ0ffgctmsS+YUxS/O2FwVJY3/FumSRJx9a/Wy5kJ075jdRx84spnbu97xKzous6ypcQ/eKNz3HVV75XcsUxleSYuO2NgLK5Y9ry+8vW5scFmLbeUOgAAq/b+q53jszHLZgkXFkWce+34KO226m1yautUUh3nXvNZzspIk8cVx1vPl7REqc02bVJxvPZ0ac65n/55QvQZ0PhVoQqLkjj7b+OjtPuy9/rz5xTGsw91a26ZAADQYgTXa6jRo0fHSSedFA8//HDO+Z49e8af//znOO200+L//u//YuTIkSvc++CDD8Zxxx0Xr732WluVC5AXtvjKwtj3yNzw+uL/Nyju/EvvKC9bdbD6xrMlccqIjXL2tu7aoypOvHBSvfcUFsUKS2W/8Z8u8bdz+0d52cr/mh77Tsc4beSGMXfmsvF2Gzk79jhwziprnT+nMB67bZ2cc7uNnBMbbbXiPtwtqX3HZIXn8dbzpXHOERs0aL/rhfMK4g8/HhQP3dQr5/wRp02Ndfqu/MPKDp1q4tRLPs8594/frxv3XtNrpTOvFy0siN/8cHC89fyyDw1LulbFyb+r/88VAIDWUVOTiWsuXDfn3OCNy+PPD42NjbZq+JfwN9yyLK58dGysX+uLiNf9Zt2oWpw/H5dde+G6Ub3c29x1+lbFnx78OLbbfV79N9Wy7uCKuPRfH+cssR4Rceuf+sT82baMAwAgf3h3uoaZPHlyXH/99fHMM8+s8Lttttkmzj333OjRY8lysqWlpXHGGWfEnnvuGZdeemlMnjw5e+3UqVPj5z//eYwYMSJOOumkKCnJj28bA7S2H54/Oca+0yk+frdTRCz5YOwfF60b913fKw4+aVpsvuPCWHdwRXamwtxZhfHW86Xx7zt6xBvP5i6z175DTfz65k/r3Xd5qW8dPSOe/lf3+OCNZXtdP/zPnvHCo13j8FOmxuY7LYh+gyqjU0lNlJdlYvxHHeKRm3vG0//qnt2TOyJiyKaL4sw/Tax3uezlvfFs6QofyJV2q4qHb16nnjsarlNJTez17fqXKt/pG/Pi2z+alrMP+Lsvl8Txu20Sex86K3YbOSc23mZhdOi0JE2urMjE2Hc6xitPdY1Hbl4nFszNffuyx4Gz49AfN2yf7F32nRsjj52eE3xfe+F68cx93eOIU6fGsG0WRo8+VVFdFTF1YnG8/GTXuPOvvXO+HBARceafJkTfASv/cwUAoHX879nSuOXyPnHUGcu2bem/QWX85bGx8drTpfGf+7vF6Nc7x5QJxdmVkAoKkugzoDI23X5h7HnQnNhhrxWX3L736l7x4uP5tXT2hLEd4oqz+scZly37AuY6fari97ePi/df6xRP3tUjRr/WOSZ+0j5qqpe81kwmiZ79FsfG25bFV/ebE7uOmBuFtT4BfO7BrvHADbnb4AAAQNoE12uI+fPnxy233BL33XdfLF68OOd3mUwmvvvd78YPfvCDKKxj7dhtttkmbrjhhrjhhhvi3nvvjWS5aWePPvpovPrqq3HGGWfE8OHDW/11AKStc2lNXHL3x3He99aP919b9qWd2dPbxXW/Xi/b7ti5OqoWZ3KC4+X16L04zrn6s9h421XP+igsjLjgxnHxs+8MjQljly17OHt6u5w9qDuVVkfZ/LrXAB80bFFccOO46NCpYXtTv/Gf0hXO1Z7F3FR9+leuNLjOZCJ+9KvJUVFeEI/cvOzDssov20vPdSpZ8uWAsgX1r3v+te/MitMunRgFjZgU86PzJ8f0SUtC6aXGvtMpLvzBkIhYsuz44sqC7Ad/ubUncfx5k2PnbzZ8hgsAAC3v/y7tE0VFSXz3lNwvMO6w1/ycUHrB3CVvFEu6rvx98r+u7RnX/aZfRDR8C5u28sTt60Rx+yROuGBytCte9pnNZjuUxWY7LPv3xsL5S97Ddu5SvdL3x88+2DX+eMrAnO2NAAAgH+TP2kc0ydSpU+P666+PI488Mu66664VQuvNNtssrr766vjRj35UZ2i9VIcOHeLHP/5x/PWvf41Bgwbl/G7GjBlxzjnnxN/+9reoqmr8nlEAq5vOXWri4rs/ie/99ItoV1z3B1yLFhbWG1rv9I25cdW/P4wtdlrY4DG79ayKS/81Nr46sv7At77Qeo8DZ8eVj4xt1AzgKROLG3xta8hkIn5y0efxsyvHR0nXuv9uKVtQWG9oXdK1Kk7944T42ZUTorj9Stb5rkNxhyR+deO4+N6ZX9T5+4pFhXWG1iXdquJ3t34aB584vVHjAQDQGjJx4x/6xVmHrh9TJtT/3raka81KQ+spE9vFOYcPiWsuWC+SJH+D3Idu6hmn7jc0Pn2/Q73XdC6tidJu9YfWc2YWxsX/b2D8/sRB9f5bBgBaU02ScSx3ACsy43o1lCRJvP322/Gvf/0rXnjhhaipWfEfYD169Igf/ehHsffee0dBI6ahbbrppnHttdfGLbfcErfddltUV1dnf3f33XfHRx99FOeff352uXGANVW74iSOOmNqfOPQWfHw//WMp+7uHjOnrOwDsarYfo/5cfBJ02LDLZu2T3TXdarj3GvGx5tHzYyH/tkzXn2qS70fKBUUJLHTN+bGfsfMiG13W9Cg5cGXN2dG+m8BMpmIrx88O3bae148cUePePy2dXJmnNdl8MaL4mvfmR37HTMjOpU0bHZ5XQoKIo46c2oM32dePHzzOvH0v7pHeVndIXnv9Spj36Nmxr5HzIzuvXyBCwAgn7z1fGl8f5eNY6e958W+R86Mzb+ycJXvExctLIj3Xukcj93aI176d9c6v7SYjz55r1OctPdGsfWuC2K/o2fG1rssyG5hVJ/K8kyMebNTPHFHj3j2wW6xuEJgDQBA/so888wzjZumRGpmzpwZzz77bDz00EPx2Wef1XlNu3bt4qCDDoqjjz46OnfuXOc1DfXxxx/HBRdcEJMmTco537Nnz7jwwgtj0003bVb/S82ZMycOOuignHNT3h0SvXrWP0McoK0lScSUCcXx0dudYu7MolgwrzA6dKqJbutUxbpDKmLDLctiJQtbNEllRSY+ea9jTBjbIRbOK4zyRQWxTu/F0WdAZQzcqDx69F7zQtQ5M4pizP86xaxp7WL+7MKITETXHlXRrWdVbLxtWXTr2TqvuWxBQYz7oENMn1Qc0ye3i3bFSfReb3H07l8ZQzZd1OJ/tgAtZZ91t067BIC8UlCQxOCNy2PA0PIo6VodnbtWRyYiFswtjAXzCmPixx3iszEdVpuwemUymST6b1ARg4aVR2m36ijpWh1FRUksmFcYC+YWxuTPiuOT9zpG1WJhNUBlUhHPxUM55+67777o1q1bOgWtBer63P/AR4+KDt07plRRfimfvSjuH3FLzjn/TYIZ13lvypQp8dxzz8V///vfeP/993P2n15eaWlp7L///nHQQQfFOuus0yJjDx06NK6++uq46KKL4sUXX8yenzFjRpx66qlxyimnxMiRI1tkLIB8l8lE9BtUGf0GNXw57uYqbp/EJtuVxSbbrXqf7DVFt55VsdM32n7/6E4lNUv2B9xh7XnWAABropqaTHw6umN8OnrN/1A8STIx8eMOMfHjla9aBAAAqwvBdZ569tln49Zbb42xY8eu9Lp+/frFwQcfHPvuu2907Njy/ygrKSmJ3/zmN3HbbbfFjTfemF2WvKqqKi6//PIYM2ZMnHrqqVFcnO5eqQAAAAAAAMDqS3Cdp/r27Rsff/xxnb8rKCiIrbfeOkaOHBlf/epXo7CV1y8tKCiIo446KjbZZJP49a9/HfPmLZsJN2rUqBgxYkRsttlmrVoDAAAAAACw+qpJCqImsYVFRHgOUA/BdZ4aNmxYjBgxIh555JHsuc022yz22muv2GOPPaJHjx5tXtN2220X1157bVxwwQUxZsyY6NChQ1x00UVCawAAAAAAAKBZBNd57Ac/+EFMmDAhhg8fHnvuuWf07ds37ZKiT58+ccUVV8Q111wTe+yxR2yxxRZplwQAAAAAAACs5gTXeax79+5x5ZVXpl3GCoqLi+MnP/lJ2mUAAAAAAAAAawiL6AMAAAAAAACQKsE1AAAAAAAAAKmyVDgAAAAAAACtKkkyUZNk0i4jLySeA9TJjGsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUlWUdgEAAAAAAACs2WoiEzWRSbuMvOA5QN3MuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFJVlHYBAAAAAAAArNlqkkzUJJm0y8gLngPUzYxrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFJVlHYBAAAAAAAArNlqkkzUJJm0y8gLngPUzYxrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFJVlHYBAAAAAAAArNlqIhM1SSbtMvJCTXgOUBczrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQVpV0AAAAAAAAAa7aaJBM1SSbtMvKC5wB1M+MaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQVpV0AAAAAAAAAa7YkImoik3YZeSFJuwDIU2ZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqSpKuwAAAAAAAADWbDVJJmqSTNpl5AXPAepmxjUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqSpKuwAAAAAAAADWbDVJJmqSTNpl5AXPAepmxjUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqSpKuwAAAAAAAADWbDVJJmqSTNpl5AXPAepmxjUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqorQLAAAAAAAAYM1Wk2SiJsmkXUZe8BygbmZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqorQLAAAAAAAAYM2WJJlIkkzaZeQFzwHqZsY1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQqqK0CwAAAAAAAGDNVhOZqIlM2mXkBc8B6mbGNQAAAAAAAACpMuMaAAAAAAAAaHHV1dXxySefxLRp02LatGmxePHi6N27d/Tp0yeGDBkSHTt2NO5qPG5LE1wDAAAAAADAWmzSpEnxox/9KMrKyrLnbr/99ujbt2+T+ps2bVo8/PDD8dhjj8WMGTPqvKZTp07x9a9/PUaOHBlDhw5t0jjGTWfc1iK4BgAAAAAAgLVUZWVlXHjhhTmhdXM8++yz8Yc//CHKy8tXel1ZWVk8+OCD8dBDD8X3v//9OPLII6OgoOm7HBu3bcZtTflZFQAAAAAAANDq/va3v8XYsWNbpK8bb7wxLrjggjrD1MLCwiguLl7hfJIk8Y9//CPOO++8qKysNG4ej9vazLgGAAAAAACgVSVJJmqSTNpl5IUkj57DM888Ew888ECL9DVq1Ki4+eabc8717NkzjjjiiNhxxx2jb9++kclkYsaMGfH+++/HLbfcEp9++mn22hdffDGuvvrqOOWUU4ybh+O2BcE1AAAAAAAArGUmTZoUl156aYv0NXny5Lj88stzzu21115x1llnrTD7t3fv3tG7d+/Yfffd47bbbosbbrgh+7v77rsvttlmm/jqV79q3Dwat61YKhwAAAAAAADWIpWVlXHBBRdk97Xu3LlzdO7cucn9XXnllTl7ZO+8885x9tln17lk9VIFBQVx1FFHxfe+972c85dddlksWrTIuHk0blsRXAMAAAAAAMBa5KqrroqPP/442z7rrLOipKSkSX2NHTs2XnnllWy7Y8eO8bOf/SyKihq28PMxxxwTgwcPzrbnzp0bjzzyiHHzZNy2JLgGAAAAAACAtcTTTz8dDz74YLb9ne98p1lLRt9222057cMOOyy6devW4PsLCwvjRz/6Uc65u+66K6qqqoybB+O2JcE1AAAAAAAArAU+//zzuOyyy7LtjTfeOE444YQm91deXh4vvvhitl1YWBgHHXRQo/vZaaedYsCAAdn29OnT47333jNuyuO2NcE1AAAAAAAArSpJMo7ljjTU3te6pKQkfvWrX0W7du2a3Oebb74ZlZWV2fb2228fXbp0aXQ/mUwm9thjj5xzL7/8snFTHretCa4BAAAAAABgDXfVVVfFJ598km2fddZZ0bdv32b1WTv03GGHHZrc14477rjSvo3b9uO2NcE1AAAAAAAArMFq72t9yCGHxK677trsfj/66KOc9sYbb9zkvjbccMMoLCzMtidMmBAVFRXGTXHctia4BgAAAAAAgDXUxIkT49JLL822N9lkk/jhD3/Y7H6TJImJEyfmnBsyZEiT+2vfvn3O/stJksTnn39u3JTGTYPgGgAAAAAAANZAFRUVceGFF8aiRYsiIqK0tDTOP//8Zu1rvdSsWbNi4cKF2Xa7du2iY8eOzeqzW7duOe0JEyYYN6Vx01CUdgEAAAAAAACs2WqSTNQkmbTLyAtt+Rxq72v9i1/8otn7Wi81derUnHZJSUlkMs17baWlpSsdw7htN24azLgGAAAAAACANcyoUaPioYceyrYPO+yw2HnnnVus/6WzuJeqHYY2RZcuXVY6hnHbbtw0CK4BAAAAAABgDTJx4sS47LLLsu3NNtssjj/++BYdY20Lcte2cdMguAYAAAAAAIA1REVFRVxwwQXZMLJLly5x3nnnRVFRy+4gXDvsLCkpaXaftUPZsrIy46Y0bhoE1wAAAAAAALCG+Otf/xqffvpptv2LX/wi+vTpk2JF0DCCawAAAAAAAFgDjBo1Kh5++OFs+7vf/W4MHz68Vcbq2LFjTnvBggXN7nP+/Pk57U6dOhk3pXHT0LJrAgAAAAAAAEAtSZKJJMmkXUaLqi6vbNJ9VYuqWriSJSZMmLDCvtY/+MEPWmWsiBUD1dphaFPMmzdvpWMYt+3GTYPgGgAAAAAAABrp9QMvT7uErIqKirjwwgtz9rU+//zzW3xf6+WtbUHu2jZuGiwVDgAAAAAAAKuxv/zlLzn7Wp9zzjnRu3fvVh2z9r7ZCxYsiCRJmtVn7VC2rr25jds246bBjGsAAAAAAABopO3vP6NJ9y2esyjePvbvLVbHk08+GY888ki2fcQRR8RXvvKVFuu/Pj169IjOnTvHwoULIyJi8eLFsWjRombtlzx79uyc9sCBA42b0rhpEFwDAAAAAABAIxV2KG7SfTUdWnaP6+VD606dOkXPnj3jwQcfbHQ/S5cZX+qpp56KLl265JwrKiqKESNGREREJpOJAQMGxJgxY7K///TTT2PzzTdv9NgRS5Y7nzhxYradyWSif//+K1xn3LYZNw2CawAAAAAAAFgDlJWVxZVXXtkifd1www0rnOvcuXM2uI6I2GijjXIC1TFjxjQ5UB07dmzU1NRk2wMHDoz27dvXea1x22bctmaPawAAAAAAAFpVTZJxLHesKXbaaaec9uuvv97kvl599dWV9m3cth+3rQmuAQAAAAAAgEbbZpttorh42ZLpb7zxRsyfP7/R/SRJEv/5z39yzg0fPty4KY/b1iwVDgAAAAAAAKupP//5zy3Sz2mnnRZvv/12tn377bdH3759V3pPhw4dYpdddolnnnkmIiKqqqrivvvui6OPPrpRY7/88ss5+y737t17pUthG7dtxm1rZlwDAAAAAAAATXLEEUfktO+8886YM2dOg++vrq6O6667LufcYYcdFoWFhcbNg3HbkuAaAAAAAAAAaJKhQ4fm7JNcVlYWf/zjH6OqqqpB9994440xbty4bLtbt24xYsQI4+bJuG1JcA0AAAAAAECrSiIiSRxJsuRZrGlOOeWU6Ny5c7b94osvxm9/+9uoqKio956ampr45z//GbfeemvO+TPPPDM6dOhg3Dwat60IrgEAAAAAAIAm69evX/z0pz/NOffss8/GUUcdFffdd19MmjQpqquro6amJqZPnx7PPPNMHH/88XHTTTfl3HPIIYfErrvuatw8G7etFKVdAAAAAAAAALB622OPPeLzzz+PG264IXtuxowZceWVV0ZERGFhYRQWFkZlZWWd9+++++7xwx/+0Lh5Om5bEFwDAAAAAAAAzXbUUUfFkCFD4qKLLoqFCxfm/K66ujqqq6tXuKegoCCOP/74+O53vxuZTMa4eTxuaxNcAwAAAAAAAC1il112iZtuuikeeeSRePjhh2PGjBl1XtepU6fYe++944ADDoghQ4YYdzUZtzVlnnnmmTVxD3hWI3PmzImDDjoo59yUd4dEr56FKVUEAACrh33W3TrtEgAAIO9VJhXxXDyUc+6+++6Lbt26pVPQWqCuz/23uv3UaNe1U0oV5ZfFc8vi7cOvyDm3pv43WV1dHZ988klMmzYtpk6dGosXL47evXtHnz59Yv3114+OHTsadzUet6WZcQ0AAAAAAECrqomImsjP5YnbWk3aBbShwsLC2GijjWKjjTYy7ho4bksrSLsAAAAAAAAAANZugmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUlWUdgEAAAAAAACs2ZIkE0mSSbuMvOA5QN3MuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVUVpFwAAAAAAAMCaLUkyUZNk0i4jLySeA9TJjGsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUlWUdgEAAAAAAACs2ZJkyYHnAPUx4xoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEhVUdoFAAAAAAAAsGZLkkwkSSbtMvKC5wB1M+MaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQVpV0AAAAAAAAAa7YkyUSSZNIuIy94DlA3M64BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASFVR2gUAAAAAAACwZqtJMlGTZNIuIy94DlA3M64BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUFaVdAAAAAAAAAGu2JFly4DlAfcy4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVRWkXAAAAAAAAwJotSTKRJJm0y8gLngPUzYxrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVUVpFwAAAAAAAMCaLUkykSSZtMvIC54D1M2MawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSVZR2AQAAAAAAAKzZki8PPAeojxnXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKSqKO0CAAAAAAAAWLMlSSaSJJN2GXnBc4C6mXENAAAAAAAAQKoE1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkqijtAgAAAAAAAFjDJV8eeA5QDzOuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEhVUdoFAAAAAAAAsKbLRJJk0i4iT3gOUBczrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIVVHaBQAAAAAAALBmS5IlB54D1MeMawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFVFaRcAAAAAAADAmi1JMpEkmbTLyAueA9TNjGsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUlWUdgEAAAAAAACs4ZLMkgPPAephxjUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqorQLAAAAAAAAYM2WJEsOPAeojxnXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKSqKO0CAAAAAAAAWMMlXx54DlAPM64BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASFVR2gUAAAAAAACwZkuSiCTJpF1GXkiStCuA/GTGNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkKqitAsAAAAAAABgDZd8eeA5QD3MuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVUVpFwAAAAAAAMCaLUkykSSZtMvIC54D1M2MawAAAAAAAABSlcqM67fffjuNYVdqq622SrsEAAAAAAAAgLVSKsH16aefHplMfi2DMGrUqLRLAAAAAAAAAFgrpbrHdZIkaQ6flW8hOgAAAAAAAMDaxB7XAAAAAAAAAKQqlRnXW265pVnOAAAAAAAAa4vkywPPAeqRSnD95z//OY1hAQAAAAAAAMhDlgoHAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWp7HENAAAAAADA2iTz5YHnAHVb7YPrWbNmxdixY+Ojjz6K6dOnx8KFC2PhwoXxhz/8IXvN9OnTo1u3btGuXbsUKwUAAAAAAACgLqtlcL1w4cIYNWpUPPbYY/HRRx/l/C5Jkshkcr+p8tBDD8W9994bu+22W3z3u9+NQYMGtWW5AAAAAAAAAKzEahdcP/7443HNNdfEvHnzImJJUN0QixYtin//+9/x73//Ow455JA47rjjori4uDVLBQAAAAAAAKABVpvguqqqKi688MJ48cUXs2F17ZnVmUxmlUF2kiRx9913x3vvvRd/+MMfoqSkpNVqBgAAAAAAAGDVCtIuoCEqKyvj/PPPjxdeeCFnKfBMJhPbbLNNHHHEEfH1r3+93tC6W7duEbFsGfEkSeKDDz6IX/ziF1FdXd1WLwMAAAAAAACAOqwWwfUtt9wSL7/8cmQymchkMlFYWBiHHXZY3HvvvXHZZZfF8ccfH5tvvnm993/729+Ov//97zF8+PAVwut//OMfbfhKAAAAAAAA1kKJI+cAVpD3wfWkSZPijjvuyIbNJSUlcdlll8UJJ5wQXbt2bXA/w4YNi9/97ndx8sknR8SyZcXvvPPOmDJlSmuVDwAAAAAAAMAq5H1wfdddd0VVVVV2pvQ555wTW2yxRZP7O/jgg+Okk07KmXl91113tWDFAAAAAAAAADRG3gfXL730UnaJ8B122CG+8pWvNLvPgw46KPr16xcRS/a9/s9//tPsPgEAAAAAAABomrwOrj/55JOYMWNGJMmSxf733XffFum3sLAwDj/88Gy/c+fOjXHjxrVI3wAAAAAAAAA0Tl4H11988UVOuzlLhNe2ySabRMSSva4jQnANAAAAAADQWhJHzgGsIK+D69mzZ2d/LiwsjB49erRY3+uuu25Oe+7cuS3WNwAAAAAAAAANl9fB9bx587I/d+zYsUX77tixY5SUlGTbZWVlLdo/AAAAAAAAAA2T18F1aWlp9ueWDpaTJMnpc/mxAAAAAAAAAGg7eR1cL780eE1NTUyePLnF+p4yZUrU1NRk2927d2+xvgEAAAAAAABouLwOrgcOHBgREZlMJiIiXnvttRbr++WXX46IJTOvIyIGDx7cYn0DAAAAAAAA0HB5H1z3798/IpYEzPfcc09UV1c3u9+qqqq45557soF4v379YsCAAc3uFwAAAAAAgDokGcfyB7CCvA6uIyL22GOPSJIkMplMTJ48Oa677rpm93nttdfGF198ke13t912a4FKAQAAAAAAAGiKvA+uDzvssOz+00mSxN133x3/+Mc/mtRXdXV1XHPNNTmzrTt27BiHHXZYi9ULAAAAAAAAQOPkfXDduXPnOPnkk7Ozo5MkiVtvvTVOOumkePnll6O8vHyVfVRXV8d//vOfOOmkk+Kuu+6KiMj2d+yxx0bXrl1b+2UAAAAAAAAAUI+itAtoiK997WsxZcqUuOGGG7Lh9Ycffhi//OUvo7CwMIYNGxbt2rXLuee6666L+fPnx6RJk+KDDz6IioqKSJIkIiLbxze/+c04+OCD03hJAAAAAAAAAHxptQiuIyKOPPLIKCgoiOuvvz67zHeSJFFVVRWjR4/OuTZJkrjjjjty2hGRc98+++wTZ5xxRhtVDwAAAAAAAEB9VpvgOiLi8MMPj8033zx+//vfx9SpU7NB9FLLt5cuBb78+SRJokOHDvGTn/wk9t1337YrHAAAAAAAYC2WRMSX8wzXeh4D1G21Cq4jIrbYYou4+eab48knn4x77rknPvvss3qvTZb7X8AePXrEgQceGPvvv3906dKlDSoFAAAAAAAAoCFWu+A6IqJdu3YxYsSIGDFiRHzxxRfx9ttvx9ixY2Pu3Lkxf/78yGQyUVJSEl26dInBgwfHlltuGYMGDVphhjYAAAAAAAAA6Vstg+vl9evXL/r16xff/OY30y4FAAAAAAAAgCYoSLsAAAAAAAAAANZuq/2MawAAAAAAAPJc8uWB5wD1WO2D6+rq6hg3blx88sknMX/+/Jg3b14UFBRESUlJdO3aNTbYYIMYPHhwFBSYXA4AAAAAAACQj1bL4DpJknjttdfiwQcfjDfeeCMqKytXen379u1j2223jZEjR8aOO+4YmUymjSoFAAAAAAAAYFVWu+D6vffei8svvzzGjx8fEUtC7FUpLy+Pl156KV566aXo379/nH766bH11lu3cqUAAAAAAAAANMRqtX72NddcE6eeemqMHz8+G1hnMpmVzqBe/vdJksTEiRPjzDPPjL/+9a9tUjMAAAAAAAAAK7dazLiuqamJyy+/PB577LFIkiQbRi8Nr/v27RtDhw6NHj16ROfOnSNJkli4cGFMnz49Pvnkk5g+fXpERE6Afd9990VlZWWcccYZqb0uAAAAAAAAAFaT4Pq2226LRx99NCew7tSpUxx88MGxzz77RL9+/VZ6/8SJE+PJJ5+M++67LxYuXJjt45FHHokBAwbEIYcc0kavBAAAAAAAYC2UZJYceA5Qj7xfKnzChAlx8803Z8PmJEliu+22i5tvvjmOPfbYVYbWEREDBgyI4447Lm699dYYPnx4dtZ2kiRx4403xrRp09rglQAAAAAAAABQl7wPrm+//faoqqqKiCVLfX/lK1+J3//+99GjR49G99WlS5f49a9/Hbvttls2vK6oqIg77rijpcsGAAAAAAAAoIHyOriuqqqK559/Pjs7uqSkJM4666xo165dk/ssLCyMn/70p9ngO0mSeO6557L7ZQMAAAAAAADQtvI6uB49enQsXLgwIpbMtj7ggAOiW7duze63pKQkDjzwwGxYPXv27BgzZkyz+wUAAAAAAACg8fI6uF669/TSgHm33XZrsb6/+tWvRsSSQDwiYsqUKS3WNwAAAAAAAAANV5R2ASszc+bMnPagQYNarO/+/fuvdCwAAAAAAABaRiZZcuA5QH3yesZ1cXFx9ufOnTs3a2/r2goLC6OkpCTbrq6ubrG+AQAAAAAAAGi4vA6u+/Xrl/25vLy8RfuuqamJsrKybLtXr14t2j8AAAAAAAAADZPXwfXyS4NXV1fH9OnTW6zvadOmRU1NTXb/bME1AAAAAAAAQDryOrju169fbLfddtn2888/32J9L99XSUlJDBs2rMX6BgAAAAAAAKDh8jq4jog49NBDsz/feeedsWjRomb3WV5eHvfdd19ERGQymdhnn31y9tMGAAAAAACgBSWOnANYQd4H1zvssEMceuihkSRJTJ8+PS6++OKorq5uVp9/+ctf4osvvohMJhOlpaVxyCGHtFC1AAAAAAAAADRW3gfXEREnnHBC7LnnnpEkSfz3v/+Nc845J2bPnt3ofhYtWhS//e1v4/HHH4+IiIKCgjj//PPtbw0AAAAAAACQotUiuM5kMnHuuefGT37ykyguLo7XX389jj322Ljrrrti7ty5q7x/wYIF8eCDD8bRRx8dzzzzTCRJEl26dIkLL7wwtt122zZ4BQAAAAAAAADUpyiNQa+++uom37vddtvFiy++GPPnz49rrrkmrr/++thwww1j2LBh0aNHj+jcuXNkMpkoKyuLmTNnxscffxxjxoyJqqqqSJIlmwZ07949fvCDH0RBQUG8/PLLERGx0047tchrAwAAAAAAAKBxUgmu77rrrshkMk2+f+m9SZJEVVVVjBkzJsaMGVPv9UsD66X3zZkzJy677LKca0aNGtXkegAAAAAAAABoulSC66ZKkiQn8K4dfi8NqGv/vq6QfPlrmxOiAwAAAAAAsApJZsmB5wD1SC24rh0yt8Z9TR0DAAAAAAAAgLaTSnB9zDHHpDEsAAAAAAAAAHlIcA0AAAAAAABAqgrSLgAAAAAAAACAtVtqe1wDAAAAAACwlki+PPAcoB5mXAMAAAAAAACQKsE1AAAAAAAAAKlarZcKnzlzZsydOzcWLFgQSdK8dRW22mqrFqoKAAAAAAAAgMZY7YLrt956Kx588MF46623Yu7cuS3W76hRo1qsLwAAAAAAAAAabrUJrmfNmhV/+MMf4o033oiIaPYM6+VlMpkW6wsAAAAAAACAxlktgutZs2bFySefHNOnT48kSSKTyeSEzUvPLd9eqq5QuiVDbwAAAAAAAFYh+fLAc4B65H1wnSRJnH/++TFt2rRsYL00eG7fvn0MHDgwOnXqFG+//XY2pO7fv3+ss846UV1dHdOmTYtp06ZFxLIQe6uttoqtt946ldcDAAAAAAAAQK68D66ff/75GD16dDawzmQyccghh8SBBx4Yffv2zYbRe+21V/aeQw45JEaOHJltf/7553H77bfHY489FplMJt5///3YZ5994pvf/Gabvx4AAAAAAAAAcuV9cH3XXXdFxLLlwH/+85/HPvvs06g++vfvHz/72c9ixx13jN/+9rdRVVUVf/zjHyMihNcAAAAAAAAAKStIu4CVWbRoUXa2dSaTiR133LHRofXydt999zj77LMjYkkQfsUVV8SECRNaqlwAAAAAAAAAmiCvg+vRo0dHkiTZPa1HjBjR7D732muvbD8VFRVx0UUXNbtPAAAAAAAAAJour4Pr6dOn57QHDx7cIv2ecMIJ0aFDh4iI+Oijj+Ltt99ukX4BAAAAAACoQ+LIOYAV5HVwPX/+/Jx2nz59GnTf0hna9SktLY199903237wwQcbXxwAAAAAAAAALSKvg+uqqqrszyUlJVFcXFzvte3atcv+XDvwrssuu+wSEUtC7v/+97+xcOHCZlQKAAAAAAAAQFPldXDdo0eP7M8LFy5c6Uzqzp07Z3+eNWvWKvvedNNNsz9XV1fHpEmTmlglAAAAAAAAAM2R18F1z549sz8nSRJz5syp99pOnTplg+2GBNcdOnTI7nMdETFlypSmFwoAAAAAAABAk+V1cL3OOuvktMeMGVPvtf3798/+3NAQOkmSyGQyjboHAAAAAACARkoyjuUPYAV5HVz36dMnCgoKsuHyE088Ue+1Q4YMiYglYfRHH30UkydPXmnfs2bNioqKimy7pqamBSoGAAAAAAAAoLHyOrju2LFjbLnllpEkSSRJEi+++GK89957dV675ZZbRkRkQ+7HH398pX2/++67ERHZ5cW7dOnSUmUDAAAAAAAA0Ah5HVxHRHz961+PiCWBdFVVVZx//vkxadKkFa7bfvvto1OnThGxJIx++OGHY+bMmfX2e9ttt2VD7oiIoUOHtnDlAAAAAAAAADRE3gfX3/jGN6Jv374RsSS8njNnTnz/+9+PSy+9NObOnZu9rl27drHPPvtk962eM2dO/PSnP41x48bl9FdVVRWXXHJJjB07Nnuua9eugmsAAAAAAACAlBSlXcCqFBUVxTnnnBOnnXZaNpSuqqqKxx57LEaOHBldu3bNXnv00UfHE088EYsWLYpMJhPjx4+P448/PjbffPMYPHhwLFy4MN57772YPn16ZDKZbH8jR46MgoK8z/ABAAAAAAAA1kh5H1xHRGy++eZx9tlnx8UXXxzV1dXZ87X3pe7atWv89Kc/jV//+teRyWSy4fR7772X3Rt76Z7WS5cJX3fddePwww9vo1cCAAAAAACw9skkSw7SfQ7l5eUxZsyYmDJlSsyfPz8qKiqic+fOUVpaGkOGDInBgwdHYWFhi45ZVlYW48aNi6lTp8b06dOjXbt20bt37+jTp0+sv/76LT6ecdt23Ja0WgTXERFf+9rXol+/fvGXv/wlPvzww4hYMbiOiNhjjz1ixowZcfXVV2dnVC9vaTtJklhnnXXid7/7XXTo0KH1XwAAAAAAAAC0sZqamnjllVfinnvuiXfeeSeqqqrqvbZDhw6x8847x8iRI2OrrbZaIWdrjA8//DAeeuihGDVqVJSXl9d5Te/evWPEiBHxrW99K3r27NnksYzb9uO2hswzzzyz2n2/5a233ooXX3wxTj755HqvGT16dPz973+P999/v87f77HHHnHSSSdFr169WqtMGmjOnDlx0EEH5Zyb8u6Q6NUz/7/5AQAAadpn3a3TLgEAAPJeZVIRz8VDOefuu+++6NatWzoFrQXq+tx/wO8viMLSkpQqyi/V8xfExHMuyDnXWv9NTp48OX7/+9/Xm5etzPDhw+P0009vdJZWXV0dN954Y9x6660Nvqdz587xy1/+MoYPH97YMo3bxuO2ptUyuG6MKVOmxOjRo2PWrFmxePHi6NmzZ2y33XbRo0ePtEvjS4JrAABoGsE1AACsmuC67QmuV66tgut33nknzj777CgrK6vz95lMJjp27Fjv7yMiunfvHn/+859j4MCBDRqzvLw8fvWrX8Wrr75a5+87dOgQlZWVUVNTU+fvjz/++DjyyCMbNJZx237c1rbaLBXeVH379o2+ffumXQYAAAAAAAC0iQ8//HCF0LqwsDD22muv2G233aJ///6x7rrrRnFxcSxYsCAmT54cn376aTz88MM5s7Nnz54dZ555Zlx//fXRtWvXVY571VVXrRCmDhs2LI488sjYeOONo2fPnlFdXR1ffPFFvPTSS3HHHXfE7Nmzs9def/31MWDAgNhtt90a9XqN2zbjtrY1PrgGAAAAAACAtUVlZWX89re/zQmt+/XrFxdccEFstNFGK1xfUlISG220UWy00Uaxzz77xMMPPxx//vOfs7N1Z8yYEddff32ceeaZKx332WefjYcffjjn3I9//OP4zne+k7NXdlFRUQwYMCAGDBgQ+++/f/z617+Ol156Kfv7Sy65JDbccMPo169fg16vcdtm3LZQkHYBAAAAAAAArOESR87Rim6//fb4/PPPs+3u3bvHZZddVmdoXVsmk4mRI0fGSSedlHP+0UcfjXnz5tV736JFi+JPf/pTzrkTTjghDj744JwwtbYOHTrEBRdcENtuu2323MKFC+OKK65YZa3Gbbtx24rgGgAAAAAAANYA1dXV8eCDD+acO+mkkxo9q/aggw6KddddN9uuqamJF198sd7rH3nkkZg7d262vemmm8Zhhx3WoLGKi4vj5z//ebRr1y577pVXXomPP/54lfcat23GbSuCawAAAAAAAFgDvPHGGzFr1qxsu6SkJPbaa69G91NYWBg777xzzrnPPvuszmurqqrizjvvzDn3wx/+cKUzgGvr06dPHHTQQTnnbr/99pXeY9y2GbctpbLH9T//+c80hl2pY445Ju0SAAAAAAAAoMnef//9nPY222wThYWFTeqr9izt5QPx5b377rsxY8aMbHujjTaKrbfeutHjHXzwwXH33XdHkixZS/2FF16IioqKaN++vXFTHLctpRZcNyb9bwuCawAAAAAAAFZn48aNy2lvs802Te6rdpbXqVOnOq97+eWXc9p77rlnk8br1atXbLHFFvHOO+9ERERFRUW8+eabsdNOOxk3xXHbUqpLhSdJkuqxtAYAAAAAAABY3Z177rnxxBNPZI/999+/yX1Nnjw5p738ntfLqx2o7rDDDk0es/a9L730Ur3XGrdtxm1Lqcy4jsiPwDgfagAAAAAAAICWUFxc3CL9VFdXrxBmbrDBBitct2jRopgwYUK23b59+xg8eHCTx91kk01y2h9//HGd1xm3bcZta6kE15blBgAAAAAAgPz04IMPxqRJk7LtoUOHxrbbbrvCdZ9//nlOe+DAgU3eU3vpOMubOHFiJEmywrLlxm2bcdua4BoAAAAAAACIysrKePDBB+Pvf/97zvnjjjuuzlBz+VnAERFdu3Zt1vilpaWRyWSyqybPnz8/5syZE927dzduCuO2tdSWCgcAAAAAAADSU1lZGVOnTo0pU6bEJ598Evfee2/MmDEj+/uCgoL42c9+FsOHD6/z/mnTpuW0S0tLm1VPQUFBlJaWxrx587Lnpk6dukKgaty2GbetCa4BAAAAAABgLXHbbbfFddddt8rrNthgg/jRj34UO+64Y73XLFq0KKfd3EA1IqJLly45gWrtMYzbduO2NcE1AAAAAAAArSoTEZkk7SryQ7q7CK9aQUFBnHbaabHffvutcs/j2mFnSUlJs8evHco2JMg1buuM29YK0i4AAAAAAAAAyA81NTVxxRVXxEUXXRTjxo1b6bWtNRN4eWVlZcZNady2ZsY1AAAAAAAArCV22GGH7IzdxYsXx9SpU2Py5Mnx+eefx/jx4yMiorq6Op588sn4z3/+E7/61a9il112SbNk1hKCawAAAAAAAFhLbLjhhrHhhhvW+btPP/00rr322njllVciYkmwff7558cvfvGL2HvvvVe4vmPHjjnt+fPnN7u+5fddjojo1KmTcVMat60JrslLh261ZRRnOqRdBgAA5LWHJ72adgkAAJD3ps+sjgFbpF0Fa6Kaioqm3VdZ2cKVtJz1118/Lrroorj++uvjtttui4glS4dfeumlMWzYsBg4cGDO9bUD1QULFjS7htqBau0xjNt247Y1wTUAAAAAAAA00vif/zLtElpFJpOJ448/Pj766KN4/fXXIyKisrIybr755jj33HNzrm2NmcC1+2hIkGvc1hm3rRWkXQAAAAAAAABruCSz5h1rsEwmE8cdd1zOueeeey6qq6tzzvXu3Tun3dxAtaamZoU++vTps8J1xm2bcduaGdcAAAAAAADQSIMu/n2T7qteuDA+//XvWrialrfxxhtHjx49YtasWRGxZL/rqVOnxrrrrpu9pvbS4XPnzm3WmPPmzYskSbLt0tLS6Nat2wrXGbdtxm1rgmsAAAAAAABopIL27Zt0X7J4cQtXssTo0aNj9OjR2Xbfvn1j1113bXJ/mUwmBgwYkA2uIyI+//zznOC6f//+OfeMHz8+qquro7CwsEljfvLJJzntgQMHRiaz4ux247bNuG3NUuEAAAAAAACwmhs/fnxcddVV2ePOO+9sdp+dOnXKaS9YsCCn3bFjx5zZwJWVlTFu3Lgmj/fBBx/ktIcOHVrndcZtm3HbmuAaAAAAAAAAVnO1Z+XOnDmz2X3WXpK6R48eK1yz00475bRff/31Jo/32muvrbRv47b9uG1JcA0AAAAAAEDrShw5RysYPHhwFBQsi/6++OKLmDJlSpP7q6ysjE8//TTnXO/evVe4bvjw4Tnt//znP00ab8aMGfHuu+9m2+3bt49tttmm3uuN2zbjtiXBNQAAAAAAAKzmSktLY9ttt80598wzzzS5v1dffTXKy8uz7XXXXTf69eu3wnWbb7559OrVK9v+8MMP4+233270ePfcc08kybJUf5dddon2K9lH3LhtM25bElwDAAAAAADAGmDPPffMad9+++0xb968RvdTVVUVN954Y865nXfeOTKZzArXFhUVxaGHHppz7tprr80JR1dl6tSp8a9//Svn3OGHH77Se4zbNuO2JcE1AAAAAAAArAG+9rWv5cyKnj9/flxwwQVRWVnZ4D6SJImrrroqZ5nwDh06xGGHHVbvPd/61reiW7du2fbo0aPjjjvuaNB4lZWVcfHFF8fixYuz54YPHx5Dhw5d5b3GbZtx24rgGgAAAAAAANYA7du3jx//+Mc5595888342c9+1qD9rhcsWBC//e1v4/777885f9RRR0XPnj3rva9jx45xxhln5Jy79tpr46677lrpjOBFixbF+eefH2+++Wb2XElJSZxyyimrrNW4bTduWxFcAwAAAAAAwBpi5513joMPPjjn3DvvvBPHHHNM/OlPf4o333wzZ+/qysrKeO+99+K6666Lww8/PJ5++umce/faa68GLSf91a9+NQ488MCcc3//+9/jxBNPjOeffz5mzJgRSZJEVVVVfP7553HXXXfFEUccEa+88krOPWeddVb07du3wa/XuG0zblsoSrsAAAAAAAAA1nDJlwet/hwymUycfPLJUVlZGQ8++GD2/NL20nOdOnWKiIiysrJ6+9p7773jpz/9aRQUNGwu7EknnRTTpk2LF198MXvuo48+ivPOOy8iliw5XllZGTU1NXXWfeKJJ8auu+7aoLGM2/bjtrY1Zsb19OnTY/To0fHqq6/GM888k3Y5AAAAAAAAkIpMJhOnnXZanH322VFSUlLnNWVlZfWG1iUlJXHmmWfG2WefHcXFxQ0et7i4OH7zm9/EscceW+fvy8vL6wxTS0tL4+KLL45DDz20wWMZt+3HbW2r7YzrBQsWxKhRo+Kll16Kjz76KObOnZvz+z333DP78z333BPjx4+Pr3/967HVVlu1dakAAAAAAADQpjKZTHzjG9+InXfeOR599NF49NFHY/z48Su9Z8iQIbH33nvHAQcckJ2R3VgFBQVxzDHHxC677BIPPPBAPPXUUzlLky+vT58+sd9++8WIESOiR48eTRrPuG07bmvKPPPMM6vVwgxz586N6667Lp588smoqqqKiFhhs/FMJhOjRo3Ktv/xj3/ELbfcEplMJjbZZJM47bTTYujQoW1aN/WbM2dOHHTQQTnndis4IIozHVKqCAAAVg8PT3w17RIAACDvTZ9ZHQO2mJBz7r777otu3bqlU9BaoK7P/Qf++sIorGfm79qmesGCmHD+r3LOtdV/k7Nnz44PPvggZs6cGfPnz49MJhNdunSJ7t27xyabbBLdu3dv8THLysri008/jWnTpsW0adOiXbt20adPn+jTp0+sv/76UVhY2OJjGrftxm1Jq9WM62effTYuv/zyWLBgQTaszmQykclkstfUDrGXlyRJjB49Ok466aQ48cQT4zvf+U6r1wwAAAAAAAD5oHv37rHzzju36ZidOnWKzTffvE3HNO7qabUJrv/973/HxRdfHEmSZMPqJEkiSZJYZ511ori4OL744ouV9rE04K6uro6//e1vUV5eHkceeWRblA8AAAAAALDWyiRLDjwHqE9B2gU0xKuvvpoTWidJEr169YqTTz457rzzzrj77rvjsMMOq/f+kSNHxoEHHhhFRUU5ffzjH/+IN954ow1fCQAAAAAAAAC15X1wXVlZGVdeeWV2CfAkSeKAAw6Im2++OQ4++ODo1avXKvvo1atXnHLKKXHrrbfGpptumhNeX3zxxVFZWdnaLwMAAAAAAACAeuR9cP3AAw/E5MmTs8uDf+c734lTTz01iouLG91Xz5494/LLL48dd9wxG4TPnDkznnjiiZYuGwAAAAAAAIAGyvvg+tlnn42IJTOt+/TpEyeccEKz+isuLo4zzjgj2rVrl511/cADD7REqQAAAAAAAAA0QV4H17Nnz44PPvggO9t66T7VzdW7d+/Yf//9s7Oux40bF3Pnzm12vwAAAAAAAAA0Xl4H12PHjo0kSbIB8/bbb99ife+222457ffff7/F+gYAAAAAAGA5iSPnAFaQ18H17Nmzc9qDBw9usb7XW2+9iIjIZDIRETFr1qwW6xsAAAAAAACAhlttguvOnTtHQUHLldujR49o3759tj1v3rwW6xsAAAAAAACAhsvr4LqwsDD78+LFi1t1rOLi4lbtHwAAAAAAAIC65XVw3aNHj+zPlZWVsWDBghbre+HChVFRUZFtd+/evcX6BgAAAAAAAKDh8jq47tWrV0773XffbbG+33rrrYiISJIkIiJ69+7dYn0DAAAAAAAA0HB5HVxvsskm0alTp8hkMhER8dhjj7VY30888UT2544dO8Ymm2zSYn0DAAAAAACwnMSRcwAryOvgul27dvGVr3wlkiSJJEnihRdeiDfffLPZ/b7xxhvx/PPPRyaTiUwmE9ttt10UFRW1QMUAAAAAAAAANFZeB9cREYceemg2YE6SJC688ML45JNPmtzf2LFj44ILLsj2FxFx2GGHtVS5AAAAAAAAADRS3gfXw4YNi/322y+SJIlMJhPz5s2Ln/zkJ3H//fdHdXV1g/uprKyMu+66K/7f//t/sXDhwmx/u+66a2y66aat+AoAAAAAAAAAWJnVYn3sE088McaOHRtjxoyJTCYT5eXl8Ze//CVuueWW2GabbWLrrbeOqVOn5twzceLEmD9/fkyaNCnefffdeO6552L+/PnZwDoiol+/fnHGGWek8ZIAAAAAAAAA+NJqEVx37NgxLrnkkjjjjDPi448/zi7zPWvWrHj66afj6aefzrk+SZI49thjVzgXEdl7u3fvHhdffHF07dq1rV4GAAAAAADAWimTLDnwHKA+eb9U+FIlJSXxl7/8JUaOHJmdNb105nSSJNlgeqml55YPrJeG1ltvvXVcd911sd5667X56wAAAAAAAAAg12ox43qp9u3bx+mnnx677rpr3HLLLfHuu+9mf7c0xK5t+fC6f//+ceihh8a+++4bhYWFbVIzAAAAAAAAACu3WgXXS+2www6xww47xIcffhgvvvhivPPOOzF27NgoKyvLua5Dhw4xePDg2HLLLWPbbbeN7bffPgoKVptJ5gAAAAAAAABrhdUyuF5q2LBhMWzYsGy7qqoq5s2bF5lMJkpLS6OoaLV+eQAAAAAAAABrhTUq2S0qKooePXqkXQYAAAAAAAAAjbBGBdcAAAAAAADkoSSz5MBzgHrY8BkAAAAAAACAVOX9jOvPPvusTcYZPHhwm4wDAAAAAAAAQK68D66PO+64yGRaf8mEUaNGtfoYAAAAAAAAAKwo74PrpZIkabW+2yIYBwAAAAAAAKBuq01w3dLhcpIkkclkWjUQBwAAAAAAAGDV8j647t27d5ND65qampg+fXq2vbSfgoKC6NmzZ4vUBwAAAAAAwCokXx54DlCPvA+u77jjjmbdP2PGjHj11VfjpZdeihdffDGSJImamprYa6+94vjjj7dMOAAAAAAAAEDKCtIuoLX17NkzRowYEb/5zW/ib3/7W/Tv3z+SJIk77rgjfvWrX0VNTU3aJQIAAAAAAACs1db44Hp5w4YNi6uuuiqGDBkSSZLECy+8EDfeeGPaZQEAAAAAAACs1daq4DoiorS0NM4555woKiqKJEnitttui3feeSftsgAAAAAAAADWWmtdcB0RscEGG8SRRx6Zbd99990pVgMAAAAAALBmyySO5Q9gRWtlcB0Rse+++0ZERJIk8dJLL8XMmTNTrggAAAAAAABg7bTWBte9e/eO9u3bR8SS8Prjjz9OuSIAAAAAAACAtdNaG1xHRBQWFmZ/njp1aoqVAAAAAAAAAKy91trgurKyMsrKyiKTyURExIIFC1KuCAAAAAAAAGDttNYG16NHj85pl5SUpFQJAAAAAAAAwNqtKO0C0vLYY49FxJL9rTOZTPTs2TPligAAAAAAANZQyZcHngPUY62ccf3yyy/HU089lV0mPCJiiy22SLEiAAAAAAAAgLXXWjXjury8PO6777646aabImLZbOstt9wySktL0y0OAAAAAAAAYC2V98H1/fff3+R7kySJuXPnxtSpU2Pq1Knx4YcfRnl5eTawXuqYY45pgUoBAAAAAAAAaIq8D66vvPLKnJC5OZJkyaYBy/d38MEHx9Zbb90i/QMAAAAAAADQeHkfXC+1NHRuiqVB9dL/u3TG9eGHHx7HH398i9QHAAAAAABA/TJNj3qAtcBqEVw3J7SufX9hYWHssssuceihh8amm27a3NIAAAAAAAAAaKa8D6732WefZt3frl276Ny5c3Tr1i2GDh0aw4YNi5KSkhaqDgAAAAAAAIDmyvvg+qyzzkq7BAAAAAAAAABaUUHaBQAAAAAAAACwdhNcAwAAAAAAAJCqvF4qfPLkyfG9730v2y4pKYkHHnggxYoAAAAAAABotOTLA88B6pHXwfXUqVMjSZb9f++wYcNSrAYAAAAAAACA1pD3wXVERCaTiYiIfv36pVkOAAAAAAAAAK0gr/e4nj17dk67tLQ0pUoAAAAAAAAAaC15HVz37ds3p11WVpZSJQAAAAAAAAC0lrwOrgcPHpzT/uSTT9IpBAAAAAAAAIBWk9fB9aBBg2LIkCEREZEkSbz33nsxceLElKsCAAAAAACgURJHzgGsIK+D64KCgjj55JMjSZLIZDKRJElcfPHFUVVVlXZpAAAAAAAAALSQvA6uIyK22267OOSQQ7Lh9QcffBBnn312LFq0KO3SAAAAAAAAAGgBeR9cR0ScdNJJcdRRR0XEkiXD//e//8UPfvCDeP3111OuDAAAAAAAAIDmKkq7gFWZOnVqRER861vfiqFDh8bf//73mDp1akyZMiXOOuusGDJkSOy+++6x6aabRo8ePaJjx46RyWQaPU6fPn1aunQAAAAAAAAAGiDvg+vDDz98hSB6aTtJkvj0009j3LhxzR5n1KhRze4DAAAAAACAFWWSJQeeA9Qn74PriCUB9fKWBtfLB9jN0ZQZ2gAAAAAAAAC0jNUiuF5VsNyc4Lm5oTcAAAAAAAAAzZP3wXXv3r3NiAYAAAAAAABYg+V9cH3HHXekXQIAAAAAAAAAragg7QIAAAAAAAAAWLsJrgEAAAAAAABIVWpLhU+dOjX7c5cuXaJjx45plQIAAAAAAABAilILrg8//PDIZDIREXHaaafFyJEj0yoFAAAAAAAAgBSlulR4kiRpDg8AAAAAAABAHkg1uF464xoAAAAAAACAtVeqwTUAAAAAAAAApLbHNQAAAAAAAGuJ5MsDzwHqYcY1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQqqK0CwAAAAAAAGDNlkmWHHgOUB8zrgEAAAAAAABIleAaAAAAAAAAgFTlxVLhL7/8csyaNSvVGo455phUxwcAAAAAAABYW6UeXCdJEi+//HK8/PLLqdYhuAYAAAAAAABIR+rBdcSS8DoNmUwmkiSJTCaTyvgAAAAAAAAA5ElwnZa0AnMAAAAAAIC1SvLlgecA9Ug9uM5kMrHNNtvE0KFD0y4FAAAAAAAAgBSkHlxHROy+++4xcuTItMsAAAAAAAAAIAUFaRcAAAAAAAAAwNpNcA0AAAAAAABAqvJiqXAAAAAAAADWYMmXB54D1MOMawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSVZR2AQAAAAAAAKzZMsmSA88B6mPGNQAAAAAAAACpSnXGdZL4SgkAAAAAAADA2i614Pq2227L/ty1a9e0ygAAAAAAAAAgZakF13379k1raAAAAAAAAADyiD2uAQAAAAAAAEhVqntcAwAAAAAAsBZIvjzwHKAeZlwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpKkq7AAAAAAAAANZsmWTJgecA9THjGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUFaVdAAAAAAAAAGu45MsDzwHqYcY1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkqSrsAAAAAAAAA1gJJ2gUA+cyMawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFVFaRcAAAAAAADAmi2TLDnwHKA+ZlwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkKqitAsAAAAAAABgDZd8eeA5QD3MuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAOD/s3fn0XWV5f7An5OcpumYdKDpQCkthVJAJmVUEFDmWaHKICJXEeSqIFfvxZ+goMgVEBAVkCvIKDLI0DKD1iJWELUic6FQWjqkc9K0TZPm7N8fgZTTDM3QZqfp57PWXqvvPnu/z3O2rHp6vufdGwAAACBVgmsAAAAAAAAAUpVNuwEAAAAAAAC6ueT9DdcBmmHFNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpyqbdAAAAAAAAAN1bJqnfcB2gOVZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqbNoNAAAAAAAA0M0l72+4DtAMK64BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUZdNuAAAAAAAAgO4tk9RvuA7QHCuuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEhVNu0GAAAAAAAA6OaS9zdcB2iGFdcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApCqbdgMAAAAAAAB0c8n7G64DNMOKawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFXZtBsAAAAAAACge8u8v+E6QHOsuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVdm0GwAAAAAAAKCbS97fcB2gGVZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqcqm3QAAAAAAAADdWyap33AdoDlWXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQqmzaDQAAAAAAANDNJe9vuA7QDCuuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEhVNu0GAAAAAAAA2AwkaTcAdGVWXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKnKpt0AAAAAAAAA3Vsmqd9wHaA5VlwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkKps2g0AAAAAAADQzSXvb7gO0AwrrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFRl024AAAAAAAAA2PjmzJkTb7/9dixdujQqKyujT58+0b9//9hmm21i1KhRkclkNmi9mpqaePvtt6O8vDwWLFgQERFlZWUxZMiQGDNmTBQVFW3Qeup2bt0NTXANAAAAAADARpVJ6jc6/zpUVVXFvffeG5MnT47Zs2c3e1xpaWkcdNBBMWHChCgrK+tQzVmzZsWkSZPiiSeeiOXLlzd5TElJSRx22GFx9NFHx4gRIzpUT93OrbuxZCZPnuyvCVK1bNmyOP744/P27V9wbBRlilPqCAAANg0Pz/5b2i0AAECXt3BxXYz8yKy8fQ888ECUlpam09BmoKnv/Xf60iWR7dU3pY66ljWrquLl31yUt29j/Tf5xBNPxPXXXx8VFRWtPqegoCBOO+20OPXUU6OwsLBN9ZIkiQcffDB++ctfRl1dXavO6dGjR3zzm9+MI488sk211O38uhubFdcAAAAAAADQjSRJEr/5zW/i9ttvb/L1goKCKC4ujpUrVzZ6LZfLxS233BIvvfRS/PjHP271baZzuVxcccUV8fjjjzf5eo8ePSJJklizZk3e/tra2rjyyitj+vTpce6557b5duXqdk7dziC4BgAAAAAAgG7ktttuaxRaDx8+PCZMmBB77bVXDBkyJDKZTFRWVsbs2bPjrbfeiokTJ8Y777zTcPw//vGPuPTSS+P73/9+FBQUrLfmXXfd1ShMHTlyZJx66qmx8847x5AhQyJJkigvL49p06bFnXfeGfPmzWs4duLEiTFixIiYMGFCm96rup1TtzMIrgEAAAAAAKCb+Mc//hG33npr3r4jjjgivvnNbzZaPV1SUhIlJSWx0047xdFHHx133XVX3HTTTQ2vP/PMM/Hkk0/GYYcd1mLNV155JW6++ea8fRMmTIgzzzyz0e3Ghw8fHsOHD49DDz00rr322pg0aVLDazfeeGPstNNOscMOO7TqvarbOXU7y/p/HgEAAAAAAAAdkdjyto2krq4urrrqqkiStUU++clPxvnnn7/eW34XFhbGqaeeGqeffnre/ptuuilqamqaPS+Xy8WVV14ZuVyuYd+xxx4bZ511VovPyM5ms3HuuefGIYccktf/FVdckTeXuunW7UyCawAAAAAAAOgGpkyZEnPnzm0YDx48OM4///xW3er7A6ecckqMGDGiYbxo0aL45z//2ezxU6dOjZkzZzaMy8rK4mtf+1qrnqFcUFAQ3/zmN6O0tLRh38yZM2Pq1KnrPVfdzqnbmQTXAAAAAAAA0A1MnDgxb3zCCSdEv3792jRHNpuNT33qU3n7nnvuuWaPv/POO/PGp59++npXd39Y796947TTTsvbd9ddd633PHU7p25nElwDAAAAAADAJm716tXx6quvNowzmUwceuih7Zpr3Wcfz549u8nj5s2bF6+//nrDeMCAAXHwwQe3ud6RRx4Zffr0aRi/+uqrMX/+/GaPV7dz6nY2wTUAAAAAAABs4l577bWora1tGI8cOTLv1tBtse55S5YsafK4dVdi77///i0+b7k5RUVFse+++7Y4t7qdX7ezCa4BAAAAAABgEzdnzpy88dixY9s916pVq1p13Lqh5x577NHumnvuuWeLc6vb+XU7WzbtBgAAAAAAAOjmkohMknYTXcRGug6VlZV545KSknbPNW/evLzx4MGDmzzuzTffzBuPGzeu3TXHjx/f4tzqdn7dzia4BgAAAAAAgE3cgAED8lbibrfddu2ea91VuCNGjGh0TGVlZSxdurRh3L9//2YD7tYYPnx49OrVq2G195IlS6Kqqir69u2rbgp10yC4BgAAAAAAgE3cYYcdFocddliH51m4cGGj4HqfffZpdNzs2bPzxh1Z4R0RkclkoqSkJO825bNnz260QljdzqmbBs+4BgAAAAAAACIi4qabboqampqGcf/+/WO33XZrdFx5eXneeEOs2O3Xr1+LNdTtvLppEFwDAAAAAAAA8dRTT8UTTzyRt++UU06JoqKiRsd+eMVuROMwtD3WXU28bg11O69uGtwqHAAAAAAAgI0reX+jy16H559/Pq688sq8fcOHD4/jjjuuyePXDTs3xkrg1gS56m6cumkQXAMAAAAAAMBm7JFHHomrrroqcrlcw76ioqK4+OKLm1xtHRFRXV2dN94QK4HXnWPlypXqplQ3DYJrAAAAAAAA2AzV1tbGr371q/j973+ft7+goCC+853vxNixY1PqjM2R4BoAAAAAAAA2M/PmzYtLLrkkXn/99bz9xcXF8f3vfz/23nvvFs8vLi7OGy9fvrzDPa07R+/evdVNqW4aBNcAAAAAAADQRnW1q9t1Xq62ZgN30nbPPPNMXH755bFixYq8/QMGDIjLLrssxo0bt945evXqlTeuqqrqcF+VlZUt1lC38+qmQXANAAAAAAAAbfTvmy9Iu4U2q6mpiRtuuCEeeOCBRq/tvvvuccEFF8TgwYNbNde6YeeGWAncniBX3Y1TNw2CawAAAAAAADau5P2N1MyZMycuvvjiePPNN/P2FxQUxBlnnBEnnXRSFBQUtHq+srKyvPGGWAm8bii7bg11O69uGgTXAAAAAAAA0EY7f+mydp23ZtWKePV3P9rA3bRs8uTJceWVV8bKlSvz9g8dOjQuvPDC2GGHHdo858iRI/PGFRUVHeoxSZJYtmxZizXU7by6aWj9zyYAAAAAAACAiIgo7NGzXVtBj6JO6zFJkrjhhhvikksuaRRaH3DAAfF///d/7QqtIyL69+8fAwYMaBhXVlbGokWL2t3r3Llzo7q6umE8aNCg6Nu3r7op1U2D4BoAAAAAAAC6mbq6urjyyivj7rvvzttfVFQU559/flx00UUdDiy33XbbvPHrr7/e7rlee+21vPHYsWPVTbluZxNcAwAAAAAAQDdSW1sbP/zhD+PRRx/N2z98+PC44YYb4qijjopMJtPhOnvvvXfe+O9//3u75/rb3/7W4tzqdn7dzia4BgAAAAAAgG4iSZL46U9/GlOmTMnbP3bs2Pj5z38eo0eP3mC11g09n3nmmairq2vzPDU1NTF16tQW51a38+t2NsE1AAAAAAAAG1UmsX1425geffTReOKJJ/L27bTTTnH11VfHwIEDN2itYcOGxfjx4xvGS5cujT/84Q9tnueRRx6JFStWNIx33HHHGDp0qLop1+1sgmsAAAAAAADoBhYtWhTXXntt3r4hQ4bED3/4ww4/z7o5J598ct745ptvjpqamlafv3Llyrj99ttbnFPd9Op2JsE1AAAAAAAAdAN33HFHXphZUFAQl1xySZSWlm60mvvuu2/e7cfLy8vjl7/8ZSTJ+peW53K5uOaaa2Lp0qUN+8aMGdOq21er2zl1O5PgGgAAAAAAADZxy5cvj0ceeSRv3wEHHBDjxo3bqHULCgri29/+dhQWFjbsmzhxYlx33XUtPoe5trY2rrrqqnjqqaca9mWz2fjOd74TBQXrjzDV7Zy6nSmbdgMAAAAAAABAx7zwwguxZs2avH39+/ePiRMndnjuXr16xcEHH9zs6+PHj4+vfOUrccMNNzTsu+++++K5556LL3zhC7HLLrvEFltsEUmSxIIFC+Kf//xn3HHHHTF//vy8ec4+++w2Be3qdk7dziK4BgAAAAAAYONK3t/YaNfhhRdeaLTvwQcf3CBzl5WVtRhcR0SceOKJMWfOnJg0aVLDvvfeey8uu+yyiIjo0aNHRNSvAG7KZz/72Tj++OPb3Ju6nVO3MwiuAQAAAAAAYBO37qrazlZQUBDf+ta3Ytttt41rr7220erv5oLUoqKiOP/88+OQQw5RtwvX7QyCawAAAAAAANjELV26NO0WIiLi6KOPjt133z0mTpwYjz/+eFRWVjZ5XGlpaRx++OFx9NFHx7Bhw9TdROpuTJnJkye7MQOpWrZsWaNbEuxfcGwUZYpT6ggAADYND8/+W9otAABAl7dwcV2M/MisvH0PPPBAlJaWptPQZqCp7/0/csol0aNX35Q66lpqV1XFS3delLevu/43WVNTEzNmzIgFCxZEeXl5ZDKZGDJkSJSVlcWYMWOiqKhI3U247oZmxTUAAAAAAACwwRUVFcX48eNj/Pjx6nbDuhtaQdoNAAAAAAAAALB5s+IaAAAAAACAjSqTJJFJPL02IlwHaIYV1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkKpt2AwAAAAAAAHRzyfsbrgM0w4prAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVdm0GwAAAAAAAKB7y0REJkm7i64hk3YD0EVZcQ0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqrJpNwAAAAAAAEA3l7y/4TpAM6y4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUpVNuwEAAAAAAAC6t0xSv+E6QHOsuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVdm0GwAAAAAAAKCbS97fcB2gGVZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqbNoNAAAAAAAA0L1lkvoN1wGaY8U1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQqmzaDQAAAAAAANDNJe9vuA7QDCuuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEhVNu0GAAAAAAAA6N4ySf2G6wDNseIaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFRl024AAAAAAACAbi5J6jdcB2iGFdcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqsmk3AAAAAAAAQPeWSeo3XAdojhXXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKQqm3YDAAAAAAAAdHPJ+xuuAzTDimsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBV2bQbAAAAAAAAoHvL5Oo3XAdojhXXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKTKM643EQceeGDaLURExBe/+MU4/fTT024DAAAAAAAA6EYE1wDQTfUszsV2u66Isi1rol/pmijulYsVywtj+bJszHyjON6d3itydZmNUnvYqNUxYnR1DBlREyUD1zTUfef1XjHrzeKNVhcAAD4sSSLKZxXFjJd7R+WSbFRVFEYmE9G3pC76D1wTY3ZaGWUjayLTzT6eLprbI957qzgWzCmKpQt6RK++ddGnf11stV11bL39qujRM0m7RQAAaERwDQBdRNmWq+Pye96MoVvVRETEnx8pjR99dUyb5shkktjzoMo4/ssLYqe9qqJHUfNfSFWvLIi/PlkSj945OP79174R0bFv63oU5WL/o5fGkacuih33WNHscatWFMSfHy6Nh2/fIt74V58O1fzAxw6oiO//+u0oKq5/v7/83pYx8ZYhG2RuAADa7msH7hCzpvfaYPMVFCYxcdY/W338nBk9Y9JvhsSzkwbEskU9Wjy2dIva2O+YJXH0lxbG8NGr29xb2u/1A7lcxPNPlsRjt28R//xTSbPHZYtyseenK+KILyyMXfdf3pFWG7z1Uq+48PPbxfJl9V81Hv/V+fEfF83ZIHMD0M347RTQAsH1JmjvvfeOgw8+uNXHT5kyJZ555pmG8YUXXtjqc2tqauInP/lJm/oDoO2GblUfWpdtWdOhOf772pmxw8eaD40/rLh3Lg48bmkceNzSeO6p/vHz724Vi+YVtav2kBGr43u/eifG7bpyvcf26pOLQz63JA753JJ49M5BceMlW8aqFYXtqhsRsedBFXHh/70dRVaNAAB0GUsWtBwWbyw11Zm45ccjYuJNZa0+Z9nCHjHpprJ4+OYhccx/LIjTvzunTSuS03qvH7Z8aWFc+fXR8Y/JzQfWH1hTUxBTHx0QUx8dEPscvjS+dtmsGLDFmnbXnv6v3nHhydvGigpfMwIA0DE+UW6CttpqqzjooINaffysWbPyguu2nLtq1SrBNcBG9rEDK+I7P5sZJQPr2j3HTntWxSW3vhV9+uWafD2Xq1/p3Nzrex9cGeN2fT2+fcJ2MXtGcZtrX/TrGU32X1cXsXpVQfTu23TdI05ZHFttWx3f+8LYNofXmUwSJ55dHqd/Z24U+kQDANBl1FRnompZ539AW760ML530rYx46Xm7+rTu1/9Z9aVyxt/9kySTDz067J4+fm+cenv3oy+pev/fJ7We/2wWdOL4+LTxkb57J5Nvt67X12T7zci4q+PDYjZ03vFj+99IwaWtT28fvruQXH9d7eK1dUFbT4XAADW5WteAEhJz151cep582PC18o7NM+2O69oFFqvqY3408SB8ewjpTHnnZ4xb1bPqF1dEH36r4lhW9XE6PGr4vCT82/pPWCLNfG/d78ZZ316fMMt/tanbMvV8f2bZkT/AWu/1Fu6MBuP3DE4/nj/wJg/u2fUrclE35I1MXRkTexxUEV89swF0e9DXwLutOeK+NZP341Lz2r9bdG3GFYT3/jJrNjzoMpWnwMAQOdYujB/BfIun6iMjx+5tENzru8Z1DXVmfj+FxqH1iPGVMfhX1gYO398eYwat6rhB49raiPefaNX/OvP/ePxOwbHvJlrf7w546U+ccnpY+PSe6a3+OidiHTe64dVVRTGD7+0TV5o3atvXRxy0qI4+HOLYvjo1VFUnMTKqoIon9UzXvpr37jvuqGxZP7aOy29N6M4Lv2PbeLyh96Iwlb+lrRySWHcdMmW8Yd7B7e+WQAAWA/BNQB0sp7FuTji1IXxuXPKO3RLvoiIHj1z8T+/mJkXWs97tyh+9NUx8dbLvRsdv6IyG2+9nI23Xu4dT907MA4/ZXF8/cezGr6gGjy0Nr70P3Pj2v/Zar21i4pzceGNb+eF1i/+tW/86MwxUbk0/yNGVUU23qqor/vQzUPi3MvfjU8es6zh9f2PWhZ/PGRZ/PXJ0hZrDhxSG5/7z/lxxCmL3BocAKCLWlKeH+butn9lHHHaoo1a8/fXl8X0aWtD60wmiVP+a26c+J/zm7w7T7ZHxDY7rYptdloVx365PO79xbC488rhDa+/+kLfmHjTkPjs2S3/yDSN9/qBXC7ip9/YOi90H7X9qrjwN2/F0K3yH0HUu28uRu+wKkbvsCoOO2VR3HrZiHjo12tvp/7GtL7x8G+GxLFfXtBizaqKwnjwxiHx0K/LYlVV+x/3AwAATXEfHwDoJNvvviLOvfzd+O0/XoqzfjAnL7RetjgbE29p+2qFCWeXx5ZjVjeMly7Mxn9/ftsmQ+vGMvHYnYPjxku2zNt72OcXRb/S9QfqE742P7bdeVXD+OW/9Ynvnjy2UWi9rpVVhXHFeVvHq3/PXw1zYgtfCu5/1NL4wc0z4vbnX4rjzliYF1q/8a/e8fwf+q+3XwAAOsfSdZ75PLCsdqPWq15ZEL+/bmjevq/+aHZ8/tymQ+t1ZXtEnHTevPjMWfPz9t/9s2FRU93y8ufOfq8f9sd7B8ULT5c2jIdtXR2XP/B6o9B6XUXFSfzH99+LfY/IXxn+4K/Koq6Zu6P/c0q/uOKc0fHF3XeO310zPC+0HrZ1dRz4mcXtfh8AAPABK643QUuWLIk33nij1ccvWpT/S9+2nFtdXd3qYwFo2RkXzIld9qlqtH/2jJ5x0enbxI4fWxERrV+dUVCYxJFfWJi378Yfjmj22XbNmXjLFnHslxbE8K3rv+AqzEbs9emKePq+Qc2e0690TXzmK2tXYyxfVhj/e87oWFPbut/E1a4uiMu/uXXc8pdXGvbtuMeK2GrbVTHrzV6Njv9/N7zT5Dx/fbIk/vc/t45zfjS7VXUBANj4lnRymPuPyf2jeuXaIHXbXVfEkV9c2MIZTTvtf+bEP6f0j5mv1f8IdOXywpj2TP/Y65CKZs/p7Pf6gdqaTNx19bCGcbYoF/99w9vRp3+uhbPWKiiIOPeqmfGPyf1j9ar6a7dwblFMm9I/PtbE43h+8Z1RseC9xv/OGLd7VXzv5hnx6G1btPOdALA5yST1G64DNEdwvQl6+umn4+mnn273+WedddYG7AaAjnjgpi3iN5eNiNXVBe8H1623+36VMahs7cro5csK408PDmxzD7m6TDz3VGleED1q3KoWzqhfHf3h25PfftWwWDivqIUzGpv3bs945YU+ec/Z3vXjy5sMrte1sqogbrxky3jst4Miog0PAQQAYKNbus7tswcN3bhh7tuv5N9taNdPVLbpOdEfyPaI2P2AyobgOiJixsu9WwyuO/u9fuCp3w3K+8HqkV9cGGM/0vJn+HX17peLvQ9bFlMeWPuD1Ref7ddkcL2ubI9cnHTevDjhnNatagcAgNbw0RIAUvDXJ0ri9quGxYxXWnNL76aN/2h+0P3iX/tGLte+EHferPzQeeCQ5m8VXlCYxGEnrV0ZvnBej3j0zrbf5jwi4v7/GxIVi5c0jDPrWbBdU52JR+4cHPf8cmij1S0AAHQNSxeuuwq55VtXd1TFovyvt7b5yMp2z7XtzvnnrvsM63V19nv9wGO3r13h3LNXXZxwzvwWjm7e4V9YFKs/tFr9w4/kaUpBQRIHfGZJfP7ceTF89OoWjwUAgLYSXANAJ1k0r0fcc11ZPH3fwHh3+vpXFa/P1uPyH+fwr2c78Jzndb6fWrm8+QT5I3tVRcnAtQ+/e/LuQVG7unW3CF/Xs48OiGcfHbDe4958qVf88f6BMfnBgY2+HAQAoGv5cNhb3Lsuevdr3e2r26vfgPwfXdbWtO+zaUTEmtr8H4L2K23+B50Rnf9eIyLmv1sU77y69gew+x6+LAZs0XKfzdlpr6rYaa/GjzNa14gxq+Pgzy+OAz+7eL3P0AYAgPYSXG+CJkyYEGeffXarj7/lllviFNdkRAAAYZJJREFU1ltvbRhPnjy51eeuWrUqjjjiiDb1B0DTLv/G6A063//+59Z5d8muq23/LbPX/fJp3rvNPyf744cvyxtPfby03XVb6z8PH7/RawAAsGEs/dCdcQaW1bbrtt1tMXRU/srfd19v/49EZ65z7rpzr6uz32tExNTHSvPG+6zz+Xxj+OFdb270GgAAILgGgE1UR1aSfFhBQRJ7H5z/3L53Xmv+y77d91/7zLtli7Px1ssdXz0OAED38eFHugxs4pnPSRLx3oyeseC9nrFsYTYKCiL6D1oTQ0bUxJZjq9sc/o7bLf8ROn97uiRO+a+50aOo5dter6u2JhMv/KGkxbnX1dnvNSJi2jP5d1radf/1P5MaALqEJKnfcB2gGYJrANjMHXXawhjxoefTvfVyr5j2bL8mjy0qzsXwrdceO/P14shb9h0R2R65+OQxS2OfQypi6MjVMWxUTRRmk1i2KBsL5hTF36f0j+eeLIlZbwq8AQC6m7q6/GdODypbG+YuKc/G/TcMjb8+Vhrls5u+w8/AoTWxx6cq4rNnl7f6Gcpbj6+OPT61LF74Q2lERMx6o1fc+4uhcfK35rWp93t+PjRmvbH2M+rHDqqIrcdXN3t8Gu81IuLdD/VYttXq6N03//bkuVzE354qiWceGhjzZ/WM+e8WRfXKwigZVBuDh9XGrvtVxh6frojtdm3/s8ABAGBjEFwDwGaqR89cHHXqovjKhe/l7b/tiuGxbhj9gZHbVEdh4dpxfvicxDGnL4wJ55THFsMarzbp1acmho2qiV32rYr/uGBuTJlYGr/5yYgWb0sOAMCmpWJRNnK5tZ8lB5bVRF1dxN0/Gxa/v64sVq8qbOHsiCXzi+KJO7eIp+8eHEedviC+9L33ItujxVMiIuIL/z03Xn6uX6xaUT//764ZFsW9c3HcmeVRsJ4bFdXVRTzwq7K4+2fDGvYV966LL/z3nBbPS+O9Vi0rjCXzixrGW227Ku/1Zx4aEHdcMTzmvlPc6NyFc3rGwjk947W/9427rh4eu+1fGV/6f+/FmJ1WNToWAADSILgGgM1Ej565GDK8JspG1sSYHVbFcf+xIC9grlsTcdV/jYrn17k94oeNGpf/pdaSBdmGuc+74t341GeWtrqfTx6zLD52YGVcetaY+MeU/us/AQCALu/Dt86OiOg/cE389Ouj45mHBrZpnro1mXjo12Xxzmu94rs3vh19S+taPH7Mjqvi+7e/FT84dWxUryyMXF0mbv7hlvHY7YPj4M8tjh32qooRo6ujT//6eaoqCmPO28Xx8nN94+m7B+etiu7Vpy5+cMebsc16At003uu70/MD6QFD1kRE/SrrWy8bEb+/bmir6057pn/8e+r4+MYVM+NTE5a0qWcAANgYBNeboHvuuSfuueeedp9/4IEHbsBuAOjKJpwzP/7jgrnrPW7GK73iph+PWG+APPD9L8Y+sGpFYRQUJnHpHW/FLvtUNeyfP6so/jGlf5S/VxQ1qzNROnhNbPuRlfGRvauiqOfaZ/j06ZeLH976Vvzwq2Pir0+Utu3NAQDQ5Swtzw9z7/3FsFi5vH7lcUFBEh89qCI+deLiGLltdWwxvCYyBREVi7Px5r/6xHNPlMafJw7IW8X877/0jyu/PjouuvWt9a6c3mmvqrjioTfi9suHx9+eKo2IiHkzi+O2n4xodf8fO6givnjBnBi9w/pXIafxXtet2atPXSRJxFXf3Dr+dP+ghv0DhtTGRw+oiKGjVkevvrmoXJKNma/1ihef7RfVK9euBK9bk4mrzxsdK6sK4+gzFq73PQMAwMYkuAaAzVhdXcTPv7tVPHbnoGju9uAf1rtv/uqP6pUF8dkzyxtC6znv9Ixf/WDLeP4P/Zucr/+ANXHqt+bFsV9a+6VYYTbiv656N752WK9mn/8HAMCmYenC/GD1gyB3211WxLd/+U6Tz3Lu1acmhm5VE/sdszRO/Pq8+N8zt4n3ZqxdWfz3P5bEgzcOic+ctWC99UfvsCou/M2MuP+GsvjNj7ZsU+9nXDg7jv/qgsis/2NxRKTzXleuyL/9eHHvXDw7aUBDaD1gSG2c/t334sDPLmky/F61oiDuv74s7v3F0FhTu/aAX188MrbbdUWM291zrwEASM96fqsKAHRnhYUR//mjWfHta96NUdutf1VJce9c3njrcaviC+fPi4iIF6f2ja8fOe79W403/W1f5dJsXHfhyPjx17aO2pq1x/QtqYuv/3h2+98IAABdwpLyxg9pPvAzi+PyB99oMshd19bbV8dPHnw9Rq7z7Oa7rx0WK6vW/zXWMw8NiK8fPL7NoXVExM0/HBnnHr59PPtwaauOT+O9Vq/I37+4vEf86sKRERExavtVcfUjr8WnTmw6tI6I6NUnF6f817y45M43o3e/tT9KrVuTiZ+dv3Xkck2fBwAbQiaxfXgDGrPiehNxww03pN1CREQMGjRo/QcB0GX840/9Y0VF/aqMbFESZSNqYtio1TFi9OoYNa66fn+PiE+fsCT2P2ppXHr26Hju/dsqNmXdFdfHf7l+5fTst3rG97+0TaxaZwVIc6ZMHBgDtlgTZ1/8XsO+PQ6sjG12XBkzXundlrcIAEAXsnSd5z5vuU11nHP5u9GjqPXfzpYMrIv/vuHtOO+I8VG7uj6BXVGRjcfvGNzsSuQkibjr6mHx258Ob/TaFsNrYvweVTFs69XRt2RNZDL1z7ieN7M4Xn2hTyycs/auPzNe6hP/+9Vt4uTz58ZJ581rcfV1Gu91VVX+5+2n7x5cP8+g2rj4jjdj8LDaVtXd+eNVcd4178Sl/zG2Yd+s6b3iuSdKY9/Dl7W6fwAA2JAE15uIcePGpd0CAJugGa/0bjYI3nr7VXHGBXNir09VRkREUXESF/3f23HFeVvH5AcGNnlO0sx3cFect3WrQ+sPPHjTFvGpzyyJ7XZZezvCI09dFNdesFWb5gEAoOv46IGVUbZVTcN49wMqorhX25cUbb19dXzqhMXx+J1bNOz762MDmg2ub/7hiHjgV0Pz9u209/I46bx5sfPHlzcbQCdJxCt/6xt3/2xoTJtS0rD/tz8dHpVLsvHVH85u9tw03mtzvZz1o9mtDq0/sM9hFbHvEUtj6qMDGvY9fsdgwTUAAKkRXHdzN9xwQ0ybNi0iInr37h1XX311yh0B0FXMfL1XXPTFbeJL/zM3Pv+f5RFR/7zp8654N976d++Y/aFn7X1g3RUeERHT/twv3pjWpx0dZOL3Nw6JC345s2HPLh9f3o55AADoKvY8uCL2PLhig8x1xGkL88LcN1/sHdWrMo3C4b88UtootP7cN+fFyefPjcL1/LYyk4nYaa+q2PHOt+Kh/xsSv754ZMNrD/9mSIzbfUUc+JklTZ6bxnvttc4dkCIiho+ujo8ftbRddY//anlecP3K832jtibTplXjAACwoXjGdTc3derUmD59ekyfPj2qq6vTbqfV6pI17d4AaItM/OZ/h8ff/9SvYU/P4iROPndek0evWtn4o8PUJ0uaOLJ1/jGlf954yzGro2Rg21aKAADQPW29w6roU7L23/lragti5qv5dxNaUxvx6x+MzNt37FfK4wvfWX9o/WGZTMRxZy6Iz587N2//TZdsGTXVLdwvfANpzXuNiCju0/gh1HseXNHsM63XZ7vdVuTVXb2qMN55tVf7JgPoBCtW5tq1rVzZ+O9PALoeK667sZUrV8Z77619duioUaNS7KZt/pI8EtHOH/d+uvBzG7YZgG4vE7ddOTw+dsAbDXs+cfiyuKIgiVwu/0u6plZcv/Gv9qy2rrd8WTbmzy6KoSPX3mJxwJA1UbGkRwtnAQCwOSgoqH+8zSvPr/2RZcXi/K+ynn+yNBbOLWoYDxpaE1/47zntrvm5b8yPyfcPivJZ9c+9XrawRzz78IA46ISmV11vKK15rxERvfo0XnG93a4r2l23sDBi9A6r4uW/rq277rO7AbqSQWPfTbsFADYiwXUXtXr16igvL+/QHG+99VYkH3oYab9+/WLWrFkdbS0iIkpKSqKkpP0r7ADoWt74V+9YXJ6NQWX1qy2KipMYsmVNzH//C7sPzJtV1OjcZQs79nGiYnE2L7guGejuGQAA1Os3IP+z4fKl+Z89/7nOHXwOPGFxu54z/YEePZP41ImL47c/Hd6wb9qU/hs9uI5Y/3uNiBg6anWjfaVbdOyORet+/q5soi4AbBBJtHvBWrfjOkCTfBLtombOnBlnnXXWBp3zvvvui/vuu2+DzHXOOefECSecsEHmasrHM0dGUabxs1UBqDdutxWxw+5rV1bMn10Uf32ytAMzZuK9t4tjUFlVw54tx1Q3Cq7ffaPxbQPXrNn4t04EAGDzVNQz/1vd3Dp3ep35Wv7n0x33rIqOWneOd17rnFtnr++9RkRsObY6MpkkkmTtZ/Bsj459853J5J+f+CId6MIWv9W+u4ouWlwX4/Z6b/0HApAqwTXt0qPHxr1tVGEmG4UZ/3kCNGfUttVx1sVr/8H18t/6dDC4jlhVlf9gvD79Gt+GcO67PaNmdSbvS7V+pXWxeH776/YrzV/hsWyRv/8BAKi37u2yB6yzunjdVcmlgzt+956BZfk1OmsF8vrea0REca8kho5aHfNmrv2xf1Mrs9ti3fc3YHDHVnADbEx9ehes/6AmrFzlVzkAm4L2/S3PZi+bFSoApOm9t/NXQg8q6/iXS/0H5gfVSxY2/pFSri4T0//VO2/flttUt7tmrz51UbZlTd6+JZ6pBwCwSfrDPQPjc+N3adiu+Vb7VsV9IEkiZr+Zfze2Aet87i0qzl+WvKam43cDqlmdP0fP4sZLn9N4rx/Yfvf8Z1rPebv9d6xLkohZ0/NXlDdXFwAANjbpYxc1cuTIuPrqq9t9/m233RbTpk1rGH/jG9+I0aNHt3u+6dOnx/XXX98w3tgrrgFo2aw3i6OuLqKwsH48bFRNDBmxOhbM6dnyic3oUZSL0duvytu3cG7j51lHRDz7WGnstNfaL8t23Xd5PPvIgHbV/cheVVH4oU8j775RHMuX+XgCALApGrr16lhRufaz3PRpfTo038zXesWS8rWfSfuUrImt1/nMOmCLNTHztbXj+bN6xvg98oPdtpo3M/8z9YAtGq/iTuO9fmCfI5bF5PsHNYxf/Eu/+MxZ5e2qO2t6cSz70A9We/eri9Hjm64LAAAbm2+Gu6jevXvHrrvu2q5zZ82aFS+++GLDeJtttonjjjsuMpn2/+p45cqVeWMrrgHSVVWRjX892y8++snlDfsOOHZp3HPd0HbN97EDKqNXn7UrSebOLIr5s5oOrv/yWGmc9YM5DeODjl8SN182IlZWFba57lGnLcwb//PP/do8BwAAXcPW26+KbFEu1tTU3+Bv1vReMfutnjFy7Op2zffgjUPyxh87qCKy6/yOfvQOK2PaM/0bxi/8oSQO/OySdtX7wN+eKs0bbz1+ZaNj0nivH9j9gIro2asuVq+q//w97U/9Y/6sohi6VU3TJ7Tg0du2yBvvvO/yvB+WAsCGlEnqN1wHaI5bhXdDv/rVryKXWxs+nHzyyR0KrSMiqqvzbwPbt2/fDs0HQMf9aWL+KucJXytv9Lzo1ijMJnHaf83L21f/vOym/79jwZye8bc/rv1ysE//XJx63rwmj23JTnstj70+XdkwzuUiHrtrcJvnAQCga+jTPxf7HLosb9+9Px/Wrrle+3ufmPz7QXn7Dj15UaPj9vh0Rd742YcHNLrldlvMmdEzpjw4MG/fngdXNDoujff6geJeSXzqxMUN41wuEzf/cMtI2vgF+JwZPePp3+V//j7s1IXNHA0AABuf4Lqbefjhh2Pq1KkN4+HDh8cnP/nJDs9bU5P/q93S0tIOzwlAx/zpoYEx7921q6L7ldbF/7vhnejRs/Ez+JqXxFk/eC/G7LD2doDVKwvivl8NaeGciJt+PCI+9Bup+OxXF8T+Ry1tddUBW9TGt695N2/fnx8ujXff6NXMGQAAbAqO/FJ+8PnH+wbFlIfa9liZ+bOK4rIzx0Qut/aHlHsfuix23req0bE77FkVW41b+1k2V5eJy746JpYvbfvdgFYuL4jLvjom6tasrTts6+rY5RPLmzy+s9/rh530rXlR3LuuYTz10QFx/w1lra67sqogrvjP0bG6eu1Xg9vttiI+emBlC2cBAMDGJbjuRv7973/HNddck7fvrLPOisLCtv9jbV0VFfm/Lh4woH3PMgVgw6mpLogbfrBl3r7dPrE8fnznWzFkxPpvUdi7X138zy9mxjGn53/h9tufDc17vl5TZr7eKx77bf7qjP/5xTtxxCkLI6LlpR6jx6+Mqx98I4aOXPujqIolhfF/P9qyhbMAANgU7LRXVRx6Sv7ny5/+5+i49xdlUb1q/XeDm/ZMv/jWkdvnfR7tP7A2vvyD2U0eX1gY8eXv5782641ecf7R28f0f/Vudd9vvtg7vnXk+Jj5Wv45X/renOhR1PTn285+rx82YIs1MeEb8/P2/eZHW8adPx0Wa2pbPrd8dlF898Tt4q1/r30ud4+eufjaZe9GB2/YBwAAHeKpNd3Eiy++GBdddFHU1a39te1xxx0X++233waZf8GCBXnjkpKSDTIvAB3z3FMl8fsbh8Rnz1z79/TOe1fFr6e8Gk/dMyieeXhAvP7PPg0rKXoU5WLbj6yMvT5dEUd+YVH0K63Lm2/ygwPinutat1Lj+u9vGaO2WxU77bkiIiIKsxHf/Mns+PQJS+Lh2wfHP/7UPyqWZCMiE0XFuRi/+4o48PglcciExfHh31TV1UVc9rXRsXBuy2E5AACbhv+48L1469+9Y8ZL9cFoLpeJWy/bMh76dVl85qz5seOeVTFs69XRb0D9Z9HKJdl48dl+8dTdg2LalPzvG3oW5+L7t77V4vObd//k8jjpvLlx19XDG/bNfac4vnXk+PjogRWx/7FLYvzHVkTZVqsbPofW1UUsmF0Ur/29b0x5cGD8Y3Lj7zmOO7M89jlsWZd6rx92wjnz4+2Xe8WzD6+9tfldVw2PvzwyII45Y0F87KCKGDi0NgoKImprMjHjpd7x50kD4rHbt4ia6vy1LOdcNivGfmTVuiUAAKBTCa43cUmSxAMPPBDXXXddXmg9duzYOPvsszdYnYUL1/6CeNCgQVFUJFwA6BoyceMlI6JncS6OOm3tc/B6Fidx1GmLGvatWF7/xVSffs3fRvzp3w+Ma769VSRJ65ZZ1K4uiB+csU1ced/02Hr76ob9O+6xInbcoz7MrqnORG1NJvr0b7ruyqqCuOxro2Pas/2bfB0AgE1P7365+PE90+Pi07aNV1/o27B/2cIecfMPRzaMe/Wpi9raTKypafqGgAOG1MZ/X/92jNt95Xprnnz+vFhTm4l7f5H/nOl/TC7JC6X79F8TERErKlv+SuzYL5fHGRe+t94VyGm81w8UFER862czo2Jxj3jpr/0a9s96o1f84r9HRUREYTaJ4j51saKi6fdbmE3ia5e9G5/+3OImXwcAgM7kVuGbsNmzZ8f3vve9+PnPf54XWg8YMCAuuuiiDRouz507t+HPY8eO3WDzArAhZOLn3x0Zl39zVCxf1vTjIfr0yzUbWi9fVhjXfGeruOKbo6K2mS/SmrN8WTa+cdT28cCvt2jy9aLipNnQ+o1/9Y7zjhsXf/uju3gAAHQ3ffrn4tJ7pscp/zU3skVNfx5ctaKw2SB3r0OWxbVPvBo77d3ys54/kMlEfPGCufGj302PspHNPzZnRWW2xdB6yJar4+I734yvXPxeFLTyo3Fnv9cPKypO4ke/mx5f+M6cyPZoXLtuTabZ0HrEmOr40e+mx6EnC60B6CRJYvvwBjRixfUmaNmyZXHbbbfFxIkT8wLriIhtttkmLr300igra91tXltj/vz5MWPGjIax4BqgK8rEH34/KJ57qiQO/dziOOykxTFqu+oWz3jn9eL4w+8HxsO3bRGrVjQdeLfG6uqCuOEHI2PygwPj8JMXxSeOWNboFuQfqKuLePWFvjHp1sHxzMMDWr26GwCATU+PoiROOm9efGrConjsti3iD/cNiiXzm/+RfZ+SNfHRAyrjM2fPb/dtq3fdb3nc+OzL8fxTpfHEbwfHq8/3Xe9n3eLedbHjXlVx6MmLYq9DlkVhO74tS+O9fqAwG/G5b86PfY9YFo/fMTj+PGlA3nOz17XtLivi4M8vikNOWhTZHh0qDQAAG1Rm8uTJftaxCUiSJF5++eV4+OGHY8qUKbF6deNfD++3335xwQUXRK9evTZY3bq6uvj+978ff/nLXxr2XX755bHHHntssBrLli2L448/Pm/f/gXHRlGmeIPVANgclQyqje13WxEDy9ZEv9I1EUlExZJsLFuUjden9YmKxRvnW6psj/rnaA8aVhsDh9RGr965qFiSjSXlPeK1f/aJ5cv8bg5gQ3l49t/SbgGg1ZIkonxWUbz5Yp+oWJKNFRWFUdw7F/0HrYnho6tj7M4rG55BvaHU1UW8+3qveO+t4qiqKIwVlYWRJJnoW7Im+pTUxchtqmPU9qvaFVa3JI33+oG6uoh3XukdC+f2iCXzi2JFZWH0G7AmBgypje12rf/3AcDmZuHiuhj5kVl5+x544IEoLS1Np6HNQFPf++912EXRo2ffZs7YvNSurornH78kb5//JsGK6y4tl8vF22+/Hc8//3w8+eSTMWvWrCaPGz58eJx99tnx8Y9/PDLre/jSh8yZMycqKyujV69eeVs2m41ly5bFG2+8Eb/97W/jpZdeajhnyJAhsfvuu3f4vQGw8VUs7hHPP13a6XXX1BbEa//0jxAAAPJlMhFDR9XE0FE1nVazsDBizI6rYsyOHVvV3FZpvNcPFBZGjN15ZYzdudNLAwBAhwiuu6DnnnsuJk+eHC+88EIsXbq02eN69+4dJ510UkyYMKFdz7OeNm1a/PSnP220v6CgIHK5pp/J9IUvfCEKN9ZPggEAAAAAAIDNkuC6C5o+fXo8+eSTzb6+9dZbx7HHHhuHHHJI9O7du911mls53VxoffLJJ8dRRx3V7noAAAAAAMBmKonIeHhtPdcBmiS47oKOOOKIuPXWW/MC5D59+sTee+8dRx11VOyyyy5tuiV4c4YPHx5Dhw6N+fPnN3tMNpuN3XffPU466aTYddddO1wTAAAAAAAAYF2C6y5o8ODB8YlPfCLefPPN2GeffWLfffeNXXbZJbLZDf8/15VXXhkVFRVRU1MTa9asidra2qitrY3i4uIoLS2NESNGRJ8+fTZ4XQAAAAAAAIAPCK67qAsuuCB69uy5QVZWt2TEiBExYsSIjVoDAAAAAAAAoCWC6y6quLg47RYAAAAAAAAAOkVB2g0AAAAAAAAAsHmz4hoAAAAAAICNK3l/w3WAZlhxDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqsmk3AAAAAAAAQPeWSeo3XAdojhXXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqrJpNwAAAAAAAEA3l0vqN1wHaIYV1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkKpt2AwAAAAAAAHRzyfsbrgM0w4prAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFKVTbsBAAAAAAAAurdMRGSStLvoGjJpNwBdlBXXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqrJpNwAAAAAAAEA3lyT1G64DNMOKawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSlU27AQAAAAAAALq3TFK/4TpAc6y4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUpVNuwEAAAAAAAC6ueT9DdcBmmHFNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpyqbdAAAAAAAAAN1bJkkikyRpt9EluA7QNCuuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEhVNu0GAAAAAAAA6OZy72+4DtAMK64BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUZdNuAAAAAAAAgO4tkySRSZK02+gSXAdomhXXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKQqm3YDAAAAAAAAdHPJ+xuuAzTDimsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUpVNuwEAAAAAAAC6uSQikiTtLroGlwGaZMU1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQqmzaDQAAAAAAANC9ZZL6DdcBmmPFNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpyqbdAAAAAAAAAN1cktRvuA7QDCuuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVGXTbgAAAAAAAIDuLZNEZHJpd9E1ZJK0O4CuyYprAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVdm0GwAAAAAAAAA2HTU1NfH2229HeXl5LFiwICIiysrKYsiQITFmzJgoKipSdxOumxbBNQAAAAAAABtXktRvpHId5s+fH+edd17Mnz8/IiL233//uPjii9s8z6xZs2LSpEnxxBNPxPLly5s8pqSkJA477LA4+uijY8SIER3qW93OrZs2twoHAAAAAACAbmru3Llx7rnnNoTW7ZEkSTzwwANxxhlnxH333ddsmBoRUVFREXfffXd86UtfikceeaTdNdXtvLpdhRXXAAAAAAAA0A09//zz8eMf/zgqKyvbPUcul4srrrgiHn/88SZf79GjRyRJEmvWrMnbX1tbG1deeWVMnz49zj333MhkMup2wbpdieAaAAAAAAAAupFVq1bFbbfdFr/73e86PNddd93VKEwdOXJknHrqqbHzzjvHkCFDIkmSKC8vj2nTpsWdd94Z8+bNazh24sSJMWLEiJgwYYK6XbBuVyK4BgAAAAAAgG6guro6Jk2aFHfddVcsXbq0w/O98sorcfPNN+ftmzBhQpx55plRWFiYt3/48OExfPjwOPTQQ+Paa6+NSZMmNbx24403xk477RQ77LCDul2oblfjGdcAAAAAAACwCXv11VfjyiuvjBNOOCGuu+66vNC6pKQkjj322DbPmcvl4sorr4xcLtew79hjj42zzjqrUZj6YdlsNs4999w45JBDGvbV1dXFFVdckTeXuunW7YoE1wAAAAAAAGxciS1v28BuvPHGeOSRR2LFihV5+0eOHBm/+MUvYvvtt2/znFOnTo2ZM2c2jMvKyuJrX/taq56hXFBQEN/85jejtLS0Yd/MmTNj6tSp6naRul2R4BoAAAAAAAC6mc985jNx4403xpZbbtmu8++888688emnnx5FRUWtPr93795x2mmn5e2766671O0idbsiwTUAAAAAAAB0E/vuu2/ceOON8fWvfz2Ki4vbNce8efPi9ddfbxgPGDAgDj744DbPc+SRR0afPn0axq+++mrMnz9f3ZTrdlWCawAAAAAAANiEbbHFFvH5z38+br755rj00ktj22237dB8zz33XN54//33b/F5y80pKiqKfffdt8W51e38ul2V4BoAAAAAAAA2Yf/v//2/+OpXvxqjR4/eIPOtG3ruscce7Z5rzz33bHFudTu/bleVTbsBAAAAAAAAurdMkkQmSdJuo0vYFK7Dm2++mTceN25cu+caP358i3Or2/l1uyorrgEAAAAAAICIiKisrIylS5c2jPv37x+DBw9u93zDhw+PXr16NYyXLFkSVVVV6qZUtysTXAMAAAAAAAARETF79uy8cUlJSYfmy2QyjeZYt4a6nVe3KxNcAwAAAAAAABERUV5enjfu27dvh+fs169fizXU7by6XZngGgAAAAAAAIiIiFWrVuWN1w1D22PdlcDr1lC38+p2ZYJrAAAAAAAAICIah50bYyVwa4JcdTdO3a4sm3YDAAAAAAAAdHNJUr/R5a9DdXV13nhDrARed46VK1eqm1LdrsyKawAAAAAAAABSJbgGAAAAAAAAIiKiuLg4b7x8+fIOz7nuHL1791Y3pbpdmVuFAwAAAAAAQBvV1dV06nmdpVevXnnjqqqqDs9ZWVnZYg11O69uVya4BgAAAAAAgDb6498vTbuFjWLdsHNDrARuT5Cr7sap25W5VTgAAAAAAAAQERFlZWV54w2xEnjdUHbdGup2Xt2uzIprAAAAAAAANq7c+1s3ctDu/69d59XUrohnX7pmwzazAY0cOTJvXFFR0aH5kiSJZcuWtVhD3c6r25VZcQ0AAAAAAABtVFhY1O6tK+vfv38MGDCgYVxZWRmLFi1q93xz586N6urqhvGgQYOib9++6qZUtysTXAMAAAAAAAANtt1227zx66+/3u65Xnvttbzx2LFj1U25blcluAYAAAAAAAAa7L333nnjv//97+2e629/+1uLc6vb+XW7KsE1AAAAAAAA0GDd0POZZ56Jurq6Ns9TU1MTU6dObXFudTu/blcluAYAAAAAAGCjyiSJ7UNbVzds2LAYP358w3jp0qXxhz/8oc3zPPLII7FixYqG8Y477hhDhw5VN+W6XZXgGgAAAAAAAMhz8skn541vvvnmqKmpafX5K1eujNtvv73FOdVNr25XJLgGAAAAAAAA8uy7774xevTohnF5eXn88pe/jKQVK8ZzuVxcc801sXTp0oZ9Y8aMadXtq9XtnLpdkeAaAAAAAAAAyFNQUBDf/va3o7CwsGHfxIkT47rrrmvxOcy1tbVx1VVXxVNPPdWwL5vNxne+850oKFh/NKlu59TtijbNrgEAAAAAAICNavz48fGVr3wlb999990Xp59+ejz55JNRXl4euVwu6urqYt68efHII4/EaaedFo888kjeOWeffXaMGzdO3S5Wt6vJpt0AAAAAAAAA0DWdeOKJMWfOnJg0aVLDvvfeey8uu+yyiIjo0aNHRNSvAG7KZz/72Tj++OPV7aJ1uxLBNQAAAAAAABtXktRvbHLXoaCgIL71rW/FtttuG9dee22sWbMm7/XmgtSioqI4//zz45BDDlG3C9ftSgTXAAAAAAAAQIuOPvro2H333WPixInx+OOPR2VlZZPHlZaWxuGHHx5HH310DBs2TN1NpG5XkJk8efKm9bMOup1ly5Y1unXB/gXHRlGmOKWOAABg0/Dw7L+l3QIAAHR5CxfXxciPzMrb98ADD0RpaWk6DW0Gmvre/4Cd/yuKevRJqaOupaZ2Rfzp31fm7dvU/pusqamJGTNmxIIFC6K8vDwymUwMGTIkysrKYsyYMVFUVKTuJlw3LVZcAwAAAAAAAK1WVFQU48ePj/Hjx6vbDeumpSDtBgAAAAAAAADYvFlxDQAAAAAAwMaVJPUbrgM0w4prAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFKVTbsBAAAAAAAAurkkInJpN9FFJGk3AF2TFdcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApCqbdgMAAAAAAAB0b5kkiUySpN1Gl+A6QNOsuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFKVTbsBAAAAAAAAurkkqd9wHaAZVlwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkKps2g0AAAAAAADQzSVJ/YbrAM2w4hoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVGXTbgAAAAAAAIBuLknqN1wHaIYV1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkSnANAAAAAAAAQKqyaTcAAAAAAABAN5d7f8N1gGZYcQ0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqrJpNwAAAAAAAED3lkkiMkmSdhtdQsZlgCZZcQ0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKQqm3YDAAAAAAAAdHNJUr/hOkAzrLgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFXZtBsAAAAAAACgm8sl9RuuAzTDimsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUiW4BgAAAAAAACBVgmsAAAAAAAAAUpVNuwEAAAAAAAC6uSSp33AdoBlWXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKnKpt0AAAAAAAAA3V0SkSRpN9FFuA7QFCuuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEhVNu0GAAAAAAAA6OaSpH7DdYBmWHENAAAAAAAAQKoE1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkSnANAAAAAAAAQKqyaTcAAAAAAABAN5dLIjJJ2l10DTnXAZpixTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqbNoNAAAAAAAA0M0lufoN1wGaYcU1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKnKpt0AAAAAAAAA3VyS1G+4DtAMK64BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUZdNuAAAAAAAAgG4ul0RkkrS76BpyrgM0xYprAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFKVTbsBAAAAAAAAurkkqd9wHaAZVlwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkKps2g0AAAAAAADQzSURkSRpd9E1uAzQJCuuAQAAAAAAAEiV4BoAAAAAAACAVAmuAQAAAAAAAEiV4BoAAAAAAACAVGXTbgAAAAAAAIBuLknqN1wHaIYV1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkSnANAAAAAAAAQKoE1wAAAAAAAACkKpt2AwAAAAAAAHRzSS4il0u7i64hcR2gKVZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqbNoNAAAAAAAA0M0lSf2G6wDNsOIaAAAAAAAAgFQJrgEAAAAAAABIleAaAAAAAAAAgFQJrgEAAAAAAABIVTbtBgAAAAAAAOjmkqR+w3WAZlhxDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqsmk3AAAAAAAAQDeXSyIiSbuLriHnOkBTrLgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSlU27AQAAAAAAALq3JMlFkuTSbqNLcB2gaVZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqbNoNAAAAAAAA0M3lkohI0u6ia8i5DtAUK64BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASFU27QYAAAAAAADo5pKkfsN1gGZYcQ0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKQqm3YDAAAAAAAAdHO5JCJyaXfRNeSStDuALsmKawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSlU27AQAAAAAAALq5JKnfcB2gGVZcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJCqbNoNAAAAAAAA0L0lSS6SXC7tNrqEJHEdoClWXAMAAAAAAACQKsE1AAAAAAAAAKkSXAMAAAAAAACQKsE1AAAAAAAAAKnKpt0AAAAAAAAA3VySRESSdhddQ+I6QFOsuAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVYJrAAAAAAAAAFIluAYAAAAAAAAgVdm0GwAAAAAAAKCbyyURmSTtLrqGxHWAplhxDQAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqBNcAAAAAAAAApCqbdgMAAAAAAAB0c0kuInJpd9E1JK4DNMWKawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSJbgGAAAAAAAAIFWCawAAAAAAAABSlU27AQAAAAAAALq3JJdEkknSbqNLSBLXAZpixTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqRJcAwAAAAAAAJAqwTUAAAAAAAAAqcqm3QAA0LXVJWticu73ERFxYMFnozDj4wMAADRnxcpcDBr7bkRELH5rVPTpbd0IAERERJKLiFzaXXQNiesATfHJGQAAAAAAAIBUCa4BAAAAAAAASJXgGgAAAAAAAIBUCa4BAAAAAAAASFU27QYAAAAAAADo3pJcEkkmSbuNLiFJXAdoihXXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKRKcA0AAAAAAABAqgTXAAAAAAAAAKQqm3YDAAAAAAAAdHNJEhG5tLvoGpIk7Q6gS7LiGgAAAAAAAIBUCa4BAAAAAAAASJVbhZO6XK7xrUFqktUpdAJAU+qSNQ1/rkmqo9DHB4AuY+HiurRbAGAdK1eu/Z5j0eK6WLnKrUAB0rZ4SePPzU19L83GVRurI/zfYkS8fy2ARnzzTOqWL1/eaN9zyeP+DwygC/pL8oi/nwG6kJEfSbsDAFoybq/30m4BgGYsX748Bg4cmHYbm5W/xpNptwB0cW4VDgAAAAAAAECqBNcAAAAAAAAApEpwDQAAAAAAAECqMpMnT/akSlK1Zs2amDNnTt6+fv36RUGB31UAAAAAANAxuVwuli9fnrdvxIgRkc1mU+qo+8vlclFZWZl2G5uU/v37y0XY7AmuAQAAAAAAAEiVn24AAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAAAAAAAAkCrBNQAAAAAAAACpElwDAI3U1dVFkiRptwEAAAAAwGZCcA0ANDJp0qSYMGFCXH311fHcc8/F6tWr024JAAAAAIBuTHANAORJkiQefvjhWLRoUUycODEuuOCCeOmll9JuCwAAAACAbiybdgMAQNfy2muvxYwZMxrGw4cPj9133z3FjgAAoHMdeOCBabcQERFf/OIX4/TTT0+7DQAA6BRWXAMAeSZNmpQ3PvLII6OgwEcGAAAAAAA2Ht9CAwANqqqqYvLkyQ3jwsLCOOyww1LsCAAAAACAzYFbhQMADZ566qlYvXp1w/gTn/hEDBw4MMWOAAAgXXvvvXccfPDBrT5+ypQp8cwzzzSML7zwwlafW1NTEz/5yU/a1B8AAHQXgmsAICIikiSJhx9+OG/fUUcd1eZ55s6dG++9917069cv+vfvH2VlZZHN+sgBAMCmaauttoqDDjqo1cfPmjUrL7huy7mrVq0SXAMAsNnyLTIAEBERr732Wrz99tsN4+HDh8fuu+/e5nmeeuqpuOWWWxrG9913XwwaNGhDtAgAAAAAQDflGdcAQERETJo0KW985JFHRkFB2z8qzJkzp+HPPXr0iAEDBnS4NwAAAAAAujfBNQAQVVVVMXny5IZxYWFhHHbYYe2aa+7cuQ1/Lisra1f4DQAAAADA5sWtwgGAeOqpp2L16tUN449//OMxcODAds21bnANAACbsiVLlsQbb7zR6uMXLVqUN27LudXV1a0+FgAAuhvBNQB0IzU1NfHqq6+2+byHHnoobzxmzJj417/+1eZ5amtrY+nSpQ3jgoKC9c6z0047RTbrIwkAAF3T008/HU8//XS7zz/rrLM2YDcAANB9+ZYYALqRJUuWxHnnndfheW655ZaONxMRL7zwQrzwwgstHvPggw9GSUnJBqkHAAAAAMCmyUMnAQAAAAAAAEiVFdcAAAAA0IwJEybE2Wef3erjb7nllrj11lsbxpMnT271uatWrYojjjiiTf0BAEB3IbgGgG5k4MCBccMNN7T6+Ntuuy2mTp3aMD7mmGPa/UVZXV1dnH/++VFdXR0REdtss018+9vfXu95ffr0aVc9AAAAAAC6D8E1AHQjRUVFMW7cuFYdu2TJkvj73/+ed+4Xv/jFGDhwYLtqv/TSSw2hdUTEPvvs0+peAAAAAADYvHnGNQBspu65556oqalpGB955JHtDq0jIv7yl7/kjffYY492zwUAAAAAwOZFcA0Am6GKiop46KGHGsaFhYXxuc99rt3zJUmSF1z37t07dthhhw71CAAAAADA5sOtwgFgM/T73/8+77behxxySJSVlbV7vpkzZ8Z7773XMP7oRz8a2ayPGQAAbPruueeeuOeee9p9/oEHHrgBuwEAgO7LimsA2MxUVVXF/fff3zDOZDJx0kkndWjOD6/ejojYb7/9OjQfAAAAAACbF8E1AGxmHnzwwVixYkXD+IADDoiRI0e2e76qqqp44oknGsb9+/ePT37ykx3qEQAAAACAzYt7eALAZmTVqlVx77335u07+eSTOzTno48+2ui240VFRR2aEwAA0nTDDTek3UJERAwaNCjtFgAAoNMIrgFgMzJp0qSorKxsGO+zzz4xduzYds9XU1OTd9vxiIijjjqq3fMBAEBXMG7cuLRbAACAzY7gGgA2E6tXr47f/e53efuOPfbYDs153333RXl5ecN4t912i1GjRnVoTgAA2FTdcMMNMW3atIiI6N27d1x99dUpdwQAAJsOwTUAbCYeffTRWLp0ad6+Cy+8MPbbb7844ogjYrfddouCgoJWz7dw4cK4/fbb8/Z95Stf2SC9AgDApmjq1Kkxe/bsiIjYfvvtU+4GAAA2LYJrANhMFBUVxYgRI2LOnDkN+2pra+OPf/xj/PGPf4yysrI4/PDD49BDD42hQ4eud74bb7wx79nWBx54YIwfP36j9A4AAF3dypUr47333msYuxMRAAC0jeAaADYTRx55ZBxxxBHx4osvxmOPPRZTpkyJ1atXN7xeXl4et9xyS9x6663x0Y9+ND772c/Gnnvu2eQq7MmTJ8fTTz/dMM5ms/HlL3+5U94HAABsaKtXr857BE57vPXWW5EkScO4X79+MWvWrI62FhERJSUlUVJSskHmAgCAriozefLkZP2HAQDdTVVVVfzxj3+Mxx57LF5//fUmjxk1alSceOKJcfDBB0dRUVFERMycOTPOPvvsvNXWp5xyiuAaAIBN1htvvBFnnXVW2m0065xzzokTTjgh7TYAAGCjav2DLAGAbqVv375xzDHHxPXXXx8333xzHHvssdGzZ8+8Y95999248sor4/Of/3zccccdMX/+/LjooovyQusdd9wxTj/99E7uHgAANh89evRIuwUAANjoBNcAQIwePTrOPffcuPvuu+P000+P0tLSvNeXLl0aN910U5x00kkxe/bshv39+vWLCy+8MLJZTx8BAICNxedtAAA2Bz71AgANSkpK4otf/GJ8/vOfjyeeeCLuu+++vKB6XV//+tejrKysEzsEAIANb+TIkXH11Ve3+/zbbrstpk2b1jD+xje+EaNHj273fNOnT4/rr7++YWzFNQAAmwPBNQDQSM+ePeOYY46Jo446Ku6///745S9/2eRxv/zlL2PFihVx1FFHWQUCAMAmq3fv3rHrrru269xZs2bFiy++2DDeZptt4rjjjotMJtPuflauXJk39lkbAIDNgVuFAwDNeuaZZ+KWW25p9vWKior42c9+FmeccUb861//6rS+AACgq/jVr34VuVyuYXzyySd3KLSOiKiurs4b9+3bt0PzAQDApkBwDQA0Ul1dHT/96U/j4osvjhUrVjTsLywsjGOPPTZKSkryjp89e3acd955cfXVVzdaHQIAAN3Vww8/HFOnTm0YDx8+PD75yU92eN6ampq8cWlpaYfnBACArk5wDQDkefXVV+Pss8+Ohx9+OG//4MGD45prrolzzz03br/99jj66KMbnTtx4sQ444wz4oUXXuisdgEAIBX//ve/45prrsnbd9ZZZ0VhYWGH566oqMgbDxgwoMNzAgBAVye4BgAiImLu3LlxySWXxDnnnBMzZ87Me2233XaLX/3qV7HTTjtFRES/fv3iW9/6Vlx11VUxfPjwvGPLy8vjO9/5TlxxxRVRVVXVWe0DAECnefHFF+PCCy+Murq6hn3HHXdc7Lfffhtk/gULFuSN173jEQAAdEeCawDYzC1fvjyuv/76OP3002Py5Ml5r2UymTjppJPiiiuuiIEDBzY6d7fddoubbropTjjhhEbP8Xv00UfjS1/6Uvz1r3/dqP0DAEBnSZIk7r///jj//POjsrKyYf/YsWPj7LPP3mB1Fi5c2PDnQYMGRVFR0QabGwAAuqps2g0AAOkoLy+PSZMmxcSJE2P58uWNXt9xxx3jG9/4Rmy33XYtzlNcXBznnHNOHHjggXH55ZfHu+++2/DaokWL4rvf/W6ceOKJceaZZ0Y266MHAACbptmzZ8cNN9yQ90zriPrbeF900UUbNFyeO3duw5/Hjh27weYFAICuzLfHALAZSZIkXnzxxbj//vvjL3/5S+RyuUbHDBw4MM4888w4+OCDo6Cg9Tdn2WGHHeLGG2+MO+64I37729/m3Tbx3nvvjenTp8dFF13U5MptAADoqpYtWxa33XZbTJw4Me8zbkTENttsE5deemmUlZVtsHrz58+PGTNmNIwF1wAAbC4E1wCwGVi8eHFMmTIlJk2a1Oj51R/o0aNHHH/88XHaaadFnz592lWnqKgozjjjjNh///3jBz/4QcyZM6fhtRdffDG++tWvxsUXXxw77LBDu+YHAIDOkCRJvPzyy/Hwww/HlClTYvXq1Y2O2W+//eKCCy6IXr16bbC6dXV18Ytf/CJv3y677LLB5gcAgK4sM3ny5CTtJgCADW/+/PnxzDPPxJ///Od45ZVXIkma/r/8fv36xTHHHBPHH398DBo0aIPVr6qqissuu6zRrRSz2Wx84xvfiKOPPnqD1QIAgI7K5XLx9ttvx/PPPx9PPvlkzJo1q8njhg8fHmeffXZ8/OMfj0wm0+r558yZE5WVldGrV6+8LZvNxrJly+L/t3fn0THf+x/HX5kkskwSSSwJjl2trVDcoqWlRATR1tLabrW3+721HO21dHEt1Wu79Gq1josuBJfatZZWyqX2vVQkhBJiSSKRjSzz+8MxP9+sMyMxqs/HOf3j85nv5/v+zHzm9OA1n883OjpakZGROnr0qHVM5cqVFRkZKVdX17t+fwAAAMD9juAaAIAHzNatW7Vo0SLFxMQUe12VKlXUu3dvde3atVR3idwpLy9PkZGRWrBgQYFjycPDwzV06NBSfRYgAAAAYK9du3YpKipKe/fuVXJycpHXeXt7q1+/furbt69Df4Zdt26dpk+fXqDfZDIV+ggfSRoxYoS6d+9udy0AAADg94ijwgEAeMAEBwcrNja20NdMJpOaNWumHj16qF27dmW+c8NkMmngwIFq1KiRxo8fr9TUVOtrP/74o8LDw9WkSZMynQMAAABQnJMnT2rTpk1Fvl6rVi317NlToaGh8vb2drjOo48+Wmh/UaF1//79Ca0BAADwh8KOawAAHkDTpk3T+vXrre0mTZqoY8eOeuqppxQYGOiUOV26dEn/+Mc/dOLECXl6eurjjz9Ws2bNnDIXAAAA4LarV6/q+eefNwTIZrNZrVu3Vvfu3RUSEmLXkeDF6devnxISEop83c3NTY8++qj69evHn5UBAADwh0NwDQDAAyg5OVljx45VmzZt1KFDBwUHBzt7SpKkmzdvas6cOXrqqaf0yCOPOHs6AAAAgCRp7NixiomJUZs2bdS2bVuFhITIza30DyqMj49XSkqKbt68qZycHGVnZys7O1uenp7y9/dXtWrVZDabS70uAAAA8HtAcA0AAAAAAIA/tKysLHl4eJTazmoAAAAA9uMZ1wAAAAAAAPhD8/T0dPYUAAAAgD88k7MnAAAAAAAAAAAAAAD4YyO4BgAAAAAAAAAAAAA4FcE1AAAAAAAAAAAAAMCpCK4BAAAAAAAAAAAAAE5FcA0AAAAAAAAAAAAAcCqCawAAAAAAAAAAAACAUxFcAwAAAAAAAAAAAACciuAaAAAAAAAAAAAAAOBUBNcAAAAAAAAAAAAAAKciuAYAAAAAAAAAAAAAOBXBNQAAAAAAAAAAAADAqQiuAQAAAAAAAAAAAABORXANAAAAAAAAAAAAAHAqgmsAAAAAAAAAAAAAgFMRXAMAAAAAAAAAAAAAnIrgGgAAAAAAAAAAAADgVATXAAAAAAAAAAAAAACnIrgGAAAAAAAAAAAAADgVwTUAAAAAAAAAAAAAwKkIrgEAAAAAAAAAAAAATkVwDQAAAAAAAAAAAABwKoJrAAAAAAAAAAAAAIBTEVwDAAAAAAAAAAAAAJyK4BoAAAAAAAAAAAAA4FRuzp4AAAAAABRm7Nix2rZtm7U9cuRIhYWFOXFGcNS9XssOHToY2lFRUWVWy1755/bdd9/Jy8vLSbMpG4cOHdLw4cOt7ZCQEM2cOdN5EwIAAAAA/C4QXAMAAACF2LBhgyZPnuzQWJPJJB8fH5nNZlWqVEn169dXw4YN1bZt2wcuoLpfpKSkaNasWdq9e7e8vLz0zDPP6IUXXpDJxCFTAAAAAAAAvwcE1wAAAEApy8vLU2pqqlJTU3Xx4kUdOXJEkuTl5aXOnTvrpZdekr+/v3Mn+YCZOHGi9u3bJ0lKS0vT3Llz5e7urj59+jh5ZkVLS0vT2bNn1aRJE2dPBQAAAAAAwOnYfgAAAADcI5mZmVqzZo0GDx6snTt3Ons6D4yMjAxraH2nLVu2OGE2JYuNjdX06dPVp08frV271tnTAQAAAAAAuC+w4xoAAACwQb169dSjR48Sr7NYLEpPT9elS5d0+fJlXbx4UWfPnjVck5KSorFjx+rjjz9WixYtymrKfxguLi6F9t+Px4RHRUVp/Pjxzp4GAAAAAADAfYfgGgAAALBB1apVFRER4dDYc+fOadGiRdq4caO1Lzs7W+PHj9eiRYvk4+NTWtP8Q/Ly8lKbNm0K7GLv3Lmzk2ZUtBs3bjh7CgAAAAAAAPel+28LAgAAAPCAqV69ukaNGqUxY8YYdgenpqZq8eLFTpzZg2PkyJEKDQ2Vr6+vgoKC9Prrrzv8QwMAAAAAAADce+y4BgAAAO6Rzp0768yZM4qMjLT2bdq0Sa+88kqRx13DNuXLl9fo0aOdPQ0AAAAAAAA4iB3XAAAAwD3Ur18/ubq6WttXr17VmTNnnDchAAAAAAAA4D5AcA0AAADcQz4+PmrcuLGhLy4uzkmzAQAAAAAAAO4PHBUOAAAA3GNVq1bV0aNHre2UlBS772GxWHTu3DmdOXNG8fHx8vPzU1BQkCpXrqxq1aoZdnUXJTc3V9HR0UpISFBSUpJu3rwpf39/BQQEqHbt2goODrZ7XsVJT0/X8ePHlZiYqKSkJPn4+KhKlSqqXbu2KlasWKq17sbtz/a3335TUlKSUlNT5enpKR8fH9WqVUt16tRRuXLlnD1NA9ay9OXl5eny5ctKSkrS1atXlZiYqPT0dPn6+srX11f16tVT9erVy+SY/xs3bigmJkYXL17U5cuX5evrqwoVKqhu3bqlvpaZmZnWtUxMTJSrq6sCAgLk7++vhg0bytfXt1TrAQAAAABQFIJrAAAA4B7z9vY2tNPT0wtcM2zYMB0+fNjanjFjhpo1ayZJio6O1uzZs3XkyJFC77948eJiw63z589r8eLF2rFjR7GheZ06ddS+fXv16tVLPj4+xb2lYh06dEjLli3T3r17lZ2dXeB1k8mk9u3b6/nnn1fDhg0dqhEXF6eXX37Z2g4KCtKSJUvsukdqaqqWLl2qbdu26fz580Ve5+7urrZt26pHjx5q0aJFkdd9+eWX+uqrr4qtuXHjRm3cuLHQ16Kiokqc84O4ls7222+/6dtvv9WOHTuUmJhY7LX+/v7q2LGjXnjhBVWqVOmua6ekpGjlypVatWpVkevZoEEDhYWFqUePHjb9QKUo+/fv14oVK7Rv3z7dvHmz0GtMJpNCQkL09NNPKyws7K7qAQAAAABQEoJrAAAA4B5LS0sztO0JEpcvX67PPvvMobo5OTn6+uuvtWTJkkJDx/xOnz6t06dPa/Xq1XrzzTfVuXNnu+qlp6fr888/1/r164u9Li8vTz/99JO2bdumv/3tb3r22WftqlMavv/+e82ZM8em3e/Z2dnaunWrtm7dqscff1zDhw9XhQoV7sEs/x9rWfpu3rypL774QqtWrZLFYrFpzLVr17RixQqtXbtWQ4cOVbdu3RyuHxcXp1GjRuny5cvFXhcdHa3o6Ght2LBBI0eOVO3ate2qk5SUpFmzZumnn34q8dq8vDwdPHhQBw8e1OrVqzV8+HA1atTIrnoAAAAAANiKZ1wDAAAA91j+3bzly5e3adz69esdDq1v3rypsWPH6ptvvik06HR3d5evr2+hxx4nJydr0qRJ+uabb2yul5mZqVGjRhUbdPr4+Mhk+v+/kuTl5enf//635s6da3Odu2WxWDR//nxNmTKl0NDaxcVFZrO5yPE7duzQkCFDdOnSpbKcpgFrWfoyMzM1dOhQrVy5ssjQ2tPTs8gj4rOzszVt2jQtXLjQofrHjx/X22+/XSC0NpvNcnMr/Pfm0dHRGjp0qGJjY22uc+nSJQ0ZMqTI0NrLy6vAiRC3xcTEaOjQodq3b5/N9QAAAAAAsAc7rgEAAIB7KCUlRdHR0Ya+hx56qMRxV65c0aefflpg3O1n0KalpenKlSv69ddfC4zNzc3V2LFjtWvXLkN/SEiIIiIi1Lx5c/n7+8vFxUV5eXk6f/68Dh48qFWrVunMmTPW6+fPny8PDw/17du32Lnm5ubqww8/1C+//GLor169unr06KE2bdooODhYbm5uys3N1blz5xQVFaXly5crIyNDkZGReuSRR0r8TErDwoULC4S4fn5+6t27t9q1a6fq1avL1dVVWVlZunTpko4dO6YlS5bo3Llz1usvXLigMWPGaM6cOYaQsU2bNgoMDDTc+9ixY9q0aZO13bhxY3Xp0sXm+bKWpc9iseijjz7SiRMnDP1VqlRRWFiY2rdvr6CgIHl5eclisSg5OVkJCQnau3evVq1apWvXrlnHzJ8/X02bNlXTpk1trp+RkaEJEyZYHxnQsWNHhYeH6+GHH5aHh4csFouuXbum/fv3a8OGDdq/f7917PXr1/Xuu+/qs88+U9WqVYutk5SUpGHDhikhIcHaZzKZ1KlTJ3Xq1EkPP/ywvLy8JN0K4mNjY7Vr1y6tWbPG+h6zs7P13nvvadq0aff9ugIAAAAAfn8IrgEAAIB7aNGiRcrLy7O2g4KCVK1atRLHzZs3T1lZWZJuhYYjR45UkyZNClxnsVgK7Bj973//awg6PTw8NGLEiEKPizaZTKpRo4Zq1KihHj16aPHixZo3b571nnPnzlXr1q1Vo0aNIue6cuXKArsy+/fvrxdffLHAjlVXV1fVqlVLL730krp166YPP/xQ0dHRmjp1arE1SsMvv/yiL7/80tDXunVrjRo1qsAueE9PT9WsWVM1a9ZUly5dtH79en3yySfWtTx9+rSWLFmigQMHWsc0aNBADRo0MNynXLlyhuC6evXqioiIsHnOrGXp2717t3bs2GHoCw0N1YgRIwq8RxcXFwUGBiowMFCNGzdWz549NWbMGOsPRiwWi+bNm6dPPvnE5vqzZ89WQkKCPD09NX78eLVq1apAzYCAAHXq1ElPP/20oqKiNHnyZOtzqa9du6YZM2ZoypQphe6yvz2vyZMnG0LroKAgTZgwodAfzri7u6tRo0Zq1KiR+vbtq6lTp2rr1q2Sbu34nzx5subPn1/kDnQAAAAAABzBUeEAAADAPfLjjz9q+fLlhr7w8PAiw6Y73T6Kuk6dOpo9e3ahobV0K+S688jmuLg4zZs3z9o2mUwaN26cTc84NplMGjBggF577TVrX05OTrGh3JUrVwz1JGngwIF69dVXSwy5KleurClTpqhWrVpKSkrSoUOHSpyjo3JzczVlyhTDjwhatmypcePGlXh0u6urqyIiIvT6668b+lesWKGcnJwyma/EWpaVJUuWGNqhoaEaNWqUTaGsv7+/JkyYIE9PT2vfkSNHDLvbS7JlyxaZTCZNnDixQGidn4uLizp27KgJEybI1dXV2r9v3z5FRUUVOW7dunXas2ePtR0QEKBPPvnEptMezGazPvjgA7Vt29baFx8fr6VLl5Y4FgAAAAAAexBcAwAAAGXs3Llzmjx5siZOnGjYDR0QEKA+ffrYfB+z2axJkybJx8fH5jGLFy9Wbm6utd2rVy899thjNo+XpOeff14NGza0tg8cOFBkMLd27VrrznDp1q7jwYMH21zLz89PI0eOtCnMvxvbt283HPdtNpttDitv6927t4KDg63t5OTkAkd4lybWsvSlpqbq6NGj1rbJZNKLL75o15wrVKigdu3aGfrsDer79eunFi1a2Hz9n/70pwL/71i2bFmh1+bm5ioyMtLQ98477ygoKMjmeq6urhoxYoQhoF+9erXh+wgAAAAAwN3iqHAAAADABhcuXNCaNWtsuvbmzZtKT0/XpUuXFB0drdOnTxe4xsPDQxMmTLA+U9YWr732ml1h09WrV7VlyxZr29XVVQMGDLB5/G0uLi4aMGCAPvjgA2vfhg0b9MYbbxiuy83N1XfffWfoe+WVVww7Q23RsGFDdejQwTD30rZ69WpDu0+fPqpQoYJd9zCZTHryyScNO08PHz6sJ554olTmeCfWsmz4+fnp+++/V2pqqlJTU5WTk1Pis6ILU7t2bUM7NjbWrjkMGjTI7pqDBg3SqlWrrD8uOHHihE6ePKn69esbrtu+fbvhiPCHHnpIbdq0sbteYGCgunXrpm+//VaSlJiYqD179jh0LwAAAAAACkNwDQAAANggNjZWM2bMKJV7VaxYUWPGjCnyuO/ClC9fXmFhYXbV2bNnj2FHZMOGDUs8BrsorVq1kqenpzUk+/nnnwuEnadOnVJiYqK1XbFiRTVv3tyhet27dy+zsDMzM1OHDx+2tl1cXOz+bG/r1KmTTpw4YW3nf754aWEty065cuVUsWJFVaxY0eF71KxZ09BOSUmxeWxoaKg8PDzsrunt7a327dsbnpm+Z8+eAsF1/ud3t2rVyuFd8O3bt7cG15K0c+dOgmsAAAAAQKkhuAYAAADuER8fH3Xp0kWDBw+267hvSWrTpo1dx1hLt561e6eQkBC7xt/Jw8NDdevW1bFjxyRJ58+fV0ZGhry9va3XHD9+vEA9e3fo3vbII4/Iw8NDN27ccHjORTlx4oTh2dY1a9a0ayf7nerVq6eZM2eW0syKxlre3+787CQpLS3N5rEtW7Z0uG7z5s0NwfWvv/5a4Jr8351mzZo5XK9BgwZycXGx/kDj5MmTDt8LAAAAAID8CK4BAACAUubq6iqz2Syz2aygoCDVr19fjRs3VuvWrR3aWSlJjRs3tntMTEyMoZ2SkmLzceeFuXPHr8ViUUxMjCFAjYuLM1xft25dh2u5ubmpXr161nC1NOX/XPLvUL0fsZb3Vnx8vH7++WedOnVKcXFxSk5OVkZGhjIyMmzaVW/PzvsGDRo4PM969eoZ2qdOnTK0U1NTdenSJUPf6dOndfHiRYdrent7Kz093XqvnJwcubnxTwsAAAAAgLvH3y4BAAAAG7Rv317jxo1zWv3AwEC7x+Q/rnj9+vVav359aU1JV65cMbRTU1MN7YCAgLu6/90c3Vyc/POsXLlymdQpTaxl2bNYLNq5c6e+/vprRUdH35OaJpPJ4SPfJRV4Lvv169cN7cKOLP/iiy8crpdfdna2rl279rtYXwAAAADA/Y/gGgAAAPgdyH8UsS3yh4+l7fauy9vyH4/syJzvZO9x6rbKH+6ZzeYyqVOaWMuydf36dU2cOFF79uy5p3V9fHwcft60VPC7m5GRYdgBnf+7XhbS09MJrgEAAAAApYLgGgAAAPgdcCTcuptAzBb5w06TyWRo23NccmHKav53HpMtFZz3/Yi1LDtJSUkaNmyYzp07V+C18uXLq0GDBgoMDFRgYKACAgIKfdb8uXPntHz58nsxXYPC1uVef9b5vzsAAAAAADiK4BoAAAB4QPn6+ioxMdHa/uc//6nHHnuszOrl31WbmZl5V/crq0DM19fX0M7IyCiTOqWJtSwbFotFU6dOLRBat2jRQt27d1fbtm0LDarzO3TokEPB9e1nZjsaNuf/XM1ms1xdXa3t/N91SdqwYYM8PDwcqgcAAAAAQFkiuAYAAAAeUPnDzrI+Njh/SJb/ucn2unPupSn/PJOSksqkTmliLcvGgQMHtGvXLkPfm2++qT59+tgVJuffxW+rnJwcpaamOvyc6/zr4ufnV2xbuvXdIbgGAAAAANyPCK4BAACAB1StWrV05swZazs+Pr5M69WpU8fQPnXqlMP3slgsdzW+ODVq1DC0Y2NjHb5XZmamli5dam17eHioX79+Dt+vKKxl2di8ebOh3a5dO/Xt29fu+1y+fNnhOcTGxqpFixYOj71T3bp1DW0/Pz8FBAQoOTnZ2hcfH88zqQEAAAAA9yWCawAAAOAB1bRpU/3000/W9i+//OLwvTIzMxUZGWltu7m56c9//rNhV2rjxo0NYw4dOqScnBy5udn/146TJ0+W2fHSTZo0MbRjY2OVlpZW4HhsWxw4cEBfffWVtd2iRYsyCa5Zy7Jx/PhxQ7tnz54O3Wf//v0Oz2Hfvn0OB9cHDhwwtBs1amRou7i4qGnTptq6dau17+jRowoJCXGoXlxcnLZs2WJtV6tWTWFhYQ7dCwAAAACA/EzOngAAAACAsvHoo48a2gcPHnT4yOe1a9dq4cKF1v+OHj1a4Cjl2rVrq0KFCtZ2SkqK9u7d61C9jRs3OjTOFgEBAapVq5a1nZ2dbQjj7LFnzx5Du1mzZncxs6KxlmUj/zHxd34vbBUfH69t27Y5PIcffvhBOTk5do9LT0/Xzz//bOhr2bJlgevyf3d++OEHWSwWu+tJ0ty5cw3fnatXrzp0HwAAAAAACkNwDQAAADygatasaQitcnNztWjRIrvvk5WVZTgOW5KefvrpAte5urqqa9euhr4FCxYoLy/PrnoJCQlat26d3fO0R0REhKEdGRmpzMxMu+6RlZWlHTt2GPo6duxo1z1sDRBZy7KRnZ1taNu7MzwrK0sfffRRgfvY4+rVq1qxYoXd45YuXaqsrCxru379+qpfv36B6zp16iSz2Wxtnz171rAD21YxMTHauXOnte3i4qIOHTrYfR8AAAAAAIpCcA0AAAA8wF544QVDe/Xq1YbwqSS5ubmaNGmSYWdqpUqV1KlTp0Kv7969u8qVK2dtx8TEaMGCBTbXu3Hjhj7++OO7CgJtERoaajga/NKlS5o1a5ZdO1HnzZunxMREa7tVq1aqWrVqsWPyH0eekZFhcz3WsvTlf9bzwYMHbR57/fp1jRw5Ur/++utdz2PBggWKjo62+fojR45oyZIlhr5evXoVeq23t3eBI9BnzJihhIQEm+tdu3ZN48aNM/Q9+eSTqlatms33AAAAAACgJATXAAAAwAOsZcuW6ty5s6Fv7NixNh0XnJKSoqlTp+p///ufoX/IkCFyd3cvdExQUJAGDx5s6Fu4cKH+85//lBhgJiUl6f3339eRI0ck3TrSu6yYzWa9/fbbhr7vv/9eM2fO1I0bN4odm5ubqwULFmj58uXWPpPJpNdee63EusHBwYb2sWPHDLtmi8Nalr78R2vPmTNHp0+fLnaMxWLR7t279Ze//MX6/vIfx20rX19fmUwmZWVladSoUTp8+HCJY3bt2qUxY8YY1qBp06ZF/gBBkgYOHKgaNWpY26mpqRoyZIhiYmJKrHf27FmNHj1a8fHx1j6z2aw33nijxLEAAAAAANjDzdkTAAAAAFB2XFxcNHz4cJ08eVJnz56VdOt45I8++kjr1q1TRESEWrRoIT8/P0m3Aq34+Hjt2bNHy5YtK7Aj+LnnntMTTzxRbM2+fftq9+7dhhBu0aJF2r59uyIiItS2bVtVqlRJrq6uysnJ0fnz57V161atWLFCqampkm49a7hLly6aM2dOaX4cBp07d9auXbsUFRVl7VuzZo327dunXr16GeaZlpamCxcu6OTJk1q1apVOnTpluFf//v1Vr169EmtWqVJF5cqV082bNyVJycnJeuutt/TUU0+pfPny1mdNN2/eXNWrVzeMZS1LX8+ePbVu3TrrEeiZmZl666231KNHD3Xt2lXVqlWTh4eH8vLydPHiRR04cEDfffedTpw4Yb1HnTp1NG7cOPXo0cPu+lWqVNFjjz2mb775RteuXdOwYcPUuXNnhYaGqkmTJvLy8pLFYlFaWpoOHTqkTZs2afv27YZ7+Pn5acSIETKZiv5dupeXl/7xj3/orbfesv5Q4sqVK3rjjTfUuXNnderUyVovLy9PV69e1fnz57Vp0yZt3rzZcES8i4uL3n33XQUFBdn9fgEAAAAAKA7BNQAAAPCA8/Ly0r/+9S+NHj1aJ0+etPYfPnzYGki6u7vLzc2t2Oc8d+/eXX/9619LrOfq6qpJkybp73//u44dO2btP3v2rGbNmqVZs2bJZDLJ29tb6enpBXYLe3t767333tPRo0ftfat2cXFx0ejRo5WXl2d45u+FCxcM8/Ty8ir22cdhYWF66aWXbKppNpsVERFh2K0dFxenuLg4w3VjxowpEFxLrGVpq1OnjgYNGqSvvvrK2nfjxg0tX77cukY+Pj7KyMgo9PneISEh+uCDDwocAW+PQYMG6cCBA9bPd/Pmzdq8ebOkW59fdnZ2kTvc/fz8NH36dMNu6qLUrl1b06dP15gxY5SSkiJJysvL08aNG7Vx40ZJt75fOTk5RdZzc3PTiBEj9OSTT9r9PgEAAAAAKAlHhQMAAAB/AIGBgZo5c6bCw8ML3ZmZnZ1dZNBpNps1ZsyYEnd13snb21tTpkxRRESEdRfxnfLy8pSWllYg6Kxevbo+//xzm3YvlwZ3d3d98MEHGjRoUKFHZufl5RUZWru7u+v111/XO++8Y/PnIkkDBgywKWgsCmtZul588UX179+/yNfT0tIKhNbu7u56+eWXNX36dFWoUOGu6ru7u2vatGnq2LFjgdcyMjKKDJGbNm2qWbNm2fX5Nm7cWJ999plCQkIKfT0zM7PIetWrV9enn36qsLAwm+sBAAAAAGAPdlwDAAAAfxBeXl5699131bt3by1evFi7d++2HudcmGrVqik8PFwREREO7Sj19vbW8OHDFRYWpmXLlmnnzp1FPs85ODhY3bp10zPPPHNXu1cd4erqqpdfflnh4eFavHixduzYocTExCKvL1++vEJDQ/Xss8+qSpUqdtfz9/fX7NmztXr1ah05ckTnzp1TQkJCoTt6i8Jalh4XFxe9+uqratWqlRYuXKj9+/cXeW2VKlUUERGhrl27qnz58qU2B09PT73//vt6/PHHrd+LoubatGlThYeHq1OnTnb9YOK2atWqacaMGdq1a5dWrlypQ4cOFRlWm0wmNWjQQM8995w6dOggV1dXu+sBAAAAAGArl6ioKEvJlwEAAAB40OTm5io6OloXL15UcnKysrKy5OvrK39/fzVq1EiVK1cu1Xo3btzQ8ePHdfXqVSUmJspkMqly5coKDg5W/fr1HQrhykJeXp5iY2N14cIFJScnKy0tTd7e3vLz89NDDz2kGjVq3DdzvY21LD1JSUk6duyYLl++rMzMTAUEBCg4OFjBwcGqUqXKPXlv8fHx+u2333TlyhWlpqbK399fFSpUUIMGDRQYGFiqtTIzM61ree3aNeXm5qp8+fKqUKGCHn744d/Fjw8AAAAAAA8GgmsAAAAAAAAAAAAAgFP9fn8GDwAAAAAAAAAAAAB4IBBcAwAAAAAAAAAAAACciuAaAAAAAAAAAAAAAOBUBNcAAAAAAAAAAAAAAKciuAYAAAAAAAAAAAAAOBXBNQAAAAAAAAAAAADAqQiuAQAAAAAAAAAAAABORXANAAAAAAAAAAAAAHAqgmsAAAAAAAAAAAAAgFMRXAMAAAAAAAAAAAAAnIrgGgAAAAAAAAAAAADgVATXAAAAAAAAAAAAAACnIrgGAAAAAAAAAAAAADgVwTUAAAAAAAAAAAAAwKkIrgEAAAAAAAAAAAAATkVwDQAAAAAAAAAAAABwKoJrAAAAAAAAAAAAAIBTEVwDAAAAAAAAAAAAAJyK4BoAAAAAAAAAAAAA4FQE1wAAAAAAAAAAAAAApyK4BgAAAAAAAAAAAAA4FcE1AAAAAAAAAAAAAMCpCK4BAAAAAAAAAAAAAE5FcA0AAAAAAAAAAAAAcCqCawAAAAAAAAAAAACAUxFcAwAAAAAAAAAAAACciuAaAAAAAAAAAAAAAOBUBNcAAAAAAAAAAAAAAKciuAYAAAAAAAAAAAAAOBXBNQAAAAAAAAAAAADAqQiuAQAAAAAAAAAAAABORXANAAAAAAAAAAAAAHAqgmsAAAAAAAAAAAAAgFMRXAMAAAAAAAAAAAAAnIrgGgAAAAAAAAAAAADgVATXAAAAAAAAAAAAAACnIrgGAAAAAAAAAAAAADjV/wEEFadJJTTwaQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ConfusionMatrixDisplay.from_estimator(clf, X_test_bow, y_test, normalize=None)\n", "plt.title(\"Confusion Matrix (Frequencies)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- LIME Explanations of Names:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explanations[0].show_in_notebook(text=True)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explanations[1].show_in_notebook(text=True)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explanations[2].show_in_notebook(text=True)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": true, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explanations[3].show_in_notebook(text=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Feature Coefficients Analysis of Logistic Regression Model (Note: The number of coefficients should be the same as the number of features used in training.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../exercise-ans/midterm/question2-2-output-featimportance.jpeg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question Three (50%)\n", "\n", "### Question 3-1\n", "\n", "This exercise requires the dataset, `demo_data/midterm/apple5000.csv`, which includes 5000 news articles from Apple Daily. Please use `spacy` and its pre-trained language model to extract word pairs of the dependency relation of `amod`. For example, in the following sequence:\n", "\n", "```\n", "\"陸軍542旅下士洪仲丘關禁閉被操死，該旅副旅長何江忠昨遭軍高檢向最高軍事法院聲押獲准。何江忠的前同事說：「他（何江忠）只能用『陰險』兩字形容，得罪他都沒好下場。」還說他常用官威逼部下，「仗勢欺人、人神共憤，大家都不喜歡他。」被他帶過的阿兵哥說，懲處到了何手上都會加重，簡直是「大魔頭」。\"\n", "```\n", "\n", "`spacy` identifies three token pairs showing a `amod` dependency relation, namely:\n", "\n", "```\n", "amod dep: 高 head: 軍事\n", "amod dep: 前 head: 同事\n", "amod dep: 大 head: 魔頭\n", "```\n", "\n", "Please note that the head and the dependent are NOT necessarily adjacent to each other. For example, in a sentence like:\n", "\n", "```\n", "\"這是一個漂亮且美麗的作品，明亮的窗戶，房子很大。\"\n", "```\n", "\n", "`spacy` identifies two token pairs showing a `amod` dependency relation, namely:\n", "\n", "```\n", "amod dep: 漂亮 head: 作品\n", "amod dep: 明亮 head: 窗戶\n", "```\n", "\n", "With the `apple5000.csv` corpus, your job is to extract all word-pairs that show a `amod` dependency relation using `spacy` dependency parser. (These two word tokens may or may not be adjacent to each other.)\n", "\n", "Please follow the following instructions for the analysis.\n", "\n", "1. Preprocess each news article by removing symbols, punctuations, digits, and English alphabets (see the sample data frame below).\n", "2. Parse all the articles using `spacy` and extract word pairs showing the `amod` dependency relation.\n", "3. In `spacy`, you can choose to use either the more efficient language model `zh_core_web_sm` or the more accurate model `zh_core_web_trf`, depending on your hardware available. The sample results below are based on `zh_core_web_trf`.\n", "4. In your final report, please include only word pairs where the nouns are of AT LEAST two syllables/characters. Your final report is a frequency list of these word pairs (see the sample data frame below for the top 50 frequent pairs).\n", "5. A sample output csv is provided in `demo_data/midterm/question3-1-output-modnounfreq.csv`.\n", "6. Please note that your results may vary due to the selection of the pretrained language models in spacy. This is OK.\n", "\n", "---------\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Examples of Raw Texts and Preprocessed Texts" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "scrolled": true, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
doc_idtexttext_pre
01【鄧玉瑩╱台中報導】台中市警二分局育才派出所爆發疑似集體索賄案，台中地檢署檢察官指揮調查局中部機動組查出，轄區警員柯文山利用職權之便，向轄區飯店、色情業者索賄，昨天深夜向地院聲請羈押獲准。派出所隨即表示，將對柯某撤職查辦、從嚴處分。\\n\\n\\n台中地檢署檢察官吳祚延指揮調查局中機組幹員，搜索台中市警二分局育才派出所，帶回警員柯文山，進行偵訊。檢調同時到台中市「合利太」飯店大樓展開搜索，除帶...鄧玉瑩 台中報導 台中市警二分局育才派出所爆發疑似集體索賄案 台中地檢署檢察官指揮調查局中部機動組查出 轄區警員柯文山利用職權之便 向轄區飯店 色情業者索賄 昨天深夜向地院聲請羈押獲准 派出所隨即表示 將對柯某撤職查辦 從嚴處分\\n台中地檢署檢察官吳祚延指揮調查局中機組幹員 搜索台中市警二分局育才派出所 帶回警員柯文山 進行偵訊 檢調同時到台中市 合利太 飯店大樓展開搜索 除帶回帳冊 飯店...
12陸軍542旅下士洪仲丘關禁閉被操死，該旅副旅長何江忠昨遭軍高檢向最高軍事法院聲押獲准。何江忠的前同事說：「他（何江忠）只能用『陰險』兩字形容，得罪他都沒好下場。」還說他常用官威逼部下，「仗勢欺人、人神共憤，大家都不喜歡他。」被他帶過的阿兵哥說，懲處到了何手上都會加重，簡直是「大魔頭」。\\n曾與何江忠共事1年的軍官昨向《蘋果》爆料，前年何江忠還在馬祖東引擔任副指揮官時，遇到年度本職學能鑑測，...陸軍 旅下士洪仲丘關禁閉被操死 該旅副旅長何江忠昨遭軍高檢向最高軍事法院聲押獲准 何江忠的前同事說 他 何江忠 只能用 陰險 兩字形容 得罪他都沒好下場 還說他常用官威逼部下 仗勢欺人 人神共憤 大家都不喜歡他 被他帶過的阿兵哥說 懲處到了何手上都會加重 簡直是 大魔頭\\n曾與何江忠共事 年的軍官昨向 蘋果 爆料 前年何江忠還在馬祖東引擔任副指揮官時 遇到年度本職學能鑑測 他卻要步兵學校裁...
23終於拿到冠軍，感覺真是棒，尤其是從蔣宸豑的手上搶過來，算是報了一箭之仇。其實我今天的推桿感覺真的很不好，有好幾次3呎內的短推都錯過，不然也不會打得這麼累。」今年第3次參賽，前兩次分別在第1輪及8強賽輸給蔣宸豑。\\n\\n\\n年齡：17歲身高：181公分體重：80公斤就讀學校：啟英高中二年級球齡：6年\\n\\n\\n \\n終於拿到冠軍 感覺真是棒 尤其是從蔣宸豑的手上搶過來 算是報了一箭之仇 其實我今天的推桿感覺真的很不好 有好幾次 呎內的短推都錯過 不然也不會打得這麼累 今年第 次參賽 前兩次分別在第 輪及 強賽輸給蔣宸豑\\n年齡 歲身高 公分體重 公斤就讀學校 啟英高中二年級球齡 年\\n\\n
34【陳毓婷╱台北報導】過去業績不甚理想的中國人壽（2823），今年初找來南山人壽的專業經理人王銘陽擔任總經理後，不但保費收入大幅成長，而且獲利也出現轉機，今年上年已經轉虧為盈，小賺667萬元，擺脫今年第一季虧損近1.6億元的陰霾。\\n\\n\\n中壽今年上半年的保費收入達155.7億元，較去年同期的96.6億元成長62%，與國內壽險業今年上半年保費收入比較，中壽首度擠進前五名。通常壽險公司在衝刺...陳毓婷 台北報導 過去業績不甚理想的中國人壽 今年初找來南山人壽的專業經理人王銘陽擔任總經理後 不但保費收入大幅成長 而且獲利也出現轉機 今年上年已經轉虧為盈 小賺 萬元 擺脫今年第一季虧損近 億元的陰霾\\n中壽今年上半年的保費收入達 億元 較去年同期的 億元成長 與國內壽險業今年上半年保費收入比較 中壽首度擠進前五名 通常壽險公司在衝刺新契約保單的情況下 成本支出會墊高 中壽今年第 季就...
45台灣國際語文教育協會假借中央機關指導名義，招攬學員參加該機構舉辦的觀光研習營，活動宣稱「參加滿三梯次可退費」，實際上卻任意改期、提高收費。學員幾經爭執、《蘋果》追查發現真相後，業者同意學員的退費要求。攝影．報導╱褚明達\\n台中市徐先生說，去年10月下旬，他看到台灣國際語文教育協會（以下簡稱台協）招攬「台灣觀光親善大使甄選研習營」學員的網路廣告，因內容豐富，他立即報名參加。台協在廣告上註明將...台灣國際語文教育協會假借中央機關指導名義 招攬學員參加該機構舉辦的觀光研習營 活動宣稱 參加滿三梯次可退費 實際上卻任意改期 提高收費 學員幾經爭執 蘋果 追查發現真相後 業者同意學員的退費要求 攝影 報導 褚明達\\n台中市徐先生說 去年 月下旬 他看到台灣國際語文教育協會 以下簡稱台協 招攬 台灣觀光親善大使甄選研習營 學員的網路廣告 因內容豐富 他立即報名參加 台協在廣告上註明將請 外...
\n", "
" ], "text/plain": [ " doc_id \\\n", "0 1 \n", "1 2 \n", "2 3 \n", "3 4 \n", "4 5 \n", "\n", " text \\\n", "0 【鄧玉瑩╱台中報導】台中市警二分局育才派出所爆發疑似集體索賄案，台中地檢署檢察官指揮調查局中部機動組查出，轄區警員柯文山利用職權之便，向轄區飯店、色情業者索賄，昨天深夜向地院聲請羈押獲准。派出所隨即表示，將對柯某撤職查辦、從嚴處分。\\n\\n\\n台中地檢署檢察官吳祚延指揮調查局中機組幹員，搜索台中市警二分局育才派出所，帶回警員柯文山，進行偵訊。檢調同時到台中市「合利太」飯店大樓展開搜索，除帶... \n", "1 陸軍542旅下士洪仲丘關禁閉被操死，該旅副旅長何江忠昨遭軍高檢向最高軍事法院聲押獲准。何江忠的前同事說：「他（何江忠）只能用『陰險』兩字形容，得罪他都沒好下場。」還說他常用官威逼部下，「仗勢欺人、人神共憤，大家都不喜歡他。」被他帶過的阿兵哥說，懲處到了何手上都會加重，簡直是「大魔頭」。\\n曾與何江忠共事1年的軍官昨向《蘋果》爆料，前年何江忠還在馬祖東引擔任副指揮官時，遇到年度本職學能鑑測，... \n", "2 終於拿到冠軍，感覺真是棒，尤其是從蔣宸豑的手上搶過來，算是報了一箭之仇。其實我今天的推桿感覺真的很不好，有好幾次3呎內的短推都錯過，不然也不會打得這麼累。」今年第3次參賽，前兩次分別在第1輪及8強賽輸給蔣宸豑。\\n\\n\\n年齡：17歲身高：181公分體重：80公斤就讀學校：啟英高中二年級球齡：6年\\n\\n\\n \\n \n", "3 【陳毓婷╱台北報導】過去業績不甚理想的中國人壽（2823），今年初找來南山人壽的專業經理人王銘陽擔任總經理後，不但保費收入大幅成長，而且獲利也出現轉機，今年上年已經轉虧為盈，小賺667萬元，擺脫今年第一季虧損近1.6億元的陰霾。\\n\\n\\n中壽今年上半年的保費收入達155.7億元，較去年同期的96.6億元成長62%，與國內壽險業今年上半年保費收入比較，中壽首度擠進前五名。通常壽險公司在衝刺... \n", "4 台灣國際語文教育協會假借中央機關指導名義，招攬學員參加該機構舉辦的觀光研習營，活動宣稱「參加滿三梯次可退費」，實際上卻任意改期、提高收費。學員幾經爭執、《蘋果》追查發現真相後，業者同意學員的退費要求。攝影．報導╱褚明達\\n台中市徐先生說，去年10月下旬，他看到台灣國際語文教育協會（以下簡稱台協）招攬「台灣觀光親善大使甄選研習營」學員的網路廣告，因內容豐富，他立即報名參加。台協在廣告上註明將... \n", "\n", " text_pre \n", "0 鄧玉瑩 台中報導 台中市警二分局育才派出所爆發疑似集體索賄案 台中地檢署檢察官指揮調查局中部機動組查出 轄區警員柯文山利用職權之便 向轄區飯店 色情業者索賄 昨天深夜向地院聲請羈押獲准 派出所隨即表示 將對柯某撤職查辦 從嚴處分\\n台中地檢署檢察官吳祚延指揮調查局中機組幹員 搜索台中市警二分局育才派出所 帶回警員柯文山 進行偵訊 檢調同時到台中市 合利太 飯店大樓展開搜索 除帶回帳冊 飯店... \n", "1 陸軍 旅下士洪仲丘關禁閉被操死 該旅副旅長何江忠昨遭軍高檢向最高軍事法院聲押獲准 何江忠的前同事說 他 何江忠 只能用 陰險 兩字形容 得罪他都沒好下場 還說他常用官威逼部下 仗勢欺人 人神共憤 大家都不喜歡他 被他帶過的阿兵哥說 懲處到了何手上都會加重 簡直是 大魔頭\\n曾與何江忠共事 年的軍官昨向 蘋果 爆料 前年何江忠還在馬祖東引擔任副指揮官時 遇到年度本職學能鑑測 他卻要步兵學校裁... \n", "2 終於拿到冠軍 感覺真是棒 尤其是從蔣宸豑的手上搶過來 算是報了一箭之仇 其實我今天的推桿感覺真的很不好 有好幾次 呎內的短推都錯過 不然也不會打得這麼累 今年第 次參賽 前兩次分別在第 輪及 強賽輸給蔣宸豑\\n年齡 歲身高 公分體重 公斤就讀學校 啟英高中二年級球齡 年\\n\\n \n", "3 陳毓婷 台北報導 過去業績不甚理想的中國人壽 今年初找來南山人壽的專業經理人王銘陽擔任總經理後 不但保費收入大幅成長 而且獲利也出現轉機 今年上年已經轉虧為盈 小賺 萬元 擺脫今年第一季虧損近 億元的陰霾\\n中壽今年上半年的保費收入達 億元 較去年同期的 億元成長 與國內壽險業今年上半年保費收入比較 中壽首度擠進前五名 通常壽險公司在衝刺新契約保單的情況下 成本支出會墊高 中壽今年第 季就... \n", "4 台灣國際語文教育協會假借中央機關指導名義 招攬學員參加該機構舉辦的觀光研習營 活動宣稱 參加滿三梯次可退費 實際上卻任意改期 提高收費 學員幾經爭執 蘋果 追查發現真相後 業者同意學員的退費要求 攝影 報導 褚明達\\n台中市徐先生說 去年 月下旬 他看到台灣國際語文教育協會 以下簡稱台協 招攬 台灣觀光親善大使甄選研習營 學員的網路廣告 因內容豐富 他立即報名參加 台協在廣告上註明將請 外... " ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "apple_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Number of MOD-NOUN Types:" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "28496" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(mod_head_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Top 50 Frequent MOD-NOUN Showing `amod` dependency relation in Apple News:" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MOD-NOUNFrequency
223新_台幣108
750女_主角81
641綜合_報導76
86月_營收71
258數位_相機48
30大_股東44
471主治_醫師43
1054男_主角39
234新_產品38
96稅後_純益37
170好_朋友37
34最高_法院36
193新_專輯36
635相關_資訊35
91稅前_盈餘35
1231前_女友33
308智慧型_手機32
130相關_單位32
342前_總統32
1489高速_公路31
10021聚酯_纖維31
1831筆記型_電腦31
98前_盈餘31
291大_聯盟30
324快速_道路29
2336小_女孩29
2759大_台北29
978前_男友28
463新_政府26
315國立_大學26
1305有限_公司26
200先發_投手26
314助理_教授26
580女_學生25
1159大_尺寸24
207發_投手24
3087基本_工資24
1883中小_企業23
2912附設_醫院23
1776大_公司22
2371知名_品牌22
888市立_醫院21
2290不_動產21
1592同性_婚姻21
708高等_法院20
1468地下_錢莊20
607小_黃瓜19
5935突發_中心19
572實際_價格19
778加護_病房18
\n", "
" ], "text/plain": [ " MOD-NOUN Frequency\n", "223 新_台幣 108\n", "750 女_主角 81\n", "641 綜合_報導 76\n", "86 月_營收 71\n", "258 數位_相機 48\n", "30 大_股東 44\n", "471 主治_醫師 43\n", "1054 男_主角 39\n", "234 新_產品 38\n", "96 稅後_純益 37\n", "170 好_朋友 37\n", "34 最高_法院 36\n", "193 新_專輯 36\n", "635 相關_資訊 35\n", "91 稅前_盈餘 35\n", "1231 前_女友 33\n", "308 智慧型_手機 32\n", "130 相關_單位 32\n", "342 前_總統 32\n", "1489 高速_公路 31\n", "10021 聚酯_纖維 31\n", "1831 筆記型_電腦 31\n", "98 前_盈餘 31\n", "291 大_聯盟 30\n", "324 快速_道路 29\n", "2336 小_女孩 29\n", "2759 大_台北 29\n", "978 前_男友 28\n", "463 新_政府 26\n", "315 國立_大學 26\n", "1305 有限_公司 26\n", "200 先發_投手 26\n", "314 助理_教授 26\n", "580 女_學生 25\n", "1159 大_尺寸 24\n", "207 發_投手 24\n", "3087 基本_工資 24\n", "1883 中小_企業 23\n", "2912 附設_醫院 23\n", "1776 大_公司 22\n", "2371 知名_品牌 22\n", "888 市立_醫院 21\n", "2290 不_動產 21\n", "1592 同性_婚姻 21\n", "708 高等_法院 20\n", "1468 地下_錢莊 20\n", "607 小_黃瓜 19\n", "5935 突發_中心 19\n", "572 實際_價格 19\n", "778 加護_病房 18" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod_head_df.sort_values(['Frequency'],ascending=[False]).head(50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3-2\n", "\n", "Following the previous question, with the extracted MODIFIER-NOUN word pairs, please create a NOUN-by-MODIFIER co-occurrence table, showing the co-occurring frequencies of a particular noun (i.e., the row) and a particular modifier (i.e., the column) (see the sample data frame below).\n", "\n", "In addition, with a co-occurrence matrix like this, we can cluster the NOUNS according their co-occurring patterns with different modifiers. That is, please perform a cluster analysis on the NOUNS, using their co-occurring frequencies with the MODIFIERS as the features. In particular, among all these modifier-noun pairs:\n", " - please include nouns whose total frequencies are > 70 (i.e., given the NOUN-by-MODIFIER matrix, you need to include only rows whose row sums are > 70)\n", " - please include modifiers whose total frequencies are > 10 (i.e., given the NOUN-by-MODIFIER matrix, you need to include only columns whose column sums are > 10)\n", " - perform the cluster analysis using the default settings used in the lecture notes (i.e., cosine similarity, linkage of ward's method).\n", " \n", " \n", ":::{important}\n", "\n", "In case you fail to create the output from Question 3-1, you can use the sample output csv, `demo_data/midterm/question3-1-output-modnounfreq.csv`, as your starting point for this exercise. \n", "\n", "The csv file is the expected output from Question 3-1, including all the MODIFIER-NOUN pairs identified by `spacy` and their frequency counts in the corpus. (As specified in Question 3-1, bigrams with one-syllable nouns have been removed from the list.)\n", "\n", ":::\n", "\n", "\n", "-------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Before filtering, the shape of the NOUN-by-MODIFIER co-occurrence matrix should be as follows: ( `Number_of_Noun_Types`, `Number_of_Modifier_Types`)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(11718, 9243)\n" ] } ], "source": [ "print(noun_by_mod.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Noun-by-Modifier Co-occurrence Matrix After Filtering" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "scrolled": true, "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
進一步世界下列親密免費單親彩色甜美暢銷優惠...以下歲地冷極大難得自由有趣原淡淡
企業0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
設計0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
價格0.000.000.000.000.000.000.000.000.005.00...0.000.000.000.000.001.000.000.000.000.00
時間0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
運動0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.004.000.000.000.00
報導0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
台幣0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
活動0.000.000.000.003.000.000.000.000.004.00...0.000.000.000.000.000.001.001.000.000.00
手機0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
盈餘0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
女友0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
醫師0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.001.000.00
產品0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.001.000.00
成績0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.001.000.000.000.000.00
基金0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
風格0.000.000.000.000.000.000.001.000.000.00...0.000.000.000.000.000.000.000.000.000.00
朋友0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
方式0.000.000.000.001.000.000.000.000.001.00...14.000.000.000.000.000.000.001.000.000.00
公司0.001.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
學生0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
國家0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.001.000.000.000.00
環境0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
功能0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
醫院0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
主角0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
問題1.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
營收0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
空間0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
品牌0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
投手0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
表現0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
市場0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.002.000.000.000.00
商品0.000.000.000.000.000.000.000.002.003.00...0.000.000.000.000.000.000.000.000.000.00
服務0.000.000.000.004.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
電腦0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
效果0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
人士0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
總統0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
生活0.000.000.000.000.000.000.000.000.000.00...0.000.000.000.000.000.000.000.000.000.00
\n", "

39 rows × 587 columns

\n", "
" ], "text/plain": [ " 進一步 世界 下列 親密 免費 單親 彩色 甜美 暢銷 優惠 ... 以下 歲 地 \\\n", "企業 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "設計 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "價格 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.00 ... 0.00 0.00 0.00 \n", "時間 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "運動 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "報導 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "台幣 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "活動 0.00 0.00 0.00 0.00 3.00 0.00 0.00 0.00 0.00 4.00 ... 0.00 0.00 0.00 \n", "手機 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "盈餘 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "女友 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "醫師 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "產品 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "成績 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "基金 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "風格 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "朋友 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "方式 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 1.00 ... 14.00 0.00 0.00 \n", "公司 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "學生 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "國家 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "環境 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "功能 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "醫院 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "主角 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "問題 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "營收 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "空間 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "品牌 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "投手 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "表現 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "市場 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "商品 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 3.00 ... 0.00 0.00 0.00 \n", "服務 0.00 0.00 0.00 0.00 4.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "電腦 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "效果 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "人士 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "總統 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "生活 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n", "\n", " 冷 極大 難得 自由 有趣 原 淡淡 \n", "企業 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "設計 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "價格 0.00 0.00 1.00 0.00 0.00 0.00 0.00 \n", "時間 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "運動 0.00 0.00 0.00 4.00 0.00 0.00 0.00 \n", "報導 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "台幣 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "活動 0.00 0.00 0.00 1.00 1.00 0.00 0.00 \n", "手機 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "盈餘 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "女友 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "醫師 0.00 0.00 0.00 0.00 0.00 1.00 0.00 \n", "產品 0.00 0.00 0.00 0.00 0.00 1.00 0.00 \n", "成績 0.00 0.00 1.00 0.00 0.00 0.00 0.00 \n", "基金 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "風格 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "朋友 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "方式 0.00 0.00 0.00 0.00 1.00 0.00 0.00 \n", "公司 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "學生 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "國家 0.00 0.00 0.00 1.00 0.00 0.00 0.00 \n", "環境 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "功能 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "醫院 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "主角 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "問題 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "營收 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "空間 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "品牌 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "投手 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "表現 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "市場 0.00 0.00 0.00 2.00 0.00 0.00 0.00 \n", "商品 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "服務 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "電腦 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "效果 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "人士 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "總統 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "生活 0.00 0.00 0.00 0.00 0.00 0.00 0.00 \n", "\n", "[39 rows x 587 columns]" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "noun_by_mod_filtered_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- After filtering, the shape of the NOUN-by-MODIFIER co-occurrence matrix should be as follows: (`Number_of_Noun_Types`, `Number_of_Modifier_Types`)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/plain": [ "(39, 587)" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "noun_by_mod_filtered_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Pairwise Cosine Similarity Matrix for Nouns Whose Frequency > 70" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../exercise-ans/midterm/question3-2-output-heatmap.jpeg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The Cluster Result, the Dengrogram:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](../exercise-ans/midterm/question3-2-output-dendrogram.jpeg)" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "python-notes", "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": "3.9.18" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "165px" }, "toc_section_display": true, "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }