NLP Project - Word2Vec (PART2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NLP Project: Sentiment Analysis with Word2Vec (Part 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** The Word2Vec model is a simple word embedding neural network, developed by Mikolov et al. (2013).**\n",
    "* Such **continuous word embedding representations** have have been proven to be able to carry **semantic meanings** and are useful in various NLP tasks.\n",
    "* The model assumes the **Distributional Hypothesis** that words are characterized by other words in their **vicinity**.\n",
    "* This idea is used to estimate the **probability of two words occurring near each other**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numpy import average\n",
    "import os\n",
    "import gensim\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from bs4 import BeautifulSoup\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "#sns.set_style(style='white')\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (8.0, 6.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load JSON File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import graphlab as gl\n",
    "gl.canvas.set_target('ipynb')\n",
    "#gl.canvas.set_target('browser')\n",
    "\n",
    "def load_json_from_file(filename):\n",
    "    \"\"\"\n",
    "    Load JSON from a file.\n",
    "    INPUT:  filename  Name of the file to be read.\n",
    "    RETURN: Output SFrame\n",
    "    \"\"\"\n",
    "    # Read the entire file into a SFrame with one row\n",
    "    sf = gl.SFrame.read_csv(filename, delimiter='\\n', header=False)\n",
    "    \n",
    "    # The dictionary can be unpacked to generate the individual columns.\n",
    "    sf = sf.unpack('X1', column_name_prefix='')\n",
    "    return sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Finished parsing file /Users/marvinbertin/Desktop/data/Sport_sentiment\n",
      "PROGRESS: Parsing completed. Parsed 100 lines in 0.706755 secs.\n",
      "------------------------------------------------------\n",
      "Inferred types from first line of file as \n",
      "column_type_hints=[dict]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n",
      "PROGRESS: Read 113078 lines. Lines per second: 76301.5\n",
      "PROGRESS: Finished parsing file /Users/marvinbertin/Desktop/data/Sport_sentiment\n",
      "PROGRESS: Parsing completed. Parsed 768312 lines in 6.16828 secs.\n"
     ]
    }
   ],
   "source": [
    "Sport = load_json_from_file('/Users/marvinbertin/Desktop/data/Sport_sentiment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sport Review Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">overall</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reviewText</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sentiment</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">summary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I love this chair for<br>using at my computer. ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Great chair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My Kindergartener uses<br>this lock to secure her ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Falls apart and gets<br>stuck ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Such a let down! I tried<br>to use this mat last ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">So slippery!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I was looking forward to<br>using this. The Picture ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Too Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Purchased my first full<br>carbon bike and was told ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Great Tool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I got these for my 5 year<br>old daughter and she ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Big Hit with my Daughter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">If I receive wat I ask<br>for, I don't like it too ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Wasn't wat i wan't</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">quality is not very good,<br>but it's good enough for ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">good enough</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">In the description it<br>says it fits a 48\" waist ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">48\" waist?  I think not</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This Watch is Sharp!, it<br>looks a lot like a Watch ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Worth Every Penny in The<br>Gold Box ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[768312 rows x 4 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\toverall\tint\n",
       "\treviewText\tstr\n",
       "\tsentiment\tint\n",
       "\tsummary\tstr\n",
       "\n",
       "Rows: 768312\n",
       "\n",
       "Data:\n",
       "+---------+-------------------------------+-----------+\n",
       "| overall |           reviewText          | sentiment |\n",
       "+---------+-------------------------------+-----------+\n",
       "|    5    | I love this chair for usin... |     1     |\n",
       "|    2    | My Kindergartener uses thi... |     0     |\n",
       "|    2    | Such a let down! I tried t... |     0     |\n",
       "|    2    | I was looking forward to u... |     0     |\n",
       "|    5    | Purchased my first full ca... |     1     |\n",
       "|    4    | I got these for my 5 year ... |     1     |\n",
       "|    2    | If I receive wat I ask for... |     0     |\n",
       "|    5    | quality is not very good, ... |     1     |\n",
       "|    2    | In the description it says... |     0     |\n",
       "|    5    | This Watch is Sharp!, it l... |     1     |\n",
       "+---------+-------------------------------+-----------+\n",
       "+-------------------------------+\n",
       "|            summary            |\n",
       "+-------------------------------+\n",
       "|          Great chair          |\n",
       "|   Falls apart and gets stuck  |\n",
       "|          So slippery!         |\n",
       "|            Too Bad            |\n",
       "|           Great Tool          |\n",
       "|    Big Hit with my Daughter   |\n",
       "|       Wasn't wat i wan't      |\n",
       "|          good enough          |\n",
       "|    48\" waist?  I think not    |\n",
       "| Worth Every Penny in The G... |\n",
       "+-------------------------------+\n",
       "[768312 rows x 4 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example of a review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I love this chair for using at my computer.  It's the second one I've bought from Amazon and I've gotten many comments from friends who see it.  I just have to make myself not lean against the back so it does what it's purported to do - help your posture!\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sport['reviewText'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine All The Reviews Into One File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BASE_DIR = \"/Users/marvinbertin/Desktop/data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mashed = \"\\n\".join(Sport['reviewText'])\n",
    "with open(os.path.join(BASE_DIR, \"txt/sportsReviews.txt\"), \"w\") as f:\n",
    "    f.write(mashed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parse Text Into Sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TrainSentences(object):\n",
    "    \"\"\"\n",
    "    Iterator class that returns Sentences from texts files in a input directory\n",
    "    \"\"\"\n",
    "    RE_WIHTE_SPACES = re.compile(\"\\s+\")\n",
    "    STOP_WORDS = set(stopwords.words(\"english\"))\n",
    "    \n",
    "    def __init__(self, filename):\n",
    "        \"\"\"\n",
    "        Initialize a TrainSentences object with a input filename that contains text files for training\n",
    "        :param filename: file name which contains the text       \n",
    "        \"\"\"\n",
    "        self.filename = filename\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        Sentences iterator that return sentences parsed from files in the input directory.\n",
    "        Each sentences is returned as list of words\n",
    "        \"\"\"\n",
    "        # read line from file (Without reading the entire file)\n",
    "        for line in file(self.filename, \"r\"):\n",
    "            # split the read line into sentences using NLTK\n",
    "            for sent in txt2sentences(line):\n",
    "                # split the sentence into words using regex\n",
    "                w =txt2words(sent,\n",
    "                             lower=True,\n",
    "                             remove_stop_words=False,\n",
    "                             remove_none_english_chars=True)\n",
    "                \n",
    "                #skip short sentences with less than 3 words\n",
    "                if len(w) < 3:\n",
    "                    continue\n",
    "                yield w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use NLTK's Punkt Sentence Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def txt2sentences(txt, remove_none_english_chars=True):\n",
    "    \"\"\"\n",
    "    Split the English text into sentences using NLTK\n",
    "    :param txt: input text.\n",
    "    :param remove_none_english_chars: if True then remove non-english chars from text\n",
    "    :return: string in which each line consists of single sentence from the original input text.\n",
    "    :rtype: str\n",
    "    \"\"\"\n",
    "    tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "\n",
    "    # split text into sentences using nltk packages\n",
    "    for s in tokenizer.tokenize(txt):\n",
    "        if remove_none_english_chars:\n",
    "            #remove none English chars\n",
    "            s = re.sub(\"[^a-zA-Z]\", \" \", s)\n",
    "        yield s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Every Sentence a List of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def txt2words(txt, lower=True, remove_none_english_chars=True, remove_stop_words=True):\n",
    "    \"\"\"\n",
    "    Split text into words list\n",
    "    :param txt: the input text\n",
    "    :param lower: if to make the  text to lowercase or not.\n",
    "    :param remove_none_english_chars: if True then remove non-english chars from text\n",
    "    :param remove_stop_words: if True then remove stop words from text\n",
    "    :return: words list create from the input text according to the input parameters.\n",
    "    :rtype: list\n",
    "    \"\"\"\n",
    "    if lower:\n",
    "        txt = txt.lower()\n",
    "    if remove_none_english_chars:\n",
    "        txt = re.sub(\"[^a-zA-Z]\", \" \", txt)\n",
    "\n",
    "    words = TrainSentences.RE_WIHTE_SPACES.split(txt.strip().lower())\n",
    "    \n",
    "    if remove_stop_words:\n",
    "        #remove stop words from text\n",
    "        words = [w for w in words if w not in TrainSentences.STOP_WORDS]\n",
    "    return words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Word2Vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sentences = TrainSentences(os.path.join(BASE_DIR, \"txt/sportsReviews.txt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_Sport = gensim.models.Word2Vec(sentences,\n",
    "                                     size=300,\n",
    "                                     min_count=40,\n",
    "                                     window=8)\n",
    "model_Sport.save(os.path.join(BASE_DIR ,\"sport_300_c_40_w_8.word2vec\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Embedded Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('fantastic', 0.7145101428031921),\n",
       " ('great', 0.6555029153823853),\n",
       " ('wonderful', 0.6375762224197388),\n",
       " ('fabulous', 0.5946256518363953),\n",
       " ('phenomenal', 0.5340671539306641),\n",
       " ('excellent', 0.5293058156967163),\n",
       " ('superb', 0.514076828956604),\n",
       " ('awesome', 0.5106169581413269),\n",
       " ('outstanding', 0.5017399191856384),\n",
       " ('amazing', 0.48741185665130615)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Sport.most_similar(\"terrific\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('terrible', 0.8401502370834351),\n",
       " ('awful', 0.6961092948913574),\n",
       " ('horrid', 0.5938861966133118),\n",
       " ('atrocious', 0.5628483295440674),\n",
       " ('lousy', 0.5514189004898071),\n",
       " ('horrendous', 0.5308316946029663),\n",
       " ('pathetic', 0.5148214101791382),\n",
       " ('dreadful', 0.4818209409713745),\n",
       " ('aweful', 0.4583595097064972),\n",
       " ('horrific', 0.44951626658439636)]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Sport.most_similar(\"horrible\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fun Analogies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "king - queen + man = woman\n"
     ]
    }
   ],
   "source": [
    "predict = model_Sport.most_similar_cosmul(positive=['king', 'man'], negative=['queen'])[0]\n",
    "print \"king - queen + man = {}\".format(predict[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " running - run + ski = skiing\n"
     ]
    }
   ],
   "source": [
    "predict = model_Sport.most_similar_cosmul(positive=['running', 'ski'], negative=['run'])[0]\n",
    "print \" running - run + ski = {}\".format(predict[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " cheapest - cheap + cool = coolest\n"
     ]
    }
   ],
   "source": [
    "predict = model_Sport.most_similar_cosmul(positive=['cheapest', 'cool'], negative=['cheap'])[0]\n",
    "print \" cheapest - cheap + cool = {}\".format(predict[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the Intruder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sleeping'"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Sport.doesnt_match(['skiing', 'swimming', 'climbing', 'sleeping'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hair'"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Sport.doesnt_match(['hand', 'head', 'foot', 'hair'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word Embedding Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word 'horrible' and 'terrible' have high similarity: 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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WBuQ0aklWl1IGwJ8u+PrB2PJ/B/59HeXqKRzfxfZsEkb3knr5QYDj+iRMA88P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vUZk8TdDfwzSUiH3TGWca8fvaJPsnDMUO+en5FakjP7Vaw9svLuPG1gBPPr+B882z0MSUlVRH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\nPUajYaYXLgPKNCrSWx+8dU+Pq1dD4pUIJ1yi6hVbCSHdYyrNLPr6dy+m3yt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t/ZTmufQq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\niIiIiGyioJGIiIiIiIiIiGzy/wETwkyCNpmKTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a5929d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"Word 'horrible' and 'terrible' have high similarity: {:.2f}\".format(model_Sport.similarity('horrible', 'terrible'))\n",
    "\n",
    "plt.figure(figsize=(20, 3))\n",
    "plt.plot(range(300), model_Sport['horrible'])\n",
    "plt.plot(range(300), model_Sport['terrible'])\n",
    "plt.legend(['horrible', 'terrible'])\n",
    "plt.title('Word Embedding Representation of Similar Words(dim: 300)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word 'potato' and 'love' have low similarity: 0.01\n"
     ]
    },
    {
     "data": {
      "image/png": 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5fx8f+vIjTV/lTmUKnBit7rp89tAYH73pUcYnZXK8HHCHQy3lxNa9aDJUo9Po\nucPjABwZbN5vaylxchoBTEheo7rI5Yvl87icogvSdpGCpcwXthJIpPM8vGuAYslirIp7gZMCQ8Kh\nWo9lWWXRCCCZXxnpCkbT43x1x39xJH6s1U0p44ylT+ViJstHNCqHp1V21s+MaU4kB3lqaGcrmrWs\nKJUs9h6L1WwvdDpwDx6gMoeHMD8SnibUituSnWjhDdpxGq3URNjpbIHrv/UkO/YbZ4Pj2mr06qUj\noveEVxHPNscNYVlWhRg0m9Po+EiSZKbAzgPNLed6x0MH+PjXt84pTpWsUvke88zhcU6MptjfPzMx\nbMmyuPEHu3hg2/IZ1J3sVCbCXsLwtHyagNeM0WqtJDo4ZiaOJ4uLzi3CxyQ8rS6SLqFoOTmNyuFp\nktOogod3DZB3RL4qvi9nodod1iq0htHMeMWi90oR8h4deIKnR3bzxadumtU0MjKRXvLFLMe1fyrn\nsls2olEk5MfDzA5pLGNWqY4nB1vQquXF488Ncv23tvHU3toGbXk7PK3NHwEkPK0WJDxNqJWEy13U\nqmTYlmWVB+a5FSoaHRmcZO+xCbY+Z/p+R8Bt9EpZtpgj4PWzKtRFPJegZDXemZXKFiiWpsT+2ezN\nzr3v6X3NLfc8OpGhUCzNGmYwkY3zVz//GFuOP1bRptkcFZPJHFufG+L+bf1Nba8whSMaBQPepa2e\nVkjTFeqkM9hRs9NocNzcO5Ppk2Og7e5PqwnVEWbiFhCXo2iUzBQolk4Oh+5isSyLB7dP9fHpKpxh\njmiUlPC0luO4jMK+MLByRCM9tg8PHlKFNF/cfhMDycoy9//67e3ceOeuJW1T2WmUK56yuaGWjWjk\n9XpoC/tnEY1iAJxIDrSiWcuKEyPmx35ooDabt1P2MhpsA2aGqwlzU66eJjc/oUomc613GqWzxbJI\nkV2h4WmO6DU6YVbJMvZqWbLBAm6umCPoC9IZ6sTCakpIYXya6DKb08gZjO88MNrUxMHOxGi2FeOB\n5BC5Up59sYPmNXabprcfpoSk46PJU3rlbSlJZwsE/F66osElToSdoc0foS+ymrHM+Iw8iXNhWRZD\ntmh0siQXdoenNdtpdDwxQLG0MkX/+Ui6roXllMfU3ZaTReRcLPpIjIGxFKGgD6hO5MuXcxqtDIHi\nZOaYnQT7nO6zgJURnpYt5jgUP8JpHRt507nXMZlP8MWnbmIkbSp35vJFhmOZ8r1lqXAWDIolq+y8\nO9VYNqIW8traAAAgAElEQVQRGLfR9Phmx2k0lBopix+nKk7SxYGx2jpiZ4AX9UcByWlUC9nCVE6j\nU1VZFmqjIjytRTmNEump3/hKdRo5q9GjcVs0Kgu4jR2IZos5gt4gXcEOoDnJsB1XSHiegbczYUln\ni+w5Gmt4GxwcwWq2XCJOGK6zWOO8ZjankfOcZcGRQamSsxRkckUiQR/tkSCTqfyS3JPypQL5Up42\nf4Q1bauxsMqD94WIJXLl8NiTJjytUCToN0PniWTznEb7Y4f4p8dv4OETW5t2jFaRcAkyy9FpBEsb\n/rmcefAp4zJ6xSUbgCrD05ycRhKe1nIcp5Hq3gyYqr7LnX2xgxStIqp7My/fdCXXbf5tYtkJvvjU\nTRRKBcbtuXAyvTT3QIesq6jMqVpBbVmJRm3huUUjC4uB1HArmrVsGK9TNHI68GjAcRqt/JvhRCLL\n939xoOkDDmeiWrSK5dxQgjAfbqGoVSVn3YPy7Aqtnuas9I7Fs5RK1pTTqMGDnlwpR8gXpCvYCZgQ\nrUbjiEZruk2I8PQBh2VZpLMFfF6Td+6pJoaoOaFxsw3+nRwU41lbNMqads/qNHK5LA7X6H4V6iOT\nKxAO+uloC1AsWeXEvc3ECc2O+MOsiawGqs9r5C7RnTxZnEb5ItFIgLaQv+I30GgOTBwCTk6XvVtA\nLBQt8svkHpV2Vyc8Sa7XxRBP5nhSD7NhdZSLzja//aqcRk5OI3Hot5xjk8fpDHawProWWBlOIz22\nF4Dzes4B4KrTf41L+i5kJD3KWCbGeNyely3RPdDBnc/uVHVXLy/RKOQnmy+WrfnZYq5ignAy3jxr\nITZpBiiDYylKNairjtjRHrCdRidBIuz7th3j7ocP8eNHDzftGMVSpVDUyGTYlmXxHX0HO4Z3N2yf\nQuspWSUS+SSrQl1A68LTKp1Gi7fRZvPFJR/YO4JFsWQRS2RJF5uTlH4qPM1xGjVeAHFWrdesskWj\naQPvXKFEsWRx7mmrCAd9PLV3pGkraPM6jexBfiw7QckqTYWnzbLq7nZZHBqYKbSNZcZ5fGBbQ9os\nGDK5IuGgj442k5R6Mt38e3navibaAhH62szEsdq8RoOu8IHkMqqStRhyhRJBv5eu9mDZ/d0M+hNm\nvBvLzExCv9JxRKNgwExBlksFtUqnkYhGv9x5gmLJ4hWXbKAt5AdqS4QtTqPFMRJL88iu+ue9iXyS\n8WyMTR0byqaBlZDTSI/vw+/1c1bXmeXnesLdgFnEcCfATizBPdDB7dpfSrFqObGsRKNo2AyEnI57\n3HYZOatbxxOnuGhkD1ByhVJZaa0GJxH2yeQ02nfMDKTu33ZsRsW9RjE9jM9dgWCxxLIT/KL/ER46\n/mjD9im0nmQ+hYVVXtVpXXja1G+iEdXTPv6Nx7nhO08vqRXYnVNiNJ5pitOoZJXIlwrTnEbNE436\nVs3uNHImTR1tAS48q5eRiQzHR5pz7WTKYXBzh6eVrBIT2Xh5gjBfTiOYPc/ezw4/yH89cxsj6eZW\ngztVKFmWSzQKAkszsU2VnUYR+iK9AAxV6TQadLmiEydLeFq+RMDvY1V7iGSm0LTcFseTpmLQePbk\nE42c+5PTHy6XvEYVotEp7jQq2Qmwg34vL7lwHZGwEY2qSYTtLLbmirmq85+dqtz35LE5w9F//Ohh\nbrr7GQbH6xvz9E+aPmRT+9KJRtuHdpZzItZDIpfkWOI4Z3WeQdAXKD8f9ptE3ulChrHJqbnYUv5O\n3fnsxGm0DHA6JecGMmrnVbig9zzg1HYa5QuliolgLSFqjlXUcRqtdNGoWCpx8ISZpKSzRe5rUvUe\np3KaQyMrqDlhb5Mtqq4lNAfHWdQb7ibg9ZNoVXiaazK52JxGk9kUExt+xgHfL9l9sLpcJo3AHcIw\nMpEpV09rZE4jRxgOVTiNmhee1meHp02vnubc89rCAS7dbBZJmhGiViyVygOf2cPTpvq4sUysIqfR\ndMHQEZLW9rQxMJqaIUI5g9OVsLK5EsjaQmM45J9yGi1B3pWULdZG/FNOo+EanUa9nWGS6cJJkRcw\nXygSChinETQnr1GxVGTQrhY0cRKKRs6CQF+X3R8ug1X7QrFEvlDC6zEhwolTPKfRM4fGGJnI8KLn\nr6UtHKjRaTR17hrtDD6ZSGUK3PqzPfzw4UOzbncWZiaT9QkjTj4jIxqZ+V8z78eWZfFfz9zG9/be\nVfc+9sT2A6B6Nlc8H7FFo0whU+E0Wsqw5wqnUQtzGu0Y3s3te+5syf10WYlG5U7JHqg6+YxO69hI\nZ7CD48nBlrWt1TguIycBYy2iUWGa02ilJ8LuH06SzRd50flriIb9/Gzr0fKAupFkpolGjYzPdlxL\nIhqdXDjfZ0ewnfZAe1MqcVWDe1V/seFpt+k78LYl8HaMc+eWg0t2o3KHs4zE0mURt5GWd2dwG3Q7\njZoRnmYPbNbM4TRyBJdIyMcLzu7F42mOaOQ+7mwhIWlXHzecGi27KPKF0ow2TyRyeIAXnNWDBRwZ\nrDxvOTsM2hH7hOooWRZj8ZmuVuf8h4M+OiJL5zRyFkva/JGyI6/68LQU4aCP9b1tFIqlipXalUix\nVKJQtAgGfKyKhgCaktdoKD1CwTLfdzyXOOkqqDkLAqu7zETQCUVuJY4Y0ttlvtdTPTxty06zSP/y\ni0wC7Eio+uppOXdaB1k0mBNnnDZX/ixnDFTv7+OoXTltU8cGgt4AAa+/qaJRvpQnX8oTX8S8xsln\n5CTudoj4bVfiNNFoKX+nbtGolU6jh/of5efHtjDRhAXOhVheolF4dtGoJ7yKDdF1jGXGyyEKpxqO\naLR5k8mVMjBa/Q+/nAg7eHI4jfb1m5W3C87s4dWXbSKRzpcrPDQSZ7LjKNyNXDFxruNELnFSrL4K\nBsdp1B5spz0YbWFOI1ci7EU4jR4f2MZTI08D4PHn2N8f59nD44tuXzW4V5CGJxNYmN9JupBp2CQq\n6xKNooE2fB4f8SYkwnZcOU44xvSBt3PPawv5aY8EOGdjFwf647OGhS2GjGtFfyGn0VCy8nue3paJ\nZI72tgBnbzD3pOkhalnb4TpdfBfmZ8uOE3zwSw9zdKhy4O0MUp1E2LBETiMnp5F9H1zTtppYdmLB\narYly2JoPM3a7jbaI6a9S7kqPBbPsOtgY0MjHQE+4J9yGsWaIBr1J06U/7awWjI5aCbOdbB61fJx\nGjl9siPsn8qJsFOZAtv2DLO2O8LZG81iis/rJRTw1ZTTCCApybDnxPkdzNUvOqk36s351Z84TtAX\npC/Si8fjIRqINjURtjN+WMy8Ro/vI+wLc3rHpornp5xGacbi7pxGS/c7zVaEp7Wuz3IWTlvh4l5W\nopGT0yhVzmlkwtN6wt2sbzc5Qk7Ylt1TDWdgok43ycAGaohxdZxG5UTYCwz2ljv7bdHo7I1dXHX5\naYSCPu59/EjDE/VmikbY6Q6tAhqb08jZV8EqNjTsrZnoI+N86CuPcGxI3FFzUXYaBaK0B6LkS/mW\nOPvc1vpcnb+LkfQY39E/IOAJUsq04fHnAYu7thxqTCMXIJUpELHdp8OTlYJEo36LOVd4msfjoTPY\n0RynUSpfkcB4Rk4jeyXRcdteck4fFvD0/sa6jdxhcbPmNHIN8EdSlaGIE7OIRl3RIGeuN2F90yuo\n5e1zOz3MV5ifo0MJLODEaOXgvsJptIQ5jcrV0wJmMt0XWY2FxfACuapik1nyhRJreyLlsd1SJsP+\nwUMH+dx3nmZkonH3V8d5Fwz4mhqe5uTvdCZOsWUUojYSSy+6am0ibfr29oizUNz6Manzmfq6jSO/\nGkG2GvF0JfKEHiJfKPGSF6zHY4frgVnYr6p6WkUBGXEazYUjBs3VLyYX2D4f+WKegdQQm9rX4/WY\nqX400EaiiUKDe15Tz2LRWGac4fQo53Q/D5/XV7Et4nMW7zOMu3IaLaVoVOE0aqHQ7TjCT3nRaCo8\nzVwEY5lxPHhYFepiQ3QdcOrmNXLseOt72uhqD9bnNLLD0/IrvHra/v440bCfdb1mBfOVl25kIpHj\nlzsbe21kbafRqrBZSW9sTiNXIrcVEqL29P5RhsbT3P3IoVY3ZdkyPTwNzKrLUuPcSIMBb13hacVS\nkf965jYyxQy/2nMVVqoDPHDB5nb2HI2hjzTfbZTM5OnpDNEeCTCWqDyHjQpRc0Kogl4zAewMdRDP\nxitWybK54qIdP5PpHJ1tQUIBHx5mVk9zVtqdvH4XbzYJh5/e11inxELhaalCiqDXTPCdRRsnJNp9\nDvKFIulsga5okDWrIkRCfg7OcBqduuFpuXyRHz96uC4Le8w+z9NXn51rpqJ62pKIRuZe1WaHB6xx\n8hotkAzbCaFf091G1BYHltJpNBrPYAHHhhq3su5MGoJ+bzk8rRlOIycJ9gW9CoDxFlZQu2Pfj7hh\ny02AEVY+9vXH+e6D+xe1z2QmTzTsLy8KLAunkd0fdkVNP71Qgt1kPsU/PvIZ7jpwb1X7v03fwc8O\nP7jYZi4JW3aewAO85IJ1Fc9HQv4FXS/FUpGiNfV9Sk67uXHCNNPZAsXSzHGas72eRPEnkoOUrBKb\n2jeUn4v628gUF3Zq748dQo/tq/mY7jlSPUVgnGOq7nNmbIsEjGiUzKeJp/J0Rc2YbSlFo/wySYTt\njH9bEfq5rESj6YmwxzIxVoW68Hl9rLdFo+OnqGjkhKd1d4RY39PGWDxTdYLb6YmwV3JOo3gyx1As\nzVkbusoJC3/jitPw+7zc8+hhSg0M9XKU8h7baTRbeNpwLD1jBb4a3E6JxcT/LiVOJZwnnhtmdOLk\nCxPdMbybb+6+bVGhT+XwtEA7HXY46GJC1AbHUnzlrt0VSaGrakc6TyRkBuX1hKfdf/QhDkwc4rI1\nF7PJp7AKZpL68svMhLHZbqOSZZHKFIiG/PR2hplIV94cG3WzdCfCBugKdlKwihW/9a/96Bn+7muP\nUSjWl4/FsiwSqTwdbQE8Hg/hkG9GEkVnoaQtZM7z+t4oXe1BDs9Syn4xuMWqucLTesLdhH1hYjkz\nUV3bYxYb3P2c83dnNITH4+HMdR0MjqUqJhS5U9hptPW5If7fg/t57Jna8zDG7Xv99MGwI/hF3Imw\nl6DcsOMUcHJKlJNhL+A0cpJgr+12O42WboDvCGr9I427v2ZncxolmuM06gi0s6ljI9Bap9GO4d08\n1r+dklViMpUjmytyYpGVHZOZPNFIbcmVm00qO/X7ao8EFhRkxzIxcqU8e8cXFtDypQIP9T/Cz489\n3JC2NpPB8RR7j01w3hnd9No5pxwiIR/pbHHe0CNnkdqDGZ9LTqO5cTuIpotx+UKxvOBXjxPvaMKk\n7KgQjZwKavMsulmWxc27vsU3n/l2zcd0O5XrKQKjxx3RaPOMbRGfuf/E06bvOX2tcTgnWpTTqFVC\nd8kqlc/zinEaKaU8SqkblVIPK6XuV0qdNW37W5RSjyqlHlJKfana/boTYRdLRWLZCbrDZsK+ProG\ngBOJUzMZdsx2Gq1qD7Gupw2LqUHZQjjhaRF/GA+eFZ3TaCo0rbP8XFd7iIvONmWqG5kDxBGNnGsw\nPS02u1As8cn/eoKv3rW79n27Vt8nW1Rhq1ac661kWdz35LEWt6bxPHLiCbYObmMwNQzADx8+VPPn\nrHQaLV40+unWozz2zCDPHKrN2ZNI52mP+An5fXVVT3v4+OOEfEHerK4jlS1iFcwEqbvby4XP6+HZ\nw+NzloltBOlsAQuIRgL0doUpMJV7CBp3s5wuGjkV1CbsvEaZXIGn942QSOfrntiksgWKJascUhQO\n+mesUpWrp9n3QDCu0tF4dlE5qabjdhqlpw1ELcsiXcjQFojQE17FZMGcg/W9c4tGzsT5jHXmvLmT\nYWdLp65o5CzyxOsY0Drn1p2XDCrD00IBHwG/d0mcRu7qaQBrIkY0WigZtrPIsLbH5TRawvC0hC2o\nHV+kwOHGCYEP+r2sarcTYTc471i6kGE0M87G9vV0h4zLuZWiUSKfxLIsMoVseaIUX0QuLWcy3O52\nGi3hdTEX5WIEtpMvkc7PK444uWFOJAcXXGhy8uSNZ2PLftH2Ydux/6svWDdjW1soQMmy5nUvO6Fp\nHUHjtE6ukPQLrSCRyhFUW/GvOzijb5xPUKqGY5PGrbipYxbRaJ7x02hmjIlcnMlcgpJV20KZezG8\n1nGvZVno8X10BNtZH107Y7vjNJrMmrZv6ovigQUdgY0kmy/hj06Cp9gyp1GmkC3n91wxohHwOiCk\ntX4J8GHgBmeDUioMfAL4Na31y4BVSqlrq9mpOxF2LDuBhUWPPWEP+8P0hrtPeadRV3uQdfbKb7UV\n1HKlPB48+Dw+Ar7AihaN9h03g6fNG7sqnu9bZTqURjpgnPC07rLTqHLfhwcmSaTz9NcxKHUndF/K\n8DTLstg18mxZSJzO0HiKodjMm3ypZDE0nuK0Ne10RoP8/On+Rec1WG7E7Vw249kYpZLFXb88yA8e\nOlBTQr/JXAIPHqKBNtodp1GdFdQsy2LnAbOaX8u5tizLFo2CBAO+msPThlLDDKVHOK/nXNoCbWaV\ny3YaJfNJrr7iNIBy25qBM2BqC/tNhR2febw63AM0Lim9u3oaQGfQiB/OtfDMoXEKRTsBd50TG2di\n3267Q8JB34xVqunhaUC5nx+soVLmQqTncRplillKVok2f4Tu8CryVhZ8+XI73IJ83A7JcSziZ9qi\nkTsZtnNuT8VE2HG7RHI9q6Cxsmg03Wk0lQjb4/GYie1ShKdNS4S9OmJCJ4cXEI2GZnMaLdEA37Is\nl9OokeFpjtPISzjoIxjwlhf0GoWTgmFD+zpW2aLReItEo2JpynWZKqTLfcZixEpHDI1GAlNj/mUw\nlpiqYOmnvS1AvlCaV7B3zkvBKjKQmj/XqjuR+eACr20lJcvi4V0DhII+Ljt3zYztTgW1+b4vJwm2\nc+2K02huYtlJfF2jeLsHZ/SNixWNTiQH8OApR+kARO2FzPnEhv2xQ4BJwF9rxWh3eFqtlYPjuUni\nuUnO7jqzIo+Wg5PTKGG3qbcrTFvYv6QhzznvJIHnbyF43laSudbMo91j30Rh6Qvt1CsavRS4F0Br\n/RhwuWtbFniJ1tq5k/qBqmby7kTYY64k2A7ro+uI5ybrnoStZMYns3S2BfD7vKyzV34HRqs7D4VS\nnoDPhEaEvMHyCvBKZP+xCTweeN76zornV3eZVdCRBopGM5xG0yaq2nZaxJO5mpNwpytyGjU+8e5c\nPDu2hxt3fIPHBp6cdfvnbn+az93+9IznR+MZCkWLjX1RXv3CjaSzRX6540TFaw7FjzCSHpvx3pVC\nWTTKxBifzFIsWSQzBUZnKX89F4l8kmigDa/HW85pVK+TbGAsVb6eaxk0ZPNFCkWL9kiAUMBbs1Nl\n96gGpvJpJDMFrLwdP55P0ttpx5Y38Wbt7DsaDtDbGcZji0bO/SCZT1EoluoOGXPIThONuoKVTqOn\nXWXv653YOAlVOyucRnOFp80UjapdHKgG93ELRaui33IGiBF/W7nP8wQz5ftNfDankSMa2f3xITuc\nzrKs8uQhewrmNHKcGLXmW8jkCmTt72h6KFfa5TQC6IgElyg8LU3A6yfgM+OzoC9Ad2gVQwvkNBoc\nT5WrAUbt6mmJJQpPS2eLFEtG7D0xmqJUakzY+lROIx8ej4dV0VBZ5JsNy3bl1iL89ttJsDdE19ER\nbMfr8TLRYNHo7gM/YcvxxxZ8nXuCksqnyuGtyXS+7nPqXNfu8LTlsABVFo3Cfjoi9v1uHnHMXYXq\nmF3afC5iroqcA8u4mM+eIzFG4xkuV32Egr4Z26sJJ3QWCxzRSHIazY3jmvEEcjOdRq57Rz1jj5H0\nGF2hToJ2vw3QXnYazT133D9xqPx3rW6hCkGjxnGvc504C3fT8Xl9BL2B8oJ7d0eI9rbgkjmNLMui\n4J8ED/g6YvQHFu4/m4FbhG3Fb8u/8EtmpRNw38UKSimv1rqktbaAYQCl1F8AUa31/1az09M3moFq\noWRRCJoL44zV6+nrMxfR5jWns2v0WdKBOM/rm2mdPFmxLIuJZI4Nq9vp6+vg+XYm/PFUvnxu5qPk\nKRHyB+nr6yASDFEoFap633KjUCxxaDDBGes6OX1Td8W2s04zjzNFq2GfzXvEDIo2rVlNyBckT65i\n3xWJX/1++vraq9pvX18HpT1TN4G8N7tk30feFkAy3tSMY6azhXIIWiRqOmSHo6Pm+bM2dfNbLzmT\nHz16hPu29/Om3zwfn9dDoVTkAz//Kuf1beajv/YXS/JZGollWWVxJ+tLkWdqpSOWLnD+5uq+n2Qh\nyapIJ319HZzm6QOg5K/udzqdLc9MDS69fl/V+3AmKKu7I4xOeCiWLLp7ovh91a0R7H1mLwAvP+dy\neto6KOEph6cRLHL6JtNP5y3mbdNirumjY+Z6W9Mb5fR1nbDL/F42da9l1+izECzwrf/dy96jMb7y\noVfPujJVDcEx876+7i7zneXWgoZiIEdvbzu7Dk6JoMFwoK7PtM/uJ9avMf13V3uIgyfirOqOErCT\nTBfs+dfpG1cRtgfm6qzVcP8+JrPFxvVpATMRaI+Y8ItINEy3IwKOGxG8t7OT7nAX9BvR6JwzevH7\nvKRyU/eNcns3rKKvr4PVq9tpjwQ4Opykr6+DXCFXtlBb/tKKvN9Uy2yfLWOLC/lSbfcjd66YbL7y\nvHn95rtbv9b0L72rIhwenKSjK0I4WO9QbmFyVpZosK2iLRu61rB7aA+d3SFC/uCM9xRLFsOxDM/b\n0MmaNZ3k7P60aHmW5Fo47spjlC+UKPl8rF0dXfR+wwNmvz3d5nys7o7w7KExenqi+GbpX48OTnLr\nz/bwm1eeyXt/7+KqjjF+xDg4LzjtbNb2dNETWUU8P9mw81ayStz7wP1s7FjH6y6+at7XZiemzmMg\nCn67b7KAUFuIVR2hmo8/aJfLXtMTZdNGM26r9XfSFOzvb8PaTtYcN322LzRPnz88JbiPlUbnbX9h\nzLVIyETNn3UgMUxnqJ02u4Jhs7j1PnPv/+2Xnj1rG3vtynLhSHDOzxD3mXvmulW97BiBgjfX+u+2\nBpayrTnLCGyeQBZfoHKMd9CVwD9XrO0eWiiaaJ3z+jZXvG9dwrhEveG593f4iSPlvwPRGn+X/VO/\niWKN495hyyxCr+5aNef7oqE2MmnTf5x1eg/dHccYjqVZvbq97jFgtWTzRTwhM7a2Sl7ikX08NfEU\nV29+WVOPO50TrvXGgre+ucViqHekEQfcLfVqrctLvkopD/AZ4Bzgd6ve6UQKn9dDLJ7h0JDRpAKF\nCMPDpgPv8pgbzDP9B+nzrK+z6SuPVKZAJlekPeJneHgST6mEz+vh8PGJ8rmZj3Quiw8fw8OT+Akw\nmU9V9b7lxsETcXL5ImeubZ/Rfr89QTlS5TmphvGE2U8qXiTsCxPPJMv7LpZK7HaF5+w9NEqAhVfe\n+vo6GB6eJJ6cuiEMx8eX7PsYGje5cYYmZh7TXTL7qWcHUKdPCXP6oFlV7gj5yKVzvOTCdTy4vZ+f\nbjnA5eetIZadIFvMMRAfWZHXVrqQLieM7x8bJpif+m537Blm87qFO+ZiqUgil2R921qGhycppMxN\nbLZzXQ2P7Ogv/z0ynqx6H0dst4ff4ylbSY+fmCjnj5iPbDHH7sE9bGxfTzHpYzg5yWgsVU6EPRgb\nI9NtbtpjsfScbXKu83o5MWivzJZKBD1W2WkUwUz8BsfHeXQn5AoljhyLlcMcHCzLYv/xOGet78Tr\nnXswMTZh2phJFk17M2Y/x8eHeXL3ccYns/i8Rng7MTjJxu7aB+3H7O/DUyoxPDyJ05xjx2O02w6M\nickMPq+H+ESKSXvwE7EXevcfbVz/MDpuBj2r2kMk0nmOHo9RyJpz2j9ufuOevJ+gPTnxBNPkMjk6\n2gKMxjLldhwfNpNJq1AoP3f62naeOTTOoaNjWF6XKymVWJF9QjXMdZ2P2CG+o/P8RmbjgCtPWGwy\nU/HeMfu7y6SyDA9PEvKb6+TgkbGy07YZJLIp2gPRirZ0+829YftBzdmrzpzxnuFYmkKxRG9niOHh\nSbK2I2p0fGnGHkfs3Id+n4dC0WLnnkH8Vt+i9zsyaq77XCbP8PAkbSE/lgX7D4/RPYuAcswOpx8e\nrb7/3jd8BA8ewjkzzukMdHAofpTBoYly6ezFkMqnsSyLsVRswTYdGZ9auDg+Msr48NQY5+DRMTZV\nuVDm5tgJuz+0LMZGE0RCPuKJbMv7CKdvzKZz+JzxZH+M9qBVdtm5GYpN5RncN3R43vb3jw2X/z4w\nfKymz5ouZPjIlk9x2ZqL+cPz31D1+2plx/4RHtreT29nmDWdwdnbaFf46h+I0xudeU4ABu3FB18h\nQMDrZzw52fLvtloWO26plYlkAsLg8RU5OjjG8PCq8rbjg1PutInJ2n4fQ6lhLCw6fZ0V7ytlTP9x\nYmyU4Y6Z+0vmUxyLT0UQHB0eppeZ+YXmYjQ+1ebhGse9/cNT44+53hfyhIiXTJ/qKRQJB3yUShZH\njo3TFp79emwUiXQeT8jc1z1HL8azaTc3b7uNdquLzaue19Rjuzk+MjU3GU/UN99djNBU7x1oC/Bb\nAEqpXwF2Ttv+VUzOo9e5wtQWxOPxmJKO2QJjGdMh94anfkQbToEKal/6wS5uuP2piuecfEZO4kWf\n18ua7ggDY6mq8q0USvlyGeWgL7hgTqNjwwk+9rXH2LLzxLyvW2r2lZNgd83Y5oTMNDI8zUngGvaF\niAQiFeFpRwYTZHLFsoW3lhAmMAMBv8eH1+Nd0uppjp0xOUuIpzsM5thw5XbHgeSEzFx9+SbAJGqG\nqbxM9VRMWCoKxRLf+PGzbNszPGNbPOuanGXGGXZdR4cHquuUE/a5bbcTQDrhafWck0yuwJ6jMaLh\n2u37TkhMe1uAYMB08dWGqOmxvRSsIhf2nl9+LpUpgO00SuZS+H1eQgFfXXH21eJYs9vCflPBxRaN\nehVPALAAACAASURBVO2cRkOTcXJ2JaPZQnSeOTzOP//3k2zZNX8fNqN6WsiEWcWzk+Vy9y84y6zO\nVWMR7x9JcscvDlSUz3XyfzjhaRG7z6jIL5QpEAn5K1bLVndF8Ps8DIw2MDzNPmZvZ6h83HIbnNw1\ngUg5DNATytAW8tMVDRJP5cr3m4lyjr2pibKTDPvwwGRFstdTMTxt0g5ZqjWE0x0COFf1tHJ4mn09\nNTMZtmVZpArpchJsh0vXXATAA0cfmvV9g/YEfK3tTHD6saWqnuack7PssMlGJcPOFaZyGgGsssMz\nJ5KzX+NOHqq5whTjuUn6E1N9lGVZHE8O0NfWWw6ZXRXqMpXLGjROcKrhpVwLJXPhDmNJFVIVfdZk\nnQnAy+Fp9jVRTRn3pcCd08ipTrhtbCt/89A/lMOV3Tj9pd/r51ji+LxjcSeRuQfPgvmPphPPTZIr\n5jgYP7Lwi+ugVLK44xcH+Px3d2ABb371OeXKxNOpJpzQqZ4W9AZp87dJTqN5cIdzjWcqx5nucLVa\nwzedNBGrIz0Vzy+UCPuAHZrm5I6cbZ4wHxU5jWoNT7P7pai/bc7XRPxhip4cfp8Z3zqLbrWGgddD\nLl/Ea4tG4dwaQv1XAPC1nf+9pHlpU4XWhqfVKxrdAWSVUluAfwP+r10x7R1KqUuBPwFeoJR6wK6u\n9tpqdxwNmxvIeNao1d2unEZr2/rwerycSJycotFEIsuTzw2x68BYRYnFcXuA7l7JWtfTRjpbrKpa\nWK5UwO81nX3QF6RoFckXCnPGpOsjMfpHktz8o2f57gP7aopdL5UsxqtMDJnKp/jm7m8zWmUeHKdy\n2vQk2GAml20hf83izXw4Fc5C/hBt/jDpQqY8MNBHzPV5uTKrl2Px2iZGmWKGiD9CRyC6pDmNnJvU\nbLHK7rwLR4cmZ922xnZarO+NcsGZ3ezrn2AikS0LX6l8elEl65vJ/z5xmMdyd3Lncw/M2BZ3fQfj\n2YmyUyDo93J4IF6VOOuIQx22WBTxh/F6vHXlYHvucIxC0eLy80wyykwN5T2dPAztkQBBO6Sl2gpq\nu0efA+CC3vPKzyUzBcJeJwmh+SxtYX9TJ4DOgKk9bPJe+ANmstYbMfeD0cTUIH4yObMdQ/b1emIB\nwWVKNDJ9a0egHQ8eJnJxnt43gs/r4YrzzXdQzcTmvieO8sOHD7H74NQqtDO5KldPswfeFZXMsgVC\nHSm2De0oP+f1eljT3Vb14kA1OMfssUX2CuGq4CQ8jpQLUHiCadrCfjqjQfKFUvn98WQOn9dT4fA6\nwy6Be2woQc6VN+9US4RdKlnlPAu1DmbdlbhSmcp7tDsRNlCe2DZTNMoWc5SsUrlyjYPq3sxpHRt5\nanhXudqkm8GxqSTYYBa6IiH/jIpwzcLJI3au7ZZtmGjkymkEU9UDY4nZx2GZOfJTOXxH38Gnt36h\nnHw2lp0gXUizITrlpF/V4Apq7olGfIGxh3uckMqny3m1oP6qRe6cRmBEmuWU06gt5KPdzmk0lBkg\nV8rPmocoaSehPavzDBL5ZEWy6+k4otOm9vUMp0ZqGiM54lSt76uGRDrP5777ND98+BCru8J85A8v\n4zI1tyMvUoVolLfvqQFfgGigTaqnzUOmNHVvnMhUXj/OgoPHY98LahgDjGYc0ai34vmFRCOnH7qo\n7wKg9pxGTvU0n8dX87jXaZPTxtmI+CPgsejq8OP1eMqi0VLkNTLhaWk8lo+wL0ou1s2vn/4KJvMJ\n9sYO1Ly/7XuH+af/fqLmvi/pSk6+YkQjrbWltX631vpX7X97tNbf1lp/TWu9XWvt11q/0v73Kq31\nndXuuy085TSKBtrKK8BgOqG+yGqOJwcbNoheTjy9f7Qc4OR2NzjVOVa1T52LcjLsKhIsOomwAUL2\n//96+xN84ptbZ329M3CNhPzc89gRvvi9HVVf2Ft2neCvv7SlLPDMx+5RzdbB7Tx64omq9r2/P057\nJFAWLqbT2xVmdCLTsGsjW8yainNePxF/hJJVKruPnHLjV15g3G+1Vm1LFzKE/SE6gh3El9Cd46z6\nzHYzGKgQjZIztnVFgxUhTpvt3DaHBydJ2KKRhVVeMWgUuWKO8cziyrsn0nnufkLj6xxjwnd4xna3\n2yuWiTEUM6Gyzz+zh3gqP+ekwI2z2uBUTfN4PLQHokzWeOOFqcpkL7JFo1oSIZadRpEAITuHTbaK\nCmqWZbF7VNPmj3Bm52nl51PZPJFABJ/HV155joYDTRaNpiYWHo+HUNi0vyPQjt/rJ56dOqeTs5R/\njiWyeNvHGZucf8Barp7mNX2rz+ujPRBlPB3n0MAk5562quxirOY7GLP76r3Hpq5XZ0DT4aqeBjMr\nmeXWb+Pru26tWJld19NGJldsWFlv55g9jtNoDtGoK9gJlgdfOEPA76Oz7KjIlf/vjAYrVqR7u8x5\nGpvMVrhZ63Ea3fvYET7/3adrGigvF0ypbvN3KluocJ0thOMqjoZNwLX7+5mRCLvsNKr+2njiuaGq\nF3VgauW4bZrTyOPx8OtnvBILi/uO/HzG+8pOo56pCUC0yUKzG+c3d9b6TgJ+b8MqqM1wGtlOO8d5\nN50p0Wj2vmM4PUrJKvGN3f9DKp8qu+g3tE/l7Gx0BTV3RaSFRSPXqvY0p1E1C5azkUxPLQiAca+k\nc7VNiptBKlvE7/MQ8PvKfbUzCZ7NLZzMp/HgKYemuB1j05nIxmkPRNnQvp6iVWQkU33BEMdZULSK\njKQbW7H0lp9odh8c46Kze/n4n1xRdovORaSaRNhlp1GAtkCETCFTc+n2lYxlWTzU/2hVIm+u5Mp1\nNe0ac/rKno4wFrUtHDpOI8eZ7bBQ9bQDE4fw4OEFq43TvJ5E2AFvgM5gR+3vtful+UQjZ3FvVafp\nf52KtEtRRTSXL+EJpQnTQZtdzGRNmxFYpxdJqoan9o6wvz9edSSDg9Mf+Dw+koXGLShWy+IDpBtM\nW8hPvlBkLBOrqJzmsCG6lnQhXf5BPrV3hGcPj8943XKi2pvhU3unqpEcGnSJRnM4jQBOLCAaWZZF\nvlQg4DiN7MnRvhNjHBlKkC/M7MydAdAH3nQxF5zZzdP7R/mXW7dVValoYDSFZcETemELrtOpVGPX\njSWyjMYznL2hc86EZ6u7wmTzxYZZFTPFLGG7k3IGzelChpJlsfdYjNVd4bLrqRqH0/d/cYDPfXub\n2XchQ8QfpiPYTq6YqwjnaCbzOY0Gxkzo0ca+KP0jifIqd75QYnQiUzEBgKlwlEMDkxWDz0ZXN7x5\n16188rHPlp1f9XDXloPlinV578zfjNN+n8dHwSoyPDlBb2eY5623Q24GF+7YHeHMcRoBtAeiNZ8P\ny7LYeWCUSMjPuaevIuj31heeFpkKT6vGaXQo1s94NsbzexU+71TllGSmQHvYrBo61017xG9XKGrO\nYNCZZDlOlkDQHMcq+mnzR8i6BlvxWSbNxzKHCD3/MY4V9Kz7f1IPsW3PcLmSZNC1ONEZ6iivDF+8\nefXU6moVTqNYWTSaGjA6k3pnIhIpO43M/grFEnnfJIXQuClz6xqAlCuoNShEzZnEOveS2cPT2vB5\nfXgKYTx2QQqnSlo8mSsXZnCec+i2J9CxRLaiP6vHabT1uSF27B9teDlzoFx9pVlMn0wna3DXOKLc\nRjtps/telskV8Hk95eTpHZHanEaHBuJ86Qe7+PGjM0Xz2bjvyWPc++R+YKZoBHBJ34X0RXp57MST\nM8J3hsYrnUZgBOClC0+zKxZGg6zvbWtYBbW5nEYTczqNFghPs8Oix7Mxbn3ue2XhYWP7LE6jTIOc\nRq5FnYkFRCN3eFo6n64UjeqcqDnnIhqZCk+zLMpVA1tFOlso981OX52x7zOzlQ9P5lNEA21s6tgA\nzF1BzbIsYrk4q0JdrIuaRaBaKqi5Rb5aQ9sWYmA0RSTk4y9/76Jy9er5aKsiXN4JeQz4AkT9bVhY\nZfGt2Uymchwbam2KhGOJ49ymv889B+ev/5QvlCjiqpA2rXy6c2/uW+UsWlX/e5srPC3iD+PBM2v1\ntHypwOHJY2xqX08xY/rtWp0s6UKaNn+Y9mC0PB6uFqdNbfOIRj7L9LftHWYOuJThaZPZJB5/njZP\nB+Ggj2LJIug1Y556rm+nzbE5FhzmwukPVkd6KFmlJXdyLzvRKBIOgD9HvpSfVTQ6rWMjAIcnj2FZ\nFjfd/Qy33PtcVfseSv1/9t40SLLsrBI8b3++e+xrZkZmVmZtWZuq1KqSSioJCYRQdwPNNHRPY3Qz\n0zNjbcNMM4DZWIMB07QNdDODQbM0NGooKG0IIVAJIdUi1b4vWbnvGRkZe4SH7/72bX7cd+97z/25\ne0RWVpnMhs9MprLICHd/z++79/vOd875Klhs7i5hulnhuD5+6XOv47En04sXGpbj4exSjT0ESaZR\nOMIy5h8xM0qSSlpM1NsWfusv3sFfv3A18bquTzYeKfQ0MgySOHE8OaDTOmQ0cZ0dy+Fnf/we3HvL\nOJa3O1jeGr4J0A7EqavDOyJ0k9jNAUqZPDNj/SegUEbAzZKoma4FRST3nHo66K6B1e0ONNPFrfvL\nkCUBxayE2pD39MPRu8++vQLLcWD7DlSBgEYA3jdNLGUxaE4SoQ6CAJs1HVMjGeyfLMB2fFRCidZ2\nw0AAYHo0WTgsxDxM4p//ZvoaLbWWcaZ6HpZnM5+zvcZmTcezx9cwUiLbXSAZ8LoAUAoa0WS947cx\nUVYZMLa8i24AZRTR7xQg/kamZ8Lx+ydZ1abJ5HD08+40Tdy5MMJkHf2StMv1RWx1PT9pTKNhoNGZ\nxSp+4xtPAEhK01zPh2V7yKoiAcDC9UNNB/fqRWF7Nl5ce23oIUup2TSR5SUPQQC02z7EQAFEBzMh\n2zKtaK46pGPf8dM7uo89eRGPfus8bJfK06KEuSQX4cIBeBf33DIWGzM8PDGhUuLF9RYD5Nu6A1UW\nIIWFJmWKUADHsFwIY1HBEQdHWXOgquH1jbfxneXn2f9e23hrz10mw3ahyAK7r/2YRgAAWwUkE57v\nMaZRS7NhWB4c1+8BjUp5GRxHzqIE0yglqTEsF7/yJ/1986hHTKVxc6UNTyw9g1944Vdxub53Ovlu\noxvE3EtCSwGn2dBgOAkaeVBlgTVNGNMoxdMrLS6vNBPvMSgs28MXn76E775zDQBw9kobp65WE+uN\n53h8cv8jcAMPz668lPj7rZqOfEZKmJPmVRG248Nx++9FrufjGy9dw3b93YGkTKKblTA3noPj+qg0\n3/1aokwjiXkahUBpn3tKn3HH9Xv2YM/30HE0HC4t4JbyQZyonMYzoUcU9e8EQCYZ4ubJ0+JMxlaK\nV0884s0lzTUS+0VnDwy3eHTL03YDRLwfkQSNyLNlh/KhtJxGd3RkpQzm8wQ06sc0Mj0TtmejrBQx\nnSWgUfeZPSjiIN9ewKbdhG45yKlSXw+j7mBMowHnfpJpNFgOdTMifl5+8elL+A+P7V3ys9c4vn0K\nnzv9+VS5IC3ql9urA19DNx1wYrS/G34SyOmYDviRTdQmngc4b0+5VtWsQeLFnvH1PMcTyWDK97HS\nXoXruzhUPogvP0Hq5Ka1t1zecIntRl7Kwfadof658diNPA1euGeEZWChD2i0VungT7557obZkGlR\nNUj9kRNKTCLOB0lG4l4iAo329hnp/j2RGQfw/kvUvudAo5wqglPIF0B9FeIbwIFQNnG9tQLD8mBY\nLnaa5q66SF84/1X85+N/tGfa3LuJU1er2KrpuLg8uOA9t1SD4/r42D2zyKkiljZ7mUbx8aZxedqV\ntSZ+7c/exNmlOp49vpZgNjmxDbyt27iwRF730L5c+Nq9C7bZsSGLPFRZgMDzuO8IWZzL28MLZ7qx\nbVR1bA9J+GkBuq1Xhmq1mZlsrne8Lw0qj9irVKxfWAmmUegB4pq4GErTbt1HQM3RoopqyxpYwG3s\naDAsF0EAbDVJokaZRgDeN18jWhj6gZ+gVLY0G6btYWo0i32T5DOthB0b6mfUzTQq5xWUcjKub7UT\n8q60rtyNxrevfZf9943S87/67BV4foCH7iH6bo73sd1OJssUNKL7CycbGC9nmE/LbphGTJ4mRcBm\ngdGB0+9JvW3h/3r0Dfzi517Dt1+/Dt8PcPpq0oBZVcSElwQN23Pw+yc+h7+8lFT/JplGu5OnXVxp\ngC8Rb5LbR4+yn9MiIRcyjQyXeFZRE9O9gkYnKmfwFxf/Go+d+8rA54W+LgVsOMEFPBG1lgXfkQDB\nwQdvI9TgNKaRFpBn1EKn530s20Nbd6CZLjSH7BVxphE1MJ+Y4DE1kmVFzbBrdVyf7VOu5zPgv6Xb\nrHMNABk5WSRppgNhLCo44p0jus9fri/hsfNfwd9c+Tv2v8+f/0ss7dEc1bQ8yKM7eKX5FICgi2kU\nAoJSBkEQwDVVgAvQstsMIGpqNgN0SvnkXizwPIo5GfV2kmnk+G7P/r6+o2G1oqU2F/wgYMyNSuPm\ndahf33gbf7v4BAIEWO2kswJuRtBElT4jewGNGh0LssRjPDzLEqCR5UFRffzGG7+Dby4+lepp5AdB\nX9N7OkhiNzJLCn5OjJFr2Kw4+J2vnsSLp5KF8YPT96Mg50MgmJwnruej0jAx1dVkoCBBP6kWQLwC\nv/7SNfzh18++KxYjk4RmJMyGrK31yrs/lyjwo/QwjQbL04De6247HQQIUFZK+Fd3/HNkxQzadgcy\nLyUYAjfiaeS4Pl47t5l6D7U9ydPinkb6TWEaRQ2BiGkE7E2C3R1PLT2LZ5ZfuOG/B5KgUVYVwXGA\nC/K9djf1goDI8HNiDqPqCDKiitU+oBFl4ZWUIqYo02gIY+jZd9bw9RcJsG28h0wjzXR7Jo8Oit0Y\nYUdMIxlZiTZb35vC9nz1En7+hV/GlQYBt69tkGbNXtkbwyIIAmxUNXZevrV1Aicqp5n3bjxok2S9\ns8ma9mnRMV024AMArCB5j3TThTS+ibawAS7T2VOutWPUMJYZS1VlxBnj8aB+RodLC6g2bAQ+j63W\n7m0h4kMTaA61l1pAc3Vw4FJZrTQ8l+y7mSzJ6XJ9QKOXT2/i5TOb+Mozl3f9/sOiZpEaPi8UoSrk\nc3A+2f9vRJ52w0yj8L0mQr+qfrXFexXfc6BRVhHByeSmjCplvHVhGz/z2y/gzDWSXO4vkKlNy61V\nlth4fjD0xgdBgLXOJtzAw6nKuffwCpLxSji9p9YeDCpQadp9R8axf6qA7brBNol6h4x9piwkgBSE\n+YyESysN/OaXjqOl25gsZ6CZLjZi+n2K+ou8iEe/dQFWeJsOz1HQKI1pZKGYk9mGsz8snFd2wzSK\n0c9PD2Eb0Y3LDTxm3NYvWl0Sj7SgifbNmKAWBAFMz2Ia2oxE5WkGLoUm2LfuJ6DmWFGF6/kDk6ir\n6xFIsdUiiZoqqqwT8H4xjeIJY/zgoH5G06NZzE+StcFAo65JOPE4MF1ArWWhHqPO73VqQr9Ybq/i\nTPU8BI5s0I2UA3pYXLhexzuXd3B0voSZyWjtrDaS5q30/kegkYmJcgalvIJSXk6AuP2CGWEnmEa5\n8PV7N/YgCPDot89DM10IPI+vPnsVv/nld/D6eZIYHgtBo6wipCZpFWMHbuD1gGmp8rQB3X0A2Kg3\nwBcaKGIy8fn1mEyMgmGaq0eH9R7lJlRicWrnLF5ce7Xv72mmg6wigg/n0/ucg8ATsdM0YRo8OA64\n8xYyGSlNz27y4T2RjZ6EYifGCtQsExIvJkZZuyZJBA4dIP+vyAIxoxxS1NDCkTKJLq02EAQBOrrD\nOtfxf6cF5WJjGbyqgxraxeVTlGm03SbX8+DMA/hf7vqXeHjuQQBAbY9eX6btAmPXcaZ5Apyq9TXC\ntl0fvqWy96CT35qazUCRYq53xPhoQQnlaclzpVt+S4GOesr509EdeGET6GYxjS7ULuMLF74KDmQ9\n0ULOD4KbLrGk58BsisRsWDQ1G+Wcws56rUueFkxcxWpnHW9sHmdnYXz9/80Li/jZ33sp1bfoajj+\nfTddePr38zPkO/7YXfsBJIclAESC8on5h2F6Jl5cew0Aadr4QdBzXlB226CJcpQNdH2rje++NbhT\nPyjaug1RII2vuXGyn61X+yfXxy9V8L/9zgtDPSK7PY3yGQkCz/XtFsclV93rgAI2RbmAEbWMn7z9\nxwEQtmt8PyrJRXDgekCjty9u49Uz6UNhXj27iT/+xjm8fbHXpFyLyWCa1hB5mq2Hno4qdNeAaXkk\nR+ciCWDDau6J8aiZLjIKaUgCERBxoxPUKnoV31h8At9e+u7wX+4TrufDdn32WajJrs+FhvZdhZnp\nWfADHzkpA47jMJubwbZeSWVWNBhoVMK4OgqREwaCP0EQ4PEXF/G3Ly/Bcb0uptHWDV9jd3h+yCJW\ndg8a7c7TiPoESmwSVjzvvJlxrUUYMZfqV2DZHnbCJsPNZJgAwJlrNfzS517Hz/zOC/h3f/warmyS\n5yrNMoE2fdzAw8YAZphuOuCEaE9wkLxHmuFACAkUnGQPBNsTr+voMFwD4ylKHYCARrpr9DyzV8PJ\nafvz+2BYPuBKaJq9Tbd+YfsOG5pQkAc3S9NCc3Q2PKZfuDbJnRQl9LjsMwxio94Gl23h1bNbuHCT\n7GvqNnmdklRmTCPOC0HU91GeRu9TXh7sT/VexfceaKSKbKxdUS7jL5+9ggDAtQ1yuGWlDCYz47je\nXkWtFT1kw4CCpt2CGXqavFM5NfB3b1a0dZt1Ui3b65us+X6Ak1d2UMxKODhbjGQ/W228sXkcNauG\ncl7poZBSk1RFEvBzP3EvPvMgSewuxfw0KNJdb7k4cWUHE0WSQCkhmNu9YH0/QEtzElK42fEcBJ7b\nHdPIckE/5jCJWnyc4zDabeQL8v4wjRzfhR/4UJk8jby25ui4uNLAaFFhIBWdRDRIonYlZgxeaUeg\nUUF6/+Rpnu+xZwBImlxuMjZRBvsmwylIlcFMIyCSqNX1CBS7WZ5GTyw9AwD4vn0fBXBjng5/9+oS\nAOAnPnkksbFvdpIgZctuQ+YlzISdQAoaAWQqVL1tDU1EKDCUAI1CoCWN3v78yXWcWazhzoUR/Kd/\n8xA+cHQCl1YauLbRwv7JPPOdySgiHNfv8RSjz0z3a3d0B4osQBL5mBH2YNBozVoGxwXIO3OJn0fj\nkSXkwkOqY2vRCO09TkNqOWTt8xyPr135Zl9Kf3cH1A0cwBOxUdWgdcgGk8uT+9HNNPIDH75E3oeT\nzZ4COr4/GI6VYBkBQKdF7tnEBBd+Vg4ZefiEHwqA3HeEMKAurzRgWC48P2CgCxBNT6Ovd6p2krwf\nfwBAkmlEmwN1naytg8X9uHviTtw6cgu59j0yFA3bAyRybnKy2SNPEzkBEi9BN10ENln/dbPOGBUt\nzWby5W55GkDYh64XoGWSeyyGgG83iES/szTPoviZtHMTJEVrnQ187vRj4MHhX9z+T8n7h/ft0W+d\nxy/8wSvMe+ZmBJOY7RE08v0Abc1BMS/3ACxBEMBEB2b5CgAiP3A5EwLPsbPRsFx85+1VWLaHk1d3\nEq9da5lswuduinP6vchhcj43QhokaVNqPjr3EFRBwXMrL8PzPTaxkMpHaVAPm0HFD302OQ746xcX\nb/j7b+sOCllioj87Tj7HIDPs04tVaKaLt4d4MVKmEZWachyHUl5m7LvuiK+rbrCM+hnRxtE9E3fi\nf7rrp/ATt/5o4vcEXkBRzvc0B77w9CU89tTF1KKO2hakga5JI+zh8rSclENezkJ3iDwtq4ooZCS0\ndAcXa1fwSy//3zhfuzTwdRKvaTgJ/5zdsFcGxQtrrzAvOH0X4MTXnr+Kf//om12TCcn3Gh/0kcuI\nCHjybHXnZ5GUhjzj84VZBAiYkXk8KEBdlosQeAET2XFsadt9i/Fay0JLdxAA2G6Y7JpUQcWmXukx\nlT5ZOYs/P/cXe56sRveB3XgZ0VB30UCxQ6aRLETyNP09KmwpkLqubWG9qrFBQjfKgusX1CdpbjyH\nlmajHQ7i2G72nr/xBsnKAImaZsSYRgEHj08SCzTTBUICBSdZu/Y0on5GeaGExfXe5zsnZUOlQZQH\n+YGPxeYSxtQRcC459wNHhsebuLo2eI+gER+aQJ+LvQyB0UOPMBpB0EsGsU0CWUgKWetpzRUAWPXO\nQT32CoTRDXz+qYu78uMdFi2HrLWSXEYmZBpRudxemUZ+ENy4PM01kBWzQyfhvVfxvQcaKSIz37y2\n5DAwKF6Q7y/Ow3ANrDQi1H0YUBAHJS7Wrrxnm1g83ji/TcyyQuPKfmPZFzdaaOkO7rllHDzHMR+V\n8+tr+PNzfwFj7CTKhd4E/SN3TeOOhRH88r98AHcujOJIOM0qPrmHbuA7dbIwP3Qr0V8rISbUPZWn\nYzjwgyBREEgij5mxHFa3taEyQN10UczKmJvI4cJyfWCxmmC6DKHdtjQqT0s/4Np2B55ENreb4WlE\nixzGNAopk5vNFjqGg1v3lRkTazdgVXyaXK1DDqCMoDCAofU+gEbdaLiWwjSaGc2hlJNRzEqMabRZ\nM8ABGCmKeGf7dCJpofKtttNhXfybIf9c62zgZOUMFor78dDMAwBuzNNhp2milJNxcKaYMBje0ZLd\nh5bVJh1fhTxDvGIwA0J6jctDJGptuwOe4xnACERSp24gbbuu4yvfvYKsIuKnf+h2FLMy/tcfPYb/\n8bO3o5SX8ch9EXjTLWdirxGOutYdI/GddEyHTaaRmadR/0MzCAK0fPJanJ40TkxlGjl65ImzR6YR\nTb7/6ZEfhuu7ePTsl1K7s5rpMDZTEASwAwuBJ+KtixX4LrkfdmBBlYWeLtOOVgeEcN+RLFRayb0+\nvj9Yns2ecRrNOtmv5Uy0X9OpnoOCglMHZwqYKKu4stZk+2s+xpCMM40838PlznkEjox5hQBB3dPG\npseyaJvkGtRwbdFCcy+gkev5xHxTJK/FyWYCQDAcA5mwc65bLoI40yjmaUSlY2mgEQU6Wzp5/jtF\nXAAAIABJREFUj0L4ObvNGinQUW9bPYMi4knUu5WnWZ6N/3LyT2F6Fn7qjp/A/ZN3AyCF3OnFKl4+\nvYmmZuPi8rubzhgPCojNju0NNGrHzt58CLBQJp/r+eBnLwKcx/xurrdXkM9KbP2/cmaTMVvOLCZB\n8XjTYjfFea0djk6Wye+OZMn3mMbqy0oZfHD6A2jaLZyvXYqxVpP+g7thGtFn84c/chC24+MLT126\noekwbcNhfhfj5QxkkR8oT6NA14Uh64D6lClSjAmUU9Ds2KmfMylP68M0UiLfkXsnjjHfzniUlFKC\n0aOZDpodG5btpYJwlE1J/TDjQYsMDtzQ/UNzNOSlHHJylk1PyygiCjkZbc3GWijz3NJ7GU19XzO2\ntwPvTp5muhZe3YimAA9jrAOEfXx9q50oSOl7s2IQQD7LIUxpenKauJQXAOZDL8S1dm8ThOYtJYUw\nY6ezkzA9C80+gN21jejn2zWd5S0HS/the3ZPHvTU9WfxxuZxbBtJoHhYxM/23Qa3iwYKtcSQeCkq\nbN8jeRoFUjc6m1iPgcI3m2lEa7d//Q/vwO/97EehZsk+sNHo/Q7jDZKV9lrf19RCTyOJkyEECiBZ\nbL/wgwCaZSEQyWtxkr2rQRwA2GS+K9dc/OaXjvd4qeXEXobKtl6B5ug4VFpgOYvCZ8AJHr77zu58\ngCm4mREzzJZht2bYQRBAc/SECfa563X83O+/jFfPRkCsYYSNPIlcU06VwCHZzAiCAB2frIvi4SVs\nVDt46s2VXX2OQdF2yWuOKiOMaeQ5Ajhwe2YaUasS4EaMsHXkpMzQSXjvVXzvgUaqBC5kGr3wVp0l\n2PFE/wCVqHUiFHdYR4oearO5aXiBh9M752/q506LV89uguOAj95NgBqaiHUHlabdG3oHUfbG0k5Y\nyBVqKOV7N/ZH7p3DL/yz+zAZ0sBnxrLIZyRmeAkkmUb7J/MYzZOFJod1Unenl3WRY34VQRBg31QO\nluMN9Sminai7D4/Bcf2BXk6ao7FO9G6ZRsU+TKO/uPjX+C9n/isU+d3L0547sYYrm+Q76Z6etlYj\n13N0X5n9/lg4vrof06hjONio6oxKSVkDqqiyoupmSboGBdWV03sel0xt1cJpN6EPxfxkHjtNUlRu\n1XSMlVS8vvUm/tuZz+NsNTKeJwBnADswmcb2ZrCmngip5p9Z+CTKobfZjXga0fUIJDuscS26H/ho\nOx0UlQIB8QIenGxivBQyjaZ352vUcTrIS7kEvZZSSONJp+8H+G9/dx6W4+Enf+AoY6pxHIeP3DWD\n3/6Zh/GJOGjUpxNL97QAQeLg6BgO68BQwHqQEXbHcOCFzByzk9STR0yjOGiksXuaVrA8du4r+NVn\nfiv1vSg4+uHZD+Jjcx/GhraFr13+28TvENNYn7GZKOuP90VSgLth8ekYKGblHqbRUj1K3DkO2Gwm\nGY9xcNcNnATTKAgCbIW9CJokAKSZMYyhQUGjkYKKI/NlaKaL566+A768lfQ0ik1Pu1C/DNM34NWm\nGaXb8JL7yPRolnUk6X5UkkkB0j21alCYtgcIDgKefKe8Yia6l7SDBZBJcZRpVLMayCoiRIELPY16\nzwgaFDRqmyFbOCyIu2n8tAng+UEPEBFPot6tefGF2iU0rCY+se9h3D91L2RBRkZU0bBa+HxsOMXZ\npd2PwB4WtGCZCRkuuwWNqLyRgEbUq4F875dq1yGOb0D1RvEjt3wWAHCtuYxCRkLbsNmgBVHgMFJQ\ncG6pluiu0m6xwHO7Ks4p2MCF666czYHnuL7X8uDM/QCA1zbfxkYoA5vuZhqpwyWt1aYJjgN+6KED\nuP3ACE5dreKtFIkVQJiznzv9eby+8Xbi547rwbI99szxHIeZsRw2av0nqG2Gn/nyamNgV5pNT5Mi\ncKGcl8k6Trk3cabRIHnasBhRSnB9l+3zGzvRfp+W+9I9Li3n1F0dPMdjVC0PlKc5vgvTs5CXcsjL\nOdieDdNxCGiUkaBbLhrh3++20+64HmzHRz4GVPQ733YTb24dh+Ga7B5WjeHPMS0wa7HclxbkcaZR\nNrZ8u5s+jGkUFuB0gEaaV1pcngYg8jXqk/PGQaOtugHdIV4vC8X9PX9nuCYzXN4rE5vuA3sBjejv\nD/quIqaRzPLm96pJT8+/bWMHK5Xovt100Ch8jkaKCjgAXuh1VWkNYxoNAo0I00jhVcjIgJMsBqib\nlgdI0bPLSdau5Wn0GWjVBdiu35MfRUBetKapNO1weYHdu4k8yTHevrrG6q9BEZe3p+W9g8LybLiB\nl2AaUTuUJ15fjsByjfx/EMpGeZ5DVhUTe2ujY8MXyL0zuSbys9v4xsvX3jVrWfOaCDwBeTnLcAnL\n8aEIyq5BI9uzmWUBjX4Nh7RwPDJEKStmI+nnewTI9ovvQdAo9DTyBWgdDp996ACyiphg6ewPfUe2\nrQiBHAYU0I320wvfB+C9l6htVDUsrrdw58FRHJwlB1qtz/jgE1d2IIk87lggXf6JcgZZRcR6kxwC\nnOBByA8/EDiOw5H5EqotkwEY1NPIdzncdXiMFUiiSBKjbpQznrjS+Obikzif+yrAuwPZFkFAjFWz\nioi7Qz+Wk30kakEQoOPomM3PDNV4A8M9jdY7mzA9CyMj/LuSp+00DTz2xEX84eNEMqKE94sefpc3\nCJh06/5IL0yL/p0+oBGliN5/K0kWmmYcNKKeN++9EbbGRjX2Gqht1HTkVJHJ/6gZNmVLTI9m2Xe0\nrUcdrZGCgkIhALgA07kpcODe9fS09c4m3tk+jX2FOdw5dhuUMPnYK2gUX49AMrFtOVGCQZk6RbkA\nnuPBuyp4xWSgBTPDHuJr1La1hAk2gNSOyzPHV3FltYkHbpvEh+6YGnodUVKdBH62Ys8MPZxtJ0zK\nw+dkN/K07boBTtUQeAI6reSREHUjo65hx9EiU9uuQsjzPRzfPoXzlSupE+PadhtZMQORF/Gjt3wW\nM7kpvLT+Ouoxbx49JokDwCSVdLwpPLJGdYcAsR3dSRy6Ky1yLvBOOGFSSxYStAFRysnwORcyH+0p\n23UDRpu8ftwvKKuKMG1vINMyAo0UAipLJl5u/x3kQ6cZ6wFIMo3e3HwHAODuzCIvkz2mm2k0EweN\nKNNI2TvTyLRdxuIFACljs++XmljSfU4zHQQ2ea+6WQfHcSjm5FCe1ntG0KCgUccm70OLuW55Wnzi\nV7d8kJ5JAs8RNsUQaeWgoAD3/ZP3sJ8V5SJ29AZ2miY+9cA8ZInHuaWb43sARH46EyHovFvQqMXA\nOCUxSjgIAnzz2rcAAPvcD+JgWDxeay2jkJVhWB5OXa1is6bjg7dN4QNHJmDaXoLdemWtCYHncGi2\nCMf1GWOmX9AiKRBCU28xi3xGTJWnAcCBwj5M56ZwunIW6/U6eI7DZLnbCHu4pLVi1KB+4Ds4UTmJ\nn/r0rRAFHl96+lJqkbqubeJE5TSeW01ObqPMq7iUfZZOUEtpenUMh8lZbMdPlXTQsFwfHEfWJg36\nHKRJYkynvxH2XkCjcjhBjZ6BcX+mtHyH5n9pXWzNIc95SSmi7XR65E7R75H3yElZVgRCIF5zlHlY\nC2Xpad4uqa9JJVGx/fBGp6cFQYDnV18Bz/H4zMKnAEQsi4GfgYJGsXyNvjdl9QKAko3ui+bqCfkX\nLdRyIdNoJjcNnuNTzbApo6gcYxoB/dn1CaZRXQ/NhVXM5KZ6/u5q4xr7/tJMmQcFbQhl9yBPA0g+\nopsutvUdVPTe/J55GglSZIT9HnkaUaDMD/xEsyhtOMa7iXrbgijwKGQk2L4DD2Qt7HR6QRF61tH1\n0E82SDyNXKiCCoXLgRM8NHQj+jc5ds9Ee9dMvB2DfCeterrfTz6FoUJNsA+VItAoH6ogPM7GS32m\nnMaD5tcZUY0Y9rsEjWhDmwIhQLR3rWx3cDm0XAlFGgmbjXxWTpyxlYbBptKJnIDM/muwXRdf/s6N\nm2IHQQA9aCGwslAkke0Tpu0iI6qpoFEQBPjydy6zCbHrnU38/Au/gncqpxOf13K8BCN1UDBgTsr8\nvTyNRlYh09N8S0Upr+BTD+wLp1OZrDDYV5gDBw5NP9o8h4JG4UZ7bOx2zOVncL56qac7Uh9iVr2X\noJS6Dx+bxkiBet70HqxbdR3rOxruXBhlBR4XStSaRlQQmEq64WF3RBI18pBRqigCAXcdGmMgiA9y\n+HePim3GEleAmLI+u/oSrEAHl+lgeYAZtu348PwAWVXC4bkSMoqI011jetn1eBa8wENRLgzVeANk\n48so0djqePiBz8axF4ukk3qjpop0g6UGiOevtdExHDz/Nlk/hmvihx48wAxqAWKEDfSXH9Lk/Z7D\nY5BEHh0r2lzfT08juuFMZonnCjMi93zsNIzENc2HI5+px8PUSJZ1MOi9BshanZ4OJ44IOWSlzLvy\nNHJ8F4+d/woCBPjswe9nEsCyUtpzJy2+HgEk5Gm6Fz1bNHkvyAUEQQDPVAGRrE8AGC2SIm6QGTbp\nypoJPyMgOnjjh+cb57fBcxx+8vuPpk636A5KmY8nDUEQJCQBFJSKm2ADu5OnbdY0cKqOwMyhrTkJ\nuRBL8mNMo46jM/lbdyG0pm2wPYd6dlxaaTBgoG13GLtOFiTcP3kv+btYst3pKiyoMTQFTCYLJPnW\nHB2FLOnyx+8NbRCMcYSRSkel0qg2TfAchzsPlsHxPuBHe8q1zRbgi5Cgohpb5wy4G+B9Q5OckYKC\nI/MlSDPXEHAeONGFnIn+jiYcmmXiZOUMMigi0EooqCTB7pZyTY9mGeODSh8VQYYqKHsDjSyPsXgB\ngFcsVixZobErS/ItF/AkiJzMwLNiViZMow41wk4BjcJzQwsnLhT7ydNi5063GTaVpy3MkL8ddrbv\nNAy8cX6rR+YWBAHOVi8iJ2WZwT0AqFwWLiyMliT82McO49Z9I1jf0QZ60u0lWpqNUk5iwG1Hd/DK\n+hv4vXc+lwqk0mjEZH9xUPZk5QyWtWV49UlMiPuQlTKYzk7iemsZ+SxZu4+/RKYHfeqBeRw7RJpP\np0OJmu14WN5qY/9UgQEcwwr0RocUSU5APlNWypDkvE8xxnEcHpy+n5i/+lcwUVYhicn0ksnT+jCN\nXM9Hm9sEBBcX6lcwNZrF931gDk3NTgVyVtqE1bHa2UhIXOkZHpeEzk2EE9RSfI2onI6CXIOMUx3H\nhywJiX17EFMmIU/rAtwoy4fKlgZFNEGt0XMd3c+H43osj0trVOqOTiYciQX4gd+3sKPFSF4m8jQA\n4EQHqiIwQI6yaExvd88OPZ9S5Wl7zNku1a9iQ9vCBybvxqES8YMbxjTyfJ+dWfF8jYFGMaaRrCbP\nzLgHZPd4cFmQMJmdwFpnvQeEa1hNCJzAfnc6ZBptxRhDnu9hrbMBPwiwtNlmOSVlGmWlLPu7ONPo\nUv0q+28KKL54ah1vXRg+ZU2Pne17iYwiwrRd/O47/xWfO/NYz79TppEUN8J+D9gQtmcnTcL1LShh\nQ+ZmM43qbQujBYVIt2ProK6lgUbkvQ8U5uH4Tl/pZsewwYkuMmIGWYHcpx2NPN+a6bIp4gCRp+3W\nCoDmLYFF9rNu0CgNbFhsLjFgkg26UMieKSkOnn9nved87Q4KnCSYRrusa7qfJyAJeD9zfJX47baC\nxHsBQD4jJhqHW3UdnGRB4mR8ePZD6PhNTB2u4p3LO7tq4FyuX8WXL3wtMfmu42jw4CKwMpAlgTX+\nDMsLQaN0tufTb63gu28TJuBicwl+4GO5tdrTfNmtRE1jsti4p9H/z6enCZIHTnQQWBn8yMMHoUgC\nxopKwkhaEWTM5KZgCjWoMumADmOXbGnbGFHKUEUF903cBbdLonZppYGf/4OX8drZdz+dwA8CvHpm\nE6os4L4jExgN5Uv1lKT0ZJc0jcaB6QIgRgurjl5DNT/we1DsI/MkuaC+Rmz8JS/i8FyRMY0sz0Ep\nL/fI0xpdTKO3tk6wTZBX9IFm2HG6qyjwuPPgKHaaJvMLiAdd6HkpxzTegzxrWrqNQiZdmtay23DD\nAr9QIBvHjfoa0YT27qMEfFvbsvBzv/8Sng1Bo6MLOfx3Hz+c+JtCVoIk8n3fk06tOTxXwngpEzM2\nVCDwJJl4X0CjcMOZzJK1RpPFnaYJzw8SoBFlGr0Trs+p0QzzC+ie2DQxRhJo15KQl/KpSWjTauHN\nzXeGgrKPX/kWVtpreHDmAdw1fgf7eVktwfTMPWmHu+nXhmuAB4/AlmEhpn9nHd88WpoNz1IBLkqI\nKYi70zT7Fjz0cOwBjaQkTdewXCyut3BwppBadKcFkzPFipKm3UrQoKnhYC9oRLZ4a8D0tOX6Njje\nB2/n4flBorjRY93IdHla8n4sNaMR8E27hZXtDv7jF4/jq89dged7BOiRIzbWbJ74s6x3IlBcj0ni\ngKiLnZVDj6kJUhRrrs48zuJJ4o61gyAADhXJc9qyk/tKtWVipKBgZpIkVXSMKwAshQMXykoJNbPO\n1utuJvzU2xY4jviuqTkH4mSko/fEaL3JEpn+1uBXYPsOxvzDADgU1WziemlMj8XlaZFfVlEuMGBu\nN2HaXoJpBMlgz0icWh6/zrxQZB3sYk6G6/nYrOlQZIHp+uNRDplGhktBI/I8dLOn4qyMHol0eAYd\nmSN78M4ASfSJKzv41UffxB89fhavnE42VtY6G2hYTdwxeiuTjPpBgO0KKep++BOzUGQBdy4Q1ujN\nYBsFQYCmRqblZeiUKdPE41e/jQv1y1ht98pXaMQZXKLAQ5EF0rAIpww6K7cyAHmhtB+WZ0PIkD39\n+mYbh2aLODhTxG37RyAKHM4sko7z0mYbnh/glrlSJNUdAhrV2hbKeZklw6pAgHPddPtOm/sH0x8A\nDx5eeTlxltDox06k0Whb4FSyj9JuOTXTTkuqqRTID3wsx2QglMXWzTQC0s2wqZzukXtnwQG4MEBS\nb7sek/zSGMSUMWPs0DR5Gg8emja8cRCBRiHTaABoFAdDWpqd+L7oqPhqzcfSKrlP/fYQ2vyhRtgA\nAY0yihhNLQrPzl0zjShodBOMsJ9ffRkA8Mj8RzCWIWfCzhDQKM5yi4PEaVItSe4GjaIcTWdFbnSW\nzednYHl2T5OiabVQUopsD6JNu00qLw8C/OnZL+LX3/htnF5bgml7uHV/GSMFBVsh0ygrZjCZnQAH\nLjFB7VIjAo0aJpFWfv7Ji/jyd4ezKhiLuM/0NMM18I2rT/RIGLOKCE7VULeaqY082jSS+ffWCJs+\nC6PhlDCdq+PwbBECz91UppHr+WhpdsSijV1Ly+i9Lssl731L+RCA/hK1lhWx1XIiOSerBsk5tS6m\nEQGNds80Uvks4JPvtVtaFmeMA2QfqhhVHCotgOd4BjiPqKRpc8tCBtsNA+euDX62aA6RkSJPo90a\nYaeDRjY4kL377YsVXN9qw3d6p5UVMjL8IGD7B2Ea2ciJOXx64ROQeBH26EWA8xMsvn7x3OrLeGn9\ndVysX2E/o7UPAY14NsyEMI0yMF2rByy+FNbg9H7Svaltd9g+SNdU2kCQtNATZuN/zzQCABggNzqD\nEh6+m+iER6nRcOwwPFDcB/AeimM2xkuEidRPOmC4Bpp2iyH1903eBQA4sX2a/Q5lg7y5C4R+WFxa\nbqDasvDArZNQJAGj4cJI6/pQ9sJtB5LjERemC+AkstgCT0DV3epZHF+99Dh++ZVfZ8AQQMAmSeQZ\n02irSR7a2dECBJ6HzJNEyvZtlPMKNNOFEyso434VQRDgpXCMLgDkSg7TmaYFKzDDB4pK1NKmqNEN\nKyfHOihddN0/OPkn+ONTf06c5nUHhT4m2HHmSyYbgkY3KFGjSUWpSB6No7Nj8PwAH7+LdLIEqXfj\n5jgOo0U1tVPt+wEW11uYHiV+U2NlldFXqbl2Qcq/v0yjTAgahUnhZmw62lPXn8XT15/DzBiZmEcT\n3amRDGphMtRtNlkk+SwMTUBBzkFz9J4N9Knrz+LPzn05sRF3x6nKWTy7+hKms5P48aM/kvi3kTBp\nbu5Bota9HnXHQEZSwbkZuLzOAIG4TKDSMBOyHBrMDLsP24h6UnXL03JSFhw45h91caUBPwhw+0L6\nONS0SDMK3dJIwjmukmSZfpfdoJHCmEb9QaO1DklCR2TyvMbN8eNMo1wMAIuMsJPPw7VWDDSyWnj6\nLQKc1JomOo6GAAFjGgFgpr7xqTP0GeyWp43mSGJ1xzyh6esh0whIdtOaTg2BlcG+Ivk9LcYqcz0f\njbaFsZKKqVHy+k4sp7q20QLHAVO5MTi+w/apjLo70KiUkyHwPJ5efg7gffgdsm5tPkpWOI6DKosw\nePIcqTbZ/0oZkgR0S7kmypkY0ygy7S4qBdIB2+XUHMN2E4moLxqwHTKVjwLZCaYRgKJUguGaMFyD\nNRKouXxa0CSIJs79mEbxpL7etrDUWsZ/fON3sGPUGMuFeomlSYr8IMDXX1zE7/7VKbieD0nk8bUX\nriY8ZKg07djYbexnJy/voNUke/v8DPlO7zhInqFzN8HXyLQ9uJ6PYk5mY7vr4iJbR+taf5o/fe7o\n5NK8KhHplN2GymcQmDkG1FGJmqtEZ+snP0CYdYos4Oi+Mpa3O2h0LJbbHJ4rIqukP7fxcD0frY6N\n0YIC3TVYc6OQkRCg//SzklLEgdwh8LkWCmO9uQ71sen0+ftqywSvho2MMMGm9yIVNIoVY9eakVkr\nk6fF2CwUNEpjGtGm1uG5EvZN5XFlrdV3v7QdD3IX23kQU8ZwDWTufQHCxEqqPI33Ffz7R98ayiIY\nYaAR2UfWqxoDELtznbhMPgjAmIFAxCh0LAHVHXL+NfuwFTuxxh5lGjF5WrjvdkJflG4ftn5B70Ga\np9FejLCrRh2nds5hf2EOB4v7kRFV5KRsqhH2cnsVv/X2H6BhNRPAXcLTKIVpRHO9DNfLBo86/pEE\ncz5PvEtXOtG69AMfLbvNpGkAaXqPqiPYCsGf7yw/jxOVMwCA85ukOXxwpkhyrrYOx3eRk7KQeBET\nmTGWJ+uOjtX2OjNOr1tNrO9ocL0A9bbFTPH7xTB52neuP48nrz+DZ1deTPw8o4jgi+Q+G57Z0wi0\nPQcCJ0DgBaiCAp7jmTXCzQwKGt0xehQAwGc6mJ/Ik+EAWu/z1NJt/NqfvYkXT/YH7lPfp2MhAPEz\nApLMDsu3e8BgOzzrbikfBNAfNKKKg7ySYc2VBgONIik5z/Hh9LTe5+PtixXWZAGo6qIBFVGONYxp\ntBiTpgFRA24sT/acQ/vIGh8mUTMc8nkzooqMmAHP8btWHcQZNDQaHQvFnIxPPTAPzw/w9RevEVZ4\nkLSaoLkuZe9s1XVAslFUCigrJXx07iGY6ECYWE1ItvsFZfLFJ0JSINjvYhqZNmEaBQh68jZag7c0\n4jlYCY3qW06bfSdU0dGt+OkXegxckwUZEi/+PWhUd8mN/YG77oDAk49HQZc4k2M+RzZKpUSonJ7f\nO56PBqUHUi3xdG4K07kpnK1dZNIHqhE/f73+rsfzvXYukqYBZDxrISulggobNR2iwGO8qCZ+fmC6\nwHSZXp187njBXTcbeGn9dTTtduKgFAUeh2aKWN3uQDddLG0SEG7fONkAIqaRzRKyeFIRTcZRsNxe\nxUpnnRV22aIdyhPS73N3t+auwxQ06p3qQDcTyjQCkrTbbb2Cc9WLOF09j5Zu9oytjkct1tmRVXJQ\nvlumkSCS1/nosf34w597BD/16duhDjA8GysqaOtOT7K5vqPBtD0cniNJw3g5E/MnIfe/IOd7NPPv\nRdANZzQzEh7kIWgUJs1TIxk8ufQMnlj6LkSBSxiZZgseY3N1M43UHLmeVoNDXsr3GDMDYJM9TlbO\npn62utnAF85/FRIv4n849i+YjJIGnWq2F1+jNKZRVsxADnIA7yc6LQCRp1UaBpsaFX8vWsAu9ulU\n0PXczTTiOR45KcveixamdxxITikbFGmdWLqnHQ4TE9oFjYNGK+01PL3+JMB7sAbI06omKTznCwRk\niYNG8Qkr8Q5VRhGIMW5XsRMv3rY6dcbcbOkOM8Euxu7RWGYEMi8lQSOWzNLvjew3t86N4zf+5wdx\newgaaY4RA43IZ9YdAxaI1G6mSPYfC1GyX2uZCEAkpaMjYbconMjh+wGub7UxN57DRI58PxSQzg4p\nbMh4WNKNrJsNvLz2OrJcEc4amYhmBMl1o8oCbIH8LDBIQVsOnVe7CzBR4Nno8/ikt6JcQIBg1yb6\nph3J02ZyU8QQm3eJnLeLaUSNYdlzZzYTzLh+oJEqi8goIpMLMU+jGBOBGkHSZK/etnB25wJWOus4\nvn0SjY6Ncl7GRCgX6p6g5rg+fvevTuEbLy9hvKTiF3/yfnzmQ/vR7Nj41msRaHm2egEcONw2dpT9\nbG1HA5zw3AsL8LnxHEp5GeeWakMp+MOCUfvDdZnLiDCKUdc/zqjrjm7ZXz4jQTMc6I4OmSN7Ek1W\nD4ZyHE2ohO8n4YHbJtlr3RU2bM5eq7HJabfMlRjQMIjV0dJsBCCsMcM1WXMjLrfrF7PcrQCAtrrY\n82/ZIdPTdpomuAzZJxtWE47vxkCjZFLtBz7WOhsMpI+D1ZGnUVQMj5dUyBKP1Urvs0LPv5mxLG7b\nPwLX8/sWF7brM/YmjUHyNEdsArIOoVxJZRoFDmHRnxnSxS+Hz2HDbMKwXNRaFg7OFKHKQg/TiIJI\n8eeLBi3eA1dCED4HrT5m+hEbPPI0SjKNAlg+uXdmn7zo61e+hSeXnoleM0WeNmxvTYtXN95AgAAf\nm/9INMVWHUXVqPU0rN7aPIHF5nWcq15MgkZpnkax6Wm8GP4MJHeOS22Yp5EYNYmoBDYBYNoa/MBn\ngwtoTGcn0bTbOFk5i8evfpv9fK1O1sHBmSKmRrNMbUD35ancJDRHR9vu4HLjGgIEuGvsdub5SCfe\nAlEzsF8Mmp5mezZeDBvG52oXE/+WVUXwBfI5/cBnvqnsb30bUugTyHEcsmKGedbczKhV4HQjAAAg\nAElEQVSHLKd9hTkoXAZcpoPZ8VzqcAwAuLraxNJmG49++wKePd6r3OgXlLlHGyJxmwOOd3vuM2WA\nHyotgAPHjMq7I5J/ZlFSyfqguShhGpH1OZObAifaPazujaqGP/ib0/ibF6O9tmE14QUe83MEev2d\nuqduMRPsGGjEccB4jnwmNeNhvKTi9GJ1YG1sxHIIjuOQl3K79jdl9yKUM5J8ykI5r+ChO6aJ1cli\nFQAHkZOT8jR6LoXP9mazCY4DyiFT6gcOfAISL0GavYor68Pkqx4qIcv1fDUGGjGmURaKyCeGmWSY\n2XtyD6SgkecTb1X6uh27w77L+VA2vWt5GrvH5D7lpNz7Mgk+Ht9zoBH1trht8gD7GdX3xuVdZSFM\nkLJNjDMmUvrBRcGIqdwE+9l9E3fB9V3WjaSJg+V47Mu+4WvY0SDwHI7ujyZsjRZU1Lo8k4IgwFZN\nx9RIBjyfpChPljMQ5RA0qhHG1YUY8vns6kvscKx2FfFH9pUQgJhfLlfIvy1Mkc+iCOQBsz2SmAPJ\nhKypEUpgMSexQ+MfH/5BcODAq+QexQ+meHTTXUs5GQemC7i82uwxM2WbhJTDVIrB34lt0nnxAx/X\n6qQzUOgDGsW9R8RwRPCNOuXTThgXgkaqqDBfmIyY6TslhJphd6/BKzFpGgAykavLn6R4kyeofePl\na/j9vz7d83N62OXEbLihk6Rwq06+i3KZh+lZMD0LLbuNfSEKLgoc/Ji8xnCNxKbt8+EkiarPkstu\n5hQF9k7tnO1J6vzAx6NnvwTN1fFjR/4xm0QSD0bP34OvUfd6pNOh1LBzuB0aJCeYRk0jmhoVe66O\nzpcgiTyefms1tStMr7ebaQSQjZ0enuev1yGLPFsPuwmVFnqxziE1wT5cXgAQUabjoNGLa6/h5c1X\nIE5dH8g0anvku6GMv1YCNIoAHEWQIfIiNFsnyaAqJgrAjq2hYlTZIXp2ZZ0lGW3dZlKGghR1wXiO\nx0xuGlvaNgNNI3YT2atoBycrqZgazbIDU3ejiYS0UKT3JTBzmCgUwfsSAslgRQEtqMZKKqRwO9F1\nsievVzXYjo+F6SKjvFe7QaM+LImO4cD1fJTzCp64/gzcwMPHph9BYIbPg5fcozOKCE/qQBUU2CYZ\nG1vMEFZat5QLAETZR+AJMKzo2aGFyG4laoZFupcceFbgcLIJIwU0oknNaMyAdzegEUCSa8d3wIFj\nnl5xppFuufD8APunyL/VOxZjO1yuX0OzY6NcUDBRDgcMdO3lxy9VcOpqFbcfGMGv/KsP4sB0AZ/5\n0AGU8zKefGMZ1aYJzdGx2LyOg6X9iWey0bEQ2CFoFL4nx3G448AoWrqD1T5n226DJuj0XkmlOqC2\ncWzsdgBDQKPY2QsQrwbb9aA7BkSOvB4FjWZyU5AFGc2ArPeP3zeX8BA6FoJGpxeruLrWxEhBwWhR\njfzdBhToFGQYLajQHYOxKeLm3P2C70wjcCWsOBd6miCSyEORhL4S30qTeKsBZCJkzagxuWN3Ur1j\n1GB6Fm4bPYKSXMS15nWWW7XZ0IxojfIch4XpItZ2tJ5neKOqIZ+RUMjKuD1kfJ9fTjcVth2/L9Oo\nGzRyPR9eeDYKqpG4btM1YXs2PIt8xpNXBo9Lp75HDavJmFGz4zmMlVRUW0Yir6R73C3hGRMHjVjx\nHgeNhjCN4vI0MNBIBkQbAYLwetKLnufXXsFzoYwMiCbnxUEjWeIh8Nye5GkbIUvnzrFb2c/GM6Nw\nA6/nemhNsWPUEqyLJNOIrNU40ygQQmsHn+ZnUQ4UyWkiptFCcR94jsdiDDSizGiav9Cgtcifnv0i\nOI7DDx38fgDAdqcBgeewbzKPyZEMaxxTBka8wXo59DM6OnKYeT7GJ7wOBY2s/p5Gr2++Dc0lU9uo\nzJeGKgsQClHxbbomHNfHHz1+Bo89eRGaZUEWou83J2V7moiOO3ioxG6iEbu3il8Gp+iYHJNRzMkw\nba8n56m1LXCZFjjOx+efusS8ZoZFfD8EujxkBI/JW2mYngWRE5CVMpjKTWIlxecKAHSXMnMyGM2Q\n55vWAJrhgFMMKLxKBtdw6AHetuvkXKSSeiBiaLpGRELoJ0+j13G1uQSBE1hO0NRsFDISsxHouDru\nuWUchuXh0kp/s/W4ETaARI0xLOi10XVuWGSgSzkvQ5EFfPTuqCZQBLXL0yhqZgRBgJ0OWRe0OVmQ\n8/j4/EfAyRau2WcGNoZ2jCrzMt3Ut9mAFuqV1u1pZNoeshK53rivW8dwEqzWRttkkuu2rfUyjdq7\nYxoZXftOTsr+/fQ0mlTRSQFAvCCPNnnZLSPwOVhilYFG/QwzKWhEN1wgkqidrJxFEATYqOqgsM2Z\na+kTv3YbzY6NQlYCHzNLHC0qcFw/QVFuaTZM20vV/3McByVDNhrZmERWzOB87TKCIIDhmnh57Q32\nu3F5FhCZYZ++WsV2KE8rZskio0wjO8Y0iidkzY6FQlaC5Vl4e+sExtRR3Dl2G8pKCTZPNrR+o8fT\ndOFH5kvw/KBn8lQ8IZkKtdpxY8D4dLvrDULvLPaRp8VBI04iD+ONy9PCpIKn/llRZz8jqmyj747x\nPmbYtGN5y2wIGpUz4MJkhPqT0MLqZknU3jy/jeOXKgnZIYCYBIWCRmSz2QzXvqhG17alVzAf+hpN\nlDOo22TzFHny3cbXHP3cjTqgcOHEoBgAFgQB+44aVrOHrnuuehFXm0u4Z+IYHp79UOo10ekxg3yv\nuoOux4wqwvEcOD6ZdJAXyQG9Ho5ib1mUARMyjag8LTaNpJRX8I8+TCZLfO353i46Pey7mUYAOTx1\nx0C9bWCtouHIvnKPSeygSCtKGNMo7A6lGWHThFWcuQazD9ipmQ5cqQ0EwFwxnO7XScrTVFmAwPOx\n7lH47KrJMfRLYbf/rnFSIC9VK1BlAQemCtBMlzE7il33aCY/BTfwGH038r2gnkahEbYQmUALnADd\n0Rmjgxbr1CfCN/Io5iQoyIOTTZb4UenGeEllbBjDJJ0gqnc/OFNgoBFd55khE37o62cLNl5dfxOT\nmXF8+siDkP0cgoDrkU2oModA1jCZnYBheVAVco8VQemRcgFh19sTWXOD3Me9TVAzbQ+8YiAnFDAW\nXh8nm9BNl+0NGepBEV5nKUO+K8PRE0BRKaegX4zkw6l0gsT2z/g1UVByvKSSYQxti13D1SaZBlQO\nx86rstAjT6NeAf/kY4ciGaYs4MceOQzH9fFXz1/F+dolBAhwZ0yaBpAGSeCQdRRnWNx58Ob4GkVM\no1BaGbKMPj77CMbVUaxpG3193ZoayRsowzqXkQDehxu4EANyH6mJOs/xOFCYR8Op4t/82G34hx9e\nSLzW7FgWo0UFxy/toKU7DEDYDdOIruVSXoLpmawIoHKvbrlDPLZrFrydGRie3sNQINck9p2ett6u\ngOOie1MxquH94HpAI+pntK8wh4OlA2jZbQbypzGNAJKLBAGwuB6dIWSimslYtUf3lcFzXKoZdhAE\nRJ7WxTTqx5QxbY9ZDEDR0YlNDKTr3bXIZzx1tTqwiJYFMr2SSpAAAhqNF1UYlpd4b9q4ouzmuNE8\nLd4DVwLvke+1oqUXgp1YYy/haSQLKObk6NqAVAa253uwPRstu81AJfrd52OSKI7jkFHEnumgg6Ie\nmkvHAeExNd3XKAKNqgngrqXZbIogy13joFE4DEWww2EWdtLTSOAEPPnqOpsoLAsy9uXnsNJeY2bQ\ndHJat9k5rUVc38WP3vJZ3DtxjPy+1cb8ZB6SyGNqJMtyRQrmx60cLjWuQuJFLJQOMM/H69vRuu0G\nM7pDY0yj5HPiBz6eXXkJIifgU/sfAQCci7EufLkDTk5+90ubLbxxfhvPvbOGWkdHq+PhK89chuv5\nyIpZ6G4EbLqej3/3x6/hPzz2Vl/Vwm6C5oIjahmBkSdTDTMa23u7GTbLzXWod72CD33MQDEn44tP\nX8J33lrped3uiEAjKk+LMY0EN3EmA6TJRc+9/YU52J6Nit4LCpusUaNiLEf2Zz2Ue2oGYRoVpRLz\nB9K95PdJmXJrOxprztG1r7dklj8NkqdZno2V9hr2F+YY0NfSbRRzcsLMmnrunrjcH9yOg2AAqWsM\n10wYSveLbk8jut/TpsEnPjDHfrd7Wlm8maGZLqyAvFY8F//UgUfABxKCyStYqfQHvihxgTKsz9fI\n+R03F5dEPmmEHeal8QmB1FOYnhWb7QZjoLWdDjsLaI1F/QyHhdbV3MuJWRiu+Z6rVOLxPQUaBUGA\ndW0T4+ook+4A8elU0UJpdVwEehEd1FAukkXTDzRi8rQYEDWbm4bIi6gYVbR0B7rl4o6DoxAFHmcX\n3523AX3o4jFa6L2GuJdMWoiKg8AVUc5lcXTkFtTMOirGDl5dfwOmZ+L2UMvbDRodni2BA/D8yXUE\nHFlMlC5KZT+2b6d28ZqajWJOwRtbx2H7Dh6e+xB4jsd4ZhS61wE4bzjTKHYIHQ7Bku7pJ3G9vCxI\nGFNHGLhXNWpYbq+xyWLUB6If0yh+/Q5MiAK3J3naWmeDUUhpUhHwSQkZgNDwzEztGvRjGl1da0GV\nBeapMF5Se+RptIhu3STQiB5y3R4KFJHOihnkpRwM14Dne9is6xgtqmi50Xe0pW8zM+zp0Why2qEi\nYQAmQKMQMAkcBbYRHlQxLXPH0eD4DtTwIO2WqL0QGr1+ZuFTfaeJUU+HPcnTYowVeqBlpQxjaGx1\nqDFdZIRdaZgA8zRKHi4/+KH9mBnL4rl31pi5ObvGPvI08rMcAgQ4sUTW8R0Hdu9nBPSXp5XkAsYy\nhFFAnycqHSGgUWjkLTqoqRdSX3u7boBXNSjIY7RA1mg30yjeiSRdQ/JeWVUio9nDZJDS8mkC7HA6\nHr57JmKMaK3wfiTv0Vwof10LGwbse2PT08h6ptPTOI5DVspAS2MahXuI7BUhiQJyQgGc6GKzSb4v\nxjQqRqARfBFrOx3mL7cwU2SgCi1EmRdMn2Kb7qE15QK8wMMPLnwSsijhhx8+jBxfZNRkGqJqgeN9\nTGTGoZsu+45VUUnt2geci8ATsFGLnquiQkAjWpgMC920AMlCUSoyuQsnm6E8LdobyO+GoJEaJqwx\nTyMAKOYHMY1UgHchchJ75uN6//hI9JGCgnrbYmwpy7PAZdso58mkmvFShviMxYCWK6tNSGLkeUTj\noWPTWJgu4PVzW3hthTAtu0GjZscC55J1FPdyuWOBFJxn36WvETX4LuZkbOsVdKQ1+J0SRsVpzOZn\noDl6D8jn+R4ev/ptNJ0dFGNgXD4jAWHRyAdJphFAJGoBAhTGNIhCMpXjOA7HDo6xYoKCRrvxNKLn\nRz5P9mLK7KMygH5MIYDI7aU28Vt6e+tkz7/nwj0jLSomydPm86RA2DFr4DkudWAHNRSfz8/iYIm8\n31KL7D/dvm40aDPtUoxJvt0w4AcBZsIcLKOIWJgp4NpGK+GPBQCuR3g1lHlMoy9oZLngxHCP4T1o\nrhbz0YvOTICc1d1nSneUlRLqVgNrO+RvZ8dyhLmMZJOM/jfNvZJMo0ie9sAh4oG1UksvBJk8Tc4h\nR+VpgsvkaZwUvW4a0B0v7ChorjGmUZLdklGEXU+HAnrNpQHCNAKSE9RadpvlJxWjylgXTLoX7ttp\nnkYuR37XM8naoEwj1/Ox3W7CtUU8/vISvvHyEvubQ+UD8AIP11sEjKDARjdotL9I7v19k3fjE/MP\nswYARAuHZsjvTo1kgFAiR9l+FDS60ljEWmcDB0sLkHiRFbmrjR12DcPlaUnPRxrnqhexpVfwwNR9\neHDmAfKzGADc5kkeQyWzpmcy1ssnPjAHSQ4AX8CTb6zg5dMbyEkZ+IHPzoCra03UWhaub7bx6194\nGxc31/Cf3vxdXGlcG/h5u6Meu7d6I3wO7AqrvVpdvkYUUOGzHfyf//19KOVkfOk7l4eyS2ttso7T\nPI3Ae6nyNNqcp35T3Y3SIIg8cFQxg8k8yTfMUO7ZsjRwgocRpcw8IF2YCXkYJVF4fsCAZPacNSXs\nnypAFLgeppHIi1AFBZqj43prGX7gMz8jx/VgWB6Z4BmTsd26r4yMIuDElZ2+TY9uplGhawjMoIhA\no6RcixIbpkayuOfwGPEJlDPMmw2Imhkdwwknp4XPeCzPzEs5HFXvAyfZeOpa0qMrHtQr9JH5DwMA\nzofrvmrWwHsKBEhkSIUkgEMoT5Po1Ntov6NqpbsPE7BtvRVN0HN9Fy3TAAciieawByPsLu8nZjTf\npylM42ZNhQe+x0Cjlt1Bx9F65CnlggyOSxbk9bYFXyshgA9PpgVB+o3b1LeYJIcGx3EoyUU0rSY2\nQ0T+wFQBR/eVsLzdGYqA9/sSTNuF7fiJTqwf+GzDiTNRItAog7TweRuBSxLr20ePAADOVi/i2dWX\nIfESfvjwZ8i96Cpus6qI+ck82WB4ChqRg4EaYVuew4oAKk+zbA+m7aGUl/DS2msQOAEPzXwQADCe\nGUOAAJmCg+U+Zthph9DhWXIALnYlRHG9PEAOw7bTwTdfv4R3QoPyTx0gXY6KRQrB7s4hjapZg8CF\nyK9rYKyoDh3TTGOxeR3/z1u/jz86+Wfkc4WdMJ+jbKDoe8xKxPAsragbY99vkqK4WdNxaLbI5Ifj\noaktH0gs4aFF9G7HUw4Ky466jt2gke4Y4MBBFRWWBO5oLTQ7NqbHsgkgaEuv4PBsCQemCnjg1kns\nhIfRkREyESIu3WrbbQicCPgCtDa5zvhBQV/3gal7IfESTu5EoNGOUcW56kUcLB7AvsJs3+sqM9Co\nf5egO+Lr0QiL4oyYwVgmnMqkk8/VstvIiCokQUKlYWAkl4cqqInnyvZsfO3K4/j0IwUEAB574mJi\nKk0/I+z4z86tEFCEFqi7DWp+SxNb23NQNxuYyk5CChMABhqZUcHUsFrkvjkytMKl1MN7tdoAJ1so\niaNsP4h3PTTTTUy6yUs5mJ4F13eRy4hwvQB26JdEfUWOlA+B80lR8an75xnYW9PJHhA3wgaAmXCC\n2kboa6R1TU+jHj9xE+icSCjv3Z5GtFOU40kSVpLJullrksKIniFjJRWWHw0aWK1oWNpoQRQ4zE/k\nY0wjsu7Z1Kk+hQ0pzAJsBVeRFTN4YOpeAARoXBiZRsfRkt340LtlRB6DYbks0VcFJXV8tQsb8ERc\niRW8jGlk7W7faDrE5LsslxkIy8kmDNOF4SQ7WLrlQpEERlHXHGPX8rRyQQEneBAgMWA8LrmL+/6U\nCwp0y00AX3whkiVNlFVYjsdMLnXTxep2B4dmij1ACc9x+GefPAIgwMX6JZTkAjOnpdHoWCgpYXcv\nxjQq5xXMTeRwaaXRw9DcS9BrK+RkPLf6CsAB7uYCOrqDuZRJgQDxXnrq+rPwRpZQioFx+YzE5Cm8\nH4JGsbN1ITTDvhabWBgP6msERPLoaHpa/wKdFtKU6czkBpnks9YdruejUjcwk51FTswmZDo0cqrI\nzMK7o+mQZ40yFSmdv5xX0OjYCVkBZRrNF2ZxMGxk0PvQ1oknR64LNLplrggOwJXVaF+nud/MWLRv\n37Z/BJ4fJJ41gExOA9AzPa2fPC3BNALgSx3YIbOFAoeBo7CG0skrgxnuI0oJtmdjtUY+P5WnAcmG\nabVlohTzBGskPI3CAteT8EMfPIogGMA0otPTxGyPPC2riBBibBPT7TVEjhcylGnRSfE0AgiY2c00\ncn03tUEXmUsnJV/RBLXoPq61I/PeqlFj+d0BKo0Nz4M00Mj2yb85ndDLz+7g/FINv/wnb6Bj6+A8\nCbLEJyby0eJ7MfSJoXtMuRs0KszjF//B/4GfvuOfg+O4kGHBAZKNhRmyr0+OZMCHoCMFbilDiQ7x\nOVomE0Lpfm5Bw7GDo5BEfleeRsrcIv7k3GOo6NE9+25ofP19+z+KqewERtURXKhdZmyGqkcAkCmR\nPHeGa2IrBI0euHUSvOBjdpRcw6mrVVbYUoYO9e86dmgUlYaJ33v+77DcXsXbWycGft7uaFpNSLyI\nwJEYaLSubfawj2m0TPI96X4HM2M5/JOPkVz28hBzZAq6jhR6GSWC5PdMh7Y8C0p47u0LAfDlLtDI\ntD0mf8yKKsZzBQQBBxvktZrhxNexzAhrKnOSnQCm47UGbeTTtR9YGUyNZlHIyqnMUOqzebVB9mhq\nc0D9LIs5OZFbigKPYwfHsNM0UydQAqT2knmJqREiptJeQKNwz6JTvGPn4b/+R3fgl37qfhSUbMJ4\nOhcDjSp1AwifGUo6oPH9Cx9D4Io403mzx4OtY5DmJ80f7564EyNKGRdrZOpvzaiDd7OsYcBxHFRF\nGMg04jkO94UMre3w+aI1X9vusCnjxZzc49kHkD21m0HELEakbOL/tQHA3CtnNvBvf/elXfsmDYvv\nKdCIMkroGGYaAs+jnFcSgEutbcHXQk+HUNefBhS4vosdo4ap3EQPi6GsFNGyO6xzMzOWxbGDJNEa\nZEzY1Gz87//5RTz9Zi+1saXZ4NQO2qVT+ONTf45fe+3/xb997hdxOXgx/Ny9TKOZ0d5CMwgC2IEJ\nuBImyhncFrKKvn3tO6iZdTw08wBmc9PgwPUwjQBCxQYAOXzmKNNI4AWInEDkaQVqhG3hlfU38cXz\nfwX5yHFsjj6FDW0L904cY4DGeMhomJjysVXTUyczpMnTxkoqijkZV3uYRuEmEW4sU2EH5etvnsYb\n6yfBgcOHpu/HmDqKulsBkG6ETacFUDmj5ugYK6lo606Pj1J3bGpb+KOTj8LxHTTtFkzXgm464AB4\nIBttUp4WmsSmoLpswl9sDbKpNbNRckONsPkgSpro5rZbmcmgSFDRu7wndFdHNpxqQIGM5SrZzKZH\nekGjrCriV3/6g3jo2DSqRg0cODYRIv67LbuDvJQHwKFWJ4ljHACj1M7p3BRuHz2KTW2Lsf9eWns9\nNLR8aOB1qaL6/zH3nlGWZed53nPyPTeHupWrOndNDyZgBhAGAEEIYJBokZRALUlLNCWtZQUuW5Rt\nUcGybEq0xEXKMiXa5FKARdMyLWaKZrBIgSAIYADMDCan7umpzl1d+datm9OJ/rFPvHWrZ4A/1v4z\n03XvPWGffb797Xe/7/uRUTLfmKdRYjxGTCPVpJ4XyWW4C9i1ehT1ArYjKmvVSybVTDkFUH1t90W+\nsvMCX2z+Bz7++DwPDvt84ZVYEx/e70x5WvC3W4cNchmVtYWT33lYi5lGYjw3Rkf4+MwHvgh5LRfL\n04IEwczI9Ow+dbOG2rwMssMf3P/yiWPfa4uYO5+tJ0CjYIfV84RmO/E+5xO7T6HEYDC28XyP+90H\nLGTn2Tu0cccGasZivpKNwN6wdO+0PC2uoHYQHG+qelrINEqUm89qWYb2iJwpJvEk08h3NEoZcY6a\nKcCfRsAqi5lGRoJppHBvr8uDQ1F9RVNlsqqJoegJptHDzVpbvQlyvs3IG/BE/QMocsxGCGNncjHj\na6IvSmqF0STJNMqc8DRyPVdIlCSdl989jGJvuHvdfZ9Mo64d0/krmRg0Shlha7ERdjajxtXUnGl5\n2sM9jZBdZF+dKU8LAaBCVgvMRf3oHQRQCq3Iby82wxbXd3u3g4/w7ZvVLq+VWV638ZQJj9YeSc35\noVl5OZsXfkBT/faBs1Vsx3vfnoZffG2bf/cfr6f+Fi5UDMPjhb2XyUh53NYC/ZHNcrAZtjNVQe1m\nO5C7ajblRL/mEqARrngXkkyjEDQKGTbT7cqZCoosoaly5B8VARzj0+fGcJFkZMSCPQQSC1OGo9Ot\nEbJ2ajnOltZpjo9PzGkhw2Oa6eT5PiNJvGuzQCPX81Pn3e7tUDHK5LUca4UVZEmOQOteYLIuT+V7\n2YzGSj3Pnd1uBFrtJUywU/02t81/uPe51O9DcHyaaRT36RRoZLuQYONIxjCak0MwwbcMPvaBBTRV\n5s0ZBUOSrRQAA3udppCPmNoJawbPE5Wz5ooZSnkdibR3T7hTbapZVusFFDfD2BvOfKYDe4Ch6GiK\nFlVPk1RRPU2SJIygAIaEFCzi0gufZJ4UMi2npcdhMw2FiR2DibZr86Mv/FP+/c3fOXFdXasnJKxT\nQMxcRsTZpF1B+K4pksLAGdIaifG4FlRDDdcUo4mDIkspQDDMGYZ9FVmSOei1+alfe5NGa4ikOpyt\n1zi7UOCwNYyA5gtToFFY7a40BXA5rsdrb445bMUVslTfQNJippGmKmSDpUEYhzNqhrJRioqSXK4I\n0KiciZmjZxYLLFSy7B8PH+rf0vOOkZdv8PbRO/zEy/8rz+++xHZvlxutW2xULrKSXxJ+b7UNRs6I\ne90HwofV3sa3DHKeWBCPnDGHgS/mXEn42eX0DIvVLO/ca5FR4jkEhM+aqkj80Gce5y985yW8osij\nrh9ufUOMiNakQ9kosXs0wBuJ+LbXP0gwjdLjsW+J8RjK9kOm6rR1xnQ77k5QZCmKf4NAQiYhoWke\njfYoBYJPXCvabF4NNkKnmUaDsZ2oiGqiKgqSo2NL4hp7Aeu/nqvEjBltkoqbSRJFuJEv8nQZ38qw\nUDEpZLVTQaOBPeR2R7C7QrAzCRqJ78V2BB+8KJ73af5rQ2ecqn6W/waZRrIkRzleaJEQMo1A5IPn\nlorxeAqMp5Ns88PWKALqp3Pxi4tzeIfncaQJX3oQ+6y9fafJf/vTX+W1Gw32h4eoksJcpsqV6mUG\nzpCrzevifbOyKWlyRldTTKNwc9OyXe7t9TizKHzJAI4nIv9cDXKAgdOP5kKxKZL2Ox7YQ37k+Z/g\n9+7+wYl+goQ8LSpOczpA/PVrB/RHNrM1HN94+08KNAq1x8szjHBrxYxgFwW673bANALYGexSzGoz\nQaPGqInneyk/o7CVjCI+PlsBPXexluWx8wFN/SGg0fX7xwzGTqRbTLbuwEY7+w4H2tu8eXSNrtVD\nkWS2JjcBPwV8HRyLADGLaTRxJ7i+y/n5Of70J88zZ1apm7XInO7Ta9+KIiuUjZ/GXskAACAASURB\nVNKJalYQU7FrZTEwQ6YRCO215VpRgno0bPGL7/46rzZfRakcMlJazJtzEdMHoB7s4hRKNj7MrEIy\nbTwMApG9sFyk1ZukWTjWAAkp1moHz0cuNtkZbXOxfI6Cnmclv4TNWJRQnAEa9awBjudQN2uYqilA\noxlyxunWnnT4F2/8HANnGJUtPx63GASLpVgSk2AahbvwM/T7ofwwGcjfCILrxdU4YSjmdCRVSE3C\nFjIvHuZp9L/80mv8q9+6eurnYUtR0WcwjcIAFwb0nVYA6CSYRpqsRjTNsDXHLUpGkfmsACrC7/q+\nT9/qUzYKaKpMtytCU9I0MqSL1zIVnqh/AIC3GtewXZsX9l4mp2V5qv74e95bOVP6xjyNEuNxmAi2\nS4Uqvg89u4vruQzsIUW9QDOorFUvm5QzYalxgfZ/cUuAvo1Rk/rlXfKmxm999S7t/oTd/j6brVsU\n9HwK2Ahb2Ne9yYArZyonFjPv1XRNRpZio9DI2D94Fnk9T98eimcxstFUmbEv+r9kFDEHF8DO8Oz2\n8yl2BcBeXxxrvbSIpiqYhholDsMp8AZi+nDfHkRg0mDssDc4YOxOOFdc5w9f28a3DTzZwnbtiA3U\njYC1NNOoqBfIaVl2g/g/GNuoihRN0CHzJvku5jSx2+T4YhHTHVrRBoE3ylHKBrTmACAMiwU0u2Ox\ni6YqsWTKU3h1s4Hj+pwLEnZJkqhlqu/b06jdn6BUBYPk6fknUp+FsTMpUXPUwAfDLeITS3ozioHj\nu9gJH4AQcKnl84wtl1c2D6N+E/36/sDmgSu+N5etRrv0kaeRc5JplDXUhOn4CNNQURUxdksPk6fl\nBWiEr6DJYsGVkqcl2DiVvAGqAB3PFtcxyCEXWlHSOg0ahXPuxZUypzW1LOLuudyF1N/7IxvX8ynn\nDcp68YSB+DciUXNcj9949ja/9oUbqQV3uFDZte5iuRYXM4+DLwvQKDebaRQa2kqqlZL95TOxPM23\nQ9AonltLhvCmChdz0y2bUfnMt57jT33iXMTKisHPhzCNehMkQA5Ljk8bYZ/iaRQBMNVsgv2TBrTC\nXeFpiVpvYEGmj+TLrBVWMNVMJCeJCnYEc1vX6tGxeqwWRJ6oKxpr+RW2ezvYnkNvaJ0qZb+0VsJy\nvGiRNQs0urhaQlu+wwPeTLEDT2MaZQwhVTjJNErI0xCgUThWonfWNlioZLlypsJOY8BR+3SZQfhu\ntkcDloPrjZlG4nft/gTX86mVMvEu9gymUSWQnea1AmgTXr+Rnu9BLETCuctUM+BLoNhRYQY9I+43\nlPJOMySTfReBRmMHM/BvS91buEEQAOI7A2G+fDsoB55s7VPMpauZMhJSmmkUzCkb1YvBb0U8D0HU\ncBN3GLA9kyDzyBkheQqDgUdGNtnrtJEkiR/6Mxsg+RSNPMtzOXw/Hkclo8hcpsqdzn0834vlaVPV\n097davGbX73LP/mF1yJPJN8SPlFJ1ls2J95rjXjuC3NlXdY4E8jckszR9YU8i7Uslu09VPYyrFwF\nSUhxFEnmF9/99/zMG/8GgG9b+9boe49Whdn4O8dCtjZyB7i9Kr4bb+octkaoikwhL8aGpmg8caHG\nxHYZDUSfDuwhnYHF1kGfS6tlDF1h5dwEyRDP4HB0wE/+yuvv6cUEggzQs/qUjZJgvrgappxnb3Bw\ngn0MApQO57jwmSzP5VAVKer/01qrN6ZSMKK8bWCPkCWZol5AVl1cz4/mJ9dzcTwnsgEx1Qzz2Tke\n9HfSoMDIiTYEzMBIWXYNPDkYj14wV5uVaJ6XNCuV0x93x4KNCjw4FN8/Gh9jSnlAeGIVsjoT2z2x\ngZ7Tctieze3OPeazcxHAEs5foVImr+UY2EJW+3ggD3vjFNBoZI8iVqr47ftXUIQb2uH7F25+VxKg\nUdimjafzKXnaKIq506x/VZFZkx7DtzX+cOvZiBn0ey/cx0eAhweDQ+rZORRZ4UpQdTUsCOVPTIxE\nEYSMrjC2YqbRKACx7u51cT2fS6vlaHOt64i4E1Y9HXvDSO5dzutYjpdiWu4NDhg543hDKewne4Qq\nqxEJJASNTqug5rhiE2yplqU0oy+/mfafFGgUJlNhcpVs1aKBF5ThA7F7olgFdEVnq7dNrWTS7IxP\nIOtx5bSToFE46ex2xSSzVM2yMpejUjC4evf08ru3d8TCaxalrN2fIOc65KQyP/Et/4Cf/Nb/iQ/W\nH2fsjZAygxTTaO94SC6jzkxwwsl9oViOEuiQbfTE3KPMZwXqW82URZnFKRrbU5fm+PanV1ldEINK\nS1Qz0BVd6G41hayh0rLEgH4k+0FGr34bfzL/1/nRj/13rBdWo9+Eu+VaTlz/rECbNB5OtvORRC1e\nsA7sATktG9H1dFc8C3Ux8EUJjMpDqaKc7VKYsbsdykeqmQo51WTojN7TGH1oD/mXb/wcrUmbP3n+\nu/j48keCY7Xoj21yphYtct4v00hTZUo5PQIFHxz2+cqbuyxWs1FFlrBJioNnx30UMi9Oq57m+T43\nHnS4dvf4PXdiUgni+CTTKBf6UwQB9aArFmELVZPjcQtNVlkvrHE8bmEHZo6u59Iat6llxASmSEoE\nVI6cEY7vUjTylHI6/eARJyeKcOFdzVR4vHYFCYk3G9d4vfE2fXvAx5c+khqfp7WKUWLojE7saJ7W\nkuMxTBhMzaRaMME2GPnC78DHj0ywAebKGapRqfE2rx6+SWvS5pnFD1HQ83xp51k+8eEyE9tlc7vJ\nv732S9iew/dv/OmZnkyhtlvSJlz5BqVpEBqFKtGi5DBgaS0EyWNey+H6LmN3TH9kp/yMSkaRjKLh\n713C9mw+lyh/DHA8EQnA+arYESvl9ChxmFWSN5+gw4Zg0nBscy+QhpwrrQvPGV98r2P1Ejt0olqY\nPvWsJUliObfI0egYy7UC4FaL+nIW0yiXrKCWExTsxqiJh4c/ykcxc6U0F1xHB88XoH0IKofjqJzN\nRmMllAaAiK0jZywqSL1H9bTjngCNTCXDRuVi6rN6EKuPEhKAiRIwDSbBoixYiM2Sc4WLr+WKiJFf\ne0sshML42Xmf1dMGnjjnfLZCRs2gy0bMNEqY5Pu+MAY3E0yjkT1CkqSoXx9mhF3Mq0iyj+8qgpGg\npH2aogpjgadR6I1SNArk3QUkzcLVRPyI/LDaog9uPuggIaRGp7WxsY/vSdTktdTf29HupU7RKNC3\nB6l5c2OtjKpIvHP3vc2wbzxoR0leUnrdCyqgOZK43nlTALv9kS1Yf7LK7iAGjYb2kO1gYSupdqpf\nk0wjzznJNALBNurbgxPmv2H77o+d5U98NK5Em82kWYuzWqsngFXLC8zd1TRo1DuFaRQypxdr2chn\naFo6F8aMaTPsRlvkRyZl4aGYqXI0EvNdXLBDPL/Yzyg2Rz1bWsfxXe53HjAcO+RNDc/3TiTTIQM7\nBB/3jweoihR5A4HwD5MM8bvDQbxAiphGU9XTZEkiY6gMp/p0PEnL06TMMGJRxvI0nXLe4MkLIr96\n8/bpErUoF5HdtEciMYPyKOHZBkIq2krsYh8PxXlrefH+zBfKSIrLyzd3T5yvbw+iTQJJklB8PWIa\nAShGYGgf5IbTsv3hLKbR2E5tQoQtjH9hHN7qCvZJcwaLvp2Y25JNkRUqmXKaadTfQ5c1rlSEvUPP\n66DIEitzIudqJphG4TWEbWSPkH2xoBv2ZVAn/PXPPMbKkngOWc2MnsNuAug4Xz7L0BlxMGzQmXTJ\nKJnUhgcgvBMRceEnf/l13r3fElU0FQfXj9+NTCARHY/ipVroa3ShfC6SAiVBo7X5QlRcZ+8Uidrt\n9l0oHaBP5vizl/4U/8NHfphL5fMM7CEL2XkeTVSl26hcQJEU3mm+Gy1ivW4F1w42dZwRh60R9XIm\nMj3WZQEaARwdi/di6Iy4FhQZCqWzLx28BkBJLyOpDpt7u/zDn3uJ/+crdyKT8lktlv2VI7nUgjlP\na9ImEzAkOwmmUXdg4csibo3dCSNnjKrIrNTzbDf6p5aSd1yPTt+KTLBBrM2yqimeacAWCkHDMKdI\nrhvW8iuMnHGqGMZwbMdVlAPmjOqboAjz+Ali/qtkynH+qFqRPF4wCi0Wq1nmq1m2DvqMnQk9q4/i\niLG9UDUjU/DTKqhZrsWF0rlUP0FcdCinZ7E9B8sTeeXF1RJ3dronWFx+AMqFaySI5Wm998k0yiVA\nnuRcPd2m5WC5jADO+iObRnuErM9mGgFcXKrh7J1j5I754oOvsnXQYzOoCHc0bDN2JxEou1G5iITE\nu4EZtjc2U0wj0wiZRgFo5IrrCT3zLq2WItBo6HeRJTlaU/vKJGLql2YUpAo32kNFRtjEGi4G15K+\nU7Pa3b0uE9vlkW/QR/Vh7ZsCjTY2NqSNjY1/vbGx8fzGxsYXNzY2zk99/r0bGxsvbWxsPLexsfFX\n3+9xdwf7qLJK3ayd+Kw2VZ2q3Z9QzmdYzM5zODqiVtJxPT9V+QfiEsynMY0AjgZtSjk9Wqh84FyV\n/sg+lbYYyo5maQR3+4dIisuCsUTJKCBJUkT9U/JtWsH1O67HUXs0s3IaJKuLxZ9/bOnDLOUWovKc\nIBbiPn40kYZN1xR+4I9dRgvmZ11OgkZaJM0oFwz6XiDB8OfA1SPZWrKFiYGjimC2NcM8bhbTCOB8\naIa9F19jMiEB6DTF9YVJcmimG4FGZp/8lHkixElF1ayQDUx6azOkYsn2B1vPsjvY54+ufgt/7Myn\nE6a3LQYjR/guuBN0WUsZLWYDFH0WaATCDPu4J4DLX/7CDXwf/vy3X0p5b9iuDZKPY8vRRFV4j+pp\nw7Ej6PsT56HljiEtf0x6GllB9bCofHIwru41mkiI0o/NcYtKpsxiro6PHyV6rUkbH59qpoosyVSM\nUgQERewRrUA5b9ANQaMk0ygBGuX1HBfL57jX3eL3730RCYlPrDzD7Z3Oe95bmBi9X7ZRcjyOEvK0\nUk7HtzJMGEZsg4Kej3Z562WTSkD3Ph63+IP7X0aWZL773HfyfRe+G9uzuYsw7/7a0RfZHezzrSsf\n48lgzE63JHX80bPfXPA2DZVRYMwa6q5jplEwOVuDCDRqR0lVCV1XsA6XmctUeX7vpdRCKnz3lwtC\n3lnK6fSHonz8tEwMYjlp3x4mtOROJA1ZL6xx3J2IHWxEcheC4mNvOHMiByFJ9vHZHxwyGKXNt6Pq\naUnWXzCOha+RRn9osxdsEPjjXJQsLRZE3Bp6PTp9K9qFB6IYWC/E13RuMV6IJCuovVfVqYa1h6RP\neLL+WJTIhy2MnUmm0ZAO3iRDtycS6tCgOJZzxe9xeP/lbJ4rZypsPmhz0BJ07oKWn8k0enWzwcG0\nQWeQiM7nxfUUtWLkaTR0RiiSgi5rjC0Xz/fJGmq0cxia6AuD6tP95QByWZHQuI6IexnFSDGNIrPo\nrCb8j0LQSC+gjgXAdhz4ZiSZRo7rcWevy0o9f6LiT7JZ8gDfMun30wB7J/JJMCjpgmWc7DtDV7iw\nXGLroPeesSjpP3Mr4YnRGdrks1q0YC4H1ef6IxtFVljKzrM/OIi8Wm6170Zly1GtVJKc9DRyLTW6\nxmQ7G4Izp0jUppuhC1bMad5cvi8WI5WCccLYVFRRlE7tm72EP9DZ4hoS0glfo9AAuT91/q12Qxi/\nagJUnzNr2J4tvGumkuqkn1HYzgVSvRvNe/hAPivzr9/8t/zI8z+RmlcvBQy1m9udqGruQjUbeQ5C\n4K0W/HOnG1d0jZhG2sm0OWsojKbYW6GnUV4uI6MgJ+Rp4bzj2wblvB4Zpj5MomaEYHsCNMqbGoam\nRLlO0rMNRMWnZNXeTuDrslAMQKOc6I93d/dTsc1yLWzPTvuAegI0ithuavA+Bc/sJNNotqfRtJ8R\nnJT4hR4wI2d0It+KqmYZJyWqtUyF9qSD7do4nsP+4JCl/GIE3A+9DjlTO+FBOZq4KT8jce4xmiS+\n59oakurw2IVyqtLTSggazfI1at+jM+mekNEBUa7x6adWGE4c/tmvvIFvpxm5AKoh+qOfCPE1Xcz7\nZ/PxYj+Uv2nZCaWcHjHnpit7gXjHf/PW7wGwMH4KSZKoZir8N0/9IH/5Az/ADz7+F1N5b0bNcKF0\nlq3eDq8diqrGXq+KbYlY1BkPGU4cFipZbE+Mb03WubxWJqMr7B2Ivw3sIVfvxH5GlmvxxuHbVIwy\nn1wVVXO/59trFHM6/+H5e/zYz798KguoFVVOK7HTGCABZ8oiHgwlkW8mZVnH3UkkB4PYB/bMQh7H\n9U/4EoWtO7DwgUox3rAKN7wNRcclBI3E8483m+M4Hq5h9gL5PYjcPKyMF4IOuiSeWXPYwZYDRmDC\nCFvSJhGo2u5P8HyfatFgfT7PcOJw+0hsPnjjDJIk5s6YdTW7ghrE4xVOytMiiZkVS9R8TsapiTvB\nx4/WSPD+jbA93wtAo/ia2v0JsiTNJFSEBVHCeCPLEtmMSn9kc9gaohkOElLqeGG7sFzCOVxHQeOV\ng9f5wquxxURzLO4pJJjktCxnimvR/GyPMilpckZXcFwfNWABhkyjiA29WhYSU0PFkntUjXLM8Nas\nFNMovOf4Wo6jvksCQkItkpQABpu4zuzxG1YBvbL+/zNoBHwGMDY3Nz8O/H3gp8IPNjY21ODf3wF8\nCvjBjY2N+nsd0PM89gcHLGXnU34QYQurUx33xrieR7s/oVIwWMjWcTyHfFG8TEdTZtgh02jxIUyj\nvt1L0ZMfOycmwVm+RhM7rh42rUME2B2IyW45Fyc0ocmYUelEi/pmZ4zr+aeCRtNu8gBnimv8yDN/\nO5UsJRe3s1oYxJMLGUPWIxPYUk7HkoPAPMlGf5tuWdXEVDMM3E5A6ZwNGhmacsKg9OxiAUmCO0Fy\nHQaJfGLw39+d4Fvi5cvYc9GzCQOuXuifoDQDHI/EfdcyFXKaQMVLAUX2tApqYfD+E+dEta5q4Hty\nODzGcT1yGY2JE5vZhc18iDwNhE+K4/p86bUd3t1q88SFWrTbEvVR8Fvf0SJWkK7oGIp+qswkuWMS\nVqk4rbV76cpX8XmntLDB4r857PLEhRqmKTGwh1SNSsRgCcGJcBd7LuinaqZC1+phu3aq8lgpr+P7\nMqZiphL15riFqZrRQv/J+mMCIBge8mhtg/axyo//u1f51T+8OfOehmOHg9YwGhPTvkaHw6OZJSeT\n43GYMPo1DRVsEySPnWDXXzCNxLOpl82oGskLey+zO9jn6fknqJlVPrL4tEigJrfQ1q9zx3qLpdwC\nf/ri98x+IMTlOzN5i/nybNP792qiJHHMNNJklWrw7oeeWJ1xj7HlpplGehFDlfE8iY8tPYPjObwc\nGE6OLQdX6yF5akSBLuV1fESSERmJz/A06luDCNgZjG3udu5jKDoZt4zn+1QyIlHuWN0AwBEebdPS\ntLAtRRXU9hhOmW+P3TG6oqcS2ZiWO6KY1fF8n+2OeJbeKBclG5VMCXxhEBourOaKIWgk7m+xIq5J\n12SW5uKYlASNFFnG0JVTmUY9XSyOn5o/KbOcy1SRkGiMRFIydsZM/AH+OBdtgoTszCghSuzah/I0\nUzH4xBMiHoZso5JRoGt1U/PQcXfMv/zNt/n1L99OXYcliXcy9HkqGyUk1aE3Gabo4eE4y2WEtMxU\nM9H782c/dYG/8t1XTsT4ZNOMoLRysBNtKHra0yiIZzlTo5KPQaOSXsDuiGu73xf9GTIpGu0R9/d7\n2I53qp8RiAWR7Y/A0U7Ik1tRRRY9qjw3HXOvnKngA5tbJyXfN1t3+Nm3/2+G9pA3bx1F4EHIPA7v\nrZjTI9CokhXnCcGC5fwStudEi+hw515CDphGSdBIhQA0ciwFQ1dOSFtXcmI8TO9IntbkoLz5LG+u\ngT3kfmsfx/WoFIwTkkVJksgHAO2stt8cosgSc6UMGTXDcn6Rrd52KjbHTKP0MR4E7+68KeaeJNBa\nLqST6pBptJYwOQ9p/7c79wGfw8KLvHO8ycS12Gzdir5XK2WoFg1ubre5un+XsWNFldPCFjK/APb6\ncb+GTCNNPTn2zVlMI8sG1SKr5igoJaTMMALLupaIu3gqpbxBrZRhtZ7n3futE1XbwhYCypLiRhIm\nSZKoleLCH+GYTzKNIJat9yYDfB8WSuIdKgaAhqeMU14l8aZlQuLhaKDahEPQUyb4jormh5tpp8vT\n2pMOI2uCZXvkMyc3/6Y948JqtsAJ+4XODJ+gN24d8fadZjRujsctDoYNXN9lNb8UbURPpB4FU8M0\nVAxd4bg7wfU8Jrab2uwMmRMhu/VMLa5SGuZSOTUbgXc7jXhhHPoavdu6ycAZnmBEATSC5/U9Hz/L\nX/veR8U5g0p6PTuOSVJQPa3diZkwh3er2FuX6dyPbTwcWxbPIiNia8Q0ag545eAN7nVjv6Crzevc\n7d7Hbc1TU+N3SJZkPrTwZKrKdNhC5tGN1i2KegF/nMMJQKPWQNz7fMWMNmJ0RVSZ+sDZKp2uOO/A\nHnL17jGVgsHKXI63jt5h7E74yOLTUcGCTHHAj/+1Z/jUUytsNwb82M+/wu++cC9VdARIyf52jwbU\nKyZrgVy16wpAP8mGOe6OY3+4xO/PLDzc1+g4MsEWzyYcF1k1i6EYOL4N+BE4Nws0CtdorUTe2h/b\nUWW8kDmTkQOgr9vCU4fgS8JeQM0go6TkaWHeUCtmIqnlzQMRt8Z9g1pRyFMLpzKN4vc6XJ8m+yy0\nAok9LAPQKDB2fuNmGjQK3/Uk0yiyMngPedrYEYBTaIINQjFRyuspMD9s2UjxEceXfFan2RnTHdrI\nukVey6XyxbCdXy6Cp2LaCzRGTb5+8y7zZRNNlem4Yp2TJJiEFcoBnJGRkiaH4LnsiT4euWM8z+f2\nToeFihnN5cWChK9MmDNr0aappFmxp9GMKubNUdpfFsS6eeiMyCX6OLQPOI1p9G6Qx2ysny7n/0bb\nNwsafQL4HMDm5uaLwIcTn10Bbm5ubnY3Nzdt4GvAJ9/rgPv9Q2zPmelnBEKeBgII6PQtfJ8ANBIP\nWM2JTpuWJO0PD4MF1kmkLdJE6+OUjvjRs1WUcoNnW78Tm6UG7V6gVwRRgnW6QtWRJQCJs6WYGr+U\nWyCjZJDybeHL5PsRbXThG2AazWrhfZ1WVcqJkP+0PM127Yj6LWfEtThDMRhnaR8lSWLOrNEcH1Mp\nGjP9goYTO7XADJtpqKzM5bm338NxPUbOOAgScZ/f3O7AJNjx78RjYM6sgqsgZWcH9RCRrWWqUV+Z\ngQ78tRsNPvvbV/knv/Aq/+DnXuRGQENsjo4x1UwkcQn7sDEQk03O1Bi7k1TlNIi9HUanvKDh7t6v\nfekWiizx/d9+KeVNAgmHfVdNGVQW9MKpwbX3DYBGKSPsxNhMyk8g1htLqs23f3g1Ah1rZiVisIQy\nqGQfQ3rMhdTTgl6gHEgrTCUbjV/f9zkeHUdsLhDyyrB9cuVj/M7XhBlfSBOdbr/w+U1+9P98iawi\nJvgk0+huZ4t//PWf5Nmd50/8LjkeRwmjX0mS0AP51FZQGreoFyKa83wlZhq90RA+Ut+xLvy9JEni\nz13+DBIS6uJ9JF/hL3/gB05IrsLm+z5XNwf4nkQmb82Ur72fZhoq44mL63kcDBvUzbloUgzH/dFA\n9ItgGsW+D+HuyAdrTyJLMi/svgQIaYaUGZClHF1X0kQyZhrNMsKO5Wmd0YD94SFniuscdwPJQlBC\nNmIaqZbwgTiNaRSARg+6e3i+P8U0mmBOvYvRZOkMo8Ropydirz/ORxO2KqsonomnDqNy9eF7GlLJ\nlysiqT+zUEgB0+E4D5ly2VMW2xPbxi3sInkajwRVLpNNUzRKRjECXw8DwECARuPo2BBXa0yCLDHT\nKsOHLtcxDZXn3t7D84Ss0vac1C5/yAINzUnD5ihDcPQooQ1Bx57VExK80PQ6YuhpUV+HAMLGeoWP\nPzZ7jg5buFFhT8SYMlQjJbfrBUwyVZGpFI1IwlPUC/TaOjh6VH5ZUxXKeZ2jzjgyqL60ejpoNHEn\nQqLo6JH0JGxJc83QY2Ta4yukcYc7dGGzXIuff+dXeKNxla/ce53D9ojHz9dYWyhwZ7eL63nYjsdw\n4lDM6hGbby5XiO4Z4iIfIVh9s3UbVVbJeXUkxcU04/iQN7VoN9qaKCekaTC7zPh7tSQAnWy/uvmb\n/NSbP4OkjygXjKiiXnIhUDC1mfI03/fZPx4yXzEjQPFccR3bsyNmEJwOGoWM8LXS4on7OiFP6+9i\nqmYqp6tlKhS0PNuDbdTVGzTl29F8tXkcg0Yg/B77UoPPXv8s6vItFhO5H8Q+OBBXvQFhcApgaCef\ng4jPTsrSoGsNRBU3NUdJryCpDq1AHta1ekiOQd7UIhDqyYs1HNfn+r3ZG4BhGW9kJwIrQACrw4nD\ncOzERv9BjAs9QULQaOiMwNVYKAcbhAkWQ9LLK5y/o6ppiI0uSYoNrx1phG/r+K6IXdPytHD8zJtz\n+Phsd0Q+MYtpFBd7cLBcO8XKmB7b4eI7qqjam/CvfvNt/vVvXaWiB5VRx8cpj9RqMBZcbSBkn5JE\ntWDQ6o0jqWbSLyxkTixXyvz4X3uGCwuBzNQapJhGxZxOLqOym2CqLObmMVWTq813U9eZbM3OCFWR\nKOV1PvroIj/0fY+xXBLjObnh5kkWvifRbIv3ZWK7PPfmIc7+eZ5/qxG9x1sHPXwrg6uKPg9Bo/u9\n+/zba7/ET77yL/inr/wMz+28yG/f/o9ISNgPLj+UsZlsSbnapfJ5VEVmMhaxqj0KGKwVEyuI/aGy\n4YkLNfxAWrvXbtMf2XzgXBVJknhpX0jTPrL4VLQ5vNPfI6Or/KU/vsHf/LNPks9q/Mazd/g3v/NO\n6nrC/MYgR39kszKXYykvwK6D0WHkcxi2494kkoMlf78egEanMpqmQKOxO8bzvYBpJP6mqG60npsl\nTwsZcZ1E3jocO0iKgyZpEUkiqwSstU4LSR+j+UJ+LkkSppIFzYosJ8INWoLKKAAAIABJREFU8Wox\nw9p8cA9tEUNHPT1aV74X0yiv5Zg356K/R55GQdzITbGFFqtZFqpZrt07TlUZjewfknNFWBX6PZhG\n0fukinNFBStO8U0MN9aS8te8qUa+TZ4yOZXRXi1mBIu2Id41P3/It39olVJOZ0hYsGc2aORPzBTT\nyAznYy+IXfaI7Uaf0cTl0loM0mSLok9rZi1ad6FaKSNsIKWSSkoZDwLiSwiuJc3GH1Y9zXZcbu10\nWFgd88u3f+UEqP/Ntm8WNCoCye1+Z2NjQz7lsx5wepYXtPudgKEzVTktbJE8rTOJFsbVQoaFoIKQ\npwclBxOgked7HAwOmc/WZ6KOIW1U0icsJphGeVOjuLrPyNzm5d23Ur8Jq4CF8odpo7mO38D34WIt\nBo1kSeZcaR1X6+FIE3pDO5IOvBfTaFYJ72RL7obPalYAWkwbYfv42J5NuaAjGUNUSWM4EC/BaZVx\n5jJVbM+hWPDoDq3IlDxsw7FzQpoWtgsrRSzHY6cxiBOSYMB3hxYHx0Nq8gqSp9LbrUXJl++DN8rj\n6b0TAAwkpU/l6AVSDSEx22sOeen6Ibe2O+w0Brx4/QDf9zkaNcXuf7hQ1guokhIdK5SnTYNG2ShY\nzX75Qjac7Xh85x9Z453Ba/ydZ/9Bym8ipDD6rpqSkhW0PD17MLPEbHLyO2jNBqzC1jrF0ygq1RgE\n9dBY1cx5PHq2mvIdCs2uQ4Q7RL3DkrbViN3WjnbqC3o+2hHWJWFI7vkefXuA5dkp0KhmVrlUPs9y\nbhF9vBgx+o464xSrCgLQ5e4xlu3hW6J/W4nJ983GVXx87ndPVjJMjsfhlNQiKwc7TIF3Ql7Pc3un\nw3zZpJjVo3sEeKRyibVC7J+xWljmE0ui2tvc4OlTY9ad3S7/5Bdf4+c/dwMsE197+LOb1Qb2kOd2\nX8TQPXyg0W8xca2UR1tYXSP0q8ibWlQZqmQUo0WOIeV4rHaFB/1dtnrb3Dk6QJJ9KnrMhosrqE0S\nTKOkEXZYrSE2wt4bi9h9rrhOI2B6LpfEMTuTrqhkFFW0mM00Wg4Svp3A1y55zpE7jhKF+DqS8jRx\nzYejQ2QU/ImZkk8Z5JD0cSQjmpannV+sktEVnrw4lzzFidiazcxebF87vItsjKl46yekaWGrm7VI\nNhHtHI1yEXCcjZhGQXKaiDFJeZ6uKTzz6ALtvsXVu8cRQyzpa7TTiOdCP4qjPr46RHHi+aaWFWN8\n4HaD3dPYBBti9lNWM1MJ2nu1sF8nE+G9EJp7h34X3YCNAwKEkA3RBwWtQG9gk7HrtCbtKO7UyybN\n7ph3t8S/L6+evmsWVhHxHS0VXyHeySvnjWj3vzPFNDq/XETXZK5vpefT37//pWhj5rVdUTHtyQtz\nXDlbZWK7bB8Oot3cYk6P6OK1fJppFDKDdvv7kZ/RueI6kh34WuhxIm4aasQ0sEZyalEbtrJRQpbk\nUz2NZrVsZjb42Ry3cH0XdekO1YIRV5xM7ADnTY3RxDnhAdIb2gzGTiqfCdk/SV+jfCRPS5+/ZQtw\n5nxNMA5STKMQNOpNhOnu8IjVoLJT2CRJ4lzpDH2ni7Z8l7xc5u9++G9gqiabrTSD9dJqCaUiQAml\n1EyxzAF2+rtIgT4tmbyHHivT1dNAPCsfUlVlw8V/XstFAFZzfIzne/SsPp5lpBZGYfz53a/f56Xr\nBydkgCHYaxh+lH9CHM+a3TFHU0yjSsQ0En+feCP8oCIvxGb6RtaONtUABtbJ/NO1w7l0iOu5WIzw\nbQPXFv0xXcY6HD/rgVlzKPV7mDxtOHbY6e/h+V4EaJ1kGgUsk+Ad/tyLWziuz9hymQyCvhjFoNFq\nfhld0SjqRSRjSCE4f7WYYTB2oriQlKeFC6ycZrJUy0Vs3p7djysYaVkkSWJlLpeqoBbm+2EcnMk0\nao+pFTMRc/Cpy3W+60OB71ICNLKZgKtFVda+fm2fwdihnNcZTVyevyrmy62DvgCNsBg5Y0xDpZzX\nadgxK2+nv8cvbf4Ge4MDrhSewB/nT1SxO60t5xYj8OtS5QKmoUagUX8SgINlM5anKTFoRAI0AuFn\n1LP6XD++wXphhcXcAmWjhKmaKcD2iQs1fuyvPMN82eTNW0cpNm3INp8MA+/Ceo7FrMghdgcHwudw\nmmmUlKcFY2h1Po8kwf1TQKNwUyf0NBqEPjqBPA1grqqx3xSFSGYxjcqhP2Yibx2MbFBsDCUp5woq\nwHWbSPoYgxj4yKk5JHUS5fTRdRWNiGl0OBCxypuYLFTSFS+7U0yjfLDpdqF0NhVHuwNLyM+DdySf\nyPfC9tTFOSzbS7FxY/uHhO+klkVComc9HDSKVBBhTjcW80v5FONmM2JjJ9ZPZtDfkocrWXHFuRnt\n/HKRQQAaaZUmn3hiiVJexw6Kk4TrH4CzxTVMNUNJL4GvTMnTxLvj2BKqrDJyZ29shT7ABaUc9aek\nTRKgkbj25IZ/cj4P88UkwzFsMWh0Mj+7vdPFdjzkpVu82bh6Ij5/s+39RYyTrQsks395c3PTS3yW\njJIFYDaFING22iK4Pbpynnr95MIiE7AY+hMHNwCAVpeKXFmZh6vgZwaAztByo98fDY6xPJszleWZ\nxyw5Ad1Xm/DI+bnUd4yCxdiFr+28xmee/nT09wcBDfVbnlzhP75wD19Rot95vsdIbuKPczxydilF\nrXt8eYPrxzeQ8y18RaYdIL9XLtZnXpu/LwLcSn1u5udhu6ivwJswZDDze5LsoUgyiwtxol3IZuEY\nChWDlfkCkj2krNcYjF0MXWFtpTyTEbFeW+L1xtvkqw7+FmimTrWY4VbzHqvFJUYTh/XF4szr+ODG\nPM++sctBd8LFJRH866UK9XqBW2+LieJTq5/m7sEHeX58iGpo1Eomre4Yb1hEznew9AHLldXUcbt2\nl4KeY22pTr1Zhm0w8hL/29/6FJ3+hLmySS6j8ed/5PfYaw7RCz6WZ7NcXkhdZz1XoxtMRHOVLFbP\nIm/mUt8ZqMGiUnNn3uO51UD2UTD4i99zhb/7hV/F8V1aHHGlLhLo/YOg9KarYntEx5krlLnbvY9Z\nUiga6YDnvRtT5Lsj56HjoRMsyLoDCydx/PtW0Odl0eeff10k8nNzMgvzRd7sioB0pr7EI2vrqC+p\nNK0m9XqB/i0BQFxeWaOeK3CmtwT3wNZGuEGlgvX5edRhQLNVRGUrsyjTG4hAuFJN9/c/+o4fxsPn\nx3/uFQCe3pjntc1DjvoWF8/GIMaDhL+ILot+GUvxWH/3lU0AWnYrdXw/8IAKx6O7KY6xtjhP0chT\nMct0iUvyGkqB4WSPjz6+RL1eoOxmolLCf+aJ/+xEn//Q3A/wlX+koRYWZz6Pn/2tt/mdrwrpycce\nX2K4tMKN1k2KFQNDPb3yVLJZrs1Pf+mz3GjeIVutwtYTdHzxLM7NrUTnXbWD0rf+GFCYn8txxxNx\n6sLKMsVCAIYWMnzXI5/kra9d4/XWG4zGYiFzphrHx9XA08eXZVDEBLm8EL/TUgBWObLFWuBV1vJF\n4vrk2gabb4txtl6rwz5MpBELC0XMvIcHLJarp4zfArVshcPxIXCRejUbfW/iTpjP11K/W3aDd1F3\nWZrPAz4t+5gMJQZInF2rRN8vGWWGzhF3GuI9unQ2OJYqpqyNswv80o9dQJHlVMzW8qvwKgz8PvV6\ngVLeYO9oQK2WT33vnWsCRLhQuHLqu7lWWeRm+w5edkIv2FPxxznawe78Yr1AvV5gri361MjJ0bHU\njjjXfKVMvV7gez95gS+/vsPLmw3OPj0HeyBn45jUCBg2Y8vFzGcoZHU64y7IHhr56HtneotwD8ZK\nV5SwzolruBNIj+drIv6Vs3ke9HaoVE1UJZ0yfPHOc6yVlrlUi/01mkFa4XsKakajmM1BC/Jljaya\nZTC2ObMUjykj6+AA1aKoajinrbDNDofeHo/U11ldKHBzWxQCqFdMNi6crnbvNAX4ILsGXdtOPY9R\nIC+6cKaKNliAd8BRxyee2WPn53htU8xBlWKG/d4hX9h6lppZwfFd9odbSNI5Pv2RM7xyfZ/Pv3if\ng+6EcsDeWKjluIeFIsmcWa4L5qrtUa8XUPMX4U1o2kc0/H18fJ5cucLu9g0woFiVqVfj61ENR4AR\nY4WFBX3m+JrLVmhZrZmf/dTzP4uMxN/8eGwtWSoYPDjsU63lURLj2PLFuFHq21TnZbq+iJmrC3PM\nBTK7WiULW20yWSPl83HQDQz1V8vRdXwo8yj/7jrsTnaivw0cER88SD8b2uDDhy5cxNAMfHMd3oCe\n3+XsWgVNlelPHEZaFx+fy/PnTj635Uu8dXQN3zL4vss/wPmVJR5buMzLO2/imWMW8mLcfOTxZX59\nV8QCKdvjwtl4LHq+x97ggJJWozXo0VPa8TgN2GfVSvbEuauBkbaZy1APFmy2JPpzvlSlnCnweutl\nhnQxCojKjxOd+WqcY1RreR45c4d377f47G9fQ5bg0nqFv/Bdj/DBy/NULBGbSyWV+fk4xT67XILX\ndrB9Mf/nTY31IBc5txqUgQ5yAVeywM1z+fwciiJzRlqEq1Cbk3mwPUbWVWolk82hAEAWKyJW+r6P\na6kogJGXyWTEuPFtA1UW40A108/Uuy1y2CuLF3jl4A32h8eAzNnl8on+WwzYEoqmcOyLtcAz60/x\n+VtfYST1U9/vuT1RuWyhQrs34dk3d9E1Bct2aTbF2mAg9WlY4hk/ceYiOT3LnFmjM7lLNWNQrxdY\nrue5dveYrx+8glxoUivHY2rUFvNstSDGxmKnBndBzrhgB7n5nMjNz69VuLHdYeJLLAe/f2J5g3ea\nIjdZrc2nx3rgTXlpLd0Pa848XAdXs6K/j70xim9w1BkxN5fny2/sosgS/+gHP87f/umv8KXXd/hz\nf+wRDjvjaFNNzjrUSwXWF4u8SxMF+Puf/iFkZP7wzte409ri6dy38SrXqdfyM+PGrPbRtaf4g9tf\n4eMXP8jvZ99kOAnmmcAw/5ELdRqhLLcgjluvFzg7X2MfOBr0kCX45IfW+Mr21/B8j2+7+PHo/Ocq\nq1xv3KJQ0aPNkzqwvlTk8PoB+aIZbSaNboh8VfJEzN04W2NtaY75XI2D4QFzZZPrd4dRjBtYbiT1\nBRhLwzjnmS+w3ehTq4n8Mtkf4yBenV8XeUv3OLBqKJYFs+4QlhdNXj8copk6RgCgVktxTCm5wWaz\nH49jT5KQVIe8Hn9voVTlWgsa1gGSBEW1FMeGXInDyT4OYp4fBnPZxTM1zi0XKecNek5QXMPKcD7I\nf9aDdabrS6n7uqKeh3fgY+eeSv29P3Yo5QwWFkR8WZnUYRPQ43XHE5fn+dxLWwyDOQ3gfoBJzZXT\nYzqvZxl7o4eOsZ2gj+eDtckw8L5drM8em8uyWB9Ienz+uUoApATrkbnCyRgTticu1Xl1s4E3zqKV\nj1leLjJfzbGj9KmZFVYX05Yif+9b/ytaPYv/+Wu3KeaN+JkEcT6TNchpJpY/YStQK3z0iRXqdTGe\njIIFDsxl51hcKGNIJiPNYiVYmygBo3sU9KfjOrStDkuFefZ6h7TdFp6i8LuvCLuBt2/1+Dtffp65\ncoa/95c+BIi5Zvp+P//qDqgWHWmH85V1Lq+lC4N8s+2bBY2eA74H+PcbGxsfBd5OfHYduLixsVEG\nhghp2k++1wG3AqZRzinRaJxEfX3fR9dk9o763A+MpnQJ1LEp/CKGh0CF7f1u9Pt3mmLRVlarM48J\noPg6nj4mq0jRd3zfZ+SLxH5rcIf7uwdRVZl37japFg0WKyIQ3NtpsVYTg+dg2MCXHdTJAs1mWmq0\noAo2glxoc/t+i/u7ogqM5nszr63REQmKM5BOvXYAXLEA3W0fzvzecDJGldX0Z4FB6e7BMY4/RFJc\ndLfAUWdEMatxdDRbJpXzxaD01T6Q5c79Y26ax/zzV/8ln1j6GJ5fQldmX289QOrfunFIpiKChOxo\nNBo9Xn0noBFXs7QCpti7t4+4vFbmwWEfbyjOe/XBLXJOjOD6vs/h4IjF3II4Z2AWunvUZGF+mWpW\nw7Mcepagc9/Z6XD9gfDKKMrpcVbSSqL8uOziBpOg4imp74xHIlAf97oz73G+qHNmocCf/JazPH/n\nFVojMYbuHuxwyRQ0xyTTaGsvPo4R+ALc29tnaUpTvnvYQ128K36zXzx1PLieR6s35sJKicHIptUd\nRd/dbYrJzp/I7O13+NxzW/gbCrJq02j0uN8QC3/NNjluDpkza+x0Djg87LLbPkSWZLyBSmPYQwt2\nxe839iKmkTuUUQKWlDsWz+Hu3n7ki5T18yeu+/ZOh9c2D3lkvcy3PbXMa5uHvHZ9nwsLMWj24ls7\n0f8f7ovj77WPaDR6NEctHnTF2AmvNQQ7RxMHzwctGI/tgTj3sO0wkXvkAqlbyH64fUcE+9W5bHSd\nq4VldFljUV6Z2edFtcpRZ3Tis+7A4ne+eoe5Uoa/8t1X2Fiv8IvXr3EDuLG9NdMzYLr5vs/Pv/Mr\n3GjeYc6scTRqkvnAC3zlrphQC8Tj1w0qq7yztQusMV8wePG4RU7L0jke4wU7oPsHXVbr65T0Al+9\n9xLV8aOgwnxmLjqWFLAHt/e7UYUje2JHn1uu+LzZ72CNLMDnwLmNpqksyEt8flfE3PPz83ANDrvH\nNBo9MlmbIaA4+qnjd9Fc4FrzXVAsJN+n0eiJHW3XRvW11O9ChWij02LN81HmdrA9i2wAhDmJa84r\nBXBgt3MElJFdl0ajR3cknnmvbWOrswxDJTRZZa/ToNHoockSng/bu+1oV9r3fd5qvI3vqCxr66fe\nW14SSdiNnS3uHgl2mz/O0bNCKZe43nAz6PC4TcMUx2oEO7TWUPRJOaOwUs/x9at7rD0iktStg33m\nJcFiub0d79Fs3j7izGKBO23BxNPdXHSNSuAhN0TEBtUTz2YvKN/rOaKfVE/MMff3DyNmAgjJ62df\n/gWuVC/zNz4YgxIHzeD8rsLt+8dIrrjGnYMmqjfB98HU5Og6wmonh3uiL+bkZbaB1x+8w5XcoxSC\n3XDX87mwdHr8A9huioViRjE5OBqmvnvYHKDIEpPhBEYBS651dOJ4F5YKvLZ5yNdef8AzVxb47Fu/\nhOM5fObCd/P6/lVeG7/B2rqPPbbYOCPG2xubB2QUEXs0GTqjHqZqcnTUJ2eotHpjGo0evi8MOu8e\nPyAfsB1X9FXGw1uQh+3GEXk3ZmXKqo3ryVgWqBIz772iV9hs3WJnvxlLmBBVL1/efhNJkjg47ERs\nazWIkds7rRSjrzcR74Mk+7zd+jpOUMFu1HFpBPFTD0Cme9stnHocp9+9I0Cjkhm/p6pvktOyvHt4\nO44fI/Gsj1rxs/F9H0vpojg5um0LsPA84ae10zrg6KhPKadz1Bry9gPBGqopcyf64tH8oyzIb3N/\nc4HCIwUajR7ncud4mTd54dabfMuKMNsdWsfIWXFvkuSzP9hivhFIfEdNRs6Y5ew5mmOXvt5i90BU\nFT0KJShj68S5pcAsdXu3DY6Im61RB3Qxtxdl8f43R03u7AXV82ydbDadY/ydP/9Btg56vH3nmGt3\nmtzcavMP//cX+BMfO8Pymng/Mpn0ODCCcXd7q8XB8ZCFSjYRz0Xs397vsrN/jC+5aBgcB1JdbxSw\nULPi2C+8scMzjy6wG4Cv/kRcX6Fk4jkCNNppNDhWA/aAo9PriHM02u3UdbWDUqo1WQD8tw63gXUW\nywaNRo83Glf55Xd/gx9++r/EnohxeNgc0EUsjh7JP8Ln+QrbrYPUWGkOWsxn6zQaPX79S7ewbJf/\n/Dsu8f8+f4+r10fwCGwf73O384CKUWbYcRnSw/CEt6Yj9Wg0emR1BbQxXzr8MtpaCfyno/PstMX9\nS7a4f2ki4sVO84ijkYhv9kA8h2pQaerqzUPygc/ZghpLeBUrPedtB/LhUjY9p4XPYr/VDGKFz8Aa\nolHluDvhCy/c4/5+j49cmaegyzzz6DzPvb3Pl166x82tFoopxvCdvV0MK0+loCP7bfJqAa+v4ksS\nn1r4o3xqAb76lgDm/CDGv5/2x1e+g4/M/RGUUQZdlWm0PTKyynA8QpElJNehcSxyXmscr2seW5tn\n3xGVIs8tF+n3hvzhzeeQJZmN3CPR9+rGPO9wk7fv3+JsYGwPYq4AuH3/OGIyHnSPBMPyULxrSpAv\nzGfqXG2+y6Jm4/lwb+uYYk5nv9FHWnAo60XaVpf9dhz3V+ayPDjoce3mIY9vLKT6YyeYC6UgZ9hp\nijgnOyq+HZR2D/CKazcO6elibDjj9Dua13Ic9o6jvzVaAyjaGLKRWAMERR8me6CAKcU5czbwOzrs\ntsR1HMTXdXTUZ6We4xZ9FGSwDXLB/OoG5vwHR/3U9eQo8RPf8iPCyzPx91ZvTK1onsgtD1rHJ2PK\nQbx+2W+GLKf0+i+rZjnqt7m7dRwxa6bbzlHgj2SJdeqdB+JYmVPWkuOBWAc0u514rgmERKHUXfeM\nU8f1QsDM9DpzuJktXr59DVV1kLQJJWX1xO/q0hKWNQBu47vxuI7y6sMehmLQnwy5cf+YrKGiJtb1\nk2CTcNwR77vqZ5DUPq7l0Gj08DwfSRKxr9HocTgUrLq13CrtUY9bh9v8zc99maG2h/EIdDs+Od/n\nxlabf/yzL2Gez9AanFyTvnZ9H7V6gI/PE9XHUp+/X6B4Vvtm5Wm/CUw2NjaeA/458MMbGxvfv7Gx\n8Vc3Nzcd4G8Bn0eAS//H5ubm3kOOBcBWZ1dohE+RL0iSRK2Y4bgby9PKBQNN0ahmKhyOGgLwSMjT\n9qPKaQ/x4bZNJH1CpRhT4YSkxgIfkDyefyCMYxvtEb2hzYXlUqQVbyd0iA8CqYvpnaz+dra4joSE\nnG9x3BuzdzykWszMpDqH1wDv7WlkKDo5LXuCwhvdnuek/Iwg1sZbnoUfyPoUJ0d3YM30MwpbKE9C\nF8lTqz/h5UCX/HrjLcCb6WkEogyvaajc3u1GEoJQL3tzW5RBPb9UjEosh+XPe0MLPwCNktRVEFRh\n23Mi6VNsjntyAXhmoYDleNwKKgxE9xK0UIoi6SP0wMh1WhITG2HPlmoUszo/+l/8EZ66XOfZ7eei\nvx+NE1WTEp5GrZSnUVhB7WSg6w4nqKs30VZuPdTTqDuw8X1Bpc1m1FRZ45EdU0Bfu9Gg3bfIyGZE\neYw8jYJ+WMjWGbtjulafo/ExVaMcLTpi2U47rp6mF6Kx4zlifPXtQUr2Nt1++znhW/KnPnGOc0tF\nJODOTtpjJKR7Ahy3HQxFj/To7xwLzwDfF1rzpBlcKCMKx+PQGWEoeqQfD029ASQkHuyKyfXSSgxK\n/t0P/Q3+66d+8FQfonLeoDewTpg0hnrzpy7V2QiqFoTmw7NKCM9qv3//i7x88Drniuv8yEf+Fhel\nj4I24Y1j8b4tJGJaqJPe67RZqJg8eXGOzqQb+baEFZcs20ORFZ5Z+jAjZ8yeJHwCLtdjM8xIntaP\n9fNJCruu6OiyxsAeiGqTZp+J0uEDtStk1AxHnRGSBEu1Ijk1SzuQyelm4I+kni63DX2NZLMfnTP0\n9pmWioYU3aE9IpPx0FZvIKNitB5BkaWUTDZ81pI+FhXBgs+Spp2zmiRJVDLlaAyHcq2kGfZWb5uB\n18Vtz1Mvnn5vYfWexqjJwbCBKqnRzjDE49SY5WkU9UEmuq6PProgqoW2xTsZgreO66Uq5oSFIQ76\nIgZlpHihH/p2Obp4nyJPo4QRdurvUxToMFZNV30M+9X3hNFs+OwmrhVJuJJVUSR1gm9r7ByK617O\nLZFRjMjXqJ4wj3+YnxHEsu6CLuazpO+CqLiqI0kSxcjT6GS8TfoavXX0Du80N9moXOSp+uOYthij\n1ZVgwVEXEo/bO53ID6KQ1Rnao2g+ypkag5HwEJQkieXcIkejY64130WVFNYLa4yHSnD9U1R+1Y48\nQWbJ04CE9CkdW5rjY9xAFthK5AfZGeMYhMTA9Kp4kwzXem/QGIqFWdKfI0z8p82ww+pDSamXJEmc\nK67THLeifo7N8+NzN/pdJM0i48fPVpEVqpkKR0HFwXLBoDOweNA7WTktbJVMmQvWp/FHxUhesVG5\nCJAyw74WzBtuS8TQB4NY2hyaYC9mF/GDwiDHgVTADj2N1JM527SRM8DYDSoOZgqsFgVDc+h3o3fV\nt41I0h02WZI4u1jkez9+lv/+L3yI//EvfYi5cobffeE+v/IFAcib6bSEuYDldP+gh2V7kTQNYr+M\nVn9CayTOm5Hj9ynMt5UAuL0RAM6DKQuB4diJxuHQHkXvvm8ZjAOZ0rSn0dAZIyFFz6o5PkaRJc4u\ninO+uPcqfXvA13ZfjL0HJw5b3W10WeNi+RyarKasF0bOGMuzKRtF+iObL76+Qymn80c/uMwHL87R\n60qoksa97gO6Vo/VQgzemAFw7wf5a7VgoBQDcEgfT8nTAjl7WG029GeZ8jQCZlZQO1tcA18K+jvN\nHA/XKHNTRTGmq+iO3Qme70V5569+UQCm3/nhtdR/f+/rW+w1h5GkPpTRlssekj6hqiycyGEi37r3\nKU8DMfeHeYdpqNiOR0YxsP0JtVIGRZYjeZouJ2WX8/iOiqTaXDlb4LNv/V886O/yxNyjqfXeauhr\n1Evn+aV8mJPE46s16VA2SnQHduo7ofw9kxM5WRiTmz0hTytlhAwu6QF75iG+Rq3eGEWWImPo8L0I\njbABSgURD/aaw4SnUfq9LhslWpNOJLEbTMbC7yzhGVYN2JwTWVxbSY/jYTkTMjXF2DjujjE0JYqn\n6/N5JH0MVgaQTnoazfChKxnF1LiwbJfRxKWUi/OhWfK0yLg5UXRnlhE2wGigMPbG/Mazs4vcQJxX\nhO9TeNz3kqeljLCDeC+roQ3C6fK0M4sFClmN84ULAFxvbqJkg3gnz64yFvrZJStnhvPx2HIwFZOR\nPeawNWJ9IZ/q14kUxkpxf7JnIGk22UCqLcsSpZweyWSTHrIZr8iKWinuAAAgAElEQVTx5JjRxOKT\nT4sc8vs//Sj/7K9/nP+PujcPsiS7y0O/k3ve/d7al66q7q7et1la0xpJSKPRjEYzQmi1hIRWEAKB\nFWGeeYT9wBABth+EiYDwezwIbDYbW7bB4AcILCE/YUkWI400+1a9d1d17Xdfcs98f5w8J5ebt6p6\nLEeIX8RETFfVXTLzLL/z/b7f933f+Rnc2urCtaUhIWzL8XBtvYPcNC2i3Td5YeT9uNt4TaDRyspK\nsLKy8tmVlZU3hv9dXllZ+fzKysq/Dn//hZWVlQdWVlZet7Ky8lsHec/t3i5m89N7CsXWShp6hsOT\nYtZnOpWfQNfuoVoRUe+YXA+H6ZzMF4YTDADwgwCOoYCILuxYks6skecUOqi+dvs7ACKXlKNz5SEb\nWAC40aagUUkYdmrTJBUT6hSEfBt36h20ezama6OdlNIb015R06pomM0hJzeAipKmQSM1XNBtz4Yl\nhBtUV0MQjNYzAsAdKByRvqbZNbgFZ9/tQyg2uXhqOgRCcGSmiK3GAI0BvY8FOQfL8XBrs4uFqSJU\nRUxYLAO0F9cfARoxzYtaCjTKUpJfDBOVWw2qZTCeAo3YoZ6oBmSFbjhq6qCqSSoIyNDhKR2r3XVc\na9/EcuVw4nsC0QIpBEl3H7bZdTLEsJuDDohAE4Ce3U9oFSX+rhtpduQ0OeGe1udOODn8t9BmspYr\n8c2gYbYgEIH337PkYK13B127lwDZqloZBAQNs4mu3YNIRGplH27crhkeLJx+pIeUAo2urbfx4vUG\nTi5UcGKhCl2VMDuRx43NTgKEubJGWR2EALtNExW1wkGjl0KhSb9JmTvbRuTowEWcGfUzdLxgMZGv\ngE2XvJzDtTsd5FQJMzGBUVEQE1pg6aiETmMscWHBxUhjQDQXVTb2B42e3n4ef379i6iqFXzm/Ccg\nizKOa/fBvnovJEIr8JMJ0IheVyBaeOzSAmzfhumZXIOAOT4wocAHZ14HgDrmIABmCtF6xRO0vj0y\nsczLefScAWRJgDJBq+b3T9FNabdtolZUIYl0LDGhYVENXRyD0esZ04YiuR7XvYiLQMcjF5vrz/e+\nCaLYmPXOo9+RUMoriX1kKrSYF1SD638AdP2TBTlT745FTa2i5/RheXZCrJUFc7/yWxM8mcoKtt7s\nDHaxPdjFhD4O7uuN6NCp76lpFH33Y6GuT8iW59o8m40BPD/gSRRzBNwJ9Q7yYtTWwgVatbCayVxJ\nuBB2CBoxgC4FljMR/KGEhZlH+CJaPSsBhHU5sBLtE65AtVFubrKWEB2Hy4vYGuyga/dSoNHeLiBs\nPWMtvkwziolrMmBblzTIgoSOnQSp//jKn+FP1v8Q+smn8Yz11/iPK38KgQj44PF3gxCCxjq9f5ZK\n9xFBIDg6V8ZOy8RaqCVVzMkJjahiTobrBXz+zRZmECDA1mAHS+UF2Bb4gbyXupeBEAON1OwC09gI\nMWwmuJ7+/yyAg1mUw1XgbhyBG7jYNnahS1piLjGb4LTezibTaEzpA3Fdow5l+CqyCFkSEkLYV3cp\nm7QsJYttE/oYuk4PpmuhUlARBAGuNG9AEqSEw008ukYSlJzKTaCslLDSvMr1Al/YpWC5s0bZv9db\nN/nr7zBntuIMApNeC8sHrVDTSJaH1ws9Y20w/VDXSi9hPFcDAsAmKdBojyIdAByeKeEXPvkALp6c\nRK8XaiqpyTyPOQwyjZH4GqerEnRVRKtrYb1Jf59XovkkizJ0SYdDBlBkgesapQt7fcPh2jR9dxBd\ng6vANELQKCbGD7A9V4cu6cjLeVikg4WpImRJhOd7uNykjKLvbD0HVWH6OCY2B9uYL85x4DAOhnLX\nLLWMv35qFZbt4R2XFiBLIu49PgGAQAkK/O+YhhgAKD5dEzyJztNaWYMQgkaQLf4dgChPYwfUyD68\nN5SbZzmoea6AYEDXCqOb3D+Z7t94ObmnMU0bdm9Z8ZOJkW81DRyeKeFoWNhamCri+KEKLq+24AcB\nZkp0/jDtnCBH75vmJrX6gAg0OqimUTpYTqAIKgLiYjJs1WGOpPFCzNJMEcRXQBQLzwt/gZcbKzg9\ndgIfO/WhxHtyMex+CjQKpUk6rNXK99Cxu6ioZa6BWeHCzfReqZoXvoYW9dp9AxB85CQdVbWcMFNZ\n2MNBrdmlhQbWjs7y6LimUbFI1wMKGjFNo+S8rqhl2J7NgY6excZQNBdruRKCgPC0IK6rWQndaAce\nHWP1jolaSeVr8+ykBqJYcE2qk8XGlqZQ9+C0e1pWcOe02BkwEsKO9iVmeNPqR2dfI+W0CQDfemUL\nzSZl0Tx15c6QDh4LBsSxQiA7U48iMOhZ7mlhvpMv0c/YSwtYlUX8n595ED/xyEMQiICXG5fhKyGg\nHmQXpThoFCsY6OF+bNoedEmDG7gIiIf5ySRgNQg6CGwV/QFdtwmTxZGiZ1IpqGj1bMqkDPfxVy6b\n2NkSQYQAn/nAESzNh87Xocvtxx47gZMLFViGiI7dT5z/r6614YkmHG0Hh0uL/Hz73YjXyjT6rkeA\ngC8ao4Idwq6vt2nPZz5KDACgULXgegFXIb/Zvo28lEscsOLRaJvwrVC5POaiwqpbr5s/Db9Xwa5H\nqxZX1+lCszxXjqGt0cS52V4NNRmy20+OlBdBhAAvbd0EAEzXRg/sntOHJqojhVXjUVMrcHwnU6Xe\n9h3IKR0KxjSyPBsDP3QU26UTgC0IWcFENw3Qe3WjewM9p8+fmzi2yavxWXEk1EBZb9HNrCDnuRsd\nqyCPc9CILgjdvgP4EgpieQg0aqRcveKOSulg1YTNHk2eGQDGIs40EkMx0jS7QSACNEnlC2TLauNX\nnvqX+O3n/yDhsvfVkGX06MJDKCtFPp4AYBC2pxXVXIJpxEQf45sZi5Yd/UzQ+yPZRkzsslpUUdAk\n9E2XLyQsCep2A1xZa+Ps4RqqehGO78DybDTMBqrh8wWiOfVK/TKA6B4D1JGqpBRRN5vo2l0UFYqs\nF3QZokBghzTrrt3jzyjONNptG/jDL9H3ffebIi2Uo7Nl2I6Pte1+eD0Wdlomjs+XMVbSsN0yUFXL\n1PbWMbDSuArNL8Nrh6KpsYMRA8z0GNNIjx26q0UNCC1u81IeOy0Ty/PlIUvrvYIlM3HgGIiLFEaf\nx+7fKMF6Fm2ri3/z8n+EIir47IVP8UpcTpXgN6fwxNhH8dnzn0pci++KgC9AUhy88ex0QgQbiIRb\n2cY3mRvH0TK975KX56KVAD3gElDQqM+FsJNzuqDk0XPoJiXWNgBfxNmxk3BcH62uxSvfJaUIwzVg\new6nDRN3NCg9w5lG3WGmkZQCcEWVtq/0NvBU/ZvwLQ3F/kl0BjavCrKYK9OkmShmogpvec5QRTAd\n3CnQbEYMjdjB8HqbHob9XoWLzmYFW2+utK7D8Z0EUwyIDp2ZTKOwgq/H7sHSdBGiQLAeat+xgwYD\nLi4cpZ/HAMx6yDQpiFFlV5c0CIEMItJxkRbCZq1LEdMoua6yinh637H9CDRqdi3+7CzX4ok/27tt\nz4FHbASOihsb9BoqBQWHw/aEW51VDhoxYBkAd/9MRz8U3WRVW2ZN3DMceH7ADxeMbZTc9xv4yurX\ncb19Eyhtwy2toW138MjCWzCdn4Ln+3j16gDELOGOcZsfkNgh7pkrtKqna1QbhwGbzDGMsXPm8pFw\n/rHKUbR6FoIYO5OFH/jUPSk8rOsjmEYMkNw1k6ARE9AEgC0j+v8sgIOJFnu2CKWzyPejdOWYMXjS\nleuNeh/FnMyvlcXhEhPDvsV/ltekROHjVovu6xNa8nDLwTCzQQ9uxQZ2zB1cGD/DGaPpYC5B7BBB\nCMGJ2jJ6Th8b/S0YroErreuYVKcRGEXkggqud27BC1sumKvd4eo8ZxoxUVLGWlP2YBrF76kNuueW\ntSIkQQJxc3ClHjrhmDsIaATQ9fez7z6DDz5EHazU1EuKORmKJHCGa3yNA+iBpNm1sNmma0BaM7Gs\nFNGxuzg6W6ZGJYYTc0+j821gOgi8ONOIzn3B0zAIl4Uh9zTXjEAXsQIoBo7M0c++1V2D6VEmUsfu\n4o5JNRabzg78wMdCaDxR06roOwP+3my+5sUivvydNRR0GQ/dQ//2zFIVqizC6kU3aC7GSBMcei2s\n8l8tKBBK9NkSAkCKDqFpN6gCZwFRppEsSDyPznJQe+7qLuy1ZTjrR9BsJnOK3TC3ZfskC1EQkZdy\n/N6y71DRouf1yMWkpuejsX8vjtE9hYlE9wid8153+CAcFYQO5p6WDs7K8iRAdLkbH3dPi+2rAiEo\nawUQycGOtYU3zl7Cj5/75NCePpOfAgHBWoppVMonmUZdpwc/8FFVy2j3bCiSwJ0lGWgkM9Cob1O3\n7VAEW5c0VLQyDNfkxZjFUA4hzTTy/QCtrk1zxTD6sQ4QtlfnQ5x8o9EfzTTS6DNg+X3amAUAijkV\ncKLXjeeinJkxZ0xvANN20TfdRH5ZCfGlwNYwXta4gyUhBKW8PFTYzIr2YBg0yklU/iXOgFUVMQSi\nozPPIAUare308Lt/+QoEn94jwx/ghevROSgevKAtJ0GjUe5psiBBJGICNOLC3QUKGu3FNALomlrU\ndBwpL2K1ewdt0HVfdIZF6wHA5iYIw0wjw3Kj5yi6OBQDjVzfRc/twLd0dEJcIgifsSdE379SULn7\nKgPJV67ZKMt0D9RKRkwwPDR8EgX85PvOQSEaAnj4wjevc8LMq7ebEKubAAJe0P1uxfcMaASMdk5j\nUQsnb2fgoJRX+MSYCqtOSp4OvnrbRDdsq1kqL4xkL200BgjCg2MrljxyplFpEtNkGSDAV65/G9fW\n2pAlAQtThdAymPAB7gc+1gcbCMw8avlsMOjkOGUutX26KE7twzTK7+OcxiI62Ay3qLmZ7Wn037Zn\no2nTATpohxvgiIkK0E1tTKui69LPuW1Tob+/d+wHoAk5iNVNaMroIXV0jk7InS5dOPNyHpdTavO1\nogqBEF6NYar/k9oUuk4v0U5Qj9nE0/eLHJXSMT+Zp4dhpwmBCKiqyYo1B41UA0IokKtKwwmdLukw\nXBN1o4lf+85v4nZ3Dc/tvoT/57nfhela6DsDPLX1DMa1Gk6PncCYPoaG1eJJKbOhLWk5KlYdou81\nPWr5SkfPjcYm0XsjHdTi9qA5TYbnR9VttuDs1unn3Xdigo+vltVG2+4mgB12qGVU/qx2vpbVRicE\njQCaHJTyCnfh69k91M0mdElDTtbhBwG+8vQa/snvfAu3Nrt4/ekp3r4FAEdn6fi4HoKzV0Kq/LFD\nFUxUdLR6NophW8m3t56B7TuQ+tMILHodcaYRP/iqEhWod82EC1A5r/L2INGjP1+e27v1JR0VTptO\nVnGY1Xe84svGaPpgl46X6q/A8R08sfRIAkTnoIJXTtjfAsDXnt+A7yjQcj5kSeTuMswdkrmnsbEA\nAGfL9wAAciQ5D0RBQCEnc6aRpogJG3qAgr22Z+Na+yal+renoIgKGl0TAaIKKgOtOnYHgUg3SN8Z\nfUiibcSEMo2Y4CVj2YjJgxAhBLmQZu4FHpzbJ9FoObAdH8V8cr2bq8RAoxTTSNkHNIq3FTIWJUu4\ngyDA9dZNiK4O4uSGwKp46JKOgpznVtLT+ckEQMmer8adQeLtacP3QJFFLE4Xsb4Rgkbhusiq3YuL\nBBBc3p7WtOhaWVaSz1sJoj1GZ+ytFOCaG9GWyw43ju8kQHOWOAceBY3iQBhbz9m9irMu2HevFFUs\nlSlodLNzG+WCgqmqjvtPTEAgBKvddfzDr/48Z7nGoxeucxMFOpcZgMvayOOJaFktoWP3hhgoHzr+\nXryz+GMwn30Lnqh+Au868hgAWr0bWC6m5QU4votrbdo+x9YNVuiQVDdx33hLV3hfZ2Pz+nj1CK6v\nd7ibZaLF1jVp1dlj7WkjmEbaKKbRTuz/Y0yjjPY0M3y2tiWiWsjhkcWHwmtIzjvONIpVrm3Hw27L\nxOzYcL6yWDoEApIEjXQ50TrN5sRcMVlsY0DrrlFHtaBCmqLAwkOH3jT0OSy6Awe6KnIbeyDWota4\ngpfrK/ADHxdnzuIn33sWZ6eWYXs2L0jd6a6jIOcxXazCtxjTaDe8zjAvGOGeBqTYWyFoVFIpACd7\nBUC2eH55UNAIoOvdWy8s0fcN7KHfxde1NIOlVlTRN11stMJ9QU8eqEpqCX1ngOV5+j2vrLXQC+c2\nbwM2Xc40GrgGn7d5KY9ejx5UjBTTaOAavL1LdAogQoDpKfpcXm3QotFbw2f5YpPKonYCOmYXihQM\nGUs5WDIWzbWbFgzLxWMPHOLt17Ik4tyRWhI0igG0CPOEQahX6sk9CGr0nV0xPveSh2Bd0iAQAT2n\nh4EzSLCWCSGYTTmofeuVbfjtCbhrx7G2kwTV2Zo8Xkn1GYIedtm6ytaCWp4+r3JewetOJhl29x6b\n4CDhiemZ8B7RvGnLWkcQAJ3dYXZvP9S6GeV2vF+w1/muBCJ6GK9E3QsAhhjaSzWaT777yOP48In3\nZYK+iqhgMjeO9f5GoiAQObrS926aLL8po9W3UC5EzGIGGgkyvb7OwEGjY4GI4Z4maaiqDMCheXVO\nkzFe1nBrq5f43Hbfhh8EiWJQnGXGQK9AoG52d3b6vNiTZhqxz2TjN6udq6BJHFAAgKl8VNhmObYF\ngxdC4kx2SQu1Ui0Nk7U00K9wBuZewZhG5RhxQBRoF0G6MFTOq2gnmEbR9QxMB//3n7wA2/Fxz2EK\n2hLJxpMvbWV+broVlu/VI4pwhBDokpZkGoX7kpqj828/0IjFqdoJBAhwfUDNTHwj+8wdtafFmEZK\nnGlE7zmRXCxMRoW5htlEgACBmeP3y7XDddSLtfyFeUmrZ/N9PLB0PHruJABaAEq38QG0IHVqnq4J\nf/qNV/FLf/BtXF1r49VbTUhjmyAguHfy3IHuxUHjews0yu/NNIojq7XYgGIH3EBlVsMGrwAfjgmq\npWOjPkBgM9AoYnMwZsi4Poa3HL4fQQD87Z2nsbrTw+J0EZIogBDCKWXsNbZvwe+XEkhtPJYrSwCo\nGDYwTOWOR9/pH6g1DYhboA+zGBxvuD0tzjTaGdQBX+AH6L3a0wB6TwbeAJBs1HETVbWCo5XDOKQc\nA5Ed9MXR8lVHZsu0Oh5jGl0NQaPlsO1AEgXUSmpC0wgA5kJAcT3GNmIAS9SeFiYFGaCRpkiYHsvB\nIl1U1crQpjUWB43CqkSaaQTQJKLr9PBrT/8mds0G3rH0NtwzcQ5XWtfxG8/9Dr6y+jU4vos3z78B\nAhEwrtfgBz7fLBjTqJrLI0DEVIuYKMOgwsCPWtYEvTeaadSLQKN86mDAFhxmk1otqJz2vNa9k7iP\nQDSn2GFjPNVeVtMq8AMfju8mFuhKQUGvG1LNnT7qZhM1rYp2z8Kvfv4Z/NsvXYZICH7knafwo+86\nnXhPVrG/GraBxu0rGf1ZDt0yvr7+TQBAf7satREkmEZRa5WZsUGX8wp8i/7bDUU499NLSUdWiyqQ\nzTQqKUVIRERjn/a0l8Nk+tx48t5orJJtJ3VIXM/Hl55aBfEUeAL9HiwZiphGdJln1RIAmJOOwq1P\nY0k+M/QdyqH7Xt90M+nrbF366to3AADWzhR8P4gqqCEzhH1+y+rAJSYCT4S1h+unLMrQ/TKEXAea\nSr9z3G5+1PdYrhyGbs5jvR62JqXAm5JSAHwBRDESVfiDgEa12KGF0ZEHYcK9Y9TRdXrAoIZyjMI+\nKsZj7Map/AQHARRJ4IfcSP9nmGmUBrGX58rwPAGKoEZMo+0eIJv4s91/A/3Mt7DTpWtH224j8AQU\nlWRSpJHo3xwcSrenca24dHtatC7Fk0rbizONTH5NpmfFNI1CK+AYaMSqZOW8isUS1eu42VmFQAj+\n+Wdej08+ThOo53dfghd4vP08HoxpNFWmc5mxL9oZlPeyUkSAgFsCv7hLE8fzE6dxfmkKga1jfY0y\nTDsDG//h/6O6OPfNngIAvNqgOg2HZ4oJAFCU3cR9S7d0MaMDiYhYKi3i8mqLM43iFV12vyNNo2zQ\naHxEe9pWAjQaZhoNMphGji2iWlTxptlLmMpN4nB5KfGezNo4zjTabAwQIGrTiYcmqZgtTON2d42b\nDuQ1GQPL5W3IDYvmW4erSRkBNl92jDok3YJQ3UJVnNwzp+sadmS/HEZc1+iF8BmfmziN+09M4uQY\nLeRda9+E4ZrYNRuYK8xAEkXooUEwywfZwSEOSLHIYm95ggUEhFehtSAsirRobho4yshqelbIggQC\nkgBoWSQKFCnQiB2+1hp07xkvJHVDS+H+PTdDx9fl1Rb6zgC6pPM8KalpFLWnFaQiegMPAhESLbXU\nwMDme67Vp9+hUKHf/dXGVRAQvGPpbRjTqnh+90UocoCBQO/1Qmkevh+gmgKNWmHO9/LVAaaqOh6+\nL8m8uff4BN/XZUHmWnIAYBoCAldC1w0NSroU9GXyBzaJ5l50CA4dyYiAgpxH1+6h7w6GcvO58TyC\ngJ4pBqaDF67XMTOWg0AIF75msdumejTFDGHgolJA3x3A8z0O0k+Uynjonln80KPHeaGchSAQfPTt\nx/HQvXM4Ol2DLuloWW14vofV7hoku4ythj3EynwtmkbxYHuDbdLvUy7TcZKlaQQAHzrxXvzMxc/h\n7Utv3VOCZLYwA8M1E8XTNGjEWxSVEtVhjYEc7AxAQqe0Tt9Go2sCnGmk87bs+JlvcbqInmlhoxE9\nq0Y3srVnwazNqaZReI5yLSxMFdHsWuia9PdZmkb0M1twPZ87VcaZRjlN5vtA4EocLASi1kWXGJn5\nJetGCGwdU9Xk2CzmZNiOD8v2sFdE7WnJcZlXcsOgUUFCd+DwojdnTskafu+vXsV208ATr1/EsWkK\naFQqBM9e3U2sjyz6TpJB0+5ZEAUyUjgboPfNjBWxjsyW8MjFeUyM03HIdD73i9M12qLsBi4CR4bR\nz95jswoGmjrMNBIkF7Pj0f3fYXuylUcrvL92aMIR14KMnyU2ursIfILlqSmcnaVM3a3+Nu+giQPW\nADCWp/vK+RMl3Nrs4p//4XdwbWcbQrGJ5crhSIbguxTfU6BR2jUqHXFkNU4ZZEwjS6CHpe2mgZsd\nWpViPfVZsVnvI3Do+6Tb0wQiYEyr4g0nloBeDV2yBchGgo1QKaho92z4foDV8OAdDEpDk45/Z60C\n0c1BKDQBBJiuZoNCtmfD8d09+zKT75vcWFn4gQ838IZQ/7im0Y6xC9kvgDXS7pfEsEROmV6FLzi4\nb+o8BCJgitDka927mvj72901rj1T0GX8yDtPIRAdBAHBv//SdVy908ZUVU+AVRMVHe2eDdvxOK1y\nqUIpyGu9df539VTrky5Rm/S0LgSLQ1M6IFsoy8O6GGW1BAQCBNUAhOz2NPYZru+iabXw7iOP411H\nHsMPn/kI7p+8gOvtm/irm/8NiiDjwZmL9H6FYBBLPBnTiG0ITHNDISpkogxp3liOBy9WASN7gEYM\ngKoWVM7UYNo+fXcASZDQDZ0HygWFL6y3M0CjnJzjGxUwWjgcAEpylISW8ypH0jf727A9G2NaDV94\n8hZevd3CPcvj+KVPX8Ibz80MJQ9MLD3ONJJEAUvTJQ4akdC57U5vA4qgYNAoIbA1BL6QqKbHmUZp\n6iwQUnBD1ku/J1CBzplsauqoKPPqQBI0qndMSKKAUky3RSBCqM8wmmnk+R5ebVxBTasOtS9ltT8A\nYd9410JVL8L2bdiew9eySNNomGnUNwI41+7BcvHk8HXlFRiWi87AzqSvs3XpmZ0XIAQy/PY4BlbE\nakkzjdpWBzYMBI7C20dGheqOgYgeegGdB2kR6MT3VEogIPjAsXejlFP4xp4G7QkhkPwciGomqvCW\nZ/G1cFTERd/ZvWAJN2NPWK3Snq1pLOItsVO5CQ5CxQVYWYUyXrU3XOqAmV7H2V4kB3qsPa2P/HQd\nXuABegeN0lPwfR9dp43A1qGnNOdyQjR3uaaRlQKNONMoua727FGgUVjFllU0ezYHuyzX4s+fac7w\nVp2weKPIAnRVREHOY1wfw83OKvzAByGEAzNXQi2UdkqPKP49ZkO+PkuwmxmU91KMCWe4Jq60ruNQ\ncQ4VtYy58TyKORmv3Gqi3jbxy3/4NG5tdvGm8zN4+MR5SILEQSNNkTA/mQ//X+QHgiGmUXjtmqTi\n4tQ9eMPsA5AFCSurLRRDAL9nx0Cj8H7vJ4RdkPNQRGWIxbg92EFVraCsFBOgUZamEWNWwJVQLahQ\nRAU/d+l/wwePvzv5WXoSAAPAwdqZEUWww+VFOL7LRayZ2cWv/9HzqLcN9IIGAlvBXC1ZmIj2zgZu\nuy+AEGAWZ0YeOoMgQG/gJPSyAJp3TeUmcKV1HS/XV1BRyzhUoPnE0VB38FrrBtZ7tEWBMTwLSh5w\nZZ74Z7UosMi6p75ggngqb/nOi3S+bhnbQEBAHHVkgTErCCFQRSXSDItFvNUp3Z7GcuXtkOE9WUoe\nIphhQqVGIAoEl1fb6Dl9XvkHKPuQtaf13ag9rawVYDsBNFGF6VoYmA7Wd/scdMlJGoIgQLMe7kGk\nC9M1caNzCwvFeeTlHC5O3QvLs6FM7MKWG1AEBd/4dgc/8Wv/Hf/1a3TcbnR34Xo+/vYyXXOnCzX8\no4/en1g7AdqWS0LB2dn8dEKvrm+4CCwdTZvqfzJxdHeHjgUziNazdHsaQAGdjt2F4ZpDoBEDTNfr\nfTx9eReeH+DBM9OYGcthbafHAfEgCLDbNjBe1jLHMWtH74aMJoAyMD7+jpO4mGIZ8WteHsfHHzsB\nQSCoqmU0zTbu9Dfg+C5KmIRlewnDHoDuYQIhI4Ho/YIDz+GWUCrSa8nSNAJoLsAKAXsFE8Nej+ka\nsTnSSYFGKilQHdbYms6YcX5YQOsMbMo0kiKmUSXsMoh3ZixMFaEsP4tf+tqv8p81O6wIG2tPc4fb\n0yzP5uLujT79fZamEUBbBweWCyIm2agABaOJSz8rsDWu67eN+vUAACAASURBVAhEmqeBaHPTgfg8\nZyBbYGuYqqaYRkwMex9do3aGphFA95e+M+DA43M7L2J16o8hFJr8mbBzjUwUPL2yg/mJPN735iNc\nC2xibgC/cht/8Myf4QvXv8Q7LwBaHJEECUpIbmCGFXtJReiSxgsdAGXIf+SR45zRflCm0Xxxluez\nvllIiK3Hw+KtyfH2tIhpxBjiY1URcqx9mZ37VBTRCTWLzAGdO/H8KS53sz2oI7B1PHr/Asb1MRCQ\nBNMo3jEBRMXTxx6cxv/x0fupdEGN7mXfTQFsFt8zoNEPnHw0s5ocj1os4a/GKoYlpQBN1DBAE5Io\n4MvfWcPlxg0QkD0XqvX6IFSbB3f5AWhli7FRFFnEgkrbQcTaJo7OxkCjIq2Odgc2P3j7/XIC+U5H\nIZgEkR1IOSNxPfE4qHMaC840spKtTayyJ4vZTKOW1aYUYkSH5b2+OxBVNcWpmwCAi5O0zUV3JxDY\nKm6ZV/iCcKuzil97+rfwW8//Pk8iXn9mGuNjAkRfwTde2IJhuVhOMTxYUrnbNtE1bIgCwfHaEggI\nvnrnb/nkaRjNUGgxqgblJH3ocMNibIImfZI3vKAIRIDg6iCqCSfIruwD4BvOB479AN6+9FZ6LwQR\nnzj9g3hg+j4AwAMz93PUnIFsrArMvvt4MdxkuiaaXQv/4vPPwuor2BkkE/9u36auCAAUQYag7aVp\nFLkKRi0IdLM0HCpKGaegsqTwduj6l3Y4i2uB7QUapZlGjMZ+u0vfd0yr8taTH/uBMyMP2AIhODJb\nwlbTwE7LwOp2D0dmipAlAZMhe8Uxog1tXl8CAgEAQWDmuFUlEIn55rUYaCQnN2jZp5tFu0WoEPsI\nJ8NRwVgLbLNl0eiYGIuJFLKoaZGoclbc6q7BcA2cqh0fei2vZJvJatEXv0WZGIdq9Pn0nB4HjYbd\n0+JOUmwcDB9aSuEa4Lh+JtOIb7KBj4q/AAQi+oYTucIw0Ig7VLVh+QNqz7xP4iIY9Dq2LHrA3Itp\n9KET78Hfv+fTOFScTbhxZbWJqSiAyDbKRWbf7sENvH01jca4gHljCLi71r4JAHA7lcR+NCriTKPJ\n3AQHAeLVXlVUQEBgpdrT9AzQjDHzfFtFz+mja5iod0woY9Q1VHUrILV1/MW1L8MKTAS2NnRIKEox\n0EiOmEa6KnLmFP/5iPY0INlWxTSNSpqOVjcC5kzPGhLbZGAX03KoFKJ5s1Q6BMM1EgxCx3NwIywI\nZZkGMIYEc7JjVP42b09LMo0ACmq+0rgML/BwboyyiAghOLVYRatn4xf/4ClsNgZ4x6UFfOrxk9Ak\nFUfLS1jrraNl0rnGALxSTuH3ie3fWUDLp858BB868V7stAw0uxZOzI9BEZWEHh9ndu3DNCKEYFyr\noW40+PpnuibadhdTuQlM5ibQMFtwGJjHnKrMYdAo8GS+PmcJxEftaTHQaDd0TstgGgFRNfePLv+/\ncDwHH3hoGWeP1PDSjQZ+/i8+D0foI+hXhw4rLNdY723i5d5zCBwF2mB+6P35NVheQgQ+Hieqy7A8\nG313gLNjJ/kYG9OqKCtFXG/fxJ2wIMVBI12Gb+VQNxvwAz9TDBUAntl+Af9l/fOAQN2HWASSBdGP\nsU3FqFhFPAXFvDrEHNkvFFHJZBqxNVeRhGHQLHyedhCuz/kU0yhsnzO8Ppami7i12QlBo+h5DsxI\nCNtwaHuaLuko5+jaoAgqeraBn//db+Hnf+dbuL5FD0u6pKPeMbn8wa5Rx9XWDfiBj5O1YwCA103f\nS+9J7TY8pQO7W8AX/nYVmiLxVrM/ffIl/OLvfxs7fVpM+Kn3vj5z78ppMo7UKGNtSkt2LvQMB4GV\ng+M7aNsdXGleg+Tl4HfpOIszujnoFcsZCnKe790jQaPdPr71Cm3DeeDUJOYnCzBtj2vL9U0XhuUN\ntRCyYLlUx+5mFrv2i4pWhumZHNCe1um92KwnmSJ900FOk/Zk/ewVbA1xbTp+9fB2ME2jdHfDQYOL\nYcc6CkTRh6L4UXtaeM5hsgKVBNOIfhGPhKBR30ajY3KQRg+FsIEk02h+UodQ3kHd2oYTnptYPh3v\nahk4BiQiQhWVBGjEjHY6Bl0LVSk5NuOf2TccQGQFhOQ4kEGvKbD1xHpfkHNAABDZwmrIXIszjbgb\nnK3j+KFkUZytB1kOavHIak+jn10IJR4M+IGPP7/+RQTEh1jb5MUYI3QnbncdBKAasoJAONh1238R\nypEX8cLgSfzlzS/j+bAVHAi7aqQcCCHwQ8OK/dp2dUmH4zsJ8AmgmmOSIGUW/LNCIAJfhySnOJTL\ns2AFyTggFHdP8xz681o1uTew1uaiSEXbTduDHxbVu7FCG1vLbu+24BITkpfHvcfHIQsSxvQaBY3c\nAQhIgp0GJE1hlufL+LlPXMTCic7/ktY04HsINProhfft+zfxyVuNsY4IIZjKT6BuNvDBh4+gZ1i4\n1V7FTH5q6AbHY7PeR1Wnk5npgJiuRR1bYgn+o8dehyAgEGubWJ6Luc/E+hAj0Gh0exoATMh0Ea9M\n9UYiqX1eYbg7TaO0Hs6oBZwdlJi9bEmKFpnyAZlGEB0EZg5zoTOdYfnwGtOwfBOvNq9iZ1DHbz73\ne7A9G37g83ZBADA9A5PFCt5xaQGSSPC6kyktg5iDWrfvoJCTUdOreMfS29Awm/j3K/+ZqsybzSFX\nrpysZ7anAUC+RBcE1xixCds6iGzxyZy18Lz/2PfjZy5+jvfisxAFER879UH8/QufxgeW38V/PpYS\nKTUcEwIR+KHm6cu7+MXffwpX71AmgBPYiTaQzsCBoBoggYDD5UUQ1cRWe1gsG6CbXCknQxIFftjv\nGRHTKA4aFXMyH19s7KbvJWO7KIKcYB0BSWeHOGhULqhAIEAVNM4SqelVbDYGqJVUDmCMCqZr9NdP\nUVH5Y+EGyMaE0YvGctWngHApJyMwc7B8iwOucTHfyAkl+dwL/gwCW4Hbqd11axpAhTSBpKaR7Xjo\nDJzEhs5iTM9mBLJ4uU41wtKaRUDUPx2vZPcMB6vbPZxeqmI8T79/z+4nHGaAuHta1J7G+quzNuf4\nGpDFNIprrc2KdMPtmy5vKWVVb8Y02uhvwYePwFH3ZRoFPfqdV7u09YiNoax1fDo/xTf8OKsri+l5\nqErHci5cAxiwsV97WlktQSBCyDRKsgmut29CJjKCQXFP5zQWbE8pK0XoksbdsOK6EpRNoKaEsM1M\n0KxaVDFW0mCGlOqrmzuAaMOUt7FYPIRzwuMIbAVfXP0yANojn67Ml2LU5bgQdvw7cfe0PdrT+jGG\nDDtYlXLUHRO+HP6cMo0EQvi9ZK5vmkjHVHw8LpWYrlHUhnazs8qLIR1rmGnUDxkSqkLtiOtc02h4\nvHMmnN3hekbxttCTi3S+dgcOPvDQUXzwrcv8kMXG3YtblEXLQaO8wvefIaZRRtK+ErpVHT9UQV7K\nJZhGDEBiDI/0s4vHmF6F6Vn8NYx1OZWnoFGAgGvp7NWeBk/akzWnyiIUSUgcQDbCA2mWphEAnB8/\ng0vT9+NWdxWfX/kTlHIyfurvXcDbH1YRTL9KReyb9w3lQ5qkoSDnca19A6Znwt2eR6eX7cADDDun\nxYO1qAHJZ0wIwZHKYbTtLp7beQkAeE5T0GX4pg7XdylbcgTT6Ont53Cjdx1CvsUB5YFtgYgepCBW\n6FSj/dW31btqTWNBmUbD1XDGOqiVKIPlSvMa/vev/gJudVY5oM3YFmmtTAbud+wOjh+qwBcc+IGf\n+Lu+4QKBAInI6LsDdO0eSkqRH0hdW0Db6KPRseAHAf7mhZsA6Lp97U4Hvknfa8eoc0DjZI0+k5n8\nFA4VZuHmdqgg9aCE97/lCH7lxx/Ez//Q9wEARJ26E+aKLhRBTrTupOOBo0dgvfI6jBvnEz/vGg7V\nlQLw7PaL6LsDVMkcl2aIa0cy5kQcrI/nOekWkbkQNLp8u4WXbzaxNF3EZDWH+VC4n7WoMfBovJKd\ng7JDdtfux5gFByseA+Banc9sU42ooxXaabHRSObEA8t9za1pQLSGsLXJDXW2nBFMo4MGkyhhZ5PL\nzav4hSd/BdKZr6IdAiNtzk6lzyauw8rGrOkZ3DWs0bVi7WkaF6VuxkAjKd8BESjgzvQB4xqhLPpO\nHzk5x1l/9LMsLE3TOdS1mP5gcg0txzSNBmY20wgAVIQix14uAeiJgggJGohsY3WHgUbRZzDW1K//\n+KPcDY5Fmqk1KrLc04BIa6jn9PFS/VWuQScUGzz3HbgmdEnnhUPWIrtcOYzHl96Gx5ceQalxEc5N\nuva+0ljh7x9v90wbVowKlg/GdY0AmpMU5cJdgaFnw0KR7teG9ElZsIKBKmczjfphKlQuJfcGxjSq\nKjVYDgWPmW5VVnva11+lLOr58gTXEp3OTaDn9LEzqHNttXgUpAg0AoDN/hY2zXWcqC4fmHF1N/E9\nAxodJGRJ5IeDdGV3OjcJL/Bw/qSOU6ckBIIHwRhtM9czHHQGDmYrVYhE5DogrH0kbsl+7+E5iGYV\nQr4DXY9uGfsOja6J1e4dKF4J8KU9QaOFHKVDC5XdkX9zt0yjgpyHLMhDh1HONEq1NbCDEqus1TR6\nmCHAUJUqHXEwza3PcEHLgeXAa1Ddoa/d+Vv8xnP/Gl2nh3sn6cZ9rUX7x1mvdlHJ44NvXcZv/fRD\nOH80ZbUbA43ibkiPL70NR8pLeGb7eXzp1lfg+A5qKQZMPkWljIeoh3aXrexr9E36uWxRTFNMAZo4\njGKvCUTAqbHjCWYXd7YJF4+BY0ATVYyV6Gd9+9VtdAcO3nFpAUHYix+3mO0MKNNIFwqYDUUde0Fr\nqE0pCAI0exY/vDJq68CkCeDAMZCTc2j3bRT0EFjiG2zYI50GjfL0oF3Ta5msmfg9YcEAB5XEWsGk\nMppda6jXOisYe+Krz9OxycAc1p7WaUXfg/QoXfvkYiRayg5LcTHfURW7MXkS5rMPI+iX71oEG6CJ\nkywJvNoCRO2G6RYBAKiNEKxl8UrjMgQi4ET16PBnaVFVg8VmSFOem8hzl5ue00fb7kAgkX5N2j0N\niICuLOH7eAU3K7Fk61JO0jGn0qS0bzqot02IAuFJFjuMrIbrTOAoXAh5VJjdHOCLHCjgTKN9Kkf7\nMY1OT9HvuTmgtN1RLifpoKL55VDTKNIJGzgGNvpbGJenAQiJBG5UTOToOscYfKxSlQYDNElNCGEb\nnjVUkWRxdK4Ex6Rz/cbuNsTKDkACXJg4g7nyOOyr94KE23y6egkgYQjAK1amm2hjG9We1o0BHPGW\nYMaEGC/S8dfp0nFnulQIu5CTOUDAEnTG+okfpLNAoyuta/z/GeDEIggC9GIGEmMlDY2OhSCsXqbf\nn43PltnGS/VXUVZKOBS6NgFUZPbobAmffPwknnh9ss39VMieeX4zBI2YkUNJHWI27gUaXQ5t0k8s\nVFFQ8pmaRvsxjQBgXEsyWpme0aQ+gclQ14W1qGW5AHKmkSvtC4AWcnKCabRRH0BXxZEgCCEEHz7x\nPiwWD+Gbm9/BV9a+jl2jgW+bX4IsSDhsPYS3nDmc+VqWbwhEgNg8nHCr9YMAf/Q3V/HHf3MNl1db\nfE3LymGOVY+CgEAR5ASABABHQ92mV5tXIBAB03m6r+Q1mTuo7Rj1kUwjdogleo+3rtYH9GcKonkb\nz518WzmwCHY8FFGB5Q2PI8ZcYYe1b2w8hYFr4JntF/h6HIFGyX2Ytc682riC5fky/7sE0yjUcdNF\nHV27h57TR0kp8LW23Q0A0cVPvOcsZsZyePEWzaFyko5rd9qAK0MRVOwadbzavAJZkBN6WRdDthEA\nfOj1F/HOB5egyiKmilVIRMT8nICf/fj9kHUbFbW856Hw/JEx+N0x3LqTBNd6AxtayKr/H6Em4qy6\nCLgyAl9AJ9ZtwJgTce3L+P1I30PmoHZ5rQ0/CPDAKVoIZU5K7KDPCisTI5hGJe7S1o3cku6CacQY\nLbe7a9AlDScmaa66UU+BRiP0Cg8aCfc0RDnkQYsxo6KmVaBLGu501/GXN/4a//KZf4Wu3UMgmzDn\nv4Gu3UfTbIOAwDHpZ1cSFvGhGY5rcNewRsfkOnO0PW2YabTtRMymjTbN1RsxN2IWA8fgz567gnq0\nnaqUV2A6JkQiDrlea5LK9ab6pgOwdjk5OQ50kb63gmEAXiU6iGxjfTd0CI19r4bVomYb6vBY4Y6X\n+xTs2n0bhGBIa4vtpz2njy/e/AoAoCCWQXJdbHeYsDftZEiDRqIg4vuPPIbvP/J2PLz0INztQ5Ch\n4aX6CoIggOd7iXZPtr7vtzayfCiL/ZzWbdwv7p+6gB8//0lM+scxsFwuZh8P2x0WwlYVEQSAablo\nd+h5M19Injt3jAY0UUM1dHO9s9vn5lvdWP7C9lyL0HXi9GzEqGX5YtvuZK4FXMvXHcB0TfzOi38I\nAHjz/BsOegvuKv5OgUZARMlLV8PYjd0ydnDPefpgb1wTcXl12I0KAKf4HZoooqQU+QKyExPBZiEI\nBJeWjgMkSNAm2cC+09mG4RqQrErmpIvHYmUGvpnDQN7gNMh0RAr9Bxv8hBDUtMoQaMT6i0cxjZiV\n7FSYVBZz8pBTUjribBSvPsMruAPThd+roKSU8MLuy9gx6nhs8WH80MkPgIDgaus6/TvXQICAX1sW\n24oxFdbrA5i2x4FCURDxydMfhi7p+LPr/3Xo+wA0UXcDj7Os4tEOXd/qO2QIVPJ8H45BnyfTNtiv\nXfIgUVKKkAWJ3+tBaEM7XtEgCgSlnIyf/sF78IG3HAVset3x59jsGYBsoSCWeDKbJYZtWC5sx+dA\nJjsY9E0XlmchQICcRLWiGCgQX1wJCKpaEjhhTKO0CDaQ0jRSosoGmxNirMLKhCn3En5ncSRkGtmO\nD4IIRNIUCsbuNj0UlQKWSguohw6eJxeqCMykg1pczDfthMIiDo68FqYRIQTlvJLog65zkcLhscPu\nYz2DadRz+rjVWcXh0uIQIwqI7LbjYOFmWD2cquV4Qttz+mhZHZSUIq9IqFmgEaciZ7WnxZKwjMSS\nMTTumTiLYo4+577pYKdNLe1ZWxNre9gI59N+TKPuwEaza0N1atjob8F0TQ6ejAJNWBQTTKPha2IW\nzoxVx4CNgyS3Y1oNbbsDl9BxNLBc3OhQ5qTq0DkSd8wYFbP5aYxpNZwdDytbIQiQBuY0UeVsAj/w\nYXv2SNBsea7M9YDWWg2IVXpYuzBxFuNlDX6virPKm+l79UtDANVYyLQlEKAIMvwggJmqQmuSCgIy\nxDQarWlE7+2ZBbpeXVulv2NMozgrjB3UGOM3nizOF2chEZHrEwLAleZ1EBAcKs7BcA2+xwG04usF\nHq+M1koaLMdD33QzxTXZ+Hx250X0nQHOjZ9KHEbLeQU/+/GLePOFpEAzQNsoCnIeL4RMo/Gyjp/6\n4AW8/y1HI2FPaXR7GouV1RbymkSBXzkP23f4NR1U0wiIMVrDfYYBRFO5iSFTgyx9NF6x9eTEYSQr\nCrrMr8X1fGw1Bpgdy+95kJdFGZ85/3GUlCL+9OoX8H89+69guAZ+8MR78TPveRjvfHAp83UsD7tn\n4iwqaimhH3f5dgt/9eRt/OWTt/DL/+5p/Op/eAZANtMoL+fwzsOP4geOPj7Urn+0En32dG6SF9kK\nuhyZLBi7sF0fkkiGBO8ZeCnm+vyeNgah7ooQ7XnlXB6BHQrd3oVzWjzUcG1I5y+z43mU8gpOLlCD\nCsZavdy6FuXKYUtMeh88Ul7EkfIint15EXV5BUQKW7CU6LsPjOjQzXLlklLkRSKZKCAEOLtcxmMP\nLMAXmIaMjmvrHYiCgMncGDYH29job2G5cjhRzLw4dQ9IqKt5fDwCaJkWYMNsYmE6j57T5/vPqBgr\na8hrEs/xAWqfPjBd5AW6zqz36Z60XDoKgIA4etTig4g5EY94cSwNGjEHNRbM5Wx+gr6GMY1292Ua\nMdCoN1LDZK+Ii94uFg9hJmT/xdvTHNeD4/qv2TkNiNaQIASN2PrhjDhzHDQIIZjNz2Db2MUXbvw1\nKmoZ//D+n0DNPAWi9/Gbz/0e6mYDJaWIbp/mM3FzA9aa1Hf6KOVokarRtaDrdL7ooZyFJmoJ0Cju\n7vj7X34O9baJRteCEBoeAXQvHrgGX9d5e5prgRCCpekiXDhDIuD0tQGqajkEjdxEu1w8xrEId3se\nZevI0HtoQg5EcuB4Lkp5hbdKBUGAptlMsP/jcVBNo07fRlGXh9Y3VpB8dvtF3OjcwrnxUzhTPg9C\ngFu9m9ydWJe0iElXHh6zD5yaouB/fwItq42N/laswJJ2Tts7L2NrWFx83/JsOL6Dwl2yawQi4Nz4\naVTyTIh7+D6x9rQ4y1QgBKoiwrA9NFuhDm4sTfUDH3Wjjgm9FmEFuz3AFyFATBTdimEhjah0z58t\nRtIgcX3TLNZhPsYE+7ev/CdsDrbx8KHvw4WJYZOb70b8nQONGIKZPpRNM9BosIM7g7BVrFfBb//5\nSwlrWRYcNJosoKKW0bG78AOfM0LiVSEAWK7RqifTaQGiquVaj36eNyhnTrp4XFgew4J2FB4cXG5e\nzfybu2UaAfQQ33cGCb2UiGmUrWkUgC6kcyW6wZUPkMRokobp/BQqZAqBWeCHT5osEdwfCm9dmr4f\n7zryGHRJw3xxFrc6q3A8Z8heMSuYphETRC7GDoFjehUfOfn+xHXHIy/RBa6fUvsHgHr4bHtthVNP\nWQxMlzN9WB9qFtPoboMKqtcSmkaapCGvyfi5j1/EL376Ek4uViEIBIVQKDMOGu32myCE0lunQ6F4\novex1UxWjhopKm0khO1E7VmihoHl8kN1HJQsKcWh6shCcR6KIGeKyWuSykUHhzSNQDUbWNh9+v/T\ntf3Hc16TuaDq3ESeXwcATFZ0NNo2/sE9n8WPnf8ENuoDVIsqpqr6kIMaS951VRyZfLH7MFHRDjT2\ns6JSUNHpO/B9OpcarNKSxTRiLkcZYtgrjSsIEOD02PHMzxEEukENMkCjmVqOb5Rdm2oaxRNrttEl\n2tN6FnRVzNRxSjKNhpO/o+UlfOTE+/Huo09wUKnVtdHp2wnnHlmQkJdzcIOwSgN9z8Tl+Wt1BAEw\nn59HgAC3u2tcEDpLCDse8YNi1qFxvkgP/sywwOI0+v1Bo/un6Jr2je2vA6Bji7Xbmi16n5mmwV6h\nSRp+8Q3/CI8svIX+m2kaDTGNNJ4M7QeaLc+XeeVqq7cLobyLKX0S0/lJzj4oDI7iPutj8NsTQ2yV\nyTwdk1JAtYRMy0WQ+k6RVlwEGnm+l6CVx9dbtgedPzwJWRLwynV6qDYcE4blJp5Px+7Svv0CHb/x\ng7QsSJgrzuJObwOO58DxKVg3W5jmphmdWLUuXWxhOUKjY3JxzQQoFM4RdoBMOxbuFQIRMFuYQd1o\n8uLPuSNjmKjoMU0jxjQKAfwUaNTomNhtmzg2X4FAyNC9ZNfDWkD2Yhpx7S0zxTQKNY0AWlADkGDM\nsWCHvsDbn2lU1GVYjgfb8bDTMuD5AT+c7hUVtYwfPfdxCCComw28ee4NeHD2dXu+ZrF0CAIR8PCh\nN9O1NubY8+TLFCB9/1uO4KF75/h6fmgq+9Dw+OFHhtrKAWAuP8MLaUxTBaCMKsY02jUasB1viGUU\nBAFvkxRzPb7vNI2w7TIGGuU1mTNi79Y5jYUqKggQ8NyOha5K+PXPvQnvfHAJNzurPIdc7d6BpPgQ\nBQIiOZAgDznHioKIHz7zQ8jLOfz5jS+gMkuvh7EegIi5G89Ji0oB9x2fwIcfOYazi3Q+mp6JB89M\nI5cLQS1Pwu2tLhamipjIjcMP6LNj7Z0sKmoZFybOYkyrJbQUgUgLkOXmFTX7cMyCEIJDkwVsNw3u\nGNU3qdZKKWaCMpOfwlyVrn+CS23FGejBmBPx2ItpBEQtastzZb4PMifb1VDTcSdlFpEOVoDr2F30\nX4OmUTUGHBwuL0BXabvpeoxpFDmnvTZgB4iYz5xp5DKmEXNPe+3vvViiLItz46fxjx/4BzhcXsSy\n+CDc+jRudW+jZbVR0cojC195OYe+M0Apr8BxfXT6NhSNjjvW1lTRymiZoeNYECTkM9pWF7/8776D\nzfog4YpquCYCBPzsosZcqAGq4wPBg4jkfv7kS5v4yV/7KkRfh+GaaA36CWHueJT0HJybZ1FShouY\n7GwDyU6YQhmuAcuzE6zheBTzB2MadQY217NMfG447v/72v8AALx98WHeWrppr8LybAQIMtvT4lHO\nKzi9VEVnk37PlxsrvJWbnSWy2sizQstoT2PtXmkpjYMGA8CzdI1GsUx1VYJpu9ip03vrC9FrW1Yb\nju9iMjfB9yaq7UqgEj3R3i8QQseaGq4PsQ4aZvQFZK877GdPbnwbz+68iGOVI3jP0ScOfuF3GX/n\nQKPvf3AJP/jwMm9hYsETo/4ObnRuISfpePu5U2h0LDx/fbgVbHWbburzkwVU1BL8wEfX7mcyjQB6\ngAZSoFGYXG2Hoq1Wq7CvG4YsifjAvW8EADwf9tCn4241jYCozaAZAxyY/aUsptrTYki4QASu95HF\nOsiKn77/J/G2GgVu2CTvh+KpTxx+BJ86/WHKMAoT9OXKYbiBFyYz+19bQZehKSLWtrMttO+bPI83\nzl4CMOy4x+mpqao4QBM/GSrgybi1lWxtGJgugpDpwxKb7wbTCKALQN8dYOAMYDoWPwAvThcT18Yq\nBTuDOv9Z3aDVrzG9EjGNtGGmUSsFGkVC2C4/xIgI24bCxTEn6by6x/R24lFWS/hnb/xZPLrwUOZ1\nse8bZxoxET12kNVEDY0mvZ8HAY2AiG10LCXoN1HR4QcBiJOHHOhodi3MjuVQLqhcMyHONNIUEaIg\nxOxzU0yj8D4sz+2dhO4VlYLCxfCBGNMoY9NkB7uGMcw0erl+GQBwujasZ8RCV0SYMaHVrQym0dZg\nG17gJSqOvD3NTTKNRonel/dhGhFC8Ma5SygoEah3s2P8FAAAIABJREFUO5xPDPDl76VE4JUu5vdM\nXJ69Qp/dvXP0QHGzvRoDTfaei/F1N6s9RZd0TOhjWO3eQRAEnMmzX3saALx+5iKqagXf2PgmFM3G\nwHRxvXUTALC9pmG8rO1pDzsqtAz3NIAyjdzAg+O7nPY/CjSbnyhwVl9LuQYi+rhn8iyA6GCy2zbh\n2HSep9kq5VwegStBDOj9HcQE5OOhp7Ti2KF0OjeV+DdAGa6yIEFXZZxerGJjh86Nvk3Xofizaltd\nlJQSaqFDTVpTb6m0AC/wsNpbp4UH38WxypGEDguLfqrYwoDbesdEq2cPAcN5KQeJ0GcgCzKOp9qW\n9gv+HazUXpKyEJYlCs6mhUhXeGtaJfzejC1IX8/WbRn0nrDxkhUsX4kzjWRBQlUrY1yrQSCRu6Qk\nClBkIbM9TSHankxpACiEe1bPcHirxOwIEex0HCkv4tPnPoZHFx7CB469a9+/f8v8G/BP3/CzOFxe\n4PlWp2/D9Xx8Z2UblYKCxy8t4uOPncC/+Owb8Bs/9WacWart867JEAURh0u0MJIAjXQZPmca1WG7\n/pCekemZEatZ76IftnG1wzGRiwEvBT0CoQJHPZAOWjrSB9WseGn3FQA0N/IDHzc7t2heIDnQxGwA\noqpV8MnTH4YX+DAqVN/LGkTjjRlqFGLso5JShKZIePTiIZRUVvm3IEsCji3Sv/vG83V4foCjc6VE\nIfZkNQkaAcAPn/kI/snrf3pIs4MVBtmaW9mHaQTQvD4AsBa2hTFmXFWt8Pc/UV1GNVwjmClG02on\nmBPx2ItpBACHQi2ZB05FhzxCCOYnCthuDLimCZDNxAAiq/Cu3YPhDKCIylAxb6+I7/usvXdmLIdm\n1+KAZn/EGn83MYppZHsOCMhdfed0PHH4EXzunh/Fj537BL/PlbwK5/p5zGn0mipqmTO80+ArB41i\nubWs0hyUAXBVtYy+O4Dt2WhaLbTtDgcuTi/nUO9Y6BlOgnWZ3l8kQYJERJ5LLE0XQUQXgZ+89r95\n5g4s28PqWggom+1IYym1r7N8KuvZ5MOxQWQ7JYJNwa//GaaR7VAR/3KGHiQDydzAw7HKERwpL+L0\n5GEEvoAWNvjeoUs66m0DhGAkW/UNZ6fhdWhnyyv1y1yDj7enHRA0ijSNonMQA40Kd9mexoLlvVmg\nkZXBNAJoIafds9FqU5B84EQgFttvJ3Pj/L3vhPtlTsrTtssYY/QdlxYwM8MMGmKgUT7GNMoAkFme\nYbgmKmoZP3L2o0OFge9m/J0DjRani3j7AwtDVOiJ3DgICK61b2DXqGOptIB7j9ObfWO9O/Q+q9s9\nSKKA6ZrOF9q21cbugIFGycRjOj8JRZC50xQQDewWNiARCUZzf9AIoIlTXs7hhd2XOUARj/TidJCo\n8UpjRLF1RjKN5MTrJit5fN/5mUwaflbokoaJ0P2L0QkHJhVPzck6Lk7fmxi0yxVKtbzauhGxqPaY\n2IQQDhAA2YfAHzzxXvzMxc8N6RPkMirfAAWCds0GKgq9T7c2k2OiZzqcacTioAr8+8VYmCzd6W2E\niHz2+07m6d9tdCPQqGXT5zmRq6Eg55GX8iAZ7Wlx5zQgyTRiIKTg07HJFjBREPkilGZsscjJuZEL\n0H2TF3CyeiwxTkt5GQSAZ0nhtVc5I+agoNGZw3TunTucBG6ZlehO0+A9+jNjeZo0OCpIIGI3XKgH\nVtSzP0rTaKaWDz9vtPbZfsEOomweMLem8QymUVEpQBKkofa0IAjwSmMFBTnPGTFZoavSENNIU0SU\n8woHjZiAZBysYWwiK6yWuJ6P7sAZCRLHD9f7iWXmQxYFA2HHUslwnPFUkPLoGxErKx6O6+HFGw1M\nVXXcM0fn9M3O7Zh72j5MIz1KtkY5Ei0U5zFwDdTNJm+hOghoJAkSHlt6GI7vQp69ib5l42bnNia1\nSfT7B2MZZUWWexoQ00pwLQ6ajVozJFHAfCWcJzoFUBgtOafJyKkSdtsmzLDinmar5DUZ9rULmOw/\ngE7fxn/5OtWey6eAgzTTiCVoDMiOu6dZvs0ZXBeOjQOBAAIBRngt7Fn5gY+uQwV1L52ewsWTkzh3\nJDnnl0L9uJud27jSpHpGx6pHeWtZHLDpcRYrYxrRMXN7q5cprklI5O5ysnbsrsVby+F3aNtJQe6s\n9aagS0NMIyaCzUCjQmrvYgxJrqGxR3taLebyFwQBtoxdTOjjtB1AEDGu1XjLGkDXknh7GvusyWJx\nXxHReLvdOl+HD56rnBs/jfcsP3GgxFYgAr/P7HDY7Fl48XoDfdOlLQ8hE4AQsqdY+F7BmC9xVm1B\nlwFHhQARu6Gm0bCeUSyPEB2YHn12XYvOD84OAF0rGSMWr7E9TTkAaPRC/RVIRMQThx8FAFxpXUel\nqIJIzp455emxE3jH0sMI60joxIb1wHQhS0LiQFaMFYvSlf/ZaXpt12/T8bE8V+agUVEuYLYwPfT5\noiAO6W8CUUGLsUHiwMioYFpCt7eToFEpp/K5cqK6jFpRhSIJyIn071tWK8GciEdBHi2EDQBvOjeN\nz7zrNN5631zi5wzAWt/tY6dlIK9JI/fVUowx3HcNDmQcNDJBozDPYXkYKwzo/1OaRuE8SLen+TZk\nUX7NrmwABR9O1o4l3qOUV4BAwJuK78Kl6fvxptlLaDENnlwaNMrD8R3kctHrBSlqrwSi+9S02rw1\njTFNp6dEfPhtdD2YiuWs/VQxAEDCtGJxugiIHjwnyj+aXQtX1too6DKXv9jpN0FEFxKRhlpl2bjI\nZ7DA2NggspVgsrPOhOoI0Ii1g3f2KNiNEsEGkgX+RxepY3RB04B+FbbU4kXunKyh3jFRLY52hbz/\nxATyYh4wSrjSus7NmyLQKGSP7cPCZHMzzjTqOd8lplHPGvpdlqYRQPdk2/U5eGp6UY4UZ/uyXHsn\nPLcV5AIc30ms449ePAQ5Z0IVlSSjUy7wcZvVnqaJKuQQwPzRcx/7XyJ+HY+/c6DRqIhb0wGUmrkw\nVYRACG5sJJM6z/exvtvH3EQeoiDwg03LamPXqKMoF4YOKQIRcKg4h43+Fj9waIoIVfNhiU3M5+eB\nQDwQaCQKIs6OnULb7iaYSyw40+guEFOGMieYRvtoGgG0DU8gBJ964hQunpzEQYOjskzTyEqKp8aD\nCU1ea9/gLjv7saji9N2sdhOBCFgsHRranNhkSwukdewuXN/FdIGi3Le3knbNfcNFYKtcNJaAvOa+\n7HQwLRt2oB91AJ4pVxB4QoKJ0nXo2J0p0YRrtjAFohrYbEVjum408K3WVwHixdrTIk2jARc5pdcT\nZ5iwMTYKNNor3r70Vnzu3h9NVAZFQUAxr8AyJP6+G40BJFHIbNnKikunpvBPP30J9xwbT/x8IgSN\ntlsGd+yZGc+HgtQiBKeAbWMXQRCEtuGhGDjf7JNJ4IXlMfzsx+/Hg2eGE9iDRoVTWuk8YEyjLAci\nqs8wrD223t9E2+7iVO14ps01i1x40AuCAL4fYKtpYKpGXTbYc2R6XHGwRg7d01hfNmP7jNqYc5oE\nMTyIZSUvyb+lv2ei3GmBz/j3KKpFBMjWdnnlVhOW4+HeYxOoahVU1DJudm7D8ExIJPswEQ/WwrrX\n+stEjle7d+5K0wgAHgzZRn7tFgbyBmzfQVWg42bpNYJGeoZ7GhC1xZqeFTGN9gDNjk5G67aGAmfF\nAnQd3W0bMCwXBBhyL9RVEX57Ahu3Nfzj334S33hxE3MTeTx8f9LaPC/n4Pgu31MYtbqqlqGJ6pCm\nEWOzXjhK5zDxpQg0Cp9R3xnAD3yU1SKmqjn8xHvODjG2uBh2+zauhLp4y+XDnN0YF8PupfYWtt5c\nC9ucs9qB2Pg8F+pM3U1w97WUi9sggyFQ0BXuZMliZbUFTRH5AZcxjThoFFrsMnbHXu1pmqSiIOdR\nNxto2x3Ynp3QQpjMjaP3/7P33lGSXfd95/e+/CpXV1fH6Z7pCXgzg8HMIGeAFAEGMJoUKZJKTFag\naEmUpVWyZMlryWkt7+5ZWxt8uNaR9+zu0Wq98qGC5bWlJUWTEhMIkiAKwAAYYGLn7spVr+rtH/fd\nV69y7q7u/n3O4SG6p7rrddWt++793u/v+ytnaxsfXakrT0sXc3CqDDPx7mPZC1bNl3GzT6fRMHiH\ndOkSvvI8n+ceunO204/0zPctPY5fuO8zOB2rBXKHDAUAg4kIF43sStNJs3C6ya5jraTsolp1vM9H\nnTvFUGGvLaF8cwWV7ZmmZi69UGvz3byxAXgXpeuZmzgTP4VzU3d4eZJnjoXB5AqiRuf36ZmVp3Ei\nxF+D7c3a35orlGHqSp0QGvH9bcIJKeYr0U1LlFaeWogiafK5wJo63fE+14jnNNp5DUBvTiORMSdi\nKERwe9BQcSy0AE3WcCZ+Eoos4Rc+ejcePcsPNrcKO3XOCT/+9Xgr8U1VZDx051xTLqgXhr2awcZO\noa3LCODZX4ZsYLeUdpuX9F6aBvB5IKqFMRuY8a53fppfq1gziVDzbvf2TsiSBF2VfZtlPh5LlfJQ\npWntEPuNfJ7hR87/AM4nLOxmeJOcxjgQ8d6Ypu9AXuYOKPH58cKwCzt4dYdn5gmH7m4pg6fvX8Jv\nfPx+fPgtNUdcrfzZJxopeq1baFABk6ooFWvX8/XUKhwA73n0BKx5vl747rXrruuv+Z4uDmtaiYox\ng497ppTqnDyic1q78jRdlaEqUken0U5OlPo1z0niXrsUWsD5qVp8glHic+9z67xixpAMbKaLHdf5\nqiLj0bvmUd6aRsWp4Nm17wDwiUY9BmF7TqNKi/K0AUUT8be3Lk/jY0lXGkWjevG03mlUyxUUB7Hi\nuDSi82vM+ErUeEfwTSSM+sZDjDGvRC3YwmnEGMMPn/sB/OSlT3jrpXFyaEQjoJZrBPATI12VcSwZ\nxNXbaa8OHuAbHLvieJO5mEA2C9vYLG43laYJlsM8a0Ns/hljCE6nAQbM6XyR3apzTysuuqfBz609\n3/RvYvHbzymDEI02fU4jYZtu3HCpkuqVJYkbeb8IR8tOptQyPNVPWAthLjCDV3Ze8zIouoV8+8sP\ne31Ngdpr5j/5BmolX/PhJGIhrak8jdfsSzAlPiYMRR/qtMSPcBqJPJV2G8BkLACnZGLXrgX05R1+\nndMBvnCaD86CMeB2vlZy+X+//Md4pfINyNM3vIWopspQZAm5QtkTTSpl/v7UlR+JU/k2pxSDEAtq\nyOf41JIw4ri9mcPslNkx68tPY6ikYMYdE6tbedzw2jwHvEBqp2CiWClhp5hG3jce25WnMcZwaqFz\nJ5ZuxJqcRgVEAmrTiYQgYUwhU87WdccSoaXnE+1L0wDA0BVUqg7KdhUbuwXYlSrmp2qdzCQmeSWp\n/oW1xBg0RfLqsoUFuF15msSYJ750cxqF3H8XN8PGBbHf8SQWPa0WL6I0TQiFJyJL2CmlsZpb6+oy\nAmqnaZ3miiUvDPtarXtai9DKVnC30ZsBqYLKwrcAAHKBf64HdRqJz2o8XP/3ib+3YBc8oaWT6/Hs\n4iycKh/DK8E76sZzImqgVK5ibTsPXZObGg/IkgRDk7GbLUFiwA8+fQd+4+P3e581Qa2DGt9Q1azg\nIa9jpaBUKXkHE/GwjpX5MCpl2Rvz3smney+IaO03gUkzgaAawCs7V/HKzlUsBOcQ0oJet7Vdn2Dj\nWd2FEO7mPrx6gz+mVW6ZCD6+kOg9z0ggxnYrp1FTHoqpoFiueJ1ZdjJF3N7M4cyxmLfJrLU2dh0B\n5TxMxcD9Z7kLq93prWDaTGCzsI1b2VUAaBCNGsKwjZoADQCZUg6oKN580gmxscnmy7ixkYWmSC0z\nLEaNmGtvb+Xw7EvrmJ0K8ByRESBLctOCW/ydWjWMvF1AqVpsmteF02glyn+WmWnkS7Ynota1aTcU\nsLIO+w0LqLbvNtcJ4YYTXaoa+e4GD2a/kDgHUzGwFF7E1d1rePODfK7q5PAG+MHGZy5/HNWXH8Ta\nzdr1ZQs2TE2uO+n2l6Wb3pzFP+NCeGFVFbGQhqmIjlOxE3jnytN4p+uA6hVRqiFKz2NGd6fRwnQQ\nssS8GApRGhoOqPjI2ffjl+7/GW89cGohimU3omGruO2tFwINna38DoZ+qgBEGPZ3X91Eya62zTMS\nRLQQdoq7KFQKfeUZCX7y0ifxY3f9iPe1yBsT7uysr0nIMESCKmIm/93i/S5XyyM7bPXTWDrkOA62\ns8WWB1/ivVGNWjl+VSrDVAzv3hj3dVB7ZfcqZCbDip+BLMnefWl5Nlx3iJFtyN8B+AG8EHDLDh9j\n5RLz1jhfe2EVDMC91gzeetkVXLQimGy3FASFGNTq0DFuuqJRQ3ma2PO1cxoxxhAOqB1Fo05Oo4Q5\nhY9aH8DH7vxo3doiBl7K+y03ZsWpqHCc9nldgicuLaC6w9d531nn+9+ArzxNkaWupZOirC9fbnY/\nDy4adc80UluUp3EYdEn3RHPA7zSabnL1xwyRXVYTjUQmccJsLq8W9/JWTiOA52425sSNi0MlGomF\nEQPzbO0rCxGU7aobQMWpdU7jg0ucGL66exVVp9peNHID2vwlamqUuwbEB6jXXKBzU3dAlRRPpfWT\ntflJZaN1sRPiNMbvYrCrrZ1GjDHvd4s20P0SMlXIEsN2tuiFp3b6oJ+OraBYKeGFrZf4z3e56fpF\no3CLOtt21MJE60WjdTcgdNqcwvHZMLbSRW+iBGqW3YjCbyajCMEWiFLHa27r8cY6Zu9xMQNO0UTJ\nKdSCBZnbXtO9IYgw7JyzhWKpgs3CFr7lqvVy/HbdBjRoKnVOI7vIJzj/jcEr5TD6y4HoRDSko7QT\nhsIULJrLKJQqmIv3Z7NuxUy8JhrdXK+Vp/Hn1FDK8n+/tnu7Lsw3Z+chM3k8J2DuomU7wzvabOwW\n627ojSRafE6FaHTOd4rTClF6kS9V6vKMAL7Y9y9mGjvMaKrslaeJm2KnTYsXlt7lNNLQFfh1iOnG\nTCP3OhRJQczg19eYa1R1HDz78jpCporTbrc8sYHL24WeykRDpoqn7jvWVBrgp85pNEBr4Ifm74dc\nCYBpfJGYWef3jxNz3U++W3H/uRn83Icu4eLp+jnY8DuNeijPO71YC8O+d+6uun8T8+h2ptS2dOet\n9y/hrfcv4R/9+MN4y73HWnbQNNV60SjjW6CFVN7VSAgQxUqp7nW9fHoaTkX2/havTbcr+Pg3n40w\nxnA8soSt4jbK1TLOxE/xnxEuH38QdsNhSzSkQWLM2yS1Gu8fOPNu/PIDn/VKoPqhndMo62vLLBCH\nLP/rn7yAdK7klabdsVTbADc6jbLlHAKKiXc9cgKfft+FrteTMOKoOBW86JbxzbQUjWph2JWqg5LN\nD9PydgFORcVsD/O0KBffzZZwayOHuUSgZRfUUSPev7/85nWU7CoeOj87soOdVgiHs2Tz96WiZqAp\n9Z8NIRiejfO5WzIzyBds5NwytahRG1cSY54ILzHW0kHdjVrHptYbwO9s8DyjC9NnAQBn4idRcSr4\nzjoXk1qdVDdiagaOB07gxkYWhRL/7OTyLZxGur88zZ2z3M+4EBE++baL+OQ7z4MxBolJeGbl6aag\n6240Hmj1Up6mKhLmEgFcW82i6jheaWjIVBFSg3WCKuDPBN2uNQ5peK1MxfAcZe02b61YnA6CAfj2\nK270RazzpjqshTwBvJ/nESyFF7yyYYAfrAE10agWhD2caPRj774Tn3zHRQC+IOxKua97aq+IfZVY\nsxdKFZTK1ZYHX2L+l/XaOsN2SnXvpxAeV/PreCN9HcfCC9BkFTEjUtdcwU+twUF9eVrRFUrFQZRT\nVXD1VtorTTtzLIp4WEfC5GPMCJbB5HKTKAkAF04m8On3XcCjdzW73xMBd52hFutEetH1r9PBbzig\ndcyT3PFEo9brvUcXH6wbUwCQ1OfhVGTv+e0S/2w0RhQ0sjAdxMnocTi24kWo+IOwGxtWtMJUWziN\nRlae1kI0sqtQFanpPifWVIosIaCadR1mV3PrCGshmIoJQ5Pr7h1TrgCY9olGoonFdIu9mJgzBxGR\nR82hEo3mXAvXXHDGmyBW5l1ByFei5u+cBtRO5V/e5nkOyRZKH9A6DLtsrMGpMpR2+O/opTwN4Aq1\nFT+Dm9nb3umfIFPKDlTLzMDqNqMlL9Oo+eYgTtgbu8T1inAjbKdLPZ1cnHJt31fcMMPuTqPapNiX\n00iUpzWIRqJz2rSRwLJ7Ovn6qm/D4S4qYpo7sY9QNBInZTezvNtLW6dR1PTCuDcL26hWHVTkHFhV\n9cbzvDtxMzOD1e08vnDty7wLnsMgRTYAuTbhBQ0V2Xyte1qxwCd1v7CZMOJgYJgNDOY4a0U0pMEp\nBvFLF38VU9UTAIC5PjIv2hEyVZi6jDW3PC1oKN4GJhbUvdDSG7v8pL3mNMrXnTKNkliw5rgTnX06\n2XPFWBA3iGvpG3hx+wpORo93PSEJuOVM+aKNmy1yovynyI0La12VPIutKCntNFeJE69uAc8SY97n\nXpGlpt8pNtZhtZb3tttw4nX1VhrbmRIunUp4bjQh+gPd84wALi589Kk78MC59uUqQTWAhDGFN9LX\nvQVePwtcVVIwW+ab96ASxPUbzsAh2AB3+Vw4mWhaiHiZRv7ytA7zUTigQbfjcIoB3LtYLzz6F5ft\nypve9/hJfPgtZzr+HWK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p1GQuyB93K7OO1awbsq7VnyIci8yCMQfMzMLenMWz38sAYJiV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b825290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print \"Word 'potato' and 'love' have low similarity: {:.2f}\".format(model_Sport.similarity('potato', 'love'))\n",
    "\n",
    "plt.figure(figsize=(20, 3))\n",
    "plt.plot(range(300), model_Sport['potato'])\n",
    "plt.plot(range(300), model_Sport['love'])\n",
    "plt.legend(['potato', 'love'])\n",
    "plt.title('Word Embedding Representation of Dissimilar Words(dim: 300)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word2Vec Sentiment Analysis\n",
    "** The DeepTextAnalyzer vectorizes the words in a review and averages them in one vector embedding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class DeepTextAnalyzer(object):\n",
    "    def __init__(self, word2vec_model):\n",
    "        \"\"\"\n",
    "        Construct a DeepTextAnalyzer using the input Word2Vec model\n",
    "        :param word2vec_model: a trained Word2Vec model\n",
    "        \"\"\"\n",
    "        self._model = word2vec_model\n",
    "\n",
    "    def txt2vectors(self,txt):\n",
    "        \"\"\"\n",
    "        Convert input text into an iterator that returns the corresponding vector representation of each\n",
    "        word in the text, if it exists in the Word2Vec model\n",
    "        :param txt: input text\n",
    "        :return: iterator of vectors created from the words in the text using the Word2Vec model.\n",
    "        \"\"\"\n",
    "        words = txt2words(txt, lower=True, remove_none_english_chars=True)\n",
    "        words = [w for w in words if w in self._model]\n",
    "        if len(words) != 0:\n",
    "            for w in words:\n",
    "                yield self._model[w]\n",
    "\n",
    "\n",
    "    def txt2avg_vector(self, txt):\n",
    "        \"\"\"\n",
    "        Calculate the average vector representation of the input text\n",
    "        :param txt: input text\n",
    "        :return the average vector of the vector representations of the words in the text  \n",
    "        \"\"\"\n",
    "        vectors = self.txt2vectors(txt)\n",
    "        vectors_sum = next(vectors, None)\n",
    "        if vectors_sum is None:\n",
    "            return None\n",
    "        count =1.0\n",
    "        for v in vectors:\n",
    "            count += 1\n",
    "            vectors_sum = np.add(vectors_sum,v)\n",
    "        \n",
    "        #calculate the average vector and replace +infy and -inf with numeric values \n",
    "        avg_vector = np.nan_to_num(vectors_sum/count)\n",
    "        return avg_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Review2Vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = DeepTextAnalyzer(model_Sport)\n",
    "Sport['vectors'] = Sport['reviewText'].apply(lambda r: dt.txt2avg_vector(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">overall</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">reviewText</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sentiment</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">summary</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">vectors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I love this chair for<br>using at my computer. ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Great chair</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0256419274956,<br>0.00641404185444, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My Kindergartener uses<br>this lock to secure her ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Falls apart and gets<br>stuck ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-0.091676287353,<br>-0.0490146353841, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Such a let down! I tried<br>to use this mat last ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">So slippery!</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-0.0615132041276,<br>0.0257563963532, ...</td>\n",
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       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I was looking forward to<br>using this. The Picture ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Too Bad</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.079170294106,<br>-0.059742808342, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Purchased my first full<br>carbon bike and was told ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Great Tool</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-0.0305297728628,<br>-0.0285292342305, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I got these for my 5 year<br>old daughter and she ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Big Hit with my Daughter</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0216339249164,<br>0.0514854006469, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">If I receive wat I ask<br>for, I don't like it too ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Wasn't wat i wan't</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0299516189843,<br>0.0517501458526, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">quality is not very good,<br>but it's good enough for ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">good enough</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.0457096733153,<br>0.0367940254509, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">In the description it<br>says it fits a 48\" waist ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">48\" waist?  I think not</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[0.00452239252627,<br>0.0481718108058, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This Watch is Sharp!, it<br>looks a lot like a Watch ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Worth Every Penny in The<br>Gold Box ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">[-0.00142007833347,<br>0.0489712171257, ...</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 5 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\toverall\tint\n",
       "\treviewText\tstr\n",
       "\tsentiment\tint\n",
       "\tsummary\tstr\n",
       "\tvectors\tarray\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+---------+-------------------------------+-----------+\n",
       "| overall |           reviewText          | sentiment |\n",
       "+---------+-------------------------------+-----------+\n",
       "|    5    | I love this chair for usin... |     1     |\n",
       "|    2    | My Kindergartener uses thi... |     0     |\n",
       "|    2    | Such a let down! I tried t... |     0     |\n",
       "|    2    | I was looking forward to u... |     0     |\n",
       "|    5    | Purchased my first full ca... |     1     |\n",
       "|    4    | I got these for my 5 year ... |     1     |\n",
       "|    2    | If I receive wat I ask for... |     0     |\n",
       "|    5    | quality is not very good, ... |     1     |\n",
       "|    2    | In the description it says... |     0     |\n",
       "|    5    | This Watch is Sharp!, it l... |     1     |\n",
       "+---------+-------------------------------+-----------+\n",
       "+-------------------------------+-------------------------------+\n",
       "|            summary            |            vectors            |\n",
       "+-------------------------------+-------------------------------+\n",
       "|          Great chair          | [0.0256419274956, 0.006414... |\n",
       "|   Falls apart and gets stuck  | [-0.091676287353, -0.04901... |\n",
       "|          So slippery!         | [-0.0615132041276, 0.02575... |\n",
       "|            Too Bad            | [0.079170294106, -0.059742... |\n",
       "|           Great Tool          | [-0.0305297728628, -0.0285... |\n",
       "|    Big Hit with my Daughter   | [0.0216339249164, 0.051485... |\n",
       "|       Wasn't wat i wan't      | [0.0299516189843, 0.051750... |\n",
       "|          good enough          | [0.0457096733153, 0.036794... |\n",
       "|    48\" waist?  I think not    | [0.00452239252627, 0.04817... |\n",
       "| Worth Every Penny in The G... | [-0.00142007833347, 0.0489... |\n",
       "+-------------------------------+-------------------------------+\n",
       "[10 rows x 5 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sport.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logisitc Classifier with Embedded Review Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sport, Nas = Sport.dropna_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sport_train, Sport_test = Sport.random_split(0.8, seed = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n",
      "PROGRESS: Logistic regression:\n",
      "PROGRESS: --------------------------------------------------------\n",
      "PROGRESS: Number of examples          : 583285\n",
      "PROGRESS: Number of classes           : 2\n",
      "PROGRESS: Number of feature columns   : 1\n",
      "PROGRESS: Number of unpacked features : 300\n",
      "PROGRESS: Number of coefficients    : 301\n",
      "PROGRESS: Starting Newton Method\n",
      "PROGRESS: --------------------------------------------------------\n",
      "PROGRESS: +-----------+----------+--------------+-------------------+---------------------+\n",
      "PROGRESS: | Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |\n",
      "PROGRESS: +-----------+----------+--------------+-------------------+---------------------+\n",
      "PROGRESS: | 1         | 2        | 360.740921   | 0.851105          | 0.851924            |\n",
      "PROGRESS: | 2         | 3        | 538.984012   | 0.854651          | 0.854727            |\n",
      "PROGRESS: | 3         | 4        | 723.032899   | 0.855714          | 0.855118            |\n",
      "PROGRESS: | 4         | 5        | 3150.463187  | 0.855777          | 0.855476            |\n",
      "PROGRESS: | 5         | 6        | 4968.021449  | 0.855755          | 0.855378            |\n",
      "PROGRESS: | 6         | 7        | 5170.309775  | 0.855758          | 0.855378            |\n",
      "PROGRESS: +-----------+----------+--------------+-------------------+---------------------+\n",
      "PROGRESS: SUCCESS: Optimal solution found.\n",
      "PROGRESS:\n"
     ]
    }
   ],
   "source": [
    "# Automatically take validation set for tuning\n",
    "word2vec_model = gl.logistic_classifier.create(Sport_train[['sentiment', 'vectors']],\n",
    "                                                  target = 'sentiment',\n",
    "                                                  features=['vectors'], \n",
    "                                                  l2_penalty=0.01,\n",
    "                                                  max_iterations = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def print_statistics(result):\n",
    "    print \"*\" * 30\n",
    "    print \"Accuracy        : \", result[\"accuracy\"]\n",
    "    print \"Precision       : \", result['precision']\n",
    "    print \"Recall          : \", result['recall']\n",
    "    print \"AUC             : \", result['auc']\n",
    "    print \"Confusion Matrix: \\n\", result[\"confusion_matrix\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      "Accuracy        :  0.854770809266\n",
      "Precision       :  0.855807793237\n",
      "Recall          :  0.867044556752\n",
      "AUC             :  0.929505492319\n",
      "Confusion Matrix: \n",
      "+--------------+-----------------+-------+\n",
      "| target_label | predicted_label | count |\n",
      "+--------------+-----------------+-------+\n",
      "|      0       |        1        | 11649 |\n",
      "|      0       |        0        | 61823 |\n",
      "|      1       |        1        | 69139 |\n",
      "|      1       |        0        | 10602 |\n",
      "+--------------+-----------------+-------+\n",
      "[4 rows x 3 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result_word2vec = word2vec_model.evaluate(Sport_test)\n",
    "print_statistics(result_word2vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
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       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word2vec_model.show(view = 'Evaluation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** what to note:**\n",
    "* word2vec model performed close to the Unigram model\n",
    "* However this performance was achieved with just 300 features (as opposed to 4 million!)\n",
    "* Averaging the word vectors removes ordering and adds unnecessary noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Maybe performing word2vec on the review summary would give better result. We saw previously that the review summary where more signal dense**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Review Summary\n",
    "** Let's train the review summary just like we trained the review. Since sentences are shorter and we have less text to train on, the word embedding was reduced to 200 features, the window to 5 and the minimum word count to 20**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mashed = \"\\n\".join(Sport['summary'])\n",
    "with open(os.path.join(BASE_DIR, \"txt/sportsSummary.txt\"), \"w\") as f:\n",
    "    f.write(mashed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sent_Summary = TrainSentences(os.path.join(BASE_DIR, \"txt/sportsSummary.txt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_Sport_Summary = gensim.models.Word2Vec(sent_Summary,\n",
    "                                     size=200,\n",
    "                                     min_count=20,\n",
    "                                     window=5)\n",
    "model_Sport_Summary.save(os.path.join(BASE_DIR ,\"sportSummary_200_c_30_w_5.word2vec\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Embedded Words\n",
    "** Despite the limited corpus the word embedding seems decent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('terrible', 0.8657429218292236),\n",
       " ('awful', 0.8092228770256042),\n",
       " ('lousy', 0.7548978328704834),\n",
       " ('horrid', 0.7292232513427734),\n",
       " ('aweful', 0.7017415761947632),\n",
       " ('unacceptable', 0.6918298006057739),\n",
       " ('pathetic', 0.6916425228118896),\n",
       " ('rude', 0.6912922263145447),\n",
       " ('horrendous', 0.690521776676178),\n",
       " ('abysmal', 0.6828699111938477)]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Sport_Summary.most_similar('horrible')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('excelent', 0.7967259287834167),\n",
       " ('wonderful', 0.7929089069366455),\n",
       " ('fantastic', 0.792600154876709),\n",
       " ('outstanding', 0.7820264101028442),\n",
       " ('great', 0.7681150436401367),\n",
       " ('incredible', 0.7647918462753296),\n",
       " ('excellent', 0.7497338056564331),\n",
       " ('fabulous', 0.7480643391609192),\n",
       " ('excellant', 0.7264809608459473),\n",
       " ('superb', 0.7246178984642029)]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Sport_Summary.most_similar(\"terrific\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Logistice Classifier on Review Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = DeepTextAnalyzer(model_Sport_Summary)\n",
    "Sport['vectorsSummary'] = Sport['summary'].apply(lambda r: dt.txt2avg_vector(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sport, Nas = Sport.dropna_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Sport_train, Sport_test = Sport.random_split(0.8, seed = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n",
      "PROGRESS: Logistic regression:\n",
      "PROGRESS: --------------------------------------------------------\n",
      "PROGRESS: Number of examples          : 573096\n",
      "PROGRESS: Number of classes           : 2\n",
      "PROGRESS: Number of feature columns   : 1\n",
      "PROGRESS: Number of unpacked features : 200\n",
      "PROGRESS: Number of coefficients    : 201\n",
      "PROGRESS: Starting Newton Method\n",
      "PROGRESS: --------------------------------------------------------\n",
      "PROGRESS: +-----------+----------+--------------+-------------------+---------------------+\n",
      "PROGRESS: | Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |\n",
      "PROGRESS: +-----------+----------+--------------+-------------------+---------------------+\n",
      "PROGRESS: | 1         | 2        | 155.606620   | 0.807762          | 0.810881            |\n",
      "PROGRESS: | 2         | 3        | 238.616211   | 0.808561          | 0.811447            |\n",
      "PROGRESS: | 3         | 4        | 319.899489   | 0.807729          | 0.810814            |\n",
      "PROGRESS: | 4         | 5        | 408.470611   | 0.807655          | 0.810481            |\n",
      "PROGRESS: | 5         | 6        | 495.020428   | 0.807608          | 0.810415            |\n",
      "PROGRESS: | 6         | 7        | 571.115985   | 0.807610          | 0.810415            |\n",
      "PROGRESS: +-----------+----------+--------------+-------------------+---------------------+\n",
      "PROGRESS: SUCCESS: Optimal solution found.\n",
      "PROGRESS:\n"
     ]
    }
   ],
   "source": [
    "# Automatically take validation set for tuning\n",
    "word2vec_model = gl.logistic_classifier.create(Sport_train[['sentiment', 'vectorsSummary']],\n",
    "                                                  target = 'sentiment',\n",
    "                                                  features=['vectorsSummary'], \n",
    "                                                  l2_penalty=0.01,\n",
    "                                                  max_iterations = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************\n",
      "Accuracy        :  0.807441860465\n",
      "Precision       :  0.809527966577\n",
      "Recall          :  0.825652832013\n",
      "AUC             :  0.886673937275\n",
      "Confusion Matrix: \n",
      "+--------------+-----------------+-------+\n",
      "| target_label | predicted_label | count |\n",
      "+--------------+-----------------+-------+\n",
      "|      0       |        1        | 15273 |\n",
      "|      0       |        0        | 56608 |\n",
      "|      1       |        0        | 13707 |\n",
      "|      1       |        1        | 64912 |\n",
      "+--------------+-----------------+-------+\n",
      "[4 rows x 3 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result2_word2vec = word2vec_model.evaluate(Sport_test)\n",
    "print_statistics(result2_word2vec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What to note:**\n",
    "* Same weaknesses as full review word2vec\n",
    "* Review summaries are too short to allow word2vect to learn more complex language structure\n",
    "\n",
    "** In this notebook, we learned that word2vec is able to learn nice structures in language. For example, words can be manipulated with arithmetic operations.**\n",
    "\n",
    "**We also learned that averaging word vectors to represent a review is not more powerful than a simple Bag of Word model.**\n",
    "\n",
    "**So in what context would word2vec be more powerful?**\n",
    "\n",
    "## [PART 3: Document Classification](http://nbviewer.jupyter.org/github/MarvinBertin/Deep-NLP-Project/blob/master/NLP%20Project%20-%20Deep%20Inverse%20Classifier%20%28PART%203%29.ipynb)"
   ]
  }
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