notebooks/supporting/stock-clustering-via-price-correlations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supporting Notebook 1 (Clustering Similar Stocks)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we want to explore ways to find similar stocks. After starting this work, others (see [\"Pairs trading with machine learing\"](https://www.quantopian.com/posts/pairs-trading-with-machine-learning) ) appear to have had the same idea and our work has some similarities with theirs.\n",
    "\n",
    "Below you see a sample visualization of stocks using this notebook. For example, in the bottom right corner you see a cluster with:\n",
    "- BMW\n",
    "- Daimler\n",
    "- Porshe\n",
    "- Continental\n",
    "- Volkswagen\n",
    "\n",
    "We can also see a cluster with:\n",
    "- Telefonica\n",
    "- Vodafone\n",
    "- Deutche Telecom\n",
    "\n",
    "Be aware that the results depend on the time period in which the clustering was done, the discretization interval (e.g. 15 minutes) and other factors as described below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"stock_clusters.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(url= \"stock_clusters.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A technique such as this would be useful for:\n",
    "\n",
    "- finding stocks that behave similarly (or dissimilarly) to one of interest\n",
    "- building trading strategies\n",
    "- identifying anomalies (e.g. if two stocks are normally correlated but fall out of line in a particular day, you might want to investigate)\n",
    "- discarding bad stocks (if stocks do not correlate with other stocks they might need investigating).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.manifold import TSNE\n",
    "import hdbscan\n",
    "import time\n",
    "import seaborn as sns\n",
    "import collections\n",
    "%matplotlib inline\n",
    "mpl.rcParams['figure.figsize'] = (5, 3) # use bigger graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in previous notebooks we load the prepared data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file = '/data/processed/cooked_v3.pkl'\n",
    "df = pd.read_pickle(input_file)\n",
    "df['CalcDateTime'] = df.index\n",
    "df['Date'] = df['CalcDateTime'].dt.strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology\n",
    "\n",
    "1. Select a time frame within which to analyze the stocks.\n",
    "- Select an interval within which to aggregate the prices (e.g. 10 minutes, 1 day).\n",
    "- Select a function of the price such as percent change or log returns.\n",
    "- Select a similarity function between the timeseries, such as dot product, cosine or correlation coefficient.\n",
    "- Select a clustering algorithm.\n",
    "- Visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217, ['2017-07-03', '2017-07-04'], ['2018-05-11', '2018-05-14'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def date_part(dt):\n",
    "    return str(dt).split(' ')[0]\n",
    "\n",
    "unique_days = sorted(list(set(map(date_part , list(df.index.unique())))))\n",
    "len(unique_days), unique_days[0:2], unique_days[-3:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_days = unique_days[0:60]\n",
    "subset_df = df[df.Date.isin(list(selected_days))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SNH', 'DBK', 'EOAN', 'DTE', 'CBK', 'RWE', 'IFX', 'SVAB', 'LHA',\n",
       "       'DAI', 'O2D', 'TKA', 'DPW', 'SIE', 'HDD', 'BAYN', 'AIXA', 'SAP',\n",
       "       'BAS', 'EVT', 'AT1', 'PSM', 'BMW', 'VOW3', 'FRE', 'SDF', 'ALV',\n",
       "       'CEC', 'GAZ', 'VNA', 'B4B', 'SHA', 'AB1', 'DLG', 'NDX1', 'UN01',\n",
       "       'NOA3', 'IGY', 'ADS', 'VODI', '1COV', 'TUI1', 'BPE5', 'HEI', 'USE',\n",
       "       'KCO', 'SZU', 'EVK', 'ADV', 'HEN3', 'MUV2', 'WDI', 'DWNI', 'MRK',\n",
       "       'PAH3', 'DEZ', 'FME', 'FNTN', 'G1A', 'RKET', 'QIA', 'DB1', 'ZAL',\n",
       "       'RIB', 'QSC', 'CON', 'SGL', 'BVB', 'SHL', 'PNE3', 'PBB', 'OSR',\n",
       "       'TINA', 'BEI', 'TEG', 'AOX', 'UTDI', 'ARL', 'MDG1', 'SANT', 'LXS',\n",
       "       'KGX', 'TTI', 'LINU', 'GYC', 'ARO', 'SOW', 'ANO', 'SZG', 'BOSS',\n",
       "       'BNR', 'LLD', 'WAF', 'DRI', 'NDA', 'SY1', 'LIN', 'ZIL2', 'CAP',\n",
       "       '3W9K'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prepare_single_stock(df, mnemonic, interval):\n",
    "    single_stock = df[df.Mnemonic == mnemonic].copy()\n",
    "    single_stock['StartEndPrice'] = 0.5*(single_stock['StartPrice'] + single_stock['EndPrice'])\n",
    "    single_stock['MaxMinPrice'] = 0.5*(single_stock['MaxPrice'] + single_stock['MinPrice'])\n",
    "    single_stock['Avg4Price'] = 0.25*(single_stock['MaxPrice'] + single_stock['MinPrice'] + \n",
    "                                      single_stock['StartPrice'] + single_stock['EndPrice'])\n",
    "    \n",
    "    resampled = pd.DataFrame({\n",
    "        'MeanAvg4Price': single_stock['Avg4Price'].resample(interval).mean(),          \n",
    "        'HasTrade': single_stock['HasTrade'].resample(interval).max(),\n",
    "        'Mnemonic': mnemonic,\n",
    "    })\n",
    "    resampled['PctChange'] = resampled['MeanAvg4Price'].pct_change().fillna(0.0)\n",
    "    resampled = resampled[resampled.HasTrade == 1.0]\n",
    "    return resampled[['Mnemonic', 'PctChange', 'MeanAvg4Price']]\n",
    "mnemonics = subset_df['Mnemonic'].unique()\n",
    "mnemonics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnemonic_2_desc = {\n",
    "    '1COV': 'COVESTRO AG  O.N.',\n",
    "     '3W9K': '3W POWER S.A. EO -,01',\n",
    "     'AB1': 'AIR BERLIN PLC    EO -,25',\n",
    "     'ADS': 'ADIDAS AG NA O.N.',\n",
    "     'ADV': 'ADVA OPT.NETW.SE  O.N.',\n",
    "     'AIXA': 'AIXTRON SE NA O.N.',\n",
    "     'ALV': 'ALLIANZ SE NA O.N.',\n",
    "     'AOX': 'ALSTRIA OFFICE REIT-AG',\n",
    "     'ARL': 'AAREAL BANK AG',\n",
    "     'AT1': 'AROUNDTOWN EO-,01',\n",
    "     'B4B': 'METRO AG   ST O.N.',\n",
    "     'BAS': 'BASF SE NA O.N.',\n",
    "     'BAYN': 'BAYER AG NA O.N.',\n",
    "     'BEI': 'BEIERSDORF AG O.N.',\n",
    "     'BMW': 'BAY.MOTOREN WERKE AG ST',\n",
    "     'BNR': 'BRENNTAG AG NA O.N.',\n",
    "     'BOSS': 'HUGO BOSS AG NA O.N.',\n",
    "     'BPE5': 'BP PLC             DL-,25',\n",
    "     'BVB': 'BORUSSIA DORTMUND',\n",
    "     'CAP': 'ENCAVIS AG  INH. O.N.',\n",
    "     'CBK': 'COMMERZBANK AG',\n",
    "     'CEC': 'CECONOMY AG  ST O.N.',\n",
    "     'CON': 'CONTINENTAL AG O.N.',\n",
    "     'DAI': 'DAIMLER AG NA O.N.',\n",
    "     'DB1': 'DEUTSCHE BOERSE NA O.N.',\n",
    "     'DBK': 'DEUTSCHE BANK AG NA O.N.',\n",
    "     'DEZ': 'DEUTZ AG O.N.',\n",
    "     'DLG': 'DIALOG SEMICOND.   LS-,10',\n",
    "     'DPW': 'DEUTSCHE POST AG NA O.N.',\n",
    "     'DRI': '1+1 DRILLISCH AG O.N.',\n",
    "     'DTE': 'DT.TELEKOM AG NA',\n",
    "     'DWNI': 'DEUTSCHE WOHNEN SE INH',\n",
    "     'EOAN': 'E.ON SE NA O.N.',\n",
    "     'EVK': 'EVONIK INDUSTRIES NA O.N.',\n",
    "     'EVT': 'EVOTEC AG O.N.',\n",
    "     'FME': 'FRESEN.MED.CARE KGAA O.N.',\n",
    "     'FNTN': 'FREENET AG NA O.N.',\n",
    "     'FRE': 'FRESENIUS SE+CO.KGAA O.N.',\n",
    "     'G1A': 'GEA GROUP AG',\n",
    "     'GAZ': 'GAZPROM ADR SP./2 RL 5L 5',\n",
    "     'GYC': 'GRAND CITY PROPERT.EO-,10',\n",
    "     'HDD': 'HEIDELBERG.DRUCKMA.O.N.',\n",
    "     'HEI': 'HEIDELBERGCEMENT AG O.N.',\n",
    "     'HEN3': 'HENKEL AG+CO.KGAA VZO',\n",
    "     'IFX': 'INFINEON TECH.AG NA O.N.',\n",
    "     'IGY': 'INNOGY SE  INH. O.N.',\n",
    "     'KCO': 'KLOECKNER + CO SE NA O.N.',\n",
    "     'KGX': 'KION GROUP AG',\n",
    "     'LHA': 'LUFTHANSA AG VNA O.N.',\n",
    "     'LIN': 'LINDE AG O.N.',\n",
    "     'LINU': 'LINDE AG O.N. Z.UMT.',\n",
    "     'LLD': 'LLOYDS BKG GRP     LS-,10',\n",
    "     'LXS': 'LANXESS AG',\n",
    "     'MDG1': 'MEDIGENE AG NA O.N.',\n",
    "     'MRK': 'MERCK KGAA O.N.',\n",
    "     'MUV2': 'MUENCH.RUECKVERS.VNA O.N.',\n",
    "     'NDA': 'AURUBIS AG',\n",
    "     'NDX1': 'NORDEX SE O.N.',\n",
    "     'NOA3': 'NOKIA OYJ EO-,06',\n",
    "     'O2D': 'TELEFONICA DTLD HLDG NA',\n",
    "     'OSR': 'OSRAM LICHT AG NA O.N.',\n",
    "     'PAH3': 'PORSCHE AUTOM.HLDG VZO',\n",
    "     'PBB': 'DT.PFANDBRIEFBK AG',\n",
    "     'PNE3': 'PNE WIND AG NA O.N.',\n",
    "     'PSM': 'PROSIEBENSAT.1  NA O.N.',\n",
    "     'QIA': 'QIAGEN NV         EO -,01',\n",
    "     'QSC': 'QSC AG NA O.N.',\n",
    "     'RIB': 'RIB SOFTWARE SE  NA EO 1',\n",
    "     'RKET': 'ROCKET INTERNET SE',\n",
    "     'RWE': 'RWE AG ST O.N.',\n",
    "     'SANT': 'S+T AG (Z.REG.MK.Z.)O.N.',\n",
    "     'SAP': 'SAP SE O.N.',\n",
    "     'SDF': 'K+S AG NA O.N.',\n",
    "     'SGL': 'SGL CARBON SE O.N.',\n",
    "     'SHA': 'SCHAEFFLER AG INH. VZO',\n",
    "     'SHL': 'SIEMENS HEALTH.AG NA O.N.',\n",
    "     'SIE': 'SIEMENS AG NA',\n",
    "     'SNH': 'STEINHOFF INT.HLDG.EO-,50',\n",
    "     'SOW': 'SOFTWARE AG  NA O.N.',\n",
    "     'SVAB': 'STOCKHOLM IT VENTURES AB',\n",
    "     'SY1': 'SYMRISE AG INH. O.N.',\n",
    "     'SZG': 'SALZGITTER AG O.N.',\n",
    "     'SZU': 'SUEDZUCKER AG  O.N.',\n",
    "     'TEG': 'TAG IMMOBILIEN AG',\n",
    "     'TKA': 'THYSSENKRUPP AG O.N.',\n",
    "     'TTI': 'TOM TAILOR HLDG  NA O.N.',\n",
    "     'TUI1': 'TUI AG NA O.N.',\n",
    "     'UN01': 'UNIPER SE  NA O.N.',\n",
    "     'USE': 'BEATE UHSE AG',\n",
    "     'UTDI': 'UTD.INTERNET AG NA',\n",
    "     'VNA': 'VONOVIA SE NA O.N.',\n",
    "     'VODI': 'VODAFONE GROUP PLC',\n",
    "     'VOW3': 'VOLKSWAGEN AG VZO O.N.',\n",
    "     'WAF': 'SILTRONIC AG NA O.N.',\n",
    "     'WDI': 'WIRECARD AG',\n",
    "     'ZAL': 'ZALANDO SE',\n",
    "     'ZIL2': 'ELRINGKLINGER AG NA O.N.',\n",
    "     'TINA': 'TINA',\n",
    "     'ANO': 'ANO',\n",
    "     'ARO': 'ARO'                   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_stocks_dfs = []\n",
    "\n",
    "interval = '60Min'\n",
    "\n",
    "for mnemonic in mnemonics:\n",
    "    single_stock = prepare_single_stock(subset_df, mnemonic, interval)\n",
    "    single_stocks_dfs.append(single_stock)\n",
    "\n",
    "# the dataframe for clustering    \n",
    "clustering_df = pd.concat(single_stocks_dfs, axis=0)\n",
    "clustering_df['CalcDateTime'] = clustering_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mnemonic</th>\n",
       "      <th>PctChange</th>\n",
       "      <th>MeanAvg4Price</th>\n",
       "      <th>CalcDateTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CalcDateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-03 08:00:00</th>\n",
       "      <td>SNH</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.460471</td>\n",
       "      <td>2017-07-03 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 09:00:00</th>\n",
       "      <td>SNH</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>4.462808</td>\n",
       "      <td>2017-07-03 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 10:00:00</th>\n",
       "      <td>SNH</td>\n",
       "      <td>0.008449</td>\n",
       "      <td>4.500512</td>\n",
       "      <td>2017-07-03 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 11:00:00</th>\n",
       "      <td>SNH</td>\n",
       "      <td>0.006072</td>\n",
       "      <td>4.527838</td>\n",
       "      <td>2017-07-03 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03 12:00:00</th>\n",
       "      <td>SNH</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>4.528175</td>\n",
       "      <td>2017-07-03 12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Mnemonic  PctChange  MeanAvg4Price        CalcDateTime\n",
       "CalcDateTime                                                              \n",
       "2017-07-03 08:00:00      SNH   0.000000       4.460471 2017-07-03 08:00:00\n",
       "2017-07-03 09:00:00      SNH   0.000524       4.462808 2017-07-03 09:00:00\n",
       "2017-07-03 10:00:00      SNH   0.008449       4.500512 2017-07-03 10:00:00\n",
       "2017-07-03 11:00:00      SNH   0.006072       4.527838 2017-07-03 11:00:00\n",
       "2017-07-03 12:00:00      SNH   0.000075       4.528175 2017-07-03 12:00:00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Mnemonic</th>\n",
       "      <th>1COV</th>\n",
       "      <th>3W9K</th>\n",
       "      <th>AB1</th>\n",
       "      <th>ADS</th>\n",
       "      <th>ADV</th>\n",
       "      <th>AIXA</th>\n",
       "      <th>ALV</th>\n",
       "      <th>ANO</th>\n",
       "      <th>AOX</th>\n",
       "      <th>ARL</th>\n",
       "      <th>...</th>\n",
       "      <th>UN01</th>\n",
       "      <th>USE</th>\n",
       "      <th>UTDI</th>\n",
       "      <th>VNA</th>\n",
       "      <th>VODI</th>\n",
       "      <th>VOW3</th>\n",
       "      <th>WAF</th>\n",
       "      <th>WDI</th>\n",
       "      <th>ZAL</th>\n",
       "      <th>ZIL2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mnemonic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1COV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048965</td>\n",
       "      <td>0.015822</td>\n",
       "      <td>0.035523</td>\n",
       "      <td>0.032499</td>\n",
       "      <td>0.173607</td>\n",
       "      <td>0.448280</td>\n",
       "      <td>0.108518</td>\n",
       "      <td>0.219470</td>\n",
       "      <td>0.274090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192464</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>0.276918</td>\n",
       "      <td>0.211907</td>\n",
       "      <td>0.256222</td>\n",
       "      <td>0.217871</td>\n",
       "      <td>0.211186</td>\n",
       "      <td>0.101065</td>\n",
       "      <td>0.207686</td>\n",
       "      <td>0.090076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3W9K</th>\n",
       "      <td>0.048965</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061478</td>\n",
       "      <td>0.136542</td>\n",
       "      <td>-0.012191</td>\n",
       "      <td>0.092599</td>\n",
       "      <td>0.143738</td>\n",
       "      <td>0.020394</td>\n",
       "      <td>0.034827</td>\n",
       "      <td>0.091008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090111</td>\n",
       "      <td>-0.355021</td>\n",
       "      <td>-0.061778</td>\n",
       "      <td>-0.027660</td>\n",
       "      <td>0.109736</td>\n",
       "      <td>-0.038468</td>\n",
       "      <td>0.055208</td>\n",
       "      <td>0.030829</td>\n",
       "      <td>0.125867</td>\n",
       "      <td>0.095154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB1</th>\n",
       "      <td>0.015822</td>\n",
       "      <td>0.061478</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.066823</td>\n",
       "      <td>0.226705</td>\n",
       "      <td>-0.030050</td>\n",
       "      <td>0.051273</td>\n",
       "      <td>0.018297</td>\n",
       "      <td>-0.069767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090725</td>\n",
       "      <td>0.124877</td>\n",
       "      <td>0.015534</td>\n",
       "      <td>0.079739</td>\n",
       "      <td>-0.140695</td>\n",
       "      <td>-0.103210</td>\n",
       "      <td>0.163032</td>\n",
       "      <td>0.020541</td>\n",
       "      <td>0.076912</td>\n",
       "      <td>0.050893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADS</th>\n",
       "      <td>0.035523</td>\n",
       "      <td>0.136542</td>\n",
       "      <td>0.007969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053431</td>\n",
       "      <td>0.094512</td>\n",
       "      <td>0.132458</td>\n",
       "      <td>-0.074142</td>\n",
       "      <td>0.077855</td>\n",
       "      <td>0.077573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009460</td>\n",
       "      <td>0.072037</td>\n",
       "      <td>0.096356</td>\n",
       "      <td>0.070037</td>\n",
       "      <td>0.130684</td>\n",
       "      <td>0.021549</td>\n",
       "      <td>0.113116</td>\n",
       "      <td>0.121943</td>\n",
       "      <td>0.144174</td>\n",
       "      <td>0.053607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADV</th>\n",
       "      <td>0.032499</td>\n",
       "      <td>-0.012191</td>\n",
       "      <td>0.066823</td>\n",
       "      <td>0.053431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097387</td>\n",
       "      <td>0.233218</td>\n",
       "      <td>0.004022</td>\n",
       "      <td>0.187164</td>\n",
       "      <td>0.142589</td>\n",
       "      <td>...</td>\n",
       "      <td>0.181224</td>\n",
       "      <td>0.021717</td>\n",
       "      <td>0.274105</td>\n",
       "      <td>0.093829</td>\n",
       "      <td>0.214735</td>\n",
       "      <td>0.227708</td>\n",
       "      <td>0.049017</td>\n",
       "      <td>0.134347</td>\n",
       "      <td>0.092583</td>\n",
       "      <td>0.137238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Mnemonic      1COV      3W9K       AB1       ADS       ADV      AIXA  \\\n",
       "Mnemonic                                                               \n",
       "1COV      1.000000  0.048965  0.015822  0.035523  0.032499  0.173607   \n",
       "3W9K      0.048965  1.000000  0.061478  0.136542 -0.012191  0.092599   \n",
       "AB1       0.015822  0.061478  1.000000  0.007969  0.066823  0.226705   \n",
       "ADS       0.035523  0.136542  0.007969  1.000000  0.053431  0.094512   \n",
       "ADV       0.032499 -0.012191  0.066823  0.053431  1.000000  0.097387   \n",
       "\n",
       "Mnemonic       ALV       ANO       AOX       ARL  ...      UN01       USE  \\\n",
       "Mnemonic                                          ...                       \n",
       "1COV      0.448280  0.108518  0.219470  0.274090  ...  0.192464  0.015148   \n",
       "3W9K      0.143738  0.020394  0.034827  0.091008  ...  0.090111 -0.355021   \n",
       "AB1      -0.030050  0.051273  0.018297 -0.069767  ...  0.090725  0.124877   \n",
       "ADS       0.132458 -0.074142  0.077855  0.077573  ...  0.009460  0.072037   \n",
       "ADV       0.233218  0.004022  0.187164  0.142589  ...  0.181224  0.021717   \n",
       "\n",
       "Mnemonic      UTDI       VNA      VODI      VOW3       WAF       WDI  \\\n",
       "Mnemonic                                                               \n",
       "1COV      0.276918  0.211907  0.256222  0.217871  0.211186  0.101065   \n",
       "3W9K     -0.061778 -0.027660  0.109736 -0.038468  0.055208  0.030829   \n",
       "AB1       0.015534  0.079739 -0.140695 -0.103210  0.163032  0.020541   \n",
       "ADS       0.096356  0.070037  0.130684  0.021549  0.113116  0.121943   \n",
       "ADV       0.274105  0.093829  0.214735  0.227708  0.049017  0.134347   \n",
       "\n",
       "Mnemonic       ZAL      ZIL2  \n",
       "Mnemonic                      \n",
       "1COV      0.207686  0.090076  \n",
       "3W9K      0.125867  0.095154  \n",
       "AB1       0.076912  0.050893  \n",
       "ADS       0.144174  0.053607  \n",
       "ADV       0.092583  0.137238  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_by_feature = 'PctChange' # this is also possible\n",
    "subset = clustering_df.pivot(index='CalcDateTime', columns='Mnemonic', values=cluster_by_feature)\n",
    "corr_mat = subset.corr().fillna(0.0)\n",
    "corr_mat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Mnemonic</th>\n",
       "      <th>Desc</th>\n",
       "      <th>Corr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mnemonic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAI</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.810825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAH3</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.762627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOW3</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.746777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CON</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.599425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.430759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPW</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.427840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAYN</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.426127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIL2</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.416700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEI</th>\n",
       "      <td>&lt;map object at 0x7f0a310693c8&gt;</td>\n",
       "      <td>0.399698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mnemonic                            Desc      Corr\n",
       "Mnemonic                                          \n",
       "BMW       <map object at 0x7f0a310693c8>  1.000000\n",
       "DAI       <map object at 0x7f0a310693c8>  0.810825\n",
       "PAH3      <map object at 0x7f0a310693c8>  0.762627\n",
       "VOW3      <map object at 0x7f0a310693c8>  0.746777\n",
       "CON       <map object at 0x7f0a310693c8>  0.599425\n",
       "BAS       <map object at 0x7f0a310693c8>  0.430759\n",
       "DPW       <map object at 0x7f0a310693c8>  0.427840\n",
       "BAYN      <map object at 0x7f0a310693c8>  0.426127\n",
       "ZIL2      <map object at 0x7f0a310693c8>  0.416700\n",
       "HEI       <map object at 0x7f0a310693c8>  0.399698"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_most_correlated(corr_mat, mnemonic, n=10):\n",
    "    results = corr_mat[[mnemonic]].sort_values(mnemonic, ascending=False).head(n).copy()\n",
    "    results['Desc'] = map(lambda m: mnemonic_2_desc[m], list(results.index))\n",
    "    results['Corr'] = results[mnemonic]\n",
    "    return results[['Desc', 'Corr']]\n",
    "\n",
    "find_most_correlated(corr_mat, 'BMW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Mnemonic</th>\n",
       "      <th>Desc</th>\n",
       "      <th>Corr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mnemonic</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ZAL</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOA3</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.367431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALV</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.346885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMW</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.322760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPW</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.314061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFX</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.312548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KGX</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.288901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOW3</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.286392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPE5</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.284564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAP</th>\n",
       "      <td>&lt;map object at 0x7f0a3105de48&gt;</td>\n",
       "      <td>0.283204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mnemonic                            Desc      Corr\n",
       "Mnemonic                                          \n",
       "ZAL       <map object at 0x7f0a3105de48>  1.000000\n",
       "NOA3      <map object at 0x7f0a3105de48>  0.367431\n",
       "ALV       <map object at 0x7f0a3105de48>  0.346885\n",
       "BMW       <map object at 0x7f0a3105de48>  0.322760\n",
       "DPW       <map object at 0x7f0a3105de48>  0.314061\n",
       "IFX       <map object at 0x7f0a3105de48>  0.312548\n",
       "KGX       <map object at 0x7f0a3105de48>  0.288901\n",
       "VOW3      <map object at 0x7f0a3105de48>  0.286392\n",
       "BPE5      <map object at 0x7f0a3105de48>  0.284564\n",
       "SAP       <map object at 0x7f0a3105de48>  0.283204"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_most_correlated(corr_mat, 'ZAL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Mnemonic</th>\n",
       "      <th>Desc</th>\n",
       "      <th>Corr</th>\n",
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       "      <th>Mnemonic</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DB1</th>\n",
       "      <td>&lt;map object at 0x7f0a3105dac8&gt;</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>&lt;map object at 0x7f0a3105dac8&gt;</td>\n",
       "      <td>0.522488</td>\n",
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       "      <th>BAYN</th>\n",
       "      <td>&lt;map object at 0x7f0a3105dac8&gt;</td>\n",
       "      <td>0.489341</td>\n",
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       "      <th>DLG</th>\n",
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       "      <td>0.429095</td>\n",
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       "      <td>0.416107</td>\n",
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       "      <th>MUV2</th>\n",
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       "      <td>0.399725</td>\n",
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       "      <td>&lt;map object at 0x7f0a3105dac8&gt;</td>\n",
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       "      <th>DPW</th>\n",
       "      <td>&lt;map object at 0x7f0a3105dac8&gt;</td>\n",
       "      <td>0.396724</td>\n",
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       "      <th>IFX</th>\n",
       "      <td>&lt;map object at 0x7f0a3105dac8&gt;</td>\n",
       "      <td>0.395663</td>\n",
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       "    <tr>\n",
       "      <th>HEI</th>\n",
       "      <td>&lt;map object at 0x7f0a3105dac8&gt;</td>\n",
       "      <td>0.382005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mnemonic                            Desc      Corr\n",
       "Mnemonic                                          \n",
       "DB1       <map object at 0x7f0a3105dac8>  1.000000\n",
       "BAS       <map object at 0x7f0a3105dac8>  0.522488\n",
       "BAYN      <map object at 0x7f0a3105dac8>  0.489341\n",
       "DLG       <map object at 0x7f0a3105dac8>  0.429095\n",
       "DBK       <map object at 0x7f0a3105dac8>  0.416107\n",
       "MUV2      <map object at 0x7f0a3105dac8>  0.399725\n",
       "SAP       <map object at 0x7f0a3105dac8>  0.398477\n",
       "DPW       <map object at 0x7f0a3105dac8>  0.396724\n",
       "IFX       <map object at 0x7f0a3105dac8>  0.395663\n",
       "HEI       <map object at 0x7f0a3105dac8>  0.382005"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_most_correlated(corr_mat, 'DB1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SAP</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPW</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.607967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFX</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.586449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAS</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.574498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAYN</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.573304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTDI</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.566911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRE</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.558952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALV</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.546060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FME</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.530520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEI</th>\n",
       "      <td>&lt;map object at 0x7f0a31096898&gt;</td>\n",
       "      <td>0.512719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mnemonic                            Desc      Corr\n",
       "Mnemonic                                          \n",
       "SAP       <map object at 0x7f0a31096898>  1.000000\n",
       "DPW       <map object at 0x7f0a31096898>  0.607967\n",
       "IFX       <map object at 0x7f0a31096898>  0.586449\n",
       "BAS       <map object at 0x7f0a31096898>  0.574498\n",
       "BAYN      <map object at 0x7f0a31096898>  0.573304\n",
       "UTDI      <map object at 0x7f0a31096898>  0.566911\n",
       "FRE       <map object at 0x7f0a31096898>  0.558952\n",
       "ALV       <map object at 0x7f0a31096898>  0.546060\n",
       "FME       <map object at 0x7f0a31096898>  0.530520\n",
       "BEI       <map object at 0x7f0a31096898>  0.512719"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_most_correlated(corr_mat, 'SAP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "enable_truncated_svd = False\n",
    "if enable_truncated_svd:\n",
    "    #corr_mat = orig_corr_mat\n",
    "    reduced_corr_mat = TruncatedSVD(n_components=20, random_state=478870).fit_transform(corr_mat)\n",
    "    tmp_corr_mat = pd.DataFrame(reduced_corr_mat)\n",
    "    tmp_corr_mat.index = list(corr_mat.index)\n",
    "    # uncomment to swap the correlation matrix\n",
    "    orig_corr_mat = corr_mat\n",
    "    corr_mat = tmp_corr_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cluster:\n",
    "    def __init__(self, cluster_id, members):\n",
    "        self.cluster_id = cluster_id\n",
    "        self.members = members\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return \"Cluster {}:\".format(self.cluster_id) + \" \" + \", \".join(self.members)\n",
    "        \n",
    "def build_clusters(data, algorithm, args, kwds, names):\n",
    "    membership_labels = algorithm(*args, **kwds).fit_predict(data)\n",
    "    d = collections.defaultdict(list)\n",
    "    i = 0\n",
    "    for label in membership_labels:\n",
    "        d[label].append(names[i])\n",
    "        i += 1\n",
    "    clusters = []\n",
    "    for k,v in d.items():\n",
    "        clusters.append(Cluster(k, v))\n",
    "    return membership_labels, clusters\n",
    "\n",
    "\n",
    "friendly_labels = []\n",
    "\n",
    "def truncate_str(v):\n",
    "    t = 12\n",
    "    if len(v) <= t:\n",
    "        return v\n",
    "    return v[0:10] + \"...\"\n",
    "\n",
    "for m in list(corr_mat.index):\n",
    "    friendly_labels.append(\"{}({})\".format(m, truncate_str(mnemonic_2_desc[m])))\n",
    "   \n",
    "membership_labels, clusters = build_clusters(corr_mat, hdbscan.HDBSCAN, (), {'min_cluster_size':2}, friendly_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Cluster 9: 1COV(COVESTRO A...), NDA(AURUBIS AG),\n",
       " Cluster -1: 3W9K(3W POWER S...), ADV(ADVA OPT.N...), ARO(ARO), AT1(AROUNDTOWN...), B4B(METRO AG  ...), BNR(BRENNTAG A...), BOSS(HUGO BOSS ...), BPE5(BP PLC    ...), CEC(CECONOMY A...), DLG(DIALOG SEM...), DRI(1+1 DRILLI...), EVK(EVONIK IND...), GAZ(GAZPROM AD...), IFX(INFINEON T...), LHA(LUFTHANSA ...), LIN(LINDE AG O...), LINU(LINDE AG O...), LLD(LLOYDS BKG...), NDX1(NORDEX SE ...), NOA3(NOKIA OYJ ...), OSR(OSRAM LICH...), PNE3(PNE WIND A...), PSM(PROSIEBENS...), QSC(QSC AG NA ...), RIB(RIB SOFTWA...), SDF(K+S AG NA ...), SNH(STEINHOFF ...), TTI(TOM TAILOR...), TUI1(TUI AG NA ...), USE(BEATE UHSE...), ZAL(ZALANDO SE), ZIL2(ELRINGKLIN...),\n",
       " Cluster 0: AB1(AIR BERLIN...), ANO(ANO), SVAB(STOCKHOLM ...),\n",
       " Cluster 2: ADS(ADIDAS AG ...), G1A(GEA GROUP AG),\n",
       " Cluster 7: AIXA(AIXTRON SE...), BVB(BORUSSIA D...), MDG1(MEDIGENE A...), SOW(SOFTWARE A...), WDI(WIRECARD AG),\n",
       " Cluster 17: ALV(ALLIANZ SE...), DPW(DEUTSCHE P...), MUV2(MUENCH.RUE...), SAP(SAP SE O.N.),\n",
       " Cluster 5: AOX(ALSTRIA OF...), DWNI(DEUTSCHE W...), GYC(GRAND CITY...), TEG(TAG IMMOBI...), VNA(VONOVIA SE...), WAF(SILTRONIC ...),\n",
       " Cluster 10: ARL(AAREAL BAN...), CBK(COMMERZBAN...), DB1(DEUTSCHE B...), DBK(DEUTSCHE B...), PBB(DT.PFANDBR...), RKET(ROCKET INT...),\n",
       " Cluster 18: BAS(BASF SE NA...), BAYN(BAYER AG N...), HEI(HEIDELBERG...), SIE(SIEMENS AG...),\n",
       " Cluster 14: BEI(BEIERSDORF...), HEN3(HENKEL AG+...), SY1(SYMRISE AG...),\n",
       " Cluster 8: BMW(BAY.MOTORE...), CON(CONTINENTA...), DAI(DAIMLER AG...), PAH3(PORSCHE AU...), VOW3(VOLKSWAGEN...),\n",
       " Cluster 1: CAP(ENCAVIS AG...), TINA(TINA),\n",
       " Cluster 6: DEZ(DEUTZ AG O...), SANT(S+T AG (Z....), SGL(SGL CARBON...),\n",
       " Cluster 15: DTE(DT.TELEKOM...), O2D(TELEFONICA...), VODI(VODAFONE G...),\n",
       " Cluster 12: EOAN(E.ON SE NA...), IGY(INNOGY SE ...), RWE(RWE AG ST ...), UN01(UNIPER SE ...),\n",
       " Cluster 4: EVT(EVOTEC AG ...), HDD(HEIDELBERG...),\n",
       " Cluster 19: FME(FRESEN.MED...), FRE(FRESENIUS ...),\n",
       " Cluster 16: FNTN(FREENET AG...), KGX(KION GROUP...), LXS(LANXESS AG), UTDI(UTD.INTERN...),\n",
       " Cluster 13: KCO(KLOECKNER ...), SZG(SALZGITTER...), TKA(THYSSENKRU...),\n",
       " Cluster 11: MRK(MERCK KGAA...), QIA(QIAGEN NV ...),\n",
       " Cluster 3: SHA(SCHAEFFLER...), SZU(SUEDZUCKER...)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 76 nearest neighbors...\n",
      "[t-SNE] Indexed 99 samples in 0.000s...\n",
      "[t-SNE] Computed neighbors for 99 samples in 0.002s...\n",
      "[t-SNE] Computed conditional probabilities for sample 99 / 99\n",
      "[t-SNE] Mean sigma: 0.622257\n",
      "[t-SNE] Computed conditional probabilities in 0.013s\n",
      "[t-SNE] Iteration 50: error = 57.6251831, gradient norm = 0.5732235 (50 iterations in 0.038s)\n",
      "[t-SNE] Iteration 100: error = 56.4043121, gradient norm = 0.6486871 (50 iterations in 0.016s)\n",
      "[t-SNE] Iteration 150: error = 57.1521149, gradient norm = 0.4838236 (50 iterations in 0.016s)\n",
      "[t-SNE] Iteration 200: error = 57.6005630, gradient norm = 0.5920968 (50 iterations in 0.016s)\n",
      "[t-SNE] Iteration 250: error = 57.6061554, gradient norm = 0.5426315 (50 iterations in 0.016s)\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 57.606155\n",
      "[t-SNE] Iteration 300: error = 0.8015581, gradient norm = 0.0108575 (50 iterations in 0.018s)\n",
      "[t-SNE] Iteration 350: error = 0.6223631, gradient norm = 0.0038253 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 400: error = 0.5671667, gradient norm = 0.0039558 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 450: error = 0.5317929, gradient norm = 0.0034445 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 500: error = 0.4747829, gradient norm = 0.0021152 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 550: error = 0.4161078, gradient norm = 0.0016711 (50 iterations in 0.031s)\n",
      "[t-SNE] Iteration 600: error = 0.3737060, gradient norm = 0.0046519 (50 iterations in 0.017s)\n",
      "[t-SNE] Iteration 650: error = 0.3585058, gradient norm = 0.0017238 (50 iterations in 0.014s)\n",
      "[t-SNE] Iteration 700: error = 0.3581345, gradient norm = 0.0002967 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 750: error = 0.3578142, gradient norm = 0.0004778 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 800: error = 0.3576140, gradient norm = 0.0003845 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 850: error = 0.3580349, gradient norm = 0.0002740 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 900: error = 0.3574553, gradient norm = 0.0003759 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 950: error = 0.3579863, gradient norm = 0.0003892 (50 iterations in 0.015s)\n",
      "[t-SNE] Iteration 1000: error = 0.3577323, gradient norm = 0.0003875 (50 iterations in 0.015s)\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.357732\n"
     ]
    },
    {
     "data": {
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+fvj5+RX3UuQ2MpvN+Pv7k5mZiaOjI3379iU8PBwHBwfWr19P586dbXJwR0VFUatWLUJCQti5c6dRHhkZiZubGwcPHuTnn38mIyODgwcPUrduXQDGjBlD9erVee2110hPTyc9PZ0ePXoQGRkJwPLly/m///s/UlJScHFxITg4mEmTJhnjjhgxwpjLw8ODmJgY3N3djfXn6dmzp92hkqGhofz000+ULVsWBwcHpk6dSrNmzW7H03nbKcgsIiIiIiIiIiIidxRXV1fi4uIASEpKonfv3ly8eJGxY8cC0LJlS5YuXWrTJyEhocDxpk6darQJCQkxxgaoW7cu33zzDQEBAWRnZ7Nnzx4Adu7cSVhYGD/88APe3t5kZ2czY8aMG17/tUycOJFu3brx448/8vLLL/P7778Xavw7jXIyi4iIiIiIiIiIyB3rgQceYMaMGUyZMgWr1XrLx09KSjJSrpjNZnx8fACYMGECb7zxBt7e3kbd4MGDb/n8AK1atWLfvn23Zey/goLMIiIiIiIiIiIickd7+OGHyc7OJikpCYCNGzdisViMx/79+4s8dnh4OHXr1qVLly5Mnz6dtLQ0IHcnc6NGjQrsN3nyZJs1HDt2zKhLTU21qZs/f/411/D999/bpNe42yhdhoiIiIiIiIiIiBS7nJwcDh06xNmzZ7FarVitVkwmU75t80uXcejQoXzbFjRGnjfffJPnnnuOH3/8kXnz5hEdHc369euvu97w8HC7nMx5CpsuIyIignfffZdKlSrx+eefX7f9nUo7mUVERERERP7LbDZjsVjw9fUlICCASZMmkZOTw8qVK42dSG5ubtStWxeLxULfvn1t+mdlZRlj+Pn50aNHD1JTU23K8x6JiYlGv7CwMB566CGbrwDPnDkTBwcH4uPjjTJvb2+OHDkCQPXq1fH398fPzw9fX1/efPNN0tPTAdi3bx+urq5YLBYCAgJ49NFH2bt3LwCrV6+mbNmyWCwWvL297Q4hEhERKQ4pKSnMnj2bpUuXsnHjRrKyspgzZ46xq/jAgQOYzWYeeOCBAseoWLEi586dsyk7e/Ys7u7u153fy8uLwYMHs2bNGrZv386ZM2fw9fUlNjb25m7sOiZOnEhcXByrVq26qw+zVJBZRERERETkv/J2HcXHx7Nq1SqWL1/O2LFjadeuHXFxccTFxREYGMjcuXOJi4vjiy++sBujdOnSxMXFsWPHDgD++c9/2pTnPWrUqAFAdnY2ixcvpkqVKmzatMlmrOrVq/Pee+8VuN6NGzeyc+dOtmzZwp49e3jllVeMurp16xIXF8f27dvp3bs377//vlEXFBREXFwc27Zt49tvv2Xr1q1Ff9JERERugTVr1nDhwgUyMjLIzs4G4Ny5c6xbt45Tp04xaNAgwsLCrrkr2c3NjSpVqrB27VogN8C8YsUKWrRocc25f/jhB+OD3r1792I2mylXrhwRERG89957/Pnnn0DuTutp06bditu95yjILCIiIiIiko+bPWTIZDLRsmXL6x7is2bNGho0aMBLL71EdHS0TV3nzp3Ztm3bdccoU6YMM2bM4JtvvuHChQt29RcvXqR8+fJ25SVLliQgIICjR48W4o5Ebr0zZ84Yu/srV65MtWrVjOuSJUvatJ09ezZhYWGsWrWKZs2aGf9fZmdn06BBAzZv3syePXto3bo1FouFevXq8dJLLwG5OySfe+45Y/d/ixYtSE5OBrD7lkHeBzKtW7cmMDDQmD8mJobWrVsDsH79ekJCQoy6MWPG0L59e9LT02ndujV169YlICCAxo0b23xd3s3NLd97ErnfWa1W9u3bR05OjlGWmZlJVFQU/fv3p02bNrRt25a33nrLqL86J/OCBQsA+OKLL3jnnXewWCwEBwfz1ltv4eXldc35v/zyS+NbSn369GHu3LmYzWbq16/PRx99RK9evahXrx5+fn4cOHCgUPd0dU7mG/nm0NSpU5k5cyYAW7duZdCgQUDu692Vr0t3EuVkFhERERERKcCVhww9+OCDN9Q3MzOTFStW0LlzZwAuXbqExWIBoFatllutCAAAIABJREFUWsYfw9HR0fTq1Yv27dvz1ltv8cknn+DomPunmtlsJiIigvHjx183T2PZsmWpWbMm+/bto2zZsuzZsweLxcLFixdJT0/Pd7fy2bNnOXDgwHV3eIncLhUrVjSCsJGRkbi5uRn5Ta8OyOZ54okn+Pzzz/n8888ZOHAgn376KYGBgTRv3px27doRHh5u/H+X942Cjz/+mAcffNC43rNnD05OTsC186YmJSWxfPlyOnToUOA9vPvuu/z8888sW7YMFxcXAObOnUtgYCCzZs0iIiKCVatW3ehTI3LfufoD3YkTJwK574V/+9vfbOpat26d74eqAD4+Pqxbt67AeTw8PNi5c6dN2ddff11g+5CQEJsPlfJERkbalSUkJBg/5+3GvpbZs2fnWz5kyBDj5yZNmtCkSRMg97mIiYm57rjFQTuZRURERETkvpWRmc0Xy3bRN3IFvf9vGZlZOZy/lH5TY+YFkxs3boyXlxehoaGAbbqMvABzeno6K1eupFOnTpQvX56GDRuyevVqm/H69OnDhg0bOHz48HXnvvIP9Lx0GQcOHGDChAnGLiiAdevWERAQQLVq1QgJCblmfkuRO9HkyZMZP3488fHxTJkyhQ8++ACA48ePU716daOdv7+/UV6tWjWjvG7dukZA+FoiIiIYN25cgfWTJk1i+fLlfP/997i6utrVN2vWTN8UECkEk8mEh4eHXSoMk8nEww8/XEyrkhuhncwiIiIiInLfGjvzF3YnnCUjK/frudk5VsI/+ol/vB5MCWfHQh0ydLW8YPKVsrKy8m27bNkyLly4gK+vLwCXL1+mfPnytG/f3mjj5OREeHg4EyZMuOa8Fy5cIDExkdq1a5OUlGRT16lTJwYPHmxcBwUFsWjRIvbv30/Tpk159tlnjWCcyJ0i76vmec6ePUunTp0AqFKlCn/7299o1qwZn3zyCRUqVAAgPDyc4OBgmjdvTtu2benXrx/lypWjf//+tG3blgULFvD444/zwgsvULt27XznGTVqFD169AByg8Tfffcd69ato3Tp0jbr+/nnn9mzZw+xsbEF7rpesWIFTz/99K17UkTuYW3atGHevHlkZGSQmZmJk5MTLi4uBAcHF/fSpBAUZBYRERERkfvS3sRz7Dl8zggw50lOyeCnbUdo6FWqUIcM3Yzo6Ghmz57Ns88+C+Tugvby8iItLc2m3YABA/Dx8eHSpUv5jnPp0iUGDx7Ms88+S5kyZeyCzJs2bco3H6WXlxd///vfmTBhAl9++eUtuiuR67NarVzavYfM8+dxq1MHl4oV7NpcncZi9uzZNl8THzJkCCNHjjS+LQDQr18/2rVrx4oVK1i8eDHTp09n+/btWCwWDhw4wI8//sjq1atp3LgxW7ZsoV69etdMlwG5+ZbfffddY7d0nlq1anHu3DlWrVpF165dbeqee+45MjIySE5OvubYwG17fRG525QpU4YBAwawZ88eTp8+TaVKlahTp46R2kbubAoyi4iIiIjIfenA0Qtw1Xl+2VkZ/Pj5q2z6yoEHKrjRp08fhg0bds1xEhMTGTJkCEuWLLmh+ZOTk1m9ejX/+te/jLLSpUvTtGlTfvjhB5u2Li4uDBkyhOHDh9uUt2zZEsg97f6ZZ55hzJgxRl1eTmar1YqLiwszZszIdx2vvPIKtWvXJjExkWPHjjFr1iymTZtGdnY2TZo0MYJ6/fr147XXXrPZ8SlSkHHjxjFv3jzMZjMODg5Mnz6dJk2acPr0aapUqcKIps3pVLU6JgcTOZlZdF+/ml79+tnsCE5PT7f5fTty5AgpKSlMmTIFgHnz5pGWloavry+Ojo40btyYqKgoqlatyhdffEFUVBStWrXC39+ftLQ0UlNTjZQZGRkZPP744zg6OnL58mVjnkWLFtG6dWtKly7NwYMH6d27N+3btyc1NZUhQ4YYAetLly5RqVIlli1bxuOPP06FChUICgoy1jp37lwaNWpEREQEr776KgsXLgRyA+cZGRk4OzsDubuz3d3db+9/DJG7iJOTE35+fsW9DCkCU1FOSb4VAgMDrXdqomoREREREbn3bf/zFONm/0pqum0qC2cnB55rV49ngmoV08pE7m5btmxh2LBhrF+/HhcXF06fPk1GRgZVq1blH//4B/98dxzW9HSmPNrK6NNt9Uq6du6ER8OGNgf/JScnA7kfpNSrV49q1aqxdu1aVqxYwRtvvMHu3bu5fPky2dnZzJkzh5ycHF544QWeeOIJRo4cSb9+/fjtt9/49NNPOXnyJDNnziQjI4P27dvzyiuvkJyczIsvvkhmZqaxFg8PD2JiYujWrRtRUVEEBgaybNkyunfvTs2aNYmPj2fy5MmMGTOGy5cv85///Ienn36aH374AYvFQuvWrY1+qampeHl5sXbtWry9venWrRsdO3akf//+pKamEhQUxIQJE2jVqhUiIncyk8kUa7VaAwuq18F/IiIiIiJyX/Kv5U6FMi6YHWy/qu5oduDxxjWKaVUid7/jx4/j7u5uHKzn7u5O1apVAZg7Zw5DvH04lZpKUmrK/zpZrVzef7DAMd977z1Kly6Nj48PkLtTOioqykg1YTab6d+/P7t27cLPz4+YmBiGDh3KxIkTqVy5MklJSSxatAh/f38aNGhAYGCgkeIiOzsbi8ViPM6dO2c3f8eOHW0OCvT19TXS2jRu3JhZs2bRqVMn9u/fb9PP1dWV4cOHM3HiRAA+/vhjFi5ciMViMfKh5wWYlyxZwptvvgnAsWPH6Nixo838x44du95TLyJSbLSTWURERERE7lvnLqXx8de/EffnKQA8qpThb70a4lGlTDGvTOTulZycTIsWLUhJSaFNmzb06NGDxx57jMTERIJatuTLpo/yj20xlHFyoVet3MP3uq1awdzn+hD8z2l24/366690796dbdu2GQf8VahQgYMHD1K2bNl813DlbmL4Xz7nvFQbefIr9/DwoHTp0pjNZgBeeOEFwsPDCQ0NJSQkhG7durFo0SK++eYb5s2bd/NPmIjIXeB6O5mVk1lERERERO5b5UuXIPLFZqRlZJGTY6VkCR0uJFIUVquV5H37STtxklKeNYmNjWXjxo2sW7eOHj168P7773P69Gm69+qF6dBRHq9ag/FxsUaQGRNUaGwfu0hOTub555/n888/NwLMV9uxYwd9+vTh0qVLvPfee/To0eOm72fdunX55kqOiIhg9OjRHDlyhC1bttz0PCIi9woFmUVERERE5L5Xwll/GokUVVZyMvFvvU1K4hFwcIDsbMpZAmj19+G0bt0af39/5syZw7Fjxzhx4gRf5eSQefEip1NTSUxOpmbFipgczFRu94Td2K+++iqdO3fm8ccftyn39fVl27ZtBAUF4e/vT1xcHGFhYaSmpt7We504cSLdunXj008/pX///sTGxt7W+URE7hbKySwiIiIiIrfMokWLMJlM7N69267uo48+okSJEly4cMEoW79+PWXLlsViseDt7W0c+AW5X2OvVKmSTa7UXbt2XXe8kJCQa64xNDQUT09PY86xY8fa1J8+fRonJyemTbP92r6Hh4eRwxVgwYIFhIaGXvsJEbkP7Js6jcsJh8hJTycnNZWEs2f4feMmjnz7HQBxcXFkZ2eTnJzM0aNHOXz8OPv/2M2gDh3ZRDYeoX1xrlAex1KlbMZdsGAB27dvZ9y4cXZzjho1ihEjRnDkyBGj7HYHmK8UFhZGTk4OK1eu/MvmFBG5kynILCIiIiIit0x0dDQtWrQgOjo637rGjRuzcOFCm/KWLVsSFxfHb7/9xtKlS/n555+Nuh49ehAXF2c88g79utZ4hTFx4kRjzDlz5nDw4P8OHPv3v/9N06ZN872H2NhYm0C3yP0uJzOTs7/+B2tWllGWkpXFu79u4bFBL1O/fn127dpFkyZN6NKli9Gm5EM1eHH8e6w5dpQqHduDyUT9+vWpXr061atXZ9iwYbzxxhucOnWKRx55xObDptTUVDp27MjQoUPp0KEDPj4+NG/eHLPZTLt27W7JfQUFBRnz9e3b167eZDIxZswYJkyYAMCbb77JkiVLgGsf4Ccicq/Sd8JEREREROSWSE5OZtOmTaxbt46nnnrKZofw/v37SU5O5rPPPmPcuHH069fPrr+rqysWi4WjR49ed67CjFcYaWlpAJS6YgdldHQ0kyZNonfv3hw5coTq1asbdcOHD2fcuHHMnTu3SPOJ3GusWVlgtdqUeZcrz7SWrTGXLEnT6C8L7Fu/fn3++OMPABISEuzqP/zww2vO/cILL/DCCy/kW7d+/Xqb69DQ0Hy/eZBfeX5rgdxvV1ypa9euxrcb3n77baO8U6dOdOrUCYCqVauybNmygm9CROQeoZ3MIiIiIiJySyxevJj27dtTp04dKlasaJOr9Ouvv6Znz560bNmSPXv2cPLkSbv+586dY+/evbRq1coomz9/vt0OxsKOdy0RERFYLBaqV69Oz549eeCBBwBITEzk+PHjPPLII3Tv3p358+fb9OvevTvbtm1j3759NzSfyL3K7OqKa40a9hUODpQPbPjXL0hERIqFgswiIiIiIlJkB49dYH1sIn8ePkd0dDQ9e/YEoGfPnjbpJvLqHBwc6Nq1K//+97+Nuo0bNxIQEEC1atVo164dlStXNuquTpfh6up63fEKIy9dxokTJ1izZg2bN28GcoPa3bt3z/ceAMxmMxEREYwfP/6G5hO5l9V+9RUcSpTA5OQEgIOzM46l3fDo26eYVyYiIn8VpcsQEREREZEblp6ZzTufb2V3wlkcHCA95RIrf1zN77/vwMHBRHZ2NiaTiYkTJ7Jz50727t3LE088AUBGRgaenp6EhYUBuTmZly5dysGDB2natCndu3fHYrEUOPeOHTuuOd6NcHNzo3Xr1mzatInmzZsTHR3NiRMnjHQYx44dY+/evdSuXdvo06dPH8aPH4+fn98NzydyL3Kr5UXDzz7hxIqVpBxOpHTdulRu2wZHN7fiXpqIiPxFtJNZRERERERu2FfL/2DXwTOkZ2aTmp7NwfiNVK/XmqHjvyUhIYHExEQ8PT3ZuHEj0dHRREZGkpCQQEJCAseOHePYsWMcOnTIZkxPT09GjhzJBx98cM25CzteYWRlZbF161a8vLz4888/SU5O5ujRo8bYo0aNstvN7OTkRHh4OJMnT77h+UTuVS4VK1Lzud7UG/U61Z95WgFmEZH7jILMIiIiIiJyw1b/epjMrBzj+tjujTzg1YSftx8jJyf3ELCuXbsSHR3N119/TZcuXWz6d+nSha+//tpu3EGDBrFhwwbj4K2rczJv3rz5uuOtWbOG6tWrG48tW7bYzZOXk7l+/fr4+/vzzDPPEB0dbTdu3j1cbcCAAWRlZRnXMTExDBw40Li+cif2wIEDiYmJsRtDROSvcubMGeN1tHLlylSrVg2LxUK5cuXw8fGxaRsZGUlUVBSQezDiggUL8h0zKyuLSpUqMXLkSLvy0aNHU7t2bWPOcePG5TuGh4eHcXgiwIIFC+wOYnz66adp2rTpjd6yiPzFTNarToH9qwQGBlr1Dy0RERERkbtTt5FLSc/Mtis3mWDhB0/haNZ+FhGRO1FkZCRubm6MGDGChIQEQkJC2LlzZ771oaGhhISE0K1bN7txli9fzrvvvsuJEyfYt28fJpMJgJEjR3LixAmmTZtGiRIluHTpEpMmTSIyMtJuDA8PDwCWLVuGj48PCxYsYOnSpcyePRuA8+fP4+/vj5ubGz/88AMPP/zwLX8+RKRwTCZTrNVqDSyoXv/yExERERGRG9ag7gM4mGzLTEDdmuUVYBYRuQ9ER0fz2muv8dBDDxnfGElJSeGf//wnn376KSVKlACgdOnS+QaY8wwfPrzAnc4LFy7kqaeeomfPnvl++0VE7hz615+IiIiIiNywgZ39cCvpjLNT7p8Uzo4OuJZwJKxbwQf2iYjIvSEtLY3Vq1fz1FNP0atXLyOt0L59+3jooYcoXbp0ocfq3r0727ZtY9++fXZ10dHR9OrVy2YOEbkzKcgsIiIiIiI37MEKJZk+8nGeb1+PFgFV6d6mDtNHtqFmlTLFvTQREfmv8+fP8/3337N+/Xqys/NLcWTKp1fB5XmWLl1KUFAQrq6udO3alUWLFuU7/qxZs7BYLNSoUYPExMR8xzKbzURERDB+/Hib8pMnT7J3715atGhBnTp1cHJysknrISJ3FgWZRURERESkSNxKOtOldS1e79uYHk/UpVxpl+JekshNM5vNWCwWAgICaNiwIZs3bwYgISEBV1dXLBYLPj4+DBo0iJycnAL7+/n58eyzz5KSkgKAm5tbvvN98cUX+Pn54e/vT4MGDYwD10Ru1meffUaVKlV4/vnn6dSpE9WrV2f79u02bSpWrMi5c+dsys6ePYu7u/s1x46Ojmb16tV4eHjQqFEjzpw5w9q1a6lVqxaHDx/m0qVLAPTr14+4uDjKli2bbxA6T58+fdiwYYNNIPqbb77h3LlzeHp64uHhQUJCgnYzi9zBFGQWERERERER+S9XV1fi4uLYvn0748ePZ9SoUUadl5cXcXFx/P777+zatYtFixYV2H/nzp04Ozszbdq0Audavnw5H330ET/++CM7duzgl19+oWzZsrflvuT+EhMTQ0REBGlpaVy8eJFLly5x4sQJ2rZta/PhiJubG1WqVGHt2rVAboB5xYoVtGjRosCxL168yMaNGzl8+DAJCQkkJCQwdepUoqOjKVmyJAMGDCAsLIy0tDQAsrOzycjIuOZ6nZycCA8PZ/LkyUZZdHQ0K1asMOaIjY1VXmaRO5iCzCIiIiIiIiL5uHjxIuXLl7crd3R0pHnz5vnmkL1Sy5Ytr9lm/PjxREVFUbVqVQBcXFx48cUXb27RIsD06dONIO+VUlNTSUhIsCn74osveOedd7BYLAQHB/PWW2/h5eVV4NjfffcdwcHBuLj879srnTt35vvvvyc9PZ1x48ZRpUoV/Pz8aNCgAS1btuSFF14wfs8LMmDAALKysoDcbw4cOnSIpk2bGvWenp6ULVuWrVu32vSLiYlh4MCBxrXF8r+zAQYOHEhMTMw15xWRW8NktVqLZeLAwECr/kcXERERERGRO4nZbMbf35+0tDSOHz/O2rVradSoEQkJCYSEhLBz505SUlJ47LHHePvtt+nQoYNNfzc3N5KTk8nKyqJr1660b9+ewYMHG+VXqlChAgcPHtTuZbnlunbtysKFC+3Ky5Qpw6xZs3jmmWeKYVUicjczmUyxVqs1sKB67WQWERERERGR+9apU6d4/vnnKVmyJKVKlcLBwYHVq1eze/duVqxYQd++fcnbnLV//34sFguPPvooTz75pF2AGXJ3ilosFgIDA3nooYcYMGDAX31LIjzzzDOUKlXKrjwjI4PHHnusGFYkIvc6x+JegIiIiIiIiEhxyMzMpFmzZhw+fJjMzEyjvFmzZvzxxx80a9aM06dPc+rUKeB/OZmvJS8nc2H4+voSGxtLcHBw0W9CJB/du3fns88+Y/v27Vy+fBmTyYSrqytjx46lYsWKxb08EbkHaSeziIiIiIiI3JeWLFlCUlKSTYAZ4OTJkyxdupTdu3eTnZ1924Jyo0aNIiIighMnTgC5u0xnzpx5W+aS+4uTkxPr1q3jH//4ByEhITz//POsWrWKESNGFPfSROQepZ3MIiIiIiIicl+Kj4+3y5MMcOnSJQYPHswDDzzAnDlzMJvNNz1XSkoK1atXN66HDRvGsGHDOHnyJG3atMFqtWIymejfv3+R57BYLMYu6oEDBzJo0CACAwOZNm0aAIMGDbq5m5C7irOzM3369KFPnz7FvRQRuQ/o4D8RERERERG5L82fP5+BAwfaBZpLly7Nv/71L7p161ZMKxMREbmz6OA/ERERERERkXx06dKF8uXL2+xUdnR0pEKFCnTq1KkYVyYiInJ3UZBZRERERERE7kvOzs788ssvPPnkkzg6OuLo6MiTTz7Jli1bcHZ2Lu7liYjIHcLNzQ2AhIQETCYTn376qVEXFhbG7NmzAQgNDcXT0xOLxYLFYuGTTz4BwMPDg9OnTwNgMpkYPny40T8qKorIyEgAIiMjqVatmtHfYrFw/vx5ADZt2sQjjzyCt7c33t7ezJgxwxgjMjKSkiVLkpSUZLfmKzVp0gSLxcJDDz1EpUqVjDkSEhJu+jlSTmYRERERERG5b1WtWpXFixeTk5MDgIOD9mKJiEjBHnjgAT7++GNefvnlfD+QnDhx4jXTLbm4uLBw4UJGjRqFu7u7XX14eLjdIZ0nTpygd+/eLFq0iIYNG3L69GnatWtHtWrVePLJJwFwd3dn0qRJfPDBBwXOvXXrVgBmz55NTEwMU6ZMKdQ9F4bePUVEREREROS+5+DgoACziIhcV6VKlXj88ceZM2dOkfo7Ojry0ksvMXny5EL3mTp1KqGhoTRs2BDIDShPmDCB999/32jTv39/5s+fz9mzZ4u0rpuld1ARERERERERERGRQnr99deJiooiOzvbri4iIsJIQ7Fjx458+w8ZMoS5c+dy4cIFu7rJkycb/YOCggCIj4+nUaNGNu0CAwOJj483rt3c3Ojfvz8ff/zxzdxakSldhoiIiIiIiIiIiMh/5eTksHjxYubOnYuTkxPZ2dlYrVaj/uGHH6ZJkybMmzfPru/10mUAlClThr59+/LJJ5/g6upqU5dfuozCGjp0KBaLpcj9b4Z2MouIiIiIiNwiZrMZi8WCn58fTz31lHFYD+TuQgoODqZu3brUrl2bd955x+YP1uXLlxMYGIiPjw8NGjQwDgWKjIwkKioKgLS0NJ544gnjgKC8+fIe77//Pl26dMFisVCrVi3Kli1r1G3evNlmraGhoSxYsACA1q1bExgYaNTFxMTQunVrVq5cafR3c3Ojbt26WCwW+vbte1uePxGR+0VCQgJ+fn42ZZGRkZQqVQqLxYKPjw+urq7Ga/CCBQuMQ+UCAgKoU6cOffv25ciRI/mOf7Ov8evXr7d5D7FYLKxevRoo+L3ueofi3S2sVis9e/akT58+fPvtt3z99dekpaUxbNgwm3ajR4/mgw8+sHkvvxF/+9vf+Pzzz7l8+fJ12/r4+BAbG2tTFhsbi6+vr01ZuXLl6N27N1OnTi3Smm6GgswiIiIiIiK3iKurK3FxcezcuZMKFSoYf+SlpqbSqVMnRo4cyZ49e9i+fTubN2/ms88+A2Dnzp2EhYXx1VdfsWvXLmJiYqhVq5bN2BkZGXTt2pVGjRoZQea8+fIeI0eO5LvvviMuLo6ZM2fSsmVLo6558+bXXHtSUhLLly+3KWvXrp3RPzAwkLlz5xIXF8cXX3xxi54xERG50tixY4mLi2PZsmV4eXkZr8F5O2MnTpzI9u3b2bNnDw0aNCA4OJiMjIzrjluU1/gr30Pi4uJo06YNUPB7HfzvULzCrOlOtWHDBpYtW2YX/J0+fTr79+83rr29vfHx8eH7778v0jwVKlSge/fufP7559dtO2TIEGbPnk1cXBwAZ86c4fXXX+fvf/+7Xdthw4Yxffp0srKyirSuolKQWURERERE5DZo1qwZR48eBWDevHk8+uijtG3bFoCSJUsyZcoU48CeCRMm8MYbb+Dt7Q3k7hIbPHiwMVZWVhY9evSgdu3aNof83EoRERGMGzfutowtIiK3lslkIjw8nMqVK9sFj/Nzu17jr3yvg5s/FO9OkF+AGXJ3OG/cuNGm7I033ihwN3lhDB8+nNOnT9uUXZmT2WKxkJCQQJUqVfjqq6948cUX8fb2pnnz5vTv35+nnnrKbkx3d3e6dOlCenr6Da2lX79+RhB76tSpzJw5E4CtW7cyaNCg6/ZXTmYREREREZFbLDs7mzVr1jBgwAAg/wN7vLy8SE5O5uLFi+zcudNIj5GfCRMm8MQTT/DRRx/ZlKempmKxWIzrUaNG0aNHjyKtuVmzZnz33XesW7eO0qVLF2kMERH5azVs2JDdu3fTuXPna7Yrymv8xo0bbd5jvv32W7y8vIzrq9/r8rz++ut06NCB/v3738Cd3DnKlCmDs7Oz3W5sR0dHPD092blzp1EWEBBATk6OcV1QapCEhATj5+TkZOPnBx98kJSUFOM6MjLS+LbS1Vq1asV//vOffOuu7vPhhx/y4Ycf5tsWctOphIaG2pTNmjXL+HnIkCHGz02aNKFJkyZMnz69wPFAO5lFRERERERuSnaOlcMnLnLybIoR9K1cuTInT57kiSeeuCVztGjRgs2bN/Pnn3/alF+dLqOoAeY8Y8aM4d13372pMUREJH/ppy5zZsthzm5NJPNi/rtMTSbTDY15I/mAb/Q1/up0GXkB5uu9113rULy7wfPPP4/ZbLYrt1qtdOnSpRhWdHdQkFlERERERKSIftuTROjYlQz/eAOvfLAGs6MzK9b8zKFDh7BarUaeyvwO7Dlw4ABubm6UKVMGX19fu/ortWrVio8++ogOHTpw/Pjx23Y/wcHBpKam8ssvv9y2OURE7kenNyaQOHc7Zzcf5szPh0lZlsjZpDM2bc6ePYu7u/sNjfvbb79Rr169QrW9Va/xeR9wXv1ed6WbPRTvSuPGjcPX15f69etjsVjYunWrUXf69GmcnJyYNm2aTR8PDw/8/f2pX78+bdu25cSJE3bjLl26lAYNGhAQEICPj4+xU3fWrFmUKFECBwcH41GmTBm+//57ypQpY/S/3oG+ee6VAxGvR0FmERERERGRIjhx5jLjZv3K+eR00jKyycjKIccKoz/7GZcSrnzyySdMmjSJrKwsnnvuOTZt2sTq1auB3F1gQ4cONQ7siYiI4L333jN2Kufk5Nj9wdy1a1dGjBhB+/btOX/+/G27rzFjxjBhwoTbNr6IyP0mPSmZ87HHsGblgBXIsVLSsQQVXcqyaukKIDfAvGLFClq0aFGoMa1WK5988gnHjx+nffvU8gyKAAAgAElEQVT2hV7LrXyNL1mypM173ZVu9lC8PFu2bGHp0qVs27aN33//ndWrV1OjRg2j/t///jdNmzYlOjraru+6dev4/fffCQwM5L333rOpy8zM5KWXXuL7779n+/bt/Pbbb7Ru3dqoHz16NBcvXmTJkiUsW7aMpKQkgoKCjPrrHeh7tXvhQMTrUZBZRERERESkCFb+coisK/Iw5klOzWTnvtM0aNCA+vXrEx0djaurK4sXL+bdd9+lbt26+Pv707hxY8LCwgCoX78+H330Eb169aJevXr4+flx4MABu7EHDx5Mly5d6NSpE2lpacZXlvMeI0eOvOn76tixI5UqVbpuu8TERDp16mRct2vXjqSkJCD3IKRly5bd9FpERO4Fl/48gzXb/v1iwnP/x9tj38FisRAcHMxbb71lk/M4PxEREQQEBFCnTh3+85//sG7dOpydnQEYOHAgMTEx1+xf2Nd4+F9O5rzHggUL7Npc+V53tasPxZs2bZrxAWpMTAwDBw406q7M/Xyl48eP4+7ujouLC5B7qF3VqlWN+ujoaCZNmsTRo0cLPICvVatW7Nu3z6bs0qVLZGVlUbFiRQBcXFyoW7euTZtSpUrx5JNP0q5dO2P+PNc70Pdq98KBiNdjuhXb1osiMDDQer1ffBERERERkTtV1Fcx/PTbUbvyEs5mXukWQFCjGvn0EhGR+82Znw9x9pfE3F3MVzA5OlApyJOyAVWKZ2F3geTkZFq0aEFKSgpt2rShR48ePPbYY0Duh53BwcHs3buX0aNHU7FiReMQXQ8PD2JiYnB3dycsLIxSpUrxwQcf2Iw9cOBAlixZwuOPP05ISAi9evXCwcGByMhI/vnPfxrB+PLly7Nu3TqbvsOGDaNmzZq89tprNuXly5fn0KFDNmk1EhISCAkJYcmSJXTo0IFdu3bx2muvERgYaHf43p3MZDLFWq3WwILqtZNZRERERESkCCx1KuHibH8wUHaOFe+aFYphRSIicidyq+uOyZxPCM5qpVStin/9gu5wly5dYu3atcyZM4dVq1axePFiZsyYQaVKlejRo4eRy3j+/Pl0794dgJ49e9rtpg4KCsJisXDx4kVGjRplN8/MmTNZs2YNjzzyCFFRUfTv39+oCw8PNw48vDrAXFR3+4GI1+NY3AsQERERERG5G7VqUJ1v1+3j5NkUMrNyvwbt4mymlaUaVdxLFfPqRETkTuHiXooKzWpwdnMiVqyYTIDVRKUnvHAs5Vzcy7ujXLx4kS+//JKMjAxycnI4ffo0hw8fpm3btowdOxZ/f3/mzJlDaGgo0dHRnDhxgrlz5wJw7Ngx9u7dS+3atYHcnMzXO0jR398ff39/+vTpg6enZ6EP4/Px8WHDhg02ZVce6FuQ0aNH061bN2M39r1EO5lFRERERESKwNnJzKTXWtEtuDY1HnCjdo1yDOpSn7Bn888rKSIi968KTWpQs19D3Ft54N7KE48XG1HW78HiXtYdZ8uWLaSnp5Pz3zMPkpKSOH78OGvXriUnJ4e4uDhq1qzJn3/+SXJyMkePHiUhIYGEhARGjRqVb27o/CQnJ7N+/XrjOm/cwrregb4FuVUHIt6JtJNZRERERESkiEqWcKJ3O296t/Mu7qWIiMgdzqlcCco3qlbcy7ijHTp0iCvPj8vIyOC7774jLS2Nzz77jLp16zJjxgymTp1Kly5dbPp27dqVHj168Oabb153HqvVyoQJE3j55ZdxdXWlVKlSNruYJ0+ezFdffWVcL1q0CA8PD+M670DfV199lSFDhpCdnU2fPn2MA31jYmKYNm0aM2fOtJv7jTfeoEGDBsZ13mGIgwYNuu6672Q6+E9ERERERERERESK3dy5czlx4oRdudlsNgLCUjx08J+IiIiIiIiIiIjc8Ro3boyjo23iBbPZjIeHhwLMdzgFmUVERERERERERKTY1alTh6ZNm+Lo6IizszNms5kaNWrQoUOH4l6aXIdyMouIiIiIiIiIiMgdoUmTJjRo0ICzZ89SqlQpSpcuXdxLkkJQkFlERERERERERETuGM7OzlSuXLm4lyE3QOkyRERERERERERERKTIFGQWERERERERERERkSJTkFlERERERERERO4Zbm5uxs/Lli2jTp06HDp0iMjISKKiogCwWq28++671K5dmzp16hAUFER8fLzRz8PDg65duxrXCxYsIDQ0NN/5fv31V1q3bk3t2rVp2LAhTz75JDt27AAgMjKSatWqYbFY8PHxITo62uh3vTVceR8As2fPJiwszG5cPz8/lixZUsRnS+TWUE5mERERERERERG556xZs4ahQ4eycuVKatasaVM3depUNm/ezPbt2ylZsiQ//vgjnTp1Ij4+nhIlSgAQGxvLrl278PHxKXCOkydP0r17d+bNm0fz5s0B2LRpE/v378ff3x+A8PBwRowYwd69e2nUqBHdunXDycmpUGu4lrxx//jjD1q2bElSUhIODtpPKsVDv3kiIiIiIiIiInJP2bBhAy+++CJLly7Fy8vLrv6DDz5gypQplCxZEoC2bdvSvHlz5s6da7QZPnw448aNu+Y8U6ZM4YUXXjACzAAtWrTg6aeftmtbu3ZtSpYsyblz5wq9hsKoV68ejo6OnD59+ob6idxKCjKLiIiIiIiIiMg9Iz09naeffppFixbh7e1tV3/x4kUuX77Mww8/bFMeGBhok66ie/fubNu2jX379hU4V3x8PA0bNizUurZt20bt2rV54IEHCr2Gwti6dSsODg5UqlTphvqJ3EoKMouIiIiIiIiIyF0vOy2LjDMpODk50bx5cz7//PObGs9sNhMREcH48eML3adJkybUq1eP1157zSibPHkyvr6+NGnShDfeeOOm1mQymWzGtVgsjBgxgvnz59vUifzVFGQWEREREREREZG7ljU7hxMr9nLwH1s5/NV2TNnwj+GT+PXXX3nvvffs2pcpU4ZSpUpx4MABm/LY2Fh8fX1tyvr06cOGDRtITEzMd25fX1+2bdtmXG/dupV33nmHCxcuGGXh4eHEx8fz7bffMmDAANLS0gq1BldXVzIyMoy6s2fP4u7ubjNuXFwcGzdupGXLltd7mkRuKwWZRURERERERETkrnVq/UGSd5/Cmm3FmpkNQPr2M0RHzWLu3Ln57miOiIhg6NChpKamArB69Wo2bdpE7969bdo5OTkRHh7O8OHD8517yJAhjB8/njZt2hhlKSkpbNq0iWrVqpGVlQXA6dOnGTp0KIGBgbz99tv4+voSHh7O0KFDiY+P5+GHH2bx4sWsWrWKYcOGYbFYyMzMpFatWqxevZrU1FQmT57M7NmzqV+/PtOmTePw4cMALF26lAYNGhAQEICPjw/Tp08v8Ll6+umnadq0qV35V199Rf369fH19SUgIICBAwdy/vz5az3tIjYci3sBIiIiIiIiIiIiRWHNzuHijpNYs3Jsy7NyYPdlVqxYQatWrezyFb/66qucO3cOf39/zGYzlStXZvHixbi6utrNMWDAAMLCwvKd/9y5c9SoUYONGzfy8MMPU7lyZdzd3fH29ub333/nt99+IygoyGj/5ptv0rt3b4KDg0lNTaVx48Y0btyYsmXL8uGHHzJu3DgWLlzI0qVLOXr0KC+//DIjRowgOTkZs9nM7t27cXFx4e9//zsuLi5kZmby0ksv8euvv1K9enW++eYbNm3aBEBiYiJDhgxhyZIlAAQHB7Nnzx7KlCnDgQMHjHzQK1asYPLkySxfvpxq1aqRnZ3NnDlzOHnyJOXKlSv6fxy5r5isVmuxTBwYGGiNiYkplrlFREREREREROTul52ayYF//Ao59vEtBxczXq82uyXzuLm5kZycbFf+5ptv4ubmxh9//METTzxh7IQODQ01dhzv2rWL8+fPExgYSEJCAgDnz5+nQYMGDB48mB9//JHVq1cDsH79eqKioli6dKnNPAsXLmTWrFl8//33NuVnz57F29ubQ4cO5Rsgv9K//vUvYmJiePDBB3FycmL06NEAtGzZkrffftsmGC5yNZPJFGu1WgMLqle6DBERERERERERuSs5lHDEXNIp37oSVUrf9vnnz59Pz5496dWrF9HR0TZ1Dz30EC1atODLL7+061euXDlGjhzJqFGjmDp1qk3dxo0bsVgsxmP//v20bduWxMRE6tSpwyuvvMJPP/0EQIUKFejUqRM1a9akV69ezJ07l5ycHLv5AKKjo+nVq5fdWuPj42nYsOHNPhVyn1OQWURERERERERE7komk4lKwQ9jcrwixGUCk5MD7q08bmrs9FOXubjzJKmJF/Ktj4mJwd3dnYceeojHH3+c3377jbNnz9q0GTVqFBMnTsw38Lt8+XIefPBBdu3aZVPesmVL4uLijIeXlxdubm7ExsYyY8YMKlWqRI8ePZg9ezYAM2fOZM2aNTzyyCNERUXRv39/u7lOnjzJ3r17adGiBXXq1MHJyYmdO3fatduxYwcWiwUvLy/mz59f2KdKREFmERERERERERG5e5Wu4061rr641iyHY1kX3Oq4U+M5Cy4PuBVpPGt2Dke/jSdx7naS1uzn6MJ4rFk5ZF3OsGkXHR3N7t278fDwwMvLi4sXL/Ltt9/atKlduzYWi4VvvvnGpnzp0qVcuHCBlStXEhERQUpKynXXZTabad26NWPHjmXKlCk2c/n7+xMeHs6qVavs1gDwzTffcO7cOTw9PfHw8CAhIcHYzezr68u2bduMceLi4ujQoYNxKKJIYSjILCIiIiIiIiIidzXXGmWp/qwfni82pspT3ri4lyzyWGe3HiE18QLWrBysmbkPrHByxV6jTU5ODt988w07duwgISGBhIQEFi9ebJcyA+CNN94gKirKuE5NTWXYsGFMnToVf39/OnfuzLhx4665pj179rB37//mj4uLo2bNmiQnJ7N+/Xq78qtFR0ezYsUKY62xsbF8/fXXQO5u6xEjRnDkyBGbNYrcCMfiXoCIiIiIiIiIiMid4sLvJ7Bm2aa3SM1Mo9GAIBxfcwbgxRdfpFq1alStWtVo06pVK3bt2sXx48dt+vr6+tKwYUNjt/A777xDly5d8PHxASAyMpKAgABCQ0OB/+VkzjNmzBg8PT159dVXOX/+PI6OjtSqVYsZM2ZgtVqZMGECL7/8Mq6urpQqVcpIo5EnISGBQ4cO0bRpU6PM09OTsmXLsnXrVjp27MipU6fo0KED2dnZlCtXDj8/P9q1a3dzT6TcV0xWq/3pm3+FwMBAa0xMTLHMLSIiIiIiIiIikp/9U38hJzXLvsLBxMOvNMFcQns25f5jMplirVZrYEH1SpchIiIiIiIiIiLyX25eFfKNmDlXcFWAWaQACjKLiIiIiIiIiIj8V8WWHjiWdMbkmBs2M5kdMDmbebBDnWJemcidSx+/iIiIiIiIiIiI/JdjKWdq9m/ExV1JpB27iHN5V8rUr4xjKefiXprIHUtBZhERERERERERkSs4OJspZ6kClirFvRSRu4LSZYiIiIiIiIiIiIhIkSnILCIiIiIiIiIiIiJFpiCziIiIiIiIiIiIiBSZgswiIiIiIiIiIiIiUmQKMouIiIiIiIiIiIhIkSnILCIiIiIiIiIiIiJFpiCziIiIiIiIiIiIiBSZgswiIiIiIiIiIiIiUmQKMouIiIiIiIiIiIhIkSnILCIiIiIiIiIiIiJFpiCziIiIiIiIiIiIiBSZgswiIiIiIiIiIiIiUmQKMouIiIiIiIiIiIhIkSnILCIiIiIiIiIiIiJFpiCziIiIiIiIiIiIiBSZgswiIiIiIiIiIiIiUmQKMouIiIiIiIiIiIhIkSnILCIiIiIiIiIiIiJFpiCziIiIiIiIiFzXmTNnsFgsWCwWKleuTLVq1Yxrk8lk/GyxWHj//fcBaN26NTExMTbjrF+/nrJly9q0X716NQBms9mmPCEhAYBNmzbxyCOP4O3tjbe3NzNmzDDGi4yMpGTJkiQlJRllbm5u+f78559/0rFjR2rXrk3Dhg3p3r07J0+eNOr/9re/Ua1aNXJycm7dEycich9wLO4FiIiIiIiIiMidr2LFisTFxQG5gV03NzdGjBgB5AZy8+oKo2XLlixdutSu3NXV1W6cEydO0Lt3bxYtWkTDhg05ffo07dq1o1q1ajz55JMAuLu7M2nSJD744IMC50xLS+PJJ5/kww8/5KmnngJyA96nTp3iwQcfJCcnh++++44aNWrw008/ERQUVOj7ERG532kns4iIiIiIiIjcsaZOnUpoaCgNGzYEcgPKEyZMMHZLA/Tv35/58+dz9uzZAseZN28ezZo1MwLMkLvT2s/PD8gNOPv6+jJ48GCio6Nv093cX/LbmX7lTnZvb2/jgwqA2bNnU6lSJZs+u3btIiEhAVdXV5vyL774AgAPDw+6du1qjLFgwQJCQ0MLHG/79u3GzxUqVMDT0xOLxUKbNm3yvYdFixZhMpnYvXu3TfnevXsJCQnBy8uLRo0aERQUxIYNG27xMyhy99BOZhERERERERG5KampqVgsFuN61KhR9OjRo8D2GzdutGn/7bff4uXlZTOOp6cn3333HfHx8bzwwgs2/QMDA4mPjzeu3dzc6N+/Px9//DFjx47Nd86dO3fSqFGjAtcUHR1Nr1696Ny5M6NHjyYzMxMnJ6dr37hcU3470xMSEoyd7KmpqTRo0IAuXbrw6KOPAtCjRw+mTJli18fLy6vA3fKxsbHs2rULHx8fu7r8xssbJzQ0lJCQELp161bgPURHR9OiRQuio6ON3628XfFRUVF06tQJyP39iomJoVWrVtd6SkTuWQoyi4iIiIiIiEi+crKz2fHLWuJ/XU92Vha1A5rQ8LEOdu3yCyZey42kyyisoUOHYrFYbHbGFlZGRgbLli3jww8/pHTp0jRp0oSVK1cSEhJSpLVI4eTtTj569OhNjTN8+HDGjRvH3Llzb9HKciUnJ7Np0ybWrVvHU089ZQSZ586dS7NmzYwAM4Cfn5+xK17kfqQgs4iIiIiIiIjka0X0Zxz+cydZmRkA/LZxOQfiY8nJMf9la/Dx8SE2NpbOnTsbZbGxsfj6+tq0K1euHL1792bq1Kn5juPr68tPP/2Ub93KlSs5f/48/v7+AKSkpODq6qogcxFZc6wA+e5Mv9K5c+fYu3evze7f+fPns2nTJuN6y5YtAOzfv99m9/unn35Ky5YtAejevTufffYZ+/bts1tLfuO5uroW6j4WL15M+/btqVOnDhUrViQ2NpZGjRoRHx9vpG8RkVwKMouIiIiIiIiIndPHE20CzADZWVlcOn+Gc0lZlClT5i9Zx5AhQ2jSpAnPPPMMFouFM2fO8Prrr/Pmm2/atR02bBiNGzcmKyvLrq53796MHz+eH374wTgwcMOGDVSoUIHo6GhmzpxJr169ALh8+TKenp6kpKRQsmTJ23uD95DMi+kkrdpHSsI5AEo4uxCzaSuObi427TZu3EjA/7N353FRVe8Dxz+XYVXUVNzRQGSRdVRcv2qo5Zb7Ei4/N6S+leSSSypmlrjvad/UUtEycMUtxVygMNMCxQVFEcUN940QBBnm9wdxYxxQNA2X5/16zau555x77rmjoj3z3Od4eZGQkMDQoUOpWLGi2pdfeQvgoeUyNBoNI0eOZMqUKbRpY5hpX9B8hRESEsKQIUMA6NGjByEhIfmWXOncuTMJCQk4OTmxfv36J7qWEC862fhPCCGEEEIIIYQQRi6fT8y3/X5mBim3rhu05Was5r5Gjx6t9r399tvY2tpia2tL9+7dgb9rMue+1q5dW+A6KlWqxPfff8+7776Li4sLjRo1ws/Pz2ADv1w2NjZ07tyZjIwMoz4rKyu2bNnC/PnzcXR0xNXVlf/973+UKFGC8PBwNfAMULx4cRo3bszmzZuJjo7G399f7cubTevv7090dHSBa3+VZGdlc/6HQzkBZj05r2w4v/Iwel22wdgmTZpw6NAh4uLiWLJkyROXSMmrT58+/PLLL5w/f/4fzwVw8+ZNdu/ejb+/P3Z2dsyYMYPVq1ej1+txc3PjwIED6tiwsDCCg4MfuvGkEC87yWQWQgghhBBCCCGEEeuSpVFMjHPTNKZmDP7Anzo+f5eS0Ol0+c4RGRmZb/udO3fybU9NTc23vWnTpvzxxx/59k2YMMHgePbs2cyePTvfOV1cXAgPDzeaI7/gYN6M1G+//VZ9nzcgmre9sBRF4eOPP2bWrFkAzJw5k9TUVPU+Fi9erK6/ZMmSzJ49m8aNGwPg4+PDzJkz8fb25syZM7Rs2ZIFCxZgYWHBzJkzDepcd+rUicuXL7Nv374C17JhwwbGjx/P/fv3MTU1ZeLEiXTq1IlvvvmGnTt3smrVKgBSUlKoVasWO3bs4Isvvsh3s7zUhOtkZ2TlBJfz0N27T2riTUo42Rhd397entGjRzNt2jRCQkIe74N8gJmZGcOGDWPq1Kk0b978H80FsHbtWvr06cOiRYvUtjfeeIOoqCg1K37Tpk1qXea0tLR/fE0hXmSSySyEEEIIIYQQQggj1RzdMbewRFEUg3YTExNc6jQpolW9+CwsLFi/fj3Xr1836tuyZQuLFi1iz549xMfHs3DhQnr16sXly5cNxl24cIHWrVsza9YsWrVqZTTP7du3iYmJ4c6dO5w+fTrfdRw6dIgRI0awceNGjh8/zqZNmxgxYgSHDx/G39+f8+fPs3PnTgDGjx+Pn58f1atXL/C+7t9MR38/26hdn5XN/VvpBZ73/vvv88svv5CUlATk1FDOm+W+d+9e4O+azLmvL7/80miugQMHGpVKKWi+RwkJCaFz584GbV27diUkJETNil+4cCHVq1enYcOGBAUFMW7cOADJfhevJEWv1z961DPg7e2tlz9UQgghhBBCCCHE8+vOzauEr/wfN68moygKVsVL0LLHf6n0umNRL+2FZW1tTWBgIKmpqUyaNMkgk7lJkyZ8/vnnBpm4n376KQATJ07Ex8eH4cOH88knn/DFF1+o2cSRkZEGmcxLly4lOjqaChUqYGZmxtixY43W0adPH5o1a4afn5/atmTJEiIjI/nuu+84fPgwvXr1Ijg4mAEDBhATE4O5uTn9+/fPN5P5zxPXuRJ+0ijQrJiZULGdM9YOZZ/OByiEKBKKosTo9Xrvgvolk1kIIYQQQgghhBD5KlWmPL4fTaDPiGn0HDqRvqNmSID5KRg0aBArV640KhsSFxdntLGct7c3cXFx6nG/fv0ICAgwCvLmFRISQs+ePenZs2eBZSgedS1PT09atWpFixYtmD9/Pubm5g+9J+saZTAtbg4meTLfTRRMS1hQ3L7MQ88VQrz4JMgshBBCCCGEEEKIh7IuVZpSZcoblc4QhXPs2DHGjRvHqFGjyM7OpmTJkvTt2zffkg+P8uabb/L9998XWAP4ypUrJCQk0LhxY5ycnDAzM+Po0aNPtO5BgwZRpUoVfHx8HjlW0ZhQtZcXJVzKoZiZoJhpKOlajqo9PVFM5PeNEC87CTILIYQQQgghhBBCPCNffvkl3t7eTJs2jZkzZ5Kenk5AQABDhw5lyZIl3L17Vx3r6upKTEyMwfkxMTG4ubmpx6NGjaJu3bp0797dqP4wwOrVq7l16xb29vbY2dmRlJSUbzZzYa5lYmKCST6bPxZEU8yMim2dqDGkETWGNKRCayc0VmaFPl8I8eKSILMQQgghhBBCCCHEM5CcnMwnn3xCeno6WVlZ5O6LtWzZMk6cOME777zDkiVL1PGjRo3ik08+4caNGwDExsYSHBzMhx9+aDDv3LlzKVmyJAMHDuTBvbZCQkIIDw8nKSmJpKQkYmJiCA0NNVrbiBEjmDJlirrhXlJSEpMnT2b48OFP8yMQQrwiChVkVhSltaIoJxRFOaUoyugCxryjKMoxRVHiFEX54ekuUwghhBBCCCGEEOLFsnXrVjQajVF7eno6a9euZfjw4Vy/fl1t79ChA35+fjRq1AgXFxfeffddvv/+eypVqmRwvqIoLF++nEuXLjFq1Ci1PSkpibNnz9KgQQO1zd7enlKlSrF//36DObRaLdOmTaN9+/a4uLjQvn17pk+fjlarfeg9ZWVlYWFhYdS+cOFCFi5cCEB0dDT+/v4G1xKiqGg0GrRaLV5eXtSuXZu9e/ca9M+dOxdLS0uDGulpaWn07t0bDw8P3N3dady4MampqQbz5b5yv6jJlZ2dzeDBg3F3d8fDw4O6dety5swZAOzs7PDw8FDPHTx4sNF6J0yYQLFixbh69araZm1tbTBmw4YNKIpCfHz8P/psniblwW+8jAYoigY4CbwFXAD+AHrq9fpjecY4AquB5nq9/paiKOX1ev3VfCf8i7e3tz46Ovqfrl8IIYQQQgghhBDiubR8+XICAgLU4FQujUbDiBEjmDp1ahGt7MlkZ2dTt25dvvvuO1xdXYt6OUIUirW1tfpncPv27UyePJmff/5Z7a9fvz7m5ub4+fkxYMAAAKZMmcK1a9eYPXs2ACdOnMDOzg4LCwuD+fITEhLCunXrWL16NSYmJly4cIHixYtTunRp7OzsiI6OxsbGpsDzJ0yYwNKlS+nZsyfTpk0zugcAX19fkpOTad68OZ9//vmTfziPQVGUGL1e711Qf2EymesBp/R6/Wm9Xp8JhAIdHxjzLvCVXq+/BfCoALMQQgghhBBCCCFefs2aNWP79u0GbXPnzuWDDz4AIC4ujubNm+Ps7IyjoyMTJ05Uyz8EBwdTrlw5atWqhaOjI61atTLKQMzKyqJcuXKMHm340LWPjw/Ozs5qtuDatWsBuHDhAh07dsTR0REHBweGDBlCZmYmAJGRkSiKwubNm9V52rVrR2RkZL5zduvWLd97Dg8Pp169eri4uKg1mB9kbm5O7969C/sxPheSk5Nxd3enQYMGEmAWL6yUlBRKly6tHicmJpKamkpQUJBB7fJLly5RpUoV9djZ2TnfDPN4ERgAACAASURBVP78XLp0iUqVKqn1zG1tbQ2uWRh+fn6sWrWKmzdvGvWlpqayZ88elixZkm8pnKJSmCBzFeB8nuMLf7Xl5QQ4KYryq6Io+xRFaZ3fRIqivKcoSrSiKNHXrl17shULIYQQQgghhBDihdCzZ0+jIEhoaCg9e/YkPT2dDh06MHr0aE6cOMGhQ4fYu3cv//vf/9Sxvr6+HDx4kISEBEaPHk2XLl04fvy42r9jxw6cnJxYs2aNUW3ilStXEhsbS2xsLN26dUOv19OlSxc6depEQkICJ0+eJDU1lcDAQPUcW1tbJk2aVOD95J0zN3Cd19GjR/noo49Yvnw58fHxHDlyhJEjR2Jubk7x4sUpVqwYlpaWfPHFF3h4eDz251mUKleuzLFjx/jqq6+KeilCFIou6x7Zuvukp6ej1WpxcXHB39+fTz/9VB0TGhpKjx49aNKkCSdOnODKlStATpB32rRpNGzYkHHjxpGQkKCekzufVqulc+fORtd955132Lx5M1qtluHDh3Pw4EGD/mbNmqnnz5kzJ9+1W1tb4+fnx7x584z6Nm7cSOvWrXFycqJs2bJGG3gWlae18Z8p4Aj4AD2BbxRFee3BQXq9frFer/fW6/Xe5cqVe0qXFkIIIYQQQgghxPOoW7du/Pjjj2q2cFJSEsnJyTRp0oQffviB//znP7Rs2RKAYsWKsWDBggJLSDRr1oz33nuPxYsXq20hISEMGTKEatWq8dtvvz10Lbt378bS0lJ9HF6j0TBnzhyWLl1KWloaAF5eXpQqVYodO3Y80f1OmzaNsWPHUrNmTbVtypQpJCcnM3/+fGbPns3JkycZMWLEE80vhHi0u3fOc2zvbGIjPiN296dYWpgR/cde4uPjCQ8Pp2/fvuqXUiEhIfTo0QMTExO6du3KmjVrgJw64qdPn2bkyJHcvHmTunXrql9wWVlZqV82hYWFGV3f1taWEydOMGXKFExMTGjRogW7du1S+yMiItTzhw0bVuB9DB48mOXLl/Pnn38atOeuGaBHjx4GGdhFybQQYy4CVfMc2/7VltcFYL9er78PnFEU5SQ5Qec/nsoqhRBCCCGEEEII8cIpU6YM9erVY9u2bXTs2JHQ0FDeeecdFEUhLi6OOnXqGIx3cHAgNTWVlJSUfOerXbs2ixYtAuDevXvs3LmTRYsWcfv2bUJCQmjUqJE6tnfv3lhZWQGwa9eufK9XsmRJqlWrxqlTp9S2wMBAPv30U9566y2j6+ed86233mLGjBkG/XFxcfkGkMuWLasGt4UQz07mvducjF5Iti7niy09oNdnkxDzDS71B9OwYUOuX7/OtWvXuHLlCgkJCeqf9czMTOzt7QkICABysom7dOlCly5dMDExYevWrQZfID2MhYUFbdq0oU2bNlSoUIENGzbQokWLx7qX1157jV69ehk8PXDz5k12797NkSNHUBQFnU6HoijMmDEDRVEea/6nrTCZzH8Ajoqi2CuKYg70ADY9MGYDOVnMKIpiQ075jNNPcZ1CCCGEEEIIIYR4Qdy6eonoyC3ERP5Ih3Zt1JIZuaUynlTekhhbtmyhWbNmWFlZ0bVrVzZs2IBOp1P785a2KFu2bKGv0bRpUwD27Nlj1Jd3zgcDzA+6ceMGWq0WJycnZs6cWejrCyGe3LXzv6HP1hm137t7lbQ/LxIfH49Op6Ns2bKEhIQwYcIEkpKS1KcskpOTOXv2LL/++iu3bt0CcoLPx44d4/XXXy/UGg4cOEBycjKQs1nm4cOHC33ugz7++GMWLVpEVlYWAGvXrqVPnz6cPXuWpKQkzp8/j729PVFRUU80/9P0yCCzXq/PAgKA7cBxYLVer49TFOULRVE6/DVsO3BDUZRjQAQwUq/X33hWixZCCCGEEEIIIcTzKTpiM6HzP+P3HRvYvyOMu2ei2R6+jQMHDpCWlqZmE7u6uhrVEj19+jTW1taULFky37kPHjyoZhKGhISwc+dO7OzsqFOnDjdu3GD37t0Friu/66WkpHDu3Dlq1Khh0B4YGEhQUNBj37ubmxsHDhwAcrKXY2Njee+990hNTX3suYQQjy899Qp6vWGQOSMzix7//ZYGjVrg6+vL8uXL0Wg0hIaGGtVU7ty5M6GhoSQmJvLGG2/g4eFBrVq18Pb2pmvXroVaw9WrV2nfvj3u7u54enpiamqqZkeDYU3mvn37PnQuGxsbOnfuTEZGBpDzc+/BNXft2lUtmaHVatV2f39/oqOjAVi4cCELFy4s1PqflPJgYfx/i7e3tz73RoUQQgghhBBCCPHiu3X1EqHzP0OXdd+gfdmmX7hvYkXnLl35/PPPgZzNs9zc3Fi8eDFvvvkm6enpdO/enVatWvHRRx8RHBxMdHQ0CxYsAODnn3/G19eXiIgIqlSpQo0aNTh//jwWFhY511i2jKioKJYuXYqPjw8zZ87E29tbXYNer6du3boMHjyYvn37otPpeP/99ylZsiSzZs0iMjKSmTNnsmXLFgDq16/PpUuXWLFiBT4+PvnO+aAjR47QuXNnNm/erAbDv/jiC7Kzs5kwYcJT+5yFEPm7fCaC5MQd6LMNfwYpJma4NhyKZfHyRbSyF5+iKDF6vb7AH4BPa+M/IYQQQgghhBBCvOISj8Wgz842avd2q86Ro3EGpTKsrKzYuHEjQUFBODs74+HhQd26dQ0y/latWqWWnJg8eTLr1q2jZs2ahIWF0bx5czXADNCxY0c2b96sZvw9SFEUwsLCWLNmDY6Ojjg5OWFpacnkyZPzHR8YGMj58+cN2nr37q1mIL755ptG53h4eDBv3jz69u2Ls7Mz//nPfzh+/Di9evUCDLMJo6Oj8ff3V8/Nm4EohHgyNrb10WjMgb/rEysmZpQs6yQB5mdMMpmFEEIIIYQQQgjxVMRE/sj+HWFkP1ATVWNqRqPW3fD6T8siWpkQ4lWRee82F05uJeX6cUw05tjYNqCSfXMUE01RL+2FJpnMQgghhBBCPKesra3V91u3bsXJyYmzZ88yYcKEfDeJunDhAh07dsTR0REHBweGDBlCZmbO7umRkZGUKlVKzbDTarXs3LkTgMuXL9OjRw8cHByoU6cObdu25eTJkyQlJeHu7q7O/80331CnTh1u3bpF//79qVKlipoReP36dezs7ABISkrCysrK4ForVqwAwM7ODg8PDzw9PXnjjTc4e/bsM/nshBDPJwf3Oigm+YcaqrvV+ZdXI4R4FZlbvkZ1z15om0/E841PqezwlgSY/wUSZBZCCCGEEKKI7dq1i8GDB7Nt27YCdx/X6/V06dKFTp06kZCQwMmTJ0lNTSUwMFAd06RJE2JjY9XXm2++iV6vp3Pnzvj4+JCYmEhMTAxTpkzhypUrBvN/9913zJ8/n+3bt1O6dGkANBoNS5cuzXc9Dg4OBtfKu3FNREQEhw8fxsfH54k2zhJCvLhes6lIw1Zd0ZiaoTE1/eu/ZjR+uwclXitb1MsTQgjxjEiQWQghhBBCiCL0yy+/8O6777JlyxYcHBwKHLd7924sLS0ZMGAAkBMAnjNnDkuXLiUtLa3A8yIiIjAzM+P9999X27y8vGjSpIl6vHr1aqZOncpPP/2EjY2N2j506FDmzJlDVlbWE91bw4YNuXjx4hOd+6rTaDRotVrc3Nzw8vJi1qxZZP9V57agrPUHM9MBNSt+0KBBaLVaXF1dDbLQ165dy759+6hfvz5arZaaNWsabE62bds2vL29cXV1pVatWgwfPtxg3rzs7Oy4fv26wfpzX1OnTjW6x/79+2Nvb49Wq8XFxUXdDE68+LSNW9F72CQatupOo9bd+L/hU/Bo0LyolyWEEOIZMi3qBQghhBBCCPGqysjIoFOnTkRGRuLi4vLQsXFxcdSpY/ioecmSJalWrRqnTp0CICoqymDjqHXr1nH06FGj8/I6e/YsAQEBHDx4kIoVKxr0VatWjcaNG/Pdd9/Rvn17g77ExESDa82fP98gcA0QHh5Op06dHnpfIn9WVlbExsYCcPXqVXr16kVKSooaiG3SpAlbtmwxOCcpKanA+b766it1TLt27dS5AZydnVm9ejVeXl7odDpOnDgBwNGjRwkICODHH3/ExcUFnU7H4sWLH3v9DzNjxgy6devGvXv3cHV1pW/fvtjb2xfqGuL5VrJMObSNpf6yEEK8KiSTWQghhBBCiL8UVCP5UbKzsxk8eDDu7u54eHhQt25dzpw5k+/Y+M1bMNVoGFmrDqZAXTc3lixZQmpqKh988AEODg4sWrSIuXPn8s033+Q7h6IoakYpwPLlywkODjYolwEYlNIoSLly5ahWrRqrV6/Ot3/MmDHMmDFDzaLN9WC5jLwB5mbNmlGlShW2bdtGz549H7kG8XDly5dn8eLFLFiwgGexcfvVq1epVKkSkJOB7OrqCsD06dMJDAxUvwDRaDR88MEHT/36APfu3QOgePHiz2R+IYQQQjxbEmQWQgghhBDiAQ+rkezj42OUMbpq1SqSk5M5fPgwR44cISwsjNdee81o3iu7Ilgy4XPcXivNzvPnUPR6RttUZG9EBI0aNaJ06dIkJCTw3//+F39/f27evKme6+rqSkxMDAAWFhasX7+eM2fOcO7cOcqWNaxzevz4cXQ6HVFRUVSvXl09Lz/FihVj69atLFy4kJUrVxr1Ozo6otVqCwxC5yciIoKzZ8+i1Wr57LPPCn2eKFj16tXR6XRcvXoV+DtrPfeVmJj4xHMPGzYMZ2dnOnfuzKJFi9SA76Oy4OfMmWOwhuTkZLUvPT3doG/VqlX5zjFy5Ei0Wi22trb06NGD8uXLP/F9CCGEEKLoSJBZCCGEEEKIPApbIzmvS5cuUalSJUxMcv55bWtrq26el0uv13P2u+/ZkXSGADcPrqWno9frMc/OZmhNN+Lj47G3t1fnsLa25pNPPlHPb9GiBWlpaaxYsQJTU1P8/f3p3Lkz/fv3x9zc3OBaISEh9OnTh5YtW3Lnzh0yMjIMyhwcPnyYqKgo9bh8+fKEh4czduxYtm/fbnR/gYGBRvV3H8XU1JS5c+eyYsUKg2C5yN+NO+kEb4kjcOGvLN10lEflKz+4yaODgwOKouQ7tqD2XOPHjyc6OpqWLVvyww8/0Lp160KtediwYQZrqFy5stqXWy4j9+Xr65vvHDNmzCA2NpbLly+za9cu9u7dW6hrCyGEEOL5IkFmIYQQQggh/pJbI3nDhg2PrJGc1zvvvMPmzZvRarUMHz6cgwcPGo3RZ2Vx4dIlbmTcw7V0GZpXqULWX6UPLpy/QPPmzZk8eTKbNm0CICgoCFtbW/WlKAphYWGsWbOGtLQ0vv32W06fPm0QiM7Nbp0+fTrBwcHY2toSGhpKWFgYO3fuxMHBATc3N8aMGWNUf9ne3p5Nmzbh5+fH77//btDn5uZG7dq1DdpyazLnvr788kuje65UqRI9e/ZU6wHn5e/vT3R0NAALFy5k4cKFAERHR+Pv7//Iz/xlcu5yCh9O383GX05zOOE6m/ecJiNTx+mLd9Qxp0+fRqPRPDTTt2zZsty6dcug7ebNmwabORbEwcGBDz74gF27dnHo0CFu3LiBm5vbQ7PgnyZra2t8fHzYs2fPv3I9IYQQQjxdsvGfEEIIIYR4Zf35558EBwcTGRmJk5MTpqamNGrUiCVLljBv3jx13LJly9TjU6dO0bZtW8zNzbG3tycsLAxbW1tOnDjB7t272b17Ny1atGDNmjW0aNFCnUMxNSXy2lWaVbYFoEXlqvz+V+kDs1IlsbI0V+s4HzlyBDs7O65evWpQgqBq1aps3rwZa2trzpw5w/jx41m0aBFWVlbY2dlx584doqOjGTJkCL/++is6nY7XX38dS0vLAstdHD16VH3v5eXFxYsXAahXr57BuPXr16vv7ezsSE9Pz3e+B0uJzJ8/P99x3377rfr+/fffV997e3sb9L0KFoUdIf1elpq9nKXLeff1ukPMGNyUa9eu8f777xMQEPDQrGRra2sqVarE7t27ad68OTdv3iQ8PJwhQ4Y89Po//vgjbdu2RVEUEhIS0Gg0vPbaa4wcOZIuXbrQuHFjnJycyM7OZvHixQa/Xk9LVlYW+/fv56OPPnrqcwshhBDi2ZNMZiGEEEII8Uq6evUqrq6ujB49mvXr1zN79mzu3bvH0KFD+f3335k8ebI6dsCAAepj/97e3mzdupXY2FjCwsLUMRYWFrRp04YZM2YwduxYNmzYYHA9RVGITLnNtvPn6LYjnNG//0Ziyh0uZmbyn359OXTokLq5XmBgILGxsaSkpDz0HoYOHcqSJUu4e/eu2hYSEkJ8fDx2dnY4ODiQkpLCunXrnsZHJp6RuNM3jMpj6LIyWRzUDzc3N958801atmxpUN/6wZrMa9euBWDFihVMnDgRrVZL8+bN+eyzzx5Z9uW7777D2dkZrVZLnz59WLlyJRqNBk9PT+bOnUvPnj2pWbMm7u7unD59ulD39GBN5tGjR+c7Lrcms6enJx4eHnTp0gXIKeGRm9W/adMmxo8fD0BycjJt27Yt1BqEEEII8e9RnsXuxIXh7e2tz308TgghhBBCiH/bBx98wJIlS7h//75Bu4ODA/v376dp06Z8/PHHDBw40KDfx8eH4OBg7Ozs1LYDBw5QsWJFKleuTHZ2Nv3798fT05MRI0aoY06ePEn79u2JmDWH86vWkPXnnwQnnaaM1otpwct45513qFGjBhMnTkSj0XDv3j3Kli1rEEDOZW1tTWpqKgCjRo0iNDQUPz8/xo8fz+uvv87+/fvV+rgRERFMnDiR3bt3P62PTjxlPQJ/5O69LKN2CzMNa6a8/ciaykK8DDQaDR4eHty/fx9TU1P69u3LsGHD2LFjh1oW6NSpU1SpUgUrKys8PT1ZsWIFkPOlYcuWLQG4fPkyGo2GcuXKAbB//36KFSuGh4eHeq3evXszcuRIGjduzIIFC9BqtWrfzp076dq1K/b29mrbnDlzaNKkCTY2Nty+fRuAzZs3M2LECHbu3EnVqlX5+uuv1SdeSpUqxZw5c2jUqBEAjRs35uLFi+rTKgDt2rVjz5496nxCCPG8UxQlRq/XexfUL+UyhBBCCCHEK2njxo1GAWaAixcvkpmZSXh4OE2bNqVcuXJ06NDhoXNdvXqVd999l4yMDCCn1ERAQIDBmJCQEDp37kzldm2p9HYb9FlZlDh+HF9fX6aRUz5i5MiR1KhRg7Jly2JlZcX06dMfeR/Dhw9nwYIFQE52a5UqVQw2YGvatCnHjh1TNyfMtWnTJqKjo/niiy9ITk7G39+frVu3AtC2bVu+/fZbg3nEs9OqgR1bfj1N5v1stc3M1IQ361WTALN4ZeRuFgk5P1N79epFSkoKn3/+Oa1atQJyvuSbOXMm3t6GMY7y5cur544bNw4bGxuGDh0K5JRiKVGihNpfGM2aNTN6GiUr6+8vgrZv364GwKtWrcqGDRtYtmwZe/fupUyZMkRHR9OlSxdiYmLUYHeJEiXYt28fDRo04ObNm1y5cuUxPyEhhHi+SSazEEIIIYR4JTk6OnLq1CmjdnNzcy5fvkzp0qWLYFXiVXQ/S8f0FdEcOHEVU1MTsnTZeNYox+h+dbEw0xT18oT4V+R9QgNyNrusW7cu169fV79sKSjInFd+Qea8Gch5FZTJvGDBgnyDzDY2NoSFheHv7094eDiOjo4ANGzYkGnTptG0aVN1/JgxY7C0tOSzzz6jcePGtG3blqtXrzJ37lwWL17MrVu3mDJlimQyCyFeGI/KZJaazEIIIYQQ4pX04YcfUqxYMYM2U1NT3njjDQkwi3+VmamGQL/6LBjZnOG96zB/eDM+828gAWbxSqtevTo6nY6rf22Q+k/8+eef+dYwL0hERITB+NwNTdPS0ujatSsbN25UA8wAx44do06dOgZzeHt7ExcXpx6/9dZb7N69m+zsbFatWoWvr+8/vi8hhHieSLkMIYQQQgjxSho8eDB//PEHYWFhmJmZodfref311/nuu++KemniFVXJpjiVbIoX9TKE+Fekp6YQ9WMop+NiQFHQZWVxLy0Vy2LWT/1aT6tchqWlJfXr12fZsmXMmjVL7SvME+JmZmY0aNCA0NBQdDodtra2hb8BIYR4AUgmsxBCCCGEeCVpNBp++OEHDh8+zOLFi/npp584cuQIFSpUKOqlCSHES02XlcWar4M4dfh3su5nkpWZQXa2jrULJ5Gt0wE55TI0Gg3ly5cv4tX+zcTEhLVr17Jnzx6Dmvmurq7ExMQYjI2JicHNzc2grUePHnz00UeSxSyEeClJJrMQQgghhHilOTo6Gjz2LIQQ4tk6c/wg6akpZGfrDNrv3rnN2ZNHsLapwvvvv09AQMBzt/ll8eLF+fHHH2nSpAkVKlSgX79+jBo1ilGjRrFt2zZKly7NgQMH+P7773lwHyofHx9Gjx4tQWYhxEtJgsxCCCGEEEIIIYT411y/fIH7mRkGbfezdExcuJppy3+kRKnS9OnTh48//vih85w/f55BgwaxadOmh47Lrcmc6+2332bSpEkAtGrVCjMzMwCaNGnCwIED1ZrMuT777DPat2+vHtvY2BAeHs4bb7yBjY0NXbp0ITk5mQYNGqAoCiVLluSHH34wysI2MTFh5MiRQE75jVw6nY769eurQekBAwYwZMgQtFotX331FRYWFvj7+z/0HoUQoqgphakd9Cx4e3vrH/xWTwghhBBCCCGEEC+3E7G/ERm23CjQbGZhyZvdBuLg7l1EKxNCCFEQRVFi9Hp9gT+gpSazEEIIIYQQQggh/jUObt6YWxYzKIWhmJhgaVUcu5rah5wphBDieSVBZiGEEEIIIYQQQvxrTM3M6P7hOKo5eaIoJigmJtg5e9Htg3FoNFLVU4jnmbW1tcFxcHAwAQEBAEyYMIEqVaqg1WrV1+3bt4mMjKRdu3bq+HLlylGrVi0cHR1p1aoVe/fuVefr378/9vb26vmNGjUyuk5ednZ2eHh4oNVq8fDwYOPGjWqfRqMxWMvUqVOBnProzs7OeHl5UbduXWJjY9VzUlNT+eCDD3BwcKB27drUqVOHb7755il9ei83+ekthBBCCCGEEEKIf5V1qTK07z8UfXY2kJPJLIR48Q0bNowRI0Y8dIyvry8LFiwAICIigi5duhAREUHNmjUBmDFjBt26dSv0NSMiIrCxseHEiRO0bNmSjh07AmBlZWUQQM5r5cqVeHt7s2zZMkaOHMmOHTsA8Pf3p3r16iQkJGBiYsK1a9dYunRpodfyKpOf4kIIIYQQQgghhCgSiomJBJiFeIU1a9aM9957j8WLF//juVJSUihduvRjndOwYUMuXrwIQGJiIr///jtBQUGY/PVzqVy5cnzyySf/eG2vAslkFkIIIYQQQgghhBBCPFJ6ejpa7d+102/evEmHDh3U4zlz5vD9998DULp0aSIiIh45Z+3atVm0aJF6PHLkSIKCggBwc3Nj5cqVDz2/WbNm6PV6Tp8+zerVqwtc65gxY/D19TU4Nzw8nE6dOgEQFxeHl5eXGmAWj0eCzEIIIYQQQgghhBBCiHxl6zJJv3sVM3NroxIUwcHBREdHq8eFKZfxIL1eb3D8pOUyEhMTadGiBT4+PlhbG681r969e5OZmUlqamqBYyZNmsSaNWu4evUqycnJhb+hV5SE5oUQQgghhBBCCCGEEEaunI3iUOTnnPxjIUf3TEOfnUXW/bSneo2DBw+q9Zj/CQcHBypUqMCxY8ceOXblypWcPn2afv368dFHHwHg6urKoUOHyP6rVnxgYCCxsbGkpKT847W9CiTILIQQQgghhBBCCCGEMHDn2nGSE7aRrcskW5eBPjsLvT6bM4cfXr7icfz8888sXryYd9999x/PdfXqVc6cOcPrr79eqPGKojBx4kT27dtHfHw8NWrUwNvbm3HjxqHT6QC4d++eUaa1yJ8EmYUQQgghhBBCCCGEEAYuJ/1MdvZ9o/Y/b53mfkb+2b1z5sxBq9Wqr6SkJKMxq1atQqvV4uTkxOTJk1m3bp1BJvPIkSMN5sjMzARySnPY2tqqrwsXLgA5NZm1Wi3NmjVj6tSpVKhQAfi7JrNWq0VRFMqXL4+XlxcxMTEcOnQIACsrK4YPH07//v2xtLRk1qxZ3LhxA3t7eywsLHjjjTeYPn06AIMGDaJy5cpERkZSqlQpypYti5OTE1qtliFDhrBw4UI0Gg1arRZ3d3fat2/P7du3C/x8N2zYgKIoxMfHG7QnJCTQrl07HBwcqFOnDs2aNeOXX34xOj8yMhJFUdi8ebPa1q5dOyIjIwu85rOkFFU03tvbW5+3ZosQQgghhBBCCCGEEOL5cHTPdDLSrhm1m2gscK73IcVKVC6CVT0Za2trUlNTAdi+fTuTJ0/m559/Vvvr16+Pubk5fn5+DBgwAICFCxeyZ88evv/+ew4cOED//v2JiYnh119/ZebMmWzZsuWh1+nXrx9OTk4EBgbmuyZfX1+Sk5Np3rw5n3/+OZCTOe3p6cnMmTPVDRWPHj1KdHQ0/fv3Nzg/MjKSPn36UKVKFfbt2wfkBJlHjBiBj4/Pk39YBVAUJUav13sX1C+ZzEIIIYQQQgghhBBCCAMlyzqBosm3z7J4+X95NU9PSkoKpUuXVo8TExNJTU0lKCiIkJAQtf29994jMTGRiIgIBg0axIIFCzAzMyv0dRo2bMjFixfz7UtNTWXPnj0sWbKE0NBQtX3lypU0bNhQDTADuLu7GwWYc3l5eVGqVCl27NhR6HU9KxJkFkIIIYQQQgghhBBCGKho3wxTU0uDQLOJiRm2Tu0wMTEtwpU9vtzSGS4uLvj7+/Ppp5+qfaGhofTo0YMmTZpw4sQJrly5AoCJiQlff/01Xbt2xdnZmaZNm6rnREVFGZT0SExMNLieTqdj165dBsHivDZu3Ejr1q1xcnKibNmyxMTEABAXF0ft2rUf694CAwMJCgp6tE8I6gAAIABJREFUrHOehRfrd4QQQgghhBBCCCGEEOKZM7cshWujj7l8JpKUGwmYW5aigp0PJcvWKOqlPVLmvducj9/EnevxmJhosLQwJyb6dzSm5vz222/07duXo0ePoigKISEhhIWFYWJiQteuXVmzZg0BAQEAan3lDz/80GD+Jk2a5FsuIzeYffHiRWrWrMlbb72V7/pCQkIYMmQIAD169CAkJIQ6deoYjevcuTMJCQk4OTmxfv36fOfKDX7v2bOn8B/QMyBBZiGEEEIIIYQQQgghhBEzi5JUdck/G/d5pcu6x/F988jKvAvo0WXfR6/XkRCzGOd6g2jYsCHXr1/n2rVrXLlyhYSEBDUYnJmZib29vRpkhpyMZhOTwhWDsLKyIjY2lrS0NFq1asVXX33F4MGDDcbcvHmT3bt3c+TIERRFQafToSgKM2bMwM3NzWCTv7CwMKKjoxkxYsRDr5ubzWxqWnShXimXIYQQQgghhBBCCCGEeCncSD6ALisD0Bu0p6de4u6dc8THx6PT6ShbtiwhISFMmDCBpKQkkpKSSE5OJjk5mbNnz/6jNRQrVowvv/ySWbNmkZWVZdC3du1a+vTpw9mzZ0lKSuL8+fPY29sTFRVFr169+PXXX9m0aZM6Pi0t7ZHXa9myJbdu3eLw4cP/aN3/hASZhRBCCCGEEEIIIYQQL4W0lAvos+8btGVkZtHjv9/S4D9v4evry/Lly9FoNISGhtK5c2eDsZ07dzbYjC8/D9ZkXrt2rdGYWrVq4enpabCZIOSUynjwml27diUkJAQrKyu2bNnCwoULqV69Og0bNiQoKIhx48Y98r4DAwM5f/68ejx+/Hg1WL1p0ybGjx8PQHJyMm3btn3kfI9L0ev1jx71DHh7e+ujo6OL5NpCCCGEEEIIIYQQQoiXz5Wzv3AxIdwo0GyiMadGrYGUKFO9iFb2YlMUJUav13sX1C+ZzEIIIYQQQgghhBBCiJdC2cremGhMAUVtUxQNFlZlsS5tX3QLe8lJkFkIIYQQQgghhBBCCPFSMDUrhku9j7AuXR1QUBQNr1XwwKnu+yiK8sjzxZORILMQQgjxirC2tjZqmzBhAjNnzjRqv3DhAh07dsTR0REHBweGDBlCZmYmV69exc7OjsuXL6tjBw0axJQpU4iMjKRUqVLUqlULZ2dnmjZtypYtW9RxJ06cwMfHB61WS82aNXnvvfeMrpuUlISVlRVarRYvLy8aNWrEiRMnAIiMjKRdu3bq2HHjxtG6dWsyMjLIyspi7NixODo6qnXRJk2a9I8+LyGEEEIIIcSLybJ4OZzrvk/tN6dQ683JVPfsjalZsaJe1ktNgsxCCCGEMKDX6+nSpQudOnUiISGBkydPkpqaSmBgIOXLl2f06NGMGDECgAMHDhAVFaUeN2nShIMHD3LixAm+/PJLAgIC2LVrFwCDBw9m2LBhxMbGcvz4cT766KN8r+/g4EBsbCyHDh2iX79+TJ482WhMUFAQv/76K2FhYVhYWDBu3DiSk5M5cuQIsbGxREVFcf/+/XxmF0IIIYQQQrwqFBMNiiLhz3+DfMpCCCGEMLB7924sLS0ZMGAAABqNhjlz5rB06VLS0tJ47733SExMJCIigkGDBrFgwQLMzMyM5tFqtYwfP54FCxYAcOnSJWxtbdV+Dw+PR64lJSWF0qVLG7TNmjWLbdu2sXnzZqysrEhLS+Obb75h/vz5WFpaAlCiRAkmTJjwpB+BEOI55+fnR/ny5XF3d3+s827cuEGzZs2wtrYmICCgwHE+Pj44Ozvj6emJi4sLAQEB3L59W+3XaDRotVrc3d1p37692peUlKSu6cGnL/LO/eAG6AWNFUIIIYR4UUiQWQghhBAG4uLiqFOnjkFbyZIlqVatGqdOncLExISvv/6arl27qmUxClK7dm3i4+MBGDZsGM2bN6dNmzbMmTPHIGCTV2JiIlqtFgcHB2bPns3HH3+s9v36668sXLiQbdu2qeU/Tp06RbVq1ShRosQ/vXUhxAuif//+hIeHP3SMnZ2dUZulpSUTJ07Mt0zQg1auXMnhw4c5fPgwFhYWdOzYUe2zsrIiNjaWo0ePUqZMGb766qvHvgchhBBCiJeJBJmFEEKIl1hSUhIbNmzg0KFDT3Xe3Ay+Dz/88KHj9Hq9+n7AgAEcP36c7t27ExkZSYMGDcjIyDA6J7dcRmJiInPnzjWo3VyjRg30ej07duwo8JrLli1Dq9VStWpVzp8//wR3J4R43jVt2pQyZco89nnFixencePG6lMPhWFubs706dM5d+5cvj9LGzZsyMWLFx97LeLpURSF//u//1OPs7KyKFeunJodHhwcTLly5dSa/VqtlmPHjhnsA+Dq6krfvn0NSi39/vvvNG3aFGdnZ2rVqoW/vz9paWkEBwermfDZ2dn069cPPz8/g7/zwDBr/cyZMzg6OrJ9+3Z1bh8fHxwdHalduzZvv/02R44cMThfq9XSo0cPo/vNvb/Ro0c/hU9PCCGEeDokyCyEEEK8hHQ6HX379qVmzZr069ePRo0akZ6ezq1btx55rqurKzExMQZtKSkpnDt3jho1aqhtJiYmmJg8/J8SBw8epGbNmupx5cqV8fPzY+PGjZiamnL06NGHnt+hQwd++eUX9bhChQps3bqVoUOHEhERAeQEns+dO8eff/4J5ASzY2NjKVWqFDqd7pH3K4QQj6LRaPDy8lKfzMil0+nYtWsXHTp0KKKVCcj58uDo0aOkp6cDsGPHDqpUqWIwxtfXl9jYWPXl6uoK/P3F5pEjR7hw4QKrV68G4MqVK3Tv3p1p06Zx4sQJDh48SOvWrdW/ayDni9T333+f+/fv8+2336IoSr7ru3DhAq1bt2bWrFm0atWKK1eu8M477zB58mQSEhI4cOAAY8aMITExUT3n+PHj6HQ6oqKiuHv3rsF8O3bswMnJiTVr1hgFtoUQQoiiIkFmIYQQ4iU0Z84c1q1bx71790hJSSEtLY3s7GwGDhz4yHNbtGhBWloaK1asAHKCKMOHD6d///4UK1b4HZkPHz7MxIkTGTRoEADh4eFqhtjly5e5ceOGURDgQXv27MHBwcGgzcnJifXr1/N///d/xMbGUqxYMQYOHEhAQAD37t1T15yZmVnotQohXgxHjx5l3bp1HD9+PN/+SZMmqZmqycnJ6vvcn0P/RN5gXnp6OlqtlooVK3LlyhXeeuutfzy/+Gfatm3Ljz/+CEBISAg9e/Z8rPM1Gg316tVTs9K/+uor+vXrR8OGDdUx3bp1o0KFCurx4MGDuXHjBitWrCjwS9dLly7RsmVLJk2apH4ZsWDBAvUL4FyNGzemU6dO6nFISAh9+vShZcuWbNy40WDOkJAQhgwZQrVq1fjtt98e6z6FEEKIZ0WCzEIIIcRL6KuvviItLc2oPSwsjCpVqmBra8vs2bMBCAoKwtbWVn0pikJYWBhr1qzB0dERJycnLC0tmTx58iOvGxUVRa1atXB2dmbQoEF8+eWXtGjRAoCffvoJd3d3vLy8aNWqFTNmzKBixYpGc+TWZPby8mLs2LF8++23RmPq1q3LsmXL6NChA4mJiUyaNIlKlSrh7u5OrVq1aNKkCf369aNy5cpATvAhOTkZgPHjx7Np0yYANm3axPjx4wv5qQohisrdu3dp0aIF9evXx8/Pjzp16tCvXz+jLM7AwEA1U7Vy5crq+39aM1mn03HkyBH1yYzcmsxnz55Fr9dLTebnQI8ePQgNDeXevXscPnyY+vXrG/SvWrXKoFxGbtZzrnv37rF//35at24N5Hyh8eD+BHn98MMPHDhwgNDQUExNTQsc169fPwICAujWrZvaFhcXR+3atR96P6tWraJHjx707NmTkJAQg3Xu3LmT9u3bG/UJIYQQRangvw2FEEII8cJKTU3Nt93c3JxDhw5hY2Ojtk2YMMFoXNWqVdm8efNDrxEZGWlw7OPjw507dwocP3v2bDWwXRA7Ozuj//HPO7+Pj4963LJlS86dO6ceT506lalTp+Z77tatW9X3X3zxhfq+Q4cO8pi7EC+A4cOHs3fvXvVpBYB9+/b9Kxt+3r9/n8DAQKpWrYqnp6dBX7Fixfjyyy/p1KnTI2vUi6cn68+b3Pl9C/fOH8fMxhb0ejw9PUlKSiIkJIS2bdsanePr68uCBQuM2nO/2Dxz5gxvv/220a9xQXI3tv3999/5z3/+U+C4N998k++///6hTwPVr1+flJQUWrZsybx584iOjsbGxoZq1apRpUoV/Pz8uHnzJmXKlGHLli00a9YMKysrunbtysSJE5k7dy4ajaZQ6xZCCCGeFclkFkIIIV5Cbdq0yfd/OO3s7ChbtmwRrEgIIZ6MXq9nxYoVBgFmgMzMTG7cuIGtrS1Lliwp9Hx2dnZ8/PHHBAcHY2try7Fjx/Id17t3bzw9PXF3d+fu3btGJQty1apVC09Pz3wzSnft2mXwpEhuaYO3335bbevevbvBOdHR0fj7+6vHWq1Wfe/v769uJPequn/rMhcWD+XOHz+ScfEkqYcj0Wdlkp50hA4dOjBixIjHKpWRd7PZmJgY9UkXNzc3o/0J8nJxcWH16tX4+voSFxdX4LhRo0ZRt25dunfvTlZWljr3gQMH1DH79+9n4sSJ6he1ISEhxMfHY2dnh4ODAykpKaxbt07t27lzJ3Z2dtSpU4cbN26we/fuQt+vEC8zjUZj8MTC1KlT+fzzzxkzZozBuNjYWPXJlDt37tC3b19q1KiBg4MDffv2Vf8sJiUloSgK8+fPV88NCAggODgYgP79+7N27Vrg4Rt95kpKSsLd3R3ISdZQFMUgqaNdu3ZqEoePjw/Ozs54enri4uJCQEAAt2/ffkqflBDPhmQyCyGEEC+hyZMnEx4ezp9//sm9e/cwMzPD3NycJUuWFLgxkRBCPI/0ej0ZGRn59pmZmXHhwoV8+5KSkh6rPa8Hn9R40INPi+QNEuRuaOrj45PvkxkFzZ37pIa3t7dBmaDY2Fj1fX7lg141N3d/T3ZGOuizcxr02YCeaz9+zYABY3jttdfw8PB45K/hg2xsbJg6dSpTpkyhQ4cOBAQEUK9ePd5++2219Mb69esNspYbNWrE119/Tbt27fj555+pVq1avnPPnTuXXr16MXDgQIKDgxk0aBD169enVatWal3m3BJX2dnZrF69miNHjqglnyIiIpg4cSK+vr5ERUVx/vx5LCwsAFi2bBkhISFSF1wI/i5llNfJkydp3bo1U6ZMUdtCQ0PVL6MGDhyIu7u7uhfJZ599hr+/P2vWrAGgfPnyzJs3j//+97+Ym5s/cg0PbvT5MLa2tkyaNIn27dvn279y5Uq8vb3JzMxkzJgxdOzYkZ9//vmRaxCiqEgmsxBCCPESsrW15fjx43z66ae0bt2aQYMGcfjwYRo3blzUSxNCiMdiYmKSbzkCRVFo3rx5EaxIFKX0pCN/B5jzyEq5QWWb1xg8eHC+5z1Yk3nv3r1GYzp16kRaWhpRUVFUqFCB0NBQRowYgbOzMzVr1mT79u1GJVrat2/P+PHjad26NTdu3Mj32oqisHz5ci5dusSoUaOoWLEiq1atYsyYMdSoUYNGjRqxdu1aAgICiIqKokqVKmqAGaBp06YcO3aMsLAwmjdvrgaYATp27MjmzZvJyMgocM+B5OTkfEuICPEqcHJyonTp0uzfv19tW716NT179uTUqVPExMTw6aefqn3jx48nOjqaxMREAMqVK0eLFi1Yvnz5I6+V30afD+Pl5UWpUqXYsWPHQ8eZm5szffp0zp07x6FDhx45rxBFRTKZhRBCiJdU2bJlGTt2bFEvQwgh/rGvv/6aRo0akZGRQUZGBpaWllhaWjJv3ryiXpr4l5lYFCP7nmEm+ZHxHUEBxezv4GveOv79+/enf//++c6Xm3kOOcHgvAGchg0bEhUVZXTOg/MNGDCAAQMGGI3Lm01tbm7OTz/9pB43aNCgwIzEffv2GRxrNBouX74M5GwkmFeZMmW4du0aUPCeA5UrVzbYm0CIl4ler+fatWsUL16c9PR0gxJDY8aMwdfXl549exIaGkr9+vXZt28fZcqUwdHRkU2bNqHVag1KzOWW3IiLi1NrtH/yySe0adMGPz+/h66lX79+BAUFGWz0+SiBgYF8+umnj3waQaPR4OXlRXx8PF5eXoWeX4h/k2QyCyGEEEIIIZ5rbm5uxMfHM2rUKNq1a8cnn3xCfHw8zs7ORb008S8rVa+dQTAZAI0ZxV0aYmL66EfZhRAvj59++gk7OzuqVatG2bJlMTExISoqitjYWGJjY/H19QVyNv5cu3Yt2dnZBqUyCqt69erUr1+fH3744aHjcjf6zC1/UxhNmzYFYM+ePY8cq9frCz2vEEVBMpmFEEIIIYQQz71KlSoZZGqKV1PJum3IvHGR1EO7wdQMdFlYVq1Jubb/LeqlCSH+RUeOHKFz585GAd2uXbsaPDUAULVqVezt7fn5559Zt26dugmrq6srsbGxZGdnY2KSk4OZnZ1NbGwsrq6uBnOMHTuWbt268cYbbxS4plGjRvHdd9/RvXt3Nm7ciKlp4UJugYGBBAUFPXS8TqfjyJEj6oaFQjyPJJNZCCGEEEIIIcQLQVFMKNfmPaoGLKRit1HYvjeHSr3GY2JuVdRLE0L8i2bOnJnvprB79uzh9OnTRu09e/Zk2LBhVK9eHVtbWwBq1KhBrVq1CAoKUscFBQVRu3ZtatSoYXC+i4sLrq6uBhu95mfu3LmULFmSgQMHFjrzuGXLlty6dYvDhw/n23///n3GjBlD1apV1RIeQjyPJMgshBBCCCGEEOKFYmr9GlZ2HpiVrljUSxFCFIGTJ0+i0+mM2jMyMmjZsiVarZbRo0er7d27dycuLs6oVMaSJUs4efIkDg4OODg4cPLkSZYsWZLvNQMDA7lw4cJD1/XgRp+FFRgYyPnz5w3aevfujaenJ+7u7ty9e5eNGzeqfW3btiU5ORmgwE0/hfi3KUVV08Xb21sfHR1dJNcWQgghhBBCCCGEEC+mUaNGMW/ePDIzMw3aLS0tOXfuHOXKlSuilQnx8lIUJUav13sX1C+ZzEIIIYQQQgghhBDihTF06FCKFy+u1lIGKFasGO+++64EmIUoIhJkFkIIIYQQQgghhBAvjMqVKxMTE4Ovry82NjY4OjoyY8YM5s2bV9RLE+KVVbitLoUQQgghhBBCCCGEeE7Y29vzww8/FPUyhBB/kUxmIYQQQgghhBBCCCGEEE9MgsxCCCGEEEIIIYQQQgghnpgEmYUQQgghhBBCCCGE+BdZW1sbtU2YMIGZM2cC0L9/f6pUqUJGRgYA169fx87ODoCkpCSsrKyoVasWNWvWpF69egQHB6vzBAcHU65cObRarfo6duxYvuvYsGEDiqIQHx9v0J6QkEC7du1wcHCgTp06NGvWjF9++SXfOfbs2UO9evVwcXHBxcWFxYsXP+7HIV4CUpNZCCGEEEIIIYQQ/8/encdFVfV/AP8MAwgIrijuMgyg7ANiioGBuIUr0iOKCW75aKhliUsaomK5lVlqau6PNhgqLqRUivjg+gg2LpCJ4BCK4YKKbCJwf3/w48Y4gyJlqH3er9e8Xsy555x7uCoy3/u930NELxipVIqNGzdi4sSJWsfkcjl+/vlnAEBGRgaGDBkCQRAwevRoAEBgYCBWrlz51HMolUp4enpCqVRi3rx5AIDi4mL069cPy5Ytw8CBAwEAFy9eRFJSErp3764x/vfff0dQUBD27NkDNzc33L59G3369EHr1q3Rr1+/P/X908uFmcxEREREREREREQvmPfffx/Lly9HaWnpE/tZWVnh888/x5dffvlM8+fn5+PYsWPYsGEDoqKixPbt27fDw8NDDDADgKOjI0aNGqU1x6pVqzBq1Ci4ubkBAMzNzbFkyRIsWrTomdZCLz8GmYmIiIiIiIiIiF4w7dq1g6enJ/7zn/88ta+bm5tGyYsdO3ZolMsoKirSGrN371707dsXtra2aNq0KZKTkwEAKSkpYtD4aVJSUtCpUyeNNnd3d6SkpNRoPL06WC6DiIiIiIiIiIjoOcvJycH+/fshCAIEQajRmFmzZmHQoEFPLT3x+Hw1KZehVCrx3nvvAQCGDRsGpVKpFTAGAH9/f6SlpcHW1ha7d++u0brpn4dBZiIiIiIiIiIioudoy5YtmDBhAqRSKQCgsLAQ33zzDd55550njrOxsYFCocB33333xH4///wz7Ozsarye3NxcxMfH48KFC5BIJCgrK4NEIsHSpUvh4OCgsclfTEwMkpKSMG3aNK157O3tkZycjEGDBoltycnJcHBwqPFa6NXAchlERERERERERETPybVr1zBx4kQUFxejoKAABQUFAIApU6ZArVY/dfzs2bOxbNmyao+r1WpMmzYNkydPrvGadu7ciZEjRyIzMxNqtRpZWVmQyWRITExEUFAQjh8/jn379on9CwsLdc4TGhqKzZs3Q6VSAQDu3LmDGTNmYPr06TVeC70aGGQmIiIiIiIiIqJnJpVKoVAo4OLiAjc3N5w4cQJARdDT2NgYCoUC9vb2CA4OxqNHjwAACQkJaNiwoUa94EOHDgEAJBIJPvzwQ3H+ZcuWISIiAgAQEREBExMT3Lx5UzxuamqKO3fuiPO0aNECrVu3Ft+XlJQAAPbs2QOJRKJRsxgA0tLS0L9/f8jlcnTq1Ak+Pj4aGbyPe//999G6dWuUl5c/03XavXu3zvIYxcXFUCgUaNOmDT7//PNqxzs4OGjVSE5PT4erqyvs7OwwdOhQTJkyBaNHjxaPP16TufLPppJSqYS/v79GW0BAAJRKJYyNjREbG4s1a9bAysoKHh4eiIyMxJw5c7TW1rJlS2zbtg3vvPMOOnbsiG7dumHMmDEYMGAAAGDfvn0IDw8HAGRnZ8PPz08c6+fnh+zs7Gq/b3rJVNaB+btfnTp1EoiIiIiIiIiI6OVUv3598eu4uDihe/fugiAIwtWrVwUHBwdBEAShtLRU8PHxEbZt2yYIgiAcOXJE6Nevn8756tWrJ1haWgq3bt0SBEEQli5dKsydO1cQBEGYO3eu0LZtW2H69Ok6z1/ZZ+nSpVrzDh06VPD09BTCw8PFtqKiIsHGxkbYu3ev2HbhwgVh06ZNOtdWVlYmtGvXTujSpYsQHx+vs091li1bJhgaGgoANF76+vrCwoULn2mul0lkZKRgb28vODk5CS4uLsKpU6cEQRCEN954Qzhz5owgCILQvn17wdHRUXBxcRFcXFyEyZMnC4IgCCEhIYKxsbGQl5cnzvfee+8JAMS/H3p6euI4FxcX4dNPPxXnrxp3PHPmjPDGG28IgiAIBQUFQlBQkODo6Cg4ODgIr7/+uvDgwQOd6//5558FAMLBgwc12n///Xdh+PDhgkwmE9zc3ISuXbsKu3fv/guu2IsNQJLwhFgvM5mJiIiIiIiIiOhPycvLQ+PGjbXapVIpXnvtNVy/fv2pc+jr62P8+PFYvny5zuNjxozBjh07kJubW+N15efn49ixY9iwYQOioqLE9u3bt8PDwwMDBw4U2xwdHTFq1Cid8yQkJMDBwQETJ06EUqms8fkBYMCAAWIt5qoMDAw0ahm/Sk6ePInY2FicPXsW58+fx6FDh9C2bVudfY8cOQKVSgWVSoUvv/xSbLe2tsbevXsBAOXl5YiPj0fr1q3F48bGxuI4lUqFmTNnisdu3ryJgwcPap1rxYoVsLCwwIULF3Dx4kVs2LABBgYGOtelVCrh6emp8ectCAIGDx6M7t27IyMjA8nJyYiKisK1a9ee7QK9ghhkJiIiIiIiIiKiZ1ZUVASFQoGOHTti3Lhx+Pjjj7X6FBcX4/Tp0+jbt6/YlpiYqFHKIT09XTwWGhqK7du34/79+1pzmZqaYsyYMVixYkWN17h371707dsXtra2aNq0KZKTkwEAKSkpWiUonkSpVGL48OHw9/fH999/L5b/qAlbW1vMnDkTxsbGkEql0NPTg4mJCT744INXdoO8GzduwNzcHPXq1QMAmJubo1WrVs80x7Bhw7Bjxw4AFUH+119/Hfr6+jUaGxYWhoULF+pcV9VAdYcOHcQ1ViUIAqKjo7F582b89NNPKC4uBgDEx8fD0NAQEyZMEPu2b9/+mephv6oYZCYiIqI/JScnB0FBQbCyskKnTp3g4eGBmJgYjT666teNHj1a48OFpaUlLCwsAFTU3Kusp+fo6ChuOlK13d7eXiurIDIyEjY2NrC1tYWPjw9SUlLE45aWlvDy8tJYV+X81fniiy9gZGSk8SGnso6gq6srOnTogO7duyM2NrYWV46IiIjo5VJWVobvv/8e06ZNw2effQYjIyOoVCpcunQJcXFxCA4OFmsPp6enQ6FQwMLCAi1btoSzs7M4j5eXl0YGqlwuF481aNAAwcHBGhmtVU2ZMgVbtmzBgwcParRmpVKJYcOGAagIWlaXhezv7w9HR0cMGTJE61hJSQkOHDiAwYMHo0GDBujSpQt++OGHGp2/Unh4OE6ePInp06dj+vTpSExMRGRk5DPN8TLp3bs3srKyYGtri3fffRdHjx6ttq+Pj4/4maBqFrutrS1u3bqFu3fvavw5Vqq8yVH5qgxIA4CHhwcMDQ1x5MgRjTFjxozB4sWL4eHhgTlz5iAtLU3nmk6cOAGZTAa5XA5vb298//33AJ795sQ/Sc3C/0REREQ6VD4uFhISgm+//RYAkJmZqbETdXl5OWJiYtC2bVscPXoUPj4+AIBNmzZp9PH29kZwcLDYNnXqVEybNg2//PILvLy8xE1eKtvT0tLQqVMnvPXWWzAwMMCqVatw4sQJnDt3DiYmJvjxxx8xcOBApKSkwMjICADw4MEDZGVloW3btvjll1+e+v0plUp07twZu3fv1thIxcvLSwwsq1QqDB48GMbGxvD19a3tpSQiIiJ6oRUXF6Nnz560KB0VAAAgAElEQVQ4d+4c8vPzYWRkhOLiYiQmJsLLywseHh64ffs2bt26BQCQy+VQqVS4ffs2Xn/9dezbt0+jNMWTvP/++3Bzc9P4/atSo0aNEBQUhFWrVj11ntzcXMTHx+PChQuQSCQoKyuDRCLB0qVL4eDgoLHJX0xMDJKSkjBt2jSteX744Qfcu3cPTk5OAIDCwkIYGxujf//+Nfp+Krm4uMDFxeWZxrxM8m7/iqxf96O44CYMDE1xMOZLXFKXISEhAYGBgVi0aJHOciRHjhyBubm5zjmHDBmCqKgonD59GmvXrtU4Vlkuozpz5sxBZGQkFi9eLLYpFApkZGTgxx9/xKFDh9C5c2ecPHkSdnZ2GmMfvzmxdetWBAQEaJ0jNDQUx44dg6GhIc6cOVPtWv4JmMlMREREtVaTx8VqUr/uk08+QbNmzTBu3DitY3Z2dtDX18ft27c12m1sbGBiYoK7d+8CABYvXoyVK1fCxMQEQEX2RLdu3bB9+3ZxzNChQ8UMh8pHHquTnp6O/Px8REZGPrHunkKhQHh4OFauXFltn5dB5e7wDg4OcHFxwWeffSZmnle3C7xardbKBI+IiMCyZcsQGhoqZpxX7i5fmWFSdR6FQgFzc3MEBgbqXFdpaSmaNWumUWOvsv2jjz6CjY2NOI+uRyIB4P79+wgODoa1tTXkcjmCg4N1PoJLRERE1Vu7di3Onj2L/Px8ABDLBwwdOhTl5eW4dOkSysrK0LRpU41x5ubmWLRoET799NMan6tJkyYYOnQoNmzYoPP4Bx98gLVr16K0tPSJ8+zcuRMjR45EZmYm1Go1srKyIJPJkJiYiKCgIBw/flwjOaKwsFDnPEqlEuvXr4darYZarcbVq1fx008/Vdv/n+hBbjquqLaguCAHgIBHJQ/we3ocOrYXMG/ePKxcuRK7du165nkDAwPx8ccfo1evXtDTe7YwZo8ePVBUVIRTp05ptJuammLIkCFYvXo13n77bRw4cEDjeFlZGXbt2oX58+fD0tISkydPRlxcHB48eAAHBwecPXtW7Ltq1SocPnxYvLnyT8YgMxEREdWKIAg1elzsafXr/ve//2H9+vX45ptvdI4/ffo09PT00KxZM432s2fPwsbGBs2bN0deXh4KCgpgZWWl0cfd3V2jZEZAQAB2794NANi/fz8GDBhQ7bqjoqIwbNgweHl54ddff0VOTk61fd3c3HDp0qVqj78MKjNBUlJS8NNPP+HgwYOYN2+eePzxx1p79uz5xPlWrVoFlUqFAwcOiJlMKpUKgYGBGvMcPHgQxsbGOms4AsBPP/0EW1tbREdHi4/fAhWZKdnZ2bhw4QJUKhUSExOrrY04duxYWFlZ4cqVK0hPT4dMJtN5Q4OIiIiqt3XrVhQVFWm137x5E/b29ggMDMSWLVt0bnA3ePBgFBYWIjExEYB2TeadO3dqjfnwww+1kgwqmZubw9/fHw8fPnzimpVKJfz9/TXaAgICoFQqYWxsjNjYWKxZswZWVlbw8PBAZGQk5syZo9G/sLAQcXFx6Nevn9hWv359eHp6Yv/+/UhKStL4vUKhUIhfjxs3DklJSU9c46si+0ochPI/fhdTZ+VCnXUTv1+Nh1BeBpVKhfbt2z/zvO3bt8fChQvx7rvv1mpdc+bMwZIlS8T3x48fF5NUSkpKkJqaqrWuw4cPw9nZGVlZWVCr1cjMzERAQABiYmLQo0cPFBcX4+uvvxb782ZDBZbLICIiomeSnp6OiRMnIj4+HhKJBB06dEBeXh4aNGig9bhYZf26zz//HGZmZmL9uspHC/Pz8/H2229jw4YNaNKkicZ5li9fjm3btsHMzAw7duyARCIR2zdt2oTLly9j//79z7T2pk2bonHjxoiKioKdnZ2Y9ayLUqlETEwM9PT0EBAQgOjoaEyaNEln36rBz1dB8+bNsW7dOnTu3BkRERHP7TyCICAkJARhYWHV1sZWKpV477338PXXX+PkyZPo1q0bCgsL8c0330CtVoulUMzMzHSu9cqVK0hOTtao0RceHg5ra2ukp6dr1IAkIiKi6hkYGOhsNzY2RkxMjEa5AUtLS1y8eFF8L5FIcO7cOfF9dU8UVWZJA4CFhYVG8O7x/+c///xzfP755xptj/d5vB4vUFHTuVLHjh21slgfZ2JigtzcXK32ysQFAFi/fr34ddXyDVXbX3VFBTc13xeXYOmqeDwoeIj6DfbAxsYW69at0znWx8dHvDnh7OyMrVu3ahz/97//rfuc/1+TuVLfvn2xaNEijT5+fn4aySqVn2UEQUB5eTn69eunVQajupsTX3/9NYKDg7Fnzx5MnToVS5YsQbNmzVC/fn2xJEd2djbGjRsn/r3y8/PD+vXr0apVK4SHh8Pd3b3GZWNeNgwyExERUY3dvXsXXbp0wd27d8VSCqmpqejVqxdOnTqFVatW4fbt23B3dwfw9Pp1kydPxqBBg3TWMq6svVxd+759+zB27Fikp6ejQYMGqF+/PjIyMjSymZOTk/HGG29ojA8MDERoaCg2b95c7fd54cIFpKWloVevXgAqshxkMlm1Qeaff/5Zq47by87KygplZWViLezKjKNKu3bt0pmp9CyWL18OfX39anfjLi4uxqFDh7B27Vrcu3cPSqUS3bp1w5UrV9CuXTuYmZk99RypqalQKBQaa60sDZKSksIgM9E/1LVr1xAaGorU1FSUlZXBz88Pn332GerVq4eEhAQsW7ZMY1PXwYMH4/fff9d65Hrbtm1YsmQJysrKoK+vj86dO2PZsmVo1KgRvL29cePGDRgbGwMArK2tsXPnTkRERGDJkiVQq9Vo3rw5gIpHt6sG1ypZWlqiU6dO4iPmO3fuRGxsLCIiIuDp6YnffvtN4/FxhUKBtWvXokuXLn/5NSN65513cPHiRRQUFGi0W1hYoGPHjnW0KnpRGJk0Q8H9TPG9nU0LbPwiCHrSelD4zINE74/fxRISEsSv1Wq1zvmq+129av+ysjKdfarOD1R8JqgUHByssQ+MLlX3jqk0cOBAMTjcsmVLREVF6RzbqlUrjRsXVb+eP3/+E8/7smO5DCIiIqqxTZs2obCwUAwwAxXZqCqVSvzgXTXj5En163bu3Ilz585VW0f3aQYOHAh3d3ds2bIFABAWFoYpU6aIj3EeOnQIx44dQ1BQkMY4f39/TJ8+HX369Kl2bqVSiYiICHHd2dnZyM7ORmZmplbf8+fPY8GCBQgNDa3V91FXyh+VIe9iDm4eSse9n7Of2l/XLvCV2eWPq669qnPnzuGLL77Apk2bqu0fGxsLHx8fGBsbIyAgAHv27NH5YWLTpk1QKBRo27YtsrKynnpuIvpnEwQBQ4YMweDBg5GWloa0tDQUFRVh+vTpOvvfu3cPycnJuH//PjIyMsT2uLg4LF++HAcPHkRKSgrOnj2Lbt26aZRX2r59u/hzs2o5AHNzc3z22Wc1Wm9ycjJSU1M12iwtLdGuXTux9AAAXLp0CQ8ePGCAmZ6bUaNG4c0334SJiQnq1asHMzMzNG7cGDExMTX6v59eba2s+0Kip5ntrqdnAAtLb40AM726GGQmIiKiGjt37pzOWnxlZWUYOHAgXnvtNYSEhGDx4sVPrV83e/Zs3Lp1C6+99ppGTT5d81cnPDwcn3/+OcrLyzF58mR07twZTk5O6NChAxYsWIC9e/eKGWSVzMzMMGPGDBgaGlY7b1RUlNYjcv7+/mLGQmJiIlxdXdGhQweEhobiyy+/FLOx9+3bh/DwcAAVj8v5+fmJc/j5+SE7++kB3eettKAEmZvO4ubhdNxX3cDto2oIj8pRkvvHDYKMjAxIpVIxy06Xpk2bijXtKuXm5la7O3iloqIijBgxAl9//TUsLCyq7adUKnHo0CExk+/OnTuIj4+HtbU1fvvtNzx48AAAMHr0aKhUKjRs2FArCG1vbw+VSqVxY6S8vBwqlQr29vZPXCcRvZri4+NhZGSE0aNHA6h4umH58uXYunWrzmzi3bt3Y8CAARg2bJhG5trChQuxbNkytG7dWpxnzJgx6NChw1PXMGbMGOzYsUPnI/iP+/DDD3XekB0+fLjGeir3EiB6XqRSKaKjo5GYmIjFixfjm2++wfXr1+Hs7FzXS6MXQIOm1pC7jEQ9k4rfA/UN6qOldR+0tNJ+YpFeTZK6qiHo7u4u/FOKnxMREb0qVqxYgY8++khrc4v69evj0KFD6Nq1ax2tjJ7F7wcu48Glm8AfcVcoZvfGpU0n0TbIBbdu3cKIESPg4eGBefPm6Xx0vJK7uzuWLFmCHj16IDc3F127dsXBgwfFMhRqtRr9+/fXqMtYmfW9atWqateYl5cHa2trZGVloV69egAqMpYTExOxceNGTJ8+HTk5OVi7di2MjIxQVlYGOzs7/Pjjj7C0tNSYa8iQIVAoFGLwf/78+Th37lytdjgnopebIJTjyy+/glqtxvLlyzWOubq6YtOmTbh3757Gz7xevXohPDwcFhYWCAgIwIULFwAATZo0wdWrV9GwYUOd53q8XEavXr2wdOlSREREwNTUFIWFhSgrK8O8efOeWC7j9OnT8Pb2xv79+6FSqRAbG4vNmzcjJycHCoUCWVlZ0NfXh52dHaKjo6utcU9E9HcRBIHZ7a8giUSSLAiCe3XHmclMRERENRYSEgITExON+o/16tWDk5MTH899iRSk39EIMANA8aOH6B32FhzsHdCzZ0/07t0bc+fOFY9Xtwv81q1bsWDBAigUCvTo0QNz5859Yp3j7OxsrF69GvHx8RrzjRgxQqNf5e7dlQFmABg0aBD279+Phw8fYuHChWjZsiUcHR3h6uoKLy8vhISEoFWrVlrn3LBhAy5fvgy5XA65XI7Lly9jw4YN4vHqdoFfs2YN1qxZU4MrSkQvupLb15D9n49x9dOhuHNoMwrTf0Z5ccFTx+Xk5CAtLQ2enp6wtbWFgYGBxk2zShcuXIBCoYBcLtfYaLRquYylS5dqjJkyZQq2bNkiPpVRHalUirCwMHz66aca7RYWFnB0dMThw4ehUqmgr6/PADMRvRAYYP5n4sZ/REREVGONGjXC6dOnMWnSJBw6dAiGhoYYMWIEli1bxl8mXyY6/qwuLTkK6Ekgn+IBPX3NPARvb+9qd4G3t7fXuXN7pcd3l2/VqhVq8iRdSEgIQkJCNNqaNGmCW7duie8XLVqktYO4Lo0bN8a2bduqPV7dLvATJkx46txE9OIrK8xD9uaPUP6wEIAAG3NTxCWk4Ma389Fq9CJIJBLk5eXh999/R4cOHXD69Glx7HfffYe7d+9CJpMBqHjKQqlUYuHChXBwcMDZs2fh4+MDJycnqFQqTJo0qcZlnxo1aoSgoKAnPtVRaeTIkfj000+1gsiVJTMsLCwwfPjwml8UIiKivxgzmYmIiOiZWFlZ4cCBAygpKUF+fj7Wrl0LMzOzul4WPYMGDs0hkT4WaJYAJpaNtALMREQvuwfnDkMoewSg4gZXN3kzFJWUIuqH/+JhdhrKysrw4YcfYtKkSVp1/JVKJeLi4sSNYJOTk8U6yLNmzcK0adNw7do1sf+z7CsAAB988AHWrl2L0tLSJ/YzMDDA1KlTtUp8DBkyBAcOHMCOHTtYj5mIiOoUP0UQERER/cM0fb09DJubQmKgB4lUDxIDKQwaGsGij01dL42I6C/3MCcTQmmJ+F4ikeDroK44eCELDt16oGnTptDT08Ps2bM1xqnVamRmZmrsNyCTydCwYUOcPn0afn5+mDJlCt58803Y29ujW7dukEql6NOnj9h/xIgRYlmgnj17aq3N3Nwc/v7+ePjw4VO/j7Fjx2oFoxs1agQPDw9YWFjAyspK57ikpCSMGzdOfF9diSAiIqI/gxv/EREREf0DCYKA4ut5eHizAAaNjWHSvhEkeix5QkSvnnun9+NughJCqWYgV2JQD61GLkDy1RwMHz4cMTExcHNzq6NVEhERvdietvEfazITERER/QNJJBIYt2kI4zYN63opRETPlZmzD+4d31VRMkOo2PVUIjVAvRZy1GspR7eWcmRmZtbxKomIiF5uLJdBREREREREryypsSlaj1kCE2s3SKQGkNQzhplrL7QYNvvpg4mIiKhGmMlMRERERERErzSDRs3RYuisul4GERHRK4uZzERERERERERERERUawwyExEREREREREREVGtMchMRERERERERERERLXGIDMRERERERERERER1RqDzERERERERERERERUawwyExG9wkxNTTXeb968GZMmTQIAREREoHXr1lAoFOLr3r17SEhIQP/+/cX+zZo1g6urK2xsbNCnTx+cOHFCnG/UqFGQyWTi+G7dummdpypLS0s4OTlBoVDAyckJe/fuFY9JpVKNtSxatAgA4O3tjQ4dOsDFxQWdO3eGSqUSx+Tn52PixImQy+Vwc3NDp06d8M033/xFV4+IiIiIiIiIakK/rhdARER1Z+rUqZg2bdoT+wQGBmLlypUAgCNHjmDIkCE4cuQI7OzsAABLly7FW2+9VeNzHjlyBObm5vj111/Ru3dvDBo0CABgbGysEUCuavv27XB3d8emTZsQFhaGn376CQAwbtw4WFlZIS0tDXp6erh16xY2btxY47UQERERERER0Z/HTGYiIqoxHx8fjB8/HuvWrfvTc+Xl5aFx48bPNMbDwwPXr18HAKSnp+N///sfIiMjoadX8d9Zs2bNMGPGjD+9NiIiIiIiIiKqOWYyExG9woqKiqBQKMT3ubm5GDhwoPh++fLl2LZtGwCgcePGOHLkyFPndHNzw9q1a8X3YWFhiIyMBAA4ODhg+/btTxzv4+MDQRCQkZGB7777rtq1zpo1C4GBgRpj4+LiMHjwYABASkoKXFxcxAAzEREREREREdUNBpmJiF4hmRl3kHDwV9y+mQ/z5qYwMtIsQbF582YkJSWJ72tSLuNxgiBovK9tuYz09HT4+vrC29sbpqamTyyXMWLECJSUlCA/P7/aPgsXLkR0dDRu3ryJ7Ozsmn9DRERERERERPSnMP2LiOgVkf7rLWxfdxqZGbkoyC9BZkYuSh+VIf3XW3/peX7++WexHvOfIZfLYWFhgdTU1Kf23b59OzIyMhASEoLJkycDAOzt7XHu3DmUl5cDAGbPng2VSoW8vLw/vTYiIiIiIiIiqjkGmYmIXhE/7E1B6aNyne1/laNHj2LdunV45513/vRcN2/exNWrV9G+ffsa9ZdIJFiwYAFOnTqFS5cuwdraGu7u7pgzZw7KysoAAMXFxVqZ1kRERET08rh27RoGDRoEGxsbyOVyvPfeeygpKQEAFBYWYsSIEXBycoKjoyM8PT2Rn58PAJBKpVAoFHB0dMSAAQNw7949jXm/+OILGBkZ4f79+2JbQkICJBIJ1q9fL7apVCpIJBIsW7ZMa20RERE6201NTcWvL1++DD8/P9jY2MDNzQ1Dhw5FTk4OEhIS0L9/f41xo0aNws6dO2txlYiIXjwMMhMRvSJu5+Q/UztQUZNZoVCIL7VardVnx44dUCgUsLW1xSeffIJdu3ZpZDKHhYVpzFH5IWDz5s1o06aN+Lp27RqAiprMCoUCPj4+WLRoESwsLAD8UZO58jVz5kyttRgbG+PDDz/E0qVLAQDr16/HnTt3xIBzr169sGTJErF/1RrP48aNE0uFrFmzBmvWrKn2uhARERHR308QBAwZMgSDBw9GWloaLl++jPz8fMyePRsAsGLFClhYWODChQu4ePEiNmzYAAMDAwAQS69dvHgRTZo0wapVqzTmViqV6Ny5M3bv3q3R7ujoqLFPiFKphIuLS63WX1xcjH79+mHixIlIS0vD2bNn8e677+LWrb/2yUIioheRpK4yvtzd3YWqdUGJiOjPWTb3RxTml2i11zc1xIfzetfBioiIiF5NUqkUTk5OePToEfT19REcHIypU6dCT08PCQkJWLZsGWJjY8X+gwcPxu+//45Tp06JbRERETA1NdXaG+HatWsIDQ1FamoqysvL0b9/fyxduhSGhoYAgP/973+YPn06rl+/DjMzM7Rs2RKLFi2Ck5OTxjw5OTkYO3YssrKy8OjRI1haWuLAgQNQq9Wws7NDhw4dxL4ffPABgoODYWlpibZt2yIxMVE8plAoUFpaiosXL/6l15BIl8OHD2PevHn473//K7bl5eVBJpMhKysLM2fORPv27fHhhx9qjTU1NRWzmtesWYPz589j9erVAID09HQMHDgQq1evxsKFC/Hjjz8CgPjvNS8vD9HR0WjevDkUCgX8/PzQtGlTrX+f1f27rTz3xo0bkZCQgK1bt2qtT9fPhlGjRqF///7PtL8JEVFdkUgkyYIguFd3nBv/ERG9Ijx7WONI3K94VFImthkYSvF6D+s6XBUREdGrp+pmtTdv3kRQUBDy8vIwb948rb737t1DcnIyTE1NkZGRASsrq2rnrczinDhxIvbu3YuysjKMHz8es2fPxtKlS5GTk4OhQ4fi22+/Rbdu3QAAx44dQ3p6ulaQOTw8HL169cJ7770HADh//rx4TC6XV7uR7oMHD5CVlYW2bdvil19+ebYLQ/QnpaSkoFOnThptDRo0QLt27XDlyhWMGTMGvXv3xs6dO+Hr64uQkBDY2Nho9C8rK8Phw4cxduxYsS0qKgrDhg2Dl5cXfv31V+Tk5IhP0wHAW2+9hejoaLi6usLNzQ316tWr1fovXryotf6qEhMTNZ60++2337RKaBARvaxYLoOI6BXRpbsM3bzlMDCUwsBACgNDKbr5yNGlu6yul0ZEpGXhwoVwcHCAs7MzFAoFTp8+DQAoLS3FRx99BBsbG7F8zsKFC8VxVeteVufgwYNwd3eHvb09XF1dtTLeFAoFhg0bptE2atQoyGQyKBQKuLi44PDhw+Ixb29vdOjQAQqFAnZ2dli3bp147P79+wgODoa1tTXkcjmCg4PFep9qtRoSiQRfffWV2H/SpEnYvHlzzS8UvfCaN2+OdevWYeXKlTr3Bdi9ezcGDBiAYcOGISoq6olzxcfHw8jICKNHjwZQkTG9fPlybNy4EYWFhVi5ciVCQkLEADMAeHp6YvDgwVpz3bhxA23atBHfOzs71+j7GTp0KHbs2AGgomzA8OHDazSO6M/IyMhAeHg4duzYgfT0dHG/jccpFApkZGQgLCwMubm56Ny5s3gzpLL0WosWLZCTk4NevXqJ45RKJYYNGwY9PT0EBAQgOjpaY96hQ4ciOjr6uf+d9/LygkqlEl8DBw58buciIvq7MchMRPSKkEgkeKOPLcIW9Ma7M7wRtqA33uhtC4lEUtdLIyLScPLkScTGxuLs2bM4f/48Dh06hLZt2wIA5syZg+zsbFy4cAEqlQqJiYl49OhRjee+ePEiJk2ahG3btiE1NRVJSUmwtv7jiY5ffvkFZWVlSExMREFBgcbYpUuXQqVS4YsvvsCECRM0jm3fvh0qlQrHjx/HjBkzxPrzY8eOhZWVFa5cuYL09HTIZDKMGzdOHNe8eXOsWLFC7E+vJisrK5SVleHmzZtaxyqDVsOHD4dSqXziPE/L4kxJSYGbm1uN1hQaGoqxY8fCx8cHCxcuRHZ2tngsPT1dYx+EquUxAgICxJq1+/fvx4ABA2p0PqLaiomJgaOjIxYtWoQTJ07g+++/h6+vr/izPy8vD7/99pv4s9zU1BRDhgzB6tWr8fbbb+PAgQMA/njCIDMzE4IgiDWZL1y4gLS0NPTq1QuWlpaIiorS+rfYokULGBgY4KeffoKvr2+tvxcHBwckJyfXejwR0cuMQWYioleMvr4UDRsbQ19fWtdLISLS6caNGzA3NxcfRzY3N0erVq1QWFiIb775Bl999RWMjIwAAGZmZoiIiKjx3EuWLMHs2bPRsWNHABWZoBMnThSPK5VKjBw5Er1798bevXt1zuHh4YHr16/rPJafn4/69etDKpXiypUrSE5OxscffyweDw8PR1JSEtLT0wEAzZo1g6+vL7Zs2VLj74FeTMXFxUhISMCJEydqPCYnJwdpaWnw9PSEra0tDAwM/tLaxl26dIGdnZ1YEqOqPn36ICMjA++88w4uXboEV1dXcfOxynIZlS8vLy9xXNOmTdG4cWNERUXBzs4OJiYmf9l6iR5XXFyMkJAQFBUViUHl8vJynDx5Etu2bUNZWRk+/PBDjBo1CiYmJjh+/Dju3r0LACgpKUFqairat2+vMaeJiQm+/PJLfPbZZygtLYVSqURERATUajXUajWys7ORnZ2NzMxMjXHz58/H4sWLIZXW/nfooKAgMVBe6b///S9rmhPRPwKDzERERET0t+rduzeysrJga2uLd999F0ePHgUAXLlyBe3atYOZmVmt535aPcwdO3Zg2LBhT8wqjYuL0yo/MGLECDg7O6NDhw74+OOPIZVKkZqaCoVCoRGQkEqlUCgUSElJEdtmzJiBZcuWVfv4N7349uzZg+bNm2PQoEHo27cvCgsLcebMGfF4RkYGpFIpmjdvrjHuu+++w927dyGTyWBpaQm1Wv3EbGZ7e3utLMiqWZwODg44e/aseOz06dNYsGCBWKLlcU2aNEFQUBD+85//oHPnzhqbqT1JYGAgQkNDWSqDnrtTp07pfOqupKQEoaGhsLW1hZGRET755BMAFVn4b7zxBpycnODq6gp3d3cEBARojXd1dYWzszOUSiWioqLg7++vcdzf31+rfE23bt10lp55XGRkJNq0aSO+qjI2NkZsbCy++uor2NjYwN7eHqtXr0azZs2eOm94eDj27dsHANi3bx/Cw8MBANnZ2fDz83vqeCKiusaN/4iIiIjouSsrLsXtIxl4cPk2IACx877Fr4bX8d9TxxAYGIhFixZplQHYtGkTVqxYgTt37uDEiRNiSY3aSkpKgrm5Odq1a4fWrVtjzJgxyM3NRZMmTQAAYWFh+Oijj3Dt2jWcPHlSHJeZmQmJRIJ69epBLrOIrG0AACAASURBVJcjMjISffv2feK5Dh06hJkzZ+Lq1avo2LEj6tevD5lMhmbNmsHdXXtT7vfffx/R0dHIysqCnt4feSBxcXEIDw9HXl4ejIyM0KFDByxduhTt2rXTmuOLL75AkyZNMGLECK1A+7Vr1+Dr6yvW2qWaU6vVCAoKQlFRkUZ77969kZ2djfz8fEyYMAGTJk3SCpYplUrExcXBw8MDAHD16lX07NlTo854Vb6+vpg5cya2bt2K4OBgrSzO0NBQdOnSBX369BHrMhcWFuqcKz4+Hl27doWJiQkePHiA9PR0nX9vdPH398eNGzfQp08fjTIbRH81IyMjnbXMAaBHjx6IjY3VaAsODkZwcLDO/vn5+Rrv9+/fDwAYOXKkVt/PP/9c/Nrb21vreHVP0EREROg8VvXcHTt2RFxcnFYfCwsLrXNVrdE/f/588euBAweK9ZpbtWollgQhInqRMZOZiIiIiJ4roVxA1rfnkPfLLQiPyiGUlqMoPRdWWY0QET4XK1euxK5du2BtbY3ffvsNDx48AACMHj0aKpUKDRs2rHEW8JPqYSqVSly6dAmWlpaQy+XIy8vDrl27xONLly7F5cuXsXjxYowZMwZARf3oO3fuYNu2bTh//jwSEhLg6uqK06dPw97eHiqVCuXl5bC0tARQ8Zi3SqXClClTcODAAcjlcjFA0KBBA3Tt2lVrXeXl5YiJiUHbtm3FrG6gIit78uTJ2LJlCy5dugSVSoURI0ZArVZrzVFaWoqNGzciKCgIUqlUoxTCwYMHYWxsrFHWg2pu8+bNOv/+5eXlwdbWFj179kTv3r0xd+5cjeNqtRqZmZkaf+YymQwNGzYUN7p8PCNSIpEgJiYG0dHRsLGx0cribNGiBXbs2IFZs2bB2toa3bp1w86dOzFp0iSt9SUnJ8Pd3R3Ozs7w8PDAuHHj0LlzZwDaNZm//PJLjbFmZmaYMWMGDA0NNdofz6j08/MTg9BVszCJauq1117TuaFr/fr18e9//7sOVkRERLXFTGYiIiIieq4K1XdR+uAhUF6RrZZx8zfoSSSQSdsjP+0OVCoV2rdvDxMTE4wdOxaTJk3C2rVrYWRkhLKysmfaNC8sLAxDhgwRa+CWl5dj3bp1GD9+PL777jtcuHABrVq1AgAcOXIECxYswDvvvKMxx6RJk7Bx40b88MMPKCgogIGBgRhsMzExwaVLlyCXy2FtbQ1XV1dERkaKYyMjI+Hm5gZra2uNYHDlY9P79+8XA32VEhIS4ODggMDAQCiVSvj4+AAAFi9ejI8++gh2dnZi38rMtsfFx8fDzc0N+vqav94LgoCQkBCEhYXB0dGxxteR/nD79m2dfweNjIwwZ84crUCYt7e3mK2oq7Z3ZbmLLl266MyIbNu2rZiBqUvXrl01bkZUJywsDGFhYVrtlpaWWlnZlXTdwLC0tBTryT6eUVn166pZmEQ1paenh9jYWPTs2ROlpaUoKytDeXk5xowZg/79+9f18oiI6BkwyExEREREz9XD24UQSsvF94UlRViw5wvkFT1AvS+N0MHFHuvWrQMALFy4EB9//DEcHR1hZmYGY2NjhISEiIHhwsJCjRqYH3zwAT744APxvbOzM7744gsMHz4chYWFkEgk6N+/PxITE9G6dWtxHgDo3r07UlNTcePGDY31SiQSfDTrIyxa+Cn2xOzBw4cP0a1bNzRo0EDMrqssR7FhwwZMnjwZ169fh1wuh4eHBzZs2KDzOsyePRuurq7i+zVr1gCoyDjt0qULDh8+jMOHD+PRo0fo3Lkz9PT0MG3atBpd4+PHj+usRb18+XLo6+tj8uTJNZqHtL355pvYsmWL1qP45eXl6NGjRx2tiujV4ebmhuzsbBw4cAC5ubnw9vaGtbV1XS+LiIiekaS6+kfPm7u7u5CUlFQn5yYiIiKiv0/+lTv4/ftfITwq12iXGOiheS9rNLBvXs3IupF75hpyT/xW8aZcgLFNE/xqfANH/3sUa9euxaJFizBq1CiEhobi+PHjAIDU1FTY29sDAP71r39h9uzZ4nyWlpZiPejHlZSUQCaT4dKlSzAzM8OQIUPEDD43Nzds2rQJLi4uuHPnDnx9fVFYWIjx48drBZ/Hjx+PHj16YNiwYWLbuXPnMGDAAJw5cwYWFhZ/9WX6xygvL0efPn1w8uRJFBQUAKh4lH/06NH46quv6nh1RERERH8PiUSSLAiC9uYi/4+ZzERERET0XNW3agKpiQFK8x4ClfkNEkDPUB+mNk3rdG2Pe3DpFnKP/6aReV105S4c7Nuhx7x5cHJywpYtWzBq1CisWrVK7GNpaQmVSvXM5/vhhx9w7949ODk5AajI1DY2Nkb//v3h4OCAs2fPwsXFBU2bNoVKpcKyZcu0MmoBwNjYGMXFxX+suagII0aMwNdff80A85+kp6eHAwcO4Ntvv8W2bdtgZGSE8ePH81F+IiIioioYZCYiIiKi50qiJ0HbIBfc/OkKCtJzAQAmlo3RvJc19Aykdbw6TbmnsjQCzGL9aEFAMx+ZWD/6r6JUKrF+/XoMHz4cAFBQUACZTIbCwkJMnz4d/v7+6Nq1q1iXubCwUOc8dnZ2uHLlivh+2rRpeOONN9CvX7+/bK3/ZAYGBggJCUFISEhdL4WIiIjohcQgMxERERE9d/r1DdFqsD0qS7VJJJI6XpFupQWaG7yJ9aOL82G8wRQ2tjZi/eg/q7CwEHFxcWJtZqCiDIOnpyf279+PwMBArFixAsHBwcjLy4O5uTnatWuHefPmac315ptvYuTIkQCA7OxsrF69Gh07doRCoRD7ODg4YPv27QgPD4e7uzsGDhyIffv2ISkpCfPnz0d2djbGjRsnbubm5+eH9evXa9SxJiIiIiLShTWZiYiIiIj+3/XdKSjMuKvVLjUxgGziay9scBwA/P39sWTJEtjY2NT1UoiIiIjoFfO0msx6f+diiIiIiIheZOZelpAY6AFVYskSfT2Y+8he6AAzACxatAg3btyo62UQERHRS0YqlUKhUIgvtVqNhIQENGzYEAqFAh07dtTYdHjz5s1o1qyZxpjU1FSUl5djypQpcHR0hJOTEzp37oyrV68CqNi/wsnJSew/ZcoUAMCoUaPQunVrPHz4EABw+/ZtWFpaAgDUajUcHR3Fc06aNElj3d7e3qhMYN24cSOcnJzg7OwMR0dH7N2797leM9LGchlERERERP+vXrP6aPe2AndO/obi7AcwaGiEJl3bwqR9o7pe2lN16NABHTp0qOtlEBER0UvG2NhYawNjtVoNLy8vxMbGoqioCK6urvD398frr78OAAgMDMTKlSs1xiiVSmRnZ+P8+fPQ09PDtWvXUL9+ffH4kSNHYG5urnV+qVSKjRs3YuLEibVa/7Vr17Bw4UKcPXsWDRs2RH5+Pm7dulWruaj2mMlMRFTHHr9rvGjRIsybNw+zZs3S6KdSqcSNn+7fv4/g4GBYW1tDLpcjODgY9+/fx4ULF8R5mjRpAplMBoVCgZ49e0KtVsPY2FjjXFu3bgUA5Ofn49///jfkcjk6deoEb29vnD59Wud6VSoVJBIJ4uLiNNpzcnIQFBQEKysrdOrUCR4eHoiJiXkOV4yI6PkybGqClv07Qja+M9oEOr0UAWYiIiKi56Xyc+T169ef2O/GjRto2bIl9PQqwo1t2rRB48aNnzr/+++/j+XLl6O0tLRW67t58ybMzMxgamoKADA1NYVMJqvVXFR7zGQmIqpjuu4aX758GX379sWnn34qtkVFRWH48OEAgLFjx8LR0VEMEs+dOxfjxo1DdHS0ONeoUaPQv39/vPXWWwAq7kTL5XKtcwHAuHHjIJPJkJaWBj09PVy9ehWpqak616tUKuHp6QmlUom+ffsCAARBwODBgxESEoJvv/0WAJCZmYl9+/b9mUtDREREREREz1lRUZG4WbBMJtNKFrp79y7S0tLQvXt3sW3Hjh04duyY+P7kyZMYOnQoPD09kZiYCF9fX7z99ttwdXUV+/j4+EAqlQIAQkJCMHXqVABAu3bt4Onpif/85z8YMGDAM6/fxcUFFhYWkMlk8PX1xZAhQ2o1D/05DDITEb2AbG1t0bhxY5w+fRpdunQBAHz33Xf44YcfcOXKFSQnJ2PHjh1i//DwcFhbWyM9PR1yufyZzpWeno7Tp09j+/bt4h1nmUym886vIAiIjo7GTz/9BC8vLxQXF8PIyAjx8fEwNDTEhAkTxL7t27fH5MmTa/PtExERERER0XP0qKwcNwqKYWqgrzPxCQASExPh4uKCtLQ0vP/++2jRooV4TFe5jDZt2uDXX39FfHw84uPj4evri+joaPj6+gKovlwGAMyaNQuDBg1Cv379dB6vbm8MiUQCqVSKuLg4nDlzBocPH8bUqVORnJyMiIiImlwK+ouwXAYRUR2rvGtc+aoMHg8fPhxRUVEAgFOnTqFJkyawsbFBamoqFAqFeAcY+KPkRkpKyhPPlZ6ernGuxMREpKSkaM1XnRMnTkAmk0Eul8Pb2xvff/89ACAlJQVubm61vQRERERERET0NzmWdRsfHDqPpScvY87RFBQUFMDZxUVj4z+VSoXCwkJIJBK0aNECK1asEAPRx44dw4YNG3Ru/BcWFoawsDDExcWhfv364tO3165dg5eXl9bGf8eOHcP48ePRrl07KBQKbNy4EdeuXdNac9OmTbFp0yZ4eXmJbbm5uRg5ciQcHR0hkUhQWFiIRYsWQU9PD4sXL4ZCocChQ4cA/PGZ2cHBAS4uLvjss89QXl7+vC/1PwozmYmI6kBB/kPkZOehYWPjau8aBwYGolu3bvjss880SmX8GbrKZTxLSQulUolhw4YBAIYNG4atW7ciICBAq19oaCiOHTsGQ0NDnDlz5s8tmoiIiIiIiP4Sl3PzoUy9hpIyzQDriDXfYYaH5gbCTZo0gUqlQlFREdq3b49p06aJQVsbGxutz5affPIJ0tPTcf78eQDA0KFDNZ603bNnj85NivX09LBx40bMnj0bb775ps51d+7cGQ8fPkRubi6ysrKQk5ODBw8eoEGDBiguLsbZs2cBAF5eXhg8eDD27NmD2NhYcXzVz903b95EUFAQ8vLyMG/evBpdN3o6BpmJiP5GgiDg0P5fcOa4GlJ9PZSVlaP0UTmKCktgbGKo0bdt27aQyWQ4evQodu3ahZMnTwIA7O3toVKpUF5eLpa3KC8vh0qlgr29/TOvycHBAefOnUNZWdkTs5nLysqwa9cu7N27FwsXLoQgCLhz5w4ePHgABwcH7Nq1S+y7atUq3L59G+7u7s+8HiIiIiIiIno+fszI0QowA0Dm/ULcKnyIZib1tI4ZGxuje/fuiI+Ph1qtBgCkpaWJdZwBYPXq1VCr1Thz5gycnZ0BAK+99hqmT58u9vH394ehYcXnXmdnZzHLuV+/fli+fDneeecdODs7Izs7W2sNFhYWaNKkCe7evYsuXbrAysoKPXv2hLW1NTZs2IBp06bhypUruH//Ph4+fIg1a9ZozbFv3z4kJSVh/vz5WLBgAd544w1ERERAIpHAz88P69evR6tWrZ7halJVLJdBRPQ3OncmC0knM1FaWo6HxaUofVQOQRCwR6mdyQxUlMyYOnUqrKys0KZNGwCAtbU1XF1dERkZKfaLjIyEm5sbrK2tn3lNcrkc7u7umDt3LgRBAFCxSWBlKYxKhw8fhrOzM7KysqBWq5GZmYmAgADExMSgR48eKC4uxtdffy32LywsfOa1EBERERER0fNzt7hEq02ip4fo0GHo3qUz/P39AQAKhQKdO3euGHP3rrg5vKWlJTw9PWFqaqoxh6urK8LDw2FiYgJ9fX306dMHkydPhpGREYCKes1Vk5oqNwT09PTEgAEDxI3/Nm/eLH72tbS0xMWLF8UxJiYmOHLkCCwtLXHs2DEkJSVhwIABMDQ0RHx8vBi0vnXrFgICAqBQKJCeni6OHzhwIObPnw8A8PDwgImJCW7evAkAOHDgAAPMfxKDzEREf6NTR6/iUUmZRtuj0hJ8MGsYnJ0ramDNnDlTPPavf/0LKSkpWqUyNmzYgMuXL0Mul0Mul+Py5cvYsGHDU8//eE3mL7/8EgCwfv165OTkwNraGo6Ojhg1ahSaN2+uMVapVIq/cFQKCAiAUqmERCLBnj17cPToUchkMrz22msICQnB4sWLda6j6h3vcePGISkpCQCwZs0anXeciYiIiIiI6M9zMG8AfT3NTfSkhvXwr1VR+PnnnxETEyO2V27817p1a/Tp00dr4z+VSiW+jI2NxY3/Pv30U+jp6cHX1xeHDx8Wxxw5ckTsP3XqVI01zJo1C0uXLn1qneSmTZuicePGiIqKgp2dHUxMTDSOe3l5aayrarkOer5YLoOI6G9UVKR91zhi6l7oG+jh3eneaNRE8z9Ic3NzPHr0SGtM48aNsW3btieea/PmzRrvLS0tUVRUpLNvgwYN8M033zxxvk2bNmm1DRw4EAMHDgQAtGzZUtyo8Gmq1u5av369+PWECRNqNJ6IiIiIiIieXS9ZcxzNuo3Scs3kJ32JBI/KBRhVafPy8kJsbCyuXr2Krl27YujQoRoJQ7rUq1cPb775Jt58801YWFhgz5498PX1feq6bGxsoFAo8N133z21b2BgIEJDQ7U+8z6LjIwMSKVSreQqqj1mMhMR/Y2sOzaHRMdPXiMjAzRsZPz3L4iIiIiIiIj+MUwN9WGsr/2htKS8HLFpN3SOkclkmDlzZrVPqlY6e/asWE+5vLwc58+fR/v27Wu8ttmzZ2PZsmVP7efv74/p06ejT58+NZ67qlu3bmHChAmYNGkSJBLJ0wdQjTDITET0N3qjjy2MjQ0hlVb8RyaRAAYGUvQf6gyJHv9zIyIiIiIioufn/sNHuP+wVKu9TADO/n6v2nETJkzAf//7X3Hjvx07dmiUYjxx4gRu3ryJAQMGwNHREc7OztDX18ekSZPEOXx8fMT+wcHBWudwcHCAm5ubRpuuzGkzMzPMmDFD3ESwqsTERI117dy5EwBQVFQEhUIBBwcH9OzZE71798bcuXMBANnZ2fDz8xPn8PPzE4Pl4eHh2LdvX7XXhf4gqdzk6e/m7u4uVNbgJCL6Jyl48BD/O3YV6it30NjcBF3fkKNFqwZ1vSwiIiIiIiJ6xRWUlGLa4Qso1REPbG5SDwu9HepgVfQykEgkyYIguFd3nDWZiYj+ZvXN6sHnzY51vQwiIiIiIiL6h6lvqA954/pIy81H1S32DKV68G5vXmfropcfy2UQERERERERERH9Q4xTyNCsfj3Uk+qhnlQPBnoSODVrgB7tuQke1R4zmYmIiIiIiIiIiP4hGhkZYEF3e6TdLUBuUQksG5qghalRXS+LXnIMMhMREREREREREf2DSCQS2DYxretl0CuE5TKIiIiIiIiIiIiIqNYYZCYiIiIiIiIiIiKiWmOQmYiIiIiIiIiIiIhqjUFmIiIiIiIiIiIiIqo1BpmJiIiIiIiIiIiIqNYYZCYiIiIiIiIiIiKiWmOQmYiIiIiIiIiIiIhqjUFmIiIiIiIiIiIiIqo1BpmJiIiIiIiIiIiIqNYYZCYiIiIiIiIiIiKiWmOQmYiIiIiIiIiIiIhqrUZBZolE0lcikfwqkUiuSCSSmU/oFyCRSASJROL+1y2RiIiIiIiIiIiIiF5UTw0ySyQSKYBVAP6PvTsPi7JcHzj+HRAQxB13VBBZZB0BUXEJNdfQJMs1FZfK1DySW6Yn+aWkuVGmBh5N6qiAmkuamporopYooCRuiCvuGwTINr8/OPPGOMOiWZren+ua62Ke5X2fGQecued577sr4Az0U6lUzgbGVQT+BRx+2osUQgghhBBCCCGEEEII8Xwqy05mH+CsRqNJ0Wg0OUAU8LqBcdOBz4Hsp7g+IYQQQgghhBBCCCGEEM+xsgSZ6wGXity//L82hUql8gTqazSaH0s6kEqlelelUh1RqVRHbt68+diLFUIIIYQQQgjx9zI2NkatVuPq6spbb71FZmYmACqVinHjxinj5s6dS3BwMADBwcHUq1cPtVqt3O7du6dz3ICAADZs2KDcd3R0ZMaMGcr9Xr16sW7dOvbs2YO/vz8AERERGBkZkZiYqIxzdXUlNTUVABsbG9zc3HBzc8PZ2ZmpU6eSnV38PqgNGzagUqlITk5+sidHCCGEEMBTKPynUqmMgPnAuNLGajSaJRqNxluj0XjXqFHjz55aCCHK7Pbt28oHnNq1a+t86Ll48SKvv/469vb22NnZ8a9//YucnBwA9uzZg0qlYunSpcqx4uPjUalUzJ07V+ccISEhyjG1H8bUajULFixQxqjVavr27aszLzAwkLVr1wLg5+fHkSNH9NYfExODj48PTk5OODk5sWTJEqWv6Ic4Z2dnIiMj//wTJoQQQgjxP+bm5sTHx3PixAlMTU0JCwsDwMzMjHXr1nHr1i2D84KCgoiPj1duVapU0elv1aoVsbGxQOF7tQoVKnDw4EGl/+DBg/j6+uod19rampCQkGLXu3v3bo4fP84vv/xCSkoK7733XrFjIyMjad26tbx/EkIIIf6ksgSZrwD1i9y3/l+bVkXAFdijUqlSgRbAD1L8TwjxPKlevbryAWfEiBHKh55jx47x5ptv0rNnT86cOcPp06fJyMhgypQpylxXV1dWr16t3I+MjMTDw0PvHFOmTFHOof0wFh8fz5gxYwA4efIk+fn57N+/n99//73Ma7927Rr9+/cnLCyM5ORkYmJiCA8P58cf/7h4RPt4Nm7cyHvvvUdubu6TPE1CCCGEECVq06YNZ8+eBaBcuXK8++67hIaGPtGxfH19lSBzbGws3bt35+bNm2g0Gs6fP4+5uTm1a9fWm+fv709SUhKnTp0q8fiWlpaEhYWxYcMG7ty5o9efkZFBTEwMy5YtIyoq6okegxBCCCEKlSXI/Ctgr1KpbFUqlSnQF/hB26nRaO5rNBorjUZjo9FobIBDQA+NRqO/FU8IIZ4zu3btonz58gwZMgQovBw0NDSUb775RrkUtGHDhmRnZ3P9+nU0Gg3btm2ja9euj32uyMhIBg4cSKdOndi4cWOZ5y1atIjAwEA8PT0BsLKyYvbs2cyaNUtvrL29PRYWFty9e/ex1yeEEEIIUZK8vDy2bt2Km5ub0jZq1ChWrlzJ/fv39caHhoYqV3a1a9dOr9/Ly4sTJ06Qk5NDbGwsLVu2xNHRkZMnTxIbG2twFzOAkZEREydO5LPPPit1zZUqVcLW1pYzZ87o9W3cuJEuXbrg4OBA9erViYuLK/V4QgghhDCs1CCzRqPJA0YDPwEngdUajSZJpVJ9qlKpevzVCxRCiKdBo9EYbE9KSsLLy0unrVKlSjRo0EDZpQPw5ptvsmbNGmJjY/H09MTMzOyx1xAdHU3fvn3p16/fY12SaWiN3t7eJCUl6Y09evQo9vb21KxZ87HXJ4QQQghR1NmzZ9m+fTtZWVmo1Wq8vb1p0KABw4YNU8ZUqlSJQYMG6aQH0yqaLmP37t16/WZmZri4uHD06FEOHTpE8+bNadmyJbGxscTGxtKqVati19a/f38OHTrE+fPnS30cxb0PjIyMVNKY9e3bV1JmCCGEEH9CubIM0mg0W4Atj7R9UsxYvz+/LCGEeDo2btzIuHHjSElJoWbNmkydOrXYDxol6d27N3369CE5OZl+/fopl3aW1ZEjR7CysqJBgwbUq1ePoUOHcufOHapVq/bYazEkNDSU5cuXc/r0aTZt2vRUjimEEEKIl1NGRgZvvPEGMTExmJqaUlBQQNOmTVm6dCnGxsZ648eOHYunp6dyZdjjaNWqFfv27SM9PZ2qVavSokULFi5cyLFjx0rMpVyuXDnGjRvH559/XuLx09PTSU1NxcHBQaf9zp077Nq1i+PHj6NSqcjPz0elUjFnzhxUKtVjPw4hhBDiZfenC/8JIcTzatu2bfTr149z586h0Wi4fv06kyZN0iko4+zsrHdp5IMHD7h48SKNGzdW2mrXro2JiQk7duygQ4cOj72WyMhIkpOTsbGxwc7OjgcPHvD999+Xaa6hNcbFxeHi4qLcDwoKIikpie+//55hw4aVWEVdCCGEEKIko0aNYt++fWRlZSlpMFavXs28efMMjq9WrRq9e/dm2bJlj30uX19fwsPDlXoX7u7uHDp0iIsXL+Lq6lri3MDAQHbu3MnNmzcN9mdkZDBy5Eh69uxJ1apVdfrWrl3LwIEDuXDhAqmpqVy6dAlbW1v279//2I9BCCGEEBJkFkK8wKZMmUJWVpZOW2ZmJvv27aOgoACADh06kJmZyXfffQdAfn4+48aNIzAwEAsLC525n376KZ9//rnBHTwlKSgoYPXq1Rw/fpzU1FRSU1PZuHFjmS/JHDVqFBEREcTHxwOF1dcnTZrExIkT9cb26NEDb29vvv3228daoxBCCCEEQE5ODtHR0Tx8+FCnPTMz02BKDK1x48Zx69YtnbaiOZnVajWpqal683x9fUlJSaFly5ZA4Q7lmjVr4u3tjZFRyR9XTU1NGTNmDDdu3NBpb9euHa6urvj4+NCgQQPCw8P15kZGRhIQEKDT1qtXL+X9mVqtVtqHDx/OkSOFJYfCwsIICwsrcV1CCCHEy0j1JJeNPw3e3t4a7X/UQgjxV6hcuTIPHjzQazcyMmLatGl88klh1p9Lly4xcuRIkpOTKSgooFu3bsydOxczMzP27NnD3Llz2bx5s84xgoODsbS0ZPz48QbPbWlpSUZGBgB79+5l0qRJHDp0SOnPz8+nXr16HDt2jMmTJ+Pv78+bb76Jn58fJ0+exMTEBICWLVuyZs0a9u3bx7hx40hPT0ej0TB27Fjef/99g2uJi4ujf//+nDx5kiVLlgAwYsQIjhw5QlhYGEuXLgUKPzxpA9dCCCGEEFCYXqJatWrk5eXp9VWsWNHgeyshxMslJCSEVatWrOIv2wAAIABJREFUYWxsjJGREeHh4fzwww/k5eUpKWwuXLhAu3btOHr0KD179iQlJYULFy4o6Wh69uzJzp07ycjIIDU1lSZNmuDo6EhOTg7e3t4sW7YMExMTnc9j169fZ9iwYVy6dInc3FxsbGzYsmWLznytDz/8kEGDBumt/datW9SpU4evvvqKESNGKO0ZGRlMmDCB7du3U7lyZVQqFSNGjOCdd975i59NIf45VCpVnEaj8S62X4LMQogXVbNmzTD0d6ZKlSrcunXrsXckCyGEEEK8DJydnTl58qROm5GREa+//jrr1q17RqsSQjwPDh48yIcffsiePXswMzPj1q1b5OTkULVqVdRqNRs2bKBJkyb07NmTt956iwEDBuDn58edO3dYvHgxrVu35t69e3Tu3JmkpCQlyOzv78+JEyfIz8+nY8eODBs2jAEDBugEmd977z2cnZ3517/+BUBiYiLu7u4680vz9ddfs2rVKoyMjNi7d6/S3rdvXxo1asSMGTMwMjLi5s2bfPPNN0yaNOkvey6F+KcpLcgs6TKEEC+szz77TC/lhYWFBdOmTZMAsxBCCCFEMZYsWYKFhQXlyhXWiTc1NaVSpUqlFtkTQrz40tLSsLKywszMDAArKyvq1q2Lubk5oaGhjBo1ii1btpCens6AAQOUeX379iUqKgqAdevW8cYbbxg8vrGxMT4+Ply5csXgua2trZX77u7uj73+yMhI5s2bx5UrV7h8+TIA586d45dfflECzAA1atSQALMQj0mCzEKIF1bHjh1ZvXo1Tk5OGBsbU69ePUJDQ5VvvoUQQgghhL7WrVtz7Ngxhg0bRtu2bRk7dixJSUnY29s/66UJIZ6xTp06cenSJRwcHBg5cqTObuBu3bpRtWpVBg8ezOLFi3XmdejQgX379pGfn09UVBR9+vQxePzs7GwOHz5Mly5d9PpGjRrFsGHDaNeuHSEhIVy9elXpO3funE4OeENFPC9dukRaWho+Pj707t2b6OhoAJKSkvDw8Cg1D7wQomTlnvUChBDir/Taa6/x2muvPetlCCGEEEL8ozg4OEiBOyGEQqPRcOXiPbKzcjkQc4hfjxxi9+7d9OnTh1mzZhEYGAgUBoKzsrJ08iND4Q7l1q1bExUVRVZWFjY2Njr92iDx+fPnee211wzuUu7cuTMpKSls27aNrVu30rRpUyVFhp2dXan1ZqKjo+nduzdQuLN66NChjBs3Tm9cSEgIa9as4caNGzqBbCFEySTILIQQQgghhBBCCCEMun0zgxXhh8n6PQeVkYr8/AJe9W/C//3f/+Hm5sa3336rBJmNjIyK3RHct29fAgICCA4O1uvTBolv3bpFq1at+OGHH+jRo4feuGrVqtG/f3/69++Pv78/+/btw8vLq0yPIzIykmvXrrFy5UoArl69ypkzZ3B2diYhIYGCggKMjIyYMmUKU6ZMwdLSsmxPkBACkHQZQgghxF/K2NhY59K9WbNmAeDn56dXmHLPnj1UrlwZtVqNk5MT48ePV/oiIiIYPXo0AMHBwVhYWHDjxg2lv+ib4OvXr9O/f38aNWqEl5cXLVu2ZP369co5/P39lbFTp06lS5cuPHz4sNg1acdHRERgZGREYmKi0u/q6kpqauqfeYqEEEIIIcRzSlOgYUX4Ye7fyyInJ58rV1O5fuMyP29O5lLqXeLj42nYsGGZjtWmTRsmT55Mv379ih1jZWXFrFmzmDlzpl7frl27yMzMBCA9PZ1z587RoEGDMp379OnTZGRkcOXKFVJTU0lNTWXy5MlERkbSuHFjvL29mTp1Kvn5+UBh2g6NRlOmYwshCkmQWQghhPgLmZubEx8fr9w++uijEse3adOG+Ph4jh07xubNmzlw4IDBcVZWVsybN0+vXaPR0LNnT9q2bUtKSgpxcXFERUUphU2KmjFjBgcOHGD9+vVK8ZbSWFtbExISUqaxQgghhBDin+3yxXtkZebA/+KtObnZrN8WSuh/3qN1Wx9+++03gzuTDVGpVIwfPx4rK6sSx/Xs2ZPMzEy9vMpxcXF4e3vj7u5Oy5YtGT58OM2aNQP0czIvWLBAZ25kZCQBAQE6bb169SIyMhKApUuXcvv2bSXg3LFjR2bPnq2MVavVys/Dhw9XNmaEhYVJaiEh/kfSZQghhBDPIXNzc9RqtcHK2gBDhw4lIiKCSZMmUa1aNaV9165dmJqaMmLECKWtYcOGfPDBBzrz582bx9atW/npp58wNzcv87q0lyWeOnVKL9eeEEIIIYR4sWRn5aJSqZT7dWs1Zni/OQA0crDi7fda6Iz38/PDz89Pp23Pnj0Gj52RkQGAjY2NklsZCoPRCQkJOscEmDBhAhMmTNA7jo2NDVlZWSU+jmnTpum1ubu7c/LkSQAqVapEeHh4sfOL5nteunSp8nPR99xCvOxkJ7MQQgjxF8rKytLZVaGtYl2au3fvcubMGdq2bWuw39LSkqFDh/Lll1/qtCclJeHp6VnisQ8cOEBYWBhbt2597FxzRkZGTJw4kc8+++yx5gkhhBBCiH+e+jZVyc8r0Gs3MTGmiXudZ7AiIcTzSoLMQgghxFN27tw5PvvsM4KDgzEzM9NJl9GnT58S5+7fvx8PDw/q1atH586dqV27drFjx4wZw7fffkt6enqxY0aNGoWHh4dyKSFA48aN0Wg07Nix4/EfHNC/f38OHTrE+fPnn2i+EEIIIf56165do2/fvtjZ2eHl5UW3bt04ffo0UPildPv27XF0dMTe3p7p06ej0WhITU3F2tqaggLdoKJarebw4cMEBwdTr149nS/Q7927R2ZmJgMGDMDNzQ1XV1dat27NhQsXlDG1a9fWmZeTk6PUrXB1daV79+7cu3dPOV9x6yvO2LFjqVevnt66xZ9X3tyE9q85YWJirLSZmBhTrUYF3L2tn+HK/qB9LXl4eODp6UlsbCwAqampytWB2tt3330HFO5+vnXrls58QzVUHB0dlffSRXczf/PNN7i5ueHu7o6rqysbN24EIDAwEFtbWzw8PHBwcGDQoEE6aevu37/PoEGDaNy4MXZ2dgwaNIj79+/rrdfZ2ZlBgwaRm5sL6NZuUavVvPrqq3/xsyrE45N0GUIIIcRTFB4eTlBQEHl5eeTn51NQUMD48eOZO3dumea3adOGzZs3c/78eVq0aEHv3r11csAVVaVKFfr378+iRYuUNhcXF77//nvl/qJFi7h16xbe3t5KW61atVi5ciUdOnSgWrVqtGvX7rEeY7ly5Rg3bhyff/75Y80TQgghxN9Do9EQEBDA4MGDiYqKAiAhIYHr169Tv359evTowddff02nTp3IzMykV69eLF68mFGjRtGgQQP279/PK6+8AkBycjLp6ek0b96crVu3EhQUpFOcGGDmzJnUqlWL48ePA3Dq1Clq166tBOWCg4OxtLTUmaetWwEwePBgFi1axJQpU8jKyipxfY8qKChg/fr11K9fn7179z72+xpRuhZtG1HHujJHDqSS+XsuTdxq4+FTXyfw/CwVfS399NNPTJ48mb179wJgZ2enExwubf6jVq5cibe3N8uXL2fChAns2LGDy5cvExISwtGjR6lcuTIZGRncvHlTmTNnzhzefPNNNBoNX3zxBe3bt+fEiROYmpoybNgwXF1dlWD3tGnTGD58OGvWrNFZb35+Ph07dmT16tUMGDAA+ONzghDPK9nJLIQQQjwl165dY+zYsWRlZZGbm6vspvn666/55ZdfHutYtra2fPTRR6UGcj/88EPCw8PJy8sDoH379mRnZ/P1118rY7RVuItycHBg3bp1vP3226W+8TYkMDCQnTt36ryhFkIIIcTzYffu3ZiYmOjki/Xw8KBNmzasWrWKVq1a0alTJwAsLCxYuHChsnuzX79+SmAaICoqir59+5Z4vrS0NOrVq6fcd3R0LHNRYYCWLVsqdShKW9+j9uzZg4uLC++//75SxE08fQ0bVafXQC8GjmiBdyub5ybA/KgHDx5QtWrVp37coq/RGzduULFiRSXtnKWlJba2tnpzVCoVQUFB1K5dm61bt3L27Fni4uL497//rYz55JNPOHLkCOfOndOZa2xsjI+PT7H1WYR4HkmQWQghhHhKfvzxR4yN9d9wZ2Zm4u/vj1qt5qOPPlLaX3vtNaytrbG2tuatt97SmzdixAj27dtHampqsee0srIiICCAhw8fAoVvZjds2MDevXuxtbXFx8eHwYMHGwxWN2vWjOXLl9OjRw/ljW1pa9IyNTVlzJgx3LhxQ2krrtL2kSNHGD58eLHHEkIIIcTTdeLECby8vAz2JSUl6fXZ2dmRkZHBgwcP6N27Nxs2bFC+wI6OjqZfv37K2NDQUOWSfe2u4aFDh/L555/TsmVLpk6dypkzZ8q81vz8fH7++Wd69OhRpvU9KjIykn79+hEQEMCPP/6opBcQLw9tDRQnJyeGDx+uE8Q9d+6cTiqM/fv3Fzu/pBoq27Zto2fPnkDhFza1atXC1taWIUOGsGnTphLX5+npSXJyMr/99htqtVrn84I2VUdSUpLOnOzsbA4fPkyXLl2Utv379ytrDAkJKduTI8TfSNJlCCGEEE+JsbGxTvXtou1DhgzRCfQWV2W7aDVuc3NzZfdCYGAggYGBQOElp0XNnz+f+fPnK/fr1KmjswPp0eMXPUenTp24ePFimdZUdA1QmBN6zJgxyv3iKm17e3vr9Innl0qlYsCAAaxYsQKAvLw86tSpQ/Pmzdm8eTMRERFMmDBBZ7faqlWrsLCwoEmTJjg5OZGdnU3FihUZOXKk8nqJiIjgyJEjLFy4EIAVK1Ywe/Zs8vPzKVeuHM2aNWPu3LlUqVIFPz8/0tLSMDc3BwpziK9du5bg4GBmz55NamoqNWvWBAp3Dmkr0xsbG+Pm5qasq2/fvjpf6mhpH9OwYcOK3RUnhBD/VNl5+Zy/l8ndrJwScxiXpFatWri6uvLzzz9Tq1YtypUrh6urq9JvKF2GWq0mJSWF7du3s3PnTpo1a8bBgwdp0qRJsefRBvauXLlCkyZN6Nix42OvNScnhy1btjB//nwqVqxI8+bN+emnn/D393/sY4l/Fo1Gw63rGTx8mKeT7uLgwYMMGjSIEydOAH8+XcaAAQPIyckhIyNDGWNsbMy2bdv49ddf+fnnnwkKCiIuLk7vPXrRtZaVNih+/vx5XnvtNdzd3ZU+SZchnneyk1kIIYR4Svz9/ZVdP0WZmprq7AASz9aGDRtQqVQkJycDJRdZAYiJicHHxwcnJyecnJxYsmSJ0hcYGMjatWt1jq+9dDI1NRWVSsVXX32l9I0ePZqIiAhlbnGFYZo3b45araZBgwbUqFFD2bWSmppaYsGYgIAANmzYoJzP0dGRGTNmKPd79erFunXr2LNnDyqVSmfnjb+/P+XLl+fEiRNkZWUBsGPHDp2AMkCfPn10ilk6OzsDhR/ijh07xsmTJ4mKiuKLL75g+fLles//tm3bCA0NZevWrSQlJXH06FF8fX25fv26MmblypXK8Ys+v1ZWVsybN0/vmPDHB0TtzVCAWfuYHBwcWLNmzRMHYIQQ4nm0O/Um43Ym8vXRcxzVVGTd7hhuZj7UG+fs7ExcXJxOW0pKCpaWllSqVAn4I2VGVFRUmd/DWFpa8sYbb7B48WLefvtttmzZUuJ47d/tCxcuoNFolBoTZVmf1k8//cS9e/dwc3PDxsaGmJgYSZnxErh7O5Ov5+xl6ZcxrAw/TG5OPsfjCt9DtWzZklu3bj21lG4rV64kJSWFwYMH88EHHyjtKpUKHx8fJk+eTFRUlE5NlEcdO3aMJk2a4OzsTHx8vE6ByoKCAr33U/Hx8Zw7d464uDh++OGHp/I4hPg7SJBZCCGEeEqsrKz45ptvMDc3x8LCgvLly1O+fHmmTJlSbPE+8feLjIykdevWOh9CtW/ojx8/zuXLl1m9ejVQmGe7f//+hIWFkZycTExMDOHh4fz4449lOlfNmjX58ssvycnJMdg/Z84cEhISOHXqFE2bNqV9+/bk5ORw+PBh4uPj+fTTT3WCujY2NgwbNoxGjRpx9uxZzp07h62trZKOpFWrVkpF9du3b1OhQgUOHjyonO/gwYP4+voCYG1tbfBSy27duimPT3sJ8uNq1KgR8+fPZ8GCBXp9ISEhzJ07VwleGxsbM3ToUBwdHUs97tChQ4mOjubOnTuPvSatyMhI/vWvf9GgQQOd50YIIf7Jzt7JYG3yZXIKNGTlFVDDzZvs7GwG/3um8oVaYmIi+/fvZ8CAAcTExLBz506gcEfxmDFjmDhxonK8N954gy1bthAdHV1qPmaAAwcOcPfuXaBwd/Fvv/1Gw4YNy7R2CwsLFixYwLx588jLyyvT+rQiIyNZunQpqamppKamcv78eXbs2GGwHoV4MWg0GlaEHeL2jQxyc/J5+LBwg8fmNYlcu3Kf5ORk8vPzqV69+lM7p0qlYvr06Rw6dIjk5GSuXr3K0aNHlf74+HiDr3eNRsOCBQtIS0ujS5cuNG7cmKZNm+psAJgxYwaenp40btxYZ66VlRWzZs1i5syZT+1xCPFXkyCzEEII8RT169eP8+fPM3fuXGbNmkVSUhJTpkx51ssS/5ORkUFMTAzLli0zmFLk0SIrixYtIjAwEE9PT6DwDf/s2bPLnGahRo0adOjQgW+//bbEcY8WhilOaQVjfH19lSBzbGws3bt35+bNm2g0Gs6fP4+5uTm1a9cGCvMJVq5cmR07duico2/fvkRFRZGdnU1iYiLNmzfX6Y+OjtbJW6jd9fwobf7BRyUlJSnPZ3EGDBigHH/ChAlKu6WlJUOHDuXLL7/Um1OWfIrZ2dns3LmT7t27069fP9ntJoR4Yey6cIOcgj+uzlCpVLw6dR6nf43FppEdLi4uTJ48mdq1a2Nubs7GjRuZMWMGjo6OuLm50axZM0aPHq3Mr1KlCi1btqRWrVo0atRI51xFczJrr7I5d+4cr7zyCm5ubjRt2hRvb2969epV5vU3bdoUd3d3IiMjy7Q+KKx5sW3bNl577TWlrUKFCrRu3ZpNmzbp1YQo+oV/0ToS4p/lcupdfv/9IUUvRsrNy2HBN6PxbeVDnz59+Pbbb5W8x4/mZDb0Bfij7yEMXQ1lbm7OuHHjmDNnDrm5uYwfPx4nJyflPUfR9yYTJkxQrlT79ddf2b17N6ampgAsW7aM06dPY2dnh52dHadPn2bZsmUGH2vPnj3JzMw0mEdaa/369Xz66acAXLp0ScltDtC5c2ed+ilC/NUkJ7MQQgjxlNWqVYv333//WS9DFJGZmcnt27fZvXs3Xbp0wcHBgerVqxMXF6ez00VbZEX7QSEpKYnBgwfrHMvb21uvOEtJJk2aRNeuXRk6dGipY7WB2ddff91gf2kFYzp37syJEyfIyckhNjaWV155hZSUFE6ePMmxY8eUXcxaU6ZMYerUqdSqVYvbt2+j0Who0qQJqampREZG0q1bN7019OnTR8mtXJKypKI4fvw4AwcOJD09nc8++4w+ffoAhZement7G5wzZswY1Gq1Xj7QkvIpam3evJl27dphbm5Or169mD59Ol988YXBgp1CCPFP8uChfrquCtVr0HXqbN5v2gjnGrppJtzc3IqtxaBVNP2SVnBwsMG8szY2NgwaNKjYYxmao82pr1U0hVNZ1mdhYWHwypZ169YpPxetCVH0/wipFfHPlfl7jl4NlOCgjQA0blKD/sP/+HLcxsam2C/DixbWzs/PNzjm0dfguHHjlJ937dplcI42LVpxqlatqtS+eJSNjY2SSxoKvyxKSEhQ7hetq6IVEBBAQEAAAPXr19dJr/HTTz+VuBYhnjbZySyEEEKIF1Zubi6jR4/GysoKJycnhg0bRoUKFYDCHbvanazaXS61atWiTp06OkVWSmKo0OOjbY0aNaJ58+asWrWq1OP92RzBZmZmuLi4cPToUQ4dOkTz5s1p2bIlsbGxxMbG0qpVK53xHh4epKWlsWzZMu7fv09BQQHLly+nS5cujB8//k/lEtfmH3yUdn1QGESIj4+na9euxX4IfFSVKlXo37+/krvzcURGRrJz505sbGzw8vLi9u3bxX5IFOJ5p/2CydXVlbfeektJD1C0vXv37ty7d0+Zk5SURPv27XF0dMTe3p7p06crf3euX7+Ov78/Hh4eODs7K18ypaamKkXf9uzZQ+XKlXV2/GlTGmjP6+Hhgaenp3JVRdG899rbd999BxQGVIrudl27dq1SMLS49QjD1LUqY2Kk/39SfoEGmyoVHvt4j9YvgMJ//0cL6hWtTeDn54ejoyMeHh40a9ZMJ6irrVegFRERoexMDg4Opl69eqjVapycnHj//feVnLVFj79582aaNm2qvCbCw8OLXX/Pnj1p0aKFXvuKFStwd3fHxcUFDw8Phg8frvM7Ip5/1g2rkpdXoNduYmKEQ5Naz2BFQggtCTILIYQQ4oX14Ycf8s0335CVlUVmZiZ5eXksXLiQmjVrMmfOHFavXo1Goym2yIqh4kNxcXG4uLgAUL16dSUHJcCdO3ewsrLSW8fHH3/M559/XmoQubjArFZZCsa0atWKffv2kZ6eTtWqVWnRooUSZH50J/P27dtp3769stNFo9GQkZGBvb0906ZNw83NrcT1Fic1NZXx48frFMjRmjx5MuPHj1eKHAJlDjBrffjhh4SHhxsstFmcBw8esH//fi5evKjk7ly0aJGkzBD/WNrd+ydOnMDU1JSwsDC99mrVqilfyGRlZdGjRw8++ugjTp06RUJCArGxsSxevBgoTL3TsWNHEhIS+O2334pNC9SmTRudIpuvvvqqznkTEhKYOXMmkydPVuZo/8Zqb0V3vMbFxfHbb7/pnaes6xGF2tS3orq5qU6g2dTYiADHuliYPP7VGobqF5TFypUrSUhIYOTIkTrpjkoTFBREfHw8v/32G8ePH2fv3r06/bm5ubz77rts2rSJhIQEjh07ZnBXJ8C9e/eIi4vj/v37pKSkKO1lKTwrnn8VKprh69cIE9M/XtflyhlRuao5Hs3qP8OVCSEkyCyEEEKIF1J2djbLli3TC2BqNBrq1avHpUuXsLW15dKlS0rfo0VWRo0aRUREhLIb6/bt20yaNEkpPuTn50d0dLRS2C8iIoJ27drprcXJyQlnZ2edS4EfXVPRwjDFKUvBGF9fX8LDw/Hw8ADA3d2dQ4cOcfHiRWU3ovacly5dwsHBgczMTNLS0pT2e/fuMWbMGINreDQns3a34rlz52jatClNmjShd+/ejBkzhiFDhujN79atG2PGjKFr1644Ozvj6+uLsbExnTt3VsYUzcmsDWAVZWVlRUBAAA8fPlTaSsunuH79etq3b4+ZmZnS9vrrr7Np0yYePnzIJ598ony58MMPP/DJJ58AcPXqVdlBKZ57bdq04ezZs3rtLVu2VHLMr1q1ilatWtGpUyegMNXAwoULleBtWloa1tbWytyyXtFhyIMHD6hatWqZxo4bN85gEdKnuZ6XgVk5Y6a0ciLAsS72VSvgWasKY7zt6Gj7+Ds7S6tfUBZFX3uPIycnh+zsbL3XT3p6Onl5eUqKKzMzs2ILxq5bt47u3bsrNQa0/kzhWfF8adfViV4DPbFztKJu/cq80tmB4WPb6ASehRB/P8nJLIQQQogX0t27d4vdOazdRdurVy+9qt09e/YkODiY/fv306ZNG1asWME777xDeno6Go2GsWPH0r17dwD8/f2Ji4vDy8sLY2Nj7OzslN2Ej5oyZQpNmzbVaZswYQLTp08nMzOTFi1a6BSGKc6yZcv44IMPsLOzAwo/yBctGOPr60tKSoqyi7BcuXLUrFmT+vXrY2T0x/4ClUqFSqVCo9Hw6quvsnz5ckaMGAHA1q1bsbW1pUePHjx48EAp1NepUydWr17Nli1b9NZV0m7kwMBA5RJ4gMGDB+vlutYqLgfno/k858+fz/z585X7xeVTLOmc1apV4+bNmwBK0RyAHj16KIVz6tata/DxCvGs5OXl6fy+5eXlsXXrVr0vqPLz8/n5558ZNmwYUJgqw8vLS2eMnZ0dGRkZPHjwgFGjRik511999VWGDBlC3bp19c6/f/9+nQJq33//PXZ2dsoXPdnZ2aSlpemkotGmJNL66quvaNOmDQC9e/dm8eLFekHysq5H/KF8OWM62tZ6osByURs3btSrX/Doa6c027Zto2fPnmUeHxoayooVK7hw4QJdu3bVeb1A4d/rHj160LBhQzp06IC/vz/9+vXT+X9NKzIykk8++YRatWrRq1cvPv74Y6BshWfFP4eDcy0cnCU9hhDPEwkyCyGEEOKFVLNmTSpUqEB2drZOu0qlolmzZkBhEblHd+w+WmSlbdu2/Prrr8WeZ9q0aUybNk2v/dHiLR4eHjppLkorDAP6wVkouWAMFD7uR4PrjwZu/fz88PPzY+PGjaSkpODi4sLcuXOBwp1d48aNo2PHjoAEXIV4XuTk5DB+/HiWLl1Kbm4ueXl5NGrUiEqVKtGmTRslmKwN9l65coUmTZoov8ul6dy5MykpKWzbto2tW7fStGlTnb9hWm3atGHz5s167UWLbx48eJBBgwYp87XpMgwxNjZmwoQJzJw5k65du5a6nho1apTp8Yiyu/8wl01n0jh+4z4WJuXYvvxbpk0sLK6qrV/g5eVlsA4B6NYiGDBgADk5OWRkZJRajLXovKCgIMaPH09ubi5vvvkmUVFR9O3bV2f80qVLOX78ODt37mTu3Lns2LFD7//S69evc+bMGVq3bo1KpcLExIQTJ07oXMkDxReeFUII8eQkXYYQQgghXkjGxsbMmTMHCwsLpU2lUmFubs5nn332DFf2/OjYsSOVKlXCxMQEY2NjTExMqFatGm3btn3WS3vmDBW9KloAbd26dXTo0EHpi4mJQa1W85///EdJ2WFqaoqbm5uSviMiIoIaNWooxa0osoDzAAAgAElEQVRCQ0N1zrlkyRKcnJxwcnLCx8eHmJgYpc/Pzw9vb2/l/pEjRwzmIy0oKGDMmDG4urri5uZGs2bNOH/+PFD4xYd2PWq1utiUKOL59M4777B06VKysrKUfOTXr18nPDycr776SrkKQhvsvXDhAhqNRsnJbCjHfEpKCpaWllSqVAko3C3av39//vvf/9KsWTP27dv3RGtt2bIlt27dUq4UKM3AgQPZt2+fTvqip7keUbyMnDw+3X+SmEu3uJOdy9mr1/glZj8DAodiY2OjU7/g0ToEoF+LYOXKlaSkpDB48GCdvPzm5uZKailD87RMTEzo0qVLsf/Wbm5uBAUFsWPHDr7//nu9/tWrV3P37l1sbW2xsbEhNTVVySv9ZwvPCiGEKJkEmYUQQgjxwhoyZAjR0dH4+PhQq1YtXnvtNWJjY/Uuw31ZWVhYMGTIELp3707btm3p2bMnAwcO1Mlb/LIqrejVG2+8gZmZGatWrSI3N5eRI0eyePFi3nnnHaW4Wd26ddm9ezfx8fFK3ts+ffoQHx/PgQMHCAkJUYJqmzdvJjw8nJiYGJKTkwkLC6N///5cu3ZNOeeNGzfYunVrieuOjo7m6tWrJCYmcvz4cdavX0+VKlWUfu164uPjWbBgwZ99msTf5Pbt20RHR+sFw7Kysor90szCwoIFCxYwb9488vLyGDBgADExMezcuVOZO2bMGCXH/K5du8jMzAQK89+eO3eOBg0aPNF6k5OTyc/PV/LnlsbExISgoCCdL16e5npE8fZcuElWXj75/7sAJjXmZxq368ab32wi8dQZpX7B/v37sbe35+rVq5w8eRKACxcukJCQoPd/qkqlYvr06Rw6dEj5ou6VV15RrsLJyspi9erVBmsYaDQaDhw4oKSE0srIyNC5Kic+Pp6GDRvqzY+MjGTbtm1Kgde4uDglL/PTKDwrhBCieBJkFkIIIcQLzd/fn8OHD3Pt2jU2bdqkFMQThYyMjLC1tcXT05MGDRoUezn0y6SsRa8WLlzI1KlTCQ4OplmzZvj6+pb5HNWrV6dx48ZKwcXPP/+cOXPmKDv7PD09GTx4sLILFQpzeBsqkFZUWloaderUUfKUWltbl7kAm3h+Xb582eCXPxqNhlOnThU7r2nTpri7uxMZGYm5uTkbN25kxowZODo6KjvdR48eDUBcXBze3t64u7vTsmVLhg8frqQWKkqbk1l7W7t2LaBbfLNPnz58++23GBsXFuHS5mTW3gx9wTFs2DBlh3Zp6yka1Bw+fDhHjhwBICwsrNi8+MKw5Nvp5Bb8kWLp3N5tNGzZDhMjFRcfFAZge/XqRWRkJGZmZqxYsYIhQ4agVqt58803Wbp0KZUrV9Y7rrm5OePGjWPOnDkAfPnll6xbtw61Wk2LFi146623dK6aCQ0NRa1W4+rqSn5+PiNHjtQ5nkajYfbs2Tg6OqJWq5k2bZpeqozU1FQuXLhAixYtlDZbW1sqV67M4cOHy1R4VgghxJNTFVcQ56/m7e2t0b4ZEEIIIYQQ4nmxcuVKdu3axbJly/D19eWrr77Cy8uL1NRU/P39dfLUTp48mcWLF3Pu3Dm9S79tbGw4cuSI0h4REcGRI0dYuHAhFy9epEePHhw6dIjy5ctTrVo1zp8/rxOs2bhxI99++y3r1q3Dz8+PuXPnMnHiRP79739TsWJFxo8fr5dv+/Lly7Ru3ZoqVarQoUMH3n77baXgpI2NDRUrVlQCf4MHDyYoKOiveArFU5aenk6tWrX0dl0aGxvTr18//vvf/z6jlYl/um8TLxB75TYFj4QFTI2N+NjXkXoVzZ/NwoQQQjx3VCpVnEaj8S6uX3YyCyGEEEKIl15WVhYJCQn88ssvREREKAWntEWvDMnPz2fHjh1YWlpy4cKFMp0nOjoad3d3GjduzMiRIylfvvxjrXPq1KnMmDGj2H5ra2tOnTrFzJkzMTIyokOHDvz8889Kf9F0GRJg/ueoWLEiQUFBOjnmoXC36JQpU57RqsSLoINNTco9cgWLsQrqWpaXALMQQojHIkFmIYQQQgjxUrtw4QL/+c9/2LNnD9u3b2fv3r0MGDBAr+jVoxYvXoybmxvLli1j1KhRBsc8qk+fPiQmJhIbG8tHH32k5Fw2VJQtLi4OFxcXnbb27duTlZXFoUOHij2HmZkZXbt2Zc6cOXz88cds2LChLE+DeM7NmDGDuXPnYmtri6WlJR07diQmJgYnJ6dnvTTxD2ZdyZz3PBtRyawcpkYqyhmpcKxekTHNGj/rpQkhhPiHkSCzEEIIIYR4aeXn57Np0yZyc3PJy8sjISEBLy8vpk6dyo4dO3SKXhV17do15s+fz+zZs+nSpQv16tVj6dKlZT6vt7c3AwcO5MsvvwRg4sSJTJo0idu3bwOFRa0iIiL08pJC4W7m2bNnGzzu0aNHuXr1KgAFBQUkJiYaLI4l/nlUKhXvv/8+KSkppKens337dskxL54K95qVmdPejeC2zsxu70aQjz0VTcs962UJIYT4h5EgsxBCCCGEeGlduXJFZwfysWPHcHV1JTc3l6SkJOCPoldFffjhh0ycOJEaNWoA8MUXXxASEsKdO3fKfO5JkyaxfPly0tPT6dGjB0OHDsXX1xcnJyfeeecdVqxYQZ06dfTmdevWTTnvo27cuEH37t1xdXXF3d2dcuXKKYXdANq1a6cUXxs0aBCgWyztyJEjDB8+XBlftMCaEOLFZaRSUcPCTILLQgghnpgU/hNCCCGEEC+tCxcu8MMPP5CTk6PX16hRIwICAp7BqoQQQgghhHi+SOE/IYQQQgghilGvXj2D7SYmJnr5kIUQQgghhBCGSZBZCCGEEEK8tMqVK4e/vz/lypXD2NgYKAwwN2rUCHt7+2e8OiGEEEIIIf4ZJOGSEEIIIYR4qdna2vLOO++QnJzMw4cPadiwIXXq1EGlUj3rpQkhhBBCCPGPIEFmIYQQQgjx0rOwsMDT0/NZL0MIIYQQQoh/JEmXIYQQQgghhBBCCCGEEOKJSZBZCCGEEEIIIYQQQgghxBOTILMQQgghhBBCCCGEEEKIJyZBZiGEEEIIIYQQQgghhBBPTILMQgghhBBCCCGEEEIIIZ6YBJmFEEIIIYQQQgghhBBCPDEJMgshhBBCCCGEEEIIIYR4YhJkFkIIIYQQQgghhBBCCPHEJMgshBBCCCGEEEIIIYQQ4olJkFkIIYQQQgghhBBCCCHEE5MgsxBCCCGEEEIIIYQQQognJkFmIYQQQgghhBBCCCGEEE9MgsxCCCGEEEIIIYQQQgghnpgEmYUQQgghhBBCCCGEEEI8MQkyCyGEEEIIIYQQQgghhHhiEmQWQgghhBBCCCGEEEII8cQkyCyEEEIIIYQQQgghhBDiiUmQWQghhBBCCCGEEEIIIcQTkyCzEEIIIYQQQgghhBBCiCcmQWbxUgkJCcHFxQV3d3fUajWHDx8mICAAtVpN48aNqVy5Mmq1GrVaTWxsLDk5OYwdO5bGjRtjb2/P66+/zuXLl5XjXbt2jb59+2JnZ4eXlxfdunXj9OnTpKam4urqqoz7z3/+g5eXF3fv3iUwMJC1a9fqrMvS0lL5OSkpifbt2+Po6Ii9vT3Tp09Ho9EAEBERwejRo/Uel42NDW3atNFpU6vVOmsQQgghhBBCCCGEEOKvUO5ZL0CIv8vBgwfZvHkzR48exczMjFu3bpGTk8P69esB2LNnD3PnzmXz5s3KnPHjx5Oens6pU6cwNjZm+fLlvPHGGxw+fBiAgIAABg8eTFRUFAAJCQlcv36d+vXrK8f473//y1dffcWuXbuoWrVqiWvMysqiR48efP3113Tq1InMzEx69erF4sWLGTVqVIlz09PTuXTpEvXr1+fkyZNP9BwJIYQQQgghhBBCCPG4ZCezeGmkpaVhZWWFmZkZAFZWVtStW7fY8ZmZmSxfvpzQ0FCMjY0BGDJkCGZmZuzatYvdu3djYmLCiBEjlDkeHh46O4pXr17NrFmz2L59O1ZWVqWucdWqVbRq1YpOnToBYGFhwcKFC5k1a1apc3v37k10dDQAkZGR9OvXr9Q5QgghhBBCCCGEEEL8WRJkFi+NTp06cenSJRwcHBg5ciR79+4tcfzZs2dp0KABlSpV0mn39vYmKSmJEydO4OXlVez8CxcuMHr0aLZv307t2rV1+iZMmKCk5VCr1Up7UlKS3jHt7OzIyMjgwYMHJa63V69erFu3DoBNmzbRvXv3EscLIYQQQgghhBBCCPE0SLoM8UJLOHOT7378jcs3M6hdzYLwFVvIunWa3bt306dPH2bNmkVgYOBfcu4aNWpQrVo1Vq9eTVBQkE7fnDlzePPNN5X7RXMyP6nq1atTtWpVoqKiaNKkCRYWFn/6mEIIIYQQQgghhBBClEZ2MosX1tFTN/h02SFOX7pHZnYeKVcfMGfFUUyq2vN///d/LFy4kO+//77Y+XZ2dly8eJH09HSd9ri4OFxcXHBxcSEuLq7Y+RYWFmzZsoWwsDBWrlxZpjU7OzvrHTMlJQVLS0u9HdWG9OnTh1GjRkmqDCGEEEIIIYQQQgjxt5Egs3hhffNDEjm5Bcr9jDtXuH3jEt9sSgIgPj6ehg0bFju/QoUKDB48mA8//JD8/HwAvvvuOzIzM2nfvj3t27fn4cOHLFmyRJmTmJjI/v37lfs1a9Zk27ZtfPzxx/z000+lrnnAgAHExMSwc+dOoLAQ4JgxY5g4cWKZHnNAQAATJ06kc+fOZRovhBBCCCGEEEIIIcSfJUFm8cK6clN3B3JebhYJ275kdegw3N3d+e233wgODi7xGDNnzqR8+fI4ODhgb2/PmjVrWL9+PSqVCpVKxfr169m5cyd2dna4uLgwefJkvfzLtra2/PDDDwwdOpRffvmlxPOZm5uzceNGZsyYgaOjI25ubjRr1ozRo0crYyIiIrC2tlZuly9fVvoqVqzIpEmTMDU1LfE8RfNADx8+nCNHjgAQFhZGWFhYiXOFEEIIIYQQQgghhChKpdFonsmJvb29NdrAlhB/hWEztnPjbpZee0ULE1ZN7/YMViSEEEIIIYQQQgghxD+PSqWK02g03sX1y05m8cLq29ERMxNjnTYzU2N6v+rwjFYkhBBCCPFiCAkJwcXFBXd3d9RqNYcPHwYgJyeHsWPH0rhxY+zt7Xn99dd1rroyNjZGrVYrt/DwcOVnS0tLHB0dUavVDBo0iKZNmxIfHw9AXl4elpaWrFixQjmWl5cXR48eVe737NmTFi1a6KwzODiYevXqoVarcXZ2JjIyUukLDAzE1tZWOb+vr2+xj3fs2LHUq1ePgoKCYscIIYQQQrzMJMgsXlgdmzdk8GvOWJqbUM7YCIvy5ejzqgOvt7V71ksTQgghxDOiDXJ6eHjg6elJbGys0peUlET79u1xdHTE3t6e6dOno73qLyIigho1aqBWq3FyciI0NFSZFxgYyNq1a3XOY2lpCUBBQQFjxozB1dVVSYN1/vx5AL755hvc3Nxwd3fH1dWVjRs3GjzerVu3MDExKTGllZ+fH46Ojri7u+Pk5MTo0aO5d+/en3y2DDt48CCbN2/m6NGjJCYmsnPnTurXrw/Axx9/THp6OqdOneLMmTP07NmTN954Q3kezc3NiY+PV27vvfee8rO3tzcrV64kPj6e7777jlatWin/PgkJCTg4OCj3f//9d86dO4eHhwcA9+7dIy4ujvv375OSkqKz3qCgIOLj49m4cSPvvfceubm5St+cOXOU8xd9LRRVUFDA+vXrqV+/Pnv37n26T6YQQgghxAtCgszihda9TSNWfNqV74I7s2p6N97q4IBKpXrWyxLipaNSqXj77beV+3l5edSoUQN/f39AN3ijvSUkJCg/V6tWTdlt9uqrr5Kamoq5ubmyM23QoEE6QYOYmBh8fHxwcnLCyclJp0CnIYZ2vwGsWLECd3d3XFxc8PDwYPjw4X9Z0EaIv4M28FlUSQHSor9rhnaempqa4ubmhlqt5qOPPiIiIkKnjgAUBj+LpkiLj49HpVKxbds2nXEqlYpx48Yp9+fOnavUTjh16hR+fn6o1WqaNGnCu+++qzP3cXaZaoOcCQkJzJw5k8mTJwOFxXZ79OjBRx99xKlTp0hISCA2NpbFixcrc/v06UN8fDwHDhwgJCSES5culXq+6Ohorl69SmJiIsePH2f9+vVUqVKFy5cvExISQkxMDImJiRw6dAh3d3eDx1izZg0tWrTQ2YVryMqVK0lMTCQxMREzMzNef/31Utf3JNLS0rCyssLMzAwAKysr6tatS2ZmJsuXLyc0NBRj48KryYYMGYKZmRm7du167PP4+voqgd/Y2FhGjBih7Gz+5Zdf8PLyUs6zbt06unfvTt++fYmKijJ4PHt7eywsLLh79+5jrWPPnj24uLjw/vvvl/pvIIQQQgjxspIgs3jhGRupqGhhirGRBJeFeFYqVKjAiRMnyMoqzJO+Y8cO6tWrpzNGG7zR3jw8PJSfe/Tooew227lzJwB2dnbEx8dz/PhxLl++zOrVqwG4du0a/fv3JywsjOTkZGJiYggPD+fHH380uLbidr9t27aN0NBQtm7dSlJSEkePHsXX15fr16//FU/R32bDhg2oVCqSk5OVNm0gsWnTpjRp0gQfHx8iIiKUPmtra73gXdHL47X3+/btW+K5lyxZogT+fXx8iImJUfq0uzA9PDxo1aoVp06dIiAgALVaTePGjalcubIS2Hx0t+HUqVOxtLTk1q1bSpuhYKp4MtrfNUM7T+vWrcvu3buJj49n1qxZZTpeZGQkrVu31gvWmZmZsW7dOp1/R60xY8You1FPnjzJBx98oPT9mV2mDx48oGrVqgCsWrWKVq1a0alTJwAsLCxYuHChwcdVvXp1GjduTFpaWqnnSEtLo06dOhgZFb7ttra2pmrVqty4cYOKFSsqr1VLS0tsbW0NHiMyMpJ58+Zx5coVndQTxTE1NWX27NlcvHiRhISEUsc/rk6dOnHp0iUcHBwYOXKk8ryfPXuWBg0aUKlSJZ3x3t7eJCUlAYXBfO3vckBAQInnKbqTOTY2lrZt22JmZkZ6ejqxsbE66S0iIyPp168f/fr1KzYQfPToUezt7alZs6bSNmHCBGU9AwYMMDhPe+yAgAB+/PFHnS81hRBCCCFEIQkyCyGE+Ft069ZNCfRqP7A/DcbGxvj4+HDlyhUAFi1aRGBgIJ6enkDhDrvZs2cXGwArbvdbSEgIc+fOVYLhxsbGDB06FEdHx6ey7meluACfnZ0dx44d4+TJk0RFRfHFF1+wfPlybGxsaNCgAfv371fGJicnk56eTvPmzQE4efIk+fn57N+/n99//93geTdv3kx4eDgxMTEkJycTFhZG//79uXbtmjJm5cqVJCQkMHjwYCZMmMD69euJj49n6dKltGnTRglsGsqbWq1aNZ30BeL5pNFoWLNmDREREezYsYPs7Gylr1y5crz77rsG/x3T0tKwtrZW7ru5uSk/P+4uU22Q08nJieHDh/Pvf/8bKEyV4eXlpTPWzs6OjIwMHjx4oNN+8eJFsrOzi915XFTv3r3ZtGkTarWacePGcezYMQA8PDyoVasWtra2DBkyhE2bNhmcf+nSJdLS0vDx8aF3795ER0eXek4o/Jvl4eGh84XSn5Wdnc3Jkye5cOEC+/fvZ8mSJdSoUYM+ffooX0yVpmi6jPXr15c4tmHDhuTk5HDt2jWSk5NxdHSkWbNmHD58mNjYWFq1agXA9evXOXPmDK1bt8bBwQETExNOnDihHCc0NBQXFxeaN2/OlClTdM5RNF3GypUr9daQk5PDli1b6NmzJ5UqVaJ58+b89NNPZXqsQgghhBAvEwkyCyGE+Ftog7jZ2dkkJiYqAUqt6OhonUvytbueS5Odnc3hw4fp0qULYDhQVHQX3aOK2/2WlJSkBKpfFBkZGcTExLBs2bJiLycHaNSoEfPnz2fBggUA9OvXT2d8VFSUzq7lyMhIBg4cSKdOnZScso/6/PPPmTNnDlZWVgB4enoyePBgFi1apDe2bdu2nD179rEe2/Dhw1m5ciX3799/rHmidOfOnVN+L0eNGlXq+Ed/l4umyoiNjcXW1hY7Ozv8/Pz0rjAYNWqUwX/HoKAg2rdvT9euXQkNDdVJW1PaLtPff/+dTZs2ERoaSmhoKKamphw4cIDk5GS2bdvGoEGDlHzBZXls7u7uNG7cmJEjR1K+fHkAg6m4tG3W1tacOnWKmTNnYmRkRIcOHfj5558xNjZm27ZtrF27FgcHB4KCgpT0II+es3fv3kDh39HHSddQ1sdVFqdPn/5/9u49Luf7f/z441KiRIhZDikppeJSiSJynJGc9hE2c/jw+Wwz5zbCBzM+GCbDsA/TDBczqxz2cUyU2Fa6lJwSl1NzZkml0/X7o1/vr0tXTh/T8Lzfbt1uvV+n9+t6d9XV9bxe7+eLFStWsHv3bvbu3cuqVauwtrbms88+Y8mSJWzevBkHBwcuXLjA3bt3DfomJCTg6ur6TOf19fVl06ZN2NjYoFKpaNmyJQcPHuTXX3/Fx8cHgB9++IHbt29jb2+PnZ0dOp3O4DqNHTuWlJQUNm/ezN///neDDzceZ+fOndy5cwd3d3fs7OyIjY2VlBlCCCGEEEZIkFkIIcSf4uzZs2zevJmEhAQAmjRporzx79q1a4n2D6fLMDc3f+T4xYGvWrVqYWNj80QrCh/2uNVvxZKTk1Gr1Tg4ODzxKsK/osjISLp06YKTkxPW1tbKz8YYDw8PZQVk3759iYiIID8/HygKej24En3jxo3069fvkbepP03wf+vWrQYrVZ9ElSpVeP/995XAuPg/hYWFHDlyxCC9yYMeFSAFw3QZxj4UeNjDv8teXl5KnUajUT6gMBYwLe3nOGTIEE6cOMHf/vY3oqOjadmyJffv33/sKtOCggLWr19PamoqhYWFytf69espKCjAx8eHGzducP36dRo3blzid+Ls2bNYWloq6R+CgoJISkoiLi6OiRMnKivxra2tDfL83rp1S/lABYpSgbz99tvMmzePSZMmERERoVxnb29vQkJC2LBhA5s3by5xPTUaDWFhYdjZ2REYGEhSUhKpqamP/TkUFBSQnJyMi4vLY9s+TlZWFv/973/Jz88nLy+Py5cvc+XKFaKiorhz5w5arZb69etTqVIlBg0axLhx4ygoKABgzZo1ZGVl0b59+2c6t6+vL6GhoUpA2cfHhzVr1vDmm29iZWUFFF2jHTt2oNPp0Ol0JCQkGP0gLTAwEC8vL7777rsnPr9Go2HlypXK2OfOnWP37t1kZWU90+MRQgghhHhVSZBZCCHEc5Wfn8+7776Lq6srQ4cOpW3btmRnZ3Pz5k0CAwMJDg5+LqkyigNfaWlpJCQksGXLFgCjgaLSVtE9avWbq6srR44cAYpuzddqtbz99ttPvML6r+Lu3btER0eTlJT02ADfgx5cAVmrVi3c3NzYu3cvWq0WU1NT3NzcAIiPj6dGjRrY2trSoUMHEhMTuXXr1jPN9d1330WtVnPw4EHmz5//1P3HjBnDqlWryMzMfKbzv4oSExOpX78+bdu2pVOnTty7d49du3YZtHlcgPR5KSgoYPPmzcyYMQM7OztGjhzJjh07Sqx6Lf45Ppx6pXbt2gwdOpTIyEhMTU05duzYY1eZpqWlkZ2dXWJFb3Z2NmfOnOHkyZMUFBRgbW3Nu+++S2xsrJL3PTs7m1GjRvHpp5+WeCxeXl4MHDiQRYsWAUU5xTdu3Ehubi5QtJlpu3btgKI8wOnp6UBRwD8pKYn69euTnp6u/I0BlEDtg06fPk1mZiaXL19WgpwhISGPXUmbl5dHSEgI9erVe6YP4B728J0Fubm5bNiwgdmzZ+Pl5cXx48eVVdizZ8+mYsWKODk54ejoyKZNmwgPD3/mjZdbtWrF2bNnlSCzjY0NBQUFStocnU7H+fPnDTZvtbe3x8rKyugHK1OnTuXLL79U8sw/mJNZrVYrP0MoCq7v2LGDbt26KWWVKlWidevWbN26lfj4eIYNG6bUqdVq5fthw4YZrOIXQgghhHjVmZb1BIQQQrxaQkNDiYiIICcnx+CW5MGDB7Ns2TKqVq2Ku7s70dHRz+V8NWrUYM6cOcyePZvAwEBGjBhBixYt6N27N2q1mps3bzJhwgSmTp1aom/x6rfi4MW5c+fo2LEjs2bNIiQkhODgYCIjI5VcsC9bgHnRokWEhIRQvnx58vPzyc7OVoLEBQUFqFQq5s2bZ7RvYmKiwQrI4pQZtWrVMviQQKPRcPLkSezs7ICijdQ2b97M8OHDDcYrDv4/uJrx4eD/unXrDFa9Pq3q1avTt29fli9f/sxjvEqys7Pp0KGDQQAZoFevXpw+fVrJN+7v709oaCiDBg3CzMzMIED6PO3du5cmTZoYrDQeNGgQ4eHhvP/++0pZ8c9x1apVDB06FCjaiLNDhw6UL1+eK1eucPPmTerUqcOCBQtYuXKl8py8d+8e9vb2ZGVlYWFhwa1bt0qkz8jLy2Pu3LksWbIEc3NzvvvuO0xMTDA3NycyMpKRI0cyYsQICgoKGDhwIB9//LHRxzNhwgQ8PDyYNGkSAQEBJCQk4OnpiYmJCQ4ODsrz8Nq1awwfPpz79+8D4O3tzccff8zVq1cJDg4mPT2dihUrUrNmzRLPXY1GU2JzvD59+hAUFGT0b9q7775LhQoVuH//Ph07djRIX9O1a1dWrlxJ7dq1mTp1Kl5eXgQGBrJlyxbi4+OZMWNGqT+7/Px8g0B93bp1lc0XW7RoQevWrZW6ChUqsHjxYhYvXmx0rEd9CGTsdaF58+YlPiTQ6XTK93Z2dkpO/gcVB/AfTs3k6enJqVOnAB6bR7r4OfSwn376Sfl+5cqVyvdardZouRBCCCHE60CCzEIIIZ6rr7/+2uhtxLt27aJq1aqMGjXKaL+NGzcSGxtrMKBmqeAAACAASURBVI6xDd6M6dmzJ9OnTycmJgY/Pz/Wrl3L8OHDuXv3Lnq9njFjxtC9e3eDPo9b/da1a1euX7/O22+/TUFBAVWrVsXNzY233nrrkXN5MGCTnp7OsGHD+PnnnwHDIM+fLTo6mkmTJpGdna0Ex1UqFdWqVSMpKQmVSkXbtm2JiYnB1tbWoK9OpyM4OFgJIgH07t2bkJAQLCws2Lt3L1C0KvOHH34gOTlZeUz79u3j888/LxFk/vTTT5kwYQI7duzA2toarVZLWFhYqSkcntX48ePx8fFRVim+zrZs2aKkOHlQVlYWjRs3pnLlyowbN45x48aVGiB9nkoLmC5btswgyAxFP8clS5Yox7t27WL06NFKDuR58+ZRpUoVduzYYTDXB1eZBgUFYW1tTfny5blx4wabNm1i2LBhzJs3j1WrVrFixQr8/PyYOnUqBQUFBAYGPvIDsMGDBzN48GDluHbt2gYbV06bNo1p06aV6NelSxclZ/yD6tevT1RUlNFzFQc/33nnnRJ1TZo04cSJEyXKH/fBXfHfIcAgoBwYGEhgYOAj+zZo0MBg889ipqamNGzY8JF9hRBCCCHE60H1PDcEeRpeXl56uYVMCCFePW+88QbXr18vUW5mZsalS5eoWbNmGczq9dOrVy8l7+uDKlWqxOHDh3Fzc+Orr77ixIkTTJgwARcXF5ydncnJyaFy5cp89NFHBgE1KArmX7lyhcOHDwOwf/9+JkyYoBxDUUqEOnXqkJiYiI2NjUH/ZcuWERoaikqlonLlyixYsIA2bdoARatp58+fb3Qlc3R0NPPnz2fbtm1K2eTJk2nVqhVdu3ZlypQp1KhRgzFjxgAwatQovv76ayXA+tZbb/H999/zxhtvGPQLDw8nOTnZ6IrQV8HSpUsJDg42usnZ6NGjCQ0NLYNZvVgFBQWsXr2au3fvKh88FD//hg4diomJSRnP8OURFxfHb7/9pvxemZqa4urqSseOHct4ZkIIIYQQ4kVQqVQJer2+1FtPJcgshBDiuRo6dCjff/99iRWUjo6OnDp16pnzcoqn06pVK+Li4kqUW1lZERkZSdu2bctgVuJFSklJoXnz5iXSvFhaWrJhwwaDPLOvsqysLPbu3UtaWhpQlM+9ffv2VKpUqYxn9vK5cuUKJ06coLCwkEaNGlGnTh35my6EEEII8ZqQILMQQogXKj09HQ8PDzIyMsjOzsbMzIzy5cuzc+dOWrVqVdbTe23MnTuXzz77rESA0cLCgqtXr2JpaVlGMxMv0uDBg/nxxx+VTfQsLCxo3rw5e/fulVW8QgghhBBCiCcmQWYhhBAv3O3bt1m5ciUxMTE4Ozvz0UcfKRvDiRcjIyMDDw8P0tPTyc7ORqVSYW5uzhdffMGIESPKenriBSnOm71ixQpyc3MZOHAgQ4cOxczMrKynJoQQQgghhHiJSJBZCCGEeE1lZGSwYsUKIiMjqVWrFqNHj1ZyIAshhBBCCCGEEE9KgsxCCCGEEEIIIYQQQgghntnjgszlXuRkhBBCCCGEEEIIIYQQQrxaJMgshBBCCCGEEEIIIYQQ4plJkFkIIYQQQgghhBBCCCHEM5MgsxBCCCGEEEIIIYQQQohnJkFmIYQQQgghhBBCCCGEEM9MgsxCCCGEEEIIIYQQQgghnpkEmYUQQgghhBBCCCGEEEI8Mwkyi1dSREQEKpWKkydPAqDT6VCpVCxevFhp8/HHHxMWFgaAXq9n5syZODo64uTkRLt27UhJSSmLqQshhBBCCCGEEEII8VKRILN4JWk0Glq3bo1Go1HK3njjDRYtWkRubm6J9kuXLiUuLo6jR49y+vRpQkJCCAwMJCcn50VO+5WTk5ODt7c3TZs2xdXVlWnTpgEQGRlJz549lXazZ8+mYcOGyvHWrVsJDAwsMZ6/vz+NGjWiadOmtGrVilOnTgGQm5vLmDFjaNiwIY6OjvTo0YNLly4BMHbsWEJDQ5Ux3nrrLYYNG6Ycjx8/ni+//BKdToe5uTlqtVr5WrNmDQB2dna4u7vTpEkT2rZty/nz540+3m+//VZp5+bmRmRk5LNeOiGEEEIIIYQQQoiXhgSZxSsnMzOT2NhYVq1axYYNG5TymjVr0qFDB7777rsSfebOncuSJUuwsLAAoHPnzvj6+rJu3boXNu9XUYUKFYiKiuLo0aNotVp27NjB4cOH8fX15fDhw0q7Q4cOUaVKFa5duwZAXFwcvr6+Rsdct24dR48eZdCgQXzyyScATJo0ibt373Lq1ClSU1Pp2bMnvXv3Rq/X06pVK+Li4gAoLCzkxo0bBqvUHzyXg4MDWq1W+Xr//feVdvv27SMpKQl/f39mzpxZYl6XLl1i1qxZxMbGkpSUxOHDh2nSpMn/eAWFEEIIIYQQQggh/vokyCxeGfkFhdzLziMiIoIuXbrg5OSEtbU1CQkJSpsJEyYwf/58CgoKlLKMjAzu3btHgwYNDMbz8vKSlBn/I5VKhaWlJQB5eXnk5eWhUqmoWbMmVapU4cyZMwBcvnyZPn36KMHguLg4WrVq9cix27Rpw5kzZ8jKymL16tUsXLgQExMTAIYMGaIEuH19fTl06BAAKSkpuLm5UblyZW7fvs39+/c5ceIEHh4eT/yYfHx8uHz5conya9euUblyZeXxWlpaYm9v/8TjCiGEEEIIIYQQQrysJMgsXnp5+YWsCE8maPJ23p36X0I+X4y6ZWcA+vXrZ5Ayo0GDBrRo0YL169eX1XRfOwUFBajVat544w06depEixYtAJQVxqdOncLR0ZGWLVsSFxdHfn4+R48epXnz5o8cd+vWrbi7u3PmzBlsbW2pUqWKQX3xhwS1a9fG1NSUCxcuEBcXh4+PDy1atODQoUPEx8fj7u6OmZkZAGlpaQbpMmJiYkqcd8eOHQapPoo1bdqUWrVqYW9vz5AhQ9i6deuzXjIhhBBCCCGEEEKIl4ppWU9AiP/V0h+1xGgvk5tXSG72XdLPapkYPJI5n31COZUelUrFiBEjlPaTJk3inXfeoW3btgBUqVKFSpUqcfbsWYPVzAkJCUob8XT0ej2//fYb169fp2XLlmi1Wu7cuUOvXr04duwYbm5u+Pr6EhcXR0FBAT4+Pnh7ezNjxgwSExNxdnamYsWKRsd+9913MTc3x87OjsWLF3P79u3Hzqf4XHFxcYwbN47Lly8TFxeHlZWVwYrp4nQZxrRr145bt25haWnJ559/XqLexMSEHTt28Ntvv7F3717Gjh1LQkIC06dPf7KLJoQQQgghhBBCCPGSkpXM4qWWmZXLgcSiADPA76lx1HXxp/2w/zB82kYuXryIvb09Fy9eVPo4OzvTuHFjg5Wmn3zyCaNGjSI7OxuAPXv2EBsby4ABA17sA3oFnD9/HmdnZzp06MCAAQOoW7cuM2fOpGrVqrRr144dO3YA/7eSuXh1ceXKlcnJySE6OrrUfMxQlJNZq9USERFBvXr1cHBw4MKFC9y9e9egXUJCAq6urgbnSk5Oxs3NjZYtW3Lo0KFH5n5+2L59+zh//jxqtVrZwPBhKpUKb29vQkJC2LBhA5s3b36isYUQQgghhBBCCCFeZhJkFi+1m3/kYGryf0/j9JMxvNmwJQCXr2cC0KdPH2bPnm3Qb/LkyVy6dEk5HjlyJM2bN8fd3Z1GjRrx+eefExkZibm5+Qt4FK8OvV5Pt27dOHPmDJmZmWRkZJCTk8Ps2bOJiIhg9+7dODs7A+Di4kJ6ejqxsbE0a9YMALVazfLlyx+bj/lBlSpVYtCgQYwbN07Jtb1mzRqysrJo3749ULSSedu2bVSvXh0TExOqV6/OnTt3OHTo0BMHmQFMTU0JDQ1lzZo13Lp1y6AuPT2dI0eOKMdarZb69es/8dhCCCGe3dixYwkNDVWO33rrLYYNG6Ycjx8/ni+//BKdToebmxsA0dHRWFlZoVarcXZ2Jjg4WGkfFhZGzZo1DVIoHT9+HIDTp0/TtWtXHB0d8fDwoG/fvly9elXpO2bMGOrUqUNhYaHR8ZydnVm4cKFSN336dOrUqYNarcbR0ZHevXsr5zImPz+fmjVrMnHixBLlkyZNwtHRUZnzrFmznvZSCiGEEEII8UwkyCxearWqW1BQqFeOffrO5A17D8qpwKleNQBGjRrFf//7X44dO6a0a9q0KYWFhQwePBgoWoE6bdo0zpw5w6lTp9i/fz/u7u4v9LG8Ck6cOMG5c+cM3lgDZGVlMXDgQDp16kRAQABQdM1btGiBtbU15cuXB4o21Tt79uxTBX4BZs+eTcWKFXFycsLR0ZFNmzYRHh6OSqUCwN3dnRs3btCyZUulj7u7O1ZWVtSoUUMpezgn81dffVXiXDY2NvTv35+lS5calOfl5REcHIyzszNqtZqNGzeyaNEiAJYvX87y5csBiI+PNwh8qNXqp3qsQgghSiq+YwWgsLCQGzduGGzeW9qdK35+fmi1WhITE9m2bRsHDx5U6oKCgtBqtcpX48aNycnJoVu3bnz44YekpqZy5MgRPvroI65fv66cOzw8nHr16rF//36DcxWPd/DgQWbNmmVwl9XYsWPRarWkpqYSFBRE+/btlTEftnv3bpycnNi0aRN6/f/9DzRlyhTS09NJTk5Gq9USExNDXl7eM1xNIYQQQgghnp7kZBYvtYoVTOndzoHwfWnczytQys3Km9Cvc6MynNnr6c6dO5iaGv+z0rhxY6ZOnWpQtn37doPjwYMHK4F/Y6Kjo42WV6hQgcWLF7N48WKj9SYmJmRkZBiUhYWFGRzb2dkp6VIeptPpDI6Nnad+/fpERUUZ7f/BBx8o33t5ebFy5UrluLQc0EIIIZ6cr68vY8eOBSAlJQU3Nzd+//13bt++jYWFBSdOnMDDw4P09HSj/c3NzVGr1Vy+fPmR51m/fj0+Pj50795dKfP391e+j46OxtXVlaCgIDQaDe3atSsxhrW1NQ0bNuT333+nXr16JeqDgoLYvn0769evZ/To0SXqNRoNo0ePZtmyZcodOVlZWfznP/9Bp9MpexpUrlxZ9gUQQgghhBAvjASZxUtvQGdnaliZsznqDHcy79OofjWGBLhSr1blsp7aa6dZs2YlVjEDVKxYkd69e5fBjIQQQrzq9Ho9tWrVwtTUlAsXLii5/i9fvsyhQ4ewsrLC3d0dMzOzUse4ffs2qamptGnTRinbuHEjsbGxyvGhQ4c4duwYnp6epY6j0Wjo378/PXr0YNKkSeTl5Sl36xS7cOECOTk5NGnSpNRxPDw8OHnyZInynJwc9uzZw4oVK7hz5w4ajQZfX1/OnDmDra0tlSvL/z5CCCGEEKJsSLoM8dJTqVS81dKObyZ15Id/d+Pzf/rSoI5VWU/rtWRubs6SJUuwsLCgXLmiPy8WFhbY2try0UcflfHshBBCvEoKCws5ePAgS5YsITQ0lFq1ahEZGakEmX18fJQNZkvL9R8TE0PTpk2pU6cOb731Fm+++aZS93C6jMft05Cbm8vPP/9Mz549qVKlCi1atGDnzp1K/caNG2nSpAkNGzbko48+UlYcG/NgGowHbdu2jXbt2mFubk6fPn2IiIhQ9iN40OrVq1Gr1dSrV88gLYcQQgghhBB/FgkyCyGeq0GDBrF//34lB/OcOXNISEiQ1VVCCCGeq+joaOLj48nNzQWgdu3abNq0iSNHjuDm5kbLli05dOhQqfmYoSgn89GjR0lJSWHVqlWPTWHk6upKQkKC0bqdO3dy584d3N3dsbOzIzY2Fo1Go9QHBQWRlJREXFwcEydO5MqVK6WeJzExERcXlxLlGo2GPXv2YGdnh6enJzdv3iQqKoqGDRty4cIF7t69C8CQIUPQarVYWVkZDUILIYQQQgjxvEmQWQjx3Hl5eREWFsauXbsYOXIklpaWZT0lIcRr7sqVK/Tr1w8HBwc8PT3p2rUrp0+fRqfTKfl4i7/WrFkDQGZmJv/85z+VPv7+/vzyyy8AXLp0iR49euDo6IiDgwOjR49Wgp3R0dGoVCq2bt2qnD8gIEDJK5+bm8uYMWNo2LAhjo6O9OjRg0uXLiltVSoV7733nnKcn59PzZo1CQgIICUlBScnJ4Mc8t26dTMIZj5ozJgx1KlTx2gqo5dZbm4uycnJ5OfnK2V2dnakpKSgUqkwMTGhevXq3LlzR8lb/Cj29vZMnDiRuXPnPrLdgAEDiIuLM9hT4MCBAxw7dgyNRsPKlSvR6XTodDrOnTvH7t27ycrKMhjDy8uLgQMHKpvDPmzz5s3s2rWL/v37G5RnZGQQExPDhQsXlHMsXboUjUaDhYUFf//73/n444/JyckBoKCgQHlOCiGEEEII8WeTILMQQgghXml6vZ5evXrh7+9PWloaCQkJzJ49m6tXrwLg4OBgkBbh/fffB2DYsGFUr16d1NRUEhISWL16NTdu3ECv19O7d2969uxJamoqp0+fJjMzk8mTJyvnrFu3LrNmzTI6n0mTJnH37l1OnTpFamoqPXv2pHfv3kqKhEqVKnHs2DElkLx7927q1KkDFK2k7d27tzJ2REQEeXl5JQKSUJROIjw8nHr16rF///7ndDX/GjIzM1GpVAZlNjY23Lt3j7p16ypl7u7uWFlZUaNGjceO+cEHH3DgwAFls9eNGzcafPgQFxeHubk527ZtY/HixTg6OtK4cWO+/vprKleuzI4dO+jWrZsyXqVKlWjdurXBhw3FJkyYwOrVq5WVxwsXLkStVuPo6MjatWuJioqiZs2aBn3Cw8Np3749FSpUUMp69OjB1q1buX//PrNmzcLGxgY3NzeaNWuGn58fgwYNonbt2o+/oEIIIYQQQvyPVKXlfPuzeXl56ePj48vk3EIIIYR4fURFRTF9+nQOHDhQok6n0xEQEMCxY8cMytPS0ujYsSNnzpzBxMTEoG7v3r189tlnBuNlZGRgb2/PxYsX+fXXX5k/fz55eXkEBwfTqVMnAgICCA4Oxtvbm3r16nHu3DmqVKmi9Pfz82P69Ol06NABS0tLRo0ahYeHB++88w7vv/8+rq6uxMTEsG3bNrKysmjWrBkbN26kX79+bN26FUdHR6OPe/78+QQFBXHw4EG++eab//VS/mXk5eXx9ddfG6xkLubg4EDPnj3LYFZCCCGEEEK8ulQqVYJer/cqrV5WMgshhBDilXbs2DE8PT1LrU9LSzNYsRoTE0NKSgpqtbpEgBkgJSWlxHhVqlTB1taWM2fOKGWTJ09m5syZBu3OnDmDra2tQYAZilIopKSkKMf9+vVjw4YN5OTkkJSURIsWLZQ6CwsL5s+fT5s2bejXr5/RADMU5e/t378/vXr1Yvv27eTl5ZV6DV425cuXx9PTE1NTU4NyU1NTfHx8ymhWQgghhBBCvL4kyPwXYCxf7fTp05k/fz4AgwcPpk6dOty/fx+AGzduYGdnB6DkkmzWrBkuLi54e3sTFhamjBMWFkbNmjUN3jwfP37c6DwiIiJQqVScPHnSoDw1NZWAgAAlJ2W7du2MrgYDiI2NxdvbG2dnZ5ydnV+pVVNCCCFeHoWFhSxbtgxnZ2emTJnCzp07uXjxotG2D6fL8PPzey5zaNOmDVD02vi0mjRpgk6nQ6PR0LVr1xL13bt3p2rVqnz00UdG++fm5vLzzz/Ts2dPqlSpQosWLdi5c+dTz+OvrFWrVvj5+WFpaYmJiQk2Nja888471KpVq6yn9koxMTFBrVbj5uZG9+7duXPnDlD0P6ibmxtQlIfcysoKtVpNkyZN6NixI9euXSsxVlZWFu+++y7u7u64ubnRunVrMjMzgcfnOS8eX61W07FjR2bNmqUcF89RrVYzY8YMrK2tlfQzhw4dQqVSKXnP//jjD6pXr67kKS/OeT5x4sQ/90IKIYQQQrziJMj8kjAxMeHbb781Wufg4EBiYiInTpxgw4YNhIaGsnr1aqU+KCjI4M1z48aNjY6j0Who3bq1weZBOTk5dOvWjX/84x9KHsvFixdz9uzZEv2vXLnCgAEDWL58OSdPniQ2NpYVK1YYbI4jhBBCvAgjR44kODiYU6dOcffuXU6cOIGHhwfXr19/ov6urq4cPXqUgoKCEnWNGzcmISHBoCwjI4MLFy7QsGFDg/KHVzM7ODhw4cIFJRdvsYSEBFxdXQ3KAgMDCQ4ONppvGaBcuXKUK2f8X7mdO3dy584d3N3dsbOzIzY2ttTNAV9WKpUKDw8P/vnPfzJmzBgGDBig5K4Wz4+5uTlarZZjx45RvXp1li5darSdn58fWq2WpKQkmjdvbrTdokWLqFWrFsnJyRw7doxVq1ZRvnz5J8pzXjy+Vqtlz549TJ48WTkunqNWq2Xq1KnY2Nhw4sQJAOLi4mjWrBlxcXEAHD58GG9vb+V3Z/fu3Tg5ObFp0ybKKo2gEEIIIcSrQILML4kxY8awcOFCo7kHH9SgQQO+/PJLvvrqq6caPzMzk9jYWFatWsWGDRuU8nXr1uHj40NgYKBS5ubmxuDBg0uMsXTpUgYPHoyHhwcANWrU4IsvvmDOnDlPNRchhBDif3HlyhVWrVpFVlaWQfmdO3dYsmQJAElJScTExJQ6hoODA15eXkybNk0JPOl0OrZv306HDh3IyspizZo1ABQUFDB+/HgGDx6MhYWFwTidO3fm9u3bJCUlAUWbwQ0aNIhx48YpAew1a9aQlZVF+/btDfoOHTqUadOm4e7u/tTXQKPRsHLlSnQ6HTqdjnPnzrF79+4S10SIp+Hj48Ply5cf2Uav13P37l2qVatWou733383+CCgUaNGVKhQgaioKCpWrMiQIUOAosUVCxcu5Ntvv32m56yvr68SVI6Li2Ps2LEGx61atVLaajQaRo8eja2tLYcOHXrqcwkhhBBCiCISZH5J2Nra0rp1a77//vvHtvXw8DBIefHw7ujFu9U/KDIyki5duuDk5IS1tbWyQislJUUJGj+OsRyVD+eYFEIIIf5sR48epWLFiiXK8/Pz+eKLL3B1dSUkJIQ333wTKJmTufiD2pUrV3L16lUaNmyofMD6xhtvoFKpCA8PZ9OmTTg6OuLk5ETFihX597//bXQ+kydPNkjVMXv2bCpWrIiTkxOOjo5s2rSJ8PBwVCqVQb+6desyatSop378WVlZ7Nixg27duilllSpVonXr1mzdupX4+HiGDRum1KnVauX7YcOGIRszC2MKCgrYu3evwcKDB8XExKBWq7G1tWXPnj0MHTq0RJuhQ4cyd+5cfHx8mDJlCqmpqcCT5TkvHl+tVjNr1qxHzrVVq1ZKUPns2bP87W9/U57XcXFx+Pr6AkV37O3Zs4fu3bvTv3//V261vxBCCCHEi2T6+CbizxAXF8fSpUu5fv06+fn5ZGdnY25u/sg+ISEh9OjRw+BNozEP3+oXFBSkrNwqTfEqDijabEij0RjdJKlXr16kpqbi5OTETz/99MgxhRBCiLJQv359JZfrg0xMTHjvvff4z3/+Y1Bu7MNXKApyPdy2WL169di6davROn9/f/z9/ZXjwMBAg9fmChUqsHjxYhYvXmy0f3GO2keNCUUrq42xsLDg1q1bJcoffN1euXKl8r1WqzVaLl5vOp2O8+fPk52djVqt5vLly7i4uNCpUyej7f38/Ni2bRsAc+fO5dNPP2X58uUGbdRqNWfPnmXXrl3s2bOH5s2bP/Hq4QfHfxxfX19mz57NuXPnsLOzo2LFiuj1ejIzM0lISFA20ty2bRvt2rXD3NycPn368PnnnxMaGmp0w08hhBBCCPFospK5DCxZsoROnTqh0WjYvXs39+/fp2XLlqW+yS3m6OiIWq3mhx9+eGS7xMREXFxcnng+t27dIioqimHDhmFnZ8e8efP44Ycf0Ov1uLq6cuTIEaVteHg4YWFhRt+8GstRaSzHpBBCCPFncnZ2xtPTEzMzM4PyChUqMHbs2DKalRAvh3v37tG1a1dcXFzo0aMHhYWF+Pn5ce7cOfR6fak5mR8UGBhY6ibRlpaW9O7dm6+//pr33nuPn3/++anynD8JR0dH7ty5w9atW/Hx8QHA09OT1atXY2dnp2y6rdFo2LNnD3Z2dnh6enLz5k2ioqKe+nxCCCGEEEKCzC/cH3/8waeffkpWVpbBqqYzZ84QFhb22P6TJ09m/vz5pdbrdDqCg4MZOXLkE8/pxx9/ZODAgZw/fx6dTsfFixext7cnJiaGAQMGcPDgQbZs2aK0Ly033ogRIwgLC1NWRN28eZMJEybw6aefPvFchBBCiOdh69atvP3225iZmVGxYkVsbW0JDw8vdfNbIUSRDz74gKioKHJycvjjjz8A+PbbbwkLC+Orr75iwYIFj90jJDY2FgcHhxLlBw8e5Pbt2wDk5uZy/Phx6tev/1R5zp9Uy5YtWbRokRJk9vHxITQ0VMnHnJGRQUxMDBcuXFByly9dulRSZgghhBBCPCMJMr9ghw8fpnz58iXKs7KyGDt2LHXr1uXLL78stb+rq2uJHMlpaWk0a9YMFxcX+vbty6hRo5SNU6BkTubiHHXFNBoNvXr1Mijr06cPGo0Gc3Nztm3bxvLly2nQoAE+Pj7MnDmTKVOmlJibjY0Na9euZfjw4Tg7O+Pr68vQoUPp3r07AFu2bGHq1KkApKen07VrV6Vv165dSU9PL/VxCyGEEE+jatWqREREcP36ddLS0tDpdHTu3LmspyXEX1pOTg6bNm3i/v37BuVZWVksXLiQZs2a0aRJE6OB2OKcyU2bNuX7779nwYIFJdqkpaXRtm1b3N3dadasGV5eXvTp0+ep85w/iVatWnHx4kW8vLyAoiDz2bNnlXzM4eHhtG/fngoVKih9evTowdatW7l//z5Tp05VFlk86n9YIYQQQghRRPVw/t4XxcvLS/86bizzyy+/0LFjxxL5FlUqFUFBQbJ6QgghhBBClIlbt25hY2NjNKd5tWrVjKZLE0IIIYQQrweVSpWg1+u9SquXlcwvmLe3NzVq1Cixy60ciQAAIABJREFUg7y5uTkjRowoo1kJIYQQQojXXbVq1ahTp06J8nLlytGuXbsymJEQQgghhHhZSJD5BVOpVOzcuRNbW1sqV65MlSpVqFixIrNmzaJ169ZlPT0hhBBCCPGaUqlUrFixAgsLC8qVK3qbYGZmRpUqVZg7d24Zz04IIYQQQvyVSbqMMqLX6/nll1+4ffs2vr6+WFlZlfWUhBBCCCGEIDk5mXnz5nH69Gl8fHwYP348devWLetpCSGEEEKIMvS4dBkSZBZCCCGEEEIIIYQQQghRKsnJLIQQQgghhBBCCCGEEOJPI0FmIYQQQgghhBBCCCGEEM9MgsxCCCGEEEIIIYQQQgghnpkEmYV4ShEREahUKk6ePKmU6XQ63NzcAIiOjsbKygq1Wo2zszPBwcFGx3mwXZMmTejYsSPXrl0DICwsjJo1a6JWq5Wv48ePo9PpMDc3R61W07hxY95//33y8vKU8QICAkqcx9/fn+L853Z2dvTp00ep+/HHHxk8ePBzuS5CCCGEEEIIIYQQ4vUkQWYhnpJGo6F169ZoNJpS2/j5+aHVaklMTGTbtm0cPHjwke2SkpJo3rw5S5cuVeqCgoLQarXKV+PGjQFwcHBAq9WSnJzMpUuX+OGHH55q/gkJCRw/fvyp+gghhBBCCCGEEEIIURoJMgvxFDIzM4mNjWXVqlVs2LDhse2LVx1fvnz5ke30ej13796lWrVqTzwXExMTvL29Hzv2w8aPH8+sWbOeqo8QQgghhBBCCCGEEKWRILN4rIfTQzwqZQNAbGws3t7eODs74+zszDfffFNWU3/uIiMj6dKlC05OTlhbW5OQkPDI9rdv3yY1NZU2bdoYrY+JiUGtVmNra8uePXsYOnSoUrdx40aDdBnZ2dkGfXNycvjll1/o0qXLUz2Gvn37cuTIEc6cOfNU/YQQQgghhBBCCCGEMEaCzOKxjKWHKC1lw5UrVxgwYADLly/n5MmTxMbGsmLFCrZv315W0/+fnNTdYvKyg7w37b98ujiGFSu/o1+/fgD069ev1JQZMTExNG3alDp16vDWW2/x5ptvGm1XnC7j4sWLDBkyhE8//VSpezhdhrm5OQBpaWmo1Wpq1aqFjY0NTZo0earHZGJiwieffMLs2bOfqp8QQgghhBBCCCGEEMZIkFk80uPSQzycsmHp0qUMHjwYDw8PAGrUqMEXX3zBnDlzXui8n4fktBtMXh5H0pkb/JGZy9ET54mL3c/7g4ZgZ2fHvHnz+OGHH9Dr9SX6+vn5cfToUVJSUli1ahVarfax5wsMDOTAgQOPbVcc4E9LSyMhIYEtW7Y89WMbOHAgBw4c4OLFi0/dVwghhBBCCCGEEEKIB0mQWTzS49JDPJyyISUlBU9PT4M2Xl5epKSkvLA5Py+rIo+Rm1egHP+eGkcdF38CPl6NTqfj4sWL2NvbExMTU+oY9vb2TJw4kblz5z72fLGxsTg4ODzx/GrUqMGcOXOeaUVy+fLlGTt2LAsXLnzqvkIIIYQQQgghhBBCPEiCzKKEe/fucerUKe7du4dGozGaHuJ/TdnwMjh/5a7BcfrJGN5s2JKrN7PILygEoE+fPqWmzCj2wQcfcODAAXQ6XYm64pzMTZs25fvvv2fBggVK3cM5mePi4kr079mzJ1lZWUqge+/evdStW1f5OnToUKnz+vvf/05+fr5yHB8fz7Bhw5RjtVqtfD9s2DDi4+Mf+TiFEEIIIYQQQgghxOtJZexW/xfBy8tLL0Grv5bCwkImTJjA0qVLMTExIT8/n/z8fGrXro1KpaKgoACVSsX+/fvp3r07x44d48aNG7Rq1Yp58+YRGBjIlClTKFeuHDNmzFDGjYqKYtq0aY9c8ftXNOTzndy4k1OivJK5KZrPu6JSqcpgVkIIIYQQQgghhBBCvFgqlSpBr9d7lVYvK5mFYs6cOXz99ddkZ2eTmZlJTk5RgHXEiBEG6SEezOP7cMqGESNGEBYWpuQgvnnzJhMmTDDY0O5l0bdjIyqYmRiUVShvQm//hhJgFkIIIYQQQgghhBDi/5Mgs1AsWLCArKwsg7L8/Hzmz5+vHPfp06dEDuAHUzbY2Niwdu1ahg8fjrOzM76+vgwdOpTu3bu/kMfwPHVpWZ9+HZ0wr2BKhfImVDAzIbBNA95p71TWUxNCCCGEEEIIIYQQ4i9D0mUIAPR6PSYmJhh7PhSnznhd5eUXcufufawszTArb/L4DkIIIYQQQgghhBBCvEIkXYZ4IiqVCldXV6N17u7uL3g2fy3lTctRs5q5BJiFEEIIIYQQQgghhDBCgsxCsWjRIiwsLJRjlUqFhYUFoaGhZTgrIYQQQgghhBBCCCHEX5kEmYWiffv2REVF0bVrV2xtbenatSvR0dG0bdu2rKcmhBBCCCGEEEIIIYT4izIt6wmIv5YWLVqwffv2sp6GEEIIIYQQQgghhBDiJSErmYUQQgghhBBCCCGEEEI8MwkyCyHEX0RERAQqlYqTJ08qZTqdDnNzc5o1a4aLiwve3t6EhYUp9WFhYXz88ccATJ8+nTp16qBWq3F0dKR3794cP37c4Bw3btygfPnyLF++3KD822+/xd3dnSZNmuDm5kZkZGSp81Sr1fTr169E+ZdffomzszPu7u40bdqUcePGkZeX9yyXQgghhHgilpaWBscPvi4OHjyYH3/8sdT2qampBAQE4ODggKenJ+3atePAgQNKfUREBE2aNMHFxQV3d3ciIiKMzuHB119nZ2c+/PBDCgsLAdDr9cycORNHR0ecnJxo164dKSkpSt/SXn8PHz5MixYtUKvVuLi4MH369Ge/SEIIIYQQL4AEmYUQ4i9Co9HQunVrNBqNQbmDgwOJiYmcOHGCDRs2EBoayurVq42OMXbsWLRaLampqQQFBdG+fXuuX7+u1G/atAkPDw+mTp1KgwYN8PT0xNPTk0mTJhEbG8tXX33FxYsXCQkJQa1Wo1ar2bNnj9J/8eLFHD16lKioKO7du6eUL1++nF27dnH48GH27NmDi4sLYWFhNG/eHB8fH8LDwwGIjo7GyspKeSMeHBxsMP9HvaH39/cnPj5eOdbpdLi5uZUY18XFhc8++6zU6xwaGkrFihX5448/DMp//fVX/P39cXR0xMPDg27dupGcnFzqOEIIIV5eOTk5dOvWjX/84x+kpaWRkJDA4sWLOXv2LABHjx4lODiYyMhITpw4wZYtWwgODiYpKcnoeMWvv8ePHyc5OZn9+/cDsHTpUuLi4jh69CinT58mJCSEwMBAcnJyuHTpErNmzSI2NpakpCQOHz5MkyZNABg0aBDffPMNWq2WY8eO0bdv3xdzYYQQQgghnpEEmYUQ4i8gMzOT2NhYVq1axYYNG0pt16BBA7788ku++uqrx44ZFBRE586dWb9+vVK2fv16srKy0Ov1HDhwgISEBGbMmIGJiYmyusvPz4/jx4+j1WrRarV07NhR6b98+XLs7OyoXbu2wWrnWbNmsWzZMqysrOjZsyf+/v7cunULrVbLhg0buHTpktLWz88PrVZLYmIi27Zt4+DBg8DTv6F/WPG48fHxrF27liNHjhhtp9FoaN68OT/99JNSdvXqVfr27cu///1vUlNTOXLkCCEhIaSlpT3RuYUQQrxc1q1bh4+PD4GBgUqZm5sbgwcPBmD+/PlMmjQJe3t7AOzt7QkJCWHevHmPHDc3N5ecnByqVasGwNy5c1myZAkWFhYAdO7cGV9fX9atW8e1a9eoXLmy8vpraWmpnO/atWvY2NgAYGJiQuPGjZ/fgxdCCCGE+BNIkFkIIf4CIiMj6dKlC05OTlhbW5OQkFBqWw8PD4OUGo/yYNuLFy9y9uxZqlevzvDhw9m4cSMAXbp0wc3NDXt7e+bMmcPVq1eNjpWZmcmpU6dYvXo1N2/eVFZcZ2RkkJmZib29PVFRUZiZmfHBBx8o/erXr8/IkSNLjGdubo5areby5cvAs7+hf1ilSpXw9PTkzJkzJerS0tLIzMxk5syZBivGlyxZwqBBg/D19VXKWrduTc+ePZ/q3EIIIV4MvV6PXq8nOztbufNGrVYzderUJ+qfkpKCh4fHI+s9PT0Nyry8vAxSXTxo4cKFqNVqbGxscHJyQq1Wk5GRwb1792jQoIHRcZo2bUqtWrWwt7dnyJAhbN26VWkzduxYGjVqRK9evVixYgU5OTlP9LiEEEIIIcqKBJmFEKKM5OTlEHU2jo3JW1i+eoVyK2y/fv1KpMx4kF6vf+JzFLct1BeyZt33NG7cGA8PD4NzmJiYsGPHDn788Ufq1avHkSNHePPNN5U37MWreRcuXIi1tTX+/v7UrVuXX375hVu3bhmcLyUlBWtra9RqNXZ2dsTFxZU6t9u3b5OamkqbNm2Uvk/zhr40N2/e5PDhw7i6upao27BhA/369cPPz49Tp04pAfXHBRuEEEL8NZw/f55u3bphZmaGubk55cqVY9++fcrdNzNmzFDaqlSqEv2NlQH06tULNzc3evfu/UzzKk6Xce3aNe7du/fIu5KKPfj66+TkxNixY5Xcy1OnTiU+Pl65I6lLly7PNC8hhBBCiBfFtKwnIIQQr6PLGVf419755BXkcfePDA7FHOJvyUFUM7eisKAQlUpV6grexMREXFxcnug8iYmJVLWvwT8jJ7L5mzAyr/5B7KGDhIeHk56ezrvvvsuxY8cwMzPjt99+Iysri5MnT3Lnzh20Wq3BWKtXryY7Oxs7Oztl9fLmzZsZPnw4lpaWnDt3DihaufzTTz8REBDAF198wblz55TxAWJiYmjatCmpqamMGTOGN99884key+OCBTExMTRr1oxy5coxceJEo0FmjUZDeHg45cqVo0+fPmzatEnZIOpBLVq0ICMjg86dO7No0aInmp8QQog/V2ZmJt7e3ty8eZOCggLy8/MBaNOmDUePHqVcOcP1M9bW1ty+fVs5vnXrFjVq1ADA1dXVYJO/8PBw4uPjlb0CGjduTEJCAk2bNlXaJCQkGH1teVD58uXp0qULBw4coF+/flSqVImzZ88arGZOSEigbdu2QNHrmLe3N97e3nTq1IkhQ4YogWYHBwc+/PBDhg8fTs2aNbl58ybW1tZPe9mEEEIIIV4IWckshBBl4KtD35KZe4/7BblcOnyW+n6OBCwZyKyIhVy8eBF7e3tiYmJK9NPpdAQHBxtNP/GwzZs3s33Hz6Tb3uXSuYvk5eTiM7YzVWyrMXtLKCEhITRq1Ih169YpKSsA/vjjD+rXr28w1o0bN9DpdFSpUgUoSklRrVo1Jd9zSEgIH374IfXr1+fIkSPo9XpycnIYM2YMe/fuNdh80M/Pj6NHj5KSksKqVauUYHbxG/oHPfiG/lHBguJxExMTSUhIMEjXUSw5OZnU1FQ6deqEnZ0dGzZsUFZzu7q6GuRw/uWXX/j8889LbA4ohBCi7Kxbt4579+5RUFBgUK7T6di3b1+J9v7+/mzcuJHc3FwAwsLCaNeuHQADBgzg4MGDbNmyRWmflZWlfB8cHMzs2bPR6XTKOf79738zfvz4R85Rr9dz8OBBHBwcAPjkk08YNWoU2dnZAOzZs4fY2FgGDBhAenq6wWuPVqtVXn+3b9+u3I2UmpqKiYkJVatWffxFEkIIIYQoIxJkFkKIF+zu/UwuZqQrxxfizlCnuT35hfnEXiha7dunTx8lAJqWlkazZs1wcXGhb9++jBo1iiFDhhgduzgnpKOjI2vXrqXHzHcpZ2mqnOMNtzrk5+YTungRPXr2QKPRkJGRwe3bt3F2dmbYsGH8/vvvJVbvzpkzh5o1a3Lp0iV0Oh0XL16kUaNGHD16lN9//50PP/yQDh068Mknn/Dbb7/h4OBAs2bNaNasmcGbdoDTp0+zZcsW7O3tCQgI4L333gPg/fffZ+TIkaW+off392ft2rXKm+7vvvtOCRY8CY1Gw/Tp09HpdOh0OtLT00lPT+f8+fOMGDGCsLAwg/QeD89bCCFE2UpOTubevXslyvPz843uVRAQEICfnx+enp6o1WoOHjzI3LlzgaJ9AbZt28by5ctp0KABPj4+zJw5kylTpgCgVquZO3cu3bt3x9nZme7du/PFF1+gVquNzq349dfNzY2CggI++ugjAEaOHEnz5s1xd3enUaNGfP7550RGRmJubk5eXh7BwcE4OzujVqvZuHGj8vr7/fff06hRI9RqNQMHDmTdunWYmJgYnDM9PZ2uXbsqx127diU9vej/i6lTpxoE0IUQQggh/myqJ8ntqVKpugCLABNgpV6vn/NQ/ThgGJAPXAeG6vX6848a08vLSx8fH/+s8xZCiJdW5v17/GPLBPILC0rUVTevyvLA2c/tXEN+Gs+9PMNgafbteyR9fxgu51LrjTeoVKkSH3zwAUFBQURHR9OjRw9l8z2AKVOmsHTpUiZMmGCQE/Krr77ixIkTLFu2zGD833//nbFjx/LLL79Qs2bNEuPPnz+fbdu2Fc0lO5uGDRty8OBB7Ozs+Omnn5g2bRp5eXmUL1+ezz77TMmPmZuby7hx4zhw4AAqlQovLy8WL16MhYVFiXGNadCgAT///DPOzs5K2bhx46hVqxYTJkzg8OHDTJgwgcuXL/PGG29Qo0YNpk6dipeX17P/AIR4DsLDw/nss88MypKSkti+fTtvv/02AKGhoUycOJGrV69iZWUF8Mjfixs3bmBjY8PixYsNVv7b2dnh6enJ5s2bAfjxxx/Ztm0bYWFhhIWF8cknn1C3bl0yMzNp0KAB06ZNUzbM1Ov1zJo1i++++w6VSkWdOnVYsmSJ0fQC27Zt41//+heFhYXk5eUxevRo/vnPfzJ9+nT+85//ULNmTaVtdHS0rOAUAKxcuZIxY8aUCDRbWloSGRlJ+/bty2hmQgghhBCvPpVKlaDX60t9g/zYILNKpTIBTgOdgEvAb0B/vV5//IE27YBf9Hp9lkql+hDw1+v1QY8aV4LMQojX2aTdc0m7dR49//c3uHw5UwIadaR/kx7P7TzTo77k+PXUEuWWZhas7DGvRP5KIcRf3zfffMO6devYt2+f8jvcokULzMzMGDp0qHKnw6OCzMuWLWP9+vWUK1eO/fv3K+V2dnYA/PzzzzRu3LhEkDk+Pp4lS5YAsG/fPvr378++fftwcXFhyZIl/Pzzz/z4449YWFiwa9cuPvzwQ1JSUqhYsaJyjry8POrXr8+vv/5K3bp1uX//PjqdjkaNGjF9+nQsLS2VvLhCPCgzMxNHR0euX7+upMwwMzPDxcWFxMTEUjf1E0IIIYQQ/7vHBZmfJLrgDZzR6/Vn9Xp9LrABMIiA6PX6fXq9vnip3GGg7rNOWAghXgejfIZSpWJlKppWoJyqHBVNK1C/al16N377uZ5nQJOemJmYGZRVMDGjv3tPCTAL8RI6ffo0M2bM4Pvvv1d+h9PS0sjMzGTmzJlKmp3H0Wg0LFiwgMuXL3Pp0iWDuvHjxzNr1qzHjtGuXTv+8Y9/8M033wAwd+5clixZgoWFBQCdO3fG19eXdevWGfS7e/cu+fn5ygZmFSpUoFGjRk80b/F6s7S05NdffyUgIAAzMzPMzc0ZMGAA0dHREmAWQgghhChjpk/Qpg5w8YHjS0CLR7T/O/Df/2VSQgjxqnvTsiZLA2by22Ut1+/dwqF6fdzeaPTc3yQ71WjAv/xHsfZoOBfuXMbaohrvuHbD19bzuZ5HCPHny8vLY8CAASxYsABbW1ulfMOGDfTr1w8/Pz9OnTrF1atXqVWrVqnjXLx4kd9//x1vb2/69u3Lxo0bDTYz69u3L19//TVnzpx57Jw8PDxYsWIFGRkZ3Lt3jwYNGhjUe3l5kZKSYlBWvXp1AgMDqV+/Ph06dCAgIID+/fsrQfOFCxeydu1aAKpVq2Z0Qzfx+qpXrx4RERFlPQ0hhBBCCPGQJwkyPzGVSvUe4AW0LaX+H8A/AIM3R0II8ToyMylPK9vmf/p5GtVw4PMOcuu5EC8bvV7PiRMnMDU1xdHRkX/961+4uroSFGSYkUyj0RAeHk65cuXo06cPmzZt4uOPPy513I0bN9K3b18A+vXrx9ChQw2CzCYmJnzyySfMnj1byfn8qDk+i5UrV5KcnMyePXuYP38+u3fvJiwsDICxY8dKugwhhBBCCCFeMk8SZL4M1HvguO7/LzOgUqk6ApOBtnq9/r6xgfR6/TfAN1CUk/mpZyuEEEII8Ro4dOgQQUFB3Lp1C71eT9WqVTE1NeXYsWMG7ZKTk0lNTaVTp05A0QaZ9vb2jwwyazQarly5oqSxSE9PJzU1FUdHR6XNwIEDmT17Nm5ubo+cZ2JiIi4uLlSpUoVKlSpx9uxZg9XMCQn/j737Dsuyfvs4/r6ZorjBgZbgAmV4q+RKC61cibPEUWlmttQ098jI8aRJ+dO00Cy1VDQ1DWeKgrnSgHClyaPhAremKCDjfv7g8fp5CzjIQu3zOg6Pw+t7fRdXHQjn/b3OM4ann8717AG+vr74+vry8ssv4+HhYQSZRURERETk4XM3CTl/AaqZTCYPk8nkAHQBwm/uYDKZagMzgbYWi+XM/d+miIiIyL/DuXPnaN68OcePH+fq1atcu3aNxMRELl68iJ2d9fmAsLAwgoODSUhIICEhgcTERBITEzl69Giucx86dIjk5GROnjxpjBkxYkSOXM729vYMHDiQKVOm5LnPzZs3M2vWLF5//XUAhgwZQv/+/UlJSQEgIiKCrVu30q1bN6txycnJREVFGddxcXFUqlTprp+PiIiIiIg8eO54ktlisWSYTKa+wI+ALfC1xWLZbzKZxgLRFoslHJgMOANL/j+f6DGLxdL2b9y3iIiIyCMpLCyMzMzMHO1Xr16lRo0alChRAoARI0awaNEi1qxZY9WvQ4cOLFq0iPr167Nx40YqVvxvPebXX3+dDh06WPXv1KkTQUFBjBkzxqr9tddeY/z48VZtixcvZuvWrVy7dg0PDw+WLVtGjRo1AOjXrx8XL17E19cXW1tbypUrxw8//ICTk5PVHBaLhY8//pg33ngDJycnihQpYnWK+eaczAArVqzA3d39Dk9NREREREQKkim/ufT+Kn9/f0t0dHSBrC0iIv8eEyZMYOHChdja2mJjY8PMmTOZOHEif/zxB8nJyZw9exYPDw8APv/8c0aOHElSUpIRGKtatSpLly4lODgYZ2dnBg8eTM+ePdmwYQNHjhzB0dGRc+fO4e/vb5wMbdOmjVVag5vHWiwWJkyYwLx58zCZTFSoUIHp06fj7e0NgLu7O0WLFsXW1tbYk5ubGzVq1MDT09OYc9euXTg4OPxTj1H+QcOHD2fSpEk52h0cHPjoo4947733CmBXIiIiIiLyb2YymWIsFot/Xvfva+E/ERGRB8mOHTtYtWoVsbGxRjD4+vXrLF++HICoqChCQkJYtWqV1bgFCxbg75/nv51AdnG0r7/+mrfeeuue9jRjxgy2b9/O7t27KVy4MOvXr6dt27bs37+fQoUKARAZGYmLi4sxJiEhgSpVqhAXF3dPa8nD6amnnmLGjBkkJydbtdvb29O4ceMC2pWIiIiIiEje7iYns4iIyEMpKSkJFxcXHB0dAXBxccHNze2+zD1gwACmTJlCRkbGPY2bNGkS06dPp3DhwgA0b96cRo0aGUXYRFq2bImfn59VmonChQvTtGlTnnjiiQLcmYiIiIiISO4UZBYRkUfWjeJp1atX5+2332bz5s13Na579+6YzWbMZjNDhgzJtc/jjz9O48aN+fbbb3PcO3z4sDHebDYTGhoKwOXLl7l69SqVK1e26u/v78/+/fuN66ZNm2I2m6lfv36uc77zzjt39XXIw8nGxoaNGzcybtw4/Pz8qFOnDiEhISxfvpz/r30hIiIiIiLyQFG6DBEReeScOXOGdevWYW9vz6ZNm9izZw+RkZEEBQUxceJEevbsedvxd5MuA7ILr7Vr147nn3/eqv3W1BbBwcH3tP9b02XkNqc82goVKsSgQYMYNGhQQW9FRERERETkjnSSWeQhYGtri9lsplatWtSpU4ft27cD2XlanZycrE5MfvPNN8a4uLg4TCYT69atA+D8+fNGv3LlylGhQgXj+vr168Y6N/5MnDgxx14CAgK4uWhnQkICPj4+QHZ+2zZt2lj179mzJ0uXLgVg1apV1K5dm1q1alGzZk1mzpwJZAfgbt6L2Wzm0qVL9/EJyr/JzJkzqVSpEu+88w5vvPEGjz32GJcvX+bDDz9k+vTpLFu27L6tVa1aNcxmM999991d9S9WrBhFihThyJEjVu0xMTFG4T8RERERERGRh41OMos8BJycnIwTjD/++CMjRowwXvu/3enGsLAwGjduTFhYGC1btqR06dJG3+DgYJydnRk8eHCu69xv6enp9OnTh127dlGxYkXS0tJISEgw7g8cONBqLyL5cejQIQYOHEhqaqpVe5cuXThx4gRxcXFUqlTpvq45atSoHCeZb2fIkCH079+fJUuW4OTkREREBFu3bjU+dBERERERERF52Ogks8hD5vLly5QsWfKO/SwWC0uWLGHu3Lls2LAhR9Dtn3blyhUyMjIoXbo0AI6Ojnh6ehbonuTRs3DhQtLT03O0p6amUqtWLX777be7Sl1xc07mZ5999rZ9vb29qVOnzl3vsV+/fjzxxBP4+vri6enJuHHj+OGHH6yKvOVHYmIirVu3Nq5bt25NYmIiAGPGjCE8PPwvzS8iIiJSUO7mjUuAWbNm4eXlhZeXF/Xq1WPr1q3GvYCAAKt0aNHR0QQEBOSYIysri/79++Pj44Ovry9PPPEEf/zxBwDu7u74+voa++jfv3+O8cHBwRQuXJgzZ84Ybc7OzlZ9VqxYgclk4uDBg/f0HEREHmQ6ySzyEEhJScFsNpOamkpSUhKbNm0y7t0oBnbDZ599RpMmTdi+fTseHh5UqVKFgIAAVq9eTadOne5qnRtGjBhBUFDQPe11y5bwa2RAAAAgAElEQVQtVnMcO3aMNm3aUKpUKdq2bUulSpV45plnaNOmDV27dsXGJvuzrilTpjB//nwASpYsSWRk5D2tKwLZ/w9nZmbmaC9UqBAjR47krbfesmoPCAjI8ctFVFRUrnPfHJyeO3eu1b3vv//e+Lu7uzv79u3Lc6zJZOKDDz7ggw8+yHWdm0/4327OW7m5ubFmzRrj+ua/jx079rZjC8KECRNYuHAhtra22NjYMHPmTIKCgoiOjjbyUUdFRRESEsKqVauYO3cuQ4YMoUKFCsYcCxcupHDhwtSoUQMvLy9SU1MpWrQob7/9tpF3+9VXX+XXX381xly6dImUlBROnz5Nz549adOmDS+88IJx39nZmeTkZCD7ZPyAAQOIj4+naNGiVK1alc8++4wDBw4Y+wIYPXo00dHR/PDDD7Ro0YKkpCTjQ4OqVauydOlSgoOD+fLLL3F1deX69eu8//77dO3a9W99xiIiIo+Ku3njctWqVcycOZOtW7fi4uJCbGws7du3Z9euXZQrVw7Irtuxdu1aWrVqlec8ixcvJjExkT179mBjY8OJEycoUqSIcT+32hm3cnFx4ZNPPmHSpEm53r/5jdMPP/zwtnOJiDwsFGQWeQBlZWWxfPly5syZA4C9vT2xsbHY2NiwY8cOXnnlFSPglFe6jLCwMLp06QJkpwr45ptv7hhkvpsf3kwm023bmjRpYgReAKsCa7Nnz2bv3r1EREQQEhLChg0bjGCd0mXI/dC+fXumT5/OtWvXrNotFovVKV8pWDt27GDVqlXExsbi6OjIuXPnuH79+h3HBQUFMX36dKu2hIQEqlSpYgSSjxw5QseOHbFYLLz66qvG91HI/t4aEBDAK6+8cse1UlNTef755/n0008JDAwEsoPeZ8+eteo3fvx4tm3bxpo1a3B0dATyLhx54/tcfHw8devW5YUXXsDe3v6OexEREZE7mzRpEpMnTzYCwHXq1KFHjx7MmDGDcePGAdlpyyZMmHDbIHNSUhLly5c3DsNUrFjxnvfSq1cv5s6dy7BhwyhVqpTVveTkZLZu3UpkZCSBgYEKMovII0PpMkQeQK+88go9evRg9erVrF69mrS0NLp27YrFYqFhw4acO3cuR6DjZpmZmSxbtoyxY8fi7u5Ov379WLduHVeuXPnLeytdujQXL140ri9cuHDHT/Jv5uvry8CBA9mwYcN9LcAmAtCgQQO6d+9OkSJFMJlM2NjYULhwYUaPHn3fczFL/iUlJeHi4mIEZV1cXHBzc7svc1euXJlPP/2UadOm5bj3P//zP7i6utK7d+87zrNw4UIaNmxoBJgh++T7jUKnAJ988glr165l5cqV95TupFq1ahQuXNjqe6mIiIjk7cYblzf+LF68OEef/fv3U7duXas2f39/9u/fb1w3bNgQBweH27412blzZ1auXInZbGbQoEFWb0QBNG3a1NjHlClTcp3D2dmZXr16MXXq1Bz3fvjhB1q2bEn16tUpXbo0MTExt/3aRUQeFjrJLPKAiY6OZvny5TlOYq5evZqdO3dSokQJMjMzKV26dI4+N2zcuBE/Pz9+/PFHo61Hjx4sX778rk7w3U5AQADz58/n2WefxWQyMW/ePJo2bXrHccnJyVZ5z/6OAmwiJpOJmTNn0r17d7777jscHBx4+eWX7ylnsvz9mjdvztixY6levTrPPvssQUFBPP3003cct3jxYqvcijt27Mi1X506dXLkONy1axezZ88mNjb2rva4b9++HL+o3mzbtm38/vvvxMTE5Miz2L17dyPo/NxzzzF58mSr+7GxsVSrVo0yZcrc1V5ERET+ba5ev8bcX5ew/VgMWZZM7BztWL9tI2WKlP7Lc48ePZrx48fnmcqiYsWK/P7772zatIlNmzbxzDPPsGTJEp555hng7tJlAPTv3x+z2Zzjbc2wsDDeffddIPuN07CwsNv+zCEi8rBQkFnkARMREZHra+NXr16lQ4cOlClThnnz5mFrawvkzMncq1cvfv31Vzp06GA1vlOnTnzxxRe3DTLfmpO5ZcuWOYpq9OnTh4MHD1KrVi1MJhP+/v589NFHd/y6LBYLH3/8MW+88QZOTk4UKVLEKq/tzTmZIbsYhoODA7179zZyy7Zu3ZrZs2fj5ubGmDFj8Pf3p23btndcW/5dTCYTTz/99F0FLeWf8+fefRxdEEbKiZM4VazAprnz2HPpIpGRkQQFBTFx4sQ7puPJLV1GbiwWi9V1cnIyL730El999ZXVK6t3Wu92qlatysWLF9mwYUOOVER5pcuYMmUKc+bM4dChQ6xcufKu1hEREXkU2dra4uvrS0ZGBh4eHnz77beUKFGCDh068Morr7C98G+c/DOJlQPmU6lJdbKyshi1YRJn5x3ilZdfoVSpUrRr1w4PDw9jTjc3N2JiYmjWrJnRFhMTQ+nSpTGZTPj6+gLQrFkzRo8ezfr16zlw4ACVK1emZMmSODg4MHToUDp06ICjoyOtWrWiVatWuLq68t5775GZmUliYiLPPfcc33//PV26dCEtLY0LFy6QkpJi1I0ICAjA2dmZEiVK0K1bN2bMmGHs58KFC2zatIm9e/diMpnIzMzEZDIxefLku/4ZRETkQaUgs8gD5sYPOBkZGVbtTk5OjBo1ir59+xpt7u7upKSk3NW8bdu2tQrI3lyI7IbcCqbdysHBIc8gT25F1G4OJN9ciOxmwcHBue7n1jEPeiEzEcndxdhfOTjxY7LSsj9Au3LgIL//z0RqDR9KwIcf4uvry7x584x0PDdOB91rOp4bfv31V2rUqGFc9+vXj3bt2hknkG64Xfofb29vNm/enOcaZcuWZcGCBTzzzDOUKlXqrt7ouJGTOTw8nNdee43Dhw9TqFChe/3yREREHno314K5kTd51KhRPPnkk6zcuJqMps5cvXwVO0d7zsefxgKkZqSxZftWQr8I5eDBgzlqwYSHhzNs2DDWrVtH6dKliYuLY+7cuQQGBtK4cWPi4+ONvqNGjaJjx45UqlSJQ4cOAXD06FHCw8OJjY2lXLlyuLm5kZWVxcqVK7FYLOzZs4fKlSszb948SpQowc6dO4Hs33eio6ON35Fu/r3mvffe44knnjB+t1u6dCkvv/wyM2fONPo8/fTTbNmyhaeeeupvedYiIv8U5WQWecC8+OKLeZ6uCwoKKoAdiYj8NX98NccIMAMcS77C0fMX+OOr7KJ8N9LnBAQE8O233wLZH3rNnz//roK3N0tISGDw4MH069cPyP5lbvfu3UyYMCFH34CAABYvXmy8PTJ37lxjvW7durF9+3ZWr15t9P/pp5+MoqsA1atX5/vvv+ell166Y9HUm7Vt2xZ/f3/mzZt3T1+biIjIo6hhw4acPHkSgEaNGrHr551kWbI4f+gUbnUrkXY5hcy0DH549xv+vPgnLVu2ZNasWQCMGTOG8PBwIPvf1169etGoUSO8vLx4/fXX+fbbb1m7di1z587l4sWLpKWlAVCoUCEcHBysakJUqlSJfv36cebMGQIDA/Hx8cHPz4+0tDSefPJJoxBg9+7djbzMd0pF6OLiQocOHYx1w8LCcn3jNCwsDMDqrdLevXsTHR0NQGhoKKGhofl7wCIi/xDTra+U/lP8/f0tN75hioi1TZs20alTJ7KysoDsAPOSJUt47rnnCnhnIiL3blv7F+CmnzcOXrrIf/buJjk9naIe7lStWpVZs2Zhb2/PW2+9xf79+7FYLEbKHhsbG+bOncuQIUOMV1EBPv/8c9zc3KhRowZeXl6kpqZStGhR3n77bXr27AmAp6cn165do3Rp6xyOO3bswMnJiQ8//JClS5dia2tLlSpVCA0NxdXVNXufBw8yYMAADh8+jL29PX5+fkydOpUDBw4QEhJinJ5av349vXv3JjIyktdee42kpCQjJ7OLiwsREREEBwfj7Oxs5GWMiYmhW7duHDhwwPhF+c033yQ6OprQ0FBmz54NZP+yeS8BbBERkYeBs7MzycnJZGZm0qVLF1577TVatmxJWloarmVcCZzZg9hF23CtUZ6jW+Op0b42yccuUeiohYgV64mKisqRLmPZsmVUqVLFap1t27YxZswYNm7cSLdu3ejUqROdOnVi2rRp/PHHH3kW7rvZiRMnaNy4MSVKlOCZZ57hpZdeonbt2sb9W08yi4g8qkwmU4zFYsmZF/DGfQWZRR5M6enpbNu2DYvFwpNPPomDg0NBb6nAnD9/3njN/dSpU9ja2hpBoEOHDnHt2jUSEhLw8PBg2rRpxgnGvn374u/vbwSbMjIyKF++PK+99lqOXNMi8vfZ1aMX6Zf+zNFuX6I49eZ9XQA7EhERkX/agbPxfLdvFYlXTvNlh4/xrOnFuVNnqVGjBpGRkUbNmSeffBL3F32JmL2KhgObc2x7PLZ2dlw9dok32/Si/zv9iIqKsvrANy99+/alVq1avP7664SHh/PNN9+wdOnSHEHmd955h61bt+Lg4MAvv/ySY560tDSjEOBXX31lVQhQQWYR+be4U5BZ6TJEHlD29vYEBATQtGnTf3WAGTByqsXFxfHmm28ycOBA4/rGa2sAZcqUYerUqbkWTgTYsGED1atXZ8mSJTkKg4nI36fiC52wcXS0arNxdKRCp44FtCMREZFHl7OzM5CdQspkMvHZZ58Z9/r27WvUTOnZsycVKlQwUjmcO3cOd3d3o+/+/ftp1qwZnp6eVKtWjXHjxln9DL1u3Trq1auHl5cXZrOZoKAgjh07xpdffmmV5u/y5ctUdH+M4d9NYP+ZQ1xM+RMbe1uq9fbn8SqViI2Nxc3NjT59+gDw2GOPsWz4XC7+71l+GreS+DV7+TPuNLaJmQQ0ufvCzpmZmSxbtoyxY8fi7u5Ov379WLduHVeuXMHb25vY2Fij74wZM9i4cSNnz57Nda4bhQAnT57MyJEjWbFixV3vQ0Tk30JBZhF5ZLi6uvLMM8/kmec0LCyMd999l8cff5wdO3b8w7sT+fcq36Y1FV/siE2hQtg4OGBTqBAVX+iIW+DzBb01ERGRR9qdDmHY2try9dc53ypKSUmhbdu2DB8+nN9//53du3ezfft2Pv/8cwD27dtHv379mDdvHgcPHiQuLo7u3buTkJBA7969OX78OBEREUB23uTHnqqKo4uT1Rq7vo7EvVVNtmzZQqFChXjrrbcA8PHxwdbWlpe7v8SFhLNcPH4OuyuQeOIkPj4+d/21b9y4ET8/P44fP05CQgJHjx6lU6dOLF++nGbNmpGamsoXX3xh9L927Vqu88TGxpKYmAhAVlYWe/bsoVKlSne9DxGRfwsFmUXkkTJs2DBCQkLIzMy0ak9NTSUiIoLAwEC6du1qFNcQkb+fyWTisRdfoP6CedSd+Tn1F8zjsc4v5FrkVERE5FGXkJCQI1gaHBxMSEgIcPsTxjePjYqKonjx4pjNZmrUqMGHH34IZJ/gLV68OK1bt+by5ctcunSJYcOGAdn1DMaMGYOPjw8bN27kjTfeYMqUKWRkZFjtp1u3bpw/f57BgwdjNpvZu3cv06dP57333sPT05OnnnqKtLQ03n//fWNM27ZteeqppzCZTISGhjJgwACio6OJ2LiRCi2q53gOqZeucbVQGrVr18bPz88oruvt7c21a9do2LAhAHZ2dpQpUwZ/f3+rtxi3bNmC2Ww2/ixdutRq/tsV2TOZTKxYsYLNmzfj4eFBvXr16NGjB5MmTcqxz1sLAdrZ2dG3b98c/fJyc3HC8PBwxowZA0BiYiKtW7e+63lERB50dgW9ARGR3GSmXuVy9BquxcdgW6Q4xeu1wcnd947jKleuTP369Vm4cKFV+6pVq2jatClOTk506tSJcePG8Z///MfI/SYifz8bOzscSpUs6G2IiIg88G6cML5xujcvTZo0YdWqVVy9ehWz2UxgYKDRPn36dNq0aUN4eDitWrUiJCQEW1tbxo4dS8+ePalatSr79u2jcePGfPvtt8bYHTt2sHPnTt5//30GDRrEuXPnuH79Om5ubmRmZjJz5kzee+895syZQ61atXLdl5+fHy1atOCZZ55hxYoVzL0QTkpGqnG/47ze/BF1kMixP9AqqhXNmzc31i9ZsiTFihVj+vTpRp7jW4v6BQQE8OefOes93GzOnDk52tq2bUvbtm0BKF++PIsWLbrtHAAtW7akZcuWed7v2bOnUQMmN2PHjs11fTc3N9asWXPH9UVEHhY6ySwiD5zM1KucnD2IS1uXkZYYz7X4aE599xF//nJ3P4SNHDmSSZMmWeWMCwsLIyIiAnd3d+rWrcv58+fZtGnT3/UliIiIiIjk24ABA3I9YZyXZMs13GtUZmtczpRweR3CcHV15cKFC4wYMYLJkyeTlZUFQFJSEoUKFcLOLvtMmouLC25ubnmuff78ecxmM9WrVzdOY0N2Mb0KFSrQtGlT2ng+g6OtdZ0Zr2f8mPPjAl588UWioqJo0KCBcXq7SZMmRg2WuLg4qwCziIg8mBRkFpEHzuXoNWQmX8KSmW60WdLTuBA5H0vmnX/Q9vLyombNmqxcuTJ7vsuX2bJlC8eOHSMhIYGEhARmzJihlBkiIiIi8kB6/PHHjRPGt2OxWJi242ve+W4kO37ewYY/d5CemcGWLVto3bo1hw8fxmw289JLL1mlgsjMzCQpKQl/f3+qVauG2Wzmu+++A6B58+akp6fz/vvv8/bbb7N582YAjhw5gq2tLW+88QYJCQkEBgYyZMgQo0h3nz59SE5ONtawsbEx0lt08m5N86pP4WBrj6OtA462DrT1ak7XJzvSq1cvfvjhB+zs7IyUGSIi8vBRugwReeBci4+2CjAbbGzJTL4IxUvccY5Ro0ZRu3ZtAKO4h6Ojo3G/Xbt2DB06lLS0NKt2EREREZH77er1a0T9sYPDF4/hlGxHliUrR59baxWMGDGCdu3a8fzzeRfKjfopih3ddmExgWdbM05uRbGQRXnvx1izcBVt2rQhLi4OgJo1a7J//37GjBnDkCFDsLGxwc/PD8j+2fnGOs7Ozhw4cIBq1apx5coVgoKCGDt2LOHh4Tz++OMsWLAAR0dHOnToQK9evYy95FU4D8DGZMPL5k509gnkUuqflHQqwaYNG8nwzMDe3p5Tp05x/vx5KlSowMGDB+/+wYqIyANDJ5lF5IFjWySPIHJmBmNGjWDw4MFG043TEu7u7lYnH2rVqkVWVhY9e/akR48eOfKtlSpVirNnzyrALCIiIiJ/qzNXz9N/zQcs2hvO1qO7iEjaxtFTx0i8fMroc+HCBVxcXKzG3XrCODeuXm48O/EFnvvoBao85220X0lLJjU9zarvqFGjgOwcwUePHgXgxx9/BLKL7dWpU8fo6+zszPr16zl+/Dg2NjYMHDiQJ554ggoVKgDg6+vL1KlTeeWVV/D09OTJJ5/kwIEDdOvW7bbPwtHOgbLOrjjY2rN+/Xp8fHyoVasWLVq0YPLkyZQrVw64c1E/gN69exMdHQ1AaGgooaGhAERHR9O7d+/b7kNERO4/0805S/9J/v7+lhv/IIiI3CwlYS+nvvsIy80/GJtscChTiYq9Q/IeKCIiIiLygPl4yxfEJO21qhcSMXIZgW+/wLxBn3PhwgUaNGjA2rVrqVKlCj179qRNmza88MIL7N+/3zhhfCPtW5s2bdi3bx9RUVG8Mvg1GgxpbrXemf0niV+9h9+27aF4oWJW95ydnY1DGr/++ivt27fn8OHDRv5lgN9//x0bGxuqVasGwOjRo7l06RLTp08nICCAkJAQ/P39/5ZnJSIiDy6TyRRjsVjy/AdAJ5lF5IHj5O5LqaYvYbJ3xORYGJOdAw5lKlEuaGRBb01ERERE5J7sPvUbtx7uqvd2U9Z+/QNms5lmzZrxwQcf5Frc7tYTxrcqXqgotqacv9afO3iKpxs8dduTwLVr18bPzy9HnZLk5GR69OhBzZo18fPz47fffiM4ONi43717d2PeZ599Nse8t54kNpvNxt9vPn0sIiKPFp1kFpEHVtb1FK6fTsDGqSgOLhULejsiIiIiIvesx7KBpGSk5mi3s7FlwQuf5cjFfC8upFxi+PqPuJaewvXMdOxMttja2jLqqX54uVb9K9sWERGxcqeTzCr8JyIPLBsHJwo9VqOgtyEiIiIikm9Puddn05FtpGdlGG12NrY0eqzuXwowA5RyKsGUVh+w6cg2fjsTj1uxsrSo+jRlnF3uPFhEROQ+UpBZRERERERE5G/SvVYHjl46wR8Xj5MdUzbhVrQsvep0uS/zF3EoTKDXcwR6PXdf5hMREckP5WQWEZE7MplMDBo0yLgOCQmxys03a9YsvLy88PLyol69emzdutW4FxAQgKenJ7Vq1eKJJ54gLi7OuOfu7o6vry++vr7UrFmT0aNHk5qa/TppQkICTk5OVpXFv/nmG65cuUKVKlWIj48HID09HV9fX3bu3Jlj319//TW+vr74+fnh4+PDDz/8AEDPnj3x8PAw5m3UqNF9fV4iIiIiNxSyc2TsM4MJbvYevep0YfTT/fnoueEUdnAq6K2JiIjcNzrJLCIid+To6Mj333/PiBEjcHGxfv1y1apVzJw5k61bt+Li4kJsbCzt27dn165dlCtXDoAFCxbg7+/PnDlzGDJkCBs2bDDGR0ZG4uLiQnJyMn369OGNN95g3rx5AFSpUsUqKH3DRx99RN++ffnxxx8JCQmhUaNG1K9f36rPiRMnmDBhArGxsRQvXpzk5GTOnj1r3J88eTIvvPDCfXtGIiIiIrdTpVQlqpSqVNDbEBER+VvoJLOIiNyRnZ0dffr0YcqUKTnuTZo0icmTJxvB5zp16tCjRw9mzJiRo2/Dhg05efJkrms4OzsTGhrKihUruHDhwm3307lzZwA+/vhjQkND+eijj3L0OXPmDEWLFsXZ2dmY38PD4/ZfqIjIP2TChAl4e3vj5+eH2Ww23sYICAjgRnFsd3d3zp07Z4yZPXu28QaGg4MDvr6+mM1mRo0axezZs3F1dcVsNuPl5cW0adOs1vviiy+MN07q16/P9u3bjXuNGze2+qDu559/5tlnnwUgIiKC9u3bG/dWr15N3bp18fb2xmw2M2zYsPv/cERERETkoaMgs4iI5CojI4OzZ8+SkZFdpOadd95hwYIF/Pnnn1b99u/fT926da3a/P392b9/f445161bZxWsuFWxYsXw8PAwUmEcPnzYKl3Gli1bjL5Tp05l2LBhjB49mlKlSuWYq1atWpQtWxYPDw9effVVVq5caXV/yJAhxrzdu3e/w9MQEbl/duzYwapVq4iNjWXPnj1ERETw2GOP3XFc7969iYuLIy4ujjJlyrBlyxbi4uKYMGECAN27dycuLo4tW7YQHBxMUlISACtWrGDOnDls376dgwcPMmPGDLp06WL1dkdSUpLVWya52b17NwMGDCAsLIz9+/cTExODu7t7/h+EiIiIiDwylC5DRESsWCwWpkyZwtixY0lNTcXBwYH09HSKFi3KK6+8wrRp03Byurccgt27d+f69eskJyfnmv7i1vVvyCtdBmQHrMuXL8++fftyvW9ra8u6dev45Zdf2LhxIwMHDiQmJsbIJa10GSJSUJKSknBxccHR0REgRxqiv8rV1ZXKlSuTlJRE+fLlmTRpEiEhIcYHcv7+/nTv3p3PP/+cDz74AMj+4G38+PE891zehcMmTZrE+++/T/Xq1YHs77NvvfXWfd27iIiIiDycdJJZRESszJ49m/fff58///yTtLQ0rly5wvXr15kyZQoDBgzgq6++4urVq0b/mjVrEhMTYzVHTEwM3t7exvWCBQs4cuQIPXr0oF+/fnmufeXKFRISEowARl4SExOZNm0au3btYs2aNezZsyfXfiaTiXr16jFixAgWLVrEsmXL7uYRyN8otxQB6enpDB8+nGrVqlGnTh0aNmzI2rVrgZzpAqKiomjTpo3VnO3bt6dBgwZWbcHBwVSoUMHqJPylS5eIioqiePHiRtuNlAC39h8+fDhgnbrg5j3cPIfZbCYiIgLIDrqZzWZ8fHwIDAzk0qVL9/cByiOhefPmHD9+nOrVq/P222+zefPm+zp/QkICmZmZ+Pj4APDbb7/d8Y2TJk2aAFgVbr3Vvn37cswjIiIiIgIKMouIyC3GjRvHtWvXcrR/9NFHlCpVis6dO/PVV18Z7UOHDmXYsGGcP38egLi4OObOncvbb79tNd5kMjFu3Dh+/vlnDh48mGP+5ORk3n77bdq3b0/JkiVvu8eBAwcycuRIKlasyKeffso777xjdQIasgPRsbGxxnVcXByVKqnYTkHKK0XA+++/T1JSEvv27SM2NpYVK1Zw5cqVu5rz0qVLxMTE8Oeff3LkyBGrewMHDjRSC8TFxVGiRAkgO5h2o+1GcPjW/hMnTrztujfPERcXZwSrnZyciIuLY9++fZQqVSrX3OTy75SZkcHenzex5IvxrF/wGQu+/IyZoaG4uroSFBTE3Llz//IaCxYswNfXl2rVqtGvXz8cHBwAcnx/zMuoUaMYN27cX96HiIiIiPz7KMgsIiJWbuTwvNW5c+fIzMxk0KBBVidL27ZtS69evWjUqBFeXl68/vrrzJ8/n/Lly+eYw8nJiUGDBjF58mSjrWnTpvj4+FCvXj0ef/xxZs6cady7NSfztGnT2LBhA8eOHeO1114DIDAwkJIlS/LNN99YrZWens7gwYPx8vLCbDazePFipk6daty/OSez2Wzm+vXrVuMTExNp3bq1cd26dWsSExMBGDNmDOHh4Xd8lmIttxQBJUqU4Msvv+Szzz4z2suWLWsUd7yT77//nsDAQLp06cKiRYv+tr3nx+0KXcq/iyUri/A5n7BtzWJOHztMYsIhtqxcwPUzh/jwww+ZPn36fXnTonv37uzdu5etW7cyePBgzpw5A9zdGyeQfcL60qVLOU7v3+Dt7Z1jHhERERERUE5mERG5ReZ6D7IAACAASURBVI0aNdi7d2+O9sqVK2Nra0vZsmVznHR+66238szLGRUVZXU9aNAg4+8JCQl57sPd3Z2UlJRc792aMzS3gG+lSpXYtGlTruPv5sSgm5sba9asMa5v/vvYsWPvOF5yat68OWPHjqV69eo8++yzBAUFUbJkSR5//HGKFSuW57imTZtia2sLZJ949/LyMu6FhYUxZswYypYtS6dOnRg5cqRxb8qUKcyfPx+AkiVLEhkZCcCWLVswm80AvPjii4waNSpH/0mTJtGiRYs893TzHADLli2jSpUqxnVmZiYbN240PgyRf7dj8fs4c+IPMtKzP8w6ff5PTCYT8Cvnko7d9zct6tevT9euXfnss88YN24cQ4cOZejQoaxdu5aSJUsSGxvL/Pnzcw0mjxo1ir59++aatmjo0KF07tyZRo0aUbVqVTIzM/nyyy95880379veRUREROThpCCziIhYCQkJoX379lYBXicnJz755JMC3JU8rNLOnuXc1u1kpadTqp4/MTExbNmyhcjISIKCgqyCwnmJjIw0CqNFRUUREhICwOnTp4mPj6dx48aYTCbs7e3Zt2+fkYd24MCBDB48OMd8TZo0YdWqVTna8+qfm7zmSElJwWw2c/LkSWrUqHHbImry73Hi8AHSr6cZ12np6SzdsJOUtHQ+XbgBHz8zs2bNynWsn58fNjbZLx927tyZTz/99K7WHD58OPXq1WP48OF07NiRxMREGjRogMlkolixYixcuJAyZcrkGBcYGMj777+f65y1a9fmk08+oXPnzqSmpgLQrl27HP127tzJnDlzCA0NJTMzk/r16xsB7VdffZV3333X6kMaEREREXn4KcgsIiJWmjdvzpo1axg5ciQHDx6kSpUqjB8//ranOkVyc3pjJEdCZ2HJysKSlcWJJcso16oFAb16EhAQgK+vLzNnzuTYsWNcvnz5tqeZc/Pdd99x8eJFPDw8ALh8+TJhYWFMmDDh7/hy7sqNnMzXrl2jRYsWzJgxg/79+xfYfuTB4ORcDFs7ezIz0gF4vJwL7738PPYOhWjaoQfVzf8tXHnz2x+3e9vjxIkTVte9e/e2un7ssces0h/17duXvn375jrXzcX+TCYTu3fvNq6fffZZI+c4ZKdIatu2bZ77guyT1PXr1weyi2HefGJ6zpw5tx0rIiIiIg8n5WQWEZEcAgIC2L59OxcuXOCXX35RgFnuWfrlyxwOnUnW9etYMjIgK4uEC+f5Zckyrvx+CMguxujp6clrr73Gu+++a+TFPnv2LEuWLLnjGmFhYaxbt46EhAQSEhKIiYl5YPIyFy5cmGnTpvHJJ5+QkZFR0NuRAuZZu9H/p8ewZmNjg0fN2gWwIxERERGR+0tBZhEREbnvLkbHYrKxtWq7lpHBuJ3bqdM0AD8/P3777TeCg4MZP348rq6u1KxZEx8fH9q0aXPHU80JCQkcPXqUBg3+ewLUw8OD4sWLs3PnTiA7x/LNxR1vdyr0dp5//nkqVqxIxYoVefHFF4H/5mS+8Wfp0qU5xtWuXRs/Pz/CwsIArNID9O7d2zjdGRoaSmhoaL72Jg+HIkWL06bHuxQq7Iy9YyHsHRxxLl6Kdr2HYO/gWNDbExERERH5y0wWi6VAFvb397fkVblaREREHm5nIqM4HPolWf+ft9VgMlE+8Hkqv/ZqwWxMpABlZWVxNvEotra2lC73WK6nm0VEREREHkQmkynGYrH453VfJ5lF5KFka2trdYpw4sSJQHaaB3///37Pi46OJiAgwLjetWsXTz31FJ6entSuXZvevXtz7do143779u2tTkZu3ryZhg0bWq2dkZFB2bJlSUxMpGfPnsYJxlWrVlG7dm1q1apFzZo1mTlzZp77v3WdG+bPn4+fnx/e3t7UqlWL3r17c+nSpXt7OCIPgJL+dSErK0e7jYM9rk81KYAdiRQ8Gxsbylb0wKX84wowi4iIiMgjRYX/ROShdKO4Vm7OnDnD2rVradWqlVX76dOnefHFF1m0aJEROF66dClXrlyhcOHCXLp0iZiYGJydnTly5AiVK1emSZMmnDhxgqNHj1KpUiUAIiIi8Pb2xs3NzZg7PT2dPn36sGvXLipWrEhaWlqer+bntg7AunXrmDJlCmvXrqVChQpkZmYyb948Tp8+TYkSJf7qIxP5R9kXLUrV/u/wv9NmYAHIysJkY4Nb20CKVqta0NsTERERERGR+0hBZhF55AwZMoQJEybkCDLPmDGDHj16WJ1MfuGFF4y/f//99wQGBlK2bFkWLVrEyJEjsbGxoXPnzixatIhhw4YBsGjRIrp27Wo195UrV8jIyKB06dIAODo64unpmev+clsHYMKECYSEhFChQgUg+7R2r169/uLTECk4rk0aU9zHm/Pbd5B1PZ2ST/hTuGKFgt6WiIiIiIiI3GdKlyEiD6WUlBSrdBmLFy827jVs2BAHBwciIyOtxuzbt4+6devmOWdYWBhdu3ala9euRqEugK5du7Jo0SIA0tLSWLNmDZ06dbIaW6pUKdq2bUulSpXo2rUrCxYsICuXVAG3W2f//v3UqVPn7h+CyEPAoWRJyj/fmgod2inALCIiIiIi8ohSkFlEHhrXz5/kyt7NpCTsNdJl3PgTFBRk1Xf06NGMHz/+ruc+ffo08fHxNG7cmOrVq2Nvb8++ffsA8Pf3Jzk5md9//521a9dSv359SpUqlWOO2bNns3HjRurVq0dISEiup5Bvt87N9u7di9lspkqVKlYBdBERERERERGRB42CzCLywLNkZXJ6+RROzh7MubWzOLVkIpb0NDIun89zTLNmzUhJSeHnn3822ry9vYmJicm1/3fffcfFixfx8PDA3d2dhISEXE8z55Yq42a+vr4MHDiQDRs2sGzZsntax9vbm9jYWGOeuLg4WrVqRUpKyu0fkIiIiIiIiIhIAVKQWUQeeJdjfuRa/C9YMq5jSU/Fcj0VsHB6+ae3HTd69Gg+/vhj47pv377MmzePnTt3Gm3ff/89p0+fJiwsjHXr1pGQkEBCQgIxMTFGigzIDjLPnz+fTZs20a5duxxrJScnExUVZVzHxcUZhQJvdrt1RowYweDBgzlx4oTRXwFmEREREREREXnQKcgsIg+8yzHrsKSnWbWlpmfy3IjPMfv5YjabGT58eI5xrVu3xtXV1bi+UWhv8ODBeHp6UqNGDX788UfOnz/P0aNHadCggdHXw8OD4sWLGwHpGjVqUKRIEZo1a0aRIkVyrGWxWPj444/x9PTEbDbzwQcfMHfuXKs+CQkJt12ndevW9O/fn1atWlGzZk0aNWqEra0tLVq0AGDMmDGEh4cDEB4ezpgxYwBITEykdevW9/JIRURERERERETuG5PFYimQhf39/S3R0dEFsraIPFyOTX+TjD/P5mg32TlQ8c2p2BcvUwC7EhERERERERH5dzCZTDEWi8U/r/s6ySwiD7wiXg3A1i5Hu22R4tgVc81lhIiIiIiIiIiI/FMUZBaRB16JJzthV7Q0JnvH7AZbO0z2jri27Y/JZCrYzYmIiIiIiIiI/MvlPBooIvKAsXUqSsU+U7iydzOpx37DvkRZitV+FrviOsUsIiIiIiIiIlLQFGQWkYeCjb0jxes0p3id5gW9FRERERERERERuYnSZYiIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIiIiIiIiIiL5piCziIiIiIiIiIiIiOSbgswiIiIiIiIiIiIikm8KMouIiIiIiIiIiIhIvinILCIiIvIvcOLECdq1a0e1atWoXLkyffv2JS0tDYBr167RvXt3fH198fHxoXHjxiQnJwNw6tQpunTpQpUqVahbty6tW7fm0KFDua6xYsUKTCYTBw8etGqPj4+nTZs2xhxNmzblp59+yjE+KioKk8nEypUrjbY2bdoQFRVlXJ87dw57e3tCQ0P/6iMREREREZH7REFmERERkUecxWKhY8eOtG/fnvj4eOLj40lJSWHo0KEATJ06lbJly7J371727dvHV199hb29PRaLhQ4dOhAQEMDhw4eJiYnho48+4vTp07muExYWRuPGjQkLCzPaUlNTef755+nTp48xx2effcaRI0dynaNixYpMmDAhz69lyZIlNGjQwGqNf5vcgvkJCQn4+Pjk6Pvzzz9Tv359zGYzNWrUIDg4mDlz5mA2mzGbzTg4OODr64vZbGb48OHMnTsXV1dXzGYzXl5eTJkyxZgrODiYkJAQ4zojIwNXV1eGDx+e514tFgvjx4+nWrVqVK9enaZNm7J//37jvru7u7G+2Wxm+/btf/XxiIiIiEgBUJBZRERE5BG3adMmChUqxKuvvgqAra0tU6ZM4ZtvviE5OZmkpCQqVKhg9Pf09MTR0ZHIyEjs7e158803jXu1atWiSZMmOdZITk5m69atfPXVVyxatMhoX7BgAQ0bNqRt27ZGm4+PDz179sx1r7Vq1aJ48eJs2LAh1/thYWF88sknnDx5khMnTtzTc3hU5BbMz0uPHj2YNWsWcXFx7Nu3j86dO/Pqq68SFxdHXFwcbm5uREZGEhcXx8SJEwEICgoiLi6Obdu2MWHCBI4fP57r3Bs2bKB69eosWbIEi8WSa58ZM2awfft2du/ezaFDhxgxYgRt27YlNTXV6HNj/bi4OBo1apSPJyIiIiIiBU1BZhEREZFH3P79+6lbt65VW7FixXB3d+d///d/6dWrF5MmTaJhw4aMHj2a+Ph4APbt25djXF5++OEHWrZsSfXq1SldujQxMTHG2nXq1Lmn/Y4aNYrx48fnaD9+/DhJSUnUq1ePzp07s3jx4nua91GQVzA/L2fOnKF8+fJA9ocLNWvWvOu1SpcuTdWqVUlKSsr1flhYGO+++y6PP/44O3bsyLXPpEmTmD59OoULFwagefPmNGrUiAULFtz1PkRERETkwacgs4iIiMgj7MKFC5w8eZLr16/n2cdsNnPkyBGGDBnChQsXeOKJJzhw4MA9rRMWFkaXLl0A6NKlS56nbDt06ICPjw8dO3bMc66nnnoKgK1bt1q1L168mM6dO99xjUdZXsH8vAwcOBBPT086dOjAzJkzrU4Q38mxY8dITU3Fz88vx73U1FQiIiIIDAyka9euuf63uHz5MlevXqVy5cpW7f7+/lYpM5o2bYrZbKZ+/fp3vTcRERERebDYFfQGREREROT+S09P580332TBggXY2tqSkpJCkSJFmDRpEiaTicuXL3Pq1Ck8PT0BcHZ2pmPHjnTs2BEbGxvWrFmD2Wxm6dKld1zrwoULbNq0ib1792IymcjMzMRkMjF58mS8vb2tivwtX76c6OhoBg8efNs5b5xmtrP774+rYWFhnDp1yjgFm5iYSHx8PNWqVcvPI3ponD99kkNxP5OVmcHcr79i6PARwH8D7bc7bT5mzBi6d+/O+vXrWbhwIWFhYVaFFHOzePFifvrpJw4ePMj06dMpVKhQjj6rVq2iadOmODk50alTJ8aNG8d//vMfbG1t7/nri4yMxMXF5Z7HiYiIiMiDQyeZRURERB5BI0eOJCwsjLS0NK5du4bFYmHq1KlMmzaNzMxMBg0aRN++fXFycmLbtm1cvHgRgOvXr/Pbb79RqVIlmjVrRlpaGrNmzTLm3bNnD1u2bLFaa+nSpbz88sscPXqUhIQEjh8/joeHB1u2bKFbt25s27aN8PBwo/+1a9fuuP/mzZtz8eJF9uzZA8ChQ4dITk7m5MmTJCQkkJCQwIgRIx7508xxW39kyYyxxG5ew9b14Wz+6Sde7t4Vd3d3Jk+ezHfffZdnPuQbqlSpwltvvcXGjRvZvXs358+fv23/oKAg9uzZw/bt2xk+fDinTp3K0ScsLIyIiAjc3d2pW7cu58+fZ9OmTVZ9ihUrRpEiRXIUeYyJicHb2/sun4CIiIiIPAwUZBYRERF5xGRlZfHFF1+QkpJi1X79+nUGDx5M6dKlsbGxYdSoUQAcPnyYp59+Gl9fX2rXro2/vz+dOnXCZDKxfPlyIiIiqFKlCt7e3owYMYJy5cpZzRsWFkaHDh2s2jp16kRYWBhOTk6sWrWK0NBQKleuTMOGDRk/fjyjR4++49cxatQoo+jc7da41ZgxY4ygdnh4OGPGjAGyTz63bt36jus+KJL/vMCOH5eRkX4diyWLuN//oJ5PFYLf7MTPP220CubnZfXq1UYQOj4+HltbW0qUKHFX6/v7+/Pyyy8zdepUq/bLly+zZcsWjh07ZgT8Z8yYket/iyFDhtC/f3/j/8WIiAi2bt1Kt27d7vYxiIiIiMhDwHSnkw9/F39/f0t0dHSBrC0iIiLyKEtNTaVIkSJkZWXluOfk5ERERARdu3Zl+fLl91yUT/45+3ZGsnX1IjLSs/NpT1u4jmcb+FCz8mPUbtKCJ1sHMW3aNA4cOMCwYcOoVq0aZcuWNcZPmTKFZcuWERsbS+HChbGzs2PChAm0aNHC6OPu7k50dLSRrmLu3LlER0czffp0IDswX6dOHeLj4/nkk09wdnbG1dWVtWvXWhUevHDhAp6enpw4cQJHR0ej3WKxMHbsWL799ltsbW0pV64c06dPx9fXN9f1ATIzM6lfvz43fld49dVXeffddzGbzcyYMQNHR0d69+59x+d3+vRpBg4cyM8//0zJkiVxcHBg6NChdOjQgaioKNq1a4eHhwepqam0adOGkJAQq/Ht27fn1KlT/Pzzz0ZbcHAwH3/8MQkJCZQpUwbITjWTnJwMZBdX9PX1JT09HTs7O1555RUGDhyIjU3Osz2HDh1iwIABxMfHU7RoUapWrcpnn33GgQMHCAkJoVOnTkaA/7fffsPT0xNbW1uqVavG7t272b17N05OTgA8//zzvPTSS3Tt2vWOz0VEREQkP0wmU4zFYvHP876CzCIiIiKPnurVqxMfH5+j/emnn75jTl55MPwW/RNbVi4k/XqaVbvJxoY6T7emYfNOBbSzB5/FYqFRo0b06NGDN998E4CjR48SHh5Ov379iIqKIiQkhFWrVpGSkkLt2rX56quvePLJJwG4dOkSvr6+ODs7s3r1aqN4YXBwMF9//TVdu3Zl0qRJgHWQ+ea/nzlzhm7duvHkk0/y4YcfWu0vNTUVX19fPv30UwIDAwGIiorCxcWFc+fOGXu74dZg/PDhw7Gzs2P8+PGsWLGCzz//nPXr1/9dj1NERETkjkFmpcsQEREReQTNmDGDwoULYzKZALCxsaFIkSL8X3v3Hl/zle9//L2yk2hM6n6pcculaHNht8JISX+oKupSU20TM9TB9OhQpxd6MtOpmmr70x/O9IzLT3XMmGoljKPquD5GjQY5o5I2SHQUEVValLikiUuSdf5I7LEl2LbYW+T1fDzyeOS71vp+v5+981gS76ys74wZM/xcGTwVfu/9le637HA41K5DFz9UVH1s2LBBwcHBroBZklq3bq3nnnuuwtiQkBA5nU4dOnTI1bZs2TINGDBAiYmJbiu2JWnkyJFavHixTpw4cdUamjRponnz5mnWrFkVvo6LFi1SfHy8K2CWpO7duysmJsaj1zdp0iT95S9/UVZWlpKTkzV79myPzgMAALhZCJkBAABuQw8//LA+/fRTDRw4UG3btlViYqI+++wzdezY0d+lwUMhPwpVryd+IUdgkAKDaykwKFiOwEB16T1EDZo293d5t7ScnByPt4LJz8/Xnj179OCDD7raUlJSlJSUpKSkpAp7TYeGhmrkyJEV9qquTEREhEpKSnT06FG39uzs7Buai7Vr19b06dP14IMPKjExUW3atPH6WgAAAFUh0N8FAAAA4OaIi4vT8uXL/V0GbsDdsXFqHnGP9n/5hUpLSxTWroNC69b3d1m3rCPf7Ffm31Yq42//rR/OSye//071Gt2lsWPHavPmzQoODta2bdskSZs2bVKHDh20Z88ePf/8864HWh45ckR79uxRt27dZIxRUFCQsrOz3VYZjx8/Xk6nUxMmTPDL65SkAQMGqF69evrlL3/ptxoAAAAuYiUzAAAAcAsL+VGoouISFNO5OwHzVRz4aqc+mjdVubs+V907ArRjZ7YWz5ys7789qNmzZ+uTTz7RsWPHXOMTEhK0fft25eTkaP78+crKypIkLVmyRPn5+QoPD1dYWJjy8vIqrGauV6+ehg4des1tKnJzc+VwOFwPCbwoOjpamZmZN/yaAwICKn2oIAAAgK/xEwkAAACAas1aq0+XL1TxhfOSpLatm+lCcbE2/H27tqxeLEkqLCys9Nzw8HAlJye7HuSXkpKitWvXKi8vT3l5ecrMzKywL7Mkvfjii3r33XdVXFxc6XWPHTumMWPGaNy4ca690S8aOnSo0tPTtWrVKldbWlqasrOzr//FAwAA3AIImQEAAABUaxfOn9WZU8ddx8YY/eLxntp78DuNmvC6OnfurKefftoVJF9uzJgxSktLU15eng4cOKAuXf75YMXw8HDVrVtXW7dudTunUaNGGjx4sM6dO+dqKyoqktPpVHR0tHr16qXevXvrtddeq3C/kJAQrVy5UjNnzlSbNm0UFRWlOXPmqHHjxjf0PqSnp2vs2LGSpPPnz+snP/mJq2/48OHauXPnDV0fAADgSkxlT6z2hbi4OJuRkeGXewMAAAC4fZSWlOjdyc+qpPhChb476zfS0y9P80NVAAAAtw9jTKa1Nu5K/axkBgAAAFCtBTgciopLkCMwyK09MChY9yX08VNVAAAANQchMwAAAIBqr9ujiYqMiZMjMFDBtULkCAxSbPxDiu3S09+lAQAA3PYC/V0AAAAAANwoR2CQej/1jIoeTdSZUydUt2ET1bqjtr/LAgAAqBEImQEAAADcNkJC6ygktI6/ywAAAKhR2C4DAAAAAAAAAOA1QmYAAAAAAAAAgNcImQEAAAAAAAAAXiNkBgAAAAAAAAB4jZAZAAAAAAAAAOA1QmYAAAAA8NLx48fldDrldDp11113qXnz5q7j8+fPa/ny5TLG6B//+IfbeXv27FH//v0VGRmpjh07qkePHkpLS6tw/Y0bN6p///6SpAULFsgYo/Xr17v6L15/6dKlkqTu3burVatWsta6xjz22GMKDQ11Hefk5Khnz55q166d2rRpoylTprjGL1iwQI0bN5bT6VR0dLSGDBmiwsJCSdLkyZM1ffr0KnrnAADA7YSQGQAAAAC81LBhQ2VlZSkrK0tjxozRCy+84DoODg5WSkqKunXrppSUFNc5Z8+e1aOPPqpnnnlG+/btU2ZmpmbOnKnc3Nxr3i82Nlapqamu45SUFHXo0MFtTL169bRlyxZJ0smTJ/Xtt9+6+oqKijRw4EAlJydr9+7d2r59u9LT0zVnzhzXmKeeekpZWVnKyclRcHCwFi9e7PX7AwAAagZCZgAAAAC4CQoKCrR582bNnz/fLRj+8MMPFR8fr4EDB7raYmJiNGLEiGteMyEhQZ999pkuXLiggoIC7d27V06n021MYmKi637Lli3TT3/6U1ffokWL1LVrV/Xu3VuSVLt2bc2aNUtTp06tcK/i4mL98MMPql+//nW9bgAAUPMQMgMAAADATfDxxx+rT58+atu2rRo2bKjMzExJZdtV3H///V5d0xijXr16ad26dfr444/dguqLHnroIaWlpamkpESpqal66qmnXH05OTnq2LGj2/jIyEgVFBTo9OnTkqTFixfL6XSqefPmOnHihAYMGOBVrQAAoOYgZAYAAACA61RUcFpfbFqrtBUfas+Oz1RSUlxhTEpKihITEyWVrS6+dMuMSw0ePFgxMTFuK46v5uJK5dTUVCUlJVXodzgc6tatm1JTU1VUVKSwsDDPX5j+uV3Gd999p9jYWE2bNu26zgcAADVPoL8LAAAAAIDq5MjBXC2fP02lJSUqKb6gLzM3KWPDf6ukuNQ15sSJE9qwYYN27twpY4xKSkpkjNG0adMUHR3t9pC/jz76SBkZGZowYYJH9+/cubN27typ2rVrq23btpWOSUxM1ODBgzV58mS39qioqAoPGMzNzVVoaKjq1Knj1m6M0YABAzRz5kwlJyd7VBsAAKiZWMkMAAAAAB6y1mpd6ru6cO6sSoovSJIunD+nk8e/07cH9rrGLV26VMOGDdOBAweUl5engwcPKjw8XJs2bdLQoUO1ZcsWrVixwjW+sLDwuuqYOnWq3nrrrSv2JyQk6Fe/+lWFlc4/+9nPtHnzZq1fv15S2YMAx48fr5dffrnS62zevFmRkZHXVRsAAKh5CJkBAAAAwENnTh7XD6fzK7SXFBfr+HffuI5TUlI0ePBgtzGPyzEcVgAAExpJREFUP/64UlJSFBISopUrV2ru3LmKiIhQfHy83njjDf3mN7/xuI6+ffuqR48eV+w3xmjChAlq1KiRW3tISIg+/vhjvfHGG2rXrp1iY2PVqVMnjRs3zjXm4p7M7du31xdffKFXX321wvXnzp2ruXPnSpIyMjI0evRoV9/lDyIEAAC3P2Ot9cuN4+LibEZGhl/uDQAAAADeKDidr4XTXlZJccU9mOs1vks/f/H/+qEqAACAm8sYk2mtjbtSPyuZAQAAAMBDoXXqq0GT5jLGuLUHBgUrulN3/xQFAADgZ4TMAAAAAHAdHkl6ViGhdRRU6w45AoMUGBSs5hH3qP0DD/m7NAAAAL8I9HcBAAAAAFCd1GvUVE//+3Qd2L1DBafy1bRlhJq2CPd3WQAAAH5DyAwAAAAA18nhCFRE1P3+LgMAAOCWwHYZAAAAAAAAAACvETIDAAAAAAAAALxGyAwAAAAAN1GPHj20bt06t7Z33nlHzz77rCQpJydHPXv2VLt27dSmTRtNmTJF1lpJ0oIFCxQQEKAdO3a4zo2JiVFeXp4k6dSpUxo+fLjuvvtuRUZGavjw4Tp16pQkKSIiQrt373a77/PPP6+3335bGzduVP/+/d36HnvsMXXp0uWKr+PIkSPq37+/OnTooKioKPXr10+SlJeXp5CQEDmdTtfH+++/78U7BQAAqitCZgAAAAC4iZKSkpSamurWlpqaqqSkJBUVFWngwIFKTk7W7t27tX37dqWnp2vOnDmusS1atNCbb75Z6bVHjRqliIgI7d27V/v27VN4eLhGjx4tSUpMTHS7b2lpqZYuXarExMQK1zl58qQyMzN16tQp5ebmVnqvSZMm6eGHH9b27du1a9cuTZ061dUXGRmprKws18fw4cM9f4MAAEC1R8gMAAAAADfRkCFDtGrVKp0/f15S2crfw4cPKyEhQYsWLVLXrl3Vu3dvSVLt2rU1a9YstwC3f//+ysnJqbAqee/evcrMzNSrr77qaps0aZIyMjK0b98+JSUlafHixa6+tLQ0tW7dWq1bt65Q47JlyzRgwIAKwfSlvv32W7Vo0cJ13L59ey/eDQAAcDsiZAYAAACAm6hBgwbq3Lmz1qxZI6lsFfOTTz4pY4xycnLUsWNHt/GRkZEqKCjQ6dOnJUkBAQF6+eWX9dZbb7mN27Vrl5xOpxwOh6vN4XDI6XQqJydHsbGxCggI0Pbt2133TUpKqrTGlJQUJSUlKSkpSSkpKZWOGTt2rEaNGqUePXrozTff1OHDh119+/btc9suY9OmTdf5LgEAgOos0N8FAAAAAMDtprDgtDI2rFDul1kKrnWHEjo7lZqSokGDBik1NVXz58+/rusNHTpUb775pvbv339d513cqiM6OlrLly/Xb3/72wpjjhw5oj179qhbt24yxigoKEjZ2dmKiYlxG/fII48oNzdXa9eu1Zo1a3TfffcpOztb0j+3ywAAADUTK5kBAAAAoAqdO1uoxTMnK3vrRhWcPK4TRw6pVuFhrV2zWp9//rkKCwtdq5ejoqKUmZnpdn5ubq5CQ0NVp04dV1tgYKBeeuklvf322662qKgoZWVlqbS01NVWWlqqrKwsRUVFSSrbl3nJkiVav3692rdvr6ZNm1aod8mSJcrPz1d4eLjCwsKUl5d3xdXMDRo00NC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      "text/plain": [
       "<Figure size 1800x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.figsize'] = (25, 16) # use bigger graphs\n",
    "\n",
    "model = TSNE(n_components=2, perplexity=25, verbose=2, random_state=686861).fit_transform(corr_mat)\n",
    "\n",
    "x_axis=model[:,0]\n",
    "y_axis=model[:,1]\n",
    "x_norm = (x_axis-np.min(x_axis)) / (np.max(x_axis) - np.min(x_axis))\n",
    "y_norm = (y_axis-np.min(y_axis)) / (np.max(y_axis) - np.min(y_axis))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "palette = sns.color_palette('deep', np.unique(membership_labels).max() + 1)\n",
    "colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in membership_labels]\n",
    "    \n",
    "ax.scatter(x_norm, y_norm, c = colors)\n",
    "names = list(corr_mat.index)\n",
    "\n",
    "for i, name in enumerate(names):\n",
    "    ax.annotate(truncate_str(mnemonic_2_desc[name]), (x_norm[i],y_norm[i]))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last run on Wed Jan 22 18:33:58 UTC 2020\r\n"
     ]
    }
   ],
   "source": [
    "!echo \"Last run on `date`\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}