notebooks/2_Predict_Canon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cafa196b",
   "metadata": {},
   "source": [
    "# Goal: \n",
    "\n",
    "Explain the canonical label of a work by the motifs within a corpus.\n",
    "\n",
    "    \n",
    "# Methodology:\n",
    "\n",
    "## Model\n",
    "We use a linear classification technique which takes the following form, $\\forall i \\in [1, n]$:\n",
    "$$y_i = \\alpha_1 X_{i1} + \\alpha_2 X_{i2} + \\ldots + \\alpha_p X_{ip} + \\beta_0C_0 + \\ldots + \\beta_mC_m = f_{\\alpha, \\beta}(X, C)$$\n",
    "where $y$ indicates the where document $i$ is considered canonical (1) or not (0) and $X_i$ is a vector of features computed from motifs of length $p$ for the document $i$, $C = (C_0, \\ldots, C_m)$ is a vector of control variables, and $n$ is the number of documents.\n",
    "\n",
    "Since the canonical label is defined at the author level within our corpus, we need to add these variables to control for the effects that might influence the use of some motifs within specific writings. Our goal is to find the motifs that explain the canonical label, independently from such effects. For example, in the notebook \"0_Pipeline_Motifs\" we have found that \"le NC de\" is highly specific to Balzac in \"Le Père Goriot\". Maybe this motifs is fully explained by the variable \"Balzac\" who is considered canonical. Thus, a model that does not control for the author variable might conclude that \"le NC de\" has a strong influence on the canonical label, while \"Balzac\" might be the variable explaining the canonical label and not the motif itself.\n",
    "\n",
    "Thus, we include as dummies the author, gender, and publication period as control variables. That way we will find the motifs that explain the canonical aspect of a writing indepently from the author, gender and publication date, focusing only on linguistic features of the writing.\n",
    "\n",
    "### Pre-selection of features\n",
    "\n",
    "In this notebook, $p$ is very large ($>200K$), thus we introduce a criterion for feature selection before estimating the parameters. Since we know that a token which as a tfidf of 0 corresponds to non-informative tokens, we propose a simple solution solution based on the sparsity of feature $X_{:,j}$. We define the sparsity level as the proportion of 0 in a vector: $\\forall j, \\operatorname{S}_j = 1/n\\sum_i \\operatorname{I}\\{X_{i,j} = 0\\}$. The more sparse the token is in the corpus, the less informative it is, since it is more common to all documents. Then, we simply select the $P$ less sparse features.\n",
    "\n",
    "\n",
    "## Estimation\n",
    "\n",
    "We apply the pipeline in order to obtain the motif annotations (or any other token). Then, we compute the $X_i$ which are defined as the TFIDF scores of each motif within each document in the corpus, where a document represents a window of motifs, as stated above.\n",
    "\n",
    "To fit the model, we simply minimize the Mean Squarred Error as in a classical logistic regression framework. However, in order to select the most important motifs, we apply the L1 regularization which forces non-informative features to have a 0 coefficient.\n",
    "\n",
    "## Evaluation\n",
    "\n",
    "\n",
    "To evaluate the model, we split the data in train/test samples where the observations in the test sample come from documents that are not in the train sample. We keep the class distributions in the train and test equal a Finally, we use the accuracy metric on the test set (as classes are balanced)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14eb49fd-1ea4-4b16-b19d-ce44daa60543",
   "metadata": {},
   "source": [
    "# Import librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92a66741-a948-4e01-9c8a-898eb1906257",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim import corpora\n",
    "from gensim import models\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import pandas as pd\n",
    "\n",
    "from motifs.dataloader import CanonData\n",
    "from motifs.features import bow_to_matrix\n",
    "from motifs.utils import filter_token_by_freq\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import RocCurveDisplay, classification_report, confusion_matrix\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "\n",
    "from motifs.pipeline import Pipeline\n",
    "from motifs.features import build_tfidf, transform_corpus_to_ngrams\n",
    "from motifs.utils import build_window_corpus, return_to_text_from_token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad3e34f-213d-4afd-bf4b-3071aa402585",
   "metadata": {},
   "source": [
    "# Load data\n",
    "\n",
    "- Get metadata\n",
    "- Load the 5-grams that were precomputed using a subcorpus of the \"canon\" corpus.\n",
    "    - This subcorpus was built by selecting at most 50 texts at random per period, which are defined in `motifs.dataloader.CanonData`, e.g 1800-1826, 1827-1850, 1851-1869, 1870-1899, 1900-1945, 1946-2024.\n",
    "    - We have in total 217 documents\n",
    "    - Remove tokens that appear only once in each document in the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c214a0c8-8440-432f-9229-8af3d2c22487",
   "metadata": {},
   "outputs": [],
   "source": [
    "NGRAMS_PATH = \"/Users/brunospilak/Documents/Sorbonne/PyMotifs/data_backup/ngrams_canon.csv\"\n",
    "METADATA_PATH = \"../data/canon/metadata.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba38e600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done in 27.54 secs\n",
      "Done in 90.07 secs\n"
     ]
    }
   ],
   "source": [
    "# Load 5-grams, about 1.6GB in RAM\n",
    "t1 = time.time()\n",
    "ngrams = pd.read_csv(NGRAMS_PATH, index_col=0)\n",
    "t2 = time.time()\n",
    "print(f\"Done in {t2-t1:.2f} secs\")\n",
    "docs = list(set(ngrams.doc.unique()))\n",
    "\n",
    "# Remove tokens that appear only once, this can take a while since ngrams has more than 15 Million rows\n",
    "t1 = time.time()\n",
    "ngrams = filter_token_by_freq(ngrams, 1, groupby=\"doc\")\n",
    "t2 = time.time()\n",
    "print(f\"Done in {t2-t1:.2f} secs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "720ea0f0-2c64-4d04-bc2e-f9c5c5ff3392",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load metadata\n",
    "metadata = CanonData(METADATA_PATH, ids=docs)\n",
    "# data = metadata.data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42101aad-870e-4fa7-9cc4-e44d350bb057",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doc</th>\n",
       "      <th>token</th>\n",
       "      <th>word</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...</td>\n",
       "      <td>le NC de le NC</td>\n",
       "      <td>les</td>\n",
       "      <td>les regards de les agens</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...</td>\n",
       "      <td>le NC de le NC</td>\n",
       "      <td>le</td>\n",
       "      <td>le nom de le conseil</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...</td>\n",
       "      <td>le NC de le NC</td>\n",
       "      <td>la</td>\n",
       "      <td>la bienfaisance de le dieu</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...</td>\n",
       "      <td>le NC de le NC</td>\n",
       "      <td>les</td>\n",
       "      <td>les habitans de le royaume</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...</td>\n",
       "      <td>le NC de le NC</td>\n",
       "      <td>les</td>\n",
       "      <td>les fers de l' étranger</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848684</th>\n",
       "      <td>1971_Veuzit-Max-du_Rien-qu'une-nuit</td>\n",
       "      <td>être un ADJ NC de</td>\n",
       "      <td>serait</td>\n",
       "      <td>serait un vrai coup de</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848685</th>\n",
       "      <td>1971_Veuzit-Max-du_Rien-qu'une-nuit</td>\n",
       "      <td>être un ADJ NC que</td>\n",
       "      <td>est</td>\n",
       "      <td>est un charmant garçon que</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848686</th>\n",
       "      <td>1971_Veuzit-Max-du_Rien-qu'une-nuit</td>\n",
       "      <td>être un ADJ NC que</td>\n",
       "      <td>est</td>\n",
       "      <td>est un vrai roman que</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848687</th>\n",
       "      <td>1971_Veuzit-Max-du_Rien-qu'une-nuit</td>\n",
       "      <td>être un NC , un</td>\n",
       "      <td>est</td>\n",
       "      <td>est une bombe , un</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4848688</th>\n",
       "      <td>1971_Veuzit-Max-du_Rien-qu'une-nuit</td>\n",
       "      <td>être un NC , un</td>\n",
       "      <td>est</td>\n",
       "      <td>est une plaisanterie , une</td>\n",
       "      <td>non-canon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4848689 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       doc  \\\n",
       "0        1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...   \n",
       "1        1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...   \n",
       "2        1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...   \n",
       "3        1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...   \n",
       "4        1816_Barthelemy-Hadot-Marie-Adelaide_La-Vierge...   \n",
       "...                                                    ...   \n",
       "4848684                1971_Veuzit-Max-du_Rien-qu'une-nuit   \n",
       "4848685                1971_Veuzit-Max-du_Rien-qu'une-nuit   \n",
       "4848686                1971_Veuzit-Max-du_Rien-qu'une-nuit   \n",
       "4848687                1971_Veuzit-Max-du_Rien-qu'une-nuit   \n",
       "4848688                1971_Veuzit-Max-du_Rien-qu'une-nuit   \n",
       "\n",
       "                      token    word                         text      label  \n",
       "0            le NC de le NC     les     les regards de les agens  non-canon  \n",
       "1            le NC de le NC      le         le nom de le conseil  non-canon  \n",
       "2            le NC de le NC      la   la bienfaisance de le dieu  non-canon  \n",
       "3            le NC de le NC     les   les habitans de le royaume  non-canon  \n",
       "4            le NC de le NC     les     les fers de l' étranger  non-canon  \n",
       "...                     ...     ...                          ...        ...  \n",
       "4848684   être un ADJ NC de  serait       serait un vrai coup de  non-canon  \n",
       "4848685  être un ADJ NC que     est  est un charmant garçon que  non-canon  \n",
       "4848686  être un ADJ NC que     est        est un vrai roman que  non-canon  \n",
       "4848687     être un NC , un     est           est une bombe , un  non-canon  \n",
       "4848688     être un NC , un     est   est une plaisanterie , une  non-canon  \n",
       "\n",
       "[4848689 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add target variable\n",
    "sns.countplot(metadata.data, x=\"label\", stat=\"proportion\")\n",
    "plt.show()\n",
    "ngrams = ngrams.set_index(\"doc\").join(metadata.data[\"label\"]).reset_index()\n",
    "ngrams"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d9b74a-59cd-49d6-8a30-f2ba2f895cae",
   "metadata": {},
   "source": [
    "# Train/test split\n",
    "\n",
    "We split with respect to the documents, having different document in train and test, but keeping the class distribution in train and test samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8a4a55f-2b4a-4e1e-a4b2-2a75efcd2332",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151 66\n",
      "0.57\n"
     ]
    }
   ],
   "source": [
    "test_size = 0.3\n",
    "random_state = 0 # For reproducibility\n",
    "# Use StratifiedShuffleSplit to keep class balance\n",
    "sss = StratifiedShuffleSplit(n_splits=1, test_size=test_size, random_state=random_state)\n",
    "train_docs, test_docs = list(sss.split(metadata.data.index, metadata.data.label))[0]\n",
    "train_docs = metadata.data.index[train_docs].tolist()\n",
    "test_docs = metadata.data.index[test_docs].tolist()\n",
    "print(len(train_docs), len(test_docs))\n",
    "\n",
    "t1 = time.time()\n",
    "train_motifs = ngrams[ngrams[\"doc\"].isin(train_docs)]\n",
    "test_motifs = ngrams[ngrams[\"doc\"].isin(test_docs)]\n",
    "t2 = time.time()\n",
    "print(f\"{t2-t1:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00d84af0-0325-4cc1-8b8d-0ea33444b9b0",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# Featurization (TFIDF)\n",
    "\n",
    "As we split the data in train/test, we need to compute the tfidf separately on the two samples. We get the idf on the train set and apply it on the test set, otherwise we would include information from the test set in the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ce97b0d-9b9e-4e5f-b105-acff1eed0341",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.36\n",
      "15.85\n"
     ]
    }
   ],
   "source": [
    "## Train set\n",
    "t1 = time.time()\n",
    "train_tfidf, model, dictionary = build_tfidf(train_motifs, normalize=False, smartirs=\"nfn\")\n",
    "t2 = time.time()\n",
    "print(f\"{t2-t1:.2f}\")\n",
    "\n",
    "# Test set\n",
    "t1 = time.time()\n",
    "test_tfidf, _, _ = build_tfidf(test_motifs, model=model, dictionary=dictionary, normalize=False, smartirs=\"nfn\") # Use train_idf\n",
    "t2 = time.time()\n",
    "print(f\"{t2-t1:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02d1d7cf-aa0b-422f-a7c8-9f45491cc03c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.65\n",
      "14.24\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((151, 241304), (66, 241304))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform to a table with documents as indices and tokens as columns\n",
    "t1 = time.time()\n",
    "train_tfidf = train_tfidf.pivot_table(values=\"tfidf\", columns=\"token\", index=\"doc\")\n",
    "t2 = time.time()\n",
    "print(f\"{t2-t1:.2f}\")\n",
    "\n",
    "t1 = time.time()\n",
    "test_tfidf = test_tfidf.pivot_table(values=\"tfidf\", columns=\"token\", index=\"doc\")\n",
    "t2 = time.time()\n",
    "print(f\"{t2-t1:.2f}\")\n",
    "\n",
    "# In the test set, remove new tokens and add missing ones\n",
    "if set(train_tfidf.columns) != set(test_tfidf.columns):\n",
    "    missing_features_in_test = pd.DataFrame(0, columns=list(set(train_tfidf.columns) - set(test_tfidf.columns)), index=test_tfidf.index)\n",
    "    test_tfidf = pd.concat([test_tfidf, missing_features_in_test], axis=1)\n",
    "    test_tfidf = test_tfidf[train_tfidf.columns]\n",
    "    \n",
    "    missing_features_in_train = list(set(test_tfidf.columns) - set(train_tfidf.columns))\n",
    "    if len(missing_features_in_train):\n",
    "        # Remove new features in test set (they are equal to 0 anyway)\n",
    "        test_tfidf.drop(missing_features_in_train, axis=1, inplace=True)\n",
    "\n",
    "assert train_tfidf.shape[1] == test_tfidf.shape[1]\n",
    "(train_tfidf.shape, test_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0400d69-fdc4-4e94-97ae-1370ae163e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for _ in range(100):\n",
    "    plt.hist(train_tfidf.iloc[:, np.random.randint(train_tfidf.shape[-1])],\n",
    "             bins=50, alpha=0.5)\n",
    "plt.show()\n",
    "for _ in range(100):\n",
    "    plt.hist(test_tfidf.iloc[:, np.random.randint(test_tfidf.shape[-1])],\n",
    "             bins=50, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c122d40c-434e-4f22-a65f-df08f0f7a89b",
   "metadata": {},
   "source": [
    "## We obtain more than 200K features, too many! We must introduce a criterion for feature selection.\n",
    "\n",
    "Since we know that a token which as a tfidf of 0 corresponds to non-informative tokens, we propose a simple solution solution based on the sparsity of each column of the `train_features` matrix. We define the sparsity level as the proportion of 0 in a column of a matrix ($\\forall j, \\operatorname{S}_j = 1/n\\sum_i \\operatorname{I}\\{M_{ij} = 0\\}$ where $n$ is the number of rows in matrix $M$ and $j$ represents a token as column). The more sparse the column of a token, the less informative is the token within the corpus, since it is more common to all documents. Then, we simply select the $n$ less sparse features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ae0bf02-8718-4e72-a3a9-34af5d7bd49b",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1000\n",
    "sparsity = (train_tfidf == 0).sum() / len(train_tfidf)\n",
    "tokens = sparsity.sort_values()[:n].index\n",
    "selected_train_tfidf = train_tfidf[tokens]\n",
    "selected_test_tfidf = test_tfidf[tokens]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229551be-b8ce-48b8-bec3-17419bdf73fe",
   "metadata": {},
   "source": [
    "### TFIDF Normalization\n",
    "\n",
    "We normalize the TFIDF vectors of each document to have unit norm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a50ab3a3-de97-4362-82d1-26c472e596b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "normalizer = Normalizer(norm=\"l2\")\n",
    "selected_train_tfidf = pd.DataFrame(\n",
    "    normalizer.fit_transform(selected_train_tfidf), \n",
    "    columns=tokens,\n",
    "    index=train_tfidf.index,\n",
    ")\n",
    "selected_test_tfidf = pd.DataFrame(\n",
    "    normalizer.transform(selected_test_tfidf), \n",
    "    columns=tokens,\n",
    "    index=test_tfidf.index,\n",
    ")\n",
    "\n",
    "x = []\n",
    "for _ in range(100):\n",
    "    i = np.random.randint(len(tokens))\n",
    "    x.append(i)\n",
    "    plt.hist(selected_train_tfidf.values[:, i],\n",
    "             bins=50, alpha=0.5)\n",
    "plt.show()\n",
    "for i in range(100):\n",
    "    plt.hist(selected_test_tfidf.values[:, x[i]],\n",
    "             bins=50, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7afb5111-f972-4119-8b9f-c60913f0d0fa",
   "metadata": {},
   "source": [
    "# Add control variables\n",
    "\n",
    "We include the *author*, *period*, and *gender* as control variables, encoded as one-hot features, cf `motifs.dataloader.CanonData`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3d7850a-fbf2-4916-8e40-df95c551124d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(151, 1116) (66, 1116)\n"
     ]
    }
   ],
   "source": [
    "encoded_cat, encoder = metadata.encode_categories()\n",
    "# Remove gender variable (included in the author) (mostly)\n",
    "encoded_cat.drop([\"female\", \"male\"], axis=1, inplace=True)\n",
    "\n",
    "train_x = encoded_cat.loc[train_tfidf.index]\n",
    "test_x = encoded_cat.loc[test_tfidf.index]\n",
    "\n",
    "train_x = pd.concat([train_x, selected_train_tfidf], axis=1)\n",
    "test_x = pd.concat([test_x, selected_test_tfidf], axis=1)\n",
    "print(train_x.shape, test_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63927787-f9af-4a07-9933-fa1773e644e9",
   "metadata": {},
   "source": [
    "# Normalization\n",
    "\n",
    "We propose to perform a MinMax scaling of the feature in the range [0,1] to ease the interpretation of the regession coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d635037d-313e-456c-ab83-0250a37ec0b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "train_features = scaler.fit_transform(train_x)\n",
    "test_features = scaler.transform(test_x)\n",
    "\n",
    "x = []\n",
    "for _ in range(100):\n",
    "    i = np.random.randint(train_features.shape[-1])\n",
    "    x.append(i)\n",
    "    plt.hist(train_features[:, i],\n",
    "             bins=50, alpha=0.5)\n",
    "plt.show()\n",
    "for i in range(100):\n",
    "    plt.hist(test_features[:, x[i]],\n",
    "             bins=50, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa67180f-811c-4847-ae56-57ffcfab1317",
   "metadata": {},
   "source": [
    "# Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1126b5b9-ea08-4b22-9b09-2ae4df9490c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = metadata.data.loc[train_tfidf.index, \"label\"].cat.codes\n",
    "test_y = metadata.data.loc[test_tfidf.index, \"label\"].cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a9d85b-8bce-47b1-ab2a-15532f6ebfe6",
   "metadata": {},
   "source": [
    "# Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e0b5e0a2-1cab-4d6f-a10b-3052626ab6c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy 98.67549668874173\n",
      "Test accuracy 71.21212121212122\n",
      "Sparisty 96.05734767025089\n"
     ]
    }
   ],
   "source": [
    "# Since we have a still a lot of features (1116) compared to the number of observations (151), we propose to use a L1 regularization to select the most important features\n",
    "# It introduces one hyperparameter: C (the strength of the penalty).\n",
    "# It will force many parameters to be 0, which introduce sparsity in the solution, thus it is easier to interpret\n",
    "clf = LogisticRegression(solver='liblinear', penalty='l1', C=0.9, random_state=random_state)\n",
    "clf.fit(train_features, train_y)\n",
    "\n",
    "print(\"Train accuracy\", clf.score(train_features, train_y) * 100)\n",
    "print(\"Test accuracy\", clf.score(test_features, test_y) * 100)\n",
    "print(\"Sparisty\", np.mean(clf.coef_ == 0) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9f8b04a2-415a-475b-b9e9-38ae8f345454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_y_proba = clf.predict_proba(train_features)\n",
    "display = RocCurveDisplay.from_predictions(\n",
    "    train_y,\n",
    "    train_y_proba[:, 1],\n",
    "    color=\"darkorange\",\n",
    "    plot_chance_level=True,\n",
    ")\n",
    "_ = display.ax_.set(\n",
    "    xlabel=\"False Positive Rate\",\n",
    "    ylabel=\"True Positive Rate\",\n",
    "    title=\"ROC curve on the train set\",\n",
    ")\n",
    "\n",
    "test_y_proba = clf.predict_proba(test_features)\n",
    "display = RocCurveDisplay.from_predictions(\n",
    "    test_y,\n",
    "    test_y_proba[:, 1],\n",
    "    color=\"darkorange\",\n",
    "    plot_chance_level=True,\n",
    ")\n",
    "_ = display.ax_.set(\n",
    "    xlabel=\"False Positive Rate\",\n",
    "    ylabel=\"True Positive Rate\",\n",
    "    title=\"ROC curve on the test set\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57f5aa4-375d-4e08-8816-87cf61fad251",
   "metadata": {},
   "source": [
    "We obtain an accuracy of 71% on the test set showing that we can classify realtively well whether a document is a canon or not based on the motifs and metadata. The classifier does not perfectly predict the target variable on the train set, probably because of the L1 regularization.\n",
    "\n",
    "On top the ROC curves and AUC of 0.80 on the set show that the predicted probability scores are relatively well calibrated. Since the classes are balanced, the default probability class threshold of 0.5 is likely a good choice."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d87ce860-44a3-4606-840b-1bb0c2b86628",
   "metadata": {},
   "source": [
    "# Features' importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6fd0b93-300d-4647-b19b-f60157219503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n"
     ]
    },
    {
     "data": {
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D+/btq0uWLNG1a9c6PIySJ08e/fXXX+3PP/zwQy1durT9+Zw5cxyeP21+fn6PPB7y8/MzLP9x3bp1S7/55htDXrtw4cL270BKdu3apYULFzYkOymzfovNPiYODAzUffv2pTp///79GhAQYFh+Wtkf9/T0tP8dmjZtqsOGDVNV1dOnT6eZ81JG+a9sh+AcjAFiUT179pSdO3fKN998I3fu3JELFy7IhQsX5M6dO/Ltt9/Kzp07pWfPnqa1L3/+/FKtWjX5+OOPDc25ffu2jBo1SgoWLCjbtm2TVatWyQ8//CAVKlQwNFdEJCEhQeLj45NN//PPP8XHx8fw/MeRMWNGQ1/f3d09xX49jx07Jv7+/oZmi4j4+fnJX3/9lWz63r17JXfu3Iblurq6yrlz51Kdf+7cOXFxMW/z26hRI2nXrp1p+YlcXFykXbt2cvnyZUNe/+TJk1K2bNlk093d3SU2NtaQTBGR2NjYh/Zne+PGDYd+WI106dIlyZYtW7LpsbGxYrPZDMu9du3aQ/sXvnfvnly7ds2w/JiYGMmaNav9eYYMGcTT01Oio6MNy0zK7M/AhQsXJF26dKnOd3Nzk0uXLhmWnyg2NlYyZMiQbPrVq1fF3d3d8Py00obUOGNfyOz3b9Y2KJFZvwOPKyAgQN555x1DXtvs7VAis/bFEpn1HUgr61/k7/3Ozp07S8mSJeX+/fsSHh4uCxculPz58xuamz59evH29hYRkdKlS8u2bdvk+vXrsnbtWrl+/bqsWrVKFixYYGgbHpeRx0TvvPOOXLhwQSZMmCCtWrWSRo0a2R+NGzc2LPfy5cuSJ08e+/Nt27bJq6++an8eEhIiUVFRhuXfv3//ofsaly5dShPjQnl6ekq9evUMee3Tp0+n+BuYKGvWrHLmzBlDspMy67fY7GPis2fPPvS8i7e3d4q/T09LWtkfDwoKkjVr1siZM2dkw4YNUqtWLRERuXjxouHng8z2X9kOwTkogFjUypUrZcGCBVK7dm1xdXW1T3d1dZVatWrJvHnzZMWKFSa20Fjx8fEyc+ZMCQwMlDlz5siUKVNk7969hu3cpKRWrVoyadIk+3ObzSY3b96UoUOHOrUdZmrQoIGMGDFC7t27JyJ/r4PTp0/LgAED5PXXXzc8v3nz5jJgwAA5f/682Gw2SUhIkF9++UX69esnbdu2NSy3bNmysmbNmlTnr169OsUTMlaSkJAg8+bNk/r160uJEiWkZMmS0qBBA1m0aJHDwKO5c+c2bMcvICBAwsPDk01fv369FCtWzJBMEZFChQrJr7/+mur8n3/+WQoVKmRYflLPP/+8fPPNN/bniQc5c+bMkYoVKxqWW6BAAdm1a1eq83ft2mX4iZcNGzbIunXr7I+EhATZsmWLwzSjmP0ZyJ0790MHft+/f7/kzJnTsPxEL730kixatMj+PHE7/PHHH0u1atUMz08rbTCT2e/frG1QIrN+B9ICs7dDiczaF0tk1ncgLaz/6OhoGTBggAQFBcmhQ4dky5Yt8vXXX0uJEiUMzU1UuXJl+e677+zPS5UqJd98843s3r1bihYtKps3b5YTJ044pS1mSkhISPWR0sVyT0vmzJntJ3cTEhJk165dDhcB3r1712Gf/GkrXry4bN68OdX5GzdulOLFixuWnxb4+vrK8ePHU50fGRnplBPQZv0Wm31M7O/vL0ePHk11/pEjRxwumHra0sr++AcffCD9+vWTAgUKyIsvvmj/m2/cuNHy5yTYDsGB2begwBgZM2bU33//PdX5O3fu1IwZMzqxRc6zbNkyLVSokPr7++ukSZM0Li7OlHacOXNGg4ODtVixYurm5qYVKlTQLFmyaJEiRQwf+DmtuH79uoaGhqqfn5+6urpq3rx5NV26dFq1alW9efOm4flxcXHaqVMndXNzsw+E7OLioq1bt05xUMqnZcWKFerm5qZTp051yLl//75OmTJF06VLp1999ZVh+WZLSEjQV155RW02m5YpU0abN2+ub7zxhpYqVUptNps2bNjQKe2YPXu25s6dW5cuXapeXl765Zdf6siRI+3/NsrYsWM1S5YsKd5yHR4erlmyZNGxY8calp/UTz/9pN7e3vrWW2+ph4eH9uzZU2vWrKleXl66a9cuw3Lfe+89zZcvn54/fz7ZvL/++kvz5cun7733nmH5iYOfP+xh5Dg4Zn8GwsLCtESJEnr79u1k827duqUlSpTQ7t27G5af6MCBA5otWzatU6eOpk+fXps0aaLFihXT7NmzGzr4c1prg5nMfv9mbYMSmfU7kBaYvR1KZNa+WCKzvgNmr/+xY8dq5syZNTg4WNesWWNYzsPs37/fsC7e8GgtW7bU+vXr6+nTp3X8+PHq7e3tcPy1YsUKLVWqlGH5s2bNUi8vL/3666+TzVu3bp16eXnprFmzDMtPC5o2bfrQMR4aNGjw0EHCnxazfovNPiZu3769VqlSJcV5CQkJWrlyZW3fvr1h+Wllf1z17+OvPXv2aHx8vH3ajh079I8//nBKvlnYDiEpm6qBZX+YplWrVvLHH3/I3Llzk1V19+7dK507d5aiRYvKF198YVILjePi4iKenp7SokWLh15RMWHCBMPbcv/+fVm6dKns379fbt68Kc8995y0atVKPD09Dc9OS3755RfZt2+ffR2EhoY6Nf/06dNy8OBBuXnzppQtW9YpVzwOHjxYxowZIz4+PhIYGCgiIidOnJCbN2/Ku+++Kx999JHhbTDL/PnzpWfPnrJ27dpkV1du3bpVGjVqJNOmTXPKlZ+LFy+WYcOG2a++ypUrlwwfPlw6duxoWOa9e/ekVq1a8vPPP0toaKgULVpURP6+ymjz5s1SuXJl2bRp00NviX6ajh8/Lh999JHDd3DAgAFSsmRJwzJv3LghFStWlNOnT0vr1q2lSJEiIvL3Oli8eLHkzZtXfvvttzTTHeDTZvZn4MKFC/Lcc8+Jq6urhIWFOaz/6dOnS3x8vOzZs0eyZ89uSH5S0dHRMm3aNIfPX7du3ZxyxVtaaoOZzH7/ZmyDkjLjdyAtMHs79CAz9sUSmfEdMHv9Jx4PhYaGOvQG8KBVq1YZkv+smzJlinTp0kU8PDxkypQpD122R48ehrQhKipKatasKcePHxdXV1eZMmWKvP322/b5jRo1koCAAJk4caIh+SIirVu3liVLlkjRokUd9kWOHTsmzZo1ky+//NKw7LRg7969UrFiRalfv77079/fYR18/PHH8s0338ivv/4qzz33nOFtMeu32Mxj4uPHj0u5cuWkSJEi0rdvX4f1P378eDl27Jjs2rVLgoKCDMlPS/vjz7JnfTuE/0MBxKKuXbsmLVu2lA0bNkimTJnsfT5evHhRrl+/LrVr15YlS5aIn5+fuQ01QEhIyCP7srTZbLJ161YntejZdO/ePfH09JTw8HCn3Wqf1uzcuVMWL14skZGRoqpSuHBhadmypbzwwgtmN81QtWrVkurVq8vAgQNTnD969Gj54YcfZMOGDU5r061bt+TmzZsP7Yf3abp3755MnDhRlixZIhEREQ5//169ekn69Omd0g4zRUdHy6BBg2TZsmX28T78/PykefPmMmrUKMmUKZPJLTSW2Z+BU6dOydtvvy0bNmywd3Fhs9mkdu3aMn36dAkICDA0H0hrnP07kBaYvR161pm5/tu3b/9YffvPnz/fsDY8ywICAmTXrl2SJUuWh/7e2mw2Q7sBu3//vhw6dEj8/f0lV65cDvP27dsnefLkkSxZshiWLyKyfPnyFL8DzZo1MzQ3rfjf//4nb775ply5csVhepYsWWTOnDnSoEEDk1rmPGYeE+/atUvat28vhw8ftm8TVVWCg4Nl/vz5Ur58eUPz08r++K5du2T58uVy+vRpuXv3rsO8Z6EQ/qxvh/A3CiAWd+TIEdm+fbucP39eRERy5MghFStWtF+FZJSYmBjx9vZONqhVfHy8xMbGWnawpSfpU96onZ1z587JhAkT5IMPPki2nqOjo2XkyJHSr18/p1xpEBgYKKtXr5bSpUsbnpWoT58+j72sM+4CehblyJFD1q9fL2XKlElx/t69e6Vu3br27RKM8+2334qrq6vUrl3bYfqGDRskISFB6tata3gbVFUuX74sqir+/v5OGfj4xx9/fKzlqlatanBLzHft2jX7AWehQoUMLzzt37//sZctVaqUZdsgYt6+kNnv/2EDPz/IqvuD8fHxcujQISlUqFCyu35v3bolkZGRUqJECUMHfzWT2ftiZn8HAJjv9u3bsmnTJqlWrVqyO45jYmLk+++/l9q1a4u7u7tT2rJ+/XqHAkCtWrUkQ4YMhmXyW+woPDzc4eR3asepRnH2/nhSS5culbZt20rt2rVl48aNUqtWLTl27JhcuHBBGjdubGghPC2dmzLDs/7+0xoKIHjqVq9eLQMGDJDw8PBkP+qxsbHy3HPPybhx4+TVV181rA0xMTGyY8cOuXv3rrzwwgvi7+9vWFZSDx7I2my2ZIPLJZ78M2rQu379+klMTIx89tlnKc5/6623xNfXV8aOHWtIflJz586VVatWyeeffy6ZM2c2PE9EknW5tGfPHrl//779dsdjx46Jq6urlCtXzrC7gE6fPv1Yy+XLl8+Q/K1bt0pYWJj89ttvKf7QVqpUSWbOnCkvvfSSIfnp06eXU6dOpdq1xLlz5yQgIEDi4uKeenbZsmUf+wT7nj17nnp+WlOqVCn56KOPpF69eg7T169fLwMGDJB9+/aZ1DJjubi4OFzllRKbzWbo4KPPqsR1r6oO38WkV70lMmr9p4U2mLkvZPb7T/r9exQj8tPC78CCBQtk2rRpsmPHjmTdD92/f18qVKggvXr1ktatWxuSbzaz98XM/g6kFVFRUbJp0ya5e/euhISEPHMDvT7Otshms8n9+/ed1KKUP4NGCQ4Olp9//tl+DPbOO+/IiBEj7IM+X7x4UQoUKCC3bt0yvC1mmDx5sqxbt062bNmS4vzQ0FBp3LixdOvWzcktcw6zf4tFzD8mxt9KlSolXbt2lW7duomPj4/s27dPAgICpGvXrpIzZ04ZPny4Ydlp6dyUGZ7195/WuJndADiHqsr3338vkZGRkjNnTqldu7Zhfc7OmDFD+vfvn+IVDV5eXjJgwACZNm2aYQWQ8PBwqVevnv3qch8fH1m+fHmyK6CNkJCQYP/35s2bZcCAATJ69GipWLGiiIhs375d3n//fRk9erRhbVi/fr3MnDkz1flt27aVzp07O2UjO23aNImMjJRcuXJJ/vz5xcvLy2G+EScetm3bZv/3hAkTxMfHRxYuXGi/yuLatWvSoUMHw07+i4gUKFAgxR3OpAfiRh5wTZo0STp37pzi1Ty+vr7StWtXmTBhgmHrID4+XtzcUv95cXV1Ney9N2rUyJDXfRIBAQGPdcCd2B+9kSIiIiQ4ODjZ9KJFi0pkZKRhudWqVXusdZDaQem/lSlTJvHx8ZH27dtLmzZt7Af7zmL2Z+DNN9985DI2m03mzp371LNPnjxp//fevXulX79+8u677zr8Do4fP14+/vjjp56dltpg5r6Q2e8/6e9wVFSUDBw4UNq3b++Qv3DhQhkzZowh+Ul/B+7cuSOffvqpBAcH2/N/++03OXTokLzzzjuG5Iv8fQFIv379Uhx7wc3NTfr37y/Tpk0ztABi5nbI7H0xs78DIub/Dmzbtk3q168vt2/fFpG/P3fz5s2zbNEtJatXr0513vbt22XKlCkOx25GWrRokXzyyScSEREhIiKFCxeWd999V9q0aWNY5pEjRxz2t7/44gvp16+ffZ9IVeXOnTuG5ZtdgFq8eLEMGTIk1fm9evWSESNGGFoAWbRo0WMtZ8S4iGb/FouYf0w8YsSIx1rugw8+MCTfzP3xpI4fPy6vvPKKiPx9oWJsbKzYbDbp3bu3VK9e3dACiNnnpszeDpn9/uGIO0Asql69evLll1+Kr6+vXL16VerVqyc7d+6UrFmzypUrV6Rw4cLy448/GnJnRK5cueTHH39MdTCpyMhIqVq1qpw7d+6pZ4uI1K5dW27evCnjxo0TDw8P+fDDD+XAgQP2HU5nKVGihMycOVOqVKniMP2nn36SLl26yB9//GFIrpeXl/zxxx+pXklx+vRpKVasmMTGxhqSn9SjfkyHDh1qaH7u3Lll48aNya54O3jwoNSqVcuwz2BqV9WrqixdulSmTJki3t7ecvHiRUPy8+fPL+vXr5dixYqlOP/IkSNSq1atx74q50m5uLhI3bp1U72lPC4uTtavX2/Zqy4nT56c6ryoqCiZNWuWxMXFOeX958iRQ5YsWSLVq1d3mL5582Zp2bKlYZ/B3r17pzrvxo0bsmTJEkPXwd27d2X16tUyb948+emnn6RevXrSsWNHqVOnjlOuujT7M9C4ceNU58XHx8vmzZud8hl84YUXZNiwYcnuQPr2229lyJAhsnv3bkPzzWyD2ftCicz+G9SoUUM6deokLVq0cJi+ZMkS+eyzz+T77783NL9Tp06SM2dO+fDDDx2mDx06VM6cOSPz5s0zJDdbtmyyc+dOKVCgQIrzT548KS+88IJcunTJkHwR87dDiczaF0tk1nfA7PVfpUoVyZo1q8yYMUM8PDzk/fffl9WrVxu+vpNKS10QJTp69KgMHDhQvv76a2nVqpWMGDFC8ufPb2jmhAkTZMiQIRIWFiaVK1cWEZGff/5Zpk+fLiNHjnzoPtO/4eLiIufPn7ePe5R45XfiQNQXLlyQXLlyGfYZXLt2barzkhagjCrCZMqUSfbt2/fQY+LSpUvbx6kzqg2psdlsEhsbK/fv3zd8O2zWb7HZx8Rly5ZNdZ7NZpOjR4/KnTt3LL8/nidPHvnuu++kZMmSUqpUKRk0aJC0aNFCtm/fLnXq1JHo6GjDss0+N2X2dsjs948HKCzJZrPphQsXVFX17bff1uDgYD1x4oSqqp45c0bLlSunb731liHZHh4e+scff6Q6//Dhw+rh4WFItqpqlixZdPfu3fbn165dU5vNptHR0YZlpsTDw0MPHDiQbPq+ffsMf/8//PBDqvN/+OEHzZIli2H5aYm3t7du27Yt2fStW7eqt7e3U9uyadMmLVeunPr4+OjQoUM1JibGsCx3d3eNiIhIdX5ERIShn8H27ds/1uNZcuXKFe3Vq5e6u7tr1apVdfv27U7J7dKli5YsWVIjIyPt0yIiIrRUqVLasWNHp7Qh0b1793TSpEnq7++vQUFB+uWXXzol99SpUzp8+HANDAzU3Llz63vvvaf37t1zSnZSZn0GklqzZo0GBwern5+fjhkzxvA8Dw8PPXz4cLLpRu8HpIU2mL0vlLQdZv4NPD099dixY8mmHz16VD09PQ3Pz5gxY4r5x44d04wZMxqWmyFDBt23b1+q8/ft26cZMmQwLD81ZmyHzN4XM/s7kJQz17+vr68eOnTI/jw2NlZdXV318uXLhmU+aNKkSVq9evVU59eoUUOnTZvmlLacPXtWO3XqpOnSpdP69euneIxmlAIFCujChQuTTV+wYIEWKFDAsNyk5wNU//4uHj9+3P78/Pnz6uLiYlh+So4cOaKNGjVSV1dXbdu2rUZFRRmW5e3trbt27Up1/q5du5x+PJjo3Llz2rVrV02XLp3Wrl3b8Dyzf4uTcuYxcWr27t2rtWvX1nTp0mnXrl2dnu/s/fEWLVro+PHjVVV1xIgR6u/vr506ddL8+fNr48aNDc1Oi+emnLkdSovv/1lGAcSiku7wFClSRNeuXeswf/PmzRoQEGBIdtGiRfXzzz9Pdf6iRYu0SJEihmSrJt/ZU/17ByixAOQsL730ktasWVPPnz9vn3b+/HmtVauWVq1a1bDcevXqaadOnVKd37FjR61bt65h+WlJmzZttECBArpy5Uo9c+aMnjlzRlesWKEBAQHatm1bp7Rh9+7dGhoaqu7u7tqtW7dkn00jBAYG6urVq1Odv3LlSsO+/3B069YtHTlypPr5+Wnp0qX1m2++cWr+9evXtUKFCurm5qYFChTQAgUKqJubm1arVk2vXbvmtHZ88cUXGhgYqDlz5tTp06ebUoA4ceKEVqtWTV1cXPTKlStOyzX7M6Cq+vPPP2uVKlU0Q4YM2r9/f7169apTcsuWLatt2rTRuLg4+7S4uDht06aNli1b1tJtMHtfKJHZf4PChQvru+++m2z6u+++q4ULFzY8P3v27Dp//vxk0+fPn6/ZsmUzLLd06dI6Y8aMVOdPnz5dS5cubVj+g8zcDpm9L2b2d0DVnPWf2vFQ0hPgRitfvryuW7cu1flff/21li9f3tA2XL9+Xfv376+enp5asWJF/fHHHw3NS0lqFyYdO3ZM3d3dDct1cXHRixcv2p8/eDzszAKIGQWoF198UT/66KNU548ePVpffPFFw9uRVExMjA4ePFi9vb31xRdf1K1btzol1+zfYlVzjokfdOLECW3VqpW6ublps2bNUiwKGcms/fErV67o2bNnVVU1Pj5ex4wZo6+++qr26dPH8DakpXNTZmyH0tL7BwUQy7LZbPYdnmzZsunBgwcd5kdFRRm2w/Xee+9pvnz5HE78J/rrr780X758+t577xmSrfr3e9+2bZvu27fP/vDy8tJvvvnGYZrRIiIitESJEpo+fXotWLCgFixYUNOnT6/Fixd/6NX5/9bWrVvV1dVV+/btm6z40qdPH3V1ddUtW7YYlu/n56eZMmV6rIfRYmNj9e2331Z3d3d1cXFRFxcXTZ8+vb799tt68+ZNQ7MjIyO1WbNm6urqqi1atHDqAWdYWJiWKFFCb9++nWzerVu3tESJEtq9e3entSephIQE/fbbb/X11183Jd9Z7t+/rzNmzNAcOXJogQIFdNGiRZqQkGBKWxISEnTDhg368ccf69SpUx96FcrT9t1332np0qU1Y8aMOmLECMO/dw+6c+eOLl68WGvUqKEZMmTQpk2b6nfffeeU7LTwGTh06JDWr19f3dzc9M0339QzZ844NX/Hjh2aLVs29ff31xo1amiNGjXU399fs2XLpjt27LB0G8zeF0pk9t/gm2++UQ8PDy1RooR27NhRO3bsqCVLllQPDw+nnAQeM2aMenh4aPfu3fXzzz/Xzz//XMPCwjRDhgyGXnU5duxYzZIlS4r7m+Hh4ZolSxYdO3asYfmJ0sJ2yMx9MVVzvwNmrn+bzaaLFi3StWvX2h8ZMmTQzz77zGGakfz8/PTUqVOpzj916pT6+fkZlj927FjNnDmzBgcH65o1awzLeZTixYvrqFGjkk3/8MMPtUSJEobl2mw2LVmypJYtW1bLli2rrq6uWrx4cfvzkiVLGl4AMbMANWvWLPXy8tKvv/462bx169apl5eXzpo1yyltuXv3ro4fP16zZMmihQsX1q+++sopuYnM/C0285g40aVLlzQsLEzTp0+v1atX1507dzo13+z9cTOZfW5K1dztUFp4//g/jAFiUUn74P/+++9l0aJFUr9+ffv8HTt2SMOGDe0DhT9NN27ckIoVK8rp06eldevWUqRIERH5e9yBxYsXS968eeW3335L1hfs05I40FFKH+3E6TabzSn976uqbNq0SY4cOSIiIsWKFZPQ0FDD+6CfNWuW9OzZU+7duycZM2YUm80m0dHRki5dOpk4caK8/fbbhmUvXLjQ/u8rV67IyJEjpXbt2g4Drm3YsEGGDBliWJ+3D4qNjbUPMlmwYMFkg7E/be+8847MnTtXqlWrJh999JGUKVPG0LwHXbhwQZ577jlxdXWVsLAwh+/g9OnTJT4+Xvbs2SPZs2d3WptOnjwp8+bNkwULFsilS5ckNDRU/ve//zkt35mWL18u77//vly/fl0GDx4sb7/9tqRPn97sZjnVzp07ZcCAAfLbb7/JW2+9JYMHD3bqQOQ7d+6U+fPny9KlS6VAgQLSoUMHad26tWTOnNkp+WZ/Bs6cOSMffPCBfPHFF1K/fn0ZPXp0qmMCGS02NlYWL17s8DvYsmVLw7fDZrfB7H2hpMz+G/z5558yY8YM+9hnxYoVk7feekvy5s3rlPzly5fL5MmTHfJ79uwpzZo1Myzz3r17UqtWLfn5558lNDRUihYtKiJ///03b94slStXlk2bNkm6dOkMa4PZ26EHOXtf7MFsZ38HzF7/Li4uj1zG6OMhHx8f+f7776VcuXIpzt+9e7eEhITIjRs3DMl3cXERT09PCQ0NFVdX11SXW7VqlSH5iVauXClvvPGGhIaG2scA+eWXX2TLli2yfPnyh44T8G887sDGRo3J+PHHH8vYsWMlR44cMnr0aGnYsKEhOQ/TunVrWbJkiRQtWtThd/jYsWPSrFkz+fLLLw3NV1VZtGiRfPDBB3L//n0ZOnSodOzY8aGfR6OY8Vts9jFxbGysjBs3TiZMmCBBQUEyZswYqVWrltPy09L+eHx8vKxevdr+9w8ODpaGDRuKm5ub4dlmnptKC9shM98/HFEAsagOHTo4PK9bt67DgV7//v1l//79sn79ekPyo6OjZdCgQbJs2TL7wGJ+fn7SvHlzGTVq1EMHBPu3Tp069VjLGT3gndnOnj0ry5cvl8jISFFVKVy4sDRp0kTy5MnjtDa8/vrrUq1aNQkLC3OYPm3aNNm8ebOsWbPGaW1xJhcXF/Hw8LCf8EjNnj17DGvDqVOn5O2335YNGzbYi4E2m01q164t06dPl4CAAMOyE8XFxcmKFStk7ty58vPPP0t8fLyMGzdOOnbsKBkzZjQ8P9Hdu3fl5MmTUrBgQafs5CUecLdo0eKh73PChAmGt8UsieugS5cuD/2s9ejRw7D8fPnySbt27VI98SIi0qBBA8PyzfwMZMiQQWw2m8OAqykx6v3jb2buC8F89+7dk4kTJ8qSJUskIiLCvi/WsmVL6dWrl+Enw83eDj3rWP8iFSpUkMaNG8uAAQNSnD9mzBhZu3at/Pbbb4bkt2/f/rEuOps/f74h+Unt3r1bJk6c6HDyuW/fvg8dpPm/Lq0UoJYvX57idtjIIniikiVLyokTJ6R79+7Sq1cvyZAhQ4rLOfO4yJnMPibOkSOH3LhxQ7p37y4tWrRIdXtQqlQpQ/LTyv74oUOHpEGDBnL+/Hl7IfDYsWPi7+8vX3/9tZQoUcLQfBHzzk2lle1QWjg3Bwogz6zY2FhxdXUVDw8PQ3NUVS5fviyqKv7+/obf+YC0xdvbW8LDwyUoKMhhemRkpJQpU0Zu3rxpUsuMZfYVV0ldu3bN/kNbqFAhp5xw2717t8ydO1e+/PJLCQoKkjZt2sgbb7whefLkkX379klwcLDhbRARuXXrlnTv3t1+V9KxY8ckMDBQunfvLrlz55aBAwcakhsSEvLIbZ3NZpOtW7cakp8WFChQ4LHWwYkTJwzJN/vKV7M/A2a/fzhiXwhmMHs79Kxj/Yt89tln0qdPH1m6dKlDTwQiIl9//bW0aNFCJkyYIF26dDGphc+2O3fuyLRp06Rfv36GvH5aKkCZJen+WErrwpk9U5jB7GPiB9d/0lOfzugZJK3sj1esWFH8/f1l4cKF9nMB165dk/bt28ulS5fk119/NTTfTGyHkBQFEFjW77//Ll9++aUcO3ZMRMR+tcfzzz9vcsuM9eOPPz7WclWrVjW4JX/fZdOjRw/p27evw/Tx48fLlClTHvtuHfy3uLm5Sffu3eWtt96yX2UiIpIuXTqnFkB69uwpv/zyi0yaNEnq1Kkj+/fvl8DAQFm7dq0MGzZM9u7d65R2AAAAmOGrr75K8XioSZMmTsk3uwuiqKgo2bRpk9y9e1dCQkKkePHihualJDQ0VFq3bi2vvfaa06/0v3TpkuzYsUPSp08vNWrUEFdXV7l37558+umnMmbMGLl//75cvnzZqW16lvzwww+PtdzLL79scEueTfQM8jdPT0/ZtWtXsu3fwYMHpXz58nL79m3DstPSuSkzPOvvP62hAGJh06ZNk507d0q9evWkefPm8vnnn8uYMWMkISFBXnvtNRkxYoQh3cFUq1btsa542rJly1PPTtS/f38ZN26ceHt7S2BgoIiIHD9+XG7duiX9+vWTsWPHGpZttoddaZD4d7HZbHL//n3D27JgwQLp1KmT1K1bV1588UUR+Xv8mfXr18vs2bOlffv2hrfhWfTmm28+chmbzSZz5841JL927dqyfft2efXVV6VNmzZSu3ZtsdlsTi+A5M+fX5YtWyYVKlQQHx8f2bdvnwQGBkpkZKQ899xzEhMT45R2AHj2pIV9IZgnICDgsf7+iWNiAE9bQkKCtGjRQr766ispXLiwvQuaP/74QyIjI6Vp06by5ZdfOuWONLO6INq2bZvUr1/ffnLPzc1N5s2bJ61btzY090E9e/aU5cuXS3R0tLzyyivSunVrqVevnqFjAImI/Pzzz1K/fn2JiYkRm80mzz//vMyfP18aNWokbm5u0qNHD2nXrp14enoa2g6zJI4L+jDOOiYGzFS6dGmZOHGiVK9e3WH61q1bpWfPnnLgwAHDstPSuSkzPOvvP60xvjN0mGLkyJHy8ccfS61ataR3795y6tQp+eSTT6R3797i4uIiEydOlHTp0j32bYlP4mGDW924cUOWLFkicXFxTz030cKFC2Xq1KkyZcoU6dq1q33n8t69ezJjxgwZMGCAFC9eXNq2bWtYG8yU2M/4g27duiWTJ0+WKVOm2ItCRmvfvr0UK1ZMpkyZYu9XsVixYvLzzz/bCyJ4+lL7DIj8PQDa5s2bJS4uzrACyIYNG+TMmTMyf/58efvtt+X27dvyxhtviEjKt38b5dKlS5ItW7Zk02NjY+mCxuLWrVv3WMsxBgaMYva+EMzVq1evVOdFRUXJrFmz+PvDUJMnT5bNmzfLunXrknU/tW7dOunQoYNMnjz5oZ/Vp6VZs2ZOGW/hQUOGDJGaNWvKjBkzxMPDQ95//33p37+/0wsgkydPlokTJ8rmzZtlyZIl0rZtW3F1dZUmTZpIq1atDLv6//3335d69erJe++9JwsXLpTx48dL48aNZfTo0U67A8hMq1evTnXe9u3bZcqUKZKQkODEFgHmGDNmjPTo0UOGDRsmFSpUEBGR3377TUaMGCFjx451uCjwad+llpbOTZnhWX//aY7CkgoWLKgrV65UVdXw8HB1dXXVL774wj5/1apVGhQU5LT23Lt3TydNmqT+/v4aFBSkX375pWFZ5cuX1wkTJqQ6f/z48Vq+fHnD8s+ePat9+/bV6OjoZPOuX7+u/fr10/PnzxuW/6D4+HidPXu25smTR/Ply6fz5s3T+Ph4p+WbbdGiRVqpUiXNmTOnRkVFqarqxIkTdc2aNSa3zPnWrFmjwcHB6ufnp2PGjHFa7saNG7VFixbq4eGhhQoV0kGDBunu3bsNz33ppZd0ypQpqqrq7e2tJ06cUFXVsLAwrV27tuH5aVmNGjU0ICDA7GYYxmazPfLh4uJidjMt6+rVqzplypRUfwdTm2e1NjzImftCZrt165auXbtWY2Jiks2Ljo7WtWvX6p07d0xomXmuXLmivXr1Und3d61atapu377d7CYZateuXRoSEpLqdzAkJETDw8NNaNmzoWTJkjp37txU58+ZM0dLlizpxBY5n6+vrx46dMj+PDY2Vl1dXfXy5csmtkr19u3bunz5ci1durSh+yKZM2e2v/9bt26pi4vLM3n8k9SRI0e0UaNG6urqqm3btrUfG1oVv8VQ1WTHPy4uLik+d8ax0bN+bupZf/9m4w4Qizp37px9rIvSpUuLi4uLw9WIzz33nJw7d84pbVm8eLF88MEHcvv2bRk2bJh06dLFkK63Eh06dEgaNmyY6vxGjRrJkCFDDMufMGGCxMTEpFg99/X1lRs3bsiECROc0g3XqlWr5L333pNLly7JoEGDpHv37uLu7m54blIJCQkSGRkpFy9eTHaVzdPu63DDhg1SoUIF8fX1FRGRGTNmyAcffCC9evWSUaNG2QcY8/Pzk0mTJj30c2Ilv/zyiwwcOFD27NkjYWFhMnDgQKcMhp6oZs2aUrNmTbl27Zp88cUXMm/ePBk7dqzhA76NHj1a6tatK4cPH5b79+/L5MmT5fDhw/Lrr78+dp+8VtW4cWNL9/nMFX3mmjZtmuzfv1+6d++ebJ6vr6/89NNPEhMTI4MHD7Z0G5Jy9r5QpkyZHnmnm5ubm+TIkUNq1qwpQ4YMET8/v6eW/9lnn8m6detSvMsqY8aMMmXKFDlz5ox069btqWWm5FFdQs6bN8/QfBGR27dvy4QJE2TcuHGSP39+WbVqldSrV8/wXLONHz9eqlevnur+cM2aNeWTTz6RL774wpB8s78DZouIiJDQ0NBU54eGhkpYWJihbTC7C6KYmBjJmjWr/XmGDBnE09NToqOjJUuWLIZkPsr58+dl6dKl8sUXX8j+/fvlhRdeMCzr2rVr9vfv6ekpGTJkkBIlShiWl5adO3dOhg4dKgsXLpTatWtLeHj4M7Eu0spvMcy1bds2s5sgImnj3JSZnvX3nxZQALGoHDlyyOHDhyVfvnwSEREh8fHxcvjwYfvAR4cOHUqxa5inaf369TJw4EA5efKk9OvXT/r06SNeXl6GZoqIuLq6yt27d1Odf+/ePXF1dTUsf/369TJz5sxU57dt21Y6d+5saAHkhx9+kAEDBsiBAwekZ8+eMmDAAHtRwJl+++03admypZw6dUr0geGGbDbbUz8Bfv78ealcubKsX79e8uTJI1OnTpXZs2dLo0aN5KOPPrIv9/zzz0u/fv2eavaj3LlzRzw8PJyaefjwYRkwYICsX79e2rZtK19++aXkyZPHqW1IKlOmTNK9e3fp3r277Nmzx/C8KlWqSHh4uHz00UdSsmRJ2bhxozz33HOyfft2KVmypOH5aZnVD3T+97//ySuvvEJXZyZZuXKljB8/PtX5Xbt2lX79+hlafEgLbRAxb19o0qRJj1wmISFBLl68KPPnz5dz58491cGIFy9e/NCLTXr16iUjRowwfFv0YNcD9+7dk4MHD8r169eT9YX9tMXHx8vs2bNl+PDh4uHhIVOmTJHWrVs/M9ulHTt2yMCBA1Od/+qrr8qcOXMMyzf7O2A2T09PuX79uuTLly/F+TExMYbvl6aFLog2bNjgcAyUkJAgW7ZskYMHD9qnGd0dZkxMjKxcuVKWLFki33//vQQGBkqrVq1k2bJlUrBgQUOzDx8+LOfPnxcREVWVo0ePSmxsrMMypUqVMrQNZoqOjpbRo0fL1KlTpUyZMrJlyxZ56aWXzG6W06SV3+KkzDgmftYZ1c3e40or56bM8qy//zTF7FtQYIz3339f/f39tVOnThoQEKADBw7UfPny6YwZM3TmzJmaN29e7d27tyHZO3bs0JCQEPXw8NBevXrppUuXDMlJzcsvv6zvv/9+qvMHDx6sL7/8smH5GTJk0FOnTqU6/9SpU5ohQwbD8uvWravp0qXTrl276l9//WVYzuMoXbq0Nm3aVA8fPqzXrl3T69evOzyMsGLFCg0ODlZVVQ8PD/utzd7e3nr8+HFVVT127Jh6eHgYkp9UfHy8jhgxQnPlyqWurq72/Pfff1/nzJljWO7p06e1ffv26ubmpo0aNdLDhw8bloWH+/HHH7VVq1ZaoUIF/fPPP1X1727ZfvrpJ5Nb5jyRkZE6ePBgbd68uV64cEFVVb/99ls9ePCgYZmurq6aK1cufe+99zQiIsKwnMdx7do13bBhg37++ee6cOFCh4ez8mfPnq0DBw7UK1euqKrq7t277Z9HI3h7ez/yd9DHx8ew/LTQBrP3hZ7E7t27NXPmzE/1Nf38/B65/v38/J5q5uOKj4/XLl266NixYw3LWLZsmRYqVEj9/f110qRJGhcXZ1hWavbt2/fIx6FDh+zbhafN3d3d3vVkSk6cOOGUfbHHYcR3ICkz9gXq1aunb731Vqrzu3btqnXr1jUsPzXO7IIorXSH6eHhoTlz5tRevXrp77//bnheosT3l9r7dmZ3oGZ0STx27FjNnDmzBgcHp5muvyIiInT9+vV669YtVVVNSEgwNC+t/BabdUyc1L1793TTpk06c+ZMe5dgZ8+e1Rs3bjgl/1mVls5NmbEdSkvvH3SBZVnDhw8XT09P2b59u3Tu3FkGDhwopUuXlv79+8utW7fk1VdflQ8//NCQ7AoVKoinp6e89dZbEhAQIEuWLElxuR49ehiS369fP2nUqJHExcVJ3759JXv27CLy990B48ePl0mTJj30iqR/y9PTU6KiolK94ioqKko8PT0Ny1+/fr24ubnJsmXLZPny5akud/XqVcPakCgiIkJWrFghQUFBhmclev311+3dvQUEBEh4eLjkz5/fYZn169dLsWLFDG/LyJEjZeHChfLxxx9L586d7dNLlCghkyZNko4dOxqSW6RIEbHZbNKnTx+pXLmyRERESERERLLlrDgAdNJB3B7laQ/y9qCVK1dKmzZtpFWrVrJ37177gLeJV6N9++23huanBT/88IPUrVtXKleuLD/++KOMGjVKsmXLJvv27ZO5c+fKihUrDMk9efKkzJ8/XxYuXCgfffSRVKlSRTp16iRNmjQxdPv7oK+//lpatWolN2/elIwZMzpc+W2z2aRt27aG5u/fv19CQ0PF19dXoqKipHPnzpI5c2ZZtWqVnD59WhYtWmRIrqurq5w7dy7V38Fz586Ji4uLIdlppQ1m7wslUlXZvXu3REVFic1mk4CAAClbtqzDZ7FYsWLywQcfPNXc+/fvy6VLl1Jd/5cuXTKs25tHcXFxkT59+khISIj079/fkIzmzZuLp6entGjRQk6dOpXqnRATJkwwJF9EpEyZMmKz2ZLdgfsgm80mpUuXlkWLFj3VLmH8/f3l6NGjEhAQkOL8I0eOOHRPZBSzvgOJzNoXGDx4sISEhMiVK1ekX79+UrRoUVFV+eOPP2T8+PGydu1ap3aLYkYXRGmlO8x169ZJjRo1DP/de9DJkyedmpcas7okHjhwoHh6ekpQUJAsXLhQFi5cmOJyq1atMiQ/qStXrsgbb7whW7duFZvNJhERERIYGCgdO3aUTJkyPfSO1X8jrfwWm3VMnOjUqVNSp04dOX36tMTFxUnNmjXFx8dHxo4dK3FxcQ/tvePfeNa7YhRJO+emzNoOpZX3j7/Z9FF7xcATKlCgwGP193rixAnD2jB16lTp16+f3L9/3357WXR0tLi5ucnHH38sPXv2NCz7lVdekVy5csns2bNTnN+pUyc5d+6cYQc8qe3cPahdu3aG5CdVvXp16d+/v9SpU8fwrJTMmTNHhg0bJuPHj5eOHTvKnDlz5Pjx4zJmzBiZM2eONG/e3ND8oKAgmTVrltSoUUN8fHxk3759EhgYKEeOHJGKFSsm65rjaXmcAywjuiBLCx6nv+lERr//smXLSu/evaVt27YOf/+9e/dK3bp17V0SWFnFihWladOm0qdPH4d1sHPnTnnttdfkzz//NLwN27ZtkwULFsjKlSvFzc1NmjdvLh07dpTy5csbnl24cGGpV6+ejB49WjJkyGB43oNCQ0Plueeek48//thh/f/666/SsmVLiYqKMiS3WrVq8uKLLzp0PZjUgAEDZOfOnYaefDO7DWlhX2jbtm3SsWNHh24oE08Az5s376mPw5VUhQoVpHHjxjJgwIAU548ZM0bWrl0rv/32m2FteJhvv/1W2rVrJ5cuXTLk9UNCQh7r779161ZD8kX+PuHzKAkJCXLhwgX55JNP5OLFi/LTTz89tfwOHTpIZGRkiq+pqvLSSy9JoUKFZP78+U8t80FmfgcSmbkvsHr1aunSpUuyEyuZMmWSWbNmyeuvv25YdqIHuyAaO3bsM9UFEUSCg4Nl9OjR0qhRI4fvwMGDByUkJMSwMenat2//WMcERm6DErVt21YuXrwoc+bMkWLFitnXwYYNG6RPnz5y6NAhQ3LTym+xWcfEiRI/e3PnzpUsWbLY87///nvp3LlzihcKPg2Pc14maVeMZcuWtVRXjCJp59yUWduhtPL+8f+Zdu8JYLAzZ87ohAkT9O2339a3335bJ06cqKdPnzY8d+vWrerq6qp9+/bV8+fP26efP39e+/Tpo66urrplyxbD25EWrFq1SoODg3X+/Pm6a9euZF0vOMMXX3yhQUFB9lu+c+fO7bRbbVPrguvQoUPq5eXllDY8a77//nv7Y8GCBZojRw4dOHCgrl27VteuXasDBw7UnDlz6oIFCwxvi6enp548eVJVHf/+x48fV3d3d8Pz0wIvLy97FyhJ18HJkyedvg5iYmL0s88+00qVKqmLi4uWKlXK8MwMGTLY37MZMmbMqJGRkarquP6joqIMXf8rVqxQNzc3nTp1qt6/f98+/f79+zplyhRNly6dfvXVV4blp5U2mCkiIkIzZMig1apV0zVr1uiRI0f0jz/+0JUrV+rLL7+sXl5ehn42Z82apV5eXvr1118nm7du3Tr18vLSWbNmGZafqHfv3g6PXr166RtvvKHe3t7arVs3w/P/KxI/L09TZGSk+vr66gsvvKDLli3T8PBwDQ8P16VLl2r58uXV19fX0C4Kzf4OJDJ7XyA2NlZXrVqlY8eO1bFjx+rq1as1NjbW8FzVtNMF0fLly7Vx48ZavHhxLV68uDZu3Njp2/+vvvpKmzZtqi+++KKWLVvW4WGUmzdv6ltvvaW5cuXSrFmz6htvvKEXL140LC81ZndJnBZkz55dw8PDVTX5dsDIY8K08lts9jFx5syZ9ciRI8nyT548qZ6enobnPw6jumJMSEjQU6dO6e3bt5/6a/+XsB2CqioFkGdUZGSkVqtWzexmmCax302jzJw5U93d3dXFxUX9/Pw0U6ZM6uLiou7u7vrpp58amp2WmNnn7L1793ThwoX2IlRsbKx9/AFnee655/Tzzz9XVccf2uHDh2uVKlWc2hZnunXrlq5du9bev2pS0dHRunbtWr1z547h7ahevbouWbIk2fTFixcbOg5QooCAAN20aZOqOv79Fy5cqMWKFTM8Py3InTu3/vLLL6rquA5WrVqlgYGBTm/P8ePHdfDgwZo5c2Z1c3MzPK9x48a6bNkyw3NS4+/vr3v27FFVx/W/ceNGzZMnj6HZ7733ntpsNs2YMaOWKVNGy5QpoxkzZlQXFxcdMGCAodlpqQ1m6datm1avXj3FeQkJCVq9enUNCwsztA2tWrVSm82mxYoV00aNGmmjRo20aNGi6uLios2bNzc0O1FISIjDo3r16vrGG2/orFmz9N69e05pg9mOHTumn3zyiXbr1k3DwsJ0/PjxyU78379/335y7mn6/ffftXjx4vb9vsR9wOLFi+vOnTufel5SaeE7oJq29wWMHAtK9e/jgAwZMmiDBg20cePGqT6MEh8fr82aNVObzaZFihTRhg0basOGDbVw4cLq4uKib7zxhuFjMKiqTp48Wb29vTUsLEzTp0+vXbt21dDQUPX19dX33nvPsNzevXurl5eXdunSRXv06KH+/v7aqFEjw/JSU6xYMXsBLOl3YMqUKYYWgNISb29vPXbsmP3fievg999/N3T8IdW08Vts9jGxn5+fHjp0KFn+Tz/9pNmyZTM8PyEhQX///Xf96quvdMWKFbp79+5k255bt27ppEmTnnp2fHy8pkuXzv75e1axHYIqBZBnVnh4uNMGPUtL7ty5o+PGjdPs2bMbnvXnn3/qhAkT9J133rHfgXLmzBnDc9OSqKiohz6M5unp6ZSc1KxZs0Z9fX31o48+0gwZMugnn3yinTp10vTp0+vGjRtNa5fRJk2alOpJB1XVGjVq6LRp0wxvh6enZ4o7e0ePHnXK1T6jR4/W4OBg/e2339THx0d/+ukn/eKLL9Tf31+nTJliaPauXbs0JCREo6Ojk827fv26hoSEGHKy60F9+/bVKlWq6F9//aU+Pj4aERGhP//8swYGBuqwYcMMz1f9+4Bi4cKF+vLLL6uLi4sWLFhQR44cafiJH1XVOXPmaL58+XTo0KG6YsUK+51IiQ+jdezYURs1aqR3795Vb29vPXHihJ46dUrLli2rPXv2NDx/x44d2qNHD61Xr57WrVtXe/bsqTt27DA8N621wQzFixfXdevWpTp/3bp1Wrx4ccPbsWzZMm3YsKEGBwdrsWLFtGHDhqYWBZ81o0ePVjc3N3VxcdEcOXJo9uzZ1cXFRdOlS6effPKJ09qxd+9eXb58uS5btkz37t3rlMy08h0wc18gNX/99ZeGhYUZvi/Url07bd++/SMfRpkwYYJmzpw5xavf165dq5kzZ9aJEycalp+oSJEi9gtykp54GzJkiKF3ohUoUECXL19uf75r1y51c3NzevF39uzZmjt3bl26dKl6eXnpl19+qSNHjrT/+1lQt25dff/991VV7ftj8fHx2rRpU3399dcNzzf7t9jsY+JmzZpp586dVfX/1v+NGze0evXqhm6DVP/uHSQgIMB+AUDiBQEFCxbUH374wdDsRMHBwbp9+3anZKVVbIegSgHEsiZPnvzQR//+/S1bALlz544OHDhQy5UrpxUrVtTVq1erquq8efM0Z86cmidPHv3oo4/MbSSc4uWXX7b//c3y448/amhoqPr7+6unp6dWrlxZN2zYYGqbjFa+fPmHnnT4+uuvtXz58oa3o3Dhwvruu+8mm/7uu+9q4cKFDc9PSEiw71gl7vB6eHjYD4CM1KJFCx0xYkSq80eNGqWtWrUyvB1xcXHaqVMndXNzU5vNpunSpVMXFxdt3bq1Q7dERti+fbt27txZfX191dPTU1u1aqVbt241NPNBKd0Fl/Tgx2jXr1/X0NBQ9fPzU1dXV82bN6+mS5dOq1atqjdv3jQ8H+bx8fGxd7uTkhMnTqi3t7fzGmSie/fu6aZNm3TmzJn2OxPPnj2rN27cMLllxtq6dau6uLjo0KFD9erVq/bpV65c0SFDhqirq6vTTr6YIa18B8zaF7h69ao2b95cs2TJojlz5tTJkydrfHy8DhkyRD09PfXFF1/UpUuXGtoGs5UsWVLnzp2b6vw5c+ZoyZIlDW9H0guy/P397RegHDt2zNCr/93c3PTs2bPJ2nLq1CnDMlNjZpfEacGBAwc0W7ZsWqdOHU2fPr02adJEixUrptmzZ7d3VWp1Zh4Tnzlzxl78cXNz0woVKmiWLFm0SJEihvYQkVa6Yly3bp1WqVJFDxw4YHhWWvasb4egyiDoFuXi4iI5c+aU9OnTpzj/7t27cv78eUsOgjxgwACZNWuWhIaGyq+//iqXLl2SDh06yG+//SbvvfeeNG3aVFxdXQ3L//HHHx9rOWcMvJhWHD58WE6fPi137951mN6gQQNDc5cvXy6DBg2S3r17S7ly5cTLy8thfqlSpQzNf1ZlypRJ9u3bJ/ny5Utx/unTp6V06dKGD3j37bffyuuvvy5BQUHy4osviojIzp07JSIiQlauXCn16tUzND/R3bt3JTIyUm7evCnBwcHi7e1teGbBggVl9erVqX7GDxw4IA0bNjR0AOakTp8+LQcPHpSbN29K2bJlpVChQobmBQcHy9GjR6Vs2bLSsWNHadmypfj6+hqamZb9/PPPsn//frl586Y899xzEhoaamje6dOnH2u51LYRVmmDmVxcXOT8+fOSLVu2FOdfuHBBcuXKZcn9wKROnTolderUkdOnT0tcXJwcO3ZMAgMDpWfPnhIXFyczZ840u4mGeeONN8TPz09mzZqV4vwuXbrIjRs3DBtwdcSIEY+13AcffGBIflr7Djh7X6Br166yfv16adq0qWzYsEEOHz4stWvXFhcXF3n//felQoUKhuanBZ6ennL06NFUt/OnTp2SokWLyu3btw1tR2BgoKxcuVLKli0rzz//vHTu3Fm6du0qGzdulObNmycbpP5pcXV1lfPnz4u/v799WsaMGWXfvn0SEBBgSOaj3Lp1S27evJnq99LKoqOjZdq0abJv3z77/li3bt0kZ86cZjftmXD//n1ZunSpw/5wq1atxNPT07DMsLAw+eOPP2TLli3J5qmqhIaGSnBwsEydOtWwNoj8fWx+69YtuX//vqRPnz7ZezZqG5RWPcvboWcdBRCLCggIkLFjx0qzZs1SnB8eHi7lypWz5IFvYGCgTJo0SRo0aCAHDx6UUqVKSfv27WXu3Ln/j703j6tpff//X3uX5omERCpRMpZ5Jikcs3OMSZTpGCJR5kLKUCfz1CjHTMIhY5T5JBqQ5kKGHGOlUl2/P/q1v20Np/fn7V5rv2s/H4/1YN/3evS61t7rXuservu6IBAImOsLhcIq68r0BQIBioqKmNsClA640tLS0LJlS8jKynKiWUZqairGjBmDuLg4CAQClD1uyr4H1vdfZb9FmR0CgaBW3v+SgKqqKm7cuIHOnTtXWv/w4UMMGDAAX79+ZW7Ly5cvsWfPHjx79gwA0KZNG8yZMwfNmzdnrs0nCgoKePbsWZUD3LS0NJiYmDAf9PPFwoULYWdnh44dO/JtSp1EKBRW+r4te/YC7N+DkmADnwiFQly/fh0NGjSotP79+/cYPHgws/dgVd9/ebj4/kePHg1VVVX4+flBU1MTMTExMDAwwI0bNzBz5kwkJSUx1ecTfX19BAcHo0+fPpXWR0ZGwsbGBmlpaUz0TU1Nq6wTCAR4/vw58vPzmd6DfLYBvtHV1UVgYCDMzc2Rnp4OAwMDuLi4YOPGjXybxhkNGjTAjRs3qnUG6devH3OHHHt7ezRv3hxr167Frl27sHTpUvTu3RtRUVEYO3Ys/Pz8mOgKhUK0a9dObPwXGxsLY2NjMSfJ6OhoJvpS+EdS3sV1lXbt2sHDwwMjRoyotP7cuXNYvnw54uPjmdoRFBRUbf20adOY6kuRIilwOxsqhTM6d+6Mhw8fVrkAUn4ymiUpKSkICAhASkoKtm3bhkaNGuHixYvQ1dVF27ZtmWi+fPlSNPHarl07yMvLY/HixZwsfgCoshOdl5eHbdu2Yfv27TAwMGBuR15eHhYsWCB64ZV5PS5YsAA6OjpwcXFhboODgwP09fVx7do16Ovr48GDB/jnn3+wZMkSbN26lbk+q0F9ddSvX7/G9xoX3hafPn3CyZMnkZKSgqVLl6JBgwaIjo5G48aNoaOjw0Szbdu2uHr1apULIJcvX2bW/n+kWbNmcHd350QLAMaOHVvjc0+fPs3MDi0tLTx//rzKBZCEhAQ0bNiQibajo2ONz/X29mZiw/bt25n83f+Uf/OAZuH5/J9c+8KFC3+6PgA8evSo0nIiwtGjR7F9+3bm3s+SYEMZfPSFAGDQoEGV9vXKOwKwIiQkpMq6u3fvYvv27SgpKWGmX0ZkZCTu3LlTYUe0np4eXr16xVy/zIZ9+/YhJSUFJ0+ehI6ODoKDg6Gvr1/l4sTP4O3bt9DT06uyXl9fH2/evGGmX1UbfPz4MVxcXBAfH4+ZM2cy0wf4awOS0BfIyspCmzZtAJTe7woKCrC2tmaiJan07NkTe/bswZ49eyqt37VrF3r27Mncjv3794ued/PmzYOmpibu3LmDkSNHYvbs2cx0165dW6Fs1KhRzPTKY2pqWuP2xcUCTHBwMPbu3Yu0tDTcvXsXLVq0gI+PD/T19Zl9J7GxsTU+l1VUAj7fxXyPic+ePVvjc1lFpcjMzET79u2rrG/Xrh0yMjKYaJdHEhY4iouLERgYiGvXruHdu3cV7rvr16//dE1Jeg7xcf1SKiJdAKmlrFu3Dnl5eVXWm5iYMJ8cvnnzJoYOHYrevXsjIiIC7u7uaNSoEWJiYuDn54eTJ08y0S0uLhYb6MrKynI2yQGgQpiVkpIS+Pv7w83NDUKhELt27eLkJbR8+XLExMTgxo0bGDJkiKjcwsICrq6unCyA3L17F9evX0fDhg0hFAohFArRp08feHh4YOHChVUOjn8WLVq0YPr3K8PHx0f0/3/++QcbNmyAlZWVaIB19+5dXLp0CatXr2ZuS2xsLCwsLKCuro709HTMnDkTDRo0wOnTp5GZmYmDBw8y0Z0xYwYcHR3Rtm1bDB8+XKzu3LlzcHd3ZzbxzTfl2z8RISQkBOrq6ujSpQuA0t0vnz59+o8mR/4vWFhYwN3dXaztl7fL3d2dWRikmrZrrhal+eTHgef379+RlpYGWVlZtGzZkskCyB9//FGj8wQCAbMFkMp23ly9ehUuLi5ITEzEsmXLsGTJEibakmQDwF9fiA8HgPJUNqH0/PlzuLi44Ny5c5gyZUqNQyT9N5SUlFTq4f/y5Uuoqqoy1z916hSmTp2KKVOm4NGjRygoKABQGgpl48aNuHDhAjPt/Pz8KkPhAkC9evUqhCZlSVpaGlavXo1jx45h7NixePLkCdNwiHy2AUnoCxCRmOe/jIwM01AvksjKlSsxYMAA/PPPP3BycoKxsTGICM+ePYOXlxdCQ0MRHh7O3I6yMVAZEydOxMSJE5nrVrYAwhWjR48W/T8/Px+7d++GiYmJaDx07949PHnyBL///jtzW/bs2YM1a9Zg0aJFcHd3F70TNDQ04OPjw2wBpFOnTjVyOmUZlYDPdzHfY+Ly9yBQuQMw66gUOTk5UFJSqrJeSUmp2jm7/4YvX75ATU1N9P/qKDuPJQ4ODggMDMQvv/yCdu3acTIO/PEe4BM+rl9KJXCYb0RKHaNHjx7k5eVFREQqKiqiBE/3798nHR0dZroCgYCGDRtGY8aMoTFjxpCsrCxZWlqKPpcdXHDq1CkyMjKiBg0a0JYtWyg/P58TXSIiXV1dunv3LhGJf/9JSUmkqqrKiQ0aGhqUmppKREQGBgaiBMTJycmkqKjIiQ3Jyck0f/58GjRoEA0aNIgWLFjAWbK5sWPH0o4dOyqU79ixg0aNGsVcf9CgQaIk4OXvgdu3b1OLFi2Yak+ZMoUEAgG1adOGRo8eTaNHjyZjY2MSCoU0ceJEptqSwrJly8je3l4s2XdRURHNmjWLnJycmGonJyeTuro6devWjY4dO0aPHz+mx48f09GjR6lr166krq5OSUlJTG2QUjmfP3+mMWPG0MGDB/k2hRMePnxIFhYWJC8vT/PmzWOabFISbeCrL1QTuEqG+erVK7K3t6d69erR8OHDOU3COX78eJo5cyYRlX7/qamp9PXrVzI3NydbW1vm+p06daKgoCCRftnvHx0dTY0bN2aqLRAIyN3dnbZt21bpsWHDBhIKhUxtICLKzs6m+fPnk5ycHJmbm9ODBw+Ya9YULu5FvvoCAoGA2rdvT6ampmRqakoyMjLUtm1b0eeyo7Zz+vRpatiwIQmFQrFDU1OTTp48ybd5dQI7OztatWpVhfI1a9bQ9OnTmeu3adOGQkJCiEj8ORwXF0eamprMdNPT02t8cAGf72K+x8RXrlwhMzMzCgsLo8+fP9Pnz58pLCyMunTpQpcvX2amKxAIKDw8nGJiYio9rl27xuw9LBQKRf1dgUBQ4RkoFApF5VygqalJf/31Fydakkhdv35JQZoDpA7h6emJOXPmQENDgxM9FRUVxMXFQV9fH6qqqqK4y+np6TA2NkZ+fj4T3enTp9fovICAACb6QKnHp7OzM+Li4uDg4ABnZ2fOE/AqKSkhPj4eBgYGYt9/TEwM+vXrh8+fPzO3oW/fvliyZAlGjx6NyZMn4+PHj1i1ahX279+Phw8fMo93eenSJYwcORKdOnVC7969AQC3b99GTEwMzp07h8GDBzPVV1FRwePHj2FoaChWnpycjE6dOiEnJ4epvrq6OqKjo9GyZUuxeyAjIwNGRkbM2mAZx48fx+HDh5GUlAQiQuvWrTF58uQqQ/PVNrS0tHDr1i0YGRmJlT9//hy9evXCP//8w1Q/KioKtra2ePr0qcjLhIhgYmKCgIAAdO3alam+lKqJi4vDiBEjkJ6ezrcpzEhJScGKFStw6tQpjB8/Hhs2bOAk/KOk2cBXX6gqypJe+/r64uHDh0zzH5TtctixYwc6deqETZs2oW/fvsz0KuPly5ewsrICESEpKQldunRBUlISGjZsiIiICOYJMJWUlPD06VPo6emJ/f6pqakwMTFh+vvr6enVyMOQ1U6J3NxcbN26Fd7e3jA0NISHhwcsLS2ZaP0ncNkGAP76Am5ubjU6j6tdAnyEICojLy8Ply5dEuX8ad26NSwtLav1zJby81BXV0dUVFSFHV9lz2TWY1JFRUUkJCSgRYsWYs/hpKQkdOjQodbmwytDEt7FfI+J27Vrh71791YIOxkZGYlZs2aJckX+bMpysFQ25co6L+nNmzfRu3dvyMrK4ubNm9We279//5+u/yNNmzbFjRs30Lp1a+Zakkhdv35JQRoCqw6xceNGjB8/nrMFEA0NDbx+/bpCDPpHjx4xyz0AsF3YqAnDhg3D1atXMWPGDJw5cwZNmjThxY4uXbrgr7/+woIFCwD8vy2evr6+nMS7BYBVq1YhNzcXQGlYtuHDh6Nv377Q1NTEsWPHmOu7uLhg8eLF8PT0rFDu7OzMfAFEU1MToaGhFcKshIaGQlNTk6k2AMjLy1e65TUxMRFaWlrM9cePH19nFjsqo6ioCAkJCRUmPRISEjiJfd+lSxfEx8fj8ePHYotQnTp1Yq4tpXo+f/7MySI0X/z+++/w8/PDwIEDERUVxcs9Jwk2APz1hX4kIiICfn5+OHXqFJo2bYqxY8di165dzPQ2b96MTZs2oUmTJjhy5Ahnced/pFmzZoiJicHRo0cRGxuLnJwc2NnZYcqUKZyEA2rSpAmSk5Mr5OK4desW88U4vhdYW7Zsia9fv2LBggWYNGkSBAJBpTHxWcW+/xGu20AZfPUF+Ax/9CN8hSAqQ0lJCWPGjKm07tWrV5w+i+siioqKuH37doUFkNu3b0NBQYG5vr6+Ph4/flwhNHJYWJgoT05tRVLexXyPiVNSUiqdAysLE80KPkMxll/U4GKB499YsmQJtm3bhp07d9bJ8E91/folBekOkDpEeY8HLnBycsL9+/dx4sQJtG7dGtHR0Xj79i1sbGxgY2PDtGOenp6OK1euoLCwEAMGDOAs4TJQutIvKysLZWXlah9urBNg37p1C0OHDoW1tTUCAwMxe/ZsPH36FHfu3MHNmzerTFDNmg8fPvxHSdH+GxQUFBAXF1ehw52YmIgOHTow97wNDAyEvb09hg4diu7duwMA7t+/j7CwMBw4cAC2trZM9e3t7fHPP//g+PHjaNCgAWJjYyEjI4PRo0ejX79+YrFZpfx8HB0dcfDgQaxYsQLdunUDUPr7e3p6YurUqbU2DwrfSEIS8DJ+tIWI8Pr1awQHB6N///44fPgwU32+EAqFUFBQgLGxcbXnsUw4KAk2APz2hd68eYPAwED4+fnhy5cvGD9+PPbu3YuYmBiYmJgw0wVKv39FRUVYWFhARkamyvNYJYCWFDw8PHDo0CH4+/tj8ODBuHDhAjIyMrB48WKsXr1a5KTCB58+fcKhQ4cwf/58Jn+/fM6DHz1gWXu+lsFnGyiDz77AvXv3cO7cORQWFmLQoEGV5gXjAhMTE2zcuBGjR48WG4/Gx8djwIABeP/+Pec2vXnzBu7u7vDz82MWg19KKZ6ennBzc8PMmTPF2oC/vz9Wr17NPC+lr68vXF1d4eXlBTs7O/j6+iIlJQUeHh7w9fXlJB8LX0jKu5jvMXG/fv2goKCA4OBgNG7cGABEfbH8/Px/3SHBkvj4eLRr1465zqdPn+Dn5yfa7dK2bVvMmDGDsyglY8aMQXh4OBo0aIC2bduiXr16YvW1vT9Y169fUpAugNQhuF4AKSwsxLx58xAYGIji4mLIysqiuLgYkydPRmBgYLUv4f+G8PBwDB8+XLSdVVZWFv7+/rC2tmai9yNBQUE1Oo+LROgpKSnw9PRETEwMcnJyYGZmBmdnZ7Rv3565NgBkZ2dXudMgLi6OuR3NmzeHt7c3fvvtN7Hy48ePw8nJCZmZmUz1gdLO3fbt20WdjTZt2mDhwoWizh9LPn/+jF9//RVRUVH4+vUrmjZtijdv3qBnz564cOEClJWVmeiWbfetDoFAgKKiIib6Zejr61drR2pqKlP9kpISbN26Fdu2bcPr168BANra2nBwcMCSJUuYPQMB1DihIYsk3Hzzo6d9VQgEAub3wI+2CIVCaGlpwdzcHMuXL+ckCTMfSELoFUmwAeCvLzRixAhERETgl19+wZQpUzBkyBDIyMigXr16nEz+2tra1sjRgcWu3bNnz2Lo0KGoV68ezp49W+25I0eO/On65SEibNy4ER4eHqJJVnl5eTg5OWH9+vVMtavi2rVr8PPzQ0hICJSUlJiFYMrIyKjReT96Zf8s+G4DZfDVFzh58iQmTJgARUVF1KtXD1++fMGmTZvg5OTERK86+ApB9PHjR/z++++4cuUK5OTk4OLigvnz58PV1RVbt25Fhw4dsHjxYkyYMIGJ/rdv33DlyhUMHDiwwvv+y5cvuHHjBqysrCAvL89EvzLy8/M52XXxI8ePH8e2bdvExkMODg6c7RT/888/4erqipSUFACl4Wjc3NxgZ2fHiT5f8Pku/hE+x8TJyckYM2YMEhMT0bx5cwDAixcv0KpVK5w5c6ZCaC7WcB2KMSoqClZWVlBUVBQtQv7999/49u0bLl++DDMzM6b6wL+Hqec7igtr6vr1SwrSBZA6xIsXL9C0aVOmk26VkZmZifj4eOTk5MDU1LSCN/7Ppk+fPmjYsCH27NkDBQUFrFq1CiEhIcjKymKqK6UiTZo0gZ+fH3755Rex8q1bt2L16tXMY66uW7cOf/zxB1xcXNCrVy8ApdutN23aBEdHR6xevZqpvqRw69YtUegPMzMzWFhYMNULDQ2tsu7u3bvYvn07SkpKmO/A2bZtm9jn79+/49GjRwgLC8PSpUuZe5yVpywUmZqaGid6pqamVdYJBAI8f/4c+fn5zDvcUqRIKeXFixeIi4vjrC8kKyuLhQsXYu7cuWJaXE/+8oFQKMSbN2/QqFEjsV0IP8J690F5CgsLkZycjJycHJiYmEBFRYUT3TJevHiBgIAABAQEIDMzExMnTsTUqVMxaNCgCl6ItQVJbANc9gU6d+6Mrl27YteuXZCRkYGHhwe2bNnCfAd6ZZiYmMDDwwOjRo0SWwDZsWMHAgICmO3Emz17NsLCwvDbb7/h0qVLePr0KaysrCAUCrFq1Sr06NGDiW4Z27Ztw9mzZ3Ht2rVK6y0sLDBmzBjMmzePqR0lJSVwd3fH3r178fbtWyQmJsLAwACrV6+Gnp5erV8EKE9eXh5ycnKY53+SInkQEa5cuYKEhAQApQswFhYWnIYjqiwU47hx45jnZezbty8MDQ1x4MAByMqWZkEoKiqCvb09UlNTERERwVRfihSJgeOk61I4Ijs7m28TeENdXZ2ePHki+pybm0syMjL0/v17Hq3ihs+fP9f44IJNmzaRvLw8zZkzh/Ly8ujly5dkbm5OWlpadPr0aeb6JSUl5O3tTTo6OiQQCEggEJCOjg75+PhQSUkJc30p/4+EhAQaPXo0ycjIkI2NDaWnp/Nmy86dO8nW1pY3fT559OgRWVlZUb169Wj27NmcaB48eJB69epF2traot/9jz/+oDNnznCiX9eJiIigKVOmUI8ePejly5dEVPqbREZG8myZFJbcvXuX7O3tSVVVlbp160Y7duyg7OxskpWVFesjSam9FBYW0vHjx8nS0pIUFRVpzJgxdOLEiTpzD9T1NqCsrExJSUmizwUFBSQrK0tv377l3JYDBw6Qjo4OHT16lJSVlenIkSO0YcMG0f9Z0bx5c7p27RoREaWlpZFAIKDly5cz0/uRrl270tmzZ6usP3fuHHXt2pW5HW5ubmRgYECHDh0iRUVFSklJISKio0ePUo8ePZjrSyHS19evdC7i48ePpK+vz4NFUrji9evX5OHhQYaGhtSoUSOaP38+5+8hBQUFevbsWYXyJ0+ekKKiImd2EBG9e/eOIiMjKTIykt69e8ep9s2bN+nvv/+utPzTp0+c2MDn9Ushku4AqaXIyMhgwIABsLOzw7hx4zjbWuvo6Fjjc1nFvC3v+VcG1+G/+KImoYeIg5jL5Xn06BGmTp2KgoICfPjwAd27d4e/vz/nyeG/fv0KALU25EwZkpT/AACysrKwdu1aBAUFwcrKCh4eHpzEOa2O1NRUdOrUqdIE8bWVtLQ0rF69GseOHcPYsWOxYcMG5h7oQMXEp/Hx8TAwMEBgYCCCgoIQHh7+0zUdHR2xfv16KCsr/+s7qbbnYTl16hSmTp2KKVOmIDg4GE+fPoWBgQF27tyJCxcu4MKFC3ybWOsZN24cunXrBmdnZ7HyzZs34++//8aJEyeY6ufm5uLYsWPw9/fHgwcPUFxcDG9vb8yYMaPWvw/5YuzYsTU+l2XM50aNGsHY2BjW1tb47bffUL9+fQB1YxdQeepqG5C08RAfIYhkZWXx4sULaGtrAyhNhh4VFcXZvV+/fn3ExMRAV1e30vrMzEx07NgRHz9+ZGqHoaEh9u3bh0GDBondAwkJCejZsydzfT4wNTWtsWc/61xgQOXtESjNQ6Grq4uCggLmNkjhHkkJxdi4cWMEBwfD0tJSrPzSpUuwsbHB27dvmduQm5uLBQsW4ODBgygpKQFQOmdpY2ODHTt2QElJibkNQqEQxsbGePr0aYXy+vXrY8WKFViyZAkTbUm4fimALN8GSGEDEUFOTg7Tp0/H/PnzMWXKFNjZ2aFTp05MdR89eiT2OTo6GkVFRTAyMgJQmnxaRkaGeQLuS5cuiSV0KikpwbVr1xAfHy8qYx33mQ9YTCb+txgaGqJdu3Y4deoUAGDChAmcL34AtX/ho4w//vijRucJBAKmCyCfP3/Gxo0bsWPHDnTq1AnXrl1D3759men9J5w8eRINGjTg2wxOeP/+Pdzc3LB//3706dMHd+7cYb7Nujw7duzAgQMHMHr0aHh6eorKu3TpwiwO+aNHj/D9+3fR/6uCyy3vfLFhwwbs3bsXNjY2OHr0qKi8d+/e2LBhA4+W1R0iIiLg6upaoXzo0KHw8vJirq+srIwZM2ZgxowZeP78Ofz8/ODp6QkXFxcMHjz4X3Nk/C/CtyNA+f4nESEkJATq6uro0qULAODhw4f49OnTf7RQ8n+hqKgIAoEAAoGA8/C3kkRdbANl+Pr6ioVbKyoqQmBgIBo2bCgq48IZBgCmTJmCKVOmcBqCiIhE4V6A0skmRUVF5rplFBUVITs7u8oFkOzsbOb58ADg1atXleY4KCkpEfWXahujR4/m2wQAEHu+/Dg/UVxcjGvXrkFPT48Hy6RwwcWLFysNxcg1EyZMgJ2dHbZu3SoWFnzp0qWYNGkSJzY4Ojri5s2bOHfuHHr37g2gNEz3woULsWTJEuzZs4e5DWlpaZWG/UxLS0NqaiouXrzITFsSrl8KpCGwaisCgYDevn1L2dnZtHXrVjIxMSGhUEhmZma0e/duTkIgeXl50YgRI+jDhw+isg8fPtCoUaNo69atzHTLQh1VdwiFQmb6Uv4ft27dIj09PTIzM6OnT5/SgQMHSFVVlcaPHy92X7DizZs3ZG1tTdra2iQjI0NCoVDskMKGTZs2UYMGDcjExITXMEedOnUiU1NT0dGpUydq0qQJycjI0L59+3iziwtycnLI1dWV1NTUyMzMjC5dusSLHQoKCqKwVyoqKqKwC4mJiaSgoMCLTXUJRUVFSktLIyLx7z8lJYXk5eV5tKzuoKCgQAkJCRXKnz17xlsbKCoqopCQEBoxYgQv+qzR09Or0cFF2JFly5aRvb09FRUVicqKiopo1qxZ5OTkxFT727dvdOjQIRo4cCApKirS2LFj6fTp01SvXr06EQKqOmp7GyAiatGihUS0AT4RCATUvn17UT9QRkaG2rZtK9Y3NDU1ZabfvXt38vT0rLJ+48aN1L17d2b6ZZiZmVFwcDARifcF3NzcqE+fPsz16zLl5x5+nI+Qk5Oj1q1b07lz5/g2UwojJCUUY0FBAS1cuJDk5ORE8yDy8vK0aNEiys/P58QGTU1NCg8Pr1B+/fp1atiwISc28Eldv35JQRoCq5ZS2TbLu3fvwtfXFydOnEBxcTHGjRuHgwcPMrNBR0cHly9fRtu2bcXK4+PjYWlpWWeSkhcWFiItLQ0tW7YU80KqC8jLy2Px4sVYv369aLU9JSUF1tbWePHiBV6+fMlUf+jQocjMzMT8+fOhra1dweN71KhRTHSLi4vx5MkTtGrVqoKnWV5eHpKTk9GuXbtqk7P+LyMUCqGoqAgLC4tqvU5Zhv4AADc3twp2aWlpYcCAATA2NmaqzTdNmjTB169fsWDBAkyaNKnK3Q4dOnRgagdfiU+llGJgYID9+/fDwsJC7Ps/ePAgPD09K2wBZ0l+fj4UFBQ405MUunXrhuHDh2PNmjVi5a6urjh37hwePnzIk2VSuEBLSwu3bt0S7YQu4/nz5+jVqxf++ecfTuxISUlBQEAAgoKC8OrVK0yaNAm2trYwNzfnZHdIUVERbty4gZSUFEyePBmqqqrIysqCmpoa5wnhpXCDJIQg+rEfWBVr165lor9//344Ojri6NGjGD58uFjduXPnMGnSJHh7e2PWrFlM9MsIDQ3FtGnTsHz5cqxbtw5ubm54/vw5Dh48iPPnz2Pw4MHMtL98+QIVFZUKY57i4mLk5uZCTU2NmbYkoa+vj7///lts91VdoK6PicuQlFCMeXl5ojCELVu25DTskpKSEh4+fIg2bdqIlT958gTdunVDbm4uZ7bwQV2/fklBugBSS5GRkcHr168r3V6cm5uLo0ePwt/fH7dv32Zmg6qqKs6dO4cBAwaIlYeHh2PkyJGinAy1lby8PCxYsABBQUEASsN/GRgYYMGCBdDR0YGLiwvPFrLn5s2b6N+/f4XykpISuLu7Y/Xq1Uz1VVVVERkZyTz0248EBgZi586duH//foWJhaKiIvTo0QOLFi2CtbU1p3Zxha2tbY0GvQEBARxYwz3Xr1/H/Pnzce/evQoDu8+fP6NXr17Yu3cv05Bg5QcSAoEA5V/1ZZ+5yAXk6+sLV1dXeHl5wc7ODr6+vkhJSYGHhwd8fX0xceJEpvqSQHBwMPbu3Yu0tDTcvXsXLVq0gI+PD/T19Zktwpbh4eGBQ4cOwd/fH4MHD8aFCxeQkZGBxYsXY/Xq1ViwYAFT/bJn/d69e/H27VvRe3D16tXQ09NjGvu9JmGQZGVl0aRJE/Tp04dZOJZz585h7NixmDx5MszNzQEA165dw5EjR3DixAmJCdPBEj7bAN/Ur18fgYGBFa4zNDQUtra2nMfeLykpwaVLl+Dn54dz585BVVUV79+/Z6qZkZGBIUOGIDMzEwUFBaLngIODAwoKCrB3716m+nwhCX0BPqnp4gPAbgFCErC2tsbhw4dhbGwsWghNSEhAYmIixo8fjyNHjnBiR2RkJNatW4eYmBjk5OTAzMwMa9asqZAT4GcSEhICZ2dnPH78uMJEa25uLszMzLB161aMGDGCmQ2SCF8OIXy8i/keE2dlZcHb2xtr1qyp9Dm8YcMGODk5oXHjxkz0K6MsFGNwcDA+ffrESSjGQ4cOYezYsbzmmRg0aBA0NTVx8OBB0f3/7ds3TJs2DR8+fMDVq1d5s40L6vr1Swx8bj+Rwo6yEFh8MnXqVNLT06NTp07Rixcv6MWLF3Ty5EnS19cnGxsbXm3jgoULF1Lnzp0pMjKSlJWVRduNz5w5Q506deLZOm5JSkqisLAwysvLIyKikpISTnTbtGlD0dHRnGiVp0+fPnTkyJEq648dO0Z9+/bl0KK6S3JyMq1cuZImTpwoeiZeuHCB4uPjmWmOGDGCvL29q6zftm0bjR49mpk+EVF6enqNDi44dOgQGRoairb86+jokK+vLyfafLN7925q2LAhbdiwgRQVFUXvgYCAABowYABz/ZKSEtqwYQMpKyuLvn8FBQVatWoVc22i0vAaBgYGdOjQIbHrP3r0KPXo0YOpdk1CIOnq6pKysjIpKirSqVOnmNly/vx56tWrFykpKZGmpiYNHDiQbty4wUxPkuCjDSxevJhycnJE/6/uYM3ixYtJU1OTvLy8KDIykiIjI2nr1q3UsGFDTvSr4927d+Tl5cVcZ9SoUWRtbU0FBQVi4XfCw8PJ0NCQuT5fSEJfQEppCJoVK1aQk5MTXbx4kRcbjh07RqNGjSITExNq06YNjRo1io4dO8aLLVwyePBgOnDgQJX1fn5+ZGlpyaFF/FFcXEzr1q2jpk2bkoyMjOg5uGrVKk76xHz1R/keEy9ZsoRmzpxZZf3s2bNp2bJlzPSrg8tQjA0bNiRlZWWaNGkS/fXXX2JhObkiLi6OmjZtSpqammRubk7m5uakqalJOjo6TMflkkJdv35JQboAUksJDAzkLJ5fVeTm5tLcuXNJXl5eFGtQTk6O5s6dKxqY1mZ0dXXp7t27RCQebzUpKYlUVVWZapeUlFBGRgZ9+/aNqc6/8f79ezI3NxfFPi37DqZPn05Llixhrn/p0iWytLQUxcDnCi0trWo1U1NTpbEeOeDGjRukqKhIFhYWJCcnJ7r/PDw8aNy4ccx0dXV16enTp1XWP3v2jJo3b85MX1LJzc3lfWGea9q0aUMhISFEJP4eiIuLI01NTc7sKCgooCdPntD9+/fp69evnOm2bNmSrl69SkTi1//s2TPS0NDgzI7qKC4uJnd3dzI2NubblFoJH21gwIAB9PHjR9H/qzoGDhzIRL88xcXFtGnTJmratKloEbJp06a0adMmXiYg+KBBgwaiPDjl74G0tDRSVFTk0zSmSPsC/HPixAkSCoWkrKxMGhoaJBQKacuWLXybxTmZmZn04sUL0ef79++Tg4MD83x42tralJSUVGV9UlISaWtrM7VBUuDTIYSIv/4o32Pitm3bUmRkZJX1t2/fJhMTE2b6ksL379/p3LlzNHnyZFJWViYtLS36/fff6fbt25zakZubS/v37ydHR0dydHSkAwcOiBxk6wJ1/folAekCiBTm5OTkUExMDMXExNSJhY8yynduync0Hj9+TGpqaky1i4uLqV69epSYmMhU59+YOnUqWVlZ0YsXL8S+g7CwME46GxoaGqJkXyoqKlS/fn2xgxVKSkoUExNTZX1MTAwpKSkx05dSSo8ePUTereXvv/v375OOjg4zXXl5+X8d8EkTgNcN6noS+Kqu/8mTJ6SsrMynaWK8fPlSuijNiLreBsrz+fNn+vz5M99mcI6GhoYo2Wv5eyAyMpIaNWrEp2lMkfYF+MfMzIxmz54tWmzcuHEj0/6/pNKnTx86ePAgERG9fv2aVFVVqWfPntSwYUNyc3NjpqugoEDPnj2rsv7p06ectoGCggJKSEig79+/c6ZZBt8OIXy9i/keEyspKVFGRkaV9RkZGXVuTJ6bm0uHDh2iYcOGkZycHBkYGPBtEnM+f/5MxcXFFcqLiorqZL+sLlO3MjLXQUpKSipNKlVSUoKXL19CV1eXuQ3KysrME+1KIl26dMFff/0lirFelhPB19cXPXv2ZKotFArRqlUr/PPPP2jVqhVTreq4fPkyLl26hGbNmomVt2rVChkZGcz1fXx8mGtURqtWrXDnzp0q7/tbt25x+rvk5eUhMzMThYWFYuW1vV3GxcXh8OHDFcobNWrENOa5jo4O4uPjYWhoWGl9bGwstLW1menzjSQkPq0JERER6NixI9TV1Zlp6Ovr4/Hjx2jRooVYeVhYWIUkeD+LsWPH1vjc06dPM7GhDBMTE0RGRla4/pMnT8LU1JSpNlDa1wkMDMTp06eRnp4OgUAAfX19/Prrr5g6daroPtXR0UF2djZze7jk4cOHcHJyQmhoaKVxr0ePHg0fHx907NiRqR18tAFJpa4k+/0RS0tL+Pj4YP/+/QBK+8M5OTlYu3Ythg0bxkyX7zZQ1/sCksDz589x7NgxUe6BJUuWYM2aNXj37h2zvE/lEQqF/9ofEggEKCoqYmpHfHw8unXrBgA4fvw42rdvj9u3b+Py5cuYM2cO1qxZw0RXT08PUVFRMDY2rrQ+KiqqwruBBZKQl/PVq1eVPgtKSkrw/ft35vp8vYv5HhMrKioiPT29yjmv9PT0CsnZaztKSkqwsrLCx48fkZGRgWfPnnGiGxQUhIYNG+KXX34BACxbtgz79++HiYkJjhw5wuxZUF0uovz8fHTt2pWTXER8Xb8UcaQLILWUL1++wN7eHufOnYOamhpmz56NtWvXijqA2dnZ0NfXZ54Al28+ffqEkydPIiUlBUuXLkWDBg0QHR2Nxo0bQ0dHh6n2xo0bMXToUDx9+hRFRUXYtm0bnj59ijt37uDmzZtMtQHA09MTS5cuxZ49e9CuXTvmepWRm5tbabKtDx8+QF5enrn+tGnTmGtUxuTJk7Fq1Sr06tWrQocvJiYGa9aswbJly5jbkZ2djenTp+PixYuV1tf29q+hoYHXr19DX19frPzRo0dM2/+wYcOwevVqDBkypEKSw2/fvmHt2rUYPnw4M32+KZ/UOT8/H7t374aJiYlo4ffevXt48uQJfv/9d54sLGXAgAGoX78+VqxYgSVLljDRcHR0xLx585Cfnw8iwoMHD3DkyBFREngWlF/QISKEhIRAXV0dXbp0AVA6Kfjp06f/aKHk/8qaNWswbdo0vHr1CiUlJTh9+jSeP3+OgwcP4vz580y1iQgjR47EhQsX0LFjR7Rv3x5EhGfPnsHW1hanT5/GmTNnmNrAJ15eXjA3N6900l1dXR2DBw/Gli1bcOjQIaZ28NEGypOfn48dO3YgPDwc7969Q0lJiVg9n4uwdQUvLy9YWVnBxMQE+fn5mDx5MpKSktCwYUOmCaD5bgN1vS8gCeTl5Yn9/nJyclBQUEBOTg4nCyAhISFV1t29exfbt2+v8Exiwffv30XjrqtXr2LkyJEAAGNjY7x+/ZqZ7tixY7Fy5UoMHjy4QpLpN2/eYNWqVcySX5dn+fLliImJwY0bNzBkyBBRuYWFBVxdXTlZAOHbIYSvdzHfY+Lu3bsjODgY/fr1q7T+4MGDosXB2k5eXh5CQkLw559/4tq1a2jevDkmTZqEkydPcqK/ceNG7NmzB0Dp82/nzp3w8fHB+fPnsXjxYmZOWXv27MGyZcsqnZNSVlaGs7Mzdu7cyXwBhK/rl/IDfG4/kcKOhQsXUuvWrenEiRN04MABatGiBf3yyy9UUFBARERv3rwhgUDAs5VsiYmJIS0tLTI0NCRZWVnRVs+VK1fS1KlTObEhOTmZ7O3tqWvXrtSmTRuaMmUKxcbGcqJdPvyTgoICZ+GfyjN06FBRsl0VFRVKTU2l4uJi+u2335jmYChPUVERnTx5ktavX0/r16+n06dPM4+7XVhYSAMGDCBZWVkaMmQILVq0iBYtWkRDhgwhWVlZ6t+/PxUWFjK1gYho8uTJ1Lt3b/r7779JWVmZLl++TMHBwWRkZETnz59nrs83S5YsoT59+oi2+yclJdGtW7fIwMCAXF1dmem+efOGmjZtSs2bN6dNmzbRmTNn6MyZM+Tp6UnNmzenpk2b0ps3b5jpSxJ2dnaVJtxes2YNTZ8+nQeL/h/p6el0/fp1Wrp0KVMdPpPAL1u2jOzt7cWeeUVFRTRr1ixycnLixIaIiAiysLAgLS0tUlRUpN69e9OlS5eY6/r7+5Oqqipdv369Qt21a9dIVVWVgoKCmNvBFwYGBtWGnYiNjSV9fX1ObOGzDUyePJkaNmxIc+bMobVr15Krq6vYUZfgM/zL9+/fKTg4mJYuXUpz587lJO41321A0vsCgwYN4uwZUAbX96BAICB3d3fatm2b6FBQUKDVq1eLlXFJQkICjR49mmRkZMjGxkYUlogl3bp1I2dnZ4qIiCAFBQV6/PgxEZUmiGcZEvbLly/Utm1bUlVVpblz55KPjw/5+PjQnDlzSFVVlUxMTOjLly/M9MvgMy9nGWfOnCF1dXXy9PQkJSUl2rJlC9nb25OcnBxdvnyZExv4eBfzPSa+fv06ycjI0JIlS8Set2/evCFHR0eSkZGha9euMdOXFCZMmCDK/TFv3jy6c+cO5zYoKiqKwpEtW7ZMNB8XHx/PNAytpOQi4uv6pYgjXQCppejq6lJ4eLjoc3Z2NnXr1o0sLS0pPz+f3rx5Q0KhkD8DOWDQoEGiia3ynZ3bt29TixYteLSMGwIDA6s9uCAuLo4aNWpEQ4YMITk5Ofr111+pTZs21LhxY0pOTmaun5SURK1atSIlJSUyNTUlU1NTUlJSIiMjI+b6hYWFtGnTJurYsSMpKSmRoqIidezYkTZt2iRaiGSFu7s7ERE1adKE7t+/T0REqqqq9Pz5cyIiCg0Npd69ezO1oYyDBw9Sr169SFtbWzTI++OPP+jMmTPMtQsKCsje3p5kZWVJIBBQvXr1SCgUkrW1NfNFsPT0dBo6dCgJhULRQEMoFNLQoUMpNTWVqfaPfP/+na5cuUJ79+4VDTRfvXrFSTJsNTW1SnMRJSYmMs+FJGnwkQS+YcOGouTD5UlISKAGDRpwagvXDB48mDw8PKqsd3d3J0tLS87s4XriT15evtpnTWpqKuf5B/hoA2pqanTr1i1ONSWN3NxcmjFjBsnIyJCMjIyoPzx//vxq28j/OpLQBiSpL/AjO3fu5GwRkK97sEWLFqSnp1ftwdUi0KtXr8je3p7q1atHw4cPp7i4OE50iYjCw8NFSeDLO58sX76cxowZw1T706dPNHfuXGrQoIGoDdSvX5/mzp1LHz58YKpdBp95OcvDl0PIj3D9LuZzTExEtHfvXpKXlyehUEgaGhpUv359EgqFJC8vT7t372auX0ZiYiLt27eP1q9fT25ubmIHayZPnkx//fUX8/FvdWhpaVF0dDQREXXq1EmUlyg5OZlpXkBJyUXE1/VLEUe6AFJLUVRUrNCx/vLlC/Xs2ZPMzc0pNTW11i+AqKmpiSa5y3d20tPTSV5enolmWYLLmhx1hU+fPtGGDRvot99+o6FDh9LKlSspKyuLE+2hQ4fSkCFD6J9//hGVvX//noYMGULDhg3jxAYuefv2LQ0ePJgWLFhARKWLHmlpaURUuihaNgmUmppKioqKzO3ZvXs3NWzYkDZs2CA2+AgICKABAwYw1y8jIyOD/vrrLzp27Filk/Es+fDhAz148IDu37/P2UCvPOnp6WRsbExKSkpikw4LFy6k2bNnM9dv3LgxBQQEVCgPCAio1clvJQUNDY1KFxvPnDnDSdJNPmncuDE9evSoyvro6Ghq3Lgxczv4mvhr1qwZXbx4scr6CxcuULNmzZjpSwpt2rSpdhdAXWDhwoXUuXNnioyMJGVlZdE9eObMGerUqRMTzdDQ0BofrJCkNsB3X4Bv+LgHJYVPnz7RsmXLSFFRkXr27EkRERG82FFUVFTh3ktLS+NsIrykpITevXtHb9++pZKSEk40y+jbty9t376diP5fRAKi0vewlZUVp7ZI4YeXL1+St7c3/f777zR37lz6448/6MWLF5zp79+/n2RkZKhx48bUsWNH6tSpk+gwNTXlzA4+mTx5MpmZmZGdnR0pKSnR+/fviai0v9C2bVtmusbGxhQcHFxl/cGDB8nIyIiZfhl8Xb8UcaQ5QGopurq6ePbsmVjse1VVVVy+fBmWlpYYM2YMM22+kw6WIS8vjy9fvlQoT0xMhJaWFhNNDQ2Nf012R0QQCATM8y9kZmZWW19VMrCfjbq6OlauXMmJ1o/cvHkT9+7dQ4MGDURlmpqa8PT0RO/evXmxiSX79u2DlZWVKJ+BkZERnj9/Dj09PXTs2BH79u2Dnp4e9u7dy0nizR07duDAgQMYPXo0PD09ReVdunSBk5MTc/0ydHV1Obvff6R+/fro2rUrL9oA4ODggC5duiAmJgaampqi8jFjxmDmzJnM9RctWoS5c+ciOjpaFGP3/v378Pf3x+rVq5nr84EkJYGfPn067OzskJKSIvb9e3p6Yvr06Uw069evX+Pr//DhAxMbyv72jzHHy9O4cWN8/PiRmX4ZfMUet7CwgLu7u5hmGUQEd3d3WFhYMNGWpDbg5eUFZ2dn7N27l9MEk9evX8f8+fNx7969SvvCvXr1wt69e9G3b1/mtpw5cwbHjh1Djx49xH6Xtm3bIiUlhYlm+VxQ1cGyP8xnG/gRvvsCfMPHPSgJbN68GZs2bUKTJk1w5MgRjBo1ijdbZGRkUL9+fbEyPT09zvQFAgGz8fe/wXdeTr6QpHcx3+jo6GDx4sW86W/YsAHu7u5wdnbmzYbc3FzcvHkTmZmZKCwsFKtbuHAhc/1du3Zh1apVePHiBU6dOiUalz58+BCTJk1ipispuYj4un4p4kgXQGoplpaWCAgIwLBhw8TKVVRUcOnSJQwePJiZNt9JB8sYOXIk1q1bh+PHjwMo7XhlZmbC2dkZ48aNY6IZHh7O5O/+X9DT06u208NqwBkbG1vjc39MhvazkZeXx9evXyuU5+TkQE5Ojpmuvr7+v3Y4BQLBTx/0OTg4iLU7BwcHUXLDtWvXYsiQIfjzzz8hJyeHwMDAn6pdGWlpaZUm9pOXl0dubi4TTUdHR6xfvx7KyspwdHSs9lxvb28mNsyYMeNfzxEIBPDz82OiX57IyEjcuXOnwv2up6eHV69eMdd3cXGBgYEBtm3bJnrmt2nTBgEBARg/fjxzfT6QpCTwW7duRZMmTeDl5SV6Fmhra2Pp0qXMEr/7+Pgw+bv/KcXFxZCVrbqbKyMjg6KiIuZ28DXxt2rVKnTu3Bndu3fHkiVLYGRkBABISEiAl5cXEhMTmb0Hajr5zQVdunRBfn4+DAwMoKSkhHr16onVs1qE8/HxwcyZM6vsC8+ePRve3t6cLIBkZ2dXmvA5Nze3xpNj/ylcJHb+N/hsA4Bk9QX4ho97UBJwcXGBoqIiDA0NERQUhKCgoErPq63JbwcOHFij8dC1a9eY2tGnTx88fvwYnp6eaN++PS5fvgwzMzPcvXsX7du3Z6rNJ5LwLuZrTFxGREREjc6rKkn6z+Ljx4/47bffmGpUx6NHjzBs2DDk5eUhNzcXDRo0wPv376GkpIRGjRpxsgCioaGBnTt3Vih3c3Njquvi4oLQ0FC0atUK1tbWYn2BP//8E82bN2fmjFQevq5fijgCIiK+jZDy8/n48SOysrLQtm3bSuu/fv2K6Oho9O/f/6drt2zZEiEhIVVObsfFxWHUqFFITU396drl+fz5M3799VdERUXh69evaNq0Kd68eYOePXviwoULUFZWZqrPNzExMWKfv3//jkePHsHb2xvu7u4YO3YsE12hUAiBQIB/e7RwsQvGxsYG0dHR8PPzE/N+njlzJjp37sxs4Ltt27Yq69LT07Fv3z4UFBQwv/4fycvLQ0JCAnR1ddGwYUPmeiYmJvDw8MCoUaOgqqqKmJgYGBgYYMeOHQgICGDibTRw4ECEhIRAQ0MDAwcOrPI8gUCA69ev/3R9ANXusCsuLsbVq1c5+/3r16+P27dvw8TEROw3uHXrFsaNG4e3b98yt6EuY29vD21tbaxfv16sfO3atXjx4gX8/f05s6VsR2RlE7K1EaFQiKFDh0JeXr7S+oKCAoSFhTFvh0pKSoiPj4eBgYFYG4yJiUG/fv3w+fNnZtpRUVGwtbXF06dPRRMQRAQTExMEBATUCY90CwsLZGZmws7ODo0bN64wETNt2jQmui1atEBYWBjatGlTaX1CQgIsLS3/dbfuz6Bfv3747bffsGDBAqiqqiI2Nhb6+vpYsGABkpKSEBYWxtwGvuCzDUhSX4Bv6uo9aGtrW6MFnoCAAA6s4Z7qPO6/fv2Kw4cP15k2UFfhe0wsFAqrrCtrmwKBgLlDjJ2dHbp27Yo5c+Yw1amKAQMGoHXr1ti7dy/U1dURExODevXqwdraGg4ODszmhSSFz58/Y/ny5Th27Jho97eGhgYmTpwId3f3CrvjpNRepAsgUn46CgoKFcJvlSctLQ0mJib49u0bJ/bcunULsbGxyMnJgZmZGWfb3SWVv/76C1u2bMGNGzeY/P2MjIwan8s6HMWnT58wbdo0nDt3TuT1WVRUhJEjRyIwMBDq6upM9cvz4cMHrF+/Hnv27EH37t2xadMm9OjRgzN9PvD19YWrqyu8vLxgZ2cHX19fpKSkwMPDA76+vpg4cSLfJnJKaGgoVqxYgaysLDg7O3PibTJhwgSoq6tj//79okkHLS0tjBo1Crq6urV20A2UTvirqKhUGPwUFxcjNzeXk4UAdXV1REVFoVWrVmLlSUlJ6NKlC9PJ77pOTUN8sW4DkjDx9/jxYyQlJYGI0Lp1a3Tq1Im5pqSgpKSEu3fvMg+7+iMKCgqIj4+HoaFhpfXJyclo3749J33hW7duYejQobC2tkZgYCBmz54tFv6lc+fOzG3gG0lqA3z0BfhGeg9KKaOoqAi7du2Cu7s71NXVsX79eibjgcrCYFdFXXEMkRS4HBNX1c/Oy8vDtm3bsH37dhgYGCA+Pp6JfhkeHh7w9vbGL7/8gvbt21fYjcp6B4aGhgbu378PIyMjaGho4O7du2jTpg3u37+PadOmISEhgam+pEBEeP/+PYgIWlpatXoHopQq4CPxiBRuOHfuHK1evVqU+PjatWs0dOhQsrKyon379jHTlaSkg1IqkpSUREpKSrzaUFxcTOfOneNMLzExkc6ePUtnz56lpKQkznSJiPLy8mjDhg2koaFBHTt2pL/++otTfb45dOgQGRoakkAgIIFAQDo6OuTr68u3WZxy69Yt6tOnDykpKdGyZcs4TYD64sULMjExoTZt2pCsrCz16NGDNDU1ycjIiLPEl3xw+vRpatWqFeXm5laoy8nJodatW9PZs2eZ2yFNAi8lMjKSVFRUaM6cOaSgoEAODg40ePBgUlZWpqioKL7Nq/WYmprS3bt3Odc1MDCgkJCQKutPnTpF+vr6nNmTnJxM9vb21LVrV2rTpg1NmTKFYmNjOdOXwm9fQBKQ3oNSDh06RAYGBqStrU27du2i79+/M9MSCAQkFAprdLDm8+fPVFxcXKG8qKiIPn/+zFxfUpCEMXFxcTEdOHCAmjVrRrq6uuTv71/pb/Oz0dPTq/Lgoi/QsGFDSkxMJCKiVq1aUVhYGBERPXv2jPd5ISlSuES6A6SWsm/fPsyfPx8dO3ZEUlISdu3ahd9//x0TJkyAjIwMDh48CA8PDzg4OPx07enTpyM5ORmRkZEV6ogIffv2RatWrZh4XW7fvr3G53IR65BPfvR8ISK8fv0arq6uSEhIwOPHjzm3KTk5Gf7+/ggMDER2dja+f//OuQ1cUVxcjAMHDsDNzQ0KCgpYt24drK2t66ynQV5eHnJyciqNAf0z+U+28LKOufz06VM4OzsjLCwMNjY2cHNzQ7NmzZhqVkZRURGOHj0qthNuypQpUFRU5NwWrrC0tMT48eNhb29fab2/vz+OHTuGS5cuMbXD09MTbm5umDlzZqVJ4OuC568kQkQICwuDn58fTp48yVwvJSUFnp6eiImJEbVBZ2dnprHH161bV6Pz1qxZw8wGSeDy5ctwc3ODu7t7pV6XrDx/FyxYgBs3buDvv/+GgoKCWN23b9/QrVs3DBw48D/qt0r5z5CUNsBXX+Dhw4dwcnJCaGhohfv88+fPGD16NHx8fDjfHcUlWVlZ8Pb2xpo1ayr9DjZs2AAnJ6cKiXFrA9u3b8esWbOgoKDwr88Z1mPisLAwuLi4IC0tDU5OTnB0dGQeirp8cvP09HS4uLjA1tZWlI/t7t27CAoKgoeHB7NQiAAQEhICZ2dnPH78GEpKSmJ1ubm5MDMzw9atWzFixAhmNvCNpIyJT58+jRUrViA7OxvLly/HggULqgyTWtuwtLSEra0tJk+ejJkzZyI2NhYLFy5EcHAwPn78iPv37/NtIjMkJReRFMlAugBSS2nbti0WLVqEmTNnIjw8HMOGDYOXl5co6WpgYCA2b96Mp0+f/nTtlJQUdO7cGUZGRlUmHYyKiqoyLMB/Q1Vht35EIBAwz0HCN2W5OMpDRGjevDmOHj0q6gCy5tu3bzhx4gR8fX1x+/Zt9O3bFxMnTsSYMWOYDTj+Lfl1GaySYB8/fhyrVq3Cp0+fsHLlSsydO5dp0nUp/4+ahr0B2IW+efHiBdasWYNDhw5h+PDh2LhxY5Vx4KWwoWnTpoiIiKg2/Ey/fv2QlZXF3Jbjx49j27ZtePbsGYDSJPAODg61Ngm8JJOWlia2CG9hYYHz58/zbRYTTE1Nq6wTCAR4/vw58vPza33s9bIQeJX1h1jmInv79i3MzMwgIyOD+fPni/WFd+3aheLiYkRHRzPrB0nDv/DfBvjuC0yePBlt2rTB6tWrK63fuHEjnj59ikOHDjHRl4R70MnJCV++fMH+/fsrrZ8zZw7U1dWxadMmJvp8oq+vj6ioKGhqalY7PmY5Jn7w4AGcnZ1x7949zJkzBytXruQkB+GPDBo0CPb29pg0aZJY+eHDh7F//35mYaEByXHI4QtJGBPfvHkTzs7OiIuLg4ODA5ydnTkNg/0jZdOvXC4AleXEHThwIN69ewcbGxvcuXMHrVq1gr+/P9OF8OvXr6Nfv36QlZVlplEd0lxEUsojXQCppSgpKYmSHQOAnJwcoqOj0a5dOwClnhBt27ZFbm4uE31p4s1SioqKcOPGDaSkpGDy5MlQVVVFVlYW1NTUoKKiwlS7vOcLUDoJoKWlBUNDQ05eQH///Td8fX1x9OhRtGzZElOmTIGzszNiY2NhYmLCVPvH5Ne3bt1C586dxTzeWSbBFgqFUFRUxKRJk6od1LFagImPjxe1da4xNTWtcYeORRJ0SUBJSQkCgQDz589H7969qzxv5MiRTPTPnj1b43NZ2QDwm4NDUVERjx49grGxcaX1z549g5mZGWe5qOoi6enpuHLlCgoLC9G/f3/enkkFBQU4efIk/Pz8cOvWLRQXF2Pr1q2ws7Njdg9KwsRfVTx+/BguLi64fv06ZsyYgb179zLTkoQ8PD/2hX6kf//+zLQzMjIwd+5cXLp0SWzCw8rKCrt27aqx087/hcqcYH6E9SJQGdHR0ahXr55ox1NoaCgCAgJgYmICV1dXzifDuGoDfPcFWrZsiZCQEHTo0KHS+ri4OIwaNYrZ5HdN7sEyWN2D7dq1w969e9GnT59K6+/cuYOZM2fiyZMnTPTrOmXjoVmzZlX7vGO9A0VJSQkxMTEV8rElJiaiU6dOyMvLY6YtSQ45fMD3mHjYsGG4evUqZsyYAVdXVzRp0oSJTk04ePAgtmzZgqSkJABA69atsXTpUkydOpU3m7hARkYGr1+/FkWB6NGjB06dOgUdHR3ebOIqFxEg3YEiafCzDCeFOZqamsjIyICuri6ysrJQVFSEzMxM0QRERkYGGjRowEy/S5cuiI+Pl6ikg1yTkZGBIUOGIDMzEwUFBRg8eDBUVVWxadMmFBQUMJ10ANgO6v+NDh064MuXL5g8eTLu3LmDtm3bAgBn4V7Cw8PFPquqquLw4cMwMDDgRL9fv34QCARISUmp8hyWXh8dOnRA165dYW9vj4kTJ0JVVZWZ1o+MHj2aMy1JJT8/HwCwZcsWbNmypdJzWE461fQ3YGlDdVv+8/Pz0bVrV6Zb/vX09BAVFVXlAkhUVBRatGjBRFsSqElYHVlZWTRp0gR9+vT56aHpwsPDMXz4cNECk6ysLPz9/WFtbf1Tdarj4cOH8PPzw5EjR2BoaIipU6fiyJEjaNasGaysrJhOvmtoaPA+8fcjaWlpWL16NY4dO4axY8fiyZMnFSaDfiZ8PwPK4LMv1KJFC1y4cAEfP35EcnIyiAitWrVC/fr1mWv/2A/ik9mzZ8PFxQXt27dHamqqaBfwiRMnkJeXBx8fH07s4LoN8N0XePXqVbX9PxUVFbx+/ZqJNiB+D/5bCCJWpKWliZwBK6NZs2ZIT09npl/X0dXVhUAgwJkzZ6o8RyAQMF8Aad68OQ4cOIDNmzeLlfv6+qJ58+ZMtT9+/IiioqIq679//46PHz8ytaE6QkND8fnzZ9jY2DD5+3yPicPCwiArK4tjx47h+PHjVZ734cMHZjYApQs8q1evFlsQv3XrFubMmYP3799Xu0vhf50f/e2fPHmCgoICnqwB/vzzT6xZswbfvn2Dq6srZs2axdQ5uLr5z/I7UKRwBNdJR6Rww7x586hVq1a0YcMG6tatG02bNo2MjY3p4sWLFBYWRu3bt6cZM2bwbWatZtSoUWRtbU0FBQWkoqJCKSkpREQUHh5OhoaGzPUDAwPp/Pnzos9Lly4ldXV16tmzJ6WnpzPVlpOTo6lTp9Lly5eppKREVC4rK0tPnjxhql0Z5b//ukBERARNnz6dVFVVSVlZmWxsbCgiIoJvszjB1NRUlFi0U6dOZGpqWuUhhR2DBw+mAwcOVFnv5+dHlpaWzPRXrFhBurq69ObNmwp1r1+/Jl1dXVqxYgUzfb6pLtli2aGrq0vKysqkqKhIp06d+qn6vXv3plGjRlFWVhZ9+PCBfv/9d9LW1v6pGv+GjIwMLVq0iBISEsTKuXgP3bhxQ3QEBgZSkyZNyMXFhUJDQyk0NJRcXFxIW1ubAgMDmdpBRJSdnU3z588nOTk5Mjc3pwcPHjDXJOL/GSBFclBTU6Pk5GQiIvL09BT97rdu3aJmzZox1+erDfBNs2bN6OLFi1XWX7hwgZPvn4jI3NycDh8+XKH8zz//pP79+zPT1dTUpJs3b1ZZf/PmTdLU1GSmX56DBw9Sr169SFtbWzQO++OPP+jMmTPMtadPn17tUdv566+/SEFBgdq1a0d2dnZkZ2dH7du3JwUFBeaJuI2NjSk4OLjK+oMHD5KRkRFTG6rDyMiIk0TwfBEYGFijgzV6enoUFBRUqX16enrM9flEIBDQ27dvRZ/5mpe5ePEidezYkdTU1GjdunWUk5PDuQ1lfP/+nXx8fEhLS4sMDQ3pyJEjvNlS15CGwKql5ObmYvHixbh79y569eqFHTt2YPv27Vi5ciW+f/+O/v3749ixY0wSEktK0kG+0dTUxJ07d2BkZARVVVXExMTAwMAA6enpMDExYbrdFgCMjIywZ88emJub4+7duxg0aBB8fHxw/vx5yMrKMk0A/erVKwQGBiIgIADfvn3DpEmTMGXKFHTv3h2PHz9mHgLrR8p//3WJ3NxcHD9+HIGBgYiMjIShoSHs7Owwbdo0XrcAs8TNzQ1Lly6FkpIS3Nzcqj137dq1HFlV9+B7y//Xr1/Rs2dPZGZmwtraWiz+/p9//onmzZvj3r17nO6OkkRKSkrg6emJ4OBgUY6Sn4GGhgbu3Lkjetbn5eVBTU0Nb9++haam5k/TqQ4rKyvcvXsXI0aMwNSpU2FlZQWBQIB69eohJiaGs/cQX7HHc3NzsXXrVnh7e8PQ0BAeHh6wtLRkolUZfD8D+GbGjBn/eo5AIICfnx8H1vCLmpoaHj58iFatWmHw4MEYPnw4HBwckJmZCSMjI2ahCPluA3wzffp0JCcnIzIyskIdEaFv375o1aoVs3xo5eErBNEvv/yCpk2b4sCBA5XW29vbIysrCxcuXGCiX8aePXuwZs0aLFq0CO7u7oiPj4eBgQECAwMRFBTEfMfWmDFjxD5///4d8fHx+PTpE8zNzZmOCSWFly9fYs+ePWL52ObMmcN8B8jKlStx6NAhPHjwoELOpzdv3qB79+6wtraGu7s7Uzuk8IuCggLi4+Mr9ImSkpLQvn170Y7B2oiMjAzevHkDLS0tAKV9gpiYGKZhQMsjKbmIyii/A2XVqlXMd6BIEUe6AFLHyM/Px/fv35lO+vCddFBSqF+/Pm7fvg0TExOxCfhbt25h3LhxePv2LVP98nlgnJ2d8fr1axw8eBBPnjzBgAEDkJ2dzVS/jOvXr8Pf3x+nT59Gfn4+nJycYG9vj9atW3OiD9TdBZDyJCcnIyAgAMHBwXjz5g2GDBnyH+WKkCLlP0EScnB8/vwZy5cvx7Fjx0ThBTQ0NDBx4kS4u7tzEobmf4FXr16hU6dOP/WdIBQK8ebNGzEnCz6ewy9evEBAQIBoMX7ChAnYvXs3YmNjOUtGzNfEX5MmTfD161csWLAAkyZNqjLERFX5Af5bJOEZwCc/TjiWp7i4GFevXq0zSTfNzc3RvHlzWFhYwM7ODk+fPoWhoSFu3ryJadOmMQtBxHcb4JuUlBR07twZRkZGWLJkiZgjgJeXFxITExEVFVXlIuXPxMjICKNGjaoQgmjZsmUIDQ3F8+fPmeiGh4dj8ODBWLRoEZYuXSqagH779i02b96Mbdu24fLlyzA3N2eiX4aJiQk2btyI0aNHi70L4+PjMWDAALx//56pfmWUlJRg7ty5aNmyJZYtW8a5fl1B6pAjBSjNRzR58mSsWLFCrHzDhg04duwY4uLieLKMPUKhEO3atRNN8sfGxsLY2LhC/i9WuUElJRdRWFgYXFxckJaWBicnJzg6OkJZWZmpppSKSBdA6igvXrzA2rVr4e/vz5kml4k3JYEJEyZAXV0d+/fvh6qqKmJjY6GlpYVRo0ZBV1eXucdVo0aNcOnSJZiamsLU1BSOjo6YOnUqUlJS0LFjR+Tk5DDV/5HPnz/jzz//hL+/P6Kjo9GuXTvExsYy0frx7/bq1QvHjx9Hs2bNxMpr66C3KnJzc/Hnn39i+fLl+PTpU52YeCkjJycHJSUlYmVcJx+uS7Rp0wYrV66sMudDcHAw3N3dkZCQwNwWIsL79+9BRNDS0mIaa1iSKCkpQWBgIE6fPo309HQIBALo6+vj119/xdSpU5l+D0KhEEFBQVBXVxeVTZo0CT4+PmIekKyS/1bGlStXEBAQgJCQEDRv3hy//vorfv31V5iZmTHV5Wvir3zicYFAIBaDuewzy/wDkvQMkCRCQ0OxYsUKZGVlwdnZmbPcaHwSGxuLKVOmIDMzE46OjqLdlwsWLMA///yDw4cPM9Hluw1IAlFRUbC1tcXTp09Fz3wigomJCQICAtC1a1dO7Lhw4QLGjRsHQ0NDdO/eHUCpV25SUhJOnTqFYcOGMdPet28fHBwc8P37d6ipqUEgEODz58+oV68e/vjjD8ydO5eZdhmKiopISEhAixYtxBZAkpKS0KFDB94Wgp8/f44BAwYwzQUjReqQIwU4deoUJkyYAAsLC1EOkNu3b+PatWs4fvx4tU4T/w07d+6EtbU1NDQ0mPz9mvBvERnKYBWZQU9Pr0ZJyFNTU5noS9oOlLqOdAGkjhITEwMzMzNOOv0/Jh3csGED06SD5YmMjMS+ffuQkpKCkydPQkdHB8HBwdDX10efPn2Yar98+RJWVlYgIiQlJaFLly5ISkpCw4YNERERwST8WHmmTJmChIQEmJqa4siRI8jMzISmpibOnj2LFStWID4+nql+dTx+/Bj+/v41StT7f0EoFFYY7JZRVwa95YmIiIC/vz9OnToFoVCI8ePHw87ODj169ODbNKakpaVh/vz5uHHjhtjW4rr2+/OBdMv/v7Nu3ToMHDgQffv2/el/m4gwYsQIXLhwAR07doSxsTGICM+ePUNcXBxGjhxZbVLS/5byE49VwVcb/PjxIw4dOgR/f3/ExsYyt4Gvib+MjIwandeiRQsm+pL0DCgqKsKNGzeQkpKCyZMnQ1VVFVlZWVBTU4OKigpzfaB0osPFxQXR0dGYP38+XFxc6vykV35+PmRkZFCvXj0mf5/vNiBJPH78GElJSSAitG7dutqkrKzgKwQRULrT8fjx40hOThZ9B7/++msFxyhWmJiYwMPDA6NGjRJbANmxYwcCAgKYeT7/GxcuXMC0adM4iwpQ16mrDjlSSnn48CH++OMPsWfgkiVLqo2e8t+irq6O79+/Y/To0bC3t2e+201KRSRlB4qUUqQLILWUfwttk5qaiiVLljAd+L9//x5ubm7Yv38/+vTpA09PT848jYDSlfapU6diypQpCA4OxtOnT2FgYICdO3fiwoULzOO9AqWD7qNHjyI2NhY5OTkwMzPDlClToKioyFz706dPWLVqFV68eIG5c+diyJAhAEpX1+Xk5LBy5UrmNvCFdNALZGVlITAwEIGBgUhOTkavXr1gZ2eH8ePH87bdsri4GHFxcWjRogUnEz+9e/cGEcHBwQGNGzeuMNDo378/cxvqKnxv+R84cGCNvH2uXbvGRL8m6Ovr4+3btxg0aBDOnTv3U/92QEAAHBwcEBoaioEDB4rVXb9+HaNHj8bOnTthY2PzU3X/14iOjma+AwTgd+KPL/h+BpSRkZGBIUOGIDMzEwUFBUhMTISBgQEcHBxQUFDAfDfy06dP4ezsjLCwMNjY2MDNzY2zSVdJo7CwEO/evauwG1NXV5cni6RI4QZfX1+4urrCy8sLdnZ28PX1RUpKCjw8PODr64uJEycy1Xd0dBT7TER4/fo1/vrrL0ybNg07d+5kqi9FihR++PbtG06cOIGAgABERERAV1cXM2bMgK2tba3ug0oSfO9AkSKOdAGkllKdB3wZrLwvJSXpoKmpKRYvXgwbGxsxb5tHjx5h6NChePPmDec2Sal7fPr0CQ8ePKh00M9q8nHo0KG4evUqGjZsCBsbG8yYMUM0+cQlixYtQvv27WFnZ4fi4mL0798fd+7cgZKSEs6fP48BAwYw1VdRUcHDhw95ufby8D3pk5KSgoCAAKSkpGDbtm1o1KgRLl68CF1dXbRt25aZLp9b/hcvXlxl3devX3H48GGJiL//7ds3hIeH//RdAJaWljA3N68yvM7GjRtx8+ZNXLp06afqSpFSHkkI+1EWc9/Pzw+ampqivuCNGzcwc+ZMJCUlMdF98eIF1qxZg0OHDmH48OHYuHEjZ3lnypCUheDExETY2dnhzp07YuV1bTcm132BdevW1ei8NWvWMNGXBCIiImp0Xr9+/RhbUpr41tXVFSkpKQCApk2bws3NDXZ2dsy1f3SEEAqF0NLSgrm5OWbMmMFJAl6++qJ8IynPYUmAjzGxpBAdHY169eqhffv2AErDYQYEBMDExASurq4V8mGwIDU1FYGBgTh48CBevnwpyss1evRoZjsxAWkbkCJZSBdAaik6OjrYvXs3Ro0aVWn948eP0blzZyaDDklJOqikpISnT59CT09PbAEkNTUVJiYmYiFxfhb/SVJpLmOfS+GHc+fOYcqUKcjJyRHFHS5DIBDgw4cPTHRHjhwJOzs7DB8+HDIyMkw0akKzZs1w5swZdOnSBWfOnMG8efMQHh6O4OBgXL9+Hbdv32aqP3DgQKxcuRIWFhZMdaoiKSkJM2bM4HXS5+bNmxg6dCh69+6NiIgIPHv2DAYGBvD09ERUVBROnjzJ3AZJ2fJfVFSEXbt2wd3dHerq6li/fj1zr0u+aNKkCcLCwqoMc8LaEUCSJp2k8A+fzwBNTU3cuXMHRkZGYn3B9PR0mJiYMEtCr6SkBIFAgPnz54vifVcGy76gpCwE9+7dG7KysnBxcYG2tnaF379jx45M9fmGr75AdWFVBAIBnj9/jvz8/Fq9AFVdOMay+1AgEKCoqIgrk5CXl4ecnBzmoZAlCb76ot++fcOVK1cwcODACrsNv3z5ghs3bsDKygry8vJM9AH+n8OS8B0A/I2JyyguLkZgYCCuXbtW6QLM9evXmep37doVLi4uGDdunGguauzYsfj777/xyy+/wMfHh6l+eYgIV69eRWBgIM6cOQNlZWW8e/eOmR7fbUASICIkJyejsLAQRkZGnCw6S6kCklIrGTFiBK1evbrK+sePH5NAIGCiLRAIRIdQKKz0s1AoZKJdHn19fbpy5QoREamoqFBKSgoREQUFBVGbNm2YaJa/1squv6yMi+uXwj+tWrUiBwcHys3N5UW/pKSE/v77bzpx4gSdPHmSHj58SCUlJZzpy8vL04sXL4iIaObMmeTg4EBERKmpqaSqqspcPzk5mSwsLCgwMJCioqIoJiZG7GBNr169qF+/fnThwgV69OgRPX78WOzggh49epCXlxcRiT8H79+/Tzo6OpzYIAkcOnSIDAwMSFtbm3bt2kXfv3/n2ySm1KtXj7Kysqqsf/XqFcnJyTHTL/+u+/EdWP5dKEUKazQ0NOjJkydEJP4MjIyMpEaNGjHTreq+57sNfP/+nXx8fEhLS4sMDQ3pyJEjzDWVlJTo2bNnzHUkFUnoC5Tn0aNHZGVlRfXq1aPZs2dzrs8lnz59qvTIysoiZ2dnUlRUpLZt2/JtZq2Hr76oj48PmZubV1k/aNAg2rlzJzP9quDyOSwp3wHfY+J58+aRsrIyjR8/nhwcHGjRokViB2vU1NQoOTmZiIg8PT3J0tKSiIhu3bpFzZo1Y67/I9evX6cpU6aQoqIiaWhocK7PR1+EL1JTU6ldu3aicVHz5s3pwYMHfJtVZ5EuPdVSli5ditzc3CrrDQ0NER4ezkQ7LS2Nyd/9T5k5cyYcHBzg7+8PgUCArKws3L17F05OTli9ejUTzfLeBFevXoWzszM2btyInj17AgDu3r2LVatWYePGjUz0pUgWr169wsKFC6GkpMS5dnh4OOzs7JCRkSEKhScQCKCvrw9/f39OPK8bN26Mp0+fQltbG2FhYdizZw+AUu83LnamZGdnIyUlBdOnTxeVlYUG5GIHxuPHj/Hw4UMYGxsz1amOuLg4HD58uEJ5o0aN8P79e2a6krLdOSwsDC4uLkhLS4OTkxMcHR15y4HDJcXFxdV6F8nIyDD1eK1fvz5UVVVha2uLqVOnomHDhsy0pEgmkvIMsLS0hI+PD/bv3y/SzMnJwdq1a5kloAdQwbtUEvjzzz+xZs0afPv2Da6urpg1axYnXogmJiZM3zeSjiT0BYDS8dnq1atx7NgxjB07Fk+ePEGrVq14tYk16urqYp9LSkrg7+8PNzc3CIVC7Nq1C9OmTWOibWpqWuPdbqyToPNtC1990T///LPaMf+iRYuwbt06zJs3j5kNldnE5XNYUr4DPsfEAHD06FEcP36c6Xu/OohI1C+4evUqhg8fDgBo3rw5Z+/HFy9eICAgAIGBgcjMzES/fv1w4MABjBs3jhP9Mvjqi/DF0qVLUVRUhEOHDkFBQQFbt27FnDlz8PDhQ75Nq5PU3jutjtO3b99q65WVlZklAJaUxNIuLi4oKSnBoEGDkJeXh379+kFeXh5OTk5YsGABc/1FixZh79696NOnj6jMysoKSkpKmDVrligZal3l4MGD6N27N1q2bMm3KcywsrJCVFQUDAwMONVNTk7G8OHD0b17d/zxxx8wNjYGEeHp06fYvn07hg0bhtjYWOZ2TZ8+HePHjxeFvCgLRXX//n1OJgJmzJgBU1NTHDlypNIk6KyRhEkfDQ0NvH79Gvr6+mLljx49go6ODjPdqkIvAeLbnVnx4MEDODs74969e5gzZ44oJ05dgYhga2tbZUgDlt89ALx+/RohISHw9/fH5s2bMWzYMNjZ2WHIkCG8hEArKirCjRs3kJKSgsmTJ0NVVRVZWVlQU1ODiooK5/ZwDR/Xz/czoAwvLy9YWVmJQp9OnjwZSUlJaNiwIY4cOcJcXxLgeyF406ZNWLZsGTZu3Ij27dtXiDWupqbG3AY+nwF89wXev38PNzc37N+/H3369MGdO3fQtWtXTrQlJfwOAJw+fRorVqxAdnY2li9fjgULFjDVHT16NLO//Z8yZMgQ7N69GyYmJiKnvHv37uHJkyeYO3cuFBUVmerz1RdNSkqqNsRehw4dmOWB+hG+nsOS8h3wNSYuQ05ODoaGhrxoA0CXLl2wYcMGWFhY4ObNmyKnwLS0NDRu3JiZbmFhIU6fPg1/f39cv34d2tramDZtGmbMmMH5b8F3XwTgJxfRrVu3cPLkSdGcYI8ePdCsWTPk5ubWCac8iYPX/SdSpHBAQUEBPXnyhO7fv09fv37lTFdBQYHi4uIqlMfExJCCggIz3by8PAoNDaUvX75UqPv8+TOFhoZSfn4+M/2aIhAISE5OjubPn8+3Kczw9fUlXV1dWrt2LZ08eZJCQ0PFDlbMmzevyu3OJSUlZG5uztn3fuLECfL29haFwiIiCgwMpDNnzjDXVlJSoqSkJOY6VXHt2jXq2bMnhYeH0/v37+nz589iBxcsWbKE+vTpQ69fvyZVVVVKSkqiW7dukYGBAbm6unJiQxlcbncWCASkpKREixYtom3btlV51FZsbW1rdHBBRkYGubm5kYGBAeno6NCKFSs4DUGWnp5OxsbGpKSkRDIyMqLQGwsXLmQa/kVS3sV8XX9l8BXy4Pv37xQcHExLly6luXPn0oEDBygvL48TbT65f/8+DRgwgBQUFGjRokWUnZ3Nix0/hoAtHx6PizBgfLcBvvoCOTk55OrqSmpqamRmZkaXLl1iplUVkhB+58aNG9S9e3dSUlKi5cuX06dPn5jqSSJ2dna0atWqCuVr1qyh6dOnM9fnqy+qoqJCUVFRVdZHRUWRiooKM30i/p/DkvAdEPE3Ji5j69at9Pvvv3MaCro8MTEx1K5dO1JTUxO75+fPn0+TJk1iplu/fn2Sl5encePG0YULF6i4uJiZVlXw3QbKuHHjBikqKpKFhQXJycmJ+gIeHh40btw4ZroCgYDevHkjVqasrEypqanMNKVUjTQJuhQpjOjXrx8UFBQQHBwsWtl/+/YtbGxskJ+fj5s3bzLR3bZtG86ePVtlWAkLCwuMGTOG0+2+VZGWloaLFy/i999/51zb3NwcAwcOxJIlS5htx/235IusQjC1a9cOHh4eGDFiRKX1586dw/LlyxEfH89EX1IYMWIEbG1tOd/aW0bZ7/+jxztxmAS9sLAQ8+bNQ2BgoCgsUnFxMSZPnozAwEBOQpEB4tudV61axXy7s56eXo3C76SmpjLRlySvV0khLS0NdnZ2uHnzJrKzs9GgQQNOdEePHg1VVVX4+flBU1NTlAT7xo0bmDlzJjPPR0l5F/N1/T/C9TNASuk7SFFREbNmzargeV2ehQsXMrXj3/q7rHakl8F3G+CrL9CkSRN8/foVCxYswKRJk6p8J3bo0IGJPgB069YNq1evrrI/ev78eaxbtw4PHjxgoj9s2DBcvXoVM2bMgKurK5o0acJER9JRV1dHVFRUhZBnSUlJ6NKlCz5//sxUn6++aI8ePTBmzBg4OztXWu/h4YHQ0FDcu3ePiT7A/3NYEr4DgL8xcRljxoxBeHg4GjRogLZt21bYiXj69Gmm+lWRn58PGRmZCvb8LLy9vTF16lRoaWkx+fs1ge82UEbPnj3x22+/wdHREaqqqqK+wIMHDzB27Fi8fPmSia6MjAwSExPFfoNmzZrh1q1b0NPTE5VxsRtWCiBdAJFSqxg7dmyNz2X9oktOTsaYMWOQmJiI5s2bAyiNvdiqVSucOXOG2TZMvgcb/yvY2toiPT0dqampyMzM5Nucn4qamhpiY2PFXqrlSUtLQ4cOHfD161fmtly7dg3Xrl3Du3fvKsRE9/f3Z6q9f/9+bNiwATNmzKg07MbIkSOZ6vM96VOezMxMxMfHIycnB6amppzF/ZaE7c58ICmT35VBRAgLC4Ofnx9OnjzJVKugoACnTp2Cv78/7t69i19++QUzZszAkCFDmOqWR1NTE3fu3IGRkZHYgCc9PR0mJibIy8tjoisp72K+rr8MPp4BZ8+erfG5rN8DfML3QrCkwHcb4KsvUH7CsSz/2Y+fWU881q9fHzExMdDV1a20PjMzEx07dsTHjx+Z6AuFQsjKykJZWbnatvDhwwcm+pJCkyZN4OnpCVtbW7HywMBAODs74+3bt5zYwXVfdP/+/XB0dMTRo0dFORfKOHfuHCZNmgRvb2/MmjWLmQ18P4cl4TuQBMrng6yMgIAAjizhh6SkJISGhiI9PV2UE3T06NGchMHiuw2UoaKigri4OOjr61foCxgbGyM/P5+JrlAorNIBovz/uXCMlCLNASKlllE+2R0RISQkBOrq6ujSpQsA4OHDh/j06dN/tFDyf8XQ0BCxsbG4cuUKEhISAABt2rSBhYUF0xjokhLrs4zv37/jzZs3yMvLg5aWFmdev/9GYGAggFJv7NpGTk5OtbtalJSUmA/4AcDNzQ3r1q1Dly5dRHlAuGTOnDkAgHXr1lWo46KjweUCx7+hq6tb5QQEC+p6Dg5JSTpZnrS0NPj7+yMwMBDZ2dminDwsePDgAQICAnD06FHo6elh+vTpOH78OC/P/5KSkkrb+suXLyvszvmZSMq7mK/r5/MZUNPY+7V9wJmens63CWLk5eUhMzMThYWFYuUsdyAA/LWBMvjqC6SlpfGiW56ioiJkZ2dX2f/Izs5GUVERM/3aPqlZUxYtWoS5c+ciOjoa3bp1A1Caj8/f37/avsrPhuu+6KxZsxAREYGRI0fC2NgYRkZGAICEhAQkJiZi/PjxzCf++X4OS8J3IAnU5WeBh4cHVq9eDSJCo0aNQETIzs6Gi4sLNm7cCCcnJ6b6fLeBMvjKRRQeHs7sb0v5z5HuAJHCFD6TDjo7O+PDhw/Yu3evaGttcXExfv/9d6ipqWHLli1M9flCVVUVN27cQOfOnSutf/jwIQYMGMDU+//r1684dOgQjh49igcPHqCwsFC0ut2sWTNYWlpi1qxZnCVh5IvKJt7Ls2bNGia6QqEQ169fr3Ky8f379xg8eDDziR9tbW1s3rwZU6dOZaojqURERFRb369fPya6jo6ONT7X29ubiQ2Sst2ZL/j2ei2joKAAJ0+ehJ+fH27duoXi4mJs3boVdnZ2TLdaC4VC6OrqYtq0aVW+iwBuvO8nTJgAdXV17N+/H6qqqoiNjYWWlhZGjRoFXV1dZoNiSXgXA/xdf11/BvxIYWFhpTshWU8GEhGSk5NRWFgIIyMjXsKOZWdnY/r06bh48WKl9az7Iny1gTL46gtIApISfkcKcPz4cWzbtg3Pnj0DUOqU5+DggPHjxzPRk4S+aBnHjx/H4cOHkZSUBCJC69atMXnyZGbXLonw/R3wNSau64SHh8PCwgKrV6+Gg4MD6tevD6B015uPjw82btyI69ev1+r3UBlOTk64f/8+Tpw4gdatWyM6OloUnt7GxgZr167l20QpHCBdAJHCjIyMDAwZMgSZmZkoKChAYmIiDAwM4ODggIKCAuzdu5epvpaWFm7duiXydCjj+fPn6NWrF/755x+m+nzB92DD29sb7u7uaNmyJUaMGIFu3bqhadOmUFRUxIcPHxAfH4/IyEicOXMG3bt3x44dO5htgV64cCEMDQ0rTLDs3LkTycnJ8PHxYaJbhqmpqdjn79+/Iy0tDbKysmjZsiWio6OZ6JZttazs8c5V2AOgNOzEgwcP0LJlS6Y6kkpl8W7L74Jh9f0PHDhQ7HN0dDSKiopEz8LExETIyMigc+fOuH79OhMbJGW7M1/wPfn98OFD+Pn54ciRIzA0NMTUqVMxYcIENGvWDDExMTAxMWGiW0Z1sZ7L4Mr7/uXLl7CysgIRieKdJyUloWHDhoiIiECjRo2Y6PL9Li6Dr+uv68+AMpKSkjBjxgzcuXNHrJyL93BaWhpGjhyJp0+fAgB0dHRw6tQpzp1PpkyZgoyMDPj4+GDAgAEICQnB27dvsWHDBnh5eeGXX35hqs9XGyiDr76AJCANvyNOYWEh0tLS0LJly1qfA+nHvmhVCAQCZn1RKZIDX2PiMvT19avtk9TWvsiECROgoaGBffv2VVo/a9YsfP36FUeOHGFmw/bt22t0HmuHGL7zYr569QqnTp1CYmIiAMDIyAhjx45luvtESkVq95tXCq84ODigS5cuiImJgaampqh8zJgxmDlzJnP9oqIiJCQkVFgASUhIqOCBV5uYMWMGHB0d0bZt20oHG+7u7kw9bf7++29ERESgbdu2ldZ369YNM2bMwN69exEQEIDIyEhmCyCnTp2qNBZ4r1694OnpyXwB5NGjRxXKvnz5AltbW4wZM4aZriSEPQAAe3t7HD58mNPt9ZLEj979379/x6NHj7B69Wq4u7sz0y2/1dbb2xuqqqoICgoSef18/PgR06dPR9++fZnZICnbnfmibdu2uHr1apULIJcvX67yGfkz6N69OxYsWIB79+5VeAdygSS9Y8sWfY4ePYrY2Fjk5OTAzs4OU6ZMgaKiIjNdvt/FZfB1/XX9GVCGra0tZGVlcf78ec5DQS5duhRFRUU4dOgQFBQUsHXrVsyZMwcPHz7kzAYAuH79OkJDQ9GlSxcIhUK0aNECgwcPhpqaGjw8PJgvgPDVBsrgqy8gCUjD75SSl5eHBQsWICgoCABEToELFiyAjo4OXFxceLbw5yMN+yKlPHyNictYtGiR2Oey53BYWBiWLl3KXJ8vHjx4gODg4Crrp06dChsbG6Y2/PHHH/96jkAgYL4AIicnhwMHDmD16tWc58XcvXs3HB0dUVhYKNqB/+XLFyxduhTe3t74/fffmdsg5f+HpEhhRIMGDSghIYGIiFRUVCglJYWIiNLS0khRUZG5/uLFi0lTU5O8vLwoMjKSIiMjaevWrdSwYUNavHgxc30+mTJlCgkEAmrTpg2NHj2aRo8eTcbGxiQUCmnixIl8m8cZ8vLylJSUVKE8KSmJ5OXlebColNjYWGrRogVv+kREcXFxzDUWLlxIGhoa1K9fP5o/fz4tXrxY7Kir3Lhxg8zMzDjRatq0KcXHx1coj4uLI21tbU5sqIvs27ePlJWV6dy5cxXqzp49S8rKyrRv3z5m+paWlqSqqkqTJ0+mixcvUklJCRERycrK0pMnT5jpShFH+i6WoqSkRM+ePeNFu3HjxhQZGSn6nJWVRUKhkHJycji1Q1VVldLS0oiISFdXl27dukVERKmpqZyMByQVLvsCfHPs2DEaNWoUmZiYUJs2bWjUqFF07Ngxvs3ijIULF1Lnzp0pMjKSlJWVRWPiM2fOUKdOnXi2rvYiEAhIKBRWe8jIyPBtJlMk/Tvge0y8c+dOsrW15U2fiMjNzY0iIiKY/G1FRUV68eJFlfUvXrwgBQUFJtpSSjl//jzJyMjQkiVLKCsrS1SelZVFixcvJllZWfrrr794tLBuId0BIoUZfCcd3Lp1K5o0aQIvLy+8fv0aQGlOgqVLl2LJkiXM9fnk0KFDGDlyJA4fPozExEQQEYyMjODm5sZLvNPk5GSkpKSgX79+UFRUFIV+YI2hoSHCwsIwf/58sfKLFy/CwMCAuX5VfP78GZ8/f+Zct2yLq6+vLx4+fMg87EJsbCw6deoEAIiPjxer4zohuiTRuHFjPH/+nBOtL1++IDs7u0J5dnY289wDkkJKSgoCAgKQkpKCbdu2oVGjRrh48SJ0dXWZ7cLg2+v10qVLePHiBQICAjB37lx8+/YNEyZMAMBN26ts511lsMoBUlN9ljYA/L2LJeX6pQAmJiZ4//49L9rv3r0T82zU1taGoqIi3r17V21elp+NkZERnj9/Dj09PXTs2BH79u2Dnp4e9u7dC21tbSaa/wttgMu+AN+MHz++TuVb+JEzZ87g2LFj6NGjh9g7uG3btkhJSeHRstpNSEhIlXV3797F9u3bJWrHKgsk/Tvga0xcxtChQ7F8+XJek6QHBATA09MTgwYNwrlz537q387Pz4ecnFyV9fXq1UNhYeFP1ZQkJCEX0ZYtW+Di4oINGzaIlWtra8Pb2xtKSkrYvHkzhg0bxkRfijjSHCBSmMF30sHyfPnyBQCYJn2VUpF//vkHEyZMwPXr1yEQCJCUlAQDAwPMmDED9evXh5eXF1N9f39/zJ8/H0uXLoW5uTkA4Nq1a/Dy8oKPjw/zUGw/xrwkIrx+/RrBwcHo378/Dh8+zFS/jIiICPj5+eHUqVNo2rQpxo4di3HjxtX6JPR8ExsbK/a57Pf39PREUVERbt26xdwGGxsbREZGwsvLC926dQMA3L9/H0uXLkXfvn1F4RhqKzdv3sTQoUPRu3dvRERE4NmzZzAwMICnpyeioqJw8uRJpvp8J50s48qVKwgICEBISAiaN2+OX3/9Fb/++ivMzMyY6PGdA6Qm+qxt4JO6fv2SxPXr17Fq1Sps3LgR7du3R7169cTqWfZLZWRkkJiYCC0tLVFZs2bNcOvWLejp6XFiA1C6EFhUVARbW1s8fPgQQ4YMwYcPHyAnJ4fAwEDR4uzPRJLagCT0BaSUwlcODiUlJcTHx8PAwACqqqqIiYmBgYEBYmJi0K9fP14ngOsaz58/h4uLC86dO4cpU6Zg3bp1aNGiBSfaJSUlSE5Oxrt37yosOnCZhJqP70BSxsQ/snnzZuzevZv3sJ3fvn1DeHj4T58EFwqF2LBhA1RUVCqt//r1K9asWVNr+4KSkItITU0Nf//9d5UhiZ8/f46uXbuK5iulsEW6ACKFGXwnHZRkhEIhBgwYgC1btlQZI742YGNjg3fv3sHX1xdt2rQRdfgvXboER0dHPHnyhLkNe/bsgbu7O7KysgCUJmZ1dXVlHu8SQAUPS6FQCC0tLZibm2P58uVMd0K9efMGgYGB8PPzw5cvXzB+/Hjs3buXkwTIkgQf3v9lVJWMvkePHvD394exsTFTfaA07rSTkxP8/f3x/ft3AICsrCzs7OywZcsWKCsrM7eBT3r27InffvsNjo6OYpMODx48wNixY/Hy5Uu+TeSUjx8/4tChQ/D390dsbGytHfBIkVIZ+fn5FTwdWU/+l03E/7jzijhIgl72DqpMlysbKiMvLw8JCQnQ1dVFw4YNOdXmA776Aqampv+6409WVhZNmjTB4MGDMXv27Go9hf8vVHYP/ohAIEBRUdFP1f0RvnNw9OvXD7/99hsWLFggcgrU19fHggULkJSUhLCwMGbaX758gYqKSoVFweLiYuTm5tYZ58CsrCysXbsWQUFBsLKygoeHB9q1a8eZ/r179zB58mRkZGRUeBZw9Rzm8zvgc0wMVHweEhHevHmD7Oxs7N69u9bmItLT06vRzm9JyR9aG1FWVkZcXFyV0UdSU1PRvn175ObmcmxZ3US6ACKFKUVFRTh27BhiYmKQk5MDMzMzzpIOSjKBgYFIT09HWFgY7t2791P/tqQMNgCgSZMmuHTpEjp27Cg2+ZiamooOHTogJyeHuQ1lZGdnQ1FRsUoPiNrEiBEjEBERgV9++QVTpkzBkCFDICMjg3r16nG+ABIVFYXjx48jMzOzwsTT6dOnmWrz7f2fkZEh9rmss6+goMBUtzJyc3NFYRZatmxZ6xc+ylBRUUFcXBz09fXFnkHp6ekwNjZGfn4+3ybyRnR0NLMdIOfPn8cvv/xSp0PdSdK7uC6Tl5eHZcuW4fjx4/jnn38q1LOedLp582a19f379+dNmwsbpPDXF3Bzc/vXc0pKSvDu3TucPn0a48aNw+7du3+qDaGhoVXWlQ+/w/pd7ODggNu3b8PHxwdDhgxBbGwsDAwMEBoaCldX10oTNP9Mbt26haFDh8La2hqBgYGYPXs2nj59ijt37uDmzZvMnOFCQkLg7OyMx48fQ0lJSawuNzcXZmZm2Lp1K0aMGMFEXxL4/PkzNm7ciB07dqBTp07YtGkT+vbty7kdnTp1QuvWreHm5gZtbe0K/QN1dXVm2pLyHfDJj8/DsufwgAEDOHFIk1J36datGyZNmoTFixdXWu/t7Y2jR4/iwYMHHFtWN5EugEiRUsuQlMEGAKiqqiI6OhqtWrUSm3yMioqClZVVpZMRUv57ZGVlsXDhQsydO1cs/jfXCyBHjx6FjY0NrKyscPnyZVhaWiIxMRFv377FmDFjmIfBk3r/S2nWrBmOHz+OXr16id0DISEhcHJyYhZ7u65PfsvKyqJx48awtbXF9OnTYWhoyLdJnCNJ72K+4TPsx7x58xAeHo7169dj6tSp2LVrF169eoV9+/bB09MTU6ZMYapfV5GEuNtSak5ERATGjx+PN2/eMNfiI/xOixYtRDk4yvcFkpOTYWZmxknokZSUFHh6eoo5BTo7O6N9+/bMNC0tLTF+/HjY29tXWu/v749jx47h0qVLTPQjIiJqdB6r98DmzZuxadMmNGnSBBs3bsSoUaOY6NQEZWVlxMTEcN4fkqTvQIrk8enTJxw6dKhCvlQpP4+goCDMnTsXW7duxaxZs0ThF4uKirBv3z4sXboUu3fvhq2tLb+G1hGkCyBSmOHh4YHGjRtjxowZYuX+/v7Izs6Gs7MzT5bVPfiKdzps2DB07twZ69evF235btGiBSZOnIiSkhLmHvgAcPLkySp3IERHRzPX54N79+7Bz88Px44dQ5s2bTB16lRMnDgR2tranC6AdOjQAbNnz8a8efNEA059fX3Mnj0b2traNfJO/G+Qev9LNlyEAnRycsL9+/dx4sQJtG7dGtHR0Xj79i1sbGxgY2ODtWvXMtGt65PfZQnYg4KCkJ6ejj59+sDe3h6//vprnd4Bymfscb7gK+zH4cOHMXnyZOjq6uLgwYMYMGAA1NTUEB0dDUNDQwQHB+PIkSO4cOECE/3yREZGYt++fUhNTcWJEyego6OD4OBg6Ovro0+fPsz1X716hVOnTiExMRFAaVLysWPHQkdHh5nmj3G3o6OjUVRUJIqBnZiYCBkZGXTu3JlZ3G0p/4/3798jPT0dAoEAenp60NTUFKvPycnBmjVrmC5G8Rl+p67m4GjatCkiIiKqnHRPTk5Gv379RGGCfzbV5eIpcxJh6QwiFAqhqKgICwsLyMjIVHke6x3pAGBubo5ly5ZhyJAhzLXKI0nfgRTJ4dq1a/Dz80NISAiUlJQ4c0rlMzQ1nzg5OcHb2xuqqqpo2bIliAipqanIycnBwoUL8ccff/BtYp2hZhnipEj5P7Bv375KtxS2bdsWe/fu5cGiukdWVhZmzpyJ9u3bo6ioCI8fP0ZQUBBnEy6bN2/G/v37MXToUBQWFmLZsmVo164dIiIisGnTJub627dvx/Tp09G4cWM8evQI3bp1g6amJlJTUzF06FDm+nzRo0cPHDhwAK9fv8bs2bNx9OhRNG3aFCUlJbhy5Qq+fv3KiR0pKSn45ZdfAABycnLIzc2FQCDA4sWLsX//fub6GhoaeP36dYXyR48eMZ34kVIz/P390a9fP8ybN4+ZxsaNG2FsbIzmzZsjJycHJiYm6NevH3r16oVVq1Yx0x01alSFw9jYGIGBgdi6dSt+++03PH/+nJk+3zRv3hxr1qxBSkoKrl69Cj09PcydOxfa2tqYM2cO/v77b75N5BS+38V8MmfOHHTp0gXx8fH48OEDPn78KDo+fPjw0/W+ffsGOzs7nDhxAgDw4cMHUdxlNTU1kWafPn1q7J3833Dq1ClYWVlBUVER0dHRKCgoAPD/QpKwZvfu3WjZsiUWLVqEQ4cO4dChQ3BwcEDLli1/erij8oSHh4uOESNGoH///nj58iWio6MRHR2NFy9eYODAgaI+ghQ2PHnyBP369UPjxo3RvXt3dOvWDY0aNYK5ubnYO0hFRYXZ4sfnz5/h7OwMQ0NDPHnyBNeuXcO5c+c4zb/QpUsX/PXXX6LPZZPvvr6+6NmzJxPNL1++1PhgxcePH6tdXPj+/Ts+fvzIVL+y49WrV1i6dCnk5eWZhh+ysbHB+PHj0aBBA6irq1d5cMGCBQuwZMkSBAYG4uHDh4iNjRU7WCFJ34EUfnnx4gXWrVsHfX19WFpaQiAQICQkhJOdf0BpWM727dvj/v37OH36tCgUekxMDDOHNElh69atuHPnDmxtbdGkSRNoa2tj+vTpuH37tnTxg2tIihRGyMvLU2pqaoXylJQUkpeXZ6a7bdu2fz127dpFp06dordv3zKzg08+ffpEy5YtI0VFRerZsydFRETwasuGDRvot99+o6FDh9LKlSspKyuLE20jIyM6fPgwERGpqKhQSkoKERGtXr2a5s2bx4kNkkJCQgItXbqUmjRpQgoKCjRixAjmmjo6OhQbG0tERO3btxf9Fnfu3CE1NTXm+kuWLKE+ffrQ69evSVVVlZKSkujWrVtkYGBArq6uzPWlSA4ZGRn0119/0bFjxygxMZFT7VevXpG9vT3Vq1ePhg8fTnFxcZzqSwpfvnyh/fv3U69evUgoFFKHDh34Nok5kvQu5gslJSVKSkriTG/9+vU0f/580ef27dvTjRs3iIho0KBBtGTJEiIq7Svq6Ogwt6dTp04UFBREROL9kOjoaGrcuDFT7fPnz5OMjAwtWbJErN+VlZVFixcvJllZWfrrr7+Y2kBE1LRpU4qPj69QHhcXR9ra2sz16yqvX78mTU1NMjY2Jh8fHwoLC6OLFy+Sl5cXGRsbk5aWFvNx0KZNm6hBgwZkYmJCZ86cYapVHZGRkaSiokJz5swhBQUFcnBwoMGDB5OysjJFRUUx0RQIBCQUCmt0sMLY2JiCg4OrrD948CAZGRkx0/+R4uJiOnDgADVr1ox0dXXJ39+fiouLOdPnE4FAUOEQCoWif6XUTvLy8ig0NJS+fPlSoe7z588UGhpK+fn5zPQLCwvp+PHjZGlpSYqKijRmzBg6ceIEycrK0pMnT5jpVkaPHj3Iy8uLiMT7Q/fv3+ekPyZFChGRdAFECjMMDQ0r7XQdPHiQ9PX1menq6en966Grq0vKysqkqKhIp06dYmYLH0jKYKOwsJDMzc05n2wsj6KiIqWnpxMRkZaWFj1+/JiIiBITE6lBgwa82cUnRUVFFBISwskCyKRJk0QdnXXr1pGWlhbZ29tTixYtaMyYMcz1CwoKyN7enmRlZUkgEFC9evVIKBSStbU1FRUVMdeXUrfhc/Kb7wFXVaSkpNDKlSupQYMGJCsry5lucnIyrVy5kiZOnCia8Ltw4UKlk7I/C0l5FxPxc/1lDBw4kC5evMhcp4zMzEyxz97e3rRt2zYiIrpy5QopKCiQvLw8CYVC8vHxYW6PoqIipaWlEZH4gJ+1MxARUf/+/WnlypVV1q9cuZL69+/P1Aai0usODw+vUH79+nVSUVFhrl9XWbZsGZmZmdG3b98q1OXl5ZGZmRm5uLgwtUEgEJCSkhKNHDmSxowZU+XBBcnJyWRvb09du3alNm3a0JQpU0ROOiy4ceOG6AgMDKQmTZqQi4sLhYaGUmhoKLm4uJC2tjYFBgYys2HFihWkq6tLb968qVD3+vVr0tXVpRUrVjDTL8+pU6fIyMiIGjRoQFu2bOGlD8In6enp1R5Saic+Pj5kbm5eZf2gQYNo586dzPS1tLSob9++tG/fPvrw4YOonI8FEGVlZZFzdPn+UFpaGvP+kCRz6tQpat++Pd9m1BmkOUCkMGPz5s3YvHkztmzZAnNzcwCl8QaXLVuGJUuWYPny5bzaV1JSAk9PTwQHB+PZs2e82vIzkaRYn1paWrhz545YIm4uMTAwwKlTp2BqaoouXbpg5syZmD17Ni5fvoyJEycyCb/xI8HBwdi7dy/S0tJw9+5dtGjRAj4+PtDX16/1ieg+fPiA/Px8UfitzZs3i+6HVatWoX79+pzYkZmZifj4eOTk5MDU1JS3+1EKN0hC8l2+k05u27YNZ8+exbVr1yqtt7CwwJgxY5iGHyvj27dvOHHiBPz9/REZGQl9fX1Mnz4dtra2nISiu3nzJoYOHYrevXsjIiICz549g4GBATw9PREVFcUsF5WkvIv5uv4yQkJCsGrVKixduhTt27dHvXr1xOo7dOjAVP9HMjIy8PDhQxgaGnKibWBggP3798PCwkIs98DBgwfh6emJp0+fMtNWU1PD33//Lcq78SPPnz9H165dmSeAtrGxQWRkJLy8vNCtWzcAwP3797F06VL07dsXQUFBTPWrg4tcVHxhZmYGFxcXjB8/vtL6o0ePYvPmzUzz4dna2orCTVVHQEAAMxskgUGDBsHe3h6TJk0SKz98+DD279+PGzduMNH9+vUrevbsiczMTFhbW4ueBQkJCfjzzz/RvHlz3Lt3D6qqqkz0gdJ3kLOzM+Li4uDg4ABnZ2dpyKU6RGJiIj59+iR69gOl80EbNmxAbm4uRo8ejRUrVjC14fv371BUVMTjx485Db0HAN26dcPq1asxYsSISuvPnz+PdevW4cGDB0z0GzRogPbt28Pa2hoTJkyAmpoaAKBevXqc5gUFgGbNmuH48ePo1auXWH8oJCQETk5OSElJ4cwWrtm3bx+uXLkCOTk5ODg4oHv37rh+/TqWLFmCxMRE2NjYYM+ePXybWSeQ5dsAKbWXpUuX4p9//sHvv/8uSj6toKAAZ2dn3hc/gNJBz7Rp02pd3D0bG5saDTa4wNraGn5+fvD09ORF39zcHGfPnoWpqSmmT5+OxYsX4+TJk4iKisLYsWOZ6+/Zswdr1qzBokWL4O7uLkr2qqGhAR8fn1q/ANKgQQPR/4VCIVxcXHixQ1dXF7q6urxoV0VtnnThm0ePHtXoPJbPSRcXFygqKsLQ0BBBQUFVTvCxmvz+888/sXr16irrFy1ahHXr1jFdALl37x78/f1x/PhxFBYWYuzYsbh69WqF5MiscXFxwYYNG+Do6Cg2yWNubo6dO3cy05WUdzFf11/GuHHjAAAzZswQlQkEAhAR0yToVdGiRQtOc6/MnDkTDg4O8Pf3h0AgQFZWFu7evQsnJ6dq2+jPoLi4uMKCU3nq1avHyfe/d+9eODk5YfLkyfj+/TsAQFZWFnZ2dtiyZQtz/erw9/dHeno65s2bh3v37nGqbWFhgdTUVKSmpjL5+6mpqTAzM6uyvkuXLsy0ywgMDGT696vjP1nYK5sUZMXdu3crzX/ZpUsX2NvbM9NVVVXF7du3sXz5chw7dkyU70NDQwPW1tZwd3dnuvgxbNgwXL16FTNmzMCZM2fQpEkTZlr/C6SkpMDHx0fkeGliYiLKyVRbcXZ2Rvv27UULIGlpaRgxYgT69u2LDh06wMPDA0pKSli0aBEzG+rVqwddXV3O+xsAkJSUhI4dO1ZZ36FDByQlJTHTz8rKwqlTp+Dn5wcHBwcMHToU1tbWvPRPJ06cCGdnZ5w4cQICgQAlJSW4ffs2nJycYGNjw7k9XOHp6Yk1a9agQ4cOSEhIQGhoKFauXIkdO3bAwcEBs2fP5swpVAog3QEihTk5OTl49uwZFBUV0apVK8jLyzPXLCkpQWBgIE6fPo309HQIBALo6+vj119/xdSpUyViUqIusGDBAhw8eBCtWrVC586doaysLFbPyvu6jJKSEpSUlEBWtnSt9+jRo6IdCLNnz4acnBxTfRMTE2zcuBGjR48W83SIj4/HgAED8P79e6b6fMD3gFMSvP9rQmBgINLT0xEWFsb5pIsU9vDt9Vq/fn3ExMRUufCXmZmJjh07Mkt+amJigufPn8PU1BR2dnaYPHkybx6fKioqiIuLg76+vthzOD09HcbGxsjPz+fFLq7g+/ozMjKqra/tieCJCBs3boSHhwfy8vIAAPLy8nBycsL69euZanfr1g2TJk3C4sWLK6339vbG0aNHmXme/khubq7Iw7Nly5YV+oR1jV27duH9+/fMkr/KyMjg9evXaNSoUaX1b9++hY6OTrVJsv+XEQqFNR7vsZ4YNTIywqhRo7B582ax8mXLliE0NFQsIT0riAjv378HEUFLS4uTsbBQKISsrCyUlZWr1eNiRz7fXLp0CSNHjkSnTp3Qu3dvAMDt27cRExODc+fOYfDgwTxbyIbmzZvj+PHj6NmzJwBgw4YNOHnyJB4/fgwA8PPzw44dO0SfWeHn54fTp08jODhYzEGPNaqqqrhx40aVzm4PHz7EgAED8PXrV+a2pKSkICAgAEFBqJczVwAAND1JREFUQXj16hUmTZoEW1tbmJubV7tT+WdRWFiIefPmITAwEMXFxZCVlUVxcTEmT56MwMBATmzgAyMjI6xYsQLTpk1DZGQk+vfvj2HDhuHYsWN1vh/EB9IFECm1DiLCiBEjcOHCBXTs2BHGxsYgIjx79gxxcXEYOXIkzpw5w7eZdYLqPH0FAgGuX7/OoTXco6ioiISEBLRo0UJs4ikpKQkdOnTAt2/f+Dbxp8P3gLOm3uV14f6TUnfhe8C1cOFC2NnZVev1xhV1ecs9IL1+SaGwsBDJycnIycmBiYkJVFRUmGsGBQVh7ty52Lp1K2bNmiVyBikqKsK+ffuwdOlS7N69G7a2tsxtkcI9MjIySExMhJaWVqX1b9++hbGxMS9e0Vxw8+ZN0f/T09Ph4uICW1tb0UTs3bt3ERQUBA8PD0ybNo2pLRcuXMC4ceNgaGiI7t27AwAePHiApKQknDp1CsOGDWOqzxc1DW/H+vuXBExNTWFlZVUhKoKLiwsuX77MNBQdnygqKiIxMRHNmzcHUBoOrlevXiIHgJSUFHTu3BmfPn1iaoepqSmSk5Px/ft3tGjRosLEM6vvv0ePHhgzZgycnZ0rrffw8EBoaCinznAlJSW4dOkS/Pz8cO7cOaiqqnLqlFnXQlP/2Abk5eVx584daQQInpCGwJJS6wgMDERERASuXbtWYTL0+vXrGD16NA4ePFirt9pJCuHh4bzqGxgYoH///ti7d6/YzqP379+jW7duzLf+6+vr4/HjxxU8XMPCwtCmTRum2nxR/jf/twEna30pUuoqbdu2xdWrV6vsXF++fBlt27Zlpr99+3Zmf/s/pa5uuS9DEq6/Lob9+BE5OTlOY20DpZOKcXFxmD9/PpYvX46WLVuCiJCamoqcnBwsXLhQuvhRiyEitG7dutr62rwjvn///qL/r1u3Dt7e3mI5OEaOHIn27dtj//79zCfghw0bhqSkJOzZs0f0HBwxYgTmzJkjmhRjwcCBA//1NxYIBFXmC/tvqQsLGzXl2bNnOH78eIXyGTNmwMfHh3uDOKJBgwZ4/fo1mjdvjpKSEkRFRYnt1i8sLAQX/tijR49mrlEZM2bMgKOjI9q2bYvhw4eL1Z07dw7u7u6cRyQQCoUYOnQohg4diuzsbAQHB3OqL4mhqVlSUFAABQUF0Wc5OTlOdyFJEUe6A0RKrcPS0hLm5uZV5hvYuHEjbt68iUuXLnFsWd0lISEBqampIg8nrgZdQqEQhoaG0NDQwNmzZ0WxZ9++fYumTZsy93rz9fWFq6srvLy8YGdnB19fX6SkpMDDwwO+vr6YOHEiU32+4SvpoxQpdZ39+/fD0dERR48erXTANWnSJHh7e2PWrFk8WcgddXXLfRl8X39dDfshSdy7dw9HjhwRxRlv3bo1Jk6ciB49evBsmRSWlN8BUR3lFwpqK0pKSoiJiangaZyYmIhOnTqJwtPVNqoKfweUJkg/fPgwCgoKmI6Hjh07hrNnz6KwsBCDBg3CnDlzmGlJMs2bN4e3tzd+++03sfLjx4/DyckJmZmZPFnGlilTpuDLly/YvXs3Tpw4gbVr1+LNmzeiHRinTp3CunXrEBMTw7Ol7LC2tsbhw4dhbGwMIyMjAKVzI4mJiRg/fjyOHDnCs4Xs+F8JTc0SoVCIWbNmQUlJCUBp+Etra+sKoYFr6/VLGtIFECm1jiZNmiAsLAydOnWqtP7Ro0cYOnQo3rx5w61hdZB3795h7NixuHPnDoRCoSjO8JQpU9CkSRN4eXkx1ZeRkUFSUhKcnJxw//59nDlzBl27duVsAQQoTUbs6uoqCjPStGlTuLm5wc7Ojrk239TVAacUKZJAXR5wVUZd23L/I3xdf10N+yFFyv8CHz58qBOeqJKQg0NSKCoqwq5du+Du7g51dXWsX7+emUPWnj17MG/ePLRq1QqKioqIi4uDo6MjtmzZwkRPklm3bh3++OMPuLi4oFevXgBKnQE2bdoER0dHrF69mmcL2ZCeng4LCwukpqZCRkYG27dvx9y5c0X1o0ePhr6+Pv744w8erWTP8ePHcfjwYSQlJYl2502ePBnjx4/n2zSm/BiNJTo6GkVFRaJxSWJiImRkZNC5c+daG5p6wIABNdqJV1uvX9KQLoBIqXXIyckhIyMD2traldZnZWVBX18fBQUFHFtW97C2tsaHDx/g6+uLZs2aoaSkBECpR6ijoyOePHnCVF8oFOLNmzdo1KgRli9fjm3btmH//v0YPHgwZwsgZeTl5SEnJ6fKZJS1EemAU4oUfqmrAy4pkoOCggLi4uIqXQjv0KEDr0nohUIhBgwYgC1bttTJWMynT5+Gq6srYmNj+TZFCsdcvnwZvr6+OHfuXK3MR/cjdTUHx4/8+eefWLNmDb59+4ZVq1aJ5QZiQdu2bTF+/HisXbsWAHDo0CHMnj0bubm5zDQlFSKCj48PvLy8kJWVBaDUKW7p0qVYuHBhrQ5HV1RUhCdPnkBLSwtNmzYVq4uJiUGzZs2gqan503UbNGiAxMRENGzYEPXr16/2O/7w4cNP15cijre3N27cuIGgoCDUr18fAPDx40dMnz4dffv2xZIlS3i2UEpdQLoAIqXWISMjgzdv3lSb9I/rye+6xt69ezFnzhw0adIEly9fRocOHSAjIyP6zlNTU9GhQwfk5OQwtUNGRgavX78WLTocOnQIM2fOxKRJkxAUFCS9BxgjHXBKkSKFD+r6lntJun5JDvsRGBiI9PR0hIWFcZqAlEv27duHK1euQE5ODg4ODujevTuuX7+OJUuWIDExETY2NtizZw8T7YiIiBqd169fPyb6UsTJyMiAv78/goKC8PHjRwwdOhTjxo2r0DZrKy9fvhTLwdGmTRvmOTgkhbCwMLi4uCAtLQ1OTk5wdHSskASaBYqKinj27Bn09PQAlCZfVlRURHp6epWOinWBr1+/AgBUVVV5toQbvnz5gvv376OwsBDdunWrco7mZxMUFISJEydCXl4eQUFB1Z4rzVfDHh0dnUpzEMbHx8PS0lK0MFgb4asNSKmINAm6lFoHEcHW1lYs6XV5pDs/2JGbm4tZs2ahqKgIc+bMQW5urijeYXnev39f5e/zM/lxfdfa2hotW7bEmDFjmGsDwD///IM1a9YgPDwc7969E+2AKaO2e5vwlfRRihQp/PGfJEBfuHAhExsePXok9rm6Lfe1EUm6/pkzZ2LWrFlITU2tNOwHn5QlAHd1deXVDlZ4enpizZo16NChAxISEhAaGoqVK1dix44dcHBwwOzZs0VemCwYMGBAlXVlnrgCgUAUHlXKz6ewsBCnT5+Gr68vbt++DQsLC7x8+RKPHj1C+/bt+TaPU5o1awZ3d3e+zeCUBw8ewNnZGffu3cOcOXNw9epVNGzYkDP9goICsYUWoVAIOTm5OrHr6Ee+ffsGIoKSkhJUVVWRkZEBPz8/mJiYwNLSkm/zmPH48WMMGzYMb9++BRFBVVUVx48fh5WVFXPt8osafC1wCIXCGoU/Yvke/PLlC1RUVCAUCsXKi4uLkZubCzU1NWbaP9qRnZ1doTw7O1u0KFgbKWsDZeH3uWwDUioi3QEipdYxffr0Gp0XEBDA2JK6x4YNG/D+/Xv4+PgAKJ0A7969O9auXQuhUIiSkhIUFxfjt99+g1AoxMmTJ3mx8+3bt0hISGCe+HHYsGFITk6GnZ0dGjduXKEDJPU24YanT58iMzMThYWFYuUjR47kySIpUtjC54BLX1+/RucJBAKkpqb+dP0fqetb7vm+/roc9oNvjIyMsGLFCkybNg2RkZHo378/hg0bhmPHjnHi/f358+dKy/Py8rBt2zZs374dBgYGiI+PZ25LXWTBggU4cuQIWrVqBWtra0ycOBGampqoV68eYmJiYGJiwreJtZ5v377hypUrGDhwYAVv/y9fvuDGjRuwsrJi5hQmFAqhqKiIWbNmVftuZuWM8GPyX6DyBMC1cSfmj1haWmLs2LGYM2cOPn36BCMjI8jJyeH9+/fw9vYWy4tRm7CyskJOTg62bt0KBQUFrF+/HnFxcUhKSuLcluLiYpw5c0bklNe2bVuMHDkSMjIyzDRDQ0OrrLt79y62b9+OkpISZuFAQ0JC4OzsjMePH1dwSs3NzYWZmRm2bt2KESNGMNEvj42NDSIjI+Hl5YVu3boBAO7fv4+lS5eib9++/7pL538VSWoDUqQLIFKkSPmJvHz5Es2aNRN9jo+Px6BBg9C1a1dcuHBBNAjPycnB7du30bJlSx6tZY+qqipu3bqFjh078m1KnSQ1NRVjxoxBXFwcBAKBaEdQ2YSbNASalNoK3wMuSaIub7kHJOv661rYD75RVFREYmKiaMelvLw87ty5w9vOp5KSEvj7+8PNzQ1CoRCurq6YNm1aBa9UKT8HWVlZODs7w8XFRazNSRdAuGPbtm04e/bs/9fevUfVmL7/A3/vXTpISEVjRFFqJqfm4MxCkUMx9TWJ0uQwtKZhSDNyqvk0lNNiOc8YKkylpjGOgyg0ZjINkVoqiiYkonKoFLV/f/SzPzZlrO/q2c/+Pvv9WmvWmv3ce7muxlT7fq7nvi4kJyc3uu7s7Ax3d3cEBAQIEt/KyuqtHoYQ6mEEDv/9LzMzM5w5cwYODg7YsWMHNm3ahIsXL+KXX35BSEiI8qa81JiZmSEpKQkffPABAKCiogLt2rVDRUWF2k4eAEB+fj7Gjh2L27dvK0/D5uXlwdLSEkeOHFHrPYm8vDwEBwfj0KFD8Pb2RlhYGLp06SJIrFGjRsHT0xMzZ85sdD0yMhLx8fE4fvy4IPFfVlVVhaCgIERGRuLZs2cAGn5PzZgxA2vWrFHLgxli0JTvAWrAFlikVRQKBY4dO4adO3eKdvpAyl4ufgBAjx49cPXqVWzevBktW7bE3bt34eXlhYCAAMF6vzo6Or71E6UZGRmC5PCCvb29Vh7z1hRfffUVrK2tkZycDGtra6Snp+PBgwdYsGAB1q5dK3Z6RIKZMGHCa9ca23BpA209cv+C2F+/trb90AQ1NTUwMDBQvtbT00O7du1EyWXfvn1YvHgxSktLsWjRIsyZM0ctrVC12Z49exAZGYl33nkH48aNw9SpUzFmzBix09IqMTExWLZsWZPr8+bNQ1hYmGAFkMLCQkH+3Ld1+vRpUeNrkqqqKmUhMikpCR4eHpDL5ejfvz/++ecfkbMTTllZmcr9gbZt28LIyAgPHjxQ683fuXPnolu3bjh37pzy9+CDBw/g4+ODuXPn4siRI4LnUFxcjNDQUOzatQsuLi64dOkSevToIWjM7OxsbN26tcn1oUOHYunSpYLm8ELLli2xdetWrFmzBgUFBQCAbt26Sbbw8YKmfA9QAxZASCvcuHEDkZGRiI6ORmlpKZydncVOSWu0adMGS5YsUVu8Tz75RG2x/s3WrVsRHByMkJAQ9OjRAy1atFBZ5y+95ldTU4M5c+Zg+/btSEtLQ0pKCszMzCCXyyGXyzF48GBERERg7ty5r/XJJ5IidW+4AgMD8d1338HIyOhfZzyoo+2Fu7s7pk2b1uiRew8PD8Hji03sr3/ChAkqbT/69u2rFW0/NMWyZcuUbS9qa2uxfPlyldYzgLDfh2fOnMHChQuRlZWFr776CgsXLnwtPglj8uTJmDx5Mm7cuIHo6GgEBASgqqoK9fX1uHLlCk+AqMG1a9feeAq8V69eWtUG5f79+wCg1jkkmsLGxgb79++Hu7s7jh8/jvnz5wMA7t27J/n94JUrV5TzD4CGB1JzcnJUHsLo1auXoDmcOXNGpfgBAKampli5ciUGDRokaOyHDx8iPDwcmzZtQp8+fZCcnIwhQ4YIGvOF8vLyN7a7ffbsGcrLy9WSywtGRkaC/31rGk34HqAGLICQZNXU1CAxMRE7d+7E2bNnUVdXh7Vr12LGjBmS/6ChScrLy7Fz507l0d73338f06ZNE+wpxNDQUEH+3P+Ntm3b4tGjRxgxYoTKdYVCAZlMxhZMzezq1av49NNPlbNV6urqlE9bmZmZobi4GHZ2dujSpQvy8vLETJVIcGJtuC5evKg82v6mIqO6Zj98//33CAoKwpQpUxo9ci91Yn/9GRkZWL9+PQAgMTERFhYWKm0/WAARztChQ1V+1w0cOPC1VjdCfh+OHTsWJ0+exPTp07F//35YWFgIFouaZm1tjf/85z/49ttvkZSUhJ07d8LHxwfz5s2Dh4cHNm7cKHaKghF7Bsfz589RWlqKzp07N7peWloq6PBjTVBRUYElS5YgPj5eeaPVxMQEXl5eWL58Odq2bStugmoSEhKCKVOmYP78+XBycsKAAQMANJwGcXR0FDk7YTk5OeHVrvuurq7K9sTq2BPr6+s3eur1yZMn0NPTEyzu6tWrsWrVKlhYWCAuLq7RE9pCsrKywvnz52Fvb9/o+vnz5wVrv0X/pQnfA9SAM0BIci5cuICdO3ciLi4ONjY2mDp1KiZNmoROnTqx562apaamws3NDW3atMFHH30EoOHvp6KiAocOHcLQoUNFzlBYffv2ha6uLr766qtGh6ALPYRdLGJtOFetWoVu3bph4sSJAKAc8PvJJ59gypQpKC8vx9KlS7F9+3ZcuHCBg1dJsl7ecIWHh6t9w6WJKisrterI/avE+vpbtmyJ3NxcdO7cGZ6ennBwcEBoaChu3rwJOzs7VFVVqSUPUj+5XA5dXV0YGRm9sdBSVlamxqwIaPhvvnv3bkRFRSEzM1PsdAQj9gyO/v37w93dHQsXLmx0PSIiAgcOHMC5c+cEiS+2srIyDBgwALdv34a3tzfee+89AA1PQ8fGxsLS0hJ//vknTExMRM5UPUpKSnDnzh307t1bOfsoPT0drVu3bvIG9f91b9veS+ib8L6+vsjIyMDOnTtVTsN+/vnn+PDDDxEdHS1IXLlcDkNDQzg7O79x2Pq+ffsEib9kyRL89NNPSE9PR4cOHVTWSkpK0K9fP/j4+GDFihWCxCfN+R6gBiyAkOTo6upizpw58Pf3Vw65Ajj0Tww9e/bEgAEDsG3bNuUv/bq6OnzxxRf4888/kZWVJWj8uro6rF+/HgkJCSgqKkJtba3KutCb7pYtW+LixYsq/x9qA7E2nM+ePVNpM3b8+HFUVlbCw8MD+fn5cHV1xdWrV2Fqaor4+PjXTuYQSYXYGy6iF3r16oWZM2fC3d0dPXr0wLFjxzBgwABcuHAB48aNU2kJQM3v0aNH+Ouvv1BbW4u+ffvC3NxcbbF37dr1Vu97cWqTqLn17dsXy5Ytg5ubW6Prhw8fRlhYGNLT0wWJv337dgQGBmLv3r1wdXVVWTt06BAmT56MdevWYdasWYLEF9u8efOQnJyMkydPNnrzddSoUXByclKeEiTpyc7OFnzOxduoqKjAZ599hkOHDin3is+fP8f48eMRHR0tWGtGPz+/tzppGRUVJUj8x48fY8CAASgqKoKPj4/ynkRubi5iYmJgaWmJc+fOvfbAIpFUsQBCkuPi4oK0tDS4ublh6tSpcHFxgUwmYwFEBIaGhrh06dJrBYC8vDz06dNH8AHhISEh2LFjBxYsWIClS5diyZIlKCwsxP79+xESEoK5c+cKGn/o0KEICQnRupkzYm8436SsrAwmJiZqa79DJAaxN1xELyQmJmLKlCmoq6uDk5MTkpKSADQ8+ZyamoqjR4+KnKF0Xbp0CWPHjlUWmYyNjZGQkAAXFxeRMyNSDxMTE2RmZjbZgqqoqAi9e/cWtAe+j48PYmNjYW9vr3Lz8erVq/D09ERcXJxgscVmZWWFH374ocmfOceOHYO/v7/ow9pJOHK5HB9//DFmzpwJLy8v0W+0X7t2Dbm5uQCA9957DzY2NqLmow4PHz7EokWLVNrQtW3bFl5eXlixYoXWnMAiAlgAIYm6efMmoqKiEBUVherqakyaNAlbt27F5cuXlcdvSXiDBg3C119//dpg8v3792PlypWCH/nu1q0bNm7ciHHjxsHY2BiXLl1SXjt37hxiY2MFjf/zzz/j22+/xddff42ePXu+NgRdqsOuNGHDSUREmkEb235oAhcXFzx58gRr166FgYEBvvvuO2RlZal16HJ8fDwOHjyI2tpaODk5wd/fX22xiYyNjXH69Gl8+OGHja5fuHABw4YNa3Q2QHNKSEhAbGwsrl27BoVCge7du2PKlCnw9PQUNO7LCgoKEBUVhYKCAmzYsAHt27fH0aNH0blzZzg4OAgSU19fHwUFBejUqVOj67du3YKNjQ2ePn0qSHwS3++//46oqCgkJiaivr4e//M//4OZM2eqbQj4C2fPnsXgwYPVGlPTKBQK3L9/HwqFAubm5nwYkLQSCyAkeSdOnEBUVBR+/fVXWFpaYuLEiZg4cSI++OADsVOTvPj4eHzzzTeYM2cO+vfvDwA4d+4ctmzZgpUrV6oUo4QoBhgZGSEnJwedO3fGO++8gyNHjuCDDz7A9evX4ejoiIcPHzZ7zJe9uNHzMm0YdqUpG04iIiJtZWZmhqSkJOXn3YqKCrRr1w4VFRVo3bq14PG3bduGgIAA2NrawtDQEFlZWQgMDMSaNWsEj00EcAbHC2fOnMGYMWMwaNAgpKamIicnB127dsXKlStx/vx5JCYmChL33XffRXx8fJM3nn///XdMmjQJxcXFgsQnzVFZWYmEhARER0fj999/h42NDWbMmIHPPvsMFhYWgsfX09PDu+++i8mTJ8PHx4cdQYi0FAsgpDXKy8vx008/ITIyEpcvX5bszWdN0lgB4GVCFwPs7Oywe/du9OvXD4MHD4arqyuCg4MRHx+POXPm4N69e80e82X/NvRKqsOuuOEkIiISl1wuR0lJCdq3b6+8ZmxsjMuXL8Pa2lrw+A4ODvD09ERoaCgA4KeffsLs2bNRWVkpeGwigDM4XhgwYAA+/fRTBAYGwtjYGJmZmejatSvS09Ph4eGBW7duCRJ3+vTpKCgowIkTJ6Cnp6eyVlNTAxcXF3Tt2hWRkZGCxCfNlJ+fj6ioKOzZswclJSUYPXo0Dh48KGjM+/fvY+/evYiLi0NaWhp69eoFb29vTJ48uckTSlIwfPjwfz3pIZPJmpzbSSQ1LICQVsrIyOAJEDX4twLAy4QoBgQHB6N169ZYvHgx4uPj4ePjAysrKxQVFWH+/PlYuXJls8ckbjiJiIjEJpfLkZKSgnbt2imvDRw4EAkJCSo3fIRqx2loaIicnBxYWVkBAOrr62FoaIjCwkK88847gsSkxlVUVCA9PR337t1DfX29ypqvr69IWamHmDM45HL5W918fP78uWA5AECrVq2QlZUFa2trlQJIYWEh7O3tBWtBdevWLXz00UfQ19dHQEAA7O3toVAokJOTg61bt6Kmpgbnz5+HpaWlIPFJc1VWViImJgaLFi1CRUWFWh9MvXHjBmJjYxEXF4fc3FwMHToUKSkpaouvTvPnz29y7fHjx4iNjUVNTQ0fDBZQdXU1Tpw4geHDh782A+fRo0c4ffo0XFxcoK+vL1KG2oUFECLSGmlpaUhLS4OtrW2TA7qbW3FxMc6ePdvoplPoIexi0uahj0RERGJ7cfO1sa2eOtpxyuVy3L17F+bm5sprL998JfU4dOgQvL298eTJE7Ru3VrlhrxMJkNZWZmI2amHWDM4Dhw40ORaWloaNm7ciPr6esFnYHTq1AkJCQkYOHCgyvfgr7/+iqCgIBQUFAgW+8aNG/jiiy+QlJSk/Fkkk8kwcuRIbN68WSuGUNN/paamIjIyEr/88gvkcjk8PT0xY8YMZatsdamrq8PRo0exbNkyresM8vz5c2zZsgUrVqxAmzZt8N1338HLy0vstCRrw4YNOHjwYJOnbJydneHu7o6AgAA1Z6adWAAhomZ17ty5t/4QU1VVhRs3bgg2fE9s0dHRmD17NvT09GBqavrapvP69esiZic8TRj6SEREpI3e9hSuUO045XI5Zs2ahZYtWyqvbdmyBT4+PmjTpo3y2rp16wSJTw26d++OsWPHIjw8XOXvgsSRl5eH4OBgZWEqLCxM8Ja4QUFB+Ouvv/Dzzz+je/fuyMjIwN27d+Hr6wtfX19lmzohlZeX49q1awAAGxsblZNpJG3FxcWIjo5GdHQ08vPzMXDgQMyYMQOenp4wMjJSay5//PEHYmJikJiYiKdPn2LChAnw9vbG6NGj1ZqHWGJiYhASEoLq6mosXboUs2bNgq6urthpSVrfvn2xbNmyJh++PXz4MMLCwpCenq7mzLQTCyBE1KxsbW3RtWtXzJw5E2PHjm30g82VK1fw008/ISoqCqtWrRLs+P2DBw9gamoKALh58yZ+/PFHVFdXY/z48RgyZIggMV9maWkJf39/LFq06F/noRARERFJxbBhw96q/Y9UW49oCiMjI2RlZfHUjciKi4sRGhqKXbt2wcXFBREREejRo4daYtfW1iIgIADR0dGoq6uDrq4u6urqMGXKFERHR0NHR0cteTQmMTEREydOFC0+CWvMmDE4efIkzMzM4Ovri+nTpys7A6jTokWLsHfvXhQXF2PkyJHw9vbGhAkTtKYofOzYMQQHB+PGjRsICgpCYGCg2otP2srExASZmZno3Llzo+tFRUXo3bs3ysvL1ZyZdmIBhIia1bNnz7Bt2zZs2bIF169fR/fu3dGxY0cYGBigvLwcubm5ePLkCdzd3bF48WL07Nmz2XPIysqCm5sbbt68CVtbW+zduxejR49GZWUl5HI5KisrkZiYiE8++aTZY7/M1NQU6enp6Natm6BxiIiIiIhe5eHhAS8vL608fasJMzgePnyI8PBwbNq0CX369MGqVavU8hBWY4qKipCdnY0nT57A0dERtra2gsd8/vw5cnNzoaenh+7duyuvHzhwACEhIcjNzUVNTY3geZA4xo8fjxkzZsDV1VXUQtugQYPg7e0NT09PmJmZiZaHuqWnp2PhwoU4d+4c/P39sWTJEq36+jWBsbExTp8+jQ8//LDR9QsXLmDYsGF4/PixmjPTTiyAkORw0JDmOH/+PM6ePYt//vkH1dXVMDMzg6OjI4YPHy7o0ecxY8ZAV1cXwcHB2LNnDw4fPgwXFxf8+OOPAIA5c+bgwoULOHfunGA5AMA333yDdu3aITg4WNA4mkYTNpxERESkOe7fvw8AvPmiZjt37kRYWBimTZuGnj17okWLFirr48ePFykz4Yk9g2P16tVYtWoVLCwsEB4ejgkTJggSR1NlZ2fD1dUVN2/eBABMmDAB27Ztg6enJ7Kzs/H555/jyy+/RKdOnUTOlEia5HI5DA0NMWvWLFhbWzf5PinPJRVb//794e7ujoULFza6HhERgQMHDgh+X4oasABCksNBQ2RmZoaUlBT06tVLOfTx77//Vlbec3Nz0b9/f1RUVAiaR11dHVxdXVFdXd3oplOqfa/F3nASERGR+CoqKrBkyRLEx8cr2zuYmJjAy8sLy5cvR9u2bcVNUAu8qQWrTCbTquG/gHpncLy4+ejs7PzGp9/37dvX7LEDAwPf+r1C7UfGjRuHmpoazJs3D3FxcYiLi4OdnR1mzJiBgIAAGBoaChKXqClXrlxBUVERamtrVa5LtRBsZWX1Vg8lSn0uqZi2b9+OwMBA7N27F66uriprhw4dwuTJk7Fu3TrMmjVLpAy1CyfekOTExMRg2bJlTa7PmzcPYWFhLIBIWFlZGSwsLAAArVq1gpGREUxMTJTrJiYmajlmGBERgePHjyt7nb46BF2qGnvCrbENJxEREUlTWVkZBgwYgNu3b8Pb2xvvvfcegIYbUNHR0UhOTsaff/6p8vmMml99fb3YKWiEV2dwXLp0SfAZHL6+vqJ93r948eJbvU/I/P7++28kJSWhT58+GDJkCOLi4rB48WJMnTpVsJhEjbl+/Trc3d2RlZUFmUyGF8+Av/j/X6qF4MLCQrFT0HqzZs1Camoqxo8fD3t7e+V9odzcXFy9ehWenp4sfqgRT4CQ5HDQEMnlcty9exfm5uYAGnovXr58WXn08+7du+jYsaPgH3ZMTEywfv16+Pn5CRpHk4k59JGIiEhbid0Sdt68eUhOTsbJkyfRoUMHlbWSkhKMGjUKTk5OWL9+vSDxiQDNmsGhbeRyOUpKStC+fXsADfuxjIwMtcweIXqZm5sbdHR0sGPHDlhbWyM9PR0PHjzAggULsHbtWv5MIMElJCQgNjYW165dg0KhQPfu3TFlyhStnM8lJp4AIcl5/vw5SktLmyyAlJaWcvaAFvDz81Nu6p8+fQp/f38YGRkBgNqG7enr62PQoEFqiaVpXt1wJicn88MlERGRmmzfvh0HDx5stLVH69atsXHjRty8eVOwE9H79+/HDz/88FrxAwAsLCywevVq+Pv7swAisH87cRsSEqKmTNTv5RkccXFxWjeDQ2wymQyPHz+GgYEBFAoFZDIZqqur8ejRI5X3tW7dWqQMSVukpaUhJSUFZmZmkMvlkMvlGDx4MCIiIjB37ty3PjFF9L/l6enJYocG4AkQkhwOGqJp06a91fuioqIEzSMiIgJ37tzBxo0bBY2jabR96CMREZHY+vbti2XLlsHNza3R9cOHDyMsLAzp6emCxNfX10dBQUGTA45v3boFGxsbzgMTmKOjo8rrZ8+e4caNG9DV1UW3bt2QkZEhUmbCE3MGBzX893+5xdaLIsirr6Xafog0h4mJCTIyMmBtbY1u3bphx44dGD58OAoKCtCzZ09UVVWJnSIRqQFPgJDkTJ8+HYGBgXBwcGh00NCKFSskO3z6/4KKigrBh14KXdh4W+np6UhJScHhw4fh4ODw2hB0qW64goODYWhoCBsbG+zatQu7du1q9H1S/fqJiIjEdu3aNfTu3bvJ9V69euHatWuCxTczM0NhYWGTBZAbN26gXbt2gsWnBo092fzo0SP4+fnB3d1dhIzUR8wZHAScOnVK7BSIAAA9evRAZmYmrK2t0a9fP6xevRp6enrYvn07unbtKnZ6JGGvFoIbI5PJ2KFGTXgChCTJx8cHsbGxTQ4aiouLEzlD7bBq1SpYWVlh0qRJABqO/v3yyy+wsLDAb7/99saNuRT820kUTSnUNDc/P7+32nBK9esnIiISm7GxMU6fPo0PP/yw0fULFy5g2LBhePz4sSDxp0+fjoKCApw4cQJ6enoqazU1NXBxcUHXrl0RGRkpSHx6s6ysLLi5uXFILhFJ3vHjx1FZWQkPDw/k5+fD1dUVV69ehampKeLj4zFixAixUySJOnDgQJNraWlp2LhxI+rr63kaVk1YACHJ4qAh8VlbWyMmJgYDBw7EiRMn4Onpifj4eCQkJKCoqAhJSUlip0hEREQkOWK3hL116xY++ugj6OvrIyAgAPb29lAoFMjJycHWrVtRU1OD8+fPw9LSUpD49GZnz56Fm5sbysvLxU6FJKq+vh5r1qzBwYMHUVtbCycnJ4SGhsLQ0FDs1IhQVlYGExMTrTklVlBQgKioKBQUFGDDhg1o3749jh49is6dO8PBwUHs9LRKXl4egoODcejQIXh7eyMsLAxdunQROy2twBZYJFkcNCS+kpIS5cb28OHD8PT0xKhRo2BlZYV+/fqJnJ36lJaWIi8vDwBgZ2cHc3NzkTMiIiIiKRO7JWynTp2QlpaGL774AosWLcKLZ+5kMhlGjhyJzZs3s/ihBq/OoVMoFLhz5w727NmDMWPGiJQVaYMVK1bg22+/hbOzMwwNDbFhwwbcu3ePp75II2hTC8YzZ85gzJgxGDRoEFJTU7FixQq0b98emZmZ2LlzJxITE8VOUSsUFxcjNDQUu3btgouLCy5duoQePXqInZZW4QkQIhJMx44dkZiYiIEDB8LOzg7Lly/Hp59+iry8PHz88cd49OiR2CkKqrKyEnPmzMHu3btRX18PANDR0YGvry82bdqEli1bipwhERERSZWmtIQtLy9XzhuxsbHRqhtPYrO2tlZ5LZfLYW5ujhEjRmDRokUwNjYWKTOSOltbWwQFBWH27NkAgJMnT2LcuHGorq6GXC4XOTsi7TFgwAB8+umnCAwMhLGxMTIzM9G1a1ekp6fDw8MDt27dEjtFSXv48CHCw8OxadMm9OnTB6tWrcKQIUPETksrsQBCksNBQ5rjyy+/xOHDh2Fra4uLFy+isLAQrVq1wt69e7F69WpkZGSInaKgZs+ejZMnT2Lz5s0YNGgQgIaWA3PnzsXIkSOxbds2kTMkIiIiKdPklrCJiYmYOHGi2GkQkQD09fWRn5+vctLLwMAA+fn56NSpk4iZEWmXVq1aISsrC9bW1ioFkMLCQtjb23P+hIBWr16NVatWwcLCAuHh4ZgwYYLYKWk1FkBIcjhoSHM8e/YMGzZswM2bN+Hn5wdHR0cAwPr162FsbIyZM2eKnKGwzMzMkJiYiGHDhqlcP3XqFDw9PVFaWipOYkREREQCe/78OXJzc6Gnp4fu3bsrrx84cAAhISHIzc1FTU2NiBkSkVB0dHRQUlKi0vrX2NgYly9ffu1kEhEJp1OnTkhISMDAgQNVCiC//vorgoKCUFBQIHaKkiWXy2FoaAhnZ2fo6Og0+b59+/apMSvtxRkgJDmNVVUbGzREwmvRogWCgoJeuz5//nwRslG/qqoqdOjQ4bXr7du3R1VVlQgZEREREQkvOzsbrq6uuHnzJoCGz+fbtm2Dp6cnsrOz8fnnn+PIkSMiZ0lEQlEoFPDz84O+vr7y2tOnT+Hv7w8jIyPlNd74IxKWl5cXFi5ciJ9//hkymQz19fX4448/EBQUBF9fX7HTkzRfX99/7U5D6sMTICRprw4aioiI4KAhEVy5cgVFRUWora1VuT5+/HiRMlIPJycnmJqaYvfu3TAwMAAAVFdX47PPPkNZWRlOnjwpcoZEREQkRWK3hB03bhxqamowb948xMXFIS4uDnZ2dpgxYwYCAgJgaGgoSFwi0gzTpk17q/dFRUUJnAmRdqutrUVAQACio6NRV1cHXV1d1NXVYcqUKYiOjn7jyQQiKWEBhCSJg4Y0w/Xr1+Hu7o6srCzIZDK8+HHzYkNeV1cnZnqCy87OhouLC2pqatC7d28AQGZmJgwMDHD8+HE4ODiInCERERFJkdgtYdu3b4+kpCT06dMHDx8+hImJCXbt2oWpU6cKEo+IiIiaVlRUhOzsbDx58gSOjo6wtbUVOyUitWIBhCSHg4Y0h5ubG3R0dLBjxw5YW1sjPT0dDx48wIIFC7B27VqtKEpVVVUhJiYGubm5AID33nsP3t7efPKRiIiI1KqxlrBdunQRJJZcLkdJSQnat28PoKH3f0ZGBm+4EBEREZHasQBCksNBQ5rDzMwMKSkp6NWrF9q0aYP09HTY2dkhJSUFCxYswMWLF8VOkYiIiEjSxGgJq6Ojg6tXr8Lc3BwKhQKWlpY4e/YsrKysVN7XunVrQfMgIiLSNoGBgW/93nXr1gmYCZHm4BB0khwOGtIcdXV1MDY2BtBQDCkuLoadnR26dOmCvLw8kbMTXkREBDp06IDp06erXI+MjERpaSkWLlwoUmZEREQkda+2hE1OTlbb6VuFQoHu3burvHZ0dFR5LZPJJN8OlYiISN3e9kFT3jcjbcICCElOdHS02CnQ/9ejRw9kZmbC2toa/fr1w+rVq6Gnp4ft27eja9euYqcnuB9++AGxsbGvXXdwcICXlxcLIERERCSIl1vCxsXFqb0l7KlTp9Qaj4iIiBrwdzDR69gCi4gEc/z4cVRWVsLDwwP5+flwdXXF1atXYWpqivj4eIwYMULsFAVlYGCAnJwcWFtbq1y/fv063n//fcEGjxIREZF2Y0tYIiIiIqIGPAFCRIJxcXFR/ruNjQ1yc3NRVlYGExMTrThuaWlpiT/++OO1Asgff/yBjh07ipQVERERSZ3YLWHr6+uxZs0aHDx4ELW1tXByckJoaCgMDQ1Fy4mIiIiItBMLIESkVu3atRM7BbX5/PPPMW/ePDx79kx52iU5ORnffPMNFixYIHJ2REREJFVit4RdsWIFvv32Wzg7O8PQ0BAbNmzAvXv3EBkZKWpeRERERKR92AKLiATz9OlTbNq0CadOncK9e/dQX1+vsp6RkSFSZuqhUCgQHByMjRs3ora2FkBDW6yFCxciJCRE5OyIiIiIhGFra4ugoCDMnj0bAHDy5EmMGzcO1dXVkMvlImdHRERERNqEBRAiEoy3tzeSkpIwceJEdOjQ4bVWDKGhoSJlpl5PnjxBTk4ODA0NYWtrC319fbFTIiIiIhKMvr4+8vPzYWlpqbxmYGCA/Px8dOrUScTMiIiIiEjbsABCRIJp06YNfvvtNwwaNEjsVIiIiIhITXR0dFBSUgJzc3PlNWNjY1y+fPm12WhERERERELiDBAiEsy7774LY2NjsdMgIiIiIjVSKBTw8/NTOfX69OlT+Pv7w8jISHlt3759YqRHRERERFqEJ0CISDBHjx7Fxo0b8f3336NLly5ip0NEREREajBt2rS3el9UVJTAmRARERGRtmMBhIgEU1paCk9PT6SmpqJly5Zo0aKFynpZWZlImREREREREREREZHUsQUWEQlm8uTJuH37NsLDwxsdgk5EREREREREREQkFJ4AISLBtGzZEmlpaejdu7fYqRAREREREREREZGWkYudABFJl729Paqrq8VOg4iIiIiIiIiIiLQQCyBEJJiVK1diwYIFOH36NB48eIBHjx6p/ENEREREREREREQkFLbAIiLByOUNNdZXZ38oFArIZDLU1dWJkRYRERERERERERFpAQ5BJyLBnDp1SuwUiIiIiIiIiIiISEvxBAgREREREREREREREUkOT4AQkWBSU1PfuD506FA1ZUJERERERERERETahidAiEgwL2aAvOzleSCcAUJERERERERERERCef3uJBFRMykvL1f55969ezh27Bg+/vhjJCUliZ0eERERERERERERSRhPgBCR2p05cwaBgYG4cOGC2KkQERERERERERGRRPEECBGpXYcOHZCXlyd2GkRERERERERERCRhHIJORIK5fPmyymuFQoE7d+5g5cqV6NOnjzhJERERERERERERkVZgCywiEoxcLodMJsOrP2b69++PyMhI2Nvbi5QZERERERERERERSR0LIEQkmH/++UfltVwuh7m5OQwMDETKiIiIiIiIiIiIiLQFCyBERERERERERERERCQ5nAFCRIKor69HdHQ09u3bh8LCQshkMlhbW2PixImYOnUqZDKZ2CkSERERERERERGRhPEECBE1O4VCATc3N/z222/o3bs37O3toVAokJOTg6ysLIwfPx779+8XO00iIiIiIiIiIiKSMJ4AIaJmFx0djdTUVCQnJ2P48OEqaykpKfjkk0+we/du+Pr6ipQhERERERERERERSR1PgBBRsxs1ahRGjBiB4ODgRtfDw8Nx5swZHD9+XM2ZERERERERERERkbaQi50AEUnP5cuXMXr06CbXx4wZg8zMTDVmRERERERERERERNqGBRAianZlZWXo0KFDk+sdOnRAeXm5GjMiIiIiIiIiIiIibcMCCBE1u7q6OujqNj1iSEdHB8+fP1djRkRERERERERERKRtOASdiJqdQqGAn58f9PX1G12vqalRc0ZERERERERERESkbVgAIaJm99lnn/3re3x9fdWQCREREREREREREWkrmUKhUIidBBERERERERERERERUXPiDBAiIiIiIiIiIiIiIpIcFkCIiIiIiIiIiIiIiEhyWAAhIiIiIiIiIiIiIiLJYQGEiIiIiIiIiIiIiIgkhwUQIiIiIiIiIiIiIiKSHBZAiIiIiIiIiIiIiIhIclgAISIiIiIiIiIiIiIiyWEBhIiIiIiIiIiIiIiIJIcFECIiIiIiIiIiIiIikpz/B8HiHQHAJuSPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coefs = pd.Series(clf.coef_[0,:], index=train_x.columns)\n",
    "selected_features = coefs[coefs != 0].sort_values(ascending=False)\n",
    "print(len(selected_features))\n",
    "# Separate positive and negative values\n",
    "positive_values = [val if val > 0 else 0 for val in selected_features]\n",
    "negative_values = [val if val < 0 else 0 for val in selected_features]\n",
    "\n",
    "# Create a figure and axis\n",
    "fig, ax = plt.subplots(figsize=(20,5))\n",
    "\n",
    "# Plot positive values with varying shades of blue\n",
    "ax.bar(range(len(positive_values)), positive_values, color=plt.cm.Blues(np.linspace(0, 1, len(positive_values)))[::-1])\n",
    "\n",
    "# Plot negative values with varying shades of red\n",
    "ax.bar(range(len(negative_values)), negative_values, color=plt.cm.Reds(np.linspace(0, 1, len(negative_values))))\n",
    "\n",
    "# Customize the plot\n",
    "ax.set_xticks(range(len(selected_features)))\n",
    "ax.set_xticklabels(selected_features.index, rotation=90)\n",
    "ax.set_title(\"Selected features coefficients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3810cb7e-593c-4de5-be91-a49b4f7af113",
   "metadata": {},
   "source": [
    "The lasso has selected 44 features: some motifs but also 2 authors (A. Dumas (père) and H. de Balzac), and 1 period (1827-1850).\n",
    "\n",
    "In our corpus, writings by A. Dumas and H. de Balzac are more likely to be regarded as canonical compared to those of other authors.\n",
    "\n",
    "On top, the period 1827-1850 has a strong positive impact on the target.\n",
    "\n",
    "Let us remember that these interpretations are based on correlation only which cannot be extrapolated as causation. A priori, the period of the publication should not have an impact on the canonical aspect of a writing.\n",
    "\n",
    "The two most important motifs for the canon label are \"ce NC de NC,\" and \"NC ADJ de le PROPN\", while the two most important motifs for the non-canon label are \"le NC; le NC\" and \"NC PROPN son NC .\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef8abba-5817-459a-9096-837e3b5fbdb4",
   "metadata": {},
   "source": [
    "# Error analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f62fc6-b6ea-4782-9907-73fab1d9c280",
   "metadata": {},
   "source": [
    "It seems that the type I and II errors are well balanced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d252e16b-6d7e-490c-801d-5ff2d9d334f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.66      0.69        32\n",
      "           1       0.70      0.76      0.73        34\n",
      "\n",
      "    accuracy                           0.71        66\n",
      "   macro avg       0.71      0.71      0.71        66\n",
      "weighted avg       0.71      0.71      0.71        66\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y, test_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f1ae43f5-ff67-4301-a851-4ed9134f722f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y_pred = clf.predict(test_features)\n",
    "correct = test_y.index[test_y_pred == test_y]\n",
    "errors = test_y.index[test_y_pred != test_y]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "769523e3-4c25-485e-9092-6200cfeea99b",
   "metadata": {},
   "source": [
    "The sparsity of the features within the correct prediction and wrong prediction seems also similar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "944b5619-e07d-421b-95ce-af4621dc5558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1db331050>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3IUOGRHdGAAAg5tgKLC0tLaqrq5PH47k4QXy8PB6Pamtrr2iO06dP6+zZsxo6dGjEeE1NjYYNG6YbbrhBc+fO1fHjxzuco7m5WaFQKGIDAACxy1ZgaWhoUGtrq1wuV8S4y+VSIBC4ojmeeOIJpaamRoSewsJCrVmzRn6/Xy+88IL+/Oc/65577lFra2u7c/h8PiUlJYW3tLQ0O6cBAAB6mD5d+cOWLl2qtWvXqqamRk6nMzw+derU8Ndjx45VVlaWRo4cqZqaGt15552XzFNSUiKv1xv+PhQKEVoAAIhhtq6wJCcnKyEhQcFgMGI8GAzK7XZf9tjly5dr6dKl+sMf/qCsrKzL1mZkZCg5OVn79u1rd7/D4dCgQYMiNgAAELtsBZbExETl5ORE3DB74Qba/Pz8Do9btmyZlixZourqauXm5n7pzzl8+LCOHz+ulJQUO+0BAIAYZftVQl6vV6tWrdLq1au1e/duzZ07V01NTSouLpYkzZgxQyUlJeH6F154QYsXL1ZVVZXS09MVCAQUCAR06tQpSdKpU6e0YMECbdmyRQcPHpTf79fkyZM1atQoFRQUdNJpAgCAnsz2PSxFRUU6duyYSktLFQgElJ2drerq6vCNuPX19YqPv5iDVq5cqZaWFj3wwAMR85SVlemZZ55RQkKCPvjgA61evVonTpxQamqq7r77bi1ZskQOh+MqTw8AAMSCOMuyrO5u4mqFQiElJSWpsbGR+1kAAOdt8nV3B5HuuPjsw883ftKNjUTnsbuu7/Q57Tx+81lCAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwD0Iqdbzin9yXeV/uS7Ot1yrnMmPdckvRF3fjvX1DlzAl9AYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGK9PdzcAAECvsMkX/vKb9ce7sZHztnx9Tne3YAtXWAAAgPEILAAAwHgEFgAAYDwCCwAAMF5UgaWiokLp6elyOp3Ky8vTtm3bOqxdtWqVJk6cqCFDhmjIkCHyeDyX1FuWpdLSUqWkpKhv377yeDzau3dvNK0BAIAYZDuwrFu3Tl6vV2VlZdq+fbvGjRungoICHT16tN36mpoaTZs2TZs2bVJtba3S0tJ0991367PPPgvXLFu2TC+++KIqKyu1detW9e/fXwUFBTpz5kz0ZwYAAGKG7cCyYsUKzZ49W8XFxRozZowqKyvVr18/VVVVtVv/+uuva968ecrOzlZmZqZeeeUVtbW1ye/3Szp/daW8vFyLFi3S5MmTlZWVpTVr1ujIkSNav379VZ0cAACIDbYCS0tLi+rq6uTxeC5OEB8vj8ej2traK5rj9OnTOnv2rIYOHSpJOnDggAKBQMScSUlJysvL63DO5uZmhUKhiA0AAMQuW4GloaFBra2tcrlcEeMul0uBQOCK5njiiSeUmpoaDigXjrMzp8/nU1JSUnhLS0uzcxoAAKCH6dJXCS1dulRr167VO++8I6fTGfU8JSUlamxsDG+HDh3qxC4BAIBpbL01f3JyshISEhQMBiPGg8Gg3G73ZY9dvny5li5dqj/+8Y/KysoKj184LhgMKiUlJWLO7OzsdudyOBxyOBx2WgcAAD2YrSssiYmJysnJCd8wKyl8A21+fn6Hxy1btkxLlixRdXW1cnNzI/aNGDFCbrc7Ys5QKKStW7dedk4AANB72P7wQ6/Xq5kzZyo3N1fjx49XeXm5mpqaVFxcLEmaMWOGhg8fLp/v/Ic8vfDCCyotLdUbb7yh9PT08H0pAwYM0IABAxQXF6dHH31Uzz//vEaPHq0RI0Zo8eLFSk1N1ZQpUzrvTAEAQI9lO7AUFRXp2LFjKi0tVSAQUHZ2tqqrq8M3zdbX1ys+/uKFm5UrV6qlpUUPPPBAxDxlZWV65plnJEkLFy5UU1OT5syZoxMnTmjChAmqrq6+qvtcAABA7LAdWCRp/vz5mj9/frv7ampqIr4/ePDgl84XFxen5557Ts8991w07QAAgBjHZwkBAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8fp0dwMAEKt+vvGT7m7hEmdb28Jfv/ynffpKQuT/b33sruu7uiXginCFBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjRRVYKioqlJ6eLqfTqby8PG3btq3D2o8++kj333+/0tPTFRcXp/Ly8ktqnnnmGcXFxUVsmZmZ0bQGAABikO3Asm7dOnm9XpWVlWn79u0aN26cCgoKdPTo0XbrT58+rYyMDC1dulRut7vDeW+66SZ9/vnn4W3z5s12WwMAADHKdmBZsWKFZs+ereLiYo0ZM0aVlZXq16+fqqqq2q2/9dZb9dOf/lRTp06Vw+HocN4+ffrI7XaHt+TkZLutAQCAGGUrsLS0tKiurk4ej+fiBPHx8ng8qq2tvapG9u7dq9TUVGVkZOjBBx9UfX19h7XNzc0KhUIRGwAAiF22AktDQ4NaW1vlcrkixl0ulwKBQNRN5OXl6dVXX1V1dbVWrlypAwcOaOLEiTp58mS79T6fT0lJSeEtLS0t6p8NAADM16e7G5Cke+65J/x1VlaW8vLydN111+l3v/udZs2adUl9SUmJvF5v+PtQKERoAYBe6ucbP2l3/Jv1x7u4E1xLtgJLcnKyEhISFAwGI8aDweBlb6i1a/Dgwbr++uu1b9++dvc7HI7L3g8DAABii62nhBITE5WTkyO/3x8ea2trk9/vV35+fqc1derUKe3fv18pKSmdNicAAOi5bD8l5PV6NXPmTOXm5mr8+PEqLy9XU1OTiouLJUkzZszQ8OHD5fP5JJ2/UXfXrl3hrz/77DPt3LlTAwYM0KhRoyRJjz/+uO677z5dd911OnLkiMrKypSQkKBp06Z11nkCAIAezHZgKSoq0rFjx1RaWqpAIKDs7GxVV1eHb8Str69XfPzFCzdHjhzRN77xjfD3y5cv1/LlyzVp0iTV1NRIkg4fPqxp06bp+PHj+trXvqYJEyZoy5Yt+trXvnaVpwcAAGJBVDfdzp8/X/Pnz29334UQckF6erosy7rsfGvXro2mDQAA0EvwWUIAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMbr090NAECPssl3xaXfrD9+DRs5b8vX51zznwGYIKorLBUVFUpPT5fT6VReXp62bdvWYe1HH32k+++/X+np6YqLi1N5eflVzwkAAHoX24Fl3bp18nq9Kisr0/bt2zVu3DgVFBTo6NGj7dafPn1aGRkZWrp0qdxud6fMCQAAehfbgWXFihWaPXu2iouLNWbMGFVWVqpfv36qqqpqt/7WW2/VT3/6U02dOlUOh6NT5gQAAL2LrcDS0tKiuro6eTyeixPEx8vj8ai2tjaqBqKZs7m5WaFQKGIDAACxy1ZgaWhoUGtrq1wuV8S4y+VSIBCIqoFo5vT5fEpKSgpvaWlpUf1sAADQM/TIlzWXlJSosbExvB06dKi7WwIAANeQrZc1JycnKyEhQcFgMGI8GAx2eEPttZjT4XB0eD8MAACIPbausCQmJionJ0d+vz881tbWJr/fr/z8/KgauBZzAgCA2GL7jeO8Xq9mzpyp3NxcjR8/XuXl5WpqalJxcbEkacaMGRo+fLh8vvNvrtTS0qJdu3aFv/7ss8+0c+dODRgwQKNGjbqiOQEAQO9mO7AUFRXp2LFjKi0tVSAQUHZ2tqqrq8M3zdbX1ys+/uKFmyNHjugb3/hG+Pvly5dr+fLlmjRpkmpqaq5oTgAA0LtF9db88+fP1/z589vddyGEXJCeni7Lsq5qTgAA0Lv1yFcJAQCA3oXAAgAAjEdgAQAAxiOwAAAA4xFYAACA8aJ6lRCA836+8ZPubsG2x+66vrtbgMGi+Z3uY53WD///1y/9aa/OxfXr3KYAcYUFAAD0AAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHh9ursBXBs/3/hJd7dg22N3Xd/dLfQKPfF3wyTfrD/e3S0AvRJXWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjBdVYKmoqFB6erqcTqfy8vK0bdu2y9a/9dZbyszMlNPp1NixY7Vhw4aI/Q899JDi4uIitsLCwmhaAwAAMch2YFm3bp28Xq/Kysq0fft2jRs3TgUFBTp69Gi79e+//76mTZumWbNmaceOHZoyZYqmTJmiv/3tbxF1hYWF+vzzz8Pbm2++Gd0ZAQCAmGP7jeNWrFih2bNnq7i4WJJUWVmpd999V1VVVXryyScvqf/FL36hwsJCLViwQJK0ZMkSbdy4US+//LIqKyvDdQ6HQ263O9rzgOG+Wf+rLy/a9NVr30gn403Err0tX5/T3S0Y7Yr+tv6PM23xkiZJkm49VCVnfNtV9xCvs1Lfq54GuCxbV1haWlpUV1cnj8dzcYL4eHk8HtXW1rZ7TG1tbUS9JBUUFFxSX1NTo2HDhumGG27Q3Llzdfx4xw8Ezc3NCoVCERsAAIhdtgJLQ0ODWltb5XK5IsZdLpcCgUC7xwQCgS+tLyws1Jo1a+T3+/XCCy/oz3/+s+655x61tra2O6fP51NSUlJ4S0tLs3MaAACghzHis4SmTp0a/nrs2LHKysrSyJEjVVNTozvvvPOS+pKSEnm93vD3oVCI0AIAQAyzdYUlOTlZCQkJCgaDEePBYLDD+0/cbreteknKyMhQcnKy9u3b1+5+h8OhQYMGRWwAACB22QosiYmJysnJkd/vD4+1tbXJ7/crPz+/3WPy8/Mj6iVp48aNHdZL0uHDh3X8+HGlpKTYaQ8AAMQo2y9r9nq9WrVqlVavXq3du3dr7ty5ampqCr9qaMaMGSopKQnXP/LII6qurtbPfvYzffzxx3rmmWf017/+VfPnz5cknTp1SgsWLNCWLVt08OBB+f1+TZ48WaNGjVJBQUEnnSYAAOjJbN/DUlRUpGPHjqm0tFSBQEDZ2dmqrq4O31hbX1+v+PiLOei2227TG2+8oUWLFumpp57S6NGjtX79et18882SpISEBH3wwQdavXq1Tpw4odTUVN19991asmSJHA5HJ50mAADoyaK66Xb+/PnhKyRfVFNTc8nY9773PX3ve99rt75v37567733omkDAAD0EnyWEAAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADBen+5uoCf4+cZPursFAAB6Na6wAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADBeVIGloqJC6enpcjqdysvL07Zt2y5b/9ZbbykzM1NOp1Njx47Vhg0bIvZblqXS0lKlpKSob9++8ng82rt3bzStAQCAGGQ7sKxbt05er1dlZWXavn27xo0bp4KCAh09erTd+vfff1/Tpk3TrFmztGPHDk2ZMkVTpkzR3/72t3DNsmXL9OKLL6qyslJbt25V//79VVBQoDNnzkR/ZgAAIGbYDiwrVqzQ7NmzVVxcrDFjxqiyslL9+vVTVVVVu/W/+MUvVFhYqAULFujGG2/UkiVLdMstt+jll1+WdP7qSnl5uRYtWqTJkycrKytLa9as0ZEjR7R+/fqrOjkAABAb+tgpbmlpUV1dnUpKSsJj8fHx8ng8qq2tbfeY2tpaeb3eiLGCgoJwGDlw4IACgYA8Hk94f1JSkvLy8lRbW6upU6deMmdzc7Oam5vD3zc2NkqSQqGQndO5YmeaTl2TeXuTpr83f2lNqKnnXVG7kvPC1THt76+n/5ufaYtXW/NpSefPpTW+7arnjNdZhazzX/+96ZRa465+zs7Q0/+trjW7f1vX4jH2wpyWZX1pra3A0tDQoNbWVrlcrohxl8uljz/+uN1jAoFAu/WBQCC8/8JYRzVf5PP59Oyzz14ynpaWdmUnAqAHebm7G4hBL0qS7rsmc0+8JrPiWrD3t/XUNepCkk6ePKmkpKTL1tgKLKYoKSmJuGrT1tam//mf/9FXv/pVxcXFdcrPCIVCSktL06FDhzRo0KBOmRPtY627FuvdtVjvrsNad63OWG/LsnTy5EmlpqZ+aa2twJKcnKyEhAQFg8GI8WAwKLfb3e4xbrf7svUX/jcYDColJSWiJjs7u905HQ6HHA5HxNjgwYPtnMoVGzRoEL/4XYS17lqsd9divbsOa921rna9v+zKygW2brpNTExUTk6O/H5/eKytrU1+v1/5+fntHpOfnx9RL0kbN24M148YMUJutzuiJhQKaevWrR3OCQAAehfbTwl5vV7NnDlTubm5Gj9+vMrLy9XU1KTi4mJJ0owZMzR8+HD5fD5J0iOPPKJJkybpZz/7me69916tXbtWf/3rX/WrX/1KkhQXF6dHH31Uzz//vEaPHq0RI0Zo8eLFSk1N1ZQpUzrvTAEAQI9lO7AUFRXp2LFjKi0tVSAQUHZ2tqqrq8M3zdbX1ys+/uKFm9tuu01vvPGGFi1apKeeekqjR4/W+vXrdfPNN4drFi5cqKamJs2ZM0cnTpzQhAkTVF1dLafT2QmnGB2Hw6GysrJLnnpC52Otuxbr3bVY767DWnetrl7vOOtKXksEAADQjfgsIQAAYDwCCwAAMB6BBQAAGI/AAgAAjNerA0tFRYXS09PldDqVl5enbdu2Xbb+rbfeUmZmppxOp8aOHasNGzZ0Uac9n521XrVqlSZOnKghQ4ZoyJAh8ng8X/pvg0h2f7cvWLt2reLi4nhLAZvsrveJEyf08MMPKyUlRQ6HQ9dffz3/PblCdte6vLxcN9xwg/r27au0tDQ99thjOnOm531uWXf4z//8T913331KTU1VXFzcFX0gcU1NjW655RY5HA6NGjVKr776auc1ZPVSa9eutRITE62qqirro48+smbPnm0NHjzYCgaD7db/5S9/sRISEqxly5ZZu3btshYtWmR95StfsT788MMu7rznsbvWP/jBD6yKigprx44d1u7du62HHnrISkpKsg4fPtzFnfdMdtf7ggMHDljDhw+3Jk6caE2ePLlrmo0Bdte7ubnZys3Ntb7zne9Ymzdvtg4cOGDV1NRYO3fu7OLOex67a/36669bDofDev31160DBw5Y7733npWSkmI99thjXdx5z7Rhwwbr6aeftt5++21LkvXOO+9ctv7TTz+1+vXrZ3m9XmvXrl3WSy+9ZCUkJFjV1dWd0k+vDSzjx4+3Hn744fD3ra2tVmpqquXz+dqt//73v2/de++9EWN5eXnWv/7rv17TPmOB3bX+onPnzlkDBw60Vq9efa1ajCnRrPe5c+es2267zXrllVesmTNnElhssLveK1eutDIyMqyWlpauajFm2F3rhx9+2PrHf/zHiDGv12t961vfuqZ9xqIrCSwLFy60brrppoixoqIiq6CgoFN66JVPCbW0tKiurk4ejyc8Fh8fL4/Ho9ra2naPqa2tjaiXpIKCgg7rcV40a/1Fp0+f1tmzZzV06NBr1WbMiHa9n3vuOQ0bNkyzZs3qijZjRjTr/R//8R/Kz8/Xww8/LJfLpZtvvlk/+clP1Nra2lVt90jRrPVtt92murq68NNGn376qTZs2KDvfOc7XdJzb3OtHyd75Kc1X62Ghga1traG3533ApfLpY8//rjdYwKBQLv1gUDgmvUZC6JZ6y964oknlJqaeskfAi4VzXpv3rxZv/71r7Vz584u6DC2RLPen376qf70pz/pwQcf1IYNG7Rv3z7NmzdPZ8+eVVlZWVe03SNFs9Y/+MEP1NDQoAkTJsiyLJ07d07/9m//pqeeeqorWu51OnqcDIVC+vvf/66+ffte1fy98goLeo6lS5dq7dq1euedd7r1oxpi1cmTJzV9+nStWrVKycnJ3d1Or9DW1qZhw4bpV7/6lXJyclRUVKSnn35alZWV3d1azKmpqdFPfvIT/fKXv9T27dv19ttv691339WSJUu6uzVEoVdeYUlOTlZCQoKCwWDEeDAYlNvtbvcYt9ttqx7nRbPWFyxfvlxLly7VH//4R2VlZV3LNmOG3fXev3+/Dh48qPvuuy881tbWJknq06eP9uzZo5EjR17bpnuwaH6/U1JS9JWvfEUJCQnhsRtvvFGBQEAtLS1KTEy8pj33VNGs9eLFizV9+nT9y7/8iyRp7Nix4c+te/rppyM+9w5Xr6PHyUGDBl311RWpl15hSUxMVE5Ojvx+f3isra1Nfr9f+fn57R6Tn58fUS9JGzdu7LAe50Wz1pK0bNkyLVmyRNXV1crNze2KVmOC3fXOzMzUhx9+qJ07d4a37373u7rjjju0c+dOpaWldWX7PU40v9/f+ta3tG/fvnAwlKRPPvlEKSkphJXLiGatT58+fUkouRAULT5Gr9Nd88fJTrl1twdau3at5XA4rFdffdXatWuXNWfOHGvw4MFWIBCwLMuypk+fbj355JPh+r/85S9Wnz59rOXLl1u7d++2ysrKeFnzFbK71kuXLrUSExOtf//3f7c+//zz8Hby5MnuOoUexe56fxGvErLH7nrX19dbAwcOtObPn2/t2bPH+v3vf28NGzbMev7557vrFHoMu2tdVlZmDRw40HrzzTetTz/91PrDH/5gjRw50vr+97/fXafQo5w8edLasWOHtWPHDkuStWLFCmvHjh3Wf//3f1uWZVlPPvmkNX369HD9hZc1L1iwwNq9e7dVUVHBy5o7y0svvWR9/etftxITE63x48dbW7ZsCe+bNGmSNXPmzIj63/3ud9b1119vJSYmWjfddJP17rvvdnHHPZedtb7uuussSZdsZWVlXd94D2X3d/v/IrDYZ3e933//fSsvL89yOBxWRkaG9eMf/9g6d+5cF3fdM9lZ67Nnz1rPPPOMNXLkSMvpdFppaWnWvHnzrP/93//t+sZ7oE2bNrX73+ILazxz5kxr0qRJlxyTnZ1tJSYmWhkZGdZvfvObTusnzrK4LgYAAMzWK+9hAQAAPQuBBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADG+3/mErEfYr05iQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    (selected_test_tfidf.loc[correct] != 0).sum(axis=1) / selected_test_tfidf.shape[-1], \n",
    "    alpha=0.5, \n",
    "    weights=np.ones(len(correct)) / len(correct)\n",
    ")\n",
    "plt.vlines(np.mean((selected_test_tfidf.loc[correct] != 0).sum(axis=1) / selected_test_tfidf.shape[-1]), 0, 0.22)\n",
    "\n",
    "plt.hist(\n",
    "    (selected_test_tfidf.loc[errors] != 0).sum(axis=1) / selected_test_tfidf.shape[-1],\n",
    "    alpha=0.5,\n",
    "    weights=np.ones(len(errors)) / len(errors)\n",
    ")\n",
    "plt.vlines(np.mean((selected_test_tfidf.loc[errors] != 0).sum(axis=1) / selected_test_tfidf.shape[-1]), 0, 0.22, \n",
    "           color=\"Orange\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b8b60b-303c-49c4-8176-97d34e77f41b",
   "metadata": {},
   "source": [
    "## FP/FN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "20b4ee66-7178-4318-b1ba-d198afe1e9bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>titre</th>\n",
       "      <th>author</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>death_date</th>\n",
       "      <th>date_publication</th>\n",
       "      <th>label</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1833_Corbiere-Edouard_La-mer-et-les-marins-Scenes-maritimes</th>\n",
       "      <td>La mer et les marins. Scènes maritimes</td>\n",
       "      <td>Corbière, Édouard</td>\n",
       "      <td>male</td>\n",
       "      <td>1793</td>\n",
       "      <td>1875</td>\n",
       "      <td>1833</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1827-1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1839_Karr-Alphonse_Clotilde</th>\n",
       "      <td>Clotilde</td>\n",
       "      <td>Karr, Alphonse</td>\n",
       "      <td>male</td>\n",
       "      <td>1808</td>\n",
       "      <td>1890</td>\n",
       "      <td>1839</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1827-1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846_Lacombe-Patrice_La-terre-paternelle</th>\n",
       "      <td>La terre paternelle</td>\n",
       "      <td>Lacombe, Patrice</td>\n",
       "      <td>male</td>\n",
       "      <td>1807</td>\n",
       "      <td>1863</td>\n",
       "      <td>1846</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1827-1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862_Erckmann-Chatrian_Contes-des-bords-du-Rhin</th>\n",
       "      <td>Contes des bords du Rhin</td>\n",
       "      <td>Erckmann, Émile</td>\n",
       "      <td>male</td>\n",
       "      <td>1822</td>\n",
       "      <td>1899</td>\n",
       "      <td>1862</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1851-1869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863_Erckmann-Chatrian_Madame-Therese-ou-Les-Volontaires-de-92-Pourquoi-Hunebourg-ne-fut-pas-rendu</th>\n",
       "      <td>Madame Thérèse ou Les Volontaires de 92 - Pour...</td>\n",
       "      <td>Chatrian, Alexandre</td>\n",
       "      <td>male</td>\n",
       "      <td>1826</td>\n",
       "      <td>1890</td>\n",
       "      <td>1863</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1851-1869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878_Gaboriau-Emile_Le-capitaine-Coutanceau</th>\n",
       "      <td>Le capitaine Coutanceau</td>\n",
       "      <td>Gaboriau, Émile</td>\n",
       "      <td>male</td>\n",
       "      <td>1832</td>\n",
       "      <td>1873</td>\n",
       "      <td>1878</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1870-1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881_Buet-Charles_Histoires-a-dormir-debout</th>\n",
       "      <td>Histoires à dormir debout, par Charles Buet (1...</td>\n",
       "      <td>Buet, Charles</td>\n",
       "      <td>male</td>\n",
       "      <td>1846</td>\n",
       "      <td>1897</td>\n",
       "      <td>1881</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1870-1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886_Arene-Paul_Contes-de-Provence</th>\n",
       "      <td>Contes de Provence</td>\n",
       "      <td>Arène, Paul</td>\n",
       "      <td>male</td>\n",
       "      <td>1886</td>\n",
       "      <td>1886</td>\n",
       "      <td>1887</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1870-1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906_Le-Rouge-Gustave_La-Reine-des-elephants</th>\n",
       "      <td>La Reine des éléphants</td>\n",
       "      <td>Le Rouge, Gustave</td>\n",
       "      <td>male</td>\n",
       "      <td>1867</td>\n",
       "      <td>1938</td>\n",
       "      <td>1906</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1900-1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907_Vanderem-Fernand_La-victime</th>\n",
       "      <td>La victime</td>\n",
       "      <td>Vandérem, Fernand</td>\n",
       "      <td>male</td>\n",
       "      <td>1864</td>\n",
       "      <td>1939</td>\n",
       "      <td>1907</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1900-1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947_Renard-Maurice_Le-Maitre-de-la-lumiere</th>\n",
       "      <td>Le Maître de la lumière</td>\n",
       "      <td>Renard, Maurice</td>\n",
       "      <td>male</td>\n",
       "      <td>1875</td>\n",
       "      <td>1939</td>\n",
       "      <td>1947</td>\n",
       "      <td>non-canon</td>\n",
       "      <td>1946-2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                titre  \\\n",
       "doc                                                                                                     \n",
       "1833_Corbiere-Edouard_La-mer-et-les-marins-Scen...             La mer et les marins. Scènes maritimes   \n",
       "1839_Karr-Alphonse_Clotilde                                                                  Clotilde   \n",
       "1846_Lacombe-Patrice_La-terre-paternelle                                          La terre paternelle   \n",
       "1862_Erckmann-Chatrian_Contes-des-bords-du-Rhin                              Contes des bords du Rhin   \n",
       "1863_Erckmann-Chatrian_Madame-Therese-ou-Les-Vo...  Madame Thérèse ou Les Volontaires de 92 - Pour...   \n",
       "1878_Gaboriau-Emile_Le-capitaine-Coutanceau                                   Le capitaine Coutanceau   \n",
       "1881_Buet-Charles_Histoires-a-dormir-debout         Histoires à dormir debout, par Charles Buet (1...   \n",
       "1886_Arene-Paul_Contes-de-Provence                                                 Contes de Provence   \n",
       "1906_Le-Rouge-Gustave_La-Reine-des-elephants                                   La Reine des éléphants   \n",
       "1907_Vanderem-Fernand_La-victime                                                           La victime   \n",
       "1947_Renard-Maurice_Le-Maitre-de-la-lumiere                                   Le Maître de la lumière   \n",
       "\n",
       "                                                                 author  \\\n",
       "doc                                                                       \n",
       "1833_Corbiere-Edouard_La-mer-et-les-marins-Scen...    Corbière, Édouard   \n",
       "1839_Karr-Alphonse_Clotilde                              Karr, Alphonse   \n",
       "1846_Lacombe-Patrice_La-terre-paternelle               Lacombe, Patrice   \n",
       "1862_Erckmann-Chatrian_Contes-des-bords-du-Rhin         Erckmann, Émile   \n",
       "1863_Erckmann-Chatrian_Madame-Therese-ou-Les-Vo...  Chatrian, Alexandre   \n",
       "1878_Gaboriau-Emile_Le-capitaine-Coutanceau             Gaboriau, Émile   \n",
       "1881_Buet-Charles_Histoires-a-dormir-debout               Buet, Charles   \n",
       "1886_Arene-Paul_Contes-de-Provence                          Arène, Paul   \n",
       "1906_Le-Rouge-Gustave_La-Reine-des-elephants          Le Rouge, Gustave   \n",
       "1907_Vanderem-Fernand_La-victime                      Vandérem, Fernand   \n",
       "1947_Renard-Maurice_Le-Maitre-de-la-lumiere             Renard, Maurice   \n",
       "\n",
       "                                                   gender birth_date  \\\n",
       "doc                                                                    \n",
       "1833_Corbiere-Edouard_La-mer-et-les-marins-Scen...   male       1793   \n",
       "1839_Karr-Alphonse_Clotilde                          male       1808   \n",
       "1846_Lacombe-Patrice_La-terre-paternelle             male       1807   \n",
       "1862_Erckmann-Chatrian_Contes-des-bords-du-Rhin      male       1822   \n",
       "1863_Erckmann-Chatrian_Madame-Therese-ou-Les-Vo...   male       1826   \n",
       "1878_Gaboriau-Emile_Le-capitaine-Coutanceau          male       1832   \n",
       "1881_Buet-Charles_Histoires-a-dormir-debout          male       1846   \n",
       "1886_Arene-Paul_Contes-de-Provence                   male       1886   \n",
       "1906_Le-Rouge-Gustave_La-Reine-des-elephants         male       1867   \n",
       "1907_Vanderem-Fernand_La-victime                     male       1864   \n",
       "1947_Renard-Maurice_Le-Maitre-de-la-lumiere          male       1875   \n",
       "\n",
       "                                                   death_date  \\\n",
       "doc                                                             \n",
       "1833_Corbiere-Edouard_La-mer-et-les-marins-Scen...       1875   \n",
       "1839_Karr-Alphonse_Clotilde                              1890   \n",
       "1846_Lacombe-Patrice_La-terre-paternelle                 1863   \n",
       "1862_Erckmann-Chatrian_Contes-des-bords-du-Rhin          1899   \n",
       "1863_Erckmann-Chatrian_Madame-Therese-ou-Les-Vo...       1890   \n",
       "1878_Gaboriau-Emile_Le-capitaine-Coutanceau              1873   \n",
       "1881_Buet-Charles_Histoires-a-dormir-debout              1897   \n",
       "1886_Arene-Paul_Contes-de-Provence                       1886   \n",
       "1906_Le-Rouge-Gustave_La-Reine-des-elephants             1938   \n",
       "1907_Vanderem-Fernand_La-victime                         1939   \n",
       "1947_Renard-Maurice_Le-Maitre-de-la-lumiere              1939   \n",
       "\n",
       "                                                    date_publication  \\\n",
       "doc                                                                    \n",
       "1833_Corbiere-Edouard_La-mer-et-les-marins-Scen...              1833   \n",
       "1839_Karr-Alphonse_Clotilde                                     1839   \n",
       "1846_Lacombe-Patrice_La-terre-paternelle                        1846   \n",
       "1862_Erckmann-Chatrian_Contes-des-bords-du-Rhin                 1862   \n",
       "1863_Erckmann-Chatrian_Madame-Therese-ou-Les-Vo...              1863   \n",
       "1878_Gaboriau-Emile_Le-capitaine-Coutanceau                     1878   \n",
       "1881_Buet-Charles_Histoires-a-dormir-debout                     1881   \n",
       "1886_Arene-Paul_Contes-de-Provence                              1887   \n",
       "1906_Le-Rouge-Gustave_La-Reine-des-elephants                    1906   \n",
       "1907_Vanderem-Fernand_La-victime                                1907   \n",
       "1947_Renard-Maurice_Le-Maitre-de-la-lumiere                     1947   \n",
       "\n",
       "                                                        label     period  \n",
       "doc                                                                       \n",
       "1833_Corbiere-Edouard_La-mer-et-les-marins-Scen...  non-canon  1827-1850  \n",
       "1839_Karr-Alphonse_Clotilde                         non-canon  1827-1850  \n",
       "1846_Lacombe-Patrice_La-terre-paternelle            non-canon  1827-1850  \n",
       "1862_Erckmann-Chatrian_Contes-des-bords-du-Rhin     non-canon  1851-1869  \n",
       "1863_Erckmann-Chatrian_Madame-Therese-ou-Les-Vo...  non-canon  1851-1869  \n",
       "1878_Gaboriau-Emile_Le-capitaine-Coutanceau         non-canon  1870-1899  \n",
       "1881_Buet-Charles_Histoires-a-dormir-debout         non-canon  1870-1899  \n",
       "1886_Arene-Paul_Contes-de-Provence                  non-canon  1870-1899  \n",
       "1906_Le-Rouge-Gustave_La-Reine-des-elephants        non-canon  1900-1945  \n",
       "1907_Vanderem-Fernand_La-victime                    non-canon  1900-1945  \n",
       "1947_Renard-Maurice_Le-Maitre-de-la-lumiere         non-canon  1946-2024  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp = test_y.index[(test_y_pred==1) & (test_y==0)]\n",
    "fn = test_y.index[(test_y_pred==0) & (test_y==1)]\n",
    "metadata.data.loc[fp]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b83f05c-5a4a-401b-a461-f9531f3ca405",
   "metadata": {},
   "source": [
    "From the metadata, it is difficult to interpret the False Positives. We notice that they are all writen by male authors, but we know that the male authors are overrepresented in the corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "42bc4f98-d822-41cd-8a26-01194bbae7b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>titre</th>\n",
       "      <th>author</th>\n",
       "      <th>gender</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>death_date</th>\n",
       "      <th>date_publication</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1823_Duras-Claire-de-Durfort_Ourika</th>\n",
       "      <td>Ourika</td>\n",
       "      <td>Duras, Claire (de)</td>\n",
       "      <td>female</td>\n",
       "      <td>1777</td>\n",
       "      <td>1828</td>\n",
       "      <td>1823</td>\n",
       "      <td>canon</td>\n",
       "      <td>1800-1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837_Sand-George_Mauprat</th>\n",
       "      <td>Mauprat</td>\n",
       "      <td>Sand, George</td>\n",
       "      <td>female</td>\n",
       "      <td>1804</td>\n",
       "      <td>1876</td>\n",
       "      <td>1837</td>\n",
       "      <td>canon</td>\n",
       "      <td>1827-1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855_Sand-George_Kourroglou</th>\n",
       "      <td>Kourroglou</td>\n",
       "      <td>Sand, George</td>\n",
       "      <td>female</td>\n",
       "      <td>1804</td>\n",
       "      <td>1876</td>\n",
       "      <td>1855</td>\n",
       "      <td>canon</td>\n",
       "      <td>1851-1869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863_Verne-Jules_Cinq-Semaines-en-ballon</th>\n",
       "      <td>Cinq Semaines en ballon</td>\n",
       "      <td>Verne, Jules</td>\n",
       "      <td>male</td>\n",
       "      <td>1828</td>\n",
       "      <td>1905</td>\n",
       "      <td>1863</td>\n",
       "      <td>canon</td>\n",
       "      <td>1851-1869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883_Renan-Ernest_Souvenirs-d'enfance-et-de-jeunesse_remastered</th>\n",
       "      <td>Souvenirs d'enfance et de jeunesse</td>\n",
       "      <td>Renan, Ernest</td>\n",
       "      <td>male</td>\n",
       "      <td>1823</td>\n",
       "      <td>1892</td>\n",
       "      <td>1883</td>\n",
       "      <td>canon</td>\n",
       "      <td>1870-1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911_Apollinaire-Guillaume_Les-exploits-d-un-jeune-don-Juan</th>\n",
       "      <td>Les exploits d’un jeune don Juan</td>\n",
       "      <td>Apollinaire, Guillaume</td>\n",
       "      <td>male</td>\n",
       "      <td>1880</td>\n",
       "      <td>1918</td>\n",
       "      <td>1911</td>\n",
       "      <td>canon</td>\n",
       "      <td>1900-1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926_Bourget-Paul_Le-danseur-mondain</th>\n",
       "      <td>Le danseur mondain</td>\n",
       "      <td>Bourget, Paul</td>\n",
       "      <td>male</td>\n",
       "      <td>1852</td>\n",
       "      <td>1935</td>\n",
       "      <td>1926</td>\n",
       "      <td>canon</td>\n",
       "      <td>1900-1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927_Gide-Andre_Les-Faux-monnayeurs</th>\n",
       "      <td>Les Faux-monnayeurs</td>\n",
       "      <td>Gide, André</td>\n",
       "      <td>male</td>\n",
       "      <td>1869</td>\n",
       "      <td>1951</td>\n",
       "      <td>1925</td>\n",
       "      <td>canon</td>\n",
       "      <td>1900-1945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                 titre  \\\n",
       "doc                                                                                      \n",
       "1823_Duras-Claire-de-Durfort_Ourika                                             Ourika   \n",
       "1837_Sand-George_Mauprat                                                       Mauprat   \n",
       "1855_Sand-George_Kourroglou                                                 Kourroglou   \n",
       "1863_Verne-Jules_Cinq-Semaines-en-ballon                       Cinq Semaines en ballon   \n",
       "1883_Renan-Ernest_Souvenirs-d'enfance-et-de-jeu...  Souvenirs d'enfance et de jeunesse   \n",
       "1911_Apollinaire-Guillaume_Les-exploits-d-un-je...    Les exploits d’un jeune don Juan   \n",
       "1926_Bourget-Paul_Le-danseur-mondain                                Le danseur mondain   \n",
       "1927_Gide-Andre_Les-Faux-monnayeurs                                Les Faux-monnayeurs   \n",
       "\n",
       "                                                                    author  \\\n",
       "doc                                                                          \n",
       "1823_Duras-Claire-de-Durfort_Ourika                     Duras, Claire (de)   \n",
       "1837_Sand-George_Mauprat                                      Sand, George   \n",
       "1855_Sand-George_Kourroglou                                   Sand, George   \n",
       "1863_Verne-Jules_Cinq-Semaines-en-ballon                      Verne, Jules   \n",
       "1883_Renan-Ernest_Souvenirs-d'enfance-et-de-jeu...           Renan, Ernest   \n",
       "1911_Apollinaire-Guillaume_Les-exploits-d-un-je...  Apollinaire, Guillaume   \n",
       "1926_Bourget-Paul_Le-danseur-mondain                         Bourget, Paul   \n",
       "1927_Gide-Andre_Les-Faux-monnayeurs                            Gide, André   \n",
       "\n",
       "                                                    gender birth_date  \\\n",
       "doc                                                                     \n",
       "1823_Duras-Claire-de-Durfort_Ourika                 female       1777   \n",
       "1837_Sand-George_Mauprat                            female       1804   \n",
       "1855_Sand-George_Kourroglou                         female       1804   \n",
       "1863_Verne-Jules_Cinq-Semaines-en-ballon              male       1828   \n",
       "1883_Renan-Ernest_Souvenirs-d'enfance-et-de-jeu...    male       1823   \n",
       "1911_Apollinaire-Guillaume_Les-exploits-d-un-je...    male       1880   \n",
       "1926_Bourget-Paul_Le-danseur-mondain                  male       1852   \n",
       "1927_Gide-Andre_Les-Faux-monnayeurs                   male       1869   \n",
       "\n",
       "                                                   death_date  \\\n",
       "doc                                                             \n",
       "1823_Duras-Claire-de-Durfort_Ourika                      1828   \n",
       "1837_Sand-George_Mauprat                                 1876   \n",
       "1855_Sand-George_Kourroglou                              1876   \n",
       "1863_Verne-Jules_Cinq-Semaines-en-ballon                 1905   \n",
       "1883_Renan-Ernest_Souvenirs-d'enfance-et-de-jeu...       1892   \n",
       "1911_Apollinaire-Guillaume_Les-exploits-d-un-je...       1918   \n",
       "1926_Bourget-Paul_Le-danseur-mondain                     1935   \n",
       "1927_Gide-Andre_Les-Faux-monnayeurs                      1951   \n",
       "\n",
       "                                                    date_publication  label  \\\n",
       "doc                                                                           \n",
       "1823_Duras-Claire-de-Durfort_Ourika                             1823  canon   \n",
       "1837_Sand-George_Mauprat                                        1837  canon   \n",
       "1855_Sand-George_Kourroglou                                     1855  canon   \n",
       "1863_Verne-Jules_Cinq-Semaines-en-ballon                        1863  canon   \n",
       "1883_Renan-Ernest_Souvenirs-d'enfance-et-de-jeu...              1883  canon   \n",
       "1911_Apollinaire-Guillaume_Les-exploits-d-un-je...              1911  canon   \n",
       "1926_Bourget-Paul_Le-danseur-mondain                            1926  canon   \n",
       "1927_Gide-Andre_Les-Faux-monnayeurs                             1925  canon   \n",
       "\n",
       "                                                       period  \n",
       "doc                                                            \n",
       "1823_Duras-Claire-de-Durfort_Ourika                 1800-1826  \n",
       "1837_Sand-George_Mauprat                            1827-1850  \n",
       "1855_Sand-George_Kourroglou                         1851-1869  \n",
       "1863_Verne-Jules_Cinq-Semaines-en-ballon            1851-1869  \n",
       "1883_Renan-Ernest_Souvenirs-d'enfance-et-de-jeu...  1870-1899  \n",
       "1911_Apollinaire-Guillaume_Les-exploits-d-un-je...  1900-1945  \n",
       "1926_Bourget-Paul_Le-danseur-mondain                1900-1945  \n",
       "1927_Gide-Andre_Les-Faux-monnayeurs                 1900-1945  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.data.loc[fn]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497b50e6-24f9-4525-9247-6b69a39a3c09",
   "metadata": {},
   "source": [
    "From the metadata, it is difficult to interpret the False Negatives, however we notice three documents written by female authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5e8ac40-6119-4483-9051-0db9d5bd5381",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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