15_topic_modeling/06_lda_earnings_calls.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Topic Modeling with Earnings Call Transcripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:06.590938Z",
     "start_time": "2020-06-20T20:13:06.588820Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:07.720988Z",
     "start_time": "2020-06-20T20:13:06.592135Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stefan/.pyenv/versions/miniconda3-latest/envs/ml4t/lib/python3.7/site-packages/patsy/constraint.py:13: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
      "  from collections import Mapping\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from collections import Counter\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter, ScalarFormatter\n",
    "import seaborn as sns\n",
    "from ipywidgets import interact, FloatRangeSlider\n",
    "\n",
    "# spacy for language processing\n",
    "import spacy\n",
    "\n",
    "# sklearn for feature extraction\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.feature_extraction import stop_words\n",
    "\n",
    "# gensim for topic models\n",
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.matutils import Sparse2Corpus\n",
    "\n",
    "# topic model viz\n",
    "import pyLDAvis\n",
    "from pyLDAvis.gensim import prepare\n",
    "\n",
    "# evaluate parameter settings\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:07.742511Z",
     "start_time": "2020-06-20T20:13:07.736315Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:07.757730Z",
     "start_time": "2020-06-20T20:13:07.745625Z"
    }
   },
   "outputs": [],
   "source": [
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:07.956985Z",
     "start_time": "2020-06-20T20:13:07.771359Z"
    }
   },
   "outputs": [],
   "source": [
    "stop_words = set(pd.read_csv('http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words', \n",
    "                         header=None, \n",
    "                         squeeze=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Load Earnings Call Transcripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:07.770103Z",
     "start_time": "2020-06-20T20:13:07.762362Z"
    }
   },
   "outputs": [],
   "source": [
    "PROJECT_DIR = Path().cwd().parent\n",
    "data_path = PROJECT_DIR / 'data' / 'earnings_calls'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The document are the result of scraping the [SeekingAlpha Earnings Transcripts](https://seekingalpha.com/earnings/earnings-call-transcripts) as described in n Chapter 3 on [Alternative Data](../../03_alternative_data/02_earnings_calls)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The transcripts consist of individual statements by company representative, an operator and usually a Q&A session with analysts. We will treat each of these statements as separate documents, ignoring operator statements, to obtain 22,766 items with mean and median word counts of 144 and 64, respectively (or as many as you were able to scrape):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:09.546498Z",
     "start_time": "2020-06-20T20:13:07.959316Z"
    }
   },
   "outputs": [],
   "source": [
    "documents = []\n",
    "for i, transcript in enumerate(data_path.iterdir()):\n",
    "    content = pd.read_csv(transcript / 'content.csv')\n",
    "    documents.extend(content.loc[(content.speaker!='Operator') & (content.content.str.len() > 5), 'content'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:09.550832Z",
     "start_time": "2020-06-20T20:13:09.547566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32047"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokens per document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:10.334604Z",
     "start_time": "2020-06-20T20:13:09.552581Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_count = pd.Series(documents).str.split().str.len()\n",
    "ax = sns.distplot(np.log(word_count), kde=False)\n",
    "ax.set_title('Log word count distribution')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:10.346042Z",
     "start_time": "2020-06-20T20:13:10.335743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   32,047.00\n",
       "mean       137.33\n",
       "std        283.60\n",
       "min          1.00\n",
       "10%          4.00\n",
       "20%         12.00\n",
       "30%         28.00\n",
       "40%         45.00\n",
       "50%         62.00\n",
       "60%         82.00\n",
       "70%        111.00\n",
       "80%        161.00\n",
       "90%        273.00\n",
       "max      5,718.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_count.describe(percentiles=np.arange(.1, 1.0, .1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:11.086387Z",
     "start_time": "2020-06-20T20:13:10.350865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 10000 15000 20000 25000 30000 "
     ]
    }
   ],
   "source": [
    "token_count = Counter()\n",
    "for i, doc in enumerate(documents, 1):\n",
    "    if i % 5000 == 0:\n",
    "        print(i, end=' ', flush=True)\n",
    "    token_count.update(doc.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most frequent tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:11.594715Z",
     "start_time": "2020-06-20T20:13:11.088529Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.DataFrame(token_count.most_common(), columns=['token', 'count'])\n",
    " .pipe(lambda x: x[~x.token.str.lower().isin(stop_words)])\n",
    " .set_index('token')\n",
    " .squeeze()\n",
    " .iloc[:50]\n",
    " .sort_values()\n",
    " .plot\n",
    " .barh(figsize=(8, 10)))\n",
    "sns.despine()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess Transcripts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use spaCy to preprocess these documents as illustrated in [Chapter 13 - Working with Text Data](../../13_working%20with_text_data) and store the cleaned and lemmatized text as a new text file. \n",
    "\n",
    "Data exploration reveals domain-specific stopwords like ’year’ and ‘quarter’ that we remove in a second step, where we also filter out statements with fewer than 10 words so that some 16,150 remain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:13:11.599365Z",
     "start_time": "2020-06-20T20:13:11.596002Z"
    }
   },
   "outputs": [],
   "source": [
    "def clean_doc(d):\n",
    "    doc = []\n",
    "    for t in d:\n",
    "        if not any([t.is_stop, t.is_digit, not t.is_alpha, t.is_punct, t.is_space, t.lemma_ == '-PRON-']):        \n",
    "            doc.append(t.lemma_)\n",
    "    return ' '.join(doc)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:15:43.129341Z",
     "start_time": "2020-06-20T20:13:11.600559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.12% 6.24% 9.36% 12.48% 15.60% 18.72% 21.84% 24.96% 28.08% 31.20% 34.32% 37.45% 40.57% 43.69% 46.81% 49.93% 53.05% 56.17% 59.29% 62.41% 65.53% 68.65% 71.77% 74.89% 78.01% 81.13% 84.25% 87.37% 90.49% 93.61% 96.73% 99.85% "
     ]
    }
   ],
   "source": [
    "nlp = spacy.load('en')\n",
    "iter_docs = (doc for doc in documents)\n",
    "clean_docs = []\n",
    "for i, document in enumerate(nlp.pipe(iter_docs, batch_size=100, n_process=8), 1):  \n",
    "    if i % 1000 == 0: \n",
    "        print(f'{i/len(documents):.2%}', end=' ', flush=True)\n",
    "    clean_docs.append(clean_doc(document))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:24:28.984432Z",
     "start_time": "2020-06-20T20:24:28.981267Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'earnings_calls')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:24:44.320316Z",
     "start_time": "2020-06-20T20:24:44.317525Z"
    }
   },
   "outputs": [],
   "source": [
    "clean_text = results_path / 'clean_text.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:24:45.181745Z",
     "start_time": "2020-06-20T20:24:45.090032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14079406"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_text.write_text('\\n'.join(clean_docs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Vectorize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:24:53.421502Z",
     "start_time": "2020-06-20T20:24:52.869046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22571"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs = []\n",
    "for line in clean_text.read_text().split('\\n'):\n",
    "    line = [t for t in line.split() if t not in stop_words]\n",
    "    if len(line) > 10:\n",
    "        docs.append(' '.join(line))\n",
    "\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:25:08.376150Z",
     "start_time": "2020-06-20T20:25:08.110459Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 10000 15000 20000 "
     ]
    }
   ],
   "source": [
    "token_count = Counter()\n",
    "for i, doc in enumerate(docs, 1):\n",
    "    if i % 5000 == 0:\n",
    "        print(i, end=' ', flush=True)\n",
    "    token_count.update(doc.split())\n",
    "token_count = pd.DataFrame(token_count.most_common(), columns=['token', 'count'])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:25:16.411481Z",
     "start_time": "2020-06-20T20:25:16.188929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (token_count.set_index('token').squeeze().iloc[:25].sort_values(\n",
    "    ascending=False).plot.bar(figsize=(14, 4), rot=25, title='Most Common Tokens'))\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_xlabel('Token')\n",
    "sns.despine()\n",
    "plt.gcf().tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:25:29.858785Z",
     "start_time": "2020-06-20T20:25:28.836804Z"
    }
   },
   "outputs": [],
   "source": [
    "frequent_words = token_count.head(50).token.tolist()\n",
    "binary_vectorizer = CountVectorizer(max_df=1.0,\n",
    "                             min_df=1,\n",
    "                             stop_words=frequent_words,\n",
    "                             max_features=None,\n",
    "                             binary=True)\n",
    "\n",
    "binary_dtm = binary_vectorizer.fit_transform(docs)\n",
    "\n",
    "n_docs, n_tokens = binary_dtm.shape\n",
    "doc_freq = pd.Series(np.array(binary_dtm.sum(axis=0)).squeeze()).div(binary_dtm.shape[0])\n",
    "max_unique_tokens = np.array(binary_dtm.sum(axis=1)).squeeze().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:25:37.989376Z",
     "start_time": "2020-06-20T20:25:37.084602Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9d0debdee8a41b491ebd5636ba86965",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatRangeSlider(value=(0.0, 1.0), description='Doc. Freq.', layout=Layout(width='800px'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_range = FloatRangeSlider(value=[0.0, 1.0],\n",
    "                            min=0,\n",
    "                            max=1,\n",
    "                            step=0.0001,\n",
    "                            description='Doc. Freq.',\n",
    "                            disabled=False,\n",
    "                            continuous_update=True,\n",
    "                            orientation='horizontal',\n",
    "                            readout=True,\n",
    "                            readout_format='.1%',\n",
    "                            layout={'width': '800px'})\n",
    "\n",
    "@interact(df_range=df_range)\n",
    "def document_frequency_simulator(df_range):\n",
    "    min_df, max_df = df_range\n",
    "    keep = doc_freq.between(left=min_df, right=max_df)\n",
    "    left = keep.sum()\n",
    "\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(14, 6))\n",
    "    updated_dtm = binary_dtm.tocsc()[:, np.flatnonzero(keep)]\n",
    "    unique_tokens_per_doc = np.array(updated_dtm.sum(axis=1)).squeeze()\n",
    "    sns.distplot(unique_tokens_per_doc, ax=axes[0], kde=False, norm_hist=False)\n",
    "    axes[0].set_title('Unique Tokens per Doc')\n",
    "    axes[0].set_yscale('log')\n",
    "    axes[0].set_xlabel('# Unique Tokens')\n",
    "    axes[0].set_ylabel('# Documents (log scale)')\n",
    "    axes[0].set_xlim(0, max_unique_tokens)    \n",
    "    axes[0].yaxis.set_major_formatter(ScalarFormatter())\n",
    "\n",
    "    term_freq = pd.Series(np.array(updated_dtm.sum(axis=0)).squeeze())\n",
    "    sns.distplot(term_freq, ax=axes[1], kde=False, norm_hist=False)\n",
    "    axes[1].set_title('Document Frequency')\n",
    "    axes[1].set_ylabel('# Tokens')\n",
    "    axes[1].set_xlabel('# Documents')\n",
    "    axes[1].set_yscale('log')\n",
    "    axes[1].set_xlim(0, n_docs)\n",
    "\n",
    "    title = f'Document/Term Frequency Distribution | # Tokens: {left:,d} ({left/n_tokens:.2%})'\n",
    "    fig.suptitle(title, fontsize=14)\n",
    "    sns.despine()\n",
    "    fig.tight_layout()\n",
    "    fig.subplots_adjust(top=.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train & Evaluate LDA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:07.429909Z",
     "start_time": "2020-06-20T20:28:07.425096Z"
    }
   },
   "outputs": [],
   "source": [
    "def show_word_list(model, corpus, top=10, save=False):\n",
    "    top_topics = model.top_topics(corpus=corpus, coherence='u_mass', topn=20)\n",
    "    words, probs = [], []\n",
    "    for top_topic, _ in top_topics:\n",
    "        words.append([t[1] for t in top_topic[:top]])\n",
    "        probs.append([t[0] for t in top_topic[:top]])\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(model.num_topics*1.2, 5))\n",
    "    sns.heatmap(pd.DataFrame(probs).T,\n",
    "                annot=pd.DataFrame(words).T,\n",
    "                fmt='',\n",
    "                ax=ax,\n",
    "                cmap='Blues',\n",
    "                cbar=False)\n",
    "    sns.despine()\n",
    "    fig.tight_layout()\n",
    "    if save:\n",
    "        fig.savefig(results_path / 'earnings_call_wordlist', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:10.227154Z",
     "start_time": "2020-06-20T20:28:10.221082Z"
    }
   },
   "outputs": [],
   "source": [
    "def show_coherence(model, corpus, tokens, top=10, cutoff=0.01):\n",
    "    top_topics = model.top_topics(corpus=corpus, coherence='u_mass', topn=20)\n",
    "    word_lists = pd.DataFrame(model.get_topics().T, index=tokens)\n",
    "    order = []\n",
    "    for w, word_list in word_lists.items():\n",
    "        target = set(word_list.nlargest(top).index)\n",
    "        for t, (top_topic, _) in enumerate(top_topics):\n",
    "            if target == set([t[1] for t in top_topic[:top]]):\n",
    "                order.append(t)\n",
    "\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(15,5))\n",
    "    title = f'# Words with Probability > {cutoff:.2%}'\n",
    "    (word_lists.loc[:, order]>cutoff).sum().reset_index(drop=True).plot.bar(title=title, ax=axes[1]);\n",
    "\n",
    "    umass = model.top_topics(corpus=corpus, coherence='u_mass', topn=20)\n",
    "    pd.Series([c[1] for c in umass]).plot.bar(title='Topic Coherence', ax=axes[0])\n",
    "    sns.despine()\n",
    "    fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:13.000753Z",
     "start_time": "2020-06-20T20:28:12.994925Z"
    }
   },
   "outputs": [],
   "source": [
    "def show_top_docs(model, corpus, docs):\n",
    "    doc_topics = model.get_document_topics(corpus)\n",
    "    df = pd.concat([pd.DataFrame(doc_topic, \n",
    "                                 columns=['topicid', 'weight']).assign(doc=i) \n",
    "                    for i, doc_topic in enumerate(doc_topics)])\n",
    "\n",
    "    for topicid, data in df.groupby('topicid'):\n",
    "        print(topicid, docs[int(data.sort_values('weight', ascending=False).iloc[0].doc)])\n",
    "        print(pd.DataFrame(lda.show_topic(topicid=topicid)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vocab Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For illustration, we create a document-term matrix containing terms appearing in between 0.5% and 50% of documents for around 1,560 features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:15.212013Z",
     "start_time": "2020-06-20T20:28:15.210063Z"
    }
   },
   "outputs": [],
   "source": [
    "min_df = .005\n",
    "max_df=.25\n",
    "ngram_range=(1, 1)\n",
    "binary = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:15.603257Z",
     "start_time": "2020-06-20T20:28:15.601327Z"
    }
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(stop_words=frequent_words,\n",
    "                             min_df=min_df,\n",
    "                             max_df=max_df,\n",
    "                             ngram_range=ngram_range,\n",
    "                             binary=binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:16.763182Z",
     "start_time": "2020-06-20T20:28:15.877847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22571, 1526)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm = vectorizer.fit_transform(docs)\n",
    "tokens = vectorizer.get_feature_names()\n",
    "dtm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:18.543706Z",
     "start_time": "2020-06-20T20:28:17.891882Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "corpus = Sparse2Corpus(dtm, documents_columns=False)\n",
    "id2word = pd.Series(tokens).to_dict()\n",
    "dictionary = Dictionary.from_corpus(corpus, id2word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:28:20.688305Z",
     "start_time": "2020-06-20T20:28:20.685696Z"
    }
   },
   "outputs": [],
   "source": [
    "num_topics=15\n",
    "chunksize=50000\n",
    "passes=25\n",
    "update_every=None\n",
    "alpha='auto'\n",
    "eta='auto'\n",
    "decay=0.5\n",
    "offset=1.0\n",
    "eval_every=None\n",
    "iterations=50\n",
    "gamma_threshold=0.001\n",
    "minimum_probability=0.01\n",
    "minimum_phi_value=0.01\n",
    "per_word_topics=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training a 15 topic model using 25 passes over the corpus takes a bit over two minutes on a 4-core i7.\n",
    "The top 10 words per topic identify several distinct themes that range from obvious financial information to clinical trials (topic 4) and supply chain issues (12)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:31:31.808221Z",
     "start_time": "2020-06-20T20:28:21.439869Z"
    }
   },
   "outputs": [],
   "source": [
    "lda = LdaModel(corpus=corpus,\n",
    "               id2word=id2word,\n",
    "               num_topics=num_topics,\n",
    "               chunksize=chunksize,\n",
    "               update_every=update_every,\n",
    "               alpha=alpha,\n",
    "               eta=eta,\n",
    "               decay=decay,\n",
    "               offset=offset,\n",
    "               eval_every=eval_every,\n",
    "               passes=passes,\n",
    "               iterations=iterations,\n",
    "               gamma_threshold=gamma_threshold,\n",
    "               minimum_probability=minimum_probability,\n",
    "               minimum_phi_value=minimum_phi_value,\n",
    "               random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:31:33.093893Z",
     "start_time": "2020-06-20T20:31:31.809058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_word_list(model=lda, corpus=corpus, save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topic Coherence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:31:34.260300Z",
     "start_time": "2020-06-20T20:31:33.094965Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K07R6FX2z2ShJEu12Oy8Zjsej0jTVarWqzgnPZjNFUaTRaOQlw935fNZisVBRFNput17XBgDUF73ixkKvkJrXLRhgAACe7tUXE/6kLEsFQW2/Kv4nZVkqyzINBoNXRwEA4NfoFXY9u1swwACAN/fVJn/na5Pf7/ffXkzY7Xa9ZLDwHKxmsFL6AAD2WdjL6BW2cvjsFgwwAODNWdjkJWm9XisMQ00mE29rPrLwHMgAAKg7K/sIvcJODp8ZWnEcx//6jwAAUzqdji6Xi67Xq4bDodrtdvXzaTweq9freV3zkYXnQAYAQN1Z2UfoFXZy+MzAGxgAAAAAAMC85t4uAgAAAAAAaoMBBgAAAAAAMI8BBgAAAAAAMI8BBgAAAAAAMO8T5nRzun2RgIUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_coherence(model=lda, corpus=corpus, tokens=tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pyLDAVis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:31:46.084441Z",
     "start_time": "2020-06-20T20:31:34.261462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el325711405093228928806557471723\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el325711405093228928806557471723_data = {\"mdsDat\": {\"x\": [114.34487915039062, 105.821044921875, -138.33447265625, -7.436697483062744, -207.55062866210938, 52.817649841308594, 168.01084899902344, -94.16960144042969, -122.84917449951172, -94.8942642211914, 24.927400588989258, -53.41346740722656, -12.255849838256836, -224.21795654296875, 36.15425491333008], \"y\": [114.30829620361328, -93.74962615966797, 95.32076263427734, 111.25969696044922, -105.47260284423828, 14.603764533996582, 6.806949615478516, -157.78955078125, -39.059417724609375, 195.40829467773438, -180.46487426757812, 26.832107543945312, -72.09320068359375, 28.070581436157227, 209.42758178710938], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [11.66164493560791, 9.881596565246582, 9.343316078186035, 8.625358581542969, 8.37745475769043, 8.006321907043457, 6.895401954650879, 6.828745365142822, 5.523187160491943, 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      "0 sure happy talk strategy Steve look big series announcement Dell Technologies World handful week ago talk Dell Technologies Cloud specifically talk build cloud platform base HCI CI stack VMware Cloud Foundation organization want deploy prem cloud hybrid cloud consistency management automation provide tool high level integration VxRail VCF product VMware VCF product think differentiated offer continue build add capability talk extension platform primary storage array continue able enable customer build edge deployment core datum center deployment offer announce data center service basically ability public cloud experience prem private cloud deliver service private datum center edge network fully manage offer subscription build VxRail VMware Cloud Foundation install manage cut like cloud service customer consume today extend cloud strategy announcement Microsoft essentially allow customer want use public cloud public cloud service VMware software define datum center run cloud include AWS marketplace today announce Dell Technologies World VMware service Azure pretty excited believe customer wide range capability multi cloud world customer today deploy average different cloud architecture today want consistency management automation deploy provision announce strategy allow vm application container edge core datum center public cloud orchestrate specifically VMware capability build highly integrate infrastructure product help customer strategy spend vast Monday Tuesday Dell Technologies World talk help\n",
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      "1 thank good afternoon like thank time join SG Blocks quarter conference host today Paul Galvin Chief Executive Officer Mahesh Shetty company President Chief Financial Officer Paul provide business update cover customer partner announcement Mahesh discuss financial result press release result cross wire afternoon pm Eastern available company website follow management prepared comment open floor question turn management remember certain statement contain release forward look statement meaning private security litigation reform act statement statement historical fact contain presentation include statement future operation financial position business strategy plan objective management future operation forward look statement case forward look statement identify terminology believe estimate continue anticipate intend plan expect predict potential negative term similar expression base forward look statement largely current expectation projection future event financial trend believe affect financial condition result operation business strategy financial need forward look statement subject number risk uncertainty assumption include set forth filing Securities Exchange Commission SEC available rely forward look statement prediction future event assure event circumstance reflect forward look statement achieve occur presentation include non gaap financial measure SG Blocks use certain non gaap financial measure assess business operation reference non gaap financial measure consider addition GAAP financial measure consider substitute result present accordance GAAP finally conference webcast webcast link available Investor Relations section website time like turn Paul Galvin Paul floor\n",
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      "2 yes think problem big problem market rebate activity rebate shoot people buy year supply lense order big rebate year supply lense month month worth actual wearing right people necessarily wear lense single day lot lense kind market people shelf forth think thing market wear growth mean talk number people contact lense wearer growth world new wearer come contact lense fantastic market kind agree like little disappointed market growth Americas hope little bit strong think personally CooperVision little bit half year feel good think market end end day kind grower Asia Pac region strong look historically kind little bit talk recession market softness mean market grow north like mean pull stat matter fact happen handy contact lens market grow kind recession market overall grow bounce pretty recessionary resistant lot trade forth tie fact global growth wearer come market world outside good growth toric multifocal people fit correctly conversion daily daily silicone hydrogel help forth pretty recession resistant mean look pretty bad market contact lens market grow grow\n",
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      "3 thank Ramon good morning comment balance outlook mention release reiterate component guidance continue expect organic revenue growth core effective tax rate approximately core constant currency EPS decline approximately free cash flow approximately billion total cash return shareholder approximately billion comprise dividend approximately billion share repurchase approximately billion organic revenue growth core constant currency EPS drop high year target imply growth rate balance year growth rate let address think start line rate organic revenue growth FLNA extraordinary expect rate growth continue balance year second represent easy lap year balance year lap difficult especially half year EPS perspective consider follow benefit lap onetime frontline bonus benefit approximately million insurance recovery current year balance year lapping gain strategic asset sale franchise gain insurance recovery notably rate net commodity inflation include impact transactional foreign exchange expect accelerate second quarter finally pace plan reinvestment accelerate course year reflect core EPS operate margin performance open question operator question\n",
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      "4 thank Greg turn oncology study Phase clinical study design open label study explore safety tolerability efficacy follow intra tumoral injection intend produce single strand antisense RNA direct inhibit expression Epidermal Growth Factor Receptor EGFR protein stimulate induce cell differentiation proliferation know significantly amplify cancer cell administer target lesion week week patient squamous cell carcinoma head neck know HNSCC patient refractory standard therapy surgery chemotherapy immunotherapy continue good progress start study site Chris Lifehouse active hope site active patient enrol near future addition regulatory approval Ministry Health Russia hope russian site active month June total anticipate clinical site Australia Russia primary outcome trial measure size size lesion technique CT MRI positive response treatment reduce overall size tumor completely ablate lesion additionally look secondary endpoint progression free survival overall survival duration response disease control rate safety tolerability addition monitor molecular marker biobsolete lesion monitor presence antisense RNA produce vector result impact egfr level study stage design allow stop study base futility success end stage patient enrol monitor primary outcome measure assume enrollment rate expect interim analysis anticipate occur end calendar year touch stand regulatory clinical perspective program treat oculopharyngeal muscular dystrophy OPMD develop therapeutic treat dysphagia associate OPMD disease symptom OPMD varied depend severity rate progression pathophysiology current symptom include ptosis dysphagia leg weakness dysphagia impair swallow function cause majority health problem patient OPMD disease progress incidence hospitalization aspiration seriousness result lung infection significantly decrease quality life life threaten addition malnutrition factor equation currently write clinical study patient clinically genetically diagnose OPMD impairment swallow function patient receive single intra muscular dose cricopharyngeal muscle throat clinical protocol design dose escalation study enroll patient increase dose time period cohort ensure stay proceed dose reach dose define dose maximally effective dose enroll additional number patient patient follow year study primary endpoint safety tolerability patient look quantitative clinical improvement swallow patient report improvement swallowing quality life meet month face face OPMD Clinical Advisory Board update clinical plan finalize clinical protocol design group comprise key opinion leader doctor help manage treatment option OPMD patient separate group expert quantitative assessment swallow input design clinical study way hope maximize opportunity success positive time forward approval turn regulatory advance program important especially gene therapy product novel silence replace approach discuss IND enable study clinical plan regulatory agency improve likelihood pass regulatory requirement enter clinic mind past quarter meet discuss plan regulatory agency key OPMD country Canada quarter France development plan receive high level enthusiasm ingenuity single vector approach silence disease cause gene simultaneously express novel healthy copy gene feedback meeting incorporate clinical study design ongoing ind enable work feel position progress clinic pathway set time complete required toxicology manufacturing work support high quality regulatory filing turn David Suhy ongoing toxicology manufacturing work\n",
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      "5 think look share balance product category man woman brand activation test membership event activity able leverage improved datum analytic digital marketing mean guest respond share store traffic plus e commerce good healthy metric highly engage guest typically business significant swing week season season holiday holiday pleased momentum consistent traffic drive big piece business new guest exist guest balance product range\n",
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      "6 somewhat encouraged opportunity little big opportunity come way hope continue far talent talent incredibly important company like retain good talent add evolve provide marketplace need evolve hunt good people help business development resume background capability experience relationship key area opportunity enormous outreach term identify specific type talent quality skill need folk talent recruitment effort board important company look generation leadership company develop talent play leadership role company change forward grow evolve need cultivate talent internally bring people opportunity forward carrier carrier goal super important attract talent outside far create additional prospect create generation leadership company interested people come organization involve path want term growth merger acquisition forth bring folk successful thing maybe differently different method different kind idea different approach solve problem develop new offering market type people interesting talent internal develop internally attract outside care people people super important care people convince stick continue career successful\n",
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      "7 okay kind leveling deposit pressure March probably characterize pretty difficult deposit environment January February come increase relate steepness curve Steve remember invest loan short end curve steepness matter Fund Banking division Signature Financial tie short end curve certainly traditional asset base lending group tie short end curve area growth Venture Banking Group come short end curve steepness matter\n",
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      "8 thank Ed thank join today KLA Tencor deliver outstanding result September quarter drive company technology market leadership compelling value diversified product service portfolio enable customer success September quarter result company benefit strategy market leadership revenue diversification furthermore demonstrate critical nature process control enable technology inflection semiconductor industry include growth market vertical NAND EUV adoption China semiconductor industry expect closing quarter Orbotech acquisition KLA Tencor extend market reach electronics value chain enhance company product service portfolio core wfe address new growth market technological complexity rise KLA extend core competency exciting new opportunity perspective current industry demand environment notwithstanding recent adjustment capacity investment memory customer delay logic spend second half believe long term factor underpin demand wafer fab equipment industry remain sound result key element include diversified end market semiconductor ongoing commitment customer drive innovation leading edge technology roadmap discipline market drive capacity planning high level investment require address increase design complexity advanced device architecture expect industry driver persist deliver long term growth value creation opportunity company industry stakeholder recent highlight demonstrate successful execution company strategic focus technology market leadership market leader process control KLA Tencor help enable growth semiconductor industry China memory foundry manufacturer region invest high level accelerate process development yield learn expect momentum China continue customer progress technology roadmap ramp new production capacity initial phase multiyear investment cycle additionally past quarter exceptional demand bare waver mask inspection product growth mask inspection business consistent planned increase CapEx lead foundry customer ramp capacity support high number customer tapeout node begin aggressive development include EUV development bare wafer KLA market lead inspection metrology tool key enabler growth wafer supply address capacity expansion memory product essential help customer segment meet stringent design specification wafer flatness cleanliness finally KLA Tencor service business continue highlight forward trajectory deliver high single digit low double digit annual revenue growth low business volatility consistent historical trend long term growth service tie expansion instal base increase uptime requirement current node production trailing edge trail edge fab currently run near utilization create new opportunity product enhancement upgrade summary turn Bren KLA Tencor deliver outstanding performance September quarter drive company strong execution market leadership momentum marketplace diversified growth market critical role company product service play enable customer success position strong finish like turn Bren review financial result Bren\n",
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      "9 couple month ago look trade war think affect market think affect small size decision start fix vessel come free quarter year bit second quarter right fix number vessel actually probably vessel come free depend thing continue charter certain period small vessel actually instal scrubber instal scrubber big vessel Newcastlemaxes Capes January mean dedicated big vessel small vessel reason flexibility fix spot market low vessel open income short period probably short period low spot rate recover continue similar policy\n",
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      "10 objective primary production cost cost quarter primary produce production roughly purify produce month January primary quality brine deteriorate quarter impact negatively low brine quality require large energy plant brine plant large reagent expect able stabilize brine cost close range think achievable time effort require stabilization brine quality\n",
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      "11 thing work diligently fact learning mvmt brand level content quality content produce Movado drive immediate result pleased business second half year continue strong momentum strong momentum business believe able talk consumer different way ampe level content use reach consumer reach change dramatically direct relationship know customer rewarding reach vehicle reach build funnel digital perspective continue build brand awareness image brand vitally important\n",
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      "12 Tracy Ward Investor Relations Tom Olinger Chief Financial Officer Hamid Moghadam Chairman CEO Gary Anderson Chief Executive Officer Europe Asia Chris Caton Senior Vice President Global Head Research Mike Curless Chief Investment Officer Ed Nekritz Chief Legal Officer Gene Reilly Chief Executive Officer Americas Colleen McKeown Chief Human Resources Officer\n",
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      "13 okay fair want ask question kind cash flow cash flow cycle able expect year know inventory build little bit hope improve result term thinking kind cash flow cycle remainder year look kind maybe little drag expect time think Marc remainder year couple quarter kind hold serve cash flow operation kind nice cash flow context\n",
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      "14 thank Wahid good afternoon AeroVironment fiscal fourth quarter result follow revenue continue operation fourth quarter fiscal million decrease million fourth quarter fiscal revenue million decrease decrease product delivery million decrease service revenue fourth quarter fiscal revenue major product program follow small UAS million TMS million HAPS million million Gross margin continue operation fourth quarter fiscal million revenue compare million revenue fourth quarter fiscal decrease gross margin primarily decrease product margin million decrease service margin million gross margin percentage revenue decrease primarily increase proportion service revenue total revenue unfavorable service revenue mix high CIS inventory reserve look rest income statement expense continue operation fourth quarter fiscal million revenue compare expense million revenue fourth quarter fiscal increase primarily CIS fix asset impairment charge rate adoption Quantix AV DSS solution slow expect fourth quarter lower future outlook unit sale result low forecast impairment charge million s Quantex AV DSS fix asset expense continue operation fourth quarter fiscal million revenue compare expense million revenue fourth quarter fiscal income continue operation fourth quarter fiscal revenue compare million fourth quarter fiscal decrease income operation primarily decrease gross margin million increase expense million increase expense million net income fourth quarter fiscal million compare net income million fourth quarter fiscal increase net income income transition service agreement buyer EES business high income investment effective income tax rate continue operation minus fourth quarter fiscal compare effective income tax rate fourth quarter fiscal decrease effective tax rate fourth quarter fiscal reduction fiscal federal statutory rate low pre tax profit equity method investment activity net tax fourth quarter fiscal loss million dilute share compare loss million net tax fourth quarter fiscal net income continue operation attributable AeroVironment fourth quarter fiscal million dilute share compare net income continue operation attributable AeroVironment million cent diluted share fourth quarter fiscal net loss discontinue operation net tax fourth quarter fiscal million compare loss discontinue operation net tax million fourth quarter fiscal year fiscal result revenue fiscal million increase million compare million fiscal increase revenue increase service revenue million increase product revenue million inception date revenue hapsmobile million total value contract hapsmobile million consist million design development agreement million preliminary design related effort million remain contract include portion currently unfunded gross margin fiscal million revenue compare million fiscal increase increase product margin million increase service margin million Gross margin percentage revenue increase primarily favorable product mix partially offset unfavorable service mix increase CIS inventory reserve charge expense fiscal million revenue compare expense million revenue fiscal increase primarily million excessive impairment charge CIS business expense relate transition service agreement buyer ees business expense fiscal million revenue compare expense million revenue fiscal net income fiscal million compare prior year net income million net income increase primarily litigation settlement income earn transition service agreement buyer EES business increase income effective income tax rate continue operation fiscal compare effective income tax rate fiscal effective income tax rate fiscal include impact time defer tax expense result measurement exist defer tax asset liability million decrease effective income tax rate reduction fiscal federal statutory rate equity method investment activity net tax fiscal loss million dilute share compare loss million net tax fiscal increased loss increase ownership hapsmobile joint venture high investment hapsmobile joint venture net income continue operation attributable AeroVironment fiscal million dilute share compare million dilute share fiscal net income discontinue operation net tax fiscal million dilute share compare loss discontinue operation net tax million fiscal dilute share fiscal include million gain net tax sale ees business funded backlog April million decrease fourth quarter fiscal increase million quarter fiscal backlog million turn balance sheet cash cash equivalent investment end fourth quarter fiscal total million increase million end fiscal cash cash equivalent investment million net account receivable include unbilled receivable retention end fourth quarter fiscal total million unbilled receivables retention balance million inclusive million related party total day sale outstanding continue operation fourth quarter fiscal year approximately day compare day fourth quarter fiscal year net inventory end fourth quarter fiscal year million compare million end fourth quarter fiscal year day inventory outstanding fourth quarter fiscal year approximately day compare day fourth quarter fiscal year account payable end fourth quarter fiscal year million compare million end fourth quarter fiscal year total day payable outstanding fourth quarter fiscal year approximately compare day fourth quarter fiscal year turn capital expenditure fourth quarter fiscal year invest approximately million property improvement capital equipment continue operation recognize million depreciation amortization expense update fiscal visibility today fourth quarter end backlog expect execute fiscal million quarter date booking anticipate execute fiscal million unfunded backlog incrementally fund contract anticipate recognize revenue balance year million add million fiscal year midpoint revenue guidance range anticipate year effective tax rate range like turn thing Wahid\n",
      "               0    1\n",
      "0        compare 0.03\n",
      "1        expense 0.03\n",
      "2  approximately 0.02\n",
      "3          total 0.02\n",
      "4          gross 0.02\n",
      "5         period 0.02\n",
      "6          prior 0.02\n",
      "7           loss 0.02\n",
      "8       decrease 0.01\n",
      "9            non 0.01\n"
     ]
    }
   ],
   "source": [
    "show_top_docs(model=lda, corpus=corpus, docs=docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review Experiment Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the impact of different parameter settings, we run a few hundred experiments for different DTM constraints and model parameters. More specifically, we let the min_df and max_df parameters range from 50-500 words and 10% to 100% of documents, respectively using alternatively binary and absolute counts. We then train LDA models with 3 to 50 topics, using 1 and 25 passes over the corpus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The script [run_experiments.py](run_experiments.py) lets you train many topic models with different hyperparameters to explore how they impact the results. The script [collect_experiments.py](collect_experiments.py) combines the results into a `results.h5` HDF store.\n",
    "\n",
    "These results are not included in the repository due to their size, but the results are displayed and you can rerun these experiments with earnings call transcripts or other text documents of your choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:35:45.866118Z",
     "start_time": "2020-06-20T20:35:45.807602Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore(results_path / 'results.h5') as store:\n",
    "    perplexity = store.get('perplexity')\n",
    "    coherence = store.get('coherence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:35:47.906108Z",
     "start_time": "2020-06-20T20:35:47.901413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 496 entries, 0 to 15\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   vocab_size  496 non-null    int64  \n",
      " 1   test_vocab  496 non-null    int64  \n",
      " 2   min_df      496 non-null    int64  \n",
      " 3   max_df      496 non-null    float64\n",
      " 4   binary      496 non-null    bool   \n",
      " 5   num_topics  496 non-null    int64  \n",
      " 6   passes      496 non-null    int64  \n",
      " 7   perplexity  496 non-null    float64\n",
      "dtypes: bool(1), float64(2), int64(5)\n",
      "memory usage: 31.5 KB\n"
     ]
    }
   ],
   "source": [
    "perplexity.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:35:48.236229Z",
     "start_time": "2020-06-20T20:35:48.223114Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8370 entries, 0 to 713\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   topic       8370 non-null   int64  \n",
      " 1   passes      8370 non-null   object \n",
      " 2   num_topics  8370 non-null   object \n",
      " 3   coherence   8370 non-null   float64\n",
      " 4   min_df      8370 non-null   int64  \n",
      " 5   max_df      8370 non-null   float64\n",
      " 6   binary      8370 non-null   bool   \n",
      "dtypes: bool(1), float64(2), int64(2), object(2)\n",
      "memory usage: 465.9+ KB\n"
     ]
    }
   ],
   "source": [
    "coherence.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter Settings: Impact on Perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:35:51.535117Z",
     "start_time": "2020-06-20T20:35:51.505808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             perplexity   R-squared:                       0.772\n",
      "Model:                            OLS   Adj. R-squared:                  0.765\n",
      "Method:                 Least Squares   F-statistic:                     75.71\n",
      "Date:                Sat, 20 Jun 2020   Prob (F-statistic):          5.53e-116\n",
      "Time:                        16:35:51   Log-Likelihood:                -2407.9\n",
      "No. Observations:                 496   AIC:                             4848.\n",
      "Df Residuals:                     480   BIC:                             4915.\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const           158.0331      5.804     27.227      0.000     146.657     169.409\n",
      "min_df_100      -34.7269      4.675     -7.428      0.000     -43.890     -25.564\n",
      "min_df_250      -77.8456      4.348    -17.903      0.000     -86.368     -69.323\n",
      "min_df_500     -108.7279      4.475    -24.295      0.000    -117.500     -99.956\n",
      "max_df_0.25     -20.5220      4.448     -4.614      0.000     -29.240     -11.804\n",
      "max_df_0.5      -30.2190      4.287     -7.050      0.000     -38.620     -21.818\n",
      "max_df_1.0      -29.6129      4.442     -6.666      0.000     -38.319     -20.906\n",
      "binary_True      40.5022      2.764     14.651      0.000      35.084      45.920\n",
      "num_topics_5      2.4029      3.747      0.641      0.521      -4.941       9.747\n",
      "num_topics_7      5.7087      3.566      1.601      0.109      -1.280      12.697\n",
      "num_topics_10    11.3472      3.353      3.385      0.001       4.776      17.918\n",
      "num_topics_15    20.6403      3.259      6.332      0.000      14.252      27.029\n",
      "num_topics_20    30.0500      3.500      8.585      0.000      23.190      36.910\n",
      "num_topics_25    39.5115      4.040      9.780      0.000      31.593      47.430\n",
      "num_topics_50    89.8369      9.679      9.282      0.000      70.866     108.808\n",
      "passes_25       -25.4013      2.789     -9.108      0.000     -30.867     -19.935\n",
      "==============================================================================\n",
      "Omnibus:                      459.795   Durbin-Watson:                   1.195\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            19757.525\n",
      "Skew:                           3.888   Prob(JB):                         0.00\n",
      "Kurtosis:                      32.926   Cond. No.                         12.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n"
     ]
    }
   ],
   "source": [
    "X = perplexity[['min_df', 'max_df', 'binary', 'num_topics','passes']]\n",
    "X = pd.get_dummies(X, columns=X.columns, drop_first=True)\n",
    "ols = sm.OLS(endog=perplexity.perplexity, exog=sm.add_constant(X))\n",
    "model = ols.fit(cov_type='HC0')\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameter Settings: Impact on Coherence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:35:55.293514Z",
     "start_time": "2020-06-20T20:35:55.240067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              coherence   R-squared:                       0.665\n",
      "Model:                            OLS   Adj. R-squared:                  0.663\n",
      "Method:                 Least Squares   F-statistic:                     237.0\n",
      "Date:                Sat, 20 Jun 2020   Prob (F-statistic):               0.00\n",
      "Time:                        16:35:55   Log-Likelihood:                -4925.0\n",
      "No. Observations:                8370   AIC:                             9980.\n",
      "Df Residuals:                    8305   BIC:                         1.044e+04\n",
      "Df Model:                          64                                         \n",
      "Covariance Type:                  HC0                                         \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const            -1.5492      0.023    -66.490      0.000      -1.595      -1.504\n",
      "topic_1          -0.1076      0.020     -5.501      0.000      -0.146      -0.069\n",
      "topic_2          -0.2316      0.020    -11.553      0.000      -0.271      -0.192\n",
      "topic_3          -0.3080      0.019    -16.228      0.000      -0.345      -0.271\n",
      "topic_4          -0.4257      0.019    -21.936      0.000      -0.464      -0.388\n",
      "topic_5          -0.4553      0.019    -23.826      0.000      -0.493      -0.418\n",
      "topic_6          -0.5660      0.021    -27.289      0.000      -0.607      -0.525\n",
      "topic_7          -0.5650      0.020    -28.721      0.000      -0.604      -0.526\n",
      "topic_8          -0.6366      0.020    -31.807      0.000      -0.676      -0.597\n",
      "topic_9          -0.7315      0.022    -32.944      0.000      -0.775      -0.688\n",
      "topic_10         -0.7038      0.020    -34.972      0.000      -0.743      -0.664\n",
      "topic_11         -0.7618      0.020    -37.648      0.000      -0.801      -0.722\n",
      "topic_12         -0.8278      0.021    -39.529      0.000      -0.869      -0.787\n",
      "topic_13         -0.9086      0.024    -37.905      0.000      -0.956      -0.862\n",
      "topic_14         -1.0062      0.028    -36.165      0.000      -1.061      -0.952\n",
      "topic_15         -0.9400      0.020    -46.426      0.000      -0.980      -0.900\n",
      "topic_16         -1.0064      0.021    -48.362      0.000      -1.047      -0.966\n",
      "topic_17         -1.0891      0.024    -45.451      0.000      -1.136      -1.042\n",
      "topic_18         -1.2143      0.035    -34.359      0.000      -1.284      -1.145\n",
      "topic_19         -1.3974      0.051    -27.558      0.000      -1.497      -1.298\n",
      "topic_20         -1.2093      0.032    -38.068      0.000      -1.272      -1.147\n",
      "topic_21         -1.3008      0.043    -30.040      0.000      -1.386      -1.216\n",
      "topic_22         -1.3990      0.048    -28.894      0.000      -1.494      -1.304\n",
      "topic_23         -1.5555      0.075    -20.642      0.000      -1.703      -1.408\n",
      "topic_24         -1.8207      0.102    -17.928      0.000      -2.020      -1.622\n",
      "topic_25         -1.2510      0.027    -47.049      0.000      -1.303      -1.199\n",
      "topic_26         -1.2884      0.027    -47.200      0.000      -1.342      -1.235\n",
      "topic_27         -1.3080      0.028    -45.896      0.000      -1.364      -1.252\n",
      "topic_28         -1.3387      0.029    -46.353      0.000      -1.395      -1.282\n",
      "topic_29         -1.3818      0.033    -41.600      0.000      -1.447      -1.317\n",
      "topic_30         -1.4258      0.036    -40.118      0.000      -1.495      -1.356\n",
      "topic_31         -1.4620      0.037    -39.411      0.000      -1.535      -1.389\n",
      "topic_32         -1.4920      0.038    -38.818      0.000      -1.567      -1.417\n",
      "topic_33         -1.5331      0.042    -36.156      0.000      -1.616      -1.450\n",
      "topic_34         -1.5724      0.045    -35.007      0.000      -1.660      -1.484\n",
      "topic_35         -1.6058      0.046    -34.745      0.000      -1.696      -1.515\n",
      "topic_36         -1.6599      0.050    -33.476      0.000      -1.757      -1.563\n",
      "topic_37         -1.7249      0.057    -30.426      0.000      -1.836      -1.614\n",
      "topic_38         -1.7716      0.061    -28.987      0.000      -1.891      -1.652\n",
      "topic_39         -1.8252      0.065    -28.099      0.000      -1.953      -1.698\n",
      "topic_40         -1.8731      0.067    -27.897      0.000      -2.005      -1.741\n",
      "topic_41         -1.9452      0.073    -26.551      0.000      -2.089      -1.802\n",
      "topic_42         -2.0258      0.081    -24.863      0.000      -2.186      -1.866\n",
      "topic_43         -2.1048      0.088    -24.050      0.000      -2.276      -1.933\n",
      "topic_44         -2.2254      0.103    -21.667      0.000      -2.427      -2.024\n",
      "topic_45         -2.3408      0.114    -20.463      0.000      -2.565      -2.117\n",
      "topic_46         -2.4815      0.135    -18.355      0.000      -2.746      -2.217\n",
      "topic_47         -2.6899      0.161    -16.751      0.000      -3.005      -2.375\n",
      "topic_48         -3.0070      0.190    -15.830      0.000      -3.379      -2.635\n",
      "topic_49         -3.3959      0.225    -15.072      0.000      -3.837      -2.954\n",
      "passes_25        -0.0874      0.010     -9.177      0.000      -0.106      -0.069\n",
      "num_topics_15     0.0485      0.015      3.204      0.001       0.019       0.078\n",
      "num_topics_20     0.0827      0.015      5.589      0.000       0.054       0.112\n",
      "num_topics_25     0.1508      0.015     10.103      0.000       0.122       0.180\n",
      "num_topics_3     -0.1450      0.029     -4.998      0.000      -0.202      -0.088\n",
      "num_topics_5     -0.0886      0.021     -4.246      0.000      -0.129      -0.048\n",
      "num_topics_50     0.4335      0.015     28.089      0.000       0.403       0.464\n",
      "num_topics_7     -0.0345      0.018     -1.886      0.059      -0.070       0.001\n",
      "min_df_100        0.2362      0.016     15.220      0.000       0.206       0.267\n",
      "min_df_250        0.4365      0.016     27.103      0.000       0.405       0.468\n",
      "min_df_500        0.5494      0.016     33.442      0.000       0.517       0.582\n",
      "max_df_0.25       0.2821      0.014     20.594      0.000       0.255       0.309\n",
      "max_df_0.5        0.2855      0.013     21.696      0.000       0.260       0.311\n",
      "max_df_1.0        0.2830      0.014     20.323      0.000       0.256       0.310\n",
      "binary_True      -0.1248      0.010    -12.994      0.000      -0.144      -0.106\n",
      "==============================================================================\n",
      "Omnibus:                     5210.657   Durbin-Watson:                   0.513\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           124064.587\n",
      "Skew:                          -2.572   Prob(JB):                         0.00\n",
      "Kurtosis:                      21.146   Cond. No.                         49.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC0)\n"
     ]
    }
   ],
   "source": [
    "X = coherence.drop('coherence', axis=1)\n",
    "X = pd.get_dummies(X, columns=X.columns, drop_first=True)\n",
    "ols = sm.OLS(endog=coherence.coherence, exog=sm.add_constant(X))\n",
    "model = ols.fit(cov_type='HC0')\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Impact on Perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:36:02.867550Z",
     "start_time": "2020-06-20T20:35:59.821106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIglOoUBEREREJMEpFIiIiIiIJDiFAhERERGRBKdQICIiIiKS4BQKREREREQSnEKBiIiIiEiCc9pdgIiIiIjI12IBi+DnUYwk8AxzYDgNu0tKCAoFIiIiIhIXolUxqv4SxGpo2m7oFSHjSg+GqWDQ3jR8SERERETiQsPyaHMgAIjssAhvjDVv14RCWJZlQ2Vdn3oKRERERCQ+xPY94LeiUOyvZfbqFWwO+OmT4uXXw0dRkJZuQ4Fdl3oKRERERCQuJI1yYrj3bDuyDdyDTH73xSo2B/wAbKsPcNfnn9hUYdelngIRERERiQvObJPMqzw0ro5iJhl4Ch0YDoMNdTUt7lfsr7Wpwq5LPQUiIiIiEjccmSbecS6SRzsxk5omGB/bLbvFfYq+sS3fnUKBiIiIiMS1m484mlNye5LjSeLknDxuO3KE3SV1ORo+JCIiIiJxLdPt4ddHjbK7jC5NPQUiIiIiIglOoUBEREREJMEpFIiIiIiIJDiFAhERERGRBKdQICIiIiKS4BQKREREREQSnEKBiIiIiEiCUygQEREREUlwCgUiIiIiIglOoUBEREREJMEpFIiIiIiIJDiFAhERERGRBKdQICIiIiKS4BQKREREREQSnEKBiIiIiEiCUygQEREREUlwCgUiIiIiIglOoUBEREREJMEpFIiIiIiIJDiFAhERERGRBKdQICIiIiKS4BQKREREREQSnEKBiIiIiEiCUygQEREREUlw7RYKKioqGD9+PMXFxWzZsoWLL76YKVOmMHv2bGKxGAALFixg8uTJ/PCHP+Ttt99ur1JEREREROQg2iUUhMNhZs2aRVJSEgB33XUXN954I3//+9+xLIvFixdTVlbGvHnzmD9/Po899hhz5swhFAq1RzkiIiIiInIQ7RIK7rnnHi666CJycnIAWLNmDaNHjwZg3LhxvPfee6xatYqRI0fidrtJTU2lb9++rF27tj3KEREREZFOyGqMYAXCTf9u2PNvaXvOtv6DixYtolu3bpx00kk8+uijAFiWhWEYAHi9Xurq6vD7/aSmpjY/zuv14vf727ocEREREemEwou3EX2/BGIWdPNAdQhiFuZRWbjOyccwDbtL7FLaPBQsXLgQwzB4//33+eKLL5gxYwaVlZXNtwcCAdLS0vD5fAQCgRbte4cEEREREUlMse1+okt37WmoDO65bXUFsYHpOAq721BZ19Xmw4eefvppnnrqKebNm8ewYcO45557GDduHMuWLQNgyZIlFBUVUVhYyIoVKwgGg9TV1VFcXExBQUFblyMiIiIinUysrOE73S6Hr817CvZnxowZzJw5kzlz5pCfn8+kSZNwOBxMnTqVKVOmYFkW06dPx+PxdEQ5IiIiIhLHHPlpRBwGRK393z44o4Mr6voMy7L2/27HqcmTJ7No0SK7yxAR6XS0/xSRziRaXEPknZ0QjmL0ScXaFYCohXNMLo6jNHSorXVIT4GIiIiIyOFwDEzHMTDd7jIShq5oLCIiIiKS4BQKREREREQSnEKBiIiIiEiCUygQEREREUlwCgUiIiIiIglOoUBEREREJMEpFIiIiIiIJDiFAhERERGRBKdQICIiIiKS4BQKREREREQSnEKBiIiIiEiCUygQEREREUlwCgUiIiIiIgmuVaHg8ccfp7Kysr1rERERERERGzhbc6fk5GSuueYacnJyOP/88xk3bhyGYbR3bSIiIiIi0gFa1VNw8cUXM3/+fK6//npeeuklJkyYwIMPPkhtbW171yciIiIiIu2sVT0FtbW1/POf/+Qf//gHqamp3HbbbUQiEa655hqeeuqp9q5RRERERETaUatCwQUXXMDZZ5/N/fffT15eXnP72rVr260wERERERHpGK0aPvQ///M/XHfddc2B4P/9v/8HwPTp09uvMhERERER6RAH7Sl45ZVXeOutt1i2bBnLli0DIBqNsmHDBi699NIOKVBERERE2p4Vs2hsgKQUtICMHDwUnHTSSWRnZ1NdXc2PfvQjAEzTpE+fPh1SnIiIiIi0vcrdUT56O0xDwMKbZjD6FDdp3XT5qkR20FDQ0NDAmDFjyMnJadFeX1/frkWJiIiISPtZ+U5TIAAI1Fp8+l6Yk37gsbkqsdNBQ8ETTzzBLbfcwqxZs1q0G4bRPK9ARERERDqPWMzCX2O1aKutitlUzf5tCwTZXt9IYaYPr9NhdzkJ4aCh4JZbbgHgvvvuIzc3t7l9zZo17VuViIiIiLQL0zTo3tOkfOeeIJDbO34OvOdt3M1fN+zCAnxOB/cXDWRIeordZXV5rRo8Nm3aNN59910AHn/8cW677bZ2LUpEpKuIRiy2F0fY9HmEYIN16AeIiHSAUePd9Mp34E016Fvg4OgTXHaXBEBdOMKTX5bw9d7SH4nyeHGJrTUlilZdp+DJJ5/kl7/8Jffddx9FRUUsWLCgvesSEen0YjGLd/8ZpLq86edt7cdhxp3twZumyXwiYq+kFIOiCW67y9iHPxIlbLU8gVIVjNhUTWJp1S/TunXrKCsr4+ijj+aLL76gpESJTUTkUMp2xJoDAUAoCJvXRm2sSEQkvuUlexiR6W3R9r1e3WyqJrG0qqfgwQcfZO7cufTs2ZNPPvmEa6+9lpdffrm9axMR6dSs/YwWsmIaQiQicjC/HZnPgi2lbA0EOTEnnVPzMu0uKSG0KhQ8/fTTNDQ0sG7dOgoKCnjmmWfauy4RkU4vp5dJaoZBXXVTEHC4oN/QVu12RUQSls/l4MpBeXaXkXBa9ev05ptv8uc//5loNMoZZ5yBYRhcc8017V2biEinZjoMTjrLw7Yvo4RDFr0HOvCmaj6BiIjEn1b9Oj3xxBMsWLCAjIwMrrnmGt588832rktEpEtwuQ3yj3AyZIRLgUBEROJWq36hTNPE7XZjGAaGYZCcnNzedYmIiIiISAdpVSgoKiripptuYvfu3cyaNYujjjqqvesSEREREZEO0qo5BTfddBNLlizhiCOOYODAgUyYMKG96xIRERERkQ5y0FDw7LPPtthOTU2ltLSUZ599lh/96EftWpiIiIiIiHSMg4aCsrKyjqpDRERERKSFupDFgi/CbKqOcUR3B71S4b9bYyQ74dwCF4O7aQGHtnLQUHDdddcBYFkWb775Jps2bWLw4MEaPiQiIiIi7e6hFSFWlcYA2FgdaXHb5+VB/nBaEukew47SupxWxatf/epXvPrqq3g8Hl588UXuuuuu9q5LRERERBJYKGo1B4L9CUZhdWm0Ayvq2lo10Xj9+vU899xzAFx22WX88Ic/bNeiRERERCSxuUzonmxQ3mAd8D49fBo+1FZa9U727duXbdu2AVBRUUFeni49LSIiItIaNZUxaisPfMZb9s8wDKYd7cLratrO8MCgjKahQqYBZw50MihToaCttKqn4NNPP+XMM8+kZ8+elJSU4Ha7OfHEEwF4991327VAERERkc4oFrVY9maI0u1NgSCnt8mY09yYpsbAt9bRuQ4empREacAiz2fgMA12B2IkOQzSk/Q+tqVWhYLf/OY3HH/88e1di4iIiEiXsWNTtDkQAJRuj7FzU5TeA1t1+CVfcTsMeqftCQC5XvUOtIdWfSr/9Kc/HVYoiEaj/OpXv2LTpk04HA7uuusuLMvi5ptvxjAMBg8ezOzZszFNkwULFjB//nycTidXX321VjYSERGRLqEhsO9Y+P21icSDVoUCwzC49tprGTBgAKbZlM5uuummA97/7bffBmD+/PksW7asORTceOONjBkzhlmzZrF48WJGjBjBvHnzWLhwIcFgkClTpjB27FjcbncbvDQRERER+/Ts72DdygixrxbIMR2Q189hb1EiB9CqUHD++ecf1h899dRTOfnkkwHYuXMn3bt35z//+Q+jR48GYNy4cSxduhTTNBk5ciRutxu3203fvn1Zu3YthYWFh/cqREREROKML93khO+52bimKRUMPNKBL11DXyQ+teqTedZZZxGJRNi2bRs9e/Zk/Pjxh3yM0+lkxowZ/OY3v2HSpElYloVhNI0H83q91NXV4ff7SU1NbX6M1+vF7/d/y5ciIiIiEl+ych0cO9HNsRPddMtVL4HEr1aFgtmzZ7Nz506WLl1KIBBgxowZrfrj99xzD2+88QYzZ84kGAw2twcCAdLS0vD5fAQCgRbte4cEERERERFpf60KBVu3buWnP/0pbrebiRMnUldXd9D7v/jii8ydOxeA5ORkDMNg+PDhLFu2DIAlS5ZQVFREYWEhK1asIBgMUldXR3FxMQUFBd/xJYmIiIiIyOFo1ZyCaDRKZWUlhmHg9/ubJxsfyOmnn84tt9zCJZdcQiQS4dZbb2XgwIHMnDmTOXPmkJ+fz6RJk3A4HEydOpUpU6ZgWRbTp0/H4/G0yQsTEREREZHWMSzLOuTaWMuXL+fWW29l165d9O/fn1tvvZUTTjihI+rbx+TJk1m0aJEtzy0i0plp/ykiIgfSquFDVVVVRKNR+vXrR2NjI7GYLtUtIiIiItJVtGr40MMPP8xzzz1HVlYW5eXlXHXVVZx44ontXZuIiIiIdICY38LwgOEyDn1n6ZJaFQoyMjLIysoCoHv37vh8vnYtSkRERETaX6zBonZBiPDmGIYbvKe7SB7VqsND6WJa9X/d5/Mxbdo0jj32WNasWUNjYyNz5swBDn5lYxERERGJX/XvRAhvbhoWboXA/2oYzxAHpk89BommVaHglFNOaf53bm5uuxUjIiIiIh0nUvaNeaIxiFTEcPt0obVE06pQcN5557V3HSIiIiLSwdyDHIS/3BMMjBRw9WrVOjTSxWjQmIiIiEiCSh7twApaBFdHMdMNvKe4MJwaOpSIFApEREREEpRhGHjHufCOc9ldithM/UMiIiIiIglOoUBEREREJMEpFIiIiIgkuE3+Oj6vqcKyLLtLEZtoToGIiIh0CnXVMdZ+HKHBH6NXvpP8Ix0YhibFfheWZfHrz1by5u4dAAxLy+APxxyH16k5BolGPQUiIiIS92Ixi/dfD7FzU5SqMovPloXZ9EXU7rI6vY+rKpoDAcAXtdW8tGOrjRWJXRQKREREJO7VVFg0BFoObdm1RaHguyptbNinrWw/bdL1KRSIiIhI3EvxGRjfOGrxpWno0Hd1fPccfM49o8lNYGKPXvYVJLbRnAIRERGJe55kg+GjXaxZHiYWhbRuBgUjNO79u8pwe3i4aCzPbCmmIRrlnF79GJ6eaXdZYgOFAhEREekU8o900meQg8YGi9QMDXZoK/m+NG47cqTdZQAQiTSwfPV9bC9ZQpqvL0VH/ZysjGF2l5UQ9I0SEbHBP3du5ZefLOMP6z6jIthodzkinYbLYygQdGGfrn2EjdteIRSupbzqM5Z8+EtisYjdZSUE9RSIiHSwF7dv5r61q5u3V1ZV8OSYcVpaUUQ6XKwhAnUhjOzkuNgH7a5Y2WK7vrGUusB20lP721NQAlEoEBHpYG+W7GixXeyvZVOgjnxfmk0ViUgiCi36kthnlU0bHhPX5UfgyE2xtaasjGFU1axr3va4M/Cl9LSxosSh/jcRkQ6WnZTcYttpGGS6PTZVIyLtyYpYhDZFiVbG7C6lheimmj2BACAYI/L8l/YV9JURw66hZ87xgIEvpRcnjvo/HA633WUlBPUUiIh0sCsGFPBJVQVlwUZMYFr+EIUCkS4oUhGj5m9BYnVN2yknOfFOjI8Vk6yS+n3baoI2VNKSx53OhOP+QDQWxmHGx3uVKBQKREQ6WF+vjwVjT2FNTRU9kpPpkWRvd72ItI/6dyLNgQCg/t0ISUVOHHFwfQVzcAb8e1uLNqNvqk3V7EuBoONp+JCIiA1cpsmIzCwFApEuLOZveQVmrP202cTsnozz7AGQ7ACHgdEvFfeFg+0uS2ykngIRERGRdpB0lINw8Z65BI5sA2ee/b0EX3OOyMY5ItvuMlrNsqy4WCGpq1IoEBFpY1bMov6/EULroziyTLynOHFkqmNWJNEkHe0EBwTXRHFkGKSMdemg9luwIhEib7xL7PMvwZuCc+JxOIbm211Wl6NQICLSxuqXRKhf0nSxnUhJlMjuGJnXeHQwIJKAkoY7SRquw63vIrp8NbHPNjRt1AWIvPIfzL55GCnJB3+gHMFTwukAACAASURBVBaduhIRaWOh9dEW29Fyi2hlfIwjFhHpbKydpS0bolGs3RX2FNOFKRSIiLQxR1bLXavhBkeqeglEEkldOMyD69dw3Ufv8eTG9YRj8XWdgs7E6J3XssHlxOjR3Z5iujD1Z4mItDHvKU4iu2NEyywMD/i+78JwKxSIJJLfrPmY98qbznB/Ul1BbTjEDUOG21xV5+QoGo5VW0dszZcYvhQcE47DSE6yu6wuR6FARKSNOTJMul2TRLQyhukzFAhEEkxDNML75S2HvLxVukuh4FsyHCau08bCaWPtLqVL0/AhEelSaitj1FTERze9o5upQCCSgNymg27fuEp5XpImxUp8U0+BiHQJVsziw8UhSrY2BYLueSbHne7G4dRBuYh0LKOknhvM3txtbKLBipHhcHFVqAex3fWYubpgocQnhQIR6RJ2bY01BwKA8l0xthdH6TdEuzmRthKLWmCAaSpsH0hkZRmRlzcxFnjGkcWOfA8DvgzhtioJLa7ENXkgjuFZdpcpsg/9WopIl9AY2HfJz4b9tInI4bMsi8+XR9j4eQTDgEFHORl6jMvusuJS9N2dzf/2RU2GbAgDe0JU5N2dCgUSlzSnQES6hB59TRx7neYwTOjZ32FfQSJdSMnWGF+ujhCLQjQC61ZGKN8VPfQDE5AVPcTJiEPdLmIThQIR6RJSUk3GnumhV76DvP4mJ5zhJq1bfOziYjv8hBdvI/JxKVYkRmx3PeG3txNZvhsrpAMriX/V5ftO3t9fm4DzuB4tto0eLecQOL5xu0i80PAhEekyMrNNiia47S6jheiGasLz18NXJwejK8uwdtVDrKkhuqoc95VHYBgaoy3xq3ueyfpPvtkWPz1xVtiiYUWUaGUMz1AH7nz7anMe1wMjO5nY5lrMXl6MggystdXESgKYA9JxDEizrTaRg1EoEBFpR9EPdzcHAgBrR6DF7daOANZ2P0af1A6uTKT1sns6OOp4F8WfNc0pKDjaSUb3+OiJA6iZHyK8sannonF5lNTzXSQNt+8QxzEwHcfA9D0NR3TDcUQ32+o5lJe3VfD3TbsBuGhADuf00dWCE5FCgYhIe3K0ogfAGT8HVyIHkn+Ek/wj4u+wIVoZaw4EX2v8KGprKOhM1lQHuPfzbc3bv/98O/m+JI7K9NlW09rqT3hx65M0Rhs4vef5nJB7um21JBJ9Y0RE2pHzhDxCG2sg0tRdYA7OILalFkJNBzHmoHTMPK+dJYp0bi6jaXGfvXrkDM8B7y3fsLLSv0/bx5V+20JBZbCM3666gVAsCMDamk9Ic3djeGaRLfUkEoUCEZF2ZPZNxX1NIbEN1RgZHsxB6eAPE11XheFzYxZk2F2iSKfmSDVIPtZBw4dfTdp3QcqJOrxprYK0fa+0PCTNvgusrapc1hwIvvZR+X8VCjqAvjUiso+NayJs3RDBk2Qw5BgX3XI0vOW7MDM8mMfm7mlIdeMsyj3wA0TksPi+58YzPEq00sI90IHp08T91hrdPY2p+bk8t6UMy7K4sF82x2XbNxk6L6XPPm09kvvaUEniafNQEA6HufXWW9mxYwehUIirr76aQYMGcfPNN2MYBoMHD2b27NmYpsmCBQuYP38+TqeTq6++mgkTJrR1OSJymLYXR1j9QfirLYvK0iCnX5SEy60fWRGJX64+Dlz7Hk9KK/zv4DyuGNgDCwuXae9JoCHpR3NGrx/yxo7nsYgxotvxTMw7y9aaEkWbh4KXXnqJjIwM7r33XqqqqjjvvPMYOnQoN954I2PGjGHWrFksXryYESNGMG/ePBYuXEgwGGTKlCmMHTsWtzu+lhMUSTQlW1tO2IuEoaIkRo++8bP8oIiItC2nabD3lZftdPngn3Fuv8sJRYPkJPe0u5yE0eah4IwzzmDSpEnN2w6HgzVr1jB69GgAxo0bx9KlSzFNk5EjR+J2u3G73fTt25e1a9dSWFjY1iWJyGFIzdj3R8GXHh8/FCIideEwJY31DHB74eMyrMpG8LnBH8LoloRjVA6GWycxOrsMd5bdJSScNg8FXm/TKhp+v58bbriBG2+8kXvuuaf5wjxer5e6ujr8fj+pqaktHuf37zsDXkQ6Vv6RTipKYpTtjGE6YMhIJ750zSkQaWtWzGL9pxF2bYniTTUYVuTSd+0QXtu5jfvWriIYi9Ej4uS3q9Pp29DyUCZWXIP7/xtqU4Wdx4c7ozz1WZiaoMVJfRxcXugiZsHjn4Z5b0eUrGSDS4e7GNlDAStRtMveZ9euXVx66aWcc845nHXWWZh7jU8LBAKkpaXh8/kIBAIt2vcOCSJiD5fb4ITveTj9oiTOmJJEwdEuu0sS6ZI2rI6w9uMINRUWOzfH+OBfISzLOvQDE1RjNML96z4jGGsa4ljijPBY331PJsY21hKrCu7TLnvUNFr8aUWI8gaLcAze2hLljY0RXt4QYcm2KJEY7A5YPPBRiPqwPpOJos1DQXl5OVdeeSW/+MUvuOCCCwA44ogjWLZsGQBLliyhqKiIwsJCVqxYQTAYpK6ujuLiYgoKCtq6HBH5lpK9hiYXi7Sjb87fCdRa1FXpAOxAqkMh6qORFm07k6L73tEAw60el4PZWBMj0vLjx4bKGOsrWzYGo7C19ht37GBVoXq2N1TbWkOiaPPhQ4888gi1tbU8/PDDPPzwwwDcdttt/N///R9z5swhPz+fSZMm4XA4mDp1KlOmTMGyLKZPn47Ho6uNiIhIYvClG1SV7tl2OCFZS2keUI/kFIakprOurqa57aSKfY8bHMf1wPDGTw/nkt3VfFLpZ2h6CqfmZWIa9v8/zs8wcZkQ3ut4vyDLQWPEYnXZnsYkB/RNsy9gzd34Hk9t/YioFWNkRi9+d9TZeJ06VmwvhtXJ+ionT57MokWL7C5DROSgnt64m1e2V5DqcjBtUB5jbFz3+2vaf8aXhoDFB/8KUltp4XBB4XEu+hbo8kEHUx5s5LHidWwO1HFCdi4XOXtgVgUxeqRgldRjZCVh9rTnSrz789TG3Ty6YVfz9vl9u/PTYb1trGiP5bui/H1NmJrGpjkFU49qmlPw5Kow7++I0u2rOQVH59ozp6DYX87U5U+1aPvxgBO4vP9oW+pJBNr7iIi0sTd3VTH36wOBBrjtk03MH3cE3T3xc/ZS7JfsNZhwXhKB2hieZAOny/4zyPGuuyeJGUcc3bKx91fzEbPtuwrvgbywtbzF9svbK7h2SK+vlv+017F5Do7N2/eA/8cj3fx4pA0FfcP+hgxta6iyoZLEoUF3IiJt7KOKuhbboZjFp5VaXU32z5tmxn0gsGIWliacHjaPo+Vhlts0iYPRQ53CMRm98TlbXrtqXPeBNlWTGNRTIGIjK2axdUOUqtIYWT1Meg9yNC/fK53XwNTkfdoG7adNpDNoWB4h8FYYKwSeoxyknuXCcGg/1RpXDOzBnau38PUo/csH5uLQPr5VUl1J/PHo83liyzJqw438IO9IxmcPsrusLk2hQMRGq5eF2fR50+oZW9ZH8ddYDCvSEJPO7pw+WXxRU89bu6pIcphcOagH/XxJdpclctgiFTH8r4abt4OfRnHlmSSP0eFDa5zWM5OCtGQ+rfIzJD2FIWnxN8Qpng1Ly+V3R51tdxkJQ99qEZtYlsWWdS2X09u8LqJQ0AW4TZNZhf34+RG9cZoGbrPlEALLsni75GVWVS6jn28wZ/a+CI9DoaE91NfF2Lk5ijvJoNcABw6nztIejkjJvstRhnfFUL/XodWHLWIW9PMl0c+XhD9kUR+2SInzoWKSuBQKRGxiGAZOF4T2ygXxPq5YDk+Kc/+rdryw5QkWbJ4LwAdlb7Kx7gt+NvyejiwtIdRUxHjnlSBfL22/ZV2UE7/v1hC9w+Dq6wBHGPbaT7kHaDriocz/PMyrxRGiMTihl4nLYbBkWxQDOG2Ag6nDXfocStzRN1vERkfs1StgGDBslHJ6IvhvyT9bbH9U/l8C4boD3Fu+rU1fRNj7WleVu2NU7rb3QkydjSPVIO2Hbhy5BmaaQcrJTjyF9ixR2VmsrYjy0oYIkRhYwNIdMf6zNUrMgqgFr2+MsqpMn0OJPzoCEbFRvyFOsnqYVJXF6JZr4k1VTv+21tRU8fjGdVT7Q5ze2ItzcvvhGe7AiIOl/74pzZ3J7sbtzdvJDi9uhy7II/aLrqsitrUOs5cPc1gmhmHgKXDg7mMRXVmGFYxiVWZhZGkA0YHsqDv0Kk3ba2McnaNwJfFFoUDEZr50E1+6wsB3URcOc9PHHxD46rTwOqMGzxIHp2zuSerZ7kM8uuNdNOBqfrf6ZwRjDRiYXJR/DS4z/urs7AYMc7K9ONrcW9Atx6Rbrr5rBxJ5dyeRt5rCahRwnJCH69Q+WJEYocfWYFUGm277oAT3/xyJma1gsD/Ds00cRlOvwP4YQKECgcQhhQIR6fRWVpU3B4KvLe9WxthPcvGebmEmxVdvwZGZo3jo+JdYV7OKvr6BZCfl2V1Sq1iWRVVp07CHzBwz7sdEp2eZTJjsYeemKJ7kponG8V6znSIf7m6xHV2+G+cpvQltqGJFtJYUn8lQvwvCMaIryzBP72tTpfEt12ty02g3L66PEIpZnD7AicuEV4sjmAacPdhFnzSFU4k/CgUi0un19fr2aevV4AUTjDj47V1VGuWvn4SpaLAwDRjX1+Tyo1IZ1f1Eu0trtWjE4r3XQ81j8rN6mBx/hhtHnK9X7001GVwYBx+CzuCb/y8dBjXhENeWrmTLkQ0AnFDh4dfr0ve9r00CkSjv7K7B4zAZm5O2z0pfdhnZw8HIHi17A07so0MuiW/x8e0REfkO+ntT+Z/8Ibi+2qUVVnfjB7v6kHy8E8Nt78FLfdji/g9DlDdYWDQNKXh7S4x/fhk55GPjyY6N0RaTdCtKYuzcFD3II2R/ojUx6t8L0/hJJO6uEOwc12uf7X/s2MKWcENz23tZQVbmxnAW5XR0efuoCoa5bOlafvvZVmZ/upmrP9hAKKYJvCLflmKrJIRIpGnYgy/NINmnLNwVXZ5fwOQ+/akrC9NtuwfHsQbufvaP291SEyO4n2PntZWd6+Al2LDvAez+2uTAIqUxqh8LYoWaths+ipAxzRM3Q5qcI7Mxe3qbJhr39GL28lG1rnyf+wW+1wsj3f6J8f/cUUlp454Lq22oa2BpaS0TemTYWJVI56VQIF1eTUWM914PEmpsWvbzyNEuBg7XR78rSnO5Sevphp52V7JHnzQTtwmhb2SAQZmdK5z2HOBg3coI0a8CjsMJPfvbH7o6k4blkeZAABDZYRHeHMM9IH7eRzM3BTN3z1V3J/XozQvbNxO1mgJghsvN8T3jYw7M/noFgtHOFbbjSTQaJBT2k5yUZXcpYhMdGUmX98WKMKHGpn9bFnzxUZi+BQ5cNg8r6UysmBWXS3t2Bj63wXVFbv76SYjaUNPKI8f1Mjl7cOfa/XrTTE78gYdNn0fAgPwjnKRoCd0ub1h6Bg+OOoGXd2zB63RxYZ8B+JzxcdX1M3p247ktZQQiTUEgJ8nFSbnpNlfVOW3c9iorPptDKFxHdrcRjDv2HpI86nFJNJ3rV0nkW2isbznEIRqFcNBSKDiIcCzGM1uKWbm1nEHb0jhnSz/Sj3Lj+74LI04mGHYmRXkOivKSsb462xovw0UOV0Z3k5HjtHTqt5V8rJPgqmhzb4Gzl4Grf/wHq8KMbhRmdLO7jGbLdkZZsjVCmsfg7sIC/rnFz+5aNwO9bmobDfaz7oAcRDBUw4ef3k001rTkbFnlJ3y2/jGKjvqZzZVJR1MokC6vzyAnNRV7xp12yzV1hvMQ/rDuM/6xYwsAy3uUs8us56crh+PINkk5XruN1gqH/Wza/gbhSD39e5+ONznX7pLERo7kCGlFFUR2hDEd4Ojngfps8MbHmffOYMWuKH9cvmcM1vJdUB/2AlBWZ7G2PMgfTkvCrZMXrVYX2N4cCL5WXVdsUzViJ/26S5c3cLgTpwtKtkbxZZgMLtTH/lD+XbKjxfa73Xdzw4YjieyMv/G6sZ1+QguLoToIbgfOib1xHmv/wXc0GuL1d6ZR698MwOdf/o0zxj1Jqre3vYUlEMuyaHgvQuPqKI5Ug5SJLlx59pwQsOrDBP+yhnAgRJUrRk7IQXQTRFeW4rnqKAxP/MwriGfv72w5a78+3PL26iB8UaGrBR+OzPQCkjxZNAYrmtt65hxvY0ViF50ulTYRi1l8uTrMB28E+WJFmEicLbXXb4iTMad5OPJYF26PziAdSk5SUovtzJAHAwNXv/jaZVjRGKGn10FVECwgGCXy2hZi2+rsLo0du5c2BwKAULiOL7f8w76CElDjx1ECb0aI7rYIfRmj5qmgbcuARj+v5AOXn4uKyphSVM7/HF3BTk8EakLE1lXZUlNnlNWKCxF2T9Y+/nA4TBcTj/sDednHkebrx/CCKxk6cIrdZYkN4usXXjqtz5dHWPNhhN3bY6z/JMLHS0KHfpDEresLjiTZ0XSmzRMz+d8dQ0g+3knSqPg6+2aVNUDDvut9xjbV2lBNS8Z+rpq2vzZpP6H1LT8bVj2Et9vT2xV2wH2DaqhxNYWSzd4Ij/b3N93o1Oeitc4c5KRPatNBvwFM7Oeg117bZw500kvDQw9bZnoBE4//I2dNXMDRQ3+CacTXvl46hsZRSJvYvrHlhZh2bYkRjVg4nDpj0xmNycrhhZNOY0NdDQN9aaSdHp+TS41uSeA0INLy7K/Rw2tTRXv0zD2BzLTBVNVuAMDjTmdQv3Nsrqr9WGELHMTVKlWObBPW7xUCDHBk2XPAWDfIR3VFy8/p5pQIRo8UzAKt8tJa6R6DuyZ42FRtkeqGHK9JzLLYWG2R7oHsFAUCkW9LoaCTiUYtHHE4gSo5xSC41yo/niQwdaKhVcJbo0TrLNwDHZit6BrvKD6ni5GZ3e0u46AMtwPX+YMIv1DcdCEAAxzH5uKIg4Msh+ni9BP/wpadi4lE6unb85Quuf53LGhR90KI0LoYhhdSv+fGc6S9X37Lsoitr8Yd82OlR4nVQMSZRvKpaTjS7PmO5aR6GeRN5cvAnqFtx3fPwf29IzDUU3BYTMNgYKbRYntQZvzsO0U6K4WCTqK+LsaK/4SpLI2RmmFwzHg3Gd3j54fkyNEulv07RCTcFAaGj3F12mUXO1LtCyGCq5qGOBgpYbjUIJIWo0dyyiEe2X5iVY2EnvwC6sLgNHCe2R/niGzb6jkUx5BMzBmjsBoiGG5HXB1gOZ3JDOz7A7vLaFf1SyKE1jWdjbcCUPtiiKz8JEwbx3VH/r2N6AclALgADPDESnFlDAYybavrrhGjeWjD52zy13Fc9xx+PHAohiN+Pq+ljSHmbyqlMhRhUs9Mjs/Wmv8ibW379u3cdNNNLFiw4Fv/jenTp3PPPffgdrdtL75CQSex6r2mQABQV22x4j8hTrkg6RCP6jjd8xycflES1eUx0jJNPJrodUiR0lhzIAD4W/YGXlq1lahhMTorm98WFpHk6PivaPjv65sCAUDEIvLyJsyjsjDj6ODlmwzDwEjRso52iJR8Y4x+BKJlMcy+9vQWWJEY0eW7AdicHGFHcoSja9z4oibRD0pwDLEvFOQlp/B/hUW2Pf/BRGIW13/4JbsamuaDvVVSzd0jB3BCjoKBSLy5//772+XvJmwoqCqLUbIlSkqaQZ+BDsw4HJKzt+rylj+8/hqLcCi+LsDlchtk99SYodayGvcMt/rSV8sLvbc0b39YUcaL27dwUb+BHV9XdfAbDWBt90O/tA6vReKXZVlYW/24U0OE8eAggIUTKykFp03LfjYzDZ7oWcfTfQIAeCMG967JZGgczXeIN6uq/E2B4OvdkgFv7KzihJx0ojELh967by0aDbKz9H1M001ezhhN4u1krrvuOi699FJGjx7NqlWr+NOf/kR6ejrbtm0jGo1yxRVXcOaZZ/Lpp59y5513YlkWubm53Hfffc33B2hsbOSee+7B5XJRWVnJVVddRWVlJePHj+faa6894PPffPPNbN26lWAwyLRp0zjzzDOZOHEir732GnfeeSebNm0C4LPPPmPWrFkcd9xxzJw5k2AwiMfj4Te/+Q15eXmteq0JGQpKtkZZ9uaenV/JlihjTvPYW9QhZOU52Llpz1nl9O5GXAUCOXzOPiaOHINoqcXOpPp9bt9WH7ChKjDS3ViVewUDA4xeukSo7GFZFuG/rydWXIMJ+IymlV8AjD5ZGK6OD7NfM5wmdWOymB/Z1dwWcFrM6xPg7mOOtK2ueJfpdpIezsYbzQAsap0VuGMefrG4kR1+iyO7m1wzyk1mHM176gyCoRreeGcadYFtAHTPLOTUsQ/jMNWz2VlceOGFvPDCC4wePZoXXniBcePGsXXrVu699178fj+TJ09uPhC///77GThwIE8//TTFxcVs2LCBe++9l9zcXB555BFef/11zjrrLOrr67n33ntJSUnhkksu4ZRTTmHo0KH7PLff72fZsmUsXLgQgKVLl7a4/Y477gBg/vz59OjRg3PPPZfp06czdepUxo8fz/vvv899993H73//+1a91oQMBRvXRPacDQFKtsYI1MbwpsXv8IjCE1xYlkX5zhjp3U2OHqsdSmdnGAYZl3loWB5hdF13kgwHjdae4Hditj0X4HJdPKRpTkEgDA4D5xn9MONonP7+bA8EWVJaTbbHzck90nGZ8V1vZxcrriFWXNO8vfdhovVlBbHSPMwc++bFBI/LIfpeyzZ/nyQcg+yfgB6vdte6SI12a97OiOSwrRRqg00/lmvKY8xbHeaGY+NzJbJ4Vbz15eZAAFBetYodJe/Qt+dEG6uSw3HSSSdx7733Ul1dzUcffUQsFuPEE08EwOfzMXDgQLZt20ZFRQUDBzadELnkkksA2LVrF3feeScpKSns3r2bY445BoChQ4eSmpoKwFFHHcWmTZv2Gwp8Ph8zZ85k5syZ+P1+zj777H3u8+qrr7J48WIefvhhDMNg/fr1zJ07l7/+9a9YloXL1frjxYQMBfs7Xoj3YwhPksHoU+K7N0MOn5li4B3vwouL+6uP48lN6wlEIpzTqx/Hd7cnFJhZSST9bKQtz/1trKkO8NPlXxKKNR28vLEzlfuK7DtTnRAaIge/vf4Qt7ez3ileRmR045Pqyua2H/Trb19BnUBx1b4Xdav9xkjC4ur4u6J5vAtH/Ptps6cX+GtVwXIe3/A71tespiC9kCsH/4JMT3yvNGcn0zQ544wzuP322zn11FPJysrio48+4rTTTsPv97N+/Xp69+5NTk4Omzdvpn///jz66KMMGDCAmTNn8uabb+Lz+ZgxYwaW1fQ9Ky4uJhAI4PF4WLVqFT/60Y/2+9ylpaWsWbOGhx56iGAwyPjx4znnnD1LWy9ZsoSnnnqKv/71r80H//n5+Vx55ZUcc8wxFBcXs3z58la/1oQMBYMKnZTtChH76qRsn8EOkn1xngqkS7EiMaxdAYxuSRjepi/y8LRM7s0ejpHmxkjT2bjWen5LWXMgAPiwoo4vaxsYlJZsY1Vdz9aAn+e2bSIYjXJ2j94M8rqaepO+wchKwuibakOFLd09YjTPb93EtvoA43J6MD6ndWNqO1JdOMKDa3fwaVWAoekpXD+0F9099vQCD+1u8s/iPdsG0D0Zyhr2tA2z6RoPndmA3t/ji+JniEYbAUjydKN3j/G21vTI2t/wadUHACwv/w+hWJBbCv9ga02tYcWiGDatdX7++edz6qmn8sYbb5CTk8PMmTO5+OKLCQaDXHfddWRlZfHrX/+aW2+9FdM0yc7O5vLLL+ecc87hhz/8IWlpaXTv3p3S0lIA0tPTmT59OpWVlZx55pkMGjRov8+bnZ1NWVkZ5557LikpKVx55ZU4nXsO3W+44QYKCgr4yU9+AsDEiROZMWMGt99+O8FgkMbGRm677bZWv07D+jq2dBKTJ09m0aJF3/nvBOpi7N4aw5tmkNPb1PKZXVBocxT/a2GiVRaeIxyknunCiIN5GLFdAUJ/XweBSNPwnDP74xiQRmjeWqyqIBjgHN8L57hedpfaKdz+6WbeKqlu0fbY8QUMTrNv+Eq8+rb7z6pQkCnvvU1dpCkEuAyTR4eNof/qAIRjGLnJWDvrwefCOaYHhk/DG1tj9qebeXuvz+4x3Xz84dj9Hxx0hJfWh3l9YwSXw2DyECf5GSaPfxpma22MwhwH04524YuDfWhnU127keKtL+FwuBncbzLelB621nPJf08gutdQVYfh4Onx7x3kEfaywg1E/nU3/z979x0YRZk3cPw7M1vTe09I6DV0gnQLIHI2TpHjxDvPV8/2esp5L56K9Szc2Tnrne0QBQX09FAPRUABKQJSEyAhDUjvZXezOzPvH0s2hASQAJlFns9fmZktvy2Znd9Tfo+2fzUER2OacDdKtzFGh/WzdF72FAAEBst07SdaPX6udLdO7YdNNDk1NkWUUVPSxIRv4+lyifEtmJ6Vhd6EAEDV8awoQOsT7k0IAHTwrDmEMiha9Bj8BNd0iWZtaY2vt2BYZLBICM6wdWUlvoQAwK1rrGws49bJfQyM6ty3uby21fbWyno8mo7JoEo/V/Q0c0XP1gndw2PFsNXTFRbSlaH97zY6DJ/UoN7k1O1ute3P1E3/Qtu30rtRW4zni0eRb/4YyfrzKYCxcuVK3nnnnTb7b7jhBiZOnNhpcZy3SYHw8+Yp09EcOo/138au0CoA3tdyeL1hDKmBxiYGem1T6x0utSUh8N0I9Lomv0kKGj0qefVO0oJs2E3+VU6vf1ggb4/qzZqSamJsZi6ME5NJz7SwdhbICTf7x3fzp3KpGlaD19qocui8t9tNXo1GTIBEpLMLAapKnakKh1JHl0CrYQmBcP64tdcDzNv1GLkOlThbMLf0utfokE5IK9rTeofbgV6Ri5QwwJiAzoKLL76Yiy++2OgwEE3lwhmjN+m4PboJrgAAIABJREFU9qq4Dxs/Gc0ULZEVWeNLCAAaZA9LC/OMC+oIuW9E6+0uwSgDW0/ykiKsSPGBnRnWcW0ur+OaNbu5deN+frlmNz9U1Bkdkk9Jg8bWYhXNY6JPQBQXRIWLykNnwQVRsYyIbFnVuntQCFMTUwyM6Kcrcri4beM+Jn69gxvWZpFV07b8b2d56Ycmvj+kUlSvs71UQ/VYsOh2ItzxxJmDua//ufGeGk3TPGze8TcWf34hn668hoPF3xod0jmlWrWzyzWMKoaR6ezF2wX7jA7phOTEga13WIOQoo0bZvdzJnoK/Iyu6qDiF2PfT4WnQqP6bRf6kaIKtiEKwZcb15IomSWsExQobr3fo/tBwjI+EclmQsuuRoq2YxqbgBRgRpJA3VWJFGbBNCYByU9aDF/IPEi9x/u+1Xs0Xsg8yHtjjB82sjzbzfu7PUdXF8aqwL0ZFvpF+1dvxrlOkSSeGzySXTVVNKkqA8MjUc6ReVjP7znE7mpvIpDX4OQvO/MN+f42unX2VrZ//pGQ+GV8Mv3Czq3eF6Pszf2QfXlLAKjzNLL2hwe5auKn2Kyil/CnWFiwhSatZU7BipK93Jw2ikS7f65erQy/Hr2hAm3fSqTgWEwT/oBkFoUkzgaRFPiRxu89NK52o7vBmq4QfLkZyc9XWm6Ws76GLyMPYQ2Tubg0gbCtVuwXaJiijGu1HdYvmp6lAezTvBcEFl3iyljjW+IkScI0Mg5Gtp5spgyMRhkYfZx7GafY0XTCbSM4PTofZbVOCABcKizOdPOYSArOiv6h4a22t1fVc6DOybDIIJIDbQZF1T6nR+erXA+5JYHY0JCQsKmBVHpc1Lg8hFo79+fPZoIou0S5o/3aHsnBoofrpyqr2N5qW9VcVNZkkhBzgUERtabrOh/mvs7Kok8INAVzXdqtjIwxfmhIM/dRCcGJ9vkLyWTBfMmf4JI/GR1KG7quo+3JQd28E72uASk4EGX4AOS+3c7JAjbnVVLgLtJw56gosTKW7v5VcchTptGwomUin2u7ijlRxj7c/z+ig40N3Gn9Hkey96Ty37hDvPjjSMIcxrZ6SYcbeHZ9IF9FK1SbNS4st9GlrgKmhZ/8zoLPhXFhrCiqarVtNKcHmo7zG3ZsbXXh7Hhj32Hey/WW11MkeGxQGmNj/Kel8cXNTWwv1TATQhQhLQc0eHeHxl3DOzceWZL4/WAzL29potoFASZwqqDrMC5FYWSiSGR/qqiI/hQWr/Zty7KZiNBexgV0jG9LPufjgrcBqHVXMT9zLt1D+hFlM7bqULNrkwaxqSof9UjxyYyILqQGRpzkXp2rrvRHKvK+JCxhNGFJY40Op126ruP5+Cu0vEPg9hYP0RsdeP77HfLeA5iuntih60xVVXnwwQfJzc1FURSeeuopGhoaePjhh1EUhdTUVJ544glkWebDDz9k0aJFmEwmbrvtNi688MLTek3+f8V5hjh3eahb2nLRbR+pEDTZf7pqPUVtu5WP3udSVdy6RpDJ/0r9fX64EIfccoVWZnPyQ5dyrk5KNS4oQN1diV2TuaKkpRKNdqD2BPfoXE5VY11pDYokMSomBIufjoW/OiGRgsMRNDRJhNo1ZnUxfq5DmE0iPUZmR2nb/5txKeLi6mxr9Kh8mF/m21Z1WJBT4jdJQaXDO2b/eDYeVml06wSYO7dhqF+0wkuTbNS4dCJsEg1u0IHgc2y4qNF6dZ1BbX0huQe/wGaNYGi/P2Cz+s9FbWb1tlbbLs3CzqptXBg/xaCIWhsZmco/hs5gdVk2CbZQJse2VB/SG514Vm1AP1SClBSH6cIMJHvn9gIWbp1PSdZCAMqzPyEs+UK6j32qU2P4KbQ9Oa0SAh+3By3vEFpmDkrfU5/7sGrVKgAWLVrExo0beeqpp5BlmTvuuIPx48fzxz/+kdWrVzNgwAAWLFjA0qVLcblczJw5k9GjR2NppzDET3XeJAWOdR72BdWwI6ySrvXBDNkURcAEHdnqHydjc6oCshuO+h0zd/VeJC7My+ad3H24VJWL4xK5v+8gv5hM6dlcgrqtDFN0LRxT0Cesj2p4T4yU2PbiVYrwj/J6tU0efr9hH4eODMXpEWznlYwehldHOVaVQ2feejce3XuqqG5UeGp9E/Mn2QyvkvKHYRbey9pOTu0+wkxDCFBSGBAtM0EkBWedR9MwuQMIUkNQJZUGpZomzfj5Os2sJjDJ4DlOSFbFe9wIJlki0u793wnyn3apc4oimxk56H5GDrrf6FDa1T2kL6uLP0PVLZQzFhcxPL4vmyptO9OOnTRrkN7BsfQOjm2z3/P5arScQgD0qlo8ribMV3deSUyA0n0fttquLlyF6mlCMfnXP4y6eWfbhKCZ24O6eWeHkoJLLrmECRMmAHD48GGioqKIjY2luroaXddpaGjAZDKxY8cOBg8ejMViwWKxkJKSQlZWFunp6R1+Tf51BXIWfRV0iDkDN7OwSw6P9/uRf6XsNzqkVpQQiZDrLJjiJeRwicBLTFh7SmR9uptXszNxqCoa8FXxIT49lG90uKi7K/B8kY9e3MiULIloV8tXqWe9ieH/rUSrchoYISh9I5C7H9VyGWDCdHU34wI6ypeHK30JAcD+OgdrSqpPcA9j/Fiq4jlmCHSNCw5UG38B+Pmht1hZ8Xvy3M+yw3EDI7us58IuJsOT0fPBR5k6Ee4kArQQgtVwYptSGRPe9gLDKIFmiSt7tLR5mWXvEKdm1/QxYzlH5msJ554L465gYsIvqWcALmIAcGoqz+9fQ7mrweDoTkw7cPCY7cJOj0HX244N1TXj57IdS6878Wep13b8szaZTMyZM4fHH3+cyZMn+4YMTZkyhYqKCjIyMqivryc4uKVFNjAwkPr6+g4/J5xHPQWfJOTDUd+z5QkF3F6Vhj3O+MWsmll7Klh7trRyul7ZQQ5V0LP17bLrjR8Co+5vuYANdyv888dI1ke4sKoSF1RZMeug5dQiDzNu8qEkSVhm9kJraAKHihzlP9UKnGrbi+r29hktJqDthZMERNmNbU9oUp18WvAv37aGyrL8txgRPcG4oM4T9U06q/Jb/2hLSFTV+teCcb/sbWZEgsLhep0+kTK6DpkVGikhEgliUu8p03QV1ePAbPbPBaOcnkbKnEUkBqYiS8b2FiqyiZt6/h97HB/zfWVLI56qaxQ2VhFlNX4I5vFIUeHoZZWttjtbcMxQ6ko2+7ZtIV0wWfzveycFB6I3Oo5/POT0Pud58+Zx7733Mn36dBwOBwsXLqRHjx4sXLiQp59+mjFjxtDQ0JJ4NDQ0tEoSOuK8OTPqHJN5ajruf+zB80OJMQGdhF7tQi93kl5rwXzMtWJGhPEVao69wA5UZSaW2RlXacOsS0du4x/VSORAS6t4dV1H19uvANJZJiVEEHTU+IUIi4nxscZP4D1W3yiZCxJanyam9TIRYTe2lVXTNTy6u9W+irpsPvjPGDb8+ASq5j7OPTvfntpinshcwd0/LmP29k94Oec7KpuMq5V/thj7H9W+5BCZjASFEKtEqE1iZKIiEoIOOFi8lk++upIPv7iYr9ffjtNVdfI7daINpSu57fup/OmHmdy1YRqF9TlGhwTAqKiurbbDzHb6hvjHZOPjMV06FkKOXICHBmOaPKbTY+h50UvE9JyOLSSViNRL6XPpv05+JwMowweA+Tht62aT93gHfPLJJ7z++usA2O12JEkiLCyMoCDv5xITE0NtbS3p6els2bIFl8tFXV0dOTk59OzZ80QPfVLnTU/B9AI7zya2DGe5qjgAiy7hWX0I0zD/6fb2sXh/uKKbFB7NCuPd5HoaFZ0r+nZnQmyCwcGBMiIWLb8OLacGTBLKmAT0Mgfa7kqQJZQRscipISd/oE72VnYRi/PKkCX4dVos13c15rOPspqZGNKDzYe9l1KRkkR5g0yofw2ZRJIk/ne4lesaVPJqdHpFKITajB92YTMFMD7uF3xT9G/fvi5OFU1TySn4lODAFPr1mGVghF55DZXcvu2jVjXBN1Tm8X1FHguGX39ODnUKskhc2EVhZV7La5KBKV3Pm5+T84rH4+T7bY/Q5PYuWlhSvoXtWa+SMdA/xvN7NDdv7v8rDtWbaJe7inkv5yX+PPBFgyODaQnpNHia+KpkLzHWIG7pOgqr4t//J3JCDJbfXwf1jRAcaMg5SpIkUobN7vTnPVVy327Iew+0nWxsNiGnJiL36dhw5UmTJvHnP/+ZX//613g8Hu6//37CwsK45557MJlMmM1mHn/8caKjo5k1axYzZ85E13XuuecerNbTmzfp39/OM2hKiZ0uFbA1rInuDWZGVh1549wauq773Y+zFGBGHhiFtr2cEdVWRlRbkdOjsHTvevI7dwLJomD5dS/0uiawKEhWb3etPtkNioRk97+v1sayWt7JaekZemN/Ef3CAhgc0flDyL7I8bD5MHgH40CFA57f5OKlSTZkP/suOjwN7Kj5Lw5PA12ZBPhHEv0/PefQK3Qgu0vX4Dy4htijzskVVbuMC+woX5fubZUQNDvQUEFWXSl9QvzjvTxVN6ab6R8ls/GwSrBFYmKaiaQQ0QL/c1TfeNiXEDSrrPGPFXCLHDV8VbKLkqYg7NQgSd5GlmLHwZPcs3NIksQNXYZzQ5dOrn97miRZbuktEI5LkiRMV09EyzyyTkFtA1LIkXUK+nR8nYKAgABefLFtUrto0aI2+6ZPn8706dM79DztOWtXbtu3b+eZZ55hwYIF5Ofnc9999yFJEj169ODhhx8+K/VVT0QZFkvftYfpW9+6KVYZGuN3CUEzy5VdUQdGou2tRu4VjuKHLe9SsPf9/L6shrWltaQEWrkyOQr/GDjU2u6atpN+9lQ3GpIU7GtnZdNKJ1Q4dKLbGcdvFI/m5qFtN1PY4O2O/zj/HZ4Y+jbxAcYvAidLCuPjppIRPppPCjag0rJAQXSkf1T4CLe0P85eAsIs/jPH5VTJkkRGoomMRP9L/oUzKyQohQB7LI2OlgaVuCjjL3J31RRx549LjiTdY7FTSDRrARgePd7Y4E5Ab3R4Lx5jIrwX38I5TZIklL7dO1RlyB+dlTP6P/7xDz799FPsdu+P3lNPPcXdd99NRkYGDz30ECtXrmTQoEFnvL7qiZguTESOtaMV1IEkoes6SlIQcv/Is/J8Z4qSGoqS6h+1v4/ni0MVPLWrpULBDxV1/G2of1T5OdrA8CCg9RySgRHGtIb0jJDZUtw6MQi34StV6C9+rFzvSwgAGtV6VhZ9wvXd7jIwqtZs1jDGDHuCrbtfwumqIC35MnqlnbmWk9MxJbYPy4v2kFXX+ns3I3kI8Tb/S/KFs6+2voBGRwnREQNRFD8bL9gOWTYxfsQzbN31PLUNhSTHjSe91/8YHRaLCre26oVzkEx8wBBGRQ/m6i43GhjZ8Xl+2IW6eiOoGoSFYLluClKYOA8I/uOsJAUpKSnMnz+f//u//wNg9+7djBgxAoBx48axbt06ZFk+4/VVT0SSJJR+kSj9WicB7x0oYWlBGVZZ5jfd4piSaNwCKIcaXTy35yD7ahsZEhHMPX2TCLP4f0vcp4UVrbY3ltdR4mgi1m78D57DrbO6QGV/pYoiW/lFVBc21B5GkuH6tFj6hxlTBWJKNxOFtRrrDmroQJRd4n+HWfxu6JDUTi0Ch6PcgEhOLCluLElx/rfqZYDJwj+HzmB7zSE0XcOtacTZQvxu9VChc2zb8zJ7sr2TJgNsMVwy+lWCA5MMjur4Kl1lLC98n+qmCsb0nsUlkaOMDslH1dv2tt7R9zG6BxlfiKM9usPZkhAAVNfiWbsF8y/O3ggJQThVZ+WKc/LkyRw82DKm7+gx+4GBgdTV1Z2V+qqnam1pDW/sL/JtP72rgF4hdroGG9Ot/+j2fLJqvZOlVpVUgwSPDkw1JJZTEWhqXf5NkcDmB4tw6brOk+td5FQfXRfFxriE7tw93NhFzA435tAlZhNj0rqRHpFhaCwnMjDiAiKlACp07/fSrENgyXbob3Bg5xBZkhgc5r8XfkLnqG8sYk/2At92o7OU3fvfYeSgBw2M6vg8mofHfrzVNz5/Xel/+b8BzzIksvOr0bTn2qRBrK3I9SUHw8NT/DYhANDrG1sSguZ9NXXHubUgGKNTmqHlo8bNNTQ0EBISQlBQ0Bmvr3qqtle1TkJ0YHtVgyFJgcOj+hKCZtsqOzdJ+ika3TofZbnJrtToHaXwy14mftMtlh3VDTg9OiGeCBLNYSzJ1Li2t06IgStG51TpxyQEXpsPa1Q5dcINqqKzoXQlL+55EP3I8tVTk2Yyq/sfDInlZEyyibEOM3k6uCVIcIOuF+PxODGZ/HHmiCB4NThKyDu4ArPJTmrSpVgMrq/vdFVybOFWh9P/et2a7a3Z3mbC7rfFX/hNUjAkPJl3hs1kdVk2sbZgJsb0MjqkE5KiwpEiw9ArWtb4kXv5R+EQQWjWKUlB37592bhxIxkZGXz77beMHDmS9PR0XnjhBVwuF01NTWekvurJNKk6eys0ogMk4oJkEq2BWNUGmmQH+pGqBX1CjVmAx25SSAm0UtDgQtFNBHsiidRtrM73kJGg8O/9HnKrNfpGyUztbsIkG3NB+/q2JjYXeS9mc6o91Ll0bh0SxOKxfXh5m5M9JSbqVFiZp1Jcr/PAaONa5C3HWb9GlrwrnBrl3wXv+hICgC8PLmZy9KXEhPrnj1py9AWoh770bUdHDBQJgeDX6hoO8uW3v/VVzdmb+xGXjf8XimLc+SgyrA8hQanU1uf59qUlTfH93aS6MMlmZMn4XtasuhLeLjhAmT6WIPZjl4oBCLV0/kJWJ9ItKIpuQVFGh/GTSJKEefoUPOu2olfXovRMRR7Sz+iwhNOk6xpa1teoWxej15ciBcWgDLkOufclSH7wv3yqOiUpmDNnDnPnzuW5556ja9euTJ48GUVRznh91RM5WKvxxHoXNS5v5Y8+UTKZ5VaiSUbDQ739MDf0DKe3QUkBwIMDuvD4jnyaquIx61ZcKrzxo5sVuR7yarxJy84yjRqXzg0DOn+8vqbr/FDUuvtzc5HKrUC41UxJrcrRLWG7yzUa3DqBZmMSmJRQmeHxMpuLNHR0pCPlPyd3NRFkMa4HQz1mCXdNV/ni299y8fC/kRTnH61wRxuefi+ybKKkfAsRYb0Y1t//60f7E93tQV23Ba2wGDkhBmXMUCSr8fNtfs5yCj5tVUaztj6PQyXrSEm4yLCYJEnmklEvsyf7PRocxaQmTiQl4WJcqpNXsx5nY9k3hJhDmdX9HsbETjYsznJXA3duW0Kj6gaScJBArP4VCTYzv0j+tWFx/RxIIUGYp4wzOgzhDNF1Dc9nD6AVbAa3dx0svbEKz9d/Rd6/GtPlfzmtxKCiooJp06bx1ltv0dTUxOOPP46iKFgsFubNm0dZWRlPPvmk7/Y//vgjL7/8MuPGdfw7dtaSgqSkJD788EMA0tLSeO+999rc5kzXVz2Rj/d5qDlSsVAH9pS3XNzKmLg4LJXpqcaOM+8dGsBfBvTigTWuVvubE4Jm3x9SuaFjC+WdFlmSiAmUKGloiSc2UGr1d4Wj5ViYFYxeruAPwy38fddrbKpcjIzChNjfcn1vYxe1mpo8k1ezHvNtp7jBpGvs3v+2XyYFFnMwFwyea3QY5yzPinVou7x13dXDpei19ZivnmhwVKdP13UKDq+konoPsVFDSIz1p++u94e4QoF6GWI8+EWrnd0WxdD+d/u2mzQPr+1/l7Wl6zBJGjXuKl7Lepz+4cMIsxhTGW99Re6RhKCZTHr0b3mw79WYZf9IZrWySrT8w8hxUchJ/r1C8LnGH9dt8lda1tetEgIfjxMtfxPa3q9Rek/q0GO73W4eeughbDZvr/wTTzzB3Llz6dOnD4sWLeIf//gHf/7zn1mwwDtP6YsvviAmJua0EgKgndIiP1M1rrZjy1sf76RATiLMJnHsyCDLMZ+SkWUrf5duJsjs/TvECr9NN/uOXd/fTMSRUSV2E9w40PhqOtsq1rK+4h08uoMmvZ4VxX/n/XU3snPfW6hqkyExjY+bygP9n6enC4Y2woAj5xNV9ZMvoXBGaftyW2/vz0fXT3w+Ohds2/MSa7c8QGbOQlZv/GOrSbRG697lSvYEWFgfCDvssCpYosZqXC9wew40VHDN92+zuMjBYa6gVvcOn/Xobgrrc05y77Mn1tZ2bt+AsF5+kxCoe7Jxv7UUdeX3uBd+hmfdVqND+lnQdR3P2tdoenkSrlem4Nn6odEh+T116+K2CUEzjxN1y+IOP/a8efOYMWMGMTExADz33HP06dPH+7yq2mpkTWNjI/Pnz+eBBx7o8PM1O2+SgjFJrQeYHzvefGzycQagd7Jwm8Q1vU2+xCA6QGJmf7Mv3gCz9+LbKANiFP4+2cbTE6z8fZINk3kff9/zMC/svh+Hvp0XJ9p4+kIrL0+2MTze+Pd0f23blW2z6/awI+t1ftj1nAEReQ2IGsWl0ZeR4Gle0xh6dfWP2vrCmSWFH1OHPDT4nG+J0zQPmXlLKDJBqeLtfc060PEfwDPNo1g4YGpZ4lpF57PChQZG1NY/c7+nvKm52IZMDQPRdDM2JYBuIX0Ni2tEeAqTYlvmNw0MTWRqvP+MfVfXb2u9vXE7utp21XDh1Gj7V6Nufg/cDnDVoa55Ca04y+iw/JpeX3pax49n2bJlREREMHZsS5nt5uRg69atvPfee/z2t7/1HVuyZAmXXnopERGnX+ra/4vgnyETupiwKLCpSCU2QGZ8isK3hSrF9RrD4hXGJPvPW3FVTzNjk01UOnRiAmuxm2yMSrRxqE6jS6iMzWTsBYVFkUgJlSh3lvDotttwaQ4ANpev4amh75IS4j8r+/UOHdRmX+SRa4X8QyvIGHhfJ0fUYuSgB4iLGkZ1XQ4JMRcQF238KqHnor11pTyz7xsONFTQLziOGo+TQ45qxkR15U89LyLQZOywQNMlo3B//BU0OsFqwTxptKHxnAl1nlq+sbtpONKsFOGBifhHSzLAp0V7OKhfjo5CMHsJlXbj0o7TomeQEmfrcpQ6JhIC+/A/PX5PgMm4SkmSJPFI3yn8LnUkLs1DDz8r86kfU9YTTYOfQc+b0fTiPe3s2w1xvQ2I5twgBcWgN1ad8HhHLF26FEmS+P7778nMzGTOnDm8+uqrbN68mVdffZU33nijVQLw2Wef8dJLL3XouY7lP1fCnWBUkolRSS0veUZf/+soOVy6gb0HFuFBZ7PFwc7aHVhkG9PTfs/U5F8ZHV4rqwve9yUEAKruYUPZSlKC/CcpGBR5ATO73snnBz/A5aqiu1Mj7MhvSoA91tDYZNlE15SphsZwrtN0nQd2/YfDzloAfqhuWVl7RcleQkw2Zvc0dnEgOSkOy20z0SuqkSJCkczn7mnXpXrYUJnHtoo11Eu6r5er0gTmWP9IdrLqSngtdwvgHS5UQzoWvYrJidcaG9gxJsb2IvOola57B8fywrC7T3CPzpUS4F+VhpqZhvXH8/V637YyqA+Syf/+p9S9K9EKNiNH90AecAWSYlwPf3uctQUc3DYfZ10BYYljsVo1SiO8LWZRDTJhLgUpcaDBUfo3Zch1eL7+K3jaaXAw2VCGXtehx124sKVXc9asWTzyyCOsX7+exYsXs2DBAsLCwnzH6+rqaGpqIj4+vkPPdSz/+086yxrcdWyp+A67EsjgyNGYZO9b4HBW4HRVEhbS3bCu/aqafazeOBtdV8m2QOaR8fkuzcF7OS8yJHI08QEphsR2LI/HycHcf8MxjbAR1o5lxmfTFSmzuCJlFgeLv2XtlrmoODEpAQzt5z8/wP6mSfOwsnQf++vKkSToHhTNxTE9sMj+dcooddX5EoL2bK851InRHJ9kUpBijZk4eqZUNTVy85bFHHbWAGDlImL0VUhHyjmHhPcxMjyfHTWH2+wbGnM9F8RcYkA0x3dd0mBssonvyg+QHBDGDV1GGB3SOUEZ2g8pIhQt/zBSbCRyb/+r9e/ZvBB17asAaIBcvAfzpf5TrEHXNfav+SOuOm8jSnFtvvfAkbylMFQjoMeNWKP9p4HPH8m9L0Hevwotf3PrxMBkQ+4yArnXmTnnaJrGE088QXx8PP/7v/8LwPDhw7nrrrvIzc0lMTHxjDwPnGdJQbmzhAe33kh1UwUAvUIH8vCgV9m19y127X8bXVcJD+nBhSNfxG7r/B/wwuJv0Y+Uq6w9phNDR6ew4YDfJAVVtfuIcjmIlaHkyIkkTgpiXOyUE9/RQElx45g26T9U1WYTEdIDs8GLGfmzP27/N1uOanUH+KJ4Dy8N+qVBEbUv2hpErDWYElf7K4P2DfG/yiRaVSHa/lVIgZHIvS5BMnh400/16eFdvoQAwEUsTuKxc5gwSyRDI8ee4N6dp39I2xazCTFthxEaTZIkrkpM56rEdKND8dHrG/Gs2YReWoGcmoQydqhftsLLaUnIaf67Sri689NW21rW1+gX/RHJ4h+T3V11hb6EoF0S1Aea6dzlZM89kiRjuvwJtL1fo245ap2Codd5z+1noOJZc3WhTZs2tXs8PT2dV1555bSfp5n//befRV8fXuZLCMC7YuPGos85sO9NmuvrV9XuJzPnPYb06/wVZoMCEnx/x6hwdBunVba1Oz7eKKHBaZgUGyMcTmpd3taQUV2nYVH8e1ErizmY2MjBRocBQGVTA0WOWnoFx2CSWyZllzjrqHI30jMoxpDqTVl1JW0SAoAfqgrZW1dKr2D/6Q1SJJnH+k3h6ayvyWuspE9wLLUeJ4cdtYyKTOO2rv5UJhO04kzcH94BRypfSbu/wDL97wZH9dPUe9pWxxoQMY5+wVYmJvySQLN/XEL0DYnj7h7jeSdvE25N5brkwYyL7mZ0WOcE9ydfox/yDmlSSytBVTFdMsrgqM4tjpo88q3lOKM8BDdJxNfJKGYrlYfK1UmuAAAgAElEQVTWULRnAbrmJrbXdcT0vMawGM0BsSjmIFR3/XFvYwv1vx4YfyRJMkrvSR0uPepvzqukoL2JZnWOEo5der6+oW33c2fokjiRwqJvOFj8HUlusIf1JE9xEWwKZXra7wmxhJ38QTqJxRzMqMGP8MOuZ5GcFSTGjaV/z98aHdY546ODPzI/+1s8ukasNZgXBl1Nl4AIXs1Zy8KCLWjopAVGMn/QNCIsgUaH6+OPNXMGhCawMOMGNF33JVFH/+1P1B+X+BICAP3Qj2hFu5Dj+xsY1U8zJb4vHx36kSbN25sZZQnkz/1uMHwid3umJw1mepJ/JP/nCt3h9CUEzdT9+edUUqDXFqP+uATdWYfS7zLkTh4Tr+s6Od/NwSk1gAJVdh1J14judyW5Gx4H3TuhreCHZ7CFpBASZ8yQMcVko0vG/eRvehq1qRZ7eC8CwrpSkbcCgOhuVxCW6B89f0LnOq+SgovirmDl4Y9p0rwtXrG2JMamTOeL7I9xuMp9t0s2aNVLRTYzfsQz1NUXIkkKQYEJJ7+TgVISLiQ5fjya5kZR/O/CwF/VuZ28nPMdniM/ECWuOv6Z+z03p41iQcEPvtvlNlTwfsFW7uzeuSfn3sGxDA9PYXNVQav9I8JT6OlHvQTHOjoJ8MeEAID2upP9YFGtn6JrYCT/HDqDz4p2Y5fNTEtM98uEwN+pWQdQN+0AQBk+AKWPn/RiWC0QaIeGluIRUkSogQGdGt3tpGnxbVBfBoC250vM019GTui8hNvtKMPZPD7/iIaoWAIjoiC3ddWk2uLNhiUFABEpFxGWOAaPqxpLgPe8njz0HgBMlpAT3VX4GTuvkoLkoG48NfRffFvyOQGmIC6MuwK7OYSLR73Mrn1v4XCVk5Y0hbQk45aYBwgOSjb0+U+FJMl+lxB8U7qPN3M3UOlupGtgFHd1H+dXQ14q3Y2+1tZmRY7aNiUKAYpOMIn2bHom/UpWle0np74cHegeFMWF0T0MieVcp7obyNvwBDWH1wMS4cES8XUaEhJS8lDkOONq0p+q7kHR3NNjgtFhnLO0knI8n37jK6Hp+fQbpPAQ5Djjy35Ksozp0rF4lq8BpwvCgjFdNNLosE6qNPvfVOYux6JbiWsoRWnuz9RV1MwvOzUpMNsiMNujcDtaGhkDovoREN6rzW0Dwnt2WlzHIysWX0IAIhkQzrOkoKqpkc9LD5PTkMyI8BSsineiaWhwKqOHPmZwdJBdX8b87O845Kjhwuju/L7rKPIbq3gp+9tW+44ef26UXTVFvLB/NQcd1YSbA6hyO0iyh3J3jwn0Dz0zpbE64oeqQh7c/blve1v1Qe7ctoSlF/yOELN/zHfoEhBBj6Bo9h9p0QK4OKYX6aEJRFkCj1rQCC6OMeaHwywrTIrtDcZWbT0leoMDbX8+BNqQu6Ugyf7RAp+/+RmqCr/xbVcEgLXLOGKTJyKLC+yzSq+tx7NiLVpRGXJSPKZJo5EC7YbFox042Kamvnag0C+SAgClexfk22ei19UjhYe2qcSnHliPtvs/YAtBGTYTOdzYwhcFW56ndG/Lonk1UdC7TEE+khhI9s7t6ZBkE2kXPELehr/Q1FhMYNQAkof8AUtADPH9b6IkcyG6rhLd/UrCUy7u1NgE4ac4b5KCClcD129aQM2RslHflufw35IsXhsy3S9WF/VoKvfu+DelLu/En4WFW7DKJpYX7/FVVllYuAWbYuamNGNbb5o0D/ft+ozKpkYAao9MQNxT5+S+XZ/x8QU3YTYocVlTlt1mX4PaxA9VBVxk0AV2e55Nv4p38jdS2FjNuKhuTEtMR5IkXh58De/mb6aiqYEpcX24KEa0zv8UWkU17gX/BteRCbypiViuu8zYoI6oObS2zb5aqY6E3hMNiOb84v5sFfrBYgC0fbl4dA3zNOMmBErRbWv/S1GnvwrpmSSZTUgRbeevaQU/4Pn3HJrn4Gk5a7H87kNDK+qUZX/SaluVoc6qE+qSIDQBZeC0To8pJG4YA65chupuxGRpqXCXmH4z8f1+A7qGbPKPBirh9Om6RmXeCkqyFtHUWIIlIJbY3jOISJ10RqoPdbbzJin4ojjTlxA021lbxM6aItLDjB+7f6ChwpcQNPuuIqdNqcWNlfmGJwX5jVW+hOBYlU2N5DVWGrYKZuJxWoYS7f4zSRsgyhrIvT3bzl1JDgjnwT7+WcVAr60HRTG0pfV41C27fAkBgJ53CO1gCXKS8V0dttA0Gsp3tNpnDz1363/r1bWo+/ORQoOQu3fxmx6ZY+ma5ksImmn5xhSRaCZ3S0Ee3AftxyygHCnFhORny1e4nZWUZX+C2lRPZNfLCAjrjupxULLtNVzBHkKcMkFuCRzVaAWbUbqPNyxWSVI4di1jy7i7MAV2QU4ZZtiCYZIkt0oImsmK/6z6LZw+XdfI+e4+aos2o6neuTgeVxX5m56mqnAV3cY+dVqJQUVFBdOmTeOtt95C13Xmzp2Lruv07t2buXPnoigK77zzDsuXLwdg/Pjx3Hnnnaf1mvzzbH4eSrSHYj/mBNYrKJaAY/Z1D4rqzLDalWwPI/g4EwyDTVaSDbwAvzJhAINCWy/k8avkIX41p+Bco3tU3EtX0PTqBzS9vLDVaqL+QnfVc2wVMVS13dt2ttQR92GytbQG20K7kTDgJgMj6jjtUAlNby5B/WYDno+/xvPvlUaHdFySLCNFRwBOkPeDVACxxrbKS5KEedIYlHFWMP8HvegT3AtuQM36ytC4mmkeJ5n//R8O73iDkqz3yfzydzRW7Sd7zb0cbthFRYBObrhKjdU7aVYKMva8GtfvhlbblsA4Qvtei5J2gd+tICz8/FTmrWiVEDTTVCe1RRupzO/4/7Xb7eahhx7CZvP2Kj333HPMnj2bRYsW4XQ6+eabbygsLOTTTz9l0aJFLF68mLVr15KVlXVar+m86SmYEteHhQU/tOot6B8SxwADx78fLdBk5f7eE3l23yqq3Q4GhiZya7fRjIzswjNH7fuftAuMDhWbYuaRvpfyt33fUOysI8IcQJW7kVhbMH/qeRE2A0/GdsXMK0OupaCxigpXA8kB4URZ/aek59G0skrvSrfh/l3hQ9u5Fy37SEUNXUfdshu5VxpysvH/O1pVIZ7lD6GX7QdzCHjGgB6NFBOJlOwfC5fZw7oy8OrluOoKkWQL1iD/iOtU6XWleNZ/C56WZEvbl4dWXoUc1XZYjBFcDcVU5n+Nx1lJSHwGgeN6of7nNdC9P9qSVIGuX27IkFFdUzm8859UFnyNubKIgECVWquOBMRs/DvRfjCcrKZoA01HleTWtSaKMxdSV7Kl5UYSVNp1wnv8AjnO2FWsE/r9lsDIvpRn/xt7WHfi+vwayQ/m3Annh5KsRW0Sgmaa6qQk6wMiUztWuGbevHnMmDGDN954A4D58+ejKApNTU2UlZURGRlJXFwc//znP1EU73fe4/FgtZ5e4ZfzJimItAbyfsYNLDn0Izn1FYwIT2FqfD+/mE/Q7OKYnoyL6ka9x0X4kXGaF8X0ZOwx+/zBBZFpLB35O+8YXVnBramYJNlv3s+UgHBSAvzjQuVYutuDe8mX6AVFAMj9umOaOsFv3rtj6ZU1IBWDvBcwgdbXu88PkgLPN8/iLt+Hywx2dw1y8A/og/9EU4oFk+5Gwj8qY0mShC3EP1YjP1W6ruP56mm03Z8DOpiS0D3jkPAm/7rqNjbAI+pKt7Hvm7vQNW88JVnvE25PJUlv+dHWi3egH9yGlDyk0+Mr2buIot1vA+AyQ/1RbSf5eglBNbnYQ9M6Pa6jyaa2QwNN5iC8K5S09MQpKcMxX3xf5wV2AqFxIwg1sLSncP5qaiw5yfHSDj3usmXLiIiIYOzYsb6kQFEUDh06xI033khQUBBpaWmYzWYiIiLQdZ2//vWv9O3bl7S00zuHnDdJAUC4JYCb0/x7IRazrLS5+G9vnz+QJAmz5M1QjZpYfCq0giK0g0XICbHIqYknv8PZimPnPl9CAKDtzkZNkJETYgxveWuPFKWCaQVIRy4K5HykuF8ZG9QR5dU7KIpW0SUwqRDRWERZ3v+hH3BjsoXTY8LzBEb0NjrMc5qevwltt3fMqirpHAzPp9b6L0yqHclkwb3yTYJiBtP1gkewBBo3h6No9798CUGzKkce0YqCVW1JuPV2VmbuDDWHvz/+QQlqizYZnhSExA0nOHaor2fAbI8mrt8N6LpKWfbHgDdxiB/4eyPDFAS/YAmIxeOqOsHxjg2vW7p0KZIk8f3335OZmcmcOXN49dVXSUxMZMWKFXz00Uc8/fTTzJs3D5fLxf33309gYCAPP/xwR1+Kz3mVFBxLP1Iazl9baI+lVdag7doHJhNKei+kIP9IFPRGB+qeHCRZQu7bHcnmH62zR/Ns3on6zQYAVEAZPxzTyEGGxKLXHD15XAXTStQ176ICUtJgzFc/i2Tyjwlp9eW7OZj9LK5oN1Y3xDUo2D1u9LLtEGvsehqqu5Fiu8vXfulRoDRYhyMXhh5nFYd+fIWeF71kXJA/A3r1Qd/fJYEatbYjNfZNDsDbCl9fuo2CH56h+/i/GREiALra/sW+pphB9QAgRaQipwzvzLB87GHdqSv54QTHjV/ETJJkel74ErXFG/E01ROWOBrFHEiXEXOISJ2Mq/4QofEjMdv9bHa0IBggtvcM8jc9jaY62xyTFRuxvTvWeLZw4ULf37NmzeKRRx5h7ty53HfffaSmphIYGIgsy+i6zu23305GRga33HJLh1/H0c7bpMCzcTvq9z+CpqEM6YcyfrjfJAd6UyM4a5FCWsYe61U1uN/9GJq8Fzzqj5lYbroGyWrsxaPe0EjTOx9D/ZFqRBu+wnTJUOSuGYZf2GqV+ajrXkevOYxeEQ70pnluvbpxh2FJgdwrDXXzTm+9cjmfeushygI1ZF0i7vAWlH0rUfpOMSS2oznrCtn79a3e1lcF3ArkWFR6VSiYAo2/KPA01aJx4snEJ+veFU5OTrsAvn0Z1CYc5mNrvbRoqNjTiVG1Fd3zGupKt3H0MJeg6EGETJ6DlrkC7CEo/aYiKcb87CX0vxFH9X7qSrYgm4IIiOhBfdkOJEkmptd0QuKGGRLXsSRZITShbY96cMwggmOMOWcKgj+KSJ1EVeE31BZtapUYyIqNkPgMIrqcuXlCt9xyC/fddx9msxm73c5f/vIXvv76azZt2kRTUxPfffcdALNnz2bw4MEdfp7zJinQdQ11/T9Q93wJpiAo6w66txSpunE7Unw0Si9ju24B1O3L8Hz7CnicSPH9MV/5NJI9DHXnPl9CAEBdA9r+fJT+xtaxV3dntyQEyrfgysWzfAmExGO57hUkg0qTVh/8jtqVj2NqrMejgM0MIU0qkjbAkHiOJifEYPrlZLRtu6lq3EqBonmH7KJTa9XpW3UAf+gDqipY1WY4hi5DfVJ3AlMzDIqqhTUwjsDI/jRU7PLtswQmtJooGZ5yiRGh/axIoQmYpz2L+sP7BDqzadSK2r1dUEzHf4jOhIiUizBf8ipl+5fhdlUTFn8BUT2uQjbZkccYP9zFZA2l18Uv43ZWoZgDkRULnqY676rwZv8shiAIwvFJkky3sU9Tmf8VJVkf0NRYiiUghtjevyKiy8Qzsk7BggULfH8vWrSo1bGJEyeyc+fO036Oo503SYG241PUTc1vbhmYCsB9DWDDrTSQv/tJGncVIJuDSOh/E1FdO3/hI72+HM/qF0Hztn7qRbuoXfs8JaYapBonsUpPrGpwyx3Mnf/xaZqHop1vUn3wW6zByZjrTNTFbsWsWohrKMPWPHa3tgh160eYxt3e6TEe2v4aRbvfATNwVGGfcMsukiq8SYGSkd7pcR1N6ZaM0i2Zsk/fgqOXp5CgTC+ni2GRtbAEtJ/Q2Ube7DeLsnQf/zeKdr+DsyaX0MTRRHSZRPHud3HUHCAk4QJie043OsSfBTlpMHLSYJI9TvQtz1FVuBqzPRJJNtNUf4jguOGkDLvX6DDPidZss62lAILJEnyCWwqC4O8kSSYydXKHqwz5m/MnKSjc0nqHpIJUBnoyuVH/xeU8MlnEVUPehscwWYIISxrXqTHq1Yd8CQFAvUkjt+y/vu2a+B30PjwDs2ZHiotC7t751UyKdr3tq6DhqMnx7rSAE3BYoFd5yxLzuqO60+PTNQ8lWYvaPVYV0Ehir95YkrsaOtG4FXPblS1lP6maFJ5yMWU5n1Jfus23LzRxDKHxxpfFbWa2hZMy9J5W+5KH3m1QND9/sslGasb9pGbcb3QogiAIwhl23iQFUkxP2L/qqB0yBCXRJLlxWdrOHq8qXN3pSYEU1xsCIqCxEoCSYO2YG2iUp5eSnHoTcvcUJKXzK/7UHF533GMeBZwmCPAAkozS99LOC8xH8n627R6SMY1IR7b6z7oASQNvZ//qlotYSbES22emgRG1kBULvS95lfryPThrDxAQ3ouAcGOHqwmCIAiCcHacN0mBMmQ6enk22r5VYA3GNO52lH5TMbkbkZZ9iH7M7HFrUEKnxyiZrJinPYe6/h/odaVIgbXgONTqNnJUpKFzH2whqTRWtr9iniSbsfe/CrnJidJ3CrIBtcAlWSGuz0wO7/xnm2PR3a7E5EcJAUBowkj6TllI8Z53UcxBJKTf3Gp4gT8IiupLUFRfo8MQBEEQBOEskvTmupzniGnTprFs2bIO31/3uEA2tVr1sDznM/I2PQ26d+iOPawHvS55GZMl5LTjPR31FZlk/fd3NFfTkGQLA6/+DyarcXE1NZSwf829OKr3I5sCMNsicNUfRDYFkDzkLqK7X2VYbEerLd5MQ0Um5oBo3I4y7CFphCaO8ZsKU4JghNM9fwqCIAg/X+dNT0EzydS2hn5Ut8sJSx7vvYi0RRIQ3t2AyNoKiuxD/18s4tDOf6KY7CQOvM3QhADAEhhLv8sW4Ko/jNkWgWyy4WooxmQNRWlnNUyjhMQNJyTOmHrkgiAIgiD8/Om6Rt6hFWTlfECjs5QAWwy9u/2K1MRJflOQ41Scd0nB8ZgsIYTGG19m8Vi2kC50G/240WG0cfTwKmtg3AluKQiCIAiC8POi6xrfbp5DUdlmVNW7kKPTVcnG7U9RcPgbxg1/ukOJwbJly/j4Y+8K4i6Xi8zMTNatW0dISAifffYZ7733HosXLwbgzTffZPny5UiSxK233srEiae3NoJICgRBEARBEAThFOQdWtEqIWimqk6KyjaSd+gr0pJOvVTptGnTmDZtGgCPPvoov/zlLwkJCSEzM5MlS5bQPOq/traWBQsWsGLFChwOB1ddddVpJwXnXt+GIAiCIAiCIBgoK+eDNglBM1V1kpXz/mk9/s6dO8nOzua6666jqqqKZ555hvvvbykHbbfbSUhIwOFw4HA4zsicSdFTIAiCIAiCIAinoNFZelrHT+b111/njjvuQFVVHnjgAe6//36s1tbzYuPj45k6dSqqqvL735/+yu0iKRAEQRAEQRCEUxBgi8Hpqjzh8Y6qra3lwIEDjBw5kh07dpCfn88jjzyCy+UiOzubJ554gpEjR1JaWsrKlSsBuOmmmxgyZAjp6ekdfl4xfEgQBEEQBEEQTkHvbr9CUWztHlMUG727dXwh0s2bNzNq1CgA0tPTWb58OQsWLOC5556je/fuPPDAA4SGhmKz2bBYLFitVoKDg6mtre3wc4LoKRAEQRAEQRCEU5KaOImCwyspKtuEetQCuIpiIz46g9TEjk/6zc3NJSkp6YS3GTZsGOvXr2f69OnIssyQIUMYPXp0h58TzsPFywRBEM5X4vwpCIJw5njXKfiKrJz3j1qnYCapiRPFOgWCIAiCIAiCcD6QJJm0pMkdKj3qj869NEYQBEEQBEEQhDNKJAWCIAiCIAiCcJ4TSYEgCIIgCIIgnOfOuYnGGRkZJCYmGh2GIAiCXwgPD+fNN9/8SbcV509BEIQWp3L+PB+cc0mBIAiCIAiCIBhN0zXWl65geeEHVLpKibDGMDX5V4yKmYR8DlYfEkmBIAiCIAiCIJwCTdd4btccdlZtxqU5fPutso30iAzu6fd0hxKDZcuW8fHHHwPgcrnIzMxk0aJFPPzww1gsFvr06cMDDzyALMv85S9/YevWrQQGBgLwyiuvEBwc3OHXJJICQRAEQRAEQTgFa0u+5B97n26VEDSzyjZu7nU/Y2JPr1Tpo48+Su/evVm8eDEPPvggQ4YM4fnnn6dr165ceeWV/OpXv+Lll18mIiLitJ6n2bnXtyEIgiAIgiAIBlpe+EG7CQGAS3PyeeH7p/X4O3fuJDs7m+uuu46SkhKGDBkCwJAhQ9iyZQuappGfn89DDz3EjBkzWLJkyWk9H4jFywRBEARBEAThlFS6Sk94vOIkx0/m9ddf54477gAgOTmZTZs2MWLECFatWoXD4aCxsZHrr7+eG2+8EVVVueGGG+jfvz+9e/fu8HOKngJBEARBEARBOAUR1pgTHo88yfETqa2t5cCBA4wcORKAJ598ktdff51bbrmFyMhIwsPDsdvt3HDDDdjtdoKCghg5ciRZWVkdfk4QSYEgCIIgCIIgnJKpyb/CKtvaPWaVbVyWPLPDj71582ZGjRrl216zZg1PPvkkb7zxBtXV1YwePZq8vDxmzpyJqqq43W62bt1Kv379OvycIIYPCYIgCIIgCMIpGRUziQ2lK9lZtQmX5vTtb64+NCpmYocfOzc3l6SkJN92ly5duOWWW7Db7WRkZDB+/HgALr/8cqZPn47ZbObKK6+kR48eHX9BiOpDgiAIgiAIgnDKvOsUfMXnhe9T4Sol0hrDZckzGRUzUaxTIAiCIAiCIAjCuUcMHxLOK//5z3949913URSFnj178sgjjyDLMldddZVvwY+kpCSeeuqpVvebNWsWCxYsaLPvkUceoVu3br59mZmZrFy5kjvvvPPsv5gTmDVrFg6HA7vd7tv35ptvYrFY2tx248aNLFq0iOeff74zQxQE4Rx1PpxHP/nkE5YuXYrL5SI7O9s3VvuZZ54hNjbWsLgE4WwSSYFw3nA6nbzwwgt89tln2O12Zs+ezapVqxgzZgxAmx+rjujTpw99+vQ57cc5E+bNm9fqh1YQBOF0nS/n0auuuoqrrrqKgwcPMnv27DPyugTB34mkQDgty5YtY+XKldTX11NVVcUdd9zB5MmT+fLLL1m4cKHvdi+++CIAd999N7qu43a7efTRR0lNTeUPf/gD9fX1OJ1O/vSnP5GRkcEXX3zBO++8gyzLDB06lHvvvZctW7Ywb948TCYTISEhPPPMMwQFBfme4/nnn2fr1q2t4ju6ddxisbBo0SJf67nH48FqtZKVlYXD4eB3v/sdHo+H2bNnM2jQoJ/0+l966SWqqqqwWCz89a9/Zf/+/b5W90mTJjFkyBByc3OJjIxk/vz5OBwOHnjgAerq6qiqquLaa69l5syZzJo1i/DwcGpra4mIiOCKK65gwoQJ5OTkMG/ePN54442f/DpP5HifS7P77ruPgoICXC4XN910E5dddhmbNm3i+eefR1EUkpOTeeyxxzCbzT/p/REE4eTEefTcOY8uW7aMpUuXomkad911F/feey/r1q0D4J577mHGjBkMGTKEhx9+mPz8fDRN4+677yYjI+MnvReCYCSRFAinrbGxkbfffpvKykquvfZaLr74YvLy8njjjTew2+089NBDrF27lpCQEIKDg3n22WfJzs6mvr6egoICysvLeeedd6ioqCAvL4/q6mrmz5/P0qVLsdvt/OlPf2LdunWsXbuWiRMnctNNN/HNN99QW1vb6sfsnnvuOWGcsiwTFRUFeFuzGhsbGT16NPv27eOmm27i2muvJS8vj5tvvpkvv/wSk+nk/x6TJk1i6tSpLFy4kNdff52LLrrId6ywsJB3332X+Ph4ZsyYwc6dOzGbzUydOpVJkyZRUlLCrFmzmDnTW7bs8ssvZ+LEiWzYsIEPPviACRMmsGTJEq655ppWz3my19lszpw5vh/uK664wvf6jv1cmrvC6+vr2bhxI0uXLgVg3bp16LrO3Llzef/994mMjOSFF17g448/Zvr06T8pBkEQfhpxHvXP82h7QkJCePXVV497/KOPPiI8PJwnn3ySqqoqrr/+epYvX97h5xOEziKSAuG0DR8+3PdDERISQmVlJZGRkcyZM4fAwEAOHDjAoEGDGDduHHl5edx+++2YTCZuu+02evTowa9//Wtmz56Nx+Nh1qxZFBQUUFlZyS233AJAQ0MDhYWF3Hrrrbz22mv85je/ITY2lvT09FZx/JSWH03T+Nvf/kZubi7z589HkiTS0tLo0qWL7++wsDDKysqIj48/6WsfNmwY4F12fM2aNa2OhYeH+x4jPj4el8tFfHw87777LitWrCAoKAiPx+O7fVpaGgAZGRk88cQTVFRUsG7dOmbPnn3KrxPaHz7U3ufSLCgoiLlz5zJ37lzq6+u54oorqKyspLS0lLvvvhvwDh0YPXr0Sd8XQRBOjTiP+ud5tD3Nz3Gs5rot+/btY8uWLezYsQPw9qZUVVURHh5+0scWzi2arvNVyV4WH9xKibOeWFsQ1yUNYWJsL2RJMjq8UyaSAuG07d69G4Dy8nLq6+ux2+289NJLrF69GoAbb7wRXdfZuHEjMTExvPXWW2zbto3nnnuOBx98kIaGBt544w1KS0uZMWMGS5YsIT4+nrfeeguz+f/Zu/PAqOp7///Pc2bLTgh72ANE1iiIoGVRWjHWaiuIoPGCFrytuAJWRWTxW7SWWun9XRW1XtQWtYhKFVvbKmhFUBHRgiBrWIQAISGBzGSZ9fz+iAxEtiQkmUnm9fgH5sxkzjvbyed1PpuDpUuX0qtXL9555x1GjRrFAw88wHPPPceSJUuqTESrzp2f2bNn43Q6WbBgAaZZuVzYG2+8wbZt23j44YfJz8/H4/HQqlWran3uX3/9NQ734ugAACAASURBVG3atOGLL744aX1g4xQXhBdeeIELLriAnJwcPvvssyp/AI+93jAMrrnmGh599FGGDBly0lCd2t7hcrvdp/y+HHPo0CE2bdrE008/jdfr5dJLL+Waa66hbdu2LFiwgOTkZFasWEFCQkKtzi8ip6fraOO4jgLhzxkqG/ylpaU4HA527NgBQEZGBm3btuW2226joqKCZ555hmbNmtX6fBKdQpbFjI1/Y23Rt5SH/AAU+8uYt3U5HxZs5zd9r65VMPD7/UyfPp28vDxM02Tu3LnY7XamT5+OYRj06NGDOXPmYJomS5YsYfHixeEbBCNGjDinz0mhQM5ZYWEhN998M263mzlz5pCUlMSAAQMYNWoUCQkJpKSkcOjQIX74wx8ydepU/vSnP2GaJnfccQddunTh6aef5q233sLhcHD33XeTlpbGLbfcwvjx4wkGg7Rv354f//jH+Hw+pk+fTkJCAg6Hg1//+tc1qnPTpk288cYbDBw4kJtvvhmACRMmMGbMGB588EFuvPFGDMPgN7/5TbW6vAGWL1/On/70JxITE5k3b95ZtxgfMWIEDz/8MO+88w6pqanYbDZ8Pt9Jrxs9ejSXXXYZb7/9do0+xzM53ffl2AYprVq1oqCggGuvvZaEhAQmTpyI0+nkoYce4he/+AWWZZGYmMjvfve7OqtJRCrpOto4rqPfN2HCBMaNG0eHDh1IT08H4IYbbmDmzJn813/9Fx6Ph5ycnCpBQpqG9/O3VgkEx1SEAnxetIflh7ZyRZueNX7fjz76iEAgwOLFi1m9ejX/8z//g9/vD89NmT17NitWrOCCCy5g0aJF4VWycnJyGDJkSLV6u05H+xTIOVm6dCk7d+7kV7/6VaRLqVenWkqvPuXn53P//ffzpz/9qcHOKSKRoeto/dB1VOrTxC9eZYv70Gmf75ncmhcG5tT4fXNzc/nDH/7A//7v//L+++/zz3/+ky+++IKVK1diGAbLly9n9erVDB06lI8++igc7O+44w5++ctfnjQksCbUUyASZf71r3/x1FNP8eijj0a6FBGRRknXUalv+RWec3r+dBISEsjLy+PHP/4xxcXFPPvss6xduzY8NC4xMRG3243H4wnvC3LsuMdTu3Meo1Ag52T06NGRLqFBNOTdrezsbLKzsxvsfCISWbqO1j1dR6W+tYlLothfdsbna+Oll15i6NCh3HvvvRw4cICbb74Zv//4EKXS0lJSUlJISkqitLS0yvETQ0JtaJCbiIiIiEgNjOswgDjz1PfW40w7N3QcUKv3PbbsMECzZs0IBAL07t2bNWvWALBy5UoGDhxIVlYW69atw+v14na7yc3NJTMzs3afzHc0p0BEREREpAaOrT70edEeKkLHl8WNM+0MSutc69WHSktLmTFjBgUFBfj9fiZMmEDfvn2ZNWsWfr+fjIwMHnnkEWw2G0uWLOG1117Dsix++ctfnnPvWKMLBZMmTWLhwoWRLkNEpNHR9VNEpO6ELIvlh7ayeO/xfQpu6DiAy1trn4IGUVxcHOkSREQaJV0/RUTqjmkYXNGmZ62WHo1GmlMgIiIiIhLjFApERERERGKcQoGIiIiISIxTKBARERERiXGNbqKxiIiIiEikhSyL5QfzeO3bnRzyltPaFc+4Thlc3rZ9o1x9SD0FIiIiIiI1ELIsHtqwlt9t3sBW91GKfT62uo/yu80bmLnhC0LnuOL/+vXrGT9+fJVj77zzDuPGjQs/XrhwIaNHj+a6667j/fffr/La999/n3vvvbdG51RPgYiIiIhIDSw/mMfaw4VUhIJVjleEgnx+uIDl+Xlc0bZDrd77+eefZ9myZcTHx4ePbd68mTfeeINj24uVlJSwaNEi3nvvPcrLy7n22msZOXIkAI888girVq2iV69eNTqvegpERERERGrgtW93nhQIjqkIBXltz85av3enTp148sknw4+Li4v5/e9/z4wZM8LH4uPjSU9Pp7y8nPLycowThisNGDCAhx9+uMbnVSgQEREREamBQ97yc3r+TLKzs7HbKwfzBINBHnroIWbMmEFiYmKV17Vr146f/OQnjBo1igkTJoSPX3XVVVVCQnVp+JCIiIiISA20dsVT7POd8fm6sGnTJvbs2cPDDz+M1+tlx44dPProo1x88cUcOnSIFStWADBp0iQGDBhAVlZWrc+lUCAiIiIiUgPjOmXwu80bTjmEKM60Ma5zRp2cJysri7///e8A7Nu3j2nTpvHQQw/xxRdfEBcXh9PpxDAMkpOTKSkpOadzKRSIiIiIiNTA5W3b8+Gh/SdNNo4zbQxq0YrL27Sv1/MPHDiQTz75hLFjx2KaJgMGDGDIkCHn9J6GZZ3jmkkNbPTo0SxdujTSZYiINDq6foqI1J2QZbE8P4/X9pywT0HnDC5v0zj3KVBPgYiIiIhIDZmGwRVtO9R66dFoU2+h4LnnnuODDz7A7/dz4403MmjQIKZPn45hGPTo0YM5c+ZgmiZLlixh8eLF2O12Jk+ezIgRI+qrJBEREREROYV6WZJ0zZo1fPXVV/zlL39h0aJFHDx4kMcee4wpU6bw6quvYlkWK1asoKCggEWLFrF48WIWLlzI/Pnz8Z1hJreIiIiIiNS9egkFq1atIjMzkzvuuIPbbruNyy67jE2bNjFo0CAAhg8fzieffMKGDRvo378/TqeT5ORkOnXqxJYtW+qjJBEREREROY16GT5UXFzM/v37efbZZ9m3bx+TJ0/GsqzwRgqJiYm43W48Hg/Jycnhj0tMTMTj8dRHSSIiIiIichr1EgpSU1PJyMjA6XSSkZGBy+Xi4MGD4edLS0tJSUkhKSmJ0tLSKsdPDAkiIiIiItEoZFksP1DM63sKOFThp3Wcg+s7t+Lyds0b5epD9TJ86MILL+Tjjz/Gsizy8/MpLy/nkksuYc2aNQCsXLmSgQMHkpWVxbp16/B6vbjdbnJzc8nMzKyPkkRERERE6kTIspj5n138/pt9bC0pp9gXYGtJOb//Zh+z/rOL0Dmu+L9+/XrGjx8PVO5qPGzYMMaPH8/48eN59913AXjppZe4/vrruf7663nqqacAcLvd3Hrrrdx0003ccsstFBQUVPuc9dJTMGLECNauXcuYMWOwLIvZs2fToUMHZs2axfz588nIyCA7Oxubzcb48ePJycnBsiymTp2Ky+Wqj5JEREREROrE8gPFfHHYQ0UwVOV4RTDE2sMeVhw4wsj05rV67+eff55ly5YRHx8PwDfffMPPf/5zJk6cGH7N3r17WbZsGa+//jqGYZCTk8Pll1/OmjVryMzM5P7772fJkiUsXLiQ6dOnV+u89bYk6f3333/SsZdffvmkY2PHjmXs2LH1VYaIiIiISJ16fU/BSYHgmIpgiCV7DtU6FHTq1Iknn3wy3JbeuHEju3btYsWKFXTu3JkZM2bQtm1b/u///g+bzQZAIBDA5XKRmZnJzp07AfB4PNjt1W/q18vwIRERERGRpupQhf+cnj+T7OzsKo35rKws7r//fl555RU6duzI008/jcPhIC0tDcuymDdvHr1796Zr1640b96c1atXc9VVV7Fw4ULGjBlT7fMqFIiIiIiI1EDrOMc5PV8TI0eOpG/fvuH/f/PNNwB4vV5+9atfUVpaypw5cwB46qmnuPXWW3n33XdZuHAhd911V7XPo1AgIiIiIlID13duRZzt1M3oOJvJ2M6t6+xckyZNYsOGDQB8+umn9OnTB8uyuP322znvvPP49a9/HR5GlJKSEl7Js0WLFlVW+TybeptTICIiIiLSFF3erjn/zj9y0mTjOJvJRS2S+FG71Do718MPP8zcuXNxOBy0bNmSuXPnsnz5cj7//HN8Ph8ff/wxANOmTeOee+5h5syZvPrqqwQCAebOnVvt8xiWdY5rJjWw0aNHs3Tp0kiXISLS6Oj6KSJSd0KWxYoDR1iy51B4n4KxnVvzo3apjXKfAvUUiIiIiIjUkGkYjExvXutVhqKN5hSIiIiIiMQ4hQIRERERkRinUCAiIiIiEuMUCkREREREYpwmGouIiIiI1FDIsvhkX5B/5AY4XGHRIs7gx93s/KCDrVGuPqSeAhERERGRGghZFn/43MfC9X52HbUo8cKuoxYL1/v5n899hM5xxf/169czfvx4ADZt2sSwYcMYP34848eP59133wXglVde4brrrmPMmDF8+OGHAJSVlTF58mRycnKYNGkSRUVF1T6negpERERERGrgk31BNhaE8AarHvcG4euCEJ/mBRnSoXbN7Oeff55ly5YRHx8PwDfffMPPf/5zJk6cGH5NUVERr776Km+99RZer5ef/OQnXHbZZSxZsoQ+ffpw5513snTpUhYsWMDMmTOrdV71FIiIiIiI1MA/cgMnBYJjvEF4d0eg1u/dqVMnnnzyyfDjjRs38u9//5ubbrqJGTNm4PF4SEtL4+2338bhcFBYWEhKSgqGYXDLLbcwefJkAPbv30/Lli2rfV6FAhERERGRGjhccebhQWd7/kyys7Ox24/3MmRlZXH//ffzyiuv0LFjR55++mkA7HY7L7/8MuPGjSM7Ozv8epvNxoQJE3j55Ze59NJLq31ehQIRERERkRpoEXfmicRne74mRo4cSd++fcP//+abb8LP/dd//Rcff/wxa9eu5bPPPgsf//Of/8wrr7zCXXfdVe3zKBSIiIiIiNTAj7vZcdlO/ZzLBld1r7tpu5MmTWLDhg0AfPrpp/Tp04edO3dy5513YlkWDocDp9OJaZo899xzvPXWWwAkJCRgs52myFPQRGMRERERkRr4QQcba/afPNnYZYN+rUwuaV/9xvjZPPzww8ydOxeHw0HLli2ZO3cuSUlJ9OzZk3HjxmEYBsOGDWPQoEFkZGTwwAMP8OabbxIMBvnNb35T7fMYlnWOayY1sNGjR7N06dJIlyEi0ujo+ikiUndClsWneUHe3XF8n4Krutu5pH3j3KdAPQUiIiIiIjVkGgZDOthrvfRotNGcAhERERGRGKdQICIiIiIS4xQKRERERERinEKBiIiIiEiMaxozI0REREREGpBlWezLDZK7KUCFxyIuyaBbHzsdutkwtPqQiIiIiEjTZlkWny/3UbA/RDBQecxbYbF+tZ/9u4MM+pGzVsHA7/czY8YM8vLy8Pl8TJ48mfT0dObOnYvNZsPpdDJv3jxatmzJRx99xNNPPw1A7969mTNnDl6vl/vuu4/Dhw+TmJjIvHnzSEtLq9a5NXxIRERERKQG9uUGqwSCY4IBKMgLkbczeOoPPItly5aRmprKq6++yvPPP8/cuXN59NFHmTVrFosWLWLkyJE8//zzeDweHn/8cZ599lmWLFlC+/btKS4u5i9/+QuZmZm8+uqrXHvttSxYsKDa51YoEBERERGpgdxNgZMCwTHBAOzYeJonz+LKK6/knnvuCT+22WzMnz+fXr16Vb53MIjL5eKrr74iMzOTefPmkZOTQ8uWLUlLS2PdunUMGzYMgOHDh/Ppp59W+9waPiQiIiIiUgMVHuucnj+dxMREADweD3fffTdTpkyhdevWAHz55Ze8/PLLvPLKK6xatYo1a9bw1ltvkZCQwE033cQFF1yAx+MhOTk5/F5ut7va51YoEBERERGpgbgkA2/F6Rv+cUm1n2h84MAB7rjjDnJycrjmmmsAePfdd3nmmWf44x//SFpaGqmpqfTr149WrVoBMHDgQDZv3kxSUhKlpaUAlJaWkpKSUu3zaviQiIiIiEgNdOtjx3aaW+s2O3TvW7v77oWFhUycOJH77ruPMWPGAPD222/z8ssvs2jRIjp27AhA37592bZtG0VFRQQCAdavX0/37t0ZMGAAH330EQArV67kwgsvrPa51VMgIiIiIlIDHbrZ2L/r5MnGNju0am/SPsNWq/d99tlnKSkpYcGCBSxYsIBgMMj27dtJT0/nrrvuAuCiiy7i7rvv5t577+XWW28FKuciZGZm0rFjRx544AFuvPFGHA4HTzzxRLXPbViWVbtBTxEyevRoli5dGukyREQaHV0/RUTqjmVZ5O0MsmPj8X0Kuve10z5D+xSIiIiIiMQEwzDo0M1Oh25NozmtOQUiIiIiIjFOoUBEREREJMYpFIiIiIiIxDiFAhERERGRGNc0ZkaIiIiIiDQgy7Lwfh2k/LMAwRILW4pB/MV2XP20+pCIiIiISJNnWRYlr/nw7QyBv/JYoNTC/Tc/3s1BUsY6axUM/H4/M2bMIC8vD5/Px+TJk+ncuTOzZs3Csix69uzJrFmzsNlsLFmyhMWLF2O325k8eTIjRozgj3/8Ix9//DEAJSUlFBYWsnr16mqdW6FARERERKQGvF8HqwSCMD/4ckN4NwaJ61fzZvayZctITU3l8ccfp7i4mFGjRtG7d2+mTZvGRRddxPTp0/nggw+44IILWLRoEW+++SZer5ecnByGDBnCL37xC37xi18A8Mtf/pJf/epX1T63QoGIiIiISA2UfxY4ORAc44fyTwO1CgVXXnkl2dnZ4cc2m40nn3wSm82Gz+ejoKCAFi1asGHDBvr374/T6cTpdNKpUye2bNlCVlYWAO+99x4pKSkMGzas2ufWRGMRERERkRoIlljn9PzpJCYmkpSUhMfj4e6772bKlCnYbDby8vK4+uqrKS4upmvXrng8HpKTk6t8nMfjCT9+7rnnuPPOO2t07nrrKbj22mvDxXbo0IHbbruN6dOnYxgGPXr0YM6cOZimecrxUCIiIiIi0cqWYhAoPX3D35ZS+4nGBw4c4I477iAnJ4drrrkGgPbt2/Pee+/x+uuv89vf/pYrrriC0tLS8MeUlpaG2907duwgJSWFzp071+i89dJT4PV6AVi0aBGLFi3iscce47HHHmPKlCm8+uqrWJbFihUrKCgoYNGiRSxevJiFCxcyf/58fD5ffZQkIiIiIlIn4i+2g+M0Tzog/pLa3XcvLCxk4sSJ3HfffYwZMwaA2267jd27dwOVPQKmaZKVlcW6devwer243W5yc3PJzMwE4JNPPmH48OE1Pne99BRs2bKF8vJyJk6cSCAQYNq0aWzatIlBgwYBMHz4cFavXo1pmmccDyUiIiIiEm1c/Wx4vznFZGMHOLuZuPraavW+zz77LCUlJSxYsIAFCxYAMGXKFKZPn47D4SA+Pp5HHnmEVq1aMX78eHJycrAsi6lTp+JyuQDYtWsXQ4YMqfG56yUUxMXFMWnSJK6//np2797Nf//3f2NZVnhppsTERNxu91nHQ4mIiIiIRBvDMEgZ58S7MUj5pyfsU3CJHVff2u9TMHPmTGbOnHnS8cWLF590bOzYsYwdO/ak43PmzKnVueslFHTt2pXOnTtjGAZdu3YlNTWVTZs2hZ8vLS0lJSWFpKSk046HEhERERGJVoZhENfPXqtVhqJRvcwpeOONN/jtb38LQH5+Ph6PhyFDhrBmzRoAVq5cycCBA884HkpERERERBpGvUSbMWPG8OCDD3LjjTdiGAa/+c1vaN68ObNmzWL+/PlkZGSQnZ2NzWY77XgoERERERFpGIZlWbVbSDVCRo8ezdKlSyNdhohIo6Prp4iInI42LxMRERERiXFNY2aEiIiIiEgDsiyL0MbDBD47iFXiw0hxYr+4LWbfFrVefSiSFApERERERGrAsiz8S7YT2lkC/lDlsdIA/r/txvymGMfY7rUKBn6/nxkzZpCXl4fP52Py5Ml07tyZWbNmYVkWPXv2ZNasWdhslfsgFBUVccMNN/DOO+/gcrlwu93cd999eDwe/H4/06dPp3///tU6t0KBiIiIiEgNhDYerhIIwvwhQjuPEtp4GFu/ljV+32XLlpGamsrjjz9OcXExo0aNonfv3kybNo2LLrqI6dOn88EHHzBy5Eg+/vhjnnjiCQoLC8Mf/+KLL3LxxRdzyy23sHPnTu69917++te/VuvcCgUiIiIiIjUQ+OzgyYHgGH+IwGcHaxUKrrzySrKzs8OPbTYbTz75JDabDZ/PR0FBAS1atADANE1efPFFrrvuuvDrb7nlFpxOJwDBYLBGq3oqFIiIiIiI1IBV4jun508nMTERAI/Hw913382UKVOw2Wzk5eXx85//nKSkJLp27QrAkCFDTvr4lJQUAAoKCrjvvvuYMWNGtc+t1YdERERERGrASHGe0/NncuDAASZMmMDPfvYzrrnmGgDat2/Pe++9x4033hjeIPh0tm7dyi233MLUqVMZNGhQtc+rUCAiIiIiUgP2i9uC4zTNaIdZ+XwtFBYWMnHiRO677z7GjBkDwG233cbu3buByp4E0zx9833Hjh3cc889PPHEE1x66aU1OreGD4mIiIiI1IDZtwXmN0UnTzZ2mJgZzTD7tqjV+z777LOUlJSwYMECFixYAMCUKVOYPn06DoeD+Ph4HnnkkdN+/BNPPIHP5+PRRx8FICkpiWeeeaZa59aOxiIiMULXTxGRuqN9CkREREREYpxhGNj6tazVKkPRSHMKRERERERinEKBiIiIiEiMUygQEREREYlxCgUiIiIiIjFOoUBEREREJMYpFIiIiIiIxDiFAhERERGRGKdQICIiIiIS4xQKRERERERinEKBiIiIiEiMUygQEREREYlxCgUiIiIiIjFOoUBEREREJMYpFIiIiIiIxDiFAhERERGRGKdQICIiIiIS4xQKRERERERinEKBiIiIiEiMUygQEREREYlxCgUiIiIiIjFOoUBEREREJMYpFIiIiIiIxDiFAhERERGRGKdQICIiIiIS4xQKRERERERinEKBiIiIiEiMUygQEREREYlxCgUiIiIiIjFOoUBEREREJMYpFIiIiIiIxDiFAhERERGRGKdQICIiIiIS4+otFBw+fJhLL72U3Nxc9uzZw4033khOTg5z5swhFAoBsGTJEkaPHs3YsWP58MMP66sUERERERE5g3oJBX6/n9mzZxMXFwfAY489xpQpU3j11VexLIsVK1ZQUFDAokWLWLx4MQsXLmT+/Pn4fL76KEdERERERM6gXkLBvHnzuOGGG2jdujUAmzZtYtCgQQAMHz6cTz75hA0bNtC/f3+cTifJycl06tSJLVu21Ec5IiIiIiJyBnUeCpYuXUpaWhrDhg0LH7MsC8MwAEhMTMTtduPxeEhOTg6/JjExEY/HU9fliIiIiIjIWdjr+g3ffPNNDMPg008/ZfPmzTzwwAMUFRWFny8tLSUlJYWkpCRKS0urHD8xJIiIiIiISMOo856CV155hZdffplFixbRq1cv5s2bx/Dhw1mzZg0AK1euZODAgWRlZbFu3Tq8Xi9ut5vc3FwyMzPruhwRERERETmLOu8pOJUHHniAWbNmMX/+fDIyMsjOzsZmszF+/HhycnKwLIupU6ficrkaohwRERERETmBYVmWFekiamL06NEsXbo00mWIiDQ6un6KiMjpaPMyEREREZEYp1AgIiIiIhLjFApERERERGKcQoGIiIiISIxTKBARERERiXEKBSIiIiIiMU6hQEREREQkxikUiIiIiIjEOIUCEREREZEYp1AgIiIiIhLjFApERERERGKcQoGIiIiISIxTKBARERERiXEKBSIiIiIiMa5aoeCFF16gqKiovmsREREREZEIsFfnRfHx8dx+++20bt2a6667juHDh2MYRn3XJiIiIiIiDaBaPQU33ngjixcv5q677mLZsmWMGDGCJ598kpKSkvquT0RERERE6lm1egpKSkr4+9//zttvv01ycjIPPfQQgUCA22+/nZdffrm+axQRERERkXpUrVAwZswYfvrTn/KHP/yBdu3ahY9v2bKl3goTEREREZGGUa3hQ7feeit33nlnOBD8+c9/BmDq1Kn1V5mIiIiIiDSIM/YU/O1vf+ODDz5gzZo1rFmzBoBgMMj27duZMGFCgxQoIiIiIiL164yhYNiwYbRq1YojR44wbtw4AEzTpGPHjg1SnIiIiIiI1L8zhoLy8nIGDx5M69atqxwvKyur16JERERERKThnDEUvPjiizz44IPMnj27ynHDMMLzCkREREREpHE7Yyh48MEHAfj9739PmzZtwsc3bdpUv1WJiIiIiEiDqdbqQ5MmTWLVqlUAvPDCCzz00EP1WpSIiIiIiDScaoWCl156iRdeeIFrr72W/fv3s2TJkvquS0REREREGki1QsHWrVspKCjg/PPPZ/PmzRw8eLC+6xIRERERkQZSrR2Nn3zySZ577jnS09P5z3/+wx133ME777xT37WJiIiIiEgDqFYoeOWVVygvL2fr1q1kZmbyl7/8pb7rEhERERGRBlKtULB8+XKeeeYZgsEgV155JYZhcPvtt9d3bSIiIiIi0gCqNafgxRdfZMmSJaSmpnL77bezfPny+q5LREREREQaSLVCgWmaOJ1ODMPAMAzi4+Pruy4REREREWkg1QoFAwcOZNq0aeTn5zN79mz69etX33WJiIiIiEgDqdacgmnTprFy5Up69+5Nt27dGDFiRH3XJSIiIiIiDeSMoeC1116r8jg5OZlDhw7x2muvMW7cuHotTEREREREGsYZQ0FBQUFD1SEiIiIiIhFyxlBw5513AmBZFsuXL2fXrl306NFDw4dERERERJqQak00njlzJu+++y4ul4u33nqLxx57rL7rEhERERGRBlKticbbtm3j9ddfB+Dmm29m7Nix9VqUiIiIiIg0nGr1FHTq1Im9e/cCcPjwYdq1a1evRYmIiIiISMOpVk/B+vXrueqqq0hPT+fgwYM4nU6GDh0KwKpVq+q1QBERERERqV/VCgVz587lkksuqe9aREREREQkAqoVCp566qkahYJgMMjMmTPZtWsXNpuNxx57DMuymD59OoZh0KNHD+bMmYNpmixZsoTFixdjt9uZPHmyVjYSEREREWlg1QoFhmFwxx130LVrV0yzchrCtGnTTvv6Dz/8EIDFixezZs2acCiYMmUKgwcPZvbs2axY7yGNcwAAIABJREFUsYILLriARYsW8eabb+L1esnJyWHIkCE4nc46+NRERERERKQ6qhUKrrvuuhq96eWXX85ll10GwP79+2nZsiX//ve/GTRoEADDhw9n9erVmKZJ//79cTqdOJ1OOnXqxJYtW8jKyqrZZyEiIiIiIrVWrdWHrrnmGgKBAHv37iU9PZ1LL730rB9jt9t54IEHmDt3LtnZ2ViWhWEYACQmJuJ2u/F4PCQnJ4c/JjExEY/HU8tPRUREREREaqNaoWDOnDns37+f1atXU1paygMPPFCtN583bx7/+te/mDVrFl6vN3y8tLSUlJQUkpKSKC0trXL8xJAgIiIiIiL1r1qh4Ntvv+Wee+7B6XTywx/+ELfbfcbXv/XWWzz33HMAxMfHYxgGffv2Zc2aNQCsXLmSgQMHkpWVxbp16/B6vbjdbnJzc8nMzDzHT0lERERERGqiWnMKgsEgRUVFGIaBx+MJTzY+nSuuuIIHH3yQm266iUAgwIwZM+jWrRuzZs1i/vz5ZGRkkJ2djc1mY/z48eTk5GBZFlOnTsXlctXJJyYiIiIiItVjWJZlne1Fa9euZcaMGRw4cIAuXbowY8YMfvCDHzREfScZPXo0S5cujci5RUQaM10/RUTkdKo1fKi4uJhgMEjnzp2pqKggFArVd10iIiIiItJAqjV8aMGCBbz++uu0aNGCwsJCbrvtNoYOHVrftYmIiIiISAOoVk9BamoqLVq0AKBly5YkJSXVa1EiIiIiItJwqtVTkJSUxKRJk7jooovYtGkTFRUVzJ8/HzjzzsYiIiIiIhL9qhUKfvSjH4X/36ZNm3orRkREREREGl61QsGoUaPquw4REREREYmQas0pEBERERGRpqtaPQUiIiIiIg3tSGGI/3zso6TYonUHk/7DnLjijUiX1SSpp0BEREREoo5lWaz9wMfRIgvLgvy9ITau8Ue6rCZLoUBEREREoo63HMrcVpVjRYe0gW59USgQERERkajjiofE5KpDhdLaqOlaX/SVFREREZGoYxgGA3/opFlLA9OEtp1M+g52RLqsJksTjUVEREQkKqW2NLnsZ3GRLiMmqKdARERERCTGKRSIiIiIiMQ4hQIRERERkRinUCAiIiIiEuMUCkREREREYpxCgYiIiIhIjFMoEBERERGJcQoFIiIiIiIxTqFARERERCTGKRSIiIiIiMQ4hQIRERERkRinUCAiIiIiEuMUCkREREREYpxCgYiIiIhIjFMoEBERERGJcQoFIiIiIiIxzh7pAkRERESkYVSUWezLDWAYBh2723DGGZEuSaKEQoGIiIhIDKgos/j3XyvwVlQ+zt0UYMQoFw5ndAWDiq8DVKwPYiYaJAyzY2+pgS0NQV9lERERkRiwd0cgHAgAyj0W+3cFI1fQKXg3B3Ev9ePPDeHdEOTIS14svxXpsmKCQoGIiIhIDDBO1SEQXZ0EeL+pGlKsUvDtDkWomtiiUCAiIiISAzp2txOXcPxxYopBehdb5Ao6BbPZySnFlhplyaWJ0pwCERERkRjgijcYMTqOvJ1BTBPSu9qibj5BwiV2fDuCBPMtMCD+Ejv2VrqH3RAUCkRERETqmPtIiNISixZtzahqeDtdBl17RW/zz0w0aP5LF8F8CyPBwJYSPV+7pi56fypEREREGqHN6/xs+08AAIcThlzlolkL3e2uLsMwsLetGgbeP5jHX/bkApDTuRuXt20fidKaNP2EioiIiNQRb7nF9vWB8GO/D7b+xx/Bihq/LSVH+PXGL9nmPso291H+38Yv2VpyJNJlNTnqKRARERGpI36fhfW9FTS95ZGppbEKfLyfwGcHwbLAZrC6xVGsDseft4DPDxdwXkpqxGpsihQKREREROpIUjOTtNYmRYeOL6PZKTO6VviJZsFtRwh8uK/Ksa7Ok79+GUkpDVVSzFAoEBEREalDF1/hJHdTgNISi3adbaR3VSiortBe90nHLil28bMD8fytbTmGafCz9p35QcvWEaiuaVMoEBERkUbF57VwOMAwo3NlGofLoOcAR6TLaJTMDkl8f49lA4O7dqVwa/seOIenk2jX17Y+KBSIiIhIo1BRZrH2Ax9F+SFcCdB/qJM2HXUXvimxndec0PB0gmvyAQtsJniDmH3SSB3WAcOu73d9qfNQ4Pf7mTFjBnl5efh8PiZPnkz37t2ZPn06hmHQo0cP5syZg2maLFmyhMWLF2O325k8eTIjRoyo63JERESkifhmrZ+i/Mqx+t4yWPeRj+wb4rDZo7PHQGrHcVkHHJd1OPsLpU7VeShYtmwZqampPP744xQXFzNq1Ch69uzJlClTGDx4MLNnz2bFihVccMEFLFq0iDfffBOv10tOTg5DhgzB6XTWdUkiIiLSBBwtClV57PdCeZlFkja4EjlndR4KrrzySrKzs8OPbTYbmzZtYtCgQQAMHz6c1atXY5om/fv3x+l04nQ66dSpE1u2bCErK6uuSxIREZEmoFW6jZKi43sAJCYbJCYrEIjUhTrfvCwxMZGkpCQ8Hg933303U6ZMwbIsDMMIP+92u/F4PCQnJ1f5OI/HU9fliIiISBPR60I7XXvZiE80aJVuMmikM9y+EJFzUy87Gh84cIAJEybws5/9jGuuuQbTPH6a0tJSUlJSSEpKorS0tMrxE0OCiIiIyIlsdoOsHzi54oY4fvBjFynN66UZEzMCh0N4/uXD8w8fgYLQ2T9AmrQ6/20qLCxk4sSJ3HfffYwZMwaA3r17s2bNGgBWrlzJwIEDycrKYt26dXi9XtxuN7m5uWRmZtZ1OSIiIiLyPUG3xZH/81L+WZDyz4Mc+T8vwWIFg1hW53MKnn32WUpKSliwYAELFiwA4KGHHuKRRx5h/vz5ZGRkkJ2djc1mY/z48eTk5GBZFlOnTsXlctV1OSIiIiLyPb5vglgVxx9bPvBuDJIwTL0vscqwLMuKdBE1MXr0aJYuXRrpMkREGh1dP6W6KsosNn7mp7gwRIu2Jn0HO3C6NHa/KalYH8D9lr/KsaSfOIgfqC2sYpXioIiIiFSx7t8+8nYFKXNb7N0eZMNq/9k/SBoVVx8b9vbHg56tjYGrnzYGi2WKgyIiIhIWClkUHqg6tjw/LxihaqS+GHaD1Iku/LtCWCFwdjMxTPUGxTKFAhEREQkzTYOkZgaeo8dHF2uVn9oL5IcIHrFwdjUxnNHV6DZMA2e36O8d2FFSzlfFHnqmxNOveVKky2myFApERESkiv7DHHzxbz/lHoukZgbn/8AR6ZIaJc8/fZSvqexlMRIg9WYX9taRDViWZRFYsY/gfwrAboBpQkWAjf3i2NDDTq/UVC5p2SaiNZ7oX/uL+M3X33Isot7avS0TurWNaE1NlUKBiEgDCwQsDuwOYoWgXRcbjii7eyiS1sbGyLEm3nJwxaMNwmoheCQUDgQAVhmUrQqQMtoZwaog+GUBwU8OVDn2dtsynnTsh92Vj8d36c4vu/dq+OJO4c+5+Zy4Is4ruw6R07UNdg11qnPqDxQRaUABv8XKt718+ZGfrz728+FfvXgrGtUicFLHLMvicH6Q4ijbPMowDOISDAWCWjpxuc9jQmWR/10P7Sk56djr6aVVH3+7i0AoOn4e/d9bJDNoWVhE/uvYFCkUiIg0oP27g7iPHP+DVu6x2LdDkzhjVcBv8fE7Xlb9zcfKZV4++YeXUEgNnpoIeSyCR6KjAXsiWxsDe7uqgSquf+TH75vpJ4/JN6haZzTlwLGdW1V5fG3HljhMNV/rg4YPiYg0oNAp2v/BoBqBsWrvjiDFBce//wX7QxzcEyK9a+Qbj42B558+yj8PggWO7ibNxjoxHJFv0YYOVxD8Ip+ERC/BlhaWLQ7bD9oQ1yfyzS7bRa2xDpUR3HAYHAaYBjfkJfCHbu7wa27o1A17lDS8x3RuRefEOL4scnNeSgKXtmkW6ZKarMj/dIqIxJD0rja2fuWnoqzysdMFHbvrUhyrvOUnB8KKUxyTk/n3BquM2ffvCFH+ZZCEwZH9fbI8fnwLN0FFkEJnkL+2K6PEbnHlukMM6Dcw4sOxDJuJ46cZ2K/uCkblMLHRIYue7iN8WVxIz+RULmrR6uxvVI/2HA2xal+QZCf8sLOdi1omc1HLZABK/RYf7PZT4oMh7W10SY2O8NIU6C+RiEgDcroMLr02jr3bAoRC0KmHjfjEyN/ZbMr8PotQCFxx0fd1Tu9qY/uGQLgHye6Adp3VS1AdwcMnh6fg4cgPIwpuKYKKIF7T4u5+RRxyVdb0L6ucJ7flcf55HSJcYaUT9yQwTIM+zZrTp1nzCFZUaUdxiF+v8hL47lv58bdBfjvChc00CFkWv17lZW9J5ff+n7kBZg11kpmm35m6oFAgItLA4uINepyvJR4bwtav/GxbX9noTu9iMuAyJzZb9ISDlOYmQ65ysXtzANOEjD52hcRqcnSzgd0PgePHXD0j3zg04iubVl8284UDAUDIgH+WHuJ8oiMURKsP9wTCgQAgz2OxqTBEVmsbmwtD4UAAELTgg91BhYI6olAgIiJN0tHDIbZ8ebzFuH93iBZbg2T0jq4/fWmtTdJaR3aZysbAKvXjX7aL0M6jGG0ScFzTldTxLspWBbC8ENc7CKu3U/GqG6N9Io6fZmC2iGvwOs3zmmN0TCL5iO+k55qlxDd4PY2N6xTt+2PHXPaTA/OpXi+1o4FYIiLSJJUUnzyUxH2KY3J6oQqLso/9uN/x4dsZ2VWy/P/cQ2j7EQhaWPtL8b+xA0cnG81yXKT+3IW5Yy+hXSUQsrD2evC/lRuROg27ifOWXvS/NothcWnh423j4hnTsWtEampMsjPspJyQkbNam5zXorLl3725Sf82x5uuSU64slt0hfzGTF9JEWky3EdCeMst0tqYmNrYpt6Ue0Ic+DZEQpJBmw5mlbHJ0aRlOxumzV9lxafWHXRbsSaOvuIjsK8ySFV8GSTleieu3pH5Gob2egDYGxfAADocrsAq82MkOL573l3l9VZeKVbIisjPp2EY2Lqm8FjXIXx9pIgSv5+BaS1x2fTzdzZtEk1+/6M4vjwYpHmwnJ67NuFfVobZuxu27p25d7CTrw+FOOq1GNDWRpI2f6wzCgUi0iR8/amPnd9Utv4Skg2GXOUkIUmdoXWtuCDE6ne9BL8blZPexeSiH7kiW9RpxCcaDB7pZOtXAQJ+iy497ZrEWwOB/FA4EBxT/mUgcqGgQyJzQof4NM0LwFB3PHNdJsdm55gdkgjtPL4xl5GeGBWBtV9q2tlfJFUkOQ2GdTDxL/wHoaKjAIQ258Koy7FlduX8Nvo9rg/6iykijZ77SCgcCADK3BY7vg6c4SOktnK/DoQDAVSO03dH4cZRx7Rub2PY1S5GjIqjay/dB6sJ4xRZz4zgCk6rBjrDgQBgVXI5qwrzw48d13TF7JJcucxmhyQc12ZEosxGqyIY4sUdB5n+5U5e232IQIQ30bP2H8L6LhAcE9q4PULVxAZdIUWkWgJ+i707gvgqLNpn2EhqFj33FE651nuZ1nqvD6fabfdUG7JJ42SFLKwjXoxmTkzDT1x/qPiq8jnDBQlDI9dsOGB5Tz5WXhb+v9HMhXNCr4YsqUl57Otv+TD/CACfFJRQ6PVzx3ntI1dQwikmZSdqonZ9UigQkbOyLIvV73o5UljZINy2PsCwq12ktoyOYJDWxiQh2aDMfbzB2lGTz+pF1952Dn7rw/ruS92ynUmzFtHxc9AYWSGLso8CeDcFMVMNki53YG8bma9naL8H35IdUOIDmwFBC4fdwHlROlanNji72TDjI9dTMKxVO17YuY3gdz98DsNkaKu2EaunKfEGQ3z0XSA45v39xRENBWZaM2wX9iG4blPlgeRE7IPPj1g9sUB/NUXkrA4fDIUDAVTeGd61OUD/YdGxjKJpGgy9ysn2rwNUlFl07GanXReNOa0PrdJtXPozF/t3B4lPNOjYXV/nc1H+aYCylZXjsYKHLY7me0mbEocRgb0U/O/uqQwEULkAPEDAwvoiD+clLTDjI9tkyEhKZn7/i1ny7U4MYFznDDolJkW0pqbCYRqkOu0U+Y6PDWwZF/m9VOyX/wDz/J7gKcPo2A7DrutNfVIoEImQA3uCfLstgDPOoEeWPaqG43yfcYrSzCgrNz7JJOuS6AgpTV2zFuodqCu+HVXnY4Q8EDho4Wjf8KHAKiw/6VgQizXNvRTv2snw+K60iovs8I0L01pyYVrLiNZwLt7cU8AbewpwmCYTurXh8naR30EYwDQM7uzZnse+/ha/ZZFgM5mcmR7psgAwW6VBK03WbggKBSIRkL8vyOfLj29sk/9tkMvHxmF3RH6ljFNJa23Ssp1J4YHKBozdUTmMRCTaWSGLspUBfFuD2FqaJP7Qjq159AQaWysD/+4TDtjBlhaZ64CZ2ZzQxsNVjj3c82jl5N6iozz76W6evnAImSnNIlJfY/d5YQn/35a88ONHNuwhIymOjOToGCd/ebvmXJiWxE5PBT2bJZCou/IxR3/VRSIg73ubAHkroGB/KGqXSzQMg0uynRzYE8RbAe0624hPjM4AI3Kiso+OD88JHAwSyA/RfLILw4jAnfjyAIFV+7EOlWN2a4ZtcBsShtpg+wGMI6WE7Ak4f5wesXH7jp90IRBvI7THDU6THUZFldV+yoNBXvt2J7P69o9IfY3dusOeKo9DwFdFngYPBUu3+vlHbgCbCaMyHbhs8MYWP+UBuLyLnRt6J0Xk90MiT6FAYsbRwyEO5QVJSTNp3d6M6EUvPuHkc0d7I9u0GbTP0CWjtrzBIP/edYDSPX6GxrWh5YD4iE7arK1AfgjDBbbU6Lnbfia+bVUDeLDAIlhkYW8RgTH7b+yo3HEXCOUexaoIgNuP42gBGEDQg7k3BAMis5Sm4bLh+HGX8GPz6BFYu7/KawJW9C4/G+0yU05u/Pc4xbH69OXBIG9sOT5v4E9f+6s8/86OAB1TDIZ21LU+Fum7LjFh/64gaz/0wXdz5zL62Oh3ceTGn2f0sXPw2yAlxZUFdelpi5qVfKTu+UJBbvt0FdsrKhuELx3ZzvyXBtPtv1Mw7I0jGIS8VuXutnsrG4VxF9pIvjr653DYWpoEDh4PBoYLbMkR6CUo9RPaVYLPsHBYYGAQ2lSEVeKr8rrQxsPws+hYX79Xs1T6NWvO10eLAbAbBqM6dIlsUSfwh0LscFfQIcFJsiP6mzMj2qay8Ugpy/Yexm4a5HRtTVbzhp0ova3o7KFuW1GIoR0boBiJOtH/WyRRy30kxNavApSXWnTIsEX1GPNtG/zhQACwa3OQngMsHBHaHt0Vb3DZKBfFBRbOOEhKUSBoylYX5IcDAUCRy8t7zn1M2tETV8/oHDL2fRVfBMKBAKBiXZC484M4OkZH/cGNhwl8cQgcBmYzF6GDZRgt4ki4KJ1AvkGwwMJwQdJPHBgR+L0vs1n8utdRPkmtIM1ncteuZIanpABgHa4Iv85Iia6gNX/AxfzzwD4KKir4YZt0uienRLokALaXlHHfup0U+QK4TIP7+3RiZHp0TNrdVBDk9S0BDnoqf1/aJJpc38tO31Y27unVgdsy0zENcERgtYbMtLOfszqvkaYpeltxEVBSHMJbbtGirYkZBVujHxMMWPi80TW8JBS0+OSfPipKK1vaRfkhTBt0Pi9Kf6S+v99SFOxrZRgGaa2j53sq9ccfOvnunN+0MKL01+VUgkdO/qUJHrFwRMEdxeDOo/iX5h5//N2/1v5SrPwymk/uS6jYwkwyIhIIAP60dwerm1c2/g+7Qvy2RwmDemeR6K0cVoQvBA4Te3bniNR3OvE2e1T1DhzzzLb94eUzvSGL/2/LPi5r2ywiDe0TlXgtHl/jw3fCqLUSX4jHP/PxvyPjaBZn4LJFrsYBbW2M6Wk/45yCIR2iI+iXleez9uvfU1j0Na1aXMBF/e4jPq5FpMtq0hrRn6T69Z9VPvZsrfwtTkg2GPoTV1Q0wr/dHuDrT/0E/NC8tcngy524omAccnFBKBwIjjmwOxi1oaBbXztffnR87GTn82wR6yWQ2DO0VVvaOuM56Ktc8jEhYOcKsz2OjMZzR87V24b3i3KcFGIQIOBojrNbKwBCe0oIrCvAcJrYLm6L2bJhx0mHthafdKzEHsKwIPlQOVaRF1uLuAat6fu2lhyt8rjCZvFtcoi+HdMwp/bHOliG0SYeIy46r6HR5kB51WFXJf4gpYEQqc7I/k5tORyqEgiO8Ydg8+EQF7ePfIN79HkORp9XdQ+CyzpH38/dJ1/9mvzCLwDYe+BDQiE/lw1+IsJVNW3R91MQAUeLQuFAAFDmtsjdGKDv4Mhu3OHzWqxf7Sf0XWnFh0Js+crP+T+IfPdyQrKJYRDe1RQgMSV6G9kdu9tJTDE5tK9yonG7zo2nMSaNk2VZhDYVEdrnwdUhiecHD+PdPd9Sur2EK9zJdLzEwIiiHsmzcXQ0SErcCWWV4doROIq130kowY5v0VYIVV4Mgt8U4bozCyOh4a6fRtrxBn8Iiz90c/Ov1uUYFlxdkMC9SZH/U9e/eQvWFReGH6c4HOGhOIbLhtE5OVKlNTohy2JASgsOlhYQNCp7Cy5ITeJwqYnXH6JNYuSu7x3P8HfwTM9JVZZlhQPBMQcL10aomtgR+StlFPCWndwt7i2P/PiS0hIrHAiOcRdHvi6oHMrUa6CdLesChEKQkmbQ4/zI7354JmmtTdJaKww0ZZbPIuSNzETS7wt8uI/gqgMABD/PJ2lIO64/YhDaFASO4N92BOvqLtgHtI5sodUU2l0SDgTHBL8+jJHoCAcCACqChLYfxXZ+w20wZRvQitCOo4Ryj/JxCy//aPPdJlwGvN2mjKHuw1ziatNg9ZzKTV26U+zzsiJ/P+3iE7gzszdxtuj8ExwIWawpLMECBrdMjviQnBMdLrd4dLWXg6UptCOZ+CQ3vdoEyD+UxqyVlcunjuxq4+dZkbl51i7J5KY+dl7fHMD33ahBlw2u62mnfXL0fB2jnWEYNG92HsVHt4aPpTU7L4IVxYbovCI1sBbtTOITDcpPGA7ToXvku/iatTCIS4CKsuPH2nSMnotKjywHnTLteMstUqJoMyCJHXllpewvLyMrNY3Qlxaly/1YPnB0NkkZ54zokp/BtYeqPv78IPitk17TWEKBkVQZ+rcn+vk4zUsbr0l2UmviEk9xMyCpYW8QGA4bzpvOI1TsZd/BXMirOlRnl8fNJS0jGwocpsnUnv2Y2rNfROs4m/JAkDs/38F2d2WwykiKY8HgHiREyUZWy7b7ORj+W21Q4UmhRSsbX7qP30F7f1eQEZ1CdInQsrk/6e5gZFd7eBiR0wZOW+RvVDQ2l1wwk9VfzuaoexepKd0ZfP6MSJfU5CkUADabwdCrnez4OoCvHDr2sNEmCibamKbBJdkuvvnCT5nbIr2rje59o+tb5oozcMXpYhcLQmUWgQMh7O1MzFPss9DQFuZu5aVd27CANIeL//f5ADr4EgHw7wlRtipA0sgI9l45TfCe0NXntEEoCMETgoGj8YRps20i/xkYz/2OfELffftXpuTxP1mDCW48jHWw8u6FeV5zzIzIrFBjNndxsdmOl/Jyw2sJmMCgFo0jeEWDD/OPhAMBwE5PBcsPFPPTjg3X83MmRd/rxbeAQ6Un96AfLrfoktpARZ2C02bgjHwzolFr3iyTq0csxu/34HA07NKtsSq6WpgRlJBkknVJ5Mfqf19KmsnFV7giXYbEOO+WICVv+iAA2CHlOmdEl9I87K3gz7u3hxt+RX4vr3fYxdRtfcOvCRZEdpMl+2UdCLyz6/jjER2wjvgIrvpuMyjTwD48PULV1c5b6eWEjg+LZ11JETsDZWT8dx+sbz3gNDHbJUauQKBPs+Y83HcAi7/diWkY5HTuFjXLaJ5K4f/f3p0H1lHVCxz/zszdb/Z9T5s06b7SjbaUAi1FdpBNEGRRBHEBRAQEQYsPEeTpE/UB1qciylKKIIushUKBtnTfl6TNvm83yc3dZs7747Y3SRNaRJqZNufzTzNzb9Jfp5lzz+/MOb8TDFPe2cPYRI8lau13R6L3jSZs2A0XAkF5q0IoR1hitPvEXI119b33drpHYdEIjU2NvecSnDA+/dhJuKXDkwnB0DG/BZIkiUiLQXCbjhav4JyoWW5Dq9q3u3k2bx/Vnm6mt6Zx1hv5OMcMbYWZvtpCIXTRf3SwzRXsd+woMXeYzjY1HTUvDqOqEzUvDjXDA4BWmoTR1IM2MgEl6dhK+G2DzC23K9Hdwa20UPa0rFxOy8o1O4wjeq2mhV9sq0IX4NZUfj5tJFNTzL2Op2Ql8dSOLtzBTBSi7dCa/VDWEOS+kxykus3tbM/JsyGAVdU6yS6F80ttpHtUbpmh8F5VhDi7wjklNlwWa0Olz6412MSf9vySvb6tjEmawtWjvk+Cwxp7UBzvZFIgSSYLVxu0/ykYK64e2KSTdLW1Oos/y9nIzvjoPO0NyS30VOrcwFjT4imOi2dUXAJ7u3o3BPvS6HzsfhXRFsQ5QseRoyMiHhSbeZ0YNd2Nmh5NnvZ37mZz22ryvcVMmXIiinLsdVq+UljMR80NBA/su3BqZg4FXjmK93lEDMHvdtXGZpP16Ab/u7uOx2abmxSkOe1kk0XHIedbegSvlel8dYL5I/Bz82zMzevffZmRozEjR87XOR78fudP2dK2BoAPG98kpAe5beJDJkc1PMikQDouGX6BCAk0kxaa/Tt61kToJsyywv3s83YyuSOFq6pLcOVZ4/ZsCPTEEoKDVuXVm5oUKIrCf0+bzVP791LX42dBZg6LsnIJV1WglzVAK4TXAx4bjstKUfPM7bh+1PgW/7P9bsSBCU9fyruMr426xdSYPo/xicn8bc6pfNBUT7bbw2wLz9Xf0tbFE3vqaA1FWJyTwldHZlgqEQsZBr6wjs1woAgFFIVW/5G/byhVPISzAAAgAElEQVQcUmQqpjNkjep30vFLCMHWtv6lRw8mCNLRZ41eh9SP4RcoTlAsMH/zWNT9Thj/qggYYC9SSbzUYdoupp+JAr8cvZUNyS0AbEpuJdIk+FbeONNC0re3oq9tAIeKZ3oaLqESUHrn7Gale0yL7aBkh5Nvl46PHRu13eirG/q/yR8h/EYlzmuH/lq2hyK8XN2CLxxhb8efYwkBwBs1y7hkxPW4bebOv/88Ml1uvpw/st+5nR1+VtS3k+Gyc2ZuCm6TK9V0hXVuX18emx//xJ46kuw2zsm3zm6oLk2lRCnEH+qzqVoIfrU2yHenO1BNTGAWFGi8ub9/PWwFOLlAjsRLR5eiKBR4R7Grqx0f4xBolDqDR/5G6Qsx7JMCoQuMDoGaZP5GQka3wPdciHCFgeKB+DMdOMdbuxEObtfpfi+MCIN7pg3PbHN/pSINBv73I7HjTU2tvPDBfsIJBufnjWCRBecZ69MFG3a19Du3oruWb2FOUqDv9xFetjd2bN/bwfUZXn43spOICsmGxvWjxpgS2+GI9sE/OD7t/NEUNgxuWr2HKn/0785w9NB/irPolyQcy9a1dHLburLYNJgV9e08OqvElFh0Q9AVhk2t3fjDAvpc8zUtPkslBZsaDfw9A3dZXlNrsKXJYHKGeW3/VRPt5MSrbGvWCUYg2aVwcoHG2DRrfx5J/z4hBG2+PTjs8cR5ss0OB4Avj7yVW7a8iyD6pH+rH7Z01DIx8dgqzHAsGtZJQahCp3NZCKML1ESFhEsc2HPMm27SvSJMuCI6siX80PlSCPsoF6rTmqPckRYD37IQB/s23a+HIUngKNUGXZA4FPTm3o5WqyPIT8avJ2QY0A6b2ltJcTg5IcXc0npCN4i8W4O+sw0lzo7n9DwSbXY6Ir3P7DNd5i3iNXa19T8h4NwGD/NandS4dEb7HSScnmhOcIehFiWAS4NA/xFObVzKkMfySUtnLCEA6IwsJNn+f7HjU7PPx2M7Pubiv1DZ3K/K6ub2bnb5/IxOGNqnSRsadB5fH6IjBGAnh1H4bK102qIJd1GceffUYA4trflZXxsKmqqwuMjG4qJh3UU47gVDHbzz0Xdp7dgJKIweeTHTJ37f7LCoDmqxhACiXYyVzWUyKRgCw/qO7/pnGKMr+vVapYkdH7UzdV4qJ2dkmzL3NNIoMBBsTWwjrBpMbk/BaBOoWdZMCsL7DfoOdj5ZuJdXqipR6xSuKBzF1UWlQx6TfaSK4gARgo1JLYS0/mUp32+qNz0pCL9QhrE92vEWLQFYupPvXDaaX1RuJ2QYJNrt3Fhi3tQhJWXg6CVASlgjJayhpA3+utkUlw3HVWMJv1eNqPOjOFTUcSnY5g39B4nzkKS4x5jNKamllMSXk+8dxYy0k4c8pi/K6lqdT+p0srwKJ+VrdPjiSQ656dG6CGjRBvXQf//RFjEE/7s+RGeo95yCSmIkjYDazfRMB5eOSB/SmI5kWpbGU1vDh+awuG0wLVOOyEtH3659zx1ICAAEu/Y9S1HB2abvHJzvHlhpaLBz0hdv2CYFwhDoLdEe7Ys5Ffxp5B4Ant+yn8sLi/mWCZ0ydST8OHEd2xLbAcgLeHk8eS4JWKsSzUG2rN4P/rXJTSzP2x890OEP5buYlJTCtCHugKsehcQrnfhXhskzBs7Xzg2Y+2ErdANjxyEj8YZgYZ2TE+ctosLfxej4RJyaeXFqU9IxdrdjlHWAAmppEkZVF/gj4LFhO3OEabEdiZrlwXnp0Cejh5qaEscJKXGsa412klMcNq4eNY8s96kmR/afWVER4YmNvU+0XtkbIaB78QJeI5EWapmdqzEibmgTx7aA6JcQ9PX90iLOLLbeHjTJLoUfn+Tk1b0RmnsMFCDVrXLWKBuJckPI44YQBi3tO3A6Eon35pkdTj/d/tpBztWZnhRMT87n3OwJ/LNuKwKYl1rEGVnWm7J6PBpWSYEQAmNnG0JR0EYn4ShRCe0x+GdOZb/3PV+1j+uLxwz5FJiNY1vYtrU9dlzt6uaVuiouS86HeIfpax4ARCBC+PVKjN1tICDOqRKOJLE3vnvAe3f62oc8KQCw56kkXu7kxNYkLnjNw4sZfgwFZrY5OP1jH3p8B1qRSdNfVCW6i23okI213DYSHQ4mOYZ+qsuhFLuK44rRGG0BFJuKEu9ARAxESwAl1WVqic/BBHWDre3dZLoc5HmtkUArisLD04tZ0+yjM6wzJz2ROLvG+nqdV/ZGaA9EByTSPAoXjLYxJvXYGBl+r7L/sPaho9xTPFncO2noF6GnuRVy4hRqu/pPu9EUmJ5l3Y+5EYkq3zrBegmL9MUIBNt4+8Nv094ZXaNVOuIiZkz6gclR9SrIWUh51SuxY6cjkay06SZGFKUoCneMWch1I2cTNnRy3Nabrnq8sm5r+QUTXWGCv9sc+xSLeGzEXTMRv1fBrvTv5NgObMYz1DqDA4e62j6qIbinFRIdOC4ahZpr7jzk8OuVGJt6tzRV0HHQxOziRJ49JPypyeZO0zG2tHBTWTyXV3oIqpAVjHa89I1NpiUFiqKgLSxAf3V/78lkJ9ok6yyAPEhN7h3tVWwqSqb5FYcOVdkd4Oa1ZTQHwyjAlUWZfL3EGovlNEXhxPTe37NKn8Eja0IYffqtdd2CXa0hfrXQRdIxMDocf4T+a26c3ZSqOYqi8P1ZDv66NcyeVgMBZHgUvjzGTobXWkmsNHzsLH86lhAA7N6/jOLC80hJNP9pJkBu5hxOmv5f7K38J057IuNLrrLU7sHpTuvEMlwMm6Qg/GZl/2EtfwRjdQ3x543g2trRPLB9Y2x6/FUjS9BM+GCbXQ6JYYUOezQShw6nVdujL3aECL+yH+f1E4Y8rr6M8kO3tImaUg03z5vA05VlqIrClSNGMTYxaYijO4QrmgQkh/uPwiouc3/t7dMzUIsSMLY0o6a5UcemWOIp0GcR1A0e2V7N2/VtZLgcfG9MLrPSE0yL5y9lDTQHo9NZBPDX8gbOy08l3WW90deN9Xq/hOCgkA5bmnROyrd+c3zhaDs7WoKxOvalKQq7W6P/qDgHnF9q3r8hO07lB7Ot8aRIOvpC4S42bv8tja0bSUsez9Rx38HpsNaIsr+nYdBzVkkKABzJo9nQ+gY13Tsob3yZS7034NDkfTRcHbUWfNOmTTz88MM8+eSTVFRUcMcdd6AoCiUlJdx7772oqsqzzz7L008/jc1m48Ybb+SUU045WuEMWpbQaAsAcGZOPiXxCWxsa2FsQhITksyZwpHQHOY35Sn8M6uHoCo4s8FNYU/vf5Fo7DElrr7ULC9GZ/uA80qWl4sK8rioYOQg32UObXIa+vomRFOf6+a1oc3OMi+oA7QUF9rJeezvCrCrvo1JyV6y3dZviJ/a18Brta0AVPuD/HjTfpYvGI/XpLr0raH+uywZRPcHsGJSkB336SPWOYd5zUpGJqn8zyIXO5oNsuIUcuNVqnwGjX7BuFQVt/3YSG6lY9+azQ9SUfMGAB2d5fQEWzll1iMmR9VfYe4i9lW/Fjt2OZItMT2nr4e3/IBqfzkAtdUVAFw56ntmhiSZ6KgkBU888QQvvfQSbne0BNwDDzzAzTffzKxZs/jxj3/M22+/zZQpU3jyySd5/vnnCQaDXH755cydOxeH4+h8mGszMohUdfU7Z5uRGfu6JD6RknhzRxnUkiRydrTxzYoD29yrCn3L+6glJo+8A7YzCgl3hxG13dEa4CK6ENU21xpTNvpSXDYc14/HKPMhWnsgwYE2KgnFYY3528srm/jVjhogOvf53kkjWJBl/v/x4Wxt77/lao9uUNbZw6TkoX3MawjBJ3UGWSIDuxEmrEaT/pFuL7sb7VS0RJiTp+GxUCf1hGyV+fkaK6t6n1hqCnyp2EZx8rGRFAB47AonZPfeQ/kJKvnmPSyShqma+g/6Hdc2fIgQBopijXvJH+liH52kj70aW8te3M4kxpdchc1mndK4TYE6qv3l6MJBgCzsdLKx9UOuRCYFw9VRSQoKCgr4zW9+w+233w7Atm3bmDlzJgDz589n1apVqKrK1KlTcTgcOBwOCgoK2LlzJ5MmTToaIWGbkAZdESIf1YGiYJuXjVZqrRJXtinp0BNB39SMEmdHOzEbY1sLRlUXan4ctkUFZoeImuzE+fXxiEAEYVdRIgLF2dtBMIQwdSfOgzrDEf62r5HK7iBz0hM4q9RaSYsuBEv31Pc5hqV76yyTFAQigk/qdGyqwglZKnZNIaQL0pVE3DoE1C6EInBrKsXxQ/8ht3RTmBUVOmAni0LS09rJT1TYWZnEU9uim9f9qzzCfy1w4rDIzuCqonDDNAeXjYtuW2YIgV1VSLDoPiTS8BOO+NlX9SqBUDsjcheREFdodkifKjF+BC3t22PHCXGFlkkImgMN3LP+WtpC0fV345Kmcffkh1AtEt9BSY5UNLWAKn0mguhU5SQxsGiINHwclaRg8eLFVFdXx46FELGFu16vl87OTrq6uoiPj4+9x+v10tXVNeBnfVFag2GWxocpP9HLzLQErizKOGp/13/CdmI2thP7dGCLEni1uoXNbd2Ma2zjvPw0bBaYf664bNHNQg/kAyEjOtf8jdo2khw2bhqdw2nZ5iVdd27Yx+a2aOP2fmMH3RGdS0ZY5//cEIKA3r8CUXfE+JR3Dy1fUHDPyiBN/uhTqsJEhR+d6GDJhyGqfF5S8RJRQtgS6/nuuOwhnzrkCwreregdbRcoJOgpFGga68K9u1nXdgnW1+vMzrXWXP3eBcXm38eSdJAhdN5adQOtHbsA2L73L5w+7wnTy1MeSjciVHaXMX7cTXyyYQn+nnrczjRmTb7T7NBi3qxdRluomZBIpJMxvNdmY0zlW1xSeLrZocWIiI66p4pFbYt52t2F78DyxT09cbSF/CQ7rFdYQjr6huTTUu1T2rO7u5uEhATi4uLo7u7ud75vkvBFu3vjfra2R/++bR1+gobBDaXW3x3v97tqebaiCYA369qo6A5w67h8k6MaaFlFE6/WROeaNwfD/GxLJVNS4kh12oc8lvqeUCwhOOiNujZLJQV2VeVLuSm8vT+CR0/EUHRmpCr8cnWQ9oBgXr55u4murIrEEgKAig7BMzsiVPl6z9mEgyvzipmdbo0Ot4LC4A+orNHxrm9ay77qf+F2pTGm6FJcTvNLz0pSX43NG2IJAYCuB9mz/3lmTb7LxKj6q+ou5+ebb6Yl2IBDdXLthNtZmDAVrzsTVbVGWwQQ1APowkEDCxFEp0T/T/l2xidNZHyi+U+thWEQ/tvLiLpGvgKcYfNw4/geGpwCXQh69DDWmkchDZUheZY1btw4Vq9eDcDKlSuZPn06kyZNYt26dQSDQTo7OykrK6O09OisyG8NhmMJwUHvNQxcLGtFBzvafY+FGKSEicm2HzLXPCIEe3xDvzA6Ygj2NCsk6kmohoZL9+KNJJGoWW8X3hMTs0mOZOEUbtxGHFtqvKyrNyhrF/x5S5iVlZEj/5CjIKR/1nPm/B4mOBVOKex9OqEAZ4+ysaBA61cyMzdeYVqW+Y/raxs/4u2PvkN51cts2/Mn3lx1I4YY5IJKw4IQgi27lvKPt87ntZVXU9e42uyQAFDVgU/8FMU6HW2Av5f/lpZgtKJPyAjyl73/jcOZQm2gi6BuTns5mFOyzyGkFMYSAogWQXircbd5QfVh7KtB1DXGjpMjCuc2Rv+vpyfny30BhrEhueN/+MMfcs899/DII49QVFTE4sWL0TSNK6+8kssvvxwhBLfccgtO59GpvhJv10iwa/hCOvF6Cg7DTYLD4IOqCKtrdVJcCueU2EjzmN+BOFQiHhyheHQlTIfWTLxTM2UPhYPquwye2BimwmeQ6lK4boqd0hSNySlxrGzsLVfqUBXGJA7948eHPg6xpckgnkziyEA5MFLc1CDY1aIz2kKbRG1sOPx0oXX1OvMLhv5D+aR8jVf3RvAf+IxNdsEFY2xsadI5WMTLa4d5+eZdy2sn25maqVHTaTA5U6MwMXrv/uIUFx/W6LhsMDtHs8R6grLKl+lbMMDXtZ+m1s1kpk41L6jjnGFEaPftxevJwemw1iro8qqX2bzrcSC6e+x7a3/A+QtfxOU0d2w2PWUKGanT2Na+np1OiKgKTo+bmaZG1V9jT02/446IwlfX/o2aQCfxNid3jVnEyemjTIquV2FcKdeXfJuf7f6433nL1N03Bg5KjPakcVNxMRfkHJ11ndKx4aj1OPLy8nj22WcBGDlyJH/9618HvOeSSy7hkksuOVohxNhVlVvH5fHougCuSHQhZ6cPfre+t5zhxkaDX57mtMR8/YPeq4igdGXjOdCx9egJXDMxfITvOnp0Q/CTD4J0HOgY+sOCJR+E+NUiFxfkp1HnD/J6bRspThvfGp1DkmNoO7R7Ww22NPV2tJU+U0cihsIreyOWSgqy4w7/u5Zl0qZL6R6Vny1wsrJSx6bCgkIbyS6Fn53sYkVlBEPAyQUaqW7zkmhViVbAyUms4fFdD1DWuY1xSdO4fvSP+FJxumlxDcZpH9gpddrlSNzR0u4rZ8Xqm/H3NKBpTmZO/CFFBWeZHVZMXdOafse6HqSpdTP52SebFFGUoihMnfZj/rzmIiIiAgiWVf+VnIQxzMlYZGpsB81KP5XqiqWx47BtPs2BTgA6I0Ee3PU2c1JHYh/kqcdQOzNnFh+3t/L2gacDY+IzOS/H3H2GDlJH5qOkJCFaD8yYsNuYveB05qTJSUPDnbWeDR5Fp2Yl8xQ9fNqElia/YE+rwdg08xuTg5btCtN3TrSGDY9hXjmzfe0ilhAcpAvY2KBz2ggb3x2bx3fH5pkTHNEpS4dj0myXT3VGkY3tzQbbmw00BcalqexsMQgbUJKsck6Jebdnplfl4rH9O/3JboULRw/9GpHDeXTHvZR1RiuQbGz9iMd2/JQfTPiFpcr+jR11BVX17xEItgBQlH82SQlFJkf1xSir/Cc7y55GVTXGl1xNQc6pZofExh2/jW0apetBPtn6CAW5p2GzyBTCWju8641+XRSCwrBKckKJuUEdsLtz64GEoNfm1o+Zk7GIiGHwSVslmqJyQnK+KVXmvjziOhyai/UtH5DvKeL11gyI+GKvt4d7aA/3WGJEXlEUlow/k6sLZxLQw4xLyDL1KX9fik3DfuW56Fv3QDCENm4USrK1nqgdJPxtiI5alIxSFM1anz/Ho2GTFACkuBVqOgfvGSoHXreSwXY/NbNjm+pR6L9zQlSm1xrXrTRFpThJoaw9GmHfWFUFTjdp4e6n8dgV7p7rpLHbwGNXiHModIUE3WFBpklPCQ7HF2ojzp5ombJ6YSMUSwgO2ta2hudf/xKTx97ImKJLTYqsv3hvHuedtpz65jW4nWmkJo8zO6QvREPLBj7eeH/s+INPfsSZC54yNeEJ6gG2+XfTY4OMSLQ4WjjSRSjUgc1tflKwr3MXL7S+iTgw9rTJDdNHXEac1xpFLwq9A6feFMSV0B0J8a0Nz7KnK1pic3xCFo9OuQinNrRtqqponFdwFecVXAVAYO/7/K1qXez1MfGZlkgI+hqxr5XIh+sJRXS0E8Zjmz3F7JAAUFxObNOt8eTi0+ib/0Hk3V+DHoa4dOwX/BI17fgYULEqa/WSjrKvTrDzqzUhgjrYVfDYoCMU7TyeNcpmuY7YuSV2/ryld7qQ2wbz8sx7kpHsUrhkrI1nd0Rine0Tc1TGp1njuqmKwo/mOnm/SqcjKJiRpbKvQ9DoF8zM1hiRZI04D5XR5/cuzhFNDqykurucR7b+kNqeShIUN9eOuInZhRebHRZ21cGIuNHs7+qtmJKsQ0TvYf3WX5GXNZ84j/mVPgBsNhd5WfPNDuML0R5s4e26F6hs/Ai3CvEHZuwJDOqbVpuWFHSFfdy9/lrqlUbwQIIO87ohM3kCHnfmkX/AENjatgbRZ1hFCIW3gnbe2vISExKyuTR/Kg4Tq+jkekdyRdF3WLb/D4SMILPTT2VRzoX8s25HLCEA2Oar573mvZyeOca0WAGuLzoRTVH4uHU/I71pfKtorqnxHMpoaiXy6nuxY/29tShpyWijrLv/g1WIkJ/Ie49GEwKArib0D59APfcBcwM7zg2rpGByhsaji13sbzcoSFTx2GBPm0GyS7FcQgCwuMhGmlvhX+URUt1wxXg7dpMXTp5XaueUQht723QKEhTSPNaZbgXgsiksGtn7a11ojb3ABrVn/3Iqa1cQ581hQum1eC3ScTnU0t2/oLanEgCf6OEPex+m0F1AdsYskyODm8bex+93/pTyzh2kRmDSgfmBAgNf137LJAXHi0DEz4/WXxOrAKN54aTu3sQgycRpMCsbXqW+pyp27NOgK2MW9qzLeL1+JwvSRw35yPahCuL6X582plHVFgLKeb+5nOqedu4cY+78/XMKvsri3IsIG2G89miZcF84OOB9vnBgqEMbwKHauLF4HjcWzzM7lEGJqvqB5yrrQCYFRxRqLaPa3U3IJkgIqKT2KAhf9Hq2Vr5D894X0RxxZI//Gp7ko1O5cjgaVkkBgNeuMD69tyM7xkILTwdzQrbGCdm9MVphG/cEp8K0LGv/6rR17KbNt5fMtBMs2dnevW8ZK7c+RIsGia3Q2LKRs0952jJzTvva37mz33GXBnsq/2mJpCDfW8R/nfAn9ux/kTWb/yt23m6LIz1FVtH4oq1reT+WEABEUHjXNZl2JZ8cVzwz3YVkmRRbUO/fSQ2LOJ4PjqJnX3Rh77PVmTw27VJsqnnt5+SU2ZyTfyX/qn4GgSBISb/5mP+q32l6UgDg0Fw4+qzBWJQ5micr19JzYNQ23uZkQbo11kFYmZI9sOjBYOek/oQQ7Fn3AAFP9ObodhigqGSWnkpH3WrKP+jdO8NXt5qJ5y3HZrEqY8cqa/fsjhJfVwU7yp4iHO5mVOH5ZKXPMDukI9KNMGs3P8S+6ldx2hOZOv47jMw7w9SYAsFWdpY/Q0+giRG5iy3RSQTYtufPbNzxOwBUxcb8mb8gN9Naj5VXVS/nnTgwDuQAYwP7mefbQ3Ki9UY8xiZMYn17by311Ah4XNbafGtU4bmEwj72Vb+K25nK5DHfxG7zmh3WAIYQ3LThOTZ31KKgcGbWWO4aa51dTo/EqfVfwO1jHB3a2OjXIcFtm1/k+dnXYDOh+stJmWfwz8on8etdAITVCfQYvdXIdnQ28ElbJbNTRwx5bH1dUfxtLhrxdQSCr619hqqe3j1zUp3W3EU2153I49Mu5cXaLWiKygW5k0hzWu/+EqEwhCMoXmsUGlCz09FOmYX+4QbQdbSp41DHyDnxRxLw7SfQUd7vXEdGJjkzrqBtzYP9zuvhLnx1a0gpXDiUIR63hl1SEAp38sYH3yAYitbUr6x9h0XzHic9ZaLJkR3e7n3LKKt8EYCeYDMfbfgpmalTTZsrawidN1fdiK9rPwDlVa+yYNYvTe98R/QAW3b/EQHogE1E2LLrD6bHdaiNojmWEADsdoLmsGaZym+NX8LDa66hIlhDkg7TRSpjir5idlj9KIrC+JIrGV9ypdmhHNav97zHpo5aAASCl+u3Mz99FPOOkcVzU1LmUJIwkT2+LQBElIJ+I91NwS7KulsYHT/0u4enubJ4YPpfWFH3EppioyyYx0t1u/q9RxeH3xtkqDgPjMJ/d9R8frTtFUKGjkPV+O4oc8uSHk5xXBq3lp5idhifKrJ6M/qqdRCOoBbnYzv3NBSH+dVqbDMnoc2YCEKgmPiU6lhid6eiaE6E3jttzZUxEUVRccYNXJQ/2Dnp8xl2SUFtw4exhACic4/317xu+aSguW1rv2MhdFo7dpmWFDS3boklBAciorzyZdM738KI0EyQDV7o1iApAidHOo78jUPN4YVAb1yGomCzW2/kDSDOnsh9c5fT0r6DQLCVrLTpaNrR2WjweLeuvWrAuXcadx8zSYFNtXHflP9lQ+uHBPUe3m9TeKmutwKUW7OTZ+JuqJnuXC4ruhGA/d2tvNVYjv/AlJcibyqzUqw1l3tuWhEvzvk6OzsbKY1LJ9lhzScFVme0dqC/2/s00yirQl+3DduJFqn0oyhgwamhfYlgN9gclij7aXMkkD/1O1St/zXCCOOMyyVn0vUApJd8mfaaD+hu3gIoZJR+GW/q8VHRzQqGXVLgdg2cz6c5EtndsYV8bxFuC005MIROZ7iDREcKGalTqKx9K/aaqtpJTRpvWmxORyIBBTa7oFWLVn3JtFmgMbF52RLnplv4AWi3wQ6X4wjfNfQW5V7EX8v+J3Y8J+N0PDZrldI7VGrSWLNDGFR3JMjS/avZ6Wsgwe6iOxIi1enh6sJZjPBaa5rT5MQcyrtb+p2bn2b+Dqz/Dk21MT0tWklpXLKfqp4ONrTXkGR38/3SU/DarJEwjvCm8OSMr/JGwy7i7E7OyBxjyrSmI0m0uy2XrBxrRHPbIOdaTYjk2CNCfiL/WoJR9gE447DNvwltwtlmh0VG6UUkFywk5K/Hk1SCcuDetTniGHv6E/S0l6HZ43B4rbdm8Fg27JKCjNSpFOYuoqLmTQB6EnJ4tPHv+Ov+gEvz8N1xS5iWan4lgy1ta/n9jp/QGmpiRFwp3xv3M8Z0X0551cu4nMlMGXsTbleqafElxo9kb3ImDZHoosNGFd43qjF7Vp8/0kXHgYTgoCaj06RoPt3Z+VeQ5sxic9tqCrwlLMw53+yQBrWqeR9PV60H4CsF0+gIB3ixdgtezcHVI2YyMdH8x7ZLdrzByuayAefXtlbx/InX4LLAyNdBt5aews7ORnZ0NqAQXcC5IOPYSgr6SnZ4+O3Ui2kP9RBnc1iu053tTuRrI2aaHcYxSbT7MJrbUPOyUFzWSPQ+jZqfBQ47hHpLeKtFBSZGNJDRsBN943JQQJtyEWqGNdaP6ev+jlH2fvQg2EnPil8Q0HoQNhue1LF4U8wrO2t3JTygC4AAABmcSURBVGF3DV5C0J1UPMTRDA/DLilQFIV5J9zP+JKr6Qz6+PW+h+nqbkBBJaD7+dOeX5qeFBhCjyUEAPu7dvPXsv/hBxMf5oQJ3zM1tr5qjf7TcnZ1bkMIYWoFHa89fkDt+vHJ0wHY09lEwAgzNj7L1AokNT0dPLDzTbb66piQUEpZyMEvyh4j35PEbaWnWqKjDbC3q4k7tr6EfmCn6A3tVfSdkb2xo4bnZl9DiolTHsKGzvuDJAQAbWE/G9trTF9Y2pemqCydbq31GF+EJIc1FnZKX4zIJ1vR3/4oeuCwY79oMWq+dcv7Km4X9ovPIPL+J+APoE4ajTbeOsm20VZJ57IbUCIhnIaKsesdHFf9BcUCbb1o2hv72ucwqEiKwIZfxs6ll1xI4YzbzQhNMsGwSwoAIobB/9bu45W67RhMB2agoJMgtqEFd5nWsTWE4IXazaxuKWd/MI14WlAUg25RyIpWB4nlH3JZ/jQS7ObvzAmQ5x3PKl8KIZKw0cNET7UlSmrePO5n/HHPQ1R07WZC8kzOLbiRiz/6P2oOzOH3ag4enXqRKYshAe7f8Xpssen69urY+T1dzdy19WVeOPE6S4y4rmreF0sIAA5dotmjh/mkrdLUDYxsikqGM56G4OBPg7JcskydZC1CN8AwUOzW/PgV4Qj6+5/0ngiFiaz8BMcV55gX1CBETzuRdx7BqFqPmjkG26nfx/EV86e9HKq7ZTu737wRPTm6aNYZMRjZ1oO2511s0y83OTpQC6bHnhQ0xBnR3Vz7aNqznMwxX8EVn29CdNJQs2ardJT9q2EH/6zbduAoOmIssNHBZGYmFZrWsX2sfBVPVh5sjKcQIgmnaKKNGWDAnyrWsL69mv+ddokp8R2qLDydED4AIsSzPTABQwhUkxODLE8+d03una//y90rYgkBQLce4qFdb/MHE0ZshRCxhGAwLSE/VT3tjPSaNzXsoHxP8pHf4z7ye44mRVH4fukp3Lf9Nfx6GBUFA4GKwlcLpltmTYHQDQiFUNzWSOglc+gbdxB5dw2EwqhjirCdeTKKzfwBgH4ikT7TcLpB6UR0W2+9U/vrPyJQt4H4kIJt/0f4Xr0NZn+VxNy5lqpZv//j/0I3eqvoBG3Q6DUotEAbD6BOvgDN34a+43V0ez0QHvCeSNAH8UMfmzT0hmVSsLuz6VNfm5hq3mjIa/U7+h33UAhKSr+Sf5s7aqnuaSfPbf5WvVU9vn7HQUOnvLuZUXHW2pyl0j9wEVrf2uBDSVEUxidksc03cKdLgCS7m1wTq7f0dXJ6MadnjuHNhujmZadmlOILB1jbVommKFyWN42xCeYv8pqXVsSLc75Bhb+VIk8qVYF2kuxu0p3W6MjoO8qIvPkh9ARQ8rOxn38aikdOtxkKRmUdRn0TakEOalaaqbGIdh+RN1bBgadvxo4y9Kw0bDMnIbqawOFFsUD1IcXtQh1VgFH+KmjrQREQ+hij5URUi0zFq1j7ME3+dZAYDc8dBr8og49+gs2ZyJhFj+NKsMbi7UBnxYBzQW8caok1yrsqioptztexzfk6aZufoG7r0n6vu5NGyeo+w8iwTApmpOSzrGbjgPOaojArxbzSgMkON82h7thxot3DxIRi3m/p3cTDrmjEW6S6R5zNSWekdwREATKd1htOWJA+irVtlf3OnZJu3nzTe8Yu5v4db7DNV8f4hGy8NgeftFWS507i9tLTcKjWuC01ReW+cWdwU3F0jc3BTnZtTwduzW6p8olem4NxCdG9dEsslJSKYIjIayshHIkeV9UR+WAd9tPNL2ZwvIusWo/+wTogumeJdsZc1CwNJTnflM630dgSSwhi52qrCS1biqhaBzYn2tzrsU27dMhjO5S26ASMPz5AjTdCm0cgaMH71g2UXvg8msmlk0P+Jpr2Lo8dCwX8fQrMRYIdNOx8msKZPzQhuoESc+fRXrWi37nkKVej2KxXFS9n4tdxerNp3v8aRriHhOyZZI6+zBLTgqWhYY3exxA7Ka2Y7406meU1mwjoEQwEKQ4314yY/ZmmTBwtNxbN486tLxM0ImiKyreLT6I4Lo1NHbX4IgEU4OoRM0m0W2OU8a7Ri7h7+yuxeedfLZhBvEXWO/R1fs5EOsIBnqveQEQYnJw+iltMHKUp8CTz+Anmf/B/VoeOuOdY5EmG1YnuViIf/BmMDaAUgciNnm88PkolGs1tGBt3InybEWoVasYItGmXoFhgHYfQDYJrVhOxdeGKJBG0VaK+9wx2PQB2N7bFP0IrWTCkMam5WWDT6FEaqU36mJCtA1cgnpzGFpwoEAmir/wtWvF8lESTF/VGuml3BGn19iYx3Xo7FWsfomjOfebFBRh6EI6wCZ0e6RmiaI5sxKy7qEClo+4jFFUjfdSFZI6xZrEBRVFIKz6btGLrrc2QhoYixCFDFxZ34YUXsnz58iO/8RjVEe5ha0c9pfHpsc6YPxJiU0cNee4kU5OWwUQMnfVt1YyOzyBRViA5rgnDgC4/xHstN3Kkl1Whr1oPoRDa1HFoJ5i3hweAMHTCT34NvXUfPqcgrAoUvRRvcCxxs07HNneaKXF9Ue2n6Ogk9MfnEZFttMetImATIMARl03Gl/8Pm9PcxLFu65+p3fQYQjFQDTuGGgYBqX6FnC4NPCk4vrEcZYifygV3bmHruhsRSiR2TjNgTJOGemCFp/2CX6KOmDWkcQ1m71On0670nyJqd6cx+YKXTYqo1+53voevvnezMocnk5A/Wh5bUTRKT/st8RnW2LhMko4lw/JJgZUl2t3MTRvZ75zH5uDE1JGf8h3msqkaM1OtMXfzWGXUNyFqm1Dys1DTrbEw9lBGbSPhF98GXxckxGE/fyFqtjWm6QhfF5Hlb4ARHT2MvPUhQu1ATVZRcqegmPD0StRtI9K2j7JUnWCsld0F7CI/tZhMzEkKvij6jjIIhalIX0unq++obS3Nr1zBuLP/js1hzlTCYHcdNVseAyUal6EeWDipQItXkBQUePytEA7AEK878dn39UsIAHQVuhyChJACzniUnIlDGtOnSZx6De0bf93vnMfEmvV9Fc9/kOaylwh2VpOUfzKe5FKa9v6DSKCVlBGLTa2tL0nHsmGbFBgt7RhllSjJCaijzKs49GlERycIgZJk/qP4z8JoaoWIbpmO4kH6znL0tVtAVdBmTUYbZa0EJloPfBUoLSDisX3pdLRJo80OCwCjbiuRVU9AdwvCnwe+IkABXxeR1z/AcfUFZocIgFFRG0sIANDWob/7Z3QATwr2i3+DOtQ7xnqSaHeJPglBr7ptS8kcc/HQxvMFU1xOgrYOOl2BAa+FAs20Vb5N+ihzNuQLdtUednpJUBN4s2egmLAQ3eEZfGG+I7EAxZODbc43LLHYGCBtzCW0N22go2YlAM74AkbMusvkqKI0m4vM0f2r8GWPu9KkaCTp+DEskwJ94yoibz0NIgGMAtTxpdjPtkYlAGEYhJf9BVGxHYwc1NLx2M49DUUzb7Otw9FrNqO/8yai1g64UXIysF96JorDnF1khRAYm19AL/sAxZGOsc0LRKc1RWreRLnmQsuMxgsh0D94G+yvgOIHoRJZWY826Wdmh4YIdhN+4TaMYCd+OyiiHMXZjjs4DQUF0TywopNZlPQUWrw7aEzcgKFESAxE6LFHO+TxwSbyP16K+8yfDmlManIBImcCdG0e8JqhB03f5O8/pY4bhbLxcBXQzPu3xaVNwO5KJRxoGfCaikpCyTnY537LhMggIWsGibkn0VHzfuxcWvF5JM6605R4DkdRNUpO/gWGHkYYYTS7NZIVSZKOnmGXFOhlHxBZcSdo0aUU3VoW3ZWbUT/ZherxkJQz19Tts8MvPYCoeQ3dZgAa7OnE2DUSbZw1dmcUQhDo2IfNlYLy0Z/QNy6LvmC3YeinEmxqwbupEPsMc+Zz6uv+jv7+76KxAiFnIj3qZDzBTHQtQOeWf5I078vYHOaVqwx211Gz8fcEfBXEeRrJ6O5GQUFXdepd79Lz6leJz5hC7uQbTav0Ieq20G10UpGuo8fy0Y24QpWMbPoSjmLrPJ4POFupTfkwdtzWp+/icwnqOjdhRk2x9IU/o/GVr6CHu/qdzyj58jGdEAAoDjtxV11L0utbaG9b3e81Z1weKYULTYoMVM1J6am/oXbrUkL+RtwJIwh0VaPZPGRPuAZP2gTTYlMUlZKTH8LfXoa/ZQdxGZMtvymUqtlBM2eQR5KkoTX8koL1z3Cw8H+Tx6A+vhqoht3R0nU1mx5j1PyHSMqdM+SxCX8b+r5/UZ4coccOYOAKv0FpyyI0zE8KQj3N7FlxMz3te1FUG5kdgvQDI4I+V4iqhNcwVIG9bAWjin9tyrxOY9fbsa9b3Aa18S2gvBOtW6cIqAftpacYfdqjeJJLhzw+gL3v3kZPRxkA/jgAhcxujeoEA59LQPteetr3Egl2UDR3iSkxKikjqI8z+iQEUQFHK63FDeSd8Q1T4hpMV/OWw77ut5tTS8HhSWfcmU/SXPYyAd9+VJuHxKyZJJvYYf4iKapK8eJf0l69kp6OfQghcHjSSC5YaHrZSndSEcXzzH/i9mk8ScV4TBx8kiRJGsywSwpQens5Td5B5p0KnYadfzMlKUAP0eiN0NOnfHHAEaZeeY8CZg99PIeo2/onetr3AiCMCPVxkBTQsBlQE29gHLi0YaODqvW/ZszC3w95jEpCJqJxFwLRf8t2pbdjqId81G39P4pPemDI4wt0VscSgoN8TkFmd/TPvtqrVw5laP0oCVlEvPEQ9g14Tc/1WGpn3ri0wy/MjMs9cYgiGcjpzSZ3knUSqC+aotpILjgVa9VEkyRJkj4Pa05UP4q0Ey4D9Qjbyh+hBvLRosRn4k8YuLC4u3uPCdEMFOyq7n9CgZAGhgKRQy5psPOQ9w4R7cSvgzcNAeiHmaERCXYMWUx92d1pA0ZRXSmlKPkn4LT1r9biNHlHzpSSwRaKKqQULhryWA7Hk1xKwYzbsbtS0exxpBadjTupBEW1kZS/gLypN5kdoiRJkiRZ3rB7UqCNPBHliv/D2PcRmYFyaqte6f8GRSNz7OXmBAekTL6a7g39y8Al5y8wJ5hDJOcvwFf3cezY7s4g/sSvQEcd8YHNdLbv7PdeM6hpRTiuew7RsJOUvX+ntfqdQd+XVnzOEEcWpdlcFMy4nYo1D2JE/LgSCslf8ACOuFxGNG6k7IO7iARasbvTKJz+A1NiPCh30jexuZJpr3qXSLgLpyeL9JILSMiaYWpcg8kouZCMkgvNDkOSJEmSjlnDfvMyX/0ndDVuxEBHVR0k5c7Dk1zyhf38f5cQgv0f309rxRsgBEkFp1I05z4UxRoPdRp3L6O14k0cnkxyJn4dV0IBEB15r9n8GP7W3SRkzSB7wjWomrnbuBt6mKY9z9PduhNv6gT0UAfBrhqS8xeQlDff1Nj0SA9hfxPO+Px+i04NPUyouxZHXC7qEG+sJB3/jvfNHyVJkqTPb9gnBZIkScOFbD8lSZKkT2ON4WdJkiRJkiRJkkwjkwJJkiRJkiRJGuZkUiBJkiRJkiRJw5xMCiRJkiRJkiRpmJNJgSRJkiRJkiQNczIpkCRJkiRJkqRhTiYFkiRJkiRJkjTMyaRAkiRJkiRJkoY5mRRIkiRJkiRJ0jAnkwJJkiRJkiRJGuZkUiBJkiRJkiRJw5wihBBmB/HvmDVrFrm5uWaHIUmSZAnJycksXbr0M71Xtp+SJEm9/p32czg45pICSZIkSZIkSZK+WHL6kCRJkiRJkiQNczIpkCRJkiRJkqRhTiYFkiRJkiRJkjTMyaRAkiRJkiRJkoY5mRRIkiRJkiRJ0jAnkwJJkiRJkiRJGuZsZgdgFl3Xufvuu9m3bx+apvHAAw9QUFBgdlgDnH/++cTHxwOQl5fHAw88YHJEvZYvX84LL7wAQDAYZMeOHaxatYqEhASTI4vatGkTDz/8ME8++SQVFRXccccdKIpCSUkJ9957L6pqbk7cN75t27Zxww03MGLECAC+8pWvcOaZZ5oWWzgc5q677qKmpoZQKMSNN97IqFGjLHUNB4sxKyvLUtcRBt7DN9xwg6Wu4+dxLLSfVm47wdrtp9XbzkNjlO3nfx6fbDslAMQw9eabb4o77rhDCCHExx9/LG644QaTIxooEAiI8847z+wwPpP77rtPPP3002aHEfP444+Ls88+W1x88cVCCCG++c1vio8//lgIIcQ999wj3njjDTPDGxDfs88+K5YuXWpqTH0tW7ZM3H///UIIIVpbW8XJJ59suWs4WIxWu46D3cNWu46fh9Xbz2Op7RTCWu2n1dtOIWT7eTTis9o1PF7bTqsbtinWwoULWbJkCQC1tbWkpaWZHNFAO3fupKenh2uvvZarrrqKjRs3mh3SoLZs2cLevXu59NJLzQ4lpqCggN/85jex423btjFz5kwA5s+fz4cffmhWaMDA+LZu3cq7777LFVdcwV133UVXV5eJ0cEZZ5zB9773vdixpmmWu4aDxWi16zjYPWy16/h5WL39PFbaTrBe+2n1thNk+/mfkm2n9GmGbVIAYLPZ+OEPf8iSJUtYvHix2eEM4HK5uO6661i6dCk/+clPuO2224hEImaHNcBjjz3GTTfdZHYY/SxevBibrXd2nBACRVEA8Hq9dHZ2mhUaMDC+SZMmcfvtt/PUU0+Rn5/Pb3/7WxOji16juLg4urq6+O53v8vNN99suWs4WIxWu46D3cNWu46fl5Xbz2Ol7QTrtZ9WbztBtp9HIz6rXcPjue20smGdFAA8+OCDvP7669xzzz34/X6zw+ln5MiRnHvuuSiKwsiRI0lKSqKpqcnssPrx+XyUl5cze/Zss0M5rL7zDru7uy0xb7evRYsWMWHChNjX27dvNzkiqKur46qrruK8887jnHPOseQ1PDRGq13Hwe7hlpaW2OtWuY6fl1Xbz2Oh7YRjo/204n1/KKvd92D99lO2ndJghm1S8I9//IPHHnsMALfbjaIoaJpmclT9LVu2jJ///OcANDQ00NXVRXp6uslR9bd27VrmzJljdhhHNG7cOFavXg3AypUrmT59uskR9XfdddexefNmAD766CPGjx9vajzNzc1ce+21/OAHP+Ciiy4CrHcNB4vRatdxsHt47ty5lrqOn4fV289joe2EY6P9tNp9Pxir3fdWbz9l2yl9GkUIIcwOwgx+v58777yT5uZmIpEI3/jGN1i4cKHZYfUTCoW48847qa2tRVEUbrvtNqZNm2Z2WP384Q9/wGazcfXVV5sdygDV1dXceuutPPvss+zbt4977rmHcDhMUVER999/v+mdmL7xbdu2jSVLlmC320lLS2PJkiXExcWZFtv999/Pa6+9RlFRUezcj370I+6//37LXMPBYrz55pt56KGHLHMdB7uHk5OTLfe7+O+yevt5LLSdYN320+pt56ExyvbzP49Ptp0SDOOkQJIkSZIkSZKkqGE7fUiSJEmSJEmSpCiZFEiSJEmSJEnSMCeTAkmSJEmSJEka5mRSIEmSJEmSJEnDnEwKJEmSJEmSJGmYk0mBJH0OzzzzDOFw+N/6nqamJu67776jE5AkSdIxQrafkmRNsiSpJH0Op556Kq+99hpOp9PsUCRJko4psv2UJGuymR2AJH3Rli9fznvvvUcgEKCyspJvfOMbvPDCC9x3330UFxfz97//nebmZi644AJuueUWsrOzqa6u5qyzzmLPnj1s376dBQsWcOuttw7685977jmampq45ZZb+N3vfsfPf/5z1q1bB8DZZ5/N1772Ne644w6EENTV1eH3+3nwwQdxOp2xzXZWrFjBo48+CkR3uvzJT37Cr3/9az7++GMMw+Css86y3IZGkiQd/2T7KUnDl0wKpONSV1cXS5cuZf/+/dxwww2kp6cP+r6qqir++Mc/EggEOO2001i5ciVut5tTTjnlUz/ULr74Yn7/+9/z3//936xYsYLq6mqeffZZIpEIl19+ObNnzwYgPz+fBx98kPfee4+HHnqIu+++G4BIJMKSJUt47rnnSE1N5dFHH6W+vp5//OMf/PWvfyUzM5Ply5cfnQsjSZJ0BLL9lKThSa4pkI5LY8aMASA7O5tQKNTvtb4z5vLz84mPjychIYG0tDSSkpJwOp0oivKZ/p6ysjKmT5+OoijY7XYmT55MWVkZQOzDberUqezbty/2PW1tbSQkJJCamgrAt7/9bXJycnjkkUd45JFHuO666/D5fJ//Hy9JkvQfkO2nJA1PMimQjkuHfig5HA6ampoA2L59+6e+79/5+YZhUFxcHHv0HQ6H2bBhA4WFhQBs27YNgPXr11NSUhL73tTUVHw+H+3t7QDcf//9bNq0iX/961888sgj/PnPf+aFF16gpqbmc8UmSZL0n5DtpyQNT3L6kDQsXHXVVfz0pz8lOzubjIyM//jnTZ8+neuvv56//OUvrFmzhksvvZRwOMwZZ5zB+PHjAVi5ciVvv/02hmHwwAMPxL5XVVXuvfdevvnNb6KqKuPGjWPSpEm8//77nHfeeSQmJjJ37lxycnL+4zglSZL+U7L9lKThQVYfkqSj4I477uDMM89k/vz5ZociSZJ0TJHtpySZQz4pkKRP8cwzz/Dyyy8POH/rrbcydepUEyKSJEk6Nsj2U5KOPfJJgSRJkiRJkiQNc3KhsSRJkiRJkiQNczIpkCRJkiRJkqRhTiYFkiRJkiRJkjTMyaRAkiRJkiRJkoY5mRRIkiRJkiRJ0jD3/+zR6C8J4s4vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 778.5x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='num_topics',\n",
    "            y='perplexity',\n",
    "            data=perplexity,\n",
    "            hue='vocab_size',\n",
    "            col='binary',\n",
    "            row='passes',\n",
    "            kind='strip');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:36:07.110720Z",
     "start_time": "2020-06-20T20:36:07.101416Z"
    }
   },
   "outputs": [],
   "source": [
    "coherence.num_topics = coherence.num_topics.apply(lambda x: f'model_{int(x):0>2}')\n",
    "perplexity.min_df = perplexity.min_df.apply(lambda x: f'min_df_{int(x):0>3}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Impact on Topic Coherence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following chart illustrate the results in terms of topic coherence (higher is better) ,and perplexity (lower is better). Coherence drops after 25-30 topics, and perplexity similarly increases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-20T20:36:12.416899Z",
     "start_time": "2020-06-20T20:36:09.230578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(16,5))\n",
    "data = coherence.sort_values('num_topics')\n",
    "sns.lineplot(x='topic', y='coherence', hue='num_topics', data=data, lw=2, ax=axes[0])\n",
    "axes[0].set_title('Topic Coherence')\n",
    "sns.stripplot(x='num_topics', y='perplexity', hue='vocab_size', data=perplexity, lw=2, ax=axes[1])\n",
    "axes[1].set_title('Perplexity')\n",
    "sns.despine()\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "name": "_merged",
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "387.145px",
    "left": "1219px",
    "right": "1064px",
    "top": "29px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}